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not supported`)}};let Ea=class{constructor(o,n,u){_I();let p,b;if(typeof o=="object"&&"location"in o)switch(this.dataLocation=o.location,p=o.type,b=o.dims,o.location){case"cpu-pinned":{const w=to.get(p);if(!w)throw new TypeError(`unsupported type "${p}" to create tensor from pinned buffer`);if(!(o.data instanceof w))throw new TypeError(`buffer should be of type ${w.name}`);this.cpuData=o.data;break}case"texture":{if(p!=="float32")throw new TypeError(`unsupported type "${p}" to create tensor from texture`);this.gpuTextureData=o.texture,this.downloader=o.download,this.disposer=o.dispose;break}case"gpu-buffer":{if(p!=="float32"&&p!=="float16"&&p!=="int32"&&p!=="int64"&&p!=="uint32"&&p!=="uint8"&&p!=="bool"&&p!=="uint4"&&p!=="int4")throw new TypeError(`unsupported type "${p}" to create tensor from gpu 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p=0;p<n.length;p++){if(u&&!n[p].includes("TRACE_FUNC")){let b=`FUNC_${a}::${n[p].trim().split(" ")[1]}`;o&&(b+=`::${o}`),Bm("CPU",b);return}n[p].includes("TRACE_FUNC")&&(u=!0)}},J2=a=>{(typeof La.trace>"u"?!La.wasm.trace:!La.trace)||ym("BEGIN",a)},q2=a=>{(typeof La.trace>"u"?!La.wasm.trace:!La.trace)||ym("END",a)};let LI=class Dm{constructor(o){this.handler=o}async run(o,n,u){J2();const p={};let b={};if(typeof o!="object"||o===null||o instanceof eo||Array.isArray(o))throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");let C=!0;if(typeof n=="object"){if(n===null)throw new TypeError("Unexpected argument[1]: cannot be null.");if(n instanceof eo)throw new TypeError("'fetches' cannot be a Tensor");if(Array.isArray(n)){if(n.length===0)throw new TypeError("'fetches' cannot be an empty array.");C=!1;for(const v of n){if(typeof v!="string")throw new TypeError("'fetches' must be a string array or an 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o=this.tensorTrackersById.get(a);o&&(this.tensorTrackersById.delete(a),o.tensorWrapper&&this.releaseTensor(o.tensorWrapper))}async ensureTensor(a,o,n,u,p){St("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${o}, dataType: ${n}, shape: ${u}, copyOld: ${p}}`);let b=this.tensorTrackersById.get(o);if(!b)throw new Error("Tensor not found.");return b.ensureTensor(a,n,u,p)}upload(a,o){let n=this.tensorTrackersById.get(a);if(!n)throw new Error("Tensor not found.");n.upload(o)}async download(a,o){St("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${a}, dstBuffer: ${o?.byteLength}}`);let n=this.tensorTrackersById.get(a);if(!n)throw new Error("Tensor not found.");return n.download(o)}releaseTensorsForSession(a){for(let o of this.freeTensors)o.sessionId===a&&o.destroy();this.freeTensors=this.freeTensors.filter(o=>o.sessionId!==a)}registerTensor(a,o,n,u){let p=this.getMLContext(a),b=s2(),C=new n2({sessionId:a,context:p,tensor:o,dataType:n,shape:u});return 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n=this.mlContextCache.findIndex(u=>u.gpuDevice===a);if(n!==-1)return this.mlContextCache[n].mlContext;{let u=await navigator.ml.createContext(a);return this.mlContextCache.push({gpuDevice:a,mlContext:u}),u}}else if(a===void 0){let n=this.mlContextCache.findIndex(u=>u.options===void 0&&u.gpuDevice===void 0);if(n!==-1)return this.mlContextCache[n].mlContext;{let u=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:u}),u}}let o=this.mlContextCache.findIndex(n=>ag(n.options,a));if(o!==-1)return this.mlContextCache[o].mlContext;{let n=await navigator.ml.createContext(a);return this.mlContextCache.push({options:a,mlContext:n}),n}}registerMLContext(a,o){this.mlContextBySessionId.set(a,o);let n=this.sessionIdsByMLContext.get(o);n||(n=new Set,this.sessionIdsByMLContext.set(o,n)),n.add(a),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(a,this.temporaryGraphInputs),this.temporaryGraphInputs=[])}onReleaseSession(a){this.sessionGraphInputs.delete(a);let o=this.mlContextBySessionId.get(a);if(!o)return;this.tensorManager.releaseTensorsForSession(a),this.mlContextBySessionId.delete(a);let n=this.sessionIdsByMLContext.get(o);if(n.delete(a),n.size===0){this.sessionIdsByMLContext.delete(o);let u=this.mlContextCache.findIndex(p=>p.mlContext===o);u!==-1&&this.mlContextCache.splice(u,1)}}getMLContext(a){return this.mlContextBySessionId.get(a)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(a){St("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${a}}`),this.tensorManager.releaseTensorId(a)}async ensureTensor(a,o,n,u,p){let b=w0.get(n);if(!b)throw new Error(`Unsupported ONNX data type: ${n}`);return this.tensorManager.ensureTensor(a??this.currentSessionId,o,b,u,p)}async createTemporaryTensor(a,o,n){St("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${o}, shape: ${n}}`);let u=w0.get(o);if(!u)throw new Error(`Unsupported ONNX data type: ${o}`);let p=this.tensorManager.reserveTensorId();await 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w=a;a.startsWith("./")&&(w=a.substring(2));let M=b.get(w);if(!M)throw new Error(`File with name ${w} not found in preloaded files.`);if(o+n>M.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let v=M.slice(o,o+n).buffer,D;switch(p.dataType){case"float32":D=new Float32Array(v);break;case"float16":D=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(v):new Uint16Array(v);break;case"int32":D=new Int32Array(v);break;case"uint32":D=new Uint32Array(v);break;case"int64":C?(D=Ac(new Uint8Array(v),!1),p.dataType="int32"):D=new BigInt64Array(v);break;case"uint64":D=new BigUint64Array(v);break;case"int8":D=new Int8Array(v);break;case"int4":case"uint4":case"uint8":D=new Uint8Array(v);break;default:throw new Error(`Unsupported data type: ${p.dataType} in creating WebNN Constant from external data.`)}return St("verbose",()=>`[WebNN] registerMLConstant {dataType: ${p.dataType}, shape: ${p.shape}}} ${C?"(Note: it was int64 data type and 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`)}get variablesInfo(){if(this.uniforms.length===0)return;let a=o=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(o)];return this.uniforms.map(o=>[a(o.type),o.length??1])}},fh=(a,o)=>new cg(a,o)}),ug,c2,dg,fg,gg,pg,pa,gh,ph,qn=IA(()=>{it(),It(),Hr(),kt(),ug=(a,o)=>{if(!a||a.length!==1)throw new Error("Transpose requires 1 input.");if(o.length!==0&&o.length!==a[0].dims.length)throw new Error(`perm size ${o.length} does not match input rank ${a[0].dims.length}`)},c2=(a,o)=>o.length!==0?o:[...new Array(a).keys()].reverse(),dg=(a,o)=>He.sortBasedOnPerm(a,c2(a.length,o)),fg=(a,o,n,u)=>{let p=`fn perm(i: ${u.type.indices}) -> ${n.type.indices} {
var a: ${n.type.indices};`;for(let b=0;b<o;++b)p+=`a[${a[b]}]=i[${b}];`;return p+="return a;}"},gg=(a,o)=>{let n=[],u=[];for(let p=0;p<a.length;++p)a[p]!==1&&n.push(a[p]),a[o[p]]!==1&&u.push(o[p]);return{newShape:n,newPerm:u}},pg=(a,o)=>{let n=0;for(let u=0;u<a.length;++u)if(o[a[u]]!==1){if(a[u]<n)return!1;n=a[u]}return!0},pa=(a,o)=>{let n=a.dataType,u=a.dims.length,p=c2(u,o),b=dg(a.dims,p),C=a.dims,w=b,M=u<2||pg(p,a.dims),v;if(M)return v=F=>{let j=nA("input",n,C,4),Z=XA("output",n,w,4);return`
${F.registerUniform("output_size","u32").declareVariables(j,Z)}
${F.mainStart()}
${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
output[global_idx] = input[global_idx];
}`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let F=He.size(b);return{outputs:[{dims:b,dataType:a.dataType}],dispatchGroup:{x:Math.ceil(F/64/4)},programUniforms:[{type:12,data:Math.ceil(F/4)}]}},getShaderSource:v};let{newShape:D,newPerm:B}=gg(a.dims,p),E=He.areEqual(B,[2,3,1]),S=He.areEqual(B,[3,1,2]);if(D.length===2||E||S){C=E?[D[0],D[1]*D[2]]:S?[D[0]*D[1],D[2]]:D,w=[C[1],C[0]];let F=16;return v=j=>{let Z=nA("a",n,C.length),R=XA("output",n,w.length);return`
${j.registerUniform("output_size","u32").declareVariables(Z,R)}
var<workgroup> tile : array<array<${R.type.value}, ${F+1}>, ${F}>;
${j.mainStart([F,F,1])}
let stride = (uniforms.output_shape[1] - 1) / ${F} + 1;
let workgroup_id_x = workgroup_index % stride;
let workgroup_id_y = workgroup_index / stride;
let input_col = workgroup_id_y * ${F}u + local_id.x;
let input_row = workgroup_id_x * ${F}u + local_id.y;
if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
tile[local_id.y][local_id.x] = ${Z.getByIndices(`${Z.type.indices}(input_row, input_col)`)};
}
workgroupBarrier();
let output_col = workgroup_id_x * ${F}u + local_id.x;
let output_row = workgroup_id_y * ${F}u + local_id.y;
if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
${R.setByIndices(`${R.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
}
}`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let j=He.size(b);return{outputs:[{dims:b,dataType:a.dataType}],dispatchGroup:{x:Math.ceil(w[1]/F),y:Math.ceil(w[0]/F)},programUniforms:[{type:12,data:j},...et(C,w)]}},getShaderSource:v}}return v=F=>{let j=nA("a",n,C.length),Z=XA("output",n,w.length);return`
${F.registerUniform("output_size","u32").declareVariables(j,Z)}
${fg(p,u,j,Z)}
${F.mainStart()}
${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let indices = ${Z.offsetToIndices("global_idx")};
let aIndices = perm(indices);
${Z.setByOffset("global_idx",j.getByIndices("aIndices"))}
}`},{name:"Transpose",shaderCache:{hint:`${o}`,inputDependencies:["rank"]},getRunData:()=>{let F=He.size(b);return{outputs:[{dims:b,dataType:a.dataType}],dispatchGroup:{x:Math.ceil(F/64)},programUniforms:[{type:12,data:F},...et(C,w)]}},getShaderSource:v}},gh=(a,o)=>{ug(a.inputs,o.perm),a.compute(pa(a.inputs[0],o.perm))},ph=a=>Ut({perm:a.perm})}),mg,hg,Cg,bg,Ig,wg,kg,Mg,Eg,vg,Qa,mh,hh,Ch,bh,Ih,wh,kh,Mh,Eh,vh,g6=IA(()=>{it(),It(),kt(),Pc(),qn(),mg={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},hg={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},Cg={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},bg={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Ig=(a,o)=>{let n=[];for(let u=o-a;u<o;++u)n.push(u);return n},wg=(a,o)=>{let n=[],u=a.length;for(let b=0;b<u;b++)o.indexOf(b)===-1&&n.push(a[b]);let p=o.map(b=>a[b]);return[n,p]},kg=(a,o)=>{let n=a.length+o.length,u=[],p=0;for(let b=0;b<n;b++)o.indexOf(b)===-1?u.push(a[p++]):u.push(1);return u},Mg=(a,o)=>{for(let n=0;n<a.length;++n)if(a[a.length-n-1]!==o-1-n)return!1;return!0},Eg=(a,o)=>{let n=[];if(!Mg(a,o)){for(let u=0;u<o;++u)a.indexOf(u)===-1&&n.push(u);a.forEach(u=>n.push(u))}return n},vg=(a,o,n,u,p,b,C)=>{let w=n[0].dims,M=He.size(b),v=He.size(C),D=nA("_A",n[0].dataType,w),B=XA("output",p,b),E=64;M===1&&(E=256);let S=`
var<workgroup> aBestValues : array<f32, ${E}>;
`,F=j=>`
${j.registerUniform("reduceSize","u32").declareVariables(D,B)}
${S}
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${j.mainStart(E)}
let outputIndex = global_idx / ${E};
let offset = outputIndex * uniforms.reduceSize;
var bestValue = f32(${Cg[u]});
let Length = uniforms.reduceSize;
for (var k = local_idx; k < Length; k = k + ${E}) {
let candidate = f32(${D.getByOffset("offset + k")});
bestValue = ${mg[u]};
}
aBestValues[local_idx] = bestValue;
workgroupBarrier();
var reduceSize = min(Length, ${E}u);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (local_idx < currentSize) {
let candidate = aBestValues[local_idx + interval];
bestValue = ${hg[u]};
aBestValues[local_idx] = bestValue;
}
reduceSize = interval;
workgroupBarrier();
}
if (local_idx == 0u) {
${B.setByOffset("outputIndex",`${u==="mean"?`${B.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${B.type.storage}(${bg[u]})`}`)};
}
}`;return{name:a,shaderCache:{hint:`${o};${E}`,inputDependencies:["type"]},getShaderSource:F,getRunData:()=>({outputs:[{dims:b,dataType:p}],dispatchGroup:{x:M},programUniforms:[{type:12,data:v}]})}},Qa=(a,o,n,u)=>{let p=a.inputs.length===1?n:sc(a.inputs,n),b=p.axes;b.length===0&&!p.noopWithEmptyAxes&&(b=a.inputs[0].dims.map((S,F)=>F));let C=He.normalizeAxes(b,a.inputs[0].dims.length),w=C,M=a.inputs[0],v=Eg(w,a.inputs[0].dims.length);v.length>0&&(M=a.compute(pa(a.inputs[0],v),{inputs:[0],outputs:[-1]})[0],w=Ig(w.length,M.dims.length));let[D,B]=wg(M.dims,w),E=D;p.keepDims&&(E=kg(D,C)),a.compute(vg(o,p.cacheKey,[M],u,a.inputs[0].dataType,E,B),{inputs:[M]})},mh=(a,o)=>{Qa(a,"ReduceMeanShared",o,"mean")},hh=(a,o)=>{Qa(a,"ReduceL1Shared",o,"l1")},Ch=(a,o)=>{Qa(a,"ReduceL2Shared",o,"l2")},bh=(a,o)=>{Qa(a,"ReduceLogSumExpShared",o,"logSumExp")},Ih=(a,o)=>{Qa(a,"ReduceMaxShared",o,"max")},wh=(a,o)=>{Qa(a,"ReduceMinShared",o,"min")},kh=(a,o)=>{Qa(a,"ReduceProdShared",o,"prod")},Mh=(a,o)=>{Qa(a,"ReduceSumShared",o,"sum")},Eh=(a,o)=>{Qa(a,"ReduceSumSquareShared",o,"sumSquare")},vh=(a,o)=>{Qa(a,"ReduceLogSumShared",o,"logSum")}}),Fa,xg,R0,sc,Sa,Bg,yg,Dg,Pg,Tg,Gg,Qg,Fg,Sg,Og,Oa,xh,Bh,yh,Dh,Ph,Th,Gh,Qh,Fh,Sh,Pc=IA(()=>{it(),It(),Hr(),kt(),g6(),Fa=a=>{if(!a||a.length===0||a.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(a.length===2&&a[1].dims.length!==1)throw new Error("Invalid axes input dims.")},xg=a=>["","",`var value = ${a.getByIndices("input_indices")};`,""],R0=(a,o,n,u,p,b,C=!1,w=!1)=>{let M=[],v=n[0].dims,D=v.length,B=He.normalizeAxes(p,D),E=!w&&B.length===0;v.forEach((j,Z)=>{E||B.indexOf(Z)>=0?C&&M.push(1):M.push(j)});let S=M.length,F=He.size(M);return{name:a,shaderCache:o,getShaderSource:j=>{let Z=[],R=nA("_A",n[0].dataType,D),z=XA("output",b,S),U=u(R,z,B),f=U[2];for(let k=0,e=0;k<D;k++)E||B.indexOf(k)>=0?(C&&e++,f=`for(var j${k}: u32 = 0; j${k} < ${v[k]}; j${k}++) {
${U[2].includes("last_index")?`let last_index = j${k};`:""}
${R.indicesSet("input_indices",k,`j${k}`)}
${f}
}`):(Z.push(`${R.indicesSet("input_indices",k,z.indicesGet("output_indices",e))};`),e++);return`
${j.registerUniform("output_size","u32").declareVariables(R,z)}
${j.mainStart()}
${j.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
var input_indices: ${R.type.indices};
let output_indices = ${z.offsetToIndices("global_idx")};
${Z.join(`
`)}
${U[0]} // init ops for reduce max/min
${U[1]}
${f}
${U[3]}
${U.length===4?z.setByOffset("global_idx","value"):U.slice(4).join(`
`)}
}`},getRunData:()=>({outputs:[{dims:M,dataType:b}],dispatchGroup:{x:Math.ceil(F/64)},programUniforms:[{type:12,data:F},...et(v,M)]})}},sc=(a,o)=>{let n=[];return a[1].dims[0]>0&&a[1].getBigInt64Array().forEach(u=>n.push(Number(u))),Ut({axes:n,keepDims:o.keepDims,noopWithEmptyAxes:o.noopWithEmptyAxes})},Sa=(a,o,n,u)=>{let p=a.inputs,b=p.length===1?n:sc(p,n);a.compute(R0(o,{hint:b.cacheKey,inputDependencies:["rank"]},[p[0]],b.noopWithEmptyAxes&&b.axes.length===0?xg:u,b.axes,p[0].dataType,b.keepDims,b.noopWithEmptyAxes),{inputs:[0]})},Bg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceLogSum",o,(n,u)=>[`var value = ${u.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,"value = log(value);"])},yg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceL1",o,(n,u)=>[`var value = ${u.type.storage}(0);`,"",`value += abs(${n.getByIndices("input_indices")});`,""])},Dg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceL2",o,(n,u)=>[`var t = ${u.type.value}(0); var value = ${u.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Pg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceLogSumExp",o,(n,u)=>[`var value = ${u.type.storage}(0);`,"",`value += exp(${n.getByIndices("input_indices")});`,"value = log(value);"])},Tg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceMax",o,(n,u,p)=>{let b=[];for(let C=0;C<n.rank;C++)(p.indexOf(C)>=0||p.length===0)&&b.push(n.indicesSet("input_indices",C,0));return[`${b.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = max(value, ${n.getByIndices("input_indices")});`,""]})},Gg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceMean",o,(n,u,p)=>{let b=1;for(let C=0;C<n.rank;C++)(p.indexOf(C)>=0||p.length===0)&&(b*=a.inputs[0].dims[C]);return["var sum = f32(0);","",`sum += f32(${n.getByIndices("input_indices")});`,`let value = ${u.type.value}(sum / ${b});`]})},Qg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceMin",o,(n,u,p)=>{let b=[];for(let C=0;C<n.rank;C++)(p.indexOf(C)>=0||p.length===0)&&b.push(`input_indices[${C}] = 0;`);return[`${b.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = min(value, ${n.getByIndices("input_indices")});`,""]})},Fg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceProd",o,(n,u)=>[`var value = ${u.type.storage}(1);`,"",`value *= ${n.getByIndices("input_indices")};`,""])},Sg=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceSum",o,(n,u)=>[`var value = ${u.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,""])},Og=(a,o)=>{Fa(a.inputs),Sa(a,"ReduceSumSquare",o,(n,u)=>[`var t = ${u.type.value}(0); var value = ${u.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += t * t;`,""])},Oa=(a,o,n)=>{if(o.length===0)return n;let u=1,p=1;for(let b=0;b<o.length;b++)o.indexOf(b)===-1?u*=a[b]:p*=a[b];return p<32&&u>1024},xh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Gg(a,o):mh(a,o)},Bh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?yg(a,o):hh(a,o)},yh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Dg(a,o):Ch(a,o)},Dh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Pg(a,o):bh(a,o)},Ph=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Tg(a,o):Ih(a,o)},Th=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Qg(a,o):wh(a,o)},Gh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Fg(a,o):kh(a,o)},Qh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Sg(a,o):Mh(a,o)},Fh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Og(a,o):Eh(a,o)},Sh=(a,o)=>{Oa(a.inputs[0].dims,o.axes,o.noopWithEmptyAxes)?Bg(a,o):vh(a,o)}}),u2,Oh,_h,ac,p6=IA(()=>{it(),Hr(),Pc(),u2=a=>{if(!a||a.length===0||a.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(a[0].dataType!==1)throw new Error("Invalid input type.")},Oh=(a,o)=>{u2(a.inputs);let n=(u,p,b)=>{let C=[];for(let w=0;w<u.rank;w++)(b.indexOf(w)>=0||b.length===0)&&C.push(`input_indices[${w}] = 0;`);return[`${C.join(`
`)}`,`var value = ${u.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${u.getByIndices("input_indices")} ${o.selectLastIndex>0?"<=":"<"} value) {
value = ${u.getByIndices("input_indices")};
best_index = i32(last_index);
}`,"",p.setByOffset("global_idx","best_index")]};a.compute(R0("ArgMin",{hint:o.cacheKey,inputDependencies:["rank"]},[a.inputs[0]],n,[o.axis],7,o.keepDims),{inputs:[0]})},_h=(a,o)=>{u2(a.inputs);let n=(u,p,b)=>{let C=[];for(let w=0;w<u.rank;w++)(b.indexOf(w)>=0||b.length===0)&&C.push(`input_indices[${w}] = 0;`);return[`${C.join(`
`)}`,`var value = ${u.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${u.getByIndices("input_indices")} ${o.selectLastIndex>0?">=":">"} value) {
value = ${u.getByIndices("input_indices")};
best_index = i32(last_index);
}`,"",p.setByOffset("global_idx","best_index")]};a.compute(R0("argMax",{hint:o.cacheKey,inputDependencies:["rank"]},[a.inputs[0]],n,[o.axis],7,o.keepDims),{inputs:[0]})},ac=a=>Ut(a)}),_g,v0,zg,Ng,Lg,Jo,Rg,zh,Tc=IA(()=>{it(),It(),yc(),kt(),_g=(a,o)=>{let n=a[0],u=a[1],p=a[2],b=a[3],C=a[4],w=a[5];if(C&&w)throw new Error("Attention cannot have both past and attention_bias");if(n.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let M=n.dims[0],v=n.dims[1],D=n.dims[2];if(p.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(u.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(u.dims[0]!==D)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(p.dims[0]!==u.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let B=p.dims[0]/3,E=B,S=E;if(o.qkvHiddenSizes.length>0){if(o.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let U of o.qkvHiddenSizes)if(U%o.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");B=o.qkvHiddenSizes[0],E=o.qkvHiddenSizes[1],S=o.qkvHiddenSizes[2]}let F=v;if(B!==E)throw new Error("qkv_hidden_sizes first element should be same as the second");if(p.dims[0]!==B+E+S)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let j=0;if(C){if(E!==S)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(C.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(C.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(C.dims[1]!==M)throw new Error('Input "past" second dimension must be batch_size');if(C.dims[2]!==o.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(C.dims[4]!==E/o.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');o.pastPresentShareBuffer||(j=C.dims[3])}let Z=F+j,R=-1,z=0;if(b)throw new Error("Mask not supported");if(C)throw new Error("past is not supported");if(w){if(w.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(w.dims[0]!==M||w.dims[1]!==o.numHeads||w.dims[2]!==v||w.dims[3]!==Z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:M,sequenceLength:v,pastSequenceLength:j,kvSequenceLength:F,totalSequenceLength:Z,maxSequenceLength:R,inputHiddenSize:D,hiddenSize:B,vHiddenSize:S,headSize:Math.floor(B/o.numHeads),vHeadSize:Math.floor(S/o.numHeads),numHeads:o.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:o.maskFilterValue,maskType:z,scale:o.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},v0=(a,o,n)=>o&&a?`
let total_sequence_length_input = u32(${o.getByOffset("0")});
let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
total_sequence_length = u32(${a?.getByOffset("batchIdx")}) + 1;
var past_sequence_length: u32 = 0;
if (is_first_prompt == false) {
past_sequence_length = total_sequence_length - sequence_length;
}
`:`
${n?"let past_sequence_length = uniforms.past_sequence_length":""};
let present_sequence_length = total_sequence_length;
`,zg=(a,o,n,u,p,b,C,w)=>{let M=Rr(C?1:b),v=64,D=b/M;D<v&&(v=32);let B=Math.ceil(b/M/v),E=[{type:12,data:o},{type:12,data:n},{type:12,data:u},{type:12,data:p},{type:12,data:D},{type:12,data:B}],S=hs(a.dataType,M),F=Qs(1,M),j=["type"];C&&j.push("type"),w&&j.push("type");let Z=R=>{let z=XA("x",a.dataType,a.dims,M),U=[z],f=C?nA("seq_lens",C.dataType,C.dims):void 0;f&&U.push(f);let k=w?nA("total_sequence_length_input",w.dataType,w.dims):void 0;k&&U.push(k);let e=Qs(a.dataType),d=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
var<workgroup> thread_max: array<f32, ${v}>;
var<workgroup> thread_sum: array<f32, ${v}>;
${R.registerUniforms(d).declareVariables(...U)}
${R.mainStart([v,1,1])}
let batchIdx = workgroup_id.z / uniforms.num_heads;
let headIdx = workgroup_id.z % uniforms.num_heads;
let sequence_length = uniforms.sequence_length;
var total_sequence_length = uniforms.total_sequence_length;
${v0(f,k,!1)}
let local_offset = local_idx * uniforms.elements_per_thread;
let offset = (global_idx / ${v}) * uniforms.total_sequence_length + local_offset;
let seq_causal_length = ${C?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
var thread_max_vector = ${F}(-3.402823e+38f);
for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
thread_max_vector = max(${F}(x[offset + i]), thread_max_vector);
}
thread_max[local_idx] = ${(()=>{switch(M){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${M}`)}})()};
workgroupBarrier();
var max_value = f32(-3.402823e+38f);
for (var i = 0u; i < ${v}; i++) {
max_value = max(thread_max[i], max_value);
}
var sum_vector = ${F}(0);
for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
sum_vector += exp(${F}(x[offset + i]) - max_value);
}
thread_sum[local_idx] = ${(()=>{switch(M){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${M}`)}})()};
workgroupBarrier();
var sum: f32 = 0;
for (var i = 0u; i < ${v}; i++) {
sum += thread_sum[i];
}
if (sum == 0) {
for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
x[offset + i] = ${z.type.value}(${e}(1.0) / ${e}(seq_causal_length));
}
} else {
for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
var f32input = ${F}(x[offset + i]);
x[offset + i] = ${z.type.value}(exp(f32input - max_value) / sum);
}
}
${C?`
for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
x[offset + total_seq_id] = ${z.type.value}(${e}(0));
}`:""};
}`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${v};${S};${M}`,inputDependencies:j},getShaderSource:Z,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:p,z:o*n},programUniforms:E})}},Ng=(a,o,n,u,p,b,C,w,M)=>{let v=C+b.kvSequenceLength,D=[b.batchSize,b.numHeads,b.sequenceLength,v],B=a>1&&u,E=b.kvNumHeads?b.kvNumHeads:b.numHeads,S=B?[b.batchSize,E,v,b.headSize]:void 0,F=b.nReps?b.nReps:1,j=b.scale===0?1/Math.sqrt(b.headSize):b.scale,Z=Rr(b.headSize),R=b.headSize/Z,z=12,U={x:Math.ceil(v/z),y:Math.ceil(b.sequenceLength/z),z:b.batchSize*b.numHeads},f=[{type:12,data:b.sequenceLength},{type:12,data:R},{type:12,data:v},{type:12,data:b.numHeads},{type:12,data:b.headSize},{type:1,data:j},{type:12,data:C},{type:12,data:b.kvSequenceLength},{type:12,data:F}],k=B&&u&&He.size(u.dims)>0,e=["type","type"];k&&e.push("type"),p&&e.push("type"),w&&e.push("type"),M&&e.push("type");let d=[{dims:D,dataType:o.dataType,gpuDataType:0}];B&&d.push({dims:S,dataType:o.dataType,gpuDataType:0});let y=Ae=>{let P=nA("q",o.dataType,o.dims,Z),O=nA("key",n.dataType,n.dims,Z),pe=[P,O];if(k){let _e=nA("past_key",u.dataType,u.dims,Z);pe.push(_e)}p&&pe.push(nA("attention_bias",p.dataType,p.dims));let ee=w?nA("seq_lens",w.dataType,w.dims):void 0;ee&&pe.push(ee);let be=M?nA("total_sequence_length_input",M.dataType,M.dims):void 0;be&&pe.push(be);let ke=XA("output",o.dataType,D),Me=[ke];B&&Me.push(XA("present_key",o.dataType,S,Z));let De=Qs(1,Z),ye=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
const TILE_SIZE = ${z}u;
var<workgroup> tileQ: array<${P.type.storage}, ${z*z}>;
var<workgroup> tileK: array<${P.type.storage}, ${z*z}>;
${Ae.registerUniforms(ye).declareVariables(...pe,...Me)}
${Ae.mainStart([z,z,1])}
// x holds the N and y holds the M
let headIdx = workgroup_id.z % uniforms.num_heads;
let kvHeadIdx = ${F===1?"headIdx":"headIdx / uniforms.n_reps"};
let kv_num_heads = ${F===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
let batchIdx = workgroup_id.z / uniforms.num_heads;
let m = workgroup_id.y * TILE_SIZE;
let n = workgroup_id.x * TILE_SIZE;
let sequence_length = uniforms.M;
var total_sequence_length = uniforms.N;
${v0(ee,be,!0)}
let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
${k&&B?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
${B?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
var value = ${De}(0);
for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
}
if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
var idx = TILE_SIZE * local_id.y + local_id.x;
${k&&B?`
if (n + local_id.y < past_sequence_length) {
tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
} else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
}`:`
if (n + local_id.y < uniforms.kv_sequence_length) {
tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
}`}
${B?`if (n + local_id.y < present_sequence_length) {
present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
}`:""}
}
workgroupBarrier();
for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
value += ${De}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
}
workgroupBarrier();
}
if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
var sum: f32 = ${(()=>{switch(Z){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${Z}`)}})()};
output[outputIdx] = ${ke.type.value} (sum * uniforms.alpha) + ${p?"attention_bias[outputIdx]":"0.0"};
}
}`};return{name:"AttentionProbs",shaderCache:{hint:`${Z};${p!==void 0};${u!==void 0};${a}`,inputDependencies:e},getRunData:()=>({outputs:d,dispatchGroup:U,programUniforms:f}),getShaderSource:y}},Lg=(a,o,n,u,p,b,C=void 0,w=void 0)=>{let M=b+p.kvSequenceLength,v=p.nReps?p.nReps:1,D=p.vHiddenSize*v,B=a>1&&u,E=p.kvNumHeads?p.kvNumHeads:p.numHeads,S=B?[p.batchSize,E,M,p.headSize]:void 0,F=[p.batchSize,p.sequenceLength,D],j=12,Z={x:Math.ceil(p.vHeadSize/j),y:Math.ceil(p.sequenceLength/j),z:p.batchSize*p.numHeads},R=[{type:12,data:p.sequenceLength},{type:12,data:M},{type:12,data:p.vHeadSize},{type:12,data:p.numHeads},{type:12,data:p.headSize},{type:12,data:D},{type:12,data:b},{type:12,data:p.kvSequenceLength},{type:12,data:v}],z=B&&u&&He.size(u.dims)>0,U=["type","type"];z&&U.push("type"),C&&U.push("type"),w&&U.push("type");let f=[{dims:F,dataType:o.dataType,gpuDataType:0}];B&&f.push({dims:S,dataType:o.dataType,gpuDataType:0});let k=e=>{let d=nA("probs",o.dataType,o.dims),y=nA("v",n.dataType,n.dims),Ae=[d,y];z&&Ae.push(nA("past_value",u.dataType,u.dims));let P=C?nA("seq_lens",C.dataType,C.dims):void 0;C&&Ae.push(P);let O=w?nA("total_sequence_length_input",w.dataType,w.dims):void 0;w&&Ae.push(O);let pe=[XA("output",o.dataType,F)];B&&pe.push(XA("present_value",o.dataType,S));let ee=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
const TILE_SIZE = ${j}u;
var<workgroup> tileQ: array<${d.type.value}, ${j*j}>;
var<workgroup> tileV: array<${d.type.value}, ${j*j}>;
${e.registerUniforms(ee).declareVariables(...Ae,...pe)}
${e.mainStart([j,j,1])}
let headIdx = workgroup_id.z % uniforms.num_heads;
let batchIdx = workgroup_id.z / uniforms.num_heads;
let kvHeadIdx = ${v===1?"headIdx":"headIdx / uniforms.n_reps"};
let kv_num_heads = ${v===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
let m = global_id.y;
let n = global_id.x;
let sequence_length = uniforms.M;
var total_sequence_length = uniforms.K;
${v0(P,O,!0)}
let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
${z&&B?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
${B?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
var value = ${d.type.storage}(0);
for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
if (m < uniforms.M && w + local_id.x < uniforms.K) {
tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
}
if (n < uniforms.N && w + local_id.y < uniforms.K) {
var idx = TILE_SIZE * local_id.y + local_id.x;
${z&&B?`
if (w + local_id.y < past_sequence_length) {
tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
} else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
}
`:`
if (w + local_id.y < uniforms.kv_sequence_length) {
tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
}`}
${B?`
if (w + local_id.y < present_sequence_length) {
present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
}`:""}
}
workgroupBarrier();
for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
}
workgroupBarrier();
}
// we need to transpose output from BNSH_v to BSND_v
if (m < uniforms.M && n < uniforms.N) {
let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
+ headIdx * uniforms.N + n;
output[outputIdx] = value;
}
}`};return{name:"AttentionScore",shaderCache:{hint:`${u!==void 0};${a}`,inputDependencies:U},getRunData:()=>({outputs:f,dispatchGroup:Z,programUniforms:R}),getShaderSource:k}},Jo=(a,o,n,u,p,b,C,w,M,v,D=void 0,B=void 0)=>{let E=Math.min(a.outputCount,1+(C?1:0)+(w?1:0)),S=E>1?v.pastSequenceLength:0,F=S+v.kvSequenceLength,j=M&&He.size(M.dims)>0?M:void 0,Z=[o,n];E>1&&C&&He.size(C.dims)>0&&Z.push(C),j&&Z.push(j),D&&Z.push(D),B&&Z.push(B);let R=a.compute(Ng(E,o,n,C,j,v,S,D,B),{inputs:Z,outputs:E>1?[-1,1]:[-1]})[0];a.compute(zg(R,v.batchSize,v.numHeads,S,v.sequenceLength,F,D,B),{inputs:D&&B?[R,D,B]:[R],outputs:[]});let z=[R,u];E>1&&w&&He.size(w.dims)>0&&z.push(w),D&&z.push(D),B&&z.push(B),a.compute(Lg(E,R,u,w,v,S,D,B),{inputs:z,outputs:E>1?[0,2]:[0]})},Rg=(a,o)=>{let n=[o.batchSize,o.numHeads,o.sequenceLength,o.headSize],u=o.sequenceLength,p=o.inputHiddenSize,b=o.headSize,C=12,w={x:Math.ceil(o.headSize/C),y:Math.ceil(o.sequenceLength/C),z:o.batchSize*o.numHeads},M=[a.inputs[0],a.inputs[1],a.inputs[2]],v=[{type:12,data:u},{type:12,data:p},{type:12,data:b},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:o.hiddenSize},{type:12,data:o.hiddenSize+o.hiddenSize+o.vHiddenSize}],D=B=>{let E=XA("output_q",M[0].dataType,n),S=XA("output_k",M[0].dataType,n),F=XA("output_v",M[0].dataType,n),j=nA("input",M[0].dataType,M[0].dims),Z=nA("weight",M[1].dataType,M[1].dims),R=nA("bias",M[2].dataType,M[2].dims),z=j.type.storage,U=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
const TILE_SIZE = ${C}u;
var<workgroup> tileInput: array<${z}, ${C*C}>;
var<workgroup> tileWeightQ: array<${z}, ${C*C}>;
var<workgroup> tileWeightK: array<${z}, ${C*C}>;
var<workgroup> tileWeightV: array<${z}, ${C*C}>;
${B.registerUniforms(U).declareVariables(j,Z,R,E,S,F)}
${B.mainStart([C,C,1])}
let batchIndex = workgroup_id.z / uniforms.num_heads;
let headNumber = workgroup_id.z % uniforms.num_heads;
let m = global_id.y;
let n = global_id.x;
let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
let biasOffsetQ = headNumber * uniforms.head_size;
let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
let biasOffsetV = uniforms.hidden_size + biasOffsetK;
var valueQ = ${z}(0);
var valueK = ${z}(0);
var valueV = ${z}(0);
for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
if (m < uniforms.M && w + local_id.x < uniforms.K) {
tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
}
if (n < uniforms.N && w + local_id.y < uniforms.K) {
let offset = n + (w + local_id.y) * uniforms.ldb;
tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
}
workgroupBarrier();
for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
let inputTileOffset = TILE_SIZE * local_id.y + k;
let weightTileOffset = TILE_SIZE * k + local_id.x;
valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
}
workgroupBarrier();
}
let headOffset = (m * uniforms.N + n) % uniforms.head_size;
valueQ += bias[headOffset + biasOffsetQ];
valueK += bias[headOffset + biasOffsetK];
valueV += bias[headOffset + biasOffsetV];
let offset = workgroup_id.z * uniforms.M * uniforms.N;
if (m < uniforms.M && n < uniforms.N) {
let outputIdx = offset + m * uniforms.N + n;
output_q[outputIdx] = valueQ;
output_k[outputIdx] = valueK;
output_v[outputIdx] = valueV;
}
}`};return a.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:a.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:a.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:a.inputs[0].dataType,gpuDataType:0}],dispatchGroup:w,programUniforms:v}),getShaderSource:D},{inputs:M,outputs:[-1,-1,-1]})},zh=(a,o)=>{let n=_g(a.inputs,o),[u,p,b]=Rg(a,n);return Jo(a,u,p,b,a.inputs[4],void 0,void 0,void 0,a.inputs[5],n)}}),jg,Wg,Vg,Nh,m6=IA(()=>{ja(),it(),It(),Hr(),kt(),jg=(a,o)=>{if(!a||a.length!==5)throw new Error("BatchNormalization requires 5 inputs");let n=(u,p,b)=>{let C=p.length;if(C!==u.length)throw new Error(`${b}: num dimensions != ${C}`);p.forEach((w,M)=>{if(w!==u[M])throw new Error(`${b}: dim[${M}] do not match`)})};if(a[0].dims.length>1){let u=o.format==="NHWC"?o.spatial?a[0].dims.slice(-1):a[0].dims.slice(-1).concat(a[0].dims.slice(1,a[0].dims.length-1)):a[0].dims.slice(1,o.spatial?2:void 0);n(a[1].dims,u,"Invalid input scale"),n(a[2].dims,u,"Invalid input B"),n(a[3].dims,u,"Invalid input mean"),n(a[4].dims,u,"Invalid input var")}else n(a[1].dims,[1],"Invalid input scale"),n(a[2].dims,[1],"Invalid input B"),n(a[3].dims,[1],"Invalid input mean"),n(a[4].dims,[1],"Invalid input var")},Wg=(a,o)=>{let{epsilon:n,spatial:u,format:p}=o,b=a[0].dims,C=u?Rr(b[b.length-1]):1,w=p==="NHWC"&&b.length>1?C:1,M=He.size(b)/C,v=u,D=v?b.length:b,B=nA("x",a[0].dataType,a[0].dims,C),E=nA("scale",a[1].dataType,a[1].dims,w),S=nA("bias",a[2].dataType,a[2].dims,w),F=nA("inputMean",a[3].dataType,a[3].dims,w),j=nA("inputVar",a[4].dataType,a[4].dims,w),Z=XA("y",a[0].dataType,D,C),R=()=>{let U="";if(u)U=`let cOffset = ${b.length===1?"0u":p==="NHWC"?`outputIndices[${b.length-1}] / ${C}`:"outputIndices[1]"};`;else if(p==="NCHW")U=`
${Z.indicesSet("outputIndices","0","0")}
let cOffset = ${Z.indicesToOffset("outputIndices")};`;else{U=`var cIndices = ${E.type.indices}(0);
cIndices[0] = outputIndices[${b.length-1}];`;for(let f=1;f<E.rank;f++)U+=`cIndices[${f}] = outputIndices[${f}];`;U+=`let cOffset = ${E.indicesToOffset("cIndices")};`}return U},z=U=>`
const epsilon = ${n};
${U.registerUniform("outputSize","u32").declareVariables(B,E,S,F,j,Z)}
${U.mainStart()}
${U.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
var outputIndices = ${Z.offsetToIndices(`global_idx * ${C}`)};
${R()}
let scale = ${E.getByOffset("cOffset")};
let bias = ${S.getByOffset("cOffset")};
let inputMean = ${F.getByOffset("cOffset")};
let inputVar = ${j.getByOffset("cOffset")};
let x = ${B.getByOffset("global_idx")};
let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
${Z.setByOffset("global_idx","value")}
}`;return{name:"BatchNormalization",shaderCache:{hint:`${o.epsilon}_${o.format}_${u}_${C}`,inputDependencies:v?["rank","type","type","type","type"]:void 0},getShaderSource:z,getRunData:()=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(M/64)},programUniforms:v?[{type:12,data:M},...et(b)]:[{type:12,data:M}]})}},Vg=a=>Ut(a),Nh=(a,o)=>{let{inputs:n,outputCount:u}=a,p=Vg({...o,outputCount:u});if(wr.webgpu.validateInputContent&&jg(n,p),o.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");a.compute(Wg(n,p))}}),Yg,Hg,Lh,h6=IA(()=>{It(),kt(),Yg=a=>{if(a[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(a[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(a[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(a[0].dims[2]!==a[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Hg=a=>{let o=a[0].dims,n=a[0].dims[2],u=He.size(o)/4,p=a[0].dataType,b=nA("input",p,o,4),C=nA("bias",p,[n],4),w=nA("residual",p,o,4),M=XA("output",p,o,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:o,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:v=>`
const channels = ${n}u / 4;
${v.declareVariables(b,C,w,M)}
${v.mainStart()}
${v.guardAgainstOutOfBoundsWorkgroupSizes(u)}
let value = ${b.getByOffset("global_idx")}
+ ${C.getByOffset("global_idx % channels")} + ${w.getByOffset("global_idx")};
${M.setByOffset("global_idx","value")}
}`}},Lh=a=>{Yg(a.inputs),a.compute(Hg(a.inputs))}}),Ug,jt,Rh,jh,Wh,Vh,Yh,Hh,Uh,Kh,Xh,Kg,Zh,Jh,qh,$h,Vo,e3,O0,A3,t3,r3,s3,a3,n3,i3,o3,l3,c3,u3,d3,f3,g3,p3,m3,d2,h3,nc,ic,C3,b3,I3,Xg,Zg,w3,Gc=IA(()=>{it(),It(),Hr(),kt(),Ug=(a,o,n,u,p,b,C)=>{let w=Math.ceil(o/4),M="";typeof p=="string"?M=`${p}(a)`:M=p("a");let v=nA("inputData",n,[w],4),D=XA("outputData",u,[w],4),B=[{name:"vec_size",type:"u32"}];return C&&B.push(...C),`
${a.registerUniforms(B).declareVariables(v,D)}
${b??""}
${a.mainStart()}
${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
let a = ${v.getByOffset("global_idx")};
${D.setByOffset("global_idx",M)}
}`},jt=(a,o,n,u,p,b=a.dataType,C,w)=>{let M=[{type:12,data:Math.ceil(He.size(a.dims)/4)}];return C&&M.push(...C),{name:o,shaderCache:{hint:p,inputDependencies:["type"]},getShaderSource:v=>Ug(v,He.size(a.dims),a.dataType,b,n,u,w),getRunData:v=>({outputs:[{dims:a.dims,dataType:b}],dispatchGroup:{x:Math.ceil(He.size(v[0].dims)/64/4)},programUniforms:M})}},Rh=a=>{a.compute(jt(a.inputs[0],"Abs","abs"))},jh=a=>{a.compute(jt(a.inputs[0],"Acos","acos"))},Wh=a=>{a.compute(jt(a.inputs[0],"Acosh","acosh"))},Vh=a=>{a.compute(jt(a.inputs[0],"Asin","asin"))},Yh=a=>{a.compute(jt(a.inputs[0],"Asinh","asinh"))},Hh=a=>{a.compute(jt(a.inputs[0],"Atan","atan"))},Uh=a=>{a.compute(jt(a.inputs[0],"Atanh","atanh"))},Kh=a=>Ut(a),Xh=(a,o)=>{let n;switch(o.to){case 10:n="vec4<f16>";break;case 1:n="vec4<f32>";break;case 12:n="vec4<u32>";break;case 6:n="vec4<i32>";break;case 9:n="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${o.to}`)}a.compute(jt(a.inputs[0],"Cast",n,void 0,o.cacheKey,o.to))},Kg=a=>{let o,n,u=a.length>=2&&a[1].data!==0,p=a.length>=3&&a[2].data!==0;switch(a[0].dataType){case 1:o=u?a[1].getFloat32Array()[0]:-34028234663852886e22,n=p?a[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:o=u?a[1].getUint16Array()[0]:64511,n=p?a[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Ut({min:o,max:n})},Zh=(a,o)=>{let n=o||Kg(a.inputs),u=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"Clip",p=>`clamp(${p}, vec4<${u}>(uniforms.min), vec4<${u}>(uniforms.max))`,void 0,n.cacheKey,void 0,[{type:a.inputs[0].dataType,data:n.min},{type:a.inputs[0].dataType,data:n.max}],[{name:"min",type:u},{name:"max",type:u}]),{inputs:[0]})},Jh=a=>{a.compute(jt(a.inputs[0],"Ceil","ceil"))},qh=a=>{a.compute(jt(a.inputs[0],"Cos","cos"))},$h=a=>{a.compute(jt(a.inputs[0],"Cosh","cosh"))},Vo=a=>Ut(a),e3=(a,o)=>{let n=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"Elu",u=>`elu_vf32(${u})`,`
const elu_alpha_ = ${n}(${o.alpha});
fn elu_f32(a: ${n}) -> ${n} {
return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
}
fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {
return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
}`,o.cacheKey))},O0=(a="f32")=>`
const r0: ${a} = 0.3275911;
const r1: ${a} = 0.254829592;
const r2: ${a} = -0.284496736;
const r3: ${a} = 1.421413741;
const r4: ${a} = -1.453152027;
const r5: ${a} = 1.061405429;
fn erf_vf32(v: vec4<${a}>) -> vec4<${a}> {
let absv = abs(v);
let x = 1.0 / (1.0 + r0 * absv);
return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,A3=a=>{let o=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"Erf",n=>`erf_vf32(${n})`,O0(o)))},t3=a=>{a.compute(jt(a.inputs[0],"Exp","exp"))},r3=a=>{a.compute(jt(a.inputs[0],"Floor","floor"))},s3=a=>{let o=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"Gelu",n=>`0.5 * ${n} * (1.0 + erf_vf32(${n} * 0.7071067811865475))`,O0(o)))},a3=(a,o)=>{let n=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"LeakyRelu",u=>`select(leaky_relu_alpha_ * ${u}, ${u}, ${u} >= vec4<${n}>(0.0))`,`const leaky_relu_alpha_ = ${n}(${o.alpha});`,o.cacheKey))},n3=a=>{a.compute(jt(a.inputs[0],"Not",o=>`!${o}`))},i3=a=>{a.compute(jt(a.inputs[0],"Neg",o=>`-${o}`))},o3=a=>{a.compute(jt(a.inputs[0],"Reciprocal",o=>`1.0/${o}`))},l3=a=>{let o=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"Relu",n=>`select(vec4<${o}>(0.0), ${n}, ${n} > vec4<${o}>(0.0))`))},c3=a=>{a.compute(jt(a.inputs[0],"Sigmoid",o=>`(1.0 / (1.0 + exp(-${o})))`))},u3=a=>Ut(a),d3=(a,o)=>{let n=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"HardSigmoid",u=>`max(vec4<${n}>(0.0), min(vec4<${n}>(1.0), ${o.alpha} * ${u} + vec4<${n}>(${o.beta})))`,void 0,o.cacheKey))},f3=a=>{a.compute(jt(a.inputs[0],"Sin","sin"))},g3=a=>{a.compute(jt(a.inputs[0],"Sinh","sinh"))},p3=a=>{a.compute(jt(a.inputs[0],"Sqrt","sqrt"))},m3=a=>{a.compute(jt(a.inputs[0],"Tan","tan"))},d2=a=>`sign(${a}) * (1 - exp(-2 * abs(${a}))) / (1 + exp(-2 * abs(${a})))`,h3=a=>{a.compute(jt(a.inputs[0],"Tanh",d2))},nc=(a="f32")=>`
const fast_gelu_a: ${a} = 0.5;
const fast_gelu_b: ${a} = 0.7978845608028654;
const fast_gelu_c: ${a} = 0.035677408136300125;
fn tanh_v(v: vec4<${a}>) -> vec4<${a}> {
return ${d2("v")};
}
`,ic=a=>`(fast_gelu_a + fast_gelu_a * tanh_v(${a} * (fast_gelu_c * ${a} * ${a} + fast_gelu_b))) * ${a}`,C3=a=>{let o=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"FastGelu",ic,nc(o),void 0,a.inputs[0].dataType))},b3=(a,o)=>{let n=Qs(a.inputs[0].dataType);return a.compute(jt(a.inputs[0],"ThresholdedRelu",u=>`select(vec4<${n}>(0.0), ${u}, ${u} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${n}>(${o.alpha});`,o.cacheKey)),0},I3=a=>{a.compute(jt(a.inputs[0],"Log","log"))},Xg=(a,o)=>`
const alpha = vec4<${a}>(${o});
const one = ${a}(1.0);
const zero = ${a}(0.0);
fn quick_gelu_impl(x: vec4<${a}>) -> vec4<${a}> {
let v = x *alpha;
var x1 : vec4<${a}>;
for (var i = 0; i < 4; i = i + 1) {
if (v[i] >= zero) {
x1[i] = one / (one + exp(-v[i]));
} else {
x1[i] = one - one / (one + exp(v[i]));
}
}
return x * x1;
}
`,Zg=a=>`quick_gelu_impl(${a})`,w3=(a,o)=>{let n=Qs(a.inputs[0].dataType);a.compute(jt(a.inputs[0],"QuickGelu",Zg,Xg(n,o.alpha),o.cacheKey,a.inputs[0].dataType))}}),Jg,qg,k3,C6=IA(()=>{It(),kt(),Gc(),Jg=a=>{if(a[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(a[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(a[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(a[0].dims[2]!==a[1].dims[0])throw new Error("last dimension of input and bias are not the same")},qg=a=>{let o=a[0].dims.slice();o[2]=o[2]/2;let n=nA("input",a[0].dataType,a[0].dims,4),u=nA("bias",a[0].dataType,[a[0].dims[2]],4),p=XA("output",a[0].dataType,o,4),b=He.size(o)/4,C=hs(a[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:o,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(b/64)}}),getShaderSource:w=>`
const M_SQRT2 = sqrt(2.0);
const halfChannels = ${a[0].dims[2]/4/2}u;
${w.declareVariables(n,u,p)}
${O0(C)}
${w.mainStart()}
${w.guardAgainstOutOfBoundsWorkgroupSizes(b)}
let biasIdx = global_idx % halfChannels;
let batchIndex = global_idx / halfChannels;
let inputOffset = biasIdx + batchIndex * halfChannels * 2;
let valueLeft = input[inputOffset] + bias[biasIdx];
let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);
${p.setByOffset("global_idx","valueLeft * geluRight")}
}`}},k3=a=>{Jg(a.inputs),a.compute(qg(a.inputs))}}),$g,ep,_a,M3,E3,v3,x3,B3,y3,D3,P3,T3,G3,b6=IA(()=>{it(),It(),kt(),$g=(a,o,n,u,p,b,C,w,M,v,D,B)=>{let E,S;typeof w=="string"?E=S=(z,U)=>`${w}((${z}),(${U}))`:typeof w=="function"?E=S=w:(E=w.scalar,S=w.vector);let F=XA("outputData",D,u.length,4),j=nA("aData",M,o.length,4),Z=nA("bData",v,n.length,4),R;if(p)if(b){let z=He.size(o)===1,U=He.size(n)===1,f=o.length>0&&o[o.length-1]%4===0,k=n.length>0&&n[n.length-1]%4===0;z||U?R=F.setByOffset("global_idx",S(z?`${j.type.value}(${j.getByOffset("0")}.x)`:j.getByOffset("global_idx"),U?`${Z.type.value}(${Z.getByOffset("0")}.x)`:Z.getByOffset("global_idx"))):R=`
let outputIndices = ${F.offsetToIndices("global_idx * 4u")};
let offsetA = ${j.broadcastedIndicesToOffset("outputIndices",F)};
let offsetB = ${Z.broadcastedIndicesToOffset("outputIndices",F)};
${F.setByOffset("global_idx",S(C||f?j.getByOffset("offsetA / 4u"):`${j.type.value}(${j.getByOffset("offsetA / 4u")}[offsetA % 4u])`,C||k?Z.getByOffset("offsetB / 4u"):`${Z.type.value}(${Z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
`}else R=F.setByOffset("global_idx",S(j.getByOffset("global_idx"),Z.getByOffset("global_idx")));else{if(!b)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let z=(U,f,k="")=>{let e=`aData[indexA${f}][componentA${f}]`,d=`bData[indexB${f}][componentB${f}]`;return`
let outputIndices${f} = ${F.offsetToIndices(`global_idx * 4u + ${f}u`)};
let offsetA${f} = ${j.broadcastedIndicesToOffset(`outputIndices${f}`,F)};
let offsetB${f} = ${Z.broadcastedIndicesToOffset(`outputIndices${f}`,F)};
let indexA${f} = offsetA${f} / 4u;
let indexB${f} = offsetB${f} / 4u;
let componentA${f} = offsetA${f} % 4u;
let componentB${f} = offsetB${f} % 4u;
${U}[${f}] = ${k}(${E(e,d)});
`};D===9?R=`
var data = vec4<u32>(0);
${z("data",0,"u32")}
${z("data",1,"u32")}
${z("data",2,"u32")}
${z("data",3,"u32")}
outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:R=`
${z("outputData[global_idx]",0)}
${z("outputData[global_idx]",1)}
${z("outputData[global_idx]",2)}
${z("outputData[global_idx]",3)}
`}return`
${a.registerUniform("vec_size","u32").declareVariables(j,Z,F)}
${B??""}
${a.mainStart()}
${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
${R}
}`},ep=(a,o,n,u,p,b,C=n.dataType)=>{let w=n.dims.map(j=>Number(j)??1),M=u.dims.map(j=>Number(j)??1),v=!He.areEqual(w,M),D=w,B=He.size(w),E=!1,S=!1,F=[v];if(v){let j=oo.calcShape(w,M,!1);if(!j)throw new Error("Can't perform binary op on the given tensors");D=j.slice(),B=He.size(D);let Z=He.size(w)===1,R=He.size(M)===1,z=w.length>0&&w[w.length-1]%4===0,U=M.length>0&&M[M.length-1]%4===0;F.push(Z),F.push(R),F.push(z),F.push(U);let f=1;for(let k=1;k<D.length;k++){let e=w[w.length-k],d=M[M.length-k];if(e===d)f*=e;else break}f%4===0?(S=!0,E=!0):(Z||R||z||U)&&(E=!0)}else E=!0;return F.push(E),{name:a,shaderCache:{hint:o+F.map(j=>j.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:j=>$g(j,w,M,D,E,v,S,p,n.dataType,u.dataType,C,b),getRunData:()=>({outputs:[{dims:D,dataType:C}],dispatchGroup:{x:Math.ceil(B/64/4)},programUniforms:[{type:12,data:Math.ceil(He.size(D)/4)},...et(w,M,D)]})}},_a=(a,o,n,u,p,b)=>{a.compute(ep(o,p??"",a.inputs[0],a.inputs[1],n,u,b))},M3=a=>{_a(a,"Add",(o,n)=>`${o}+${n}`)},E3=a=>{_a(a,"Div",(o,n)=>`${o}/${n}`)},v3=a=>{_a(a,"Equal",{scalar:(o,n)=>`u32(${o}==${n})`,vector:(o,n)=>`vec4<u32>(${o}==${n})`},void 0,void 0,9)},x3=a=>{_a(a,"Mul",(o,n)=>`${o}*${n}`)},B3=a=>{let o=nA("input",a.inputs[0].dataType,a.inputs[0].dims).type.value;_a(a,"Pow",{scalar:(n,u)=>`pow_custom(${n},${u})`,vector:(n,u)=>`pow_vector_custom(${n},${u})`},`
fn pow_custom(a : ${o}, b : ${o}) -> ${o} {
if (b == ${o}(0.0)) {
return ${o}(1.0);
} else if (a < ${o}(0.0) && f32(b) != floor(f32(b))) {
return ${o}(pow(f32(a), f32(b))); // NaN
}
return select(sign(a), ${o}(1.0), round(f32(abs(b) % ${o}(2.0))) != 1.0) * ${o}(${o==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
}
fn pow_vector_custom(a : vec4<${o}>, b : vec4<${o}>) -> vec4<${o}> {
// TODO: implement vectorized pow
return vec4<${o}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
}
`)},y3=a=>{_a(a,"Sub",(o,n)=>`${o}-${n}`)},D3=a=>{_a(a,"Greater",{scalar:(o,n)=>`u32(${o}>${n})`,vector:(o,n)=>`vec4<u32>(${o}>${n})`},void 0,void 0,9)},P3=a=>{_a(a,"Less",{scalar:(o,n)=>`u32(${o}<${n})`,vector:(o,n)=>`vec4<u32>(${o}<${n})`},void 0,void 0,9)},T3=a=>{_a(a,"GreaterOrEqual",{scalar:(o,n)=>`u32(${o}>=${n})`,vector:(o,n)=>`vec4<u32>(${o}>=${n})`},void 0,void 0,9)},G3=a=>{_a(a,"LessOrEqual",{scalar:(o,n)=>`u32(${o}<=${n})`,vector:(o,n)=>`vec4<u32>(${o}<=${n})`},void 0,void 0,9)}}),Ap,tp,rp,sp,Q3,F3,I6=IA(()=>{it(),It(),Hr(),kt(),Ap=(a,o)=>{if(!a||a.length<1)throw new Error("too few inputs");let n=0,u=a[n],p=u.dataType,b=u.dims.length;a.forEach((C,w)=>{if(w!==n){if(C.dataType!==p)throw new Error("input tensors should be one type");if(C.dims.length!==b)throw new Error("input tensors should have the same shape");C.dims.forEach((M,v)=>{if(v!==o&&M!==u.dims[v])throw new Error("non concat dimensions must match")})}})},tp=(a,o)=>`
fn calculateInputIndex(index: u32) -> u32 {
let sizeInConcatAxis = array<u32, ${a}u>(${o});
for (var i: u32 = 0u; i < ${a}; i += 1u ) {
if (index < sizeInConcatAxis[i]) {
return i;
}
}
return ${a}u;
}`,rp=(a,o)=>{let n=a.length,u=[];for(let p=0;p<n;++p){let b=o.setByOffset("global_idx",a[p].getByIndices("indices"));n===1?u.push(b):p===0?u.push(`if (inputIndex == ${p}u) { ${b} }`):p===n-1?u.push(`else { ${b} }`):u.push(`else if (inputIndex == ${p}) { ${b} }`)}return u.join(`
`)},sp=(a,o,n,u)=>{let p=He.size(n),b=new Array(a.length),C=new Array(a.length),w=0,M=[],v=[],D=[{type:12,data:p}];for(let j=0;j<a.length;++j)w+=a[j].dims[o],b[j]=w,v.push(a[j].dims.length),C[j]=nA(`input${j}`,u,v[j]),M.push("rank"),D.push({type:12,data:b[j]});for(let j=0;j<a.length;++j)D.push(...et(a[j].dims));D.push(...et(n));let B=XA("output",u,n.length),E=B.indicesGet("indices",o),S=Array.from(Array(b.length).keys()).map(j=>`uniforms.sizeInConcatAxis${j}`).join(","),F=j=>`
${(()=>{j.registerUniform("outputSize","u32");for(let Z=0;Z<a.length;Z++)j.registerUniform(`sizeInConcatAxis${Z}`,"u32");return j.declareVariables(...C,B)})()}
${tp(b.length,S)}
${j.mainStart()}
${j.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
var indices = ${B.offsetToIndices("global_idx")};
let inputIndex = calculateInputIndex(${E});
if (inputIndex != 0u) {
let sizeInConcatAxis = array<u32, ${b.length}u>(${S});
${E} -= sizeInConcatAxis[inputIndex - 1u];
}
${rp(C,B)}
}`;return{name:"Concat",shaderCache:{hint:`${o}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:n,dataType:u}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:D}),getShaderSource:F}},Q3=(a,o)=>{let n=a.inputs,u=n[0].dims,p=He.normalizeAxis(o.axis,u.length);Ap(n,p);let b=u.slice();b[p]=n.reduce((w,M)=>w+(M.dims.length>p?M.dims[p]:0),0);let C=n.filter(w=>He.size(w.dims)>0);a.compute(sp(C,p,b,n[0].dataType),{inputs:C})},F3=a=>Ut({axis:a.axis})}),yi,Di,Pi,Qc,Gi=IA(()=>{it(),It(),yi=(a,o,n="f32")=>{switch(a.activation){case"Relu":return`value = max(value, ${o}(0.0));`;case"Sigmoid":return`value = (${o}(1.0) / (${o}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${o}(${n}(uniforms.clip_min)), ${o}(${n}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${o}(0.0), min(${o}(1.0), ${n}(uniforms.alpha) * value + ${n}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${n}(uniforms.alpha) * value, value, value >= ${o}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
`;case"":return"";default:throw new Error(`Unsupported activation ${a.activation}`)}},Di=(a,o)=>{a.activation==="Clip"?o.push({type:1,data:a.clipMax},{type:1,data:a.clipMin}):a.activation==="HardSigmoid"?o.push({type:1,data:a.alpha},{type:1,data:a.beta}):a.activation==="LeakyRelu"&&o.push({type:1,data:a.alpha})},Pi=(a,o)=>{a.activation==="Clip"?o.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):a.activation==="HardSigmoid"?o.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):a.activation==="LeakyRelu"&&o.push({name:"alpha",type:"f32"})},Qc=a=>{let o=a?.activation||"";if(o==="HardSigmoid"){let[n,u]=a?.activation_params||[.2,.5];return{activation:o,alpha:n,beta:u}}else if(o==="Clip"){let[n,u]=a?.activation_params||[nh,ih];return{activation:o,clipMax:u,clipMin:n}}else if(o==="LeakyRelu"){let[n]=a?.activation_params||[.01];return{activation:o,alpha:n}}return{activation:o}}}),Is,S3,Fc=IA(()=>{Is=(a,o)=>{switch(a){case 1:return o;case 2:return`vec2<${o}>`;case 3:return`vec3<${o}>`;case 4:return`vec4<${o}>`;default:throw new Error(`${a}-component is not supported.`)}},S3=a=>`
${a?"value = value + getBiasByOutputCoords(coords);":""}
`}),O3,w6=IA(()=>{O3=a=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
i32(${a}.x), i32(${a}.y), i32(${a}.z), 1));
}
`}),Ho,Sc,Oc=IA(()=>{it(),It(),kt(),Gi(),Ho=(a,o,n,u,p)=>{let b=u-n;return`
${Array.from({length:n}).map((C,w)=>`
if (${ZA(o.shape,w,o.rank)} != 1) {
${o.indicesSet(a,w,ZA(p,w+b,u))}
} else {
${o.indicesSet(a,w,0)}
}`).join("")}
`},Sc=(a,o,n,u,p=!1,b)=>{let C=a[0].dims,w=a[1].dims,M=C[C.length-2],v=w[w.length-1],D=C[C.length-1],B=Rr(v),E=Rr(D),S=Rr(M),F=He.size(n)/B/S,j=a.length>2,Z=u?u.slice(0,-2):n.slice(0,-2),R=[He.size(Z),M,v],z=[{type:12,data:F},{type:12,data:M},{type:12,data:v},{type:12,data:D}];Di(o,z),z.push(...et(Z,C,w)),j&&z.push(...et(a[2].dims)),z.push(...et(R));let U=f=>{let k=Dc("batch_dims",a[0].dataType,Z.length),e=nA("a",a[0].dataType,C.length,E),d=nA("b",a[1].dataType,w.length,B),y=XA("output",a[0].dataType,R.length,B),Ae=hs(y.type.tensor),P=yi(o,y.type.value,Ae),O=[e,d],pe="";if(j){let ke=p?B:1;O.push(nA("bias",a[2].dataType,a[2].dims.length,ke)),pe=`${p?`value += bias[col / ${ke}];`:`value += ${y.type.value}(bias[row + i]);`}`}let ee=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Pi(o,ee);let be=()=>{let ke=`var a_data: ${e.type.value};`;for(let Me=0;Me<E;Me++)ke+=`
let b_data${Me} = b[(b_offset + (k + ${Me}) * uniforms.N + col) / ${B}];`;for(let Me=0;Me<S;Me++){ke+=`a_data = a[(a_offset + (row + ${Me}) * uniforms.K + k) / ${E}];`;for(let De=0;De<E;De++)ke+=`
values[${Me}] = fma(${d.type.value}(a_data${E===1?"":`[${De}]`}), b_data${De}, values[${Me}]);
`}return ke};return`
${f.registerUniforms(ee).registerInternalVariables(k).declareVariables(...O,y)}
${f.mainStart()}
${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let col = (global_idx % (uniforms.N / ${B})) * ${B};
var index1 = global_idx / (uniforms.N / ${B});
let stride1 = uniforms.M / ${S};
let row = (index1 % stride1) * ${S};
let batch = index1 / stride1;
${n.length===2?"":`let batch_indices = ${k.offsetToIndices("batch")};`}
var a_indices: ${e.type.indices};
${Ho("a_indices",e,e.rank-2,k.rank,"batch_indices")}
${e.indicesSet("a_indices",e.rank-2,0)}
${e.indicesSet("a_indices",e.rank-1,0)}
let a_offset = ${e.indicesToOffset("a_indices")};
var b_indices: ${d.type.indices};
${Ho("b_indices",d,d.rank-2,k.rank,"batch_indices")}
${d.indicesSet("b_indices",d.rank-2,0)}
${d.indicesSet("b_indices",d.rank-1,0)}
let b_offset = ${d.indicesToOffset("b_indices")};
var values: array<${y.type.value}, ${S}>;
for (var k: u32 = 0u; k < uniforms.K; k = k + ${E}) {
${be()}
}
for (var i = 0u; i < ${S}u; i++) {
var value = values[i];
${pe}
${P}
let cur_indices = ${y.type.indices}(batch, row + i, col);
let offset = ${y.indicesToOffset("cur_indices")};
${y.setByOffset(`offset / ${B}`,"value")};
}
}
`};return{name:"MatMulNaive",shaderCache:{hint:`${o.activation};${B};${E};${S};${p}`,inputDependencies:j?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:b?b(n):n,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(F/64)},programUniforms:z}),getShaderSource:U}}}),ap,np,oc,f2,ip,lc,op,j0,_c=IA(()=>{it(),It(),kt(),Gi(),Oc(),Fc(),ap=(a,o)=>a?`
mm_Asub[inputRow][inputCol] = mm_readA(batch,
kStart + inputRow,
globalRowStart / innerElementSize + inputCol${o?", batchIndices":""});
`:`
mm_Asub[inputRow][inputCol] = mm_readA(batch,
globalRow + innerRow,
kStart / innerElementSize + inputCol${o?", batchIndices":""});
`,np=(a,o)=>a?`
let ACached0 = mm_Asub[k * innerElementSize][localRow];
let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
${o===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
for (var i = 0; i < rowPerThread; i = i + 1) {
acc[i] = BCached0 * ACached0[i] + acc[i];
acc[i] = BCached1 * ACached1[i] + acc[i];
acc[i] = BCached2 * ACached2[i] + acc[i];
${o===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
}`:`
for (var i = 0; i < rowPerThread; i = i + 1) {
let ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached0 * ACached.x + acc[i];
acc[i] = BCached1 * ACached.y + acc[i];
acc[i] = BCached2 * ACached.z + acc[i];
${o===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
}`,oc=(a,o,n="f32",u,p=!1,b=32,C=!1,w=32)=>{let M=o[1]*a[1],v=o[0]*a[0],D=p?M:b,B=p?b:M,E=D/o[0],S=b/o[1];if(!((p&&E===4&&a[1]===4||!p&&(E===3||E===4))&&D%o[0]===0&&b%o[1]===0&&a[0]===4))throw new Error(`If transposeA ${p} is true, innerElementSize ${E} and workPerThread[1] ${a[1]} must be 4.
Otherwise, innerElementSize ${E} must be 3 or 4.
tileAWidth ${D} must be divisible by workgroupSize[0]${o[0]}. tileInner ${b} must be divisible by workgroupSize[1] ${o[1]}. colPerThread ${a[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${E}<${n}>, ${D/E}>, ${B}>;
var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${v/a[0]}>, ${b}>;
const rowPerThread = ${a[1]};
const colPerThread = ${a[0]};
const innerElementSize = ${E};
const tileInner = ${b};
@compute @workgroup_size(${o[0]}, ${o[1]}, ${o[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(global_invocation_id) globalId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
let localRow = i32(localId.y);
let tileRow = localRow * rowPerThread;
let tileCol = i32(localId.x);
let globalRow =i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x);
let batch = ${C?"0":"i32(globalId.z)"};
${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""}
let globalRowStart = i32(workgroupId.y) * ${M};
let num_tiles = ${C?`${Math.ceil(w/b)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
var kStart = ${C?`i32(globalId.z) * ${w}`:"0"};
var acc: array<vec4<${n}>, rowPerThread>;
// Loop over shared dimension.
let tileRowB = localRow * ${S};
for (var t = 0; t < num_tiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
${ap(p,u)}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${u?", batchIndices":""});
}
kStart = kStart + tileInner;
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
${E===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}
${np(p,E)}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
}
}`},f2=(a,o)=>a?`
mm_Asub[inputRow][inputCol] = mm_readA(batch,
kStart + inputRow,
globalRowStart + inputCol${o?", batchIndices":""});
`:`
mm_Asub[inputRow][inputCol] = mm_readA(batch,
globalRowStart + inputRow,
kStart + inputCol${o?", batchIndices":""});
`,ip=a=>a?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",lc=(a,o,n="f32",u,p=!1,b=32,C=!1,w=32,M=!1)=>{let v=a[1]*o[1],D=a[0]*o[0],B=p?v:b,E=p?b:v;if(!(E%o[1]===0&&B%o[0]===0&&b%o[1]===0))throw new Error(`tileAHight ${E} must be divisible by workgroupSize[1]${o[1]}, tileAWidth ${B} must be divisible by workgroupSize[0]${o[0]}, tileInner ${b} must be divisible by workgroupSize[1]${o[1]}`);let S=E/o[1],F=B/o[0],j=b/o[1],Z=M?`
let localRow = i32(localId.y);
let localCol = i32(localId.x);
let globalRowStart = i32(workgroupId.y) * ${v};
let globalColStart = i32(workgroupId.x) * ${D};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
// Load one tile of A into local memory.
for (var inputRow = localRow; inputRow < ${E}; inputRow = inputRow + ${o[1]}) {
for (var inputCol = localCol; inputCol < ${B}; inputCol = inputCol + ${o[0]}) {
${f2(p,u)}
}
}
// Load one tile of B into local memory.
for (var inputRow = localRow; inputRow < ${b}; inputRow = inputRow + ${o[1]}) {
for (var inputCol = localCol; inputCol < ${D}; inputCol = inputCol + ${o[0]}) {
mm_Bsub[inputRow][inputCol] = mm_readB(batch,
kStart + inputRow,
globalColStart + inputCol${u?", batchIndices":""});
}
}
kStart = kStart + tileInner;
workgroupBarrier();
// Compute acc values for a single thread.
var BCached : array<${n}, colPerThread>;
for (var k = 0; k < tileInner; k = k + 1) {
for (var inner = 0; inner < colPerThread; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][localCol + inner * ${o[0]}];
}
for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
let ACached = ${p?`mm_Asub[k][localRow + innerRow * ${o[1]}];`:`mm_Asub[localRow + innerRow * ${o[1]}][k];`}
for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] +
ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
let gRow = globalRowStart + localRow + innerRow * ${o[1]};
for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
let gCol = globalColStart + localCol + innerCol * ${o[0]};
mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
}
}
`:`
let tileRow = i32(localId.y) * rowPerThread;
let tileCol = i32(localId.x) * colPerThread;
let globalRow = i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x) * colPerThread;
let globalRowStart = i32(workgroupId.y) * ${v};
let tileRowA = i32(localId.y) * ${S};
let tileColA = i32(localId.x) * ${F};
let tileRowB = i32(localId.y) * ${j};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${F}; innerCol = innerCol + 1) {
let inputRow = tileRowA + innerRow;
let inputCol = tileColA + innerCol;
${f2(p,u)}
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < ${j}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(batch,
kStart + inputRow,
globalCol + innerCol${u?", batchIndices":""});
}
}
kStart = kStart + tileInner;
workgroupBarrier();
// Compute acc values for a single thread.
var BCached : array<${n}, colPerThread>;
for (var k = 0; k < tileInner; k = k + 1) {
for (var inner = 0; inner < colPerThread; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
${ip(p)}
for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
mm_write(batch, globalRow + innerRow, globalCol + innerCol,
acc[innerRow][innerCol]);
}
}
`;return`
var<workgroup> mm_Asub : array<array<${n}, ${B}>, ${E}>;
var<workgroup> mm_Bsub : array<array<${n}, ${D}>, ${b}>;
const rowPerThread = ${a[1]};
const colPerThread = ${a[0]};
const tileInner = ${b};
@compute @workgroup_size(${o[0]}, ${o[1]}, ${o[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(global_invocation_id) globalId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
let batch = ${C?"0":"i32(globalId.z)"};
${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""}
let num_tiles = ${C?`${Math.ceil(w/b)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
var kStart = ${C?`i32(globalId.z) * ${w}`:"0"};
var acc : array<array<${n}, colPerThread>, rowPerThread>;
${Z}
}
`},op=(a,o,n,u,p=!1)=>{let[b,C,w,M]=u,v=hs(u[0].type.tensor);return`
fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${b.type.indices}) -> ${Is(a,v)} {
var value = ${Is(a,v)}(0.0);
let col = colIn * ${a};
if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
{
var aIndices: ${C.type.indices};
${Ho("aIndices",C,C.rank-2,b.rank,"batchIndices")}
${C.indicesSet("aIndices",C.rank-2,"u32(row)")}
${C.indicesSet("aIndices",C.rank-1,"u32(colIn)")}
value = ${C.getByIndices("aIndices")};
}
return value;
}
fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${b.type.indices}) -> ${Is(a,v)} {
var value = ${Is(a,v)}(0.0);
let col = colIn * ${a};
if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
{
var bIndices: ${w.type.indices};
${Ho("bIndices",w,w.rank-2,b.rank,"batchIndices")}
${w.indicesSet("bIndices",w.rank-2,"u32(row)")}
${w.indicesSet("bIndices",w.rank-1,"u32(colIn)")}
value = ${w.getByIndices("bIndices")};
}
return value;
}
fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Is(a,v)}) {
let col = colIn * ${a};
if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
var value = valueIn;
let coords = vec3<i32>(batch, row, colIn);
${o?`value = value + ${p?"bias[colIn]":`${Is(a,v)}(bias[row])`};`:""}
${n}
${M.setByIndices("vec3<u32>(coords)","value")}
}
}
`},j0=(a,o,n,u,p=!1,b)=>{let C=a[0].dims,w=a[1].dims,M=C.slice(0,-2),v=w.slice(0,-2),D=u?u.slice(0,-2):n.slice(0,-2),B=He.size(D),E=C[C.length-2],S=C[C.length-1],F=w[w.length-1],j=S%4===0&&F%4===0,Z=E<=8?[4,1,1]:[4,4,1],R=[8,8,1],z=[Math.ceil(F/R[0]/Z[0]),Math.ceil(E/R[1]/Z[1]),Math.ceil(B/R[2]/Z[2])],U=j?4:1,f=[...M,E,S/U],k=f.length,e=[...v,S,F/U],d=e.length,y=[B,E,F/U],Ae=[{type:6,data:E},{type:6,data:F},{type:6,data:S}];Di(o,Ae),Ae.push(...et(D,f,e));let P=["rank","rank"],O=a.length>2;O&&(Ae.push(...et(a[2].dims)),P.push("rank")),Ae.push(...et(y));let pe=ee=>{let be=D.length,ke=Dc("batchDims",a[0].dataType,be,1),Me=hs(a[0].dataType),De=nA("a",a[0].dataType,k,U),ye=nA("b",a[1].dataType,d,U),_e=XA("result",a[0].dataType,y.length,U),Ne=[De,ye];if(O){let xe=p?U:1;Ne.push(nA("bias",a[2].dataType,a[2].dims.length,xe))}let Pe=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Pi(o,Pe);let Ce=hs(_e.type.tensor),ie=yi(o,_e.type.value,Ce),se=op(U,O,ie,[ke,De,ye,_e],p);return`
${ee.registerUniforms(Pe).registerInternalVariables(ke).declareVariables(...Ne,_e)}
${se}
${j?oc(Z,R,Me,ke):lc(Z,R,Me,ke)}
`};return{name:"MatMul",shaderCache:{hint:`${Z};${o.activation};${j};${p}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:b?b(n):n,dataType:a[0].dataType}],dispatchGroup:{x:z[0],y:z[1],z:z[2]},programUniforms:Ae}),getShaderSource:pe}}}),lp,_3,k6=IA(()=>{it(),Bn(),kt(),Gi(),Fc(),w6(),_c(),lp=(a,o,n,u,p=!1,b,C=4,w=4,M=4,v="f32")=>{let D=Ae=>{switch(Ae){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${v}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Ae} is not supported.`)}},B=Ae=>{switch(Ae){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${Ae} is not supported.`)}},E=a?`
let coord = vec4<i32>(batch, xRow, xCol, xCh);
`:`
let coord = vec4<i32>(batch, xCh, xRow, xCol);
`,S=a?`
let coords = vec4<i32>(
batch,
row / outWidth,
row % outWidth,
col);
`:`
let coords = vec4<i32>(
batch,
row,
col / outWidth,
col % outWidth);
`,F=a?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",j=a?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Z=a?"row":"col",R=a?"col":"row",z=`
let inChannels = i32(uniforms.w_shape[2]);
let outWidth = ${a?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
let outRow = ${Z} / outWidth;
let outCol = ${Z} % outWidth;
let WRow = ${R} / (i32(uniforms.w_shape[1]) * inChannels);
let WCol = ${R} / inChannels % i32(uniforms.w_shape[1]);
let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
let xCh = ${R} % inChannels;
var resData = ${Is(C,v)}(0.0);
// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (xRow >= 0 && xRow < ${F} && xCol >= 0 && xCol < ${j}) {
${E}
let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
${D(C)}
}
return resData;`,U=a?o&&u?`
let col = colIn * ${C};
${z}`:`
let col = colIn * ${C};
if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
${z}
}
return ${Is(C,v)}(0.0);`:u&&n?`
let col = colIn * ${C};
${z}`:`
let col = colIn * ${C};
if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
${z}
}
return ${Is(C,v)}(0.0);`,f=a?u&&n?B(w):`
let col = colIn * ${w};
if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
${B(w)}
}
return ${Is(w,v)}(0.0);`:`
let col = colIn * ${w};
if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
${B(w)}
}
return ${Is(w,v)}(0.0);`,k=Is(M,v),e=Is(a?C:w,v),d=Is(a?w:C,v),y=yi(b,k,v);return`
fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${e} {
${a?U:f}
}
fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${d} {
${a?f:U}
}
fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {
let col = colIn * ${M};
if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
{
var value = valueIn;
let outWidth = ${a?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
${S}
${S3(p)}
${y}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}`},_3=(a,o,n,u,p,b,C,w,M)=>{let v=o.format==="NHWC",D=v?a[0].dims[3]:a[0].dims[1],B=n[0],E=v?n[2]:n[3],S=v?n[1]:n[2],F=v?n[3]:n[1],j=v&&(D%4===0||D%3===0)&&F%4===0,Z=v?F:E*S,R=v?E*S:F,z=[8,8,1],U=u<=8?[4,1,1]:[4,4,1],f=[Math.ceil(Z/z[0]/U[0]),Math.ceil(R/z[1]/U[1]),Math.ceil(B/z[2]/U[2])];St("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${f}`);let k=j?v&&D%4!==0?3:4:1,e=z[1]*U[1],d=z[0]*U[0],y=Math.max(z[0]*k,z[1]),Ae=u%e===0,P=p%d===0,O=b%y===0,pe=j?[k,4,4]:[1,1,1],ee=[{type:6,data:u},{type:6,data:p},{type:6,data:b},{type:6,data:[o.pads[0],o.pads[1]]},{type:6,data:o.strides},{type:6,data:o.dilations}];Di(o,ee),ee.push(...et(a[0].dims,a[1].dims));let be=["rank","rank"];C&&(ee.push(...et(a[2].dims)),be.push("rank")),ee.push(...et(n));let ke=Me=>{let De=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Pi(o,De);let ye=j?4:1,_e=hs(a[0].dataType),Ne=`
fn setOutputAtIndex(flatIndex : i32, value : ${j?`vec4<${_e}>`:_e}) {
result[flatIndex] = ${j?`vec4<${_e}>`:_e}(value);
}
fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${j?`vec4<${_e}>`:_e}) {
let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
setOutputAtIndex(flatIndex ${j?"/ 4":""}, value);
}`,Pe=nA("x",a[0].dataType,a[0].dims.length,k===3?1:k),Ce=nA("w",a[1].dataType,a[1].dims.length,ye),ie=[Pe,Ce],se=XA("result",a[0].dataType,n.length,ye);if(C){let xe=nA("bias",a[2].dataType,a[2].dims.length,ye);ie.push(xe),Ne+=`
fn getBiasByOutputCoords(coords : vec4<i32>) -> ${j?`vec4<${_e}>`:_e} {
return bias[coords.${v?"w":"y"}${j?"/ 4":""}];
}`}return`
${O3("uniforms.result_strides")}
//struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
// outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
// dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
${Me.registerUniforms(De).declareVariables(...ie,se)}
${Ne}
${lp(v,Ae,P,O,C,o,pe[0],pe[1],pe[2],_e)}
${j?oc(U,z,_e,void 0,!v,y):lc(U,z,_e,void 0,!v,y,!1,void 0,w)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${o.cacheKey};${k};${j};${Ae};${P};${O};${e};${d};${y}`,inputDependencies:be},getRunData:()=>({outputs:[{dims:M?M(n):n,dataType:a[0].dataType}],dispatchGroup:{x:f[0],y:f[1],z:f[2]},programUniforms:ee}),getShaderSource:ke}}}),cp,g2,So,up,p2,dp,z3,N3,M6=IA(()=>{it(),Bn(),It(),kt(),Gi(),Fc(),cp=a=>{let o=1;for(let n=0;n<a.length;n++)o*=a[n];return o},g2=a=>typeof a=="number"?[a,a,a]:a,So=(a,o)=>o<=1?a:a+(a-1)*(o-1),up=(a,o,n,u=1)=>{let p=So(o,u);return Math.floor((a[0]*(n-1)-n+p)/2)},p2=(a,o,n,u,p)=>{p==null&&(p=up(a,o[0],u[0]));let b=[0,0,0,n];for(let C=0;C<3;C++)a[C]+2*p>=o[C]&&(b[C]=Math.trunc((a[C]-o[C]+2*p)/u[C]+1));return b},dp=(a,o,n,u,p,b,C,w,M,v)=>{let D,B,E,S;if(a==="VALID"&&(a=0),typeof a=="number"){D={top:a,bottom:a,left:a,right:a,front:a,back:a};let F=p2([o,n,u,1],[w,M,v],1,[p,b,C],a);B=F[0],E=F[1],S=F[2]}else if(Array.isArray(a)){if(!a.every((j,Z,R)=>j===R[0]))throw Error(`Unsupported padding parameter: ${a}`);D={top:a[0],bottom:a[1],left:a[2],right:a[3],front:a[4],back:a[5]};let F=p2([o,n,u,1],[w,M,v],1,[p,b,C],a[0]);B=F[0],E=F[1],S=F[2]}else if(a==="SAME_UPPER"){B=Math.ceil(o/p),E=Math.ceil(n/b),S=Math.ceil(u/C);let F=(B-1)*p+w-o,j=(E-1)*b+M-n,Z=(S-1)*C+v-u,R=Math.floor(F/2),z=F-R,U=Math.floor(j/2),f=j-U,k=Math.floor(Z/2),e=Z-k;D={top:U,bottom:f,left:k,right:e,front:R,back:z}}else throw Error(`Unknown padding parameter: ${a}`);return{padInfo:D,outDepth:B,outHeight:E,outWidth:S}},z3=(a,o,n,u,p,b=!1,C="channelsLast")=>{let w,M,v,D,B;if(C==="channelsLast")[w,M,v,D,B]=a;else if(C==="channelsFirst")[w,B,M,v,D]=a;else throw new Error(`Unknown dataFormat ${C}`);let[E,,S,F,j]=o,[Z,R,z]=g2(n),[U,f,k]=g2(u),e=So(S,U),d=So(F,f),y=So(j,k),{padInfo:Ae,outDepth:P,outHeight:O,outWidth:pe}=dp(p,M,v,D,Z,R,z,e,d,y),ee=b?E*B:E,be=[0,0,0,0,0];return C==="channelsFirst"?be=[w,ee,P,O,pe]:C==="channelsLast"&&(be=[w,P,O,pe,ee]),{batchSize:w,dataFormat:C,inDepth:M,inHeight:v,inWidth:D,inChannels:B,outDepth:P,outHeight:O,outWidth:pe,outChannels:ee,padInfo:Ae,strideDepth:Z,strideHeight:R,strideWidth:z,filterDepth:S,filterHeight:F,filterWidth:j,effectiveFilterDepth:e,effectiveFilterHeight:d,effectiveFilterWidth:y,dilationDepth:U,dilationHeight:f,dilationWidth:k,inShape:a,outShape:be,filterShape:o}},N3=(a,o,n,u,p,b)=>{let C=b==="channelsLast";C?a[0].dims[3]:a[0].dims[1];let w=[64,1,1],M={x:n.map((Z,R)=>R)},v=[Math.ceil(cp(M.x.map(Z=>n[Z]))/w[0]),1,1];St("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${v}`);let D=1,B=He.size(n),E=[{type:12,data:B},{type:12,data:u},{type:12,data:p},{type:12,data:o.strides},{type:12,data:o.dilations}];Di(o,E),E.push(...et(a[0].dims,a[1].dims));let S=["rank","rank"],F=a.length===3;F&&(E.push(...et(a[2].dims)),S.push("rank")),E.push(...et(n));let j=Z=>{let R=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:u.length},{name:"pads",type:"u32",length:p.length},{name:"strides",type:"u32",length:o.strides.length},{name:"dilations",type:"u32",length:o.dilations.length}];Pi(o,R);let z=1,U=hs(a[0].dataType),f=nA("x",a[0].dataType,a[0].dims.length,D),k=nA("W",a[1].dataType,a[1].dims.length,z),e=[f,k],d=XA("result",a[0].dataType,n.length,z),y="";if(F){let O=nA("bias",a[2].dataType,a[2].dims.length,z);e.push(O),y+=`
fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${U} {
return bias[${C?ZA("coords",4,5):ZA("coords",1,5)}];
}`}let Ae=Is(D,U),P=yi(o,Ae,U);return`
${y}
fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
return ${f.getByIndices("aIndices")};
}
fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
return ${k.getByIndices("aIndices")};
}
${Z.registerUniforms(R).declareVariables(...e,d)}
${Z.mainStart()}
${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let coords = ${d.offsetToIndices("global_idx")};
let batch = ${ZA("coords",0,f.rank)};
let d2 = ${C?ZA("coords",f.rank-1,f.rank):ZA("coords",1,f.rank)};
let xFRCCorner = vec3<u32>(${C?ZA("coords",1,f.rank):ZA("coords",2,f.rank)},
${C?ZA("coords",2,f.rank):ZA("coords",3,f.rank)},
${C?ZA("coords",3,f.rank):ZA("coords",4,f.rank)}) * uniforms.strides - uniforms.pads;
let xFCorner = xFRCCorner.x;
let xRCorner = xFRCCorner.y;
let xCCorner = xFRCCorner.z;
let xShapeY = ${C?ZA("uniforms.x_shape",1,f.rank):ZA("uniforms.x_shape",2,f.rank)};
let xShapeZ = ${C?ZA("uniforms.x_shape",2,f.rank):ZA("uniforms.x_shape",3,f.rank)};
let xShapeW = ${C?ZA("uniforms.x_shape",3,f.rank):ZA("uniforms.x_shape",4,f.rank)};
let xShapeU = ${C?ZA("uniforms.x_shape",4,f.rank):ZA("uniforms.x_shape",1,f.rank)};
let inputDepthNearestVec4 = (xShapeU / 4) * 4;
let inputDepthVec4Remainder = xShapeU % 4;
var value = 0.0;
for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
let xF = xFCorner + wF * uniforms.dilations[0];
if (xF < 0 || xF >= xShapeY) {
continue;
}
for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
let xR = xRCorner + wR * uniforms.dilations[1];
if (xR < 0 || xR >= xShapeZ) {
continue;
}
for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
let xC = xCCorner + wC * uniforms.dilations[2];
if (xC < 0 || xC >= xShapeW) {
continue;
}
for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
${C?`let xValues = vec4<f32>(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3));
`:`let xValues = vec4<f32>(
getX(batch, d1, xF, xR, xC),
getX(batch, d1 + 1, xF, xR, xC),
getX(batch, d1 + 2, xF, xR, xC),
getX(batch, d1 + 3, xF, xR, xC));
`}
let wValues = vec4<f32>(
getW(d2, d1, wF, wR, wC),
getW(d2, d1 + 1, wF, wR, wC),
getW(d2, d1 + 2, wF, wR, wC),
getW(d2, d1 + 3, wF, wR, wC));
value += dot(xValues, wValues);
}
if (inputDepthVec4Remainder == 1) {
${C?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
* getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
* getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
} else if (inputDepthVec4Remainder == 2) {
${C?`let xValues = vec2<f32>(
getX(batch, xF, xR, xC, inputDepthNearestVec4),
getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
`:`let xValues = vec2<f32>(
getX(batch, inputDepthNearestVec4, xF, xR, xC),
getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
`}
let wValues = vec2<f32>(
getW(d2, inputDepthNearestVec4, wF, wR, wC),
getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
value += dot(xValues, wValues);
} else if (inputDepthVec4Remainder == 3) {
${C?`let xValues = vec3<f32>(
getX(batch, xF, xR, xC, inputDepthNearestVec4),
getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
`:`let xValues = vec3<f32>(
getX(batch, inputDepthNearestVec4, xF, xR, xC),
getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
`}
let wValues = vec3<f32>(
getW(d2, inputDepthNearestVec4, wF, wR, wC),
getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
value += dot(xValues, wValues);
}
}
}
}
${F?"value = value + getBiasByOutputCoords(coords)":""};
${P}
result[global_idx] = f32(value);
}`};return{name:"Conv3DNaive",shaderCache:{hint:`${o.cacheKey};${C};${D};${F}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:n,dataType:a[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:E}),getShaderSource:j}}}),L3,R3,E6=IA(()=>{it(),It(),kt(),Gi(),L3=(a,o,n,u)=>{let p=a.length>2,b=p?"value += b[output_channel];":"",C=a[0].dims,w=a[1].dims,M=o.format==="NHWC",v=M?n[3]:n[1],D=v/o.group,B=M&&D>=4?Rr(v):1,E=He.size(n)/B,S=[{type:12,data:E},{type:12,data:o.dilations},{type:12,data:[o.strides[0],o.strides[1]]},{type:12,data:[o.pads[0],o.pads[1]]},{type:12,data:D}];Di(o,S),S.push(...et(C,[w[0],w[1],w[2],w[3]/B]));let F=p?["rank","rank","rank"]:["rank","rank"];S.push(...et([n[0],n[1],n[2],n[3]/B]));let j=Z=>{let R=XA("output",a[0].dataType,n.length,B),z=hs(R.type.tensor),U=yi(o,R.type.value,z),f=nA("x",a[0].dataType,C.length),k=nA("w",a[1].dataType,w.length,B),e=[f,k];p&&e.push(nA("b",a[2].dataType,a[2].dims,B));let d=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:o.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Pi(o,d);let y=M?`
for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
continue;
}
for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
continue;
}
for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
let input_channel = in_channel_offset + wInChannel;
let xVal = ${f.get("batch","xHeight","xWidth","input_channel")};
let wVal = ${k.get("wHeight","wWidth","wInChannel","output_channel")};
value += xVal * wVal;
}
}
}
`:`
for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
let input_channel = in_channel_offset + wInChannel;
for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
continue;
}
for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
continue;
}
let xVal = ${f.get("batch","input_channel","xHeight","xWidth")};
let wVal = ${k.get("output_channel","wInChannel","wHeight","wWidth")};
value += xVal * wVal;
}
}
}
`;return`
${Z.registerUniforms(d).declareVariables(...e,R)}
${Z.mainStart()}
${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let outputIndices = ${R.offsetToIndices("global_idx")};
let batch: u32 = outputIndices[0];
let output_channel: u32 = outputIndices[${M?3:1}];
let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${M?1:2}], outputIndices[${M?2:3}]) * uniforms.strides - uniforms.pads;
let group_id: u32 = output_channel * ${B} / uniforms.output_channels_per_group;
var in_channel_offset = group_id * uniforms.w_shape[${M?2:1}];
var value: ${R.type.value} = ${R.type.value}(0);
${y}
${b}
${U}
${R.setByOffset("global_idx","value")}
}`};return{name:"GroupedConv",shaderCache:{hint:`${o.cacheKey}_${B}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:u?u(n):n,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:S}),getShaderSource:j}},R3=(a,o,n,u)=>{let p=a.length>2,b=Rr(n[3]),C=Rr(n[2]),w=He.size(n)/b/C,M=[a[0].dims[0],a[0].dims[1],a[0].dims[2],a[0].dims[3]/b],v=[a[1].dims[0],a[1].dims[1],a[1].dims[2],a[1].dims[3]/b],D=[n[0],n[1],n[2],n[3]/b],B=[{type:12,data:w},{type:6,data:[o.strides[0],o.strides[1]]},{type:6,data:[o.pads[0],o.pads[1]]}];Di(o,B),B.push(...et(M,v,D));let E=(C-1)*o.strides[1]+v[1],S=F=>{let j=XA("output",a[0].dataType,D.length,b),Z=hs(j.type.tensor),R=yi(o,j.type.value,Z),z=nA("x",a[0].dataType,M.length,b),U=nA("w",a[1].dataType,v.length,b),f=[z,U];p&&f.push(nA("b",a[2].dataType,a[2].dims,b));let k=p?"value += b[output_channel];":"",e=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Pi(o,e),`
${F.registerUniforms(e).declareVariables(...f,j)}
${F.mainStart()}
${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let width0 = uniforms.output_shape[3];
let output_channel = global_idx % width0;
var index1 = global_idx / width0;
let width1 = uniforms.output_shape[2] / ${C}u;
let col = (index1 % width1) * ${C}u;
index1 = index1 / width1;
let row = index1 % uniforms.output_shape[1];
let batch = index1 / uniforms.output_shape[1];
let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;
var x_vals: array<${z.type.value}, ${E}>;
var values: array<${j.type.value}, ${C}>;
let input_channel = output_channel;
// Use constant instead of uniform can give better performance for w's height/width.
for (var w_height: u32 = 0u; w_height < ${v[0]}; w_height++) {
let x_height = x_corner.x + i32(w_height);
if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
for (var i = 0; i < ${E}; i++) {
let x_width = x_corner.y + i;
if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
x_vals[i] = ${z.get("batch","u32(x_height)","u32(x_width)","input_channel")};
} else {
x_vals[i] = ${z.type.value}(0);
}
}
for (var w_width: u32 = 0u; w_width < ${v[1]}; w_width++) {
let w_val = ${U.get("w_height","w_width","0","output_channel")};
for (var i = 0u; i < ${C}u; i++) {
values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
}
}
}
}
for (var i = 0u; i < ${C}u; i++) {
var value = values[i];
${k}
${R}
${j.set("batch","row","col + i","output_channel","value")};
}
}`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${o.cacheKey};${b};${C};${E};${v[0]};${v[1]}`,inputDependencies:p?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:u?u(n):n,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:B}),getShaderSource:S}}}),fp,x0,gp,B0,cc,m2,pp,mp,uc,v6=IA(()=>{It(),k6(),M6(),_c(),E6(),Gi(),Oc(),qn(),fp=(a,o,n,u,p,b)=>{let C=a[0],w=a.slice(b?1:2,b?3:4),M=w.length,v=o[0],D=o.slice(2).map((E,S)=>E+(E-1)*(n[S]-1)),B=w.map((E,S)=>E+u[S]+u[S+M]).map((E,S)=>Math.floor((E-D[S]+p[S])/p[S]));return B.splice(0,0,C),B.splice(b?3:1,0,v),B},x0=[2,3,1,0],gp=(a,o)=>{if(!a||a.length!==2&&a.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(a[0].dims.length>5)throw new Error("greater than 5D is not supported");if(a[0].dims.length!==a[1].dims.length)throw new Error("filter does not have same dimension as input");let n=a[0].dims[o.format==="NHWC"?a[0].dims.length-1:1],u=a[1].dims[1]*o.group;if(n!==u)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(a.length===3&&(a[2].dims.length!==1||a[1].dims[0]!==a[2].dims[0]))throw new Error("invalid bias");let p=a[0].dims.length-2;if(o.dilations.length!==p)throw new Error(`dilations should be ${p}D`);if(o.strides.length!==p)throw new Error(`strides should be ${p}D`);if(o.pads.length!==p*2)throw new Error(`pads should be ${p*2}D`);if(o.kernelShape.length!==0&&o.kernelShape.length!==a[1].dims.length-2)throw new Error("invalid kernel shape")},B0=(a,o)=>{let n=a.kernelShape.slice();n.length<o[1].dims.length-2&&n.push(...Array(o[1].dims.length-2-n.length).fill(0));for(let b=2;b<o[1].dims.length;++b)n[b-2]===0&&(n[b-2]=o[1].dims[b]);let u=a.pads.slice();L0.adjustPadsBasedOnAutoPad(o[0].dims,a.strides,a.dilations,n,u,a.format==="NHWC",a.autoPad);let p=Object.assign({},a);return Object.assign(p,{kernelShape:n,pads:u}),p},cc=a=>{let o=Qc(a),n=a.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][a.auto_pad],p=a.dilations,b=a.group,C=a.kernel_shape,w=a.pads,M=a.strides,v=a.w_is_const();return{autoPad:u,format:n,dilations:p,group:b,kernelShape:C,pads:w,strides:M,wIsConst:v,...o,cacheKey:`${a.format};${o.activation};`}},m2=(a,o,n,u)=>{let p=n.format==="NHWC",b=fp(o[0].dims,o[1].dims,n.dilations,n.pads,n.strides,p);if(n.group!==1){let e=[o[0]];if(p){let d=a.kernelCustomData.wT??a.compute(pa(o[1],x0),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!a.kernelCustomData.wT&&(a.kernelCustomData.wT=d),e.push(d)}else e.push(o[1]);o.length===3&&e.push(o[2]),!a.adapterInfo.isArchitecture("ampere")&&p&&o[1].dims[0]===n.group&&o[1].dims[1]===1&&n.dilations[0]===1&&n.dilations[1]===1?a.compute(R3(e,n,b,u),{inputs:e}):a.compute(L3(e,n,b,u),{inputs:e});return}let C=o.length===3,w=o[0].dims[p?1:2],M=o[0].dims[p?2:3],v=o[0].dims[p?3:1],D=o[1].dims[2],B=o[1].dims[3],E=b[p?1:2],S=b[p?2:3],F=b[p?3:1],j=p&&D===w&&B===M&&n.pads[0]===0&&n.pads[1]===0;if(j||D===1&&B===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let e=b[0],d,y,Ae,P=[];if(p){let ee=a.kernelCustomData.wT??a.compute(pa(o[1],x0),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!a.kernelCustomData.wT&&(a.kernelCustomData.wT=ee),j){let be=w*M*v;d=o[0].reshape([1,e,be]),y=ee.reshape([1,be,F]),Ae=[1,e,F]}else d=o[0].reshape([e,w*M,v]),y=ee.reshape([1,v,F]),Ae=[e,E*S,F];P.push(d),P.push(y)}else d=o[0].reshape([e,v,w*M]),y=o[1].reshape([1,F,v]),Ae=[e,F,E*S],P.push(y),P.push(d);C&&P.push(o[2]);let O=Ae[2],pe=P[0].dims[P[0].dims.length-1];O<8&&pe<8?a.compute(Sc(P,n,b,Ae,p,u),{inputs:P}):a.compute(j0(P,n,b,Ae,p,u),{inputs:P});return}let Z=!0,R=a.kernelCustomData.wT??a.compute(pa(o[1],x0),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!a.kernelCustomData.wT&&(a.kernelCustomData.wT=R);let z=[o[0],R];C&&z.push(o[2]);let U=p?E*S:F,f=p?F:E*S,k=D*B*v;a.compute(_3(z,n,b,U,f,k,C,Z,u),{inputs:z})},pp=(a,o)=>{let n=o.format==="NHWC",u=[a.inputs[0].reshape(n?[a.inputs[0].dims[0],1,a.inputs[0].dims[1],a.inputs[0].dims[2]]:[a.inputs[0].dims[0],a.inputs[0].dims[1],1,a.inputs[0].dims[2]]),a.inputs[1].reshape([a.inputs[1].dims[0],a.inputs[1].dims[1],1,a.inputs[1].dims[2]])];a.inputs.length===3&&u.push(a.inputs[2]);let p=[0,o.pads[0],0,o.pads[1]],b=[1].concat(o.strides),C=[1].concat(o.dilations),w=[1].concat(o.kernelShape),M=B0({...o,pads:p,strides:b,dilations:C,kernelShape:w},u);m2(a,u,M,v=>n?[v[0],v[2],v[3]]:[v[0],v[1],v[3]])},mp=(a,o,n)=>{let u=n.format==="NHWC"?"channelsLast":"channelsFirst",p=B0(n,o),b=n.autoPad==="NOTSET"?n.pads:n.autoPad,C=z3(o[0].dims,o[1].dims,n.strides,n.dilations,b,!1,u);a.compute(N3(o,p,C.outShape,[C.filterDepth,C.filterHeight,C.filterWidth],[C.padInfo.front,C.padInfo.top,C.padInfo.left],u))},uc=(a,o)=>{if(gp(a.inputs,o),a.inputs[0].dims.length===3)pp(a,o);else if(a.inputs[0].dims.length===5)mp(a,a.inputs,o);else{let n=B0(o,a.inputs);m2(a,a.inputs,n)}}}),j3,x6=IA(()=>{it(),Bn(),It(),kt(),j3=(a,o,n)=>{let u=a.length>2,p=o.outputShape,b=o.format==="NHWC",C=o.group,w=a[1].dims,M=w[2]/C,v=w[3],D=b?Rr(M):1,B=b&&v===1&&M>=4,E=B?Math.floor(M/4)*4:Math.floor(M/D)*D,S=M-E,F=b?Rr(v):1,j=b?v===1?D:F:1,Z=He.size(p)/F,R=[Math.ceil(Z/64),1,1];St("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${R}`);let z=["rank","rank"],U=[o.strides[0],o.strides[1]],f=[o.kernelShape[b?1:2],o.kernelShape[b?2:3]],k=[o.dilations[0],o.dilations[1]],e=[f[0]+(o.dilations[0]<=1?0:(o.kernelShape[b?1:2]-1)*(o.dilations[0]-1)),f[1]+(o.dilations[1]<=1?0:(o.kernelShape[b?2:3]-1)*(o.dilations[1]-1))],d=[e[0]-1-Math.floor((o.pads[0]+o.pads[2])/2),e[1]-1-Math.floor((o.pads[1]+o.pads[3])/2)],y=[{type:12,data:Z},{type:12,data:U},{type:12,data:f},{type:12,data:k},{type:12,data:e},{type:6,data:d},{type:12,data:E},{type:12,data:M},{type:12,data:v},...et(a[0].dims,a[1].dims)];u&&(y.push(...et(a[2].dims)),z.push("rank")),y.push(...et(p));let Ae=P=>{let O=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:U.length},{name:"filter_dims",type:"u32",length:f.length},{name:"dilations",type:"u32",length:f.length},{name:"effective_filter_dims",type:"u32",length:e.length},{name:"pads",type:"i32",length:d.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],pe=hs(a[0].dataType),ee=b?1:2,be=b?2:3,ke=b?3:1,Me=nA("W",a[1].dataType,a[1].dims.length,j),De=nA("Dy",a[0].dataType,a[0].dims.length,D),ye=[De,Me];u&&ye.push(nA("bias",a[2].dataType,[p[ke]].length,F));let _e=XA("result",a[0].dataType,p.length,F),Ne=()=>{let ie="";if(B)D===4?ie+=`
let xValue = ${De.getByOffset("x_offset")};
let wValue = ${Me.getByOffset("w_offset")};
dotProd = dotProd + dot(xValue, wValue);
x_offset += 1u;
w_offset += 1u;`:D===2?ie+=`
dotProd = dotProd + dot(vec4<${pe}>(${De.getByOffset("x_offset")}, ${De.getByOffset("x_offset + 1u")}), vec4<${pe}>(${Me.getByOffset("w_offset")}, ${Me.getByOffset("w_offset + 1u")}));
x_offset += 2u;
w_offset += 2u;`:D===1&&(ie+=`
dotProd = dotProd + dot(vec4<${pe}>(${De.getByOffset("x_offset")}, ${De.getByOffset("x_offset + 1u")}, ${De.getByOffset("x_offset + 2u")}, ${De.getByOffset("x_offset + 3u")}), vec4<${pe}>(${Me.getByOffset("w_offset")}, ${Me.getByOffset("w_offset + 1u")}, ${Me.getByOffset("w_offset + 2u")}, ${Me.getByOffset("w_offset + 3u")}));
x_offset += 4u;
w_offset += 4u;`);else if(ie+=`
let xValue = ${b?De.getByOffset(`${De.indicesToOffset(`${De.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${D}`):De.get("batch","inputChannel","idyR","idyC")};
`,D===1)ie+=`
let w_offset = ${Me.indicesToOffset(`${Me.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
let wValue = ${Me.getByOffset(`w_offset / ${j}`)};
dotProd = dotProd + xValue * wValue;`;else for(let se=0;se<D;se++)ie+=`
let wValue${se} = ${Me.getByOffset(`${Me.indicesToOffset(`${Me.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${se}, wOutChannel)`)} / ${j}`)};
dotProd = dotProd + xValue[${se}] * wValue${se};`;return ie},Pe=()=>{if(S===0)return"";if(!B)throw new Error(`packInputAs4 ${B} is not true.`);let ie="";if(D===1){ie+="dotProd = dotProd";for(let se=0;se<S;se++)ie+=`
+ ${De.getByOffset(`x_offset + ${se}`)} * ${Me.getByOffset(`w_offset + ${se}`)}`;ie+=";"}else if(D===2){if(S!==2)throw new Error(`Invalid inputChannelsRemainder ${S}.`);ie+=`
let xValue = ${De.getByOffset("x_offset")};
let wValue = ${Me.getByOffset("w_offset")};
dotProd = dotProd + dot(xValue, wValue);`}return ie},Ce=`
let outputIndices = ${_e.offsetToIndices(`global_idx * ${F}`)};
let batch = ${_e.indicesGet("outputIndices",0)};
let d1 = ${_e.indicesGet("outputIndices",ke)};
let r = ${_e.indicesGet("outputIndices",ee)};
let c = ${_e.indicesGet("outputIndices",be)};
let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
let groupId = d1 / uniforms.output_channels_per_group;
let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = ${_e.type.value}(0.0);
var wR: u32 = 0;
if (uniforms.dilations.x == 1) {
// Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
}
for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
if (wR % uniforms.dilations.x != 0) {
continue;
}
let dyR = (${pe}(dyRCorner) + ${pe}(wR)) / ${pe}(uniforms.strides[0]);
let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
if (dyR < 0.0 || dyR >= ${pe}(uniforms.Dy_shape[${ee}]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR: u32 = u32(dyR);
var wC: u32 = 0;
if (uniforms.dilations.y == 1) {
// Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
}
for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
if (wC % uniforms.dilations.y != 0) {
continue;
}
let dyC = (${pe}(dyCCorner) + ${pe}(wC)) / ${pe}(uniforms.strides.y);
let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
if (dyC < 0.0 || dyC >= ${pe}(uniforms.Dy_shape[${be}]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC: u32 = u32(dyC);
var inputChannel = groupId * uniforms.input_channels_per_group;
${B?`
var x_offset = ${De.indicesToOffset(`${De.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${D};
var w_offset = ${Me.indicesToOffset(`${Me.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${j};
`:""}
for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${B?4:D}) {
${Ne()}
inputChannel = inputChannel + ${B?4:D};
}
${Pe()}
wC = wC + uniforms.strides.y - 1;
}
wR = wR + uniforms.strides[0] - 1;
}
let value = dotProd${u?` + bias[d1 / ${F}]`:""};
${_e.setByOffset("global_idx","value")};
`;return`
${P.registerUniforms(O).declareVariables(...ye,_e)}
${P.mainStart()}
${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
${Ce}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${o.cacheKey};${D}${j}${F}${B}${S}`,inputDependencies:z},getRunData:()=>({dispatchGroup:{x:R[0],y:R[1],z:R[2]},outputs:[{dims:n?n(p):p,dataType:a[0].dataType}],programUniforms:y}),getShaderSource:Ae}}}),hp,Cp,bp,h2,W3,Ip,C2,wp,V3,B6=IA(()=>{x6(),Gi(),qn(),hp=(a,o,n,u,p,b)=>(a-1)*o+n+(u-1)*p+1-b,Cp=(a,o,n,u,p)=>{let b=Math.floor(a/2);o==="SAME_UPPER"?(n[u]=b,n[p]=a-b):o==="SAME_LOWER"&&(n[u]=a-b,n[p]=b)},bp=(a,o,n,u,p,b,C,w,M,v)=>{let D=a.length-2,B=v.length===0;M.length<D&&M.push(...Array(D-M.length).fill(0));let E=a[0],S=o[w?3:1]*p;for(let F=0,j=a.length-D-(w?1:0);F<D;++F,++j){let Z=a[j],R=B?Z*C[F]:v[F],z=hp(Z,C[F],b[F],o[j],n[F],R);Cp(z,u,b,F,F+D),B&&v.push(C[F]*(Z-1)+M[F]+(o[j]-1)*n[F]+1-b[F]-b[F+D])}v.splice(0,0,E),v.splice(w?3:1,0,S)},h2=(a,o)=>{let n=a.kernelShape.slice();if(a.kernelShape.length===0||a.kernelShape.reduce((B,E)=>B*E,1)===0){n.length=0;for(let B=2;B<o[1].dims.length;++B)n.push(o[1].dims[B])}let u=a.format==="NHWC";n.splice(0,0,o[1].dims[0]),n.splice(u?3:1,0,o[1].dims[1]);let p=a.pads.slice(),b=a.outputShape.slice(),C=a.outputPadding.slice(),w=o[0].dims,M=a.dilations.slice();if(M.reduce((B,E)=>B+E,0)===0){let B=o[0].dims.length-2;M=new Array(B).fill(1)}let v=a.strides.slice();if(v.reduce((B,E)=>B+E,0)===0){let B=o[0].dims.length-2;v=new Array(B).fill(1)}bp(w,n,M,a.autoPad,a.group,p,v,u,C,b);let D=Object.assign({},a);return Object.assign(D,{kernelShape:n,pads:p,outputPadding:C,outputShape:b,dilations:M,strides:v}),D},W3=a=>{let o=Qc(a),n=a.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof a.autoPad>"u"?0:a.autoPad],p=a.dilations,b=a.group,C=a.kernelShape,w=a.pads,M=a.strides,v=a.wIsConst(),D=a.outputPadding,B=a.outputShape;return{autoPad:u,format:n,dilations:p,group:b,kernelShape:C,outputPadding:D,outputShape:B,pads:w,strides:M,wIsConst:v,...o,cacheKey:`${a.format};${o.activation};`}},Ip=(a,o)=>{if(!a||a.length!==2&&a.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(a[0].dims.length!==4&&a[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(a[0].dims.length!==a[1].dims.length)throw new Error("filter does not have same dimension as input");let n=a[0].dims[o.format==="NHWC"?a[0].dims.length-1:1],u=a[1].dims[0];if(n!==u)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let p=a[1].dims[1]*o.group;if(a.length===3&&(a[2].dims.length!==1||a[2].dims[0]!==p))throw new Error("invalid bias");let b=a[0].dims.length-2;if(o.dilations.reduce((C,w)=>C+w,0)>0&&o.dilations.length!==b)throw new Error(`dilations should be ${b}D`);if(o.strides.reduce((C,w)=>C+w,0)>0&&o.strides.length!==b)throw new Error(`strides should be ${b}D`);if(o.pads.reduce((C,w)=>C+w,0)>0&&o.pads.length!==b*2)throw new Error(`pads should be ${b*2}D`);if(o.outputPadding.length!==b&&o.outputPadding.length!==0)throw new Error(`output_padding should be ${b}D`);if(o.kernelShape.reduce((C,w)=>C+w,0)>0&&o.kernelShape.length!==0&&o.kernelShape.length!==a[1].dims.length-2)throw new Error("invalid kernel shape");if(o.outputShape.length!==0&&o.outputShape.length!==a[0].dims.length-2)throw new Error("invalid output shape")},C2=(a,o,n,u)=>{let p=a.kernelCustomData.wT??a.compute(pa(o[1],[2,3,0,1]),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!a.kernelCustomData.wT&&(a.kernelCustomData.wT=p);let b=[o[0],p];o.length===3&&b.push(o[2]),a.compute(j3(b,n,u),{inputs:b})},wp=(a,o)=>{let n=o.format==="NHWC",u=[a.inputs[0].reshape(n?[a.inputs[0].dims[0],1,a.inputs[0].dims[1],a.inputs[0].dims[2]]:[a.inputs[0].dims[0],a.inputs[0].dims[1],1,a.inputs[0].dims[2]]),a.inputs[1].reshape([a.inputs[1].dims[0],a.inputs[1].dims[1],1,a.inputs[1].dims[2]])];a.inputs.length===3&&u.push(a.inputs[2]);let p=o.kernelShape;(p.length===0||p[0]===0)&&(p=[a.inputs[1].dims[2]]);let b=o.dilations;(b.length===0||b[0]===0)&&(b=[1]);let C=o.strides;(C.length===0||C[0]===0)&&(C=[1]);let w=o.pads;w.length===0&&(w=[0,0]),w=[0,w[0],0,w[1]],C=[1].concat(C),b=[1].concat(b),p=[1].concat(p);let M=o.outputPadding;M=[0].concat(M);let v=h2({...o,pads:w,strides:C,dilations:b,kernelShape:p,outputPadding:M},u);C2(a,u,v,D=>n?[D[0],D[2],D[3]]:[D[0],D[1],D[3]])},V3=(a,o)=>{if(Ip(a.inputs,o),a.inputs[0].dims.length===3)wp(a,o);else{let n=h2(o,a.inputs);C2(a,a.inputs,n)}}}),kp,Y3,H3,y6=IA(()=>{it(),It(),Hr(),kt(),kp=(a,o,n,u)=>{let p=He.size(o),b=o.length,C=nA("input",a,b),w=XA("output",a,b),M=n.dataType===6?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),v=He.normalizeAxis(M,b),D=B=>{let E=` i32(${C.indicesGet("inputIndices","uniforms.axis")}) `,S=ZA("uniforms.input_shape","uniforms.axis",b),F=u.reverse?E+(u.exclusive?" + 1":""):"0",j=u.reverse?S:E+(u.exclusive?"":" + 1");return`
${B.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(C,w)}
${B.mainStart()}
${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
var inputIndices = ${w.offsetToIndices("global_idx")};
var sum = ${w.type.value}(0);
let first : i32 = ${F};
let last : i32 = ${j};
for (var i : i32 = first; i < last; i++) {
${C.indicesSet("inputIndices","uniforms.axis","u32(i)")};
sum = sum + ${C.getByIndices("inputIndices")};
}
${w.setByOffset("global_idx","sum")};
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${Ep(w,E,F,j)}
${R.mainStart()}
${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let indices = ${j.offsetToIndices("global_idx")};
let aIndices = perm(indices);
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${v.mainStart()}
${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
var outputIndices = ${C.offsetToIndices("global_idx")};
${p.map((f,k)=>`var input${k}Indices: ${p[k].type.indices};`).join(`
`)}
${U.join(`
`)};
${C.setByOffset("global_idx","sum")};
}`};return{name:"Einsum",shaderCache:{hint:n.equation,inputDependencies:a.map(()=>"rank")},getRunData:()=>{let v=w.filter(B=>n.symbolToInfo.has(B)).map(B=>({type:12,data:n.symbolToInfo.get(B)?.dimValue||0}));v.push({type:12,data:b});let D=a.map((B,E)=>[...et(B)]).reduce((B,E)=>B.concat(E),v);return D.push(...et(u)),{outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:D}},getShaderSource:M}},X3=(a,o)=>{let n=new Dp(a.inputs,o.equation),u=n.outputDims,p=a.inputs.map((b,C)=>b.dims);a.compute(Pp(p,a.inputs[0].dataType,n,u))},Z3=a=>{let o=a.equation.replace(/\s+/g,"");return Ut({equation:o})}}),Tp,w2,Gp,Qp,J3,T6=IA(()=>{it(),It(),kt(),Tp=a=>{if(!a||a.length!==2)throw new Error("Expand requires 2 input.");let o=a[0].dims,n=Array.from(a[1].getBigInt64Array(),Number),u=n.length<o.length?0:n.length-o.length,p=o.length<n.length?0:o.length-n.length;for(;u<n.length&&p<o.length;++u,++p)if(n[u]!==o[p]&&n[u]!==1&&o[p]!==1)throw new Error("Expand requires shape to be broadcastable to input")},w2=(a,o)=>{let n=a.length-o.length,u=[];for(let p=0;p<n;++p)u.push(a[p]);for(let p=0;p<o.length;++p)u.push(o[p]===1?a[p+n]:o[p]);return u},Gp=(a,o)=>a.length>o.length?w2(a,o):w2(o,a),Qp=a=>{let o=a[0].dims,n=Array.from(a[1].getBigInt64Array(),Number),u=Gp(o,n),p=a[0].dataType,b=p===9||He.size(o)===1,C=p===9||o.length>0&&o[o.length-1]%4===0?4:1,w=b||u.length>0&&u[u.length-1]%4===0?4:1,M=Math.ceil(He.size(u)/w),v=B=>{let E=nA("input",p,o.length,C),S=XA("output",p,u.length,w),F;if(p===9){let j=(Z,R,z="")=>`
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let component${R} = offset${R} % 4u;
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let outputOffset = global_idx * ${w};
var data = vec4<u32>(0);
${j("data",0,"u32")}
${j("data",1,"u32")}
${j("data",2,"u32")}
${j("data",3,"u32")}
${S.setByOffset("global_idx","data")}
}`}else F=`
let outputIndices = ${S.offsetToIndices(`global_idx * ${w}`)};
let inputOffset = ${E.broadcastedIndicesToOffset("outputIndices",S)};
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}`;return`
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${B.mainStart()}
${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
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let bias${S}_offset: u32 = (global_idx * 4 + ${S}) % uniforms.bias_size;
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${C.mainStart(lo)}
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let x = ${w.getByOffset("global_idx")};
${E}
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var idx${z} = ${F.getByIndices(`indicesIndices${z}`)};
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}
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let outputIndices${f} = ${j.offsetToIndices(`outputOffset + ${f}u`)};
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let index${f} = offset${f} / 4u;
let component${f} = offset${f} % 4u;
${U}[${f}] = ${k}(${S.getByOffset(`index${f}`)}[component${f}]);
`;R=`
let outputOffset = global_idx * ${M};
var value = vec4<u32>(0);
${z("value",0,"u32")}
${z("value",1,"u32")}
${z("value",2,"u32")}
${z("value",3,"u32")}
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`}else R=`
let outputIndices = ${j.offsetToIndices("global_idx")};
${Z("")};
let value = ${S.getByIndices("dataIndices")};
${j.setByOffset("global_idx","value")};
`;return`
${E.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(S,F,j)}
${E.mainStart()}
${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
${R}
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${E.registerUniforms(Z).declareVariables(...j)}
${E.mainStart()}
${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let batch_idx = global_idx / uniforms.num_slices_per_batch;
let base_offset = batch_idx * uniforms.input_batch_stride;
let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
var relative_slice_offset = 0;
for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
let input_dim_idx = uniforms.batch_dims + dim_idx;
if (index < 0) {
${p.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
}
${n.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
}
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${f.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(k,e,d)}
${f.mainStart()}
${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
}`};a.compute({name:"GatherND",shaderCache:{hint:o.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Z,dataType:p}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:z}),getShaderSource:U},{inputs:[n[0],F]})},tC=a=>({batchDims:a.batch_dims,cacheKey:""})}),zp,Np,rC,sC,S6=IA(()=>{it(),It(),Hr(),kt(),zp=(a,o)=>{if(a.length<3||a.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let n=He.normalizeAxis(o.quantizeAxis,a[0].dims.length),u=o.blockSize,p=a[0],b=a[2],C=a.length===4?a[3]:void 0;if(b.dims.length!==p.dims.length||!p.dims.map((w,M)=>M===n?Math.ceil(w/u)===b.dims[M]:w===b.dims[M]).reduce((w,M)=>w&&M,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(C){if(C.dataType!==p.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(C.dims.length!==b.dims.length||!C.dims.map((w,M)=>w===b.dims[M]).reduce((w,M)=>w&&M,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Np=(a,o)=>{let n=a[0].dims,u=a[1].dims,p=n.length,b=He.normalizeAxis(o.gatherAxis,p),C=He.normalizeAxis(o.quantizeAxis,p),w=n.slice(0);w.splice(b,1,...u);let M=He.size(w),v=a[2].dataType,D=a[0].dataType===22,B=[{type:12,data:M},{type:12,data:C},{type:12,data:b},{type:12,data:o.blockSize},...et(...a.map((S,F)=>S.dims),w)],E=S=>{let F=nA("data",a[0].dataType,a[0].dims.length),j=nA("inputIndices",a[1].dataType,a[1].dims.length),Z=nA("scales",a[2].dataType,a[2].dims.length),R=a.length>3?nA("zeroPoint",a[3].dataType,a[3].dims.length):void 0,z=XA("output",v,w.length),U=[F,j,Z];R&&U.push(R);let f=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
${S.registerUniforms(f).declareVariables(...U,z)}
${S.mainStart()}
let output_indices = ${z.offsetToIndices("global_idx")};
var indices_indices = ${j.type.indices}(0);
${u.length>1?`
for (var i: u32 = 0; i < ${u.length}; i++) {
let index = ${z.indicesGet("output_indices","uniforms.gather_axis + i")};
${j.indicesSet("indices_indices","i","index")};
}`:`indices_indices = ${z.indicesGet("output_indices","uniforms.gather_axis")};`};
var data_indices = ${F.type.indices}(0);
for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
let index = ${z.indicesGet("output_indices","i")};
${F.indicesSet("data_indices","i","index")};
}
var index_from_indices = ${j.getByIndices("indices_indices")};
if (index_from_indices < 0) {
index_from_indices += ${n[b]};
}
${F.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
for (var i = uniforms.gather_axis + 1; i < ${w.length}; i++) {
let index = ${z.indicesGet("output_indices",`i + ${u.length} - 1`)};
${F.indicesSet("data_indices","i","index")};
}
let data_offset = ${F.indicesToOffset("data_indices")};
let data_index = data_offset % 8;
// Convert 4-bit packed data to 8-bit packed data.
let packed_4bit_quantized_data = ${F.getByOffset("data_offset / 8")};
let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
let quantized_data_vec = ${D?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
let quantized_data = quantized_data_vec[data_index / 2];
var scale_indices = data_indices;
let quantize_axis_index = ${Z.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
${Z.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
var scale = ${Z.getByIndices("scale_indices")};
${R?`
let zero_point_indices = scale_indices;
let zero_point_offset = ${R.indicesToOffset("zero_point_indices")};
let zero_point_index = zero_point_offset % 8;
let packed_4bit_zero_points = ${R.getByOffset("zero_point_offset / 8")};
let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
let zero_point_vec = ${D?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
let dequantized_data = ${Qs(v)}(quantized_data - zero_point) * scale;
${z.setByOffset("global_idx","dequantized_data")};
}`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${o.cacheKey};${a.filter((S,F)=>F!==1).map(S=>S.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:a.length},(S,F)=>"rank")},getRunData:()=>({outputs:[{dims:w,dataType:v}],dispatchGroup:{x:Math.ceil(M/64)},programUniforms:B}),getShaderSource:E}},rC=(a,o)=>{let n=a.inputs;zp(n,o),a.compute(Np(a.inputs,o))},sC=a=>Ut({blockSize:a.blockSize,gatherAxis:a.gatherAxis,quantizeAxis:a.quantizeAxis})}),Lp,Rp,aC,nC,O6=IA(()=>{it(),It(),Hr(),kt(),Lp=a=>{if(!a||a.length!==2)throw new Error("GatherElements requires 2 inputs.");if(a[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(a[0].dims.length!==a[1].dims.length)throw new Error(`GatherElements requires that the data input and
indices input tensors be of same rank.`)},Rp=(a,o)=>{let n=a[0].dims,u=a[0].dataType,p=n.length,b=a[1].dims,C=a[1].dataType,w=He.normalizeAxis(o.axis,p),M=n[w],v=b.slice(0),D=He.size(v),B=nA("input",u,p),E=nA("indicesInput",C,b.length),S=XA("output",u,v.length),F=[{type:12,data:D},{type:6,data:M},{type:12,data:w}];return F.push(...et(n,b,v)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:v,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(D/64)},programUniforms:F}),getShaderSource:j=>`
${j.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(B,E,S)}
${j.mainStart()}
${j.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
let outputIndices = ${S.offsetToIndices("global_idx")};
var idx = ${E.getByOffset("global_idx")};
if (idx < 0) {
idx = idx + uniforms.axisDimLimit;
}
var inputIndices = ${B.type.indices}(outputIndices);
${B.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
let value = ${B.getByIndices("inputIndices")};
${S.setByOffset("global_idx","value")};
}`}},aC=a=>Ut({axis:a.axis}),nC=(a,o)=>{let n=a.inputs;Lp(n),a.compute(Rp(a.inputs,o))}}),jp,Wp,iC,oC,_6=IA(()=>{it(),It(),kt(),jp=a=>{if(!a)throw new Error("Input is missing");if(a.length<2||a.length>3)throw new Error("Invaid input number.");if(a.length===3&&a[2].dims.length>2)throw new Error("Invalid input shape of C");if(a[0].dataType!==a[1].dataType||a.length===3&&a[0].dataType!==a[2].dataType)throw new Error("Input types are mismatched")},Wp=(a,o)=>{let n=a[0].dims.slice(),u=a[1].dims.slice(),[p,b,C]=ah.getShapeOfGemmResult(n,o.transA,u,o.transB,a.length===3?a[2].dims:void 0),w=[p,b];if(!w)throw new Error("Can't use gemm on the given tensors");let M=16,v=Math.ceil(b/M),D=Math.ceil(p/M),B=!0,E=He.size(w),S=[{type:12,data:B?v:E},{type:12,data:p},{type:12,data:b},{type:12,data:C},{type:1,data:o.alpha},{type:1,data:o.beta}],F=["type","type"];a.length===3&&(S.push(...et(a[2].dims)),F.push("rank")),S.push(...et(w));let j=R=>{let z="";o.transA&&o.transB?z="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":o.transA&&!o.transB?z="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!o.transA&&o.transB?z="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!o.transA&&!o.transB&&(z="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let U=o.alpha===1?"":"value *= uniforms.alpha;",f=nA("a",a[0].dataType,a[0].dims),k=nA("b",a[1].dataType,a[1].dims),e=f.type.value,d=null,y=[f,k];a.length===3&&(d=nA("c",a[2].dataType,a[2].dims.length),y.push(d));let Ae=XA("output",a[0].dataType,w.length);y.push(Ae);let P=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
${R.registerUniforms(P).declareVariables(...y)}
${R.mainStart()}
${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let m = global_idx / uniforms.N;
let n = global_idx % uniforms.N;
var value = ${e}(0);
for (var k: u32 = 0u; k < uniforms.K; k++) {
${z}
}
${U}
${d!=null?`let cOffset = ${d.broadcastedIndicesToOffset("vec2(m, n)",Ae)}; value += ${e}(uniforms.beta) * ${d.getByOffset("cOffset")};`:""}
output[global_idx] = value;
}`},Z=R=>{let z=nA("a",a[0].dataType,a[0].dims),U=nA("b",a[1].dataType,a[1].dims),f=null,k=[z,U];a.length===3&&(f=nA("c",a[2].dataType,a[2].dims.length),k.push(f));let e=XA("output",a[0].dataType,w.length);k.push(e);let d=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],y="",Ae="";o.transA&&o.transB?(Ae=`
var col = tile_row_start + local_id.x;
var row = k_start + local_id.y;
if (col < uniforms.M && row < uniforms.K) {
tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
} else {
tile_a[local_id.y][local_id.x] = ${z.type.value}(0);
}
col = k_start + local_id.x;
row = tile_col_start + local_id.y;
if (col < uniforms.K && row < uniforms.N) {
tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
} else {
tile_b[local_id.y][local_id.x] = ${U.type.value}(0);
}
`,y="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):o.transA&&!o.transB?(Ae=`
var col = tile_row_start + local_id.x;
var row = k_start + local_id.y;
if (col < uniforms.M && row < uniforms.K) {
tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
} else {
tile_a[local_id.y][local_id.x] = ${z.type.value}(0);
}
col = tile_col_start + local_id.x;
row = k_start + local_id.y;
if (col < uniforms.N && row < uniforms.K) {
tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
} else {
tile_b[local_id.y][local_id.x] = ${U.type.value}(0);
}
`,y="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!o.transA&&o.transB?(Ae=`
var col = k_start + local_id.x;
var row = tile_row_start + local_id.y;
if (col < uniforms.K && row < uniforms.M) {
tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
} else {
tile_a[local_id.y][local_id.x] = ${z.type.value}(0);
}
col = k_start + local_id.x;
row = tile_col_start + local_id.y;
if (col < uniforms.K && row < uniforms.N) {
tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
} else {
tile_b[local_id.y][local_id.x] = ${U.type.value}(0);
}
`,y="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!o.transA&&!o.transB&&(Ae=`
var col = k_start + local_id.x;
var row = tile_row_start + local_id.y;
if (col < uniforms.K && row < uniforms.M) {
tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
} else {
tile_a[local_id.y][local_id.x] = ${z.type.value}(0);
}
col = tile_col_start + local_id.x;
row = k_start + local_id.y;
if (col < uniforms.N && row < uniforms.K) {
tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
} else {
tile_b[local_id.y][local_id.x] = ${U.type.value}(0);
}
`,y="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let P=o.alpha===1?"":"value *= uniforms.alpha;";return`
${R.registerUniforms(d).declareVariables(...k)}
var<workgroup> tile_a: array<array<${z.type.storage}, ${M}>, ${M}>;
var<workgroup> tile_b: array<array<${U.type.storage}, ${M}>, ${M}>;
${R.mainStart([M,M,1])}
let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${M};
let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${M};
let num_tiles = (uniforms.K - 1) / ${M} + 1;
var k_start = 0u;
var value = ${e.type.value}(0);
for (var t: u32 = 0u; t < num_tiles; t++) {
${Ae}
k_start = k_start + ${M};
workgroupBarrier();
for (var k: u32 = 0u; k < ${M}; k++) {
${y}
}
workgroupBarrier();
}
${P}
let m = tile_row_start + local_id.y;
let n = tile_col_start + local_id.x;
${f!=null?`let cOffset = ${f.broadcastedIndicesToOffset("vec2(m, n)",e)}; value += ${e.type.value}(uniforms.beta) * ${f.getByOffset("cOffset")};`:""}
if (m < uniforms.M && n < uniforms.N) {
output[m * uniforms.N + n] = value;
}
}`};return B?{name:"GemmShared",shaderCache:{hint:`${o.cacheKey}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:w,dataType:a[0].dataType}],dispatchGroup:{x:v*D},programUniforms:S}),getShaderSource:Z}:{name:"Gemm",shaderCache:{hint:`${o.cacheKey}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:w,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:S}),getShaderSource:j}},iC=a=>{let o=a.transA,n=a.transB,u=a.alpha,p=a.beta;return{transA:o,transB:n,alpha:u,beta:p,cacheKey:`${a.transA};${a.transB};${a.alpha===1}`}},oC=(a,o)=>{jp(a.inputs),a.compute(Wp(a.inputs,o))}}),tn,kn,hi,Ci,Vp,Yp,Hp,Up,Kp,Xp,Zp,Jp,lC,cC,z6=IA(()=>{it(),It(),Hr(),kt(),[tn,kn,hi,Ci]=[0,1,2,3],Vp=a=>{if(a[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(a[0].dims.length!==a[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(a[0].dims.length-2!==a[1].dims[a[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${a[0].dims.length-2}`);if(a[0].dims[0]!==a[1].dims[0])throw new Error("grid batch size must match input batch size")},Yp=`
fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
let cubic_alpha = -0.75f;
let x_abs = abs(x);
var coeffs: vec4<f32>;
coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
return coeffs;
}
`,Hp=a=>`
fn gs_bicubic_interpolate(p: mat4x4<${a}>, x: f32, y: f32) -> ${a} {
var v: vec4<f32>;
var coeffs = gs_get_cubic_coeffs(x);
for (var i = 0; i < 4; i++) {
v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
}
coeffs = gs_get_cubic_coeffs(y);
let pixel = ${a}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
return pixel;
}
`,Up=a=>`
fn gs_denormalize(n: f32, length: i32) -> f32 {
${a.alignCorners===0?`
// alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
return ((n + 1.0) * f32(length) - 1.0) / 2.0;
`:`
// alignCorners: true => [-1, 1] to [0, length - 1]
return (n + 1.0) / 2.0 * (f32(length - 1));
`}
}
`,Kp=a=>`
${a.paddingMode==="reflection"?`
fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
var dx = 0.0;
var fx = f32(x);
let range = x_max - x_min;
if (fx < x_min) {
dx = x_min - fx;
let n = u32(dx / range);
let r = dx - f32(n) * range;
if (n % 2 == 0) {
fx = x_min + r;
} else {
fx = x_max - r;
}
} else if (fx > x_max) {
dx = fx - x_max;
let n = u32(dx / range);
let r = dx - f32(n) * range;
if (n % 2 == 0) {
fx = x_max - r;
} else {
fx = x_min + r;
}
}
return u32(fx);
}`:""}
`,Xp=(a,o,n)=>`
fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${o} {
var pixel = ${o}(0);
var indices = vec4<u32>(0);
indices[${tn}] = batch;
indices[${kn}] = channel;`+(()=>{switch(n.paddingMode){case"zeros":return`
if (r >= 0 && r < H && c >=0 && c < W) {
indices[${hi}] = u32(r);
indices[${Ci}] = u32(c);
} else {
return ${o}(0);
}
`;case"border":return`
indices[${hi}] = u32(clamp(r, 0, H - 1));
indices[${Ci}] = u32(clamp(c, 0, W - 1));
`;case"reflection":return`
indices[${hi}] = gs_reflect(r, border[1], border[3]);
indices[${Ci}] = gs_reflect(c, border[0], border[2]);
`;default:throw new Error(`padding mode ${n.paddingMode} is not supported`)}})()+`
return ${a.getByIndices("indices")};
}
`,Zp=(a,o,n)=>(()=>{switch(n.mode){case"nearest":return`
let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${tn}], indices[${kn}], border);
`;case"bilinear":return`
let x1 = i32(floor(x));
let y1 = i32(floor(y));
let x2 = x1 + 1;
let y2 = y1 + 1;
let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${tn}], indices[${kn}], border);
let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${tn}], indices[${kn}], border);
let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${tn}], indices[${kn}], border);
let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${tn}], indices[${kn}], border);
let dx2 = ${o}(f32(x2) - x);
let dx1 = ${o}(x - f32(x1));
let dy2 = ${o}(f32(y2) - y);
let dy1 = ${o}(y - f32(y1));
let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
`;case"bicubic":return`
let x0 = i32(floor(x)) - 1;
let y0 = i32(floor(y)) - 1;
var p: mat4x4<${o}>;
for (var h = 0; h < 4; h++) {
for (var w = 0; w < 4; w++) {
p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${tn}], indices[${kn}], border);
}
}
let dx = x - f32(x0 + 1);
let dy = y - f32(y0 + 1);
let result = gs_bicubic_interpolate(p, dx, dy);
`;default:throw new Error(`mode ${n.mode} is not supported`)}})()+`${a.setByOffset("global_idx","result")}`,Jp=(a,o)=>{let n=nA("x",a[0].dataType,a[0].dims.length),u=[a[1].dims[0],a[1].dims[1],a[1].dims[2]],p=nA("grid",a[1].dataType,u.length,2),b=[a[0].dims[0],a[0].dims[1],a[1].dims[1],a[1].dims[2]];o.format==="NHWC"&&(b=[a[0].dims[0],a[1].dims[1],a[1].dims[2],a[0].dims[3]],[tn,kn,hi,Ci]=[0,3,1,2]);let C=XA("output",a[0].dataType,b.length),w=n.type.value,M=He.size(b),v=[{type:12,data:M},...et(a[0].dims,u,b)],D=B=>`
${B.registerUniform("output_size","u32").declareVariables(n,p,C)}
${Yp}
${Hp(w)}
${Up(o)}
${Kp(o)}
${Xp(n,w,o)}
${B.mainStart()}
${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let H_in = i32(uniforms.x_shape[${hi}]);
let W_in = i32(uniforms.x_shape[${Ci}]);
${o.alignCorners===0?`
let x_min = -0.5;
let x_max = f32(W_in) - 0.5;
let y_min = -0.5;
let y_max = f32(H_in) - 0.5;
`:`
let x_min = 0.0;
let x_max = f32(W_in) - 1.0;
let y_min = 0.0;
let y_max = f32(H_in) - 1.0;
`};
let border = vec4<f32>(x_min, y_min, x_max, y_max);
let indices = ${C.offsetToIndices("global_idx")};
var grid_indices = vec3<u32>(indices[${tn}], indices[${hi}], indices[${Ci}]);
let nxy = ${p.getByIndices("grid_indices")};
var x = gs_denormalize(f32(nxy[0]), W_in);
var y = gs_denormalize(f32(nxy[1]), H_in);
${Zp(C,w,o)}
}`;return{name:"GridSample",shaderCache:{hint:`${o.cacheKey}`,inputDependencies:["type","type"]},getRunData:B=>{let E=He.size(b);return{outputs:[{dims:b,dataType:B[0].dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:v}},getShaderSource:D}},lC=(a,o)=>{Vp(a.inputs),a.compute(Jp(a.inputs,o))},cC=a=>Ut({alignCorners:a.align_corners,mode:a.mode,paddingMode:a.padding_mode,format:a.format})}),js,qp,uC,k2,$p,Yo,dC,fC=IA(()=>{it(),It(),Hr(),yc(),Tc(),kt(),qn(),js=(a,o)=>a.length>o&&a[o].dims.length>0?a[o]:void 0,qp=(a,o)=>{let n=a[0],u=js(a,1),p=js(a,2),b=js(a,3),C=js(a,4),w=js(a,5),M=js(a,6),v=js(a,7);if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let D=n.dims[0],B=n.dims[1],E=n.dims.length===3?n.dims[2]:o.numHeads*n.dims[4],S=B,F=0,j=0,Z=Math.floor(E/o.numHeads);if(M&&v&&He.size(M.dims)&&He.size(v.dims)){if(M.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(M.dims[0]!==D||M.dims[1]!==o.numHeads||M.dims[3]!==Z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(v.dims[0]!==D||v.dims[1]!==o.numHeads||v.dims[3]!==Z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(M.dims[2]!==v.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(v.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');F=M.dims[2],j=M.dims[2]}else if(M&&He.size(M.dims)||v&&He.size(v.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let R;if(u&&He.size(u.dims)>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(u.dims.length<3||u.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==u.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(u.dims.length===3){if(u.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');R=2,S=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==o.numHeads||u.dims[3]!==2||u.dims[4]!==Z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(p)throw new Error('Expect "value" be none when "key" has packed kv format.');R=5,S=u.dims[1]}else{if(u.dims[1]!==o.numHeads||u.dims[3]!==Z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');R=0,S=u.dims[2]}}else{if(n.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(n.dims[2]!==o.numHeads||n.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');R=3}if(b&&He.size(b.dims)>0){if(b.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(u&&u.dims.length===5&&u.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let z=F+S,U=0;if(C&&He.size(C.dims)>0){U=8;let d=C.dims;throw d.length===1?d[0]===D?U=1:d[0]===3*D+2&&(U=3):d.length===2&&d[0]===D&&d[1]===z&&(U=5),U===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let f=!1,k=E;if(p&&He.size(p.dims)>0){if(p.dims.length!==3&&p.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==p.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(p.dims.length===3){if(S!==p.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');k=p.dims[2]}else{if(S!==p.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');k=p.dims[1]*p.dims[3],f=!0}}let e=!1;if(C&&He.size(C.dims)>0)throw new Error("Key padding mask is not supported");if(w&&He.size(w.dims)>0){if(w.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(w.dims[0]!==D||w.dims[1]!==o.numHeads||w.dims[2]!==B||w.dims[3]!==z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:D,sequenceLength:B,pastSequenceLength:F,kvSequenceLength:S,totalSequenceLength:z,maxSequenceLength:j,inputHiddenSize:0,hiddenSize:E,vHiddenSize:k,headSize:Z,vHeadSize:Math.floor(k/o.numHeads),numHeads:o.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:o.maskFilterValue,maskType:U,scale:o.scale,broadcastResPosBias:e,passPastInKv:f,qkvFormat:R}},uC=a=>Ut({...a}),k2=Ut({perm:[0,2,1,3]}),$p=(a,o,n,u,p,b,C)=>{let w=[u,p,b],M=He.size(w),v=[{type:12,data:M},{type:12,data:C},{type:12,data:b}],D=B=>{let E=XA("qkv_with_bias",o.dataType,w),S=nA("qkv",o.dataType,w),F=nA("bias",n.dataType,w),j=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
${B.registerUniforms(j).declareVariables(S,F,E)}
${B.mainStart()}
${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;
qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
}`};return a.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:w,dataType:o.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(M/64)},programUniforms:v}),getShaderSource:D},{inputs:[o,n],outputs:[-1]})[0]},Yo=(a,o,n,u,p,b,C,w)=>{let M=b;if(C&&He.size(C.dims)>0){if(u===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return M=$p(a,b,C,o,u,n*p,w),M=M.reshape([o,u,n,p]),n===1||u===1?M:a.compute(pa(M,k2.perm),{inputs:[M],outputs:[-1]})[0]}else return b.dims.length===3&&(M=b.reshape([o,u,n,p])),n===1||u===1?M:a.compute(pa(M,k2.perm),{inputs:[M],outputs:[-1]})[0]},dC=(a,o)=>{let n=qp(a.inputs,o),u=a.inputs[0],p=js(a.inputs,1),b=js(a.inputs,2),C=js(a.inputs,3),w=js(a.inputs,4),M=js(a.inputs,5),v=js(a.inputs,6),D=js(a.inputs,7);if(u.dims.length===5)throw new Error("Packed QKV is not implemented");if(p?.dims.length===5)throw new Error("Packed KV is not implemented");let B=p&&b&&p.dims.length===4&&b.dims.length===4,E=Yo(a,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,u,C,0);if(B)return Jo(a,E,p,b,w,void 0,v,D,M,n);if(!p||!b)throw new Error("key and value must be provided");let S=Yo(a,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,p,C,n.hiddenSize),F=Yo(a,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,b,C,2*n.hiddenSize);Jo(a,E,S,F,w,void 0,v,D,M,n)}}),e4,A4,t4,r4,dc,gC,pC,mC=IA(()=>{it(),It(),Hr(),kt(),e4=a=>{if(!a||a.length<1)throw new Error("too few inputs")},A4=(a,o)=>{let n=[],u=o.numOutputs;return a[1].dims[0]>0&&(a[1].getBigInt64Array().forEach(p=>n.push(Number(p))),u=n.length),Ut({numOutputs:u,axis:o.axis,splitSizes:n})},t4=a=>`
fn calculateOutputIndex(index: u32) -> u32 {
for (var i: u32 = 0u; i < ${a}u; i += 1u ) {
if (index < ${ZA("uniforms.size_in_split_axis","i",a)}) {
return i;
}
}
return ${a}u;
}`,r4=a=>{let o=a.length,n=[];for(let u=0;u<o;++u){let p=a[u].setByIndices("indices","input[global_idx]");o===1?n.push(p):u===0?n.push(`if (output_number == ${u}u) { ${p} }`):u===o-1?n.push(`else { ${p} }`):n.push(`else if (output_number == ${u}) { ${p} }`)}return`
fn writeBufferData(output_number: u32, indices: ${a[0].type.indices}, global_idx: u32) {
${n.join(`
`)}
}`},dc=(a,o)=>{let n=a[0].dims,u=He.size(n),p=a[0].dataType,b=He.normalizeAxis(o.axis,n.length),C=new Array(o.numOutputs),w=nA("input",p,n.length),M=new Array(o.numOutputs),v=[],D=[],B=0,E=[{type:12,data:u}];for(let F=0;F<o.numOutputs;F++){B+=o.splitSizes[F],M[F]=B;let j=n.slice();j[b]=o.splitSizes[F],D.push(j),C[F]=XA(`output${F}`,p,j.length),v.push({dims:D[F],dataType:a[0].dataType})}E.push({type:12,data:M},...et(n,...D));let S=F=>`
${F.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",M.length).declareVariables(w,...C)}
${t4(M.length)}
${r4(C)}
${F.mainStart()}
${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}
var indices = ${w.offsetToIndices("global_idx")};
var index = ${w.indicesGet("indices",b)};
let output_number = calculateOutputIndex(index);
if (output_number != 0) {
index -= ${ZA("uniforms.size_in_split_axis","output_number - 1u",M.length)};
${w.indicesSet("indices",b,"index")};
}
writeBufferData(output_number, indices, global_idx);
}`;return{name:"Split",shaderCache:{hint:o.cacheKey,inputDependencies:["rank"]},getShaderSource:S,getRunData:()=>({outputs:v,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:E})}},gC=(a,o)=>{e4(a.inputs);let n=a.inputs.length===1?o:A4(a.inputs,o);a.compute(dc(a.inputs,n),{inputs:[0]})},pC=a=>{let o=a.axis,n=a.splitSizes,u=a.numOutputs<0?n.length:a.numOutputs;if(u!==n.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Ut({axis:o,numOutputs:u,splitSizes:n})}}),s4,W0,hC,CC=IA(()=>{it(),It(),Hr(),kt(),s4=(a,o)=>{let[n,u,p,b]=a,{numHeads:C,rotaryEmbeddingDim:w}=o;if(n.dims.length!==3&&n.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${n.dims.length}`);if(!He.areEqual(u.dims,[])&&!He.areEqual(u.dims,[1])&&u.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${u.dims.length}`);if(p.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${p.dims.length}`);if(b.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${b.dims.length}`);if(!He.areEqual(p.dims,b.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(w>0&&C===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let M=n.dims[0],v=n.dims[n.dims.length-2],D=p.dims[0],B=He.sizeFromDimension(n.dims,1)/v,E=w===0?p.dims[1]*2:B/C;if(w>E)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(u.dims.length===2){if(M!==u.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${u.dims[0]}`);if(v!==u.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${u.dims[1]}`)}if(E/2!==p.dims[1]&&w/2!==p.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${p.dims[1]}`);if(v>D)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},W0=(a,o)=>{let{interleaved:n,numHeads:u,rotaryEmbeddingDim:p,scale:b}=o,C=a[0].dims[0],w=He.sizeFromDimension(a[0].dims,1),M=a[0].dims[a[0].dims.length-2],v=w/M,D=a[2].dims[1],B=p===0?D*2:v/u,E=new Array(C,M,v/B,B-D),S=He.computeStrides(E),F=[{type:1,data:b},{type:12,data:E},{type:12,data:S},...a[0].dims.length===3?new Array({type:12,data:[w,v,B,1]}):[],...a[0].dims.length===4?new Array({type:12,data:[w,B,M*B,1]}):[],...et(a[0].dims,a[1].dims,a[2].dims,a[3].dims,a[0].dims)],j=Z=>{let R=nA("input",a[0].dataType,a[0].dims.length),z=nA("position_ids",a[1].dataType,a[1].dims.length),U=nA("cos_cache",a[2].dataType,a[2].dims.length),f=nA("sin_cache",a[3].dataType,a[3].dims.length),k=XA("output",a[0].dataType,a[0].dims.length);return Z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:E.length},{name:"global_strides",type:"u32",length:S.length},{name:"input_output_strides",type:"u32",length:S.length}]),`
${Z.declareVariables(R,z,U,f,k)}
${Z.mainStart(lo)}
let half_rotary_emb_dim = uniforms.${U.name}_shape[1];
let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
let size = uniforms.global_shape[0] * uniforms.global_strides[0];
${Z.guardAgainstOutOfBoundsWorkgroupSizes("size")}
if (bsnh[3] < half_rotary_emb_dim) {
let position_ids_idx =
${z.broadcastedIndicesToOffset("bsnh.xy",XA("",z.type.tensor,2))};
let position_id =
u32(${z.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${n});
let j = i + select(half_rotary_emb_dim, 1, ${n});
let re = ${R.getByOffset("i")} * ${U.get("position_id","bsnh[3]")} -
${R.getByOffset("j")} * ${f.get("position_id","bsnh[3]")};
${k.setByOffset("i","re")}
let im = ${R.getByOffset("i")} * ${f.get("position_id","bsnh[3]")} +
${R.getByOffset("j")} * ${U.get("position_id","bsnh[3]")};
${k.setByOffset("j","im")}
} else {
let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
${k.setByOffset("k",R.getByOffset("k"))}
}
}`};return{name:"RotaryEmbedding",shaderCache:{hint:Ut({interleaved:n}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:j,getRunData:()=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(He.size(E)/lo)},programUniforms:F})}},hC=(a,o)=>{s4(a.inputs,o),a.compute(W0(a.inputs,o))}}),a4,n4,M2,i4,bC,N6=IA(()=>{Hr(),it(),Tc(),fC(),mC(),qn(),CC(),kt(),a4=(a,o)=>{if(o.doRotary&&a.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let n=a[0],u=a[1],p=a[2],b=a[3],C=a[4];if(o.doRotary!==0&&a.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(o.localWindowSize!==-1)throw new Error("Local attention is not supported");if(o.softcap!==0)throw new Error("Softcap is not supported");if(o.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(o.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let w=!1,M=n.dims[0],v=n.dims[1],D=n.dims.length===3?w?n.dims[2]/3:n.dims[2]:o.numHeads*n.dims[4],B=v,E=0,S=!u||u.dims.length===0,F=Math.floor(S?D/(o.numHeads+2*o.kvNumHeads):D/o.numHeads);S&&(D=F*o.numHeads);let j=b&&b.dims.length!==0,Z=C&&C.dims.length!==0;if(j&&b.dims.length===4&&b.dims[0]===M&&b.dims[1]!==o.kvNumHeads&&b.dims[2]===o.kvNumHeads&&b.dims[3]===F)throw new Error("BSNH pastKey/pastValue is not supported");if(j&&Z){if(b.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(C.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');E=b.dims[2]}else if(j||Z)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let R=1;if(u&&u.dims.length>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(u.dims.length<3||u.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==u.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(u.dims.length===3){if(n.dims[2]%u.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');B=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==o.numHeads||u.dims[3]!==2||u.dims[4]!==F)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(p)throw new Error('Expect "value" be none when "key" has packed kv format.');B=u.dims[1]}else{if(u.dims[1]!==o.numHeads||u.dims[3]!==F)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');B=u.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(n.dims.length===5&&(n.dims[2]!==o.numHeads||n.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');R=3}let z=0,U=!1,f=o.kvNumHeads?F*o.kvNumHeads:D;if(p&&p.dims.length>0){if(p.dims.length!==3&&p.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==p.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(p.dims.length===3){if(B!==p.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');f=p.dims[2]}else{if(B!==p.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');f=p.dims[1]*p.dims[3],U=!0}}let k=a.length>4?a[5]:void 0;if(k&&k.dims.length!==1&&k.dims[0]!==M)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:M,sequenceLength:v,pastSequenceLength:E,kvSequenceLength:B,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:D,vHiddenSize:f,headSize:F,vHeadSize:Math.floor(f/o.kvNumHeads),numHeads:o.numHeads,kvNumHeads:o.kvNumHeads,nReps:o.numHeads/o.kvNumHeads,pastPresentShareBuffer:!1,maskType:z,scale:o.scale,broadcastResPosBias:!1,passPastInKv:U,qkvFormat:R}},n4=Ut({perm:[0,2,1,3]}),M2=(a,o,n)=>{let u=o,p=n.kvNumHeads;return o.dims.length===3&&n.kvSequenceLength!==0&&(u=o.reshape([n.batchSize,n.kvSequenceLength,p,n.headSize]),u=a.compute(pa(u,n4.perm),{inputs:[u],outputs:[-1]})[0]),u},i4=(a,o,n,u)=>{let p=7,b=["type","type"],C=[a*o],w=a*o,M=[{type:12,data:w},{type:12,data:o},{type:12,data:a}],v=D=>{let B=nA("seq_lens",n.dataType,n.dims),E=nA("total_seq_lens",u.dataType,u.dims),S=XA("pos_ids",p,C),F=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
${D.registerUniforms(F).declareVariables(B,E,S)}
${D.mainStart()}
${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let total_sequence_length = u32(${E.getByOffset("0")});
let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
let batch_idx = global_idx / uniforms.sequence_length;
let sequence_idx = i32(global_idx % uniforms.sequence_length);
var pos_id: i32 = 0;
let seqlen = ${B.getByOffset("batch_idx")};
let total_seqlen = seqlen + 1;
if (is_first_prompt) {
if (sequence_idx < total_seqlen) {
pos_id = sequence_idx;
} else {
pos_id = 1;
}
${S.setByOffset("global_idx","pos_id")}
} else if (is_subsequent_prompt) {
let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
if (past_seqlen + sequence_idx < total_seqlen) {
pos_id = past_seqlen + sequence_idx;
} else {
pos_id = 1;
}
${S.setByOffset("global_idx","pos_id")}
} else if (global_idx < uniforms.batch_size) {
${S.setByOffset("global_idx","seqlen")}
};
}
`};return{name:"GeneratePositionIds",shaderCache:{hint:`${a};${o}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:C,dataType:p}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:M}),getShaderSource:v}},bC=(a,o)=>{let n=a4(a.inputs,o);if(a.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(a.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let u=a.inputs[0],p=a.inputs[1]&&a.inputs[1].dims.length>0?a.inputs[1]:void 0,b=a.inputs[2]&&a.inputs[2].dims.length>0?a.inputs[2]:void 0,C=a.inputs[3]&&a.inputs[3].dims.length!==0?a.inputs[3]:void 0,w=a.inputs[4]&&a.inputs[4].dims.length!==0?a.inputs[4]:void 0,M=a.inputs.length>4?a.inputs[5]:void 0,v=a.inputs.length>5?a.inputs[6]:void 0,D=n.kvNumHeads?n.kvNumHeads:n.numHeads,B=Ut({axis:2,numOutputs:3,splitSizes:[n.numHeads*n.headSize,D*n.headSize,D*n.headSize]}),[E,S,F]=!p&&!b?a.compute(dc([u],B),{inputs:[u],outputs:[-1,-1,-1]}):[u,p,b],j,Z;if(o.doRotary){let f=a.compute(i4(n.batchSize,n.sequenceLength,M,v),{inputs:[M,v],outputs:[-1]})[0],k=a.inputs[7],e=a.inputs[8],d=Ut({interleaved:o.rotaryInterleaved!==0,numHeads:n.numHeads,rotaryEmbeddingDim:0,scale:o.scale}),y=[E,f,k,e],Ae=[-1];j=a.compute(W0(y,d),{inputs:y,outputs:Ae})[0],y.splice(0,1,S);let P=Ut({interleaved:o.rotaryInterleaved!==0,numHeads:n.kvNumHeads,rotaryEmbeddingDim:0,scale:o.scale});Z=a.compute(W0(y,P),{inputs:y,outputs:Ae})[0]}let R=Yo(a,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,o.doRotary?j:E,void 0,0),z=M2(a,o.doRotary?Z:S,n),U=M2(a,F,n);Jo(a,R,z,U,void 0,void 0,C,w,void 0,n,M,v)}}),E2,o4,l4,IC,L6=IA(()=>{it(),It(),qn(),kt(),E2=(a,o,n,u,p,b,C,w)=>{let M=Rr(b),v=M===1?"f32":`vec${M}f`,D=M===1?"vec2f":`mat2x${M}f`,B=p*C,E=64;B===1&&(E=256);let S=[p,C,b/M],F=[p,C,2],j=["rank","type","type"],Z=[];Z.push(...et(S,F));let R=z=>{let U=nA("x",o.dataType,3,M),f=nA("scale",n.dataType,n.dims),k=nA("bias",u.dataType,u.dims),e=XA("output",1,3,2),d=[U,f,k,e];return`
var<workgroup> workgroup_shared : array<${D}, ${E}>;
const workgroup_size = ${E}u;
${z.declareVariables(...d)}
${z.mainStart(E)}
let batch = workgroup_index / uniforms.x_shape[1];
let channel = workgroup_index % uniforms.x_shape[1];
let hight = uniforms.x_shape[2];
// initialize workgroup memory
var sum = ${v}(0);
var squared_sum = ${v}(0);
for (var h = local_idx; h < hight; h += workgroup_size) {
let value = ${v}(${U.get("batch","channel","h")});
sum += value;
squared_sum += value * value;
}
workgroup_shared[local_idx] = ${D}(sum, squared_sum);
workgroupBarrier();
for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) {
if (local_idx < currSize) {
workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
}
workgroupBarrier();
}
if (local_idx == 0) {
let sum_final = ${Jn("workgroup_shared[0][0]",M)} / f32(hight * ${M});
let squared_sum_final = ${Jn("workgroup_shared[0][1]",M)} / f32(hight * ${M});
let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${w}));
let channel_scale = inv_std_dev * f32(scale[channel]);
let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
output[workgroup_index] = vec2f(channel_scale, channel_shift);
}
}`};return a.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${M};${w};${E}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:F,dataType:1}],dispatchGroup:{x:B},programUniforms:Z}),getShaderSource:R},{inputs:[o,n,u],outputs:[-1]})[0]},o4=(a,o,n)=>{let u=o[0].dims,p=u,b=2,C=u[0],w=u[1],M=He.sizeFromDimension(u,b),v=Rr(M),D=He.size(p)/v,B=E2(a,o[0],o[1],o[2],C,M,w,n.epsilon),E=[C,w,M/v],S=[C,w],F=["type","none"],j=Z=>{let R=nA("x",o[0].dataType,E.length,v),z=nA("scale_shift",1,S.length,2),U=XA("output",o[0].dataType,E.length,v),f=[R,z,U];return`
${Z.registerUniform("output_size","u32").declareVariables(...f)}
${Z.mainStart()}
${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let outputIndices = ${U.offsetToIndices("global_idx")};
let batch = outputIndices[0];
let channel = outputIndices[1];
let scale_shift = ${z.getByIndices("vec2<u32>(batch, channel)")};
let value = ${R.getByOffset("global_idx")} * ${U.type.value}(scale_shift.x) + ${U.type.value}(scale_shift.y);
${U.setByOffset("global_idx","value")};
}`};a.compute({name:"InstanceNormalization",shaderCache:{hint:`${v}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:p,dataType:o[0].dataType}],dispatchGroup:{x:Math.ceil(D/64)},programUniforms:[{type:12,data:D},...et(E,S,E)]}),getShaderSource:j},{inputs:[o[0],B]})},l4=(a,o,n)=>{let u=o[0].dims,p=u,b=u[0],C=u[u.length-1],w=He.sizeFromDimension(u,1)/C,M=Rr(C),v=He.size(p)/M,D=[{type:12,data:w},{type:12,data:Math.floor(C/M)}],B=["type","type"],E=!1,S=[0,u.length-1];for(let R=0;R<u.length-2;R++)E=E||u[R+1]!==1,S.push(R+1);E=E&&u[u.length-1]!==1;let F=E?a.compute(pa(a.inputs[0],S),{inputs:[a.inputs[0]],outputs:[-1]})[0]:a.inputs[0].reshape(Array.from({length:u.length},(R,z)=>u[S[z]])),j=E2(a,F,o[1],o[2],b,w,C,n.epsilon),Z=R=>{let z=hs(o[0].dataType),U=M===1?"vec2f":`mat${M}x2f`,f=d=>{let y=d===0?"x":"y",Ae=M===1?"f32":`vec${M}f`;switch(M){case 1:return`${z}(${Ae}(scale.${y}))`;case 2:return`vec2<${z}>(${Ae}(scale[0].${y}, scale[1].${y}))`;case 4:return`vec4<${z}>(${Ae}(scale[0].${y}, scale[1].${y}, scale[2].${y}, scale[3].${y}))`;default:throw new Error(`Not supported compoents ${M}`)}},k=nA("input",o[0].dataType,o[0].dims,M),e=XA("output",o[0].dataType,p,M);return`
@group(0) @binding(0) var<storage, read> input : array<${k.type.storage}>;
@group(0) @binding(1) var<storage, read> scale_input : array<${U}>;
@group(0) @binding(2) var<storage, read_write> output : array<${e.type.storage}>;
struct Uniforms {H: u32, C : u32};
@group(0) @binding(3) var<uniform> uniforms: Uniforms;
${R.mainStart()}
let current_image_number = global_idx / (uniforms.C * uniforms.H);
let current_channel_number = global_idx % uniforms.C;
let scale_offset = current_image_number * uniforms.C + current_channel_number;
let scale = scale_input[scale_offset];
output[global_idx] = fma(input[global_idx], ${f(0)}, ${f(1)});
}`};a.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${M}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:p,dataType:o[0].dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:D}),getShaderSource:Z},{inputs:[o[0],j]})},IC=(a,o)=>{o.format==="NHWC"?l4(a,a.inputs,o):o4(a,a.inputs,o)}}),c4,u4,wC,R6=IA(()=>{it(),It(),kt(),c4=a=>{if(!a||a.length<2)throw new Error("layerNorm requires at least 2 inputs.")},u4=(a,o,n)=>{let u=o.simplified,p=a[0].dims,b=a[1],C=!u&&a[2],w=p,M=He.normalizeAxis(o.axis,p.length),v=He.sizeToDimension(p,M),D=He.sizeFromDimension(p,M),B=He.size(b.dims),E=C?He.size(C.dims):0;if(B!==D||C&&E!==D)throw new Error(`Size of X.shape()[axis:] == ${D}.
Size of scale and bias (if provided) must match this.
