diff --git "a/demo/yolo/dist/index.min.js.map" "b/demo/yolo/dist/index.min.js.map"
new file mode 100644--- /dev/null
+++ "b/demo/yolo/dist/index.min.js.map"
@@ -0,0 +1 @@
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x=ae(\"x\",t.dataType,t.dims,o),A=\"thread_max_vector\";o===2?A=\"max(thread_max_vector.x, thread_max_vector.y)\":o===4&&(A=\"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))\");let S=gt(t.dataType),k=[{name:\"d_inv\",type:S},{name:\"d_comp\",type:\"u32\"},{name:\"elements_per_wg\",type:\"u32\"}];return`\n  var<workgroup> wgMax: array<f32, ${c}>;\n  var<workgroup> wgSum: array<f32, ${c}>;\n  ${_.registerUniforms(k).declareVariables(x)}\n  ${_.mainStart([c,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 = ${ut(\"f32\",o,\"-3.402823e+38f\")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      thread_max_vector = max(${Rt(S,o,\"x[offset + i]\")}, thread_max_vector);\n    }\n    wgMax[local_idx] = ${A};\n    workgroupBarrier();\n\n    var maxValue = -3.402823e+38f;\n   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}`};e.compute({name:\"AttentionProbsSoftmax\",shaderCache:{hint:`${c};${v};${o}`},getShaderSource:$,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})},{inputs:[t],outputs:[]})},sy=(e,t,r,u,o,c)=>{let p=[o.batchSize,o.numHeads,o.sequenceLength,o.kvSequenceLength+o.pastSequenceLength],h=c.scale===0?1/Math.sqrt(o.headSize):c.scale,l=et(o.headSize),v=o.headSize/l,$=12,_={x:Math.ceil(o.totalSequenceLength/$),y:Math.ceil(o.sequenceLength/$),z:o.batchSize*o.numHeads},x=[{type:12,data:o.sequenceLength},{type:12,data:v},{type:12,data:o.totalSequenceLength},{type:12,data:o.kvSequenceLength},{type:t.dataType,data:h}],A=[t,r],S=M=>{let R=X(\"q\",t.dataType,t.dims,l),P=X(\"key\",r.dataType,r.dims,l),W=ae(\"output\",t.dataType,p),U=Ne(t.dataType),q=[{name:\"M\",type:\"u32\"},{name:\"K\",type:\"u32\"},{name:\"N\",type:\"u32\"},{name:\"kv_sequence_length\",type:\"u32\"},{name:\"alpha\",type:U}];return`\n  const beta: ${U} = 1.0;\n  const TILE_SIZE = ${$}u;\n\n  var<workgroup> tileQ: 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o=[u.batchSize,u.sequenceLength,u.vHiddenSize],c=12,p={x:Math.ceil(u.vHeadSize/c),y:Math.ceil(u.sequenceLength/c),z:u.batchSize*u.numHeads},h=[{type:12,data:u.sequenceLength},{type:12,data:u.totalSequenceLength},{type:12,data:u.vHeadSize},{type:12,data:u.numHeads},{type:12,data:u.vHiddenSize}],l=v=>{let $=X(\"probs\",t.dataType,t.dims),_=X(\"v\",r.dataType,r.dims),x=ae(\"output\",t.dataType,o),A=[{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 = ${c}u;\n  var<workgroup> tileQ: array<${$.type.value}, ${c*c}>;\n  var<workgroup> tileK: array<${$.type.value}, ${c*c}>;\n  ${v.registerUniforms(A).declareVariables($,_,x)}\n  ${v.mainStart([c,c,1])}\n   let headIdx = workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   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uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`};return e.compute({name:\"AttentionScore\",shaderCache:{inputDependencies:[\"type\",\"type\"]},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType,gpuDataType:0}],dispatchGroup:p,programUniforms:h}),getShaderSource:l},{inputs:[t,r],outputs:[0]})[0]},Sn=(e,t,r,u,o,c,p,h,l,v,$)=>{let _=sy(e,t,r,l,v,$);uy(e,_,u,v)},ly=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],u=t.sequenceLength,o=t.inputHiddenSize,c=t.headSize,p=12,h={x:Math.ceil(t.headSize/p),y:Math.ceil(t.sequenceLength/p),z:t.batchSize*t.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],v=[{type:12,data:u},{type:12,data:o},{type:12,data:c},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],$=_=>{let x=ae(\"output_q\",l[0].dataType,r),A=ae(\"output_k\",l[0].dataType,r),S=ae(\"output_v\",l[0].dataType,r),k=X(\"input\",l[0].dataType,l[0].dims),M=X(\"weight\",l[1].dataType,l[1].dims),R=X(\"bias\",l[2].dataType,l[2].dims),P=k.type.storage,W=[{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 = ${p}u;\n  var<workgroup> tileInput: array<${P}, ${p*p}>;\n  var<workgroup> tileWeightQ: array<${P}, ${p*p}>;\n  var<workgroup> tileWeightK: array<${P}, ${p*p}>;\n  var<workgroup> tileWeightV: array<${P}, ${p*p}>;\n  ${_.registerUniforms(W).declareVariables(k,M,R,x,A,S)}\n  ${_.mainStart([p,p,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 = ${P}(0);\n    var valueK = ${P}(0);\n    var valueV = ${P}(0);\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m < uniforms.M && w + local_id.x < uniforms.K) {\n        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`};return e.compute({name:\"AttentionPrepare\",shaderCache:{inputDependencies:[\"type\",\"type\",\"type\"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:h,programUniforms:v}),getShaderSource:$},{inputs:l,outputs:[-1,-1,-1]})},Gl=(e,t)=>{let r=ay(e.inputs,t),[u,o,c]=ly(e,r);return Sn(e,u,o,c,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var dy,cy,py,Ll,Hl=ne(()=>{\"use strict\";It();$e();Pe();nt();Te();dy=(e,t)=>{if(!e||e.length!==5)throw new Error(\"BatchNormalization requires 5 inputs\");let r=(u,o,c)=>{let p=o.length;if(p!==u.length)throw new Error(`${c}: num dimensions != ${p}`);o.forEach((h,l)=>{if(h!==u[l])throw new Error(`${c}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let 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dimensions\");if(e[0].dims[2]!==e[1].dims[0])throw new Error(\"last dimension of input and bias are not the same\")},my=e=>{let t=e[0].dims,r=e[0].dims[2],u=Q.size(t)/4,o=e[0].dataType,c=X(\"input\",o,t,4),p=X(\"bias\",o,[r],4),h=X(\"residual\",o,t,4),l=ae(\"output\",o,t,4);return{name:\"BiasAdd\",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:$=>`\n  const channels = ${r}u / 4;\n  ${$.declareVariables(c,p,h,l)}\n\n  ${$.mainStart()}\n    ${$.guardAgainstOutOfBoundsWorkgroupSizes(u)}\n    let value = ${c.getByOffset(\"global_idx\")}\n      + ${p.getByOffset(\"global_idx % channels\")} + ${h.getByOffset(\"global_idx\")};\n    ${l.setByOffset(\"global_idx\",\"value\")}\n  }`}},Fl=e=>{fy(e.inputs),e.compute(my(e.inputs))}});var hy,je,Kl,Yl,Ql,Zl,Xl,Jl,ed,td,rd,gy,id,nd,ad,od,In,sd,Tn,ud,ld,dd,cd,pd,fd,md,hd,gd,yd,vd,bd,wd,_d,$d,Cd,xd,Sd,$a,Ca,Id,Td,Ad,An=ne(()=>{\"use strict\";$e();Pe();nt();Te();hy=(e,t,r,u,o,c)=>{let 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3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},kn=e=>`\n      ${e?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n      `});var Pn,xa=ne(()=>{\"use strict\";Pn=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var Ay,Ey,xi,Fd,ky,Si,Oy,Rn,Ii=ne(()=>{\"use strict\";$e();Pe();Te();rr();On();Ay=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?\", batchIndices\":\"\"});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?\", batchIndices\":\"\"});\n        `,Ey=(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          ${t===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n        }`,xi=(e,t,r=\"f32\",u,o=!1,c=32,p=!1,h=32)=>{let l=t[1]*e[1],v=t[0]*e[0],$=o?l:c,_=o?c:l,x=$/t[0],A=c/t[1];if(!((o&&x===4&&e[1]===4||!o&&(x===3||x===4))&&$%t[0]===0&&c%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${x} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${x} must be 3 or 4.\n  tileAWidth ${$} must be divisible by workgroupSize[0]${t[0]}. tileInner ${c} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${x}<${r}>, ${$/x}>, ${_}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${v/e[0]}>, ${c}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${x};\nconst tileInner = ${c};\n\n@compute @workgroup_size(${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 = ${p?\"0\":\"i32(globalId.z)\"};\n  ${u?`let batchIndices = ${u.offsetToIndices(\"u32(batch)\")};`:\"\"}\n  let globalRowStart = i32(workgroupId.y) * ${l};\n\n  let num_tiles = ${p?`${Math.ceil(h/c)}`:\"(uniforms.dim_inner - 1) / tileInner + 1\"};\n  var kStart = ${p?`i32(globalId.z) * ${h}`:\"0\"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${A};\n  for (var t = 0; t < num_tiles; 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          ${Ay(o,u)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${A}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${u?\", 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          ${x===3?\"\":\"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];\"}\n\n          ${Ey(o,x)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Fd=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?\", batchIndices\":\"\"});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?\", batchIndices\":\"\"});\n            `,ky=e=>e?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\",Si=(e,t,r=\"f32\",u,o=!1,c=32,p=!1,h=32,l=!1)=>{let v=e[1]*t[1],$=e[0]*t[0],_=o?v:c,x=o?c:v;if(!(x%t[1]===0&&_%t[0]===0&&c%t[1]===0))throw new Error(`tileAHight ${x} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${_} must be divisible by workgroupSize[0]${t[0]}, tileInner ${c} must be divisible by workgroupSize[1]${t[1]}`);let A=x/t[1],S=_/t[0],k=c/t[1],M=l?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${v};\n    let globalColStart = i32(workgroupId.x) * ${$};\n\n    // Loop over shared dimension.\n    for (var t = 0; t < num_tiles; t = t + 1) {\n      // Load one tile of A into local memory.\n      for (var inputRow = localRow; inputRow < ${x}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${_}; inputCol = inputCol + ${t[0]}) {\n          ${Fd(o,u)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${$}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${u?\", 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 = ${o?`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) * ${v};\n\nlet tileRowA = i32(localId.y) * ${A};\nlet tileColA = i32(localId.x) * ${S};\nlet tileRowB = i32(localId.y) * ${k};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${A}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${S}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Fd(o,u)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${k}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${u?\", 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      ${ky(o)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${_}>, ${x}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${$}>, ${c}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${c};\n\n@compute @workgroup_size(${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 = ${p?\"0\":\"i32(globalId.z)\"};\n    ${u?`let batchIndices = ${u.offsetToIndices(\"u32(batch)\")};`:\"\"}\n    let num_tiles = ${p?`${Math.ceil(h/c)}`:\"(uniforms.dim_inner - 1) / tileInner + 1\"};\n    var kStart = ${p?`i32(globalId.z) * ${h}`:\"0\"};\n\n    var acc : array<array<${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    ${M}\n  }\n`},Oy=(e,t,r,u,o,c=!1)=>{let[p,h,l]=o,[v,$,_,x]=u,A=Dr(p,l),S=Dr(h,l),k=Ne(u[0].type.tensor),M=()=>{let W=$.rank,U=v.rank,q=`var aIndices: ${$.type.indices};`;for(let Y=W-2-1,J=U-1;Y>=0;Y--,J--)q+=`\naIndices[${Y}] = ${U>1?`batchIndices[${J}]`:\"batchIndices\"};`;return A.forEach(Y=>{q+=`\naIndices[${Y}] = 0;`}),q+=`\naIndices[${W-2}] = u32(row);\n                   aIndices[${W-1}] = u32(colIn);`,q},R=()=>{let W=_.rank,U=v.rank,q=`var bIndices: ${_.type.indices};`;for(let Y=W-2-1,J=U-1;Y>=0;Y--,J--)q+=`\nbIndices[${Y}] = ${U>1?`batchIndices[${J}]`:\"batchIndices\"};`;return S.forEach(Y=>{q+=`\nbIndices[${Y}] = 0;`}),q+=`\nbIndices[${W-2}] = u32(row);\n                   bIndices[${W-1}] = u32(colIn);`,q};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${v.type.indices}) -> ${lt(e,k)} {\n      var value = ${lt(e,k)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n      {\n        ${M()}\n        value = ${$.getByIndices(\"aIndices\")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${v.type.indices}) -> ${lt(e,k)} {\n      var value = ${lt(e,k)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n      {\n        ${R()}\n        value = ${_.getByIndices(\"bIndices\")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${lt(e,k)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${c?\"bias[colIn]\":`${lt(e,k)}(bias[row])`};`:\"\"}\n        ${r}\n        ${x.setByIndices(\"vec3<u32>(coords)\",\"value\")}\n      }\n    }\n    `},Rn=(e,t,r,u,o=!1)=>{let c=e[0].dims,p=e[1].dims,h=c.slice(0,-2),l=p.slice(0,-2),v=u?u.slice(0,-2):r.slice(0,-2),$=Q.size(v),_=c[c.length-2],x=c[c.length-1],A=p[p.length-1],S=x%4===0&&A%4===0,k=_<=8?[4,1,1]:[4,4,1],M=[8,8,1],R=[Math.ceil(A/M[0]/k[0]),Math.ceil(_/M[1]/k[1]),Math.ceil($/M[2]/k[2])],P=S?4:1,W=[...h,_,x/P],U=W.length,q=[...l,x,A/P],Y=q.length,J=[$,_,A/P],oe=[{type:6,data:_},{type:6,data:A},{type:6,data:x}];Lt(t,oe),oe.push(...se(v,W,q));let ge=[\"rank\",\"rank\"],H=e.length>2;H&&(oe.push(...se(e[2].dims)),ge.push(\"rank\")),oe.push(...se(J));let le=Ae=>{let Ue=v.length,We=$n(\"batchDims\",e[0].dataType,Ue,1),Ee=Ne(e[0].dataType),ee=X(\"a\",e[0].dataType,U,P),Ve=X(\"b\",e[1].dataType,Y,P),xe=ae(\"result\",e[0].dataType,J.length,P),Ce=[ee,Ve];if(H){let re=o?P:1;Ce.push(X(\"bias\",e[2].dataType,e[2].dims.length,re))}let be=[{name:\"dim_a_outer\",type:\"i32\"},{name:\"dim_b_outer\",type:\"i32\"},{name:\"dim_inner\",type:\"i32\"}];Ht(t,be);let Se=Ne(xe.type.tensor),De=Gt(t,xe.type.value,Se),Ye=Oy(P,H,De,[We,ee,Ve,xe],[h,l,v],o);return`\n  ${Ae.registerUniforms(be).registerInternalVariables(We).declareVariables(...Ce,xe)}\n  ${Ye}\n  ${S?xi(k,M,Ee,We):Si(k,M,Ee,We)}\n                   `};return{name:\"MatMul\",shaderCache:{hint:`${k};${t.activation};${S};${o}`,inputDependencies:ge},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:R[0],y:R[1],z:R[2]},programUniforms:oe}),getShaderSource:le}}});var Py,qd,Kd=ne(()=>{\"use strict\";$e();tr();Te();rr();On();xa();Ii();Py=(e,t,r,u,o=!1,c,p=4,h=4,l=4,v=\"f32\")=>{let $=H=>{switch(H){case 1:return\"resData = x[xIndex];\";case 3:return`resData = vec3<${v}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${H} is not supported.`)}},_=H=>{switch(H){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 ${H} is not supported.`)}},x=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,A=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    `,S=e?\"i32(uniforms.x_shape[1])\":\"i32(uniforms.x_shape[2])\",k=e?\"i32(uniforms.x_shape[2])\":\"i32(uniforms.x_shape[3])\",M=e?\"row\":\"col\",R=e?\"col\":\"row\",P=`\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 = ${M} / outWidth;\n    let outCol = ${M} % outWidth;\n\n    let WRow = ${R} / (i32(uniforms.w_shape[1]) * inChannels);\n    let WCol = ${R} / inChannels % i32(uniforms.w_shape[1]);\n    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n    let xCh = ${R} % inChannels;\n    var resData = ${lt(p,v)}(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 < ${S} && xCol >= 0 && xCol < ${k}) {\n      ${x}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${$(p)}\n    }\n    return resData;`,W=e?t&&u?`\n    let col = colIn * ${p};\n    ${P}`:`\n    let col = colIn * ${p};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n      ${P}\n    }\n    return ${lt(p,v)}(0.0);`:u&&r?`\n    let col = colIn * ${p};\n    ${P}`:`\n    let col = colIn * ${p};\n    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n      ${P}\n    }\n    return ${lt(p,v)}(0.0);`,U=`${_(h)}`,q=lt(l,v),Y=e?lt(p,v):lt(h,v),J=e?lt(h,v):lt(p,v),oe=Gt(c,q,v);return`\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Y} {\n      ${e?W:U}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${J} {\n      ${e?U:W}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${q}) {\n      let col = colIn * ${l};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n      {\n      var value = valueIn;\n      let outWidth = ${e?\"i32(uniforms.result_shape[2])\":\"i32(uniforms.result_shape[3])\"};\n      ${A}\n      ${kn(o)}\n      ${oe}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},qd=(e,t,r,u,o,c,p,h)=>{let l=t.format===\"NHWC\",v=l?e[0].dims[3]:e[0].dims[1],$=r[0],_=l?r[2]:r[3],x=l?r[1]:r[2],A=l?r[3]:r[1],S=l&&(v%4===0||v%3===0)&&A%4===0,k=l?A:_*x,M=l?_*x:A,R=[8,8,1],P=u<=8?[4,1,1]:[4,4,1],W=[Math.ceil(k/R[0]/P[0]),Math.ceil(M/R[1]/P[1]),Math.ceil($/R[2]/P[2])];Fe(\"verbose\",()=>`[conv2d_mm_webgpu] dispatch = ${W}`);let U=S?l&&v%4!==0?3:4:1,q=R[1]*P[1],Y=R[0]*P[0],J=Math.max(R[0]*U,R[1]),oe=u%q===0,ge=o%Y===0,H=c%J===0,le=S?[U,4,4]:[1,1,1],Ae=[{type:6,data:u},{type:6,data:o},{type:6,data:c},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Lt(t,Ae),Ae.push(...se(e[0].dims,e[1].dims));let Ue=[\"rank\",\"rank\"];p&&(Ae.push(...se(e[2].dims)),Ue.push(\"rank\")),Ae.push(...se(r));let We=Ee=>{let ee=[{name:\"dim_a_outer\",type:\"i32\"},{name:\"dim_b_outer\",type:\"i32\"},{name:\"dim_inner\",type:\"i32\"},{name:\"pad\",type:\"i32\",length:2},{name:\"stride\",type:\"i32\",length:2},{name:\"dilation\",type:\"i32\",length:2}];Ht(t,ee);let Ve=S?4:1,xe=Ne(e[0].dataType),Ce=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${S?`vec4<${xe}>`:xe}) {\n        result[flatIndex] = ${S?`vec4<${xe}>`:xe}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${S?`vec4<${xe}>`:xe}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${S?\"/ 4\":\"\"}, value);\n      }`,be=X(\"x\",e[0].dataType,e[0].dims.length,U===3?1:U),Se=X(\"w\",e[1].dataType,e[1].dims.length,Ve),De=[be,Se],Ye=ae(\"result\",e[0].dataType,r.length,Ve);if(p){let re=X(\"bias\",e[2].dataType,e[2].dims.length,Ve);De.push(re),Ce+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${S?`vec4<${xe}>`:xe} {\n          return bias[coords.${l?\"w\":\"y\"}${S?\"/ 4\":\"\"}];\n        }`}return`\n        ${Pn(\"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        ${Ee.registerUniforms(ee).declareVariables(...De,Ye)}\n        ${Ce}\n        ${Py(l,oe,ge,H,p,t,le[0],le[1],le[2],xe)}\n        ${S?xi(P,R,xe,void 0,!l,J):Si(P,R,xe,void 0,!l,J,!1,void 0,h)}`};return{name:\"Conv2DMatMul\",shaderCache:{hint:`${t.cacheKey};${U};${S};${oe};${ge};${H};${q};${Y};${J}`,inputDependencies:Ue},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:W[0],y:W[1],z:W[2]},programUniforms:Ae}),getShaderSource:We}}});var Sa,Yd=ne(()=>{\"use strict\";$e();Pe();Te();Ta();rr();Sa=(e,t,r)=>{let u=e.length>2,o=u?\"value += b[output_channel];\":\"\",c=e[0].dims,p=e[1].dims,h=p[0]/t.group,l=t.format===\"NHWC\",v=Ia(c,p,t.dilations,t.pads,t.strides,l),$=Q.size(v),_=[{type:12,data:$},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:h}];Lt(t,_),_.push(...se(c,p,v));let x=[\"rank\",\"rank\"];u&&(_.push(...se(e[2].dims)),x.push(\"rank\")),_.push(...se(v));let A=S=>{let k=ae(\"output\",e[0].dataType,v.length),M=Ne(k.type.tensor),R=Gt(t,k.type.value,M),P=X(\"x\",e[0].dataType,c.length),W=X(\"w\",e[1].dataType,p.length),U=[P,W];u&&U.push(X(\"b\",e[2].dataType,e[2].dims));let q=[{name:\"output_size\",type:\"u32\"},{name:\"dilations\",type:\"u32\",length:t.dilations.length},{name:\"strides\",type:\"u32\",length:2},{name:\"pads\",type:\"u32\",length:2},{name:\"output_channels_per_group\",type:\"u32\"}];return Ht(t,q),`\n  ${S.registerUniforms(q).declareVariables(...U,k)}\n\n  ${S.mainStart()}\n    ${S.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n\n    let outputIndices = ${k.offsetToIndices(\"global_idx\")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${l?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * uniforms.strides - uniforms.pads;\n    let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n    var value: ${k.type.value} = ${k.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n      let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n        if (xHeight < 0u || xHeight >= uniforms.x_shape[${l?1:2}]) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n          if (xWidth < 0u || xWidth >= uniforms.x_shape[${l?2:3}]) {\n            continue;\n          }\n\n          let xVal = ${l?P.get(\"batch\",\"xHeight\",\"xWidth\",\"input_channel\"):P.get(\"batch\",\"input_channel\",\"xHeight\",\"xWidth\")};\n          let wVal = ${W.get(\"output_channel\",\"wInChannel\",\"wHeight\",\"wWidth\")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${o}\n    ${R}\n    ${k.setByOffset(\"global_idx\",\"value\")}\n  }`};return{name:\"GroupedConv\",shaderCache:{hint:t.cacheKey,inputDependencies:x},getRunData:()=>({outputs:[{dims:r?r(v):v,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($/64)},programUniforms:_}),getShaderSource:A}}});var Aa,Ry,Qd,Ea=ne(()=>{\"use strict\";$e();Pe();Ii();Te();rr();Aa=(e,t,r,u,o=!1)=>{let c=e[0].dims,p=e[1].dims,h=c[c.length-2],l=p[p.length-1],v=c[c.length-1],$=et(l),_=et(v),x=et(h),A=Q.size(r)/$/x,S=e.length>2,k=u?u.slice(0,-2):r.slice(0,-2),R=[Q.size(k),h,l],P=[{type:12,data:A},{type:12,data:h},{type:12,data:l},{type:12,data:v}];Lt(t,P),P.push(...se(k,c,p)),S&&P.push(...se(e[2].dims)),P.push(...se(R));let W=U=>{let q=$n(\"batch_dims\",e[0].dataType,k.length),Y=X(\"a\",e[0].dataType,c.length,_),J=X(\"b\",e[1].dataType,p.length,$),oe=ae(\"output\",e[0].dataType,R.length,$),ge=Ne(oe.type.tensor),H=Gt(t,oe.type.value,ge),le=[Y,J],Ae=\"\";if(S){let be=o?$:1;le.push(X(\"bias\",e[2].dataType,e[2].dims.length,be)),Ae=`${o?`value += bias[col / ${be}];`:`value += ${oe.type.value}(bias[row + i]);`}`}let Ue=c.slice(0,-2),We=p.slice(0,-2),Ee=Dr(Ue,k),ee=Dr(We,k),Ve=[{name:\"output_size\",type:\"u32\"},{name:\"M\",type:\"u32\"},{name:\"N\",type:\"u32\"},{name:\"K\",type:\"u32\"}];Ht(t,Ve);let xe=(be,Se)=>{let De=be.rank,Ye=be.name;if(De===2)return`var ${Ye}_indices = ${be.type.indices}(0u, 0u);`;let re=q.rank,pe=`var ${Ye}_indices: ${be.type.indices};`;for(let _e=De-2-1,at=re-1;_e>=0;_e--,at--)pe+=`\n${Ye}_indices[${_e}] = ${re>1?`batch_indices[${at}]`:\"batch_indices\"};`;return Se.forEach(_e=>{pe+=`\n${Ye}_indices[${_e}] = 0;`}),pe+=`${Ye}_indices[${De-2}] = 0u;\n                     ${Ye}_indices[${De-1}] = 0u;`,pe},Ce=()=>{let be=`var a_data: ${Y.type.value};`;for(let Se=0;Se<_;Se++)be+=`\n              let b_data${Se} = b[(b_offset + (k + ${Se}) * uniforms.N + col) / ${$}];`;for(let Se=0;Se<x;Se++){be+=`a_data = a[(a_offset + (row + ${Se}) * uniforms.K + k) / ${_}];`;for(let De=0;De<_;De++)be+=`\n            values[${Se}] = fma(${J.type.value}(a_data${_===1?\"\":`[${De}]`}), b_data${De}, values[${Se}]);\n`}return be};return`\n  ${U.registerUniforms(Ve).registerInternalVariables(q).declareVariables(...le,oe)}\n  ${U.mainStart()}\n    ${U.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n    let col = (global_idx % (uniforms.N / ${$})) * ${$};\n    var index1 = global_idx / (uniforms.N / ${$});\n    let stride1 = uniforms.M / ${x};\n    let row = (index1 % stride1) * ${x};\n    let batch = index1 / stride1;\n\n    ${r.length===2?\"\":`let batch_indices = ${q.offsetToIndices(\"batch\")};`}\n    ${xe(Y,Ee)}\n    let a_offset = ${Y.indicesToOffset(\"a_indices\")};\n    ${xe(J,ee)}\n    let b_offset = ${J.indicesToOffset(\"b_indices\")};\n    var values: array<${oe.type.value}, ${x}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${_}) {\n      ${Ce()}\n    }\n    for (var i = 0u; i < ${x}u; i++) {\n      var value = values[i];\n      ${Ae}\n      ${H}\n      let cur_indices = ${oe.type.indices}(batch, row + i, col);\n      let offset = ${oe.indicesToOffset(\"cur_indices\")};\n      ${oe.setByOffset(`offset / ${$}`,\"value\")};\n    }\n  }\n  `};return{name:\"MatMulNaive\",shaderCache:{hint:`${t.activation};${$};${_};${x};${o}`,inputDependencies:S?[\"rank\",\"rank\",\"rank\"]:[\"rank\",\"rank\"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(A/64)},programUniforms:P}),getShaderSource:W}},Ry=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.\")},Qd=e=>{Ry(e.inputs);let t=Mt.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],u=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&u<8?e.compute(Aa(e.inputs,{activation:\"\"},t)):e.compute(Rn(e.inputs,{activation:\"\"},t))}});var Ia,Zd,By,Xd,ka,zy,Dy,Oa,Ta=ne(()=>{\"use strict\";Pe();Kd();Ii();Yd();rr();Ea();Mr();Ia=(e,t,r,u,o,c)=>{let p=e[0],h=e.slice(c?1:2,c?3:4),l=h.length,v=t[0],_=t.slice(2).map((S,k)=>S+(S-1)*(r[k]-1)),A=h.map((S,k)=>S+u[k]+u[k+l]).map((S,k)=>Math.floor((S-_[k]+o[k])/o[k]));return A.splice(0,0,p),A.splice(c?3:1,0,v),A},Zd=[2,3,1,0],By=(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],u=e[1].dims[1]*t.group;if(r!==u)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 o=e[0].dims.length-2;if(t.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(t.strides.length!==o)throw new Error(`strides should be ${o}D`);if(t.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error(\"invalid kernel shape\")},Xd=(e,t)=>{let r=e.kernelShape.slice();for(let c=2;c<t[1].dims.length;++c)r[c-2]===0&&(r[c-2]=t[1].dims[c]);let u=e.pads.slice();hr.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,u,e.format===\"NHWC\",e.autoPad);let o=Object.assign({},e);return Object.assign(o,{kernelShape:r,pads:u}),o},ka=e=>{let t=En(e),r=e.format,u=[\"NOTSET\",\"VALID\",\"SAME_UPPER\",\"SAME_LOWER\"][e.auto_pad],o=e.dilations,c=e.group,p=e.kernel_shape,h=e.pads,l=e.strides,v=e.w_is_const();return{autoPad:u,format:r,dilations:o,group:c,kernelShape:p,pads:h,strides:l,wIsConst:v,...t,cacheKey:`${e.format};${t.activation};`}},zy=(e,t,r)=>{let u=Xd(r,t),o=r.format===\"NHWC\";if(r.group!