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workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${n.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / 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e=(e,A,t="")=>{let n=`aData[indexA${A}][componentA${A}]`,r=`bData[indexB${A}][componentB${A}]`;return`\n            let outputIndices${A} = ${B.offsetToIndices(`global_idx * 4u + ${A}u`)};\n            let offsetA${A} = ${y.broadcastedIndicesToOffset(`outputIndices${A}`,B)};\n            let offsetB${A} = ${v.broadcastedIndicesToOffset(`outputIndices${A}`,B)};\n            let indexA${A} = offsetA${A} / 4u;\n            let indexB${A} = offsetB${A} / 4u;\n            let componentA${A} = offsetA${A} % 4u;\n            let componentB${A} = offsetB${A} % 4u;\n            ${e}[${A}] = ${t}(${p(n,r)});\n          `};f=9===l?`\n            var data = vec4<u32>(0);\n            ${e("data",0,"u32")}\n            ${e("data",1,"u32")}\n            ${e("data",2,"u32")}\n            ${e("data",3,"u32")}\n            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:`\n            ${e("outputData[global_idx]",0)}\n            ${e("outputData[global_idx]",1)}\n            ${e("outputData[global_idx]",2)}\n            ${e("outputData[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables(y,v,B)}\n\n        ${c??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${f}\n      }`},Kn=(e,A,t,n,r,i,a=t.dataType)=>{let s=!fA.areEqual(t.dims,n.dims),o=t.dims,u=fA.size(t.dims),l=!1,d=!1,c=[s];if(s){let e=hA.calcShape(t.dims,n.dims,!1);if(!e)throw new Error("Can't perform binary op on the given tensors");o=e,u=fA.size(o);let A=1===fA.size(t.dims),r=1===fA.size(n.dims),i=t.dims.length>0&&t.dims[t.dims.length-1]%4==0,a=n.dims.length>0&&n.dims[n.dims.length-1]%4==0;c.push(A),c.push(r),c.push(i),c.push(a);let s=1;for(let e=1;e<o.length;e++){let A=t.dims[t.dims.length-e]??1;if(A!==(n.dims[n.dims.length-e]??1))break;s*=A}s%4==0?(d=!0,l=!0):(A||r||i||a)&&(l=!0)}else l=!0;c.push(l);let p=kA(t.dims.length)&&kA(n.dims.length)&&kA(o.length);return{name:e,shaderCache:{hint:A+c.map((e=>e.toString())).join("_"),inputDependencies:p?["rank","rank"]:["dims","dims"]},getShaderSource:e=>Ln(e,t.dims,n.dims,o,l,s,d,r,t.dataType,n.dataType,a,p,i),getRunData:()=>({outputs:[{dims:o,dataType:a}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:p?[{type:"uint32",data:Math.ceil(fA.size(o)/4)},...CA(t.dims),...CA(n.dims),...CA(o)]:[{type:"uint32",data:Math.ceil(fA.size(o)/4)}]})}},zn=(e,A,t,n,r,i)=>{e.compute(Kn(A,r??"",e.inputs[0],e.inputs[1],t,n,i))},Nn=e=>{zn(e,"Add",((e,A)=>`${e}+${A}`))},Vn=e=>{zn(e,"Div",((e,A)=>`${e}/${A}`))},Gn=e=>{zn(e,"Equal",{scalar:(e,A)=>`u32(${e}==${A})`,vector:(e,A)=>`vec4<u32>(${e}==${A})`},void 0,void 0,9)},Wn=e=>{zn(e,"Mul",((e,A)=>`${e}*${A}`))},qn=e=>{let A=_A("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;zn(e,"Pow",{scalar:(e,A)=>`pow_custom(${e},${A})`,vector:(e,A)=>`pow_vector_custom(${e},${A})`},`\n    fn pow_custom(a : ${A}, b : ${A}) -> ${A} {\n      if (b == ${A}(0.0)) {\n        return ${A}(1.0);\n      } else if (a < ${A}(0.0) && f32(b) != floor(f32(b))) {\n        return ${A}(pow(f32(a), f32(b))); // NaN\n      }\n      return select(sign(a), ${A}(1.0), round(f32(abs(b) % ${A}(2.0))) != 1.0) * ${A}(${"i32"===A?"round":""}(pow(f32(abs(a)), f32(b))));\n    }\n    fn pow_vector_custom(a : vec4<${A}>, b : vec4<${A}>) -> vec4<${A}> {\n      // TODO: implement vectorized pow\n      return vec4<${A}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));\n    }\n      `)},jn=e=>{zn(e,"Sub",((e,A)=>`${e}-${A}`))},Yn=e=>{zn(e,"Greater",{scalar:(e,A)=>`u32(${e}>${A})`,vector:(e,A)=>`vec4<u32>(${e}>${A})`},void 0,void 0,9)},Xn=e=>{zn(e,"Less",{scalar:(e,A)=>`u32(${e}<${A})`,vector:(e,A)=>`vec4<u32>(${e}<${A})`},void 0,void 0,9)},Jn=e=>{zn(e,"GreaterOrEqual",{scalar:(e,A)=>`u32(${e}>=${A})`,vector:(e,A)=>`vec4<u32>(${e}>=${A})`},void 0,void 0,9)},Zn=e=>{zn(e,"LessOrEqual",{scalar:(e,A)=>`u32(${e}<=${A})`,vector:(e,A)=>`vec4<u32>(${e}<=${A})`},void 0,void 0,9)}})),Ps=L((()=>{Us(),Qs(),xs(),er=e=>{if(!e||e.length<1)throw new Error("too few inputs");let A=e[0].dataType,t=e[0].dims.length;for(let n of e){if(n.dataType!==A)throw new Error("input tensors should be one type");if(n.dims.length!==t)throw new Error("input tensors should have the same shape")}},Ar=(e,A)=>`\n  fn calculateInputIndex(index: u32) -> u32 {\n    let sizeInConcatAxis = array<u32, ${e}u>(${A});\n    for (var i: u32 = 0u; i < ${e}; i += 1u ) {\n      if (index < sizeInConcatAxis[i]) {\n        return i;\n      }\n    }\n    return ${e}u;\n  }`,tr=(e,A)=>{let t=e.length,n=[];for(let r=0;r<t;++r){let i=A.setByOffset("global_idx",e[r].getByIndices("indices"));1===t?n.push(i):0===r?n.push(`if (inputIndex == ${r}u) { ${i} }`):r===t-1?n.push(`else { ${i} }`):n.push(`else if (inputIndex == ${r}) { ${i} }`)}return n.join("\n")},nr=(e,A)=>{let t=e[0].dims.slice();if(A>=t.length||A<-1*t.length)throw new Error("axis specified for concat doesn't match input dimensionality");let n=A<0?t.length+A:A,r=t.slice(0);for(let A=1;A<e.length;A++){let i=e[A].dims.slice();for(let e=0;e<t.length;e++)if(e===n)r[n]+=i[e];else if(t[e]!==i[e])throw new Error("non concat dimensions must match")}let i=fA.size(r),a=new Array(e.length),s=new Array(e.length),o=e[0].dataType,u=0,l=[],d=[],c=[],p=[{type:"uint32",data:i}];for(let A=0;A<e.length;++A)u+=e[A].dims[n],a[A]=u,c.push(kA(e[A].dims.length)),d.push(c[A]?e[A].dims.length:e[A].dims),s[A]=_A(`input${A}`,o,d[A]),l.push(c[A]?"rank":"dims"),p.push({type:"uint32",data:a[A]});for(let A=0;A<e.length;++A)c[A]&&p.push(...CA(e[A].dims));let h=kA(r.length);h&&p.push(...CA(r));let f=h?r.length:r,g=FA("output",o,f),m=g.indicesGet("indices",n),w=Array.from(Array(a.length).keys()).map((e=>`uniforms.sizeInConcatAxis${e}`)).join(",");return{name:"Concat",shaderCache:{hint:`${A}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:A=>`\n\n  ${(()=>{A.registerUniform("outputSize","u32");for(let t=0;t<e.length;t++)A.registerUniform(`sizeInConcatAxis${t}`,"u32");return A.declareVariables(...s,g)})()}\n\n  ${Ar(a.length,w)}\n\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n    var indices = ${g.offsetToIndices("global_idx")};\n\n    let inputIndex = calculateInputIndex(${m});\n    if (inputIndex != 0u) {\n      let sizeInConcatAxis = array<u32, ${a.length}u>(${w});\n      ${m} -= sizeInConcatAxis[inputIndex - 1u];\n    }\n\n    ${tr(s,g)}\n  }`}},rr=(e,A)=>{er(e.inputs),e.compute(nr(e.inputs,A.axis))},ir=e=>cA({axis:e.axis})})),Ls=L((()=>{Us(),ar=(e,A)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${A}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${A}(1.0) / (${A}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${A}(${e.clipMin});const clip_max_=${A}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},sr=e=>{let A=e?.activation||"";if("Clip"===A){let[t,n]=e?.activation_params||[wA,BA];return{activation:A,clipMax:n,clipMin:t,activationCacheKey:`${A}:${t},${n}`}}return{activation:A,activationCacheKey:A}}})),Ks=L((()=>{or=(e,A)=>{switch(e){case 1:return A;case 2:return`vec2<${A}>`;case 3:return`vec3<${A}>`;case 4:return`vec4<${A}>`;default:throw new Error(`${e}-component is not supported.`)}},ur=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `})),zs=L((()=>{lr=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`})),Ns=L((()=>{Us(),xs(),Ls(),Ks(),dr=(e,A)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${A?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${A?", batchIndices":""});\n        `,cr=(e,A)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${3===A?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${3===A?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${3===A?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,pr=(e,A,t="f32",n,r=!1,i=32,a=!1,s=32)=>{let o=A[1]*e[1],u=A[0]*e[0],l=r?o:i,d=r?i:o,c=l/A[0],p=i/A[1];if((!r||4!==c||4!==e[1])&&(r||3!==c&&4!==c)||l%A[0]!=0||i%A[1]!=0||4!==e[0])throw new Error(`If transposeA ${r} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${c} must be 3 or 4.\n  tileAWidth ${l} must be divisible by workgroupSize[0]${A[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${A[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${c}<${t}>, ${l/c}>, ${d}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${t}>, ${u/e[0]}>, ${i}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${c};\nconst tileInner = ${i};\n\n@compute @workgroup_size(${A[0]}, ${A[1]}, ${A[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${a?"0":"i32(globalId.z)"};\n  ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${o};\n\n  let numTiles = ${a?`${Math.ceil(s/i)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n  var kStart = ${a?`i32(globalId.z) * ${s}`:"0"};\n\n  var acc: array<vec4<${t}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${p};\n  for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let inputRow = tileRow + innerRow;\n          let inputCol = tileCol;\n          ${dr(r,n)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${3===c?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${cr(r,c)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},hr=(e,A)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${A?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${A?", batchIndices":""});\n            `,fr=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",gr=(e,A,t="f32",n,r=!1,i=32,a=!1,s=32,o=!1)=>{let u=e[1]*A[1],l=e[0]*A[0],d=r?u:i,c=r?i:u;if(c%A[1]!=0||d%A[0]!=0||i%A[1]!=0)throw new Error(`tileAHight ${c} must be divisible by workgroupSize[1]${A[1]}, tileAWidth ${d} must be divisible by workgroupSize[0]${A[0]}, tileInner ${i} must be divisible by workgroupSize[1]${A[1]}`);let p=c/A[1],h=d/A[0],f=i/A[1],g=o?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${u};\n    let globalColStart = i32(workgroupId.x) * ${l};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${A[1]}) {\n        for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${A[0]}) {\n          ${hr(r,n)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${A[1]}) {\n            for (var inputCol = localCol; inputCol < ${l}; inputCol = inputCol + ${A[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${n?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${t}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${A[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${r?`mm_Asub[k][localRow + innerRow * ${A[1]}];`:`mm_Asub[localRow + innerRow * ${A[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${A[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${A[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${u};\n\nlet tileRowA = i32(localId.y) * ${p};\nlet tileColA = i32(localId.x) * ${h};\nlet tileRowB = i32(localId.y) * ${f};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${h}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${hr(r,n)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${n?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${t}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${fr(r)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${t}, ${d}>, ${c}>;\n  var<workgroup> mm_Bsub : array<array<${t}, ${l}>, ${i}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${i};\n\n@compute @workgroup_size(${A[0]}, ${A[1]}, ${A[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${a?"0":"i32(globalId.z)"};\n    ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""}\n    let numTiles = ${a?`${Math.ceil(s/i)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n    var kStart = ${a?`i32(globalId.z) * ${s}`:"0"};\n\n    var acc : array<array<${t}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${g}\n  }\n`},mr=(e,A,t,n,r,i=!1)=>{let[a,s,o]=r,[u,l,d,c]=n,p=HA(a,o),h=HA(s,o),f=vA(n[0].type.tensor);return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${or(e,f)} {\n      var value = ${or(e,f)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimAOuter && col < uniforms.dimInner)\n      {\n        ${(()=>{let e=l.rank,A=u.rank,t=`var aIndices: ${l.type.indices};`;for(let n=e-2-1,r=A-1;n>=0;n--,r--)t+=`\naIndices[${n}] = ${A>1?`batchIndices[${r}]`:"batchIndices"};`;return p.forEach((e=>{t+=`\naIndices[${e}] = 0;`})),t+=`\naIndices[${e-2}] = u32(row);\n                   aIndices[${e-1}] = u32(colIn);`,t})()}\n        value = ${l.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${or(e,f)} {\n      var value = ${or(e,f)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimInner && col < uniforms.dimBOuter)\n      {\n        ${(()=>{let e=d.rank,A=u.rank,t=`var bIndices: ${d.type.indices};`;for(let n=e-2-1,r=A-1;n>=0;n--,r--)t+=`\nbIndices[${n}] = ${A>1?`batchIndices[${r}]`:"batchIndices"};`;return h.forEach((e=>{t+=`\nbIndices[${e}] = 0;`})),t+=`\nbIndices[${e-2}] = u32(row);\n                   bIndices[${e-1}] = u32(colIn);`,t})()}\n        value = ${d.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${or(e,f)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${A?`value = value + ${i?"bias[colIn]":`${or(e,f)}(bias[row])`};`:""}\n        ${t}\n        ${c.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},wr=(e,A,t,n,r=!1)=>{let i=e[0].dims,a=e[1].dims,s=i.slice(0,-2),o=a.slice(0,-2),u=n?n.slice(0,-2):t.slice(0,-2),l=kA(u.length),d=l?u.length:u,c=SA("batchDims",e[0].dataType,d,1),p=fA.size(u),h=i[i.length-2],f=i[i.length-1],g=a[a.length-1],m=f%4==0&&g%4==0,w=h<=8?[4,1,1]:[4,4,1],B=[8,8,1],y=[Math.ceil(g/B[0]/w[0]),Math.ceil(h/B[1]/w[1]),Math.ceil(p/B[2]/w[2])],v=vA(e[0].dataType),b=m?4:1,C=[...s,h,f/b],$=kA(C.length),I=$?C.length:C,E=[...o,f,g/b],Q=kA(E.length),U=Q?E.length:E,x=[p,h,g/b],_=_A("a",e[0].dataType,I,b),F=_A("b",e[1].dataType,U,b),S=FA("result",e[0].dataType,x.length,b),T=[_,F],O=[{type:"int32",data:h},{type:"int32",data:g},{type:"int32",data:f}];l&&O.push(...CA(u)),$&&O.push(...CA(C)),Q&&O.push(...CA(E));let H=[];H.push($?"rank":"dims"),H.push(Q?"rank":"dims");let k=e.length>2,{activationFunction:D,applyActivation:M}=ar(A,S.type.value),R=mr(b,k,M,[c,_,F,S],[s,o,u],r);if(k){let A=r?b:1;T.push(_A("bias",e[2].dataType,e[2].dims.length,A)),O.push(...CA(e[2].dims)),H.push("rank")}O.push(...CA(x));return{name:"MatMul",shaderCache:{hint:A.activationCacheKey+`${w}${m}${r}`,inputDependencies:H},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:y[0],y:y[1],z:y[2]},programUniforms:O}),getShaderSource:e=>`\n  ${e.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(c).declareVariables(...T,S)}\n  ${D}\n  ${R}\n  ${m?pr(w,B,v,c):gr(w,B,v,c)}\n                   `}}})),Vs=L((()=>{Cs(),xs(),Ls(),Ks(),zs(),Ns(),Br=(e,A,t,n,r=!1,i,a=4,s=4,o=4,u="f32")=>{let l=e?"\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    ":"\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    ",d=e?"\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    ":"\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    ",c=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",p=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",h=e?"row":"col",f=e?"col":"row",g=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${h} / outWidth;\n    let outCol = ${h} % outWidth;\n\n    let WRow = ${f} / (filterDims[1] * inChannels);\n    let WCol = ${f} / inChannels % filterDims[1];\n    let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];\n    let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];\n    let xCh = ${f} % inChannels;\n    var resData = ${or(a,u)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the 'same' padding type.\n    if (xRow >= 0 && xRow < ${c} && xCol >= 0 && xCol < ${p}) {\n      ${l}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${(e=>{switch(e){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(a)}\n    }\n    return resData;`,m=e?A&&n?`\n    let col = colIn * ${a};\n    ${g}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${g}\n    }\n    return ${or(a,u)}(0.0);`:n&&t?`\n    let col = colIn * ${a};\n    ${g}`:`\n    let col = colIn * ${a};\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n      ${g}\n    }\n    return ${or(a,u)}(0.0);`,w=`${(e=>{switch(e){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 ${e} is not supported.`)}})(s)}`,B=or(o,u),y=or(e?a:s,u),v=or(e?s:a,u),{activationFunction:b,applyActivation:C}=ar(i,B);return`\n    ${b}\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${y} {\n      ${e?m:w}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${v} {\n      ${e?w:m}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${B}) {\n      let col = colIn * ${o};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${d}\n      ${ur(r)}\n      ${C}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},yr=(e,A,t,n,r,i,a,s)=>{let o="NHWC"===A.format,u=o?e[0].dims[3]:e[0].dims[1],l=t[0],d=o?t[2]:t[3],c=o?t[1]:t[2],p=o?t[3]:t[1],h=o&&(u%4==0||u%3==0)&&p%4==0,f=o?p:d*c,g=o?d*c:p,m=[8,8,1],w=n<=8?[4,1,1]:[4,4,1],B=[Math.ceil(f/m[0]/w[0]),Math.ceil(g/m[1]/w[1]),Math.ceil(l/m[2]/w[2])];nA("verbose",(()=>`[conv2d_mm_webgpu] dispatch = ${B}`));let y=h?o&&u%4!=0?3:4:1,v=m[1]*w[1],b=m[0]*w[0],C=Math.max(m[0]*y,m[1]),$=n%v==0,I=r%b==0,E=i%C==0,Q=h?[y,4,4]:[1,1,1],U=vA(e[0].dataType),x=h?4:1,_=[{type:"int32",data:n},{type:"int32",data:r},{type:"int32",data:i}],F=[_A("x",e[0].dataType,e[0].dims.length,3===y?1:y),_A("w",e[1].dataType,e[1].dims.length,x)];_.push(...CA(e[0].dims)),_.push(...CA(e[1].dims));let S=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${h?`vec4<${U}>`:U}) {\n        result[flatIndex] = ${h?`vec4<${U}>`:U}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${h?`vec4<${U}>`:U}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${h?"/ 4":""}, value);\n      }`;if(a){let A=_A("bias",e[2].dataType,e[2].dims.length,x);F.push(A),_.push(...CA(e[2].dims)),S+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${h?`vec4<${U}>`:U} {\n          return bias[coords.${o?"w":"y"}${h?"/ 4":""}];\n        }`}let T=FA("result",e[0].dataType,t.length,x);return _.push(...CA(t)),{name:"Conv2DMatMul",shaderCache:{hint:A.cacheKey},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:B[0],y:B[1],z:B[2]},programUniforms:_}),getShaderSource:e=>`\n        ${lr("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${e.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...F,T)}\n        const filterDims : vec2<i32> = vec2<i32>(${A.kernelShape[0]}, ${A.kernelShape[1]});\n        const pad : vec2<i32> = vec2<i32>(${A.pads[0]}, ${A.pads[1]});\n        const stride : vec2<i32> = vec2<i32>(${A.strides[0]}, ${A.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${A.dilations[0]}, ${A.dilations[1]});\n        ${S}\n        ${Br(o,$,I,E,a,A,Q[0],Q[1],Q[2],U)}\n            ${h?pr(w,m,U,void 0,!o,C):gr(w,m,U,void 0,!o,C,!1,void 0,s)}`}}})),Gs=L((()=>{Us(),xs(),qs(),Ls(),vr=(e,A,t)=>{let n=e.length>2,r=n?"value += b[output_channel];":"",i=e[0].dims,a=e[1].dims,s=a[0]/A.group,o="NHWC"===A.format,u=Ir(i,a,A.dilations,A.pads,A.strides,o),l=fA.size(u),d=FA("output",e[0].dataType,u),{activationFunction:c,applyActivation:p}=ar(A,d.type.value),h=_A("x",e[0].dataType,i),f=_A("w",e[1].dataType,a),g=[h,f];n&&g.push(_A("b",e[2].dataType,e[2].dims));return{name:"GroupedConv",shaderCache:{hint:A.cacheKey},getRunData:()=>({outputs:[{dims:t?t(u):u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)}}),getShaderSource:e=>`\n  const strides: vec2<u32> = vec2(${A.strides[0]}u, ${A.strides[1]}u);\n  const pads: vec2<u32> = vec2(${A.pads[0]}u, ${A.pads[1]}u);\n\n  ${e.declareVariables(...g,d)}\n\n  ${c}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(l)}\n\n    let outputIndices = ${d.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${o?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${o?1:2}], outputIndices[${o?2:3}]) * strides - pads;\n    let group_id: u32 = output_channel / ${s}u;\n\n    var value: ${d.type.value} = ${d.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < ${a[1]}u; wInChannel++) {\n      let input_channel = group_id * ${a[1]}u + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < ${a[2]}u; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * ${A.dilations[0]}u;\n\n        if (xHeight < 0u || xHeight >= ${i[o?1:2]}u) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < ${a[3]}u; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * ${A.dilations[1]}u;\n          if (xWidth < 0u || xWidth >= ${i[o?2:3]}u) {\n            continue;\n          }\n\n          let xVal = ${o?h.get("batch","xHeight","xWidth","input_channel"):h.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${f.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${r}\n    ${p}\n    ${d.setByOffset("global_idx","value")}\n  }`}}})),Ws=L((()=>{Us(),Ns(),xs(),Ls(),br=(e,A,t,n,r=!1)=>{let i=e[0].dims,a=e[1].dims,s=i[i.length-2],o=a[a.length-1],u=i[i.length-1],l=$A(o),d=$A(u),c=$A(s),p=fA.size(t)/l/c,h=e.length>2,f=n?n.slice(0,-2):t.slice(0,-2),g=[fA.size(f),s,o],m=[{type:"uint32",data:p},{type:"uint32",data:s},{type:"uint32",data:o},{type:"uint32",data:u},...CA(f),...CA(i),...CA(a)];h&&m.push(...CA(e[2].dims)),m.push(...CA(g));return{name:"MatMulNaive",shaderCache:{hint:`${A.activationCacheKey}_${l}_${d}_${c}_${r}`,inputDependencies:h?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:m}),getShaderSource:n=>{let s=SA("batch_dims",e[0].dataType,f.length),o=_A("a",e[0].dataType,i.length,d),u=_A("b",e[1].dataType,a.length,l),p=FA("output",e[0].dataType,g.length,l),{activationFunction:m,applyActivation:w}=ar(A,p.type.value),B=[o,u],y="";if(h){let A=r?l:1;B.push(_A("bias",e[2].dataType,e[2].dims.length,A)),y=""+(r?`value += bias[col / ${A}];`:`value += ${p.type.value}(bias[row + i]);`)}let v=i.slice(0,-2),b=a.slice(0,-2),C=HA(v,f),$=HA(b,f),I=(e,A)=>{let t=e.rank,n=e.name;if(2===t)return`var ${n}_indices = ${e.type.indices}(0u, 0u);`;let r=s.rank,i=`var ${n}_indices: ${e.type.indices};`;for(let e=t-2-1,A=r-1;e>=0;e--,A--)i+=`\n${n}_indices[${e}] = ${r>1?`batch_indices[${A}]`:"batch_indices"};`;return A.forEach((e=>{i+=`\n${n}_indices[${e}] = 0;`})),i+=`${n}_indices[${t-2}] = 0u;\n                     ${n}_indices[${t-1}] = 0u;`,i};return`\n  ${n.registerUniform("outputSize","u32").registerUniform("M","u32").registerUniform("N","u32").registerUniform("K","u32").registerInternalVariables(s).declareVariables(...B,p)}\n  ${m}\n  ${n.mainStart()}\n    ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    let col = (global_idx % (uniforms.N / ${l})) * ${l};\n    var index1 = global_idx / (uniforms.N / ${l});\n    let stride1 = uniforms.M / ${c};\n    let row = (index1 % stride1) * ${c};\n    let batch = index1 / stride1;\n\n    ${2===t.length?"":`let batch_indices = ${s.offsetToIndices("batch")};`}\n    ${I(o,C)}\n    let a_offset = ${o.indicesToOffset("a_indices")};\n    ${I(u,$)}\n    let b_offset = ${u.indicesToOffset("b_indices")};\n    var values: array<${p.type.value}, ${c}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${d}) {\n      ${(()=>{let e=`var a_data: ${o.type.value};`;for(let A=0;A<d;A++)e+=`\n              let b_data${A} = b[(b_offset + (k + ${A}) * uniforms.N + col) / ${l}];`;for(let A=0;A<c;A++){e+=`a_data = a[(a_offset + (row + ${A}) * uniforms.K + k) / ${d}];`;for(let t=0;t<d;t++)e+=`\n            values[${A}] = fma(${u.type.value}(a_data${1===d?"":`[${t}]`}), b_data${t}, values[${A}]);\n`}return e})()}\n    }\n    for (var i = 0u; i < ${c}u; i++) {\n      var value = values[i];\n      ${y}\n      ${w}\n      let cur_indices = ${p.type.indices}(batch, row + i, col);\n      let offset = ${p.indicesToOffset("cur_indices")};\n      ${p.setByOffset(`offset / ${l}`,"value")};\n    }\n  }\n  `}}},Cr=e=>{if(!e||2!==e.length)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},$r=e=>{Cr(e.inputs);let A=hA.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!A)throw new Error("Can't use matmul on the given tensors");let t=A[A.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];t<8&&n<8?e.compute(br(e.inputs,{activation:"",activationCacheKey:""},A)):e.compute(wr(e.inputs,{activation:"",activationCacheKey:""},A))}})),qs=L((()=>{Us(),Qs(),Vs(),Ns(),Gs(),Ls(),Ws(),_s(),Ir=(e,A,t,n,r,i)=>{let a=e[0],s=e.slice(i?1:2,i?3:4),o=s.length,u=A[0],l=A.slice(2).map(((e,A)=>e+(e-1)*(t[A]-1))),d=s.map(((e,A)=>e+n[A]+n[A+o])).map(((e,A)=>Math.floor((e-l[A]+r[A])/r[A])));return d.splice(0,0,a),d.splice(i?3:1,0,u),d},Er=[2,3,1,0],Qr=(e,A)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length&&3!==e[0].dims.length)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");if(e[0].dims["NHWC"===A.format?e[0].dims.length-1:1]!==e[1].dims[1]*A.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(3===e.length&&(1!==e[2].dims.length||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let t=e[0].dims.length-2;if(A.dilations.length!==t)throw new Error(`dilations should be ${t}D`);if(A.strides.length!==t)throw new Error(`strides should be ${t}D`);if(A.pads.length!==2*t)throw new Error(`pads should be ${2*t}D`);if(0!==A.kernelShape.length&&A.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Ur=(e,A)=>{let t=e.kernelShape.slice();for(let e=2;e<A[1].dims.length;++e)0===t[e-2]&&(t[e-2]=A[1].dims[e]);let n=e.pads.slice();gA.adjustPadsBasedOnAutoPad(A[0].dims,e.strides,e.dilations,t,n,"NHWC"===e.format,e.autoPad);let r=Object.assign({},e);return Object.assign(r,{kernelShape:t,pads:n,cacheKey:e.cacheKey}),r},xr=e=>{let A=sr(e),t=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],r=e.dilations,i=e.group,a=e.kernel_shape,s=e.pads,o=e.strides,u=e.w_is_const();return cA({autoPad:n,format:t,dilations:r,group:i,kernelShape:a,pads:s,strides:o,wIsConst:u,...A})},_r=(e,A,t)=>{let n=Ur(t,A),r="NHWC"===t.format;if(1!==t.group)return void e.compute(vr(A,n));let i=3===A.length,a=A[0].dims[r?1:2],s=A[0].dims[r?2:3],o=A[0].dims[r?3:1],u=A[1].dims[2],l=A[1].dims[3],d=Ir(A[0].dims,A[1].dims,t.dilations,n.pads,t.strides,r),c=d[r?1:2],p=d[r?2:3],h=d[r?3:1],f=r&&u===a&&l===s&&0===t.pads[0]&&0===t.pads[1];if(f||1===u&&1===l&&1===t.dilations[0]&&1===t.dilations[1]&&1===t.strides[0]&&1===t.strides[1]&&0===t.pads[0]&&0===t.pads[1]){let u,l,g,m=d[0],w=[];if(r){let n=e.kernelCustomData.wT??e.compute(LA(A[1],Er),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=n),f){let e=a*s*o;u=A[0].reshape([1,m,e]),l=n.reshape([1,e,h]),g=[1,m,h]}else u=A[0].reshape([m,a*s,o]),l=n.reshape([1,o,h]),g=[m,c*p,h];w.push(u),w.push(l)}else u=A[0].reshape([m,o,a*s]),l=A[1].reshape([1,h,o]),g=[m,h,c*p],w.push(l),w.push(u);i&&w.push(A[2]);let B=g[2],y=w[0].dims[w[0].dims.length-1];return void(B<8&&y<8?e.compute(br(w,n,d,g,r),{inputs:w}):e.compute(wr(w,n,d,g,r),{inputs:w}))}let g=e.kernelCustomData.wT??e.compute(LA(A[1],Er),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=g);let m=[A[0],g];i&&m.push(A[2]);let w=r?c*p:h,B=r?h:c*p,y=u*l*o;e.compute(yr(m,n,d,w,B,y,i,!0),{inputs:m})},Fr=(e,A)=>{let t="NHWC"===A.format,n=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];3===e.inputs.length&&n.push(e.inputs[2]);let r=[0,A.pads[0],0,A.pads[1]],i=[1].concat(A.strides),a=[1].concat(A.dilations),s=[1].concat(A.kernelShape),o=Ur({...A,pads:r,strides:i,dilations:a,kernelShape:s},n);e.compute(vr(n,o,(e=>t?[e[0],e[2],e[3]]:[])))},Sr=(e,A)=>{Qr(e.inputs,A),3===e.inputs[0].dims.length?Fr(e,A):_r(e,e.inputs,A)}})),js=L((()=>{Cs(),xs(),Ls(),Ks(),zs(),Ns(),Tr=(e,A=!1,t,n=4)=>{let r=or(n,"f32"),i=e?"\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    ":"\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    ",a=e?"row":"col",s=e?"col":"row",o=`\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${a} / outWidth;\n      let outCol = ${a} % outWidth;\n\n      let WRow = ${s} / (filterDims[1] * inChannels);\n      let WCol = ${s} / inChannels % filterDims[1];\n      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);\n      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);\n      if (xR < 0.0 || xR >= f32(${e?"outBackprop[1]":"outBackprop[2]"}) || fract(xR) > 0.0) {\n        return ${r}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${e?"outBackprop[2]":"outBackprop[3]"}) || fract(xC) > 0.0) {\n        return ${r}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${s} % inChannels;\n      ${e?"\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      ":"\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      "}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${n}];`,u=e?`\n      let col = colIn * ${n};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${o}\n      }\n      return ${r}(0.0);`:`\n      let col = colIn * ${n};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${o}\n      }\n      return ${r}(0.0);`,l=`\n      let col = colIn * ${n};\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);\n      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];\n      if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${(e=>{switch(e){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return"\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return vec4<f32>(v0, v1, v2, v3);\n            ";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(n)}\n      }\n      return ${r}(0.0);\n      `,{activationFunction:d,applyActivation:c}=ar(t,r);return`\n      ${d}\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${r} {\n    ${e?u:l}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${r} {\n    ${e?l:u}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${r}) {\n    let col = colIn * ${n};\n    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${i}\n      ${ur(A)}\n      ${c}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${n}] = value;\n    }\n  }`},Or=(e,A,t,n,r,i,a,s)=>{let o="NHWC"===A.format,u=o?e[0].dims[3]:e[0].dims[1],l=t[0],d=o?t[2]:t[3],c=o?t[1]:t[2],p=o?t[3]:t[1],h=o?u%4==0&&p%4==0:d%4==0&&p%4==0,f=o?p:d*c,g=o?d*c:p,m=h?