Got scale size of ${B} and bias size of ${E}`);let S=[];for(let k=0;k<p.length;++k)k<M?S.push(p[k]):S.push(1);let F=Rr(D),j=["type","type"],Z=[{type:12,data:v},{type:1,data:D},{type:12,data:Math.floor(D/F)},{type:1,data:o.epsilon}];C&&j.push("type");let R=n>1,z=n>2,U=k=>{let e=hs(a[0].dataType),d=[nA("x",a[0].dataType,a[0].dims,F),nA("scale",b.dataType,b.dims,F)];C&&d.push(nA("bias",C.dataType,C.dims,F)),d.push(XA("output",a[0].dataType,w,F)),R&&d.push(XA("mean_data_output",1,S)),z&&d.push(XA("inv_std_output",1,S));let y=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
${k.registerUniforms(y).declareVariables(...d)}
${k.mainStart()}
${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
let offset = global_idx * uniforms.norm_size_vectorized;
var mean_vector = ${rc("f32",F)};
var mean_square_vector = ${rc("f32",F)};
for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
let value = ${ro(e,F,"x[h + offset]")};
mean_vector += value;
mean_square_vector += value * value;
}
let mean = ${Jn("mean_vector",F)} / uniforms.norm_size;
let inv_std_dev = inverseSqrt(${Jn("mean_square_vector",F)} / uniforms.norm_size ${u?"":"- mean * mean"} + uniforms.epsilon);
for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
let f32input = ${ro(e,F,"x[j + offset]")};
let f32scale = ${ro(e,F,"scale[j]")};
output[j + offset] = ${d[0].type.value}((f32input ${u?"":"- mean"}) * inv_std_dev * f32scale
${C?`+ ${ro(e,F,"bias[j]")}`:""}
);
}
${R?"mean_data_output[global_idx] = mean":""};
${z?"inv_std_output[global_idx] = inv_std_dev":""};
}`},f=[{dims:w,dataType:a[0].dataType}];return R&&f.push({dims:S,dataType:1}),z&&f.push({dims:S,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${F};${n};${u}`,inputDependencies:j},getRunData:()=>({outputs:f,dispatchGroup:{x:Math.ceil(v/64)},programUniforms:Z}),getShaderSource:U}},wC=(a,o)=>{c4(a.inputs),a.compute(u4(a.inputs,o,a.outputCount))}}),d4,kC,j6=IA(()=>{It(),Oc(),_c(),d4=a=>{if(!a||a.length!==2)throw new Error("MatMul requires 2 inputs.");if(a[0].dims[a[0].dims.length-1]!==a[1].dims[a[1].dims.length-2])throw new Error("shared dimension does not match.")},kC=a=>{d4(a.inputs);let o=oo.calcShape(a.inputs[0].dims,a.inputs[1].dims,!0);if(!o)throw new Error("Can't use matmul on the given tensors");let n=o[o.length-1],u=a.inputs[0].dims[a.inputs[0].dims.length-1];if(n<8&&u<8)a.compute(Sc(a.inputs,{activation:""},o));else{let p=o[o.length-2],b=He.size(a.inputs[0].dims.slice(0,-2)),C=He.size(a.inputs[1].dims.slice(0,-2));if(b!==1&&p===1&&C===1){let w=a.inputs[0].reshape([1,b,u]),M=a.inputs[1].reshape([1,u,n]),v=[1,b,n],D=[w,M];a.compute(j0(D,{activation:""},o,v),{inputs:D})}else a.compute(j0(a.inputs,{activation:""},o))}}}),f4,g4,p4,MC,EC,W6=IA(()=>{it(),It(),Hr(),kt(),f4=(a,o)=>{if(a.length<3||a.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let n=a[0],u=n.dims.length;if(n.dims[u-1]!==o.k)throw new Error("The last dim of input shape does not match the k value");let p=Math.floor((o.k+o.blockSize-1)/o.blockSize),b=o.blockSize/8*o.bits,C=a[1];if(!He.areEqual(C.dims,[o.n,p,b]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let w=a[2].dims;if(He.size(w)!==o.n*p)throw new Error("scales input size error.");if(a.length===4){let M=a[3].dims,v=o.bits>4?o.n*p:o.n*Math.floor((p+1)/2);if(He.size(M)!==v)throw new Error("zeroPoints input size error.")}},g4=(a,o)=>{let n=a[0].dims,u=n.length,p=n[u-2],b=o.k,C=o.n,w=n.slice(0,u-2),M=He.size(w),v=a[1].dims[2]/4,D=a[0].dataType,B=Rr(o.k),E=Rr(v),S=Rr(C),F=w.concat([p,C]),j=p>1&&C/S%2===0?2:1,Z=He.size(F)/S/j,R=64,z=[],U=[M,p,b/B],f=He.convertShape(a[1].dims).slice();f.splice(-1,1,v/E),z.push(...et(U)),z.push(...et(f)),z.push(...et(a[2].dims)),a.length===4&&z.push(...et(He.convertShape(a[3].dims)));let k=[M,p,C/S];z.push(...et(k));let e=d=>{let y=U.length,Ae=nA("a",a[0].dataType,y,B),P=nA("b",12,f.length,E),O=nA("scales",a[2].dataType,a[2].dims.length),pe=[Ae,P,O],ee=a.length===4?nA("zero_points",12,a[3].dims.length):void 0;ee&&pe.push(ee);let be=k.length,ke=XA("output",a[0].dataType,be,S),Me=hs(a[0].dataType),De=(()=>{switch(B){case 1:return`array<${Me}, 8>`;case 2:return`mat4x2<${Me}>`;case 4:return`mat2x4<${Me}>`;default:throw new Error(`${B}-component is not supported.`)}})(),ye=()=>{let Pe=`
// reuse a data
var input_offset = ${Ae.indicesToOffset(`${Ae.type.indices}(batch, row, word_offset)`)};
var a_data: ${De};
for (var j: u32 = 0; j < ${8/B}; j++) {
a_data[j] = ${Ae.getByOffset("input_offset")};
input_offset++;
}
`;for(let Ce=0;Ce<S*j;Ce++)Pe+=`
b_value = ${E===1?`b${Ce}_data`:`b${Ce}_data[i]`};
b_value_lower = unpack4xU8(b_value & b_mask);
b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
b_quantized_values = ${De}(${Array.from({length:4},(ie,se)=>`${Me}(b_value_lower[${se}]), ${Me}(b_value_upper[${se}])`).join(", ")});
b_dequantized_values = ${B===1?`${De}(${Array.from({length:8},(ie,se)=>`(b_quantized_values[${se}] - ${ee?`zero_point${Ce}`:"zero_point"}) * scale${Ce}`).join(", ")});`:`(b_quantized_values - ${De}(${Array(8).fill(`${ee?`zero_point${Ce}`:"zero_point"}`).join(",")})) * scale${Ce};`};
workgroup_shared[local_id.x * ${j} + ${Math.floor(Ce/S)}]${S>1?`[${Ce%S}]`:""} += ${Array.from({length:8/B},(ie,se)=>`${B===1?`a_data[${se}] * b_dequantized_values[${se}]`:`dot(a_data[${se}], b_dequantized_values[${se}])`}`).join(" + ")};
`;return Pe},_e=()=>{let Pe=`
var col_index = col * ${S};
${ee?`
let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
var zero_point_byte_count: u32;
var zero_point_word_index: u32;
var zero_point_byte_offset: u32;
let zero_point_nibble_offset: u32 = block & 0x1u;
var zero_point_bits_offset: u32;
var zero_point_word: u32;`:`
// The default zero point is 8 for unsigned 4-bit quantization.
let zero_point = ${Me}(8);`}
`;for(let Ce=0;Ce<S*j;Ce++)Pe+=`
let scale${Ce} = ${O.getByOffset("col_index * nBlocksPerCol + block")};
${ee?`
zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
zero_point_word_index = zero_point_byte_count >> 0x2u;
zero_point_byte_offset = zero_point_byte_count & 0x3u;
zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
zero_point_word = ${ee.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
let zero_point${Ce} = ${Me}((zero_point_word) & 0xFu);`:""}
col_index += 1;`;return Pe},Ne=()=>{let Pe=`col_index = col * ${S};`;for(let Ce=0;Ce<S*j;Ce++)Pe+=`
let b${Ce}_data = ${P.getByIndices(`${P.type.indices}(col_index, block, word)`)};
col_index += 1;`;return Pe+=`
var b_value: u32;
let b_mask: u32 = 0x0F0F0F0Fu;
var b_value_lower: vec4<u32>;
var b_value_upper: vec4<u32>;
var b_quantized_values: ${De};
var b_dequantized_values: ${De};`,Pe};return`
var<workgroup> workgroup_shared: array<${ke.type.value}, ${j*R}>;
${d.declareVariables(...pe,ke)}
${d.mainStart([R,1,1])}
let output_indices = ${ke.offsetToIndices(`(global_idx / ${R}) * ${j}`)};
let col = output_indices[2];
let row = output_indices[1];
let batch = output_indices[0];
let nBlocksPerCol = uniforms.b_shape[1];
for (var block = local_id.x; block < nBlocksPerCol; block += ${R}) {
//process one block
var word_offset: u32 = block * ${o.blockSize/B};
${_e()}
for (var word: u32 = 0; word < ${v}; word += ${E}) {
${Ne()}
for (var i: u32 = 0; i < ${E}; i++) {
${ye()}
word_offset += ${8/B};
}
}
}
workgroupBarrier();
if (local_id.x < ${j}) {
var output_value: ${ke.type.value} = ${ke.type.value}(0);
var workgroup_shared_offset: u32 = local_id.x;
for (var b: u32 = 0u; b < ${R}u; b++) {
output_value += workgroup_shared[workgroup_shared_offset];
workgroup_shared_offset += ${j};
}
${ke.setByIndices(`${ke.type.indices}(batch, row, col + local_id.x)`,"output_value")};
}
}`};return{name:"MatMulNBits",shaderCache:{hint:`${o.blockSize};${o.bits};${B};${E};${S};${j};${R}`,inputDependencies:Array(a.length).fill("rank")},getRunData:()=>({outputs:[{dims:F,dataType:D}],dispatchGroup:{x:Z},programUniforms:z}),getShaderSource:e}},p4=(a,o)=>{let n=a[0].dims,u=n.length,p=n[u-2],b=o.k,C=o.n,w=n.slice(0,u-2),M=He.size(w),v=a[1].dims[2]/4,D=a[0].dataType,B=Rr(o.k),E=Rr(v),S=w.concat([p,C]),F=128,j=C%8===0?8:C%4===0?4:1,Z=F/j,R=Z*E*8,z=R/B,U=R/o.blockSize,f=He.size(S)/j,k=[],e=[M,p,b/B],d=He.convertShape(a[1].dims).slice();d.splice(-1,1,v/E),k.push(...et(e)),k.push(...et(d)),k.push(...et(a[2].dims)),a.length===4&&k.push(...et(He.convertShape(a[3].dims)));let y=[M,p,C];k.push(...et(y));let Ae=P=>{let O=e.length,pe=nA("a",a[0].dataType,O,B),ee=nA("b",12,d.length,E),be=nA("scales",a[2].dataType,a[2].dims.length),ke=[pe,ee,be],Me=a.length===4?nA("zero_points",12,a[3].dims.length):void 0;Me&&ke.push(Me);let De=y.length,ye=XA("output",a[0].dataType,De),_e=hs(a[0].dataType),Ne=()=>{switch(B){case 1:return`
let a_data0 = vec4<${_e}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
let a_data1 = vec4<${_e}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
let a_data0 = vec4<${_e}>(sub_a[word_offset], sub_a[word_offset + 1]);
let a_data1 = vec4<${_e}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
let a_data0 = sub_a[word_offset];
let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${B}-component is not supported.`)}};return`
var<workgroup> sub_a: array<${pe.type.value}, ${z}>;
var<workgroup> inter_results: array<array<${ye.type.value}, ${Z}>, ${j}>;
${P.declareVariables(...ke,ye)}
${P.mainStart([Z,j,1])}
let output_indices = ${ye.offsetToIndices(`workgroup_index * ${j}`)};
let col = output_indices[2];
let row = output_indices[1];
let batch = output_indices[0];
let n_blocks_per_col = uniforms.b_shape[1];
let num_tiles = (n_blocks_per_col - 1) / ${U} + 1;
// Loop over shared dimension.
for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
let a_col_start = tile * ${z};
// load one tile A data into shared memory.
for (var a_offset = local_idx; a_offset < ${z}; a_offset += ${F})
{
let a_col = a_col_start + a_offset;
if (a_col < uniforms.a_shape[2])
{
sub_a[a_offset] = ${pe.getByIndices(`${pe.type.indices}(batch, row, a_col)`)};
} else {
sub_a[a_offset] = ${pe.type.value}(0);
}
}
workgroupBarrier();
// each thread process one block
let b_row = col + local_id.y;
let block = tile * ${U} + local_id.x;
${Me?`
let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
let zero_point_word_index = zero_point_byte_count >> 0x2u;
let zero_point_byte_offset = zero_point_byte_count & 0x3u;
let zero_point_nibble_offset: u32 = block & 0x1u;
let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
let zero_point_word = ${Me.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
let zero_point = ${_e}((zero_point_word) & 0xFu);`:`
// The default zero point is 8 for unsigned 4-bit quantization.
let zero_point = ${_e}(8);`}
let scale = ${be.getByOffset("b_row * n_blocks_per_col + block")};
let b_data = ${ee.getByIndices(`${ee.type.indices}(b_row, block, 0)`)};
var word_offset = local_id.x * ${o.blockSize/B};
for (var i: u32 = 0; i < ${E}; i++) {
${Ne()}
let b_value = ${E===1?"b_data":"b_data[i]"};
let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
let b_quantized_values = mat2x4<${_e}>(${Array.from({length:4},(Pe,Ce)=>`${_e}(b_value_lower[${Ce}]), ${_e}(b_value_upper[${Ce}])`).join(", ")});
let b_dequantized_values = (b_quantized_values - mat2x4<${_e}>(${Array(8).fill("zero_point").join(",")})) * scale;
inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(Pe,Ce)=>`${`dot(a_data${Ce}, b_dequantized_values[${Ce}])`}`).join(" + ")};
word_offset += ${8/B};
}
workgroupBarrier();
}
if (local_idx < ${j}) {
var output_value: ${ye.type.value} = ${ye.type.value}(0);
for (var b = 0u; b < ${Z}; b++) {
output_value += inter_results[local_idx][b];
}
if (col + local_idx < uniforms.output_shape[2])
{
${ye.setByIndices(`${ye.type.indices}(batch, row, col + local_idx)`,"output_value")}
}
}
}`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${o.blockSize};${B};${E};${Z};${j}`,inputDependencies:Array(a.length).fill("rank")},getRunData:()=>({outputs:[{dims:S,dataType:D}],dispatchGroup:{x:f},programUniforms:k}),getShaderSource:Ae}},MC=(a,o)=>{f4(a.inputs,o),o.blockSize===32&&a.adapterInfo.isVendor("intel")&&a.adapterInfo.isArchitecture("gen-12lp")?a.compute(p4(a.inputs,o)):a.compute(g4(a.inputs,o))},EC=a=>Ut(a)}),m4,h4,C4,b4,I4,w4,k4,M4,vC,V6=IA(()=>{it(),It(),kt(),m4=a=>{if(!a||a.length<1)throw new Error("Too few inputs");if(a[0].dataType!==1&&a[0].dataType!==10)throw new Error("Input type must be float or float16.");if(a.length>=2){let o=a[0].dims.length*2===a[1].dims[0];if(a.length===4&&(o=a[3].dims[0]*2===a[1].dims[0]),!o)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},h4=(a,o,n)=>{let u="";for(let p=o-1;p>=0;--p)u+=`
k = i32(${a.indicesGet("indices",p)}) - ${ZA("uniforms.pads",p,n)};
if (k < 0) {
break;
}
if (k >= i32(${ZA("uniforms.x_shape",p,o)})) {
break;
}
offset += k * i32(${ZA("uniforms.x_strides",p,o)});
`;return`
value = ${a.type.value}(uniforms.constant_value);
for (var i = 0; i < 1; i++) {
var offset = 0;
var k = 0;
${u}
value = x[offset];
}
`},C4=(a,o,n)=>{let u="";for(let p=o-1;p>=0;--p)u+=`
k = i32(${a.indicesGet("indices",p)}) - ${ZA("uniforms.pads",p,n)};
if (k < 0) {
k = -k;
}
{
let _2n_1 = 2 * (i32(${ZA("uniforms.x_shape",p,o)}) - 1);
k = k % _2n_1;
if(k >= i32(${ZA("uniforms.x_shape",p,o)})) {
k = _2n_1 - k;
}
}
offset += k * i32(${ZA("uniforms.x_strides",p,o)});
`;return`
var offset = 0;
var k = 0;
${u}
value = x[offset];
`},b4=(a,o,n)=>{let u="";for(let p=o-1;p>=0;--p)u+=`
k = i32(${a.indicesGet("indices",p)}) - ${ZA("uniforms.pads",p,n)};
if (k < 0) {
k = 0;
}
if (k >= i32(${ZA("uniforms.x_shape",p,o)})) {
k = i32(${ZA("uniforms.x_shape",p,o)}) - 1;
}
offset += k * i32(${ZA("uniforms.x_strides",p,o)});
`;return`
var offset = 0;
var k = 0;
${u}
value = x[offset];
`},I4=(a,o,n)=>{let u="";for(let p=o-1;p>=0;--p)u+=`
k = i32(${a.indicesGet("indices",p)}) - ${ZA("uniforms.pads",p,n)};
if (k < 0) {
k += i32(${ZA("uniforms.x_shape",p,o)}]);
}
if (k >= i32(${ZA("uniforms.x_shape",p,o)})) {
k -= i32(${ZA("uniforms.x_shape",p,o)});
}
offset += k * i32(${ZA("uniforms.x_strides",p,o)});
`;return`
var offset = 0;
var k = 0;
${u}
value = x[offset];
`},w4=(a,o,n)=>{switch(n.mode){case 0:return h4(a,o,n.pads.length);case 1:return C4(a,o,n.pads.length);case 2:return b4(a,o,n.pads.length);case 3:return I4(a,o,n.pads.length);default:throw new Error("Invalid mode")}},k4=(a,o)=>{let n=He.padShape(a[0].dims.slice(),o.pads),u=a[0].dims,p=He.size(n),b=[{type:12,data:p},{type:6,data:o.pads}],C=a.length>=3&&a[2].data;o.mode===0&&b.push({type:C?a[2].dataType:1,data:o.value}),b.push(...et(a[0].dims,n));let w=["rank"],M=v=>{let D=XA("output",a[0].dataType,n.length),B=nA("x",a[0].dataType,u.length),E=B.type.value,S=w4(D,u.length,o),F=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:o.pads.length}];return o.mode===0&&F.push({name:"constant_value",type:C?E:"f32"}),`
${v.registerUniforms(F).declareVariables(B,D)}
${v.mainStart()}
${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let indices = ${D.offsetToIndices("global_idx")};
var value = ${E}(0);
${S}
output[global_idx] = value;
}`};return{name:"Pad",shaderCache:{hint:`${o.mode}${C}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:n,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(He.size(n)/64)},programUniforms:b}),getShaderSource:M}},M4=(a,o)=>{if(a.length>1){let n=a[1].getBigInt64Array(),u=a.length>=3&&a[2].data?a[2].dataType===10?a[2].getUint16Array()[0]:a[2].getFloat32Array()[0]:0,p=a[0].dims.length,b=new Int32Array(2*p).fill(0);if(a.length>=4){let w=a[3].getBigInt64Array();for(let M=0;M<w.length;M++)b[Number(w[M])]=Number(n[M]),b[Number(w[M])+p]=Number(n[M+w.length])}else n.forEach((w,M)=>b[Number(M)]=Number(w));let C=[];return b.forEach(w=>C.push(w)),{mode:o.mode,value:u,pads:C}}else return o},vC=(a,o)=>{m4(a.inputs);let n=M4(a.inputs,o);a.compute(k4(a.inputs,n),{inputs:[0]})}}),_o,v2,x2,B2,y2,E4,v4,D2,P2,xC,BC,T2,yC,DC,G2,PC,TC,GC,QC,Y6=IA(()=>{ja(),it(),It(),kt(),_o=a=>{if(wr.webgpu.validateInputContent&&(!a||a.length!==1))throw new Error("Pool ops requires 1 input.")},v2=(a,o,n)=>{let u=o.format==="NHWC",p=a.dims.slice();u&&p.splice(1,0,p.pop());let b=Object.hasOwnProperty.call(o,"dilations"),C=o.kernelShape.slice(),w=o.strides.slice(),M=b?o.dilations.slice():[],v=o.pads.slice();L0.adjustPoolAttributes(n,p,C,w,M,v);let D=L0.computePoolOutputShape(n,p,w,M,C,v,o.autoPad),B=Object.assign({},o);b?Object.assign(B,{kernelShape:C,strides:w,pads:v,dilations:M,cacheKey:o.cacheKey}):Object.assign(B,{kernelShape:C,strides:w,pads:v,cacheKey:o.cacheKey});let E=D.slice();return E.push(E.splice(1,1)[0]),[B,u?E:D]},x2=(a,o)=>{let n=o.format==="NHWC",u=He.size(a),p=He.size(o.kernelShape),b=[{type:12,data:u},{type:12,data:p}],C=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(o.kernelShape.length<=2){let w=o.kernelShape[o.kernelShape.length-1],M=o.strides[o.strides.length-1],v=o.pads[o.pads.length/2-1],D=o.pads[o.pads.length-1],B=!!(v+D);b.push({type:12,data:w},{type:12,data:M},{type:12,data:v},{type:12,data:D}),C.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let E=!1;if(o.kernelShape.length===2){let S=o.kernelShape[o.kernelShape.length-2],F=o.strides[o.strides.length-2],j=o.pads[o.pads.length/2-2],Z=o.pads[o.pads.length-2];E=!!(j+Z),b.push({type:12,data:S},{type:12,data:F},{type:12,data:j},{type:12,data:Z}),C.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[b,C,!0,B,E]}else{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let w=He.computeStrides(o.kernelShape);b.push({type:12,data:w},{type:12,data:o.pads},{type:12,data:o.strides}),C.push({name:"kernelStrides",type:"u32",length:w.length},{name:"pads",type:"u32",length:o.pads.length},{name:"strides",type:"u32",length:o.strides.length});let M=o.pads.reduce((v,D)=>v+D);return[b,C,!!M,!1,!1]}},B2=(a,o,n,u,p,b,C,w,M,v,D,B)=>{let E=p.format==="NHWC",S=o.type.value,F=XA("output",o.type.tensor,u);if(p.kernelShape.length<=2){let j="",Z="",R="",z=n-(E?2:1);if(D?j=`
for (var i: u32 = 0u; i < uniforms.kw; i++) {
xIndices[${z}] = indices[${z}] * uniforms.sw - uniforms.pwStart + i;
if (xIndices[${z}] < 0 || xIndices[${z}]
>= uniforms.x_shape[${z}]) {
pad++;
continue;
}
let x_val = x[${o.indicesToOffset("xIndices")}];
${b}
}`:j=`
for (var i: u32 = 0u; i < uniforms.kw; i++) {
xIndices[${z}] = indices[${z}] * uniforms.sw - uniforms.pwStart + i;
let x_val = x[${o.indicesToOffset("xIndices")}];
${b}
}`,p.kernelShape.length===2){let U=n-(E?3:2);B?Z=`
for (var j: u32 = 0u; j < uniforms.kh; j++) {
xIndices[${U}] = indices[${U}] * uniforms.sh - uniforms.phStart + j;
if (xIndices[${U}] < 0 || xIndices[${U}] >= uniforms.x_shape[${U}]) {
pad += i32(uniforms.kw);
continue;
}
`:Z=`
for (var j: u32 = 0u; j < uniforms.kh; j++) {
xIndices[${U}] = indices[${U}] * uniforms.sh - uniforms.phStart + j;
`,R=`
}
`}return`
${a.registerUniforms(M).declareVariables(o,F)}
${a.mainStart()}
${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
let indices = ${F.offsetToIndices("global_idx")};
var xIndices = ${F.offsetToIndices("global_idx")};
var value = ${S}(${w});
var pad = 0;
${Z}
${j}
${R}
${C}
output[global_idx] = value;
}`}else{if(E)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let j=p.kernelShape.length,Z=p.pads.length,R="";return v?R=`
if (xIndices[j] >= uniforms.x_shape[j]) {
pad++;
isPad = true;
break;
}
}
if (!isPad) {
let x_val = x[${o.indicesToOffset("xIndices")}];
${b}
}`:R=`
}
let x_val = x[${o.indicesToOffset("xIndices")}];
${b}
`,`
${a.registerUniforms(M).declareVariables(o,F)}
${a.mainStart()}
${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
let indices = ${F.offsetToIndices("global_idx")};
var xIndices = ${F.offsetToIndices("global_idx")};
var offsets: array<u32, ${j}>;
var value = ${S}(${w});
var pad = 0;
var isPad = false;
for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
var offset = i;
for (var j = 0u; j < ${j-1}u; j++) {
offsets[j] = offset / ${ZA("uniforms.kernelStrides","j",j)};
offset -= offsets[j] * ${ZA("uniforms.kernelStrides","j",j)};
}
offsets[${j-1}] = offset;
isPad = false;
for (var j = ${n-j}u; j < ${n}u; j++) {
xIndices[j] = indices[j] * ${ZA("uniforms.strides",`j - ${n-j}u`,j)}
+ offsets[j - ${n-j}u] - ${ZA("uniforms.pads","j - 2u",Z)};
${R}
}
${C}
output[global_idx] = value;
}`}},y2=a=>`${a.format};${a.ceilMode};${a.autoPad};${a.kernelShape.length}`,E4=a=>`${y2(a)};${a.countIncludePad}`,v4=a=>`${y2(a)};${a.storageOrder};${a.dilations}`,D2=a=>({format:a.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][a.auto_pad],ceilMode:a.ceil_mode,kernelShape:a.kernel_shape,strides:a.strides,pads:a.pads}),P2=(a,o,n,u)=>{let[p,b]=v2(o,u,n),C=nA("x",o.dataType,o.dims.length),w=C.type.value,M="value += x_val;",v="";p.countIncludePad?v+=`value /= ${w}(uniforms.kernelSize);`:v+=`value /= ${w}(i32(uniforms.kernelSize) - pad);`;let[D,B,E,S,F]=x2(b,p);D.push(...et(o.dims,b));let j=["rank"];return{name:a,shaderCache:{hint:`${u.cacheKey};${E};${S};${F}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:b,dataType:o.dataType}],dispatchGroup:{x:Math.ceil(He.size(b)/64)},programUniforms:D}),getShaderSource:Z=>B2(Z,C,o.dims.length,b.length,p,M,v,0,B,E,S,F)}},xC=a=>{let o=a.count_include_pad!==0,n=D2(a);if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let u={countIncludePad:o,...n,cacheKey:""};return{...u,cacheKey:E4(u)}},BC=(a,o)=>{_o(a.inputs),a.compute(P2("AveragePool",a.inputs[0],!1,o))},T2={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},yC=a=>{let o=a.format;return{format:o,...T2,cacheKey:o}},DC=(a,o)=>{_o(a.inputs),a.compute(P2("GlobalAveragePool",a.inputs[0],!0,o))},G2=(a,o,n,u)=>{let[p,b]=v2(o,u,n),C=`
value = max(x_val, value);
`,w="",M=nA("x",o.dataType,o.dims.length),v=["rank"],[D,B,E,S,F]=x2(b,p);return D.push(...et(o.dims,b)),{name:a,shaderCache:{hint:`${u.cacheKey};${E};${S};${F}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:b,dataType:o.dataType}],dispatchGroup:{x:Math.ceil(He.size(b)/64)},programUniforms:D}),getShaderSource:j=>B2(j,M,o.dims.length,b.length,p,C,w,o.dataType===10?-65504:-1e5,B,E,S,F)}},PC=(a,o)=>{_o(a.inputs),a.compute(G2("MaxPool",a.inputs[0],!1,o))},TC=a=>{let o=a.storage_order,n=a.dilations,u=D2(a);if(o!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(u.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let p={storageOrder:o,dilations:n,...u,cacheKey:""};return{...p,cacheKey:v4(p)}},GC=a=>{let o=a.format;return{format:o,...T2,cacheKey:o}},QC=(a,o)=>{_o(a.inputs),a.compute(G2("GlobalMaxPool",a.inputs[0],!0,o))}}),x4,B4,FC,SC,H6=IA(()=>{it(),It(),Hr(),kt(),x4=(a,o)=>{if(a.length<2||a.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(a.length===3&&a[1].dims===a[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(a.length===3&&a[0].dataType!==a[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(a[0].dataType===6&&a.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(a[1].dims.length!==0&&a[1].dims.length!==1&&a[1].dims.length!==a[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(a.length>2){if(a[0].dataType!==a[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(a[1].dims.length!==a[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!a[1].dims.map((n,u)=>n===a[2].dims[u]).reduce((n,u)=>n&&u,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(o.blockSize>0){if(a[1].dims.length===0||a[1].dims.length===1&&a[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!a[1].dims.map((p,b)=>b===o.axis||p===a[0].dims[b]).reduce((p,b)=>p&&b,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(a[1].dims.length!==a[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let n=a[0].dims[o.axis],u=a[1].dims[o.axis];if(o.blockSize<Math.ceil(n/u)||o.blockSize>Math.ceil(n/(u-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},B4=(a,o)=>{let n=He.normalizeAxis(o.axis,a[0].dims.length),u=a[0].dataType,p=u===3,b=a[0].dims,C=a[1].dataType,w=He.size(b),M=u===3||u===2,v=M?[Math.ceil(He.size(a[0].dims)/4)]:a[0].dims,D=a[1].dims,B=a.length>2?a[2]:void 0,E=B?M?[Math.ceil(He.size(B.dims)/4)]:B.dims:void 0,S=D.length===0||D.length===1&&D[0]===1,F=S===!1&&D.length===1,j=Rr(w),Z=S&&(!M||j===4),R=Z?j:1,z=Z&&!M?j:1,U=nA("input",M?12:u,v.length,z),f=nA("scale",C,D.length),k=B?nA("zero_point",M?12:u,E.length):void 0,e=XA("output",C,b.length,R),d=[U,f];k&&d.push(k);let y=[v,D];B&&y.push(E);let Ae=[{type:12,data:w/R},{type:12,data:n},{type:12,data:o.blockSize},...et(...y,b)],P=O=>{let pe=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
${O.registerUniforms(pe).declareVariables(...d,e)}
${O.mainStart()}
${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let output_indices = ${e.offsetToIndices("global_idx")};
// Set input x
${M?`
let input = ${U.getByOffset("global_idx / 4")};
let x_vec = ${p?"unpack4xI8(input)":"unpack4xU8(input)"};
let x_value = ${R===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${U.getByOffset("global_idx")};`};
// Set scale input
${S?`let scale_value= ${f.getByOffset("0")}`:F?`
let scale_index = ${e.indicesGet("output_indices","uniforms.axis")};
let scale_value= ${f.getByOffset("scale_index")};`:`
var scale_indices: ${f.type.indices} = output_indices;
let index = ${f.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
${f.indicesSet("scale_indices","uniforms.axis","index")};
let scale_value= ${f.getByIndices("scale_indices")};`};
// Set zero-point input
${k?S?M?`
let zero_point_input = ${k.getByOffset("0")};
let zero_point_vec = ${p?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${k.getByOffset("0")}`:F?M?`
let zero_point_index = ${e.indicesGet("output_indices","uniforms.axis")};
let zero_point_input = ${k.getByOffset("zero_point_index / 4")};
let zero_point_vec = ${p?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
let zero_point_index = ${e.indicesGet("output_indices","uniforms.axis")};
let zero_point_value = ${k.getByOffset("zero_point_index")};`:M?`
let zero_point_offset = ${f.indicesToOffset("scale_indices")};
let zero_point_input = ${k.getByOffset("zero_point_offset / 4")};
let zero_point_vec = ${p?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${k.getByIndices("scale_indices")};`:`let zero_point_value = ${M?p?"i32":"u32":U.type.value}(0);`};
// Compute and write output
${e.setByOffset("global_idx",`${e.type.value}(x_value - zero_point_value) * scale_value`)};
}`};return{name:"DequantizeLinear",shaderCache:{hint:o.cacheKey,inputDependencies:k?["rank","rank","rank"]:["rank","rank"]},getShaderSource:P,getRunData:()=>({outputs:[{dims:b,dataType:C}],dispatchGroup:{x:Math.ceil(w/R/64),y:1,z:1},programUniforms:Ae})}},FC=(a,o)=>{x4(a.inputs,o),a.compute(B4(a.inputs,o))},SC=a=>Ut({axis:a.axis,blockSize:a.blockSize})}),y4,D4,OC,U6=IA(()=>{ja(),it(),kt(),y4=(a,o,n)=>{let u=a===o,p=a<o&&n<0,b=a>o&&n>0;if(u||p||b)throw new Error("Range these inputs' contents are invalid.")},D4=(a,o,n,u)=>{let p=Math.abs(Math.ceil((o-a)/n)),b=[p],C=p,w=[{type:12,data:C},{type:u,data:a},{type:u,data:n},...et(b)],M=v=>{let D=XA("output",u,b.length),B=D.type.value,E=[{name:"outputSize",type:"u32"},{name:"start",type:B},{name:"delta",type:B}];return`
${v.registerUniforms(E).declareVariables(D)}
${v.mainStart()}
${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
output[global_idx] = uniforms.start + ${B}(global_idx) * uniforms.delta;
}`};return{name:"Range",shaderCache:{hint:`${u}`},getShaderSource:M,getRunData:()=>({outputs:[{dims:b,dataType:u}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:w})}},OC=a=>{let o=0,n=0,u=0;a.inputs[0].dataType===6?(o=a.inputs[0].getInt32Array()[0],n=a.inputs[1].getInt32Array()[0],u=a.inputs[2].getInt32Array()[0]):a.inputs[0].dataType===1&&(o=a.inputs[0].getFloat32Array()[0],n=a.inputs[1].getFloat32Array()[0],u=a.inputs[2].getFloat32Array()[0]),wr.webgpu.validateInputContent&&y4(o,n,u),a.compute(D4(o,n,u,a.inputs[0].dataType),{inputs:[]})}}),P4,Q2,F2,T4,_C,zC,K6=IA(()=>{it(),It(),Hr(),kt(),P4=(a,o,n,u)=>{if(a!=="none"&&u!=="i32"&&u!=="u32"&&u!=="f32")throw new Error(`Input ${u} is not supported with reduction ${a}.`);let p=`{
var oldValue = 0;
loop {
let newValueF32 =`,b=`;
let newValue = bitcast<i32>(newValueF32);
let res = atomicCompareExchangeWeak(&${o}, oldValue, newValue);
if res.exchanged {
break;
}
oldValue = res.old_value;
}
}`;switch(a){case"none":return`${o}=${n};`;case"add":return u==="i32"||u==="u32"?`atomicAdd(&${o}, bitcast<${u}>(${n}));`:`
${p}bitcast<${u}>(oldValue) + (${n})${b}`;case"max":return u==="i32"||u==="u32"?`atomicMax(&${o}, bitcast<${u}>(${n}));`:`
${p}max(bitcast<f32>(oldValue), (${n}))${b}`;case"min":return u==="i32"||u==="u32"?`atomicMin(&${o}, bitcast<${u}>(${n}));`:`${p}min(bitcast<${u}>(oldValue), (${n}))${b}`;case"mul":return`${p}(bitcast<${u}>(oldValue) * (${n}))${b}`;default:throw new Error(`Reduction ${a} is not supported.`)}},Q2=(a,o)=>`${a===1?`
let element_count_dim = uniforms.output_strides;
let dim_value = uniforms.output_shape;`:`
let element_count_dim = uniforms.output_strides[${o?"i - indices_start":"i"}];
let dim_value = uniforms.output_shape[${o?"i - indices_start":"i"} + uniforms.last_index_dimension];`}
if (index >= 0) {
if (index >= i32(dim_value)) {
index = i32(dim_value - 1);
}
} else {
if (index < -i32(dim_value)) {
index = 0;
} else {
index += i32(dim_value);
}
}
data_offset += u32((u32(index) * element_count_dim));`,F2=(a,o,n)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) {
let value = updates[uniforms.num_updates_elements * ${n?"global_idx":"idx"} + i];
${P4(a.reduction,"output[data_offset + i]","value",o)}
}`,T4=(a,o)=>{let n=a[0].dims,u=a[1].dims,p=n,b=1,C=Math.ceil(He.size(u)/b),w=u[u.length-1],M=He.sizeFromDimension(n,w),v=He.sizeFromDimension(u,0)/w,D=[{type:12,data:C},{type:12,data:w},{type:12,data:M},...et(a[1].dims,a[2].dims,p)],B=E=>{let S=nA("indices",a[1].dataType,a[1].dims.length),F=nA("updates",a[2].dataType,a[2].dims.length,b),j=o.reduction!=="none"&&o.reduction!==""?dh("output",a[0].dataType,p.length):XA("output",a[0].dataType,p.length,b);return`
${E.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(S,F,j)}
${E.mainStart()}
${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
var hasDuplicates = false;
if (${o.reduction==="none"}) {
for (var i = 0; i < ${v}; i = i + 1) {
for (var j = i + 1; j < ${v}; j = j + 1) {
var index_i = i32(indices[i].x);
var index_j = i32(indices[j].x);
if (index_i == index_j) {
hasDuplicates = true;
break;
}
}
if (hasDuplicates) {
break;
}
}
}
if (${o.reduction==="none"} && hasDuplicates) {
if (global_idx != 0u) {
return;
}
// Process each index-update pair individually when duplicates exist
for (var idx = 0u; idx < ${v}u; idx++) {
var data_offset = 0u;
for (var i = 0u; i < uniforms.last_index_dimension; i++) {
var index = i32(indices[idx * uniforms.last_index_dimension + i].x);
${Q2(n.length,!1)}
}
${F2(o,j.type.value,!1)}
}
return;
}
var data_offset = 0u;
var indices_start = uniforms.last_index_dimension * global_idx;
var indices_end = indices_start + uniforms.last_index_dimension;
for (var i = indices_start; i < indices_end; i++) {
var index = i32(indices[i].x);
${Q2(n.length,!0)}
}
${F2(o,j.type.value,!0)}
}`};return{name:"ScatterND",shaderCache:{hint:`${o.cacheKey}_${o.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:p,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:D}),getShaderSource:B}},_C=a=>Ut({reduction:a.reduction}),zC=(a,o)=>{a.compute(T4(a.inputs,o),{inputs:[a.inputs[1],a.inputs[2]],outputs:[]})}}),G4,Q4,F4,S2,S4,O4,_4,z4,N4,L4,R4,j4,O2,W4,V4,Y4,H4,U4,NC,LC,X6=IA(()=>{it(),It(),Hr(),kt(),G4=(a,o)=>{if(a.every(n=>n>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),a.length>0){if(o.mode==="linear"){if(!(a.length===2||a.length===3||a.length===4&&a[0]===1&&a[1]===1||a.length===4&&a[0]===1&&a[3]===1||a.length===5&&a[0]===1&&a[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(o.mode==="cubic"&&!(a.length===2||a.length===4&&a[0]===1&&a[1]===1||a.length===4&&a[0]===1&&a[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Q4=(a,o,n)=>{o.every(p=>p>=0&&p<n||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let u=new Array(n).fill(1);return o.forEach((p,b)=>u[p]=a[b]),u},F4=(a,o,n,u,p,b)=>{let[C,w,M]=n>10?[1,2,3]:[-1,a.length>1?1:-1,-1],v=a[0].dims.length;if(C>0&&a.length>C&&a[C].dims.length>0)a[C].getFloat32Array().forEach(D=>b.push(D));else if(o.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(w>0&&a.length>w&&a[w].dims.length===1&&a[w].dims[0]>0){if(a[w].getFloat32Array().forEach(D=>u.push(D)),u.length!==0&&u.length!==v&&n>=18&&u.length!==o.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");G4(u,o),o.axes.length>0&&Q4(u,o.axes,v).forEach((D,B)=>u[B]=D)}if(M>0&&a.length>M&&a[M].dims.length===1&&a[M].dims[0]>0&&(a[M].getBigInt64Array().forEach(D=>p.push(Number(D))),p.length!==0&&p.length!==v&&n>=18&&p.length!==o.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(o.axes.length>0){if(u.length!==0&&u.length!==o.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(p.length!==0&&p.length!==o.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof u<"u"&&typeof p<"u"&&u.length>0&&p.length>v)throw new Error("Resize requires only of scales or sizes to be specified")},S2=(a,o,n,u)=>`
// The whole part and the fractional part are calculated separately due to inaccuracy of floating
// point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
// offset-by-one error later in floor().
let big = (${a}) * (${o});
let whole = ${u}(big / (${n}));
let fract = ${u}(big % (${n})) / ${u}(${n});
return whole + fract;
`,S4=(a,o)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${o} { `+(()=>{switch(a){case"asymmetric":return`
if (xScale < 1.0 || floor(xScale) != xScale) {
return ${o}(xResized) / ${o}(xScale);
} else {
${S2("xResized","lengthOriginal","lengthResized",o)}
}
`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
return (${o}(xResized) + 0.5) / ${o}(xScale) - 0.5;
} else {
return 0.0;
}`;case"tf_half_pixel_for_nn":return`return (${o}(xResized) + 0.5) / ${o}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
return 0.0;
} else {
${S2("xResized","lengthOriginal - 1","lengthResized - 1",o)}
}`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
return ${o}(roiStart) * ${o}(lengthOriginal - 1) +
(${o}(xResized) * ${o}(roiEnd - roiStart) * ${o}(lengthOriginal - 1)) /
${o}(lengthResized - 1);
} else {
return 0.5 * ${o}(roiStart + roiEnd) * ${o}(lengthOriginal - 1);
}`;case"half_pixel_symmetric":return`const outputWidth = ${o}xScale * ${o}(lengthResized);
const adjustment = ${o}(lengthResized) / outputWidth;
const center = ${o}(lengthOriginal) / 2;
const offset = center * (1 - adjustment);
return offset + ((${o}(xResized) + 0.5) / ${o}(xScale)) - 0.5;`;case"half_pixel":return`return ((${o}(xResized) + 0.5) / ${o}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${a} is not supported`)}})()+"}",O4=(a,o,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(a){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(o<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${a} is not supported`)}})()+"}",_4=(a,o,n)=>{let u=new Array(n).fill(0).concat(new Array(n).fill(1)),p=a.length===0?u:a.slice();return o.length>0?(o.forEach((b,C)=>{u[b]=p[C],u[C+n]=p[o.length+C]}),u):p},z4=(a,o,n,u)=>{let p=[];if(n.length>0)if(u.length>0){if(a.forEach(b=>p.push(b)),Math.max(...u)>a.length)throw new Error("axes is out of bound");u.forEach((b,C)=>p[b]=n[C])}else n.forEach(b=>p.push(b));else{if(o.length===0)throw new Error("Resize requires either scales or sizes.");p=a.map((b,C)=>Math.round(b*o[C]))}return p},N4=(a,o,n)=>{let u=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map(b=>o[b]),Number.MAX_VALUE):Math.min(...o,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map(b=>o[b]),Number.MIN_VALUE):Math.max(...o,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();o.fill(1,0,o.length);let p=a.slice();return n.axes.length>0?(n.axes.forEach(b=>o[b]=u),n.axes.forEach(b=>p[b]=Math.round(a[b]*o[b]))):(o.fill(u,0,o.length),p.forEach((b,C)=>p[C]=Math.round(b*o[C]))),p},L4=(a,o,n,u,p)=>`
fn calculateOriginalIndicesFromOutputIndices(output_indices: ${a.type.indices}) -> array<${a.type.value}, ${n.length}> {
var original_indices: array<${a.type.value}, ${n.length}>;
for (var i:u32 = 0; i < ${n.length}; i++) {
var output_index = ${a.indicesGet("output_indices","i")};
var scale = ${ZA("uniforms.scales","i",u)};
var roi_low = ${ZA("uniforms.roi","i",p)};
var roi_hi = ${ZA("uniforms.roi",`i + ${o.length}`,p)};
if (scale == 1.0) {
original_indices[i] = ${a.type.value}(output_index);
} else {
var input_shape_i = ${ZA("uniforms.input_shape","i",o.length)};
var output_shape_i = ${ZA("uniforms.output_shape","i",n.length)};
original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
input_shape_i, roi_low, roi_hi);
}
}
return original_indices;
}`,R4=(a,o,n,u,p,b,C)=>`
fn calculateInputIndicesFromOutputIndices(output_indices: ${o.type.indices}) -> ${a.type.indices} {
var input_indices: ${a.type.indices};
for (var i:u32 = 0; i < ${u.length}; i++) {
var output_index = ${o.indicesGet("output_indices","i")};
var input_index: u32;
var scale = ${ZA("uniforms.scales","i",p)};
if (scale == 1.0) {
input_index = output_index;
} else {
var roi_low = ${ZA("uniforms.roi","i",b)};
var roi_hi = ${ZA("uniforms.roi",`i + ${n.length}`,b)};
var input_shape_i = ${ZA("uniforms.input_shape","i",n.length)};
var output_shape_i = ${ZA("uniforms.output_shape","i",u.length)};
var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
input_shape_i, roi_low, roi_hi);
if (!${C} || (original_idx >= 0 && original_idx < ${o.type.value}(input_shape_i))) {
if (original_idx < 0) {
input_index = 0;
} else if (original_idx > ${o.type.value}(input_shape_i - 1)) {
input_index = input_shape_i - 1;
} else {
input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
}
} else {
input_index = u32(original_idx);
}
}
${a.indicesSet("input_indices","i","input_index")}
}
return input_indices;
}`,j4=(a,o)=>`
fn checkInputIndices(input_indices: ${a.type.indices}) -> bool {
for (var i:u32 = 0; i < ${o.length}; i++) {
var input_index = ${a.indicesGet("input_indices","i")};
if (input_index < 0 || input_index >= ${ZA("uniforms.input_shape","i",o.length)}) {
return false;
}
}
return true;
}`,O2=(a,o,n,u)=>a.rank>u?`
${a.indicesSet("input_indices",o,"channel")};
${a.indicesSet("input_indices",n,"batch")};
`:"",W4=(a,o,n,u,p)=>{let[b,C,w,M]=n.length===2?[-1,0,1,-1]:[0,2,3,1],v=a.type.value;return`
fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${v} {
var input_indices: ${a.type.indices};
${a.indicesSet("input_indices",C,`max(0, min(row, ${n[C]} - 1))`)};
${a.indicesSet("input_indices",w,`max(0, min(col, ${n[w]} - 1))`)};
${O2(a,M,b,2)}
return ${a.getByIndices("input_indices")};
}
fn bilinearInterpolation(output_indices: ${o.type.indices}) -> ${v} {
var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
var row:${v} = originalIndices[${C}];
var col:${v} = originalIndices[${w}];
${u?`if (row < 0 || row > (${n[C]} - 1) || col < 0 || col > (${n[w]} - 1)) {
return ${p};
}`:""};
row = max(0, min(row, ${n[C]} - 1));
col = max(0, min(col, ${n[w]} - 1));
var row1: u32 = u32(row);
var col1: u32 = u32(col);
var row2: u32 = u32(row + 1);
var col2: u32 = u32(col + 1);
var channel: u32 = ${n.length>2?`u32(originalIndices[${M}])`:"0"};
var batch: u32 = ${n.length>2?`u32(originalIndices[${b}])`:"0"};
var x11: ${v} = getInputValue(batch, channel, row1, col1);
var x12: ${v} = getInputValue(batch, channel, row1, col2);
var x21: ${v} = getInputValue(batch, channel, row2, col1);
var x22: ${v} = getInputValue(batch, channel, row2, col2);
var dx1: ${v} = abs(row - ${v}(row1));
var dx2: ${v} = abs(${v}(row2) - row);
var dy1: ${v} = abs(col - ${v}(col1));
var dy2: ${v} = abs(${v}(col2) - col);
if (row1 == row2) {
dx1 = 0.5;
dx2 = 0.5;
}
if (col1 == col2) {
dy1 = 0.5;
dy2 = 0.5;
}
return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
}`},V4=(a,o,n,u,p,b,C,w,M,v)=>{let D=n.length===2,[B,E]=D?[0,1]:[2,3],S=a.type.value,F=j=>{let Z=j===B?"row":"col";return`
fn ${Z}CubicInterpolation(input_indices: ${a.type.indices}, output_indices: ${o.type.indices}) -> ${S} {
var output_index = ${o.indicesGet("output_indices",j)};
var originalIdx: ${S} = getOriginalCoordinateFromResizedCoordinate(output_index, ${p[j]},
${u[j]}, ${n[j]}, ${b[j]}, ${b[j]} + ${n.length});
var fractOriginalIdx: ${S} = originalIdx - floor(originalIdx);
var coefs = getCubicInterpolationCoefs(fractOriginalIdx);
if (${w} && (originalIdx < 0 || originalIdx > (${n[j]} - 1))) {
return ${M};
}
var data: array<${S}, 4> = array<${S}, 4>(0.0, 0.0, 0.0, 0.0);
for (var i: i32 = -1; i < 3; i++) {
var ${Z}: ${S} = originalIdx + ${S}(i);
if (${Z} < 0 || ${Z} >= ${n[j]}) {
${v?`coefs[i + 1] = 0.0;
continue;`:w?`return ${M};`:`${Z} = max(0, min(${Z}, ${n[j]} - 1));`};
}
var input_indices_copy: ${a.type.indices} = input_indices;
${a.indicesSet("input_indices_copy",j,`u32(${Z})`)};
data[i + 1] = ${j===B?a.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
}
return cubicInterpolation1D(data, coefs);
}`};return`
${F(B)};
${F(E)};
fn getCubicInterpolationCoefs(s: ${S}) -> array<${S}, 4> {
var absS = abs(s);
var coeffs: array<${S}, 4> = array<${S}, 4>(0.0, 0.0, 0.0, 0.0);
var oneMinusAbsS: ${S} = 1.0 - absS;
var twoMinusAbsS: ${S} = 2.0 - absS;
var onePlusAbsS: ${S} = 1.0 + absS;
coeffs[0] = ((${C} * onePlusAbsS - 5 * ${C}) * onePlusAbsS + 8 * ${C}) * onePlusAbsS - 4 * ${C};
coeffs[1] = ((${C} + 2) * absS - (${C} + 3)) * absS * absS + 1;
coeffs[2] = ((${C} + 2) * oneMinusAbsS - (${C} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
coeffs[3] = ((${C} * twoMinusAbsS - 5 * ${C}) * twoMinusAbsS + 8 * ${C}) * twoMinusAbsS - 4 * ${C};
return coeffs;
}
fn cubicInterpolation1D(x: array<${S}, 4>, coefs: array<${S}, 4>) -> ${S} {
var coefsSum: ${S} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
}
fn bicubicInterpolation(output_indices: ${o.type.indices}) -> ${S} {
var input_indices: ${a.type.indices} = output_indices;
return colCubicInterpolation(input_indices, output_indices);
}
`},Y4=(a,o,n,u,p)=>{let[b,C,w,M,v]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],D=a.type.value;return`
fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${D} {
var input_indices: ${a.type.indices};
${a.indicesSet("input_indices",C,`max(0, min(depth, ${n[C]} - 1))`)};
${a.indicesSet("input_indices",w,`max(0, min(height, ${n[w]} - 1))`)};
${a.indicesSet("input_indices",M,`max(0, min(width, ${n[M]} - 1))`)};
${O2(a,v,b,3)}
return ${a.getByIndices("input_indices")};
}
fn trilinearInterpolation(output_indices: ${o.type.indices}) -> ${D} {
var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
var depth:${D} = originalIndices[${C}];
var height:${D} = originalIndices[${w}];
var width:${D} = originalIndices[${M}];
${u?`if (depth < 0 || depth > (${n[C]} - 1) || height < 0 || height > (${n[w]} - 1) || width < 0 || (width > ${n[M]} - 1)) {
return ${p};
}`:""};
depth = max(0, min(depth, ${n[C]} - 1));
height = max(0, min(height, ${n[w]} - 1));
width = max(0, min(width, ${n[M]} - 1));
var depth1: u32 = u32(depth);
var height1: u32 = u32(height);
var width1: u32 = u32(width);
var depth2: u32 = u32(depth + 1);
var height2: u32 = u32(height + 1);
var width2: u32 = u32(width + 1);
var channel: u32 = ${n.length>3?`u32(originalIndices[${v}])`:"0"};
var batch: u32 = ${n.length>3?`u32(originalIndices[${b}])`:"0"};
var x111: ${D} = getInputValue(batch, channel, depth1, height1, width1);
var x112: ${D} = getInputValue(batch, channel, depth1, height1, width2);
var x121: ${D} = getInputValue(batch, channel, depth1, height2, width1);
var x122: ${D} = getInputValue(batch, channel, depth1, height2, width2);
var x211: ${D} = getInputValue(batch, channel, depth2, height1, width1);
var x212: ${D} = getInputValue(batch, channel, depth2, height1, width2);
var x221: ${D} = getInputValue(batch, channel, depth2, height2, width1);
var x222: ${D} = getInputValue(batch, channel, depth2, height2, width2);
var dx1: ${D} = abs(depth - ${D}(depth1));
var dx2: ${D} = abs(${D}(depth2) - depth);
var dy1: ${D} = abs(height - ${D}(height1));
var dy2: ${D} = abs(${D}(height2) - height);
var dz1: ${D} = abs(width - ${D}(width1));
var dz2: ${D} = abs(${D}(width2) - width);
if (depth1 == depth2) {
dx1 = 0.5;
dx2 = 0.5;
}
if (height1 == height2) {
dy1 = 0.5;
dy2 = 0.5;
}
if (width1 == width2) {
dz1 = 0.5;
dz2 = 0.5;
}
return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
}`},H4=(a,o,n,u,p,b)=>{let C=a.dims,w=_4(b,o.axes,C.length),M=z4(C,u,p,o.axes),v=u.slice();u.length===0&&(v=C.map((z,U)=>z===0?1:M[U]/z),o.keepAspectRatioPolicy!=="stretch"&&(M=N4(C,v,o)));let D=XA("output",a.dataType,M.length),B=nA("input",a.dataType,C.length),E=He.size(M),S=C.length===M.length&&C.every((z,U)=>z===M[U]),F=o.coordinateTransformMode==="tf_crop_and_resize",j=o.extrapolationValue,Z=B.type.value,R=z=>`
${S?"":`
${S4(o.coordinateTransformMode,Z)};
${(()=>{switch(o.mode){case"nearest":return`
${j4(B,C)};
${O4(o.nearestMode,n,Z)};
${R4(B,D,C,M,v.length,w.length,F)};
`;case"linear":return`
${L4(D,C,M,v.length,w.length)};
${(()=>{if(C.length===2||C.length===4)return`${W4(B,D,C,F,j)}`;if(C.length===3||C.length===5)return`${Y4(B,D,C,F,j)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
`;case"cubic":return`
${(()=>{if(C.length===2||C.length===4)return`${V4(B,D,C,M,v,w,o.cubicCoeffA,F,o.extrapolationValue,o.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
`;default:throw Error("Invalid resize mode")}})()};
`}
${z.registerUniform("output_size","u32").registerUniform("scales","f32",v.length).registerUniform("roi","f32",w.length).declareVariables(B,D)}
${z.mainStart()}
${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
${S?"output[global_idx] = input[global_idx];":`
let output_indices = ${D.offsetToIndices("global_idx")};
var input_indices: ${B.type.indices};
${(()=>{switch(o.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
if (checkInputIndices(input_indices)) {
output[global_idx] = ${B.getByIndices("input_indices")};
} else {
output[global_idx] = ${o.extrapolationValue};
}`;case"linear":return`output[global_idx] = ${C.length===2||C.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${o.mode}`)}})()};
`}
}`;return{name:"Resize",shaderCache:{hint:`${o.cacheKey}|${n}|${v.length>0?o.mode==="cubic"?v:v.length:""}|${p.length>0?p:""}|${w.length>0?w:""}|${S}|${o.mode==="nearest"?C.length:C}`,inputDependencies:["rank"]},getShaderSource:R,getRunData:()=>({outputs:[{dims:M,dataType:a.dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:[{type:12,data:E},{type:1,data:v},{type:1,data:w},...et(C,M)]})}},U4=a=>{let o=a.customDataBuffer;return new Uint32Array(o,o.byteOffset,1)[0]},NC=(a,o)=>{let n=[],u=[],p=[],b=U4(a);if(o.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");F4(a.inputs,o,b,n,u,p),a.compute(H4(a.inputs[0],o,b,n,u,p),{inputs:[0]})},LC=a=>{let o=a.antialias,n=a.axes,u=a.coordinateTransformMode,p=a.cubicCoeffA,b=a.excludeOutside!==0,C=a.extrapolationValue,w=a.keepAspectRatioPolicy,M=a.mode,v=a.nearestMode===""?"simple":a.nearestMode;return Ut({antialias:o,axes:n,coordinateTransformMode:u,cubicCoeffA:p,excludeOutside:b,extrapolationValue:C,keepAspectRatioPolicy:w,mode:M,nearestMode:v})}}),K4,X4,RC,Z6=IA(()=>{it(),It(),kt(),K4=a=>{if(!a||a.length<3)throw new Error("layerNorm requires at least 3 inputs.");let o=a[0],n=a[1],u=a[2];if(o.dataType!==n.dataType||o.dataType!==u.dataType)throw new Error("All inputs must have the same data type");if(o.dims.length!==3&&o.dims.length!==2)throw new Error("Input must be 2D or 3D");if(n.dims.length!==3&&n.dims.length!==2)throw new Error("Skip must be 2D or 3D");let p=o.dims[o.dims.length-1],b=o.dims[o.dims.length-2];if(n.dims[n.dims.length-1]!==p)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==b)throw new Error("Skip must have the same sequence length as input");if(u.dims.length!==1)throw new Error("Gamma must be 1D");if(u.dims[u.dims.length-1]!==p)throw new Error("Gamma must have the same hidden size as input");if(a.length>3){let C=a[3];if(C.dims.length!==1)throw new Error("Beta must be 1D");if(C.dims[C.dims.length-1]!==p)throw new Error("Beta must have the same hidden size as input")}if(a.length>4){let C=a[4];if(C.dims.length!==1)throw new Error("Bias must be 1D");if(C.dims[C.dims.length-1]!==p)throw new Error("Bias must have the same hidden size as input")}},X4=(a,o,n,u)=>{let p=o.simplified,b=a[0].dims,C=He.size(b),w=b,M=C,v=b.slice(-1)[0],D=u?b.slice(0,-1).concat(1):[],B=!p&&a.length>3,E=a.length>4,S=u&&n>1,F=u&&n>2,j=n>3,Z=64,R=Rr(v),z=[{type:12,data:M},{type:12,data:R},{type:12,data:v},{type:1,data:o.epsilon}],U=k=>{let e=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],d=[nA("x",a[0].dataType,a[0].dims,R),nA("skip",a[1].dataType,a[1].dims,R),nA("gamma",a[2].dataType,a[2].dims,R)];B&&d.push(nA("beta",a[3].dataType,a[3].dims,R)),E&&d.push(nA("bias",a[4].dataType,a[4].dims,R)),d.push(XA("output",a[0].dataType,w,R)),S&&d.push(XA("mean_output",1,D)),F&&d.push(XA("inv_std_output",1,D)),j&&d.push(XA("input_skip_bias_sum",a[0].dataType,w,R));let y=hs(a[0].dataType),Ae=hs(1,R);return`
${k.registerUniforms(e).declareVariables(...d)}
var<workgroup> sum_shared : array<${Ae}, ${Z}>;
var<workgroup> sum_squared_shared : array<${Ae}, ${Z}>;
${k.mainStart([Z,1,1])}
let ix = local_id.x;
let iy = global_id.x / ${Z};
let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
var stride = hidden_size_vectorized / ${Z};
let offset = ix * stride + iy * hidden_size_vectorized;
let offset1d = stride * ix;
if (ix == ${Z-1}) {
stride = hidden_size_vectorized - stride * ix;
}
for (var i: u32 = 0; i < stride; i++) {
let skip_value = skip[offset + i];
let bias_value = ${E?"bias[offset1d + i]":y+"(0.0)"};
let input_value = x[offset + i];
let value = input_value + skip_value + bias_value;
${j?"input_skip_bias_sum[offset + i] = value;":""}
output[offset + i] = value;
let f32_value = ${ro(y,R,"value")};
sum_shared[ix] += f32_value;
sum_squared_shared[ix] += f32_value * f32_value;
}
workgroupBarrier();
var reduce_size : u32 = ${Z};
for (var curr_size = reduce_size >> 1; curr_size > 0; curr_size = reduce_size >> 1) {
reduce_size = curr_size + (reduce_size & 1);
if (ix < curr_size) {
sum_shared[ix] += sum_shared[ix + reduce_size];
sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
}
workgroupBarrier();
}
let sum = sum_shared[0];
let square_sum = sum_squared_shared[0];
let mean = ${Jn("sum",R)} / f32(uniforms.hidden_size);
let inv_std_dev = inverseSqrt(${Jn("square_sum",R)} / f32(uniforms.hidden_size) ${p?"":"- mean * mean"} + uniforms.epsilon);
${S?"mean_output[global_idx] = mean;":""}
${F?"inv_std_output[global_idx] = inv_std_dev;":""}
for (var i: u32 = 0; i < stride; i++) {
output[offset + i] = (output[offset + i] ${p?"":`- ${y}(mean)`}) *
${y}(inv_std_dev) * gamma[offset1d + i]
${B?"+ beta[offset1d + i]":""};
}
}`},f=[{dims:w,dataType:a[0].dataType}];return n>1&&f.push({dims:D,dataType:1}),n>2&&f.push({dims:D,dataType:1}),n>3&&f.push({dims:b,dataType:a[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${R};${S};${F};${j}`,inputDependencies:a.map((k,e)=>"type")},getShaderSource:U,getRunData:()=>({outputs:f,dispatchGroup:{x:Math.ceil(M/v)},programUniforms:z})}},RC=(a,o)=>{K4(a.inputs);let n=[0];a.outputCount>1&&n.push(-3),a.outputCount>2&&n.push(-3),a.outputCount>3&&n.push(3),a.compute(X4(a.inputs,o,a.outputCount,!1),{outputs:n})}}),Z4,zo,J4,_2,q4,$4,jC,WC,J6=IA(()=>{it(),It(),Hr(),kt(),Z4=(a,o)=>{if(!a||a.length<1)throw new Error("too few inputs");if(o.axes.length!==0){if(o.axes.length!==o.starts.length||o.axes.length!==o.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(o.starts.length!==o.ends.length)throw new Error("starts and ends must have the same length");a.slice(1).forEach((n,u)=>{if(a[u+1].dataType!==6&&a[u+1].dataType!==7)throw new Error(`Input ${u} must be an array of int32 or int64`)})},zo=(a,o)=>{let n=[];if(a.length>o)if(a[o].dataType===7)a[o].getBigInt64Array().forEach(u=>n.push(Number(u)));else if(a[o].dataType===6)a[o].getInt32Array().forEach(u=>n.push(Number(u)));else throw new Error(`Input ${o} must be an array of int32 or int64`);return n},J4=(a,o)=>{if(a.length>1){let n=zo(a,1),u=zo(a,2),p=zo(a,3);return p.length===0&&(p=[...Array(a[0].dims.length).keys()]),Ut({starts:n,ends:u,axes:p})}else return o},_2=(a,o,n,u,p)=>{let b=a;return a<0&&(b+=n[u[o]]),p[o]<0?Math.max(0,Math.min(b,n[u[o]]-1)):Math.max(0,Math.min(b,n[u[o]]))},q4=(a,o,n)=>`fn calculateInputIndices(output_indices: ${o.type.indices}) -> ${a.type.indices} {
var input_indices: ${a.type.indices};
var carry = 0u;
for (var i = ${n.length}; i >= 0; i--) {
let input_shape_i = ${ZA("uniforms.input_shape","i",n.length)};
let steps_i = ${ZA("uniforms.steps","i",n.length)};
let signs_i = ${ZA("uniforms.signs","i",n.length)};
let starts_i = ${ZA("uniforms.starts","i",n.length)};
var output_index = ${o.indicesGet("output_indices","i")};
var input_index = output_index * steps_i + starts_i + carry;
carry = input_index / input_shape_i;
input_index = input_index % input_shape_i;
if (signs_i < 0) {
input_index = input_shape_i - input_index - 1u + starts_i;
}
${a.indicesSet("input_indices","i","input_index")};
}
return input_indices;
}`,$4=(a,o)=>{let n=a[0].dims,u=He.size(n),p=o.axes.length>0?He.normalizeAxes(o.axes,n.length):[...Array(n.length).keys()],b=zo(a,4);b.forEach(R=>R!==0||(()=>{throw new Error("step cannot be 0")})),b.length===0&&(b=Array(p.length).fill(1));let C=o.starts.map((R,z)=>_2(R,z,n,p,b)),w=o.ends.map((R,z)=>_2(R,z,n,p,b));if(p.length!==C.length||p.length!==w.length)throw new Error("start, ends and axes should have the same number of elements");if(p.length!==n.length)for(let R=0;R<n.length;++R)p.includes(R)||(C.splice(R,0,0),w.splice(R,0,n[R]),b.splice(R,0,1));let M=b.map(R=>Math.sign(R));b.forEach((R,z,U)=>{if(R<0){let f=(w[z]-C[z])/R,k=C[z],e=k+f*b[z];C[z]=e,w[z]=k,U[z]=-R}});let v=n.slice(0);p.forEach((R,z)=>{v[R]=Math.ceil((w[R]-C[R])/b[R])});let D={dims:v,dataType:a[0].dataType},B=XA("output",a[0].dataType,v.length),E=nA("input",a[0].dataType,a[0].dims.length),S=He.size(v),F=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:C.length},{name:"signs",type:"i32",length:M.length},{name:"steps",type:"u32",length:b.length}],j=[{type:12,data:S},{type:12,data:C},{type:6,data:M},{type:12,data:b},...et(a[0].dims,v)],Z=R=>`
${R.registerUniforms(F).declareVariables(E,B)}
${q4(E,B,n)}
${R.mainStart()}
${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
let output_indices = ${B.offsetToIndices("global_idx")};
let input_indices = calculateInputIndices(output_indices);
${B.setByOffset("global_idx",E.getByIndices("input_indices"))}
}`;return{name:"Slice",shaderCache:{hint:`${M.length}_${C.length}_${b.length}`,inputDependencies:["rank"]},getShaderSource:Z,getRunData:()=>({outputs:[D],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:j})}},jC=(a,o)=>{Z4(a.inputs,o);let n=J4(a.inputs,o);a.compute($4(a.inputs,n),{inputs:[0]})},WC=a=>{let o=a.starts,n=a.ends,u=a.axes;return Ut({starts:o,ends:n,axes:u})}}),em,Am,VC,YC,q6=IA(()=>{it(),It(),Hr(),qn(),kt(),em=a=>{if(!a||a.length!==1)throw new Error("Softmax op requires 1 input.")},Am=(a,o)=>{let n=a.inputs[0],u=n.dims,p=He.size(u),b=u.length,C=He.normalizeAxis(o.axis,b),w=C<u.length-1,M,v=[];w?(v=Array.from({length:b},(d,y)=>y),v[C]=b-1,v[b-1]=C,M=a.compute(pa(n,v),{inputs:[n],outputs:[-1]})[0]):M=n;let D=M.dims,B=D[b-1],E=p/B,S=Rr(B),F=B/S,j=64;E===1&&(j=256);let Z=(d,y)=>y===4?`max(max(${d}.x, ${d}.y), max(${d}.z, ${d}.w))`:y===2?`max(${d}.x, ${d}.y)`:y===3?`max(max(${d}.x, ${d}.y), ${d}.z)`:d,R=nA("x",M.dataType,M.dims,S),z=XA("result",M.dataType,M.dims,S),U=R.type.value,f=hs(M.dataType)==="f32"?`var threadMax = ${U}(-3.402823e+38f);`:`var threadMax = ${U}(-65504.0h);`,k=d=>`
var<workgroup> rowMaxShared : ${U};
var<workgroup> rowSumShared : ${U};
var<workgroup> threadShared : array<${U}, ${j}>;
fn getValue(row: i32, col: i32, row_stride: i32) -> ${U} {
let index = row * row_stride + col;
return x[index];
}
fn setValue(row: i32, col: i32, row_stride: i32, value: ${U}) {
let index = row * row_stride + col;
result[index] = value;
}
${d.registerUniform("packedCols","i32").declareVariables(R,z)}
${d.mainStart(j)}
let gindex = i32(global_idx);
let lindex = i32(local_idx);
const wg = ${j};
let row = gindex / wg;
let cols = uniforms.packedCols;
let row_stride : i32 = uniforms.packedCols;
// find the rows max
${f}
for (var col = lindex; col < cols; col += wg) {
let value = getValue(row, col, row_stride);
threadMax = max(threadMax, value);
}
if (lindex < cols) {
threadShared[lindex] = threadMax;
}
workgroupBarrier();
var reduceSize = min(cols, wg);
for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {
reduceSize = currSize + (reduceSize & 1);
if (lindex < currSize) {
threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
}
workgroupBarrier();
}
if (lindex == 0) {
rowMaxShared = ${U}(${Z("threadShared[0]",S)});
}
workgroupBarrier();
// find the rows sum
var threadSum = ${U}(0.0);
for (var col = lindex; col < cols; col += wg) {
let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
threadSum += subExp;
}
threadShared[lindex] = threadSum;
workgroupBarrier();
for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) {
if (lindex < currSize) {
threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
}
workgroupBarrier();
}
if (lindex == 0) {
rowSumShared = ${U}(${Jn("threadShared[0]",S)});
}
workgroupBarrier();
// calculate final value for each element in the row
for (var col = lindex; col < cols; col += wg) {
let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
setValue(row, col, row_stride, value);
}
}`,e=a.compute({name:"Softmax",shaderCache:{hint:`${S};${j}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:D,dataType:M.dataType}],dispatchGroup:{x:E},programUniforms:[{type:6,data:F}]}),getShaderSource:k},{inputs:[M],outputs:[w?-1:0]})[0];w&&a.compute(pa(e,v),{inputs:[e]})},VC=(a,o)=>{em(a.inputs),Am(a,o)},YC=a=>Ut({axis:a.axis})}),z2,tm,rm,sm,HC,$6=IA(()=>{it(),It(),kt(),z2=a=>Array.from(a.getBigInt64Array(),Number),tm=a=>{if(!a||a.length!==2)throw new Error("Tile requires 2 inputs.");if(a[0].dataType!==1&&a[0].dataType!==10&&a[0].dataType!==6&&a[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(a[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(a[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(z2(a[1]).length!==a[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},rm=(a,o)=>{let n=[];for(let u=0;u<a.length;++u)n.push(a[u]*o[u]);return n},sm=(a,o)=>{let n=a[0].dims,u=o??z2(a[1]),p=rm(n,u),b=He.size(p),C=a[0].dataType,w=nA("input",C,n.length),M=XA("output",C,p.length),v=D=>`
const inputShape = ${w.indices(...n)};
${D.registerUniform("output_size","u32").declareVariables(w,M)}
${D.mainStart()}
${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
let output_indices = ${M.offsetToIndices("global_idx")};
var input_indices: ${w.type.indices};
for (var i = 0; i < ${n.length}; i++) {
let input_dim_i = ${w.indicesGet("uniforms.input_shape","i")};
let input_dim_value = ${M.indicesGet("output_indices","i")} % input_dim_i;
${w.indicesSet("input_indices","i","input_dim_value")}
}
${M.setByOffset("global_idx",w.getByIndices("input_indices"))}
}`;return{name:"Tile",shaderCache:{hint:`${u}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:12,data:b},...et(a[0].dims,p)]}),getShaderSource:v}},HC=a=>{tm(a.inputs),a.compute(sm(a.inputs),{inputs:[0]})}}),am,nm,UC,e5=IA(()=>{it(),It(),kt(),am=(a,o,n,u,p)=>{let b=XA("output_data",p,n.length,4),C=nA("a_data",o[1].dataType,o[1].dims.length,4),w=nA("b_data",o[2].dataType,o[2].dims.length,4),M=nA("c_data",o[0].dataType,o[0].dims.length,4),v,D=(B,E,S)=>`select(${E}, ${B}, ${S})`;if(!u)v=b.setByOffset("global_idx",D(C.getByOffset("global_idx"),w.getByOffset("global_idx"),M.getByOffset("global_idx")));else{let B=(E,S,F="")=>{let j=`a_data[index_a${S}][component_a${S}]`,Z=`b_data[index_b${S}][component_b${S}]`,R=`bool(c_data[index_c${S}] & (0xffu << (component_c${S} * 8)))`;return`
let output_indices${S} = ${b.offsetToIndices(`global_idx * 4u + ${S}u`)};
let offset_a${S} = ${C.broadcastedIndicesToOffset(`output_indices${S}`,b)};
let offset_b${S} = ${w.broadcastedIndicesToOffset(`output_indices${S}`,b)};
let offset_c${S} = ${M.broadcastedIndicesToOffset(`output_indices${S}`,b)};
let index_a${S} = offset_a${S} / 4u;
let index_b${S} = offset_b${S} / 4u;
let index_c${S} = offset_c${S} / 4u;
let component_a${S} = offset_a${S} % 4u;
let component_b${S} = offset_b${S} % 4u;
let component_c${S} = offset_c${S} % 4u;
${E}[${S}] = ${F}(${D(j,Z,R)});
`};p===9?v=`
var data = vec4<u32>(0);
${B("data",0,"u32")}
${B("data",1,"u32")}
${B("data",2,"u32")}
${B("data",3,"u32")}
output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:v=`
${B("output_data[global_idx]",0)}
${B("output_data[global_idx]",1)}
${B("output_data[global_idx]",2)}
${B("output_data[global_idx]",3)}
`}return`
${a.registerUniform("vec_size","u32").declareVariables(M,C,w,b)}
${a.mainStart()}
${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
${v}
}`},nm=a=>{let o=a[1].dims,n=a[2].dims,u=a[0].dims,p=a[1].dataType,b=!(He.areEqual(o,n)&&He.areEqual(n,u)),C=o,w=He.size(o);if(b){let v=oo.calcShape(oo.calcShape(o,n,!1),u,!1);if(!v)throw new Error("Can't perform where op on the given tensors");C=v,w=He.size(C)}let M=Math.ceil(w/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:v=>am(v,a,C,b,p),getRunData:()=>({outputs:[{dims:C,dataType:p}],dispatchGroup:{x:Math.ceil(w/64/4)},programUniforms:[{type:12,data:M},...et(u,o,n,C)]})}},UC=a=>{a.compute(nm(a.inputs))}}),KC,A5=IA(()=>{p6(),Tc(),m6(),h6(),C6(),b6(),I6(),v6(),B6(),y6(),D6(),P6(),T6(),G6(),Q6(),F6(),S6(),O6(),_6(),z6(),N6(),L6(),R6(),j6(),W6(),fC(),V6(),Y6(),H6(),U6(),K6(),Pc(),X6(),CC(),Z6(),J6(),q6(),mC(),$6(),qn(),Gc(),e5(),KC=new Map([["Abs",[Rh]],["Acos",[jh]],["Acosh",[Wh]],["Add",[M3]],["ArgMax",[_h,ac]],["ArgMin",[Oh,ac]],["Asin",[Vh]],["Asinh",[Yh]],["Atan",[Hh]],["Atanh",[Uh]],["Attention",[zh]],["AveragePool",[BC,xC]],["BatchNormalization",[Nh]],["BiasAdd",[Lh]],["BiasSplitGelu",[k3]],["Cast",[Xh,Kh]],["Ceil",[Jh]],["Clip",[Zh]],["Concat",[Q3,F3]],["Conv",[uc,cc]],["ConvTranspose",[V3,W3]],["Cos",[qh]],["Cosh",[$h]],["CumSum",[Y3,H3]],["DepthToSpace",[U3,K3]],["DequantizeLinear",[FC,SC]],["Div",[E3]],["Einsum",[X3,Z3]],["Elu",[e3,Vo]],["Equal",[v3]],["Erf",[A3]],["Exp",[t3]],["Expand",[J3]],["FastGelu",[q3]],["Floor",[r3]],["FusedConv",[uc,cc]],["Gather",[eC,$3]],["GatherElements",[nC,aC]],["GatherBlockQuantized",[rC,sC]],["GatherND",[AC,tC]],["Gelu",[s3]],["Gemm",[oC,iC]],["GlobalAveragePool",[DC,yC]],["GlobalMaxPool",[QC,GC]],["Greater",[D3]],["GreaterOrEqual",[T3]],["GridSample",[lC,cC]],["GroupQueryAttention",[bC]],["HardSigmoid",[d3,u3]],["InstanceNormalization",[IC]],["LayerNormalization",[wC]],["LeakyRelu",[a3,Vo]],["Less",[P3]],["LessOrEqual",[G3]],["Log",[I3]],["MatMul",[kC]],["MatMulNBits",[MC,EC]],["MaxPool",[PC,TC]],["Mul",[x3]],["MultiHeadAttention",[dC,uC]],["Neg",[i3]],["Not",[n3]],["Pad",[vC]],["Pow",[B3]],["QuickGelu",[w3,Vo]],["Range",[OC]],["Reciprocal",[o3]],["ReduceMin",[Th]],["ReduceMean",[xh]],["ReduceMax",[Ph]],["ReduceSum",[Qh]],["ReduceProd",[Gh]],["ReduceL1",[Bh]],["ReduceL2",[yh]],["ReduceLogSum",[Sh]],["ReduceLogSumExp",[Dh]],["ReduceSumSquare",[Fh]],["Relu",[l3]],["Resize",[NC,LC]],["RotaryEmbedding",[hC]],["ScatterND",[zC,_C]],["Sigmoid",[c3]],["Sin",[f3]],["Sinh",[g3]],["Slice",[jC,WC]],["SkipLayerNormalization",[RC]],["Split",[gC,pC]],["Sqrt",[p3]],["Softmax",[VC,YC]],["Sub",[y3]],["Tan",[m3]],["Tanh",[h3]],["ThresholdedRelu",[b3,Vo]],["Tile",[HC]],["Transpose",[gh,ph]],["Where",[UC]]])}),XC,t5=IA(()=>{ja(),Bn(),kt(),XC=class{constructor(a){this.backend=a,this.repo=new Map,this.attributesBound=!1}getArtifact(a){return this.repo.get(a)}setArtifact(a,o){this.repo.set(a,o)}run(a,o,n,u,p){Ra(a.programInfo.name);let b=this.backend.device,C=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let w=[];for(let v of o)w.push({binding:w.length,resource:{buffer:v.buffer}});for(let v of n)w.push({binding:w.length,resource:{buffer:v.buffer}});p&&w.push({binding:w.length,resource:p});let M=b.createBindGroup({layout:a.computePipeline.getBindGroupLayout(0),entries:w,label:a.programInfo.name});if(this.backend.sessionStatus==="capturing"){let v={kernelId:this.backend.currentKernelId,computePipeline:a.computePipeline,bindGroup:M,dispatchGroup:u};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(v)}C.setPipeline(a.computePipeline),C.setBindGroup(0,M),C.dispatchWorkgroups(...u),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),va(a.programInfo.name)}dispose(){}build(a,o){Ra(a.name);let n=this.backend.device,u=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"}].forEach(v=>{n.features.has(v.feature)&&u.push(`enable ${v.extension};`)});let p=fh(o,this.backend.device.limits),b=a.getShaderSource(p),C=`${u.join(`
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${p.additionalImplementations}
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(should not happen)");let a=this.kernelCustomData.get(this.currentKernelId);return a||(a={},this.kernelCustomData.set(this.currentKernelId,a)),a}async initialize(a,o){this.env=a;let n=[],u={requiredLimits:{maxComputeWorkgroupStorageSize:o.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.limits.maxStorageBufferBindingSize,maxBufferSize:o.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:o.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:o.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:o.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:o.limits.maxComputeWorkgroupSizeZ},requiredFeatures:n},p=b=>o.features.has(b)&&n.push(b)&&!0;p("chromium-experimental-timestamp-query-inside-passes")||p("timestamp-query"),p("shader-f16"),p("subgroups"),this.device=await o.requestDevice(u),this.adapterInfo=new lm(o.info||await o.requestAdapterInfo()),this.gpuDataManager=uh(this),this.programManager=new XC(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,xc(a.logLevel,!!a.debug),this.device.onuncapturederror=b=>{b.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${b.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:o,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let a=this.getCommandEncoder(),o={};this.queryType==="at-passes"&&(o.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=a.beginComputePass(o)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Ra(),this.endComputePass();let a;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),a=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(a,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,a,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&a.mapAsync(GPUMapMode.READ).then(()=>{let o=new BigUint64Array(a.getMappedRange()),n=this.pendingQueries.get(a);for(let u=0;u<o.length/2;u++){let p=n[u],b=p.kernelId,C=this.kernels.get(b),w=C.kernelType,M=C.kernelName,v=p.programName,D=p.inputTensorViews,B=p.outputTensorViews,E=o[u*2],S=o[u*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=E);let F=Number(E-this.queryTimeBase),j=Number(S-this.queryTimeBase);if(!Number.isSafeInteger(F)||!Number.isSafeInteger(j))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:D.map(Z=>({dims:Z.dims,dataType:vn(Z.dataType)})),outputsMetadata:B.map(Z=>({dims:Z.dims,dataType:vn(Z.dataType)})),kernelId:b,kernelType:w,kernelName:M,programName:v,startTime:F,endTime:j});else{let Z="";D.forEach((z,U)=>{Z+=`input[${U}]: [${z.dims}] | ${vn(z.dataType)}, `});let R="";B.forEach((z,U)=>{R+=`output[${U}]: [${z.dims}] | ${vn(z.dataType)}, `}),console.log(`[profiling] kernel "${b}|${w}|${M}|${v}" ${Z}${R}execution time: ${j-F} ns`)}Zo("GPU",`${v}::${E}::${S}`)}a.unmap(),this.pendingQueries.delete(a)}),va()}run(a,o,n,u,p,b){Ra(a.name);let C=[];for(let z=0;z<o.length;++z){let U=o[z].data;if(U===0)continue;let f=this.gpuDataManager.get(U);if(!f)throw new Error(`no GPU data for input: ${U}`);C.push(f)}let{outputs:w,dispatchGroup:M,programUniforms:v}=a.getRunData(o),D=n.length===0?w.map((z,U)=>U):n;if(D.length!==w.length)throw new Error(`Output size ${D.length} must be equal to ${w.length}.`);let B=[],E=[];for(let z=0;z<w.length;++z){if(!Number.isInteger(D[z])||D[z]<-3||D[z]>=b)throw new Error(`Invalid output index: ${D[z]}`);if(D[z]===-3)continue;let U=D[z]===-1,f=D[z]===-2,k=U||f?p(w[z].dataType,w[z].dims):u(D[z],w[z].dataType,w[z].dims);if(B.push(k),k.data===0)continue;let e=this.gpuDataManager.get(k.data);if(!e)throw new Error(`no GPU data for output: ${k.data}`);if(U&&this.temporaryData.push(e),f){let d=this.kernelPersistentData.get(this.currentKernelId);d||(d=[],this.kernelPersistentData.set(this.currentKernelId,d)),d.push(e)}E.push(e)}if(C.length!==o.length||E.length!==B.length){if(E.length===0)return va(a.name),B;throw new Error(`Program ${a.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let S;if(v){let z=0,U=[];v.forEach(d=>{let y=typeof d.data=="number"?[d.data]:d.data;if(y.length===0)return;let Ae=d.type===10?2:4,P,O;d.type===10?(O=y.length>4?16:y.length>2?8:y.length*Ae,P=y.length>4?16:Ae*y.length):(O=y.length<=2?y.length*Ae:16,P=16),z=Math.ceil(z/O)*O,U.push(z);let pe=d.type===10?8:4;z+=y.length>4?Math.ceil(y.length/pe)*P:y.length*Ae});let f=16;z=Math.ceil(z/f)*f;let k=new ArrayBuffer(z);v.forEach((d,y)=>{let Ae=U[y],P=typeof d.data=="number"?[d.data]:d.data;if(d.type===6)new Int32Array(k,Ae,P.length).set(P);else if(d.type===12)new Uint32Array(k,Ae,P.length).set(P);else if(d.type===10)new Uint16Array(k,Ae,P.length).set(P);else if(d.type===1)new Float32Array(k,Ae,P.length).set(P);else throw new Error(`Unsupported uniform type: ${vn(d.type)}`)});let e=this.gpuDataManager.create(z,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(e.buffer,0,k,0,z),this.gpuDataManager.release(e.id),S={offset:0,size:z,buffer:e.buffer}}let F=this.programManager.normalizeDispatchGroupSize(M),j=F[1]===1&&F[2]===1,Z=om(a,o,j),R=this.programManager.getArtifact(Z);if(R||(R=this.programManager.build(a,F),this.programManager.setArtifact(Z,R),St("info",()=>`[artifact] key: ${Z}, programName: ${a.name}`)),v&&R.uniformVariablesInfo){if(v.length!==R.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${R.uniformVariablesInfo.length}, got ${v.length} in program "${R.programInfo.name}".`);for(let z=0;z<v.length;z++){let U=v[z],f=U.type,k=typeof U.data=="number"?1:U.data.length,[e,d]=R.uniformVariablesInfo[z];if(f!==e||k!==d)throw new Error(`Uniform variable ${z} mismatch: expect type ${e} with size ${d}, got type ${f} with size ${k} in program 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o)this.gpuDataManager.release(n.id);this.kernelPersistentData.delete(a)}this.kernelCustomData.delete(a),this.kernels.delete(a)}computeKernel(a,o,n){let u=this.kernels.get(a);if(!u)throw new Error(`kernel not created: ${a}`);let p=u.kernelType,b=u.kernelName,C=u.kernelEntry,w=u.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${p}] ${b}" is not allowed to be called recursively`);this.currentKernelId=a,w[0]&&(w[1]=w[0](w[1]),w[0]=void 0),St("info",()=>`[WebGPU] Start to run kernel "[${p}] ${b}"...`);let M=this.env.debug;this.temporaryData=[];try{return M&&this.device.pushErrorScope("validation"),C(o,w[1]),0}catch(v){return n.push(Promise.resolve(`[WebGPU] Kernel "[${p}] ${b}" failed. ${v}`)),1}finally{M&&n.push(this.device.popErrorScope().then(v=>v?`GPU validation error for kernel "[${p}] ${b}": ${v.message}`:null));for(let v of this.temporaryData)this.gpuDataManager.release(v.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(a,o,n,u){let p=this.sessionExternalDataMapping.get(a);p||(p=new Map,this.sessionExternalDataMapping.set(a,p));let b=p.get(o),C=this.gpuDataManager.registerExternalBuffer(n,u,b);return p.set(o,[C,n]),C}unregisterBuffers(a){let o=this.sessionExternalDataMapping.get(a);o&&(o.forEach(n=>this.gpuDataManager.unregisterExternalBuffer(n[0])),this.sessionExternalDataMapping.delete(a))}getBuffer(a){let o=this.gpuDataManager.get(a);if(!o)throw new Error(`no GPU data for buffer: ${a}`);return o.buffer}createDownloader(a,o,n){return async()=>{let u=await tc(this,a,o);return Bc(u.buffer,n)}}writeTimestamp(a){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,a)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){St("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){St("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){St("info","replay"),this.sessionStatus="replaying";let a=this.capturedCommandList.get(this.currentSessionId),o=this.capturedPendingKernels.get(this.currentSessionId),n=a.length;this.pendingKernels=[];for(let u=0;u<n;u++){let p=this.getComputePassEncoder(),b=a[u];this.writeTimestamp(this.pendingDispatchNumber*2),p.setPipeline(b.computePipeline),p.setBindGroup(0,b.bindGroup),p.dispatchWorkgroups(...b.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(o[u]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(a){this.unregisterBuffers(a),this.capturedCommandList.has(a)&&this.capturedCommandList.delete(a),this.capturedPendingKernels.has(a)&&this.capturedPendingKernels.delete(a),this.gpuDataManager.onReleaseSession(a)}onRunStart(a){this.currentSessionId=a,this.setQueryType()}}}),qC={};fo(qC,{init:()=>$C});var D0,cm,$C,s5=IA(()=>{it(),Bn(),It(),d6(),D0=class 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iA=C.webnnEnsureTensor,rA=C.webnnIsInt64Supported;if(!iA||!rA)throw new Error('preferredLocation "ml-tensor" is not supported without using WebNN.');if(Ei(Pe,xe)===void 0||!Ec(ye))throw new Error(`Unsupported data type: ${ye}`);if(ye==="int64"&&!rA(a))throw new Error('preferredLocation "ml-tensor" for int64 output is not supported by current WebNN Context.');let CA=await iA(a,_e,Pe,se,!1);De=!0,pe.push([ye,se,{mlTensor:CA,download:C.webnnCreateMLTensorDownloader(_e,ye),dispose:()=>{C.webnnReleaseTensorId(_e),C._OrtReleaseTensor(be)}},"ml-tensor"])}else{let iA=kc(ye),rA=new iA(xe);new Uint8Array(rA.buffer,rA.byteOffset,rA.byteLength).set(C.HEAPU8.subarray(_e,_e+rA.byteLength)),pe.push([ye,se,rA,"cpu"])}}finally{C.stackRestore(ke),ye==="string"&&_e&&C._free(_e),De||C._OrtReleaseTensor(be),C.webnnOnRunEnd?.(v)}}return E&&!S&&(C._OrtClearBoundOutputs(E.handle)!==0&&sr("Can't clear bound 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No instance will be fused."),Ae=new Set);const O=k.class_queries_logits??k.logits,ee=(k.masks_queries_logits??k.pred_masks).sigmoid();let[be,ke,Me]=O.dims;if(Me-=1,P!==null&&P.length!==be)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let De=[];for(let ye=0;ye<be;++ye){let _e=P!==null?P[ye]:null,Ne=O[ye],Pe=ee[ye],[Ce,ie,se]=F(Ne,Pe,e,Me);if(se.length===0){let[iA,rA]=_e??Pe.dims.slice(-2),CA=new p.Tensor("int32",new Int32Array(iA*rA).fill(-1),[iA,rA]);De.push({segmentation:CA,segments_info:[]});continue}let[xe,je]=Z(Ce,ie,se,d,y,Ae,_e);De.push({segmentation:xe,segments_info:je})}return De}function U(k,e=.5,d=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class f extends u.Callable{constructor(e){super(),this.image_mean=e.image_mean??e.mean,this.image_std=e.image_std??e.std,this.resample=e.resample??2,this.do_rescale=e.do_rescale??!0,this.rescale_factor=e.rescale_factor??1/255,this.do_normalize=e.do_normalize,this.do_thumbnail=e.do_thumbnail,this.size=e.size??e.image_size,this.do_resize=e.do_resize??this.size!==void 0,this.size_divisibility=e.size_divisibility??e.size_divisor,this.do_center_crop=e.do_center_crop,this.crop_size=e.crop_size,this.do_convert_rgb=e.do_convert_rgb??!0,this.do_crop_margin=e.do_crop_margin,this.pad_size=e.pad_size,this.do_pad=e.do_pad,this.min_pixels=e.min_pixels,this.max_pixels=e.max_pixels,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=e.do_flip_channel_order??!1,this.config=e}async thumbnail(e,d,y=2){const Ae=e.height,P=e.width,O=d.height,pe=d.width;let ee=Math.min(Ae,O),be=Math.min(P,pe);return ee===Ae&&be===P?e:(Ae>P?be=Math.floor(P*ee/Ae):P>Ae&&(ee=Math.floor(Ae*be/P)),await e.resize(be,ee,{resample:y}))}async crop_margin(e,d=200){const y=e.clone().grayscale(),Ae=(0,b.min)(y.data)[0],O=(0,b.max)(y.data)[0]-Ae;if(O===0)return e;const pe=d/255;let ee=y.width,be=y.height,ke=0,Me=0;const De=y.data;for(let ye=0;ye<y.height;++ye){const _e=ye*y.width;for(let Ne=0;Ne<y.width;++Ne)(De[_e+Ne]-Ae)/O<pe&&(ee=Math.min(ee,Ne),be=Math.min(be,ye),ke=Math.max(ke,Ne),Me=Math.max(Me,ye))}return e=await e.crop([ee,be,ke,Me]),e}pad_image(e,d,y,{mode:Ae="constant",center:P=!1,constant_values:O=0}={}){const[pe,ee,be]=d;let ke,Me;if(typeof y=="number"?(ke=y,Me=y):y==="square"?ke=Me=Math.max(pe,ee):(ke=y.width,Me=y.height),ke!==ee||Me!==pe){const De=new Float32Array(ke*Me*be);if(Array.isArray(O))for(let Ne=0;Ne<De.length;++Ne)De[Ne]=O[Ne%be];else O!==0&&De.fill(O);const[ye,_e]=P?[Math.floor((ke-ee)/2),Math.floor((Me-pe)/2)]:[0,0];for(let Ne=0;Ne<pe;++Ne){const Pe=(Ne+_e)*ke,Ce=Ne*ee;for(let ie=0;ie<ee;++ie){const se=(Pe+ie+ye)*be,xe=(Ce+ie)*be;for(let je=0;je<be;++je)De[se+je]=e[xe+je]}}if(Ae==="symmetric"){if(P)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const Ne=pe-1,Pe=ee-1;for(let Ce=0;Ce<Me;++Ce){const ie=Ce*ke,se=(0,C.calculateReflectOffset)(Ce,Ne)*ee;for(let xe=0;xe<ke;++xe){if(Ce<pe&&xe<ee)continue;const je=(ie+xe)*be,iA=(se+(0,C.calculateReflectOffset)(xe,Pe))*be;for(let rA=0;rA<be;++rA)De[je+rA]=e[iA+rA]}}}e=De,d=[Me,ke,be]}return[e,d]}rescale(e){for(let d=0;d<e.length;++d)e[d]=this.rescale_factor*e[d]}get_resize_output_image_size(e,d){const[y,Ae]=e.size;let P,O;if(this.do_thumbnail){const{height:pe,width:ee}=d;P=Math.min(pe,ee)}else Number.isInteger(d)?(P=d,O=this.config.max_size??P):d!==void 0&&(P=d.shortest_edge,O=d.longest_edge);if(P!==void 0||O!==void 0){const pe=P===void 0?1:Math.max(P/y,P/Ae),ee=y*pe,be=Ae*pe,ke=O===void 0?1:Math.min(O/ee,O/be);let Me=Math.floor(Number((ee*ke).toFixed(2))),De=Math.floor(Number((be*ke).toFixed(2)));return this.size_divisibility!==void 0&&([Me,De]=D([Me,De],this.size_divisibility)),[Me,De]}else if(d!==void 0&&d.width!==void 0&&d.height!==void 0){let pe=d.width,ee=d.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let be=ee/Ae,ke=pe/y;Math.abs(1-ke)<Math.abs(1-be)?be=ke:ke=be,ee=v(be*Ae,this.config.ensure_multiple_of),pe=v(ke*y,this.config.ensure_multiple_of)}return[pe,ee]}else{if(this.size_divisibility!==void 0)return D([y,Ae],this.size_divisibility);if(this.min_pixels!==void 0&&this.max_pixels!==void 0){const pe=this.config.patch_size*this.config.merge_size;return R(Ae,y,pe,this.min_pixels,this.max_pixels)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(d)}`)}}async resize(e){const[d,y]=this.get_resize_output_image_size(e,this.size);return await e.resize(d,y,{resample:this.resample})}async preprocess(e,{do_normalize:d=null,do_pad:y=null,do_convert_rgb:Ae=null,do_convert_grayscale:P=null,do_flip_channel_order:O=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[pe,ee]=e.size;if(Ae??this.do_convert_rgb?e=e.rgb():P&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let ye,_e;Number.isInteger(this.crop_size)?(ye=this.crop_size,_e=this.crop_size):(ye=this.crop_size.width,_e=this.crop_size.height),e=await e.center_crop(ye,_e)}const be=[e.height,e.width];let ke=Float32Array.from(e.data),Me=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(ke),d??this.do_normalize){let ye=this.image_mean;Array.isArray(this.image_mean)||(ye=new Array(e.channels).fill(ye));let _e=this.image_std;if(Array.isArray(this.image_std)||(_e=new Array(e.channels).fill(_e)),ye.length!==e.channels||_e.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${ye.length}) and \`image_std\` (${_e.length}) must match the number of channels in the image (${e.channels}).`);for(let Ne=0;Ne<ke.length;Ne+=e.channels)for(let Pe=0;Pe<e.channels;++Pe)ke[Ne+Pe]=(ke[Ne+Pe]-ye[Pe])/_e[Pe]}if(y??this.do_pad){if(this.pad_size)[ke,Me]=this.pad_image(ke,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){const[ye,_e]=D([Me[1],Me[0]],this.size_divisibility);[ke,Me]=this.pad_image(ke,Me,{width:ye,height:_e})}}if(O??this.do_flip_channel_order){if(Me[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let ye=0;ye<ke.length;ye+=3){const _e=ke[ye];ke[ye]=ke[ye+2],ke[ye+2]=_e}}const De=new p.Tensor("float32",ke,Me).permute(2,0,1);return{original_size:[ee,pe],reshaped_input_size:be,pixel_values:De}}async _call(e,...d){Array.isArray(e)||(e=[e]);const y=await Promise.all(e.map(P=>this.preprocess(P)));return{pixel_values:(0,p.stack)(y.map(P=>P.pixel_values),0),original_sizes:y.map(P=>P.original_size),reshaped_input_sizes:y.map(P=>P.reshaped_input_size)}}static async from_pretrained(e,d={}){const y=await(0,w.getModelJSON)(e,M.IMAGE_PROCESSOR_NAME,!0,d);return new this(y)}}}),"./src/base/processing_utils.js":((a,o,n)=>{n.r(o),n.d(o,{Processor:()=>C});var u=n("./src/utils/constants.js"),p=n("./src/utils/generic.js"),b=n("./src/utils/hub.js");class C extends p.Callable{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;static uses_chat_template_file=!1;constructor(M,v,D){super(),this.config=M,this.components=v,this.chat_template=D}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(M,v={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(M,{tokenize:!1,chat_template:this.chat_template??void 0,...v})}batch_decode(...M){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...M)}decode(...M){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...M)}async _call(M,...v){for(const D of[this.image_processor,this.feature_extractor,this.tokenizer])if(D)return D(M,...v);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(M,v={}){const[D,B,E]=await Promise.all([this.uses_processor_config?(0,b.getModelJSON)(M,u.PROCESSOR_NAME,!0,v):{},Promise.all(this.classes.filter(S=>S in this).map(async S=>{const F=await this[S].from_pretrained(M,v);return[S.replace(/_class$/,""),F]})).then(Object.fromEntries),this.uses_chat_template_file?(0,b.getModelText)(M,u.CHAT_TEMPLATE_NAME,!0,v):null]);return new this(D,B,E)}}}),"./src/configs.js":((a,o,n)=>{n.r(o),n.d(o,{AutoConfig:()=>D,PretrainedConfig:()=>v,getCacheShapes:()=>w});var u=n("./src/utils/core.js"),p=n("./src/utils/hub.js");async function b(B,E){return await(0,p.getModelJSON)(B,"config.json",!0,E)}function C(B){const E={};let 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u=n("./src/utils/generic.js");n("./src/utils/tensor.js");var p=n("./src/utils/maths.js");class b extends u.Callable{_call(e,d){throw Error("`_call` should be implemented in a subclass")}}class C extends u.Callable{_call(e,d){throw Error("`_call` should be implemented in a subclass")}}class w extends u.Callable{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,d){let y=d;for(const Ae of this.processors)y=Ae(e,y);return y}[Symbol.iterator](){return this.processors.values()}}class M extends b{constructor(e){super(),this.bos_token_id=e}_call(e,d){for(let y=0;y<e.length;++y)if(e[y].length===1){const Ae=d[y].data;Ae.fill(-1/0),Ae[this.bos_token_id]=0}return d}}class v extends b{constructor(e,d){super(),this.max_length=e,this.eos_token_id=Array.isArray(d)?d:[d]}_call(e,d){for(let y=0;y<e.length;++y)if(e[y].length===this.max_length-1){const Ae=d[y].data;Ae.fill(-1/0);for(const P of this.eos_token_id)Ae[P]=0}return d}}class D 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extends b{constructor(e,d,y){super(),this.prompt_length_to_skip=e,this.min_new_tokens=d,this.eos_token_id=Array.isArray(y)?y:[y]}_call(e,d){for(let y=0;y<e.length;++y)if(e[y].length-this.prompt_length_to_skip<this.min_new_tokens){const P=d[y].data;for(const O of this.eos_token_id)P[O]=-1/0}return d}}class Z extends b{constructor(e,d){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(d)?d:[d]}_call(e,d){for(let y=0;y<e.length;++y){const Ae=d[y].data,P=e[y];for(const O of this.bad_words_ids){if(P.length<O.length-1)continue;let pe=!0;for(let ee=1;ee<=O.length-1;++ee)if(O.at(-ee-1)!=P.at(-ee)){pe=!1;break}pe&&(Ae[O.at(-1)]=-1/0)}}return d}}class R extends b{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,d){if(d.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${d.dims[0]} for the logits and ${e.length} for the input ids.`);const y=e.length,Ae=d.slice([0,y],null),P=d.slice([y,d.dims[0]],null);for(let O=0;O<P.data.length;++O)P.data[O]+=(Ae.data[O]-P.data[O])*this.guidance_scale;return P}}class z extends C{constructor(e){super(),this.temperature=e}_call(e,d){const y=d.data;for(let Ae=0;Ae<y.length;++Ae)y[Ae]/=this.temperature;return d}}class U extends C{constructor(e,{filter_value:d=-1/0,min_tokens_to_keep:y=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(y)||y<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${y}`);this.top_p=e,this.filter_value=d,this.min_tokens_to_keep=y}}class f extends C{constructor(e,{filter_value:d=-1/0,min_tokens_to_keep:y=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,y),this.filter_value=d}}}),"./src/generation/logits_sampler.js":((a,o,n)=>{n.r(o),n.d(o,{LogitsSampler:()=>C});var u=n("./src/utils/generic.js"),p=n("./src/utils/tensor.js"),b=n("./src/utils/maths.js");n("./src/generation/configuration_utils.js");class C extends u.Callable{constructor(B){super(),this.generation_config=B}async _call(B){return this.sample(B)}async sample(B){throw Error("sample should be implemented in subclasses.")}getLogits(B,E){let S=B.dims.at(-1),F=B.data;if(E===-1)F=F.slice(-S);else{let j=E*S;F=F.slice(j,j+S)}return F}randomSelect(B){let E=0;for(let F=0;F<B.length;++F)E+=B[F];let S=Math.random()*E;for(let F=0;F<B.length;++F)if(S-=B[F],S<=0)return F;return 0}static getSampler(B){if(B.do_sample)return new M(B);if(B.num_beams>1)return new v(B);if(B.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${B.num_return_sequences}.`);return new w(B)}}class w extends C{async sample(B){const E=(0,b.max)(B.data)[1];return[[BigInt(E),0]]}}class M extends C{async sample(B){let E=B.dims.at(-1);this.generation_config.top_k>0&&(E=Math.min(this.generation_config.top_k,E));const[S,F]=await(0,p.topk)(B,E),j=(0,b.softmax)(S.data);return Array.from({length:this.generation_config.num_beams},()=>{const Z=this.randomSelect(j);return[F.data[Z],Math.log(j[Z])]})}}class v extends C{async sample(B){let E=B.dims.at(-1);this.generation_config.top_k>0&&(E=Math.min(this.generation_config.top_k,E));const[S,F]=await(0,p.topk)(B,E),j=(0,b.softmax)(S.data);return Array.from({length:this.generation_config.num_beams},(Z,R)=>[F.data[R],Math.log(j[R])])}}}),"./src/generation/stopping_criteria.js":((a,o,n)=>{n.r(o),n.d(o,{EosTokenCriteria:()=>w,InterruptableStoppingCriteria:()=>M,MaxLengthCriteria:()=>C,StoppingCriteria:()=>p,StoppingCriteriaList:()=>b});var u=n("./src/utils/generic.js");class p extends u.Callable{_call(D,B){throw Error("StoppingCriteria needs to be subclassed")}}class b extends u.Callable{constructor(){super(),this.criteria=[]}push(D){this.criteria.push(D)}extend(D){D instanceof b?D=D.criteria:D instanceof p&&(D=[D]),this.criteria.push(...D)}_call(D,B){const E=new Array(D.length).fill(!1);for(const S of this.criteria){const F=S(D,B);for(let j=0;j<E.length;++j)E[j]||=F[j]}return E}[Symbol.iterator](){return this.criteria.values()}}class C extends p{constructor(D,B=null){super(),this.max_length=D,this.max_position_embeddings=B}_call(D){return D.map(B=>B.length>=this.max_length)}}class w extends p{constructor(D){super(),Array.isArray(D)||(D=[D]),this.eos_token_id=D}_call(D,B){return D.map(E=>{const S=E.at(-1);return this.eos_token_id.some(F=>S==F)})}}class M extends p{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(D,B){return new Array(D.length).fill(this.interrupted)}}}),"./src/generation/streamers.js":((a,o,n)=>{n.r(o),n.d(o,{BaseStreamer:()=>C,TextStreamer:()=>M,WhisperTextStreamer:()=>v});var u=n("./src/utils/core.js"),p=n("./src/tokenizers.js"),b=n("./src/env.js");class C{put(B){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const w=b.apis.IS_PROCESS_AVAILABLE?D=>process.stdout.write(D):D=>console.log(D);class M extends C{constructor(B,{skip_prompt:E=!1,callback_function:S=null,token_callback_function:F=null,skip_special_tokens:j=!0,decode_kwargs:Z={},...R}={}){super(),this.tokenizer=B,this.skip_prompt=E,this.callback_function=S??w,this.token_callback_function=F,this.decode_kwargs={skip_special_tokens:j,...Z,...R},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(B){if(B.length>1)throw Error("TextStreamer only supports batch size of 1");const E=this.next_tokens_are_prompt;if(E&&(this.next_tokens_are_prompt=!1,this.skip_prompt))return;const S=B[0];this.token_callback_function?.(S),this.token_cache=(0,u.mergeArrays)(this.token_cache,S);const F=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let j;E||F.endsWith(`
`)?(j=F.slice(this.print_len),this.token_cache=[],this.print_len=0):F.length>0&&(0,p.is_chinese_char)(F.charCodeAt(F.length-1))?(j=F.slice(this.print_len),this.print_len+=j.length):(j=F.slice(this.print_len,F.lastIndexOf(" ")+1),this.print_len+=j.length),this.on_finalized_text(j,!1)}end(){let B;this.token_cache.length>0?(B=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):B="",this.next_tokens_are_prompt=!0,this.on_finalized_text(B,!0)}on_finalized_text(B,E){B.length>0&&this.callback_function?.(B),E&&this.callback_function===w&&b.apis.IS_PROCESS_AVAILABLE&&this.callback_function?.(`
`)}}class v extends M{constructor(B,{skip_prompt:E=!1,callback_function:S=null,token_callback_function:F=null,on_chunk_start:j=null,on_chunk_end:Z=null,on_finalize:R=null,time_precision:z=.02,skip_special_tokens:U=!0,decode_kwargs:f={}}={}){super(B,{skip_prompt:E,skip_special_tokens:U,callback_function:S,token_callback_function:F,decode_kwargs:f}),this.timestamp_begin=B.timestamp_begin,this.on_chunk_start=j,this.on_chunk_end=Z,this.on_finalize=R,this.time_precision=z,this.waiting_for_timestamp=!1}put(B){if(B.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const E=B[0];if(E.length===1){const S=Number(E[0])-this.timestamp_begin;if(S>=0){const F=S*this.time_precision;this.waiting_for_timestamp?this.on_chunk_end?.(F):this.on_chunk_start?.(F),this.waiting_for_timestamp=!this.waiting_for_timestamp,this.token_callback_function?.(E);return}}return 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When 'free_dimension_overrides' is not set, you may experience significant performance degradation.`);const Jt=R.apis.IS_NODE_ENV&&R.env.useFSCache,vr=(0,M.getModelFile)(N,at,!0,ue,Jt),yr=ue.use_external_data_format??Ge.use_external_data_format;let Yr=[];if(yr){let xt;typeof yr=="object"?yr.hasOwnProperty(Rt)?xt=yr[Rt]:yr.hasOwnProperty(L)?xt=yr[L]:xt=!1:xt=yr;const Nr=+xt;if(Nr>M.MAX_EXTERNAL_DATA_CHUNKS)throw new Error(`The number of external data chunks (${Nr}) exceeds the maximum allowed value (${M.MAX_EXTERNAL_DATA_CHUNKS}).`);for(let ms=0;ms<Nr;++ms){const gi=`${Rt}_data${ms===0?"":"_"+ms}`,Rs=`${ue.subfolder??""}/${gi}`;Yr.push(new Promise(async(Ta,Zi)=>{const Ji=await(0,M.getModelFile)(N,Rs,!0,ue,Jt);Ta(Ji instanceof Uint8Array?{path:gi,data:Ji}:gi)}))}}else tt.externalData!==void 0&&(Yr=tt.externalData.map(async xt=>{if(typeof xt.data=="string"){const Nr=await(0,M.getModelFile)(N,xt.data,!0,ue);return{...xt,data:Nr}}return xt}));if(Yr.length>0){const xt=await Promise.all(Yr);R.apis.IS_NODE_ENV||(tt.externalData=xt)}if(Ye==="webgpu"){const xt=(0,u.getCacheShapes)(ue.config,{prefix:"present"});if(Object.keys(xt).length>0&&!(0,p.isONNXProxy)()){const Nr={};for(const ms in xt)Nr[ms]="gpu-buffer";tt.preferredOutputLocation=Nr}}return{buffer_or_path:await vr,session_options:tt,session_config:bt}}async function Ae(N,L,ue){return Object.fromEntries(await Promise.all(Object.keys(L).map(async Ge=>{const{buffer_or_path:Re,session_options:Ye,session_config:sA}=await y(N,L[Ge],ue),mA=await(0,p.createInferenceSession)(Re,Ye,sA);return[Ge,mA]})))}async function P(N,L,ue){return Object.fromEntries(await Promise.all(Object.keys(L).map(async Ge=>{const Re=await(0,M.getModelJSON)(N,L[Ge],!1,ue);return[Ge,Re]})))}function O(N,L){const ue=Object.create(null),Ge=[];for(const sA of N.inputNames){const mA=L[sA];if(!(mA instanceof E.Tensor)){Ge.push(sA);continue}ue[sA]=(0,p.isONNXProxy)()?mA.clone():mA}if(Ge.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${Ge.join(", ")}.`);const Re=Object.keys(L).length,Ye=N.inputNames.length;if(Re>Ye){let sA=Object.keys(L).filter(mA=>!N.inputNames.includes(mA));console.warn(`WARNING: Too many inputs were provided (${Re} > ${Ye}). The following inputs will be ignored: "${sA.join(", ")}".`)}return ue}async function pe(N,L){const ue=O(N,L);try{const Ge=Object.fromEntries(Object.entries(ue).map(([Ye,sA])=>[Ye,sA.ort_tensor])),Re=await(0,p.runInferenceSession)(N,Ge);return ee(Re)}catch(Ge){const Re=Object.fromEntries(Object.entries(ue).map(([Ye,sA])=>{const mA={type:sA.type,dims:sA.dims,location:sA.location};return mA.location!=="gpu-buffer"&&(mA.data=sA.data),[Ye,mA]}));throw console.error(`An error occurred during model execution: "${Ge}".`),console.error("Inputs given to model:",Re),Ge}}function ee(N){for(let L in N)(0,p.isONNXTensor)(N[L])?N[L]=new E.Tensor(N[L]):typeof N[L]=="object"&&ee(N[L]);return N}function be(N){if(N instanceof E.Tensor)return N;if(N.length===0)throw Error("items must be non-empty");if(Array.isArray(N[0])){if(N.some(L=>L.length!==N[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have 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N.encode_text({input_ids:L.input_ids})}if(ue.inputNames.includes("token_type_ids")&&!Ge.token_type_ids){if(!Ge.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");Ge.token_type_ids=(0,E.zeros_like)(Ge.input_ids)}if(ue.inputNames.includes("pixel_mask")&&!Ge.pixel_mask){if(!Ge.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Re=Ge.pixel_values.dims;Ge.pixel_mask=(0,E.ones)([Re[0],Re[2],Re[3]])}return await pe(ue,Ge)}async function ye(N,L){const ue=await N.encode(L);return await N.decode(ue)}async function _e(N,L,ue=!1){const Ge=N.sessions[ue?"decoder_model_merged":"model"],{past_key_values:Re,...Ye}=L;if(Ge.inputNames.includes("use_cache_branch")&&(Ye.use_cache_branch=ke(!!Re)),Ge.inputNames.includes("position_ids")&&Ye.attention_mask&&!Ye.position_ids){const mA=["paligemma","gemma3_text","gemma3"].includes(N.config.model_type)?1:0;Ye.position_ids=iA(Ye,Re,mA)}N.addPastKeyValues(Ye,Re);const sA=(0,w.pick)(Ye,Ge.inputNames);return await pe(Ge,sA)}function Ne({modality_token_id:N,inputs_embeds:L,modality_features:ue,input_ids:Ge,attention_mask:Re}){const Ye=Ge.tolist().map(RA=>RA.reduce((pt,mt,bt)=>(mt==N&&pt.push(bt),pt),[])),sA=Ye.reduce((RA,pt)=>RA+pt.length,0),mA=ue.dims[0];if(sA!==mA)throw new Error(`Number of tokens and features do not match: tokens: ${sA}, features ${mA}`);let EA=0;for(let RA=0;RA<Ye.length;++RA){const pt=Ye[RA],mt=L[RA];for(let bt=0;bt<pt.length;++bt)mt[pt[bt]].data.set(ue[EA++].data)}return{inputs_embeds:L,attention_mask:Re}}function Pe({image_token_id:N,inputs_embeds:L,image_features:ue,input_ids:Ge,attention_mask:Re}){return Ne({modality_token_id:N,inputs_embeds:L,modality_features:ue,input_ids:Ge,attention_mask:Re})}function Ce({audio_token_id:N,inputs_embeds:L,audio_features:ue,input_ids:Ge,attention_mask:Re}){return Ne({modality_token_id:N,inputs_embeds:L,modality_features:ue,input_ids:Ge,attention_mask:Re})}async function ie(N,{encode_function:L,merge_function:ue,modality_input_name:Ge,modality_output_name:Re,input_ids:Ye=null,attention_mask:sA=null,position_ids:mA=null,inputs_embeds:EA=null,past_key_values:RA=null,generation_config:pt=null,logits_processor:mt=null,...bt}){const rt=bt[Ge];if(!EA){if(EA=await N.encode_text({input_ids:Ye,...bt}),rt&&Ye.dims[1]!==1){const at=await L({[Ge]:rt,...bt});({inputs_embeds:EA,attention_mask:sA}=ue({[Re]:at,inputs_embeds:EA,input_ids:Ye,attention_mask:sA}))}else if(RA&&rt&&Ye.dims[1]===1){const at=Ye.dims[1],tt=Object.values(RA)[0].dims.at(-2);sA=(0,E.cat)([(0,E.ones)([Ye.dims[0],tt]),sA.slice(null,[sA.dims[1]-at,sA.dims[1]])],1)}}if(!mA&&N.config.model_type==="qwen2_vl"){const{image_grid_thw:at,video_grid_thw:tt}=bt;[mA]=N.get_rope_index(Ye,at,tt,sA)}return await _e(N,{inputs_embeds:EA,past_key_values:RA,attention_mask:sA,position_ids:mA,generation_config:pt,logits_processor:mt},!0)}async function se(N,L){return await ie(N,{...L,modality_input_name:"audio_values",modality_output_name:"audio_features",encode_function:N.encode_audio.bind(N),merge_function:N._merge_input_ids_with_audio_features.bind(N)})}async function xe(N,L){return await ie(N,{...L,modality_input_name:"pixel_values",modality_output_name:"image_features",encode_function:N.encode_image.bind(N),merge_function:N._merge_input_ids_with_image_features.bind(N)})}function je(N,L=0){const[ue,Ge]=N.dims,Re=N.data,Ye=new BigInt64Array(Re.length);for(let sA=0;sA<ue;++sA){const mA=sA*Ge;let EA=BigInt(L);for(let RA=0;RA<Ge;++RA){const pt=mA+RA;Re[pt]===0n?Ye[pt]=BigInt(1):(Ye[pt]=EA,EA+=Re[pt])}}return{data:Ye,dims:N.dims}}function iA(N,L=null,ue=0){const{input_ids:Ge,inputs_embeds:Re,attention_mask:Ye}=N,{data:sA,dims:mA}=je(Ye,ue);let EA=new E.Tensor("int64",sA,mA);if(L){const RA=-(Ge??Re).dims.at(1);EA=EA.slice(null,[RA,null])}return EA}function rA(N,L,ue,Ge){const Re=ue.past_key_values?Object.values(ue.past_key_values)[0].dims.at(-2):0;if(!ue.attention_mask){let Ye;for(const sA of["input_ids","inputs_embeds","position_ids"])if(ue[sA]){Ye=ue[sA].dims;break}if(!Ye)throw new Error("attention_mask is not provided, and unable to infer its shape from model inputs.");ue.attention_mask=(0,E.ones)([Ye[0],Re+Ye[1]])}if(ue.past_key_values){const{input_ids:Ye,attention_mask:sA}=ue;sA&&sA.dims[1]>Ye.dims[1]||Re<Ye.dims[1]&&(ue.input_ids=Ye.slice(null,[Re,null]))}return ue}function CA(N,L,ue,Ge){return ue.past_key_values&&(L=L.map(Re=>[Re.at(-1)])),{...ue,decoder_input_ids:be(L)}}function $e(N,...L){return N.config.is_encoder_decoder?CA(N,...L):rA(N,...L)}function we(N,L,ue,Ge){const Re=!!ue.past_key_values;return Ge.guidance_scale!==null&&Ge.guidance_scale>1&&(Re?ue.input_ids=(0,E.cat)([ue.input_ids,ue.input_ids],0):(ue.input_ids=(0,E.cat)([ue.input_ids,(0,E.full_like)(ue.input_ids,BigInt(Ge.pad_token_id))],0),ue.attention_mask=(0,E.cat)([ue.attention_mask,(0,E.full_like)(ue.attention_mask,0n)],0))),(Re||!ue.pixel_values)&&(ue.pixel_values=(0,E.full)([0,0,3,384,384],1)),Re&&(ue.images_seq_mask=new E.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),ue.images_emb_mask=new E.Tensor("bool",new Array(0).fill(!1),[1,1,0])),ue}class ae extends C.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(L,ue,Ge){super(),this.config=L,this.sessions=ue,this.configs=Ge;const Re=d.get(this.constructor),Ye=k.get(Re);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ye){case f.DecoderOnly:this.can_generate=!0,this._forward=_e,this._prepare_inputs_for_generation=rA;break;case f.Seq2Seq:case f.Vision2Seq:case f.Musicgen:this.can_generate=!0,this._forward=Me,this._prepare_inputs_for_generation=CA;break;case f.EncoderDecoder:this._forward=Me;break;case f.ImageTextToText:this.can_generate=!0,this._forward=xe,this._prepare_inputs_for_generation=$e;break;case f.AudioTextToText:this.can_generate=!0,this._forward=se,this._prepare_inputs_for_generation=$e;break;case f.Phi3V:case f.ImageAudioTextToText:this.can_generate=!0,this._prepare_inputs_for_generation=$e;break;case f.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=we;break;case f.AutoEncoder:this._forward=ye;break;default:this._forward=De;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const L=[];for(const ue of Object.values(this.sessions))ue?.handler?.dispose&&L.push(ue.handler.dispose());return await Promise.all(L)}static async from_pretrained(L,{progress_callback:ue=null,config:Ge=null,cache_dir:Re=null,local_files_only:Ye=!1,revision:sA="main",model_file_name:mA=null,subfolder:EA="onnx",device:RA=null,dtype:pt=null,use_external_data_format:mt=null,session_options:bt={}}={}){let rt={progress_callback:ue,config:Ge,cache_dir:Re,local_files_only:Ye,revision:sA,model_file_name:mA,subfolder:EA,device:RA,dtype:pt,use_external_data_format:mt,session_options:bt};const Rt=d.get(this),at=k.get(Rt);Ge=rt.config=await u.AutoConfig.from_pretrained(L,rt);let tt;if(at===f.DecoderOnly)tt=await Promise.all([Ae(L,{model:rt.model_file_name??"model"},rt),P(L,{generation_config:"generation_config.json"},rt)]);else if(at===f.Seq2Seq||at===f.Vision2Seq)tt=await Promise.all([Ae(L,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},rt),P(L,{generation_config:"generation_config.json"},rt)]);else if(at===f.MaskGeneration)tt=await Promise.all([Ae(L,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},rt)]);else if(at===f.EncoderDecoder)tt=await Promise.all([Ae(L,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},rt)]);else if(at===f.ImageTextToText){const Qt={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ge.is_encoder_decoder&&(Qt.model="encoder_model"),tt=await Promise.all([Ae(L,Qt,rt),P(L,{generation_config:"generation_config.json"},rt)])}else if(at===f.AudioTextToText){const Qt={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};tt=await Promise.all([Ae(L,Qt,rt),P(L,{generation_config:"generation_config.json"},rt)])}else if(at===f.ImageAudioTextToText){const Qt={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};tt=await Promise.all([Ae(L,Qt,rt),P(L,{generation_config:"generation_config.json"},rt)])}else if(at===f.Musicgen)tt=await Promise.all([Ae(L,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},rt),P(L,{generation_config:"generation_config.json"},rt)]);else if(at===f.MultiModality)tt=await Promise.all([Ae(L,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},rt),P(L,{generation_config:"generation_config.json"},rt)]);else if(at===f.Phi3V)tt=await Promise.all([Ae(L,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},rt),P(L,{generation_config:"generation_config.json"},rt)]);else if(at===f.AutoEncoder)tt=await Promise.all([Ae(L,{encoder_model:"encoder_model",decoder_model:"decoder_model"},rt)]);else if(at===f.Supertonic)tt=await Promise.all([Ae(L,{text_encoder:"text_encoder",latent_denoiser:"latent_denoiser",voice_decoder:"voice_decoder"},rt)]);else{if(at!==f.EncoderOnly){const Qt=Rt??Ge?.model_type;Qt!=="custom"&&console.warn(`Model type for '${Qt}' not found, assuming encoder-only architecture. Please report this at ${v.GITHUB_ISSUE_URL}.`)}tt=await Promise.all([Ae(L,{model:rt.model_file_name??"model"},rt)])}return new this(Ge,...tt)}async _call(L){return await this.forward(L)}async forward(L){return await this._forward(this,L)}get generation_config(){return this.configs?.generation_config??null}_get_logits_processor(L,ue,Ge=null){const Re=new D.LogitsProcessorList;if(L.repetition_penalty!==null&&L.repetition_penalty!==1&&Re.push(new D.RepetitionPenaltyLogitsProcessor(L.repetition_penalty)),L.no_repeat_ngram_size!==null&&L.no_repeat_ngram_size>0&&Re.push(new D.NoRepeatNGramLogitsProcessor(L.no_repeat_ngram_size)),L.bad_words_ids!==null&&Re.push(new D.NoBadWordsLogitsProcessor(L.bad_words_ids,L.eos_token_id)),L.min_length!==null&&L.eos_token_id!==null&&L.min_length>0&&Re.push(new D.MinLengthLogitsProcessor(L.min_length,L.eos_token_id)),L.min_new_tokens!==null&&L.eos_token_id!==null&&L.min_new_tokens>0&&Re.push(new D.MinNewTokensLengthLogitsProcessor(ue,L.min_new_tokens,L.eos_token_id)),L.forced_bos_token_id!==null&&Re.push(new D.ForcedBOSTokenLogitsProcessor(L.forced_bos_token_id)),L.forced_eos_token_id!==null&&Re.push(new D.ForcedEOSTokenLogitsProcessor(L.max_length,L.forced_eos_token_id)),L.begin_suppress_tokens!==null){const Ye=ue>1||L.forced_bos_token_id===null?ue:ue+1;Re.push(new D.SuppressTokensAtBeginLogitsProcessor(L.begin_suppress_tokens,Ye))}return L.guidance_scale!==null&&L.guidance_scale>1&&Re.push(new D.ClassifierFreeGuidanceLogitsProcessor(L.guidance_scale)),L.temperature===0&&L.do_sample&&(console.warn("`do_sample` changed to false because `temperature: 0` implies greedy sampling (always selecting the most likely token), which is incompatible with `do_sample: true`."),L.do_sample=!1),L.do_sample&&L.temperature!==null&&L.temperature!==1&&Re.push(new D.TemperatureLogitsWarper(L.temperature)),Ge!==null&&Re.extend(Ge),Re}_prepare_generation_config(L,ue,Ge=B.GenerationConfig){const Re={...this.config};for(const sA of["decoder","generator","text_config"])sA in Re&&Object.assign(Re,Re[sA]);const Ye=new Ge(Re);return Object.assign(Ye,this.generation_config??{}),L&&Object.assign(Ye,L),ue&&Object.assign(Ye,(0,w.pick)(ue,Object.getOwnPropertyNames(Ye))),Ye}_get_stopping_criteria(L,ue=null){const Ge=new j.StoppingCriteriaList;return L.max_length!==null&&Ge.push(new j.MaxLengthCriteria(L.max_length,this.config.max_position_embeddings??null)),L.eos_token_id!==null&&Ge.push(new j.EosTokenCriteria(L.eos_token_id)),ue&&Ge.extend(ue),Ge}_validate_model_class(){if(!this.can_generate){const L=[Sl,Ol,Fl,Ql],ue=d.get(this.constructor),Ge=new Set,Re=this.config.model_type;for(const sA of L){const mA=sA.get(Re);mA&&Ge.add(mA[0])}let Ye=`The current model class (${ue}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ge.size>0&&(Ye+=` Please use the following class instead: ${[...Ge].join(", ")}`),Error(Ye)}}prepare_inputs_for_generation(...L){return this._prepare_inputs_for_generation(this,...L)}_update_model_kwargs_for_generation({generated_input_ids:L,outputs:ue,model_inputs:Ge,is_encoder_decoder:Re}){return Ge.past_key_values=this.getPastKeyValues(ue,Ge.past_key_values),Ge.input_ids=new E.Tensor("int64",L.flat(),[L.length,1]),Re||(Ge.attention_mask=(0,E.cat)([Ge.attention_mask,(0,E.ones)([Ge.attention_mask.dims[0],1])],1)),Ge.position_ids=null,Ge}_prepare_model_inputs({inputs:L,bos_token_id:ue,model_kwargs:Ge}){const Re=(0,w.pick)(Ge,this.forward_params),Ye=this.main_input_name;if(Ye in Re){if(L)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Re[Ye]=L;return{inputs_tensor:Re[Ye],model_inputs:Re,model_input_name:Ye}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:L,model_inputs:ue,model_input_name:Ge,generation_config:Re}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ue.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:sA,pixel_values:mA,attention_mask:EA,...RA}=ue,pt=await this._prepare_inputs_embeds(ue);ue={...RA,...(0,w.pick)(pt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ye}=await De(this,ue);if(Re.guidance_scale!==null&&Re.guidance_scale>1)Ye=(0,E.cat)([Ye,(0,E.full_like)(Ye,0)],0),"attention_mask"in ue&&(ue.attention_mask=(0,E.cat)([ue.attention_mask,(0,E.zeros_like)(ue.attention_mask)],0));else if(ue.decoder_input_ids){const sA=be(ue.decoder_input_ids).dims[0];if(sA!==Ye.dims[0]){if(Ye.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ye.dims[0]}) than the decoder inputs (${sA}).