diff --git "a/assets/index-CadZQhnH.js" "b/assets/index-CadZQhnH.js"
deleted file mode 100644--- "a/assets/index-CadZQhnH.js"
+++ /dev/null
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Da=!1,ch(e,r,s);Da=(e.flags&131072)!==0}else Da=!1,Cn&&(r.flags&1048576)!==0&&oc(r,Wl,r.index);switch(r.lanes=0,r.tag){case 16:e:{e=r.pendingProps;var u=r.elementType,m=u._init;if(u=m(u._payload),r.type=u,typeof u=="function")Vl(u)?(e=Ai(u,e),r.tag=1,r=Ff(null,r,u,e,s)):(r.tag=0,r=il(null,r,u,e,s));else{if(u!=null){if(m=u.$$typeof,m===A){r.tag=11,r=yu(null,r,u,e,s);break e}else if(m===x){r.tag=14,r=Df(null,r,u,e,s);break e}}throw r=le(u)||u,Error(i(306,r,""))}}return r;case 0:return il(e,r,r.type,r.pendingProps,s);case 1:return u=r.type,m=Ai(u,r.pendingProps),Ff(e,r,u,m,s);case 3:e:{if(Be(r,r.stateNode.containerInfo),e===null)throw Error(i(387));u=r.pendingProps;var b=r.memoizedState;m=b.element,tu(e,r),Ho(r,u,null,s);var R=r.memoizedState;if(u=R.cache,Is(r,Pa,u),u!==b.cache&&pc(r,[Pa],s,!0),Wo(),u=R.element,b.isDehydrated)if(b={element:u,isDehydrated:!1,cache:R.cache},r.updateQueue.baseState=b,r.memoizedState=b,r.flags&256){r=Bf(e,r,u,s);break e}else 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Ir(e){e.flags|=4}function jf(e,r){if(r.type!=="stylesheet"||(r.state.loading&4)!==0)e.flags&=-16777217;else if(e.flags|=16777216,!Gg(r)){if(r=Sr.current,r!==null&&((bn&4194048)===bn?Zr!==null:(bn&62914560)!==bn&&(bn&536870912)===0||r!==Zr))throw Go=Zl,Nd;e.flags|=8192}}function wu(e,r){r!==null&&(e.flags|=4),e.flags&16384&&(r=e.tag!==22?Y():536870912,e.lanes|=r,co|=r)}function ol(e,r){if(!Cn)switch(e.tailMode){case"hidden":r=e.tail;for(var s=null;r!==null;)r.alternate!==null&&(s=r),r=r.sibling;s===null?e.tail=null:s.sibling=null;break;case"collapsed":s=e.tail;for(var u=null;s!==null;)s.alternate!==null&&(u=s),s=s.sibling;u===null?r||e.tail===null?e.tail=null:e.tail.sibling=null:u.sibling=null}}function Hn(e){var r=e.alternate!==null&&e.alternate.child===e.child,s=0,u=0;if(r)for(var m=e.child;m!==null;)s|=m.lanes|m.childLanes,u|=m.subtreeFlags&65011712,u|=m.flags&65011712,m.return=e,m=m.sibling;else 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m=r.type;if(e!==null&&r.stateNode!=null)e.memoizedProps!==u&&Ir(r);else{if(!u){if(r.stateNode===null)throw Error(i(166));return Hn(r),null}e=ie.current,Yi(r)?Dd(r):(e=Lg(m,u,s),r.stateNode=e,Ir(r))}return Hn(r),null;case 5:if(X(r),s=r.type,e!==null&&r.stateNode!=null)e.memoizedProps!==u&&Ir(r);else{if(!u){if(r.stateNode===null)throw Error(i(166));return Hn(r),null}if(e=ie.current,Yi(r))Dd(r);else{switch(m=bd(ce.current),e){case 1:e=m.createElementNS("http://www.w3.org/2000/svg",s);break;case 2:e=m.createElementNS("http://www.w3.org/1998/Math/MathML",s);break;default:switch(s){case"svg":e=m.createElementNS("http://www.w3.org/2000/svg",s);break;case"math":e=m.createElementNS("http://www.w3.org/1998/Math/MathML",s);break;case"script":e=m.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild);break;case"select":e=typeof 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-`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = min(value, ${n.getByIndices("input_indices")});`,""]})},d0=(t,a)=>{Ys(t.inputs),Zs(t,"ReduceProd",a,(n,i)=>[`var value = ${i.type.storage}(1);`,"",`value *= ${n.getByIndices("input_indices")};`,""])},f0=(t,a)=>{Ys(t.inputs),Zs(t,"ReduceSum",a,(n,i)=>[`var value = ${i.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,""])},p0=(t,a)=>{Ys(t.inputs),Zs(t,"ReduceSumSquare",a,(n,i)=>[`var t = ${i.type.value}(0); var value = ${i.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += t * t;`,""])},Js=(t,a,n)=>{if(a.length===0)return n;let i=1,l=1;for(let o=0;o<a.length;o++)a.indexOf(o)===-1?i*=t[o]:l*=t[o];return l<32&&i>1024},QM=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?u0(t,a):RM(t,a)},XM=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?s0(t,a):NM(t,a)},YM=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?i0(t,a):jM(t,a)},ZM=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?o0(t,a):UM(t,a)},JM=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?l0(t,a):VM(t,a)},ew=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?c0(t,a):GM(t,a)},tw=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?d0(t,a):WM(t,a)},nw=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?f0(t,a):HM(t,a)},aw=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?p0(t,a):qM(t,a)},rw=(t,a)=>{Js(t.inputs[0].dims,a.axes,a.noopWithEmptyAxes)?r0(t,a):KM(t,a)}}),e_,sw,iw,V_,_E=it(()=>{jt(),xa(),_g(),e_=t=>{if(!t||t.length===0||t.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(t[0].dataType!==1)throw new Error("Invalid input type.")},sw=(t,a)=>{e_(t.inputs);let n=(i,l,o)=>{let c=[];for(let d=0;d<i.rank;d++)(o.indexOf(d)>=0||o.length===0)&&c.push(`input_indices[${d}] = 0;`);return[`${c.join(`
-`)}`,`var value = ${i.getByIndices("input_indices")};
-var best_index : i32 = 0;`,`if (${i.getByIndices("input_indices")} ${a.selectLastIndex>0?"<=":"<"} value) {
-         value = ${i.getByIndices("input_indices")};
-         best_index = i32(last_index);
-       }`,"",l.setByOffset("global_idx","best_index")]};t.compute(im("ArgMin",{hint:a.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],n,[a.axis],7,a.keepDims),{inputs:[0]})},iw=(t,a)=>{e_(t.inputs);let n=(i,l,o)=>{let c=[];for(let d=0;d<i.rank;d++)(o.indexOf(d)>=0||o.length===0)&&c.push(`input_indices[${d}] = 0;`);return[`${c.join(`
-`)}`,`var value = ${i.getByIndices("input_indices")};
-var best_index : i32 = 0;`,`if (${i.getByIndices("input_indices")} ${a.selectLastIndex>0?">=":">"} value) {
-         value = ${i.getByIndices("input_indices")};
-         best_index = i32(last_index);
-       }`,"",l.setByOffset("global_idx","best_index")]};t.compute(im("argMax",{hint:a.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],n,[a.axis],7,a.keepDims),{inputs:[0]})},V_=t=>$n(t)}),h0,Gh,m0,_0,g0,Xp,y0,ow,gg=it(()=>{jt(),Yt(),hg(),Jt(),h0=(t,a)=>{let n=t[0],i=t[1],l=t[2],o=t[3],c=t[4],d=t[5];if(c&&d)throw new Error("Attention cannot have both past and attention_bias");if(n.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let f=n.dims[0],p=n.dims[1],g=n.dims[2];if(l.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(i.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(i.dims[0]!==g)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(l.dims[0]!==i.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let h=l.dims[0]/3,_=h,w=_;if(a.qkvHiddenSizes.length>0){if(a.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let D of a.qkvHiddenSizes)if(D%a.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");h=a.qkvHiddenSizes[0],_=a.qkvHiddenSizes[1],w=a.qkvHiddenSizes[2]}let M=p;if(h!==_)throw new Error("qkv_hidden_sizes first element should be same as the second");if(l.dims[0]!==h+_+w)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let $=0;if(c){if(_!==w)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(c.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(c.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(c.dims[1]!==f)throw new Error('Input "past" second dimension must be batch_size');if(c.dims[2]!==a.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(c.dims[4]!==_/a.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');a.pastPresentShareBuffer||($=c.dims[3])}let L=M+$,E=-1,S=0;if(o)throw new Error("Mask not supported");if(c)throw new Error("past is not supported");if(d){if(d.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(d.dims[0]!==f||d.dims[1]!==a.numHeads||d.dims[2]!==p||d.dims[3]!==L)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:f,sequenceLength:p,pastSequenceLength:$,kvSequenceLength:M,totalSequenceLength:L,maxSequenceLength:E,inputHiddenSize:g,hiddenSize:h,vHiddenSize:w,headSize:Math.floor(h/a.numHeads),vHeadSize:Math.floor(w/a.numHeads),numHeads:a.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:a.maskFilterValue,maskType:S,scale:a.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Gh=(t,a,n)=>a&&t?`
-      let total_sequence_length_input = u32(${a.getByOffset("0")});
-      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
-      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
-      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
-      total_sequence_length = u32(${t==null?void 0:t.getByOffset("batchIdx")}) + 1;
-      var past_sequence_length: u32 = 0;
-      if (is_first_prompt == false) {
-        past_sequence_length = total_sequence_length - sequence_length;
-      }
-       `:`
-    ${n?"let past_sequence_length = uniforms.past_sequence_length":""};
-    let present_sequence_length = total_sequence_length;
-    `,m0=(t,a,n,i,l,o,c,d)=>{let f=ya(c?1:o),p=64,g=o/f;g<p&&(p=32);let h=Math.ceil(o/f/p),_=[{type:12,data:a},{type:12,data:n},{type:12,data:i},{type:12,data:l},{type:12,data:g},{type:12,data:h}],w=Qa(t.dataType,f),M=br(1,f),$=["type"];c&&$.push("type"),d&&$.push("type");let L=E=>{let S=Et("x",t.dataType,t.dims,f),D=[S],A=c?Ge("seq_lens",c.dataType,c.dims):void 0;A&&D.push(A);let C=d?Ge("total_sequence_length_input",d.dataType,d.dims):void 0;C&&D.push(C);let T=br(t.dataType),x=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
-  var<workgroup> thread_max: array<f32, ${p}>;
-  var<workgroup> thread_sum: array<f32, ${p}>;
-  ${E.registerUniforms(x).declareVariables(...D)}
-  ${E.mainStart([p,1,1])}
-    let batchIdx = workgroup_id.z / uniforms.num_heads;
-    let headIdx = workgroup_id.z % uniforms.num_heads;
-    let sequence_length = uniforms.sequence_length;
-    var total_sequence_length = uniforms.total_sequence_length;
-    ${Gh(A,C,!1)}
-    let local_offset = local_idx * uniforms.elements_per_thread;
-    let offset = (global_idx / ${p}) * uniforms.total_sequence_length + local_offset;
-    let seq_causal_length = ${c?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
-    var thread_max_vector = ${M}(-3.402823e+38f);
-    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
-      thread_max_vector = max(${M}(x[offset + i]), thread_max_vector);
-    }
-    thread_max[local_idx] = ${(()=>{switch(f){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${f}`)}})()};
-    workgroupBarrier();
-
-    var max_value =  f32(-3.402823e+38f);
-    for (var i = 0u; i < ${p}; i++) {
-      max_value = max(thread_max[i], max_value);
-    }
-
-    var sum_vector = ${M}(0);
-    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
-      sum_vector += exp(${M}(x[offset + i]) - max_value);
-    }
-    thread_sum[local_idx] = ${(()=>{switch(f){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${f}`)}})()};
-    workgroupBarrier();
-
-    var sum: f32 = 0;
-    for (var i = 0u; i < ${p}; i++) {
-      sum += thread_sum[i];
-    }
-
-    if (sum == 0) {
-      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
-        x[offset + i] = ${S.type.value}(${T}(1.0) / ${T}(seq_causal_length));
-      }
-    } else {
-      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
-        var f32input = ${M}(x[offset + i]);
-        x[offset + i] = ${S.type.value}(exp(f32input - max_value) / sum);
-      }
-    }
-      ${c?`
-        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
-          x[offset + total_seq_id] = ${S.type.value}(${T}(0));
-        }`:""};
-  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${p};${w};${f}`,inputDependencies:$},getShaderSource:L,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:l,z:a*n},programUniforms:_})}},_0=(t,a,n,i,l,o,c,d,f)=>{let p=c+o.kvSequenceLength,g=[o.batchSize,o.numHeads,o.sequenceLength,p],h=t>1&&i,_=o.kvNumHeads?o.kvNumHeads:o.numHeads,w=h?[o.batchSize,_,p,o.headSize]:void 0,M=o.nReps?o.nReps:1,$=o.scale===0?1/Math.sqrt(o.headSize):o.scale,L=ya(o.headSize),E=o.headSize/L,S=12,D={x:Math.ceil(p/S),y:Math.ceil(o.sequenceLength/S),z:o.batchSize*o.numHeads},A=[{type:12,data:o.sequenceLength},{type:12,data:E},{type:12,data:p},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:1,data:$},{type:12,data:c},{type:12,data:o.kvSequenceLength},{type:12,data:M}],C=h&&i&&Fe.size(i.dims)>0,T=["type","type"];C&&T.push("type"),l&&T.push("type"),d&&T.push("type"),f&&T.push("type");let x=[{dims:g,dataType:a.dataType,gpuDataType:0}];h&&x.push({dims:w,dataType:a.dataType,gpuDataType:0});let O=N=>{let G=Ge("q",a.dataType,a.dims,L),te=Ge("key",n.dataType,n.dims,L),se=[G,te];if(C){let ve=Ge("past_key",i.dataType,i.dims,L);se.push(ve)}l&&se.push(Ge("attention_bias",l.dataType,l.dims));let Q=d?Ge("seq_lens",d.dataType,d.dims):void 0;Q&&se.push(Q);let le=f?Ge("total_sequence_length_input",f.dataType,f.dims):void 0;le&&se.push(le);let re=Et("output",a.dataType,g),H=[re];h&&H.push(Et("present_key",a.dataType,w,L));let ee=br(1,L),oe=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
-  const TILE_SIZE = ${S}u;
-
-  var<workgroup> tileQ: array<${G.type.storage}, ${S*S}>;
-  var<workgroup> tileK: array<${G.type.storage}, ${S*S}>;
-  ${N.registerUniforms(oe).declareVariables(...se,...H)}
-  ${N.mainStart([S,S,1])}
-    // x holds the N and y holds the M
-    let headIdx = workgroup_id.z % uniforms.num_heads;
-    let kvHeadIdx = ${M===1?"headIdx":"headIdx / uniforms.n_reps"};
-    let kv_num_heads = ${M===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
-    let batchIdx = workgroup_id.z / uniforms.num_heads;
-    let m = workgroup_id.y * TILE_SIZE;
-    let n = workgroup_id.x * TILE_SIZE;
-    let sequence_length = uniforms.M;
-    var total_sequence_length = uniforms.N;
-    ${Gh(Q,le,!0)}
-    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
-    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
-    ${C&&h?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
-    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
-    ${h?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
-    var value = ${ee}(0);
-    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
-      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
-        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
-      }
-      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
-        var idx = TILE_SIZE * local_id.y + local_id.x;
-      ${C&&h?`
-              if (n + local_id.y < past_sequence_length) {
-                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
-              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
-                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
-              }`:`
-          if (n + local_id.y < uniforms.kv_sequence_length) {
-            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
-          }`}
-      ${h?`if (n + local_id.y < present_sequence_length) {
-        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
-      }`:""}
-      }
-      workgroupBarrier();
-
-      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
-          value += ${ee}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
-      }
-
-      workgroupBarrier();
-    }
-
-    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
-      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
-      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
-      var sum: f32 = ${(()=>{switch(L){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${L}`)}})()};
-        output[outputIdx] = ${re.type.value} (sum * uniforms.alpha) + ${l?"attention_bias[outputIdx]":"0.0"};
-    }
-  }`};return{name:"AttentionProbs",shaderCache:{hint:`${L};${l!==void 0};${i!==void 0};${t}`,inputDependencies:T},getRunData:()=>({outputs:x,dispatchGroup:D,programUniforms:A}),getShaderSource:O}},g0=(t,a,n,i,l,o,c=void 0,d=void 0)=>{let f=o+l.kvSequenceLength,p=l.nReps?l.nReps:1,g=l.vHiddenSize*p,h=t>1&&i,_=l.kvNumHeads?l.kvNumHeads:l.numHeads,w=h?[l.batchSize,_,f,l.headSize]:void 0,M=[l.batchSize,l.sequenceLength,g],$=12,L={x:Math.ceil(l.vHeadSize/$),y:Math.ceil(l.sequenceLength/$),z:l.batchSize*l.numHeads},E=[{type:12,data:l.sequenceLength},{type:12,data:f},{type:12,data:l.vHeadSize},{type:12,data:l.numHeads},{type:12,data:l.headSize},{type:12,data:g},{type:12,data:o},{type:12,data:l.kvSequenceLength},{type:12,data:p}],S=h&&i&&Fe.size(i.dims)>0,D=["type","type"];S&&D.push("type"),c&&D.push("type"),d&&D.push("type");let A=[{dims:M,dataType:a.dataType,gpuDataType:0}];h&&A.push({dims:w,dataType:a.dataType,gpuDataType:0});let C=T=>{let x=Ge("probs",a.dataType,a.dims),O=Ge("v",n.dataType,n.dims),N=[x,O];S&&N.push(Ge("past_value",i.dataType,i.dims));let G=c?Ge("seq_lens",c.dataType,c.dims):void 0;c&&N.push(G);let te=d?Ge("total_sequence_length_input",d.dataType,d.dims):void 0;d&&N.push(te);let se=[Et("output",a.dataType,M)];h&&se.push(Et("present_value",a.dataType,w));let Q=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
-  const TILE_SIZE = ${$}u;
-  var<workgroup> tileQ: array<${x.type.value}, ${$*$}>;
-  var<workgroup> tileV: array<${x.type.value}, ${$*$}>;
-  ${T.registerUniforms(Q).declareVariables(...N,...se)}
-  ${T.mainStart([$,$,1])}
-   let headIdx = workgroup_id.z % uniforms.num_heads;
-   let batchIdx = workgroup_id.z / uniforms.num_heads;
-   let kvHeadIdx = ${p===1?"headIdx":"headIdx / uniforms.n_reps"};
-   let kv_num_heads = ${p===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
-   let m = global_id.y;
-   let n = global_id.x;
-   let sequence_length = uniforms.M;
-   var total_sequence_length = uniforms.K;
-   ${Gh(G,te,!0)}
-   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
-   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
-   ${S&&h?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
-   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
-   ${h?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
-   var value = ${x.type.storage}(0);
-   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
-      if (m < uniforms.M && w + local_id.x < uniforms.K) {
-        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
-      }
-      if (n < uniforms.N && w + local_id.y < uniforms.K) {
-        var idx = TILE_SIZE * local_id.y + local_id.x;
-        ${S&&h?`
-        if (w + local_id.y < past_sequence_length) {
-          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
-        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
-          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
-        }
-      `:`
-            if (w + local_id.y < uniforms.kv_sequence_length) {
-              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
-            }`}
-        ${h?`
-            if (w + local_id.y < present_sequence_length) {
-          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
-        }`:""}
-      }
-     workgroupBarrier();
-     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
-       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
-     }
-     workgroupBarrier();
-   }
-
-   // we need to transpose output from BNSH_v to BSND_v
-   if (m < uniforms.M && n < uniforms.N) {
-     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
-       + headIdx * uniforms.N + n;
-     output[outputIdx] = value;
-   }
-  }`};return{name:"AttentionScore",shaderCache:{hint:`${i!==void 0};${t}`,inputDependencies:D},getRunData:()=>({outputs:A,dispatchGroup:L,programUniforms:E}),getShaderSource:C}},Xp=(t,a,n,i,l,o,c,d,f,p,g=void 0,h=void 0)=>{let _=Math.min(t.outputCount,1+(c?1:0)+(d?1:0)),w=_>1?p.pastSequenceLength:0,M=w+p.kvSequenceLength,$=f&&Fe.size(f.dims)>0?f:void 0,L=[a,n];_>1&&c&&Fe.size(c.dims)>0&&L.push(c),$&&L.push($),g&&L.push(g),h&&L.push(h);let E=t.compute(_0(_,a,n,c,$,p,w,g,h),{inputs:L,outputs:_>1?[-1,1]:[-1]})[0];t.compute(m0(E,p.batchSize,p.numHeads,w,p.sequenceLength,M,g,h),{inputs:g&&h?[E,g,h]:[E],outputs:[]});let S=[E,i];_>1&&d&&Fe.size(d.dims)>0&&S.push(d),g&&S.push(g),h&&S.push(h),t.compute(g0(_,E,i,d,p,w,g,h),{inputs:S,outputs:_>1?[0,2]:[0]})},y0=(t,a)=>{let n=[a.batchSize,a.numHeads,a.sequenceLength,a.headSize],i=a.sequenceLength,l=a.inputHiddenSize,o=a.headSize,c=12,d={x:Math.ceil(a.headSize/c),y:Math.ceil(a.sequenceLength/c),z:a.batchSize*a.numHeads},f=[t.inputs[0],t.inputs[1],t.inputs[2]],p=[{type:12,data:i},{type:12,data:l},{type:12,data:o},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:12,data:a.hiddenSize},{type:12,data:a.hiddenSize+a.hiddenSize+a.vHiddenSize}],g=h=>{let _=Et("output_q",f[0].dataType,n),w=Et("output_k",f[0].dataType,n),M=Et("output_v",f[0].dataType,n),$=Ge("input",f[0].dataType,f[0].dims),L=Ge("weight",f[1].dataType,f[1].dims),E=Ge("bias",f[2].dataType,f[2].dims),S=$.type.storage,D=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
-  const TILE_SIZE = ${c}u;
-  var<workgroup> tileInput: array<${S}, ${c*c}>;
-  var<workgroup> tileWeightQ: array<${S}, ${c*c}>;
-  var<workgroup> tileWeightK: array<${S}, ${c*c}>;
-  var<workgroup> tileWeightV: array<${S}, ${c*c}>;
-  ${h.registerUniforms(D).declareVariables($,L,E,_,w,M)}
-  ${h.mainStart([c,c,1])}
-    let batchIndex = workgroup_id.z / uniforms.num_heads;
-    let headNumber = workgroup_id.z % uniforms.num_heads;
-    let m = global_id.y;
-    let n = global_id.x;
-
-    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
-    let biasOffsetQ = headNumber * uniforms.head_size;
-    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
-    let biasOffsetV = uniforms.hidden_size + biasOffsetK;
-
-    var valueQ = ${S}(0);
-    var valueK = ${S}(0);
-    var valueV = ${S}(0);
-    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
-      if (m < uniforms.M && w + local_id.x < uniforms.K) {
-        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
-      }
-      if (n < uniforms.N && w + local_id.y < uniforms.K) {
-        let offset = n + (w + local_id.y) * uniforms.ldb;
-        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
-        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
-        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
-      }
-      workgroupBarrier();
-      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
-        let inputTileOffset = TILE_SIZE * local_id.y + k;
-        let weightTileOffset = TILE_SIZE * k + local_id.x;
-        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
-        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
-        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
-      }
-
-      workgroupBarrier();
-    }
-
-    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
-    valueQ += bias[headOffset + biasOffsetQ];
-    valueK += bias[headOffset + biasOffsetK];
-    valueV += bias[headOffset + biasOffsetV];
-
-    let offset = workgroup_id.z * uniforms.M * uniforms.N;
-    if (m < uniforms.M && n < uniforms.N) {
-      let outputIdx = offset + m * uniforms.N + n;
-      output_q[outputIdx] = valueQ;
-      output_k[outputIdx] = valueK;
-      output_v[outputIdx] = valueV;
-    }
-  }`};return t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:d,programUniforms:p}),getShaderSource:g},{inputs:f,outputs:[-1,-1,-1]})},ow=(t,a)=>{let n=h0(t.inputs,a),[i,l,o]=y0(t,n);return Xp(t,i,l,o,t.inputs[4],void 0,void 0,void 0,t.inputs[5],n)}}),b0,v0,M0,lw,gE=it(()=>{si(),jt(),Yt(),xa(),Jt(),b0=(t,a)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let n=(i,l,o)=>{let c=l.length;if(c!==i.length)throw new Error(`${o}: num dimensions != ${c}`);l.forEach((d,f)=>{if(d!==i[f])throw new Error(`${o}: dim[${f}] do not match`)})};if(t[0].dims.length>1){let i=a.format==="NHWC"?a.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,a.spatial?2:void 0);n(t[1].dims,i,"Invalid input scale"),n(t[2].dims,i,"Invalid input B"),n(t[3].dims,i,"Invalid input mean"),n(t[4].dims,i,"Invalid input var")}else n(t[1].dims,[1],"Invalid input scale"),n(t[2].dims,[1],"Invalid input B"),n(t[3].dims,[1],"Invalid input mean"),n(t[4].dims,[1],"Invalid input var")},v0=(t,a)=>{let{epsilon:n,spatial:i,format:l}=a,o=t[0].dims,c=i?ya(o[o.length-1]):1,d=l==="NHWC"&&o.length>1?c:1,f=Fe.size(o)/c,p=i,g=p?o.length:o,h=Ge("x",t[0].dataType,t[0].dims,c),_=Ge("scale",t[1].dataType,t[1].dims,d),w=Ge("bias",t[2].dataType,t[2].dims,d),M=Ge("inputMean",t[3].dataType,t[3].dims,d),$=Ge("inputVar",t[4].dataType,t[4].dims,d),L=Et("y",t[0].dataType,g,c),E=()=>{let D="";if(i)D=`let cOffset = ${o.length===1?"0u":l==="NHWC"?`outputIndices[${o.length-1}] / ${c}`:"outputIndices[1]"};`;else if(l==="NCHW")D=`
-            ${L.indicesSet("outputIndices","0","0")}
-            let cOffset = ${L.indicesToOffset("outputIndices")};`;else{D=`var cIndices = ${_.type.indices}(0);
-                       cIndices[0] = outputIndices[${o.length-1}];`;for(let A=1;A<_.rank;A++)D+=`cIndices[${A}] = outputIndices[${A}];`;D+=`let cOffset = ${_.indicesToOffset("cIndices")};`}return D},S=D=>`
-  const epsilon = ${n};
-  ${D.registerUniform("outputSize","u32").declareVariables(h,_,w,M,$,L)}
-  ${D.mainStart()}
-  ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-    var outputIndices = ${L.offsetToIndices(`global_idx * ${c}`)};
-    ${E()}
-    let scale = ${_.getByOffset("cOffset")};
-    let bias = ${w.getByOffset("cOffset")};
-    let inputMean = ${M.getByOffset("cOffset")};
-    let inputVar = ${$.getByOffset("cOffset")};
-    let x = ${h.getByOffset("global_idx")};
-    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
-    ${L.setByOffset("global_idx","value")}
-  }`;return{name:"BatchNormalization",shaderCache:{hint:`${a.epsilon}_${a.format}_${i}_${c}`,inputDependencies:p?["rank","type","type","type","type"]:void 0},getShaderSource:S,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:p?[{type:12,data:f},...Dt(o)]:[{type:12,data:f}]})}},M0=t=>$n(t),lw=(t,a)=>{let{inputs:n,outputCount:i}=t,l=M0({...a,outputCount:i});if(na.webgpu.validateInputContent&&b0(n,l),a.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");t.compute(v0(n,l))}}),w0,T0,uw,yE=it(()=>{Yt(),Jt(),w0=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(t[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},T0=t=>{let a=t[0].dims,n=t[0].dims[2],i=Fe.size(a)/4,l=t[0].dataType,o=Ge("input",l,a,4),c=Ge("bias",l,[n],4),d=Ge("residual",l,a,4),f=Et("output",l,a,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:a,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:p=>`
-  const channels = ${n}u / 4;
-  ${p.declareVariables(o,c,d,f)}
-
-  ${p.mainStart()}
-    ${p.guardAgainstOutOfBoundsWorkgroupSizes(i)}
-    let value = ${o.getByOffset("global_idx")}
-      + ${c.getByOffset("global_idx % channels")} + ${d.getByOffset("global_idx")};
-    ${f.setByOffset("global_idx","value")}
-  }`}},uw=t=>{w0(t.inputs),t.compute(T0(t.inputs))}}),x0,Pn,cw,dw,fw,pw,hw,mw,_w,gw,yw,E0,bw,vw,Mw,ww,Vp,Tw,nm,xw,Ew,Sw,Cw,Pw,Aw,$w,kw,Ow,Dw,Iw,zw,Lw,Fw,Bw,Rw,t_,Nw,G_,W_,jw,Uw,Vw,S0,C0,Gw,yg=it(()=>{jt(),Yt(),xa(),Jt(),x0=(t,a,n,i,l,o,c)=>{let d=Math.ceil(a/4),f="";typeof l=="string"?f=`${l}(a)`:f=l("a");let p=Ge("inputData",n,[d],4),g=Et("outputData",i,[d],4),h=[{name:"vec_size",type:"u32"}];return c&&h.push(...c),`
-      ${t.registerUniforms(h).declareVariables(p,g)}
-
-  ${o??""}
-
-  ${t.mainStart()}
-    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
-
-    let a = ${p.getByOffset("global_idx")};
-    ${g.setByOffset("global_idx",f)}
-  }`},Pn=(t,a,n,i,l,o=t.dataType,c,d)=>{let f=[{type:12,data:Math.ceil(Fe.size(t.dims)/4)}];return c&&f.push(...c),{name:a,shaderCache:{hint:l,inputDependencies:["type"]},getShaderSource:p=>x0(p,Fe.size(t.dims),t.dataType,o,n,i,d),getRunData:p=>({outputs:[{dims:t.dims,dataType:o}],dispatchGroup:{x:Math.ceil(Fe.size(p[0].dims)/64/4)},programUniforms:f})}},cw=t=>{t.compute(Pn(t.inputs[0],"Abs","abs"))},dw=t=>{t.compute(Pn(t.inputs[0],"Acos","acos"))},fw=t=>{t.compute(Pn(t.inputs[0],"Acosh","acosh"))},pw=t=>{t.compute(Pn(t.inputs[0],"Asin","asin"))},hw=t=>{t.compute(Pn(t.inputs[0],"Asinh","asinh"))},mw=t=>{t.compute(Pn(t.inputs[0],"Atan","atan"))},_w=t=>{t.compute(Pn(t.inputs[0],"Atanh","atanh"))},gw=t=>$n(t),yw=(t,a)=>{let n;switch(a.to){case 10:n="vec4<f16>";break;case 1:n="vec4<f32>";break;case 12:n="vec4<u32>";break;case 6:n="vec4<i32>";break;case 9:n="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${a.to}`)}t.compute(Pn(t.inputs[0],"Cast",n,void 0,a.cacheKey,a.to))},E0=t=>{let a,n,i=t.length>=2&&t[1].data!==0,l=t.length>=3&&t[2].data!==0;switch(t[0].dataType){case 1:a=i?t[1].getFloat32Array()[0]:-34028234663852886e22,n=l?t[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:a=i?t[1].getUint16Array()[0]:64511,n=l?t[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return $n({min:a,max:n})},bw=(t,a)=>{let n=a||E0(t.inputs),i=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"Clip",l=>`clamp(${l}, vec4<${i}>(uniforms.min), vec4<${i}>(uniforms.max))`,void 0,n.cacheKey,void 0,[{type:t.inputs[0].dataType,data:n.min},{type:t.inputs[0].dataType,data:n.max}],[{name:"min",type:i},{name:"max",type:i}]),{inputs:[0]})},vw=t=>{t.compute(Pn(t.inputs[0],"Ceil","ceil"))},Mw=t=>{t.compute(Pn(t.inputs[0],"Cos","cos"))},ww=t=>{t.compute(Pn(t.inputs[0],"Cosh","cosh"))},Vp=t=>$n(t),Tw=(t,a)=>{let n=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"Elu",i=>`elu_vf32(${i})`,`
-  const elu_alpha_ = ${n}(${a.alpha});
-
-  fn elu_f32(a: ${n}) -> ${n} {
-  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
-  }
-
-  fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {
-  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
-  }`,a.cacheKey))},nm=(t="f32")=>`
-const r0: ${t} = 0.3275911;
-const r1: ${t} = 0.254829592;
-const r2: ${t} = -0.284496736;
-const r3: ${t} = 1.421413741;
-const r4: ${t} = -1.453152027;
-const r5: ${t} = 1.061405429;
-
-fn erf_vf32(v: vec4<${t}>) -> vec4<${t}> {
-  let absv = abs(v);
-  let x = 1.0 / (1.0 + r0 * absv);
-  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
-}`,xw=t=>{let a=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"Erf",n=>`erf_vf32(${n})`,nm(a)))},Ew=t=>{t.compute(Pn(t.inputs[0],"Exp","exp"))},Sw=t=>{t.compute(Pn(t.inputs[0],"Floor","floor"))},Cw=t=>{let a=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"Gelu",n=>`0.5 * ${n} * (1.0 + erf_vf32(${n} * 0.7071067811865475))`,nm(a)))},Pw=(t,a)=>{let n=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"LeakyRelu",i=>`select(leaky_relu_alpha_ * ${i}, ${i}, ${i} >= vec4<${n}>(0.0))`,`const leaky_relu_alpha_ = ${n}(${a.alpha});`,a.cacheKey))},Aw=t=>{t.compute(Pn(t.inputs[0],"Not",a=>`!${a}`))},$w=t=>{t.compute(Pn(t.inputs[0],"Neg",a=>`-${a}`))},kw=t=>{t.compute(Pn(t.inputs[0],"Reciprocal",a=>`1.0/${a}`))},Ow=t=>{let a=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"Relu",n=>`select(vec4<${a}>(0.0), ${n}, ${n} > vec4<${a}>(0.0))`))},Dw=t=>{t.compute(Pn(t.inputs[0],"Sigmoid",a=>`(1.0 / (1.0 + exp(-${a})))`))},Iw=t=>$n(t),zw=(t,a)=>{let n=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"HardSigmoid",i=>`max(vec4<${n}>(0.0), min(vec4<${n}>(1.0), ${a.alpha} * ${i} + vec4<${n}>(${a.beta})))`,void 0,a.cacheKey))},Lw=t=>{t.compute(Pn(t.inputs[0],"Sin","sin"))},Fw=t=>{t.compute(Pn(t.inputs[0],"Sinh","sinh"))},Bw=t=>{t.compute(Pn(t.inputs[0],"Sqrt","sqrt"))},Rw=t=>{t.compute(Pn(t.inputs[0],"Tan","tan"))},t_=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,Nw=t=>{t.compute(Pn(t.inputs[0],"Tanh",t_))},G_=(t="f32")=>`
-const fast_gelu_a: ${t} = 0.5;
-const fast_gelu_b: ${t} = 0.7978845608028654;
-const fast_gelu_c: ${t} = 0.035677408136300125;
-
-fn tanh_v(v: vec4<${t}>) -> vec4<${t}> {
-  return ${t_("v")};
-}
-`,W_=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,jw=t=>{let a=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"FastGelu",W_,G_(a),void 0,t.inputs[0].dataType))},Uw=(t,a)=>{let n=br(t.inputs[0].dataType);return t.compute(Pn(t.inputs[0],"ThresholdedRelu",i=>`select(vec4<${n}>(0.0), ${i}, ${i} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${n}>(${a.alpha});`,a.cacheKey)),0},Vw=t=>{t.compute(Pn(t.inputs[0],"Log","log"))},S0=(t,a)=>`
-const alpha = vec4<${t}>(${a});
-const one = ${t}(1.0);
-const zero = ${t}(0.0);
-
-fn quick_gelu_impl(x: vec4<${t}>) -> vec4<${t}> {
-  let v = x *alpha;
-  var x1 : vec4<${t}>;
-  for (var i = 0; i < 4; i = i + 1) {
-    if (v[i] >= zero) {
-      x1[i] = one / (one + exp(-v[i]));
-    } else {
-      x1[i] = one - one / (one + exp(v[i]));
-    }
-  }
-  return x * x1;
-}
-`,C0=t=>`quick_gelu_impl(${t})`,Gw=(t,a)=>{let n=br(t.inputs[0].dataType);t.compute(Pn(t.inputs[0],"QuickGelu",C0,S0(n,a.alpha),a.cacheKey,t.inputs[0].dataType))}}),P0,A0,Ww,bE=it(()=>{Yt(),Jt(),yg(),P0=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(t[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},A0=t=>{let a=t[0].dims.slice();a[2]=a[2]/2;let n=Ge("input",t[0].dataType,t[0].dims,4),i=Ge("bias",t[0].dataType,[t[0].dims[2]],4),l=Et("output",t[0].dataType,a,4),o=Fe.size(a)/4,c=Qa(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:a,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:d=>`
-  const M_SQRT2 = sqrt(2.0);
-  const halfChannels = ${t[0].dims[2]/4/2}u;
-
-  ${d.declareVariables(n,i,l)}
-
-  ${nm(c)}
-
-  ${d.mainStart()}
-    ${d.guardAgainstOutOfBoundsWorkgroupSizes(o)}
-    let biasIdx = global_idx % halfChannels;
-    let batchIndex = global_idx / halfChannels;
-    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
-    let valueLeft = input[inputOffset] + bias[biasIdx];
-    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
-    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);
-
-    ${l.setByOffset("global_idx","valueLeft * geluRight")}
-  }`}},Ww=t=>{P0(t.inputs),t.compute(A0(t.inputs))}}),$0,k0,ei,Hw,qw,Kw,Qw,Xw,Yw,Zw,Jw,e1,t1,vE=it(()=>{jt(),Yt(),Jt(),$0=(t,a,n,i,l,o,c,d,f,p,g,h)=>{let _,w;typeof d=="string"?_=w=(S,D)=>`${d}((${S}),(${D}))`:typeof d=="function"?_=w=d:(_=d.scalar,w=d.vector);let M=Et("outputData",g,i.length,4),$=Ge("aData",f,a.length,4),L=Ge("bData",p,n.length,4),E;if(l)if(o){let S=Fe.size(a)===1,D=Fe.size(n)===1,A=a.length>0&&a[a.length-1]%4===0,C=n.length>0&&n[n.length-1]%4===0;S||D?E=M.setByOffset("global_idx",w(S?`${$.type.value}(${$.getByOffset("0")}.x)`:$.getByOffset("global_idx"),D?`${L.type.value}(${L.getByOffset("0")}.x)`:L.getByOffset("global_idx"))):E=`
-            let outputIndices = ${M.offsetToIndices("global_idx * 4u")};
-            let offsetA = ${$.broadcastedIndicesToOffset("outputIndices",M)};
-            let offsetB = ${L.broadcastedIndicesToOffset("outputIndices",M)};
-            ${M.setByOffset("global_idx",w(c||A?$.getByOffset("offsetA / 4u"):`${$.type.value}(${$.getByOffset("offsetA / 4u")}[offsetA % 4u])`,c||C?L.getByOffset("offsetB / 4u"):`${L.type.value}(${L.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
-          `}else E=M.setByOffset("global_idx",w($.getByOffset("global_idx"),L.getByOffset("global_idx")));else{if(!o)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let S=(D,A,C="")=>{let T=`aData[indexA${A}][componentA${A}]`,x=`bData[indexB${A}][componentB${A}]`;return`
-            let outputIndices${A} = ${M.offsetToIndices(`global_idx * 4u + ${A}u`)};
-            let offsetA${A} = ${$.broadcastedIndicesToOffset(`outputIndices${A}`,M)};
-            let offsetB${A} = ${L.broadcastedIndicesToOffset(`outputIndices${A}`,M)};
-            let indexA${A} = offsetA${A} / 4u;
-            let indexB${A} = offsetB${A} / 4u;
-            let componentA${A} = offsetA${A} % 4u;
-            let componentB${A} = offsetB${A} % 4u;
-            ${D}[${A}] = ${C}(${_(T,x)});
-          `};g===9?E=`
-            var data = vec4<u32>(0);
-            ${S("data",0,"u32")}
-            ${S("data",1,"u32")}
-            ${S("data",2,"u32")}
-            ${S("data",3,"u32")}
-            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:E=`
-            ${S("outputData[global_idx]",0)}
-            ${S("outputData[global_idx]",1)}
-            ${S("outputData[global_idx]",2)}
-            ${S("outputData[global_idx]",3)}
-          `}return`
-        ${t.registerUniform("vec_size","u32").declareVariables($,L,M)}
-
-        ${h??""}
-
-        ${t.mainStart()}
-        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
-        ${E}
-      }`},k0=(t,a,n,i,l,o,c=n.dataType)=>{let d=n.dims.map($=>Number($)??1),f=i.dims.map($=>Number($)??1),p=!Fe.areEqual(d,f),g=d,h=Fe.size(d),_=!1,w=!1,M=[p];if(p){let $=Pd.calcShape(d,f,!1);if(!$)throw new Error("Can't perform binary op on the given tensors");g=$.slice(),h=Fe.size(g);let L=Fe.size(d)===1,E=Fe.size(f)===1,S=d.length>0&&d[d.length-1]%4===0,D=f.length>0&&f[f.length-1]%4===0;M.push(L),M.push(E),M.push(S),M.push(D);let A=1;for(let C=1;C<g.length;C++){let T=d[d.length-C],x=f[f.length-C];if(T===x)A*=T;else break}A%4===0?(w=!0,_=!0):(L||E||S||D)&&(_=!0)}else _=!0;return M.push(_),{name:t,shaderCache:{hint:a+M.map($=>$.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:$=>$0($,d,f,g,_,p,w,l,n.dataType,i.dataType,c,o),getRunData:()=>({outputs:[{dims:g,dataType:c}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(Fe.size(g)/4)},...Dt(d,f,g)]})}},ei=(t,a,n,i,l,o)=>{t.compute(k0(a,l??"",t.inputs[0],t.inputs[1],n,i,o))},Hw=t=>{ei(t,"Add",(a,n)=>`${a}+${n}`)},qw=t=>{ei(t,"Div",(a,n)=>`${a}/${n}`)},Kw=t=>{ei(t,"Equal",{scalar:(a,n)=>`u32(${a}==${n})`,vector:(a,n)=>`vec4<u32>(${a}==${n})`},void 0,void 0,9)},Qw=t=>{ei(t,"Mul",(a,n)=>`${a}*${n}`)},Xw=t=>{let a=Ge("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;ei(t,"Pow",{scalar:(n,i)=>`pow_custom(${n},${i})`,vector:(n,i)=>`pow_vector_custom(${n},${i})`},`
-    fn pow_custom(a : ${a}, b : ${a}) -> ${a} {
-      if (b == ${a}(0.0)) {
-        return ${a}(1.0);
-      } else if (a < ${a}(0.0) && f32(b) != floor(f32(b))) {
-        return ${a}(pow(f32(a), f32(b))); // NaN
-      }
-      return select(sign(a), ${a}(1.0), round(f32(abs(b) % ${a}(2.0))) != 1.0) * ${a}(${a==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
-    }
-    fn pow_vector_custom(a : vec4<${a}>, b : vec4<${a}>) -> vec4<${a}> {
-      // TODO: implement vectorized pow
-      return vec4<${a}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
-    }
-      `)},Yw=t=>{ei(t,"Sub",(a,n)=>`${a}-${n}`)},Zw=t=>{ei(t,"Greater",{scalar:(a,n)=>`u32(${a}>${n})`,vector:(a,n)=>`vec4<u32>(${a}>${n})`},void 0,void 0,9)},Jw=t=>{ei(t,"Less",{scalar:(a,n)=>`u32(${a}<${n})`,vector:(a,n)=>`vec4<u32>(${a}<${n})`},void 0,void 0,9)},e1=t=>{ei(t,"GreaterOrEqual",{scalar:(a,n)=>`u32(${a}>=${n})`,vector:(a,n)=>`vec4<u32>(${a}>=${n})`},void 0,void 0,9)},t1=t=>{ei(t,"LessOrEqual",{scalar:(a,n)=>`u32(${a}<=${n})`,vector:(a,n)=>`vec4<u32>(${a}<=${n})`},void 0,void 0,9)}}),O0,D0,I0,z0,n1,a1,ME=it(()=>{jt(),Yt(),xa(),Jt(),O0=(t,a)=>{if(!t||t.length<1)throw new Error("too few inputs");let n=0,i=t[n],l=i.dataType,o=i.dims.length;t.forEach((c,d)=>{if(d!==n){if(c.dataType!==l)throw new Error("input tensors should be one type");if(c.dims.length!==o)throw new Error("input tensors should have the same shape");c.dims.forEach((f,p)=>{if(p!==a&&f!==i.dims[p])throw new Error("non concat dimensions must match")})}})},D0=(t,a)=>`
-  fn calculateInputIndex(index: u32) -> u32 {
-    let sizeInConcatAxis = array<u32, ${t}u>(${a});
-    for (var i: u32 = 0u; i < ${t}; i += 1u ) {
-      if (index < sizeInConcatAxis[i]) {
-        return i;
-      }
-    }
-    return ${t}u;
-  }`,I0=(t,a)=>{let n=t.length,i=[];for(let l=0;l<n;++l){let o=a.setByOffset("global_idx",t[l].getByIndices("indices"));n===1?i.push(o):l===0?i.push(`if (inputIndex == ${l}u) { ${o} }`):l===n-1?i.push(`else { ${o} }`):i.push(`else if (inputIndex == ${l}) { ${o} }`)}return i.join(`
-`)},z0=(t,a,n,i)=>{let l=Fe.size(n),o=new Array(t.length),c=new Array(t.length),d=0,f=[],p=[],g=[{type:12,data:l}];for(let $=0;$<t.length;++$)d+=t[$].dims[a],o[$]=d,p.push(t[$].dims.length),c[$]=Ge(`input${$}`,i,p[$]),f.push("rank"),g.push({type:12,data:o[$]});for(let $=0;$<t.length;++$)g.push(...Dt(t[$].dims));g.push(...Dt(n));let h=Et("output",i,n.length),_=h.indicesGet("indices",a),w=Array.from(Array(o.length).keys()).map($=>`uniforms.sizeInConcatAxis${$}`).join(","),M=$=>`
-
-  ${(()=>{$.registerUniform("outputSize","u32");for(let L=0;L<t.length;L++)$.registerUniform(`sizeInConcatAxis${L}`,"u32");return $.declareVariables(...c,h)})()}
-
-  ${D0(o.length,w)}
-
-  ${$.mainStart()}
-    ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-
-    var indices = ${h.offsetToIndices("global_idx")};
-
-    let inputIndex = calculateInputIndex(${_});
-    if (inputIndex != 0u) {
-      let sizeInConcatAxis = array<u32, ${o.length}u>(${w});
-      ${_} -= sizeInConcatAxis[inputIndex - 1u];
-    }
-
-    ${I0(c,h)}
-  }`;return{name:"Concat",shaderCache:{hint:`${a}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:n,dataType:i}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:g}),getShaderSource:M}},n1=(t,a)=>{let n=t.inputs,i=n[0].dims,l=Fe.normalizeAxis(a.axis,i.length);O0(n,l);let o=i.slice();o[l]=n.reduce((d,f)=>d+(f.dims.length>l?f.dims[l]:0),0);let c=n.filter(d=>Fe.size(d.dims)>0);t.compute(z0(c,l,o,n[0].dataType),{inputs:c})},a1=t=>$n({axis:t.axis})}),Bu,Ru,Nu,bg,Uu=it(()=>{jt(),Yt(),Bu=(t,a,n="f32")=>{switch(t.activation){case"Relu":return`value = max(value, ${a}(0.0));`;case"Sigmoid":return`value = (${a}(1.0) / (${a}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${a}(${n}(uniforms.clip_min)), ${a}(${n}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${a}(0.0), min(${a}(1.0), ${n}(uniforms.alpha) * value + ${n}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${n}(uniforms.alpha) * value, value, value >= ${a}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
-              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
-        `;case"":return"";default:throw new Error(`Unsupported activation ${t.activation}`)}},Ru=(t,a)=>{t.activation==="Clip"?a.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?a.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&a.push({type:1,data:t.alpha})},Nu=(t,a)=>{t.activation==="Clip"?a.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?a.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&a.push({name:"alpha",type:"f32"})},bg=t=>{let a=(t==null?void 0:t.activation)||"";if(a==="HardSigmoid"){let[n,i]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:a,alpha:n,beta:i}}else if(a==="Clip"){let[n,i]=(t==null?void 0:t.activation_params)||[AM,$M];return{activation:a,clipMax:i,clipMin:n}}else if(a==="LeakyRelu"){let[n]=(t==null?void 0:t.activation_params)||[.01];return{activation:a,alpha:n}}return{activation:a}}}),ar,r1,vg=it(()=>{ar=(t,a)=>{switch(t){case 1:return a;case 2:return`vec2<${a}>`;case 3:return`vec3<${a}>`;case 4:return`vec4<${a}>`;default:throw new Error(`${t}-component is not supported.`)}},r1=t=>`
-      ${t?"value = value + getBiasByOutputCoords(coords);":""}
-      `}),s1,wE=it(()=>{s1=t=>`
-fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
-  return dot(coords, vec4<i32>(
-      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
-}
-fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
-  return dot(coords, vec4<i32>(
-    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
-}
-`}),Wp,Mg,wg=it(()=>{jt(),Yt(),Jt(),Uu(),Wp=(t,a,n,i,l)=>{let o=i-n;return`
-      ${Array.from({length:n}).map((c,d)=>`
-      if (${Pt(a.shape,d,a.rank)} != 1) {
-        ${a.indicesSet(t,d,Pt(l,d+o,i))}
-      } else {
-        ${a.indicesSet(t,d,0)}
-      }`).join("")}
-`},Mg=(t,a,n,i,l=!1,o)=>{let c=t[0].dims,d=t[1].dims,f=c[c.length-2],p=d[d.length-1],g=c[c.length-1],h=ya(p),_=ya(g),w=ya(f),M=Fe.size(n)/h/w,$=t.length>2,L=i?i.slice(0,-2):n.slice(0,-2),E=[Fe.size(L),f,p],S=[{type:12,data:M},{type:12,data:f},{type:12,data:p},{type:12,data:g}];Ru(a,S),S.push(...Dt(L,c,d)),$&&S.push(...Dt(t[2].dims)),S.push(...Dt(E));let D=A=>{let C=mg("batch_dims",t[0].dataType,L.length),T=Ge("a",t[0].dataType,c.length,_),x=Ge("b",t[1].dataType,d.length,h),O=Et("output",t[0].dataType,E.length,h),N=Qa(O.type.tensor),G=Bu(a,O.type.value,N),te=[T,x],se="";if($){let re=l?h:1;te.push(Ge("bias",t[2].dataType,t[2].dims.length,re)),se=`${l?`value += bias[col / ${re}];`:`value += ${O.type.value}(bias[row + i]);`}`}let Q=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Nu(a,Q);let le=()=>{let re=`var a_data: ${T.type.value};`;for(let H=0;H<_;H++)re+=`
-              let b_data${H} = b[(b_offset + (k + ${H}) * uniforms.N + col) / ${h}];`;for(let H=0;H<w;H++){re+=`a_data = a[(a_offset + (row + ${H}) * uniforms.K + k) / ${_}];`;for(let ee=0;ee<_;ee++)re+=`
-            values[${H}] = fma(${x.type.value}(a_data${_===1?"":`[${ee}]`}), b_data${ee}, values[${H}]);
-`}return re};return`
-  ${A.registerUniforms(Q).registerInternalVariables(C).declareVariables(...te,O)}
-  ${A.mainStart()}
-    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-    let col = (global_idx % (uniforms.N / ${h})) * ${h};
-    var index1 = global_idx / (uniforms.N / ${h});
-    let stride1 = uniforms.M / ${w};
-    let row = (index1 % stride1) * ${w};
-    let batch = index1 / stride1;
-
-    ${n.length===2?"":`let batch_indices = ${C.offsetToIndices("batch")};`}
-
-    var a_indices: ${T.type.indices};
-    ${Wp("a_indices",T,T.rank-2,C.rank,"batch_indices")}
-    ${T.indicesSet("a_indices",T.rank-2,0)}
-    ${T.indicesSet("a_indices",T.rank-1,0)}
-    let a_offset = ${T.indicesToOffset("a_indices")};
-
-    var b_indices: ${x.type.indices};
-    ${Wp("b_indices",x,x.rank-2,C.rank,"batch_indices")}
-    ${x.indicesSet("b_indices",x.rank-2,0)}
-    ${x.indicesSet("b_indices",x.rank-1,0)}
-    let b_offset = ${x.indicesToOffset("b_indices")};
-    var values: array<${O.type.value}, ${w}>;
-    for (var k: u32 = 0u; k < uniforms.K; k = k + ${_}) {
-      ${le()}
-    }
-    for (var i = 0u; i < ${w}u; i++) {
-      var value = values[i];
-      ${se}
-      ${G}
-      let cur_indices = ${O.type.indices}(batch, row + i, col);
-      let offset = ${O.indicesToOffset("cur_indices")};
-      ${O.setByOffset(`offset / ${h}`,"value")};
-    }
-  }
-  `};return{name:"MatMulNaive",shaderCache:{hint:`${a.activation};${h};${_};${w};${l}`,inputDependencies:$?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:o?o(n):n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(M/64)},programUniforms:S}),getShaderSource:D}}}),L0,F0,H_,n_,B0,q_,R0,om,Tg=it(()=>{jt(),Yt(),Jt(),Uu(),wg(),vg(),L0=(t,a)=>t?`
-        mm_Asub[inputRow][inputCol] = mm_readA(batch,
-          kStart + inputRow,
-          globalRowStart / innerElementSize + inputCol${a?", batchIndices":""});
-        `:`
-        mm_Asub[inputRow][inputCol] = mm_readA(batch,
-          globalRow + innerRow,
-          kStart / innerElementSize + inputCol${a?", batchIndices":""});
-        `,F0=(t,a)=>t?`
-        let ACached0 = mm_Asub[k * innerElementSize][localRow];
-        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
-        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
-        ${a===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
-        for (var i = 0; i < rowPerThread; i = i + 1) {
-          acc[i] = BCached0 * ACached0[i] + acc[i];
-          acc[i] = BCached1 * ACached1[i] + acc[i];
-          acc[i] = BCached2 * ACached2[i] + acc[i];
-          ${a===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
-        }`:`
-        for (var i = 0; i < rowPerThread; i = i + 1) {
-          let ACached = mm_Asub[tileRow + i][k];
-          acc[i] = BCached0 * ACached.x + acc[i];
-          acc[i] = BCached1 * ACached.y + acc[i];
-          acc[i] = BCached2 * ACached.z + acc[i];
-          ${a===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
-        }`,H_=(t,a,n="f32",i,l=!1,o=32,c=!1,d=32)=>{let f=a[1]*t[1],p=a[0]*t[0],g=l?f:o,h=l?o:f,_=g/a[0],w=o/a[1];if(!((l&&_===4&&t[1]===4||!l&&(_===3||_===4))&&g%a[0]===0&&o%a[1]===0&&t[0]===4))throw new Error(`If transposeA ${l} is true, innerElementSize ${_} and workPerThread[1] ${t[1]} must be 4.
-      Otherwise, innerElementSize ${_} must be 3 or 4.