[8,8,1]:[f<=4||g<=4?4:16,f>4&&g<=4?4:16,1],w=h?[4,4,1]:[f<=4?1:4,f>4&&g<=4?1:4,1],B=[Math.ceil(f/m[0]/w[0]),Math.ceil(g/m[1]/w[1]),Math.ceil(l/m[2]/w[2])];nA("verbose",(()=>`[conv_backprop_mm_webgpu] dispatch = ${B}`));let y=h?4:1,v=Math.max(m[0]*y,m[1]),b=h?4:1,C=[{type:"int32",data:n},{type:"int32",data:r},{type:"int32",data:i}],$=_A("x",e[0].dataType,e[0].dims.length,b),I=_A("w",e[1].dataType,e[1].dims.length,1),E=FA("result",e[0].dataType,t.length,b),Q=[$,I];C.push(...CA(e[0].dims)),C.push(...CA(e[1].dims));let U="";if(a){let A=_A("bias",e[2].dataType,e[2].dims.length,b);Q.push(A),C.push(...CA(e[2].dims)),U+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${h?"vec4<f32>":"f32"} {\n          return bias[coords.${o?"w":"y"}${h?"/ 4":""}];\n        }`}return C.push(...CA(t)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:A.cacheKey},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:B[0],y:B[1],z:B[2]},programUniforms:C}),getShaderSource:t=>`\n        ${lr("uniforms.result_strides")}\n        ${t.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...Q,E)};\n        const outBackprop : vec4<i32> = vec4<i32>(${e[0].dims.join(",")});\n        const filterDims : vec2<i32> = vec2<i32>(${A.kernelShape[o?1:2]}, ${A.kernelShape[o?2:3]});\n        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(\n              ${A.dilations[0]<=1?0:(A.kernelShape[o?1:2]-1)*(A.dilations[0]-1)},\n              ${A.dilations[1]<=1?0:(A.kernelShape[o?2:3]-1)*(A.dilations[1]-1)});\n        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${A.pads[0]+A.pads[2]})/2,\n                                         i32(effectiveFilterDims[1]) - 1 - (${A.pads[1]+A.pads[3]})/2);\n        const strides : vec2<i32> = vec2<i32>(${A.strides[0]}, ${A.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${A.dilations[0]}, ${A.dilations[1]});\n        const dimAOuter : i32 = ${n};\n        const dimBOuter : i32 = ${r};\n        const dimInner : i32 = ${i};\n        ${U}\n        ${Tr(o,a,A,y)}\n        ${h?pr(w,m,"f32",void 0,!o,v):gr(w,m,"f32",void 0,!o,v,!1,void 0,s)}`}}})),Ys=L((()=>{Cs(),Us(),xs(),Hr=(e,A,t,n,r,i,a=!1,s)=>{let o="NHWC"===t.format,u=o?1:2,l=o?2:3,d=o?3:1,c=fA.size(n),p=a?2:1,h=t.group,f=A[1].dims,g=f[0]/h,m=f[1],w=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${s}>`:s}) {\n    result[flatIndex] = ${a?`vec4<${s}>`:s}(value);\n  }`;r&&(w+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${a?`vec4<${s}>`:s} {\n      return bias[coords.${o?"w":"y"}${a?"/ 4":""}];\n    }`);let B=a?4:1,y=_A("W",A[1].dataType,A[1].dims,B),v=_A("Dy",A[0].dataType,A[0].dims,B),b=[v,y];r&&b.push(_A("bias",A[2].dataType,[n[d]],B));let C=FA("result",A[0].dataType,n,B),$=`{\n        let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / outShape[1];\n        let r = ${i?"global_id.z":"workgroup_id.z"} % outShape[1];\n        let c = ${i?"global_id.y":"workgroup_id.y"} * ${p};\n        let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${s}>, ${p}>;\n        for (var i = 0; i < ${p}; i++) {\n          dotProd[i] = vec4<${s}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {\n          var dyR = (${s}(dyCorner.x) + ${s}(wR)) / ${s}(strides.x);\n          let wRPerm = filterDims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${s}(outBackprop[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {\n            let dyC = (${s}(dyCorner.y) + ${s}(wC)) / ${s}(strides.y);\n            let dyC2 = (${s}(dyCorner.y) + 1.0 + ${s}(wC)) / ${s}(strides.y);\n            let wCPerm = filterDims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${s}(outBackprop[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${s}(outBackprop[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${v.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${s}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${v.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${s}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = outBackprop[${d}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${v.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${s}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${y.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${v.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${s}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${p}; i = i + 1) {\n          let value = dotProd[i] + ${r?"bias[c+i]":`vec4<${s}>(0.0)`};\n          ${C.set("batch","r","c + i","d1","value")};\n        }\n      }`,I=`\n          let outputIndices = ${C.offsetToIndices("global_idx")};\n          let batch = ${C.indicesGet("outputIndices",0)};\n          let d1 = ${C.indicesGet("outputIndices",d)};\n          let r = ${C.indicesGet("outputIndices",u)};\n          let c = ${C.indicesGet("outputIndices",l)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / ${m};\n          let wOutChannel = d1 - groupId * ${m};\n          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n          // ? = to be determined. : = across all values in that axis.\n          var dotProd = ${s}(0.0);\n          for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {\n            if (wR % dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${s}(dyRCorner) + ${s}(wR)) / ${s}(strides[0]);\n            let wRPerm = filterDims.x - 1 - wR / dilations.x;\n            if (dyR < 0.0 || dyR >= ${s}(outBackprop[${u}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {\n              if (wC % dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(strides.y);\n              let wCPerm = filterDims.y - 1 - wC / dilations.y;\n              if (dyC < 0.0 || dyC >= ${s}(outBackprop[${l}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * ${g};\n              for (var d2: u32 = 0; d2 < ${g}; d2 = d2 + 1) {\n                let xValue = ${o?v.get("batch","idyR","idyC","inputChannel"):v.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${y.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${r?"bias[d1]":`${s}(0.0)`};\n          ${C.setByOffset("global_idx","value")};\n        `;return`\n  ${e.declareVariables(...b,C)}\n  ${w}\n  const outShape : vec4<u32> = vec4<u32>(${n.join(",")});\n  const outBackprop : vec4<u32> = vec4<u32>(${A[0].dims.join(",")});\n  const strides : vec2<u32> = vec2<u32>(${t.strides[0]}, ${t.strides[1]});\n  const filterDims : vec2<u32> = vec2<u32>(${t.kernelShape[o?1:2]}, ${t.kernelShape[o?2:3]});\n  const dilations : vec2<u32> = vec2<u32>(${t.dilations[0]}, ${t.dilations[1]});\n  const effectiveFilterDims : vec2<u32> = filterDims + 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new Error('Input "bias" is expected to have 1 dimension');if(r&&5===t.dims.length&&2===t.dims[3])throw new Error("bias is not allowed for packed kv.")}let w=0;if(a){w=8;let e=a.dims;throw 1===e.length?e[0]===d?w=1:e[0]===3*d+2&&(w=3):2===e.length&&e[0]===d&&e[1]===h&&(w=5),8===w?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let B=!1,y=p;if(r){if(3!==r.dims.length&&4!==r.dims.length)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==r.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(3===r.dims.length){if(h!==r.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');y=r.dims[2]}else{if(h!==r.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');y=r.dims[1]*r.dims[3],B=!0}}let v=f+h;if(a)throw new Error("Key padding mask is not 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n=FA("qkv_with_bias",A.dataType,s),r=_A("qkv",A.dataType,s),i=_A("bias",t.dataType,s);return`\n  ${e.registerUniforms([{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}]).declareVariables(r,i,n)}\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`}},{inputs:[A,t],outputs:[-1]})[0]},Oi=(e,A,t,n,r,i,a,s)=>{let o=i;if(a){if(1===n)throw new Error("AddBiasReshape is not implemented. 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xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${A.indicesToOffset("xIndices")}];\n                  ${i}\n                }`,2===r.kernelShape.length){let e=t-(c?3:2);u=d?`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${e}] < 0 || xIndices[${e}] >= uniforms.x_shape[${e}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;\n                `,f="\n              }\n            "}return`\n            ${e.registerUniforms(o).declareVariables(A,h)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let 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${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${h.offsetToIndices("global_idx")};\n              var xIndices = ${h.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${n}>;\n\n              var value = ${p}(${s});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${n-1}u; j++) {\n                  offsets[j] = offset / ${UA("uniforms.kernelStrides","j",n)};\n                  offset -= offsets[j] * ${UA("uniforms.kernelStrides","j",n)};\n                }\n                offsets[${n-1}] = offset;\n\n                isPad = false;\n                for (var j = ${t-n}u; j < ${t}u; j++) {\n                  xIndices[j] = indices[j] * ${UA("uniforms.strides",`j - ${t-n}u`,n)}\n                    + offsets[j - ${t-n}u] - 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invalid.")},da=(e,A,t,n)=>{let r=Math.abs(Math.ceil((A-e)/t)),i=[r],a=r,s=Ne(n),o=[{type:"uint32",data:a},{type:s,data:e},{type:s,data:t},...CA(i)];return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:e=>{let A=FA("output",n,i.length),t=A.type.value,r=[{name:"outputSize",type:"u32"},{name:"start",type:t},{name:"delta",type:t}];return`\n        ${e.registerUniforms(r).declareVariables(A)}\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${t}(global_idx) * uniforms.delta;\n      }`},getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:o})}},ca=e=>{let A=0,t=0,n=0;6===e.inputs[0].dataType?(A=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):1===e.inputs[0].dataType&&(A=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),l.webgpu.validateInputContent&&la(A,t,n),e.compute(da(A,t,n,e.inputs[0].dataType),{inputs:[]})}})),lo=L((()=>{Us(),Qs(),xs(),pa=(e,A)=>{if(e.every((e=>e>0||(()=>{throw new Error("Resize requires scales input values to be positive")}))),e.length>0)if("linear"===A.mode){if(!(2===e.length||3===e.length||4===e.length&&1===e[0]&&1===e[1]||4===e.length&&1===e[0]&&1===e[3]||5===e.length&&1===e[0]&&1===e[1]))throw new Error("For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1")}else if("cubic"===A.mode&&!(2===e.length||4===e.length&&1===e[0]&&1===e[1]||4===e.length&&1===e[0]&&1===e[3]))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")},ha=(e,A,t)=>{A.every((e=>e>=0&&e<t||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")})));let n=new Array(t).fill(1);return A.forEach(((A,t)=>n[A]=e[t])),n},fa=(e,A,t,n,r,i)=>{let[a,s,o]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],u=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach((e=>i.push(e)));else if("tf_crop_and_resize"===A.coordinateTransformMode)throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(s>0&&e.length>s&&e[s].dims.length>0){if(e[s].getFloat32Array().forEach((e=>n.push(e))),0!==n.length&&n.length!==u&&t>=18&&n.length!==A.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");pa(n,A),A.axes.length>0&&ha(n,A.axes,u).forEach(((e,A)=>n[A]=e))}if(o>0&&e.length>o&&(e[o].getBigInt64Array().forEach((e=>r.push(Number(e)))),r.length!==u||t>=18&&r.length===A.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(A.axes.length>0){if(n.length!==A.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(r.length!==A.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof r<"u"&&n.length>0&&r.length>u)throw new Error("Resize requires only of scales or sizes to be specified")},ga=(e,A)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${A} { `+(()=>{switch(e){case"asymmetric":return`return ${A}(xResized) / ${A}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${A}(xResized) + 0.5) / ${A}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${A}(xResized) + 0.5) / ${A}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${A}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${A}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${A}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${A}(roiStart) * ${A}(lengthOriginal - 1) +\n                        (${A}(xResized) * ${A}(roiEnd - roiStart) * ${A}(lengthOriginal - 1)) /\n                        ${A}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${A}(roiStart + roiEnd) * ${A}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${A}xScale * ${A}(lengthResized);\n                  const adjustment = ${A}(lengthResized) / outputWidth;\n                  const center = ${A}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${A}(xResized) + 0.5) / ${A}(xScale)) - 0.5;`;case"half_pixel":return`return ((${A}(xResized) + 0.5) / ${A}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",ma=(e,A,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{switch(e){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);                   }";default:if(A<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",wa=(e,A,t)=>{let n=new Array(t).fill(0).concat(new Array(t).fill(1)),r=0===e.length?n:e.slice();return A.length>0?(A.forEach(((e,i)=>{n[e]=r[i],n[i+t]=r[A.length+i]})),n):r},Ba=(e,A,t,n)=>{let r=[];if(t.length>0)if(n.length>0){if(e.forEach((e=>r.push(e))),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach(((e,A)=>r[e]=t[A]))}else t.forEach((e=>r.push(e)));else{if(0===A.length)throw new Error("Resize requires either scales or sizes.");r=e.map(((e,t)=>Math.round(e*A[t])))}return r},ya=(e,A,t)=>{let n=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map((e=>A[e])),Number.MAX_VALUE):Math.min(...A,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map((e=>A[e])),Number.MIN_VALUE):Math.max(...A,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();A.fill(1,0,A.length);let r=e.slice();return t.axes.length>0?(t.axes.forEach((e=>A[e]=n)),t.axes.forEach((t=>r[t]=Math.round(e[t]*A[t])))):(A.fill(n,0,A.length),r.forEach(((e,t)=>r[t]=Math.round(e*A[t])))),r},va=(e,A,t,n,r)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> {\n      var original_indices: array<${e.type.value}, ${t.length}>;\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${UA("uniforms.scales","i",n)};\n        var roi_low = ${UA("uniforms.roi","i",r)};\n        var roi_hi = ${UA("uniforms.roi",`i + ${A.length}`,r)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${UA("uniforms.input_shape","i",A.length)};\n          var output_shape_i = ${UA("uniforms.output_shape","i",t.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,ba=(e,A,t,n,r,i,a)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${A.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${n.length}; i++) {\n        var output_index = ${A.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${UA("uniforms.scales","i",r)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${UA("uniforms.roi","i",i)};\n          var roi_hi = ${UA("uniforms.roi",`i + ${t.length}`,i)};\n          var input_shape_i = ${UA("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${UA("uniforms.output_shape","i",n.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${a} || (original_idx >= 0 && original_idx < ${A.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${A.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,Ca=(e,A)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${A.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${UA("uniforms.input_shape","i",A.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,$a=(e,A,t,n)=>e.rank>n?`\n    ${e.indicesSet("input_indices",A,"channel")};\n    ${e.indicesSet("input_indices",t,"batch")};\n`:"",Ia=(e,A,t,n,r)=>{let[i,a,s,o]=2===t.length?[-1,0,1,-1]:[0,2,3,1],u=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",a,`max(0, min(row, ${t[a]} - 1))`)};\n      ${e.indicesSet("input_indices",s,`max(0, min(col, ${t[s]} - 1))`)};\n      ${$a(e,o,i,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${A.type.indices}) -> ${u} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${u} = originalIndices[${a}];\n      var col:${u} = originalIndices[${s}];\n      ${n?`if (row < 0 || row > (${t[a]} - 1) || col < 0 || col > (${t[s]} - 1)) {\n        return ${r};\n      }`:""};\n      row = max(0, min(row, ${t[a]} - 1));\n      col = max(0, min(col, ${t[s]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${t.length>2?`u32(originalIndices[${o}])`:"0"};\n      var batch: u32 =  ${t.length>2?`u32(originalIndices[${i}])`:"0"};\n      var x11: ${u} = getInputValue(batch, channel, row1, col1);\n      var x12: ${u} = getInputValue(batch, channel, row1, col2);\n      var x21: ${u} = getInputValue(batch, channel, row2, col1);\n      var x22: ${u} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${u} = abs(row - ${u}(row1));\n      var dx2: ${u} = abs(${u}(row2) - row);\n      var dy1: ${u} = abs(col - ${u}(col1));\n      var dy2: ${u} = abs(${u}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},Ea=(e,A,t,n,r,i,a,s,o,u)=>{let l=2===t.length,[d,c]=l?[0,1]:[2,3],p=e.type.value,h=a=>{let l=a===d?"row":"col";return`\n      fn ${l}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${A.type.indices}) -> ${p} {\n        var output_index = ${A.indicesGet("output_indices",a)};\n        var originalIdx: ${p} = getOriginalCoordinateFromResizedCoordinate(output_index, ${r[a]},\n        ${n[a]}, ${t[a]}, ${i[a]}, ${i[a]} + ${t.length});\n        var fractOriginalIdx: ${p} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${s} && (originalIdx < 0 || originalIdx > (${t[a]} - 1))) {\n          return ${o};\n        }\n        var data: array<${p}, 4> = array<${p}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${l}: ${p} = originalIdx + ${p}(i);\n          if (${l} < 0 || ${l} >= ${t[a]}) {\n            ${u?"coefs[i + 1] = 0.0;\n                        continue;":s?`return ${o};`:`${l} = max(0, min(${l}, ${t[a]} - 1));`};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",a,`u32(${l})`)};\n          data[i + 1] = ${a===d?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${h(d)};\n    ${h(c)};\n  fn getCubicInterpolationCoefs(s: ${p}) -> array<${p}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${p}, 4> = array<${p}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${p} = 1.0 - absS;\n    var twoMinusAbsS: ${p} = 2.0 - absS;\n    var onePlusAbsS: ${p} = 1.0 + absS;\n    coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};\n    coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${p}, 4>, coefs: array<${p}, 4>) -> ${p} {\n    var coefsSum: ${p} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${A.type.indices}) -> ${p} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},Qa=(e,A,t,n,r)=>{let[i,a,s,o,u]=3===t.length?[-1,0,1,2,-1]:[0,2,3,4,1],l=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${l} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",a,`max(0, min(depth, ${t[a]} - 1))`)};\n      ${e.indicesSet("input_indices",s,`max(0, min(height, ${t[s]} - 1))`)};\n      ${e.indicesSet("input_indices",o,`max(0, min(width, ${t[o]} - 1))`)};\n      ${$a(e,u,i,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${A.type.indices}) -> ${l} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${l} = originalIndices[${a}];\n      var height:${l} = originalIndices[${s}];\n      var width:${l} = originalIndices[${o}];\n      ${n?`if (depth < 0 || depth > (${t[a]} - 1) || height < 0 || height > (${t[s]} - 1) || width < 0 || (width > ${t[o]} - 1)) {\n      return ${r};\n        }`:""};\n\n    depth = max(0, min(depth, ${t[a]} - 1));\n      height = max(0, min(height, ${t[s]} - 1));\n      width = max(0, min(width, ${t[o]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${t.length>3?`u32(originalIndices[${u}])`:"0"};\n      var batch: u32 =  ${t.length>3?`u32(originalIndices[${i}])`:"0"};\n\n      var x111: ${l} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${l} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${l} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${l} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${l} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${l} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${l} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${l} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${l} = abs(depth - ${l}(depth1));\n      var dx2: ${l} = abs(${l}(depth2) - depth);\n      var dy1: ${l} = abs(height - ${l}(height1));\n      var dy2: ${l} = abs(${l}(height2) - height);\n      var dz1: ${l} = abs(width - ${l}(width1));\n      var dz2: ${l} = abs(${l}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},Ua=(e,A,t,n,r,i)=>{let a=e.dims,s=wa(i,A.axes,a.length),o=Ba(a,n,r,A.axes),u=n.slice();0===n.length&&(u=a.map(((e,A)=>0===e?1:o[A]/e)),"stretch"!==A.keepAspectRatioPolicy&&(o=ya(a,u,A)));let l=FA("output",e.dataType,o.length),d=_A("input",e.dataType,a.length),c=fA.size(o),p=a.length===o.length&&a.every(((e,A)=>e===o[A])),h="tf_crop_and_resize"===A.coordinateTransformMode,f=A.extrapolationValue,g=d.type.value;return{name:"Resize",shaderCache:{hint:`${A.cacheKey}|${t}|${u.length>0?u:""}|${r.length>0?r:""}|${s.length>0?s:""}|${p}|${a}`,inputDependencies:["rank"]},getShaderSource:e=>`\n      ${p?"":`\n      ${ga(A.coordinateTransformMode,g)};\n      ${(()=>{switch(A.mode){case"nearest":return`\n              ${Ca(d,a)};\n              ${ma(A.nearestMode,t,g)};\n              ${ba(d,l,a,o,u.length,s.length,h)};\n              `;case"linear":return`\n              ${va(l,a,o,u.length,s.length)};\n              ${(()=>{if(2===a.length||4===a.length)return`${Ia(d,l,a,h,f)}`;if(3===a.length||5===a.length)return`${Qa(d,l,a,h,f)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(2===a.length||4===a.length)return`${Ea(d,l,a,o,u,s,A.cubicCoeffA,h,A.extrapolationValue,A.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${e.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",s.length).declareVariables(d,l)}\n      ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${p?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${l.offsetToIndices("global_idx")};\n        var input_indices: ${d.type.indices};\n        ${(()=>{switch(A.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${d.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${A.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${2===a.length||4===a.length?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${A.mode}`)}})()};\n`}\n      }`,getRunData:()=>({outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:"uint32",data:c},{type:"float32",data:u},{type:"float32",data:s},...CA(a),...CA(o)]})}},xa=e=>{let A=e.customDataBuffer;return new Uint32Array(A,A.byteOffset,1)[0]},_a=(e,A)=>{let t=[],n=[],r=[],i=xa(e);if(0!==A.antialias)throw 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type.")},Va=(e,t)=>{Ua(e.inputs);let r=(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};\nvar best_index : i32 = 0;`,`if (${o.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {\n         value = ${o.getByIndices("input_indices")};\n         best_index = i32(last_index);\n       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(qr("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Na=(e,t)=>{Ua(e.inputs);let r=(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};\nvar best_index : i32 = 0;`,`if (${o.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {\n         value = ${o.getByIndices("input_indices")};\n         best_index = i32(last_index);\n       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(qr("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},zn=e=>ge(e)});var Ud,Vd,Nd,Wd,Kr,Hd,Ha,Un=j(()=>{"use strict";Ne();Nr();ve();Ud=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5];if(u&&l)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error(\'Input "input" must have 3 dimensions\');let a=r.dims[0],p=r.dims[1],h=r.dims[2];if(n.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimensions\');if(o.dims.length!==2)throw new Error(\'Input "weights" is expected to have 2 dimensions\');if(o.dims[0]!==h)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(n.dims[0]!==o.dims[1])throw new Error(\'Input "bias" dimension 0 should have same length as dimension 1 of input "weights"\');let g=n.dims[0]/3,b=g,w=b;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let E of t.qkvHiddenSizes)if(E%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");g=t.qkvHiddenSizes[0],b=t.qkvHiddenSizes[1],w=t.qkvHiddenSizes[2]}let y=p;if(g!==b)throw new Error("qkv_hidden_sizes first element should be same as the second");if(n.dims[0]!==g+b+w)throw new Error(\'Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes\');let _=0;if(u){if(b!==w)throw new Error(\'Input "past" expect k_hidden_size == v_hidden_size\');if(u.dims.length!==5)throw new Error(\'Input "past" must have 5 dimensions\');if(u.dims[0]!==2)throw new Error(\'Input "past" first dimension must be 2\');if(u.dims[1]!==a)throw new Error(\'Input "past" second dimension must be batch_size\');if(u.dims[2]!==t.numHeads)throw new Error(\'Input "past" third dimension must be num_heads\');if(u.dims[4]!==b/t.numHeads)throw new Error(\'Input "past" fifth dimension must be k_hidden_size / num_heads\');t.pastPresentShareBuffer||(_=u.dims[3])}let I=y+_,$=-1,x=0;if(s)throw new Error("Mask not supported");if(u)throw new Error("past is not supported");if(l)throw new Error("relativePositionBias is not supported");return{batchSize:a,sequenceLength:p,pastSequenceLength:_,kvSequenceLength:y,totalSequenceLength:I,maxSequenceLength:$,inputHiddenSize:h,hiddenSize:g,vHiddenSize:w,headSize:Math.floor(g/t.numHeads),vHeadSize:Math.floor(w/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:x,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Vd=(e,t,r,o)=>{let n=Fe(o),s=64,u=o/n;u<s?s=1:u/8<64&&(s=Math.ceil(u/8));let l=Math.ceil(o/n/s),p=[{type:Xe(t.dataType),data:1/o},{type:"uint32",data:u},{type:"uint32",data:l}],h=Le(t.dataType,n),g=b=>{let w=F("x",t.dataType,t.dims,n),y="thread_max_vector";n===2?y="max(thread_max_vector.x, thread_max_vector.y)":n===4&&(y="max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))");let _=lt(t.dataType),I=[{name:"d_inv",type:_},{name:"d_comp",type:"u32"},{name:"elements_per_wg",type:"u32"}];return`\n  var<workgroup> wgMax: array<f32, ${s}>;\n  var<workgroup> wgSum: array<f32, ${s}>;\n  ${b.registerUniforms(I).declareVariables(w)}\n  ${b.mainStart([s,1,1])}\n    let localOffset = local_idx * uniforms.elements_per_wg;\n    let offset: u32 = workgroup_id.x * uniforms.d_comp + localOffset;\n\n    var thread_max_vector = ${Ze("f32",n,"-3.402823e+38f")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      thread_max_vector = max(${at(_,n,"x[offset + i]")}, thread_max_vector);\n    }\n    wgMax[local_idx] = ${y};\n    workgroupBarrier();\n\n    var maxValue = -3.402823e+38f;\n    for (var i = 0u; i < ${s}; i++) {\n      maxValue = max(wgMax[i], maxValue);\n    }\n\n    var sumVector = ${Ze("f32",n,"0")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      sumVector += exp(${at(_,n,"x[offset + i]")} - maxValue);\n    }\n    wgSum[local_idx] = ${Je("sumVector",n)};\n    workgroupBarrier();\n\n    var sum: f32 = 0;\n    for (var i = 0u; i < ${s}; i++) {\n      sum += wgSum[i];\n    }\n\n    if (sum == 0) {\n      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n        x[offset + i] = ${Ze("f32",n,"uniforms.d_inv")};\n      }\n    } else {\n      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n        let f32input = ${at(_,n,"x[offset + i]")};\n        x[offset + i] = ${w.type.value}(exp(f32input - maxValue) / sum);\n      }\n    }\n  }`};e.compute({name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${h};${n}`},getShaderSource:g,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:p})},{inputs:[t],outputs:[]})},Nd=(e,t,r,o,n,s)=>{let u=[n.batchSize,n.numHeads,n.sequenceLength,n.kvSequenceLength+n.pastSequenceLength],l=s.scale===0?1/Math.sqrt(n.headSize):s.scale,a=Fe(n.headSize),p=n.headSize/a,h=12,g={x:Math.ceil(n.totalSequenceLength/h),y:Math.ceil(n.sequenceLength/h),z:n.batchSize*n.numHeads},b=Xe(t.dataType),w=[{type:"uint32",data:n.sequenceLength},{type:"uint32",data:p},{type:"uint32",data:n.totalSequenceLength},{type:"uint32",data:n.kvSequenceLength},{type:b,data:l}],y=[t,r],_=$=>{let x=M("q",t.dataType,t.dims,a),E=M("key",r.dataType,r.dims,a),A=F("output",t.dataType,u),z=Le(t.dataType),R=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:z}];return`\n  const beta: ${z} = 1.0;\n  const TILE_SIZE = ${h}u;\n\n  var<workgroup> tileQ: array<${x.type.storage}, ${h*h}>;\n  var<workgroup> tileK: array<${x.type.storage}, ${h*h}>;\n  ${$.registerUniforms(R).declareVariables(x,E,A)}\n  ${$.mainStart([h,h,1])}\n    // x holds the N and y holds the M\n    let headIdx = workgroup_id.z;\n    let m = workgroup_id.y * TILE_SIZE;\n    let n = workgroup_id.x * TILE_SIZE;\n    let lm = m + local_id.y;\n    let ln = n + local_id.x;\n\n    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;\n\n    var value = ${Ze(z,a)};\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m + local_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      workgroupBarrier();\n\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = headIdx * uniforms.M * uniforms.N;\n    if (lm < uniforms.M && ln < uniforms.N) {\n      let outputIdx = headOffset + lm * uniforms.N + ln;\n      output[outputIdx] = ${Je("value",a)} * uniforms.alpha;\n    }\n  }`},I=e.compute({name:"AttentionProbs",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:w}),getShaderSource:_},{inputs:y,outputs:[-1]})[0];return Vd(e,I,n.batchSize*n.numHeads*n.sequenceLength,n.totalSequenceLength),I},Wd=(e,t,r,o)=>{let n=[o.batchSize,o.sequenceLength,o.vHiddenSize],s=12,u={x:Math.ceil(o.vHeadSize/s),y:Math.ceil(o.sequenceLength/s),z:o.batchSize*o.numHeads},l=[{type:"uint32",data:o.sequenceLength},{type:"uint32",data:o.totalSequenceLength},{type:"uint32",data:o.vHeadSize},{type:"uint32",data:o.numHeads},{type:"uint32",data:o.vHiddenSize}],a=p=>{let h=M("probs",t.dataType,t.dims),g=M("v",r.dataType,r.dims),b=F("output",t.dataType,n),w=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n  const TILE_SIZE = ${s}u;\n  var<workgroup> tileQ: array<${h.type.value}, ${s*s}>;\n  var<workgroup> tileK: array<${h.type.value}, ${s*s}>;\n  ${p.registerUniforms(w).declareVariables(h,g,b)}\n  ${p.mainStart([s,s,1])}\n   let headIdx = workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${h.