`);Ye=(0,E.cat)(Array.from({length:sA},()=>Ye),0)}}return ue.encoder_outputs=Ye,ue}_prepare_decoder_input_ids_for_generation({batch_size:L,model_input_name:ue,model_kwargs:Ge,decoder_start_token_id:Re,bos_token_id:Ye,generation_config:sA}){let{decoder_input_ids:mA,...EA}=Ge;if(!(mA instanceof E.Tensor)){if(mA)Array.isArray(mA[0])||(mA=Array.from({length:L},()=>mA));else if(Re??=Ye,this.config.model_type==="musicgen")mA=Array.from({length:L*this.config.decoder.num_codebooks},()=>[Re]);else if(Array.isArray(Re)){if(Re.length!==L)throw new Error(`\`decoder_start_token_id\` expcted to have length ${L} but got ${Re.length}`);mA=Re}else mA=Array.from({length:L},()=>[Re]);mA=be(mA)}return Ge.decoder_attention_mask=(0,E.ones_like)(mA),{input_ids:mA,model_inputs:EA}}async generate({inputs:L=null,generation_config:ue=null,logits_processor:Ge=null,stopping_criteria:Re=null,streamer:Ye=null,...sA}){this._validate_model_class(),ue=this._prepare_generation_config(ue,sA);let{inputs_tensor:mA,model_inputs:EA,model_input_name:RA}=this._prepare_model_inputs({inputs:L,model_kwargs:sA});const pt=this.config.is_encoder_decoder;pt&&("encoder_outputs"in EA||(EA=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:mA,model_inputs:EA,model_input_name:RA,generation_config:ue})));let mt;pt?{input_ids:mt,model_inputs:EA}=this._prepare_decoder_input_ids_for_generation({batch_size:EA[RA].dims.at(0),model_input_name:RA,model_kwargs:EA,decoder_start_token_id:ue.decoder_start_token_id,bos_token_id:ue.bos_token_id,generation_config:ue}):mt=EA[RA];let bt=mt.dims.at(-1);ue.max_new_tokens!==null&&(ue.max_length=bt+ue.max_new_tokens);const rt=this._get_logits_processor(ue,bt,Ge),Rt=this._get_stopping_criteria(ue,Re),at=EA[RA].dims.at(0),tt=Z.LogitsSampler.getSampler(ue),Qt=new Array(at).fill(0),Jt=mt.tolist();Ye&&Ye.put(Jt);let vr,yr={};for(;;){if(EA=this.prepare_inputs_for_generation(Jt,EA,ue),vr=await this.forward(EA),ue.output_attentions&&ue.return_dict_in_generate){const Rs=this.getAttentions(vr);for(const Ta in Rs)Ta in yr||(yr[Ta]=[]),yr[Ta].push(Rs[Ta])}const xt=vr.logits.slice(null,-1,null),Nr=rt(Jt,xt),ms=[];for(let Rs=0;Rs<Nr.dims.at(0);++Rs){const Ta=Nr[Rs],Zi=await tt(Ta);for(const[Ji,g0]of Zi){const Po=BigInt(Ji);Qt[Rs]+=g0,Jt[Rs].push(Po),ms.push([Po]);break}}if(Ye&&Ye.put(ms),Rt(Jt).every(Rs=>Rs))break;EA=this._update_model_kwargs_for_generation({generated_input_ids:ms,outputs:vr,model_inputs:EA,is_encoder_decoder:pt})}Ye&&Ye.end();const Yr=this.getPastKeyValues(vr,EA.past_key_values,!0),Zr=new E.Tensor("int64",Jt.flat(),[Jt.length,Jt[0].length]);if(ue.return_dict_in_generate)return{sequences:Zr,past_key_values:Yr,...yr};for(const xt of Object.values(vr))xt.location==="gpu-buffer"&&xt.dispose();return Zr}getPastKeyValues(L,ue,Ge=!1){const Re=Object.create(null);for(const Ye in L)if(Ye.startsWith("present")){const sA=Ye.replace("present_conv","past_conv").replace("present","past_key_values"),mA=Ye.includes("encoder");if(mA&&ue?Re[sA]=ue[sA]:Re[sA]=L[Ye],ue&&(!mA||Ge)){const EA=ue[sA];EA.location==="gpu-buffer"&&EA.dispose()}}return Re}getAttentions(L){const ue={};for(const Ge of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Re in L)Re.startsWith(Ge)&&(Ge in ue||(ue[Ge]=[]),ue[Ge].push(L[Re]));return ue}addPastKeyValues(L,ue){if(ue)Object.assign(L,ue);else{const Ge=this.sessions.decoder_model_merged??this.sessions.model,Re=(L[this.main_input_name]??L.attention_mask)?.dims?.[0]??1,Ye=Ge?.config?.kv_cache_dtype??"float32",sA=Ye==="float16"?E.DataTypeMap.float16:E.DataTypeMap.float32,mA=(0,u.getCacheShapes)(this.config,{batch_size:Re});for(const EA in mA){const RA=mA[EA].reduce((pt,mt)=>pt*mt,1);L[EA]=new E.Tensor(Ye,new sA(RA),mA[EA])}}}async encode_image({pixel_values:L}){return(await pe(this.sessions.vision_encoder,{pixel_values:L})).image_features}async encode_text({input_ids:L}){return(await pe(this.sessions.embed_tokens,{input_ids:L})).inputs_embeds}async encode_audio({audio_values:L}){return(await pe(this.sessions.audio_encoder,{audio_values:L})).audio_features}}class ze{}class Ue extends ze{constructor({last_hidden_state:L,hidden_states:ue=null,attentions:Ge=null}){super(),this.last_hidden_state=L,this.hidden_states=ue,this.attentions=Ge}}class Ze extends ae{}class qe extends Ze{}class AA extends Ze{async _call(L){return new ps(await super._call(L))}}class H extends Ze{async _call(L){return new ht(await super._call(L))}}class lA extends Ze{async _call(L){return new is(await super._call(L))}}class We extends Ze{async _call(L){return new vs(await super._call(L))}}class le extends ae{}class wA extends le{}class aA extends le{async _call(L){return new ps(await super._call(L))}}class FA extends le{async _call(L){return new ht(await super._call(L))}}class pA extends le{async _call(L){return new is(await super._call(L))}}class SA extends le{async _call(L){return new vs(await super._call(L))}}class TA extends ae{}class cA extends TA{}class oA extends TA{async _call(L){return new ps(await super._call(L))}}class zA extends TA{async _call(L){return new ht(await super._call(L))}}class UA extends TA{async _call(L){return new is(await super._call(L))}}class KA extends ae{}class HA extends KA{}class Vt extends KA{}class ts extends ae{}class ut extends ts{}class ls extends ae{}class ma extends ls{}class ha extends ls{async _call(L){return new ps(await super._call(L))}}class xs extends ls{async _call(L){return new ht(await super._call(L))}}class pr extends ls{async _call(L){return new is(await super._call(L))}}class Ur extends ls{async _call(L){return new vs(await super._call(L))}}class Bs extends ae{}class Et extends Bs{}class VA extends Bs{async _call(L){return new ps(await super._call(L))}}class Kt extends Bs{async _call(L){return new ht(await super._call(L))}}class Ss extends Bs{async _call(L){return new is(await super._call(L))}}class Ws extends Bs{async _call(L){return new vs(await super._call(L))}}class Cs extends ae{}class ot extends Cs{}class Vs extends Cs{async _call(L){return new ps(await super._call(L))}}class Y extends Cs{async _call(L){return new ht(await super._call(L))}}class fe extends Cs{async _call(L){return new is(await super._call(L))}}class ne extends Cs{async _call(L){return new vs(await super._call(L))}}class me extends ae{}class ve extends me{}class Se extends me{async _call(L){return new ps(await super._call(L))}}class Ke extends me{async _call(L){return new ht(await super._call(L))}}class GA extends me{async _call(L){return new is(await super._call(L))}}class YA extends me{async _call(L){return new vs(await super._call(L))}}class BA extends ae{}class nt extends BA{}class yA extends BA{async _call(L){return new ps(await super._call(L))}}class ft extends BA{async _call(L){return new ht(await super._call(L))}}class lt extends BA{async _call(L){return new is(await super._call(L))}}class Dr extends BA{async _call(L){return new vs(await super._call(L))}}class kr extends ae{}class mr extends kr{}class hr extends kr{async _call(L){return new ps(await super._call(L))}}class Ys extends kr{async _call(L){return new ht(await super._call(L))}}class Ot extends kr{async _call(L){return new is(await super._call(L))}}class Hs extends kr{async _call(L){return new vs(await super._call(L))}}class qr extends ae{}class rs extends qr{}class ws extends qr{async _call(L){return new ht(await super._call(L))}}class Pr extends qr{async _call(L){return new is(await super._call(L))}}class or extends qr{async _call(L){return new vs(await super._call(L))}}class cs extends qr{async _call(L){return new ps(await super._call(L))}}class qA extends ae{}class $r extends qA{}class us extends qA{async _call(L){return new ps(await super._call(L))}}class ta extends qA{async _call(L){return new ht(await super._call(L))}}class Us extends qA{async _call(L){return new is(await super._call(L))}}class Os extends ae{}class Tr extends Os{}class uA extends Os{async _call(L){return new ps(await super._call(L))}}class bA extends Os{async _call(L){return new ht(await super._call(L))}}class NA extends Os{async _call(L){return new vs(await super._call(L))}}class Yt extends ae{}class Ca extends Yt{}class ra extends Yt{async _call(L){return new ps(await super._call(L))}}class Ks extends Yt{async _call(L){return new ht(await super._call(L))}}class rn extends Yt{async _call(L){return new is(await super._call(L))}}class Oe extends Yt{async _call(L){return new vs(await super._call(L))}}class X extends ae{}class ge extends X{}class Ie extends X{async _call(L){return new ps(await super._call(L))}}class Be extends X{async _call(L){return new ht(await super._call(L))}}class Ve extends X{async _call(L){return new vs(await super._call(L))}}class tA extends ae{}class DA extends tA{}class vA extends tA{async _call(L){return new ht(await super._call(L))}}class Je extends tA{async _call(L){return new vs(await super._call(L))}}class kA extends tA{async _call(L){return new ps(await super._call(L))}}class gt extends ae{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]}class dt extends gt{}class ur extends gt{}class Gr extends ae{}class nr extends Gr{}class _t extends Gr{}class Gt extends ae{}class jr extends Gt{}class ks extends Gt{}class qt extends ae{}class _s extends qt{}class $t extends qt{}class lr extends qt{async _call(L){return new ht(await super._call(L))}}class yt extends ae{}class dr extends yt{}class Xs extends yt{}class cr extends yt{async _call(L){return new ht(await super._call(L))}}class Ms extends yt{}class Mr extends ae{}class er extends Mr{}class jA extends Mr{}class vt extends ae{}class Qr extends vt{}class xa extends vt{}class sa extends ae{}class yn extends sa{}class $n extends sa{async _call(L){return new ps(await super._call(L))}}class Ba extends sa{async _call(L){return new ht(await super._call(L))}}class Dn extends sa{async _call(L){return new is(await super._call(L))}}class ei extends sa{async _call(L){return new vs(await super._call(L))}}class aa extends ae{}class Wa extends aa{}class Fr extends aa{async _call(L){return new ps(await super._call(L))}}class PA extends aa{async _call(L){return new ht(await super._call(L))}}class ds extends aa{async _call(L){return new is(await super._call(L))}}class Pn extends aa{async _call(L){return new vs(await super._call(L))}}class na extends ae{}class Ai extends na{}class Tn extends na{async _call(L){return new ps(await super._call(L))}}class Gn extends na{async _call(L){return new ht(await super._call(L))}}class Qn extends na{async _call(L){return new is(await super._call(L))}}class ti extends na{async _call(L){return new vs(await super._call(L))}}class ya extends ae{}class Ft extends ya{}class sn extends ya{}class ba extends ae{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]}class Ia extends ba{}class Zs extends ba{_prepare_generation_config(L,ue){return super._prepare_generation_config(L,ue,z.WhisperGenerationConfig)}_retrieve_init_tokens(L){const ue=[L.decoder_start_token_id];let Ge=L.language;const Re=L.task;if(L.is_multilingual){Ge||(console.warn("No language specified - defaulting to English (en)."),Ge="en");const sA=`<|${(0,U.whisper_language_to_code)(Ge)}|>`;ue.push(L.lang_to_id[sA]),ue.push(L.task_to_id[Re??"transcribe"])}else if(Ge||Re)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!L.return_timestamps&&L.no_timestamps_token_id&&ue.at(-1)!==L.no_timestamps_token_id?ue.push(L.no_timestamps_token_id):L.return_timestamps&&ue.at(-1)===L.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),ue.pop()),ue.filter(Ye=>Ye!=null)}async generate({inputs:L=null,generation_config:ue=null,logits_processor:Ge=null,stopping_criteria:Re=null,...Ye}){ue=this._prepare_generation_config(ue,Ye);const sA=Ye.decoder_input_ids??this._retrieve_init_tokens(ue);if(ue.return_timestamps&&(Ge??=new D.LogitsProcessorList,Ge.push(new D.WhisperTimeStampLogitsProcessor(ue,sA))),ue.begin_suppress_tokens&&(Ge??=new D.LogitsProcessorList,Ge.push(new D.SuppressTokensAtBeginLogitsProcessor(ue.begin_suppress_tokens,sA.length))),ue.return_token_timestamps){if(!ue.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");ue.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),ue.output_attentions=!0,ue.return_dict_in_generate=!0}const mA=await super.generate({inputs:L,generation_config:ue,logits_processor:Ge,decoder_input_ids:sA,...Ye});return ue.return_token_timestamps&&(mA.token_timestamps=this._extract_token_timestamps(mA,ue.alignment_heads,ue.num_frames)),mA}_extract_token_timestamps(L,ue,Ge=null,Re=.02){if(!L.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");Ge==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let Ye=this.config.median_filter_width;Ye===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Ye=7);const sA=L.cross_attentions,mA=Array.from({length:this.config.decoder_layers},(at,tt)=>(0,E.cat)(sA.map(Qt=>Qt[tt]),2)),EA=(0,E.stack)(ue.map(([at,tt])=>{if(at>=mA.length)throw new Error(`Layer index ${at} is out of bounds for cross attentions (length ${mA.length}).`);return Ge?mA[at].slice(null,tt,null,[0,Ge]):mA[at].slice(null,tt)})).transpose(1,0,2,3),[RA,pt]=(0,E.std_mean)(EA,-2,0,!0),mt=EA.clone();for(let at=0;at<mt.dims[0];++at){const tt=mt[at];for(let Qt=0;Qt<tt.dims[0];++Qt){const Jt=tt[Qt],vr=RA[at][Qt][0].data,yr=pt[at][Qt][0].data;for(let Yr=0;Yr<Jt.dims[0];++Yr){let Zr=Jt[Yr].data;for(let xt=0;xt<Zr.length;++xt)Zr[xt]=(Zr[xt]-yr[xt])/vr[xt];Zr.set((0,F.medianFilter)(Zr,Ye))}}}const bt=[(0,E.mean)(mt,1)],rt=L.sequences.dims,Rt=new E.Tensor("float32",new Float32Array(rt[0]*rt[1]),rt);for(let at=0;at<rt[0];++at){const tt=bt[at].neg().squeeze_(0),[Qt,Jt]=(0,F.dynamic_time_warping)(tt.tolist()),vr=Array.from({length:Qt.length-1},(Zr,xt)=>Qt[xt+1]-Qt[xt]),yr=(0,w.mergeArrays)([1],vr).map(Zr=>!!Zr),Yr=[];for(let Zr=0;Zr<yr.length;++Zr)yr[Zr]&&Yr.push(Jt[Zr]*Re);Rt[at].data.set(Yr,1)}return Rt}}class zs extends Zs{}class an extends ae{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]}class ri extends an{}class Js extends an{}class Va extends ae{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]}class Fn extends ae{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Ya extends Fn{_merge_input_ids_with_image_features(L){const ue=L.image_features.dims.at(-1),Ge=L.image_features.view(-1,ue);return Pe({image_token_id:this.config.image_token_index,...L,image_features:Ge})}}class dA extends Ya{}class si extends Ya{}class QA extends ae{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds"}class nn extends QA{_merge_input_ids_with_image_features({inputs_embeds:L,image_features:ue,input_ids:Ge,attention_mask:Re}){return{inputs_embeds:(0,E.cat)([ue,L],1),attention_mask:(0,E.cat)([(0,E.ones)(ue.dims.slice(0,2)),Re],1)}}async _prepare_inputs_embeds({input_ids:L,pixel_values:ue,inputs_embeds:Ge,attention_mask:Re}){if(!L&&!ue)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Ye,sA;return L&&(Ye=await this.encode_text({input_ids:L})),ue&&(sA=await this.encode_image({pixel_values:ue})),Ye&&sA?{inputs_embeds:Ge,attention_mask:Re}=this._merge_input_ids_with_image_features({inputs_embeds:Ye,image_features:sA,input_ids:L,attention_mask:Re}):Ge=Ye||sA,{inputs_embeds:Ge,attention_mask:Re}}async forward({input_ids:L,pixel_values:ue,attention_mask:Ge,decoder_input_ids:Re,decoder_attention_mask:Ye,encoder_outputs:sA,past_key_values:mA,inputs_embeds:EA,decoder_inputs_embeds:RA}){if(EA||({inputs_embeds:EA,attention_mask:Ge}=await this._prepare_inputs_embeds({input_ids:L,pixel_values:ue,inputs_embeds:EA,attention_mask:Ge})),!sA){let{last_hidden_state:bt}=await De(this,{inputs_embeds:EA,attention_mask:Ge});sA=bt}if(!RA){if(!Re)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");RA=await this.encode_text({input_ids:Re})}return await _e(this,{inputs_embeds:RA,attention_mask:Ye,encoder_attention_mask:Ge,encoder_hidden_states:sA,past_key_values:mA},!0)}}class on extends ae{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]}class Kr extends on{_merge_input_ids_with_image_features(L){const ue=L.image_features.dims.at(-1),Ge=L.image_features.view(-1,ue);return Pe({image_token_id:this.config.image_token_index,...L,image_features:Ge})}}class ss extends Fn{_merge_input_ids_with_image_features(L){const ue=L.image_features.dims.at(-1),Ge=L.image_features.view(-1,ue);return Pe({image_token_id:this.config.image_token_index,...L,image_features:Ge})}}class ia extends ss{}class qs extends ae{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","input_features","input_features_mask","past_key_values"]}class Ha extends qs{async forward({input_ids:L=null,attention_mask:ue=null,pixel_values:Ge=null,input_features:Re=null,input_features_mask:Ye=null,position_ids:sA=null,inputs_embeds:mA=null,per_layer_inputs:EA=null,past_key_values:RA=null,generation_config:pt=null,logits_processor:mt=null,...bt}){if((!mA||!EA)&&({inputs_embeds:mA,per_layer_inputs:EA}=await pe(this.sessions.embed_tokens,{input_ids:L}),L.dims[1]!==1)){if(Ge){const{image_features:Rt}=await pe(this.sessions.vision_encoder,{pixel_values:Ge});({inputs_embeds:mA,attention_mask:ue}=this._merge_input_ids_with_image_features({image_features:Rt,inputs_embeds:mA,input_ids:L,attention_mask:ue}))}if(Re){const{audio_features:Rt}=await pe(this.sessions.audio_encoder,{input_features:Re,input_features_mask:Ye});({inputs_embeds:mA,attention_mask:ue}=this._merge_input_ids_with_audio_features({audio_features:Rt,inputs_embeds:mA,input_ids:L,attention_mask:ue}))}}return await _e(this,{inputs_embeds:mA,per_layer_inputs:EA,past_key_values:RA,attention_mask:ue,position_ids:sA,generation_config:pt,logits_processor:mt},!0)}_merge_input_ids_with_image_features(L){const ue=L.image_features.dims.at(-1),Ge=L.image_features.view(-1,ue);return Pe({image_token_id:this.config.image_token_id,...L,image_features:Ge})}_merge_input_ids_with_audio_features(L){const ue=L.audio_features.dims.at(-1),Ge=L.audio_features.view(-1,ue);return Ce({audio_token_id:this.config.audio_token_id,...L,audio_features:Ge})}}class Cr extends ae{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"]}class ln extends Cr{async encode_image({pixel_values:L,pixel_attention_mask:ue}){return(await pe(this.sessions.vision_encoder,{pixel_values:L,pixel_attention_mask:ue})).image_features}_merge_input_ids_with_image_features(L){const ue=L.image_features.dims.at(-1),Ge=L.image_features.view(-1,ue);return Pe({image_token_id:this.config.image_token_id,...L,image_features:Ge})}}class Ua extends ln{}class wa extends ae{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"]}class oa extends wa{async forward({input_ids:L=null,attention_mask:ue=null,pixel_values:Ge=null,image_sizes:Re=null,position_ids:Ye=null,inputs_embeds:sA=null,past_key_values:mA=null,generation_config:EA=null,logits_processor:RA=null,...pt}){if(!sA){let bt;if(Ge&&L.dims[1]!==1){if(!Re)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:bt}=await pe(this.sessions.vision_encoder,{pixel_values:Ge,image_sizes:Re}))}else{const rt=this.config.normalized_config.hidden_size;bt=new E.Tensor("float32",[],[0,rt])}({inputs_embeds:sA}=await pe(this.sessions.prepare_inputs_embeds,{input_ids:L,image_features:bt}))}return await _e(this,{inputs_embeds:sA,past_key_values:mA,attention_mask:ue,position_ids:Ye,generation_config:EA,logits_processor:RA},!1)}}class la extends ae{}class cn extends la{}class Ka extends la{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"text_model"})}}class MA extends la{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"text_model"})}}class ys extends la{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"vision_model"})}}class ai extends la{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"vision_model"})}}class Sn extends ae{}class Da extends Sn{}class $s extends Sn{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"text_model"})}}class JA extends la{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"vision_model"})}}class ni extends ae{}class On extends ni{}class Pa extends ae{}class Xa extends Pa{async forward(L){const ue=!L.input_ids,Ge=!L.pixel_values;if(ue&&Ge)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(ue&&(L.input_ids=(0,E.ones)([L.pixel_values.dims[0],1])),Ge){const{image_size:RA}=this.config.vision_config;L.pixel_values=(0,E.full)([0,3,RA,RA],0)}const{text_embeddings:Re,image_embeddings:Ye,l2norm_text_embeddings:sA,l2norm_image_embeddings:mA}=await super.forward(L),EA={};return ue||(EA.text_embeddings=Re,EA.l2norm_text_embeddings=sA),Ge||(EA.image_embeddings=Ye,EA.l2norm_image_embeddings=mA),EA}}class Ds extends Pa{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"text_model"})}}class fr extends Pa{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"vision_model"})}}class Za extends ae{}class _n extends Za{}class st extends Za{}class Ps extends ae{}class Ja extends Ps{}class Ns extends Ps{}class un extends ae{}class Sr extends un{}class Ts extends un{}class Er extends ae{}class Gs extends Er{}class dn extends Er{}class qa extends ae{}class zn extends qa{}class fn extends qa{}class Ar extends ae{}class G extends Ar{}class W extends Ar{}class q extends ae{}class re extends q{}class de extends q{}class Qe extends ae{}class eA extends Qe{}class gA extends Qe{}class xA extends ae{}class OA extends xA{}class ct extends xA{}class Dt extends ae{}class zt extends Dt{}class Or extends ae{}class fs extends Or{}class gn extends Or{}class _r extends ae{}class Qi extends _r{}class ii extends _r{}class Nn extends ae{}class Es extends Nn{}class oi extends Nn{}class Fi extends ae{}class ca extends Fi{}class go extends Fi{}class li extends ae{}class Si extends li{}class pn extends li{}class Oi extends ae{}class po extends Oi{}class mo extends Oi{}class es extends ae{}class Ln extends es{}class as extends es{}class ci extends ae{}class ui extends ci{}class ho extends ci{}class Xt extends ae{}class _i extends Xt{}class $a extends Xt{}class en extends ae{}class Co extends en{}class bo extends en{}class di extends ae{}class Io extends di{}class An extends di{}class Ls extends ae{}class zi extends Ls{}class fi extends Ls{}class Wr extends ae{}class Lt extends Wr{}class Rn extends Wr{}class Ni extends ae{}class Li extends Ni{}class wo extends Ni{}class Ri extends ae{}class ko extends Ri{}class ji extends Ri{}class Wi extends ae{}class Mo extends Wi{}class Vi extends Wi{}class jn extends ae{}class As extends jn{}class Yi extends jn{}class Hi extends ae{}class mn extends Hi{}class Eo extends Hi{}class hn extends ae{}class $A extends hn{}class A extends hn{}class t extends ae{}class r extends t{}class s extends t{}class i extends ae{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]}class l extends i{get_rope_index(L,ue,Ge,Re){const{vision_config:Ye,image_token_id:sA,video_token_id:mA,vision_start_token_id:EA}=this.config,RA=Ye.spatial_merge_size??2,pt=[];if(ue||Ge){let mt=L.tolist();Re||(Re=(0,E.ones_like)(L));const bt=Re.tolist(),rt=Array.from({length:3},Jt=>Array.from({length:L.dims[0]},vr=>Array.from({length:L.dims[1]},yr=>1))),Rt=ue?ue.tolist():[],at=Ge?Ge.tolist():[];let tt=0,Qt=0;for(let Jt=0;Jt<mt.length;++Jt){const vr=mt[Jt].filter((xr,os)=>bt[Jt][os]==1),Yr=vr.reduce((xr,os,Hn)=>(os==EA&&xr.push(Hn),xr),[]).map(xr=>vr[xr+1]),Zr=Yr.filter(xr=>xr==sA).length,xt=Yr.filter(xr=>xr==mA).length;let Nr=[],ms=0,gi=Zr,Rs=xt;for(let xr=0;xr<Yr.length;++xr){const os=vr.findIndex((mi,Ga)=>Ga>ms&&mi==sA),Hn=vr.findIndex((mi,Ga)=>Ga>ms&&mi==mA),pi=gi>0&&os!==-1?os:vr.length+1,qi=Rs>0&&Hn!==-1?Hn:vr.length+1;let p0,_l,zl,Nl;pi<qi?([_l,zl,Nl]=Rt[tt],++tt,--gi,p0=pi):([_l,zl,Nl]=at[Qt],++Qt,--Rs,p0=qi);const[wI,Ll,m0]=[Number(_l),Math.floor(Number(zl)/RA),Math.floor(Number(Nl)/RA)],Rl=p0-ms,Ff=Nr.length>0?(0,F.max)(Nr.at(-1))[0]+1:0;Nr.push(Array.from({length:3*Rl},(mi,Ga)=>Ff+Ga%Rl));const jl=Rl+Ff,h0=wI*Ll*m0,kI=Array.from({length:h0},(mi,Ga)=>jl+Math.floor(Ga/(Ll*m0))),MI=Array.from({length:h0},(mi,Ga)=>jl+Math.floor(Ga/m0)%Ll),EI=Array.from({length:h0},(mi,Ga)=>jl+Ga%m0);Nr.push([kI,MI,EI].flat()),ms=p0+h0}if(ms<vr.length){const xr=Nr.length>0?(0,F.max)(Nr.at(-1))[0]+1:0,os=vr.length-ms;Nr.push(Array.from({length:3*os},(Hn,pi)=>xr+pi%os))}const Ta=Nr.reduce((xr,os)=>xr+os.length,0),Zi=new Array(Ta);let Ji=0;for(let xr=0;xr<3;++xr)for(let os=0;os<Nr.length;++os){const Hn=Nr[os],pi=Hn.length/3;for(let qi=xr*pi;qi<(xr+1)*pi;++qi)Zi[Ji++]=Hn[qi]}let g0=0;const Po=bt[Jt];for(let xr=0;xr<Po.length;++xr)if(Po[xr]==1){for(let os=0;os<3;++os)rt[os][Jt][xr]=Zi[os*Ta/3+g0];++g0}const II=(0,F.max)(Zi)[0];pt.push(II+1-mt[Jt].length)}return[new E.Tensor("int64",rt.flat(1/0),[3,L.dims[0],L.dims[1]]),new E.Tensor("int64",pt,[pt.length,1])]}else if(Re){const{data:mt,dims:bt}=je(Re),rt=BigInt64Array.from({length:3*mt.length},(at,tt)=>mt[tt%mt.length]),Rt=Array.from({length:bt[0]},(at,tt)=>(0,F.max)(mt.subarray(bt[1]*tt,bt[1]*(tt+1)))[0]+1n+BigInt(bt[1]));return[new E.Tensor("int64",rt,[3,...bt]),new E.Tensor("int64",Rt,[Rt.length,1])]}else{const[mt,bt]=L.dims,rt=BigInt64Array.from({length:3*mt*bt},(Rt,at)=>BigInt(Math.floor(at%bt/mt)));return[new E.Tensor("int64",rt,[3,...L.dims]),(0,E.zeros)([mt,1])]}}async encode_image({pixel_values:L,image_grid_thw:ue}){return(await pe(this.sessions.vision_encoder,{pixel_values:L,grid_thw:ue})).image_features}_merge_input_ids_with_image_features(L){return Pe({image_token_id:this.config.image_token_id,...L})}prepare_inputs_for_generation(L,ue,Ge){if(ue.attention_mask&&!ue.position_ids)if(!ue.past_key_values)[ue.position_ids,ue.rope_deltas]=this.get_rope_index(ue.input_ids,ue.image_grid_thw,ue.video_grid_thw,ue.attention_mask);else{ue.pixel_values=null;const Re=BigInt(Object.values(ue.past_key_values)[0].dims.at(-2)),Ye=ue.rope_deltas.map(sA=>Re+sA);ue.position_ids=(0,E.stack)([Ye,Ye,Ye],0)}return ue}}class c extends ae{}class g extends c{}class m extends c{}class I extends ae{}class h extends I{}class x extends I{}class T extends ae{}class _ extends T{}class V extends T{}class K extends ae{}class J extends K{}class te extends K{}class ce extends ae{}class he extends ce{}class Ee extends ce{}class Te extends ae{}class Fe extends Te{}class Le extends Te{async _call(L){return new ht(await super._call(L))}}class Xe extends ae{}class fA extends Xe{}class hA extends Xe{async _call(L){return new ht(await super._call(L))}}class _A extends ae{}class LA extends _A{}class At extends ae{}class Mt extends At{}class Pt extends At{async _call(L){return new ht(await super._call(L))}}class Nt extends ae{}class tr extends Nt{}class rr extends ae{}class br extends rr{}class Ir extends rr{async _call(L){return new ht(await super._call(L))}}class zr extends ae{}class Vr extends zr{}class Xr extends ae{}class ns extends Xr{}class gs extends Xr{async _call(L){return new ht(await super._call(L))}}class bs extends ae{}class ua extends bs{async _call(L){return new Gf(await super._call(L))}}class ea extends ae{}class da extends ea{}class Cn extends ea{async _call(L){return new ht(await super._call(L))}}class bn extends ae{}class In extends bn{}class Wn extends bn{async _call(L){return new ht(await super._call(L))}}class wn extends ae{}class vo extends wn{}class xo extends wn{}class Ui extends ae{}class Bo extends Ui{}class Uc extends Ui{}class X0 extends ae{}class Kc extends X0{}class Xc extends X0{async _call(L){return new ht(await super._call(L))}}class qo extends ae{}class Zc extends qo{}class Jc extends qo{async _call(L){return new J0(await super._call(L))}}class Z0 extends qo{async _call(L){return new qc(await super._call(L))}}class J0 extends ze{constructor({logits:L,pred_boxes:ue}){super(),this.logits=L,this.pred_boxes=ue}}class qc extends ze{constructor({logits:L,pred_boxes:ue,pred_masks:Ge}){super(),this.logits=L,this.pred_boxes=ue,this.pred_masks=Ge}}class q0 extends ae{}class $c extends q0{}class e1 extends q0{async _call(L){return new yo(await super._call(L))}}class yo extends ze{constructor({logits:L,pred_boxes:ue}){super(),this.logits=L,this.pred_boxes=ue}}class $0 extends ae{}class A1 extends $0{}class t1 extends $0{async _call(L){return new r1(await super._call(L))}}class r1 extends yo{}class el extends ae{}class s1 extends el{}class a1 extends el{async _call(L){return new n1(await super._call(L))}}class n1 extends yo{}class Al extends ae{}class i1 extends Al{}class o1 extends Al{async _call(L){return new yo(await super._call(L))}}class tl extends ae{}class l1 extends tl{}class c1 extends tl{async _call(L){return new u1(await super._call(L))}}class u1 extends J0{}class rl extends ae{}class d1 extends rl{}class f1 extends rl{async _call(L){return new ht(await super._call(L))}}class sl extends ae{}class g1 extends sl{}class p1 extends sl{async _call(L){return new ht(await super._call(L))}}class al extends ae{}class m1 extends al{}class h1 extends al{async _call(L){return new ht(await super._call(L))}}class $o extends ae{}class C1 extends $o{}class b1 extends $o{async _call(L){return new ht(await super._call(L))}}class I1 extends $o{}class nl extends ae{}class w1 extends nl{}class k1 extends nl{}class il extends ae{}class M1 extends il{}class E1 extends il{}class v1 extends ae{}class x1 extends v1{}class e0 extends ae{}class B1 extends e0{}class y1 extends e0{}class D1 extends e0{}class P1 extends ae{}class T1 extends P1{}class G1 extends ae{}class Q1 extends G1{}class F1 extends ae{}class S1 extends F1{}class ol extends ae{}class O1 extends ol{}class _1 extends ol{}class ll extends ae{}class z1 extends ll{}class N1 extends ll{}class L1 extends ae{}class R1 extends L1{}class cl extends ae{}class j1 extends cl{}class W1 extends cl{async _call(L){return new ht(await super._call(L))}}class ul extends ae{}class V1 extends ul{}class Y1 extends ul{async _call(L){return new ht(await super._call(L))}}class dl extends ae{}class H1 extends dl{}class U1 extends dl{async _call(L){return new ht(await super._call(L))}}class fl extends ae{}class K1 extends fl{}class X1 extends fl{async _call(L){return new ht(await super._call(L))}}class Z1 extends ae{}class J1 extends Z1{}class q1 extends ae{}class $1 extends q1{}class eu extends ae{}class Au extends eu{}class gl extends ae{}class tu extends gl{}class ru extends gl{async _call(L){return new su(await super._call(L))}}class su extends ze{constructor({logits:L,pred_boxes:ue}){super(),this.logits=L,this.pred_boxes=ue}}class au extends ae{}class nu extends au{async get_image_embeddings({pixel_values:L}){return await De(this,{pixel_values:L})}async forward(L){!L.image_embeddings||!L.image_positional_embeddings?L={...L,...await this.get_image_embeddings(L)}:L={...L},L.input_labels??=(0,E.ones)(L.input_points.dims.slice(0,-1));const ue={image_embeddings:L.image_embeddings,image_positional_embeddings:L.image_positional_embeddings};return L.input_points&&(ue.input_points=L.input_points),L.input_labels&&(ue.input_labels=L.input_labels),L.input_boxes&&(ue.input_boxes=L.input_boxes),await pe(this.sessions.prompt_encoder_mask_decoder,ue)}async _call(L){return new iu(await super._call(L))}}class iu extends ze{constructor({iou_scores:L,pred_masks:ue}){super(),this.iou_scores=L,this.pred_masks=ue}}class ou extends ze{constructor({iou_scores:L,pred_masks:ue,object_score_logits:Ge}){super(),this.iou_scores=L,this.pred_masks=ue,this.object_score_logits=Ge}}class lu extends ae{}class A0 extends lu{async get_image_embeddings({pixel_values:L}){return await De(this,{pixel_values:L})}async forward(L){const{num_feature_levels:ue}=this.config.vision_config;if(Array.from({length:ue},(sA,mA)=>`image_embeddings.${mA}`).some(sA=>!L[sA])?L={...L,...await this.get_image_embeddings(L)}:L={...L},L.input_points){if(L.input_boxes&&L.input_boxes.dims[1]!==1)throw new Error("When both `input_points` and `input_boxes` are provided, the number of boxes per image must be 1.");const sA=L.input_points.dims;L.input_labels??=(0,E.ones)(sA.slice(0,-1)),L.input_boxes??=(0,E.full)([sA[0],0,4],0)}else if(L.input_boxes){const sA=L.input_boxes.dims;L.input_labels=(0,E.full)([sA[0],sA[1],0],-1n),L.input_points=(0,E.full)([sA[0],1,0,2],0)}else throw new Error("At least one of `input_points` or `input_boxes` must be provided.");const Re=this.sessions.prompt_encoder_mask_decoder,Ye=(0,w.pick)(L,Re.inputNames);return await pe(Re,Ye)}async _call(L){return new ou(await super._call(L))}}class cu extends A0{}class uu extends A0{}class pl extends ae{}class du extends pl{}class fu extends pl{}class ml extends ae{}class gu extends ml{}class pu extends ml{}class Vn extends ae{}class mu extends Vn{}class hu extends Vn{async _call(L){return new Yn(await super._call(L))}}class Cu extends Vn{async _call(L){return new ht(await super._call(L))}}class bu extends Vn{async _call(L){return new is(await super._call(L))}}class Iu extends ae{}class wu extends Iu{async _call(L){return new Yn(await super._call(L))}}class hl extends ae{}class ku extends hl{}class Mu extends hl{async _call(L){return new is(await super._call(L))}}class Eu extends ae{}class vu extends Eu{}class t0 extends ae{}class xu extends t0{}class Bu extends t0{async _call(L){return new Yn(await super._call(L))}}class yu extends t0{async _call(L){return new ht(await super._call(L))}}class Do extends ae{}class Du extends Do{}class Pu extends Do{async _call(L){return new Yn(await super._call(L))}}class Tu extends Do{async _call(L){return new ht(await super._call(L))}}class Gu extends Do{async _call(L){return new is(await super._call(L))}}class r0 extends ae{}class Qu extends r0{}class Fu extends r0{async _call(L){return new Yn(await super._call(L))}}class Su extends r0{async _call(L){return new ht(await super._call(L))}}class y8 extends ae{}class Ou extends Vn{}class _u extends Vn{async _call(L){return new Yn(await super._call(L))}}class zu extends Vn{async _call(L){return new ht(await super._call(L))}}class Ki extends ae{}class Nu extends Ki{}class Lu extends Ki{async _call(L){return new Yn(await super._call(L))}}class Ru extends Ki{async _call(L){return new ht(await super._call(L))}}class ju extends Ki{async _call(L){return new Tf(await super._call(L))}}class Wu extends Ki{async _call(L){return new is(await super._call(L))}}class Vu extends ae{}class Yu extends Vu{}class s0 extends ae{}class D8 extends s0{}class Hu extends s0{}class Uu extends s0{async generate_speech(L,ue,{threshold:Ge=.5,minlenratio:Re=0,maxlenratio:Ye=20,vocoder:sA=null}={}){const mA={input_ids:L},{encoder_outputs:EA,encoder_attention_mask:RA}=await De(this,mA),pt=EA.dims[1]/this.config.reduction_factor,mt=Math.floor(pt*Ye),bt=Math.floor(pt*Re),rt=this.config.num_mel_bins;let Rt=[],at=null,tt=null,Qt=0;for(;;){++Qt;const yr=ke(!!tt);let Yr;tt?Yr=tt.output_sequence_out:Yr=new E.Tensor("float32",new Float32Array(rt),[1,1,rt]);let Zr={use_cache_branch:yr,output_sequence:Yr,encoder_attention_mask:RA,speaker_embeddings:ue,encoder_hidden_states:EA};this.addPastKeyValues(Zr,at),tt=await pe(this.sessions.decoder_model_merged,Zr),at=this.getPastKeyValues(tt,at);const{prob:xt,spectrum:Nr}=tt;if(Rt.push(Nr),Qt>=bt&&(Array.from(xt.data).filter(ms=>ms>=Ge).length>0||Qt>=mt))break}const Jt=(0,E.cat)(Rt),{waveform:vr}=await pe(sA.sessions.model,{spectrogram:Jt});return{spectrogram:Jt,waveform:vr}}}class Ku extends ae{main_input_name="spectrogram"}class Xu extends ae{}class Cl extends Xu{async generate_speech({input_ids:L,attention_mask:ue,style:Ge,num_inference_steps:Re=5,speed:Ye=1.05}){const{sampling_rate:sA,chunk_compress_factor:mA,base_chunk_size:EA,latent_dim:RA}=this.config,{last_hidden_state:pt,durations:mt}=await pe(this.sessions.text_encoder,{input_ids:L,attention_mask:ue,style:Ge});mt.div_(Ye);const bt=mt.max().item()*sA,rt=EA*mA,Rt=Math.floor((bt+rt-1)/rt),at=L.dims[0],tt=(0,E.ones)([at,Rt]),Qt=(0,E.full)([at],Re);let Jt=(0,E.randn)([at,RA*mA,Rt]);for(let yr=0;yr<Re;++yr){const Yr=(0,E.full)([at],yr);({denoised_latents:Jt}=await pe(this.sessions.latent_denoiser,{style:Ge,noisy_latents:Jt,latent_mask:tt,encoder_outputs:pt,attention_mask:ue,timestep:Yr,num_inference_steps:Qt}))}const{waveform:vr}=await pe(this.sessions.voice_decoder,{latents:Jt});return{waveform:vr,durations:mt}}}class Zu extends ae{}class Ju extends Zu{}class bl extends ae{}class qu extends bl{}class $u extends bl{}class Il extends ae{}class ed extends Il{}class Ad extends Il{}class wl extends ae{}class td extends wl{}class rd extends wl{}class kl extends ae{}class sd extends kl{}class ad extends kl{}class Ml extends ae{}class nd extends Ml{}class id extends Ml{}class El extends ae{}class od extends El{}class ld extends El{}class a0 extends ae{}class cd extends a0{}class ud extends a0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"text_model"})}}class dd extends a0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"audio_model"})}}class fd extends ae{}class vl extends fd{async _call(L){return new Qf(await super._call(L))}}class n0 extends ae{}class P8 extends n0{}class gd extends n0{}class pd extends n0{}class xl extends ae{}class md extends xl{}class hd extends xl{}class Bl extends ae{}class Cd extends Bl{}class bd extends Bl{async _call(L){return new ht(await super._call(L))}}class yl extends ae{}class T8 extends yl{}class G8 extends yl{}class Dl extends ae{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(L){const[ue,Ge]=L.dims,Re=this.config.decoder.num_codebooks,Ye=Ge-Re;let sA=0;for(let RA=0;RA<L.size;++RA){if(L.data[RA]===this.config.decoder.pad_token_id)continue;const pt=RA%Ge,mt=Math.floor(RA/Ge)%Re,bt=pt-mt;bt>0&&bt<=Ye&&(L.data[sA++]=L.data[RA])}const mA=Math.floor(ue/Re),EA=sA/(mA*Re);return new E.Tensor(L.type,L.data.slice(0,sA),[mA,Re,EA])}prepare_inputs_for_generation(L,ue,Ge){let Re=structuredClone(L);for(let sA=0;sA<Re.length;++sA)for(let mA=0;mA<Re[sA].length;++mA)sA%this.config.decoder.num_codebooks>=mA&&(Re[sA][mA]=BigInt(this.config.decoder.pad_token_id));return Ge.guidance_scale!==null&&Ge.guidance_scale>1&&(Re=Re.concat(Re)),super.prepare_inputs_for_generation(Re,ue,Ge)}async generate(L){const ue=await super.generate(L),Ge=this._apply_and_filter_by_delay_pattern_mask(ue).unsqueeze_(0),{audio_values:Re}=await pe(this.sessions.encodec_decode,{audio_codes:Ge});return Re}}class i0 extends ae{}class Id extends i0{}class wd extends i0{async _call(L){return new ht(await super._call(L))}}class kd extends i0{}class o0 extends ae{}class Md extends o0{}class Ed extends o0{async _call(L){return new ht(await super._call(L))}}class vd extends o0{}class l0 extends ae{}class xd extends l0{}class Bd extends l0{async _call(L){return new ht(await super._call(L))}}class yd extends l0{}class c0 extends ae{}class Dd extends c0{}class Pd extends c0{async _call(L){return new ht(await super._call(L))}}class Td extends c0{}class Gd extends ae{}class Qd extends Gd{}class Fd extends ae{}class Sd extends Fd{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...L){super(...L),this._generation_mode="text"}async forward(L){const ue=this._generation_mode??"text";let Ge;if(ue==="text"||!L.past_key_values){const EA=this.sessions.prepare_inputs_embeds,RA=(0,w.pick)(L,EA.inputNames);Ge=await pe(EA,RA)}else{const EA=this.sessions.gen_img_embeds,RA=(0,w.pick)({image_ids:L.input_ids},EA.inputNames);Ge=await pe(EA,RA)}const Re={...L,...Ge},Ye=await _e(this,Re),sA=this.sessions[ue==="text"?"lm_head":"gen_head"];if(!sA)throw new Error(`Unable to find "${sA}" generation head`);const mA=await pe(sA,(0,w.pick)(Ye,sA.inputNames));return{...Ge,...Ye,...mA}}async generate(L){return this._generation_mode="text",super.generate(L)}async generate_images(L){this._generation_mode="image";const ue=(L.inputs??L[this.main_input_name]).dims[1],Re=(await super.generate(L)).slice(null,[ue,null]),Ye=this.sessions.image_decode,{decoded_image:sA}=await pe(Ye,{generated_tokens:Re}),mA=sA.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),EA=[];for(const RA of mA){const pt=S.RawImage.fromTensor(RA);EA.push(pt)}return EA}}class Od extends ze{constructor({char_logits:L,bpe_logits:ue,wp_logits:Ge}){super(),this.char_logits=L,this.bpe_logits=ue,this.wp_logits=Ge}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class _d extends ae{}class zd extends _d{async _call(L){return new Od(await super._call(L))}}class Pl extends ae{}class Nd extends Pl{}class Ld extends Pl{}class Tl extends ae{}class Rd extends Tl{}class jd extends Tl{}class Wd extends ae{forward_params=["input_ids","attention_mask","position_ids","audio_values","past_key_values"]}class Gl extends Wd{_merge_input_ids_with_audio_features(L){const ue=L.audio_features.dims.at(-1),Ge=L.audio_features.view(-1,ue);return Ce({audio_token_id:this.config.ignore_index??this.config.audio_token_id,...L,audio_features:Ge})}}class Vd extends Gl{}class u0 extends ae{main_input_name="input_values";forward_params=["input_values"]}class Yd extends ze{constructor({audio_codes:L}){super(),this.audio_codes=L}}class Hd extends ze{constructor({audio_values:L}){super(),this.audio_values=L}}class Ud extends u0{async encode(L){return new Yd(await pe(this.sessions.encoder_model,L))}async decode(L){return new Hd(await pe(this.sessions.decoder_model,L))}}class Kd extends u0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"encoder_model"})}}class Xd extends u0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"decoder_model"})}}class d0 extends ae{main_input_name="input_values";forward_params=["input_values"]}class Zd extends ze{constructor({audio_codes:L}){super(),this.audio_codes=L}}class Jd extends ze{constructor({audio_values:L}){super(),this.audio_values=L}}class qd extends d0{async encode(L){return new Zd(await pe(this.sessions.encoder_model,L))}async decode(L){return new Jd(await pe(this.sessions.decoder_model,L))}}class $d extends d0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"encoder_model"})}}class ef extends d0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"decoder_model"})}}class f0 extends ae{main_input_name="input_values";forward_params=["input_values"]}class Af extends f0{async encode(L){return await pe(this.sessions.encoder_model,L)}async decode(L){return await pe(this.sessions.decoder_model,L)}}class tf extends f0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"encoder_model"})}}class rf extends f0{static async from_pretrained(L,ue={}){return super.from_pretrained(L,{...ue,model_file_name:ue.model_file_name??"decoder_model"})}}class Zt{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(L,{progress_callback:ue=null,config:Ge=null,cache_dir:Re=null,local_files_only:Ye=!1,revision:sA="main",model_file_name:mA=null,subfolder:EA="onnx",device:RA=null,dtype:pt=null,use_external_data_format:mt=null,session_options:bt={}}={}){const rt={progress_callback:ue,config:Ge,cache_dir:Re,local_files_only:Ye,revision:sA,model_file_name:mA,subfolder:EA,device:RA,dtype:pt,use_external_data_format:mt,session_options:bt};if(rt.config=await u.AutoConfig.from_pretrained(L,rt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const Rt=rt.config.model_type;for(const at of this.MODEL_CLASS_MAPPINGS){let tt=at.get(Rt);if(!tt){for(const Qt of at.values())if(Qt[0]===Rt){tt=Qt;break}if(!tt)continue}return await tt[1].from_pretrained(L,rt)}if(this.BASE_IF_FAIL)return Pf.has(Rt)||console.warn(`Unknown model class "${Rt}", attempting to construct from base class.`),await ae.from_pretrained(L,rt);throw Error(`Unsupported model type: ${Rt}`)}}const Q8=new Map([["bert",["BertModel",qe]],["neobert",["NeoBertModel",wA]],["modernbert",["ModernBertModel",cA]],["nomic_bert",["NomicBertModel",ut]],["roformer",["RoFormerModel",ma]],["electra",["ElectraModel",ot]],["esm",["EsmModel",$r]],["convbert",["ConvBertModel",Et]],["camembert",["CamembertModel",ve]],["deberta",["DebertaModel",nt]],["deberta-v2",["DebertaV2Model",mr]],["mpnet",["MPNetModel",Ca]],["albert",["AlbertModel",DA]],["distilbert",["DistilBertModel",rs]],["roberta",["RobertaModel",yn]],["xlm",["XLMModel",Wa]],["xlm-roberta",["XLMRobertaModel",Ai]],["clap",["ClapModel",cd]],["clip",["CLIPModel",cn]],["clipseg",["CLIPSegModel",_n]],["chinese_clip",["ChineseCLIPModel",On]],["siglip",["SiglipModel",Da]],["jina_clip",["JinaCLIPModel",Xa]],["mobilebert",["MobileBertModel",Tr]],["squeezebert",["SqueezeBertModel",ge]],["wav2vec2",["Wav2Vec2Model",mu]],["wav2vec2-bert",["Wav2Vec2BertModel",Qu]],["unispeech",["UniSpeechModel",xu]],["unispeech-sat",["UniSpeechSatModel",Du]],["hubert",["HubertModel",Ou]],["wavlm",["WavLMModel",Nu]],["audio-spectrogram-transformer",["ASTModel",Ft]],["vits",["VitsModel",vl]],["pyannote",["PyAnnoteModel",ku]],["wespeaker-resnet",["WeSpeakerResNetModel",vu]],["detr",["DetrModel",Zc]],["rt_detr",["RTDetrModel",$c]],["rt_detr_v2",["RTDetrV2Model",A1]],["rf_detr",["RFDetrModel",s1]],["d_fine",["DFineModel",i1]],["table-transformer",["TableTransformerModel",l1]],["vit",["ViTModel",Fe]],["ijepa",["IJepaModel",fA]],["pvt",["PvtModel",Mt]],["vit_msn",["ViTMSNModel",br]],["vit_mae",["ViTMAEModel",tr]],["groupvit",["GroupViTModel",Vr]],["fastvit",["FastViTModel",ns]],["mobilevit",["MobileViTModel",da]],["mobilevitv2",["MobileViTV2Model",In]],["owlvit",["OwlViTModel",vo]],["owlv2",["Owlv2Model",Bo]],["beit",["BeitModel",Kc]],["deit",["DeiTModel",d1]],["hiera",["HieraModel",g1]],["convnext",["ConvNextModel",j1]],["convnextv2",["ConvNextV2Model",V1]],["dinov2",["Dinov2Model",H1]],["dinov2_with_registers",["Dinov2WithRegistersModel",K1]],["dinov3_vit",["DINOv3ViTModel",J1]],["dinov3_convnext",["DINOv3ConvNextModel",$1]],["resnet",["ResNetModel",m1]],["swin",["SwinModel",C1]],["swin2sr",["Swin2SRModel",w1]],["donut-swin",["DonutSwinModel",R1]],["yolos",["YolosModel",tu]],["dpt",["DPTModel",M1]],["glpn",["GLPNModel",z1]],["hifigan",["SpeechT5HifiGan",Ku]],["efficientnet",["EfficientNetModel",Cd]],["decision_transformer",["DecisionTransformerModel",Qd]],["patchtst",["PatchTSTForPrediction",Nd]],["patchtsmixer",["PatchTSMixerForPrediction",Rd]],["mobilenet_v1",["MobileNetV1Model",Id]],["mobilenet_v2",["MobileNetV2Model",Md]],["mobilenet_v3",["MobileNetV3Model",xd]],["mobilenet_v4",["MobileNetV4Model",Dd]],["maskformer",["MaskFormerModel",O1]],["mgp-str",["MgpstrForSceneTextRecognition",zd]],["style_text_to_speech_2",["StyleTextToSpeech2Model",Yu]]]),F8=new Map([["t5",["T5Model",dt]],["longt5",["LongT5Model",nr]],["mt5",["MT5Model",jr]],["bart",["BartModel",_s]],["mbart",["MBartModel",dr]],["marian",["MarianModel",du]],["whisper",["WhisperModel",Ia]],["m2m_100",["M2M100Model",gu]],["blenderbot",["BlenderbotModel",er]],["blenderbot-small",["BlenderbotSmallModel",Qr]]]),S8=new Map([["mimi",["MimiModel",Ud]],["dac",["DacModel",qd]],["snac",["SnacModel",Af]]]),O8=new Map([["bloom",["BloomModel",_]],["jais",["JAISModel",Sr]],["gpt2",["GPT2Model",Ja]],["gptj",["GPTJModel",G]],["gpt_bigcode",["GPTBigCodeModel",re]],["gpt_neo",["GPTNeoModel",Gs]],["gpt_neox",["GPTNeoXModel",zn]],["codegen",["CodeGenModel",eA]],["llama",["LlamaModel",OA]],["nanochat",["NanoChatModel",fs]],["arcee",["ArceeModel",Qi]],["lfm2",["Lfm2Model",Es]],["smollm3",["SmolLM3Model",ca]],["exaone",["ExaoneModel",Ln]],["olmo",["OlmoModel",_i]],["olmo2",["Olmo2Model",Co]],["mobilellm",["MobileLLMModel",ui]],["granite",["GraniteModel",Io]],["granitemoehybrid",["GraniteMoeHybridModel",zi]],["cohere",["CohereModel",Lt]],["gemma",["GemmaModel",Li]],["gemma2",["Gemma2Model",ko]],["vaultgemma",["VaultGemmaModel",Mo]],["gemma3_text",["Gemma3Model",As]],["helium",["HeliumModel",Si]],["glm",["GlmModel",po]],["openelm",["OpenELMModel",mn]],["qwen2",["Qwen2Model",$A]],["qwen3",["Qwen3Model",r]],["phi",["PhiModel",g]],["phi3",["Phi3Model",h]],["mpt",["MptModel",J]],["opt",["OPTModel",he]],["mistral",["MistralModel",qu]],["ministral",["MinistralModel",ed]],["ministral3",["Ministral3Model",td]],["ernie4_5",["Ernie4_5Model",sd]],["starcoder2",["Starcoder2Model",nd]],["falcon",["FalconModel",od]],["stablelm",["StableLmModel",md]],["modernbert-decoder",["ModernBertDecoderModel",HA]]]),Ql=new Map([["speecht5",["SpeechT5ForSpeechToText",Hu]],["whisper",["WhisperForConditionalGeneration",Zs]],["lite-whisper",["LiteWhisperForConditionalGeneration",zs]],["moonshine",["MoonshineForConditionalGeneration",Js]]]),sf=new Map([["speecht5",["SpeechT5ForTextToSpeech",Uu]]]),af=new Map([["vits",["VitsModel",vl]],["musicgen",["MusicgenForConditionalGeneration",Dl]],["supertonic",["SupertonicForConditionalGeneration",Cl]]]),nf=new Map([["bert",["BertForSequenceClassification",H]],["neobert",["NeoBertForSequenceClassification",FA]],["modernbert",["ModernBertForSequenceClassification",zA]],["roformer",["RoFormerForSequenceClassification",xs]],["electra",["ElectraForSequenceClassification",Y]],["esm",["EsmForSequenceClassification",ta]],["convbert",["ConvBertForSequenceClassification",Kt]],["camembert",["CamembertForSequenceClassification",Ke]],["deberta",["DebertaForSequenceClassification",ft]],["deberta-v2",["DebertaV2ForSequenceClassification",Ys]],["mpnet",["MPNetForSequenceClassification",Ks]],["albert",["AlbertForSequenceClassification",vA]],["distilbert",["DistilBertForSequenceClassification",ws]],["roberta",["RobertaForSequenceClassification",Ba]],["xlm",["XLMForSequenceClassification",PA]],["xlm-roberta",["XLMRobertaForSequenceClassification",Gn]],["bart",["BartForSequenceClassification",lr]],["mbart",["MBartForSequenceClassification",cr]],["mobilebert",["MobileBertForSequenceClassification",bA]],["squeezebert",["SqueezeBertForSequenceClassification",Be]]]),of=new Map([["bert",["BertForTokenClassification",lA]],["neobert",["NeoBertForTokenClassification",pA]],["modernbert",["ModernBertForTokenClassification",UA]],["roformer",["RoFormerForTokenClassification",pr]],["electra",["ElectraForTokenClassification",fe]],["esm",["EsmForTokenClassification",Us]],["convbert",["ConvBertForTokenClassification",Ss]],["camembert",["CamembertForTokenClassification",GA]],["deberta",["DebertaForTokenClassification",lt]],["deberta-v2",["DebertaV2ForTokenClassification",Ot]],["mpnet",["MPNetForTokenClassification",rn]],["distilbert",["DistilBertForTokenClassification",Pr]],["roberta",["RobertaForTokenClassification",Dn]],["xlm",["XLMForTokenClassification",ds]],["xlm-roberta",["XLMRobertaForTokenClassification",Qn]]]),Fl=new Map([["t5",["T5ForConditionalGeneration",ur]],["longt5",["LongT5ForConditionalGeneration",_t]],["mt5",["MT5ForConditionalGeneration",ks]],["bart",["BartForConditionalGeneration",$t]],["mbart",["MBartForConditionalGeneration",Xs]],["marian",["MarianMTModel",fu]],["m2m_100",["M2M100ForConditionalGeneration",pu]],["blenderbot",["BlenderbotForConditionalGeneration",jA]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",xa]]]),Sl=new Map([["bloom",["BloomForCausalLM",V]],["gpt2",["GPT2LMHeadModel",Ns]],["jais",["JAISLMHeadModel",Ts]],["gptj",["GPTJForCausalLM",W]],["gpt_bigcode",["GPTBigCodeForCausalLM",de]],["gpt_neo",["GPTNeoForCausalLM",dn]],["gpt_neox",["GPTNeoXForCausalLM",fn]],["codegen",["CodeGenForCausalLM",gA]],["llama",["LlamaForCausalLM",ct]],["nanochat",["NanoChatForCausalLM",gn]],["llama4_text",["Llama4ForCausalLM",zt]],["arcee",["ArceeForCausalLM",ii]],["lfm2",["Lfm2ForCausalLM",oi]],["smollm3",["SmolLM3ForCausalLM",go]],["exaone",["ExaoneForCausalLM",as]],["olmo",["OlmoForCausalLM",$a]],["olmo2",["Olmo2ForCausalLM",bo]],["mobilellm",["MobileLLMForCausalLM",ho]],["granite",["GraniteForCausalLM",An]],["granitemoehybrid",["GraniteMoeHybridForCausalLM",fi]],["cohere",["CohereForCausalLM",Rn]],["gemma",["GemmaForCausalLM",wo]],["gemma2",["Gemma2ForCausalLM",ji]],["vaultgemma",["VaultGemmaForCausalLM",Vi]],["gemma3_text",["Gemma3ForCausalLM",Yi]],["helium",["HeliumForCausalLM",pn]],["glm",["GlmForCausalLM",mo]],["openelm",["OpenELMForCausalLM",Eo]],["qwen2",["Qwen2ForCausalLM",A]],["qwen3",["Qwen3ForCausalLM",s]],["phi",["PhiForCausalLM",m]],["phi3",["Phi3ForCausalLM",x]],["mpt",["MptForCausalLM",te]],["opt",["OPTForCausalLM",Ee]],["mbart",["MBartForCausalLM",Ms]],["mistral",["MistralForCausalLM",$u]],["ministral",["MinistralForCausalLM",Ad]],["ministral3",["Ministral3ForCausalLM",rd]],["ernie4_5",["Ernie4_5ForCausalLM",ad]],["starcoder2",["Starcoder2ForCausalLM",id]],["falcon",["FalconForCausalLM",ld]],["trocr",["TrOCRForCausalLM",Ju]],["stablelm",["StableLmForCausalLM",hd]],["modernbert-decoder",["ModernBertDecoderForCausalLM",Vt]],["phi3_v",["Phi3VForCausalLM",oa]]]),_8=new Map([["multi_modality",["MultiModalityCausalLM",Sd]]]),lf=new Map([["bert",["BertForMaskedLM",AA]],["neobert",["NeoBertForMaskedLM",aA]],["modernbert",["ModernBertForMaskedLM",oA]],["roformer",["RoFormerForMaskedLM",ha]],["electra",["ElectraForMaskedLM",Vs]],["esm",["EsmForMaskedLM",us]],["convbert",["ConvBertForMaskedLM",VA]],["camembert",["CamembertForMaskedLM",Se]],["deberta",["DebertaForMaskedLM",yA]],["deberta-v2",["DebertaV2ForMaskedLM",hr]],["mpnet",["MPNetForMaskedLM",ra]],["albert",["AlbertForMaskedLM",kA]],["distilbert",["DistilBertForMaskedLM",cs]],["roberta",["RobertaForMaskedLM",$n]],["xlm",["XLMWithLMHeadModel",Fr]],["xlm-roberta",["XLMRobertaForMaskedLM",Tn]],["mobilebert",["MobileBertForMaskedLM",uA]],["squeezebert",["SqueezeBertForMaskedLM",Ie]]]),cf=new Map([["bert",["BertForQuestionAnswering",We]],["neobert",["NeoBertForQuestionAnswering",SA]],["roformer",["RoFormerForQuestionAnswering",Ur]],["electra",["ElectraForQuestionAnswering",ne]],["convbert",["ConvBertForQuestionAnswering",Ws]],["camembert",["CamembertForQuestionAnswering",YA]],["deberta",["DebertaForQuestionAnswering",Dr]],["deberta-v2",["DebertaV2ForQuestionAnswering",Hs]],["mpnet",["MPNetForQuestionAnswering",Oe]],["albert",["AlbertForQuestionAnswering",Je]],["distilbert",["DistilBertForQuestionAnswering",or]],["roberta",["RobertaForQuestionAnswering",ei]],["xlm",["XLMForQuestionAnswering",Pn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",ti]],["mobilebert",["MobileBertForQuestionAnswering",NA]],["squeezebert",["SqueezeBertForQuestionAnswering",Ve]]]),Ol=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Va]],["idefics3",["Idefics3ForConditionalGeneration",ln]],["smolvlm",["SmolVLMForConditionalGeneration",Ua]]]),uf=new Map([["llava",["LlavaForConditionalGeneration",Ya]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",dA]],["moondream1",["Moondream1ForConditionalGeneration",si]],["florence2",["Florence2ForConditionalGeneration",nn]],["qwen2-vl",["Qwen2VLForConditionalGeneration",l]],["idefics3",["Idefics3ForConditionalGeneration",ln]],["smolvlm",["SmolVLMForConditionalGeneration",Ua]],["paligemma",["PaliGemmaForConditionalGeneration",Kr]],["llava_qwen2",["LlavaQwen2ForCausalLM",ss]],["gemma3n",["Gemma3nForConditionalGeneration",Ha]],["mistral3",["Mistral3ForConditionalGeneration",ia]]]),df=new Map([["ultravox",["UltravoxModel",Gl]],["voxtral",["VoxtralForConditionalGeneration",Vd]]]),z8=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Va]]]),ff=new Map([["vit",["ViTForImageClassification",Le]],["ijepa",["IJepaForImageClassification",hA]],["pvt",["PvtForImageClassification",Pt]],["vit_msn",["ViTMSNForImageClassification",Ir]],["fastvit",["FastViTForImageClassification",gs]],["mobilevit",["MobileViTForImageClassification",Cn]],["mobilevitv2",["MobileViTV2ForImageClassification",Wn]],["beit",["BeitForImageClassification",Xc]],["deit",["DeiTForImageClassification",f1]],["hiera",["HieraForImageClassification",p1]],["convnext",["ConvNextForImageClassification",W1]],["convnextv2",["ConvNextV2ForImageClassification",Y1]],["dinov2",["Dinov2ForImageClassification",U1]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",X1]],["resnet",["ResNetForImageClassification",h1]],["swin",["SwinForImageClassification",b1]],["segformer",["SegformerForImageClassification",gd]],["efficientnet",["EfficientNetForImageClassification",bd]],["mobilenet_v1",["MobileNetV1ForImageClassification",wd]],["mobilenet_v2",["MobileNetV2ForImageClassification",Ed]],["mobilenet_v3",["MobileNetV3ForImageClassification",Bd]],["mobilenet_v4",["MobileNetV4ForImageClassification",Pd]]]),gf=new Map([["detr",["DetrForObjectDetection",Jc]],["rt_detr",["RTDetrForObjectDetection",e1]],["rt_detr_v2",["RTDetrV2ForObjectDetection",t1]],["rf_detr",["RFDetrForObjectDetection",a1]],["d_fine",["DFineForObjectDetection",o1]],["table-transformer",["TableTransformerForObjectDetection",c1]],["yolos",["YolosForObjectDetection",ru]]]),pf=new Map([["owlvit",["OwlViTForObjectDetection",xo]],["owlv2",["Owlv2ForObjectDetection",Uc]],["grounding-dino",["GroundingDinoForObjectDetection",Au]]]),Xi=new Map([["detr",["DetrForSegmentation",Z0]],["clipseg",["CLIPSegForImageSegmentation",st]]]),mf=new Map([["segformer",["SegformerForSemanticSegmentation",pd]],["sapiens",["SapiensForSemanticSegmentation",B1]],["swin",["SwinForSemanticSegmentation",I1]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",kd]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",vd]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",yd]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",Td]]]),hf=new Map([["detr",["DetrForSegmentation",Z0]],["maskformer",["MaskFormerForInstanceSegmentation",_1]]]),Cf=new Map([["sam",["SamModel",nu]],["sam2",["Sam2Model",A0]],["edgetam",["EdgeTamModel",cu]],["sam3_tracker",["Sam3TrackerModel",uu]]]),bf=new Map([["wav2vec2",["Wav2Vec2ForCTC",hu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Fu]],["unispeech",["UniSpeechForCTC",Bu]],["unispeech-sat",["UniSpeechSatForCTC",Pu]],["wavlm",["WavLMForCTC",Lu]],["hubert",["HubertForCTC",_u]],["parakeet_ctc",["ParakeetForCTC",wu]]]),If=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Cu]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Su]],["unispeech",["UniSpeechForSequenceClassification",yu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Tu]],["wavlm",["WavLMForSequenceClassification",Ru]],["hubert",["HubertForSequenceClassification",zu]],["audio-spectrogram-transformer",["ASTForAudioClassification",sn]]]),wf=new Map([["wavlm",["WavLMForXVector",ju]]]),kf=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Gu]],["wavlm",["WavLMForAudioFrameClassification",Wu]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",bu]],["pyannote",["PyAnnoteForAudioFrameClassification",Mu]]]),Mf=new Map([["vitmatte",["VitMatteForImageMatting",ua]]]),N8=new Map([["patchtst",["PatchTSTForPrediction",Ld]],["patchtsmixer",["PatchTSMixerForPrediction",jd]]]),Ef=new Map([["swin2sr",["Swin2SRForImageSuperResolution",k1]]]),vf=new Map([["dpt",["DPTForDepthEstimation",E1]],["depth_anything",["DepthAnythingForDepthEstimation",x1]],["glpn",["GLPNForDepthEstimation",N1]],["sapiens",["SapiensForDepthEstimation",y1]],["depth_pro",["DepthProForDepthEstimation",T1]],["metric3d",["Metric3DForDepthEstimation",Q1]],["metric3dv2",["Metric3Dv2ForDepthEstimation",S1]]]),xf=new Map([["sapiens",["SapiensForNormalEstimation",D1]]]),Bf=new Map([["vitpose",["VitPoseForPoseEstimation",LA]]]),yf=new Map([["clip",["CLIPVisionModelWithProjection",ai]],["siglip",["SiglipVisionModel",JA]],["jina_clip",["JinaCLIPVisionModel",fr]]]),Df=[[Q8,f.EncoderOnly],[F8,f.EncoderDecoder],[O8,f.DecoderOnly],[S8,f.AutoEncoder],[nf,f.EncoderOnly],[of,f.EncoderOnly],[Fl,f.Seq2Seq],[Ql,f.Seq2Seq],[Sl,f.DecoderOnly],[_8,f.MultiModality],[lf,f.EncoderOnly],[cf,f.EncoderOnly],[Ol,f.Vision2Seq],[uf,f.ImageTextToText],[df,f.AudioTextToText],[ff,f.EncoderOnly],[Xi,f.EncoderOnly],[hf,f.EncoderOnly],[mf,f.EncoderOnly],[Mf,f.EncoderOnly],[N8,f.EncoderOnly],[Ef,f.EncoderOnly],[vf,f.EncoderOnly],[xf,f.EncoderOnly],[Bf,f.EncoderOnly],[gf,f.EncoderOnly],[pf,f.EncoderOnly],[Cf,f.MaskGeneration],[bf,f.EncoderOnly],[If,f.EncoderOnly],[sf,f.Seq2Seq],[af,f.EncoderOnly],[wf,f.EncoderOnly],[kf,f.EncoderOnly],[yf,f.EncoderOnly]];for(const[N,L]of Df)for(const[ue,Ge]of N.values())k.set(ue,L),d.set(Ge,ue),e.set(ue,Ge);const L8=[["MusicgenForConditionalGeneration",Dl,f.Musicgen],["Phi3VForCausalLM",oa,f.Phi3V],["CLIPTextModelWithProjection",MA,f.EncoderOnly],["SiglipTextModel",$s,f.EncoderOnly],["JinaCLIPTextModel",Ds,f.EncoderOnly],["ClapTextModelWithProjection",ud,f.EncoderOnly],["ClapAudioModelWithProjection",dd,f.EncoderOnly],["DacEncoderModel",$d,f.EncoderOnly],["DacDecoderModel",ef,f.EncoderOnly],["MimiEncoderModel",Kd,f.EncoderOnly],["MimiDecoderModel",Xd,f.EncoderOnly],["SnacEncoderModel",tf,f.EncoderOnly],["SnacDecoderModel",rf,f.EncoderOnly],["Gemma3nForConditionalGeneration",Ha,f.ImageAudioTextToText],["SupertonicForConditionalGeneration",Cl,f.Supertonic]];for(const[N,L,ue]of L8)k.set(N,ue),d.set(L,N),e.set(N,L);const Pf=new Map([["modnet",Xi],["birefnet",Xi],["isnet",Xi],["ben",Xi]]);for(const[N,L]of Pf.entries())L.set(N,["PreTrainedModel",ae]),k.set(N,f.EncoderOnly),d.set(ae,N),e.set(N,ae);class R8 extends Zt{static MODEL_CLASS_MAPPINGS=Df.map(L=>L[0]);static BASE_IF_FAIL=!0}class j8 extends Zt{static MODEL_CLASS_MAPPINGS=[nf]}class W8 extends Zt{static MODEL_CLASS_MAPPINGS=[of]}class V8 extends Zt{static MODEL_CLASS_MAPPINGS=[Fl]}class Y8 extends Zt{static MODEL_CLASS_MAPPINGS=[Ql]}class H8 extends Zt{static MODEL_CLASS_MAPPINGS=[sf]}class U8 extends Zt{static MODEL_CLASS_MAPPINGS=[af]}class K8 extends Zt{static MODEL_CLASS_MAPPINGS=[Sl]}class X8 extends Zt{static MODEL_CLASS_MAPPINGS=[lf]}class Z8 extends Zt{static MODEL_CLASS_MAPPINGS=[cf]}class J8 extends Zt{static MODEL_CLASS_MAPPINGS=[Ol]}class q8 extends Zt{static MODEL_CLASS_MAPPINGS=[ff]}class $8 extends Zt{static MODEL_CLASS_MAPPINGS=[Xi]}class eI extends Zt{static MODEL_CLASS_MAPPINGS=[mf]}class AI extends Zt{static MODEL_CLASS_MAPPINGS=[hf]}class tI extends Zt{static MODEL_CLASS_MAPPINGS=[gf]}class rI extends Zt{static MODEL_CLASS_MAPPINGS=[pf]}class sI extends Zt{static MODEL_CLASS_MAPPINGS=[Cf]}class aI extends Zt{static MODEL_CLASS_MAPPINGS=[bf]}class nI extends Zt{static MODEL_CLASS_MAPPINGS=[If]}class iI extends Zt{static MODEL_CLASS_MAPPINGS=[wf]}class oI extends Zt{static MODEL_CLASS_MAPPINGS=[kf]}class lI extends Zt{static MODEL_CLASS_MAPPINGS=[z8]}class cI extends Zt{static MODEL_CLASS_MAPPINGS=[Mf]}class uI extends Zt{static MODEL_CLASS_MAPPINGS=[Ef]}class dI extends Zt{static MODEL_CLASS_MAPPINGS=[vf]}class fI extends Zt{static MODEL_CLASS_MAPPINGS=[xf]}class gI extends Zt{static MODEL_CLASS_MAPPINGS=[Bf]}class pI extends Zt{static MODEL_CLASS_MAPPINGS=[yf]}class mI extends Zt{static MODEL_CLASS_MAPPINGS=[uf]}class hI extends Zt{static MODEL_CLASS_MAPPINGS=[df]}class CI extends ze{constructor({logits:L,past_key_values:ue,encoder_outputs:Ge,decoder_attentions:Re=null,cross_attentions:Ye=null}){super(),this.logits=L,this.past_key_values=ue,this.encoder_outputs=Ge,this.decoder_attentions=Re,this.cross_attentions=Ye}}class ht extends ze{constructor({logits:L,...ue}){super(),this.logits=L;const Ge=Object.values(ue);Ge.length>0&&(this.attentions=Ge)}}class Tf extends ze{constructor({logits:L,embeddings:ue}){super(),this.logits=L,this.embeddings=ue}}class is extends ze{constructor({logits:L}){super(),this.logits=L}}class ps extends ze{constructor({logits:L}){super(),this.logits=L}}class vs extends ze{constructor({start_logits:L,end_logits:ue}){super(),this.start_logits=L,this.end_logits=ue}}class Yn extends ze{constructor({logits:L}){super(),this.logits=L}}class bI extends ze{constructor({logits:L,past_key_values:ue}){super(),this.logits=L,this.past_key_values=ue}}class Gf extends ze{constructor({alphas:L}){super(),this.alphas=L}}class Qf extends ze{constructor({waveform:L,spectrogram:ue}){super(),this.waveform=L,this.spectrogram=ue}}}),"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":((a,o,n)=>{n.r(o),n.d(o,{ASTFeatureExtractor:()=>b});var u=n("./src/base/feature_extraction_utils.js");n("./src/utils/tensor.js");var p=n("./src/utils/audio.js");class b extends u.FeatureExtractor{constructor(w){super(w);const M=this.config.sampling_rate,v=(0,p.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(M/2),M,null,"kaldi",!0);this.mel_filters=v,this.window=(0,p.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(w,M){return(0,p.spectrogram)(w,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:M,transpose:!0})}async _call(w){(0,u.validate_audio_inputs)(w,"ASTFeatureExtractor");const M=await this._extract_fbank_features(w,this.config.max_length);if(this.config.do_normalize){const v=this.std*2,D=M.data;for(let B=0;B<D.length;++B)D[B]=(D[B]-this.mean)/v}return{input_values:M.unsqueeze_(0)}}}}),"./src/models/auto/feature_extraction_auto.js":((a,o,n)=>{n.r(o),n.d(o,{AutoFeatureExtractor:()=>C});var u=n("./src/utils/constants.js"),p=n("./src/utils/hub.js");n("./src/base/feature_extraction_utils.js");var b=n("./src/models/feature_extractors.js");class C{static async from_pretrained(M,v={}){const D=await(0,p.getModelJSON)(M,u.FEATURE_EXTRACTOR_NAME,!0,v),B=D.feature_extractor_type,E=b[B];if(!E)throw new Error(`Unknown feature_extractor_type: '${B}'. Please report this at ${u.GITHUB_ISSUE_URL}.`);return new E(D)}}}),"./src/models/auto/image_processing_auto.js":((a,o,n)=>{n.r(o),n.d(o,{AutoImageProcessor:()=>w});var u=n("./src/utils/constants.js"),p=n("./src/utils/hub.js"),b=n("./src/base/image_processors_utils.js"),C=n("./src/models/image_processors.js");class w{static async from_pretrained(v,D={}){const B=await(0,p.getModelJSON)(v,u.IMAGE_PROCESSOR_NAME,!0,D),E=B.image_processor_type??B.feature_extractor_type;let S=C[E?.replace(/Fast$/,"")];return S||(E!==void 0&&console.warn(`Image processor type '${E}' not found, assuming base ImageProcessor. Please report this at ${u.GITHUB_ISSUE_URL}.`),S=b.ImageProcessor),new S(B)}}}),"./src/models/auto/processing_auto.js":((a,o,n)=>{n.r(o),n.d(o,{AutoProcessor:()=>v});var u=n("./src/utils/constants.js"),p=n("./src/utils/hub.js"),b=n("./src/base/processing_utils.js"),C=n("./src/models/processors.js"),w=n("./src/models/image_processors.js"),M=n("./src/models/feature_extractors.js");class v{static async from_pretrained(B,E={}){const S=await(0,p.getModelJSON)(B,u.IMAGE_PROCESSOR_NAME,!0,E),{image_processor_type:F,feature_extractor_type:j,processor_class:Z}=S;if(Z&&C[Z])return C[Z].from_pretrained(B,E);if(!F&&!j)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const R={};if(F){const U=w[F.replace(/Fast$/,"")];if(!U)throw new Error(`Unknown image_processor_type: '${F}'.`);R.image_processor=new U(S)}if(j){const U=w[j];if(U)R.image_processor=new U(S);else{const f=M[j];if(!f)throw new Error(`Unknown feature_extractor_type: '${j}'.`);R.feature_extractor=new f(S)}}const z={};return new b.Processor(z,R,null)}}}),"./src/models/beit/image_processing_beit.js":((a,o,n)=>{n.r(o),n.d(o,{BeitFeatureExtractor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}}),"./src/models/bit/image_processing_bit.js":((a,o,n)=>{n.r(o),n.d(o,{BitImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}}),"./src/models/chinese_clip/image_processing_chinese_clip.js":((a,o,n)=>{n.r(o),n.d(o,{ChineseCLIPFeatureExtractor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}}),"./src/models/clap/feature_extraction_clap.js":((a,o,n)=>{n.r(o),n.d(o,{ClapFeatureExtractor:()=>b});var u=n("./src/base/feature_extraction_utils.js");n("./src/utils/tensor.js");var p=n("./src/utils/audio.js");class b extends u.FeatureExtractor{constructor(w){super(w),this.mel_filters=(0,p.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,p.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,p.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(w,M,v,D){let B;const E=w.length-M;if(E>0)if(v==="rand_trunc"){const S=Math.floor(Math.random()*(E+1));w=w.subarray(S,S+M),B=await this._extract_fbank_features(w,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${v}" not implemented`);else{if(E<0){let S=new Float64Array(M);if(S.set(w),D==="repeat")for(let F=w.length;F<M;F+=w.length)S.set(w.subarray(0,Math.min(w.length,M-F)),F);else if(D==="repeatpad")for(let F=w.length;F<-E;F+=w.length)S.set(w,F);w=S}if(v==="fusion")throw new Error(`Truncation strategy "${v}" not implemented`);B=await this._extract_fbank_features(w,this.mel_filters_slaney,this.config.nb_max_samples)}return B.unsqueeze_(0)}async _extract_fbank_features(w,M,v=null){return(0,p.spectrogram)(w,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:M,log_mel:"dB",max_num_frames:v,do_pad:!1,transpose:!0})}async _call(w,{max_length:M=null}={}){return(0,u.validate_audio_inputs)(w,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(w,M??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}}),"./src/models/clip/image_processing_clip.js":((a,o,n)=>{n.r(o),n.d(o,{CLIPFeatureExtractor:()=>b,CLIPImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/convnext/image_processing_convnext.js":((a,o,n)=>{n.r(o),n.d(o,{ConvNextFeatureExtractor:()=>b,ConvNextImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{constructor(w){super(w),this.crop_pct=this.config.crop_pct??224/256}async resize(w){const M=this.size?.shortest_edge;if(M===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(M<384){const v=Math.floor(M/this.crop_pct),[D,B]=this.get_resize_output_image_size(w,{shortest_edge:v});w=await w.resize(D,B,{resample:this.resample}),w=await w.center_crop(M,M)}else w=await w.resize(M,M,{resample:this.resample});return w}}class b extends p{}}),"./src/models/dac/feature_extraction_dac.js":((a,o,n)=>{n.r(o),n.d(o,{DacFeatureExtractor:()=>p});var u=n("./src/models/encodec/feature_extraction_encodec.js");class p extends u.EncodecFeatureExtractor{}}),"./src/models/deit/image_processing_deit.js":((a,o,n)=>{n.r(o),n.d(o,{DeiTFeatureExtractor:()=>b,DeiTImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/detr/image_processing_detr.js":((a,o,n)=>{n.r(o),n.d(o,{DetrFeatureExtractor:()=>C,DetrImageProcessor:()=>b});var u=n("./src/base/image_processors_utils.js"),p=n("./src/utils/tensor.js");class b extends u.ImageProcessor{async _call(M){const v=await super._call(M),D=[v.pixel_values.dims[0],64,64],B=(0,p.full)(D,1n);return{...v,pixel_mask:B}}post_process_object_detection(...M){return(0,u.post_process_object_detection)(...M)}post_process_panoptic_segmentation(...M){return(0,u.post_process_panoptic_segmentation)(...M)}post_process_instance_segmentation(...M){return(0,u.post_process_instance_segmentation)(...M)}}class C extends b{}}),"./src/models/dinov3_vit/image_processing_dinov3_vit.js":((a,o,n)=>{n.r(o),n.d(o,{DINOv3ViTImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}}),"./src/models/donut/image_processing_donut.js":((a,o,n)=>{n.r(o),n.d(o,{DonutFeatureExtractor:()=>b,DonutImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{pad_image(w,M,v,D={}){const[B,E,S]=M;let F=this.image_mean;Array.isArray(this.image_mean)||(F=new Array(S).fill(F));let j=this.image_std;Array.isArray(j)||(j=new Array(S).fill(F));const Z=F.map((R,z)=>-R/j[z]);return super.pad_image(w,M,v,{center:!0,constant_values:Z,...D})}}class b extends p{}}),"./src/models/dpt/image_processing_dpt.js":((a,o,n)=>{n.r(o),n.d(o,{DPTFeatureExtractor:()=>b,DPTImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/efficientnet/image_processing_efficientnet.js":((a,o,n)=>{n.r(o),n.d(o,{EfficientNetImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{constructor(C){super(C),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(w=>w*w))}}}),"./src/models/encodec/feature_extraction_encodec.js":((a,o,n)=>{n.r(o),n.d(o,{EncodecFeatureExtractor:()=>b});var u=n("./src/base/feature_extraction_utils.js"),p=n("./src/utils/tensor.js");class b extends u.FeatureExtractor{async _call(w){(0,u.validate_audio_inputs)(w,"EncodecFeatureExtractor"),w instanceof Float64Array&&(w=new Float32Array(w));const M=this.config.feature_size;if(w.length%M!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${M}).`);const v=[1,M,w.length/M];return{input_values:new p.Tensor("float32",w,v)}}}}),"./src/models/feature_extractors.js":((a,o,n)=>{n.r(o),n.d(o,{ASTFeatureExtractor:()=>u.ASTFeatureExtractor,ClapFeatureExtractor:()=>b.ClapFeatureExtractor,DacFeatureExtractor:()=>C.DacFeatureExtractor,EncodecFeatureExtractor:()=>p.EncodecFeatureExtractor,Gemma3nAudioFeatureExtractor:()=>w.Gemma3nAudioFeatureExtractor,ImageFeatureExtractor:()=>R.ImageProcessor,MoonshineFeatureExtractor:()=>M.MoonshineFeatureExtractor,ParakeetFeatureExtractor:()=>v.ParakeetFeatureExtractor,PyAnnoteFeatureExtractor:()=>D.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>B.SeamlessM4TFeatureExtractor,SnacFeatureExtractor:()=>E.SnacFeatureExtractor,SpeechT5FeatureExtractor:()=>S.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>F.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>j.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>Z.WhisperFeatureExtractor});var u=n("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),p=n("./src/models/encodec/feature_extraction_encodec.js"),b=n("./src/models/clap/feature_extraction_clap.js"),C=n("./src/models/dac/feature_extraction_dac.js"),w=n("./src/models/gemma3n/feature_extraction_gemma3n.js"),M=n("./src/models/moonshine/feature_extraction_moonshine.js"),v=n("./src/models/parakeet/feature_extraction_parakeet.js"),D=n("./src/models/pyannote/feature_extraction_pyannote.js"),B=n("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),E=n("./src/models/snac/feature_extraction_snac.js"),S=n("./src/models/speecht5/feature_extraction_speecht5.js"),F=n("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),j=n("./src/models/wespeaker/feature_extraction_wespeaker.js"),Z=n("./src/models/whisper/feature_extraction_whisper.js"),R=n("./src/base/image_processors_utils.js")}),"./src/models/florence2/processing_florence2.js":((a,o,n)=>{n.r(o),n.d(o,{Florence2Processor:()=>C});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");class C extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;constructor(M,v,D){super(M,v,D);const{tasks_answer_post_processing_type:B,task_prompts_without_inputs:E,task_prompts_with_input:S}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(B??{})),this.task_prompts_without_inputs=new Map(Object.entries(E??{})),this.task_prompts_with_input=new Map(Object.entries(S??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(M){typeof M=="string"&&(M=[M]);const v=[];for(const D of M)if(this.task_prompts_without_inputs.has(D))v.push(this.task_prompts_without_inputs.get(D));else{for(const[B,E]of this.task_prompts_with_input)if(D.includes(B)){v.push(E.replaceAll("{input}",D).replaceAll(B,""));break}v.length!==M.length&&v.push(D)}return v}post_process_generation(M,v,D){const B=this.tasks_answer_post_processing_type.get(v)??"pure_text";M=M.replaceAll("<s>","").replaceAll("</s>","");let E;switch(B){case"pure_text":E=M;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const S=B==="ocr"?"quad_boxes":"bboxes",F=M.matchAll(this.regexes[S]),j=[],Z=[];for(const[R,z,...U]of F)j.push(z?z.trim():j.at(-1)??""),Z.push(U.map((f,k)=>(Number(f)+.5)/this.size_per_bin*D[k%2]));E={labels:j,[S]:Z};break;default:throw new Error(`Task "${v}" (of type "${B}") not yet implemented.`)}return{[v]:E}}async _call(M,v=null,D={}){if(!M&&!v)throw new Error("Either text or images must be provided");const B=await this.image_processor(M,D),E=v?this.tokenizer(this.construct_prompts(v),D):{};return{...B,...E}}}}),"./src/models/gemma3n/feature_extraction_gemma3n.js":((a,o,n)=>{n.r(o),n.d(o,{Gemma3nAudioFeatureExtractor:()=>C});var u=n("./src/base/feature_extraction_utils.js"),p=n("./src/utils/tensor.js"),b=n("./src/utils/audio.js");class C extends u.FeatureExtractor{constructor(M){super(M);const{fft_length:v,feature_size:D,min_frequency:B,max_frequency:E,sampling_rate:S,frame_length:F}=this.config,j=(0,b.mel_filter_bank)(Math.floor(1+v/2),D,B,E,S,null,"htk",!1);this.mel_filters=j,this.window=(0,b.window_function)(F,"hann")}async _extract_fbank_features(M,v){return(0,b.spectrogram)(M,this.window,this.config.frame_length,this.config.hop_length,{fft_length:this.config.fft_length,center:!1,onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,remove_dc_offset:!1,transpose:!0})}async _call(M,{max_length:v=48e4,truncation:D=!0,padding:B=!0,pad_to_multiple_of:E=128}={}){if((0,u.validate_audio_inputs)(M,"Gemma3nAudioFeatureExtractor"),D&&M.length>v&&(M=M.slice(0,v)),B&&M.length%E!==0){const j=E-M.length%E,Z=new Float64Array(M.length+j);Z.set(M),this.config.padding_value!==0&&Z.fill(this.config.padding_value,M.length),M=Z}const S=await this._extract_fbank_features(M,this.config.max_length),F=(0,p.full)([1,S.dims[0]],!0);return{input_features:S.unsqueeze_(0),input_features_mask:F}}}}),"./src/models/gemma3n/processing_gemma3n.js":((a,o,n)=>{n.r(o),n.d(o,{Gemma3nProcessor:()=>w});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/models/auto/feature_extraction_auto.js"),C=n("./src/tokenizers.js");n("./src/utils/image.js"),n("./src/utils/audio.js");class w extends u.Processor{static image_processor_class=p.AutoImageProcessor;static feature_extractor_class=b.AutoFeatureExtractor;static tokenizer_class=C.AutoTokenizer;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(v,D,B){super(v,D,B),this.audio_seq_length=this.config.audio_seq_length,this.image_seq_length=this.config.image_seq_length;const{audio_token_id:E,boa_token:S,audio_token:F,eoa_token:j,image_token_id:Z,boi_token:R,image_token:z,eoi_token:U}=this.tokenizer.config;this.audio_token_id=E,this.boa_token=S,this.audio_token=F;const f=F.repeat(this.audio_seq_length);this.full_audio_sequence=`
${S}${f}${j}
`,this.image_token_id=Z,this.boi_token=R,this.image_token=z;const k=z.repeat(this.image_seq_length);this.full_image_sequence=`
${R}${k}${U}
`}async _call(v,D=null,B=null,E={}){typeof v=="string"&&(v=[v]);let S;B&&(S=await this.feature_extractor(B,E),v=v.map(Z=>Z.replaceAll(this.audio_token,this.full_audio_sequence)));let F;return D&&(F=await this.image_processor(D,E),v=v.map(Z=>Z.replaceAll(this.image_token,this.full_image_sequence))),{...this.tokenizer(v,E),...F,...S}}}}),"./src/models/glpn/image_processing_glpn.js":((a,o,n)=>{n.r(o),n.d(o,{GLPNFeatureExtractor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}}),"./src/models/grounding_dino/image_processing_grounding_dino.js":((a,o,n)=>{n.r(o),n.d(o,{GroundingDinoImageProcessor:()=>b});var u=n("./src/base/image_processors_utils.js"),p=n("./src/utils/tensor.js");class b extends u.ImageProcessor{async _call(w){const M=await super._call(w),v=M.pixel_values.dims,D=(0,p.ones)([v[0],v[2],v[3]]);return{...M,pixel_mask:D}}}}),"./src/models/grounding_dino/processing_grounding_dino.js":((a,o,n)=>{n.r(o),n.d(o,{GroundingDinoProcessor:()=>M});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js"),C=n("./src/base/image_processors_utils.js");function w(v,D){const E=v.dims.at(-1)-1,S=v.tolist();S.fill(!1,0,1),S.fill(!1,E);const F=D.tolist();return S.map((j,Z)=>j?Z:null).filter(j=>j!==null).map(j=>F[j])}class M extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;async _call(D,B,E={}){const S=D?await this.image_processor(D,E):{};return{...B?this.tokenizer(B,E):{},...S}}post_process_grounded_object_detection(D,B,{box_threshold:E=.25,text_threshold:S=.25,target_sizes:F=null}={}){const{logits:j,pred_boxes:Z}=D,R=j.dims[0];if(F!==null&&F.length!==R)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const z=j.dims.at(1),U=j.sigmoid(),f=U.max(-1).tolist(),k=Z.tolist().map(d=>d.map(y=>(0,C.center_to_corners_format)(y))),e=[];for(let d=0;d<R;++d){const y=F!==null?F[d]:null;y!==null&&(k[d]=k[d].map(ee=>ee.map((be,ke)=>be*y[(ke+1)%2])));const Ae=f[d],P=[],O=[],pe=[];for(let ee=0;ee<z;++ee){const be=Ae[ee];if(be<=E)continue;const ke=k[d][ee],Me=U[d][ee];P.push(be),pe.push(ke);const De=w(Me.gt(S),B[d]);O.push(De)}e.push({scores:P,boxes:pe,labels:this.batch_decode(O)})}return e}}}),"./src/models/idefics3/image_processing_idefics3.js":((a,o,n)=>{n.r(o),n.d(o,{Idefics3ImageProcessor:()=>b});var u=n("./src/base/image_processors_utils.js"),p=n("./src/utils/tensor.js");class b extends u.ImageProcessor{constructor(w){super(w),this.do_image_splitting=w.do_image_splitting??!0,this.max_image_size=w.max_image_size}get_resize_for_vision_encoder(w,M){let[v,D]=w.dims.slice(-2);const B=D/v;return D>=v?(D=Math.ceil(D/M)*M,v=Math.floor(D/B),v=Math.ceil(v/M)*M):(v=Math.ceil(v/M)*M,D=Math.floor(v*B),D=Math.ceil(D/M)*M),{height:v,width:D}}async _call(w,{do_image_splitting:M=null,return_row_col_info:v=!1}={}){let D;if(!Array.isArray(w))D=[[w]];else{if(w.length===0||!w[0])throw new Error("No images provided.");Array.isArray(w[0])?D=w:D=[w]}let B=[],E=[],S=[];const F=[],j=[];for(const d of D){let y=await Promise.all(d.map(O=>this.preprocess(O)));F.push(...y.map(O=>O.original_size)),j.push(...y.map(O=>O.reshaped_input_size)),y.forEach(O=>O.pixel_values.unsqueeze_(0));const{longest_edge:Ae}=this.max_image_size;let P;if(M??this.do_image_splitting){let O=new Array(y.length),pe=new Array(y.length);P=await Promise.all(y.map(async(ee,be)=>{const ke=this.get_resize_for_vision_encoder(ee.pixel_values,Ae),Me=await(0,p.interpolate_4d)(ee.pixel_values,{size:[ke.height,ke.width]}),{frames:De,num_splits_h:ye,num_splits_w:_e}=await this.split_image(Me,this.max_image_size);return O[be]=ye,pe[be]=_e,(0,p.cat)(De,0)})),E.push(O),S.push(pe)}else{const O=[Ae,Ae];P=await Promise.all(y.map(pe=>(0,p.interpolate_4d)(pe.pixel_values,{size:O}))),E.push(new Array(y.length).fill(0)),S.push(new Array(y.length).fill(0))}B.push((0,p.cat)(P,0))}const Z=B.length,[R,z,U,f]=B[0].dims;let k,e;if(Z===1)k=B[0].unsqueeze_(0),e=(0,p.full)([Z,R,U,f],!0);else{const d=Math.max(...B.map(P=>P.dims.at(0)));e=(0,p.full)([Z,d,U,f],!0);const y=e.data,Ae=d*U*f;for(let P=0;P<Z;++P){const O=B[P].dims[0];if(O<d){B[P]=(0,p.cat)([B[P],(0,p.full)([d-O,z,U,f],0)],0);const pe=P*Ae+O*U*f,ee=(P+1)*Ae;y.fill(!1,pe,ee)}}k=(0,p.stack)(B,0)}return{pixel_values:k,pixel_attention_mask:e,original_sizes:F,reshaped_input_sizes:j,...v?{rows:E,cols:S}:{}}}async split_image(w,{longest_edge:M}){const v=M,D=M,B=[],[E,S]=w.dims.slice(-2);let F=0,j=0;if(E>v||S>D){F=Math.ceil(E/v),j=Math.ceil(S/D);const Z=Math.ceil(E/F),R=Math.ceil(S/j);for(let f=0;f<F;++f)for(let k=0;k<j;++k){let e,d,y,Ae;f===F-1?(d=E-Z,Ae=E):(d=f*Z,Ae=(f+1)*Z),k===j-1?(e=S-R,y=S):(e=k*R,y=(k+1)*R);const P=[d,e],O=[Ae,y],pe=await(0,p.slice)(w,P,O,[2,3]);B.push(pe)}const z=v,U=D;(E!==z||S!==U)&&(w=await(0,p.interpolate_4d)(w,{size:[z,U]}))}return B.push(w),{frames:B,num_splits_h:F,num_splits_w:j}}}}),"./src/models/idefics3/processing_idefics3.js":((a,o,n)=>{n.r(o),n.d(o,{Idefics3Processor:()=>D});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");n("./src/utils/image.js");var C=n("./src/utils/core.js");function w(B,E,S,F,j,Z){let R="";for(let z=0;z<E;++z){for(let U=0;U<S;++U)R+=F+`<row_${z+1}_col_${U+1}>`+j.repeat(B);R+=`
`}return R+=`
${F}${Z}`+j.repeat(B)+`${F}`,R}function M(B,E,S,F){return`${E}${F}`+S.repeat(B)+`${E}`}function v(B,E,S,F,j,Z){return B===0&&E===0?M(S,F,j,Z):w(S,B,E,F,j,Z)}class D extends u.Processor{static image_processor_class=p.AutoImageProcessor;static tokenizer_class=b.AutoTokenizer;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(E,S=null,F={}){F.return_row_col_info??=!0;let j;S&&(j=await this.image_processor(S,F)),Array.isArray(E)||(E=[E]);const Z=j.rows??[new Array(E.length).fill(0)],R=j.cols??[new Array(E.length).fill(0)],z=this.config.image_seq_len,U=[],f=[];for(let e=0;e<E.length;++e){const d=E[e],y=Z[e],Ae=R[e];U.push((0,C.count)(d,this.image_token));const P=y.map((ee,be)=>v(ee,Ae[be],z,this.fake_image_token,this.image_token,this.global_img_token)),O=d.split(this.image_token);if(O.length===0)throw new Error("The image token should be present in the text.");let pe=O[0];for(let ee=0;ee<P.length;++ee)pe+=P[ee]+O[ee+1];f.push(pe)}return{...this.tokenizer(f),...j}}}}),"./src/models/image_processors.js":((a,o,n)=>{n.r(o),n.d(o,{BeitFeatureExtractor:()=>u.BeitFeatureExtractor,BitImageProcessor:()=>p.BitImageProcessor,CLIPFeatureExtractor:()=>C.CLIPFeatureExtractor,CLIPImageProcessor:()=>C.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>b.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>w.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>w.ConvNextImageProcessor,DINOv3ViTImageProcessor:()=>D.DINOv3ViTImageProcessor,DPTFeatureExtractor:()=>E.DPTFeatureExtractor,DPTImageProcessor:()=>E.DPTImageProcessor,DeiTFeatureExtractor:()=>M.DeiTFeatureExtractor,DeiTImageProcessor:()=>M.DeiTImageProcessor,DetrFeatureExtractor:()=>v.DetrFeatureExtractor,DetrImageProcessor:()=>v.DetrImageProcessor,DonutFeatureExtractor:()=>B.DonutFeatureExtractor,DonutImageProcessor:()=>B.DonutImageProcessor,EfficientNetImageProcessor:()=>S.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>F.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>j.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>Z.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>z.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>U.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>f.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>k.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>k.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>e.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>e.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>d.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>d.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>y.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>y.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>Ae.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>Ae.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>P.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>P.MobileViTImageProcessor,NougatImageProcessor:()=>O.NougatImageProcessor,OwlViTFeatureExtractor:()=>ee.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>ee.OwlViTImageProcessor,Owlv2ImageProcessor:()=>pe.Owlv2ImageProcessor,Phi3VImageProcessor:()=>be.Phi3VImageProcessor,PixtralImageProcessor:()=>ke.PixtralImageProcessor,PvtImageProcessor:()=>Me.PvtImageProcessor,Qwen2VLImageProcessor:()=>De.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>ye.RTDetrImageProcessor,Sam2ImageProcessor:()=>Ne.Sam2ImageProcessor,Sam3ImageProcessor:()=>Pe.Sam3ImageProcessor,SamImageProcessor:()=>_e.SamImageProcessor,SegformerFeatureExtractor:()=>Ce.SegformerFeatureExtractor,SegformerImageProcessor:()=>Ce.SegformerImageProcessor,SiglipImageProcessor:()=>ie.SiglipImageProcessor,SmolVLMImageProcessor:()=>se.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>xe.Swin2SRImageProcessor,VLMImageProcessor:()=>R.VLMImageProcessor,ViTFeatureExtractor:()=>je.ViTFeatureExtractor,ViTImageProcessor:()=>je.ViTImageProcessor,VitMatteImageProcessor:()=>iA.VitMatteImageProcessor,VitPoseImageProcessor:()=>rA.VitPoseImageProcessor,YolosFeatureExtractor:()=>CA.YolosFeatureExtractor,YolosImageProcessor:()=>CA.YolosImageProcessor});var u=n("./src/models/beit/image_processing_beit.js"),p=n("./src/models/bit/image_processing_bit.js"),b=n("./src/models/chinese_clip/image_processing_chinese_clip.js"),C=n("./src/models/clip/image_processing_clip.js"),w=n("./src/models/convnext/image_processing_convnext.js"),M=n("./src/models/deit/image_processing_deit.js"),v=n("./src/models/detr/image_processing_detr.js"),D=n("./src/models/dinov3_vit/image_processing_dinov3_vit.js"),B=n("./src/models/donut/image_processing_donut.js"),E=n("./src/models/dpt/image_processing_dpt.js"),S=n("./src/models/efficientnet/image_processing_efficientnet.js"),F=n("./src/models/glpn/image_processing_glpn.js"),j=n("./src/models/grounding_dino/image_processing_grounding_dino.js"),Z=n("./src/models/idefics3/image_processing_idefics3.js"),R=n("./src/models/janus/image_processing_janus.js"),z=n("./src/models/jina_clip/image_processing_jina_clip.js"),U=n("./src/models/llava_onevision/image_processing_llava_onevision.js"),f=n("./src/models/mask2former/image_processing_mask2former.js"),k=n("./src/models/maskformer/image_processing_maskformer.js"),e=n("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),d=n("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),y=n("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),Ae=n("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),P=n("./src/models/mobilevit/image_processing_mobilevit.js"),O=n("./src/models/nougat/image_processing_nougat.js"),pe=n("./src/models/owlv2/image_processing_owlv2.js"),ee=n("./src/models/owlvit/image_processing_owlvit.js"),be=n("./src/models/phi3_v/image_processing_phi3_v.js"),ke=n("./src/models/pixtral/image_processing_pixtral.js"),Me=n("./src/models/pvt/image_processing_pvt.js"),De=n("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),ye=n("./src/models/rt_detr/image_processing_rt_detr.js"),_e=n("./src/models/sam/image_processing_sam.js"),Ne=n("./src/models/sam2/image_processing_sam2.js"),Pe=n("./src/models/sam3/image_processing_sam3.js"),Ce=n("./src/models/segformer/image_processing_segformer.js"),ie=n("./src/models/siglip/image_processing_siglip.js"),se=n("./src/models/smolvlm/image_processing_smolvlm.js"),xe=n("./src/models/swin2sr/image_processing_swin2sr.js"),je=n("./src/models/vit/image_processing_vit.js"),iA=n("./src/models/vitmatte/image_processing_vitmatte.js"),rA=n("./src/models/vitpose/image_processing_vitpose.js"),CA=n("./src/models/yolos/image_processing_yolos.js")}),"./src/models/janus/image_processing_janus.js":((a,o,n)=>{n.r(o),n.d(o,{VLMImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{constructor(C){super({do_pad:!0,pad_size:{width:C.image_size,height:C.image_size},...C}),this.constant_values=this.config.background_color.map(w=>w*this.rescale_factor)}pad_image(C,w,M,v){return super.pad_image(C,w,M,{constant_values:this.constant_values,center:!0,...v})}}}),"./src/models/janus/processing_janus.js":((a,o,n)=>{n.r(o),n.d(o,{VLChatProcessor:()=>v});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js"),C=n("./src/utils/core.js"),w=n("./src/utils/tensor.js"),M=n("./src/utils/image.js");class v extends u.Processor{static image_processor_class=p.AutoImageProcessor;static tokenizer_class=b.AutoTokenizer;static uses_processor_config=!0;constructor(B,E,S){super(B,E,S),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(B,{images:E=null,chat_template:S="default"}={}){E?Array.isArray(E)||(E=[E]):E=await Promise.all(B.filter(P=>P.images).flatMap(P=>P.images).map(P=>M.RawImage.read(P)));const F=this.tokenizer,j=F.apply_chat_template(B,{tokenize:!1,add_generation_prompt:!0,chat_template:S}),Z=P=>F.encode(P,{add_special_tokens:!1}),R=j.split(this.image_tag),z=R.length-1;if(E.length!==z)throw new Error(`Number of images provided (${E.length}) does not match number of "${this.image_tag}" image tags (${z})`);const[U,f,k]=F.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let e=Z(R[0]),d=new Array(e.length).fill(!1);for(let P=1;P<R.length;++P){const O=new Array(this.num_image_tokens).fill(U),pe=Z(R[P]);e=(0,C.mergeArrays)(e,[f],O,[k],pe);const ee=new Array(this.num_image_tokens).fill(!0);d=(0,C.mergeArrays)(d,[!1],ee,[!1],new Array(pe.length).fill(!1))}const y=[1,e.length],Ae={input_ids:new w.Tensor("int64",e,y),attention_mask:new w.Tensor("int64",new Array(e.length).fill(1),y),images_seq_mask:new w.Tensor("bool",d,y),images_emb_mask:new w.Tensor("bool",new Array(z*this.num_image_tokens).fill(!0),[1,z,this.num_image_tokens])};if(E&&E.length>0){const P=await this.image_processor(E);return P.pixel_values.unsqueeze_(0),{...Ae,...P}}return Ae}}}),"./src/models/jina_clip/image_processing_jina_clip.js":((a,o,n)=>{n.r(o),n.d(o,{JinaCLIPImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{constructor(C){const{resize_mode:w,fill_color:M,interpolation:v,size:D,...B}=C,E=w==="squash"?{width:D,height:D}:w==="shortest"?{shortest_edge:D}:{longest_edge:D},S=v==="bicubic"?3:2;super({...B,size:E,resample:S,do_center_crop:!0,crop_size:D,do_normalize:!0})}}}),"./src/models/jina_clip/processing_jina_clip.js":((a,o,n)=>{n.r(o),n.d(o,{JinaCLIPProcessor:()=>C});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");class C extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;async _call(M=null,v=null,D={}){if(!M&&!v)throw new Error("Either text or images must be provided");const B=M?this.tokenizer(M,D):{},E=v?await this.image_processor(v,D):{};return{...B,...E}}}}),"./src/models/llava/processing_llava.js":((a,o,n)=>{n.r(o),n.d(o,{LlavaProcessor:()=>C});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");class C extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;static uses_processor_config=!0;async _call(M,v=null,D={}){const B=await this.image_processor(M,D);if(v){const[S,F]=B.pixel_values.dims.slice(-2),{image_token:j,patch_size:Z,num_additional_image_tokens:R}=this.config,z=Math.floor(S/Z)*Math.floor(F/Z)+R;v=structuredClone(v),Array.isArray(v)||(v=[v]);for(let U=0;U<v.length;++U)v[U]=v[U].replace(j,j.repeat(z))}const E=v?this.tokenizer(v,D):{};return{...B,...E}}}}),"./src/models/llava_onevision/image_processing_llava_onevision.js":((a,o,n)=>{n.r(o),n.d(o,{LlavaOnevisionImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}}),"./src/models/mask2former/image_processing_mask2former.js":((a,o,n)=>{n.r(o),n.d(o,{Mask2FormerImageProcessor:()=>p});var u=n("./src/models/maskformer/image_processing_maskformer.js");class p extends u.MaskFormerImageProcessor{}}),"./src/models/maskformer/image_processing_maskformer.js":((a,o,n)=>{n.r(o),n.d(o,{MaskFormerFeatureExtractor:()=>b,MaskFormerImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{post_process_panoptic_segmentation(...w){return(0,u.post_process_panoptic_segmentation)(...w)}post_process_instance_segmentation(...w){return(0,u.post_process_instance_segmentation)(...w)}}class b extends p{}}),"./src/models/mgp_str/processing_mgp_str.js":((a,o,n)=>{n.r(o),n.d(o,{MgpstrProcessor:()=>M});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js"),C=n("./src/utils/maths.js");const w={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class M extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(D,B){if(!w.hasOwnProperty(B))throw new Error(`Format ${B} is not supported.`);const[E,S]=w[B],F=this[E].bind(this),[j,Z]=D.dims,R=[],z=[],U=D.tolist();for(let k=0;k<j;++k){const e=U[k],d=[],y=[];for(let P=1;P<Z;++P){const[O,pe]=(0,C.max)((0,C.softmax)(e[P]));if(y.push(O),pe==S)break;d.push(pe)}const Ae=y.length>0?y.reduce((P,O)=>P*O,1):0;z.push(d),R.push(Ae)}return[F(z),R]}char_decode(D){return this.char_tokenizer.batch_decode(D).map(B=>B.replaceAll(" ",""))}bpe_decode(D){return this.bpe_tokenizer.batch_decode(D)}wp_decode(D){return this.wp_tokenizer.batch_decode(D).map(B=>B.replaceAll(" ",""))}batch_decode([D,B,E]){const[S,F]=this._decode_helper(D,"char"),[j,Z]=this._decode_helper(B,"bpe"),[R,z]=this._decode_helper(E,"wp"),U=[],f=[];for(let k=0;k<S.length;++k){const[e,d]=(0,C.max)([F[k],Z[k],z[k]]);U.push([S[k],j[k],R[k]][d]),f.push(e)}return{generated_text:U,scores:f,char_preds:S,bpe_preds:j,wp_preds:R}}static async from_pretrained(...D){const B=await super.from_pretrained(...D),E=await b.AutoTokenizer.from_pretrained("Xenova/gpt2"),S=await b.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return B.components={image_processor:B.image_processor,char_tokenizer:B.tokenizer,bpe_tokenizer:E,wp_tokenizer:S},B}async _call(D,B=null){const E=await this.image_processor(D);return B&&(E.labels=this.tokenizer(B).input_ids),E}}}),"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":((a,o,n)=>{n.r(o),n.d(o,{MobileNetV1FeatureExtractor:()=>b,MobileNetV1ImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":((a,o,n)=>{n.r(o),n.d(o,{MobileNetV2FeatureExtractor:()=>b,MobileNetV2ImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":((a,o,n)=>{n.r(o),n.d(o,{MobileNetV3FeatureExtractor:()=>b,MobileNetV3ImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":((a,o,n)=>{n.r(o),n.d(o,{MobileNetV4FeatureExtractor:()=>b,MobileNetV4ImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/mobilevit/image_processing_mobilevit.js":((a,o,n)=>{n.r(o),n.d(o,{MobileViTFeatureExtractor:()=>b,MobileViTImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{}class b extends p{}}),"./src/models/moonshine/feature_extraction_moonshine.js":((a,o,n)=>{n.r(o),n.d(o,{MoonshineFeatureExtractor:()=>b});var u=n("./src/base/feature_extraction_utils.js"),p=n("./src/utils/tensor.js");class b extends u.FeatureExtractor{async _call(w){(0,u.validate_audio_inputs)(w,"MoonshineFeatureExtractor"),w instanceof Float64Array&&(w=new Float32Array(w));const M=[1,w.length];return{input_values:new p.Tensor("float32",w,M)}}}}),"./src/models/moonshine/processing_moonshine.js":((a,o,n)=>{n.r(o),n.d(o,{MoonshineProcessor:()=>C});var u=n("./src/models/auto/feature_extraction_auto.js"),p=n("./src/tokenizers.js"),b=n("./src/base/processing_utils.js");class C extends b.Processor{static tokenizer_class=p.AutoTokenizer;static feature_extractor_class=u.AutoFeatureExtractor;async _call(M){return await this.feature_extractor(M)}}}),"./src/models/nougat/image_processing_nougat.js":((a,o,n)=>{n.r(o),n.d(o,{NougatImageProcessor:()=>p});var u=n("./src/models/donut/image_processing_donut.js");class p extends u.DonutImageProcessor{}}),"./src/models/owlv2/image_processing_owlv2.js":((a,o,n)=>{n.r(o),n.d(o,{Owlv2ImageProcessor:()=>p});var u=n("./src/models/owlvit/image_processing_owlvit.js");class p extends u.OwlViTImageProcessor{}}),"./src/models/owlvit/image_processing_owlvit.js":((a,o,n)=>{n.r(o),n.d(o,{OwlViTFeatureExtractor:()=>b,OwlViTImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{post_process_object_detection(...w){return(0,u.post_process_object_detection)(...w)}}class b extends p{}}),"./src/models/owlvit/processing_owlvit.js":((a,o,n)=>{n.r(o),n.d(o,{OwlViTProcessor:()=>C});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");class C extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor}}),"./src/models/paligemma/processing_paligemma.js":((a,o,n)=>{n.r(o),n.d(o,{PaliGemmaProcessor:()=>M});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");const C="<image>";function w(v,D,B,E,S){return`${E.repeat(B*S)}${D}${v}
`}class M extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;static uses_processor_config=!1;async _call(D,B=null,E={}){B||(console.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),B=""),Array.isArray(D)||(D=[D]),Array.isArray(B)||(B=[B]);const S=this.tokenizer.bos_token,F=this.image_processor.config.image_seq_length;let j;B.some(z=>z.includes(C))?j=B.map(z=>{const U=z.replaceAll(C,C.repeat(F)),f=U.lastIndexOf(C),k=f===-1?0:f+C.length;return U.slice(0,k)+S+U.slice(k)+`