-  tileAWidth ${g} must be divisible by workgroupSize[0]${a[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${a[1]}. colPerThread ${t[0]} must be 4.`);return`
-var<workgroup> mm_Asub: array<array<vec${_}<${n}>, ${g/_}>, ${h}>;
-var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${p/t[0]}>, ${o}>;
-
-const rowPerThread = ${t[1]};
-const colPerThread = ${t[0]};
-const innerElementSize = ${_};
-const tileInner = ${o};
-
-@compute @workgroup_size(${a[0]}, ${a[1]}, ${a[2]})
-fn main(@builtin(local_invocation_id) localId : vec3<u32>,
-        @builtin(global_invocation_id) globalId : vec3<u32>,
-        @builtin(workgroup_id) workgroupId : vec3<u32>) {
-  let localRow = i32(localId.y);
-  let tileRow = localRow * rowPerThread;
-  let tileCol = i32(localId.x);
-
-  let globalRow =i32(globalId.y) * rowPerThread;
-  let globalCol = i32(globalId.x);
-  let batch = ${c?"0":"i32(globalId.z)"};
-  ${i?`let batchIndices = ${i.offsetToIndices("u32(batch)")};`:""}
-  let globalRowStart = i32(workgroupId.y) * ${f};
-
-  let num_tiles = ${c?`${Math.ceil(d/o)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
-  var kStart = ${c?`i32(globalId.z) * ${d}`:"0"};
-
-  var acc: array<vec4<${n}>, rowPerThread>;
-
-  // Loop over shared dimension.
-  let tileRowB = localRow * ${w};
-  for (var t = 0; t < num_tiles; t = t + 1) {
-      // Load one tile of A into local memory.
-      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
-          let inputRow = tileRow + innerRow;
-          let inputCol = tileCol;
-          ${L0(l,i)}
-      }
-
-      // Load one tile of B into local memory.
-      for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {
-          let inputRow = tileRowB + innerRow;
-          let inputCol = tileCol;
-          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${i?", batchIndices":""});
-      }
-      kStart = kStart + tileInner;
-      workgroupBarrier();
-
-      // Compute acc values for a single thread.
-      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
-          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
-          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
-          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
-          ${_===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}
-
-          ${F0(l,_)}
-      }
-
-      workgroupBarrier();
-  }
-
-  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
-      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
-  }
-}`},n_=(t,a)=>t?`
-            mm_Asub[inputRow][inputCol] = mm_readA(batch,
-              kStart + inputRow,
-              globalRowStart + inputCol${a?", batchIndices":""});
-            `:`
-            mm_Asub[inputRow][inputCol] = mm_readA(batch,
-              globalRowStart + inputRow,
-              kStart + inputCol${a?", batchIndices":""});
-            `,B0=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",q_=(t,a,n="f32",i,l=!1,o=32,c=!1,d=32,f=!1)=>{let p=t[1]*a[1],g=t[0]*a[0],h=l?p:o,_=l?o:p;if(!(_%a[1]===0&&h%a[0]===0&&o%a[1]===0))throw new Error(`tileAHight ${_} must be divisible by workgroupSize[1]${a[1]}, tileAWidth ${h} must be divisible by workgroupSize[0]${a[0]}, tileInner ${o} must be divisible by workgroupSize[1]${a[1]}`);let w=_/a[1],M=h/a[0],$=o/a[1],L=f?`
-    let localRow = i32(localId.y);
-    let localCol = i32(localId.x);
-    let globalRowStart = i32(workgroupId.y) * ${p};
-    let globalColStart = i32(workgroupId.x) * ${g};
-
-    // Loop over shared dimension.
-    for (var t = 0; t < num_tiles; t = t + 1) {
-      // Load one tile of A into local memory.
-      for (var inputRow = localRow; inputRow < ${_}; inputRow = inputRow + ${a[1]}) {
-        for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${a[0]}) {
-          ${n_(l,i)}
-        }
-      }
-      // Load one tile of B into local memory.
-      for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${a[1]}) {
-            for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${a[0]}) {
-          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
-            kStart + inputRow,
-            globalColStart + inputCol${i?", batchIndices":""});
-        }
-      }
-      kStart = kStart + tileInner;
-      workgroupBarrier();
-
-      // Compute acc values for a single thread.
-      var BCached : array<${n}, colPerThread>;
-      for (var k = 0; k < tileInner; k = k + 1) {
-        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
-          BCached[inner] = mm_Bsub[k][localCol + inner * ${a[0]}];
-        }
-        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
-          let ACached = ${l?`mm_Asub[k][localRow + innerRow * ${a[1]}];`:`mm_Asub[localRow + innerRow * ${a[1]}][k];`}
-          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
-            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
-                ACached * BCached[innerCol];
-          }
-        }
-      }
-      workgroupBarrier();
-    }
-    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
-      let gRow = globalRowStart + localRow + innerRow * ${a[1]};
-      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
-        let gCol = globalColStart + localCol + innerCol * ${a[0]};
-        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
-      }
-    }
-    `:`
-let tileRow = i32(localId.y) * rowPerThread;
-let tileCol = i32(localId.x) * colPerThread;
-
-let globalRow = i32(globalId.y) * rowPerThread;
-let globalCol = i32(globalId.x) * colPerThread;
-let globalRowStart = i32(workgroupId.y) * ${p};
-
-let tileRowA = i32(localId.y) * ${w};
-let tileColA = i32(localId.x) * ${M};
-let tileRowB = i32(localId.y) * ${$};
-// Loop over shared dimension.
-for (var t = 0; t < num_tiles; t = t + 1) {
-  // Load one tile of A into local memory.
-  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {
-    for (var innerCol = 0; innerCol < ${M}; innerCol = innerCol + 1) {
-      let inputRow = tileRowA + innerRow;
-      let inputCol = tileColA + innerCol;
-      ${n_(l,i)}
-    }
-  }
-
-  // Load one tile of B into local memory.
-  for (var innerRow = 0; innerRow < ${$}; innerRow = innerRow + 1) {
-    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
-      let inputRow = tileRowB + innerRow;
-      let inputCol = tileCol + innerCol;
-      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
-        kStart + inputRow,
-        globalCol + innerCol${i?", batchIndices":""});
-    }
-  }
-  kStart = kStart + tileInner;
-  workgroupBarrier();
-
-  // Compute acc values for a single thread.
-  var BCached : array<${n}, colPerThread>;
-  for (var k = 0; k < tileInner; k = k + 1) {
-    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
-      BCached[inner] = mm_Bsub[k][tileCol + inner];
-    }
-
-    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
-      ${B0(l)}
-      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
-        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
-      }
-    }
-  }
-
-  workgroupBarrier();
-}
-
-for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
-  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
-    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
-        acc[innerRow][innerCol]);
-  }
-}
-`;return`
-  var<workgroup> mm_Asub : array<array<${n}, ${h}>, ${_}>;
-  var<workgroup> mm_Bsub : array<array<${n}, ${g}>, ${o}>;
-  const rowPerThread = ${t[1]};
-  const colPerThread = ${t[0]};
-  const tileInner = ${o};
-
-@compute @workgroup_size(${a[0]}, ${a[1]}, ${a[2]})
-fn main(@builtin(local_invocation_id) localId : vec3<u32>,
-        @builtin(global_invocation_id) globalId : vec3<u32>,
-        @builtin(workgroup_id) workgroupId : vec3<u32>) {
-    let batch = ${c?"0":"i32(globalId.z)"};
-    ${i?`let batchIndices = ${i.offsetToIndices("u32(batch)")};`:""}
-    let num_tiles = ${c?`${Math.ceil(d/o)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
-    var kStart = ${c?`i32(globalId.z) * ${d}`:"0"};
-
-    var acc : array<array<${n}, colPerThread>, rowPerThread>;
-    ${L}
-  }
-`},R0=(t,a,n,i,l=!1)=>{let[o,c,d,f]=i,p=Qa(i[0].type.tensor);return`
-    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${o.type.indices}) -> ${ar(t,p)} {
-      var value = ${ar(t,p)}(0.0);
-      let col = colIn * ${t};
-      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
-      {
-        var aIndices: ${c.type.indices};
-        ${Wp("aIndices",c,c.rank-2,o.rank,"batchIndices")}
-        ${c.indicesSet("aIndices",c.rank-2,"u32(row)")}
-        ${c.indicesSet("aIndices",c.rank-1,"u32(colIn)")}
-        value = ${c.getByIndices("aIndices")};
-      }
-      return value;
-    }
-
-    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${o.type.indices}) -> ${ar(t,p)} {
-      var value = ${ar(t,p)}(0.0);
-      let col = colIn * ${t};
-      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
-      {
-        var bIndices: ${d.type.indices};
-        ${Wp("bIndices",d,d.rank-2,o.rank,"batchIndices")}
-        ${d.indicesSet("bIndices",d.rank-2,"u32(row)")}
-        ${d.indicesSet("bIndices",d.rank-1,"u32(colIn)")}
-        value = ${d.getByIndices("bIndices")};
-      }
-      return value;
-    }
-
-    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ar(t,p)}) {
-      let col = colIn * ${t};
-      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
-        var value = valueIn;
-        let coords = vec3<i32>(batch, row, colIn);
-        ${a?`value = value + ${l?"bias[colIn]":`${ar(t,p)}(bias[row])`};`:""}
-        ${n}
-        ${f.setByIndices("vec3<u32>(coords)","value")}
-      }
-    }
-    `},om=(t,a,n,i,l=!1,o)=>{let c=t[0].dims,d=t[1].dims,f=c.slice(0,-2),p=d.slice(0,-2),g=i?i.slice(0,-2):n.slice(0,-2),h=Fe.size(g),_=c[c.length-2],w=c[c.length-1],M=d[d.length-1],$=w%4===0&&M%4===0,L=_<=8?[4,1,1]:[4,4,1],E=[8,8,1],S=[Math.ceil(M/E[0]/L[0]),Math.ceil(_/E[1]/L[1]),Math.ceil(h/E[2]/L[2])],D=$?4:1,A=[...f,_,w/D],C=A.length,T=[...p,w,M/D],x=T.length,O=[h,_,M/D],N=[{type:6,data:_},{type:6,data:M},{type:6,data:w}];Ru(a,N),N.push(...Dt(g,A,T));let G=["rank","rank"],te=t.length>2;te&&(N.push(...Dt(t[2].dims)),G.push("rank")),N.push(...Dt(O));let se=Q=>{let le=g.length,re=mg("batchDims",t[0].dataType,le,1),H=Qa(t[0].dataType),ee=Ge("a",t[0].dataType,C,D),oe=Ge("b",t[1].dataType,x,D),ve=Et("result",t[0].dataType,O.length,D),U=[ee,oe];if(te){let Me=l?D:1;U.push(Ge("bias",t[2].dataType,t[2].dims.length,Me))}let j=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Nu(a,j);let z=Qa(ve.type.tensor),V=Bu(a,ve.type.value,z),ie=R0(D,te,V,[re,ee,oe,ve],l);return`
-  ${Q.registerUniforms(j).registerInternalVariables(re).declareVariables(...U,ve)}
-  ${ie}
-  ${$?H_(L,E,H,re):q_(L,E,H,re)}
-                   `};return{name:"MatMul",shaderCache:{hint:`${L};${a.activation};${$};${l}`,inputDependencies:G},getRunData:()=>({outputs:[{dims:o?o(n):n,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:N}),getShaderSource:se}}}),N0,i1,TE=it(()=>{jt(),To(),Jt(),Uu(),vg(),wE(),Tg(),N0=(t,a,n,i,l=!1,o,c=4,d=4,f=4,p="f32")=>{let g=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},h=N=>{switch(N){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 ${N} is not supported.`)}},_=t?`
-    let coord = vec4<i32>(batch, xRow, xCol, xCh);
-    `:`
-    let coord = vec4<i32>(batch, xCh, xRow, xCol);
-    `,w=t?`
-    let coords = vec4<i32>(
-      batch,
-      row / outWidth,
-      row % outWidth,
-      col);
-    `:`
-    let coords = vec4<i32>(
-      batch,
-      row,
-      col / outWidth,
-      col % outWidth);
-    `,M=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",$=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",L=t?"row":"col",E=t?"col":"row",S=`
-    let inChannels = i32(uniforms.w_shape[2]);
-    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
-    let outRow = ${L} / outWidth;
-    let outCol = ${L} % outWidth;
-
-    let WRow = ${E} / (i32(uniforms.w_shape[1]) * inChannels);
-    let WCol = ${E} / inChannels % i32(uniforms.w_shape[1]);
-    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
-    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
-    let xCh = ${E} % inChannels;
-    var resData = ${ar(c,p)}(0.0);
-    // The bounds checking is always needed since we use it to pad zero for
-    // the 'same' padding type.
-    if (xRow >= 0 && xRow < ${M} && xCol >= 0 && xCol < ${$}) {
-      ${_}
-      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
-      ${g(c)}
-    }
-    return resData;`,D=t?a&&i?`
-    let col = colIn * ${c};
-    ${S}`:`
-    let col = colIn * ${c};
-    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
-      ${S}
-    }
-    return ${ar(c,p)}(0.0);`:i&&n?`
-    let col = colIn * ${c};
-    ${S}`:`
-    let col = colIn * ${c};
-    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
-      ${S}
-    }
-    return ${ar(c,p)}(0.0);`,A=t?i&&n?h(d):`
-    let col = colIn * ${d};
-    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
-      ${h(d)}
-    }
-    return ${ar(d,p)}(0.0);`:`
-    let col = colIn * ${d};
-    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
-      ${h(d)}
-    }
-    return ${ar(d,p)}(0.0);`,C=ar(f,p),T=ar(t?c:d,p),x=ar(t?d:c,p),O=Bu(o,C,p);return`
-    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${T} {
-      ${t?D:A}
-    }
-
-    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${x} {
-      ${t?A:D}
-    }
-
-    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${C}) {
-      let col = colIn * ${f};
-      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
-      {
-      var value = valueIn;
-      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
-      ${w}
-      ${r1(l)}
-      ${O}
-      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
-      }
-    }`},i1=(t,a,n,i,l,o,c,d,f)=>{let p=a.format==="NHWC",g=p?t[0].dims[3]:t[0].dims[1],h=n[0],_=p?n[2]:n[3],w=p?n[1]:n[2],M=p?n[3]:n[1],$=p&&(g%4===0||g%3===0)&&M%4===0,L=p?M:_*w,E=p?_*w:M,S=[8,8,1],D=i<=8?[4,1,1]:[4,4,1],A=[Math.ceil(L/S[0]/D[0]),Math.ceil(E/S[1]/D[1]),Math.ceil(h/S[2]/D[2])];vn("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${A}`);let C=$?p&&g%4!==0?3:4:1,T=S[1]*D[1],x=S[0]*D[0],O=Math.max(S[0]*C,S[1]),N=i%T===0,G=l%x===0,te=o%O===0,se=$?[C,4,4]:[1,1,1],Q=[{type:6,data:i},{type:6,data:l},{type:6,data:o},{type:6,data:[a.pads[0],a.pads[1]]},{type:6,data:a.strides},{type:6,data:a.dilations}];Ru(a,Q),Q.push(...Dt(t[0].dims,t[1].dims));let le=["rank","rank"];c&&(Q.push(...Dt(t[2].dims)),le.push("rank")),Q.push(...Dt(n));let re=H=>{let ee=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Nu(a,ee);let oe=$?4:1,ve=Qa(t[0].dataType),U=`
-      fn setOutputAtIndex(flatIndex : i32, value : ${$?`vec4<${ve}>`:ve}) {
-        result[flatIndex] = ${$?`vec4<${ve}>`:ve}(value);
-      }
-      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${$?`vec4<${ve}>`:ve}) {
-        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
-        setOutputAtIndex(flatIndex ${$?"/ 4":""}, value);
-      }`,j=Ge("x",t[0].dataType,t[0].dims.length,C===3?1:C),z=Ge("w",t[1].dataType,t[1].dims.length,oe),V=[j,z],ie=Et("result",t[0].dataType,n.length,oe);if(c){let Me=Ge("bias",t[2].dataType,t[2].dims.length,oe);V.push(Me),U+=`
-        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${$?`vec4<${ve}>`:ve} {
-          return bias[coords.${p?"w":"y"}${$?"/ 4":""}];
-        }`}return`
-        ${s1("uniforms.result_strides")}
-        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
-        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
-        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
-        ${H.registerUniforms(ee).declareVariables(...V,ie)}
-        ${U}
-        ${N0(p,N,G,te,c,a,se[0],se[1],se[2],ve)}
-        ${$?H_(D,S,ve,void 0,!p,O):q_(D,S,ve,void 0,!p,O,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${a.cacheKey};${C};${$};${N};${G};${te};${T};${x};${O}`,inputDependencies:le},getRunData:()=>({outputs:[{dims:f?f(n):n,dataType:t[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Q}),getShaderSource:re}}}),j0,a_,Ip,U0,r_,V0,o1,l1,xE=it(()=>{jt(),To(),Yt(),Jt(),Uu(),vg(),j0=t=>{let a=1;for(let n=0;n<t.length;n++)a*=t[n];return a},a_=t=>typeof t=="number"?[t,t,t]:t,Ip=(t,a)=>a<=1?t:t+(t-1)*(a-1),U0=(t,a,n,i=1)=>{let l=Ip(a,i);return Math.floor((t[0]*(n-1)-n+l)/2)},r_=(t,a,n,i,l)=>{l==null&&(l=U0(t,a[0],i[0]));let o=[0,0,0,n];for(let c=0;c<3;c++)t[c]+2*l>=a[c]&&(o[c]=Math.trunc((t[c]-a[c]+2*l)/i[c]+1));return o},V0=(t,a,n,i,l,o,c,d,f,p)=>{let g,h,_,w;if(t==="VALID"&&(t=0),typeof t=="number"){g={top:t,bottom:t,left:t,right:t,front:t,back:t};let M=r_([a,n,i,1],[d,f,p],1,[l,o,c],t);h=M[0],_=M[1],w=M[2]}else if(Array.isArray(t)){if(!t.every(($,L,E)=>$===E[0]))throw Error(`Unsupported padding parameter: ${t}`);g={top:t[0],bottom:t[1],left:t[2],right:t[3],front:t[4],back:t[5]};let M=r_([a,n,i,1],[d,f,p],1,[l,o,c],t[0]);h=M[0],_=M[1],w=M[2]}else if(t==="SAME_UPPER"){h=Math.ceil(a/l),_=Math.ceil(n/o),w=Math.ceil(i/c);let M=(h-1)*l+d-a,$=(_-1)*o+f-n,L=(w-1)*c+p-i,E=Math.floor(M/2),S=M-E,D=Math.floor($/2),A=$-D,C=Math.floor(L/2),T=L-C;g={top:D,bottom:A,left:C,right:T,front:E,back:S}}else throw Error(`Unknown padding parameter: ${t}`);return{padInfo:g,outDepth:h,outHeight:_,outWidth:w}},o1=(t,a,n,i,l,o=!1,c="channelsLast")=>{let d,f,p,g,h;if(c==="channelsLast")[d,f,p,g,h]=t;else if(c==="channelsFirst")[d,h,f,p,g]=t;else throw new Error(`Unknown dataFormat ${c}`);let[_,,w,M,$]=a,[L,E,S]=a_(n),[D,A,C]=a_(i),T=Ip(w,D),x=Ip(M,A),O=Ip($,C),{padInfo:N,outDepth:G,outHeight:te,outWidth:se}=V0(l,f,p,g,L,E,S,T,x,O),Q=o?_*h:_,le=[0,0,0,0,0];return c==="channelsFirst"?le=[d,Q,G,te,se]:c==="channelsLast"&&(le=[d,G,te,se,Q]),{batchSize:d,dataFormat:c,inDepth:f,inHeight:p,inWidth:g,inChannels:h,outDepth:G,outHeight:te,outWidth:se,outChannels:Q,padInfo:N,strideDepth:L,strideHeight:E,strideWidth:S,filterDepth:w,filterHeight:M,filterWidth:$,effectiveFilterDepth:T,effectiveFilterHeight:x,effectiveFilterWidth:O,dilationDepth:D,dilationHeight:A,dilationWidth:C,inShape:t,outShape:le,filterShape:a}},l1=(t,a,n,i,l,o)=>{let c=o==="channelsLast";c?t[0].dims[3]:t[0].dims[1];let d=[64,1,1],f={x:n.map((L,E)=>E)},p=[Math.ceil(j0(f.x.map(L=>n[L]))/d[0]),1,1];vn("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${p}`);let g=1,h=Fe.size(n),_=[{type:12,data:h},{type:12,data:i},{type:12,data:l},{type:12,data:a.strides},{type:12,data:a.dilations}];Ru(a,_),_.push(...Dt(t[0].dims,t[1].dims));let w=["rank","rank"],M=t.length===3;M&&(_.push(...Dt(t[2].dims)),w.push("rank")),_.push(...Dt(n));let $=L=>{let E=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:i.length},{name:"pads",type:"u32",length:l.length},{name:"strides",type:"u32",length:a.strides.length},{name:"dilations",type:"u32",length:a.dilations.length}];Nu(a,E);let S=1,D=Qa(t[0].dataType),A=Ge("x",t[0].dataType,t[0].dims.length,g),C=Ge("W",t[1].dataType,t[1].dims.length,S),T=[A,C],x=Et("result",t[0].dataType,n.length,S),O="";if(M){let te=Ge("bias",t[2].dataType,t[2].dims.length,S);T.push(te),O+=`
-        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${D} {
-          return bias[${c?Pt("coords",4,5):Pt("coords",1,5)}];
-        }`}let N=ar(g,D),G=Bu(a,N,D);return`
-            ${O}
-            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
-              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
-              return ${A.getByIndices("aIndices")};
-            }
-            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
-              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
-              return ${C.getByIndices("aIndices")};
-            }
-          ${L.registerUniforms(E).declareVariables(...T,x)}
-          ${L.mainStart()}
-          ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-              let coords = ${x.offsetToIndices("global_idx")};
-              let batch = ${Pt("coords",0,A.rank)};
-              let d2 = ${c?Pt("coords",A.rank-1,A.rank):Pt("coords",1,A.rank)};
-              let xFRCCorner = vec3<u32>(${c?Pt("coords",1,A.rank):Pt("coords",2,A.rank)},
-              ${c?Pt("coords",2,A.rank):Pt("coords",3,A.rank)},
-              ${c?Pt("coords",3,A.rank):Pt("coords",4,A.rank)}) * uniforms.strides - uniforms.pads;
-              let xFCorner = xFRCCorner.x;
-              let xRCorner = xFRCCorner.y;
-              let xCCorner = xFRCCorner.z;
-              let xShapeY = ${c?Pt("uniforms.x_shape",1,A.rank):Pt("uniforms.x_shape",2,A.rank)};
-              let xShapeZ = ${c?Pt("uniforms.x_shape",2,A.rank):Pt("uniforms.x_shape",3,A.rank)};
-              let xShapeW = ${c?Pt("uniforms.x_shape",3,A.rank):Pt("uniforms.x_shape",4,A.rank)};
-              let xShapeU = ${c?Pt("uniforms.x_shape",4,A.rank):Pt("uniforms.x_shape",1,A.rank)};
-              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
-              let inputDepthVec4Remainder = xShapeU % 4;
-
-              var value = 0.0;
-              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
-                let xF = xFCorner + wF * uniforms.dilations[0];
-                if (xF < 0 || xF >= xShapeY) {
-                  continue;
-                }
-
-                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
-                  let xR = xRCorner + wR * uniforms.dilations[1];
-                  if (xR < 0 || xR >= xShapeZ) {
-                    continue;
-                  }
-
-                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
-                    let xC = xCCorner + wC * uniforms.dilations[2];
-                    if (xC < 0 || xC >= xShapeW) {
-                      continue;
-                    }
-
-                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
-                      ${c?`let xValues = vec4<f32>(
-                               getX(batch, xF, xR, xC, d1),
-                               getX(batch, xF, xR, xC, d1 + 1),
-                               getX(batch, xF, xR, xC, d1 + 2),
-                               getX(batch, xF, xR, xC, d1 + 3));
-                            `:`let xValues = vec4<f32>(
-                               getX(batch, d1, xF, xR, xC),
-                               getX(batch, d1 + 1, xF, xR, xC),
-                               getX(batch, d1 + 2, xF, xR, xC),
-                               getX(batch, d1 + 3, xF, xR, xC));
-                            `}
-                            let wValues = vec4<f32>(
-                              getW(d2, d1, wF, wR, wC),
-                              getW(d2, d1 + 1, wF, wR, wC),
-                              getW(d2, d1 + 2, wF, wR, wC),
-                              getW(d2, d1 + 3, wF, wR, wC));
-                      value += dot(xValues, wValues);
-                    }
-                    if (inputDepthVec4Remainder == 1) {
-                        ${c?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
-                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
-                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
-                    } else if (inputDepthVec4Remainder == 2) {
-                      ${c?`let xValues = vec2<f32>(
-                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
-                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
-                      `:`let xValues = vec2<f32>(
-                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
-                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
-                    `}
-                    let wValues = vec2<f32>(
-                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
-                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
-                      value += dot(xValues, wValues);
-                    } else if (inputDepthVec4Remainder == 3) {
-                      ${c?`let xValues = vec3<f32>(
-                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
-                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
-                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
-                      `:`let xValues = vec3<f32>(
-                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
-                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
-                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
-                    `}
-                    let wValues = vec3<f32>(
-                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
-                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
-                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
-                      value += dot(xValues, wValues);
-                    }
-                  }
-                }
-              }
-              ${M?"value = value + getBiasByOutputCoords(coords)":""};
-              ${G}
-              result[global_idx] = f32(value);
-          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${a.cacheKey};${c};${g};${M}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:p[0],y:p[1],z:p[2]},programUniforms:_}),getShaderSource:$}}}),u1,c1,EE=it(()=>{jt(),Yt(),Jt(),Uu(),u1=(t,a,n,i)=>{let l=t.length>2,o=l?"value += b[output_channel];":"",c=t[0].dims,d=t[1].dims,f=a.format==="NHWC",p=f?n[3]:n[1],g=p/a.group,h=f&&g>=4?ya(p):1,_=Fe.size(n)/h,w=[{type:12,data:_},{type:12,data:a.dilations},{type:12,data:[a.strides[0],a.strides[1]]},{type:12,data:[a.pads[0],a.pads[1]]},{type:12,data:g}];Ru(a,w),w.push(...Dt(c,[d[0],d[1],d[2],d[3]/h]));let M=l?["rank","rank","rank"]:["rank","rank"];w.push(...Dt([n[0],n[1],n[2],n[3]/h]));let $=L=>{let E=Et("output",t[0].dataType,n.length,h),S=Qa(E.type.tensor),D=Bu(a,E.type.value,S),A=Ge("x",t[0].dataType,c.length),C=Ge("w",t[1].dataType,d.length,h),T=[A,C];l&&T.push(Ge("b",t[2].dataType,t[2].dims,h));let x=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:a.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Nu(a,x);let O=f?`
-      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
-        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
-
-        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
-          continue;
-        }
-
-        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
-          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
-          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
-            continue;
-          }
-
-          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
-            let input_channel = in_channel_offset + wInChannel;
-            let xVal = ${A.get("batch","xHeight","xWidth","input_channel")};
-            let wVal = ${C.get("wHeight","wWidth","wInChannel","output_channel")};
-            value += xVal * wVal;
-          }
-        }
-      }
-      `:`
-      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
-        let input_channel = in_channel_offset + wInChannel;
-        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
-          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
-
-          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
-            continue;
-          }
-
-          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
-            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
-            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
-              continue;
-            }
-
-            let xVal = ${A.get("batch","input_channel","xHeight","xWidth")};
-            let wVal = ${C.get("output_channel","wInChannel","wHeight","wWidth")};
-            value += xVal * wVal;
-          }
-        }
-      }
-      `;return`
-  ${L.registerUniforms(x).declareVariables(...T,E)}
-
-  ${L.mainStart()}
-    ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-
-    let outputIndices = ${E.offsetToIndices("global_idx")};
-    let batch: u32 = outputIndices[0];
-    let output_channel: u32 = outputIndices[${f?3:1}];
-    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${f?1:2}], outputIndices[${f?2:3}]) * uniforms.strides - uniforms.pads;
-    let group_id: u32 = output_channel * ${h} / uniforms.output_channels_per_group;
-    var in_channel_offset = group_id * uniforms.w_shape[${f?2:1}];
-
-    var value: ${E.type.value} = ${E.type.value}(0);
-    ${O}
-    ${o}
-    ${D}
-    ${E.setByOffset("global_idx","value")}
-  }`};return{name:"GroupedConv",shaderCache:{hint:`${a.cacheKey}_${h}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:i?i(n):n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:w}),getShaderSource:$}},c1=(t,a,n,i)=>{let l=t.length>2,o=ya(n[3]),c=ya(n[2]),d=Fe.size(n)/o/c,f=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/o],p=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/o],g=[n[0],n[1],n[2],n[3]/o],h=[{type:12,data:d},{type:6,data:[a.strides[0],a.strides[1]]},{type:6,data:[a.pads[0],a.pads[1]]}];Ru(a,h),h.push(...Dt(f,p,g));let _=(c-1)*a.strides[1]+p[1],w=M=>{let $=Et("output",t[0].dataType,g.length,o),L=Qa($.type.tensor),E=Bu(a,$.type.value,L),S=Ge("x",t[0].dataType,f.length,o),D=Ge("w",t[1].dataType,p.length,o),A=[S,D];l&&A.push(Ge("b",t[2].dataType,t[2].dims,o));let C=l?"value += b[output_channel];":"",T=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Nu(a,T),`
-  ${M.registerUniforms(T).declareVariables(...A,$)}
-  ${M.mainStart()}
-    ${M.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-    let width0 = uniforms.output_shape[3];
-    let output_channel = global_idx % width0;
-    var index1 = global_idx / width0;
-    let width1 = uniforms.output_shape[2] / ${c}u;
-    let col = (index1 % width1) * ${c}u;
-    index1 = index1 / width1;
-    let row = index1 % uniforms.output_shape[1];
-    let batch = index1 / uniforms.output_shape[1];
-
-    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;
-
-    var x_vals: array<${S.type.value}, ${_}>;
-    var values: array<${$.type.value}, ${c}>;
-    let input_channel = output_channel;
-    // Use constant instead of uniform can give better performance for w's height/width.
-    for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) {
-      let x_height = x_corner.x + i32(w_height);
-      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
-        for (var i = 0; i < ${_}; i++) {
-          let x_width = x_corner.y + i;
-          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
-            x_vals[i] = ${S.get("batch","u32(x_height)","u32(x_width)","input_channel")};
-          } else {
-            x_vals[i] = ${S.type.value}(0);
-          }
-        }
-        for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {
-          let w_val = ${D.get("w_height","w_width","0","output_channel")};
-          for (var i = 0u; i < ${c}u; i++) {
-            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
-          }
-        }
-      }
-    }
-
-    for (var i = 0u; i < ${c}u; i++) {
-      var value = values[i];
-      ${C}
-      ${E}
-      ${$.set("batch","row","col + i","output_channel","value")};
-    }
-  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${a.cacheKey};${o};${c};${_};${p[0]};${p[1]}`,inputDependencies:l?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i?i(n):n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}),getShaderSource:w}}}),G0,Wh,W0,Hh,K_,s_,H0,q0,Q_,SE=it(()=>{Yt(),TE(),xE(),Tg(),EE(),Uu(),wg(),Sl(),G0=(t,a,n,i,l,o)=>{let c=t[0],d=t.slice(o?1:2,o?3:4),f=d.length,p=a[0],g=a.slice(2).map((_,w)=>_+(_-1)*(n[w]-1)),h=d.map((_,w)=>_+i[w]+i[w+f]).map((_,w)=>Math.floor((_-g[w]+l[w])/l[w]));return h.splice(0,0,c),h.splice(o?3:1,0,p),h},Wh=[2,3,1,0],W0=(t,a)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length>5)throw new Error("greater than 5D is not supported");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let n=t[0].dims[a.format==="NHWC"?t[0].dims.length-1:1],i=t[1].dims[1]*a.group;if(n!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let l=t[0].dims.length-2;if(a.dilations.length!==l)throw new Error(`dilations should be ${l}D`);if(a.strides.length!==l)throw new Error(`strides should be ${l}D`);if(a.pads.length!==l*2)throw new Error(`pads should be ${l*2}D`);if(a.kernelShape.length!==0&&a.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},Hh=(t,a)=>{let n=t.kernelShape.slice();n.length<a[1].dims.length-2&&n.push(...Array(a[1].dims.length-2-n.length).fill(0));for(let o=2;o<a[1].dims.length;++o)n[o-2]===0&&(n[o-2]=a[1].dims[o]);let i=t.pads.slice();sm.adjustPadsBasedOnAutoPad(a[0].dims,t.strides,t.dilations,n,i,t.format==="NHWC",t.autoPad);let l=Object.assign({},t);return Object.assign(l,{kernelShape:n,pads:i}),l},K_=t=>{let a=bg(t),n=t.format,i=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],l=t.dilations,o=t.group,c=t.kernel_shape,d=t.pads,f=t.strides,p=t.w_is_const();return{autoPad:i,format:n,dilations:l,group:o,kernelShape:c,pads:d,strides:f,wIsConst:p,...a,cacheKey:`${t.format};${a.activation};`}},s_=(t,a,n,i)=>{let l=n.format==="NHWC",o=G0(a[0].dims,a[1].dims,n.dilations,n.pads,n.strides,l);if(n.group!==1){let T=[a[0]];if(l){let x=t.kernelCustomData.wT??t.compute(as(a[1],Wh),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=x),T.push(x)}else T.push(a[1]);a.length===3&&T.push(a[2]),!t.adapterInfo.isArchitecture("ampere")&&l&&a[1].dims[0]===n.group&&a[1].dims[1]===1&&n.dilations[0]===1&&n.dilations[1]===1?t.compute(c1(T,n,o,i),{inputs:T}):t.compute(u1(T,n,o,i),{inputs:T});return}let c=a.length===3,d=a[0].dims[l?1:2],f=a[0].dims[l?2:3],p=a[0].dims[l?3:1],g=a[1].dims[2],h=a[1].dims[3],_=o[l?1:2],w=o[l?2:3],M=o[l?3:1],$=l&&g===d&&h===f&&n.pads[0]===0&&n.pads[1]===0;if($||g===1&&h===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let T=o[0],x,O,N,G=[];if(l){let Q=t.kernelCustomData.wT??t.compute(as(a[1],Wh),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=Q),$){let le=d*f*p;x=a[0].reshape([1,T,le]),O=Q.reshape([1,le,M]),N=[1,T,M]}else x=a[0].reshape([T,d*f,p]),O=Q.reshape([1,p,M]),N=[T,_*w,M];G.push(x),G.push(O)}else x=a[0].reshape([T,p,d*f]),O=a[1].reshape([1,M,p]),N=[T,M,_*w],G.push(O),G.push(x);c&&G.push(a[2]);let te=N[2],se=G[0].dims[G[0].dims.length-1];te<8&&se<8?t.compute(Mg(G,n,o,N,l,i),{inputs:G}):t.compute(om(G,n,o,N,l,i),{inputs:G});return}let L=!0,E=t.kernelCustomData.wT??t.compute(as(a[1],Wh),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=E);let S=[a[0],E];c&&S.push(a[2]);let D=l?_*w:M,A=l?M:_*w,C=g*h*p;t.compute(i1(S,n,o,D,A,C,c,L,i),{inputs:S})},H0=(t,a)=>{let n=a.format==="NHWC",i=[t.inputs[0].reshape(n?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&i.push(t.inputs[2]);let l=[0,a.pads[0],0,a.pads[1]],o=[1].concat(a.strides),c=[1].concat(a.dilations),d=[1].concat(a.kernelShape),f=Hh({...a,pads:l,strides:o,dilations:c,kernelShape:d},i);s_(t,i,f,p=>n?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},q0=(t,a,n)=>{let i=n.format==="NHWC"?"channelsLast":"channelsFirst",l=Hh(n,a),o=n.autoPad==="NOTSET"?n.pads:n.autoPad,c=o1(a[0].dims,a[1].dims,n.strides,n.dilations,o,!1,i);t.compute(l1(a,l,c.outShape,[c.filterDepth,c.filterHeight,c.filterWidth],[c.padInfo.front,c.padInfo.top,c.padInfo.left],i))},Q_=(t,a)=>{if(W0(t.inputs,a),t.inputs[0].dims.length===3)H0(t,a);else if(t.inputs[0].dims.length===5)q0(t,t.inputs,a);else{let n=Hh(a,t.inputs);s_(t,t.inputs,n)}}}),d1,CE=it(()=>{jt(),To(),Yt(),Jt(),d1=(t,a,n)=>{let i=t.length>2,l=a.outputShape,o=a.format==="NHWC",c=a.group,d=t[1].dims,f=d[2]/c,p=d[3],g=o?ya(f):1,h=o&&p===1&&f>=4,_=h?Math.floor(f/4)*4:Math.floor(f/g)*g,w=f-_,M=o?ya(p):1,$=o?p===1?g:M:1,L=Fe.size(l)/M,E=[Math.ceil(L/64),1,1];vn("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${E}`);let S=["rank","rank"],D=[a.strides[0],a.strides[1]],A=[a.kernelShape[o?1:2],a.kernelShape[o?2:3]],C=[a.dilations[0],a.dilations[1]],T=[A[0]+(a.dilations[0]<=1?0:(a.kernelShape[o?1:2]-1)*(a.dilations[0]-1)),A[1]+(a.dilations[1]<=1?0:(a.kernelShape[o?2:3]-1)*(a.dilations[1]-1))],x=[T[0]-1-Math.floor((a.pads[0]+a.pads[2])/2),T[1]-1-Math.floor((a.pads[1]+a.pads[3])/2)],O=[{type:12,data:L},{type:12,data:D},{type:12,data:A},{type:12,data:C},{type:12,data:T},{type:6,data:x},{type:12,data:_},{type:12,data:f},{type:12,data:p},...Dt(t[0].dims,t[1].dims)];i&&(O.push(...Dt(t[2].dims)),S.push("rank")),O.push(...Dt(l));let N=G=>{let te=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:D.length},{name:"filter_dims",type:"u32",length:A.length},{name:"dilations",type:"u32",length:A.length},{name:"effective_filter_dims",type:"u32",length:T.length},{name:"pads",type:"i32",length:x.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],se=Qa(t[0].dataType),Q=o?1:2,le=o?2:3,re=o?3:1,H=Ge("W",t[1].dataType,t[1].dims.length,$),ee=Ge("Dy",t[0].dataType,t[0].dims.length,g),oe=[ee,H];i&&oe.push(Ge("bias",t[2].dataType,[l[re]].length,M));let ve=Et("result",t[0].dataType,l.length,M),U=()=>{let V="";if(h)g===4?V+=`
-        let xValue = ${ee.getByOffset("x_offset")};
-        let wValue = ${H.getByOffset("w_offset")};
-        dotProd = dotProd + dot(xValue, wValue);
-        x_offset += 1u;
-        w_offset += 1u;`:g===2?V+=`
-          dotProd = dotProd + dot(vec4<${se}>(${ee.getByOffset("x_offset")}, ${ee.getByOffset("x_offset + 1u")}), vec4<${se}>(${H.getByOffset("w_offset")}, ${H.getByOffset("w_offset + 1u")}));
-          x_offset += 2u;
-          w_offset += 2u;`:g===1&&(V+=`
-          dotProd = dotProd + dot(vec4<${se}>(${ee.getByOffset("x_offset")}, ${ee.getByOffset("x_offset + 1u")}, ${ee.getByOffset("x_offset + 2u")}, ${ee.getByOffset("x_offset + 3u")}), vec4<${se}>(${H.getByOffset("w_offset")}, ${H.getByOffset("w_offset + 1u")}, ${H.getByOffset("w_offset + 2u")}, ${H.getByOffset("w_offset + 3u")}));
-          x_offset += 4u;
-          w_offset += 4u;`);else if(V+=`
-                  let xValue = ${o?ee.getByOffset(`${ee.indicesToOffset(`${ee.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${g}`):ee.get("batch","inputChannel","idyR","idyC")};
-        `,g===1)V+=`
-          let w_offset = ${H.indicesToOffset(`${H.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
-          let wValue = ${H.getByOffset(`w_offset / ${$}`)};
-          dotProd = dotProd + xValue * wValue;`;else for(let ie=0;ie<g;ie++)V+=`
-            let wValue${ie} = ${H.getByOffset(`${H.indicesToOffset(`${H.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${ie}, wOutChannel)`)} / ${$}`)};
-            dotProd = dotProd + xValue[${ie}] * wValue${ie};`;return V},j=()=>{if(w===0)return"";if(!h)throw new Error(`packInputAs4 ${h} is not true.`);let V="";if(g===1){V+="dotProd = dotProd";for(let ie=0;ie<w;ie++)V+=`
-            + ${ee.getByOffset(`x_offset + ${ie}`)} * ${H.getByOffset(`w_offset + ${ie}`)}`;V+=";"}else if(g===2){if(w!==2)throw new Error(`Invalid inputChannelsRemainder ${w}.`);V+=`
-          let xValue = ${ee.getByOffset("x_offset")};
-          let wValue = ${H.getByOffset("w_offset")};
-          dotProd = dotProd + dot(xValue, wValue);`}return V},z=`
-            let outputIndices = ${ve.offsetToIndices(`global_idx * ${M}`)};
-            let batch = ${ve.indicesGet("outputIndices",0)};
-            let d1 = ${ve.indicesGet("outputIndices",re)};
-            let r = ${ve.indicesGet("outputIndices",Q)};
-            let c = ${ve.indicesGet("outputIndices",le)};
-            let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
-            let dyRCorner = dyCorner.x;
-            let dyCCorner = dyCorner.y;
-            let groupId = d1 / uniforms.output_channels_per_group;
-            let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
-            // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
-            // ? = to be determined. : = across all values in that axis.
-            var dotProd = ${ve.type.value}(0.0);
-            var wR: u32 = 0;
-            if (uniforms.dilations.x == 1) {
-              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
-              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
-            }
-            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
-              if (wR % uniforms.dilations.x != 0) {
-                continue;
-              }
-              let dyR = (${se}(dyRCorner) + ${se}(wR)) / ${se}(uniforms.strides[0]);
-              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
-              if (dyR < 0.0 || dyR >= ${se}(uniforms.Dy_shape[${Q}]) || fract(dyR) > 0.0 ||
-                  wRPerm < 0) {
-                continue;
-              }
-              let idyR: u32 = u32(dyR);
-              var wC: u32 = 0;
-              if (uniforms.dilations.y == 1) {
-                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
-                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
-              }
-              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
-                if (wC % uniforms.dilations.y != 0) {
-                  continue;
-                }
-                let dyC = (${se}(dyCCorner) + ${se}(wC)) / ${se}(uniforms.strides.y);
-                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
-                if (dyC < 0.0 || dyC >= ${se}(uniforms.Dy_shape[${le}]) ||
-                    fract(dyC) > 0.0 || wCPerm < 0) {
-                  continue;
-                }
-                let idyC: u32 = u32(dyC);
-                var inputChannel = groupId * uniforms.input_channels_per_group;
-                ${h?`
-                var x_offset = ${ee.indicesToOffset(`${ee.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${g};
-                var w_offset = ${H.indicesToOffset(`${H.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${$};
-                  `:""}
-                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${h?4:g}) {
-                  ${U()}
-                  inputChannel = inputChannel + ${h?4:g};
-                }
-                ${j()}
-                wC = wC + uniforms.strides.y - 1;
-              }
-              wR = wR + uniforms.strides[0] - 1;
-            }
-            let value = dotProd${i?` + bias[d1 / ${M}]`:""};
-            ${ve.setByOffset("global_idx","value")};
-          `;return`
-    ${G.registerUniforms(te).declareVariables(...oe,ve)}
-      ${G.mainStart()}
-      ${G.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
-    ${z}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${a.cacheKey};${g}${$}${M}${h}${w}`,inputDependencies:S},getRunData:()=>({dispatchGroup:{x:E[0],y:E[1],z:E[2]},outputs:[{dims:n?n(l):l,dataType:t[0].dataType}],programUniforms:O}),getShaderSource:N}}}),K0,Q0,X0,i_,f1,Y0,o_,Z0,p1,PE=it(()=>{CE(),Uu(),Sl(),K0=(t,a,n,i,l,o)=>(t-1)*a+n+(i-1)*l+1-o,Q0=(t,a,n,i,l)=>{let o=Math.floor(t/2);a==="SAME_UPPER"?(n[i]=o,n[l]=t-o):a==="SAME_LOWER"&&(n[i]=t-o,n[l]=o)},X0=(t,a,n,i,l,o,c,d,f,p)=>{let g=t.length-2,h=p.length===0;f.length<g&&f.push(...Array(g-f.length).fill(0));let _=t[0],w=a[d?3:1]*l;for(let M=0,$=t.length-g-(d?1:0);M<g;++M,++$){let L=t[$],E=h?L*c[M]:p[M],S=K0(L,c[M],o[M],a[$],n[M],E);Q0(S,i,o,M,M+g),h&&p.push(c[M]*(L-1)+f[M]+(a[$]-1)*n[M]+1-o[M]-o[M+g])}p.splice(0,0,_),p.splice(d?3:1,0,w)},i_=(t,a)=>{let n=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((h,_)=>h*_,1)===0){n.length=0;for(let h=2;h<a[1].dims.length;++h)n.push(a[1].dims[h])}let i=t.format==="NHWC";n.splice(0,0,a[1].dims[0]),n.splice(i?3:1,0,a[1].dims[1]);let l=t.pads.slice(),o=t.outputShape.slice(),c=t.outputPadding.slice(),d=a[0].dims,f=t.dilations.slice();if(f.reduce((h,_)=>h+_,0)===0){let h=a[0].dims.length-2;f=new Array(h).fill(1)}let p=t.strides.slice();if(p.reduce((h,_)=>h+_,0)===0){let h=a[0].dims.length-2;p=new Array(h).fill(1)}X0(d,n,f,t.autoPad,t.group,l,p,i,c,o);let g=Object.assign({},t);return Object.assign(g,{kernelShape:n,pads:l,outputPadding:c,outputShape:o,dilations:f,strides:p}),g},f1=t=>{let a=bg(t),n=t.format,i=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],l=t.dilations,o=t.group,c=t.kernelShape,d=t.pads,f=t.strides,p=t.wIsConst(),g=t.outputPadding,h=t.outputShape;return{autoPad:i,format:n,dilations:l,group:o,kernelShape:c,outputPadding:g,outputShape:h,pads:d,strides:f,wIsConst:p,...a,cacheKey:`${t.format};${a.activation};`}},Y0=(t,a)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let n=t[0].dims[a.format==="NHWC"?t[0].dims.length-1:1],i=t[1].dims[0];if(n!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let l=t[1].dims[1]*a.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==l))throw new Error("invalid bias");let o=t[0].dims.length-2;if(a.dilations.reduce((c,d)=>c+d,0)>0&&a.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(a.strides.reduce((c,d)=>c+d,0)>0&&a.strides.length!==o)throw new Error(`strides should be ${o}D`);if(a.pads.reduce((c,d)=>c+d,0)>0&&a.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(a.outputPadding.length!==o&&a.outputPadding.length!==0)throw new Error(`output_padding should be ${o}D`);if(a.kernelShape.reduce((c,d)=>c+d,0)>0&&a.kernelShape.length!==0&&a.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(a.outputShape.length!==0&&a.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape")},o_=(t,a,n,i)=>{let l=t.kernelCustomData.wT??t.compute(as(a[1],[2,3,0,1]),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=l);let o=[a[0],l];a.length===3&&o.push(a[2]),t.compute(d1(o,n,i),{inputs:o})},Z0=(t,a)=>{let n=a.format==="NHWC",i=[t.inputs[0].reshape(n?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&i.push(t.inputs[2]);let l=a.kernelShape;(l.length===0||l[0]===0)&&(l=[t.inputs[1].dims[2]]);let o=a.dilations;(o.length===0||o[0]===0)&&(o=[1]);let c=a.strides;(c.length===0||c[0]===0)&&(c=[1]);let d=a.pads;d.length===0&&(d=[0,0]),d=[0,d[0],0,d[1]],c=[1].concat(c),o=[1].concat(o),l=[1].concat(l);let f=a.outputPadding;f=[0].concat(f);let p=i_({...a,pads:d,strides:c,dilations:o,kernelShape:l,outputPadding:f},i);o_(t,i,p,g=>n?[g[0],g[2],g[3]]:[g[0],g[1],g[3]])},p1=(t,a)=>{if(Y0(t.inputs,a),t.inputs[0].dims.length===3)Z0(t,a);else{let n=i_(a,t.inputs);o_(t,t.inputs,n)}}}),J0,h1,m1,AE=it(()=>{jt(),Yt(),xa(),Jt(),J0=(t,a,n,i)=>{let l=Fe.size(a),o=a.length,c=Ge("input",t,o),d=Et("output",t,o),f=n.dataType===6?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),p=Fe.normalizeAxis(f,o),g=h=>{let _=` i32(${c.indicesGet("inputIndices","uniforms.axis")}) `,w=Pt("uniforms.input_shape","uniforms.axis",o),M=i.reverse?_+(i.exclusive?" + 1":""):"0",$=i.reverse?w:_+(i.exclusive?"":" + 1");return`
-                ${h.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(c,d)}
-                ${h.mainStart()}
-                  ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-                  var inputIndices = ${d.offsetToIndices("global_idx")};
-                  var sum = ${d.type.value}(0);
-                  let first : i32 = ${M};
-                  let last : i32 = ${$};
-                  for (var i : i32 = first; i < last; i++) {
-                    ${c.indicesSet("inputIndices","uniforms.axis","u32(i)")};
-                    sum = sum + ${c.getByIndices("inputIndices")};
-                  }
-                  ${d.setByOffset("global_idx","sum")};
-                }`};return{name:"CumSum",shaderCache:{hint:i.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:a,dataType:t}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:12,data:p},...Dt(a,a)]}),getShaderSource:g}},h1=(t,a)=>{let n=t.inputs[0].dims,i=t.inputs[0].dataType,l=t.inputs[1];t.compute(J0(i,n,l,a),{inputs:[0]})},m1=t=>{let a=t.exclusive===1,n=t.reverse===1;return $n({exclusive:a,reverse:n})}}),eb,tb,nb,_1,g1,$E=it(()=>{jt(),Yt(),xa(),Jt(),eb=t=>{if(!t||t.length!==1)throw new Error("DepthToSpace requires 1 input.");if(t[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},tb=(t,a,n,i)=>{let l=[];l.push(`fn perm(i: ${i.type.indices}) -> ${n.type.indices} {
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-          for (var i: u32 = 0; i < ${i.length}; i++) {
-            let index = ${S.indicesGet("output_indices","uniforms.gather_axis + i")};
-            ${$.indicesSet("indices_indices","i","index")};
-          }`:`indices_indices = ${S.indicesGet("output_indices","uniforms.gather_axis")};`};
-        var data_indices = ${M.type.indices}(0);
-        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
-          let index = ${S.indicesGet("output_indices","i")};
-          ${M.indicesSet("data_indices","i","index")};
-        }
-        var index_from_indices = ${$.getByIndices("indices_indices")};
-        if (index_from_indices < 0) {
-          index_from_indices += ${n[o]};
-        }
-        ${M.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
-        for (var i = uniforms.gather_axis + 1; i < ${d.length}; i++) {
-          let index = ${S.indicesGet("output_indices",`i + ${i.length} - 1`)};
-          ${M.indicesSet("data_indices","i","index")};
-        }
-        let data_offset = ${M.indicesToOffset("data_indices")};
-        let data_index = data_offset % 8;
-        // Convert 4-bit packed data to 8-bit packed data.