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / uniforms.num_heads;\n   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;\n   if (m < uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`};return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:t.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:a},{inputs:[t,r],outputs:[0]})[0]},Kr=(e,t,r,o,n,s,u,l,a,p,h)=>{let g=Nd(e,t,r,a,p,h);Wd(e,g,o,p)},Hd=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,n=t.inputHiddenSize,s=t.headSize,u=12,l={x:Math.ceil(t.headSize/u),y:Math.ceil(t.sequenceLength/u),z:t.batchSize*t.numHeads},a=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:"uint32",data:o},{type:"uint32",data:n},{type:"uint32",data:s},{type:"uint32",data:t.numHeads},{type:"uint32",data:t.headSize},{type:"uint32",data:t.hiddenSize},{type:"uint32",data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],h=g=>{let b=F("output_q",a[0].dataType,r),w=F("output_k",a[0].dataType,r),y=F("output_v",a[0].dataType,r),_=M("input",a[0].dataType,a[0].dims),I=M("weight",a[1].dataType,a[1].dims),$=M("bias",a[2].dataType,a[2].dims),x=_.type.storage,E=[{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`\n  const TILE_SIZE = ${u}u;\n  var<workgroup> tileInput: array<${x}, ${u*u}>;\n  var<workgroup> tileWeightQ: array<${x}, ${u*u}>;\n  var<workgroup> tileWeightK: array<${x}, ${u*u}>;\n  var<workgroup> tileWeightV: array<${x}, ${u*u}>;\n  ${g.registerUniforms(E).declareVariables(_,I,$,b,w,y)}\n  ${g.mainStart([u,u,1])}\n    let batchIndex = workgroup_id.z / uniforms.num_heads;\n    let headNumber = workgroup_id.z % uniforms.num_heads;\n    let m = workgroup_id.y * TILE_SIZE + local_id.y;\n    let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n    let biasOffsetQ = headNumber * uniforms.head_size;\n    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n    let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n    var valueQ = ${x}(0);\n    var valueK = ${x}(0);\n    var valueV = ${x}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m < uniforms.M && w + local_id.x < uniforms.K) {\n        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:p}),getShaderSource:h},{inputs:a,outputs:[-1,-1,-1]})},Ha=(e,t)=>{let r=Ud(e.inputs,t),[o,n,s]=Hd(e,r);return Kr(e,o,n,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var Gd,Ld,Fd,Ga,La=j(()=>{"use strict";Lt();$e();je();ve();Gd=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,n,s)=>{let u=n.length;if(u!==o.length)throw new Error(`${s}: num dimensions != ${u}`);n.forEach((l,a)=>{if(l!==o[a])throw new Error(`${s}: dim[${a}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input 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o.join(`\n`)},al=(e,t)=>{let r=e[0].dims.slice();if(t>=r.length||t<-1*r.length)throw new Error("axis specified for concat doesn\'t match input dimensionality");let o=t<0?r.length+t:t,n=r.slice(0);for(let A=1;A<e.length;A++){let z=e[A].dims.slice();for(let R=0;R<r.length;R++)if(R===o)n[o]+=z[R];else if(r[R]!==z[R])throw new Error("non concat dimensions must match")}let s=U.size(n),u=new Array(e.length),l=new Array(e.length),a=e[0].dataType,p=0,h=[],g=[],b=[],w=[{type:"uint32",data:s}];for(let A=0;A<e.length;++A)p+=e[A].dims[o],u[A]=p,b.push(Re(e[A].dims.length)),g.push(b[A]?e[A].dims.length:e[A].dims),l[A]=M(`input${A}`,a,g[A]),h.push(b[A]?"rank":"dims"),w.push({type:"uint32",data:u[A]});for(let A=0;A<e.length;++A)b[A]&&w.push(...L(e[A].dims));let y=Re(n.length);y&&w.push(...L(n));let _=y?n.length:n,I=F("output",a,_),$=I.indicesGet("indices",o),x=Array.from(Array(u.length).keys()).map(A=>`uniforms.sizeInConcatAxis${A}`).join(","),E=A=>`\n\n  ${(()=>{A.registerUniform("outputSize","u32");for(let z=0;z<e.length;z++)A.registerUniform(`sizeInConcatAxis${z}`,"u32");return A.declareVariables(...l,I)})()}\n\n  ${nl(u.length,x)}\n\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n    var indices = ${I.offsetToIndices("global_idx")};\n\n    let inputIndex = calculateInputIndex(${$});\n    if (inputIndex != 0u) {\n      let sizeInConcatAxis = array<u32, ${u.length}u>(${x});\n      ${$} -= sizeInConcatAxis[inputIndex - 1u];\n    }\n\n    ${ol(l,I)}\n  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:w}),getShaderSource:E}},zi=(e,t)=>{rl(e.inputs),e.compute(al(e.inputs,t.axis))},Ui=e=>ge({axis:e.axis})});var gt,Qr,It=j(()=>{"use strict";$e();gt=(e,t)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${t}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${t}(${e.clipMin});const clip_max_=${t}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},Qr=e=>{let t=e?.activation||"";if(t==="Clip"){let[r,o]=e?.activation_params||[Gr,Lr];return{activation:t,clipMax:o,clipMin:r,activationCacheKey:`${t}:${r},${o}`}}return{activation:t,activationCacheKey:t}}});var Ke,Xr,Jr=j(()=>{"use strict";Ke=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Xr=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `});var en,Nn=j(()=>{"use strict";en=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var il,sl,mr,Ni,ul,fr,dl,tn,hr=j(()=>{"use strict";$e();ve();It();Jr();il=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `,sl=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,mr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32)=>{let a=t[1]*e[1],p=t[0]*e[0],h=n?a:s,g=n?s:a,b=h/t[0],w=s/t[1];if(!((n&&b===4&&e[1]===4||!n&&(b===3||b===4))&&h%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${n} is true, innerElementSize ${b} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${b} must be 3 or 4.\n  tileAWidth ${h} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${b}<${r}>, ${h/b}>, ${g}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${p/e[0]}>, ${s}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${b};\nconst tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${u?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${a};\n\n  let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n  var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${w};\n  for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let inputRow = tileRow + innerRow;\n          let inputCol = tileCol;\n          ${il(n,o)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${b===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${sl(n,b)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Ni=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,ul=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32,a=!1)=>{let p=e[1]*t[1],h=e[0]*t[0],g=n?p:s,b=n?s:p;if(!(b%t[1]===0&&g%t[0]===0&&s%t[1]===0))throw new Error(`tileAHight ${b} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}, tileInner ${s} must be divisible by workgroupSize[1]${t[1]}`);let w=b/t[1],y=g/t[0],_=s/t[1],I=a?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${p};\n    let globalColStart = i32(workgroupId.x) * ${h};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < numTiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${b}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) {\n          ${Ni(n,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${o?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${r}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${n?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${t[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${p};\n\nlet tileRowA = i32(localId.y) * ${w};\nlet tileColA = i32(localId.x) * ${y};\nlet tileRowB = i32(localId.y) * ${_};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${y}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Ni(n,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${o?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${r}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${ul(n)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${g}>, ${b}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${h}>, ${s}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${u?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n    var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n    var acc : array<array<${r}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${I}\n  }\n`},dl=(e,t,r,o,n,s=!1)=>{let[u,l,a]=n,[p,h,g,b]=o,w=Ft(u,a),y=Ft(l,a),_=Le(o[0].type.tensor),I=()=>{let E=h.rank,A=p.rank,z=`var aIndices: ${h.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\naIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return w.forEach(R=>{z+=`\naIndices[${R}] = 0;`}),z+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,z},$=()=>{let E=g.rank,A=p.rank,z=`var bIndices: ${g.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\nbIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return y.forEach(R=>{z+=`\nbIndices[${R}] = 0;`}),z+=`\nbIndices[${E-2}] = u32(row);\n                   bIndices[${E-1}] = u32(colIn);`,z};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n      var value = ${Ke(e,_)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimAOuter && col < uniforms.dimInner)\n      {\n        ${I()}\n        value = ${h.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n      var value = ${Ke(e,_)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimInner && col < uniforms.dimBOuter)\n      {\n        ${$()}\n        value = ${g.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ke(e,_)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${s?"bias[colIn]":`${Ke(e,_)}(bias[row])`};`:""}\n        ${r}\n        ${b.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},tn=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s.slice(0,-2),a=u.slice(0,-2),p=o?o.slice(0,-2):r.slice(0,-2),h=Re(p.length),g=h?p.length:p,b=Fr("batchDims",e[0].dataType,g,1),w=U.size(p),y=s[s.length-2],_=s[s.length-1],I=u[u.length-1],$=_%4===0&&I%4===0,x=y<=8?[4,1,1]:[4,4,1],E=[8,8,1],A=[Math.ceil(I/E[0]/x[0]),Math.ceil(y/E[1]/x[1]),Math.ceil(w/E[2]/x[2])],z=Le(e[0].dataType),R=$?4:1,V=[...l,y,_/R],T=Re(V.length),N=T?V.length:V,te=[...a,_,I/R],Y=Re(te.length),K=Y?te.length:te,Q=[w,y,I/R],Z=M("a",e[0].dataType,N,R),Ee=M("b",e[1].dataType,K,R),Pe=F("result",e[0].dataType,Q.length,R),fe=[Z,Ee],Ie=[{type:"int32",data:y},{type:"int32",data:I},{type:"int32",data:_}];h&&Ie.push(...L(p)),T&&Ie.push(...L(V)),Y&&Ie.push(...L(te));let he=[];he.push(T?"rank":"dims"),he.push(Y?"rank":"dims");let ye=e.length>2,{activationFunction:We,applyActivation:De}=gt(t,Pe.type.value),Ge=dl(R,ye,De,[b,Z,Ee,Pe],[l,a,p],n);if(ye){let ee=n?R:1;fe.push(M("bias",e[2].dataType,e[2].dims.length,ee)),Ie.push(...L(e[2].dims)),he.push("rank")}Ie.push(...L(Q));let G=ee=>`\n  ${ee.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(b).declareVariables(...fe,Pe)}\n  ${We}\n  ${Ge}\n  ${$?mr(x,E,z,b):fr(x,E,z,b)}\n                   `;return{name:"MatMul",shaderCache:{hint:t.activationCacheKey+`${x}${$}${n}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Ie}),getShaderSource:G}}});var ll,Wi,Hi=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();ll=(e,t,r,o,n=!1,s,u=4,l=4,a=4,p="f32")=>{let h=Y=>{switch(Y){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},g=Y=>{switch(Y){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 ${Y} is not supported.`)}},b=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,w=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,y=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",_=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",I=e?"row":"col",$=e?"col":"row",x=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${I} / outWidth;\n    let outCol = ${I} % outWidth;\n\n    let WRow = ${$} / (filterDims[1] * inChannels);\n    let WCol = ${$} / inChannels % filterDims[1];\n    let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];\n    let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];\n    let xCh = ${$} % inChannels;\n    var resData = ${Ke(u,p)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the \'same\' padding type.\n    if (xRow >= 0 && xRow < ${y} && xCol >= 0 && xCol < ${_}) {\n      ${b}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${h(u)}\n    }\n    return resData;`,E=e?t&&o?`\n    let col = colIn * ${u};\n    ${x}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${x}\n    }\n    return ${Ke(u,p)}(0.0);`:o&&r?`\n    let col = colIn * ${u};\n    ${x}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n      ${x}\n    }\n    return ${Ke(u,p)}(0.0);`,A=`${g(l)}`,z=Ke(a,p),R=e?Ke(u,p):Ke(l,p),V=e?Ke(l,p):Ke(u,p),{activationFunction:T,applyActivation:N}=gt(s,z);return`\n    ${T}\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n      ${e?E:A}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${V} {\n      ${e?A:E}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${z}) {\n      let col = colIn * ${a};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${w}\n      ${Xr(n)}\n      ${N}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Wi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a&&(p%4===0||p%3===0)&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let A=y?a&&p%4!==0?3:4:1,z=$[1]*x[1],R=$[0]*x[0],V=Math.max($[0]*A,$[1]),T=o%z===0,N=n%R===0,te=s%V===0,Y=y?[A,4,4]:[1,1,1],K=Le(e[0].dataType),Q=y?4:1,Z=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],Ee=M("x",e[0].dataType,e[0].dims.length,A===3?1:A),Pe=M("w",e[1].dataType,e[1].dims.length,Q),fe=[Ee,Pe];Z.push(...L(e[0].dims)),Z.push(...L(e[1].dims));let Ie=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${K}>`:K}) {\n        result[flatIndex] = ${y?`vec4<${K}>`:K}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${K}>`:K}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);\n      }`;if(u){let ye=M("bias",e[2].dataType,e[2].dims.length,Q);fe.push(ye),Z.push(...L(e[2].dims)),Ie+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?`vec4<${K}>`:K} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}let he=F("result",e[0].dataType,r.length,Q);return Z.push(...L(r)),{name:"Conv2DMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Z}),getShaderSource:ye=>`\n        ${en("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${ye.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...fe,he)}\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[0]}, ${t.kernelShape[1]});\n        const pad : vec2<i32> = vec2<i32>(${t.pads[0]}, ${t.pads[1]});\n        const stride : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        ${Ie}\n        ${ll(a,T,N,te,u,t,Y[0],Y[1],Y[2],K)}\n            ${y?mr(x,$,K,void 0,!a,V):fr(x,$,K,void 0,!a,V,!1,void 0,l)}`}}});var Wn,Gi=j(()=>{"use strict";$e();ve();Gn();It();Wn=(e,t,r)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,l=u[0]/t.group,a=t.format==="NHWC",p=Hn(s,u,t.dilations,t.pads,t.strides,a),h=U.size(p),g=F("output",e[0].dataType,p),{activationFunction:b,applyActivation:w}=gt(t,g.type.value),y=M("x",e[0].dataType,s),_=M("w",e[1].dataType,u),I=[y,_];o&&I.push(M("b",e[2].dataType,e[2].dims));let $=x=>`\n  const strides: vec2<u32> = vec2(${t.strides[0]}u, ${t.strides[1]}u);\n  const pads: vec2<u32> = vec2(${t.pads[0]}u, ${t.pads[1]}u);\n\n  ${x.declareVariables(...I,g)}\n\n  ${b}\n\n  ${x.mainStart()}\n    ${x.guardAgainstOutOfBoundsWorkgroupSizes(h)}\n\n    let outputIndices = ${g.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${a?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${a?1:2}], outputIndices[${a?2:3}]) * strides - pads;\n    let group_id: u32 = output_channel / ${l}u;\n\n    var value: ${g.type.value} = ${g.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < ${u[1]}u; wInChannel++) {\n      let input_channel = group_id * ${u[1]}u + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < ${u[2]}u; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * ${t.dilations[0]}u;\n\n        if (xHeight < 0u || xHeight >= ${s[a?1:2]}u) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < ${u[3]}u; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * ${t.dilations[1]}u;\n          if (xWidth < 0u || xWidth >= ${s[a?2:3]}u) {\n            continue;\n          }\n\n          let xVal = ${a?y.get("batch","xHeight","xWidth","input_channel"):y.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${_.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${n}\n    ${w}\n    ${g.setByOffset("global_idx","value")}\n  }`;return{name:"GroupedConv",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r?r(p):p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)}}),getShaderSource:$}}});var Ln,cl,Li,Fn=j(()=>{"use strict";$e();hr();ve();It();Ln=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s[s.length-2],a=u[u.length-1],p=s[s.length-1],h=Fe(a),g=Fe(p),b=Fe(l),w=U.size(r)/h/b,y=e.length>2,_=o?o.slice(0,-2):r.slice(0,-2),$=[U.size(_),l,a],x=[{type:"uint32",data:w},{type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},...L(_),...L(s),...L(u)];y&&x.push(...L(e[2].dims)),x.push(...L($));let E=A=>{let z=Fr("batch_dims",e[0].dataType,_.length),R=M("a",e[0].dataType,s.length,g),V=M("b",e[1].dataType,u.length,h),T=F("output",e[0].dataType,$.length,h),{activationFunction:N,applyActivation:te}=gt(t,T.type.value),Y=[R,V],K="";if(y){let he=n?h:1;Y.push(M("bias",e[2].dataType,e[2].dims.length,he)),K=`${n?`value += bias[col / ${he}];`:`value += ${T.type.value}(bias[row + i]);`}`}let Q=s.slice(0,-2),Z=u.slice(0,-2),Ee=Ft(Q,_),Pe=Ft(Z,_),fe=(he,ye)=>{let We=he.rank,De=he.name;if(We===2)return`var ${De}_indices = ${he.type.indices}(0u, 0u);`;let Ge=z.rank,G=`var ${De}_indices: ${he.type.indices};`;for(let ee=We-2-1,be=Ge-1;ee>=0;ee--,be--)G+=`\n${De}_indices[${ee}] = ${Ge>1?`batch_indices[${be}]`:"batch_indices"};`;return ye.forEach(ee=>{G+=`\n${De}_indices[${ee}] = 0;`}),G+=`${De}_indices[${We-2}] = 0u;\n                     ${De}_indices[${We-1}] = 0u;`,G},Ie=()=>{let he=`var a_data: ${R.type.value};`;for(let ye=0;ye<g;ye++)he+=`\n              let b_data${ye} = b[(b_offset + (k + ${ye}) * uniforms.N + col) / ${h}];`;for(let ye=0;ye<b;ye++){he+=`a_data = a[(a_offset + (row + ${ye}) * uniforms.K + k) / ${g}];`;for(let We=0;We<g;We++)he+=`\n            values[${ye}] = fma(${V.type.value}(a_data${g===1?"":`[${We}]`}), b_data${We}, values[${ye}]);\n`}return he};return`\n  ${A.registerUniform("outputSize","u32").registerUniform("M","u32").registerUniform("N","u32").registerUniform("K","u32").registerInternalVariables(z).declareVariables(...Y,T)}\n  ${N}\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    let col = (global_idx % (uniforms.N / ${h})) * ${h};\n    var index1 = global_idx / (uniforms.N / ${h});\n    let stride1 = uniforms.M / ${b};\n    let row = (index1 % stride1) * ${b};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${z.offsetToIndices("batch")};`}\n    ${fe(R,Ee)}\n    let a_offset = ${R.indicesToOffset("a_indices")};\n    ${fe(V,Pe)}\n    let b_offset = ${V.indicesToOffset("b_indices")};\n    var values: array<${T.type.value}, ${b}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${g}) {\n      ${Ie()}\n    }\n    for (var i = 0u; i < ${b}u; i++) {\n      var value = values[i];\n      ${K}\n      ${te}\n      let cur_indices = ${T.type.indices}(batch, row + i, col);\n      let offset = ${T.indicesToOffset("cur_indices")};\n      ${T.setByOffset(`offset / ${h}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activationCacheKey}_${h}_${g}_${b}_${n}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:x}),getShaderSource:E}},cl=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Li=e=>{cl(e.inputs);let t=dt.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Ln(e.inputs,{activation:"",activationCacheKey:""},t)):e.compute(tn(e.inputs,{activation:"",activationCacheKey:""},t))}});var Hn,Fi,pl,ji,jn,ml,fl,qn,Gn=j(()=>{"use strict";$e();je();Hi();hr();Gi();It();Fn();jt();Hn=(e,t,r,o,n,s)=>{let u=e[0],l=e.slice(s?1:2,s?3:4),a=l.length,p=t[0],g=t.slice(2).map((y,_)=>y+(y-1)*(r[_]-1)),w=l.map((y,_)=>y+o[_]+o[_+a]).map((y,_)=>Math.floor((y-g[_]+n[_])/n[_]));return w.splice(0,0,u),w.splice(s?3:1,0,p),w},Fi=[2,3,1,0],pl=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ji=(e,t)=>{let r=e.kernelShape.slice();for(let s=2;s<t[1].dims.length;++s)r[s-2]===0&&(r[s-2]=t[1].dims[s]);let o=e.pads.slice();Bt.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let n=Object.assign({},e);return Object.assign(n,{kernelShape:r,pads:o,cacheKey:e.cacheKey}),n},jn=e=>{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],n=e.dilations,s=e.group,u=e.kernel_shape,l=e.pads,a=e.strides,p=e.w_is_const();return ge({autoPad:o,format:r,dilations:n,group:s,kernelShape:u,pads:l,strides:a,wIsConst:p,...t})},ml=(e,t,r)=>{let o=ji(r,t),n=r.format==="NHWC";if(r.group!==1){e.compute(Wn(t,o));return}let s=t.length===3,u=t[0].dims[n?1:2],l=t[0].dims[n?2:3],a=t[0].dims[n?3:1],p=t[1].dims[2],h=t[1].dims[3],g=Hn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,n),b=g[n?1:2],w=g[n?2:3],y=g[n?3:1],_=n&&p===u&&h===l&&r.pads[0]===0&&r.pads[1]===0;if(_||p===1&&h===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=g[0],V,T,N,te=[];if(n){let Q=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Q),_){let Z=u*l*a;V=t[0].reshape([1,R,Z]),T=Q.reshape([1,Z,y]),N=[1,R,y]}else V=t[0].reshape([R,u*l,a]),T=Q.reshape([1,a,y]),N=[R,b*w,y];te.push(V),te.push(T)}else V=t[0].reshape([R,a,u*l]),T=t[1].reshape([1,y,a]),N=[R,y,b*w],te.push(T),te.push(V);s&&te.push(t[2]);let Y=N[2],K=te[0].dims[te[0].dims.length-1];Y<8&&K<8?e.compute(Ln(te,o,g,N,n),{inputs:te}):e.compute(tn(te,o,g,N,n),{inputs:te});return}let I=!0,$=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=$);let x=[t[0],$];s&&x.push(t[2]);let E=n?b*w:y,A=n?y:b*w,z=p*h*a;e.compute(Wi(x,o,g,E,A,z,s,I),{inputs:x})},fl=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let n=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),u=[1].concat(t.dilations),l=[1].concat(t.kernelShape),a=ji({...t,pads:n,strides:s,dilations:u,kernelShape:l},o);e.compute(Wn(o,a,p=>r?[p[0],p[2],p[3]]:[]))},qn=(e,t)=>{pl(e.inputs,t),e.inputs[0].dims.length===3?fl(e,t):ml(e,e.inputs,t)}});var hl,qi,Ki=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();hl=(e,t=!1,r,o=4)=>{let n=Ke(o,"f32"),s=x=>{switch(x){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${x} is not supported.`)}},u=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,l=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,a=e?"outBackprop[1]":"outBackprop[2]",p=e?"outBackprop[2]":"outBackprop[3]",h=e?"row":"col",g=e?"col":"row",b=`\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${h} / outWidth;\n      let outCol = ${h} % outWidth;\n\n      let WRow = ${g} / (filterDims[1] * inChannels);\n      let WCol = ${g} / inChannels % filterDims[1];\n      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);\n      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);\n      if (xR < 0.0 || xR >= f32(${a}) || fract(xR) > 0.0) {\n        return ${n}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${p}) || fract(xC) > 0.0) {\n        return ${n}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${g} % inChannels;\n      ${u}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${o}];`,w=e?`\n      let col = colIn * ${o};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${b}\n      }\n      return ${n}(0.0);`:`\n      let col = colIn * ${o};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${b}\n      }\n      return ${n}(0.0);`,y=`\n      let col = colIn * ${o};\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);\n      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];\n      if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${s(o)}\n      }\n      return ${n}(0.0);\n      `,{activationFunction:_,applyActivation:I}=gt(r,n);return`\n      ${_}\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {\n    ${e?w:y}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {\n    ${e?y:w}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {\n    let col = colIn * ${o};\n    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${l}\n      ${Xr(t)}\n      ${I}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${o}] = value;\n    }\n  }`},qi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a?p%4===0&&w%4===0:g%4===0&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=y?[8,8,1]:[_<=4||I<=4?4:16,_>4&&I<=4?4:16,1],x=y?[4,4,1]:[_<=4?1:4,_>4&&I<=4?1:4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let A=y?4:1,z=Math.max($[0]*A,$[1]),R=y?4:1,V=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],T=M("x",e[0].dataType,e[0].dims.length,R),N=M("w",e[1].dataType,e[1].dims.length,1),te=F("result",e[0].dataType,r.length,R),Y=[T,N];V.push(...L(e[0].dims)),V.push(...L(e[1].dims));let K="";if(u){let Q=M("bias",e[2].dataType,e[2].dims.length,R);Y.push(Q),V.push(...L(e[2].dims)),K+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?"vec4<f32>":"f32"} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}return V.push(...L(r)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:V}),getShaderSource:Q=>`\n        ${en("uniforms.result_strides")}\n        ${Q.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...Y,te)};\n        const outBackprop : vec4<i32> = vec4<i32>(${e[0].dims.join(",")});\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[a?1:2]}, ${t.kernelShape[a?2:3]});\n        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(\n              ${t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)},\n              ${t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1)});\n        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${t.pads[0]+t.pads[2]})/2,\n                                         i32(effectiveFilterDims[1]) - 1 - (${t.pads[1]+t.pads[3]})/2);\n        const strides : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        const dimAOuter : i32 = ${o};\n        const dimBOuter : i32 = ${n};\n        const dimInner : i32 = ${s};\n        ${K}\n        ${hl(a,u,t,A)}\n        ${y?mr(x,$,"f32",void 0,!a,z):fr(x,$,"f32",void 0,!a,z,!1,void 0,l)}`}}});var gl,Kn,Yi=j(()=>{"use strict";Ct();$e();ve();gl=(e,t,r,o,n,s,u=!1,l)=>{let a=r.format==="NHWC",p=a?1:2,h=a?2:3,g=a?3:1,b=U.size(o),w=u?2:1,y=r.group,_=t[1].dims,I=_[0]/y,$=_[1],x=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${l}>`:l}) {\n    result[flatIndex] = ${u?`vec4<${l}>`:l}(value);\n  }`;n&&(x+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${u?`vec4<${l}>`:l} {\n      return bias[coords.${a?"w":"y"}${u?"/ 4":""}];\n    }`);let E=u?4:1,A=M("W",t[1].dataType,t[1].dims,E),z=M("Dy",t[0].dataType,t[0].dims,E),R=[z,A];n&&R.push(M("bias",t[2].dataType,[o[g]],E));let V=F("result",t[0].dataType,o,E),T=`{\n        let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / outShape[1];\n        let r = ${s?"global_id.z":"workgroup_id.z"} % outShape[1];\n        let c = ${s?"global_id.y":"workgroup_id.y"} * ${w};\n        let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${l}>, ${w}>;\n        for (var i = 0; i < ${w}; i++) {\n          dotProd[i] = vec4<${l}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {\n          var dyR = (${l}(dyCorner.x) + ${l}(wR)) / ${l}(strides.x);\n          let wRPerm = filterDims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${l}(outBackprop[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {\n            let dyC = (${l}(dyCorner.y) + ${l}(wC)) / ${l}(strides.y);\n            let dyC2 = (${l}(dyCorner.y) + 1.0 + ${l}(wC)) / ${l}(strides.y);\n            let wCPerm = filterDims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${l}(outBackprop[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${l}(outBackprop[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${z.