`}):(console.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),j=B.map(z=>w(z,S,F,C,D.length)));const Z=this.tokenizer(j,E);return{...await this.image_processor(D,E),...Z}}}}),"./src/models/parakeet/feature_extraction_parakeet.js":((a,o,n)=>{n.r(o),n.d(o,{ParakeetFeatureExtractor:()=>w});var u=n("./src/base/feature_extraction_utils.js"),p=n("./src/utils/tensor.js"),b=n("./src/utils/audio.js");const C=1e-5;class w extends u.FeatureExtractor{constructor(v){super(v),this.config.mel_filters??=(0,b.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,this.config.sampling_rate/2,this.config.sampling_rate,"slaney","slaney");const D=(0,b.window_function)(this.config.win_length,"hann",{periodic:!1});this.window=new Float64Array(this.config.n_fft);const B=Math.floor((this.config.n_fft-this.config.win_length)/2);this.window.set(D,B)}async _extract_fbank_features(v){const D=this.config.preemphasis;v=new Float64Array(v);for(let E=v.length-1;E>=1;--E)v[E]-=D*v[E-1];return await(0,b.spectrogram)(v,this.window,this.window.length,this.config.hop_length,{fft_length:this.config.n_fft,power:2,mel_filters:this.config.mel_filters,log_mel:"log",mel_floor:-1/0,pad_mode:"constant",center:!0,transpose:!0,mel_offset:2**-24})}async _call(v){(0,u.validate_audio_inputs)(v,"ParakeetFeatureExtractor");const D=await this._extract_fbank_features(v),B=Math.floor((v.length+Math.floor(this.config.n_fft/2)*2-this.config.n_fft)/this.config.hop_length),E=D.data;E.fill(0,B*D.dims[1]);const[S,F]=D.dims,j=new Float64Array(F),Z=new Float64Array(F);for(let U=0;U<B;++U){const f=U*F;for(let k=0;k<F;++k){const e=E[f+k];j[k]+=e,Z[k]+=e*e}}const R=B>1?B-1:1;for(let U=0;U<F;++U){const f=j[U]/B,k=(Z[U]-B*f*f)/R,d=1/(Math.sqrt(k)+C);for(let y=0;y<B;++y){const Ae=y*F+U;E[Ae]=(E[Ae]-f)*d}}const z=new BigInt64Array(S);return z.fill(1n,0,B),{input_features:D.unsqueeze_(0),attention_mask:new p.Tensor("int64",z,[1,S])}}}}),"./src/models/phi3_v/image_processing_phi3_v.js":((a,o,n)=>{n.r(o),n.d(o,{Phi3VImageProcessor:()=>D});var u=n("./src/base/image_processors_utils.js"),p=n("./src/utils/tensor.js");const b=336,C=[2,3],{ceil:w,floor:M,sqrt:v}=Math;class D extends u.ImageProcessor{constructor(E){super({...E,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=E.num_crops}calc_num_image_tokens_from_image_size(E,S){const{num_img_tokens:F}=this.config;return M((M(S/b)*M(E/b)+1)*F+1+(M(S/b)+1)*v(F))}get_resize_output_image_size(E,S){const F=this._num_crops,[j,Z]=E.size;let R=j/Z,z=1;for(;z*Math.ceil(z/R)<=F;)z+=1;z-=1;const U=Math.floor(z*336),f=Math.floor(U/R);return[U,f]}pad_image(E,S,F,j={}){const[Z,R]=S,z=b*w(Z/b),U=b*w(R/b),f=[1,1,1].map((k,e)=>(k-this.image_mean[e])/this.image_std[e]);return super.pad_image(E,S,{width:U,height:z},{center:!0,constant_values:f,...j})}async _call(E,{num_crops:S=null}={}){if(this._num_crops=S??=this.config.num_crops,S<4||v(S)%1!==0)throw new Error("num_crops must be a square number >= 4");Array.isArray(E)||(E=[E]);const F=E.length,j=await Promise.all(E.map(d=>this.preprocess(d))),Z=j.map(d=>d.original_size),R=j.map(d=>d.reshaped_input_size),z=[];for(const{pixel_values:d}of j){d.unsqueeze_(0);const[y,Ae]=d.dims.slice(-2),P=await(0,p.interpolate_4d)(d,{size:[b,b],mode:"bicubic"});if(S>0){const O=[],pe=v(S),ee=M(Ae/pe),be=M(y/pe);for(let Me=0;Me<pe;++Me)for(let De=0;De<pe;++De){let ye,_e,Ne,Pe;Me===pe-1?(_e=y-be,Pe=y):(_e=Me*be,Pe=(Me+1)*be),De===pe-1?(ye=Ae-ee,Ne=Ae):(ye=De*ee,Ne=(De+1)*ee);const Ce=[_e,ye],ie=[Pe,Ne],se=await(0,p.slice)(d,Ce,ie,C);O.push(se)}const ke=await(0,p.interpolate_4d)((0,p.cat)(O,0),{size:[b,b],mode:"bicubic"});z.push((0,p.cat)([P,ke],0))}else z.push(P)}const U=(0,p.stack)(z,0),f=R.map(d=>d.map(y=>b*w(y/b))),k=new p.Tensor("int64",f.flat(),[F,2]),e=f.map(([d,y])=>this.calc_num_image_tokens_from_image_size(y,d));return{pixel_values:U,original_sizes:Z,reshaped_input_sizes:R,image_sizes:k,num_img_tokens:e}}}}),"./src/models/phi3_v/processing_phi3_v.js":((a,o,n)=>{n.r(o),n.d(o,{Phi3VProcessor:()=>M});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");n("./src/utils/image.js");const C="<|image|>",w=/<\|image_\d+\|>/g;class M extends u.Processor{static image_processor_class=p.AutoImageProcessor;static tokenizer_class=b.AutoTokenizer;async _call(D,B=null,{padding:E=!0,truncation:S=!0,num_crops:F=null}={}){Array.isArray(D)||(D=[D]);let j,Z;if(B){Z=await this.image_processor(B,{num_crops:F});const{num_img_tokens:R}=Z,z=D.map((f,k)=>f.split(w).join(C.repeat(R[k])));j=this.tokenizer(z,{padding:E,truncation:S});const U=this.tokenizer.model.convert_tokens_to_ids([C])[0];j.input_ids.map_(f=>f==U?-f:f)}else j=this.tokenizer(D);return{...j,...Z}}}}),"./src/models/pixtral/image_processing_pixtral.js":((a,o,n)=>{n.r(o),n.d(o,{PixtralImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{get_resize_output_image_size(C,w){const{longest_edge:M}=w;if(M===void 0)throw new Error("size must contain 'longest_edge'");const[v,D]=C.size,B=Math.max(v,D)/M;let E=v,S=D;B>1&&(E=Math.floor(v/B),S=Math.floor(D/B));const{patch_size:F,spatial_merge_size:j}=this.config;if(!j)throw new Error("config must contain 'spatial_merge_size'");const Z=F*j,R=Math.floor((E-1)/Z)+1,z=Math.floor((S-1)/Z)+1;return[R*Z,z*Z]}}}),"./src/models/pixtral/processing_pixtral.js":((a,o,n)=>{n.r(o),n.d(o,{PixtralProcessor:()=>C});var u=n("./src/base/processing_utils.js"),p=n("./src/models/auto/image_processing_auto.js"),b=n("./src/tokenizers.js");class C extends u.Processor{static tokenizer_class=b.AutoTokenizer;static image_processor_class=p.AutoImageProcessor;static uses_processor_config=!0;async _call(M,v=null,D={}){const B=await this.image_processor(M,D);if(v){const[S,F]=B.pixel_values.dims.slice(-2),{image_token:j,image_break_token:Z,image_end_token:R,patch_size:z,spatial_merge_size:U}=this.config,f=z*U,k=Math.floor(S/f),e=Math.floor(F/f);v=structuredClone(v),Array.isArray(v)||(v=[v]);for(let d=0;d<v.length;++d){const y=j.repeat(e),Ae=y+Z,P=y+R,O=Ae.repeat(k-1)+P;v[d]=v[d].replace(j,O)}}const E=v?this.tokenizer(v,D):{};return{...B,...E}}}}),"./src/models/processors.js":((a,o,n)=>{n.r(o),n.d(o,{Florence2Processor:()=>u.Florence2Processor,Gemma3nProcessor:()=>p.Gemma3nProcessor,GroundingDinoProcessor:()=>b.GroundingDinoProcessor,Idefics3Processor:()=>C.Idefics3Processor,JinaCLIPProcessor:()=>M.JinaCLIPProcessor,LlavaProcessor:()=>v.LlavaProcessor,MgpstrProcessor:()=>D.MgpstrProcessor,MoonshineProcessor:()=>B.MoonshineProcessor,OwlViTProcessor:()=>E.OwlViTProcessor,PaliGemmaProcessor:()=>S.PaliGemmaProcessor,Phi3VProcessor:()=>F.Phi3VProcessor,PixtralProcessor:()=>j.PixtralProcessor,PyAnnoteProcessor:()=>Z.PyAnnoteProcessor,Qwen2VLProcessor:()=>R.Qwen2VLProcessor,Sam2Processor:()=>U.Sam2Processor,Sam2VideoProcessor:()=>U.Sam2VideoProcessor,SamProcessor:()=>z.SamProcessor,SmolVLMProcessor:()=>f.SmolVLMProcessor,SpeechT5Processor:()=>k.SpeechT5Processor,UltravoxProcessor:()=>e.UltravoxProcessor,VLChatProcessor:()=>w.VLChatProcessor,VoxtralProcessor:()=>d.VoxtralProcessor,Wav2Vec2Processor:()=>y.Wav2Vec2Processor,Wav2Vec2ProcessorWithLM:()=>Ae.Wav2Vec2ProcessorWithLM,WhisperProcessor:()=>P.WhisperProcessor});var 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Me=[y*O/ee,Ae*pe/ee];k.push(Me),d.push(P),e.push(be)}z.push({bbox:f,scores:e,labels:d,keypoints:k})}F.push(z)}return F}}}),"./src/models/voxtral/processing_voxtral.js":((a,o,n)=>{n.r(o),n.d(o,{VoxtralProcessor:()=>B});var u=n("./src/models/auto/feature_extraction_auto.js"),p=n("./src/tokenizers.js"),b=n("./src/base/processing_utils.js"),C=n("./src/utils/tensor.js");const w="[AUDIO]",M="[BEGIN_AUDIO]",v=375;function D(E,S){const F=[];for(let j=0;j<E.length;j+=S)F.push(E.subarray(j,Math.min(j+S,E.length)));return F}class B extends b.Processor{static tokenizer_class=p.AutoTokenizer;static feature_extractor_class=u.AutoFeatureExtractor;static uses_processor_config=!1;async _call(S,F=null,j={}){if(Array.isArray(S))throw new Error("Batched inputs are not supported yet.");const Z={};if(F){if(!S.includes(w))throw new Error(`The input text does not contain the audio token ${w}.`);Array.isArray(F)||(F=[F]);const z=S.split(w),U=z.length-1;if(U!==F.length)throw new Error(`The number of audio inputs (${F.length}) does not match the number of audio tokens in the text (${U}).`);const f=this.feature_extractor.config.n_samples,k=F.map(P=>D(P,f)),e=k.map(P=>P.length),d=k.flat(),y=(await Promise.all(d.map(P=>this.feature_extractor(P,j)))).map(P=>P.input_features);Z.audio_values=y.length>1?(0,C.cat)(y,0):y[0];let Ae=z[0];for(let P=0;P<e.length;++P){Ae+=M;for(let O=0;O<e[P];++O)Ae+=w.repeat(v);Ae+=z[P+1]}S=Ae}return{...this.tokenizer(S,{add_special_tokens:!1,...j}),...Z}}}}),"./src/models/wav2vec2/feature_extraction_wav2vec2.js":((a,o,n)=>{n.r(o),n.d(o,{Wav2Vec2FeatureExtractor:()=>b});var u=n("./src/base/feature_extraction_utils.js"),p=n("./src/utils/tensor.js");class b extends u.FeatureExtractor{_zero_mean_unit_var_norm(w){const v=w.reduce((B,E)=>B+E,0)/w.length,D=w.reduce((B,E)=>B+(E-v)**2,0)/w.length;return w.map(B=>(B-v)/Math.sqrt(D+1e-7))}async _call(w){(0,u.validate_audio_inputs)(w,"Wav2Vec2FeatureExtractor"),w instanceof Float64Array&&(w=new Float32Array(w));let M=w;this.config.do_normalize&&(M=this._zero_mean_unit_var_norm(M));const v=[1,M.length];return{input_values:new p.Tensor("float32",M,v),attention_mask:new p.Tensor("int64",new BigInt64Array(M.length).fill(1n),v)}}}}),"./src/models/wav2vec2/processing_wav2vec2.js":((a,o,n)=>{n.r(o),n.d(o,{Wav2Vec2Processor:()=>C});var u=n("./src/tokenizers.js"),p=n("./src/models/auto/feature_extraction_auto.js"),b=n("./src/base/processing_utils.js");class C extends b.Processor{static tokenizer_class=u.AutoTokenizer;static feature_extractor_class=p.AutoFeatureExtractor;async _call(M){return await this.feature_extractor(M)}}}),"./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js":((a,o,n)=>{n.r(o),n.d(o,{Wav2Vec2ProcessorWithLM:()=>C});var u=n("./src/tokenizers.js"),p=n("./src/models/auto/feature_extraction_auto.js"),b=n("./src/base/processing_utils.js");class C extends b.Processor{static tokenizer_class=u.AutoTokenizer;static feature_extractor_class=p.AutoFeatureExtractor;async _call(M){return await this.feature_extractor(M)}}}),"./src/models/wespeaker/feature_extraction_wespeaker.js":((a,o,n)=>{n.r(o),n.d(o,{WeSpeakerFeatureExtractor:()=>b});var u=n("./src/base/feature_extraction_utils.js");n("./src/utils/tensor.js");var p=n("./src/utils/audio.js");class b extends u.FeatureExtractor{constructor(w){super(w);const M=this.config.sampling_rate,v=(0,p.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(M/2),M,null,"kaldi",!0);this.mel_filters=v,this.window=(0,p.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(w){return w=w.map(M=>M*32768),(0,p.spectrogram)(w,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(w){(0,u.validate_audio_inputs)(w,"WeSpeakerFeatureExtractor");const M=(await this._extract_fbank_features(w)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const v=M.mean(1).data,D=M.data,[B,E,S]=M.dims;for(let F=0;F<B;++F){const j=F*E*S,Z=F*S;for(let R=0;R<E;++R){const z=j+R*S;for(let U=0;U<S;++U)D[z+U]-=v[Z+U]}}}return{input_features:M}}}}),"./src/models/whisper/common_whisper.js":((a,o,n)=>{n.r(o),n.d(o,{WHISPER_LANGUAGE_MAPPING:()=>p,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>b,whisper_language_to_code:()=>C});const u=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],p=new Map(u),b=new Map([...u.map(([w,M])=>[M,w]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function C(w){w=w.toLowerCase();let M=b.get(w);if(M===void 0){const v=w.match(/^<\|([a-z]{2})\|>$/);if(v&&(w=v[1]),p.has(w))M=w;else{const B=w.length===2?p.keys():p.values();throw new Error(`Language "${w}" is not supported. Must be one of: ${JSON.stringify(Array.from(B))}`)}}return M}}),"./src/models/whisper/feature_extraction_whisper.js":((a,o,n)=>{n.r(o),n.d(o,{WhisperFeatureExtractor:()=>C});var u=n("./src/base/feature_extraction_utils.js");n("./src/utils/tensor.js");var p=n("./src/utils/audio.js"),b=n("./src/utils/maths.js");class C extends u.FeatureExtractor{constructor(M){super(M),this.config.mel_filters??=(0,p.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=(0,p.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(M){const v=await(0,p.spectrogram)(M,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:Math.min(Math.floor(M.length/this.config.hop_length),this.config.nb_max_frames)}),D=v.data,B=(0,b.max)(D)[0];for(let E=0;E<D.length;++E)D[E]=(Math.max(D[E],B-8)+4)/4;return v}async _call(M,{max_length:v=null}={}){(0,u.validate_audio_inputs)(M,"WhisperFeatureExtractor");let D;const B=v??this.config.n_samples;return M.length>B?(M.length>this.config.n_samples&&console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),D=M.slice(0,B)):(D=new Float32Array(B),D.set(M)),{input_features:(await this._extract_fbank_features(D)).unsqueeze_(0)}}}}),"./src/models/whisper/generation_whisper.js":((a,o,n)=>{n.r(o),n.d(o,{WhisperGenerationConfig:()=>p});var u=n("./src/generation/configuration_utils.js");class p extends u.GenerationConfig{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}}),"./src/models/whisper/processing_whisper.js":((a,o,n)=>{n.r(o),n.d(o,{WhisperProcessor:()=>C});var u=n("./src/models/auto/feature_extraction_auto.js"),p=n("./src/tokenizers.js"),b=n("./src/base/processing_utils.js");class C extends b.Processor{static tokenizer_class=p.AutoTokenizer;static feature_extractor_class=u.AutoFeatureExtractor;async _call(M){return await this.feature_extractor(M)}}}),"./src/models/yolos/image_processing_yolos.js":((a,o,n)=>{n.r(o),n.d(o,{YolosFeatureExtractor:()=>b,YolosImageProcessor:()=>p});var u=n("./src/base/image_processors_utils.js");class p extends u.ImageProcessor{post_process_object_detection(...w){return(0,u.post_process_object_detection)(...w)}}class b extends p{}}),"./src/ops/registry.js":((a,o,n)=>{n.r(o),n.d(o,{TensorOpRegistry:()=>C});var u=n("./src/backends/onnx.js"),p=n("./src/utils/tensor.js");const b=async(w,M,v)=>{const D=await(0,u.createInferenceSession)(new Uint8Array(w),M);return(async B=>{const E=(0,u.isONNXProxy)(),S=Object.fromEntries(Object.entries(B).map(([j,Z])=>[j,(E?Z.clone():Z).ort_tensor])),F=await(0,u.runInferenceSession)(D,S);return Array.isArray(v)?v.map(j=>new p.Tensor(F[j])):new p.Tensor(F[v])})};class C{static session_options={};static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=b([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=b([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=b([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=b([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=b([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=b([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=b([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=b([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}}),"./src/pipelines.js":((a,o,n)=>{n.r(o),n.d(o,{AudioClassificationPipeline:()=>pe,AutomaticSpeechRecognitionPipeline:()=>be,BackgroundRemovalPipeline:()=>ye,DepthEstimationPipeline:()=>xe,DocumentQuestionAnsweringPipeline:()=>Ce,FeatureExtractionPipeline:()=>P,FillMaskPipeline:()=>U,ImageClassificationPipeline:()=>Me,ImageFeatureExtractionPipeline:()=>O,ImageSegmentationPipeline:()=>De,ImageToImagePipeline:()=>se,ImageToTextPipeline:()=>ke,ObjectDetectionPipeline:()=>Ne,Pipeline:()=>j,QuestionAnsweringPipeline:()=>z,SummarizationPipeline:()=>k,Text2TextGenerationPipeline:()=>f,TextClassificationPipeline:()=>Z,TextGenerationPipeline:()=>y,TextToAudioPipeline:()=>ie,TokenClassificationPipeline:()=>R,TranslationPipeline:()=>e,ZeroShotAudioClassificationPipeline:()=>ee,ZeroShotClassificationPipeline:()=>Ae,ZeroShotImageClassificationPipeline:()=>_e,ZeroShotObjectDetectionPipeline:()=>Pe,pipeline:()=>rA});var u=n("./src/tokenizers.js"),p=n("./src/models.js"),b=n("./src/models/auto/processing_auto.js");n("./src/base/processing_utils.js");var C=n("./src/utils/generic.js"),w=n("./src/utils/core.js"),M=n("./src/utils/maths.js"),v=n("./src/utils/audio.js"),D=n("./src/utils/tensor.js"),B=n("./src/utils/image.js");async function E($e){return Array.isArray($e)||($e=[$e]),await Promise.all($e.map(we=>B.RawImage.read(we)))}async function S($e,we){return Array.isArray($e)||($e=[$e]),await Promise.all($e.map(ae=>typeof ae=="string"||ae instanceof URL?(0,v.read_audio)(ae,we):ae instanceof Float64Array?new Float32Array(ae):ae))}function F($e,we){we&&($e=$e.map(qe=>qe|0));const[ae,ze,Ue,Ze]=$e;return{xmin:ae,ymin:ze,xmax:Ue,ymax:Ze}}class j extends C.Callable{constructor({task:we,model:ae,tokenizer:ze=null,processor:Ue=null}){super(),this.task=we,this.model=ae,this.tokenizer=ze,this.processor=Ue}async dispose(){await this.model.dispose()}}class Z extends j{constructor(we){super(we)}async _call(we,{top_k:ae=1}={}){const ze=this.tokenizer(we,{padding:!0,truncation:!0}),Ue=await this.model(ze),Ze=this.model.config.problem_type==="multi_label_classification"?H=>H.sigmoid():H=>new D.Tensor("float32",(0,M.softmax)(H.data),H.dims),qe=this.model.config.id2label,AA=[];for(const H of Ue.logits){const lA=Ze(H),We=await(0,D.topk)(lA,ae),le=We[0].tolist(),aA=We[1].tolist().map((FA,pA)=>({label:qe?qe[FA]:`LABEL_${FA}`,score:le[pA]}));ae===1?AA.push(...aA):AA.push(aA)}return Array.isArray(we)||ae===1?AA:AA[0]}}class R extends j{constructor(we){super(we)}async _call(we,{ignore_labels:ae=["O"]}={}){const ze=Array.isArray(we),Ue=this.tokenizer(ze?we:[we],{padding:!0,truncation:!0}),qe=(await this.model(Ue)).logits,AA=this.model.config.id2label,H=[];for(let lA=0;lA<qe.dims[0];++lA){const We=Ue.input_ids[lA],le=qe[lA],wA=[];for(let aA=0;aA<le.dims[0];++aA){const FA=le[aA],pA=(0,M.max)(FA.data)[1],SA=AA?AA[pA]:`LABEL_${pA}`;if(ae.includes(SA))continue;const TA=this.tokenizer.decode([We[aA].item()],{skip_special_tokens:!0});if(TA==="")continue;const cA=(0,M.softmax)(FA.data);wA.push({entity:SA,score:cA[pA],index:aA,word:TA})}H.push(wA)}return ze?H:H[0]}}class z extends j{constructor(we){super(we)}async _call(we,ae,{top_k:ze=1}={}){const Ue=this.tokenizer(we,{text_pair:ae,padding:!0,truncation:!0}),{start_logits:Ze,end_logits:qe}=await this.model(Ue),AA=Ue.input_ids.tolist(),H=Ue.attention_mask.tolist(),lA=this.tokenizer.all_special_ids,We=[];for(let le=0;le<Ze.dims[0];++le){const wA=AA[le],aA=wA.findIndex(oA=>oA==this.tokenizer.sep_token_id);H[le].map((oA,zA)=>oA==1&&(zA===0||zA>aA&&lA.findIndex(UA=>UA==wA[zA])===-1));const FA=Ze[le].tolist(),pA=qe[le].tolist();for(let oA=1;oA<FA.length;++oA)(H[le]==0||oA<=aA||lA.findIndex(zA=>zA==wA[oA])!==-1)&&(FA[oA]=-1/0,pA[oA]=-1/0);const SA=(0,M.softmax)(FA).map((oA,zA)=>[oA,zA]),TA=(0,M.softmax)(pA).map((oA,zA)=>[oA,zA]);SA[0][0]=0,TA[0][0]=0;const cA=(0,w.product)(SA,TA).filter(oA=>oA[0][1]<=oA[1][1]).map(oA=>[oA[0][1],oA[1][1],oA[0][0]*oA[1][0]]).sort((oA,zA)=>zA[2]-oA[2]);for(let oA=0;oA<Math.min(cA.length,ze);++oA){const[zA,UA,KA]=cA[oA],HA=wA.slice(zA,UA+1),Vt=this.tokenizer.decode(HA,{skip_special_tokens:!0});We.push({answer:Vt,score:KA})}}return ze===1?We[0]:We}}class U extends j{constructor(we){super(we)}async _call(we,{top_k:ae=5}={}){const ze=this.tokenizer(we,{padding:!0,truncation:!0}),{logits:Ue}=await this.model(ze),Ze=[],qe=ze.input_ids.tolist();for(let AA=0;AA<qe.length;++AA){const H=qe[AA],lA=H.findIndex(FA=>FA==this.tokenizer.mask_token_id);if(lA===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const We=Ue[AA][lA],le=await(0,D.topk)(new D.Tensor("float32",(0,M.softmax)(We.data),We.dims),ae),wA=le[0].tolist(),aA=le[1].tolist();Ze.push(aA.map((FA,pA)=>{const SA=H.slice();return SA[lA]=FA,{score:wA[pA],token:Number(FA),token_str:this.tokenizer.decode([FA]),sequence:this.tokenizer.decode(SA,{skip_special_tokens:!0})}}))}return Array.isArray(we)?Ze:Ze[0]}}class f extends j{_key="generated_text";constructor(we){super(we)}async _call(we,ae={}){Array.isArray(we)||(we=[we]),this.model.config.prefix&&(we=we.map(H=>this.model.config.prefix+H));const ze=this.model.config.task_specific_params;ze&&ze[this.task]&&ze[this.task].prefix&&(we=we.map(H=>ze[this.task].prefix+H));const Ue=this.tokenizer,Ze={padding:!0,truncation:!0};let qe;this instanceof e&&"_build_translation_inputs"in Ue?qe=Ue._build_translation_inputs(we,Ze,ae):qe=Ue(we,Ze);const AA=await this.model.generate({...qe,...ae});return Ue.batch_decode(AA,{skip_special_tokens:!0}).map(H=>({[this._key]:H}))}}class k extends f{_key="summary_text";constructor(we){super(we)}}class e extends f{_key="translation_text";constructor(we){super(we)}}function d($e){return Array.isArray($e)&&$e.every(we=>"role"in we&&"content"in we)}class y extends j{constructor(we){super(we)}async _call(we,ae={}){let ze=!1,Ue=!1,Ze=ae.add_special_tokens??(this.tokenizer.add_bos_token||this.tokenizer.add_eos_token)??!1,qe;if(typeof we=="string")qe=we=[we];else if(Array.isArray(we)&&we.every(aA=>typeof aA=="string"))ze=!0,qe=we;else{if(d(we))we=[we];else if(Array.isArray(we)&&we.every(d))ze=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Ue=!0,qe=we.map(aA=>this.tokenizer.apply_chat_template(aA,{tokenize:!1,add_generation_prompt:!0})),Ze=!1}const AA=Ue?!1:ae.return_full_text??!0;this.tokenizer.padding_side="left";const H=this.tokenizer(qe,{add_special_tokens:Ze,padding:!0,truncation:!0}),lA=await this.model.generate({...H,...ae}),We=this.tokenizer.batch_decode(lA,{skip_special_tokens:!0});let le;!AA&&H.input_ids.dims.at(-1)>0&&(le=this.tokenizer.batch_decode(H.input_ids,{skip_special_tokens:!0}).map(aA=>aA.length));const wA=Array.from({length:we.length},aA=>[]);for(let aA=0;aA<We.length;++aA){const FA=Math.floor(aA/lA.dims[0]*we.length);le&&(We[aA]=We[aA].slice(le[FA])),wA[FA].push({generated_text:Ue?[...we[FA],{role:"assistant",content:We[aA]}]:We[aA]})}return!ze&&wA.length===1?wA[0]:wA}}class Ae extends j{constructor(we){super(we),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([ae,ze])=>[ae.toLowerCase(),ze])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(we,ae,{hypothesis_template:ze="This example is {}.",multi_label:Ue=!1}={}){const Ze=Array.isArray(we);Ze||(we=[we]),Array.isArray(ae)||(ae=[ae]);const qe=ae.map(lA=>ze.replace("{}",lA)),AA=Ue||ae.length===1,H=[];for(const lA of we){const We=[];for(const aA of qe){const FA=this.tokenizer(lA,{text_pair:aA,padding:!0,truncation:!0}),pA=await this.model(FA);AA?We.push([pA.logits.data[this.contradiction_id],pA.logits.data[this.entailment_id]]):We.push(pA.logits.data[this.entailment_id])}const wA=(AA?We.map(aA=>(0,M.softmax)(aA)[1]):(0,M.softmax)(We)).map((aA,FA)=>[aA,FA]).sort((aA,FA)=>FA[0]-aA[0]);H.push({sequence:lA,labels:wA.map(aA=>ae[aA[1]]),scores:wA.map(aA=>aA[0])})}return Ze?H:H[0]}}class P extends j{constructor(we){super(we)}async _call(we,{pooling:ae="none",normalize:ze=!1,quantize:Ue=!1,precision:Ze="binary"}={}){const qe=this.tokenizer(we,{padding:!0,truncation:!0}),AA=await this.model(qe);let H=AA.last_hidden_state??AA.logits??AA.token_embeddings;switch(ae){case"none":break;case"mean":H=(0,D.mean_pooling)(H,qe.attention_mask);break;case"first_token":case"cls":H=H.slice(null,0);break;case"last_token":case"eos":H=H.slice(null,-1);break;default:throw Error(`Pooling method '${ae}' not supported.`)}return ze&&(H=H.normalize(2,-1)),Ue&&(H=(0,D.quantize_embeddings)(H,Ze)),H}}class O extends j{constructor(we){super(we)}async _call(we,{pool:ae=null}={}){const ze=await E(we),{pixel_values:Ue}=await this.processor(ze),Ze=await this.model({pixel_values:Ue});let qe;if(ae){if(!("pooler_output"in Ze))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");qe=Ze.pooler_output}else qe=Ze.last_hidden_state??Ze.logits??Ze.image_embeds;return qe}}class pe extends j{constructor(we){super(we)}async _call(we,{top_k:ae=5}={}){const ze=this.processor.feature_extractor.config.sampling_rate,Ue=await S(we,ze),Ze=this.model.config.id2label,qe=[];for(const AA of Ue){const H=await this.processor(AA),We=(await this.model(H)).logits[0],le=await(0,D.topk)(new D.Tensor("float32",(0,M.softmax)(We.data),We.dims),ae),wA=le[0].tolist(),FA=le[1].tolist().map((pA,SA)=>({label:Ze?Ze[pA]:`LABEL_${pA}`,score:wA[SA]}));qe.push(FA)}return Array.isArray(we)?qe:qe[0]}}class ee extends j{constructor(we){super(we)}async _call(we,ae,{hypothesis_template:ze="This is a sound of {}."}={}){const Ue=!Array.isArray(we);Ue&&(we=[we]);const Ze=ae.map(We=>ze.replace("{}",We)),qe=this.tokenizer(Ze,{padding:!0,truncation:!0}),AA=this.processor.feature_extractor.config.sampling_rate,H=await S(we,AA),lA=[];for(const We of H){const le=await this.processor(We),wA=await this.model({...qe,...le}),aA=(0,M.softmax)(wA.logits_per_audio.data);lA.push([...aA].map((FA,pA)=>({score:FA,label:ae[pA]})))}return Ue?lA[0]:lA}}class be extends j{constructor(we){super(we)}async _call(we,ae={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(we,ae);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":case"parakeet_ctc":return this._call_wav2vec2(we,ae);case"moonshine":return this._call_moonshine(we,ae);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(we,ae){ae.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),ae.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const ze=!Array.isArray(we);ze&&(we=[we]);const Ue=this.processor.feature_extractor.config.sampling_rate,Ze=await S(we,Ue),qe=[];for(const AA of Ze){const H=await this.processor(AA),We=(await this.model(H)).logits[0],le=[];for(const aA of We)le.push((0,M.max)(aA.data)[1]);const wA=this.tokenizer.decode(le,{skip_special_tokens:!0}).trim();qe.push({text:wA})}return ze?qe[0]:qe}async _call_whisper(we,ae){const ze=ae.return_timestamps??!1,Ue=ae.chunk_length_s??0,Ze=ae.force_full_sequences??!1;let qe=ae.stride_length_s??null;const AA={...ae};ze==="word"&&(AA.return_token_timestamps=!0,AA.return_timestamps=!1);const H=!Array.isArray(we);H&&(we=[we]);const lA=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,We=this.processor.feature_extractor.config.hop_length,le=this.processor.feature_extractor.config.sampling_rate,wA=await S(we,le),aA=[];for(const FA of wA){let pA=[];if(Ue>0){if(qe===null)qe=Ue/6;else if(Ue<=qe)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const cA=le*Ue,oA=le*qe,zA=cA-2*oA;let UA=0;for(;;){const KA=UA+cA,HA=FA.subarray(UA,KA),Vt=await this.processor(HA),ts=UA===0,ut=KA>=FA.length;if(pA.push({stride:[HA.length,ts?0:oA,ut?0:oA],input_features:Vt.input_features,is_last:ut}),ut)break;UA+=zA}}else pA=[{stride:[FA.length,0,0],input_features:(await this.processor(FA)).input_features,is_last:!0}];for(const cA of pA){AA.num_frames=Math.floor(cA.stride[0]/We);const oA=await this.model.generate({inputs:cA.input_features,...AA});ze==="word"?(cA.tokens=oA.sequences.tolist()[0],cA.token_timestamps=oA.token_timestamps.tolist()[0].map(zA=>(0,M.round)(zA,2))):cA.tokens=oA[0].tolist(),cA.stride=cA.stride.map(zA=>zA/le)}const[SA,TA]=this.tokenizer._decode_asr(pA,{time_precision:lA,return_timestamps:ze,force_full_sequences:Ze});aA.push({text:SA,...TA})}return H?aA[0]:aA}async _call_moonshine(we,ae){const ze=!Array.isArray(we);ze&&(we=[we]);const Ue=this.processor.feature_extractor.config.sampling_rate,Ze=await S(we,Ue),qe=[];for(const AA of Ze){const H=await this.processor(AA),lA=Math.floor(AA.length/Ue)*6,We=await this.model.generate({max_new_tokens:lA,...ae,...H}),le=this.processor.batch_decode(We,{skip_special_tokens:!0})[0];qe.push({text:le})}return ze?qe[0]:qe}}class ke extends j{constructor(we){super(we)}async _call(we,ae={}){const ze=Array.isArray(we),Ue=await E(we),{pixel_values:Ze}=await this.processor(Ue),qe=[];for(const AA of Ze){AA.dims=[1,...AA.dims];const H=await this.model.generate({inputs:AA,...ae}),lA=this.tokenizer.batch_decode(H,{skip_special_tokens:!0}).map(We=>({generated_text:We.trim()}));qe.push(lA)}return ze?qe:qe[0]}}class Me extends j{constructor(we){super(we)}async _call(we,{top_k:ae=5}={}){const ze=await E(we),{pixel_values:Ue}=await this.processor(ze),Ze=await this.model({pixel_values:Ue}),qe=this.model.config.id2label,AA=[];for(const H of Ze.logits){const lA=await(0,D.topk)(new D.Tensor("float32",(0,M.softmax)(H.data),H.dims),ae),We=lA[0].tolist(),wA=lA[1].tolist().map((aA,FA)=>({label:qe?qe[aA]:`LABEL_${aA}`,score:We[FA]}));AA.push(wA)}return Array.isArray(we)?AA:AA[0]}}class De extends j{constructor(we){super(we),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(we,{threshold:ae=.5,mask_threshold:ze=.5,overlap_mask_area_threshold:Ue=.8,label_ids_to_fuse:Ze=null,target_sizes:qe=null,subtask:AA=null}={}){if(Array.isArray(we)&&we.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const lA=await E(we),We=lA.map(cA=>[cA.height,cA.width]),le=await this.processor(lA),{inputNames:wA,outputNames:aA}=this.model.sessions.model;if(!wA.includes("pixel_values")){if(wA.length!==1)throw Error(`Expected a single input name, but got ${wA.length} inputs: ${wA}.`);const cA=wA[0];if(cA in le)throw Error(`Input name ${cA} already exists in the inputs.`);le[cA]=le.pixel_values}const FA=await this.model(le);let pA=null;if(AA!==null)pA=this.subtasks_mapping[AA];else if(this.processor.image_processor){for(const[cA,oA]of Object.entries(this.subtasks_mapping))if(oA in this.processor.image_processor){pA=this.processor.image_processor[oA].bind(this.processor.image_processor),AA=cA;break}}const SA=this.model.config.id2label,TA=[];if(AA)if(AA==="panoptic"||AA==="instance"){const cA=pA(FA,ae,ze,Ue,Ze,qe??We)[0],oA=cA.segmentation;for(const zA of cA.segments_info){const UA=new Uint8ClampedArray(oA.data.length);for(let HA=0;HA<oA.data.length;++HA)oA.data[HA]===zA.id&&(UA[HA]=255);const KA=new B.RawImage(UA,oA.dims[1],oA.dims[0],1);TA.push({score:zA.score,label:SA[zA.label_id],mask:KA})}}else if(AA==="semantic"){const{segmentation:cA,labels:oA}=pA(FA,qe??We)[0];for(const zA of oA){const UA=new Uint8ClampedArray(cA.data.length);for(let HA=0;HA<cA.data.length;++HA)cA.data[HA]===zA&&(UA[HA]=255);const KA=new B.RawImage(UA,cA.dims[1],cA.dims[0],1);TA.push({score:null,label:SA[zA],mask:KA})}}else throw Error(`Subtask ${AA} not supported.`);else{const oA=FA[aA[0]];for(let zA=0;zA<We.length;++zA){const UA=We[zA],KA=oA[zA];KA.data.some(Vt=>Vt<-1e-5||Vt>1+1e-5)&&KA.sigmoid_();const HA=await B.RawImage.fromTensor(KA.mul_(255).to("uint8")).resize(UA[1],UA[0]);TA.push({label:null,score:null,mask:HA})}}return TA}}class ye extends De{constructor(we){super(we)}async _call(we,ae={}){if(Array.isArray(we)&&we.length!==1)throw Error("Background removal pipeline currently only supports a batch size of 1.");const Ue=await E(we),Ze=await super._call(we,ae);return Ue.map((AA,H)=>{const lA=AA.clone();return lA.putAlpha(Ze[H].mask),lA})}}class _e extends j{constructor(we){super(we)}async _call(we,ae,{hypothesis_template:ze="This is a photo of {}"}={}){const Ue=Array.isArray(we),Ze=await E(we),qe=ae.map(wA=>ze.replace("{}",wA)),AA=this.tokenizer(qe,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:H}=await this.processor(Ze),lA=await this.model({...AA,pixel_values:H}),We=this.model.config.model_type==="siglip"?wA=>wA.sigmoid().data:wA=>(0,M.softmax)(wA.data),le=[];for(const wA of lA.logits_per_image){const FA=[...We(wA)].map((pA,SA)=>({score:pA,label:ae[SA]}));FA.sort((pA,SA)=>SA.score-pA.score),le.push(FA)}return Ue?le:le[0]}}class Ne extends j{constructor(we){super(we)}async _call(we,{threshold:ae=.9,percentage:ze=!1}={}){const Ue=Array.isArray(we);if(Ue&&we.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ze=await E(we),qe=ze?null:Ze.map(aA=>[aA.height,aA.width]),{pixel_values:AA,pixel_mask:H}=await this.processor(Ze),lA=await this.model({pixel_values:AA,pixel_mask:H}),We=this.processor.image_processor.post_process_object_detection(lA,ae,qe),le=this.model.config.id2label,wA=We.map(aA=>aA.boxes.map((FA,pA)=>({score:aA.scores[pA],label:le[aA.classes[pA]],box:F(FA,!ze)})));return Ue?wA:wA[0]}}class Pe extends j{constructor(we){super(we)}async _call(we,ae,{threshold:ze=.1,top_k:Ue=null,percentage:Ze=!1}={}){const qe=Array.isArray(we),AA=await E(we),H=this.tokenizer(ae,{padding:!0,truncation:!0}),lA=await this.processor(AA),We=[];for(let le=0;le<AA.length;++le){const wA=AA[le],aA=Ze?null:[[wA.height,wA.width]],FA=lA.pixel_values[le].unsqueeze_(0),pA=await this.model({...H,pixel_values:FA});let SA;if("post_process_grounded_object_detection"in this.processor){const TA=this.processor.post_process_grounded_object_detection(pA,H.input_ids,{box_threshold:ze,text_threshold:ze,target_sizes:aA})[0];SA=TA.boxes.map((cA,oA)=>({score:TA.scores[oA],label:TA.labels[oA],box:F(cA,!Ze)}))}else{const TA=this.processor.image_processor.post_process_object_detection(pA,ze,aA,!0)[0];SA=TA.boxes.map((cA,oA)=>({score:TA.scores[oA],label:ae[TA.classes[oA]],box:F(cA,!Ze)}))}SA.sort((TA,cA)=>cA.score-TA.score),Ue!==null&&(SA=SA.slice(0,Ue)),We.push(SA)}return qe?We:We[0]}}class Ce extends j{constructor(we){super(we)}async _call(we,ae,ze={}){const Ue=(await E(we))[0],{pixel_values:Ze}=await this.processor(Ue),qe=`<s_docvqa><s_question>${ae}</s_question><s_answer>`,AA=this.tokenizer(qe,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,H=await this.model.generate({inputs:Ze,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:AA,...ze}),We=this.tokenizer.batch_decode(H)[0].match(/<s_answer>(.*?)<\/s_answer>/);let le=null;return We&&We.length>=2&&(le=We[1].trim()),[{answer:le}]}}class ie extends j{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(we){super(we),this.vocoder=we.vocoder??null}async _prepare_speaker_embeddings(we){if((typeof we=="string"||we instanceof URL)&&(we=new Float32Array(await(await fetch(we)).arrayBuffer())),we instanceof Float32Array)we=new D.Tensor("float32",we,[we.length]);else if(!(we instanceof D.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");return we}async _call(we,{speaker_embeddings:ae=null,num_inference_steps:ze,speed:Ue}={}){return this.processor?this._call_text_to_spectrogram(we,{speaker_embeddings:ae}):this.model.config.model_type==="supertonic"?this._call_supertonic(we,{speaker_embeddings:ae,num_inference_steps:ze,speed:Ue}):this._call_text_to_waveform(we)}async _call_supertonic(we,{speaker_embeddings:ae,num_inference_steps:ze,speed:Ue}){if(!ae)throw new Error("Speaker embeddings must be provided for Supertonic models.");ae=await this._prepare_speaker_embeddings(ae);const{sampling_rate:Ze,style_dim:qe}=this.model.config;ae=ae.view(1,-1,qe);const AA=this.tokenizer(we,{padding:!0,truncation:!0}),{waveform:H}=await this.model.generate_speech({...AA,style:ae,num_inference_steps:ze,speed:Ue});return new v.RawAudio(H.data,Ze)}async _call_text_to_waveform(we){const ae=this.tokenizer(we,{padding:!0,truncation:!0}),{waveform:ze}=await this.model(ae),Ue=this.model.config.sampling_rate;return new v.RawAudio(ze.data,Ue)}async _call_text_to_spectrogram(we,{speaker_embeddings:ae}){this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await p.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"}));const{input_ids:ze}=this.tokenizer(we,{padding:!0,truncation:!0});ae=await this._prepare_speaker_embeddings(ae),ae=ae.view(1,-1);const{waveform:Ue}=await this.model.generate_speech(ze,ae,{vocoder:this.vocoder}),Ze=this.processor.feature_extractor.config.sampling_rate;return new v.RawAudio(Ue.data,Ze)}}class se extends j{constructor(we){super(we)}async _call(we){const ae=await E(we),ze=await this.processor(ae),Ue=await this.model(ze),Ze=[];for(const qe of Ue.reconstruction){const AA=qe.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ze.push(B.RawImage.fromTensor(AA))}return Ze.length>1?Ze:Ze[0]}}class xe extends j{constructor(we){super(we)}async _call(we){const ae=await E(we),ze=await this.processor(ae),{predicted_depth:Ue}=await this.model(ze),Ze=[];for(let qe=0;qe<ae.length;++qe){const AA=Ue[qe],[H,lA]=AA.dims.slice(-2),[We,le]=ae[qe].size,wA=(await(0,D.interpolate_4d)(AA.view(1,1,H,lA),{size:[le,We],mode:"bilinear"})).view(le,We),aA=wA.min().item(),FA=wA.max().item(),pA=wA.sub(aA).div_(FA-aA).mul_(255).to("uint8").unsqueeze(0),SA=B.RawImage.fromTensor(pA);Ze.push({predicted_depth:wA,depth:SA})}return Ze.length>1?Ze:Ze[0]}}const je=Object.freeze({"text-classification":{tokenizer:u.AutoTokenizer,pipeline:Z,model:p.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:u.AutoTokenizer,pipeline:R,model:p.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:u.AutoTokenizer,pipeline:z,model:p.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:u.AutoTokenizer,pipeline:U,model:p.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:u.AutoTokenizer,pipeline:k,model:p.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:u.AutoTokenizer,pipeline:e,model:p.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:u.AutoTokenizer,pipeline:f,model:p.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:u.AutoTokenizer,pipeline:y,model:p.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:u.AutoTokenizer,pipeline:Ae,model:p.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:pe,model:p.AutoModelForAudioClassification,processor:b.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:u.AutoTokenizer,pipeline:ee,model:p.AutoModel,processor:b.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:u.AutoTokenizer,pipeline:be,model:[p.AutoModelForSpeechSeq2Seq,p.AutoModelForCTC],processor:b.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:u.AutoTokenizer,pipeline:ie,model:[p.AutoModelForTextToWaveform,p.AutoModelForTextToSpectrogram],processor:[b.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:u.AutoTokenizer,pipeline:ke,model:p.AutoModelForVision2Seq,processor:b.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Me,model:p.AutoModelForImageClassification,processor:b.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:De,model:[p.AutoModelForImageSegmentation,p.AutoModelForSemanticSegmentation,p.AutoModelForUniversalSegmentation],processor:b.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:ye,model:[p.AutoModelForImageSegmentation,p.AutoModelForSemanticSegmentation,p.AutoModelForUniversalSegmentation],processor:b.AutoProcessor,default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{tokenizer:u.AutoTokenizer,pipeline:_e,model:p.AutoModel,processor:b.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Ne,model:p.AutoModelForObjectDetection,processor:b.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:u.AutoTokenizer,pipeline:Pe,model:p.AutoModelForZeroShotObjectDetection,processor:b.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:u.AutoTokenizer,pipeline:Ce,model:p.AutoModelForDocumentQuestionAnswering,processor:b.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:se,model:p.AutoModelForImageToImage,processor:b.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:xe,model:p.AutoModelForDepthEstimation,processor:b.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:u.AutoTokenizer,pipeline:P,model:p.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:b.AutoProcessor,pipeline:O,model:[p.AutoModelForImageFeatureExtraction,p.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),iA=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function rA($e,we=null,{progress_callback:ae=null,config:ze=null,cache_dir:Ue=null,local_files_only:Ze=!1,revision:qe="main",device:AA=null,dtype:H=null,subfolder:lA="onnx",use_external_data_format:We=null,model_file_name:le=null,session_options:wA={}}={}){$e=iA[$e]??$e;const aA=je[$e.split("_",1)[0]];if(!aA)throw Error(`Unsupported pipeline: ${$e}. Must be one of [${Object.keys(je)}]`);we||(we=aA.default.model,console.log(`No model specified. Using default model: "${we}".`));const FA={progress_callback:ae,config:ze,cache_dir:Ue,local_files_only:Ze,revision:qe,device:AA,dtype:H,subfolder:lA,use_external_data_format:We,model_file_name:le,session_options:wA},pA=new Map([["tokenizer",aA.tokenizer],["model",aA.model],["processor",aA.processor]]),SA=await CA(pA,we,FA);SA.task=$e,(0,w.dispatchCallback)(ae,{status:"ready",task:$e,model:we});const TA=aA.pipeline;return new TA(SA)}async function CA($e,we,ae){const ze=Object.create(null),Ue=[];for(const[Ze,qe]of $e.entries()){if(!qe)continue;let AA;Array.isArray(qe)?AA=new Promise(async(H,lA)=>{let We;for(const le of qe){if(le===null){H(null);return}try{H(await le.from_pretrained(we,ae));return}catch(wA){if(wA.message?.includes("Unsupported model type"))We=wA;else if(wA.message?.includes("Could not locate file"))We=wA;else{lA(wA);return}}}lA(We)}):AA=qe.from_pretrained(we,ae),ze[Ze]=AA,Ue.push(AA)}await Promise.all(Ue);for(const[Ze,qe]of Object.entries(ze))ze[Ze]=await qe;return ze}}),"./src/tokenizers.js":((a,o,n)=>{n.r(o),n.d(o,{AlbertTokenizer:()=>Ss,AutoTokenizer:()=>rn,BartTokenizer:()=>BA,BertTokenizer:()=>Kt,BlenderbotSmallTokenizer:()=>bA,BlenderbotTokenizer:()=>uA,BloomTokenizer:()=>lt,CLIPTokenizer:()=>ta,CamembertTokenizer:()=>ve,CodeGenTokenizer:()=>us,CodeLlamaTokenizer:()=>mr,CohereTokenizer:()=>ra,ConvBertTokenizer:()=>fe,DebertaTokenizer:()=>ot,DebertaV2Tokenizer:()=>Vs,DistilBertTokenizer:()=>me,ElectraTokenizer:()=>Ke,EsmTokenizer:()=>qr,FalconTokenizer:()=>Ot,GPT2Tokenizer:()=>YA,GPTNeoXTokenizer:()=>Hs,GemmaTokenizer:()=>ws,Grok1Tokenizer:()=>Pr,HerbertTokenizer:()=>Y,LlamaTokenizer:()=>kr,M2M100Tokenizer:()=>qA,MBart50Tokenizer:()=>yA,MBartTokenizer:()=>nt,MPNetTokenizer:()=>Ys,MarianTokenizer:()=>Os,MgpstrTokenizer:()=>Ks,MobileBertTokenizer:()=>Ws,NllbTokenizer:()=>cs,NougatTokenizer:()=>Yt,PreTrainedTokenizer:()=>VA,Qwen2Tokenizer:()=>rs,RoFormerTokenizer:()=>ne,RobertaTokenizer:()=>ft,SiglipTokenizer:()=>Us,SpeechT5Tokenizer:()=>NA,SqueezeBertTokenizer:()=>Cs,T5Tokenizer:()=>GA,TokenizerModel:()=>O,VitsTokenizer:()=>Ca,Wav2Vec2CTCTokenizer:()=>Tr,WhisperTokenizer:()=>$r,XLMRobertaTokenizer:()=>hr,XLMTokenizer:()=>Se,is_chinese_char:()=>U});var u=n("./src/utils/generic.js"),p=n("./src/utils/core.js"),b=n("./src/utils/hub.js"),C=n("./src/utils/maths.js"),w=n("./src/utils/tensor.js"),M=n("./src/utils/data-structures.js"),v=n("./node_modules/@huggingface/jinja/dist/index.js"),D=n("./src/models/whisper/common_whisper.js");async function B(Oe,X){const ge=await Promise.all([(0,b.getModelJSON)(Oe,"tokenizer.json",!0,X),(0,b.getModelJSON)(Oe,"tokenizer_config.json",!0,X)]);return X.legacy!==null&&(ge[1].legacy=X.legacy),ge}function E(Oe,X){const ge=[];let Ie=0;for(const Be of Oe.matchAll(X)){const Ve=Be[0];Ie<Be.index&&ge.push(Oe.slice(Ie,Be.index)),Ve.length>0&&ge.push(Ve),Ie=Be.index+Ve.length}return Ie<Oe.length&&ge.push(Oe.slice(Ie)),ge}function S(Oe,X=!0){if(Oe.Regex!==void 0){let ge=Oe.Regex.replace(/\\([#&~])/g,"$1");for(const[Ie,Be]of Ae)ge=ge.replaceAll(Ie,Be);return new RegExp(ge,"gu")}else if(Oe.String!==void 0){const ge=(0,p.escapeRegExp)(Oe.String);return new RegExp(X?ge:`(${ge})`,"gu")}else return console.warn("Unknown pattern type:",Oe),null}function F(Oe){return new Map(Object.entries(Oe))}function j(Oe){const X=Oe.dims;switch(X.length){case 1:return Oe.tolist();case 2:if(X[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return Oe.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${X.length}.`)}}function Z(Oe){return Oe.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function R(Oe){return Oe.replace(new RegExp("\\p{M}","gu"),"")}function z(Oe){return R(Oe.toLowerCase())}function U(Oe){return Oe>=19968&&Oe<=40959||Oe>=13312&&Oe<=19903||Oe>=131072&&Oe<=173791||Oe>=173824&&Oe<=177983||Oe>=177984&&Oe<=178207||Oe>=178208&&Oe<=183983||Oe>=63744&&Oe<=64255||Oe>=194560&&Oe<=195103}function f(Oe,X,ge){const Ie=[];let Be=0;for(;Be<Oe.length;){if(Ie.push(Oe[Be]),(X.get(Oe[Be])??ge)!==ge){++Be;continue}for(;++Be<Oe.length&&(X.get(Oe[Be])??ge)===ge;)X.get(Ie.at(-1))!==ge&&(Ie[Ie.length-1]+=Oe[Be])}return Ie}function k(Oe){return Oe.match(/\S+/g)||[]}const e="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",d=new RegExp(`^[${e}]+$`,"gu"),y=".,!?…。，、।۔،",Ae=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],["(?i:[sdmt]|ll|ve|re)","(?:[sS]|[dD]|[mM]|[tT]|[lL][lL]|[vV][eE]|[rR][eE])"],["[^\\r\\n\\p{L}\\p{N}]?+","[^\\r\\n\\p{L}\\p{N}]?"],["[^\\s\\p{L}\\p{N}]++","[^\\s\\p{L}\\p{N}]+"],[` ?[^(\\s|[${y}])]+`,` ?[^\\s${y}]+`]]);class P{constructor(X){this.content=X.content,this.id=X.id,this.single_word=X.single_word??!1,this.lstrip=X.lstrip??!1,this.rstrip=X.rstrip??!1,this.special=X.special??!1,this.normalized=X.normalized??null}}class O extends u.Callable{constructor(X){super(),this.config=X,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(X,...ge){switch(X.type){case"WordPiece":return new pe(X);case"Unigram":return new ee(X,...ge);case"BPE":return new Me(X);default:if(X.vocab)return Array.isArray(X.vocab)?new ee(X,...ge):Object.hasOwn(X,"continuing_subword_prefix")&&Object.hasOwn(X,"unk_token")?Object.hasOwn(X,"merges")?new Me(X):new pe(X):new De(X,...ge);throw new Error(`Unknown TokenizerModel type: ${X.type}`)}}_call(X){return X=this.encode(X),this.fuse_unk&&(X=f(X,this.tokens_to_ids,this.unk_token_id)),X}encode(X){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(X){return X.map(ge=>this.tokens_to_ids.get(ge)??this.unk_token_id)}convert_ids_to_tokens(X){return X.map(ge=>this.vocab[ge]??this.unk_token)}}class pe extends O{constructor(X){super(X),this.tokens_to_ids=F(X.vocab),this.unk_token_id=this.tokens_to_ids.get(X.unk_token),this.unk_token=X.unk_token,this.max_input_chars_per_word=X.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[ge,Ie]of this.tokens_to_ids)this.vocab[Ie]=ge}encode(X){const ge=[];for(const Ie of X){const Be=[...Ie];if(Be.length>this.max_input_chars_per_word){ge.push(this.unk_token);continue}let Ve=!1,tA=0;const DA=[];for(;tA<Be.length;){let vA=Be.length,Je=null;for(;tA<vA;){let kA=Be.slice(tA,vA).join("");if(tA>0&&(kA=this.config.continuing_subword_prefix+kA),this.tokens_to_ids.has(kA)){Je=kA;break}--vA}if(Je===null){Ve=!0;break}DA.push(Je),tA=vA}Ve?ge.push(this.unk_token):ge.push(...DA)}return ge}}class ee extends O{constructor(X,ge){super(X);const Ie=X.vocab.length;this.vocab=new Array(Ie),this.scores=new Array(Ie);for(let Be=0;Be<Ie;++Be)[this.vocab[Be],this.scores[Be]]=X.vocab[Be];this.unk_token_id=X.unk_id,this.unk_token=this.vocab[X.unk_id],this.tokens_to_ids=new Map(this.vocab.map((Be,Ve)=>[Be,Ve])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=ge.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,C.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new M.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(X){const ge=X.chars,Ie=1;let Be=0;for(;Be<ge.length;){let Ve=!1;const tA=ge.slice(Be).join(""),DA=this.trie.commonPrefixSearch(tA);for(const vA of DA){const Je=this.tokens_to_ids.get(vA),kA=this.scores[Je],gt=(0,p.len)(vA);X.insert(Be,gt,kA,Je),!Ve&&gt===Ie&&(Ve=!0)}Ve||X.insert(Be,Ie,this.unk_score,this.unk_token_id),Be+=Ie}}tokenize(X){const ge=new M.TokenLattice(X,this.bos_token_id,this.eos_token_id);return this.populateNodes(ge),ge.tokens()}encode(X){const ge=[];for(const Ie of X){const Be=this.tokenize(Ie);ge.push(...Be)}return ge}}const be=(()=>{const Oe=[...Array.from({length:94},(Be,Ve)=>Ve+33),...Array.from({length:12},(Be,Ve)=>Ve+161),...Array.from({length:82},(Be,Ve)=>Ve+174)],X=Oe.slice();let ge=0;for(let Be=0;Be<256;++Be)Oe.includes(Be)||(Oe.push(Be),X.push(256+ge),ge+=1);const Ie=X.map(Be=>String.fromCharCode(Be));return Object.fromEntries(Oe.map((Be,Ve)=>[Be,Ie[Ve]]))})(),ke=(0,p.reverseDictionary)(be);class Me extends O{constructor(X){super(X),this.tokens_to_ids=F(X.vocab),this.unk_token_id=this.tokens_to_ids.get(X.unk_token),this.unk_token=X.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[Ie,Be]of this.tokens_to_ids)this.vocab[Be]=Ie;const ge=Array.isArray(X.merges[0]);this.merges=ge?X.merges:X.merges.map(Ie=>Ie.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((Ie,Be)=>[JSON.stringify(Ie),Be])),this.end_of_word_suffix=X.end_of_word_suffix,this.continuing_subword_suffix=X.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new M.LRUCache(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(X){if(X.length===0)return[];const ge=this.cache.get(X);if(ge!==void 0)return ge;const Ie=Array.from(X);this.end_of_word_suffix&&(Ie[Ie.length-1]+=this.end_of_word_suffix);let Be=[];if(Ie.length>1){const Ve=new M.PriorityQueue((vA,Je)=>vA.score<Je.score);let tA={token:Ie[0],bias:0,prev:null,next:null},DA=tA;for(let vA=1;vA<Ie.length;++vA){const Je={bias:vA/Ie.length,token:Ie[vA],prev:DA,next:null};DA.next=Je,this._add_node(Ve,DA),DA=Je}for(;!Ve.isEmpty();){const vA=Ve.pop();if(vA.deleted||!vA.next||vA.next.deleted)continue;if(vA.deleted=!0,vA.next.deleted=!0,vA.prev){const kA={...vA.prev};vA.prev.deleted=!0,vA.prev=kA,kA.prev?kA.prev.next=kA:tA=kA}const Je={token:vA.token+vA.next.token,bias:vA.bias,prev:vA.prev,next:vA.next.next};Je.prev?(Je.prev.next=Je,this._add_node(Ve,Je.prev)):tA=Je,Je.next&&(Je.next.prev=Je,this._add_node(Ve,Je))}for(let vA=tA;vA!==null;vA=vA.next)Be.push(vA.token)}else Be=Ie;if(this.continuing_subword_suffix)for(let Ve=0;Ve<Be.length-1;++Ve)Be[Ve]+=this.continuing_subword_suffix;return X.length<this.max_length_to_cache&&this.cache.put(X,Be),Be}_add_node(X,ge){const Ie=this.bpe_ranks.get(JSON.stringify([ge.token,ge.next.token]));Ie!==void 0&&(ge.score=Ie+ge.bias,X.push(ge))}encode(X){const ge=[];for(const Ie of X){if(this.ignore_merges&&this.tokens_to_ids.has(Ie)){ge.push(Ie);continue}const Be=this.bpe(Ie);for(const Ve of Be)if(this.tokens_to_ids.has(Ve))ge.push(Ve);else if(this.byte_fallback){const tA=Array.from(this.text_encoder.encode(Ve)).map(DA=>`<0x${DA.toString(16).toUpperCase().padStart(2,"0")}>`);tA.every(DA=>this.tokens_to_ids.has(DA))?ge.push(...tA):ge.push(this.unk_token)}else ge.push(this.unk_token)}return ge}}class De extends O{constructor(X,ge){super(X),this.tokens_to_ids=F(ge.target_lang?X.vocab[ge.target_lang]:X.vocab),this.bos_token=ge.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=ge.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=ge.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=ge.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[Ie,Be]of this.tokens_to_ids)this.vocab[Be]=Ie}encode(X){return X}}class ye extends u.Callable{constructor(X){super(),this.config=X}static fromConfig(X){if(X===null)return null;switch(X.type){case"BertNormalizer":return new $e(X);case"Precompiled":return new ut(X);case"Sequence":return new CA(X);case"Replace":return new _e(X);case"NFC":return new Pe(X);case"NFD":return new Ce(X);case"NFKC":return new ie(X);case"NFKD":return new se(X);case"Strip":return new xe(X);case"StripAccents":return new je(X);case"Lowercase":return new iA(X);case"Prepend":return new rA(X);default:throw new Error(`Unknown Normalizer type: ${X.type}`)}}normalize(X){throw Error("normalize should be implemented in subclass.")}_call(X){return this.normalize(X)}}class _e extends ye{normalize(X){const ge=S(this.config.pattern);return ge===null?X:X.replaceAll(ge,this.config.content)}}class Ne extends ye{form=void 0;normalize(X){return X=X.normalize(this.form),X}}class Pe extends Ne{form="NFC"}class Ce extends Ne{form="NFD"}class ie extends Ne{form="NFKC"}class se extends Ne{form="NFKD"}class xe extends ye{normalize(X){return this.config.strip_left&&this.config.strip_right?X=X.trim():(this.config.strip_left&&(X=X.trimStart()),this.config.strip_right&&(X=X.trimEnd())),X}}class je extends ye{normalize(X){return X=R(X),X}}class iA extends ye{normalize(X){return X=X.toLowerCase(),X}}class rA extends ye{normalize(X){return X=this.config.prepend+X,X}}class CA extends ye{constructor(X){super(X),this.normalizers=X.normalizers.map(ge=>ye.fromConfig(ge))}normalize(X){return this.normalizers.reduce((ge,Ie)=>Ie.normalize(ge),X)}}class $e extends ye{_tokenize_chinese_chars(X){const ge=[];for(let Ie=0;Ie<X.length;++Ie){const Be=X[Ie],Ve=Be.charCodeAt(0);U(Ve)?(ge.push(" "),ge.push(Be),ge.push(" ")):ge.push(Be)}return ge.join("")}stripAccents(X){return X.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(X){switch(X){case" ":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(X)}}_clean_text(X){const ge=[];for(const Ie of X){const Be=Ie.charCodeAt(0);Be===0||Be===65533||this._is_control(Ie)||(/^\s$/.test(Ie)?ge.push(" "):ge.push(Ie))}return ge.join("")}normalize(X){return this.config.clean_text&&(X=this._clean_text(X)),this.config.handle_chinese_chars&&(X=this._tokenize_chinese_chars(X)),this.config.lowercase?(X=X.toLowerCase(),this.config.strip_accents!==!1&&(X=this.stripAccents(X))):this.config.strip_accents&&(X=this.stripAccents(X)),X}}class we extends u.Callable{static fromConfig(X){if(X===null)return null;switch(X.type){case"BertPreTokenizer":return new ae(X);case"Sequence":return new ls(X);case"Whitespace":return new ma(X);case"WhitespaceSplit":return new ha(X);case"Metaspace":return new Vt(X);case"ByteLevel":return new ze(X);case"Split":return new Ue(X);case"Punctuation":return new Ze(X);case"Digits":return new qe(X);case"Replace":return new xs(X);case"FixedLength":return new pr(X);default:throw new Error(`Unknown PreTokenizer type: ${X.type}`)}}pre_tokenize_text(X,ge){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(X,ge){return(Array.isArray(X)?X.map(Ie=>this.pre_tokenize_text(Ie,ge)):this.pre_tokenize_text(X,ge)).flat()}_call(X,ge){return this.pre_tokenize(X,ge)}}class ae extends we{constructor(X){super(),this.pattern=new RegExp(`[^\\s${e}]+|[${e}]`,"gu")}pre_tokenize_text(X,ge){return X.trim().match(this.pattern)||[]}}class ze extends we{constructor(X){super(),this.config=X,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=be,this.text_encoder=new TextEncoder}pre_tokenize_text(X,ge){return this.add_prefix_space&&!X.startsWith(" ")&&(X=" "+X),(this.use_regex?X.match(this.pattern)||[]:[X]).map(Be=>Array.from(this.text_encoder.encode(Be),Ve=>this.byte_encoder[Ve]).join(""))}}class Ue extends we{constructor(X){super(),this.config=X,this.pattern=S(this.config.pattern,this.config.invert)}pre_tokenize_text(X,ge){return this.pattern===null?[]:this.config.invert?X.match(this.pattern)||[]:this.config.behavior?.toLowerCase()==="removed"?X.split(this.pattern).filter(Ie=>Ie):E(X,this.pattern)}}class Ze extends we{constructor(X){super(),this.config=X,this.pattern=new RegExp(`[^${e}]+|[${e}]+`,"gu")}pre_tokenize_text(X,ge){return X.match(this.pattern)||[]}}class qe extends we{constructor(X){super(),this.config=X;const ge=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(ge,"gu")}pre_tokenize_text(X,ge){return X.match(this.pattern)||[]}}class AA extends u.Callable{constructor(X){super(),this.config=X}static fromConfig(X){if(X===null)return null;switch(X.type){case"TemplateProcessing":return new We(X);case"ByteLevel":return new le(X);case"RobertaProcessing":return new lA(X);case"BertProcessing":return new H(X);case"Sequence":return new wA(X);default:throw new Error(`Unknown PostProcessor type: ${X.type}`)}}post_process(X,...ge){throw Error("post_process should be implemented in subclass.")}_call(X,...ge){return this.post_process(X,...ge)}}class H extends AA{constructor(X){super(X),this.cls=X.cls[0],this.sep=X.sep[0]}post_process(X,ge=null,{add_special_tokens:Ie=!0}={}){Ie&&(X=(0,p.mergeArrays)([this.cls],X,[this.sep]));let Be=new Array(X.length).fill(0);if(ge!==null){const Ve=Ie&&this instanceof lA?[this.sep]:[],tA=Ie?[this.sep]:[];X=(0,p.mergeArrays)(X,Ve,ge,tA),Be=(0,p.mergeArrays)(Be,new Array(ge.length+Ve.length+tA.length).fill(1))}return{tokens:X,token_type_ids:Be}}}class lA extends H{}class We extends AA{constructor(X){super(X),this.single=X.single,this.pair=X.pair}post_process(X,ge=null,{add_special_tokens:Ie=!0}={}){const Be=ge===null?this.single:this.pair;let Ve=[],tA=[];for(const DA of Be)"SpecialToken"in DA?Ie&&(Ve.push(DA.SpecialToken.id),tA.push(DA.SpecialToken.type_id)):"Sequence"in DA&&(DA.Sequence.id==="A"?(Ve=(0,p.mergeArrays)(Ve,X),tA=(0,p.mergeArrays)(tA,new Array(X.length).fill(DA.Sequence.type_id))):DA.Sequence.id==="B"&&(Ve=(0,p.mergeArrays)(Ve,ge),tA=(0,p.mergeArrays)(tA,new Array(ge.length).fill(DA.Sequence.type_id))));return{tokens:Ve,token_type_ids:tA}}}class le extends AA{post_process(X,ge=null){return ge&&(X=(0,p.mergeArrays)(X,ge)),{tokens:X}}}class wA extends AA{constructor(X){super(X),this.processors=X.processors.map(ge=>AA.fromConfig(ge))}post_process(X,ge=null,Ie={}){let Be;for(const Ve of this.processors)if(Ve instanceof le)X=Ve.post_process(X).tokens,ge&&(ge=Ve.post_process(ge).tokens);else{const tA=Ve.post_process(X,ge,Ie);X=tA.tokens,Be=tA.token_type_ids}return{tokens:X,token_type_ids:Be}}}class aA extends u.Callable{constructor(X){super(),this.config=X,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=X.trim_offsets}static fromConfig(X){if(X===null)return null;switch(X.type){case"WordPiece":return new cA(X);case"Metaspace":return new ts(X);case"ByteLevel":return new oA(X);case"Replace":return new FA(X);case"ByteFallback":return new pA(X);case"Fuse":return new SA(X);case"Strip":return new TA(X);case"Sequence":return new UA(X);case"CTC":return new zA(X);case"BPEDecoder":return new KA(X);default:throw new Error(`Unknown Decoder type: ${X.type}`)}}_call(X){return this.decode(X)}decode(X){return this.decode_chain(X).join("")}decode_chain(X){throw Error("`decode_chain` should be implemented in subclass.")}}class FA extends aA{decode_chain(X){const ge=S(this.config.pattern);return ge===null?X:X.map(Ie=>Ie.replaceAll(ge,this.config.content))}}class pA extends aA{constructor(X){super(X),this.text_decoder=new TextDecoder}decode_chain(X){const ge=[];let Ie=[];for(const Be of X){let Ve=null;if(Be.length===6&&Be.startsWith("<0x")&&Be.endsWith(">")){const tA=parseInt(Be.slice(3,5),16);isNaN(tA)||(Ve=tA)}if(Ve!==null)Ie.push(Ve);else{if(Ie.length>0){const tA=this.text_decoder.decode(Uint8Array.from(Ie));ge.push(tA),Ie=[]}ge.push(Be)}}if(Ie.length>0){const Be=this.text_decoder.decode(Uint8Array.from(Ie));ge.push(Be),Ie=[]}return ge}}class SA extends aA{decode_chain(X){return[X.join("")]}}class TA extends aA{constructor(X){super(X),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(X){return X.map(ge=>{let Ie=0;for(let Ve=0;Ve<this.start&&ge[Ve]===this.content;++Ve){Ie=Ve+1;continue}let Be=ge.length;for(let Ve=0;Ve<this.stop;++Ve){const tA=ge.length-Ve-1;if(ge[tA]===this.content){Be=tA;continue}else break}return ge.slice(Ie,Be)})}}class cA extends aA{constructor(X){super(X),this.cleanup=X.cleanup}decode_chain(X){return X.map((ge,Ie)=>(Ie!==0&&(ge.startsWith(this.config.prefix)?ge=ge.replace(this.config.prefix,""):ge=" "+ge),this.cleanup&&(ge=Z(ge)),ge))}}class oA extends aA{constructor(X){super(X),this.byte_decoder=ke,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(X){const ge=X.join(""),Ie=new Uint8Array([...ge].map(Ve=>this.byte_decoder[Ve]));return this.text_decoder.decode(Ie)}decode_chain(X){const ge=[];let Ie=[];for(const Be of X)this.added_tokens.find(Ve=>Ve.content===Be)!==void 0?(Ie.length>0&&(ge.push(this.convert_tokens_to_string(Ie)),Ie=[]),ge.push(Be)):Ie.push(Be);return Ie.length>0&&ge.push(this.convert_tokens_to_string(Ie)),ge}}class zA extends aA{constructor(X){super(X),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(X){if(X.length===0)return"";const ge=[X[0]];for(let Ve=1;Ve<X.length;++Ve)X[Ve]!==ge.at(-1)&&ge.push(X[Ve]);let Be=ge.filter(Ve=>Ve!==this.pad_token).join("");return this.cleanup&&(Be=Z(Be).replaceAll(this.word_delimiter_token," ").trim()),Be}decode_chain(X){return[this.convert_tokens_to_string(X)]}}class UA extends aA{constructor(X){super(X),this.decoders=X.decoders.map(ge=>aA.fromConfig(ge))}decode_chain(X){return this.decoders.reduce((ge,Ie)=>Ie.decode_chain(ge),X)}}class KA extends aA{constructor(X){super(X),this.suffix=this.config.suffix}decode_chain(X){return X.map((ge,Ie)=>ge.replaceAll(this.suffix,Ie===X.length-1?"":" "))}}class HA extends aA{decode_chain(X){let ge="";for(let Ie=1;Ie<X.length;Ie+=2)ge+=X[Ie];return[ge]}}class Vt extends we{constructor(X){super(),this.replacement=X.replacement,this.strRep=X.str_rep||this.replacement,this.prepend_scheme=X.prepend_scheme??"always"}pre_tokenize_text(X,{section_index:ge=void 0}={}){let Ie=X.replaceAll(" ",this.strRep);return!Ie.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&ge===0)&&(Ie=this.strRep+Ie),[Ie]}}class ts extends aA{constructor(X){super(X),this.replacement=X.replacement}decode_chain(X){const ge=[];for(let Ie=0;Ie<X.length;++Ie){let Be=X[Ie].replaceAll(this.replacement," ");Ie==0&&Be.startsWith(" ")&&(Be=Be.substring(1)),ge.push(Be)}return ge}}class ut extends ye{constructor(X){super(X),this.charsmap=X.precompiled_charsmap}normalize(X){return X=X.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),X=X.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),X.includes("～")?X=X.split("～").map(Ie=>Ie.normalize("NFKC")).join("～"):X=X.normalize("NFKC"),X}}class ls extends we{constructor(X){super(),this.tokenizers=X.pretokenizers.map(ge=>we.fromConfig(ge))}pre_tokenize_text(X,ge){return this.tokenizers.reduce((Ie,Be)=>Be.pre_tokenize(Ie,ge),[X])}}class ma extends we{constructor(X){super()}pre_tokenize_text(X,ge){return X.match(/\w+|[^\w\s]+/g)||[]}}class ha extends we{constructor(X){super()}pre_tokenize_text(X,ge){return k(X)}}class xs extends we{constructor(X){super(),this.config=X,this.pattern=S(this.config.pattern),this.content=this.config.content}pre_tokenize_text(X,ge){return this.pattern===null?[X]:[X.replaceAll(this.pattern,this.config.content)]}}class pr extends we{constructor(X){super(),this._length=X.length}pre_tokenize_text(X,ge){const Ie=[];for(let Be=0;Be<X.length;Be+=this._length)Ie.push(X.slice(Be,Be+this._length));return Ie}}const Ur=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Bs(Oe,X,ge,Ie){for(const Be of Object.keys(Oe)){const Ve=X-Oe[Be].length,tA=ge(Be),DA=new Array(Ve).fill(tA);Oe[Be]=Ie==="right"?(0,p.mergeArrays)(Oe[Be],DA):(0,p.mergeArrays)(DA,Oe[Be])}}function Et(Oe,X){for(const ge of Object.keys(Oe))Oe[ge].length=X}class VA extends u.Callable{return_token_type_ids=!1;padding_side="right";constructor(X,ge){super(),this.config=ge,this.normalizer=ye.fromConfig(X.normalizer),this.pre_tokenizer=we.fromConfig(X.pre_tokenizer),this.model=O.fromConfig(X.model,ge),this.post_processor=AA.fromConfig(X.post_processor),this.decoder=aA.fromConfig(X.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const Ie of X.added_tokens){const Be=new P(Ie);this.added_tokens.push(Be),this.model.tokens_to_ids.set(Be.content,Be.id),this.model.vocab[Be.id]=Be.content,Be.special&&(this.special_tokens.push(Be.content),this.all_special_ids.push(Be.id))}if(this.additional_special_tokens=ge.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_splitter=new M.DictionarySplitter(this.added_tokens.map(Ie=>Ie.content)),this.added_tokens_map=new Map(this.added_tokens.map(Ie=>[Ie.content,Ie])),this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=ge.model_max_length,this.remove_space=ge.remove_space,this.clean_up_tokenization_spaces=ge.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ge.do_lowercase_and_remove_accent??!1,ge.padding_side&&(this.padding_side=ge.padding_side),this.add_bos_token=ge.add_bos_token,this.add_eos_token=ge.add_eos_token,this.legacy=!1,this.chat_template=ge.chat_template??null,Array.isArray(this.chat_template)){const Ie=Object.create(null);for(const{name:Be,template:Ve}of this.chat_template){if(typeof Be!="string"||typeof Ve!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');Ie[Be]=Ve}this.chat_template=Ie}this._compiled_template_cache=new Map}getToken(...X){for(const ge of X){const Ie=this.config[ge];if(Ie)if(typeof Ie=="object"){if(Ie.__type==="AddedToken")return Ie.content;throw Error(`Unknown token: ${Ie}`)}else return Ie}return null}static async from_pretrained(X,{progress_callback:ge=null,config:Ie=null,cache_dir:Be=null,local_files_only:Ve=!1,revision:tA="main",legacy:DA=null}={}){const vA=await B(X,{progress_callback:ge,config:Ie,cache_dir:Be,local_files_only:Ve,revision:tA,legacy:DA});return new this(...vA)}_call(X,{text_pair:ge=null,add_special_tokens:Ie=!0,padding:Be=!1,truncation:Ve=null,max_length:tA=null,return_tensor:DA=!0,return_token_type_ids:vA=null}={}){const Je=Array.isArray(X);let kA;if(Je){if(X.length===0)throw Error("text array must be non-empty");if(ge!==null){if(Array.isArray(ge)){if(X.length!==ge.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");kA=X.map((dt,ur)=>this._encode_plus(dt,{text_pair:ge[ur],add_special_tokens:Ie,return_token_type_ids:vA}))}else kA=X.map(dt=>this._encode_plus(dt,{add_special_tokens:Ie,return_token_type_ids:vA}))}else{if(X==null)throw Error("text may not be null or undefined");if(Array.isArray(ge))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");kA=[this._encode_plus(X,{text_pair:ge,add_special_tokens:Ie,return_token_type_ids:vA})]}if(tA===null?tA=this.model_max_length:Ve===null&&(Be===!0?(console.warn("`max_length` is ignored when `padding: true` and there is no truncation strategy. To pad to max length, use `padding: 'max_length'`."),tA=this.model_max_length):Be===!1&&(console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."),Ve=!0)),Be===!0&&(tA=Math.min((0,C.max)(kA.map(dt=>dt.input_ids.length))[0],tA??1/0)),tA=Math.min(tA,this.model_max_length??1/0),Be||Ve)for(let dt=0;dt<kA.length;++dt)kA[dt].input_ids.length!==tA&&(kA[dt].input_ids.length>tA?Ve&&Et(kA[dt],tA):Be&&Bs(kA[dt],tA,ur=>ur==="input_ids"?this.pad_token_id:0,this.padding_side));const gt={};if(DA){if(!(Be&&Ve)&&kA.some(ur=>{for(const Gr of Object.keys(ur))if(ur[Gr].length!==kA[0][Gr]?.length)return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const dt=[kA.length,kA[0].input_ids.length];for(const ur of Object.keys(kA[0]))gt[ur]=new w.Tensor("int64",BigInt64Array.from(kA.flatMap(Gr=>Gr[ur]).map(BigInt)),dt)}else{for(const dt of Object.keys(kA[0]))gt[dt]=kA.map(ur=>ur[dt]);if(!Je)for(const dt of Object.keys(gt))gt[dt]=gt[dt][0]}return gt}_encode_text(X){if(X===null)return null;const ge=this.added_tokens_splitter.split(X);for(let Be=0;Be<ge.length;++Be){const Ve=this.added_tokens_map.get(ge[Be]);Ve&&(Ve.lstrip&&Be>0&&(ge[Be-1]=ge[Be-1].trimEnd()),Ve.rstrip&&Be<ge.length-1&&(ge[Be+1]=ge[Be+1].trimStart()))}return ge.flatMap((Be,Ve)=>{if(Be.length===0)return[];if(this.added_tokens_map.has(Be))return[Be];if(this.remove_space===!0&&(Be=Be.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(Be=z(Be)),this.normalizer!==null&&(Be=this.normalizer(Be)),Be.length===0)return[];const tA=this.pre_tokenizer!==null?this.pre_tokenizer(Be,{section_index:Ve}):[Be];return this.model(tA)})}_encode_plus(X,{text_pair:ge=null,add_special_tokens:Ie=!0,return_token_type_ids:Be=null}={}){const{tokens:Ve,token_type_ids:tA}=this._tokenize_helper(X,{pair:ge,add_special_tokens:Ie}),DA=this.model.convert_tokens_to_ids(Ve),vA={input_ids:DA,attention_mask:new Array(DA.length).fill(1)};return(Be??this.return_token_type_ids)&&tA&&(vA.token_type_ids=tA),vA}_tokenize_helper(X,{pair:ge=null,add_special_tokens:Ie=!1}={}){const Be=this._encode_text(X),Ve=this._encode_text(ge);return this.post_processor?this.post_processor(Be,Ve,{add_special_tokens:Ie}):{tokens:(0,p.mergeArrays)(Be??[],Ve??[])}}tokenize(X,{pair:ge=null,add_special_tokens:Ie=!1}={}){return this._tokenize_helper(X,{pair:ge,add_special_tokens:Ie}).tokens}encode(X,{text_pair:ge=null,add_special_tokens:Ie=!0,return_token_type_ids:Be=null}={}){return this._encode_plus(X,{text_pair:ge,add_special_tokens:Ie,return_token_type_ids:Be}).input_ids}batch_decode(X,ge={}){return X instanceof w.Tensor&&(X=X.tolist()),X.map(Ie=>this.decode(Ie,ge))}decode(X,ge={}){if(X instanceof w.Tensor&&(X=j(X)),!Array.isArray(X)||X.length===0||!(0,p.isIntegralNumber)(X[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(X,ge)}decode_single(X,{skip_special_tokens:ge=!1,clean_up_tokenization_spaces:Ie=null}){let Be=this.model.convert_ids_to_tokens(X);ge&&(Be=Be.filter(tA=>!this.special_tokens.includes(tA)));let Ve=this.decoder?this.decoder(Be):Be.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Ve=Ve.replaceAll(this.decoder.end_of_word_suffix," "),ge&&(Ve=Ve.trim())),(Ie??this.clean_up_tokenization_spaces)&&(Ve=Z(Ve)),Ve}get_chat_template({chat_template:X=null,tools:ge=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const Ie=this.chat_template;if(X!==null&&Object.hasOwn(Ie,X))X=Ie[X];else if(X===null)if(ge!==null&&"tool_use"in Ie)X=Ie.tool_use;else if("default"in Ie)X=Ie.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(Ie).sort()}.`)}else if(X===null)if(this.chat_template)X=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return X}apply_chat_template(X,{tools:ge=null,documents:Ie=null,chat_template:Be=null,add_generation_prompt:Ve=!1,tokenize:tA=!0,padding:DA=!1,truncation:vA=!1,max_length:Je=null,return_tensor:kA=!0,return_dict:gt=!1,tokenizer_kwargs:dt={},...ur}={}){if(Be=this.get_chat_template({chat_template:Be,tools:ge}),typeof Be!="string")throw Error(`chat_template must be a string, but got ${typeof Be}`);let Gr=this._compiled_template_cache.get(Be);Gr===void 0&&(Gr=new v.Template(Be),this._compiled_template_cache.set(Be,Gr));const nr=Object.create(null);for(const Gt of Ur){const jr=this.getToken(Gt);jr&&(nr[Gt]=jr)}const _t=Gr.render({messages:X,add_generation_prompt:Ve,tools:ge,documents:Ie,...nr,...ur});if(tA){const Gt=this._call(_t,{add_special_tokens:!1,padding:DA,truncation:vA,max_length:Je,return_tensor:kA,...dt});return gt?Gt:Gt.input_ids}return _t}}class Kt extends VA{return_token_type_ids=!0}class Ss extends VA{return_token_type_ids=!0}class Ws extends VA{return_token_type_ids=!0}class Cs extends VA{return_token_type_ids=!0}class ot extends VA{return_token_type_ids=!0}class Vs extends VA{return_token_type_ids=!0}class Y extends VA{return_token_type_ids=!0}class fe extends VA{return_token_type_ids=!0}class ne extends VA{return_token_type_ids=!0}class me extends VA{}class ve extends VA{}class Se extends VA{return_token_type_ids=!0;constructor(X,ge){super(X,ge),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ke extends VA{return_token_type_ids=!0}class GA extends VA{}class YA extends VA{}class BA extends VA{}class nt extends VA{constructor(X,ge){super(X,ge),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Ie=>this.languageRegex.test(Ie)),this.lang_to_token=Ie=>Ie}_build_translation_inputs(X,ge,Ie){return or(this,X,ge,Ie)}}class yA extends nt{}class ft extends VA{}class lt extends VA{}const Dr="▁";class kr extends VA{padding_side="left";constructor(X,ge){super(X,ge),this.legacy=ge.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Vt({replacement:Dr,prepend_scheme:"first"}))}_encode_text(X){if(X===null)return null;if(this.legacy||X.length===0)return super._encode_text(X);let ge=super._encode_text(Dr+X.replaceAll(Dr," "));return ge.length>1&&ge[0]===Dr&&this.special_tokens.includes(ge[1])&&(ge=ge.slice(1)),ge}}class mr extends VA{}class hr extends VA{}class Ys extends VA{}class Ot extends VA{}class Hs extends VA{}class qr extends VA{}class rs extends VA{}class ws extends VA{}class Pr extends VA{}function or(Oe,X,ge,Ie){if(!("language_codes"in Oe)||!Array.isArray(Oe.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Oe)||!(Oe.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Oe)||typeof Oe.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const Be=Ie.src_lang,Ve=Ie.tgt_lang;if(!Oe.language_codes.includes(Ve))throw new Error(`Target language code "${Ve}" is not valid. Must be one of: {${Oe.language_codes.join(", ")}}`);if(Be!==void 0){if(!Oe.language_codes.includes(Be))throw new Error(`Source language code "${Be}" is not valid. Must be one of: {${Oe.language_codes.join(", ")}}`);for(const tA of Oe.post_processor.config.single)if("SpecialToken"in tA&&Oe.languageRegex.test(tA.SpecialToken.id)){tA.SpecialToken.id=Oe.lang_to_token(Be);break}}return Ie.forced_bos_token_id=Oe.model.convert_tokens_to_ids([Oe.lang_to_token(Ve)])[0],Oe._call(X,ge)}class cs extends VA{constructor(X,ge){super(X,ge),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Ie=>this.languageRegex.test(Ie)),this.lang_to_token=Ie=>Ie}_build_translation_inputs(X,ge,Ie){return or(this,X,ge,Ie)}}class qA extends VA{constructor(X,ge){super(X,ge),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Ie=>this.languageRegex.test(Ie)).map(Ie=>Ie.slice(2,-2)),this.lang_to_token=Ie=>`__${Ie}__`}_build_translation_inputs(X,ge,Ie){return or(this,X,ge,Ie)}}class $r extends VA{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(X,{return_timestamps:ge=!1,return_language:Ie=!1,time_precision:Be=null,force_full_sequences:Ve=!0}={}){if(Be===null)throw Error("Must specify time_precision");let tA=null;const DA=ge==="word";function vA(){return{language:tA,timestamp:[null,null],text:""}}const Je=[];let kA=vA(),gt=0;const dt=this.timestamp_begin,Gr=dt+1500;let nr=[],_t=[],Gt=!1,jr=null;const ks=new Set(this.all_special_ids);for(const $t of X){const lr=$t.tokens,yt=DA?$t.token_timestamps:null;let dr=null,Xs=dt;if("stride"in $t){const[Mr,er,jA]=$t.stride;if(gt-=er,jr=Mr-jA,er&&(Xs=er/Be+dt),jA)for(let vt=lr.length-1;vt>=0;--vt){const Qr=Number(lr[vt]);if(Qr>=dt){if(dr!==null&&(Qr-dt)*Be<jr)break;dr=Qr}}}let cr=[],Ms=[];for(let Mr=0;Mr<lr.length;++Mr){const er=Number(lr[Mr]);if(ks.has(er)){const jA=this.decode([er]),vt=D.WHISPER_LANGUAGE_MAPPING.get(jA.slice(2,-2));if(vt!==void 0){if(tA!==null&&vt!==tA&&!ge){nr.push(cr);const Qr=this.findLongestCommonSequence(nr)[0],xa=this.decode(Qr);kA.text=xa,Je.push(kA),nr=[],cr=[],kA=vA()}tA=kA.language=vt}}else if(er>=dt&&er<=Gr){const jA=(er-dt)*Be+gt,vt=(0,C.round)(jA,2);if(dr!==null&&er>=dr)Gt=!0;else if(Gt||nr.length>0&&er<Xs)Gt=!1;else if(kA.timestamp[0]===null)kA.timestamp[0]=vt;else if(vt!==kA.timestamp[0]){kA.timestamp[1]=vt,nr.push(cr),DA&&_t.push(Ms);const[Qr,xa]=this.findLongestCommonSequence(nr,_t),sa=this.decode(Qr);kA.text=sa,DA&&(kA.words=this.collateWordTimestamps(Qr,xa,tA)),Je.push(kA),nr=[],cr=[],_t=[],Ms=[],kA=vA()}}else if(cr.push(er),DA){let jA=(0,C.round)(yt[Mr]+gt,2),vt;if(Mr+1<yt.length){vt=(0,C.round)(yt[Mr+1]+gt,2);const Qr=this.decode([er]);d.test(Qr)&&(vt=(0,C.round)(Math.min(jA+Be,vt),2))}else vt=null;Ms.push([jA,vt])}}if("stride"in $t){const[Mr,er,jA]=$t.stride;gt+=Mr-jA}cr.length>0?(nr.push(cr),DA&&_t.push(Ms)):nr.every(Mr=>Mr.length===0)&&(kA=vA(),nr=[],cr=[],_t=[],Ms=[])}if(nr.length>0){if(Ve&&ge)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[$t,lr]=this.findLongestCommonSequence(nr,_t),yt=this.decode($t);kA.text=yt,DA&&(kA.words=this.collateWordTimestamps($t,lr,tA)),Je.push(kA)}let qt=Object.create(null);const _s=Je.map($t=>$t.text).join("");if(ge||Ie){for(let $t=0;$t<Je.length;++$t){const lr=Je[$t];ge||delete lr.timestamp,Ie||delete lr.language}if(DA){const $t=[];for(const lr of Je)for(const yt of lr.words)$t.push(yt);qt={chunks:$t}}else qt={chunks:Je}}return[_s,qt]}findLongestCommonSequence(X,ge=null){let Ie=X[0],Be=Ie.length,Ve=[];const tA=Array.isArray(ge)&&ge.length>0;let DA=tA?[]:null,vA=tA?ge[0]:null;for(let Je=1;Je<X.length;++Je){const kA=X[Je];let gt=0,dt=[Be,Be,0,0];const ur=kA.length;for(let qt=1;qt<Be+ur;++qt){const _s=Math.max(0,Be-qt),$t=Math.min(Be,Be+ur-qt),lr=Ie.slice(_s,$t),yt=Math.max(0,qt-Be),dr=Math.min(ur,qt),Xs=kA.slice(yt,dr);if(lr.length!==Xs.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let cr;tA?cr=lr.filter((er,jA)=>er===Xs[jA]&&vA[_s+jA]<=ge[Je][yt+jA]).length:cr=lr.filter((er,jA)=>er===Xs[jA]).length;const Ms=qt/1e4,Mr=cr/qt+Ms;cr>1&&Mr>gt&&(gt=Mr,dt=[_s,$t,yt,dr])}const[Gr,nr,_t,Gt]=dt,jr=Math.floor((nr+Gr)/2),ks=Math.floor((Gt+_t)/2);Ve.push(...Ie.slice(0,jr)),Ie=kA.slice(ks),Be=Ie.length,tA&&(DA.push(...vA.slice(0,jr)),vA=ge[Je].slice(ks))}return Ve.push(...Ie),tA?(DA.push(...vA),[Ve,DA]):[Ve,[]]}collateWordTimestamps(X,ge,Ie){const[Be,Ve,tA]=this.combineTokensIntoWords(X,Ie),DA=[];for(let vA=0;vA<Be.length;++vA){const Je=tA[vA];DA.push({text:Be[vA],timestamp:[ge[Je.at(0)][0],ge[Je.at(-1)][1]]})}return DA}combineTokensIntoWords(X,ge,Ie=`"'“¡¿([{-`,Be=`"'.。,，!！?？:：”)]}、`){ge=ge??"english";let Ve,tA,DA;return["chinese","japanese","thai","lao","myanmar"].includes(ge)?[Ve,tA,DA]=this.splitTokensOnUnicode(X):[Ve,tA,DA]=this.splitTokensOnSpaces(X),this.mergePunctuations(Ve,tA,DA,Ie,Be)}decode(X,ge){let Ie;return ge?.decode_with_timestamps?(X instanceof w.Tensor&&(X=j(X)),Ie=this.decodeWithTimestamps(X,ge)):Ie=super.decode(X,ge),Ie}decodeWithTimestamps(X,ge){const Ie=ge?.time_precision??.02,Be=Array.from(this.all_special_ids).at(-1)+1;let Ve=[[]];for(let tA of X)if(tA=Number(tA),tA>=Be){const DA=((tA-Be)*Ie).toFixed(2);Ve.push(`<|${DA}|>`),Ve.push([])}else Ve[Ve.length-1].push(tA);return Ve=Ve.map(tA=>typeof tA=="string"?tA:super.decode(tA,ge)),Ve.join("")}splitTokensOnUnicode(X){const ge=this.decode(X,{decode_with_timestamps:!0}),Ie="�",Be=[],Ve=[],tA=[];let DA=[],vA=[],Je=0;for(let kA=0;kA<X.length;++kA){const gt=X[kA];DA.push(gt),vA.push(kA);const dt=this.decode(DA,{decode_with_timestamps:!0});(!dt.includes(Ie)||ge[Je+dt.indexOf(Ie)]===Ie)&&(Be.push(dt),Ve.push(DA),tA.push(vA),DA=[],vA=[],Je+=dt.length)}return[Be,Ve,tA]}splitTokensOnSpaces(X){const[ge,Ie,Be]=this.splitTokensOnUnicode(X),Ve=[],tA=[],DA=[],vA=new RegExp(`^[${e}]$`,"gu");for(let Je=0;Je<ge.length;++Je){const kA=ge[Je],gt=Ie[Je],dt=Be[Je],ur=gt[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),Gr=kA.startsWith(" "),nr=kA.trim(),_t=vA.test(nr);if(ur||Gr||_t||Ve.length===0)Ve.push(kA),tA.push(gt),DA.push(dt);else{const Gt=Ve.length-1;Ve[Gt]+=kA,tA[Gt].push(...gt),DA[Gt].push(...dt)}}return[Ve,tA,DA]}mergePunctuations(X,ge,Ie,Be,Ve){const tA=structuredClone(X),DA=structuredClone(ge),vA=structuredClone(Ie);let Je=tA.length-2,kA=tA.length-1;for(;Je>=0;)tA[Je].startsWith(" ")&&Be.includes(tA[Je].trim())?(tA[kA]=tA[Je]+tA[kA],DA[kA]=(0,p.mergeArrays)(DA[Je],DA[kA]),vA[kA]=(0,p.mergeArrays)(vA[Je],vA[kA]),tA[Je]="",DA[Je]=[],vA[Je]=[]):kA=Je,--Je;for(Je=0,kA=1;kA<tA.length;)!tA[Je].endsWith(" ")&&Ve.includes(tA[kA])?(tA[Je]+=tA[kA],DA[Je]=(0,p.mergeArrays)(DA[Je],DA[kA]),vA[Je]=(0,p.mergeArrays)(vA[Je],vA[kA]),tA[kA]="",DA[kA]=[],vA[kA]=[]):Je=kA,++kA;return[tA.filter(gt=>gt),DA.filter(gt=>gt.length>0),vA.filter(gt=>gt.length>0)]}}class us extends VA{}class ta extends VA{}class Us extends VA{}class Os extends VA{constructor(X,ge){super(X,ge),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(Ie=>this.languageRegex.test(Ie)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(X){if(X===null)return null;const[ge,...Ie]=X.trim().split(this.languageRegex);if(Ie.length===0)return super._encode_text(ge);if(Ie.length===2){const[Be,Ve]=Ie;return this.supported_language_codes.includes(Be)||console.warn(`Unsupported language code "${Be}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,p.mergeArrays)([Be],super._encode_text(Ve))}}}class Tr extends VA{}class uA extends VA{}class bA extends VA{}class NA extends VA{}class Yt extends VA{}class Ca extends VA{constructor(X,ge){super(X,ge),this.decoder=new HA({})}}class ra extends VA{}class Ks extends VA{}class rn{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:GA,DistilBertTokenizer:me,CamembertTokenizer:ve,DebertaTokenizer:ot,DebertaV2Tokenizer:Vs,BertTokenizer:Kt,HerbertTokenizer:Y,ConvBertTokenizer:fe,RoFormerTokenizer:ne,XLMTokenizer:Se,ElectraTokenizer:Ke,MobileBertTokenizer:Ws,SqueezeBertTokenizer:Cs,AlbertTokenizer:Ss,GPT2Tokenizer:YA,BartTokenizer:BA,MBartTokenizer:nt,MBart50Tokenizer:yA,RobertaTokenizer:ft,WhisperTokenizer:$r,CodeGenTokenizer:us,CLIPTokenizer:ta,SiglipTokenizer:Us,MarianTokenizer:Os,BloomTokenizer:lt,NllbTokenizer:cs,M2M100Tokenizer:qA,LlamaTokenizer:kr,CodeLlamaTokenizer:mr,XLMRobertaTokenizer:hr,MPNetTokenizer:Ys,FalconTokenizer:Ot,GPTNeoXTokenizer:Hs,EsmTokenizer:qr,Wav2Vec2CTCTokenizer:Tr,BlenderbotTokenizer:uA,BlenderbotSmallTokenizer:bA,SpeechT5Tokenizer:NA,NougatTokenizer:Yt,VitsTokenizer:Ca,Qwen2Tokenizer:rs,GemmaTokenizer:ws,Grok1Tokenizer:Pr,CohereTokenizer:ra,MgpstrTokenizer:Ks,PreTrainedTokenizer:VA};static async from_pretrained(X,{progress_callback:ge=null,config:Ie=null,cache_dir:Be=null,local_files_only:Ve=!1,revision:tA="main",legacy:DA=null}={}){const[vA,Je]=await B(X,{progress_callback:ge,config:Ie,cache_dir:Be,local_files_only:Ve,revision:tA,legacy:DA}),kA=Je.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let gt=this.TOKENIZER_CLASS_MAPPING[kA];return gt||(console.warn(`Unknown tokenizer class "${kA}", attempting to construct from base class.`),gt=VA),new gt(vA,Je)}}}),"./src/utils/audio.js":((a,o,n)=>{n.r(o),n.d(o,{RawAudio:()=>pe,hamming:()=>E,hanning:()=>B,mel_filter_bank:()=>U,read_audio:()=>v,spectrogram:()=>y,window_function:()=>Ae});var u=n("./src/utils/hub.js"),p=n("./src/utils/maths.js"),b=n("./src/utils/core.js"),C=n("./src/env.js"),w=n("./src/utils/tensor.js"),M=n("?7992");async function v(ee,be){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. 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d=this.channels,y=this.toCanvas(),Ae=M(k,e).getContext("2d");return Ae.drawImage(y,R,z,k,e,0,0,k,e),new F(Ae.getImageData(0,0,k,e).data,k,e,4).convert(d)}else{const d=this.toSharp().extract({left:R,top:z,width:k,height:e});return await D(d)}}async center_crop(R,z){if(this.width===R&&this.height===z)return this;const U=(this.width-R)/2,f=(this.height-z)/2;if(B){const k=this.channels,e=this.toCanvas(),d=M(R,z).getContext("2d");let y=0,Ae=0,P=0,O=0;return U>=0?y=U:P=-U,f>=0?Ae=f:O=-f,d.drawImage(e,y,Ae,R,z,P,O,R,z),new F(d.getImageData(0,0,R,z).data,R,z,4).convert(k)}else{let k=this.toSharp();if(U>=0&&f>=0)k=k.extract({left:Math.floor(U),top:Math.floor(f),width:R,height:z});else if(U<=0&&f<=0){const e=Math.floor(-f),d=Math.floor(-U);k=k.extend({top:e,left:d,right:R-this.width-d,bottom:z-this.height-e})}else{let e=[0,0],d=0;f<0?(e[0]=Math.floor(-f),e[1]=z-this.height-e[0]):d=Math.floor(f);let 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type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...ie){return(0,p.isONNXTensor)(ie[0])?this.ort_tensor=ie[0]:this.ort_tensor=new p.Tensor(ie[0],ie[1],ie[2]),new Proxy(this,{get:(se,xe)=>{if(typeof xe=="string"){let je=Number(xe);if(Number.isInteger(je))return se._getitem(je)}return se[xe]},set:(se,xe,je)=>se[xe]=je})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[ie,...se]=this.dims;if(se.length>0){const xe=se.reduce((je,iA)=>je*iA);for(let je=0;je<ie;++je)yield this._subarray(je,xe,se)}else yield*this.data}_getitem(ie){const[se,...xe]=this.dims;if(ie=k(ie,se),xe.length>0){const je=xe.reduce((iA,rA)=>iA*rA);return this._subarray(ie,je,xe)}else return new w(this.type,[this.data[ie]],xe)}indexOf(ie){const se=this.data;for(let xe=0;xe<se.length;++xe)if(se[xe]==ie)return xe;return-1}_subarray(ie,se,xe){const 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Blob([w]).stream().pipeThrough(new DecompressionStream("gzip")),v=[];for await(const B of M)v.push(B);const D=await new 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A}e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A)}e[c+2152>>2]=r}wn=fA+2|0,Fe=32;e:{for(;;){Te=Fe;A:{r:{a:{if(r=e[33691],(i=e[33285])?A=0:(i=e[33285],A=e[33283],A=e[A>>2]==e[A+4>>2]),r|!A|i||!(e[32524]<0)){if(!xA(e[c+2156>>2])){if((i=(0|(A=e[49828]))>0)&(0|(r=A))<(0|(A=e[33284])))break a;if(!((0|(r=e[49845]))<=0|(0|A)<(0|r))){e[49845]=0,f[134760]=1,e[33285]=e[c+2152>>2],s=16384;break e}}Fe=e[c+2156>>2],e[c+2156>>2]=e[c+2152>>2];n:{o:{c:{u:{l:{if((0|(r=e[32524]))>=0){if(d[r+134736|0])break l;e[32524]=-1}if(r=e[33285])break c;if(A=e[33283],e[A>>2]!=e[A+4>>2])break u;r=32;break n}e[33691]|r||(e[c+2156>>2]=f[134736],r=1),e[32524]=r+1,r=f[r+134736|0];break n}if(!(r=e[33285]))break o}e[33285]=0;break n}e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A)}e[c+2152>>2]=r,e[33691]=0;n:if(!(I|!e[47203])){o:{if((0|(A=e[c+2156>>2]))!=60){if((0|r)!=35&r-97>>>0>25|(0|A)!=38)break n;for(i=e[33285],l=0;;){c:{if(e[c+2156>>2]=r,!i){if(A=e[33283],e[A>>2]==e[A+4>>2])break c;r=e[c+2156>>2]}if(!(!((r=!!(0|xA(r)))|(0|(A=e[c+2156>>2]))==35)|l>>>0>19)){f[(c+112|0)+l|0]=A,l=l+1|0,(r=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A),i=e[33285]);continue}}break}f[(c+112|0)+l|0]=0;c:{u:{if(!(r=e[33285])){if(r=0,A=e[33283],e[A>>2]==e[A+4>>2])break c;if(!(r=e[33285]))break u}e[33285]=0;break c}e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A)}e[c+2152>>2]=r,e[c+100>>2]=e[c+2156>>2],e[c+104>>2]=r,e[c+96>>2]=c+112,dA(134736,84252,c+96|0);c:{if(e[c+2156>>2]==59){l=c+2156|0,V=c+2152|0,H=i=H-32|0;u:if(d[0|(r=c+112|0)]!=35)A=-1,(0|(r=fs(130752,r)))!=-1&&(e[l>>2]=r,e[V>>2]||(e[V>>2]=32),A=r);else{if(d[0|(A=r+1|0)]==120){e[i>>2]=l,A=KA(r+2|0,90005,i);break u}e[i+16>>2]=l,A=KA(A,90070,i+16|0)}if(H=i+32|0,(0|A)>0)break c}e[32524]=0,e[c+2156>>2]=38,e[c+2152>>2]=32;break n}if((0|(A=e[c+2156>>2]))>32)break n;if(!(r=e[33692]-20|0)|(0|r)==16)break o;break n}if((0|r)!=47&&!OA(r)&&(0|(A=e[c+2152>>2]))!=63&&(0|A)!=33)break n;if((0|(A=e[c+2148>>2]))>780){e[33691]=e[c+2156>>2],f[0|(A=A+189424|0)]=32,f[A+1|0]=0,e[33285]=e[c+2152>>2],s=16384;break e}for(V=e[33285],l=0,r=e[c+2152>>2];e[c+2156>>2]=r,i=0,V||(A=e[33283],i=e[A>>2]==e[A+4>>2],r=e[c+2156>>2]),!((0|r)==62|i|l>>>0>499);)e[(c+144|0)+(l<<2)>>2]=r,l=l+1|0,(r=e[33285])?(V=0,e[33285]=0):(e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A),V=e[33285]);e[(i=c+144|0)+(l<<2)>>2]=0,e[c+2152>>2]=32,Le=c+2148|0,Te=e[32525],r=0,H=Ee=H-560|0;c:if(W(i,84333,3)&&W(i,84477,4)){for(A=(i+(po(i)<<2)|0)-4|0,(0|(hA=e[A>>2]))==47&&(e[A>>2]=32);;){if(A=e[i+(r<<2)>>2]){if(fr(A))A=r;else if(f[(Ee+512|0)+r|0]=Ps(A<<24>>24),A=39,(0|(r=r+1|0))!=39)continue}else A=r;break}if(f[(Ee+512|0)+A|0]=0,d[Ee+512|0]!=47){if((0|(V=fs(130480,Ee+512|0)))!=16&&(r=e[Le>>2],e[Le>>2]=r+1,f[r+189424|0]=32),(0|hA)==47&&(r=0,!(502241>>>V&1)))break c}else(0|(r=fs(130480,Ee+512|1)))!=16&&(l=e[Le>>2],e[Le>>2]=l+1,f[l+189424|0]=32),V=r+32|0;l=i+(A<<2)|0,A=e[33708],I=O(A,76)+133076|0,r=262174;u:{l:{i:{p:switch(V-1|0){case 33:C:if(!((0|A)<=1)){for(;;){if(e[O(r=A-1|0,76)+133152>>2]==2)break C;if(e[33708]=r,i=A>>>0>2,A=r,!i)break}A=1}r=ne(l,34,A);break c;case 32:C:if(!((0|A)<=1)){for(;;){if(e[O(r=A-1|0,76)+133152>>2]==1)break C;if(e[33708]=r,i=A>>>0>2,A=r,!i)break}A=1}r=ne(l,33,A)+524328|0;break c;case 9:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(A=(I=r<<6)+134912|0)>>2]=10,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1,A=yt(l,88301),i=yt(l,88390),JA(A,88479)?JA(A,88528)||(e[28+(134912+(r<<6)|0)>>2]=Ia(i,130224)):e[24+(I+134912|0)>>2]=Ia(i,130192),hr(Le,e[33709]);break i;case 2:for((0|(A=e[33709]))<=18&&(e[33709]=A+1),e[(A=(bn=A<<6)+134912|0)>>2]=3,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1,Nt=1;;){if(i=yt(l,e[(tr=Nt<<2)+130448>>2])){hA=0,A=e[tr+131072>>2];C:if(V=e[A>>2])for(;;){for(r=0;I=f[r+V|0],(Te=e[(r<<2)+i>>2])&&(r=r+1|0,(0|I)==(0|Te)););h:switch(Te-34|0){case 0:case 5:if(!I)break C;break;default:break h}if(!(V=e[A+((hA=hA+1|0)<<3)>>2]))break}vo=tr+(bn+134912|0)|0;C:if((0|(A=e[4+(A+(hA<<3)|0)>>2]))>=0)A=(0|O(A,e[4+(tr+134912|0)>>2]))/100|0;else{for(;i=(A=i)+4|0,fr(e[A>>2]););for(In=e[A>>2]==43,i=((Wn=e[(A=A+(In<<2)|0)>>2]==45)<<2)+A|0,hA=Ee+96|0,H=V=(H=I=H-16|0)-224|0,Je(V+16|0,0,144),A=V+160|4,e[V+24>>2]=A,e[V+60>>2]=A,e[V+92>>2]=-1,e[V+64>>2]=60,e[V+20>>2]=A,e[V+48>>2]=19,r=i;r=(A=r)+4|0,Te=(Te=e[A>>2])?_r(124960,Te):0;);e[V+100>>2]=A,ys(r=V+16|0,0,0),TA(V,r,1,1),r=e[V+8>>2],xo=e[V+12>>2],Te=e[V>>2],Ui=e[V+4>>2],hA&&(Bo=hA,hA=e[V+136>>2]+(e[V+20>>2]-e[V+60>>2]|0)|0,e[Bo>>2]=hA?A+(hA<<2)|0:i),e[(A=I)+8>>2]=r,e[A+12>>2]=xo,e[A>>2]=Te,e[A+4>>2]=Ui,H=V+224|0,he=Us(e[A>>2],e[A+4>>2],e[A+8>>2],e[A+12>>2]),H=A+16|0;h:{r=100;b:if((0|(A=i))!=(0|(i=e[Ee+96>>2]))){r=Wn?-1:In;m:{if((0|(A=e[i>>2]))!=115){if((0|A)!=37)break m;if(ee(he=r?+(0|r)*he+100:he)<2147483648){r=~~he;break b}r=-2147483648;break b}if(e[i+4>>2]==116){F(+(Mt=he*+(0|r)/12)),A=0|B(1),B(0);x:{if((r=(A=A>>>20&2047)-969|0)>>>0>=63){if(he=Mt+1,(0|r)<0)break x;if(F(+Mt),r=0|B(1),i=0|B(0),!(A>>>0<1033)){if(he=0,!i&(0|r)==-1048576||(he=Mt+1,A>>>0>=2047))break x;if((0|r)>0|(0|r)>=0){P[(A=H-16|0)+8>>3]=3105036184601418e216,he=3105036184601418e216*P[A+8>>3];break x}if(!(r>>>0<3230714880)){P[(A=H-16|0)+8>>3]=12882297539194267e-247,he=12882297539194267e-247*P[A+8>>3];break x}}I=A,A=!(i<<1)&(0|(A=r<<1|i>>>31))==-2129002496|A>>>0<2165964800?I:0}bs=(Mt=(he=Mt-((Xr=(he=P[14416])+Mt)-he))*he)*Mt*(he*P[14421]+P[14420]),Mt*=he*P[14419]+P[14418],he*=P[14417],F(+Xr),B(1),I=0|B(0),he=bs+(Mt+(he+P[(i=I<<4&2032)+115376>>3])),Te=e[(i=i+115384|0)>>2],V=e[i+4>>2],i=(r=Te)+(Te=0)|0,r=(I<<13)+V|0,r=i>>>0<Te>>>0?r+1|0:r,A?(E(0,0|i),E(1,0|r),he=(Mt=+S())*he+Mt):-2147483648&I?(E(0,0|i),E(1,r+1071644672|0),(he=(Xr=(Mt=+S())*he)+Mt)<1&&(e[(A=H-16|0)+8>>2]=0,e[A+12>>2]=1048576,P[A+8>>3]=22250738585072014e-324*P[A+8>>3],he=(he=(bs=he+1)+(Xr+(Mt-he)+(he+(1-bs)))+-1)==0?0:he),he*=22250738585072014e-324):(E(0,0|i),E(1,r+-1048576|0),he=(Mt=+S())*he+Mt,he+=he)}if(ee(he*=100)<2147483648){r=~~he;break b}r=-2147483648;break b}}if((0|Nt)!=1)break h;if(r)r=(A=ee(he=he*+(0|r)*100)<2147483648?~~he:-2147483648)+100|0;else{if(ee(he*=100)<2147483648){r=~~he;break b}r=-2147483648}}A=(0|O(r,e[tr+134848>>2]))/100|0;break C}A=ee(he)<2147483648?~~he:-2147483648,r&&(A=e[tr+134848>>2]+O(A,r)|0)}e[vo+4>>2]=A}if((0|(Nt=Nt+1|0))==5)break}hr(Le,e[33709]);break i;case 11:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(A=134912+(r<<6)|0)>>2]=12,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1,A=(A=yt(l,88658))?Ia(A,130400):3,i=134912+(r<<6)|0,e[e[47192]+148>>2]!=1?(e[52+(134912+(r<<6)|0)>>2]=A,A=d[A+102776|0]):(e[20+(134912+(r<<6)|0)>>2]=d[A+102764|0],A=d[A+102770|0]),e[i+12>>2]=A,hr(Le,e[33709]);break i;case 34:case 41:case 43:if(!((0|(A=e[33709]))<=0)){if(l=V-32|0,V=0,i=0,r=0,A>>>0>=4)for(I=-4&A,hA=0;Nt=2|r,tr=1|r,i=(0|l)==e[134912+((Te=3|r)<<6)>>2]?Te:(0|l)==e[134912+(Nt<<6)>>2]?Nt:(0|l)==e[134912+(tr<<6)>>2]?tr:(0|l)==e[134912+(r<<6)>>2]?r:i,r=r+4|0,(0|I)!=(0|(hA=hA+4|0)););if(I=3&A)for(;i=(0|l)==e[134912+(r<<6)>>2]?r:i,r=r+1|0,(0|I)!=(0|(V=V+1|0)););(0|i)<=0||(e[33709]=i,A=i)}hr(Le,A);break i;case 7:if(A=yt(l,88741),r=yt(l,88860),(0|Ia(A,130176))!=1)break i;A=e[Le>>2],e[Le>>2]=A+1,f[A+189424|0]=91,A=e[Le>>2],e[Le>>2]=A+1,f[A+189424|0]=91,A=ya((A=e[Le>>2])+189424|0,r,800-A|0)+e[Le>>2]|0,e[Le>>2]=A+1,f[A+189424|0]=93,A=e[Le>>2],e[Le>>2]=A+1,f[A+189424|0]=93;break i;case 35:e[33692]==36&&(f[e[Le>>2]+189424|0]=0,(i=fs(131104,r=(A=e[33707])+189424|0))&&(e[Le>>2]=Cr(i,r)+A)),A=e[Le>>2],e[Le>>2]=A+1,f[A+189424|0]=1,A=e[Le>>2],e[Le>>2]=A+1,f[A+189424|0]=89,e[33692]=0;break i;case 8:if(!(A=yt(l,89299)))break i;f[134824]=1,r=e[Le>>2],e[Le>>2]=ya(r+189424|0,A,800-r|0)+e[Le>>2];break i;case 13:f[134824]=1;break i;case 40:case 45:f[134824]=0;break i;case 4:if(!(A=yt(l,89360)))break i;if(ya(Ee+352|0,A,160),d[Ee+352|0]&&!Ar(199328,Ee+352|0)){f[134760]=1,f[199328]=0,r=16384;break c}if((0|(A=dn(Ee+352|0)))<0)break i;e[Ee+20>>2]=A,e[Ee+16>>2]=1,dA(A=Ee+352|0,89460,Ee+16|0),PA(e[Le>>2]+189424|0,A),e[Le>>2]=e[Le>>2]+MA(A);break i;case 10:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(A=134912+(r<<6)|0)>>2]=11,e[A+4>>2]=-1,e[A+8>>2]=-1,e[A+52>>2]=-1,e[A+56>>2]=-1,e[A+44>>2]=-1,e[A+48>>2]=-1,e[A+36>>2]=-1,e[A+40>>2]=-1,e[A+28>>2]=-1,e[A+32>>2]=-1,e[A+20>>2]=-1,e[A+24>>2]=-1,e[A+12>>2]=-1,e[A+16>>2]=-1,e[A+60>>2]=-1;C:if(A=yt(l,89514)){if(ya(Ee+352|0,A,160),e[34441]){if((0|(A=dn(Ee+352|0)))<0||0|$A[e[34441]](1,A+e[33282]|0,Te))break C;e[Ee+68>>2]=A,e[Ee+64>>2]=1,dA(Ee+352|0,89658,Ee- -64|0)}else{if(!Te|d[Ee+352|0]==47?A=Gn(Ee+352|0):(e[Ee+48>>2]=Te,e[Ee+52>>2]=Ee+352,dA(A=Ee+96|0,89564,Ee+48|0),A=Gn(A)),(0|A)<0)break C;e[Ee+36>>2]=A,e[Ee+32>>2]=1,dA(Ee+352|0,89623,Ee+32|0)}A=Ee+352|0,PA(e[Le>>2]+189424|0,A),e[Le>>2]=e[Le>>2]+MA(A),e[4+(134912+(r<<6)|0)>>2]=1}if(hr(Le,e[33709]),(0|hA)==47){xa(11,Le),r=16384;break c}f[134772]=1,r=16384;break c;case 42:xa(43,Le),f[134772]=0,r=16384;break c;case 12:C:{if(A=yt(l,89714)){if(r=16384,(I=(0|(A=Ia(A,130336)))<0?2:A)>>>0<=2&&(A=e[Le>>2],e[Ee+84>>2]=I,e[Ee+80>>2]=1,dA(A+189424|0,89770,Ee+80|0),e[Le>>2]=e[Le>>2]+3,r=0),A=e[102784+(I<<2)>>2],!(i=yt(l,89907)))break C;break l}if(r=16384,i=yt(l,89907))break l;A=21;break u}if(I>>>0<3)break i;break u;case 0:(A=yt(l,89965))&&(ya(r=Ee+352|0,A,160),dn(r)),r=ne(l,1,e[33708])?147456:0;break c;case 1:r=ne(l,2,A)?147456:0;break c;case 5:r=0,e[I>>2]==6&&(r=ne(l,38,A),A=e[33708]),r=524358+(ne(l,6,A)|r)|0;break c;case 6:r=0,(0|(i=e[I>>2]))==6&&(r=ne(l,38,A),i=e[I>>2]),(0|i)==7&&(r=ne(l,39,e[33708])|r),r=524358+(r|ne(l,7,e[33708]))|0;break c;case 37:if(r=524328,e[I>>2]!=6)break c;r=ne(l,38,A)+524328|0;break c;case 38:if(r=524358,(-2&e[I>>2])!=6)break c;r=ne(l,39,A)+524358|0;break c;case 14:case 46:break c;case 3:break p;default:break i}A=yt(l,88893),r=yt(l,88992),i=yt(l,89153),l=Ia(A,130272),r=Ia(r,130320),A=Ds(i,0),e[Ee>>2]=1,i=(0|A)<2?193:A- -64|0,r=(0|(A=(0|r)==1?19:l))==64?i:A,e[Ee+4>>2]=r,dA(A=Ee+352|0,89230,Ee),PA(e[Le>>2]+189424|0,A),A=e[Le>>2]+MA(A)|0,e[Le>>2]=A,e[33707]=A,e[33692]=r}r=0;break c}A=Ds(i,1),i=e[33722],eA(1,e[33713]),(0|(A=(0|(i=(0|O(A,i))/100<<8))/(0|O(e[36429],10))|0))<=199&&(A=(0|i)/(0|O(e[36428],10))|0),r=r||16384}i=A>>>5|0,l=A,r=((A=(0|A)>4095)?i>>>0>=4095?4095:i:l)+(A?8388608|r:r)|0}if(H=Ee+560|0,r){if(A=e[c+2148>>2]+189424|0,f[0|A]=32,f[A+1|0]=0,!(131072&r)){s=r;break e}PA(189360,134784),s=r;break e}e[c+2156>>2]=32;c:{u:{if(!(r=e[33285])){if(I=0,A=e[33283],e[A>>2]==e[A+4>>2])continue;if(!(r=e[33285]))break u}e[33285]=0;break c}e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A)}e[c+2152>>2]=r,I=0;continue}e[c+2156>>2]=A+57344}if(d[134824])continue;if(i=e[c+2156>>2],!((0|(A=e[c+2152>>2]))!=10|e[47268]!=-1)){(0|(s=or(i)))==16384?(A=e[c+2148>>2],k[fA+(A<<1)>>1]=e[33284]-e[47353],e[Vr>>2]=A,s=524328,A=Cr(e[c+2156>>2],A+189424|0)+e[c+2148>>2]|0):A=e[c+2148>>2],f[0|(A=A+189424|0)]=32,f[A+1|0]=0;break e}n:if((0|i)==1){if((0|A)!=66){if((0|A)!=86)break n;for(A=e[c+2148>>2],e[c+2148>>2]=A+1,f[A+189424|0]=0;;){o:{c:{u:{if(!(r=e[33285])){if(A=e[33283],e[A>>2]==e[A+4>>2])break o;if(!(r=e[33285]))break u}e[33285]=0;break c}e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A)}if(e[c+2156>>2]=r,!(fr(r)||(0|(A=e[c+2148>>2]))>=799)){e[c+2148>>2]=A+1,f[A+189424|0]=e[c+2156>>2];continue}}break}f[e[c+2148>>2]+189424|0]=0,s=147456;break e}r=e[c+2148>>2],f[0|(A=r+189424|0)]=32,f[A+1|0]=32,f[A+2|0]=32,f[A+3|0]=0,e[c+2148>>2]=r+3;o:{c:{u:{l:{i:{p:{if(!(r=e[33285])){if(A=e[33283],e[A>>2]==e[A+4>>2])break l;if(!(r=e[33285]))break p}e[33285]=0;break i}e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A)}if(e[c+2152>>2]=r,i=0,(0|r)!=48)break u;break c}r=e[c+2152>>2]}if(e[47208]=0,e[47201]=1,(0|r)==49)break o;for(i=e[33285],l=0;;){u:{if(!i){if(A=e[33283],e[A>>2]==e[A+4>>2])break u;r=e[c+2152>>2]}if(!(fr(r)|l>>>0>58)){e[188832+(l<<2)>>2]=e[c+2152>>2],(r=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A),i=e[33285]),l=l+1|0,e[c+2152>>2]=r,A=e[c+2148>>2],e[c+2148>>2]=A+1,f[A+189424|0]=32;continue}}break}i=2,e[188832+(l<<2)>>2]=0}e[47201]=i}o:{if(!(r=e[33285])){if(A=e[33283],e[A>>2]==e[A+4>>2])continue;if(!(r=e[33285]))break o}e[33285]=0,e[c+2152>>2]=r;continue}e[33284]=e[33284]+1,A=e[33283],e[c+2152>>2]=$A[e[A+8>>2]](A);continue}br=br+1|0,r=0,A=e[K+340>>2];n:if(l=y[A>>1]){for(;;){if((65535&l)!=(0|i)){if(l=y[A+((r=r+2|0)<<1)>>1])continue;break n}break}o:switch(0|(A=y[A+(r<<1|2)>>1])){case 1:continue;case 0:break n;default:break o}e[c+2156>>2]=A,i=A}xA(i)?(te=1,r=e[c+2156>>2]):LA?(e[33285]=e[c+2152>>2],r=1328,e[c+2156>>2]=1328,e[c+2152>>2]=32,LA=0):(0|(r=e[c+2156>>2]))!=3851?(LA=0,(0|r)!=3405|e[c+2152>>2]!=8205||(r=3406,e[c+2156>>2]=3406)):(r=32,e[c+2156>>2]=32,LA=0);n:if(Gs(r)){if(e[K+8216>>2]=e[K+8216>>2]+1,e[33692]|e[47200]!=2||Gs(Fe)||(e[c+2544>>2]=0,e[c+2548>>2]=0,e[c+2304>>2]=84731,!Ot(K,c+2304|0,c+2160|0,c+2544|0,0,0))||(Vt(K,A=c+2160|0,c+2544|0,-1,0),jr(r=A,A=c+2336|0),e[c+80>>2]=A,dA(r=c+2240|0,85451,c+80|0),A=e[c+2148>>2],(0|(r=MA(r)+A|0))>=800))break n;PA(A+189424|0,c+2240|0),e[c+2148>>2]=r}else OA(e[c+2156>>2])&&(e[K+8220>>2]=e[K+8220>>2]+1);if(r=e[c+2152>>2],A=e[c+2156>>2],e[47204])if((0|I)>0)I=I-1|0;else{if(!((0|A)!=91|(0|r)!=91)){i=0,I=-1;break A}I=(0|A)==93&&(0|r)==93?2:I}if((0|A)==10){for(i=e[33285],l=0;;){n:{if(!i){if(A=e[33283],e[A>>2]==e[A+4>>2])break n;r=e[c+2152>>2]}if(fr(r)){l=(e[c+2152>>2]==10)+l|0,(r=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,A=e[33283],r=0|$A[e[A+8>>2]](A),i=e[33285]),e[c+2152>>2]=r;continue}}break}if((0|l)>0){s&&Je(A=Ir+189424|0,32,jA(c+2336|0,A)),A=e[c+2148>>2]+189424|0,f[0|A]=32,f[A+1|0]=0,e[33285]=e[c+2152>>2],s=e[47203]?524358:O((0|l)>=3?3:l,30)+524328|0;break e}if(A=e[47268]<(0|br),br=0,!A){A=e[c+2148>>2]+189424|0,f[0|A]=32,f[A+1|0]=0,e[33285]=e[c+2152>>2],s=262174;break e}}if(i=0,e[33692]|I)break A;if(A=0,!s)break r;if(fr(e[c+2156>>2])){A=s;break r}if(Ft(e[c+2156>>2])&&de(e[c+2156>>2]))break r;e[33691]=e[c+2156>>2],f[0|(A=Ir+189424|0)]=32,f[A+1|0]=0,e[33285]=e[c+2152>>2];break e}LA&&(e[c+2148>>2]=Cr(1328,e[c+2148>>2]+189424|0)+e[c+2148>>2]),s&&Je(A=Ir+189424|0,32,jA(c+2336|0,A)),A=e[c+2148>>2]+189424|0,f[0|A]=32,f[A+1|0]=0}s=589864;break e}if(!((0|(r=e[c+2156>>2]))!=46|e[c+2152>>2]!=46)){r:{a:{n:{if(!(r=e[33285])){if(r=e[33283],e[r>>2]==e[r+4>>2])break r;if(!(r=e[33285]))break n}e[33285]=0;break a}e[33284]=e[33284]+1,r=e[33283],r=0|$A[e[r+8>>2]](r)}if(g=r,(0|r)==46)for(e[c+2152>>2]=32,e[c+2156>>2]=8230,g=e[33285];;){a:{n:{if(!g){if(r=e[33283],e[r>>2]==e[r+4>>2]){g=46;break r}if(!(g=e[33285]))break n}e[33285]=0,r=0;break a}e[33284]=e[33284]+1,r=e[33283],g=0|$A[e[r+8>>2]](r),r=e[33285]}if((0|g)!=46)break r;e[c+2152>>2]=32,e[c+2156>>2]=8230,g=r}}(0|(r=e[c+2156>>2]))!=8230?e[33285]=g:(e[c+2152>>2]=g,r=8230)}if(hA=0,(0|(V=or(r)))!=16384){r:if(536621&V)for(r=e[33285];;){if(!r&&(r=e[33283],e[r>>2]==e[r+4>>2])||!(536621&or(e[c+2152>>2])))break r;(g=e[33285])?(e[33285]=0,r=0):(e[33284]=e[33284]+1,r=e[33283],g=0|$A[e[r+8>>2]](r),r=e[33285]),e[c+2152>>2]=g}if(1048576&V){e[m+780>>2]=V>>>12&15,LA=1,I=0,s=A;continue}(fr(e[c+2152>>2])|32768&V||en(e[c+2152>>2])||(0|(r=e[c+2152>>2]))==63||(l=0,e[33285]||(r=e[33283],l=e[r>>2]==e[r+4>>2],r=e[c+2152>>2]),l||(0|r)==1))&&(hA=1)}if((0|(r=e[c+2156>>2]))==57404&&(e[c+2156>>2]=60,r=60),e[47201]){s=0;r:if(!(1<<(l=Qr(r))&1879048255&&l>>>0<=30)){if(!((l=Qr(r))>>>0>27)){if(116672&(I=1<<l))break r;if(134227968&I){s=!(1024&aA(r,l));break r}}s=1}if(!(d[134772]|!s)&&(e[47201]==1||_r(188832,e[c+2156>>2]))){e[K+288>>2]=0,i=e[c+2156>>2],f[c+2336|0]=0,I=e[c+2152>>2],r=0;r:{a:if(!((0|(s=e[34064]))<=0))for(;;){if((0|i)==e[136272+(r<<4)>>2]){if(e[136276+(r<<4)>>2])break r;if(yA(0,r))break a;break r}if((0|s)==(0|(r=r+1|0)))break}r=-1}r:if((0|r)>=0)e[c>>2]=r,dA(c+2336|0,86007,c),e[33285]=I;else if((l=!hA)|(0|i)!=46|(0|I)==46||(e[c+2600>>2]=0,e[c+2604>>2]=0,e[c+2540>>2]=86036,!Ot(K,c+2540|0,c+2544|0,c+2600|0,0,0))?r=mr(c+2240|0,K,i,0):(Vt(K,r=c+2544|0,c+2600|0,-1,0),jr(s=r,r=c+2160|0),e[c+64>>2]=r,dA(r=c+2304|0,85451,c- -64|0)),s=r,l|!e[c+2148>>2]|2&d[K+76|0]){for(r=e[33285],l=1;;){a:{n:{if(!r){if(r=e[33283],e[r>>2]==e[r+4>>2]|(0|i)==60|(0|i)!=(0|I))break a;if(l=l+1|0,I=e[33285])break n;e[33284]=e[33284]+1,r=e[33283],I=0|$A[e[r+8>>2]](r),r=e[33285];continue}if((0|i)==60|(0|i)!=(0|I))break a;I=r,l=l+1|0}r=0,e[33285]=0;continue}break}if(e[c+2152>>2]=I,hA&&(e[33285]=I),(0|l)==1){e[c+16>>2]=s,dA(c+2336|0,86219,c+16|0);break r}if((0|l)<=3){if(f[c+2336|0]=0,(0|(r=e[50786]))<=299&&(e[c+2336>>2]=d[86728]|d[86729]<<8|d[86730]<<16|d[86731]<<24,k[c+2340>>1]=d[86732]|d[86733]<<8),(0|l)>0){for(;e[c+32>>2]=s,dA(r=c+2160|0,86219,c+32|0),I=l>>>0>1,As(c+2336|0,r),l=l-1|0,I;);r=e[50786]}if((0|r)>299)break r;e[c+2160>>2]=d[86857]|d[86858]<<8|d[86859]<<16|d[86860]<<24,r=d[86860]|d[86861]<<8|d[86862]<<16|d[86863]<<24,f[c+2163|0]=r,f[c+2164|0]=r>>>8,f[c+2165|0]=r>>>16,f[c+2166|0]=r>>>24,As(c+2336|0,c+2160|0);break r}e[c+56>>2]=s,e[c+52>>2]=l,e[c+48>>2]=s,dA(c+2336|0,86932,c+48|0)}else e[33691]=i,e[33285]=I,k[c+2336>>1]=32;if(l=MA(s=c+2336|0),PA((r=e[c+2148>>2])+189424|0,s),e[c+2148>>2]=r+l,hA){if((0|i)==45){s=16384;break e}if(s=or(i),!(2&d[K+76|0]|(0|r)<=0)){s=(-32769&s)==266270?262148:(28672&s)==4096?266244:262148;break e}if(!(524288&s)){s=(28672&s)==4096?266244:262148;break e}if((0|s)>=0)break e}i=e[c+2156>>2]}}if(i|!(2097152&V)||(r=PA(e[c+2148>>2]+189424|0,mr(c+2336|0,K,e[c+2156>>2],1)),d[0|r]?(e[c+2148>>2]=e[c+2148>>2]+MA(r),V&=-28673,i=e[c+2156>>2]):i=0),I=0,hA){r:if(fr(g=e[c+2152>>2]))for(l=e[33285],r=0;;){if(!l&&(s=e[33283],e[s>>2]==e[s+4>>2])||!fr(g))break r;r=((0|g)==10)+r|0,(g=e[33285])?(e[33285]=0,l=0):(e[33284]=e[33284]+1,s=e[33283],g=0|$A[e[s+8>>2]](s),l=e[33285])}else r=0;s=(0|(I=e[c+2156>>2]))==46&&(0|r)<2?4194304|V:V;r:{if(!r){l=1,(0|I)!=44|(0|Fe)!=46|e[K+212>>2]!=26741|Te-48>>>0>=10||g-48>>>0>=10&&!de(g)||(e[c+2156>>2]=1367,l=0),(0|(V=e[c+2156>>2]))!=46|(0|g)!=39||(I=l,V=e[33283],(0|(Ee=e[V>>2]))==e[V+4>>2]?l=0:(l=0|$A[e[V+8>>2]](V),e[V>>2]=Ee),V=e[c+2156>>2],l=(0|l)!=115&I);a:if((0|V)!=46)l&=te;else{n:if(1&f[K+106|0]){if(!(Fe-48>>>0<10)&&((I=Fe-73|0)>>>0>15|!(1<<I&40969)||!(1<<(I=Te-73|0)&40969&&I>>>0<=15||fr(Te))))break n;l=Fe-48>>>0>=10?0:!de(g)&(0|g)!=45&l}if(de(g)&&(l=d[K+208|0]!=0&l),te){V=e[c+2156>>2];break a}V=32,e[c+2156>>2]=32,l=0}if(!(!l|(0|V)!=46|!e[47203]|(0|g)!=60)){Ir=e[c+2148>>2],A=s;break r}if(!l)break r}if(A=e[c+2148>>2]+189424|0,f[0|A]=32,f[A+1|0]=0,e[33285]=g,Fe-48>>>0<10&&(s=Ft(g)?s:-4194305&s),(0|r)<2)break e;s=(0|s)==536621?536656:(0|s)==532520?532555:524358;break e}(e[33285]||(I=0,r=e[33283],e[r>>2]!=e[r+4>>2]))&&(I=0,fr(e[c+2152>>2])&&(e[33285]=g))}s=A}if(e[33712]!=1){if((0|i)!=(0|(r=e[c+2156>>2])))l=e[c+2148>>2],(0|r)==57404&&(r=60,e[c+2156>>2]=60);else{A:{if(en(i))r=57384;else{if(r=45,e[c+2156>>2]==45)break A;r=32}e[c+2156>>2]=r}l=e[c+2148>>2]}e[c+2148>>2]=Cr(r,l+189424|0)+e[c+2148>>2],fr(e[c+2156>>2])||en(e[c+2156>>2])||(A=e[c+2148>>2],k[fA+(A<<1)>>1]=e[33284]-e[47353],(0|A)<=(l+1|0)||Je(wn+(l<<1)|0,255,A+~l<<1)),r=e[c+2148>>2],e[Vr>>2]=r;A:{r:{if((0|r)>725){if(!Ft(e[c+2156>>2]))break r;r=e[c+2148>>2]}if((0|r)<796)continue;break A}if(r=e[c+2148>>2],!(e[c+2156>>2]-48>>>0>=10)&&(0|r)<796)continue}break}}f[0|(A=r+189424|0)]=32,f[A+1|0]=0,e[33285]=e[c+2152>>2],s=16384}H=c+2608|0,LA=s,gs&&(A=e[m+780>>2],e[gs>>2]=A||LA>>>12&7),A=(m+5184|0)+(e[m+6800>>2]<<1)|0,k[A+6>>1]=0,k[A+2>>1]=0,k[A+4>>1]=32767,V=O(4095&LA,8388608&LA?320:10),s=189424;e:{A:{r:if(A=d[189424]){for(;;){if(!!(255&(A=A<<24>>24))&A>>>0<33){if(A=d[0|(s=s+1|0)])continue;break r}break}if(d[0|s])break A}V=(0|(r=V-(A=e[47566])|0))>0?r:0,e[47566]=V+A,LA=d[190268]?524288|LA:LA,e[K+8240>>2]=LA;break e}e[47566]=V,A=d[190268],e[K+8240>>2]=LA,A&&(At=1,e[47568]=e[47568]+1,(0|(A=e[47569]))<=0||(A=A-1|0,e[47569]=A,A||(f[190280]=0)))}e[49572]=1,e[47572]=655360,e[47573]=0,e[K+8184>>2]=0,e[K+8188>>2]=0,A=0,e[K+288>>2]=0,e[(r=K- -8192|0)>>2]=0,e[r+4>>2]=0,e[K+8200>>2]=0,e[K+8224>>2]=0,e[K+8228>>2]=0,e[(r=K+8232|0)>>2]=0,e[r+4>>2]=0,f[m+786|0]=32,k[m+784>>1]=8192,e[m+6812>>2]=32,k[m+1588>>1]=3,e[m+1584>>2]=0,s=0;e:if(!((0|(r=e[m+6800>>2]))<=0)){for(;;){if(k[(m+5184|0)+(s<<1)>>1]>0)break e;if((0|r)==(0|(s=s+1|0)))break}s=r}if(r=y[(m+5184|0)+(s<<1)>>1],k[m+1592>>1]=r,r)for(;A=!!(65535&~r)+A|0,r=y[(m+5184|0)+((s=s+1|0)<<1)>>1];);for(f[m+1594|0]=A,l=3,fA=1,s=0;;){Te=e[m+6808>>2],Mr(m+6808|0,(Le=(m+784|0)+l|0)-1|0),!d[K+170|0]|e[m+6808>>2]-48>>>0>=10||Ft(Te)&&(e[m+6808>>2]=97),ce?e[m+6812>>2]=ce:x&&Mr(m+6812|0,x+189423|0),r=x;e:{A:if(s||(r=jA(m+6816|0,x+189424|0)+x|0,s=e[m+6816>>2])){if(te=jA(m+6804|0,A=r+189424|0),(0|s)==1){if(i=r-1|0,ce=32,Fe=0,e[m+6812>>2]!=32){r=i,A=32;break A}x=0,s=r;r:{a:switch(d[0|A]-43|0){case 0:s=r+1|0,x=64;break r;case 2:break a;default:break r}s=r+1|0,x=96}if(f[0|(A=s+189424|0)]-48>>>0>=10)g=s+1|0,te=-1;else{for(te=Js(A);s=(A=s)+1|0,f[A+189424|0]-48>>>0<10;);g=s,s=A}if((0|(ce=e[47350]))>247)A=0;else if(A=0,!((0|(s=f[s+189424|0]))<0)&&(s=Ba(84868,255&s,14))){r=(A=s-84868|0)+1|0,(0|te)==-1&&(te=e[105536+(r<<2)>>2],x=0);r:{a:switch(A-8|0){case 0:e[49574]=0,e[49573]=te;break r;case 4:break a;default:break r}(0|te)>=3?f[199304]=1:f[199304]=0}A=1,e[47350]=ce+1,e[198304+(ce<<2)>>2]=(r+x|0)+(te<<8),r=g}ce=e[m+6812>>2],Je(i+189424|0,32,r-i|0),ns=A+ns|0,s=0;break e}ce=0,(0|s)==32|e[49573]!=36?(Fe=0,A=s):(e[m+6812>>2]!=32|e[m+6804>>2]!=32||(e[49573]=20),Fe=0,A=Ln(s,K))}else e[m+6804>>2]=32,Fe=1,ce=0,te=0,A=32;A:if(rr){if(rr=1,fA=8,s=0,(0|A)!=93|e[m+6804>>2]!=93)break A;r=r+1|0,A=32,rr=0}else if((240&(s=e[49573]))!=64)if(rr=0,16&s)s=0;else{r:{a:{n:{o:{if(!((0|(g=(0|A)==8242||(0|A)==8217||(0|A)==146||(0|A)==180?39:A))!=8216&(0|g)!=63))if(Ft(e[m+6808>>2])){if(g=A,Ft(e[m+6804>>2])){g=39;break o}}else g=A;c:{if((0|g)!=1367){if((0|g)==1328){_|=1024,g=32;break o}if((A=g-44032|0)>>>0>11183)break o;if(i=((s=((I=65535&A)>>>0)/28|0)>>>0)%21|0,A=A-O(s,28)&65535,g-50500>>>0>587)break c;s=A?A+4519|0:0,i=i+4449|0;break a}_|=131072,Ir=e[m+6804>>2],A=e[m+6812>>2],g=32;break n}s=50500+(A+O(i,28)|0)|0,i=(I>>>0)/588|4352;break a}if(Ir=e[m+6804>>2],A=e[m+6812>>2],!((s=g-12592|0)>>>0>51)){i=4352|d[s+103296|0],s=0;break a}}c=r+189424|0;n:if(!((0|(s=e[K+212>>2]))!=28268&(0|s)!=24934|(0|g)!=39)&&!OA(A)&&(jA(m+6820|0,c+1|0),pn(e[m+6820>>2]))){i=601,s=0;o:switch(Ir-110|0){case 6:break a;case 0:break o;default:break n}if(e[K+212>>2]!=24934)break a;f[0|c]=32;break a}if(e[m+6824>>2]=32,(0|(A=e[49897]))>0)e[49897]=A-1,s=0;else{if(!g){s=0,i=0;break r}n:{o:{c:{u:if((hA=e[K+180>>2])&&(I=g,(Ee=Gs(g))&&(I=Ln(g,K)),!di(hA)))for(;;){e[m+16>>2]=0,e[m+624>>2]=I,A=jA(m+16|0,hA)+hA|0;l:if(e[m+624>>2]==e[m+16>>2]){if(d[0|A]){for(i=1,Nt=0,s=c;tr=jA(m+16|0,A),br=jA(m+624|0,s),Vr=Ln(e[m+624>>2],K),e[m+624>>2]=Vr,s=s+br|0,Nt=(br=(0|Vr)==e[m+16>>2])+Nt|0,i&=br,d[0|(A=A+tr|0)];);if(!i)break l;e[49897]=Nt}if(!(A=A+1|0))break u;if(8&d[188788]&&(e[m>>2]=hA,e[m+4>>2]=A,Xt(e[47195],85187,m)),A=jA(m+6828|0,A)+A|0,d[0|A])break c;s=0;break o}for(;s=A,A=A+1|0,d[0|s];);for(;d[0|(s=(A=s)+1|0)];);if(di(hA=A+2|0))break}s=0,i=g;break n}jA(m+6824|0,A),Ee&&Gs(Ir)&&(e[m+6824>>2]=Ja(e[m+6824>>2])),s=e[m+6824>>2]}i=e[m+6828>>2],_|=2097152,Ee&&(i=Ja(i))}if((0|i)!=8)break a}i=r;break e}s?e[m+6804>>2]=s:s=0}Ft(i)||pn(i)||_r(e[K+336>>2],i)||!Ft(e[m+6808>>2])|!(!d[K+170|0]|i-48>>>0>=10)&e[m+6804>>2]-48>>>0>=10||(i=32,Xe=1);r:{a:{n:{o:{c:{if(e[m+6808>>2]-48>>>0<10){if(i-48>>>0<10){A=T;break c}if(1<<(A=i-32|0)&20481&&A>>>0<=14)break o;Xe=1}else if(A=0,e[m+6812>>2]!=44||(A=T,(0|i)!=44))break c;i=32;break o}if((0|i)==91&&((0|(g=e[m+6804>>2]))==2||(i=91,(0|g)==91&&e[47204])))break n;T=A}if(Ft(i)){o:{c:{u:{if(Ft(e[m+6808>>2])){if(!d[K+171|0]||(A=e[m+6808>>2],!((0|i)>12352)&&(0|A)<12353))break u}else A=e[m+6808>>2];if(zr=_r(e[K+336>>2],A)?zr:0,(0|(A=e[m+6808>>2]))!=32&&!_r(e[K+336>>2],A)){A=32,J=en(e[m+6808>>2])?J:256|J;break c}_=Gs(i)?2|_:_,e[m+6808>>2]!=32|f[Le-2|0]-48>>>0>=10|e[m+6812>>2]-48>>>0<10||(f[(m+784|0)+l|0]=32,A=1588+(O(_A,12)+m|0)|0,k[A>>1]=y[A>>1]+1,l=l+1|0)}if(A=32,(0|i)==32)break o;if(zr=zr+1|0,(0|(g=e[K+600>>2]))<=0){A=i;break o}if(!((0|i)<=591&(0|(I=e[m+6808>>2]))>=(0|g))){if((0|i)<(0|g)){A=i;break o}if((0|zr)<2){A=i;break o}if(!((0|I)<=591)){A=i;break o}}if(!Ft(I)){A=i;break o}_|=16384,J|=128}Xe=1}if(ua=ua+1|0,Gs(A)){if(g=Ln(A,K),e[K- -64>>2]){A=ea?g:712,s=ea?s:g,ea=1;break A}if(de(e[m+6812>>2])){if(e[m+6808>>2]==32){A=g;break A}if(A=32,e[K+212>>2]!=26465)break r;for(i=85240,I=(m+784|0)+l|0,te=0;;){if(c=MA(i),d[0|(Te=I-c|0)]==32&&!Kr(Te+1|0,i,c=c-1|0)){if((0|(i=f[i+c|0]))==(0|g)){A=g;break A}if((0|i)==65&&Ua(K,g)){A=g;break A}}if(i=e[131184+((te=te+1|0)<<2)>>2],(0|te)==11)break}break r}if(A=32,(0|g)==32)break A;if(!Gs(e[m+6812>>2])){A=g;break A}if(!de(e[m+6804>>2])){A=g;break A}if(jA(m+16|0,189424+(r+te|0)|0),!(e[K+212>>2]!=28268|(0|zr)!=2|(0|g)!=106|e[m+6812>>2]!=73)){A=g;break A}if(e[m+6808>>2]==32){A=g;break A}if(!Ft(e[m+16>>2])){A=g;break A}J|=256,ce=32,Xe=1;break A}if(!fA){fA=0;break A}if((0|zr)<3){fA=0;break A}if((0|A)!=115){fA=0;break A}if(e[K+212>>2]!=25966){fA=0;break A}if(e[m+6804>>2]!=32){fA=0;break A}if(fA|=4,A=32,d[(i=l+m|0)+783|0]!=39)break A;f[i+783|0]=32;break A}A=32;o:{c:{u:{l:{i:switch(i-39|0){default:if((0|i)==95)break A;case 1:case 2:case 3:case 4:case 5:if(i-48>>>0>=10||d[K+170|0]&&Ft(e[m+6808>>2])&&!((g=e[m+6804>>2])-48>>>0<10|g-2406>>>0<10))break o;if((0|(I=e[m+6808>>2]))==32)break c;if(g=e[m+6808>>2],I-48>>>0<10)break u;if((0|(I=g))==(0|(g=e[K+128>>2])))break l;Xe=1;break A;case 6:if(!pn(e[m+6812>>2])&&Ft(e[m+6804>>2])){if(e[m+6808>>2]!=32){Xe=1;break A}if(_|=128,(0|_A)<=0)break A;i=1572+(O(_A,12)+m|0)|0,e[i>>2]=16384|e[i>>2];break A}if(i=e[m+6804>>2],!(e[m+6812>>2]!=32|(0|i)!=32)){Pt=4;break A}if((0|i)==45){r=r+1|0,Pt=4;break A}if(A=45,e[m+6808>>2]!=32||!Ft(Te)||Ft(e[m+6812>>2]))break A;f[(m+784|0)+l|0]=32,i=1588+(O(_A,12)+m|0)|0,k[i>>1]=y[i>>1]+1,l=l+1|0;break A;case 7:if(e[m+6808>>2]==46){Xe=1;break A}if(A=46,(0|_A)<=0||(i=1572+(O(_A,12)+m|0)|0,1&f[i+1|0])||!Ft(e[m+6812>>2]))break A;e[i>>2]=65536|e[i>>2],A=(A=pn(e[m+6804>>2]))||e[m+6804>>2]==45?32:46;break A;case 0:break i}i:{if((0|(g=e[m+6812>>2]))!=46||(i=115,e[m+6804>>2]!=115)){if(!xA(g))break i;i=e[m+6804>>2]}if(Ft(i))break a}if(1&(i=e[K+88>>2])){if(Ft(e[m+6804>>2]))break a;i=e[K+88>>2]}if(2&i&&Ft(e[m+6812>>2]))break a;if(!(!_r(e[K+332>>2],e[m+6812>>2])|(0|Te)!=32)){r=(e[m+6804>>2]==32)+r|0;break a}if(g=(0|(i=e[m+6808>>2]))!=115|da,da=0,!(1&g))break A;da=!!(0|pn(i)),Pt=4;break A}if((0|g)==44&T){Xe=1;break A}T=1;break o}if((0|g)!=32)break o}Ft(Te)&&(Ft(e[m+6812>>2])||(f[(m+784|0)+l|0]=32,A=1588+(O(_A,12)+m|0)|0,k[A>>1]=y[A>>1]+1,l=l+1|0))}A=i;break A}rr=1,i=r+1|0,T=A;break e}A=39,da=0;break A}Xe=1,ce=32}else{if(A-48>>>0<10){s=0,i=(0|(g=e[49574]+1|0))>(15&e[49573]),e[49574]=i?0:g,A=i?32:A,Xe|=i,rr=0;break A}s=0,e[49574]=0,A=(i=e[m+6808>>2]-48>>>0<10)?32:A,Xe|=i,rr=0}if(pn(A)){if(e[m+6808>>2]==32){_|=262144,i=r;break e}if(i=e[m+6816>>2]-9>>>0<2,I=1&Xe){te=0;A:if(!((0|h)>(0|(A=r-1|0))))for(;;){if(!(g=k[(m+5184|0)+(A<<1)>>1]))break A;if(te=((0|g)>0)+te|0,!((0|h)<=(0|(A=A-1|0))))break}f[1594+(O(_A,12)+m|0)|0]=te}if(J=i?262144|J:J,f[(m+784|0)+l|0]=32,A=l+1|0,!((0|_A)>298||(i=(m+1584|0)+O(_A,12)|0,(0|(g=y[i+4>>1]))>=(0|A)))){if((0|ns)<=0?h=e[i>>2]:(h=198300+(e[47350]<<2)|0,e[h>>2]=128|e[h>>2],ns=0,h=64|e[i>>2]),c=e[47352],f[i+6|0]=c,e[i>>2]=h|(ua?fA:-2&fA)|(d[199304]?2048:0)|_,(0|c)>0){for(;_=(h=m+784|0)+A|0,h=h+(A=A-1|0)|0,f[0|_]=d[0|h],(0|A)>(0|g););f[0|h]=32,k[i+4>>1]=g+1,A=l+2|0}g=(m+1584|0)+O(_A=_A+1|0,12)|0,e[g>>2]=0,k[g+4>>1]=A,l=r;A:if(!((0|(i=e[m+6800>>2]))<=(0|r))){for(;;){if(k[(m+5184|0)+(l<<1)>>1]>0)break A;if((0|i)==(0|(l=l+1|0)))break}l=i}if(te=y[(m+5184|0)+(l<<1)>>1],k[g+8>>1]=te,ua=0,i=0,te)for(;i=!!(65535&~te)+i|0,te=y[(m+5184|0)+((l=l+1|0)<<1)>>1];);f[g+10|0]=i,e[47352]=0,fA=1,_=J,J=0,ea=0}Xe=0,s=I?0:s,i=I?x:r}else(0|l)>795?(i=r,r=h,A=l):(A=Cr(A,(m+784|0)+l|0)+l|0,i=r,r=h);e[47352]<(0|Pt)&&(e[47352]=Pt),Pt=0,h=r,l=A}if(Fe||(x=i,!((0|l)<799)))break}(0|ns)<=0|_A||(A=198300+(e[47350]<<2)|0,e[A>>2]=128|e[A>>2],e[m+1584>>2]=64|e[m+1584>>2],_A=1),A=(m+784|0)+l|0,e[K+8204>>2]=A-1,r=0,f[0|A]=0,f[m+1590|0]=0,f[1590+(O(_A,12)+m|0)|0]=8;e:if((0|_A)<=0)e[m+1584>>2]=512|e[m+1584>>2],l=e[49572];else{A=_A-1|0;A:if((0|_A)!=1)for(s=A;;){if(!en(f[y[1588+(O(s,12)+m|0)>>1]+(m+784|0)|0])){r=s;break A}if(g=(0|s)>1,s=s-1|0,!g)break}if(r=(m+1584|0)+O(r,12)|0,e[r>>2]=16|e[r>>2],4194304&LA&&(A=(m+1584|0)+O(A,12)|0,256&(r=e[A>>2])||(e[A>>2]=65536|r)),e[m+1584>>2]=512|e[m+1584>>2],!((0|_A)<=0|(0|(l=e[49572]))>990))for(g=3|(A=m+624|0),I=2|A,ce=m+754|0,Fe=!(4194304&LA),x=0,h=0;;){e[47354]=e[47354]+1;A:{if((0|(A=e[49827]))<=0||(A=A-1|0,e[49827]=A,A)){if(d[190280])break A}else f[190280]=0;A=y[1588+(O(x,12)+m|0)>>1]+(m+784|0)|0;r:if(!(f[0|A]-48>>>0>=10)&&(r=m+624|0,s=A,e[K+112>>2]!=1227133512)){for(;;){a:{if(f[0|s]-48>>>0<10)f[0|r]=d[0|s],r=r+1|0,s=s+1|0;else{if(e[K+124>>2]!=f[0|s]|d[s+1|0]!=32||(l=s+2|0,d[s+3|0]==32|f[0|l]-48>>>0>=10|d[s+4|0]==32))break a;x=x+1|0,s=l}if(r>>>0<ce>>>0)continue;break r}break}Je(A+(r=r-(T=m+624|0)|0)|0,32,(s=(l=s-A|0)-r|0)>>>0<=l>>>0?s:0),qA(A,T,r)}for(r=0;s=r,r=r+1|0,f[A+s|0]-48>>>0<10;);r:if(s-5>>>0<=27){for(f[m+626|0]=32,k[m+624>>1]=8224,d[0|A]!=48&e[K+132>>2]>=(0|s)||(r=(m+1584|0)+O(x,12)|0,e[r>>2]=524288|e[r>>2]),J=(m+1584|0)+O(x,12)|0,te=0,l=g;r=A,!((A=f[0|A])-48>>>0>=10&(0|A)!=e[K+128>>2])&&(f[0|l]=A,A=l+1|0,T=s,(0|(s=s-1|0))<=0?l=A:e[K+112>>2]>>>s&1?(c=e[J+4>>2],_=(m+16|0)+O(te,12)|0,e[_>>2]=e[J>>2],e[_+4>>2]=c,e[_+8>>2]=e[J+8>>2],te=te+1|0,(0|(_=e[K+124>>2]))!=32&&(f[l+1|0]=_,A=l+2|0),f[0|A]=32,l=A+1|0,8&d[J+2|0]||((Pt=e[K+112>>2])>>>T-2&1&&(f[A+1|0]=48,f[A+2|0]=48,Pt=e[K+112>>2],l=A+3|0),Pt>>>T-3&1&&(f[0|l]=48,l=l+1|0))):l=A,A=r+1|0,l>>>0<ce>>>0););if(s=e[J+4>>2],A=(m+16|0)+O(te,12)|0,e[A>>2]=e[J>>2],e[A+4>>2]=s,s=e[J+20>>2],e[A+16>>2]=e[J+16>>2],e[A+20>>2]=s,s=e[J+12>>2],e[A+8>>2]=e[J+8>>2],e[A+12>>2]=s,s=1,(0|te)>0)for(;A=(m+16|0)+O(s,12)|0,e[A>>2]=-262209&e[A>>2],(0|te)>=(0|(s=s+1|0)););if(A=d[r+4|0]|d[r+5|0]<<8|d[r+6|0]<<16|d[r+7|0]<<24,s=d[0|r]|d[r+1|0]<<8|d[r+2|0]<<16|d[r+3|0]<<24,f[0|l]=s,f[l+1|0]=s>>>8,f[l+2|0]=s>>>16,f[l+3|0]=s>>>24,f[l+4|0]=A,f[l+5|0]=A>>>8,f[l+6|0]=A>>>16,f[l+7|0]=A>>>24,A=d[r+12|0]|d[r+13|0]<<8|d[r+14|0]<<16|d[r+15|0]<<24,r=d[r+8|0]|d[r+9|0]<<8|d[r+10|0]<<16|d[r+11|0]<<24,f[l+8|0]=r,f[l+9|0]=r>>>8,f[l+10|0]=r>>>16,f[l+11|0]=r>>>24,f[l+12|0]=A,f[l+13|0]=A>>>8,f[l+14|0]=A>>>16,f[l+15|0]=A>>>24,f[l+16|0]=0,l>>>0<=g>>>0)break r;for(A=d[J+6|0],te=0,s=g;;){for(h=ma(K,s,(m+16|0)+O(te,12)|0,255&A);A=d[0|s],s=s+1|0,(0|A)!=32;);if(A=0,f[J+6|0]=0,te=te+1|0,!(s>>>0<l>>>0))break}}else{if(e[47352]=0,h=ma(K,A,r=(m+1584|0)+O(x,12)|0,d[r+6|0]),(0|(s=e[47352]))>d[r+18|0]&&(f[r+18|0]=s,e[47352]=0),!(!(4096&h)|d[0|A]==32))for(;Je(m+624|0,0,150),e[m+624>>2]=538976288,e[m+628>>2]=538976288,f[m+632|0]=32,ma(K,qA(I,A,s=jA(m+16|0,A)),r,0),d[0|(A=A+s|0)]!=32;);50331648&h&&(V=(A=Fe|(~e[33264]+_A|0)!=(0|x))?V:10,A|!gs||(e[gs>>2]=4,V=10))}if(128&h&&!((0|(r=e[33264]))<=0)){if(A=0,s=r,l=3&r)for(;T=(m+1584|0)+O(s+x|0,12)|0,e[T>>2]=1048576|e[T>>2],s=s-1|0,(0|l)!=(0|(A=A+1|0)););if(r>>>0>=4)for(;A=(m+1584|0)+O(s+x|0,12)|0,e[A>>2]=1048576|e[A>>2],e[(r=A-12|0)>>2]=1048576|e[r>>2],e[(r=A-24|0)>>2]=1048576|e[r>>2],e[(A=A-36|0)>>2]=1048576|e[A>>2],s=s-4|0;);e[33264]=s}}if(l=e[49572],(0|_A)<=(0|(x=x+1|0)))break e;if(!((0|l)<991))break}}if((0|(s=e[47351]))<(0|(g=e[47350]))){for(ce=e[47202],Fe=e[49846],te=e[47352];;){A=(r=e[198304+(s<<2)>>2])>>8;e:{A:switch((31&r)-9|0){case 0:ce=A;break e;case 4:Fe=A;break e;case 3:break A;default:break e}te=r>>>0>=256?A+te|0:0}if(!(!(128&r)&(0|g)>(0|(s=s+1|0))))break}e[47352]=te,e[47351]=s,e[49846]=Fe,e[47202]=ce}e[49572]=l+2,e[(A=190288+(l<<3)|0)>>2]=589824,k[A+4>>1]=i,e[A+8>>2]=589824,k[A+12>>1]=i,r=_A&&e[47199]?V:10,e[33285]?A=0:(A=e[33283],A=e[A>>2]==e[A+4>>2]),V=A?r:V,x=At,g=0,_A=0,H=I=H-32192|0,e[I+24>>2]=0,e[I+28>>2]=0,e[I+16>>2]=0,e[I+20>>2]=0,e[I+8>>2]=0,e[I+12>>2]=0,e[I>>2]=0,e[I+4>>2]=0,i=e[49572],c=y[190284+(i<<3)>>1];e:{if((0|(s=i-3|0))<0)A=s;else{for(;;){if(g=(0|(r=127&d[(A=190288+(s<<3)|0)+3|0]))<(0|g)?g:r,y[A+4>>1])A=s;else if(A=-1,r=(0|s)>0,s=s-1|0,r)continue;break}if(g>>>0>3)break e}for(;;){if((0|(A=A-1|0))<0)break e;if(64&d[0|(r=190288+(A<<3)|0)]){f[r+3|0]=4;break e}if(!