-        let packed_4bit_quantized_data = ${M.getByOffset("data_offset / 8")};
-        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
-        let quantized_data_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
-        let quantized_data = quantized_data_vec[data_index / 2];
-        var scale_indices = data_indices;
-        let quantize_axis_index = ${L.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
-        ${L.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
-        var scale = ${L.getByIndices("scale_indices")};
-        ${E?`
-              let zero_point_indices = scale_indices;
-              let zero_point_offset = ${E.indicesToOffset("zero_point_indices")};
-              let zero_point_index = zero_point_offset % 8;
-              let packed_4bit_zero_points = ${E.getByOffset("zero_point_offset / 8")};
-              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
-              let zero_point_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
-              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
-        let dequantized_data = ${br(p)}(quantized_data - zero_point) * scale;
-        ${S.setByOffset("global_idx","dequantized_data")};
-    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${a.cacheKey};${t.filter((w,M)=>M!==1).map(w=>w.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:t.length},(w,M)=>"rank")},getRunData:()=>({outputs:[{dims:d,dataType:p}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:h}),getShaderSource:_}},S1=(t,a)=>{let n=t.inputs;mb(n,a),t.compute(_b(t.inputs,a))},C1=t=>$n({blockSize:t.blockSize,gatherAxis:t.gatherAxis,quantizeAxis:t.quantizeAxis})}),gb,yb,P1,A1,FE=it(()=>{jt(),Yt(),xa(),Jt(),gb=t=>{if(!t||t.length!==2)throw new Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and
-                     indices input tensors be of same rank.`)},yb=(t,a)=>{let n=t[0].dims,i=t[0].dataType,l=n.length,o=t[1].dims,c=t[1].dataType,d=Fe.normalizeAxis(a.axis,l),f=n[d],p=o.slice(0),g=Fe.size(p),h=Ge("input",i,l),_=Ge("indicesInput",c,o.length),w=Et("output",i,p.length),M=[{type:12,data:g},{type:6,data:f},{type:12,data:d}];return M.push(...Dt(n,o,p)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:p,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:M}),getShaderSource:$=>`
-      ${$.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(h,_,w)}
-      ${$.mainStart()}
-      ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-
-      let outputIndices = ${w.offsetToIndices("global_idx")};
-
-      var idx = ${_.getByOffset("global_idx")};
-      if (idx < 0) {
-        idx = idx + uniforms.axisDimLimit;
-      }
-      var inputIndices = ${h.type.indices}(outputIndices);
-      ${h.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
-      let value = ${h.getByIndices("inputIndices")};
-
-      ${w.setByOffset("global_idx","value")};
-  }`}},P1=t=>$n({axis:t.axis}),A1=(t,a)=>{let n=t.inputs;gb(n),t.compute(yb(t.inputs,a))}}),bb,vb,$1,k1,BE=it(()=>{jt(),Yt(),Jt(),bb=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},vb=(t,a)=>{let n=t[0].dims.slice(),i=t[1].dims.slice(),[l,o,c]=PM.getShapeOfGemmResult(n,a.transA,i,a.transB,t.length===3?t[2].dims:void 0),d=[l,o];if(!d)throw new Error("Can't use gemm on the given tensors");let f=16,p=Math.ceil(o/f),g=Math.ceil(l/f),h=!0,_=Fe.size(d),w=[{type:12,data:h?p:_},{type:12,data:l},{type:12,data:o},{type:12,data:c},{type:1,data:a.alpha},{type:1,data:a.beta}],M=["type","type"];t.length===3&&(w.push(...Dt(t[2].dims)),M.push("rank")),w.push(...Dt(d));let $=E=>{let S="";a.transA&&a.transB?S="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":a.transA&&!a.transB?S="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!a.transA&&a.transB?S="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!a.transA&&!a.transB&&(S="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let D=a.alpha===1?"":"value *= uniforms.alpha;",A=Ge("a",t[0].dataType,t[0].dims),C=Ge("b",t[1].dataType,t[1].dims),T=A.type.value,x=null,O=[A,C];t.length===3&&(x=Ge("c",t[2].dataType,t[2].dims.length),O.push(x));let N=Et("output",t[0].dataType,d.length);O.push(N);let G=[{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`
-  ${E.registerUniforms(G).declareVariables(...O)}
-
-  ${E.mainStart()}
-    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-
-    let m = global_idx / uniforms.N;
-    let n = global_idx % uniforms.N;
-
-    var value = ${T}(0);
-    for (var k: u32 = 0u; k < uniforms.K; k++) {
-      ${S}
-    }
-
-    ${D}
-    ${x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",N)}; value += ${T}(uniforms.beta) * ${x.getByOffset("cOffset")};`:""}
-    output[global_idx] = value;
-  }`},L=E=>{let S=Ge("a",t[0].dataType,t[0].dims),D=Ge("b",t[1].dataType,t[1].dims),A=null,C=[S,D];t.length===3&&(A=Ge("c",t[2].dataType,t[2].dims.length),C.push(A));let T=Et("output",t[0].dataType,d.length);C.push(T);let x=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],O="",N="";a.transA&&a.transB?(N=`
-      var col = tile_row_start + local_id.x;
-      var row = k_start + local_id.y;
-      if (col < uniforms.M && row < uniforms.K) {
-        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
-      } else {
-        tile_a[local_id.y][local_id.x] = ${S.type.value}(0);
-      }
-
-      col = k_start + local_id.x;
-      row = tile_col_start + local_id.y;
-      if (col < uniforms.K && row < uniforms.N) {
-        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
-      } else {
-        tile_b[local_id.y][local_id.x] = ${D.type.value}(0);
-      }
-      `,O="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):a.transA&&!a.transB?(N=`
-      var col = tile_row_start + local_id.x;
-      var row = k_start + local_id.y;
-      if (col < uniforms.M && row < uniforms.K) {
-        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
-      } else {
-        tile_a[local_id.y][local_id.x] = ${S.type.value}(0);
-      }
-
-      col = tile_col_start + local_id.x;
-      row = k_start + local_id.y;
-      if (col < uniforms.N && row < uniforms.K) {
-        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
-      } else {
-        tile_b[local_id.y][local_id.x] = ${D.type.value}(0);
-      }
-      `,O="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!a.transA&&a.transB?(N=`
-      var col = k_start + local_id.x;
-      var row = tile_row_start + local_id.y;
-      if (col < uniforms.K && row < uniforms.M) {
-        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
-      } else {
-        tile_a[local_id.y][local_id.x] = ${S.type.value}(0);
-      }
-
-      col = k_start + local_id.x;
-      row = tile_col_start + local_id.y;
-      if (col < uniforms.K && row < uniforms.N) {
-        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
-      } else {
-        tile_b[local_id.y][local_id.x] = ${D.type.value}(0);
-      }
-      `,O="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!a.transA&&!a.transB&&(N=`
-      var col = k_start + local_id.x;
-      var row = tile_row_start + local_id.y;
-      if (col < uniforms.K && row < uniforms.M) {
-        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
-      } else {
-        tile_a[local_id.y][local_id.x] = ${S.type.value}(0);
-      }
-
-      col = tile_col_start + local_id.x;
-      row = k_start + local_id.y;
-      if (col < uniforms.N && row < uniforms.K) {
-        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
-      } else {
-        tile_b[local_id.y][local_id.x] = ${D.type.value}(0);
-      }
-      `,O="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let G=a.alpha===1?"":"value *= uniforms.alpha;";return`
-  ${E.registerUniforms(x).declareVariables(...C)}
-  var<workgroup> tile_a: array<array<${S.type.storage}, ${f}>, ${f}>;
-  var<workgroup> tile_b: array<array<${D.type.storage}, ${f}>, ${f}>;
-  ${E.mainStart([f,f,1])}
-    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${f};
-    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${f};
-    let num_tiles = (uniforms.K - 1) / ${f} + 1;
-    var k_start = 0u;
-    var value = ${T.type.value}(0);
-    for (var t: u32 = 0u; t < num_tiles; t++) {
-      ${N}
-      k_start = k_start + ${f};
-      workgroupBarrier();
-
-      for (var k: u32 = 0u; k < ${f}; k++) {
-        ${O}
-      }
-      workgroupBarrier();
-    }
-
-    ${G}
-    let m = tile_row_start + local_id.y;
-    let n = tile_col_start + local_id.x;
-    ${A!=null?`let cOffset = ${A.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${T.type.value}(uniforms.beta) * ${A.getByOffset("cOffset")};`:""}
-    if (m < uniforms.M && n < uniforms.N) {
-      output[m * uniforms.N + n] = value;
-    }
-  }`};return h?{name:"GemmShared",shaderCache:{hint:`${a.cacheKey}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:p*g},programUniforms:w}),getShaderSource:L}:{name:"Gemm",shaderCache:{hint:`${a.cacheKey}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:w}),getShaderSource:$}},$1=t=>{let a=t.transA,n=t.transB,i=t.alpha,l=t.beta;return{transA:a,transB:n,alpha:i,beta:l,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},k1=(t,a)=>{bb(t.inputs),t.compute(vb(t.inputs,a))}}),zi,vo,$u,ku,Mb,wb,Tb,xb,Eb,Sb,Cb,Pb,O1,D1,RE=it(()=>{jt(),Yt(),xa(),Jt(),[zi,vo,$u,ku]=[0,1,2,3],Mb=t=>{if(t[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(t[0].dims.length!==t[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(t[0].dims.length-2!==t[1].dims[t[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${t[0].dims.length-2}`);if(t[0].dims[0]!==t[1].dims[0])throw new Error("grid batch size must match input batch size")},wb=`
-  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
-    let cubic_alpha = -0.75f;
-    let x_abs = abs(x);
-    var coeffs: vec4<f32>;
-    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
-    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
-    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
-    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
-    return coeffs;
-  }
-`,Tb=t=>`
-  fn gs_bicubic_interpolate(p: mat4x4<${t}>, x: f32, y: f32) -> ${t} {
-    var v: vec4<f32>;
-    var coeffs = gs_get_cubic_coeffs(x);
-    for (var i = 0; i < 4; i++) {
-      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
-    }
-    coeffs = gs_get_cubic_coeffs(y);
-    let pixel = ${t}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
-    return pixel;
-  }
-`,xb=t=>`
-  fn gs_denormalize(n: f32, length: i32) -> f32 {
-    ${t.alignCorners===0?`
-    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
-    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
-    `:`
-    // alignCorners: true => [-1, 1] to [0, length - 1]
-    return (n + 1.0) / 2.0 * (f32(length - 1));
-    `}
-  }
-`,Eb=t=>`
-  ${t.paddingMode==="reflection"?`
-      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
-        var dx = 0.0;
-        var fx = f32(x);
-        let range = x_max - x_min;
-        if (fx < x_min) {
-          dx = x_min - fx;
-          let n = u32(dx / range);
-          let r = dx - f32(n) * range;
-          if (n % 2 == 0) {
-            fx = x_min + r;
-          } else {
-            fx = x_max - r;
-          }
-        } else if (fx > x_max) {
-          dx = fx - x_max;
-          let n = u32(dx / range);
-          let r = dx - f32(n) * range;
-          if (n % 2 == 0) {
-            fx = x_max - r;
-          } else {
-            fx = x_min + r;
-          }
-        }
-        return u32(fx);
-      }`:""}
-`,Sb=(t,a,n)=>`
-  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${a} {
-     var pixel = ${a}(0);
-     var indices = vec4<u32>(0);
-     indices[${zi}] = batch;
-     indices[${vo}] = channel;`+(()=>{switch(n.paddingMode){case"zeros":return`
-          if (r >= 0 && r < H && c >=0 && c < W) {
-            indices[${$u}] = u32(r);
-            indices[${ku}] = u32(c);
-          } else {
-            return ${a}(0);
-          }
-        `;case"border":return`
-          indices[${$u}] = u32(clamp(r, 0, H - 1));
-          indices[${ku}] = u32(clamp(c, 0, W - 1));
-        `;case"reflection":return`
-          indices[${$u}] = gs_reflect(r, border[1], border[3]);
-          indices[${ku}] = gs_reflect(c, border[0], border[2]);
-        `;default:throw new Error(`padding mode ${n.paddingMode} is not supported`)}})()+`
-    return ${t.getByIndices("indices")};
-  }
-`,Cb=(t,a,n)=>(()=>{switch(n.mode){case"nearest":return`
-          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${zi}], indices[${vo}], border);
-        `;case"bilinear":return`
-          let x1 = i32(floor(x));
-          let y1 = i32(floor(y));
-          let x2 = x1 + 1;
-          let y2 = y1 + 1;
-
-          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${zi}], indices[${vo}], border);
-          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${zi}], indices[${vo}], border);
-          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${zi}], indices[${vo}], border);
-          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${zi}], indices[${vo}], border);
-
-          let dx2 = ${a}(f32(x2) - x);
-          let dx1 = ${a}(x - f32(x1));
-          let dy2 = ${a}(f32(y2) - y);
-          let dy1 = ${a}(y - f32(y1));
-          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
-        `;case"bicubic":return`
-          let x0 = i32(floor(x)) - 1;
-          let y0 = i32(floor(y)) - 1;
-          var p: mat4x4<${a}>;
-          for (var h = 0; h < 4; h++) {
-            for (var w = 0; w < 4; w++) {
-              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${zi}], indices[${vo}], border);
-            }
-          }
-
-          let dx = x - f32(x0 + 1);
-          let dy = y - f32(y0 + 1);
-          let result = gs_bicubic_interpolate(p, dx, dy);
-        `;default:throw new Error(`mode ${n.mode} is not supported`)}})()+`${t.setByOffset("global_idx","result")}`,Pb=(t,a)=>{let n=Ge("x",t[0].dataType,t[0].dims.length),i=[t[1].dims[0],t[1].dims[1],t[1].dims[2]],l=Ge("grid",t[1].dataType,i.length,2),o=[t[0].dims[0],t[0].dims[1],t[1].dims[1],t[1].dims[2]];a.format==="NHWC"&&(o=[t[0].dims[0],t[1].dims[1],t[1].dims[2],t[0].dims[3]],[zi,vo,$u,ku]=[0,3,1,2]);let c=Et("output",t[0].dataType,o.length),d=n.type.value,f=Fe.size(o),p=[{type:12,data:f},...Dt(t[0].dims,i,o)],g=h=>`
-  ${h.registerUniform("output_size","u32").declareVariables(n,l,c)}
-  ${wb}
-  ${Tb(d)}
-  ${xb(a)}
-  ${Eb(a)}
-  ${Sb(n,d,a)}
-
-  ${h.mainStart()}
-    ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-      let H_in = i32(uniforms.x_shape[${$u}]);
-      let W_in = i32(uniforms.x_shape[${ku}]);
-
-      ${a.alignCorners===0?`
-      let x_min = -0.5;
-      let x_max = f32(W_in) - 0.5;
-      let y_min = -0.5;
-      let y_max = f32(H_in) - 0.5;
-      `:`
-      let x_min = 0.0;
-      let x_max = f32(W_in) - 1.0;
-      let y_min = 0.0;
-      let y_max = f32(H_in) - 1.0;
-      `};
-      let border = vec4<f32>(x_min, y_min, x_max, y_max);
-
-      let indices = ${c.offsetToIndices("global_idx")};
-      var grid_indices = vec3<u32>(indices[${zi}], indices[${$u}], indices[${ku}]);
-      let nxy = ${l.getByIndices("grid_indices")};
-      var x = gs_denormalize(f32(nxy[0]), W_in);
-      var y = gs_denormalize(f32(nxy[1]), H_in);
-
-      ${Cb(c,d,a)}
-  }`;return{name:"GridSample",shaderCache:{hint:`${a.cacheKey}`,inputDependencies:["type","type"]},getRunData:h=>{let _=Fe.size(o);return{outputs:[{dims:o,dataType:h[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:p}},getShaderSource:g}},O1=(t,a)=>{Mb(t.inputs),t.compute(Pb(t.inputs,a))},D1=t=>$n({alignCorners:t.align_corners,mode:t.mode,paddingMode:t.padding_mode,format:t.format})}),Ar,Ab,I1,d_,$b,Gp,z1,L1=it(()=>{jt(),Yt(),xa(),hg(),gg(),Jt(),Sl(),Ar=(t,a)=>t.length>a&&t[a].dims.length>0?t[a]:void 0,Ab=(t,a)=>{let n=t[0],i=Ar(t,1),l=Ar(t,2),o=Ar(t,3),c=Ar(t,4),d=Ar(t,5),f=Ar(t,6),p=Ar(t,7);if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let g=n.dims[0],h=n.dims[1],_=n.dims.length===3?n.dims[2]:a.numHeads*n.dims[4],w=h,M=0,$=0,L=Math.floor(_/a.numHeads);if(f&&p&&Fe.size(f.dims)&&Fe.size(p.dims)){if(f.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(f.dims[0]!==g||f.dims[1]!==a.numHeads||f.dims[3]!==L)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[0]!==g||p.dims[1]!==a.numHeads||p.dims[3]!==L)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(f.dims[2]!==p.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(p.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');M=f.dims[2],$=f.dims[2]}else if(f&&Fe.size(f.dims)||p&&Fe.size(p.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let E;if(i&&Fe.size(i.dims)>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(i.dims.length<3||i.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(i.dims.length===3){if(i.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');E=2,w=i.dims[1]}else if(i.dims.length===5){if(i.dims[2]!==a.numHeads||i.dims[3]!==2||i.dims[4]!==L)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(l)throw new Error('Expect "value" be none when "key" has packed kv format.');E=5,w=i.dims[1]}else{if(i.dims[1]!==a.numHeads||i.dims[3]!==L)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');E=0,w=i.dims[2]}}else{if(n.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(n.dims[2]!==a.numHeads||n.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');E=3}if(o&&Fe.size(o.dims)>0){if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(i&&i.dims.length===5&&i.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let S=M+w,D=0;if(c&&Fe.size(c.dims)>0){D=8;let x=c.dims;throw x.length===1?x[0]===g?D=1:x[0]===3*g+2&&(D=3):x.length===2&&x[0]===g&&x[1]===S&&(D=5),D===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let A=!1,C=_;if(l&&Fe.size(l.dims)>0){if(l.dims.length!==3&&l.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==l.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(l.dims.length===3){if(w!==l.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');C=l.dims[2]}else{if(w!==l.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');C=l.dims[1]*l.dims[3],A=!0}}let T=!1;if(c&&Fe.size(c.dims)>0)throw new Error("Key padding mask is not supported");if(d&&Fe.size(d.dims)>0){if(d.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(d.dims[0]!==g||d.dims[1]!==a.numHeads||d.dims[2]!==h||d.dims[3]!==S)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:g,sequenceLength:h,pastSequenceLength:M,kvSequenceLength:w,totalSequenceLength:S,maxSequenceLength:$,inputHiddenSize:0,hiddenSize:_,vHiddenSize:C,headSize:L,vHeadSize:Math.floor(C/a.numHeads),numHeads:a.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:a.maskFilterValue,maskType:D,scale:a.scale,broadcastResPosBias:T,passPastInKv:A,qkvFormat:E}},I1=t=>$n({...t}),d_=$n({perm:[0,2,1,3]}),$b=(t,a,n,i,l,o,c)=>{let d=[i,l,o],f=Fe.size(d),p=[{type:12,data:f},{type:12,data:c},{type:12,data:o}],g=h=>{let _=Et("qkv_with_bias",a.dataType,d),w=Ge("qkv",a.dataType,d),M=Ge("bias",n.dataType,d),$=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
-  ${h.registerUniforms($).declareVariables(w,M,_)}
-  ${h.mainStart()}
-    ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;
-
-    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
-  }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:d,dataType:a.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:p}),getShaderSource:g},{inputs:[a,n],outputs:[-1]})[0]},Gp=(t,a,n,i,l,o,c,d)=>{let f=o;if(c&&Fe.size(c.dims)>0){if(i===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return f=$b(t,o,c,a,i,n*l,d),f=f.reshape([a,i,n,l]),n===1||i===1?f:t.compute(as(f,d_.perm),{inputs:[f],outputs:[-1]})[0]}else return o.dims.length===3&&(f=o.reshape([a,i,n,l])),n===1||i===1?f:t.compute(as(f,d_.perm),{inputs:[f],outputs:[-1]})[0]},z1=(t,a)=>{let n=Ab(t.inputs,a),i=t.inputs[0],l=Ar(t.inputs,1),o=Ar(t.inputs,2),c=Ar(t.inputs,3),d=Ar(t.inputs,4),f=Ar(t.inputs,5),p=Ar(t.inputs,6),g=Ar(t.inputs,7);if(i.dims.length===5)throw new Error("Packed QKV is not implemented");if((l==null?void 0:l.dims.length)===5)throw new Error("Packed KV is not implemented");let h=l&&o&&l.dims.length===4&&o.dims.length===4,_=Gp(t,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,i,c,0);if(h)return Xp(t,_,l,o,d,void 0,p,g,f,n);if(!l||!o)throw new Error("key and value must be provided");let w=Gp(t,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,l,c,n.hiddenSize),M=Gp(t,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,o,c,2*n.hiddenSize);Xp(t,_,w,M,d,void 0,p,g,f,n)}}),kb,Ob,Db,Ib,X_,F1,B1,R1=it(()=>{jt(),Yt(),xa(),Jt(),kb=t=>{if(!t||t.length<1)throw new Error("too few inputs")},Ob=(t,a)=>{let n=[],i=a.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(l=>n.push(Number(l))),i=n.length),$n({numOutputs:i,axis:a.axis,splitSizes:n})},Db=t=>`
-fn calculateOutputIndex(index: u32) -> u32 {
-    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
-    if (index < ${Pt("uniforms.size_in_split_axis","i",t)}) {
-        return i;
-    }
-    }
-    return ${t}u;
-}`,Ib=t=>{let a=t.length,n=[];for(let i=0;i<a;++i){let l=t[i].setByIndices("indices","input[global_idx]");a===1?n.push(l):i===0?n.push(`if (output_number == ${i}u) { ${l} }`):i===a-1?n.push(`else { ${l} }`):n.push(`else if (output_number == ${i}) { ${l} }`)}return`
-      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
-        ${n.join(`
-`)}
-      }`},X_=(t,a)=>{let n=t[0].dims,i=Fe.size(n),l=t[0].dataType,o=Fe.normalizeAxis(a.axis,n.length),c=new Array(a.numOutputs),d=Ge("input",l,n.length),f=new Array(a.numOutputs),p=[],g=[],h=0,_=[{type:12,data:i}];for(let M=0;M<a.numOutputs;M++){h+=a.splitSizes[M],f[M]=h;let $=n.slice();$[o]=a.splitSizes[M],g.push($),c[M]=Et(`output${M}`,l,$.length),p.push({dims:g[M],dataType:t[0].dataType})}_.push({type:12,data:f},...Dt(n,...g));let w=M=>`
-  ${M.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",f.length).declareVariables(d,...c)}
-  ${Db(f.length)}
-  ${Ib(c)}
-
-  ${M.mainStart()}
-    ${M.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}
-
-    var indices = ${d.offsetToIndices("global_idx")};
-    var index = ${d.indicesGet("indices",o)};
-    let output_number = calculateOutputIndex(index);
-    if (output_number != 0) {
-      index -= ${Pt("uniforms.size_in_split_axis","output_number - 1u",f.length)};
-      ${d.indicesSet("indices",o,"index")};
-    }
-    writeBufferData(output_number, indices, global_idx);
-  }`;return{name:"Split",shaderCache:{hint:a.cacheKey,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:p,dispatchGroup:{x:Math.ceil(i/64)},programUniforms:_})}},F1=(t,a)=>{kb(t.inputs);let n=t.inputs.length===1?a:Ob(t.inputs,a);t.compute(X_(t.inputs,n),{inputs:[0]})},B1=t=>{let a=t.axis,n=t.splitSizes,i=t.numOutputs<0?n.length:t.numOutputs;if(i!==n.length)throw new Error("numOutputs and splitSizes lengh must be equal");return $n({axis:a,numOutputs:i,splitSizes:n})}}),zb,lm,N1,j1=it(()=>{jt(),Yt(),xa(),Jt(),zb=(t,a)=>{let[n,i,l,o]=t,{numHeads:c,rotaryEmbeddingDim:d}=a;if(n.dims.length!==3&&n.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${n.dims.length}`);if(!Fe.areEqual(i.dims,[])&&!Fe.areEqual(i.dims,[1])&&i.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${i.dims.length}`);if(l.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${l.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(!Fe.areEqual(l.dims,o.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(d>0&&c===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let f=n.dims[0],p=n.dims[n.dims.length-2],g=l.dims[0],h=Fe.sizeFromDimension(n.dims,1)/p,_=d===0?l.dims[1]*2:h/c;if(d>_)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(i.dims.length===2){if(f!==i.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${i.dims[0]}`);if(p!==i.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${i.dims[1]}`)}if(_/2!==l.dims[1]&&d/2!==l.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${l.dims[1]}`);if(p>g)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},lm=(t,a)=>{let{interleaved:n,numHeads:i,rotaryEmbeddingDim:l,scale:o}=a,c=t[0].dims[0],d=Fe.sizeFromDimension(t[0].dims,1),f=t[0].dims[t[0].dims.length-2],p=d/f,g=t[2].dims[1],h=l===0?g*2:p/i,_=new Array(c,f,p/h,h-g),w=Fe.computeStrides(_),M=[{type:1,data:o},{type:12,data:_},{type:12,data:w},...t[0].dims.length===3?new Array({type:12,data:[d,p,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[d,h,f*h,1]}):[],...Dt(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],$=L=>{let E=Ge("input",t[0].dataType,t[0].dims.length),S=Ge("position_ids",t[1].dataType,t[1].dims.length),D=Ge("cos_cache",t[2].dataType,t[2].dims.length),A=Ge("sin_cache",t[3].dataType,t[3].dims.length),C=Et("output",t[0].dataType,t[0].dims.length);return L.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:_.length},{name:"global_strides",type:"u32",length:w.length},{name:"input_output_strides",type:"u32",length:w.length}]),`
-        ${L.declareVariables(E,S,D,A,C)}
-
-        ${L.mainStart(Ad)}
-          let half_rotary_emb_dim = uniforms.${D.name}_shape[1];
-          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
-          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
-          ${L.guardAgainstOutOfBoundsWorkgroupSizes("size")}
-
-          if (bsnh[3] < half_rotary_emb_dim) {
-            let position_ids_idx =
-                ${S.broadcastedIndicesToOffset("bsnh.xy",Et("",S.type.tensor,2))};
-            let position_id =
-                u32(${S.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
-            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${n});
-            let j = i + select(half_rotary_emb_dim, 1, ${n});
-            let re = ${E.getByOffset("i")} * ${D.get("position_id","bsnh[3]")} -
-                ${E.getByOffset("j")} * ${A.get("position_id","bsnh[3]")};
-            ${C.setByOffset("i","re")}
-            let im = ${E.getByOffset("i")} * ${A.get("position_id","bsnh[3]")} +
-                ${E.getByOffset("j")} * ${D.get("position_id","bsnh[3]")};
-            ${C.setByOffset("j","im")}
-          } else {
-            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
-            ${C.setByOffset("k",E.getByOffset("k"))}
-          }
-        }`};return{name:"RotaryEmbedding",shaderCache:{hint:$n({interleaved:n}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Fe.size(_)/Ad)},programUniforms:M})}},N1=(t,a)=>{zb(t.inputs,a),t.compute(lm(t.inputs,a))}}),Lb,Fb,f_,Bb,U1,NE=it(()=>{xa(),jt(),gg(),L1(),R1(),Sl(),j1(),Jt(),Lb=(t,a)=>{if(a.doRotary&&t.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let n=t[0],i=t[1],l=t[2],o=t[3],c=t[4];if(a.doRotary!==0&&t.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(a.localWindowSize!==-1)throw new Error("Local attention is not supported");if(a.softcap!==0)throw new Error("Softcap is not supported");if(a.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(a.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=!1,f=n.dims[0],p=n.dims[1],g=n.dims.length===3?d?n.dims[2]/3:n.dims[2]:a.numHeads*n.dims[4],h=p,_=0,w=!i||i.dims.length===0,M=Math.floor(w?g/(a.numHeads+2*a.kvNumHeads):g/a.numHeads);w&&(g=M*a.numHeads);let $=o&&o.dims.length!==0,L=c&&c.dims.length!==0;if($&&o.dims.length===4&&o.dims[0]===f&&o.dims[1]!==a.kvNumHeads&&o.dims[2]===a.kvNumHeads&&o.dims[3]===M)throw new Error("BSNH pastKey/pastValue is not supported");if($&&L){if(o.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');_=o.dims[2]}else if($||L)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let E=1;if(i&&i.dims.length>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(i.dims.length<3||i.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(i.dims.length===3){if(n.dims[2]%i.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');h=i.dims[1]}else if(i.dims.length===5){if(i.dims[2]!==a.numHeads||i.dims[3]!==2||i.dims[4]!==M)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(l)throw new Error('Expect "value" be none when "key" has packed kv format.');h=i.dims[1]}else{if(i.dims[1]!==a.numHeads||i.dims[3]!==M)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');h=i.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(n.dims.length===5&&(n.dims[2]!==a.numHeads||n.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');E=3}let S=0,D=!1,A=a.kvNumHeads?M*a.kvNumHeads:g;if(l&&l.dims.length>0){if(l.dims.length!==3&&l.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==l.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(l.dims.length===3){if(h!==l.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=l.dims[2]}else{if(h!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=l.dims[1]*l.dims[3],D=!0}}let C=t.length>4?t[5]:void 0;if(C&&C.dims.length!==1&&C.dims[0]!==f)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:f,sequenceLength:p,pastSequenceLength:_,kvSequenceLength:h,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:g,vHiddenSize:A,headSize:M,vHeadSize:Math.floor(A/a.kvNumHeads),numHeads:a.numHeads,kvNumHeads:a.kvNumHeads,nReps:a.numHeads/a.kvNumHeads,pastPresentShareBuffer:!1,maskType:S,scale:a.scale,broadcastResPosBias:!1,passPastInKv:D,qkvFormat:E}},Fb=$n({perm:[0,2,1,3]}),f_=(t,a,n)=>{let i=a,l=n.kvNumHeads;return a.dims.length===3&&n.kvSequenceLength!==0&&(i=a.reshape([n.batchSize,n.kvSequenceLength,l,n.headSize]),i=t.compute(as(i,Fb.perm),{inputs:[i],outputs:[-1]})[0]),i},Bb=(t,a,n,i)=>{let l=7,o=["type","type"],c=[t*a],d=t*a,f=[{type:12,data:d},{type:12,data:a},{type:12,data:t}],p=g=>{let h=Ge("seq_lens",n.dataType,n.dims),_=Ge("total_seq_lens",i.dataType,i.dims),w=Et("pos_ids",l,c),M=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
-  ${g.registerUniforms(M).declareVariables(h,_,w)}
-  ${g.mainStart()}
-    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-    let total_sequence_length = u32(${_.getByOffset("0")});
-    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
-    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
-    let batch_idx = global_idx / uniforms.sequence_length;
-    let sequence_idx = i32(global_idx % uniforms.sequence_length);
-    var pos_id: i32 = 0;
-    let seqlen = ${h.getByOffset("batch_idx")};
-    let total_seqlen = seqlen + 1;
-    if (is_first_prompt) {
-      if (sequence_idx < total_seqlen) {
-        pos_id = sequence_idx;
-      } else {
-        pos_id = 1;
-      }
-      ${w.setByOffset("global_idx","pos_id")}
-    } else if (is_subsequent_prompt) {
-      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
-      if (past_seqlen + sequence_idx < total_seqlen) {
-        pos_id = past_seqlen + sequence_idx;
-      } else {
-        pos_id = 1;
-      }
-      ${w.setByOffset("global_idx","pos_id")}
-    } else if (global_idx < uniforms.batch_size) {
-      ${w.setByOffset("global_idx","seqlen")}
-    };
-  }
-  `};return{name:"GeneratePositionIds",shaderCache:{hint:`${t};${a}`,inputDependencies:o},getRunData:()=>({outputs:[{dims:c,dataType:l}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:f}),getShaderSource:p}},U1=(t,a)=>{var A;let n=Lb(t.inputs,a);if(t.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((A=t.inputs[1])==null?void 0:A.dims.length)===5)throw new Error("Packed KV is not implemented");let i=t.inputs[0],l=t.inputs[1]&&t.inputs[1].dims.length>0?t.inputs[1]:void 0,o=t.inputs[2]&&t.inputs[2].dims.length>0?t.inputs[2]:void 0,c=t.inputs[3]&&t.inputs[3].dims.length!==0?t.inputs[3]:void 0,d=t.inputs[4]&&t.inputs[4].dims.length!==0?t.inputs[4]:void 0,f=t.inputs.length>4?t.inputs[5]:void 0,p=t.inputs.length>5?t.inputs[6]:void 0,g=n.kvNumHeads?n.kvNumHeads:n.numHeads,h=$n({axis:2,numOutputs:3,splitSizes:[n.numHeads*n.headSize,g*n.headSize,g*n.headSize]}),[_,w,M]=!l&&!o?t.compute(X_([i],h),{inputs:[i],outputs:[-1,-1,-1]}):[i,l,o],$,L;if(a.doRotary){let C=t.compute(Bb(n.batchSize,n.sequenceLength,f,p),{inputs:[f,p],outputs:[-1]})[0],T=t.inputs[7],x=t.inputs[8],O=$n({interleaved:a.rotaryInterleaved!==0,numHeads:n.numHeads,rotaryEmbeddingDim:0,scale:a.scale}),N=[_,C,T,x],G=[-1];$=t.compute(lm(N,O),{inputs:N,outputs:G})[0],N.splice(0,1,w);let te=$n({interleaved:a.rotaryInterleaved!==0,numHeads:n.kvNumHeads,rotaryEmbeddingDim:0,scale:a.scale});L=t.compute(lm(N,te),{inputs:N,outputs:G})[0]}let E=Gp(t,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,a.doRotary?$:_,void 0,0),S=f_(t,a.doRotary?L:w,n),D=f_(t,M,n);Xp(t,E,S,D,void 0,void 0,c,d,void 0,n,f,p)}}),p_,Rb,Nb,V1,jE=it(()=>{jt(),Yt(),Sl(),Jt(),p_=(t,a,n,i,l,o,c,d)=>{let f=ya(o),p=f===1?"f32":`vec${f}f`,g=f===1?"vec2f":`mat2x${f}f`,h=l*c,_=64;h===1&&(_=256);let w=[l,c,o/f],M=[l,c,2],$=["rank","type","type"],L=[];L.push(...Dt(w,M));let E=S=>{let D=Ge("x",a.dataType,3,f),A=Ge("scale",n.dataType,n.dims),C=Ge("bias",i.dataType,i.dims),T=Et("output",1,3,2),x=[D,A,C,T];return`
-  var<workgroup> workgroup_shared : array<${g}, ${_}>;
-  const workgroup_size = ${_}u;
-  ${S.declareVariables(...x)}
-  ${S.mainStart(_)}
-    let batch = workgroup_index / uniforms.x_shape[1];
-    let channel = workgroup_index % uniforms.x_shape[1];
-    let hight = uniforms.x_shape[2];
-    // initialize workgroup memory
-    var sum = ${p}(0);
-    var squared_sum = ${p}(0);
-    for (var h = local_idx; h < hight; h += workgroup_size) {
-      let value = ${p}(${D.get("batch","channel","h")});
-      sum += value;
-      squared_sum += value * value;
-    }
-    workgroup_shared[local_idx] = ${g}(sum, squared_sum);
-    workgroupBarrier();
-
-    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
-      if (local_idx < currSize) {
-        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
-      }
-      workgroupBarrier();
-    }
-    if (local_idx == 0) {
-      let sum_final = ${El("workgroup_shared[0][0]",f)} / f32(hight * ${f});
-      let squared_sum_final = ${El("workgroup_shared[0][1]",f)} / f32(hight * ${f});
-
-      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${d}));
-      let channel_scale = inv_std_dev * f32(scale[channel]);
-      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
-      output[workgroup_index] = vec2f(channel_scale, channel_shift);
-    }
-  }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${f};${d};${_}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:M,dataType:1}],dispatchGroup:{x:h},programUniforms:L}),getShaderSource:E},{inputs:[a,n,i],outputs:[-1]})[0]},Rb=(t,a,n)=>{let i=a[0].dims,l=i,o=2,c=i[0],d=i[1],f=Fe.sizeFromDimension(i,o),p=ya(f),g=Fe.size(l)/p,h=p_(t,a[0],a[1],a[2],c,f,d,n.epsilon),_=[c,d,f/p],w=[c,d],M=["type","none"],$=L=>{let E=Ge("x",a[0].dataType,_.length,p),S=Ge("scale_shift",1,w.length,2),D=Et("output",a[0].dataType,_.length,p),A=[E,S,D];return`
-  ${L.registerUniform("output_size","u32").declareVariables(...A)}
-  ${L.mainStart()}
-  ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-      let outputIndices = ${D.offsetToIndices("global_idx")};
-      let batch = outputIndices[0];
-      let channel = outputIndices[1];
-      let scale_shift = ${S.getByIndices("vec2<u32>(batch, channel)")};
-      let value = ${E.getByOffset("global_idx")} * ${D.type.value}(scale_shift.x) + ${D.type.value}(scale_shift.y);
-      ${D.setByOffset("global_idx","value")};
-  }`};t.compute({name:"InstanceNormalization",shaderCache:{hint:`${p}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:l,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},...Dt(_,w,_)]}),getShaderSource:$},{inputs:[a[0],h]})},Nb=(t,a,n)=>{let i=a[0].dims,l=i,o=i[0],c=i[i.length-1],d=Fe.sizeFromDimension(i,1)/c,f=ya(c),p=Fe.size(l)/f,g=[{type:12,data:d},{type:12,data:Math.floor(c/f)}],h=["type","type"],_=!1,w=[0,i.length-1];for(let E=0;E<i.length-2;E++)_=_||i[E+1]!==1,w.push(E+1);_=_&&i[i.length-1]!==1;let M=_?t.compute(as(t.inputs[0],w),{inputs:[t.inputs[0]],outputs:[-1]})[0]:t.inputs[0].reshape(Array.from({length:i.length},(E,S)=>i[w[S]])),$=p_(t,M,a[1],a[2],o,d,c,n.epsilon),L=E=>{let S=Qa(a[0].dataType),D=f===1?"vec2f":`mat${f}x2f`,A=x=>{let O=x===0?"x":"y",N=f===1?"f32":`vec${f}f`;switch(f){case 1:return`${S}(${N}(scale.${O}))`;case 2:return`vec2<${S}>(${N}(scale[0].${O}, scale[1].${O}))`;case 4:return`vec4<${S}>(${N}(scale[0].${O}, scale[1].${O}, scale[2].${O}, scale[3].${O}))`;default:throw new Error(`Not supported compoents ${f}`)}},C=Ge("input",a[0].dataType,a[0].dims,f),T=Et("output",a[0].dataType,l,f);return`
-  @group(0) @binding(0) var<storage, read> input : array<${C.type.storage}>;
-  @group(0) @binding(1) var<storage, read> scale_input : array<${D}>;
-  @group(0) @binding(2) var<storage, read_write> output : array<${T.type.storage}>;
-  struct Uniforms {H: u32, C : u32};
-  @group(0) @binding(3) var<uniform> uniforms: Uniforms;
-
-  ${E.mainStart()}
-    let current_image_number = global_idx / (uniforms.C * uniforms.H);
-    let current_channel_number = global_idx % uniforms.C;
-
-    let scale_offset = current_image_number * uniforms.C + current_channel_number;
-    let scale = scale_input[scale_offset];
-    output[global_idx] = fma(input[global_idx], ${A(0)}, ${A(1)});
-  }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${f}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:l,dataType:a[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:g}),getShaderSource:L},{inputs:[a[0],$]})},V1=(t,a)=>{a.format==="NHWC"?Nb(t,t.inputs,a):Rb(t,t.inputs,a)}}),jb,Ub,G1,UE=it(()=>{jt(),Yt(),Jt(),jb=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Ub=(t,a,n)=>{let i=a.simplified,l=t[0].dims,o=t[1],c=!i&&t[2],d=l,f=Fe.normalizeAxis(a.axis,l.length),p=Fe.sizeToDimension(l,f),g=Fe.sizeFromDimension(l,f),h=Fe.size(o.dims),_=c?Fe.size(c.dims):0;if(h!==g||c&&_!==g)throw new Error(`Size of X.shape()[axis:] == ${g}.
-       Size of scale and bias (if provided) must match this.
-       Got scale size of ${h} and bias size of ${_}`);let w=[];for(let C=0;C<l.length;++C)C<f?w.push(l[C]):w.push(1);let M=ya(g),$=["type","type"],L=[{type:12,data:p},{type:1,data:g},{type:12,data:Math.floor(g/M)},{type:1,data:a.epsilon}];c&&$.push("type");let E=n>1,S=n>2,D=C=>{let T=Qa(t[0].dataType),x=[Ge("x",t[0].dataType,t[0].dims,M),Ge("scale",o.dataType,o.dims,M)];c&&x.push(Ge("bias",c.dataType,c.dims,M)),x.push(Et("output",t[0].dataType,d,M)),E&&x.push(Et("mean_data_output",1,w)),S&&x.push(Et("inv_std_output",1,w));let O=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
-  ${C.registerUniforms(O).declareVariables(...x)}
-  ${C.mainStart()}
-    ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
-    let offset = global_idx * uniforms.norm_size_vectorized;
-    var mean_vector = ${j_("f32",M)};
-    var mean_square_vector = ${j_("f32",M)};
-
-    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
-      let value = ${Cd(T,M,"x[h + offset]")};
-      mean_vector += value;
-      mean_square_vector += value * value;
-    }
-    let mean = ${El("mean_vector",M)} / uniforms.norm_size;
-    let inv_std_dev = inverseSqrt(${El("mean_square_vector",M)} / uniforms.norm_size ${i?"":"- mean * mean"} + uniforms.epsilon);
-
-    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
-      let f32input = ${Cd(T,M,"x[j + offset]")};
-      let f32scale = ${Cd(T,M,"scale[j]")};
-      output[j + offset] = ${x[0].type.value}((f32input ${i?"":"- mean"}) * inv_std_dev * f32scale
-        ${c?`+ ${Cd(T,M,"bias[j]")}`:""}
-      );
-    }
-
-    ${E?"mean_data_output[global_idx] = mean":""};
-    ${S?"inv_std_output[global_idx] = inv_std_dev":""};
-  }`},A=[{dims:d,dataType:t[0].dataType}];return E&&A.push({dims:w,dataType:1}),S&&A.push({dims:w,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${M};${n};${i}`,inputDependencies:$},getRunData:()=>({outputs:A,dispatchGroup:{x:Math.ceil(p/64)},programUniforms:L}),getShaderSource:D}},G1=(t,a)=>{jb(t.inputs),t.compute(Ub(t.inputs,a,t.outputCount))}}),Vb,W1,VE=it(()=>{Yt(),wg(),Tg(),Vb=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},W1=t=>{Vb(t.inputs);let a=Pd.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!a)throw new Error("Can't use matmul on the given tensors");let n=a[a.length-1],i=t.inputs[0].dims[t.inputs[0].dims.length-1];if(n<8&&i<8)t.compute(Mg(t.inputs,{activation:""},a));else{let l=a[a.length-2],o=Fe.size(t.inputs[0].dims.slice(0,-2)),c=Fe.size(t.inputs[1].dims.slice(0,-2));if(o!==1&&l===1&&c===1){let d=t.inputs[0].reshape([1,o,i]),f=t.inputs[1].reshape([1,i,n]),p=[1,o,n],g=[d,f];t.compute(om(g,{activation:""},a,p),{inputs:g})}else t.compute(om(t.inputs,{activation:""},a))}}}),Gb,Wb,Hb,H1,q1,GE=it(()=>{jt(),Yt(),xa(),Jt(),Gb=(t,a)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let n=t[0],i=n.dims.length;if(n.dims[i-1]!==a.k)throw new Error("The last dim of input shape does not match the k value");let l=Math.floor((a.k+a.blockSize-1)/a.blockSize),o=a.blockSize/8*a.bits,c=t[1];if(!Fe.areEqual(c.dims,[a.n,l,o]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let d=t[2].dims;if(Fe.size(d)!==a.n*l)throw new Error("scales input size error.");if(t.length===4){let f=t[3].dims,p=a.bits>4?a.n*l:a.n*Math.floor((l+1)/2);if(Fe.size(f)!==p)throw new Error("zeroPoints input size error.")}},Wb=(t,a)=>{let n=t[0].dims,i=n.length,l=n[i-2],o=a.k,c=a.n,d=n.slice(0,i-2),f=Fe.size(d),p=t[1].dims[2]/4,g=t[0].dataType,h=ya(a.k),_=ya(p),w=ya(c),M=d.concat([l,c]),$=l>1&&c/w%2===0?2:1,L=Fe.size(M)/w/$,E=64,S=[],D=[f,l,o/h],A=Fe.convertShape(t[1].dims).slice();A.splice(-1,1,p/_),S.push(...Dt(D)),S.push(...Dt(A)),S.push(...Dt(t[2].dims)),t.length===4&&S.push(...Dt(Fe.convertShape(t[3].dims)));let C=[f,l,c/w];S.push(...Dt(C));let T=x=>{let O=D.length,N=Ge("a",t[0].dataType,O,h),G=Ge("b",12,A.length,_),te=Ge("scales",t[2].dataType,t[2].dims.length),se=[N,G,te],Q=t.length===4?Ge("zero_points",12,t[3].dims.length):void 0;Q&&se.push(Q);let le=C.length,re=Et("output",t[0].dataType,le,w),H=Qa(t[0].dataType),ee=(()=>{switch(h){case 1:return`array<${H}, 8>`;case 2:return`mat4x2<${H}>`;case 4:return`mat2x4<${H}>`;default:throw new Error(`${h}-component is not supported.`)}})(),oe=()=>{let j=`
-          // reuse a data
-            var input_offset = ${N.indicesToOffset(`${N.type.indices}(batch, row, word_offset)`)};
-            var a_data: ${ee};
-            for (var j: u32 = 0; j < ${8/h}; j++) {
-              a_data[j] = ${N.getByOffset("input_offset")};
-              input_offset++;
-            }
-          `;for(let z=0;z<w*$;z++)j+=`
-            b_value = ${_===1?`b${z}_data`:`b${z}_data[i]`};
-            b_value_lower = unpack4xU8(b_value & b_mask);
-            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
-            b_quantized_values = ${ee}(${Array.from({length:4},(V,ie)=>`${H}(b_value_lower[${ie}]), ${H}(b_value_upper[${ie}])`).join(", ")});
-            b_dequantized_values = ${h===1?`${ee}(${Array.from({length:8},(V,ie)=>`(b_quantized_values[${ie}] - ${Q?`zero_point${z}`:"zero_point"}) * scale${z}`).join(", ")});`:`(b_quantized_values - ${ee}(${Array(8).fill(`${Q?`zero_point${z}`:"zero_point"}`).join(",")})) * scale${z};`};
-            workgroup_shared[local_id.x * ${$} + ${Math.floor(z/w)}]${w>1?`[${z%w}]`:""} += ${Array.from({length:8/h},(V,ie)=>`${h===1?`a_data[${ie}] * b_dequantized_values[${ie}]`:`dot(a_data[${ie}], b_dequantized_values[${ie}])`}`).join(" + ")};
-          `;return j},ve=()=>{let j=`
-            var col_index = col * ${w};
-            ${Q?`
-            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
-            var zero_point_byte_count: u32;
-            var zero_point_word_index: u32;
-            var zero_point_byte_offset: u32;
-            let zero_point_nibble_offset: u32 = block & 0x1u;
-            var zero_point_bits_offset: u32;
-            var zero_point_word: u32;`:`
-            // The default zero point is 8 for unsigned 4-bit quantization.
-            let zero_point = ${H}(8);`}
-            `;for(let z=0;z<w*$;z++)j+=`
-            let scale${z} = ${te.getByOffset("col_index * nBlocksPerCol + block")};
-            ${Q?`
-            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
-            zero_point_word_index = zero_point_byte_count >> 0x2u;
-            zero_point_byte_offset = zero_point_byte_count & 0x3u;
-            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
-            zero_point_word = ${Q.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
-            let zero_point${z} = ${H}((zero_point_word) & 0xFu);`:""}
-            col_index += 1;`;return j},U=()=>{let j=`col_index = col * ${w};`;for(let z=0;z<w*$;z++)j+=`
-            let b${z}_data = ${G.getByIndices(`${G.type.indices}(col_index, block, word)`)};
-            col_index += 1;`;return j+=`
-            var b_value: u32;
-            let b_mask: u32 = 0x0F0F0F0Fu;
-            var b_value_lower: vec4<u32>;
-            var b_value_upper: vec4<u32>;
-            var b_quantized_values: ${ee};
-            var b_dequantized_values: ${ee};`,j};return`
-        var<workgroup> workgroup_shared: array<${re.type.value}, ${$*E}>;
-        ${x.declareVariables(...se,re)}
-        ${x.mainStart([E,1,1])}
-          let output_indices = ${re.offsetToIndices(`(global_idx / ${E}) * ${$}`)};
-          let col = output_indices[2];
-          let row = output_indices[1];
-          let batch = output_indices[0];
-          let nBlocksPerCol = uniforms.b_shape[1];
-
-          for (var block = local_id.x; block < nBlocksPerCol; block += ${E}) {
-            //process one block
-            var word_offset: u32 = block * ${a.blockSize/h};
-            ${ve()}
-            for (var word: u32 = 0; word < ${p}; word += ${_}) {
-              ${U()}
-              for (var i: u32 = 0; i < ${_}; i++) {
-                ${oe()}
-                word_offset += ${8/h};
-              }
-            }
-          }
-          workgroupBarrier();
-
-          if (local_id.x < ${$}) {
-            var output_value: ${re.type.value} = ${re.type.value}(0);
-            var workgroup_shared_offset: u32 = local_id.x;
-            for (var b: u32 = 0u; b < ${E}u; b++) {
-              output_value += workgroup_shared[workgroup_shared_offset];
-              workgroup_shared_offset += ${$};
-            }
-            ${re.setByIndices(`${re.type.indices}(batch, row, col + local_id.x)`,"output_value")};
-          }
-        }`};return{name:"MatMulNBits",shaderCache:{hint:`${a.blockSize};${a.bits};${h};${_};${w};${$};${E}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:M,dataType:g}],dispatchGroup:{x:L},programUniforms:S}),getShaderSource:T}},Hb=(t,a)=>{let n=t[0].dims,i=n.length,l=n[i-2],o=a.k,c=a.n,d=n.slice(0,i-2),f=Fe.size(d),p=t[1].dims[2]/4,g=t[0].dataType,h=ya(a.k),_=ya(p),w=d.concat([l,c]),M=128,$=c%8===0?8:c%4===0?4:1,L=M/$,E=L*_*8,S=E/h,D=E/a.blockSize,A=Fe.size(w)/$,C=[],T=[f,l,o/h],x=Fe.convertShape(t[1].dims).slice();x.splice(-1,1,p/_),C.push(...Dt(T)),C.push(...Dt(x)),C.push(...Dt(t[2].dims)),t.length===4&&C.push(...Dt(Fe.convertShape(t[3].dims)));let O=[f,l,c];C.push(...Dt(O));let N=G=>{let te=T.length,se=Ge("a",t[0].dataType,te,h),Q=Ge("b",12,x.length,_),le=Ge("scales",t[2].dataType,t[2].dims.length),re=[se,Q,le],H=t.length===4?Ge("zero_points",12,t[3].dims.length):void 0;H&&re.push(H);let ee=O.length,oe=Et("output",t[0].dataType,ee),ve=Qa(t[0].dataType),U=()=>{switch(h){case 1:return`
-          let a_data0 = vec4<${ve}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
-          let a_data1 = vec4<${ve}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
-          let a_data0 = vec4<${ve}>(sub_a[word_offset], sub_a[word_offset + 1]);
-          let a_data1 = vec4<${ve}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
-          let a_data0 = sub_a[word_offset];
-          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${h}-component is not supported.`)}};return`
-        var<workgroup> sub_a: array<${se.type.value}, ${S}>;
-        var<workgroup> inter_results: array<array<${oe.type.value}, ${L}>, ${$}>;
-        ${G.declareVariables(...re,oe)}
-        ${G.mainStart([L,$,1])}
-          let output_indices = ${oe.offsetToIndices(`workgroup_index * ${$}`)};
-          let col = output_indices[2];
-          let row = output_indices[1];
-          let batch = output_indices[0];
-          let n_blocks_per_col = uniforms.b_shape[1];
-          let num_tiles =  (n_blocks_per_col - 1) / ${D} + 1;
-
-          // Loop over shared dimension.
-          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
-            let a_col_start = tile * ${S};
-            // load one tile A data into shared memory.
-            for (var a_offset = local_idx; a_offset < ${S}; a_offset += ${M})
-            {
-              let a_col = a_col_start + a_offset;
-              if (a_col < uniforms.a_shape[2])
-              {
-                sub_a[a_offset] = ${se.getByIndices(`${se.type.indices}(batch, row, a_col)`)};
-              } else {
-                sub_a[a_offset] = ${se.type.value}(0);
-              }
-            }
-            workgroupBarrier();
-
-            // each thread process one block
-            let b_row = col + local_id.y;
-            let block = tile * ${D} + local_id.x;
-            ${H?`
-            let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
-            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
-            let zero_point_word_index = zero_point_byte_count >> 0x2u;
-            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
-            let zero_point_nibble_offset: u32 = block & 0x1u;
-            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
-            let zero_point_word = ${H.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
-            let zero_point = ${ve}((zero_point_word) & 0xFu);`:`
-            // The default zero point is 8 for unsigned 4-bit quantization.