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${l}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = outBackprop[${g}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${w}; i = i + 1) {\n          let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${l}>(0.0)`};\n          ${V.set("batch","r","c + i","d1","value")};\n        }\n      }`,N=`\n          let outputIndices = ${V.offsetToIndices("global_idx")};\n          let batch = ${V.indicesGet("outputIndices",0)};\n          let d1 = ${V.indicesGet("outputIndices",g)};\n          let r = ${V.indicesGet("outputIndices",p)};\n          let c = ${V.indicesGet("outputIndices",h)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / ${$};\n          let wOutChannel = d1 - groupId * ${$};\n          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n          // ? = to be determined. : = across all values in that axis.\n          var dotProd = ${l}(0.0);\n          for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {\n            if (wR % dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${l}(dyRCorner) + ${l}(wR)) / ${l}(strides[0]);\n            let wRPerm = filterDims.x - 1 - wR / dilations.x;\n            if (dyR < 0.0 || dyR >= ${l}(outBackprop[${p}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {\n              if (wC % dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${l}(dyCCorner) + ${l}(wC)) / ${l}(strides.y);\n              let wCPerm = filterDims.y - 1 - wC / dilations.y;\n              if (dyC < 0.0 || dyC >= ${l}(outBackprop[${h}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * ${I};\n              for (var d2: u32 = 0; d2 < ${I}; d2 = d2 + 1) {\n                let xValue = ${a?z.get("batch","idyR","idyC","inputChannel"):z.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${A.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${n?"bias[d1]":`${l}(0.0)`};\n          ${V.setByOffset("global_idx","value")};\n        `;return`\n  ${e.declareVariables(...R,V)}\n  ${x}\n  const outShape : vec4<u32> = vec4<u32>(${o.join(",")});\n  const outBackprop : vec4<u32> = vec4<u32>(${t[0].dims.join(",")});\n  const strides : vec2<u32> = vec2<u32>(${r.strides[0]}, ${r.strides[1]});\n  const filterDims : vec2<u32> = vec2<u32>(${r.kernelShape[a?1:2]}, ${r.kernelShape[a?2:3]});\n  const dilations : vec2<u32> = vec2<u32>(${r.dilations[0]}, ${r.dilations[1]});\n  const effectiveFilterDims : vec2<u32> = filterDims + vec2<u32>(\n          ${r.dilations[0]<=1?0:(r.kernelShape[a?1:2]-1)*(r.dilations[0]-1)},\n          ${r.dilations[1]<=1?0:(r.kernelShape[a?2:3]-1)*(r.dilations[1]-1)});\n  const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${r.pads[0]+r.pads[2]})/2,\n                                     i32(effectiveFilterDims[1]) - 1 - (${r.pads[1]+r.pads[3]})/2);\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(b)};\n  ${u?T:N}}`},Kn=(e,t,r)=>{let o=e.length>2,n=t.outputShape,s=U.size(n),u=[Math.ceil(s/64),1,1];Be("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${u}`);let l=Le(e[0].dataType);return{name:"ConvTranspose2D",shaderCache:{hint:t.cacheKey},getRunData:()=>({dispatchGroup:{x:u[0],y:u[1],z:u[2]},outputs:[{dims:r?r(n):n,dataType:e[0].dataType}]}),getShaderSource:a=>gl(a,e,t,n,o,u[1]===1&&u[2]===1,!1,l)}}});var yl,bl,wl,Zi,Qi,vl,$l,Sl,xl,Xi,Ji=j(()=>{"use strict";je();Ki();Yi();It();jt();yl=(e,t,r,o,n,s)=>(e-1)*t+r+(o-1)*n+1-s,bl=(e,t,r,o,n)=>{let s=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=s,r[n]=e-s):t==="SAME_LOWER"&&(r[o]=e-s,r[n]=s)},wl=(e,t,r,o,n,s,u,l,a,p)=>{let h=e.length-2,g=p.length===0;if(a.length===0)for(let y=0;y<h;++y)a.push(0);let b=e[0],w=t[l?3:1]*n;for(let y=0,_=e.length-h-(l?1:0);y<h;++y,++_){let I=e[_],$=g?I*u[y]:p[y],x=yl(I,u[y],s[y],t[_],r[y],$);bl(x,o,s,y,y+h),g&&p.push(u[y]*(I-1)+a[y]+(t[_]-1)*r[y]+1-s[y]-s[y+h])}p.splice(0,0,b),p.splice(l?3:1,0,w)},Zi=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((b,w)=>b*w,1)===0){r.length=0;for(let b=2;b<t[1].dims.length;++b)r.push(t[1].dims[b])}let o=e.format==="NHWC";r.splice(0,0,t[1].dims[0]),r.splice(o?3:1,0,t[1].dims[1]);let n=e.pads.slice(),s=e.outputShape.slice(),u=e.outputPadding.slice(),l=t[0].dims,a=e.dilations.slice();if(a.reduce((b,w)=>b+w,0)===0){let b=t[0].dims.length-2;a=new Array(b).fill(1)}let p=e.strides.slice();if(p.reduce((b,w)=>b+w,0)===0){let b=t[0].dims.length-2;p=new Array(b).fill(1)}wl(l,r,a,e.autoPad,e.group,n,p,o,u,s);let h=Object.assign({},e),g=e.cacheKey+[r.join("n,"),n.join(","),p.join(","),u.join(","),s.join(","),a.join(",")].join("_");return Object.assign(h,{kernelShape:r,pads:n,outputPadding:u,outputShape:s,dilations:a,strides:p,cacheKey:g}),h},Qi=e=>{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],n=e.dilations,s=e.group,u=e.kernelShape,l=e.pads,a=e.strides,p=e.wIsConst(),h=e.outputPadding,g=e.outputShape;return ge({autoPad:o,format:r,dilations:n,group:s,kernelShape:u,outputPadding:h,outputShape:g,pads:l,strides:a,wIsConst:p,...t})},vl=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[0];if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let n=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==n))throw new Error("invalid bias");let s=e[0].dims.length-2;if(t.dilations.reduce((h,g)=>h+g,0)>0&&t.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(t.strides.reduce((h,g)=>h+g,0)>0&&t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.reduce((h,g)=>h+g,0)>0&&t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.outputPadding.length!==s&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${s}D`);if(t.kernelShape.reduce((h,g)=>h+g,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},$l=[2,3,1,0],Sl=(e,t,r)=>{let o=Zi(r,t),n=r.format==="NHWC",s=o.outputShape,u=s[n?3:1],l=t[0].dims[n?3:1];if(o.group!==1||u===1&&l===1){e.compute(Kn(t,o));return}let a=s[n?1:2],p=s[n?2:3],h=t[1].dims[2],g=t[1].dims[3],b=n?a*p:u,w=n?u:a*p,y=h*g*l,_=!0,I=e.kernelCustomData.wT??e.compute(it(t[1],$l),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let $=[t[0],I],x=t.length===3;x&&(!n&&t[2].dims.length===1?$.push(t[2].reshape([t[2].dims[0],1,1])):$.push(t[2])),e.compute(qi($,o,s,b,w,y,x,_),{inputs:$})},xl=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];o.length===3&&o.push(e.inputs[2]);let n=t.kernelShape;(n.length===0||n[0]===0)&&(n=[e.inputs[1].dims[2]]);let s=t.dilations;(s.length===0||s[0]===0)&&(s=[1]);let 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k];":t.transA&&!t.transB?w="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?w="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(w="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let y=t.alpha===1?"":"value *= uniforms.alpha;",_=M("a",e[0].dataType,e[0].dims),I=M("b",e[1].dataType,e[1].dims),$=_.type.value,x=null,E=[_,I];e.length===3&&(x=M("c",e[2].dataType,e[2].dims.length),E.push(x));let A=F("output",e[0].dataType,l.length);E.push(A);let z=[{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`\n  ${b.registerUniforms(z).declareVariables(...E)}\n\n  ${b.mainStart()}\n    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let m = global_idx / uniforms.N;\n    let n = global_idx % uniforms.N;\n\n    var value = ${$}(0);\n    for (var k: u32 = 0u; k < uniforms.K; k++) {\n      ${w}\n    }\n\n    ${y}\n    ${(()=>x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",A)}; value += ${$}(uniforms.beta) * ${x.getByOffset("cOffset")};`:"")()}\n    output[global_idx] = value;\n  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:g}},ys=e=>{let t=e.transA,r=e.transB,o=e.alpha,n=e.beta;return{transA:t,transB:r,alpha:o,beta:n,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},bs=(e,t)=>{Dl(e.inputs),e.compute(Ml(e.inputs,t))}});var zl,Ul,Vl,vs,$s=j(()=>{"use strict";Ne();$e();ve();zl=(e,t)=>{let r=e[0].dims,o=r,n=2,s=U.sizeToDimension(r,n),u=U.sizeFromDimension(r,n),l=Fe(u),a=u/l,p=[r[0],r[1],a],h=["rank","type","type"],g=[{type:"uint32",data:u},{type:"uint32",data:a}];g.push(...L(p),...L(p));let b=w=>{let y=M("x",e[0].dataType,p.length,l),_=M("scale",e[1].dataType,e[1].dims),I=M("bias",e[2].dataType,e[2].dims),$=F("output",e[0].dataType,p.length,l),x=[y,_,I,$],E=y.type.value,A=l===1?"f32":`vec${l}<f32>`,z=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${A}, ${z}>;\n  const workgroupSize = ${z}u;\n  ${w.registerUniforms(R).declareVariables(...x)}\n  ${w.mainStart(z)}\n    let norm = global_idx / workgroupSize;\n    let batch = norm / uniforms.x_shape[1];\n    let channel = norm % uniforms.x_shape[1];\n    let localIndex = local_id.x;\n\n    // initialize workgroup memory\n    var initial = ${A}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${A}(${y.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the mean of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      meanShared = ${Je("workgroupShared[0]",l)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${A}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${A}(${y.get("batch","channel","h")}) - ${A}(meanShared);\n      initial = initial + deviation * deviation;\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the sum of square of deviation of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      squaredNormShared = ${Je("workgroupShared[0]",l)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${_.getByOffset("channel")});\n    let channelShift = f32(${I.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${y.get("batch","channel","h")} * ${E}(${A}(channelScale)) + ${E}(${A}(channelShift));\n      ${$.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:s},programUniforms:g}),getShaderSource:b}},Ul=(e,t,r,o,n,s,u,l)=>{let a=Fe(u),p=64,h=a===1?"vec2f":`mat2x${a}f`,g=a===1?"f32":`vec${a}f`,b=(R,V)=>`${h}(${R}, ${V})`,w=n*u/a,y=Math.ceil(s/p),_=["type"],I=[{type:"uint32",data:y},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(s*u/a)}],$=R=>{let V=M("input",t.dataType,t.dims,a);return`\n  ${R.declareVariables(V)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${h}>;\n  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n  @group(0) @binding(2) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart(p)}\n    let currentImageNumber = global_idx / ${p} / uniforms.C;\n    let currentChannelNumber = (global_idx / ${p}) % uniforms.C;\n    let wgId = global_idx % ${p};\n    let wgOffset = wgId * uniforms.wg_size;\n    if (wgOffset >= uniforms.H) {\n        return;\n    }\n    let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n    var sum = ${Ze("f32",a)};\n    var squaredSum = ${Ze("f32",a)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${g}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${b("sum","squaredSum")};\n  }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${a}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:[n,u,p,2],dataType:1}],dispatchGroup:{x:n*u/a},programUniforms:I}),getShaderSource:$},{inputs:[t],outputs:[-1]})[0],E=[{type:"uint32",data:w},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(p*u/a)}],A=["type","type","type"],z=R=>{let V=M("scale",r.dataType,r.dims,a),T=M("bias",o.dataType,o.dims,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${h}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${V.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${T.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${h}>;\n  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n  @group(0) @binding(4) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart()}\n    ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n    let currentImageNumber = global_idx / uniforms.C;\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let offset = currentImageNumber * uniforms.image_size;\n    var sum = ${Ze("f32",a)};\n    var squaredSum = ${Ze("f32",a)};\n    for (var i: u32 = 0; i < ${p}; i++) {\n        let value = input[offset + i + currentChannelNumber * ${p}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${l}));\n    let channelScale = invStdDev * ${g}(scale[currentChannelNumber]);\n    let channelShift = ${g}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${b("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${a};${l}`,inputDependencies:A},getRunData:()=>({outputs:[{dims:[n,u,2],dataType:1}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:E}),getShaderSource:z},{inputs:[x,r,o],outputs:[-1]})[0]},Vl=(e,t,r)=>{let o=t[0].dims,n=o,s=o[0],u=o[o.length-1],l=U.sizeFromDimension(o,1)/u,a=Fe(u),p=U.size(n)/a,h=[{type:"uint32",data:l},{type:"uint32",data:Math.floor(u/a)}],g=["type","type"],b=Ul(e,t[0],t[1],t[2],s,l,u,r.epsilon),w=y=>{let _=Le(t[0].dataType),I=a===1?"vec2f":`mat2x${a}f`,$=a===1?_:`vec${a}<${_}>`,x=M("input",t[0].dataType,t[0].dims,a),E=F("output",t[0].dataType,n,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${I}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${y.mainStart()}\n    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n    let scale = scaleInput[scaleOffset];\n    output[global_idx] = fma(input[global_idx], ${$}(scale[0]), ${$}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${a}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:w},{inputs:[t[0],b]})},vs=(e,t)=>{t.format==="NHWC"?Vl(e,e.inputs,t):e.compute(zl(e.inputs,t))}});var Nl,Wl,Ss,xs=j(()=>{"use strict";Ne();$e();ve();Nl=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Wl=(e,t,r)=>{let o=e[0].dims,n=e[1],s=e[2],u=o,l=U.normalizeAxis(t.axis,o.length),a=U.sizeToDimension(o,l),p=U.sizeFromDimension(o,l),h=U.size(n.dims),g=s?U.size(s.dims):0;if(h!==p||s&&g!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${h} and bias size of ${g}`);let b=[];for(let A=0;A<o.length;++A)A<l?b.push(o[A]):b.push(1);let w=Fe(p),y=["type","type"],_=[{type:"uint32",data:a},{type:"float32",data:p},{type:"uint32",data:Math.floor(p/w)},{type:"float32",data:t.epsilon}];s&&y.push("type");let I=r>1,$=r>2,x=A=>{let z=Le(e[0].dataType),R=[M("x",e[0].dataType,e[0].dims,w),M("scale",n.dataType,n.dims,w)];s&&R.push(M("bias",s.dataType,s.dims,w)),R.push(F("output",e[0].dataType,u,w)),I&&R.push(F("mean_data_output",1,b)),$&&R.push(F("inv_std_output",1,b));let V=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${A.registerUniforms(V).declareVariables(...R)}\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var meanVector = ${Ze("f32",w)};\n    var meanSquareVector = ${Ze("f32",w)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${at(z,w,"x[h + offset]")};\n      meanVector += value;\n      meanSquareVector += value * value;\n    }\n    let mean = ${Je("meanVector",w)} / uniforms.norm_size;\n    let invStdDev =\n        inverseSqrt(${Je("meanSquareVector",w)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${at(z,w,"x[j + offset]")};\n      let f32scale = ${at(z,w,"scale[j]")};\n      output[j + offset] = ${R[0].type.value}((f32input - mean) * invStdDev * f32scale\n        ${s?`+ ${at(z,w,"bias[j]")}`:""}\n      );\n    }\n\n    ${I?"mean_data_output[global_idx] = mean":""};\n    ${$?"inv_std_output[global_idx] = invStdDev":""};\n  }`},E=[{dims:u,dataType:e[0].dataType}];return I&&E.push({dims:b,dataType:1}),$&&E.push({dims:b,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${w};${r}`,inputDependencies:y},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:_}),getShaderSource:x}},Ss=(e,t)=>{Nl(e.inputs),e.compute(Wl(e.inputs,t,e.outputCount))}});var Hl,Cs,_s,Gl,Xn,Is,As=j(()=>{"use strict";$e();je();Nr();Un();ve();jt();Hl=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5],a=e[6],p=e[7];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let h=!1,g=r.dims[0],b=r.dims[1],w=r.dims.length===3?h?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],y=b,_=0,I=0,$=Math.floor(w/t.numHeads);if(a&&p){if(a.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');_=a.dims[2],I=a.dims[2]}else if(a||p)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,y=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==$)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(n)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,y=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==$)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,y=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(s){if(s.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(n&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(u){E=8;let T=u.dims;throw T.length===1?T[0]===g?E=1:T[0]===3*g+2&&(E=3):T.length===2&&T[0]===g&&T[1]===y&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let A=!1,z=w;if(n){if(n.dims.length!==3&&n.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==n.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(n.dims.length===3){if(y!==n.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');z=n.dims[2]}else{if(y!==n.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');z=n.dims[1]*n.dims[3],A=!0}}let R=_+y,V=!1;if(u)throw new Error("Key padding mask is not supported");if(l)throw new Error("extraAddQk is not supported");if(a)throw new Error("pastKey is not supported");if(p)throw new Error("pastValue is not supported");return{batchSize:g,sequenceLength:b,pastSequenceLength:_,kvSequenceLength:y,totalSequenceLength:R,maxSequenceLength:I,inputHiddenSize:0,hiddenSize:w,vHiddenSize:z,headSize:$,vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:V,passPastInKv:A,qkvFormat:x}},Cs=e=>ge({...e}),_s=ge({perm:[0,2,1,3]}),Gl=(e,t,r,o,n,s,u)=>{let l=[o,n,s],a=U.size(l),p=[{type:"uint32",data:a},{type:"uint32",data:u},{type:"uint32",data:s}],h=g=>{let b=F("qkv_with_bias",t.dataType,l),w=M("qkv",t.dataType,l),y=M("bias",r.dataType,l),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${g.registerUniforms(_).declareVariables(w,y,b)}\n  ${g.mainStart()}\n    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:l,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:h},{inputs:[t,r],outputs:[-1]})[0]},Xn=(e,t,r,o,n,s,u,l)=>{let a=s;if(u){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return a=Gl(e,s,u,t,o,r*n,l),a=a.reshape([t,o,r,n]),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]}else return s.dims.length===3&&(a=s.reshape([t,o,r,n])),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]},Is=(e,t)=>{let r=Hl(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let o=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,n=Xn(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],e.inputs[3],0);if(o)return Kr(e,n,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t);let s=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,e.inputs[1],e.inputs[3],r.hiddenSize),u=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,e.inputs[2],e.inputs[3],2*r.hiddenSize);Kr(e,n,s,u,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],r,t)}});var Ll,Fl,jl,ql,Kl,Yl,Zl,Ql,Ts,Es=j(()=>{"use strict";Ne();$e();ve();Ll=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1)throw new Error("Input type must be float.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Fl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n            k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n              break;\n            }\n            offset += k * i32(${ce("uniforms.x_strides",n,t)});\n        `;return`\n          value = ${e.type.value}(uniforms.constant_value);\n          for (var i = 0; i < 1; i++) {\n            var offset = 0;\n            var k = 0;\n            ${o}\n            value = x[offset];\n          }\n      `},jl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${ce("uniforms.x_shape",n,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},ql=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                  k = i32(${ce("uniforms.x_shape",n,t)}) - 1;\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},Kl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0)  {\n                  k += i32(${ce("uniforms.x_shape",n,t)}]);\n                }\n                if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                  k -= i32(${ce("uniforms.x_shape",n,t)});\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},Yl=(e,t,r)=>{switch(r.mode){case 0:return Fl(e,t,r.pads.length);case 1:return jl(e,t,r.pads.length);case 2:return ql(e,t,r.pads.length);case 3:return Kl(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Zl=(e,t)=>{let r=U.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,s=[{type:"uint32",data:U.size(r)},{type:"uint32",data:t.pads}];if(t.mode===0){let a=Xe(e[0].dataType);s.push({type:a,data:t.value})}s.push(...L(e[0].dims),...L(r));let u=["rank"],l=a=>{let p=F("output",e[0].dataType,r.length),h=M("x",e[0].dataType,o.length),g=h.type.value,b=Yl(p,o.length,t),w=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&w.push({name:"constant_value",type:g}),`\n            ${a.registerUniforms(w).declareVariables(h,p)}\n            ${a.mainStart()}\n            ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${p.offsetToIndices("global_idx")};\n\n            var value = ${g}(0);\n            ${b}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(U.size(r)/64)},programUniforms:s}),getShaderSource:l}},Ql=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,n=e[0].dims.length,s=new Int32Array(2*n).fill(0);if(e.length>=4){let l=e[3].getBigInt64Array();for(let a=0;a<l.length;a++)s[Number(l[a])]=Number(r[a]),s[Number(l[a])+n]=Number(r[a+l.length])}else r.forEach((l,a)=>s[Number(a)]=Number(l));let u=[];return s.forEach(l=>u.push(l)),{mode:t.mode,value:o,pads:u}}else return t},Ts=(e,t)=>{Ll(e.inputs);let r=Ql(e.inputs,t);e.compute(Zl(e.inputs,r),{inputs:[0]})}});var nn,Os,Ps,ks,Rs,Xl,Jl,Bs,Ds,Ms,zs,Us,Vs,Ns,Ws,Hs,Gs,Ls,Fs,js=j(()=>{"use strict";Lt();$e();ve();nn=e=>{if(Gt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Os=(e,t,r)=>{let o=t.format==="NHWC",n=e.dims.slice();o&&n.splice(1,0,n.pop());let s=Object.hasOwnProperty.call(t,"dilations"),u=t.kernelShape.slice(),l=t.strides.slice(),a=s?t.dilations.slice():[],p=t.pads.slice();Bt.adjustPoolAttributes(r,n,u,l,a,p);let h=Bt.computePoolOutputShape(r,n,l,a,u,p,t.autoPad),g=Object.assign({},t);s?Object.assign(g,{kernelShape:u,strides:l,pads:p,dilations:a,cacheKey:t.cacheKey}):Object.assign(g,{kernelShape:u,strides:l,pads:p,cacheKey:t.cacheKey});let b=h.slice();return b.push(b.splice(1,1)[0]),[g,o?b:h]},Ps=(e,t)=>{let r=t.format==="NHWC",o=U.size(e),n=U.size(t.kernelShape),s=[{type:"uint32",data:o},{type:"uint32",data:n}],u=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let l=t.kernelShape[t.kernelShape.length-1],a=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],h=t.pads[t.pads.length-1],g=!!(p+h);s.push({type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},{type:"uint32",data:h}),u.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let b=!1;if(t.kernelShape.length===2){let w=t.kernelShape[t.kernelShape.length-2],y=t.strides[t.strides.length-2],_=t.pads[t.pads.length/2-2],I=t.pads[t.pads.length-2];b=!!(_+I),s.push({type:"uint32",data:w},{type:"uint32",data:y},{type:"uint32",data:_},{type:"uint32",data:I}),u.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,u,!0,g,b]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let l=U.computeStrides(t.kernelShape);s.push({type:"uint32",data:l},{type:"uint32",data:t.pads},{type:"uint32",data:t.strides}),u.push({name:"kernelStrides",type:"u32",length:l.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let a=t.pads.reduce((p,h)=>p+h);return[s,u,!!a,!1,!1]}},ks=(e,t,r,o,n,s,u,l,a,p,h,g)=>{let b=n.format==="NHWC",w=t.type.value,y=F("output",t.type.tensor,o);if(n.kernelShape.length<=2){let _="",I="",$="",x=r-(b?2:1);if(h?_=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${x}] < 0 || xIndices[${x}]\n                      >= uniforms.x_shape[${x}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`:_=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`,n.kernelShape.length===2){let A=r-(b?3:2);g?I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${A}] < 0 || xIndices[${A}] >= uniforms.x_shape[${A}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n                `,$=`\n              }\n            `}return`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var value = ${w}(${l});\n              var pad = 0;\n              ${I}\n              ${_}\n              ${$}\n              ${u}\n\n              output[global_idx] = value;\n            }`}else{if(b)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=n.kernelShape.length,I=n.pads.length,$="";return p?$=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${s}\n              }`:$=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${s}\n            `,`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${_}>;\n\n              var value = ${w}(${l});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${_-1}u; j++) {\n                  offsets[j] = offset / ${ce("uniforms.kernelStrides","j",_)};\n                  offset -= offsets[j] * ${ce("uniforms.kernelStrides","j",_)};\n                }\n                offsets[${_-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-_}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${ce("uniforms.strides",`j - ${r-_}u`,_)}\n                    + offsets[j - ${r-_}u] - ${ce("uniforms.pads","j - 2u",I)};\n                  ${$}\n              }\n              ${u}\n\n              output[global_idx] = value;\n            }`}},Rs=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Xl=e=>`${Rs(e)};${e.countIncludePad}`,Jl=e=>`${Rs(e)};${e.storageOrder};${e.dilations}`,Bs=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),Ds=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=M("x",t.dataType,t.dims.length),l=u.type.value,a="value += x_val;",p="";n.countIncludePad?p+=`value /= ${l}(uniforms.kernelSize);`:p+=`value /= ${l}(i32(uniforms.kernelSize) - pad);`;let[h,g,b,w,y]=Ps(s,n);h.push(...L(t.dims),...L(s));let _=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:I=>ks(I,u,t.dims.length,s.length,n,a,p,0,g,b,w,y)}},Ms=e=>{let t=e.count_include_pad!==0,r=Bs(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:Xl(o)}},zs=(e,t)=>{nn(e.inputs),e.compute(Ds("AveragePool",e.inputs[0],!1,t))},Us={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Vs=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Ns=(e,t)=>{nn(e.inputs),e.compute(Ds("GlobalAveragePool",e.inputs[0],!0,t))},Ws=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=`\n      value = max(x_val, value);\n    `,l="",a=M("x",t.dataType,t.dims.length),p=["rank"],[h,g,b,w,y]=Ps(s,n);return h.push(...L(t.dims),...L(s)),{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:_=>ks(_,a,t.dims.length,s.length,n,u,l,-1e5,g,b,w,y)}},Hs=(e,t)=>{nn(e.inputs),e.compute(Ws("MaxPool",e.inputs[0],!1,t))},Gs=e=>{let t=e.storage_order,r=e.dilations,o=Bs(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let n={storageOrder:t,dilations:r,...o,cacheKey:""};return{...n,cacheKey:Jl(n)}},Ls=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Fs=(e,t)=>{nn(e.inputs),e.compute(Ws("GlobalMaxPool",e.inputs[0],!0,t))}});var tc,rc,qs,Ks=j(()=>{"use strict";Lt();Ne();ve();tc=(e,t,r)=>{let o=e===t,n=e<t&&r<0,s=e>t&&r>0;if(o||n||s)throw new Error("Range these inputs\' contents are invalid.")},rc=(e,t,r,o)=>{let n=Math.abs(Math.ceil((t-e)/r)),s=[n],u=n,l=Xe(o),a=[{type:"uint32",data:u},{type:l,data:e},{type:l,data:r},...L(s)],p=h=>{let g=F("output",o,s.length),b=g.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:b},{name:"delta",type:b}];return`\n        ${h.registerUniforms(w).declareVariables(g)}\n        ${h.mainStart()}\n        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${b}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:s,dataType:o}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:a})}},qs=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),Gt.webgpu.validateInputContent&&tc(t,r,o),e.compute(rc(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var nc,oc,ac,ic,sc,uc,dc,lc,cc,pc,mc,Ys,fc,hc,gc,yc,bc,Zs,Qs,Xs=j(()=>{"use strict";$e();je();ve();nc=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},oc=(e,t,r)=>{t.every(n=>n>=0&&n<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((n,s)=>o[n]=e[s]),o},ac=(e,t,r,o,n,s)=>{let[u,l,a]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(u>0&&e.length>u&&e[u].dims.length>0)e[u].getFloat32Array().forEach(h=>s.push(h));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(l>0&&e.length>l&&e[l].dims.length>0){if(e[l].getFloat32Array().forEach(h=>o.push(h)),o.length!==0&&o.length!==p&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");nc(o,t),t.axes.length>0&&oc(o,t.axes,p).forEach((h,g)=>o[g]=h)}if(a>0&&e.length>a&&(e[a].getBigInt64Array().forEach(h=>n.push(Number(h))),n.length!==p||r>=18&&n.length===t.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(t.axes.length>0){if(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(n.length!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof n<"u"&&o.length>0&&n.