(d[r+3|0]<4))break}}if(A=e[K+292>>2],s=0,(0|i)<=0)g=0;else for(T=-1,g=0;;){r=A,e[K+292>>2]!=(0|A)&&(k[(A=190288+(s<<3)|0)>>1]=32|y[A>>1]),(0|g)>0&&(J=e[(h=190288+(s<<3)|0)+4>>2],e[(l=(A=s-g<<3)+190288|0)>>2]=e[h>>2],e[l+4>>2]=J,(0|T)!=-1&&(k[4+(A+190288|0)>>1]=T),T=-1);e:{if(d[2+((l=s<<3)+190288|0)|0]==21){if(A=d[(h=l+190288|0)+7|0],2&d[0|h])break e;A:if((0|A)!=(0|r)){if(h=d[10+(l+190288|0)|0]-9|0){if((0|h)==12)break A;break e}if(d[18+(l+190288|0)|0]!=21)break e}(0|T)==-1&&(T=(A=y[4+(l+190288|0)>>1])||-1),g=g+1|0}A=r}if((0|i)==(0|(s=s+1|0)))break}if(e[49572]=i-g,as(A),(r=e[K+36>>2])&&!((0|(A=(g=e[49572])-1|0))<0))for(ce=256&r,_=4&r,Fe=8&r,J=15&r,Xe=16&r,At=2&r,r=r>>>8&1,s=0;;){if(l=g,i=s,g=A,(0|(A=d[(h=(fA=A<<3)+190288|0)+2|0]))==21){e:{A:{if((0|(s=l-2|0))>=0)for(;;){if(d[2+((A=s<<3)+190288|0)|0]==21)break A;if(A=(0|s)>0,s=s-1|0,!A)break}A=e[K+292>>2];break e}A=d[7+(A+190288|0)|0]}as(A),A=d[h+2|0]}if(s=i,(A=e[144464+((255&A)<<2)>>2])&&(s=r,!(32&d[0|h]))){s=d[A+11|0],T=0,At&&((0|(l=d[0|A]))!=118&(0|l)!=82||(i=Xe?0:i,T=1));e:{A:{r:{a:switch((l=253&s)-4|0){case 1:break r;case 0:break a;default:break A}if(J&&(s=1,!i)||(0|(s=i))!=2||(s=2,!(A=d[A+13|0])))break e;f[h+2|0]=A;break e}if(J&&(s=2,!i)||(0|(s=i))!=1||(s=1,!(A=d[A+13|0])))break e;f[h+2|0]=A;break e}s=0,Fe&&(s=l?i:0)}s=A=T?0:s,y[4+(fA+190288|0)>>1]&&(s=A=_?0:A,ce&&(s=A||1))}if(!((0|(A=g-1|0))>=0))break}if(as(e[K+292>>2]),e[49572]<=0)Xe=-2,s=0;else{for(s=-1,i=0,h=0,Fe=0;;){A=h<<3,(0|s)!=-1&&(k[4+(A+190288|0)>>1]=s),d[(T=A+190288|0)+2|0]==21&&as(d[7+(A+190288|0)|0]),J=e[49572];e:{if(!(32&d[0|(l=A+190288|0)])){i=(0|(r=J-1|0))>(0|h)?e[144464+(d[10+(A+190288|0)|0]<<2)>>2]:i,!(y[l+12>>1]|(0|r)==(0|h))&&(te=0,d[i+11|0]|!i)||(te=1),g=d[T+2|0];A:if(!((0|(Xe=e[49848]))<=0))for(r=A+190288|0,s=0;;){if(ce=O(s,3),d[ce+199408|0]==(255&g)&&!((_=d[2+(ce+199408|0)|0])&(1^te)|(4&d[r+3|0]?2&_:0)|(y[r+4>>1]?0:4&_))){if(g=d[1+(ce+199408|0)|0],f[T+2|0]=g,!(2&d[e[144464+(g<<2)>>2]+4|0])|d[r+3|0]<2)break A;f[r+3|0]=0;break A}if((0|Xe)==(0|(s=s+1|0)))break}if(!(255&g)){s=y[4+(A+190288|0)>>1];break e}}s=e[l+4>>2],A=(I+32|0)+(Fe<<5)|0,r=e[l>>2],e[A>>2]=r,e[A+4>>2]=s,r=e[144464+(r>>>14&1020)>>2],e[A+8>>2]=r,f[A+17|0]=d[r+11|0],Fe=Fe+1|0,s=-1}if(!((0|Fe)<1e3&(0|J)>(0|(h=h+1|0))))break}if(g=0,s=0,!((0|(Xe=Fe-2|0))<=0))for(;;){e:if(y[4+((I+32|0)+(g<<5)|0)>>1]){for(r=(0|g)>(0|Xe)?g:Xe,s=0,A=g;;){if((0|A)!=(0|r)){if(s=(0|s)>(0|(l=d[3+((i=I+32|0)+(A<<5)|0)|0]))?s:l,!y[4+(i+((A=A+1|0)<<5)|0)>>1])continue}else A=r;break}if((0|A)<=(0|g))break e;if(r=~g+A|0,i=0,l=A-g&7)for(;f[6+((I+32|0)+(g<<5)|0)|0]=s,g=g+1|0,(0|l)!=(0|(i=i+1|0)););if(r>>>0<7)break e;for(;f[(r=(I+32|0)+(g<<5)|0)+6|0]=s,f[r+38|0]=s,f[r+70|0]=s,f[r+102|0]=s,f[r+134|0]=s,f[r+166|0]=s,f[r+198|0]=s,f[r+230|0]=s,(0|(g=g+8|0))!=(0|A););}else A=g+1|0;if(g=A,!((0|Xe)>(0|A)))break}}for(e[I+40>>2]=e[36125],as(e[K+292>>2]),Te=(0|s)<4,h=1,ce=1,J=0,r=0,s=0,At=0;;){e:{A:{r:{a:{if(r){if(i=(A=I+32|0)+((T=s-1|0)<<5)|0,J=d[2+(A+(s<<5)|0)|0],(0|T)>0){if(A=h-(s=(0|h)>0)|0,T>>>0>=(g=s?h:2)>>>0)for(;l=(s=(I+32|0)+(g<<5)|0)-32|0,h=e[s+12>>2],e[l+8>>2]=e[s+8>>2],e[l+12>>2]=h,h=e[s+4>>2],e[l>>2]=e[s>>2],e[l+4>>2]=h,h=e[s+28>>2],e[l+24>>2]=e[s+24>>2],e[l+28>>2]=h,h=e[s+20>>2],e[l+16>>2]=e[s+16>>2],e[l+20>>2]=h,(0|T)>=(0|(g=g+1|0)););h=A}te=e[144464+(J<<2)>>2],e[i>>2]=0,e[i+4>>2]=0,e[i+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+8>>2]=0,e[i+12>>2]=0,f[i+2|0]=r,A=e[144464+(r<<2)>>2],e[i+8>>2]=A,J=i}else{if((0|s)>=(0|Xe)|(0|At)>=997)break a;l=d[(i=(g=s<<5)+(I+32|0)|0)+2|0],A=e[144464+(l<<2)>>2],e[i+8>>2]=A,T=y[i+4>>1],(0|l)==21&&as(d[7+(g+(I+32|0)|0)|0]),h=T?s:h,te=e[144464+(d[i+34|0]<<2)>>2],e[i+40>>2]=te,T=s}if(!A){r=0,s=T+1|0;continue}if(ut(K,256,i,I+32040|0,I),(0|(s=e[I+32052>>2]))>0&&(g=(I+32|0)+(T<<5)|0,te=e[144464+(s<<2)>>2],e[g+40>>2]=te,f[g+34|0]=s,f[g+49|0]=d[te+11|0]),s=0,r)r=A;else if((0|(g=e[I+32056>>2]))<=0)r=A;else{r=e[144464+(g<<2)>>2],e[i+8>>2]=r,s=d[i+2|0],f[i+2|0]=g,g=y[i>>1];n:if(d[r+11|0]!=2)k[i>>1]=65531&g;else{if(k[i>>1]=4|g,d[A+11|0]==2)break n;f[i+3|0]=0}ut(K,256,i,I+32040|0,I)}if((0|(l=e[I+32048>>2]))<=0)g=r;else{if(g=e[144464+(l<<2)>>2],f[i+2|0]=l,e[i+8>>2]=g,A=d[g+11|0],Fe=1,(0|l)==1){fA=(0|A)==2;break A}l=y[i>>1];n:if((0|A)!=2)k[i>>1]=65531&l;else{if(k[i>>1]=4|l,d[r+11|0]==2)break n;f[i+3|0]=0}ut(K,256,i,I+32040|0,I)}if(fA=0,(0|(A=d[g+11|0]))!=2){Fe=0;break A}if(fA=1,Fe=0,A=2,d[i+3|0]>1){_A=0;break A}l=i+3|0,_A=_A+1|0,r=i;n:{if(8&(_=e[K+12>>2])){for(;;){o:switch(_=r,r=r+32|0,d[_+49|0]){case 0:break A;case 2:break o;default:continue}break}if(d[0|(r=_+35|0)]>1)break A;if(d[i+6|0]<=3&&(f[0|l]=0),d[_+38|0]<4)break n;break A}if(1&_A|(0|_A)<2)break A;if(2&_)break r;if(Te)r=l;else if(r=l,y[i+36>>1])break r}f[0|r]=0;break A}e[36423]=At+2,k[(A=145840+(At<<5)|0)>>1]=0,f[A+2|0]=9,f[A+20|0]=2,e[A+12>>2]=V,k[A+4>>1]=c,f[A+17|0]=0,f[A+18|0]=0,e[A+8>>2]=e[36125],k[A+32>>1]=0,f[A+34|0]=9,f[A+52|0]=0,e[A+44>>2]=0,k[A+36>>1]=0,f[A+49|0]=0,f[A+50|0]=0,e[A+40>>2]=e[36126],as(e[K+292>>2]),H=I+32192|0;break e}_A=1}if(!(8&(r=y[i+32>>1]))|(0|T)<=0||(l=d[te+11|0])>>>0>15|!(1<<l&457)||(s=d[te+10|0],k[i+32>>1]=8^r),Ee=y[i+36>>1]){A:if(r=e[K+4>>2]){r:switch(0|A){default:s=512&r?11:s;break;case 0:break A;case 2:break r}if(d[te+11|0]==2){(l=12&r)&&(s=(0|l)!=12?23:11);r:if(fA){a:switch(3&r){case 2:s=10;break r;case 0:break r;default:break a}s=23}d[i+35|0]<4||(s=256&r?10:s)}}if(!((0|i)==(0|J)|(0|At)<=0)){A:{r:{a:switch(0|(r=7&e[K>>2])){case 0:break A;case 1:break a;default:break r}if(s-12>>>0>4294967293)break A}s=d[r+101916|0]}s=e[47205]>0?24:s}}if(e[i+72>>2]=e[144464+(d[i+66|0]<<2)>>2],r=e[I+32060>>2],r=s||r||s,!Fe){f[(l=(_=At<<5)+145840|0)+17|0]=A,e[l+8>>2]=g,f[l+16|0]=0,k[l>>1]=y[i>>1],f[l+3|0]=15&d[i+3|0],f[l+6|0]=d[i+6|0],s=d[i+7|0],k[l+4>>1]=0,f[l+7|0]=s,Fe=d[g+10|0],f[l+2|0]=Fe;A:if(s=y[i+4>>1]){if(k[l+4>>1]=s,x=1&x?5:1,f[(i=_+145840|0)+20|0]=x,s=ce,ce=0,!s){x=0;break A}f[i+20|0]=8|x,x=0}else f[20+(_+145840|0)|0]=0;e[(s=_+145840|0)+12>>2]=e[I+32084>>2]<<1,!Ee|(0|Fe)!=24||(0|(i=e[47205]))<=0||(e[l+8>>2]=e[36126],e[s+12>>2]=O(i,14)),(1<<A&428?A>>>0<=8:0)|2&d[g+7|0]&&(e[s+12>>2]=128,f[l+16|0]=0),f[(A=_+145840|0)+21|0]=255,f[A+22|0]=255,k[A+18>>1]=5120,At=At+1|0}s=T+1|0;continue}break}k[88922]=1,e[44462]=0,ns&&(k[145776+(e[36423]<<5)>>1]=2,A=198304+(e[47350]<<2)|0,e[A>>2]=128,e[(A=A-4|0)>>2]=128|e[A>>2]),f[190268]=LA>>>19&1,Cn&&(e[Cn>>2]=LA<<14>>31&189360)}H=m+6832|0,Xe=e[47192],fA=e[t+12>>2],r=0,l=0,h=0,_=0,J=0,Pt=0,V=0,H=c=H-6e3|0;e:if(!((0|(i=(At=e[36423])-1|0))<=0)){for(;f[2+(c+O(r,6)|0)|0]=0,4&d[(A=r<<5)+145840|0]?(s=c+O(_,6)|0,f[s+1|0]=0,A=A+145840|0,f[s+3|0]=d[A+49|0],A=d[A+3|0],f[0|s]=A,_=_+1|0,Pt=(A>>>0>3)+Pt|0):d[e[8+(A+145840|0)>>2]+10|0]!=27|(0|_)<=0||(A=(c+O(_,6)|0)-4|0,f[0|A]=4|d[0|A]),(0|i)!=(0|(r=r+1|0)););if(f[c+O(_,6)|0]=0,_)if(e[Xe+148>>2]==1){if(!((0|At)<=0)){for(A=-2&At,s=1&At,r=145840;h=d[r+17|0]==2&&d[r+3|0]>3?l:h,h=d[r+49|0]==2&&d[r+35|0]>3?1|l:h,r=r- -64|0,l=l+2|0,(0|A)!=(0|(J=J+2|0)););!s|d[r+17|0]!=2||(h=d[r+3|0]>3?l:h)}if(f[(g=(A=h<<5)+145840|0)+3|0]=7,e[Xe+212>>2]==30313&&(d[(A=A+145840|0)+7|0]||(f[A+7|0]=q(55),At=e[36423])),!((0|At)<=0)){for(J=0,r=145840,A=145840,l=T=e[36125],i=0,ce=1;;){if(d[r+17|0]?x=e[36125]:(x=e[36125],T=(s=d[e[r+8>>2]+14|0]>50)?x:T,ce|=s),s=d[r+20|0]?x:l,4&d[0|r]){x=d[r+7|0],l=e[144464+(x<<2)>>2];A:{if((0|(_=e[Xe+212>>2]))==6840683){if(e[s>>2]!=49||(_=e[l>>2]-49|0)>>>0>5|!(1<<_&41))break A;f[A+7|0]=q(50),_=e[Xe+212>>2]}if(!((0|_)!=6516078&(0|_)!=31336)){_=0,x||(i=q(1&(_=i|ce)?13621:12593),f[r+7|0]=i,l=e[144464+(i<<2)>>2]),(0|h)!=(0|J)|(1024|e[l>>2])!=13621||(f[g+3|0]=6),e[T>>2]==3420466&&(f[A+7|0]=q(e[l>>2]==3420466?13619:12594));r:{if(e[s>>2]==12597){if((0|(x=e[l>>2]))!=12597)break r;f[A+7|0]=q(13109)}x=e[l>>2]}i=_,(0|x)==12593&&((0|(x=e[T>>2]))==13621&&(f[r+7|0]=q(12850),x=e[T>>2]),(0|x)==13619&&(f[r+7|0]=q(13107),x=e[T>>2]),(0|x)==3420466&&(f[r+7|0]=q(13364)),f[r+3|0]=0)}}ce=0,T=l,A=r}else l=s;if(r=r+32|0,!((0|(J=J+1|0))<(0|(s=e[36423]))))break}if(l=0,r=145840,!((0|s)<=0))for(;4&d[0|r]&&((A=d[r+7|0])||(f[r+7|0]=17,A=17),A=e[144464+(A<<2)>>2],f[r+21|0]=d[A+12|0],f[r+22|0]=d[A+13|0]),r=r+32|0,(0|s)!=(0|(l=l+1|0)););}}else{if(A=e[Xe+152>>2],A=Xe+O(Te=(0|A)>7?1:A,6)|0,K=d[0|(Te?A+637:Xe+157)],LA=d[0|(Te?636+(A+fA|0):156+(Xe+fA|0))],f[133068]=(0|fA)==4,!((0|_)<=0)){for(Fe=_-1|0,te=fA-1>>>0>1,s=0,i=0;;){m=c+O(i,6)|0,V=((A=d[0|m])<<24>>24>3)+V|0;A:if((0|A)==6){A=i-3|0,r=i;r:{for(;;){if((0|r)<=(0|s)|(0|A)>=(0|r))break r;a:switch(g=c+O(r=r-1|0,6)|0,d[0|g]-4|0){case 2:break r;case 0:break a;default:continue}break}f[0|g]=3}r=i;r:{for(;;){if((0|_)<=(0|(r=r+1|0)))break r;a:switch(d[c+O(r,6)|0]-4|0){case 0:break r;case 2:break a;default:continue}break}f[m+2|0]=2,f[0|m]=5,A=s;break A}if(d[0|m]==6){f[m+2|0]=2,I=0;r:if((0|_)<=(0|(A=i+1|0)))g=i,rr=0;else if(rr=1,(0|(l=f[c+O(A,6)|0]))>4)g=i;else{for(h=(Pt-V|0)>1,g=i;;){if(r=A,(255&l)==4&&(A=h+1|0,h=1,!((0|A)<=1))){A=r;break r}if(rr=(0|_)>(0|(A=r+1|0)),(0|A)==(0|_))break;if(g=r,(0|(l=f[c+O(A,6)|0]))>4)break r}g=Fe,A=_}l=-1,x=0,h=0,ce=-1,J=0,T=-1;r:{if((0|(r=s))<(0|A)){for(;l=(Ee=(0|(T=f[c+O(r,6)|0]))>3)&&(0|l)<0?r-s|0:l,I=(J=(0|h)>(0|T))?I:(0|h)<(0|T)?r:x,ce=Ee?r:ce,x=J?x:r,Ee=(0|r)!=(0|g),h=J?h:T,r=r+1|0,Ee;);if(J=x,T=ce,(0|l)>=0)break r}l=A,x=J,ce=T}e[33269]=g-x,e[33268]=l,e[33270]=x,e[33271]=I;r:if(d[133068])e[33270]=A,e[33271]=A;else if((0|ce)>=0){if((0|A)!=(0|_))break r;f[c+O(ce,6)|0]=7}else f[c+O(x,6)|0]=7;ha(c,Te,s,A,LA),!rr&!!(0|fA)||(LA=te?d[Xe+156|0]:d[Xe+157|0])}else A=s}else A=s;if((0|A)>=(0|i))s=A;else if(4&d[m+2|0]){for(s=i+1|0,l=-1,ce=0,x=0,h=0,r=A,I=-1;l=(J=(0|(g=f[c+O(r,6)|0]))>3)&&(0|l)<0?r-A|0:l,ce=(T=(0|g)<(0|h))?ce:(0|g)>(0|h)?r:x,I=J?r:I,x=T?x:r,J=(0|r)!=(0|i),h=T?h:g,r=r+1|0,J;);e[33269]=i-x,e[33270]=x,e[33271]=ce,e[33268]=(0|l)<0?s:l,d[133068]?(e[33270]=s,e[33271]=s):(0|I)>=0?f[c+O(I,6)|0]=7:f[c+O(x,6)|0]=7,ha(c,Te,A,s,K)}else s=A;if((0|_)==(0|(i=i+1|0)))break}if(!((0|s)>=(0|_))){for(l=-1,ce=0,x=0,h=0,r=s,I=-1;l=(g=(0|(A=f[c+O(r,6)|0]))>3)&&(0|l)<0?r-s|0:l,ce=(i=(0|A)<(0|h))?ce:(0|A)>(0|h)?r:x,I=g?r:I,x=i?x:r,h=i?h:A,(0|_)!=(0|(r=r+1|0)););e[33270]=x,e[33271]=ce,e[33269]=~x+_,e[33268]=(0|l)<0?_:l,d[133068]?(e[33270]=_,e[33271]=_):(0|I)>=0?f[c+O(I,6)|0]=7:f[c+O(x,6)|0]=7,ha(c,Te,s,_,LA)}}if((0|At)<=0)break e;for(r=0,J=0;;){if(T=s=(g=r<<5)+145840|0,A=c+O(J,6)|0,l=d[0|A],f[s+3|0]=l,4&d[0|s]){i=g+145840|0,s=d[A+4|0],f[i+21|0]=s,h=d[A+5|0],f[i+16|0]=0,f[i+22|0]=h;A:{if(1&(x=d[A+2|0]))A=2;else{if(l>>>0<6)break A;A=d[A+1|0]}f[i+16|0]=A}s>>>0<=(255&h)>>>0?(A=h,h=s):(f[i+21|0]=h,f[i+22|0]=s,A=s),(s=d[7+(g+145840|0)|0])&&(A=(255&A)+(255&h)>>>1|0,s=e[144464+(s<<2)>>2],f[i+22|0]=A+d[s+13|0],f[i+21|0]=A+d[s+12|0]),2&x&&(f[T+3|0]=8|l),J=J+1|0}if((0|At)==(0|(r=r+1|0)))break}}}if(H=c+6e3|0,J=e[47192],r=0,te=0,I=0,V=0,H=c=H-160|0,e[36423]>=2)for(fA=e[30450],l=1;;){if(l=(A=l)+1|0,_=d[(i=(h=A<<5)+145840|0)+3|0],2&(x=y[i>>1])){for(;(31&(g=e[198304+(V<<2)>>2]))==2&&(Hs(127&g,g>>>8|0),e[36432]=110,e[36433]=100,e[36434]=450,e[36430]=5,x=e[50786],T=e[32972],(0|(s=e[T+84>>2]))>0&&(x=(0|O(s,x))/100|0),ce=d[((0|(s=(0|x)>=359?359:x))<=80?80:s)+101856|0],s=(0|(s=(0|x)>=450?450:x))>399?6:(0|s)>379?7:ce,e[32526]=(0|O(s,e[T+72>>2]))/256,e[32527]=(0|O(s,e[T+76>>2]))/256,e[32528]=(0|O(s,e[T+80>>2]))/256,s>>>0>7||(T=s-1|0,e[32528]=T,e[32526]=s,e[32527]=T)),V=V+1|0,!(128&g););x=y[i>>1]}T=l<<5,ce=A-1|0,s=7&_;e:{A:{r:{a:{n:{o:{c:{u:{l:{i:switch(LA=d[17+(h+145840|0)|0],0|(g=4&x?2:LA)){case 2:break n;case 3:case 8:break o;case 5:break c;case 6:case 7:break u;case 4:break l;case 0:break i;default:break e}r=0;break e}if((0|(A=d[17+(145840+(ce<<5)|0)|0]))!=6?(s=(0|A)==4?60:e[34063]>0||s>>>0<4?48:60,f[18+(h+145840|0)|0]=s):(s=25,f[18+(h+145840|0)|0]=25),!(16&d[0|J])|!d[20+(h+145840|0)|0]||(f[18+(h+145840|0)|0]=60,s=60),64&d[e[8+(h+145840|0)>>2]+6|0]&&(s=s+30|0,f[18+(h+145840|0)|0]=s),r=0,!(8&x))break e;f[18+(h+145840|0)|0]=d[J+164|0]+s;break e}!(i=d[(A=h+145840|0)+20|0])|1&f[e[A+8>>2]+7|0]&d[17+(145840+(ce<<5)|0)|0]==2||(f[18+(h+145840|0)|0]=15),s=d[17+(T+145840|0)|0],8&d[e[8+(h+145840|0)>>2]+4|0]|s|d[17+(145840+(ce<<5)|0)|0]!=8||(f[18+(h+145840|0)|0]=25),64&d[e[8+((A=ce<<5)+145840|0)>>2]+5|0]&&(f[18+(h+145840|0)|0]=30),!i|!(16&e[J>>2])||(f[18+(h+145840|0)|0]=30);u:if(d[20+(T+145840|0)|0]|!(32&d[e[8+(h+145840|0)>>2]+4|0])|(0|s)!=4)e[12+(h+145840|0)>>2]=256;else{if(i=h+145840|0,d[17+(A+145840|0)|0]==2){e[i+12>>2]=200;break u}e[i+12>>2]=150}if((0|g)!=7||(te|=(0|s)==2,(254&d[17+(A+145840|0)|0])!=2))break e;e[12+(h+145840|0)>>2]=e[12+(A+145840|0)>>2]+255>>>1;break e}(254&(s=d[17+((A=ce<<5)+145840|0)|0]))==6|(0|s)==3|32&e[e[8+(A+145840|0)>>2]+4>>2]&&(f[18+(h+145840|0)|0]=30);c:if((254&(i=d[17+(T+145840|0)|0]))==2){te=d[20+(T+145840|0)|0]&&(0|i)!=2?te:1,f[(i=h+145840|0)+18|0]=40,_=0;u:{l:switch(0|s){case 0:if((A=e[12+(A+145840|0)>>2])>>>0>39)break u;_=40-A|0;break u;case 2:break u;default:break l}if(d[20+(h+145840|0)|0])break c;_=20;l:switch(s-3|0){case 1:if(_=0,!(8&d[e[8+(A+145840|0)>>2]+4|0]))break u;break c;case 0:break u;case 5:break l;default:break c}_=12}f[i+18|0]=_}if(!(16&d[0|J])|!d[20+(h+145840|0)|0]||d[(A=h+145840|0)+18|0]>19)break e;f[A+18|0]=20;break e}i=d[J+296|0],g=s=h+145840|0,e[s+12>>2]=256,f[s+19|0]=i;o:if(d[s+20|0]){x=25;c:switch(d[17+(145840+(ce<<5)|0)|0]-2|0){case 0:if(x=12,1&f[e[8+(h+145840|0)>>2]+7|0])break o;break;case 1:break c;default:break o}f[18+(h+145840|0)|0]=x}if((0|(T=d[17+(T+145840|0)|0]))==2){I=1;break e}if(f[(i=h+145840|0)+22|0]=r,(254&d[17+((s=ce<<5)+145840|0)|0])==2)break a;if(s=r,(0|(g=e[36423]))<=(0|A))break A;for(;;){if(d[17+((s=A<<5)+145840|0)|0]==2){s=d[22+(s+145840|0)|0],f[i+22|0]=s;break A}if((0|g)==(0|(A=A+1|0)))break}break r}if(Xe=h+145840|0,Fe=s^s>>>0<2,s=(At=8&_)?25:d[296+(Fe+J|0)|0]-I|0,f[Xe+19|0]=s,(e[36423]-3|0)>(0|A)||(0|(g=255&s))<=(0|(s=e[J+52>>2]))||(f[Xe+19|0]=s),s=0,_=0,!(x=d[i+52|0]))for(;g=e[i+40>>2],_=d[i+49|0]==2?(~e[g+4>>2]>>>20&1)+_|0:_,s=d[g+10|0]==27?2:s,g=i,i=i+32|0,!(x=d[g+84|0]););g=Xe+96|0,A=(Te=A+2<<5)+145840|0,e[34063]=_,m=d[e[i+40>>2]+10|0],T=T+145840|0,d[17+(Te+145840|0)|0]|d[e[T+8>>2]+10|0]!=23?(i=g,g=A,A=T):i=h+145968|0,T=d[e[g+8>>2]+15|0];n:if(_)T=d[e[J+96>>2]+(d[e[A+8>>2]+15|0]+O(T,10)|0)|0],d[A+17|0]!=8|(254&d[g+17|0])!=4||(T=8&d[e[i+8>>2]+4|0]?T-15|0:T);else{if(K=e[J+100>>2],Te=d[e[A+8>>2]+15|0],i=d[A+20|0],T=d[K+(Te+O(i|d[g+20|0]?(0|T)==1:T,10)|0)|0],!i|!(32&d[0|J]))break n;T=d[1+(K+O(Te,10)|0)|0]+T>>>1|0}i=x>>>1|0,Te=!_,x=(0|(T=(0|O(e[130104+(_?(0|_)==1?4:8:0)>>2],T))/128|0))<=8?8:T;n:if((0|Fe)!=7)At&&(x=e[J+200>>2]+x|0);else{if(x=(T=e[J+200>>2])+x|0,!At)break n;x=((0|T)/2|0)+x|0}T=i&Te|(0|m)==27,(i=y[304+(J+(Fe<<1)|0)>>1])||(i=y[J+316>>1]),x=O(i<<16>>16,x),(_=d[(i=h+145840|0)+7|0])&&(Fe=d[e[144464+(_<<2)>>2]+14|0])&&(x=(0|O(x,Fe))/100|0),(T|(0|s)==2)==1&&(2097152&(s=e[J+12>>2])||(x=(0|O(262144&s?282:256+((280-(d[e[8+(h+145840|0)>>2]+14|0]<<1)|0)/3|0)&65535,x))/256|0)),Fe=h+145840|0,s=O(e[32526],e[J+196>>2]),At=(0|LA)!=2?256:(0|((0|s)>(0|x)?x:s))/128|0,e[Fe+12>>2]=At,(s=d[Fe+16|0])>>>0>=19&&(_i(84371,28,fA),f[Fe+16|0]=0,_=d[i+7|0],s=0),x=s+1|0,(s=255&_)?(Pa(s,c+8|0),s=ci(e[c+132>>2])):s=e[129280+((255&x)<<2)>>2],T=h+145840|0,1&(I|te)&&(i=(h=ce<<5)+145840|0,I=d[0|s],s=d[T+21|0],s=((0|O(I,d[T+22|0]-s|0))/256|0)+s|0,f[i+22|0]=s,r=(s-(r=(0|s)==255?255:r)|0)>16?s-16|0:r,f[i+21|0]=r,i=0,(0|r)<(0|s)&&(f[Fe+16|0]=x,i=2),e[(r=h+145840|0)+12>>2]=At,f[r+16|0]=i,s=d[Xe+19|0],f[r+19|0]=d[r+17|0]!=3&&s>>>0>18?18:s),r=(0|LA)!=2,i=-2&(s=y[A>>1]),k[A>>1]=i;n:{o:{c:switch(d[A+17|0]-3|0){case 5:if(d[g+17|0]==2)break n;i=1|s;break o;case 0:break c;default:break n}if(k[A>>1]=1|s,d[g+17|0]!=2&&e[e[A+8>>2]>>2]!=12146)break n}k[A>>1]=i}r?(0|(A=r<<4))<=((s=d[T+22|0])-(i=d[T+21|0])|0)||(i=(0|(A=s-A|0))>0?A:0,f[T+21|0]=i):(s=d[T+22|0],i=d[T+21|0]),A=255&i,r=((0|O(d[e[129280+(d[Fe+16|0]<<2)>>2]+127|0],s-A|0))/256|0)+A|0,I=0,te=0;break e}i=e[12+(s+145840|0)>>2],e[g+12>>2]=i,(0|LA)==3&&(i=e[32526],e[g+12>>2]=i),s=r;a:switch(T-5|0){case 0:e[g+12>>2]=(O(i,160)>>>0)/100;break r;case 2:break a;default:break A}e[g+12>>2]=(O(i,120)>>>0)/100}s=r}te=0,f[(A=h+145840|0)+16|0]=0,i=A,s=(A=255&s)-16|0,f[i+21|0]=A>>>0>=s>>>0?s:0}if(!(e[36423]>(0|l)))break}if(H=c+160|0,15&(i=e[47197])|e[36456]){A=0,g=0,H=s=H-80|0;e:if((r=e[33222])||(e[33223]=500,r=HA(500),e[33222]=r,r)){if(!((e[36423]-2|0)<2)){for(A=i>>8,J=128&i?0:A,_=A&i<<24>>31,T=2&i,i=s+32|1,h=1;;){if(Tr(s,e[(l=(ce=h<<5)+145840|0)+8>>2],l,T,s+72|0),A=s+32|0,(13&(r=d[l+20|0]))==1&&(f[s+32|0]=32,A=i),!J|(0|J)!=32&!!(0|r)|h>>>0<2||(jA(s+76|0,s),e[s+76>>2]-880>>>0>4294967103||(A=Cr(J,A)+A|0)),4&d[0|l]&&((r=d[3+(ce+145840|0)|0])>>>0<2||(r=r>>>0>=5?5:r,r=T?r>>>0>3?712:716:f[r+94144|0],e[s+76>>2]=r,A=Cr(r,A)+A|0)),x=0,e[s+72>>2]=0,d[0|(r=s)])for(;r=jA(s+76|0,r)+r|0,e[s+72>>2]>>>x-1&1|!_|(0|x)<=0||(I=e[s+76>>2])-880>>>0>4294967103||oi(I)&&(A=Cr(_,A)+A|0),x=x+1|0,A=Cr(e[s+76>>2],A)+A|0,d[0|r];);if(d[e[l+8>>2]+10|0]!=21&&(8&(r=y[l>>1])&&(A=Tr(A,e[36128],l,T,0),r=y[l>>1]),!(4&r)|d[17+(ce+145840|0)|0]==2||(A=Tr(A,e[36136],l,T,0)),(r=d[7+(ce+145840|0)|0])&&(A=Tr(A,e[144464+(r<<2)>>2],l,T,0))),(A=(l=A-(s+32|0)|0)+g|0)>>>0<Ae[33223])r=e[33222];else{if(r=A+500|0,e[33223]=r,!(r=lt(e[33222],r))){e[33223]=0,r=86135;break e}e[33222]=r}if(f[(x=l)+(l=s+32|0)|0]=0,PA(r+g|0,l),g=A,!((0|(h=h+1|0))<(e[36423]-2|0)))break}if(!r){r=86135;break e}}f[A+r|0]=0}else e[33223]=0,r=86135;H=s+80|0,15&d[188788]&&(e[t>>2]=r,Xt(e[47195],84367,t)),(A=e[36456])&&$A[0|A](r)}d[190280]?(e[36423]=0,A=1):(SA(0),(A=e[t+8>>2])?(H=r=H+-64|0,Lt(r,A,60),aa(r,1),s=0,(A=UA(r,0))&&(s=A,d[202976]&&(s=UA(202976,2))),H=r- -64|0,e[44468]=s):s=e[44468],A=1,s&&(r=e[32972],(s=HA(1344))&&(r=qA(s,r,1344),s=216192+(e[50758]<<4)|0,e[s>>2]=11,e[s+8>>2]=r,r=e[50758]+1|0,e[50758]=(0|r)<=169?r:0),e[44468]=0))}else A=0,f[190280]=0;else A=0,e[36423]=0,e[50758]=0,e[50757]=0;return H=t+16|0,A}function aA(A,t){var r=0,s=0,i=0,l=0;r=1073741825;e:{A:{r:{a:{n:{o:{c:{u:{l:{i:{p:{C:{h:{b:{m:{x:{I:{B:{N:{L:{U:{y:{E:{Q:{F:{Ae:{R:{q:{_:{oe:{j:{Fe:{K:{f:{g:{re:{k:{se:{w:{s:{tA:{te:{t:{pe:{W:{me:{be:{rA:{de:{z:{Se:{v:{Oe:{$:{Ie:{we:{_e:{ze:{Ne:{Le:{Re:{je:{We:{ke:{sA:{ae:{Ve:{Ye:{Me:{He:{G:{Ee:{V:{mA:{hA:{CA:{bA:{IA:{wA:{kA:{MA:{EA:{vA:{xA:{BA:{yA:{DA:{PA:{TA:{GA:{QA:{FA:{SA:{OA:{_A:{zA:{NA:{LA:{RA:{jA:{WA:{VA:{YA:switch(0|t){case 0:t=A-9>>>0<5?1073741825:0,t=(A=(0|A)==133)?1073741825:t;break F;case 1:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{fe:{ge:{if((0|(t=-256&A))<=2047){if(!t)break ge;if((0|t)==1536)break fe;if((0|t)!=1792||(r=0,s=1,(0|A)!=1807))break d;break t}if((0|t)<=69631){if((0|t)==2048)break le;if((0|t)!=8192)break d;switch(r=131076,A-8204|0){case 1:break ne;case 0:break t;default:break ee}}if((0|t)==69632)break Z;if((0|t)!=917504)break d;switch(r=8388608,A-917505|0){case 62:break T;case 58:break O;case 57:break D;case 45:break P;case 43:break X;case 32:break Y;case 0:break t;default:break S}}if(r=16,(0|A)!=173)break d;break t}if(r=0,s=1,A-1536>>>0<6)break t;s=(0|A)==1757,t=(A=(0|A)==1564)?2:0;break Q}if(r=0,s=1,(0|A)!=2274)break d;break t}return le=64,4}if(r=1073741826,(-2&A)==8206)break t;if(A-8234>>>0<5)return le=0,2;if(r=128,A-8289>>>0<4||(r=2,A-8294>>>0<4))break t;if(r=8388608,A-8298>>>0>=6)break d;break t}t=!(A-69821&-17),A=0;break E}le=536870976;break y}le=268435520;break y}le=-2147483584;break y}le=134217792;break y}le=67108928;break y}le=1073741888;break y}if(r=131072,s=64,A-917536>>>0<96)break t}break s;case 2:d:{S:{T:{O:{D:{P:{X:{if((0|(t=-256&A))<=130303){if((0|t)<=127743){if((0|t)<=64767){if((0|t)<=11007){if((0|t)==8192)break X;if((0|t)!=9216)break d;if(A-9255>>>0>=25)break P;break w}if((0|t)==11008)break D;if((0|t)!=11776||(r=-2147483648,A-11845>>>0>=59))break d;break t}if((0|t)<=126975){if((0|t)==64768)break O;if((0|t)!=65280||(r=4194304,A-65520>>>0>=9))break d;break t}if((0|t)==126976|(0|t)==127232|(0|t)==127488)break te;break d}if((0|t)<=129023){if((0|t)<=128255){if((0|t)==127744|(0|t)==128e3)break te;break d}if((0|t)==128256|(0|t)==128512|(0|t)==128768)break te;break d}if((0|t)<=129535){if((0|t)==129024|(0|t)==129280)break te;break d}if((0|t)==129536|(0|t)==129792|(0|t)==130048)break te;break d}if((0|t)<=919039){if((0|t)<=917759){if((0|t)<=130815){if((0|t)==130304)break te;if((0|t)!=130560)break d;break te}if((0|t)==130816)break te;if((0|t)!=917504)break d;if((-128&A)!=917632)break T;break se}if((0|t)<=918271){if((0|t)==917760)break S;if(r=4194304,(0|t)!=918016)break d;break t}if((0|t)==918272|(0|t)==918528)break se;if(r=4194304,(0|t)!=918784)break d;break t}if((0|t)<=920319){if((0|t)<=919551){if((0|t)==919040)break se;if(r=4194304,(0|t)!=919296)break d;break t}if((0|t)==919552|(0|t)==919808)break se;if(r=4194304,(0|t)!=920064)break d;break t}if((0|t)<=920831){if((0|t)==920320)break se;if(r=4194304,(0|t)!=920576)break d;break t}if((0|t)==920832|(0|t)==921088)break se;if(r=4194304,(0|t)!=921344)break d;break t}if(r=4194304,(0|A)!=8293)break d;break t}if(r=-2147483648,A-9291>>>0>=21)break d;break t}if((-16&A)==11248|A-11219>>>0<25|(0|A)==11209|A-11194>>>0<3||(0|(t=-2&A))==11124)break w;if(r=-2147483648,(0|t)!=11158)break d;break t}if(r=65536,A-64976>>>0>=32)break d;break t}if((0|A)==917504)break se;if(r=4194304,A-917506>>>0>=30)break d;break t}if(r=4194304,A>>>0>917999)break t}t=(A=!(65534&~A))>>>16|0,A<<=16;break E;case 6:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{if((0|(t=-256&A))<=7679){if((0|t)<=767){if(!t)break le;if((0|t)==256)break ne;if((0|t)!=512)break s;if((0|A)!=585)break ee;break U}if((0|t)==768)break Z;if((0|t)==1024)break Y;if((0|t)!=7424||(r=16777216,(0|A)!=7574))break s;break t}if((0|t)<=119807){if((0|t)==7680)break X;if((0|t)==8448)break P;if((0|t)!=65280||(r=256,A-65345>>>0>=6))break s;break t}if((0|t)<=120319){if((0|t)==119808)break D;if((0|t)!=120064)break s;if(A>>>0>=120070)break O;break k}if((0|t)==120320)break T;if((0|t)!=120576)break s;if(A>>>0>=120597)break S;break k}if(r=768,A-97>>>0<6)break t;if(r=16777216,A-105>>>0>=2)break s;break t}r=(t=(0|A)==329)>>>9|0,t=(A=(0|A)==303)?16777216:t<<23;break L}if((0|A)==616)break U;if(r=16777216,(0|A)!=669)break s;break t}r=128;Z:switch(A-976|0){case 35:break d;case 0:case 1:case 2:case 5:case 32:case 33:break t;default:break Z}if((-2&A)!=1012)break s;break t}s=(A=!(A-1110&-3))>>>8|0,A<<=24;break N}s=(t=(0|A)==7883)>>>8|0,t=(A=(0|A)==7725)?16777216:t<<24;break Q}if(A-8458>>>0<10)break k;if((t=A-8495|0)>>>0<11)break VA;break pe}if((0|(t=-2&A))==119842)break re;if(A-119808>>>0<85)break k;if(A-119894>>>0<2|A-119946>>>0<2)break re;if((0|A)==119995|A-119896>>>0<69|A-119982>>>0<12)break k;if((0|t)==119998)break re;if(A-119997>>>0<7)break k;if(r=16777344,(0|t)==120050)break t;if(r=128,A>>>0<=120004)break s;break t}if((-2&A)==120102)break re;if(A-120094>>>0<28)break k;O:{if((0|A)<=120257){if(A-120154>>>0<2)break re;if(r=16777344,A-120206>>>0>=2)break O;break t}if(A-120258>>>0<2)break re;if(r=16777344,A-120310>>>0<2)break t}if(r=128,A>>>0<=120145)break s;break t}if(A-120362>>>0<2|A-120414>>>0<2)break re;if(r=16777344,A-120466>>>0<2)break t;if(A-120540>>>0<31|A>>>0>120571|A>>>0<120486)break k;if(r=128,A-120514>>>0>=25)break s;break t}if(A-120772>>>0<8|A-120746>>>0<25|A-120714>>>0<31|A-120688>>>0<25||(0|A)!=120597&A>>>0<120629|A-120656>>>0<31)break k;if(r=128,A-120630>>>0<25)break t;break s}break U;case 7:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{fe:{ge:{M:{ce:{J:{H:{ie:{ue:{ve:{xe:{if((0|(t=-256&A))<=11263){if((0|t)<=3583){if((0|t)<=1535){if((0|t)==512)break xe;if((0|t)==768)break ve;if((0|t)!=1280||(r=4096,(0|A)!=1369))break s;break t}if((0|t)==1536)break ue;if((0|t)==1792)break ie;if((0|t)!=2304||(r=4096,(0|A)!=2417))break s;break t}if((0|t)<=7167){if((0|t)==3584)break H;if((0|t)==6144)break J;if((0|t)!=6656||(r=8192,(0|A)!=6823))break s;break t}if((0|t)==7168)break ce;if((0|t)==7424)break M;if((0|t)!=8192)break s;if(r=16793600,!(t=A-8305|0))break t;if((0|t)==14)break ge;break fe}if((0|t)<=43263){if((0|t)<=40959){if((0|t)==11264)break le;if((0|t)==11776)break ne;if((0|t)!=12288)break s;switch(r=8192,A-12293|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 28:case 29:case 30:case 31:case 32:case 33:case 34:case 35:case 36:case 37:case 38:case 39:case 40:case 41:case 42:case 43:break Z;case 0:case 44:case 45:case 46:case 47:case 48:break t;default:break ee}}if((0|t)==40960)break Y;if((0|t)==42496)break X;if((0|t)!=42752)break s;if(A-42775>>>0>=9)break P;break g}if((0|t)<=65279){if((0|t)==43264)break D;if((0|t)==43520)break O;if((0|t)!=43776||(r=20480,(-4&A)!=43868))break s;break t}if((0|t)==65280)break T;if((0|t)==92928)break S;if((0|t)!=93952)break s;if(r=4096,A-94099>>>0<13)break t;if(r=8192,(-2&A)!=94176)break s;break t}if(r=16797696,(0|A)==690)break t;if(A-688>>>0<9)return le=0,20480;if(A-697>>>0<7)break g;if((0|(t=-2&A))==704)return le=0,20480;if(r=4096,A-710>>>0<10||(r=12288,(0|t)==720)||(r=20480,A-736>>>0<5))break t;r=(A=(-3&A)==748)>>>20|0,A<<=12;break B}t=(0|A)==890?20480:0,t=(A=(0|A)==884)?4096:t;break F}if(r=8192,(0|A)==1600)break t;if(r=4096,A-1765>>>0>=2)break s;break t}if(r=4096,(-2&A)==2036)break t;if(r=8192,(0|A)!=2042)break s;break t}t=(A=!(A-3654&-129))>>>19|0,A<<=13;break E}if(r=8192,(0|A)!=6211)break s;break t}if(r=12288,(0|A)==7291)break t;if(r=4096,A-7288>>>0>=6)break s;break t}if(r=16797696,(0|A)==7522||(r=20480,A-7468>>>0<63))break t;r=16384;M:switch(A-7588|0){default:if((0|A)==7544)break t;case 1:case 2:case 3:if(A-7579>>>0>=37)break s;break t;case 0:case 4:break M}return le=0,16793600}return le=0,16384}if(r=16384,A-8336>>>0>=13)break s;break t}t=(r=(0|A)==11389)>>>18|0,r=(A=(0|A)==11388)?16793600:r<<14;break I}if(r=-2147479552,(0|A)!=11823)break s;break t}if(A-12445>>>0<2)break t;if((0|A)==12540)break d}if(A-12541>>>0>=2)break s;break t}if(r=8192,(0|A)!=40981)break s;break t}if((0|A)==42508)break x;if((0|A)==42623)break g;if(r=20480,(-2&A)!=42652)break s;break t}if((0|A)==42864)return le=0,16384;if((0|A)==42888)break g;if(r=20480,(-2&A)!=43e3)break s;break t}r=(t=(0|A)==43494)>>>19|0,t=(A=(0|A)==43471)?8192:t<<13;break L}if((0|A)==43632||(0|A)==43741)break x;if(r=8192,A-43763>>>0>=2)break s;break t}if(r=12288,(0|A)==65392)break t;if(r=135168,(-2&A)!=65438)break s;break t}if(r=8192,(-2&A)==92994)break t;break s}return le=0,12288;case 8:r=128;d:{S:{T:{O:{D:{P:{X:{Y:{Z:{if((0|(t=-256&A))<=12543){if((0|t)<=5887){if((0|t)<=3583){if(!t)break Z;if((0|t)!=1536||(t=8388608,(0|A)!=1651))break v;break de}if((0|t)==3584)break Y;if((0|t)!=4352||(r=4194304,A-4447>>>0>=2))break v;break z}if((0|t)<=8447){if((0|t)==5888)break X;if((0|t)!=6400||(t=A-6581|0)>>>0>=6)break v;r=e[(t=81432+(t<<3)|0)>>2],s=e[t+4>>2];break z}if((0|t)==8448)break P;if((0|t)!=12288||(t=2048,(0|A)!=12294))break v;break de}if((0|t)<=68863){if((0|t)<=63999){if((0|t)==12544)break D;if((0|t)!=43520)break v;if((t=A-43701|0)>>>0<8)break S;break Se}if((0|t)==64e3)break d;if((0|t)!=65280||(t=4194304,(0|A)!=65440))break v;break de}if((0|t)<=100095){if((0|t)==68864)break O;if((0|t)!=70400||(r=8192,(0|A)!=70493))break v;break be}if((0|t)==100096)break T;if((0|t)!=126464)break v;break z}s=(t=!(A-170&-17))>>>18|0,r=t<<14;break z}if(r=33554432,A-3648>>>0<5)break z;if(t=0,l=33554432,(0|A)==3759)break de;if(A-3776>>>0>=5)break v;break z}if(r=8388608,A-6051>>>0>=2)break v;break z}if(A-8501>>>0>=4)break v;break z}if(t=4194304,(0|A)!=12644)break v;break de}if(r=4096,(-2&A)!=68898)break v;break z}if(r=2048,A-100333>>>0<5)break z;break v}if(r=33554432,!(211>>>t&1))break Se;break z}if((t=A-64014|0)>>>0>=28)break v;r=e[(t=81480+(t<<3)|0)>>2],s=e[t+4>>2];break z;case 10:d:{S:{T:{O:{D:{P:{if((0|(t=-256&A))<=119807){if((0|t)<=8447){if(!t)break P;if((0|t)!=768)break s;switch(r=128,A-976|0){case 0:case 1:case 2:case 36:case 37:break t;default:break s}}if((0|t)==8448)break D;if((0|t)!=65280||(r=256,A-65313>>>0>=6))break s;break t}if((0|t)<=120319){if((0|t)==119808)break O;if((0|t)!=120064)break s;if(A>>>0>=120070)break T;break k}if((0|t)==120320)break S;if((0|t)!=120576)break s;if(A-120772>>>0>=8)break d;break k}if(r=768,A-65>>>0>=6)break s;break t}r=128;D:switch(A-8450|0){case 0:case 5:break t;default:break D}if(A-8458>>>0<10)break k;if((t=A-8469|0)>>>0<20)break WA;if((-4&A)==8508)break t;break Oe}if(A-119982>>>0<12|A>>>0>120004|A-119977>>>0<4|A-119973>>>0<2||(0|A)==119970|(-2&A)==119966|A-119808>>>0<85)break k;if(r=128,A-119894>>>0>=71)break s;break t}if(A-120138>>>0<7|A>>>0>120145|(0|A)==120134|A-120128>>>0<5||A-120123>>>0<4|A-120094>>>0<28|(0|A)!=120070&A>>>0<120075|A-120086>>>0<7)break k;if(r=128,A-120077>>>0>=8)break s;break t}if(A-120540>>>0<31|A>>>0<120486)break k;if(r=128,A-120488>>>0>=25)break s;break t}if(A-120714>>>0<31|A-120598>>>0<31)break k;if(r=128,A-120656>>>0<31)break t;break s;case 11:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{fe:{ge:{M:{ce:{J:{if((0|(t=-256&A))<=43263){if((0|t)<=3839){if((0|t)<=3071){if((0|t)==2304)break J;if((0|t)!=2816)break f;if((0|A)>3005)break M;if((0|A)!=2878)break ce;return le=0,132096}if((0|t)==3072)break ge;if((0|t)!=3328)break f;switch(r=132096,A-3535|0){case 0:case 16:break t;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break f;default:break fe}}if((0|t)<=6911){if((0|t)==3840)break le;if((0|t)!=4096)break f;if(r=1024,A-4139>>>0<2)break t;switch(A-4145|0){case 0:case 7:case 10:case 11:case 37:case 38:case 49:case 54:case 55:case 82:case 83:break t;case 86:case 87:case 88:case 89:case 90:case 91:case 94:case 105:case 106:break d;default:break ne}}if((0|t)==6912)break ee;if((0|t)==7168)break Z;if((0|t)!=12288||(r=135168,(-2&A)!=12334))break f;break t}if((0|t)<=70399){if((0|t)<=43775){if((0|t)==43264)break Y;if((0|t)!=43520)break f;return le=0,A-43643&-3?1024:4096}if((0|t)==43776)break X;if((0|t)==69888)break P;if((0|t)!=70144||(r=4096,(0|A)!=70197))break f;break t}if((0|t)<=70911){if((0|t)==70400)break D;if((0|t)!=70656)break f;t=(0|A)==70845?132096:1024,t=(A=(0|A)==70832)?132096:t;break F}if((0|t)==70912)break O;if((0|t)==71168)break T;if((0|t)!=119040)break f;switch(r=131072,A-119141|0){case 8:break g;case 1:break s;case 0:break t;default:break S}}t=(0|A)==2519?132096:1024,t=(A=(0|A)==2494)?132096:t;break F}if(r=132096,(0|A)!=2903)break f;break t}if((0|A)==3006)return le=0,132096;if(r=132096,(0|A)!=3031)break f;break t}if((A=A-3266|0)>>>0>20||(r=132096,!(1<<A&1572865)))break f;break t}if((0|A)==3390)break t;if((0|A)!=3415)break f;break t}if(r=4096,(-2&A)!=3902)break f;break t}r=(A=(0|A)==4252)>>>22|0,A<<=10;break B}r=1024;ee:switch(A-6965|0){default:if((0|A)==6916)break t;break;case 0:case 6:break t;case 1:case 2:case 3:case 4:case 5:break ee}if(A-6973>>>0<5)break t;ee:switch(A-6979|0){case 1:break g;case 0:break t;default:break ee}if((0|A)==7042|(0|A)==7073|(-2&A)==7078)break t;if((0|A)==7082)break g;if((0|A)==7143|A-7146>>>0<3)break t;r=(A=(0|A)==7150)>>>22|0,A<<=10;break B}t=(0|A)==7415?4096:1024,t=(A=(0|A)==7393)?4096:t;break F}t=(0|A)==43456?4096:1024,t=(A=(0|A)==43347)?4096:t;break F}if(r=4096,(0|A)!=44012)break f;break t}if(r=4096,(0|A)!=70080)break f;break t}r=132096;D:switch(A-70462|0){case 0:case 25:break t;case 15:break D;default:break f}break g}if(r=132096,(0|A)!=71087)break f;break t}if(r=4096,(0|A)!=71350)break f;break t}if(r=135168,A-119150>>>0<5)break t;break f}break g;case 12:t=((0|A)==8419)<<6,A=0;break E;case 13:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{fe:{ge:{M:{ce:{J:{H:{ie:{ue:{ve:{xe:{Ue:{Be:{Ke:{aA:{Xe:{nA:{Ze:{iA:{oA:{ye:{Je:{qe:{lA:{De:{Pe:{cA:{$e:{uA:{eA:{he:{Te:{Ge:{Qe:{AA:{dA:{Ce:{fA:{gA:{pA:{if((0|(t=-256&A))<=43775){if((0|t)<=5887){if((0|t)<=2559){if((0|t)<=1535){if((0|t)==768)break pA;if((0|t)==1024)break gA;if((0|t)!=1280)break s;if(A-1425>>>0>=17)break fA;break g}if((0|t)<=2047){if((0|t)==1536)break Ce;if((0|t)!=1792)break s;if(r=1024,(0|A)==1809)break t;if((-16&A)!=1840)break dA;return le=0,5120}if((0|t)==2048)break AA;if((0|t)!=2304)break s;if(A>>>0>=2307)break Qe;break f}if((0|t)<=3583){if((0|t)<=3071){if((0|t)==2560)break Ge;if((0|t)!=2816)break s;switch(r=1024,A-2876|0){case 0:break g;case 3:break t;case 1:case 2:break he;default:break Te}}if((0|t)==3072)break eA;if((0|t)!=3328)break s;if((0|(t=-2&A))!=3328)break uA;break f}if((0|t)<=4095){if((0|t)==3584)break $e;if((0|t)!=3840)break s;if((0|(t=-2&A))!=3864)break cA;break g}if((0|t)==4096)break Pe;if((0|t)!=4864||(r=1024,(0|A)!=4959))break s;break t}if((0|t)<=8191){if((0|t)<=6655){if((0|t)==5888)break De;if((0|t)==6144)break lA;if((0|t)!=6400)break s;if((t=A-6432|0)>>>0<=18&&(r=1024,1<<t&262535))break t;if(r=4096,A-6457>>>0>=3)break s;break t}if((0|t)<=7167){if((0|t)==6656)break qe;if((0|t)!=6912)break s;if(r=1024,(-4&A)==6912)break t;if((0|A)!=6964)break Je;break g}if((0|t)==7168)break ye;if((0|t)!=7424)break s;if(r=4096,A-7620>>>0<12||(r=1024,A-7655>>>0<14))break t;if((A=A-7669|0)>>>0>=11)break s;t=e[(A=82104+(A<<3)|0)>>2];break m}if((0|t)<=42495){if((0|t)<=11519){if((0|t)==8192)break oA;if((0|t)!=11264||(r=4096,A-11503>>>0>=3))break s;break t}if((0|t)==11520)break iA;if((0|t)!=12288)break s;if(A-12330>>>0>=4)break Ze;break g}if((0|t)<=43263){if((0|t)==42496)break nA;if((0|t)!=43008)break s;if(A-43045>>>0>=2)break Xe;break f}if((0|t)==43264)break aA;if((0|t)!=43520)break s;switch(r=1024,A-43561|0){case 83:case 150:case 152:break g;case 0:case 1:case 2:case 3:case 4:case 5:case 8:case 9:case 12:case 13:case 26:case 35:case 135:case 137:case 138:case 139:case 142:case 143:case 149:break t;default:break Ke}}if((0|t)<=71423){if((0|t)<=69375){if((0|t)<=66047){if((0|t)==43776)break Be;if((0|t)==64256)break Ue;if((0|t)!=65024)break s;if(r=536870912,A-65024>>>0<15||(s=64,(0|A)==65039))break t;if(r=4096,s=0,(-16&A)!=65056)break s;break t}if((0|t)<=68095){if((0|t)==66048)break xe;if((0|t)!=66304||(r=1024,A-66422>>>0>=5))break s;break t}if((0|t)==68096)break ve;if((0|t)!=68864||(r=5120,(-4&A)!=68900))break s;break t}if((0|t)<=70399){if((0|t)<=69887){if((0|t)==69376)break ue;if((0|t)!=69632)break s;if(A-69688>>>0>=14)break ie;break f}if((0|t)==69888)break H;if((0|t)!=70144)break s;if(r=1024,A-70191>>>0<3)break t;switch(A-70196|0){case 2:break g;case 0:case 3:case 10:break t;case 1:case 4:case 5:case 6:case 7:case 8:case 9:break ce;default:break J}}if((0|t)<=70911){if((0|t)==70400)break M;if((0|t)!=70656)break s;if((-8&A)!=70712)break ge;break f}if((0|t)==70912)break fe;if((0|t)!=71168)break s;if(r=1024,A-71219>>>0<8)break t;switch(A-71229|0){case 2:break g;case 0:case 3:break t;case 1:break ne;default:break le}}if((0|t)<=92927){if((0|t)<=72703){if((0|t)==71424)break _A;if((0|t)==71680)break ee;if((0|t)!=72192)break s;if(A-72193>>>0>=10)break Z;break f}if((0|t)<=73215){if((0|t)==72704)break Y;if((0|t)!=72960)break s;if((t=A-73009|0)>>>0<19)break SA;break $}if((0|t)==73216)break X;if((0|t)!=92672||(r=4096,A-92912>>>0>=5))break s;break t}if((0|t)<=122879){if((0|t)<=113663){if((0|t)==92928)break P;if((0|t)!=93952||(r=4096,A-94095>>>0>=4))break s;break t}if((0|t)==113664)break D;if((0|t)!=119040)break s;switch(r=4096,A-119143|0){case 0:case 1:case 2:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 30:case 31:case 32:case 33:case 34:case 35:case 36:case 67:case 68:case 69:case 70:break t;default:break s}}if((0|t)<=125183){if((0|t)==122880)break O;if((0|t)!=124928||(r=4096,A-125136>>>0>=7))break s;break t}if((0|t)==125184)break T;if((0|t)!=917760||(r=536870912,A-917760>>>0>=240))break s;break t}if(A-768>>>0<69)break g;if(r=21504,(0|A)==837)break t;if(A-838>>>0<9)break g;if(r=4194304,(0|A)==847)break t;if((-8&A)==848)break g;if(r=4096,A-861>>>0>=6)break s;break t}if(r=4096,A-1155>>>0>=5)break s;break t}if(r=4096,A-1443>>>0<13||(r=5120,A-1456>>>0<14))break t;if((A=A-1471|0)>>>0>=9)break s;t=e[(A=81944+(A<<3)|0)>>2];break m}if(A-1552>>>0<11)break f;if(r=5120,A-1611>>>0<8)break t;if(A-1619>>>0<4)break f;Ce:switch(A-1623|0){case 1:break g;case 0:break t;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 25:case 127:case 128:case 129:case 130:case 131:case 132:case 133:break f;default:break Ce}if(r=4096,A-1759>>>0<2)break t;if((t=A-1761|0)>>>0<8)break jA;break Ie}if(A-1856>>>0<11)break g;if(r=5120,A-1958>>>0<11)break t;if(r=4096,A-2027>>>0>=9)break s;break t}r=1024;AA:switch((-2&A)-2070|0){case 2:break g;case 0:break t;default:break AA}if(A-2260>>>0<12|A-2089>>>0<4|A-2075>>>0<9|A-2085>>>0<3)break f;if(A-2275>>>0<7)return le=0,5120;if(r=4096,A-2282>>>0<6||(r=5120,A-2288>>>0<15))break t;if(r=1024,(0|A)!=2303)break s;break t}r=1024;Qe:switch(A-2362|0){case 2:break g;case 0:break t;default:break Qe}if(A-2369>>>0<8)break f;r=4096;Qe:switch(A-2381|0){case 0:case 4:case 5:case 6:case 7:case 111:break t;case 8:case 9:case 10:case 21:case 22:case 52:break f;default:break Qe}if(A-2497>>>0<4)break f;if((0|A)==2509)break t;if(r=1024,(-2&A)!=2530)break s;break t}if(A-2561>>>0<2)break f;r=4096;Ge:switch(A-2620|0){case 0:case 17:case 128:case 145:break t;case 5:case 6:case 11:case 12:case 15:case 16:case 21:case 52:case 53:case 57:case 69:case 70:case 133:case 134:case 135:case 136:case 137:case 139:case 140:case 166:case 167:case 190:case 191:case 192:break f;default:break Ge}if(A-2813>>>0>=3)break s;break t}if((0|A)==2817)break f}if(A-2881>>>0<4)break f;r=4096;he:switch(A-2893|0){case 0:break t;case 9:break f;default:break he}if((-2&A)==2914)break f;r=1024;he:switch(A-3008|0){default:if((0|A)!=2946)break s;break t;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:break s;case 0:break t;case 13:break he}break g}r=1024;eA:switch(A-3072|0){case 77:case 188:case 205:break g;case 0:case 62:case 63:case 64:case 70:case 71:case 72:case 74:case 75:case 76:case 85:case 86:case 98:case 99:case 129:case 191:case 198:case 204:break t;default:break eA}if((-2&A)!=3298)break s;break t}if(r=4096,A-3387>>>0<2)break t;if(A-3393>>>0<4)break f;if((0|A)==3405)break t;if((0|t)==3426)break f;switch(A-3530|0){case 0:break t;case 8:case 9:case 10:case 12:break f;default:break s}}if((t=A-3633|0)>>>0<10)break RA;break we}if((r=A-3893|0)>>>0>4|!(1<<r&21))break _e;break g}r=1024;Pe:switch(A-4141|0){case 10:case 12:case 13:break g;case 0:case 1:case 2:case 3:case 5:case 6:case 7:case 8:case 9:case 16:case 17:case 43:case 44:case 49:case 50:case 51:case 68:case 69:case 70:case 71:case 85:case 88:case 89:break t;default:break Pe}t=(r=(0|A)==4253)>>>22|0,r=(A=(0|A)==4237)?4096:r<<10;break I}r=1024;De:{Pe:switch(A-5906|0){case 0:case 1:case 32:case 33:break t;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 28:case 29:case 30:case 31:break De;default:break Pe}switch(A-5970|0){case 0:case 1:case 32:case 33:break t;default:break De}}if(r=4194304,(-2&A)==6068)break t;if((t=A-6071|0)>>>0<16)break LA;break ze}if(r=536870912,A-6155>>>0<3||(r=67109888,A-6277>>>0<2))break t;if(r=1024,(0|A)!=6313)break s;break t}if(A-6679>>>0<2)break f;r=1024;qe:switch(A-6683|0){case 0:case 59:case 61:case 62:case 63:case 64:case 65:case 66:case 67:case 71:case 74:case 75:case 76:case 77:case 78:case 79:case 80:case 81:case 88:case 89:break t;default:break qe}if(r=4096,A-6832>>>0<14)break t;if((A=A-6773|0)>>>0>=11)break s;t=e[(A=82016+(A<<3)|0)>>2];break m}if(A-6966>>>0<5)break t;Je:switch(A-6972|0){case 0:case 6:break t;default:break Je}if(A-7019>>>0<9)break g;switch(A-7040|0){case 43:break g;case 0:case 1:case 34:case 35:case 36:case 37:case 40:case 41:case 44:case 45:case 104:case 105:case 109:case 111:case 112:case 113:break t;default:break s}}if(r=1024,A-7212>>>0<8)break t;r=12288;ye:switch(A-7222|0){case 1:break g;case 0:break t;default:break ye}if(A-7380>>>0<13)break g;r=4096;ye:switch(A-7376|0){case 0:case 1:case 2:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 29:case 36:break t;default:break ye}if((-2&A)!=7416)break s;break t}if(r=128,A-8400>>>0<13)break t;if((A=A-8417|0)>>>0>=15)break s;t=e[(A=82192+(A<<3)|0)>>2];break m}if(r=1024,(-32&A)!=11744)break s;break t}if(r=4096,A-12441>>>0>=2)break s;break t}if((0|A)==42607)break g;if(A-42612>>>0<8)break f;if((0|(A&=-2))==42620)break g;if((0|A)==42654)break d;if(r=4096,(0|A)!=42736)break s;break t}r=4096;Xe:switch(A-43204|0){case 0:break t;case 1:break f;default:break Xe}if(A-43232>>>0>=18)break s;break t}if(A-43302>>>0<5)break f;if(A-43307>>>0<3)break g;if(A-43335>>>0<11|A-43392>>>0<3)break f;if(r=4096,(0|A)==43443||(r=1024,A-43446>>>0<4))break t;s=(t=(0|A)==43493)>>>20|0,t=(A=(0|A)==43452)?1024:t<<12;break Q}if((-2&A)==43756)break t;if(r=4096,(0|A)!=43766)break s;break t}r=1024;Be:switch(A-44005|0){case 0:case 3:break t;case 8:break Be;default:break s}break g}if(r=5120,(0|A)!=64286)break s;break t}if(r=4096,(0|A)!=66272)break s;break t}if((-4&A)==68108)break f;if((t=A-68097|0)>>>0<6)break NA;break Ne}if(r=4096,A-69446>>>0>=11)break s;break t}if(A-69811>>>0<4)break f;if(r=1024,(0|A)==69633)break t;if(r=4096,A-69817>>>0>=2)break s;break t}r=1024;H:switch(A-69888|0){case 51:case 52:case 115:break g;case 0:case 1:case 2:case 39:case 40:case 41:case 42:case 43:case 45:case 46:case 47:case 48:case 49:case 50:break t;default:break H}if((-2&A)==70016|A-70070>>>0<9)break t;if(r=4096,A-70090>>>0>=3)break s;break t}if((0|A)==70367)break t}if(A-70371>>>0<6)break t;if(r=4096,A-70377>>>0>=2)break s;break t}if((-2&A)==70400)break f;r=4096;M:switch(A-70460|0){case 0:case 42:case 43:case 44:case 45:case 46:case 47:case 48:case 52:case 53:case 54:case 55:case 56:break t;case 4:break M;default:break s}break f}if((0|A)==70722)break g;if(A-70723>>>0<2)break f;if((t=A-70835|0)>>>0<=13)break S;break Le}if((t=A-71090|0)>>>0<12)break zA;break Re}switch(A-71339|0){case 0:case 2:break t;default:break ne}}if(A-71344>>>0<6)break t;if(r=4096,(0|A)!=71351)break s;break t}if(r=1024,A-71727>>>0<10)break t;if(r=4096,A-71737>>>0>=2)break s;break t}if((0|A)==72244)break g;if(A-72245>>>0<10)break f;if(r=4096,(0|A)==72263)break t;if(A-72273>>>0<11)break f;if(r=1024,A-72330>>>0<13)break t;r=(t=(0|A)==72345)>>>20|0,t=(A=(0|A)==72344)?8192:t<<12;break L}if((t=A-72752|0)>>>0<16)break OA;break je}if(r=1024,A-73459>>>0>=2)break s;break t}if(r=1024,A-92976>>>0>=7)break s;break t}if(r=1024,(0|A)!=113822)break s;break t}if(r=1024,A-122888>>>0<17)break t;if((A=A-122880|0)>>>0>=43)break s;t=e[(A=82816+(A<<3)|0)>>2];break m}if(r=12288,A-125252>>>0<3||(r=1024,(0|A)==125255)||(r=4096,A-125256>>>0<3))break t;break s}if(!(1<<t&12479))break Le;break f}break f;case 14:d:{if((0|(t=-256&A))!=120576){if((0|t)!=65280){if(t||(r=768,s=66,A-48>>>0>=10))break d;break t}if(r=256,A-65296>>>0>=10)break d;break t}if(r=128,A-120782>>>0<50)break t}break s;case 15:d:{if((0|(t=-256&A))!=12288){if((0|t)!=8448)break d;t=(A=(0|(r=-16&A))==8560)>>>18|0,s=A<<14,r=(A=(0|r)==8544)?32768:s;break I}if(A-12321>>>0<9||A-12344>>>0<3)return le=0,2048;if(r=2048,(0|A)==12295)break t}break s;case 16:d:{S:{T:{if((0|(t=-256&A))<=9215){if((0|t)==4864)break T;if((0|t)!=6400||(r=134217728,(0|A)!=6618))break d;break t}if((0|t)==9216)break S;if((0|t)!=127232)break d;if(r=0,s=-2147483648,(0|A)==127232)break t;if(s=268435456,A-127233>>>0>=10)break d;break t}if(r=134217728,A-4969>>>0>=9)break d;break t}if(r=0,s=-2147483648,A-9352>>>0<20)break t}break s;case 17:s=(A=(0|A)==8256)>>>25|0,A<<=7;break N;case 18:d:{S:{T:{O:{if((0|(t=-256&A))<=11775){if((0|t)<=6143){if(r=-2147483624,!t)break t;if((0|t)!=1280||(r=24,(0|A)!=1418))break d;break t}if((0|t)==6144)break O;if((0|t)!=8192)break d;if(r=-2147483624,(-2&A)==8208)break t;A=A-8211>>>0<2,t=-2147483640;break b}if((0|t)<=65023){if((0|t)==11776)break T;if((0|t)!=12288)break d;t=(0|A)==12336,r=(A=(0|A)==12316)||t?-2147483640:8,le=A?0:t?130:0;break h}if((0|t)==65024)break S;if((0|t)!=65280||(r=24,(0|A)!=65293))break d;break t}if(r=24,(0|A)!=6150)break d;break t}if(r=-2147483624,(0|A)==11799)break t;A=(-2&A)==11834,t=-2147483640;break b}if(r=8,s=8388608,A-65073>>>0<2||(r=152,s=0,(0|A)==65123))break t}return le=0,8;case 19:d:{S:{T:{O:{D:{P:{if((0|(t=-256&A))<=11775){if((0|t)<=8959){if(t)break P;break w}if((0|t)==8960)break D;if((0|t)==9984)break O;if((0|t)!=10496)break s;break K}if((0|t)<=64767){if(r=-2147483648,(0|t)==11776)break t;if((0|t)!=12288)break s;switch(r=-2147483616,A-12301|0){case 0:case 2:break t;default:break T}}if((0|t)==64768)break S;if((0|t)==65024)break d;if((0|t)!=65280)break s;if(r=32,(0|A)==65379)break t;break s}if((0|t)!=8192)break s;P:switch(A-8318|0){default:if(r=-2147483648,(0|A)!=8262)break s;break t;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break s;case 0:case 16:break P}break k}r=-2147483520;D:switch(A-8969|0){case 1:break s;case 0:case 2:break t;default:break D}if((0|A)!=9002)break s;return le=0,-2139095040}if(r=-2147483520,(0|A)==10182)break t;break Fe}return le=0,(-2&A)==12318?-2147483616:-2147483648}if(r=-2147483648,(0|A)!=64830)break s;break t}s=(A=!(A-65090&-3))>>>27|0,A<<=5;break N;case 20:case 21:if(!(A&=-256))break C;if(r=-2147483616,(0|A)==8192)break t;break tA;case 22:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{fe:{ge:{M:{ce:{J:{H:{ie:{ue:{ve:{xe:{Ue:{Be:{Ke:{aA:{Xe:{nA:{Ze:{iA:{oA:{ye:{Je:{qe:{lA:{De:{Pe:{cA:{$e:{uA:{eA:{he:{Te:{Ge:{Qe:{AA:{dA:{Ce:{if((0|(t=-256&A))<=43519){if((0|t)<=5887){if((0|t)<=2303){if((0|t)<=1535){if(!t)break Ce;if((0|t)==768)break dA;if((0|t)!=1280)break s;if(r=0,s=538968064,A-1371>>>0<2)break t;switch(s=268435456,A-1373|0){case 0:break t;case 1:break Qe;default:break AA}}if((0|t)==1536)break Te;if((0|t)==1792)break he;if((0|t)!=2048)break s;if((t=A-2103|0)>>>0<8)break QA;break We}if((0|t)<=3839){if((0|t)==2304)break eA;if((0|t)==3328)break uA;if((0|t)!=3584||(r=64,(-2&A)!=3674))break s;break t}if((0|t)<=4863){if((0|t)==3840)break $e;if((0|t)!=4096)break s;if(r=268435520,(-2&A)==4170)break t;if(r=0,s=16777216,(0|A)!=4347)break s;break t}if((0|t)==4864)break cA;if((0|t)!=5632)break s;switch(r=64,A-5741|0){case 1:break n;case 0:break t;default:break Pe}}if((0|t)<=11263){if((0|t)<=6655){if((0|t)==5888)break De;if((0|t)==6144)break TA;if((0|t)!=6400)break s;t=(0|A)==6469,r=(A=(0|A)==6468)||t?268435520:0,le=A?536870912:t?1073741824:0;break h}if((0|t)<=7167){if((0|t)==6656)break lA;if((0|t)!=6912)break s;if((0|(t=-2&A))!=7002)break qe;break j}if((0|t)==7168)break Je;if((0|t)!=8192)break s;switch(r=-2147483520,A-8214|0){case 1:break w;case 0:break t;case 16:break ye;default:break oA}}if((0|t)<=41983){if((0|t)==11264)break iA;if((0|t)==11776)break Ze;if((0|t)!=12288)break s;switch(r=-2147483584,s=272629760,A-12289|0){case 2:break w;case 0:break t;case 1:break Xe;default:break nA}}if((0|t)<=43007){if((0|t)==41984)break aA;if((0|t)!=42496)break s;switch(r=64,s=268435456,A-42739|0){case 4:break o;case 0:break n;case 3:break r;case 2:break t;case 1:break Be;default:break Ke}}if((0|t)==43008)break Ue;if((0|t)!=43264)break s;switch(r=4096,A-43310|0){case 0:break t;case 1:break j;default:break xe}}if((0|t)<=70655){if((0|t)<=67839){if((0|t)<=65279){if((0|t)==43520)break ve;if((0|t)==43776)break ue;if((0|t)!=65024)break s;if(r=0,s=268435456,(0|(l=-2&A))==65040)break t;if((t=A-65042|0)>>>0<8)break DA;break ke}if((0|t)==65280)break ie;if((0|t)==66304)break H;if((0|t)!=67584||(r=64,(0|A)!=67671))break s;break t}if((0|t)<=69375){if((0|t)==67840)break J;if((0|t)==68096)break ce;if((0|t)!=68352)break s;switch(r=64,A-68410|0){case 0:case 1:case 2:case 3:case 4:case 5:case 95:case 96:case 97:case 98:break t;default:break s}}if((0|t)<=69887){if((0|t)==69376)break M;if((0|t)!=69632)break s;if(A-69703>>>0>=2)break ge;break j}if((0|t)==69888)break fe;if((0|t)!=70144)break s;if((t=A-70200|0)>>>0<=4)break le;if((0|A)!=70313)break s;break j}if((0|t)<=74751){if((0|t)<=71423){if((0|t)==70656)break ne;if((0|t)==70912)break ee;if((0|t)!=71168||(r=268435520,A-71233>>>0>=2))break s;break t}if((0|t)<=72703){if((0|t)==71424)break Z;if((0|t)!=72192)break s;if(A-72258>>>0>=2)break Y;break j}if((0|t)==72704)break X;if((0|t)!=73216||(r=268435520,A-73463>>>0>=2))break s;break t}if((0|t)<=93695){if((0|t)==74752)break P;if((0|t)==92672)break O;if((0|t)!=92928)break s;if(r=268435520,A-92983>>>0<2)break t;t=(0|A)==92996?268435520:0,t=(A=(0|A)==92985)?64:t;break F}if((0|t)<=121343){if((0|t)==93696)break D;if((0|t)!=113664||(r=268435520,s=-2147483648,(0|A)!=113823))break s;break t}if((0|t)==121344)break yA;if((0|t)!=125184)break s;r=((0|A)==125279)<<30,t=0,le=(A=(0|A)==125278)?536870912:r;break i}r=-1879048128,s=536870912;Ce:{fA:{gA:{pA:{HA:switch(A-33|0){default:switch(A-183|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:break Ce;case 8:break gA;case 0:break pA;default:break fA}case 2:case 9:le=66;break u;case 11:return le=268435456,-2147483584;case 13:return le=-2147483648,-1879048128;case 25:return le=134217728,-2147483584;case 26:return le=67108864,-2147483584;case 1:case 6:break C;case 0:break t;case 3:case 4:case 5:case 7:case 8:case 10:case 12:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 27:case 28:case 29:break Ce;case 30:break HA}return le=1073741824,-1879048128}return le=0,134230016}le=1078984704;break u}if((0|A)==161)break sA}break w}t=(0|A)==903,r=(A=(0|A)==894)?64:t?134217792:0,le=A?1073741824:t?67108864:0;break h}if((0|A)==1417)break Ge;if((0|A)!=1475)break s;break oe}le=1075838976;break c}le=-2143289344;break a}r=64,s=268435456;Te:switch(A-1548|0){case 15:break r;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 16:case 17:break s;case 0:break t;case 19:break Te;case 18:break j;default:break T}break o}if((t=A-1792|0)>>>0<6)break FA;if(r=64,s=134217728,(-2&A)==1798)break t;s=67108864;he:{Te:{Ge:switch(A-1800|0){default:switch(A-2040|0){case 1:break he;case 0:break Te;default:break s}case 3:break s;case 0:break t;case 1:break Ge;case 2:case 4:break oe}le=1073741824;break A}le=268435456;break A}le=536870912;break a}t=(0|A)==2405,r=(A=(0|A)==2404)||t?268435520:0,le=A?-2143289344:t?16777216:0;break h}if(r=0,s=-2143289344,(0|A)!=3572)break s;break t}r=64;$e:switch(A-3848|0){case 5:le=-2143289344;break A;case 6:le=16777216;break A;case 0:break t;default:break $e}if(A-3854>>>0<5)break t;if(r=0,s=268435456,(0|A)!=3860)break s;break t}if((t=A-4961|0)>>>0<4)break GA;if(r=64,s=134217728,A-4965>>>0<2)break t;t=(0|A)==4968,r=(A=(0|A)==4967)||t?268435520:0,le=A?1073741824:t?16777216:0;break h}if(A-5867>>>0>=3)break s;break t}if(r=268435520,A-5941>>>0<2)break t;if((A=A-6100|0)>>>0>=7)break s;t=e[(A=83240+(A<<3)|0)>>2];break m}if(r=268435520,(-4&A)!=6824)break s;break t}if(r=64,(0|A)==7005)break t;if(r=268435520,(0|t)!=7006)break s;break t}if(A-7227>>>0<2)break j;if(r=64,A-7229>>>0<3||(r=268435520,(-2&A)==7294))break t;if(r=4096,(0|A)!=7379)break s;break t}le=33554432;break u}if((-8&A)==8224)break w;if(A-8242>>>0<3)break t;if(A-8240>>>0<9)break w;if((t=A-8251|0)>>>0<21)break PA;break ae}if((0|A)==11513){le=-2147483648;break c}if(r=0,s=1073741824,(-2&A)==11514)break t;if(s=-2147483648,(0|A)!=11518)break s;break t}r=-1879048128;Ze:switch(A-11822|0){case 4:case 6:le=268435456;break u;case 5:le=-2147483648;break u;case 7:le=67108864;break u;case 14:return le=-2147483648,-1879048128;case 19:return le=268435456,-2147483584;case 30:case 32:return le=0,-2147483584;case 0:break t;default:break Ze}break w}if((0|A)==12349)break _;if((0|A)!=12539)break s;return le=0,16}return le=-2143289344,-1879048128}t=(0|A)==42239,r=(A=(0|A)==42238)?64:t?268435520:0,le=A?268435456:t?-2147483648:0;break h}Ke:switch(A-42509|0){case 1:break n;case 0:break t;case 2:break Ke;default:break s}break o}le=134217728;break A}if(A-43126>>>0<2)break j;if(r=268435520,A-43214>>>0>=2)break s;break t}if((0|A)==43463)break oe;if(r=268435520,(-2&A)!=43464)break s;break t}if(A-43613>>>0<3)break j;if(r=64,(0|A)==43743)break t;if(r=268435520,(-2&A)!=43760)break s;break t}if(r=268435520,(0|A)!=44011)break s;break t}r=268435520,s=541065216;ie:switch(A-65281|0){case 1:case 6:return le=0,32;case 11:le=272629760;break A;case 13:le=-2143289344;break a;case 100:return le=0,16;case 25:le=138412032;break A;case 26:le=71303168;break A;case 30:le=1077936128;break a;case 96:break n;case 59:break k;case 0:break t;case 99:break ie;default:break s}le=268435456;break A}r=(t=(0|A)==66512)>>>26|0,t=(A=(0|A)==66463)?64:t<<6;break L}if(r=64,(0|A)!=67871)break s;break t}if(r=268435520,(-2&A)==68182)break t;if(r=64,A-68336>>>0>=6)break s;break t}if(r=268435520,A-69461>>>0>=5)break s;break t}if(r=64,A-69705>>>0<5)break t;if(r=268435520,A-69822>>>0>=4)break s;break t}if(A-69953>>>0<2)break j;if((t=A-70085|0)>>>0<=26)break S;break Ve}if((0|t)!=2)break j;break oe}if(r=268435520,A-70731>>>0<2)break t;s=(t=(0|A)==70747)>>>26|0,t=(A=(0|A)==70733)?64:t<<6,le=A?268435456:s;break i}r=268435520;ee:switch((-2&A)-71106|0){case 0:break t;case 2:break d;default:break ee}if(r=8192,A-71110>>>0<3)break t;if(r=268435520,A-71113>>>0>=15)break s;break t}if(r=268435520,A-71484>>>0>=3)break s;break t}if(r=268435520,A-72347>>>0<2)break t;if(r=64,A-72353>>>0>=2)break s;break t}if(r=268435520,A-72769>>>0<2)break t;r=(t=(0|A)==72817)>>>26|0,t=(A=(0|A)==72771)?64:t<<6;break L}if(r=64,s=134217728,A-74865>>>0<2)break t;if(s=0,A-74864>>>0>=5)break s;break t}t=(0|A)==93848?268435520:0,t=(A=(0|A)==93847)?64:t;break F}if(r=268435520,(-2&A)==92782)break t;if(s=-2147483648,(0|A)!=92917)break s;break t}if((0|A)==1748)break n;break s}if(!(1<<t&100663555))break Ve;break j}break oe;case 23:d:{S:{T:{O:{D:{P:{if((0|(t=-256&A))<=11775){if((0|t)<=8959){if(t)break P;break w}if((0|t)==8960)break D;if((0|t)==9984)break O;if((0|t)!=10496)break s;break K}if((0|t)<=64767){if((0|t)==11776)break T;if((0|t)!=12288)break s;if((A=A-12300|0)>>>0<=17&&(r=-2147483616,1<<A&131077))break t;break w}if((0|t)==64768)break S;if((0|t)==65024)break d;if((0|t)!=65280)break s;if(r=32,(0|A)==65378)break t;break s}if((0|t)!=8192)break s;r=-2147483616;P:switch(A-8218|0){case 1:case 2:case 3:break s;case 0:case 4:break t;default:break P}P:switch(A-8317|0){default:if((0|A)!=8261)break s;break w;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break s;case 0:case 16:break P}break k}r=-2147483520;D:switch(A-8968|0){case 1:break s;case 0:case 2:break t;default:break D}if((0|A)!=9001)break s;return le=0,-2139095040}if(r=-2147483520,(0|A)==10181)break t;break Fe}return le=0,(0|A)==11842?-2147483616:-2147483648}if(r=-2147483648,(0|A)!=64831)break s;break t}r=(A=!(A-65089&-3))>>>27|0,A<<=5;break B;case 24:r=(A=A>>>0<256)>>>1|0,A<<=31;break B;case 25:d:{S:{T:{O:{D:{P:{X:{Y:{if((0|(t=-256&A))<=12287){if((0|t)<=767){if(!t)break Y;if((0|t)!=512)break d;if(A-751>>>0>=17)break X;break g}if((0|t)==768)break P;if((0|t)!=7936)break d;switch(r=4096,A-8125|0){case 0:case 2:case 3:case 4:case 16:case 17:case 18:case 32:case 33:case 34:case 48:case 49:case 50:case 64:case 65:break t;default:break d}}if((0|t)<=43775){if((0|t)==12288)break D;if((0|t)!=42752||(r=4096,(-2&A)!=42784))break d;break t}if((0|t)==43776)break O;if((0|t)==65280)break T;if(r=0,s=78,(0|t)!=127744)break d;break t}Y:switch(A-168|0){default:r=-2147479424;Z:switch(A-94|0){case 0:break t;case 2:break Z;default:break d}return le=0,-2147479552;case 0:case 7:break g;case 1:case 2:case 3:case 4:case 5:case 6:case 8:case 9:case 10:case 11:case 13:case 14:case 15:break d;case 12:case 16:break Y}break g}if((0|A)==749|A-741>>>0<7)break g;if(A-706>>>0>=4)break S;break g}if((A=A-885|0)>>>0>16||(r=4096,!(1<<A&98305)))break d;break t}if(r=67112960,A-12443>>>0>=2)break d;break t}if(r=4096,(0|A)!=43867)break d;break t}r=4224;T:switch(A-65342|0){default:if((0|A)!=65507)break d;break;case 0:break t;case 1:break d;case 2:break T}break g}if(r=4096,A-722>>>0<14)break t}break s;case 26:d:{S:{T:{O:{D:{P:{if((0|(t=-256&A))<=9471){if((0|t)<=8447){if(t)break d;break w}if((0|t)==8448)break P;if((0|t)==8704)break D;if(r=-2147483648,(0|t)!=8960)break s;break t}if((0|t)<=10495){if((0|t)==9472)break O;if((0|t)==9728)break T;if((0|t)==9984)break w;break s}if((0|t)==10496)break S;if((0|t)==10752)break w;if(r=-2147483648,(0|t)!=11008)break s;break t}if((0|A)==8472)return le=0,67108864;if((0|A)==8596)break q;if(r=-2147483648,A>>>0<=8591)break s;break t}if(r=-2147483640,(0|A)==8722)break t;t=-2147483648,le=(A=A-8942>>>0<4)?33554432:0;break i}if(r=-2147483648,s=130,A-9723>>>0<2)break t;t=-2147483648,le=(A=A-9725>>>0<2)?134:0;break i}if(r=-2147483648,s=128,(0|A)==9839)break t;break w}if(r=-2147483648,s=130,(-2&A)==10548)break t;t=-2147483648,le=(A=(0|A)==10626)?134217728:0;break i}if((0|t)==8192)break Ye;break s;case 27:d:{S:{T:{O:{D:{P:{X:{Y:{Z:{ee:{ne:{le:{fe:{ge:{M:{ce:{J:{H:{ie:{ue:{ve:{xe:{Ue:{Be:{if((0|(t=-256&A))<=12287){if((0|t)<=9727){if((0|t)<=8959){if(!t)break Be;if((0|t)!=8448)break s;switch(r=0,s=130,A-8482|0){case 7:break k;case 0:break t;case 1:case 2:case 3:case 4:case 5:case 6:case 8:case 9:case 10:case 11:break ue;case 12:break xe;default:break Ue}}if((0|t)==8960)break ie;if((0|t)==9216)break H;if((0|t)!=9472)break s;switch(r=-2147483520,(-2&A)-9632|0){case 0:break t;case 10:break q;default:break J}}if((0|t)<=11007){if((0|t)==9728)break ce;if((0|t)==9984)break M;if(r=-2147483648,(0|t)!=10240)break s;break t}if((0|t)==11008)break ge;if((0|t)==11776)break fe;if((0|t)!=12032)break s;if(r=1048576,A>>>0<12246)break t;switch(r=262144,(-2&A)-12272|0){case 0:break t;case 2:break d;default:break le}}if((0|t)<=127999){if((0|t)<=127231){if((0|t)==12288)break ne;if((0|t)==12800)break ee;if((0|t)!=126976)break s;t=(0|A)==127183,r=0,le=(A=(0|A)==126980)||t?134:128;break h}if((0|t)==127232)break Z;if((0|t)==127488)break Y;if((0|t)!=127744)break s;if(A>>>0>=127777)break X;break R}if((0|t)<=128767){if((0|t)==128e3)break P;if((0|t)==128256)break D;if((0|t)!=128512)break s;if((t=A-128581|0)>>>0<11)break CA;break Me}if((0|t)==128768)break O;if((0|t)==129280)break T;if((0|t)!=129536)break s;le=128;break c}t=(0|A)==174,r=-2147483648,le=(A=(0|A)==169)||t?130:0;break h}switch(A-8616|0){case 0:break w;case 1:case 2:break ve;default:break ue}}return le=0,67108864}return le=130,-2147483520}if(r=-2147483520,A-8597>>>0<5)break t;if(A-8604>>>0<18)break K;if((t=A-8624|0)>>>0<8)break BA;break He}if(A>>>0<8968)break w;if((0|(t=-2&A))==8986)break l;if(A-8972>>>0<20|A-8994>>>0<6)break w;if((0|A)==9e3)break q;if(A-9003>>>0<81)break w;if(r=-2147483648,s=128,(0|A)==9096)break t;if(A-9085>>>0<30)break w;if(r=-2147483520,s=0,(0|t)==9140)break t;ie:switch(A-9143|0){case 0:case 25:break t;case 24:break q;default:break ie}if(A-9140>>>0<40)break w;if((0|A)==9186)break t;if((t=A-9193|0)>>>0<4)break l;r=-2147483648,s=134;ie:switch(A-9200|0){case 0:case 3:break t;default:break ie}if(t>>>0<11)break q;if(s=130,A-9208>>>0<3)break t;if(s=0,A>>>0<=9186)break s;break t}if(r=-2147483648,A-9216>>>0<75||(r=33792,s=130,(0|A)==9410)||(s=0,A-9398>>>0<26))break t;if(r=17408,A-9424>>>0>=26)break s;break t}if(A-9646>>>0<8)break K;if(s=130,(0|A)==9654)break t;if((-4&A)==9660)break K;J:switch(A-9664|0){case 0:break t;case 6:case 7:case 10:case 11:case 15:case 16:case 17:case 18:case 19:case 34:case 36:break K;default:break J}return le=0,A-9703>>>0<6?-2147483520:-2147483648}ce:switch((-16&A)-9728>>>4|0){case 0:if(A>>>0<9733)break q;r=-2147483520,s=128;J:switch(A-9733|0){case 0:break t;case 9:break J;case 1:break K;default:break G}break q;case 2:if((t=A-9760|0)>>>0<11)break vA;if(r=-2147483648,s=130,A>>>0<=9773)break G;break t;case 3:if(r=-2147483648,s=130,A-9784>>>0>=3)break G;break t;case 4:r=-2147483520,s=130;J:switch(A-9792|0){case 0:case 2:break t;default:break J}if(r=-2147483648,s=134,A>>>0<=9799)break G;break t;case 5:if(r=-2147483648,s=134,A>>>0<9812)break t;if(s=130,(0|A)!=9823)break G;break t;case 6:if((0|A)==9734)break K;if((0|A)==9824)return le=130,-2147483520;if(r=-2147483520,s=128,A-9825>>>0<2)break t;if((t=A-9827|0)>>>0<6)break EA;break Ee;case 8:if(r=-2147483648,A>>>0<=9861)break G;break t;case 10:r=-2147483648,s=130;J:switch(A-9888|0){case 1:break l;case 0:break t;default:break J}if(s=134,(-2&A)!=9898)break G;break t;case 11:if(r=-2147483648,s=130,(-2&A)==9904)break t;if(s=134,A-9917>>>0>=2)break G;break t;case 12:if((-2&A)==9924)break l;r=-2147483648,s=130;J:switch(A-9928|0){case 0:case 7:break t;case 6:break J;default:break G}break l;case 14:t=(0|A)==9962,r=-2147483648,le=(A=(0|A)==9961)?130:t?134:128;break h;case 13:break wA;case 9:break kA;case 15:break ce;case 7:break MA;case 1:break xA;default:break G}if((0|A)==9972|A>>>0<9970)break q;if(r=-2147483648,s=134,A>>>0<9974||(0|A)!=9974&&(s=130,A>>>0<9977))break t;if((A=A-9977|0)>>>0<5)break IA;break G}M:{ce:{J:{H:{ie:switch((-16&A)-9984>>>4|0){case 0:if(r=0,s=130,(0|A)==9986||(s=128,A>>>0<9989)||(s=134,(0|A)==9989))break M;s=150;ue:switch((-2&A)-9994|0){case 0:break M;case 2:break J;default:break ue}if(s=130,A-9992>>>0<6)break M;t=(0|A)==9999,r=0,s=(A=(0|A)==9998)?128:t?130:0;break M;case 1:if(r=0,s=128,A>>>0<10002)break M;if((A=A-10002|0)>>>0>11||(s=130,!(1<<A&2069)))break H;break M;case 2:t=(0|A)==10024,r=0,s=(A=(0|A)==10017)?130:t?134:0;break M;case 4:r=0,s=130;ue:switch(A-10052|0){case 0:case 3:break M;case 8:case 10:break ue;default:break H}s=134;break M;case 5:if(r=0,s=1073741958,A-10067>>>0<2)break M;s=(A=A-10069&-3)?0:536871046;break M;case 6:if((t=A-10082|0)>>>0<3)break ce;if(r=0,s=128,A>>>0<=10084)break H;break M;case 9:if(r=0,s=134,A-10133>>>0>=3)break H;break M;case 10:if(r=0,s=130,(0|A)!=10145)break H;break M;case 11:t=(0|A)==10175,r=0,s=(A=(0|A)==10160)||t?134:0;break M;case 3:break ie;default:break H}if(r=0,s=130,A-10035>>>0<2)break M}r=0,s=0;break M}s=146;break M}r=e[(A=83992+(t<<3)|0)>>2],s=e[A+4>>2]}return le=s,-2147483648|r}if(r=-2147483648,s=130,A-11013>>>0<3||(s=134,A-11035>>>0<2))break t;t=(0|A)==11093,r=-2147483648,le=(A=(0|A)==11088)||t?134:0;break h}if(A-11904>>>0>=26)break S;return le=0,1048576}if(A-12276>>>0>=8)break s;break t}if((A=A-12306|0)>>>0>14||(r=-2147483648,!(1<<A&16387)))break s;break t}t=0,le=(A=A-12951&-3)?0:130;break i}if(r=0,s=128,(0|A)==127279)break t;if(A-127280>>>0<26||A-127312>>>0<26)return le=0,33792;if((t=A-127344|0)>>>0<=15&&(r=33792,s=130,1<<t&49155)||(r=33792,s=0,t>>>0<26))break t;if((0|A)==127374)break R;if(r=0,s=134,A-127377>>>0<10)break t;if(s=102,A>>>0<=127461)break s;break t}r=0,s=134;Y:{Z:switch(A-127489|0){case 0:case 25:break t;case 1:break _;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:break Y;default:break Z}switch(A-127535|0){case 0:break t;case 8:break _;default:break Y}}if((-2&A)==127568|A-127538>>>0<9)break t;if(s=128,A>>>0<=127583)break s;break t}if((0|A)==127777)break _;if(A-127789>>>0<9|A-127799>>>0<70)break R;if((0|A)==127877)break Ae;if(A-127870>>>0<22)break R;if(A-127780>>>0<112)break _;r=0,s=130;X:switch(A-127894|0){case 44:case 45:case 46:case 49:break Ae;case 0:case 1:case 3:case 4:case 5:break t;default:break X}if(A-127904>>>0<42)break R;if(s=150,(0|A)==127946||(s=146,A-127947>>>0<2))break t;if(A-127951>>>0<5)break R;if(s=134,A-127968>>>0<17||(s=130,A-127902>>>0<83))break t;if((t=A-127987|0)>>>0<5)break bA;break V}r=0,s=130;P:switch(A-128063|0){case 0:case 2:break t;default:break P}if((0|A)==128124|(-5&A)-128129>>>0<3|(0|A)==128110|A-128112>>>0<9||(-2&A)==128066|A-128102>>>0<4|A-128070>>>0<11)break Ae;P:switch(A-128253|0){case 1:le=128;break c;case 0:break _;default:break P}if(s=150,(0|A)==128170)break t;break R}if(A>>>0<128318)break R;if(r=0,A>>>0<128326)break t;if(A-128329>>>0<2)break _;if(A-128331>>>0<4|A-128336>>>0<24)break R;if(A-128367>>>0<2)break _;if((-2&A)==128372){le=146;break c}if(A-128371>>>0<7)break _;if(s=150,!(t=A-128378|0))break t;if((0|t)==13|A-128394>>>0<4)break _;if(s=146,(0|A)==128400||(s=150,A-128405>>>0<2))break t;s=134;D:switch(A-128420|0){case 0:break t;case 1:case 4:case 13:case 14:case 24:case 30:case 31:case 32:case 45:case 46:case 47:case 56:case 57:case 58:case 61:case 63:case 68:case 75:case 79:case 86:break _;default:break D}A=A>>>0>128506,t=0;break p}if(r=0,s=128,A-128981>>>0>=4)break s;break t}if(A>>>0<129292)break s;if(A-129328>>>0<10)break Ae;r=0,s=150;T:switch(A-129304|0){case 35:break s;case 0:case 1:case 2:case 3:case 4:case 6:case 7:case 14:break t;default:break T}if(A-129341>>>0<2)break Ae;if(s=0,(0|A)==129350||(s=198,(-4&A)==129456))break t;if((t=A-129461|0)>>>0<5)break hA;break mA}if(r=1048576,A-11931>>>0<89)break t;break s}return le=0,524288;case 29:return le=16777216,1073741825;case 28:break t;case 30:break YA;default:break s}return le=0,(0|A)==32?1073741825:1}if(!(1079>>>t&1))break pe;t=e[(A=81344+(t<<3)|0)>>2];break m}if(557553>>>t&1)break k;if((-4&A)!=8508)break Oe;break t}if(!(207>>>t&1))break Ie;break f}if(r=1024,!(1017>>>t&1))break we;break t}if(r=1024,!(32895>>>t&1))break ze;break t}if(r=1024,!(55>>>t&1))break Ne;break t}if(r=1024,!(3087>>>t&1))break Re;break t}if((A=A-71453|0)>>>0>=15)break s;t=e[(A=82312+(A<<3)|0)>>2];break m}if(!(49023>>>t&1))break je;t=e[(A=82432+(t<<3)|0)>>2];break m}if(!(514623>>>t&1))break $;t=e[(A=82664+(t<<3)|0)>>2];break m}t=e[(A=83160+(t<<3)|0)>>2];break m}if(r=268435520,!(197>>>t&1))break We;break t}t=e[(A=83208+(t<<3)|0)>>2];break m}if((A=A-6145|0)>>>0>=10)break s;t=e[(A=83296+(A<<3)|0)>>2];break m}if(!(1077711>>>t&1))break ae;t=e[(A=83376+(t<<3)|0)>>2];break m}if(!(159>>>t&1))break ke;t=e[(A=83544+(t<<3)|0)>>2];break m}if((A=A-121479|0)>>>0>=4)break s;t=e[(A=83608+(A<<3)|0)>>2];break m}if(!(195>>>t&1))break He;break K}if((A=A-9745|0)>>>0>=13)break G;t=e[(A=83640+(A<<3)|0)>>2];break m}if(1101>>>t&1)break q;if(r=-2147483648,s=130,A>>>0<=9773)break G;break t}if(!(45>>>t&1))break Ee;t=e[(A=83744+(t<<3)|0)>>2];break m}if((A=A-9851|0)>>>0>=5)break G;t=e[(A=83792+(A<<3)|0)>>2];break m}if((A=A-9874|0)>>>0>=11)break G;t=e[(A=83832+(A<<3)|0)>>2];break m}if((A=A-9937|0)>>>0>=4)break G;t=e[(A=83920+(A<<3)|0)>>2];break m}t=e[(A=83952+(A<<3)|0)>>2];break m}if(!(23>>>t&1))break V;t=e[(A=84016+(t<<3)|0)>>2];break m}if(r=0,s=150,!(1991>>>t&1))break Me;break t}if(s=150,27>>>t&1)break t}t=0,le=(A=A-129489>>>0<13)?150:134;break i}A=A-127992>>>0<3,t=0;break p}if(A-9837>>>0<2)break t}le=128;break u}if(A-8623>>>0<13)break w;if(A-8636>>>0<18)break K;if((t=A-8656|0)>>>0<22&&(s=0,3157995>>>t&1)||(r=-2147483648,s=0,A-8661>>>0<31))break t;break s}if(A>>>0<128592)break R;if(r=0,s=0,A>>>0<128640)break t;if(!((t=A-128675|0)>>>0>29|!(1<<t&537788417)))break Ae;if(A>>>0<128710)break R;if(s=150,(0|A)==128716)break t;if(A-128715>>>0<5)break _;if(A-128720>>>0<3)break R;if(!((t=A-128736|0)>>>0>=10|!(575>>>t&1)))break _;if(s=134,A-128747>>>0<2)break t;s=130;Me:switch(A-128752|0){case 0:case 3:break t;default:break Me}A=A-128756>>>0<6,t=0;break p}r=-2147483648;Ye:switch(A-8260|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:break s;case 0:case 14:break t;default:break Ye}if((0|(A=A-8315|0))!=16&&A)break s;return le=0,8}if(r=268435520,s=1073741824,(0|A)!=69955)break s;break t}if(A-8266>>>0<8)break w;if(r=-2147483640,(0|A)==8275)break t;if(r=-2147483648,A-8277>>>0>=10)break s;break t}le=542113792;break u}if(r=-2147483648,s=0,A-65093>>>0<2||(r=64,s=268435456,(0|l)==65104))break t;r=268435520,s=-2147483648;ke:switch(A-65106|0){case 3:le=134217728;break A;case 5:le=536870912;break a;case 4:break o;case 2:break r;case 0:break t;case 15:case 22:break ke;default:break s}break k}if((0|A)==2142)break oe;if(r=64,A-2096>>>0<15)break t;break s}if(r=1024,A-72850>>>0<22)break t;if((A=A-72874|0)>>>0>=13)break s;t=e[(A=82560+(A<<3)|0)>>2];break m}if(r=4096,A-71103>>>0<2)break t;if(r=1024,(-2&A)!=71132)break s;break t}if((0|A)==70726)break g;if(r=4096,(-2&A)!=70850)break s;break t}if(r=4096,A-68325>>>0>=2)break s;break t}if((0|A)==6109)break g;if(r=4096,A-6089>>>0>=11)break s;break t}_e:switch(A-3959|0){case 0:case 2:return le=0,8389632;default:break _e}if((0|t)==3968|A-3953>>>0<14)break f;if(!((t=A-3970|0)>>>0>=6|!(55>>>t&1)))break g;if(A-3981>>>0<11)break f;if(r=1024,A-3993>>>0<36)break t;if(r=4096,(0|A)!=4038)break s;break t}if(A-3655>>>0<6)break g;if(!((t=A-3761|0)>>>0>11|!(1<<t&3577)))break f;r=1024;we:switch(A-3661|0){case 1:break g;case 0:break t;default:break we}if(r=4096,A-3784>>>0<5)break t;if(r=1024,(0|A)!=3789)break s;break t}if(A-1770>>>0<3)break t;if(r=1024,(0|A)!=1773)break s;break t}if((-2&A)==73028)break g;r=1024;$:switch(A-73104|0){default:if((0|A)!=73031)break s;break t;case 2:case 3:case 4:case 6:break s;case 0:case 1:case 5:break t;case 7:break $}break g}if((A=A-8492|0)>>>0>=30)break s;t=e[(A=81704+(A<<3)|0)>>2];break m}r=0;break z}s=(t=(-3&A)==43712)>>>20|0,r=t<<12}if((0|(i=-65536&A))==131072)break rA;if((0|i)==65536)break be;if(t=r,l=s,i)break me}if(A-13312>>>0<6582||A-19968>>>0<20976)break e;if(i=2048,A-63744>>>0<366)return le=l,2048|t;if(r=t,s=l,A-64112>>>0>=106)break me;break W}if(i=2099200,A-183984>>>0<7473|A-178208>>>0<5762|A-177984>>>0<222|A-131072>>>0<42711||A-173824>>>0<4149||(i=2048,A-194560>>>0<542))break W;break me}if(i=2048,A-110960>>>0<396|A-94208>>>0<6125|A-100352>>>0<755)break W}i=0}return le=s,r|i}if((-4&A)==8508)break k;if(r=128,!(A-8517>>>0<3)&&(r=16777344,(-2&A)!=8520))break s}le=s;break h}t=(A=A>>>0>131069)?65536:0,le=A?0:128;break i}if((0|A)==11776)break w}le=0;break c}le=0;break u}return le=0,4194304}return le=0,128}return le=0,16777344}return le=0,4096}return le=0,1024}return le=0,-2147483520}return le=0,A-10214>>>0<10?-2147483520:-2147483648}le=0;break a}le=0;break A}le=130;break c}le=130;break u}le=134;break c}return le=150,0}le=0;break i}le=A?0:s;break i}return le=t,A}return 131072}return le=0,16777216}le=A?0:r;break i}return le=s,A}return le=r,A}le=A?0:t;break h}return le=0,8192}le=e[A+4>>2];break i}le=A?8388608:0;break i}return r}return le=0,-2147483616}le=A?134:128}return t}le=134}return-2147483648}return 0}le=1073741824;break a}le=-2147483648}return 268435520}le=67108864}return 64}return le=l,2099200|t}function FA(A){var t,r=0,s=0,i=0,l=0,c=0;(t=HA(8244))&&(e[t+328>>2]=2,f[132848]=0,e[t+684>>2]=0,e[t+688>>2]=0,e[t+320>>2]=0,e[t+324>>2]=0,f[t+268|0]=0,f[t+228|0]=0,e[t+8216>>2]=0,e[t+8220>>2]=0,e[t+224>>2]=104944,e[t+216>>2]=383,e[t+220>>2]=96,Je(t+344|0,0,292),e[t+8196>>2]=0,e[(r=t+8188|0)>>2]=0,e[r+4>>2]=0,e[t+8180>>2]=0,e[t+8184>>2]=0,f[t+460|0]=22,f[t+461|0]=129,f[t+466|0]=38,f[t+462|0]=38,f[t+463|0]=36,f[t+464|0]=22,f[t+465|0]=224,f[t+456|0]=22,f[t+457|0]=22,f[t+458|0]=44,f[t+459|0]=22,f[t+454|0]=46,f[t+455|0]=129,f[t+446|0]=22,f[t+447|0]=38,f[t+448|0]=28,f[t+449|0]=193,f[t+450|0]=38,f[t+451|0]=22,f[t+452|0]=46,f[t+453|0]=46,f[t+441|0]=129,f[t+442|0]=38,f[t+443|0]=22,f[t+444|0]=38,f[t+445|0]=193,e[t+332>>2]=104912,e[t+336>>2]=104916,e[t+340>>2]=105232,f[t+296|0]=18,f[t+297|0]=18,k[t+304>>1]=182,k[t+306>>1]=140,f[t+298|0]=20,k[t+308>>1]=220,k[t+310>>1]=220,k[t+312>>1]=220,f[t+299|0]=20,f[t+300|0]=20,k[t+314>>1]=240,f[t+301|0]=22,k[t+316>>1]=260,k[t+318>>1]=280,f[t+302|0]=22,f[t+303|0]=20,r=Je(t,0,212),e[r+200>>2]=20,e[r+192>>2]=25966,e[r+196>>2]=500,e[r+80>>2]=95,e[r+16>>2]=1,e[r+20>>2]=3,e[r+8>>2]=2,e[r+52>>2]=19,f[r+168|0]=3,e[r+92>>2]=2,e[r+72>>2]=4,e[r+40>>2]=115,e[r+44>>2]=95,e[r+140>>2]=105244,zt(r,201),e[r+120>>2]=2,e[r+124>>2]=44,e[r+164>>2]=100,e[r+128>>2]=46,e[r+132>>2]=14,e[r+112>>2]=1227133512,e[r+116>>2]=49,e[r+104>>2]=1,s=e[26313],e[r+636>>2]=e[26312],e[r+640>>2]=s,s=e[26315],e[r+644>>2]=e[26314],e[r+648>>2]=s,s=e[26317],e[r+652>>2]=e[26316],e[r+656>>2]=s,s=e[26319],e[r+660>>2]=e[26318],e[r+664>>2]=s,s=e[26321],e[r+668>>2]=e[26320],e[r+672>>2]=s,s=e[26323],e[r+676>>2]=e[26322],e[r+680>>2]=s,s=d[104928]|d[104929]<<8,f[r+160|0]=s,f[r+161|0]=s>>>8,s=d[104924]|d[104925]<<8|d[104926]<<16|d[104927]<<24,f[r+156|0]=s,f[r+157|0]=s>>>8,f[r+158|0]=s>>>16,f[r+159|0]=s>>>24),i=PA(t+228|0,A),r=0;e:{A:if(s=d[0|A]){for(;r=(s<<24>>24)+(r<<8)|0,s=d[0|(A=A+1|0)];);r:{a:{n:{o:{c:{u:{l:{i:{p:{C:{h:{b:{m:{x:{I:{B:{N:{L:{U:{y:{E:{Q:{F:{Ae:{R:{q:{_:{oe:{j:{Fe:{K:{f:{g:{re:{k:{se:{w:{s:{tA:{te:{t:{pe:{W:{me:{be:{rA:{de:{z:{Se:{v:{Oe:{$:{Ie:{we:{_e:{ze:{Ne:{Le:{Re:{je:{We:{ke:{sA:{ae:{Ve:{Ye:{Me:{He:{G:{Ee:{if((0|r)<=28008){if((0|r)<=26464){if((0|r)<=25696){V:switch(r-24934|0){case 20:break i;case 1:case 2:case 3:case 4:case 5:case 6:case 9:case 10:case 11:case 14:case 15:case 16:case 17:case 18:case 19:break A;case 8:break $;case 12:break We;case 7:break ke;case 0:break sA;case 13:break Ee;default:break V}V:switch(r-25189|0){case 1:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 11:case 12:case 13:break A;case 14:break pe;case 2:break Re;case 0:break je;case 9:break Ee;default:break V}switch(r-25441|0){case 18:break B;case 0:break $;case 24:break Ne;default:break A}}V:switch(r-25964|0){case 1:case 4:case 5:case 6:break A;case 8:break Se;case 9:break Oe;case 7:break $;case 3:break Ie;case 2:break we;case 0:break He;default:break V}V:switch(r-26209|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:break A;case 17:break de;case 8:break z;case 0:break v;default:break V}switch(r-25697|0){case 4:break _e;case 0:break ze;default:break A}}if((0|r)<=27488){V:switch(r-26729|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 13:case 14:case 15:break A;case 16:break tA;case 12:break te;case 11:break t;case 9:break pe;case 0:break W;default:break V}V:switch(r-26977|0){case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 15:case 16:case 17:break A;case 19:break k;case 18:break se;case 3:break w;case 14:break s;case 0:break $;default:break V}switch(r-26465|0){case 20:break W;case 13:break me;case 0:case 3:break be;default:break A}}V:switch(r-27489|0){case 13:break p;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 12:case 15:case 16:case 17:case 18:case 19:case 21:case 22:case 23:break A;case 24:break oe;case 20:break j;case 14:break Fe;case 11:break K;case 10:break f;case 0:break g;default:break V}switch(r-27745|0){case 19:break q;case 0:break _;case 21:break Ye;default:break A}}if((0|r)<=29792){if((0|r)<=28768){V:switch(r-28009|0){case 3:break p;case 11:break Ae;case 2:break R;case 1:case 4:case 5:case 6:case 7:case 8:case 12:case 13:case 14:case 15:break A;case 10:break w;case 9:break W;case 0:case 16:break ae;default:break V}V:switch(r-28258|0){case 0:break Q;case 10:break F;case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 9:break A;case 3:break W;default:break V}switch(r-28525|0){case 0:break E;case 5:break W;default:break A}}if((0|r)<=29539){V:switch(r-28769|0){case 19:break U;case 11:break y;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 20:case 21:case 22:case 23:break A;case 0:break W;case 24:break ae;default:break V}V:switch(r-29295|0){case 6:break N;case 0:break L;case 1:case 2:case 3:case 4:case 5:break A;default:break V}if((0|r)==29045)break ae;break A}switch(r-29540|0){case 19:break C;case 18:break h;case 13:break b;case 8:break x;case 5:break I;case 7:break B;case 14:break pe;case 0:break c;default:break A}}if((0|r)>6514801)break G;if((0|r)<=30058)switch(r-29793|0){case 19:break l;case 17:break i;case 0:case 4:break p;case 13:break C;case 7:break ae;default:break A}if((0|r)<=30312)switch(r-30059|0){case 0:break u;case 15:break ae;case 7:break c;default:break A}if((0|r)==30313)break o;if((0|r)==31336)break n;if((0|r)!=6451321)break A}if(e[t+296>>2]=303174162,e[t+300>>2]=370545684,e[t+600>>2]=2432,e[t+8>>2]=0,e[t+12>>2]=65540,e[t+100>>2]=e[t+96>>2],A=e[25889],e[t+304>>2]=e[25888],e[t+308>>2]=A,A=e[25891],e[t+312>>2]=e[25890],e[t+316>>2]=A,YA(t),f[t+345|0]=2|d[t+345|0],f[t+406|0]=16|d[t+406|0],f[t+407|0]=16|d[t+407|0],f[t+408|0]=16|d[t+408|0],f[t+409|0]=16|d[t+409|0],f[t+410|0]=16|d[t+410|0],f[t+411|0]=16|d[t+411|0],f[t+412|0]=16|d[t+412|0],f[t+413|0]=16|d[t+413|0],f[t+414|0]=16|d[t+414|0],f[t+415|0]=16|d[t+415|0],f[t+416|0]=16|d[t+416|0],f[t+417|0]=16|d[t+417|0],f[t+418|0]=16|d[t+418|0],f[t+419|0]=16|d[t+419|0],f[t+420|0]=16|d[t+420|0],f[t+456|0]=4|d[t+456|0],f[t+457|0]=4|d[t+457|0],e[t+112>>2]=613567144,e[t+104>>2]=16,(0|r)!=6451321)break e;e[t+104>>2]=1,e[t+108>>2]=512,r=6451321;break e}if((0|r)>7364975)break Ve;if((0|r)>6840682)break Me;if((0|r)==6514802)break Le;if((0|r)==6516078)break n;if((0|r)!=6779491)break A}if(e[t+600>>2]=896,e[t+328>>2]=8,e[t+296>>2]=336858127,e[t+300>>2]=353768980,e[t+332>>2]=103632,A=e[25905],e[t+304>>2]=e[25904],e[t+308>>2]=A,A=e[25907],e[t+312>>2]=e[25906],e[t+316>>2]=A,Je(t+344|0,0,256),f[t+388|0]=129,f[t+389|0]=129,f[t+390|0]=129,f[t+391|0]=129,f[t+420|0]=129,f[t+421|0]=129,f[t+422|0]=129,f[t+423|0]=129,f[t+360|0]=129,f[t+392|0]=129,f[t+393|0]=129,f[t+417|0]=129,f[t+418|0]=129,f[t+419|0]=129,f[t+420|0]=129,f[t+408|0]=6,f[t+409|0]=4,f[t+410|0]=6,f[t+411|0]=6,f[t+412|0]=6,f[t+413|0]=193,f[t+414|0]=6,f[t+415|0]=6,f[t+406|0]=6,f[t+407|0]=129,f[t+398|0]=4,f[t+399|0]=193,f[t+400|0]=6,f[t+401|0]=193,f[t+402|0]=6,f[t+403|0]=4,f[t+404|0]=4,f[t+405|0]=4,f[t+394|0]=4,f[t+395|0]=4,f[t+396|0]=4,f[t+397|0]=193,e[t+44>>2]=130,e[t+8>>2]=2,e[t+12>>2]=6,e[t+16>>2]=0,e[t+20>>2]=2,e[t+104>>2]=264,e[t+108>>2]=6146,f[t+391|0]=193,f[t+389|0]=193,f[t+390|0]=193,f[t+421|0]=193,e[t+100>>2]=e[t+96>>2],f[t+416|0]=4|d[t+416|0],(0|r)!=6779491)break e;e[t+40>>2]=1,r=6779491;break e}if((0|r)==6840683)break rA;if((0|r)==6972015)break re;if((0|r)!=7107687)break A}e[t+296>>2]=134875662,e[t+300>>2]=252968960,e[t+328>>2]=5,f[t+169|0]=1,e[t+132>>2]=33,e[t+104>>2]=99336,e[t+8>>2]=0,e[t+12>>2]=262182,A=e[26069],e[t+304>>2]=e[26068],e[t+308>>2]=A,A=e[26071],e[t+312>>2]=e[26070],e[t+316>>2]=A;break e}if((0|r)<=7564649){if((0|r)==7364976)break $;if((0|r)==7435619)break ae;if((0|r)!=7563374)break A;e[t+148>>2]=1,e[t+112>>2]=24,e[t+104>>2]=1,e[t+100>>2]=e[t+96>>2],r=7563374;break e}if((0|r)==7564650)break m;if((0|r)==7959909)break n;if((0|r)!=1885958500)break A}e[t+104>>2]=0;break e}e[t+4>>2]=48,e[t+8>>2]=0,e[t+144>>2]=1,e[t+104>>2]=16779472,e[t+32>>2]=1,e[t+24>>2]=1,A=e[25881],e[t+304>>2]=e[25880],e[t+308>>2]=A,A=e[25883],e[t+312>>2]=e[25882],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=24934;break e}e[t+600>>2]=4608,e[t+296>>2]=303173650,e[t+300>>2]=303174162,e[t+8>>2]=0,e[t+12>>2]=36,e[t+104>>2]=1024,e[t+100>>2]=e[t+96>>2],e[t+40>>2]=1,A=e[25865],e[t+304>>2]=e[25864],e[t+308>>2]=A,A=e[25867],e[t+312>>2]=e[25866],e[t+316>>2]=A,r=24941;break e}for(e[t+600>>2]=1536,e[t+224>>2]=0,e[t+216>>2]=1631,e[t+220>>2]=1536,e[t+104>>2]=2884720,e[t+328>>2]=7,e[t+40>>2]=1,H=r=H-16|0,e[r+12>>2]=-1,A=89684;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=1|d[0|l]),A=A+i|0,s;);for(e[r+12>>2]=-1,A=89743;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=2|d[0|l]),A=A+i|0,s;);for(e[r+12>>2]=-1,A=89795;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=4|d[0|l]),A=A+i|0,s;);for(e[r+12>>2]=-1,A=89941;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=16|d[0|l]),A=A+i|0,s;);for(e[r+12>>2]=-1,A=90045;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=32|d[0|l]),A=A+i|0,s;);for(e[r+12>>2]=-1,A=90045;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=8|d[0|l]),A=A+i|0,s;);for(e[r+12>>2]=-1,A=90045;i=jA(r+12|0,A),(0|(s=e[r+12>>2]))>=33&&(f[0|(l=(t+s|0)-1192|0)]=64|d[0|l]),A=A+i|0,s;);H=r+16|0,r=24946;break e}e[t+600>>2]=1056,e[t+12>>2]=34,e[t+216>>2]=1118,e[t+220>>2]=1072,Je(t+344|0,0,256),f[t+406|0]=4,f[t+366|0]=4,f[t+367|0]=4,f[t+369|0]=4,f[t+370|0]=4,f[t+371|0]=4,f[t+372|0]=4,f[t+361|0]=4,f[t+362|0]=4,f[t+363|0]=4,f[t+364|0]=4,f[t+373|0]=4,f[t+380|0]=4,f[t+381|0]=4,f[t+382|0]=4,f[t+383|0]=4,f[t+375|0]=4,f[t+376|0]=4,f[t+377|0]=4,f[t+378|0]=4,f[t+384|0]=4,f[t+360|0]=129,e[t+328>>2]=6,e[t+296>>2]=134744588,e[t+300>>2]=286261248,e[t+40>>2]=1,e[t+8>>2]=0,e[t+104>>2]=1032,e[t+108>>2]=66,A=e[25885],e[t+304>>2]=e[25884],e[t+308>>2]=A,A=e[25887],e[t+312>>2]=e[25886],e[t+316>>2]=A,r=25189;break e}Ys(t),e[t+328>>2]=6,e[t+56>>2]=2,e[t+36>>2]=263,e[t+40>>2]=1074,e[t+124>>2]=32,e[t+104>>2]=184554728,e[t+8>>2]=2,f[t+386|0]=64&d[t+386|0]|129,r=25191;break e}e[t+12>>2]=262182,e[t+40>>2]=1,r=6514802;break e}e[t+328>>2]=14,e[t+296>>2]=303173393,e[t+300>>2]=336986112,e[t+104>>2]=1024,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=22,e[t+44>>2]=120,A=e[25893],e[t+304>>2]=e[25892],e[t+308>>2]=A,A=e[25895],e[t+312>>2]=e[25894],e[t+316>>2]=A,f[t+463|0]=64&d[t+463|0]|129,f[t+465|0]=64&d[t+465|0]|129,r=25465;break e}e[t+8>>2]=0,e[t+104>>2]=184618072,e[t+32>>2]=1,A=e[26101],e[t+304>>2]=e[26100],e[t+308>>2]=A,A=e[26103],e[t+312>>2]=e[26102],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=25697;break e}e[t+296>>2]=336860180,e[t+300>>2]=336991764,e[t+8>>2]=0,e[t+104>>2]=16846872,e[t>>2]=8,e[t+4>>2]=48,e[t+80>>2]=87,e[t+32>>2]=1,e[t+36>>2]=256,e[t+40>>2]=2,A=e[25897],e[t+304>>2]=e[25896],e[t+308>>2]=A,A=e[25899],e[t+312>>2]=e[25898],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=25701;break e}e[t+132>>2]=33,e[t+104>>2]=16779328,e[t+8>>2]=0,e[t+12>>2]=8,e[t+204>>2]=101,e[t+60>>2]=2,e[t+40>>2]=2,A=e[25901],e[t+304>>2]=e[25900],e[t+308>>2]=A,A=e[25903],e[t+312>>2]=e[25902],e[t+316>>2]=A,f[t+441|0]=64|d[t+441|0],f[t+445|0]=64|d[t+445|0],f[t+449|0]=64|d[t+449|0],f[t+455|0]=64|d[t+455|0],f[t+461|0]=64|d[t+461|0],f[t+465|0]=64|d[t+465|0],r=25966;break e}e[t+328>>2]=4,e[t+296>>2]=336858640,e[t+300>>2]=353768980,e[t+104>>2]=16782344,e[t+20>>2]=2,e[t+12>>2]=22,e[t+4>>2]=2,e[t+8>>2]=2,e[t+332>>2]=103640,A=e[25997],e[t+304>>2]=e[25996],e[t+308>>2]=A,A=e[25999],e[t+312>>2]=e[25998],e[t+316>>2]=A,r=25967;break e}e[t+296>>2]=269422096,e[t+300>>2]=370545684,e[t+104>>2]=86017320,e[t+108>>2]=6144,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=534,e[t+100>>2]=e[t+96>>2],e[t+44>>2]=120,A=e[25913],e[t+304>>2]=e[25912],e[t+308>>2]=A,A=e[25915],e[t+312>>2]=e[25914],e[t+316>>2]=A;$:{Ie:{if((0|r)<=26976){if((0|r)==24942)break Ie;if((0|r)!=25441)break $;e[t+12>>2]=566,e[t+336>>2]=103664,r=25441;break e}if((0|r)!=26977){if((0|r)!=7364976)break $;e[t+8>>2]=3,e[t+12>>2]=310,r=7364976;break e}e[t+104>>2]=85984264,r=26977;break e}e[t+104>>2]=153093416,e[t+108>>2]=2048,e[t+140>>2]=103676,r=24942;break e}e[t+40>>2]=2;break e}e[t+296>>2]=303173648,e[t+300>>2]=303174162,e[t+104>>2]=3147080,e[t+12>>2]=65792,e[t+84>>2]=1,A=e[25921],e[t+304>>2]=e[25920],e[t+308>>2]=A,A=e[25923],e[t+312>>2]=e[25922],e[t+316>>2]=A,r=25973;break e}e[t+600>>2]=1536,e[t+216>>2]=1740,e[t+220>>2]=1568,e[t+104>>2]=96,e[t+224>>2]=103696,e[t+340>>2]=103872,e[t+40>>2]=1,r=26209;break e}e[t+328>>2]=5}e[t+104>>2]=86024,e[t+164>>2]=130,f[t+465|0]=64&d[t+465|0]|129;break e}e[t+296>>2]=303173650,e[t+300>>2]=303174162,e[t+8>>2]=3,e[t+12>>2]=36,e[t+144>>2]=2,e[t+104>>2]=118658312,e[t+28>>2]=1,e[t+100>>2]=e[t+96>>2],A=e[25865],e[t+304>>2]=e[25864],e[t+308>>2]=A,A=e[25867],e[t+312>>2]=e[25866],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=26226;break e}k[t+170>>1]=257,e[t+148>>2]=1,e[t+12>>2]=2,r=6840683;break e}e[t+144>>2]=2,e[t+104>>2]=2098176,e[t+8>>2]=0,e[t+12>>2]=32,e[t+40>>2]=3,e[t+28>>2]=1;break e}e[t+8>>2]=3,e[t+100>>2]=e[t+96>>2],r=26478;break e}e[t+328>>2]=18,e[t+296>>2]=320081425,e[t+300>>2]=353768980,e[t+600>>2]=2304,e[t+112>>2]=84648,e[t+104>>2]=16,e[t+8>>2]=6,e[t+12>>2]=65540,e[t+100>>2]=e[t+96>>2],A=e[25973],e[t+304>>2]=e[25972],e[t+308>>2]=A,A=e[25975],e[t+312>>2]=e[25974],e[t+316>>2]=A;W:{me:{be:{if((0|r)<=28529){if((0|r)==26485)break be;if((0|r)!=28261)break W;A=e[25861],e[t+304>>2]=e[25860],e[t+308>>2]=A,A=e[25863],e[t+312>>2]=e[25862],e[t+316>>2]=A,e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+132>>2]=22,e[t+112>>2]=-1431655768,e[t+108>>2]=32768|e[t+108>>2],YA(t);break e}if((0|r)==28530)break me;if((0|r)!=28769)break W;e[t+600>>2]=2560,YA(t);break e}A=e[25861],e[t+304>>2]=e[25860],e[t+308>>2]=A,A=e[25863],e[t+312>>2]=e[25862],e[t+316>>2]=A,e[t+600>>2]=2688,e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+8>>2]=2,YA(t);break e}e[t+600>>2]=2816}YA(t);break e}f[0|i]=104,f[i+1|0]=98,f[i+2|0]=115,f[i+3|0]=0,(0|r)!=29554?(A=e[26093],e[t+304>>2]=e[26092],e[t+308>>2]=A,A=e[26095],e[t+312>>2]=e[26094],e[t+316>>2]=A):(A=e[25977],e[t+304>>2]=e[25976],e[t+308>>2]=A,A=e[25979],e[t+312>>2]=e[25978],e[t+316>>2]=A),e[t+328>>2]=3,e[t+296>>2]=336859409,e[t+300>>2]=353768980,k[t+168>>1]=261,e[t+8>>2]=0,e[t+12>>2]=16,e[t+144>>2]=1,e[t+184>>2]=1056,e[t+104>>2]=33572172,e[t+108>>2]=330,e[t+36>>2]=3,f[t+465|0]=64&d[t+465|0]|129,f[t+458|0]=64&d[t+458|0]|129;break e}e[t+104>>2]=17990912,e[t+8>>2]=3,e[t+12>>2]=36,r=26740;break e}e[t+328>>2]=3,e[t+296>>2]=320016657,e[t+300>>2]=353768980,e[t+124>>2]=32,e[t+128>>2]=44,e[t+104>>2]=186758144,e[t+12>>2]=1081398,e[t+16>>2]=2,e[t+4>>2]=32,e[t+8>>2]=0,e[t+116>>2]=899,e[t+120>>2]=1,f[t+169|0]=1,e[t+76>>2]=2,A=e[25981],e[t+304>>2]=e[25980],e[t+308>>2]=A,A=e[25983],e[t+312>>2]=e[25982],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,zt(t,3),r=26741;break e}for(e[t+600>>2]=1328,e[t+8>>2]=3,A=e[25985],e[t+304>>2]=e[25984],e[t+308>>2]=A,A=e[25987],e[t+312>>2]=e[25986],e[t+316>>2]=A,i=Je(t+344|0,0,256),f[t+429|0]=129,f[t+416|0]=129,f[t+403|0]=129,f[t+399|0]=129,f[t+400|0]=129,f[t+397|0]=129,f[t+393|0]=129,s=103952,l=50,c=50;f[0|(A=i+c|0)]=2|d[0|A],A=i+d[s+1|0]|0,f[0|A]=2|d[0|A],A=i+d[s+2|0]|0,f[0|A]=2|d[0|A],c=d[0|(s=s+3|0)],A=103952,(0|s)!=103982;);for(s=t+344|0;f[0|(i=s+l|0)]=4|d[0|i],i=s+d[A+1|0]|0,f[0|i]=4|d[0|i],i=s+d[A+2|0]|0,f[0|i]=4|d[0|i],l=d[0|(A=A+3|0)],(0|A)!=103982;);f[t+168|0]=6,e[t+104>>2]=5128,f[t+413|0]=4|d[t+413|0];break e}e[t+328>>2]=4,e[t+296>>2]=336858640,e[t+300>>2]=353768980,e[t+104>>2]=16782440,e[t+20>>2]=2,e[t+12>>2]=22,e[t+4>>2]=2,e[t+8>>2]=2,e[t+332>>2]=104e3,A=e[25997],e[t+304>>2]=e[25996],e[t+308>>2]=A,A=e[25999],e[t+312>>2]=e[25998],e[t+316>>2]=A,r=26991;break e}e[t+296>>2]=303174160,e[t+300>>2]=353768980,e[t+104>>2]=16781320,e[t+144>>2]=2,e[t+8>>2]=2,e[t+12>>2]=22,A=e[26005],e[t+304>>2]=e[26004],e[t+308>>2]=A,A=e[26007],e[t+312>>2]=e[26006],e[t+316>>2]=A;break e}for(e[t+8>>2]=0,e[t+12>>2]=16,e[t+56>>2]=2,e[t+28>>2]=17,A=e[26009],e[t+304>>2]=e[26008],e[t+308>>2]=A,A=e[26011],e[t+312>>2]=e[26010],e[t+316>>2]=A,A=0,s=t+344|0;f[0|(i=A+s|0)]=231&d[0|i],f[0|(i=s+(1|A)|0)]=231&d[0|i],f[0|(i=s+(2|A)|0)]=231&d[0|i],f[0|(i=s+(3|A)|0)]=231&d[0|i],(0|(A=A+4|0))!=256;);e[t+104>>2]=2280,e[t+108>>2]=2,e[t+608>>2]=104048,f[t+451|0]=16|d[t+451|0],f[t+456|0]=16|d[t+456|0],f[t+459|0]=16|d[t+459|0],f[t+460|0]=16|d[t+460|0],f[t+450|0]=8|d[t+450|0],f[t+462|0]=8|d[t+462|0],f[t+458|0]=8|d[t+458|0],f[t+465|0]=64&d[t+465|0]|129;break e}e[t+296>>2]=269618961,e[t+300>>2]=370546196,e[t+12>>2]=131110,e[t+144>>2]=2,e[t+104>>2]=184559112,e[t+108>>2]=8192,e[t+16>>2]=0,e[t+20>>2]=2,e[t+4>>2]=1,e[t+8>>2]=2,e[t+100>>2]=e[t+96>>2],e[t+140>>2]=103676,e[t+68>>2]=2,e[t+56>>2]=1,e[t+44>>2]=130,e[t+28>>2]=2,A=e[26025],e[t+304>>2]=e[26024],e[t+308>>2]=A,A=e[26027],e[t+312>>2]=e[26026],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=26996;break e}e[t+4>>2]=524,e[t+8>>2]=2,e[t+196>>2]=368,e[t+104>>2]=0,e[t+336>>2]=104128,e[t- -64>>2]=1,A=e[26029],e[t+304>>2]=e[26028],e[t+308>>2]=A,A=e[26031],e[t+312>>2]=e[26030],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=6972015;break e}for(e[t+296>>2]=303174162,e[t+300>>2]=370545684,A=e[25873],e[t+304>>2]=e[25872],e[t+308>>2]=A,A=e[25875],e[t+312>>2]=e[25874],e[t+316>>2]=A,i=Je(t+344|0,0,256),f[t+431|0]=1,f[t+429|0]=1,f[t+411|0]=1,f[t+405|0]=1,f[t+400|0]=1,f[t+396|0]=1,f[t+392|0]=1,A=104160,s=49;f[0|(s=s+i|0)]=4|d[0|s],s=i+d[A+1|0]|0,f[0|s]=4|d[0|s],s=i+d[A+2|0]|0,f[0|s]=4|d[0|s],s=d[0|(A=A+3|0)],(0|A)!=104193;);e[t+600>>2]=4256,e[t+8>>2]=0,e[t+12>>2]=16,f[t+168|0]=7,e[t+132>>2]=32,f[t+392|0]=128|d[t+392|0],f[t+396|0]=128|d[t+396|0],f[t+400|0]=128|d[t+400|0],f[t+405|0]=128|d[t+405|0],f[t+411|0]=128|d[t+411|0],f[t+429|0]=128|d[t+429|0],f[t+431|0]=128|d[t+431|0],e[t+188>>2]=1056,e[t+192>>2]=29301,e[t+104>>2]=19964960;break e}for(e[t+600>>2]=1056,i=Je(t+344|0,0,256),f[t+393|0]=1,f[t+365|0]=1,f[t+360|0]=1,f[t+545|0]=1,f[t+529|0]=1,f[t+391|0]=1,f[t+389|0]=1,f[t+390|0]=1,f[t+387|0]=1,f[t+379|0]=1,f[t+374|0]=1,f[t+368|0]=1,f[t+489|0]=1,f[t+487|0]=1,f[t+398|0]=1,A=104224,s=17;f[0|(s=s+i|0)]=4|d[0|s],s=i+d[A+1|0]|0,f[0|s]=4|d[0|s],s=i+d[A+2|0]|0,f[0|s]=4|d[0|s],s=d[0|(A=A+3|0)],(0|A)!=104251;);f[t+360|0]=128|d[t+360|0],f[t+365|0]=128|d[t+365|0],f[t+393|0]=128|d[t+393|0],f[t+368|0]=128|d[t+368|0],f[t+374|0]=128|d[t+374|0],f[t+379|0]=128|d[t+379|0],f[t+387|0]=128|d[t+387|0],f[t+389|0]=128|d[t+389|0],f[t+390|0]=128|d[t+390|0],f[t+391|0]=128|d[t+391|0],f[t+529|0]=128|d[t+529|0],f[t+545|0]=128|d[t+545|0],f[t+489|0]=128|d[t+489|0],f[t+487|0]=128|d[t+487|0],f[t+398|0]=128|d[t+398|0],A=e[26055],e[t+312>>2]=e[26054],e[t+316>>2]=A,A=e[26053],e[t+304>>2]=e[26052],e[t+308>>2]=A,e[t+296>>2]=353636370,e[t+300>>2]=336925972,e[t+200>>2]=0,e[t+8>>2]=7,e[t+12>>2]=2097184,f[t+168|0]=2,e[t+104>>2]=50176,e[t+84>>2]=1,zt(t,3);break e}e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+104>>2]=184618072,e[t+8>>2]=12,e[t+12>>2]=32,A=e[25861],e[t+304>>2]=e[25860],e[t+308>>2]=A,A=e[25863],e[t+312>>2]=e[25862],e[t+316>>2]=A,r=27500;break e}e[t+184>>2]=42752,e[t+600>>2]=4352,Je(t+344|0,0,256),f[t+456|0]=1,f[t+457|0]=1,f[t+458|0]=1,f[t+459|0]=1,f[t+449|0]=1,f[t+450|0]=1,f[t+451|0]=1,f[t+452|0]=1,f[t+453|0]=1,f[t+454|0]=1,f[t+455|0]=1,f[t+456|0]=1,f[t+441|0]=1,f[t+442|0]=1,f[t+443|0]=1,f[t+444|0]=1,f[t+445|0]=1,f[t+446|0]=1,f[t+447|0]=1,f[t+448|0]=1,f[t+460|0]=65,f[t+461|0]=65,f[t+532|0]=32,f[t+527|0]=32,f[t+519|0]=32,f[t+515|0]=32,f[t+349|0]=32,f[t+350|0]=32,f[t+346|0]=32,e[t+132>>2]=20,e[t+112>>2]=286331152,e[t+104>>2]=1024,e[t+108>>2]=16384,e[t+40>>2]=1,e[t+8>>2]=8,f[t+458|0]=65,f[t+453|0]=65,f[t+447|0]=65,f[t+448|0]=65,f[t+443|0]=65,f[t+444|0]=65,r=27503;break e}e[t+328>>2]=10,e[t+296>>2]=336859666,e[t+300>>2]=353768980,f[t+168|0]=2,e[t+104>>2]=263264,e[t+8>>2]=7,A=e[26065],e[t+304>>2]=e[26064],e[t+308>>2]=A,A=e[26067],e[t+312>>2]=e[26066],e[t+316>>2]=A,r=27509;break e}e[t+104>>2]=1,r=27513;break e}e[t+116>>2]=5e3,e[t+104>>2]=16777216,e[t+24>>2]=1,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=32,e[t+328>>2]=5,r=27745;break e}e[t+116>>2]=5e3,e[t+104>>2]=99336,e[t+108>>2]=256,e[t+24>>2]=1,e[t+16>>2]=0,e[t+20>>2]=2,e[t+8>>2]=2,e[t+12>>2]=32,e[t+328>>2]=5,r=27764;break e}e[t+328>>2]=6,e[t+296>>2]=336859409,e[t+300>>2]=353768980,e[t+600>>2]=1056,e[t+104>>2]=2114600,e[t+108>>2]=138,e[t+8>>2]=4,e[t+632>>2]=104288,e[t+604>>2]=104288,A=e[26093],e[t+304>>2]=e[26092],e[t+308>>2]=A,A=e[26095],e[t+312>>2]=e[26094],e[t+316>>2]=A,r=28011;break e}e[t+328>>2]=4,e[t+104>>2]=1,e[t+8>>2]=2,e[t+36>>2]=256,r=28020;break e}e[t+4>>2]=48,e[t+8>>2]=0,e[t+12>>2]=128,e[t+104>>2]=2169880,e[t+32>>2]=1,e[t+36>>2]=256,e[t+24>>2]=1,e[t+136>>2]=85767,A=e[26097],e[t+304>>2]=e[26096],e[t+308>>2]=A,A=e[26099],e[t+312>>2]=e[26098],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=28268;break e}e[t+8>>2]=0,e[t+104>>2]=71752,A=e[26101],e[t+304>>2]=e[26100],e[t+308>>2]=A,A=e[26103],e[t+312>>2]=e[26102],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=28258;break e}e[t+296>>2]=336858898,e[t+300>>2]=370546196,e[t+104>>2]=1088,e[t+108>>2]=512,e[t+8>>2]=2,e[t+12>>2]=524310,A=e[26105],e[t+304>>2]=e[26104],e[t+308>>2]=A,A=e[26107],e[t+312>>2]=e[26106],e[t+316>>2]=A,r=28525;break e}e[t+328>>2]=3,e[t+296>>2]=320015633,e[t+300>>2]=353768980,f[t+168|0]=7,e[t+8>>2]=2,e[t+12>>2]=6,e[t+104>>2]=20488,e[t+108>>2]=192,e[t+36>>2]=9,e[t+60>>2]=260,A=e[26109],e[t+304>>2]=e[26108],e[t+308>>2]=A,A=e[26111],e[t+312>>2]=e[26110],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=28780;break e}for(e[t+296>>2]=353569552,e[t+300>>2]=353768980,e[t+116>>2]=5e3,e[t+104>>2]=33570920,e[t+108>>2]=14336,e[t+8>>2]=3,e[t+12>>2]=139286,e[t+100>>2]=e[t+96>>2],A=e[26113],e[t+304>>2]=e[26112],e[t+308>>2]=A,A=e[26115],e[t+312>>2]=e[26114],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,A=0,s=t+344|0;f[0|(i=A+s|0)]=253&d[0|i],f[0|(i=s+(1|A)|0)]=253&d[0|i],f[0|(i=s+(2|A)|0)]=253&d[0|i],f[0|(i=s+(3|A)|0)]=253&d[0|i],(0|(A=A+4|0))!=256;);f[t+442|0]=2|d[t+442|0],f[t+443|0]=2|d[t+443|0],f[t+444|0]=2|d[t+444|0],f[t+446|0]=2|d[t+446|0],f[t+447|0]=2|d[t+447|0],f[t+450|0]=2|d[t+450|0],f[t+451|0]=2|d[t+451|0],f[t+453|0]=2|d[t+453|0],f[t+454|0]=2|d[t+454|0],f[t+456|0]=2|d[t+456|0],f[t+457|0]=2|d[t+457|0],f[t+459|0]=2|d[t+459|0],f[t+460|0]=2|d[t+460|0],f[t+462|0]=2|d[t+462|0],f[t+464|0]=2|d[t+464|0],f[t+466|0]=2|d[t+466|0],e[t+144>>2]=2,e[t+68>>2]=2;break e}e[t+296>>2]=303172879,e[t+300>>2]=353768980,e[t+328>>2]=3,e[t+8>>2]=3,e[t+12>>2]=262,e[t+104>>2]=16805928,e[t+108>>2]=30,A=e[26117],e[t+304>>2]=e[26116],e[t+308>>2]=A,A=e[26119],e[t+312>>2]=e[26118],e[t+316>>2]=A,r=29295;break e}Ai(t),r=29301;break e}for(e[t+328>>2]=3,e[t+296>>2]=336859153,e[t+300>>2]=353768980,k[t+168>>1]=261,e[t+8>>2]=0,e[t+12>>2]=22,e[t+124>>2]=0,e[t+128>>2]=44,e[t+104>>2]=16794624,e[t+108>>2]=128,e[t+36>>2]=3,e[t+60>>2]=4,A=e[25869],e[t+304>>2]=e[25868],e[t+308>>2]=A,A=e[25871],e[t+312>>2]=e[25870],e[t+316>>2]=A,(0|r)==25459&&(e[t+108>>2]=136),f[t+465|0]=64&d[t+465|0]|129,f[t+458|0]=64&d[t+458|0]|129,A=0,s=t+344|0;f[0|(i=A+s|0)]=223&d[0|i],f[0|(i=s+(1|A)|0)]=223&d[0|i],f[0|(i=s+(2|A)|0)]=223&d[0|i],f[0|(i=s+(3|A)|0)]=223&d[0|i],(0|(A=A+4|0))!=256;);f[t+442|0]=32|d[t+442|0],f[t+444|0]=32|d[t+444|0],f[t+447|0]=32|d[t+447|0],f[t+450|0]=32|d[t+450|0],f[t+452|0]=32|d[t+452|0],f[t+453|0]=32|d[t+453|0],f[t+454|0]=32|d[t+454|0],f[t+458|0]=32|d[t+458|0],f[t+462|0]=32|d[t+462|0],f[t+463|0]=32|d[t+463|0],f[t+466|0]=32|d[t+466|0],f[t+441|0]=32|d[t+441|0],f[t+445|0]=32|d[t+445|0],f[t+449|0]=32|d[t+449|0],f[t+455|0]=32|d[t+455|0],f[t+461|0]=32|d[t+461|0],f[t+465|0]=32|d[t+465|0];break e}for(e[t+296>>2]=303174162,e[t+300>>2]=370545684,e[t+600>>2]=3456,f[t+169|0]=1,e[t+8>>2]=0,e[t+12>>2]=22,e[t+100>>2]=e[t+96>>2],A=e[25873],e[t+304>>2]=e[25872],e[t+308>>2]=A,A=e[25875],e[t+312>>2]=e[25874],e[t+316>>2]=A,Je(t+344|0,0,256),f[t+365|0]=1,f[t+366|0]=1,f[t+357|0]=1,f[t+358|0]=1,f[t+359|0]=1,f[t+360|0]=1,f[t+361|0]=1,f[t+362|0]=1,f[t+363|0]=1,f[t+364|0]=1,f[t+349|0]=1,f[t+350|0]=1,f[t+351|0]=1,f[t+352|0]=1,f[t+353|0]=1,f[t+354|0]=1,f[t+355|0]=1,f[t+356|0]=1,A=74,s=74;f[(i=t+s|0)+344|0]=1|d[i+344|0],f[i+345|0]=1|d[i+345|0],f[i+346|0]=1|d[i+346|0],(0|(s=s+3|0))!=116;);for(;f[(s=A+t|0)+344|0]=2|d[s+344|0],f[s+345|0]=2|d[s+345|0],f[s+346|0]=2|d[s+346|0],(0|(A=A+3|0))!=116;);for(s=26;f[(A=t+s|0)+344|0]=4|d[A+344|0],f[A+345|0]=4|d[A+345|0],f[A+346|0]=4|d[A+346|0],f[A+347|0]=4|d[A+347|0],f[A+348|0]=4|d[A+348|0],(0|(s=s+5|0))!=71;);e[t+112>>2]=84648,e[t+104>>2]=270589952,e[t+108>>2]=65536,e[t+40>>2]=1,e[t+204>>2]=e[t+600>>2]+74;break e}e[t+8>>2]=2,e[t+12>>2]=32,e[t+328>>2]=3,e[t+124>>2]=32,e[t+104>>2]=16864280,e[t+108>>2]=256,e[t+68>>2]=2,e[t+36>>2]=259,e[t+40>>2]=118,e[t+28>>2]=1,f[t+458|0]=128|d[t+458|0],r=29548;break e}e[t+296>>2]=370544658,e[t+300>>2]=370546196,e[t+164>>2]=130,e[t+8>>2]=0,e[t+12>>2]=86,e[t+104>>2]=87064,f[t+169|0]=1,e[t+152>>2]=3,A=e[26121],e[t+304>>2]=e[26120],e[t+308>>2]=A,A=e[26123],e[t+312>>2]=e[26122],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=7564650;break e}e[t+296>>2]=269487120,e[t+300>>2]=320148500,e[t+8>>2]=3,e[t+12>>2]=278,e[t+144>>2]=2,e[t+104>>2]=32872,A=e[26125],e[t+304>>2]=e[26124],e[t+308>>2]=A,A=e[26127],e[t+312>>2]=e[26126],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=29553;break e}e[t+296>>2]=336859152,e[t+300>>2]=353768980,e[t+8>>2]=0,e[t+144>>2]=1,e[t+104>>2]=6408,A=e[26129],e[t+304>>2]=e[26128],e[t+308>>2]=A,A=e[26131],e[t+312>>2]=e[26130],e[t+316>>2]=A,f[t+465|0]=64&d[t+465|0]|129,r=29558;break e}e[t+296>>2]=320015376,e[t+300>>2]=353768980,f[t+168|0]=4,e[t+12>>2]=22,e[t+4>>2]=1,e[t+8>>2]=2,e[t+104>>2]=1248,e[t+100>>2]=e[t+96>>2],A=e[26133],e[t+304>>2]=e[26132],e[t+308>>2]=A,A=e[26135],e[t+312>>2]=e[26134],e[t+316>>2]=A;break e}e[t+296>>2]=303174162,e[t+300>>2]=370545684,f[t+169|0]=1,e[t+8>>2]=0,e[t+12>>2]=22,e[t+112>>2]=5288,e[t+100>>2]=e[t+96>>2],A=e[25877],e[t+304>>2]=e[25876],e[t+308>>2]=A,A=e[25879],e[t+312>>2]=e[25878],e[t+316>>2]=A;p:switch(r-29793|0){default:if((0|r)!=27502){if((0|r)!=28012)break r;A=e[26137],e[t+304>>2]=e[26136],e[t+308>>2]=A,A=e[26139],e[t+312>>2]=e[26138],e[t+316>>2]=A,e[t+600>>2]=3328,e[t+296>>2]=320017171,e[t+300>>2]=320017171,e[t+104>>2]=2098176,e[t+108>>2]=131072,e[t+8>>2]=13;break r}e[t+104>>2]=1,e[t+600>>2]=3200;break r;case 4:break p;case 1:case 2:case 3:break r;case 0:break a}e[t+104>>2]=1,e[t+108>>2]=524288,e[t+600>>2]=3072;break r}e[t+328>>2]=10,e[t+296>>2]=353636370,e[t+300>>2]=336925972,f[t+173|0]=1,e[t+8>>2]=7,e[t+12>>2]=32,f[t+168|0]=2,e[t+84>>2]=1,A=e[26141],e[t+304>>2]=e[26140],e[t+308>>2]=A,e[t+104>>2]=(0|r)==24954?2118920:2114824,A=e[26143],e[t+312>>2]=e[26142],e[t+316>>2]=A;break e}Ys(t),e[t+296>>2]=303173650,e[t+300>>2]=303174162,e[t+104>>2]=2131208,e[t+8>>2]=3,e[t+12>>2]=32,A=e[25865],e[t+304>>2]=e[25864],e[t+308>>2]=A,A=e[25867],e[t+312>>2]=e[25866],e[t+316>>2]=A,r=29812;break e}Ai(t),r=30059;break e}e[t+112>>2]=21160,e[t+104>>2]=16,e[t+600>>2]=1536,e[t+40>>2]=1;break e}e[t+296>>2]=269488144,e[t+300>>2]=370546198,e[t+8>>2]=0,e[t>>2]=33,e[t+148>>2]=1,e[t+104>>2]=12615688,e[t+16>>2]=2,e[t+100>>2]=e[t+96>>2],e[t+632>>2]=104592,e[t+604>>2]=104592,A=e[26145],e[t+304>>2]=e[26144],e[t+308>>2]=A,A=e[26147],e[t+312>>2]=e[26146],e[t+316>>2]=A,r=30313;break e}if(e[t+296>>2]=370544662,e[t+300>>2]=370546198,e[t+8>>2]=3,e[t+12>>2]=2,e[t+148>>2]=1,e[t+184>>2]=12544,k[t+170>>1]=257,e[t+176>>2]=1,f[t+172|0]=1,e[t>>2]=33,e[t+4>>2]=0,e[t+100>>2]=e[t+96>>2],A=e[26225],e[t+304>>2]=e[26224],e[t+308>>2]=A,A=e[26227],e[t+312>>2]=e[26226],e[t+316>>2]=A,(0|r)!=7959909)break e;e[t+112>>2]=24,e[t+104>>2]=1,e[t+108>>2]=1048576,r=7959909;break e}A=e[25873],e[t+304>>2]=e[25872],e[t+308>>2]=A,A=e[25875],e[t+312>>2]=e[25874],e[t+316>>2]=A,e[t+600>>2]=2944,e[t+104>>2]=2097152,e[t+108>>2]=262144,e[t+48>>2]=1}YA(t),f[t+422|0]=2|d[t+422|0];break e}e[t+40>>2]=1}return e[t+212>>2]=r,8&(A=e[t+104>>2])&&(e[t+124>>2]=46,e[t+128>>2]=44),4&A&&(e[t+124>>2]=0),t}function pA(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0;e:{r=e[32538],e[47354]=0,e[47568]=0,e[49828]=0,e[47569]=0,f[199328]=0,e[49827]=0,e[49845]=0,f[190280]=0,f[190268]=1,e[47202]=0,e[49573]=0,e[49846]=0,f[199304]=0,f[199388]=0,e[33691]=0,e[33285]=0,e[33708]=1,e[33709]=1,e[33288]=0,t=e[33730],e[33712]=e[33729],e[33713]=t,t=e[33732],e[33714]=e[33731],e[33715]=t,t=e[33734],e[33716]=e[33733],e[33717]=t,t=e[33736],e[33718]=e[33735],e[33719]=t,t=e[33738],e[33720]=e[33737],e[33721]=t,t=e[33740],e[33722]=e[33739],e[33723]=t,t=e[33742],e[33724]=e[33741],e[33725]=t,e[33726]=e[33743],f[134784]=0,f[134824]=0,f[134772]=0,f[134760]=0,e[33284]=-1,e[33692]=0,e[32525]=0,e[47201]=e[33717],e[47200]=e[33718],ho(),e[34438]=0,e[34437]=0,t=e[33730],e[34048]=e[33729],e[34049]=t,t=e[33732],e[34050]=e[33731],e[34051]=t,t=e[33734],e[34052]=e[33733],e[34053]=t,t=e[33736],e[34054]=e[33735],e[34055]=t,t=e[33738],e[34056]=e[33737],e[34057]=t,t=e[33740],e[34058]=e[33739],e[34059]=t,t=e[33742],e[34060]=e[33741],e[34061]=t,e[34062]=e[33743];A:{r:{if(1&r){if(e[e[32539]>>2]=0,e[47569]|e[49845]|e[49827])break r;break A}if(e[e[32539]>>2]=0,!(e[49845]|e[49827]||e[47569]))break A}f[190280]=1}e[49828]=0,t=268436735;A:if(!(!e[34391]|!e[34388])&&(e[47204]=0,e[47203]=0,e[47199]=0,e[34439]=0,(e[47192]||!(t=sa(86228)))&&((t=e[33283])||((t=HA(16))&&(e[t>>2]=0,e[t+4>>2]=0,e[t+8>>2]=0,e[t+12>>2]=0),e[33283]=t),l=268439807,(r=e[e[47192]+328>>2])>>>0>20|!e[129104+(r<<3)>>2]?t=l:(A?(l=4,i=MA(A)+1|0):(l=2,i=0),e[t+8>>2]=l,e[t>>2]=A,e[t+12>>2]=e[129108+(r<<3)>>2],e[t+4>>2]=A?A+i|0:0,t=0),!t))){wA(0);r:{for(;;){e[34436]=0,A=e[34391],e[51290]=A,e[54046]=A+e[34390],e[50767]<=102399&&(e[50767]=102400);a:if(!((c=e[51290])>>>0>=Ae[54046]))for(;;){if((((0|(A=(i=e[50757])-(g=e[50758])|0))<=0?A+170|0:A)-171|0)>=-1){if((0|(A=e[54731]))<=0)break a;l=0,e[50763]=0,e[50762]=0,e[50765]=2147483647,A=d[218920]?e[54732]:A;n:{for(;;){if(r=A-1|0,e[54732]=r,(0|A)<=0)break n;if(e[51290]=c+1,t=(A=e[51293])+1|0,e[51293]=(0|t)<=5499?t:0,t=(A=O(e[50755],k[205184+(A<<1)>>1]))>>8,f[0|c]=t,i=e[51290],e[51290]=i+1,f[0|i]=A>>>16,(i=e[50756])?(A=r,(r=e[i+4>>2])&&($A[0|r](t<<16>>16),A=e[54732])):A=r,i=(r=e[51292])+1|0,e[51292]=i,k[205184+(r<<1)>>1]=t,(0|i)>=5500&&(e[51292]=0),c=e[51290],!(Ae[54046]>=c+2>>>0))break}l=1}f[218920]=l;break a}A=e[(r=216192+(i<<4)|0)+4>>2];n:{o:{c:{u:{l:{i:{p:{C:{h:{b:{m:{x:{I:{B:{N:{L:switch((255&(t=e[r>>2]))-1|0){case 9:break c;case 7:break u;case 10:break l;case 11:break i;case 13:break p;case 0:break C;case 1:break h;case 2:break b;case 3:break m;case 6:break x;case 5:break I;case 4:break B;case 15:break N;case 8:break L;default:break o}if(!e[50759])break o;t=e[r+12>>2],r=e[r+8>>2],e[50768]=0,c=r||99232,e[50766]=c,e[50769]=A?2097152/(0|A)|0:0,A=e[50971],i=(0|O(A,e[50788]))/50|0,r=O(i-A|0,-18),A=((0|(A=e[50785]))>=101?101:A)-e[50790]|0,t=(l=r+((0|O(e[50970],d[((0|A)>0?A:0)+105680|0]))/128|0)|0)+((0|O(i,(0|(A=t>>16))<(0|(r=65535&t))?A:r))/2|0)|0,e[50770]=t,A=(l+((0|O(i,(0|A)>(0|r)?A:r))/2|0)|0)-t|0,e[50771]=A,r=d[0|c],e[33072]=e[50976],e[50767]=t+(O(A,r)>>8);break o}$A[e[e[50756]>>2]](A,e[r+8>>2]),fe(A);break o}if(d[218920]||(e[54731]=e[54731]-A),e[50781]=100,e[50773]=0,NA(),e[50763]=0,e[50762]=0,e[50765]=2147483647,!A)break o;for(A=d[218920]?e[54732]:A;;){if(r=A-1|0,e[54732]=r,(0|A)<=0)break o;if(A=e[51290],e[51290]=A+1,i=(t=e[51293])+1|0,e[51293]=(0|i)<=5499?i:0,i=A,t=(A=O(e[50755],k[205184+(t<<1)>>1]))>>8,f[0|i]=t,i=e[51290],e[51290]=i+1,f[0|i]=A>>>16,(i=e[50756])?(A=r,(r=e[i+4>>2])&&($A[0|r](t<<16>>16),A=e[54732])):A=r,i=(r=e[51292])+1|0,e[51292]=i,k[205184+(r<<1)>>1]=t,(0|i)>=5500&&(e[51292]=0),i=1,!(Ae[54046]>=e[51290]+2>>>0))break}break n}for(e[50773]=0,e[54731]=e[54729],NA(),t=e[r+12>>2],i=e[r+8>>2],d[218920]?A=e[54733]:e[54734]=0,g=t>>8,l=255&t,e[50762]=0,e[50763]=0;;){if(r=A-1|0,e[54733]=r,(0|A)<=0)break o;if(t=(A=e[54734])+1|0,l?c=O(l,f[A+i|0]):(c=d[A+i|0]|f[t+i|0]<<8,t=A+2|0),e[54734]=t,m=(A=e[51293])+1|0,e[51293]=m,t=(0|(A=(0|(A=((0|O(g,O(e[33037],O(e[33038],c))>>10))/32|0)+(O(e[50755],k[205184+(A<<1)>>1])>>8)|0))<=-32768?-32768:A))>=32767?32767:A,(0|m)>=5500&&(e[51293]=0),f[e[51290]]=t,f[e[51290]+1|0]=t>>>8,(c=e[50756])?(A=r,(r=e[c+12>>2])&&($A[0|r](t<<16>>16),A=e[54733])):A=r,r=e[51290],e[51290]=r+2,m=(c=e[51292])+1|0,e[51292]=m,k[205184+(c<<1)>>1]=(0|O(t,3))/4,(0|m)>=5500&&(e[51292]=0),!(Ae[54046]>=r+4>>>0))break}i=1;break n}t=e[r+12>>2],i=A>>>16|0,e[50777]=i,A&=65535,e[50773]=A,l=255&t,e[50774]=l,e[50775]=t>>8,l||(e[50777]=i<<1,e[50773]=A<<1),e[50778]=0,e[50776]=0,e[50772]=e[r+8>>2];break o}e[50773]=0}if(e[54731]=e[54729],t=e[50759],d[218920]){if(!t)break o}else{if(!t)break o;for(l=e[r+12>>2],c=e[r+8>>2],r=A>>16,e[55912]=255&r,f[218960]=1,e[55908]=0,67108864&A&&(e[55908]=3,e[55909]=e[110496+(r>>>6&12)>>2]),134217728&A&&(e[55908]=4,e[55909]=e[110512+(r>>>6&12)>>2]),A&=65504;;){if((0|g)!=(0|(i=(0|(r=i+1|0))<=169?r:0)))if((0|(r=e[216192+(i<<4)>>2]))!=3){if(r-5>>>0>1)continue}else f[218960]=0;break}for(e[55913]=e[50762],A=(A=A+32&131008)||64,e[50763]=A+e[50763],e[55684]=O(k[101997],7800)+(y[102024]<<8)<<8,e[55704]=O(k[101998],9e3)+(y[102025]<<8)<<8,_=e[50980],s=+(0|A),x=+(A>>>2|0),A=0;(0|A)!=7&&(i=k[(m=(r=A<<1)+t|0)+218>>1]<<8,g=O(A,80)+222176|0,m=k[m+164>>1],T=i+O(m,k[2+(r+c|0)>>1])<<8,e[g>>2]=T,h=+(0|T),P[g+16>>3]=h,P[g+48>>3]=16*(+(i+O(m,k[2+(r+l|0)>>1])<<8)-h)/x),i=(r=O(A,80))+222176|0,m=k[(T=t+(A<<1)|0)+182>>1],K=O(m,d[(g=A+c|0)+18|0])<<6,e[i+4>>2]=K,h=+(0|K),P[i+24>>3]=h,V=i,i=A+l|0,P[V+56>>3]=64*(+(O(m,d[i+18|0])<<6)-h)/s,(0|A)>(0|_)|A>>>0>5||(m=r+222176|0,K=k[T+200>>1],T=O(K,d[g+26|0])<<10,e[m+8>>2]=T,h=+(0|T),P[m+32>>3]=h,V=m- -64|0,m=K<<10,P[V>>3]=64*(+(0|O(m,d[i+26|0]))-h)/s,A>>>0<=2?(r=r+222176|0,g=O(m,d[g+32|0]),e[r+12>>2]=g,h=+(0|g),P[r+40>>3]=h,P[r+72>>3]=64*(+(0|O(m,d[i+32|0]))-h)/s):e[r+222188>>2]=T),(0|(A=A+1|0))!=8;);}for(;;){if(A=e[50762],!d[218960]&(0|A)==e[50763])break o;b:{m:{if(63&A){if(7&A)break b;x:if(!((0|(r=e[54736]))<=0||(0|(t=e[54735]))<=0))for(i=e[50826],A=1;;){if(e[(c=(l=A<<2)+i|0)>>2]=e[c>>2]+e[l+203312>>2],A>>>0>28|(0|A)>=(0|r))break x;if(l=(0|A)<(0|t),A=A+1|0,!l)break}if((0|(A=e[33073]))>255)break b;e[33073]=A+1;break b}if(A)if(e[50759]){if(r=e[50768]+e[50769]|0,e[50768]=r,r=(t=e[50766])?O(d[t+((0|(r>>=8))>=127?127:r)|0],e[50771])>>8:0,e[55911]=e[55911]+e[55915],t=(0|(t=e[55914]))<=23551?t:0,e[55914]=t+e[50761],t=(r+e[50770]|0)+O(e[33072],d[110528+(t>>6)|0]-128|0)|0,e[50767]=t,!(i=e[51291])&(0|(r=i?i<<12:t))>102399||(t=(0|r)<=102400?102400:r,e[50767]=t),(0|A)!=e[55913]){if(A=0,!((0|(g=e[50980]))<0)){for(;c=O(A,80),s=P[(r=c+222176|0)+48>>3]+P[r+16>>3],P[r+16>>3]=s,x=P[r+56>>3]+P[r+24>>3],P[r+24>>3]=x,h=P[r- -64>>3]+P[r+32>>3],P[r+32>>3]=h,i=ee(s)<2147483648?~~s:-2147483648,e[r>>2]=i,i=ee(h)<2147483648?~~h:-2147483648,e[r+8>>2]=i,l=ee(x)<2147483648?~~x:-2147483648,e[r+4>>2]=(0|l)>0?l:0,(0|A)>2||(s=P[(r=c+222176|0)+72>>3]+P[r+40>>3],P[r+40>>3]=s,i=ee(s)<2147483648?~~s:-2147483648),e[c+222188>>2]=i,(0|g)>=(0|(A=A+1|0)););if((0|A)>=8)break m}for(;(0|A)!=7&&(r=O(A,80)+222176|0,s=P[r+48>>3]+P[r+16>>3],P[r+16>>3]=s,i=ee(s)<2147483648?~~s:-2147483648,e[r>>2]=i),r=O(A,80)+222176|0,s=P[r+56>>3]+P[r+24>>3],P[r+24>>3]=s,i=ee(s)<2147483648?~~s:-2147483648,e[r+4>>2]=(0|i)>0?i:0,(0|(A=A+1|0))!=8;);}}else t=e[50767];else e[50826]=218976,e[54742]=0,e[54736]=kr(e[50767]<<4,218976,0),t=e[50767],e[54737]=890/(t>>12),e[54739]=(0|O(e[50781],O(e[50779],t>>8)))/8e4}if(e[55906]=t>>11,e[54735]=e[54736],e[55904]=O(e[50760],t>>7),e[55905]=e[50754]/(t>>12),r=1^(A=e[54742]),e[54742]=r,e[50826]=O(A,1600)+218976,e[54736]=kr(t<<4,O(r,1600)+218976|0,1),!(!(t=e[50759])|!e[51022]))for(x=P[25430],h=P[25429],A=1;e[(r=t+(A<<2)|0)+272>>2]&&(i=e[r+308>>2],r=O(A,40)+203456|0,I=ds(x*+k[2+(O(A,80)+222176|0)>>1]),I*=s=$r(h*+(0|i)),I+=I,P[r+8>>3]=I,s*=-s,P[r+16>>3]=s,P[r>>3]=1-I-s),(0|(A=A+1|0))!=9;);}if(l=e[50762]+1|0,e[50762]=l,c=(A=e[50765])+e[55904]|0,e[50765]=c,(0|c)<0&(0|A)>0){if(m=e[55905],t=e[50800]+((0|m)/-2|0)|0,e[55907]=t,(0|(T=e[50763]))<(0|l))break o;if(_=e[54738]+1|0,e[54738]=_,g=e[50767],!((0|(A=(r=e[50980])+1|0))>8)&&(i=g<<3,1&r&&(e[203264+(A<<2)>>2]=(1+(e[O(A,80)+222176>>2]/(0|i)|0)|0)/2,A=r+2|0),(0|r)!=7))for(;r=203264+(A<<2)|0,K=O(A,80)+222176|0,e[r>>2]=(1+(e[K>>2]/(0|i)|0)|0)/2,e[r+4>>2]=(1+(e[K+80>>2]/(0|i)|0)|0)/2,(0|(A=A+2|0))!=9;);A=(0|O(e[50781],O(e[50779],g>>8)))/8e4|0,e[54739]=A;b:if(!((0|(r=e[55908]))<=0)){m:switch(r-3|0){case 0:if((T-l|0)>=m<<1)break b;e[55908]=2,A=(0|O(e[55909],A))/256|0,e[54739]=A;break b;case 1:e[55908]=2,A=(0|O(e[55909],A))/256|0,e[54739]=A;break b;default:break m}e[55908]=r-1}(r=e[55910])&&(i=A,A=e[55911]>>8,A=(0|O(i,d[r+((0|A)>=127?127:A)|0]))/128|0,e[54739]=A),(0|(r=e[e[32972]+92>>2]))>7||(r=15&(i=d[e[55912]+(106336+(r<<3)|0)|0]),(i=i>>>4|0)&&((0|i)!=15?(0|_)%(0|i)|0||(e[54739]=(0|O(A,r))/16):(e[55912]=0,e[54739]=(0|O(A,r))/16)))}else t=e[55907];if(l=t+1|0,e[55907]=l,r=c>>>16|0,i=0,!((0|l)<0|(0|l)>=e[50799])){if(!((0|(A=(t=e[50980])+1|0))>8)){if(g=1&(c=8-t|0),(0|t)!=7)for(m=-2&c,t=0;i=O(e[(T=(c=A<<2)+4|0)+203216>>2],k[106400+(O(r,e[T+203264>>2])>>>4&4094)>>1])+(O(e[c+203216>>2],k[106400+(O(r,e[c+203264>>2])>>>4&4094)>>1])+i|0)|0,A=A+2|0,(0|m)!