==1){e.compute(Sa(t,u));return}let c=t.length===3,p=t[0].dims[o?1:2],h=t[0].dims[o?2:3],l=t[0].dims[o?3:1],v=t[1].dims[2],$=t[1].dims[3],_=Ia(t[0].dims,t[1].dims,r.dilations,u.pads,r.strides,o),x=_[o?1:2],A=_[o?2:3],S=_[o?3:1],k=o&&v===p&&$===h&&r.pads[0]===0&&r.pads[1]===0;if(k||v===1&&$===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 Y=_[0],J,oe,ge,H=[];if(o){let Ue=e.kernelCustomData.wT??e.compute(Bt(t[1],Zd),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ue),k){let We=p*h*l;J=t[0].reshape([1,Y,We]),oe=Ue.reshape([1,We,S]),ge=[1,Y,S]}else J=t[0].reshape([Y,p*h,l]),oe=Ue.reshape([1,l,S]),ge=[Y,x*A,S];H.push(J),H.push(oe)}else J=t[0].reshape([Y,l,p*h]),oe=t[1].reshape([1,S,l]),ge=[Y,S,x*A],H.push(oe),H.push(J);c&&H.push(t[2]);let le=ge[2],Ae=H[0].dims[H[0].dims.length-1];le<8&&Ae<8?e.compute(Aa(H,u,_,ge,o),{inputs:H}):e.compute(Rn(H,u,_,ge,o),{inputs:H});return}let M=!0,R=e.kernelCustomData.wT??e.compute(Bt(t[1],Zd),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=R);let P=[t[0],R];c&&P.push(t[2]);let W=o?x*A:S,U=o?S:x*A,q=v*$*l;e.compute(qd(P,u,_,W,U,q,c,M),{inputs:P})},Dy=(e,t)=>{let r=t.format===\"NHWC\",u=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&u.push(e.inputs[2]);let o=[0,t.pads[0],0,t.pads[1]],c=[1].concat(t.strides),p=[1].concat(t.dilations),h=[1].concat(t.kernelShape),l=Xd({...t,pads:o,strides:c,dilations:p,kernelShape:h},u);e.compute(Sa(u,l,v=>r?[v[0],v[2],v[3]]:[]))},Oa=(e,t)=>{By(e.inputs,t),e.inputs[0].dims.length===3?Dy(e,t):zy(e,e.inputs,t)}});var My,Jd,ec=ne(()=>{\"use strict\";$e();tr();Te();rr();On();xa();Ii();My=(e,t=!1,r,u,o=4)=>{let c=R=>{switch(R){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 ${u}(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${R} is not supported.`)}},p=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,h=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,l=e?\"i32(uniforms.x_shape[1])\":\"i32(uniforms.x_shape[2])\",v=e?\"i32(uniforms.x_shape[2])\":\"i32(uniforms.x_shape[3])\",$=e?\"row\":\"col\",_=e?\"col\":\"row\",x=`\n      let inChannels = ${e?\"i32(uniforms.x_shape[3])\":\"i32(uniforms.x_shape[1])\"};\n      let outWidth = ${e?\"i32(uniforms.result_shape[2])\":\"i32(uniforms.result_shape[3])\"};\n      let outRow = ${$} / outWidth;\n      let outCol = ${$} % outWidth;\n\n      let WRow = ${_} / (uniforms.filter_dims[1] * inChannels);\n      let WCol = ${_} / inChannels % uniforms.filter_dims[1];\n      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n      if (xR < 0.0 || xR >= f32(${l}) || fract(xR) > 0.0) {\n        return ${u}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${v}) || fract(xC) > 0.0) {\n        return ${u}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${_} % inChannels;\n      ${p}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${o}];`,A=e?`\n      let col = colIn * ${o};\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n        ${x}\n      }\n      return ${u}(0.0);`:`\n      let col = colIn * ${o};\n      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n        ${x}\n      }\n      return ${u}(0.0);`,S=`\n      let col = colIn * ${o};\n      let inChannels = ${e?\"i32(uniforms.x_shape[3])\":\"i32(uniforms.x_shape[1])\"};\n      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n      if (${e?\"row < uniforms.dim_inner && col < uniforms.dim_b_outer\":\"row < uniforms.dim_inner && col < uniforms.dim_a_outer\"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${c(o)}\n      }\n      return ${u}(0.0);\n      `,k=Gt(r,u);return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${u} {\n    ${e?A:S}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${u} {\n    ${e?S:A}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${u}) {\n    let col = colIn * ${o};\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n      var value = valueInput;\n      let outWidth = ${e?\"i32(uniforms.result_shape[2])\":\"i32(uniforms.result_shape[3])\"};\n      ${h}\n      ${kn(t)}\n      ${k}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${o}] = value;\n    }\n  }`},Jd=(e,t,r,u,o,c,p,h)=>{let l=t.format===\"NHWC\",v=l?e[0].dims[3]:e[0].dims[1],$=r[0],_=l?r[2]:r[3],x=l?r[1]:r[2],A=l?r[3]:r[1],S=l?v%4===0&&A%4===0:_%4===0&&A%4===0,k=l?A:_*x,M=l?_*x:A,R=S?[8,8,1]:[k<=4||M<=4?4:16,k>4&&M<=4?4:16,1],P=S?[4,4,1]:[k<=4?1:4,k>4&&M<=4?1:4,1],W=[Math.ceil(k/R[0]/P[0]),Math.ceil(M/R[1]/P[1]),Math.ceil($/R[2]/P[2])];Fe(\"verbose\",()=>`[conv_backprop_mm_webgpu] dispatch = ${W}`);let U=S?4:1,q=Math.max(R[0]*U,R[1]),Y=S?4:1,J=[t.kernelShape[l?1:2],t.kernelShape[l?2:3]],oe=[J[0]+(t.dilations[0]<=1?0:(J[0]-1)*(t.dilations[0]-1)),J[1]+(t.dilations[1]<=1?0:(J[1]-1)*(t.dilations[1]-1))],ge=[oe[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),oe[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],H=[{type:6,data:u},{type:6,data:o},{type:6,data:c},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:J},{type:6,data:ge}];Lt(t,H),H.push(...se(e[0].dims,e[1].dims));let le=[\"rank\",\"rank\"];p&&(H.push(...se(e[2].dims)),le.push(\"rank\")),H.push(...se(r));let Ae=Ue=>{let We=X(\"x\",e[0].dataType,e[0].dims.length,Y),Ee=X(\"w\",e[1].dataType,e[1].dims.length,1),ee=ae(\"result\",e[0].dataType,r.length,Y),Ve=[We,Ee],xe=\"\";if(p){let Se=X(\"bias\",e[2].dataType,e[2].dims.length,Y);Ve.push(Se),xe+=`\n          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${Se.type.value} {\n            return bias[coords.${l?\"w\":\"y\"}${S?\"/ 4\":\"\"}];\n          }`}let Ce=[{name:\"dim_a_outer\",type:\"i32\"},{name:\"dim_b_outer\",type:\"i32\"},{name:\"dim_inner\",type:\"i32\"},{name:\"strides\",type:\"i32\",length:2},{name:\"dilations\",type:\"i32\",length:2},{name:\"filter_dims\",type:\"i32\",length:J.length},{name:\"pads\",type:\"i32\",length:ge.length}];Ht(t,Ce);let be=Ne(e[0].dataType,1);if(be!==\"f16\"&&be!==\"f32\")throw new Error(`elemType ${be} is not supported.`);return`\n        ${Pn(\"uniforms.result_strides\")}\n        ${Ue.registerUniforms(Ce).declareVariables(...Ve,ee)};\n        ${xe}\n        ${My(l,p,t,We.type.value,U)}\n        ${S?xi(P,R,be,void 0,!l,q):Si(P,R,be,void 0,!l,q,!1,void 0,h)}`};return{name:\"Conv2DTransposeMatMul\",shaderCache:{hint:`${t.cacheKey};${P};${R};${S}`,inputDependencies:le},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:W[0],y:W[1],z:W[2]},programUniforms:H}),getShaderSource:Ae}}});var jy,Pa,tc=ne(()=>{\"use strict\";$e();tr();Pe();Te();jy=(e,t,r,u,o,c=!1,p,h,l=!1)=>{let v=l?1:2,$=l?2:3,_=l?3:1,x=c?2:1,A=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${c?`vec4<${p}>`:p}) {\n    result[flatIndex] = ${c?`vec4<${p}>`:p}(value);\n  }`;u&&(A+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${c?`vec4<${p}>`:p} {\n      return bias[coords.${l?\"w\":\"y\"}${c?\"/ 4\":\"\"}];\n    }`);let S=c?4:1,k=X(\"W\",t[1].dataType,t[1].dims.length,S),M=X(\"Dy\",t[0].dataType,t[0].dims.length,S),R=[M,k];u&&R.push(X(\"bias\",t[2].dataType,[r[_]].length,S));let P=ae(\"result\",t[0].dataType,r.length,S),W=`{\n        let batch: u32 = ${o?\"global_id.z\":\"workgroup_id.z\"} / uniforms.result_shape[1];\n        let r = ${o?\"global_id.z\":\"workgroup_id.z\"} % uniforms.result_shape[1];\n        let c = ${o?\"global_id.y\":\"workgroup_id.y\"} * ${x};\n        let d1: u32 = ${o?\"global_id.x\":\"workgroup_id.x\"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${p}>, ${x}>;\n        for (var i = 0; i < ${x}; i++) {\n          dotProd[i] = vec4<${p}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n          var dyR = (${p}(dyCorner.x) + ${p}(wR)) / ${p}(uniforms.strides.x);\n          let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${p}(uniforms.Dy_shape[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n            let dyC = (${p}(dyCorner.y) + ${p}(wC)) / ${p}(uniforms.strides.y);\n            let dyC2 = (${p}(dyCorner.y) + 1.0 + ${p}(wC)) / ${p}(uniforms.strides.y);\n            let wCPerm = uniforms.filter_dims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${p}(uniforms.Dy_shape[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${p}(uniforms.Dy_shape[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 = uniforms.Dy_shape[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1\",\"d2\")};\n                let wValue1 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 1\",\"d2\")};\n                let wValue2 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 2\",\"d2\")};\n                let wValue3 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 3\",\"d2\")};\n\n                var xValue = ${M.get(\"batch\",\"idyR\",\"idyC\",\"d2\")};\n                let tmpval = vec4<${p}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${M.get(\"batch\",\"idyR\",\"idyC2\",\"d2\")};\n\n                dotProd[1] = dotProd[1] + vec4<${p}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = uniforms.Dy_shape[${_}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1\",\"d2\")};\n                let wValue1 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 1\",\"d2\")};\n                let wValue2 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 2\",\"d2\")};\n                let wValue3 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 3\",\"d2\")};\n\n                var xValue = ${M.get(\"batch\",\"idyR\",\"idyC\",\"d2\")};\n                let tmpval = vec4<${p}>(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 = uniforms.Dy_shape[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1\",\"d2\")};\n                let wValue1 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 1\",\"d2\")};\n                let wValue2 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 2\",\"d2\")};\n                let wValue3 = ${k.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 3\",\"d2\")};\n\n                var xValue = ${M.get(\"batch\",\"idyR\",\"idyC2\",\"d2\")};\n                let tmpval = vec4<${p}>(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 < ${x}; i = i + 1) {\n          let value = dotProd[i] + ${u?\"bias[c+i]\":`vec4<${p}>(0.0)`};\n          ${P.set(\"batch\",\"r\",\"c + i\",\"d1\",\"value\")};\n        }\n      }`,U=`\n          let outputIndices = ${P.offsetToIndices(\"global_idx\")};\n          let batch = ${P.indicesGet(\"outputIndices\",0)};\n          let d1 = ${P.indicesGet(\"outputIndices\",_)};\n          let r = ${P.indicesGet(\"outputIndices\",v)};\n          let c = ${P.indicesGet(\"outputIndices\",$)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / uniforms.output_channels_per_group;\n          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\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 = ${p}(0.0);\n          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n            if (wR % uniforms.dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${p}(dyRCorner) + ${p}(wR)) / ${p}(uniforms.strides[0]);\n            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n            if (dyR < 0.0 || dyR >= ${p}(uniforms.Dy_shape[${v}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n              if (wC % uniforms.dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${p}(dyCCorner) + ${p}(wC)) / ${p}(uniforms.strides.y);\n              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n              if (dyC < 0.0 || dyC >= ${p}(uniforms.Dy_shape[${$}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * uniforms.input_channels_per_group;\n              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n                let xValue = ${l?M.get(\"batch\",\"idyR\",\"idyC\",\"inputChannel\"):M.get(\"batch\",\"inputChannel\",\"idyR\",\"idyC\")};\n                let wValue = ${k.get(\"inputChannel\",\"wOutChannel\",\"u32(wRPerm)\",\"u32(wCPerm)\")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${u?\"bias[d1]\":`${p}(0.0)`};\n          ${P.setByOffset(\"global_idx\",\"value\")};\n        `;return`\n  ${e.registerUniforms(h).declareVariables(...R,P)}\n  ${A}\n\n    ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")};\n  ${c?W:U}}`},Pa=(e,t,r)=>{let u=e.length>2,o=t.outputShape,c=Q.size(o),p=[Math.ceil(c/64),1,1];Fe(\"verbose\",()=>`[conv2d_backprop_webgpu] dispatch = ${p}`);let h=t.format===\"NHWC\",l=[\"rank\",\"rank\"],v=[t.strides[0],t.strides[1]],$=[t.kernelShape[h?1:2],t.kernelShape[h?2:3]],_=[t.dilations[0],t.dilations[1]],x=[$[0]+(t.dilations[0]<=1?0:(t.kernelShape[h?1:2]-1)*(t.dilations[0]-1)),$[1]+(t.dilations[1]<=1?0:(t.kernelShape[h?2:3]-1)*(t.dilations[1]-1))],A=[x[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),x[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],S=!1,k=t.group,M=e[1].dims,R=M[0]/k,P=M[1],W=[{type:6,data:c},{type:12,data:v},{type:12,data:$},{type:12,data:_},{type:12,data:x},{type:6,data:A},{type:12,data:R},{type:12,data:P},...se(e[0].dims,e[1].dims)];u&&(W.push(...se(e[2].dims)),l.push(\"rank\")),W.push(...se(o));let U=p[1]===1&&p[2]===1,q=Y=>{let J=[{name:\"output_size\",type:\"u32\"},{name:\"strides\",type:\"u32\",length:v.length},{name:\"filter_dims\",type:\"u32\",length:$.length},{name:\"dilations\",type:\"u32\",length:$.length},{name:\"effective_filter_dims\",type:\"u32\",length:x.length},{name:\"pads\",type:\"i32\",length:A.length},{name:\"input_channels_per_group\",type:\"u32\"},{name:\"output_channels_per_group\",type:\"u32\"}],oe=Ne(e[0].dataType);return`${jy(Y,e,o,u,U,S,oe,J,h)}`};return{name:\"ConvTranspose2D\",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:l},getRunData:()=>({dispatchGroup:{x:p[0],y:p[1],z:p[2]},outputs:[{dims:r?r(o):o,dataType:e[0].dataType}],programUniforms:W}),getShaderSource:q}}});var Uy,Wy,Ny,rc,ic,Vy,Gy,Ly,Hy,nc,ac=ne(()=>{\"use strict\";ec();tc();rr();Mr();Uy=(e,t,r,u,o,c)=>(e-1)*t+r+(u-1)*o+1-c,Wy=(e,t,r,u,o)=>{let c=Math.floor(e/2);t===\"SAME_UPPER\"?(r[u]=c,r[o]=e-c):t===\"SAME_LOWER\"&&(r[u]=e-c,r[o]=c)},Ny=(e,t,r,u,o,c,p,h,l,v)=>{let $=e.length-2,_=v.length===0;if(l.length===0)for(let S=0;S<$;++S)l.push(0);let x=e[0],A=t[h?3:1]*o;for(let S=0,k=e.length-$-(h?1:0);S<$;++S,++k){let M=e[k],R=_?M*p[S]:v[S],P=Uy(M,p[S],c[S],t[k],r[S],R);Wy(P,u,c,S,S+$),_&&v.push(p[S]*(M-1)+l[S]+(t[k]-1)*r[S]+1-c[S]-c[S+$])}v.splice(0,0,x),v.splice(h?3:1,0,A)},rc=(e,t)=>{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((_,x)=>_*x,1)===0){r.length=0;for(let _=2;_<t[1].dims.length;++_)r.push(t[1].dims[_])}let u=e.format===\"NHWC\";r.splice(0,0,t[1].dims[0]),r.splice(u?3:1,0,t[1].dims[1]);let o=e.pads.slice(),c=e.outputShape.slice(),p=e.outputPadding.slice(),h=t[0].dims,l=e.dilations.slice();if(l.reduce((_,x)=>_+x,0)===0){let _=t[0].dims.length-2;l=new Array(_).fill(1)}let v=e.strides.slice();if(v.reduce((_,x)=>_+x,0)===0){let _=t[0].dims.length-2;v=new Array(_).fill(1)}Ny(h,r,l,e.autoPad,e.group,o,v,u,p,c);let $=Object.assign({},e);return 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q=[{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  ${x.registerUniforms(q).declareVariables(...W)}\n\n  ${x.mainStart()}\n    ${x.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n\n    let m = global_idx / uniforms.N;\n    let n = global_idx % uniforms.N;\n\n    var value = ${R}(0);\n    for (var k: u32 = 0u; k < uniforms.K; k++) {\n      ${A}\n    }\n\n    ${S}\n    ${(()=>P!=null?`let cOffset = ${P.broadcastedIndicesToOffset(\"vec2(m, n)\",U)}; value += ${R}(uniforms.beta) * ${P.getByOffset(\"cOffset\")};`:\"\")()}\n    output[global_idx] = value;\n  }`};return{name:\"Gemm\",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:v}),getShaderSource:_}},Sc=e=>{let t=e.transA,r=e.transB,u=e.alpha,o=e.beta;return{transA:t,transB:r,alpha:u,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Ic=(e,t)=>{n0(e.inputs),e.compute(a0(e.inputs,t))}});var o0,s0,u0,Ac,Ec=ne(()=>{\"use strict\";$e();Pe();Te();o0=(e,t)=>{let r=e[0].dims,u=r,o=2,c=Q.sizeToDimension(r,o),p=Q.sizeFromDimension(r,o),h=et(p),l=p/h,v=[r[0],r[1],l],$=[\"rank\",\"type\",\"type\"],_=[{type:12,data:p},{type:12,data:l}];_.push(...se(v,v));let x=A=>{let S=X(\"x\",e[0].dataType,v.length,h),k=X(\"scale\",e[1].dataType,e[1].dims),M=X(\"bias\",e[2].dataType,e[2].dims),R=ae(\"output\",e[0].dataType,v.length,h),P=[S,k,M,R],W=S.type.value,U=h===1?\"f32\":`vec${h}<f32>`,q=64,Y=[{name:\"normSize\",type:\"u32\"},{name:\"normPackedSize\",type:\"u32\"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${U}, ${q}>;\n  const workgroupSize = ${q}u;\n  ${A.registerUniforms(Y).declareVariables(...P)}\n  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}`},P=e.compute({name:\"InstanceNormComputeMean\",shaderCache:{hint:`${l}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:[o,p,v,2],dataType:1}],dispatchGroup:{x:o*p/l},programUniforms:M}),getShaderSource:R},{inputs:[t],outputs:[-1]})[0],W=[{type:12,data:A},{type:12,data:c},{type:12,data:Math.floor(p/l)},{type:12,data:Math.floor(v*p/l)}],U=[\"type\",\"type\",\"type\"],q=Y=>{let J=X(\"scale\",r.dataType,r.dims,l),oe=X(\"bias\",u.dataType,u.dims,l);return`\n  @group(0) @binding(0) var<storage, read> input : array<${$}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${J.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${oe.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${$}>;\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  ${Y.mainStart()}\n    ${Y.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 = ${ut(\"f32\",l)};\n    var squaredSum = ${ut(\"f32\",l)};\n    for (var i: u32 = 0; i < ${v}; i++) {\n        let value = input[offset + i + currentChannelNumber * ${v}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${h}));\n    let channelScale = invStdDev * ${_}(scale[currentChannelNumber]);\n    let channelShift = ${_}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${x(\"channelScale\",\"channelShift\")};\n  }`};return e.compute({name:\"InstanceNormComputeChannelScaleShift\",shaderCache:{hint:`${l};${h}`,inputDependencies:U},getRunData:()=>({outputs:[{dims:[o,p,2],dataType:1}],dispatchGroup:{x:Math.ceil(A/64)},programUniforms:W}),getShaderSource:q},{inputs:[P,r,u],outputs:[-1]})[0]},u0=(e,t,r)=>{let u=t[0].dims,o=u,c=u[0],p=u[u.length-1],h=Q.sizeFromDimension(u,1)/p,l=et(p),v=Q.size(o)/l,$=[{type:12,data:h},{type:12,data:Math.floor(p/l)}],_=[\"type\",\"type\"],x=s0(e,t[0],t[1],t[2],c,h,p,r.epsilon),A=S=>{let k=Ne(t[0].dataType),M=l===1?\"vec2f\":`mat2x${l}f`,R=l===1?k:`vec${l}<${k}>`,P=X(\"input\",t[0].dataType,t[0].dims,l),W=ae(\"output\",t[0].dataType,o,l);return`\n  @group(0) @binding(0) var<storage, read> input : array<${P.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${M}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${W.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${S.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], ${R}(scale[0]), ${R}(scale[1]));\n  }`};e.compute({name:\"InstanceNormalizationNHWC\",shaderCache:{hint:`${l}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:$}),getShaderSource:A},{inputs:[t[0],x]})},Ac=(e,t)=>{t.format===\"NHWC\"?u0(e,e.inputs,t):e.compute(o0(e.inputs,t))}});var l0,d0,kc,Oc=ne(()=>{\"use strict\";$e();Pe();Te();l0=e=>{if(!e||e.length<2)throw new Error(\"layerNorm requires at least 2 inputs.\")},d0=(e,t,r)=>{let u=e[0].dims,o=e[1],c=e[2],p=u,h=Q.normalizeAxis(t.axis,u.length),l=Q.sizeToDimension(u,h),v=Q.sizeFromDimension(u,h),$=Q.size(o.dims),_=c?Q.size(c.dims):0;if($!==v||c&&_!==v)throw new Error(`Size of X.shape()[axis:] == ${v}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${$} and bias size of ${_}`);let x=[];for(let U=0;U<u.length;++U)U<h?x.push(u[U]):x.push(1);let A=et(v),S=[\"type\",\"type\"],k=[{type:12,data:l},{type:1,data:v},{type:12,data:Math.floor(v/A)},{type:1,data:t.epsilon}];c&&S.push(\"type\");let M=r>1,R=r>2,P=U=>{let q=Ne(e[0].dataType),Y=[X(\"x\",e[0].dataType,e[0].dims,A),X(\"scale\",o.dataType,o.dims,A)];c&&Y.push(X(\"bias\",c.dataType,c.dims,A)),Y.push(ae(\"output\",e[0].dataType,p,A)),M&&Y.push(ae(\"mean_data_output\",1,x)),R&&Y.push(ae(\"inv_std_output\",1,x));let J=[{name:\"norm_count\",type:\"u32\"},{name:\"norm_size\",type:\"f32\"},{name:\"norm_size_vectorized\",type:\"u32\"},{name:\"epsilon\",type:\"f32\"}];return`\n  ${U.registerUniforms(J).declareVariables(...Y)}\n  ${U.mainStart()}\n    ${U.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.norm_count\")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var mean_vector = ${ut(\"f32\",A)};\n    var mean_square_vector = ${ut(\"f32\",A)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${Rt(q,A,\"x[h + offset]\")};\n      mean_vector += value;\n      mean_square_vector += value * value;\n    }\n    let mean = ${yt(\"mean_vector\",A)} / uniforms.norm_size;\n    let inv_std_dev = inverseSqrt(${yt(\"mean_square_vector\",A)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${Rt(q,A,\"x[j + offset]\")};\n      let f32scale = ${Rt(q,A,\"scale[j]\")};\n      output[j + offset] = ${Y[0].type.value}((f32input - mean) * inv_std_dev * f32scale\n        ${c?`+ ${Rt(q,A,\"bias[j]\")}`:\"\"}\n      );\n    }\n\n    ${M?\"mean_data_output[global_idx] = mean\":\"\"};\n    ${R?\"inv_std_output[global_idx] = inv_std_dev\":\"\"};\n  }`},W=[{dims:p,dataType:e[0].dataType}];return M&&W.push({dims:x,dataType:1}),R&&W.push({dims:x,dataType:1}),{name:\"LayerNormalization\",shaderCache:{hint:`${A};${r}`,inputDependencies:S},getRunData:()=>({outputs:W,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:k}),getShaderSource:P}},kc=(e,t)=>{l0(e.inputs),e.compute(d0(e.inputs,t,e.outputCount))}});var c0,p0,Pc,Rc,Bc=ne(()=>{\"use strict\";$e();Pe();nt();Te();c0=(e,t)=>{if(e.length<3||e.length>4)throw new Error(\"MatMulNBits requires 3 or 4 inputs\");let r=e[0],u=r.dims.length;if(r.dims[u-1]!==t.k)throw new Error(\"The last dim of input shape does not match the k value\");let o=Math.floor((t.k+t.blockSize-1)/t.blockSize),c=t.blockSize/8*t.bits,p=e[1];if(!Q.areEqual(p.dims,[t.n,o,c]))throw new Error(\"The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize\");let l=e[2].dims;if(Q.size(l)!==t.n*o)throw new Error(\"scales input size error.\");if(e.length===4){let $=e[3].dims,_=t.bits>4?t.n*o:t.n*Math.floor((o+1)/2);if(Q.size($)!==_)throw new Error(\"zeroPoints input size error.\")}},p0=(e,t)=>{let r=e[0],u=e[1],o=e[2],c=r.dims.length,p=r.dims.slice(0,c-1).concat(t.n),h=Q.size(p),l=[{type:12,data:h},{type:12,data:t.k},{type:12,data:t.n},{type:12,data:t.accuracyLevel},{type:12,data:t.bits},{type:12,data:t.blockSize}];l.push(...se(r.dims)),l.push(...se(Q.convertShape(u.dims))),l.push(...se(o.dims)),e.length===4&&l.push(...se(Q.convertShape(e[3].dims))),l.push(...se(p));let v=$=>{let _=X(\"a\",e[0].dataType,e[0].dims.length),x=X(\"b\",12,e[1].dims.length),A=X(\"scales\",e[2].dataType,e[2].dims.length),S=[_,x,A],k=e.length===4?X(\"zero_points\",12,e[3].dims.length):void 0;k&&S.push(k);let M=ae(\"output\",e[0].dataType,p.length),R=[{name:\"output_size\",type:\"u32\"},{name:\"k\",type:\"u32\"},{name:\"n\",type:\"u32\"},{name:\"accuracy_level\",type:\"u32\"},{name:\"bits\",type:\"u32\"},{name:\"block_size\",type:\"u32\"}],P=Math.floor((t.k+t.blockSize-1)/t.blockSize),U=t.blockSize/8*t.bits/4,q=Ne(e[0].dataType);return`\n        fn ortUnpack8x4snorm(value: u32) -> array<${q}, 8>{\n          var result = array<${q}, 8>();\n          var offset: u32 = 0;\n          let count: u32 = 4;\n          for (var i: u32 = 0; i < 8u; i++) {\n            result[i] = ${q}(extractBits(value, offset, count));\n            offset += count;\n          }\n          return result;\n        }\n        ${$.registerUniforms(R).declareVariables(...S,M)}\n        ${$.mainStart()}\n          ${$.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n          var value: ${q} = 0.0;\n          let output_indices = ${M.offsetToIndices(\"global_idx\")};\n          var a_indices: ${_.type.indices} = output_indices;\n          var n = ${M.indicesGet(\"output_indices\",c-1)};\n          // Two zero points are packed into one byte because uniforms.bits <= 4.\n          // zero_point_offset is either 0 or 4. It is bit offset within one byte.\n          // TODO support zero_point_offset for bits > 4\n          ${k?`\n            var zero_point_index: u32 = n * ((${P} + 1) / 2) / 4;\n            var zero_point_word: u32 = ${k.getByOffset(\"zero_point_index\")};\n            var zero_point_offset: u32 = 0;`:\"\"}\n          var scale_idex = n * ${P};\n          var b_indices: ${x.type.indices};\n          ${x.indicesSet(\"b_indices\",\"0\",\"n\")};\n          var block_offset: u32 = 0;\n          for (var block: u32 = 0; block < ${P}; block++) {\n            // The scale and zero points are computed per block.\n            let scale = ${A.getByOffset(\"scale_idex\")};\n            // The default zero point is 8 for unsigned 4-bit quantization.\n            let zero_point: ${q} = ${k?`${q}(extractBits(zero_point_word, zero_point_offset, 4))`:8};\n            ${x.indicesSet(\"b_indices\",\"1\",\"block\")};\n            var word_offset: u32 = block_offset;\n            for (var word: u32 = 0; word < ${U}; word++) {\n              ${x.indicesSet(\"b_indices\",\"2\",\"word\")};\n              let b_value = ${x.getByIndices(\"b_indices\")};\n              let b_quantized_values: array<${q}, 8> = ortUnpack8x4snorm(b_value);\n              // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n              var offset: u32 = word_offset;\n              for (var i: u32 = 0; i < 8; i++) {\n                ${_.indicesSet(\"a_indices\",c-1,\"offset\")};\n                let a_value = ${_.getByIndices(\"a_indices\")};\n                let b_quantized_value = b_quantized_values[i];\n                let b_dequantized_value = (b_quantized_value - zero_point) * scale;\n                value += a_value * b_dequantized_value;\n                offset++;\n              }\n              word_offset += 8;\n            }\n            scale_idex++;\n            ${k?