-            let zero_point = ${ve}(8);`}
-            let scale = ${le.getByOffset("b_row * n_blocks_per_col + block")};
-            let b_data = ${Q.getByIndices(`${Q.type.indices}(b_row, block, 0)`)};
-            var word_offset = local_id.x * ${a.blockSize/h};
-            for (var i: u32 = 0; i < ${_}; i++) {
-              ${U()}
-              let b_value = ${_===1?"b_data":"b_data[i]"};
-              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
-              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
-              let b_quantized_values = mat2x4<${ve}>(${Array.from({length:4},(j,z)=>`${ve}(b_value_lower[${z}]), ${ve}(b_value_upper[${z}])`).join(", ")});
-              let b_dequantized_values = (b_quantized_values - mat2x4<${ve}>(${Array(8).fill("zero_point").join(",")})) * scale;
-              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(j,z)=>`${`dot(a_data${z}, b_dequantized_values[${z}])`}`).join(" + ")};
-              word_offset += ${8/h};
-            }
-            workgroupBarrier();
-          }
-
-          if (local_idx < ${$}) {
-            var output_value: ${oe.type.value} = ${oe.type.value}(0);
-            for (var b = 0u; b < ${L}; b++) {
-              output_value += inter_results[local_idx][b];
-            }
-            if (col + local_idx < uniforms.output_shape[2])
-            {
-              ${oe.setByIndices(`${oe.type.indices}(batch, row, col + local_idx)`,"output_value")}
-            }
-          }
-        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${a.blockSize};${h};${_};${L};${$}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:w,dataType:g}],dispatchGroup:{x:A},programUniforms:C}),getShaderSource:N}},H1=(t,a)=>{Gb(t.inputs,a),a.blockSize===32&&t.adapterInfo.isVendor("intel")&&t.adapterInfo.isArchitecture("gen-12lp")?t.compute(Hb(t.inputs,a)):t.compute(Wb(t.inputs,a))},q1=t=>$n(t)}),qb,Kb,Qb,Xb,Yb,Zb,Jb,ev,K1,WE=it(()=>{jt(),Yt(),Jt(),qb=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let a=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(a=t[3].dims[0]*2===t[1].dims[0]),!a)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Kb=(t,a,n)=>{let i="";for(let l=a-1;l>=0;--l)i+=`
-            k = i32(${t.indicesGet("indices",l)}) - ${Pt("uniforms.pads",l,n)};
-            if (k < 0) {
-              break;
-            }
-            if (k >= i32(${Pt("uniforms.x_shape",l,a)})) {
-              break;
-            }
-            offset += k * i32(${Pt("uniforms.x_strides",l,a)});
-        `;return`
-          value = ${t.type.value}(uniforms.constant_value);
-          for (var i = 0; i < 1; i++) {
-            var offset = 0;
-            var k = 0;
-            ${i}
-            value = x[offset];
-          }
-      `},Qb=(t,a,n)=>{let i="";for(let l=a-1;l>=0;--l)i+=`
-                k = i32(${t.indicesGet("indices",l)}) - ${Pt("uniforms.pads",l,n)};
-                if (k < 0) {
-                  k = -k;
-                }
-                {
-                  let _2n_1 = 2 * (i32(${Pt("uniforms.x_shape",l,a)}) - 1);
-                  k = k % _2n_1;
-                  if(k >= i32(${Pt("uniforms.x_shape",l,a)})) {
-                    k = _2n_1 - k;
-                  }
-                }
-                offset += k * i32(${Pt("uniforms.x_strides",l,a)});
-            `;return`
-              var offset = 0;
-              var k = 0;
-              ${i}
-              value = x[offset];
-          `},Xb=(t,a,n)=>{let i="";for(let l=a-1;l>=0;--l)i+=`
-                k = i32(${t.indicesGet("indices",l)}) - ${Pt("uniforms.pads",l,n)};
-                if (k < 0) {
-                  k = 0;
-                }
-                if (k >= i32(${Pt("uniforms.x_shape",l,a)})) {
-                  k = i32(${Pt("uniforms.x_shape",l,a)}) - 1;
-                }
-                offset += k * i32(${Pt("uniforms.x_strides",l,a)});
-            `;return`
-              var offset = 0;
-              var k = 0;
-              ${i}
-              value = x[offset];
-          `},Yb=(t,a,n)=>{let i="";for(let l=a-1;l>=0;--l)i+=`
-                k = i32(${t.indicesGet("indices",l)}) - ${Pt("uniforms.pads",l,n)};
-                if (k < 0)  {
-                  k += i32(${Pt("uniforms.x_shape",l,a)}]);
-                }
-                if (k >= i32(${Pt("uniforms.x_shape",l,a)})) {
-                  k -= i32(${Pt("uniforms.x_shape",l,a)});
-                }
-                offset += k * i32(${Pt("uniforms.x_strides",l,a)});
-            `;return`
-              var offset = 0;
-              var k = 0;
-              ${i}
-              value = x[offset];
-          `},Zb=(t,a,n)=>{switch(n.mode){case 0:return Kb(t,a,n.pads.length);case 1:return Qb(t,a,n.pads.length);case 2:return Xb(t,a,n.pads.length);case 3:return Yb(t,a,n.pads.length);default:throw new Error("Invalid mode")}},Jb=(t,a)=>{let n=Fe.padShape(t[0].dims.slice(),a.pads),i=t[0].dims,l=Fe.size(n),o=[{type:12,data:l},{type:6,data:a.pads}],c=t.length>=3&&t[2].data;a.mode===0&&o.push({type:c?t[2].dataType:1,data:a.value}),o.push(...Dt(t[0].dims,n));let d=["rank"],f=p=>{let g=Et("output",t[0].dataType,n.length),h=Ge("x",t[0].dataType,i.length),_=h.type.value,w=Zb(g,i.length,a),M=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:a.pads.length}];return a.mode===0&&M.push({name:"constant_value",type:c?_:"f32"}),`
-            ${p.registerUniforms(M).declareVariables(h,g)}
-            ${p.mainStart()}
-            ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-
-            let indices = ${g.offsetToIndices("global_idx")};
-
-            var value = ${_}(0);
-            ${w}
-            output[global_idx] = value;
-        }`};return{name:"Pad",shaderCache:{hint:`${a.mode}${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Fe.size(n)/64)},programUniforms:o}),getShaderSource:f}},ev=(t,a)=>{if(t.length>1){let n=t[1].getBigInt64Array(),i=t.length>=3&&t[2].data?t[2].dataType===10?t[2].getUint16Array()[0]:t[2].getFloat32Array()[0]:0,l=t[0].dims.length,o=new Int32Array(2*l).fill(0);if(t.length>=4){let d=t[3].getBigInt64Array();for(let f=0;f<d.length;f++)o[Number(d[f])]=Number(n[f]),o[Number(d[f])+l]=Number(n[f+d.length])}else n.forEach((d,f)=>o[Number(f)]=Number(d));let c=[];return o.forEach(d=>c.push(d)),{mode:a.mode,value:i,pads:c}}else return a},K1=(t,a)=>{qb(t.inputs);let n=ev(t.inputs,a);t.compute(Jb(t.inputs,n),{inputs:[0]})}}),Lp,h_,m_,__,g_,tv,nv,y_,b_,Q1,X1,v_,Y1,Z1,M_,J1,eT,tT,nT,HE=it(()=>{si(),jt(),Yt(),Jt(),Lp=t=>{if(na.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},h_=(t,a,n)=>{let i=a.format==="NHWC",l=t.dims.slice();i&&l.splice(1,0,l.pop());let o=Object.hasOwnProperty.call(a,"dilations"),c=a.kernelShape.slice(),d=a.strides.slice(),f=o?a.dilations.slice():[],p=a.pads.slice();sm.adjustPoolAttributes(n,l,c,d,f,p);let g=sm.computePoolOutputShape(n,l,d,f,c,p,a.autoPad),h=Object.assign({},a);o?Object.assign(h,{kernelShape:c,strides:d,pads:p,dilations:f,cacheKey:a.cacheKey}):Object.assign(h,{kernelShape:c,strides:d,pads:p,cacheKey:a.cacheKey});let _=g.slice();return _.push(_.splice(1,1)[0]),[h,i?_:g]},m_=(t,a)=>{let n=a.format==="NHWC",i=Fe.size(t),l=Fe.size(a.kernelShape),o=[{type:12,data:i},{type:12,data:l}],c=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(a.kernelShape.length<=2){let d=a.kernelShape[a.kernelShape.length-1],f=a.strides[a.strides.length-1],p=a.pads[a.pads.length/2-1],g=a.pads[a.pads.length-1],h=!!(p+g);o.push({type:12,data:d},{type:12,data:f},{type:12,data:p},{type:12,data:g}),c.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let _=!1;if(a.kernelShape.length===2){let w=a.kernelShape[a.kernelShape.length-2],M=a.strides[a.strides.length-2],$=a.pads[a.pads.length/2-2],L=a.pads[a.pads.length-2];_=!!($+L),o.push({type:12,data:w},{type:12,data:M},{type:12,data:$},{type:12,data:L}),c.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[o,c,!0,h,_]}else{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=Fe.computeStrides(a.kernelShape);o.push({type:12,data:d},{type:12,data:a.pads},{type:12,data:a.strides}),c.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:a.pads.length},{name:"strides",type:"u32",length:a.strides.length});let f=a.pads.reduce((p,g)=>p+g);return[o,c,!!f,!1,!1]}},__=(t,a,n,i,l,o,c,d,f,p,g,h)=>{let _=l.format==="NHWC",w=a.type.value,M=Et("output",a.type.tensor,i);if(l.kernelShape.length<=2){let $="",L="",E="",S=n-(_?2:1);if(g?$=`
-                for (var i: u32 = 0u; i < uniforms.kw; i++) {
-                  xIndices[${S}] = indices[${S}] * uniforms.sw - uniforms.pwStart + i;
-                  if (xIndices[${S}] < 0 || xIndices[${S}]
-                      >= uniforms.x_shape[${S}]) {
-                    pad++;
-                    continue;
-                  }
-                  let x_val = x[${a.indicesToOffset("xIndices")}];
-                  ${o}
-                }`:$=`
-                for (var i: u32 = 0u; i < uniforms.kw; i++) {
-                  xIndices[${S}] = indices[${S}] * uniforms.sw - uniforms.pwStart + i;
-                  let x_val = x[${a.indicesToOffset("xIndices")}];
-                  ${o}
-                }`,l.kernelShape.length===2){let D=n-(_?3:2);h?L=`
-                for (var j: u32 = 0u; j < uniforms.kh; j++) {
-                  xIndices[${D}] = indices[${D}] * uniforms.sh - uniforms.phStart + j;
-                  if (xIndices[${D}] < 0 || xIndices[${D}] >= uniforms.x_shape[${D}]) {
-                    pad += i32(uniforms.kw);
-                    continue;
-                  }
-              `:L=`
-                for (var j: u32 = 0u; j < uniforms.kh; j++) {
-                  xIndices[${D}] = indices[${D}] * uniforms.sh - uniforms.phStart + j;
-                `,E=`
-              }
-            `}return`
-            ${t.registerUniforms(f).declareVariables(a,M)}
-
-            ${t.mainStart()}
-              ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-
-              let indices = ${M.offsetToIndices("global_idx")};
-              var xIndices = ${M.offsetToIndices("global_idx")};
-
-              var value = ${w}(${d});
-              var pad = 0;
-              ${L}
-              ${$}
-              ${E}
-              ${c}
-
-              output[global_idx] = value;
-            }`}else{if(_)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let $=l.kernelShape.length,L=l.pads.length,E="";return p?E=`
-                if (xIndices[j] >= uniforms.x_shape[j]) {
-                  pad++;
-                  isPad = true;
-                  break;
-                }
-              }
-              if (!isPad) {
-                let x_val = x[${a.indicesToOffset("xIndices")}];
-                ${o}
-              }`:E=`
-              }
-              let x_val = x[${a.indicesToOffset("xIndices")}];
-              ${o}
-            `,`
-            ${t.registerUniforms(f).declareVariables(a,M)}
-
-            ${t.mainStart()}
-              ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-              let indices = ${M.offsetToIndices("global_idx")};
-              var xIndices = ${M.offsetToIndices("global_idx")};
-
-              var offsets: array<u32, ${$}>;
-
-              var value = ${w}(${d});
-              var pad = 0;
-              var isPad = false;
-
-              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
-                var offset = i;
-                for (var j = 0u; j < ${$-1}u; j++) {
-                  offsets[j] = offset / ${Pt("uniforms.kernelStrides","j",$)};
-                  offset -= offsets[j] * ${Pt("uniforms.kernelStrides","j",$)};
-                }
-                offsets[${$-1}] = offset;
-
-                isPad = false;
-                for (var j = ${n-$}u; j < ${n}u; j++) {
-                  xIndices[j] = indices[j] * ${Pt("uniforms.strides",`j - ${n-$}u`,$)}
-                    + offsets[j - ${n-$}u] - ${Pt("uniforms.pads","j - 2u",L)};
-                  ${E}
-              }
-              ${c}
-
-              output[global_idx] = value;
-            }`}},g_=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,tv=t=>`${g_(t)};${t.countIncludePad}`,nv=t=>`${g_(t)};${t.storageOrder};${t.dilations}`,y_=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),b_=(t,a,n,i)=>{let[l,o]=h_(a,i,n),c=Ge("x",a.dataType,a.dims.length),d=c.type.value,f="value += x_val;",p="";l.countIncludePad?p+=`value /= ${d}(uniforms.kernelSize);`:p+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[g,h,_,w,M]=m_(o,l);g.push(...Dt(a.dims,o));let $=["rank"];return{name:t,shaderCache:{hint:`${i.cacheKey};${_};${w};${M}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:o,dataType:a.dataType}],dispatchGroup:{x:Math.ceil(Fe.size(o)/64)},programUniforms:g}),getShaderSource:L=>__(L,c,a.dims.length,o.length,l,f,p,0,h,_,w,M)}},Q1=t=>{let a=t.count_include_pad!==0,n=y_(t);if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let i={countIncludePad:a,...n,cacheKey:""};return{...i,cacheKey:tv(i)}},X1=(t,a)=>{Lp(t.inputs),t.compute(b_("AveragePool",t.inputs[0],!1,a))},v_={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Y1=t=>{let a=t.format;return{format:a,...v_,cacheKey:a}},Z1=(t,a)=>{Lp(t.inputs),t.compute(b_("GlobalAveragePool",t.inputs[0],!0,a))},M_=(t,a,n,i)=>{let[l,o]=h_(a,i,n),c=`
-      value = max(x_val, value);
-    `,d="",f=Ge("x",a.dataType,a.dims.length),p=["rank"],[g,h,_,w,M]=m_(o,l);return g.push(...Dt(a.dims,o)),{name:t,shaderCache:{hint:`${i.cacheKey};${_};${w};${M}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:o,dataType:a.dataType}],dispatchGroup:{x:Math.ceil(Fe.size(o)/64)},programUniforms:g}),getShaderSource:$=>__($,f,a.dims.length,o.length,l,c,d,a.dataType===10?-65504:-1e5,h,_,w,M)}},J1=(t,a)=>{Lp(t.inputs),t.compute(M_("MaxPool",t.inputs[0],!1,a))},eT=t=>{let a=t.storage_order,n=t.dilations,i=y_(t);if(a!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(i.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let l={storageOrder:a,dilations:n,...i,cacheKey:""};return{...l,cacheKey:nv(l)}},tT=t=>{let a=t.format;return{format:a,...v_,cacheKey:a}},nT=(t,a)=>{Lp(t.inputs),t.compute(M_("GlobalMaxPool",t.inputs[0],!0,a))}}),av,rv,aT,rT,qE=it(()=>{jt(),Yt(),xa(),Jt(),av=(t,a)=>{if(t.length<2||t.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(t.length===3&&t[1].dims===t[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(t[0].dataType===6&&t.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(t[1].dims.length!==0&&t[1].dims.length!==1&&t[1].dims.length!==t[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(t.length>2){if(t[0].dataType!==t[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(t[1].dims.length!==t[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!t[1].dims.map((n,i)=>n===t[2].dims[i]).reduce((n,i)=>n&&i,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(a.blockSize>0){if(t[1].dims.length===0||t[1].dims.length===1&&t[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!t[1].dims.map((l,o)=>o===a.axis||l===t[0].dims[o]).reduce((l,o)=>l&&o,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(t[1].dims.length!==t[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let n=t[0].dims[a.axis],i=t[1].dims[a.axis];if(a.blockSize<Math.ceil(n/i)||a.blockSize>Math.ceil(n/(i-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},rv=(t,a)=>{let n=Fe.normalizeAxis(a.axis,t[0].dims.length),i=t[0].dataType,l=i===3,o=t[0].dims,c=t[1].dataType,d=Fe.size(o),f=i===3||i===2,p=f?[Math.ceil(Fe.size(t[0].dims)/4)]:t[0].dims,g=t[1].dims,h=t.length>2?t[2]:void 0,_=h?f?[Math.ceil(Fe.size(h.dims)/4)]:h.dims:void 0,w=g.length===0||g.length===1&&g[0]===1,M=w===!1&&g.length===1,$=ya(d),L=w&&(!f||$===4),E=L?$:1,S=L&&!f?$:1,D=Ge("input",f?12:i,p.length,S),A=Ge("scale",c,g.length),C=h?Ge("zero_point",f?12:i,_.length):void 0,T=Et("output",c,o.length,E),x=[D,A];C&&x.push(C);let O=[p,g];h&&O.push(_);let N=[{type:12,data:d/E},{type:12,data:n},{type:12,data:a.blockSize},...Dt(...O,o)],G=te=>{let se=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
-      ${te.registerUniforms(se).declareVariables(...x,T)}
-      ${te.mainStart()}
-          ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-          let output_indices = ${T.offsetToIndices("global_idx")};
-
-          // Set input x
-          ${f?`
-            let input = ${D.getByOffset("global_idx / 4")};
-            let x_vec = ${l?"unpack4xI8(input)":"unpack4xU8(input)"};
-            let x_value = ${E===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${D.getByOffset("global_idx")};`};
-
-          // Set scale input
-          ${w?`let scale_value= ${A.getByOffset("0")}`:M?`
-            let scale_index = ${T.indicesGet("output_indices","uniforms.axis")};
-            let scale_value= ${A.getByOffset("scale_index")};`:`
-            var scale_indices: ${A.type.indices} = output_indices;
-            let index = ${A.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
-            ${A.indicesSet("scale_indices","uniforms.axis","index")};
-            let scale_value= ${A.getByIndices("scale_indices")};`};
-
-          // Set zero-point input
-          ${C?w?f?`
-                let zero_point_input = ${C.getByOffset("0")};
-                let zero_point_vec =  ${l?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
-                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${C.getByOffset("0")}`:M?f?`
-                let zero_point_index = ${T.indicesGet("output_indices","uniforms.axis")};
-                let zero_point_input = ${C.getByOffset("zero_point_index / 4")};
-                let zero_point_vec =  ${l?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
-                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
-                let zero_point_index = ${T.indicesGet("output_indices","uniforms.axis")};
-                let zero_point_value = ${C.getByOffset("zero_point_index")};`:f?`
-                let zero_point_offset = ${A.indicesToOffset("scale_indices")};
-                let zero_point_input = ${C.getByOffset("zero_point_offset / 4")};
-                let zero_point_vec = ${l?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
-                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${C.getByIndices("scale_indices")};`:`let zero_point_value = ${f?l?"i32":"u32":D.type.value}(0);`};
-      // Compute and write output
-      ${T.setByOffset("global_idx",`${T.type.value}(x_value - zero_point_value) * scale_value`)};
-      }`};return{name:"DequantizeLinear",shaderCache:{hint:a.cacheKey,inputDependencies:C?["rank","rank","rank"]:["rank","rank"]},getShaderSource:G,getRunData:()=>({outputs:[{dims:o,dataType:c}],dispatchGroup:{x:Math.ceil(d/E/64),y:1,z:1},programUniforms:N})}},aT=(t,a)=>{av(t.inputs,a),t.compute(rv(t.inputs,a))},rT=t=>$n({axis:t.axis,blockSize:t.blockSize})}),sv,iv,sT,KE=it(()=>{si(),jt(),Jt(),sv=(t,a,n)=>{let i=t===a,l=t<a&&n<0,o=t>a&&n>0;if(i||l||o)throw new Error("Range these inputs' contents are invalid.")},iv=(t,a,n,i)=>{let l=Math.abs(Math.ceil((a-t)/n)),o=[l],c=l,d=[{type:12,data:c},{type:i,data:t},{type:i,data:n},...Dt(o)],f=p=>{let g=Et("output",i,o.length),h=g.type.value,_=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return`
-        ${p.registerUniforms(_).declareVariables(g)}
-        ${p.mainStart()}
-        ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-        output[global_idx] = uniforms.start + ${h}(global_idx) * uniforms.delta;
-      }`};return{name:"Range",shaderCache:{hint:`${i}`},getShaderSource:f,getRunData:()=>({outputs:[{dims:o,dataType:i}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:d})}},sT=t=>{let a=0,n=0,i=0;t.inputs[0].dataType===6?(a=t.inputs[0].getInt32Array()[0],n=t.inputs[1].getInt32Array()[0],i=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(a=t.inputs[0].getFloat32Array()[0],n=t.inputs[1].getFloat32Array()[0],i=t.inputs[2].getFloat32Array()[0]),na.webgpu.validateInputContent&&sv(a,n,i),t.compute(iv(a,n,i,t.inputs[0].dataType),{inputs:[]})}}),ov,w_,T_,lv,iT,oT,QE=it(()=>{jt(),Yt(),xa(),Jt(),ov=(t,a,n,i)=>{if(t!=="none"&&i!=="i32"&&i!=="u32"&&i!=="f32")throw new Error(`Input ${i} is not supported with reduction ${t}.`);let l=`{
-                var oldValue = 0;
-                loop {
-                  let newValueF32 =`,o=`;
-                  let newValue = bitcast<i32>(newValueF32);
-                  let res = atomicCompareExchangeWeak(&${a}, oldValue, newValue);
-                  if res.exchanged {
-                    break;
-                  }
-                  oldValue = res.old_value;
-                }
-              }`;switch(t){case"none":return`${a}=${n};`;case"add":return i==="i32"||i==="u32"?`atomicAdd(&${a}, bitcast<${i}>(${n}));`:`
-              ${l}bitcast<${i}>(oldValue) + (${n})${o}`;case"max":return i==="i32"||i==="u32"?`atomicMax(&${a}, bitcast<${i}>(${n}));`:`
-                ${l}max(bitcast<f32>(oldValue), (${n}))${o}`;case"min":return i==="i32"||i==="u32"?`atomicMin(&${a}, bitcast<${i}>(${n}));`:`${l}min(bitcast<${i}>(oldValue), (${n}))${o}`;case"mul":return`${l}(bitcast<${i}>(oldValue) * (${n}))${o}`;default:throw new Error(`Reduction ${t} is not supported.`)}},w_=(t,a)=>`${t===1?`
-    let element_count_dim = uniforms.output_strides;
-    let dim_value = uniforms.output_shape;`:`
-    let element_count_dim = uniforms.output_strides[${a?"i - indices_start":"i"}];
-    let dim_value = uniforms.output_shape[${a?"i - indices_start":"i"} + uniforms.last_index_dimension];`}
-    
-    if (index >= 0) {
-      if (index >= i32(dim_value)) {
-        index = i32(dim_value - 1);
-      }
-    } else {
-      if (index < -i32(dim_value)) {
-        index = 0;
-      } else {
-        index += i32(dim_value);
-      }
-    }
-    data_offset += u32((u32(index) * element_count_dim));`,T_=(t,a,n)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) {
-        let value = updates[uniforms.num_updates_elements * ${n?"global_idx":"idx"} + i];
-        ${ov(t.reduction,"output[data_offset + i]","value",a)}
-      }`,lv=(t,a)=>{let n=t[0].dims,i=t[1].dims,l=n,o=1,c=Math.ceil(Fe.size(i)/o),d=i[i.length-1],f=Fe.sizeFromDimension(n,d),p=Fe.sizeFromDimension(i,0)/d,g=[{type:12,data:c},{type:12,data:d},{type:12,data:f},...Dt(t[1].dims,t[2].dims,l)],h=_=>{let w=Ge("indices",t[1].dataType,t[1].dims.length),M=Ge("updates",t[2].dataType,t[2].dims.length,o),$=a.reduction!=="none"&&a.reduction!==""?zM("output",t[0].dataType,l.length):Et("output",t[0].dataType,l.length,o);return`
-      ${_.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(w,M,$)}
-      ${_.mainStart()}
-        ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-  var hasDuplicates = false;
-  if (${a.reduction==="none"}) {
-    for (var i = 0; i < ${p}; i = i + 1) {
-      for (var j = i + 1; j < ${p}; j = j + 1) {
-        var index_i = i32(indices[i].x);
-        var index_j = i32(indices[j].x);
-        if (index_i == index_j) {
-          hasDuplicates = true;
-          break;
-        }
-      }
-      if (hasDuplicates) {
-        break;
-      }
-    }
-  }
-
-  if (${a.reduction==="none"} && hasDuplicates) {
-    if (global_idx != 0u) {
-      return;
-    }
-    // Process each index-update pair individually when duplicates exist
-    for (var idx = 0u; idx < ${p}u; idx++) {
-      var data_offset = 0u;
-      for (var i = 0u; i < uniforms.last_index_dimension; i++) {
-        var index = i32(indices[idx * uniforms.last_index_dimension + i].x);
-        ${w_(n.length,!1)}
-      }
-      ${T_(a,$.type.value,!1)}
-    }
-    return;
-  }
-
-  var data_offset = 0u;
-  var indices_start = uniforms.last_index_dimension * global_idx;
-  var indices_end = indices_start + uniforms.last_index_dimension;
-  for (var i = indices_start; i < indices_end; i++) {
-    var index = i32(indices[i].x);
-    ${w_(n.length,!0)}
-  }
-  ${T_(a,$.type.value,!0)}
-  }`};return{name:"ScatterND",shaderCache:{hint:`${a.cacheKey}_${a.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:h}},iT=t=>$n({reduction:t.reduction}),oT=(t,a)=>{t.compute(lv(t.inputs,a),{inputs:[t.inputs[1],t.inputs[2]],outputs:[]})}}),uv,cv,dv,x_,fv,pv,hv,mv,_v,gv,yv,bv,E_,vv,Mv,wv,Tv,xv,lT,uT,XE=it(()=>{jt(),Yt(),xa(),Jt(),uv=(t,a)=>{if(t.every(n=>n>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(a.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
-            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(a.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},cv=(t,a,n)=>{a.every(l=>l>=0&&l<n||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let i=new Array(n).fill(1);return a.forEach((l,o)=>i[l]=t[o]),i},dv=(t,a,n,i,l,o)=>{let[c,d,f]=n>10?[1,2,3]:[-1,t.length>1?1:-1,-1],p=t[0].dims.length;if(c>0&&t.length>c&&t[c].dims.length>0)t[c].getFloat32Array().forEach(g=>o.push(g));else if(a.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&t.length>d&&t[d].dims.length===1&&t[d].dims[0]>0){if(t[d].getFloat32Array().forEach(g=>i.push(g)),i.length!==0&&i.length!==p&&n>=18&&i.length!==a.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");uv(i,a),a.axes.length>0&&cv(i,a.axes,p).forEach((g,h)=>i[h]=g)}if(f>0&&t.length>f&&t[f].dims.length===1&&t[f].dims[0]>0&&(t[f].getBigInt64Array().forEach(g=>l.push(Number(g))),l.length!==0&&l.length!==p&&n>=18&&l.length!==a.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(a.axes.length>0){if(i.length!==0&&i.length!==a.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(l.length!==0&&l.length!==a.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof i<"u"&&typeof l<"u"&&i.length>0&&l.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},x_=(t,a,n,i)=>`
-  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
-  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
-  // offset-by-one error later in floor().
-  let big = (${t}) * (${a});
-  let whole = ${i}(big / (${n}));
-  let fract = ${i}(big % (${n})) / ${i}(${n});
-  return whole + fract;
-`,fv=(t,a)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
-     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${a} { `+(()=>{switch(t){case"asymmetric":return`
-          if (xScale < 1.0 || floor(xScale) != xScale) {
-            return ${a}(xResized) / ${a}(xScale);
-          } else {
-            ${x_("xResized","lengthOriginal","lengthResized",a)}
-          }
-        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
-                    return (${a}(xResized) + 0.5) / ${a}(xScale) - 0.5;
-                  } else {
-                    return 0.0;
-                  }`;case"tf_half_pixel_for_nn":return`return (${a}(xResized) + 0.5) / ${a}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
-                    return 0.0;
-                  } else {
-                    ${x_("xResized","lengthOriginal - 1","lengthResized - 1",a)}
-                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
-                    return ${a}(roiStart) * ${a}(lengthOriginal - 1) +
-                        (${a}(xResized) * ${a}(roiEnd - roiStart) * ${a}(lengthOriginal - 1)) /
-                        ${a}(lengthResized - 1);
-                  } else {
-                    return 0.5 * ${a}(roiStart + roiEnd) * ${a}(lengthOriginal - 1);
-                  }`;case"half_pixel_symmetric":return`const outputWidth = ${a}xScale * ${a}(lengthResized);
-                  const adjustment = ${a}(lengthResized) / outputWidth;
-                  const center = ${a}(lengthOriginal) / 2;
-                  const offset = center * (1 - adjustment);
-                  return offset + ((${a}(xResized) + 0.5) / ${a}(xScale)) - 0.5;`;case"half_pixel":return`return ((${a}(xResized) + 0.5) / ${a}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",pv=(t,a,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(t){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(a<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",hv=(t,a,n)=>{let i=new Array(n).fill(0).concat(new Array(n).fill(1)),l=t.length===0?i:t.slice();return a.length>0?(a.forEach((o,c)=>{i[o]=l[c],i[c+n]=l[a.length+c]}),i):l},mv=(t,a,n,i)=>{let l=[];if(n.length>0)if(i.length>0){if(t.forEach(o=>l.push(o)),Math.max(...i)>t.length)throw new Error("axes is out of bound");i.forEach((o,c)=>l[o]=n[c])}else n.forEach(o=>l.push(o));else{if(a.length===0)throw new Error("Resize requires either scales or sizes.");l=t.map((o,c)=>Math.round(o*a[c]))}return l},_v=(t,a,n)=>{let i=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map(o=>a[o]),Number.MAX_VALUE):Math.min(...a,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map(o=>a[o]),Number.MIN_VALUE):Math.max(...a,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();a.fill(1,0,a.length);let l=t.slice();return n.axes.length>0?(n.axes.forEach(o=>a[o]=i),n.axes.forEach(o=>l[o]=Math.round(t[o]*a[o]))):(a.fill(i,0,a.length),l.forEach((o,c)=>l[c]=Math.round(o*a[c]))),l},gv=(t,a,n,i,l)=>`
-    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${n.length}> {
-      var original_indices: array<${t.type.value}, ${n.length}>;
-      for (var i:u32 = 0; i < ${n.length}; i++) {
-        var output_index = ${t.indicesGet("output_indices","i")};
-        var scale = ${Pt("uniforms.scales","i",i)};
-        var roi_low = ${Pt("uniforms.roi","i",l)};
-        var roi_hi = ${Pt("uniforms.roi",`i + ${a.length}`,l)};
-        if (scale == 1.0) {
-          original_indices[i] = ${t.type.value}(output_index);
-        } else {
-          var input_shape_i = ${Pt("uniforms.input_shape","i",a.length)};
-          var output_shape_i = ${Pt("uniforms.output_shape","i",n.length)};
-          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
-                                                                           input_shape_i, roi_low, roi_hi);
-        }
-      }
-      return original_indices;
-    }`,yv=(t,a,n,i,l,o,c)=>`
-    fn calculateInputIndicesFromOutputIndices(output_indices: ${a.type.indices}) -> ${t.type.indices} {
-      var input_indices: ${t.type.indices};
-      for (var i:u32 = 0; i < ${i.length}; i++) {
-        var output_index = ${a.indicesGet("output_indices","i")};
-        var input_index: u32;
-        var scale = ${Pt("uniforms.scales","i",l)};
-        if (scale == 1.0) {
-          input_index = output_index;
-        } else {
-          var roi_low = ${Pt("uniforms.roi","i",o)};
-          var roi_hi = ${Pt("uniforms.roi",`i + ${n.length}`,o)};
-          var input_shape_i = ${Pt("uniforms.input_shape","i",n.length)};
-          var output_shape_i = ${Pt("uniforms.output_shape","i",i.length)};
-          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
-                                                                        input_shape_i, roi_low, roi_hi);
-          if (!${c} || (original_idx >= 0 && original_idx < ${a.type.value}(input_shape_i))) {
-            if (original_idx < 0) {
-              input_index = 0;
-            } else if (original_idx > ${a.type.value}(input_shape_i - 1)) {
-              input_index = input_shape_i - 1;
-            } else {
-              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
-            }
-          } else {
-            input_index = u32(original_idx);
-          }
-        }
-        ${t.indicesSet("input_indices","i","input_index")}
-      }
-      return input_indices;
-    }`,bv=(t,a)=>`
-    fn checkInputIndices(input_indices: ${t.type.indices}) -> bool {
-      for (var i:u32 = 0; i < ${a.length}; i++) {
-        var input_index = ${t.indicesGet("input_indices","i")};
-        if (input_index < 0 || input_index >= ${Pt("uniforms.input_shape","i",a.length)}) {
-          return false;
-        }
-      }
-      return true;
-    }`,E_=(t,a,n,i)=>t.rank>i?`
-    ${t.indicesSet("input_indices",a,"channel")};
-    ${t.indicesSet("input_indices",n,"batch")};
-`:"",vv=(t,a,n,i,l)=>{let[o,c,d,f]=n.length===2?[-1,0,1,-1]:[0,2,3,1],p=t.type.value;return`
-    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${p} {
-      var input_indices: ${t.type.indices};
-      ${t.indicesSet("input_indices",c,`max(0, min(row, ${n[c]} - 1))`)};
-      ${t.indicesSet("input_indices",d,`max(0, min(col, ${n[d]} - 1))`)};
-      ${E_(t,f,o,2)}
-      return ${t.getByIndices("input_indices")};
-    }
-
-    fn bilinearInterpolation(output_indices: ${a.type.indices}) -> ${p} {
-      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
-      var row:${p} = originalIndices[${c}];
-      var col:${p} = originalIndices[${d}];
-      ${i?`if (row < 0 || row > (${n[c]} - 1) || col < 0 || col > (${n[d]} - 1)) {
-        return ${l};
-      }`:""};
-      row = max(0, min(row, ${n[c]} - 1));
-      col = max(0, min(col, ${n[d]} - 1));
-      var row1: u32 = u32(row);
-      var col1: u32 = u32(col);
-      var row2: u32 = u32(row + 1);
-      var col2: u32 = u32(col + 1);
-      var channel: u32 = ${n.length>2?`u32(originalIndices[${f}])`:"0"};
-      var batch: u32 =  ${n.length>2?`u32(originalIndices[${o}])`:"0"};
-      var x11: ${p} = getInputValue(batch, channel, row1, col1);
-      var x12: ${p} = getInputValue(batch, channel, row1, col2);
-      var x21: ${p} = getInputValue(batch, channel, row2, col1);
-      var x22: ${p} = getInputValue(batch, channel, row2, col2);
-      var dx1: ${p} = abs(row - ${p}(row1));
-      var dx2: ${p} = abs(${p}(row2) - row);
-      var dy1: ${p} = abs(col - ${p}(col1));
-      var dy2: ${p} = abs(${p}(col2) - col);
-      if (row1 == row2) {
-        dx1 = 0.5;
-        dx2 = 0.5;
-      }
-      if (col1 == col2) {
-        dy1 = 0.5;
-        dy2 = 0.5;
-      }
-      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
-    }`},Mv=(t,a,n,i,l,o,c,d,f,p)=>{let g=n.length===2,[h,_]=g?[0,1]:[2,3],w=t.type.value,M=$=>{let L=$===h?"row":"col";return`
-      fn ${L}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${a.type.indices}) -> ${w} {
-        var output_index = ${a.indicesGet("output_indices",$)};
-        var originalIdx: ${w} = getOriginalCoordinateFromResizedCoordinate(output_index, ${l[$]},
-        ${i[$]}, ${n[$]}, ${o[$]}, ${o[$]} + ${n.length});
-        var fractOriginalIdx: ${w} = originalIdx - floor(originalIdx);
-        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);
-
-        if (${d} && (originalIdx < 0 || originalIdx > (${n[$]} - 1))) {
-          return ${f};
-        }
-        var data: array<${w}, 4> = array<${w}, 4>(0.0, 0.0, 0.0, 0.0);
-        for (var i: i32 = -1; i < 3; i++) {
-          var ${L}: ${w} = originalIdx + ${w}(i);
-          if (${L} < 0 || ${L} >= ${n[$]}) {
-            ${p?`coefs[i + 1] = 0.0;
-                        continue;`:d?`return ${f};`:`${L} = max(0, min(${L}, ${n[$]} - 1));`};
-          }
-        var input_indices_copy: ${t.type.indices} = input_indices;
-          ${t.indicesSet("input_indices_copy",$,`u32(${L})`)};
-          data[i + 1] = ${$===h?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
-        }
-        return cubicInterpolation1D(data, coefs);
-      }`};return`
-    ${M(h)};
-    ${M(_)};
-  fn getCubicInterpolationCoefs(s: ${w}) -> array<${w}, 4> {
-    var absS = abs(s);
-    var coeffs: array<${w}, 4> = array<${w}, 4>(0.0, 0.0, 0.0, 0.0);
-    var oneMinusAbsS: ${w} = 1.0 - absS;
-    var twoMinusAbsS: ${w} = 2.0 - absS;
-    var onePlusAbsS: ${w} = 1.0 + absS;
-    coeffs[0] = ((${c} * onePlusAbsS - 5 * ${c}) * onePlusAbsS + 8 * ${c}) * onePlusAbsS - 4 * ${c};
-    coeffs[1] = ((${c} + 2) * absS - (${c} + 3)) * absS * absS + 1;
-    coeffs[2] = ((${c} + 2) * oneMinusAbsS - (${c} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
-    coeffs[3] = ((${c} * twoMinusAbsS - 5 * ${c}) * twoMinusAbsS + 8 * ${c}) * twoMinusAbsS - 4 * ${c};
-    return coeffs;
-  }
-
-  fn cubicInterpolation1D(x: array<${w}, 4>, coefs: array<${w}, 4>) -> ${w} {
-    var coefsSum: ${w} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
-    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
-  }
-
-  fn bicubicInterpolation(output_indices: ${a.type.indices}) -> ${w} {
-    var input_indices: ${t.type.indices} = output_indices;
-    return colCubicInterpolation(input_indices, output_indices);
-  }
-    `},wv=(t,a,n,i,l)=>{let[o,c,d,f,p]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=t.type.value;return`
-    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} {
-      var input_indices: ${t.type.indices};
-      ${t.indicesSet("input_indices",c,`max(0, min(depth, ${n[c]} - 1))`)};
-      ${t.indicesSet("input_indices",d,`max(0, min(height, ${n[d]} - 1))`)};
-      ${t.indicesSet("input_indices",f,`max(0, min(width, ${n[f]} - 1))`)};
-      ${E_(t,p,o,3)}
-      return ${t.getByIndices("input_indices")};
-    }
-
-    fn trilinearInterpolation(output_indices: ${a.type.indices}) -> ${g} {
-      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
-      var depth:${g} = originalIndices[${c}];
-      var height:${g} = originalIndices[${d}];
-      var width:${g} = originalIndices[${f}];
-      ${i?`if (depth < 0 || depth > (${n[c]} - 1) || height < 0 || height > (${n[d]} - 1) || width < 0 || (width > ${n[f]} - 1)) {
-      return ${l};
-        }`:""};
-
-    depth = max(0, min(depth, ${n[c]} - 1));
-      height = max(0, min(height, ${n[d]} - 1));
-      width = max(0, min(width, ${n[f]} - 1));
-      var depth1: u32 = u32(depth);
-      var height1: u32 = u32(height);
-      var width1: u32 = u32(width);
-      var depth2: u32 = u32(depth + 1);
-      var height2: u32 = u32(height + 1);
-      var width2: u32 = u32(width + 1);
-      var channel: u32 = ${n.length>3?`u32(originalIndices[${p}])`:"0"};
-      var batch: u32 =  ${n.length>3?`u32(originalIndices[${o}])`:"0"};
-
-      var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);
-      var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);
-      var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);
-      var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);
-      var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);
-      var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);
-      var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);
-      var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);
-      var dx1: ${g} = abs(depth - ${g}(depth1));
-      var dx2: ${g} = abs(${g}(depth2) - depth);
-      var dy1: ${g} = abs(height - ${g}(height1));
-      var dy2: ${g} = abs(${g}(height2) - height);
-      var dz1: ${g} = abs(width - ${g}(width1));
-      var dz2: ${g} = abs(${g}(width2) - width);
-      if (depth1 == depth2) {
-        dx1 = 0.5;
-        dx2 = 0.5;
-      }
-      if (height1 == height2) {
-        dy1 = 0.5;
-        dy2 = 0.5;
-      }
-      if (width1 == width2) {
-        dz1 = 0.5;
-        dz2 = 0.5;
-      }
-      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
-              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
-    }`},Tv=(t,a,n,i,l,o)=>{let c=t.dims,d=hv(o,a.axes,c.length),f=mv(c,i,l,a.axes),p=i.slice();i.length===0&&(p=c.map((S,D)=>S===0?1:f[D]/S),a.keepAspectRatioPolicy!=="stretch"&&(f=_v(c,p,a)));let g=Et("output",t.dataType,f.length),h=Ge("input",t.dataType,c.length),_=Fe.size(f),w=c.length===f.length&&c.every((S,D)=>S===f[D]),M=a.coordinateTransformMode==="tf_crop_and_resize",$=a.extrapolationValue,L=h.type.value,E=S=>`
-      ${w?"":`
-      ${fv(a.coordinateTransformMode,L)};
-      ${(()=>{switch(a.mode){case"nearest":return`
-              ${bv(h,c)};
-              ${pv(a.nearestMode,n,L)};
-              ${yv(h,g,c,f,p.length,d.length,M)};
-              `;case"linear":return`
-              ${gv(g,c,f,p.length,d.length)};
-              ${(()=>{if(c.length===2||c.length===4)return`${vv(h,g,c,M,$)}`;if(c.length===3||c.length===5)return`${wv(h,g,c,M,$)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
-            `;case"cubic":return`
-            ${(()=>{if(c.length===2||c.length===4)return`${Mv(h,g,c,f,p,d,a.cubicCoeffA,M,a.extrapolationValue,a.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
-            `;default:throw Error("Invalid resize mode")}})()};
-      `}
-      ${S.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",d.length).declareVariables(h,g)}
-      ${S.mainStart()}
-        ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
-        ${w?"output[global_idx] = input[global_idx];":`
-        let output_indices = ${g.offsetToIndices("global_idx")};
-        var input_indices: ${h.type.indices};
-        ${(()=>{switch(a.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
-                if (checkInputIndices(input_indices)) {
-                  output[global_idx] = ${h.getByIndices("input_indices")};
-                } else {
-                  output[global_idx] = ${a.extrapolationValue};
-                }`;case"linear":return`output[global_idx] = ${c.length===2||c.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${a.mode}`)}})()};
-`}
-      }`;return{name:"Resize",shaderCache:{hint:`${a.cacheKey}|${n}|${p.length>0?a.mode==="cubic"?p:p.length:""}|${l.length>0?l:""}|${d.length>0?d:""}|${w}|${a.mode==="nearest"?c.length:c}`,inputDependencies:["rank"]},getShaderSource:E,getRunData:()=>({outputs:[{dims:f,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},{type:1,data:p},{type:1,data:d},...Dt(c,f)]})}},xv=t=>{let a=t.customDataBuffer;return new Uint32Array(a,a.byteOffset,1)[0]},lT=(t,a)=>{let n=[],i=[],l=[],o=xv(t);if(a.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");dv(t.inputs,a,o,n,i,l),t.compute(Tv(t.inputs[0],a,o,n,i,l),{inputs:[0]})},uT=t=>{let a=t.antialias,n=t.axes,i=t.coordinateTransformMode,l=t.cubicCoeffA,o=t.excludeOutside!==0,c=t.extrapolationValue,d=t.keepAspectRatioPolicy,f=t.mode,p=t.nearestMode===""?"simple":t.nearestMode;return $n({antialias:a,axes:n,coordinateTransformMode:i,cubicCoeffA:l,excludeOutside:o,extrapolationValue:c,keepAspectRatioPolicy:d,mode:f,nearestMode:p})}}),Ev,Sv,cT,YE=it(()=>{jt(),Yt(),Jt(),Ev=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let a=t[0],n=t[1],i=t[2];if(a.dataType!==n.dataType||a.dataType!==i.dataType)throw new Error("All inputs must have the same data type");if(a.dims.length!==3&&a.dims.length!==2)throw new Error("Input must be 2D or 3D");if(n.dims.length!==3&&n.dims.length!==2)throw new Error("Skip must be 2D or 3D");let l=a.dims[a.dims.length-1],o=a.dims[a.dims.length-2];if(n.dims[n.dims.length-1]!==l)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==o)throw new Error("Skip must have the same sequence length as input");if(i.dims.length!==1)throw new Error("Gamma must be 1D");if(i.dims[i.dims.length-1]!==l)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let c=t[3];if(c.dims.length!==1)throw new Error("Beta must be 1D");if(c.dims[c.dims.length-1]!==l)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let c=t[4];if(c.dims.length!==1)throw new Error("Bias must be 1D");if(c.dims[c.dims.length-1]!==l)throw new Error("Bias must have the same hidden size as input")}},Sv=(t,a,n,i)=>{let l=a.simplified,o=t[0].dims,c=Fe.size(o),d=o,f=c,p=o.slice(-1)[0],g=i?o.slice(0,-1).concat(1):[],h=!l&&t.length>3,_=t.length>4,w=i&&n>1,M=i&&n>2,$=n>3,L=64,E=ya(p),S=[{type:12,data:f},{type:12,data:E},{type:12,data:p},{type:1,data:a.epsilon}],D=C=>{let T=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],x=[Ge("x",t[0].dataType,t[0].dims,E),Ge("skip",t[1].dataType,t[1].dims,E),Ge("gamma",t[2].dataType,t[2].dims,E)];h&&x.push(Ge("beta",t[3].dataType,t[3].dims,E)),_&&x.push(Ge("bias",t[4].dataType,t[4].dims,E)),x.push(Et("output",t[0].dataType,d,E)),w&&x.push(Et("mean_output",1,g)),M&&x.push(Et("inv_std_output",1,g)),$&&x.push(Et("input_skip_bias_sum",t[0].dataType,d,E));let O=Qa(t[0].dataType),N=Qa(1,E);return`
-
-      ${C.registerUniforms(T).declareVariables(...x)}
-      var<workgroup> sum_shared : array<${N}, ${L}>;
-      var<workgroup> sum_squared_shared : array<${N}, ${L}>;
-
-      ${C.mainStart([L,1,1])}
-        let ix = local_id.x;
-        let iy = global_id.x / ${L};
-
-        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
-        var stride = hidden_size_vectorized / ${L};
-        let offset = ix * stride + iy * hidden_size_vectorized;
-        let offset1d = stride * ix;
-        if (ix == ${L-1}) {
-          stride = hidden_size_vectorized - stride * ix;
-        }
-        for (var i: u32 = 0; i < stride; i++) {
-          let skip_value = skip[offset + i];
-          let bias_value = ${_?"bias[offset1d + i]":O+"(0.0)"};
-          let input_value = x[offset + i];
-          let value = input_value + skip_value + bias_value;
-          ${$?"input_skip_bias_sum[offset + i] = value;":""}
-          output[offset + i] = value;
-          let f32_value = ${Cd(O,E,"value")};
-          sum_shared[ix] += f32_value;
-          sum_squared_shared[ix] += f32_value * f32_value;
-        }
-        workgroupBarrier();
-
-        var reduce_size : u32 = ${L};
-        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
-          reduce_size = curr_size + (reduce_size & 1);
-          if (ix < curr_size) {
-            sum_shared[ix] += sum_shared[ix + reduce_size];
-            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
-          }
-          workgroupBarrier();
-        }
-
-        let sum = sum_shared[0];
-        let square_sum = sum_squared_shared[0];
-        let mean = ${El("sum",E)} / f32(uniforms.hidden_size);
-        let inv_std_dev = inverseSqrt(${El("square_sum",E)} / f32(uniforms.hidden_size) ${l?"":"- mean * mean"} + uniforms.epsilon);
-        ${w?"mean_output[global_idx] = mean;":""}
-        ${M?"inv_std_output[global_idx] = inv_std_dev;":""}
-
-        for (var i: u32 = 0; i < stride; i++) {
-          output[offset + i] = (output[offset + i] ${l?"":`- ${O}(mean)`}) *
-            ${O}(inv_std_dev) * gamma[offset1d + i]
-            ${h?"+ beta[offset1d + i]":""};
-        }
-      }`},A=[{dims:d,dataType:t[0].dataType}];return n>1&&A.push({dims:g,dataType:1}),n>2&&A.push({dims:g,dataType:1}),n>3&&A.push({dims:o,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${E};${w};${M};${$}`,inputDependencies:t.map((C,T)=>"type")},getShaderSource:D,getRunData:()=>({outputs:A,dispatchGroup:{x:Math.ceil(f/p)},programUniforms:S})}},cT=(t,a)=>{Ev(t.inputs);let n=[0];t.outputCount>1&&n.push(-3),t.outputCount>2&&n.push(-3),t.outputCount>3&&n.push(3),t.compute(Sv(t.inputs,a,t.outputCount,!1),{outputs:n})}}),Cv,Fp,Pv,S_,Av,$v,dT,fT,ZE=it(()=>{jt(),Yt(),xa(),Jt(),Cv=(t,a)=>{if(!t||t.length<1)throw new Error("too few inputs");if(a.axes.length!==0){if(a.axes.length!==a.starts.length||a.axes.length!==a.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(a.starts.length!==a.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((n,i)=>{if(t[i+1].dataType!==6&&t[i+1].dataType!==7)throw new Error(`Input ${i} must be an array of int32 or int64`)})},Fp=(t,a)=>{let n=[];if(t.length>a)if(t[a].dataType===7)t[a].getBigInt64Array().forEach(i=>n.push(Number(i)));else if(t[a].dataType===6)t[a].getInt32Array().forEach(i=>n.push(Number(i)));else throw new Error(`Input ${a} must be an array of int32 or int64`);return n},Pv=(t,a)=>{if(t.length>1){let n=Fp(t,1),i=Fp(t,2),l=Fp(t,3);return l.length===0&&(l=[...Array(t[0].dims.length).keys()]),$n({starts:n,ends:i,axes:l})}else return a},S_=(t,a,n,i,l)=>{let o=t;return t<0&&(o+=n[i[a]]),l[a]<0?Math.max(0,Math.min(o,n[i[a]]-1)):Math.max(0,Math.min(o,n[i[a]]))},Av=(t,a,n)=>`fn calculateInputIndices(output_indices: ${a.type.indices}) -> ${t.type.indices} {
-          var input_indices: ${t.type.indices};
-          var carry = 0u;
-          for (var i = ${n.length}; i >= 0; i--) {
-            let input_shape_i = ${Pt("uniforms.input_shape","i",n.length)};
-            let steps_i = ${Pt("uniforms.steps","i",n.length)};
-            let signs_i = ${Pt("uniforms.signs","i",n.length)};
-            let starts_i = ${Pt("uniforms.starts","i",n.length)};
-            var output_index = ${a.indicesGet("output_indices","i")};
-            var input_index = output_index * steps_i + starts_i + carry;
-            carry = input_index / input_shape_i;
-            input_index = input_index % input_shape_i;
-            if (signs_i < 0) {
-              input_index = input_shape_i - input_index - 1u + starts_i;
-            }
-            ${t.indicesSet("input_indices","i","input_index")};
-          }
-          return input_indices;
-      }`,$v=(t,a)=>{let n=t[0].dims,i=Fe.size(n),l=a.axes.length>0?Fe.normalizeAxes(a.axes,n.length):[...Array(n.length).keys()],o=Fp(t,4);o.forEach(E=>E!==0||(()=>{throw new Error("step cannot be 0")})),o.length===0&&(o=Array(l.length).fill(1));let c=a.starts.map((E,S)=>S_(E,S,n,l,o)),d=a.ends.map((E,S)=>S_(E,S,n,l,o));if(l.length!==c.length||l.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(l.length!==n.length)for(let E=0;E<n.length;++E)l.includes(E)||(c.splice(E,0,0),d.splice(E,0,n[E]),o.splice(E,0,1));let f=o.map(E=>Math.sign(E));o.forEach((E,S,D)=>{if(E<0){let A=(d[S]-c[S])/E,C=c[S],T=C+A*o[S];c[S]=T,d[S]=C,D[S]=-E}});let p=n.slice(0);l.forEach((E,S)=>{p[E]=Math.ceil((d[E]-c[E])/o[E])});let g={dims:p,dataType:t[0].dataType},h=Et("output",t[0].dataType,p.length),_=Ge("input",t[0].dataType,t[0].dims.length),w=Fe.size(p),M=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:c.length},{name:"signs",type:"i32",length:f.length},{name:"steps",type:"u32",length:o.length}],$=[{type:12,data:w},{type:12,data:c},{type:6,data:f},{type:12,data:o},...Dt(t[0].dims,p)],L=E=>`
-      ${E.registerUniforms(M).declareVariables(_,h)}
-        ${Av(_,h,n)}
-        ${E.mainStart()}
-          ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
-          let output_indices = ${h.offsetToIndices("global_idx")};
-          let input_indices = calculateInputIndices(output_indices);
-          ${h.setByOffset("global_idx",_.getByIndices("input_indices"))}
-      }`;return{name:"Slice",shaderCache:{hint:`${f.length}_${c.length}_${o.length}`,inputDependencies:["rank"]},getShaderSource:L,getRunData:()=>({outputs:[g],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:$})}},dT=(t,a)=>{Cv(t.inputs,a);let n=Pv(t.inputs,a);t.compute($v(t.inputs,n),{inputs:[0]})},fT=t=>{let a=t.starts,n=t.ends,i=t.axes;return $n({starts:a,ends:n,axes:i})}}),kv,Ov,pT,hT,JE=it(()=>{jt(),Yt(),xa(),Sl(),Jt(),kv=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Ov=(t,a)=>{let n=t.inputs[0],i=n.dims,l=Fe.size(i),o=i.length,c=Fe.normalizeAxis(a.axis,o),d=c<i.length-1,f,p=[];d?(p=Array.from({length:o},(x,O)=>O),p[c]=o-1,p[o-1]=c,f=t.compute(as(n,p),{inputs:[n],outputs:[-1]})[0]):f=n;let g=f.dims,h=g[o-1],_=l/h,w=ya(h),M=h/w,$=64;_===1&&($=256);let L=(x,O)=>O===4?`max(max(${x}.x, ${x}.y), max(${x}.z, ${x}.w))`:O===2?`max(${x}.x, ${x}.y)`:O===3?`max(max(${x}.x, ${x}.y), ${x}.z)`:x,E=Ge("x",f.dataType,f.dims,w),S=Et("result",f.dataType,f.dims,w),D=E.type.value,A=Qa(f.dataType)==="f32"?`var threadMax = ${D}(-3.402823e+38f);`:`var threadMax = ${D}(-65504.0h);`,C=x=>`
-      var<workgroup> rowMaxShared : ${D};
-      var<workgroup> rowSumShared : ${D};
-      var<workgroup> threadShared : array<${D}, ${$}>;
-
-      fn getValue(row: i32, col: i32, row_stride: i32) -> ${D} {
-        let index = row * row_stride + col;
-        return x[index];
-      }
-
-      fn setValue(row: i32, col: i32, row_stride: i32, value: ${D}) {
-        let index = row * row_stride + col;
-        result[index] = value;
-      }
-      ${x.registerUniform("packedCols","i32").declareVariables(E,S)}
-      ${x.mainStart($)}
-        let gindex = i32(global_idx);
-        let lindex = i32(local_idx);
-        const wg = ${$};
-        let row = gindex / wg;
-        let cols = uniforms.packedCols;
-        let row_stride : i32 = uniforms.packedCols;
-
-        // find the rows max
-        ${A}
-        for (var col = lindex; col < cols; col += wg) {
-          let value = getValue(row, col, row_stride);
-          threadMax = max(threadMax, value);
-        }
-        if (lindex < cols) {
-          threadShared[lindex] = threadMax;
-        }
-        workgroupBarrier();
-
-        var reduceSize = min(cols, wg);
-        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {
-          reduceSize = currSize + (reduceSize & 1);
-          if (lindex < currSize) {
-            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
-          }
-          workgroupBarrier();
-        }
-        if (lindex == 0) {
-          rowMaxShared = ${D}(${L("threadShared[0]",w)});
-        }
-        workgroupBarrier();
-
-        // find the rows sum
-        var threadSum = ${D}(0.0);
-        for (var col = lindex; col < cols; col += wg) {
-          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
-          threadSum += subExp;
-        }
-        threadShared[lindex] = threadSum;
-        workgroupBarrier();
-
-        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
-          if (lindex < currSize) {
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-            ${h("output_data[global_idx]",1)}
-            ${h("output_data[global_idx]",2)}
-            ${h("output_data[global_idx]",3)}
-          `}return`
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t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{var i;let a=new BigUint64Array(t.getMappedRange()),n=this.pendingQueries.get(t);for(let l=0;l<a.length/2;l++){let o=n[l],c=o.kernelId,d=this.kernels.get(c),f=d.kernelType,p=d.kernelName,g=o.programName,h=o.inputTensorViews,_=o.outputTensorViews,w=a[l*2],M=a[l*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=w);let 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T=this.gpuDataManager.create(S,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(T.buffer,0,C,0,S),this.gpuDataManager.release(T.id),w={offset:0,size:S,buffer:T.buffer}}let M=this.programManager.normalizeDispatchGroupSize(f),$=M[1]===1&&M[2]===1,L=Rv(t,a,$),E=this.programManager.getArtifact(L);if(E||(E=this.programManager.build(t,M),this.programManager.setArtifact(L,E),vn("info",()=>`[artifact] key: ${L}, programName: ${t.name}`)),p&&E.uniformVariablesInfo){if(p.length!==E.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${E.uniformVariablesInfo.length}, got ${p.length} in program "${E.programInfo.name}".`);for(let S=0;S<p.length;S++){let D=p[S],A=D.type,C=typeof D.data=="number"?1:D.data.length,[T,x]=E.uniformVariablesInfo[S];if(A!==T||C!==x)throw new Error(`Uniform variable ${S} mismatch: expect type ${T} with size ${x}, got type ${A} with size ${C} in program "${E.programInfo.name}".`)}}if(vn("info",()=>`[ProgramManager] run "${t.name}" (key=${L}) with ${M[0]}x${M[1]}x${M[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let S={kernelId:this.currentKernelId,programName:E.programInfo.name,inputTensorViews:a,outputTensorViews:h};this.pendingKernels.push(S),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(S)}return this.programManager.run(E,c,_,M,w),xs(t.name),h}upload(t,a){this.gpuDataManager.upload(t,a)}memcpy(t,a){this.gpuDataManager.memcpy(t,a)}async download(t,a){await this.gpuDataManager.download(t,a)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,a,n,i){let l=gT.get(t);if(!l)throw new Error(`kernel not implemented: ${t}`);let o={kernelType:t,kernelName:i,kernelEntry:l[0],attributes:[l[1],n]};this.kernels.set(a,o)}releaseKernel(t){let a=this.kernelPersistentData.get(t);if(a){for(let n of a)this.gpuDataManager.release(n.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,a,n){let i=this.kernels.get(t);if(!i)throw new Error(`kernel not created: ${t}`);let l=i.kernelType,o=i.kernelName,c=i.kernelEntry,d=i.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${l}] ${o}" is not allowed to be called recursively`);this.currentKernelId=t,d[0]&&(d[1]=d[0](d[1]),d[0]=void 0),vn("info",()=>`[WebGPU] Start to run kernel "[${l}] ${o}"...`);let f=this.env.debug;this.temporaryData=[];try{return f&&this.device.pushErrorScope("validation"),c(a,d[1]),0}catch(p){return n.push(Promise.resolve(`[WebGPU] Kernel "[${l}] ${o}" failed. ${p}`)),1}finally{f&&n.push(this.device.popErrorScope().then(p=>p?`GPU validation error for kernel "[${l}] ${o}": ${p.message}`:null));for(let p of this.temporaryData)this.gpuDataManager.release(p.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,a,n,i){let l=this.sessionExternalDataMapping.get(t);l||(l=new Map,this.sessionExternalDataMapping.set(t,l));let o=l.get(a),c=this.gpuDataManager.registerExternalBuffer(n,i,o);return l.set(a,[c,n]),c}unregisterBuffers(t){let a=this.sessionExternalDataMapping.get(t);a&&(a.forEach(n=>this.gpuDataManager.unregisterExternalBuffer(n[0])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let a=this.gpuDataManager.get(t);if(!a)throw new Error(`no GPU data for buffer: ${t}`);return a.buffer}createDownloader(t,a,n){return async()=>{let i=await N_(this,t,a);return pg(i.buffer,n)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){var t;this.queryType="none",(((t=this.env.webgpu.profiling)==null?void 0:t.mode)==="default"||(typeof 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t=this.capturedCommandList.get(this.currentSessionId),a=this.capturedPendingKernels.get(this.currentSessionId),n=t.length;this.pendingKernels=[];for(let i=0;i<n;i++){let l=this.getComputePassEncoder(),o=t[i];this.writeTimestamp(this.pendingDispatchNumber*2),l.setPipeline(o.computePipeline),l.setBindGroup(0,o.bindGroup),l.dispatchWorkgroups(...o.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(a[i]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),MT={};$d(MT,{init:()=>wT});var Kh,jv,wT,s2=it(()=>{jt(),To(),Yt(),pE(),Kh=class TT{constructor(a,n,i,l){this.module=a,this.dataType=n,this.data=i,this.dims=l}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let a=Fe.size(this.dims);return a===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,a)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let a=Fe.size(this.dims);return a===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,a)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let a=Fe.size(this.dims);return a===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,a)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let a=Fe.size(this.dims);return a===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,a)}reshape(a){if(Fe.size(a)!==Fe.size(this.dims))throw new Error("Invalid new shape");return new TT(this.module,this.dataType,this.data,a)}},jv=class{constructor(t,a,n){this.module=t,this.backend=a,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=a.adapterInfo;let i=t.PTR_SIZE,l=n/t.PTR_SIZE,o=i===4?"i32":"i64";this.opKernelContext=Number(t.getValue(i*l++,o));let c=Number(t.getValue(i*l++,o));this.outputCount=Number(t.getValue(i*l++,o)),this.customDataOffset=Number(t.getValue(i*l++,"*")),this.customDataSize=Number(t.getValue(i*l++,o));let d=[];for(let f=0;f<c;f++){let p=Number(t.getValue(i*l++,o)),g=Number(t.getValue(i*l++,"*")),h=Number(t.getValue(i*l++,o)),_=[];for(let w=0;w<h;w++)_.push(Number(t.getValue(i*l++,o)));d.push(new Kh(t,p,g,_))}this.inputs=d}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(t,a){var c;let n=((c=a==null?void 0:a.inputs)==null?void 0:c.map(d=>typeof d=="number"?this.inputs[d]:d))??this.inputs,i=(a==null?void 0:a.outputs)??[],l=(d,f,p)=>new Kh(this.module,f,this.output(d,p),p),o=(d,f)=>{let p=Lu(d,f);if(!p)throw new Error(`Unsupported data type: ${d}`);let g=p>0?this.backend.gpuDataManager.create(p).id:0;return new Kh(this.module,d,g,f)};return this.backend.run(t,n,i,l,o,this.outputCount)}output(t,a){let n=this.module.stackSave();try{let i=this.module.PTR_SIZE,l=i===4?"i32":"i64",o=this.module.stackAlloc((1+a.length)*i);this.module.setValue(o,a.length,l);for(let c=0;c<a.length;c++)this.module.setValue(o+i*(c+1),a[c],l);return this.module._JsepOutput(this.opKernelContext,t,o)}catch(i){throw new Error(`Failed to generate kernel's output[${t}] with dims [${a}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${i}`)}finally{this.module.stackRestore(n)}}},wT=async(t,a,n,i)=>{let l=a.jsepInit;if(!l)throw new Error("Failed to initialize JSEP. 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Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);T.push(Q)}}let x=null;return T.some(O=>O==="gpu-buffer"||O==="ml-tensor")&&(d=l._OrtCreateBinding(o),d===0&&Bn("Can't create IO binding."),x={handle:d,outputPreferredLocations:T,outputPreferredLocationsEncoded:T.map(O=>B_(O))}),wl.set(o,[o,p,g,x,E,!1]),[o,S,D,A,C]}catch($){throw p.forEach(L=>l._OrtFree(L)),g.forEach(L=>l._OrtFree(L)),d!==0&&l._OrtReleaseBinding(d)!==0&&Bn("Can't release IO binding."),o!==0&&l._OrtReleaseSession(o)!==0&&Bn("Can't release session."),$}finally{l._free(n),c!==0&&l._OrtReleaseSessionOptions(c)!==0&&Bn("Can't release session options."),f.forEach($=>l._free($)),(M=l.unmountExternalData)==null||M.call(l)}},Cg=t=>{var f,p,g;let a=Xn(),n=wl.get(t);if(!n)throw new Error(`cannot release session. invalid session id: ${t}`);let[i,l,o,c,d]=n;c&&(d&&a._OrtClearBoundOutputs(c.handle)!==0&&Bn("Can't clear bound outputs."),a._OrtReleaseBinding(c.handle)!==0&&Bn("Can't release IO 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-* @license
-* Copyright 2021 Google LLC. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 (the "License");
-* you may not use this file except in compliance with the License.