=(0|(t=t+2|0)););g&&(i=O(e[(A<<=2)+203216>>2],k[106400+(O(r,e[A+203264>>2])>>>4&4094)>>1])+i|0)}i=O(d[l+132160|0],(0|i)/e[55906]|0)}if(A=1,(0|(l=e[54737]))<=0)t=r;else for(c=e[50826],t=r;i=O(e[c+(A<<2)>>2],k[106400+((65504&t)>>>4|0)>>1])+i|0,t=t+r|0,(0|l)>=(0|(A=A+1|0)););if((0|(l=e[54735]))>=(0|A))for(c=e[50826];i=i-O(e[c+(A<<2)>>2],k[106400+((65504&t)>>>4|0)>>1])|0,t=t+r|0,(0|l)>=(0|(A=A+1|0)););if(l=(0|(A=e[54728]))==64?i:O(A,i>>6),e[51022]){if(e[50759])for(r=st(e[33209],0,1103515245,0),A=le,A=zi(r=r+12345|0,A=r>>>0<12345?A+1|0:A),e[33209]=A,x=+((16383&A)- -8192|0),t=e[50759],c=0,A=1;(i=e[272+(t+(A<<2)|0)>>2])&&(g=e[O(A,80)+222180>>2],r=O(A,40)+203456|0,s=P[r+32>>3],h=P[r+24>>3],P[r+32>>3]=h,s=s*P[r+16>>3]+(P[r>>3]*x+h*P[r+8>>3]),P[r+24>>3]=s,r=ee(s)<2147483648?~~s:-2147483648,c=O(r,O(i,g>>14))+c|0),(0|(A=A+1|0))!=9;);else c=0;l=l+c|0}t=0,(0|(A=e[50776]))>=e[50773]||(r=e[50778],t=e[50772],(c=e[50774])?(i=A+1|0,e[50776]=i,A=O(c,f[t+(A+r|0)|0])):(c=d[0|(t=t+(A+r|0)|0)],t=f[t+1|0],i=A+2|0,e[50776]=i,A=c|t<<8),t=(0|O(O(A,e[50780])>>10,e[50775]))/32|0,(0|(A=e[50777]))>(r+i|0)||(e[50778]=r+((0|O(A,3))/-4|0))),r=(A=e[51293])+1|0,e[51293]=r,A=((O(e[54739],l>>8)>>13)+t|0)+(O(e[50755],k[205184+(A<<1)>>1])>>8)|0,(0|r)>=5500&&(e[51293]=0),r=e[33073];b:{m:{if((0|(t=O(r,A)))>=8388608){if((0|r)>=(0|(c=8388608/(0|A)|0)))break m;break b}if((0|t)>-8388353||(0|r)<(0|(c=-8388608/(0|A)|0)))break b}r=c-1|0,e[33073]=r,t=O(A,r)}if(A=e[51290],e[51290]=A+1,r=A,A=t>>8,f[0|r]=A,r=e[51290],e[51290]=r+1,f[0|r]=t>>>16,(r=e[50756])&&(r=e[r+8>>2])&&$A[0|r](A<<16>>16),t=(r=e[51292])+1|0,e[51292]=t,k[205184+(r<<1)>>1]=A,(0|t)>=5500&&(e[51292]=0),!(Ae[54046]>=e[51290]+2>>>0))break}i=1;break n}e[50773]=0}e[54731]=e[54729],i=1,g=65535&A,t=d[218920],c=e[r+8>>2],l=e[r+12>>2],s=0,h=0,H=r=H+-64|0,m=e[50759];C:if((0|(A=e[m+132>>2]))!=6){if(!t){for(A-1>>>0<=4&&(e[55921]=A,e[55964]=e[110896+(A<<2)>>2]),A=e[m+88>>2],e[54741]=1,e[55922]=(0|A)/32,t=e[50758],A=e[50757];;){h:if((0|t)!=(0|(A=(0|(A=A+1|0))<=169?A:0)))if((0|(T=e[216192+(A<<4)>>2]))!=1){if(T-5>>>0>1)continue}else{if(e[54741]=0,A=e[8+(216192+(A<<4)|0)>>2],!(y[l+4>>1]!=y[A+4>>1]|y[A+6>>1]!=y[l+6>>1]|y[A+8>>1]!=y[l+8>>1]|y[A+10>>1]!=y[l+10>>1])&&y[A+12>>1]==y[l+12>>1])break h;e[54741]=2}break}for((y[c+4>>1]!=y[113564]|y[c+6>>1]!=y[113565]|y[c+8>>1]!=y[113566]|y[c+10>>1]!=y[113567]||y[c+12>>1]!=y[113568])&&(ct(),e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,e[55988]=0,e[55989]=0,e[55990]=0,e[55991]=0,e[56004]=0,e[56005]=0,e[56006]=0,e[56007]=0,e[56020]=0,e[56021]=0,e[56022]=0,e[56023]=0,e[56036]=0,e[56037]=0,e[56038]=0,e[56039]=0,e[56052]=0,e[56053]=0,e[56054]=0,e[56055]=0,e[56068]=0,e[56069]=0,e[56070]=0,e[56071]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0),A=y[l+4>>1]|y[l+6>>1]<<16,t=y[l>>1]|y[l+2>>1]<<16,k[113562]=t,k[113563]=t>>>16,k[113564]=A,k[113565]=A>>>16,A=y[l+60>>1]|y[l+62>>1]<<16,t=y[l+56>>1]|y[l+58>>1]<<16,k[113590]=t,k[113591]=t>>>16,k[113592]=A,k[113593]=A>>>16,A=y[l+52>>1]|y[l+54>>1]<<16,t=y[l+48>>1]|y[l+50>>1]<<16,k[113586]=t,k[113587]=t>>>16,k[113588]=A,k[113589]=A>>>16,A=y[l+44>>1]|y[l+46>>1]<<16,t=y[l+40>>1]|y[l+42>>1]<<16,k[113582]=t,k[113583]=t>>>16,k[113584]=A,k[113585]=A>>>16,A=y[l+36>>1]|y[l+38>>1]<<16,t=y[l+32>>1]|y[l+34>>1]<<16,k[113578]=t,k[113579]=t>>>16,k[113580]=A,k[113581]=A>>>16,A=y[l+28>>1]|y[l+30>>1]<<16,t=y[l+24>>1]|y[l+26>>1]<<16,k[113574]=t,k[113575]=t>>>16,k[113576]=A,k[113577]=A>>>16,A=y[l+20>>1]|y[l+22>>1]<<16,t=y[l+16>>1]|y[l+18>>1]<<16,k[113570]=t,k[113571]=t>>>16,k[113572]=A,k[113573]=A>>>16,A=y[l+12>>1]|y[l+14>>1]<<16,t=y[l+8>>1]|y[l+10>>1]<<16,k[113566]=t,k[113567]=t>>>16,k[113568]=A,k[113569]=A>>>16,x=+(0|g),(V=1&k[c>>1])?(A=d[c+39|0],e[56680]=A,P[28364]=A>>>0,P[28354]=+(d[l+39|0]-A<<6)/x,A=d[c+40|0],P[28366]=A>>>0,P[28356]=+(d[l+40|0]-A<<6)/x,A=d[c+41|0],e[56682]=A,P[28368]=A>>>0,P[28358]=+(d[l+41|0]-A<<6)/x,t=d[c+42|0],e[56684]=t,P[28370]=t>>>0,A=d[c+43|0],s=+(d[l+43|0]-A<<6)/x,h=+(d[l+42|0]-t<<6)/x,I=+(A>>>0)):(e[56728]=0,e[56729]=0,A=0,e[56680]=0,e[56708]=0,e[56709]=0,e[56732]=0,e[56733]=0,e[56712]=0,e[56713]=0,e[56682]=0,e[56736]=0,e[56737]=0,e[56716]=0,e[56717]=0,e[56684]=0,e[56740]=0,e[56741]=0,I=0),e[56688]=A,P[28360]=h,P[28372]=I,P[28362]=s,e[56692]=0,e[56748]=0,e[56749]=0,e[56694]=0,e[56752]=0,e[56753]=0,e[56696]=0,e[56756]=0,e[56757]=0,e[56700]=0,e[56760]=0,e[56761]=0,e[56704]=0,e[56764]=0,e[56765]=0,e[50764]=g,A=1;_=k[(T=(g=A<<1)+m|0)+164>>1],t=(Ee=O(A,80))+222896|0,h=+k[T+218>>1],s=.00390625*+(0|O(_,k[2+(c+g|0)>>1]))+h,P[t+16>>3]=s,K=ee(s)<2147483648?~~s:-2147483648,e[t>>2]=K,P[t+48>>3]=64*(.00390625*+(0|O(_,k[2+(l+g|0)>>1]))+h-s)/x,A>>>0<=3&&(t=Ee+222896|0,s=.00390625*+k[T+200>>1]*+(d[35+(A+c|0)|0]<<1),P[t+24>>3]=s,g=ee(s)<2147483648?~~s:-2147483648,e[t+4>>2]=g,P[t+56>>3]=64*(+(d[35+(A+l|0)|0]<<1)-s)/x),(0|(A=A+1|0))!=6;);if(s=+((A=d[c+40|0])<<1),P[27864]=s,t=e[56618],A||(s=+(0|t),P[27864]=s),A=ee(s)<2147483648?~~s:-2147483648,e[55724]=A,g=d[l+40|0],e[55730]=0,e[55731]=1079394304,e[55738]=0,e[55739]=0,e[55725]=89,A=1,P[27868]=64*(+(0|(g?g<<1:t))-s)/x,V)for(;t=O(A,80)+222896|0,m=d[(g=A+c|0)+56|0]<<2,e[t+12>>2]=m,s=+(0|m),P[t+40>>3]=s,m=A+l|0,P[t+72>>3]=64*(+(d[m+56|0]<<2)-s)/x,g=d[g+49|0],e[t+8>>2]=g,s=+(g>>>0),P[t+32>>3]=s,P[t- -64>>3]=64*(+d[m+49|0]-s)/x,(0|(A=A+1|0))!=7;);e[56606]=0}for(;;){if((0|(K=e[50764]))>(0|(l=e[56606]))){for(A=e[50767],e[56609]=e[55724],e[56619]=e[55725],e[56610]=e[55744],e[56611]=e[55764],e[56612]=e[55784],e[56613]=e[55804],V=O(A,10),e[56607]=(0|V)/4096,e[56620]=e[55745],e[56621]=e[55765],e[56622]=e[55785],e[56614]=e[55824],e[56630]=e[55746],e[56631]=e[55766],e[56632]=e[55786],e[56633]=e[55806],e[56634]=e[55826],e[56635]=e[55846],Ee=e[56680],e[56608]=Ee,c=e[56694],e[56656]=c,g=e[56696],e[56653]=g,m=e[56700],e[56655]=m,T=e[56684],e[56649]=T,e[56651]=e[56704],e[56654]=e[56688],e[56652]=e[56682],e[56650]=e[56692],A=0;t=O(A,80)+222896|0,s=P[t+48>>3]+P[t+16>>3],P[t+16>>3]=s,x=P[t+56>>3]+P[t+24>>3],P[t+24>>3]=x,h=P[t+72>>3]+P[t+40>>3],P[t+40>>3]=h,I=P[t- -64>>3]+P[t+32>>3],P[t+32>>3]=I,_=ee(s)<2147483648?~~s:-2147483648,e[t>>2]=_,_=ee(x)<2147483648?~~x:-2147483648,e[t+4>>2]=_,_=ee(h)<2147483648?~~h:-2147483648,e[t+12>>2]=_,_=ee(I)<2147483648?~~I:-2147483648,e[t+8>>2]=_,(0|(A=A+1|0))!=9;);for(s=P[28354]+P[28364],P[28364]=s,P[28366]=P[28356]+P[28366],x=P[28358]+P[28368],P[28368]=x,h=P[28360]+P[28370],P[28370]=h,I=P[28362]+P[28372],P[28372]=I,A=ee(s)<2147483648?~~s:-2147483648,e[56680]=A,A=ee(x)<2147483648?~~x:-2147483648,e[56682]=A,A=ee(h)<2147483648?~~h:-2147483648,e[56684]=A,A=ee(I)<2147483648?~~I:-2147483648,e[56688]=A,s=P[28374]+0,P[28374]=s,A=ee(s)<2147483648?~~s:-2147483648,e[56692]=A,s=P[28376]+0,P[28376]=s,A=ee(s)<2147483648?~~s:-2147483648,e[56694]=A,s=P[28378]+0,P[28378]=s,A=ee(s)<2147483648?~~s:-2147483648,e[56696]=A,s=P[28380]+0,P[28380]=s,A=ee(s)<2147483648?~~s:-2147483648,e[56700]=A,s=P[28382]+0,P[28382]=s,A=ee(s)<2147483648?~~s:-2147483648,e[56704]=A,e[56659]=e[55724],e[56669]=e[55725],e[56660]=e[55744],e[56670]=e[55745],e[56661]=e[55764],e[56671]=e[55765],e[56662]=e[55784],e[56672]=e[55785],e[56663]=e[55804],e[56664]=e[55824],e[56665]=e[55844],A=e[50768]+e[50769]|0,e[50768]=A,A>>=8,e[50767]=e[50770]+(O(e[50771],d[e[50766]+((0|A)>=127?127:A)|0])>>8),A=K-l|0,e[55923]=(0|A)>=64?64:A,e[55961]=(0|V)/40960,A=Ee-7|0,e[56658]=(0|A)>0?A:0,P[27974]=T>>>0<=87?.001*+k[111136+(T<<1)>>1]*.05:0,P[27975]=g>>>0<=87?.001*+k[111136+(g<<1)>>1]*.25:0,P[27973]=c>>>0<=87?.001*+k[111136+(c<<1)>>1]:0,P[27971]=m>>>0<=87?.001*+k[111136+(m<<1)>>1]*.05:0,s=(A=e[56629])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.6:0,P[r>>3]=s,s=(A=e[56630])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.4:0,P[r+8>>3]=s,s=(A=e[56631])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.15:0,P[r+16>>3]=s,s=(A=e[56632])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.06:0,P[r+24>>3]=s,s=(A=e[56633])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.04:0,P[r+32>>3]=s,s=(A=e[56634])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.022:0,P[r+40>>3]=s,s=(A=e[56635])>>>0<=87?.001*+k[111136+(A<<1)>>1]*.03:0,P[r+48>>3]=s,s=(A=(0|(A=e[56657]-3|0))<=0?57:A)>>>0<=87?.001*+k[111136+(A<<1)>>1]:0,P[27977]=s/+e[55964],x=P[27968],h=P[27967],A=1;t=(l=A<<6)+223664|0,I=(s=$r(h*+e[(g=(c=A<<2)+226428|0)+48>>2]))*-s,P[t+216>>3]=I,s*=ds(x*+e[g+8>>2]),s+=s,P[t+208>>3]=s,ce=1-s-I,P[t+200>>3]=ce,A>>>0<=5&&(g=e[(c=c+226428|0)+208>>2],t=l+223664|0,te=(J=$r(h*+e[c+248>>2]))*ds(x*+(0|g)),te+=te,P[t+1488>>3]=te,J*=-J,P[t+1496>>3]=J,he=1-te-J,P[t+1480>>3]=he,P[t+256>>3]=.015625*(J-I),P[t+248>>3]=.015625*(te-s),P[t+240>>3]=.015625*(he-ce)),(0|(A=A+1|0))!=10;);for(J=(s=$r(h*+e[56619]))*-s,P[27985]=J,te=(s*=ds(x*+(0-e[56609]|0)))+s,P[27984]=te,s=1-te-J,P[27983]=s,s!=0&&(s=1/s,P[27983]=s,J*=I=-s,P[27985]=J,te*=I,P[27984]=te),ce=(I=$r(h*+e[56669]))*-I,P[28145]=ce,he=(I*=ds(x*+(0-e[56659]|0)))+I,P[28144]=he,I=1-he-ce,P[28143]=I,I!=0&&(I=1/I,P[28143]=I,ce*=Le=-I,P[28145]=ce,he*=Le,P[28144]=he),P[27990]=.015625*(ce-J),P[27989]=.015625*(he-te),P[27988]=.015625*(I-s),A=0;c=e[(l=226428+(A<<2)|0)+8>>2],t=223664+(A<<6)|0,I=(s=$r(h*+e[l+128>>2]))*ds(x*+(0|c)),I+=I,P[t+848>>3]=I,s*=-s,P[t+856>>3]=s,P[t+840>>3]=P[r+(A<<3)>>3]*(1-I-s),(0|(A=A+1|0))!=7;);if(h=(s=$r(h*+(e[55918]/2|0)))*-s,P[28137]=h,s*=ds(0*x),s+=s,P[28136]=s,P[28135]=1-s-h,A=1,(0|Ur())!=1)continue;break C}break}A=1,e[54741]>0&&(e[54741]=0,e[55963]=64,e[56606]=l+-64,e[55923]=64,(0|Ur())==1)||(A=0)}else{if(H=T=H-752|0,!t){Je(A=T+376|0,0,376),Pr(m,c,A),Pr(m,l,A=Je(T,0,376)),t=e[50768]+O(e[50769],g>>>6|0)|0,e[50768]=t,t>>=8,t=e[50770]+(O(e[50771],d[e[50766]+((0|t)>=127?127:t)|0])>>8)|0,e[50767]=t,P[A+368>>3]=(0|t)/4096|0,e[50773]&&(P[A+736>>3]=P[A+736>>3]/5,P[A+360>>3]=P[A+360>>3]/5),t=e[e[56797]+4>>2],$A[e[e[t>>2]>>2]](t,A+376|0,110,110,-1,0),l=g-110|0,m=e[50758],t=e[50757];h:{for(;;){if((0|m)!=(0|(t=(t+1|0)%170|0))&&!((_=e[216192+(t<<4)>>2])-5>>>0<2)){if(c=1,(0|_)!=1)continue;break h}break}l=g-220|0,c=0}(0|l)>0&&(t=e[e[56797]+4>>2],$A[e[e[t>>2]>>2]](t,A,l,l||1,-1,0)),c||(e[A+352>>2]=0,e[A+356>>2]=0,P[A>>3]=P[A+368>>3],t=e[e[56797]+4>>2],$A[e[e[t>>2]>>2]](t,A,55,55,-1,0),e[A+360>>2]=0,e[A+364>>2]=0,t=e[e[56797]+4>>2],$A[e[e[t>>2]>>2]](t,A,55,55,-1,0))}A=e[e[56797]+8>>2],t=e[51290],g=0|$A[e[e[A>>2]>>2]](A,e[54046]-t>>>1|0,t),m=e[51290];h:if(g&&!((0|(t=e[50776]))>=(0|(Ee=e[50773]))))for(Te=e[50777],Xe=(0|O(Te,3))/-4|0,s=.0009765625*+e[50780],_=e[50772],l=e[50778],fA=e[50775],K=e[50774],A=0;;){if(V=d[_+(c=t+l|0)|0],K?V=O(K,V<<24>>24):(t=t+1|0,e[50776]=t,V|=f[_+(c=t+l|0)|0]<<8),V=ee(x=s*+(0|V))<2147483648?~~x:-2147483648,k[(Fe=m+(A<<1)|0)>>1]=y[Fe>>1]+((0|O(V,fA))/40|0),(0|c)>=(0|Te)&&(l=l+Xe|0,e[50778]=l),t=t+1|0,e[50776]=t,(0|t)>=(0|Ee))break h;if(!(g>>>0>(A=A+1|0)>>>0))break}A=m+(g<<1)|0,e[51290]=A,H=T+752|0,A=Ae[54046]<=A>>>0}if(H=r- -64|0,A)break n;break o}e[50781]=A||100;break o}Hs(A,e[r+8>>2]);break o}A=e[r+8>>2],e[50759]=qA(203816,A,1344),e[50801]=e[A+108>>2]?105792:106064,A=(0|O(e[A+120>>2],26))/100|0,e[33038]=A,(0|(i=e[50754]))<=11e3&&(f[203300]=1,e[33038]=A<<1),e[54728]=e[50982],A=e[50979],t=e[50978],Je(205184,0,11e3),e[51293]=0,A=(t=(c=(0|(l=e[50789]))>0)?130:(0|t)>=5499?5499:t)?c?l:(0|A)>=100?100:A:0,e[50755]=A,t=(0|O(t,i))/1e3|0,e[51292]=t,e[54729]=(0|A)>20?t<<1:A?t:0,e[33037]=(0|O(500-A|0,(0|O(d[e[50797]+105596|0],(0|O(e[50787],55))/100|0))/16|0))/500,A=256,(0|(t=(0|(t=e[50785]))>=101?101:t))>=51&&(A=256+(((O(t,25)-1250&65535)>>>0)/50|0)|0),k[101990]=(0|O(k[102026],A))/256,k[101991]=(0|O(k[102027],A))/256,k[101992]=(0|O(k[102028],A))/256,k[101993]=(0|O(k[102029],A))/256,k[101994]=(0|O(k[102030],A))/256,k[101995]=(0|O(k[102031],A))/256,A=e[50790],k[101999]=(0|O(k[102035],O(A,-6)+256|0))/256,k[102e3]=(0|O(k[102036],O(A,-3)+256|0))/256,Xs(8,0,e[50986],0,e[51290]),fe(e[r+8>>2]);break o}if(!e[50759])break o;t=e[r+12>>2],r=e[r+8>>2],e[55911]=0,e[55915]=A?2097152/(0|A)|0:0,e[55910]=r,A=(0|O(t,e[33037]))/16|0,e[50779]=A,e[50780]=(0|O(O(A,e[50985]),15))/100;break o}Xs(t>>8,A,e[r+8>>2],e[r+12>>2],c)}i=0,A=e[50757]+1|0,e[50757]=(0|A)<=169?A:0}if(f[218920]=i,!((c=e[51290])>>>0<Ae[54046]))break}l=e[34391],r=(e[51290]-l|0)/2|0,e[34439]=r+e[34439],t=e[34436],A=e[34388]+O(t,36)|0,e[A+4>>2]=0,e[A>>2]=0,e[A+24>>2]=e[34438];a:if(2&(A=e[32538])){if(c=e[34388],(-2&A)==2&&(!(r=t?c:0)|e[r>>2]!=8||(0|(r=e[r+28>>2]))!=e[34389]&&(e[34389]=r)),i=1,(0|t)<2)break a;for(;;){r=t?c+O(i,36)|0:0;n:{o:switch(0|A){case 2:case 3:if(!r|e[r>>2]!=8||(0|(r=e[r+28>>2]))==e[34389])break n;e[34389]=r;break n;case 0:break o;default:break n}(A=e[34440])?($A[0|A](l,0,r),t=e[34436],A=e[32538]):A=0}if(!((0|(i=i+1|0))<(0|t)))break}}else if((A=e[34440])&&0|$A[0|A](l,r,e[34388]))break r;if(!SA(1)&&!(170-((0|(A=e[50757]-e[50758]|0))<=0?A+170|0:A)|0||(A=e[34388],e[A>>2]=0,e[A+4>>2]=e[34437],e[A+24>>2]=e[34438],wA(1))))break}if(t=0,2&d[130152]||!(A=e[34440])||!(0|$A[0|A](0,0,e[34388])))break A}wA(2),t=268439295}if((0|t)<=268437502){if(!t|(0|t)==268436479|(0|t)!=268437247)break e;return}}}function SA(A){var t,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0;H=t=H-720|0,r=e[47198],A||(e[36443]=0,e[36442]=1,e[36444]=0,e[36440]=0,e[36441]=0,A=e[50758],e[36454]=A,e[36427]=-1,e[36424]=-1,e[36446]=0,e[36447]=0,e[36439]=-1,e[36426]=0,e[36455]=A,e[36448]=0,e[36449]=0,e[36450]=0,e[36451]=0,e[36452]=0,e[36453]=0,xs(),e[36427]=-1,A=216192+(e[50758]<<4)|0,e[A>>2]=5,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,e[36426]=0,e[36438]&&(e[36438]=0,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0));e:{A:if(!((0|(A=e[36442]))>997|e[36423]<=(0|A)))for(V=r>>>1&1,K=t+48|4,J=t+348|0,te=t+652|0,ce=e[32322],x=e[32320],he=t+60|0,Ee=t+648|0,Te=t+56|0,Fe=t+620|0,T=t- -64|0;;){if(s=145840+(A<<5)|0,!(A=e[50756])|!e[A>>2]||(e[t+12>>2]=0,Tr(A=t+16|0,e[s+8>>2],s,0,t+12|0),r=ui(A),i=d[s+17|0],A=216192+(e[50758]<<4)|0,e[A>>2]=16,e[A+8>>2]=i,e[A+4>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),A=1,(0|(i=(0|(r=e[50757]-e[50758]|0))<=0?r+170|0:r))<=(0|((r=d[s+17|0])?(0|r)==2?25:15:10)))break e;if(I=e[36442],2&d[0|s])for(h=2047&y[s+4>>1];;){if(i=e[36443],g=127&(r=e[198304+(i<<2)>>2])){A=r>>>8|0,e[36443]=i+1;r:{a:{n:switch((31&r)-2|0){case 0:Hs(96&r|8,A),ws(2);break r;case 5:if((0|A)>=e[34064]||!e[(g=(i=A<<4)+136272|0)+4>>2])break r;_t(10,0),A=216192+(e[50758]<<4)|0,e[A>>2]=6,g=e[g+4>>2],e[A+8>>2]=e[8+(i+136272|0)>>2]+44,e[A+12>>2]=5376,e[A+4>>2]=g;break a;case 8:if((0|((0|(i=e[50757]-e[50758]|0))<=0?i+170:i))<6)break r;g=e[47353],i=216192+(e[50758]<<4)|0,e[i>>2]=778,e[i+8>>2]=A,e[i+4>>2]=g+h&16777215;break a;case 9:if((0|((0|(i=e[50757]-e[50758]|0))<=0?i+170:i))<6)break r;g=e[33284],i=216192+(e[50758]<<4)|0,e[i>>2]=1034,e[i+8>>2]=A,e[i+4>>2]=g+1&16777215;break a;default:break n}_t(10,0),i=216192+(e[50758]<<4)|0,e[i>>2]=12,e[i+8>>2]=A,e[i+4>>2]=g}A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0}if(!(128&r))continue}break}(A=d[s+20|0])&&(1&f[e[47192]+48|0]&d[s+17|0]==2|1&f[e[s+8>>2]+7|0]||(e[36426]=0),r=e[47353]+(2047&y[s+4>>1])|0,e[36445]=r,4&A&&((0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(i=e[47568],A=216192+(e[50758]<<4)|0,e[A>>2]=522,e[A+8>>2]=i,e[A+4>>2]=16777215&r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)),1&f[s+20|0]&&(r=y[s+4>>1],i=e[36444],e[36444]=i+1,(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(h=e[36445],g=e[47355],A=216192+(e[50758]<<4)|0,e[A>>2]=266,e[A+8>>2]=i+g,e[A+4>>2]=16777215&h|(63488&r)<<13,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0))),(0|(A=e[36441]))>0&&(r=216192+(e[36425]<<4)|0,e[r+4>>2]||(e[r+4>>2]=A),e[36441]=0),A=I+1<<5,r=I-1<<5,!(i=d[s+18|0])|2&d[e[s+8>>2]+7|0]||_t(i,1),g=A+145840|0,l=r+145840|0,h=1;r:{a:{if(e[47198]&&(m=e[s+8>>2],d[m+10|0]!=15)){n:if(d[s+17|0]==2)switch(d[l+17|0]-3|0){case 0:case 5:break a;default:break n}h=0,Tr(t+704|0,m,s,V,0),(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(r=e[36445],A=216192+(e[50758]<<4)|0,e[A>>2]=1802,e[A+4>>2]=16777215&r,r=e[t+708>>2],e[A+8>>2]=e[t+704>>2],e[A+12>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)}n:switch(d[s+17|0]){case 0:_t(e[s+12>>2],0),f[s+23|0]=d[e[s+8>>2]+14|0];break r;case 4:r=e[s+8>>2],(0|(A=d[g+17|0]))==2|!d[g+20|0]&(0|A)==3||(k[s>>1]=8192|y[s>>1]),2&d[r+7|0]&&(e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[T>>2]=0,e[T+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,ut(0,1,s,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[T>>2]=e[t+640>>2],e[36424]<0&&(i=d[g+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=i,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,h=d[g+22|0],i=d[g+21|0],g=e[129280+(d[s+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(m=e[36440]))<=0||e[(A=216192+(A<<4)|0)+4>>2]||(e[A+4>>2]=m),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,h|=i<<16,i=(0|i)==255,e[A+12>>2]=i?3604556:h,e[A+8>>2]=i?x:g,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),cA(r,0,t+48|0,s,0)),ut(0,0,s,t+552|0,145784),e[t+552>>2]=4|e[t+552>>2],(0|(A=e[36440]))<=0||(0|(r=e[36424]))<0||(e[(r=216192+(r<<4)|0)+4>>2]||(e[r+4>>2]=A),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],xs(),e[36427]=-1,e[36422]=0,(A=e[t+624>>2])&&(i=A,A=e[t+644>>2],cs(i,2,e[t+596>>2]<<1,e[t+552>>2],0,A?(A<<5)/100|0:32)),e[36426]=0;break r;case 6:ut(0,0,s,t+552|0,145784),8&d[0|s]&&(A=e[s+12>>2],(0|(r=e[36440]))<=0||(0|(i=e[36424]))<0||(e[(i=216192+(i<<4)|0)+4>>2]||(e[i+4>>2]=r),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],xs(),e[36427]=-1,e[36422]=0,(r=e[t+624>>2])&&(i=A,A=e[t+644>>2],cs(r,2,e[t+596>>2]<<1,e[t+552>>2],i,A?(A<<5)/100|0:32))),A=e[s+12>>2],(0|(r=e[36440]))<=0||(0|(i=e[36424]))<0||(e[(i=216192+(i<<4)|0)+4>>2]||(e[i+4>>2]=r),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],xs(),e[36427]=-1,e[36422]=0,(r=e[t+624>>2])&&(i=A,A=e[t+644>>2],cs(r,2,e[t+596>>2]<<1,e[t+552>>2],i,A?(A<<5)/100|0:32)),e[36426]=0;break r;case 5:i=e[s+8>>2],e[K+40>>2]=0,e[(A=K)+32>>2]=0,e[A+36>>2]=0,e[A+24>>2]=0,e[A+28>>2]=0,e[A+16>>2]=0,e[A+20>>2]=0,e[A+8>>2]=0,e[A+12>>2]=0,e[A>>2]=0,e[A+4>>2]=0,e[t+48>>2]=4;o:{c:{u:{l:switch(d[g+17|0]-2|0){case 0:r=d[s+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,r=d[s+22|0],m=d[s+21|0],h=e[129280+(d[s+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(c=e[36440]))<=0||e[(A=216192+(A<<4)|0)+4>>2]||(e[A+4>>2]=c),A=(c=!(255&~m))?x:h,h=1;break c;case 1:break l;default:break u}if(!d[g+20|0]){r=d[g+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,r=d[g+22|0],m=d[g+21|0],h=e[129280+(d[g+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(c=e[36440]))<=0||e[(A=216192+(A<<4)|0)+4>>2]||(e[A+4>>2]=c),A=(c=!(255&~m))?x:h,h=1;break c}}if(h=0,e[36424]>=0)break o;r=d[g+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,r=d[s+22|0],m=d[s+21|0],h=e[129280+(d[s+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(c=e[36440]))<=0||e[(A=216192+(A<<4)|0)+4>>2]||(e[A+4>>2]=c),A=(c=!(255&~m))?x:h,h=0}_=e[50758],e[36424]=_,e[36440]=0,e[(_=216192+(_<<4)|0)>>2]=9,e[_+4>>2]=0,e[_+12>>2]=c?3604556:255&r|(255&m)<<16,e[_+8>>2]=A,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0}o:if(!(2&d[i+7|0])&d[l+17|0]!=2)8&d[0|s]&&_t(50,0);else{if(ut(0,1,s,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],cA(i,0,t+48|0,s,0),!(8&d[0|s]))break o;_t(25,1),cA(i,0,t+48|0,s,0)}o:if(h){if(e[36455]!=e[36454])break o;e[36455]=e[50758]}else k[s>>1]=8192|y[s>>1];if(ut(0,0,s,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],e[t+76>>2]=e[t+636>>2],e[t+80>>2]=e[t+656>>2],cA(i,0,t+48|0,s,0),d[s+20|0]|d[84+(145840+(I<<5)|0)|0]||((0|(A=d[g+17|0]))==7&&(_t(20,0),A=d[g+17|0]),(255&A)!=6))break r;_t(12,0);break r;case 7:o:{c:{u:{l:{i:switch((A=d[g+17|0])-2|0){case 1:break l;case 0:break i;default:break u}r=d[s+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,h=d[s+22|0],A=d[s+21|0],i=e[129280+(d[s+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(m=e[36440]))<=0||e[(r=216192+(r<<4)|0)+4>>2]||(e[r+4>>2]=m),r=(m=!(255&~A))?x:i;break c}r=d[g+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,h=d[g+22|0],A=d[g+21|0],i=e[129280+(d[g+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(m=e[36440]))<=0||e[(r=216192+(r<<4)|0)+4>>2]||(e[r+4>>2]=m),r=(m=!(255&~A))?x:i;break c}if(e[36424]>=0)break o;r=d[s+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,h=d[s+22|0],A=d[s+21|0],i=e[129280+(d[s+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(m=e[36440]))<=0||e[(r=216192+(r<<4)|0)+4>>2]||(e[r+4>>2]=m),r=(m=!(255&~A))?x:i}i=e[50758],e[36424]=i,e[36440]=0,e[(i=216192+(i<<4)|0)>>2]=9,e[i+4>>2]=0,e[i+12>>2]=m?3604556:(255&A)<<16|h,e[i+8>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,A=d[g+17|0]}o:{c:{u:switch((255&A)-2|0){case 1:if(d[g+20|0])break c;break;case 0:break u;default:break c}if(e[36455]!=e[36454])break o;e[36455]=e[50758];break o}k[s>>1]=8192|y[s>>1]}ut(0,0,s,t+552|0,145784),e[t+56>>2]=0,e[t+60>>2]=0,e[T>>2]=0,e[T+4>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[t+88>>2]=0,e[t+56>>2]=e[t+620>>2],e[T>>2]=e[t+640>>2],e[t+80>>2]=e[t+656>>2],e[t+48>>2]=0,e[t+52>>2]=0,e[t+76>>2]=e[t+636>>2],e[t+92>>2]=e[t+596>>2]<<1,8&d[0|s]&&cA(e[s+8>>2],0,t+48|0,s,0),cA(e[s+8>>2],0,t+48|0,s,0);break r;case 8:if(e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[T>>2]=0,e[T+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,1&f[0|s]||(r=d[s+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,i=d[s+22|0],r=d[s+21|0],h=e[129280+(d[s+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(m=e[36440]))<=0||e[(A=216192+(A<<4)|0)+4>>2]||(e[A+4>>2]=m),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,i|=r<<16,r=(0|r)==255,e[A+12>>2]=r?3604556:i,e[A+8>>2]=r?x:h,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),d[l+17|0]==8&&(e[36426]=0),ut(0,0,s,t+552|0,145784),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],e[t+92>>2]=e[t+596>>2]<<1,d[g+17|0]==2){e[36455]==e[36454]&&(e[36455]=e[50758]),cA(e[s+8>>2],0,t+48|0,s,0);break r}if(!(!(1&f[0|s])|d[l+17|0]!=2)){cA(e[s+8>>2],0,t+48|0,s,0);break r}e[36426]=0,cA(e[s+8>>2],0,t+48|0,s,0),e[36426]=0;break r;case 3:e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[T>>2]=0,e[T+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,i=e[e[s+8>>2]+4>>2],1&f[0|s]||(r=d[s+19|0],A=e[50758],e[36425]=A,e[36441]=0,e[(A=216192+(A<<4)|0)+12>>2]=r,e[A+8>>2]=0,e[A>>2]=8,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,h=d[s+22|0],r=d[s+21|0],m=e[129280+(d[s+16|0]<<2)>>2],(0|(A=e[36424]))<0|(0|(c=e[36440]))<=0||e[(A=216192+(A<<4)|0)+4>>2]||(e[A+4>>2]=c),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,h|=r<<16,r=(0|r)==255,e[A+12>>2]=r?3604556:h,e[A+8>>2]=r?x:m,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),d[l+17|0]==8&&(e[36426]=0),d[g+17|0]!=2|e[36455]!=e[36454]||(e[36455]=e[50758]),ut(0,0,s,t+552|0,145784),(0|(A=e[t+584>>2]-d[s+18|0]|0))>0&&_t(A,1),e[t+56>>2]=e[t+620>>2],e[t+64>>2]=e[t+640>>2],e[t+76>>2]=e[t+636>>2],e[t+80>>2]=e[t+656>>2],e[t+92>>2]=e[t+596>>2]<<1,cA(e[s+8>>2],0,t+48|0,s,i<<24>>31&5);break r;case 2:break n;default:break r}m=e[s+8>>2]}I=d[s+3|0],e[t+88>>2]=0,e[t+92>>2]=0,e[t+80>>2]=0,e[t+84>>2]=0,e[t+72>>2]=0,e[t+76>>2]=0,e[T>>2]=0,e[T+4>>2]=0,e[t+56>>2]=0,e[t+60>>2]=0,e[t+48>>2]=0,e[t+52>>2]=0,ut(0,0,s,t+552|0,145784),A=e[t+628>>2],e[t+56>>2]=A,e[t+92>>2]=e[t+596>>2]<<1;a:{if(!A||(c=0,i=Ee,r=he,2&d[t+552|0])){if(d[l+17|0]?(c=0,ut(0,0,l,t+400|0,0),A=e[t+476>>2],e[t+56>>2]=A,!A|!(2&d[t+400|0])||(e[t+72>>2]=e[t+496>>2],c=1),r=e[t+512>>2],e[t+84>>2]=e[t+508>>2],e[t+88>>2]=r):c=0,A)break a;e[t+48>>2]=1,e[t+52>>2]=1,i=Fe,r=Te}e[r>>2]=e[i>>2]}e[t+64>>2]=e[t+640>>2],r=d[s+16|0],A=0,(i=d[s+7|0])?(Pa(i,t+96|0),r=ci(e[t+220>>2]),(0|(i=e[t+224>>2]))<=0||(A=ci(i))):r=e[129280+(r<<2)>>2],e[36455]==e[36454]&&(e[36455]=e[50758]),i=(i=15&I)>>>0<2?1:i>>>0>6?3:2;a:{n:switch(d[l+17|0]-3|0){case 2:case 4:c=d[s+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[(l=216192+(l<<4)|0)+12>>2]=c,e[l+8>>2]=A,e[l>>2]=8,e[l+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,c=d[s+22|0],l=d[s+21|0],(0|(A=e[36440]))<=0||(0|(I=e[36424]))<0||e[(I=216192+(I<<4)|0)+4>>2]||(e[I+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,c|=l<<16,l=(0|l)==255,e[A+12>>2]=l?3604556:c,e[A+8>>2]=l?x:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,cA(m,1,t+48|0,s,i);break a;case 0:case 5:c=d[s+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[(l=216192+(l<<4)|0)+12>>2]=c,e[l+8>>2]=A,e[l>>2]=8,e[l+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,cA(m,1,t+48|0,s,i),c=d[s+22|0],l=d[s+21|0],(0|(A=e[36440]))<=0||(0|(I=e[36424]))<0||e[(I=216192+(I<<4)|0)+4>>2]||(e[I+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,c|=l<<16,l=(0|l)==255,e[A+12>>2]=l?3604556:c,e[A+8>>2]=l?x:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0;break a;default:break n}c?(c=d[s+22|0],(0|(l=e[36440]))<=0||(0|(I=e[36424]))<0||e[(I=216192+(I<<4)|0)+4>>2]||(e[I+4>>2]=l),l=e[50758],e[36424]=l,e[36440]=0,e[(l=216192+(l<<4)|0)>>2]=9,e[l+4>>2]=0,e[l+12>>2]=(c|c<<16)-983040,e[l+8>>2]=ce,l=e[50758]+1|0,e[50758]=(0|l)<=169?l:0,c=d[s+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[(l=216192+(l<<4)|0)>>2]=8,e[l+4>>2]=0,e[l+12>>2]=c-1,e[l+8>>2]=A,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,cA(m,1,t+48|0,s,i),l=d[s+21|0],c=d[s+22|0],(0|(A=e[36440]))<=0||(0|(I=e[36424]))<0||e[(I=216192+(I<<4)|0)+4>>2]||(e[I+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,c|=l<<16,l=(0|l)==255,e[A+12>>2]=l?3604556:c,e[A+8>>2]=l?x:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0):(1&f[0|s]||(c=d[s+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[(l=216192+(l<<4)|0)+12>>2]=c,e[l+8>>2]=A,e[l>>2]=8,e[l+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,c=d[s+22|0],l=d[s+21|0],(0|(A=e[36440]))<=0||(0|(I=e[36424]))<0||e[(I=216192+(I<<4)|0)+4>>2]||(e[I+4>>2]=A),A=e[50758],e[36424]=A,e[36440]=0,e[(A=216192+(A<<4)|0)>>2]=9,e[A+4>>2]=0,c|=l<<16,l=(0|l)==255,e[A+12>>2]=l?3604556:c,e[A+8>>2]=l?x:r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),cA(m,1,t+48|0,s,i))}!e[47198]|1^h||(Tr(t+704|0,e[s+8>>2],s,V,0),(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))<6||(r=e[36445],A=216192+(e[50758]<<4)|0,e[A>>2]=1802,e[A+4>>2]=16777215&r,r=e[t+708>>2],e[A+8>>2]=e[t+704>>2],e[A+12>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)),e[t+56>>2]=e[t+620>>2],e[t+84>>2]=0,e[t+88>>2]=0,e[t+64>>2]=e[t+640>>2],r=e[t+632>>2],e[t+68>>2]=r,A=te;a:{if(!r&&(!d[g+17|0]||(e[t+72>>2]=0,ut(0,0,g,t+248|0,0),e[t+52>>2]=1,A=e[t+368>>2],e[t+84>>2]=e[t+364>>2],e[t+88>>2]=A,r=e[t+328>>2],e[t+68>>2]=r,A=J,!r)))break a;e[t+72>>2]=e[A>>2]}cA(m,2,t+48|0,s,i)}if(A=e[36442]+1|0,e[36442]=A,(0|A)>997)break A;if(!(e[36423]>(0|A)))break}(0|(A=e[36440]))<=0||(0|(r=e[36424]))<0||(e[(r=216192+(r<<4)|0)+4>>2]||(e[r+4>>2]=A),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],xs(),e[36427]=-1,A=0,e[36423]<=0||(r=e[47568],i=e[33284],(0|((0|(A=e[50757]-e[50758]|0))<=0?A+170:A))>=6&&(A=216192+(e[50758]<<4)|0,e[A>>2]=1290,e[A+8>>2]=r,e[A+4>>2]=16777215&i,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),e[36423]=0,A=0)}return H=t+720|0,A}function TA(A,t,r,s){var i,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0;H=i=H-48|0;e:{if(r>>>0<=2){for(fA=e[(r<<=2)+124732>>2],hA=e[r+124720>>2];(0|(r=e[t+4>>2]))==e[t+104>>2]?r=Ie(t):(e[t+4>>2]=r+1,r=d[0|r]),(0|r)==32|r-9>>>0<5;);_=1;A:{r:switch(r-43|0){case 0:case 2:break r;default:break A}_=(0|r)==45?-1:1,(0|(r=e[t+4>>2]))==e[t+104>>2]?r=Ie(t):(e[t+4>>2]=r+1,r=d[0|r])}A:{r:{for(;;){if(f[g+84056|0]==(32|r)){if(g>>>0>6||((0|(r=e[t+4>>2]))==e[t+104>>2]?r=Ie(t):(e[t+4>>2]=r+1,r=d[0|r])),(0|(g=g+1|0))!=8)continue;break r}break}if((0|g)!=3){if((0|g)==8)break r;if(!s|g>>>0<4)break A;if((0|g)==8)break r}if((0|(r=e[t+116>>2]))>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1),!(!s|g>>>0<4))for(r=(0|r)<0;r||(e[t+4>>2]=e[t+4>>2]-1),(g=g-1|0)>>>0>3;);}H=x=H-16|0,Z(pe(pe(0|_)*pe(1/0))),(t=2147483647&(m=B(2)))-8388608>>>0<=2130706431?(r=t,r<<=25,s=(t=t>>>7|0)+1065353216|0):(r=m<<25,s=m>>>7|2147418112,t>>>0>=2139095040||(r=0,s=0,t&&(vt(x,r=t,0,0,0,(t=be(t))+81|0),h=e[x>>2],I=e[x+4>>2],r=e[x+8>>2],s=65536^e[x+12>>2]|16265-t<<16))),e[i>>2]=h,e[i+4>>2]=I,e[i+8>>2]=r,e[i+12>>2]=-2147483648&m|s,H=x+16|0,h=e[i+8>>2],I=e[i+12>>2],m=e[i>>2],T=e[i+4>>2];break e}A:{r:{a:if(!g){for(g=0;;){if(f[g+84473|0]!=(32|r))break a;if(g>>>0>1||((0|(r=e[t+4>>2]))==e[t+104>>2]?r=Ie(t):(e[t+4>>2]=r+1,r=d[0|r])),(0|(g=g+1|0))==3)break}break r}a:switch(0|g){case 0:if((0|r)==48){if((0|(g=e[t+4>>2]))==e[t+104>>2]?g=Ie(t):(e[t+4>>2]=g+1,g=d[0|g]),(-33&g)==88){H=c=H-432|0,(0|(r=e[t+4>>2]))==e[t+104>>2]?g=Ie(t):(e[t+4>>2]=r+1,g=d[0|r]);n:{o:{for(;;){if((0|g)!=48){if((0|g)!=46)break n;if((0|(r=e[t+4>>2]))!=e[t+104>>2]){e[t+4>>2]=r+1,g=d[0|r];break o}break}(0|(r=e[t+4>>2]))!=e[t+104>>2]?(Xe=1,e[t+4>>2]=r+1,g=d[0|r]):(Xe=1,g=Ie(t))}g=Ie(t)}if(J=1,(0|g)==48){for(;te=(r=te)-1|0,ce=ce-!r|0,(0|(r=e[t+4>>2]))==e[t+104>>2]?g=Ie(t):(e[t+4>>2]=r+1,g=d[0|r]),(0|g)==48;);Xe=1}}for(T=1073676288;;){n:{r=32|g;o:{if(!((_A=g-48|0)>>>0<10)){if((0|g)!=46&r-97>>>0>=6)break n;if((0|g)==46){if(J)break n;J=1,te=h,ce=I;break o}}r=(0|g)>57?r-87|0:_A,(0|I)<=0&h>>>0<=7|(0|I)<0?l=r+(l<<4)|0:!I&h>>>0<=28?(ss(c+48|0,r),Et(c+32|0,Fe,Le,m,T,0,0,0,1073414144),Fe=e[c+32>>2],Le=e[c+36>>2],m=e[c+40>>2],T=e[c+44>>2],Et(c+16|0,e[c+48>>2],e[c+52>>2],e[c+56>>2],e[c+60>>2],Fe,Le,m,T),Y(c,e[c+16>>2],e[c+20>>2],e[c+24>>2],e[c+28>>2],x,K,Ee,Te),Ee=e[c+8>>2],Te=e[c+12>>2],x=e[c>>2],K=e[c+4>>2]):V|!r||(Et(c+80|0,Fe,Le,m,T,0,0,0,1073610752),Y(c- -64|0,e[c+80>>2],e[c+84>>2],e[c+88>>2],e[c+92>>2],x,K,Ee,Te),Ee=e[c+72>>2],Te=e[c+76>>2],V=1,x=e[c+64>>2],K=e[c+68>>2]),I=(h=h+1|0)?I:I+1|0,Xe=1}(0|(r=e[t+4>>2]))!=e[t+104>>2]?(e[t+4>>2]=r+1,g=d[0|r]):g=Ie(t);continue}break}n:if(Xe){if((0|I)<=0&h>>>0<=7|(0|I)<0)for(m=h,T=I;l<<=4,(0|(m=m+1|0))!=8|(T=m?T:T+1|0););o:{c:{u:{if((-33&g)==80){if(m=rs(t,s),T=r=le,m|(0|r)!=-2147483648)break o;if(s){if((0|(r=e[t+116>>2]))>0|(0|r)>=0)break u;break c}x=0,K=0,ys(t,0,0),r=0,t=0;break n}if(m=0,T=0,e[t+116>>2]<0)break o}e[t+4>>2]=e[t+4>>2]-1}m=0,T=0}if(l)if(r=m+((t=J?te:h)<<2)|0,t=(I=(J?ce:I)<<2|t>>>30)+T|0,(h=r-32|0)>>>0>0-fA>>>0&(0|(t=I=(r>>>0<m>>>0?t+1|0:t)-(r>>>0<32)|0))>=0|(0|t)>0)e[56798]=68,ss(c+160|0,_),Et(c+144|0,e[c+160>>2],e[c+164>>2],e[c+168>>2],e[c+172>>2],-1,-1,-1,2147418111),Et(c+128|0,e[c+144>>2],e[c+148>>2],e[c+152>>2],e[c+156>>2],-1,-1,-1,2147418111),x=e[c+128>>2],K=e[c+132>>2],r=e[c+140>>2],t=e[c+136>>2];else if((0|I)>=(0|(r=(t=fA-226|0)>>31))&t>>>0<=h>>>0|(0|r)<(0|I)){if((0|l)>=0)for(;Y(c+416|0,x,K,Ee,Te,0,0,0,-1073807360),Y(c+400|0,x,K,Ee,Te,(t=r=(0|(t=ei(x,K,Ee,Te,1073610752)))>=0)?e[c+416>>2]:x,t?e[c+420>>2]:K,t?e[c+424>>2]:Ee,t?e[c+428>>2]:Te),h=(t=h)-1|0,I=I-!t|0,Ee=e[c+408>>2],Te=e[c+412>>2],x=e[c+400>>2],K=e[c+404>>2],(0|(l=r|l<<1))>=0;);t=I-((fA>>31)+(h>>>0<fA>>>0)|0)|0,(0|(r=(r=32+(h-fA|0)|0)>>>0<hA>>>0&(0|(t=r>>>0<32?t+1|0:t))<=0|(0|t)<0?(0|r)>0?r:0:hA))>=113?(ss(c+384|0,_),te=e[c+392>>2],ce=e[c+396>>2],Fe=e[c+384>>2],Le=e[c+388>>2],m=0,t=0):(qt(c+352|0,Zs(1,144-r|0)),ss(c+336|0,_),Fe=e[c+336>>2],Le=e[c+340>>2],te=e[c+344>>2],ce=e[c+348>>2],Fi(c+368|0,e[c+352>>2],e[c+356>>2],e[c+360>>2],e[c+364>>2],Fe,Le,te,ce),he=e[c+376>>2],LA=e[c+380>>2],m=e[c+372>>2],t=e[c+368>>2]),oa(c+320|0,(s=!(1&l)&!!(0|Gt(x,K,Ee,Te,0,0,0,0))&(0|r)<32)+l|0),Et(c+304|0,Fe,Le,te,ce,e[c+320>>2],e[c+324>>2],e[c+328>>2],e[c+332>>2]),r=t,Y(c+272|0,e[c+304>>2],e[c+308>>2],e[c+312>>2],e[c+316>>2],t,m,he,LA),Et(c+288|0,Fe,Le,te,ce,(t=s)?0:x,t?0:K,t?0:Ee,t?0:Te),Y(c+256|0,e[c+288>>2],e[c+292>>2],e[c+296>>2],e[c+300>>2],e[c+272>>2],e[c+276>>2],e[c+280>>2],e[c+284>>2]),fn(c+240|0,e[c+256>>2],e[c+260>>2],e[c+264>>2],e[c+268>>2],r,m,he,LA),Gt(t=e[c+240>>2],s=e[c+244>>2],r=e[c+248>>2],m=e[c+252>>2],0,0,0,0)||(e[56798]=68),tA(c+224|0,t,s,r,m,h),x=e[c+224>>2],K=e[c+228>>2],r=e[c+236>>2],t=e[c+232>>2]}else e[56798]=68,ss(c+208|0,_),Et(c+192|0,e[c+208>>2],e[c+212>>2],e[c+216>>2],e[c+220>>2],0,0,0,65536),Et(c+176|0,e[c+192>>2],e[c+196>>2],e[c+200>>2],e[c+204>>2],0,0,0,65536),x=e[c+176>>2],K=e[c+180>>2],r=e[c+188>>2],t=e[c+184>>2];else qt(c+112|0,0*+(0|_)),x=e[c+112>>2],K=e[c+116>>2],r=e[c+124>>2],t=e[c+120>>2]}else{o:{c:{if((0|(r=e[t+116>>2]))>0|(0|r)>=0){if(r=e[t+4>>2],e[t+4>>2]=r-1,!s)break c;if(e[t+4>>2]=r-2,!J)break o;e[t+4>>2]=r-3;break o}if(s)break o}ys(t,0,0)}qt(c+96|0,0*+(0|_)),x=e[c+96>>2],K=e[c+100>>2],r=e[c+108>>2],t=e[c+104>>2]}e[i+16>>2]=x,e[i+20>>2]=K,e[i+24>>2]=t,e[i+28>>2]=r,H=c+432|0,h=e[i+24>>2],I=e[i+28>>2],m=e[i+16>>2],T=e[i+20>>2];break e}e[t+116>>2]<0||(e[t+4>>2]=e[t+4>>2]-1)}g=t,he=_,c=s,t=0,_=0,H=l=H-8976|0,LA=(_A=0-fA|0)-hA|0;n:{o:{for(;;){if((0|r)!=48){if((0|r)!=46)break n;if((0|(r=e[g+4>>2]))!=e[g+104>>2]){e[g+4>>2]=r+1,r=d[0|r];break o}break}(0|(t=e[g+4>>2]))!=e[g+104>>2]?(e[g+4>>2]=t+1,r=d[0|t]):r=Ie(g),t=1}r=Ie(g)}if(V=1,(0|r)==48){for(;h=(t=h)-1|0,I=I-!t|0,(0|(t=e[g+4>>2]))==e[g+104>>2]?r=Ie(g):(e[g+4>>2]=t+1,r=d[0|t]),(0|r)==48;);t=1}}e[l+784>>2]=0;n:{o:{c:{u:{l:{if((s=(0|r)==46)|(x=r-48|0)>>>0<=9)for(;;){i:{if(1&s){if(!V){h=m,I=T,V=1;break i}s=!t;break l}T=(m=m+1|0)?T:T+1|0,(0|_)<=2044?(Xe=(0|r)==48?Xe:m,t=(l+784|0)+(_<<2)|0,J&&(x=(O(e[t>>2],10)+r|0)-48|0),e[t>>2]=x,t=1,J=(r=(0|(s=J+1|0))==9)?0:s,_=r+_|0):(0|r)!=48&&(e[l+8960>>2]=1|e[l+8960>>2],Xe=18396)}if((0|(r=e[g+4>>2]))==e[g+104>>2]?r=Ie(g):(e[g+4>>2]=r+1,r=d[0|r]),!((s=(0|r)==46)|(x=r-48|0)>>>0<10))break}if(h=V?h:m,I=V?I:T,!(!t|(-33&r)!=69)){if(x=rs(g,c),K=t=le,!(x|(0|t)!=-2147483648)){if(!c)break c;x=0,K=0,e[g+116>>2]<0||(e[g+4>>2]=e[g+4>>2]-1)}I=I+K|0,I=(h=h+x|0)>>>0<x>>>0?I+1|0:I;break o}if(s=!t,(0|r)<0)break u}e[g+116>>2]<0||(e[g+4>>2]=e[g+4>>2]-1)}if(!s)break o;e[56798]=28}m=0,T=0,ys(g,0,0),r=0,t=0;break n}if(t=e[l+784>>2])if(m>>>0>9&(0|T)>=0|(0|T)>0|(0|m)!=(0|h)|(0|I)!=(0|T)|(t>>>hA|0?(0|hA)<=30:0))if(h>>>0>_A>>>1>>>0&(0|I)>=0|(0|I)>0)e[56798]=68,ss(l+96|0,he),Et(l+80|0,e[l+96>>2],e[l+100>>2],e[l+104>>2],e[l+108>>2],-1,-1,-1,2147418111),Et(l- -64|0,e[l+80>>2],e[l+84>>2],e[l+88>>2],e[l+92>>2],-1,-1,-1,2147418111),m=e[l+64>>2],T=e[l+68>>2],r=e[l+76>>2],t=e[l+72>>2];else if((r=h>>>0<(t=fA-226|0)>>>0)&(0|I)<=(0|(t>>=31))|(0|t)>(0|I))e[56798]=68,ss(l+144|0,he),Et(l+128|0,e[l+144>>2],e[l+148>>2],e[l+152>>2],e[l+156>>2],0,0,0,65536),Et(l+112|0,e[l+128>>2],e[l+132>>2],e[l+136>>2],e[l+140>>2],0,0,0,65536),m=e[l+112>>2],T=e[l+116>>2],r=e[l+124>>2],t=e[l+120>>2];else{if(J){if((0|J)<=8){for(g=e[(t=(l+784|0)+(_<<2)|0)>>2];g=O(g,10),(0|(J=J+1|0))!=9;);e[t>>2]=g}_=_+1|0}if(V=h,!((0|Xe)>(0|h)|(0|Xe)>=9|(0|h)>17)){if((0|V)==9){ss(l+192|0,he),oa(l+176|0,e[l+784>>2]),Et(l+160|0,e[l+192>>2],e[l+196>>2],e[l+200>>2],e[l+204>>2],e[l+176>>2],e[l+180>>2],e[l+184>>2],e[l+188>>2]),m=e[l+160>>2],T=e[l+164>>2],r=e[l+172>>2],t=e[l+168>>2];break n}if((0|V)<=8){ss(l+272|0,he),oa(l+256|0,e[l+784>>2]),Et(l+240|0,e[l+272>>2],e[l+276>>2],e[l+280>>2],e[l+284>>2],e[l+256>>2],e[l+260>>2],e[l+264>>2],e[l+268>>2]),ss(l+224|0,e[124720+(0-V<<2)>>2]),ts(l+208|0,e[l+240>>2],e[l+244>>2],e[l+248>>2],e[l+252>>2],e[l+224>>2],e[l+228>>2],e[l+232>>2],e[l+236>>2]),m=e[l+208>>2],T=e[l+212>>2],r=e[l+220>>2],t=e[l+216>>2];break n}if(t=27+(O(V,-3)+hA|0)|0,!((r=e[l+784>>2])>>>t|0&&(0|t)<=30)){ss(l+352|0,he),oa(l+336|0,r),Et(l+320|0,e[l+352>>2],e[l+356>>2],e[l+360>>2],e[l+364>>2],e[l+336>>2],e[l+340>>2],e[l+344>>2],e[l+348>>2]),ss(l+304|0,e[124648+(V<<2)>>2]),Et(l+288|0,e[l+320>>2],e[l+324>>2],e[l+328>>2],e[l+332>>2],e[l+304>>2],e[l+308>>2],e[l+312>>2],e[l+316>>2]),m=e[l+288>>2],T=e[l+292>>2],r=e[l+300>>2],t=e[l+296>>2];break n}}for(;!e[(l+784|0)+((_=(r=_)-1|0)<<2)>>2];);if(J=0,t=(0|V)%9|0){if(s=0,t=(0|V)<0?t+9|0:t,r){for(I=1e9/(0|(h=e[124720+(0-t<<2)>>2]))|0,x=0,g=0;m=(m=x)+(_=((T=e[(x=(l+784|0)+(g<<2)|0)>>2])>>>0)/(h>>>0)|0)|0,e[x>>2]=m,s=(m=!m&(0|s)==(0|g))?s+1&2047:s,V=m?V-9|0:V,x=O(I,T-O(h,_)|0),(0|(g=g+1|0))!=(0|r););x&&(e[(l+784|0)+(r<<2)>>2]=x,r=r+1|0)}else r=0;V=9+(V-t|0)|0}else s=0;for(;;){g=(l+784|0)+(s<<2)|0;o:{for(;;){if(((0|V)!=36|Ae[g>>2]>=10384593)&(0|V)>=36)break o;for(_=r+2047|0,x=0,t=r;r=t,h=x,x=(t=e[(_=(l+784|0)+((m=2047&_)<<2)|0)>>2])<<29,t=T=t>>>3|0,!(I=(h=h+x|0)>>>0<x>>>0?t+1|0:t)&h>>>0<1000000001?x=0:h=(t=h)-st(x=Vi(t,I,1e9),le,1e9,0)|0,e[_>>2]=h,t=(0|m)!=(r-1&2047)||(0|s)==(0|m)||h?r:m,_=m-1|0,(0|s)!=(0|m););if(J=J-29|0,x)break}(0|(s=s-1&2047))==(0|t)&&(g=r=(h=l+784|0)+((t+2046&2047)<<2)|0,I=e[r>>2],r=t-1&2047,e[g>>2]=I|e[h+(r<<2)>>2]),V=V+9|0,e[(l+784|0)+(s<<2)>>2]=x;continue}break}o:{c:for(;;){for(h=r+1&2047,x=(l+784|0)+((r-1&2047)<<2)|0;;){m=(0|V)>45?9:1;u:{for(;;){t=s,g=0;l:{for(;;){if((0|(s=t+g&2047))!=(0|r)&&!((s=e[(l+784|0)+(s<<2)>>2])>>>0<(I=e[124672+(g<<2)>>2])>>>0)){if(s>>>0>I>>>0)break l;if((0|(g=g+1|0))!=4)continue}break}if((0|V)==36){for(h=0,I=0,g=0,m=0,T=0;(0|(s=t+g&2047))==(0|r)&&(e[780+(l+((r=r+1&2047)<<2)|0)>>2]=0),oa(l+768|0,e[(l+784|0)+(s<<2)>>2]),Et(l+752|0,h,I,m,T,0,0,1342177280,1075633366),Y(l+736|0,e[l+752>>2],e[l+756>>2],e[l+760>>2],e[l+764>>2],e[l+768>>2],e[l+772>>2],e[l+776>>2],e[l+780>>2]),m=e[l+744>>2],T=e[l+748>>2],h=e[l+736>>2],I=e[l+740>>2],(0|(g=g+1|0))!=4;);if(ss(l+720|0,he),Et(l+704|0,h,I,m,T,e[l+720>>2],e[l+724>>2],e[l+728>>2],e[l+732>>2]),m=e[l+712>>2],T=e[l+716>>2],h=0,I=0,x=e[l+704>>2],K=e[l+708>>2],(0|(s=(_=(0|(g=(c=J+113|0)-fA|0))<(0|hA))?(0|g)>0?g:0:hA))<=112)break u;break o}}if(J=m+J|0,s=r,(0|t)!=(0|r))break}for(T=1e9>>>m|0,_=~(-1<<m),g=0,s=t;I=(I=g)+((c=e[(g=(l+784|0)+(t<<2)|0)>>2])>>>m|0)|0,e[g>>2]=I,s=(I=!I&(0|t)==(0|s))?s+1&2047:s,V=I?V-9|0:V,g=O(T,c&_),(0|r)!=(0|(t=t+1&2047)););if(!g)continue;if((0|s)!=(0|h)){e[(l+784|0)+(r<<2)>>2]=g,r=h;continue c}e[x>>2]=1|e[x>>2];continue}break}break}qt(l+656|0,Zs(1,225-s|0)),Fi(l+688|0,e[l+656>>2],e[l+660>>2],e[l+664>>2],e[l+668>>2],x,K,m,T),Fe=e[l+696>>2],Le=e[l+700>>2],Ee=e[l+688>>2],Te=e[l+692>>2],qt(l+640|0,Zs(1,113-s|0)),BA(l+672|0,x,K,m,T,e[l+640>>2],e[l+644>>2],e[l+648>>2],e[l+652>>2]),fn(l+624|0,x,K,m,T,h=e[l+672>>2],I=e[l+676>>2],te=e[l+680>>2],ce=e[l+684>>2]),Y(l+608|0,Ee,Te,Fe,Le,e[l+624>>2],e[l+628>>2],e[l+632>>2],e[l+636>>2]),m=e[l+616>>2],T=e[l+620>>2],x=e[l+608>>2],K=e[l+612>>2]}if((0|(V=t+4&2047))!=(0|r)){o:if((V=e[(l+784|0)+(V<<2)>>2])>>>0<=499999999){if(!V&(t+5&2047)==(0|r))break o;qt(l+496|0,.25*+(0|he)),Y(l+480|0,h,I,te,ce,e[l+496>>2],e[l+500>>2],e[l+504>>2],e[l+508>>2]),te=e[l+488>>2],ce=e[l+492>>2],h=e[l+480>>2],I=e[l+484>>2]}else(0|V)==5e8?(At=+(0|he),(t+5&2047)!=(0|r)?(qt(l+560|0,.75*At),Y(l+544|0,h,I,te,ce,e[l+560>>2],e[l+564>>2],e[l+568>>2],e[l+572>>2]),te=e[l+552>>2],ce=e[l+556>>2],h=e[l+544>>2],I=e[l+548>>2]):(qt(l+528|0,.5*At),Y(l+512|0,h,I,te,ce,e[l+528>>2],e[l+532>>2],e[l+536>>2],e[l+540>>2]),te=e[l+520>>2],ce=e[l+524>>2],h=e[l+512>>2],I=e[l+516>>2])):(qt(l+592|0,.75*+(0|he)),Y(l+576|0,h,I,te,ce,e[l+592>>2],e[l+596>>2],e[l+600>>2],e[l+604>>2]),te=e[l+584>>2],ce=e[l+588>>2],h=e[l+576>>2],I=e[l+580>>2]);(0|s)>111||(BA(l+464|0,h,I,te,ce,0,0,0,1073676288),Gt(e[l+464>>2],e[l+468>>2],e[l+472>>2],e[l+476>>2],0,0,0,0)||(Y(l+448|0,h,I,te,ce,0,0,0,1073676288),te=e[l+456>>2],ce=e[l+460>>2],h=e[l+448>>2],I=e[l+452>>2]))}Y(l+432|0,x,K,m,T,h,I,te,ce),fn(l+416|0,e[l+432>>2],e[l+436>>2],e[l+440>>2],e[l+444>>2],Ee,Te,Fe,Le),m=e[l+424>>2],T=e[l+428>>2],x=e[l+416>>2],K=e[l+420>>2],(LA-2|0)>=(2147483647&c)||(e[l+408>>2]=m,e[l+412>>2]=2147483647&T,e[l+400>>2]=x,e[l+404>>2]=K,Et(l+384|0,x,K,m,T,0,0,0,1073610752),m=(t=(0|(t=ei(e[l+400>>2],e[l+404>>2],e[l+408>>2],e[l+412>>2],1081081856)))>=0)?e[l+392>>2]:m,T=t?e[l+396>>2]:T,x=t?e[l+384>>2]:x,K=t?e[l+388>>2]:K,J=t+J|0,!(!!(0|Gt(h,I,te,ce,0,0,0,0))&(t?_&(0|s)!=(0|g):_))&(J+110|0)<=(0|LA)||(e[56798]=68)),tA(l+368|0,x,K,m,T,J),m=e[l+368>>2],T=e[l+372>>2],r=e[l+380>>2],t=e[l+376>>2]}else ss(l+48|0,he),oa(l+32|0,t),Et(l+16|0,e[l+48>>2],e[l+52>>2],e[l+56>>2],e[l+60>>2],e[l+32>>2],e[l+36>>2],e[l+40>>2],e[l+44>>2]),m=e[l+16>>2],T=e[l+20>>2],r=e[l+28>>2],t=e[l+24>>2];else qt(l,0*+(0|he)),m=e[l>>2],T=e[l+4>>2],r=e[l+12>>2],t=e[l+8>>2]}e[i+40>>2]=t,e[i+44>>2]=r,e[i+32>>2]=m,e[i+36>>2]=T,H=l+8976|0,h=e[i+40>>2],I=e[i+44>>2],m=e[i+32>>2],T=e[i+36>>2];break e;case 3:break r;default:break a}(0|(r=e[t+116>>2]))>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1);break A}if((0|(r=e[t+4>>2]))==e[t+104>>2]?r=Ie(t):(e[t+4>>2]=r+1,r=d[0|r]),(0|r)!=40){if(I=2147450880,e[t+116>>2]<0)break e;e[t+4>>2]=e[t+4>>2]-1;break e}for(g=1;(0|(r=e[t+4>>2]))==e[t+104>>2]?r=Ie(t):(e[t+4>>2]=r+1,r=d[0|r]),r-48>>>0<10|r-65>>>0<26|(0|r)==95||!(r-97>>>0>=26);)g=g+1|0;if(I=2147450880,(0|r)==41)break e;(0|(r=e[t+116>>2]))>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1);r:{if(s){if(g)break r;break e}break A}for(;g=g-1|0,(0|r)>0|(0|r)>=0&&(e[t+4>>2]=e[t+4>>2]-1),g;);break e}e[56798]=28,ys(t,0,0)}I=0}e[A>>2]=m,e[A+4>>2]=T,e[A+8>>2]=h,e[A+12>>2]=I,H=i+48|0}function cA(A,t,r,s,i){var l,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0;if(H=l=H-112|0,e[r+8>>2]){fA=e[50754]/70|0,K=(x=e[s+12>>2])||256;e:if((0|t)!=2){if((0|t)==1){A:if(d[A+11|0]!=3)switch(d[s-15|0]-3|0){case 0:case 5:break A;default:break e}K=(0|(x=e[e[47192]+44>>2]))<(0|K)?K:x}}else{if((0|(x=e[e[47192]+80>>2]))<=0|!(8&d[0|s]|x>>>0<=d[A+14|0]|32&d[A+6|0]))break e;fA<<=1}if(e[36436]=0,ce=A,hA=t,h=s,H=V=H-16|0,A=e[34460]+e[r+8>>2]|0,t=(t=d[A+2|0])>>>0>=24?24:t,e[V+12>>2]=t,g=e[r+12>>2]+e[r+24>>2]|0,e[36422]=g,t){for(x=A+4|0,c=1&k[A+4>>1];A=145488+(I<<3)|0,s=x+(c?I<<6:O(I,44))|0,e[A+4>>2]=s,_=y[s>>1],k[A+2>>1]=_,k[A>>1]=d[s+16|0],m=2&_?I:m,(0|(I=I+1|0))!=(0|t););x=145488,(0|m)<=0||((0|hA)!=1?(t=t-m|0,e[V+12>>2]=t,x=145488+(m<<3)|0):(t=m+1|0,e[V+12>>2]=t,x=145488))}else t=0,x=145488;if(!(!e[r+4>>2]|e[r+20>>2]|d[ce+11|0]!=2)){if(c=e[r+36>>2],s=e[r+40>>2],A=0,(0|(t=e[V+12>>2]))>=2){m=c>>>12|0,_=s>>>26&7,he=s>>>18&248,J=O(te=63&s,50),Te=63&(Fe=c>>>6|0),I=c<<1&126,Le=O(s>>>16&31,50)-750|0,Xe=O(s>>>11&31,50)-750|0,Ee=O(s>>>6&31,50)-750|0;e:{A:if((0|hA)!=1){if(!(m|te))break e;if(8&m?(t=e[4+(x+((g=t-1|0)<<3)|0)>>2],k[t>>1]<0?A=t:(A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,g=y[t+20>>1]|y[t+22>>1]<<16,A=177888+(A<<6)|0,c=y[t+16>>1]|y[t+18>>1]<<16,k[A+16>>1]=c,k[A+18>>1]=c>>>16,k[A+20>>1]=g,k[A+22>>1]=g>>>16,g=y[t+4>>1]|y[t+6>>1]<<16,c=y[t>>1]|y[t+2>>1]<<16,k[A>>1]=c,k[A+2>>1]=c>>>16,k[A+4>>1]=g,k[A+6>>1]=g>>>16,g=y[t+12>>1]|y[t+14>>1]<<16,c=y[t+8>>1]|y[t+10>>1]<<16,k[A+8>>1]=c,k[A+10>>1]=c>>>16,k[A+12>>1]=g,k[A+14>>1]=g>>>16,g=y[t+28>>1]|y[t+30>>1]<<16,c=y[t+24>>1]|y[t+26>>1]<<16,k[A+24>>1]=c,k[A+26>>1]=c>>>16,k[A+28>>1]=g,k[A+30>>1]=g>>>16,g=y[t+36>>1]|y[t+38>>1]<<16,c=y[t+32>>1]|y[t+34>>1]<<16,k[A+32>>1]=c,k[A+34>>1]=c>>>16,k[A+36>>1]=g,k[A+38>>1]=g>>>16,g=y[t+44>>1]|y[t+46>>1]<<16,c=y[t+40>>1]|y[t+42>>1]<<16,k[A+40>>1]=c,k[A+42>>1]=c>>>16,k[A+44>>1]=g,k[A+46>>1]=g>>>16,g=y[t+52>>1]|y[t+54>>1]<<16,c=y[t+48>>1]|y[t+50>>1]<<16,k[A+48>>1]=c,k[A+50>>1]=c>>>16,k[A+52>>1]=g,k[A+54>>1]=g>>>16,g=y[t+60>>1]|y[t+62>>1]<<16,t=y[t+56>>1]|y[t+58>>1]<<16,k[A+56>>1]=t,k[A+58>>1]=t>>>16,k[A+60>>1]=g,k[A+62>>1]=g>>>16,f[A+16|0]=0,k[A>>1]=32768|y[A>>1],g=e[V+12>>2]-1|0),e[4+(x+(g<<3)|0)>>2]=A,t=1792,(0|(g=k[A+4>>1]))<300||(t=1536,g>>>0<400||(t=g>>>0<500?1280:1024)),e[36436]=t,c=35):(e[V+12>>2]=t+1,k[(A=(g=x+(t<<3)|0)-8|0)>>1]=I,t=e[A+4>>2],A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,(A=(Fe=A<<6)+177888|0)&&(c=y[t+4>>1]|y[t+6>>1]<<16,T=y[t>>1]|y[t+2>>1]<<16,k[A>>1]=T,k[A+2>>1]=T>>>16,k[A+4>>1]=c,k[A+6>>1]=c>>>16,c=y[t+60>>1]|y[t+62>>1]<<16,T=y[t+56>>1]|y[t+58>>1]<<16,k[A+56>>1]=T,k[A+58>>1]=T>>>16,k[A+60>>1]=c,k[A+62>>1]=c>>>16,c=y[t+52>>1]|y[t+54>>1]<<16,T=y[t+48>>1]|y[t+50>>1]<<16,k[A+48>>1]=T,k[A+50>>1]=T>>>16,k[A+52>>1]=c,k[A+54>>1]=c>>>16,c=y[t+44>>1]|y[t+46>>1]<<16,T=y[t+40>>1]|y[t+42>>1]<<16,k[A+40>>1]=T,k[A+42>>1]=T>>>16,k[A+44>>1]=c,k[A+46>>1]=c>>>16,c=y[t+36>>1]|y[t+38>>1]<<16,T=y[t+32>>1]|y[t+34>>1]<<16,k[A+32>>1]=T,k[A+34>>1]=T>>>16,k[A+36>>1]=c,k[A+38>>1]=c>>>16,c=y[t+28>>1]|y[t+30>>1]<<16,T=y[t+24>>1]|y[t+26>>1]<<16,k[A+24>>1]=T,k[A+26>>1]=T>>>16,k[A+28>>1]=c,k[A+30>>1]=c>>>16,c=y[t+20>>1]|y[t+22>>1]<<16,T=y[t+16>>1]|y[t+18>>1]<<16,k[A+16>>1]=T,k[A+18>>1]=T>>>16,k[A+20>>1]=c,k[A+22>>1]=c>>>16,c=y[t+12>>1]|y[t+14>>1]<<16,t=y[t+8>>1]|y[t+10>>1]<<16,k[A+8>>1]=t,k[A+10>>1]=t>>>16,k[A+12>>1]=c,k[A+14>>1]=c>>>16,f[Fe+177904|0]=0,k[A>>1]=32768|y[A>>1]),k[g>>1]=0,e[g+4>>2]=A,I>>>0>=37&&(e[36422]=(I+e[36422]|0)-36),c=Te<<1,te&&X(A,J,Ee,Xe,_,Le,he,m)),e[e[32972]+132>>2]||(t=d[A+17|0])&&(t=k[102896+(((0|(t=(c<<6>>>0)/(t>>>0)|0))>=199?199:t)<<1)>>1],f[A+18|0]=(0|O(t,d[A+18|0]))/512,f[A+19|0]=(0|O(t,d[A+19|0]))/512,f[A+20|0]=(0|O(t,d[A+20|0]))/512,f[A+21|0]=(0|O(t,d[A+21|0]))/512,f[A+22|0]=(0|O(t,d[A+22|0]))/512,f[A+23|0]=(0|O(t,d[A+23|0]))/512,f[A+24|0]=(0|O(t,d[A+24|0]))/512,f[A+25|0]=(0|O(t,d[A+25|0]))/512),s-536870912>>>0<=1073741823){if(_=e[44469],(0|(g=e[V+12>>2]))>0)for(A=O(s>>>29|0,10)+102854|0,he=k[A+4>>1],te=k[A+2>>1],J=k[A>>1],Le=k[A+6>>1],Xe=k[A+8>>1],c=0;t=e[(Ee=x+(c<<3)|0)+4>>2],k[t>>1]<0?A=t:(A=(Te=(_=(0|(A=_+1|0))<=169?A:0)<<6)+177888|0)?(s=y[t+4>>1]|y[t+6>>1]<<16,g=y[t>>1]|y[t+2>>1]<<16,k[A>>1]=g,k[A+2>>1]=g>>>16,k[A+4>>1]=s,k[A+6>>1]=s>>>16,s=y[t+60>>1]|y[t+62>>1]<<16,g=y[t+56>>1]|y[t+58>>1]<<16,k[A+56>>1]=g,k[A+58>>1]=g>>>16,k[A+60>>1]=s,k[A+62>>1]=s>>>16,s=y[t+52>>1]|y[t+54>>1]<<16,g=y[t+48>>1]|y[t+50>>1]<<16,k[A+48>>1]=g,k[A+50>>1]=g>>>16,k[A+52>>1]=s,k[A+54>>1]=s>>>16,s=y[t+44>>1]|y[t+46>>1]<<16,g=y[t+40>>1]|y[t+42>>1]<<16,k[A+40>>1]=g,k[A+42>>1]=g>>>16,k[A+44>>1]=s,k[A+46>>1]=s>>>16,s=y[t+36>>1]|y[t+38>>1]<<16,g=y[t+32>>1]|y[t+34>>1]<<16,k[A+32>>1]=g,k[A+34>>1]=g>>>16,k[A+36>>1]=s,k[A+38>>1]=s>>>16,s=y[t+28>>1]|y[t+30>>1]<<16,g=y[t+24>>1]|y[t+26>>1]<<16,k[A+24>>1]=g,k[A+26>>1]=g>>>16,k[A+28>>1]=s,k[A+30>>1]=s>>>16,s=y[t+20>>1]|y[t+22>>1]<<16,g=y[t+16>>1]|y[t+18>>1]<<16,k[A+16>>1]=g,k[A+18>>1]=g>>>16,k[A+20>>1]=s,k[A+22>>1]=s>>>16,s=y[t+12>>1]|y[t+14>>1]<<16,t=y[t+8>>1]|y[t+10>>1]<<16,k[A+8>>1]=t,k[A+10>>1]=t>>>16,k[A+12>>1]=s,k[A+14>>1]=s>>>16,f[Te+177904|0]=0,k[A>>1]=32768|y[A>>1],g=e[V+12>>2]):A=0,e[Ee+4>>2]=A,k[A+8>>1]=(0|O(he,k[A+8>>1]))/256,k[A+6>>1]=(0|O(te,k[A+6>>1]))/256,k[A+4>>1]=(0|O(J,k[A+4>>1]))/256,k[A+12>>1]=(0|O(Xe,k[A+12>>1]))/256,k[A+10>>1]=(0|O(Le,k[A+10>>1]))/256,(0|g)>(0|(c=c+1|0)););e[44469]=_}if(!A)break e}else{t=e[x+4>>2],(0|(g=k[t>>1]))<0?A=t:(s=(0|(s=e[44469]+1|0))<=169?s:0,e[44469]=s,(s=(g=s<<6)+177888|0)&&(A=y[t+4>>1]|y[t+6>>1]<<16,T=y[t>>1]|y[t+2>>1]<<16,k[s>>1]=T,k[s+2>>1]=T>>>16,k[s+4>>1]=A,k[s+6>>1]=A>>>16,A=y[t+60>>1]|y[t+62>>1]<<16,T=y[t+56>>1]|y[t+58>>1]<<16,k[s+56>>1]=T,k[s+58>>1]=T>>>16,k[s+60>>1]=A,k[s+62>>1]=A>>>16,A=y[t+52>>1]|y[t+54>>1]<<16,T=y[t+48>>1]|y[t+50>>1]<<16,k[s+48>>1]=T,k[s+50>>1]=T>>>16,k[s+52>>1]=A,k[s+54>>1]=A>>>16,A=y[t+44>>1]|y[t+46>>1]<<16,T=y[t+40>>1]|y[t+42>>1]<<16,k[s+40>>1]=T,k[s+42>>1]=T>>>16,k[s+44>>1]=A,k[s+46>>1]=A>>>16,A=y[t+36>>1]|y[t+38>>1]<<16,T=y[t+32>>1]|y[t+34>>1]<<16,k[s+32>>1]=T,k[s+34>>1]=T>>>16,k[s+36>>1]=A,k[s+38>>1]=A>>>16,A=y[t+28>>1]|y[t+30>>1]<<16,T=y[t+24>>1]|y[t+26>>1]<<16,k[s+24>>1]=T,k[s+26>>1]=T>>>16,k[s+28>>1]=A,k[s+30>>1]=A>>>16,A=y[t+20>>1]|y[t+22>>1]<<16,T=y[t+16>>1]|y[t+18>>1]<<16,k[s+16>>1]=T,k[s+18>>1]=T>>>16,k[s+20>>1]=A,k[s+22>>1]=A>>>16,A=y[t+12>>1]|y[t+14>>1]<<16,t=y[t+8>>1]|y[t+10>>1]<<16,k[s+8>>1]=t,k[s+10>>1]=t>>>16,k[s+12>>1]=A,k[s+14>>1]=A>>>16,f[g+177904|0]=0,g=-32768|y[s>>1],k[s>>1]=g,A=s)),e[x+4>>2]=A,k[x>>1]=I||50,k[x+2>>1]=16384|y[x+2>>1],k[A>>1]=16384|g,g=e[x+12>>2],t=d[g+17|0],s=e[32972],e[s+132>>2]&&(f[A+39|0]=d[g+39|0]-4);r:if(te){if(2048&c){t=(O(t,31&Fe)>>>0)/30|0,e[s+132>>2]||(s=d[A+17|0])&&(t=k[102896+(((0|(t=(t<<6>>>0)/(s>>>0)|0))>=199?199:t)<<1)>>1],f[A+18|0]=(0|O(t,d[A+18|0]))/512,f[A+19|0]=(0|O(t,d[A+19|0]))/512,f[A+20|0]=(0|O(t,d[A+20|0]))/512,f[A+21|0]=(0|O(t,d[A+21|0]))/512,f[A+22|0]=(0|O(t,d[A+22|0]))/512,f[A+23|0]=(0|O(t,d[A+23|0]))/512,f[A+24|0]=(0|O(t,d[A+24|0]))/512,f[A+25|0]=(0|O(t,d[A+25|0]))/512),X(A,J,Ee,Xe,_,Le,he,m);break r}if(X(A,J,Ee,Xe,_,Le,he,m),e[e[32972]+132>>2]||!(t=d[A+17|0]))break r;t=k[102896+(((0|(t=(Te<<7>>>0)/(t>>>0)|0))>=199?199:t)<<1)>>1],f[A+18|0]=(0|O(t,d[A+18|0]))/512,f[A+19|0]=(0|O(t,d[A+19|0]))/512,f[A+20|0]=(0|O(t,d[A+20|0]))/512,f[A+21|0]=(0|O(t,d[A+21|0]))/512,f[A+22|0]=(0|O(t,d[A+22|0]))/512,f[A+23|0]=(0|O(t,d[A+23|0]))/512,f[A+24|0]=(0|O(t,d[A+24|0]))/512,f[A+25|0]=(0|O(t,d[A+25|0]))/512}else if(s=e[s+132>>2],8&m){if(s||!(s=d[A+17|0]))break r;t=((16320&O(t,48))>>>0)/(s>>>0)|0,t=k[102896+((t>>>0>=199?199:t)<<1)>>1],f[A+18|0]=(0|O(t,d[A+18|0]))/512,f[A+19|0]=(0|O(t,d[A+19|0]))/512,f[A+20|0]=(0|O(t,d[A+20|0]))/512,f[A+21|0]=(0|O(t,d[A+21|0]))/512,f[A+22|0]=(0|O(t,d[A+22|0]))/512,f[A+23|0]=(0|O(t,d[A+23|0]))/512,f[A+24|0]=(0|O(t,d[A+24|0]))/512,f[A+25|0]=(0|O(t,d[A+25|0]))/512}else s||(t=d[A+17|0])&&(t=k[102896+(((t=1792/(t>>>0)|0)>>>0>=199?199:t)<<1)>>1],f[A+18|0]=(0|O(t,d[A+18|0]))/512,f[A+19|0]=(0|O(t,d[A+19|0]))/512,f[A+20|0]=(0|O(t,d[A+20|0]))/512,f[A+21|0]=(0|O(t,d[A+21|0]))/512,f[A+22|0]=(0|O(t,d[A+22|0]))/512,f[A+23|0]=(0|O(t,d[A+23|0]))/512,f[A+24|0]=(0|O(t,d[A+24|0]))/512,f[A+25|0]=(0|O(t,d[A+25|0]))/512);if(!(8&m))break A;t=2816,(0|(s=k[A+4>>1]))<300||(t=2560,s>>>0<400||(t=s>>>0<500?2304:2048)),e[36436]=t}4&m&&(k[A>>1]=32|y[A>>1]),2&m&&(k[A>>1]=16|y[A>>1])}64&m&&_t(20,0),A=I&m<<27>>31}else A=0;g=A+e[36422]|0,e[36422]=g,t=e[V+12>>2]}if((0|(s=t-1|0))<=0)c=0;else{if(A=0,I=0,c=0,t-2>>>0>=3)for(he=-4&s,_=0;c=(((k[(m=I<<3)+x>>1]+c|0)+k[x+(8|m)>>1]|0)+k[x+(16|m)>>1]|0)+k[x+(24|m)>>1]|0,I=I+4|0,(0|he)!=(0|(_=_+4|0)););if(m=3&s)for(;c=k[x+(I<<3)>>1]+c|0,I=I+1|0,(0|m)!=(0|(A=A+1|0)););}if(A=t,(m=e[r+20>>2])&&(A=s,I=m+e[34460]|0,(_=d[I+2|0])&&(te=y[I+4>>1],k[x+(s<<3)>>1]=d[I+20|0],m=1,A=t,(0|_)!=1))){if(he=I+4|0,te&=1,Le=1&(I=_-1|0),(0|_)!=2)for(Xe=-2&I,_=0;Ee=he+(m<<6)|0,Te=he+O(m,44)|0,Fe=d[(J=te?Ee:Te)+16|0],e[(I=x+(A<<3)|0)+4>>2]=J,k[I>>1]=Fe,k[I+2>>1]=y[J>>1],Ee=d[(J=te?Ee- -64|0:Te+44|0)+16|0],e[I+12>>2]=J,k[I+8>>1]=Ee,k[I+10>>1]=y[J>>1],m=m+2|0,A=A+2|0,(0|Xe)!=(0|(_=_+2|0)););Le&&(m=he+(te?m<<6:O(m,44))|0,_=d[m+16|0],e[(I=x+(A<<3)|0)+4>>2]=m,k[I>>1]=_,k[I+2>>1]=y[m>>1],A=A+1|0)}e:if(!((0|c)<=0)){A:{r:switch(hA-1|0){case 1:if(m=(0|(m=(e[r+44>>2]+g|0)-45|0))<=10?10:m,8&d[0|h]&&(m=m+(d[e[36128]+14|0]<<1)|0),(0|s)<=0)break e;if(h=(m<<8)/(0|c)|0,I=0,(0|t)!=2)for(t=-2&s,m=0;k[(c=(g=I<<3)+x|0)>>1]=(0|O(h,k[c>>1]))/256,k[(g=x+(8|g)|0)>>1]=(0|O(h,k[g>>1]))/256,I=I+2|0,(0|t)!=(0|(m=m+2|0)););if(!(1&s))break e;k[(t=x+(I<<3)|0)>>1]=(0|O(h,k[t>>1]))/256;break e;case 0:if(e[r>>2]!=1||(0|(m=e[r+44>>2]))>129)break A;k[x>>1]=(0|O(m,k[x>>1]))/130;break A;default:break r}(0|(m=e[r+44>>2]))<=0||(g=(m-c|0)+g|0,e[36422]=g)}if(!(!g|(0|s)<=0)){if(h=(c+g<<8)/(0|c)|0,I=0,(0|t)!=2)for(t=-2&s,m=0;k[(c=(g=I<<3)+x|0)>>1]=(0|O(h,k[c>>1]))/256,k[(g=x+(8|g)|0)>>1]=(0|O(h,k[g>>1]))/256,I=I+2|0,(0|t)!=(0|(m=m+2|0)););1&s&&(k[(t=x+(I<<3)|0)>>1]=(0|O(h,k[t>>1]))/256)}}if(e[l+108>>2]=A,H=V+16|0,x){if((0|(A=e[r+16>>2]))!=e[36438]&&(e[36438]=A,t=216192+(e[50758]<<4)|0,e[t>>2]=14,e[t+4>>2]=A,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),I=(A=e[e[32972]+132>>2])?1:3,t=e[x+4>>2],e[r+28>>2]|!d[145748]||(f[145748]=0,I=A?2:4),(s=e[36426])&&(!((2&(A=y[s>>1]))>>>1|d[s+16|0]<2)|16&A||(g=216192+(e[36439]<<4)|0,e[g+12>>2]=t,8&A&&(A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,(A=(m=A<<6)+177888|0)&&(h=y[t+4>>1]|y[t+6>>1]<<16,c=y[t>>1]|y[t+2>>1]<<16,k[A>>1]=c,k[A+2>>1]=c>>>16,k[A+4>>1]=h,k[A+6>>1]=h>>>16,h=y[t+60>>1]|y[t+62>>1]<<16,c=y[t+56>>1]|y[t+58>>1]<<16,k[A+56>>1]=c,k[A+58>>1]=c>>>16,k[A+60>>1]=h,k[A+62>>1]=h>>>16,h=y[t+52>>1]|y[t+54>>1]<<16,c=y[t+48>>1]|y[t+50>>1]<<16,k[A+48>>1]=c,k[A+50>>1]=c>>>16,k[A+52>>1]=h,k[A+54>>1]=h>>>16,h=y[t+44>>1]|y[t+46>>1]<<16,c=y[t+40>>1]|y[t+42>>1]<<16,k[A+40>>1]=c,k[A+42>>1]=c>>>16,k[A+44>>1]=h,k[A+46>>1]=h>>>16,h=y[t+36>>1]|y[t+38>>1]<<16,c=y[t+32>>1]|y[t+34>>1]<<16,k[A+32>>1]=c,k[A+34>>1]=c>>>16,k[A+36>>1]=h,k[A+38>>1]=h>>>16,h=y[t+28>>1]|y[t+30>>1]<<16,c=y[t+24>>1]|y[t+26>>1]<<16,k[A+24>>1]=c,k[A+26>>1]=c>>>16,k[A+28>>1]=h,k[A+30>>1]=h>>>16,h=y[t+20>>1]|y[t+22>>1]<<16,c=y[t+16>>1]|y[t+18>>1]<<16,k[A+16>>1]=c,k[A+18>>1]=c>>>16,k[A+20>>1]=h,k[A+22>>1]=h>>>16,h=y[t+12>>1]|y[t+14>>1]<<16,c=y[t+8>>1]|y[t+10>>1]<<16,k[A+8>>1]=c,k[A+10>>1]=c>>>16,k[A+12>>1]=h,k[A+14>>1]=h>>>16,f[m+177904|0]=0,k[A>>1]=32768|y[A>>1]),k[(m=m+177888|0)+8>>1]=y[s+8>>1],f[m+21|0]=d[s+21|0],k[m+10>>1]=y[s+10>>1],f[m+22|0]=d[s+22|0],k[m+12>>1]=y[s+12>>1],f[m+23|0]=d[s+23|0],k[m+14>>1]=y[s+14>>1],f[m+24|0]=d[s+24|0],f[m+25|0]=d[s+25|0],e[g+12>>2]=A))),(0|hA)!=2|d[ce+11|0]!=2||(xs(),e[36427]=e[50758]),!((0|(ce=e[l+108>>2]))<2)){for(A=e[36433],h=(O(256-A|0,K)+(A<<8)|0)/256|0,A=e[36432],g=(O(256-A|0,K)+(A<<8)|0)/256|0,c=e[50754],A=0,s=1;m=y[(V=(x+(s<<3)|0)-8|0)+2>>1],m=(0|O((0|O(c,k[V>>1]))/1e3|0,4&m?g:16384&m?h:K))/256|0,e[(s<<2)+l>>2]=m,A=A+m|0,(0|ce)!=(0|(s=s+1|0)););if(!((0|A)<=0|(0|A)>=(0|fA)|(0|ce)<2)){if(s=1,h=1&(m=ce-1|0),(0|ce)!=2)for(g=-2&m,K=0;e[(m=(s<<2)+l|0)>>2]=(0|O(e[m>>2],fA))/(0|A),e[m+4>>2]=(0|O(e[m+4>>2],fA))/(0|A),s=s+2|0,(0|g)!=(0|(K=K+2|0)););h&&(e[(s=(s<<2)+l|0)>>2]=(0|O(e[s>>2],fA))/(0|A))}if(K=0,!((0|ce)<2))for(ce=hA+256|0,s=1;A=e[4+(x+(s<<3)|0)>>2],!(m=e[r+28>>2])|128&d[0|t]||(e[36422]=0,cs(m,ce,0,c=e[r>>2],0,h=(h=e[r+32>>2])?(h<<5)/100|0:32),f[145748]=1,e[r+28>>2]=0),(0|i)<0||(i=64&d[0|t]?6:i,(e[l+108>>2]-1|0)==(0|s)&&(i=(m=i)|(3840&(i=e[36436])?i:0))),m=e[(s<<2)+l>>2],e[36440]=m+e[36440],e[36441]=m+e[36441],m?(h=e[50758],e[36439]=h,(0|i)>=0&&(e[(h=216192+(h<<4)|0)>>2]=I,e[h+12>>2]=A,e[h+8>>2]=t,e[h+4>>2]=m+(i<<16),t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),e[36426]=A,K=m+K|0):e[36426]=0,t=A,(0|(s=s+1|0))<e[l+108>>2];);}!e[36438]|(0|hA)==1||(e[36438]=0,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)}}H=l+112|0}function oA(A,t,r,s){var i,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0;H=i=H-1856|0,e[i+164>>2]=0,r?T=e[r>>2]:Je(r=i- -64|0,0,96),e[33264]=0,e[i+1824>>2]=0,e[i+1828>>2]=0,e[i+1832>>2]=0,e[i+1836>>2]=0,f[i+1616|0]=0,f[i+992|0]=0,f[i+1200|0]=0,f[i+784|0]=0;e:if(e[A+688>>2]){for(e[i+1840>>2]=t,d[0|(l=t)]==32&&(l=t+1|0,e[i+1840>>2]=l),_=i+416|1,Fe=i+1844|1,Xe=i+1848|1,fA=i+1852|1,e[i+1820>>2]=l,jA(i+168|0,l),l=e[i+1820>>2];(32|d[0|l])!=32;)l=jA(i+164|0,l)+e[i+1820>>2]|0,e[i+1820>>2]=l,J=J+1|0;qA(i+256|0,t,Le=(0|(g=l-t|0))>=159?159:g),!(Ee=4194304&T)|(0|J)!=1?(ce=((0|(l=e[47202]))==36)<<2,(0|J)==1|(0|l)!=36||(l=e[i+1840>>2]-1|0,e[i+1840>>2]=l,f[0|l]=95,ce=0,c=!!(0|Ot(A,i+1840|0,i+1616|0,i+1832|0,0,r)),l=e[47202])):(c=1,jA(i+172|0,l+1|0),Ft(e[i+172>>2])&&(0|uA(e[i+164>>2]))==(0|uA(e[i+172>>2]))&&(c=0),ce=(0|(l=e[47202]))==36?4:c,c=0);A:{r:{a:{n:{o:if(16&l)V=15&l,ce=0;else{if(te=1,c||(te=!!(0|Ot(A,i+1840|0,i+1616|0,i+1832|0,2,r))),50331648&(l=e[i+1832>>2])&&(g=e[i+1820>>2],d[g+1|0]==46&&(f[g+1|0]=32,l=e[i+1832>>2])),536870912&l){if(!s)break e;PA(s,e[i+1840>>2]);break e}if(8192&l|!(128&l)|te)s=e[33264];else if(l=e[i+1840>>2],e[i+1820>>2]=l,!((0|(s=e[33264]))<=0))for(c=0;d[0|l]==32&&(f[0|l]=45,c=c+1|0,l=e[i+1820>>2],s=e[33264]),l=l+1|0,e[i+1820>>2]=l,(0|s)>(0|c););c:if(!(s|(0|J)!=1)&&(g=jA(i+576|0,x=e[i+1840>>2]),d[g+x|0]==32)){c=i+1408|0,s=x;u:{l:{i:{for(;;){p:if(Ft(e[i+576>>2])){C:{if(d[(m=s+g|0)+1|0]==46){V=0;h:switch(d[(l=g+2|0)+s|0]-32|0){case 0:break C;case 7:break h;default:break p}if(V=1,g=l,d[m+3|0]==115)break C;break p}if(V=1,(0|h)<=0)break i}if(!((0|g)<=0)){if(K=3&g,I=0,g>>>0<4)l=0;else for(he=-4&g,l=0,m=0;f[0|c]=d[s+l|0],f[c+1|0]=d[(1|l)+s|0],f[c+2|0]=d[(2|l)+s|0],f[c+3|0]=d[(3|l)+s|0],l=l+4|0,c=c+4|0,(0|he)!=(0|(m=m+4|0)););if(K)for(;f[0|c]=d[s+l|0],l=l+1|0,c=c+1|0,(0|K)!=(0|(I=I+1|0)););}if(h=h+1|0,V)s=s+g|0;else if(g=jA(i+576|0,s=3+(s+g|0)|0),d[s+g|0]==32)continue}break}if(!((0|h)<2)){(g=(g=c-(l=i+1408|0)|0)+(l=qA(x,l,g))|0)>>>0<s>>>0&&Je(g,32,(i+1408|0)+s-(l+c)|0),e[33264]=(h<<1)-2,e[i+1836>>2]=0;break l}}if(!h)break c;if(e[i+1832>>2]=0,e[i+1836>>2]=0,!e[33264])break u}e[i+1832>>2]=128}ce=1}if(d[i+1616|0]==21){PA(189088,i+1616|0),l=0;break e}if(hA=d[i+1833|0],c=1,!te){if(e[i+168>>2]-48>>>0<10){if(QA(A,84174,189088),l=0,d[189088]==21)break e;if(!(!(128&d[A+109|0])|32&d[r+2|0])){f[189088]=21,f[189089]=0;break e}c=!!(0|ls(A,e[i+1840>>2],i+1616|0,i+1832|0,r,0))}else c=0;if(!(c|(3&T)==2)&&(16777216&(s=e[A+104>>2])||(c=0,!(!(33554432&s)|!(1&T))))&&(16&T||(c=0,!(1&f[r+13|0])))){he=e[i+1840>>2],l=0,h=0,m=0,H=K=H-224|0,f[0|(I=i+1616|0)]=0,e[K+216>>2]=0,e[K+220>>2]=0;c:if(!(f[he-2|0]-48>>>0<10|(1&f[0|r]?0:2&d[A+107|0])||(s=d[0|(g=he+1|0)],(!(2561&y[A+106>>1])|!(1&f[r+2|0]))&(0|s)==32))){if((0|(h=d[0|he]))!=32){for(V=32767,s=0;;){if(!(l=Ba(101868,h<<24>>24,8))){h=0;break c}if(x=0,(0|(l=e[(l<<2)-305584>>2]))==(0|s)&&!((0|(x=m+1|0))<=2)){h=0;break c}u:{l:{i:{p:{if(!((0|s)<2)){if((0|s)==10|(0|s)==100)break p;if(!((0|s)>(0|l))){h=0;break c}}if(!s)break l;if((0|s)<(0|l))break i;break l}if((0|s)>=(0|l))break l}if(h=0,(0|Te)%10|(0|O(s,10))<(0|l))break c;l=l-s|0,V=s;break u}if((0|l)>=(0|V)){h=0;break c}Te=s+Te|0}if(h=d[0|g],g=c=g+1|0,s=l,m=x,(0|h)==32)break}s=d[0|c]}else c=g;if((s<<24>>24)-48>>>0<10)h=0;else if((0|(s=l+Te|0))<e[A+120>>2])h=0;else if((0|s)>e[A+116>>2])h=0;else if(QA(A,85600,K+176|0),l=I,4&d[A+107|0]||(l=PA(I,l=K+176|0)+MA(l)|0),e[K+4>>2]=e[A+140>>2],e[K>>2]=s,dA(K+16|0,85839,K),h=0,d[0|c]!=46){ur(A,he,c,r,1)&&(e[r>>2]=32768|e[r>>2]),g=0;u:if(8&d[A+107|0]){if(m=e[r>>2],e[A+212>>2]==26741){if(32768&m)break u;if(!(16384&m))break c;g=1,m=0;l:{i:switch(d[0|c]-97|0){case 0:case 4:break i;default:break l}i:{p:{C:{h:switch((V=d[c+1|0])-116|0){case 6:break l;case 1:case 2:case 3:case 4:case 5:break p;case 0:break h;default:break C}if(d[c+2|0]!=116)break i;break l}if((0|V)==32)break l}if(!((0|s)%1e3|0)&&(0|V)==108)break l}m=1}if(m)break u;break c}e[r>>2]=32768|m}e[(s=A+8232|0)>>2]=0,e[s+4>>2]=0,ls(A,K+16|2,l,K+216|0,r,g),h=1,4&d[A+107|0]&&As(I,K+176|0)}}H=K+224|0,h?(e[i+1832>>2]=8192|e[i+1832>>2],c=1):c=0}}if(V=te?ce:32&hA?1:ce,ce=0,!(!(1&T)|(0|J)<2)&&OA(e[i+168>>2])){c:{if(1&f[188785]){if(!(!(s=8192&(l=e[i+1832>>2]))|c))break c;ce=s>>>2^2048;break o}if(c)break n;l=e[i+1832>>2]}if(!(128&l|J>>>0>3)&&!((0|(l=e[A+8220>>2]))<4)&&(s=1,(0|l)>=e[A+8216>>2]))break A}}if(I=0,(0|V)<=0)break a;s=V;break A}if((0|(s=V))>0)break A;I=0,x=0,m=0,h=0;break r}if(c)x=0,m=0,h=0;else{s=e[i+1840>>2],e[i+1820>>2]=s,l=999,h=0,x=0,_=0;a:{n:{o:{for(;;){c:{u:{l:{if(l-1>>>0>=2){if((0|J)<2||(jA(i+1408|0,s),(0|(l=e[i+1408>>2]))<577&e[A+600>>2]>0)||(l=uA(l),(e[l+4>>2]!=e[A+600>>2]?l:0)|e[A+40>>2]==1))break l;l=d[0|s],e[i+1408>>2]=l<<24>>24;i:switch(l-32|0){default:if(!l)break l;break;case 0:case 7:break l;case 1:case 2:case 3:case 4:case 5:case 6:break i}I=jA(i+1408|0,s),te=9;i:{p:{C:{h:if(-33&(l=e[i+1408>>2])){for(c=0,m=0;;){b:{m:{if((0|l)==39){if((0|x)>0|(0|c)>1)break h;if(m=c?m:39,e[A+40>>2]!=3)break m;break b}m=c?m:l}c=c+1|0}if(!Ua(A,l)){if((0|(l=e[i+1408>>2]))!=39&&!OA(l))break l;if(I=jA(i+1408|0,s+I|0)+I|0,-33&(l=e[i+1408>>2]))continue;break h}break}if((0|c)<=2)break C;te=c}else m=0;if((0|(l=e[A+40>>2]))!=2)break p;H=l=H-208|0,f[0|l]=0,c=d[0|(g=s-1|0)],f[0|g]=32,s=VA(A,s,l,200,0,-2147483648,0),f[0|g]=c,H=l+208|0,s=!s|(32768&s)>>>15;break i}l=e[A+40>>2],te=c}s=(f[A+168|0]+1|0)<(te-((0|l)==(0|m))|0)}if(!s)break l;s=e[i+1820>>2]}if(d[0|s]!=39)break u;h=67108864,ce=0}if(I=0,nr(A,i+992|0,0,x),s=e[i+1820>>2],(0|(l=d[0|s]))!=32)break c;x=0,m=0;break r}if(ce=0,s=Ws(A,s,i+992|0,1&(_|=(0|x)>0))+e[i+1820>>2]|0,e[i+1820>>2]=s,d[i+992|0]==21)break o;for(x=x+1|0,c=0;c=(l=c)+1|0,d[s+l|0]!=32;);h=67108864;continue}break}if(!d[i+992|0]|(0|l)==39||(f[s-1|0]=32,s=e[i+1820>>2]),g=VA(A,s,i+1616|0,200,i+784|0,T,i+1832|0),(0|(s=d[i+1616|0]))==21){PA(189088,i+1616|0),l=0;break e}if(!(s|d[i+784|0])&&(jA(i+1408|0,e[i+1820>>2]),(0|J)==1&&(Ft(e[i+1408>>2])||_n(e[i+1408>>2])))){Ha(A,e[i+1820>>2],i+1616|0,V)&&PA(189088,i+1616|0),l=0;break e}e[i+172>>2]=f[e[i+1820>>2]-1|0];c:if(1024&g)for(he=i+176|1,V=0,l=1,_=0,x=0;;){if((te=131072&g)|!(1&l)||(f[i+1408|0]=0,!(s=VA(A,e[i+1820>>2],i+1408|0,200,i+576|0,805306368|T,i+1832|0)))){2048&g&&(e[A+8184>>2]=1),f[e[i+1820>>2]-1|0]=e[i+172>>2];u:{l:{i:{p:if(te){if(f[i+176|0]=0,l=e[i+1820>>2],s=1,m=63&g){if(K=1&g,x=m-1|0,c=0,(0|m)!=1)for(Te=m-K|0,m=0;I=l,e[i+1820>>2]=l+1,f[0|(Ee=(i+176|0)+s|0)]=(0|c)!=(0|x)?d[0|l]:0,l=l+2|0,e[i+1820>>2]=l,f[Ee+1|0]=(0|x)!=(1|c)?d[I+1|0]:0,c=c+2|0,s=s+2|0,(0|Te)!=(0|(m=m+2|0)););K&&(m=l+1|0,e[i+1820>>2]=m,f[(i+176|0)+s|0]=(0|c)!=(0|x)?d[0|l]:0,s=s+1|0,l=m)}f[(i+176|0)+s|0]=0}else{if(l=e[i+1820>>2],!(m=15&g))break i;if(s=0,c=m,I=3&g)for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128||(c=c-1|0,(0|I)!=(0|(s=s+1|0))););if(m>>>0<4)break p;for(;;)if(l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])!=128){for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128;);for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128;);for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128;);if(s=(0|c)>4,c=c-4|0,!s)break}}if(s=l-1|0,e[i+172>>2]=f[0|s],f[0|s]=32,s=T|=8388608,!te)break l;if(Lt(i+576|0,i+784|0,12),e[i+1852>>2]=he,s=PA(i+1200|0,l=i+1616|0),Ot(A,i+1852|0,l,i+1832|0,0,r)&&PA(s,i+1616|0),!(32&d[i+1833|0]))break u;f[0|s]=0,Ha(A,e[i+1852>>2],s,1);break u}s=l-1|0,e[i+172>>2]=f[0|s],f[0|s]=32,s=8388608|T}T=s,As(i+1200|0,i+784|0)}if(f[i+784|0]=0,m=1,s=Ot(A,i+1820|0,i+1616|0,i+1824|0,1024,r),e[i+1832>>2]||(l=e[i+1828>>2],e[i+1832>>2]=e[i+1824>>2],e[i+1836>>2]=l,m=_),s){I=0,x=g;break a}if(I=VA(A,e[i+1820>>2],i+1616|0,200,i+784|0,8404992&T,i+1832|0),l=1,x=g,_=m,d[i+1616|0]==21){f[e[i+1820>>2]-1|0]=e[i+172>>2],PA(189088,i+1616|0),l=0;break e}}else l=i+416|0,Vs(A,e[i+1820>>2],s,l),I=VA(A,e[i+1820>>2],i+1616|0,200,i+784|0,268435456|T,i+1832|0),qA(e[i+1820>>2],l,MA(l)),1024&I||(PA(i+1616|0,i+1408|0),l=PA(i+784|0,i+576|0),8&d[188788]&&(jr(m=l,l=i+576|0),g=e[47195],e[i+48>>2]=l,Xt(g,85205,i+48|0)),I=s),l=0;if(s=(c=1024&I)>>>10|0,V>>>0>48)break c;if(V=V+1|0,g=I,!c)break}else I=g,x=0,_=0,s=0;if(s|!I)break n;for(g=PA(i+1408|0,i+1616|0),c=Vs(A,e[i+1820>>2],I,i+416|0),m=_,l=I;;){c:{if(f[i+1616|0]=0,d[i+1200|0]){if(f[e[i+1820>>2]-1|0]=e[i+172>>2],s=Ot(A,i+1840|0,i+1616|0,i+1824|0,c,r),f[e[i+1820>>2]-1|0]=32,d[i+1616|0]==21){A=i+416|0,qA(e[i+1820>>2],A,MA(A)),PA(189088,i+1616|0),l=0;break e}if(e[i+1832>>2]||(_=e[i+1828>>2],e[i+1832>>2]=e[i+1824>>2],e[i+1836>>2]=_),s){f[i+1200|0]=0;break c}m=e[i+1824>>2]?1:m}if(s=Ot(A,i+1820|0,i+1616|0,i+1824|0,c,r),d[i+1616|0]==21){A=i+416|0,qA(e[i+1820>>2],A,MA(A)),PA(189088,i+1616|0),l=0;break e}if(e[i+1832>>2]||(_=e[i+1828>>2],e[i+1832>>2]=e[i+1824>>2],e[i+1836>>2]=_),!s)if(16384&l)PA(i+1616|0,g);else{T|=c<<11&8192|l<<9&134217728;u:if(524288&l){if(_=PA(i+576|0,s=i+784|0),l=VA(A,e[i+1820>>2],i+1616|0,200,s,T,i+1832|0),As(s,_),s=0,!l){l=0;break u}if(1024&l)break u;s=1,c=Vs(A,e[i+1820>>2],l,0)}else l=0,VA(A,e[i+1820>>2],i+1616|0,200,0,T,i+1832|0),s=0;if(d[i+1616|0]==21){PA(189088,i+1616|0),A=i+416|0,qA(e[i+1820>>2],A,MA(A)),f[e[i+1820>>2]-1|0]=e[i+172>>2],l=0;break e}if(s)continue}}break}65536&I||(Pn(A,i+1616|0,200,i+784|0),f[i+784|0]=0),s=i+416|0,qA(e[i+1820>>2],s,MA(s));break a}PA(189088,A=i+992|0),l=!Kr(1|A,84744,3)<<12;break e}I=0,m=_}f[e[i+1820>>2]-1|0]=e[i+172>>2]}}if(s=e[i+164>>2],e[i+1852>>2]=8026656,e[i+1848>>2]=8022304,e[i+1844>>2]=7566112,4&T){r:{if((l=255&s)&&((0|l)==102||(Fe=Xe,bo(s<<24>>24))))break r;Fe=fA}VA(A,Fe,189088,200,0,0,0)}for(s=0,l=i+1200|0,_=d[i+784|0];;){r:{a:{n:switch(0|(g=d[0|l])){case 0:break r;case 6:case 7:break n;default:break a}s=g}l=l+1|0;continue}break}r:if(s|m){if(e[A+32>>2]|65536&x){for(c=0,Vt(A,i+1616|0,i+1832|0,3,0),l=i+1200|0;;){a:switch(d[0|l]){case 6:c&&(f[0|l]=5),c=1;default:l=l+1|0;continue;case 0:break a}break}e[i+24>>2]=i+1616,e[i+20>>2]=i+1200,e[i+16>>2]=i+992,zs(189088,200,85233,i+16|0),f[189287]=0,Vt(A,189088,i+1832|0,-1,0);break r}e[i+8>>2]=i+1616,e[i+4>>2]=i+1200,e[i>>2]=i+992,zs(189088,200,85233,i),f[189287]=0,Vt(A,189088,i+1832|0,-1,0)}else Vt(A,s=i+1616|0,i+1832|0,-1,!!(0|_)<<1),e[i+40>>2]=s,e[i+36>>2]=i+1200,e[i+32>>2]=i+992,zs(189088,200,85233,i+32|0),f[189287]=0;d[i+784|0]&&(s=MA(189088),f[983+(i-s|0)|0]=0,PA(s+189088|0,i+784|0)),16&(s=T|ce)&&(e[i+1832>>2]=-268435457&e[i+1832>>2]);r:if(!(128&s)|!(16&d[A+14|0]))if(3072&s){if(Ks(A,6),!(2048&s))break r;e[i+1832>>2]=268435456|e[i+1832>>2]}else 16&d[O(e[33264],12)+r|0]&&(1536&(r=e[i+1832>>2])?Ks(A,4):2048&r&&Ks(A,3));else Ks(A,3);8192&I&&(e[A+8192>>2]=2,e[A+8184>>2]=2);r:{if(8&(r=e[i+1836>>2]))e[A+8184>>2]=0,e[A+8188>>2]=3,s=A+8196|0;else if(1&r)e[A+8192>>2]=0,e[A+8184>>2]=2,s=A+8196|0;else if(2&r)e[A+8192>>2]=2,e[A+8184>>2]=0,e[A+8188>>2]=0,s=A+8196|0;else{if(!(4&r))break r;e[A+8184>>2]=0,e[A+8192>>2]=0,e[A+8196>>2]=2,s=A+8188|0}e[s>>2]=0}!d[e[i+1820>>2]]|256&r||((0|(r=e[A+8184>>2]))>0&&(e[A+8184>>2]=r-1),(0|(r=e[A+8192>>2]))>0&&(e[A+8192>>2]=r-1),(0|(r=e[A+8196>>2]))>0&&(e[A+8196>>2]=r-1),(0|(r=e[A+8188>>2]))<=0||(e[A+8188>>2]=r-1)),(0|J)!=1|e[A+212>>2]!=25966||!OA(e[i+168>>2])|e[i+168>>2]==105||(e[i+1832>>2]=16777216|e[i+1832>>2]);r:if(2&d[A+68|0]&&98304&(s=e[i+1832>>2])&&!((0|(r=MA(189088)-1|0))<=0))for(l=0;;){if(A=l+1|0,d[l+189088|0]==6){r=f[0|(A=A+189088|0)];a:{if(65536&s){if((0|q(69))==(0|r)?(J=q(101),f[0|A]=J):J=d[0|A],c=111,(0|q(79))==J<<24>>24)break a;break r}if((0|q(101))==(0|r)?(J=q(69),f[0|A]=J):J=d[0|A],c=79,(0|q(111))!=J<<24>>24)break r}f[0|A]=q(c);break r}if((0|r)==(0|(l=A)))break}A=e[i+1832>>2],qA(t,i+256|0,Le),l=A|h;break e}if(l=0,f[i+1616|0]=0,Ha(A,e[i+1840>>2],i+1616|0,s)){if(g=PA(189088,i+1616|0),!Ee){if(s=e[i+164>>2],e[i+1408>>2]=8026656,e[i+576>>2]=8022304,e[i+416>>2]=7566112,4&T){t=i+576|1,r=i+1408|1;A:{if((l=255&s)&&((0|l)==102||(_=t,bo(s<<24>>24))))break A;_=r}VA(A,_,g,200,0,0,0)}l=128&e[i+1832>>2]}}else l=((0|J)>1)<<12}else f[189088]=0;return H=i+1856|0,l}function zA(A,t,r,s,i,l,c,g){var m,I,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0,Mt=0,Pt=0,Nt=0,tr=0,rr=0,br=0,Ir=0,zr=0,Vr=0,Xr=0,ns=0,gs=0,bs=0,ua=0,ea=0,da=0,Cn=0,bn=0,In=0,Wn=0,wn=0;H=m=H-384|0,I=e[t>>2];e:{A:{r:{a:{if(i){if(d[0|i]!=7)break a;e[t>>2]=(s||1)+I;break r}e[l>>2]=0,e[t>>2]=I+1;break e}Mt=86135,Vr=268435456&c,Xr=134217728&c,ns=8388608&c,gs=16384&c,bs=8192&c,tr=r-1|0,rr=s-r|0,ua=2&c,ea=128&c,da=c>>>31|0,Cn=-2147483648&c,br=m+96|1;a:for(;;)for(e[m+268>>2]=0,J=(T=e[t>>2])+s|0,Xe=-2,Fe=-6,r=i,fA=Cn,_A=0,Pt=0,Le=1,h=0,hA=0,At=0;;){_=T,te=h;n:{o:{c:{u:{l:{i:{p:{C:{h:{b:{m:{x:{I:{B:{N:{L:{for(;he=r,r=r+1|0,!((x=d[0|he])>>>0>9);){V=r;U:switch(0|x){case 0:if(!(r=Ir)){Ir=0,r=86135;break N}for(;;){x=1;y:{E:switch(0|(h=d[0|r])){case 0:case 3:break L;case 5:break E;default:break y}x=2}r=(r+x|0)+(((0|h)==9)<<1)|0}case 1:if(At=1,!da)continue;break l;case 2:At=2;continue;case 4:Ir=r;continue;case 5:r=he+2|0,h=e[A+320>>2];y:{if((T=d[he+1|0])>>>0>=32){if(!(h>>>T-32&1))break y;break l}if(!(h>>>T&1))break l}Le=Le+1|0;continue;case 9:r=he+3|0;continue;case 8:At=1,Pt=1,fA=0;break;case 3:break B;default:break U}}V=0,h=te,T=_;U:switch(0|At){case 0:y:{E:{if((0|(h=d[0|J]))!=(0|x)){if((0|h)!=69)break l;if((0|x)==101)break E;break l}if(V=0,(192&x)==128)break y}V=21}J=J+1|0,_A=_A+1|0;break n;case 1:break I;case 2:break U;default:break b}if(e[m+264>>2]=e[m+268>>2],!d[J-1|0])break l;Fe=(0|(h=Fe+6|0))>=19?19:h,Te=J+1|0,Ee=jA(m+268|0,J),ce=d[0|J],V=20,h=te;U:{y:switch(x-11|0){case 6:r=he+2|0,h=e[m+268>>2],T=f[he+1|0];E:if(x=e[604+(((T=((0|T)<65?191:-65)+T|0)<<2)+A|0)>>2])h=!!(0|_r(x,h));else{if((0|T)>7)break l;Q:{if((0|(x=e[A+600>>2]))>0){if((h=h-x|0)-1>>>0<255)break Q;break l}if((x=h-192|0)>>>0<=413){h=d[344+(d[x+94240|0]+A|0)|0]&1<<T;break E}if(h>>>0>255)break l}h=d[344+(A+h|0)|0]&1<<T}if(!h)break l;J=J+Ee|0,V=((0|T)==2?19:20)-Fe|0;break n;case 7:if(r=he+2|0,h=f[he+1|0],!(h=e[4788+((((0|h)<65?191:-65)+h<<2)+A|0)>>2]))break l;E:for(;;){if((0|(V=d[0|h]))==7)break l;if((0|V)==126){V=20-Fe|0;break n}Q:if(ce){if(K=J,x=h,(0|V)==(0|ce))for(;;){if((0|(V=d[0|(x=x+1|0)]))!=(0|(h=d[0|(K=K+1|0)])))break Q;if(!h)break}}else x=h,K=J;if(V){for(;;)if(T=d[0|x],x=h=x+1|0,!T)continue E}break}if((0|(h=K-J|0))<0)break l;J=h+J|0,V=20-Fe|0;break n;case 14:x=e[m+268>>2];E:{Q:if(h=e[A+604>>2])h=!!(0|_r(h,x));else{F:{if((0|(h=e[A+600>>2]))>0){if((K=x-h|0)-1>>>0<255)break F;break E}if((h=x-192|0)>>>0<=413){h=1&f[344+(d[h+94240|0]+A|0)|0];break Q}if(K=x,x>>>0>255)break o}h=1&f[344+(A+K|0)|0]}if(h)break l;x=e[m+268>>2]}if(!Xr)break o;if((0|x)==32)break l;break o;case 4:if((h=e[m+268>>2])-48>>>0<10|h-2406>>>0<10)break o;if(!d[A+170|0])break l;V=20-Fe|0;break n;case 5:if(oi(e[m+268>>2]))break l;break c;case 0:if(e[m+268>>2]==e[m+264>>2])break c;break l;case 17:r=he+2|0,h=32768,V=0;E:{Q:switch((x=d[he+1|0])-1|0){case 0:break b;case 1:break Q;default:break E}if(V=1,h=te,!ns)break b;break l}if((0|(h=240&x))==16){if(V=23,h=te,g>>>(15&x)&16384)break b;break l}if((0|x)!=3&(0|h)!=32||(qA(h=m+96|0,tr,T=1+(e[t>>2]+(_A+rr|0)|0)|0),f[0|(h=h+T|0)]=32,f[h+1|0]=0,e[33265]=0,e[33266]=0,e[m+16>>2]=br,Ot(A,m+16|0,m+272|0,133060,0,0),V=23,!((0|x)!=3|(0|(K=e[33265]))>=0|16384&e[33266])))break n;if(h=te,T=_,K>>>(15&x)&16384)break b;break l;case 34:E:{if(h=ce-32|0){if((0|h)==13)break E;break l}if(!gs)break l}K=22-Fe|0;break u;case 10:if(h=1,d[0|r]==21)break U;break i;case 18:if((0|(K=e[m+268>>2]))!=32)for(x=J+Ee|0;;){E:{Q:if(h=e[A+632>>2])h=!!(0|_r(h,K));else{F:{if((0|(h=e[A+600>>2]))>0){if((K=K-h|0)-1>>>0<255)break F;break E}if((h=K-192|0)>>>0<=413){h=128&d[344+(d[h+94240|0]+A|0)|0];break Q}if(K>>>0>255)break E}h=128&d[344+(A+K|0)|0]}if(h)break l}if(x=jA(m+268|0,x)+x|0,(0|(K=e[m+268>>2]))==32)break}K=19-Fe|0;break u;case 49:break p;case 2:break C;case 3:break h;case 1:break b;case 13:break m;case 12:break y;default:break x}jA(m+272|0,r),Ee=-1;y:if((0|(h=e[m+272>>2]))!=(0|(x=e[m+268>>2])))if(-33&x)for(;;){ce=J,Ee=-1;E:if((0|h)==18&&(h=f[he+2|0],V=e[4788+((((0|h)<65?191:-65)+h<<2)+A|0)>>2])){for(;;){if((0|(T=d[0|V]))==7)break E;if((0|T)==126){Ee=0;break E}Q:if((0|(J=d[0|ce]))==(0|T)){if(h=ce,x=V,J)for(;;){if((0|(T=d[0|(x=x+1|0)]))!=(0|(V=d[0|(h=h+1|0)])))break Q;if(!V)break}}else x=V,h=ce;if(!T){h=h-ce|0;break}for(;h=d[0|x],x=V=x+1|0,h;);}Ee=h}if(T=jA(m+268|0,ce),(0|(h=e[m+272>>2]))==(0|(x=e[m+268>>2]))|!(-33&x))break y;if(J=T+ce|0,(0|Ee)!=-1)break}else ce=J;else ce=J;J=(0|h)==(0|x)||(0|Ee)>=0?ce:Te,V=0;break n}for(;h=h+1|0,d[0|(r=r+1|0)]==21;);break i}r=r+1|0}V=he}if(!(d[_-1|0]!=32&Pt|fA||((0|(x=Pt?Le+4|0:Le))>=(0|Nt)&&(bn=hA,In=te,Nt=x,Wn=_A,Mt=r),!(8&e[47197])|Vr|(0|x)<=0))){for(jr(r,he=m+272|0),Te=e[47195],T=m+16|0,r=0,ce=0,fA=0,hA=0,H=J=H-496|0,f[J+80|0]=0,(0|s)>0?(qA(J+288|0,I,s),te=s):te=0,f[te+(h=J+288|0)|0]=0,_=MA(h)+h|0,Ee=(0|c)<0;;){if(K=d[0|i],te=i,i=i+1|0,K>>>0>9)for(;;){B:{N:{L:{U:{y:{E:switch((h=255&K)-14|0){case 4:break L;case 3:break U;case 0:break y;case 14:break E;default:break N}if(te=te+2|0,K=32,!Ee&(0|(i=d[0|i]))==1)break B;f[0|_]=36,PA(h=_+1|0,i=G(128960,i)),_=MA(i)+h|0;break B}h=d[te+2|0],K=d[0|i],e[J+36>>2]=127&d[te+3|0],e[J+32>>2]=4&h?80:83,dA(J+48|0,85131,J+32|0),1&h&&(i=MA(i=J+48|0)+i|0,f[0|i]=101,f[i+1|0]=0),2&(i=127&h)&&(h=MA(h=J+48|0)+h|0,f[0|h]=105,f[h+1|0]=0),4&i&&(h=MA(h=J+48|0)+h|0,f[0|h]=112,f[h+1|0]=0),8&i&&(h=MA(h=J+48|0)+h|0,f[0|h]=118,f[h+1|0]=0),16&i&&(h=MA(h=J+48|0)+h|0,f[0|h]=100,f[h+1|0]=0),32&i&&(h=MA(h=J+48|0)+h|0,f[0|h]=102,f[h+1|0]=0),i>>>0>=64&&(i=MA(i=J+48|0)+i|0,f[0|i]=113,f[i+1|0]=0),1&K&&(i=MA(i=J+48|0)+i|0,f[0|i]=116,f[i+1|0]=0),te=te+4|0,_=PA(_,i=J+48|0)+MA(i)|0,K=32;break B}te=te+2|0,K=d[f[0|i]+93871|0];break B}i=f[0|i],f[0|_]=76,h=((i=i+((0|i)<65?191:-65)|0)>>>0)/10|0,f[_+1|0]=h+48,K=i-O(h,10)|48,(0|hA)==1&&(f[0|_]=K,K=76),te=te+2|0,_=_+2|0;break B}K=h>>>0<=31?d[h+93904|0]:(0|h)==32?95:K,te=i}if(f[0|_]=K,i=te+1|0,_=_+1|0,!((K=d[0|te])>>>0>=10))break}h=1;B:switch(0|K){case 1:h=r;case 8:f[0|_]=0,_=J+80|0,r=h,hA=1;continue;case 2:f[0|_]=0,te=MA(h=J+288|0)+h|0,_=d[84899]|d[84900]<<8,f[0|te]=_,f[te+1|0]=_>>>8,hA=2,f[te+2|0]=d[84901],_=MA(h)+h|0;continue;case 5:ce=f[0|i],i=te+2|0;continue;case 9:fA=(d[0|i]+O(d[te+2|0],255)|0)-256|0,i=te+3|0;continue;case 0:case 3:break B;default:continue}break}if(f[0|_]=0,_=T,(0|fA)>0&&(e[J+16>>2]=fA,dA(T,85581,J+16|0),_=T+7|0),(0|ce)>0&&(e[J>>2]=ce,dA(_,85694,J),_=MA(_)+_|0),1&((0|(i=MA(J+80|0)))>0|r)){1&r&&(f[0|_]=95,_=_+1|0);B:if(!((0|(te=i-1|0))<0|_>>>0>=T>>>0))for(;;){if(f[0|_]=d[(J+80|0)+te|0],_=_+1|0,(0|te)<=0)break B;if(te=te-1|0,!(_>>>0<T>>>0))break}f[0|_]=41,f[_+1|0]=32,_=_+2|0}f[0|_]=0,f[(r=J+288|0)+((T+3|0)-_|0)|0]=0,As(_,r),(0|(r=MA(T)))<=7&&(Je(r+T|0,32,8-r|0),r=8),f[r+T|0]=0,H=J+496|0,e[m+4>>2]=T,e[m>>2]=(0|s)>1?x+35|0:x,e[m+8>>2]=he,Xt(Te,89088,m)}r=V;break l}if(!d[0|_])break l;Xe=(0|(h=Xe+2|0))>=19?19:h,jA(m+264|0,_),h=Mr(m+268|0,T=_-1|0),ce=d[0|T],Te=T;I:{B:{N:{L:{U:{y:switch(x-10|0){case 13:if(h=(0|(LA=d[0|r]))==(0|(x=d[0|_])),Ee=-1,(0|x)==32|(0|x)==(0|LA))break L;if(x)break U;break N;case 7:r=he+2|0,V=e[m+268>>2],_=f[he+1|0];E:if(x=e[604+(((_=((0|_)<65?191:-65)+_|0)<<2)+A|0)>>2])x=!!(0|_r(x,V));else{if((0|_)>7)break l;Q:{if((0|(x=e[A+600>>2]))>0){if((V=V-x|0)-1>>>0<255)break Q;break l}if((x=V-192|0)>>>0<=413){x=d[344+(d[x+94240|0]+A|0)|0]&1<<_;break E}if(V>>>0>255)break l}x=d[344+(A+V|0)|0]&1<<_}if(!x)break l;T=1+(T-h|0)|0,V=((0|_)==2?19:20)-Xe|0;break I;case 8:if(r=he+2|0,h=f[he+1|0],!(h=e[4788+((((0|h)<65?191:-65)+h<<2)+A|0)>>2]))break l;E:{for(;;){if((0|(ce=d[0|h]))==7)break l;if((0|ce)==126){Ee=0;break E}x=T;Q:{F:{if((0|(he=(Ee=MA(h))-1|0))>0)for(x=_-Ee|0,V=0,K=T;;){if(!d[0|(K=K-1|0)])break F;if((0|he)==(0|(V=V+1|0)))break}Ae:if(!((0|(V=d[0|x]))!=(0|ce)|!V))for(;;){if((0|(ce=d[0|(h=h+1|0)]))!=(0|(V=d[0|(x=x+1|0)])))break Ae;if(!V)break}if(!ce)break Q}for(x=h;V=d[0|x],x=h=x+1|0,V;);continue}break}if((0|Ee)<0)break l}V=20-Fe|0,T=1+(T-Ee|0)|0;break I;case 15:x=e[m+268>>2];E:{Q:if(_=e[A+604>>2])_=!!(0|_r(_,x));else{F:{if((0|(_=e[A+600>>2]))>0){if((x=x-_|0)-1>>>0<255)break F;break E}if((_=x-192|0)>>>0<=413){_=1&f[344+(d[_+94240|0]+A|0)|0];break Q}if(x>>>0>255)break E}_=1&f[344+(A+x|0)|0]}if(_)break l}V=20-Xe|0,T=1+(T-h|0)|0;break I;case 1:if(e[m+268>>2]!=e[m+264>>2])break l;V=21-Xe|0,T=1+(T-h|0)|0;break I;case 5:if(!((_=e[m+268>>2])-48>>>0<10|_-2406>>>0<10))break l;V=21-Xe|0,T=1+(T-h|0)|0;break I;case 6:if(oi(e[m+268>>2]))break l;V=21-Fe|0,T=1+(T-h|0)|0;break I;case 18:if(r=he+2|0,!((0|(x=d[he+1|0]))==3|(240&x)==32)||(qA(h=m+96|0,tr,T=1+(e[t>>2]+(_A+rr|0)|0)|0),f[0|(h=h+T|0)]=32,f[h+1|0]=0,e[33265]=0,e[33266]=0,e[m+16>>2]=br,Ot(A,m+16|0,m+272|0,133060,0,0),V=23,!((0|x)!=3|(0|(K=e[33265]))>=0|16384&e[33266])))break n;if(h=te,T=_,K>>>(15&x)&16384)break b;break l;case 11:if(x=1,d[0|r]==21)for(;x=x+1|0,d[0|(r=r+1|0)]==21;);if(e[A+8208>>2]<(0|x))break l;V=18+(x-Xe|0)|0;break I;case 0:if(V=19,h=te,T=_,e[A+8212>>2]>0)break b;break l;case 19:if(V=3,(0|(K=e[m+268>>2]))==32)break I;for(x=1+(T-h|0)|0;;){E:{Q:if(h=e[A+632>>2])h=!!(0|_r(h,K));else{F:{if((0|(h=e[A+600>>2]))>0){if((K=K-h|0)-1>>>0<255)break F;break E}if((h=K-192|0)>>>0<=413){h=128&d[344+(d[h+94240|0]+A|0)|0];break Q}if(K>>>0>255)break E}h=128&d[344+(A+K|0)|0]}if(h)break l}if(x=x-Mr(m+268|0,x-1|0)|0,(0|(K=e[m+268>>2]))==32)break}break I;case 16:if(V=1,h=te,T=_,e[A+8184>>2])break b;break l;case 9:if(V=1,h=te,T=_,ua)break b;break l;case 36:for(;;){if(V=50,h=te,!(_=(255&ce)-32|0))break l;if((0|_)==14)break b;ce=d[0|(Te=Te-1|0)]}case 35:break y;default:break B}y:{if(h=ce-32|0){if((0|h)==13)break y;break l}if(!ea)break l}V=22-Fe|0;break I}U:{for(;;){Ee=-1,K=_,_=_-1|0;y:if((0|LA)==18&&(h=f[he+2|0],V=e[4788+((((0|h)<65?191:-65)+h<<2)+A|0)>>2]))for(wn=K+1|0;;){if((0|(Te=d[0|V]))==7){Ee=-1;break y}if((0|Te)==126){Ee=0;break y}x=K;E:{if((0|(zr=(Ee=MA(V))-1|0))>0)for(x=wn-Ee|0,ce=0,h=K;;){if(!d[0|(h=h-1|0)])break E;if((0|zr)==(0|(ce=ce+1|0)))break}Q:if(!((0|(h=d[0|x]))!=(0|Te)|!h))for(;;){if((0|(Te=d[0|(V=V+1|0)]))!=(0|(h=d[0|(x=x+1|0)])))break Q;if(!h)break}if(!Te)break y}for(x=V;h=d[0|x],x=V=x+1|0,h;);}if(h=(0|(x=d[0|_]))==(0|LA),(0|x)==32|(0|x)==(0|LA))break U;if(!x){_=K;break N}if((0|Ee)!=-1)break}_=K;break N}_=K}T=h?_:T}V=0,T=(0|Ee)<0?T:_+1|0;break I}if((0|x)!=(0|ce))break l;V=4,(0|x)!=32&&(V=(192&x)!=128?21-Xe|0:0)}h=te;break b}if((0|x)!=(0|ce))break l;K=(192&x)!=128?21-Fe|0:0;break u}if(V=1,bs)break l}Le=V+Le|0;continue}if(h=f[he+1|0],te=d[he+3|0],T=d[he+2|0],1&!(e[A+8208>>2]|4&T)&f[A+84|0])break l;r=he+4|0,K=0,te=127&te|(127&T)<<8|h<<16;break u}C:if(!(Te>>>0<=(x=e[t>>2]+s|0)>>>0)){for(;;){if(d[0|x]!=101){if(h=x>>>0<J>>>0,x=x+1|0,h)continue;break C}break}K=0,hA=x;break u}K=0;break u}V=-20;break n}if(ce=0,(0|(x=e[m+268>>2]))!=32)for(K=J+Ee|0,V=0;;){if(!V){i:{p:if(T=e[A+632>>2])x=!!(0|_r(T,x));else{C:{if((0|(T=e[A+600>>2]))>0){if((x=x-T|0)-1>>>0<255)break C;break i}if((T=x-192|0)>>>0<=413){x=128&d[344+(d[T+94240|0]+A|0)|0];break p}if(x>>>0>255)break i}x=128&d[344+(A+x|0)|0]}x&&(ce=ce+1|0)}x=e[m+268>>2]}i:if(T=e[A+632>>2])V=!!(0|_r(T,x));else{p:{if((0|(T=e[A+600>>2]))>0){if(V=0,(x=x-T|0)-1>>>0<255)break p;break i}if((T=x-192|0)>>>0<=413){V=128&d[344+(d[T+94240|0]+A|0)|0];break i}if(V=0,x>>>0>255)break i}V=128&d[344+(A+x|0)|0]}if(K=jA(m+268|0,K)+K|0,(0|(x=e[m+268>>2]))==32)break}if(!((0|h)>(0|ce))){K=18+(h-Fe|0)|0;break u}}for(;h=d[0|r],r=i=r+1|0,h;);if(d[0|i]!=7)continue a;if(A=s+Wn|0,e[t>>2]=e[t>>2]+(A||1),Nt)break A;break r}h=te,J=Te,T=_,Le=K+Le|0;continue}J=J+Ee|0,V=21-Fe|0;break n}J=J+Ee|0,V=20-Fe|0}h=te,T=_,Le=V+Le|0}}Mt=86135}e[l+12>>2]=bn,e[l+8>>2]=In,e[l+4>>2]=Mt,e[l>>2]=Nt}H=m+384|0}function UA(A,t){var r,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0,Mt=0,Pt=0,Nt=0;H=r=H-1168|0,e[r+928>>2]=0,e[r+932>>2]=0,e[r+920>>2]=0,e[r+924>>2]=0,e[r+912>>2]=0,e[r+916>>2]=0,e[r+904>>2]=0,e[r+908>>2]=0,e[r+896>>2]=0,e[r+900>>2]=0;e:{A:{if(A){if(d[0|A]|8&t)break A;break e}if(!(8&t))break e}if(Lt(r+1088|0,A,40),16&t){if((0|Ns(PA(r+704|0,A)))<=0)break e;K=8&t}else(K=8&t)|d[r+1088|0]||(k[r+1088>>1]=d[85055]|d[85056]<<8,f[r+1090|0]=d[85057]),e[r+496>>2]=137584,e[r+500>>2]=47,e[r+504>>2]=47,dA(s=r+512|0,85286,r+496|0),e[r+484>>2]=r+1088,e[r+480>>2]=s,dA(s=r+704|0,85425,r+480|0),(0|Ns(s))>0||(e[r+468>>2]=47,e[r+472>>2]=47,e[r+464>>2]=137584,dA(s=r+512|0,85648,r+464|0),e[r+452>>2]=r+1088,e[r+448>>2]=s,dA(r+704|0,85425,r+448|0));if(i=K?86012:85055,!(J=us(r+704|0,85712))){if(s=0,3&t)break e;i=(0|qs(s=r+1088|0))<0?i:s}if((V=2&t)||(s=e[47192])&&(Co(s),e[47192]=0),he=PA(r+992|0,i),ce=PA(r+944|0,i),V?((s=Ls(200992,43))&&(f[0|s]=0),e[r+432>>2]=A+3,dA(A=r+704|0,86030,r+432|0),As(200992,A)):(e[32972]=199592,Lt(200992,A,40),f[201088]=0,f[201040]=0,e[50299]=200992,e[50298]=201088,e[50297]=201040),Ke(V),J){for(Te=e[30450],Fe=r+548|0,Le=r+544|0,Xe=r+540|0,fA=r+536|0,hA=r+532|0,_A=r+528|0,LA=12|(A=r+512|0),At=8|A,Mt=4|A;gt(r+704|0,190,J);){A=r+704|0;A:{if(d[r+704|0]!=35){r:if(!((0|(A=MA(r+704|0)-1|0))<=0))for(;;){if(!((0|(i=f[0|(s=(r+704|0)+A|0)]))==32|i-9>>>0<5))break r;if(f[0|s]=0,!((0|(A=A-1|0))>0))break}if(!(A=ve(r+704|0)))break A}f[0|A]=0}A=r+704|0;A:if(s=d[r+704|0])for(;;){if((0|(s=s<<24>>24))==32|s-9>>>0<5)break A;if(!(s=d[0|(A=A+1|0)]))break}if(f[0|A]=0,d[r+704|0])if(A=A+1|0,s=fs(129744,r+704|0)){l=0,H=i=H-416|0;A:if(c=e[47192]){r:switch(s-19|0){case 16:if(e[i+32>>2]=i+412,(0|KA(A,84249,i+32|0))!=1)break A;e[c+324>>2]=e[i+412>>2];break A;case 8:ti(A,c+320|0,27);break A;case 2:if(e[i+48>>2]=188784,KA(A,84249,i+48|0),!(A=d[188784]))break A;e[c+152>>2]=A;break A;case 11:if(d[0|A]){for(g=e[30450];;)if(s=A,A=A+1|0,!((0|(l=f[0|s]))==32|l-9>>>0<5)){for(l=Js(s),e[i+412>>2]=l,(0|l)>0&&(l>>>0<=31?e[c+104>>2]=e[c+104>>2]|1<<l:l>>>0<=63?e[c+108>>2]=e[c+108>>2]|1<<l-32:(e[i+64>>2]=l,Xt(g,84700,i- -64|0)),s=A);s=(A=s)+1|0,(l=f[0|A])-48>>>0<10|(32|l)-97>>>0<26;);if(!l)break}}8&(A=e[c+104>>2])&&(e[c+124>>2]=46,e[c+128>>2]=44),4&A&&(e[c+124>>2]=0);break A;default:if((65280&s)!=256)break A;e[i+16>>2]=24+(c+((255&s)<<2)|0),KA(A,84249,i+16|0);break A;case 1:e[i+144>>2]=c,e[i+148>>2]=c+4,KA(A,85642,i+144|0);break A;case 3:if(s=0,Je(l=i+160|0,0,240),e[i+132>>2]=i+360,e[i+128>>2]=i+320,e[i+124>>2]=i+280,e[i+120>>2]=i+240,e[i+116>>2]=i+200,e[i+112>>2]=l,l=KA(A,85037,i+112|0),e[i+412>>2]=l,e[c+152>>2]=0,(0|l)<=0)break A;for(h=e[30450];;){a:if(Ar(g=(i+160|0)+O(s,40)|0,85301)){n:{if((0|(I=e[34454]))>0)for(_=e[34455],A=0;;){if(!Ar(g,_+O(A,68)|0))break n;if((0|I)==(0|(A=A+1|0)))break}e[i+96>>2]=g,Xt(h,85562,i+96|0),l=e[i+412>>2];break a}f[156+(s+c|0)|0]=A}if(!((0|l)>(0|(s=s+1|0))))break}break A;case 9:e[i+88>>2]=c+20,e[i+84>>2]=c+16,e[i+80>>2]=c+8,KA(A,84778,i+80|0);break A;case 10:ti(A,c+12|0,29);break A;case 5:if((0|(h=Va(A,i+160|0)))<=0)break A;if(s=0,A=0,h>>>0>=4)for(_=-4&h,g=c+304|0;I=i+160|0,k[g+(A<<1)>>1]=e[I+(A<<2)>>2],k[g+((m=1|A)<<1)>>1]=e[I+(m<<2)>>2],k[g+((m=2|A)<<1)>>1]=e[I+(m<<2)>>2],k[g+((m=3|A)<<1)>>1]=e[I+(m<<2)>>2],A=A+4|0,(0|_)!=(0|(l=l+4|0)););if(!(l=3&h))break A;for(;k[304+(c+(A<<1)|0)>>1]=e[(i+160|0)+(A<<2)>>2],A=A+1|0,(0|l)!=(0|(s=s+1|0)););break A;case 6:if((0|(h=Va(A,i+160|0)))<=0)break A;if(s=0,A=0,h>>>0>=4)for(_=-4&h,g=c+296|0;I=i+160|0,f[A+g|0]=e[I+(A<<2)>>2],f[(m=1|A)+g|0]=e[I+(m<<2)>>2],f[(m=2|A)+g|0]=e[I+(m<<2)>>2],f[(m=3|A)+g|0]=e[I+(m<<2)>>2],A=A+4|0,(0|_)!=(0|(l=l+4|0)););if(!(l=3&h))break A;for(;f[296+(A+c|0)|0]=e[(i+160|0)+(A<<2)>>2],A=A+1|0,(0|l)!=(0|(s=s+1|0)););break A;case 7:if((0|(h=Va(A,i+160|0)))<=0)break A;if(s=0,A=0,h>>>0>=4)for(_=-4&h,g=c+304|0;m=I=g+(A<<1)|0,x=y[I>>1],I=i+160|0,k[m>>1]=x+y[I+(A<<2)>>1],k[(x=g+((m=1|A)<<1)|0)>>1]=y[x>>1]+y[I+(m<<2)>>1],k[(x=g+((m=2|A)<<1)|0)>>1]=y[x>>1]+y[I+(m<<2)>>1],k[(x=g+((m=3|A)<<1)|0)>>1]=y[x>>1]+y[I+(m<<2)>>1],A=A+4|0,(0|_)!=(0|(l=l+4|0)););if(!(l=3&h))break A;for(;k[(g=c+(A<<1)|0)+304>>1]=y[g+304>>1]+y[(i+160|0)+(A<<2)>>1],A=A+1|0,(0|l)!=(0|(s=s+1|0)););break A;case 4:f[c+169|0]=1;break A;case 0:break r}f[c+208|0]=1}else e[i>>2]=G(129568,s),Xt(e[30450],89101,i);H=i+416|0}else{A:switch(fs(131904,r+704|0)-1|0){case 1:if(V||(f[r+1040|0]=0,e[r+512>>2]=5,e[r+32>>2]=r+1040,e[r+36>>2]=r+512,KA(A,86237,r+32|0),e[r+1040>>2]==1769103734&e[r+1044>>2]==7630433))continue;if((A=MA(r+1040|0)+2|0)>>>0<99-te>>>0&&(f[0|(s=te+201088|0)]=e[r+512>>2],PA(s+1|0,r+1040|0),te=A+te|0),!Pt){if(A=0,(s=g=r+1040|0)||(s=e[57150])){if(A=86875,e[(i=H-32|0)+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+8>>2]=0,e[i+12>>2]=0,e[i>>2]=0,e[i+4>>2]=0,c=0,l=d[86875])if(d[86876]){for(;e[(c=i+(l>>>3&28)|0)>>2]=e[c>>2]|1<<l,l=d[A+1|0],A=A+1|0,l;);r:if(l=d[0|(A=s)])for(;;){if(!(e[i+(l>>>3&28)>>2]>>>l&1))break r;if(l=d[A+1|0],A=A+1|0,!l)break}c=A-s|0}else{for(A=s;i=A,A=A+1|0,d[0|i]==(0|l););c=i-s|0}if(d[0|(A=c+s|0)]){s=86875,H=l=H-32|0,i=f[86875];r:if(d[86876]&&i){if(Je(l,0,32),i=d[86875])for(;e[(c=l+(i>>>3&28)|0)>>2]=e[c>>2]|1<<i,i=d[s+1|0],s=s+1|0,i;);if(s=A,i=d[0|A])for(;;){if(e[l+(i>>>3&28)>>2]>>>i&1)break r;if(i=d[s+1|0],s=s+1|0,!i)break}}else s=Dn(A,i);H=l+32|0,d[0|(s=(s-A|0)+A|0)]?(e[57150]=s+1,f[0|s]=0):e[57150]=0}else e[57150]=0,A=0}s=PA(he,A),PA(ce,A),qs(PA(r+896|0,A)),e[47192]=FA(s),Lt(e[32972]+40|0,g,20)}Pt=1;continue;case 0:if(V)continue;for(;s=A,A=A+1|0,(0|(i=f[0|s]))==32|i-9>>>0<5;);Lt(201040,s,40);continue;case 2:e[r+1152>>2]=0,s=r+512|0,e[r+48>>2]=s,e[r+52>>2]=r+1152,KA(A,86237,r+48|0),f[201200]=fs(132112,s),f[201201]=e[r+1152>>2];continue;case 4:e[r+64>>2]=ce,KA(A,86939,r- -64|0);continue;case 3:e[r+80>>2]=r+896,KA(A,86939,r+80|0);continue;case 8:if(e[r+1152>>2]=100,e[r+1164>>2]=100,e[r+1148>>2]=100,e[r+112>>2]=r+1144,e[r+1144>>2]=0,e[r+96>>2]=r+512,e[r+100>>2]=r+1152,e[r+104>>2]=r+1164,e[r+108>>2]=r+1148,(0|KA(A,91156,r+96|0))<2||(A=e[r+512>>2])>>>0>8||((0|(s=e[r+1152>>2]))>=0&&(i=e[32972]+(A<<1)|0,s=ee(T=2.56001*+(0|s))<2147483648?~~T:-2147483648,k[i+236>>1]=s,k[i+164>>1]=s),(0|(s=e[r+1164>>2]))>=0&&(i=e[32972]+(A<<1)|0,s=ee(T=2.56001*+(0|s))<2147483648?~~T:-2147483648,k[i+254>>1]=s,k[i+182>>1]=s),(0|(i=e[r+1148>>2]))<0?s=e[32972]:(g=(s=e[32972])+(A<<1)|0,i=ee(T=2.56001*+(0|i))<2147483648?~~T:-2147483648,k[g+200>>1]=i),k[218+((A<<1)+s|0)>>1]=e[r+1144>>2],A))continue;k[s+200>>1]=(0|O(k[s+200>>1],105))/100;continue;case 9:if(e[r+132>>2]=r+696,e[r+128>>2]=r+700,(0|KA(A,87106,r+128|0))!=2)continue;if(A=e[32972],s=e[r+700>>2],e[A+64>>2]=(s<<12)-36864,e[A+68>>2]=O(e[r+696>>2]-s|0,108),ee(T=256*(+(s-82|0)/82*.25+1))<2147483648){e[A+116>>2]=~~T;continue}e[A+116>>2]=-2147483648;continue;case 35:Nt||qs(r+896|0),e[r+1164>>2]=0,f[r+1156|0]=d[91267],e[r+1152>>2]=d[91263]|d[91264]<<8|d[91265]<<16|d[91266]<<24,e[r+144>>2]=r+1164,e[r+148>>2]=r+512,e[r+152>>2]=r+1152,(0|KA(A,91302,r+144|0))<2|e[49848]>59||(A=Fn(r+512|0))&&(f[O(e[49848],3)+199408|0]=A,A=Fn(r+1152|0),s=e[49848],i=O(s,3)+199408|0,f[i+1|0]=A,e[49848]=s+1,f[i+2|0]=e[r+1164>>2]),Nt=1;continue;case 10:e[r+1140>>2]=0,s=e[32972],e[s+100>>2]=0,e[r+164>>2]=s+100,e[r+160>>2]=s+96,KA(A,87106,r+160|0);continue;case 11:if(e[r+176>>2]=r+1140,(0|KA(A,87268,r+176|0))!=1)continue;e[e[32972]+88>>2]=e[r+1140>>2]<<5;continue;case 12:if(e[r+192>>2]=r+1140,(0|KA(A,87268,r+192|0))!=1)continue;e[e[32972]+92>>2]=e[r+1140>>2];continue;case 13:if(e[r+208>>2]=r+1140,(0|KA(A,87268,r+208|0))!=1)continue;s=e[32972],(0|(A=e[r+1140>>2]))>=5&&(e[s+108>>2]=1,e[r+1140>>2]=4,A=4),e[s+104>>2]=A+1;continue;case 14:for(e[r+552>>2]=-1,e[r+556>>2]=-1,e[r+544>>2]=-1,e[r+548>>2]=-1,e[r+536>>2]=-1,e[r+540>>2]=-1,e[r+528>>2]=-1,e[r+532>>2]=-1,e[r+240>>2]=_A,e[r+244>>2]=hA,e[r+248>>2]=fA,e[r+252>>2]=Xe,e[r+256>>2]=Le,e[r+260>>2]=Fe,e[r+520>>2]=-1,e[r+524>>2]=-1,e[r+512>>2]=-1,e[r+516>>2]=-1,e[r+228>>2]=Mt,e[r+232>>2]=At,e[r+236>>2]=LA,e[r+224>>2]=r+512,KA(A,84222,r+224|0),_=e[32972],A=0,l=e[r+516>>2],i=0;;){if(s=i,c=l,g=A,(0|(i=e[(l=(A<<=2)+(r+512|0)|0)>>2]))==-1&&(i=8e3,e[l>>2]=8e3,g&&(e[(r+512|0)+(4|A)>>2]=e[508+(A+r|0)>>2])),l=e[(r+512|0)+(4|A)>>2],!((0|s)>=(0|(i=(0|i)/8|0))||(0|(I=i-s|0))<=0||(h=s+1|0,A=s,1&I&&(f[344+(s+_|0)|0]=(0|c)>=255?255:c,A=h),(0|i)==(0|h))))for(h=l-c|0;x=_+344|0,m=c+((0|O(h,A-s|0))/(0|I)|0)|0,f[x+A|0]=(0|m)>=255?255:m,m=c+((0|O(h,(Ee=A+1|0)-s|0))/(0|I)|0)|0,f[x+Ee|0]=(0|m)>=255?255:m,(0|i)!=(0|(A=A+2|0)););if(A=g+2|0,!(g>>>0<10))break}continue;case 15:if(e[r+272>>2]=r+1140,(0|KA(A,87268,r+272|0))!=1)continue;e[e[32972]+112>>2]=(e[r+1140>>2]<<6)/100;continue;case 16:s=e[32972],e[(i=s+300|0)>>2]=0,e[i+4>>2]=0,e[(l=s+292|0)>>2]=0,e[l+4>>2]=0,e[(c=s+284|0)>>2]=0,e[c+4>>2]=0,e[(g=s+276|0)>>2]=0,e[g+4>>2]=0,e[r+316>>2]=s+304,e[r+312>>2]=i,e[r+308>>2]=s+296,e[r+304>>2]=l,e[r+300>>2]=s+288,e[r+296>>2]=c,e[r+292>>2]=s+280,e[r+288>>2]=g,s=KA(A,84553,r+288|0),A=e[32972],e[A+272>>2]=s,e[A+276>>2]=0-e[A+276>>2],e[A+284>>2]=0-e[A+284>>2],e[A+292>>2]=0-e[A+292>>2],e[A+300>>2]=0-e[A+300>>2];continue;case 17:s=e[32972],e[(i=s+336|0)>>2]=0,e[i+4>>2]=0,e[(l=s+328|0)>>2]=0,e[l+4>>2]=0,e[(c=s+320|0)>>2]=0,e[c+4>>2]=0,e[(g=s+312|0)>>2]=0,e[g+4>>2]=0,e[r+348>>2]=s+340,e[r+344>>2]=i,e[r+340>>2]=s+332,e[r+336>>2]=l,e[r+332>>2]=s+324,e[r+328>>2]=c,e[r+324>>2]=s+316,e[r+320>>2]=g,A=KA(A,84553,r+320|0),e[e[32972]+308>>2]=A;continue;case 36:s=e[32972],e[r+352>>2]=s+120,e[r+356>>2]=s+124,e[r+1140>>2]=KA(A,87106,r+352|0);continue;case 33:e[r+368>>2]=e[32972]+84,KA(A,87268,r+368|0),ws(3);continue;case 31:s=e[32972],e[(i=s+156|0)>>2]=0,e[i+4>>2]=0,e[(l=s+148|0)>>2]=0,e[l+4>>2]=0,e[(c=s+140|0)>>2]=0,e[c+4>>2]=0,e[(g=s+132|0)>>2]=0,e[g+4>>2]=0,e[r+412>>2]=s+160,e[r+408>>2]=i,e[r+404>>2]=s+152,e[r+400>>2]=l,e[r+396>>2]=s+144,e[r+392>>2]=c,e[r+388>>2]=s+136,e[r+384>>2]=g,KA(A,84553,r+384|0),A=e[32972],e[A+152>>2]=e[A+152>>2]-40;continue;case 32:e[r+416>>2]=145740,KA(A,87268,r+416|0),ws(3);continue;case 6:case 7:continue;default:break A}e[r+16>>2]=r+704,Xt(Te,87359,r+16|0)}}Er(J)}A:{if((A=e[47192])|V){if(V)break A}else A=FA(he),e[47192]=A;r:{if(!K){if((0|(A=qs(r+896|0)))<0&&(e[r>>2]=r+896,Xt(e[30450],87567,r),A=0),e[e[32972]+60>>2]=A,s=e[47192],e[s+292>>2]=A,GA(s,ce,4&t),d[132848])break r;Co(e[47192]),s=0;break e}e[e[32972]+60>>2]=0,e[A+292>>2]=0}f[te+201088|0]=0}s=e[32972]}return H=r+1168|0,s}function KA(A,t,r){var s,i,l,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0;H=l=H-16|0,e[l+12>>2]=r,H=c=H-144|0,s=Je(c,0,144),e[s+76>>2]=-1,e[s+44>>2]=A,e[s+32>>2]=18,e[s+84>>2]=A,c=t,Fe=r,A=0,H=i=H-304|0;e:{A:{r:if(e[s+4>>2]||(cn(s),e[s+4>>2])){if(!(t=d[0|c]))break e;a:{n:{o:{c:{for(;;){u:{l:if((0|(t&=255))==32|t-9>>>0<5){for(;t=c,c=c+1|0,(0|(r=d[t+1|0]))==32|r-9>>>0<5;);for(ys(s,0,0);(0|(r=e[s+4>>2]))==e[s+104>>2]?r=Ie(s):(e[s+4>>2]=r+1,r=d[0|r]),(0|r)==32|r-9>>>0<5;);c=e[s+4>>2],(0|(r=e[s+116>>2]))>0|(0|r)>=0&&(c=c-1|0,e[s+4>>2]=c),c=r=c-e[s+44>>2]|0,h=te+e[s+124>>2]|0,h=(g=r>>31)+((r=V+e[s+120>>2]|0)>>>0<V>>>0?h+1|0:h)|0,te=(V=r+c|0)>>>0<r>>>0?h+1|0:h}else{i:{p:{C:{if(d[0|c]==37){if((0|(t=d[c+1|0]))==42)break C;if((0|t)!=37)break p}if(ys(s,0,0),d[0|c]!=37)(0|(t=e[s+4>>2]))==e[s+104>>2]?t=Ie(s):(e[s+4>>2]=t+1,t=d[0|t]);else{for(;(0|(t=e[s+4>>2]))==e[s+104>>2]?t=Ie(s):(e[s+4>>2]=t+1,t=d[0|t]),(0|t)==32|t-9>>>0<5;);c=c+1|0}if(d[0|c]!=(0|t)){if((0|(r=e[s+116>>2]))>0|(0|r)>=0&&(e[s+4>>2]=e[s+4>>2]-1),(0|t)>=0||(m=0,Le))break e;break r}I=(g=t=e[s+4>>2]-e[s+44>>2]|0)>>31,t=te+e[s+124>>2]|0,m=((r=V+e[s+120>>2]|0)>>>0<V>>>0?t+1|0:t)+I|0,te=(V=r+g|0)>>>0<r>>>0?m+1|0:m,t=c;break l}x=0,t=c+2|0;break i}d[c+2|0]!=36|t-48>>>0>=10?(x=e[Fe>>2],Fe=Fe+4|0,t=c+1|0):(t=d[c+1|0]-48|0,e[(r=H-16|0)+12>>2]=Fe,t=(t>>>0>1?(t<<2)-4|0:0)+Fe|0,e[r+8>>2]=t+4,x=e[t>>2],t=c+3|0)}if(T=0,c=0,d[0|t]-48>>>0<10)for(;c=(d[0|t]+O(c,10)|0)-48|0,r=d[t+1|0],t=t+1|0,r-48>>>0<10;);(0|(K=d[0|t]))==109&&(_=0,T=!!(0|x),K=d[t+1|0],A=0,t=t+1|0),t=(r=t)+1|0,g=3,m=T;i:{p:switch(K-65|0){case 39:g=r+2|0,t=(r=d[r+1|0]==104)?g:t,g=r?-2:-1;break i;case 43:g=r+2|0,t=(r=d[r+1|0]==108)?g:t,g=r?3:1;break i;case 51:case 57:g=1;break i;case 11:g=2;break i;case 41:break i;case 0:case 2:case 4:case 5:case 6:case 18:case 23:case 26:case 32:case 34:case 35:case 36:case 37:case 38:case 40:case 45:case 46:case 47:case 50:case 52:case 55:break p;default:break a}g=0,t=r}m=g,Ee=(g=(47&(r=d[0|t]))==3)?1:m;i:if((0|(he=g?32|r:r))!=91){p:{if((0|he)!=110){if((0|he)!=99)break p;c=(0|c)<=1?1:c;break i}zn(x,Ee,V,te);break l}for(ys(s,0,0);(0|(r=e[s+4>>2]))==e[s+104>>2]?r=Ie(s):(e[s+4>>2]=r+1,r=d[0|r]),(0|r)==32|r-9>>>0<5;);r=e[s+4>>2],(0|(g=e[s+116>>2]))>0|(0|g)>=0&&(r=r-1|0,e[s+4>>2]=r),g=r=r-e[s+44>>2]|0,h=te+e[s+124>>2]|0,te=(m=r>>31)+((r=V+e[s+120>>2]|0)>>>0<V>>>0?h+1|0:h)|0,te=(V=r+g|0)>>>0<r>>>0?te+1|0:te}if(J=c,ys(s,c,ce=c>>31),(0|(r=e[s+4>>2]))==e[s+104>>2]){if((0|Ie(s))<0)break n}else e[s+4>>2]=r+1;(0|(r=e[s+116>>2]))>0|(0|r)>=0&&(e[s+4>>2]=e[s+4>>2]-1),r=16;i:{p:{C:{h:{b:switch(he-88|0){default:if((r=he-65|0)>>>0>6|!(1<<r&113))break i;case 9:case 13:case 14:case 15:if(TA(i+8|0,s,Ee,0),r=e[s+4>>2]-e[s+44>>2]|0,e[s+120>>2]!=(0-r|0)|e[s+124>>2]!=(0-((r>>31)+!!(0|r)|0)|0))break C;break o;case 3:case 11:case 27:if((16|he)==115){if(Je(i+32|0,-1,257),f[i+32|0]=0,(0|he)!=115)break p;f[i+65|0]=0,f[i+46|0]=0,k[i+42>>1]=0,k[i+44>>1]=0;break p}Je(i+32|0,I=(0|(g=d[t+1|0]))==94,257),f[i+32|0]=0,r=I?t+2|0:t+1|0;m:{x:{I:{if((0|(t=d[(I?2:1)+t|0]))!=45){if((0|t)==93)break I;g=(0|g)!=94,t=r;break m}g=(0|g)!=94,f[i+78|0]=g;break x}g=(0|g)!=94,f[i+126|0]=g}t=r+1|0}for(;;){if((0|(r=d[0|t]))==45){if(r=45,!(!(I=d[t+1|0])|(0|I)==93)){if(m=t+1|0,I>>>0<=(t=d[t-1|0])>>>0)r=I;else for(;f[(t=t+1|0)+(i+32|0)|0]=g,(r=d[0|m])>>>0>t>>>0;);t=m}}else{if(!r)break n;if((0|r)==93)break p}f[33+(r+i|0)|0]=g,t=t+1|0}case 23:r=8;break h;case 12:case 29:r=10;break h;case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 10:case 16:case 18:case 19:case 20:case 21:case 22:case 25:case 26:case 28:case 30:case 31:break i;case 0:case 24:case 32:break h;case 17:break b}r=0}I=0,h=0,g=0,m=0,K=0,H=Te=H-16|0;h:if((0|r)!=1&r>>>0<=36){for(;(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),(0|c)==32|c-9>>>0<5;);b:{m:switch(c-43|0){case 0:case 2:break m;default:break b}K=(0|c)==45?-1:0,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c])}b:{m:{x:{I:{if(!(!!(0|r)&(0|r)!=16|(0|c)!=48)){if((0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),(-33&c)==88){if(r=16,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),d[c+121329|0]<16)break x;(0|(r=e[s+116>>2]))>0|(0|r)>=0&&(e[s+4>>2]=e[s+4>>2]-1),ys(s,0,0);break h}if(r)break I;r=8;break x}if(!((r=r||10)>>>0>d[c+121329|0])){(0|(r=e[s+116>>2]))>0|(0|r)>=0&&(e[s+4>>2]=e[s+4>>2]-1),ys(s,0,0),e[56798]=28;break h}}if((0|r)==10){if((g=c-48|0)>>>0<=9){for(r=0;m=(r=O(r,10)+g|0)>>>0<429496729,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),m&(g=c-48|0)>>>0<=9;);I=r}I:if(!(g>>>0>9)){for(r=st(I,0,10,0),m=le;;){if(h=m,m=(0|(h=(I=r+g|0)>>>0<g>>>0?h+1|0:h))==429496729&I>>>0>=2576980378|h>>>0>429496729,(0|(r=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=r+1,c=d[0|r]),m|(g=c-48|0)>>>0>9)break I;if(r=st(I,h,10,0),!((0|(m=le))==-1&~g>>>0>=r>>>0|(0|m)!=-1))break}r=10;break m}if(r=10,g>>>0<=9)break m;break b}}if(r-1&r){if((m=d[c+121329|0])>>>0<r>>>0){for(;I=(g=O(r,g)+m|0)>>>0<119304647,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),I&(m=d[c+121329|0])>>>0<r>>>0;);I=g}if(r>>>0<=m>>>0)break m;for(;;){if(g=st(I,h,r,0),(0|(J=le))==-1&~(m&=255)>>>0<g>>>0||(h=J,h=(I=g+m|0)>>>0<m>>>0?h+1|0:h,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),r>>>0<=(m=d[c+121329|0])>>>0))break m;if(Fr(Te,r,0,0,0,I,h,0,0),e[Te+8>>2]|e[Te+12>>2])break}}else{if(J=f[84400+(O(r,23)>>>5&7)|0],(g=d[c+121329|0])>>>0<r>>>0){for(;I=(m=m<<J|g)>>>0<134217728,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),I&(g=d[c+121329|0])>>>0<r>>>0;);I=m}if(!(r>>>0<=g>>>0||(ce=31&J,(63&J)>>>0>=32?(m=0,ce=-1>>>ce|0):ce=(m=-1>>>ce|0)|(1<<ce)-1<<32-ce,!m&I>>>0>ce>>>0)))for(;;){if(Xe=255&g,g=I,c=31&J,(63&J)>>>0>=32?(h=g<<c,c=0):(h=(1<<c)-1&g>>>32-c|h<<c,c=g<<c),I=Xe|c,(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),r>>>0<=(g=d[c+121329|0])>>>0)break m;if(!((0|m)==(0|h)&I>>>0<=ce>>>0|m>>>0>h>>>0))break}}}if(!(d[c+121329|0]>=r>>>0)){for(;(0|(c=e[s+4>>2]))==e[s+104>>2]?c=Ie(s):(e[s+4>>2]=c+1,c=d[0|c]),d[c+121329|0]<r>>>0;);e[56798]=68,K=0,I=-1,h=-1}}(0|(r=e[s+116>>2]))>0|(0|r)>=0&&(e[s+4>>2]=e[s+4>>2]-1),I=(r=I^K)-K|0,h=((c=K>>31)^h)-((r>>>0<K>>>0)+c|0)|0}else e[56798]=28;if(H=Te+16|0,r=e[s+4>>2]-e[s+44>>2]|0,e[s+120>>2]==(0-r|0)&e[s+124>>2]==(0-((r>>31)+!!(0|r)|0)|0))break o;if(!(!x|(0|he)!=112)){e[x>>2]=I;break i}zn(x,Ee,I,h);break i}if(!x)break i;c=e[i+16>>2],r=e[i+20>>2],g=e[i+8>>2],T=e[i+12>>2];C:switch(0|Ee){case 0:H=h=H-32|0;h:if(0|(m=(I=2147483647&r)-1065418752|0)>>>0<(J=I-1082064896|0)>>>0){if(I=(33554431&r)<<7|c>>>25,J=m=0,!(!m&(0|(c&=33554431))==16777216?!(g|T):!m&c>>>0<16777216)){m=I+1073741825|0;break h}if(m=I+1073741824|0,16777216^c|g|T|J)break h;m=(1&I)+m|0}else(!c&(0|I)==2147418112?!(g|T):I>>>0<2147418112)?(m=2139095040,I>>>0>1082064895||(m=0,(I=I>>>16|0)>>>0<16145||(vt(h+16|0,g,T,c,m=65535&r|65536,I-16129|0),cr(h,g,T,c,m,16257-I|0),c=e[h+8>>2],m=(33554431&e[h+12>>2])<<7|c>>>25,I=e[h>>2]|!!(e[h+16>>2]|e[h+24>>2]|e[h+20>>2]|e[h+28>>2]),T=e[h+4>>2],(!(g=0)&(0|(c&=33554431))==16777216?!(I|T):!g&c>>>0<16777216)?16777216^c|I|g|T||(m=(1&m)+m|0):m=m+1|0))):m=4194303&((33554431&r)<<7|c>>>25)|2143289344;H=h+32|0,e[x>>2]=-2147483648&r|m;break i;case 1:P[x>>3]=Us(g,T,c,r);break i;case 2:break C;default:break i}e[x>>2]=g,e[x+4>>2]=T,e[x+8>>2]=c,e[x+12>>2]=r;break i}g=(Te=(0|he)!=99)?31:c+1|0;p:if((0|Ee)!=1){if(T){if(c=0,!(r=HA(g)))break c;for(;;){for(A=r;;){if((0|(r=e[s+4>>2]))==e[s+104>>2]?r=Ie(s):(e[s+4>>2]=r+1,r=d[0|r]),!d[33+(r+i|0)|0]){g=0,_=A;break p}if(f[A+c|0]=r,(0|g)==(0|(c=c+1|0)))break}if(m=1,!(r=lt(A,g=g<<1|1)))break}_=A,A=0;break a}if(c=0,x)for(;;){if((0|(A=e[s+4>>2]))==e[s+104>>2]?A=Ie(s):(e[s+4>>2]=A+1,A=d[0|A]),!d[33+(A+i|0)|0]){g=0,_=A=x;break p}f[c+x|0]=A,c=c+1|0}for(;(0|(A=e[s+4>>2]))==e[s+104>>2]?A=Ie(s):(e[s+4>>2]=A+1,A=d[0|A]),d[33+(A+i|0)|0];);A=0,_=0,g=0}else{if(r=x,T&&!(r=HA(g<<2)))break c;for(e[i+296>>2]=0,e[i+300>>2]=0,c=0;;){A=r;C:{for(;;){if((0|(r=e[s+4>>2]))==e[s+104>>2]?r=Ie(s):(e[s+4>>2]=r+1,r=d[0|r]),!d[33+(r+i|0)|0])break C;f[i+27|0]=r,I=i+28|0,r=e[(m=(r=i+296|0)||228604)>>2];h:{b:{m:{x:{if(!(_=i+27|0)){if(r)break x;r=0;break h}if(!r){if((0|(h=(r=d[0|_])<<24>>24))>=0){I&&(e[I>>2]=r),r=!!(0|h);break h}if(!e[e[56841]>>2]){if(r=1,!I)break m;e[I>>2]=57343&h,r=1;break h}if((r=r-194|0)>>>0>50)break x;r=e[124752+(r<<2)>>2];break b}if(K=1,!(((Ee=(h=d[0|_])>>>3|0)-16|(r>>26)+Ee)>>>0>7))for(;;){if(K=K-1|0,(0|(r=h-128|r<<6))>=0){e[m>>2]=0,I&&(e[I>>2]=r),r=1-K|0;break h}if(!K)break b;if((192&(h=d[0|(_=_+1|0)]))!=128)break}}e[m>>2]=0,e[56798]=25,r=-1}break h}e[m>>2]=r,r=-2}if((0|r)!=-2){if(_=0,(0|r)==-1)break n;if(A&&(e[(c<<2)+A>>2]=e[i+28>>2],c=c+1|0),!(!T|(0|c)!=(0|g)))break}}if(m=1,r=lt(A,(g=g<<1|1)<<2))continue;break a}break}if(_=0,g=A,i+296|0&&e[i+296>>2])break n}if(r=e[s+4>>2],(0|(I=e[s+116>>2]))>0|(0|I)>=0&&(r=r-1|0,e[s+4>>2]=r),r=(I=r-e[s+44>>2]|0)+e[s+120>>2]|0,h=e[s+124>>2]+(I>>31)|0,!((h=r>>>0<I>>>0?h+1|0:h)|r)|!(Te|(0|r)==(0|J)&(0|h)==(0|ce)))break u;T&&(e[x>>2]=A),(0|he)!=99&&(g&&(e[(c<<2)+g>>2]=0),_?f[c+_|0]=0:_=0),A=g}c=r=e[s+4>>2]-e[s+44>>2]|0,m=te+e[s+124>>2]|0,te=(g=r>>31)+((r=V+e[s+120>>2]|0)>>>0<V>>>0?m+1|0:m)|0,te=(V=r+c|0)>>>0<r>>>0?te+1|0:te,Le=!!(0|x)+Le|0}if(c=t+1|0,t=d[t+1|0])continue;break e}break}A=g;break o}m=1,_=0,A=0;break a}m=T;break A}m=T}if(Le)break A}Le=-1}m&&(fe(_),fe(A))}return H=i+304|0,H=s+144|0,H=l+16|0,Le}function HA(A){var t,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0;H=t=H-16|0;e:{A:{r:{a:{n:{o:{c:{u:{l:{if((A|=0)>>>0<=244){if(3&(r=(g=e[57152])>>>(s=(m=A>>>0<11?16:A+11&-8)>>>3|0)|0)){r=(A=(s=s+(1&~r)|0)<<3)+228648|0,i=e[A+228656>>2],(0|r)!=(0|(A=e[i+8>>2]))?(e[A+12>>2]=r,e[r+8>>2]=A):e[57152]=es(-2,s)&g,A=i+8|0,r=s<<3,e[i+4>>2]=3|r,e[(r=r+i|0)+4>>2]=1|e[r+4>>2];break e}if((x=e[57154])>>>0>=m>>>0)break l;if(r){r=(A=(i=fi(0-(A=(0-(A=2<<s)|A)&r<<s)&A))<<3)+228648|0,l=e[A+228656>>2],(0|r)!=(0|(A=e[l+8>>2]))?(e[A+12>>2]=r,e[r+8>>2]=A):(g=es(-2,i)&g,e[57152]=g),e[l+4>>2]=3|m,i=(A=i<<3)-m|0,e[(s=l+m|0)+4>>2]=1|i,e[A+l>>2]=i,x&&(r=228648+(-8&x)|0,c=e[57157],(A=1<<(x>>>3))&g?A=e[r+8>>2]:(e[57152]=A|g,A=r),e[r+8>>2]=c,e[A+12>>2]=c,e[c+12>>2]=r,e[c+8>>2]=A),A=l+8|0,e[57157]=s,e[57154]=i;break e}if(!(h=e[57153]))break l;for(s=e[228912+(fi(0-h&h)<<2)>>2],c=(-8&e[s+4>>2])-m|0,r=s;(A=e[r+16>>2])||(A=e[r+20>>2]);)c=(i=(r=(-8&e[A+4>>2])-m|0)>>>0<c>>>0)?r:c,s=i?A:s,r=A;if(I=e[s+24>>2],(0|(i=e[s+12>>2]))!=(0|s)){A=e[s+8>>2],e[A+12>>2]=i,e[i+8>>2]=A;break A}if(!(A=e[(r=s+20|0)>>2])){if(!(A=e[s+16>>2]))break u;r=s+16|0}for(;l=r,i=A,(A=e[(r=A+20|0)>>2])||(r=i+16|0,A=e[i+16>>2]););e[l>>2]=0;break A}if(m=-1,!(A>>>0>4294967231)&&(m=-8&(A=A+11|0),h=e[57153])){c=0-m|0,g=0,m>>>0<256||(g=31,m>>>0>16777215||(g=62+((m>>>38-(A=be(A>>>8|0))&1)-(A<<1)|0)|0));i:{p:{if(r=e[228912+(g<<2)>>2])for(A=0,s=m<<((0|g)!=31?25-(g>>>1|0):0);;){if(!((l=(-8&e[r+4>>2])-m|0)>>>0>=c>>>0||(i=r,c=l,l))){c=0,A=r;break p}if(l=e[r+20>>2],r=e[16+((s>>>29&4)+r|0)>>2],A=l?(0|l)==(0|r)?A:l:A,s<<=1,!r)break}else A=0;if(!(A|i)){if(i=0,!(A=(0-(A=2<<g)|A)&h))break l;A=e[228912+(fi(A&0-A)<<2)>>2]}if(!A)break i}for(;c=(s=(r=(-8&e[A+4>>2])-m|0)>>>0<c>>>0)?r:c,i=s?A:i,A=(r=e[A+16>>2])||e[A+20>>2];);}if(!(!i|e[57154]-m>>>0<=c>>>0)){if(g=e[i+24>>2],(0|i)!=(0|(s=e[i+12>>2]))){A=e[i+8>>2],e[A+12>>2]=s,e[s+8>>2]=A;break r}if(!(A=e[(r=i+20|0)>>2])){if(!(A=e[i+16>>2]))break c;r=i+16|0}for(;l=r,s=A,(A=e[(r=A+20|0)>>2])||(r=s+16|0,A=e[s+16>>2]););e[l>>2]=0;break r}}}if((A=e[57154])>>>0>=m>>>0){i=e[57157],(r=A-m|0)>>>0>=16?(e[(s=i+m|0)+4>>2]=1|r,e[A+i>>2]=r,e[i+4>>2]=3|m):(e[i+4>>2]=3|A,e[(A=A+i|0)+4>>2]=1|e[A+4>>2],s=0,r=0),e[57154]=r,e[57157]=s,A=i+8|0;break e}if((I=e[57155])>>>0>m>>>0){r=I-m|0,e[57155]=r,A=(s=e[57158])+m|0,e[57158]=A,e[A+4>>2]=1|r,e[s+4>>2]=3|m,A=s+8|0;break e}if(A=0,h=m+47|0,e[57270]?s=e[57272]:(e[57273]=-1,e[57274]=-1,e[57271]=4096,e[57272]=4096,e[57270]=t+12&-16^1431655768,e[57275]=0,e[57263]=0,s=4096),(r=(l=h+s|0)&(c=0-s|0))>>>0<=m>>>0||(i=e[57262])&&i>>>0<(g=(s=e[57260])+r|0)>>>0|s>>>0>=g>>>0)break e;l:{if(!(4&d[229052])){i:{p:{C:{h:{if(i=e[57158])for(A=229056;;){if((s=e[A>>2])>>>0<=i>>>0&i>>>0<s+e[A+4>>2]>>>0)break h;if(!(A=e[A+8>>2]))break}if((0|(s=re(0)))==-1||(g=r,(A=(i=e[57271])-1|0)&s&&(g=(r-s|0)+(A+s&0-i)|0),g>>>0<=m>>>0)||(i=e[57262])&&i>>>0<(c=(A=e[57260])+g|0)>>>0|A>>>0>=c>>>0)break i;if((0|s)!=(0|(A=re(g))))break C;break l}if((0|(s=re(g=c&l-I)))==(e[A>>2]+e[A+4>>2]|0))break p;A=s}if((0|A)==-1)break i;if(m+48>>>0<=g>>>0){s=A;break l}if((0|re(s=(s=e[57272])+(h-g|0)&0-s))==-1)break i;g=s+g|0,s=A;break l}if((0|s)!=-1)break l}e[57263]=4|e[57263]}if((0|(s=re(r)))==-1|(0|(A=re(0)))==-1|A>>>0<=s>>>0||(g=A-s|0)>>>0<=m+40>>>0)break a}A=e[57260]+g|0,e[57260]=A,A>>>0>Ae[57261]&&(e[57261]=A);l:{if(l=e[57158]){for(A=229056;;){if(((i=e[A>>2])+(r=e[A+4>>2])|0)==(0|s))break l;if(!(A=e[A+8>>2]))break}break o}for((A=e[57156])>>>0<=s>>>0&&A||(e[57156]=s),A=0,e[57265]=g,e[57264]=s,e[57160]=-1,e[57161]=e[57270],e[57267]=0;r=(i=A<<3)+228648|0,e[i+228656>>2]=r,e[i+228660>>2]=r,(0|(A=A+1|0))!=32;);r=(i=g-40|0)-(A=s+8&7?-8-s&7:0)|0,e[57155]=r,A=A+s|0,e[57158]=A,e[A+4>>2]=1|r,e[4+(s+i|0)>>2]=40,e[57159]=e[57274];break n}if(8&d[A+12|0]|i>>>0>l>>>0|s>>>0<=l>>>0)break o;e[A+4>>2]=r+g,s=(A=l+8&7?-8-l&7:0)+l|0,e[57158]=s,A=(r=e[57155]+g|0)-A|0,e[57155]=A,e[s+4>>2]=1|A,e[4+(r+l|0)>>2]=40,e[57159]=e[57274];break n}i=0;break A}s=0;break r}Ae[57156]>s>>>0&&(e[57156]=s),r=s+g|0,A=229056;o:{c:{u:{l:{i:{p:{for(;;){if((0|r)!=e[A>>2]){if(A=e[A+8>>2])continue;break p}break}if(!(8&d[A+12|0]))break i}for(A=229056;;){if((r=e[A>>2])>>>0<=l>>>0&&(c=r+e[A+4>>2]|0)>>>0>l>>>0)break l;A=e[A+8>>2]}}if(e[A>>2]=s,e[A+4>>2]=e[A+4>>2]+g,e[(h=(s+8&7?-8-s&7:0)+s|0)+4>>2]=3|m,A=(g=r+(r+8&7?-8-r&7:0)|0)-(I=m+h|0)|0,(0|l)==(0|g)){e[57158]=I,A=e[57155]+A|0,e[57155]=A,e[I+4>>2]=1|A;break c}if(e[57157]==(0|g)){e[57157]=I,A=e[57154]+A|0,e[57154]=A,e[I+4>>2]=1|A,e[A+I>>2]=A;break c}if((3&(c=e[g+4>>2]))==1){l=-8&c;i:if(c>>>0<=255){if(i=e[g+8>>2],r=c>>>3|0,(0|(s=e[g+12>>2]))==(0|i)){e[57152]=e[57152]&es(-2,r);break i}e[i+12>>2]=s,e[s+8>>2]=i}else{if(m=e[g+24>>2],(0|g)==(0|(s=e[g+12>>2])))if((r=e[(c=g+20|0)>>2])||(r=e[(c=g+16|0)>>2])){for(;i=c,(r=e[(c=(s=r)+20|0)>>2])||(c=s+16|0,r=e[s+16>>2]););e[i>>2]=0}else s=0;else r=e[g+8>>2],e[r+12>>2]=s,e[s+8>>2]=r;if(m){i=e[g+28>>2];p:{if(e[(r=228912+(i<<2)|0)>>2]==(0|g)){if(e[r>>2]=s,s)break p;e[57153]=e[57153]&es(-2,i);break i}if(e[m+(e[m+16>>2]==(0|g)?16:20)>>2]=s,!s)break i}e[s+24>>2]=m,(r=e[g+16>>2])&&(e[s+16>>2]=r,e[r+24>>2]=s),(r=e[g+20>>2])&&(e[s+20>>2]=r,e[r+24>>2]=s)}}c=e[(g=l+g|0)+4>>2],A=A+l|0}if(e[g+4>>2]=-2&c,e[I+4>>2]=1|A,e[A+I>>2]=A,A>>>0<=255){r=228648+(-8&A)|0,(s=e[57152])&(A=1<<(A>>>3))?A=e[r+8>>2]:(e[57152]=A|s,A=r),e[r+8>>2]=I,e[A+12>>2]=I,e[I+12>>2]=r,e[I+8>>2]=A;break c}if(c=31,A>>>0<=16777215&&(c=62+((A>>>38-(r=be(A>>>8|0))&1)-(r<<1)|0)|0),e[I+28>>2]=c,e[I+16>>2]=0,e[I+20>>2]=0,r=228912+(c<<2)|0,(i=e[57153])&(s=1<<c)){for(c=A<<((0|c)!=31?25-(c>>>1|0):0),s=e[r>>2];;){if(r=s,(-8&e[s+4>>2])==(0|A))break u;if(s=c>>>29|0,c<<=1,!(s=e[(i=(4&s)+r|0)+16>>2]))break}e[i+16>>2]=I}else e[57153]=s|i,e[r>>2]=I;e[I+24>>2]=r,e[I+12>>2]=I,e[I+8>>2]=I;break c}for(r=(i=g-40|0)-(A=s+8&7?-8-s&7:0)|0,e[57155]=r,A=A+s|0,e[57158]=A,e[A+4>>2]=1|r,e[4+(s+i|0)>>2]=40,e[57159]=e[57274],e[(i=(A=(c+(c-39&7?39-c&7:0)|0)-47|0)>>>0<l+16>>>0?l:A)+4>>2]=27,A=e[57267],e[i+16>>2]=e[57266],e[i+20>>2]=A,A=e[57265],e[i+8>>2]=e[57264],e[i+12>>2]=A,e[57266]=i+8,e[57265]=g,e[57264]=s,e[57267]=0,A=i+24|0;e[A+4>>2]=7,r=A+8|0,A=A+4|0,r>>>0<c>>>0;);if((0|i)==(0|l))break n;if(e[i+4>>2]=-2&e[i+4>>2],c=i-l|0,e[l+4>>2]=1|c,e[i>>2]=c,c>>>0<=255){r=228648+(-8&c)|0,(s=e[57152])&(A=1<<(c>>>3))?A=e[r+8>>2]:(e[57152]=A|s,A=r),e[r+8>>2]=l,e[A+12>>2]=l,e[l+12>>2]=r,e[l+8>>2]=A;break n}if(A=31,c>>>0<=16777215&&(A=62+((c>>>38-(A=be(c>>>8|0))&1)-(A<<1)|0)|0),e[l+28>>2]=A,e[l+16>>2]=0,e[l+20>>2]=0,r=228912+(A<<2)|0,(i=e[57153])&(s=1<<A)){for(A=c<<((0|A)!=31?25-(A>>>1|0):0),i=e[r>>2];;){if((0|c)==(-8&e[(r=i)+4>>2]))break o;if(s=A>>>29|0,A<<=1,!(i=e[(s=(4&s)+r|0)+16>>2]))break}e[s+16>>2]=l}else e[57153]=s|i,e[r>>2]=l;e[l+24>>2]=r,e[l+12>>2]=l,e[l+8>>2]=l;break n}A=e[r+8>>2],e[A+12>>2]=I,e[r+8>>2]=I,e[I+24>>2]=0,e[I+12>>2]=r,e[I+8>>2]=A}A=h+8|0;break e}A=e[r+8>>2],e[A+12>>2]=l,e[r+8>>2]=l,e[l+24>>2]=0,e[l+12>>2]=r,e[l+8>>2]=A}if(!((A=e[57155])>>>0<=m>>>0)){r=A-m|0,e[57155]=r,A=(s=e[57158])+m|0,e[57158]=A,e[A+4>>2]=1|r,e[s+4>>2]=3|m,A=s+8|0;break e}}e[56798]=48,A=0;break e}r:if(g){r=e[i+28>>2];a:{if(e[(A=228912+(r<<2)|0)>>2]==(0|i)){if(e[A>>2]=s,s)break a;h=es(-2,r)&h,e[57153]=h;break r}if(e[g+(e[g+16>>2]==(0|i)?16:20)>>2]=s,!s)break r}e[s+24>>2]=g,(A=e[i+16>>2])&&(e[s+16>>2]=A,e[A+24>>2]=s),(A=e[i+20>>2])&&(e[s+20>>2]=A,e[A+24>>2]=s)}r:if(c>>>0<=15)A=c+m|0,e[i+4>>2]=3|A,e[(A=A+i|0)+4>>2]=1|e[A+4>>2];else if(e[i+4>>2]=3|m,e[(l=i+m|0)+4>>2]=1|c,e[l+c>>2]=c,c>>>0<=255)r=228648+(-8&c)|0,(s=e[57152])&(A=1<<(c>>>3))?A=e[r+8>>2]:(e[57152]=A|s,A=r),e[r+8>>2]=l,e[A+12>>2]=l,e[l+12>>2]=r,e[l+8>>2]=A;else{A=31,c>>>0<=16777215&&(A=62+((c>>>38-(A=be(c>>>8|0))&1)-(A<<1)|0)|0),e[l+28>>2]=A,e[l+16>>2]=0,e[l+20>>2]=0,r=228912+(A<<2)|0;a:{if((s=1<<A)&h){for(A=c<<((0|A)!=31?25-(A>>>1|0):0),m=e[r>>2];;){if((-8&e[(r=m)+4>>2])==(0|c))break a;if(s=A>>>29|0,A<<=1,!(m=e[(s=(4&s)+r|0)+16>>2]))break}e[s+16>>2]=l}else e[57153]=s|h,e[r>>2]=l;e[l+24>>2]=r,e[l+12>>2]=l,e[l+8>>2]=l;break r}A=e[r+8>>2],e[A+12>>2]=l,e[r+8>>2]=l,e[l+24>>2]=0,e[l+12>>2]=r,e[l+8>>2]=A}A=i+8|0;break e}A:if(I){r=e[s+28>>2];r:{if(e[(A=228912+(r<<2)|0)>>2]==(0|s)){if(e[A>>2]=i,i)break r;e[57153]=es(-2,r)&h;break A}if(e[I+(e[I+16>>2]==(0|s)?16:20)>>2]=i,!i)break A}e[i+24>>2]=I,(A=e[s+16>>2])&&(e[i+16>>2]=A,e[A+24>>2]=i),(A=e[s+20>>2])&&(e[i+20>>2]=A,e[A+24>>2]=i)}c>>>0<=15?(A=c+m|0,e[s+4>>2]=3|A,e[(A=A+s|0)+4>>2]=1|e[A+4>>2]):(e[s+4>>2]=3|m,e[(i=s+m|0)+4>>2]=1|c,e[i+c>>2]=c,x&&(r=228648+(-8&x)|0,l=e[57157],(A=1<<(x>>>3))&g?A=e[r+8>>2]:(e[57152]=A|g,A=r),e[r+8>>2]=l,e[A+12>>2]=l,e[l+12>>2]=r,e[l+8>>2]=A),e[57157]=i,e[57154]=c),A=s+8|0}return H=t+16|0,0|A}function Vt(A,t,r,s,i){var l,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0;H=l=H-544|0,Je(l+320|0,0,100),Je(l+208|0,0,100),I=r?e[r>>2]:I,V=e[A+12>>2],h=e[36115];e:{A:{r:{a:{for(;;){if(c=(0|(c=d[t+g|0]))>=(0|h)?13:c,f[l+g|0]=c,!c){c=g;break a}if(m=(0|h)<=(0|(m=d[(c=1|g)+t|0]))?13:m,f[c+l|0]=m,!m)break a;if((0|(g=g+2|0))==200)break}g=198,J=d[l+199|0];break r}if(!c)break A;J=d[l+(g=c-1|0)|0],(0|c)!=1&&(g=c-2|0)}if(Ee=8&I,e[l+536>>2]=(Ee>>>3|0?3:7)&I,I=d[l+g|0],h=1,T=(0|(x=ft(A,l,l+432|0,l+540|0,l+536|0,1)))>=0?x:0,ce=d[0|l])for(g=l,c=ce;c=e[144464+((255&c)<<2)>>2],d[c+11|0]==2&&(1048576&(c=e[c+4>>2])||(m=(2097152&c)>>>21|0,m|=c=d[e[144464+(d[0|(_=g+1|0)]<<2)>>2]+10|0]==12,f[(l+208|0)+h|0]=m,K=e[144464+(d[(c?2:1)+g|0]<<2)>>2],m=d[K+11|0]-10>>>0<4294967289|!(32&d[K+6|0])&d[e[144464+(d[(c?3:2)+g|0]<<2)>>2]+11|0]==2?m:m?2:1,g=c?_:g,f[(l+320|0)+h|0]=m,h=h+1|0)),c=d[0|(g=g+1|0)];);g=r=r?T:x;r:{a:{n:{o:{c:{u:{l:{i:{p:{C:{h:{b:switch(e[A+8>>2]-1|0){case 11:if((0|(r=e[l+540>>2]))<2)break p;if(g=1,T=1&(c=r-1|0),(0|r)!=2)break h;h=0;break C;case 8:if((0|(c=e[l+540>>2]))<2)break a;if(h=3&(m=c-1|0),g=1,c-2>>>0>=3)for(T=-4&m,m=0;I=f[0|(c=(l+432|0)+g|0)],f[0|c]=(0|I)<0?4:I,I=f[c+1|0],f[c+1|0]=(0|I)<0?4:I,I=f[c+2|0],f[c+2|0]=(0|I)<0?4:I,I=c,c=f[c+3|0],f[I+3|0]=(0|c)<0?4:c,g=g+4|0,(0|T)!=(0|(m=m+4|0)););if(!h)break a;for(c=0;m=f[0|(I=(l+432|0)+g|0)],f[0|I]=(0|m)<0?4:m,g=g+1|0,(0|h)!