`\n            if (zero_point_offset == 28) {\n              zero_point_offset = 0;\n              zero_point_index++;\n              zero_point_word = ${k.getByOffset(\"zero_point_index\")};\n            } else {\n              zero_point_offset += 4;\n            }`:\"\"}\n            block_offset += uniforms.block_size;\n          }\n          ${M.setByOffset(\"global_idx\",\"value\")};\n        }\n        `};return{name:\"MatMulNBits\",shaderCache:{hint:`${t.cacheKey};${e.length}`,inputDependencies:Array(e.length).fill(\"rank\")},getRunData:()=>({outputs:[{dims:p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:l}),getShaderSource:v}},Pc=(e,t)=>{c0(e.inputs,t),e.compute(p0(e.inputs,t))},Rc=e=>Oe(e)});var f0,Dc,zc,m0,Da,Mc,jc=ne(()=>{\"use strict\";$e();Pe();nt();gn();_a();Te();Mr();f0=(e,t)=>{let r=e[0],u=e[1],o=e[2],c=e[3],p=e[4],h=e[5],l=e[6],v=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 $=!1,_=r.dims[0],x=r.dims[1],A=r.dims.length===3?$?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],S=x,k=0,M=0,R=Math.floor(A/t.numHeads);if(l&&v){if(l.dims.length!==4)throw new Error('Input \"past_key\" is expected to have 4 dimensions');if(v.dims.length!==4)throw new Error('Input \"past_value\" is expected to have 4 dimensions');k=l.dims[2],M=l.dims[2]}else if(l||v)throw new Error('Input \"past_key\" and \"past_value\" shall be both present or both absent');let P;if(u){if(r.dims.length!==3)throw new Error('Input \"query\" is expected to have 3 dimensions when key is given');if(u.dims.length<3||u.dims.length>5)throw new Error('Input \"key\" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==u.dims[0])throw new Error('Input \"query\" and \"key\" shall have same dim 0 (batch size)');if(u.dims.length===3){if(u.dims[2]!==r.dims[2])throw new Error('Input \"query\" and \"key\" shall have same dim 2 (hidden_size)');P=2,S=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==t.numHeads||u.dims[3]!==2||u.dims[4]!==R)throw new Error('Expect \"key\" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect \"value\" be none when \"key\" has packed kv format.');P=5,S=u.dims[1]}else{if(u.dims[1]!==t.numHeads||u.dims[3]!==R)throw new Error('Expect \"key\" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');P=0,S=u.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');P=3}if(c){if(c.dims.length!==1)throw new Error('Input \"bias\" is expected to have 1 dimension');if(o&&r.dims.length===5&&r.dims[3]===2)throw new Error(\"bias is not allowed for packed kv.\")}let W=0;if(p){W=8;let oe=p.dims;throw oe.length===1?oe[0]===_?W=1:oe[0]===3*_+2&&(W=3):oe.length===2&&oe[0]===_&&oe[1]===S&&(W=5),W===8?new Error('Input \"key_padding_mask\" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error(\"Mask not supported\")}let U=!1,q=A;if(o){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input \"value\" is expected to have 3 or 4 dimensions');if(r.dims[0]!==o.dims[0])throw new Error('Input \"query\" and \"value\" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(S!==o.dims[1])throw new Error('Input \"key\" and \"value\" shall have the same dim 1 (kv_sequence_length)');q=o.dims[2]}else{if(S!==o.dims[2])throw new Error('Input \"past_key\" and \"past_value\" shall have the same dim 2 (kv_sequence_length)');q=o.dims[1]*o.dims[3],U=!0}}let Y=k+S,J=!1;if(p)throw new Error(\"Key padding mask is not supported\");if(h)throw new Error(\"extraAddQk is not supported\");if(l)throw new Error(\"pastKey is not supported\");if(v)throw new Error(\"pastValue is not supported\");return{batchSize:_,sequenceLength:x,pastSequenceLength:k,kvSequenceLength:S,totalSequenceLength:Y,maxSequenceLength:M,inputHiddenSize:0,hiddenSize:A,vHiddenSize:q,headSize:R,vHeadSize:Math.floor(q/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:W,scale:t.scale,broadcastResPosBias:J,passPastInKv:U,qkvFormat:P}},Dc=e=>Oe({...e}),zc=Oe({perm:[0,2,1,3]}),m0=(e,t,r,u,o,c,p)=>{let h=[u,o,c],l=Q.size(h),v=[{type:12,data:l},{type:12,data:p},{type:12,data:c}],$=_=>{let x=ae(\"qkv_with_bias\",t.dataType,h),A=X(\"qkv\",t.dataType,h),S=X(\"bias\",r.dataType,h),k=[{name:\"output_size\",type:\"u32\"},{name:\"bias_offset\",type:\"u32\"},{name:\"hidden_size\",type:\"u32\"}];return`\n  ${_.registerUniforms(k).declareVariables(A,S,x)}\n  ${_.mainStart()}\n    ${_.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`};return e.compute({name:\"MultiHeadAttentionAddBias\",shaderCache:{inputDependencies:[\"type\",\"type\"]},getRunData:()=>({outputs:[{dims:h,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:v}),getShaderSource:$},{inputs:[t,r],outputs:[-1]})[0]},Da=(e,t,r,u,o,c,p,h)=>{let l=c;if(p){if(u===1)throw new Error(\"AddBiasReshape is not implemented. Please export your model with packed QKV or KV\");return l=m0(e,c,p,t,u,r*o,h),l=l.reshape([t,u,r,o]),e.compute(Bt(l,zc.perm),{inputs:[l],outputs:[-1]})[0]}else return c.dims.length===3&&(l=c.reshape([t,u,r,o])),e.compute(Bt(l,zc.perm),{inputs:[l],outputs:[-1]})[0]},Mc=(e,t)=>{let r=f0(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 u=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,o=Da(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],e.inputs[3],0);if(u)return Sn(e,o,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t);let c=Da(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,e.inputs[1],e.inputs[3],r.hiddenSize),p=Da(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,e.inputs[2],e.inputs[3],2*r.hiddenSize);Sn(e,o,c,p,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],r,t)}});var h0,g0,y0,v0,b0,w0,_0,$0,Uc,Wc=ne(()=>{\"use strict\";$e();Pe();Te();h0=e=>{if(!e||e.length<1)throw new Error(\"Too few inputs\");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error(\"Input type must be float or float16.\");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].\")}},g0=(e,t,r)=>{let u=\"\";for(let o=t-1;o>=0;--o)u+=`\n            k = i32(${e.indicesGet(\"indices\",o)}) - ${we(\"uniforms.pads\",o,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${we(\"uniforms.x_shape\",o,t)})) {\n              break;\n            }\n            offset += k * i32(${we(\"uniforms.x_strides\",o,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            ${u}\n            value = x[offset];\n          }\n      `},y0=(e,t,r)=>{let u=\"\";for(let o=t-1;o>=0;--o)u+=`\n                k = i32(${e.indicesGet(\"indices\",o)}) - ${we(\"uniforms.pads\",o,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${we(\"uniforms.x_shape\",o,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${we(\"uniforms.x_shape\",o,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${we(\"uniforms.x_strides\",o,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${u}\n              value = x[offset];\n          `},v0=(e,t,r)=>{let u=\"\";for(let o=t-1;o>=0;--o)u+=`\n                k = i32(${e.indicesGet(\"indices\",o)}) - ${we(\"uniforms.pads\",o,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${we(\"uniforms.x_shape\",o,t)})) {\n                  k = i32(${we(\"uniforms.x_shape\",o,t)}) - 1;\n                }\n                offset += k * i32(${we(\"uniforms.x_strides\",o,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${u}\n              value = x[offset];\n          `},b0=(e,t,r)=>{let u=\"\";for(let o=t-1;o>=0;--o)u+=`\n                k = i32(${e.indicesGet(\"indices\",o)}) - ${we(\"uniforms.pads\",o,r)};\n                if (k < 0)  {\n                  k += i32(${we(\"uniforms.x_shape\",o,t)}]);\n                }\n                if (k >= i32(${we(\"uniforms.x_shape\",o,t)})) {\n                  k -= i32(${we(\"uniforms.x_shape\",o,t)});\n                }\n                offset += k * i32(${we(\"uniforms.x_strides\",o,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${u}\n              value = x[offset];\n          `},w0=(e,t,r)=>{switch(r.mode){case 0:return g0(e,t,r.pads.length);case 1:return y0(e,t,r.pads.length);case 2:return v0(e,t,r.pads.length);case 3:return b0(e,t,r.pads.length);default:throw new Error(\"Invalid mode\")}},_0=(e,t)=>{let r=Q.padShape(e[0].dims.slice(),t.pads),u=e[0].dims,o=Q.size(r),c=[{type:12,data:o},{type:12,data:t.pads}];t.mode===0&&c.push({type:e[0].dataType,data:t.value}),c.push(...se(e[0].dims,r));let p=[\"rank\"],h=l=>{let v=ae(\"output\",e[0].dataType,r.length),$=X(\"x\",e[0].dataType,u.length),_=$.type.value,x=w0(v,u.length,t),A=[{name:\"output_size\",type:\"u32\"},{name:\"pads\",type:\"i32\",length:t.pads.length}];return t.mode===0&&A.push({name:\"constant_value\",type:_}),`\n            ${l.registerUniforms(A).declareVariables($,v)}\n            ${l.mainStart()}\n            ${l.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n\n            let indices = ${v.offsetToIndices(\"global_idx\")};\n\n            var value = ${_}(0);\n            ${x}\n            output[global_idx] = value;\n        }`};return{name:\"Pad\",shaderCache:{hint:`${t.mode}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Q.size(r)/64)},programUniforms:c}),getShaderSource:h}},$0=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),u=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,o=e[0].dims.length,c=new Int32Array(2*o).fill(0);if(e.length>=4){let h=e[3].getBigInt64Array();for(let l=0;l<h.length;l++)c[Number(h[l])]=Number(r[l]),c[Number(h[l])+o]=Number(r[l+h.length])}else r.forEach((h,l)=>c[Number(l)]=Number(h));let p=[];return c.forEach(h=>p.push(h)),{mode:t.mode,value:u,pads:p}}else return t},Uc=(e,t)=>{h0(e.inputs);let r=$0(e.inputs,t);e.compute(_0(e.inputs,r),{inputs:[0]})}});var zn,Nc,Vc,Gc,Lc,C0,x0,Hc,Fc,qc,Kc,Yc,Qc,Zc,Xc,Jc,ep,tp,rp,ip=ne(()=>{\"use strict\";It();$e();Pe();Te();zn=e=>{if(Me.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error(\"Pool ops requires 1 input.\")},Nc=(e,t,r)=>{let u=t.format===\"NHWC\",o=e.dims.slice();u&&o.splice(1,0,o.pop());let c=Object.hasOwnProperty.call(t,\"dilations\"),p=t.kernelShape.slice(),h=t.strides.slice(),l=c?t.dilations.slice():[],v=t.pads.slice();hr.adjustPoolAttributes(r,o,p,h,l,v);let $=hr.computePoolOutputShape(r,o,h,l,p,v,t.autoPad),_=Object.assign({},t);c?Object.assign(_,{kernelShape:p,strides:h,pads:v,dilations:l,cacheKey:t.cacheKey}):Object.assign(_,{kernelShape:p,strides:h,pads:v,cacheKey:t.cacheKey});let x=$.slice();return x.push(x.splice(1,1)[0]),[_,u?x:$]},Vc=(e,t)=>{let r=t.format===\"NHWC\",u=Q.size(e),o=Q.size(t.kernelShape),c=[{type:12,data:u},{type:12,data:o}],p=[{name:\"outputSize\",type:\"u32\"},{name:\"kernelSize\",type:\"u32\"}];if(t.kernelShape.length<=2){let h=t.kernelShape[t.kernelShape.length-1],l=t.strides[t.strides.length-1],v=t.pads[t.pads.length/2-1],$=t.pads[t.pads.length-1],_=!!(v+$);c.push({type:12,data:h},{type:12,data:l},{type:12,data:v},{type:12,data:$}),p.push({name:\"kw\",type:\"u32\"},{name:\"sw\",type:\"u32\"},{name:\"pwStart\",type:\"u32\"},{name:\"pwEnd\",type:\"u32\"});let x=!1;if(t.kernelShape.length===2){let A=t.kernelShape[t.kernelShape.length-2],S=t.strides[t.strides.length-2],k=t.pads[t.pads.length/2-2],M=t.pads[t.pads.length-2];x=!!(k+M),c.push({type:12,data:A},{type:12,data:S},{type:12,data:k},{type:12,data:M}),p.push({name:\"kh\",type:\"u32\"},{name:\"sh\",type:\"u32\"},{name:\"phStart\",type:\"u32\"},{name:\"phEnd\",type:\"u32\"})}return[c,p,!0,_,x]}else{if(r)throw new Error(\"Pooling with kernelShape.length > 2 is not supported for NHWC format.\");let h=Q.computeStrides(t.kernelShape);c.push({type:12,data:h},{type:12,data:t.pads},{type:12,data:t.strides}),p.push({name:\"kernelStrides\",type:\"u32\",length:h.length},{name:\"pads\",type:\"u32\",length:t.pads.length},{name:\"strides\",type:\"u32\",length:t.strides.length});let l=t.pads.reduce((v,$)=>v+$);return[c,p,!!l,!1,!1]}},Gc=(e,t,r,u,o,c,p,h,l,v,$,_)=>{let x=o.format===\"NHWC\",A=t.type.value,S=ae(\"output\",t.type.tensor,u);if(o.kernelShape.length<=2){let k=\"\",M=\"\",R=\"\",P=r-(x?2:1);if($?k=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${P}] < 0 || xIndices[${P}]\n                      >= uniforms.x_shape[${P}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset(\"xIndices\")}];\n                  ${c}\n                }`:k=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${P}] = indices[${P}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset(\"xIndices\")}];\n                  ${c}\n                }`,o.kernelShape.length===2){let U=r-(x?3:2);_?M=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${U}] = indices[${U}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${U}] < 0 || xIndices[${U}] >= uniforms.x_shape[${U}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:M=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${U}] = indices[${U}] * uniforms.sh - uniforms.phStart + j;\n                `,R=`\n              }\n            `}return`\n            ${e.registerUniforms(l).declareVariables(t,S)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\n\n              let indices = ${S.offsetToIndices(\"global_idx\")};\n              var xIndices = ${S.offsetToIndices(\"global_idx\")};\n\n              var value = ${A}(${h});\n              var pad = 0;\n              ${M}\n              ${k}\n              ${R}\n              ${p}\n\n              output[global_idx] = value;\n            }`}else{if(x)throw new Error(\"Pooling with kernelShape.length > 2 is not supported for NHWC format.\");let k=o.kernelShape.length,M=o.pads.length,R=\"\";return v?R=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset(\"xIndices\")}];\n                ${c}\n              }`:R=`\n              }\n              let x_val = x[${t.indicesToOffset(\"xIndices\")}];\n              ${c}\n            `,`\n            ${e.registerUniforms(l).declareVariables(t,S)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\n              let indices = ${S.offsetToIndices(\"global_idx\")};\n              var xIndices = ${S.offsetToIndices(\"global_idx\")};\n\n              var offsets: array<u32, ${k}>;\n\n              var value = ${A}(${h});\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 < ${k-1}u; j++) {\n                  offsets[j] = offset / ${we(\"uniforms.kernelStrides\",\"j\",k)};\n                  offset -= offsets[j] * ${we(\"uniforms.kernelStrides\",\"j\",k)};\n                }\n                offsets[${k-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-k}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${we(\"uniforms.strides\",`j - ${r-k}u`,k)}\n                    + offsets[j - ${r-k}u] - ${we(\"uniforms.pads\",\"j - 2u\",M)};\n                  ${R}\n              }\n              ${p}\n\n              output[global_idx] = value;\n            }`}},Lc=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,C0=e=>`${Lc(e)};${e.countIncludePad}`,x0=e=>`${Lc(e)};${e.storageOrder};${e.dilations}`,Hc=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}),Fc=(e,t,r,u)=>{let[o,c]=Nc(t,u,r),p=X(\"x\",t.dataType,t.dims.length),h=p.type.value,l=\"value += x_val;\",v=\"\";o.countIncludePad?v+=`value /= ${h}(uniforms.kernelSize);`:v+=`value /= ${h}(i32(uniforms.kernelSize) - pad);`;let[$,_,x,A,S]=Vc(c,o);$.push(...se(t.dims,c));let k=[\"rank\"];return{name:e,shaderCache:{hint:`${u.cacheKey};${x};${A};${S}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Q.size(c)/64)},programUniforms:$}),getShaderSource:M=>Gc(M,p,t.dims.length,c.length,o,l,v,0,_,x,A,S)}},qc=e=>{let t=e.count_include_pad!==0,r=Hc(e);if(r.ceilMode!==0)throw new Error(\"using ceil() in shape computation is not yet supported for AveragePool\");let u={countIncludePad:t,...r,cacheKey:\"\"};return{...u,cacheKey:C0(u)}},Kc=(e,t)=>{zn(e.inputs),e.compute(Fc(\"AveragePool\",e.inputs[0],!1,t))},Yc={autoPad:\"\",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Qc=e=>{let t=e.format;return{format:t,...Yc,cacheKey:t}},Zc=(e,t)=>{zn(e.inputs),e.compute(Fc(\"GlobalAveragePool\",e.inputs[0],!0,t))},Xc=(e,t,r,u)=>{let[o,c]=Nc(t,u,r),p=`\n      value = max(x_val, value);\n    `,h=\"\",l=X(\"x\",t.dataType,t.dims.length),v=[\"rank\"],[$,_,x,A,S]=Vc(c,o);return $.push(...se(t.dims,c)),{name:e,shaderCache:{hint:`${u.cacheKey};${x};${A};${S}`,inputDependencies:v},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Q.size(c)/64)},programUniforms:$}),getShaderSource:k=>Gc(k,l,t.dims.length,c.length,o,p,h,-1e5,_,x,A,S)}},Jc=(e,t)=>{zn(e.inputs),e.compute(Xc(\"MaxPool\",e.inputs[0],!1,t))},ep=e=>{let t=e.storage_order,r=e.dilations,u=Hc(e);if(t!==0)throw new Error(\"column major storage order is not yet supported for MaxPool\");if(u.ceilMode!==0)throw new Error(\"using ceil() in shape computation is not yet supported for MaxPool\");let o={storageOrder:t,dilations:r,...u,cacheKey:\"\"};return{...o,cacheKey:x0(o)}},tp=e=>{let t=e.format;return{format:t,...Yc,cacheKey:t}},rp=(e,t)=>{zn(e.inputs),e.compute(Xc(\"GlobalMaxPool\",e.inputs[0],!0,t))}});var I0,T0,np,ap=ne(()=>{\"use strict\";It();$e();Te();I0=(e,t,r)=>{let u=e===t,o=e<t&&r<0,c=e>t&&r>0;if(u||o||c)throw new Error(\"Range these inputs' contents are invalid.\")},T0=(e,t,r,u)=>{let o=Math.abs(Math.ceil((t-e)/r)),c=[o],p=o,h=[{type:12,data:p},{type:u,data:e},{type:u,data:r},...se(c)],l=v=>{let $=ae(\"output\",u,c.length),_=$.type.value,x=[{name:\"outputSize\",type:\"u32\"},{name:\"start\",type:_},{name:\"delta\",type:_}];return`\n        ${v.registerUniforms(x).declareVariables($)}\n        ${v.mainStart()}\n        ${v.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\n        output[global_idx] = uniforms.start + ${_}(global_idx) * uniforms.delta;\n      }`};return{name:\"Range\",shaderCache:{hint:`${u}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:c,dataType:u}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h})}},np=e=>{let t=0,r=0,u=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],u=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],u=e.inputs[2].getFloat32Array()[0]),Me.webgpu.validateInputContent&&I0(t,r,u),e.compute(T0(t,r,u,e.inputs[0].dataType),{inputs:[]})}});var A0,E0,k0,O0,P0,R0,B0,z0,D0,M0,j0,op,U0,W0,N0,V0,G0,sp,up,lp=ne(()=>{\"use strict\";$e();Pe();nt();Te();A0=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error(\"Resize requires scales input values to be positive\")})),e.length>0){if(t.mode===\"linear\"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode===\"cubic\"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error(\"Resize requires scales input size to be 2 or 4 for cubic mode\")}},E0=(e,t,r)=>{t.every(o=>o>=0&&o<r||(()=>{throw new Error(\"Resize requires axes input values to be positive and less than rank\")}));let u=new Array(r).fill(1);return t.forEach((o,c)=>u[o]=e[c]),u},k0=(e,t,r,u,o,c)=>{let[p,h,l]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],v=e[0].dims.length;if(p>0&&e.length>p&&e[p].dims.length>0)e[p].getFloat32Array().forEach($=>c.push($));else if(t.coordinateTransformMode===\"tf_crop_and_resize\")throw new Error(\"Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize\");if(h>0&&e.length>h&&e[h].dims.length>0){if(e[h].getFloat32Array().forEach($=>u.push($)),u.length!==0&&u.length!==v&&r>=18&&u.length!==t.axes.length)throw new Error(\"Resize requires scales input size to be same as input rank or axes size for opset 18 and up\");A0(u,t),t.axes.length>0&&E0(u,t.axes,v).forEach(($,_)=>u[_]=$)}if(l>0&&e.length>l&&(e[l].getBigInt64Array().forEach($=>o.push(Number($))),o.length!==v||r>=18&&o.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(u.length!==t.axes.length)throw new Error('Resize requires \"scales\" input size to be of axes rank when axes attributes is specified');if(o.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 u<\"u\"&&typeof o<\"u\"&&u.length>0&&o.length>v)throw new Error(\"Resize requires only of scales or sizes to be specified\")},O0=(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`)}})()+\"}\",P0=(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`)}})()+\"}\",R0=(e,t,r)=>{let u=new Array(r).fill(0).concat(new Array(r).fill(1)),o=e.length===0?u:e.slice();return t.length>0?(t.forEach((c,p)=>{u[c]=o[p],u[p+r]=o[t.length+p]}),u):o},B0=(e,t,r,u)=>{let o=[];if(r.length>0)if(u.length>0){if(e.forEach(c=>o.push(c)),Math.max(...u)>e.length)throw new Error(\"axes is out of bound\");u.forEach((c,p)=>o[c]=r[p])}else r.forEach(c=>o.push(c));else{if(t.length===0)throw new Error(\"Resize requires either scales or sizes.\");o=e.map((c,p)=>Math.round(c*t[p]))}return o},z0=(e,t,r)=>{let u=(()=>{switch(r.keepAspectRatioPolicy){case\"not_larger\":return r.axes.length>0?Math.min(...r.axes.map(c=>t[c]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case\"not_smaller\":return r.axes.length>0?Math.max(...r.axes.map(c=>t[c]),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 o=e.slice();return r.axes.length>0?(r.axes.forEach(c=>t[c]=u),r.axes.forEach(c=>o[c]=Math.round(e[c]*t[c]))):(t.fill(u,0,t.length),o.forEach((c,p)=>o[p]=Math.round(c*t[p]))),o},D0=(e,t,r,u,o)=>`\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 = ${we(\"uniforms.scales\",\"i\",u)};\n        var roi_low = ${we(\"uniforms.roi\",\"i\",o)};\n        var roi_hi = ${we(\"uniforms.roi\",`i + ${t.length}`,o)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${we(\"uniforms.input_shape\",\"i\",t.length)};\n          var output_shape_i = ${we(\"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    }`,M0=(e,t,r,u,o,c,p)=>`\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 < ${u.length}; i++) {\n        var output_index = ${t.indicesGet(\"output_indices\",\"i\")};\n        var input_index: u32;\n        var scale = ${we(\"uniforms.scales\",\"i\",o)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${we(\"uniforms.roi\",\"i\",c)};\n          var roi_hi = ${we(\"uniforms.roi\",`i + ${r.length}`,c)};\n          var input_shape_i = ${we(\"uniforms.input_shape\",\"i\",r.length)};\n          var output_shape_i = ${we(\"uniforms.output_shape\",\"i\",u.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${p} || (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    }`,j0=(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 >= ${we(\"uniforms.input_shape\",\"i\",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,op=(e,t,r,u)=>e.rank>u?`\n    ${e.indicesSet(\"input_indices\",t,\"channel\")};\n    ${e.indicesSet(\"input_indices\",r,\"batch\")};\n`:\"\",U0=(e,t,r,u,o)=>{let[p,h,l,v]=r.length===2?[-1,0,1,-1]:[0,2,3,1],$=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${$} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet(\"input_indices\",h,`max(0, min(row, ${r[h]} - 1))`)};\n      ${e.indicesSet(\"input_indices\",l,`max(0, min(col, ${r[l]} - 1))`)};\n      ${op(e,v,p,2)}\n      return ${e.getByIndices(\"input_indices\")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${$} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${$} = originalIndices[${h}];\n      var col:${$} = originalIndices[${l}];\n      ${u?`if (row < 0 || row > (${r[h]} - 1) || col < 0 || col > (${r[l]} - 1)) {\n        return ${o};\n      }`:\"\"};\n      row = max(0, min(row, ${r[h]} - 1));\n      col = max(0, min(col, ${r[l]} - 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 = ${r.length>2?`u32(originalIndices[${v}])`:\"0\"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${p}])`:\"0\"};\n      var x11: ${$} = getInputValue(batch, channel, row1, col1);\n      var x12: ${$} = getInputValue(batch, channel, row1, col2);\n      var x21: ${$} = getInputValue(batch, channel, row2, col1);\n      var x22: ${$} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${$} = abs(row - ${$}(row1));\n      var dx2: ${$} = abs(${$}(row2) - row);\n      var dy1: ${$} = abs(col - ${$}(col1));\n      var dy2: ${$} = abs(${$}(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    }`},W0=(e,t,r,u,o,c,p,h,l,v)=>{let $=r.length===2,_=!0,[x,A]=$?[0,1]:_?[2,3]:[1,2],S=e.type.value,k=M=>{let R=M===x?\"row\":\"col\";return`\n      fn ${R}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${S} {\n        var output_index = ${t.indicesGet(\"output_indices\",M)};\n        var originalIdx: ${S} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[M]},\n        ${u[M]}, ${r[M]}, ${c[M]}, ${c[M]} + ${r.length});\n        var fractOriginalIdx: ${S} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${h} && (originalIdx < 0 || originalIdx > (${r[M]} - 1))) {\n          return ${l};\n        }\n        var data: array<${S}, 4> = array<${S}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${R}: ${S} = originalIdx + ${S}(i);\n          if (${R} < 0 || ${R} >= ${r[M]}) {\n            ${(()=>v?`coefs[i + 1] = 0.0;\n                        continue;`:h?`return ${l};`:`${R} = max(0, min(${R}, ${r[M]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet(\"input_indices_copy\",M,`u32(${R})`)};\n          data[i + 1] = ${M===x?e.getByIndices(\"input_indices_copy\"):\"rowCubicInterpolation(input_indices_copy, output_indices)\"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${k(x)};\n    ${k(A)};\n  fn getCubicInterpolationCoefs(s: ${S}) -> array<${S}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${S}, 4> = array<${S}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${S} = 1.0 - absS;\n    var twoMinusAbsS: ${S} = 2.0 - absS;\n    var onePlusAbsS: ${S} = 1.0 + absS;\n    coeffs[0] = ((${p} * onePlusAbsS - 5 * ${p}) * onePlusAbsS + 8 * ${p}) * onePlusAbsS - 4 * ${p};\n    coeffs[1] = ((${p} + 2) * absS - (${p} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${p} + 2) * oneMinusAbsS - (${p} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${p} * twoMinusAbsS - 5 * ${p}) * twoMinusAbsS + 8 * ${p}) * twoMinusAbsS - 4 * ${p};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${S}, 4>, coefs: array<${S}, 4>) -> ${S} {\n    var coefsSum: ${S} = 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}) -> ${S} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},N0=(e,t,r,u,o)=>{let[p,h,l,v,$]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],_=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${_} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet(\"input_indices\",h,`max(0, min(depth, ${r[h]} - 1))`)};\n      ${e.indicesSet(\"input_indices\",l,`max(0, min(height, ${r[l]} - 1))`)};\n      ${e.indicesSet(\"input_indices\",v,`max(0, min(width, ${r[v]} - 1))`)};\n      ${op(e,$,p,3)}\n      return ${e.getByIndices(\"input_indices\")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${_} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${_} = originalIndices[${h}];\n      var height:${_} = originalIndices[${l}];\n      var width:${_} = originalIndices[${v}];\n      ${u?