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},ic=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n                        ${t}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n                  const adjustment = ${t}(lengthResized) / outputWidth;\n                  const center = ${t}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",sc=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){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(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",uc=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),n=e.length===0?o:e.slice();return t.length>0?(t.forEach((s,u)=>{o[s]=n[u],o[u+r]=n[t.length+u]}),o):n},dc=(e,t,r,o)=>{let n=[];if(r.length>0)if(o.length>0){if(e.forEach(s=>n.push(s)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((s,u)=>n[s]=r[u])}else r.forEach(s=>n.push(s));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");n=e.map((s,u)=>Math.round(s*t[u]))}return n},lc=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let n=e.slice();return r.axes.length>0?(r.axes.forEach(s=>t[s]=o),r.axes.forEach(s=>n[s]=Math.round(e[s]*t[s]))):(t.fill(o,0,t.length),n.forEach((s,u)=>n[u]=Math.round(s*t[u]))),n},cc=(e,t,r,o,n)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n      var original_indices: array<${e.type.value}, ${r.length}>;\n      for (var i:u32 = 0; i < ${r.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${ce("uniforms.scales","i",o)};\n        var roi_low = ${ce("uniforms.roi","i",n)};\n        var roi_hi = ${ce("uniforms.roi",`i + ${t.length}`,n)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${ce("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${ce("uniforms.output_shape","i",r.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,pc=(e,t,r,o,n,s,u)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${o.length}; i++) {\n        var output_index = ${t.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${ce("uniforms.scales","i",n)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${ce("uniforms.roi","i",s)};\n          var roi_hi = ${ce("uniforms.roi",`i + ${r.length}`,s)};\n          var input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${ce("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${u} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,mc=(e,t)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${ce("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,Ys=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",fc=(e,t,r,o,n)=>{let[u,l,a,p]=r.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(row, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(col, ${r[a]} - 1))`)};\n      ${Ys(e,p,u,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${h} = originalIndices[${l}];\n      var col:${h} = originalIndices[${a}];\n      ${o?`if (row < 0 || row > (${r[l]} - 1) || col < 0 || col > (${r[a]} - 1)) {\n        return ${n};\n      }`:""};\n      row = max(0, min(row, ${r[l]} - 1));\n      col = max(0, min(col, ${r[a]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${r.length>2?`u32(originalIndices[${p}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${u}])`:"0"};\n      var x11: ${h} = getInputValue(batch, channel, row1, col1);\n      var x12: ${h} = getInputValue(batch, channel, row1, col2);\n      var x21: ${h} = getInputValue(batch, channel, row2, col1);\n      var x22: ${h} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${h} = abs(row - ${h}(row1));\n      var dx2: ${h} = abs(${h}(row2) - row);\n      var dy1: ${h} = abs(col - ${h}(col1));\n      var dy2: ${h} = abs(${h}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},hc=(e,t,r,o,n,s,u,l,a,p)=>{let h=r.length===2,g=!0,[b,w]=h?[0,1]:g?[2,3]:[1,2],y=e.type.value,_=I=>{let $=I===b?"row":"col";return`\n      fn ${$}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${y} {\n        var output_index = ${t.indicesGet("output_indices",I)};\n        var originalIdx: ${y} = getOriginalCoordinateFromResizedCoordinate(output_index, ${n[I]},\n        ${o[I]}, ${r[I]}, ${s[I]}, ${s[I]} + ${r.length});\n        var fractOriginalIdx: ${y} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${l} && (originalIdx < 0 || originalIdx > (${r[I]} - 1))) {\n          return ${a};\n        }\n        var data: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${$}: ${y} = originalIdx + ${y}(i);\n          if (${$} < 0 || ${$} >= ${r[I]}) {\n            ${(()=>p?`coefs[i + 1] = 0.0;\n                        continue;`:l?`return ${a};`:`${$} = max(0, min(${$}, ${r[I]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",I,`u32(${$})`)};\n          data[i + 1] = ${I===b?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${_(b)};\n    ${_(w)};\n  fn getCubicInterpolationCoefs(s: ${y}) -> array<${y}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${y} = 1.0 - absS;\n    var twoMinusAbsS: ${y} = 2.0 - absS;\n    var onePlusAbsS: ${y} = 1.0 + absS;\n    coeffs[0] = ((${u} * onePlusAbsS - 5 * ${u}) * onePlusAbsS + 8 * ${u}) * onePlusAbsS - 4 * ${u};\n    coeffs[1] = ((${u} + 2) * absS - (${u} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${u} + 2) * oneMinusAbsS - (${u} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${u} * twoMinusAbsS - 5 * ${u}) * twoMinusAbsS + 8 * ${u}) * twoMinusAbsS - 4 * ${u};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${y}, 4>, coefs: array<${y}, 4>) -> ${y} {\n    var coefsSum: ${y} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},gc=(e,t,r,o,n)=>{let[u,l,a,p,h]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(depth, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(height, ${r[a]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(width, ${r[p]} - 1))`)};\n      ${Ys(e,h,u,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${g} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${g} = originalIndices[${l}];\n      var height:${g} = originalIndices[${a}];\n      var width:${g} = originalIndices[${p}];\n      ${o?`if (depth < 0 || depth > (${r[l]} - 1) || height < 0 || height > (${r[a]} - 1) || width < 0 || (width > ${r[p]} - 1)) {\n      return ${n};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[l]} - 1));\n      height = max(0, min(height, ${r[a]} - 1));\n      width = max(0, min(width, ${r[p]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${r.length>3?`u32(originalIndices[${h}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${u}])`:"0"};\n\n      var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${g} = abs(depth - ${g}(depth1));\n      var dx2: ${g} = abs(${g}(depth2) - depth);\n      var dy1: ${g} = abs(height - ${g}(height1));\n      var dy2: ${g} = abs(${g}(height2) - height);\n      var dz1: ${g} = abs(width - ${g}(width1));\n      var dz2: ${g} = abs(${g}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},yc=(e,t,r,o,n,s)=>{let u=e.dims,l=uc(s,t.axes,u.length),a=dc(u,o,n,t.axes),p=o.slice();o.length===0&&(p=u.map((x,E)=>x===0?1:a[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(a=lc(u,p,t)));let h=F("output",e.dataType,a.length),g=M("input",e.dataType,u.length),b=U.size(a),w=u.length===a.length&&u.every((x,E)=>x===a[E]),y=t.coordinateTransformMode==="tf_crop_and_resize",_=t.extrapolationValue,I=g.type.value,$=x=>`\n      ${w?"":`\n      ${ic(t.coordinateTransformMode,I)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${mc(g,u)};\n              ${sc(t.nearestMode,r,I)};\n              ${pc(g,h,u,a,p.length,l.length,y)};\n              `;case"linear":return`\n              ${cc(h,u,a,p.length,l.length)};\n              ${(()=>{if(u.length===2||u.length===4)return`${fc(g,h,u,y,_)}`;if(u.length===3||u.length===5)return`${gc(g,h,u,y,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(u.length===2||u.length===4)return`${hc(g,h,u,a,p,l,t.cubicCoeffA,y,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",l.length).declareVariables(g,h)}\n      ${x.mainStart()}\n        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${w?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${h.offsetToIndices("global_idx")};\n        var input_indices: ${g.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${g.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${u.length===2||u.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${p.length>0?p:""}|${n.length>0?n:""}|${l.length>0?l:""}|${w}|${u}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:"uint32",data:b},{type:"float32",data:p},{type:"float32",data:l},...L(u),...L(a)]})}},bc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Zs=(e,t)=>{let r=[],o=[],n=[],s=bc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");ac(e.inputs,t,s,r,o,n),e.compute(yc(e.inputs[0],t,s,r,o,n),{inputs:[0]})},Qs=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,n=e.cubicCoeffA,s=e.excludeOutside!==0,u=e.extrapolationValue,l=e.keepAspectRatioPolicy,a=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return ge({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:n,excludeOutside:s,extrapolationValue:u,keepAspectRatioPolicy:l,mode:a,nearestMode:p})}});var wc,vc,Js,eu,tu=j(()=>{"use strict";Ne();$e();je();ve();wc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let n=t.dims[t.dims.length-1],s=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==n)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==n)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let u=e[3];if(u.dims.length!==1)throw new Error("Beta must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let u=e[4];if(u.dims.length!==1)throw new Error("Bias must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Bias must have the same hidden size as input")}},vc=(e,t,r,o)=>{let n=e[0].dims,s=U.size(n),u=n,l=s,a=n.slice(-1)[0],p=o?n.slice(0,-1).concat(1):[],h=e.length>3,g=e.length>4,b=o&&r>1,w=o&&r>2,y=r>3,_=Fe(a),I=[M("x",e[0].dataType,e[0].dims,_),M("skip",e[1].dataType,e[1].dims,_),M("gamma",e[2].dataType,e[2].dims,_)];h&&I.push(M("beta",e[3].dataType,e[3].dims,_)),g&&I.push(M("bias",e[4].dataType,e[4].dims,_)),I.push(F("output",e[0].dataType,u,_)),b&&I.push(F("meanOutput",1,p)),w&&I.push(F("invStdOutput",1,p)),y&&I.push(F("inputSkipBiasSum",e[0].dataType,u,_));let $=Le(e[0].dataType),x=A=>`\n      const hiddenSize: f32 = ${a};\n      const hiddenSizeVectorized: u32 = ${a/_};\n      const epsilon: f32 = ${t.epsilon};\n\n      ${A.declareVariables(...I)}\n\n      ${A.mainStart()}\n        ${A.guardAgainstOutOfBoundsWorkgroupSizes(l/a)}\n        let offset = global_idx * hiddenSizeVectorized;\n        var sum = ${Ze("f32",_)};\n        var squareSum = ${Ze("f32",_)};\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          let skipValue = skip[offset + i];\n          let biasValue = ${g?"bias[i]":"0.0"};\n          let inputValue = x[offset + i];\n          let value = inputValue + skipValue + biasValue;\n          ${y?"inputSkipBiasSum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32Value = ${at($,_,"value")};\n          sum += f32Value;\n          squareSum += f32Value * f32Value;\n        }\n        let mean = ${Je("sum",_)} / hiddenSize;\n        let invStdDev = inverseSqrt(${Je("squareSum",_)} / hiddenSize - mean * mean + epsilon);\n        ${b?"meanOutput[global_idx] = mean;":""}\n        ${w?"invStdOutput[global_idx] = invStdDev;":""}\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          output[offset + i] = (output[offset + i] - ${$}(mean)) * ${$}(invStdDev) * gamma[i]\n           + ${h?"beta[i]":"0.0"};\n        }\n      }`,E=[{dims:u,dataType:e[0].dataType}];return r>1&&E.push({dims:p,dataType:1}),r>2&&E.push({dims:p,dataType:1}),r>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:t.cacheKey},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/a/64)}})}},Js=(e,t)=>{wc(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(vc(e.inputs,t,e.outputCount,!1),{outputs:o})},eu=e=>{let t=e.epsilon;return ge({epsilon:t})}});var $c,on,Sc,ru,xc,_c,nu,ou,au=j(()=>{"use strict";Ne();$e();je();ve();$c=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},on=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Sc=(e,t)=>{if(e.length>1){let r=on(e,1),o=on(e,2),n=on(e,3);return n.length===0&&(n=[...Array(e[0].dims.length).keys()]),ge({starts:r,ends:o,axes:n})}else return t},ru=(e,t,r,o,n)=>{let s=e;return e<0&&(s+=r[o[t]]),n[t]<0?Math.max(0,Math.min(s,r[o[t]]-1)):Math.max(0,Math.min(s,r[o[t]]))},xc=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n          var input_indices: ${e.type.indices};\n          var carry = 0u;\n          for (var i = ${r.length}; i >= 0; i--) {\n            let input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n            let steps_i = ${ce("uniforms.steps","i",r.length)};\n            let signs_i = ${ce("uniforms.signs","i",r.length)};\n   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elements");if(n.length!==r.length)for(let $=0;$<r.length;++$)n.includes($)||(u.splice($,0,0),l.splice($,0,r[$]),s.splice($,0,1));let a=s.map($=>Math.sign($));s.forEach(($,x,E)=>{if($<0){let A=(l[x]-u[x])/$,z=u[x],R=z+A*s[x];u[x]=R,l[x]=z,E[x]=-$}});let p=r.slice(0);n.forEach(($,x)=>{p[$]=Math.ceil((l[$]-u[$])/s[$])});let h={dims:p,dataType:e[0].dataType},g=F("output",e[0].dataType,p.length),b=M("input",e[0].dataType,e[0].dims.length),w=U.size(p),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:u.length},{name:"signs",type:"i32",length:a.length},{name:"steps",type:"u32",length:s.length}],_=[{type:"uint32",data:w},{type:"uint32",data:u},{type:"int32",data:a},{type:"uint32",data:s},...L(e[0].dims),...L(p)],I=$=>`\n      ${$.registerUniforms(y).declareVariables(b,g)}\n        ${xc(b,g,r)}\n        ${$.mainStart()}\n          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n          let output_indices = ${g.offsetToIndices("global_idx")};\n          let input_indices = calculateInputIndices(output_indices);\n          ${g.setByOffset("global_idx",b.getByIndices("input_indices"))}\n      }`;return{name:"Slice",shaderCache:{hint:`${a.length}_${u.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[h],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:_})}},nu=(e,t)=>{$c(e.inputs,t);let r=Sc(e.inputs,t);e.compute(_c(e.inputs,r),{inputs:[0]})},ou=e=>{let t=e.starts,r=e.ends,o=e.axes;return ge({starts:t,ends:r,axes:o})}});var Cc,Ic,iu,su,uu=j(()=>{"use strict";$e();je();ve();Cc=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ic=(e,t)=>{let r=e.dims,o=U.size(r),n=64,s=t.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let u=r[s],l=o/u,a=Fe(u),p=u/a,h=(I,$)=>$===4?`max(max(${I}.x, ${I}.y), max(${I}.z, ${I}.w))`:$===2?`max(${I}.x, ${I}.y)`:$===3?`max(max(${I}.x, ${I}.y), ${I}.z)`:I,g=M("x",e.dataType,e.dims,a),b=F("result",e.dataType,e.dims,a),w=g.type.value,y=Le(e.dataType)==="f32"?`var threadMax = ${w}(-3.402823e+38f);`:`var threadMax = ${w}(-65504.0h);`,_=I=>`\n      var<workgroup> rowMaxShared : ${w};\n      var<workgroup> rowSumShared : ${w};\n      var<workgroup> threadShared : array<${w}, ${n}>;\n\n      fn getValue(row: i32, col: i32, row_stride: i32) -> ${w} {\n        let index = row * row_stride + col;\n        return x[index];\n      }\n\n      fn setValue(row: i32, col: i32, row_stride: i32, value: ${w}) {\n        let index = row * row_stride + col;\n        result[index] = value;\n      }\n      ${I.registerUniform("packedCols","i32").declareVariables(g,b)}\n      ${I.mainStart()}\n        let gindex = i32(global_idx);\n        let lindex = i32(local_idx);\n        const wg = ${n};\n        let row = gindex / wg;\n        let cols = uniforms.packedCols;\n        let row_stride : i32 = uniforms.packedCols;\n\n        // find the rows max\n        ${y}\n        for (var col = lindex; col < cols; col += wg) {\n          let value = getValue(row, col, row_stride);\n          threadMax = max(threadMax, value);\n        }\n        if (lindex < cols) {\n          threadShared[lindex] = threadMax;\n        }\n        workgroupBarrier();\n\n        var reduceSize = min(cols, wg);\n        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {\n          reduceSize = currSize + (reduceSize & 1);\n          if (lindex < currSize) {\n            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowMaxShared = ${w}(${h("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // find the rows sum\n        var threadSum = ${w}(0.0);\n        for (var col = lindex; col < cols; col += wg) {\n          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n          threadSum += subExp;\n        }\n        threadShared[lindex] = threadSum;\n        workgroupBarrier();\n\n        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {\n          if (lindex < currSize) {\n            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowSumShared = ${w}(${Je("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // calculate final value for each element in the row\n        for (var col = lindex; col < cols; col += wg) {\n          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n          setValue(row, col, row_stride, value);\n        }\n      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= 0; i < ${t.length}; i++) {\n        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};\n        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;\n\n        ${u.indicesSet("input_indices","i","input_dim_value")}\n      }\n      ${l.setByOffset("global_idx",u.getByIndices("input_indices"))}\n    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:"uint32",data:n},...L(e[0].dims),...L(o)]}),getShaderSource:a}},mu=e=>{kc(e.inputs),e.compute(Bc(e.inputs),{inputs:[0]})}});var Dc,Mc,hu,gu=j(()=>{"use strict";Ne();$e();ve();Dc=(e,t,r,o,n)=>{let s=F("output_data",n,r.length,4),u=M("a_data",t[1].dataType,t[1].dims.length,4),l=M("b_data",t[2].dataType,t[2].dims.length,4),a=M("c_data",t[0].dataType,t[0].dims.length,4),p,h=(g,b,w)=>`select(${b}, ${g}, 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an,wu=j(()=>{"use strict";Lt();Ct();ve();an=class{constructor(t){this.backend=t;this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,r){this.repo.set(t,r)}run(t,r,o,n,s){kt(t.programInfo.name);let u=this.backend.device,l=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2),l.setPipeline(t.computePipeline);let a=[];for(let h of r)a.push({binding:a.length,resource:{buffer:h.buffer}});for(let h of o)a.push({binding:a.length,resource:{buffer:h.buffer}});s&&a.push({binding:a.length,resource:s});let 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'})),uu=L((()=>{ue(),su(),ms(),Do=()=>!!l.wasm.proxy&&typeof document<"u",Ro=!1,Po=!1,Lo=!1,zo=new Map,No=(e,A)=>{let t=zo.get(e);t?t.push(A):zo.set(e,[A])},Vo=()=>{if(Ro||!Po||Lo||!Mo)throw new Error("worker not 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e=>{if(Do())return Vo(),new Promise(((A,t)=>{No("init-ep",[A,t]);let n={type:"init-ep",in:{epName:e,env:l}};Mo.postMessage(n)}));await Qo(l,e)},Yo=async e=>Do()?(Vo(),new Promise(((A,t)=>{No("copy-from",[A,t]);let n={type:"copy-from",in:{buffer:e}};Mo.postMessage(n,[e.buffer])}))):_o(e),Xo=async(e,A)=>{if(Do()){if(A?.preferredOutputLocation)throw new Error('session option "preferredOutputLocation" is not supported for proxy.');return Vo(),new Promise(((t,n)=>{No("create",[t,n]);let r={type:"create",in:{model:e,options:A}},i=[];e instanceof Uint8Array&&i.push(e.buffer),Mo.postMessage(r,i)}))}return Fo(e,A)},Jo=async e=>{if(Do())return Vo(),new Promise(((A,t)=>{No("release",[A,t]);let n={type:"release",in:e};Mo.postMessage(n)}));So(e)},Zo=async(e,A,t,n,r,i)=>{if(Do()){if(t.some((e=>"cpu"!==e[3])))throw new Error("input tensor on GPU is not supported for proxy.");if(r.some((e=>e)))throw new Error("pre-allocated output tensor is not supported for proxy.");return Vo(),new Promise(((r,a)=>{No("run",[r,a]);let s=t,o={type:"run",in:{sessionId:e,inputIndices:A,inputs:s,outputIndices:n,options:i}};Mo.postMessage(o,ko(s))}))}return Oo(e,A,t,n,r,i)},eu=async e=>{if(Do())return Vo(),new Promise(((A,t)=>{No("end-profiling",[A,t]);let n={type:"end-profiling",in:e};Mo.postMessage(n)}));Ho(e)}})),lu=L((()=>{ue(),uu(),vs(),bs(),Au=(e,A)=>{switch(e.location){case"cpu":return[e.type,e.dims,e.data,"cpu"];case"gpu-buffer":return[e.type,e.dims,{gpuBuffer:e.gpuBuffer},"gpu-buffer"];default:throw new Error(`invalid data location: ${e.location} for ${A()}`)}},tu=e=>{switch(e[3]){case"cpu":return new I(e[0],e[2],e[1]);case"gpu-buffer":{let A=e[0];if(!qe(A))throw new Error(`not supported data type: ${A} for deserializing GPU tensor`);let{gpuBuffer:t,download:n,dispose:r}=e[2];return I.fromGpuBuffer(t,{dataType:A,dims:e[1],download:n,dispose:r})}default:throw new Error(`invalid data location: ${e[3]}`)}},nu=class{async fetchModelAndCopyToWasmMemory(e){return Yo(await Ye(e))}async loadModel(e,A){let t;U(),t="string"==typeof e?typeof process<"u"&&process.versions&&process.versions.node?await Ye(e):await this.fetchModelAndCopyToWasmMemory(e):e,[this.sessionId,this.inputNames,this.outputNames]=await Xo(t,A),x()}async dispose(){return Jo(this.sessionId)}async run(e,A,t){U();let n=[],r=[];Object.entries(e).forEach((e=>{let A=e[0],t=e[1],i=this.inputNames.indexOf(A);if(-1===i)throw new Error(`invalid input '${A}'`);n.push(t),r.push(i)}));let i=[],a=[];Object.entries(A).forEach((e=>{let A=e[0],t=e[1],n=this.outputNames.indexOf(A);if(-1===n)throw new Error(`invalid output '${A}'`);i.push(t),a.push(n)}));let s=n.map(((e,A)=>Au(e,(()=>`input "${this.inputNames[r[A]]}"`)))),o=i.map(((e,A)=>e?Au(e,(()=>`output "${this.outputNames[a[A]]}"`)):null)),u=await Zo(this.sessionId,r,s,a,o,t),l={};for(let e=0;e<u.length;e++)l[this.outputNames[a[e]]]=i[e]??tu(u[e]);return x(),l}startProfiling(){}endProfiling(){eu(this.sessionId)}}})),du=L((()=>{ue(),uu(),lu(),ru=()=>{if(("number"!=typeof l.wasm.initTimeout||l.wasm.initTimeout<0)&&(l.wasm.initTimeout=0),"boolean"!=typeof l.wasm.simd&&(l.wasm.simd=!0),"boolean"!=typeof l.wasm.proxy&&(l.wasm.proxy=!1),"boolean"!=typeof l.wasm.trace&&(l.wasm.trace=!1),"number"!=typeof l.wasm.numThreads||!Number.isInteger(l.wasm.numThreads)||l.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(l.wasm.numThreads=1);let e=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;l.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},iu=class{async init(e){ru(),await qo(),await jo(e)}async createInferenceSessionHandler(e,A){let t=new nu;return await t.loadModel(e,A),Promise.resolve(t)}}})),cu={};z(cu,{wasmBackend:()=>pu});var pu,hu=L((()=>{du(),pu=new iu}));ue(),ue(),ue();var fu=oe;{let e=(hu(),N(cu)).wasmBackend;i("webgpu",e,5),i("cpu",e,10),i("wasm",e,10)}Object.defineProperty(l.versions,"web",{value:"1.17.1",enumerable:!0});var gu=t(3823);const{strategies:mu}=H.segmentation,{fillInsideCircle:wu}=mu,{annotation:Bu}=H,{state:yu}=Bu,{Events:vu}=O.Enums,{Events:bu}=H.Enums,{segmentation:Cu}=H,{filterAnnotationsForDisplay:$u}=H.utilities.planar,{IslandRemoval:Iu}=H.utilities,{triggerSegmentationDataModified:Eu}=Cu.triggerSegmentationEvents,Qu="ONNX_MODEL_LOADING_STARTED",Uu="ONNX_MODEL_LOADING_COMPLETED",xu="ONNX_MODEL_COMPONENT_LOADED";var _u;!function(e){e.Log="status",e.Encoder="encoder",e.Decoder="decoder"}(_u||(_u={}));class Fu{static{this.MarkerInclude="MarkerInclude"}static{this.MarkerExclude="MarkerExclude"}static{this.BoxPrompt="BoxPrompt"}static{this.viewportOptions={displayArea:{storeAsInitialCamera:!0,interpolationType:O.Enums.InterpolationType.NEAREST,imageArea:[1,1],imageCanvasPoint:{imagePoint:[.5,.5],canvasPoint:[.5,.5]}},background:[0,0,.2]}}static{this.MODELS={sam_l:[{name:"sam-l-encoder",url:"/sam_l/vit_l_encoder.onnx",size:1224,key:"encoder",feedType:"images"},{name:"sam-l-decoder",url:"/sam_l/vit_l_decoder.onnx",size:17,key:"decoder"}],sam_h:[{name:"sam-h-encoder",url:"/sam_h/vit_h_encoder.onnx",size:18,key:"encoder",feedType:"images"},{name:"sam-h-decoder",url:"/sam_h/vit_h_decoder.onnx",size:1,key:"decoder"}]}}constructor(e={listeners:null,getPromptAnnotations:null,promptAnnotationTypes:null,models:null,modelName:null,islandFillOptions:{maxInternalRemove:16,fillInternalEdge:!0},autoSegmentMode:!1,numRandomPoints:2,searchBreadth:3}){this.maxWidth=1024,this.maxHeight=1024,this.modelWidth=1024,this.modelHeight=1024,this.canvas=document.createElement("canvas"),this.canvasMask=document.createElement("canvas"),this.sessions=[],this.points=[],this.labels=[],this.worldPoints=new Array,this._searchBreadth=3,this.excludeTool=Fu.MarkerExclude,this.listeners=[console.log],this.desiredImage={imageId:null,sampleImageId:null,imageIndex:-1,decoder:null,encoder:null},this.imageEncodings=new Map,this.boxRadius=5,this.isGpuInUse=!1,this.annotationsNeedUpdating=!1,this.promptAnnotationTypes=[Fu.MarkerInclude,Fu.MarkerExclude,Fu.BoxPrompt],this._enabled=!1,this._autoSegmentMode=!1,this.imageIdsRunAgainst=new Map,this.numRandomPoints=25,this.islandFillOptions={maxInternalRemove:16,fillInternalEdge:!0},this.pCutoff=64,this.getPromptAnnotations=(e=this.viewport)=>{const A=[],{element:t}=e;for(const e of this.promptAnnotationTypes)A.push(...yu.getAnnotations(e,t));return $u(this.viewport,A)},this.viewportRenderedListener=e=>{const{viewport:A,currentImage:t,desiredImage:n}=this;if(n.imageId=A.getCurrentImageId()||A.getViewReferenceId(),n.imageIndex=A.getCurrentImageIdIndex(),!n.imageId)return;if(n.imageId.startsWith("volumeId:")?n.sampleImageId=A.getImageIds(A.getVolumeId())[0]:n.sampleImageId=n.imageId,n.imageId===t?.imageId)return;if(this._enabled&&this._autoSegmentMode){if("isInAcquisitionPlane"in A&&!A.isInAcquisitionPlane())return void console.warn("Non acquisition plane viewports and auto segment mode is not yet supported");const e=H.segmentation.activeSegmentation.getActiveSegmentation(A.id),t=H.segmentation.segmentIndex.getActiveSegmentIndex(e.segmentationId),n=A.getImageIds(),r=A.getCurrentImageIdIndex(),i=[];let a=!1,s=!1;for(let e=1;e<=this._searchBreadth;e++){if(!a){const A=r-e;if(A>=0){const e=n[A],r=O.cache.getImageByReferencedImageId(e),s=r?.voxelManager;if(s){let e=!1;s.forEach((({value:A,pointIJK:n})=>{if(A===t){const A=O.utilities.imageToWorldCoords(r.imageId,[n[0],n[1]]);i.push(A),e=!0}})),e&&(a=!0)}}}if(!s){const A=r+e;if(A<n.length){const e=n[A],r=O.cache.getImageByReferencedImageId(e),a=r?.voxelManager;if(a){let e=!1;a.forEach((({value:A,pointIJK:n})=>{if(A===t){const A=O.utilities.imageToWorldCoords(r.imageId,[n[0],n[1]]);i.push(A),e=!0}})),e&&(s=!0)}}}if(a&&s)break}this.randomPoints=i.length>0?i.sort((()=>Math.random()-.5)).slice(0,Math.min(i.length,this.numRandomPoints)):[]}const{canvasMask:r}=this;r.getContext("2d").clearRect(0,0,r.width,r.height),this.tryLoad({resetImage:!0})},this.annotationModifiedListener=H.utilities.debounce((e=>{this.getPromptAnnotations().length&&(this.annotationsNeedUpdating=!0,this.tryLoad())}),300),e.listeners&&(this.listeners=[...e.listeners]),e.getPromptAnnotations&&(this.getPromptAnnotations=e.getPromptAnnotations),this.promptAnnotationTypes=e.promptAnnotationTypes||this.promptAnnotationTypes,e.models&&Object.assign(Fu.MODELS,e.models),this.config=this.getConfig(e.modelName),e.islandFillOptions&&(this.islandFillOptions={maxInternalRemove:e.islandFillOptions.maxInternalRemove??this.islandFillOptions.maxInternalRemove,fillInternalEdge:e.islandFillOptions.fillInternalEdge??this.islandFillOptions.fillInternalEdge}),this._autoSegmentMode=e.autoSegmentMode||!1,this.numRandomPoints=e.numRandomPoints||this.numRandomPoints,this._searchBreadth=e.searchBreadth||this._searchBreadth}set enabled(e){this._enabled=e}get enabled(){return this._enabled}set autoSegmentMode(e){this._autoSegmentMode=e,e&&(this._enabled=!0)}get autoSegmentMode(){return this._autoSegmentMode}set numSamplePoints(e){this.numRandomPoints=e}initModel(){return this.loadingAI||(this.loadingAI=this.load()),this.loadingAI}setPCutoff(e){this.pCutoff=e,this.annotationsNeedUpdating=!0,this.tryLoad()}initViewport(e){const{desiredImage:A}=this;this.viewport&&this.disconnectViewport(this.viewport),this.currentImage=null,this.viewport=e;const t=new H.LabelmapBaseTool({},{configuration:{strategies:{FILL_INSIDE_CIRCLE:wu},activeStrategy:"FILL_INSIDE_CIRCLE",preview:{enabled:!0}}});this.tool=t,A.imageId=e.getCurrentImageId?.()||e.getViewReferenceId(),A.imageId.startsWith("volumeId:")?A.sampleImageId=e.getImageIds(e.getVolumeId())[0]:A.sampleImageId=A.imageId,e.element.addEventListener(vu.IMAGE_RENDERED,this.viewportRenderedListener);const n=this.annotationModifiedListener;O.eventTarget.addEventListener(bu.ANNOTATION_ADDED,n),O.eventTarget.addEventListener(bu.ANNOTATION_MODIFIED,n),O.eventTarget.addEventListener(bu.ANNOTATION_COMPLETED,n),A.imageId&&this.tryLoad()}acceptPreview(e){this.tool.acceptPreview(e)}rejectPreview(e){this.tool.rejectPreview(e)}restoreCachedPromptAnnotations(e){if(!this._cachedPromptAnnotations)return[];const A=[],{include:t,exclude:n}=this._cachedPromptAnnotations;if(t){const n=H.utilities.moveAnnotationToViewPlane(t,e);A.push(n)}if(n){const t=H.utilities.moveAnnotationToViewPlane(n,e);A.push(t)}return A}removePromptAnnotationsWithCache(e){const A=[Fu.MarkerInclude,Fu.MarkerExclude,Fu.BoxPrompt];let t=null,n=null;const r=H.annotation.state.getAllAnnotations();for(const e of r){const r=e.metadata.toolName;A.includes(r)&&(r!==Fu.MarkerInclude||t?r!