-* You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-* =============================================================================
-*//**
- * @license
- * Copyright 2020 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- *//**
- * @license
- * Copyright 2019 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
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No instance will be fused."),N=new Set);const te=C.class_queries_logits??C.logits,Q=(C.masks_queries_logits??C.pred_masks).sigmoid();let[le,re,H]=te.dims;if(H-=1,G!==null&&G.length!==le)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let ee=[];for(let oe=0;oe<le;++oe){let ve=G!==null?G[oe]:null,U=te[oe],j=Q[oe],[z,V,ie]=M(U,j,T,H);if(ie.length===0){let[ye,Be]=ve??j.dims.slice(-2),Ke=new l.Tensor("int32",new Int32Array(ye*Be).fill(-1),[ye,Be]);ee.push({segmentation:Ke,segments_info:[]});continue}let[Me,ce]=L(z,V,ie,x,O,N,ve);ee.push({segmentation:Me,segments_info:ce})}return ee}function D(C,T=.5,x=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class A extends i.Callable{constructor(T){super(),this.image_mean=T.image_mean??T.mean,this.image_std=T.image_std??T.std,this.resample=T.resample??2,this.do_rescale=T.do_rescale??!0,this.rescale_factor=T.rescale_factor??1/255,this.do_normalize=T.do_normalize,this.do_thumbnail=T.do_thumbnail,this.size=T.size??T.image_size,this.do_resize=T.do_resize??this.size!==void 0,this.size_divisibility=T.size_divisibility??T.size_divisor,this.do_center_crop=T.do_center_crop,this.crop_size=T.crop_size,this.do_convert_rgb=T.do_convert_rgb??!0,this.do_crop_margin=T.do_crop_margin,this.pad_size=T.pad_size,this.do_pad=T.do_pad,this.min_pixels=T.min_pixels,this.max_pixels=T.max_pixels,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=T.do_flip_channel_order??!1,this.config=T}async thumbnail(T,x,O=2){const N=T.height,G=T.width,te=x.height,se=x.width;let Q=Math.min(N,te),le=Math.min(G,se);return Q===N&&le===G?T:(N>G?le=Math.floor(G*Q/N):G>N&&(Q=Math.floor(N*le/G)),await T.resize(le,Q,{resample:O}))}async crop_margin(T,x=200){const O=T.clone().grayscale(),N=(0,o.min)(O.data)[0],te=(0,o.max)(O.data)[0]-N;if(te===0)return T;const se=x/255;let Q=O.width,le=O.height,re=0,H=0;const ee=O.data;for(let oe=0;oe<O.height;++oe){const ve=oe*O.width;for(let U=0;U<O.width;++U)(ee[ve+U]-N)/te<se&&(Q=Math.min(Q,U),le=Math.min(le,oe),re=Math.max(re,U),H=Math.max(H,oe))}return T=await T.crop([Q,le,re,H]),T}pad_image(T,x,O,{mode:N="constant",center:G=!1,constant_values:te=0}={}){const[se,Q,le]=x;let re,H;if(typeof O=="number"?(re=O,H=O):O==="square"?re=H=Math.max(se,Q):(re=O.width,H=O.height),re!==Q||H!==se){const ee=new Float32Array(re*H*le);if(Array.isArray(te))for(let U=0;U<ee.length;++U)ee[U]=te[U%le];else te!==0&&ee.fill(te);const[oe,ve]=G?[Math.floor((re-Q)/2),Math.floor((H-se)/2)]:[0,0];for(let U=0;U<se;++U){const j=(U+ve)*re,z=U*Q;for(let V=0;V<Q;++V){const ie=(j+V+oe)*le,Me=(z+V)*le;for(let ce=0;ce<le;++ce)ee[ie+ce]=T[Me+ce]}}if(N==="symmetric"){if(G)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const U=se-1,j=Q-1;for(let z=0;z<H;++z){const V=z*re,ie=(0,c.calculateReflectOffset)(z,U)*Q;for(let Me=0;Me<re;++Me){if(z<se&&Me<Q)continue;const ce=(V+Me)*le,ye=(ie+(0,c.calculateReflectOffset)(Me,j))*le;for(let Be=0;Be<le;++Be)ee[ce+Be]=T[ye+Be]}}}T=ee,x=[H,re,le]}return[T,x]}rescale(T){for(let x=0;x<T.length;++x)T[x]=this.rescale_factor*T[x]}get_resize_output_image_size(T,x){const[O,N]=T.size;let G,te;if(this.do_thumbnail){const{height:se,width:Q}=x;G=Math.min(se,Q)}else Number.isInteger(x)?(G=x,te=this.config.max_size??G):x!==void 0&&(G=x.shortest_edge,te=x.longest_edge);if(G!==void 0||te!==void 0){const se=G===void 0?1:Math.max(G/O,G/N),Q=O*se,le=N*se,re=te===void 0?1:Math.min(te/Q,te/le);let H=Math.floor(Number((Q*re).toFixed(2))),ee=Math.floor(Number((le*re).toFixed(2)));return this.size_divisibility!==void 0&&([H,ee]=g([H,ee],this.size_divisibility)),[H,ee]}else if(x!==void 0&&x.width!==void 0&&x.height!==void 0){let se=x.width,Q=x.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let le=Q/N,re=se/O;Math.abs(1-re)<Math.abs(1-le)?le=re:re=le,Q=p(le*N,this.config.ensure_multiple_of),se=p(re*O,this.config.ensure_multiple_of)}return[se,Q]}else{if(this.size_divisibility!==void 0)return g([O,N],this.size_divisibility);if(this.min_pixels!==void 0&&this.max_pixels!==void 0){const se=this.config.patch_size*this.config.merge_size;return E(N,O,se,this.min_pixels,this.max_pixels)}else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(x)}`)}}async resize(T){const[x,O]=this.get_resize_output_image_size(T,this.size);return await T.resize(x,O,{resample:this.resample})}async preprocess(T,{do_normalize:x=null,do_pad:O=null,do_convert_rgb:N=null,do_convert_grayscale:G=null,do_flip_channel_order:te=null}={}){this.do_crop_margin&&(T=await this.crop_margin(T));const[se,Q]=T.size;if(N??this.do_convert_rgb?T=T.rgb():G&&(T=T.grayscale()),this.do_resize&&(T=await this.resize(T)),this.do_thumbnail&&(T=await this.thumbnail(T,this.size,this.resample)),this.do_center_crop){let oe,ve;Number.isInteger(this.crop_size)?(oe=this.crop_size,ve=this.crop_size):(oe=this.crop_size.width,ve=this.crop_size.height),T=await T.center_crop(oe,ve)}const le=[T.height,T.width];let re=Float32Array.from(T.data),H=[T.height,T.width,T.channels];if(this.do_rescale&&this.rescale(re),x??this.do_normalize){let oe=this.image_mean;Array.isArray(this.image_mean)||(oe=new Array(T.channels).fill(oe));let ve=this.image_std;if(Array.isArray(this.image_std)||(ve=new Array(T.channels).fill(oe)),oe.length!==T.channels||ve.length!==T.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${oe.length}) and \`image_std\` (${ve.length}) must match the number of channels in the image (${T.channels}).`);for(let U=0;U<re.length;U+=T.channels)for(let j=0;j<T.channels;++j)re[U+j]=(re[U+j]-oe[j])/ve[j]}if(O??this.do_pad){if(this.pad_size)[re,H]=this.pad_image(re,[T.height,T.width,T.channels],this.pad_size);else if(this.size_divisibility){const[oe,ve]=g([H[1],H[0]],this.size_divisibility);[re,H]=this.pad_image(re,H,{width:oe,height:ve})}}if(te??this.do_flip_channel_order){if(H[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let oe=0;oe<re.length;oe+=3){const ve=re[oe];re[oe]=re[oe+2],re[oe+2]=ve}}const ee=new l.Tensor("float32",re,H).permute(2,0,1);return{original_size:[Q,se],reshaped_input_size:le,pixel_values:ee}}async _call(T,...x){Array.isArray(T)||(T=[T]);const O=await Promise.all(T.map(G=>this.preprocess(G)));return{pixel_values:(0,l.stack)(O.map(G=>G.pixel_values),0),original_sizes:O.map(G=>G.original_size),reshaped_input_sizes:O.map(G=>G.reshaped_input_size)}}static async from_pretrained(T,x){const O=await(0,d.getModelJSON)(T,f.IMAGE_PROCESSOR_NAME,!0,x);return new this(O)}}},"./src/base/processing_utils.js":(t,a,n)=>{n.r(a),n.d(a,{Processor:()=>c});var i=n("./src/utils/constants.js"),l=n("./src/utils/generic.js"),o=n("./src/utils/hub.js");class c extends l.Callable{constructor(f,p){super(),this.config=f,this.components=p}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(f,p={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(f,{tokenize:!1,...p})}batch_decode(...f){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...f)}decode(...f){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...f)}async _call(f,...p){for(const g of[this.image_processor,this.feature_extractor,this.tokenizer])if(g)return g(f,...p);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(f,p){const[g,h]=await Promise.all([this.uses_processor_config?(0,o.getModelJSON)(f,i.PROCESSOR_NAME,!0,p):{},Promise.all(this.classes.filter(_=>_ in this).map(async _=>{const w=await this[_].from_pretrained(f,p);return[_.replace(/_class$/,""),w]})).then(Object.fromEntries)]);return new this(g,h)}}fe(c,"classes",["image_processor_class","tokenizer_class","feature_extractor_class"]),fe(c,"uses_processor_config",!1)},"./src/configs.js":(t,a,n)=>{n.r(a),n.d(a,{AutoConfig:()=>p,PretrainedConfig:()=>f,getKeyValueShapes:()=>d});var i=n("./src/utils/core.js"),l=n("./src/utils/hub.js");async function o(g,h){return await(0,l.getModelJSON)(g,"config.json",!0,h)}function c(g){const h={};let _={};switch(g.model_type){case"llava":case"paligemma":case"gemma3":case"florence2":case"llava_onevision":case"idefics3":case"ultravox":case"smolvlm":_=c(g.text_config);break;case"moondream1":_=c(g.phi_config);break;case"musicgen":_=c(g.decoder);break;case"multi_modality":_=c(g.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":h.num_heads="n_head",h.num_layers="n_layer",h.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":h.num_heads="num_attention_heads",h.num_layers="num_hidden_layers",h.hidden_size="hidden_size";break;case"llama":case"olmo":case"olmo2":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":case"phi":case"phi3":case"phi3_v":h.num_heads="num_key_value_heads",h.num_layers="num_hidden_layers",h.hidden_size="hidden_size",h.num_attention_heads="num_attention_heads";break;case"qwen3":case"gemma":case"gemma2":case"gemma3_text":case"glm":case"helium":h.num_heads="num_key_value_heads",h.num_layers="num_hidden_layers",h.dim_kv="head_dim";break;case"openelm":h.num_heads="num_kv_heads",h.num_layers="num_transformer_layers",h.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":h.num_heads="num_heads",h.num_layers="num_layers",h.hidden_size="hidden_size";break;case"bloom":h.num_heads="n_head",h.num_layers="n_layer",h.hidden_size="hidden_size";break;case"mpt":h.num_heads="n_heads",h.num_layers="n_layers",h.hidden_size="d_model";break;case"exaone":h.num_heads="num_key_value_heads",h.num_layers="num_layers",h.dim_kv="head_dim",h.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":h.num_decoder_layers="num_decoder_layers",h.num_decoder_heads="num_heads",h.decoder_dim_kv="d_kv",h.num_encoder_layers="num_layers",h.num_encoder_heads="num_heads",h.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"lite-whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":h.num_decoder_layers="decoder_layers",h.num_decoder_heads="decoder_attention_heads",h.decoder_hidden_size="d_model",h.num_encoder_layers="encoder_layers",h.num_encoder_heads="encoder_attention_heads",h.encoder_hidden_size="d_model";break;case"speecht5":h.num_decoder_layers="decoder_layers",h.num_decoder_heads="decoder_attention_heads",h.decoder_hidden_size="hidden_size",h.num_encoder_layers="encoder_layers",h.num_encoder_heads="encoder_attention_heads",h.encoder_hidden_size="hidden_size";break;case"trocr":h.num_encoder_layers=h.num_decoder_layers="decoder_layers",h.num_encoder_heads=h.num_decoder_heads="decoder_attention_heads",h.encoder_hidden_size=h.decoder_hidden_size="d_model";break;case"musicgen_decoder":h.num_encoder_layers=h.num_decoder_layers="num_hidden_layers",h.num_encoder_heads=h.num_decoder_heads="num_attention_heads",h.encoder_hidden_size=h.decoder_hidden_size="hidden_size";break;case"moonshine":h.num_decoder_layers="decoder_num_hidden_layers",h.num_decoder_heads="decoder_num_key_value_heads",h.num_encoder_layers="encoder_num_hidden_layers",h.num_encoder_heads="encoder_num_key_value_heads",h.encoder_hidden_size=h.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const M=c(g.decoder),$="num_decoder_layers"in M,L=(0,i.pick)(g,["model_type","is_encoder_decoder"]);return $?(L.num_decoder_layers=M.num_decoder_layers,L.num_decoder_heads=M.num_decoder_heads,L.decoder_hidden_size=M.decoder_hidden_size,L.num_encoder_layers=M.num_encoder_layers,L.num_encoder_heads=M.num_encoder_heads,L.encoder_hidden_size=M.encoder_hidden_size):(L.num_layers=M.num_layers,L.num_heads=M.num_heads,L.hidden_size=M.hidden_size),L}const w={..._,...(0,i.pick)(g,["model_type","multi_query","is_encoder_decoder"])};for(const M in h)w[M]=g[h[M]];return w}function d(g,{prefix:h="past_key_values",batch_size:_=1}={}){const w={},M=g.normalized_config;if(M.is_encoder_decoder&&"num_encoder_heads"in M&&"num_decoder_heads"in M){const $=M.encoder_dim_kv??M.encoder_hidden_size/M.num_encoder_heads,L=M.decoder_dim_kv??M.decoder_hidden_size/M.num_decoder_heads,E=[_,M.num_encoder_heads,0,$],S=[_,M.num_decoder_heads,0,L];for(let D=0;D<M.num_decoder_layers;++D)w[`${h}.${D}.encoder.key`]=E,w[`${h}.${D}.encoder.value`]=E,w[`${h}.${D}.decoder.key`]=S,w[`${h}.${D}.decoder.value`]=S}else{const $=M.num_heads,L=M.num_layers,E=M.dim_kv??M.hidden_size/(M.num_attention_heads??$);if(M.model_type==="falcon"){const S=[_*$,0,E];for(let D=0;D<L;++D)w[`${h}.${D}.key`]=S,w[`${h}.${D}.value`]=S}else if(M.multi_query){const S=[_*$,0,2*E];for(let D=0;D<L;++D)w[`${h}.${D}.key_value`]=S}else if(M.model_type==="bloom"){const S=[_*$,E,0],D=[_*$,0,E];for(let A=0;A<L;++A)w[`${h}.${A}.key`]=S,w[`${h}.${A}.value`]=D}else if(M.model_type==="openelm")for(let S=0;S<L;++S){const D=[_,$[S],0,E];w[`${h}.${S}.key`]=D,w[`${h}.${S}.value`]=D}else{const S=[_,$,0,E];for(let D=0;D<L;++D)w[`${h}.${D}.key`]=S,w[`${h}.${D}.value`]=S}}return w}class f{constructor(h){fe(this,"model_type",null);fe(this,"is_encoder_decoder",!1);fe(this,"max_position_embeddings");fe(this,"transformers.js_config");Object.assign(this,h),this.normalized_config=c(this)}static async from_pretrained(h,{progress_callback:_=null,config:w=null,cache_dir:M=null,local_files_only:$=!1,revision:L="main"}={}){w&&!(w instanceof f)&&(w=new f(w));const E=w??await o(h,{progress_callback:_,config:w,cache_dir:M,local_files_only:$,revision:L});return new this(E)}}class p{static async from_pretrained(...h){return f.from_pretrained(...h)}}},"./src/env.js":(t,a,n)=>{var O,N;n.r(a),n.d(a,{apis:()=>L,env:()=>T});var i=n("?569f"),l=n("?3f59"),o=n("?154a");const c="3.5.2",d=typeof window<"u"&&typeof window.document<"u",f=typeof self<"u"&&((O=self.constructor)==null?void 0:O.name)==="DedicatedWorkerGlobalScope",p=typeof self<"u"&&"caches"in self,g=typeof navigator<"u"&&"gpu"in navigator,h=typeof navigator<"u"&&"ml"in navigator,_=typeof process<"u",w=_&&((N=process==null?void 0:process.release)==null?void 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l{constructor(c){fe(this,"max_length",20);fe(this,"max_new_tokens",null);fe(this,"min_length",0);fe(this,"min_new_tokens",null);fe(this,"early_stopping",!1);fe(this,"max_time",null);fe(this,"do_sample",!1);fe(this,"num_beams",1);fe(this,"num_beam_groups",1);fe(this,"penalty_alpha",null);fe(this,"use_cache",!0);fe(this,"temperature",1);fe(this,"top_k",50);fe(this,"top_p",1);fe(this,"typical_p",1);fe(this,"epsilon_cutoff",0);fe(this,"eta_cutoff",0);fe(this,"diversity_penalty",0);fe(this,"repetition_penalty",1);fe(this,"encoder_repetition_penalty",1);fe(this,"length_penalty",1);fe(this,"no_repeat_ngram_size",0);fe(this,"bad_words_ids",null);fe(this,"force_words_ids",null);fe(this,"renormalize_logits",!1);fe(this,"constraints",null);fe(this,"forced_bos_token_id",null);fe(this,"forced_eos_token_id",null);fe(this,"remove_invalid_values",!1);fe(this,"exponential_decay_length_penalty",null);fe(this,"suppress_tokens",null);fe(this,"streamer",null);fe(this,"begin_suppress_tokens",null);fe(this,"forced_decoder_ids",null);fe(this,"guidance_scale",null);fe(this,"num_return_sequences",1);fe(this,"output_attentions",!1);fe(this,"output_hidden_states",!1);fe(this,"output_scores",!1);fe(this,"return_dict_in_generate",!1);fe(this,"pad_token_id",null);fe(this,"bos_token_id",null);fe(this,"eos_token_id",null);fe(this,"encoder_no_repeat_ngram_size",0);fe(this,"decoder_start_token_id",null);fe(this,"generation_kwargs",{});Object.assign(this,(0,i.pick)(c,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(t,a,n)=>{n.r(a),n.d(a,{ClassifierFreeGuidanceLogitsProcessor:()=>E,ForcedBOSTokenLogitsProcessor:()=>f,ForcedEOSTokenLogitsProcessor:()=>p,LogitsProcessor:()=>o,LogitsProcessorList:()=>d,LogitsWarper:()=>c,MinLengthLogitsProcessor:()=>M,MinNewTokensLengthLogitsProcessor:()=>$,NoBadWordsLogitsProcessor:()=>L,NoRepeatNGramLogitsProcessor:()=>_,RepetitionPenaltyLogitsProcessor:()=>w,SuppressTokensAtBeginLogitsProcessor:()=>g,TemperatureLogitsWarper:()=>S,TopKLogitsWarper:()=>A,TopPLogitsWarper:()=>D,WhisperTimeStampLogitsProcessor:()=>h});var i=n("./src/utils/generic.js");n("./src/utils/tensor.js");var l=n("./src/utils/maths.js");class o extends i.Callable{_call(T,x){throw Error("`_call` should be implemented in a subclass")}}class c extends i.Callable{_call(T,x){throw Error("`_call` should be implemented in a subclass")}}class d extends i.Callable{constructor(){super(),this.processors=[]}push(T){this.processors.push(T)}extend(T){this.processors.push(...T)}_call(T,x){let O=x;for(const N of this.processors)O=N(T,O);return O}[Symbol.iterator](){return this.processors.values()}}class f extends o{constructor(T){super(),this.bos_token_id=T}_call(T,x){for(let O=0;O<T.length;++O)if(T[O].length===1){const N=x[O].data;N.fill(-1/0),N[this.bos_token_id]=0}return x}}class p extends o{constructor(T,x){super(),this.max_length=T,this.eos_token_id=Array.isArray(x)?x:[x]}_call(T,x){for(let O=0;O<T.length;++O)if(T[O].length===this.max_length-1){const N=x[O].data;N.fill(-1/0);for(const G of this.eos_token_id)N[G]=0}return x}}class g extends o{constructor(T,x){super(),this.begin_suppress_tokens=T,this.begin_index=x}_call(T,x){for(let O=0;O<T.length;++O)if(T[O].length===this.begin_index){const N=x[O].data;for(const G of this.begin_suppress_tokens)N[G]=-1/0}return x}}class h extends o{constructor(T,x){super(),this.eos_token_id=Array.isArray(T.eos_token_id)?T.eos_token_id[0]:T.eos_token_id,this.no_timestamps_token_id=T.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=x.length,x.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=T.max_initial_timestamp_index}_call(T,x){for(let O=0;O<T.length;++O){const N=x[O].data;if(N[this.no_timestamps_token_id]=-1/0,T[O].length===this.begin_index-1){N.fill(-1/0),N[this.timestamp_begin]=0;continue}const G=T[O].slice(this.begin_index),te=G.length>=1&&G[G.length-1]>=this.timestamp_begin,se=G.length<2||G[G.length-2]>=this.timestamp_begin;if(te&&(se?N.subarray(this.timestamp_begin).fill(-1/0):N.subarray(0,this.eos_token_id).fill(-1/0)),T[O].length===this.begin_index&&this.max_initial_timestamp_index!==null){const H=this.timestamp_begin+this.max_initial_timestamp_index;N.subarray(H+1).fill(-1/0)}const Q=(0,l.log_softmax)(N),le=Math.log(Q.subarray(this.timestamp_begin).map(Math.exp).reduce((H,ee)=>H+ee)),re=(0,l.max)(Q.subarray(0,this.timestamp_begin))[0];le>re&&N.subarray(0,this.timestamp_begin).fill(-1/0)}return x}}class _ extends o{constructor(T){super(),this.no_repeat_ngram_size=T}getNgrams(T){const x=T.length,O=[];for(let G=0;G<x+1-this.no_repeat_ngram_size;++G){const te=[];for(let se=0;se<this.no_repeat_ngram_size;++se)te.push(T[G+se]);O.push(te.map(Number))}const N=new Map;for(const G of O){const te=G.slice(0,G.length-1),se=JSON.stringify(te),Q=N.get(se)??[];Q.push(G[G.length-1]),N.set(se,Q)}return N}getGeneratedNgrams(T,x){const O=x.slice(x.length+1-this.no_repeat_ngram_size,x.length);return T.get(JSON.stringify(O.map(Number)))??[]}calcBannedNgramTokens(T){const x=[];if(T.length+1<this.no_repeat_ngram_size)return x;{const O=this.getNgrams(T);return this.getGeneratedNgrams(O,T)}}_call(T,x){for(let O=0;O<T.length;++O){const N=x[O].data,G=this.calcBannedNgramTokens(T[O]);for(const te of G)N[te]=-1/0}return x}}class w extends o{constructor(T){super(),this.penalty=T}_call(T,x){for(let O=0;O<T.length;++O){const N=x[O].data;for(const G of new Set(T[O])){const te=Number(G);N[te]<0?N[te]*=this.penalty:N[te]/=this.penalty}}return x}}class M extends o{constructor(T,x){super(),this.min_length=T,this.eos_token_id=Array.isArray(x)?x:[x]}_call(T,x){for(let O=0;O<T.length;++O)if(T[O].length<this.min_length){const N=x[O].data;for(const G of this.eos_token_id)N[G]=-1/0}return x}}class $ extends o{constructor(T,x,O){super(),this.prompt_length_to_skip=T,this.min_new_tokens=x,this.eos_token_id=Array.isArray(O)?O:[O]}_call(T,x){for(let O=0;O<T.length;++O)if(T[O].length-this.prompt_length_to_skip<this.min_new_tokens){const G=x[O].data;for(const te of this.eos_token_id)G[te]=-1/0}return x}}class L extends o{constructor(T,x){super(),this.bad_words_ids=T,this.eos_token_id=Array.isArray(x)?x:[x]}_call(T,x){for(let O=0;O<T.length;++O){const N=x[O].data,G=T[O];for(const te of this.bad_words_ids){if(G.length<te.length-1)continue;let se=!0;for(let Q=1;Q<=te.length-1;++Q)if(te.at(-Q-1)!=G.at(-Q)){se=!1;break}se&&(N[te.at(-1)]=-1/0)}}return x}}class E extends o{constructor(T){if(super(),T<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${T}.`);this.guidance_scale=T}_call(T,x){if(x.dims[0]!==2*T.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${x.dims[0]} for the logits and ${T.length} for the input ids.`);const O=T.length,N=x.slice([0,O],null),G=x.slice([O,x.dims[0]],null);for(let te=0;te<G.data.length;++te)G.data[te]+=(N.data[te]-G.data[te])*this.guidance_scale;return G}}class S extends c{constructor(T){super(),this.temperature=T}_call(T,x){const O=x.data;for(let N=0;N<O.length;++N)O[N]/=this.temperature;return x}}class D extends c{constructor(T,{filter_value:x=-1/0,min_tokens_to_keep:O=1}={}){if(super(),T<0||T>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${T}`);if(!Number.isInteger(O)||O<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${O}`);this.top_p=T,this.filter_value=x,this.min_tokens_to_keep=O}}class A extends c{constructor(T,{filter_value:x=-1/0,min_tokens_to_keep:O=1}={}){if(super(),!Number.isInteger(T)||T<0)throw new Error(`\`top_k\` must be a positive integer, but is ${T}`);this.top_k=Math.max(T,O),this.filter_value=x}}},"./src/generation/logits_sampler.js":(t,a,n)=>{n.r(a),n.d(a,{LogitsSampler:()=>c});var i=n("./src/utils/generic.js"),l=n("./src/utils/tensor.js"),o=n("./src/utils/maths.js");n("./src/generation/configuration_utils.js");class c extends i.Callable{constructor(h){super(),this.generation_config=h}async _call(h){return this.sample(h)}async sample(h){throw Error("sample should be implemented in subclasses.")}getLogits(h,_){let w=h.dims.at(-1),M=h.data;if(_===-1)M=M.slice(-w);else{let $=_*w;M=M.slice($,$+w)}return M}randomSelect(h){let _=0;for(let M=0;M<h.length;++M)_+=h[M];let w=Math.random()*_;for(let M=0;M<h.length;++M)if(w-=h[M],w<=0)return M;return 0}static getSampler(h){if(h.do_sample)return new f(h);if(h.num_beams>1)return new p(h);if(h.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${h.num_return_sequences}.`);return new d(h)}}class d extends c{async sample(h){const _=(0,o.max)(h.data)[1];return[[BigInt(_),0]]}}class f extends c{async sample(h){let _=h.dims.at(-1);this.generation_config.top_k>0&&(_=Math.min(this.generation_config.top_k,_));const[w,M]=await(0,l.topk)(h,_),$=(0,o.softmax)(w.data);return Array.from({length:this.generation_config.num_beams},()=>{const L=this.randomSelect($);return[M.data[L],Math.log($[L])]})}}class p extends c{async sample(h){let _=h.dims.at(-1);this.generation_config.top_k>0&&(_=Math.min(this.generation_config.top_k,_));const[w,M]=await(0,l.topk)(h,_),$=(0,o.softmax)(w.data);return Array.from({length:this.generation_config.num_beams},(L,E)=>[M.data[E],Math.log($[E])])}}},"./src/generation/stopping_criteria.js":(t,a,n)=>{n.r(a),n.d(a,{EosTokenCriteria:()=>d,InterruptableStoppingCriteria:()=>f,MaxLengthCriteria:()=>c,StoppingCriteria:()=>l,StoppingCriteriaList:()=>o});var i=n("./src/utils/generic.js");class l extends i.Callable{_call(g,h){throw Error("StoppingCriteria needs to be subclassed")}}class o extends i.Callable{constructor(){super(),this.criteria=[]}push(g){this.criteria.push(g)}extend(g){g instanceof o?g=g.criteria:g instanceof l&&(g=[g]),this.criteria.push(...g)}_call(g,h){const _=new Array(g.length).fill(!1);for(const w of this.criteria){const M=w(g,h);for(let $=0;$<_.length;++$)_[$]||(_[$]=M[$])}return _}[Symbol.iterator](){return this.criteria.values()}}class c extends l{constructor(g,h=null){super(),this.max_length=g,this.max_position_embeddings=h}_call(g){return g.map(h=>h.length>=this.max_length)}}class d extends l{constructor(g){super(),Array.isArray(g)||(g=[g]),this.eos_token_id=g}_call(g,h){return g.map(_=>{const w=_.at(-1);return this.eos_token_id.some(M=>w==M)})}}class f extends l{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(g,h){return new Array(g.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(t,a,n)=>{n.r(a),n.d(a,{BaseStreamer:()=>c,TextStreamer:()=>f,WhisperTextStreamer:()=>p});var i=n("./src/utils/core.js"),l=n("./src/tokenizers.js"),o=n("./src/env.js");class c{put(h){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const d=o.apis.IS_PROCESS_AVAILABLE?g=>process.stdout.write(g):g=>console.log(g);class f extends c{constructor(h,{skip_prompt:_=!1,callback_function:w=null,token_callback_function:M=null,skip_special_tokens:$=!0,decode_kwargs:L={},...E}={}){super(),this.tokenizer=h,this.skip_prompt=_,this.callback_function=w??d,this.token_callback_function=M,this.decode_kwargs={skip_special_tokens:$,...L,...E},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(h){var L;if(h.length>1)throw Error("TextStreamer only supports batch size of 1");const _=this.next_tokens_are_prompt;if(_&&(this.next_tokens_are_prompt=!1,this.skip_prompt))return;const w=h[0];(L=this.token_callback_function)==null||L.call(this,w),this.token_cache=(0,i.mergeArrays)(this.token_cache,w);const M=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let $;_||M.endsWith(`
-`)?($=M.slice(this.print_len),this.token_cache=[],this.print_len=0):M.length>0&&(0,l.is_chinese_char)(M.charCodeAt(M.length-1))?($=M.slice(this.print_len),this.print_len+=$.length):($=M.slice(this.print_len,M.lastIndexOf(" ")+1),this.print_len+=$.length),this.on_finalized_text($,!1)}end(){let h;this.token_cache.length>0?(h=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):h="",this.next_tokens_are_prompt=!0,this.on_finalized_text(h,!0)}on_finalized_text(h,_){var w,M;h.length>0&&((w=this.callback_function)==null||w.call(this,h)),_&&this.callback_function===d&&o.apis.IS_PROCESS_AVAILABLE&&((M=this.callback_function)==null||M.call(this,`
-`))}}class p extends f{constructor(h,{skip_prompt:_=!1,callback_function:w=null,token_callback_function:M=null,on_chunk_start:$=null,on_chunk_end:L=null,on_finalize:E=null,time_precision:S=.02,skip_special_tokens:D=!0,decode_kwargs:A={}}={}){super(h,{skip_prompt:_,skip_special_tokens:D,callback_function:w,token_callback_function:M,decode_kwargs:A}),this.timestamp_begin=h.timestamp_begin,this.on_chunk_start=$,this.on_chunk_end=L,this.on_finalize=E,this.time_precision=S,this.waiting_for_timestamp=!1}put(h){var w,M,$;if(h.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const _=h[0];if(_.length===1){const L=Number(_[0])-this.timestamp_begin;if(L>=0){const E=L*this.time_precision;this.waiting_for_timestamp?(w=this.on_chunk_end)==null||w.call(this,E):(M=this.on_chunk_start)==null||M.call(this,E),this.waiting_for_timestamp=!this.waiting_for_timestamp,($=this.token_callback_function)==null||$.call(this,_);return}}return super.put(h)}end(){var h;super.end(),(h=this.on_finalize)==null||h.call(this)}}},"./src/models.js":(t,a,n)=>{n.r(a),n.d(a,{ASTForAudioClassification:()=>Ku,ASTModel:()=>qu,ASTPreTrainedModel:()=>Cl,AlbertForMaskedLM:()=>ue,AlbertForQuestionAnswering:()=>ne,AlbertForSequenceClassification:()=>F,AlbertModel:()=>Ee,AlbertPreTrainedModel:()=>Wr,AutoModel:()=>za,AutoModelForAudioClassification:()=>lp,AutoModelForAudioFrameClassification:()=>cp,AutoModelForAudioTextToText:()=>Pu,AutoModelForCTC:()=>ho,AutoModelForCausalLM:()=>pl,AutoModelForDepthEstimation:()=>vh,AutoModelForDocumentQuestionAnswering:()=>pd,AutoModelForImageClassification:()=>gr,AutoModelForImageFeatureExtraction:()=>fp,AutoModelForImageMatting:()=>hd,AutoModelForImageSegmentation:()=>ip,AutoModelForImageTextToText:()=>mo,AutoModelForImageToImage:()=>bh,AutoModelForMaskGeneration:()=>fd,AutoModelForMaskedLM:()=>dd,AutoModelForNormalEstimation:()=>Mh,AutoModelForObjectDetection:()=>Hs,AutoModelForPoseEstimation:()=>dp,AutoModelForQuestionAnswering:()=>_r,AutoModelForSemanticSegmentation:()=>op,AutoModelForSeq2SeqLM:()=>po,AutoModelForSequenceClassification:()=>Ws,AutoModelForSpeechSeq2Seq:()=>ud,AutoModelForTextToSpectrogram:()=>cd,AutoModelForTextToWaveform:()=>rp,AutoModelForTokenClassification:()=>fo,AutoModelForUniversalSegmentation:()=>yh,AutoModelForVision2Seq:()=>sp,AutoModelForXVector:()=>up,AutoModelForZeroShotObjectDetection:()=>Cu,BartForConditionalGeneration:()=>hn,BartForSequenceClassification:()=>da,BartModel:()=>Sn,BartPretrainedModel:()=>un,BaseModelOutput:()=>Oe,BeitForImageClassification:()=>Jl,BeitModel:()=>jd,BeitPreTrainedModel:()=>Zl,BertForMaskedLM:()=>Ve,BertForQuestionAnswering:()=>dt,BertForSequenceClassification:()=>et,BertForTokenClassification:()=>at,BertModel:()=>Le,BertPreTrainedModel:()=>We,BlenderbotForConditionalGeneration:()=>Oa,BlenderbotModel:()=>Rn,BlenderbotPreTrainedModel:()=>wn,BlenderbotSmallForConditionalGeneration:()=>ba,BlenderbotSmallModel:()=>fr,BlenderbotSmallPreTrainedModel:()=>Ja,BloomForCausalLM:()=>cc,BloomModel:()=>Xr,BloomPreTrainedModel:()=>Os,CLIPModel:()=>Il,CLIPPreTrainedModel:()=>Ps,CLIPSegForImageSegmentation:()=>Ui,CLIPSegModel:()=>Oo,CLIPSegPreTrainedModel:()=>ji,CLIPTextModel:()=>tc,CLIPTextModelWithProjection:()=>zl,CLIPVisionModel:()=>nc,CLIPVisionModelWithProjection:()=>Ll,CamembertForMaskedLM:()=>Tt,CamembertForQuestionAnswering:()=>Tn,CamembertForSequenceClassification:()=>an,CamembertForTokenClassification:()=>Zt,CamembertModel:()=>St,CamembertPreTrainedModel:()=>At,CausalLMOutput:()=>bs,CausalLMOutputWithPast:()=>Th,ChineseCLIPModel:()=>Ni,ChineseCLIPPreTrainedModel:()=>Bl,ClapAudioModelWithProjection:()=>zf,ClapModel:()=>Df,ClapPreTrainedModel:()=>yu,ClapTextModelWithProjection:()=>If,CodeGenForCausalLM:()=>mi,CodeGenModel:()=>qr,CodeGenPreTrainedModel:()=>hi,CohereForCausalLM:()=>Ul,CohereModel:()=>kd,CoherePreTrainedModel:()=>yi,ConvBertForMaskedLM:()=>Xa,ConvBertForQuestionAnswering:()=>Y,ConvBertForSequenceClassification:()=>q,ConvBertForTokenClassification:()=>_e,ConvBertModel:()=>Nt,ConvBertPreTrainedModel:()=>Ln,ConvNextForImageClassification:()=>rf,ConvNextModel:()=>af,ConvNextPreTrainedModel:()=>Oc,ConvNextV2ForImageClassification:()=>ah,ConvNextV2Model:()=>sf,ConvNextV2PreTrainedModel:()=>Dc,DFineForObjectDetection:()=>wc,DFineModel:()=>Mc,DFinePreTrainedModel:()=>vc,DPTForDepthEstimation:()=>Sc,DPTModel:()=>wa,DPTPreTrainedModel:()=>er,DacDecoderModel:()=>Yf,DacDecoderOutput:()=>nd,DacEncoderModel:()=>zr,DacEncoderOutput:()=>hh,DacModel:()=>mr,DacPreTrainedModel:()=>xu,DebertaForMaskedLM:()=>$r,DebertaForQuestionAnswering:()=>cr,DebertaForSequenceClassification:()=>Nr,DebertaForTokenClassification:()=>vr,DebertaModel:()=>ka,DebertaPreTrainedModel:()=>En,DebertaV2ForMaskedLM:()=>Na,DebertaV2ForQuestionAnswering:()=>Vn,DebertaV2ForSequenceClassification:()=>sr,DebertaV2ForTokenClassification:()=>ir,DebertaV2Model:()=>rr,DebertaV2PreTrainedModel:()=>Wa,DecisionTransformerModel:()=>Jr,DecisionTransformerPreTrainedModel:()=>ll,DeiTForImageClassification:()=>Ci,DeiTModel:()=>ao,DeiTPreTrainedModel:()=>qo,DepthAnythingForDepthEstimation:()=>su,DepthAnythingPreTrainedModel:()=>Yo,DepthProForDepthEstimation:()=>qd,DepthProPreTrainedModel:()=>Pc,DetrForObjectDetection:()=>Ud,DetrForSegmentation:()=>_c,DetrModel:()=>Go,DetrObjectDetectionOutput:()=>ds,DetrPreTrainedModel:()=>eu,DetrSegmentationOutput:()=>gc,Dinov2ForImageClassification:()=>ou,Dinov2Model:()=>so,Dinov2PreTrainedModel:()=>Ic,Dinov2WithRegistersForImageClassification:()=>Jo,Dinov2WithRegistersModel:()=>lu,Dinov2WithRegistersPreTrainedModel:()=>zc,DistilBertForMaskedLM:()=>Ye,DistilBertForQuestionAnswering:()=>Za,DistilBertForSequenceClassification:()=>Yn,DistilBertForTokenClassification:()=>Ya,DistilBertModel:()=>jr,DistilBertPreTrainedModel:()=>aa,DonutSwinModel:()=>kc,DonutSwinPreTrainedModel:()=>nf,EfficientNetForImageClassification:()=>vu,EfficientNetModel:()=>Qc,EfficientNetPreTrainedModel:()=>Kc,ElectraForMaskedLM:()=>tt,ElectraFo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P.encode_text({input_ids:I.input_ids})}if(W.inputNames.includes("token_type_ids")&&!me.token_type_ids){if(!me.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");me.token_type_ids=(0,_.zeros_like)(me.input_ids)}if(W.inputNames.includes("pixel_mask")&&!me.pixel_mask){if(!me.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Ae=me.pixel_values.dims;me.pixel_mask=(0,_.ones)([Ae[0],Ae[2],Ae[3]])}return await Q(W,me)}async function ve(P,I){const W=await P.encode(I);return await P.decode(W)}async function U(P,I,W=!1){const me=P.sessions[W?"decoder_model_merged":"model"],{past_key_values:Ae,...Pe}=I;if(me.inputNames.includes("use_cache_branch")&&(Pe.use_cache_branch=H(!!Ae)),me.inputNames.includes("position_ids")&&Pe.attention_mask&&!Pe.position_ids){const Ze=["paligemma","gemma3_text","gemma3"].includes(P.config.model_type)?1:0;Pe.position_ids=Be(Pe,Ae,Ze)}P.addPastKeyValues(Pe,Ae);const Ue=(0,d.pick)(Pe,me.inputNames);return await Q(me,Ue)}function j({modality_token_id:P,inputs_embeds:I,modality_features:W,input_ids:me,attention_mask:Ae}){const Pe=me.tolist().map(ht=>ht.reduce(($t,Lt,vt)=>(Lt==P&&$t.push(vt),$t),[])),Ue=Pe.reduce((ht,$t)=>ht+$t.length,0),Ze=W.dims[0];if(Ue!==Ze)throw new Error(`Number of tokens and features do not match: tokens: ${Ue}, features ${Ze}`);let st=0;for(let ht=0;ht<Pe.length;++ht){const $t=Pe[ht],Lt=I[ht];for(let vt=0;vt<$t.length;++vt)Lt[$t[vt]].data.set(W[st++].data)}return{inputs_embeds:I,attention_mask:Ae}}function z({image_token_id:P,inputs_embeds:I,image_features:W,input_ids:me,attention_mask:Ae}){return j({modality_token_id:P,inputs_embeds:I,modality_features:W,input_ids:me,attention_mask:Ae})}function V({audio_token_id:P,inputs_embeds:I,audio_features:W,input_ids:me,attention_mask:Ae}){return j({modality_token_id:P,inputs_embeds:I,modality_features:W,input_ids:me,attention_mask:Ae})}async function ie(P,{encode_function:I,merge_function:W,modality_input_name:me,modality_output_name:Ae,input_ids:Pe=null,attention_mask:Ue=null,position_ids:Ze=null,inputs_embeds:st=null,past_key_values:ht=null,generation_config:$t=null,logits_processor:Lt=null,...vt}){const qt=vt[me];if(!st){if(st=await P.encode_text({input_ids:Pe,...vt}),qt&&Pe.dims[1]!==1){const Kt=await I({[me]:qt,...vt});({inputs_embeds:st,attention_mask:Ue}=W({[Ae]:Kt,inputs_embeds:st,input_ids:Pe,attention_mask:Ue}))}else if(ht&&qt&&Pe.dims[1]===1){const Kt=Pe.dims[1],xt=Object.values(ht)[0].dims.at(-2);Ue=(0,_.cat)([(0,_.ones)([Pe.dims[0],xt]),Ue.slice(null,[Ue.dims[1]-Kt,Ue.dims[1]])],1)}}if(!Ze&&P.config.model_type==="qwen2_vl"){const{image_grid_thw:Kt,video_grid_thw:xt}=vt;[Ze]=P.get_rope_index(Pe,Kt,xt,Ue)}return await U(P,{inputs_embeds:st,past_key_values:ht,attention_mask:Ue,position_ids:Ze,generation_config:$t,logits_processor:Lt},!0)}async function Me(P,I){return await ie(P,{...I,modality_input_name:"audio_values",modality_output_name:"audio_features",encode_function:P.encode_audio.bind(P),merge_function:P._merge_input_ids_with_audio_features.bind(P)})}async function ce(P,I){return await ie(P,{...I,modality_input_name:"pixel_values",modality_output_name:"image_features",encode_function:P.encode_image.bind(P),merge_function:P._merge_input_ids_with_image_features.bind(P)})}function ye(P,I=0){const[W,me]=P.dims,Ae=P.data,Pe=new BigInt64Array(Ae.length);for(let Ue=0;Ue<W;++Ue){const Ze=Ue*me;let st=BigInt(I);for(let ht=0;ht<me;++ht){const $t=Ze+ht;Ae[$t]===0n?Pe[$t]=BigInt(1):(Pe[$t]=st,st+=Ae[$t])}}return{data:Pe,dims:P.dims}}function Be(P,I=null,W=0){const{input_ids:me,inputs_embeds:Ae,attention_mask:Pe}=P,{data:Ue,dims:Ze}=ye(Pe,W);let st=new _.Tensor("int64",Ue,Ze);if(I){const ht=-(me??Ae).dims.at(1);st=st.slice(null,[ht,null])}return st}function Ke(P,I,W,me){if(W.past_key_values){const Ae=Object.values(W.past_key_values)[0].dims.at(-2),{input_ids:Pe,attention_mask:Ue}=W;if(!(Ue&&Ue.dims[1]>Pe.dims[1])){if(Ae<Pe.dims[1])W.input_ids=Pe.slice(null,[Ae,null]);else if(P.config.image_token_index!=null&&Pe.data.some(Ze=>Ze==P.config.image_token_index)){const Ze=P.config.num_image_tokens;if(!Ze)throw new Error("`num_image_tokens` is missing in the model configuration.");const st=Pe.dims[1]-(Ae-Ze);W.input_ids=Pe.slice(null,[-st,null]),W.attention_mask=(0,_.ones)([1,Ae+st])}}}return W}function be(P,I,W,me){return W.past_key_values&&(I=I.map(Ae=>[Ae.at(-1)])),{...W,decoder_input_ids:re(I)}}function X(P,...I){return P.config.is_encoder_decoder?be(P,...I):Ke(P,...I)}function de(P,I,W,me){const Ae=!!W.past_key_values;return me.guidance_scale!==null&&me.guidance_scale>1&&(Ae?W.input_ids=(0,_.cat)([W.input_ids,W.input_ids],0):(W.input_ids=(0,_.cat)([W.input_ids,(0,_.full_like)(W.input_ids,BigInt(me.pad_token_id))],0),W.attention_mask=(0,_.cat)([W.attention_mask,(0,_.full_like)(W.attention_mask,0n)],0))),(Ae||!W.pixel_values)&&(W.pixel_values=(0,_.full)([0,0,3,384,384],1)),Ae&&(W.images_seq_mask=new _.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),W.images_emb_mask=new _.Tensor("bool",new Array(0).fill(!1),[1,1,0])),W}class J extends c.Callable{constructor(W,me,Ae){super();fe(this,"main_input_name","input_ids");fe(this,"forward_params",["input_ids","attention_mask"]);this.config=W,this.sessions=me,this.configs=Ae;const Pe=x.get(this.constructor),Ue=C.get(Pe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ue){case A.DecoderOnly:this.can_generate=!0,this._forward=U,this._prepare_inputs_for_generation=Ke;break;case A.Seq2Seq:case A.Vision2Seq:case A.Musicgen:this.can_generate=!0,this._forward=ee,this._prepare_inputs_for_generation=be;break;case A.EncoderDecoder:this._forward=ee;break;case A.ImageTextToText:this.can_generate=!0,this._forward=ce,this._prepare_inputs_for_generation=X;break;case A.AudioTextToText:this.can_generate=!0,this._forward=Me,this._prepare_inputs_for_generation=X;break;case A.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=X;break;case A.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=de;break;case A.AutoEncoder:this._forward=ve;break;default:this._forward=oe;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var me;const W=[];for(const Ae of Object.values(this.sessions))(me=Ae==null?void 0:Ae.handler)!=null&&me.dispose&&W.push(Ae.handler.dispose());return await Promise.all(W)}static async from_pretrained(W,{progress_callback:me=null,config:Ae=null,cache_dir:Pe=null,local_files_only:Ue=!1,revision:Ze="main",model_file_name:st=null,subfolder:ht="onnx",device:$t=null,dtype:Lt=null,use_external_data_format:vt=null,session_options:qt={}}={}){let Rt={progress_callback:me,config:Ae,cache_dir:Pe,local_files_only:Ue,revision:Ze,model_file_name:st,subfolder:ht,device:$t,dtype:Lt,use_external_data_format:vt,session_options:qt};const Kt=x.get(this),xt=C.get(Kt);Ae=Rt.config=await i.AutoConfig.from_pretrained(W,Rt);let sn;if(xt===A.DecoderOnly)sn=await Promise.all([N(W,{model:Rt.model_file_name??"model"},Rt),G(W,{generation_config:"generation_config.json"},Rt)]);else if(xt===A.Seq2Seq||xt===A.Vision2Seq)sn=await Promise.all([N(W,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Rt),G(W,{generation_config:"generation_config.json"},Rt)]);else if(xt===A.MaskGeneration)sn=await Promise.all([N(W,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Rt)]);else if(xt===A.EncoderDecoder)sn=await Promise.all([N(W,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Rt)]);else if(xt===A.ImageTextToText){const Dn={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ae.is_encoder_decoder&&(Dn.model="encoder_model"),sn=await Promise.all([N(W,Dn,Rt),G(W,{generation_config:"generation_config.json"},Rt)])}else if(xt===A.AudioTextToText){const Dn={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};sn=await Promise.all([N(W,Dn,Rt),G(W,{generation_config:"generation_config.json"},Rt)])}else if(xt===A.Musicgen)sn=await Promise.all([N(W,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Rt),G(W,{generation_config:"generation_config.json"},Rt)]);else if(xt===A.MultiModality)sn=await Promise.all([N(W,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},Rt),G(W,{generation_config:"generation_config.json"},Rt)]);else if(xt===A.Phi3V)sn=await Promise.all([N(W,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},Rt),G(W,{generation_config:"generation_config.json"},Rt)]);else if(xt===A.AutoEncoder)sn=await Promise.all([N(W,{encoder_model:"encoder_model",decoder_model:"decoder_model"},Rt)]);else{if(xt!==A.EncoderOnly){const Dn=Kt??(Ae==null?void 0:Ae.model_type);Dn!=="custom"&&console.warn(`Model type for '${Dn}' not found, assuming encoder-only architecture. Please report this at ${p.GITHUB_ISSUE_URL}.`)}sn=await Promise.all([N(W,{model:Rt.model_file_name??"model"},Rt)])}return new this(Ae,...sn)}async _call(W){return await this.forward(W)}async forward(W){return await this._forward(this,W)}get generation_config(){var W;return((W=this.configs)==null?void 0:W.generation_config)??null}_get_logits_warper(W){const me=new g.LogitsProcessorList;return W.temperature!==null&&W.temperature!==1&&me.push(new g.TemperatureLogitsWarper(W.temperature)),W.top_k!==null&&W.top_k!==0&&me.push(new g.TopKLogitsWarper(W.top_k)),W.top_p!==null&&W.top_p<1&&me.push(new g.TopPLogitsWarper(W.top_p)),me}_get_logits_processor(W,me,Ae=null){const Pe=new g.LogitsProcessorList;if(W.repetition_penalty!==null&&W.repetition_penalty!==1&&Pe.push(new g.RepetitionPenaltyLogitsProcessor(W.repetition_penalty)),W.no_repeat_ngram_size!==null&&W.no_repeat_ngram_size>0&&Pe.push(new g.NoRepeatNGramLogitsProcessor(W.no_repeat_ngram_size)),W.bad_words_ids!==null&&Pe.push(new g.NoBadWordsLogitsProcessor(W.bad_words_ids,W.eos_token_id)),W.min_length!==null&&W.eos_token_id!==null&&W.min_length>0&&Pe.push(new g.MinLengthLogitsProcessor(W.min_length,W.eos_token_id)),W.min_new_tokens!==null&&W.eos_token_id!==null&&W.min_new_tokens>0&&Pe.push(new g.MinNewTokensLengthLogitsProcessor(me,W.min_new_tokens,W.eos_token_id)),W.forced_bos_token_id!==null&&Pe.push(new g.ForcedBOSTokenLogitsProcessor(W.forced_bos_token_id)),W.forced_eos_token_id!==null&&Pe.push(new g.ForcedEOSTokenLogitsProcessor(W.max_length,W.forced_eos_token_id)),W.begin_suppress_tokens!==null){const Ue=me>1||W.forced_bos_token_id===null?me:me+1;Pe.push(new g.SuppressTokensAtBeginLogitsProcessor(W.begin_suppress_tokens,Ue))}return W.guidance_scale!==null&&W.guidance_scale>1&&Pe.push(new g.ClassifierFreeGuidanceLogitsProcessor(W.guidance_scale)),Ae!==null&&Pe.extend(Ae),Pe}_prepare_generation_config(W,me,Ae=h.GenerationConfig){const Pe={...this.config};for(const Ze of["decoder","generator","text_config"])Ze in Pe&&Object.assign(Pe,Pe[Ze]);const Ue=new Ae(Pe);return Object.assign(Ue,this.generation_config??{}),W&&Object.assign(Ue,W),me&&Object.assign(Ue,(0,d.pick)(me,Object.getOwnPropertyNames(Ue))),Ue}_get_stopping_criteria(W,me=null){const Ae=new $.StoppingCriteriaList;return W.max_length!==null&&Ae.push(new $.MaxLengthCriteria(W.max_length,this.config.max_position_embeddings??null)),W.eos_token_id!==null&&Ae.push(new $.EosTokenCriteria(W.eos_token_id)),me&&Ae.extend(me),Ae}_validate_model_class(){if(!this.can_generate){const W=[lo,ap,ad,Oi],me=x.get(this.constructor),Ae=new Set,Pe=this.config.model_type;for(const Ze of W){const st=Ze.get(Pe);st&&Ae.add(st[0])}let Ue=`The current model class (${me}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ae.size>0&&(Ue+=` Please use the following class instead: ${[...Ae].join(", ")}`),Error(Ue)}}prepare_inputs_for_generation(...W){return this._prepare_inputs_for_generation(this,...W)}_update_model_kwargs_for_generation({generated_input_ids:W,outputs:me,model_inputs:Ae,is_encoder_decoder:Pe}){return Ae.past_key_values=this.getPastKeyValues(me,Ae.past_key_values),Ae.input_ids=new _.Tensor("int64",W.flat(),[W.length,1]),Pe||(Ae.attention_mask=(0,_.cat)([Ae.attention_mask,(0,_.ones)([Ae.attention_mask.dims[0],1])],1)),Ae.position_ids=null,Ae}_prepare_model_inputs({inputs:W,bos_token_id:me,model_kwargs:Ae}){const Pe=(0,d.pick)(Ae,this.forward_params),Ue=this.main_input_name;if(Ue in Pe){if(W)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Pe[Ue]=W;return{inputs_tensor:Pe[Ue],model_inputs:Pe,model_input_name:Ue}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:W,model_inputs:me,model_input_name:Ae,generation_config:Pe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!me.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Ze,pixel_values:st,attention_mask:ht,...$t}=me,Lt=await this._prepare_inputs_embeds(me);me={...$t,...(0,d.pick)(Lt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ue}=await oe(this,me);if(Pe.guidance_scale!==null&&Pe.guidance_scale>1)Ue=(0,_.cat)([Ue,(0,_.full_like)(Ue,0)],0),"attention_mask"in me&&(me.attention_mask=(0,_.cat)([me.attention_mask,(0,_.zeros_like)(me.attention_mask)],0));else if(me.decoder_input_ids){const Ze=re(me.decoder_input_ids).dims[0];if(Ze!==Ue.dims[0]){if(Ue.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ue.dims[0]}) than the decoder inputs (${Ze}).`);Ue=(0,_.cat)(Array.from({length:Ze},()=>Ue),0)}}return me.encoder_outputs=Ue,me}_prepare_decoder_input_ids_for_generation({batch_size:W,model_input_name:me,model_kwargs:Ae,decoder_start_token_id:Pe,bos_token_id:Ue,generation_config:Ze}){let{decoder_input_ids:st,...ht}=Ae;if(!(st instanceof _.Tensor)){if(st)Array.isArray(st[0])||(st=Array.from({length:W},()=>st));else if(Pe??(Pe=Ue),this.config.model_type==="musicgen")st=Array.from({length:W*this.config.decoder.num_codebooks},()=>[Pe]);else if(Array.isArray(Pe)){if(Pe.length!==W)throw new Error(`\`decoder_start_token_id\` expcted to have length ${W} but got ${Pe.length}`);st=Pe}else st=Array.from({length:W},()=>[Pe]);st=re(st)}return Ae.decoder_attention_mask=(0,_.ones_like)(st),{input_ids:st,model_inputs:ht}}async generate({inputs:W=null,generation_config:me=null,logits_processor:Ae=null,stopping_criteria:Pe=null,streamer:Ue=null,...Ze}){this._validate_model_class(),me=this._prepare_generation_config(me,Ze);let{inputs_tensor:st,model_inputs:ht,model_input_name:$t}=this._prepare_model_inputs({inputs:W,model_kwargs:Ze});const Lt=this.config.is_encoder_decoder;Lt&&("encoder_outputs"in ht||(ht=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:st,model_inputs:ht,model_input_name:$t,generation_config:me})));let vt;Lt?{input_ids:vt,model_inputs:ht}=this._prepare_decoder_input_ids_for_generation({batch_size:ht[$t].dims.at(0),model_input_name:$t,model_kwargs:ht,decoder_start_token_id:me.decoder_start_token_id,bos_token_id:me.bos_token_id,generation_config:me}):vt=ht[$t];let qt=vt.dims.at(-1);me.max_new_tokens!