`if (depth < 0 || depth > (${r[h]} - 1) || height < 0 || height > (${r[l]} - 1) || width < 0 || (width > ${r[v]} - 1)) {\n      return ${o};\n        }`:\"\"};\n\n    depth = max(0, min(depth, ${r[h]} - 1));\n      height = max(0, min(height, ${r[l]} - 1));\n      width = max(0, min(width, ${r[v]} - 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 = ${r.length>3?`u32(originalIndices[${$}])`:\"0\"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${p}])`:\"0\"};\n\n      var x111: ${_} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${_} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${_} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${_} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${_} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${_} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${_} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${_} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${_} = abs(depth - ${_}(depth1));\n      var dx2: ${_} = abs(${_}(depth2) - depth);\n      var dy1: ${_} = abs(height - ${_}(height1));\n      var dy2: ${_} = abs(${_}(height2) - height);\n      var dz1: ${_} = abs(width - ${_}(width1));\n      var dz2: ${_} = abs(${_}(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 * 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${(()=>{if(p.length===2||p.length===4)return`${U0(_,$,p,S,k)}`;if(p.length===3||p.length===5)return`${N0(_,$,p,S,k)}`;throw Error(\"Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.\")})()};\n            `;case\"cubic\":return`\n            ${(()=>{if(p.length===2||p.length===4)return`${W0(_,$,p,l,v,h,t.cubicCoeffA,S,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      ${P.registerUniform(\"output_size\",\"u32\").registerUniform(\"scales\",\"f32\",v.length).registerUniform(\"roi\",\"f32\",h.length).declareVariables(_,$)}\n      ${P.mainStart()}\n        ${P.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\n        ${A?\"output[global_idx] = input[global_idx];\":`\n        let output_indices = ${$.offsetToIndices(\"global_idx\")};\n        var input_indices: 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U=[{name:\"output_size\",type:\"u32\"},{name:\"components\",type:\"u32\"},{name:\"hidden_size\",type:\"u32\"},{name:\"epsilon\",type:\"f32\"}],q=[X(\"x\",e[0].dataType,e[0].dims,k),X(\"skip\",e[1].dataType,e[1].dims,k),X(\"gamma\",e[2].dataType,e[2].dims,k)];$&&q.push(X(\"beta\",e[3].dataType,e[3].dims,k)),_&&q.push(X(\"bias\",e[4].dataType,e[4].dims,k)),q.push(ae(\"output\",e[0].dataType,p,k)),x&&q.push(ae(\"mean_output\",1,v)),A&&q.push(ae(\"inv_std_output\",1,v)),S&&q.push(ae(\"input_skip_bias_sum\",e[0].dataType,p,k));let Y=Ne(e[0].dataType);return`\n\n      ${W.registerUniforms(U).declareVariables(...q)}\n\n      ${W.mainStart()}\n        ${W.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size / uniforms.hidden_size\")}\n        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n        let offset = global_idx * hidden_size_vectorized;\n        var sum = ${ut(\"f32\",k)};\n        var squareSum = ${ut(\"f32\",k)};\n        for (var i: u32 = 0; 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}`},P=[{dims:p,dataType:e[0].dataType}];return r>1&&P.push({dims:v,dataType:1}),r>2&&P.push({dims:v,dataType:1}),r>3&&P.push({dims:o,dataType:e[0].dataType}),{name:\"SkipLayerNormalization\",shaderCache:{hint:`${k};${x};${A};${S}`,inputDependencies:e.map((W,U)=>\"type\")},getShaderSource:R,getRunData:()=>({outputs:P,dispatchGroup:{x:Math.ceil(h/l/64)},programUniforms:M})}},dp=(e,t)=>{L0(e.inputs);let u=[0];e.outputCount>1&&u.push(-3),e.outputCount>2&&u.push(-3),e.outputCount>3&&u.push(3),e.compute(H0(e.inputs,t,e.outputCount,!1),{outputs:u})}});var F0,Dn,q0,pp,K0,Y0,fp,mp,hp=ne(()=>{\"use strict\";$e();Pe();nt();Te();F0=(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 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 ${R.mainStart()}\n          ${R.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\n          let output_indices = ${_.offsetToIndices(\"global_idx\")};\n          let input_indices = calculateInputIndices(output_indices);\n          ${_.setByOffset(\"global_idx\",x.getByIndices(\"input_indices\"))}\n      }`;return{name:\"Slice\",shaderCache:{hint:`${l.length}_${p.length}_${c.length}`,inputDependencies:[\"rank\"]},getShaderSource:M,getRunData:()=>({outputs:[$],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:k})}},fp=(e,t)=>{F0(e.inputs,t);let r=q0(e.inputs,t);e.compute(Y0(e.inputs,r),{inputs:[0]})},mp=e=>{let t=e.starts,r=e.ends,u=e.axes;return Oe({starts:t,ends:r,axes:u})}});var Q0,Z0,gp,yp,vp=ne(()=>{\"use strict\";$e();Pe();nt();Te();Q0=e=>{if(!e||e.length!==1)throw new Error(\"Softmax op requires 1 input.\")},Z0=(e,t)=>{let r=e.dims,u=Q.size(r),o=64,c=t.axis;if(c<0&&(c=r.length+c),c<r.length-1)throw new Error(\"softmax only supports last axis for now.\");let 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}`;return{name:\"Softmax\",shaderCache:{hint:`${l}`,inputDependencies:[\"type\"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:h},programUniforms:[{type:12,data:v}]}),getShaderSource:k}},gp=(e,t)=>{Q0(e.inputs),e.compute(Z0(e.inputs[0],t))},yp=e=>Oe({axis:e.axis})});var X0,J0,ev,tv,rv,bp,wp,_p=ne(()=>{\"use strict\";$e();Pe();nt();Te();X0=e=>{if(!e||e.length<1)throw new Error(\"too few inputs\")},J0=(e,t)=>{let r=[],u=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>r.push(Number(o))),u=r.length),Oe({numOutputs:u,axis:t.axis,splitSizes:r})},ev=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n    if (index < ${we(\"uniforms.size_in_split_axis\",\"i\",e)}) {\n        return i;\n    }\n    }\n    return ${e}u;\n}`,tv=e=>{let t=e.length,r=[];for(let u=0;u<t;++u){let o=e[u].setByIndices(\"indices\",\"input[global_idx]\");t===1?r.push(o):u===0?r.push(`if (output_number == ${u}u) { ${o} 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 ${x}[${A}] = ${S}(${$(k,M,R)});\n          `};o===9?v=`\n            var data = vec4<u32>(0);\n            ${_(\"data\",0,\"u32\")}\n            ${_(\"data\",1,\"u32\")}\n            ${_(\"data\",2,\"u32\")}\n            ${_(\"data\",3,\"u32\")}\n            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:v=`\n            ${_(\"output_data[global_idx]\",0)}\n            ${_(\"output_data[global_idx]\",1)}\n            ${_(\"output_data[global_idx]\",2)}\n            ${_(\"output_data[global_idx]\",3)}\n          `}return`\n        ${e.registerUniform(\"vec_size\",\"u32\").declareVariables(l,p,h,c)}\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.vec_size\")}\n        ${v}\n      }`},sv=e=>{let t=e[1].dims,r=e[2].dims,u=e[0].dims,o=e[1].dataType,c=!(Q.areEqual(t,r)&&Q.areEqual(r,u)),p=t,h=Q.size(t);if(c){let v=Mt.calcShape(Mt.calcShape(t,r,!1),u,!1);if(!v)throw new Error(\"Can't perform where op on the 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Mn,Ep=ne(()=>{\"use strict\";It();tr();Te();Mn=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,u,o,c){Ct(t.programInfo.name);let p=this.backend.device,h=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let l=[];for(let $ of r)l.push({binding:l.length,resource:{buffer:$.buffer}});for(let $ of u)l.push({binding:l.length,resource:{buffer:$.buffer}});c&&l.push({binding:l.length,resource:c});let v=p.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:l,label:t.programInfo.name});if(this.backend.sessionStatus===\"capturing\"){let 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u=[],o={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:u};r.features.has(\"chromium-experimental-timestamp-query-inside-passes\")?u.push(\"chromium-experimental-timestamp-query-inside-passes\"):r.features.has(\"timestamp-query\")&&u.push(\"timestamp-query\"),r.features.has(\"shader-f16\")&&u.push(\"shader-f16\"),this.device=await r.requestDevice(o),this.gpuDataManager=pl(this),this.programManager=new Mn(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,sl(t.logLevel,!!t.debug),this.device.onuncapturederror=c=>{c.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${c.error.message}`)},Object.defineProperty(this.env.webgpu,\"device\",{value:this.device}),this.setQueryType()}dispose(){typeof this.querySet<\"u\"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),r={};this.queryType===\"at-passes\"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Ct(),this.endComputePass();let t;this.queryType!==\"none\"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!==\"none\"&&t.mapAsync(GPUMapMode.READ).then(()=>{let r=new BigUint64Array(t.getMappedRange()),u=this.pendingQueries.get(t);for(let o=0;o<r.length/2;o++){let c=u[o],p=c.kernelId,h=this.kernels.get(p),l=h.kernelType,v=h.kernelName,$=c.programName,_=c.inputTensorViews,x=c.outputTensorViews,A=r[o*2],S=r[o*2+1];typeof this.queryTimeBase>\"u\"&&(this.queryTimeBase=A);let k=Number(A-this.queryTimeBase),M=Number(S-this.queryTimeBase);if(!Number.isSafeInteger(k)||!Number.isSafeInteger(M))throw new RangeError(\"incorrect timestamp range\");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:_.map(R=>({dims:R.dims,dataType:er(R.dataType)})),outputsMetadata:x.map(R=>({dims:R.dims,dataType:er(R.dataType)})),kernelId:p,kernelType:l,kernelName:v,programName:$,startTime:k,endTime:M});else{let R=\"\";_.forEach((W,U)=>{R+=`input[${U}]: [${W.dims}] | ${er(W.dataType)}, `});let P=\"\";x.forEach((W,U)=>{P+=`output[${U}]: [${W.dims}] | ${er(W.dataType)}, `}),console.log(`[profiling] kernel \"${p}|${l}|${v}|${$}\" ${R}${P}execution time: ${M-k} 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J=this.kernelPersistentData.get(this.currentKernelId);J||(J=[],this.kernelPersistentData.set(this.currentKernelId,J)),J.push(Y)}x.push(Y)}if(p.length!==r.length||x.length!==_.length){if(x.length===0)return ht(t.name),_;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. 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Y=this.gpuDataManager.create(P,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Y.buffer,0,q,0,P),this.gpuDataManager.release(Y.id),A={offset:0,size:P,buffer:Y.buffer}}let S=this.programManager.normalizeDispatchGroupSize(l),k=S[1]===1&&S[2]===1,M=lv(t,r,k),R=this.programManager.getArtifact(M);if(R||(R=this.programManager.build(t,S),this.programManager.setArtifact(M,R),Fe(\"info\",()=>`[artifact] key: ${M}, programName: ${t.name}`)),Fe(\"info\",()=>`[ProgramManager] run \"${t.name}\" (key=${M}) with ${S[0]}x${S[1]}x${S[2]}`),this.queryType!==\"none\"||this.sessionStatus===\"capturing\"){let P={kernelId:this.currentKernelId,programName:R.programInfo.name,inputTensorViews:r,outputTensorViews:_};this.pendingKernels.push(P),this.sessionStatus===\"capturing\"&&this.capturedPendingKernels.get(this.currentSessionId).push(P)}return this.programManager.run(R,p,x,S,A),ht(t.name),_}upload(t,r){this.gpuDataManager.upload(t,r)}memcpy(t,r){this.gpuDataManager.memcpy(t,r)}async download(t,r){await this.gpuDataManager.download(t,r)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,r,u,o){let c=Tp.get(t);if(!c)throw new Error(`kernel not implemented: ${t}`);let p={kernelType:t,kernelName:o,kernelEntry:c[0],attributes:[c[1],u]};this.kernels.set(r,p)}releaseKernel(t){let r=this.kernelPersistentData.get(t);if(r){for(let u of r)this.gpuDataManager.release(u.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,r,u){let o=this.kernels.get(t);if(!o)throw new Error(`kernel not created: ${t}`);let c=o.kernelType,p=o.kernelName,h=o.kernelEntry,l=o.attributes;if(this.currentKernelId!==null)throw new Error(`kernel \"[${c}] ${p}\" is not allowed to be called recursively`);this.currentKernelId=t,l[0]&&(l[1]=l[0](l[1]),l[0]=void 0),Fe(\"info\",()=>`[WebGPU] Start to run kernel \"[${c}] ${p}\"...`);let v=this.env.debug;this.temporaryData=[];try{return v&&this.device.pushErrorScope(\"validation\"),h(r,l[1]),0}catch($){return u.push(Promise.resolve(`[WebGPU] Kernel \"[${c}] ${p}\" failed. ${$}`)),1}finally{v&&u.push(this.device.popErrorScope().then($=>$?`GPU validation error for kernel \"[${c}] ${p}\": ${$.message}`:null));for(let $ of this.temporaryData)this.gpuDataManager.release($.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,r,u,o){let c=this.sessionExternalDataMapping.get(t);c||(c=new Map,this.sessionExternalDataMapping.set(t,c));let p=c.get(r),h=this.gpuDataManager.registerExternalBuffer(u,o,p?.[1]);return c.set(r,[h,u]),h}unregisterBuffers(t){let r=this.sessionExternalDataMapping.get(t);r&&(r.forEach(u=>this.gpuDataManager.unregisterExternalBuffer(u[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let r=this.gpuDataManager.get(t);if(!r)throw new Error(`no GPU data for buffer: ${t}`);return r.buffer}createDownloader(t,r,u){return async()=>{let o=await fa(this,t,r);return ul(o.buffer,u)}}writeTimestamp(t){this.queryType===\"inside-passes\"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){this.queryType=\"none\",(this.env.webgpu.profiling?.mode===\"default\"||(typeof this.env.trace>\"u\"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has(\"chromium-experimental-timestamp-query-inside-passes\")?this.queryType=\"inside-passes\":this.device.features.has(\"timestamp-query\")&&(this.queryType=\"at-passes\"),this.queryType!==\"none\"&&typeof this.querySet>\"u\"&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Fe(\"info\",\"captureBegin\"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus=\"capturing\"}captureEnd(){Fe(\"info\",\"captureEnd\"),this.flush(),this.sessionStatus=\"default\"}replay(){Fe(\"info\",\"replay\"),this.sessionStatus=\"replaying\";let t=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),u=t.length;this.pendingKernels=[];for(let o=0;o<u;o++){let c=this.getComputePassEncoder(),p=t[o];this.writeTimestamp(this.pendingDispatchNumber*2),c.setPipeline(p.computePipeline),c.setBindGroup(0,p.bindGroup),c.dispatchWorkgroups(...p.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!==\"none\"&&this.pendingKernels.push(r[o]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType===\"at-passes\")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus=\"default\"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}});var Op={};Br(Op,{init:()=>dv});var Ti,Ma,dv,Pp=ne(()=>{\"use strict\";$e();kp();tr();Pe();Ti=class e{constructor(t,r,u,o){this.module=t;this.dataType=r;this.data=u;this.dims=o}getFloat32Array(){if(this.dataType!==1)throw new Error(\"Invalid data type\");let t=Q.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error(\"Invalid data type\");let t=Q.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error(\"Invalid data type\");let t=Q.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(Q.size(t)!==Q.size(this.dims))throw new Error(\"Invalid new shape\");return new e(this.module,this.dataType,this.data,t)}},Ma=class{constructor(t,r,u){this.module=t;this.backend=r;this.customDataOffset=0;this.customDataSize=0;let o=t.HEAPU32,c=u>>>2;this.opKernelContext=o[c++];let p=o[c++];this.outputCount=o[c++],this.customDataOffset=o[c++],this.customDataSize=o[c++];let h=[];for(let l=0;l<p;l++){let v=o[c++],$=o[c++],_=o[c++],x=[];for(let A=0;A<_;A++)x.push(o[c++]);h.push(new Ti(t,v,$,x))}this.inputs=h}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(t,r){let u=r?.inputs?.map(h=>typeof h==\"number\"?this.inputs[h]:h)??this.inputs,o=r?.outputs??[],c=(h,l,v)=>new Ti(this.module,l,this.output(h,v),v),p=(h,l)=>{let v=_i(h);if(!v)throw new Error(`Unsupported data type: ${h}`);let $=v*Q.size(l),_=$>0?this.backend.gpuDataManager.create($).id:0;return new Ti(this.module,h,_,l)};return this.backend.run(t,u,o,c,p)}output(t,r){let u=this.module.stackSave();try{let o=this.module.stackAlloc((1+r.length)*4),c=o>>2;this.module.HEAPU32[c++]=r.length;for(let p=0;p<r.length;p++)this.module.HEAPU32[c++]=r[p];return this.module._JsepOutput(this.opKernelContext,t,o)}catch(o){throw new Error(`Failed to generate kernel's output[${t}] with dims [${r}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${o}`)}finally{this.module.stackRestore(u)}}},dv=async(e,t,r)=>{let u=e.jsepInit;if(!u)throw new Error(\"Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.\");let o=new jn;await o.initialize(t,r),u(o,c=>o.alloc(c),c=>o.free(c),(c,p,h,l=!1)=>{if(l)Fe(\"verbose\",()=>`[WebGPU] jsepCopyGpuToGpu: src=${c}, dst=${p}, size=${h}`),o.memcpy(c,p);else{Fe(\"verbose\",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${c}, gpuDataId=${p}, size=${h}`);let v=e.HEAPU8.subarray(c>>>0,(c>>>0)+h);o.upload(p,v)}},async(c,p,h)=>{Fe(\"verbose\",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${c}, dataOffset=${p}, size=${h}`),await o.download(c,()=>e.HEAPU8.subarray(p>>>0,(p>>>0)+h))},(c,p,h)=>o.createKernel(c,p,h,e.UTF8ToString(e._JsepGetNodeName(p))),c=>o.releaseKernel(c),(c,p,h,l)=>{Fe(\"verbose\",()=>`[WebGPU] jsepRun: sessionHandle=${h}, kernel=${c}, contextDataOffset=${p}`);let v=new Ma(e,o,p);return o.computeKernel(c,v,l)},()=>o.captureBegin(),()=>o.captureEnd(),()=>o.replay())}});var cv,Bp,zp,gr,pv,ja,Dp,Mp,Rp,jp,Up,Wp,Np=ne(()=>{\"use strict\";rl();nl();$e();zr();fn();ca();cv=(e,t)=>{Qe()._OrtInit(e,t)!==0&&He(\"Can't initialize onnxruntime.\")},Bp=async e=>{cv(e.wasm.numThreads,$i(e.logLevel))},zp=async(e,t)=>{if(t===\"webgpu\"||t===\"webnn\"){if(typeof navigator>\"u\"||!navigator.gpu)throw new Error(\"WebGPU is not supported in current environment\");let r=await navigator.gpu.requestAdapter();if(!r)throw new Error('Failed to get GPU adapter. You may need to enable flag \"--enable-unsafe-webgpu\" if you are using Chrome.');if(!e.wasm.simd)throw new Error(\"Not supported for WebGPU=ON and SIMD=OFF. Please set `env.wasm.simd` to true when using `webgpu` EP\");let u=(Pp(),Jt(Op)).init;await u(Qe(),e,r)}},gr=new Map,pv=e=>{let t=Qe(),r=t.stackSave();try{let u=t.stackAlloc(8);return t._OrtGetInputOutputCount(e,u,u+4)!==0&&He(\"Can't get session input/output count.\"),[t.HEAP32[u/4],t.HEAP32[u/4+1]]}finally{t.stackRestore(r)}},ja=e=>{let t=Qe(),r=t._malloc(e.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return t.HEAPU8.set(e,r),[r,e.byteLength]},Dp=async(e,t)=>{let r,u,o=Qe();Array.isArray(e)?[r,u]=e:e.buffer===o.HEAPU8.buffer?[r,u]=[e.byteOffset,e.byteLength]:[r,u]=ja(e);let c=0,p=0,h=0,l=[],v=[],$=[];try{if([p,l]=il(t),t?.externalData&&o.mountExternalData){let P=[];for(let W of t.externalData){let U=typeof W==\"string\"?W:W.path;P.push(Ci(typeof W==\"string\"?W:W.data).then(q=>{o.mountExternalData(U,q)}))}await Promise.all(P)}c=await o._OrtCreateSession(r,u,p),c===0&&He(\"Can't create a session.\");let[_,x]=pv(c),A=!!t?.enableGraphCapture,S=[],k=[],M=[];for(let P=0;P<_;P++){let W=o._OrtGetInputName(c,P);W===0&&He(\"Can't get an input name.\"),v.push(W),S.push(o.UTF8ToString(W))}for(let P=0;P<x;P++){let W=o._OrtGetOutputName(c,P);W===0&&He(\"Can't get an output name.\"),$.push(W);let U=o.UTF8ToString(W);k.push(U);{if(A&&t?.preferredOutputLocation===void 0){M.push(\"gpu-buffer\");continue}let q=typeof t?.preferredOutputLocation==\"string\"?t.preferredOutputLocation:t?.preferredOutputLocation?.[U]??\"cpu\";if(q!==\"cpu\"&&q!==\"cpu-pinned\"&&q!==\"gpu-buffer\")throw new Error(`Not supported preferred output location: ${q}.`);if(A&&q!==\"gpu-buffer\")throw new Error(`Not supported preferred output location: ${q}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);M.push(q)}}let R=null;return M.some(P=>P===\"gpu-buffer\")&&(h=o._OrtCreateBinding(c),h===0&&He(\"Can't create IO binding.\"),R={handle:h,outputPreferredLocations:M,outputPreferredLocationsEncoded:M.map(P=>da(P))}),gr.set(c,[c,v,$,R,A,!1]),[c,S,k]}catch(_){throw v.forEach(x=>o._OrtFree(x)),$.forEach(x=>o._OrtFree(x)),h!==0&&o._OrtReleaseBinding(h),c!==0&&o._OrtReleaseSession(c),_}finally{o._free(r),p!==0&&o._OrtReleaseSessionOptions(p),l.forEach(_=>o._free(_)),o.unmountExternalData?.()}},Mp=e=>{let t=Qe(),r=gr.get(e);if(!r)throw new Error(`cannot release session. invalid session id: ${e}`);let[u,o,c,p,h]=r;p&&(h&&t._OrtClearBoundOutputs(p.handle),t._OrtReleaseBinding(p.handle)),t.jsepOnReleaseSession?.(e),o.forEach(l=>t._OrtFree(l)),c.forEach(l=>t._OrtFree(l)),t._OrtReleaseSession(u),gr.delete(e)},Rp=(e,t,r,u,o,c=!1)=>{if(!e){t.push(0);return}let p=Qe(),h=e[0],l=e[1],v=e[3],$,_;if(h===\"string\"&&v===\"gpu-buffer\")throw new Error(\"String tensor is not supported on GPU.\");if(c&&v!==\"gpu-buffer\")throw new Error(`External buffer must be provided for input/output index ${o} when enableGraphCapture is true.`);if(v===\"gpu-buffer\"){let S=e[2].gpuBuffer,k=_i(la(h));_=l.reduce((M,R)=>M*R,1)*k,$=p.jsepRegisterBuffer(u,o,S,_)}else{let S=e[2];if(Array.isArray(S)){_=4*S.length,$=p._malloc(_),r.push($);let k=$/4;for(let M=0;M<S.length;M++){if(typeof S[M]!=\"string\")throw new TypeError(`tensor data at index ${M} is not a string`);p.HEAPU32[k++]=Ke(S[M],r)}}else _=S.byteLength,$=p._malloc(_),r.push($),p.HEAPU8.set(new Uint8Array(S.buffer,S.byteOffset,_),$)}let x=p.stackSave(),A=p.stackAlloc(4*l.length);try{let S=A/4;l.forEach(M=>p.HEAP32[S++]=M);let k=p._OrtCreateTensor(la(h),$,_,A,l.length,da(v));k===0&&He(`Can't create tensor for input/output. session=${u}, index=${o}.`),t.push(k)}finally{p.stackRestore(x)}},jp=async(e,t,r,u,o,c)=>{let p=Qe(),h=gr.get(e);if(!h)throw new Error(`cannot run inference. invalid session id: ${e}`);let l=h[0],v=h[1],$=h[2],_=h[3],x=h[4],A=h[5],S=t.length,k=u.length,M=0,R=[],P=[],W=[],U=[],q=p.stackSave(),Y=p.stackAlloc(S*4),J=p.stackAlloc(S*4),oe=p.stackAlloc(k*4),ge=p.stackAlloc(k*4);try{[M,R]=tl(c);for(let ee=0;ee<S;ee++)Rp(r[ee],P,U,e,t[ee],x);for(let ee=0;ee<k;ee++)Rp(o[ee],W,U,e,S+u[ee],x);let H=Y/4,le=J/4,Ae=oe/4,Ue=ge/4;for(let ee=0;ee<S;ee++)p.HEAPU32[H++]=P[ee],p.HEAPU32[le++]=v[t[ee]];for(let ee=0;ee<k;ee++)p.HEAPU32[Ae++]=W[ee],p.HEAPU32[Ue++]=$[u[ee]];if(_&&!A){let{handle:ee,outputPreferredLocations:Ve,outputPreferredLocationsEncoded:xe}=_;if(v.length!==S)throw new Error(`input count from feeds (${S}) is expected to be always equal to model's input count (${v.length}).`);for(let Ce=0;Ce<S;Ce++){let be=t[Ce];await p._OrtBindInput(ee,v[be],P[Ce])!==0&&He(`Can't bind input[${Ce}] for session=${e}.`)}for(let Ce=0;Ce<k;Ce++){let be=u[Ce];o[Ce]?.[3]?p._OrtBindOutput(ee,$[be],W[Ce],0)!==0&&He(`Can't bind pre-allocated output[${Ce}] for session=${e}.`):p._OrtBindOutput(ee,$[be],0,xe[be])!==0&&He(`Can't bind output[${Ce}] to ${Ve[Ce]} for session=${e}.`)}gr.set(e,[l,v,$,_,x,!0])}p.jsepOnRunStart?.(l);let We;_?We=await p._OrtRunWithBinding(l,_.handle,k,oe,M):We=await p._OrtRun(l,J,Y,S,ge,k,oe,M),We!==0&&He(\"failed to call OrtRun().\");let Ee=[];for(let ee=0;ee<k;ee++){let Ve=p.HEAPU32[oe/4+ee];if(Ve===W[ee]){Ee.push(o[ee]);continue}let xe=p.stackSave(),Ce=p.stackAlloc(4*4),be=!1,Se,De=0;try{p._OrtGetTensorData(Ve,Ce,Ce+4,Ce+8,Ce+12)!==0&&He(`Can't access output tensor data on index ${ee}.`);let re=Ce/4,pe=p.HEAPU32[re++];De=p.HEAPU32[re++];let _e=p.HEAPU32[re++],at=p.HEAPU32[re++],rt=[];for(let Xe=0;Xe<at;Xe++)rt.push(p.HEAPU32[_e/4+Xe]);p._OrtFree(_e);let vt=rt.reduce((Xe,Ze)=>Xe*Ze,1);Se=er(pe);let 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T=ie(\"x\",r.dataType,r.dims,s),O=\"thread_max_vector\";s===2?O=\"max(thread_max_vector.x, thread_max_vector.y)\":s===4&&(O=\"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))\");let A=ft(r.dataType),P=[{name:\"d_inv\",type:A},{name:\"d_comp\",type:\"u32\"},{name:\"elements_per_wg\",type:\"u32\"}];return`\\n  var<workgroup> wgMax: array<f32, ${c}>;\\n  var<workgroup> wgSum: array<f32, ${c}>;\\n  ${$.registerUniforms(P).declareVariables(T)}\\n  ${$.mainStart([c,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 = ${at(\"f32\",s,\"-3.402823e+38f\")};\\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\\n      thread_max_vector = max(${It(A,s,\"x[offset + i]\")}, thread_max_vector);\\n    }\\n    wgMax[local_idx] = ${O};\\n    workgroupBarrier();\\n\\n    var maxValue = -3.402823e+38f;\\n    for (var i = 0u; i < ${c}; i++) {\\n      maxValue = max(wgMax[i], maxValue);\\n    }\\n\\n    var sumVector = ${at(\"f32\",s,\"0\")};\\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\\n      sumVector += exp(${It(A,s,\"x[offset + i]\")} - maxValue);\\n    }\\n    wgSum[local_idx] = ${pt(\"sumVector\",s)};\\n    workgroupBarrier();\\n\\n    var sum: f32 = 0;\\n    for (var i = 0u; i < ${c}; 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] = ${at(A,s,\"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 = ${It(A,s,\"x[offset + i]\")};\\n        x[offset + i] = ${T.type.value}(exp(f32input - maxValue) / sum);\\n      }\\n    }\\n  }`};e.compute({name:\"AttentionProbsSoftmax\",shaderCache:{hint:`${c};${w};${s}`},getShaderSource:C,getRunData:()=>({outputs:[],dispatchGroup:{x:t},programUniforms:d})},{inputs:[r],outputs:[]})},ag=(e,r,t,u,s,c)=>{let m=[s.batchSize,s.numHeads,s.sequenceLength,s.kvSequenceLength+s.pastSequenceLength],y=c.scale===0?1/Math.sqrt(s.headSize):c.scale,d=Je(s.headSize),w=s.headSize/d,C=12,$={x:Math.ceil(s.totalSequenceLength/C),y:Math.ceil(s.sequenceLength/C),z:s.batchSize*s.numHeads},T=[{type:12,data:s.sequenceLength},{type:12,data:w},{type:12,data:s.totalSequenceLength},{type:12,data:s.kvSequenceLength},{type:r.dataType,data:y}],O=[r,t],A=z=>{let D=X(\"q\",r.dataType,r.dims,d),k=X(\"key\",t.dataType,t.dims,d),G=ie(\"output\",r.dataType,m),W=Ve(r.dataType),q=[{name:\"M\",type:\"u32\"},{name:\"K\",type:\"u32\"},{name:\"N\",type:\"u32\"},{name:\"kv_sequence_length\",type:\"u32\"},{name:\"alpha\",type:W}];return`\\n  const beta: ${W} = 1.0;\\n  const TILE_SIZE = ${C}u;\\n\\n  var<workgroup> tileQ: array<${D.type.storage}, ${C*C}>;\\n  var<workgroup> tileK: array<${D.type.storage}, ${C*C}>;\\n  ${z.registerUniforms(q).declareVariables(D,k,G)}\\n  ${z.mainStart([C,C,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 = ${at(W,d)};\\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] = ${pt(\"value\",d)} * uniforms.alpha;\\n    }\\n  }`},P=e.compute({name:\"AttentionProbs\",shaderCache:{hint:`${d}`,inputDependencies:[\"type\",\"type\"]},getRunData:()=>({outputs:[{dims:m,dataType:r.dataType,gpuDataType:0}],dispatchGroup:$,programUniforms:T}),getShaderSource:A},{inputs:O,outputs:[-1]})[0];return og(e,P,s.batchSize*s.numHeads*s.sequenceLength,s.totalSequenceLength),P},sg=(e,r,t,u)=>{let s=[u.batchSize,u.sequenceLength,u.vHiddenSize],c=12,m={x:Math.ceil(u.vHeadSize/c),y:Math.ceil(u.sequenceLength/c),z:u.batchSize*u.numHeads},y=[{type:12,data:u.sequenceLength},{type:12,data:u.totalSequenceLength},{type:12,data:u.vHeadSize},{type:12,data:u.numHeads},{type:12,data:u.vHiddenSize}],d=w=>{let C=X(\"probs\",r.dataType,r.dims),$=X(\"v\",t.dataType,t.dims),T=ie(\"output\",r.dataType,s),O=[{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 = ${c}u;\\n  var<workgroup> tileQ: array<${C.type.value}, ${c*c}>;\\n  var<workgroup> tileK: array<${C.type.value}, ${c*c}>;\\n  ${w.registerUniforms(O).declareVariables(C,$,T)}\\n  ${w.mainStart([c,c,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 = ${C.