=(0|(c=c+1|0)););break a;case 7:if(!d[l+322|0]|f[l+321|0]>0)break a;case 0:if(e[l+536>>2]|e[l+540>>2]<3)break a;if(e[l+536>>2]=2,g=4,r)break r;f[l+434|0]=4;break r;case 1:if(e[l+536>>2])break r;m:{x:{if((0|(h=e[l+540>>2]))>=3){r=h-2|0,e[l+536>>2]=r,g=r;I:if(512&V&&(c=e[144464+(J<<2)>>2],(0|(m=d[c+11|0]))!=2)){g=e[c>>2],c=l;B:{N:{if((0|(T=e[A+212>>2]))!=26977){if((0|T)!=24942)break N;L:switch(g-110|0){case 0:case 5:if(g=r,d[e[144464+(I<<2)>>2]+11|0]==2)break I;break;default:break L}g=h-1|0;break B}if((0|g)==115&&(g=r,d[e[144464+(I<<2)>>2]+11|0]==2))break I;g=h-1|0;break B}N:{L:{if((0|g)==115){if(g=r,(0|(m=d[e[144464+(I<<2)>>2]+11|0]))!=8)break L;break I}if((0|m)!=8)break N;m=d[e[144464+(I<<2)>>2]+11|0]}if(g=r,(255&m)==2)break I}g=h-1|0}e[c+536>>2]=g}if(524288&V&&(f[(c=h-1|0)+(h=l+208|0)|0]<=f[r+h|0]||(e[l+536>>2]=c,g=c)),d[(l+432|0)+g|0]>1){c=g;break m}if(c=2,r=g-1|0,g>>>0>=2)break x;e[l+536>>2]=g+1;break m}r=1}c=r,e[l+536>>2]=c}if(g=4,f[0|(r=(l+432|0)+c|0)]>=0||f[(c=(l+432|0)+c|0)-1|0]>=4&f[c+1|0]>3)break r;f[0|r]=4;break r;case 2:if(e[l+536>>2])break r;for(r=(g=e[l+540>>2])-1|0,r&=r>>31;;){if((0|(g=g-1|0))<=0)break n;if(!(f[0|(c=(l+432|0)+g|0)]>=0))break}e[l+536>>2]=g,g=4,f[0|c]=4;break r;case 3:if(e[l+536>>2])break a;if(c=(0|(g=e[l+540>>2]-3|0))<=1?1:g,e[l+536>>2]=c,g=4,r)break r;f[c+(l+432|0)|0]=4;break r;case 4:if(e[l+536>>2])break r;if(c=(r=e[l+540>>2])-3|0,e[l+536>>2]=c,(0|r)<=15){m:{x:switch(d[e[144464+(J<<2)>>2]+11|0]-2|0){case 0:c=f[r+94176|0];break m;case 2:c=f[r+94192|0];break m;default:break x}c=f[r+94160|0]}e[l+536>>2]=c}g=4,f[(l+432|0)+c|0]=4;break r;case 5:if(e[l+536>>2])break r;if(h=-1,m=0,!((0|(r=(c=e[l+540>>2])-1|0))<2)){if(g=1,_=1&c,(0|c)!=3)for(K=(-2&c)-4|0,c=0;f[(l+432|0)+g|0]<0&&(m=(I=(0|(T=f[(l+320|0)+g|0]))<(0|h))?m:g,h=I?h:T),f[(I=g+1|0)+(l+432|0)|0]<0&&(m=(T=(0|(he=f[I+(l+320|0)|0]))<(0|h))?m:I,h=T?h:he),g=g+2|0,I=(0|c)!=(0|K),c=c+2|0,I;);!_|f[(l+432|0)+g|0]>=0||(h=(c=(0|(I=f[(l+320|0)+g|0]))<(0|h))?h:I,m=c?m:g)}e[l+536>>2]=m,d[r+(l+320|0)|0]!=2|(0|h)>1?(0|h)>0||(m=1,e[l+536>>2]=1):(e[l+536>>2]=r,m=r),g=4,f[(l+432|0)+m|0]=4;break r;case 14:break o;case 12:break c;case 6:break b;default:break r}if(e[l+536>>2])break r;h=(r=e[l+540>>2])-1|0,e[l+536>>2]=h;b:if(!((0|r)<2))for(g=1;;){if(d[(l+432|0)+g|0]==1){h=g-1|0,e[l+536>>2]=h;break b}if((0|r)==(0|(g=g+1|0)))break}g=4,f[(l+432|0)+h|0]=4;break r}for(_=-2&c,h=0,m=0;c=d[0|(K=(I=l+432|0)+g|0)],te=K,he=f[(K=l+208|0)+g|0]>0,f[0|te]=he||(0|c)==4?3:c,Te=(0|(I=d[0|(te=(c=g+1|0)+I|0)]))==4?3:I,I=f[c+K|0]>0,f[0|te]=I?3:Te,h=I?c:he?g:h,g=g+2|0,(0|_)!=(0|(m=m+2|0)););}if(T&&(I=(0|(c=d[0|(m=(l+432|0)+g|0)]))==4?3:c,c=f[(l+208|0)+g|0]>0,f[0|m]=c?3:I,h=c?g:h),c=e[l+536>>2])break u;if((0|h)>0){e[l+536>>2]=h,c=h;break u}if((0|r)<6)break i;c=r-3|0;break l}if(c=e[l+536>>2])break u}c=r-1|0}e[l+536>>2]=c}g=4,f[(l+432|0)+c|0]=4;break r}if(e[l+536>>2])break r;c=1,e[l+536>>2]=1,d[l+209|0]|e[l+540>>2]<3|f[l+210|0]<=0||(c=2,e[l+536>>2]=2),g=4,f[l+432|c]=4;break r}if(e[l+536>>2]||(0|(c=e[l+540>>2]))<3)break a;if(Je(l+432|1,0,c-1|0),e[l+536>>2]=2,r||(f[l+434|0]=4),g=4,c>>>0<4)break r;f[431+(c+l|0)|0]=3;break r}e[l+536>>2]=r,g=4;break r}g=r}!(256&V)|2&i||(0|(r=e[l+540>>2]))<3|(0|x)>2||d[0|(c=(r=r+(l+432|0)|0)-1|0)]!=4|d[e[144464+(J<<2)>>2]+11|0]!=2||(f[0|c]=1,f[r-2|0]=4);r:{a:{if(Ee)x=e[l+540>>2];else{if(r=f[l+433|0],!(!(4096&V)|(0|(x=e[l+540>>2]))!=3)){if((0|r)==4){f[l+434|0]=3;break a}if(d[l+434|0]==4){f[l+433|0]=3;break a}}if(!(!(8192&V)|(0|r)>=0|(0|x)<4|f[l+434|0]<4)){f[l+433|0]=3;break a}}if(m=0,(0|x)<2)break r}for(h=(0|g)<4?4:3,K=128&V,he=64&V,te=32&V,T=x-1|0,Te=16&V,Fe=!(32768&V),J=0,I=0,g=1;;){a:{n:if((0|(c=f[0|(_=(l+432|0)+g|0)]))>=0)m=h;else{m=3;o:{c:if(!(!(!Te|(0|h)>3)&(0|g)==(0|T))){if(!(1&(J|Fe)))break o;if(!(f[431+(l+g|0)|0]>1)){if((0|(c=f[(r=g+1|0)+(l+432|0)|0]))>=2){if((0|h)!=4||(m=4,c>>>0>=3))break c}else if(!(!te|(0|h)!=3)){h=3;break a}if(!he|g>>>0<2||(m=d[(l+320|0)+g|0]))break o;if((0|T)>(0|(c=g))){for(;;){if(f[(l+320|0)+c|0]>0)break a;if((0|T)==(0|(c=c+1|0)))break}if(m)break o}if(f[r+(l+320|0)|0]<=0)break o;break a}m=h}c=d[0|_];break n}f[0|_]=h,J=1,m=3,c=h}n:{if(c<<24>>24>=4){if(r=I||g,!I|!K)break n;f[0|_]=3}h=m;break a}h=m,I=r}if(m=1,(0|x)==(0|(g=g+1|0)))break}}if(s=!Ee|(0|s)>=0?s:e[((0|x)<3?16:20)+A>>2],h=0,c=0,m){if(J=3&(r=x-1|0),m=0,x-2>>>0<3)g=1;else for(Ee=-4&r,g=1,I=0;c=(r=(0|(c=(_=(0|(c=(T=(0|(c=(x=(0|(T=f[(r=l+432|0)+g|0]))<(0|c))?c:T))>(0|(_=f[(K=g+1|0)+r|0])))?c:_))>(0|(te=f[(he=g+2|0)+r|0])))?c:te))>(0|(Te=f[(te=g+3|0)+r|0])))?c:Te,h=r?_?T?x?h:g:K:he:te,g=g+4|0,(0|Ee)!=(0|(I=I+4|0)););if(J)for(;c=(r=(0|(I=f[(l+432|0)+g|0]))<(0|c))?c:I,h=r?h:g,g=g+1|0,(0|J)!=(0|(m=m+1|0)););}if((0|s)<0?s=c:(0|s)<=(0|c)&(0|c)>4||(f[(l+432|0)+h|0]=s),J=t+197|0,x=1,!(1&i)&&(r=e[144464+(ce<<2)>>2])){if(c=l,!((0|(m=d[r+11|0]))!=1&(0|ce)!=15))for(;r=d[0|(c=c+1|0)],(0|(m=d[e[144464+(r<<2)>>2]+11|0]))==1|(0|r)==15;);!(48&(r=e[A+4>>2]))|(0|m)!=2||(f[0|t]=(32&r)>>>5|0&&f[l+433|0]>3?11:23,t=t+1|0)}r:if(!(t>>>0>=J>>>0))for(T=65536&V,_=2&V,Ee=4&V,g=l;;){if(!(c=d[0|g]))break r;if(i=g,g=g+1|0,r=e[144464+(c<<2)>>2]){a:{n:{o:switch(d[r+11|0]){case 0:e[A+8200>>2]=0;break a;case 2:if(!(16&d[r+6|0]))break n;break;default:break o}if(d[0|g]!=20)break a}if((0|(ce=e[l+540>>2]))<(0|x))break e;I=f[0|(V=(l+432|0)+x|0)],e[A+8200>>2]=I;n:{o:{if(!((0|(r=I))>1)){if(m=ce-1|0,!(!Ee|(0|x)<2|(0|s)<2)&&(r=0,(0|m)==(0|x)))break o;if(r=1,!((0|x)==1|_|(ce-2|0)==(0|x)&f[m+(l+432|0)|0]<2|(0|m)==(0|x)||f[431+(l+x|0)|0]>=0&&(r=I,T))){r=0,f[0|V]=0;break o}}if(r&&(0|r)<2)break n}f[0|t]=d[r+94151|0],t=t+1|0,I=f[0|V]}m=(0|s)>(0|I),d[0|g]==12&&1&(ce=e[A+28>>2])&&(g=(16&ce?(0|h)!=(0|x):(0|r)<4)?i+2|0:g),s=m?s:I,x=x+1|0}if((0|c)!=1&&(f[0|t]=c,t=t+1|0),!(t>>>0<J>>>0))break}}f[0|t]=0}return void(H=l+544|0)}ie(86136,86634,1353,94208),j()}function ts(A,t,r,s,i,l,c,g,m){var I,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0,Mt=0,Pt=0,Nt=0,tr=0,rr=0,br=0,Ir=0,zr=0,Vr=0,Xr=0;H=I=H-336|0,_=g,K=65535&m,V=s,T=65535&i,Ee=-2147483648&(i^m);e:{if(!((ce=m>>>16&32767)-32767>>>0>4294934529&(J=i>>>16&32767)-32767>>>0>=4294934530)){if(!(!s&(0|(h=2147483647&i))==2147418112?!(t|r):h>>>0<2147418112)){te=s,Ee=32768|i;break e}if(!(!g&(0|(i=2147483647&m))==2147418112?!(l|c):i>>>0<2147418112)){te=g,Ee=32768|m,t=l,r=c;break e}if(!(t|s|2147418112^h|r)){if(!(l|g|2147418112^i|c)){t=0,r=0,Ee=2147450880;break e}Ee|=2147418112,t=0,r=0;break e}if(!(l|g|2147418112^i|c)){t=0,r=0;break e}if(!(t|s|r|h)){te=(t=!(l|g|i|c))?0:te,Ee=t?2147450880:Ee,t=0,r=0;break e}if(!(l|g|i|c)){Ee|=2147418112,t=0,r=0;break e}(0|h)==65535|h>>>0<65535&&(m=s=!(T|V),h=s?t:V,g=s<<=6,vt(I+320|0,t,r,V,T,(s=s+((0|(m=be(m?r:T)))==32?be(h)+32|0:m)|0)-15|0),Te=16-s|0,V=e[I+328>>2],T=e[I+332>>2],r=e[I+324>>2],t=e[I+320>>2]),i>>>0>65535||(g=s=!(_|K),m=s?l:_,i=s<<=6,vt(I+304|0,l,c,_,K,(s=s+((0|(g=be(g?c:K)))==32?be(m)+32|0:g)|0)-15|0),Te=(s+Te|0)-16|0,_=e[I+312>>2],K=e[I+316>>2],l=e[I+304>>2],c=e[I+308>>2])}if(hA=i=65536|K,_A=_,h=i<<15|(s=_)>>>17,Fr(I+288|0,s=i=s<<15|c>>>17,g=h,0,0,m=0-s|0,h=1963258675-(h+!!(0|s)|0)|0,0,0),Fr(I+272|0,0-(s=e[I+296>>2])|0,0-(e[I+300>>2]+!!(0|s)|0)|0,0,0,m,h,0,0),Fr(I+256|0,m=(s=e[I+280>>2])<<1|e[I+276>>2]>>>31,s=e[I+284>>2]<<1|s>>>31,0,0,i,g,0,0),Fr(I+240|0,m,s,0,0,0-(h=e[I+264>>2])|0,0-(e[I+268>>2]+!!(0|h)|0)|0,0,0),Fr(I+224|0,h=(m=e[I+248>>2])<<1|e[I+244>>2]>>>31,s=e[I+252>>2]<<1|m>>>31,0,0,i,g,0,0),Fr(I+208|0,h,s,0,0,0-(m=e[I+232>>2])|0,0-(e[I+236>>2]+!!(0|m)|0)|0,0,0),Fr(I+192|0,m=(s=e[I+216>>2])<<1|e[I+212>>2]>>>31,s=e[I+220>>2]<<1|s>>>31,0,0,i,g,0,0),Fr(I+176|0,m,s,0,0,0-(h=e[I+200>>2])|0,0-(e[I+204>>2]+!!(0|h)|0)|0,0,0),Fr(I+160|0,m=i,s=g,0,0,g=(_=(i=e[I+184>>2])<<1|e[I+180>>2]>>>31)-1|0,i=(e[I+188>>2]<<1|i>>>31)-!_|0,0,0),Fr(I+144|0,l<<15,c<<15|l>>>17,0,0,s=g,i,0,0),he=I+112|0,Xe=e[I+168>>2],g=e[I+172>>2],x=(_=e[I+160>>2])+(m=e[I+152>>2])|0,h=(K=e[I+164>>2])+e[I+156>>2]|0,m=h=m>>>0>x>>>0?h+1|0:h,h=(h=(0|K)==(0|h)&x>>>0<_>>>0|h>>>0<K>>>0)>>>0>(K=h+Xe|0)>>>0?g+1|0:g,Fr(he,s,i,0,0,0-(g=(_=!m&x>>>0>1|!!(0|m))+K|0)|0,0-(!!(0|g)+(h=_>>>0>g>>>0?h+1|0:h)|0)|0,0,0),Fr(I+128|0,1-x|0,0-((x>>>0>1)+m|0)|0,0,0,s,i,0,0),tr=(J-ce|0)+Te|0,he=i=e[I+116>>2],_=(s=e[I+112>>2])<<1,J=h=i<<1|s>>>31,s=h,fA=g=e[I+140>>2],s=s+(h=g<<1|(i=e[I+136>>2])>>>31)|0,i=s=(g=(m=i<<1|e[I+132>>2]>>>31)+_|0)>>>0<m>>>0?s+1|0:s,Le=s=s-(g>>>0<13927)|0,Xe=s,rr=h=65536|T,br=V,zr=(s=V)<<1,Vr=h=h<<1|s>>>31,At=h,Mt=s=st(Le,x=0,h,0),Fe=h=le,Te=t<<1,ce=s=r<<1|t>>>31,K=h=0,Le=(0|i)==(0|Le)&(m=g-13927|0)>>>0<g>>>0|i>>>0>Le>>>0,i=(0|i)==(0|J)&g>>>0<_>>>0|i>>>0<J>>>0,s=e[I+120>>2],h=g=e[I+124>>2]<<1|s>>>31,h=(s=(x=fA>>>31|0)+(s=s<<1|he>>>31)|0)>>>0<x>>>0?h+1|0:h,x=(g=s)>>>0>(s=s+i|0)>>>0?h+1|0:h,x=(i=s)>>>0>(s=s+Le|0)>>>0?x+1|0:x,i=s-1|0,h=st(ce,K,Le=x-!s|0,J=0),g=le+Fe|0,he=(0|Fe)==(0|(g=(s=h+Mt|0)>>>0<h>>>0?g+1|0:g))&s>>>0<Mt>>>0|g>>>0<Fe>>>0,fA=i,i=st(i,h=0,LA=(Ir=r>>>31|0)|V<<1,Fe=0),h=le+g|0,x=0,_=h=i>>>0>(V=i+s|0)>>>0?h+1|0:h,x=(i=s=(0|h)==(0|g)&s>>>0>V>>>0|g>>>0>h>>>0)>>>0>(s=s+he|0)>>>0?1:x,i=st(At,K,Le,J),h=le+x|0,he=s=i+s|0,s=s>>>0<i>>>0?h+1|0:h,i=st(At,K,fA,Fe),T=le,g=i,i=st(LA,Fe,Le,J),h=le+T|0,i=h=i>>>0>(x=g+i|0)>>>0?h+1|0:h,s=s+(h=(0|T)==(0|h)&g>>>0>x>>>0|h>>>0<T>>>0)|0,he=T=he+i|0,T=s=T>>>0<i>>>0?s+1|0:s,h=x+_|0,i=h=(s=(i=0)+V|0)>>>0<i>>>0?h+1|0:h,g=(0|h)==(0|_)&s>>>0<V>>>0|h>>>0<_>>>0,h=T,x=g,Pt=g=g+he|0,x=h=x>>>0>g>>>0?h+1|0:h,he=s,T=s,V=i,Mt=m,s=st(m,0,LA,Fe),g=le,i=s,m=st(Xe,te,ce,te),h=le+g|0,m=(0|g)==(0|(h=(s=s+m|0)>>>0<m>>>0?h+1|0:h))&s>>>0<i>>>0|g>>>0>h>>>0,g=h,i=st(fA,Fe,Nt=-2&Te,0),h=le+h|0,i=h=i>>>0>(_=i+s|0)>>>0?h+1|0:h,s=(0|h)==(0|g)&s>>>0>_>>>0|g>>>0>h>>>0,g=0,s=((m=s+m|0)>>>0<s>>>0?1:g)+V|0,h=x,m=s=(T=m+T|0)>>>0<m>>>0?s+1|0:s,g=s=(0|s)==(0|V)&T>>>0<he>>>0|s>>>0<V>>>0,Xr=s=s+Pt|0,he=h=g>>>0>s>>>0?h+1|0:h,s=st(At,K,Mt,te),Pt=le,At=s,g=st(Le,J,Nt,te),h=le+Pt|0,V=s=s+g|0,x=s+(K=st(Xe,te,LA,Fe))|0,s=(g=s>>>0<g>>>0?h+1|0:h)+le|0,s=x>>>0<K>>>0?s+1|0:s,J=x,K=x+(h=st(ce,te,fA,Fe))|0,x=le+s|0,LA=(0|s)==(0|(x=h>>>0>K>>>0?x+1|0:x))&K>>>0<J>>>0|s>>>0>x>>>0,h=((s=(s=(s=(0|s)==(0|g)&V>>>0>J>>>0|s>>>0<g>>>0)+(h=(0|g)==(0|Pt)&V>>>0<At>>>0|g>>>0<Pt>>>0)|0)+LA|0)|(fA=0))+m|0,V=h=(g=x)>>>0>(J=g+T|0)>>>0?h+1|0:h,s=(0|m)==(0|h)&T>>>0>J>>>0|m>>>0>h>>>0,h=he,g=s,fA=s=s+Xr|0,m=h=g>>>0>s>>>0?h+1|0:h,s=st(Xe,te,Nt,te),Xe=le,T=s,g=st(ce,te,Mt,te),h=le+Xe|0,g=(0|(h=(s=s+g|0)>>>0<g>>>0?h+1|0:h))==(0|Xe)&s>>>0<T>>>0|h>>>0<Xe>>>0,T=h,s=h+_|0,h=(g|(he=0))+i|0,T=(0|i)==(0|(h=s>>>0<T>>>0?h+1|0:h))&s>>>0<_>>>0|i>>>0>h>>>0,x=(g=h)+(h=K)|0,h=0,h=((i=s=(0|g)==(0|(x=(_=(K=0)+s|0)>>>0<K>>>0?x+1|0:x))&s>>>0>_>>>0|g>>>0>x>>>0)>>>0>(s=s+T|0)>>>0?1:h)+V|0,x=m,i=h=(i=s)>>>0>(s=s+J|0)>>>0?h+1|0:h,m=x=(m=g=(0|V)==(0|h)&s>>>0<J>>>0|h>>>0<V>>>0)>>>0>(g=g+fA|0)>>>0?x+1|0:x,(0|x)==131071|x>>>0<131071?(br=zr|Ir,rr=Fe|Vr,Fr(I+80|0,s,i,g,m,l,c,_A,hA),K=x=e[I+84>>2],h=t<<17,V=(r=(_=0)-(T=e[I+88>>2])|0)-(x=!!(x|(t=e[I+80>>2])))|0,T=(h-(e[I+92>>2]+(T>>>0>_>>>0)|0)|0)-(r>>>0<x>>>0)|0,_=0-t|0,K=0-(!!(0|t)+K|0)|0,t=tr+16382|0):(Fr(I+96|0,s=(1&i)<<31|s>>>1,i=g<<31|i>>>1,g=(1&m)<<31|g>>>1,m=m>>>1|0,l,c,_A,hA),ce=V=e[I+100>>2],V=(_=0-(Te=e[I+104>>2])|0)-(T=!!(V|(x=e[I+96>>2])))|0,T=((t<<16)-(e[I+108>>2]+(K>>>0<Te>>>0)|0)|0)-(T>>>0>_>>>0)|0,_=0-x|0,K=0-(!!(0|x)+ce|0)|0,Te=t,ce=r,t=tr+16383|0),(0|t)>=32767)Ee|=2147418112,t=0,r=0;else{if((0|t)>0)x=T<<1|V>>>31,V=V<<1|K>>>31,T=x,Te=g,ce=65535&m|t<<16,x=K<<1|_>>>31,m=_<<1;else{if((0|t)<=-113){t=0,r=0;break e}cr(I- -64|0,s,i,g,m,1-t|0),vt(I+48|0,Te,ce,br,rr,t+112|0),Fr(I+32|0,l,c,_A,hA,s=e[I+64>>2],i=e[I+68>>2],Te=e[I+72>>2],ce=e[I+76>>2]),t=e[I+40>>2],_=(r=e[I+56>>2])-(V=t<<1|(x=e[I+36>>2])>>>31)|0,T=e[I+60>>2]-((e[I+44>>2]<<1|t>>>31)+(r>>>0<V>>>0)|0)|0,h=(t=e[I+32>>2])<<1,V=_-(t=(0|(g=x<<1|t>>>31))==(0|(m=e[I+52>>2]))&h>>>0>(r=e[I+48>>2])>>>0|g>>>0>m>>>0)|0,T=T-(t>>>0>_>>>0)|0,x=m-((r>>>0<h>>>0)+g|0)|0,m=r-h|0}t=m,Fr(I+16|0,l,c,_A,hA,3,0,0,0),Fr(I,l,c,_A,hA,5,0,0,0),g=x+(r=0)|0,g=m>>>0>(t=t+(h=1&s)|0)>>>0?g+1|0:g,m=t,c=(0|c)==(0|g)&t>>>0>l>>>0|c>>>0<g>>>0,x=T,t=(0|r)==(0|g)&t>>>0<h>>>0|r>>>0>g>>>0,h=i,r=h=(r=t=(t=(0|(x=t>>>0>(l=t+V|0)>>>0?x+1|0:x))==(0|hA))&(0|l)==(0|_A)?c:t&l>>>0>_A>>>0|x>>>0>hA>>>0)>>>0>(t=t+s|0)>>>0?h+1|0:h,s=(0|i)==(0|h)&t>>>0<s>>>0|i>>>0>h>>>0,h=ce,h=(i=s)>>>0>(s=s+Te|0)>>>0?h+1|0:h,c=s,T=(0|(i=e[I+20>>2]))==(0|g)&Ae[I+16>>2]<m>>>0|i>>>0<g>>>0,i=e[I+28>>2],i=h>>>0<2147418112&((0|(s=e[I+24>>2]))==(0|l)&(0|i)==(0|x)?T:(0|i)==(0|x)&s>>>0<l>>>0|i>>>0<x>>>0),s=r,h=(r=t=(0|r)==(0|(s=(T=i)>>>0>(i=t+i|0)>>>0?s+1|0:s))&t>>>0>i>>>0|r>>>0>s>>>0)>>>0>(t=t+c|0)>>>0?h+1|0:h,c=t,g=(0|(r=e[I+4>>2]))==(0|g)&Ae[I>>2]<m>>>0|r>>>0<g>>>0,r=e[I+12>>2],r=x=(r=t=h>>>0<2147418112&((0|(t=e[I+8>>2]))==(0|l)&(0|r)==(0|x)?g:(0|r)==(0|x)&t>>>0<l>>>0|r>>>0<x>>>0))>>>0>(t=t+i|0)>>>0?s+1|0:s,i=(0|s)==(0|x)&t>>>0<i>>>0|s>>>0>x>>>0,s=h,l=i,te|=i=i+c|0,Ee|=s=l>>>0>i>>>0?s+1|0:s}}e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=te,e[A+12>>2]=Ee,H=I+336|0}function ut(A,t,r,s,i){var l,c,g,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0;if(H=c=H-80|0,g=e[r+8>>2],!i|!y[r+4>>1]||(e[i+8>>2]=0),l=Je(s,0,152),e[l+44>>2]=d[g+14|0],e[l+40>>2]=d[g+15|0],s=y[g+8>>1]){for(s=e[34459]+(s<<1)|0,te=256&t,ce=r+32|0,Te=r-32|0,Fe=r- -64|0,Le=r+96|0,Xe=r+-64|0,fA=r-28|0,hA=1&t,_A=r-24|0,he=e[30450];;){m=15&(T=(t=y[s>>1])>>>8|0);e:{A:{r:{a:{n:{o:{c:{u:{l:{i:{p:{C:switch(0|(h=t>>>12|0)){case 10:break c;case 9:break u;case 6:break l;case 2:case 3:break i;case 1:break p;case 0:break C;case 11:case 12:case 13:case 14:case 15:break o;default:break n}I=255&t;C:{h:{b:switch(0|m){case 13:if(I)break h;m=s,t=0;break C;case 0:m=s;m:switch(I-1|0){case 1:break e;case 0:break A;default:break m}e[c+20>>2]=mo(c+75|0,e[g>>2]),e[c+16>>2]=t,Xt(he,85851,c+16|0);break e;case 5:if(d[e[144464+(d[r+34|0]<<2)>>2]+11|0]!=2)break e;e[l+20>>2]=I;break e;case 12:break b;default:break a}e[l+44>>2]=e[l+44>>2]+(t<<24>>31&-256|I);break e}f[l+132|0]=d[s+3|0],m=s+2|0,f[l+133|0]=d[0|m],t=2,I>>>0<3||(f[l+134|0]=d[s+5|0],m=s+4|0,f[l+135|0]=d[0|m],t=4,I>>>0<5||(f[l+136|0]=d[s+7|0],m=s+6|0,f[l+137|0]=d[0|m],t=6,I>>>0<7||(f[l+138|0]=d[s+9|0],m=s+8|0,f[l+139|0]=d[0|m],t=8,I>>>0<9||(f[l+140|0]=d[s+11|0],m=s+10|0,f[l+141|0]=d[0|m],t=10,I>>>0<11||(f[l+142|0]=d[s+13|0],m=s+12|0,f[l+143|0]=d[0|m],t=12,I>>>0<13||(f[l+144|0]=d[s+15|0],m=s+14|0,f[l+145|0]=d[0|m],t=14,I>>>0<15||(f[l+146|0]=d[s+17|0],m=s+16|0,f[l+147|0]=d[0|m],t=16)))))))}f[132+(t+l|0)|0]=0,I=V;break A}if(!A|m>>>0>7||(h=r,d[e[144464+(d[r+2|0]<<2)>>2]+11|0]!=2&&(h=ce,d[e[144464+(d[r+34|0]<<2)>>2]+11|0]!=2))||!(1&(m=e[A+56>>2]))&&16&d[0|r])break e;I=15&d[h+3|0],I=2&m&&d[h+6|0]<=I>>>0?4:I;p:{C:{h:{b:switch((m=7&T)-3|0){case 1:break C;case 0:break b;default:break h}if(I>>>0>3)break p;break e}if(e[102832+(m<<2)>>2]>(0|I))break p;break e}if(d[h+6|0]>I>>>0)break e}e[l+8>>2]=255&t,I=1;break r}if((57344&t)==8192){for(LA=e[32972],T=1,Ee=0;;){x=255&t,m=(_=4095&t)>>>8|0;i:if(_>>>0<=3583){(0|(I=(m>>>0)%7|0))==6&&(I=y[s+2>>1]),J=0,m=r;p:{C:{h:switch(0|I){case 6:if(h=0,y[r+36>>1]|y[r+68>>1])break i;case 3:m=Fe;break C;case 9:if(h=0,y[r+36>>1]|y[r+68>>1])break i;if(m=Le,!y[r+100>>1])break p;break i;case 7:if(h=0,y[r+36>>1])break i;for(I=1;;){if(d[e[144464+(d[(m=(I<<5)+r|0)+2|0]<<2)>>2]+11|0]==2)break C;if(y[4+(((I=I+1|0)<<5)+r|0)>>1])break}break i;case 5:if(h=0,y[r+4>>1])break i;case 0:J=1,m=Te;break C;case 4:if(h=0,y[r+36>>1])break i;case 2:m=ce;break C;case 8:if(h=0,!i)break i;if(J=1,e[(m=i)+8>>2])break p;break i;case 10:break h;default:break C}if(h=0,y[r+4>>1]|y[fA>>1])break i;J=1,m=Xe;break p}C:switch(0|I){case 0:case 5:break C;default:break p}m=(d[m+2|0]==1?-32:0)+m|0}if(te?(I=e[144464+(d[m+2|0]<<2)>>2],e[m+8>>2]=I):I=e[m+8>>2],_>>>0<=1791){if(h=1,e[e[144464+(x<<2)>>2]>>2]==e[I>>2])break i;if(!(!J|d[I+11|0]!=2)){h=(0|x)==d[I+13|0];break i}h=(0|x)==d[I+12|0];break i}x=31&_,h=0;p:switch(_>>>5&7){case 0:h=(0|x)==d[I+11|0];break i;case 1:h=(0|x)==(15&y[I+6>>1]);break i;case 2:h=e[I+4>>2]>>>x&1;break i;case 4:break p;default:break i}p:switch(0|x){case 0:case 1:case 2:case 3:case 4:if(d[e[144464+(d[m+2|0]<<2)>>2]+11|0]!=2){if(d[e[144464+(d[m+34|0]<<2)>>2]+11|0]!=2)break i;m=m+32|0}I=15&d[m+3|0],I=!A|!(2&d[A+56|0])?I:d[m+6|0]<=I>>>0?4:I;C:{h:switch(x-3|0){case 1:h=d[m+6|0]<=I>>>0;break i;case 0:if(h=1,I>>>0<=3)break C;break i;default:break h}if(h=1,e[102832+(x<<2)>>2]>(0|I))break i}h=0;break i;case 17:if(!d[I+11|0]){h=1;break i}h=(32&d[r+1|0])>>>5|0;break i;case 18:h=y[m+4>>1]!=0;break i;case 19:if(h=1,y[m+36>>1])break i;h=!d[e[m+40>>2]+11|0];break i;case 9:if(y[m+4>>1])break i;for(;;){if(h=!!(0|(I=12&d[m-29|0])),I)break i;if(y[(m=m-32|0)+4>>1])break}break i;case 10:h=d[I+11|0]!=2;break i;case 11:for(;;){if(h=!!(0|(I=y[m+36>>1])),I)break i;if(I=m,m=m+32|0,d[e[I+40>>2]+11|0]==2)break}break i;case 12:if(h=1,(254&d[I+11|0])==2)break i;h=(16&d[I+4|0])>>>4|0;break i;case 13:for(;h=(d[e[m+8>>2]+11|0]==2)+h|0,I=y[m+4>>1],m=m-32|0,!I;);h=(0|h)==1;break i;case 14:for(;h=(d[e[m+8>>2]+11|0]==2)+h|0,I=y[m+4>>1],m=m-32|0,!I;);h=(0|h)==2;break i;case 16:break p;default:break i}h=(16&d[0|m])>>>4|0}else if(h=0,(0|m)==15){p:switch(x-1|0){case 0:h=hA;break i;case 1:break p;default:break i}h=e[LA+132>>2]!=0}i:if(1970>>>(m=(I=65535&t)>>>12|0)&1)m=f[m+102848|0];else{p:switch(0|m){case 0:if(m=1,(3840&I)!=3328)break i;m=1+(1+(255&I)>>>1|0)|0;break i;case 6:m=(I>>>9&7)-5>>>0<2?12:1;break i;case 2:case 3:m=(0|(m=3840&I))==3328||(0|m)==1536?2:1;break i;default:break p}m=4,(I=y[s+4>>1])>>>0>61439||(m=(0|I)==2?3:2)}if(s=((m=y[(s=(m<<1)+s|0)>>1]==3)<<1)+s|0,m^=h,T=Ee?m|T:m&T,Ee=4096&t,(57344&(t=y[s>>1]))!=8192)break}if(!(1&T))if((63488&t)!=26624){i:if(1970>>>(m=t>>>12|0)&1)m=f[m+102848|0];else{p:switch(0|m){case 0:if(m=1,(3840&t)!=3328)break i;m=1+(1+(255&t)>>>1|0)|0;break i;case 6:m=(t>>>9&7)-5>>>0<2?12:1;break i;case 2:case 3:m=(0|(t&=3840))==3328||(0|t)==1536?2:1;break i;default:break p}m=4,(t=y[s+4>>1])>>>0>61439||(m=(0|t)==2?3:2)}s=(((65024&y[(t=(m<<1)+s|0)>>1])==24576)<<1)+t|0}else s=((255&t)<<1)+s|0}m=s-2|0,I=V;break A}l:switch(m>>>1|0){case 0:s=(((255&t)<<1)+s|0)-2|0;break e;case 5:e[l>>2]=2|e[l>>2],((t=d[e[r+40>>2]+12|0])-28&255)>>>0<=5&&(m=y[(t=((t<<2)+s|0)-112|0)+4>>1],t=y[t+2>>1],e[l+96>>2]=t>>>4<<24>>24,e[l+76>>2]=(15&t)<<18|m<<2),s=s+24|0;break e;case 6:break l;default:break e}((t=d[e[_A>>2]+13|0])-28&255)>>>0<=5&&(m=y[(t=((t<<2)+s|0)-112|0)+4>>1],t=y[t+2>>1],e[l+100>>2]=t>>>4<<24>>24,e[l+80>>2]=(15&t)<<18|m<<2),s=s+24|0;break e}t=y[(s=s+2|0)>>1]|t<<16&983040;u:switch(m-1|0){case 0:if((0|K)>9)break e;e[(c+32|0)+(K<<2)>>2]=s,s=(e[34459]+(t<<1)|0)-2|0,K=K+1|0;break e;case 1:e[l+124>>2]=t;break e;case 2:break u;default:break e}e[l+128>>2]=t;break e}e[(m=(((0|m)!=1)<<3)+l|0)+108>>2]=y[s+2>>1]|(255&t)<<16,t=y[s+4>>1]<<16,s=s+6|0,e[m+112>>2]=t|y[s>>1];break e}if(I=y[(m=s+2|0)>>1],_=t>>>4|0,e[(T=((x=h-11|0)<<2)+l|0)+88>>2]=255&_,e[T+68>>2]=t<<18&3932160|I<<2,(0|(s=y[s+4>>1]))==2){s=m;break e}if(I=t>>>0<=53247?s>>>0>61439?2:1:V-((0|x)==4)|0,h-13>>>0>1)break A;e[T+88>>2]=_<<24>>24;break A}e[c+4>>2]=mo(c+75|0,e[g>>2]),e[c>>2]=t,Xt(he,85851,c);break e}e[4+((m<<2)+l|0)>>2]=I,I=te&&(0|m)==1?1:V}m=s}(0|I)!=1|(0|K)<=0?(s=m,V=I):(s=e[(c+32|0)+((K=K-1|0)<<2)>>2],V=0)}if(s=s+2|0,(0|V)==1)break}!i|d[r+17|0]!=2||(A=e[r+4>>2],e[i>>2]=e[r>>2],e[i+4>>2]=A,A=e[r+28>>2],e[i+24>>2]=e[r+24>>2],e[i+28>>2]=A,A=e[r+20>>2],e[i+16>>2]=e[r+16>>2],e[i+20>>2]=A,A=e[r+12>>2],e[i+8>>2]=e[r+8>>2],e[i+12>>2]=A),f[r+23|0]=e[l+44>>2],(A=e[l+68>>2])?(e[r+24>>2]=A,A=l+88|0):(e[r+24>>2]=e[l+72>>2],A=l+92|0),e[r+28>>2]=e[A>>2]}H=c+80|0}function ls(A,t,r,s,i,l){var c,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0;H=c=H-848|0;e:if(!(!e[A+104>>2]|8&d[i+2|0]|e[47202]==193)){for(e[s>>2]=0,e[33272]=0,e[33274]=l,f[c+192|0]=0,e[33273]=c+192;h=(l=h)+1|0,f[0|(ce=t+l|0)]-48>>>0<10;);e[56798]=0,Fe=c+188|0,T=-2147483648,H=te=H-16|0;A:if(g=d[0|t]){x=t;r:{for(;;){if(!((0|(g=g<<24>>24))==32|g-9>>>0<5))break r;if(g=d[x+1|0],x=x+1|0,!g)break}break A}r:switch((g=d[0|x])-43|0){case 0:case 2:break r;default:break A}K=(0|g)==45?-1:0,x=x+1|0}else x=t;for(;g=-48,(((_=f[0|x])-48&255)>>>0<10||(g=-87,(_-97&255)>>>0<26||(g=-55,!((_-65&255)>>>0>25))))&&!((0|(_=g+_|0))>=10);)Fr(te,10,0,0,0,V,J,0,0),g=1,e[te+8>>2]|e[te+12>>2]||(Ee=st(V,J,10,0),(0|(Te=le))==-1&~_>>>0<Ee>>>0||(g=Te,J=(V=_+Ee|0)>>>0<_>>>0?g+1|0:g,he=1,g=I)),x=x+1|0,I=g;Fe&&(e[Fe>>2]=he?x:t);A:{r:{if(I)e[56798]=68,V=-2147483648,J=0;else if(!J&V>>>0<2147483648)break r;if(!K){e[56798]=68,T=2147483647;break A}if(!(!J&V>>>0<=2147483648)){e[56798]=68;break A}}T=(K^V)-K|0}if(H=te+16|0,K=T,!(e[56798]|e[c+188>>2]==(0|t))){A:{r:{a:{if(!(!((0|(_=64&d[A+109|0]?4:3))!=(0|l)|e[A+124>>2]!=f[t-2|0])&f[t-3|0]-48>>>0<10)){n:{if(e[A+124>>2]!=32){if(!(16&d[A+105|0]))break a;if((0|l)==3)break n;break a}if((0|l)!=3)break a}if(4&d[i+2|0]|f[t-2|0]-48>>>0>=10)break a}f[133104]=0,f[c+288|0]=0,Le=1;break r}if(f[133104]=0,e[33275]=0,f[c+288|0]=0,Xe=1,he=0,d[0|t]==48)break A}he=ur(A,t,ce,i,0)}d[0|ce]!=46|f[t+h|0]-48>>>0<10|1&f[i+13|0]|f[ce+2|0]-48>>>0<10||(f[0|ce]=0);A:if(!he||(Ee=1,e[A+212>>2]==26741)){if(T=c+256|0,e[c+844>>2]=T,m=h,64&d[i+1|0]&&(f[c+256|0]=45,T=c+256|1,e[c+844>>2]=T,m=l+2|0),x=d[t+m|0]){for(;!((255&x)==32|(0|m)>28)&&(g=e[c+844>>2],e[c+844>>2]=g+1,f[0|g]=x,x=d[(m=m+1|0)+t|0]););T=e[c+844>>2]}if(f[0|T]=0,Ee=1,g=f[c+256|0]){if(!(I=e[A+136>>2])||Ar(c+256|0,I)){if(g-48>>>0<10||(e[c+176>>2]=c+256,dA(g=c+800|0,88653,c+176|0),!QA(A,g,133104)))break A;e[s>>2]=128|e[s>>2],e[c+160>>2]=c+256,dA(g=c+800|0,88773,c+160|0),QA(A,g,133116),Ee=0}he=2}}Fe=e[i>>2],f[c+352|0]=0,f[c+624|0]=0;A:{r:{a:if(!(!Xe|d[0|t]!=48||(0|(g=f[t+1|0]))==32|(0|g)==e[A+128>>2])){n:{if((0|l)==2){if(d[t+3|0]!=58|f[t+5|0]-48>>>0>=10||!((0|(g=f[t+7|0]))==32|g-9>>>0<5))break n;break a}if((0|l)>3)break r}if(d[0|t]==48&&!((0|(g=l-1|0))<=0))for(m=0;;){if(QA(A,88875,MA(I=c+288|0)+I|0),d[(m=m+1|0)+t|0]!=48)break a;if(!((0|g)>(0|m)))break}}a:{n:{if(!((0|(g=f[0|ce]))==32&&16&d[A+105|0])){if(Te=2,V=l+2|0,(0|g)==e[A+124>>2])break n;J=1,T=0,g=0;break a}Te=1,V=l+2|0}if(4&d[i+14|0])T=1,g=0,J=1;else for(g=0,x=1,m=l,J=1;;){I=g,g=x,x=(te=m+Te|0)+t|0,m=0;n:{for(;;){if(T=1,!(f[m+x|0]-48>>>0>=10)){if((0|_)!=(0|(m=m+1|0)))continue;break n}break}g=I;break a}if(f[x+_|0]-48>>>0<10){g=I;break a}if(m=0,f[x-1|0]-48>>>0<10){g=I;break a}n:{for(;;){if(d[(m+te|0)+t|0]==48){if((0|_)!=(0|(m=m+1|0)))continue;break n}break}J=0}if((0|(I=f[(m=_+te|0)+t|0]))!=e[A+124>>2]&(!(16&d[A+105|0])|(0|I)!=32))break a;if(V=m+2|0,4&d[2+(O(x=g+1|0,12)+i|0)|0])break}}m=!K;a:if(!(!J|!(64&d[1+(O(g,12)+i|0)|0])|e[A+212>>2]!=26741)){n:switch(d[0|(I=t+V|0)]-97|0){case 0:case 4:break n;default:break a}n:{o:{c:{u:switch((x=d[I+1|0])-116|0){case 6:break a;case 1:case 2:case 3:case 4:case 5:break o;case 0:break u;default:break c}if(d[I+2|0]!=116)break n;break a}if((0|x)==32)break a}if(!(!!((0|K)%1e3|0)&(0|g)!=1)&&(0|x)==108)break a}e[33274]=1|e[33274]}V=32768&Fe,m&=Le;a:if(e[A+128>>2]!=f[0|ce]|f[t+h|0]-48>>>0>=10){n:{if(!m){if(m=0,I=1,!((0|g)>0&T))break n;K=(h=ot(A,K,g,J,c+624|0))?0:K,m=!!(0|h),x=0;break a}m=1,K=0,e[33275]==1&&(e[c+144>>2]=g+1,dA(h=c+800|0,89026,c+144|0),QA(A,h,c+688|0)||(e[c+128>>2]=g,dA(h=c+800|0,89026,c+128|0),QA(A,h,c+624|0)))}I=1,x=0}else QA(A,88882,c+624|0),I=0,x=256;T=V?2:he;a:{if(g|d[c+624|0]|d[0|ce]!=46){if(g)break a}else QA(A,89192,c+624|0);if(e[c+844>>2]=t,f[t+1|0]-48>>>0<10)for(;h=e[c+844>>2],e[c+844>>2]=h+1,f[h+2|0]-48>>>0<10;);if(f[e[c+844>>2]-1|0]-48>>>0>=10||(e[c+416>>2]=e[c+844>>2]-1,Ot(A,c+416|0,c+192|0,s,4,i)&&(e[33272]=2)),d[c+192|0]|d[e[c+844>>2]]==48||Ot(A,c+844|0,c+192|0,s,4,i)&&(e[33272]=1),Xe){if(!T&I&&(e[c+112>>2]=K,dA(i=c+800|0,89214,c+112|0),QA(A,i,r)))break A;if(1&f[A+110|0]){for(h=t;(32|d[0|h])!=32;)h=h+1|0;e[c+416>>2]=h,d[h+1|0]==37&&(QA(A,89328,r),i=MA(r),f[e[c+416>>2]+1|0]=32,r=r+i|0)}}}Se(A,K,c+416|0,m,g,T|x|Le),!(2&d[A+109|0])|(0|g)<=0?(e[c+60>>2]=15,e[c- -64>>2]=c+624,e[c+56>>2]=c+416,e[c+52>>2]=c+352,e[c+48>>2]=c+288,dA(r,89415,c+48|0)):(e[c+88>>2]=15,e[c+96>>2]=c+416,e[c+92>>2]=c+352,e[c+84>>2]=c+624,e[c+80>>2]=c+288,dA(r,89346,c+80|0));a:if(!I)for(;;){for(l=l+1|0,I=0;I=(h=I)+1|0,f[(g=l+h|0)+t|0]-48>>>0<10;);i=2;n:{o:{c:{u:{l:{i:switch((I=57344&e[A+104>>2])+-8192>>>13|0){case 6:break o;case 2:break c;case 0:case 4:case 5:break u;case 1:break l;case 3:break i;default:break n}i=5}if((0|(I=d[0|(m=t+l|0)]))==48)for(;QA(A,88875,g=c+688|0),As(r,g),h=h-1|0,(0|(I=d[0|(m=(l=l+1|0)+t|0)]))==48;);if((0|i)<(0|h)|(I<<24>>24)-48>>>0>=10)break n;i=c+688|0,Se(A,Js(m),i,0,0,0),As(r,i),l=l+h|0;break n}if(Se(A,Js(i=t+l|0),c+416|0,0,0,0),!((0|I)==8192&d[0|i]!=48)){if(e[c+16>>2]=h,dA(i=c+800|0,89508,c+16|0),!QA(A,i,c+688|0))break n;As((0|I)==49152?r:c+416|0,c+688|0)}As(r,c+416|0),l=g;break n}if((0|h)>4||d[0|(i=t+l|0)]==48)break n;Se(A,l=Js(i),i=c+688|0,0,0,0),As(r,i),l=g;break n}if(!((0|h)<=1))for(;;){if(e[c+32>>2]=f[t+l|0],dA(i=c+800|0,89575,c+32|0),!QA(A,i,c+688|0))break n;if(As(r,c+688|0),l=l+1|0,!((0|(h=h-1|0))>1))break}}n:if(!((I=d[0|(h=t+l|0)])-48>>>0>=10||MA(r)>>>0>=190))for(;;){if(i=c+688|0,Ss(A,f[0|h]-48|0,0,2,i),g=MA(r),e[c>>2]=15,e[c+4>>2]=i,dA(r+g|0,89594,c),(I=d[0|(h=(l=l+1|0)+t|0)])-48>>>0>=10)break n;if(!(MA(r)>>>0<=189))break}if(QA(A,89678,c+688|0)&&As(r,c+688|0),e[A+128>>2]!=(0|I)|f[1+(t+l|0)|0]-48>>>0>=10)break a;QA(A,88882,i=c+688|0),As(r,i)}if(!(i=d[0|r])|(0|i)==21||(i=jA(c+184|0,t=1+(t+l|0)|0),l=e[c+184>>2],!(2&d[A+106|0])|(0|l)!=32||(jA(c+184|0,t+i|0),l=e[c+184>>2]),OA(l)|J||(A=MA(r)+r|0,f[0|A]=11,f[A+1|0]=0)),e[s>>2]=-2147483648|e[s>>2],e[33275]=e[33275]-1,m=1,Ee)break e;e[33264]=1;break e}e[s>>2]=-129&e[s>>2],m=0;break e}m=1}}return H=c+848|0,m}function ma(A,t,r,s){var i,l,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0;if(H=i=H-416|0,he=y[r+8>>1],V=d[r+10|0],64&(l=e[r>>2])){for(e[r>>2]=-65&l,f[199388]=1,m=e[47202],T=e[49846],h=e[47352],x=e[47351],I=e[47350];;){c=(g=e[198304+(x<<2)>>2])>>8;e:{A:switch((31&g)-9|0){case 0:m=c;break e;case 4:T=c;break e;case 3:break A;default:break e}h=g>>>0>=256?c+h|0:0}if(!(!(128&g)&(0|I)>(0|(x=x+1|0))))break}e[47352]=h,e[47351]=x,e[49846]=T,e[47202]=m}T=0;e:if(!((0|(c=e[49572]))>997)){if(1048576&l||!d[0|t])d[199388]&&(e[49572]=c+1,f[199388]=0,e[(A=190288+(c<<3)|0)>>2]=983042,e[A+4>>2]=0),f[189088]=0;else if(!((0|c)>990)){(0|(c=e[A+8224>>2]))>0&&(e[A+8224>>2]=c-1),m=512&l?s:s+4|0,h=(240&e[47202])==16,(te=2&l)&&((0|(g=e[47200]))<3||(0|(c=e[47350]))>243||(d[199388]&&(e[(x=198300+(c<<2)|0)>>2]=-129&e[x>>2]),e[47350]=c+1,f[199388]=1,Ee=(0|g)==3?20:g,e[198304+(c<<2)>>2]=Ee<<8|193)),I=h?m:s;A:{r:{if(!(8&l)){for(;s=d[(c=_)+t|0],f[c+(i+240|0)|0]=s,223&s&&(_=c+1|0,c>>>0<160););if(f[i+66|0]=0,_=i- -64|2,T=Oe(e[47192],t,r,_),e[i+412>>2]=T,!(4096&T))break r;qA(t,i+240|0,c);break e}a:if(Kr(t,87276,3))qr(t,189088,i+240|0);else{if(x=0,223&(c=d[0|(_=t+3|0)]))for(;f[(i+240|0)+x|0]=Ps(c<<24>>24),x=x+1|0,223&(c=d[0|(_=_+1|0)]););f[(s=i+240|0)+x|0]=0,c=0;n:if(!((0|(r=e[34461]))<=0)){for(;;){if(!Ar(s,O(c,44)+137856|0)){e[34457]=c;break n}if((0|r)==(0|(c=c+1|0)))break}c=r}if((0|(r=(0|r)==(0|c)?-1:c))<=0)break a;as(r),f[189090]=0,f[189089]=r,f[189088]=21}T=-2147483648,e[i+412>>2]=-2147483648,s=-1;break A}if(!(!(8388608&T)|8&d[r+12|0])){for(H=h=H-208|0,x=e[A+60>>2],g=t;s=g,g=g+1|0,d[0|s]!=32;);jA(h+204|0,g);r:if(OA(e[h+204>>2])){m=PA(h,189088),g=(!(256&x)|(J=oA(A,g,r+12|0,0))>>>15)&!(67108864&J)&d[189088]!=21;a:{n:{if(512&x){if(!(g&!(16&d[r+12|0])))break n;break a}if(g)break a}PA(189088,m);break r}if(f[0|s]=45,e[r>>2]=-2&e[r>>2],T=0,g=189088,K=oA(e[47192],t,r,0),e[i+412>>2]=K,!((0|x)<=0)&&(m=d[189088])){for(;T=(d[e[144464+(m<<2)>>2]+11|0]==2)+T|0,m=d[0|(g=g+1|0)];);if(!((31&x)>=(0|T))){f[0|s]=32,e[i+412>>2]=oA(e[47192],t,r,0);break r}}e[i+412>>2]=128|(K||J),e[33264]=1}H=h+208|0}if(s=-1,d[189088]==21){if(x=PA(i+16|0,132848),h=e[i+412>>2],g=qA(t,i+240|0,c),m=i- -64|1,(0|(s=nn(d[189089]?189089:87315,188772,189296)))<0||(e[r>>2]=4194304|e[r>>2],d[i+66|0]?(k[i+64>>1]=8192,h=oA(e[47193],m,r,0)):h=Oe(e[47193],g,r,_)),d[189088]==21&&(c=qA(g,i+240|0,c),(0|(s=nn(d[189089]?189089:87315,188772,189296)))<0||(e[r>>2]=4194304|e[r>>2],d[i+66|0]?(k[i+64>>1]=8192,h=oA(e[47193],m,r,0)):h=Oe(e[47193],c,r,_)),T=4096,d[189088]==21))break e;e[i+412>>2]=h,(0|s)>=0||(f[189090]=0,k[94544]=3341,(0|s)==-1&&(PA(132848,x),as(e[e[32972]+60>>2]),s=e[e[32972]+60>>2]))}T=e[i+412>>2],128&l||(I=268435456&T&&(0|I)<=1?1:I,!(256&T)|528&l|e[A+8224>>2]|2&d[r-11|0]||(e[A+8224>>2]=3,I=(0|I)<=4?4:I)),I=(0|I)<=0&&e[49846]>2?1:I}if(r=d[199388],!((0|I)<=0|(0|(_=e[49572]))>990)){e[49572]=_+1,g=1&r,r=0,k[(c=190288+(_<<3)|0)>>1]=g?2:0,f[c+7|0]=0,f[c+3|0]=0,g=I>>>0>1,f[c+2|0]=g?9:11,k[c+4>>1]=0,e[A+8236>>2]=0;A:if(!(!(c=g?I-2|0:0)|(0|(_=e[49572]))>990))for(m=A+8236|0;;){if(e[49572]=_+1,k[(g=190288+(_<<3)|0)>>1]=0,f[g+7|0]=0,f[g+3|0]=0,h=c>>>0>1,f[g+2|0]=h?9:11,k[g+4>>1]=0,e[m>>2]=0,_=e[49572],(0|(c=h?c-2|0:0))<=0)break A;if(!((0|_)<991))break}e[A+8228>>2]=0,e[A+8232>>2]=0}f[199388]=1&r,!te|e[47200]!=1||(e[49572]=_+2,f[199388]=0,k[(c=190288+(_<<3)|0)>>1]=1&r?2:0,f[c+7|0]=0,k[c+2>>1]=10,k[c+4>>1]=0,k[c+12>>1]=0,e[c+8>>2]=1179648,f[c+15|0]=0,1&l&&Ft(f[t+1|0])&&(r=d[199388],f[199388]=0,t=e[49572],e[49572]=t+2,k[(t=190288+(t<<3)|0)>>1]=r?2:0,f[t+7|0]=0,k[t+2>>1]=10,k[t+4>>1]=0,k[t+12>>1]=0,e[t+8>>2]=1179648,f[t+15|0]=0)),c=V>>>0<31;A:if(!((0|s)<0))if(t=e[49572],r=d[190290+((I=t-1|0)<<3)|0],d[189088]!=9|d[189089]!=21)(0|r)!=21&&(g=d[199388],f[199388]=0,f[(r=190288+(t<<3)|0)+7|0]=0,k[r+2>>1]=21,k[r+4>>1]=0,k[r>>1]=g?2:0,I=t),e[49572]=I+1,f[190295+(I<<3)|0]=s;else{if((0|r)!=21)break A;e[49572]=I}t=2047&he,r=(c?V:31)<<11,h=(Te=128&l)?d[e[144464+(d[189088]<<2)>>2]+11|0]?189088:189089:189088,(I=d[0|h])|!(1&f[199388])||(I=23,f[0|h]=23,f[h+1|0]=0),Fe=t|r,m=e[49572];A:if(I)if((0|m)>994)t=0;else for(fA=((-1610612736&T)==-2147483648)<<4,he=Fe+1|0,hA=A+8233|0,t=0,g=1,J=0,Le=1,V=-1,te=-1,c=0;;){x=h+1|0;r:{if((0|(K=255&I))!=255){if(r=e[144464+(K<<2)>>2])break r;e[i>>2]=K,H=r=H-16|0,e[r+12>>2]=i,Li(132552,87474,i),H=r+16|0,m=e[49572]}if(!(I=d[0|x]))break A;if(h=x,(0|m)<995)continue;break A}r:if((0|(ce=255&I))!=21)if((0|(h=d[r+11|0]))!=1){r=he;a:switch(ce-12|0){case 8:f[(r=190288+((V=m-1|0)<<3)|0)+3|0]=g,k[r>>1]=4|y[r>>1],r=c;break r;case 0:k[(r=190280+(m<<3)|0)>>1]=8|y[r>>1],r=c;break r;case 10:T|=16384,e[i+412>>2]=T,r=c;break r;case 3:break r;default:break a}K=d[199388],f[199388]=0,f[(r=(ce=m<<3)+190288|0)+7|0]=0,f[r+2|0]=I,k[r+4>>1]=c,c=(K?2:0)|fA,k[r>>1]=c,(0|h)==2?((0|g)>=4&&(f[189076]=1),(0|V)<0||(0|(t=m-1|0))!=(0|V)&&(f[190291+(t<<3)|0]=g),k[r>>1]=4|c,Xe=(t=(0|g)>(0|te))?m:Xe,te=t?g:te,c=1,J&&(f[r+7|0]=J),V=m,J=0,t=g):(!Le|!(64&d[0|hA])||(k[r>>1]=8|c),c=g),m=m+1|0,e[49572]=m,f[ce+190291|0]=t,r=0,Le=0,g=c}else{if(!y[r+8>>1]){g=d[r+14|0],r=c;break r}if((0|V)<0){r=c,J=K;break r}f[190295+(V<<3)|0]=I,r=c}else e[(r=190288+(m<<3)|0)>>2]=1376256,k[r+4>>1]=0,f[r+7|0]=d[h+1|0],e[49572]=m+1,as(d[h+1|0]),x=h+2|0,m=e[49572],r=c;if(!(I=d[0|x]))break A;if(h=x,c=r,!((0|m)<995))break}else t=0;131072&l&&(r=m+1|0,e[49572]=r,g=d[199388],f[199388]=0,f[(c=190288+(m<<3)|0)+7|0]=0,k[c+2>>1]=27,k[c+4>>1]=0,k[c>>1]=g?2:0,m=r),Te||(k[190292+(_<<3)>>1]=Fe),e[A+8228>>2]=0,d[e[144464+(d[190282+(m<<3)|0]<<2)>>2]+11|0]!=2|(0|t)<4||(e[A+8228>>2]=1),(0|s)>=0&&(PA(132848,i+16|0),as(e[e[32972]+60>>2]),r=d[199388],f[199388]=0,s=e[49572],k[(t=190288+(s<<3)|0)>>1]=r?2:0,k[t+2>>1]=21,k[t+4>>1]=0,f[t+7|0]=e[e[32972]+60>>2],m=s+1|0,e[49572]=m),(0|Ee)>0&&(f[199388]=0,e[49572]=m+1,e[(t=190288+(m<<3)|0)>>2]=655362,r=e[47350],e[47350]=r+1,f[t+7|0]=0,k[t+4>>1]=0,e[198304+(r<<2)>>2]=Ee<<8|225),1024&T&&(k[(t=190288+(Xe<<3)|0)>>1]=64|y[t>>1]),e[A+8232>>2]=T}}return H=i+416|0,T}function ha(A,t,r,s,i){var l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0;e:if(t){if(te=i<<4,c=(t=e[33268])+r|0,!((0|t)<=0||(l=d[0|(m=te+129360|0)],x=d[m+1|0]-l<<8,x=(0|t)!=1?(0|x)/(0|t)|0:x,(0|r)>=(0|c))))for(T=x>>>0>255,K=x>>>8|0,l<<=8,m=0-x>>>8|0,Ee=(0|x)<=0,t=r;Ee?(I=(h=(h=(0|(g=(0|(g=e[101024+(f[O(t,6)+A|0]<<2)>>2]))<(0|m)?m:g))>=18?18:g)+(g=(0|(g=(0|(l=l+x|0))/256|0))>0?g:0)|0)>>>0>=254?254:h,g=g>>>0>=254?254:g):(g=O(t,6)+A|0,f[g+2|0]=T|d[g+2|0],g=(h=(0|(g=(0|l)/256|0))>0?g:0)>>>0>=254?254:h,I=(h=h+K|0)>>>0>=254?254:h,l=l+x|0),h=O(t,6)+A|0,f[h+5|0]=g,f[h+4|0]=I,(0|c)!=(0|(t=t+1|0)););if(2&d[188785]?(m=e[33271],e[33270]=m):m=e[33270],K=(T=d[(l=(t=i<<4)+129360|0)+3|0])-(g=d[l+2|0])<<8,Ee=e[l+4>>2],(0|r)>0?(Fe=101056,ce=5,h=(0|K)/(d[t+129368|0]-1|0)|0):(Fe=e[(t=t+129360|0)+12>>2],ce=d[t+10|0],h=0),(0|c)<(0|m)){for(x=(0|r)<=0,J=((t=K>>31)^K)-t|0,_=129360+(i<<4)|0,g<<=8,Te=T<<8,T=0;;){A:{if(I=O(c,6)+A|0,!(!(1&x)&(0|(l=f[0|I]))<4)){r=0,t=c;r:if(1&((0|l)==5|x)){for(;(0|(h=f[O(t,6)+A|0]))<=6&&(r=((0|h)>3)+r|0,(0|m)!=(0|(t=t+1|0))););if(V=0,(0|(he=(0|(t=d[_+8|0]))>(0|r)?r:t))<2){h=0,T=g;break r}h=(0|K)/(he-1|0)|0,T=g}else(0|he)>0?T=h+T|0:(T=Te+(O(J,f[V+Fe|0])>>6)|0,(0|ce)>(0|(V=V+1|0))||(Fe=e[_+12>>2],V=0));if(he=he-1|0,!((0|l)<4)){f[0|I]=6,r=(t=(0|(t=(0|T)/256|0))>0?t:0)+(((r=(l=e[Ee+(l<<2)>>2])>>31)^l)-r|0)|0;break A}}(0|l)!=3?(t=(0|T)/256|0,(63&d[I-6|0])>>>0>=3?(r=(l=e[Ee+(l<<2)>>2])>>31,r=(t=(0|(t=t-f[_+9|0]|0))>0?t:0)+((r^l)-r|0)|0):r=(t=(0|t)>0?t:0)+(((r=(l=e[Ee+(l<<2)>>2])>>31)^l)-r|0)|0):r=(t=(0|(t=(0|T)/256|0))>0?t:0)+(((r=(l=e[Ee+12>>2])>>31)^l)-r|0)|0}if(f[I+5|0]=t>>>0>=254?254:t,x=0,t=(0|r)>0?r:0,f[I+4|0]=t>>>0>=254?254:t,f[I+2|0]=d[I+2|0]|l>>>31,(0|m)==(0|(c=c+1|0)))break}c=m}if(!(d[133068]||((268435455&i)==3&&(t=O(c,6)+A|0,f[t+2|0]=2|d[t+2|0]),e[33269]?(t=d[(l=100768+(i<<4)|0)+5|0],g=l+3|0,r=d[l+4|0]-t|0):(t=d[(r=100768+(i<<4)|0)+2|0],g=te+100768|0,r=d[r+1|0]-t|0),l=O(c,6)+A|0,f[l+5|0]=t>>>0>=254?254:t,f[l+2|0]=d[l+2|0]|r>>>31,t=(h=t)+(((t=r>>31)^r)-t|0)|0,f[l+4|0]=t>>>0>=254?254:t,r=O(m,6)+A|0,f[r+1|0]=d[0|g],t=c+1|0,d[0|r]==4&&(f[0|r]=6),(0|(r=s-t|0))<=0||(i=d[(c=100768+(i<<4)|0)+12|0],c=d[c+13|0]-i<<8,c=(0|r)!=1?(0|c)/(0|r)|0:c,(0|t)>=(0|s)))))for(h=c>>>0>255,x=c>>>8|0,r=i<<8,i=0-c>>>8|0,T=(0|c)<=0;T?(g=(m=(g=(0|(l=(0|i)>(0|(l=e[101024+(f[O(t,6)+A|0]<<2)>>2]))?i:l))>=18?18:l)+(l=(0|(l=(0|(r=r+c|0))/256|0))>0?l:0)|0)>>>0>=254?254:m,m=l>>>0>=254?254:l):(l=O(t,6)+A|0,f[l+2|0]=h|d[l+2|0],m=(l=(0|(l=(0|r)/256|0))>0?l:0)>>>0>=254?254:l,g=(l=l+x|0)>>>0>=254?254:l,r=r+c|0),l=O(t,6)+A|0,f[l+5|0]=m,f[l+4|0]=g,(0|s)!=(0|(t=t+1|0)););}else{if(t=(ce=e[34455])+O(i,68)|0,c=d[t+24|0],m=d[t+25|0],l=(t=e[33268])+r|0,!((0|t)<=0||(I=m-c<<8,I=(0|t)!=1?(0|I)/(0|t)|0:I,(0|r)>=(0|l))))for(h=I>>>0>255,x=I>>>8|0,t=c<<8,m=0-I>>>8|0,T=(0|I)<=0;T?(c=(c=(K=(0|(c=(0|(c=e[101024+(f[O(r,6)+A|0]<<2)>>2]))<(0|m)?m:c))>=18?18:c)+(g=(0|(c=(0|(t=t+I|0))/256|0))>0?c:0)|0)>>>0>=254?254:c,g=g>>>0>=254?254:g):(c=O(r,6)+A|0,f[c+2|0]=h|d[c+2|0],g=(c=(0|(c=(0|t)/256|0))>0?c:0)>>>0>=254?254:c,c=(c=c+x|0)>>>0>=254?254:c,t=t+I|0),K=O(r,6)+A|0,f[K+5|0]=g,f[K+4|0]=c,(0|l)!=(0|(r=r+1|0)););2&d[188785]?(r=e[33271],e[33270]=r):r=e[33270],m=r,t=ce+O(i,68)|0;A:if((0|(x=d[t+33|0]))!=255)for(;;){if((0|(m=m-1|0))<(0|l)){m=r;break A}if(!(f[O(m,6)+A|0]<4))break}if((0|r)>(0|l)){for(hA=((c=(Xe=(T=d[t+32|0])-(I=d[0|(g=t+31|0)])<<8)>>31)^Xe)-c|0,Te=(0|(t=d[0|(h=t+30|0)]))==255,K=x<<8,Ee=I<<8,_A=T<<8,te=ce+O(i,68)|0,LA=(0|t)!=255,T=0,x=0,t=1,I=0;;){A:{if(_=O(l,6)+A|0,!(!(1&t)&(0|(J=f[0|_]))<4)){r:if(1&((0|J)==5|t)){a:if(LA){if(Le=1,t=0,c=h,!((0|m)<=(0|(I=l+1|0)))){for(;;){if(c=h,(0|(x=f[O(I,6)+A|0]))>6)break a;if(t=((0|x)>3)+t|0,(0|m)==(0|(I=I+1|0)))break}c=h}}else{if(t=0,I=l,c=g,(0|m)<=(0|l))break a;for(;;){if(c=g,(0|(x=f[O(I,6)+A|0]))>6)break a;if(t=((0|x)>3)+t|0,(0|m)==(0|(I=I+1|0)))break}c=g}if(x=d[0|c]<<8,fA=0,(0|(I=(0|t)<(0|(c=d[te+34|0]))?t:c))<2){Fe=0;break r}Fe=(0|Xe)/(I-1|0)|0}else(0|l)!=(0|m)?Le?(Te=1,I=I+1|0,Le=0,x=Ee):(0|I)>0?(x=x+Fe|0,Le=0):(Le=0,x=(O(f[16+(te+fA|0)|0],hA)>>6)+_A|0,fA=(0|(t=fA+1|0))<d[te+35|0]?t:0):(Te=2,x=K);if(I=I-1|0,!((0|J)<4)){t=c=l+1|0;r:if(!((0|r)<=(0|l)))for(;;){if(f[O(t,6)+A|0]>1)break r;if(!((0|r)>=(0|(t=t+1|0))))break}f[0|_]=6,f[_+1|0]=d[te+26|0],T=0,V=d[te+27|0],l=(0|(l=(0|x)/256|0))>0?l:0,f[_+5|0]=l>>>0>=254?254:l,l=l+V|0,f[_+4|0]=l>>>0>=254?254:l,he=t-c|0;break A}}if((0|J)>=2){t=c=l+1|0;r:if(!((0|r)<=(0|l)))for(;;){if(f[O(t,6)+A|0]>1)break r;if(!((0|r)>=(0|(t=t+1|0))))break}T=0,l=(0|(l=(0|x)/256|0))>0?l:0,f[_+5|0]=l>>>0>=254?254:l,V=e[100976+(J<<2)>>2],f[_+2|0]=d[_+2|0]|V>>>31,l=(0|(l=(J=l)+(((l=V>>31)^V)-l|0)|0))>0?l:0,f[_+4|0]=l>>>0>=254?254:l,he=t-c|0}else(0|he)<=1?(V=f[36+(te+Te|0)|0],t=0):(V=f[(t=te+Te|0)+36|0],t=(f[t+39|0]-V|0)/(he-1|0)|0),c=e[100976+(J<<2)>>2],f[_+2|0]=d[_+2|0]|c>>>31,t=(0|(t=(((0|x)/256|0)+V|0)+O(t,T)|0))>0?t:0,f[_+5|0]=t>>>0>=254?254:t,t=(0|(t=t+(((J=c)^(c>>=31))-c|0)|0))>0?t:0,f[_+4|0]=t>>>0>=254?254:t,T=T+1|0,c=l+1|0}if(t=0,(0|r)==(0|(l=c)))break}l=r}if(d[133068]||(c=O(l,6)+A|0,e[33269]?(g=ce+O(i,68)|0,t=d[g+47|0],m=d[g+46|0]-t|0,g=g+45|0):(g=ce+O(i,68)|0,t=d[g+44|0],m=d[g+43|0]-t|0,g=g+42|0),g=d[0|g],h=(((h=m>>31)^m)-h|0)+t|0,f[c+4|0]=h>>>0>=254?254:h,f[c+5|0]=t>>>0>=254?254:t,t=O(l,6)+A|0,f[t+2|0]=d[t+2|0]|m>>>31,r=O(r,6)+A|0,f[r+1|0]=g,t=l+1|0,d[0|r]==4&&(f[0|r]=6),(0|(r=s-t|0))<=0)||(c=ce+O(i,68)|0,i=d[c+48|0],c=d[c+49|0]-i<<8,c=(0|r)!=1?(0|c)/(0|r)|0:c,(0|t)>=(0|s)))break e;for(h=c>>>0>255,x=c>>>8|0,r=i<<8,i=0-c>>>8|0,T=(0|c)<=0;T?(g=(m=(g=(0|(l=(0|i)>(0|(l=e[101024+(f[O(t,6)+A|0]<<2)>>2]))?i:l))>=18?18:l)+(l=(0|(l=(0|(r=r+c|0))/256|0))>0?l:0)|0)>>>0>=254?254:m,m=l>>>0>=254?254:l):(l=O(t,6)+A|0,f[l+2|0]=h|d[l+2|0],m=(l=(0|(l=(0|r)/256|0))>0?l:0)>>>0>=254?254:l,g=(l=l+x|0)>>>0>=254?254:l,r=r+c|0),l=O(t,6)+A|0,f[l+5|0]=m,f[l+4|0]=g,(0|s)!=(0|(t=t+1|0)););}}function xs(){var A,t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0;if((0|(A=e[36455]))!=(0|(m=e[36454]))){e:if(!((0|(_=e[36427]))<0|(0|m)==(0|_))){r=x=e[8+(216192+((I=_)<<4)|0)>>2];A:{for(;;){if((s=e[(c=216192+((I=(0|(s=I-1|0))<0?169:s)<<4)|0)>>2])-5>>>0<2)break A;r:{if((0|s)<=4){if(e[c+12>>2]!=(0|r))break A;if(s=y[c+4>>1],e[c+12>>2]=x,r=e[c+8>>2],16&(t=y[r>>1]))break r;for(K=32&t?(O(s,12)>>>0)/10|0:s,i=0,s=r,g=0;;){t=y[r>>1];a:if(!(g>>>0<3&&8&t)){if(t=t<<16>>16,(0|(V=(h=k[2+((T=g<<1)+r|0)>>1])-(l=k[(T=x+T|0)+2>>1])|0))>(0|(h=(0|O(K,(0|O(e[200944+(g<<2)>>2],(0|V)>0?h+(l<<1)|0:(h<<1)+l|0))/3e3|0))/256|0)))i||((0|t)<0?s=r:(s=0,t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,(t=(i=t<<6)+177888|0)&&(s=y[r+4>>1]|y[r+6>>1]<<16,l=y[r>>1]|y[r+2>>1]<<16,k[t>>1]=l,k[t+2>>1]=l>>>16,k[t+4>>1]=s,k[t+6>>1]=s>>>16,s=y[r+60>>1]|y[r+62>>1]<<16,l=y[r+56>>1]|y[r+58>>1]<<16,k[t+56>>1]=l,k[t+58>>1]=l>>>16,k[t+60>>1]=s,k[t+62>>1]=s>>>16,s=y[r+52>>1]|y[r+54>>1]<<16,l=y[r+48>>1]|y[r+50>>1]<<16,k[t+48>>1]=l,k[t+50>>1]=l>>>16,k[t+52>>1]=s,k[t+54>>1]=s>>>16,s=y[r+44>>1]|y[r+46>>1]<<16,l=y[r+40>>1]|y[r+42>>1]<<16,k[t+40>>1]=l,k[t+42>>1]=l>>>16,k[t+44>>1]=s,k[t+46>>1]=s>>>16,s=y[r+36>>1]|y[r+38>>1]<<16,l=y[r+32>>1]|y[r+34>>1]<<16,k[t+32>>1]=l,k[t+34>>1]=l>>>16,k[t+36>>1]=s,k[t+38>>1]=s>>>16,s=y[r+28>>1]|y[r+30>>1]<<16,l=y[r+24>>1]|y[r+26>>1]<<16,k[t+24>>1]=l,k[t+26>>1]=l>>>16,k[t+28>>1]=s,k[t+30>>1]=s>>>16,s=y[r+20>>1]|y[r+22>>1]<<16,l=y[r+16>>1]|y[r+18>>1]<<16,k[t+16>>1]=l,k[t+18>>1]=l>>>16,k[t+20>>1]=s,k[t+22>>1]=s>>>16,s=y[r+12>>1]|y[r+14>>1]<<16,l=y[r+8>>1]|y[r+10>>1]<<16,k[t+8>>1]=l,k[t+10>>1]=l>>>16,k[t+12>>1]=s,k[t+14>>1]=s>>>16,f[i+177904|0]=0,k[t>>1]=32768|y[t>>1],s=t))),t=h+y[T+2>>1]|0;else{if((0-h|0)<=(0|V))break a;i||((0|t)<0?s=r:(s=0,t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,(t=(i=t<<6)+177888|0)&&(s=y[r+4>>1]|y[r+6>>1]<<16,l=y[r>>1]|y[r+2>>1]<<16,k[t>>1]=l,k[t+2>>1]=l>>>16,k[t+4>>1]=s,k[t+6>>1]=s>>>16,s=y[r+60>>1]|y[r+62>>1]<<16,l=y[r+56>>1]|y[r+58>>1]<<16,k[t+56>>1]=l,k[t+58>>1]=l>>>16,k[t+60>>1]=s,k[t+62>>1]=s>>>16,s=y[r+52>>1]|y[r+54>>1]<<16,l=y[r+48>>1]|y[r+50>>1]<<16,k[t+48>>1]=l,k[t+50>>1]=l>>>16,k[t+52>>1]=s,k[t+54>>1]=s>>>16,s=y[r+44>>1]|y[r+46>>1]<<16,l=y[r+40>>1]|y[r+42>>1]<<16,k[t+40>>1]=l,k[t+42>>1]=l>>>16,k[t+44>>1]=s,k[t+46>>1]=s>>>16,s=y[r+36>>1]|y[r+38>>1]<<16,l=y[r+32>>1]|y[r+34>>1]<<16,k[t+32>>1]=l,k[t+34>>1]=l>>>16,k[t+36>>1]=s,k[t+38>>1]=s>>>16,s=y[r+28>>1]|y[r+30>>1]<<16,l=y[r+24>>1]|y[r+26>>1]<<16,k[t+24>>1]=l,k[t+26>>1]=l>>>16,k[t+28>>1]=s,k[t+30>>1]=s>>>16,s=y[r+20>>1]|y[r+22>>1]<<16,l=y[r+16>>1]|y[r+18>>1]<<16,k[t+16>>1]=l,k[t+18>>1]=l>>>16,k[t+20>>1]=s,k[t+22>>1]=s>>>16,s=y[r+12>>1]|y[r+14>>1]<<16,l=y[r+8>>1]|y[r+10>>1]<<16,k[t+8>>1]=l,k[t+10>>1]=l>>>16,k[t+12>>1]=s,k[t+14>>1]=s>>>16,f[i+177904|0]=0,k[t>>1]=32768|y[t>>1],s=t))),t=y[T+2>>1]-h|0}i=1,k[2+((g<<1)+s|0)>>1]=t,e[c+8>>2]=s}if((0|(g=g+1|0))==6)break}x=s}if((0|m)!=(0|I))continue;break A}break}x=r}for(s=0;;){if((r=e[(I=216192+(_<<4)|0)>>2])-5>>>0<2)break e;if((0|r)<=4){if(r=e[I+8>>2],t=y[I+4>>1],s){if((0|r)!=(0|s))break e;e[I+8>>2]=x}else x=r;if(16&(s=y[x>>1]))break e;for(T=32&s?(O(t,6)>>>0)/5|0:t,i=0,r=s=e[I+12>>2],g=0;;){A:{r:if((0|(m=(c=k[2+((t=g<<1)+s|0)>>1])-(t=k[(h=t+x|0)+2>>1])|0))>(0|(c=(0|O(T,(0|O(e[200944+(g<<2)>>2],(0|m)>0?c+(t<<1)|0:(c<<1)+t|0))/3e3|0))/256|0))){if(!i){if(k[s>>1]<0){r=s,t=t+c|0;break r}r=(0|(r=e[44469]+1|0))<=169?r:0,e[44469]=r,i=y[s+20>>1]|y[s+22>>1]<<16,t=(r=177888+(r<<6)|0)+16|0,m=y[s+16>>1]|y[s+18>>1]<<16,k[t>>1]=m,k[t+2>>1]=m>>>16,k[t+4>>1]=i,k[t+6>>1]=i>>>16,t=y[s+4>>1]|y[s+6>>1]<<16,i=y[s>>1]|y[s+2>>1]<<16,k[r>>1]=i,k[r+2>>1]=i>>>16,k[r+4>>1]=t,k[r+6>>1]=t>>>16,t=y[s+12>>1]|y[s+14>>1]<<16,i=y[s+8>>1]|y[s+10>>1]<<16,k[r+8>>1]=i,k[r+10>>1]=i>>>16,k[r+12>>1]=t,k[r+14>>1]=t>>>16,t=y[s+28>>1]|y[s+30>>1]<<16,i=y[s+24>>1]|y[s+26>>1]<<16,k[r+24>>1]=i,k[r+26>>1]=i>>>16,k[r+28>>1]=t,k[r+30>>1]=t>>>16,t=y[s+36>>1]|y[s+38>>1]<<16,i=y[s+32>>1]|y[s+34>>1]<<16,k[r+32>>1]=i,k[r+34>>1]=i>>>16,k[r+36>>1]=t,k[r+38>>1]=t>>>16,t=y[s+44>>1]|y[s+46>>1]<<16,i=y[s+40>>1]|y[s+42>>1]<<16,k[r+40>>1]=i,k[r+42>>1]=i>>>16,k[r+44>>1]=t,k[r+46>>1]=t>>>16,t=y[s+52>>1]|y[s+54>>1]<<16,i=y[s+48>>1]|y[s+50>>1]<<16,k[r+48>>1]=i,k[r+50>>1]=i>>>16,k[r+52>>1]=t,k[r+54>>1]=t>>>16,t=y[s+60>>1]|y[s+62>>1]<<16,i=y[s+56>>1]|y[s+58>>1]<<16,k[r+56>>1]=i,k[r+58>>1]=i>>>16,k[r+60>>1]=t,k[r+62>>1]=t>>>16,f[r+16|0]=0,k[r>>1]=32768|y[r>>1],t=y[h+2>>1]}t=t+c|0}else{if((0|m)>=(0-c|0))break A;i||(k[s>>1]<0?r=s:(r=(0|(r=e[44469]+1|0))<=169?r:0,e[44469]=r,i=y[s+20>>1]|y[s+22>>1]<<16,t=(r=177888+(r<<6)|0)+16|0,m=y[s+16>>1]|y[s+18>>1]<<16,k[t>>1]=m,k[t+2>>1]=m>>>16,k[t+4>>1]=i,k[t+6>>1]=i>>>16,t=y[s+4>>1]|y[s+6>>1]<<16,i=y[s>>1]|y[s+2>>1]<<16,k[r>>1]=i,k[r+2>>1]=i>>>16,k[r+4>>1]=t,k[r+6>>1]=t>>>16,t=y[s+12>>1]|y[s+14>>1]<<16,i=y[s+8>>1]|y[s+10>>1]<<16,k[r+8>>1]=i,k[r+10>>1]=i>>>16,k[r+12>>1]=t,k[r+14>>1]=t>>>16,t=y[s+28>>1]|y[s+30>>1]<<16,i=y[s+24>>1]|y[s+26>>1]<<16,k[r+24>>1]=i,k[r+26>>1]=i>>>16,k[r+28>>1]=t,k[r+30>>1]=t>>>16,t=y[s+36>>1]|y[s+38>>1]<<16,i=y[s+32>>1]|y[s+34>>1]<<16,k[r+32>>1]=i,k[r+34>>1]=i>>>16,k[r+36>>1]=t,k[r+38>>1]=t>>>16,t=y[s+44>>1]|y[s+46>>1]<<16,i=y[s+40>>1]|y[s+42>>1]<<16,k[r+40>>1]=i,k[r+42>>1]=i>>>16,k[r+44>>1]=t,k[r+46>>1]=t>>>16,t=y[s+52>>1]|y[s+54>>1]<<16,i=y[s+48>>1]|y[s+50>>1]<<16,k[r+48>>1]=i,k[r+50>>1]=i>>>16,k[r+52>>1]=t,k[r+54>>1]=t>>>16,t=y[s+60>>1]|y[s+62>>1]<<16,i=y[s+56>>1]|y[s+58>>1]<<16,k[r+56>>1]=i,k[r+58>>1]=i>>>16,k[r+60>>1]=t,k[r+62>>1]=t>>>16,f[r+16|0]=0,k[r>>1]=32768|y[r>>1],t=y[h+2>>1])),t=t-c|0}i=1,k[2+((g<<1)+r|0)>>1]=t,e[I+12>>2]=r}if((0|(g=g+1|0))==6)break}x=r}if((0|A)==(0|(_=(0|(r=_+1|0))<=169?r:0)))break}}e[36454]=A}}function pr(A,t){var r,s,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0;H=r=H-48|0,F(+A),l=0|B(1),i=0|B(0),s=l;e:{A:{r:{if((m=2147483647&l)>>>0<=1074752122){if((1048575&l)==598523)break r;if(m>>>0<=1073928572){if((0|s)>0|(0|s)>=0){g=(A+=-1.5707963267341256)+-6077100506506192e-26,P[t>>3]=g,P[t+8>>3]=A-g-6077100506506192e-26,l=1;break e}g=(A+=1.5707963267341256)+6077100506506192e-26,P[t>>3]=g,P[t+8>>3]=A-g+6077100506506192e-26,l=-1;break e}if((0|s)>0|(0|s)>=0){g=(A+=-3.1415926534682512)+-12154201013012384e-26,P[t>>3]=g,P[t+8>>3]=A-g-12154201013012384e-26,l=2;break e}g=(A+=3.1415926534682512)+12154201013012384e-26,P[t>>3]=g,P[t+8>>3]=A-g+12154201013012384e-26,l=-2;break e}if(m>>>0<=1075594811){if(m>>>0<=1075183036){if((0|m)==1074977148)break r;if((0|s)>0|(0|s)>=0){g=(A+=-4.712388980202377)+-18231301519518578e-26,P[t>>3]=g,P[t+8>>3]=A-g-18231301519518578e-26,l=3;break e}g=(A+=4.712388980202377)+18231301519518578e-26,P[t>>3]=g,P[t+8>>3]=A-g+18231301519518578e-26,l=-3;break e}if((0|m)==1075388923)break r;if((0|s)>0|(0|s)>=0){g=(A+=-6.2831853069365025)+-2430840202602477e-25,P[t>>3]=g,P[t+8>>3]=A-g-2430840202602477e-25,l=4;break e}g=(A+=6.2831853069365025)+2430840202602477e-25,P[t>>3]=g,P[t+8>>3]=A-g+2430840202602477e-25,l=-4;break e}if(m>>>0>1094263290)break A}i=(Ee=(g=A+-1.5707963267341256*(x=.6366197723675814*A+6755399441055744-6755399441055744))-(_=6077100506506192e-26*x))<-.7853981633974483,l=ee(x)<2147483648?~~x:-2147483648,i?(l=l-1|0,_=6077100506506192e-26*(x+=-1),g=A+-1.5707963267341256*x):Ee>.7853981633974483&&(l=l+1|0,_=6077100506506192e-26*(x+=1),g=A+-1.5707963267341256*x),A=g-_,P[t>>3]=A,F(+A),i=0|B(1),B(0),((c=m>>>20|0)-(i>>>20&2047)|0)<17||(_=g,A=(g-=A=6077100506303966e-26*x)-(_=20222662487959506e-37*x-(_-g-A)),P[t>>3]=A,F(+A),i=0|B(1),B(0),(c-(i>>>20&2047)|0)<50||(_=g,A=(g-=A=20222662487111665e-37*x)-(_=84784276603689e-45*x-(_-g-A)),P[t>>3]=A)),P[t+8>>3]=g-A-_;break e}if(m>>>0>=2146435072)A-=A,P[t>>3]=A,P[t+8>>3]=A,l=0;else{for(E(0,0|i),E(1,1048575&s|1096810496),A=+S(),l=0,i=1;c=(r+16|0)+(l<<3)|0,g=+(0|(l=ee(A)<2147483648?~~A:-2147483648)),P[c>>3]=g,A=16777216*(A-g),l=1,c=i,i=0,c;);for(P[r+32>>3]=A,l=2;l=(i=l)-1|0,P[(r+16|0)+(i<<3)>>3]==0;);if(Te=r+16|0,c=0,H=I=H-560|0,m=O(he=(0|(m=((l=(m>>>20|0)-1046|0)-3|0)/24|0))>0?m:0,-24)+l|0,((V=e[28105])+(h=(te=i+1|0)-1|0)|0)>=0)for(l=V+te|0,i=he-h|0;P[(I+320|0)+(c<<3)>>3]=(0|i)<0?0:+e[112432+(i<<2)>>2],i=i+1|0,(0|l)!=(0|(c=c+1|0)););for(J=m-24|0,l=0,c=(0|V)>0?V:0,K=(0|te)<=0;;){if(K)A=0;else for(T=l+h|0,i=0,A=0;A=P[(i<<3)+Te>>3]*P[(I+320|0)+(T-i<<3)>>3]+A,(0|te)!=(0|(i=i+1|0)););if(P[(l<<3)+I>>3]=A,i=(0|l)==(0|c),l=l+1|0,i)break}fA=47-m|0,Fe=48-m|0,hA=m-25|0,l=V;A:{for(;;){if(A=P[(l<<3)+I>>3],i=0,c=l,!(T=(0|l)<=0))for(;K=(I+480|0)+(i<<2)|0,h=ee(g=5960464477539063e-23*A)<2147483648?~~g:-2147483648,h=ee(A=-16777216*(g=+(0|h))+A)<2147483648?~~A:-2147483648,e[K>>2]=h,A=P[((c=c-1|0)<<3)+I>>3]+g,(0|l)!=(0|(i=i+1|0)););A=Zs(A,J),A+=-8*Me(.125*A),A-=+(0|(K=ee(A)<2147483648?~~A:-2147483648));r:{a:{n:{if(Le=(0|J)<=0){if(J)break n;h=e[476+((l<<2)+I|0)>>2]>>23}else ce=c=(l<<2)+I|0,c=(h=e[c+476>>2])-((i=h>>Fe)<<Fe)|0,e[ce+476>>2]=c,K=i+K|0,h=c>>fA;if((0|h)<=0)break r;break a}if(h=2,!(A>=.5)){h=0;break r}}if(i=0,c=0,!T)for(;Xe=e[(ce=(I+480|0)+(i<<2)|0)>>2],T=16777215,c||(T=16777216,Xe)?(e[ce>>2]=T-Xe,c=1):c=0,(0|l)!=(0|(i=i+1|0)););a:if(!Le){i=8388607;n:switch(0|hA){case 1:i=4194303;break;case 0:break n;default:break a}e[(T=(l<<2)+I|0)+476>>2]=e[T+476>>2]&i}K=K+1|0,(0|h)==2&&(A=1-A,h=2,c&&(A-=Zs(1,J)))}if(A!=0)break;if(c=0,!((0|V)>=(0|(i=l)))){for(;c=e[(I+480|0)+((i=i-1|0)<<2)>>2]|c,(0|i)>(0|V););if(c){for(m=J;m=m-24|0,!e[(I+480|0)+((l=l-1|0)<<2)>>2];);break A}}for(i=1;c=i,i=i+1|0,!e[(I+480|0)+(V-c<<2)>>2];);for(c=l+c|0;;){if(h=l+te|0,l=l+1|0,P[(I+320|0)+(h<<3)>>3]=e[112432+(he+l<<2)>>2],i=0,A=0,(0|te)>0)for(;A=P[(i<<3)+Te>>3]*P[(I+320|0)+(h-i<<3)>>3]+A,(0|te)!=(0|(i=i+1|0)););if(P[(l<<3)+I>>3]=A,!((0|l)<(0|c)))break}l=c}(A=Zs(A,24-m|0))>=16777216?(J=(I+480|0)+(l<<2)|0,i=ee(g=5960464477539063e-23*A)<2147483648?~~g:-2147483648,c=ee(A=-16777216*+(0|i)+A)<2147483648?~~A:-2147483648,e[J>>2]=c,l=l+1|0):(i=ee(A)<2147483648?~~A:-2147483648,m=J),e[(I+480|0)+(l<<2)>>2]=i}if(A=Zs(1,m),!((0|l)<0)){for(i=l;c=i,P[(i<<3)+I>>3]=A*+e[(I+480|0)+(i<<2)>>2],i=i-1|0,A*=5960464477539063e-23,c;);if(T=0,!((0|l)<0))for(m=(0|V)>0?V:0,c=l;;){for(J=m>>>0<T>>>0?m:T,V=l-c|0,i=0,A=0;A=P[115200+(i<<3)>>3]*P[(i+c<<3)+I>>3]+A,te=(0|i)!=(0|J),i=i+1|0,te;);if(P[(I+160|0)+(V<<3)>>3]=A,c=c-1|0,i=(0|l)!=(0|T),T=T+1|0,!i)break}}if(A=0,(0|l)>=0)for(i=l;c=i,i=i-1|0,A+=P[(I+160|0)+(c<<3)>>3],c;);if(P[r>>3]=h?-A:A,A=P[I+160>>3]-A,i=1,(0|l)>0)for(;A+=P[(I+160|0)+(i<<3)>>3],c=(0|i)!=(0|l),i=i+1|0,c;);P[r+8>>3]=h?-A:A,H=I+560|0,l=7&K,A=P[r>>3],(0|s)<0?(P[t>>3]=-A,P[t+8>>3]=-P[r+8>>3],l=0-l|0):(P[t>>3]=A,P[t+8>>3]=P[r+8>>3])}}return H=r+48|0,l}function Ur(){var A=0,t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0,Mt=0,Pt=0,Nt=0,tr=0,rr=0,br=0,Ir=0,zr=0,Vr=0,Xr=0,ns=0,gs=0,bs=0,ua=0,ea=0,da=0;e[55925]=0,A=e[56772],e[56772]=A+1,r=e[55961],T=e[55922],V=na(39.89822670059037*(t=+(0|A))),I=na(22.30530784048753*t),t=+(0|T)/50*(+(0|r)/100)*(na(14.765485471872028*t)+(V+I))*10,A=ee(t)<2147483648?~~t:-2147483648,e[56607]=A+e[56607];e:if(!(e[55923]<=0)){for(;;){for(r=st(e[33209],0,1103515245,0),A=le,A=zi(r=r+12345|0,A=r>>>0<12345?A+1|0:A),e[33209]=A,A=8191+((A>>>0)%16383|0)|0,e[55929]=A,At=+(0|A),t=.75*P[28387]+At,P[28387]=t,Mt=(0|(A=e[55924]))>(0|(Fe=e[55928]))?.5*t:t,rr=(i=.033*+e[56652])>0?1-i:1,r=e[56650]<<2,fA=(0|(Xe=e[55921]))==1&&(0|r)>263?263:r,br=111136+((Pt=e[56651])<<1)|0,Ir=111136+((hA=e[56658])<<1)|0,zr=(0|hA)>0,Nt=e[55918],Vr=O(Nt,40),Xr=e[55925],ns=P[27967],tr=e[56607],t=P[27979],J=P[27969],g=P[27970],_A=e[56780],he=e[56654],x=P[27976],_=P[27972],te=P[28388],ce=P[28389],T=e[55927],s=P[28383],h=e[55926],Ee=e[55956],gs=P[27975],I=P[28131],l=P[28130],c=P[28123],K=P[28121],m=P[28122],Le=P[28120],Te=P[28119],bs=P[28129],ua=P[28128],ea=P[28127],da=ds(0*P[27968]),LA=0;;){V=l;A:{r:switch(Xe-1|0){case 0:te=0,l=K*c,c=m,m=s=l+(Te*(te=(0|A)<=2?P[111312+(A<<3)>>3]:te)+Le*c);break A;case 1:if(s=0,(0|A)>=(0|T)){ce=0;break A}l=P[27965]-P[27966],P[27965]=l,s=.028*(ce=l+ce);break A;case 2:if(!h){Ee=100,s=0;break A}Ee=100,r=ee(l=+(0|A)/+(0|h)*100)<2147483648?~~l:-2147483648,s=+k[110928+((0|r)%100<<1)>>1],s=t*((+k[110928+((r+1|0)%100<<1)>>1]-s)*(l-+(0|r))+s);break A;case 3:break r;default:break A}h?(Ee=256,r=ee(l=+(0|A)/+(0|h)*256)<2147483648?~~l:-2147483648,s=+k[111344+((0|r)%256<<1)>>1],s=t*((+k[111344+((r+1|0)%256<<1)>>1]-s)*(l-+(0|r))+s)):(Ee=256,s=0)}if((0|A)>=(0|h)&&((0|tr)>0?(A=(0|Vr)/(0|tr)|0,x=0,_=0,_=hA>>>0<=87?.001*+k[Ir>>1]:_,x=Pt>>>0<=87?.001*+k[br>>1]*.1:x,Fe=A>>zr,T=(0|(r=(h=(A-1|0)<=(0|fA))?A-2|0:fA))<=40?40:r,l=+k[111776+((T=h||(0|r)<40?T:fA)<<1)>>1],P[27966]=l,K=l,l=+(0|T),P[27965]=K*l*.333,he=(0|(r=A-T|0))>(0|he)?he:r,_A=0-(r=(0|_A)<0?0-he|0:he)|0,Te=(l*=.00833)*l,Te*=1-(Le=(K=(l=$r(ns*+((0|Nt)/(0|T)|0)))*da)+K)-(K=l*-l),A=(0|(h=A+r|0))!=4):(e[55930]=0,e[55931]=0,e[55932]=0,e[55933]=0,Fe=4,x=0,_=0,h=4,A=0),g=(A|=!Xr)?i:g,J=A?rr:J,A=0),A=A+1|0,l=s=bs*I+(ea*s+ua*V),I=V,(0|(LA=LA+1|0))==4)break}if(e[55926]=h,e[55956]=Ee,e[55924]=A,P[28383]=s,e[55927]=T,P[28389]=ce,P[28388]=te,P[27972]=_,P[27976]=x,e[55928]=Fe,e[56654]=he,e[56780]=_A,P[27970]=g,P[27969]=J,P[28122]=m,P[28120]=Le,P[28119]=Te,P[28123]=c,P[28121]=K,P[28131]=I,P[28130]=s,(0|Xe)==5&&(s=6e3*((t=+(0|A)/+(0|h))+t+-1),P[28383]=s),t=s*J+P[28384]*g,P[28383]=t,P[28384]=t,(0|A)<(0|T)&&(t=x*At+t,P[28383]=t),g=Mt*P[27974],V=t*P[27973]+g,I=0,e[55916]!=2&&(I=P[27987],m=P[27986],P[27987]=m,t=g+t*_,P[27986]=t,g=P[28059],c=P[28058],P[28059]=c,t=g*P[28057]+(P[28055]*(I*P[27985]+(P[27983]*t+m*P[27984]))+c*P[28056]),P[28058]=t,I=P[28049],g=P[28051],m=P[28048],c=P[28047],i=P[28050],P[28051]=i,t=I*g+(c*t+m*i),P[28050]=t,I=P[28043],g=P[28041],m=P[28040],c=P[28039],i=P[28042],P[28043]=i,t=g*I+(c*t+m*i),P[28042]=t,I=P[28035],g=P[28033],m=P[28032],c=P[28031],i=P[28034],P[28035]=i,t=g*I+(c*t+m*i),P[28034]=t,I=P[28027],g=P[28025],m=P[28024],c=P[28023],i=P[28026],P[28027]=i,t=g*I+(c*t+m*i),P[28026]=t,I=P[28019],g=P[28017],m=P[28016],c=P[28015],i=P[28018],P[28019]=i,t=g*I+(c*t+m*i),P[28018]=t,I=P[28011],g=P[28009],m=P[28008],c=P[28007],i=P[28010],P[28011]=i,t=g*I+(c*t+m*i),P[28010]=t,I=P[28003],g=P[28001],m=P[28e3],c=P[27999],i=P[28002],P[28003]=i,t=g*I+(c*t+m*i),P[28002]=t,I=P[27995],g=P[27993],m=P[27992],c=P[27991],i=P[27994],P[27995]=i,I=g*I+(c*t+m*i),P[27994]=I),t=P[28385],P[28385]=V,g=P[28075],m=P[28074],P[28075]=m,c=P[28067],i=P[28066],P[28067]=i,g=g*P[28073]+(P[28071]*V+m*P[28072]),P[28074]=g,m=c*P[28065]+(P[28063]*V+i*P[28064]),P[28066]=m,c=P[28081],i=P[28083],l=P[28079],s=P[28080],x=P[28082],P[28083]=x,V=c*i+(l*(t=gs*Mt+V-t)+s*x),P[28082]=V,c=P[28091],i=P[28089],l=P[28087],s=P[28088],x=P[28090],P[28091]=x,c=i*c+(l*t+s*x),P[28090]=c,i=P[28099],l=P[28097],s=P[28095],x=P[28096],_=P[28098],P[28099]=_,i=l*i+(s*t+x*_),P[28098]=i,l=P[28107],s=P[28105],x=P[28103],_=P[28104],J=P[28106],P[28107]=J,l=s*l+(x*t+_*J),P[28106]=l,s=P[28115],x=P[28113],_=P[28111],J=P[28112],te=P[28114],P[28115]=te,s=x*s+(_*t+J*te),P[28114]=s,x=P[28139],_=P[28137],J=P[28136],te=P[28135],ce=P[27971],K=P[28138],P[28139]=K,t=_*x+(te*(t*ce-(s-(l-(i-(c-(V-(I+g+m)))))))+J*K),P[28138]=t,t=P[27977]*(t*+e[50779]),s=+(0|(A=ee(t)<2147483648?~~t:-2147483648)),(0|(A=e[50776]))<e[50773]&&(h=A+1|0,r=e[50772],(T=e[50774])?(e[50776]=h,A=O(T,f[A+r|0])):(T=d[A+r|0],r=f[r+h|0],e[50776]=A+2,A=T|r<<8),s+=+((0|O(e[50775],(0|O(A,e[50780]))/1024|0))/40|0)),(0|(A=e[55962]))<=63&&(e[55962]=A+1,s=s*+(0|A)*.015625),(0|(A=e[55963]))<=0||(A=A-1|0,e[55963]=A,s=s*+(0|A)*.015625,A||(e[55962]=0)),r=(A=e[51293])+1|0,e[51293]=r,h=O(k[205184+(A<<1)>>1],e[50755])>>8,A=ee(s)<2147483648?~~s:-2147483648,(0|r)>=5500&&(e[51293]=0),r=e[51290],e[51290]=r+1,A=(0|(A=(0|(A=A+h|0))<=-32768?-32768:A))>=32767?32767:A,f[0|r]=A,r=e[51290],e[51290]=r+1,f[0|r]=A>>>8,h=(r=e[51292])+1|0,e[51292]=h,k[205184+(r<<1)>>1]=A,(0|h)>=5500&&(e[51292]=0),h=1,e[56606]=e[56606]+1,Ae[54046]<e[51290]+2>>>0)break e;if(A=e[55925]+1|0,e[55925]=A,!((0|A)<e[55923]))break}h=0}return h}function Bs(A,t,r,s,i,l,c){var g,m,I,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0;H=g=H-80|0,e[g+76>>2]=t,I=g+55|0,m=g+56|0;e:{A:{r:{a:{n:for(;;){if(T=t,(2147483647^te)<(0|h))break a;te=h+te|0;o:{c:{u:{if(x=d[0|(h=T)])for(;;){l:{i:if(t=255&x){if((0|t)!=37)break l;for(x=h;;){if(d[x+1|0]!=37){t=x;break i}if(h=h+1|0,V=d[x+2|0],x=t=x+2|0,(0|V)!=37)break}}else t=h;if((0|(h=h-T|0))>(0|(Xe=2147483647^te)))break a;if(A&&Wr(A,T,h),h)continue n;e[g+76>>2]=t,h=t+1|0,ce=-1,d[t+2|0]!=36|f[t+1|0]-48>>>0>=10||(ce=f[t+1|0]-48|0,Te=1,h=t+3|0),e[g+76>>2]=h,K=0;i:if((t=(x=f[0|h])-32|0)>>>0>31)J=h;else if(J=h,75913&(t=1<<t))for(;;){if(J=h+1|0,e[g+76>>2]=J,K|=t,(t=(x=f[h+1|0])-32|0)>>>0>=32)break i;if(h=J,!(75913&(t=1<<t)))break}i:if((0|x)!=42){if((0|(he=Xa(g+76|0)))<0)break a;x=e[g+76>>2]}else{if(d[J+2|0]!=36|f[J+1|0]-48>>>0>=10){if(Te)break u;if(x=J+1|0,!A){e[g+76>>2]=x,Te=0,he=0;break i}t=e[r>>2],e[r>>2]=t+4,Te=0,t=e[t>>2]}else e[((f[J+1|0]<<2)+i|0)-192>>2]=10,x=J+3|0,Te=1,t=e[((f[J+1|0]<<3)+s|0)-384>>2];if(e[g+76>>2]=x,he=t,(0|t)>=0)break i;he=0-he|0,K|=8192}if(h=0,_=-1,d[0|x]==46)if(d[x+1|0]!=42)e[g+76>>2]=x+1,_=Xa(g+76|0),t=e[g+76>>2],Le=1;else{if(d[x+3|0]!=36|f[x+2|0]-48>>>0>=10){if(Te)break u;t=x+2|0,_=0,A&&(x=e[r>>2],e[r>>2]=x+4,_=e[x>>2])}else e[((f[x+2|0]<<2)+i|0)-192>>2]=10,t=x+4|0,_=e[((f[x+2|0]<<3)+s|0)-384>>2];e[g+76>>2]=t,Le=~_>>>31|0}else t=x,Le=0;for(;;){if(Ee=h,J=28,V=t,(h=f[0|t])-123>>>0<4294967238)break r;if(t=V+1|0,!((h=d[123983+(h+O(Ee,58)|0)|0])-1>>>0<8))break}e[g+76>>2]=t;i:{p:{if((0|h)!=27){if(!h)break r;if((0|ce)>=0){e[(ce<<2)+i>>2]=h,h=e[(x=(ce<<3)+s|0)+4>>2],e[g+64>>2]=e[x>>2],e[g+68>>2]=h;break p}if(!A)break o;Ve(g- -64|0,h,r,c);break i}if((0|ce)>=0)break r}if(h=0,!A)continue n}x=-65537&K,K=8192&K?x:K,ce=0,Fe=84065,J=m;i:{p:{C:{h:{b:{m:{x:{I:{B:{N:{L:{U:{y:{E:{Q:{F:switch(h=f[0|V],(h=Ee&&(15&h)==3?-33&h:h)-88|0){case 11:break i;case 9:case 13:case 14:case 15:break p;case 27:break x;case 12:case 17:break N;case 23:break L;case 0:case 32:break U;case 24:break y;case 22:break E;case 29:break Q;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 16:case 18:case 19:case 20:case 21:case 25:case 26:case 28:case 30:case 31:break c;default:break F}F:switch(h-65|0){case 0:case 4:case 5:case 6:break p;case 2:break b;case 1:case 3:break c;default:break F}if((0|h)==83)break m;break c}x=e[g+64>>2],V=e[g+68>>2],Fe=84065;break B}h=0;E:switch(255&Ee){case 0:case 1:case 6:e[e[g+64>>2]>>2]=te;continue n;case 2:T=e[g+64>>2],e[T>>2]=te,e[T+4>>2]=te>>31;continue n;case 3:k[e[g+64>>2]>>1]=te;continue n;case 4:f[e[g+64>>2]]=te;continue n;case 7:break E;default:continue n}T=e[g+64>>2],e[T>>2]=te,e[T+4>>2]=te>>31;continue n}_=_>>>0<=8?8:_,K|=8,h=120}if(T=m,fA=32&h,(x=e[g+64>>2])|(V=e[g+68>>2]))for(;f[0|(T=T-1|0)]=fA|d[124512+(15&x)|0],hA=!V&x>>>0>15|!!(0|V),Ee=V,V=V>>>4|0,x=(15&Ee)<<28|x>>>4,hA;);if(!(e[g+64>>2]|e[g+68>>2])|!(8&K))break I;Fe=84065+(h>>>4|0)|0,ce=2;break I}if(h=m,V=T=e[g+68>>2],T|(x=e[g+64>>2]))for(;f[0|(h=h-1|0)]=7&x|48,Ee=!V&x>>>0>7|!!(0|V),V=(T=V)>>>3|0,x=(7&T)<<29|x>>>3,Ee;);if(T=h,!(8&K))break I;_=(0|(h=m-T|0))<(0|_)?_:h+1|0;break I}x=e[g+64>>2],V=h=e[g+68>>2],(0|h)<0?(V=T=0-(V+!!(0|x)|0)|0,x=0-x|0,e[g+64>>2]=x,e[g+68>>2]=T,ce=1,Fe=84065):2048&K?(ce=1,Fe=84066):Fe=(ce=1&K)?84067:84065}T=$s(x,V,m)}if((0|_)<0&&Le)break a;if(K=Le?-65537&K:K,!(_|!!((h=e[g+64>>2])|(x=e[g+68>>2])))){T=m,_=0;break c}_=(0|(h=!(h|x)+(m-T|0)|0))<(0|_)?_:h;break c}if(J=(h=(h=Ba(T=(h=e[g+64>>2])||84639,0,V=_>>>0>=2147483647?2147483647:_))?h-T|0:V)+T|0,(0|_)>=0){K=x,_=h;break c}if(K=x,_=h,d[0|J])break a;break c}if(_){x=e[g+64>>2];break h}h=0,Sr(A,32,he,0,K);break C}e[g+12>>2]=0,e[g+8>>2]=e[g+64>>2],x=g+8|0,e[g+64>>2]=x,_=-1}h=0;h:{for(;;){if(!(T=e[x>>2]))break h;if(!((T=(0|(V=yn(g+4|0,T)))<0)|V>>>0>_-h>>>0)){if(x=x+4|0,_>>>0>(h=h+V|0)>>>0)continue;break h}break}if(T)break A}if(J=61,(0|h)<0)break r;if(Sr(A,32,he,h,K),h)for(J=0,x=e[g+64>>2];;){if(!(T=e[x>>2])||(J=(T=yn(g+4|0,T))+J|0)>>>0>h>>>0)break C;if(Wr(A,g+4|0,T),x=x+4|0,!(h>>>0>J>>>0))break}else h=0}Sr(A,32,he,h,8192^K),h=(0|h)<(0|he)?he:h;continue n}if((0|_)<0&&Le)break a;if(J=61,(0|(h=0|$A[0|l](A,P[g+64>>3],he,_,K,h)))>=0)continue n;break r}f[g+55|0]=e[g+64>>2],_=1,T=I,K=x;break c}x=d[h+1|0],h=h+1|0}if(A)break e;if(!Te)break o;for(h=1;;){if(A=e[(h<<2)+i>>2]){if(Ve((h<<3)+s|0,A,r,c),te=1,(0|(h=h+1|0))!=10)continue;break e}break}if(te=1,h>>>0>=10)break e;for(;;){if(e[(h<<2)+i>>2])break u;if((0|(h=h+1|0))==10)break}break e}J=28;break r}if((0|(x=(0|_)>(0|(V=J-T|0))?_:V))>(2147483647^ce))break a;if(J=61,(0|Xe)<(0|(h=(0|(_=x+ce|0))<(0|he)?he:_)))break r;Sr(A,32,h,_,K),Wr(A,Fe,ce),Sr(A,48,h,_,65536^K),Sr(A,48,x,V,0),Wr(A,T,V),Sr(A,32,h,_,8192^K);continue}break}te=0;break e}J=61}e[56798]=J}te=-1}return H=g+80|0,te}function Et(A,t,r,s,i,l,c,g,m){var I,h,x,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0,Mt=0,Pt=0,Nt=0,tr=0,rr=0,br=0,Ir=0,zr=0,Vr=0,Xr=0,ns=0,gs=0,bs=0;H=I=H-96|0,Fe=65535&m,J=-2147483648&(i^m),_A=ce=65535&i;e:{if(!((h=m>>>16&32767)-32767>>>0>4294934529&(x=i>>>16&32767)-32767>>>0>=4294934530)){if(T=s,!(!s&(0|(he=Ee=2147483647&i))==2147418112?!(t|r):he>>>0<2147418112)){V=s,J=32768|i;break e}if(!(!(i=g)&(0|(te=Ee=2147483647&m))==2147418112?!(l|c):te>>>0<2147418112)){V=g,J=32768|m,t=l,r=c;break e}if(!(t|T|2147418112^he|r)){if(!(i|l|c|te)){J=2147450880,t=0,r=0;break e}J|=2147418112,t=0,r=0;break e}if(!(i|l|2147418112^te|c)){if(i=t|T,s=r|he,t=0,r=0,!(s|i)){J=2147450880;break e}J|=2147418112;break e}if(!(t|T|r|he)){t=0,r=0;break e}if(!(i|l|c|te)){t=0,r=0;break e}(0|he)==65535|he>>>0<65535&&(Ee=(T=!(s|ce))<<6,i=be(m=T?t:s)+32|0,vt(I+80|0,t,r,s,ce,(m=Ee+((0|(m=be(T?r:ce)))==32?i:m)|0)-15|0),Te=16-m|0,s=e[I+88>>2],_A=e[I+92>>2],r=e[I+84>>2],t=e[I+80>>2]),te>>>0>65535||(ce=(m=!(g|Fe))<<6,T=be(i=m?l:g)+32|0,vt(I- -64|0,l,c,g,Fe,(i=ce+((0|(i=be(m?c:Fe)))==32?T:i)|0)-15|0),Te=16+(Te-i|0)|0,g=e[I+72>>2],Fe=e[I+76>>2],l=e[I+64>>2],c=e[I+68>>2])}if(i=l,l=c<<15|l>>>17,zr=r,Ee=st(Le=-32768&(m=i<<15),i=0,r,0),hA=i=le,Vr=l,he=t,t=st(l,0,t,0),l=le+i|0,r=t>>>0>(m=t+Ee|0)>>>0?l+1|0:l,T=0,t=st(he,_,Le,_),i=(l=m)+le|0,ce=i=t>>>0>(te=T+t|0)>>>0?i+1|0:i,Xr=(0|l)==(0|i)&T>>>0>te>>>0|i>>>0<l>>>0,ns=s,Xe=st(Le,_,s,0),gs=le,t=st(zr,_,Vr,_),T=le+gs|0,T=t>>>0>(fA=t+Xe|0)>>>0?T+1|0:T,t=Fe<<15|g>>>17,s=st(LA=g<<15|c>>>17,0,he,_),l=le+T|0,Mt=l=s>>>0>(At=s+fA|0)>>>0?l+1|0:l,l=(s=(0|r)==(0|hA)&m>>>0<Ee>>>0|r>>>0<hA>>>0)+l|0,Fe=l=r>>>0>(Pt=r+At|0)>>>0?l+1|0:l,c=Pt,r=l,_A=st(Le,_,Nt=65536|_A,K),bs=le,s=st(ns,V,Vr,_),i=le+bs|0,m=i=s>>>0>(tr=s+_A|0)>>>0?i+1|0:i,t=st(rr=-2147483648|t,0,he,_),l=le+i|0,l=t>>>0>(br=t+tr|0)>>>0?l+1|0:l,t=st(LA,V,zr,_),Ir=l,l=l+le|0,Ee=t>>>0>(hA=t+br|0)>>>0?l+1|0:l,i=r+hA|0,l=Le=(t=0)>>>0>(he=t+c|0)>>>0?i+1|0:i,r=(t=he+Xr|0)>>>0<he>>>0?l+1|0:l,Te=((x+h|0)+Te|0)-16383|0,s=st(rr,V,zr,_),g=le,i=st(Nt,V,Vr,_),l=le+g|0,K=(0|g)==(0|(l=i>>>0>(c=i+s|0)>>>0?l+1|0:l))&s>>>0>c>>>0|l>>>0<g>>>0,g=l,i=st(LA,V,ns,V),l=le+l|0,i=l=(s=i+c|0)>>>0<i>>>0?l+1|0:l,c=(0|l)==(0|g)&s>>>0<c>>>0|l>>>0<g>>>0,l=0,l=(g=c)>>>0>(c=c+K|0)>>>0?1:l,g=c,c=st(rr,V,Nt,V),l=le+l|0,Xr=g=g+c|0,c=c>>>0>g>>>0?l+1|0:l,g=s,_=i,i=(0|T)==(0|gs)&Xe>>>0>fA>>>0|T>>>0<gs>>>0,l=0,i=((K=T=(0|T)==(0|Mt)&fA>>>0>At>>>0|T>>>0>Mt>>>0)>>>0>(T=i+T|0)>>>0?1:l)+_|0,l=c,K=i=(s=s+T|0)>>>0<T>>>0?i+1|0:i,Xe=s,i=s=(0|i)==(0|_)&s>>>0<g>>>0|i>>>0<_>>>0,T=s=s+Xr|0,c=l=i>>>0>s>>>0?l+1|0:l,i=st(LA,V,Nt,V),_=le,s=st(rr,V,ns,V),l=le+_|0,s=l=s>>>0>(g=s+i|0)>>>0?l+1|0:l,i=(l=(0|_)==(0|l)&i>>>0>g>>>0|l>>>0<_>>>0)+c|0,c=i=s>>>0>(fA=s+T|0)>>>0?i+1|0:i,l=g+K|0,i=l=(s=(i=0)+Xe|0)>>>0<i>>>0?l+1|0:l,g=(0|K)==(0|l)&s>>>0<Xe>>>0|l>>>0<K>>>0,l=c,l=(c=g+(_=fA)|0)>>>0<g>>>0?l+1|0:l,Xe=c,g=s,T=i,i=(s=(s=(s=(0|m)==(0|bs)&_A>>>0>tr>>>0|m>>>0<bs>>>0)+(m=(0|m)==(0|Ir)&tr>>>0>br>>>0|m>>>0>Ir>>>0)|0)+(i=(0|Ee)==(0|Ir)&hA>>>0<br>>>0|Ee>>>0<Ir>>>0)|0)+T|0,l=c=l,T=c=(g=(0|(i=(s=m=(K=Ee)+g|0)>>>0<K>>>0?i+1|0:i))==(0|T)&g>>>0>s>>>0|i>>>0<T>>>0)+Xe|0,c=l=g>>>0>c>>>0?l+1|0:l,g=s,l=0,m=i,i=i+((_=K=(0|Fe)==(0|Le)&he>>>0<Pt>>>0|Fe>>>0>Le>>>0)>>>0>(K=K+((0|Fe)==(0|Mt)&At>>>0>Pt>>>0|Fe>>>0<Mt>>>0)|0)>>>0?1:l)|0,l=c,m=l=(c=g=(0|(i=(s=s+K|0)>>>0<K>>>0?i+1|0:i))==(0|m)&s>>>0<g>>>0|i>>>0<m>>>0)>>>0>(g=g+T|0)>>>0?l+1|0:l,65536&l?Te=Te+1|0:(T=ce>>>31|0,l=m<<1|g>>>31,g=g<<1|i>>>31,m=l,l=i<<1|s>>>31,s=s<<1|r>>>31,i=l,l=ce<<1|te>>>31,te<<=1,ce=l,l=r<<1|t>>>31,t=t<<1|T,r=l|(c=0)),(0|Te)>=32767)J|=2147418112,t=0,r=0;else{A:{if((0|Te)<=0){if((c=1-Te|0)>>>0<=127){vt(I+48|0,te,ce,t,r,l=Te+127|0),vt(I+32|0,s,i,g,m,l),cr(I+16|0,te,ce,t,r,c),cr(I,s,i,g,m,c),te=e[I+32>>2]|e[I+16>>2]|!!(e[I+48>>2]|e[I+56>>2]|e[I+52>>2]|e[I+60>>2]),ce=e[I+36>>2]|e[I+20>>2],t=e[I+40>>2]|e[I+24>>2],r=e[I+44>>2]|e[I+28>>2],s=e[I>>2],i=e[I+4>>2],c=e[I+8>>2],l=e[I+12>>2];break A}t=0,r=0;break e}c=g,l=65535&m|Te<<16}V|=c,J|=l,(!t&(0|r)==-2147483648?!(te|ce):(0|r)>0|(0|r)>=0)?t|te|-2147483648^r|ce?(t=s,r=i):(T=J,J=(s=(0|(l=i))==(0|(r=(r=t=1&s)>>>0>(t=t+s|0)>>>0?l+1|0:l))&t>>>0<s>>>0|r>>>0<l>>>0)>>>0>(V=s+V|0)>>>0?T+1|0:T):(s=(0|i)==(0|(r=(t=s+1|0)?i:i+1|0))&t>>>0<s>>>0|r>>>0<i>>>0,i=J,J=(V=s+V|0)>>>0<s>>>0?i+1|0:i)}}e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=V,e[A+12>>2]=J,H=I+96|0}function VA(A,t,r,s,i,l,c){var g,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0;H=g=H-480|0,e[g+476>>2]=0,e[g+456>>2]=0,e[g+460>>2]=0,e[g+448>>2]=0,e[g+452>>2]=0,e[g+440>>2]=0,e[g+444>>2]=0,e[g+432>>2]=0,e[g+436>>2]=0,m=0;e:if(e[A+684>>2]){for(V=c?e[c>>2]:V;h=d[t+m|0],f[(g+112|0)+m|0]=h,I=m+1|0,h&&(h=m>>>0<158,m=I,h););if(f[I+(g+112|0)|0]=0,!((te=268435456&l)|!(8&e[47197]))){I=0;A:if(223&(h=d[0|t]))for(m=0;;){if(f[(g+272|0)+m|0]=h,!(223&(h=d[(I=m+1|0)+t|0])))break A;if(x=m>>>0<118,m=I,!x)break}f[(m=g+272|0)+I|0]=0,e[g+48>>2]=m,Xt(e[47195],(0|l)>=0?87019:86877,g+48|0)}e[g+464>>2]=t,e[A+8208>>2]=0,e[A+8212>>2]=0,i&&(f[0|i]=0);A:{r:if(223&(m=d[0|t]))for(ce=536870912&l,he=4096&l,Ee=g+105|0,I=t,h=0;;){x=jA(g+476|0,I),J=!!(0|Ft(e[g+476>>2]))+J|0,_=d[(m=(T=255&m)+A|0)+7668|0];a:if(!((K=e[g+476>>2])-48>>>0<10|K-2406>>>0<10)|(J?d[A+170|0]:0)){n:if((h=e[g+476>>2]-e[A+600>>2]|0)>>>0>127||!(h=e[6192+((h<<2)+A|0)>>2])){if(_){for(K=5168+((T<<2)+A|0)|0,_=_+(m=d[m+7924|0])|0,Te=T|d[I+1|0]<<8,h=0;e[(I=(m<<2)+A|0)+7184>>2]==(0|Te)&&(e[g+472>>2]=e[g+464>>2],zA(A,g+472|0,t,2,e[I+6704>>2],g+432|0,l,V),(0|(I=e[g+432>>2]))>0&&(I=I+35|0,e[g+432>>2]=I),h=1,zA(A,g+464|0,t,1,e[K>>2],g+448|0,l,V),e[g+448>>2]>(0|I)||(I=e[g+444>>2],e[g+456>>2]=e[g+440>>2],e[g+460>>2]=I,I=e[g+436>>2],e[g+448>>2]=e[g+432>>2],e[g+452>>2]=I,e[g+464>>2]=e[g+472>>2])),_>>>0>(m=m+1|0)>>>0;);if(h)break n}o:{c:{if(!(m=e[5168+((T<<2)+A|0)>>2])){if(zA(A,g+464|0,t,0,e[A+5168>>2],g+448|0,l,V),e[g+448>>2])break o;if(16&d[188808])break c;if(h=jA(g+468|0,T=(I=e[g+464>>2])-1|0),m=e[g+468>>2],!(e[A+600>>2]<=0|(0|m)>577)){if(oi(m)){e[g+32>>2]=21,dA(r,87049,g+32|0);break A}m=e[g+468>>2]}if((0|m)==57384&&((0|(_=e[A+92>>2]))<=e[47352]||(e[47352]=_)),en(m)&&((0|(m=e[A+72>>2]))<=e[47352]||(e[47352]=m)),!((_=(m=e[g+468>>2])-192|0)>>>0>413)&&(_=d[_+94240|0])&&(h=h-1|0,!(d[I-2|0]==32&d[h+I|0]==32))){for(e[g+472>>2]=T,f[0|T]=_;x=d[(m=I)+h|0],f[0|m]=x,I=m+1|0,(0|x)!=32;);if((0|h)>0&&Je(m,32,h),e[A+24>>2]&&!((0|Qi(94222,e[g+468>>2]))<=0)){e[g+464>>2]=T,h=0;break a}h=0,f[0|r]=0,e[g+464>>2]=t,e[A+8208>>2]=0,e[A+8212>>2]=0;break a}if(!(m=uA(m))||(0|(I=e[m+4>>2]))==e[A+600>>2])break c;if((0|I)==e[A+188>>2]){e[g+4>>2]=Qe(g- -64|0,e[A+192>>2]),e[g>>2]=21,dA(r,87218,g);break A}if(!(4&d[m+16|0]))break c;e[g+20>>2]=Qe(g- -64|0,e[m+12>>2]),e[g+16>>2]=21,dA(r,87218,g+16|0);break A}if(zA(A,g+464|0,t,1,m,g+448|0,l,V),e[g+448>>2])break o}c:if(!((m=e[g+476>>2])-768>>>0<112)){if(Ft(m)){if(f[(x+e[g+464>>2]|0)-1|0]<33&(0|J)<=1)break c;if(f[0|r]=0,!c)break r;e[c>>2]=4096|e[c>>2];break r}vA(A,e[g+476>>2],-1,g+272|0,0),d[g+272|0]&&(e[g+448>>2]=1,e[g+452>>2]=g+272)}e[g+464>>2]=(x+e[g+464>>2]|0)-1;break n}e[A+288>>2]=0}else zA(A,g+464|0,t,x,h,g+448|0,l,V);if(I=(m=e[g+452>>2])||86135,e[g+452>>2]=I,h=0,!(e[g+448>>2]<=0)){if(m=1|e[g+456>>2],(0|l)<0)break e;if(!(d[0|I]!=21|he)){PA(r,I);break A}if(!(!(8&e[47197])|te))n:if(m=e[47195],(0|(x=e[m+76>>2]))>=0&(!x|e[56823]!=(-1073741825&x)))T=e[(x=m+76|0)>>2],e[x>>2]=T||1073741823,e[m+80>>2]==10||(0|(T=e[m+20>>2]))==e[m+16>>2]?la(m):(e[m+20>>2]=T+1,f[0|T]=10),e[x>>2]=0;else{if(e[m+80>>2]!=10&&(0|(x=e[m+20>>2]))!=e[m+16>>2]){e[m+20>>2]=x+1,f[0|x]=10;break n}la(m)}if(x=-32769&(m=e[g+456>>2]),e[g+456>>2]=x,!(!i|!x|(1024&m?ce:0))){A=e[g+464>>2],PA(i,I),m=x|(Fe=(r=A)-qA(t,A=g+112|0,MA(A))|0,(1151&m)==1024?Fe:0);break e}(m=e[g+460>>2])&&(f[0|m]=69),Pn(A,r,s,I)}}else f[g+104|0]=95,qA(Ee,I,x),m=1,f[105+(g+x|0)|0]=0,QA(A,g+104|0,g- -64|0),h-1>>>0<=4294967293&&(m=MA(m=g- -64|0)+m|0,f[0|m]=11,f[m+1|0]=0,m=0),Pn(A,r,s,g- -64|0),e[g+464>>2]=I+x,h=m;if(I=e[g+464>>2],!(223&(m=d[0|I])))break}qA(t,A=g+112|0,MA(A))}m=0}return H=g+480|0,m}function Kt(A,t){var r,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0;H=r=H-2976|0,e[t>>2]=1,s=e[A+20>>2],e[(g=r+2960|0)>>2]=e[A+16>>2],e[g+4>>2]=s,s=e[A+12>>2],e[(g=r+2952|0)>>2]=e[A+8>>2],e[g+4>>2]=s,s=e[A+4>>2],e[r+2944>>2]=e[A>>2],e[r+2948>>2]=s,e[50303]||kA();e:if(A=e[r+2948>>2],d[0|A]&&A||((A=e[r+2944>>2])||(A=(A=e[r+2952>>2])||85055,e[r+2944>>2]=A),Lt(s=r+80|0,A,60),aa(s,0),!(l=ks(201216,s))||(e[r+2948>>2]=e[l+4>>2]+1,d[r+2958|0]|d[r+2956|0]|d[r+2957|0]))){J=r+1536|0,H=V=H-336|0;A:if(!(!(A=e[(K=r+2944|0)+4>>2])|!d[0|A])){if((0|(te=MA(A)))>=0){for(s=te>>>0>=79?79:te,h=1;A=Ps(f[e[K+4>>2]+i|0]),f[(V+256|0)+i|0]=A,h=((255&A)==45)+h|0,A=(0|s)!=(0|i),i=i+1|0,A;);if((0|h)!=1)break A}h=1}if((0|(x=e[50303]))<=0)e[J>>2]=0,A=0;else{for(g=(0|h)>=0;;){I=e[201216+(Ee<<2)>>2];A:if(Kr(e[I+8>>2],88032,3)){if((A=e[K+4>>2])&&Kr(A,91687,3)){if(g){A=100;r:if(h){if(s=0,i=e[I+4>>2],!(ce=d[0|i])){if(!Kr(V+256|0,90013,9))break r;break A}for(;;){for(Te=1,m=i+1|0,he=1,Fe=0,i=0;(0|i)<(0|te)&&(0|(A=f[(V+256|0)+i|0]))!=45||(A=0),Fe=((T=(0|(_=d[i+m|0]))==45)&!!(0|(he=(T?0:_)<<24>>24==(0|A)?he:0)))+Fe|0,i=i+1|0,Te=T+Te|0,_;);if(i=i+m|0,(T=he+Fe|0)&&(s=(0|(A=O((m=(0|(A=h-T|0))<=0?5:5-A|0)-((0|(A=Te-T|0))>0?A:0)|0,100)-(ce<<24>>24<<1)|0))>(0|s)?A:s),!(ce=d[0|i]))break}if(!(A=s))break A}(s=e[K>>2])&&(A=Ar(s,e[I>>2])?Ar(s,e[I+8>>2])?A:A+400|0:A+500|0),((i=d[K+12|0])-1&255)>>>0>1||((s=d[I+12|0])-1&255)>>>0>1||(A=(0|s)!=(0|i)?A-50|0:A+50|0),i=d[K+13|0],A=d[I+12|0]!=2|i>>>0>12?A:d[I+13|0]>12?A+5|0:A,(s=d[I+13|0])&&((s=((i?O(i,100):3e3)>>>0)/(s>>>0)|0)>>>0<=99&&(s=1e4/(s>>>0)|0),A=(m=A)+((A=5-(((s-100&65535)>>>0)/10|0)|0)>>31&A)|0,A=i?A+10|0:A),A=(0|A)<=1?1:A}else{if(Kr(e[I+8>>2],V+256|0,te))break A;A=100}e[J+(c<<2)>>2]=I,e[I+16>>2]=A}else e[J+(c<<2)>>2]=I;c=c+1|0}if((0|x)==(0|(Ee=Ee+1|0)))break}e[J+(c<<2)>>2]=0,A=0,c&&(ta(J,c,8),A=c)}if(H=V+336|0,h=A,A||(e[t>>2]=0,A=ks(201216,85055),e[r+1536>>2]=A,h=!!(0|A)),t=d[r+2957|0],g=2,(0|(A=d[r+2956|0]))!=2&&(g=2,(t-1&255)>>>0<12||(Xe=(0|A)!=1,g=(0|A)==1)),x=(c=e[132136+(g<<2)>>2])+(_=t>>>0<60)|0,A=0,(0|h)>0)for(s=0;;){l=e[(r+1536|0)+(Le<<2)>>2];A:{r:{a:{if(Xe){if(_||(t=0,s))break r}else{if(t=d[l+12|0],s|_)break a;t=(0|t)!=(0|g)}if(i=0,t|d[l+13|0]<60)break A;break r}if((0|t)!=(0|g)){i=s;break A}}e[(r+80|0)+(s<<2)>>2]=l,i=s+1|0}A:if(d[l+15|0]){if(T=0,t=A,s=i,!((0|A)>11))for(;;){if((i=d[0|x])||(x=c,i=d[0|c]),A=e[l+12>>2],m=O(t,24)+202624|0,e[m+8>>2]=e[l+8>>2],e[m+12>>2]=A,A=e[l+4>>2],e[m>>2]=e[l>>2],e[m+4>>2]=A,A=e[l+20>>2],e[m+16>>2]=e[l+16>>2],e[m+20>>2]=A,f[m+14|0]=i,e[(r+80|0)+(s<<2)>>2]=m,x=x+1|0,s=s+1|0,A=t+1|0,(T=T+1|0)>>>0>=d[l+15|0])break A;if(i=(0|t)<11,t=A,!i)break}}else s=i;if((0|(Le=Le+1|0))==(0|h))break}else{if(!l)break e;s=0}A:if(!(!(i=d[0|x])|(0|A)>=12))for(;;){if(t=e[l+12>>2],c=O(A,24)+202624|0,e[c+8>>2]=e[l+8>>2],e[c+12>>2]=t,t=e[l+4>>2],e[c>>2]=e[l>>2],e[c+4>>2]=t,t=e[l+20>>2],e[c+16>>2]=e[l+16>>2],e[c+20>>2]=t,f[c+14|0]=i,e[(r+80|0)+(s<<2)>>2]=c,s=s+1|0,!(i=d[0|(x=x+1|0)]))break A;if(t=(0|A)<11,A=A+1|0,!t)break}s?(A=e[(r+80|0)+(d[r+2958|0]%(0|s)<<2)>>2],(t=d[A+14|0])?(f[202976]=0,e[r+48>>2]=47,dA(r+2971|0,91351,r+48|0),f[r+2971|0]=0,t>>>0<=9?(e[r+20>>2]=t,e[r+16>>2]=r+2971,dA(202976,91378,r+16|0)):(e[r+36>>2]=t-10,e[r+32>>2]=r+2971,dA(202976,91503,r+32|0)),A=e[A+8>>2],e[r+4>>2]=202976,e[r>>2]=A,A=202912,dA(202912,87760,r)):A=e[A+8>>2]):A=0}else{if(A=e[l+8>>2],!d[202976])break e;e[r+64>>2]=A,e[r+68>>2]=202976,A=202912,dA(202912,87760,r- -64|0)}return H=r+2976|0,A}function Ss(A,t,r,s,i){var l,c,g=0,m=0,I=0,h=0,x=0,T=0,_=0;H=l=H-464|0,f[l+432|0]=0,f[l+368|0]=0,f[l+304|0]=0,f[l+292|0]=0,x=(0|t)/10|0,g=e[33273];e:{if(!(c=2&s)|e[33272]!=2){_=32&s?113:111,h=1&s,T=t-O(x,10)|0;A:{r:{a:{n:{o:{c:{u:{l:if(d[0|g])g=0;else{i:{if(8&s){if(e[l+288>>2]=t,dA(m=l+452|0,91198,l+288|0),g=QA(A,m,l+304|0)){m=0;break l}e[l+272>>2]=t,dA(m=l+452|0,91314,l+272|0),g=QA(A,m,l+304|0),m=0}else{if(!h)break i;if(I=PA(l+432|0,133104),4&s){if(e[l+260>>2]=_,e[l+256>>2]=t,dA(m=l+452|0,91324,l+256|0),g=QA(A,m,l+304|0),d[133116]&&g)break u;if(m=g,g)break l}e[l+244>>2]=_,e[l+240>>2]=t,dA(m=l+452|0,91384,l+240|0),m=g=QA(A,m,l+304|0)}if(g)break l}i:{if(c){if(!(1&f[133096]))break i;e[l+208>>2]=t,dA(g=l+452|0,91498,l+208|0),g=QA(A,g,l+304|0)}else I=e[A+108>>2],e[l+224>>2]=t,dA(g=l+452|0,(0|r)>=2?91700:(262144&I)>>>18|0?91534:91700,l+224|0),g=QA(A,g,l+304|0);if(g)break l}!h|!(32&d[A+109|0])?(e[l+192>>2]=t,dA(g=l+452|0,91766,l+192|0),g=QA(A,g,l+304|0)):g=0}if(!(16&s)|(0|t)>9)break o;g=m;break c}if(PA(I,133116),!(16&s)|(0|t)>9)break n}QA(A,88875,l+368|0);break r}if(!g)break a;g=m}f[l+368|0]=0;break r}a:if(h&&(e[l+180>>2]=_,e[l+176>>2]=x,dA(g=l+452|0,91846,l+176|0),QA(A,g,l+368|0))){if(m=1,!T|!(16&d[A+109|0]))break a;As(l+368|0,133104)}else m||(e[l+160>>2]=x,dA(m=l+452|0,512&s?91936:92016,l+160|0),QA(A,m,l+368|0),m=0);if(g=T,d[l+368|0]||(g=T,16&d[A+106|0]&&(e[l+144>>2]=254&x,dA(g=l+452|0,92016,l+144|0),QA(A,g,l+368|0),g=(0|t)%20|0)),f[l+304|0]=0,x=g,(0|g)<=0)g=m;else{if(c&&(g=e[33273],d[0|g])){PA(l+304|0,g),f[l+432|0]=0,I=h;break A}if(I=0,8&s&&(e[l+128>>2]=x,dA(s=l+452|0,91314,l+128|0),I=QA(A,s,l+304|0)),!h|16&d[A+104|0]||(e[l+116>>2]=_,e[l+112>>2]=x,dA(s=l+452|0,91384,l+112|0),m=(I=QA(A,s,l+304|0))?1:m),g=m,!I){a:{if(!c|!(1&e[33274])){if(!(16&d[A+104|0])&&c)break a;m=e[A+108>>2],e[l+96>>2]=x,dA(s=l+452|0,(0|r)>=2?91700:(262144&m)>>>18|0?91534:91700,l+96|0),r=QA(A,s,l+304|0)}else e[l+80>>2]=x,dA(r=l+452|0,91498,l+80|0),r=QA(A,r,l+304|0);if(r)break r}e[l+64>>2]=x,dA(r=l+452|0,91766,l- -64|0),QA(A,r,l+304|0)}}}I=h,d[l+432|0]|g|!h||((0|t)<20|(16&d[A+104|0]?0:T)||(QA(A,92162,l+432|0),I=1,!d[l+432|0]))&&(QA(A,92205,l+432|0),I=1)}if(!(!(r=f[l+304|0])|!(48&(t=e[A+104>>2]))|!d[l+368|0])){if(QA(A,90824,l+292|0),!I|!(8&d[A+109|0])||(f[l+292|0]=0),16&d[A+104|0]){e[l+28>>2]=l+432,e[l+24>>2]=l+368,e[l+20>>2]=l+292,e[l+16>>2]=l+304,dA(i,91059,l+16|0),s=1;break e}e[l+12>>2]=l+432,e[l+8>>2]=l+304,e[l+4>>2]=l+292,e[l>>2]=l+368,dA(i,91059,l),s=1;break e}512&t&&(!r|(0|(t=MA(l+368|0)-1|0))<0||(g=d[e[144464+(f[0|(t=t+(l+368|0)|0)]<<2)>>2]+11|0]!=2,(0|(s=d[e[144464+(r<<2)>>2]+11|0]))==1&&(s=d[e[144464+(f[l+305|0]<<2)>>2]+11|0]),g|(255&s)!=2||(f[0|t]=0))),!(8&d[A+110|0])|!d[l+432|0]?(e[l+56>>2]=l+432,e[l+52>>2]=l+304,e[l+48>>2]=l+368,dA(i,92282,l+48|0)):(e[l+36>>2]=l+304,e[l+32>>2]=l+368,(0|(t=dA(i,90368,l+32|0)))>0&&(t=d[e[144464+(d[(r=t-1|0)+i|0]<<2)>>2]+11|0]==2?r:t),PA(t+i|0,l+432|0))}else PA(i,g);s=0}e:if(268435456&(A=e[A+104>>2])){if((0|MA(i))<=0)break e;for(t=0,A=0;d[0|(r=A+i|0)]==6&&(t&&(f[0|r]=5),t=1),A=A+1|0,(0|MA(i))>(0|A););}else if(256&A&&(t=0,!((0|(A=(h=MA(i))-1|0))<0))){if(A)for(T=-2&h,g=0;d[0|(m=A+i|0)]==6?(r=1,t&&(f[0|m]=5)):r=t,d[0|(m=m-1|0)]==6?(t=1,r&&(f[0|m]=5)):t=r,A=A-2|0,(0|T)!=(0|(g=g+2|0)););1&h&&(!t|d[0|(A=A+i|0)]!=6||(f[0|A]=5))}return H=l+464|0,s}function Ws(A,t,r,s){var i,l,c=0,g=0,m=0,I=0,h=0,x=0,T=0;if(H=i=H-352|0,f[i+304|0]=0,f[i+224|0]=0,f[i+64|0]=0,m=e[e[47192]+292>>2],T=jA(i+348|0,t),(1048320&(c=e[i+348>>2]))==57344&&(c&=255,e[i+348>>2]=c),2&s&&Gs(c)&&QA(A,85437,i+304|0),c=Ln(e[i+348>>2],A),e[i+348>>2]=c,x=1&s,vA(A,c,f[0|(l=t+T|0)],i+224|0,x),!(t=d[i+224|0])){e:if((t=_n(e[i+348>>2]))&&(e[i+348>>2]=16383&t,4&s)){A:switch(1073741823&(t>>=14)){case 0:case 3:break e;default:break A}QA(A,t=e[131232+(t<<2)>>2],i+304|0),d[i+304|0]||(f[i+306|0]=Rn(84744),h=t,t=i+304|3,QA(e[47194],h,t),d[i+307|0]&&(k[i+304>>1]=5385,t=MA(t)+(i+304|0)|0,f[t+5|0]=0,f[t+4|0]=m,f[t+3|0]=21))}vA(A,e[i+348>>2],f[0|l],i+224|0,x),t=d[i+224|0]}e:{A:{if(t&=255){if((0|t)!=21)break A;PA(r,i+224|0),T=0;break e}if(t=1632,!((0|(c=e[i+348>>2]))<1632)){for(I=103360;;){if((0|c)>=(t+10|0)){if(!(t=e[(I=I+4|0)>>2]))break A;if((0|t)<=(0|c))continue;break A}break}(0|(t=48+(c-t|0)|0))<=0||vA(A,t,0,i+224|0,x)}}A:{r:{a:{n:{if(t=uA(e[i+348>>2])){if(c=e[t+4>>2],!t|1&(I=e[t+16>>2])||(g=e[47192],e[g+600>>2]==(0|c)|e[g+188>>2]==(0|c)|e[g+184>>2]==(0|c)||(f[i+144|0]=0,QA(g,e[t>>2],i- -64|0)?(0|(g=e[47192]))!=(0|A)&&(m=e[A+292>>2],PA(i+144|0,i- -64|0),f[i+66|0]=e[g+292>>2]):(f[i+66|0]=Rn(84744),QA(e[47194],e[t>>2],i+144|0)),d[i+144|0]&&(k[i+64>>1]=5385,PA(3|(g=i- -64|0),h=i+144|0),g=MA(h)+g|0,f[g+5|0]=0,f[g+4|0]=m,f[g+3|0]=21))),d[i+224|0])break A;if(!c||(m=e[47192],e[m+188>>2]!=(0|c)))break n;t=e[m+192>>2];break r}if(d[i+224|0])break A;I=0,c=0;break a}if((t=e[t+12>>2])&&!(2&I))break r}t=25966}if((e[A+212>>2]==(0|t)&(0|t)!=27503||(f[i+226|0]=Rn(Qe(i+47|0,t)),(t=e[47194])&&((0|(m=e[i+348>>2]))>55215||(0|(g=m-44032|0))<0?vA(t,m,f[0|l],i+224|3,x):(f[i+52|0]=32,t=h=i+53|0,m-50500>>>0>=588&&(t=Cr(4352+((g>>>0)/588|0)|0,h)+h|0),Cr(4449+(((m=(g>>>0)/28|0)>>>0)%21|0)|0,t),Cr(4519+(g-O(m,28)|0)|0,t+3|0),f[t+6|0]=32,f[t+7|0]=0,f[i+227|0]=0,t=i+224|3,VA(e[47194],h,t,77,0,0,0),Vt(e[47194],t,0,-1,0)),t=i+224|3,d[i+227|0]==21&&(f[i+226|0]=Rn(i+224|4),vA(e[47194],e[i+348>>2],f[0|l],t,x)),as(e[e[32972]+60>>2]),d[i+227|0]&&(k[i+224>>1]=5385,t=MA(t)+(i+224|0)|0,f[t+3|0]=21,x=e[A+292>>2],f[t+5|0]=0,f[t+4|0]=x)),!d[i+224|0]))&&(16&I||(OA(e[i+348>>2])&&QA(e[47192],85683,i+224|0),d[i+224|0]||(fr(e[i+348>>2])||QA(e[47192],85778,i+224|0),d[i+224|0]||qr(85992,i+224|0,0))),!(8&I)||4&s)){if(t=e[i+348>>2],(0|c)!=10240?(e[i+32>>2]=t,dA(i+52|0,86013,i+32|0)):(c=i+52|0,1&t&&(f[i+52|0]=49,c=i+53|0),2&t&&(f[0|c]=50,c=c+1|0),4&t&&(f[0|c]=51,c=c+1|0,t=e[i+348>>2]),8&t&&(f[0|c]=52,c=c+1|0,t=e[i+348>>2]),16&t&&(f[0|c]=53,c=c+1|0,t=e[i+348>>2]),32&t&&(f[0|c]=54,c=c+1|0,t=e[i+348>>2]),64&t&&(f[0|c]=55,c=c+1|0,t=e[i+348>>2]),128&t&&(f[0|c]=56,c=c+1|0),f[0|c]=0),t=i+224|0,I=d[i+52|0])for(c=i+52|0;t=MA(t)+t|0,f[0|t]=23,t=t+1|0,vA(e[47192],I<<24>>24,0,t,1),(s=d[0|t])&&(0|s)!=21||(0|(s=f[0|c]))<97||qr(e[130860+((255&s)<<2)>>2],t,0),I=d[0|(c=c+1|0)];);t=MA(t)+t|0,f[0|t]=9,f[t+1|0]=0}}t=MA(r),2&d[A+144|0]?(e[i+16>>2]=255,e[i+28>>2]=i+304,e[i+24>>2]=i+224,e[i+20>>2]=i- -64,dA(i+144|0,86210,i+16|0)):(e[i>>2]=255,e[i+12>>2]=i+224,e[i+8>>2]=i+304,e[i+4>>2]=i- -64,dA(i+144|0,86210,i)),MA(i+144|0)+t>>>0>199||PA(t+r|0,i+144|0)}return H=i+352|0,T}function Cs(A,t,r,s,i,l,c){var g,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0,Xe=0,fA=0,hA=0,_A=0,LA=0,At=0;H=g=H-528|0,te=c?e[c>>2]:0,Le=e[i+4>>2];e:{A:{if(e[A+220>>2]>0){Lt(I=g+352|0,t,160),H=_=H-176|0,K=1-(V=e[A+220>>2])|0,J=e[A+224>>2],ce=e[A+216>>2],x=I;r:{a:{for(;;){if(he=jA(_+172|0,x),m=e[_+172>>2]){if((0|m)<(0|V)|(0|m)>(0|ce))break a;if(J){if((0|(m=f[J+(m-V|0)|0]))<=0)break a}else m=m+K|0;if(x=x+he|0,f[T+_|0]=m,m=160,(0|(T=T+1|0))!=160)continue}else m=T;break}if(J=0,f[m+_|0]=0,K=f[0|_],e[_+172>>2]=K,K){for(Xe=2+(ce-V|0)|0,V=m=_;;){ce=m+1|0;n:{if((he=e[A+8180>>2])&&(T=0,!((0|(x=k[he>>1]))>(0|(Te=(f[0|ce]<<8)+K|0)))))for(;;){if((0|x)==(0|Te)){K=T+Xe|0,e[_+172>>2]=K,m=m+2|0;break n}if(!((0|Te)>=(0|(x=k[he+((T=T+1|0)<<1)>>1]))))break}m=ce}if(Ee=63&K|Ee<<6,(0|(T=J+6|0))<8?J=T:(J=J-2|0,f[0|V]=Ee>>J,V=V+1|0),K=f[0|m],e[_+172>>2]=K,!K)break}(0|J)<=0||(f[0|V]=Ee<<8-J,V=V+1|0)}else V=_;f[0|V]=0,qA(I,_,m=V-_|0),V=64|m;break r}V=MA(I)}H=_+176|0,_=I}else V=MA(t),_=t;if(I=d[0|_]){for(m=0,T=_;h=1023&(h=(h<<3)+I|0)^h>>>8,m=m+1|0,I=d[0|(T=T+1|0)];);m=m+h&1023}else m=0;if(h=e[692+((m<<2)+A|0)>>2]){if(m=d[0|h])break A;m=0;break e}if(m=0,!i)break e;e[i>>2]=0;break e}for(he=1073741824&Le,Te=2048&l,Le=512&te,Xe=65536&te,fA=1&te,hA=2&te,te=8&l,_A=1024&l,ce=4&l,LA=63&V,At=A+8233|0;;){l=(255&m)+h|0;A:{r:if((127&(m=d[h+1|0]))==(0|V)&&!Kr(_,h+2|0,LA)){h=2+((63&m)+h|0)|0;a:{if(m<<24>>24<0)J=0,f[0|s]=0;else{if((0|(J=MA(h)))>=160)break a;PA(s,h),h=1+(h+J|0)|0}if(I=0,l>>>0<=h>>>0)m=r,x=0;else{T=0,x=0;n:{for(;;){h=(m=h)+1|0;o:if((m=d[0|m])>>>0>=100){if(K=e[A+320>>2],m>>>0>=132){T|=K>>>m-132&1;break o}T|=!(K>>>m-100&1)}else{if(m>>>0>=81){K=m-80|0,Ee=l-h|0;c:if(c)for(m=0;;){if(Fe=O(m,12)+c|0,!d[Fe+10|0])break c;if(T=!!(12&d[Fe+1|0])|T,Fe=(0|m)!=(0|K),m=m+1|0,!Fe)break}if(On(r,h,Ee)|1&T)break r;e[33264]=K,m=r+Ee|0,x|=128,h=l;break n}m>>>0>=65?(x=15&m|-16&x,x=12&~m?x:512|x):m>>>0>=32?I|=1<<m-32:x|=1<<m}if(!(l>>>0>h>>>0))break}if(m=r,1&T)break A}if(65536&I&&!ce||_A&&49152&I)break A}if(ce&&(16384&I||!te&&32768&I)||(hA?0:512&I)|(fA?0:1024&I)|(Xe?0:33554432&x)||!(!(131072&I)|Ae[e[47192]+8204>>2]<=m>>>0|he)|(Le?0:262144&I)|(8&d[e[47192]+8242|0]?0:8192&I)||16&I&&(!e[A+8184>>2]&(!te|!e[A+8192>>2])||!(!te|e[A+212>>2]!=25966)&&2097152&e[A+8232>>2])||(e[A+8188>>2]?0:64&I)|(!e[A+8196>>2]|Te?32&I:0)||!(!(65536&x)|e[A+212>>2]!=26741|128&d[0|At])|(e[47192]!=(0|A)?524288&I:0))break A;n:{o:{c:{if(!i){if(!J)break c;break n}if(e[i+4>>2]=I,e[i>>2]=1073741824|x,J)break o}if(m=0,!(8&d[188788]))break e;Qn(i,A=g+272|0),e[g>>2]=t,e[g+4>>2]=A,Xt(e[47195],89330,g);break e}e[i>>2]=-1073741824|x}if(8&d[188788]&&(jr(s,g- -64|0),d[e[47192]+172|0]==(x>>>29&1)&&(!c|!(128&x)?(e[g+48>>2]=t,Xt(e[47195],89426,g+48|0)):(qA(A=g+352|0,s=r,r=m-r|0),f[351+(r+g|0)|0]=0,e[g+32>>2]=t,e[g+36>>2]=A,Xt(e[47195],89397,g+32|0)),Qn(i,A=g+272|0),t=e[47195],e[g+16>>2]=g- -64,e[g+20>>2]=A,Xt(t,89534,g+16|0))),d[jA(g- -64|0,_)+_|0]|!i||Ft(e[g+64>>2]))break e;e[i>>2]=134217728|e[i>>2];break e}ie(89236,86634,2467,94846),j()}h=l}if(!(m=d[0|h]))break}m=0}return H=g+528|0,m}function ot(A,t,r,s,i){var l,c=0,g=0;H=l=H-304|0,f[l+278|0]=0;e:{if((0|t)>0&&(1&s&&(2&s&&(e[l+164>>2]=r,e[l+160>>2]=t,dA(c=l+290|0,89701,l+160|0),c=QA(A,c,l+224|0))||1&f[133096]&&(e[l+148>>2]=r,e[l+144>>2]=t,dA(c=l+290|0,89757,l+144|0),c=QA(A,c,l+224|0))||(e[l+132>>2]=r,e[l+128>>2]=t,dA(c=l+290|0,89894,l+128|0),c=QA(A,c,l+224|0)))||(e[l+116>>2]=r,e[l+112>>2]=t,dA(c=l+290|0,89974,l+112|0),c=QA(A,c,l+224|0))))break e;if((0|(g=(0|t)%100|0))>=20&&QA(A,90022,l+278|0),1&s){if(2&s){c=g-11|0;A:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(c>>>0<9)break r;if(s=90418,(0|(c=(0|t)%10|0))==1)break A;if(c-2>>>0>=3)break r;s=90453;break A;case 1:if(t-2>>>0>=3)break r;s=90453;break A;case 2:if(c>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;s=90453;break A;case 3:if(s=90508,c>>>0<9)break A;s=(s=(0|t)%10|0)?(0|s)==1?90453:90586:90508;break A;case 4:break a;default:break r}if(!(c>>>0<9)){if(s=90537,(0|(c=(0|t)%10|0))==1)break A;if(!(c-2>>>0>=3)){s=90453;break A}}}s=90586}if(e[l+100>>2]=r,e[l+96>>2]=s,dA(s=l+290|0,90058,l+96|0),c=0,QA(A,s,l+224|0))break e}if(s=g-11|0,1&f[133096]){A:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(s>>>0<9)break r;if(c=90418,(0|(g=(0|t)%10|0))==1)break A;if(g-2>>>0>=3)break r;c=90453;break A;case 1:if(t-2>>>0>=3)break r;c=90453;break A;case 2:if(s>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;c=90453;break A;case 3:if(c=90508,s>>>0<9)break A;c=(c=(0|t)%10|0)?(0|c)==1?90453:90586:90508;break A;case 4:break a;default:break r}if(!(s>>>0<9)){if(c=90537,(0|(g=(0|t)%10|0))==1)break A;if(!(g-2>>>0>=3)){c=90453;break A}}}c=90586}if(e[l+84>>2]=r,e[l+80>>2]=c,dA(g=l+290|0,90110,l+80|0),c=0,QA(A,g,l+224|0))break e}A:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(s>>>0<9)break r;if(c=90418,(0|(g=(0|t)%10|0))==1)break A;if(g-2>>>0>=3)break r;c=90453;break A;case 1:if(t-2>>>0>=3)break r;c=90453;break A;case 2:if(s>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;c=90453;break A;case 3:if(c=90508,s>>>0<9)break A;c=(c=(0|t)%10|0)?(0|c)==1?90453:90586:90508;break A;case 4:break a;default:break r}if(!(s>>>0<9)){if(c=90537,(0|(g=(0|t)%10|0))==1)break A;if(!(g-2>>>0>=3)){c=90453;break A}}}c=90586}if(e[l+68>>2]=r,e[l+64>>2]=c,dA(g=l+290|0,90139,l- -64|0),c=0,QA(A,g,l+224|0))break e}else s=g-11|0;A:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(s>>>0<9)break r;if(c=90418,(0|(s=(0|t)%10|0))==1)break A;if(s-2>>>0>=3)break r;c=90453;break A;case 1:if(t-2>>>0>=3)break r;c=90453;break A;case 2:if(s>>>0<9|((0|t)%10|0)-2>>>0>=3)break r;c=90453;break A;case 3:if(c=90508,s>>>0<9)break A;c=(s=(0|t)%10|0)?(0|s)==1?90453:90586:90508;break A;case 4:break a;default:break r}if(!(s>>>0<9)){if(c=90537,(0|(s=(0|t)%10|0))==1)break A;if(!(s-2>>>0>=3)){c=90453;break A}}}c=90586}e[l+52>>2]=r,e[l+48>>2]=c,dA(s=l+290|0,90218,l+48|0),c=0,QA(A,s,l+224|0)||((0|r)<4||(e[l+32>>2]=r-1,dA(s=l+290|0,89026,l+32|0),QA(A,s,l+176|0)||(QA(A,90273,l+224|0),e[33275]=3)),d[l+224|0]||(e[l+16>>2]=t,dA(s=l+290|0,90303,l+16|0),(c=QA(A,s,l+224|0))||QA(A,90347,l+224|0),e[33275]=2))}return e[l+4>>2]=l+224,e[l>>2]=l+278,dA(i,90368,l),H=l+304|0,!((0|t)!=1|(0|r)!=1)&&(t=1,32&d[A+106|0])||(t=c),t}function Vs(A,t,r,s){var i,l=0,c=0,g=0,m=0,I=0,h=0,x=0;H=i=H+-64|0,k[i+48>>1]=0,e[i+40>>2]=0,e[i+44>>2]=0,e[i+32>>2]=0,e[i+36>>2]=0,e[i+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+8>>2]=0,e[i+12>>2]=0,e[i>>2]=0,e[i+4>>2]=0,l=t;e:{for(;;){A:{r:{if((0|(c=d[0|l]))!=69){if((0|c)!=32)break r;if(s&&(f[qA(c=s,t,s=(0|(s=l-t|0))>=159?159:s)+s|0]=0),s=63&r)break A;break e}f[0|l]=101}l=l+1|0;continue}break}if(1&r){A:if((l=l-1|0)>>>0<t>>>0)c=s;else for(c=s;;){if((192&d[0|l])!=128)break A;if(c=c+1|0,!((l=l-1|0)>>>0>=t>>>0))break}g=s-1|0}else g=s,c=s;if((0|s)!=1)for(;;){s=g;A:if(!((l=l-1|0)>>>0<t>>>0))for(;;){if((192&d[0|l])!=128)break A;if(c=c+1|0,!((l=l-1|0)>>>0>=t>>>0))break}A:if(!((l=l-1|0)>>>0<t>>>0))for(;;){if((192&d[0|l])!=128)break A;if(c=c+1|0,!((l=l-1|0)>>>0>=t>>>0))break}if(g=s-2|0,!((0|s)>2))break}if((0|c)<=0)g=0;else{if(m=3&(g=(s=(t=c-1|0)>>>0>=48?48:t)+1|0),t=0,c=0,s>>>0>=3)for(x=-4&g,s=0;I=l+c|0,f[c+i|0]=d[0|I],f[0|I]=32,I=(h=1|c)+l|0,f[i+h|0]=d[0|I],f[0|I]=32,I=(h=2|c)+l|0,f[i+h|0]=d[0|I],f[0|I]=32,I=(h=3|c)+l|0,f[i+h|0]=d[0|I],f[0|I]=32,c=c+4|0,(0|x)!=(0|(s=s+4|0)););if(m)for(;s=l+c|0,f[c+i|0]=d[0|s],f[0|s]=32,c=c+1|0,(0|m)!=(0|(t=t+1|0)););}}if(f[i+g|0]=0,m=65520&r,!(512&r)|d[0|(s=l-1|0)]!=105||(f[0|s]=121),c=4|m,256&r){e:{A:{r:{if((0|(t=e[A+212>>2]))!=25966){if((0|t)!=28268)break r;if(f[0|s]<0||128&(t=f[0|(g=l-2|0)]))break e;if(m=e[A+632>>2])t=!!(0|_r(m,t));else{if((0|(m=e[A+600>>2]))>0&&(t=t-m|0)-1>>>0>254)break e;t=128&d[344+(A+t|0)|0]}if(!t)break e;if(t=f[0|s],m=e[A+612>>2])t=!!(0|_r(m,t));else{a:{if((0|(m=e[A+600>>2]))>0){if((t=t-m|0)-1>>>0<255)break a;break e}if((0|t)<0)break e}t=4&d[344+(A+t|0)|0]}if(!t)break e;t=f[l-3|0];a:{if(m=e[A+632>>2])t=!!(0|_r(m,t));else{n:{if((0|(m=e[A+600>>2]))>0){if((t=t-m|0)-1>>>0<255)break n;break a}if((0|t)<0)break a}t=128&d[344+(A+t|0)|0]}if(t)break e}f[0|l]=d[0|s],f[0|s]=d[0|g],f[l+1|0]=32;break e}if(g=f[l-2|0],t=e[A+632>>2])t=!!(0|_r(t,g));else{a:{if((0|(t=e[A+600>>2]))>0){if((g=g-t|0)-1>>>0<255)break a;break A}if((0|g)<0)break A}t=128&d[344+(A+g|0)|0]}if(!t)break A;if(g=f[0|s],t=e[A+608>>2])t=!!(0|_r(t,g));else{if((0|(t=e[A+600>>2]))>0){if((g=g-t|0)-1>>>0>=255)break A}else if((0|g)<0)break A;t=2&d[344+(A+g|0)|0]}if(!t)break A;c=Kr(87771,l-3|0,3)?20|m:c;break e}c=e[A+204>>2]?20|m:c;break e}(d[0|s]==99||(g=d[(t=l-2|0)+1|0]<<8,(d[0|t]|g)==29554|(g|d[0|t])==29289||(d[0|(t=l-2|0)]|d[t+1|0]<<8)==29301||!Kr(88115,l-3|0,3)||(d[0|(t=l-2|0)]|d[t+1|0]<<8)==29550|d[0|s]==117||!Kr(88384,l-5|0,5)||(d[0|(t=l-4|0)]|d[t+1|0]<<8|d[t+2|0]<<16|d[t+3|0]<<24)==1735287154||(d[0|t]|d[t+1|0]<<8|d[t+2|0]<<16|d[t+3|0]<<24)==1735549292))&&(c=20|m)}16&c&&(Cr(e[A+204>>2],l),8&d[188788]&&_i(88683,6,e[47195]))}return e[A+8184>>2]|!(2048&r)||(e[A+8184>>2]=1),y[i>>1]!=115&&Kr(i,88850,3)||(c|=8),H=i- -64|0,d[0|i]==39?65531&c:c}function Y(A,t,r,s,i,l,c,g,m){var I,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0;H=I=H-112|0,h=2147483647&m;e:{if(T=!(t|r),(s|(x=2147483647&i)?x-2147418112>>>0<2147549184:T)||!(!g&(0|(V=h-2147418112|0))==-2147418112?l|c:(0|V)==-2147418112&!!(0|g)|V>>>0>2147549184)){if(!(!s&(0|x)==2147418112?T:x>>>0<2147418112)){g=s,m=32768|i,l=t,c=r;break e}if(!(!g&(0|h)==2147418112?!(l|c):h>>>0<2147418112)){m|=32768;break e}if(!(t|s|2147418112^x|r)){_=s,g=(s=!(t^l|s^g|r^c|i^m^-2147483648))?0:_,m=s?2147450880:i,l=s?0:t,c=s?0:r;break e}if(!(l|g|2147418112^h|c))break e;if(!(t|s|r|x)){if(l|g|c|h)break e;l&=t,c&=r,g&=s,m&=i;break e}if(!(l|g|c|h)){l=t,c=r,g=s,m=i;break e}}x=(T=te=(_=(0|h)==(0|x))&(0|s)==(0|g)?(0|r)==(0|c)&t>>>0<l>>>0|r>>>0<c>>>0:_&s>>>0<g>>>0|h>>>0>x>>>0)?l:t,V=T?c:r,ce=_=T?m:i,T=T?g:s,J=65535&_,s=te?s:g,he=i=te?i:m,_=i>>>16&32767,(K=ce>>>16&32767)||(m=i=!(T|J),h=i?x:T,g=i<<=6,vt(I+96|0,x,V,T,J,(i=i+((0|(m=be(m?V:J)))==32?be(h)+32|0:m)|0)-15|0),T=e[I+104>>2],J=e[I+108>>2],V=e[I+100>>2],K=16-i|0,x=e[I+96>>2]),l=te?t:l,c=te?r:c,g=s,m=65535&he,_||(i=t=!(g|m),h=t?l:g,r=t<<=6,vt(I+80|0,l,c,g,m,(t=t+((0|(i=be(i?c:m)))==32?be(h)+32|0:i)|0)-15|0),_=16-t|0,g=e[I+88>>2],m=e[I+92>>2],c=e[I+84>>2],l=e[I+80>>2]),r=m<<3|g>>>29,t=g<<3|c>>>29,r|=524288,g=T<<3|V>>>29,m=J<<3|T>>>29,te=ce^he,i=c<<3|l>>>29,s=l<<3,(0|_)!=(0|K)&&((l=K-_|0)>>>0>127?(t=0,r=0,i=0,s=1):(vt(I- -64|0,s,i,t,r,128-l|0),cr(I+48|0,s,i,t,r,l),t=e[I+56>>2],r=e[I+60>>2],i=e[I+52>>2],s=e[I+48>>2]|!!(e[I+64>>2]|e[I+72>>2]|e[I+68>>2]|e[I+76>>2]))),T=s,h=i,_=g,J=524288|m,i=V<<3|x>>>29,V=x<<3,x=i;A:if((0|te)<0){if(l=0,c=0,g=0,m=0,!(T^V|t^_|h^x|r^J))break e;if(s=V-T|0,i=x-((T>>>0>V>>>0)+h|0)|0,g=(l=_-t|0)-(c=(0|h)==(0|x)&T>>>0>V>>>0|h>>>0>x>>>0)|0,m=t=(J-((t>>>0>_>>>0)+r|0)|0)-(l>>>0<c>>>0)|0,t>>>0>524287)break A;l=t=!(g|m),c=t?s:g,r=t<<=6,vt(I+32|0,s,i,g,m,t=(t=t+((0|(l=be(l?i:m)))==32?be(c)+32|0:l)|0)-12|0),K=K-t|0,g=e[I+40>>2],m=e[I+44>>2],s=e[I+32>>2],i=e[I+36>>2]}else i=h+x|0,l=(0|h)==(0|(i=(s=T+V|0)>>>0<V>>>0?i+1|0:i))&s>>>0<T>>>0|i>>>0<h>>>0,h=r+J|0,h=(t=t+_|0)>>>0<_>>>0?h+1|0:h,1048576&(m=(g=t+l|0)>>>0<t>>>0?h+1|0:h)&&(s=1&T|(1&i)<<31|s>>>1,i=g<<31|i>>>1,K=K+1|0,g=(1&m)<<31|g>>>1,m=m>>>1|0);if(r=0,x=-2147483648&ce,(0|K)>=32767)g=r,m=2147418112|x,l=0,c=0;else if(_=0,(0|K)>0?_=K:(vt(I+16|0,s,i,g,m,K+127|0),cr(I,s,i,g,m,1-K|0),s=e[I>>2]|!!(e[I+16>>2]|e[I+24>>2]|e[I+20>>2]|e[I+28>>2]),i=e[I+4>>2],g=e[I+8>>2],m=e[I+12>>2]),V=7&s,s=(0|(t=g<<29|i>>>3))==(0|(c=(s=(7&i)<<29|s>>>3)>>>0>(l=(V>>>0>4)+s|0)>>>0?t+1|0:t))&s>>>0>l>>>0|t>>>0>c>>>0,t=r|(7&m)<<29|g>>>3,m=x|m>>>3&65535|_<<16,m=t>>>0>(g=s+t|0)>>>0?m+1|0:m,(0|V)!=4){if(!V)break e}else h=c+(t=0)|0,m=(t=(0|t)==(0|(c=(s=l)>>>0>(l=l+(r=1&l)|0)>>>0?h+1|0:h))&r>>>0>l>>>0|t>>>0>c>>>0)>>>0>(g=t+g|0)>>>0?m+1|0:m}e[A>>2]=l,e[A+4>>2]=c,e[A+8>>2]=g,e[A+12>>2]=m,H=I+112|0}function fe(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0;e:if(A|=0){l=(s=A-8|0)+(A=-8&(t=e[A-4>>2]))|0;A:if(!(1&t)){if(!(3&t)||(s=s-(t=e[s>>2])|0)>>>0<Ae[57156])break e;if(A=A+t|0,e[57157]==(0|s)){if(!(3&~(t=e[l+4>>2])))return e[57154]=A,e[l+4>>2]=-2&t,e[s+4>>2]=1|A,void(e[A+s>>2]=A)}else{if(t>>>0<=255){if(i=e[s+8>>2],t=t>>>3|0,(0|(r=e[s+12>>2]))==(0|i)){e[57152]=e[57152]&es(-2,t);break A}e[i+12>>2]=r,e[r+8>>2]=i;break A}if(g=e[s+24>>2],(0|s)==(0|(t=e[s+12>>2])))if((r=e[(i=s+20|0)>>2])||(r=e[(i=s+16|0)>>2])){for(;c=i,(r=e[(i=(t=r)+20|0)>>2])||(i=t+16|0,r=e[t+16>>2]););e[c>>2]=0}else t=0;else r=e[s+8>>2],e[r+12>>2]=t,e[t+8>>2]=r;if(!g)break A;i=e[s+28>>2];r:{if(e[(r=228912+(i<<2)|0)>>2]==(0|s)){if(e[r>>2]=t,t)break r;e[57153]=e[57153]&es(-2,i);break A}if(e[g+(e[g+16>>2]==(0|s)?16:20)>>2]=t,!t)break A}if(e[t+24>>2]=g,(r=e[s+16>>2])&&(e[t+16>>2]=r,e[r+24>>2]=t),!(r=e[s+20>>2]))break A;e[t+20>>2]=r,e[r+24>>2]=t}}if(!(s>>>0>=l>>>0)&&1&(t=e[l+4>>2])){A:{if(!(2&t)){if(e[57158]==(0|l)){if(e[57158]=s,A=e[57155]+A|0,e[57155]=A,e[s+4>>2]=1|A,e[57157]!=(0|s))break e;return e[57154]=0,void(e[57157]=0)}if(e[57157]==(0|l))return e[57157]=s,A=e[57154]+A|0,e[57154]=A,e[s+4>>2]=1|A,void(e[A+s>>2]=A);A=(-8&t)+A|0;r:if(t>>>0<=255){if(i=e[l+8>>2],t=t>>>3|0,(0|(r=e[l+12>>2]))==(0|i)){e[57152]=e[57152]&es(-2,t);break r}e[i+12>>2]=r,e[r+8>>2]=i}else{if(g=e[l+24>>2],(0|l)==(0|(t=e[l+12>>2])))if((r=e[(i=l+20|0)>>2])||(r=e[(i=l+16|0)>>2])){for(;c=i,(r=e[(i=(t=r)+20|0)>>2])||(i=t+16|0,r=e[t+16>>2]););e[c>>2]=0}else t=0;else r=e[l+8>>2],e[r+12>>2]=t,e[t+8>>2]=r;if(g){i=e[l+28>>2];a:{if(e[(r=228912+(i<<2)|0)>>2]==(0|l)){if(e[r>>2]=t,t)break a;e[57153]=e[57153]&es(-2,i);break r}if(e[g+(e[g+16>>2]==(0|l)?16:20)>>2]=t,!t)break r}e[t+24>>2]=g,(r=e[l+16>>2])&&(e[t+16>>2]=r,e[r+24>>2]=t),(r=e[l+20>>2])&&(e[t+20>>2]=r,e[r+24>>2]=t)}}if(e[s+4>>2]=1|A,e[A+s>>2]=A,e[57157]!=(0|s))break A;return void(e[57154]=A)}e[l+4>>2]=-2&t,e[s+4>>2]=1|A,e[A+s>>2]=A}if(A>>>0<=255)return t=228648+(-8&A)|0,(r=e[57152])&(A=1<<(A>>>3))?A=e[t+8>>2]:(e[57152]=A|r,A=t),e[t+8>>2]=s,e[A+12>>2]=s,e[s+12>>2]=t,void(e[s+8>>2]=A);i=31,A>>>0<=16777215&&(i=62+((A>>>38-(t=be(A>>>8|0))&1)-(t<<1)|0)|0),e[s+28>>2]=i,e[s+16>>2]=0,e[s+20>>2]=0,c=228912+(i<<2)|0;A:{r:{if((r=e[57153])&(t=1<<i)){for(i=A<<((0|i)!=31?25-(i>>>1|0):0),t=e[c>>2];;){if(r=t,(-8&e[t+4>>2])==(0|A))break r;if(t=i>>>29|0,i<<=1,!(t=e[(c=r+(4&t)|0)+16>>2]))break}e[c+16>>2]=s,e[s+24>>2]=r}else e[57153]=t|r,e[c>>2]=s,e[s+24>>2]=c;e[s+12>>2]=s,e[s+8>>2]=s;break A}A=e[r+8>>2],e[A+12>>2]=s,e[r+8>>2]=s,e[s+24>>2]=0,e[s+12>>2]=r,e[s+8>>2]=A}A=e[57160]-1|0,e[57160]=A||-1}}}function ne(A,t,r){var s,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0;H=s=H-176|0;e:{if(32&t)_=r-((0|r)>1)|0;else{l=yt(A,93302);A:{r:{a:{if((0|t)!=2){if(l)break a;t=0;break e}if(_=r+1|0,x=(V=O(r,76)+133152|0)+56|0,T=yt(A,89360),c=yt(A,93318),g=yt(A,93426),m=yt(A,93499),l)break r;break A}_=r+1|0,x=(V=O(r,76)+133152|0)+56|0,m=0}for(A=0,I=(0|(i=e[l-4>>2]))!=34?(0|i)==39?i:0:i,K=O(r,76)+133208|0;;){if(i=A,!(A=e[l>>2]))break A;r:{if(!I){if((0|A)==32|A-9>>>0<5)break A;if((0|A)!=47)break r;break A}if((0|i)!=92&&(0|A)==(0|I))break A}if(l=l+4|0,!((0|(h=Cr(A,h+K|0)+h|0))<16))break}}I=0,f[h+x|0]=0,h=O(r,76)+133168|0,l=0;A:if(T)for(A=0,x=(0|(i=e[T-4>>2]))!=34?(0|i)==39?i:0:i;;){if(i=A,!(A=e[T>>2]))break A;r:{if(!x){if((0|A)==32|A-9>>>0<5)break A;if((0|A)!=47)break r;break A}if((0|i)!=92&&(0|A)==(0|x))break A}if(T=T+4|0,!((0|(l=Cr(A,l+h|0)+l|0))<36))break}if(f[l+h|0]=0,!(!c|e[c>>2]-48>>>0>=10)){for(;I=(e[c>>2]+O(I,10)|0)-48|0,e[(c=c+4|0)>>2]-48>>>0<10;);(0|I)<=0||(I=I-1|0)}if(i=O(r,76)+133152|0,e[i+4>>2]=I,A=0,l=0,!(!g|e[g>>2]-48>>>0>=10))for(;l=(e[g>>2]+O(l,10)|0)-48|0,e[(g=g+4|0)>>2]-48>>>0<10;);e[i+12>>2]=l,l=O(r,76)+133152|0;A:{r:if(m){for(;r=f[A+93099|0],(i=e[(A<<2)+m>>2])&&(A=A+1|0,(0|r)==(0|i)););a:{n:switch(i-34|0){case 0:case 5:break n;default:break a}if(!r){A=0;break A}}for(A=0;r=f[A+93116|0],(i=e[(A<<2)+m>>2])&&(A=A+1|0,(0|r)==(0|i)););a:{n:switch(i-34|0){case 0:case 5:break n;default:break a}if(!r){A=1;break A}}for(A=0;r=f[A+93197|0],(i=e[(A<<2)+m>>2])&&(A=A+1|0,(0|r)==(0|i)););a:switch(i-34|0){case 0:case 5:break a;default:break r}if(!r){A=2;break A}}A=3}e[l+8>>2]=e[131156+(A<<3)>>2],e[V>>2]=t}if(PA(137776,133168),r=PA(s+96|0,133208),f[s+157|0]=e[33291],f[s+156|0]=e[33290],A=e[33289],e[s+152>>2]=0,f[s+158|0]=A,(0|_)>0)for(c=0;;){if(g=1,t=O(c,76)+133152|0,d[0|(A=t+16|0)]&&ks(0,A)&&(PA(137776,A),g=0,f[0|r]=0,f[s+158|0]=0,k[s+156>>1]=0),d[0|(A=t+56|0)]){m=PA(r,A),i=e[33679];A:if(d[0|(A=i)])for(;;){if(!Ar(A=A+1|0,m)){PA(m,i+1|0);break A}if(A=1+(MA(A)+A|0)|0,!d[0|A])break}g&&(f[137776]=0)}if((A=e[t+8>>2])&&(f[s+156|0]=A),(A=e[t+12>>2])&&(f[s+157|0]=A),(A=e[t+4>>2])&&(f[s+158|0]=A),(0|_)==(0|(c=c+1|0)))break}e[s+148>>2]=r,e[s+144>>2]=137776,(A=Kt(s+144|0,s+172|0))?Ls(A,43)||(t=d[s+156|0],!d[134672]|((0|t)!=d[134724]?t:0)||(e[s>>2]=A,e[s+4>>2]=134672,dA(t=s+16|0,93533,s),A=137776,Lt(137776,t,40))):A=92003,t=0,Ar(A,134784)&&(PA(134784,A),t=131072)}return H=s+176|0,t}function me(A,t){var r,s=0,i=0,l=0,c=0,g=0;r=A+t|0;e:{A:if(!(1&(s=e[A+4>>2]))){if(!(3&s))break e;t=(s=e[A>>2])+t|0;r:{if((0|(A=A-s|0))!=e[57157]){if(s>>>0<=255){if(l=e[A+8>>2],s=s>>>3|0,(0|(i=e[A+12>>2]))!=(0|l))break r;e[57152]=e[57152]&es(-2,s);break A}if(g=e[A+24>>2],(0|(s=e[A+12>>2]))==(0|A))if((i=e[(l=A+20|0)>>2])||(i=e[(l=A+16|0)>>2])){for(;c=l,(i=e[(l=(s=i)+20|0)>>2])||(l=s+16|0,i=e[s+16>>2]););e[c>>2]=0}else s=0;else i=e[A+8>>2],e[i+12>>2]=s,e[s+8>>2]=i;if(!g)break A;l=e[A+28>>2];a:{if(e[(i=228912+(l<<2)|0)>>2]==(0|A)){if(e[i>>2]=s,s)break a;e[57153]=e[57153]&es(-2,l);break A}if(e[g+(e[g+16>>2]==(0|A)?16:20)>>2]=s,!s)break A}if(e[s+24>>2]=g,(i=e[A+16>>2])&&(e[s+16>>2]=i,e[i+24>>2]=s),!(i=e[A+20>>2]))break A;e[s+20>>2]=i,e[i+24>>2]=s;break A}if(3&~(s=e[r+4>>2]))break A;return e[57154]=t,e[r+4>>2]=-2&s,e[A+4>>2]=1|t,void(e[r>>2]=t)}e[l+12>>2]=i,e[i+8>>2]=l}A:{if(!(2&(s=e[r+4>>2]))){if(e[57158]==(0|r)){if(e[57158]=A,t=e[57155]+t|0,e[57155]=t,e[A+4>>2]=1|t,e[57157]!=(0|A))break e;return e[57154]=0,void(e[57157]=0)}if(e[57157]==(0|r))return e[57157]=A,t=e[57154]+t|0,e[57154]=t,e[A+4>>2]=1|t,void(e[A+t>>2]=t);t=(-8&s)+t|0;r:if(s>>>0<=255){if(l=e[r+8>>2],s=s>>>3|0,(0|(i=e[r+12>>2]))==(0|l)){e[57152]=e[57152]&es(-2,s);break r}e[l+12>>2]=i,e[i+8>>2]=l}else{if(g=e[r+24>>2],(0|r)==(0|(s=e[r+12>>2])))if((l=e[(i=r+20|0)>>2])||(l=e[(i=r+16|0)>>2])){for(;c=i,(l=e[(i=(s=l)+20|0)>>2])||(i=s+16|0,l=e[s+16>>2]););e[c>>2]=0}else s=0;else i=e[r+8>>2],e[i+12>>2]=s,e[s+8>>2]=i;if(g){l=e[r+28>>2];a:{if(e[(i=228912+(l<<2)|0)>>2]==(0|r)){if(e[i>>2]=s,s)break a;e[57153]=e[57153]&es(-2,l);break r}if(e[g+(e[g+16>>2]==(0|r)?16:20)>>2]=s,!s)break r}e[s+24>>2]=g,(i=e[r+16>>2])&&(e[s+16>>2]=i,e[i+24>>2]=s),(i=e[r+20>>2])&&(e[s+20>>2]=i,e[i+24>>2]=s)}}if(e[A+4>>2]=1|t,e[A+t>>2]=t,e[57157]!=(0|A))break A;return void(e[57154]=t)}e[r+4>>2]=-2&s,e[A+4>>2]=1|t,e[A+t>>2]=t}if(t>>>0<=255)return s=228648+(-8&t)|0,(i=e[57152])&(t=1<<(t>>>3))?t=e[s+8>>2]:(e[57152]=t|i,t=s),e[s+8>>2]=A,e[t+12>>2]=A,e[A+12>>2]=s,void(e[A+8>>2]=t);l=31,t>>>0<=16777215&&(l=62+((t>>>38-(s=be(t>>>8|0))&1)-(s<<1)|0)|0),e[A+28>>2]=l,e[A+16>>2]=0,e[A+20>>2]=0,c=228912+(l<<2)|0;A:{if((i=e[57153])&(s=1<<l)){for(l=t<<((0|l)!=31?25-(l>>>1|0):0),s=e[c>>2];;){if(i=s,(-8&e[s+4>>2])==(0|t))break A;if(s=l>>>29|0,l<<=1,!(s=e[(c=i+(4&s)|0)+16>>2]))break}e[c+16>>2]=A,e[A+24>>2]=i}else e[57153]=s|i,e[c>>2]=A,e[A+24>>2]=c;return e[A+12>>2]=A,void(e[A+8>>2]=A)}t=e[i+8>>2],e[t+12>>2]=A,e[i+8>>2]=A,e[A+24>>2]=0,e[A+12>>2]=i,e[A+8>>2]=t}}function ve(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0;if(!(t=f[88105]))return A;if(A=Ls(A,t)){if(!d[88106])return A;if(d[A+1|0]){if(!d[88107]){i=!!(0|(r=d[A+1|0]));e:if(r&&(0|(t=r|d[0|A]<<8))!=(0|(g=d[88106]|d[88105]<<8)))for(r=A+1|0;;){if(i=!!(0|(s=d[(A=r)+1|0])),!s)break e;if(r=A+1|0,(0|g)==(0|(t=s|t<<8&65280)))break}return i?A:0}if(d[A+2|0]){if(!d[88108]){r=A+2|0,i=!!(0|(t=d[A+2|0]));e:if(t&&(0|(t=d[A+1|0]<<16|d[0|A]<<24|t<<8))!=(0|(g=d[88106]<<16|d[88105]<<24|d[88107]<<8)))for(;;){if(A=r+1|0,i=!!(0|(s=d[r+1|0])),!s)break e;if(r=A,(0|g)==(0|(t=(t|s)<<8)))break}else A=r;return i?A-2|0:0}if(d[A+3|0]){if(!d[88109]){r=A+3|0,i=!!(0|(t=d[A+3|0]));e:if(t&&(0|(t=t|d[A+1|0]<<16|d[0|A]<<24|d[A+2|0]<<8))!=(0|(g=(A=d[88105]|d[88106]<<8|d[88107]<<16|d[88108]<<24)<<24|(65280&A)<<8|A>>>8&65280|A>>>24)))for(;;){if(A=r+1|0,i=!!(0|(s=d[r+1|0])),!s)break e;if(r=A,(0|g)==(0|(t=s|t<<8)))break}else A=r;return i?A-3|0:0}g=A,H=m=H-1056|0,e[(A=m+1048|0)>>2]=0,e[A+4>>2]=0,e[(A=m+1040|0)>>2]=0,e[A+4>>2]=0,e[m+1032>>2]=0,e[m+1036>>2]=0,e[m+1024>>2]=0,e[m+1028>>2]=0;e:{A:{r:{a:{if(t=d[88105]){for(;;){if(!d[l+g|0])break A;if(l=l+1|0,e[((255&t)<<2)+m>>2]=l,e[(A=(m+1024|0)+(t>>>3&28)|0)>>2]=e[A>>2]|1<<t,!(t=d[l+88105|0]))break}if(A=1,I=-1,l>>>0>1)break a}else I=-1,A=1;c=-1,r=1;break r}for(s=1,t=1;;){a:if((0|(c=d[88105+(t+I|0)|0]))!=(0|(i=d[A+88105|0])))i>>>0<c>>>0?(s=A-I|0,r=A,t=1):(I=r,r=r+1|0,s=1,t=1);else{if((0|t)==(0|s)){r=r+s|0,t=1;break a}t=t+1|0}if(!(l>>>0>(A=r+t|0)>>>0))break}if(r=1,c=-1,l>>>0<=1)A=s;else{for(A=0,i=1,t=1;;){a:if((0|(x=d[88105+(t+c|0)|0]))!=(0|(h=d[r+88105|0])))h>>>0>x>>>0?(i=r-c|0,A=r,t=1):(c=A,A=A+1|0,i=1,t=1);else{if((0|t)==(0|i)){A=A+i|0,t=1;break a}t=t+1|0}if(!(l>>>0>(r=A+t|0)>>>0))break}A=s,r=i}}for(t=A,Kr(88105,(s=(A=c+1>>>0>I+1>>>0)?r:t)+88105|0,x=(h=A?c:I)+1|0)?(s=((A=~h+l|0)>>>0<h>>>0?h:A)+1|0,i=0):i=l-s|0,_=l-1|0,T=63|l,c=0,A=g;;){if(!(g-A>>>0>=l>>>0))if(r=Ba(g,0,T)){if(g=r,r-A>>>0<l>>>0)break A}else g=g+T|0;r=d[A+_|0],t=l;r:{if(e[(m+1024|0)+(r>>>3&28)>>2]>>>r&1)if((0|(r=e[(r<<2)+m>>2]))==(0|l)){a:{if(I=d[(r=(t=x)>>>0>c>>>0?t:c)+88105|0])for(;;){if(d[A+r|0]!=(255&I))break a;if(!(I=d[(r=r+1|0)+88105|0]))break}for(;;){if(t>>>0<=c>>>0)break e;if(d[(t=t-1|0)+88105|0]!=d[A+t|0])break}t=s,c=i;break r}t=r-h|0}else t=(r=l-r|0)>>>0>c>>>0?r:c;c=0}A=A+t|0}}A=0}H=m+1056|0,r=A}}}}return r}function Se(A,t,r,s,i,l){var c,g,m,I,h=0,x=0,T=0;if(H=c=H-560|0,f[c+448|0]=0,f[c+144|0]=0,f[c+120|0]=0,m=34&l,g=t-O(x=(0|t)/100|0,100)|0,1&(I=64&d[A+106|0]?(0|t)>999|l:0)|(0|t)>99){e:{A:{r:{if(!(!m|g)){if(!QA(A,90606,c+304|0))break r;break e}if(g)break A}if(QA(A,90691,c+304|0))break e}QA(A,90725,c+304|0)}h=l,(0|t)<1e3||(h=l,!(8&d[A+105|0])|t-2e3>>>0<4294967196&&(f[c+208|0]=0,ot(A,h=(x>>>0)/10|0,s=16384&e[A+108>>2]?0:i+1|0,!((t>>>0)%1e3|0)|m,c+272|0)||Ss(A,h,i,e[A+212>>2]==28012?520:(0|s)<4?(e[A+108>>2]>>>s&1)<<3:0,c+208|0),2&d[A+109|0]?(e[c+108>>2]=15,e[c+100>>2]=15,e[c+104>>2]=c+208,e[c+96>>2]=c+272,dA(c+144|0,90761,c+96|0)):(e[c+92>>2]=15,e[c+84>>2]=15,e[c+88>>2]=c+272,e[c+80>>2]=c+208,dA(c+144|0,90761,c+80|0)),s=1,1&(!!(0|(x=x-O(h,10)|0))|I)||(f[c+304|0]=0),h=1|l)),f[c+208|0]=0;e:if(!(~I&(0|x)<=0))if(!(4&d[A+106|0])|!(1&h|d[c+144|0])||QA(A,90824,c+120|0),!m|(16&d[A+109|0]?0:g)||(e[c+64>>2]=x,dA(s=c+548|0,90875,c- -64|0),T=QA(A,s,c+208|0),!(4096&e[A+108>>2])|(0|g)<=0||As(c+208|0,133104)),s=1,1&(~I|!!(0|x))){A:{r:{a:{n:{if(!(131072&e[A+108>>2])||1&h|(0|x)!=1){if(g|T||(e[c+48>>2]=x,dA(s=c+548|0,90985,c+48|0),T=QA(A,s,c+208|0)),T||(e[c+32>>2]=x,dA(s=c+548|0,91027,c+32|0),QA(A,s,c+208|0)))break n;if((0|x)!=1)break r;break a}if(!T)break a}f[c+304|0]=0;break A}if(s=1,4&d[A+105|0])break e}Ss(A,x,i,0,c+208|0)}s=1}else QA(A,88875,c+208|0);e[c+28>>2]=c+304,e[c+24>>2]=c+208,e[c+20>>2]=c+120,e[c+16>>2]=c+144,dA(c+448|0,91059,c+16|0)}else h=l;f[c+132|0]=0;e:{A:{if((0|g)>0)16&d[A+109|0]&&2&h||(!(1&h)|i&&(0|t)<=100||!(64&(s=e[A+104>>2]))&(!(8388608&s)|g>>>0>9)||QA(A,90824,c+132|0),!(1&h|d[c+144|0])|!(524288&e[A+104>>2])|x||QA(A,90824,c+132|0)),f[c+336|0]=0;else if(f[c+336|0]=0,!g&s)break A;if(i?(l=e[A+108>>2],t=(0|i)<4?(l>>>i&1)<<3:0):(s=m?3:2,t=32&l|((0|t)<100?1&h?s:4|s:s),l=e[A+108>>2]),t=(0|i)==1&&e[A+212>>2]==28012?520|t:t,1048576&l&&(s=16|t,t=(0|x)>0||1&h?s:t),!Ss(A,g,i,256&h|t,c+336|0)|!(128&d[A+104|0]))break e;f[c+132|0]=0;break e}d[133104]&&((0|(A=MA(c+448|0)))<=0||d[(A=A+c|0)+447|0]==10&&(f[A+447|0]=0),PA(c+336|0,133104))}e[c+8>>2]=15,e[c+12>>2]=c+336,e[c+4>>2]=c+132,e[c>>2]=c+448,dA(r,91101,c),H=c+560|0}function Ke(A){var t,r,s,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0;for(i=e[32972],e[i+64>>2]=290816,e[i+68>>2]=4104,e[i+96>>2]=0,e[i+100>>2]=0,e[i+120>>2]=90,e[i+124>>2]=100,e[i+112>>2]=64,e[i+116>>2]=256,e[i+104>>2]=5,e[i+108>>2]=0,e[i+84>>2]=100,e[i+88>>2]=64,l=e[50754],e[i+132>>2]=0,e[i+136>>2]=0,e[i+128>>2]=l,e[i+140>>2]=0,e[i+144>>2]=0,e[i+148>>2]=0,e[i+152>>2]=0,e[i+156>>2]=0,e[i+160>>2]=0,e[36435]=450,e[i+92>>2]=2,e[50870]=0,e[50871]=0,e[50872]=0,e[50873]=0,e[50880]=0,e[50881]=0,e[50882]=0,e[50883]=0,e[50890]=0,e[50891]=0,e[50892]=0,e[50893]=0,c=-3.141592653589793/+e[50754],P[25429]=c,m=-2*c,P[25430]=m,t=(c=$r(200*c))*-c,P[25434]=t,P[25439]=t,P[25444]=t,c=(m=c*ds(2e3*m))+m,P[25433]=c,P[25438]=c,P[25443]=c,m=1-c-t,P[25432]=m,P[25437]=m,P[25442]=m,e[50902]=0,e[50903]=0,e[50900]=0,e[50901]=0,P[25449]=t,P[25448]=c,e[50912]=0,e[50913]=0,P[25447]=m,e[50910]=0,e[50911]=0,P[25454]=t,P[25453]=c,e[50922]=0,e[50923]=0,P[25452]=m,e[50920]=0,e[50921]=0,P[25459]=t,P[25458]=c,e[50932]=0,e[50933]=0,P[25457]=m,e[50930]=0,e[50931]=0,P[25464]=t,P[25463]=c,P[25462]=m,e[50942]=0,e[50943]=0,e[50940]=0,e[50941]=0,P[25469]=t,P[25468]=c,P[25467]=m,e[50952]=0,e[50953]=0,e[50950]=0,e[50951]=0,P[25474]=t,P[25473]=c,P[25472]=m,r=e[32972],l=0;k[(I=(l<<1)+r|0)+236>>1]=256,k[I+164>>1]=256,i=d[l+105376|0]<<1,k[I+254>>1]=i,k[I+182>>1]=i,k[I+200>>1]=d[l+105385|0]<<1,e[(i=(g=l<<2)+r|0)+308>>2]=e[g+105408>>2],e[i+272>>2]=0,k[I+218>>1]=0,e[g+200944>>2]=(0|O(e[g+105456>>2],22050))/e[50754],(0|(l=l+1|0))!=9;);for(_=e[32961],l=0,s=r+344|0;;){if(i=h,x=_,I=l,(0|(h=e[(l=(g=l<<2)+131840|0)>>2]))==-1&&(h=8e3,e[l>>2]=8e3,I&&(e[131840+(4|g)>>2]=e[g+131836>>2])),_=e[131840+(4|g)>>2],!((0|i)>=(0|(h=(0|h)/8|0))||(0|(T=h-i|0))<=0||(g=i+1|0,l=i,1&T&&(f[344+(i+r|0)|0]=(0|x)>=255?255:x,l=g),(0|g)==(0|h))))for(V=_-x|0;g=((0|O(l-i|0,V))/(0|T)|0)+x|0,f[l+s|0]=(0|g)>=255?255:g,K=((0|O((g=l+1|0)-i|0,V))/(0|T)|0)+x|0,f[g+s|0]=(0|K)>=255?255:K,(0|h)!=(0|(l=l+2|0)););if(l=I+2|0,!(I>>>0<10))break}l=e[32972],e[l+80>>2]=232,e[l+72>>2]=256,e[l+76>>2]=238,A||(e[49848]=0),k[l+200>>1]=(0|O(k[l+200>>1],105))/100}function GA(A,t,r){var s,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0;H=s=H-288|0,(0|t)!=132848&&_s(132848,t,40),(0|(i=A+228|0))!=(0|t)&&_s(i,t,40),e[s+88>>2]=t,e[s+84>>2]=47,e[s+80>>2]=137584,dA(i=s+96|0,84089,s+80|0),l=Ns(i),(i=e[A+688>>2])&&(fe(i),e[A+688>>2]=0),i=us(s+96|0,84577);e:if((0|l)>0&&i)if(r=HA(l),e[A+688>>2]=r,r)if(I=ri(r,l,i),Er(i),I>>>0<=1032)e[s+16>>2]=s+96,Xt(e[30450],85164,s+16|0),r=2;else if(i=e[A+688>>2],r=e[i+4>>2],!((0|(l=e[i>>2]))!=1024|(0|r)<=0)&(0|r)<134217729){i=r+i|0,e[A+684>>2]=i,x=Je(A+5168|0,0,1024),Je(A+7664|0,0,260),Je(A+7924|0,255,256),Je(A+4788|0,0,380),Je(A+6192|0,0,512);A:if((0|(r=d[0|i]))!=7)for(;;){if((0|(l=255&r))!=6){if(!l)break A;l=e[A+684>>2],e[s+72>>2]=r<<24>>24,e[s+64>>2]=132848,e[s+68>>2]=i-l,Xt(e[30450],88950,s- -64|0);break}r:{a:{n:{o:{c:switch((l=d[0|(r=i+1|0)])-18|0){case 0:break o;case 2:break c;default:break n}for(r=4+(-4&r)|0,e[A+180>>2]=r;r=(i=r)+1|0,!di(i););for(;l=d[0|i],r=i,i=i+1|0,(0|l)!=7;);break r}if(r=i+3|0,(0|(i=((0|(i=f[i+2|0]))<65?191:-65)+i|0))>94)break a;e[4788+((i<<2)+A|0)>>2]=r;break a}r=1+((g=MA(r))+r|0)|0;n:switch(0|g){case 1:e[5168+((l<<2)+A|0)>>2]=r;break a;case 0:e[x>>2]=r;break a;default:break n}g=d[i+2|0],(0|l)!=1?(i=e[A+7664>>2],d[0|(h=(c=A+l|0)+7924|0)]==255&&(f[0|h]=i),f[0|(c=c+7668|0)]=d[0|c]+1,e[(c=(i<<2)+A|0)+6704>>2]=r,e[A+7664>>2]=i+1,e[c+7184>>2]=l|g<<8):e[6188+((g<<2)+A|0)>>2]=r}if(d[0|r]!=7)for(;r=1+(MA(r)+r|0)|0,d[0|r]!=7;);}r=d[0|(i=r+1|0)]}for(i=e[A+688>>2]+8|0;;){for(e[692+((r=m<<2)+A|0)>>2]=i;l=d[0|i];)i=i+l|0;for(i=i+1|0,e[692+((4|r)+A|0)>>2]=i;r=d[0|i];)i=r+i|0;if(i=i+1|0,(0|(m=m+2|0))==1024)break}r=0,(0|(A=e[A+324>>2]))<=0|A>>>0<=I>>>0||(e[s+48>>2]=t,Xt(e[30450],85519,s+48|0))}else e[s+40>>2]=r,e[s+36>>2]=l,e[s+32>>2]=s+96,Xt(e[30450],85349,s+32|0),r=2;else Er(i),r=3;else{if(r||(e[s>>2]=s+96,Xt(e[30450],84963,s)),r=1,!i)break e;Er(i)}return H=s+288|0,r}function YA(A){var t,r=0,s=0;for(t=Je(A+344|0,0,256),f[A+364|0]=1,f[A+356|0]=1,f[A+357|0]=1,f[A+358|0]=1,f[A+359|0]=1,f[A+360|0]=1,f[A+361|0]=1,f[A+362|0]=1,f[A+363|0]=1,f[A+348|0]=1,f[A+349|0]=1,f[A+350|0]=1,f[A+351|0]=1,f[A+352|0]=1,f[A+353|0]=1,f[A+354|0]=1,f[A+355|0]=1,f[A+431|0]=3,f[A+429|0]=3,f[A+430|0]=3,f[A+406|0]=3,f[A+407|0]=3,f[A+408|0]=3,f[A+409|0]=3,f[A+410|0]=3,f[A+411|0]=3,f[A+412|0]=3,f[A+413|0]=3,f[A+414|0]=3,f[A+415|0]=3,f[A+416|0]=3,f[A+417|0]=3,f[A+418|0]=3,f[A+419|0]=3,f[A+420|0]=3,f[A+421|0]=3,f[A+440|0]=3,f[A+441|0]=3,f[A+442|0]=3,f[A+443|0]=3,s=21;f[(r=A+s|0)+344|0]=4|d[r+344|0],(0|(r=s+1|0))!=58;)f[0|(r=r+t|0)]=4|d[0|r],f[(r=s+t|0)+2|0]=4|d[r+2|0],f[r+3|0]=4|d[r+3|0],s=s+4|0;f[A+346|0]=4|d[A+346|0],f[A+347|0]=4|d[A+347|0],f[A+432|0]=4|d[A+432|0],f[A+433|0]=4|d[A+433|0],f[A+434|0]=4|d[A+434|0],f[A+435|0]=4|d[A+435|0],f[A+436|0]=4|d[A+436|0],f[A+437|0]=4|d[A+437|0],f[A+438|0]=4|d[A+438|0],f[A+439|0]=4|d[A+439|0],f[A+467|0]=4|d[A+467|0],f[A+468|0]=4|d[A+468|0],f[A+470|0]=4|d[A+470|0],f[A+471|0]=4|d[A+471|0],f[A+348|0]=64|d[A+348|0],f[A+349|0]=64|d[A+349|0],f[A+350|0]=64|d[A+350|0],f[A+351|0]=64|d[A+351|0],f[A+352|0]=64|d[A+352|0],f[A+353|0]=64|d[A+353|0],f[A+354|0]=64|d[A+354|0],f[A+355|0]=64|d[A+355|0],f[A+356|0]=64|d[A+356|0],f[A+357|0]=64|d[A+357|0],f[A+358|0]=64|d[A+358|0],f[A+359|0]=64|d[A+359|0],f[A+360|0]=64|d[A+360|0],f[A+361|0]=64|d[A+361|0],f[A+362|0]=64|d[A+362|0],f[A+363|0]=64|d[A+363|0],f[A+364|0]=64|d[A+364|0],f[A+406|0]=64|d[A+406|0],f[A+407|0]=64|d[A+407|0],f[A+408|0]=64|d[A+408|0],f[A+409|0]=64|d[A+409|0],f[A+410|0]=64|d[A+410|0],f[A+411|0]=64|d[A+411|0],f[A+412|0]=64|d[A+412|0],f[A+413|0]=64|d[A+413|0],f[A+414|0]=64|d[A+414|0],f[A+415|0]=64|d[A+415|0],f[A+416|0]=64|d[A+416|0],f[A+417|0]=64|d[A+417|0],f[A+418|0]=64|d[A+418|0],f[A+419|0]=64|d[A+419|0],f[A+420|0]=64|d[A+420|0],f[A+440|0]=64|d[A+440|0],f[A+441|0]=64|d[A+441|0],f[A+429|0]=64|d[A+429|0],f[A+430|0]=64|d[A+430|0],f[A+431|0]=64|d[A+431|0],f[A+442|0]=64|d[A+442|0],f[A+443|0]=64|d[A+443|0],e[A+40>>2]=1,e[A+204>>2]=e[A+600>>2]+77}function BA(A,t,r,s,i,l,c,g,m){var I,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0;H=I=H-128|0;e:{A:{if(Gt(l,c,g,m,0,0,0,0)){x=65535&m;r:a:{if((0|(_=m>>>16&32767))!=32767){if(h=4,_)break a;h=l|g|c|x?3:2;break r}h=!(l|g|c|x)}if((0|(V=32767&(ce=i>>>16|0)))!=32767&&h)break A}Et(I+16|0,t,r,s,i,l,c,g,m),ts(I,t=e[I+16>>2],s=e[I+20>>2],i=e[I+24>>2],r=e[I+28>>2],t,s,i,r),s=e[I+8>>2],i=e[I+12>>2],g=e[I>>2],m=e[I+4>>2];break e}if(_=g,(0|Gt(t,r,h=s,K=2147483647&i,l,c,g,T=2147483647&m))<=0){if(Gt(t,r,h,K,l,c,_,T)){g=t,m=r;break e}Et(I+112|0,t,r,s,i,0,0,0,0),s=e[I+120>>2],i=e[I+124>>2],g=e[I+112>>2],m=e[I+116>>2]}else{if(J=m>>>16&32767,V?