==Fu.MarkerExclude||n||(n=e):t=e,H.annotation.state.removeAnnotation(e.annotationUID))}this._cachedPromptAnnotations={include:t,exclude:n},e.render()}async interpolateScroll(e=this.viewport,A=1){const{element:t}=e;this.tool.acceptPreview(t);const n=this.getPromptAnnotations(e),r=e.getCurrentImageIdIndex(),i=e.getViewReference({sliceIndex:r+A});if(!i||i.sliceIndex===r)return void console.warn("No next image in direction",A,r);e.scroll(A),await new Promise((e=>window.setTimeout(e,250)));let a=[];if(n.length)for(const A of n){const t=structuredClone(A);H.annotation.state.removeAnnotation(A.annotationUID),Object.assign(t.metadata,i),H.utilities.moveAnnotationToViewPlane(t,e),a.push(t)}else a=this.restoreCachedPromptAnnotations(e);for(const A of a)yu.addAnnotation(A,e.element);e.render()}log(e,...A){for(const t of this.listeners)t(e,...A)}disconnectViewport(e){e.element.removeEventListener(vu.IMAGE_RENDERED,this.viewportRenderedListener);const A=this.annotationModifiedListener;O.eventTarget.removeEventListener(bu.ANNOTATION_MODIFIED,A),O.eventTarget.removeEventListener(bu.ANNOTATION_COMPLETED,A)}async load(){const{sessions:e}=this;let A;this.canvas.style.cursor="wait";for(let t=0;t<2;t++)e.push({sessionIndex:t,encoder:null,decoder:null,imageEmbeddings:null,isLoading:!1,canvas:0===t?this.canvas:document.createElement("canvas")}),0===t?(A=this.loadModels(Fu.MODELS[this.config.model],e[t]).catch((e=>{this.log(_u.Log,"Couldn't load models",e)})),await A):e[t].encoder=e[0].encoder,e[t].loader=A}clear(e){this.points=[],this.labels=[],this.getPromptAnnotations(e).forEach((e=>yu.removeAnnotation(e.annotationUID))),this.tool&&this.tool.rejectPreview(this.viewport.element)}async cacheImageEncodings(e=this.viewport.getCurrentImageIdIndex(),A=0,t=1e6){const{viewport:n,imageEncodings:r}=this;if(A>=t)return;const i=(A+e)%t,a=n.getViewReference({sliceIndex:i});if(!a)return t=i,this.cacheImageEncodings(e,A,t);const s=a.referencedImageId||n.getViewReferenceId({sliceIndex:i});if(r.has(s)||await this.loadStorageImageEncoding(e,s,i),r.has(s))return void this.cacheImageEncodings(e,A+1,t);if(this.tryLoad(),this.isGpuInUse)return void setTimeout((()=>this.cacheImageEncodings(e,A)),500);this.log(_u.Log,"Caching",i,s);const o=n.getImageIds()[0];this.handleImage({imageId:s,sampleImageId:o},this.sessions[1]).then((()=>{this.cacheImageEncodings(e,A+1,t)}))}async handleImage({imageId:e,sampleImageId:A},t){if(e===t.imageId||this.isGpuInUse||!this.enabled)return;const{viewport:n,desiredImage:r}=this;this.isGpuInUse=!0,t.imageId=e,t.sampleImageId=A;try{const A=r.imageId===e,{canvas:i}=t;A&&(this.log(_u.Encoder,`Loading image on ${t.sessionIndex}`),this.log(_u.Decoder,"Awaiting image"),i.style.cursor="wait"),this.points=[],this.labels=[];const a=this.maxWidth,s=this.maxHeight;i.width=a,i.height=s,t.imageEmbeddings=void 0;const o=i.style.width,u=i.getContext("2d",{willReadFrequently:!0});u.clearRect(0,0,a,s);const l={canvas:i,imageId:e,viewportOptions:{...n.defaultOptions,...Fu.viewportOptions},viewReference:null,renderingEngineId:n.getRenderingEngine().id};if(e.startsWith("volumeId:")){const e=n.getViewReference();l.viewReference=e,l.imageId=null}t.canvasPosition=await O.utilities.loadImageToCanvas(l),i.style.width=o,i.style.height=o,A&&this.log(_u.Encoder,`Rendered image on ${t.sessionIndex}`),this.imageImageData=u.getImageData(0,0,a,s);const d=await this.restoreImageEncoding(t,e);if(d)t.imageEmbeddings=d,r.imageId===e&&(this.log(_u.Encoder,"Cached Image"),i.style.cursor="default");else{const A=await fu.Tensor.fromImage(this.imageImageData,{resizedWidth:this.modelWidth,resizedHeight:this.modelHeight}),{feedType:n="input_image"}=this.config.encoder,a="images"===n&&{images:A}||"pixelValues"===n&&{pixel_values:A}||{input_image:A};await t.loader;const s=await t.encoder;if(!s)return void this.log(_u.Log,"****** No session");const o=performance.now();t.imageEmbeddings=s.run(a);const u=await t.imageEmbeddings;this.storeImageEncoding(t,e,u),r.imageId===e&&(this.log(_u.Encoder,`Image Ready ${t.sessionIndex} ${(performance.now()-o).toFixed(1)} ms`),i.style.cursor="default")}}finally{this.isGpuInUse=!1}this.tryLoad()}async runDecode(){const{canvas:e}=this;if(!this.isGpuInUse&&this.currentImage?.imageEmbeddings){this.isGpuInUse=!0;try{this.canvas.style.cursor="wait",await this.decode(this.points,this.labels)}finally{e.style.cursor="default",this.isGpuInUse=!1}}}tryLoad(e={resetImage:!1}){const{viewport:A,desiredImage:t}=this;t.imageId&&!e.resetImage||(t.imageId=A.getCurrentImageId()||A.getViewReferenceId(),this.currentImage=null);const[n]=this.sessions;if(n?.imageId===t.imageId)return this.currentImage!==n&&(this.currentImage=n),void(this._enabled&&this._autoSegmentMode?(this.points=[],this.labels=[],this.randomPoints?.length&&(this.randomPoints.forEach((e=>{const A=this.mapAnnotationPoint(e,this.currentImage.canvasPosition);this.points.push(...A),this.labels.push(1)})),this.runDecode())):this.updateAnnotations());this.handleImage(t,n)}mapAnnotationPoint(e,A){const{origin:t,downVector:n,rightVector:r}=A,i=gu.eR.sub([0,0,0],e,t);return[Math.round(gu.eR.dot(i,r)/gu.eR.sqrLen(r)),Math.round(gu.eR.dot(i,n)/gu.eR.sqrLen(n))]}updateAnnotations(e=this.currentImage){if(this.isGpuInUse||!this.annotationsNeedUpdating||!this.currentImage)return;const A=this.getPromptAnnotations();if(this.annotationsNeedUpdating=!1,this.points=[],this.labels=[],this.worldPoints=[],A?.length&&e?.canvasPosition){for(const t of A){const A=t.data.handles.points[0],n=this.mapAnnotationPoint(A,e.canvasPosition);if(this.points.push(...n),t.metadata.toolName===Fu.BoxPrompt)this.labels.push(2,3),this.points.push(...this.mapAnnotationPoint(t.data.handles.points[3],e.canvasPosition));else{const e=t.metadata.toolName===this.excludeTool?0:1;e&&this.worldPoints.push(A),this.labels.push(e)}}this.runDecode()}}async restoreImageEncoding(e,A){if(!this.sharedImageEncoding)return;this.imageEncodings.has(A)||await this.loadStorageImageEncoding(e,A);const t=this.imageEncodings.get(A);if(t){const e=this.sharedImageEncoding.image_embeddings?"image_embeddings":"embeddings";return this.sharedImageEncoding[e].cpuData.set(t),this.sharedImageEncoding}}async loadStorageImageEncoding(e,A,t=null){try{const r=await this.getDirectoryForImageId(e,A),i=this.getFileNameForImageId(A,this.config.model);if(!r||!i)return null;const a=await Tu(r,i);if(!a)return null;this.log(_u.Log,"Loading from storage",t||A,i);const s=await a.getFile();if(s){const e=await(n=s,new Promise((e=>{const A=new FileReader;A.readAsArrayBuffer(n),A.onload=function(){const t=A.result,n=new Float32Array(t);e(n)}})));this.imageEncodings.set(A,e)}}catch(e){this.log(_u.Log,"Unable to fetch file",A,e)}var n}async storeImageEncoding(e,A,t){this.sharedImageEncoding||(this.sharedImageEncoding=t);const n=(t.image_embeddings||t.embeddings)?.cpuData;if(!n)return void console.log("Unable to store data",t);const r=new Float32Array(n);this.imageEncodings.set(A,r);try{const t=await this.getDirectoryForImageId(e,A),n=this.getFileNameForImageId(A,this.config.model);if(!t||!n)return;const i=await t.getFileHandle(n,{create:!0}),a=await i.createWritable();await a.write(r),await a.close()}catch(e){this.log(_u.Log,"Unable to write",A,e)}}createLabelmap(e,A,t,n){const{canvas:r,viewport:i}=this,a=this.tool.addPreview(i.element),{previewSegmentIndex:s,memo:o,segmentationId:u,segmentIndex:l,segmentationVoxelManager:d}=a,c=o?.voxelManager,{dimensions:p}=c,{data:h}=e,{origin:f,rightVector:g,downVector:m}=A,w=gu.eR.create(),B=gu.eR.create(),y=i.getDefaultImageData();for(let e=0;e<r.height;e++){gu.eR.scaleAndAdd(w,f,m,e);for(let A=0;A<r.width;A++){gu.eR.scaleAndAdd(B,w,g,A);const t=y.worldToIndex(B).map(Math.round);if(-1!==t.findIndex(((e,A)=>e<0||e>=p[A])))continue;const n=h[4*(A+e*this.maxWidth)],r=d.getAtIJKPoint(t);0!==r&&255!==r||(n>this.pCutoff?c.setAtIJKPoint(t,s):c.setAtIJKPoint(t,null))}}this._autoSegmentMode&&this.tool.doneEditMemo(),this.tool._previewData.isDrag=!0;const v=c.sourceVoxelManager||c;if(this.islandFillOptions){const e=new Iu(this.islandFillOptions);e.initialize(i,v,{previewSegmentIndex:s,segmentIndex:l,points:this.worldPoints.map((e=>y.worldToIndex(e).map(Math.round)))})&&(e.floodFillSegmentIsland(),e.removeExternalIslands(),e.removeInternalIslands())}Eu(u)}async decode(e,A,t=this.currentImage){const{canvas:n,canvasMask:r,imageImageData:i,desiredImage:a,boxRadius:s}=this,o=n.getContext("2d",{willReadFrequently:!0});if(o.clearRect(0,0,n.width,n.height),n.width=i.width,n.height=i.height,r.width=i.width,r.height=i.height,t&&t.imageId===a.imageId){if(o.putImageData(i,0,0),e.length){t.imageEmbeddings||await t.encoder;const n=await t.imageEmbeddings,i=t.decoder,a=function(e,A,t,n=[1024,1024]){const r=new fu.Tensor(new Float32Array(65536),[1,1,256,256]),i=new fu.Tensor(new Float32Array([0]),[1]),a=new fu.Tensor(new Float32Array(n),[2]),s=new fu.Tensor(new Float32Array(A),[1,A.length/2,2]),o=new fu.Tensor(new Float32Array(t),[1,t.length]),u=e.image_embeddings?"image_embeddings":"embeddings";return{image_embeddings:(l=e[u],new fu.Tensor(l.type,Float32Array.from(l.data),l.dims)),point_coords:s,point_labels:o,mask_input:r,has_mask_input:i,orig_im_size:a};var l}(n,e,A),u=await i.run(a);for(let t=0;t<e.length;t+=2){const n=A[t/2];o.fillStyle=n?"blue":"pink",o.fillRect(e[t]-s,e[t+1]-s,2*s,2*s)}const l=u.masks;this.maskImageData=l.toImageData(),this.createLabelmap(this.maskImageData,t.canvasPosition,e,A),o.globalAlpha=.3;const{data:d}=this.maskImageData,c=[];for(let e=0;e<d.length;e+=4){const A=d[e];A>0&&(A<255&&(d[e]=0,A>192?d[e+1]=255:d[e+2]=A+64),c[A]=1+(c[A]||0))}const p=await createImageBitmap(this.maskImageData);o.drawImage(p,0,0);const h=r.getContext("2d");h.globalAlpha=.9,h.drawImage(p,0,0)}}else this.log(_u.Log,"***** Image not current, need to wait for current image")}async fetchAndCacheModel(e,A){try{const t=await caches.open("onnx");let n=await t.match(e);null==n?((0,O.triggerEvent)(O.eventTarget,xu,{name:A,url:e,status:"loading",source:"network"}),await t.add(e),n=await t.match(e),this.log(_u.Log,`${A} (network)`)):((0,O.triggerEvent)(O.eventTarget,xu,{name:A,url:e,status:"loaded",source:"cache"}),this.log(_u.Log,`${A} (cached)`));return await n.arrayBuffer()}catch(t){return this.log(_u.Log,`${A} (network)`),(0,O.triggerEvent)(O.eventTarget,xu,{name:A,url:e,status:"error",error:t}),await fetch(e).then((e=>e.arrayBuffer()))}}async loadModels(e,A=this.currentImage){const t=await caches.open("onnx");let n=0;const r=[];for(const A of Object.values(e)){void 0===await t.match(A.url)&&(n+=A.size),r.push(A.url)}(0,O.triggerEvent)(O.eventTarget,Qu,{modelConfig:this.config.model,totalSize:n,urls:r}),n>0?this.log(_u.Log,`downloading ${n} MB from network ... it might take a while`):this.log(_u.Log,"loading...");const i=performance.now();for(const t of Object.values(e)){const e={executionProviders:[this.config.provider],enableMemPattern:!1,enableCpuMemArena:!1,extra:{session:{disable_prepacking:"1",use_device_allocator_for_initializers:"1",use_ort_model_bytes_directly:"1",use_ort_model_bytes_for_initializers:"1"}},interOpNumThreads:4,intraOpNumThreads:2},n=await this.fetchAndCacheModel(t.url,t.name),r={...e,...t.opt||{}};this.config[t.key]=t,A[t.key]=await fu.InferenceSession.create(n,r)}const a=performance.now()-i;(0,O.triggerEvent)(O.eventTarget,Uu,{modelConfig:this.config.model,loadTimeMs:a,urls:r}),this.log(_u.Log,`ready, ${a.toFixed(1)}ms`,r.join(", "))}async getDirectoryForImageId(e,A){if(!(-1!==A.indexOf("/studies/")&&-1!==A.indexOf("/instances/")||(A=e.sampleImageId)&&-1!==A.indexOf("/studies/")&&-1!==A.indexOf("/instances/")))return null;const t=A.split("/studies/")[1].split("/instances/")[0].split("/"),[n,r,i]=t,a=await window.navigator.storage.getDirectory(),s=await Su(a,this.config.model),o=await Su(s,n);return await Su(o,i)}getFileNameForImageId(e,A){if(e.startsWith("volumeId:")){const t=e.indexOf("sliceIndex="),n=e.indexOf("&focalPoint=");return e.substring(t,n).replace("&",".").replace("sliceIndex=","volume.")+A}const t=e.indexOf("/instances/");if(-1!=t){const n=t+11,r=e.indexOf("/",n);return e.substring(n,r)+A}}getConfig(e="sam_b"){if(this.config)return this.config;const A={model:e,provider:"webgpu",device:"gpu",threads:4,local:null,isSlimSam:!1},t=window.location.search.substring(1).split("&");for(let e=0;e<t.length;e++){const n=t[e].split("=");n[0]in A&&(A[n[0]]=decodeURIComponent(n[1]))}return A.threads=parseInt(String(A.threads)),A.local=parseInt(A.local),fu.env.wasm.wasmPaths="ort/",fu.env.wasm.numThreads=A.threads,fu.env.wasm.proxy="wasm"==A.provider,this.config=A,A}}async function Su(e,A){return await Tu(e,A)||e.getDirectoryHandle(A,{create:!0})}async function Tu(e,A){for await(const[t,n]of e)if(t===A)return n}class Ou extends H.LabelmapBaseTool{static{this.toolName="LabelmapSlicePropagation"}constructor(e={},A={supportedInteractionTypes:["Mouse","Touch"],configuration:{sourceViewportId:"",autoSegmentMode:!0,modelName:"sam_b",enabled:!1,models:{sam_b:[{name:"sam-b-encoder",url:"https://huggingface.co/schmuell/sam-b-fp16/resolve/main/sam_vit_b_01ec64.encoder-fp16.onnx",size:180,key:"encoder"},{name:"sam-b-decoder",url:"https://huggingface.co/schmuell/sam-b-fp16/resolve/main/sam_vit_b_01ec64.decoder.onnx",size:17,key:"decoder"}]},numRandomPoints:5,searchBreadth:10}}){super(e,A),this._initialized=!1,this._modelInitialized=!1,this._init=async()=>{const{configuration:e}=this;if(!e.enabled||!e.sourceViewportId)return;this.segmentAI||(this.segmentAI=new Fu({autoSegmentMode:e.autoSegmentMode,models:e.models,modelName:e.modelName,numRandomPoints:e.numRandomPoints,searchBreadth:e.searchBreadth})),this.segmentAI.enabled=!0,this._modelInitialized||(await this.segmentAI.initModel(),this._modelInitialized=!0);const{sourceViewportId:A}=this.configuration,t=(0,O.getEnabledElementByViewportId)(A);if(!t)return void console.debug("No enabled element found for viewportId:",A);const{viewport:n}=t;this.segmentAI.initViewport(n),this._initialized=!0},this.onSetToolConfiguration=()=>{this._init()},this.onSetToolEnabled=async()=>{this.configuration.enabled=!0,this.segmentAI&&(this.segmentAI.enabled=!0),this._init()},this.onSetToolDisabled=()=>{this.configuration.enabled=!1,this.segmentAI&&(this.segmentAI.enabled=!1)},this.onSetToolPassive=()=>{this.configuration.enabled=!1,this.segmentAI&&(this.segmentAI.enabled=!1)},this.acceptPreview=()=>{const{sourceViewportId:e}=this.configuration,A=(0,O.getEnabledElementByViewportId)(e);if(!A)return;const{viewport:t}=A;this.segmentAI.tool.acceptPreview(t.element)},this.rejectPreview=()=>{const{sourceViewportId:e}=this.configuration,A=(0,O.getEnabledElementByViewportId)(e);A&&this.segmentAI.rejectPreview(A.viewport.element)}}}const Hu=Ou;class ku extends H.LabelmapBaseTool{static{this.toolName="MarkerLabelmap"}constructor(e={},A={supportedInteractionTypes:["Mouse","Touch"],configuration:{sourceViewportId:"",modelName:"sam_b",enabled:!1,models:{sam_b:[{name:"sam-b-encoder",url:"https://huggingface.co/schmuell/sam-b-fp16/resolve/main/sam_vit_b_01ec64.encoder-fp16.onnx",size:180,key:"encoder"},{name:"sam-b-decoder",url:"https://huggingface.co/schmuell/sam-b-fp16/resolve/main/sam_vit_b_01ec64.decoder.onnx",size:17,key:"decoder"}]},numRandomPoints:5,searchBreadth:10}}){super(e,A),this._initialized=!1,this._modelInitialized=!1,this._toolsAdded=!1,this._init=async()=>{const{configuration:e}=this;if(!e.enabled||!e.sourceViewportId)return;this.segmentAI||(this.segmentAI=new Fu({models:e.models,modelName:e.modelName})),this._toolsAdded||this._addToolInstances(),this.segmentAI.enabled=!0,this._modelInitialized||(await this.segmentAI.initModel(),this._modelInitialized=!0);const{sourceViewportId:A}=this.configuration,t=(0,O.getEnabledElementByViewportId)(A);if(!t)return void console.debug("No enabled element found for viewportId:",A);const{viewport:n}=t;this.segmentAI.initViewport(n),this._initialized=!0},this._getToolGroupId=()=>H.ToolGroupManager.getToolGroupForViewport(this.configuration.sourceViewportId),this._addToolInstances=()=>{const e=this._getToolGroupId();if(!e)return void console.debug("Tool group not found");(0,H.addTool)(H.ProbeTool);const A=Fu.MarkerInclude,t=Fu.MarkerExclude;e.addToolInstance(A,H.ProbeTool.toolName,{getTextLines:()=>null}),e.addToolInstance(t,H.ProbeTool.toolName,{getTextLines:()=>null}),H.annotation.config.style.setToolGroupToolStyles(e.id,{[A]:{color:"rgb(0, 255, 0)",colorHighlighted:"rgb(0, 255, 0)",colorSelected:"rgb(0, 255, 0)"},[t]:{color:"rgb(255, 0, 0)",colorHighlighted:"rgb(255, 0, 0)",colorSelected:"rgb(255, 0, 0)"}}),this._toolsAdded=!0},this.onSetToolConfiguration=()=>{this._init()},this.onSetToolEnabled=async()=>{this.configuration.enabled=!0,this.segmentAI&&(this.segmentAI.enabled=!0),this._initialized||await this._init()},this.onSetToolActive=()=>{this.configuration.enabled=!0,this.segmentAI&&(this.segmentAI.enabled=!0),this._initialized||this._init()},this.interpolateScroll=()=>{this.segmentAI.interpolateScroll()},this.onSetToolDisabled=()=>{this.configuration.enabled=!1,this.segmentAI&&(this.segmentAI.enabled=!1)},this.clearMarkers=()=>{if(!this.segmentAI)return;const{sourceViewportId:e}=this.configuration,A=(0,O.getEnabledElementByViewportId)(e);if(!A)return;const{viewport:t}=A;this.segmentAI.clear(t),t.render()},this.removePromptAnnotations=()=>{if(!this.segmentAI)return;const{viewportId:e}=this.configuration,A=(0,O.getEnabledElementByViewportId)(e);A&&this.segmentAI.removePromptAnnotationsWithCache(A.viewport)},this.acceptPreview=()=>{if(!this.segmentAI)return;const{sourceViewportId:e}=this.configuration,A=(0,O.getEnabledElementByViewportId)(e);A&&this.segmentAI.acceptPreview(A.viewport.element)},this.rejectPreview=()=>{if(!this.segmentAI)return;const{sourceViewportId:e}=this.configuration,A=(0,O.getEnabledElementByViewportId)(e);A&&this.segmentAI.rejectPreview(A.viewport.element)}}}const Du=ku},19742:(e,A,t)=>{"use strict";t.d(A,{FC:()=>n.F,Mr:()=>r.M});var n=t(55500),r=t(55509);t(56074),t(89926),t(80068),t(87142)},7927:(e,A,t)=>{"use strict";t.d(A,{G:()=>c});var n=t(15327),r=t(4667);let i=!1;const{triggerSegmentationEvents:a}=r.segmentation,{getOrCreateSegmentationVolume:s}=r.utilities.segmentation,{triggerSegmentationDataModified:o}=a,{WorkerTypes:u}=r.Enums,l=(0,n.getWebWorkerManager)(),d=(e,A)=>{(0,n.triggerEvent)(e,n.Enums.Events.WEB_WORKER_PROGRESS,{progress:A,type:u.INTERPOLATE_LABELMAP})};const c=async function({segmentationId:e,segmentIndex:A,configuration:r={preview:!1}}){!function(){if(i)return;i=!0,(0,n.getWebWorkerManager)().registerWorker("interpolation",(()=>new Worker(new URL(t.p+t.u(2007),t.b),{name:"interpolation",type:void 0})),{maxWorkerInstances:1,autoTerminateOnIdle:{enabled:!0,idleTimeThreshold:2e3}})}(),d(n.eventTarget,0);const a=s(e),{voxelManager:u,imageData:c}=a,p={scalarData:u.getCompleteScalarDataArray(),dimensions:c.getDimensions(),spacing:c.getSpacing(),origin:c.getOrigin(),direction:c.getDirection()};try{const{data:t}=await l.executeTask("interpolation","interpolateLabelmap",{segmentationInfo:p,configuration:{...r,label:A}});u.setCompleteScalarDataArray(t),o(e,u.getArrayOfModifiedSlices(),A),d(n.eventTarget,100)}catch(e){console.warn("Warning: Failed to perform morphological contour interpolation",e),d(n.eventTarget,100)}}},56983:(e,A,t)=>{"use strict";t.r(A),t.d(A,{canComputeRequestedRepresentation:()=>te,computeContourData:()=>k,computeLabelmapData:()=>X,computeSurfaceData:()=>B,init:()=>le,updateSurfaceData:()=>ue});var n=t(15327),r=t(4667);function i(e){const A=new Map;for(const[t,n]of e){const e=Number(t);for(const[t,r]of n)r&&(A.has(e)||A.set(e,[]),A.get(e).push(r))}return A}const{WorkerTypes:a}=r.Enums,{getAnnotation:s}=r.annotation.state,o=(0,n.getWebWorkerManager)(),u=(e,A,t)=>{(0,n.triggerEvent)(e,n.Enums.Events.WEB_WORKER_PROGRESS,{progress:A,type:a.POLYSEG_CONTOUR_TO_SURFACE,id:t})};const{getSegmentation:l}=r.segmentation.state,{getSegmentIndexColor:d}=r.segmentation.config.color;async function c(e,A,t={}){const r=l(e),i=new Map,a=Object.keys(A).map((async e=>{const a=A[e],s=a.segmentIndex,o=d(t.viewport.id,r.segmentationId,s).slice(0,3);if(!o)throw new Error("No color found for segment index, unable to create surface");const u={id:`segmentation_${r.segmentationId}_surface_${s}`,color:o,frameOfReferenceUID:"test-frameOfReferenceUID",points:a.data.points,polys:a.data.polys,segmentIndex:s},l=u.id;return i.set(s,l),n.geometryLoader.createAndCacheGeometry(l,{type:n.Enums.GeometryType.SURFACE,geometryData:u})}));return await Promise.all(a),{geometryIds:i}}const{WorkerTypes:p}=r.Enums,{computeVolumeLabelmapFromStack:h}=r.utilities.segmentation,f=(0,n.getWebWorkerManager)(),g=(e,A,t)=>{(0,n.triggerEvent)(e,n.Enums.Events.WEB_WORKER_PROGRESS,{progress:A,type:p.POLYSEG_LABELMAP_TO_SURFACE,id:t})};const{getUniqueSegmentIndices:m}=r.utilities.segmentation,{getSegmentation:w}=r.segmentation.state;async function B(e,A={}){const t=A.segmentIndices?.length?A.segmentIndices:m(e);let r;const i=w(e),a=i.representationData;try{a.Contour?r=await async function(e,A={}){const t=w(e),r=t.representationData.Contour,i=A.segmentIndices||m(e),a=i.map((async e=>{const A=await async function(e,A){const{annotationUIDsMap:t}=e,r=[],i=[],a=t.get(A);for(const e of a){const A=s(e),{polyline:t}=A.data.contour;i.push(t.length),t.forEach((e=>r.push(...e)))}u(n.eventTarget,0,A);const l=await o.executeTask("polySeg","convertContourToSurface",{polylines:r,numPointsArray:i},{callbacks:[e=>{u(n.eventTarget,e,A)}]});return u(n.eventTarget,100,A),l}(r,e);return{segmentIndex:e,data:A}}));return await Promise.all(a)}(e,{segmentIndices:t,...A}):a.Labelmap&&(r=await y(i.segmentationId,{segmentIndices:t,...A}))}catch(e){throw console.error(e),e}if(!r)throw new Error("Not enough data to convert to surface, currently only support converting volume labelmap to surface if available");return await c(e,r,A)}async function y(e,A={}){const t=w(e);if(!t?.representationData?.Labelmap)return void console.warn("Only support surface update from labelmaps");const r=t.representationData.Labelmap,i=A.segmentIndices||m(e),a=i.map((e=>{const A=async function(e,A){let t;if(e.volumeId)t=e.volumeId;else{const{imageIds:A}=e;({volumeId:t}=await h({imageIds:A}))}const r=n.cache.getVolume(t),i=r.voxelManager.getCompleteScalarDataArray(),{dimensions:a,spacing:s,origin:o,direction:u}=r;g(n.eventTarget,0,A);const l=await f.executeTask("polySeg","convertLabelmapToSurface",{scalarData:i,dimensions:a,spacing:s,origin:o,direction:u,segmentIndex:A},{callbacks:[e=>{g(n.eventTarget,e,A)}]});return g(n.eventTarget,100,A),l}(r,e);return A})),s=await Promise.allSettled(a),o=s.filter((e=>"rejected"===e.status));if(o.length>0)throw console.error(o),new Error("Failed to convert labelmap to surface");return s.map(((e,A)=>{if("fulfilled"===e.status)return{segmentIndex:i[A],data:e.value}})).filter(Boolean)}let v=!1;function b(){if(v)return;v=!0;(0,n.getWebWorkerManager)().registerWorker("polySeg",(()=>new Worker(new URL(t.p+t.u(572),t.b),{name:"polySeg",type:void 0})),{maxWorkerInstances:1,autoTerminateOnIdle:{enabled:!0,idleTimeThreshold:2e3}})}const C=(0,n.getWebWorkerManager)(),{WorkerTypes:$}=r.Enums,{pointToString:I}=r.utilities,E=new Map,Q=new Map,U=(e,A)=>{(0,n.triggerEvent)(e,n.Enums.Events.WEB_WORKER_PROGRESS,{progress:A,type:$.SURFACE_CLIPPING})};async function x(e,A){b();const t=A.getSlicesClippingPlanes?.();if(!t)return;const r=A.getSliceIndex();t.sort(((e,A)=>Math.abs(e.sliceIndex-r)-Math.abs(A.sliceIndex-r))),U(n.eventTarget,0),await async function(e){const A=e.filter((e=>!Q.has(e.id)));if(!A.length)return;const t=await C.executeTask("polySeg","getSurfacesAABBs",{surfacesInfo:A},{callbacks:[({progress:e})=>{U(n.eventTarget,e)}]});t.forEach(((e,A)=>{Q.set(A,e)}))}(e);const i=new Map;e.forEach((e=>{i.set(e.id,Q.get(e.id))}));const a=A.getCamera();return await C.executeTask("polySeg","cutSurfacesIntoPlanes",{surfacesInfo:e,planesInfo:t,surfacesAABB:i},{callbacks:[({progress:e})=>{U(n.eventTarget,e)},({sliceIndex:e,polyDataResults:t})=>{t.forEach(((t,n)=>{const r=Number(n),i=function(e,A,t){return`${e.id}-${I(A)}-${t}`}(A,a.viewPlaneNormal,e);!function(e,A,t){const{points:n,lines:r,numberOfCells:i}=t;let a=E.get(e);a||(a=new Map,E.set(e,a));a.set(A,{points:n,lines:r,numberOfCells:i})}(r,i,t)}))}]}).catch((e=>{console.error(e)})),U(n.eventTarget,100),E}const{addAnnotation:_}=r.annotation.state;const F=e=>{const{numberOfCells:A,lines:t}=e,n=[],r=[];for(let e=0;e<t.length;){const i=t[e];if(r.push(i),n.push(t.slice(e+1,e+i+1)),e+=i+1,n.length===A)break}return{lineSegments:n,linesNumberOfPoints:r}},{getUniqueSegmentIndices:S}=r.utilities.segmentation,{getSegmentation:T}=r.segmentation.state,{segmentationStyle:O}=r.segmentation,{SegmentationRepresentations:H}=r.Enums;async function k(e,A={}){const t=A.segmentIndices?.length?A.segmentIndices:S(e);let a;const s=T(e).representationData;try{s.Surface?a=await async function(e,A={}){if(!A.viewport)throw new Error("Viewport is required to compute contour from surface");const{viewport:t}=A,r=A.segmentIndices?.length?A.segmentIndices:S(e),a=new Map,s=new Map,o=T(e),u=o.representationData.Surface,l=[];u.geometryIds.forEach(((e,A)=>{if(r.includes(A)){a.set(A,e);const t=n.cache.getGeometry(e)?.data;t&&l.push({id:e,points:t.points,polys:t.polys,segmentIndex:A})}})),a.forEach(((e,A)=>{s.set(e,A)}));const d=await x(l,t),c=i(d);return c}(e,{segmentIndices:t,...A}):s.Labelmap&&(a=await async function(e,A={}){if(!A.viewport)throw new Error("Viewport is required to compute contour from labelmap");const t=await y(e,A);if(!t?.length)return void console.error("Failed to convert labelmap to surface or labelmap is empty");const{viewport:n}=A,r=t.map((e=>({id:e.segmentIndex.toString(),points:e.data.points,polys:e.data.polys,segmentIndex:e.segmentIndex}))),a=await x(r,n),s=i(a);return s}(e,{segmentIndices:t,...A}))}catch(e){throw console.error(e),e}if(!a)throw new Error("Not enough data to convert to contour, currently only support converting volume labelmap to contour if available");const{viewport:o}=A,u=function(e,A,t){const i=new Map;for(const[a,s]of e)for(const e of s){const{points:s}=e,{lineSegments:o,linesNumberOfPoints:u}=F(e);for(let e=0;e<o.length;e++){const l=o[e],d=[];for(let A=0;A<u[e];A++){const e=l[A];d.push([s[3*e],s[3*e+1],s[3*e+2]])}if(d.length<3)continue;const c={annotationUID:n.utilities.uuidv4(),data:{contour:{closed:!0,polyline:d},segmentation:{segmentationId:t,segmentIndex:a},handles:{}},handles:{},highlighted:!1,autoGenerated:!1,invalidated:!1,isLocked:!1,isVisible:!0,metadata:{toolName:r.PlanarFreehandContourSegmentationTool.toolName,...A.getViewReference()}};_(c,A.element);const p=i?.get(a)||new Set;p.add(c.annotationUID),i.set(a,p)}}return i}(a,o,e);return O.setStyle({segmentationId:e,type:H.Contour},{fillAlpha:0}),{annotationUIDsMap:u}}var D=t(3823);const{WorkerTypes:M}=r.Enums,{getAnnotation:R}=r.annotation.state,P=(0,n.getWebWorkerManager)(),L=(e,A)=>{(0,n.triggerEvent)(e,n.Enums.Events.WEB_WORKER_PROGRESS,{progress:A,type:M.POLYSEG_CONTOUR_TO_LABELMAP})};async function K(e,A={}){const t=A.viewport,r=t.getVolumeId();if(!n.utilities.getViewportImageIds(t))throw new Error("No imageIds found, labelmap computation from contour requires viewports with imageIds");const i=n.utilities.uuidv4(),a=n.volumeLoader.createAndCacheDerivedLabelmapVolume(r,{volumeId:i}),{dimensions:s,origin:o,direction:u,spacing:l,voxelManager:d}=a,{segmentIndices:c,annotationUIDsInSegmentMap:p}=N(e,A);L(n.eventTarget,0);const h=await P.executeTask("polySeg","convertContourToVolumeLabelmap",{segmentIndices:c,dimensions:s,scalarData:d.getCompleteScalarDataArray?.