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:s,dataType:r.dataType,gpuDataType:0}],dispatchGroup:m,programUniforms:y}),getShaderSource:d},{inputs:[r,t],outputs:[0]})[0]},oi=(e,r,t,u,s,c,m,y,d,w,C)=>{let $=ag(e,r,t,d,w,C);sg(e,$,u,w)},ug=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],u=r.sequenceLength,s=r.inputHiddenSize,c=r.headSize,m=12,y={x:Math.ceil(r.headSize/m),y:Math.ceil(r.sequenceLength/m),z:r.batchSize*r.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],w=[{type:12,data:u},{type:12,data:s},{type:12,data:c},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],C=$=>{let T=ie(\"output_q\",d[0].dataType,t),O=ie(\"output_k\",d[0].dataType,t),A=ie(\"output_v\",d[0].dataType,t),P=X(\"input\",d[0].dataType,d[0].dims),z=X(\"weight\",d[1].dataType,d[1].dims),D=X(\"bias\",d[2].dataType,d[2].dims),k=P.type.storage,G=[{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 = ${m}u;\\n  var<workgroup> tileInput: array<${k}, ${m*m}>;\\n  var<workgroup> tileWeightQ: array<${k}, ${m*m}>;\\n  var<workgroup> tileWeightK: array<${k}, ${m*m}>;\\n  var<workgroup> tileWeightV: array<${k}, ${m*m}>;\\n  ${$.registerUniforms(G).declareVariables(P,z,D,T,O,A)}\\n  ${$.mainStart([m,m,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 = ${k}(0);\\n    var valueK = ${k}(0);\\n    var valueV = ${k}(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:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:y,programUniforms:w}),getShaderSource:C},{inputs:d,outputs:[-1,-1,-1]})},nl=(e,r)=>{let t=ig(e.inputs,r),[u,s,c]=ug(e,t);return oi(e,u,s,c,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t,r)}});var lg,dg,cg,il,ol=ae(()=>{\"use 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var\")},dg=(e,r)=>{let{epsilon:t,spatial:u,format:s}=r,c=e[0].dims,m=u?Je(c[c.length-1]):1,y=s===\"NHWC\"&&c.length>1?m:1,d=Z.size(c)/m,w=u,C=w?c.length:c,$=X(\"x\",e[0].dataType,e[0].dims,m),T=X(\"scale\",e[1].dataType,e[1].dims,y),O=X(\"bias\",e[2].dataType,e[2].dims,y),A=X(\"inputMean\",e[3].dataType,e[3].dims,y),P=X(\"inputVar\",e[4].dataType,e[4].dims,y),z=ie(\"y\",e[0].dataType,C,m),D=()=>{let G=\"\";if(u)G=`let cOffset = ${c.length===1?\"0u\":s===\"NHWC\"?`outputIndices[${c.length-1}] / ${m}`:\"outputIndices[1]\"};`;else if(s===\"NCHW\")G=`\\n            ${z.indicesSet(\"outputIndices\",\"0\",\"0\")}\\n            let cOffset = ${z.indicesToOffset(\"outputIndices\")};`;else{G=`var cIndices = ${T.type.indices}(0);\\n                       cIndices[0] = outputIndices[${c.length-1}];`;for(let W=1;W<T.rank;W++)G+=`cIndices[${W}] = outputIndices[${W}];`;G+=`let cOffset = ${T.indicesToOffset(\"cIndices\")};`}return G},k=G=>`\\n  const epsilon = ${t};\\n  ${G.registerUniform(\"outputSize\",\"u32\").declareVariables($,T,O,A,P,z)}\\n  ${G.mainStart()}\\n  ${G.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\\n    var outputIndices = ${z.offsetToIndices(`global_idx * ${m}`)};\\n    ${D()}\\n    let scale = ${T.getByOffset(\"cOffset\")};\\n    let bias = ${O.getByOffset(\"cOffset\")};\\n    let inputMean = ${A.getByOffset(\"cOffset\")};\\n    let inputVar = ${P.getByOffset(\"cOffset\")};\\n    let x = ${$.getByOffset(\"global_idx\")};\\n    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\\n    ${z.setByOffset(\"global_idx\",\"value\")}\\n  }`;return{name:\"BatchNormalization\",shaderCache:{hint:`${r.epsilon}_${r.format}_${u}_${m}`,inputDependencies:w?[\"rank\",\"type\",\"type\",\"type\",\"type\"]:void 0},getShaderSource:k,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:w?[{type:12,data:d},...se(c)]:[{type:12,data:d}]})}},cg=e=>Oe(e),il=(e,r)=>{let{inputs:t,outputCount:u}=e,s=cg({...r,outputCount:u});if(Sr.webgpu.validateInputContent&&lg(t,s),r.trainingMode)throw new Error(\"BatchNormalization trainingMode is not supported yet.\");e.compute(dg(t,s))}});var fg,pg,al,sl=ae(()=>{\"use strict\";ke();Ae();fg=e=>{if(e[0].dims.length!==3)throw new Error(\"input should have 3 dimensions\");if(![320,640,1280].includes(e[0].dims[2]))throw new Error(\"number of channels should be 320, 640 or 1280\");if(e[1].dims.length!==1)throw new Error(\"bias is expected to have 1 dimensions\");if(e[0].dims[2]!==e[1].dims[0])throw new Error(\"last dimension of input and bias are not the same\")},pg=e=>{let r=e[0].dims,t=e[0].dims[2],u=Z.size(r)/4,s=e[0].dataType,c=X(\"input\",s,r,4),m=X(\"bias\",s,[t],4),y=X(\"residual\",s,r,4),d=ie(\"output\",s,r,4);return{name:\"BiasAdd\",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:C=>`\\n  const channels = ${t}u / 4;\\n  ${C.declareVariables(c,m,y,d)}\\n\\n  ${C.mainStart()}\\n    ${C.guardAgainstOutOfBoundsWorkgroupSizes(u)}\\n    let value = ${c.getByOffset(\"global_idx\")}\\n      + ${m.getByOffset(\"global_idx % channels\")} + ${y.getByOffset(\"global_idx\")};\\n    ${d.setByOffset(\"global_idx\",\"value\")}\\n  }`}},al=e=>{fg(e.inputs),e.compute(pg(e.inputs))}});var mg,ze,ul,ll,dl,cl,fl,pl,ml,hl,gl,hg,yl,bl,vl,wl,ai,_l,si,$l,Cl,Sl,xl,Il,Al,Tl,El,Pl,Ol,kl,Rl,Dl,Bl,Ml,zl,Ul,jl,Yi,Zi,Vl,Nl,Wl,ui=ae(()=>{\"use strict\";$e();ke();nt();Ae();mg=(e,r,t,u,s,c)=>{let m=Math.ceil(r/4),y=\"\";typeof s==\"string\"?y=`${s}(a)`:y=s(\"a\");let d=X(\"inputData\",t,[m],4),w=ie(\"outputData\",u,[m],4);return`\\n      ${e.registerUniform(\"vec_size\",\"u32\").declareVariables(d,w)}\\n\\n  ${c??\"\"}\\n\\n  ${e.mainStart()}\\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.vec_size\")}\\n\\n    let a = ${d.getByOffset(\"global_idx\")};\\n    ${w.setByOffset(\"global_idx\",y)}\\n  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${e.activation}`)}},zt=(e,r)=>{e.activation===\"Clip\"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation===\"HardSigmoid\"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation===\"LeakyRelu\"&&r.push({type:1,data:e.alpha})},Ut=(e,r)=>{e.activation===\"Clip\"?r.push({name:\"clip_max\",type:\"f32\"},{name:\"clip_min\",type:\"f32\"}):e.activation===\"HardSigmoid\"?r.push({name:\"alpha\",type:\"f32\"},{name:\"beta\",type:\"f32\"}):e.activation===\"LeakyRelu\"&&r.push({name:\"alpha\",type:\"f32\"})},li=e=>{let r=e?.activation||\"\";if(r===\"HardSigmoid\"){let[t,u]=e?.activation_params||[.2,.5];return{activation:r,alpha:t,beta:u}}else if(r===\"Clip\"){let[t,u]=e?.activation_params||[Jn,ei];return{activation:r,clipMax:u,clipMin:t}}else if(r===\"LeakyRelu\"){let[t]=e?.activation_params||[.01];return{activation:r,alpha:t}}return{activation:r}}});var st,di,ci=ae(()=>{\"use strict\";st=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},di=e=>`\\n      ${e?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\\n      `});var fi,Qi=ae(()=>{\"use strict\";fi=e=>`\\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\\n  return dot(coords, vec4<i32>(\\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\\n}\\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\\n  return dot(coords, vec4<i32>(\\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\\n}\\n`});var Ag,Tg,fn,ad,Eg,pn,Pg,pi,mn=ae(()=>{\"use strict\";$e();ke();Ae();Yt();ci();Ag=(e,r)=>e?`\\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\\n          kStart + inputRow,\\n          globalRowStart / innerElementSize + inputCol${r?\", batchIndices\":\"\"});\\n        `:`\\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\\n          globalRow + innerRow,\\n          kStart / innerElementSize + inputCol${r?\", batchIndices\":\"\"});\\n        `,Tg=(e,r)=>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        ${r===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          ${r===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          ${r===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\\n        }`,fn=(e,r,t=\"f32\",u,s=!1,c=32,m=!1,y=32)=>{let d=r[1]*e[1],w=r[0]*e[0],C=s?d:c,$=s?c:d,T=C/r[0],O=c/r[1];if(!((s&&T===4&&e[1]===4||!s&&(T===3||T===4))&&C%r[0]===0&&c%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${s} is true, innerElementSize ${T} and workPerThread[1] ${e[1]} must be 4.\\n      Otherwise, innerElementSize ${T} must be 3 or 4.\\n  tileAWidth ${C} must be divisible by workgroupSize[0]${r[0]}. tileInner ${c} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return`\\nvar<workgroup> mm_Asub: array<array<vec${T}<${t}>, ${C/T}>, ${$}>;\\nvar<workgroup> mm_Bsub: array<array<vec4<${t}>, ${w/e[0]}>, ${c}>;\\n\\nconst rowPerThread = ${e[1]};\\nconst colPerThread = ${e[0]};\\nconst innerElementSize = ${T};\\nconst tileInner = ${c};\\n\\n@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[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 = ${m?\"0\":\"i32(globalId.z)\"};\\n  ${u?`let batchIndices = ${u.offsetToIndices(\"u32(batch)\")};`:\"\"}\\n  let globalRowStart = i32(workgroupId.y) * ${d};\\n\\n  let num_tiles = ${m?`${Math.ceil(y/c)}`:\"(uniforms.dim_inner - 1) / tileInner + 1\"};\\n  var kStart = ${m?`i32(globalId.z) * ${y}`:\"0\"};\\n\\n  var acc: array<vec4<${t}>, rowPerThread>;\\n\\n  // Loop over shared dimension.\\n  let tileRowB = localRow * ${O};\\n  for (var t = 0; t < num_tiles; 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          ${Ag(s,u)}\\n      }\\n\\n      // Load one tile of B into local memory.\\n      for (var innerRow = 0; innerRow < ${O}; innerRow = innerRow + 1) {\\n          let inputRow = tileRowB + innerRow;\\n          let inputCol = tileCol;\\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${u?\", 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          ${T===3?\"\":\"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];\"}\\n\\n          ${Tg(s,T)}\\n      }\\n\\n      workgroupBarrier();\\n  }\\n\\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\\n  }\\n}`},ad=(e,r)=>e?`\\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\\n              kStart + inputRow,\\n              globalRowStart + inputCol${r?\", batchIndices\":\"\"});\\n            `:`\\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\\n              globalRowStart + inputRow,\\n              kStart + inputCol${r?\", batchIndices\":\"\"});\\n            `,Eg=e=>e?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\",pn=(e,r,t=\"f32\",u,s=!1,c=32,m=!1,y=32,d=!1)=>{let w=e[1]*r[1],C=e[0]*r[0],$=s?w:c,T=s?c:w;if(!(T%r[1]===0&&$%r[0]===0&&c%r[1]===0))throw new Error(`tileAHight ${T} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${$} must be divisible by workgroupSize[0]${r[0]}, tileInner ${c} must be divisible by workgroupSize[1]${r[1]}`);let O=T/r[1],A=$/r[0],P=c/r[1],z=d?`\\n    let localRow = i32(localId.y);\\n    let localCol = i32(localId.x);\\n    let globalRowStart = i32(workgroupId.y) * ${w};\\n    let globalColStart = i32(workgroupId.x) * ${C};\\n\\n    // Loop over shared dimension.\\n    for (var t = 0; t < num_tiles; t = t + 1) {\\n      // Load one tile of A into local memory.\\n      for (var inputRow = localRow; inputRow < ${T}; inputRow = inputRow + ${r[1]}) {\\n        for (var inputCol = localCol; inputCol < ${$}; inputCol = inputCol + ${r[0]}) {\\n          ${ad(s,u)}\\n        }\\n      }\\n      // Load one tile of B into local memory.\\n      for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${r[1]}) {\\n            for (var inputCol = localCol; inputCol < ${C}; inputCol = inputCol + ${r[0]}) {\\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\\n            kStart + inputRow,\\n            globalColStart + inputCol${u?\", 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 * ${r[0]}];\\n        }\\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\\n          let ACached = ${s?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[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 * ${r[1]};\\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\\n        let gCol = globalColStart + localCol + innerCol * ${r[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) * ${w};\\n\\nlet tileRowA = i32(localId.y) * ${O};\\nlet tileColA = i32(localId.x) * ${A};\\nlet tileRowB = i32(localId.y) * ${P};\\n// Loop over shared dimension.\\nfor (var t = 0; t < num_tiles; t = t + 1) {\\n  // Load one tile of A into local memory.\\n  for (var innerRow = 0; innerRow < ${O}; innerRow = innerRow + 1) {\\n    for (var innerCol = 0; innerCol < ${A}; innerCol = innerCol + 1) {\\n      let inputRow = tileRowA + innerRow;\\n      let inputCol = tileColA + innerCol;\\n      ${ad(s,u)}\\n    }\\n  }\\n\\n  // Load one tile of B into local memory.\\n  for (var innerRow = 0; innerRow < ${P}; innerRow = innerRow + 1) {\\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\\n      let inputRow = tileRowB + innerRow;\\n      let inputCol = tileCol + innerCol;\\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\\n        kStart + inputRow,\\n        globalCol + innerCol${u?\", 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      ${Eg(s)}\\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\\n      }\\n    }\\n  }\\n\\n  workgroupBarrier();\\n}\\n\\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\\n        acc[innerRow][innerCol]);\\n  }\\n}\\n`;return`\\n  var<workgroup> mm_Asub : array<array<${t}, ${$}>, ${T}>;\\n  var<workgroup> mm_Bsub : array<array<${t}, ${C}>, ${c}>;\\n  const rowPerThread = ${e[1]};\\n  const colPerThread = ${e[0]};\\n  const tileInner = ${c};\\n\\n@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[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 = ${m?\"0\":\"i32(globalId.z)\"};\\n    ${u?`let batchIndices = ${u.offsetToIndices(\"u32(batch)\")};`:\"\"}\\n    let num_tiles = ${m?`${Math.ceil(y/c)}`:\"(uniforms.dim_inner - 1) / tileInner + 1\"};\\n    var kStart = ${m?`i32(globalId.z) * ${y}`:\"0\"};\\n\\n    var acc : array<array<${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    ${z}\\n  }\\n`},Pg=(e,r,t,u,s,c=!1)=>{let[m,y,d]=s,[w,C,$,T]=u,O=Ir(m,d),A=Ir(y,d),P=Ve(u[0].type.tensor),z=()=>{let G=C.rank,W=w.rank,q=`var aIndices: ${C.type.indices};`;for(let Y=G-2-1,J=W-1;Y>=0;Y--,J--)q+=`\\naIndices[${Y}] = ${W>1?`batchIndices[${J}]`:\"batchIndices\"};`;return O.forEach(Y=>{q+=`\\naIndices[${Y}] = 0;`}),q+=`\\naIndices[${G-2}] = u32(row);\\n                   aIndices[${G-1}] = u32(colIn);`,q},D=()=>{let G=$.rank,W=w.rank,q=`var bIndices: ${$.type.indices};`;for(let Y=G-2-1,J=W-1;Y>=0;Y--,J--)q+=`\\nbIndices[${Y}] = ${W>1?`batchIndices[${J}]`:\"batchIndices\"};`;return A.forEach(Y=>{q+=`\\nbIndices[${Y}] = 0;`}),q+=`\\nbIndices[${G-2}] = u32(row);\\n                   bIndices[${G-1}] = u32(colIn);`,q};return`\\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${w.type.indices}) -> ${st(e,P)} {\\n      var value = ${st(e,P)}(0.0);\\n      let col = colIn * ${e};\\n      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\\n      {\\n        ${z()}\\n        value = ${C.getByIndices(\"aIndices\")};\\n      }\\n      return value;\\n    }\\n\\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${w.type.indices}) -> ${st(e,P)} {\\n      var value = ${st(e,P)}(0.0);\\n      let col = colIn * ${e};\\n      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\\n      {\\n        ${D()}\\n        value = ${$.getByIndices(\"bIndices\")};\\n      }\\n      return value;\\n    }\\n\\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${st(e,P)}) {\\n      let col = colIn * ${e};\\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\\n        var value = valueIn;\\n        let coords = vec3<i32>(batch, row, colIn);\\n        ${r?`value = value + ${c?\"bias[colIn]\":`${st(e,P)}(bias[row])`};`:\"\"}\\n        ${t}\\n        ${T.setByIndices(\"vec3<u32>(coords)\",\"value\")}\\n      }\\n    }\\n    `},pi=(e,r,t,u,s=!1)=>{let c=e[0].dims,m=e[1].dims,y=c.slice(0,-2),d=m.slice(0,-2),w=u?u.slice(0,-2):t.slice(0,-2),C=Z.size(w),$=c[c.length-2],T=c[c.length-1],O=m[m.length-1],A=T%4===0&&O%4===0,P=$<=8?[4,1,1]:[4,4,1],z=[8,8,1],D=[Math.ceil(O/z[0]/P[0]),Math.ceil($/z[1]/P[1]),Math.ceil(C/z[2]/P[2])],k=A?4:1,G=[...y,$,T/k],W=G.length,q=[...d,T,O/k],Y=q.length,J=[C,$,O/k],oe=[{type:6,data:$},{type:6,data:O},{type:6,data:T}];zt(r,oe),oe.push(...se(w,G,q));let ge=[\"rank\",\"rank\"],L=e.length>2;L&&(oe.push(...se(e[2].dims)),ge.push(\"rank\")),oe.push(...se(J));let le=Te=>{let Ue=w.length,je=ri(\"batchDims\",e[0].dataType,Ue,1),Ee=Ve(e[0].dataType),ee=X(\"a\",e[0].dataType,W,k),Ne=X(\"b\",e[1].dataType,Y,k),Se=ie(\"result\",e[0].dataType,J.length,k),Ce=[ee,Ne];if(L){let re=s?k:1;Ce.push(X(\"bias\",e[2].dataType,e[2].dims.length,re))}let ve=[{name:\"dim_a_outer\",type:\"i32\"},{name:\"dim_b_outer\",type:\"i32\"},{name:\"dim_inner\",type:\"i32\"}];Ut(r,ve);let xe=Ve(Se.type.tensor),Me=Mt(r,Se.type.value,xe),Ke=Pg(k,L,Me,[je,ee,Ne,Se],[y,d,w],s);return`\\n  ${Te.registerUniforms(ve).registerInternalVariables(je).declareVariables(...Ce,Se)}\\n  ${Ke}\\n  ${A?fn(P,z,Ee,je):pn(P,z,Ee,je)}\\n                   `};return{name:\"MatMul\",shaderCache:{hint:`${P};${r.activation};${A};${s}`,inputDependencies:ge},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:D[0],y:D[1],z:D[2]},programUniforms:oe}),getShaderSource:le}}});var Og,sd,ud=ae(()=>{\"use strict\";$e();Kt();Ae();Yt();ci();Qi();mn();Og=(e,r,t,u,s=!1,c,m=4,y=4,d=4,w=\"f32\")=>{let C=L=>{switch(L){case 1:return\"resData = x[xIndex];\";case 3:return`resData = vec3<${w}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${L} is not supported.`)}},$=L=>{switch(L){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 ${L} is not supported.`)}},T=e?`\\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\\n    `:`\\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\\n    `,O=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?\"i32(uniforms.x_shape[1])\":\"i32(uniforms.x_shape[2])\",P=e?\"i32(uniforms.x_shape[2])\":\"i32(uniforms.x_shape[3])\",z=e?\"row\":\"col\",D=e?\"col\":\"row\",k=`\\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 = ${z} / outWidth;\\n    let outCol = ${z} % outWidth;\\n\\n    let WRow = ${D} / (i32(uniforms.w_shape[1]) * inChannels);\\n    let WCol = ${D} / inChannels % i32(uniforms.w_shape[1]);\\n    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\\n    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\\n    let xCh = ${D} % inChannels;\\n    var resData = ${st(m,w)}(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 < ${A} && xCol >= 0 && xCol < ${P}) {\\n      ${T}\\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\\n      ${C(m)}\\n    }\\n    return resData;`,G=e?r&&u?`\\n    let col = colIn * ${m};\\n    ${k}`:`\\n    let col = colIn * ${m};\\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\\n      ${k}\\n    }\\n    return ${st(m,w)}(0.0);`:u&&t?`\\n    let col = colIn * ${m};\\n    ${k}`:`\\n    let col = colIn * ${m};\\n    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\\n      ${k}\\n    }\\n    return ${st(m,w)}(0.0);`,W=`${$(y)}`,q=st(d,w),Y=e?st(m,w):st(y,w),J=e?st(y,w):st(m,w),oe=Mt(c,q,w);return`\\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Y} {\\n      ${e?G:W}\\n    }\\n\\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${J} {\\n      ${e?W:G}\\n    }\\n\\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${q}) {\\n      let col = colIn * ${d};\\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\\n      {\\n      var value = valueIn;\\n      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ee=[{name:\"dim_a_outer\",type:\"i32\"},{name:\"dim_b_outer\",type:\"i32\"},{name:\"dim_inner\",type:\"i32\"},{name:\"pad\",type:\"i32\",length:2},{name:\"stride\",type:\"i32\",length:2},{name:\"dilation\",type:\"i32\",length:2}];Ut(r,ee);let Ne=A?4:1,Se=Ve(e[0].dataType),Ce=`\\n      fn setOutputAtIndex(flatIndex : i32, value : ${A?`vec4<${Se}>`:Se}) {\\n        result[flatIndex] = ${A?`vec4<${Se}>`:Se}(value);\\n      }\\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${A?`vec4<${Se}>`:Se}) {\\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\\n        setOutputAtIndex(flatIndex ${A?\"/ 4\":\"\"}, value);\\n      }`,ve=X(\"x\",e[0].dataType,e[0].dims.length,W===3?1:W),xe=X(\"w\",e[1].dataType,e[1].dims.length,Ne),Me=[ve,xe],Ke=ie(\"result\",e[0].dataType,t.length,Ne);if(m){let re=X(\"bias\",e[2].dataType,e[2].dims.length,Ne);Me.push(re),Ce+=`\\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${A?`vec4<${Se}>`:Se} {\\n    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b[output_channel];\":\"\",c=e[0].dims,m=e[1].dims,y=m[0]/r.group,d=r.format===\"NHWC\",w=Ji(c,m,r.dilations,r.pads,r.strides,d),C=Z.size(w),$=[{type:12,data:C},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:y}];zt(r,$),$.push(...se(c,m,w));let T=[\"rank\",\"rank\"];u&&($.push(...se(e[2].dims)),T.push(\"rank\")),$.push(...se(w));let O=A=>{let P=ie(\"output\",e[0].dataType,w.length),z=Ve(P.type.tensor),D=Mt(r,P.type.value,z),k=X(\"x\",e[0].dataType,c.length),G=X(\"w\",e[1].dataType,m.length),W=[k,G];u&&W.push(X(\"b\",e[2].dataType,e[2].dims));let q=[{name:\"output_size\",type:\"u32\"},{name:\"dilations\",type:\"u32\",length:r.dilations.length},{name:\"strides\",type:\"u32\",length:2},{name:\"pads\",type:\"u32\",length:2},{name:\"output_channels_per_group\",type:\"u32\"}];return Ut(r,q),`\\n  ${A.registerUniforms(q).declareVariables(...W,P)}\\n\\n  ${A.mainStart()}\\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\\n\\n    let outputIndices = ${P.offsetToIndices(\"global_idx\")};\\n    let batch: u32 = outputIndices[0];\\n    let output_channel: u32 = outputIndices[${d?3:1}];\\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${d?1:2}], outputIndices[${d?2:3}]) * uniforms.strides - uniforms.pads;\\n    let group_id: u32 = output_channel / uniforms.output_channels_per_group;\\n\\n    var value: ${P.type.value} = ${P.type.value}(0);\\n    for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\\n      let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\\n      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\\n        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\\n\\n        if (xHeight < 0u || xHeight >= uniforms.x_shape[${d?1:2}]) {\\n          continue;\\n        }\\n\\n        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\\n          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\\n          if (xWidth < 0u || xWidth >= uniforms.x_shape[${d?2:3}]) {\\n            continue;\\n          }\\n\\n          let xVal = ${d?k.get(\"batch\",\"xHeight\",\"xWidth\",\"input_channel\"):k.get(\"batch\",\"input_channel\",\"xHeight\",\"xWidth\")};\\n          let wVal = ${G.get(\"output_channel\",\"wInChannel\",\"wHeight\",\"wWidth\")};\\n          value += xVal*wVal;\\n        }\\n      }\\n    }\\n    ${s}\\n    ${D}\\n    ${P.setByOffset(\"global_idx\",\"value\")}\\n  }`};return{name:\"GroupedConv\",shaderCache:{hint:r.cacheKey,inputDependencies:T},getRunData:()=>({outputs:[{dims:t?t(w):w,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:$}),getShaderSource:O}}});var to,kg,dd,ro=ae(()=>{\"use strict\";$e();ke();mn();Ae();Yt();to=(e,r,t,u,s=!1)=>{let c=e[0].dims,m=e[1].dims,y=c[c.length-2],d=m[m.length-1],w=c[c.length-1],C=Je(d),$=Je(w),T=Je(y),O=Z.size(t)/C/T,A=e.length>2,P=u?u.slice(0,-2):t.slice(0,-2),D=[Z.size(P),y,d],k=[{type:12,data:O},{type:12,data:y},{type:12,data:d},{type:12,data:w}];zt(r,k),k.push(...se(P,c,m)),A&&k.push(...se(e[2].dims)),k.push(...se(D));let G=W=>{let q=ri(\"batch_dims\",e[0].dataType,P.length),Y=X(\"a\",e[0].dataType,c.length,$),J=X(\"b\",e[1].dataType,m.length,C),oe=ie(\"output\",e[0].dataType,D.length,C),ge=Ve(oe.type.tensor),L=Mt(r,oe.type.value,ge),le=[Y,J],Te=\"\";if(A){let ve=s?C:1;le.push(X(\"bias\",e[2].dataType,e[2].dims.length,ve)),Te=`${s?`value += bias[col / ${ve}];`:`value += ${oe.type.value}(bias[row + i]);`}`}let Ue=c.slice(0,-2),je=m.slice(0,-2),Ee=Ir(Ue,P),ee=Ir(je,P),Ne=[{name:\"output_size\",type:\"u32\"},{name:\"M\",type:\"u32\"},{name:\"N\",type:\"u32\"},{name:\"K\",type:\"u32\"}];Ut(r,Ne);let Se=(ve,xe)=>{let Me=ve.rank,Ke=ve.name;if(Me===2)return`var ${Ke}_indices = ${ve.type.indices}(0u, 0u);`;let re=q.rank,fe=`var ${Ke}_indices: ${ve.type.indices};`;for(let _e=Me-2-1,it=re-1;_e>=0;_e--,it--)fe+=`\\n${Ke}_indices[${_e}] = ${re>1?