(m=r,g=t):(Et(I+96|0,t,r,h,K,0,0,0,1081540608),h=e[I+104>>2],K=g=e[I+108>>2],V=(g>>>16|0)-120|0,m=e[I+100>>2],g=e[I+96>>2]),J||(Et(I+80|0,l,c,_,T,0,0,0,1081540608),_=e[I+88>>2],T=l=e[I+92>>2],J=(l>>>16|0)-120|0,c=e[I+84>>2],l=e[I+80>>2]),te=_,he=65535&T|65536,K=65535&K|65536,(0|V)>(0|J)){for(;;){if(_=(T=h-te|0)-(x=(0|c)==(0|m)&l>>>0>g>>>0|c>>>0>m>>>0)|0,(0|(x=(K-((h>>>0<te>>>0)+he|0)|0)-(x>>>0>T>>>0)|0))>0|(0|x)>=0){if(h=g,!((g=g-l|0)|_|(m=m-((l>>>0>h>>>0)+c|0)|0)|x)){Et(I+32|0,t,r,s,i,0,0,0,0),s=e[I+40>>2],i=e[I+44>>2],g=e[I+32>>2],m=e[I+36>>2];break e}x=x<<1|_>>>31,h=_<<1|m>>>31}else x=K<<1|h>>>31,h=h<<1|m>>>31;if(K=x,x=m<<1|g>>>31,g<<=1,m=x,!((0|(V=V-1|0))>(0|J)))break}V=J}if(_=(T=h-te|0)-(x=(0|c)==(0|m)&l>>>0>g>>>0|c>>>0>m>>>0)|0,T=x=(K-((h>>>0<te>>>0)+he|0)|0)-(x>>>0>T>>>0)|0,(0|x)<0)_=h,T=K;else if(h=g,!((g=g-l|0)|_|(m=m-((l>>>0>h>>>0)+c|0)|0)|T)){Et(I+48|0,t,r,s,i,0,0,0,0),s=e[I+56>>2],i=e[I+60>>2],g=e[I+48>>2],m=e[I+52>>2];break e}if((0|T)==65535|T>>>0<65535)for(;t=m>>>31|0,V=V-1|0,K=m<<1|g>>>31,g<<=1,m=K,r=t,t=T<<1|_>>>31,_=r|_<<1,T=t,t>>>0<65536;);t=32768&ce,(0|V)<=0?(Et(I- -64|0,g,m,_,65535&T|(t|V+120)<<16,0,0,0,1065811968),s=e[I+72>>2],i=e[I+76>>2],g=e[I+64>>2],m=e[I+68>>2]):(s=_,i=65535&T|(t|V)<<16)}}e[A>>2]=g,e[A+4>>2]=m,e[A+8>>2]=s,e[A+12>>2]=i,H=I+128|0}function nt(A,t,r){var s,i,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0;if(H=s=H-1040|0,(0|(c=ni(A,589824,0)))>=0&&((l=Dt(1,2072))?e[l+8>>2]=c:(ye(0|c),l=0)),i=l,l){e:if(l=ln(i))for(_=e[30450],T=(s+96|0)+t|0;;){if((0|(c=e[50303]))>=348){e[s+4>>2]=350,e[s>>2]=c+1,Xt(_,91860,s);break e}if(d[l+19|0]!=46)if(e[s+88>>2]=l+19,e[s+84>>2]=47,e[s+80>>2]=A,dA(l=s+96|0,91924,s+80|0),(0|(l=Ns(l)))!=-31){if(!((0|l)<=0)&&(x=us(s+96|0,85712))){g=0,f[s+832|0]=0,f[s+752|0]=0,e[s+360>>2]=0,e[s+356>>2]=4,I=0;A:for(;;){for(h=299-I|0;gt(s+912|0,120,x);){l=s+912|0;r:{if(d[s+912|0]!=35){a:if(!((0|(l=MA(s+912|0)-1|0))<=0))for(;;){if(!((0|(c=f[0|(m=(s+912|0)+l|0)]))==32|c-9>>>0<5))break a;if(f[0|m]=0,!((0|(l=l-1|0))>0))break}if(!(l=ve(s+912|0)))break r}f[0|l]=0}l=s+912|0;r:if(c=d[s+912|0])for(;;){if(fr(c<<24>>24))break r;if(!(c=d[0|(l=l+1|0)]))break}if(f[0|l]=0,d[s+912|0]){l=l+1|0;r:switch(fs(131904,s+912|0)-1|0){case 0:for(;c=l,l=l+1|0,(0|(m=f[0|c]))==32|m-9>>>0<5;);Lt(s+832|0,c,80);continue;case 1:if(f[s+672|0]=0,e[s+364>>2]=5,c=s+672|0,e[s+16>>2]=c,e[s+20>>2]=s+364,KA(l,86237,s+16|0),(c=MA(c)+2|0)>>>0>=h>>>0)continue;f[0|(l=(s+368|0)+I|0)]=e[s+364>>2],PA(l+1|0,s+672|0),g=g+1|0,I=c+I|0;continue A;case 2:if(e[s+52>>2]=s+360,e[s+48>>2]=s+752,KA(l,86237,s+48|0),!r)continue;e[s+32>>2]=T,Xt(_,92042,s+32|0);continue;case 5:break r;default:continue}e[s+64>>2]=s+356,KA(l,87268,s- -64|0)}}break}f[(s+368|0)+I|0]=0,c=fs(132112,s+752|0),g?(m=MA(T)+I|0,h=qA((g=Dt(28+(MA(s+832|0)+m|0)|0,1))+24|0,s+368|0,l=I+1|0),e[g+4>>2]=h,l=PA(l+h|0,T),e[g>>2]=l,e[g+8>>2]=l,d[s+832|0]&&(e[g>>2]=PA(2+(m+h|0)|0,s+832|0)),l=e[s+360>>2],f[g+14|0]=0,f[g+12|0]=c,f[g+13|0]=l,f[g+15|0]=e[s+356>>2],Er(x),l=e[50303],e[50303]=l+1,e[201216+(l<<2)>>2]=g):Er(x)}}else nt(s+96|0,t,r);if(!(l=ln(i)))break}Io(e[i+8>>2]),fe(i)}H=s+1040|0}function yA(A,t){var r,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0;H=r=H-352|0;e:if(A||(A=e[136284+(t<<4)>>2])){d[0|A]!=47&&(e[r+12>>2]=A,e[r+4>>2]=47,e[r+8>>2]=47,e[r>>2]=137584,dA(A=r+16|0,84114,r)),f[r+240|0]=0;A:{r:{if(s=us(A,84577)){if((0|Oi(s,20))==-1)break A;if(l=Si(s),i=Si(s),g=Si(s),!((0|l)!=65537|(0|i)!=e[50754])&(0|g)==i<<1)break r;Er(s),k[r+256>>1]=d[84864]|d[84865]<<8,A=d[84852]|d[84853]<<8|d[84854]<<16|d[84855]<<24,e[r+240>>2]=d[84848]|d[84849]<<8|d[84850]<<16|d[84851]<<24,e[r+244>>2]=A,A=d[84860]|d[84861]<<8|d[84862]<<16|d[84863]<<24,e[r+248>>2]=d[84856]|d[84857]<<8|d[84858]<<16|d[84859]<<24,e[r+252>>2]=A,H=l=H-16|0;a:{if((s=MA(A=r+240|0))>>>0>=6&&!Kr(g=(A+s|0)-6|0,84274,6)){for(I=100;;){for(m=0,H=s=H-16|0,d[227196]||(f[227197]=xe(),f[227196]=1),x=+Ce(),ee(c=x/1e3)<9223372036854776e3?(h=ee(c)>=1?~~(c>0?ke(Me(23283064365386963e-26*c),4294967295):De(23283064365386963e-26*(c-+(~~c>>>0>>>0))))>>>0:0,i=~~c>>>0):(h=-2147483648,i=0),e[s>>2]=i,e[s+4>>2]=h,c=1e3*(x-(+(st(i,h,1e3,0)>>>0)+4294967296*+(0|le)))*1e3,i=ee(c)<2147483648?~~c:-2147483648,e[s+8>>2]=i,i=g+(s>>>4|0)^O(e[s+8>>2],65537);f[g+m|0]=65+(15&i|i<<1&32),i=i>>>5|0,(0|(m=m+1|0))!=6;);if(H=s+16|0,e[l>>2]=384,(0|(s=ni(A,194,l)))>=0)break a;if(I=I-1|0,e[56798]!=20||!I)break}qA(g,84274,6)}else e[56798]=28;s=-1}H=l+16|0,(0|s)<0||Io(s)}if(!(s=us(A,84577))){A=Ts(0,e[56798],A);break e}}if((0|(l=Ns(A)))<0){Er(s),A=Ts(0,0-l|0,A);break e}if((0|Oi(s,0))==-1){t=e[56798],Er(s),A=Ts(0,t,A);break e}if(!(i=lt(e[(g=136280+(t<<4)|0)>>2],l))){Er(s),A=48;break e}if((0|ri(i,l,s))!=(0|l)){t=e[56798],Er(s),d[r+240|0]&&go(r+240|0),fe(i),A=Ts(0,t,A);break e}Er(s),d[r+240|0]&&go(r+240|0),e[136276+(t<<4)>>2]=(d[i+40|0]|d[i+41|0]<<8|d[i+42|0]<<16|d[i+43|0]<<24)/2,e[g>>2]=i,A=0;break e}t=e[56798],Er(s),A=Ts(0,t,A)}else A=28;return H=r+352|0,A}function ft(A,t,r,s,i,l){var c,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0;for(f[0|r]=1,c=1&l,he=1,K=-1,J=-1,h=1,l=t;;){Ee=ce-2|0,te=x,_=J;e:{A:{for(;;){if(!(T=d[0|l])){J=_,x=te;break e}if(l=l+1|0,V=e[144464+(T<<2)>>2]){r:{if((0|(g=d[V+11|0]))!=1){if(16&d[V+6|0]|(0|g)!=2)break r;f[0|(x=r+h|0)]=K,g=(0|K)<4|(0|_)>(0|K),!(2&d[V+4|0])|!c|(0|K)>=0||(f[0|x]=1),x=g?te:h,J=g?_:K,K=-1,h=h+1|0;break A}if(!y[V+8>>1]){g=0,I=h;a:if((0|T)!=8)g=d[V+14|0],e[i>>2]&&g>>>0>=4||(_=(0|g)<(0|_)?_:g,K=g);else{for(;;){if(T=g,I=I-1|0,e[i>>2]|(0|I)<=0||(0|(V=f[0|(m=r+I|0)]))>3)break a;if(g=T+1|0,!(V>>>0<2))break}if(f[0|m]=4,te=(0|_)<4?I:te,_=(0|_)<=4?4:_,I>>>0<2)break a;if(V=3&(I=~T+ce|0),g=1,Ee-T>>>0>=3)for(T=-4&I,I=0;d[0|(m=r+g|0)]==4&&(f[0|m]=3),d[m+1|0]==4&&(f[m+1|0]=3),d[m+2|0]==4&&(f[m+2|0]=3),d[m+3|0]==4&&(f[m+3|0]=3),g=g+4|0,(0|T)!=(0|(I=I+4|0)););if(m=0,!V)break a;for(;d[0|(T=r+g|0)]==4&&(f[0|T]=3),g=g+1|0,(0|V)!=(0|(m=m+1|0)););}if(he)continue;break e}}break}}J=_,x=te,(0|T)==20&&(f[r+h|0]=c&&(0|K)<0?1:K,h=h+1|0)}if(f[0|t]=T,ce=h-1|0,t=t+1|0,he=(0|h)<99)continue}break}f[r+h|0]=1,f[0|t]=0;e:if((0|(t=e[i>>2]))>0)J=4,(0|t)>=(0|h)&&(e[i>>2]=ce,t=ce),f[t+r|0]=4,x=e[i>>2];else if((0|J)==5&&(J=4,!((0|h)<2))){if(l=1,_=1&(t=h-1|0),(0|h)!=2)for(te=-2&t,I=0;;){m=4;A:{r:{a:switch(d[0|(t=(g=l)+r|0)]-4|0){case 1:break r;case 0:break a;default:break A}m=2&d[A+14|0]?1:3,g=x}f[0|t]=m,x=g}m=4;A:{r:{a:switch(d[0|(t=(g=l+1|0)+r|0)]-4|0){case 1:break r;case 0:break a;default:break A}m=2&d[A+14|0]?1:3,g=x}f[0|t]=m,x=g}if(l=l+2|0,(0|te)==(0|(I=I+2|0)))break}if(_){m=4;A:{r:switch(d[0|(t=r+l|0)]-4|0){case 1:break A;case 0:break r;default:break e}m=2&d[A+14|0]?1:3,l=x}f[0|t]=m,x=l}}return e[i>>2]=x,e[s>>2]=h,J}function lt(A,t){var r,s,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0;if(!A)return HA(t);if(t>>>0>=4294967232)return e[56798]=48,0;r=t>>>0<11?16:t+11&-8,c=-8&(s=e[(g=A-8|0)+4>>2]);e:if(3&s){m=c+g|0;A:if(c>>>0>=r>>>0){if((l=c-r|0)>>>0<16)break A;e[g+4>>2]=1&s|r|2,e[(i=g+r|0)+4>>2]=3|l,e[m+4>>2]=1|e[m+4>>2],me(i,l)}else if(e[57158]!=(0|m))if(e[57157]!=(0|m)){if(2&(l=e[m+4>>2])||(h=c+(-8&l)|0)>>>0<r>>>0)break e;T=h-r|0;r:if(l>>>0<=255){if(c=e[m+8>>2],i=l>>>3|0,(0|(l=e[m+12>>2]))==(0|c)){e[57152]=e[57152]&es(-2,i);break r}e[c+12>>2]=l,e[l+8>>2]=c}else{if(x=e[m+24>>2],(0|(I=e[m+12>>2]))==(0|m))if((i=e[(c=m+20|0)>>2])||(i=e[(c=m+16|0)>>2])){for(;l=c,I=i,(i=e[(c=i+20|0)>>2])||(c=I+16|0,i=e[I+16>>2]););e[l>>2]=0}else I=0;else i=e[m+8>>2],e[i+12>>2]=I,e[I+8>>2]=i;if(x){l=e[m+28>>2];a:{if(e[(i=228912+(l<<2)|0)>>2]==(0|m)){if(e[i>>2]=I,I)break a;e[57153]=e[57153]&es(-2,l);break r}if(e[(e[x+16>>2]==(0|m)?16:20)+x>>2]=I,!I)break r}e[I+24>>2]=x,(i=e[m+16>>2])&&(e[I+16>>2]=i,e[i+24>>2]=I),(i=e[m+20>>2])&&(e[I+20>>2]=i,e[i+24>>2]=I)}}T>>>0<=15?(e[g+4>>2]=1&s|h|2,e[(i=g+h|0)+4>>2]=1|e[i+4>>2]):(e[g+4>>2]=1&s|r|2,e[(l=g+r|0)+4>>2]=3|T,e[(i=g+h|0)+4>>2]=1|e[i+4>>2],me(l,T))}else{if((l=c+e[57154]|0)>>>0<r>>>0)break e;(i=l-r|0)>>>0>=16?(e[g+4>>2]=1&s|r|2,e[(c=g+r|0)+4>>2]=1|i,e[(l=l+g|0)>>2]=i,e[l+4>>2]=-2&e[l+4>>2]):(e[g+4>>2]=l|1&s|2,e[(i=l+g|0)+4>>2]=1|e[i+4>>2],i=0,c=0),e[57157]=c,e[57154]=i}else{if((c=c+e[57155]|0)>>>0<=r>>>0)break e;e[g+4>>2]=1&s|r|2,i=c-r|0,e[(l=g+r|0)+4>>2]=1|i,e[57155]=i,e[57158]=l}i=g}else{if(r>>>0<256||c>>>0>=r+4>>>0&&(i=g,c-r>>>0<=e[57272]<<1>>>0))break e;i=0}return i?i+8|0:(g=HA(t))?(qA(g,A,t>>>0>(i=(3&(i=e[A-4>>2])?-4:-8)+(-8&i)|0)>>>0?i:t),fe(A),g):0}function Dr(){var A,t,r=0,s=0;return A=Es(12),e[A>>2]=22050,t=Es(432),e[(s=t)+4>>2]=0,e[s+8>>2]=0,e[s>>2]=132304,e[s+32>>2]=0,e[s+12>>2]=0,e[s+16>>2]=0,e[s+20>>2]=0,e[s+24>>2]=0,Je(s+40|0,0,376),e[s+420>>2]=0,e[s+424>>2]=-1,f[s+416|0]=1,r=Je(Es(408),0,408),e[s+28>>2]=r,f[r+8|0]=1,e[A+4>>2]=s,r=Es(1096),e[r+8>>2]=22050,e[r+4>>2]=22050,e[r>>2]=132352,e[r+64>>2]=22050,e[r+56>>2]=0,e[r+60>>2]=0,e[r+32>>2]=0,e[r+36>>2]=0,e[r+24>>2]=22050,e[r+16>>2]=0,e[r+20>>2]=0,e[r+40>>2]=0,e[r+44>>2]=0,f[r+48|0]=0,e[r+128>>2]=0,e[r+132>>2]=0,k[r+96>>1]=0,e[r+72>>2]=22050,e[r+136>>2]=0,e[r+140>>2]=0,k[r+168>>1]=0,e[r+144>>2]=22050,e[r+200>>2]=0,e[r+204>>2]=0,e[r+208>>2]=0,e[r+212>>2]=0,e[r+216>>2]=22050,k[r+240>>1]=0,e[r+280>>2]=0,e[r+284>>2]=0,e[r+272>>2]=0,e[r+276>>2]=0,e[r+288>>2]=22050,k[r+312>>1]=0,e[r+344>>2]=0,e[r+348>>2]=0,e[r+352>>2]=0,e[r+356>>2]=0,e[r+360>>2]=22050,k[r+384>>1]=0,e[r+416>>2]=0,e[r+420>>2]=0,e[r+424>>2]=0,e[r+428>>2]=0,e[r+432>>2]=22050,k[r+456>>1]=0,e[r+488>>2]=0,e[r+492>>2]=0,e[r+496>>2]=0,e[r+500>>2]=0,e[r+504>>2]=22050,k[r+528>>1]=1,e[r+560>>2]=0,e[r+564>>2]=0,e[r+568>>2]=0,e[r+572>>2]=0,k[r+600>>1]=0,e[r+576>>2]=22050,e[r+640>>2]=0,e[r+644>>2]=0,e[r+632>>2]=0,e[r+636>>2]=0,k[r+680>>1]=0,e[r+656>>2]=22050,e[r+648>>2]=22050,e[r+720>>2]=0,e[r+724>>2]=0,e[r+712>>2]=0,e[r+716>>2]=0,k[r+752>>1]=0,e[r+728>>2]=22050,e[r+792>>2]=0,e[r+796>>2]=0,e[r+784>>2]=0,e[r+788>>2]=0,k[r+824>>1]=0,e[r+800>>2]=22050,e[r+864>>2]=0,e[r+868>>2]=0,e[r+856>>2]=0,e[r+860>>2]=0,e[r+872>>2]=22050,k[r+896>>1]=0,e[r+936>>2]=0,e[r+940>>2]=0,e[r+928>>2]=0,e[r+932>>2]=0,k[r+968>>1]=0,e[r+944>>2]=22050,e[r+1008>>2]=0,e[r+1012>>2]=0,e[r+1e3>>2]=0,e[r+1004>>2]=0,k[r+1040>>1]=0,e[r+1016>>2]=22050,e[r+1088>>2]=0,e[(s=r+1080|0)>>2]=0,e[s+4>>2]=0,e[(s=r+1072|0)>>2]=0,e[s+4>>2]=0,e[A+8>>2]=r,$A[e[e[r>>2]+4>>2]](r,t),A}function kr(A,t,r){var s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0;e:if(x=e[50759]){if(s=e[50980],i=O(s,80)+222176|0,(0|(m=(0|(i=(0|(i=(e[i+12>>2]+e[i>>2]|0)/(0|A)|0))<(0|(h=((0|O(e[50754],19))/40<<16)/(0|A)|0))?i:h))>=399?399:i))>=0&&(Je(t,0,4+(m<<2)|0),s=e[50980]),(0|s)>=0)for(T=e[50801];;){if(l=O(c,80)+222176|0,e[l+4>>2]&&(g=e[l>>2])){if(_=g+e[l+12>>2]|0,s=1+((g-e[l+8>>2]|0)/(0|A)|0)|0,(0|g)>(0|(s=O(i=(0|s)<=1?1:s,A))))for(;e[(I=(i<<2)+t|0)>>2]=e[I>>2]+O(e[l+4>>2],d[((g-s|0)/(e[l+8>>2]>>8)|0)+T|0]),i=i+1|0,(0|g)>(0|(s=A+s|0)););if(!((0|s)>=(0|_)))for(;e[(I=(i<<2)+t|0)>>2]=e[I>>2]+O(e[l+4>>2],d[((s-g|0)/(e[l+12>>2]>>8)|0)+T|0]),i=i+1|0,(0|_)>(0|(s=A+s|0)););}if(!((0|(c=c+1|0))<=e[50980]))break}if(i=1,!((0|(l=65536e3/(0|A)|0))<=0||(0|(s=O(e[55565],10)))<=0))for(l=(0|s)/(0|l)|0;e[(g=(i<<2)+t|0)>>2]=e[g>>2]+s,i=i+1|0,(0|(s=s-l|0))>0;);if((0|c)<=8)for(;i=(s=c<<2)+203216|0,l=O(c,80)+222176|0,g=e[l+4>>2]>>14,e[i>>2]=(0|O(O(g,g),5))/2,r?s=e[s+203264>>2]:(g=s+203264|0,s=e[l>>2]/(0|A)|0,e[g>>2]=s),(0|s)>=(0|h)&&(e[i>>2]=0),(0|(c=c+1|0))!=9;);if(i=0,(0|m)>=0)for(s=0;l=e[(c=(s<<2)+t|0)>>2]>>15,l=O(l,l)>>8,e[c>>2]=l,(0|i)<=524287999&&(e[c>>2]=O(l,d[344+((i>>19)+x|0)|0])>>13),i=A+i|0,c=(0|s)!=(0|m),s=s+1|0,c;);if(e[t+4>>2]=(0|O(e[t+4>>2],d[203300]?6:10))/8,1&r)for(A=e[50826],s=1;;){if(e[(r=s<<2)+203312>>2]=e[t+r>>2]-e[A+r>>2]>>3,(0|(r=s+1|0))==30)break e;e[(r<<=2)+203312>>2]=e[t+r>>2]-e[A+r>>2]>>3,s=s+2|0}}else m=1;return m}function mr(A,t,r,s){var i,l=0,c=0;H=i=H-176|0,f[0|A]=0,k[i+80>>1]=24320,e[i+104>>2]=0,e[i+108>>2]=0,f[82+(Cr(r,l=i+80|2)+i|0)|0]=0;e:{A:{if(!s){if(s=i+80|1,e[i+12>>2]=s,Ot(t,i+12|0,i+16|0,i+104|0,0,0)||(e[i+12>>2]=l,Ot(t,i+12|0,i+16|0,i+104|0,0,0)||(f[i+81|0]=32,VA(t,l,i+16|0,60,0,0,0))),(r=d[i+16|0])&&(0|r)!=21)break A;r:{if(e[t+212>>2]!=25966){if(nn(85719,188772,189296),f[i+81|0]=95,e[i+12>>2]=s,Ot(e[47193],i+12|0,i+16|0,i+104|0,0,0)||(e[i+12>>2]=l,Ot(e[47193],i+12|0,i+16|0,i+104|0,0,0)),d[i+16|0])break r;as(e[e[32972]+60>>2]),r=d[i+16|0]}if(255&r)break A;t=d[87124]|d[87125]<<8|d[87126]<<16|d[87127]<<24,r=d[87120]|d[87121]<<8|d[87122]<<16|d[87123]<<24,f[0|A]=r,f[A+1|0]=r>>>8,f[A+2|0]=r>>>16,f[A+3|0]=r>>>24,f[A+4|0]=t,f[A+5|0]=t>>>8,f[A+6|0]=t>>>16,f[A+7|0]=t>>>24,f[A+16|0]=d[87136],t=d[87132]|d[87133]<<8|d[87134]<<16|d[87135]<<24,r=d[87128]|d[87129]<<8|d[87130]<<16|d[87131]<<24,f[A+8|0]=r,f[A+9|0]=r>>>8,f[A+10|0]=r>>>16,f[A+11|0]=r>>>24,f[A+12|0]=t,f[A+13|0]=t>>>8,f[A+14|0]=t>>>16,f[A+15|0]=t>>>24;break e}s=i+16|0,l=i+104|0,H=r=H-112|0,(c=e[47193])?(Vt(c,s,l,-1,0),jr(l=s,s=r+48|0),t=e[t+212>>2],f[r+43|0]=t>>>24,f[0|(c=(l=r+43|0)+(t>>>0>16777215)|0)]=t>>>16,f[0|(c=c+!!(16711680&t)|0)]=t>>>8,f[0|(c=c+!!(65280&t)|0)]=t,f[c+!!(255&t)|0]=0,e[r+16>>2]=85719,e[r+24>>2]=l,e[r+20>>2]=s,dA(A,85662,r+16|0)):(Vt(t,s,l,-1,0),jr(s,t=r+48|0),e[r>>2]=t,dA(A,85451,r)),H=r+112|0,as(e[e[32972]+60>>2]);break e}if(e[i+12>>2]=l,Ot(t,i+12|0,i+16|0,i+104|0,0,0),!d[i+16|0])break e}Vt(r=t,t=i+16|0,i+104|0,-1,0),jr(r=t,t=i+112|0),e[i>>2]=t,dA(A,85451,i)}return H=i+176|0,A}function hr(A,t){var r,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0;if(H=r=H-112|0,e[r+72>>2]=-1,e[(s=r- -64|0)>>2]=-1,e[s+4>>2]=-1,e[r+56>>2]=-1,e[r+60>>2]=-1,e[r+48>>2]=-1,e[r+52>>2]=-1,e[r+40>>2]=-1,e[r+44>>2]=-1,e[r+32>>2]=-1,e[r+36>>2]=-1,e[r+24>>2]=-1,e[r+28>>2]=-1,e[r+16>>2]=-1,e[r+20>>2]=-1,(0|t)>0){for(l=e[r+72>>2],c=e[r+68>>2],g=e[r+64>>2],m=e[r+60>>2],I=e[r+56>>2],h=e[r+52>>2],x=e[r+48>>2],T=e[r+44>>2],_=e[r+40>>2],V=e[r+36>>2],K=e[r+32>>2],J=e[r+28>>2],te=e[r+24>>2],ce=e[r+20>>2],he=e[r+16>>2];i=l,l=(0|(l=e[(s=134912+(Ee<<6)|0)+60>>2]))<0?i:l,i=c,c=(0|(c=e[s+56>>2]))<0?i:c,i=g,g=(0|(g=e[s+52>>2]))<0?i:g,i=m,m=(0|(m=e[s+48>>2]))<0?i:m,i=I,I=(0|(I=e[s+44>>2]))<0?i:I,i=h,h=(0|(h=e[s+40>>2]))<0?i:h,i=x,x=(0|(x=e[s+36>>2]))<0?i:x,i=T,T=(0|(T=e[s+32>>2]))<0?i:T,i=_,_=(0|(_=e[s+28>>2]))<0?i:_,i=V,V=(0|(V=e[s+24>>2]))<0?i:V,i=K,K=(0|(K=e[s+20>>2]))<0?i:K,i=J,J=(0|(J=e[s+16>>2]))<0?i:J,i=te,te=(0|(te=e[s+12>>2]))<0?i:te,i=ce,ce=(0|(ce=e[s+8>>2]))<0?i:ce,he=(0|(s=e[s+4>>2]))<0?he:s,(0|(Ee=Ee+1|0))!=(0|t););e[r+72>>2]=l,e[r+68>>2]=c,e[r+64>>2]=g,e[r+60>>2]=m,e[r+56>>2]=I,e[r+52>>2]=h,e[r+48>>2]=x,e[r+44>>2]=T,e[r+40>>2]=_,e[r+36>>2]=V,e[r+32>>2]=K,e[r+28>>2]=J,e[r+24>>2]=te,e[r+20>>2]=ce,e[r+16>>2]=he}for(t=0;;){if((0|(s=e[(l=t<<2)+(r+16|0)>>2]))!=e[(l=l+134848|0)>>2]){f[r+80|0]=0;e:{A:switch(t-1|0){case 4:e[47201]=s-1;break e;case 5:e[47200]=s;break e;case 0:case 1:case 2:case 3:case 11:break A;default:break e}e[r+4>>2]=s,e[r>>2]=1,e[r+8>>2]=f[t+102812|0],dA(r+80|0,91942,r)}e[l>>2]=s,s=r+80|0,PA(e[A>>2]+189424|0,s),e[A>>2]=e[A>>2]+MA(s)}if((0|(t=t+1|0))==15)break}H=r+112|0}function Ys(A){var t,r,s,i=0,l=0,c=0,g=0;for(i=17,e[A+328>>2]=17,e[A+224>>2]=0,e[A+216>>2]=1105,e[A+220>>2]=1072,e[A+600>>2]=1056,e[A+8180>>2]=105296,c=Je(A+344|0,0,256),f[A+393|0]=1,f[A+365|0]=1,f[A+360|0]=1,f[A+545|0]=1,f[A+529|0]=1,f[A+391|0]=1,f[A+379|0]=1,f[A+374|0]=1,f[A+489|0]=1,f[A+487|0]=1,f[A+398|0]=1,f[A+387|0]=1,f[A+388|0]=2,f[A+389|0]=1,f[A+390|0]=1,f[A+385|0]=2,f[A+383|0]=2,f[A+368|0]=1,f[A+369|0]=2,l=104224;f[0|(i=i+c|0)]=4|d[0|i],i=c+d[l+1|0]|0,f[0|i]=4|d[0|i],i=c+d[l+2|0]|0,f[0|i]=4|d[0|i],i=d[0|(l=l+3|0)],(0|l)!=104251;);f[A+386|0]=8|d[A+386|0],f[A+382|0]=8|d[A+382|0],f[A+384|0]=8|d[A+384|0],f[A+369|0]=16|d[A+369|0],f[A+370|0]=16|d[A+370|0],f[A+371|0]=16|d[A+371|0],l=d[A+361|0],c=d[A+362|0],i=d[A+363|0],g=d[A+364|0],t=d[A+366|0],r=d[A+367|0],f[A+372|0]=16|d[A+372|0],f[A+373|0]=16|d[A+373|0],f[A+375|0]=16|d[A+375|0],f[A+376|0]=16|d[A+376|0],f[A+377|0]=16|d[A+377|0],f[A+378|0]=16|d[A+378|0],f[A+380|0]=16|d[A+380|0],f[A+381|0]=16|d[A+381|0],f[A+383|0]=16|d[A+383|0],f[A+385|0]=16|d[A+385|0],s=d[A+388|0],f[A+367|0]=48|r,f[A+366|0]=40|t,f[A+364|0]=48|g,f[A+363|0]=48|i,f[A+362|0]=48|c,f[A+361|0]=48|l,f[A+388|0]=80|s,l=d[A+390|0],c=d[A+391|0],i=d[A+393|0],f[A+360|0]=128|d[A+360|0],g=d[A+365|0],f[A+393|0]=192|i,f[A+365|0]=128|g,f[A+368|0]=128|d[A+368|0],f[A+374|0]=128|d[A+374|0],f[A+379|0]=128|d[A+379|0],f[A+387|0]=128|d[A+387|0],i=d[A+389|0],f[A+391|0]=192|c,f[A+390|0]=192|l,f[A+389|0]=128|i,f[A+529|0]=128|d[A+529|0],f[A+545|0]=128|d[A+545|0],f[A+489|0]=128|d[A+489|0],f[A+487|0]=128|d[A+487|0],f[A+398|0]=128|d[A+398|0]}function Ot(A,t,r,s,i,l){var c,g=0,m=0,I=0,h=0,x=0,T=0;H=c=H-192|0,g=x=e[t>>2];e:{A:{for(;m=1,(0|(I=f[0|g]))>=0||(m=2,I>>>0<4294967264||(m=I>>>0<4294967280?3:4)),!(d[0|(I=m+g|0)]!=32|d[I+1|0]!=46);){if(h-160>>>0<4294967135)break A;qA((T=c+32|0)+h|0,g,m),f[(m=m+h|0)+T|0]=46,g=I+3|0,h=m+1|0}if(h){for(m=0;I=m,m=m+1|0,223&d[g+I|0];);if(!((T=I+h|0)+1>>>0>160)&&(qA((m=c+32|0)+h|0,g,I),f[m+T|0]=0,Cs(A,m,g,r,s,i,l))){e[s>>2]=128|e[s>>2],e[33264]=h,A=1;break e}}}for(g=0;;){if(x=(m=x)+1|0,223&(m=d[0|m]))if(!g|(0|m)!=46|f[31+(g+c|0)|0]-48>>>0>=10){if(f[(c+32|0)+g|0]=m,m=159,(0|(g=g+1|0))!=159)continue}else m=g;else m=g;break}f[(g=c+32|0)+m|0]=0,g=Cs(A,g,x,r,s,i,l);A:if(8&d[s+3|0]){if(!Ar(r,I=A+268|0)){if(I=e[A+288>>2]+1|0,e[A+288>>2]=I,(0|I)<4)break A;f[0|r]=0;break A}Lt(I,r,20),e[A+288>>2]=1}else e[A+288>>2]=0;A:{if(!g){if(g=0,8&d[s+5|0]&&(I=jA(c+28|0,g=c+32|d[c+32|0]==95),bA(A,e[c+28>>2],r),g=g+I|0),!(m>>>0<2|g)){if(f[0|r]=0,!(16&i&&d[0|(g=31+(m+c|0)|0)]==101)&&(!(4096&i)||d[0|(g=(m=(c+32|0)+m|0)-1|0)]!=d[m-2|0]))break A;f[0|g]=0,g=Cs(A,c+32|0,x,r,s,i,l)}if(!g)break A}if(h=e[s>>2],d[A+172|0]&&(h^=536870912,e[s>>2]=h),A=1,!(536870912&h))break e;2&i&&(k[66448]=8192,e[c+16>>2]=r,dA(132898,87470,c+16|0),A=e[t>>2],e[t>>2]=132898,8&d[188788]&&(qA(t=c+32|0,s=A,A=g-A|0),f[A+t|0]=0,e[c+4>>2]=132898,A=e[47195],e[c>>2]=t,Xt(A,87652,c)))}f[0|r]=0,A=0}return H=c+192|0,A}function Hs(A,t){var r=0,s=0,i=0;r=31&A;e:{A:{r:{if((0|(A&=96))==96)A=-1;else{if((0|A)!=64)break r;A=1}if(r>>>0>=15)break e;t=e[203136+(r<<2)>>2]+O(A,t)|0;break A}if(r>>>0>=15)break e}A=e[(s=r<<2)+105616>>2],e[s+203136>>2]=(0|t)>=0?(0|A)>(0|t)?t:A:0}e:{A:{r:{a:{n:switch(r-1|0){case 5:if(!(A=e[50759]))break A;e[54728]=e[50982],t=e[50979],r=e[50978],Je(205184,0,11e3),e[51293]=0,t=(r=(i=(0|(s=e[50789]))>0)?130:(0|r)>=5499?5499:r)?i?s:(0|t)>=100?100:t:0,e[50755]=t,r=(0|O(r,e[50754]))/1e3|0,e[51292]=r,e[54729]=(0|t)>20?r<<1:t?r:0,e[33037]=(0|O(500-t|0,(0|O(d[e[50797]+105596|0],(0|O(e[50787],55))/100|0))/16|0))/500;break a;case 0:break n;case 2:case 12:break e;case 4:break r;default:break A}if(!(A=e[50759]))break A}return t=256,(0|(r=(0|(r=e[50785]))>=101?101:r))>=51&&(t=256+(((O(r,25)-1250&65535)>>>0)/50|0)|0),k[A+164>>1]=(0|O(k[A+236>>1],t))/256,k[A+166>>1]=(0|O(k[A+238>>1],t))/256,k[A+168>>1]=(0|O(k[A+240>>1],t))/256,k[A+170>>1]=(0|O(k[A+242>>1],t))/256,k[A+172>>1]=(0|O(k[A+244>>1],t))/256,k[A+174>>1]=(0|O(k[A+246>>1],t))/256,A=e[50790],k[102e3]=(0|O(k[102036],O(A,-3)+256|0))/256,void(k[101999]=(0|O(k[102035],O(A,-6)+256|0))/256)}e[50759]&&(e[54728]=e[50982],A=e[50979],t=e[50978],Je(205184,0,11e3),e[51293]=0,A=(t=(s=(0|(r=e[50789]))>0)?130:(0|t)>=5499?5499:t)?s?r:(0|A)>=100?100:A:0,e[50755]=A,t=(0|O(t,e[50754]))/1e3|0,e[51292]=t,e[54729]=(0|A)>20?t<<1:A?t:0,e[33037]=(0|O(500-A|0,(0|O(d[e[50797]+105596|0],(0|O(e[50787],55))/100|0))/16|0))/500)}return}e[33037]=(0|O(d[e[50797]+105596|0],(0|O(e[50787],55))/100|0))/16}function qr(A,t,r){var s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0;r&&(e[r>>2]=0);e:{A:if(!((0|(s=f[0|A]))<0)){for(;;){if((0|(l=255&s))==32|l-9>>>0<5){if((0|(s=f[0|(A=A+1|0)]))>=0)continue;break A}break}if(!(255&s))break e}for(;;){if((0|(s=m=255&s))==32|s-9>>>0<5)break e;if((0|m)!=124||(0|(s=d[0|(l=A+1|0)]))==124){A:{if((0|(x=e[36115]))>=2){for(s=1,l=-1,I=0;;){r:if(!(!(h=e[144464+(s<<2)>>2])|d[h+11|0]==15)){g=e[h>>2];a:{n:{if(m>>>0>=33){if(T=0,c=0,(255&g)==(0|m)&&(c=1,(i=d[A+1|0])>>>0<33|(0|i)!=(g>>>8&255)||(c=2,(i=d[A+2|0])>>>0<33|(0|i)!=(g>>>16&255)||(c=(i=(i=d[A+3|0])>>>0>32&(0|i)==(g>>>24|0))?4:3,T=0-i|0))),(0|l)>=(0|c))break r;if(i=4,!(1&T))break n;break a}if(c=0,(0|l)>=0)break r}if(g>>>((i=c)<<3)&255)break r}I=d[h+10|0],l=i}if((0|x)==(0|(s=s+1|0)))break}if(I)break A}return r&&jA(r,A),void(f[0|t]=0)}f[0|t]=I,A=((0|l)<=1?1:l)+A|0,t=l=t+1|0;A:if((0|I)==21){r:if((0|(i=d[0|A]))==32|i-9>>>0<5)s=l;else if(s=l,i)for(;;){if(f[0|s]=Ps(i),s=s+1|0,(0|(i=d[0|(A=A+1|0)]))==32|i-9>>>0<5)break r;if(!i)break}if(f[0|s]=0,!i){if(t=s,Ar(l,85593))break A;return void(f[0|l]=0)}f[0|s]=124,t=s+1|0}s=d[0|A]}else A=l;if(!(255&s))break}}f[0|t]=0}function rs(A,t){var r=0,s=0,i=0,l=0,c=0;e:{A:{r:{a:{n:switch((0|(r=e[A+4>>2]))==e[A+104>>2]?r=Ie(A):(e[A+4>>2]=r+1,r=d[0|r]),r-43|0){case 0:case 2:break n;default:break a}if(l=(0|r)==45,c=!t,(0|(r=e[A+4>>2]))==e[A+104>>2]?r=Ie(A):(e[A+4>>2]=r+1,r=d[0|r]),c|(t=r-58|0)>>>0>4294967285)break r;if(e[A+116>>2]<0)break A;e[A+4>>2]=e[A+4>>2]-1;break A}t=r-58|0}if(!(t>>>0<4294967286)){if((t=r-48|0)>>>0<10){for(;i=(0|(s=(s=O(s,10)+r|0)-48|0))<214748364,(0|(t=e[A+4>>2]))==e[A+104>>2]?r=Ie(A):(e[A+4>>2]=t+1,r=d[0|t]),i&(t=r-48|0)>>>0<=9;);i=s>>31}r:if(!(t>>>0>=10))for(;;){if(t=(s=st(s,i,10,0))+r|0,r=le,r=t>>>0<s>>>0?r+1|0:r,s=t-48|0,i=r-(t>>>0<48)|0,(0|(t=e[A+4>>2]))==e[A+104>>2]?r=Ie(A):(e[A+4>>2]=t+1,r=d[0|t]),(t=r-48|0)>>>0>9)break r;if(!(s>>>0<2061584302&(0|i)<=21474836|(0|i)<21474836))break}if(t>>>0<10)for(;(0|(t=e[A+4>>2]))==e[A+104>>2]?t=Ie(A):(e[A+4>>2]=t+1,t=d[0|t]),t-48>>>0<10;);(0|(t=e[A+116>>2]))>0|(0|t)>=0&&(e[A+4>>2]=e[A+4>>2]-1),A=s,s=l?0-A|0:A,i=l?0-(!!(0|A)+i|0)|0:i;break e}}if(i=-2147483648,!(e[A+116>>2]<0))return e[A+4>>2]=e[A+4>>2]-1,le=-2147483648,0}return le=i,s}function ws(A){var t=0,r=0,s=0,i=0;if(e[36432]=110,e[36433]=100,e[36434]=450,e[36430]=5,t=e[203136+((0|A)==2?32:8)>>2],s=e[32972],(0|(r=e[s+84>>2]))>0&&(t=(0|O(t,r))/100|0),r=(0|t)>=359?359:t,r=(0|(t=(0|t)>=450?450:t))>399?6:(0|t)>379?7:d[((0|r)<=80?80:r)+101856|0],1&A&&(e[32526]=(0|O(r,e[s+72>>2]))/256,e[32527]=(0|O(r,e[s+76>>2]))/256,e[32528]=(0|O(r,e[s+80>>2]))/256,r>>>0>7||(i=r-1|0,e[32528]=i,e[32526]=r,e[32527]=i)),2&A){A=e[s+72>>2];e:{A:{r:{a:{n:{o:{c:{u:{if((0|t)>=351)s=t-350|0,e[36432]=85-(((255&s)>>>0)/3|0)&255,s=60-(s>>>3|0)|0;else{if((0|t)<251)break u;s=t-250|0,e[36432]=110-(s>>>2|0),s=110-(s>>>1|0)|0}if(e[36433]=s,A=(0|O(A,r))/256|0,e[36431]=110+((0|O(A,150))/128|0),t>>>0<=349)break c;if(r=t-350|0,e[36431]=d[r+102224|0],t>>>0<390)break n;if(e[36434]=450+((t+112<<24>>24)/-2<<24>>24),t>>>0<441)break o;e[36434]=860-t,A=12;break A}A=(0|O(A,r))/256|0,e[36431]=(0|t)>=170?110+((0|O(A,150))/128|0)|0:128+((A<<7)/130|0)|0}A=(A<<8)/115|0;break A}if(A=12,t>>>0>430)break A;if(A=13,t>>>0<=400)break a;break A}if(A=(A<<8)/115|0,e[36428]=A,t>>>0<375)break r}A=14;break A}if((0|t)<351)break e;A=d[r+102336|0]}e[36428]=A}e[36429]=(0|A)<=16?16:A}}function Pr(A,t,r){var s,i,l,c;s=.000244140625*+e[50767],P[r>>3]=s,P[r+40>>3]=.015625*+e[A+112>>2],P[r+48>>3]=.015625*+e[A+276>>2],P[r+56>>3]=.00390625*+(0|O(k[A+166>>1],k[t+4>>1]))+ +k[A+220>>1],P[r+64>>3]=.00390625*+(0|O(k[A+168>>1],k[t+6>>1]))+ +k[A+222>>1],P[r+72>>3]=.00390625*+(0|O(k[A+170>>1],k[t+8>>1]))+ +k[A+224>>1],P[r+80>>3]=.00390625*+(0|O(k[A+172>>1],k[t+10>>1]))+ +k[A+226>>1],P[r+88>>3]=.00390625*+(0|O(k[A+174>>1],k[t+12>>1]))+ +k[A+228>>1],i=k[A+230>>1],l=k[A+176>>1],c=k[t+14>>1],e[r+112>>2]=0,e[r+116>>2]=1080623104,e[r+104>>2]=0,e[r+108>>2]=1081032704,P[r+96>>3]=.00390625*+(0|O(l,c))+ +(0|i),d[t+40|0]?(e[r+184>>2]=0,e[r+188>>2]=1072693248,P[r+104>>3]=d[t+40|0]<<1):(e[r+184>>2]=0,e[r+188>>2]=0),P[r+120>>3]=.00390625*+k[A+202>>1]*+(d[t+35|0]<<1),P[r+128>>3]=.00390625*+k[A+204>>1]*+(d[t+36|0]<<1),P[r+136>>3]=.00390625*+k[A+206>>1]*+(d[t+37|0]<<1),t=d[t+38|0],A=k[A+208>>1],e[r+176>>2]=0,e[r+180>>2]=1079574528,e[r+160>>2]=0,e[r+164>>2]=1083129856,e[r+152>>2]=0,e[r+156>>2]=1083129856,e[r+352>>2]=0,e[r+356>>2]=1072693248,e[r+168>>2]=0,e[r+172>>2]=1079574528,P[r+144>>3]=.00390625*+(0|A)*+(t<<1),A=e[50779],P[r+368>>3]=s,P[r+360>>3]=+(0|A)/100*3}function or(A){var t=0;aA(A,Qr(A));e:{A:{r:{a:{n:{o:{c:{u:{l:{i:{if((0|(A=-1048576&le))<268435455|(0|A)<=268435455){p:{C:{if((0|A)<33554431|(0|A)<=33554431){if((0|A)<8388607|(0|A)<=8388607){if(t=524328,!0&(0|A)==-2147483648)break e;if(0|(0|A)!=-2143289344)break A;return 557096}if(!0&(0|A)==8388608)break C;if(0|(0|A)!=16777216)break A;return 524358}if((0|A)>71303167)break p;if(!0&(0|A)==33554432)break r;if(0|(0|A)!=67108864)break A}return 266270}if(!0&(0|A)==71303168)break i;if(!0&(0|A)==134217728)break a;if(0|(0|A)!=138412032)break A;return 294942}if((0|A)<542113791|(0|A)<=542113791){if((0|A)<536870911|(0|A)<=536870911){if(!0&(0|A)==268435456)break n;if(0|(0|A)!=272629760)break A;return 299028}if(!0&(0|A)==536870912)break c;if(!0&(0|A)==538968064)break o;if(0|(0|A)!=541065216)break A;return 569389}if((0|A)<1075838975|(0|A)<=1075838975){if(!0&(0|A)==542113792)break i;if(0|(0|A)!=1073741824)break A;return 532520}if(!0&(0|A)==1075838976)break u;if(!0&(0|A)==1077936128)break l;if(0|(0|A)!=1078984704)break A}return 299038}return 565288}return 1581096}return 536621}return 1585197}return 266260}return 262174}return 2396190}t=16384}return t}function cs(A,t,r,s,i,l){var c,g,m=0,I=0,h=0,x=0;if(g=8388607&A,I=e[34456],m=d[0|(A=g+I|0)]|d[A+1|0]<<8){c=!(h=d[A+2|0]),A=e[36434]<<c,(0|r)<=0?r=m:(r=(0|O(e[50754],r))/1e3<<c,A=(0|A)<(0|(x=(0|O(r,A))/(0|m)|0))?x:A),i=(0|i)>0?(0|O(r,i))/256|0:r,r=(0|O(i,e[36431]))/256|0,r=(0|A)<(0|(r=(4&s)>>>2|0&&(0|r)>(0|i)?i:r))?r:A,h||(m=m>>>1|0,r=(0|r)/2|0);e:if(!((0|l)<0)){if(s=g+4|0,256&t)A=e[50758],e[36439]=A,e[(t=216192+(A<<4)|0)>>2]=7,e[t+8>>2]=s+I,e[t+4>>2]=m<<16|r,l=h|l<<8;else{if(A=e[50758],e[36439]=A,e[(A=216192+(A<<4)|0)>>2]=6,l=h|l<<8,e[A+12>>2]=l,e[A+8>>2]=s+I,I=A,A=O(t=m>>>2|0,3),i=(0|r)>(0|m),e[I+4>>2]=i?A:r,I=e[50758]+1|0,e[50758]=(0|I)<=169?I:0,(0|A)<(0|(r=i?r-A|0:0)))for(i=t<<1,h=s+(h?t:i)|0;t=e[50758],e[36439]=t,e[(t=216192+(t<<4)|0)>>2]=6,e[t+4>>2]=i,e[t+12>>2]=l,e[t+8>>2]=h+e[34456],t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,(0|A)<(0|(r=r-i|0)););if((0|r)<=0)break e;A=e[50758],e[36439]=A,e[(t=216192+(A<<4)|0)>>2]=6,e[t+4>>2]=r,e[t+8>>2]=e[34456]+(s+(m-r<<c)|0)}e[12+(216192+(A<<4)|0)>>2]=l,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0}}}function qA(A,t,r){var s,i=0,l=0;if(r>>>0>=512)return je(0|A,0|t,0|r),A;s=A+r|0;e:if(3&(A^t))if(s>>>0<4)r=A;else if((i=s-4|0)>>>0<A>>>0)r=A;else for(r=A;f[0|r]=d[0|t],f[r+1|0]=d[t+1|0],f[r+2|0]=d[t+2|0],f[r+3|0]=d[t+3|0],t=t+4|0,i>>>0>=(r=r+4|0)>>>0;);else{A:if(3&A)if(r)for(r=A;;){if(f[0|r]=d[0|t],t=t+1|0,!(3&(r=r+1|0)))break A;if(!(r>>>0<s>>>0))break}else r=A;else r=A;if(!((i=-4&s)>>>0<64||(l=i+-64|0)>>>0<r>>>0))for(;e[r>>2]=e[t>>2],e[r+4>>2]=e[t+4>>2],e[r+8>>2]=e[t+8>>2],e[r+12>>2]=e[t+12>>2],e[r+16>>2]=e[t+16>>2],e[r+20>>2]=e[t+20>>2],e[r+24>>2]=e[t+24>>2],e[r+28>>2]=e[t+28>>2],e[r+32>>2]=e[t+32>>2],e[r+36>>2]=e[t+36>>2],e[r+40>>2]=e[t+40>>2],e[r+44>>2]=e[t+44>>2],e[r+48>>2]=e[t+48>>2],e[r+52>>2]=e[t+52>>2],e[r+56>>2]=e[t+56>>2],e[r+60>>2]=e[t+60>>2],t=t- -64|0,l>>>0>=(r=r- -64|0)>>>0;);if(r>>>0>=i>>>0)break e;for(;e[r>>2]=e[t>>2],t=t+4|0,i>>>0>(r=r+4|0)>>>0;);}if(r>>>0<s>>>0)for(;f[0|r]=d[0|t],t=t+1|0,(0|s)!=(0|(r=r+1|0)););return A}function $r(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0;F(+A),t=0|B(1),B(0);e:{if((s=(t=t>>>20&2047)-969|0)>>>0<63)m=t;else{if((0|s)<0)return A+1;if(!(t>>>0<1033)){if(F(+A),s=0|B(1),r=0,!(0|B(0))&(0|s)==-1048576)break e;return t>>>0>=2047?A+1:(0|s)<0?(P[(t=H-16|0)+8>>3]=12882297539194267e-247,12882297539194267e-247*P[t+8>>3]):(P[(t=H-16|0)+8>>3]=3105036184601418e216,3105036184601418e216*P[t+8>>3])}}if(r=P[14409],l=(r=(A=(r=(i=P[14408]*A+r)-r)*P[14411]+(r*P[14410]+A))*A)*r*(A*P[14415]+P[14414]),r*=A*P[14413]+P[14412],F(+i),B(1),g=0|B(0),A=l+(r+(P[(s=g<<4&2032)+115376>>3]+A)),c=e[(s=s+115384|0)>>2],t=(g<<13)+(t=e[s+4>>2])|0,t=(s=(s=c)+(c=0)|0)>>>0<c>>>0?t+1|0:t,!m)return-2147483648&g?(E(0,0|s),E(1,t+1071644672|0),(A=(i=(r=+S())*A)+r)<1&&(e[(t=H-16|0)+8>>2]=0,e[t+12>>2]=1048576,P[t+8>>3]=22250738585072014e-324*P[t+8>>3],A=(A=(l=A+1)+(i+(r-A)+(A+(1-l)))+-1)==0?0:A),A*=22250738585072014e-324):(E(0,0|s),E(1,t-1058013184|0),A=5486124068793689e288*((r=+S())*A+r)),A;E(0,0|s),E(1,0|t),r=(r=+S())*A+r}return r}function us(A,t){var r,s=0,i=0,l=0,c=0;H=r=H-16|0;e:{if(Ls(84270,f[0|t])){if(i=2,Ls(t,43)||(i=d[0|t]!=114),i=Ls(t,120)?128|i:i,l=i=Ls(t,101)?524288|i:i,c=64|i,l=(0|(i=d[0|t]))==114?l:c,l=(0|i)==119?512|l:l,e[r>>2]=438,e[r+4>>2]=0,(A=0|Pe(-100,0|A,32768|((0|i)==97?1024|l:l),0|r))>>>0>=4294963201&&(e[56798]=0-A,A=-1),(0|A)<0)break e;H=i=H-32|0;A:{r:{if(Ls(84270,f[0|t])){if(s=HA(1176))break r}else e[56798]=28;t=0;break A}Je(s,0,144),Ls(t,43)||(e[s>>2]=d[0|t]==114?8:4),d[0|t]==97?(1024&(t=0|Ne(0|A,3,0))||(t|=1024,e[i+16>>2]=t,e[i+20>>2]=t>>31,Ne(0|A,4,i+16|0)),t=128|e[s>>2],e[s>>2]=t):t=e[s>>2],e[s+80>>2]=-1,e[s+48>>2]=1024,e[s+60>>2]=A,e[s+44>>2]=s+152,8&t||(e[i>>2]=i+24,e[i+4>>2]=0,0|se(0|A,21523,0|i)||(e[s+80>>2]=10)),e[s+40>>2]=10,e[s+36>>2]=11,e[s+32>>2]=12,e[s+12>>2]=13,d[227205]||(e[s+76>>2]=-1),e[s+56>>2]=e[56816],(t=e[56816])&&(e[t+52>>2]=s),e[56816]=s,t=s}if(H=i+32|0,s=t)break e;ye(0|A)}else e[56798]=28;s=0}return H=r+16|0,s}function ta(A,t,r){var s,i=0,l=0,c=0,g=0,m=0;if(c=A,H=s=H-208|0,e[s+8>>2]=1,e[s+12>>2]=0,g=t<<2){for(e[s+16>>2]=4,e[s+20>>2]=4,t=4,i=4,l=2;A=t,t=(i+4|0)+t|0,e[(s+16|0)+(l<<2)>>2]=t,l=l+1|0,i=A,t>>>0<g>>>0;);if((A=(c+g|0)-4|0)>>>0<=c>>>0)l=0,t=1,A=0;else{for(l=1,t=1;3&~l?(Ae[(s+16|0)+((i=t-1|0)<<2)>>2]>=A-c>>>0?$t(c,r,s+8|0,t,0,s+16|0):Ya(c,r,t,s+16|0),(0|t)!=1?(ii(s+8|0,i),t=1):(ii(s+8|0,1),t=0)):(Ya(c,r,t,s+16|0),Nn(s+8|0,2),t=t+2|0),l=1|(i=e[s+8>>2]),e[s+8>>2]=l,A>>>0>(c=c+4|0)>>>0;);l=i>>>0>1,A=e[s+12>>2]!=0}if($t(c,r,s+8|0,t,0,s+16|0),l|(0|t)!=1|A)for(;(0|t)<=1?(Nn(i=s+8|0,A=gA(i)),l=e[s+8>>2],A=A+t|0):(ii(i=s+8|0,2),e[s+8>>2]=7^e[s+8>>2],Nn(i,1),$t((m=c-4|0)-e[(g=s+16|0)+((A=t-2|0)<<2)>>2]|0,r,i,t-1|0,1,g),ii(i,1),l=1|e[s+8>>2],e[s+8>>2]=l,$t(m,r,i,A,1,g)),t=A,c=c-4|0,e[s+12>>2]|(0|t)!=1|(0|l)!=1;);}H=s+208|0}function Us(A,t,r,s){var i,l=0,c=0,g=0;H=i=H-32|0,g=l=2147483647&s,c=l-1006698496|0;e:if(0|(l=l-1140785152|0)>>>0>c>>>0){if(l=r<<4|t>>>28,r=s<<4|r>>>28,(0|(t&=268435455))==134217728&!!(0|A)|t>>>0>134217728){c=r+1073741824|0,c=(l=l+1|0)?c:c+1|0;break e}if(c=r+1073741824|0,A|(0|t)!=134217728)break e;c=(A=1&l)>>>0>(l=A+l|0)>>>0?c+1|0:c}else(!r&(0|g)==2147418112?!(A|t):g>>>0<2147418112)?(l=0,c=2146435072,g>>>0>1140785151||(c=0,(g=g>>>16|0)>>>0<15249||(vt(i+16|0,A,t,r,l=65535&s|65536,g-15233|0),cr(i,A,t,r,l,15361-g|0),l=(t=e[i+8>>2])<<4,t=e[i+12>>2]<<4|t>>>28,r=e[i>>2],g=c=e[i+4>>2],l|=c>>>28,c=t,(0|(A=268435455&g))==134217728&!!(0|(t=r|!!(e[i+16>>2]|e[i+24>>2]|e[i+20>>2]|e[i+28>>2])))|A>>>0>134217728?c=(l=l+1|0)?c:c+1|0:t|(0|A)!=134217728||(c=(A=l)>>>0>(l=l+(1&l)|0)>>>0?c+1|0:c)))):(l=r<<4|t>>>28,c=524287&(A=s<<4|r>>>28)|2146959360);return H=i+32|0,E(0,0|l),E(1,-2147483648&s|c),+S()}function Os(A){var t,r=0,s=0,i=0,l=0,c=0;if(F(+A),c=0|B(1),i=0|B(0),(0|(l=c>>>20&2047))==2047)return(A*=1)/A;if(!(s=i<<1)&(0|(r=c<<1|i>>>31))==2145386496|r>>>0<2145386496)return!s&(0|r)==2145386496?0*A:A;if(l)r=1048575&c|1048576;else{if(l=0,s=i<<12,(0|(r=c<<12|i>>>20))>0|(0|r)>=0)for(;l=l-1|0,r=r<<1|s>>>31,s<<=1,(0|r)>0|(0|r)>=0;);s=31&(r=1-l|0),(63&r)>>>0>=32?(r=i<<s,i=0):(r=(1<<s)-1&i>>>32-s|c<<s,i<<=s)}if(s=i,(0|l)>1023){for(;;){if(!((0|(i=r+-1048576|0))<0||(r=i)|s))return 0*A;if(r=r<<1|s>>>31,s<<=1,!((0|(l=l-1|0))>1023))break}l=1023}if(!((0|(i=r+-1048576|0))<0||(r=i)|s))return 0*A;if((0|r)==1048575|r>>>0<1048575)for(;l=l-1|0,i=r>>>0<524288,r=r<<1|s>>>31,s<<=1,i;);return t=-2147483648&c,(0|l)>0?r=r+-1048576|l<<20:(i=1-l|0,c=r,l=s,s=31&i,(63&i)>>>0>=32?(r=0,s=c>>>s|0):(r=c>>>s|0,s=((1<<s)-1&c)<<32-s|l>>>s)),E(0,0|s),E(1,r|t),+S()}function Tr(A,t,r,s,i){var l,c=0,g=0;H=l=H-160|0;e:{A:{r:{a:switch((c=d[t+10|0])-15|0){case 6:break r;case 0:break a;default:break A}f[0|A]=0;break e}e[l>>2]=O(d[r+7|0],44)+137856,dA(A,86002,l),A=MA(A)+A|0;break e}if(s){if(f[l+140|0]=0,r?ut(0,0,r,l+8|0,0):Pa(c,l+8|0),r=l+140|0,c=d[l+140|0]){if((0|c)==32){f[0|A]=0;break e}224&(c=c<<24>>24)||(i&&(e[i>>2]=c),r=l+141|0)}if(!((0|(i=MA(r)))<=0)){A=PA(A,r)+i|0,f[0|A]=0;break e}}i=0;A:if(!(!(r=255&(c=e[t>>2]))|(0|r)==47)){if(s){if((0|(g=255&c))==95)break A;r:{a:{if((0|g)==35){if(g=3,d[t+11|0]!=2)break a;break A}if((g=r-32|0)>>>0>95)break r}r=y[93952+(g<<1)>>1]}i=Cr(r,A)}else f[0|A]=c,i=1;for(;;){if(!(r=255&(c>>=8))|(0|r)==47)break A;if(s){if((0|r)==35&d[t+11|0]==2)break A;if(r-48>>>0<10)continue;(g=r-32|0)>>>0<=95&&(r=y[93952+(g<<1)>>1]),i=Cr(r,A+i|0)+i|0}else f[A+i|0]=c,i=i+1|0}}f[0|(A=A+i|0)]=0}return H=l+160|0,A}function uA(A){var t,r=0;t=A,r=131280;e:{A:{if(!((0|A)<=1023||(r=131300,A>>>0<1328||(r=131320,A>>>0<1424||(r=131340,A>>>0<1536||(r=131360,A>>>0<1792||(r=131380,A>>>0<1872||(r=131400,A>>>0<2432||(r=131420,A>>>0<2560||(r=131440,A>>>0<2688||(r=131460,A>>>0<2816||(r=131480,A>>>0<2944||(r=131500,A>>>0<3072||(r=131520,A>>>0<3200||(r=131540,A>>>0<3328||(r=131560,A>>>0<3456||(r=131580,A>>>0<3584||(r=131600,A>>>0<3712||(r=131620,A>>>0<3840||(r=131640,A>>>0<4096||(r=131660,A>>>0<4256||(r=131680,A>>>0<4352||(r=131700,A>>>0<4608||(r=131720,A>>>0<5024||(r=131740,A>>>0<10496||(r=131760,A>>>0<12544||(r=131780,A>>>0<40960))))))))))))))))))))))))))){if(A>>>0>=55296)break A;r=131800}if((0|t)>=y[(A=r)+8>>1])break e}A=0}return A}function bA(A,t,r){var s,i=0,l=0,c=0,g=0;H=s=H-208|0,f[s+80|0]=0;e:{if((i=t-224|0)>>>0<=158)t=101072+(i<<1)|0;else{if((t=t-592|0)>>>0>88)break e;t=101392+(t<<1)|0}if(t=y[t>>1]){if(c=t<<16>>16,g=(i=63&t)>>>0>37?i+59|0:k[101584+(i<<1)>>1],i=t>>>6|0,(0|c)<0)i=59+(63&i)|0,t=t>>>12&7;else{if(!(l=31&i))break e;i=0,t=t>>>11&15}(l=QA(A,e[129920+(l<<3)>>2],s+112|0))&&Za(A,g,s+176|0)&&(t&&4096&QA(A,e[129920+(t<<3)>>2],s+80|0)&&(r=MA(t=PA(r,s+80|0)),f[s+80|0]=0,r=t+r|0),i?(Za(t=A,i,A=s+144|0),e[s+68>>2]=s+80,e[s- -64>>2]=A,e[s+60>>2]=6,e[s+52>>2]=23,e[s+56>>2]=s+176,e[s+48>>2]=s+112,dA(r,84101,s+48|0)):(0|c)<0?PA(r,s+176|0):1&e[A+144>>2]|4096&l?(e[s+36>>2]=23,e[s+40>>2]=6,e[s+44>>2]=s+176,e[s+32>>2]=s+112,dA(r,84430,s+32|0)):(e[s+16>>2]=23,e[s+8>>2]=23,e[s>>2]=4,e[s+12>>2]=s+112,e[s+4>>2]=s+176,dA(r,84802,s)))}}H=s+208|0}function NA(){ct(),e[55928]=0,e[55926]=0,e[55927]=0,e[55924]=0,e[56244]=0,e[56245]=0,e[56246]=0,e[56247]=0,e[56260]=0,e[56261]=0,e[56262]=0,e[56263]=0,e[56276]=0,e[56277]=0,e[56278]=0,e[56279]=0,e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,e[55988]=0,e[55989]=0,e[55990]=0,e[55991]=0,e[56004]=0,e[56005]=0,e[56006]=0,e[56007]=0,e[56020]=0,e[56021]=0,e[56022]=0,e[56023]=0,e[56036]=0,e[56037]=0,e[56038]=0,e[56039]=0,e[56052]=0,e[56053]=0,e[56054]=0,e[56055]=0,e[56068]=0,e[56069]=0,e[56070]=0,e[56071]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0}function Yt(A,t){var r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0;e:{if((0|(l=e[A+4>>2]))==e[A>>2])if((c=e[A+8>>2])>>>0<(r=e[A+12>>2])>>>0)r=(i=(1+(r-c>>2)|0)/2<<2)+c|0,(0|l)!=(0|c)&&(Be(r=r-(s=c-l|0)|0,l,s),l=e[A+8>>2]),e[A+4>>2]=r,e[A+8>>2]=i+l;else{if((s=(0|r)==(0|l)?1:r-l>>1)>>>0>=1073741824)break e;if(h=(m=Es(r=s<<2))+r|0,g=r=(s+3&-4)+m|0,(0|l)!=(0|c)){if(x=-4&(c=c-l|0),i=r,s=l,c=1+((I=c-4|0)>>>2|0)&7)for(g=0;e[i>>2]=e[s>>2],s=s+4|0,i=i+4|0,(0|c)!=(0|(g=g+1|0)););if(g=r+x|0,!(I>>>0<28))for(;e[i>>2]=e[s>>2],e[i+4>>2]=e[s+4>>2],e[i+8>>2]=e[s+8>>2],e[i+12>>2]=e[s+12>>2],e[i+16>>2]=e[s+16>>2],e[i+20>>2]=e[s+20>>2],e[i+24>>2]=e[s+24>>2],e[i+28>>2]=e[s+28>>2],s=s+32|0,(0|g)!=(0|(i=i+32|0)););}e[A+12>>2]=h,e[A+8>>2]=g,e[A+4>>2]=r,e[A>>2]=m,l&&(fe(l),r=e[A+4>>2])}else r=l;return e[r-4>>2]=e[t>>2],void(e[A+4>>2]=e[A+4>>2]-4)}li(),j()}function Ca(A,t,r){var s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0;e:{A:{r:{a:{n:{o:{c:{u:{l:{if(t){if(!r)break l;break u}return lA=(t=A)-O(A=(A>>>0)/(r>>>0)|0,r)|0,We=0,le=0,A}if(!A)break c;break o}if(!((s=r-1|0)&r))break n;l=0-(c=(be(r)+33|0)-be(t)|0)|0;break r}return lA=0,We=t-O(A=(t>>>0)/0|0,0)|0,le=0,A}if((s=32-be(t)|0)>>>0<31)break a;break A}if(lA=A&s,We=0,(0|r)==1)break e;return r=31&(s=fi(r)),(63&s)>>>0>=32?A=t>>>r|0:(i=t>>>r|0,A=((1<<r)-1&t)<<32-r|A>>>r),le=i,A}c=s+1|0,l=63-s|0}if(s=31&(i=63&c),i>>>0>=32?(i=0,g=t>>>s|0):(i=t>>>s|0,g=((1<<s)-1&t)<<32-s|A>>>s),s=31&(l&=63),l>>>0>=32?(t=A<<s,A=0):(t=(1<<s)-1&A>>>32-s|t<<s,A<<=s),c)for(h=(0|(s=r-1|0))==-1?-1:0;m=i<<1|g>>>31,g=(i=g<<1|t>>>31)-(I=r&(l=h-(m+(i>>>0>s>>>0)|0)>>31))|0,i=m-(i>>>0<I>>>0)|0,t=t<<1|A>>>31,A=x|A<<1,x=m=1&l,c=c-1|0;);return lA=g,We=i,le=t<<1|A>>>31,m|A<<1}lA=A,We=t,A=0,t=0}return le=t,A}function ra(A,t){var r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0;e:{if((0|(r=e[A+8>>2]))==e[A+12>>2])if((s=e[A+4>>2])>>>0>(c=e[A>>2])>>>0)i=Be((l=(1+(s-c>>2)|0)/-2<<2)+s|0,s,r=r-s|0)+r|0,e[A+8>>2]=i,e[A+4>>2]=l+e[A+4>>2];else{if((l=(0|r)==(0|c)?1:r-c>>1)>>>0>=1073741824)break e;if(h=(g=Es(i=l<<2))+i|0,i=l=(-4&l)+g|0,(0|r)!=(0|s)){if(x=-4&(r=r-s|0),I=1+((m=r-4|0)>>>2|0)&7)for(i=0,r=l;e[r>>2]=e[s>>2],s=s+4|0,r=r+4|0,(0|I)!=(0|(i=i+1|0)););else r=l;if(i=l+x|0,!(m>>>0<28))for(;e[r>>2]=e[s>>2],e[r+4>>2]=e[s+4>>2],e[r+8>>2]=e[s+8>>2],e[r+12>>2]=e[s+12>>2],e[r+16>>2]=e[s+16>>2],e[r+20>>2]=e[s+20>>2],e[r+24>>2]=e[s+24>>2],e[r+28>>2]=e[s+28>>2],s=s+32|0,(0|i)!=(0|(r=r+32|0)););}e[A+12>>2]=h,e[A+8>>2]=i,e[A+4>>2]=l,e[A>>2]=g,c&&(fe(c),i=e[A+8>>2])}else i=r;return e[i>>2]=e[t>>2],void(e[A+8>>2]=e[A+8>>2]+4)}li(),j()}function Ks(A,t){var r,s=0,i=0,l=0,c=0,g=0;i=189088,H=r=H-320|0,e[r+312>>2]=0,l=ft(A,c=PA(r+112|0,189088),r,r+316|0,r+312|0,0),s=e[r+316>>2];e:if((0|t)<=3){if((0|s)<2)break e;if(l=3&(t=s-1|0),A=1,s-2>>>0>=3)for(g=-4&t,t=0;f[0|(s=A+r|0)]>=4&&(f[0|s]=3),f[(s=A+r|0)+1|0]>=4&&(f[s+1|0]=3),f[s+2|0]>=4&&(f[s+2|0]=3),f[s+3|0]>=4&&(f[s+3|0]=3),A=A+4|0,(0|g)!=(0|(t=t+4|0)););if(!l)break e;for(t=0;f[0|(s=A+r|0)]>=4&&(f[0|s]=3),A=A+1|0,(0|l)!=(0|(t=t+1|0)););}else if(A=1,!((0|s)<=1)){for(;;){if((0|l)>f[0|(g=A+r|0)]){if((0|s)!=(0|(A=A+1|0)))continue;break e}break}f[0|g]=t}if(A=d[0|c])for(t=1;s=e[144464+((255&A)<<2)>>2],d[s+11|0]!=2|16&d[s+6|0]||(l=255&(s=f[t+r|0]),(0|s)<2&&l||(f[0|i]=d[l+94151|0],i=i+1|0,A=d[0|c]),t=t+1|0),f[0|i]=A,i=i+1|0,A=d[0|(c=c+1|0)];);f[0|i]=0,H=r+320|0}function rn(A){var t=0,r=0,s=0,i=0,l=0,c=0;r=t=e[(A|=0)>>2],e[A>>2]=t+1;e:{A:{r:{a:{n:{o:{c:switch(((l=d[0|t])>>>4|0)-8|0){case 0:case 1:case 2:case 3:break A;case 7:break n;case 6:break o;case 4:case 5:break c;default:break e}if((s=t+2|0)>>>0>=(i=e[A+4>>2])>>>0)break a;if(e[A>>2]=s,(192&(r=d[r+1|0]))!=128)break r;return 63&r|l<<6&1984}if((s=t+3|0)>>>0>=(i=e[A+4>>2])>>>0)break a;if(r=t+2|0,e[A>>2]=r,(192&(t=d[t+1|0]))!=128){s=r;break r}if(e[A>>2]=s,(192&(r=d[0|r]))!=128)break r;return 63&r|(63&t|l<<6&960)<<6}if(!((i=e[A+4>>2])>>>0<=(r=t+4|0)>>>0)){if(s=t+2|0,e[A>>2]=s,(192&(i=d[t+1|0]))!=128||(s=t+3|0,e[A>>2]=s,(192&(c=d[t+2|0]))!=128)||(e[A>>2]=r,t=d[0|s],s=r,(192&t)!=128))break r;return 0|((A=63&t|c<<6&4032|(63&i|l<<6&960)<<12)>>>0>=1114112?65533:A)}}e[A>>2]=i;break A}e[A>>2]=s-1}l=65533}return 0|l}function Oe(A,t,r,s){var i,l,c=0,g=0,m=0,I=0,h=0,x=0;if(H=i=H-432|0,!(!s|!(536870912&(l=oA(A,t,r,s))))&&(k[i+48>>1]=8192,s=PA(i+48|2,s),d[0|s])){for(m=i+224|0,c=1,I=200;;){if(jA(i+44|0,s),t=Gs(e[i+44>>2]),g=e[r>>2],t?(e[r>>2]=2|g,Cr(Ps(e[i+44>>2]),s)):e[r>>2]=-3&g,x=e[33264],oA(A,s,r,0),1&c?(e[i+16>>2]=189088,g=zs(m,I,84130,i+16|0)):(e[i+32>>2]=15,e[i+36>>2]=189088,g=zs(m,I,84434,i+32|0)),c=(t=e[33264])+1|0,e[33264]=c,t>>>0<=2147483646){for(;;)if(t=s,s=s+1|0,(0|(h=f[0|t]))==32|h-9>>>0<5){for(;t=(s=t)+1|0,(0|(h=f[0|s]))==32|h-9>>>0<5;);if(c=c-1|0,e[33264]=c,!((0|c)>0))break}}if(m=m+g|0,e[33264]=x,!(d[0|s]&&(c=0,(0|(I=I-g|0))>1)))break}(i+224|0)!=(0|m)&&(e[i>>2]=i+224,zs(189088,200,84130,i))}return H=i+432|0,l}function X(A,t,r,s,i,l,c,g){var m,I;m=e[32972],I=e[m+116>>2],k[A+8>>1]=y[A+8>>1]+l,l=32&g?0-l|0:l,k[A+10>>1]=l+y[A+10>>1],k[A+12>>1]=l+y[A+12>>1],s=(0|s)>(0|(l=((l=(0|O(t,I))/256|0)-(t=k[A+6>>1])|0)/2|0))?l:s,k[A+6>>1]=((0|r)<(0|s)?s:r)+t;e:{A:switch(i-1|0){case 0:t=(0|(t=235-(r=k[A+4>>1])|0))<=-100?-100:t,k[A+4>>1]=((0|t)>=-60?-60:t)+r;break e;case 1:t=(0|(t=(0|(t=235-(r=k[A+4>>1])|0))<=-300?-300:t))>=-150?-150:t,k[A+4>>1]=t+r,k[A+2>>1]=t+y[A+2>>1];break e;case 2:break A;default:break e}t=(0|(t=(0|(t=100-(r=k[A+4>>1])|0))<=-400?-400:t))>-300?-400:t,k[A+4>>1]=t+r,k[A+2>>1]=t+y[A+2>>1]}e[m+132>>2]||(f[A+20|0]=(O(d[A+20|0],c)>>>0)/100,f[A+21|0]=(O(d[A+21|0],c)>>>0)/100,f[A+22|0]=(O(d[A+22|0],c)>>>0)/100,f[A+23|0]=(O(d[A+23|0],c)>>>0)/100,f[A+24|0]=(O(d[A+24|0],c)>>>0)/100,f[A+25|0]=(O(d[A+25|0],c)>>>0)/100)}function ge(A){var t,r=0,s=0,i=0,l=0;H=t=H-48|0;e:{if(A){d[0|A]||(A=ia(84285),d[0|A]&&A||(A=ia(121696),d[0|A]&&A||(A=ia(84614),d[0|A]&&A||(A=84891))));A:{for(;;){if(!(!(s=d[A+r|0])|(0|s)==47)){if(i=23,(0|(r=r+1|0))!=23)continue;break A}break}i=r}s=84891;A:{r:{if(r=d[0|A],(d[A+i|0]|(0|r)==46||(s=A,(0|r)==67))&&!d[s+1|0]||!Ar(s,84891)||!Ar(s,85136)){if(r=121652,d[s+1|0]==46)break r;A=0;break A}if(r=e[56851])for(;;){if(!Ar(s,r+8|0))break r;if(!(r=e[r+32>>2]))break}(A=HA(36))&&(r=e[30414],e[A>>2]=e[30413],e[A+4>>2]=r,qA(r=A+8|0,s,i),f[r+i|0]=0,e[A+32>>2]=e[56851],e[56851]=A),r=A||121652}A=r}if((0|A)==-1)break e;e[56809]=A}else A=e[56809];l=A?A+8|0:84309}return H=t+48|0,l}function Ie(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0;m=!!((t=e[A+112>>2])|(s=e[A+116>>2])),i=t,c=t=(l=e[A+4>>2])-(g=e[A+44>>2])|0,r=t+e[A+120>>2]|0,t=e[A+124>>2]+(t>>31)|0;e:{if(!(((0|(t=r>>>0<c>>>0?t+1|0:t))>=(0|s)&r>>>0>=i>>>0|(0|t)>(0|s))&m)){if((0|(m=gn(A)))>=0)break e;l=e[A+4>>2],g=e[A+44>>2]}return e[A+112>>2]=-1,e[A+116>>2]=-1,e[A+104>>2]=l,s=(c=r)+(r=g-l|0)|0,t=(r>>31)+t|0,e[A+120>>2]=s,e[A+124>>2]=r>>>0>s>>>0?t+1|0:t,-1}return t=(s=r+1|0)?t:t+1|0,l=e[A+4>>2],g=e[A+8>>2],c=i=e[A+116>>2],i|(r=e[A+112>>2])&&(i=r-s|0,(0|(r=c-(t+(r>>>0<s>>>0)|0)|0))>=(0|(c=(I=g-l|0)>>31))&i>>>0>=I>>>0|(0|r)>(0|c)||(g=i+l|0)),e[A+104>>2]=g,s=(i=(r=e[A+44>>2])-l|0)+s|0,t=(i>>31)+t|0,e[A+120>>2]=s,e[A+124>>2]=s>>>0<i>>>0?t+1|0:t,r>>>0>=l>>>0&&(f[l-1|0]=m),m}function Be(A,t,r){var s=0,i=0;e:if((0|A)!=(0|t)){if(t-(i=A+r|0)>>>0<=0-(r<<1)>>>0)return qA(A,t,r);if(s=3&(A^t),A>>>0<t>>>0){if(s)s=A;else{if(3&A)for(s=A;;){if(!r)break e;if(f[0|s]=d[0|t],t=t+1|0,r=r-1|0,!(3&(s=s+1|0)))break}else s=A;if(!(r>>>0<=3))for(;e[s>>2]=e[t>>2],t=t+4|0,s=s+4|0,(r=r-4|0)>>>0>3;);}if(r)for(;f[0|s]=d[0|t],s=s+1|0,t=t+1|0,r=r-1|0;);}else{if(!s){if(3&i)for(;;){if(!r)break e;if(f[0|(s=(r=r-1|0)+A|0)]=d[t+r|0],!(3&s))break}if(!(r>>>0<=3))for(;e[(r=r-4|0)+A>>2]=e[t+r>>2],r>>>0>3;);}if(!r)break e;for(;f[(r=r-1|0)+A|0]=d[t+r|0],r;);}}return A}function Ve(A,t,r,s){e:switch(t-9|0){case 0:return t=e[r>>2],e[r>>2]=t+4,void(e[A>>2]=e[t>>2]);case 6:return t=e[r>>2],e[r>>2]=t+4,t=k[t>>1],e[A>>2]=t,void(e[A+4>>2]=t>>31);case 7:return t=e[r>>2],e[r>>2]=t+4,e[A>>2]=y[t>>1],void(e[A+4>>2]=0);case 8:return t=e[r>>2],e[r>>2]=t+4,t=f[0|t],e[A>>2]=t,void(e[A+4>>2]=t>>31);case 9:return t=e[r>>2],e[r>>2]=t+4,e[A>>2]=d[0|t],void(e[A+4>>2]=0);case 16:return t=e[r>>2]+7&-8,e[r>>2]=t+8,void(P[A>>3]=P[t>>3]);case 17:$A[0|s](A,r);default:return;case 1:case 4:case 14:return t=e[r>>2],e[r>>2]=t+4,t=e[t>>2],e[A>>2]=t,void(e[A+4>>2]=t>>31);case 2:case 5:case 11:case 15:return t=e[r>>2],e[r>>2]=t+4,e[A>>2]=e[t>>2],void(e[A+4>>2]=0);case 3:case 10:case 12:case 13:break e}t=e[r>>2]+7&-8,e[r>>2]=t+8,r=e[t+4>>2],e[A>>2]=e[t>>2],e[A+4>>2]=r}function tA(A,t,r,s,i,l){var c;H=c=H-80|0;e:if((0|l)>=16384){if(Et(c+32|0,t,r,s,i,0,0,0,2147352576),s=e[c+40>>2],i=e[c+44>>2],t=e[c+32>>2],r=e[c+36>>2],l>>>0<32767){l=l-16383|0;break e}Et(c+16|0,t,r,s,i,0,0,0,2147352576),l=((0|l)>=49149?49149:l)-32766|0,s=e[c+24>>2],i=e[c+28>>2],t=e[c+16>>2],r=e[c+20>>2]}else(0|l)>-16383||(Et(c- -64|0,t,r,s,i,0,0,0,7471104),s=e[c+72>>2],i=e[c+76>>2],t=e[c+64>>2],r=e[c+68>>2],l>>>0>4294934644?l=l+16269|0:(Et(c+48|0,t,r,s,i,0,0,0,7471104),l=((0|l)<=-48920?-48920:l)+32538|0,s=e[c+56>>2],i=e[c+60>>2],t=e[c+48>>2],r=e[c+52>>2]));Et(c,t,r,s,i,0,0,0,l+16383<<16),t=e[c+12>>2],e[A+8>>2]=e[c+8>>2],e[A+12>>2]=t,t=e[c+4>>2],e[A>>2]=e[c>>2],e[A+4>>2]=t,H=c+80|0}function DA(A,t){var r,s,i=0;H=r=H+-64|0,i=e[A>>2],s=e[i-4>>2],i=e[i-8>>2],e[r+32>>2]=0,e[r+36>>2]=0,e[r+40>>2]=0,e[r+44>>2]=0,e[r+48>>2]=0,e[r+52>>2]=0,f[r+55|0]=0,f[r+56|0]=0,f[r+57|0]=0,f[r+58|0]=0,f[r+59|0]=0,f[r+60|0]=0,f[r+61|0]=0,f[r+62|0]=0,e[r+24>>2]=0,e[r+28>>2]=0,e[r+20>>2]=0,e[r+16>>2]=125084,e[r+12>>2]=A,e[r+8>>2]=t,A=A+i|0,i=0;e:if(ca(s,t,0))e[r+56>>2]=1,$A[e[e[s>>2]+20>>2]](s,r+8|0,A,A,1,0),i=e[r+32>>2]==1?A:0;else{$A[e[e[s>>2]+24>>2]](s,r+8|0,A,1,0);A:switch(e[r+44>>2]){case 0:i=e[r+48>>2]==1&&e[r+36>>2]==1&&e[r+40>>2]==1?e[r+28>>2]:0;break e;case 1:break A;default:break e}e[r+32>>2]!=1&&e[r+48>>2]|e[r+36>>2]!=1|e[r+40>>2]!=1||(i=e[r+24>>2])}return H=r- -64|0,i}function vA(A,t,r,s,i){var l,c,g=0;H=l=H-80|0,k[l+72>>1]=0,e[l+64>>2]=0,e[l+68>>2]=0,f[0|s]=0,g=Cr(t,c=2|(g=l- -64|0))+g|0,f[g+2|0]=32;e:if((0|r)!=-1)t>>>0>=33&&!fr(t)?(f[g+3|0]=(0|r)==32?32:31,f[l+65|0]=95,QA(A,l- -64|1,l+16|0)||(f[l+65|0]=32,QA(A,c,l+16|0)||VA(A,c,l+16|0,40,0,268435456,0)),d[l+16|0]||bA(A,t,l+16|0),t=PA(s,l+16|0),!(r=d[0|t])|(0|r)==21||(e[l+56>>2]=0,e[l+60>>2]=0,Vt(A,t,l+56|0,-1,1&i))):(e[l>>2]=t,dA(t=l- -64|1,85485,l),QA(A,t,s));else{if(QA(A,c,s)||(f[l+65|0]=95,QA(A,l- -64|1,l+16|0)|e[A+212>>2]==25966))break e;Rn(85055),QA(e[47194],c,l+16|0)&&(f[0|s]=21,f[s+1|0]=0),as(e[e[32972]+60>>2])}H=l+80|0}function Je(A,t,r){var s=0,i=0,l=0,c=0;if(r&&(f[0|A]=t,f[(s=A+r|0)-1|0]=t,!(r>>>0<3||(f[A+2|0]=t,f[A+1|0]=t,f[s-3|0]=t,f[s-2|0]=t,r>>>0<7||(f[A+3|0]=t,f[s-4|0]=t,r>>>0<9||(i=(s=0-A&3)+A|0,t=O(255&t,16843009),e[i>>2]=t,e[(r=(s=r-s&-4)+i|0)-4>>2]=t,s>>>0<9||(e[i+8>>2]=t,e[i+4>>2]=t,e[r-8>>2]=t,e[r-12>>2]=t,s>>>0<25||(e[i+24>>2]=t,e[i+20>>2]=t,e[i+16>>2]=t,e[i+12>>2]=t,e[r-16>>2]=t,e[r-20>>2]=t,e[r-24>>2]=t,e[r-28>>2]=t,(r=s-(c=4&i|24)|0)>>>0<32))))))))for(s=st(t,0,1,1),l=le,t=i+c|0;e[t+24>>2]=s,e[t+28>>2]=l,e[t+16>>2]=s,e[t+20>>2]=l,e[t+8>>2]=s,e[t+12>>2]=l,e[t>>2]=s,e[t+4>>2]=l,t=t+32|0,(r=r-32|0)>>>0>31;);return A}function kA(){var A,t=0,r=0,s=0,i=0,l=0;if(H=A=H-208|0,(0|(s=e[50303]))>0)for(;(i=e[(r=201216+(t<<2)|0)>>2])&&(fe(i),e[r>>2]=0),(0|s)!=(0|(t=t+1|0)););if(e[50303]=0,e[A+16>>2]=137584,e[A+20>>2]=47,dA(t=A+32|0,87827,A+16|0),nt(t,MA(t)+1|0,0),e[A+4>>2]=47,e[A>>2]=137584,dA(t,87933,A),nt(t,MA(t)+1|0,1),t=e[50303],e[(r=t<<2)+201216>>2]=0,r=lt(s=e[50741],r+4|0)){if(e[50741]=r,ta(201216,t,7),s=e[50741],r=0,t=e[50304])for(i=0;l=e[t+4>>2],d[0|l]&&Ar(l+1|0,86589)&&Kr(e[t+8>>2],88032,3)&&(e[(r<<2)+s>>2]=t,r=r+1|0),t=e[201216+((i=i+1|0)<<2)>>2];);e[(r<<2)+s>>2]=0}return H=A+208|0,s}function gt(A,t,r){var s=0,i=0,l=0,c=0;if(l=t-1|0,(0|t)>=2){t=A;e:{for(;;){A:{r:{if((0|(s=e[r+4>>2]))!=(0|(i=e[r+8>>2]))){if((c=Ba(s,10,i-s|0))?i=1+(c-(s=e[r+4>>2])|0)|0:(s=e[r+4>>2],i=e[r+8>>2]-s|0),qA(t,s,s=i>>>0<l>>>0?i:l),i=s+e[r+4>>2]|0,e[r+4>>2]=i,t=t+s|0,c||!(l=l-s|0))break A;if((0|i)!=e[r+8>>2]){e[r+4>>2]=i+1,s=d[0|i];break r}}if(!((0|(s=gn(r)))>=0)){if(s=0,(0|A)==(0|t))break e;if(16&d[0|r])break A;break e}}if(f[0|t]=s,t=t+1|0,(255&s)!=10&&(l=l-1|0))continue}break}A?(f[0|t]=0,s=A):s=0}}else if(t=e[r+72>>2],e[r+72>>2]=t-1|t,!l)return f[0|A]=0,A;return s}function dt(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0;if(t=O(A,44),(0|(A=e[t+137896>>2]))>0&&dt(A-1|0),A=e[36115],!((0|(t=e[(r=t+137856|0)+36>>2]))<=0)){if(i=e[r+32>>2],g=1&t,(0|t)!=1)for(m=-2&t,r=0;t=d[(l=(s=r<<4)+i|0)+10|0],e[144464+(t<<2)>>2]=l,(0|A)>=(0|t)?t=A:Je(144464+((A=A+1|0)<<2)|0,0,t-A<<2),A=d[(s=(16|s)+i|0)+10|0],e[144464+(A<<2)>>2]=s,(0|A)<=(0|t)?A=t:Je(144464+((t=t+1|0)<<2)|0,0,A-t<<2),r=r+2|0,(0|m)!=(0|(c=c+2|0)););else r=0;g&&(t=d[(r=(r<<4)+i|0)+10|0],e[144464+(t<<2)>>2]=r,(0|A)>=(0|t)||(Je(144464+((A=A+1|0)<<2)|0,0,t-A<<2),A=t))}e[36115]=A}function ur(A,t,r,s,i){var l,c=0,g=0,m=0;H=l=H-16|0;e:if(1&f[A+106|0]&&(c=d[0|r],!(!(1&f[s+2|0])&(0|c)!=46||256&(m=e[s+12>>2])|!(!(2&m)||i)||(jA(l+12|0,(0|c)!=46?r:r+2|0),!(c=d[0|r])|!d[r+1|0])))){if(!(!(m=e[l+12>>2])|2&d[s+2|0])){if(!Ft(m))break e;c=d[0|r]}(0|c)==46&&(f[0|r]=32),g=2,e[A+212>>2]!=26741|i||(r=Ft(e[l+12>>2])?Oe(A,r+2|0,0,0):0,128&d[A+8233|0]&&(!(!(i=e[l+12>>2])|2&d[s+2|0])&i-48>>>0>=10||(g=0)),g=32768&r?0:g,131072&r&&(g=163840&e[A+8232>>2]?34:d[t-2|0]!=45?g:0))}return H=l+16|0,g}function Gr(A,t,r,s,i){var l,c=0,g=0;if(H=l=H-208|0,e[l+204>>2]=r,Je(r=l+160|0,0,40),e[l+200>>2]=e[l+204>>2],(0|Bs(0,t,l+200|0,l+80|0,r,s,i))<0)i=-1;else{e[A+76>>2]>=0,c=e[A>>2],e[A+72>>2]<=0&&(e[A>>2]=-33&c);e:{A:{if(e[A+48>>2]){if(e[A+16>>2])break A}else e[A+48>>2]=80,e[A+28>>2]=0,e[A+16>>2]=0,e[A+20>>2]=0,g=e[A+44>>2],e[A+44>>2]=l;if(r=-1,qa(A))break e}r=Bs(A,t,l+200|0,l+80|0,l+160|0,s,i)}g&&($A[e[A+36>>2]](A,0,0),e[A+48>>2]=0,e[A+44>>2]=g,e[A+28>>2]=0,t=e[A+20>>2],e[A+16>>2]=0,e[A+20>>2]=0,r=t?r:-1),t=A,A=e[A>>2],e[t>>2]=A|32&c,i=32&A?-1:r}return H=l+208|0,i}function nr(A,t,r,s){var i,l=0,c=0,g=0,m=0,I=0,h=0,x=0;if(H=i=H-208|0,c=d[0|t])for(;f[l+i|0]=c,m=((255&c)==6&(0|g)!=21)+m|0,g=c<<24>>24,c=d[(l=l+1|0)+t|0];);if(f[l+i|0]=0,l=d[0|i])for(h=m-2|0,g=0,x=(0|s)<2,c=0;;){e:{A:if((255&l)!=6|x|(0|c)==21){if((0|(s=255&l))==255){if(!I|(0|r)<2)break e;s=r>>>0>2?11:(0|g)%3|0?23:11}c=s,s=g}else{if(l=g+1|0,d[A+169|0]){c=(0|l)>1?5:6,s=l;break A}if(c=6,s=m,(0|l)==(0|m))break A;c=(0|l)%3|0||(0|g)==(0|h)?5:6,s=l}g=s,f[0|t]=c,t=t+1|0}if(!(l=d[(I=I+1|0)+i|0]))break}(0|r)>=2&&(f[0|t]=11,t=t+1|0),f[0|t]=0,H=i+208|0}function _t(A,t){var r=0;r=0,A&&(r=e[50754],r=(A=(A=(0|O(e[145712+(t?12:((0|A)>199)<<2)>>2],A))/256|0)>>>0>(t=e[36430])>>>0?A:t)>>>0<=89999?(O(A,r)>>>0)/1e3|0:(O(A,(0|r)/25|0)>>>0)/40|0),(0|(A=e[36440]))<=0||(0|(t=e[36424]))<0||(e[(t=216192+(t<<4)|0)+4>>2]||(e[t+4>>2]=A),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],xs(),e[36427]=-1,A=216192+(e[50758]<<4)|0,e[A>>2]=5,e[A+4>>2]=r,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,e[36426]=0,e[36438]&&(e[36438]=0,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=0,A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0)}function Gt(A,t,r,s,i,l,c,g){var m,I=0,h=0,x=0;I=1,m=h=2147483647&s;e:if(!((x=(0|h)==2147418112)&!r?A|t:x&!!(0|r)|h>>>0>2147418112)&&!((x=(0|(h=2147483647&g))==2147418112)&!c?i|l:x&!!(0|c)|h>>>0>2147418112)){if(!(A|i|r|c|t|l|h|m))return 0;if((0|(I=s&g))>0|(0|I)>=0){if(I=-1,(0|r)==(0|c)&(0|s)==(0|g)?(0|t)==(0|l)&A>>>0<i>>>0|t>>>0<l>>>0:r>>>0<c>>>0&(0|s)<=(0|g)|(0|s)<(0|g))break e;return!!(A^i|r^c|t^l|s^g)}I=-1,((0|r)==(0|c)&(0|s)==(0|g)?(0|t)==(0|l)&A>>>0>i>>>0|t>>>0>l>>>0:r>>>0>c>>>0&(0|s)>=(0|g)|(0|s)>(0|g))||(I=!!(A^i|r^c|t^l|s^g))}return I}function jr(A,t){var r=0,s=0,i=0,l=0;for(r=d[85836]|d[85837]<<8,f[0|t]=r,f[t+1|0]=r>>>8,f[t+2|0]=d[85838];;)if(i=d[0|A],A=r=A+1|0,(0|i)!=255){if(!i)break;if(!(s=e[144464+(i<<2)>>2]))continue;if(d[s+11|0]==1&&(l=d[s+14|0],!(y[s+8>>1]|l>>>0>4))){if(l>>>0<2)continue;f[0|t]=d[l+93943|0],t=t+1|0;continue}if(255&(A=e[s>>2]))for(;f[0|t]=A,t=t+1|0,s=65280&A,A=A>>>8|0,s;);if(A=r,(0|i)!=21||(32|(r=f[0|A]))-97>>>0>=26)continue;for(;f[0|t]=r,t=t+1|0,(32|(r=f[0|(A=A+1|0)]))-97>>>0<26;);}f[0|t]=0}function ks(A,t){var r,s,i=0,l=0,c=0;H=r=H-112|0,A||(e[50303]||kA(),A=201216),Lt(i=r+16|0,t,40),e[r>>2]=47,e[r+4>>2]=i,dA(t=r- -64|0,87599,r),s=MA(t),c=-1;e:{A:{r:{if(l=e[A>>2]){t=0,i=-1;a:{for(;;){if(an(r+16|0,e[l>>2])){if(an(r+16|0,l=e[l+8>>2])?c=an(r- -64|0,l+(MA(l)-s|0)|0)?c:t:i=t,l=e[((t=t+1|0)<<2)+A>>2])continue;break a}break}if((0|t)>=0)break A;t=(0|i)<0?c:i;break r}if((0|(t=i))>=0)break r}t=c}if(i=0,(0|t)<0)break e}i=e[(t<<2)+A>>2]}return H=r+112|0,i}function qt(A,t){var r,s,i=0,l=0,c=0,g=0,m=0;H=r=H-16|0,F(+t),s=0|B(1),l=0|B(0),(0|(c=(i=2147483647&s)+-1048576|0))==2145386495|c>>>0<2145386495?(g=l<<28,c=i>>>4|0,i=(15&i)<<28|l>>>4,l=c+1006632960|0):(0|i)==2146435072|i>>>0>2146435072?(g=l<<28,i=(15&s)<<28|l>>>4,l=s>>>4|2147418112):i|l?(vt(r,l,c=i,0,0,(i=i?be(i):be(l)+32|0)+49|0),m=e[r>>2],g=e[r+4>>2],c=15372-i<<16,i=e[r+8>>2],l=c|65536^e[r+12>>2]):(i=0,l=0),e[A>>2]=m,e[A+4>>2]=g,e[A+8>>2]=i,e[A+12>>2]=-2147483648&s|l,H=r+16|0}function _s(A,t,r){var s=0,i=0;e:{A:{r:{if(!(3&((i=A)^t))){s=!!(0|r);a:if(!(!(3&t)|!r))for(;;){if(s=d[0|t],f[0|i]=s,!s)break e;if(i=i+1|0,s=!!(0|(r=r-1|0)),!(3&(t=t+1|0)))break a;if(!r)break}if(!s)break A;if(!d[0|t])break e;if(!(r>>>0<4))for(;;){if(~(s=e[t>>2])&s-16843009&-2139062144)break r;if(e[i>>2]=s,i=i+4|0,t=t+4|0,!((r=r-4|0)>>>0>3))break}}if(!r)break A}for(;;){if(s=d[0|t],f[0|i]=s,!s)break e;if(i=i+1|0,t=t+1|0,!(r=r-1|0))break}}r=0}return Je(i,0,r),A}function $t(A,t,r,s,i,l){var c,g=0,m=0,I=0;H=c=H-240|0,g=e[r>>2],e[c+232>>2]=g,r=e[r+4>>2],e[c>>2]=A,e[c+236>>2]=r,I=1;e:{A:{r:{if(r|(0|g)!=1){for(g=A;;){if((0|An(r=g-e[(m=(s<<2)+l|0)>>2]|0,A,t))<=0){r=g;break r}a:{if(!((0|s)<2|i)&&(i=e[m-8>>2],(0|An(m=g-4|0,r,t))>=0||(0|An(m-i|0,r,t))>=0))break a;if(e[(I<<2)+c>>2]=r,Nn(g=c+232|0,i=gA(g)),I=I+1|0,s=s+i|0,i=0,g=r,e[c+236>>2]|e[c+232>>2]!=1)continue;break A}break}r=g;break A}r=A}if(i)break e}si(c,I),Ya(r,t,s,l)}H=c+240|0}function lr(A,t){var r=0;e[4+((r=A<<2)+134912|0)>>2]=t,e[r+136192>>2]=t,r=28;e:{A:{r:switch(A-1|0){case 0:e[50792]=t,e[50786]=t,ws(3);break A;case 1:e[50787]=t,e[33037]=(0|O(d[e[50797]+105596|0],(0|O(e[50787],55))/100|0))/16;break A;case 2:A=(0|t)>=99?99:t,e[50785]=(0|A)>0?A:0;break A;case 3:e[50788]=(0|t)>=99?99:t;break A;case 12:e[47268]=t;break A;case 6:e[47205]=t;break A;case 9:break A;case 8:break r;default:break e}(A=255&t)&&(e[e[47192]+152>>2]=A),e[47196]=t}r=0}return r}function yt(A,t){var r=0,s=0,i=0;e:if(e[A>>2])for(;;){if(fr(e[A-4>>2])){if(s=0,(0|(r=f[0|t]))==e[A>>2])for(;(0|(r=f[(s=s+1|0)+t|0]))==e[(A=A+4|0)>>2];);if(!r){for(;t=A,A=A+4|0,fr(e[t>>2]););for(i=t+((e[t>>2]==61)<<2)|0;i=(A=i)+4|0,fr(e[A>>2]););A:switch((t=e[A>>2])-34|0){case 0:case 5:break e;default:break A}return fr(t)||e[A>>2]==47?102808:A}}if(!e[(A=A+4|0)>>2])break}return i}function dr(A,t,r,s){var i,l,c=0,g=0,m=0;return!d[A+25|0]|P[A+8>>3]!=r|P[A+16>>3]!=s?(P[A+16>>3]=s,P[A+8>>3]=r,s=(c=$r(-3.141592653589793/(g=+e[A>>2])*s))*-c,P[A+48>>3]=s,c*=ds(-6.283185307179586/g*r),c+=c,P[A+40>>3]=c,g=1-c-s,P[A+32>>3]=g,!(m=d[A+24|0])|r==0||(g=1/g,P[A+32>>3]=g,s*=r=-g,P[A+48>>3]=s,c*=r,P[A+40>>3]=c,m=1)):(m=d[A+24|0],s=P[A+48>>3],c=P[A+40>>3],g=P[A+32>>3]),f[A+25|0]=1,r=P[A+64>>3],i=P[A+56>>3],P[A+64>>3]=i,l=t,t=s*r+(g*t+c*i),P[A+56>>3]=m?l:t,t}function Xs(A,t,r,s,i){var l=0,c=0,g=0,m=0,I=0;if((g=e[34388])&&!((0|(c=e[34436]))>=(e[34393]-2|0))){if(e[34436]=c+1,l=O(c,36)+g|0,e[l>>2]=A,e[l+4>>2]=e[34437],I=e[34438],e[l+12>>2]=t>>>24,e[l+8>>2]=16777215&t,e[l+24>>2]=I,t=e[50754],i=e[34439]+((i-e[34392]|0)/2|0)|0,e[l+20>>2]=i,t=ee(m=1e3*+(0|i)/+(0|t))<2147483648?~~m:-2147483648,e[l+16>>2]=t,A-3>>>0<=1)return void(e[28+(O(c,36)+g|0)>>2]=e[33282]+r);t=28+(O(c,36)+g|0)|0,e[t>>2]=r,(0|A)==7&&(e[t+4>>2]=s)}}function cr(A,t,r,s,i,l){var c=0,g=0,m=0,I=0;64&l?(t=31&(r=l+-64|0),(63&r)>>>0>=32?(r=0,t=i>>>t|0):(r=i>>>t|0,t=((1<<t)-1&i)<<32-t|s>>>t),s=0,i=0):l&&(m=s,c=31&(g=64-l|0),(63&g)>>>0>=32?(g=m<<c,I=0):(g=(1<<c)-1&m>>>32-c|i<<c,I=m<<c),m=t,t=31&l,(63&l)>>>0>=32?(c=0,t=r>>>t|0):(c=r>>>t|0,t=((1<<t)-1&r)<<32-t|m>>>t),t|=I,r=c|g,c=s,s=31&l,(63&l)>>>0>=32?(g=0,s=i>>>s|0):(g=i>>>s|0,s=((1<<s)-1&i)<<32-s|c>>>s),i=g),e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=s,e[A+12>>2]=i}function Ms(A){var t=0,r=0;if(!A){if(e[33174]&&(t=Ms(e[33174])),e[33136]&&(t=Ms(e[33136])|t),A=e[56816])for(;e[A+20>>2]!=e[A+28>>2]&&(t=Ms(A)|t),A=e[A+56>>2];);return t}return e[A+76>>2]>=0,e[A+20>>2]==e[A+28>>2]||($A[e[A+36>>2]](A,0,0),e[A+20>>2])?((0|(t=e[A+8>>2]))!=(0|(r=e[A+4>>2]))&&(t=r-t|0,$A[e[A+40>>2]](A,t,t>>31,1)),t=0,e[A+28>>2]=0,e[A+16>>2]=0,e[A+20>>2]=0,e[A+4>>2]=0,e[A+8>>2]=0):t=-1,t}function Mr(A,t){var r=0,s=0,i=0,l=0,c=0;if((192&(r=d[0|t]))==128)for(;(192&(r=d[0|(t=t-1|0)]))==128;);e:if(128&(r=r<<24>>24)){if(s=1,(0|(i=224&r))!=192)if((240&r)!=224){if(s=3,(248&r)!=240){r&=255,s=0;break e}}else s=2,c=1;r=d[s+93846|0]&r,(l=d[t+1|0])?(r=63&l|r<<6,(0|i)!=192&&((i=d[t+2|0])?(r=63&i|r<<6,c||((t=d[t+3|0])?r=63&t|r<<6:s=2)):s=1)):s=0}return e[A>>2]=r,s+1|0}function er(A,t,r,s){var i,l=0,c=0;return H=i=H-224|0,A?(e[i>>2]=137584,e[i+4>>2]=47,e[i+8>>2]=t,dA(t=i+16|0,85430,i),(0|(l=Ns(t)))<0?t=Ts(s,0-l|0,i+16|0):(t=us(i+16|0,85659))?((c=e[A>>2])&&fe(c),l?(c=HA(l),e[A>>2]=c,c?(0|ri(c,l,t))==(0|l)?(Er(t),t=0,r&&(e[r>>2]=l)):(r=e[56798],Er(t),fe(e[A>>2]),e[A>>2]=0,t=Ts(s,r,i+16|0)):(Er(t),t=48)):(t=0,e[A>>2]=0)):t=Ts(s,e[56798],i+16|0)):t=28,H=i+224|0,t}function jA(A,t){var r=0,s=0,i=0,l=0,c=0;if((192&(r=d[0|t]))==128)for(;(192&(r=d[0|(t=t+1|0)]))==128;);e:if(128&(r=r<<24>>24)){if(s=1,(0|(i=224&r))!=192)if((240&r)!=224){if(s=3,(248&r)!=240){r&=255,s=0;break e}}else s=2,c=1;r=d[s+93846|0]&r,(l=d[t+1|0])?(r=63&l|r<<6,(0|i)!=192&&((i=d[t+2|0])?(r=63&i|r<<6,c||((t=d[t+3|0])?r=63&t|r<<6:s=2)):s=1)):s=0}return e[A>>2]=r,s+1|0}function vt(A,t,r,s,i,l){var c=0,g=0,m=0;64&l?(s=t,t=31&(i=l+-64|0),(63&i)>>>0>=32?(i=s<<t,s=0):(i=(1<<t)-1&s>>>32-t|r<<t,s<<=t),t=0,r=0):l&&(c=s,s=31&l,(63&l)>>>0>=32?(g=c<<s,m=0):(g=(1<<s)-1&c>>>32-s|i<<s,m=c<<s),c=t,s=31&(i=64-l|0),(63&i)>>>0>=32?(i=0,t=r>>>s|0):(i=r>>>s|0,t=((1<<s)-1&r)<<32-s|c>>>s),s=m|t,i|=g,t=31&l,(63&l)>>>0>=32?(g=c<<t,t=0):(g=(1<<t)-1&c>>>32-t|r<<t,t=c<<t),r=g),e[A>>2]=t,e[A+4>>2]=r,e[A+8>>2]=s,e[A+12>>2]=i}function Qr(A){var t=0;return A>>>0<=55295?t=d[e[125552+(A>>>6&67108860)>>2]+(255&A)|0]:(t=4,A>>>0<57344||(A>>>0<63488?t=3:A>>>0<=195327?t=d[e[126416+(A-63488>>>6&67108860)>>2]+(255&A)|0]:(t=2,A>>>0<917504||(A>>>0<=918015?t=d[e[128476+(A-917504>>>6&67108860)>>2]+(255&A)|0]:A>>>0<983040||(A>>>0<1048574?t=3:A>>>0<1048576||(t=3,A>>>0<1114110||(t=A>>>0<1114112?2:5))))))),255&t}function xa(A,t){var r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0;if(!((0|(r=e[33709]))<=0)){if(i=(0|A)>31?A-32|0:A,A=0,r>>>0>=4)for(h=-4&r;g=2|A,m=1|A,s=e[134912+((c=3|A)<<6)>>2]==(0|i)?c:e[134912+(g<<6)>>2]==(0|i)?g:e[134912+(m<<6)>>2]==(0|i)?m:e[134912+(A<<6)>>2]==(0|i)?A:s,A=A+4|0,(0|h)!=(0|(l=l+4|0)););if(l=3&r)for(;s=e[134912+(A<<6)>>2]==(0|i)?A:s,A=A+1|0,(0|l)!=(0|(I=I+1|0)););(0|s)<=0||(e[33709]=s,r=s)}hr(t,r)}function sa(A){var t,r=0,s=0,i=0;for(H=t=H-96|0,Lt(t,A,60),aa(t,1);i=Ps(f[0|(s=t+r|0)]),f[0|s]=i,r=r+1|0,255&i;);e[t+92>>2]=0,e[t+84>>2]=0,e[t+88>>2]=0,e[t+76>>2]=0,e[t+80>>2]=0,e[t+72>>2]=A;e:{A:{r:{if(UA(t,1)){if(d[202976])break r;break A}if(e[50303]||kA(),r=268437247,!(A=ks(201216,t))||!UA(e[A+8>>2],0))break e;if(!d[202976])break A}UA(202976,2)}Or(e[32972]),e[t+76>>2]=e[32972]+40,sn(t+72|0,202976),r=0}return H=t+96|0,r}function yn(A,t){if(!A)return 0;e:{A:{if(A){if(t>>>0<=127)break A;if(e[e[56841]>>2]){if(t>>>0<=2047){f[A+1|0]=63&t|128,f[0|A]=t>>>6|192,A=2;break e}if(!((-8192&t)!=57344&t>>>0>=55296)){f[A+2|0]=63&t|128,f[0|A]=t>>>12|224,f[A+1|0]=t>>>6&63|128,A=3;break e}if(t-65536>>>0<=1048575){f[A+3|0]=63&t|128,f[0|A]=t>>>18|240,f[A+2|0]=t>>>6&63|128,f[A+1|0]=t>>>12&63|128,A=4;break e}}else if((-128&t)==57216)break A;e[56798]=25,A=-1}else A=1;break e}f[0|A]=t,A=1}return A}function $n(A){var t=0,r=0,s=0,i=0;if(e[A+20>>2]=0,(s=(r=e[A+8>>2])-(t=e[A+4>>2])|0)>>>0>=9)for(;fe(e[t>>2]),t=e[A+4>>2]+4|0,e[A+4>>2]=t,(s=(r=e[A+8>>2])-t|0)>>>0>8;);i=512;e:switch((s>>>2|0)-1|0){case 1:i=1024;case 0:e[A+16>>2]=i;break;default:break e}if((0|t)!=(0|r)){for(;fe(e[t>>2]),(0|r)!=(0|(t=t+4|0)););(0|(t=e[A+8>>2]))!=(0|(r=e[A+4>>2]))&&(e[A+8>>2]=t+(3+(r-t|0)&-4))}(A=e[A>>2])&&fe(A)}function Ba(A,t,r){var s=0,i=0;s=!!(0|r);e:{A:{r:if(!(!(3&A)|!r))for(i=255&t;;){if((0|i)==d[0|A])break A;if(s=!!(0|(r=r-1|0)),!(3&(A=A+1|0)))break r;if(!r)break}if(!s)break e;if(!(d[0|A]==(255&t)|r>>>0<4))for(s=O(255&t,16843009);;){if(~(i=s^e[A>>2])&i-16843009&-2139062144)break A;if(A=A+4|0,!((r=r-4|0)>>>0>3))break}if(!r)break e}for(t&=255;;){if((0|t)==d[0|A])return A;if(A=A+1|0,!(r=r-1|0))break}}return 0}function Dn(A,t){var r=0,s=0;e:{if(s=255&t){if(3&A)for(;;){if(!(r=d[0|A])|(0|r)==(255&t))break e;if(!(3&(A=A+1|0)))break}A:if(!(~(r=e[A>>2])&r-16843009&-2139062144))for(s=O(s,16843009);;){if(~(r^=s)&r-16843009&-2139062144)break A;if(r=e[A+4>>2],A=A+4|0,r-16843009&~r&-2139062144)break}for(;(s=d[0|(r=A)])&&(A=r+1|0,(0|s)!=(255&t)););return r}return MA(A)+A|0}return A}function ei(A,t,r,s,i){var l,c=0,g=0;g=-1;e:if(!(((c=(0|(l=2147483647&s))==2147418112)&!r?A|t:c&!!(0|r)|l>>>0>2147418112)||(c=2147483647&i)>>>0>2147418112&(0|c)!=2147418112)){if(!(A|r|c|l|t))return 0;if((0|(c=s&i))>0|(0|c)>=0){if((!!(0|r)|(0|s)!=(0|i))&(0|s)<(0|i))break e;return!!(A|r|s^i|t)}(!r&(0|s)==(0|i)?A|t:!!(0|r)&(0|s)>=(0|i)|(0|s)>(0|i))||(g=!!(A|r|s^i|t))}return g}function aa(A,t){var r,s=0;H=r=H+-64|0,f[202976]=0,e[r+48>>2]=47,dA(r+59|0,91351,r+48|0),t||(f[r+59|0]=0);e:{A:{if(A&&(A=Ls(A,43))){if(f[0|A]=0,f[0|(A=A+1|0)]-48>>>0>=10)break A;s=Js(A)}if((0|s)<=0)break e;if(s>>>0<=9){e[r+4>>2]=s,e[r>>2]=r+59,dA(202976,91378,r);break e}e[r+20>>2]=s-10,e[r+16>>2]=r+59,dA(202976,91503,r+16|0);break e}e[r+36>>2]=A,e[r+32>>2]=r+59,dA(202976,85425,r+32|0)}H=r- -64|0}function Wa(A){var t,r=0,s=0,i=0;H=t=H-80|0,r=Kt(A,t+12|0),e[t+12>>2]?(Lt(s=t+16|0,r,60),r=0,aa(s,1),!UA(s,0)|!d[202976]||UA(202976,2),Or(e[32972]),sn(A,86012)):r=268437247,H=t+80|0;e:{A:{r:{a:{if((0|r)<=268437502){if(!r)break e;if((0|r)==268436479)break A;if((0|r)!=268437247)break a;return 2}if((0|r)==268437503|(0|r)==268437759)break r;if((0|r)==268439295)break e}return-1}return 2}i=1}return i}function Fr(A,t,r,s,i,l,c,g,m){var I,h,x;m=st(t,r,g,m),g=le,i=st(s,i,l,c),s=le+g|0,g=i>>>0>(m=i+m|0)>>>0?s+1|0:s,I=c,h=r,c=(r=st(c,i=0,r,s=0))+m|0,m=le+g|0,x=c,r=r>>>0>c>>>0?m+1|0:m,c=st(l,0,t,0),g=le,s=st(l,m=0,h,s),l=le+m|0,l=s>>>0>(g=g+s|0)>>>0?l+1|0:l,s=r,l=l>>>0>(m=l+x|0)>>>0?s+1|0:s,r=st(t,0,I,i)+g|0,i=le,g=(i=r>>>0<g>>>0?i+1|0:i)+m|0,m=l,e[A+8>>2]=g,e[A+12>>2]=i>>>0>g>>>0?m+1|0:m,e[A>>2]=c,e[A+4>>2]=r}function PA(A,t){var r=0,s=0;e:{if(3&((s=A)^t))r=d[0|t];else{if(3&t)for(;;){if(r=d[0|t],f[0|s]=r,!r)break e;if(s=s+1|0,!(3&(t=t+1|0)))break}if(!(~(r=e[t>>2])&r-16843009&-2139062144))for(;e[s>>2]=r,r=e[t+4>>2],s=s+4|0,t=t+4|0,!(r-16843009&~r&-2139062144););}if(f[0|s]=r,255&r)for(;r=d[t+1|0],f[s+1|0]=r,s=s+1|0,t=t+1|0,r;);}return A}function ds(A){var t,r=0,s=0;H=t=H-16|0,F(+A),s=0|B(1),B(0);e:if((s&=2147483647)>>>0<=1072243195){if(r=1,s>>>0<1044816030)break e;r=Da(A,0)}else if(r=A-A,!(s>>>0>=2146435072)){A:switch(3&pr(A,t)){case 0:r=Da(P[t>>3],P[t+8>>3]);break e;case 1:r=-wa(P[t>>3],P[t+8>>3],1);break e;case 2:r=-Da(P[t>>3],P[t+8>>3]);break e;default:break A}r=wa(P[t>>3],P[t+8>>3],1)}return H=t+16|0,A=r}function Pn(A,t,r,s){var i=0,l=0,c=0;if(!((MA(s)+MA(t)|0)>=(0|r))){for(c=e[36115],r=s;i=d[0|r];)if(r=r+1|0,!((0|i)>=(0|c))){e:{A:switch(i=e[144464+(i<<2)>>2],d[i+11|0]-1|0){case 1:break e;case 0:break A;default:continue}l=d[i+14|0]<4|l;continue}1&(d[i+4|0]>>>1|l)||(e[A+8212>>2]=e[A+8212>>2]+1),e[A+8208>>2]=e[A+8208>>2]+1,l=0}t&&As(t,s)}}function na(A){var t,r=0;H=t=H-16|0,F(+A),r=0|B(1),B(0);e:if((r&=2147483647)>>>0<=1072243195){if(r>>>0<1045430272)break e;A=wa(A,0,0)}else if(r>>>0>=2146435072)A-=A;else{A:switch(3&pr(A,t)){case 0:A=wa(P[t>>3],P[t+8>>3],1);break e;case 1:A=Da(P[t>>3],P[t+8>>3]);break e;case 2:A=-wa(P[t>>3],P[t+8>>3],1);break e;default:break A}A=-Da(P[t>>3],P[t+8>>3])}return H=t+16|0,A}function Ai(A){var t=0;e[A+296>>2]=303173648,e[A+300>>2]=370677780,t=e[26341],e[A+304>>2]=e[26340],e[A+308>>2]=t,t=e[26343],e[A+312>>2]=e[26342],e[A+316>>2]=t,Ys(A),e[A+56>>2]=2,e[A+36>>2]=3,e[A+40>>2]=1074,f[A+168|0]=5,e[A+132>>2]=32,e[A+104>>2]=1032,e[A+108>>2]=66,e[A+8>>2]=5,e[A+12>>2]=32,f[A+365|0]=64|d[A+365|0],f[A+368|0]=64|d[A+368|0],f[A+396|0]=64|d[A+396|0],f[A+399|0]=64|d[A+399|0]}function Tn(A,t,r){var s=0,i=0,l=0;e:{if(!(s=e[r+16>>2])){if(qa(r))break e;s=e[r+16>>2]}if(s-(l=e[r+20>>2])>>>0<t>>>0)return 0|$A[e[r+36>>2]](r,A,t);A:if(e[r+80>>2]<0)s=0;else{for(i=t;;){if(!(s=i)){s=0;break A}if(d[(i=s-1|0)+A|0]==10)break}if((i=0|$A[e[r+36>>2]](r,A,s))>>>0<s>>>0)break e;A=A+s|0,t=t-s|0,l=e[r+20>>2]}qA(l,A,t),e[r+20>>2]=e[r+20>>2]+t,i=t+s|0}return i}function Gn(A){var t,r=0,s=0;e:{if((0|(t=e[34064]))>0)for(;;){if((s=e[136284+(r<<4)>>2])&&!Ar(A,s)){if(e[136276+(r<<4)>>2])return r;if(s=-1,yA(0,r))break e;return r}if((0|t)==(0|(r=r+1|0)))break}s=-1,yA(A,t)||(r=lt(e[12+(136272+(e[34064]<<4)|0)>>2],MA(A)+1|0),s=e[34064],e[12+(136272+(s<<4)|0)>>2]=r,PA(r,A),e[34064]=s+1)}return s}function Qn(A,t){var r,s=0,i=0,l=0,c=0;for(H=r=H-16|0,f[0|t]=0,(s=15&e[A>>2])&&(t=(i=MA(t=PA(t,G(128496,64|s))))+t|0),s=8;;){e:{A:{if(s>>>0<=29){if(e[A>>2]>>>s&1)break A;break e}if(!(e[A+4>>2]>>>s-32&1)|s>>>0<32)break e}(0|(i=(c=MA(l=G(128496,s))+1|0)+i|0))>=80||(e[r>>2]=l,dA(t,84439,r),t=t+c|0)}if((0|(s=s+1|0))==64)break}H=r+16|0}function ti(A,t,r){var s,i=0,l=0,c=0;if(H=s=H-16|0,d[0|A]){for(c=e[30450];;)if(l=A,A=A+1|0,!((0|(i=f[0|l]))==32|i-9>>>0<5)){for((0|(i=Js(l)))>0&&((0|i)<32?e[t>>2]=e[t>>2]|1<<i:(l=G(129568,r),e[s+4>>2]=i,e[s>>2]=l,Xt(c,84902,s)),l=A);l=(A=l)+1|0,(i=f[0|A])-48>>>0<10|(32|i)-97>>>0<26;);if(!i)break}}H=s+16|0}function ya(A,t,r){var s=0,i=0,l=0,c=0;e:if(t&&!((0|(i=r-4|0))<=0))for(l=(0|(r=e[t-4>>2]))!=34?(0|r)==39?r:0:r,r=0;;){if(c=r,!(r=e[t>>2]))break e;A:{if(!l){if((0|r)==32|r-9>>>0<5)break e;if((0|r)!=47)break A;break e}if((0|c)!=92&&(0|r)==(0|l))break e}if(t=t+4|0,!((0|i)>(0|(s=Cr(r,A+s|0)+s|0))))break}return f[A+s|0]=0,s}function Ft(A){var t=0;e:if(!OA(A)){t=0;A:if(!(A>>>0<768)){if(A-2305>>>0<=1270){if((124&A)>>>0<100)break e;if(t=1,Qi(93850,A))break A;return A-3450>>>0<6}if((0|A)==1541|A-1456>>>0<19|(0|A)==1648||(0|(t=-256&A))==10240|(0|t)==4352|A-3904>>>0<125|A>>>0<880)break e;t=1,A-1611>>>0<20||(t=A-12353>>>0<30400)}return t}return 1}function sn(A,t){var r=0;A?((r=e[A+4>>2])&&PA(133208,r),(r=e[A>>2])&&Lt(133168,r,40),e[33289]=d[A+14|0],e[33291]=d[A+13|0],e[33290]=d[A+12|0],Lt(134672,d[0|t]!=33|d[t+1|0]!=118?t:(d[t+2|0]==47?3:0)+t|0,40),A=e[50298],e[33678]=e[50297],e[33679]=A,A=e[50302],e[33682]=e[50301],e[33683]=A,A=e[50300],e[33680]=e[50299],e[33681]=A):Je(133152,0,76)}function ba(A,t,r,s){f[A+53|0]=1;e:if(e[A+4>>2]==(0|r)){f[A+52|0]=1;A:{if(!(r=e[A+16>>2])){if(e[A+36>>2]=1,e[A+24>>2]=s,e[A+16>>2]=t,(0|s)!=1)break e;if(e[A+48>>2]==1)break A;break e}if((0|t)==(0|r)){if((0|(r=e[A+24>>2]))==2&&(e[A+24>>2]=s,r=s),e[A+48>>2]!=1)break e;if((0|r)==1)break A;break e}e[A+36>>2]=e[A+36>>2]+1}f[A+54|0]=1}}function Ia(A,t){var r=0,s=0,i=0,l=0,c=0;e:if(r=e[t>>2])for(;;){s=0;A:if(A){for(;c=f[s+r|0],(i=e[(s<<2)+A>>2])&&(s=s+1|0,(0|c)==(0|i)););r:switch(i-34|0){case 0:case 5:break r;default:break A}if(!c)break e}if(!(r=e[((l=l+1|0)<<3)+t>>2]))break}return e[4+((l<<3)+t|0)>>2]}function Zs(A,t){e:if((0|t)>=1024){if(A*=898846567431158e293,t>>>0<2047){t=t-1023|0;break e}A*=898846567431158e293,t=((0|t)>=3069?3069:t)-2046|0}else(0|t)>-1023||(A*=2004168360008973e-307,t>>>0>4294965304?t=t+969|0:(A*=2004168360008973e-307,t=((0|t)<=-2960?-2960:t)+1938|0));return E(0,0),E(1,t+1023<<20),A*+S()}function zs(A,t,r,s){var i,l,c,g=0;return H=l=H-16|0,e[l+12>>2]=s,H=i=H-160|0,c=t?A:i+158|0,e[i+144>>2]=c,g=-1,A=t-1|0,e[i+148>>2]=A>>>0<=t>>>0?A:0,A=Je(i,0,144),e[A+76>>2]=-1,e[A+36>>2]=17,e[A+80>>2]=-1,e[A+44>>2]=A+159,e[A+84>>2]=A+144,(0|t)<0?e[56798]=61:(f[0|c]=0,g=Gr(A,r,s,15,16)),H=A+160|0,H=l+16|0,g}function an(A,t){var r=0,s=0,i=0;e:if(r=d[0|A])for(;;){if(!(s=d[0|t])){i=r;break e}if((0|r)!=(0|s)&&(0|(s=r-65>>>0<26?32|r:r))!=(0|((r=d[0|t])-65>>>0<26?32|r:r))){i=d[0|A];break e}if(t=t+1|0,r=d[A+1|0],A=A+1|0,!r)break}return(i=(A=255&i)-65>>>0<26?32|A:A)-((A=d[0|t])-65>>>0<26?32|A:A)|0}function ri(A,t,r){var s=0,i=0;if(s=e[r+72>>2],e[r+72>>2]=s-1|s,(0|(s=e[r+4>>2]))==(0|(i=e[r+8>>2]))?s=t:(qA(A,s,s=t>>>0>(s=i-s|0)>>>0?s:t),e[r+4>>2]=s+e[r+4>>2],A=A+s|0,s=t-s|0),s)for(;;){if(cn(r)||!(i=0|$A[e[r+32>>2]](r,A,s)))return t-s|0;if(A=A+i|0,!(s=s-i|0))break}return t}function Js(A){for(var t=0,r=0,s=0,i=0;A=(t=A)+1|0,(0|(r=f[0|t]))==32|r-9>>>0<5;);e:{A:{r:switch((r=f[0|t])-43|0){case 0:break A;case 2:break r;default:break e}i=1}r=f[0|A],t=A}if(r-48>>>0<10)for(;s=48+(O(s,10)-f[0|t]|0)|0,A=f[t+1|0],t=t+1|0,A-48>>>0<10;);return i?s:0-s|0}function Va(A,t){var r,s,i,l=0;return H=r=H-32|0,e[t>>2]=0,e[t+4>>2]=0,e[(l=s=t+24|0)>>2]=0,e[l+4>>2]=0,e[(l=i=t+16|0)>>2]=0,e[l+4>>2]=0,e[(l=t+8|0)>>2]=0,e[l+4>>2]=0,e[r+28>>2]=t+28,e[r+24>>2]=s,e[r+20>>2]=t+20,e[r+16>>2]=i,e[r+12>>2]=t+12,e[r+8>>2]=l,e[r+4>>2]=t+4,e[r>>2]=t,A=KA(A,84553,r),H=r+32|0,A}function Fn(A){var t=0,r=0,s=0;if((t=d[0|A])&&((r=d[A+1|0])?(r=t|r<<8,(t=d[A+2|0])&&(r|=t<<16,(A=d[A+3|0])&&(r|=A<<24))):r=t),(0|(t=e[36115]))>0)for(A=0;;){if(!(!(s=e[144464+(A<<2)>>2])|e[s>>2]!=(0|r)))return d[s+10|0];if((0|t)==(0|(A=A+1|0)))break}return 0}function Ya(A,t,r,s){var i,l=0,c=0,g=0,m=0,I=0;H=i=H-240|0,e[i>>2]=A,g=1;e:if(!((0|r)<2))for(l=A;;){if((0|An(A,c=(l=l-4|0)-e[((m=r-2|0)<<2)+s>>2]|0,t))>=0&&(0|An(A,l,t))>=0)break e;if(I=c,l=(c=(0|An(c,l,t))>=0)?I:l,e[(g<<2)+i>>2]=l,g=g+1|0,!((0|(r=c?r-1|0:m))>1))break}si(i,g),H=i+240|0}function dA(A,t,r){var s,i,l,c=0;return H=i=H-16|0,e[i+12>>2]=r,H=s=H-160|0,qA(l=s+8|0,124528,144),e[s+52>>2]=A,e[s+28>>2]=A,c=(c=-2-A|0)>>>0>2147483647?2147483647:c,e[s+56>>2]=c,A=A+c|0,e[s+36>>2]=A,e[s+24>>2]=A,A=Li(l,t,r),c&&(t=e[s+28>>2],f[t-((0|t)==e[s+24>>2])|0]=0),H=s+160|0,H=i+16|0,A}function si(A,t){var r,s=0,i=0,l=0,c=0,g=0;if(s=4,H=r=H-256|0,(0|t)>=2)for(e[(g=(t<<2)+A|0)>>2]=r;;){for(l=s>>>0>=256?256:s,qA(e[g>>2],e[A>>2],l),i=0;c=(i<<2)+A|0,i=i+1|0,qA(e[c>>2],e[(i<<2)+A>>2],l),e[c>>2]=e[c>>2]+l,(0|t)!=(0|i););if(!(s=s-l|0))break}H=r+256|0}function QA(A,t,r){var s,i=0;return H=s=H-96|0,e[s+88>>2]=0,e[s+92>>2]=1073741824,e[s+84>>2]=t,t=Ot(A,s+84|0,r,s+88|0,2,0),536870912&(i=e[s+88>>2])?(t=e[47202],e[47202]=0,f[s+2|0]=32,k[s>>1]=8192,Lt(i=3|s,e[s+84>>2],77),A=Oe(A,i,0,0),PA(r,189088),e[47202]=t):A=t?i:0,H=s+96|0,A}function nn(A,t,r){var s=0,i=0,l=0;i=qs(A),s=e[t>>2];e:{A:if((0|i)>=0){if(s){if(!Ar(A,r))break A;(l=e[s+688>>2])&&fe(l),fe(s),e[t>>2]=0}e[t>>2]=FA(A),A=PA(r,A),GA(r=e[t>>2],r+228|0,0)&&(as(e[e[32972]+60>>2]),f[0|A]=0,i=-1),s=e[t>>2],e[s+292>>2]=i}else if(!s)break e;f[s+268|0]=0}return i}function on(A){var t=0,r=0;return(0|(t=e[A+76>>2]))>=0&(!t|e[56823]!=(-1073741825&t))?(r=e[(t=A+76|0)>>2],e[t>>2]=r||1073741823,(0|(r=e[A+4>>2]))==e[A+8>>2]?A=gn(A):(e[A+4>>2]=r+1,A=d[0|r]),e[t>>2]=0,A):(0|(t=e[A+4>>2]))!=e[A+8>>2]?(e[A+4>>2]=t+1,d[0|t]):gn(A)}function Kr(A,t,r){var s=0,i=0;e:{A:{if(r>>>0>=4){if(3&(A|t))break A;for(;;){if(e[A>>2]!=e[t>>2])break A;if(t=t+4|0,A=A+4|0,!((r=r-4|0)>>>0>3))break}}if(!r)break e}for(;;){if((0|(s=d[0|A]))==(0|(i=d[0|t]))){if(t=t+1|0,A=A+1|0,r=r-1|0)continue;break e}break}return s-i|0}return 0}function ss(A,t){var r,s=0,i=0,l=0;H=r=H-16|0,t?(vt(r,i=((s=t>>31)^t)-s|0,0,0,0,(s=be(i))+81|0),i=0+e[r+8>>2]|0,s=(65536^e[r+12>>2])+(16414-s<<16)|0,l=-2147483648&t|(s=i>>>0<l>>>0?s+1|0:s),s=e[r+4>>2],t=e[r>>2]):t=0,e[A>>2]=t,e[A+4>>2]=s,e[A+8>>2]=i,e[A+12>>2]=l,H=r+16|0}function ia(A){var t,r=0,s=0,i=0;if((0|(r=Dn(A,61)))==(0|A))return 0;e:if(!d[(t=r-A|0)+A|0]&&(r=e[56800])&&(s=e[r>>2])){for(;;){if(On(A,s,t)||(s=e[r>>2]+t|0,d[0|s]!=61)){if(s=e[r+4>>2],r=r+4|0,s)continue;break e}break}i=s+1|0}return i}function qs(A){var t=0,r=0;e:if(!((0|(r=e[34461]))<=0)){for(;;){if(!Ar(A,O(t,44)+137856|0)){e[34457]=t;break e}if((0|r)==(0|(t=t+1|0)))break}return-1}return(A=(0|t)==(0|r))?-1:((0|(A=A?-1:t))!=e[36114]&&(e[36115]=0,dt(A),e[36114]=A,e[36115]=e[36115]+1),t)}function Ha(A,t,r,s){var i=0,l=0;e:if((32|d[0|t])!=32){if(i=((0|s)>2)<<1,l=Ws(A,t,r,i=(0|s)>1?4|i:i),d[0|r]!=21)for(i|=1,t=t+l|0,l=1;;){if((32|d[0|t])==32)break e;if(t=Ws(A,t,r,i)+t|0,l=l+1|0,d[0|r]==21)break}return PA(189088,r),0}return nr(A,r,s,l),t}function Cr(A,t){var r,s=0,i=0,l=0;if(A>>>0<=127)return f[0|t]=A,1;if(A>>>0>=1114112)return f[0|t]=32,1;for(s=O(r=A>>>0<2048?1:A>>>0<65536?2:3,6),f[0|t]=d[r+93842|0]|A>>>s;s=s-6|0,f[(i=i+1|0)+t|0]=A>>>s&63|128,(0|(l=l+1|0))!=(0|r););return r+1|0}function ln(A){var t=0,r=0;e:{if((0|(t=e[A+12>>2]))>=e[A+16>>2]){if(t=0,(0|(r=0|ze(e[A+8>>2],A+24|0,2048)))<=0){if(!r|(0|r)==-44)break e;return e[56798]=0-r,0}e[A+16>>2]=r}r=t,t=A+t|0,e[A+12>>2]=r+y[t+40>>1],r=e[t+36>>2],e[A>>2]=e[t+32>>2],e[A+4>>2]=r,t=t+24|0}return t}function Ua(A,t){var r,s=0;if(s=e[A+632>>2])return!!(0|_r(s,t));e:{A:{if((0|(r=e[A+600>>2]))>0){if(s=0,(t=t-r|0)-1>>>0<255)break A;break e}if((s=t-192|0)>>>0<=413)return 128&d[344+(d[s+94240|0]+A|0)|0];if(s=0,t>>>0>255)break e}s=128&d[344+(A+t|0)|0]}return s}function wa(A,t,r){var s,i,l;return 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RAHAgIQEBAQEBAaGhoXEwcQEBAQEBAQEBAQGhoaFxMCBwcHBwcHBwcHBwcHBwICAhgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYAgICAgICAgICAgICAgICAg0NDQ0NDQ0NDQ0NDQ0MDAwMDQwMDA0NDQ0NDQ0NDQ0NDQICAgICAgICAgICAgICAhsbChsbGxsKGxsGCgoKBgYKCgoGGwobGxoKCgoKChsbGxsbGwobChsKGwoKCgobBgoKCgoGCAgICAYbGwYGCgoaGhoaGgoGBgYGGxobGwYbEBAQEBAQEBAQEBAQEBAQEA8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PCgYPDw8PEBsbAgICAhoaGhoaGxsbGxsaGhsbGxsaGxsaGxsaGxsbGxsbGxobGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGhobGxobGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGxsbGxsbGxsXExcTGxsbGxsbGxsbGxsbGxsbGxsbGxsaGhsbGxsbGxsXExsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxobGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwICAgICAgICAgICAgICAgICAgICAgICAgIbGxsbGxsbGxsbGwICAgICAgICAgICAgICAgICAgICAhAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxAQEBAQEBAQEBAQEBAQEBAQEBAQEBAbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsaGxsbGxsbGxsbGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaGhoaGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbFxMXExcTFxMXExcTFxMQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaFxMaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaFxMXExcTFxMXExoaGhoaGhoaGhoaGhoaGhobGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoXExcTFxMXExcTFxMXExcTFxMXExcTGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaFxMXExoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaFxMaGhsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxoaGhoaGhoaGhoaGhoaGhoaGhoaGhsbGhoaGhoaGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbAgIbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwICGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwIbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGwIKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgIGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgIKBgoKCgYGCgYKBgoGCgoKCgYKBgYKBgYGBgYGBwcKCgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYKBgoGCgYGGxsbGxsbCgYKBg0NDQoGAgICAgIWFhYWEBYWBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYCBgICAgICBgICCAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCAgICAgICBxYCAgICAgICAgICAgICAg0ICAgICAgICAgICAgICAgICAgICAgICAICAgICAgICAggICAgICAgCCAgICAgICAIICAgICAgIAggICAgICAgCCAgICAgICAIICAgICAgIAggICAgICAgCCAgICAgICAINDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDRYWFRQVFBYWFhUUFhUUFhYWFhYWFhYWEhYWEhYVFBYWFRQXExcTFxMXExYWFhYWBxYWFhYWFhYWFhYSEhYWFhYSFhcWFhYWFhYWFhYWFhYCAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsCGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsCAgICAgICAgICAgIbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbGxsbAgICAgICAgICAgICAgICAgICAgICAgICAgIbGxsbGxsbGxsbGxsCAgICHhYWFhsHCA8XExcTFxMXExcTGxsXE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for(I=J,s=x;;){if(V=(0|s)>=29?29:s,!(I>>>0>(g=m-4|0)>>>0)){for(s=0;h=e[g>>2],Fe=s,s=31&V,(63&V)>>>0>=32?(_=h<<s,s=0):(_=(1<<s)-1&h>>>32-s,s=h<<s),h=_+K|0,s=Vi(Fe=Fe+s|0,s>>>0>Fe>>>0?h+1|0:h,1e9),e[g>>2]=Fe-st(s,le,1e9,0),I>>>0<=(g=g-4|0)>>>0;);s&&(e[(I=I-4|0)>>2]=s)}for(;I>>>0<(g=m)>>>0&&!e[(m=g-4|0)>>2];);if(s=e[c+44>>2]-V|0,e[c+44>>2]=s,m=g,!((0|s)>0))break}if((0|s)<0)for(he=1+((T+25>>>0)/9|0)|0,K=(0|Te)==102;;){if(_=(0|(s=0-s|0))>=9?9:s,g>>>0<=I>>>0)m=e[I>>2];else{for(V=1e9>>>_|0,h=~(-1<<_),s=0,m=I;Fe=s,s=e[m>>2],e[m>>2]=Fe+(s>>>_|0),s=O(V,s&h),(m=m+4|0)>>>0<g>>>0;);m=e[I>>2],s&&(e[g>>2]=s,g=g+4|0)}if(s=_+e[c+44>>2]|0,e[c+44>>2]=s,I=(!m<<2)+I|0,g=g-(m=K?J:I)>>2>(0|he)?m+(he<<2)|0:g,!((0|s)<0))break}if(s=0,!(g>>>0<=I>>>0||(s=O(J-I>>2,9),m=10,(h=e[I>>2])>>>0<10)))for(;s=s+1|0,h>>>0>=(m=O(m,10))>>>0;);if((0|(m=(T-((0|Te)!=102?s:0)|0)-((0|Te)==103&!!(0|T))|0))<(O(g-J>>2,9)-9|0)){if(x=((((0|x)<0?4:292)+c|0)+((h=(0|(V=m+9216|0))/9|0)<<2)|0)-4048|0,m=10,(0|(_=V-O(h,9)|0))<=7)for(;m=O(m,10),(0|(_=_+1|0))!=8;);if(!(!(K=(V=e[x>>2])-O(m,he=(V>>>0)/(m>>>0)|0)|0)&(0|(h=x+4|0))==(0|g))&&(!(1&he)&&(t=9007199254740992,!(1&f[x-4|0])|(0|m)!=1e9|I>>>0>=x>>>0)||(t=9007199254740994),te=(0|g)==(0|h)?1:1.5,te=(h=m>>>1|0)>>>0>K>>>0?.5:(0|h)==(0|K)?te:1.5,d[0|Le]!=45|Xe||(te=-te,t=-t),h=V-K|0,e[x>>2]=h,t+te!=t)){if(s=m+h|0,e[x>>2]=s,s>>>0>=1e9)for(;e[x>>2]=0,(x=x-4|0)>>>0<I>>>0&&(e[(I=I-4|0)>>2]=0),s=e[x>>2]+1|0,e[x>>2]=s,s>>>0>999999999;);if(s=O(J-I>>2,9),m=10,!((h=e[I>>2])>>>0<10))for(;s=s+1|0,h>>>0>=(m=O(m,10))>>>0;);}g=g>>>0>(m=x+4|0)>>>0?m:g}for(;h=g,!(V=g>>>0<=I>>>0)&&!e[(g=h-4|0)>>2];);if((0|Te)==103){if(T=((g=(0|(m=T||1))>(0|s)&(0|s)>-5)?~s:-1)+m|0,l=(g?-1:-2)+l|0,!(x=8&i)){if(g=-9,!V&&(x=e[h-4>>2])&&(_=10,g=0,!((x>>>0)%10|0))){for(;m=g,g=g+1|0,!((x>>>0)%((_=O(_,10))>>>0)|0););g=~m}m=O(h-J>>2,9),(-33&l)!=70?(x=0,T=(0|(g=(0|(g=((s+m|0)+g|0)-9|0))>0?g:0))>(0|T)?T:g):(x=0,T=(0|(g=(0|(g=(g+m|0)-9|0))>0?g:0))>(0|T)?T:g)}}else x=8&i;if(_=-1,(0|((V=x|T)?2147483645:2147483646))<(0|T))break e;if(K=1+(!!(0|V)+T|0)|0,(0|(m=-33&l))!=70){if((Ee-(g=$s(((g=s>>31)^s)-g|0,0,Ee))|0)<=1)for(;f[0|(g=g-1|0)]=48,(Ee-g|0)<2;);if(f[0|(he=g-2|0)]=l,f[g-1|0]=(0|s)<0?45:43,(0|(g=Ee-he|0))>(2147483647^K))break e}else{if((2147483647^K)<(0|s))break e;g=(0|s)>0?s:0}if((0|(s=g+K|0))>(2147483647^ce))break e;Sr(A,32,r,K=s+ce|0,i),Wr(A,Le,ce),Sr(A,48,r,K,65536^i);r:{a:{n:{if((0|m)==70){for(s=8|(l=c+16|0),x=9|l,I=m=I>>>0>J>>>0?J:I;;){g=$s(e[I>>2],0,x);o:if((0|m)==(0|I))(0|g)==(0|x)&&(f[c+24|0]=48,g=s);else{if(c+16>>>0>=g>>>0)break o;for(;f[0|(g=g-1|0)]=48,c+16>>>0<g>>>0;);}if(Wr(A,g,x-g|0),!(J>>>0>=(I=I+4|0)>>>0))break}if(V&&Wr(A,85998,1),(0|T)<=0|I>>>0>=h>>>0)break n;for(;;){if((g=$s(e[I>>2],0,x))>>>0>c+16>>>0)for(;f[0|(g=g-1|0)]=48,c+16>>>0<g>>>0;);if(Wr(A,g,(0|T)>=9?9:T),g=T-9|0,h>>>0<=(I=I+4|0)>>>0)break a;if(s=(0|T)>9,T=g,!s)break}break a}o:if(!((0|T)<0))for(J=I>>>0<h>>>0?h:I+4|0,s=8|(l=c+16|0),h=9|l,m=I;;){(0|h)==(0|(g=$s(e[m>>2],0,h)))&&(f[c+24|0]=48,g=s);c:if((0|m)==(0|I))Wr(A,g,1),g=g+1|0,x|T&&Wr(A,85998,1);else{if(c+16>>>0>=g>>>0)break c;for(;f[0|(g=g-1|0)]=48,c+16>>>0<g>>>0;);}if(Wr(A,g,(0|(l=h-g|0))>(0|T)?T:l),T=T-l|0,J>>>0<=(m=m+4|0)>>>0)break o;if(!((0|T)>=0))break}Sr(A,48,T+18|0,18,0),Wr(A,he,Ee-he|0);break r}g=T}Sr(A,48,g+9|0,9,0)}Sr(A,32,r,K,8192^i),_=(0|r)<(0|K)?K:r;break e}if(x=(l<<26>>31&9)+Le|0,!(s>>>0>11)){for(g=12-s|0,te=16;te*=16,g=g-1|0;);t=d[0|x]!=45?t+te-te:-(te+(-t-te))}for((0|Ee)==(0|(g=$s(((g=e[c+44>>2])^(m=g>>31))-m|0,0,Ee)))&&(f[c+15|0]=48,g=c+15|0),J=2|ce,I=32&l,m=e[c+44>>2],f[0|(T=g-2|0)]=l+15,f[g-1|0]=(0|m)<0?45:43,g=8&i,m=c+16|0;l=m,h=ee(t)<2147483648?~~t:-2147483648,f[0|m]=I|d[h+124512|0],!((0|s)>0|g)&(t=16*(t-+(0|h)))==0|((m=l+1|0)-(c+16|0)|0)!=1||(f[l+1|0]=46,m=l+2|0),t!=0;);_=-1,(2147483645-(l=(g=Ee-T|0)+J|0)|0)<(0|s)||(Sr(A,32,r,l=(s=!s||((I=m-(c+16|0)|0)-2|0)>=(0|s)?I=m-(c+16|0)|0:s+2|0)+l|0,i),Wr(A,x,J),Sr(A,48,r,l,65536^i),Wr(A,c+16|0,I),Sr(A,48,s-I|0,0,0),Wr(A,T,g),Sr(A,32,r,l,8192^i),_=(0|r)<(0|l)?l:r)}else Sr(A,32,r,g=ce+3|0,-65537&i),Wr(A,Le,ce),s=32&l,Wr(A,t!=t?s?85596:85774:s?85247:85460,3),Sr(A,32,r,g,8192^i),_=(0|r)<(0|g)?g:r;return H=c+560|0,0|_},function(A,t){var r;A|=0,r=t|=0,t=e[t>>2]+7&-8,e[r>>2]=t+16,P[A>>3]=Us(e[t>>2],e[t+4>>2],e[t+8>>2],e[t+12>>2])},function(A,t,r){t|=0,r|=0;var s,i,l=0,c=0;return s=e[84+(A|=0)>>2],c=e[s+4>>2],i=e[A+28>>2],(l=(l=e[A+20>>2]-i|0)>>>0>c>>>0?c:l)&&(qA(e[s>>2],i,l),e[s>>2]=l+e[s>>2],c=e[s+4>>2]-l|0,e[s+4>>2]=c),l=e[s>>2],(c=r>>>0>c>>>0?c:r)&&(qA(l,t,c),l=c+e[s>>2]|0,e[s>>2]=l,e[s+4>>2]=e[s+4>>2]-c),f[0|l]=0,t=e[A+44>>2],e[A+28>>2]=t,e[A+20>>2]=t,0|r},function(A,t,r){r|=0;var s,i,l=0;return qA(t|=0,s=e[84+(A|=0)>>2],r=r>>>0>(l=(i=Ba(s,0,l=r+256|0))?i-s|0:l)>>>0?l:r),t=s+l|0,e[A+84>>2]=t,e[A+8>>2]=t,e[A+4>>2]=r+s,0|r},function(A,t,r){t|=0,r|=0;var s,i=0,l=0,c=0;i=e[84+(A|=0)>>2],s=e[i>>2]?i:84412,i=0;e:if(e[A+48>>2])for(;;){if(!(l=e[(i<<2)+s>>2]))break e;if(f[e[A+44>>2]+i|0]=(0|l)>=128?64:l,!((i=i+1|0)>>>0<Ae[A+48>>2]))break}return l=e[A+44>>2],e[A+4>>2]=l,e[A+84>>2]=(i<<2)+s,e[A+8>>2]=i+l,!r|!i||(e[A+4>>2]=l+1,f[0|t]=d[0|l],c=1),0|c},Yi,mn,Eo,Eo,function(A,t,r){r|=0;var s,i=0;return H=s=H+-64|0,i=1,ca(A|=0,t|=0,0)||(i=0,t&&(i=0,(t=DA(t,125132))&&(Je(4|(i=s+8|0),0,52),e[s+56>>2]=1,e[s+20>>2]=-1,e[s+16>>2]=A,e[s+8>>2]=t,$A[e[e[t>>2]+28>>2]](t,i,e[r>>2],1),(0|(A=e[s+32>>2]))==1&&(e[r>>2]=e[s+24>>2]),i=(0|A)==1))),H=s- -64|0,0|i},function(A,t,r,s,i,l){r|=0,s|=0,i|=0,l|=0,ca(A|=0,e[8+(t|=0)>>2],l)&&ba(t,r,s,i)},function(A,t,r,s,i){if(r|=0,s|=0,i|=0,ca(A|=0,e[8+(t|=0)>>2],i))e[t+28>>2]==1|e[t+4>>2]!=(0|r)||(e[t+28>>2]=s);else e:if(ca(A,e[t>>2],i)){if(!(e[t+16>>2]!=(0|r)&e[t+20>>2]!=(0|r))){if((0|s)!=1)break e;return void(e[t+32>>2]=1)}e[t+20>>2]=r,e[t+32>>2]=s,e[t+40>>2]=e[t+40>>2]+1,e[t+36>>2]!=1|e[t+24>>2]!=2||(f[t+54|0]=1),e[t+44>>2]=4}},function(A,t,r,s){r|=0,s|=0,ca(A|=0,e[8+(t|=0)>>2],0)&&un(t,r,s)},mn,function(A,t,r,s,i,l){r|=0,s|=0,i|=0,l|=0,ca(A|=0,e[8+(t|=0)>>2],l)?ba(t,r,s,i):(A=e[A+8>>2],$A[e[e[A>>2]+20>>2]](A,t,r,s,i,l))},function(A,t,r,s,i){if(r|=0,s|=0,i|=0,ca(A|=0,e[8+(t|=0)>>2],i))e[t+28>>2]==1|e[t+4>>2]!=(0|r)||(e[t+28>>2]=s);else e:{if(ca(A,e[t>>2],i)){if(!(e[t+16>>2]!=(0|r)&e[t+20>>2]!=(0|r))){if((0|s)!=1)break e;return void(e[t+32>>2]=1)}e[t+32>>2]=s;A:if(e[t+44>>2]!=4){if(k[t+52>>1]=0,A=e[A+8>>2],$A[e[e[A>>2]+20>>2]](A,t,r,r,1,i),d[t+53|0]){if(e[t+44>>2]=3,!d[t+52|0])break A;break e}e[t+44>>2]=4}if(e[t+20>>2]=r,e[t+40>>2]=e[t+40>>2]+1,e[t+36>>2]!=1|e[t+24>>2]!=2)break e;return void(f[t+54|0]=1)}A=e[A+8>>2],$A[e[e[A>>2]+24>>2]](A,t,r,s,i)}},function(A,t,r,s){r|=0,s|=0,ca(A|=0,e[8+(t|=0)>>2],0)?un(t,r,s):(A=e[A+8>>2],$A[e[e[A>>2]+28>>2]](A,t,r,s))},mn,function(A){return 84787},mn,function(A){return 85058},mn,function(A){return 84147},function(A){var t;return t=A|=0,A=e[A>>2],e[t>>2]=A+1,0|((0|(A=f[0|A]))<0?65533:255&A)},function(A,t,r,s,i,l){A|=0,t|=0,r|=0,s|=0,i|=0,l|=0;var c,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0;if(c=Es(408),e[c+4>>2]=s,e[c>>2]=r,t?(qA(c+16|0,t,376),P[c+392>>3]=(P[t+368>>3]-P[t>>3])/+(r>>>0),t=0):t=1,e[c+400>>2]=i,f[c+8|0]=t,l){if(r=e[A+24>>2])for(t=e[A+20>>2],s=e[A+8>>2];(i=e[e[(t>>>8&16777212)+s>>2]+((1023&t)<<2)>>2])&&(fe(i),r=e[A+24>>2],s=e[A+8>>2],t=e[A+20>>2]),t=t+1|0,e[A+20>>2]=t,r=r-1|0,e[A+24>>2]=r,t>>>0>=2048&&(fe(e[s>>2]),s=e[A+8>>2]+4|0,e[A+8>>2]=s,t=e[A+20>>2]-1024|0,e[A+20>>2]=t,r=e[A+24>>2]),r;);t=e[A+28>>2],e[A+420>>2]=e[t>>2],(r=e[A+32>>2])&&(f[t+8|0]=d[r+8|0],qA(t+16|0,A+40|0,376),(t=e[A+32>>2])&&fe(t),e[A+32>>2]=0)}if(r=(s=e[A+24>>2])+e[A+20>>2]|0,i=e[A+12>>2],(0|r)==(0|((0|(t=e[A+8>>2]))!=(0|i)?(i-t<<8)-1:0))){H=g=H-32|0;e:{A:{r:{a:{if((t=e[16+(l=A+4|0)>>2])>>>0>=1024){if(e[l+16>>2]=t-1024,t=e[l+4>>2],T=e[t>>2],i=t+4|0,e[l+4>>2]=i,(0|(t=e[l+8>>2]))==e[l+12>>2])if((m=e[l>>2])>>>0<i>>>0)r=Be((s=(1+(i-m>>2)|0)/-2<<2)+i|0,i,t=t-i|0)+t|0,e[l+8>>2]=r,e[l+4>>2]=s+e[l+4>>2];else{if((r=(0|t)==(0|m)?1:t-m>>1)>>>0>=1073741824)break a;if(_=(s=r<<2)+(I=Es(s))|0,r=s=I+(-4&r)|0,(0|t)!=(0|i)){if(V=-4&(t=t-i|0),x=1+((h=t-4|0)>>>2|0)&7)for(r=0,t=s;e[t>>2]=e[i>>2],i=i+4|0,t=t+4|0,(0|x)!=(0|(r=r+1|0)););else t=s;if(r=s+V|0,!(h>>>0<28))for(;e[t>>2]=e[i>>2],e[t+4>>2]=e[i+4>>2],e[t+8>>2]=e[i+8>>2],e[t+12>>2]=e[i+12>>2],e[t+16>>2]=e[i+16>>2],e[t+20>>2]=e[i+20>>2],e[t+24>>2]=e[i+24>>2],e[t+28>>2]=e[i+28>>2],i=i+32|0,(0|r)!=(0|(t=t+32|0)););}e[l+12>>2]=_,e[l+8>>2]=r,e[l+4>>2]=s,e[l>>2]=I,m&&(fe(m),r=e[l+8>>2])}else r=t;e[r>>2]=T,e[l+8>>2]=e[l+8>>2]+4;break e}if((m=(i=e[l+8>>2])-e[l+4>>2]>>2)>>>0<(s=(t=e[l+12>>2])-(r=e[l>>2])|0)>>2>>>0){if((0|t)!=(0|i)){e[g+8>>2]=Es(4096),ra(l,g+8|0);break e}if(e[g+8>>2]=Es(4096),Yt(l,g+8|0),t=e[l+4>>2],T=e[t>>2],i=t+4|0,e[l+4>>2]=i,(0|(t=e[l+8>>2]))==e[l+12>>2])if((m=e[l>>2])>>>0<i>>>0)r=Be((s=(1+(i-m>>2)|0)/-2<<2)+i|0,i,t=t-i|0)+t|0,e[l+8>>2]=r,e[l+4>>2]=s+e[l+4>>2];else{if((r=(0|t)==(0|m)?1:t-m>>1)>>>0>=1073741824)break a;if(_=(s=r<<2)+(I=Es(s))|0,r=s=I+(-4&r)|0,(0|t)!=(0|i)){if(V=-4&(t=t-i|0),x=1+((h=t-4|0)>>>2|0)&7)for(r=0,t=s;e[t>>2]=e[i>>2],i=i+4|0,t=t+4|0,(0|x)!=(0|(r=r+1|0)););else t=s;if(r=s+V|0,!(h>>>0<28))for(;e[t>>2]=e[i>>2],e[t+4>>2]=e[i+4>>2],e[t+8>>2]=e[i+8>>2],e[t+12>>2]=e[i+12>>2],e[t+16>>2]=e[i+16>>2],e[t+20>>2]=e[i+20>>2],e[t+24>>2]=e[i+24>>2],e[t+28>>2]=e[i+28>>2],i=i+32|0,(0|r)!=(0|(t=t+32|0)););}e[l+12>>2]=_,e[l+8>>2]=r,e[l+4>>2]=s,e[l>>2]=I,m&&(fe(m),r=e[l+8>>2])}else r=t;e[r>>2]=T,e[l+8>>2]=e[l+8>>2]+4;break e}if(e[g+24>>2]=l+12,!((t=(0|t)==(0|r)?1:s>>1)>>>0>=1073741824)){if(t=Es(r=t<<2),e[g+8>>2]=t,s=t+(m<<2)|0,e[g+16>>2]=s,e[g+20>>2]=t+r,e[g+12>>2]=s,e[g+4>>2]=Es(4096),ra(g+8|0,g+4|0),(0|(i=e[l+8>>2]))==e[l+4>>2]){t=i;break A}for(;Yt(g+8|0,i=i-4|0),e[l+4>>2]!=(0|i););break r}}li(),j()}t=e[l+8>>2]}r=e[l>>2],e[l>>2]=e[g+8>>2],e[g+8>>2]=r,e[l+4>>2]=e[g+12>>2],e[g+12>>2]=i,e[l+8>>2]=e[g+16>>2],e[g+16>>2]=t,s=e[l+12>>2],e[l+12>>2]=e[g+20>>2],e[g+20>>2]=s,(0|t)!=(0|i)&&(e[g+16>>2]=t+(3+(i-t|0)&-4)),r&&fe(r)}H=g+32|0,r=(s=e[A+24>>2])+e[A+20>>2]|0,t=e[A+8>>2]}e[e[t+(r>>>8&16777212)>>2]+((1023&r)<<2)>>2]=c,e[A+24>>2]=s+1},function(A){var t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0;r=e[420+(A|=0)>>2]+1|0,e[A+420>>2]=r;e:{if(t=e[A+32>>2]){if(r>>>0>(g=e[t+4>>2])>>>0){(r=e[A+28>>2])&&(fe(r),t=e[A+32>>2]),e[A+32>>2]=0,e[A+28>>2]=t;break e}for(l=A+40|0,c=t+16|0,m=e[A+28>>2]+16|0,I=+(r>>>0)/+(g>>>0),t=0;;){if(s=P[(r=t<<3)+c>>3],i=P[r+m>>3],P[r+l>>3]=s==s?(s-i)*I+i:i,(0|(r=1|t))==47)break e;s=P[(r<<=3)+c>>3],i=P[r+m>>3],P[r+l>>3]=s==s?(s-i)*I+i:i,t=t+2|0}}if(t=e[A+28>>2],r>>>0>Ae[t>>2]){if(l=e[A+24>>2]){if(f[A+416|0]=0,c=e[A+8>>2],r=e[A+20>>2],t=e[e[c+(r>>>8&16777212)>>2]+((1023&r)<<2)>>2],e[A+32>>2]=t,e[A+24>>2]=l-1,r=r+1|0,e[A+20>>2]=r,r>>>0>=2048&&(fe(e[c>>2]),e[A+8>>2]=e[A+8>>2]+4,e[A+20>>2]=e[A+20>>2]-1024,t=e[A+32>>2]),d[t+8|0])qA(t+16|0,e[A+28>>2]+16|0,376),t=e[A+32>>2],e[t+368>>2]=0,e[t+372>>2]=0,s=P[A+40>>3],e[t+392>>2]=0,e[t+396>>2]=0,P[t+16>>3]=s;else if(r=e[A+28>>2],d[r+8|0]&&(qA(r+16|0,t+16|0,376),t=e[A+28>>2],e[t+368>>2]=0,e[t+372>>2]=0,!(t=e[A+32>>2])))break e;(0|(r=e[t+400>>2]))!=-1&&(e[A+424>>2]=r),e[A+420>>2]=0,P[t+16>>3]=P[t+392>>3]*+Ae[t+4>>2]+P[t+16>>3];break e}f[A+416|0]=1}else s=P[t+392>>3]+P[A+40>>3],P[A+40>>3]=s,P[t+16>>3]=s}return 0|(d[A+416|0]?0:A+40)},function(A){return e[424+(A|=0)>>2]},function(A){var t=0;return e[(A|=0)>>2]=132304,(t=e[A+28>>2])&&fe(t),(t=e[A+32>>2])&&fe(t),$n(A+4|0),0|A},function(A){var t=0;e[(A|=0)>>2]=132304,(t=e[A+28>>2])&&fe(t),(t=e[A+32>>2])&&fe(t),$n(A+4|0),fe(A)},function(A,t,r){t|=0,r|=0;var s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0;if(!e[1088+(A|=0)>>2])return 0;e:if(t){for(I=A+648|0,m=A- -64|0;;){if(s=e[A+1088>>2],!(s=0|$A[e[e[s>>2]+4>>2]](s)))break e;if(i=Os(P[A+32>>3]+P[s+16>>3]/+e[A+24>>2]),P[A+32>>3]=i,i=na(6.283185307179586*i),i=Os(P[A+16>>3]+P[s>>3]*(.06*i*P[s+8>>3]+1)/+e[A+8>>2]),P[A+16>>3]=i,g=st(e[56848],e[56849],1284865837,1481765933),l=le,l=(g=g+1|0)?l:l+1|0,e[56848]=g,e[56849]=l,c=.75*P[A+40>>3]+ +(l>>>1|0)/2147483647,P[A+40>>3]=c,h=P[s+24>>3],l=i>=P[s+32>>3],f[A+48|0]=l,c=(T=h)*(h=.2*c),c=dr(m+512|0,dr(m+440|0,i=P[s+352>>3]*(h*P[s+48>>3]+P[s+40>>3]*(i+i+-1+(l?c:.01*c)))*.5,P[s+104>>3],P[s+168>>3]),P[s+112>>3],P[s+176>>3]),c=dr(m+8|0,dr(m+80|0,dr(m+152|0,dr(m+224|0,dr(m+296|0,dr(m+368|0,c==c?(c-i)*P[s+184>>3]+i:i,P[s+96>>3],P[s+160>>3]),P[s+88>>3],P[s+152>>3]),P[s+80>>3],P[s+144>>3]),P[s+72>>3],P[s+136>>3]),P[s+64>>3],P[s+128>>3]),P[s+56>>3],P[s+120>>3]),g=st(e[56848],e[56849],1284865837,1481765933),l=le,l=(g=g+1|0)?l:l+1|0,e[56848]=g,e[56849]=l,i=.75*P[A+56>>3]+ +(l>>>1|0)/2147483647,P[A+56>>3]=i,l=(x<<1)+r|0,h=dr(I+8|0,i=P[s+352>>3]*(P[s+192>>3]*(.3*i))*.5,P[s+200>>3],P[s+248>>3]),_=P[s+296>>3],V=dr(I+80|0,i,P[s+208>>3],P[s+256>>3]),K=P[s+304>>3],J=dr(I+152|0,i,P[s+216>>3],P[s+264>>3]),te=P[s+312>>3],ce=dr(I+224|0,i,P[s+224>>3],P[s+272>>3]),he=P[s+320>>3],Ee=dr(I+296|0,i,P[s+232>>3],P[s+280>>3]),Te=P[s+328>>3],T=c,c=(dr(I+368|0,i,P[s+240>>3],P[s+288>>3])-i)*P[s+336>>3]+(Te*(Ee-i)+(he*(ce-i)+(te*(J-i)+(K*(V-i)+(_*(h-i)+0))))),i=(T+(i==i?(i-c)*P[s+344>>3]+c:c))*P[s+360>>3]*4e3,s=(0|(s=ee(i)<2147483648?~~i:-2147483648))>=32e3?32e3:s,k[l>>1]=(0|s)<=-32e3?-32e3:s,(0|(x=x+1|0))==(0|t))break}x=t}return 0|(t>>>0>x>>>0?x:t)},function(A,t){t|=0,e[1088+(A|=0)>>2]=t},Yi,mn,Hi,function(A,t,r,s){return le=0,0}],hn.grow=function(A){var t=this.length;return this.length=this.length+A,t},hn.set=function(A,t){this[A]=t},hn.get=function(A){return this[A]},hn);return{v:function(){var A,t=0;H=A=H-16|0,0|Ze(A+12|0,A+8|0)||(t=HA(4+(e[A+12>>2]<<2)|0),e[56800]=t,t&&(!(t=HA(e[A+8>>2]))||(e[e[56800]+(e[A+12>>2]<<2)>>2]=0,0|Ue(e[56800],0|t)))&&(e[56800]=0)),H=A+16|0,e[56841]=227236,e[56823]=42},w:jn,x:Mo,y:function(A,t){t|=0,e[(A|=0)>>2]=t},z:function(A,t){return t|=0,f[e[(A|=0)+4>>2]+t|0]},A:ji,B:Ni,C:function(A){return d[(A|=0)+12|0]},D:function(A,t){t|=0,f[(A|=0)+12|0]=t},E:function(A){return d[(A|=0)+13|0]},F:function(A,t){t|=0,f[(A|=0)+13|0]=t},G:function(A){return d[(A|=0)+14|0]},H:function(A,t){t|=0,f[(A|=0)+14|0]=t},I:function(A){return d[(A|=0)+15|0]},J:function(A,t){t|=0,f[(A|=0)+15|0]=t},K:Ri,L:function(A,t){t|=0,e[(A|=0)+16>>2]=t},M:ko,N:function(A,t){t|=0,e[(A|=0)+20>>2]=t},O:jn,P:Mo,Q:Wi,R:ji,S:wo,T:Ri,U:ko,V:function(A){return e[(A|=0)+24>>2]},W:jn,X:function(){var A,t=0,r=0,s=0,i=0,l=0,c=0,g=0,m=0,I=0,h=0,x=0,T=0,_=0,V=0,K=0,J=0,te=0,ce=0,he=0,Ee=0,Te=0,Fe=0,Le=0;if(A=Es(20),e[A+16>>2]=0,e[A+8>>2]=175,e[A+12>>2]=50,!(t=e[33208])){H=t=(H=he=H-16|0)-80|0;e:{if((r=ia(84292))&&(e[t+32>>2]=r,zs(137584,160,85959,t+32|0),(0|Ns(137584))==-31||(e[t+16>>2]=r,zs(137584,160,86031,t+16|0),(0|Ns(137584))==-31)))break e;(r=ia(84619))&&(e[t>>2]=r,zs(137584,160,85959,t),(0|Ns(137584))==-31)||(r=d[84826]|d[84827]<<8|d[84828]<<16|d[84829]<<24,e[34396]=d[84822]|d[84823]<<8|d[84824]<<16|d[84825]<<24,e[34397]=r,k[68804]=d[84846]|d[84847]<<8,r=d[84842]|d[84843]<<8|d[84844]<<16|d[84845]<<24,e[34400]=d[84838]|d[84839]<<8|d[84840]<<16|d[84841]<<24,e[34401]=r,r=d[84834]|d[84835]<<8|d[84836]<<16|d[84837]<<24,e[34398]=d[84830]|d[84831]<<8|d[84832]<<16|d[84833]<<24,e[34399]=r)}if(H=t+80|0,e[he+12>>2]=0,i=he+12|0,H=g=H-16|0,e[g+12>>2]=22050,ge(85144)||ge(85315)||ge(85473)||ge(85698),x=g+12|0,H=l=H-16|0,e[l+12>>2]=0,!((t=er(137832,84262,0,i))||(t=er(137836,84420,0,i))||(t=er(137840,84813,0,i))||(t=er(137820,85016,l+12|0,i))))if(e[34454]=Ae[l+12>>2]/68,r=e[34460],e[34456]=r,r&&(0|(I=d[0|r]|d[r+1|0]<<8|d[r+2|0]<<16|d[r+3|0]<<24))==83969){if(T=d[r+4|0]|d[r+5|0]<<8|d[r+6|0]<<16|d[r+7|0]<<24,r=e[34458],V=d[0|r],e[34461]=V,V)for(t=r+4|0,I=0;s=O(I,44)+137856|0,i=d[0|t],e[s+36>>2]=i,e[s+40>>2]=d[t+1|0],c=d[t+8|0]|d[t+9|0]<<8|d[t+10|0]<<16|d[t+11|0]<<24,r=d[t+4|0]|d[t+5|0]<<8|d[t+6|0]<<16|d[t+7|0]<<24,f[0|s]=r,f[s+1|0]=r>>>8,f[s+2|0]=r>>>16,f[s+3|0]=r>>>24,f[s+4|0]=c,f[s+5|0]=c>>>8,f[s+6|0]=c>>>16,f[s+7|0]=c>>>24,c=d[t+16|0]|d[t+17|0]<<8|d[t+18|0]<<16|d[t+19|0]<<24,r=d[t+12|0]|d[t+13|0]<<8|d[t+14|0]<<16|d[t+15|0]<<24,f[s+8|0]=r,f[s+9|0]=r>>>8,f[s+10|0]=r>>>16,f[s+11|0]=r>>>24,f[s+12|0]=c,f[s+13|0]=c>>>8,f[s+14|0]=c>>>16,f[s+15|0]=c>>>24,c=d[t+24|0]|d[t+25|0]<<8|d[t+26|0]<<16|d[t+27|0]<<24,r=d[t+20|0]|d[t+21|0]<<8|d[t+22|0]<<16|d[t+23|0]<<24,f[s+16|0]=r,f[s+17|0]=r>>>8,f[s+18|0]=r>>>16,f[s+19|0]=r>>>24,f[s+20|0]=c,f[s+21|0]=c>>>8,f[s+22|0]=c>>>16,f[s+23|0]=c>>>24,c=d[t+32|0]|d[t+33|0]<<8|d[t+34|0]<<16|d[t+35|0]<<24,r=d[t+28|0]|d[t+29|0]<<8|d[t+30|0]<<16|d[t+31|0]<<24,f[s+24|0]=r,f[s+25|0]=r>>>8,f[s+26|0]=r>>>16,f[s+27|0]=r>>>24,f[s+28|0]=c,f[s+29|0]=c>>>8,f[s+30|0]=c>>>16,f[s+31|0]=c>>>24,r=t+36|0,e[s+32>>2]=r,t=r+(i<<4)|0,(0|V)!=(0|(I=I+1|0)););(0|V)<=e[34457]&&(e[34457]=0),t=0,x&&(e[x>>2]=T)}else e:{A:{if(i){if(r=e[i>>2]){fe(e[r+4>>2]),t=e[i>>2];break A}if(t=HA(16),e[i>>2]=t,t)break A;t=48}else t=268436223;break e}e[t>>2]=1,e[t+4>>2]=ui(137584),r=e[i>>2],e[r+12>>2]=83969,e[r+8>>2]=I,t=268436223}if(H=l+16|0,r=t,!t){if(x=e[g+12>>2],e[50754]=x,e[50759]=0,e[50760]=134217728/(0|x),e[50762]=0,e[50763]=0,e[50765]=2147483647,e[50781]=100,e[50779]=32,e[50761]=(x<<6)/(0|x),t=e[26385],e[50784]=e[26384],e[50785]=t,t=e[26387],e[50786]=e[26386],e[50787]=t,t=e[26389],e[50788]=e[26388],e[50789]=t,t=e[26391],e[50790]=e[26390],e[50791]=t,t=e[26393],e[50792]=e[26392],e[50793]=t,t=e[26395],e[50794]=e[26394],e[50795]=t,t=e[26397],e[50796]=e[26396],e[50797]=t,e[50798]=e[26398],T=(0|(t=(0|(i=O(x,60)))/12800|0))>=128?128:t,e[50799]=T,e[50800]=(0|T)/2,!((0|x)==22050|(0|i)<12800)){if(x=1&(t=(0|T)<=1?1:T),te=+(0|T),I=0,(0|T)>=2)for(T=2147483646&t,t=0;K=I+132160|0,m=127*(1-ds(6.283185307179586*+(0|I)/te)),_=ee(m)<2147483648?~~m:-2147483648,f[0|K]=_,K=(i=1|I)+132160|0,m=127*(1-ds(6.283185307179586*+(0|i)/te)),_=ee(m)<2147483648?~~m:-2147483648,f[0|K]=_,I=I+2|0,(0|T)!=(0|(t=t+2|0)););x&&(t=I+132160|0,m=127*(1-ds(6.283185307179586*+(0|I)/te)),K=ee(m)<2147483648?~~m:-2147483648,f[0|t]=K)}if(e[50801]=105792,e[56797]=Dr(),e[55964]=38,e[55921]=1,e[55918]=22050,e[56606]=0,e[55960]=110928,e[55958]=0,e[55959]=1074266112,e[55956]=100,e[55922]=20,e[55923]=220,e[55916]=1,e[55917]=0,ct(),e[56244]=0,e[56245]=0,e[55928]=0,e[55926]=0,e[55927]=0,e[55924]=0,e[56246]=0,e[56247]=0,e[56260]=0,e[56261]=0,e[56262]=0,e[56263]=0,e[56276]=0,e[56277]=0,e[56278]=0,e[56279]=0,e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,te=-3.141592653589793/+(0|(t=e[55918])),P[27967]=te,i=(0|O(t,630))/1e4|0,e[55920]=i,t=(0|O(t,950))/1e4|0,e[55919]=t,m=-2*te,P[27968]=m,te=(Fe=$r(te*+(0|i)))*-Fe,P[28129]=te,m=Fe*ds(m*+(0|t)),m+=m,P[28128]=m,P[28127]=1-m-te,e[55990]=0,e[55991]=0,e[55988]=0,e[55989]=0,e[56006]=0,e[56007]=0,e[56004]=0,e[56005]=0,e[56022]=0,e[56023]=0,e[56020]=0,e[56021]=0,e[56038]=0,e[56039]=0,e[56036]=0,e[56037]=0,e[56054]=0,e[56055]=0,e[56052]=0,e[56053]=0,e[56070]=0,e[56071]=0,e[56068]=0,e[56069]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0,e[56639]=59,e[56640]=59,e[56629]=0,e[56630]=59,e[56619]=89,e[56620]=160,e[56609]=280,e[56610]=688,e[56611]=1064,e[56621]=70,e[56631]=59,e[56612]=2806,e[56613]=3260,e[56622]=160,e[56623]=200,e[56632]=59,e[56633]=59,e[56641]=89,e[56642]=149,e[56643]=200,e[56644]=200,e[56634]=59,e[56635]=59,e[56624]=200,e[56625]=500,e[56614]=3700,e[56615]=6500,e[56645]=500,e[56646]=0,e[56616]=7e3,e[56626]=500,e[56636]=0,e[56647]=0,e[56637]=0,e[56627]=500,e[56617]=8e3,e[56669]=89,e[56648]=0,e[56638]=0,e[56628]=89,e[56618]=280,e[56657]=62,e[56655]=0,e[56656]=0,e[56653]=50,e[56654]=0,e[56651]=0,e[56652]=0,e[56649]=0,e[56650]=40,e[56607]=1e3,e[56608]=59,H=l=H-416|0,e[l+16>>2]=137584,e[l+20>>2]=47,e[l+24>>2]=85952,dA(t=l+240|0,85699,l+16|0),c=us(t,86034)){if(gt(l+240|0,170,c))for(x=5|(t=l+240|0),T=10|t;d[l+240|0]!=47&&(e[l+240>>2]!=1701736308?Kr(l+240|0,86614,9)||(e[l+4>>2]=l+32,e[l>>2]=l+239,(0|KA(T,86829,l))==2&&(i=e[34064],e[(V=136272+(i<<4)|0)>>2]=f[l+239|0],t=ui(l+32|0),e[34064]=i+1,e[V+12>>2]=t,e[V+4>>2]=0)):(H=t=H-48|0,e[32960]=-1,e[32961]=-1,e[32970]=-1,e[32971]=-1,e[32968]=-1,e[32969]=-1,e[32966]=-1,e[32967]=-1,e[32964]=-1,e[32965]=-1,e[32962]=-1,e[32963]=-1,e[t+36>>2]=131876,e[t+32>>2]=131872,e[t+28>>2]=131868,e[t+24>>2]=131864,e[t+20>>2]=131860,e[t+16>>2]=131856,e[t+12>>2]=131852,e[t+8>>2]=131848,e[t+4>>2]=131844,e[t>>2]=131840,KA(x,84222,t),H=t+48|0)),gt(l+240|0,170,c););Er(c)}H=l+416|0,e[50297]=0,e[50298]=0,e[50301]=0,e[50302]=0,e[50299]=0,e[50300]=0,sn(0,85698),e[36425]=0,e[36424]=0,e[36426]=0,e[36427]=-1,ho(),Ke(0),s=e[25690],e[34062]=s,h=e[25689],l=e[25688],e[34060]=l,e[34061]=h,J=e[25687],c=e[25686],e[34058]=c,e[34059]=J,ce=e[25685],V=e[25684],e[34056]=V,e[34057]=ce,Ee=e[25683],x=e[25682],e[34054]=x,e[34055]=Ee,Te=e[25681],T=e[25680],e[34052]=T,e[34053]=Te,_=e[25679],i=e[25678],e[34050]=i,e[34051]=_,K=e[25677],t=e[25676],e[34048]=t,e[34049]=K,e[33729]=t,e[33730]=K,e[33731]=i,e[33732]=_,e[33733]=T,e[33734]=Te,e[33735]=x,e[33736]=Ee,e[33737]=V,e[33738]=ce,e[33739]=c,e[33740]=J,e[33741]=l,e[33742]=h,e[33743]=s,lr(1,175),lr(2,100),lr(6,e[47200]),lr(5,e[47201]),lr(7,0),e[47198]=0,e[47197]=0,m=+Ce()/1e3,i=st(t=ee(m)<9223372036854776e3?~~m>>>0:0,0,1103515245,0),t=le,t=(i=i+12345|0)>>>0<12345?t+1|0:t,e[33209]=zi(i,t)}if(H=g+16|0,r){K=e[30450],Ee=e[he+12>>2],g=(h=H-560|0)+48|0,H=s=(H=h)-16|0;e:{A:switch(0|es(r-268435967|0,24)){case 0:Lt(g,84133,512);break e;case 1:Lt(g,84580,512);break e;case 2:Lt(g,84747,512);break e;case 3:Lt(g,85084,512);break e;case 4:Lt(g,85251,512);break e;case 5:Lt(g,85380,512);break e;case 6:Lt(g,85607,512);break e;case 7:Lt(g,85722,512);break e;case 8:Lt(g,85913,512);break e;case 9:Lt(g,86046,512);break e;case 10:Lt(g,86153,512);break e;case 11:Lt(g,86678,512);break e;case 12:Lt(g,86773,512);break e;case 14:Lt(g,86958,512);break e;case 15:Lt(g,87071,512);break e;default:break A}if(1879048192&r)e[s>>2]=r,zs(g,512,87182,s);else{if(I=0,l=y[123728+((r>>>0<=153?r:0)<<1)>>1]+121804|0,r=e[e[56841]+20>>2]){Te=e[r+4>>2],J=e[r>>2],ce=e[J>>2]+1794895138|0,_=$a(e[J+8>>2],ce),i=$a(e[J+12>>2],ce),t=$a(e[J+16>>2],ce);A:if(!(Te>>>2>>>0<=_>>>0||3&(t|i)|(r=Te-(_<<2)|0)>>>0<=i>>>0|t>>>0>=r>>>0))for(x=t>>>2|0,T=i>>>2|0;;){if(V=$a(e[(r=((t=(i=(c=_>>>1|0)+Le|0)<<1)+T<<2)+J|0)>>2],ce),(r=$a(e[r+4>>2],ce))>>>0>=Te>>>0|V>>>0>=Te-r>>>0|d[(r+V|0)+J|0])break A;if(!(r=Ar(l,r+J|0))){if(t=$a(e[(r=(t+x<<2)+J|0)>>2],ce),(r=$a(e[r+4>>2],ce))>>>0>=Te>>>0|t>>>0>=Te-r>>>0)break A;I=d[(t+r|0)+J|0]?0:r+J|0;break A}if((0|_)==1)break A;_=(r=(0|r)<0)?c:_-c|0,Le=r?Le:i}}if((r=MA(t=I||l))>>>0>=512){qA(g,t,511),f[g+511|0]=0;break e}qA(g,t,r+1|0)}}H=s+16|0;e:if(Ee){A:switch(e[Ee>>2]){case 0:e[h+16>>2]=e[Ee+4>>2],e[h+20>>2]=h+48,Xt(K,87384,h+16|0);break e;case 1:break A;default:break e}t=e[Ee+12>>2],r=e[Ee+8>>2],e[h+36>>2]=e[Ee+4>>2],le=r,e[h+40>>2]=t,e[h+44>>2]=le,e[h+32>>2]=h+48,Xt(K,87521,h+32|0)}else e[h>>2]=h+48,Xt(K,87700,h);H=h+560|0,(0|he)!=-12&&(r=e[he+12>>2])&&(fe(e[r+4>>2]),fe(e[he+12>>2]),e[he+12>>2]=0)}r=e[24806],e[34389]=0,e[32538]=r,r=(1e3+((r=O(e[50754],100))-((0|r)%1e3|0)|0)|0)/500|0,e[34390]=r,r=lt(e[34391],r),e[34392]=r,r&&(e[34391]=r,e[34393]=40,(r=lt(e[34388],1440))&&(e[34388]=r)),e[47198]=0,H=he+16|0,t=e[50754],e[33208]=t}return e[A+4>>2]=t,e[A>>2]=kA(),0|A},Y:function(A,t,r){A|=0,t|=0,r|=0,e[34440]=r,eA(3,e[A+12>>2]),eA(1,e[A+8>>2]),(A=e[A+16>>2])?Wa(A):Ka(1024),pA(t),e[34440]=0},Z:function(A,t,r){return A|=0,t|=0,r|=0,e[34440]=0,(A=us(r,1032))?(e[47195]=A,e[47197]=130,A||(e[47195]=e[30450]),pA(t),e[47195]=0,e[47197]=0,e[47195]=e[30450],Er(A),0):-1},_:function(A){return 36},$:function(A,t,r){var s;return A|=0,t|=0,H=s=H-32|0,(r|=0)?(e[s+24>>2]=0,e[s+28>>2]=0,e[s+16>>2]=0,e[s+20>>2]=0,e[s+12>>2]=r,e[s+8>>2]=t,f[s+21|0]=0,t=Wa(s+8|0)):t=Ka(t),e[A+16>>2]=201188,H=s+32|0,0|t},aa:function(A,t,r,s){var i;return A|=0,t|=0,H=i=H-32|0,(r|=0)|(s|=0)?(e[i+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+12>>2]=r,e[i+8>>2]=t,f[i+22|0]=0,f[i+20|0]=s,t=Wa(i+8|0)):t=Ka(t),e[A+16>>2]=201188,H=i+32|0,0|t},ba:function(A,t,r,s,i){var l;return A|=0,t|=0,H=l=H-32|0,(s|=0)|(i|=0)|(r|=0)?(e[l+24>>2]=0,e[l+28>>2]=0,e[l+16>>2]=0,e[l+20>>2]=0,e[l+12>>2]=r,e[l+8>>2]=t,f[l+21|0]=i,f[l+20|0]=s,t=Wa(l+8|0)):t=Ka(t),e[A+16>>2]=201188,H=l+32|0,0|t},ca:function(A,t,r,s,i,l){var c;return A|=0,t|=0,H=c=H-32|0,(s|=0)|(i|=0)|(l|=0)|(r|=0)?(e[c+24>>2]=0,e[c+28>>2]=0,e[c+16>>2]=0,e[c+20>>2]=0,e[c+12>>2]=r,e[c+8>>2]=t,f[c+22|0]=l,f[c+21|0]=i,f[c+20|0]=s,t=Wa(c+8|0)):t=Ka(t),e[A+16>>2]=201188,H=c+32|0,0|t},da:function(A,t){return t|=0,e[e[(A|=0)>>2]+(t<<2)>>2]},ea:function(A,t,r){t|=0,r|=0,e[e[(A|=0)>>2]+(t<<2)>>2]=r},fa:Wi,ga:ji,ha:Ni,ia:wo,ja:function(A,t){t|=0,e[(A|=0)+12>>2]=t},ka:jn,la:function(){return 0},ma:function(){return 1},na:function(){return 2},oa:function(){return 3},pa:function(){return 4},qa:function(){return 5},ra:function(){return 6},sa:function(){return 7},ta:function(){return 8},ua:$A,va:function(){return 227192},wa:fe,xa:HA,ya:function(A){return(A|=0)?0|!!(0|DA(A,125228)):0}}})(n)})(o)},instantiate:function(a,o){return{then:function(n){var u=new uo.Module(a);n({instance:new uo.Instance(u,o)})}}},RuntimeError:Error};typeof uo!="object"&&xn("no native wasm support detected");var fb=!1;function Y2(a,o){a||xn(o)}var Fs,ao,H0,Wt,Jr,gb,pb,Im=typeof TextDecoder<"u"?new TextDecoder("utf8"):void 0;function so(a,o,n){for(var u=o+n,p=o;a[p]&&!(p>=u);)++p;if(p-o>16&&a.buffer&&Im)return Im.decode(a.subarray(o,p));for(var b="";o<p;){var C=a[o++];if(128&C){var w=63&a[o++];if((224&C)!=192){var M=63&a[o++];if((C=(240&C)==224?(15&C)<<12|w<<6|M:(7&C)<<18|w<<12|M<<6|63&a[o++])<65536)b+=String.fromCharCode(C);else{var v=C-65536;b+=String.fromCharCode(55296|v>>10,56320|1023&v)}}else b+=String.fromCharCode((31&C)<<6|w)}else b+=String.fromCharCode(C)}return b}function no(a,o){return a?so(ao,a,o):""}function Hc(a,o,n,u){if(!(u>0))return 0;for(var p=n,b=n+u-1,C=0;C<a.length;++C){var 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`){++p;continue}if(D===u)break;let B=p-1;for(;B>=0&&/\S/.test(n[B]);)B--;B=Math.max(o,B+1);const E=B5(n,B);if(!E){++p;continue}if((/https?[,:]\/\//.test(E)||E.includes("@"))&&!X2(E.at(-1))){p=B+E.length;continue}if(D5(E)){++p;continue}if(/^([A-Za-z]\.)+$/.test(E)&&D<u&&/[A-Z]/.test(n[D])){++p;continue}if(w==="."&&D<u&&/[a-z]/.test(n[D])){++p;continue}const S=n.substring(o,v+1).trim();if(S==="..."||S==="…"){++p;continue}S&&this._sentences.push(S),p=o=v+1}else++p}this._buffer=n.substring(o),this._sentences.length>0&&this._resolve()}async*[Symbol.asyncIterator](){if(this._resolver)throw new Error("Another iterator is already active.");for(;;)if(this._sentences.length>0)yield this._sentences.shift();else{if(this._closed)break;await new Promise((o=>{this._resolver=o}))}}[Symbol.iterator](){this.flush();const o=this._sentences[Symbol.iterator]();return this._sentences=[],o}get sentences(){return this._sentences}}const Ro=Object.freeze({af_heart:{name:"Heart",language:"en-us",gender:"Female",traits:"❤️",targetQuality:"A",overallGrade:"A"},af_alloy:{name:"Alloy",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C"},af_aoede:{name:"Aoede",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_bella:{name:"Bella",language:"en-us",gender:"Female",traits:"🔥",targetQuality:"A",overallGrade:"A-"},af_jessica:{name:"Jessica",language:"en-us",gender:"Female",targetQuality:"C",overallGrade:"D"},af_kore:{name:"Kore",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_nicole:{name:"Nicole",language:"en-us",gender:"Female",traits:"🎧",targetQuality:"B",overallGrade:"B-"},af_nova:{name:"Nova",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C"},af_river:{name:"River",language:"en-us",gender:"Female",targetQuality:"C",overallGrade:"D"},af_sarah:{name:"Sarah",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_sky:{name:"Sky",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C-"},am_adam:{name:"Adam",language:"en-us",gender:"Male",targetQuality:"D",overallGrade:"F+"},am_echo:{name:"Echo",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_eric:{name:"Eric",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_fenrir:{name:"Fenrir",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_liam:{name:"Liam",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_michael:{name:"Michael",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_onyx:{name:"Onyx",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_puck:{name:"Puck",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_santa:{name:"Santa",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D-"},bf_emma:{name:"Emma",language:"en-gb",gender:"Female",traits:"🚺",targetQuality:"B",overallGrade:"B-"},bf_isabella:{name:"Isabella",language:"en-gb",gender:"Female",targetQuality:"B",overallGrade:"C"},bm_george:{name:"George",language:"en-gb",gender:"Male",targetQuality:"B",overallGrade:"C"},bm_lewis:{name:"Lewis",language:"en-gb",gender:"Male",targetQuality:"C",overallGrade:"D+"},bf_alice:{name:"Alice",language:"en-gb",gender:"Female",traits:"🚺",targetQuality:"C",overallGrade:"D"},bf_lily:{name:"Lily",language:"en-gb",gender:"Female",traits:"🚺",targetQuality:"C",overallGrade:"D"},bm_daniel:{name:"Daniel",language:"en-gb",gender:"Male",traits:"🚹",targetQuality:"C",overallGrade:"D"},bm_fable:{name:"Fable",language:"en-gb",gender:"Male",traits:"🚹",targetQuality:"B",overallGrade:"C"}}),Z2=new Map;async function G5(a){if(Z2.has(a))return Z2.get(a);const o=new Float32Array(await(async function(n){if(G0&&Object.hasOwn(G0,"readFile")){const w=typeof __dirname<"u"?__dirname:import.meta.dirname,M=G0.resolve(w,`../voices/${n}.bin`),{buffer:v}=await G0.readFile(M);return v}const u=`https://huggingface.co/onnx-community/Kokoro-82M-v1.0-ONNX/resolve/main/voices/${n}.bin`;let p;try{p=await caches.open("kokoro-voices");const w=await p.match(u);if(w)return await w.arrayBuffer()}catch(w){console.warn("Unable to open cache",w)}const b=await fetch(u),C=await b.arrayBuffer();if(p)try{await p.put(u,new Response(C,{headers:b.headers}))}catch(w){console.warn("Unable to cache file",w)}return C})(a));return Z2.set(a,o),o}class B8{constructor(o,n){this.model=o,this.tokenizer=n}static async from_pretrained(o,{dtype:n="fp32",device:u=null,progress_callback:p=null}={}){const b=f5.from_pretrained(o,{progress_callback:p,dtype:n,device:u}),C=u5.from_pretrained(o,{progress_callback:p}),w=await Promise.all([b,C]);return new B8(...w)}get voices(){return Ro}list_voices(){console.table(Ro)}_validate_voice(o){if(!Ro.hasOwnProperty(o))throw console.error(`Voice "${o}" not found. Available voices:`),console.table(Ro),new Error(`Voice "${o}" not found. Should be one of: ${Object.keys(Ro).join(", ")}.`);return o.at(0)}async generate(o,{voice:n="af_heart",speed:u=1}={}){const p=this._validate_voice(n),b=await vm(o,p),{input_ids:C}=this.tokenizer(b,{truncation:!0});return this.generate_from_ids(C,{voice:n,speed:u})}async generate_from_ids(o,{voice:n="af_heart",speed:u=1}={}){const p=256*Math.min(Math.max(o.dims.at(-1)-2,0),509),b=(await G5(n)).slice(p,p+256),C={input_ids:o,style:new mm("float32",b,[1,256]),speed:new mm("float32",[u],[1])},{waveform:w}=await this.model(C);return new d5(w.data,24e3)}async*stream(o,{voice:n="af_heart",speed:u=1,split_pattern:p=null}={}){const b=this._validate_voice(n);let C;if(o instanceof xm)C=o;else{if(typeof o!="string")throw new Error("Invalid input type. Expected string or TextSplitterStream.");{C=new xm;const w=p?o.split(p).map((M=>M.trim())).filter((M=>M.length>0)):[o];C.push(...w)}}for await(const w of C){const M=await vm(w,b),{input_ids:v}=this.tokenizer(M,{truncation:!0}),D=await this.generate_from_ids(v,{voice:n,speed:u});yield{text:w,phonemes:M,audio:D}}}}const F5={set wasmPaths(a){hm.backends.onnx.wasm.wasmPaths=a},get wasmPaths(){return hm.backends.onnx.wasm.wasmPaths}};export{B8 as KokoroTTS,xm as TextSplitterStream,F5 as env};
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