(),origin:o,direction:u,spacing:l,annotationUIDsInSegmentMap:p},{callbacks:[e=>{L(n.eventTarget,e)}]});return L(n.eventTarget,100),d.setCompleteScalarDataArray(h),a.modified(),{volumeId:a.volumeId}}async function z(e,A={}){if(!A.viewport)throw new Error("No viewport provided, labelmap computation from contour requires viewports");const t=A.viewport.getImageIds();if(!t)throw new Error("No imageIds found, labelmap computation from contour requires viewports with imageIds");t.forEach((e=>{if(!n.cache.getImageLoadObject(e))throw new Error("ImageIds 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r=A.segmentIndices?.length?A.segmentIndices:j(e),i=Y(e),a=i.representationData.Contour,s=t?K:z,o=await s(a,{segmentIndices:r,viewport:A.viewport});return o}(e,{segmentIndices:t,...A}):a.Surface&&(r=await async function(e,A={}){const{viewport:t}=A,r=!t||t instanceof n.VolumeViewport,i=A.segmentIndices?.length?A.segmentIndices:j(e),a=Y(e),s=new Map,o=a.representationData.Surface;if(o.geometryIds.forEach(((e,A)=>{i.includes(A)&&s.set(A,e)})),r&&!t)throw new Error("Cannot compute labelmap from surface segmentation without providing the viewport");let u;if(r){const e=t.getVolumeId();u=n.volumeLoader.createAndCacheDerivedLabelmapVolume(e)}else{const e=A.viewport.getImageIds(),t=n.imageLoader.createAndCacheDerivedLabelmapImages(e).map((e=>e.imageId));u=await n.volumeLoader.createAndCacheVolumeFromImages("generatedSegmentationVolumeId",t)}const l=await async function(e,A){const{geometryIds:t}=e;if(!t?.size)throw new Error("No geometry IDs found for surface representation");const r=new 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QAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAArACsAHgArAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAKwArACsAKwArACsAKwArAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAB4ABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAFAABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQAUAAEAAQABAAEAAQABAAEAFAAUABQAFAAUABQAFAAUABQAFAABAAEAA0ADQBLAEsASwBLAEsASwBLAEsASwBLAB4AUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAArAFAAUABQAFAAUABQAFAAUAArACsAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQACsAUAArACsAKwBQAFAAUABQACsAKwAEAFAABAAEAAQABAAEAAQABAArACsABAAEACsAKwAEAAQABABQACsAKwArACsAKwArACsAKwAEACsAKwArACsAUABQACsAUABQAFAABAAEACsAKwBLAEsASwBLAEsASwBLAEsASwBLAFAAUAAaABoAUABQAFAAUABQAEwAHgAbAFAAHgAEACsAKwAEAAQABAArAFAAUABQAFAAUABQACsAKwArACsAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQACsAUABQACsAUABQACsAUABQACsAKwAEACsABAAEAAQABAAEACsAKwArACsABAAEACsAKwAEAAQABAArACsAKwAEACsAKwArACsAKwArACsAUABQAFAAUAArAFAAKwArACsAKwArACsAKwBLAEsASwBLAEsASwBLAEsASwBLAAQABABQAFAAUAAEAB4AKwArACsAKwArACsAKwArACsAKwAEAAQABAArAFAAUABQAFAAUABQAFAAUABQACsAUABQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQACsAUABQACsAUABQAFAAUABQACsAKwAEAFAABAAEAAQABAAEAAQABAAEACsABAAEAAQAKwAEAAQABAArACsAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwBQAFAABAAEACsAKwBLAEsASwBLAEsASwBLAEsASwBLAB4AGwArACsAKwArACsAKwArAFAABAAEAAQABAAEAAQAKwAEAAQABAArAFAAUABQAFAAUABQAFAAUAArACsAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABAArACsABAAEACsAKwAEAAQABAArACsAKwArACsAKwArAAQABAAEACsAKwArACsAUABQACsAUABQAFAABAAEACsAKwBLAEsASwBLAEsASwBLAEsASwBLAB4AUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArAAQAUAArAFAAUABQAFAAUABQACsAKwArAFAAUABQACsAUABQAFAAUAArACsAKwBQAFAAKwBQACsAUABQACsAKwArAFAAUAArACsAKwBQAFAAUAArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArAAQABAAEAAQABAArACsAKwAEAAQABAArAAQABAAEAAQAKwArAFAAKwArACsAKwArACsABAArACsAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAUABQAFAAHgAeAB4AHgAeAB4AGwAeACsAKwArACsAKwAEAAQABAAEAAQAUABQAFAAUABQAFAAUABQACsAUABQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAUAAEAAQABAAEAAQABAAEACsABAAEAAQAKwAEAAQABAAEACsAKwArACsAKwArACsABAAEACsAUABQAFAAKwArACsAKwArAFAAUAAEAAQAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAKwAOAFAAUABQAFAAUABQAFAAHgBQAAQABAAEAA4AUABQAFAAUABQAFAAUABQACsAUABQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAKwArAAQAUAAEAAQABAAEAAQABAAEACsABAAEAAQAKwAEAAQABAAEACsAKwArACsAKwArACsABAAEACsAKwArACsAKwArACsAUAArAFAAUAAEAAQAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwBQAFAAKwArACsAKwArACsAKwArACsAKwArACsAKwAEAAQABAAEAFAAUABQAFAAUABQAFAAUABQACsAUABQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAFAABAAEAAQABAAEAAQABAArAAQABAAEACsABAAEAAQABABQAB4AKwArACsAKwBQAFAAUAAEAFAAUABQAFAAUABQAFAAUABQAFAABAAEACsAKwBLAEsASwBLAEsASwBLAEsASwBLAFAAUABQAFAAUABQAFAAUABQABoAUABQAFAAUABQAFAAKwAEAAQABAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQACsAUAArACsAUABQAFAAUABQAFAAUAArACsAKwAEACsAKwArACsABAAEAAQABAAEAAQAKwAEACsABAAEAAQABAAEAAQABAAEACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArAAQABAAeACsAKwArACsAKwArACsAKwArACsAKwArAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXAAqAFwAXAAqACoAKgAqACoAKgAqACsAKwArACsAGwBcAFwAXABcAFwAXABcACoAKgAqACoAKgAqACoAKgAeAEsASwBLAEsASwBLAEsASwBLAEsADQANACsAKwArACsAKwBcAFwAKwBcACsAXABcAFwAXABcACsAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcACsAXAArAFwAXABcAFwAXABcAFwAXABcAFwAKgBcAFwAKgAqACoAKgAqACoAKgAqACoAXAArACsAXABcAFwAXABcACsAXAArACoAKgAqACoAKgAqACsAKwBLAEsASwBLAEsASwBLAEsASwBLACsAKwBcAFwAXABcAFAADgAOAA4ADgAeAA4ADgAJAA4ADgANAAkAEwATABMAEwATAAkAHgATAB4AHgAeAAQABAAeAB4AHgAeAB4AHgBLAEsASwBLAEsASwBLAEsASwBLAFAAUABQAFAAUABQAFAAUABQAFAADQAEAB4ABAAeAAQAFgARABYAEQAEAAQAUABQAFAAUABQAFAAUABQACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAKwAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQADQAEAAQABAAEAAQADQAEAAQAUABQAFAAUABQAAQABAAEAAQABAAEAAQABAAEAAQABAArAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAArAA0ADQAeAB4AHgAeAB4AHgAEAB4AHgAeAB4AHgAeACsAHgAeAA4ADgANAA4AHgAeAB4AHgAeAAkACQArACsAKwArACsAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgBcAEsASwBLAEsASwBLAEsASwBLAEsADQANAB4AHgAeAB4AXABcAFwAXABcAFwAKgAqACoAKgBcAFwAXABcACoAKgAqAFwAKgAqACoAXABcACoAKgAqACoAKgAqACoAXABcAFwAKgAqACoAKgBcAFwAXABcAFwAXABcAFwAXABcAFwAXABcACoAKgAqACoAKgAqACoAKgAqACoAKgAqAFwAKgBLAEsASwBLAEsASwBLAEsASwBLACoAKgAqACoAKgAqAFAAUABQAFAAUABQACsAUAArACsAKwArACsAUAArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAHgBQAFAAUABQAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFAAUABQAFAAUABQAFAAUABQACsAUABQAFAAUAArACsAUABQAFAAUABQAFAAUAArAFAAKwBQAFAAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAKwArAFAAUABQAFAAUABQAFAAKwBQACsAUABQAFAAUAArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsABAAEAAQAHgANAB4AHgAeAB4AHgAeAB4AUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAHgAeAB4AHgAeAB4AHgAeAB4AHgArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwBQAFAAUABQAFAAUAArACsADQBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAHgAeAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAANAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAWABEAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAA0ADQANAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAAQABAAEACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAANAA0AKwArACsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEACsAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUAArAAQABAArACsAKwArACsAKwArACsAKwArACsAKwBcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqAA0ADQAVAFwADQAeAA0AGwBcACoAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwAeAB4AEwATAA0ADQAOAB4AEwATAB4ABAAEAAQACQArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAFAAUABQAFAAUAAEAAQAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQAUAArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwAEAAQABAAEAAQABAAEAAQABAAEAAQABAArACsAKwArAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsAKwArACsAHgArACsAKwATABMASwBLAEsASwBLAEsASwBLAEsASwBcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXAArACsAXABcAFwAXABcACsAKwArACsAKwArACsAKwArACsAKwBcAFwAXABcAFwAXABcAFwAXABcAFwAXAArACsAKwArAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAXAArACsAKwAqACoAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABAArACsAHgAeAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcACoAKgAqACoAKgAqACoAKgAqACoAKwAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKwArAAQASwBLAEsASwBLAEsASwBLAEsASwArACsAKwArACsAKwBLAEsASwBLAEsASwBLAEsASwBLACsAKwArACsAKwArACoAKgAqACoAKgAqACoAXAAqACoAKgAqACoAKgArACsABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsABAAEAAQABAAEAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABABQAFAAUABQAFAAUABQACsAKwArACsASwBLAEsASwBLAEsASwBLAEsASwANAA0AHgANAA0ADQANAB4AHgAeAB4AHgAeAB4AHgAeAB4ABAAEAAQABAAEAAQABAAEAAQAHgAeAB4AHgAeAB4AHgAeAB4AKwArACsABAAEAAQAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAAEAAQABAAEAAQABABQAFAASwBLAEsASwBLAEsASwBLAEsASwBQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsAKwArACsAKwArACsAKwAeAB4AHgAeAFAAUABQAFAABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsAKwArAA0ADQANAA0ADQBLAEsASwBLAEsASwBLAEsASwBLACsAKwArAFAAUABQAEsASwBLAEsASwBLAEsASwBLAEsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAA0ADQBQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwBQAFAAUAAeAB4AHgAeAB4AHgAeAB4AKwArACsAKwArACsAKwArAAQABAAEAB4ABAAEAAQABAAEAAQABAAEAAQABAAEAAQABABQAFAAUABQAAQAUABQAFAAUABQAFAABABQAFAABAAEAAQAUAArACsAKwArACsABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsABAAEAAQABAAEAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AKwArAFAAUABQAFAAUABQACsAKwBQAFAAUABQAFAAUABQAFAAKwBQACsAUAArAFAAKwAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeACsAKwAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgArAB4AHgAeAB4AHgAeAB4AHgBQAB4AHgAeAFAAUABQACsAHgAeAB4AHgAeAB4AHgAeAB4AHgBQAFAAUABQACsAKwAeAB4AHgAeAB4AHgArAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AKwArAFAAUABQACsAHgAeAB4AHgAeAB4AHgAOAB4AKwANAA0ADQANAA0ADQANAAkADQANAA0ACAAEAAsABAAEAA0ACQANAA0ADAAdAB0AHgAXABcAFgAXABcAFwAWABcAHQAdAB4AHgAUABQAFAANAAEAAQAEAAQABAAEAAQACQAaABoAGgAaABoAGgAaABoAHgAXABcAHQAVABUAHgAeAB4AHgAeAB4AGAAWABEAFQAVABUAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4ADQAeAA0ADQANAA0AHgANAA0ADQAHAB4AHgAeAB4AKwAEAAQABAAEAAQABAAEAAQABAAEAFAAUAArACsATwBQAFAAUABQAFAAHgAeAB4AFgARAE8AUABPAE8ATwBPAFAAUABQAFAAUAAeAB4AHgAWABEAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArABsAGwAbABsAGwAbABsAGgAbABsAGwAbABsAGwAbABsAGwAbABsAGwAbABsAGgAbABsAGwAbABoAGwAbABoAGwAbABsAGwAbABsAGwAbABsAGwAbABsAGwAbABsAGwAbAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQAHgAeAFAAGgAeAB0AHgBQAB4AGgAeAB4AHgAeAB4AHgAeAB4AHgBPAB4AUAAbAB4AHgBQAFAAUABQAFAAHgAeAB4AHQAdAB4AUAAeAFAAHgBQAB4AUABPAFAAUAAeAB4AHgAeAB4AHgAeAFAAUABQAFAAUAAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAFAAHgBQAFAAUABQAE8ATwBQAFAAUABQAFAATwBQAFAATwBQAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAFAAUABQAFAATwBPAE8ATwBPAE8ATwBPAE8ATwBQAFAAUABQAFAAUABQAFAAUAAeAB4AUABQAFAAUABPAB4AHgArACsAKwArAB0AHQAdAB0AHQAdAB0AHQAdAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB0AHgAdAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAdAB4AHQAdAB4AHgAeAB0AHQAeAB4AHQAeAB4AHgAdAB4AHQAbABsAHgAdAB4AHgAeAB4AHQAeAB4AHQAdAB0AHQAeAB4AHQAeAB0AHgAdAB0AHQAdAB0AHQAeAB0AHgAeAB4AHgAeAB0AHQAdAB0AHgAeAB4AHgAdAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAdAB4AHgAeAB0AHgAeAB4AHgAeAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAdAB0AHgAeAB0AHQAdAB0AHgAeAB0AHQAeAB4AHQAdAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB0AHQAeAB4AHQAdAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHQAeAB4AHgAdAB4AHgAeAB4AHgAeAB4AHQAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AFAAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeABYAEQAWABEAHgAeAB4AHgAeAB4AHQAeAB4AHgAeAB4AHgAeACUAJQAeAB4AHgAeAB4AHgAeAB4AHgAWABEAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AJQAlACUAJQAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAFAAHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHgAeAB4AHgAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAdAB0AHQAeAB4AHQAdAB0AHQAeAB4AHgAeAB4AHgAeAB4AHgAeAB0AHQAeAB0AHQAdAB0AHQAdAB0AHgAeAB4AHgAeAB4AHgAeAB0AHQAeAB4AHQAdAB4AHgAeAB4AHQAdAB4AHgAeAB4AHQAdAB0AHgAeAB0AHgAeAB0AHQAdAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAdAB0AHQAdAB4AHgAeAB4AHgAeAB4AHgAeAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAlACUAJQAlAB4AHQAdAB4AHgAdAB4AHgAeAB4AHQAdAB4AHgAeAB4AJQAlAB0AHQAlAB4AJQAlACUAIAAlACUAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAlACUAJQAeAB4AHgAeAB0AHgAdAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAdAB0AHgAdAB0AHQAeAB0AJQAdAB0AHgAdAB0AHgAdAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeACUAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHQAdAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAlACUAJQAlACUAJQAlACUAJQAlACUAJQAdAB0AHQAdACUAHgAlACUAJQAdACUAJQAdAB0AHQAlACUAHQAdACUAHQAdACUAJQAlAB4AHQAeAB4AHgAeAB0AHQAlAB0AHQAdAB0AHQAdACUAJQAlACUAJQAdACUAJQAgACUAHQAdACUAJQAlACUAJQAlACUAJQAeAB4AHgAlACUAIAAgACAAIAAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB0AHgAeAB4AFwAXABcAFwAXABcAHgATABMAJQAeAB4AHgAWABEAFgARABYAEQAWABEAFgARABYAEQAWABEATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwBPAE8ATwAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeABYAEQAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAWABEAFgARABYAEQAWABEAFgARAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AFgARABYAEQAWABEAFgARABYAEQAWABEAFgARABYAEQAWABEAFgARABYAEQAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAWABEAFgARAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AFgARAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAdAB0AHQAdAB0AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgArACsAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AKwAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AUABQAFAAUAAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAEAAQABAAeAB4AKwArACsAKwArABMADQANAA0AUAATAA0AUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAUAANACsAKwArACsAKwArACsAKwArACsAKwArACsAKwAEAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQACsAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQACsAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXAA0ADQANAA0ADQANAA0ADQAeAA0AFgANAB4AHgAXABcAHgAeABcAFwAWABEAFgARABYAEQAWABEADQANAA0ADQATAFAADQANAB4ADQANAB4AHgAeAB4AHgAMAAwADQANAA0AHgANAA0AFgANAA0ADQANAA0ADQANAA0AHgANAB4ADQANAB4AHgAeACsAKwArACsAKwArACsAKwArACsAKwArACsAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACsAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAKwArACsAKwArACsAKwArACsAKwArACsAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwAlACUAJQAlACUAJQAlACUAJQAlACUAJQArACsAKwArAA0AEQARACUAJQBHAFcAVwAWABEAFgARABYAEQAWABEAFgARACUAJQAWABEAFgARABYAEQAWABEAFQAWABEAEQAlAFcAVwBXAFcAVwBXAFcAVwBXAAQABAAEAAQABAAEACUAVwBXAFcAVwA2ACUAJQBXAFcAVwBHAEcAJQAlACUAKwBRAFcAUQBXAFEAVwBRAFcAUQBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFEAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBRAFcAUQBXAFEAVwBXAFcAVwBXAFcAUQBXAFcAVwBXAFcAVwBRAFEAKwArAAQABAAVABUARwBHAFcAFQBRAFcAUQBXAFEAVwBRAFcAUQBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFEAVwBRAFcAUQBXAFcAVwBXAFcAVwBRAFcAVwBXAFcAVwBXAFEAUQBXAFcAVwBXABUAUQBHAEcAVwArACsAKwArACsAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAKwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAKwAlACUAVwBXAFcAVwAlACUAJQAlACUAJQAlACUAJQAlACsAKwArACsAKwArACsAKwArACsAKwArAFEAUQBRAFEAUQBRAFEAUQBRAFEAUQBRAFEAUQBRAFEAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQArAFcAVwBXAFcAVwBXAFcAVwBXAFcAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQBPAE8ATwBPAE8ATwBPAE8AJQBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXACUAJQAlAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAEcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAKwArACsAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQAlACUAJQArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAADQATAA0AUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABLAEsASwBLAEsASwBLAEsASwBLAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAFAABAAEAAQABAAeAAQABAAEAAQABAAEAAQABAAEAAQAHgBQAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AUABQAAQABABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAeAA0ADQANAA0ADQArACsAKwArACsAKwArACsAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAFAAUABQAFAAUABQAFAAUABQAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AUAAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgBQAB4AHgAeAB4AHgAeAFAAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgArACsAHgAeAB4AHgAeAB4AHgAeAB4AKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwAeAB4AUABQAFAAUABQAFAAUABQAFAAUABQAAQAUABQAFAABABQAFAAUABQAAQAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABAAeAB4AHgAeAAQAKwArACsAUABQAFAAUABQAFAAHgAeABoAHgArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAADgAOABMAEwArACsAKwArACsAKwArACsABAAEAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABAAEACsAKwArACsAKwArACsAKwANAA0ASwBLAEsASwBLAEsASwBLAEsASwArACsAKwArACsAKwAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABABQAFAAUABQAFAAUAAeAB4AHgBQAA4AUABQAAQAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAAEAA0ADQBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQAKwArACsAKwArACsAKwArACsAKwArAB4AWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYAFgAWABYACsAKwArAAQAHgAeAB4AHgAeAB4ADQANAA0AHgAeAB4AHgArAFAASwBLAEsASwBLAEsASwBLAEsASwArACsAKwArAB4AHgBcAFwAXABcAFwAKgBcAFwAXABcAFwAXABcAFwAXABcAEsASwBLAEsASwBLAEsASwBLAEsAXABcAFwAXABcACsAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsAKwArACsAKwArACsAKwArAFAAUABQAAQAUABQAFAAUABQAFAAUABQAAQABAArACsASwBLAEsASwBLAEsASwBLAEsASwArACsAHgANAA0ADQBcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAKgAqACoAXAAqACoAKgBcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXAAqAFwAKgAqACoAXABcACoAKgBcAFwAXABcAFwAKgAqAFwAKgBcACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFwAXABcACoAKgBQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAA0ADQBQAFAAUAAEAAQAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUAArACsAUABQAFAAUABQAFAAKwArAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAHgAeACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAAQADQAEAAQAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAVABVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBUAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVAFUAVQBVACsAKwArACsAKwArACsAKwArACsAKwArAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAWQBZAFkAKwArACsAKwBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAWgBaAFoAKwArACsAKwAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYABgAGAAYAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXACUAJQBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAJQAlACUAJQAlACUAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAKwArACsAKwArAFYABABWAFYAVgBWAFYAVgBWAFYAVgBWAB4AVgBWAFYAVgBWAFYAVgBWAFYAVgBWAFYAVgArAFYAVgBWAFYAVgArAFYAKwBWAFYAKwBWAFYAKwBWAFYAVgBWAFYAVgBWAFYAVgBWAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAEQAWAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUAAaAB4AKwArAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQAGAARABEAGAAYABMAEwAWABEAFAArACsAKwArACsAKwAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACUAJQAlACUAJQAWABEAFgARABYAEQAWABEAFgARABYAEQAlACUAFgARACUAJQAlACUAJQAlACUAEQAlABEAKwAVABUAEwATACUAFgARABYAEQAWABEAJQAlACUAJQAlACUAJQAlACsAJQAbABoAJQArACsAKwArAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArAAcAKwATACUAJQAbABoAJQAlABYAEQAlACUAEQAlABEAJQBXAFcAVwBXAFcAVwBXAFcAVwBXABUAFQAlACUAJQATACUAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXABYAJQARACUAJQAlAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwAWACUAEQAlABYAEQARABYAEQARABUAVwBRAFEAUQBRAFEAUQBRAFEAUQBRAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAFcAVwBXAEcARwArACsAVwBXAFcAVwBXAFcAKwArAFcAVwBXAFcAVwBXACsAKwBXAFcAVwBXAFcAVwArACsAVwBXAFcAKwArACsAGgAbACUAJQAlABsAGwArAB4AHgAeAB4AHgAeAB4AKwArACsAKwArACsAKwArACsAKwAEAAQABAAQAB0AKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsADQANAA0AKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArAB4AHgAeAB4AHgAeAB4AHgAeAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgBQAFAAHgAeAB4AKwAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAAQAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwAEAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAA0AUABQAFAAUAArACsAKwArAFAAUABQAFAAUABQAFAAUAANAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwArACsAKwAeACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAUABQAFAAUABQAFAAKwArAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUAArACsAKwBQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwANAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAeAB4AUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArAFAAUAArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArAA0AUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwAeAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAUABQAFAAUABQAAQABAAEACsABAAEACsAKwArACsAKwAEAAQABAAEAFAAUABQAFAAKwBQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArAAQABAAEACsAKwArACsABABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAA0ADQANAA0ADQANAA0ADQAeACsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAeAFAAUABQAFAAUABQAFAAUAAeAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAArACsAKwArAFAAUABQAFAAUAANAA0ADQANAA0ADQAUACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsADQANAA0ADQANAA0ADQBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAB4AHgAeAB4AKwArACsAKwArACsAKwArACsAKwArACsAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArACsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArAFAAUABQAFAAUABQAAQABAAEAAQAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUAArAAQABAANACsAKwBQAFAAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAAQABAAEAAQABAAEAAQABAAEAAQABABQAFAAUABQAB4AHgAeAB4AHgArACsAKwArACsAKwAEAAQABAAEAAQABAAEAA0ADQAeAB4AHgAeAB4AKwArACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsABABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAAQABAAEAAQABAAEAAQABAAEAAQABAAeAB4AHgANAA0ADQANACsAKwArACsAKwArACsAKwArACsAKwAeACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAKwArACsAKwBLAEsASwBLAEsASwBLAEsASwBLACsAKwArACsAKwArAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEACsASwBLAEsASwBLAEsASwBLAEsASwANAA0ADQANAFAABAAEAFAAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAeAA4AUAArACsAKwArACsAKwArACsAKwAEAFAAUABQAFAADQANAB4ADQAEAAQABAAEAB4ABAAEAEsASwBLAEsASwBLAEsASwBLAEsAUAAOAFAADQANAA0AKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAAQABAAEAAQABAANAA0AHgANAA0AHgAEACsAUABQAFAAUABQAFAAUAArAFAAKwBQAFAAUABQACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAA0AKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAAQABAAEAAQAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsABAAEAAQABAArAFAAUABQAFAAUABQAFAAUAArACsAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwBQAFAAUABQAFAAUABQACsAUABQACsAUABQAFAAUABQACsABAAEAFAABAAEAAQABAAEAAQABAArACsABAAEACsAKwAEAAQABAArACsAUAArACsAKwArACsAKwAEACsAKwArACsAKwBQAFAAUABQAFAABAAEACsAKwAEAAQABAAEAAQABAAEACsAKwArAAQABAAEAAQABAArACsAKwArACsAKwArACsAKwArACsABAAEAAQABAAEAAQABABQAFAAUABQAA0ADQANAA0AHgBLAEsASwBLAEsASwBLAEsASwBLAA0ADQArAB4ABABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwAEAAQABAAEAFAAUAAeAFAAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAArACsABAAEAAQABAAEAAQABAAEAAQADgANAA0AEwATAB4AHgAeAA0ADQANAA0ADQANAA0ADQANAA0ADQANAA0ADQANAFAAUABQAFAABAAEACsAKwAEAA0ADQAeAFAAKwArACsAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsADgAOAA4ADgAOAA4ADgAOAA4ADgAOAA4ADgArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAFAAKwArACsAKwArACsAKwBLAEsASwBLAEsASwBLAEsASwBLACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAXABcAFwAKwArACoAKgAqACoAKgAqACoAKgAqACoAKgAqACoAKgAqACsAKwArACsASwBLAEsASwBLAEsASwBLAEsASwBcAFwADQANAA0AKgBQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAeACsAKwArACsASwBLAEsASwBLAEsASwBLAEsASwBQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAKwArAFAAKwArAFAAUABQAFAAUABQAFAAUAArAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQAKwAEAAQAKwArAAQABAAEAAQAUAAEAFAABAAEAA0ADQANACsAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQACsAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAArACsABAAEAAQABAAEAAQABABQAA4AUAAEACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFAABAAEAAQABAAEAAQABAAEAAQABABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAFAABAAEAAQABAAOAB4ADQANAA0ADQAOAB4ABAArACsAKwArACsAKwArACsAUAAEAAQABAAEAAQABAAEAAQABAAEAAQAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAAEAA0ADQANAFAADgAOAA4ADQANACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQAFAAUAArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAEAAQABAAEACsABAAEAAQABAAEAAQABAAEAFAADQANAA0ADQANACsAKwArACsAKwArACsAKwArACsASwBLAEsASwBLAEsASwBLAEsASwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwAOABMAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwArAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAArAAQABAAEAAQABAAEAAQABAAEAAQABAAEAAQABAArACsAKwArACsAKwArACsAKwBQAFAAUABQAFAAUABQACsAUABQACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQABAArACsAKwAEACsABAAEACsABAAEAAQABAAEAAQABABQAAQAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwArACsAUABQAFAAUABQAFAAKwBQAFAAKwBQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAAEAAQABAAEAAQAKwAEAAQAKwAEAAQABAAEAAQAUAArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAABAAEAAQABAAeAB4AKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwBQACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAB4AHgAeAB4AHgAeAB4AHgAaABoAGgAaAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgAeAB4AHgArACsAKwArACsAKwArACsAKwArACsAKwArAA0AUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQACsADQANAA0ADQANACsAKwArACsAKwArACsAKwArACsAKwBQAFAAUABQACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAASABIAEgAQwBDAEMAUABQAFAAUABDAFAAUABQAEgAQwBIAEMAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAASABDAEMAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAKwAJAAkACQAJAAkACQAJABYAEQArACsAKwArACsAKwArAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABIAEMAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUABQAFAAUAArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwArAEsASwBLAEsASwBLAEsASwBLAEsAKwArACsAKwANAA0AKwArACsAKwArACsAKwArACsAKwArACsAKwArACsAKwB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this.reconsumeCodePoint(e),this.consumeNumericToken();if(ye(i,a,s))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();if(a===le&&62===s)return this.consumeCodePoint(),this.consumeCodePoint(),De;break;case 46:if(ve(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case 47:if(42===this.peekCodePoint(0))for(this.consumeCodePoint();;){var o=this.consumeCodePoint();if(42===o&&47===(o=this.consumeCodePoint()))return this.consumeToken();if(o===ce)return this.consumeToken()}break;case 58:return Me;case 59:return Re;case 60:if(33===this.peekCodePoint(0)&&this.peekCodePoint(1)===le&&this.peekCodePoint(2)===le)return this.consumeCodePoint(),this.consumeCodePoint(),ke;break;case 64:var u=this.peekCodePoint(0),l=this.peekCodePoint(1),d=this.peekCodePoint(2);if(ye(u,l,d))return{type:7,value:this.consumeName()};break;case 91:return Pe;case 92:if(Be(e,this.peekCodePoint(0)))return 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0===e?-1:e},e.prototype.reconsumeCodePoint=function(e){this._value.unshift(e)},e.prototype.peekCodePoint=function(e){return e>=this._value.length?-1:this._value[e]},e.prototype.consumeUnicodeRangeToken=function(){for(var e=[],A=this.consumeCodePoint();he(A)&&e.length<6;)e.push(A),A=this.consumeCodePoint();for(var t=!1;63===A&&e.length<6;)e.push(A),A=this.consumeCodePoint(),t=!0;if(t)return{type:30,start:parseInt(c.apply(void 0,e.map((function(e){return 63===e?48:e}))),16),end:parseInt(c.apply(void 0,e.map((function(e){return 63===e?70:e}))),16)};var n=parseInt(c.apply(void 0,e),16);if(this.peekCodePoint(0)===le&&he(this.peekCodePoint(1))){this.consumeCodePoint(),A=this.consumeCodePoint();for(var r=[];he(A)&&r.length<6;)r.push(A),A=this.consumeCodePoint();return{type:30,start:n,end:parseInt(c.apply(void 0,r),16)}}return{type:30,start:n,end:n}},e.prototype.consumeIdentLikeToken=function(){var e=this.consumeName();return"url"===e.toLowerCase()&&40===this.peekCodePoint(0)?(this.consumeCodePoint(),this.consumeUrlToken()):40===this.peekCodePoint(0)?(this.consumeCodePoint(),{type:19,value:e}):{type:20,value:e}},e.prototype.consumeUrlToken=function(){var e=[];if(this.consumeWhiteSpace(),this.peekCodePoint(0)===ce)return{type:22,value:""};var A=this.peekCodePoint(0);if(39===A||34===A){var t=this.consumeStringToken(this.consumeCodePoint());return 0===t.type&&(this.consumeWhiteSpace(),this.peekCodePoint(0)===ce||41===this.peekCodePoint(0))?(this.consumeCodePoint(),{type:22,value:t.value}):(this.consumeBadUrlRemnants(),Oe)}for(;;){var n=this.consumeCodePoint();if(n===ce||41===n)return{type:22,value:c.apply(void 0,e)};if(fe(n))return this.consumeWhiteSpace(),this.peekCodePoint(0)===ce||41===this.peekCodePoint(0)?(this.consumeCodePoint(),{type:22,value:c.apply(void 0,e)}):(this.consumeBadUrlRemnants(),Oe);if(34===n||39===n||40===n||we(n))return this.consumeBadUrlRemnants(),Oe;if(92===n){if(!Be(n,this.peekCodePoint(0)))return this.consumeBadUrlRemnants(),Oe;e.push(this.consumeEscapedCodePoint())}else e.push(n)}},e.prototype.consumeWhiteSpace=function(){for(;fe(this.peekCodePoint(0));)this.consumeCodePoint()},e.prototype.consumeBadUrlRemnants=function(){for(;;){var e=this.consumeCodePoint();if(41===e||e===ce)return;Be(e,this.peekCodePoint(0))&&this.consumeEscapedCodePoint()}},e.prototype.consumeStringSlice=function(e){for(var A="";e>0;){var t=Math.min(5e4,e);A+=c.apply(void 0,this._value.splice(0,t)),e-=t}return this._value.shift(),A},e.prototype.consumeStringToken=function(e){for(var A="",t=0;;){var n=this._value[t];if(n===ce||void 0===n||n===e)return{type:0,value:A+=this.consumeStringSlice(t)};if(10===n)return this._value.splice(0,t),He;if(92===n){var r=this._value[t+1];r!