`batch_indices[${it}]`:\"batch_indices\"};`;return xe.forEach(_e=>{fe+=`\\n${Ke}_indices[${_e}] = 0;`}),fe+=`${Ke}_indices[${Me-2}] = 0u;\\n                     ${Ke}_indices[${Me-1}] = 0u;`,fe},Ce=()=>{let ve=`var a_data: ${Y.type.value};`;for(let xe=0;xe<$;xe++)ve+=`\\n              let b_data${xe} = b[(b_offset + (k + ${xe}) * uniforms.N + col) / ${C}];`;for(let xe=0;xe<T;xe++){ve+=`a_data = a[(a_offset + (row + ${xe}) * uniforms.K + k) / ${$}];`;for(let Me=0;Me<$;Me++)ve+=`\\n            values[${xe}] = fma(${J.type.value}(a_data${$===1?\"\":`[${Me}]`}), b_data${Me}, values[${xe}]);\\n`}return ve};return`\\n  ${W.registerUniforms(Ne).registerInternalVariables(q).declareVariables(...le,oe)}\\n  ${W.mainStart()}\\n    ${W.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\\n    let col = (global_idx % (uniforms.N / ${C})) * ${C};\\n    var index1 = global_idx / (uniforms.N / ${C});\\n    let stride1 = uniforms.M / ${T};\\n    let row = (index1 % stride1) * ${T};\\n    let batch = index1 / stride1;\\n\\n    ${t.length===2?\"\":`let batch_indices = ${q.offsetToIndices(\"batch\")};`}\\n    ${Se(Y,Ee)}\\n    let a_offset = ${Y.indicesToOffset(\"a_indices\")};\\n    ${Se(J,ee)}\\n    let b_offset = ${J.indicesToOffset(\"b_indices\")};\\n    var values: array<${oe.type.value}, ${T}>;\\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${$}) {\\n      ${Ce()}\\n    }\\n    for (var i = 0u; i < ${T}u; i++) {\\n      var value = values[i];\\n      ${Te}\\n      ${L}\\n      let cur_indices = ${oe.type.indices}(batch, row + i, col);\\n      let offset = ${oe.indicesToOffset(\"cur_indices\")};\\n      ${oe.setByOffset(`offset / ${C}`,\"value\")};\\n    }\\n  }\\n  `};return{name:\"MatMulNaive\",shaderCache:{hint:`${r.activation};${C};${$};${T};${s}`,inputDependencies:A?[\"rank\",\"rank\",\"rank\"]:[\"rank\",\"rank\"]},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(O/64)},programUniforms:k}),getShaderSource:G}},kg=e=>{if(!e||e.length!==2)throw new Error(\"MatMul requires 2 inputs.\");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error(\"shared dimension does not match.\")},dd=e=>{kg(e.inputs);let r=Pt.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error(\"Can\\'t use matmul on the given tensors\");let t=r[r.length-1],u=e.inputs[0].dims[e.inputs[0].dims.length-1];t<8&&u<8?e.compute(to(e.inputs,{activation:\"\"},r)):e.compute(pi(e.inputs,{activation:\"\"},r))}});var Ji,cd,Rg,fd,no,Dg,Bg,io,eo=ae(()=>{\"use strict\";ke();ud();mn();ld();Yt();ro();Ar();Ji=(e,r,t,u,s,c)=>{let m=e[0],y=e.slice(c?1:2,c?3:4),d=y.length,w=r[0],$=r.slice(2).map((A,P)=>A+(A-1)*(t[P]-1)),O=y.map((A,P)=>A+u[P]+u[P+d]).map((A,P)=>Math.floor((A-$[P]+s[P])/s[P]));return O.splice(0,0,m),O.splice(c?3:1,0,w),O},cd=[2,3,1,0],Rg=(e,r)=>{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 t=e[0].dims[r.format===\"NHWC\"?e[0].dims.length-1:1],u=e[1].dims[1]*r.group;if(t!==u)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 s=e[0].dims.length-2;if(r.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(r.strides.length!==s)throw new Error(`strides should be ${s}D`);if(r.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error(\"invalid kernel shape\")},fd=(e,r)=>{let t=e.kernelShape.slice();for(let c=2;c<r[1].dims.length;++c)t[c-2]===0&&(t[c-2]=r[1].dims[c]);let u=e.pads.slice();sr.adjustPadsBasedOnAutoPad(r[0].dims,e.strides,e.dilations,t,u,e.format===\"NHWC\",e.autoPad);let s=Object.assign({},e);return Object.assign(s,{kernelShape:t,pads:u}),s},no=e=>{let r=li(e),t=e.format,u=[\"NOTSET\",\"VALID\",\"SAME_UPPER\",\"SAME_LOWER\"][e.auto_pad],s=e.dilations,c=e.group,m=e.kernel_shape,y=e.pads,d=e.strides,w=e.w_is_const();return{autoPad:u,format:t,dilations:s,group:c,kernelShape:m,pads:y,strides:d,wIsConst:w,...r,cacheKey:`${e.format};${r.activation};`}},Dg=(e,r,t)=>{let u=fd(t,r),s=t.format===\"NHWC\";if(t.group!==1){e.compute(Xi(r,u));return}let c=r.length===3,m=r[0].dims[s?1:2],y=r[0].dims[s?2:3],d=r[0].dims[s?3:1],w=r[1].dims[2],C=r[1].dims[3],$=Ji(r[0].dims,r[1].dims,t.dilations,u.pads,t.strides,s),T=$[s?1:2],O=$[s?2:3],A=$[s?3:1],P=s&&w===m&&C===y&&t.pads[0]===0&&t.pads[1]===0;if(P||w===1&&C===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let Y=$[0],J,oe,ge,L=[];if(s){let Ue=e.kernelCustomData.wT??e.compute(At(r[1],cd),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ue),P){let je=m*y*d;J=r[0].reshape([1,Y,je]),oe=Ue.reshape([1,je,A]),ge=[1,Y,A]}else J=r[0].reshape([Y,m*y,d]),oe=Ue.reshape([1,d,A]),ge=[Y,T*O,A];L.push(J),L.push(oe)}else J=r[0].reshape([Y,d,m*y]),oe=r[1].reshape([1,A,d]),ge=[Y,A,T*O],L.push(oe),L.push(J);c&&L.push(r[2]);let le=ge[2],Te=L[0].dims[L[0].dims.length-1];le<8&&Te<8?e.compute(to(L,u,$,ge,s),{inputs:L}):e.compute(pi(L,u,$,ge,s),{inputs:L});return}let z=!0,D=e.kernelCustomData.wT??e.compute(At(r[1],cd),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=D);let k=[r[0],D];c&&k.push(r[2]);let G=s?T*O:A,W=s?A:T*O,q=w*C*d;e.compute(sd(k,u,$,G,W,q,c,z),{inputs:k})},Bg=(e,r)=>{let t=r.format===\"NHWC\",u=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&u.push(e.inputs[2]);let s=[0,r.pads[0],0,r.pads[1]],c=[1].concat(r.strides),m=[1].concat(r.dilations),y=[1].concat(r.kernelShape),d=fd({...r,pads:s,strides:c,dilations:m,kernelShape:y},u);e.compute(Xi(u,d,w=>t?[w[0],w[2],w[3]]:[]))},io=(e,r)=>{Rg(e.inputs,r),e.inputs[0].dims.length===3?Bg(e,r):Dg(e,e.inputs,r)}});var Mg,pd,md=ae(()=>{\"use strict\";$e();Kt();Ae();Yt();ci();Qi();mn();Mg=(e,r=!1,t,u,s=4)=>{let c=D=>{switch(D){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 ${u}(v0, v1, v2, v3);\\n            `;default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=e?`\\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\\n      `:`\\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\\n      `,y=e?`\\n    let coords = vec4<i32>(\\n      batch,\\n      row / outWidth,\\n      row % outWidth,\\n      col);\\n    `:`\\n    let coords = vec4<i32>(\\n      batch,\\n      row,\\n      col / outWidth,\\n      col % outWidth);\\n    `,d=e?\"i32(uniforms.x_shape[1])\":\"i32(uniforms.x_shape[2])\",w=e?\"i32(uniforms.x_shape[2])\":\"i32(uniforms.x_shape[3])\",C=e?\"row\":\"col\",$=e?\"col\":\"row\",T=`\\n      let inChannels = ${e?\"i32(uniforms.x_shape[3])\":\"i32(uniforms.x_shape[1])\"};\\n      let outWidth = ${e?\"i32(uniforms.result_shape[2])\":\"i32(uniforms.result_shape[3])\"};\\n      let outRow = ${C} / outWidth;\\n      let outCol = ${C} % outWidth;\\n\\n      let WRow = ${$} / (uniforms.filter_dims[1] * inChannels);\\n      let WCol = ${$} / inChannels % uniforms.filter_dims[1];\\n      let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\\n      let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\\n      if (xR < 0.0 || xR >= f32(${d}) || fract(xR) > 0.0) {\\n        return ${u}(0.0);\\n      }\\n      if (xC < 0.0 || xC >= f32(${w}) || fract(xC) > 0.0) {\\n        return ${u}(0.0);\\n      }\\n      let iXR = i32(xR);\\n      let iXC = i32(xC);\\n      let xCh = ${$} % inChannels;\\n      ${m}\\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${s}];`,O=e?`\\n      let col = colIn * ${s};\\n      if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\\n        ${T}\\n      }\\n      return ${u}(0.0);`:`\\n      let col = colIn * ${s};\\n      if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\\n        ${T}\\n      }\\n      return ${u}(0.0);`,A=`\\n      let col = colIn * ${s};\\n      let inChannels = ${e?\"i32(uniforms.x_shape[3])\":\"i32(uniforms.x_shape[1])\"};\\n      let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\\n      let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\\n      if (${e?\"row < uniforms.dim_inner && col < uniforms.dim_b_outer\":\"row < uniforms.dim_inner && col < uniforms.dim_a_outer\"}  && coordX >= 0 && coordY >= 0) {\\n        let rowInner = row % inChannels;\\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\\n        ${c(s)}\\n      }\\n      return ${u}(0.0);\\n      `,P=Mt(t,u);return`\\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${u} {\\n    ${e?O:A}\\n  }\\n\\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${u} {\\n    ${e?A:O}\\n  }\\n\\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${u}) {\\n    let col = colIn * ${s};\\n    if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\\n      var value = valueInput;\\n      let outWidth = ${e?\"i32(uniforms.result_shape[2])\":\"i32(uniforms.result_shape[3])\"};\\n      ${y}\\n      ${di(r)}\\n      ${P}\\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${s}] = value;\\n    }\\n  }`},pd=(e,r,t,u,s,c,m,y)=>{let d=r.format===\"NHWC\",w=d?e[0].dims[3]:e[0].dims[1],C=t[0],$=d?t[2]:t[3],T=d?t[1]:t[2],O=d?t[3]:t[1],A=d?w%4===0&&O%4===0:$%4===0&&O%4===0,P=d?O:$*T,z=d?$*T:O,D=A?[8,8,1]:[P<=4||z<=4?4:16,P>4&&z<=4?4:16,1],k=A?[4,4,1]:[P<=4?1:4,P>4&&z<=4?1:4,1],G=[Math.ceil(P/D[0]/k[0]),Math.ceil(z/D[1]/k[1]),Math.ceil(C/D[2]/k[2])];Le(\"verbose\",()=>`[conv_backprop_mm_webgpu] dispatch = ${G}`);let W=A?4:1,q=Math.max(D[0]*W,D[1]),Y=A?4:1,J=[r.kernelShape[d?1:2],r.kernelShape[d?2:3]],oe=[J[0]+(r.dilations[0]<=1?0:(J[0]-1)*(r.dilations[0]-1)),J[1]+(r.dilations[1]<=1?0:(J[1]-1)*(r.dilations[1]-1))],ge=[oe[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),oe[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],L=[{type:6,data:u},{type:6,data:s},{type:6,data:c},{type:6,data:r.strides},{type:6,data:r.dilations},{type:6,data:J},{type:6,data:ge}];zt(r,L),L.push(...se(e[0].dims,e[1].dims));let le=[\"rank\",\"rank\"];m&&(L.push(...se(e[2].dims)),le.push(\"rank\")),L.push(...se(t));let Te=Ue=>{let je=X(\"x\",e[0].dataType,e[0].dims.length,Y),Ee=X(\"w\",e[1].dataType,e[1].dims.length,1),ee=ie(\"result\",e[0].dataType,t.length,Y),Ne=[je,Ee],Se=\"\";if(m){let xe=X(\"bias\",e[2].dataType,e[2].dims.length,Y);Ne.push(xe),Se+=`\\n          fn getBiasByOutputCoords(coords : vec4<i32>) -> ${xe.type.value} {\\n            return bias[coords.${d?\"w\":\"y\"}${A?\"/ 4\":\"\"}];\\n          }`}let Ce=[{name:\"dim_a_outer\",type:\"i32\"},{name:\"dim_b_outer\",type:\"i32\"},{name:\"dim_inner\",type:\"i32\"},{name:\"strides\",type:\"i32\",length:2},{name:\"dilations\",type:\"i32\",length:2},{name:\"filter_dims\",type:\"i32\",length:J.length},{name:\"pads\",type:\"i32\",length:ge.length}];Ut(r,Ce);let ve=Ve(e[0].dataType,1);if(ve!==\"f16\"&&ve!==\"f32\")throw new Error(`elemType ${ve} is not supported.`);return`\\n        ${fi(\"uniforms.result_strides\")}\\n        ${Ue.registerUniforms(Ce).declareVariables(...Ne,ee)};\\n        ${Se}\\n        ${Mg(d,m,r,je.type.value,W)}\\n        ${A?fn(k,D,ve,void 0,!d,q):pn(k,D,ve,void 0,!d,q,!1,void 0,y)}`};return{name:\"Conv2DTransposeMatMul\",shaderCache:{hint:`${r.cacheKey};${k};${D};${A}`,inputDependencies:le},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:G[0],y:G[1],z:G[2]},programUniforms:L}),getShaderSource:Te}}});var zg,oo,hd=ae(()=>{\"use strict\";$e();Kt();ke();Ae();zg=(e,r,t,u,s,c=!1,m,y,d=!1)=>{let w=d?1:2,C=d?2:3,$=d?3:1,T=c?2:1,O=`\\n  fn setOutputAtIndex(flatIndex : u32, value : ${c?`vec4<${m}>`:m}) {\\n    result[flatIndex] = ${c?`vec4<${m}>`:m}(value);\\n  }`;u&&(O+=`\\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${c?`vec4<${m}>`:m} {\\n      return bias[coords.${d?\"w\":\"y\"}${c?\"/ 4\":\"\"}];\\n    }`);let A=c?4:1,P=X(\"W\",r[1].dataType,r[1].dims.length,A),z=X(\"Dy\",r[0].dataType,r[0].dims.length,A),D=[z,P];u&&D.push(X(\"bias\",r[2].dataType,[t[$]].length,A));let k=ie(\"result\",r[0].dataType,t.length,A),G=`{\\n        let batch: u32 = ${s?\"global_id.z\":\"workgroup_id.z\"} / uniforms.result_shape[1];\\n        let r = ${s?\"global_id.z\":\"workgroup_id.z\"} % uniforms.result_shape[1];\\n        let c = ${s?\"global_id.y\":\"workgroup_id.y\"} * ${T};\\n        let d1: u32 = ${s?\"global_id.x\":\"workgroup_id.x\"} * 4;\\n\\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(uniforms.pads);\\n\\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\\n        // ? = to be determined. : = across all values in that axis.\\n        var dotProd: array<vec4<${m}>, ${T}>;\\n        for (var i = 0; i < ${T}; i++) {\\n          dotProd[i] = vec4<${m}>(0.0);\\n        }\\n        for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\\n          var dyR = (${m}(dyCorner.x) + ${m}(wR)) / ${m}(uniforms.strides.x);\\n          let wRPerm = uniforms.filter_dims[0] - 1 - wR;\\n          if (dyR < 0.0 || dyR >= ${m}(uniforms.Dy_shape[1]) ||\\n              fract(dyR) > 0.0 || wRPerm < 0) {\\n            continue;\\n          }\\n          let idyR: u32 = u32(dyR);\\n\\n          for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\\n            let dyC = (${m}(dyCorner.y) + ${m}(wC)) / ${m}(uniforms.strides.y);\\n            let dyC2 = (${m}(dyCorner.y) + 1.0 + ${m}(wC)) / ${m}(uniforms.strides.y);\\n            let wCPerm = uniforms.filter_dims[1] - 1 - wC;\\n            if (wCPerm < 0) {\\n              continue;\\n            }\\n            var bDyCVal = true;\\n            var bDyCVal2 = true;\\n            if (dyC < 0.0 || dyC >= ${m}(uniforms.Dy_shape[2]) ||\\n                fract(dyC) > 0.0) {\\n              bDyCVal = false;\\n            }\\n            if (dyC2 < 0.0 || dyC2 >= ${m}(uniforms.Dy_shape[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 = uniforms.Dy_shape[3];\\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\\n                let wValue0 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1\",\"d2\")};\\n                let wValue1 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 1\",\"d2\")};\\n                let wValue2 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 2\",\"d2\")};\\n                let wValue3 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 3\",\"d2\")};\\n\\n                var xValue = ${z.get(\"batch\",\"idyR\",\"idyC\",\"d2\")};\\n                let tmpval = vec4<${m}>(dot(xValue, wValue0),\\n                                      dot(xValue, wValue1),\\n                                      dot(xValue, wValue2),\\n                                      dot(xValue, wValue3));\\n                dotProd[0] = dotProd[0] + tmpval;\\n\\n                xValue =  ${z.get(\"batch\",\"idyR\",\"idyC2\",\"d2\")};\\n\\n                dotProd[1] = dotProd[1] + vec4<${m}>(dot(xValue, wValue0),\\n                                                    dot(xValue, wValue1),\\n                                                    dot(xValue, wValue2),\\n                                                    dot(xValue, wValue3));\\n              }\\n            } else if (bDyCVal) {\\n              let d2Length = uniforms.Dy_shape[${$}];\\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\\n                let wValue0 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1\",\"d2\")};\\n                let wValue1 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 1\",\"d2\")};\\n                let wValue2 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 2\",\"d2\")};\\n                let wValue3 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 3\",\"d2\")};\\n\\n                var xValue = ${z.get(\"batch\",\"idyR\",\"idyC\",\"d2\")};\\n                let tmpval = vec4<${m}>(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 = uniforms.Dy_shape[3];\\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\\n                let wValue0 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1\",\"d2\")};\\n                let wValue1 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 1\",\"d2\")};\\n                let wValue2 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 2\",\"d2\")};\\n                let wValue3 = ${P.get(\"u32(wRPerm)\",\"u32(wCPerm)\",\"d1 + 3\",\"d2\")};\\n\\n                var xValue = ${z.get(\"batch\",\"idyR\",\"idyC2\",\"d2\")};\\n                let tmpval = vec4<${m}>(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 < ${T}; i = i + 1) {\\n          let value = dotProd[i] + ${u?\"bias[c+i]\":`vec4<${m}>(0.0)`};\\n          ${k.set(\"batch\",\"r\",\"c + i\",\"d1\",\"value\")};\\n        }\\n      }`,W=`\\n          let outputIndices = ${k.offsetToIndices(\"global_idx\")};\\n          let batch = ${k.indicesGet(\"outputIndices\",0)};\\n          let d1 = ${k.indicesGet(\"outputIndices\",$)};\\n          let r = ${k.indicesGet(\"outputIndices\",w)};\\n          let c = ${k.indicesGet(\"outputIndices\",C)};\\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;\\n          let dyRCorner = dyCorner.x;\\n          let dyCCorner = dyCorner.y;\\n          let groupId = d1 / uniforms.output_channels_per_group;\\n          let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\\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 = ${m}(0.0);\\n          for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\\n            if (wR % uniforms.dilations.x != 0) {\\n              continue;\\n            }\\n            let dyR = (${m}(dyRCorner) + ${m}(wR)) / ${m}(uniforms.strides[0]);\\n            let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\\n            if (dyR < 0.0 || dyR >= ${m}(uniforms.Dy_shape[${w}]) || fract(dyR) > 0.0 ||\\n                wRPerm < 0) {\\n              continue;\\n            }\\n            let idyR: u32 = u32(dyR);\\n\\n            for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\\n              if (wC % uniforms.dilations.y != 0) {\\n                continue;\\n              }\\n              let dyC = (${m}(dyCCorner) + ${m}(wC)) / ${m}(uniforms.strides.y);\\n              let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\\n              if (dyC < 0.0 || dyC >= ${m}(uniforms.Dy_shape[${C}]) ||\\n                  fract(dyC) > 0.0 || wCPerm < 0) {\\n                continue;\\n              }\\n              let idyC: u32 = u32(dyC);\\n              var inputChannel = groupId * uniforms.input_channels_per_group;\\n              for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\\n                let xValue = ${d?z.get(\"batch\",\"idyR\",\"idyC\",\"inputChannel\"):z.get(\"batch\",\"inputChannel\",\"idyR\",\"idyC\")};\\n                let wValue = ${P.get(\"inputChannel\",\"wOutChannel\",\"u32(wRPerm)\",\"u32(wCPerm)\")};\\n                dotProd = dotProd + xValue * wValue;\\n                inputChannel = inputChannel + 1;\\n              }\\n            }\\n          }\\n          let value = dotProd + ${u?\"bias[d1]\":`${m}(0.0)`};\\n          ${k.setByOffset(\"global_idx\",\"value\")};\\n        `;return`\\n  ${e.registerUniforms(y).declareVariables(...D,k)}\\n  ${O}\\n\\n    ${e.mainStart()}\\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")};\\n  ${c?G:W}}`},oo=(e,r,t)=>{let u=e.length>2,s=r.outputShape,c=Z.size(s),m=[Math.ceil(c/64),1,1];Le(\"verbose\",()=>`[conv2d_backprop_webgpu] dispatch = ${m}`);let y=r.format===\"NHWC\",d=[\"rank\",\"rank\"],w=[r.strides[0],r.strides[1]],C=[r.kernelShape[y?1:2],r.kernelShape[y?2:3]],$=[r.dilations[0],r.dilations[1]],T=[C[0]+(r.dilations[0]<=1?0:(r.kernelShape[y?1:2]-1)*(r.dilations[0]-1)),C[1]+(r.dilations[1]<=1?0:(r.kernelShape[y?2:3]-1)*(r.dilations[1]-1))],O=[T[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),T[1]-1-Math.floor(r.pads[1]+r.pads[3])/2],A=!1,P=r.group,z=e[1].dims,D=z[0]/P,k=z[1],G=[{type:6,data:c},{type:12,data:w},{type:12,data:C},{type:12,data:$},{type:12,data:T},{type:6,data:O},{type:12,data:D},{type:12,data:k},...se(e[0].dims,e[1].dims)];u&&(G.push(...se(e[2].dims)),d.push(\"rank\")),G.push(...se(s));let W=m[1]===1&&m[2]===1,q=Y=>{let J=[{name:\"output_size\",type:\"u32\"},{name:\"strides\",type:\"u32\",length:w.length},{name:\"filter_dims\",type:\"u32\",length:C.length},{name:\"dilations\",type:\"u32\",length:C.length},{name:\"effective_filter_dims\",type:\"u32\",length:T.length},{name:\"pads\",type:\"i32\",length:O.length},{name:\"input_channels_per_group\",type:\"u32\"},{name:\"output_channels_per_group\",type:\"u32\"}],oe=Ve(e[0].dataType);return`${zg(Y,e,s,u,W,A,oe,J,y)}`};return{name:\"ConvTranspose2D\",shaderCache:{hint:`${r.cacheKey};`,inputDependencies:d},getRunData:()=>({dispatchGroup:{x:m[0],y:m[1],z:m[2]},outputs:[{dims:t?t(s):s,dataType:e[0].dataType}],programUniforms:G}),getShaderSource:q}}});var Ug,jg,Vg,gd,yd,Ng,Wg,Gg,Hg,bd,vd=ae(()=>{\"use strict\";md();hd();Yt();Ar();Ug=(e,r,t,u,s,c)=>(e-1)*r+t+(u-1)*s+1-c,jg=(e,r,t,u,s)=>{let c=Math.floor(e/2);r===\"SAME_UPPER\"?(t[u]=c,t[s]=e-c):r===\"SAME_LOWER\"&&(t[u]=e-c,t[s]=c)},Vg=(e,r,t,u,s,c,m,y,d,w)=>{let 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d=Z.size(y),w=[{type:12,data:d},{type:12,data:s},{type:12,data:c},{type:12,data:m},{type:1,data:r.alpha},{type:1,data:r.beta}],C=[\"type\",\"type\"];e.length===3&&(w.push(...se(e[2].dims)),C.push(\"rank\")),w.push(...se(y));let $=T=>{let O=\"\";r.transA&&r.transB?O=\"value += a[k * uniforms.M + m] * b[n * uniforms.K + k];\":r.transA&&!r.transB?O=\"value += a[k * uniforms.M + m] * b[k * uniforms.N + n];\":!r.transA&&r.transB?O=\"value += a[m * uniforms.K + k] * b[n * uniforms.K + k];\":!r.transA&&!r.transB&&(O=\"value += a[m * uniforms.K + k] * b[k * uniforms.N + n];\");let A=r.alpha===1?\"\":\"value *= uniforms.alpha;\",P=X(\"a\",e[0].dataType,e[0].dims),z=X(\"b\",e[1].dataType,e[1].dims),D=P.type.value,k=null,G=[P,z];e.length===3&&(k=X(\"c\",e[2].dataType,e[2].dims.length),G.push(k));let W=ie(\"output\",e[0].dataType,y.length);G.push(W);let q=[{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  ${T.registerUniforms(q).declareVariables(...G)}\\n\\n  ${T.mainStart()}\\n    ${T.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\\n\\n    let m = global_idx / uniforms.N;\\n    let n = global_idx % uniforms.N;\\n\\n    var value = ${D}(0);\\n    for (var k: u32 = 0u; k < uniforms.K; k++) {\\n      ${O}\\n    }\\n\\n    ${A}\\n    ${(()=>k!=null?`let cOffset = ${k.broadcastedIndicesToOffset(\"vec2(m, n)\",W)}; value += ${D}(uniforms.beta) * ${k.getByOffset(\"cOffset\")};`:\"\")()}\\n    output[global_idx] = value;\\n  }`};return{name:\"Gemm\",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:y,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:w}),getShaderSource:$}},jd=e=>{let r=e.transA,t=e.transB,u=e.alpha,s=e.beta;return{transA:r,transB:t,alpha:u,beta:s,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Vd=(e,r)=>{ny(e.inputs),e.compute(iy(e.inputs,r))}});var oy,ay,sy,Wd,Gd=ae(()=>{\"use strict\";$e();ke();Ae();oy=(e,r)=>{let t=e[0].dims,u=t,s=2,c=Z.sizeToDimension(t,s),m=Z.sizeFromDimension(t,s),y=Je(m),d=m/y,w=[t[0],t[1],d],C=[\"rank\",\"type\",\"type\"],$=[{type:12,data:m},{type:12,data:d}];$.push(...se(w,w));let T=O=>{let A=X(\"x\",e[0].dataType,w.length,y),P=X(\"scale\",e[1].dataType,e[1].dims),z=X(\"bias\",e[2].dataType,e[2].dims),D=ie(\"output\",e[0].dataType,w.length,y),k=[A,P,z,D],G=A.type.value,W=y===1?\"f32\":`vec${y}<f32>`,q=64,Y=[{name:\"normSize\",type:\"u32\"},{name:\"normPackedSize\",type:\"u32\"}];return`\\n  var<workgroup> meanShared : f32;\\n  var<workgroup> squaredNormShared : f32;\\n  var<workgroup> workgroupShared : array<${W}, ${q}>;\\n  const workgroupSize = ${q}u;\\n  ${O.registerUniforms(Y).declareVariables(...k)}\\n  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}`},k=e.compute({name:\"InstanceNormComputeMean\",shaderCache:{hint:`${d}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[s,m,w,2],dataType:1}],dispatchGroup:{x:s*m/d},programUniforms:z}),getShaderSource:D},{inputs:[r],outputs:[-1]})[0],G=[{type:12,data:O},{type:12,data:c},{type:12,data:Math.floor(m/d)},{type:12,data:Math.floor(w*m/d)}],W=[\"type\",\"type\",\"type\"],q=Y=>{let J=X(\"scale\",t.dataType,t.dims,d),oe=X(\"bias\",u.dataType,u.dims,d);return`\\n  @group(0) @binding(0) var<storage, read> input : array<${C}>;\\n  @group(0) @binding(1) var<storage, read> scale : array<${J.type.storage}>;\\n  @group(0) @binding(2) var<storage, read> bias : array<${oe.type.storage}>;\\n  @group(0) @binding(3) var<storage, read_write> output : array<${C}>;\\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  ${Y.mainStart()}\\n    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e.compute({name:\"InstanceNormComputeChannelScaleShift\",shaderCache:{hint:`${d};${y}`,inputDependencies:W},getRunData:()=>({outputs:[{dims:[s,m,2],dataType:1}],dispatchGroup:{x:Math.ceil(O/64)},programUniforms:G}),getShaderSource:q},{inputs:[k,t,u],outputs:[-1]})[0]},sy=(e,r,t)=>{let u=r[0].dims,s=u,c=u[0],m=u[u.length-1],y=Z.sizeFromDimension(u,1)/m,d=Je(m),w=Z.size(s)/d,C=[{type:12,data:y},{type:12,data:Math.floor(m/d)}],$=[\"type\",\"type\"],T=ay(e,r[0],r[1],r[2],c,y,m,t.epsilon),O=A=>{let P=Ve(r[0].dataType),z=d===1?\"vec2f\":`mat2x${d}f`,D=d===1?P:`vec${d}<${P}>`,k=X(\"input\",r[0].dataType,r[0].dims,d),G=ie(\"output\",r[0].dataType,s,d);return`\\n  @group(0) @binding(0) var<storage, read> input : array<${k.type.storage}>;\\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${z}>;\\n  @group(0) @binding(2) var<storage, read_write> output : array<${G.type.storage}>;\\n  struct Uniforms {H: u32, C : u32};\\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\\n\\n  ${A.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], ${D}(scale[0]), ${D}(scale[1]));\\n  }`};e.compute({name:\"InstanceNormalizationNHWC\",shaderCache:{hint:`${d}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:s,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:C}),getShaderSource:O},{inputs:[r[0],T]})},Wd=(e,r)=>{r.format===\"NHWC\"?sy(e,e.inputs,r):e.compute(oy(e.inputs,r))}});var uy,ly,Hd,Ld=ae(()=>{\"use strict\";$e();ke();Ae();uy=e=>{if(!e||e.length<2)throw new Error(\"layerNorm requires at least 2 inputs.