==null&&(me.max_length=qt+me.max_new_tokens);const Rt=this._get_logits_processor(me,qt,Ae),Kt=this._get_stopping_criteria(me,Pe),xt=ht[$t].dims.at(0),sn=L.LogitsSampler.getSampler(me),Dn=new Array(xt).fill(0),Kn=vt.tolist();Ue&&Ue.put(Kn);let ea,ta={};for(;;){if(ht=this.prepare_inputs_for_generation(Kn,ht,me),ea=await this.forward(ht),me.output_attentions&&me.return_dict_in_generate){const Ba=this.getAttentions(ea);for(const Fr in Ba)Fr in ta||(ta[Fr]=[]),ta[Fr].push(Ba[Fr])}const Xt=ea.logits.slice(null,-1,null),ga=Rt(Kn,Xt),Pr=[];for(let Ba=0;Ba<ga.dims.at(0);++Ba){const Fr=ga[Ba],Au=await sn(Fr);for(const[en,hl]of Au){const go=BigInt(en);Dn[Ba]+=hl,Kn[Ba].push(go),Pr.push([go]);break}}if(Ue&&Ue.put(Pr),Kt(Kn).every(Ba=>Ba))break;ht=this._update_model_kwargs_for_generation({generated_input_ids:Pr,outputs:ea,model_inputs:ht,is_encoder_decoder:Lt})}Ue&&Ue.end();const _a=this.getPastKeyValues(ea,ht.past_key_values,!0),Fa=new _.Tensor("int64",Kn.flat(),[Kn.length,Kn[0].length]);if(me.return_dict_in_generate)return{sequences:Fa,past_key_values:_a,...ta};for(const Xt of Object.values(ea))Xt.location==="gpu-buffer"&&Xt.dispose();return Fa}getPastKeyValues(W,me,Ae=!1){const Pe=Object.create(null);for(const Ue in W)if(Ue.startsWith("present")){const Ze=Ue.replace("present","past_key_values"),st=Ue.includes("encoder");if(st&&me?Pe[Ze]=me[Ze]:Pe[Ze]=W[Ue],me&&(!st||Ae)){const ht=me[Ze];ht.location==="gpu-buffer"&&ht.dispose()}}return Pe}getAttentions(W){const me={};for(const Ae of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Pe in W)Pe.startsWith(Ae)&&(Ae in me||(me[Ae]=[]),me[Ae].push(W[Pe]));return me}addPastKeyValues(W,me){var Ae,Pe,Ue;if(me)Object.assign(W,me);else{const Ze=this.sessions.decoder_model_merged??this.sessions.model,st=((Ae=Ze==null?void 0:Ze.config)==null?void 0:Ae.kv_cache_dtype)??"float32",ht=st==="float16"?new _.DataTypeMap.float16:[],$t=((Ue=(Pe=W[this.main_input_name]??W.attention_mask)==null?void 0:Pe.dims)==null?void 0:Ue[0])??1,Lt=(0,i.getKeyValueShapes)(this.config,{batch_size:$t});for(const vt in Lt)W[vt]=new _.Tensor(st,ht,Lt[vt])}}async encode_image({pixel_values:W}){const me=(await Q(this.sessions.vision_encoder,{pixel_values:W})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${me.dims[1]}).`),this.config.num_image_tokens=me.dims[1]),me}async encode_text({input_ids:W}){return(await Q(this.sessions.embed_tokens,{input_ids:W})).inputs_embeds}async encode_audio({audio_values:W}){return(await Q(this.sessions.audio_encoder,{audio_values:W})).audio_features}}class Ie{}class Oe extends Ie{constructor({last_hidden_state:I,hidden_states:W=null,attentions:me=null}){super(),this.last_hidden_state=I,this.hidden_states=W,this.attentions=me}}class We extends J{}class Le extends We{}class Ve extends We{async _call(I){return new La(await super._call(I))}}class et extends We{async _call(I){return new Wt(await super._call(I))}}class at extends We{async _call(I){return new $a(await super._call(I))}}class dt extends We{async _call(I){return new Ka(await super._call(I))}}class ft extends J{}class Qe extends ft{}class ut extends ft{async _call(I){return new La(await super._call(I))}}class bt extends ft{async _call(I){return new Wt(await super._call(I))}}class Mt extends ft{async _call(I){return new $a(await super._call(I))}}class yt extends J{}class lt extends yt{}class He extends J{}class It extends He{}class tn extends He{async _call(I){return new La(await super._call(I))}}class xn extends He{async _call(I){return new Wt(await super._call(I))}}class _n extends He{async _call(I){return new $a(await super._call(I))}}class sa extends He{async _call(I){return new Ka(await super._call(I))}}class Ln extends J{}class Nt extends Ln{}class Xa extends Ln{async _call(I){return new La(await super._call(I))}}class q extends Ln{async _call(I){return new Wt(await super._call(I))}}class _e extends Ln{async _call(I){return new $a(await super._call(I))}}class Y extends Ln{async _call(I){return new Ka(await super._call(I))}}class pe extends J{}class Ce extends pe{}class tt extends pe{async _call(I){return new La(await super._call(I))}}class Re extends pe{async _call(I){return new Wt(await super._call(I))}}class Gt extends pe{async _call(I){return new $a(await super._call(I))}}class fn extends pe{async _call(I){return new Ka(await super._call(I))}}class At extends J{}class St extends At{}class Tt extends At{async _call(I){return new La(await super._call(I))}}class an extends At{async _call(I){return new Wt(await super._call(I))}}class Zt extends At{async _call(I){return new $a(await super._call(I))}}class Tn extends At{async _call(I){return new Ka(await super._call(I))}}class En extends J{}class ka extends En{}class $r extends En{async _call(I){return new La(await super._call(I))}}class Nr extends En{async _call(I){return new Wt(await super._call(I))}}class vr extends En{async _call(I){return new $a(await super._call(I))}}class cr extends En{async _call(I){return new Ka(await super._call(I))}}class Wa extends J{}class rr extends Wa{}class Na extends Wa{async _call(I){return new La(await super._call(I))}}class sr extends Wa{async _call(I){return new Wt(await super._call(I))}}class ir extends Wa{async _call(I){return new $a(await super._call(I))}}class Vn extends Wa{async _call(I){return new Ka(await super._call(I))}}class aa extends J{}class jr extends aa{}class Yn extends aa{async _call(I){return new Wt(await super._call(I))}}class Ya extends aa{async _call(I){return new $a(await super._call(I))}}class Za extends aa{async _call(I){return new Ka(await super._call(I))}}class Ye extends aa{async _call(I){return new La(await super._call(I))}}class _t extends J{}class Ct extends _t{}class gn extends _t{async _call(I){return new La(await super._call(I))}}class or extends _t{async _call(I){return new Wt(await super._call(I))}}class Ea extends _t{async _call(I){return new $a(await super._call(I))}}class ra extends J{}class Mr extends ra{}class Gn extends ra{async _call(I){return new La(await super._call(I))}}class ia extends ra{async _call(I){return new Wt(await super._call(I))}}class dr extends ra{async _call(I){return new Ka(await super._call(I))}}class Ur extends J{}class ii extends Ur{}class ja extends Ur{async _call(I){return new La(await super._call(I))}}class oi extends Ur{async _call(I){return new Wt(await super._call(I))}}class Li extends Ur{async _call(I){return new $a(await super._call(I))}}class rs extends Ur{async _call(I){return new Ka(await super._call(I))}}class Vr extends J{}class Gr extends Vr{}class Fi extends Vr{async _call(I){return new La(await super._call(I))}}class Ua extends Vr{async _call(I){return new Wt(await super._call(I))}}class Es extends Vr{async _call(I){return new Ka(await super._call(I))}}class Wr extends J{}class Ee extends Wr{}class F extends Wr{async _call(I){return new Wt(await super._call(I))}}class ne extends Wr{async _call(I){return new Ka(await super._call(I))}}class ue extends Wr{async _call(I){return new La(await super._call(I))}}class ge extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class xe extends ge{}class Ne extends ge{}class rt extends J{}class pt extends rt{}class ct extends rt{}class gt extends J{}class Ft extends gt{}class pn extends gt{}class un extends J{}class Sn extends un{}class hn extends un{}class da extends un{async _call(I){return new Wt(await super._call(I))}}class fa extends J{}class Sa extends fa{}class oa extends fa{}class Mn extends fa{async _call(I){return new Wt(await super._call(I))}}class wr extends fa{}class wn extends J{}class Rn extends wn{}class Oa extends wn{}class Ja extends J{}class fr extends Ja{}class ba extends Ja{}class Ca extends J{}class va extends Ca{}class on extends Ca{async _call(I){return new La(await super._call(I))}}class Nn extends Ca{async _call(I){return new Wt(await super._call(I))}}class Zn extends Ca{async _call(I){return new $a(await super._call(I))}}class pa extends Ca{async _call(I){return new Ka(await super._call(I))}}class kr extends J{}class Ss extends kr{}class xo extends kr{async _call(I){return new La(await super._call(I))}}class li extends kr{async _call(I){return new Wt(await super._call(I))}}class Bi extends kr{async _call(I){return new $a(await super._call(I))}}class Eo extends kr{async _call(I){return new Ka(await super._call(I))}}class ui extends J{}class Vu extends ui{}class Gu extends ui{async _call(I){return new La(await super._call(I))}}class So extends ui{async _call(I){return new Wt(await super._call(I))}}class Wu extends ui{async _call(I){return new $a(await super._call(I))}}class Hu extends ui{async _call(I){return new Ka(await super._call(I))}}class Cl extends J{}class qu extends Cl{}class Ku extends Cl{}class Co extends J{constructor(){super(...arguments);fe(this,"requires_attention_mask",!1);fe(this,"main_input_name","input_features");fe(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Qu extends Co{}class Pl extends Co{_prepare_generation_config(I,W){return super._prepare_generation_config(I,W,S.WhisperGenerationConfig)}_retrieve_init_tokens(I){const W=[I.decoder_start_token_id];let me=I.language;const Ae=I.task;if(I.is_multilingual){me||(console.warn("No language specified - defaulting to English (en)."),me="en");const Ue=`<|${(0,D.whisper_language_to_code)(me)}|>`;W.push(I.lang_to_id[Ue]),W.push(I.task_to_id[Ae??"transcribe"])}else if(me||Ae)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!I.return_timestamps&&I.no_timestamps_token_id&&W.at(-1)!==I.no_timestamps_token_id?W.push(I.no_timestamps_token_id):I.return_timestamps&&W.at(-1)===I.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),W.pop()),W.filter(Pe=>Pe!=null)}async generate({inputs:I=null,generation_config:W=null,logits_processor:me=null,stopping_criteria:Ae=null,...Pe}){W=this._prepare_generation_config(W,Pe);const Ue=Pe.decoder_input_ids??this._retrieve_init_tokens(W);if(W.return_timestamps&&(me??(me=new g.LogitsProcessorList),me.push(new g.WhisperTimeStampLogitsProcessor(W,Ue))),W.begin_suppress_tokens&&(me??(me=new g.LogitsProcessorList),me.push(new g.SuppressTokensAtBeginLogitsProcessor(W.begin_suppress_tokens,Ue.length))),W.return_token_timestamps){if(!W.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");W.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),W.output_attentions=!0,W.return_dict_in_generate=!0}const Ze=await super.generate({inputs:I,generation_config:W,logits_processor:me,decoder_input_ids:Ue,...Pe});return W.return_token_timestamps&&(Ze.token_timestamps=this._extract_token_timestamps(Ze,W.alignment_heads,W.num_frames)),Ze}_extract_token_timestamps(I,W,me=null,Ae=.02){if(!I.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");me==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let Pe=this.config.median_filter_width;Pe===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Pe=7);const Ue=I.cross_attentions,Ze=Array.from({length:this.config.decoder_layers},(Kt,xt)=>(0,_.cat)(Ue.map(sn=>sn[xt]),2)),st=(0,_.stack)(W.map(([Kt,xt])=>{if(Kt>=Ze.length)throw new Error(`Layer index ${Kt} is out of bounds for cross attentions (length ${Ze.length}).`);return me?Ze[Kt].slice(null,xt,null,[0,me]):Ze[Kt].slice(null,xt)})).transpose(1,0,2,3),[ht,$t]=(0,_.std_mean)(st,-2,0,!0),Lt=st.clone();for(let Kt=0;Kt<Lt.dims[0];++Kt){const xt=Lt[Kt];for(let sn=0;sn<xt.dims[0];++sn){const Dn=xt[sn],Kn=ht[Kt][sn][0].data,ea=$t[Kt][sn][0].data;for(let ta=0;ta<Dn.dims[0];++ta){let _a=Dn[ta].data;for(let Fa=0;Fa<_a.length;++Fa)_a[Fa]=(_a[Fa]-ea[Fa])/Kn[Fa];_a.set((0,M.medianFilter)(_a,Pe))}}}const vt=[(0,_.mean)(Lt,1)],qt=I.sequences.dims,Rt=new _.Tensor("float32",new Float32Array(qt[0]*qt[1]),qt);for(let Kt=0;Kt<qt[0];++Kt){const xt=vt[Kt].neg().squeeze_(0),[sn,Dn]=(0,M.dynamic_time_warping)(xt.tolist()),Kn=Array.from({length:sn.length-1},(_a,Fa)=>sn[Fa+1]-sn[Fa]),ea=(0,d.mergeArrays)([1],Kn).map(_a=>!!_a),ta=[];for(let _a=0;_a<ea.length;++_a)ea[_a]&&ta.push(Dn[_a]*Ae);Rt[Kt].data.set(ta,1)}return Rt}}class Al extends Pl{}class Po extends J{constructor(){super(...arguments);fe(this,"requires_attention_mask",!1);fe(this,"main_input_name","input_values");fe(this,"forward_params",["input_values","decoder_input_ids","past_key_values"])}}class Ao extends Po{}class Xu extends Po{}class $l extends J{constructor(){super(...arguments);fe(this,"main_input_name","pixel_values");fe(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class kl extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class ci extends kl{_merge_input_ids_with_image_features({inputs_embeds:I,image_features:W,input_ids:me,attention_mask:Ae}){const Pe=this.config.image_token_index,Ze=me.tolist().map(vt=>vt.findIndex(qt=>qt==Pe)),st=Ze.every(vt=>vt===-1),ht=Ze.every(vt=>vt!==-1);if(!st&&!ht)throw new Error("Every input should contain either 0 or 1 image token.");if(st)return{inputs_embeds:I,attention_mask:Ae};const $t=[],Lt=[];for(let vt=0;vt<Ze.length;++vt){const qt=Ze[vt],Rt=I[vt],Kt=W[vt],xt=Ae[vt];$t.push((0,_.cat)([Rt.slice([0,qt]),Kt,Rt.slice([qt+1,Rt.dims[0]])],0)),Lt.push((0,_.cat)([xt.slice([0,qt]),(0,_.ones)([Kt.dims[0]]),xt.slice([qt+1,xt.dims[0]])],0))}return{inputs_embeds:(0,_.stack)($t,0),attention_mask:(0,_.stack)(Lt,0)}}}class di extends ci{}class Yu extends ci{}class Ol extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);fe(this,"main_input_name","inputs_embeds")}}class Zu extends Ol{_merge_input_ids_with_image_features({inputs_embeds:I,image_features:W,input_ids:me,attention_mask:Ae}){return{inputs_embeds:(0,_.cat)([W,I],1),attention_mask:(0,_.cat)([(0,_.ones)(W.dims.slice(0,2)),Ae],1)}}async _prepare_inputs_embeds({input_ids:I,pixel_values:W,inputs_embeds:me,attention_mask:Ae}){if(!I&&!W)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Pe,Ue;return I&&(Pe=await this.encode_text({input_ids:I})),W&&(Ue=await this.encode_image({pixel_values:W})),Pe&&Ue?{inputs_embeds:me,attention_mask:Ae}=this._merge_input_ids_with_image_features({inputs_embeds:Pe,image_features:Ue,input_ids:I,attention_mask:Ae}):me=Pe||Ue,{inputs_embeds:me,attention_mask:Ae}}async forward({input_ids:I,pixel_values:W,attention_mask:me,decoder_input_ids:Ae,decoder_attention_mask:Pe,encoder_outputs:Ue,past_key_values:Ze,inputs_embeds:st,decoder_inputs_embeds:ht}){if(st||({inputs_embeds:st,attention_mask:me}=await this._prepare_inputs_embeds({input_ids:I,pixel_values:W,inputs_embeds:st,attention_mask:me})),!Ue){let{last_hidden_state:vt}=await oe(this,{inputs_embeds:st,attention_mask:me});Ue=vt}if(!ht){if(!Ae)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");ht=await this.encode_text({input_ids:Ae})}return await U(this,{inputs_embeds:ht,attention_mask:Pe,encoder_attention_mask:me,encoder_hidden_states:Ue,past_key_values:Ze},!0)}}class Ju extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class ec extends Ju{_merge_input_ids_with_image_features(I){const W=I.image_features.dims.at(-1),me=I.image_features.view(-1,W);return z({image_token_id:this.config.image_token_index,...I,image_features:me})}}class $o extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"])}}class Ri extends $o{async encode_image({pixel_values:I,pixel_attention_mask:W}){return(await Q(this.sessions.vision_encoder,{pixel_values:I,pixel_attention_mask:W})).image_features}_merge_input_ids_with_image_features(I){const W=I.image_features.dims.at(-1),me=I.image_features.view(-1,W);return z({image_token_id:this.config.image_token_id,...I,image_features:me})}}class Dl extends Ri{}class ko extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"])}}class Cs extends ko{async forward({input_ids:I=null,attention_mask:W=null,pixel_values:me=null,image_sizes:Ae=null,position_ids:Pe=null,inputs_embeds:Ue=null,past_key_values:Ze=null,generation_config:st=null,logits_processor:ht=null,...$t}){if(!Ue){let vt;if(me&&I.dims[1]!==1){if(!Ae)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:vt}=await Q(this.sessions.vision_encoder,{pixel_values:me,image_sizes:Ae}))}else{const qt=this.config.normalized_config.hidden_size;vt=new _.Tensor("float32",[],[0,qt])}({inputs_embeds:Ue}=await Q(this.sessions.prepare_inputs_embeds,{input_ids:I,image_features:vt}))}return await U(this,{inputs_embeds:Ue,past_key_values:Ze,attention_mask:W,position_ids:Pe,generation_config:st,logits_processor:ht},!1)}}class Ps extends J{}class Il extends Ps{}class tc extends Ps{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"text_model"})}}class zl extends Ps{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"text_model"})}}class nc extends Ps{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"vision_model"})}}class Ll extends Ps{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"vision_model"})}}class Tr extends J{}class fi extends Tr{}class Fl extends Tr{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"text_model"})}}class ac extends Ps{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"vision_model"})}}class Bl extends J{}class Ni extends Bl{}class Or extends J{}class As extends Or{async forward(I){const W=!I.input_ids,me=!I.pixel_values;if(W&&me)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(W&&(I.input_ids=(0,_.ones)([I.pixel_values.dims[0],1])),me){const{image_size:ht}=this.config.vision_config;I.pixel_values=(0,_.full)([0,3,ht,ht],0)}const{text_embeddings:Ae,image_embeddings:Pe,l2norm_text_embeddings:Ue,l2norm_image_embeddings:Ze}=await super.forward(I),st={};return W||(st.text_embeddings=Ae,st.l2norm_text_embeddings=Ue),me||(st.image_embeddings=Pe,st.l2norm_image_embeddings=Ze),st}}class Rl extends Or{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"text_model"})}}class pi extends Or{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"vision_model"})}}class ji extends J{}class Oo extends ji{}class Ui extends ji{}class Vi extends J{}class Do extends Vi{}class Io extends Vi{}class $s extends J{}class Ot extends $s{}class rc extends $s{}class zo extends J{}class Nl extends zo{}class Gi extends zo{}class Wn extends J{}class Hr extends Wn{}class Wi extends Wn{}class Hi extends J{}class Lo extends Hi{}class Fo extends Hi{}class ks extends J{}class jl extends ks{}class ss extends ks{}class hi extends J{}class qr extends hi{}class mi extends hi{}class _i extends J{}class Kr extends _i{}class is extends _i{}class gi extends J{}class Bo extends gi{}class Qr extends gi{}class qi extends J{}class y extends qi{}class k extends qi{}class B extends J{}class K extends B{}class ae extends B{}class we extends J{}class je extends we{}class nt extends we{}class ot extends J{}class wt extends ot{}class zt extends ot{}class Ut extends J{}class mn extends Ut{}class ha extends Ut{}class xr extends J{}class Ki extends xr{}class sc extends xr{}class yi extends J{}class kd extends yi{}class Ul extends yi{}class bi extends J{}class Yp extends bi{}class pr extends bi{}class Vl extends J{}class os extends Vl{}class Od extends Vl{}class Ro extends J{}class vi extends Ro{}class ic extends Ro{}class Gl extends J{}class Qi extends Gl{}class Xi extends Gl{}class No extends J{}class Wl extends No{}class Er extends No{}class hr extends J{}class Mi extends hr{}class ls extends hr{}class us extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"])}}class wi extends us{get_rope_index(I,W,me,Ae){const{vision_config:Pe,image_token_id:Ue,video_token_id:Ze,vision_start_token_id:st}=this.config,ht=Pe.spatial_merge_size??2,$t=[];if(W||me){let Lt=I.tolist();Ae||(Ae=(0,_.ones_like)(I));const vt=Ae.tolist(),qt=Array.from({length:3},Dn=>Array.from({length:I.dims[0]},Kn=>Array.from({length:I.dims[1]},ea=>1))),Rt=W?W.tolist():[],Kt=me?me.tolist():[];let xt=0,sn=0;for(let Dn=0;Dn<Lt.length;++Dn){const Kn=Lt[Dn].filter((Un,Ta)=>vt[Dn][Ta]==1),ta=Kn.reduce((Un,Ta,vs)=>(Ta==st&&Un.push(vs),Un),[]).map(Un=>Kn[Un+1]),_a=ta.filter(Un=>Un==Ue).length,Fa=ta.filter(Un=>Un==Ze).length;let Xt=[],ga=0,Pr=_a,_o=Fa;for(let Un=0;Un<ta.length;++Un){const Ta=Kn.findIndex((Ii,Br)=>Br>ga&&Ii==Ue),vs=Kn.findIndex((Ii,Br)=>Br>ga&&Ii==Ze),es=Pr>0&&Ta!==-1?Ta:Kn.length+1,qs=_o>0&&vs!==-1?vs:Kn.length+1;let Ks,md,mp,_p;es<qs?([md,mp,_p]=Rt[xt],++xt,--Pr,Ks=es):([md,mp,_p]=Kt[sn],++sn,--_o,Ks=qs);const[xh,_d,yo]=[Number(md),Math.floor(Number(mp)/ht),Math.floor(Number(_p)/ht)],In=Ks-ga,gd=Xt.length>0?(0,M.max)(Xt.at(-1))[0]+1:0;Xt.push(Array.from({length:3*In},(Ii,Br)=>gd+Br%In));const Ra=In+gd,yd=xh*_d*yo,gp=Array.from({length:yd},(Ii,Br)=>Ra+Math.floor(Br/(_d*yo))),yp=Array.from({length:yd},(Ii,Br)=>Ra+Math.floor(Br/yo)%_d),bd=Array.from({length:yd},(Ii,Br)=>Ra+Br%yo);Xt.push([gp,yp,bd].flat()),ga=Ks+yd}if(ga<Kn.length){const Un=Xt.length>0?(0,M.max)(Xt.at(-1))[0]+1:0,Ta=Kn.length-ga;Xt.push(Array.from({length:3*Ta},(vs,es)=>Un+es%Ta))}const Ba=Xt.reduce((Un,Ta)=>Un+Ta.length,0),Fr=new Array(Ba);let Au=0;for(let Un=0;Un<3;++Un)for(let Ta=0;Ta<Xt.length;++Ta){const vs=Xt[Ta],es=vs.length/3;for(let qs=Un*es;qs<(Un+1)*es;++qs)Fr[Au++]=vs[qs]}let en=0;const hl=vt[Dn];for(let Un=0;Un<hl.length;++Un)if(hl[Un]==1){for(let Ta=0;Ta<3;++Ta)qt[Ta][Dn][Un]=Fr[Ta*Ba/3+en];++en}const go=(0,M.max)(Fr)[0];$t.push(go+1-Lt[Dn].length)}return[new _.Tensor("int64",qt.flat(1/0),[3,I.dims[0],I.dims[1]]),new _.Tensor("int64",$t,[$t.length,1])]}else if(Ae){const{data:Lt,dims:vt}=ye(Ae),qt=BigInt64Array.from({length:3*Lt.length},(Kt,xt)=>Lt[xt%Lt.length]),Rt=Array.from({length:vt[0]},(Kt,xt)=>(0,M.max)(Lt.subarray(vt[1]*xt,vt[1]*(xt+1)))[0]+1n+BigInt(vt[1]));return[new _.Tensor("int64",qt,[3,...vt]),new _.Tensor("int64",Rt,[Rt.length,1])]}else{const[Lt,vt]=I.dims,qt=BigInt64Array.from({length:3*Lt*vt},(Rt,Kt)=>BigInt(Math.floor(Kt%vt/Lt)));return[new _.Tensor("int64",qt,[3,...I.dims]),(0,_.zeros)([Lt,1])]}}async encode_image({pixel_values:I,image_grid_thw:W}){return(await Q(this.sessions.vision_encoder,{pixel_values:I,grid_thw:W})).image_features}_merge_input_ids_with_image_features(I){return z({image_token_id:this.config.image_token_id,...I})}prepare_inputs_for_generation(I,W,me){if(W.attention_mask&&!W.position_ids)if(!W.past_key_values)[W.position_ids,W.rope_deltas]=this.get_rope_index(W.input_ids,W.image_grid_thw,W.video_grid_thw,W.attention_mask);else{W.pixel_values=null;const Ae=BigInt(Object.values(W.past_key_values)[0].dims.at(-2)),Pe=W.rope_deltas.map(Ue=>Ae+Ue);W.position_ids=(0,_.stack)([Pe,Pe,Pe],0)}return W}}class oc extends J{}class lc extends oc{}class uc extends oc{}class Ha extends J{}class la extends Ha{}class Cn extends Ha{}class Os extends J{}class Xr extends Os{}class cc extends Os{}class Ds extends J{}class Dd extends Ds{}class Id extends Ds{}class Yi extends J{}class jo extends Yi{}class zd extends Yi{}class Zi extends J{}class dc extends Zi{}class Ti extends Zi{async _call(I){return new Wt(await super._call(I))}}class Yr extends J{}class Is extends Yr{}class cs extends Yr{async _call(I){return new Wt(await super._call(I))}}class fc extends J{}class pc extends fc{}class Ji extends J{}class Hl extends Ji{}class xi extends Ji{async _call(I){return new Wt(await super._call(I))}}class qa extends J{}class ql extends qa{}class hc extends J{}class Zp extends hc{}class Jp extends hc{async _call(I){return new Wt(await super._call(I))}}class eh extends J{}class Pa extends eh{}class Kl extends J{}class Uo extends Kl{}class Vo extends Kl{async _call(I){return new Wt(await super._call(I))}}class mc extends J{}class eo extends mc{async _call(I){return new hp(await super._call(I))}}class Ei extends J{}class th extends Ei{}class Ld extends Ei{async _call(I){return new Wt(await super._call(I))}}class Fd extends J{}class Bd extends Fd{}class Si extends Fd{async _call(I){return new Wt(await super._call(I))}}class Ql extends J{}class Xl extends Ql{}class Rd extends Ql{}class to extends J{}class Nd extends to{}class Yl extends to{}class Zl extends J{}class jd extends Zl{}class Jl extends Zl{async _call(I){return new Wt(await super._call(I))}}class eu extends J{}class Go extends eu{}class Ud extends eu{async _call(I){return new ds(await super._call(I))}}class _c extends eu{async _call(I){return new gc(await super._call(I))}}class ds extends Ie{constructor({logits:I,pred_boxes:W}){super(),this.logits=I,this.pred_boxes=W}}class gc extends Ie{constructor({logits:I,pred_boxes:W,pred_masks:me}){super(),this.logits=I,this.pred_boxes=W,this.pred_masks=me}}class tu extends J{}class zs extends tu{}class Ls extends tu{async _call(I){return new Fs(await super._call(I))}}class Fs extends Ie{constructor({logits:I,pred_boxes:W}){super(),this.logits=I,this.pred_boxes=W}}class nu extends J{}class yc extends nu{}class Wo extends nu{async _call(I){return new Ho(await super._call(I))}}class Ho extends Fs{}class bc extends J{}class Vd extends bc{}class no extends bc{async _call(I){return new au(await super._call(I))}}class au extends Fs{}class vc extends J{}class Mc extends vc{}class wc extends vc{async _call(I){return new Fs(await super._call(I))}}class fs extends J{}class rn extends fs{}class Fn extends fs{async _call(I){return new Ma(await super._call(I))}}class Ma extends ds{}class qo extends J{}class ao extends qo{}class Ci extends qo{async _call(I){return new Wt(await super._call(I))}}class Ko extends J{}class Qo extends Ko{}class ro extends Ko{async _call(I){return new Wt(await super._call(I))}}class Gd extends J{}class ma extends Gd{}class Tc extends Gd{async _call(I){return new Wt(await super._call(I))}}class Xo extends J{}class Wd extends Xo{}class Hd extends Xo{async _call(I){return new Wt(await super._call(I))}}class nh extends Xo{}class ru extends J{}class xc extends ru{}class Ec extends ru{}class er extends J{}class wa extends er{}class Sc extends er{}class Yo extends J{}class su extends Yo{}class Zo extends J{}class ps extends Zo{}class iu extends Zo{}class Cc extends Zo{}class Pc extends J{}class qd extends Pc{}class Kd extends J{}class Qd extends Kd{}class Xd extends J{}class Yd extends Xd{}class Ac extends J{}class $c extends Ac{}class Zd extends Ac{}class Jd extends J{}class ef extends Jd{}class tf extends Jd{}class nf extends J{}class kc extends nf{}class Oc extends J{}class af extends Oc{}class rf extends Oc{async _call(I){return new Wt(await super._call(I))}}class Dc extends J{}class sf extends Dc{}class ah extends Dc{async _call(I){return new Wt(await super._call(I))}}class Ic extends J{}class so extends Ic{}class ou extends Ic{async _call(I){return new Wt(await super._call(I))}}class zc extends J{}class lu extends zc{}class Jo extends zc{async _call(I){return new Wt(await super._call(I))}}class of extends J{}class lf extends of{}class Lc extends J{}class uf extends Lc{}class cf extends Lc{async _call(I){return new df(await super._call(I))}}class df extends Ie{constructor({logits:I,pred_boxes:W}){super(),this.logits=I,this.pred_boxes=W}}class Fc extends J{}class ff extends Fc{async get_image_embeddings({pixel_values:I}){return await oe(this,{pixel_values:I})}async forward(I){if((!I.image_embeddings||!I.image_positional_embeddings)&&(I={...I,...await this.get_image_embeddings(I)}),!I.input_labels&&I.input_points){const me=I.input_points.dims.slice(0,-1),Ae=me.reduce((Pe,Ue)=>Pe*Ue,1);I.input_labels=new _.Tensor("int64",new BigInt64Array(Ae).fill(1n),me)}const W={image_embeddings:I.image_embeddings,image_positional_embeddings:I.image_positional_embeddings};return I.input_points&&(W.input_points=I.input_points),I.input_labels&&(W.input_labels=I.input_labels),I.input_boxes&&(W.input_boxes=I.input_boxes),await Q(this.sessions.prompt_encoder_mask_decoder,W)}async _call(I){return new pf(await super._call(I))}}class pf extends Ie{constructor({iou_scores:I,pred_masks:W}){super(),this.iou_scores=I,this.pred_masks=W}}class uu extends J{}class hf extends uu{}class mf extends uu{}class _f extends J{}class Bc extends _f{}class gf extends _f{}class Bs extends J{}class Rc extends Bs{}class yf extends Bs{async _call(I){return new bs(await super._call(I))}}class bf extends Bs{async _call(I){return new Wt(await super._call(I))}}class rh extends Bs{async _call(I){return new $a(await super._call(I))}}class vf extends J{}class Mf extends vf{}class el extends vf{async _call(I){return new $a(await super._call(I))}}class Nc extends J{}class cu extends Nc{}class du extends J{}class wf extends du{}class fu extends du{async _call(I){return new bs(await super._call(I))}}class Tf extends du{async _call(I){return new Wt(await super._call(I))}}class tl extends J{}class sh extends tl{}class io extends tl{async _call(I){return new bs(await super._call(I))}}class nl extends tl{async _call(I){return new Wt(await super._call(I))}}class pu extends tl{async _call(I){return new $a(await super._call(I))}}class Pi extends J{}class hu extends Pi{}class xf extends Pi{async _call(I){return new bs(await super._call(I))}}class Ef extends Pi{async _call(I){return new Wt(await super._call(I))}}class al extends J{}class Sf extends Bs{}class Sr extends Bs{async _call(I){return new bs(await super._call(I))}}class Zr extends Bs{async _call(I){return new Wt(await super._call(I))}}class Cr extends J{}class Cf extends Cr{}class Rs extends Cr{async _call(I){return new bs(await super._call(I))}}class hs extends Cr{async _call(I){return new Wt(await super._call(I))}}class Aa extends Cr{async _call(I){return new pp(await super._call(I))}}class mu extends Cr{async _call(I){return new $a(await super._call(I))}}class jc extends J{}class Uc extends jc{}class _u extends J{}class ih extends _u{}class Ai extends _u{}class gu extends _u{async generate_speech(I,W,{threshold:me=.5,minlenratio:Ae=0,maxlenratio:Pe=20,vocoder:Ue=null}={}){const Ze={input_ids:I},{encoder_outputs:st,encoder_attention_mask:ht}=await oe(this,Ze),$t=st.dims[1]/this.config.reduction_factor,Lt=Math.floor($t*Pe),vt=Math.floor($t*Ae),qt=this.config.num_mel_bins;let Rt=[],Kt=null,xt=null,sn=0;for(;;){++sn;const ea=H(!!xt);let ta;xt?ta=xt.output_sequence_out:ta=new _.Tensor("float32",new Float32Array(qt),[1,1,qt]);let _a={use_cache_branch:ea,output_sequence:ta,encoder_attention_mask:ht,speaker_embeddings:W,encoder_hidden_states:st};this.addPastKeyValues(_a,Kt),xt=await Q(this.sessions.decoder_model_merged,_a),Kt=this.getPastKeyValues(xt,Kt);const{prob:Fa,spectrum:Xt}=xt;if(Rt.push(Xt),sn>=vt&&(Array.from(Fa.data).filter(ga=>ga>=me).length>0||sn>=Lt))break}const Dn=(0,_.cat)(Rt),{waveform:Kn}=await Q(Ue.sessions.model,{spectrogram:Dn});return{spectrogram:Dn,waveform:Kn}}}class Pf extends J{constructor(){super(...arguments);fe(this,"main_input_name","spectrogram")}}class Af extends J{}class $f extends Af{}class rl extends J{}class kf extends rl{}class Vc extends rl{}class Gc extends J{}class Of extends Gc{}class oh extends Gc{}class Wc extends J{}class Da extends Wc{}class Va extends Wc{}class yu extends J{}class Df extends yu{}class If extends yu{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"text_model"})}}class zf extends yu{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"audio_model"})}}class Lf extends J{}class sl extends Lf{async _call(I){return new jn(await super._call(I))}}class il extends J{}class lh extends il{}class Ff extends il{}class Bf extends il{}class bu extends J{}class Hc extends bu{}class qc extends bu{}class Kc extends J{}class Qc extends Kc{}class vu extends Kc{async _call(I){return new Wt(await super._call(I))}}class Mu extends J{}class uh extends Mu{}class Rf extends Mu{}class Xc extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(W){const[me,Ae]=W.dims,Pe=this.config.decoder.num_codebooks,Ue=Ae-Pe;let Ze=0;for(let $t=0;$t<W.size;++$t){if(W.data[$t]===this.config.decoder.pad_token_id)continue;const Lt=$t%Ae,vt=Math.floor($t/Ae)%Pe,qt=Lt-vt;qt>0&&qt<=Ue&&(W.data[Ze++]=W.data[$t])}const st=Math.floor(me/Pe),ht=Ze/(st*Pe);return new _.Tensor(W.type,W.data.slice(0,Ze),[st,Pe,ht])}prepare_inputs_for_generation(W,me,Ae){let Pe=structuredClone(W);for(let Ze=0;Ze<Pe.length;++Ze)for(let st=0;st<Pe[Ze].length;++st)Ze%this.config.decoder.num_codebooks>=st&&(Pe[Ze][st]=BigInt(this.config.decoder.pad_token_id));return Ae.guidance_scale!==null&&Ae.guidance_scale>1&&(Pe=Pe.concat(Pe)),super.prepare_inputs_for_generation(Pe,me,Ae)}async generate(W){const me=await super.generate(W),Ae=this._apply_and_filter_by_delay_pattern_mask(me).unsqueeze_(0),{audio_values:Pe}=await Q(this.sessions.encodec_decode,{audio_codes:Ae});return Pe}}class Dr extends J{}class Yc extends Dr{}class ch extends Dr{async _call(I){return new Wt(await super._call(I))}}class Nf extends Dr{}class Ir extends J{}class jf extends Ir{}class wu extends Ir{async _call(I){return new Wt(await super._call(I))}}class ol extends Ir{}class Hn extends J{}class dh extends Hn{}class fh extends Hn{async _call(I){return new Wt(await super._call(I))}}class Uf extends Hn{}class $i extends J{}class Ns extends $i{}class Vf extends $i{async _call(I){return new Wt(await super._call(I))}}class Gf extends $i{}class ll extends J{}class Jr extends ll{}class Wf extends J{}class Zc extends Wf{constructor(...W){super(...W);fe(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(W){const me=this._generation_mode??"text";let Ae;if(me==="text"||!W.past_key_values){const ht=this.sessions.prepare_inputs_embeds,$t=(0,d.pick)(W,ht.inputNames);Ae=await Q(ht,$t)}else{const ht=this.sessions.gen_img_embeds,$t=(0,d.pick)({image_ids:W.input_ids},ht.inputNames);Ae=await Q(ht,$t)}const Pe={...W,...Ae},Ue=await U(this,Pe),Ze=this.sessions[me==="text"?"lm_head":"gen_head"];if(!Ze)throw new Error(`Unable to find "${Ze}" generation head`);const st=await Q(Ze,(0,d.pick)(Ue,Ze.inputNames));return{...Ae,...Ue,...st}}async generate(W){return this._generation_mode="text",super.generate(W)}async generate_images(W){this._generation_mode="image";const me=(W.inputs??W[this.main_input_name]).dims[1],Pe=(await super.generate(W)).slice(null,[me,null]),Ue=this.sessions.image_decode,{decoded_image:Ze}=await Q(Ue,{generated_tokens:Pe}),st=Ze.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),ht=[];for(const $t of st){const Lt=w.RawImage.fromTensor($t);ht.push(Lt)}return ht}}class Hf extends Ie{constructor({char_logits:I,bpe_logits:W,wp_logits:me}){super(),this.char_logits=I,this.bpe_logits=W,this.wp_logits=me}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class Jc extends J{}class ed extends Jc{async _call(I){return new Hf(await super._call(I))}}class ul extends J{}class qf extends ul{}class ms extends ul{}class ua extends J{}class td extends ua{}class Kf extends ua{}class Ia extends J{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","position_ids","audio_values","past_key_values"])}}class ph extends Ia{_merge_input_ids_with_audio_features(I){const W=I.audio_features.dims.at(-1),me=I.audio_features.view(-1,W);return V({audio_token_id:this.config.ignore_index,...I,audio_features:me})}}class Tu extends J{constructor(){super(...arguments);fe(this,"main_input_name","input_values");fe(this,"forward_params",["input_values"])}}class Qf extends Ie{constructor({audio_codes:I}){super(),this.audio_codes=I}}class Jn extends Ie{constructor({audio_values:I}){super(),this.audio_values=I}}class lr extends Tu{async encode(I){return new Qf(await Q(this.sessions.encoder_model,I))}async decode(I){return new Jn(await Q(this.sessions.decoder_model,I))}}class _s extends Tu{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"encoder_model"})}}class Xf extends Tu{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"decoder_model"})}}class xu extends J{constructor(){super(...arguments);fe(this,"main_input_name","input_values");fe(this,"forward_params",["input_values"])}}class hh extends Ie{constructor({audio_codes:I}){super(),this.audio_codes=I}}class nd extends Ie{constructor({audio_values:I}){super(),this.audio_values=I}}class mr extends xu{async encode(I){return new hh(await Q(this.sessions.encoder_model,I))}async decode(I){return new nd(await Q(this.sessions.decoder_model,I))}}class zr extends xu{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"encoder_model"})}}class Yf extends xu{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"decoder_model"})}}class tr extends J{constructor(){super(...arguments);fe(this,"main_input_name","input_values");fe(this,"forward_params",["input_values"])}}class Zf extends tr{async encode(I){return await Q(this.sessions.encoder_model,I)}async decode(I){return await Q(this.sessions.decoder_model,I)}}class js extends tr{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"encoder_model"})}}class ki extends tr{static async from_pretrained(I,W={}){return super.from_pretrained(I,{...W,model_file_name:W.model_file_name??"decoder_model"})}}class nn{static async from_pretrained(I,{progress_callback:W=null,config:me=null,cache_dir:Ae=null,local_files_only:Pe=!1,revision:Ue="main",model_file_name:Ze=null,subfolder:st="onnx",device:ht=null,dtype:$t=null,use_external_data_format:Lt=null,session_options:vt={}}={}){const qt={progress_callback:W,config:me,cache_dir:Ae,local_files_only:Pe,revision:Ue,model_file_name:Ze,subfolder:st,device:ht,dtype:$t,use_external_data_format:Lt,session_options:vt};if(qt.config=await i.AutoConfig.from_pretrained(I,qt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const Rt=qt.config.model_type;for(const Kt of this.MODEL_CLASS_MAPPINGS){let xt=Kt.get(Rt);if(!xt){for(const sn of Kt.values())if(sn[0]===Rt){xt=sn;break}if(!xt)continue}return await xt[1].from_pretrained(I,qt)}if(this.BASE_IF_FAIL)return Gs.has(Rt)||console.warn(`Unknown model class "${Rt}", attempting to construct from base class.`),await J.from_pretrained(I,qt);throw Error(`Unsupported model type: ${Rt}`)}}fe(nn,"MODEL_CLASS_MAPPINGS",null),fe(nn,"BASE_IF_FAIL",!1);const Jf=new Map([["bert",["BertModel",Le]],["modernbert",["ModernBertModel",Qe]],["nomic_bert",["NomicBertModel",lt]],["roformer",["RoFormerModel",It]],["electra",["ElectraModel",Ce]],["esm",["EsmModel",Ct]],["convbert",["ConvBertModel",Nt]],["camembert",["CamembertModel",St]],["deberta",["DebertaModel",ka]],["deberta-v2",["DebertaV2Model",rr]],["mpnet",["MPNetModel",ii]],["albert",["AlbertModel",Ee]],["distilbert",["DistilBertModel",jr]],["roberta",["RobertaModel",va]],["xlm",["XLMModel",Ss]],["xlm-roberta",["XLMRobertaModel",Vu]],["clap",["ClapModel",Df]],["clip",["CLIPModel",Il]],["clipseg",["CLIPSegModel",Oo]],["chinese_clip",["ChineseCLIPModel",Ni]],["siglip",["SiglipModel",fi]],["jina_clip",["JinaCLIPModel",As]],["mobilebert",["MobileBertModel",Mr]],["squeezebert",["SqueezeBertModel",Gr]],["wav2vec2",["Wav2Vec2Model",Rc]],["wav2vec2-bert",["Wav2Vec2BertModel",hu]],["unispeech",["UniSpeechModel",wf]],["unispeech-sat",["UniSpeechSatModel",sh]],["hubert",["HubertModel",Sf]],["wavlm",["WavLMModel",Cf]],["audio-spectrogram-transformer",["ASTModel",qu]],["vits",["VitsModel",sl]],["pyannote",["PyAnnoteModel",Mf]],["wespeaker-resnet",["WeSpeakerResNetModel",cu]],["detr",["DetrModel",Go]],["rt_detr",["RTDetrModel",zs]],["rt_detr_v2",["RTDetrV2Model",yc]],["rf_detr",["RFDetrModel",Vd]],["d_fine",["DFineModel",Mc]],["table-transformer",["TableTransformerModel",rn]],["vit",["ViTModel",dc]],["ijepa",["IJepaModel",Is]],["pvt",["PvtModel",Hl]],["vit_msn",["ViTMSNModel",Zp]],["vit_mae",["ViTMAEModel",ql]],["groupvit",["GroupViTModel",Pa]],["fastvit",["FastViTModel",Uo]],["mobilevit",["MobileViTModel",th]],["mobilevitv2",["MobileViTV2Model",Bd]],["owlvit",["OwlViTModel",Xl]],["owlv2",["Owlv2Model",Nd]],["beit",["BeitModel",jd]],["deit",["DeiTModel",ao]],["hiera",["HieraModel",Qo]],["convnext",["ConvNextModel",af]],["convnextv2",["ConvNextV2Model",sf]],["dinov2",["Dinov2Model",so]],["dinov2_with_registers",["Dinov2WithRegistersModel",lu]],["resnet",["ResNetModel",ma]],["swin",["SwinModel",Wd]],["swin2sr",["Swin2SRModel",xc]],["donut-swin",["DonutSwinModel",kc]],["yolos",["YolosModel",uf]],["dpt",["DPTModel",wa]],["glpn",["GLPNModel",ef]],["hifigan",["SpeechT5HifiGan",Pf]],["efficientnet",["EfficientNetModel",Qc]],["decision_transformer",["DecisionTransformerModel",Jr]],["patchtst",["PatchTSTForPrediction",qf]],["patchtsmixer",["PatchTSMixerForPrediction",td]],["mobilenet_v1",["MobileNetV1Model",Yc]],["mobilenet_v2",["MobileNetV2Model",jf]],["mobilenet_v3",["MobileNetV3Model",dh]],["mobilenet_v4",["MobileNetV4Model",Ns]],["maskformer",["MaskFormerModel",$c]],["mgp-str",["MgpstrForSceneTextRecognition",ed]],["style_text_to_speech_2",["StyleTextToSpeech2Model",Uc]]]),ep=new Map([["t5",["T5Model",xe]],["longt5",["LongT5Model",pt]],["mt5",["MT5Model",Ft]],["bart",["BartModel",Sn]],["mbart",["MBartModel",Sa]],["marian",["MarianModel",hf]],["whisper",["WhisperModel",Qu]],["m2m_100",["M2M100Model",Bc]],["blenderbot",["BlenderbotModel",Rn]],["blenderbot-small",["BlenderbotSmallModel",fr]]]),gs=new Map([["mimi",["MimiModel",lr]],["dac",["DacModel",mr]],["snac",["SnacModel",Zf]]]),mh=new Map([["bloom",["BloomModel",Xr]],["jais",["JAISModel",Ot]],["gpt2",["GPT2Model",Do]],["gptj",["GPTJModel",Lo]],["gpt_bigcode",["GPTBigCodeModel",jl]],["gpt_neo",["GPTNeoModel",Nl]],["gpt_neox",["GPTNeoXModel",Hr]],["codegen",["CodeGenModel",qr]],["llama",["LlamaModel",Kr]],["exaone",["ExaoneModel",K]],["olmo",["OlmoModel",wt]],["olmo2",["Olmo2Model",mn]],["mobilellm",["MobileLLMModel",je]],["granite",["GraniteModel",Ki]],["cohere",["CohereModel",kd]],["gemma",["GemmaModel",Yp]],["gemma2",["Gemma2Model",os]],["gemma3_text",["Gemma3Model",vi]],["helium",["HeliumModel",Bo]],["glm",["GlmModel",y]],["openelm",["OpenELMModel",Qi]],["qwen2",["Qwen2Model",Wl]],["qwen3",["Qwen3Model",Mi]],["phi",["PhiModel",lc]],["phi3",["Phi3Model",la]],["mpt",["MptModel",Dd]],["opt",["OPTModel",jo]],["mistral",["MistralModel",kf]],["starcoder2",["Starcoder2Model",Of]],["falcon",["FalconModel",Da]],["stablelm",["StableLmModel",Hc]]]),Oi=new Map([["speecht5",["SpeechT5ForSpeechToText",Ai]],["whisper",["WhisperForConditionalGeneration",Pl]],["lite-whisper",["LiteWhisperForConditionalGeneration",Al]],["moonshine",["MoonshineForConditionalGeneration",Xu]]]),cl=new Map([["speecht5",["SpeechT5ForTextToSpeech",gu]]]),dl=new Map([["vits",["VitsModel",sl]],["musicgen",["MusicgenForConditionalGeneration",Xc]]]),oo=new Map([["bert",["BertForSequenceClassification",et]],["modernbert",["ModernBertForSequenceClassification",bt]],["roformer",["RoFormerForSequenceClassification",xn]],["electra",["ElectraForSequenceClassification",Re]],["esm",["EsmForSequenceClassification",or]],["convbert",["ConvBertForSequenceClassification",q]],["camembert",["CamembertForSequenceClassification",an]],["deberta",["DebertaForSequenceClassification",Nr]],["deberta-v2",["DebertaV2ForSequenceClassification",sr]],["mpnet",["MPNetForSequenceClassification",oi]],["albert",["AlbertForSequenceClassification",F]],["distilbert",["DistilBertForSequenceClassification",Yn]],["roberta",["RobertaForSequenceClassification",Nn]],["xlm",["XLMForSequenceClassification",li]],["xlm-roberta",["XLMRobertaForSequenceClassification",So]],["bart",["BartForSequenceClassification",da]],["mbart",["MBartForSequenceClassification",Mn]],["mobilebert",["MobileBertForSequenceClassification",ia]],["squeezebert",["SqueezeBertForSequenceClassification",Ua]]]),tp=new Map([["bert",["BertForTokenClassification",at]],["modernbert",["ModernBertForTokenClassification",Mt]],["roformer",["RoFormerForTokenClassification",_n]],["electra",["ElectraForTokenClassification",Gt]],["esm",["EsmForTokenClassification",Ea]],["convbert",["ConvBertForTokenClassification",_e]],["camembert",["CamembertForTokenClassification",Zt]],["deberta",["DebertaForTokenClassification",vr]],["deberta-v2",["DebertaV2ForTokenClassification",ir]],["mpnet",["MPNetForTokenClassification",Li]],["distilbert",["DistilBertForTokenClassification",Ya]],["roberta",["RobertaForTokenClassification",Zn]],["xlm",["XLMForTokenClassification",Bi]],["xlm-roberta",["XLMRobertaForTokenClassification",Wu]]]),ad=new Map([["t5",["T5ForConditionalGeneration",Ne]],["longt5",["LongT5ForConditionalGeneration",ct]],["mt5",["MT5ForConditionalGeneration",pn]],["bart",["BartForConditionalGeneration",hn]],["mbart",["MBartForConditionalGeneration",oa]],["marian",["MarianMTModel",mf]],["m2m_100",["M2M100ForConditionalGeneration",gf]],["blenderbot",["BlenderbotForConditionalGeneration",Oa]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",ba]]]),lo=new Map([["bloom",["BloomForCausalLM",cc]],["gpt2",["GPT2LMHeadModel",Io]],["jais",["JAISLMHeadModel",rc]],["gptj",["GPTJForCausalLM",Fo]],["gpt_bigcode",["GPTBigCodeForCausalLM",ss]],["gpt_neo",["GPTNeoForCausalLM",Gi]],["gpt_neox",["GPTNeoXForCausalLM",Wi]],["codegen",["CodeGenForCausalLM",mi]],["llama",["LlamaForCausalLM",is]],["exaone",["ExaoneForCausalLM",ae]],["olmo",["OlmoForCausalLM",zt]],["olmo2",["Olmo2ForCausalLM",ha]],["mobilellm",["MobileLLMForCausalLM",nt]],["granite",["GraniteForCausalLM",sc]],["cohere",["CohereForCausalLM",Ul]],["gemma",["GemmaForCausalLM",pr]],["gemma2",["Gemma2ForCausalLM",Od]],["gemma3_text",["Gemma3ForCausalLM",ic]],["helium",["HeliumForCausalLM",Qr]],["glm",["GlmForCausalLM",k]],["openelm",["OpenELMForCausalLM",Xi]],["qwen2",["Qwen2ForCausalLM",Er]],["qwen3",["Qwen3ForCausalLM",ls]],["phi",["PhiForCausalLM",uc]],["phi3",["Phi3ForCausalLM",Cn]],["mpt",["MptForCausalLM",Id]],["opt",["OPTForCausalLM",zd]],["mbart",["MBartForCausalLM",wr]],["mistral",["MistralForCausalLM",Vc]],["starcoder2",["Starcoder2ForCausalLM",oh]],["falcon",["FalconForCausalLM",Va]],["trocr",["TrOCRForCausalLM",$f]],["stablelm",["StableLmForCausalLM",qc]],["phi3_v",["Phi3VForCausalLM",Cs]]]),_h=new Map([["multi_modality",["MultiModalityCausalLM",Zc]]]),Eu=new Map([["bert",["BertForMaskedLM",Ve]],["modernbert",["ModernBertForMaskedLM",ut]],["roformer",["RoFormerForMaskedLM",tn]],["electra",["ElectraForMaskedLM",tt]],["esm",["EsmForMaskedLM",gn]],["convbert",["ConvBertForMaskedLM",Xa]],["camembert",["CamembertForMaskedLM",Tt]],["deberta",["DebertaForMaskedLM",$r]],["deberta-v2",["DebertaV2ForMaskedLM",Na]],["mpnet",["MPNetForMaskedLM",ja]],["albert",["AlbertForMaskedLM",ue]],["distilbert",["DistilBertForMaskedLM",Ye]],["roberta",["RobertaForMaskedLM",on]],["xlm",["XLMWithLMHeadModel",xo]],["xlm-roberta",["XLMRobertaForMaskedLM",Gu]],["mobilebert",["MobileBertForMaskedLM",Gn]],["squeezebert",["SqueezeBertForMaskedLM",Fi]]]),np=new