==ce&&void 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e.create(A).parseComponentValues()},e.prototype.parseComponentValue=function(){for(var e=this.consumeToken();31===e.type;)e=this.consumeToken();if(32===e.type)throw new SyntaxError("Error parsing CSS component value, unexpected EOF");this.reconsumeToken(e);var A=this.consumeComponentValue();do{e=this.consumeToken()}while(31===e.type);if(32===e.type)return A;throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one")},e.prototype.parseComponentValues=function(){for(var e=[];;){var A=this.consumeComponentValue();if(32===A.type)return e;e.push(A),e.push()}},e.prototype.consumeComponentValue=function(){var e=this.consumeToken();switch(e.type){case 11:case 28:case 2:return this.consumeSimpleBlock(e.type);case 19:return this.consumeFunction(e)}return e},e.prototype.consumeSimpleBlock=function(e){for(var A={type:e,values:[]},t=this.consumeToken();;){if(32===t.type||eA(t,e))return 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Di(t.left+$/2,t.top+v/2),this.topRightBorderStroke=r>0||i>0?Ki(t.left+m,t.top+v/2,s-b/2,o-v/2,ci.TOP_RIGHT):new Di(t.left+t.width-b/2,t.top+v/2),this.bottomRightBorderStroke=l>0||d>0?Ki(t.left+B,t.top+w,l-b/2,d-C/2,ci.BOTTOM_RIGHT):new Di(t.left+t.width-b/2,t.top+t.height-C/2),this.bottomLeftBorderStroke=p>0||h>0?Ki(t.left+$/2,t.top+y,p-$/2,h-C/2,ci.BOTTOM_LEFT):new Di(t.left+$/2,t.top+t.height-C/2),this.topLeftBorderBox=r>0||i>0?Ki(t.left,t.top,r,i,ci.TOP_LEFT):new Di(t.left,t.top),this.topRightBorderBox=s>0||o>0?Ki(t.left+m,t.top,s,o,ci.TOP_RIGHT):new Di(t.left+t.width,t.top),this.bottomRightBorderBox=l>0||d>0?Ki(t.left+B,t.top+w,l,d,ci.BOTTOM_RIGHT):new Di(t.left+t.width,t.top+t.height),this.bottomLeftBorderBox=p>0||h>0?Ki(t.left,t.top+y,p,h,ci.BOTTOM_LEFT):new Di(t.left,t.top+t.height),this.topLeftPaddingBox=r>0||i>0?Ki(t.left+$,t.top+v,Math.max(0,r-$),Math.max(0,i-v),ci.TOP_LEFT):new Di(t.left+$,t.top+v),this.topRightPaddingBox=s>0||o>0?Ki(t.left+Math.min(m,t.width-b),t.top+v,m>t.width+b?0:Math.max(0,s-b),Math.max(0,o-v),ci.TOP_RIGHT):new Di(t.left+t.width-b,t.top+v),this.bottomRightPaddingBox=l>0||d>0?Ki(t.left+Math.min(B,t.width-$),t.top+Math.min(w,t.height-C),Math.max(0,l-b),Math.max(0,d-C),ci.BOTTOM_RIGHT):new Di(t.left+t.width-b,t.top+t.height-C),this.bottomLeftPaddingBox=p>0||h>0?Ki(t.left+$,t.top+Math.min(y,t.height-C),Math.max(0,p-$),Math.max(0,h-C),ci.BOTTOM_LEFT):new Di(t.left+$,t.top+t.height-C),this.topLeftContentBox=r>0||i>0?Ki(t.left+$+U,t.top+v+I,Math.max(0,r-($+U)),Math.max(0,i-(v+I)),ci.TOP_LEFT):new Di(t.left+$+U,t.top+v+I),this.topRightContentBox=s>0||o>0?Ki(t.left+Math.min(m,t.width+$+U),t.top+v+I,m>t.width+$+U?0:s-$+U,o-(v+I),ci.TOP_RIGHT):new Di(t.left+t.width-(b+E),t.top+v+I),this.bottomRightContentBox=l>0||d>0?Ki(t.left+Math.min(B,t.width-($+U)),t.top+Math.min(w,t.height+v+I),Math.max(0,l-(b+E)),d-(C+Q),ci.BOTTOM_RIGHT):new Di(t.left+t.width-(b+E),t.top+t.height-(C+Q)),this.bottomLeftContentBox=p>0||h>0?Ki(t.left+$+U,t.top+y,Math.max(0,p-($+U)),h-(C+Q),ci.BOTTOM_LEFT):new Di(t.left+$+U,t.top+t.height-(C+Q))};!function(e){e[e.TOP_LEFT=0]="TOP_LEFT",e[e.TOP_RIGHT=1]="TOP_RIGHT",e[e.BOTTOM_RIGHT=2]="BOTTOM_RIGHT",e[e.BOTTOM_LEFT=3]="BOTTOM_LEFT"}(ci||(ci={}));var Ki=function(e,A,t,n,r){var i=(Math.sqrt(2)-1)/3*4,a=t*i,s=n*i,o=e+t,u=A+n;switch(r){case ci.TOP_LEFT:return new Ri(new Di(e,u),new Di(e,u-s),new Di(o-a,A),new Di(o,A));case ci.TOP_RIGHT:return new Ri(new Di(e,A),new Di(e+a,A),new Di(o,u-s),new Di(o,u));case ci.BOTTOM_RIGHT:return new Ri(new Di(o,A),new Di(o,A+s),new Di(e+a,u),new Di(e,u));case ci.BOTTOM_LEFT:default:return new Ri(new Di(o,u),new Di(o-a,u),new Di(e,A+s),new Di(e,A))}},zi=function(e){return[e.topLeftBorderBox,e.topRightBorderBox,e.bottomRightBorderBox,e.bottomLeftBorderBox]},Ni=function(e){return[e.topLeftPaddingBox,e.topRightPaddingBox,e.bottomRightPaddingBox,e.bottomLeftPaddingBox]},Vi=function(e,A,t){this.offsetX=e,this.offsetY=A,this.matrix=t,this.type=0,this.target=6},Gi=function(e,A){this.path=e,this.target=A,this.type=1},Wi=function(e){this.opacity=e,this.type=2,this.target=6},qi=function(e){return 1===e.type},ji=function(e,A){return e.length===A.length&&e.some((function(e,t){return e===A[t]}))},Yi=function(e){this.element=e,this.inlineLevel=[],this.nonInlineLevel=[],this.negativeZIndex=[],this.zeroOrAutoZIndexOrTransformedOrOpacity=[],this.positiveZIndex=[],this.nonPositionedFloats=[],this.nonPositionedInlineLevel=[]},Xi=function(){function e(e,A){if(this.container=e,this.parent=A,this.effects=[],this.curves=new Li(this.container),this.container.styles.opacity<1&&this.effects.push(new Wi(this.container.styles.opacity)),null!==this.container.styles.transform){var t=this.container.bounds.left+this.container.styles.transformOrigin[0].number,n=this.container.bounds.top+this.container.styles.transformOrigin[1].number,r=this.container.styles.transform;this.effects.push(new Vi(t,n,r))}if(0!==this.container.styles.overflowX){var i=zi(this.curves),a=Ni(this.curves);ji(i,a)?this.effects.push(new Gi(i,6)):(this.effects.push(new Gi(i,2)),this.effects.push(new Gi(a,4)))}}return e.prototype.getEffects=function(e){for(var A=-1===[2,3].indexOf(this.container.styles.position),t=this.parent,n=this.effects.slice(0);t;){var r=t.effects.filter((function(e){return!qi(e)}));if(A||0!==t.container.styles.position||!t.parent){if(n.unshift.apply(n,r),A=-1===[2,3].indexOf(t.container.styles.position),0!==t.container.styles.overflowX){var i=zi(t.curves),a=Ni(t.curves);ji(i,a)||n.unshift(new Gi(a,6))}}else n.unshift.apply(n,r);t=t.parent}return n.filter((function(A){return pn(A.target,e)}))},e}(),Ji=function(e,A,t,n){e.container.elements.forEach((function(r){var i=pn(r.flags,4),a=pn(r.flags,2),s=new Xi(r,e);pn(r.styles.display,2048)&&n.push(s);var o=pn(r.flags,8)?[]:n;if(i||a){var u=i||r.styles.isPositioned()?t:A,l=new Yi(s);if(r.styles.isPositioned()||r.styles.opacity<1||r.styles.isTransformed()){var d=r.styles.zIndex.order;if(d<0){var c=0;u.negativeZIndex.some((function(e,A){return d>e.element.container.styles.zIndex.order?(c=A,!1):c>0})),u.negativeZIndex.splice(c,0,l)}else if(d>0){var p=0;u.positiveZIndex.some((function(e,A){return d>=e.element.container.styles.zIndex.order?(p=A+1,!1):p>0})),u.positiveZIndex.splice(p,0,l)}else u.zeroOrAutoZIndexOrTransformedOrOpacity.push(l)}else r.styles.isFloating()?u.nonPositionedFloats.push(l):u.nonPositionedInlineLevel.push(l);Ji(s,l,i?l:t,o)}else r.styles.isInlineLevel()?A.inlineLevel.push(s):A.nonInlineLevel.push(s),Ji(s,A,t,o);pn(r.flags,8)&&Zi(r,o)}))},Zi=function(e,A){for(var t=e instanceof hr?e.start:1,n=e instanceof hr&&e.reversed,r=0;r<A.length;r++){var i=A[r];i.container instanceof pr&&"number"==typeof i.container.value&&0!==i.container.value&&(t=i.container.value),i.listValue=ui(t,i.container.styles.listStyleType,!0),t+=n?-1:1}},ea=function(e,A){switch(A){case 0:return ta(e.topLeftBorderBox,e.topLeftPaddingBox,e.topRightBorderBox,e.topRightPaddingBox);case 1:return ta(e.topRightBorderBox,e.topRightPaddingBox,e.bottomRightBorderBox,e.bottomRightPaddingBox);case 2:return ta(e.bottomRightBorderBox,e.bottomRightPaddingBox,e.bottomLeftBorderBox,e.bottomLeftPaddingBox);default:return ta(e.bottomLeftBorderBox,e.bottomLeftPaddingBox,e.topLeftBorderBox,e.topLeftPaddingBox)}},Aa=function(e,A){var t=[];return Pi(e)?t.push(e.subdivide(.5,!1)):t.push(e),Pi(A)?t.push(A.subdivide(.5,!0)):t.push(A),t},ta=function(e,A,t,n){var r=[];return Pi(e)?r.push(e.subdivide(.5,!1)):r.push(e),Pi(t)?r.push(t.subdivide(.5,!0)):r.push(t),Pi(n)?r.push(n.subdivide(.5,!0).reverse()):r.push(n),Pi(A)?r.push(A.subdivide(.5,!1).reverse()):r.push(A),r},na=function(e){var A=e.bounds,t=e.styles;return A.add(t.borderLeftWidth,t.borderTopWidth,-(t.borderRightWidth+t.borderLeftWidth),-(t.borderTopWidth+t.borderBottomWidth))},ra=function(e){var A=e.styles,t=e.bounds,n=oA(A.paddingLeft,t.width),r=oA(A.paddingRight,t.width),i=oA(A.paddingTop,t.width),a=oA(A.paddingBottom,t.width);return t.add(n+A.borderLeftWidth,i+A.borderTopWidth,-(A.borderRightWidth+A.borderLeftWidth+n+r),-(A.borderTopWidth+A.borderBottomWidth+i+a))},ia=function(e,A,t){var n=function(e,A){return 0===e?A.bounds:2===e?ra(A):na(A)}(ua(e.styles.backgroundOrigin,A),e),r=function(e,A){return 0===e?A.bounds:2===e?ra(A):na(A)}(ua(e.styles.backgroundClip,A),e),i=oa(ua(e.styles.backgroundSize,A),t,n),a=i[0],s=i[1],o=sA(ua(e.styles.backgroundPosition,A),n.width-a,n.height-s);return[la(ua(e.styles.backgroundRepeat,A),o,i,n,r),Math.round(n.left+o[0]),Math.round(n.top+o[1]),a,s]},aa=function(e){return qe(e)&&e.value===NA.AUTO},sa=function(e){return"number"==typeof e},oa=function(e,A,t){var n=A[0],r=A[1],i=A[2],a=e[0],s=e[1];if(!a)return[0,0];if(tA(a)&&s&&tA(s))return[oA(a,t.width),oA(s,t.height)];var o=sa(i);if(qe(a)&&(a.value===NA.CONTAIN||a.value===NA.COVER))return sa(i)?t.width/t.height<i!=(a.value===NA.COVER)?[t.width,t.width/i]:[t.height*i,t.height]:[t.width,t.height];var u=sa(n),l=sa(r),d=u||l;if(aa(a)&&(!s||aa(s)))return u&&l?[n,r]:o||d?d&&o?[u?n:r*i,l?r:n/i]:[u?n:t.width,l?r:t.height]:[t.width,t.height];if(o){var c=0,p=0;return tA(a)?c=oA(a,t.width):tA(s)&&(p=oA(s,t.height)),aa(a)?c=p*i:s&&!aa(s)||(p=c/i),[c,p]}var h=null,f=null;if(tA(a)?h=oA(a,t.width):s&&tA(s)&&(f=oA(s,t.height)),null===h||s&&!aa(s)||(f=u&&l?h/n*r:t.height),null!==f&&aa(a)&&(h=u&&l?f/r*n:t.width),null!==h&&null!==f)return[h,f];throw new Error("Unable to calculate background-size for element")},ua=function(e,A){var t=e[A];return void 0===t?e[0]:t},la=function(e,A,t,n,r){var i=A[0],a=A[1],s=t[0],o=t[1];switch(e){case 2:return[new Di(Math.round(n.left),Math.round(n.top+a)),new Di(Math.round(n.left+n.width),Math.round(n.top+a)),new Di(Math.round(n.left+n.width),Math.round(o+n.top+a)),new Di(Math.round(n.left),Math.round(o+n.top+a))];case 3:return[new Di(Math.round(n.left+i),Math.round(n.top)),new Di(Math.round(n.left+i+s),Math.round(n.top)),new Di(Math.round(n.left+i+s),Math.round(n.height+n.top)),new Di(Math.round(n.left+i),Math.round(n.height+n.top))];case 1:return[new Di(Math.round(n.left+i),Math.round(n.top+a)),new Di(Math.round(n.left+i+s),Math.round(n.top+a)),new Di(Math.round(n.left+i+s),Math.round(n.top+a+o)),new Di(Math.round(n.left+i),Math.round(n.top+a+o))];default:return[new Di(Math.round(r.left),Math.round(r.top)),new Di(Math.round(r.left+r.width),Math.round(r.top)),new Di(Math.round(r.left+r.width),Math.round(r.height+r.top)),new Di(Math.round(r.left),Math.round(r.height+r.top))]}},da="Hidden Text",ca=function(){function e(e){this._data={},this._document=e}return e.prototype.parseMetrics=function(e,A){var t=this._document.createElement("div"),n=this._document.createElement("img"),r=this._document.createElement("span"),i=this._document.body;t.style.visibility="hidden",t.style.fontFamily=e,t.style.fontSize=A,t.style.margin="0",t.style.padding="0",t.style.whiteSpace="nowrap",i.appendChild(t),n.src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7",n.width=1,n.height=1,n.style.margin="0",n.style.padding="0",n.style.verticalAlign="baseline",r.style.fontFamily=e,r.style.fontSize=A,r.style.margin="0",r.style.padding="0",r.appendChild(this._document.createTextNode(da)),t.appendChild(r),t.appendChild(n);var a=n.offsetTop-r.offsetTop+2;t.removeChild(r),t.appendChild(this._document.createTextNode(da)),t.style.lineHeight="normal",n.style.verticalAlign="super";var s=n.offsetTop-t.offsetTop+2;return i.removeChild(t),{baseline:a,middle:s}},e.prototype.getMetrics=function(e,A){var t=e+" "+A;return void 0===this._data[t]&&(this._data[t]=this.parseMetrics(e,A)),this._data[t]},e}(),pa=function(e,A){this.context=e,this.options=A},ha=function(e){function A(A,t){var n=e.call(this,A,t)||this;return n._activeEffects=[],n.canvas=t.canvas?t.canvas:document.createElement("canvas"),n.ctx=n.canvas.getContext("2d"),t.canvas||(n.canvas.width=Math.floor(t.width*t.scale),n.canvas.height=Math.floor(t.height*t.scale),n.canvas.style.width=t.width+"px",n.canvas.style.height=t.height+"px"),n.fontMetrics=new ca(document),n.ctx.scale(n.options.scale,n.options.scale),n.ctx.translate(-t.x,-t.y),n.ctx.textBaseline="bottom",n._activeEffects=[],n.context.logger.debug("Canvas renderer initialized ("+t.width+"x"+t.height+") with scale "+t.scale),n}return r(A,e),A.prototype.applyEffects=function(e){for(var A=this;this._activeEffects.length;)this.popEffect();e.forEach((function(e){return A.applyEffect(e)}))},A.prototype.applyEffect=function(e){this.ctx.save(),function(e){return 2===e.type}(e)&&(this.ctx.globalAlpha=e.opacity),function(e){return 0===e.type}(e)&&(this.ctx.translate(e.offsetX,e.offsetY),this.ctx.transform(e.matrix[0],e.matrix[1],e.matrix[2],e.matrix[3],e.matrix[4],e.matrix[5]),this.ctx.translate(-e.offsetX,-e.offsetY)),qi(e)&&(this.path(e.path),this.ctx.clip()),this._activeEffects.push(e)},A.prototype.popEffect=function(){this._activeEffects.pop(),this.ctx.restore()},A.prototype.renderStack=function(e){return a(this,void 0,void 0,(function(){return s(this,(function(A){switch(A.label){case 0:return e.element.container.styles.isVisible()?[4,this.renderStackContent(e)]:[3,2];case 1:A.sent(),A.label=2;case 2:return[2]}}))}))},A.prototype.renderNode=function(e){return a(this,void 0,void 0,(function(){return s(this,(function(A){switch(A.label){case 0:return pn(e.container.flags,16),e.container.styles.isVisible()?[4,this.renderNodeBackgroundAndBorders(e)]:[3,3];case 1:return A.sent(),[4,this.renderNodeContent(e)];case 2:A.sent(),A.label=3;case 3:return[2]}}))}))},A.prototype.renderTextWithLetterSpacing=function(e,A,t){var n=this;0===A?this.ctx.fillText(e.text,e.bounds.left,e.bounds.top+t):tr(e.text).reduce((function(A,r){return n.ctx.fillText(r,A,e.bounds.top+t),A+n.ctx.measureText(r).width}),e.bounds.left)},A.prototype.createFontStyle=function(e){var A=e.fontVariant.filter((function(e){return"normal"===e||"small-caps"===e})).join(""),t=Ba(e.fontFamily).join(", "),n=Ge(e.fontSize)?""+e.fontSize.number+e.fontSize.unit:e.fontSize.number+"px";return[[e.fontStyle,A,e.fontWeight,n,t].join(" "),t,n]},A.prototype.renderTextNode=function(e,A){return a(this,void 0,void 0,(function(){var t,n,r,i,a,o,u,l,d=this;return s(this,(function(s){return t=this.createFontStyle(A),n=t[0],r=t[1],i=t[2],this.ctx.font=n,this.ctx.direction=1===A.direction?"rtl":"ltr",this.ctx.textAlign="left",this.ctx.textBaseline="alphabetic",a=this.fontMetrics.getMetrics(r,i),o=a.baseline,u=a.middle,l=A.paintOrder,e.textBounds.forEach((function(e){l.forEach((function(t){switch(t){case 0:d.ctx.fillStyle=mA(A.color),d.renderTextWithLetterSpacing(e,A.letterSpacing,o);var n=A.textShadow;n.length&&e.text.trim().length&&(n.slice(0).reverse().forEach((function(t){d.ctx.shadowColor=mA(t.color),d.ctx.shadowOffsetX=t.offsetX.number*d.options.scale,d.ctx.shadowOffsetY=t.offsetY.number*d.options.scale,d.ctx.shadowBlur=t.blur.number,d.renderTextWithLetterSpacing(e,A.letterSpacing,o)})),d.ctx.shadowColor="",d.ctx.shadowOffsetX=0,d.ctx.shadowOffsetY=0,d.ctx.shadowBlur=0),A.textDecorationLine.length&&(d.ctx.fillStyle=mA(A.textDecorationColor||A.color),A.textDecorationLine.forEach((function(A){switch(A){case 1:d.ctx.fillRect(e.bounds.left,Math.round(e.bounds.top+o),e.bounds.width,1);break;case 2:d.ctx.fillRect(e.bounds.left,Math.round(e.bounds.top),e.bounds.width,1);break;case 3:d.ctx.fillRect(e.bounds.left,Math.ceil(e.bounds.top+u),e.bounds.width,1)}})));break;case 1:A.webkitTextStrokeWidth&&e.text.trim().length&&(d.ctx.strokeStyle=mA(A.webkitTextStrokeColor),d.ctx.lineWidth=A.webkitTextStrokeWidth,d.ctx.lineJoin=window.chrome?"miter":"round",d.ctx.strokeText(e.text,e.bounds.left,e.bounds.top+o)),d.ctx.strokeStyle="",d.ctx.lineWidth=0,d.ctx.lineJoin="miter"}}))})),[2]}))}))},A.prototype.renderReplacedElement=function(e,A,t){if(t&&e.intrinsicWidth>0&&e.intrinsicHeight>0){var n=ra(e),r=Ni(A);this.path(r),this.ctx.save(),this.ctx.clip(),this.ctx.drawImage(t,0,0,e.intrinsicWidth,e.intrinsicHeight,n.left,n.top,n.width,n.height),this.ctx.restore()}},A.prototype.renderNodeContent=function(e){return a(this,void 0,void 0,(function(){var t,n,r,i,a,o,l,d,c,p,h,f,g,m,w,B,y,v;return s(this,(function(s){switch(s.label){case 0:this.applyEffects(e.getEffects(4)),t=e.container,n=e.curves,r=t.styles,i=0,a=t.textNodes,s.label=1;case 1:return i<a.length?(o=a[i],[4,this.renderTextNode(o,r)]):[3,4];case 2:s.sent(),s.label=3;case 3:return i++,[3,1];case 4:if(!(t instanceof lr))return[3,8];s.label=5;case 5:return s.trys.push([5,7,,8]),[4,this.context.cache.match(t.src)];case 6:return w=s.sent(),this.renderReplacedElement(t,n,w),[3,8];case 7:return s.sent(),this.context.logger.error("Error loading image "+t.src),[3,8];case 8:if(t instanceof dr&&this.renderReplacedElement(t,n,t.canvas),!(t instanceof cr))return[3,12];s.label=9;case 9:return s.trys.push([9,11,,12]),[4,this.context.cache.match(t.svg)];case 10:return w=s.sent(),this.renderReplacedElement(t,n,w),[3,12];case 11:return s.sent(),this.context.logger.error("Error loading svg "+t.svg.substring(0,255)),[3,12];case 12:return t instanceof $r&&t.tree?[4,new A(this.context,{scale:this.options.scale,backgroundColor:t.backgroundColor,x:0,y:0,width:t.width,height:t.height}).render(t.tree)]:[3,14];case 13:l=s.sent(),t.width&&t.height&&this.ctx.drawImage(l,0,0,t.width,t.height,t.bounds.left,t.bounds.top,t.bounds.width,t.bounds.height),s.label=14;case 14:if(t instanceof vr&&(d=Math.min(t.bounds.width,t.bounds.height),t.type===mr?t.checked&&(this.ctx.save(),this.path([new Di(t.bounds.left+.39363*d,t.bounds.top+.79*d),new Di(t.bounds.left+.16*d,t.bounds.top+.5549*d),new Di(t.bounds.left+.27347*d,t.bounds.top+.44071*d),new Di(t.bounds.left+.39694*d,t.bounds.top+.5649*d),new Di(t.bounds.left+.72983*d,t.bounds.top+.23*d),new Di(t.bounds.left+.84*d,t.bounds.top+.34085*d),new Di(t.bounds.left+.39363*d,t.bounds.top+.79*d)]),this.ctx.fillStyle=mA(yr),this.ctx.fill(),this.ctx.restore()):t.type===wr&&t.checked&&(this.ctx.save(),this.ctx.beginPath(),this.ctx.arc(t.bounds.left+d/2,t.bounds.top+d/2,d/4,0,2*Math.PI,!0),this.ctx.fillStyle=mA(yr),this.ctx.fill(),this.ctx.restore())),fa(t)&&t.value.length){switch(c=this.createFontStyle(r),y=c[0],p=c[1],h=this.fontMetrics.getMetrics(y,p).baseline,this.ctx.font=y,this.ctx.fillStyle=mA(r.color),this.ctx.textBaseline="alphabetic",this.ctx.textAlign=ma(t.styles.textAlign),v=ra(t),f=0,t.styles.textAlign){case 1:f+=v.width/2;break;case 2:f+=v.width}g=v.add(f,0,0,-v.height/2+1),this.ctx.save(),this.path([new Di(v.left,v.top),new Di(v.left+v.width,v.top),new Di(v.left+v.width,v.top+v.height),new Di(v.left,v.top+v.height)]),this.ctx.clip(),this.renderTextWithLetterSpacing(new Zn(t.value,g),r.letterSpacing,h),this.ctx.restore(),this.ctx.textBaseline="alphabetic",this.ctx.textAlign="left"}if(!pn(t.styles.display,2048))return[3,20];if(null===t.styles.listStyleImage)return[3,19];if(0!==(m=t.styles.listStyleImage).type)return[3,18];w=void 0,B=m.url,s.label=15;case 15:return s.trys.push([15,17,,18]),[4,this.context.cache.match(B)];case 16:return w=s.sent(),this.ctx.drawImage(w,t.bounds.left-(w.width+10),t.bounds.top),[3,18];case 17:return s.sent(),this.context.logger.error("Error loading list-style-image "+B),[3,18];case 18:return[3,20];case 19:e.listValue&&-1!==t.styles.listStyleType&&(y=this.createFontStyle(r)[0],this.ctx.font=y,this.ctx.fillStyle=mA(r.color),this.ctx.textBaseline="middle",this.ctx.textAlign="right",v=new u(t.bounds.left,t.bounds.top+oA(t.styles.paddingTop,t.bounds.width),t.bounds.width,xt(r.lineHeight,r.fontSize.number)/2+1),this.renderTextWithLetterSpacing(new Zn(e.listValue,v),r.letterSpacing,xt(r.lineHeight,r.fontSize.number)/2+2),this.ctx.textBaseline="bottom",this.ctx.textAlign="left"),s.label=20;case 20:return[2]}}))}))},A.prototype.renderStackContent=function(e){return a(this,void 0,void 0,(function(){var A,t,n,r,i,a,o,u,l,d,c,p,h,f,g;return s(this,(function(s){switch(s.label){case 0:return pn(e.element.container.flags,16),[4,this.renderNodeBackgroundAndBorders(e.element)];case 1:s.sent(),A=0,t=e.negativeZIndex,s.label=2;case 2:return A<t.length?(g=t[A],[4,this.renderStack(g)]):[3,5];case 3:s.sent(),s.label=4;case 4:return A++,[3,2];case 5:return[4,this.renderNodeContent(e.element)];case 6:s.sent(),n=0,r=e.nonInlineLevel,s.label=7;case 7:return n<r.length?(g=r[n],[4,this.renderNode(g)]):[3,10];case 8:s.sent(),s.label=9;case 9:return n++,[3,7];case 10:i=0,a=e.nonPositionedFloats,s.label=11;case 11:return i<a.length?(g=a[i],[4,this.renderStack(g)]):[3,14];case 12:s.sent(),s.label=13;case 13:return i++,[3,11];case 14:o=0,u=e.nonPositionedInlineLevel,s.label=15;case 15:return o<u.length?(g=u[o],[4,this.renderStack(g)]):[3,18];case 16:s.sent(),s.label=17;case 17:return o++,[3,15];case 18:l=0,d=e.inlineLevel,s.label=19;case 19:return l<d.length?(g=d[l],[4,this.renderNode(g)]):[3,22];case 20:s.sent(),s.label=21;case 21:return l++,[3,19];case 22:c=0,p=e.zeroOrAutoZIndexOrTransformedOrOpacity,s.label=23;case 23:return c<p.length?(g=p[c],[4,this.renderStack(g)]):[3,26];case 24:s.sent(),s.label=25;case 25:return c++,[3,23];case 26:h=0,f=e.positiveZIndex,s.label=27;case 27:return h<f.length?(g=f[h],[4,this.renderStack(g)]):[3,30];case 28:s.sent(),s.label=29;case 29:return h++,[3,27];case 30:return[2]}}))}))},A.prototype.mask=function(e){this.ctx.beginPath(),this.ctx.moveTo(0,0),this.ctx.lineTo(this.canvas.width,0),this.ctx.lineTo(this.canvas.width,this.canvas.height),this.ctx.lineTo(0,this.canvas.height),this.ctx.lineTo(0,0),this.formatPath(e.slice(0).reverse()),this.ctx.closePath()},A.prototype.path=function(e){this.ctx.beginPath(),this.formatPath(e),this.ctx.closePath()},A.prototype.formatPath=function(e){var A=this;e.forEach((function(e,t){var n=Pi(e)?e.start:e;0===t?A.ctx.moveTo(n.x,n.y):A.ctx.lineTo(n.x,n.y),Pi(e)&&A.ctx.bezierCurveTo(e.startControl.x,e.startControl.y,e.endControl.x,e.endControl.y,e.end.x,e.end.y)}))},A.prototype.renderRepeat=function(e,A,t,n){this.path(e),this.ctx.fillStyle=A,this.ctx.translate(t,n),this.ctx.fill(),this.ctx.translate(-t,-n)},A.prototype.resizeImage=function(e,A,t){var n;if(e.width===A&&e.height===t)return e;var r=(null!==(n=this.canvas.ownerDocument)&&void 0!==n?n:document).createElement("canvas");return r.width=Math.max(1,A),r.height=Math.max(1,t),r.getContext("2d").drawImage(e,0,0,e.width,e.height,0,0,A,t),r},A.prototype.renderBackgroundImage=function(e){return a(this,void 0,void 0,(function(){var A,t,n,r,i,a;return s(this,(function(o){switch(o.label){case 0:A=e.styles.backgroundImage.length-1,t=function(t){var r,i,a,o,u,l,d,c,p,h,f,g,m,w,B,y,v,b,C,$,I,E,Q,U,x,_,F,S,T,O,H;return s(this,(function(s){switch(s.label){case 0:if(0!==t.type)return[3,5];r=void 0,i=t.url,s.label=1;case 1:return s.trys.push([1,3,,4]),[4,n.context.cache.match(i)];case 2:return r=s.sent(),[3,4];case 3:return s.sent(),n.context.logger.error("Error loading background-image "+i),[3,4];case 4:return r&&(a=ia(e,A,[r.width,r.height,r.width/r.height]),y=a[0],E=a[1],Q=a[2],C=a[3],$=a[4],w=n.ctx.createPattern(n.resizeImage(r,C,$),"repeat"),n.renderRepeat(y,w,E,Q)),[3,6];case 5:1===t.type?(o=ia(e,A,[null,null,null]),y=o[0],E=o[1],Q=o[2],C=o[3],$=o[4],u=_A(t.angle,C,$),l=u[0],d=u[1],c=u[2],p=u[3],h=u[4],(f=document.createElement("canvas")).width=C,f.height=$,g=f.getContext("2d"),m=g.createLinearGradient(d,p,c,h),xA(t.stops,l).forEach((function(e){return m.addColorStop(e.stop,mA(e.color))})),g.fillStyle=m,g.fillRect(0,0,C,$),C>0&&$>0&&(w=n.ctx.createPattern(f,"repeat"),n.renderRepeat(y,w,E,Q))):function(e){return 2===e.type}(t)&&(B=ia(e,A,[null,null,null]),y=B[0],v=B[1],b=B[2],C=B[3],$=B[4],I=0===t.position.length?[iA]:t.position,E=oA(I[0],C),Q=oA(I[I.length-1],$),U=function(e,A,t,n,r){var i=0,a=0;switch(e.size){case 0:0===e.shape?i=a=Math.min(Math.abs(A),Math.abs(A-n),Math.abs(t),Math.abs(t-r)):1===e.shape&&(i=Math.min(Math.abs(A),Math.abs(A-n)),a=Math.min(Math.abs(t),Math.abs(t-r)));break;case 2:if(0===e.shape)i=a=Math.min(FA(A,t),FA(A,t-r),FA(A-n,t),FA(A-n,t-r));else if(1===e.shape){var s=Math.min(Math.abs(t),Math.abs(t-r))/Math.min(Math.abs(A),Math.abs(A-n)),o=SA(n,r,A,t,!0),u=o[0],l=o[1];a=s*(i=FA(u-A,(l-t)/s))}break;case 1:0===e.shape?i=a=Math.max(Math.abs(A),Math.abs(A-n),Math.abs(t),Math.abs(t-r)):1===e.shape&&(i=Math.max(Math.abs(A),Math.abs(A-n)),a=Math.max(Math.abs(t),Math.abs(t-r)));break;case 3:if(0===e.shape)i=a=Math.max(FA(A,t),FA(A,t-r),FA(A-n,t),FA(A-n,t-r));else if(1===e.shape){s=Math.max(Math.abs(t),Math.abs(t-r))/Math.max(Math.abs(A),Math.abs(A-n));var d=SA(n,r,A,t,!1);u=d[0],l=d[1],a=s*(i=FA(u-A,(l-t)/s))}}return Array.isArray(e.size)&&(i=oA(e.size[0],n),a=2===e.size.length?oA(e.size[1],r):i),[i,a]}(t,E,Q,C,$),x=U[0],_=U[1],x>0&&_>0&&(F=n.ctx.createRadialGradient(v+E,b+Q,0,v+E,b+Q,x),xA(t.stops,2*x).forEach((function(e){return F.addColorStop(e.stop,mA(e.color))})),n.path(y),n.ctx.fillStyle=F,x!==_?(S=e.bounds.left+.5*e.bounds.width,T=e.bounds.top+.5*e.bounds.height,H=1/(O=_/x),n.ctx.save(),n.ctx.translate(S,T),n.ctx.transform(1,0,0,O,0,0),n.ctx.translate(-S,-T),n.ctx.fillRect(v,H*(b-T)+T,C,$*H),n.ctx.restore()):n.ctx.fill())),s.label=6;case 6:return A--,[2]}}))},n=this,r=0,i=e.styles.backgroundImage.slice(0).reverse(),o.label=1;case 1:return r<i.length?(a=i[r],[5,t(a)]):[3,4];case 2:o.sent(),o.label=3;case 3:return r++,[3,1];case 4:return[2]}}))}))},A.prototype.renderSolidBorder=function(e,A,t){return a(this,void 0,void 0,(function(){return s(this,(function(n){return this.path(ea(t,A)),this.ctx.fillStyle=mA(e),this.ctx.fill(),[2]}))}))},A.prototype.renderDoubleBorder=function(e,A,t,n){return a(this,void 0,void 0,(function(){var r,i;return s(this,(function(a){switch(a.label){case 0:return A<3?[4,this.renderSolidBorder(e,t,n)]:[3,2];case 1:return a.sent(),[2];case 2:return r=function(e,A){switch(A){case 0:return ta(e.topLeftBorderBox,e.topLeftBorderDoubleOuterBox,e.topRightBorderBox,e.topRightBorderDoubleOuterBox);case 1:return ta(e.topRightBorderBox,e.topRightBorderDoubleOuterBox,e.bottomRightBorderBox,e.bottomRightBorderDoubleOuterBox);case 2:return ta(e.bottomRightBorderBox,e.bottomRightBorderDoubleOuterBox,e.bottomLeftBorderBox,e.bottomLeftBorderDoubleOuterBox);default:return ta(e.bottomLeftBorderBox,e.bottomLeftBorderDoubleOuterBox,e.topLeftBorderBox,e.topLeftBorderDoubleOuterBox)}}(n,t),this.path(r),this.ctx.fillStyle=mA(e),this.ctx.fill(),i=function(e,A){switch(A){case 0:return ta(e.topLeftBorderDoubleInnerBox,e.topLeftPaddingBox,e.topRightBorderDoubleInnerBox,e.topRightPaddingBox);case 1:return ta(e.topRightBorderDoubleInnerBox,e.topRightPaddingBox,e.bottomRightBorderDoubleInnerBox,e.bottomRightPaddingBox);case 2:return ta(e.bottomRightBorderDoubleInnerBox,e.bottomRightPaddingBox,e.bottomLeftBorderDoubleInnerBox,e.bottomLeftPaddingBox);default:return ta(e.bottomLeftBorderDoubleInnerBox,e.bottomLeftPaddingBox,e.topLeftBorderDoubleInnerBox,e.topLeftPaddingBox)}}(n,t),this.path(i),this.ctx.fill(),[2]}}))}))},A.prototype.renderNodeBackgroundAndBorders=function(e){return a(this,void 0,void 0,(function(){var A,t,n,r,i,a,o,u,l=this;return s(this,(function(s){switch(s.label){case 0:return this.applyEffects(e.getEffects(2)),A=e.container.styles,t=!gA(A.backgroundColor)||A.backgroundImage.length,n=[{style:A.borderTopStyle,color:A.borderTopColor,width:A.borderTopWidth},{style:A.borderRightStyle,color:A.borderRightColor,width:A.borderRightWidth},{style:A.borderBottomStyle,color:A.borderBottomColor,width:A.borderBottomWidth},{style:A.borderLeftStyle,color:A.borderLeftColor,width:A.borderLeftWidth}],r=ga(ua(A.backgroundClip,0),e.curves),t||A.boxShadow.length?(this.ctx.save(),this.path(r),this.ctx.clip(),gA(A.backgroundColor)||(this.ctx.fillStyle=mA(A.backgroundColor),this.ctx.fill()),[4,this.renderBackgroundImage(e.container)]):[3,2];case 1:s.sent(),this.ctx.restore(),A.boxShadow.slice(0).reverse().forEach((function(A){l.ctx.save();var t,n,r,i,a,s=zi(e.curves),o=A.inset?0:1e4,u=(t=s,n=-o+(A.inset?1:-1)*A.spread.number,r=(A.inset?1:-1)*A.spread.number,i=A.spread.number*(A.inset?-2:2),a=A.spread.number*(A.inset?-2:2),t.map((function(e,A){switch(A){case 0:return e.add(n,r);case 1:return e.add(n+i,r);case 2:return e.add(n+i,r+a);case 3:return e.add(n,r+a)}return e})));A.inset?(l.path(s),l.ctx.clip(),l.mask(u)):(l.mask(s),l.ctx.clip(),l.path(u)),l.ctx.shadowOffsetX=A.offsetX.number+o,l.ctx.shadowOffsetY=A.offsetY.number,l.ctx.shadowColor=mA(A.color),l.ctx.shadowBlur=A.blur.number,l.ctx.fillStyle=A.inset?mA(A.color):"rgba(0,0,0,1)",l.ctx.fill(),l.ctx.restore()})),s.label=2;case 2:i=0,a=0,o=n,s.label=3;case 3:return a<o.length?0!==(u=o[a]).style&&!gA(u.color)&&u.width>0?2!==u.style?[3,5]:[4,this.renderDashedDottedBorder(u.color,u.width,i,e.curves,2)]:[3,11]:[3,13];case 4:return s.sent(),[3,11];case 5:return 3!==u.style?[3,7]:[4,this.renderDashedDottedBorder(u.color,u.width,i,e.curves,3)];case 6:return s.sent(),[3,11];case 7:return 4!==u.style?[3,9]:[4,this.renderDoubleBorder(u.color,u.width,i,e.curves)];case 8:return s.sent(),[3,11];case 9:return[4,this.renderSolidBorder(u.color,i,e.curves)];case 10:s.sent(),s.label=11;case 11:i++,s.label=12;case 12:return a++,[3,3];case 13:return[2]}}))}))},A.prototype.renderDashedDottedBorder=function(e,A,t,n,r){return a(this,void 0,void 0,(function(){var i,a,o,u,l,d,c,p,h,f,g,m,w,B,y,v;return s(this,(function(s){return this.ctx.save(),i=function(e,A){switch(A){case 0:return Aa(e.topLeftBorderStroke,e.topRightBorderStroke);case 1:return Aa(e.topRightBorderStroke,e.bottomRightBorderStroke);case 2:return Aa(e.bottomRightBorderStroke,e.bottomLeftBorderStroke);default:return Aa(e.bottomLeftBorderStroke,e.topLeftBorderStroke)}}(n,t),a=ea(n,t),2===r&&(this.path(a),this.ctx.clip()),Pi(a[0])?(o=a[0].start.x,u=a[0].start.y):(o=a[0].x,u=a[0].y),Pi(a[1])?(l=a[1].end.x,d=a[1].end.y):(l=a[1].x,d=a[1].y),c=0===t||2===t?Math.abs(o-l):Math.abs(u-d),this.ctx.beginPath(),3===r?this.formatPath(i):this.formatPath(a.slice(0,2)),p=A<3?3*A:2*A,h=A<3?2*A:A,3===r&&(p=A,h=A),f=!0,c<=2*p?f=!1:c<=2*p+h?(p*=g=c/(2*p+h),h*=g):(m=Math.floor((c+h)/(p+h)),w=(c-m*p)/(m-1),h=(B=(c-(m+1)*p)/m)<=0||Math.abs(h-w)<Math.abs(h-B)?w:B),f&&(3===r?this.ctx.setLineDash([0,p+h]):this.ctx.setLineDash([p,h])),3===r?(this.ctx.lineCap="round",this.ctx.lineWidth=A):this.ctx.lineWidth=2*A+1.1,this.ctx.strokeStyle=mA(e),this.ctx.stroke(),this.ctx.setLineDash([]),2===r&&(Pi(a[0])&&(y=a[3],v=a[0],this.ctx.beginPath(),this.formatPath([new Di(y.end.x,y.end.y),new Di(v.start.x,v.start.y)]),this.ctx.stroke()),Pi(a[1])&&(y=a[1],v=a[2],this.ctx.beginPath(),this.formatPath([new Di(y.end.x,y.end.y),new 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