\")},ly=(e,r,t)=>{let 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${W.registerUniforms(J).declareVariables(...Y)}\\n  ${W.mainStart()}\\n    ${W.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.norm_count\")}\\n    let offset = global_idx * uniforms.norm_size_vectorized;\\n    var mean_vector = ${at(\"f32\",O)};\\n    var mean_square_vector = ${at(\"f32\",O)};\\n\\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\\n      let value = ${It(q,O,\"x[h + offset]\")};\\n      mean_vector += value;\\n      mean_square_vector += value * value;\\n    }\\n    let mean = ${pt(\"mean_vector\",O)} / uniforms.norm_size;\\n    let inv_std_dev = inverseSqrt(${pt(\"mean_square_vector\",O)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\\n\\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\\n      let f32input = ${It(q,O,\"x[j + offset]\")};\\n      let f32scale = ${It(q,O,\"scale[j]\")};\\n      output[j + offset] = ${Y[0].type.value}((f32input - mean) * inv_std_dev * f32scale\\n        ${c?`+ 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z=ie(\"output\",e[0].dataType,m.length),D=[{name:\"output_size\",type:\"u32\"},{name:\"k\",type:\"u32\"},{name:\"n\",type:\"u32\"},{name:\"accuracy_level\",type:\"u32\"},{name:\"bits\",type:\"u32\"},{name:\"block_size\",type:\"u32\"}],k=Math.floor((r.k+r.blockSize-1)/r.blockSize),W=r.blockSize/8*r.bits/4,q=Ve(e[0].dataType);return`\\n        fn ortUnpack8x4snorm(value: u32) -> array<${q}, 8>{\\n          var result = array<${q}, 8>();\\n          var offset: u32 = 0;\\n          let count: u32 = 4;\\n          for (var i: u32 = 0; i < 8u; i++) {\\n            result[i] = ${q}(extractBits(value, offset, count));\\n            offset += count;\\n          }\\n          return result;\\n        }\\n        ${C.registerUniforms(D).declareVariables(...A,z)}\\n        ${C.mainStart()}\\n          ${C.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\\n          var value: ${q} = 0.0;\\n          let output_indices = ${z.offsetToIndices(\"global_idx\")};\\n          var a_indices: ${$.type.indices} = output_indices;\\n          var n = ${z.indicesGet(\"output_indices\",c-1)};\\n          // Two zero points are packed into one byte because uniforms.bits <= 4.\\n          // zero_point_offset is either 0 or 4. It is bit offset within one byte.\\n          // TODO support zero_point_offset for bits > 4\\n          ${P?`\\n            var zero_point_index: u32 = n * ((${k} + 1) / 2) / 4;\\n            var zero_point_word: u32 = ${P.getByOffset(\"zero_point_index\")};\\n            var zero_point_offset: u32 = 0;`:\"\"}\\n          var scale_idex = n * ${k};\\n          var b_indices: ${T.type.indices};\\n          ${T.indicesSet(\"b_indices\",\"0\",\"n\")};\\n          var block_offset: u32 = 0;\\n          for (var block: u32 = 0; block < ${k}; block++) {\\n            // The scale and zero points are computed per block.\\n            let scale = ${O.getByOffset(\"scale_idex\")};\\n            // The default zero point is 8 for unsigned 4-bit quantization.\\n            let zero_point: ${q} = ${P?`${q}(extractBits(zero_point_word, zero_point_offset, 4))`:8};\\n            ${T.indicesSet(\"b_indices\",\"1\",\"block\")};\\n            var word_offset: u32 = block_offset;\\n            for (var word: u32 = 0; word < ${W}; word++) {\\n              ${T.indicesSet(\"b_indices\",\"2\",\"word\")};\\n              let b_value = ${T.getByIndices(\"b_indices\")};\\n              let b_quantized_values: array<${q}, 8> = ortUnpack8x4snorm(b_value);\\n              // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\\n              var offset: u32 = word_offset;\\n              for (var i: u32 = 0; i < 8; i++) {\\n                ${$.indicesSet(\"a_indices\",c-1,\"offset\")};\\n                let a_value = ${$.getByIndices(\"a_indices\")};\\n                let b_quantized_value = b_quantized_values[i];\\n                let b_dequantized_value = (b_quantized_value - zero_point) * scale;\\n                value += a_value * b_dequantized_value;\\n                offset++;\\n              }\\n              word_offset += 8;\\n            }\\n            scale_idex++;\\n            ${P?`\\n            if (zero_point_offset == 28) {\\n              zero_point_offset = 0;\\n              zero_point_index++;\\n              zero_point_word = ${P.getByOffset(\"zero_point_index\")};\\n            } else {\\n              zero_point_offset += 4;\\n            }`:\"\"}\\n            block_offset += uniforms.block_size;\\n          }\\n          ${z.setByOffset(\"global_idx\",\"value\")};\\n        }\\n        `};return{name:\"MatMulNBits\",shaderCache:{hint:`${r.cacheKey};${e.length}`,inputDependencies:Array(e.length).fill(\"rank\")},getRunData:()=>({outputs:[{dims:m,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:d}),getShaderSource:w}},Fd=(e,r)=>{dy(e.inputs,r),e.compute(cy(e.inputs,r))},qd=e=>Oe(e)});var fy,Zd,Yd,py,lo,Qd,Xd=ae(()=>{\"use strict\";$e();ke();nt();Zn();Ki();Ae();Ar();fy=(e,r)=>{let t=e[0],u=e[1],s=e[2],c=e[3],m=e[4],y=e[5],d=e[6],w=e[7];if(t.dims.length!==3&&t.dims.length!==5)throw new Error(\"Input query is expected to have 3 or 5 dimensions\");let C=!1,$=t.dims[0],T=t.dims[1],O=t.dims.length===3?C?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],A=T,P=0,z=0,D=Math.floor(O/r.numHeads);if(d&&w){if(d.dims.length!==4)throw new Error(\\'Input \"past_key\" is expected to have 4 dimensions\\');if(w.dims.length!==4)throw new Error(\\'Input \"past_value\" is expected to have 4 dimensions\\');P=d.dims[2],z=d.dims[2]}else if(d||w)throw new Error(\\'Input \"past_key\" and \"past_value\" shall be both present or both absent\\');let k;if(u){if(t.dims.length!==3)throw new Error(\\'Input \"query\" is expected to have 3 dimensions when key is given\\');if(u.dims.length<3||u.dims.length>5)throw new Error(\\'Input \"key\" is expected to have 3, 4, or 5 dimensions\\');if(t.dims[0]!==u.dims[0])throw new Error(\\'Input \"query\" and \"key\" shall have same dim 0 (batch size)\\');if(u.dims.length===3){if(u.dims[2]!==t.dims[2])throw new Error(\\'Input \"query\" and \"key\" shall have same dim 2 (hidden_size)\\');k=2,A=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==r.numHeads||u.dims[3]!==2||u.dims[4]!==D)throw new Error(\\'Expect \"key\" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\\');if(s)throw new Error(\\'Expect \"value\" be none when \"key\" has packed kv format.\\');k=5,A=u.dims[1]}else{if(u.dims[1]!==r.numHeads||u.dims[3]!==D)throw new Error(\\'Expect \"key\" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\\');k=0,A=u.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error(\\'Input \"query\" is expected to have 3 or 5 dimensions when key is empty\\');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error(\\'Expect \"query\" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\\');k=3}if(c){if(c.dims.length!==1)throw new Error(\\'Input \"bias\" is expected to have 1 dimension\\');if(s&&t.dims.length===5&&t.dims[3]===2)throw new Error(\"bias is not allowed for packed kv.\")}let G=0;if(m){G=8;let oe=m.dims;throw oe.length===1?oe[0]===$?G=1:oe[0]===3*$+2&&(G=3):oe.length===2&&oe[0]===$&&oe[1]===A&&(G=5),G===8?new Error(\\'Input \"key_padding_mask\" shape shall be (batch_size) or (batch_size, kv_sequence_length)\\'):new Error(\"Mask not supported\")}let W=!1,q=O;if(s){if(s.dims.length!==3&&s.dims.length!==4)throw new Error(\\'Input \"value\" is expected to have 3 or 4 dimensions\\');if(t.dims[0]!==s.dims[0])throw new Error(\\'Input \"query\" and \"value\" shall have same dim 0 (batch_size)\\');if(s.dims.length===3){if(A!==s.dims[1])throw new Error(\\'Input \"key\" and \"value\" shall have the same dim 1 (kv_sequence_length)\\');q=s.dims[2]}else{if(A!==s.dims[2])throw new Error(\\'Input \"past_key\" and \"past_value\" shall have the same dim 2 (kv_sequence_length)\\');q=s.dims[1]*s.dims[3],W=!0}}let Y=P+A,J=!1;if(m)throw new Error(\"Key padding mask is not supported\");if(y)throw new Error(\"extraAddQk is not supported\");if(d)throw new Error(\"pastKey is not supported\");if(w)throw new Error(\"pastValue is not supported\");return{batchSize:$,sequenceLength:T,pastSequenceLength:P,kvSequenceLength:A,totalSequenceLength:Y,maxSequenceLength:z,inputHiddenSize:0,hiddenSize:O,vHiddenSize:q,headSize:D,vHeadSize:Math.floor(q/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:G,scale:r.scale,broadcastResPosBias:J,passPastInKv:W,qkvFormat:k}},Zd=e=>Oe({...e}),Yd=Oe({perm:[0,2,1,3]}),py=(e,r,t,u,s,c,m)=>{let y=[u,s,c],d=Z.size(y),w=[{type:12,data:d},{type:12,data:m},{type:12,data:c}],C=$=>{let T=ie(\"qkv_with_bias\",r.dataType,y),O=X(\"qkv\",r.dataType,y),A=X(\"bias\",t.dataType,y),P=[{name:\"output_size\",type:\"u32\"},{name:\"bias_offset\",type:\"u32\"},{name:\"hidden_size\",type:\"u32\"}];return`\\n  ${$.registerUniforms(P).declareVariables(O,A,T)}\\n  ${$.mainStart()}\\n    ${$.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:y,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:w}),getShaderSource:C},{inputs:[r,t],outputs:[-1]})[0]},lo=(e,r,t,u,s,c,m,y)=>{let d=c;if(m){if(u===1)throw new Error(\"AddBiasReshape is not implemented. Please export your model with packed QKV or KV\");return d=py(e,c,m,r,u,t*s,y),d=d.reshape([r,u,t,s]),e.compute(At(d,Yd.perm),{inputs:[d],outputs:[-1]})[0]}else return c.dims.length===3&&(d=c.reshape([r,u,t,s])),e.compute(At(d,Yd.perm),{inputs:[d],outputs:[-1]})[0]},Qd=(e,r)=>{let t=fy(e.inputs,r);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 u=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,s=lo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,e.inputs[0],e.inputs[3],0);if(u)return oi(e,s,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],t,r);let c=lo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,e.inputs[1],e.inputs[3],t.hiddenSize),m=lo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,e.inputs[2],e.inputs[3],2*t.hiddenSize);oi(e,s,c,m,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],t,r)}});var my,hy,gy,yy,by,vy,wy,_y,Jd,ec=ae(()=>{\"use strict\";$e();ke();Ae();my=e=>{if(!e||e.length<1)throw new Error(\"Too few inputs\");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error(\"Input type must be float or float16.\");if(e.length>=2){let r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error(\"The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].\")}},hy=(e,r,t)=>{let u=\"\";for(let s=r-1;s>=0;--s)u+=`\\n            k = i32(${e.indicesGet(\"indices\",s)}) - ${we(\"uniforms.pads\",s,t)};\\n            if (k < 0) {\\n              break;\\n            }\\n            if (k >= i32(${we(\"uniforms.x_shape\",s,r)})) {\\n              break;\\n            }\\n            offset += k * i32(${we(\"uniforms.x_strides\",s,r)});\\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            ${u}\\n            value = x[offset];\\n          }\\n      `},gy=(e,r,t)=>{let u=\"\";for(let s=r-1;s>=0;--s)u+=`\\n                k = i32(${e.indicesGet(\"indices\",s)}) - ${we(\"uniforms.pads\",s,t)};\\n                if (k < 0) {\\n                  k = -k;\\n                }\\n                {\\n                  let _2n_1 = 2 * (i32(${we(\"uniforms.x_shape\",s,r)}) - 1);\\n                  k = k % _2n_1;\\n                  if(k >= i32(${we(\"uniforms.x_shape\",s,r)})) {\\n                    k = _2n_1 - k;\\n                  }\\n                }\\n                offset += k * i32(${we(\"uniforms.x_strides\",s,r)});\\n            `;return`\\n              var offset = 0;\\n              var k = 0;\\n              ${u}\\n              value = x[offset];\\n          `},yy=(e,r,t)=>{let u=\"\";for(let s=r-1;s>=0;--s)u+=`\\n                k = i32(${e.indicesGet(\"indices\",s)}) - ${we(\"uniforms.pads\",s,t)};\\n                if (k < 0) {\\n                  k = 0;\\n                }\\n                if (k >= i32(${we(\"uniforms.x_shape\",s,r)})) {\\n                  k = i32(${we(\"uniforms.x_shape\",s,r)}) - 1;\\n                }\\n                offset += k * i32(${we(\"uniforms.x_strides\",s,r)});\\n            `;return`\\n              var offset = 0;\\n              var k = 0;\\n              ${u}\\n              value = x[offset];\\n          `},by=(e,r,t)=>{let u=\"\";for(let s=r-1;s>=0;--s)u+=`\\n                k = i32(${e.indicesGet(\"indices\",s)}) - ${we(\"uniforms.pads\",s,t)};\\n                if (k < 0)  {\\n                  k += i32(${we(\"uniforms.x_shape\",s,r)}]);\\n                }\\n                if (k >= i32(${we(\"uniforms.x_shape\",s,r)})) {\\n                  k -= i32(${we(\"uniforms.x_shape\",s,r)});\\n                }\\n                offset += k * i32(${we(\"uniforms.x_strides\",s,r)});\\n            `;return`\\n              var offset = 0;\\n              var k = 0;\\n              ${u}\\n              value = x[offset];\\n          `},vy=(e,r,t)=>{switch(t.mode){case 0:return hy(e,r,t.pads.length);case 1:return gy(e,r,t.pads.length);case 2:return yy(e,r,t.pads.length);case 3:return by(e,r,t.pads.length);default:throw new Error(\"Invalid mode\")}},wy=(e,r)=>{let t=Z.padShape(e[0].dims.slice(),r.pads),u=e[0].dims,s=Z.size(t),c=[{type:12,data:s},{type:12,data:r.pads}];r.mode===0&&c.push({type:e[0].dataType,data:r.value}),c.push(...se(e[0].dims,t));let m=[\"rank\"],y=d=>{let w=ie(\"output\",e[0].dataType,t.length),C=X(\"x\",e[0].dataType,u.length),$=C.type.value,T=vy(w,u.length,r),O=[{name:\"output_size\",type:\"u32\"},{name:\"pads\",type:\"i32\",length:r.pads.length}];return r.mode===0&&O.push({name:\"constant_value\",type:$}),`\\n            ${d.registerUniforms(O).declareVariables(C,w)}\\n            ${d.mainStart()}\\n            ${d.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\\n\\n            let indices = ${w.offsetToIndices(\"global_idx\")};\\n\\n            var value = ${$}(0);\\n            ${T}\\n            output[global_idx] = value;\\n        }`};return{name:\"Pad\",shaderCache:{hint:`${r.mode}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Z.size(t)/64)},programUniforms:c}),getShaderSource:y}},_y=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),u=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,s=e[0].dims.length,c=new Int32Array(2*s).fill(0);if(e.length>=4){let y=e[3].getBigInt64Array();for(let d=0;d<y.length;d++)c[Number(y[d])]=Number(t[d]),c[Number(y[d])+s]=Number(t[d+y.length])}else t.forEach((y,d)=>c[Number(d)]=Number(y));let m=[];return c.forEach(y=>m.push(y)),{mode:r.mode,value:u,pads:m}}else return r},Jd=(e,r)=>{my(e.inputs);let t=_y(e.inputs,r);e.compute(wy(e.inputs,t),{inputs:[0]})}});var hi,tc,rc,nc,ic,$y,Cy,oc,ac,sc,uc,lc,dc,cc,fc,pc,mc,hc,gc,yc=ae(()=>{\"use strict\";xr();$e();ke();Ae();hi=e=>{if(Sr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error(\"Pool ops requires 1 input.\")},tc=(e,r,t)=>{let u=r.format===\"NHWC\",s=e.dims.slice();u&&s.splice(1,0,s.pop());let c=Object.hasOwnProperty.call(r,\"dilations\"),m=r.kernelShape.slice(),y=r.strides.slice(),d=c?r.dilations.slice():[],w=r.pads.slice();sr.adjustPoolAttributes(t,s,m,y,d,w);let C=sr.computePoolOutputShape(t,s,y,d,m,w,r.autoPad),$=Object.assign({},r);c?Object.assign($,{kernelShape:m,strides:y,pads:w,dilations:d,cacheKey:r.cacheKey}):Object.assign($,{kernelShape:m,strides:y,pads:w,cacheKey:r.cacheKey});let T=C.slice();return T.push(T.splice(1,1)[0]),[$,u?T:C]},rc=(e,r)=>{let t=r.format===\"NHWC\",u=Z.size(e),s=Z.size(r.kernelShape),c=[{type:12,data:u},{type:12,data:s}],m=[{name:\"outputSize\",type:\"u32\"},{name:\"kernelSize\",type:\"u32\"}];if(r.kernelShape.length<=2){let y=r.kernelShape[r.kernelShape.length-1],d=r.strides[r.strides.length-1],w=r.pads[r.pads.length/2-1],C=r.pads[r.pads.length-1],$=!!(w+C);c.push({type:12,data:y},{type:12,data:d},{type:12,data:w},{type:12,data:C}),m.push({name:\"kw\",type:\"u32\"},{name:\"sw\",type:\"u32\"},{name:\"pwStart\",type:\"u32\"},{name:\"pwEnd\",type:\"u32\"});let T=!1;if(r.kernelShape.length===2){let O=r.kernelShape[r.kernelShape.length-2],A=r.strides[r.strides.length-2],P=r.pads[r.pads.length/2-2],z=r.pads[r.pads.length-2];T=!!(P+z),c.push({type:12,data:O},{type:12,data:A},{type:12,data:P},{type:12,data:z}),m.push({name:\"kh\",type:\"u32\"},{name:\"sh\",type:\"u32\"},{name:\"phStart\",type:\"u32\"},{name:\"phEnd\",type:\"u32\"})}return[c,m,!0,$,T]}else{if(t)throw new Error(\"Pooling with kernelShape.length > 2 is not supported for NHWC format.\");let y=Z.computeStrides(r.kernelShape);c.push({type:12,data:y},{type:12,data:r.pads},{type:12,data:r.strides}),m.push({name:\"kernelStrides\",type:\"u32\",length:y.length},{name:\"pads\",type:\"u32\",length:r.pads.length},{name:\"strides\",type:\"u32\",length:r.strides.length});let d=r.pads.reduce((w,C)=>w+C);return[c,m,!!d,!1,!1]}},nc=(e,r,t,u,s,c,m,y,d,w,C,$)=>{let T=s.format===\"NHWC\",O=r.type.value,A=ie(\"output\",r.type.tensor,u);if(s.kernelShape.length<=2){let P=\"\",z=\"\",D=\"\",k=t-(T?2:1);if(C?P=`\\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\\n                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;\\n                  if (xIndices[${k}] < 0 || xIndices[${k}]\\n                      >= uniforms.x_shape[${k}]) {\\n                    pad++;\\n                    continue;\\n                  }\\n                  let x_val = x[${r.indicesToOffset(\"xIndices\")}];\\n                  ${c}\\n                }`:P=`\\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\\n                  xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;\\n                  let x_val = x[${r.indicesToOffset(\"xIndices\")}];\\n                  ${c}\\n                }`,s.kernelShape.length===2){let W=t-(T?3:2);$?z=`\\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\\n                  xIndices[${W}] = indices[${W}] * uniforms.sh - uniforms.phStart + j;\\n                  if (xIndices[${W}] < 0 || xIndices[${W}] >= uniforms.x_shape[${W}]) {\\n                    pad += i32(uniforms.kw);\\n                    continue;\\n                  }\\n              `:z=`\\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\\n                  xIndices[${W}] = indices[${W}] * uniforms.sh - uniforms.phStart + j;\\n                `,D=`\\n              }\\n            `}return`\\n            ${e.registerUniforms(d).declareVariables(r,A)}\\n\\n            ${e.mainStart()}\\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\\n\\n              let indices = ${A.offsetToIndices(\"global_idx\")};\\n              var xIndices = ${A.offsetToIndices(\"global_idx\")};\\n\\n              var value = ${O}(${y});\\n              var pad = 0;\\n              ${z}\\n              ${P}\\n              ${D}\\n              ${m}\\n\\n              output[global_idx] = value;\\n            }`}else{if(T)throw new Error(\"Pooling with kernelShape.length > 2 is not supported for NHWC format.\");let P=s.kernelShape.length,z=s.pads.length,D=\"\";return w?D=`\\n                if (xIndices[j] >= uniforms.x_shape[j]) {\\n                  pad++;\\n                  isPad = true;\\n                  break;\\n                }\\n              }\\n              if (!isPad) {\\n                let x_val = x[${r.indicesToOffset(\"xIndices\")}];\\n                ${c}\\n              }`:D=`\\n              }\\n              let x_val = x[${r.indicesToOffset(\"xIndices\")}];\\n              ${c}\\n            `,`\\n            ${e.registerUniforms(d).declareVariables(r,A)}\\n\\n            ${e.mainStart()}\\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\\n              let indices = ${A.offsetToIndices(\"global_idx\")};\\n              var xIndices = ${A.offsetToIndices(\"global_idx\")};\\n\\n              var offsets: array<u32, ${P}>;\\n\\n              var value = ${O}(${y});\\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 < ${P-1}u; j++) {\\n                  offsets[j] = offset / ${we(\"uniforms.kernelStrides\",\"j\",P)};\\n                  offset -= offsets[j] * ${we(\"uniforms.kernelStrides\",\"j\",P)};\\n                }\\n                offsets[${P-1}] = offset;\\n\\n                isPad = false;\\n                for (var j = ${t-P}u; j < ${t}u; j++) {\\n                  xIndices[j] = indices[j] * ${we(\"uniforms.strides\",`j - ${t-P}u`,P)}\\n                    + offsets[j - ${t-P}u] - ${we(\"uniforms.pads\",\"j - 2u\",z)};\\n                  ${D}\\n              }\\n              ${m}\\n\\n              output[global_idx] = value;\\n            }`}},ic=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,$y=e=>`${ic(e)};${e.countIncludePad}`,Cy=e=>`${ic(e)};${e.storageOrder};${e.dilations}`,oc=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}),ac=(e,r,t,u)=>{let[s,c]=tc(r,u,t),m=X(\"x\",r.dataType,r.dims.length),y=m.type.value,d=\"value += x_val;\",w=\"\";s.countIncludePad?w+=`value /= ${y}(uniforms.kernelSize);`:w+=`value /= ${y}(i32(uniforms.kernelSize) - pad);`;let[C,$,T,O,A]=rc(c,s);C.push(...se(r.dims,c));let P=[\"rank\"];return{name:e,shaderCache:{hint:`${u.cacheKey};${T};${O};${A}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:c,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Z.size(c)/64)},programUniforms:C}),getShaderSource:z=>nc(z,m,r.dims.length,c.length,s,d,w,0,$,T,O,A)}},sc=e=>{let r=e.count_include_pad!==0,t=oc(e);if(t.ceilMode!==0)throw new Error(\"using ceil() in shape computation is not yet supported for AveragePool\");let u={countIncludePad:r,...t,cacheKey:\"\"};return{...u,cacheKey:$y(u)}},uc=(e,r)=>{hi(e.inputs),e.compute(ac(\"AveragePool\",e.inputs[0],!1,r))},lc={autoPad:\"\",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},dc=e=>{let r=e.format;return{format:r,...lc,cacheKey:r}},cc=(e,r)=>{hi(e.inputs),e.compute(ac(\"GlobalAveragePool\",e.inputs[0],!0,r))},fc=(e,r,t,u)=>{let[s,c]=tc(r,u,t),m=`\\n      value = max(x_val, value);\\n    `,y=\"\",d=X(\"x\",r.dataType,r.dims.length),w=[\"rank\"],[C,$,T,O,A]=rc(c,s);return C.push(...se(r.dims,c)),{name:e,shaderCache:{hint:`${u.cacheKey};${T};${O};${A}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:c,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Z.size(c)/64)},programUniforms:C}),getShaderSource:P=>nc(P,d,r.dims.length,c.length,s,m,y,-1e5,$,T,O,A)}},pc=(e,r)=>{hi(e.inputs),e.compute(fc(\"MaxPool\",e.inputs[0],!1,r))},mc=e=>{let r=e.storage_order,t=e.dilations,u=oc(e);if(r!==0)throw new Error(\"column major storage order is not yet supported for MaxPool\");if(u.ceilMode!==0)throw new Error(\"using ceil() in shape computation is not yet supported for MaxPool\");let s={storageOrder:r,dilations:t,...u,cacheKey:\"\"};return{...s,cacheKey:Cy(s)}},hc=e=>{let r=e.format;return{format:r,...lc,cacheKey:r}},gc=(e,r)=>{hi(e.inputs),e.compute(fc(\"GlobalMaxPool\",e.inputs[0],!0,r))}});var xy,Iy,bc,vc=ae(()=>{\"use strict\";xr();$e();Ae();xy=(e,r,t)=>{let u=e===r,s=e<r&&t<0,c=e>r&&t>0;if(u||s||c)throw new Error(\"Range these inputs\\' contents are invalid.\")},Iy=(e,r,t,u)=>{let s=Math.abs(Math.ceil((r-e)/t)),c=[s],m=s,y=[{type:12,data:m},{type:u,data:e},{type:u,data:t},...se(c)],d=w=>{let C=ie(\"output\",u,c.length),$=C.type.value,T=[{name:\"outputSize\",type:\"u32\"},{name:\"start\",type:$},{name:\"delta\",type:$}];return`\\n        ${w.registerUniforms(T).declareVariables(C)}\\n        ${w.mainStart()}\\n        ${w.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\\n        output[global_idx] = uniforms.start + ${$}(global_idx) * uniforms.delta;\\n      }`};return{name:\"Range\",shaderCache:{hint:`${u}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:c,dataType:u}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:y})}},bc=e=>{let r=0,t=0,u=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],u=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],u=e.inputs[2].getFloat32Array()[0]),Sr.webgpu.validateInputContent&&xy(r,t,u),e.compute(Iy(r,t,u,e.inputs[0].dataType),{inputs:[]})}});var Ay,Ty,Ey,Py,Oy,ky,Ry,Dy,By,My,zy,wc,Uy,jy,Vy,Ny,Wy,_c,$c,Cc=ae(()=>{\"use strict\";$e();ke();nt();Ae();Ay=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error(\"Resize requires scales input values to be positive\")})),e.length>0){if(r.mode===\"linear\"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.mode===\"cubic\"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error(\"Resize requires scales input size to be 2 or 4 for cubic mode\")}},Ty=(e,r,t)=>{r.every(s=>s>=0&&s<t||(()=>{throw new Error(\"Resize requires axes input values to be positive and less than rank\")}));let u=new Array(t).fill(1);return r.forEach((s,c)=>u[s]=e[c]),u},Ey=(e,r,t,u,s,c)=>{let[m,y,d]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],w=e[0].dims.length;if(m>0&&e.length>m&&e[m].dims.length>0)e[m].getFloat32Array().forEach(C=>c.push(C));else if(r.coordinateTransformMode===\"tf_crop_and_resize\")throw new Error(\"Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize\");if(y>0&&e.length>y&&e[y].dims.length>0){if(e[y].getFloat32Array().forEach(C=>u.push(C)),u.length!==0&&u.length!==w&&t>=18&&u.length!==r.axes.length)throw new Error(\"Resize requires scales input size to be same as input rank or axes size for opset 18 and up\");Ay(u,r),r.axes.length>0&&Ty(u,r.axes,w).forEach((C,$)=>u[$]=C)}if(d>0&&e.length>d&&(e[d].getBigInt64Array().forEach(C=>s.push(Number(C))),s.length!==w||t>=18&&s.length===r.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(r.axes.length>0){if(u.length!