Map([["bert",["BertForQuestionAnswering",dt]],["roformer",["RoFormerForQuestionAnswering",sa]],["electra",["ElectraForQuestionAnswering",fn]],["convbert",["ConvBertForQuestionAnswering",Y]],["camembert",["CamembertForQuestionAnswering",Tn]],["deberta",["DebertaForQuestionAnswering",cr]],["deberta-v2",["DebertaV2ForQuestionAnswering",Vn]],["mpnet",["MPNetForQuestionAnswering",rs]],["albert",["AlbertForQuestionAnswering",ne]],["distilbert",["DistilBertForQuestionAnswering",Za]],["roberta",["RobertaForQuestionAnswering",pa]],["xlm",["XLMForQuestionAnswering",Eo]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Hu]],["mobilebert",["MobileBertForQuestionAnswering",dr]],["squeezebert",["SqueezeBertForQuestionAnswering",Es]]]),ap=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",$l]],["idefics3",["Idefics3ForConditionalGeneration",Ri]],["smolvlm",["SmolVLMForConditionalGeneration",Dl]]]),gh=new Map([["llava",["LlavaForConditionalGeneration",ci]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",di]],["moondream1",["Moondream1ForConditionalGeneration",Yu]],["florence2",["Florence2ForConditionalGeneration",Zu]],["qwen2-vl",["Qwen2VLForConditionalGeneration",wi]],["idefics3",["Idefics3ForConditionalGeneration",Ri]],["smolvlm",["SmolVLMForConditionalGeneration",Dl]],["paligemma",["PaliGemmaForConditionalGeneration",ec]]]),kn=new Map([["ultravox",["UltravoxModel",ph]]]),qn=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",$l]]]),cn=new Map([["vit",["ViTForImageClassification",Ti]],["ijepa",["IJepaForImageClassification",cs]],["pvt",["PvtForImageClassification",xi]],["vit_msn",["ViTMSNForImageClassification",Jp]],["fastvit",["FastViTForImageClassification",Vo]],["mobilevit",["MobileViTForImageClassification",Ld]],["mobilevitv2",["MobileViTV2ForImageClassification",Si]],["beit",["BeitForImageClassification",Jl]],["deit",["DeiTForImageClassification",Ci]],["hiera",["HieraForImageClassification",ro]],["convnext",["ConvNextForImageClassification",rf]],["convnextv2",["ConvNextV2ForImageClassification",ah]],["dinov2",["Dinov2ForImageClassification",ou]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Jo]],["resnet",["ResNetForImageClassification",Tc]],["swin",["SwinForImageClassification",Hd]],["segformer",["SegformerForImageClassification",Ff]],["efficientnet",["EfficientNetForImageClassification",vu]],["mobilenet_v1",["MobileNetV1ForImageClassification",ch]],["mobilenet_v2",["MobileNetV2ForImageClassification",wu]],["mobilenet_v3",["MobileNetV3ForImageClassification",fh]],["mobilenet_v4",["MobileNetV4ForImageClassification",Vf]]]),bn=new Map([["detr",["DetrForObjectDetection",Ud]],["rt_detr",["RTDetrForObjectDetection",Ls]],["rt_detr_v2",["RTDetrV2ForObjectDetection",Wo]],["rf_detr",["RFDetrForObjectDetection",no]],["d_fine",["DFineForObjectDetection",wc]],["table-transformer",["TableTransformerForObjectDetection",Fn]],["yolos",["YolosForObjectDetection",cf]]]),On=new Map([["owlvit",["OwlViTForObjectDetection",Rd]],["owlv2",["Owlv2ForObjectDetection",Yl]],["grounding-dino",["GroundingDinoForObjectDetection",lf]]]),Ga=new Map([["detr",["DetrForSegmentation",_c]],["clipseg",["CLIPSegForImageSegmentation",Ui]]]),Us=new Map([["segformer",["SegformerForSemanticSegmentation",Bf]],["sapiens",["SapiensForSemanticSegmentation",ps]],["swin",["SwinForSemanticSegmentation",nh]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",Nf]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",ol]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",Uf]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",Gf]]]),uo=new Map([["detr",["DetrForSegmentation",_c]],["maskformer",["MaskFormerForInstanceSegmentation",Zd]]]),rd=new Map([["sam",["SamModel",ff]]]),ys=new Map([["wav2vec2",["Wav2Vec2ForCTC",yf]],["wav2vec2-bert",["Wav2Vec2BertForCTC",xf]],["unispeech",["UniSpeechForCTC",fu]],["unispeech-sat",["UniSpeechSatForCTC",io]],["wavlm",["WavLMForCTC",Rs]],["hubert",["HubertForCTC",Sr]]]),ca=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",bf]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Ef]],["unispeech",["UniSpeechForSequenceClassification",Tf]],["unispeech-sat",["UniSpeechSatForSequenceClassification",nl]],["wavlm",["WavLMForSequenceClassification",hs]],["hubert",["HubertForSequenceClassification",Zr]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ku]]]),Vs=new Map([["wavlm",["WavLMForXVector",Aa]]]),Di=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",pu]],["wavlm",["WavLMForAudioFrameClassification",mu]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",rh]],["pyannote",["PyAnnoteForAudioFrameClassification",el]]]),sd=new Map([["vitmatte",["VitMatteForImageMatting",eo]]]),Lr=new Map([["patchtst",["PatchTSTForPrediction",ms]],["patchtsmixer",["PatchTSMixerForPrediction",Kf]]]),co=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ec]]]),fl=new Map([["dpt",["DPTForDepthEstimation",Sc]],["depth_anything",["DepthAnythingForDepthEstimation",su]],["glpn",["GLPNForDepthEstimation",tf]],["sapiens",["SapiensForDepthEstimation",iu]],["depth_pro",["DepthProForDepthEstimation",qd]],["metric3d",["Metric3DForDepthEstimation",Qd]],["metric3dv2",["Metric3Dv2ForDepthEstimation",Yd]]]),ur=new Map([["sapiens",["SapiensForNormalEstimation",Cc]]]),id=new Map([["vitpose",["VitPoseForPoseEstimation",pc]]]),od=new Map([["clip",["CLIPVisionModelWithProjection",Ll]],["siglip",["SiglipVisionModel",ac]],["jina_clip",["JinaCLIPVisionModel",pi]]]),Su=[[Jf,A.EncoderOnly],[ep,A.EncoderDecoder],[mh,A.DecoderOnly],[gs,A.AutoEncoder],[oo,A.EncoderOnly],[tp,A.EncoderOnly],[ad,A.Seq2Seq],[Oi,A.Seq2Seq],[lo,A.DecoderOnly],[_h,A.MultiModality],[Eu,A.EncoderOnly],[np,A.EncoderOnly],[ap,A.Vision2Seq],[gh,A.ImageTextToText],[kn,A.AudioTextToText],[cn,A.EncoderOnly],[Ga,A.EncoderOnly],[uo,A.EncoderOnly],[Us,A.EncoderOnly],[sd,A.EncoderOnly],[Lr,A.EncoderOnly],[co,A.EncoderOnly],[fl,A.EncoderOnly],[ur,A.EncoderOnly],[id,A.EncoderOnly],[bn,A.EncoderOnly],[On,A.EncoderOnly],[rd,A.MaskGeneration],[ys,A.EncoderOnly],[ca,A.EncoderOnly],[cl,A.Seq2Seq],[dl,A.EncoderOnly],[Vs,A.EncoderOnly],[Di,A.EncoderOnly],[od,A.EncoderOnly]];for(const[P,I]of Su)for(const[W,me]of P.values())C.set(W,I),x.set(me,W),T.set(W,me);const ld=[["MusicgenForConditionalGeneration",Xc,A.Musicgen],["Phi3VForCausalLM",Cs,A.Phi3V],["CLIPTextModelWithProjection",zl,A.EncoderOnly],["SiglipTextModel",Fl,A.EncoderOnly],["JinaCLIPTextModel",Rl,A.EncoderOnly],["ClapTextModelWithProjection",If,A.EncoderOnly],["ClapAudioModelWithProjection",zf,A.EncoderOnly],["DacEncoderModel",zr,A.EncoderOnly],["DacDecoderModel",Yf,A.EncoderOnly],["MimiEncoderModel",_s,A.EncoderOnly],["MimiDecoderModel",Xf,A.EncoderOnly],["SnacEncoderModel",js,A.EncoderOnly],["SnacDecoderModel",ki,A.EncoderOnly]];for(const[P,I,W]of ld)C.set(P,W),x.set(I,P),T.set(P,I);const Gs=new Map([["modnet",Ga],["birefnet",Ga],["isnet",Ga],["ben",Ga]]);for(const[P,I]of Gs.entries())I.set(P,["PreTrainedModel",J]),C.set(P,A.EncoderOnly),x.set(J,P),T.set(P,J);class za extends nn{}fe(za,"MODEL_CLASS_MAPPINGS",Su.map(I=>I[0])),fe(za,"BASE_IF_FAIL",!0);class Ws extends nn{}fe(Ws,"MODEL_CLASS_MAPPINGS",[oo]);class fo extends nn{}fe(fo,"MODEL_CLASS_MAPPINGS",[tp]);class po extends nn{}fe(po,"MODEL_CLASS_MAPPINGS",[ad]);class ud extends nn{}fe(ud,"MODEL_CLASS_MAPPINGS",[Oi]);class cd extends nn{}fe(cd,"MODEL_CLASS_MAPPINGS",[cl]);class rp extends nn{}fe(rp,"MODEL_CLASS_MAPPINGS",[dl]);class pl extends nn{}fe(pl,"MODEL_CLASS_MAPPINGS",[lo]);class dd extends nn{}fe(dd,"MODEL_CLASS_MAPPINGS",[Eu]);class _r extends nn{}fe(_r,"MODEL_CLASS_MAPPINGS",[np]);class sp extends nn{}fe(sp,"MODEL_CLASS_MAPPINGS",[ap]);class gr extends nn{}fe(gr,"MODEL_CLASS_MAPPINGS",[cn]);class ip extends nn{}fe(ip,"MODEL_CLASS_MAPPINGS",[Ga]);class op extends nn{}fe(op,"MODEL_CLASS_MAPPINGS",[Us]);class yh extends nn{}fe(yh,"MODEL_CLASS_MAPPINGS",[uo]);class Hs extends nn{}fe(Hs,"MODEL_CLASS_MAPPINGS",[bn]);class Cu extends nn{}fe(Cu,"MODEL_CLASS_MAPPINGS",[On]);class fd extends nn{}fe(fd,"MODEL_CLASS_MAPPINGS",[rd]);class ho extends nn{}fe(ho,"MODEL_CLASS_MAPPINGS",[ys]);class lp extends nn{}fe(lp,"MODEL_CLASS_MAPPINGS",[ca]);class up extends nn{}fe(up,"MODEL_CLASS_MAPPINGS",[Vs]);class cp extends nn{}fe(cp,"MODEL_CLASS_MAPPINGS",[Di]);class pd extends nn{}fe(pd,"MODEL_CLASS_MAPPINGS",[qn]);class hd extends nn{}fe(hd,"MODEL_CLASS_MAPPINGS",[sd]);class bh extends nn{}fe(bh,"MODEL_CLASS_MAPPINGS",[co]);class vh extends nn{}fe(vh,"MODEL_CLASS_MAPPINGS",[fl]);class Mh extends nn{}fe(Mh,"MODEL_CLASS_MAPPINGS",[ur]);class dp extends nn{}fe(dp,"MODEL_CLASS_MAPPINGS",[id]);class fp extends nn{}fe(fp,"MODEL_CLASS_MAPPINGS",[od]);class mo extends nn{}fe(mo,"MODEL_CLASS_MAPPINGS",[gh]);class Pu extends nn{}fe(Pu,"MODEL_CLASS_MAPPINGS",[kn]);class wh extends Ie{constructor({logits:I,past_key_values:W,encoder_outputs:me,decoder_attentions:Ae=null,cross_attentions:Pe=null}){super(),this.logits=I,this.past_key_values=W,this.encoder_outputs=me,this.decoder_attentions=Ae,this.cross_attentions=Pe}}class Wt extends Ie{constructor({logits:I,...W}){super(),this.logits=I;const me=Object.values(W);me.length>0&&(this.attentions=me)}}class pp extends Ie{constructor({logits:I,embeddings:W}){super(),this.logits=I,this.embeddings=W}}class $a extends Ie{constructor({logits:I}){super(),this.logits=I}}class La extends Ie{constructor({logits:I}){super(),this.logits=I}}class Ka extends Ie{constructor({start_logits:I,end_logits:W}){super(),this.start_logits=I,this.end_logits=W}}class bs extends Ie{constructor({logits:I}){super(),this.logits=I}}class Th extends Ie{constructor({logits:I,past_key_values:W}){super(),this.logits=I,this.past_key_values=W}}class hp extends Ie{constructor({alphas:I}){super(),this.alphas=I}}class jn extends Ie{constructor({waveform:I,spectrogram:W}){super(),this.waveform=I,this.spectrogram=W}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(t,a,n)=>{n.r(a),n.d(a,{ASTFeatureExtractor:()=>o});var i=n("./src/base/feature_extraction_utils.js");n("./src/utils/tensor.js");var l=n("./src/utils/audio.js");class o extends i.FeatureExtractor{constructor(d){super(d);const f=this.config.sampling_rate,p=(0,l.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(f/2),f,null,"kaldi",!0);this.mel_filters=p,this.window=(0,l.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(d,f){return(0,l.spectrogram)(d,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:f,transpose:!0})}async _call(d){(0,i.validate_audio_inputs)(d,"ASTFeatureExtractor");const f=await this._extract_fbank_features(d,this.config.max_length);if(this.config.do_normalize){const p=this.std*2,g=f.data;for(let h=0;h<g.length;++h)g[h]=(g[h]-this.mean)/p}return{input_values:f.unsqueeze_(0)}}}},"./src/models/auto/feature_extraction_auto.js":(t,a,n)=>{n.r(a),n.d(a,{AutoFeatureExtractor:()=>c});var i=n("./src/utils/constants.js"),l=n("./src/utils/hub.js");n("./src/base/feature_extraction_utils.js");var o=n("./src/models/feature_extractors.js");class c{static async from_pretrained(f,p={}){const g=await(0,l.getModelJSON)(f,i.FEATURE_EXTRACTOR_NAME,!0,p),h=g.feature_extractor_type,_=o[h];if(!_)throw new Error(`Unknown feature_extractor_type: '${h}'. Please report this at ${i.GITHUB_ISSUE_URL}.`);return new _(g)}}},"./src/models/auto/image_processing_auto.js":(t,a,n)=>{n.r(a),n.d(a,{AutoImageProcessor:()=>d});var i=n("./src/utils/constants.js"),l=n("./src/utils/hub.js"),o=n("./src/base/image_processors_utils.js"),c=n("./src/models/image_processors.js");class d{static async from_pretrained(p,g={}){const h=await(0,l.getModelJSON)(p,i.IMAGE_PROCESSOR_NAME,!0,g),_=h.image_processor_type??h.feature_extractor_type;let w=c[_];return w||(_!==void 0&&console.warn(`Image processor type '${_}' not found, assuming base ImageProcessor. Please report this at ${i.GITHUB_ISSUE_URL}.`),w=o.ImageProcessor),new w(h)}}},"./src/models/auto/processing_auto.js":(t,a,n)=>{n.r(a),n.d(a,{AutoProcessor:()=>p});var i=n("./src/utils/constants.js"),l=n("./src/utils/hub.js"),o=n("./src/base/processing_utils.js"),c=n("./src/models/processors.js"),d=n("./src/models/image_processors.js"),f=n("./src/models/feature_extractors.js");class p{static async from_pretrained(h,_={}){const w=await(0,l.getModelJSON)(h,i.IMAGE_PROCESSOR_NAME,!0,_),{image_processor_type:M,feature_extractor_type:$,processor_class:L}=w;if(L&&c[L])return c[L].from_pretrained(h,_);if(!M&&!$)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const E={};if(M){const D=d[M];if(!D)throw new Error(`Unknown image_processor_type: '${M}'.`);E.image_processor=new D(w)}if($){const D=d[$];if(D)E.image_processor=new D(w);else{const A=f[$];if(!A)throw new Error(`Unknown feature_extractor_type: '${$}'.`);E.feature_extractor=new A(w)}}const S={};return new o.Processor(S,E)}}},"./src/models/beit/image_processing_beit.js":(t,a,n)=>{n.r(a),n.d(a,{BeitFeatureExtractor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(t,a,n)=>{n.r(a),n.d(a,{BitImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(t,a,n)=>{n.r(a),n.d(a,{ChineseCLIPFeatureExtractor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(t,a,n)=>{n.r(a),n.d(a,{ClapFeatureExtractor:()=>o});var i=n("./src/base/feature_extraction_utils.js");n("./src/utils/tensor.js");var l=n("./src/utils/audio.js");class o extends i.FeatureExtractor{constructor(d){super(d),this.mel_filters=(0,l.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,l.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,l.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(d,f,p,g){let h;const _=d.length-f;if(_>0)if(p==="rand_trunc"){const w=Math.floor(Math.random()*(_+1));d=d.subarray(w,w+f),h=await this._extract_fbank_features(d,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${p}" not implemented`);else{if(_<0){let w=new Float64Array(f);if(w.set(d),g==="repeat")for(let M=d.length;M<f;M+=d.length)w.set(d.subarray(0,Math.min(d.length,f-M)),M);else if(g==="repeatpad")for(let M=d.length;M<-_;M+=d.length)w.set(d,M);d=w}if(p==="fusion")throw new Error(`Truncation strategy "${p}" not implemented`);h=await this._extract_fbank_features(d,this.mel_filters_slaney,this.config.nb_max_samples)}return h.unsqueeze_(0)}async _extract_fbank_features(d,f,p=null){return(0,l.spectrogram)(d,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:f,log_mel:"dB",max_num_frames:p,do_pad:!1,transpose:!0})}async _call(d,{max_length:f=null}={}){return(0,i.validate_audio_inputs)(d,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(d,f??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},"./src/models/clip/image_processing_clip.js":(t,a,n)=>{n.r(a),n.d(a,{CLIPFeatureExtractor:()=>o,CLIPImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/convnext/image_processing_convnext.js":(t,a,n)=>{n.r(a),n.d(a,{ConvNextFeatureExtractor:()=>o,ConvNextImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{constructor(d){super(d),this.crop_pct=this.config.crop_pct??224/256}async resize(d){var p;const f=(p=this.size)==null?void 0:p.shortest_edge;if(f===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(f<384){const g=Math.floor(f/this.crop_pct),[h,_]=this.get_resize_output_image_size(d,{shortest_edge:g});d=await d.resize(h,_,{resample:this.resample}),d=await d.center_crop(f,f)}else d=await d.resize(f,f,{resample:this.resample});return d}}class o extends l{}},"./src/models/dac/feature_extraction_dac.js":(t,a,n)=>{n.r(a),n.d(a,{DacFeatureExtractor:()=>l});var i=n("./src/models/encodec/feature_extraction_encodec.js");class l extends i.EncodecFeatureExtractor{}},"./src/models/deit/image_processing_deit.js":(t,a,n)=>{n.r(a),n.d(a,{DeiTFeatureExtractor:()=>o,DeiTImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/detr/image_processing_detr.js":(t,a,n)=>{n.r(a),n.d(a,{DetrFeatureExtractor:()=>c,DetrImageProcessor:()=>o});var i=n("./src/base/image_processors_utils.js"),l=n("./src/utils/tensor.js");class o extends i.ImageProcessor{async _call(f){const p=await super._call(f),g=[p.pixel_values.dims[0],64,64],h=(0,l.full)(g,1n);return{...p,pixel_mask:h}}post_process_object_detection(...f){return(0,i.post_process_object_detection)(...f)}post_process_panoptic_segmentation(...f){return(0,i.post_process_panoptic_segmentation)(...f)}post_process_instance_segmentation(...f){return(0,i.post_process_instance_segmentation)(...f)}}class c extends o{}},"./src/models/donut/image_processing_donut.js":(t,a,n)=>{n.r(a),n.d(a,{DonutFeatureExtractor:()=>o,DonutImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{pad_image(d,f,p,g={}){const[h,_,w]=f;let M=this.image_mean;Array.isArray(this.image_mean)||(M=new Array(w).fill(M));let $=this.image_std;Array.isArray($)||($=new Array(w).fill(M));const L=M.map((E,S)=>-E/$[S]);return super.pad_image(d,f,p,{center:!0,constant_values:L,...g})}}class o extends l{}},"./src/models/dpt/image_processing_dpt.js":(t,a,n)=>{n.r(a),n.d(a,{DPTFeatureExtractor:()=>o,DPTImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/efficientnet/image_processing_efficientnet.js":(t,a,n)=>{n.r(a),n.d(a,{EfficientNetImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{constructor(c){super(c),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(d=>d*d))}}},"./src/models/encodec/feature_extraction_encodec.js":(t,a,n)=>{n.r(a),n.d(a,{EncodecFeatureExtractor:()=>o});var i=n("./src/base/feature_extraction_utils.js"),l=n("./src/utils/tensor.js");class o extends i.FeatureExtractor{async _call(d){(0,i.validate_audio_inputs)(d,"EncodecFeatureExtractor"),d instanceof Float64Array&&(d=new Float32Array(d));const f=this.config.feature_size;if(d.length%f!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${f}).`);const p=[1,f,d.length/f];return{input_values:new l.Tensor("float32",d,p)}}}},"./src/models/feature_extractors.js":(t,a,n)=>{n.r(a),n.d(a,{ASTFeatureExtractor:()=>i.ASTFeatureExtractor,ClapFeatureExtractor:()=>o.ClapFeatureExtractor,DacFeatureExtractor:()=>c.DacFeatureExtractor,EncodecFeatureExtractor:()=>l.EncodecFeatureExtractor,ImageFeatureExtractor:()=>$.ImageProcessor,MoonshineFeatureExtractor:()=>d.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>f.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>p.SeamlessM4TFeatureExtractor,SnacFeatureExtractor:()=>g.SnacFeatureExtractor,SpeechT5FeatureExtractor:()=>h.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>_.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>w.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>M.WhisperFeatureExtractor});var i=n("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),l=n("./src/models/encodec/feature_extraction_encodec.js"),o=n("./src/models/clap/feature_extraction_clap.js"),c=n("./src/models/dac/feature_extraction_dac.js"),d=n("./src/models/moonshine/feature_extraction_moonshine.js"),f=n("./src/models/pyannote/feature_extraction_pyannote.js"),p=n("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),g=n("./src/models/snac/feature_extraction_snac.js"),h=n("./src/models/speecht5/feature_extraction_speecht5.js"),_=n("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),w=n("./src/models/wespeaker/feature_extraction_wespeaker.js"),M=n("./src/models/whisper/feature_extraction_whisper.js"),$=n("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(t,a,n)=>{n.r(a),n.d(a,{Florence2Processor:()=>c});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js");class c extends i.Processor{constructor(f,p){super(f,p);const{tasks_answer_post_processing_type:g,task_prompts_without_inputs:h,task_prompts_with_input:_}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(g??{})),this.task_prompts_without_inputs=new Map(Object.entries(h??{})),this.task_prompts_with_input=new Map(Object.entries(_??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(f){typeof f=="string"&&(f=[f]);const p=[];for(const g of f)if(this.task_prompts_without_inputs.has(g))p.push(this.task_prompts_without_inputs.get(g));else{for(const[h,_]of this.task_prompts_with_input)if(g.includes(h)){p.push(_.replaceAll("{input}",g).replaceAll(h,""));break}p.length!==f.length&&p.push(g)}return p}post_process_generation(f,p,g){const h=this.tasks_answer_post_processing_type.get(p)??"pure_text";f=f.replaceAll("<s>","").replaceAll("</s>","");let _;switch(h){case"pure_text":_=f;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const w=h==="ocr"?"quad_boxes":"bboxes",M=f.matchAll(this.regexes[w]),$=[],L=[];for(const[E,S,...D]of M)$.push(S?S.trim():$.at(-1)??""),L.push(D.map((A,C)=>(Number(A)+.5)/this.size_per_bin*g[C%2]));_={labels:$,[w]:L};break;default:throw new Error(`Task "${p}" (of type "${h}") not yet implemented.`)}return{[p]:_}}async _call(f,p=null,g={}){if(!f&&!p)throw new Error("Either text or images must be provided");const h=await this.image_processor(f,g),_=p?this.tokenizer(p,g):{};return{...h,..._}}}fe(c,"tokenizer_class",o.AutoTokenizer),fe(c,"image_processor_class",l.AutoImageProcessor)},"./src/models/glpn/image_processing_glpn.js":(t,a,n)=>{n.r(a),n.d(a,{GLPNFeatureExtractor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/grounding_dino/image_processing_grounding_dino.js":(t,a,n)=>{n.r(a),n.d(a,{GroundingDinoImageProcessor:()=>o});var i=n("./src/base/image_processors_utils.js"),l=n("./src/utils/tensor.js");class o extends i.ImageProcessor{async _call(d){const f=await super._call(d),p=f.pixel_values.dims,g=(0,l.ones)([p[0],p[2],p[3]]);return{...f,pixel_mask:g}}}},"./src/models/grounding_dino/processing_grounding_dino.js":(t,a,n)=>{n.r(a),n.d(a,{GroundingDinoProcessor:()=>f});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js"),c=n("./src/base/image_processors_utils.js");function d(p,g){const _=p.dims.at(-1)-1,w=p.tolist();w.fill(!1,0,1),w.fill(!1,_);const M=g.tolist();return w.map(($,L)=>$?L:null).filter($=>$!==null).map($=>M[$])}class f extends i.Processor{async _call(g,h,_={}){const w=g?await this.image_processor(g,_):{};return{...h?this.tokenizer(h,_):{},...w}}post_process_grounded_object_detection(g,h,{box_threshold:_=.25,text_threshold:w=.25,target_sizes:M=null}={}){const{logits:$,pred_boxes:L}=g,E=$.dims[0];if(M!==null&&M.length!==E)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const S=$.dims.at(1),D=$.sigmoid(),A=D.max(-1).tolist(),C=L.tolist().map(x=>x.map(O=>(0,c.center_to_corners_format)(O))),T=[];for(let x=0;x<E;++x){const O=M!==null?M[x]:null;O!==null&&(C[x]=C[x].map(Q=>Q.map((le,re)=>le*O[(re+1)%2])));const N=A[x],G=[],te=[],se=[];for(let Q=0;Q<S;++Q){const le=N[Q];if(le<=_)continue;const re=C[x][Q],H=D[x][Q];G.push(le),se.push(re);const ee=d(H.gt(w),h[x]);te.push(ee)}T.push({scores:G,boxes:se,labels:this.batch_decode(te)})}return T}}fe(f,"tokenizer_class",o.AutoTokenizer),fe(f,"image_processor_class",l.AutoImageProcessor)},"./src/models/idefics3/image_processing_idefics3.js":(t,a,n)=>{n.r(a),n.d(a,{Idefics3ImageProcessor:()=>o});var i=n("./src/base/image_processors_utils.js"),l=n("./src/utils/tensor.js");class o extends i.ImageProcessor{constructor(d){super(d),this.do_image_splitting=d.do_image_splitting??!0,this.max_image_size=d.max_image_size}get_resize_for_vision_encoder(d,f){let[p,g]=d.dims.slice(-2);const h=g/p;return g>=p?(g=Math.ceil(g/f)*f,p=Math.floor(g/h),p=Math.ceil(p/f)*f):(p=Math.ceil(p/f)*f,g=Math.floor(p*h),g=Math.ceil(g/f)*f),{height:p,width:g}}async _call(d,{do_image_splitting:f=null,return_row_col_info:p=!1}={}){let g;if(!Array.isArray(d))g=[[d]];else{if(d.length===0||!d[0])throw new Error("No images provided.");Array.isArray(d[0])?g=d:g=[d]}let h=[],_=[],w=[];const M=[],$=[];for(const x of g){let O=await Promise.all(x.map(te=>this.preprocess(te)));M.push(...O.map(te=>te.original_size)),$.push(...O.map(te=>te.reshaped_input_size)),O.forEach(te=>te.pixel_values.unsqueeze_(0));const{longest_edge:N}=this.max_image_size;let G;if(f??this.do_image_splitting){let te=new Array(O.length),se=new Array(O.length);G=await Promise.all(O.map(async(Q,le)=>{const re=this.get_resize_for_vision_encoder(Q.pixel_values,N),H=await(0,l.interpolate_4d)(Q.pixel_values,{size:[re.height,re.width]}),{frames:ee,num_splits_h:oe,num_splits_w:ve}=await this.split_image(H,this.max_image_size);return te[le]=oe,se[le]=ve,(0,l.cat)(ee,0)})),_.push(te),w.push(se)}else{const te=[N,N];G=await Promise.all(O.map(se=>(0,l.interpolate_4d)(se.pixel_values,{size:te}))),_.push(new Array(O.length).fill(0)),w.push(new Array(O.length).fill(0))}h.push((0,l.cat)(G,0))}const L=h.length,[E,S,D,A]=h[0].dims;let C,T;if(L===1)C=h[0].unsqueeze_(0),T=(0,l.full)([L,E,D,A],!0);else{const x=Math.max(...h.map(G=>G.dims.at(0)));T=(0,l.full)([L,x,D,A],!0);const O=T.data,N=x*D*A;for(let G=0;G<L;++G){const te=h[G].dims[0];if(te<x){h[G]=(0,l.cat)([h[G],(0,l.full)([x-te,S,D,A],0)],0);const se=G*N+te*D*A,Q=(G+1)*N;O.fill(!1,se,Q)}}C=(0,l.stack)(h,0)}return{pixel_values:C,pixel_attention_mask:T,original_sizes:M,reshaped_input_sizes:$,...p?{rows:_,cols:w}:{}}}async split_image(d,{longest_edge:f}){const p=f,g=f,h=[],[_,w]=d.dims.slice(-2);let M=0,$=0;if(_>p||w>g){M=Math.ceil(_/p),$=Math.ceil(w/g);const L=Math.ceil(_/M),E=Math.ceil(w/$);for(let A=0;A<M;++A)for(let C=0;C<$;++C){let T,x,O,N;A===M-1?(x=_-L,N=_):(x=A*L,N=(A+1)*L),C===$-1?(T=w-E,O=w):(T=C*E,O=(C+1)*E);const G=[x,T],te=[N,O],se=await(0,l.slice)(d,G,te,[2,3]);h.push(se)}const S=p,D=g;(_!==S||w!==D)&&(d=await(0,l.interpolate_4d)(d,{size:[S,D]}))}return h.push(d),{frames:h,num_splits_h:M,num_splits_w:$}}}},"./src/models/idefics3/processing_idefics3.js":(t,a,n)=>{n.r(a),n.d(a,{Idefics3Processor:()=>g});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js");n("./src/utils/image.js");var c=n("./src/utils/core.js");function d(h,_,w,M,$,L){let E="";for(let S=0;S<_;++S){for(let D=0;D<w;++D)E+=M+`<row_${S+1}_col_${D+1}>`+$.repeat(h);E+=`
-`}return E+=`
-${M}${L}`+$.repeat(h)+`${M}`,E}function f(h,_,w,M){return`${_}${M}`+w.repeat(h)+`${_}`}function p(h,_,w,M,$,L){return h===0&&_===0?f(w,M,$,L):d(w,h,_,M,$,L)}class g extends i.Processor{constructor(){super(...arguments);fe(this,"fake_image_token","<fake_token_around_image>");fe(this,"image_token","<image>");fe(this,"global_img_token","<global-img>")}async _call(w,M=null,$={}){$.return_row_col_info??($.return_row_col_info=!0);let L;M&&(L=await this.image_processor(M,$)),Array.isArray(w)||(w=[w]);const E=L.rows??[new Array(w.length).fill(0)],S=L.cols??[new Array(w.length).fill(0)],D=this.config.image_seq_len,A=[],C=[];for(let x=0;x<w.length;++x){const O=w[x],N=E[x],G=S[x];A.push((0,c.count)(O,this.image_token));const te=N.map((le,re)=>p(le,G[re],D,this.fake_image_token,this.image_token,this.global_img_token)),se=O.split(this.image_token);if(se.length===0)throw new Error("The image token should be present in the text.");let Q=se[0];for(let le=0;le<te.length;++le)Q+=te[le]+se[le+1];C.push(Q)}return{...this.tokenizer(C),...L}}}fe(g,"image_processor_class",l.AutoImageProcessor),fe(g,"tokenizer_class",o.AutoTokenizer),fe(g,"uses_processor_config",!0)},"./src/models/image_processors.js":(t,a,n)=>{n.r(a),n.d(a,{BeitFeatureExtractor:()=>i.BeitFeatureExtractor,BitImageProcessor:()=>l.BitImageProcessor,CLIPFeatureExtractor:()=>c.CLIPFeatureExtractor,CLIPImageProcessor:()=>c.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>o.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>d.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>d.ConvNextImageProcessor,DPTFeatureExtractor:()=>h.DPTFeatureExtractor,DPTImageProcessor:()=>h.DPTImageProcessor,DeiTFeatureExtractor:()=>f.DeiTFeatureExtractor,DeiTImageProcessor:()=>f.DeiTImageProcessor,DetrFeatureExtractor:()=>p.DetrFeatureExtractor,DetrImageProcessor:()=>p.DetrImageProcessor,DonutFeatureExtractor:()=>g.DonutFeatureExtractor,DonutImageProcessor:()=>g.DonutImageProcessor,EfficientNetImageProcessor:()=>_.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>w.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>M.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>$.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>E.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>S.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>D.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>A.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>A.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>C.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>C.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>T.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>T.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>x.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>x.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>O.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>O.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>N.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>N.MobileViTImageProcessor,NougatImageProcessor:()=>G.NougatImageProcessor,OwlViTFeatureExtractor:()=>se.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>se.OwlViTImageProcessor,Owlv2ImageProcessor:()=>te.Owlv2ImageProcessor,Phi3VImageProcessor:()=>Q.Phi3VImageProcessor,PvtImageProcessor:()=>le.PvtImageProcessor,Qwen2VLImageProcessor:()=>re.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>H.RTDetrImageProcessor,SamImageProcessor:()=>ee.SamImageProcessor,SegformerFeatureExtractor:()=>oe.SegformerFeatureExtractor,SegformerImageProcessor:()=>oe.SegformerImageProcessor,SiglipImageProcessor:()=>ve.SiglipImageProcessor,SmolVLMImageProcessor:()=>U.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>j.Swin2SRImageProcessor,VLMImageProcessor:()=>L.VLMImageProcessor,ViTFeatureExtractor:()=>z.ViTFeatureExtractor,ViTImageProcessor:()=>z.ViTImageProcessor,VitMatteImageProcessor:()=>V.VitMatteImageProcessor,VitPoseImageProcessor:()=>ie.VitPoseImageProcessor,YolosFeatureExtractor:()=>Me.YolosFeatureExtractor,YolosImageProcessor:()=>Me.YolosImageProcessor});var i=n("./src/models/beit/image_processing_beit.js"),l=n("./src/models/bit/image_processing_bit.js"),o=n("./src/models/chinese_clip/image_processing_chinese_clip.js"),c=n("./src/models/clip/image_processing_clip.js"),d=n("./src/models/convnext/image_processing_convnext.js"),f=n("./src/models/deit/image_processing_deit.js"),p=n("./src/models/detr/image_processing_detr.js"),g=n("./src/models/donut/image_processing_donut.js"),h=n("./src/models/dpt/image_processing_dpt.js"),_=n("./src/models/efficientnet/image_processing_efficientnet.js"),w=n("./src/models/glpn/image_processing_glpn.js"),M=n("./src/models/grounding_dino/image_processing_grounding_dino.js"),$=n("./src/models/idefics3/image_processing_idefics3.js"),L=n("./src/models/janus/image_processing_janus.js"),E=n("./src/models/jina_clip/image_processing_jina_clip.js"),S=n("./src/models/llava_onevision/image_processing_llava_onevision.js"),D=n("./src/models/mask2former/image_processing_mask2former.js"),A=n("./src/models/maskformer/image_processing_maskformer.js"),C=n("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),T=n("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),x=n("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),O=n("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),N=n("./src/models/mobilevit/image_processing_mobilevit.js"),G=n("./src/models/nougat/image_processing_nougat.js"),te=n("./src/models/owlv2/image_processing_owlv2.js"),se=n("./src/models/owlvit/image_processing_owlvit.js"),Q=n("./src/models/phi3_v/image_processing_phi3_v.js"),le=n("./src/models/pvt/image_processing_pvt.js"),re=n("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),H=n("./src/models/rt_detr/image_processing_rt_detr.js"),ee=n("./src/models/sam/image_processing_sam.js"),oe=n("./src/models/segformer/image_processing_segformer.js"),ve=n("./src/models/siglip/image_processing_siglip.js"),U=n("./src/models/smolvlm/image_processing_smolvlm.js"),j=n("./src/models/swin2sr/image_processing_swin2sr.js"),z=n("./src/models/vit/image_processing_vit.js"),V=n("./src/models/vitmatte/image_processing_vitmatte.js"),ie=n("./src/models/vitpose/image_processing_vitpose.js"),Me=n("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(t,a,n)=>{n.r(a),n.d(a,{VLMImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{constructor(c){super({do_pad:!0,pad_size:{width:c.image_size,height:c.image_size},...c}),this.constant_values=this.config.background_color.map(d=>d*this.rescale_factor)}pad_image(c,d,f,p){return super.pad_image(c,d,f,{constant_values:this.constant_values,center:!0,...p})}}},"./src/models/janus/processing_janus.js":(t,a,n)=>{n.r(a),n.d(a,{VLChatProcessor:()=>p});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js"),c=n("./src/utils/core.js"),d=n("./src/utils/tensor.js"),f=n("./src/utils/image.js");class p extends i.Processor{constructor(h,_){super(h,_),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(h,{images:_=null,chat_template:w="default"}={}){_?Array.isArray(_)||(_=[_]):_=await Promise.all(h.filter(G=>G.images).flatMap(G=>G.images).map(G=>f.RawImage.read(G)));const M=this.tokenizer,$=M.apply_chat_template(h,{tokenize:!1,add_generation_prompt:!0,chat_template:w}),L=G=>M.encode(G,{add_special_tokens:!1}),E=$.split(this.image_tag),S=E.length-1;if(_.length!==S)throw new Error(`Number of images provided (${_.length}) does not match number of "${this.image_tag}" image tags (${S})`);const[D,A,C]=M.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let T=L(E[0]),x=new Array(T.length).fill(!1);for(let G=1;G<E.length;++G){const te=new Array(this.num_image_tokens).fill(D),se=L(E[G]);T=(0,c.mergeArrays)(T,[A],te,[C],se);const Q=new Array(this.num_image_tokens).fill(!0);x=(0,c.mergeArrays)(x,[!1],Q,[!1],new Array(se.length).fill(!1))}const O=[1,T.length],N={input_ids:new d.Tensor("int64",T,O),attention_mask:new d.Tensor("int64",new Array(T.length).fill(1),O),images_seq_mask:new d.Tensor("bool",x,O),images_emb_mask:new d.Tensor("bool",new Array(S*this.num_image_tokens).fill(!0),[1,S,this.num_image_tokens])};if(_&&_.length>0){const G=await this.image_processor(_);return G.pixel_values.unsqueeze_(0),{...N,...G}}return N}}fe(p,"image_processor_class",l.AutoImageProcessor),fe(p,"tokenizer_class",o.AutoTokenizer),fe(p,"uses_processor_config",!0)},"./src/models/jina_clip/image_processing_jina_clip.js":(t,a,n)=>{n.r(a),n.d(a,{JinaCLIPImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{constructor(c){const{resize_mode:d,fill_color:f,interpolation:p,size:g,...h}=c,_=d==="squash"?{width:g,height:g}:d==="shortest"?{shortest_edge:g}:{longest_edge:g},w=p==="bicubic"?3:2;super({...h,size:_,resample:w,do_center_crop:!0,crop_size:g,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(t,a,n)=>{n.r(a),n.d(a,{JinaCLIPProcessor:()=>c});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js");class c extends i.Processor{async _call(f=null,p=null,g={}){if(!f&&!p)throw new Error("Either text or images must be provided");const h=f?this.tokenizer(f,g):{},_=p?await this.image_processor(p,g):{};return{...h,..._}}}fe(c,"tokenizer_class",o.AutoTokenizer),fe(c,"image_processor_class",l.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(t,a,n)=>{n.r(a),n.d(a,{LlavaOnevisionImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(t,a,n)=>{n.r(a),n.d(a,{Mask2FormerImageProcessor:()=>l});var i=n("./src/models/maskformer/image_processing_maskformer.js");class l extends i.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(t,a,n)=>{n.r(a),n.d(a,{MaskFormerFeatureExtractor:()=>o,MaskFormerImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{post_process_panoptic_segmentation(...d){return(0,i.post_process_panoptic_segmentation)(...d)}post_process_instance_segmentation(...d){return(0,i.post_process_instance_segmentation)(...d)}}class o extends l{}},"./src/models/mgp_str/processing_mgp_str.js":(t,a,n)=>{n.r(a),n.d(a,{MgpstrProcessor:()=>f});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js"),c=n("./src/utils/maths.js");const d={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class f extends i.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(g,h){if(!d.hasOwnProperty(h))throw new Error(`Format ${h} is not supported.`);const[_,w]=d[h],M=this[_].bind(this),[$,L]=g.dims,E=[],S=[],D=g.tolist();for(let C=0;C<$;++C){const T=D[C],x=[],O=[];for(let G=1;G<L;++G){const[te,se]=(0,c.max)((0,c.softmax)(T[G]));if(O.push(te),se==w)break;x.push(se)}const N=O.length>0?O.reduce((G,te)=>G*te,1):0;S.push(x),E.push(N)}return[M(S),E]}char_decode(g){return this.char_tokenizer.batch_decode(g).map(h=>h.replaceAll(" ",""))}bpe_decode(g){return this.bpe_tokenizer.batch_decode(g)}wp_decode(g){return this.wp_tokenizer.batch_decode(g).map(h=>h.replaceAll(" ",""))}batch_decode([g,h,_]){const[w,M]=this._decode_helper(g,"char"),[$,L]=this._decode_helper(h,"bpe"),[E,S]=this._decode_helper(_,"wp"),D=[],A=[];for(let C=0;C<w.length;++C){const[T,x]=(0,c.max)([M[C],L[C],S[C]]);D.push([w[C],$[C],E[C]][x]),A.push(T)}return{generated_text:D,scores:A,char_preds:w,bpe_preds:$,wp_preds:E}}static async from_pretrained(...g){const h=await super.from_pretrained(...g),_=await o.AutoTokenizer.from_pretrained("Xenova/gpt2"),w=await o.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return h.components={image_processor:h.image_processor,char_tokenizer:h.tokenizer,bpe_tokenizer:_,wp_tokenizer:w},h}async _call(g,h=null){const _=await this.image_processor(g);return h&&(_.labels=this.tokenizer(h).input_ids),_}}fe(f,"tokenizer_class",o.AutoTokenizer),fe(f,"image_processor_class",l.AutoImageProcessor)},"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":(t,a,n)=>{n.r(a),n.d(a,{MobileNetV1FeatureExtractor:()=>o,MobileNetV1ImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":(t,a,n)=>{n.r(a),n.d(a,{MobileNetV2FeatureExtractor:()=>o,MobileNetV2ImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":(t,a,n)=>{n.r(a),n.d(a,{MobileNetV3FeatureExtractor:()=>o,MobileNetV3ImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":(t,a,n)=>{n.r(a),n.d(a,{MobileNetV4FeatureExtractor:()=>o,MobileNetV4ImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/mobilevit/image_processing_mobilevit.js":(t,a,n)=>{n.r(a),n.d(a,{MobileViTFeatureExtractor:()=>o,MobileViTImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class o extends l{}},"./src/models/moonshine/feature_extraction_moonshine.js":(t,a,n)=>{n.r(a),n.d(a,{MoonshineFeatureExtractor:()=>o});var i=n("./src/base/feature_extraction_utils.js"),l=n("./src/utils/tensor.js");class o extends i.FeatureExtractor{async _call(d){(0,i.validate_audio_inputs)(d,"MoonshineFeatureExtractor"),d instanceof Float64Array&&(d=new Float32Array(d));const f=[1,d.length];return{input_values:new 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re=await(0,l.interpolate_4d)((0,l.cat)(te,0),{size:[o,o],mode:"bicubic"});S.push((0,l.cat)([G,re],0))}else S.push(G)}const D=(0,l.stack)(S,0),A=E.map(x=>x.map(O=>o*d(O/o))),C=new l.Tensor("int64",A.flat(),[M,2]),T=A.map(([x,O])=>this.calc_num_image_tokens_from_image_size(O,x));return{pixel_values:D,original_sizes:L,reshaped_input_sizes:E,image_sizes:C,num_img_tokens:T}}}},"./src/models/phi3_v/processing_phi3_v.js":(t,a,n)=>{n.r(a),n.d(a,{Phi3VProcessor:()=>f});var i=n("./src/base/processing_utils.js"),l=n("./src/models/auto/image_processing_auto.js"),o=n("./src/tokenizers.js");n("./src/utils/image.js");const c="<|image|>",d=/<\|image_\d+\|>/g;class f extends i.Processor{async _call(g,h=null,{padding:_=!0,truncation:w=!0,num_crops:M=null}={}){Array.isArray(g)||(g=[g]);let $,L;if(h){L=await this.image_processor(h,{num_crops:M});const{num_img_tokens:E}=L,S=g.map((A,C)=>A.split(d).join(c.repeat(E[C])));$=this.tokenizer(S,{padding:_,truncation:w});const 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i=n("./src/models/florence2/processing_florence2.js"),l=n("./src/models/grounding_dino/processing_grounding_dino.js"),o=n("./src/models/idefics3/processing_idefics3.js"),c=n("./src/models/janus/processing_janus.js"),d=n("./src/models/jina_clip/processing_jina_clip.js"),f=n("./src/models/mgp_str/processing_mgp_str.js"),p=n("./src/models/moonshine/processing_moonshine.js"),g=n("./src/models/owlvit/processing_owlvit.js"),h=n("./src/models/phi3_v/processing_phi3_v.js"),_=n("./src/models/paligemma/processing_paligemma.js"),w=n("./src/models/pyannote/processing_pyannote.js"),M=n("./src/models/qwen2_vl/processing_qwen2_vl.js"),$=n("./src/models/sam/processing_sam.js"),L=n("./src/models/smolvlm/processing_smolvlm.js"),E=n("./src/models/speecht5/processing_speecht5.js"),S=n("./src/models/ultravox/processing_ultravox.js"),D=n("./src/models/wav2vec2/processing_wav2vec2.js"),A=n("./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js"),C=n("./src/models/whisper/processing_whisper.js")},"./src/models/pvt/image_processing_pvt.js":(t,a,n)=>{n.r(a),n.d(a,{PvtImageProcessor:()=>l});var i=n("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/pyannote/feature_extraction_pyannote.js":(t,a,n)=>{n.r(a),n.d(a,{PyAnnoteFeatureExtractor:()=>c});var i=n("./src/base/feature_extraction_utils.js"),l=n("./src/utils/tensor.js"),o=n("./src/utils/maths.js");class c extends i.FeatureExtractor{async _call(f){(0,i.validate_audio_inputs)(f,"PyAnnoteFeatureExtractor"),f instanceof Float64Array&&(f=new Float32Array(f));const p=[1,1,f.length];return{input_values:new l.Tensor("float32",f,p)}}samples_to_frames(f){return(f-this.config.offset)/this.config.step}post_process_speaker_diarization(f,p){const g=p/this.samples_to_frames(p)/this.config.sampling_rate,h=[];for(const _ of f.tolist()){const w=[];let M=-1;for(let $=0;$<_.length;++$){const 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super._call(f);if(p&&(_.input_points=this.reshape_input_points(p,_.original_sizes,_.reshaped_input_sizes)),g){if(!_.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");_.input_labels=this.add_input_labels(g,_.input_points)}return h&&(_.input_boxes=this.reshape_input_points(h,_.original_sizes,_.reshaped_input_sizes,!0)),_}async post_process_masks(f,p,g,{mask_threshold:h=0,binarize:_=!0,pad_size:w=null}={}){const M=[];w=w??this.pad_size;const $=[w.height,w.width];for(let L=0;L<p.length;++L){const E=p[L],S=g[L];let D=await(0,o.interpolate_4d)(f[L],{mode:"bilinear",size:$});if(D=D.slice(null,null,[0,S[0]],[0,S[1]]),D=await(0,o.interpolate_4d)(D,{mode:"bilinear",size:E}),_){const A=D.data,C=new Uint8Array(A.length);for(let T=0;T<A.length;++T)A[T]>h&&(C[T]=1);D=new o.Tensor("bool",C,D.dims)}M.push(D)}return 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this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=d([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=d([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=d([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=d([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=d([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=d([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=d([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}fe(f,"session_options",{})},"./src/pipelines.js":(t,a,n)=>{n.r(a),n.d(a,{AudioClassificationPipeline:()=>se,AutomaticSpeechRecognitionPipeline:()=>le,BackgroundRemovalPipeline:()=>oe,DepthEstimationPipeline:()=>Me,DocumentQuestionAnsweringPipeline:()=>z,FeatureExtractionPipeline:()=>G,FillMaskPipeline:()=>D,ImageClassificationPipeline:()=>H,ImageFeatureExtractionPipeline:()=>te,ImageSegmentationPipeline:()=>ee,ImageToImagePipeline:()=>ie,ImageToTextPipeline:()=>re,ObjectDetectionPipeline:()=>U,Pipeline:()=>$,QuestionAnsweringPipeline:()=>S,SummarizationPipeline:()=>C,Text2TextGenerationPipeline:()=>A,TextClassificationPipeline:()=>L,TextGenerationPipeline:()=>O,TextToAudioPipeline:()=>V,TokenClassificationPipeline:()=>E,TranslationPipeline:()=>T,ZeroShotAudioClassificationPipeline:()=>Q,ZeroShotClassificationPipeline:()=>N,ZeroShotImageClassificationPipeline:()=>ve,ZeroShotObjectDetectionPipeline:()=>j,pipeline:()=>Be});var i=n("./src/tokenizers.js"),l=n("./src/models.js"),o=n("./src/models/auto/processing_auto.js");n("./src/base/processing_utils.js");var c=n("./src/utils/generic.js"),d=n("./src/utils/core.js"),f=n("./src/utils/maths.js"),p=n("./src/utils/audio.js"),g=n("./src/utils/tensor.js"),h=n("./src/utils/image.js");async function _(be){return Array.isArray(be)||(be=[be]),await Promise.all(be.map(X=>h.RawImage.read(X)))}async function w(be,X){return Array.isArray(be)||(be=[be]),await Promise.all(be.map(de=>typeof de=="string"||de instanceof URL?(0,p.read_audio)(de,X):de instanceof Float64Array?new Float32Array(de):de))}function M(be,X){X&&(be=be.map(We=>We|0));const[de,J,Ie,Oe]=be;return{xmin:de,ymin:J,xmax:Ie,ymax:Oe}}class $ extends c.Callable{constructor({task:X,model:de,tokenizer:J=null,processor:Ie=null}){super(),this.task=X,this.model=de,this.tokenizer=J,this.processor=Ie}async dispose(){await this.model.dispose()}}class L extends ${constructor(X){super(X)}async _call(X,{top_k:de=1}={}){const J=this.tokenizer(X,{padding:!0,truncation:!0}),Ie=await this.model(J),Oe=this.model.config.problem_type==="multi_label_classification"?Ve=>Ve.sigmoid():Ve=>new g.Tensor("float32",(0,f.softmax)(Ve.data),Ve.dims),We=this.model.config.id2label,Le=[];for(const Ve of Ie.logits){const et=Oe(Ve),at=await(0,g.topk)(et,de),dt=at[0].tolist(),Qe=at[1].tolist().map((ut,bt)=>({label:We?We[ut]:`LABEL_${ut}`,score:dt[bt]}));de===1?Le.push(...Qe):Le.push(Qe)}return Array.isArray(X)||de===1?Le:Le[0]}}class E extends ${constructor(X){super(X)}async _call(X,{ignore_labels:de=["O"]}={}){const J=Array.isArray(X),Ie=this.tokenizer(J?X:[X],{padding:!0,truncation:!0}),We=(await this.model(Ie)).logits,Le=this.model.config.id2label,Ve=[];for(let et=0;et<We.dims[0];++et){const at=Ie.input_ids[et],dt=We[et],ft=[];for(let Qe=0;Qe<dt.dims[0];++Qe){const ut=dt[Qe],bt=(0,f.max)(ut.data)[1],Mt=Le?Le[bt]:`LABEL_${bt}`;if(de.includes(Mt))continue;const yt=this.tokenizer.decode([at[Qe].item()],{skip_special_tokens:!0});if(yt==="")continue;const lt=(0,f.softmax)(ut.data);ft.push({entity:Mt,score:lt[bt],index:Qe,word:yt})}Ve.push(ft)}return J?Ve:Ve[0]}}class S extends ${constructor(X){super(X)}async _call(X,de,{top_k:J=1}={}){const Ie=this.tokenizer(X,{text_pair:de,padding:!0,truncation:!0}),{start_logits:Oe,end_logits:We}=await this.model(Ie),Le=Ie.input_ids.tolist(),Ve=Ie.attention_mask.tolist(),et=this.tokenizer.all_special_ids,at=[];for(let dt=0;dt<Oe.dims[0];++dt){const ft=Le[dt],Qe=ft.findIndex(He=>He==this.tokenizer.sep_token_id);Ve[dt].map((He,It)=>He==1&&(It===0||It>Qe&&et.findIndex(tn=>tn==ft[It])===-1));const ut=Oe[dt].tolist(),bt=We[dt].tolist();for(let He=1;He<ut.length;++He)(Ve[dt]==0||He<=Qe||et.findIndex(It=>It==ft[He])!