==r.axes.length)throw new Error(\\'Resize requires \"scales\" input size to be of axes rank when axes attributes is specified\\');if(s.length!==r.axes.length)throw new Error(\\'Resize requires \"sizes\" input size to be of rank axes rank when axes attributes is specified\\')}if(typeof u<\"u\"&&typeof s<\"u\"&&u.length>0&&s.length>w)throw new Error(\"Resize requires only of scales or sizes to be specified\")},Py=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case\"asymmetric\":return`return ${r}(xResized) / ${r}(xScale);`;case\"pytorch_half_pixel\":return`if (lengthResized > 1) {\\n                    return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;\\n                  } else {\\n                    return 0.0;\\n                  }`;case\"tf_half_pixel_for_nn\":return`return (${r}(xResized) + 0.5) / ${r}(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 = ${r}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\\n                    let fract =\\n                        ${r}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${r}(lengthResized - 1);\\n                    return whole + fract;\\n                  }`;case\"tf_crop_and_resize\":return`if (lengthResized > 1) {\\n                    return ${r}(roiStart) * ${r}(lengthOriginal - 1) +\\n                        (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /\\n                        ${r}(lengthResized - 1);\\n                  } else {\\n                    return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);\\n                  }`;case\"half_pixel_symmetric\":return`const outputWidth = ${r}xScale * ${r}(lengthResized);\\n                  const adjustment = ${r}(lengthResized) / outputWidth;\\n                  const center = ${r}(lengthOriginal) / 2;\\n                  const offset = center * (1 - adjustment);\\n                  return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case\"half_pixel\":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+\"}\",Oy=(e,r,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);                   }\";case\"simple\":default:if(r<11)return\"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }\";throw new Error(`Nearest mode ${e} is not supported`)}})()+\"}\",ky=(e,r,t)=>{let u=new Array(t).fill(0).concat(new Array(t).fill(1)),s=e.length===0?u:e.slice();return r.length>0?(r.forEach((c,m)=>{u[c]=s[m],u[m+t]=s[r.length+m]}),u):s},Ry=(e,r,t,u)=>{let s=[];if(t.length>0)if(u.length>0){if(e.forEach(c=>s.push(c)),Math.max(...u)>e.length)throw new Error(\"axes is out of bound\");u.forEach((c,m)=>s[c]=t[m])}else t.forEach(c=>s.push(c));else{if(r.length===0)throw new Error(\"Resize requires either scales or sizes.\");s=e.map((c,m)=>Math.round(c*r[m]))}return s},Dy=(e,r,t)=>{let u=(()=>{switch(t.keepAspectRatioPolicy){case\"not_larger\":return t.axes.length>0?Math.min(...t.axes.map(c=>r[c]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case\"not_smaller\":return t.axes.length>0?Math.max(...t.axes.map(c=>r[c]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let s=e.slice();return t.axes.length>0?(t.axes.forEach(c=>r[c]=u),t.axes.forEach(c=>s[c]=Math.round(e[c]*r[c]))):(r.fill(u,0,r.length),s.forEach((c,m)=>s[m]=Math.round(c*r[m]))),s},By=(e,r,t,u,s)=>`\\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 = ${we(\"uniforms.scales\",\"i\",u)};\\n        var roi_low = ${we(\"uniforms.roi\",\"i\",s)};\\n        var roi_hi = ${we(\"uniforms.roi\",`i + ${r.length}`,s)};\\n        if (scale == 1.0) {\\n          original_indices[i] = ${e.type.value}(output_index);\\n        } else {\\n          var input_shape_i = ${we(\"uniforms.input_shape\",\"i\",r.length)};\\n          var output_shape_i = ${we(\"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    }`,My=(e,r,t,u,s,c,m)=>`\\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {\\n      var input_indices: ${e.type.indices};\\n      for (var i:u32 = 0; i < ${u.length}; i++) {\\n        var output_index = ${r.indicesGet(\"output_indices\",\"i\")};\\n        var input_index: u32;\\n        var scale = ${we(\"uniforms.scales\",\"i\",s)};\\n        if (scale == 1.0) {\\n          input_index = output_index;\\n        } else {\\n          var roi_low = ${we(\"uniforms.roi\",\"i\",c)};\\n          var roi_hi = ${we(\"uniforms.roi\",`i + ${t.length}`,c)};\\n          var input_shape_i = ${we(\"uniforms.input_shape\",\"i\",t.length)};\\n          var output_shape_i = ${we(\"uniforms.output_shape\",\"i\",u.length)};\\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\\n                                                                        input_shape_i, roi_low, roi_hi);\\n          if (!${m} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {\\n            if (original_idx < 0) {\\n              input_index = 0;\\n            } else if (original_idx > ${r.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    }`,zy=(e,r)=>`\\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\\n      for (var i:u32 = 0; i < ${r.length}; i++) {\\n        var input_index = ${e.indicesGet(\"input_indices\",\"i\")};\\n        if (input_index < 0 || input_index >= ${we(\"uniforms.input_shape\",\"i\",r.length)}) {\\n          return false;\\n        }\\n      }\\n      return true;\\n    }`,wc=(e,r,t,u)=>e.rank>u?`\\n    ${e.indicesSet(\"input_indices\",r,\"channel\")};\\n    ${e.indicesSet(\"input_indices\",t,\"batch\")};\\n`:\"\",Uy=(e,r,t,u,s)=>{let[m,y,d,w]=t.length===2?[-1,0,1,-1]:[0,2,3,1],C=e.type.value;return`\\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${C} {\\n      var input_indices: ${e.type.indices};\\n      ${e.indicesSet(\"input_indices\",y,`max(0, min(row, ${t[y]} - 1))`)};\\n      ${e.indicesSet(\"input_indices\",d,`max(0, min(col, ${t[d]} - 1))`)};\\n      ${wc(e,w,m,2)}\\n      return ${e.getByIndices(\"input_indices\")};\\n    }\\n\\n    fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${C} {\\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\\n      var row:${C} = originalIndices[${y}];\\n      var col:${C} = originalIndices[${d}];\\n      ${u?`if (row < 0 || row > (${t[y]} - 1) || col < 0 || col > (${t[d]} - 1)) {\\n        return ${s};\\n      }`:\"\"};\\n      row = max(0, min(row, ${t[y]} - 1));\\n      col = max(0, min(col, ${t[d]} - 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 = ${t.length>2?`u32(originalIndices[${w}])`:\"0\"};\\n      var batch: u32 =  ${t.length>2?`u32(originalIndices[${m}])`:\"0\"};\\n      var x11: ${C} = getInputValue(batch, channel, row1, col1);\\n      var x12: ${C} = getInputValue(batch, channel, row1, col2);\\n      var x21: ${C} = getInputValue(batch, channel, row2, col1);\\n      var x22: ${C} = getInputValue(batch, channel, row2, col2);\\n      var dx1: ${C} = abs(row - ${C}(row1));\\n      var dx2: ${C} = abs(${C}(row2) - row);\\n      var dy1: ${C} = abs(col - ${C}(col1));\\n      var dy2: ${C} = abs(${C}(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    }`},jy=(e,r,t,u,s,c,m,y,d,w)=>{let C=t.length===2,$=!0,[T,O]=C?[0,1]:$?[2,3]:[1,2],A=e.type.value,P=z=>{let D=z===T?\"row\":\"col\";return`\\n      fn ${D}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${r.type.indices}) -> ${A} {\\n        var output_index = ${r.indicesGet(\"output_indices\",z)};\\n        var originalIdx: ${A} = getOriginalCoordinateFromResizedCoordinate(output_index, ${s[z]},\\n        ${u[z]}, ${t[z]}, ${c[z]}, ${c[z]} + ${t.length});\\n        var fractOriginalIdx: ${A} = originalIdx - floor(originalIdx);\\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\\n\\n        if (${y} && (originalIdx < 0 || originalIdx > (${t[z]} - 1))) {\\n          return ${d};\\n        }\\n        var data: array<${A}, 4> = array<${A}, 4>(0.0, 0.0, 0.0, 0.0);\\n        for (var i: i32 = -1; i < 3; i++) {\\n          var ${D}: ${A} = originalIdx + ${A}(i);\\n          if (${D} < 0 || ${D} >= ${t[z]}) {\\n            ${(()=>w?`coefs[i + 1] = 0.0;\\n                        continue;`:y?`return ${d};`:`${D} = max(0, min(${D}, ${t[z]} - 1));`)()};\\n          }\\n        var input_indices_copy: ${e.type.indices} = input_indices;\\n          ${e.indicesSet(\"input_indices_copy\",z,`u32(${D})`)};\\n          data[i + 1] = ${z===T?e.getByIndices(\"input_indices_copy\"):\"rowCubicInterpolation(input_indices_copy, output_indices)\"};\\n        }\\n        return cubicInterpolation1D(data, coefs);\\n      }`};return`\\n    ${P(T)};\\n    ${P(O)};\\n  fn getCubicInterpolationCoefs(s: ${A}) -> array<${A}, 4> {\\n    var absS = abs(s);\\n    var coeffs: array<${A}, 4> = array<${A}, 4>(0.0, 0.0, 0.0, 0.0);\\n    var oneMinusAbsS: ${A} = 1.0 - absS;\\n    var twoMinusAbsS: ${A} = 2.0 - absS;\\n    var onePlusAbsS: ${A} = 1.0 + absS;\\n    coeffs[0] = ((${m} * onePlusAbsS - 5 * ${m}) * onePlusAbsS + 8 * ${m}) * onePlusAbsS - 4 * ${m};\\n    coeffs[1] = ((${m} + 2) * absS - (${m} + 3)) * absS * absS + 1;\\n    coeffs[2] = ((${m} + 2) * oneMinusAbsS - (${m} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\\n    coeffs[3] = ((${m} * twoMinusAbsS - 5 * ${m}) * twoMinusAbsS + 8 * ${m}) * twoMinusAbsS - 4 * ${m};\\n    return coeffs;\\n  }\\n\\n  fn cubicInterpolation1D(x: array<${A}, 4>, coefs: array<${A}, 4>) -> ${A} {\\n    var coefsSum: ${A} = 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: ${r.type.indices}) -> ${A} {\\n    var input_indices: ${e.type.indices} = output_indices;\\n    return colCubicInterpolation(input_indices, output_indices);\\n  }\\n    `},Vy=(e,r,t,u,s)=>{let[m,y,d,w,C]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],$=e.type.value;return`\\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${$} {\\n      var input_indices: ${e.type.indices};\\n      ${e.indicesSet(\"input_indices\",y,`max(0, min(depth, ${t[y]} - 1))`)};\\n      ${e.indicesSet(\"input_indices\",d,`max(0, min(height, ${t[d]} - 1))`)};\\n      ${e.indicesSet(\"input_indices\",w,`max(0, min(width, ${t[w]} - 1))`)};\\n      ${wc(e,C,m,3)}\\n      return ${e.getByIndices(\"input_indices\")};\\n    }\\n\\n    fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${$} {\\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\\n      var depth:${$} = originalIndices[${y}];\\n      var height:${$} = originalIndices[${d}];\\n      var width:${$} = originalIndices[${w}];\\n      ${u?`if (depth < 0 || depth > (${t[y]} - 1) || height < 0 || height > (${t[d]} - 1) || width < 0 || (width > ${t[w]} - 1)) {\\n      return ${s};\\n        }`:\"\"};\\n\\n    depth = max(0, min(depth, ${t[y]} - 1));\\n      height = max(0, min(height, ${t[d]} - 1));\\n      width = max(0, min(width, ${t[w]} - 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 = ${t.length>3?`u32(originalIndices[${C}])`:\"0\"};\\n      var batch: u32 =  ${t.length>3?`u32(originalIndices[${m}])`:\"0\"};\\n\\n      var x111: ${$} = getInputValue(batch, channel, depth1, height1, width1);\\n      var x112: ${$} = getInputValue(batch, channel, depth1, height1, width2);\\n      var x121: ${$} = getInputValue(batch, channel, depth1, height2, width1);\\n      var x122: ${$} = getInputValue(batch, channel, depth1, height2, width2);\\n      var x211: ${$} = getInputValue(batch, channel, depth2, height1, width1);\\n      var x212: ${$} = getInputValue(batch, channel, depth2, height1, width2);\\n      var x221: ${$} = getInputValue(batch, channel, depth2, height2, width1);\\n      var x222: ${$} = getInputValue(batch, channel, depth2, height2, width2);\\n      var dx1: ${$} = abs(depth - ${$}(depth1));\\n      var dx2: ${$} = abs(${$}(depth2) - depth);\\n      var dy1: ${$} = abs(height - ${$}(height1));\\n      var dy2: ${$} = abs(${$}(height2) - height);\\n      var dz1: ${$} = abs(width - ${$}(width1));\\n      var dz2: ${$} = abs(${$}(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    }`},Ny=(e,r,t,u,s,c)=>{let m=e.dims,y=ky(c,r.axes,m.length),d=Ry(m,u,s,r.axes),w=u.slice();u.length===0&&(w=m.map((k,G)=>k===0?1:d[G]/k),r.keepAspectRatioPolicy!==\"stretch\"&&(d=Dy(m,w,r)));let C=ie(\"output\",e.dataType,d.length),$=X(\"input\",e.dataType,m.length),T=Z.size(d),O=m.length===d.length&&m.every((k,G)=>k===d[G]),A=r.coordinateTransformMode===\"tf_crop_and_resize\",P=r.extrapolationValue,z=$.type.value,D=k=>`\\n      ${O?\"\":`\\n      ${Py(r.coordinateTransformMode,z)};\\n      ${(()=>{switch(r.mode){case\"nearest\":return`\\n              ${zy($,m)};\\n              ${Oy(r.nearestMode,t,z)};\\n              ${My($,C,m,d,w.length,y.length,A)};\\n              `;case\"linear\":return`\\n              ${By(C,m,d,w.length,y.length)};\\n              ${(()=>{if(m.length===2||m.length===4)return`${Uy($,C,m,A,P)}`;if(m.length===3||m.length===5)return`${Vy($,C,m,A,P)}`;throw Error(\"Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.\")})()};\\n            `;case\"cubic\":return`\\n            ${(()=>{if(m.length===2||m.length===4)return`${jy($,C,m,d,w,y,r.cubicCoeffA,A,r.extrapolationValue,r.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      ${k.registerUniform(\"output_size\",\"u32\").registerUniform(\"scales\",\"f32\",w.length).registerUniform(\"roi\",\"f32\",y.length).declareVariables($,C)}\\n      ${k.mainStart()}\\n        ${k.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size\")}\\n        ${O?\"output[global_idx] = input[global_idx];\":`\\n        let output_indices = ${C.offsetToIndices(\"global_idx\")};\\n        var input_indices: ${$.type.indices};\\n        ${(()=>{switch(r.mode){case\"nearest\":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\\n                if (checkInputIndices(input_indices)) {\\n                  output[global_idx] = ${$.getByIndices(\"input_indices\")};\\n                } else {\\n                  output[global_idx] = ${r.extrapolationValue};\\n                }`;case\"linear\":return`output[global_idx] = ${m.length===2||m.length===4?\"bilinearInterpolation\":\"trilinearInterpolation\"}(output_indices);`;case\"cubic\":return\"output[global_idx] = bicubicInterpolation(output_indices);\";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()};\\n`}\\n      }`;return{name:\"Resize\",shaderCache:{hint:`${r.cacheKey}|${t}|${w.length>0?w:\"\"}|${s.length>0?s:\"\"}|${y.length>0?y:\"\"}|${O}|${m}`,inputDependencies:[\"rank\"]},getShaderSource:D,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(T/64)},programUniforms:[{type:12,data:T},{type:1,data:w},{type:1,data:y},...se(m,d)]})}},Wy=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},_c=(e,r)=>{let t=[],u=[],s=[],c=Wy(e);if(r.antialias!==0)throw Error(\"Only default value (0) for Antialias attribute is supported\");Ey(e.inputs,r,c,t,u,s),e.compute(Ny(e.inputs[0],r,c,t,u,s),{inputs:[0]})},$c=e=>{let r=e.antialias,t=e.axes,u=e.coordinateTransformMode,s=e.cubicCoeffA,c=e.excludeOutside!==0,m=e.extrapolationValue,y=e.keepAspectRatioPolicy,d=e.mode,w=e.nearestMode===\"\"?\"simple\":e.nearestMode;return 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input\");if(e.length>3){let m=e[3];if(m.dims.length!==1)throw new Error(\"Beta must be 1D\");if(m.dims[m.dims.length-1]!==s)throw new Error(\"Beta must have the same hidden size as input\")}if(e.length>4){let m=e[4];if(m.dims.length!==1)throw new Error(\"Bias must be 1D\");if(m.dims[m.dims.length-1]!==s)throw new Error(\"Bias must have the same hidden size as input\")}},Hy=(e,r,t,u)=>{let s=e[0].dims,c=Z.size(s),m=s,y=c,d=s.slice(-1)[0],w=u?s.slice(0,-1).concat(1):[],C=e.length>3,$=e.length>4,T=u&&t>1,O=u&&t>2,A=t>3,P=Je(d),z=[{type:12,data:y},{type:12,data:P},{type:12,data:d},{type:1,data:r.epsilon}],D=G=>{let W=[{name:\"output_size\",type:\"u32\"},{name:\"components\",type:\"u32\"},{name:\"hidden_size\",type:\"u32\"},{name:\"epsilon\",type:\"f32\"}],q=[X(\"x\",e[0].dataType,e[0].dims,P),X(\"skip\",e[1].dataType,e[1].dims,P),X(\"gamma\",e[2].dataType,e[2].dims,P)];C&&q.push(X(\"beta\",e[3].dataType,e[3].dims,P)),$&&q.push(X(\"bias\",e[4].dataType,e[4].dims,P)),q.push(ie(\"output\",e[0].dataType,m,P)),T&&q.push(ie(\"mean_output\",1,w)),O&&q.push(ie(\"inv_std_output\",1,w)),A&&q.push(ie(\"input_skip_bias_sum\",e[0].dataType,m,P));let Y=Ve(e[0].dataType);return`\\n\\n      ${G.registerUniforms(W).declareVariables(...q)}\\n\\n      ${G.mainStart()}\\n        ${G.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.output_size / uniforms.hidden_size\")}\\n        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\\n        let offset = global_idx * hidden_size_vectorized;\\n        var sum = ${at(\"f32\",P)};\\n        var squareSum = ${at(\"f32\",P)};\\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\\n          let skip_value = skip[offset + i];\\n          let bias_value = ${$?\"bias[i]\":\"0.0\"};\\n          let input_value = x[offset + i];\\n          let value = input_value + skip_value + bias_value;\\n          ${A?\"input_skip_bias_sum[offset + i] = value;\":\"\"}\\n          output[offset + i] = value;\\n          let f32_value = ${It(Y,P,\"value\")};\\n          sum += f32_value;\\n          squareSum += f32_value * f32_value;\\n        }\\n        let mean = ${pt(\"sum\",P)} / f32(uniforms.hidden_size);\\n        let inv_std_dev = inverseSqrt(${pt(\"squareSum\",P)} / f32(uniforms.hidden_size) - mean * mean + uniforms.epsilon);\\n        ${T?\"mean_output[global_idx] = mean;\":\"\"}\\n        ${O?\"inv_std_output[global_idx] = inv_std_dev;\":\"\"}\\n        for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\\n          output[offset + i] = (output[offset + i] - ${Y}(mean)) * ${Y}(inv_std_dev) * gamma[i] + ${C?\"beta[i]\":\"0.0\"};\\n        }\\n      }`},k=[{dims:m,dataType:e[0].dataType}];return t>1&&k.push({dims:w,dataType:1}),t>2&&k.push({dims:w,dataType:1}),t>3&&k.push({dims:s,dataType:e[0].dataType}),{name:\"SkipLayerNormalization\",shaderCache:{hint:`${P};${T};${O};${A}`,inputDependencies:e.map((G,W)=>\"type\")},getShaderSource:D,getRunData:()=>({outputs:k,dispatchGroup:{x:Math.ceil(y/d/64)},programUniforms:z})}},Sc=(e,r)=>{Gy(e.inputs);let u=[0];e.outputCount>1&&u.push(-3),e.outputCount>2&&u.push(-3),e.outputCount>3&&u.push(3),e.compute(Hy(e.inputs,r,e.outputCount,!1),{outputs:u})}});var Ly,gi,Fy,Ic,qy,Ky,Ac,Tc,Ec=ae(()=>{\"use strict\";$e();ke();nt();Ae();Ly=(e,r)=>{if(!e||e.length<1)throw new Error(\"too few inputs\");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error(\"axes, starts and ends must have the same length\")}else if(r.starts.length!==r.ends.length)throw new Error(\"starts and ends must have the same length\");e.slice(1).forEach((t,u)=>{if(e[u+1].dataType!==6&&e[u+1].dataType!==7)throw new Error(`Input ${u} must be an array of int32 or int64`)})},gi=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(u=>t.push(Number(u)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(u=>t.push(Number(u)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},Fy=(e,r)=>{if(e.length>1){let t=gi(e,1),u=gi(e,2),s=gi(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),Oe({starts:t,ends:u,axes:s})}else return r},Ic=(e,r,t,u,s)=>{let c=e;return e<0&&(c+=t[u[r]]),s[r]<0?Math.max(0,Math.min(c,t[u[r]]-1)):Math.max(0,Math.min(c,t[u[r]]))},qy=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {\\n          var input_indices: ${e.type.indices};\\n          var carry = 0u;\\n          for (var i = ${t.length}; i >= 0; i--) {\\n            let input_shape_i = ${we(\"uniforms.input_shape\",\"i\",t.length)};\\n            let steps_i = ${we(\"uniforms.steps\",\"i\",t.length)};\\n            let signs_i = ${we(\"uniforms.signs\",\"i\",t.length)};\\n            let starts_i = ${we(\"uniforms.starts\",\"i\",t.length)};\\n            var output_index = ${r.indicesGet(\"output_indices\",\"i\")};\\n            var input_index = output_index * steps_i + starts_i + carry;\\n            carry = input_index / input_shape_i;\\n            input_index = input_index % input_shape_i;\\n            if (signs_i < 0) {\\n              input_index = input_shape_i - input_index - 1u + starts_i;\\n            }\\n            ${e.indicesSet(\"input_indices\",\"i\",\"input_index\")};\\n          }\\n          return input_indices;\\n      }`,Ky=(e,r)=>{let t=e[0].dims,u=Z.size(t),s=r.axes.length>0?Z.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],c=gi(e,4);c.forEach(D=>D!==0||(()=>{throw new Error(\"step cannot be 0\")})),c.length===0&&(c=Array(s.length).fill(1));let m=r.starts.map((D,k)=>Ic(D,k,t,s,c)),y=r.ends.map((D,k)=>Ic(D,k,t,s,c));if(s.length!==m.length||s.length!==y.length)throw new Error(\"start, ends and axes should have the same number of elements\");if(s.length!==t.length)for(let D=0;D<t.length;++D)s.includes(D)||(m.splice(D,0,0),y.splice(D,0,t[D]),c.splice(D,0,1));let d=c.map(D=>Math.sign(D));c.forEach((D,k,G)=>{if(D<0){let W=(y[k]-m[k])/D,q=m[k],Y=q+W*c[k];m[k]=Y,y[k]=q,G[k]=-D}});let w=t.slice(0);s.forEach((D,k)=>{w[D]=Math.ceil((y[D]-m[D])/c[D])});let C={dims:w,dataType:e[0].dataType},$=ie(\"output\",e[0].dataType,w.length),T=X(\"input\",e[0].dataType,e[0].dims.length),O=Z.size(w),A=[{name:\"outputSize\",type:\"u32\"},{name:\"starts\",type:\"u32\",length:m.length},{name:\"signs\",type:\"i32\",length:d.length},{name:\"steps\",type:\"u32\",length:c.length}],P=[{type:12,data:O},{type:12,data:m},{type:6,data:d},{type:12,data:c},...se(e[0].dims,w)],z=D=>`\\n      ${D.registerUniforms(A).declareVariables(T,$)}\\n        ${qy(T,$,t)}\\n        ${D.mainStart()}\\n          ${D.guardAgainstOutOfBoundsWorkgroupSizes(\"uniforms.outputSize\")}\\n          let output_indices = ${$.offsetToIndices(\"global_idx\")};\\n          let input_indices = calculateInputIndices(output_indices);\\n          ${$.setByOffset(\"global_idx\",T.getByIndices(\"input_indices\"))}\\n      }`;return{name:\"Slice\",shaderCache:{hint:`${d.length}_${m.length}_${c.length}`,inputDependencies:[\"rank\"]},getShaderSource:z,getRunData:()=>({outputs:[C],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:P})}},Ac=(e,r)=>{Ly(e.inputs,r);let t=Fy(e.inputs,r);e.compute(Ky(e.inputs,t),{inputs:[0]})},Tc=e=>{let r=e.starts,t=e.ends,u=e.axes;return Oe({starts:r,ends:t,axes:u})}});var Yy,Zy,Pc,Oc,kc=ae(()=>{\"use strict\";$e();ke();nt();Ae();Yy=e=>{if(!e||e.length!==1)throw new Error(\"Softmax op requires 1 input.\")},Zy=(e,r)=>{let t=e.dims,u=Z.size(t),s=64,c=r.axis;if(c<0&&(c=t.length+c),c<t.length-1)throw new Error(\"softmax only supports last axis for now.\");let m=t[c],y=u/m,d=Je(m),w=m/d,C=(z,D)=>D===4?`max(max(${z}.x, ${z}.y), max(${z}.z, ${z}.w))`:D===2?`max(${z}.x, ${z}.y)`:D===3?`max(max(${z}.x, ${z}.y), ${z}.z)`:z,$=X(\"x\",e.dataType,e.dims,d),T=ie(\"result\",e.dataType,e.dims,d),O=$.type.value,A=Ve(e.dataType)===\"f32\"?`var threadMax = ${O}(-3.402823e+38f);`:`var threadMax = ${O}(-65504.0h);`,P=z=>`\\n      var<workgroup> rowMaxShared : ${O};\\n      var<workgroup> rowSumShared : ${O};\\n      var<workgroup> threadShared : array<${O}, ${s}>;\\n\\n      fn getValue(row: i32, col: i32, row_stride: i32) -> ${O} {\\n        let index = row * row_stride + col;\\n        return x[index];\\n      }\\n\\n      fn setValue(row: i32, col: i32, row_stride: i32, value: ${O}) {\\n        let index = row * row_stride + col;\\n        result[index] = value;\\n      }\\n      ${z.registerUniform(\"packedCols\",\"i32\").declareVariables($,T)}\\n      ${z.mainStart()}\\n        let gindex = i32(global_idx);\\n        let lindex = i32(local_idx);\\n        const wg = ${s};\\n        let row = gindex / wg;\\n        let cols = uniforms.packedCols;\\n        let row_stride : i32 = uniforms.packedCols;\\n\\n        // find the rows max\\n        ${A}\\n        for (var col = lindex; col < cols; col += wg) {\\n          let value = getValue(row, col, row_stride);\\n          threadMax = max(threadMax, value);\\n        }\\n        if (lindex < cols) {\\n          threadShared[lindex] = threadMax;\\n        }\\n        workgroupBarrier();\\n\\n        var reduceSize = min(cols, wg);\\n        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {\\n          reduceSize = currSize + (reduceSize & 1);\\n          if (lindex < currSize) {\\n            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\\n    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c=u[s],m=c.kernelId,y=this.kernels.get(m),d=y.kernelType,w=y.kernelName,C=c.programName,$=c.inputTensorViews,T=c.outputTensorViews,O=t[s*2],A=t[s*2+1];typeof this.queryTimeBase>\"u\"&&(this.queryTimeBase=O);let P=Number(O-this.queryTimeBase),z=Number(A-this.queryTimeBase);if(!Number.isSafeInteger(P)||!Number.isSafeInteger(z))throw new RangeError(\"incorrect timestamp range\");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:$.map(D=>({dims:D.dims,dataType:Ft(D.dataType)})),outputsMetadata:T.map(D=>({dims:D.dims,dataType:Ft(D.dataType)})),kernelId:m,kernelType:d,kernelName:w,programName:C,startTime:P,endTime:z});else{let D=\"\";$.forEach((G,W)=>{D+=`input[${W}]: [${G.dims}] | ${Ft(G.dataType)}, `});let k=\"\";T.forEach((G,W)=>{k+=`output[${W}]: [${G.dims}] | ${Ft(G.dataType)}, `}),console.log(`[profiling] kernel \"${m}|${d}|${w}|${C}\" ${D}${k}execution time: ${z-P} 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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'});var yr,Tt,Ai,Wn,Nn,Ua,Wa,jr,Ur,hv,Un,Gp,Lp,Hp,Fp,qp,Kp,Yp,Na=ne(()=>{\"use strict\";It();Np();zr();yr=()=>!!Me.wasm.proxy&&typeof document<\"u\",Ai=!1,Wn=!1,Nn=!1,Wa=new Map,jr=(e,t)=>{let r=Wa.get(e);r?r.push(t):Wa.set(e,[t])},Ur=()=>{if(Ai||!Wn||Nn||!Tt)throw new Error(\"worker not 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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//# sourceMappingURL=ort.webgpu.min.js.map\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn 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