==-1)&&(ut[He]=-1/0,bt[He]=-1/0);const Mt=(0,f.softmax)(ut).map((He,It)=>[He,It]),yt=(0,f.softmax)(bt).map((He,It)=>[He,It]);Mt[0][0]=0,yt[0][0]=0;const lt=(0,d.product)(Mt,yt).filter(He=>He[0][1]<=He[1][1]).map(He=>[He[0][1],He[1][1],He[0][0]*He[1][0]]).sort((He,It)=>It[2]-He[2]);for(let He=0;He<Math.min(lt.length,J);++He){const[It,tn,xn]=lt[He],_n=ft.slice(It,tn+1),sa=this.tokenizer.decode(_n,{skip_special_tokens:!0});at.push({answer:sa,score:xn})}}return J===1?at[0]:at}}class D extends ${constructor(X){super(X)}async _call(X,{top_k:de=5}={}){const J=this.tokenizer(X,{padding:!0,truncation:!0}),{logits:Ie}=await this.model(J),Oe=[],We=J.input_ids.tolist();for(let Le=0;Le<We.length;++Le){const Ve=We[Le],et=Ve.findIndex(ut=>ut==this.tokenizer.mask_token_id);if(et===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const at=Ie[Le][et],dt=await(0,g.topk)(new g.Tensor("float32",(0,f.softmax)(at.data),at.dims),de),ft=dt[0].tolist(),Qe=dt[1].tolist();Oe.push(Qe.map((ut,bt)=>{const Mt=Ve.slice();return Mt[et]=ut,{score:ft[bt],token:Number(ut),token_str:this.tokenizer.decode([ut]),sequence:this.tokenizer.decode(Mt,{skip_special_tokens:!0})}}))}return Array.isArray(X)?Oe:Oe[0]}}class A extends ${constructor(de){super(de);fe(this,"_key","generated_text")}async _call(de,J={}){Array.isArray(de)||(de=[de]),this.model.config.prefix&&(de=de.map(et=>this.model.config.prefix+et));const Ie=this.model.config.task_specific_params;Ie&&Ie[this.task]&&Ie[this.task].prefix&&(de=de.map(et=>Ie[this.task].prefix+et));const Oe=this.tokenizer,We={padding:!0,truncation:!0};let Le;this instanceof T&&"_build_translation_inputs"in Oe?Le=Oe._build_translation_inputs(de,We,J):Le=Oe(de,We);const Ve=await this.model.generate({...Le,...J});return Oe.batch_decode(Ve,{skip_special_tokens:!0}).map(et=>({[this._key]:et}))}}class C extends A{constructor(de){super(de);fe(this,"_key","summary_text")}}class T extends A{constructor(de){super(de);fe(this,"_key","translation_text")}}function x(be){return Array.isArray(be)&&be.every(X=>"role"in X&&"content"in X)}class O extends ${constructor(X){super(X)}async _call(X,de={}){let J=!1,Ie=!1,Oe;if(typeof X=="string")Oe=X=[X];else if(Array.isArray(X)&&X.every(Qe=>typeof Qe=="string"))J=!0,Oe=X;else{if(x(X))X=[X];else if(Array.isArray(X)&&X.every(x))J=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Ie=!0,Oe=X.map(Qe=>this.tokenizer.apply_chat_template(Qe,{tokenize:!1,add_generation_prompt:!0}))}const We=de.add_special_tokens??!1,Le=Ie?!1:de.return_full_text??!0;this.tokenizer.padding_side="left";const Ve=this.tokenizer(Oe,{add_special_tokens:We,padding:!0,truncation:!0}),et=await this.model.generate({...Ve,...de}),at=this.tokenizer.batch_decode(et,{skip_special_tokens:!0});let dt;!Le&&Ve.input_ids.dims.at(-1)>0&&(dt=this.tokenizer.batch_decode(Ve.input_ids,{skip_special_tokens:!0}).map(Qe=>Qe.length));const ft=Array.from({length:X.length},Qe=>[]);for(let Qe=0;Qe<at.length;++Qe){const ut=Math.floor(Qe/et.dims[0]*X.length);dt&&(at[Qe]=at[Qe].slice(dt[ut])),ft[ut].push({generated_text:Ie?[...X[ut],{role:"assistant",content:at[Qe]}]:at[Qe]})}return!J&&ft.length===1?ft[0]:ft}}class N extends ${constructor(X){super(X),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([de,J])=>[de.toLowerCase(),J])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(X,de,{hypothesis_template:J="This example is {}.",multi_label:Ie=!1}={}){const Oe=Array.isArray(X);Oe||(X=[X]),Array.isArray(de)||(de=[de]);const We=de.map(et=>J.replace("{}",et)),Le=Ie||de.length===1,Ve=[];for(const et of X){const at=[];for(const Qe of We){const ut=this.tokenizer(et,{text_pair:Qe,padding:!0,truncation:!0}),bt=await this.model(ut);Le?at.push([bt.logits.data[this.contradiction_id],bt.logits.data[this.entailment_id]]):at.push(bt.logits.data[this.entailment_id])}const ft=(Le?at.map(Qe=>(0,f.softmax)(Qe)[1]):(0,f.softmax)(at)).map((Qe,ut)=>[Qe,ut]).sort((Qe,ut)=>ut[0]-Qe[0]);Ve.push({sequence:et,labels:ft.map(Qe=>de[Qe[1]]),scores:ft.map(Qe=>Qe[0])})}return Oe?Ve:Ve[0]}}class G extends ${constructor(X){super(X)}async _call(X,{pooling:de="none",normalize:J=!1,quantize:Ie=!1,precision:Oe="binary"}={}){const We=this.tokenizer(X,{padding:!0,truncation:!0}),Le=await this.model(We);let Ve=Le.last_hidden_state??Le.logits??Le.token_embeddings;if(de!=="none")if(de==="mean")Ve=(0,g.mean_pooling)(Ve,We.attention_mask);else if(de==="cls")Ve=Ve.slice(null,0);else throw Error(`Pooling method '${de}' not supported.`);return J&&(Ve=Ve.normalize(2,-1)),Ie&&(Ve=(0,g.quantize_embeddings)(Ve,Oe)),Ve}}class te extends ${constructor(X){super(X)}async _call(X,{pool:de=null}={}){const J=await _(X),{pixel_values:Ie}=await this.processor(J),Oe=await this.model({pixel_values:Ie});let We;if(de){if(!("pooler_output"in Oe))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");We=Oe.pooler_output}else We=Oe.last_hidden_state??Oe.logits??Oe.image_embeds;return We}}class se extends ${constructor(X){super(X)}async _call(X,{top_k:de=5}={}){const J=this.processor.feature_extractor.config.sampling_rate,Ie=await w(X,J),Oe=this.model.config.id2label,We=[];for(const Le of Ie){const Ve=await this.processor(Le),at=(await this.model(Ve)).logits[0],dt=await(0,g.topk)(new g.Tensor("float32",(0,f.softmax)(at.data),at.dims),de),ft=dt[0].tolist(),ut=dt[1].tolist().map((bt,Mt)=>({label:Oe?Oe[bt]:`LABEL_${bt}`,score:ft[Mt]}));We.push(ut)}return Array.isArray(X)?We:We[0]}}class Q extends ${constructor(X){super(X)}async _call(X,de,{hypothesis_template:J="This is a sound of {}."}={}){const Ie=!Array.isArray(X);Ie&&(X=[X]);const Oe=de.map(at=>J.replace("{}",at)),We=this.tokenizer(Oe,{padding:!0,truncation:!0}),Le=this.processor.feature_extractor.config.sampling_rate,Ve=await w(X,Le),et=[];for(const at of Ve){const dt=await this.processor(at),ft=await this.model({...We,...dt}),Qe=(0,f.softmax)(ft.logits_per_audio.data);et.push([...Qe].map((ut,bt)=>({score:ut,label:de[bt]})))}return Ie?et[0]:et}}class le extends ${constructor(X){super(X)}async _call(X,de={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(X,de);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(X,de);case"moonshine":return this._call_moonshine(X,de);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(X,de){de.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),de.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const J=!Array.isArray(X);J&&(X=[X]);const Ie=this.processor.feature_extractor.config.sampling_rate,Oe=await w(X,Ie),We=[];for(const Le of Oe){const Ve=await this.processor(Le),at=(await this.model(Ve)).logits[0],dt=[];for(const Qe of at)dt.push((0,f.max)(Qe.data)[1]);const ft=this.tokenizer.decode(dt);We.push({text:ft})}return J?We[0]:We}async _call_whisper(X,de){const J=de.return_timestamps??!1,Ie=de.chunk_length_s??0,Oe=de.force_full_sequences??!1;let We=de.stride_length_s??null;const Le={...de};J==="word"&&(Le.return_token_timestamps=!0,Le.return_timestamps=!1);const Ve=!Array.isArray(X);Ve&&(X=[X]);const et=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,at=this.processor.feature_extractor.config.hop_length,dt=this.processor.feature_extractor.config.sampling_rate,ft=await w(X,dt),Qe=[];for(const ut of ft){let bt=[];if(Ie>0){if(We===null)We=Ie/6;else if(Ie<=We)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const lt=dt*Ie,He=dt*We,It=lt-2*He;let tn=0;for(;;){const xn=tn+lt,_n=ut.subarray(tn,xn),sa=await this.processor(_n),Ln=tn===0,Nt=xn>=ut.length;if(bt.push({stride:[_n.length,Ln?0:He,Nt?0:He],input_features:sa.input_features,is_last:Nt}),Nt)break;tn+=It}}else bt=[{stride:[ut.length,0,0],input_features:(await this.processor(ut)).input_features,is_last:!0}];for(const lt of bt){Le.num_frames=Math.floor(lt.stride[0]/at);const He=await this.model.generate({inputs:lt.input_features,...Le});J==="word"?(lt.tokens=He.sequences.tolist()[0],lt.token_timestamps=He.token_timestamps.tolist()[0].map(It=>(0,f.round)(It,2))):lt.tokens=He[0].tolist(),lt.stride=lt.stride.map(It=>It/dt)}const[Mt,yt]=this.tokenizer._decode_asr(bt,{time_precision:et,return_timestamps:J,force_full_sequences:Oe});Qe.push({text:Mt,...yt})}return Ve?Qe[0]:Qe}async _call_moonshine(X,de){const J=!Array.isArray(X);J&&(X=[X]);const Ie=this.processor.feature_extractor.config.sampling_rate,Oe=await w(X,Ie),We=[];for(const Le of Oe){const Ve=await this.processor(Le),et=Math.floor(Le.length/Ie)*6,at=await this.model.generate({max_new_tokens:et,...de,...Ve}),dt=this.processor.batch_decode(at,{skip_special_tokens:!0})[0];We.push({text:dt})}return J?We[0]:We}}class re extends ${constructor(X){super(X)}async _call(X,de={}){const J=Array.isArray(X),Ie=await _(X),{pixel_values:Oe}=await this.processor(Ie),We=[];for(const Le of Oe){Le.dims=[1,...Le.dims];const Ve=await this.model.generate({inputs:Le,...de}),et=this.tokenizer.batch_decode(Ve,{skip_special_tokens:!0}).map(at=>({generated_text:at.trim()}));We.push(et)}return J?We:We[0]}}class H extends ${constructor(X){super(X)}async _call(X,{top_k:de=5}={}){const J=await _(X),{pixel_values:Ie}=await this.processor(J),Oe=await this.model({pixel_values:Ie}),We=this.model.config.id2label,Le=[];for(const Ve of Oe.logits){const et=await(0,g.topk)(new g.Tensor("float32",(0,f.softmax)(Ve.data),Ve.dims),de),at=et[0].tolist(),ft=et[1].tolist().map((Qe,ut)=>({label:We?We[Qe]:`LABEL_${Qe}`,score:at[ut]}));Le.push(ft)}return Array.isArray(X)?Le:Le[0]}}class ee extends ${constructor(X){super(X),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(X,{threshold:de=.5,mask_threshold:J=.5,overlap_mask_area_threshold:Ie=.8,label_ids_to_fuse:Oe=null,target_sizes:We=null,subtask:Le=null}={}){if(Array.isArray(X)&&X.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const et=await _(X),at=et.map(lt=>[lt.height,lt.width]),dt=await this.processor(et),{inputNames:ft,outputNames:Qe}=this.model.sessions.model;if(!ft.includes("pixel_values")){if(ft.length!==1)throw Error(`Expected a single input name, but got ${ft.length} inputs: ${ft}.`);const lt=ft[0];if(lt in dt)throw Error(`Input name ${lt} already exists in the inputs.`);dt[lt]=dt.pixel_values}const ut=await this.model(dt);let bt=null;if(Le!==null)bt=this.subtasks_mapping[Le];else if(this.processor.image_processor){for(const[lt,He]of Object.entries(this.subtasks_mapping))if(He in this.processor.image_processor){bt=this.processor.image_processor[He].bind(this.processor.image_processor),Le=lt;break}}const Mt=this.model.config.id2label,yt=[];if(Le)if(Le==="panoptic"||Le==="instance"){const lt=bt(ut,de,J,Ie,Oe,We??at)[0],He=lt.segmentation;for(const It of lt.segments_info){const tn=new Uint8ClampedArray(He.data.length);for(let _n=0;_n<He.data.length;++_n)He.data[_n]===It.id&&(tn[_n]=255);const xn=new h.RawImage(tn,He.dims[1],He.dims[0],1);yt.push({score:It.score,label:Mt[It.label_id],mask:xn})}}else if(Le==="semantic"){const{segmentation:lt,labels:He}=bt(ut,We??at)[0];for(const It of He){const tn=new Uint8ClampedArray(lt.data.length);for(let _n=0;_n<lt.data.length;++_n)lt.data[_n]===It&&(tn[_n]=255);const xn=new h.RawImage(tn,lt.dims[1],lt.dims[0],1);yt.push({score:null,label:Mt[It],mask:xn})}}else throw Error(`Subtask ${Le} not supported.`);else{const He=ut[Qe[0]];for(let It=0;It<at.length;++It){const tn=at[It],xn=He[It];xn.data.some(sa=>sa<-1e-5||sa>1+1e-5)&&xn.sigmoid_();const _n=await h.RawImage.fromTensor(xn.mul_(255).to("uint8")).resize(tn[1],tn[0]);yt.push({label:null,score:null,mask:_n})}}return yt}}class oe extends ee{constructor(X){super(X)}async _call(X,de={}){if(Array.isArray(X)&&X.length!==1)throw Error("Background removal pipeline currently only supports a batch size of 1.");const Ie=await _(X),Oe=await super._call(X,de);return Ie.map((Le,Ve)=>{const et=Le.clone();return et.putAlpha(Oe[Ve].mask),et})}}class ve extends ${constructor(X){super(X)}async _call(X,de,{hypothesis_template:J="This is a photo of {}"}={}){const Ie=Array.isArray(X),Oe=await 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this.model({pixel_values:Le,pixel_mask:Ve}),at=this.processor.image_processor.post_process_object_detection(et,de,We),dt=this.model.config.id2label,ft=at.map(Qe=>Qe.boxes.map((ut,bt)=>({score:Qe.scores[bt],label:dt[Qe.classes[bt]],box:M(ut,!J)})));return Ie?ft:ft[0]}}class j extends ${constructor(X){super(X)}async _call(X,de,{threshold:J=.1,top_k:Ie=null,percentage:Oe=!1}={}){const We=Array.isArray(X),Le=await _(X),Ve=this.tokenizer(de,{padding:!0,truncation:!0}),et=await this.processor(Le),at=[];for(let dt=0;dt<Le.length;++dt){const ft=Le[dt],Qe=Oe?null:[[ft.height,ft.width]],ut=et.pixel_values[dt].unsqueeze_(0),bt=await this.model({...Ve,pixel_values:ut});let Mt;if("post_process_grounded_object_detection"in this.processor){const yt=this.processor.post_process_grounded_object_detection(bt,Ve.input_ids,{box_threshold:J,text_threshold:J,target_sizes:Qe})[0];Mt=yt.boxes.map((lt,He)=>({score:yt.scores[He],label:yt.labels[He],box:M(lt,!Oe)}))}else{const yt=this.processor.image_processor.post_process_object_detection(bt,J,Qe,!0)[0];Mt=yt.boxes.map((lt,He)=>({score:yt.scores[He],label:de[yt.classes[He]],box:M(lt,!Oe)}))}Mt.sort((yt,lt)=>lt.score-yt.score),Ie!==null&&(Mt=Mt.slice(0,Ie)),at.push(Mt)}return We?at:at[0]}}class z extends ${constructor(X){super(X)}async _call(X,de,J={}){const Ie=(await _(X))[0],{pixel_values:Oe}=await this.processor(Ie),We=`<s_docvqa><s_question>${de}</s_question><s_answer>`,Le=this.tokenizer(We,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,Ve=await this.model.generate({inputs:Oe,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Le,...J}),at=this.tokenizer.batch_decode(Ve)[0].match(/<s_answer>(.*?)<\/s_answer>/);let dt=null;return at&&at.length>=2&&(dt=at[1].trim()),[{answer:dt}]}}class V extends ${constructor(de){super(de);fe(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=de.vocoder??null}async _call(de,{speaker_embeddings:J=null}={}){return this.processor?this._call_text_to_spectrogram(de,{speaker_embeddings:J}):this._call_text_to_waveform(de)}async _call_text_to_waveform(de){const J=this.tokenizer(de,{padding:!0,truncation:!0}),{waveform:Ie}=await this.model(J),Oe=this.model.config.sampling_rate;return new p.RawAudio(Ie.data,Oe)}async _call_text_to_spectrogram(de,{speaker_embeddings:J}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await l.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof J=="string"||J instanceof URL)&&(J=new Float32Array(await(await fetch(J)).arrayBuffer())),J instanceof Float32Array)J=new g.Tensor("float32",J,[1,J.length]);else if(!(J instanceof g.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Ie}=this.tokenizer(de,{padding:!0,truncation:!0}),{waveform:Oe}=await this.model.generate_speech(Ie,J,{vocoder:this.vocoder}),We=this.processor.feature_extractor.config.sampling_rate;return new p.RawAudio(Oe.data,We)}}class ie extends ${constructor(X){super(X)}async _call(X){const de=await _(X),J=await this.processor(de),Ie=await this.model(J),Oe=[];for(const We of Ie.reconstruction){const Le=We.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Oe.push(h.RawImage.fromTensor(Le))}return Oe.length>1?Oe:Oe[0]}}class Me extends ${constructor(X){super(X)}async _call(X){const de=await _(X),J=await this.processor(de),{predicted_depth:Ie}=await this.model(J),Oe=[];for(let We=0;We<de.length;++We){const Le=Ie[We],[Ve,et]=Le.dims.slice(-2),[at,dt]=de[We].size,ft=(await(0,g.interpolate_4d)(Le.view(1,1,Ve,et),{size:[dt,at],mode:"bilinear"})).view(dt,at),Qe=ft.min().item(),ut=ft.max().item(),bt=ft.sub(Qe).div_(ut-Qe).mul_(255).to("uint8").unsqueeze(0),Mt=h.RawImage.fromTensor(bt);Oe.push({predicted_depth:ft,depth:Mt})}return Oe.length>1?Oe:Oe[0]}}const ce=Object.freeze({"text-classification":{tokenizer:i.AutoTokenizer,pipeline:L,model:l.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:i.AutoTokenizer,pipeline:E,model:l.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:i.AutoTokenizer,pipeline:S,model:l.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:i.AutoTokenizer,pipeline:D,model:l.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:i.AutoTokenizer,pipeline:C,model:l.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:i.AutoTokenizer,pipeline:T,model:l.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:i.AutoTokenizer,pipeline:A,model:l.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:i.AutoTokenizer,pipeline:O,model:l.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:i.AutoTokenizer,pipeline:N,model:l.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:se,model:l.AutoModelForAudioClassification,processor:o.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:i.AutoTokenizer,pipeline:Q,model:l.AutoModel,processor:o.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:i.AutoTokenizer,pipeline:le,model:[l.AutoModelForSpeechSeq2Seq,l.AutoModelForCTC],processor:o.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:i.AutoTokenizer,pipeline:V,model:[l.AutoModelForTextToWaveform,l.AutoModelForTextToSpectrogram],processor:[o.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:i.AutoTokenizer,pipeline:re,model:l.AutoModelForVision2Seq,processor:o.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:H,model:l.AutoModelForImageClassification,processor:o.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ee,model:[l.AutoModelForImageSegmentation,l.AutoModelForSemanticSegmentation,l.AutoModelForUniversalSegmentation],processor:o.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:oe,model:[l.AutoModelForImageSegmentation,l.AutoModelForSemanticSegmentation,l.AutoModelForUniversalSegmentation],processor:o.AutoProcessor,default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{tokenizer:i.AutoTokenizer,pipeline:ve,model:l.AutoModel,processor:o.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:U,model:l.AutoModelForObjectDetection,processor:o.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:i.AutoTokenizer,pipeline:j,model:l.AutoModelForZeroShotObjectDetection,processor:o.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:i.AutoTokenizer,pipeline:z,model:l.AutoModelForDocumentQuestionAnswering,processor:o.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:ie,model:l.AutoModelForImageToImage,processor:o.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Me,model:l.AutoModelForDepthEstimation,processor:o.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:i.AutoTokenizer,pipeline:G,model:l.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:o.AutoProcessor,pipeline:te,model:[l.AutoModelForImageFeatureExtraction,l.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),ye=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function Be(be,X=null,{progress_callback:de=null,config:J=null,cache_dir:Ie=null,local_files_only:Oe=!1,revision:We="main",device:Le=null,dtype:Ve=null,subfolder:et="onnx",use_external_data_format:at=null,model_file_name:dt=null,session_options:ft={}}={}){be=ye[be]??be;const Qe=ce[be.split("_",1)[0]];if(!Qe)throw Error(`Unsupported pipeline: ${be}. 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Use `tokenizer.batch_decode(...)` for batched inputs.");return Ee.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${F.length}.`)}}function L(Ee){return Ee.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function E(Ee){return Ee.replace(new RegExp("\\p{M}","gu"),"")}function S(Ee){return E(Ee.toLowerCase())}function D(Ee){return Ee>=19968&&Ee<=40959||Ee>=13312&&Ee<=19903||Ee>=131072&&Ee<=173791||Ee>=173824&&Ee<=177983||Ee>=177984&&Ee<=178207||Ee>=178208&&Ee<=183983||Ee>=63744&&Ee<=64255||Ee>=194560&&Ee<=195103}function A(Ee,F,ne){const ue=[];let ge=0;for(;ge<Ee.length;){if(ue.push(Ee[ge]),(F.get(Ee[ge])??ne)!==ne){++ge;continue}for(;++ge<Ee.length&&(F.get(Ee[ge])??ne)===ne;)F.get(ue.at(-1))!==ne&&(ue[ue.length-1]+=Ee[ge])}return ue}function C(Ee){return Ee.match(/\S+/g)||[]}const T="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",x=new RegExp(`^[${T}]+$`,"gu"),O=".,!?…。，、।۔،",N=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${O}])]+`,` ?[^\\s${O}]+`]]);class G{constructor(F){this.content=F.content,this.id=F.id,this.single_word=F.single_word??!1,this.lstrip=F.lstrip??!1,this.rstrip=F.rstrip??!1,this.special=F.special??!1,this.normalized=F.normalized??null}}class te extends i.Callable{constructor(F){super(),this.config=F,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(F,...ne){switch(F.type){case"WordPiece":return new se(F);case"Unigram":return new Q(F,...ne);case"BPE":return new H(F);default:if(F.vocab)return Array.isArray(F.vocab)?new Q(F,...ne):Object.hasOwn(F,"continuing_subword_prefix")&&Object.hasOwn(F,"unk_token")?Object.hasOwn(F,"merges")?new H(F):new se(F):new ee(F,...ne);throw new Error(`Unknown TokenizerModel type: ${F.type}`)}}_call(F){return F=this.encode(F),this.fuse_unk&&(F=A(F,this.tokens_to_ids,this.unk_token_id)),F}encode(F){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(F){return F.map(ne=>this.tokens_to_ids.get(ne)??this.unk_token_id)}convert_ids_to_tokens(F){return F.map(ne=>this.vocab[ne]??this.unk_token)}}class se extends te{constructor(F){super(F),this.tokens_to_ids=M(F.vocab),this.unk_token_id=this.tokens_to_ids.get(F.unk_token),this.unk_token=F.unk_token,this.max_input_chars_per_word=F.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[ne,ue]of this.tokens_to_ids)this.vocab[ue]=ne}encode(F){const ne=[];for(const ue of F){const ge=[...ue];if(ge.length>this.max_input_chars_per_word){ne.push(this.unk_token);continue}let xe=!1,Ne=0;const rt=[];for(;Ne<ge.length;){let pt=ge.length,ct=null;for(;Ne<pt;){let gt=ge.slice(Ne,pt).join("");if(Ne>0&&(gt=this.config.continuing_subword_prefix+gt),this.tokens_to_ids.has(gt)){ct=gt;break}--pt}if(ct===null){xe=!0;break}rt.push(ct),Ne=pt}xe?ne.push(this.unk_token):ne.push(...rt)}return ne}}class Q extends te{constructor(F,ne){super(F);const ue=F.vocab.length;this.vocab=new Array(ue),this.scores=new Array(ue);for(let ge=0;ge<ue;++ge)[this.vocab[ge],this.scores[ge]]=F.vocab[ge];this.unk_token_id=F.unk_id,this.unk_token=this.vocab[F.unk_id],this.tokens_to_ids=new Map(this.vocab.map((ge,xe)=>[ge,xe])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=ne.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,c.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new f.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(F){const ne=F.chars,ue=1;let ge=0;for(;ge<ne.length;){let xe=!1;const Ne=ne.slice(ge).join(""),rt=this.trie.commonPrefixSearch(Ne);for(const pt of rt){const ct=this.tokens_to_ids.get(pt),gt=this.scores[ct],Ft=(0,l.len)(pt);F.insert(ge,Ft,gt,ct),!xe&&Ft===ue&&(xe=!0)}xe||F.insert(ge,ue,this.unk_score,this.unk_token_id),ge+=ue}}tokenize(F){const ne=new f.TokenLattice(F,this.bos_token_id,this.eos_token_id);return this.populateNodes(ne),ne.tokens()}encode(F){const ne=[];for(const ue of F){const ge=this.tokenize(ue);ne.push(...ge)}return ne}}const le=(()=>{const Ee=[...Array.from({length:94},(ge,xe)=>xe+33),...Array.from({length:12},(ge,xe)=>xe+161),...Array.from({length:82},(ge,xe)=>xe+174)],F=Ee.slice();let ne=0;for(let ge=0;ge<256;++ge)Ee.includes(ge)||(Ee.push(ge),F.push(256+ne),ne+=1);const ue=F.map(ge=>String.fromCharCode(ge));return Object.fromEntries(Ee.map((ge,xe)=>[ge,ue[xe]]))})(),re=(0,l.reverseDictionary)(le);class H extends te{constructor(F){super(F),this.tokens_to_ids=M(F.vocab),this.unk_token_id=this.tokens_to_ids.get(F.unk_token),this.unk_token=F.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ue,ge]of this.tokens_to_ids)this.vocab[ge]=ue;const ne=Array.isArray(F.merges[0]);this.merges=ne?F.merges:F.merges.map(ue=>ue.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ue,ge)=>[JSON.stringify(ue),ge])),this.end_of_word_suffix=F.end_of_word_suffix,this.continuing_subword_suffix=F.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new f.LRUCache(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(F){if(F.length===0)return[];const ne=this.cache.get(F);if(ne!==void 0)return ne;const ue=Array.from(F);this.end_of_word_suffix&&(ue[ue.length-1]+=this.end_of_word_suffix);let ge=[];if(ue.length>1){const xe=new f.PriorityQueue((pt,ct)=>pt.score<ct.score);let Ne={token:ue[0],bias:0,prev:null,next:null},rt=Ne;for(let pt=1;pt<ue.length;++pt){const ct={bias:pt/ue.length,token:ue[pt],prev:rt,next:null};rt.next=ct,this._add_node(xe,rt),rt=ct}for(;!xe.isEmpty();){const pt=xe.pop();if(pt.deleted||!pt.next||pt.next.deleted)continue;if(pt.deleted=!0,pt.next.deleted=!0,pt.prev){const gt={...pt.prev};pt.prev.deleted=!0,pt.prev=gt,gt.prev?gt.prev.next=gt:Ne=gt}const ct={token:pt.token+pt.next.token,bias:pt.bias,prev:pt.prev,next:pt.next.next};ct.prev?(ct.prev.next=ct,this._add_node(xe,ct.prev)):Ne=ct,ct.next&&(ct.next.prev=ct,this._add_node(xe,ct))}for(let pt=Ne;pt!==null;pt=pt.next)ge.push(pt.token)}else ge=ue;if(this.continuing_subword_suffix)for(let xe=0;xe<ge.length-1;++xe)ge[xe]+=this.continuing_subword_suffix;return F.length<this.max_length_to_cache&&this.cache.put(F,ge),ge}_add_node(F,ne){const ue=this.bpe_ranks.get(JSON.stringify([ne.token,ne.next.token]));ue!==void 0&&(ne.score=ue+ne.bias,F.push(ne))}encode(F){const ne=[];for(const ue of F){if(this.ignore_merges&&this.tokens_to_ids.has(ue)){ne.push(ue);continue}const ge=this.bpe(ue);for(const xe of ge)if(this.tokens_to_ids.has(xe))ne.push(xe);else if(this.byte_fallback){const Ne=Array.from(this.text_encoder.encode(xe)).map(rt=>`<0x${rt.toString(16).toUpperCase().padStart(2,"0")}>`);Ne.every(rt=>this.tokens_to_ids.has(rt))?ne.push(...Ne):ne.push(this.unk_token)}else ne.push(this.unk_token)}return ne}}class ee extends te{constructor(F,ne){super(F),this.tokens_to_ids=M(ne.target_lang?F.vocab[ne.target_lang]:F.vocab),this.bos_token=ne.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=ne.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=ne.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=ne.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[ue,ge]of this.tokens_to_ids)this.vocab[ge]=ue}encode(F){return F}}class oe extends i.Callable{constructor(F){super(),this.config=F}static fromConfig(F){if(F===null)return null;switch(F.type){case"BertNormalizer":return new be(F);case"Precompiled":return new Nt(F);case"Sequence":return new Ke(F);case"Replace":return new ve(F);case"NFC":return new j(F);case"NFD":return new z(F);case"NFKC":return new V(F);case"NFKD":return new ie(F);case"Strip":return new Me(F);case"StripAccents":return new ce(F);case"Lowercase":return new ye(F);case"Prepend":return new Be(F);default:throw new Error(`Unknown Normalizer type: ${F.type}`)}}normalize(F){throw Error("normalize should be implemented in subclass.")}_call(F){return this.normalize(F)}}class ve extends oe{normalize(F){const ne=w(this.config.pattern);return ne===null?F:F.replaceAll(ne,this.config.content)}}class U extends oe{constructor(){super(...arguments);fe(this,"form")}normalize(ne){return ne=ne.normalize(this.form),ne}}class j extends U{constructor(){super(...arguments);fe(this,"form","NFC")}}class z extends U{constructor(){super(...arguments);fe(this,"form","NFD")}}class V extends U{constructor(){super(...arguments);fe(this,"form","NFKC")}}class ie extends U{constructor(){super(...arguments);fe(this,"form","NFKD")}}class Me extends oe{normalize(F){return this.config.strip_left&&this.config.strip_right?F=F.trim():(this.config.strip_left&&(F=F.trimStart()),this.config.strip_right&&(F=F.trimEnd())),F}}class ce extends oe{normalize(F){return F=E(F),F}}class ye extends oe{normalize(F){return F=F.toLowerCase(),F}}class Be extends oe{normalize(F){return F=this.config.prepend+F,F}}class Ke extends oe{constructor(F){super(F),this.normalizers=F.normalizers.map(ne=>oe.fromConfig(ne))}normalize(F){return this.normalizers.reduce((ne,ue)=>ue.normalize(ne),F)}}class be extends oe{_tokenize_chinese_chars(F){const ne=[];for(let ue=0;ue<F.length;++ue){const ge=F[ue],xe=ge.charCodeAt(0);D(xe)?(ne.push(" "),ne.push(ge),ne.push(" ")):ne.push(ge)}return ne.join("")}stripAccents(F){return F.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(F){switch(F){case"	":case`
-`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(F)}}_clean_text(F){const ne=[];for(const ue of F){const ge=ue.charCodeAt(0);ge===0||ge===65533||this._is_control(ue)||(/^\s$/.test(ue)?ne.push(" "):ne.push(ue))}return ne.join("")}normalize(F){return this.config.clean_text&&(F=this._clean_text(F)),this.config.handle_chinese_chars&&(F=this._tokenize_chinese_chars(F)),this.config.lowercase?(F=F.toLowerCase(),this.config.strip_accents!==!1&&(F=this.stripAccents(F))):this.config.strip_accents&&(F=this.stripAccents(F)),F}}class X extends i.Callable{static fromConfig(F){if(F===null)return null;switch(F.type){case"BertPreTokenizer":return new de(F);case"Sequence":return new Xa(F);case"Whitespace":return new q(F);case"WhitespaceSplit":return new _e(F);case"Metaspace":return new sa(F);case"ByteLevel":return new J(F);case"Split":return new Ie(F);case"Punctuation":return new Oe(F);case"Digits":return new We(F);case"Replace":return new Y(F);default:throw new Error(`Unknown PreTokenizer type: ${F.type}`)}}pre_tokenize_text(F,ne){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(F,ne){return(Array.isArray(F)?F.map(ue=>this.pre_tokenize_text(ue,ne)):this.pre_tokenize_text(F,ne)).flat()}_call(F,ne){return this.pre_tokenize(F,ne)}}class de extends X{constructor(F){super(),this.pattern=new RegExp(`[^\\s${T}]+|[${T}]`,"gu")}pre_tokenize_text(F,ne){return F.trim().match(this.pattern)||[]}}class J extends X{constructor(F){super(),this.config=F,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=le,this.text_encoder=new TextEncoder}pre_tokenize_text(F,ne){return this.add_prefix_space&&!F.startsWith(" ")&&(F=" "+F),(this.use_regex?F.match(this.pattern)||[]:[F]).map(ge=>Array.from(this.text_encoder.encode(ge),xe=>this.byte_encoder[xe]).join(""))}}class Ie extends X{constructor(F){super(),this.config=F,this.pattern=w(this.config.pattern,this.config.invert)}pre_tokenize_text(F,ne){var ue;return this.pattern===null?[]:this.config.invert?F.match(this.pattern)||[]:((ue=this.config.behavior)==null?void 0:ue.toLowerCase())==="removed"?F.split(this.pattern).filter(ge=>ge):_(F,this.pattern)}}class Oe extends X{constructor(F){super(),this.config=F,this.pattern=new RegExp(`[^${T}]+|[${T}]+`,"gu")}pre_tokenize_text(F,ne){return F.match(this.pattern)||[]}}class We extends X{constructor(F){super(),this.config=F;const ne=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(ne,"gu")}pre_tokenize_text(F,ne){return F.match(this.pattern)||[]}}class Le extends i.Callable{constructor(F){super(),this.config=F}static fromConfig(F){if(F===null)return null;switch(F.type){case"TemplateProcessing":return new at(F);case"ByteLevel":return new dt(F);case"RobertaProcessing":return new et(F);case"BertProcessing":return new Ve(F);case"Sequence":return new ft(F);default:throw new Error(`Unknown PostProcessor type: ${F.type}`)}}post_process(F,...ne){throw Error("post_process should be implemented in subclass.")}_call(F,...ne){return this.post_process(F,...ne)}}class Ve extends Le{constructor(F){super(F),this.cls=F.cls[0],this.sep=F.sep[0]}post_process(F,ne=null,{add_special_tokens:ue=!0}={}){ue&&(F=(0,l.mergeArrays)([this.cls],F,[this.sep]));let ge=new Array(F.length).fill(0);if(ne!==null){const xe=ue&&this instanceof et?[this.sep]:[],Ne=ue?[this.sep]:[];F=(0,l.mergeArrays)(F,xe,ne,Ne),ge=(0,l.mergeArrays)(ge,new Array(ne.length+xe.length+Ne.length).fill(1))}return{tokens:F,token_type_ids:ge}}}class et extends Ve{}class at extends Le{constructor(F){super(F),this.single=F.single,this.pair=F.pair}post_process(F,ne=null,{add_special_tokens:ue=!0}={}){const ge=ne===null?this.single:this.pair;let xe=[],Ne=[];for(const rt of ge)"SpecialToken"in rt?ue&&(xe.push(rt.SpecialToken.id),Ne.push(rt.SpecialToken.type_id)):"Sequence"in rt&&(rt.Sequence.id==="A"?(xe=(0,l.mergeArrays)(xe,F),Ne=(0,l.mergeArrays)(Ne,new Array(F.length).fill(rt.Sequence.type_id))):rt.Sequence.id==="B"&&(xe=(0,l.mergeArrays)(xe,ne),Ne=(0,l.mergeArrays)(Ne,new Array(ne.length).fill(rt.Sequence.type_id))));return{tokens:xe,token_type_ids:Ne}}}class dt extends Le{post_process(F,ne=null){return ne&&(F=(0,l.mergeArrays)(F,ne)),{tokens:F}}}class ft extends Le{constructor(F){super(F),this.processors=F.processors.map(ne=>Le.fromConfig(ne))}post_process(F,ne=null,ue={}){let ge;for(const xe of this.processors)if(xe instanceof dt)F=xe.post_process(F).tokens,ne&&(ne=xe.post_process(ne).tokens);else{const Ne=xe.post_process(F,ne,ue);F=Ne.tokens,ge=Ne.token_type_ids}return{tokens:F,token_type_ids:ge}}}class Qe extends i.Callable{constructor(F){super(),this.config=F,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=F.trim_offsets}static fromConfig(F){if(F===null)return null;switch(F.type){case"WordPiece":return new lt(F);case"Metaspace":return new Ln(F);case"ByteLevel":return new He(F);case"Replace":return new ut(F);case"ByteFallback":return new bt(F);case"Fuse":return new Mt(F);case"Strip":return new yt(F);case"Sequence":return new tn(F);case"CTC":return new It(F);case"BPEDecoder":return new xn(F);default:throw new Error(`Unknown Decoder type: ${F.type}`)}}_call(F){return this.decode(F)}decode(F){return this.decode_chain(F).join("")}decode_chain(F){throw Error("`decode_chain` should be implemented in subclass.")}}class ut extends Qe{decode_chain(F){const ne=w(this.config.pattern);return ne===null?F:F.map(ue=>ue.replaceAll(ne,this.config.content))}}class bt extends Qe{constructor(F){super(F),this.text_decoder=new TextDecoder}decode_chain(F){const ne=[];let ue=[];for(const ge of F){let xe=null;if(ge.length===6&&ge.startsWith("<0x")&&ge.endsWith(">")){const Ne=parseInt(ge.slice(3,5),16);isNaN(Ne)||(xe=Ne)}if(xe!==null)ue.push(xe);else{if(ue.length>0){const Ne=this.text_decoder.decode(Uint8Array.from(ue));ne.push(Ne),ue=[]}ne.push(ge)}}if(ue.length>0){const ge=this.text_decoder.decode(Uint8Array.from(ue));ne.push(ge),ue=[]}return ne}}class Mt extends Qe{decode_chain(F){return[F.join("")]}}class yt extends Qe{constructor(F){super(F),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(F){return F.map(ne=>{let ue=0;for(let xe=0;xe<this.start&&ne[xe]===this.content;++xe){ue=xe+1;continue}let ge=ne.length;for(let xe=0;xe<this.stop;++xe){const Ne=ne.length-xe-1;if(ne[Ne]===this.content){ge=Ne;continue}else break}return ne.slice(ue,ge)})}}class lt extends Qe{constructor(F){super(F),this.cleanup=F.cleanup}decode_chain(F){return F.map((ne,ue)=>(ue!==0&&(ne.startsWith(this.config.prefix)?ne=ne.replace(this.config.prefix,""):ne=" "+ne),this.cleanup&&(ne=L(ne)),ne))}}class He extends Qe{constructor(F){super(F),this.byte_decoder=re,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(F){const ne=F.join(""),ue=new Uint8Array([...ne].map(xe=>this.byte_decoder[xe]));return this.text_decoder.decode(ue)}decode_chain(F){const ne=[];let ue=[];for(const ge of F)this.added_tokens.find(xe=>xe.content===ge)!==void 0?(ue.length>0&&(ne.push(this.convert_tokens_to_string(ue)),ue=[]),ne.push(ge)):ue.push(ge);return ue.length>0&&ne.push(this.convert_tokens_to_string(ue)),ne}}class It extends Qe{constructor(F){super(F),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(F){if(F.length===0)return"";const ne=[F[0]];for(let xe=1;xe<F.length;++xe)F[xe]!==ne.at(-1)&&ne.push(F[xe]);let ge=ne.filter(xe=>xe!==this.pad_token).join("");return this.cleanup&&(ge=L(ge).replaceAll(this.word_delimiter_token," ").trim()),ge}decode_chain(F){return[this.convert_tokens_to_string(F)]}}class tn extends Qe{constructor(F){super(F),this.decoders=F.decoders.map(ne=>Qe.fromConfig(ne))}decode_chain(F){return this.decoders.reduce((ne,ue)=>ue.decode_chain(ne),F)}}class xn extends Qe{constructor(F){super(F),this.suffix=this.config.suffix}decode_chain(F){return F.map((ne,ue)=>ne.replaceAll(this.suffix,ue===F.length-1?"":" "))}}class _n extends Qe{decode_chain(F){let ne="";for(let ue=1;ue<F.length;ue+=2)ne+=F[ue];return[ne]}}class sa extends X{constructor(F){super(),this.addPrefixSpace=F.add_prefix_space,this.replacement=F.replacement,this.strRep=F.str_rep||this.replacement,this.prepend_scheme=F.prepend_scheme??"always"}pre_tokenize_text(F,{section_index:ne=void 0}={}){let ue=F.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!ue.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&ne===0)&&(ue=this.strRep+ue),[ue]}}class Ln extends Qe{constructor(F){super(F),this.addPrefixSpace=F.add_prefix_space,this.replacement=F.replacement}decode_chain(F){const ne=[];for(let ue=0;ue<F.length;++ue){let ge=F[ue].replaceAll(this.replacement," ");this.addPrefixSpace&&ue==0&&ge.startsWith(" ")&&(ge=ge.substring(1)),ne.push(ge)}return ne}}class Nt extends oe{constructor(F){super(F),this.charsmap=F.precompiled_charsmap}normalize(F){return F=F.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),F=F.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),F.includes("～")?F=F.split("～").map(ue=>ue.normalize("NFKC")).join("～"):F=F.normalize("NFKC"),F}}class Xa extends X{constructor(F){super(),this.tokenizers=F.pretokenizers.map(ne=>X.fromConfig(ne))}pre_tokenize_text(F,ne){return this.tokenizers.reduce((ue,ge)=>ge.pre_tokenize(ue,ne),[F])}}class q extends X{constructor(F){super()}pre_tokenize_text(F,ne){return F.match(/\w+|[^\w\s]+/g)||[]}}class _e extends X{constructor(F){super()}pre_tokenize_text(F,ne){return C(F)}}class Y extends X{constructor(F){super(),this.config=F,this.pattern=w(this.config.pattern),this.content=this.config.content}pre_tokenize_text(F,ne){return this.pattern===null?[F]:[F.replaceAll(this.pattern,this.config.content)]}}const pe=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ce(Ee,F,ne,ue){for(const ge of Object.keys(Ee)){const xe=F-Ee[ge].length,Ne=ne(ge),rt=new Array(xe).fill(Ne);Ee[ge]=ue==="right"?(0,l.mergeArrays)(Ee[ge],rt):(0,l.mergeArrays)(rt,Ee[ge])}}function tt(Ee,F){for(const ne of Object.keys(Ee))Ee[ne].length=F}class Re extends i.Callable{constructor(ne,ue){super();fe(this,"return_token_type_ids",!1);fe(this,"padding_side","right");this._tokenizer_config=ue,this.normalizer=oe.fromConfig(ne.normalizer),this.pre_tokenizer=X.fromConfig(ne.pre_tokenizer),this.model=te.fromConfig(ne.model,ue),this.post_processor=Le.fromConfig(ne.post_processor),this.decoder=Qe.fromConfig(ne.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ge of ne.added_tokens){const xe=new G(ge);this.added_tokens.push(xe),this.model.tokens_to_ids.set(xe.content,xe.id),this.model.vocab[xe.id]=xe.content,xe.special&&(this.special_tokens.push(xe.content),this.all_special_ids.push(xe.id))}if(this.additional_special_tokens=ue.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_splitter=new f.DictionarySplitter(this.added_tokens.map(ge=>ge.content)),this.added_tokens_map=new Map(this.added_tokens.map(ge=>[ge.content,ge])),this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=ue.model_max_length,this.remove_space=ue.remove_space,this.clean_up_tokenization_spaces=ue.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ue.do_lowercase_and_remove_accent??!1,ue.padding_side&&(this.padding_side=ue.padding_side),this.legacy=!1,this.chat_template=ue.chat_template??null,Array.isArray(this.chat_template)){const ge=Object.create(null);for(const{name:xe,template:Ne}of this.chat_template){if(typeof xe!="string"||typeof Ne!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ge[xe]=Ne}this.chat_template=ge}this._compiled_template_cache=new Map}getToken(...ne){for(const ue of ne){const ge=this._tokenizer_config[ue];if(ge)if(typeof ge=="object"){if(ge.__type==="AddedToken")return ge.content;throw Error(`Unknown token: ${ge}`)}else return ge}return null}static async from_pretrained(ne,{progress_callback:ue=null,config:ge=null,cache_dir:xe=null,local_files_only:Ne=!1,revision:rt="main",legacy:pt=null}={}){const ct=await h(ne,{progress_callback:ue,config:ge,cache_dir:xe,local_files_only:Ne,revision:rt,legacy:pt});return new this(...ct)}_call(ne,{text_pair:ue=null,add_special_tokens:ge=!0,padding:xe=!1,truncation:Ne=null,max_length:rt=null,return_tensor:pt=!0,return_token_type_ids:ct=null}={}){const gt=Array.isArray(ne);let Ft;if(gt){if(ne.length===0)throw Error("text array must be non-empty");if(ue!==null){if(Array.isArray(ue)){if(ne.length!==ue.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Ft=ne.map((un,Sn)=>this._encode_plus(un,{text_pair:ue[Sn],add_special_tokens:ge,return_token_type_ids:ct}))}else Ft=ne.map(un=>this._encode_plus(un,{add_special_tokens:ge,return_token_type_ids:ct}))}else{if(ne==null)throw Error("text may not be null or undefined");if(Array.isArray(ue))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Ft=[this._encode_plus(ne,{text_pair:ue,add_special_tokens:ge,return_token_type_ids:ct})]}if(rt===null?rt=this.model_max_length:Ne===null&&(xe===!0?(console.warn("`max_length` is ignored when `padding: true` and there is no truncation strategy. To pad to max length, use `padding: 'max_length'`."),rt=this.model_max_length):xe===!1&&(console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."),Ne=!0)),xe===!0&&(rt=Math.min((0,c.max)(Ft.map(un=>un.input_ids.length))[0],rt??1/0)),rt=Math.min(rt,this.model_max_length??1/0),xe||Ne)for(let un=0;un<Ft.length;++un)Ft[un].input_ids.length!==rt&&(Ft[un].input_ids.length>rt?Ne&&tt(Ft[un],rt):xe&&Ce(Ft[un],rt,Sn=>Sn==="input_ids"?this.pad_token_id:0,this.padding_side));const pn={};if(pt){if(!(xe&&Ne)&&Ft.some(Sn=>{var hn;for(const da of Object.keys(Sn))if(Sn[da].length!==((hn=Ft[0][da])==null?void 0:hn.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const un=[Ft.length,Ft[0].input_ids.length];for(const Sn of Object.keys(Ft[0]))pn[Sn]=new d.Tensor("int64",BigInt64Array.from(Ft.flatMap(hn=>hn[Sn]).map(BigInt)),un)}else{for(const un of Object.keys(Ft[0]))pn[un]=Ft.map(Sn=>Sn[un]);if(!gt)for(const un of Object.keys(pn))pn[un]=pn[un][0]}return pn}_encode_text(ne){if(ne===null)return null;const ue=this.added_tokens_splitter.split(ne);for(let xe=0;xe<ue.length;++xe){const Ne=this.added_tokens_map.get(ue[xe]);Ne&&(Ne.lstrip&&xe>0&&(ue[xe-1]=ue[xe-1].trimEnd()),Ne.rstrip&&xe<ue.length-1&&(ue[xe+1]=ue[xe+1].trimStart()))}return ue.flatMap((xe,Ne)=>{if(xe.length===0)return[];if(this.added_tokens_map.has(xe))return[xe];if(this.remove_space===!0&&(xe=xe.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(xe=S(xe)),this.normalizer!==null&&(xe=this.normalizer(xe)),xe.length===0)return[];const rt=this.pre_tokenizer!==null?this.pre_tokenizer(xe,{section_index:Ne}):[xe];return this.model(rt)})}_encode_plus(ne,{text_pair:ue=null,add_special_tokens:ge=!0,return_token_type_ids:xe=null}={}){const{tokens:Ne,token_type_ids:rt}=this._tokenize_helper(ne,{pair:ue,add_special_tokens:ge}),pt=this.model.convert_tokens_to_ids(Ne),ct={input_ids:pt,attention_mask:new Array(pt.length).fill(1)};return(xe??this.return_token_type_ids)&&rt&&(ct.token_type_ids=rt),ct}_tokenize_helper(ne,{pair:ue=null,add_special_tokens:ge=!1}={}){const xe=this._encode_text(ne),Ne=this._encode_text(ue);return this.post_processor?this.post_processor(xe,Ne,{add_special_tokens:ge}):{tokens:(0,l.mergeArrays)(xe??[],Ne??[])}}tokenize(ne,{pair:ue=null,add_special_tokens:ge=!1}={}){return this._tokenize_helper(ne,{pair:ue,add_special_tokens:ge}).tokens}encode(ne,{text_pair:ue=null,add_special_tokens:ge=!0,return_token_type_ids:xe=null}={}){return this._encode_plus(ne,{text_pair:ue,add_special_tokens:ge,return_token_type_ids:xe}).input_ids}batch_decode(ne,ue={}){return ne instanceof d.Tensor&&(ne=ne.tolist()),ne.map(ge=>this.decode(ge,ue))}decode(ne,ue={}){if(ne instanceof d.Tensor&&(ne=$(ne)),!Array.isArray(ne)||ne.length===0||!(0,l.isIntegralNumber)(ne[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(ne,ue)}decode_single(ne,{skip_special_tokens:ue=!1,clean_up_tokenization_spaces:ge=null}){let xe=this.model.convert_ids_to_tokens(ne);ue&&(xe=xe.filter(rt=>!this.special_tokens.includes(rt)));let Ne=this.decoder?this.decoder(xe):xe.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Ne=Ne.replaceAll(this.decoder.end_of_word_suffix," "),ue&&(Ne=Ne.trim())),(ge??this.clean_up_tokenization_spaces)&&(Ne=L(Ne)),Ne}get_chat_template({chat_template:ne=null,tools:ue=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ge=this.chat_template;if(ne!==null&&Object.hasOwn(ge,ne))ne=ge[ne];else if(ne===null)if(ue!==null&&"tool_use"in ge)ne=ge.tool_use;else if("default"in ge)ne=ge.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(ge).sort()}.`)}else if(ne===null)if(this.chat_template)ne=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return ne}apply_chat_template(ne,{tools:ue=null,documents:ge=null,chat_template:xe=null,add_generation_prompt:Ne=!1,tokenize:rt=!0,padding:pt=!1,truncation:ct=!1,max_length:gt=null,return_tensor:Ft=!0,return_dict:pn=!1,tokenizer_kwargs:un={},...Sn}={}){if(xe=this.get_chat_template({chat_template:xe,tools:ue}),typeof xe!="string")throw Error(`chat_template must be a string, but got ${typeof xe}`);let hn=this._compiled_template_cache.get(xe);hn===void 0&&(hn=new p.Template(xe),this._compiled_template_cache.set(xe,hn));const da=Object.create(null);for(const Sa of pe){const oa=this.getToken(Sa);oa&&(da[Sa]=oa)}const fa=hn.render({messages:ne,add_generation_prompt:Ne,tools:ue,documents:ge,...da,...Sn});if(rt){const Sa=this._call(fa,{add_special_tokens:!1,padding:pt,truncation:ct,max_length:gt,return_tensor:Ft,...un});return pn?Sa:Sa.input_ids}return fa}}class Gt extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class fn extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class At extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class St extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class Tt extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class an extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class Zt extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class Tn extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class En extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class ka extends Re{}class $r extends Re{}class Nr extends Re{constructor(ne,ue){super(ne,ue);fe(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class vr extends Re{constructor(){super(...arguments);fe(this,"return_token_type_ids",!0)}}class cr extends Re{}class Wa extends Re{}class rr extends Re{}class Na extends Re{constructor(F,ne){super(F,ne),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)),this.lang_to_token=ue=>ue}_build_translation_inputs(F,ne,ue){return ra(this,F,ne,ue)}}class sr extends Na{}class ir extends Re{}class Vn extends Re{}const aa="▁";class jr extends Re{constructor(ne,ue){super(ne,ue);fe(this,"padding_side","left");this.legacy=ue.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new sa({replacement:aa,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(ne){if(ne===null)return null;if(this.legacy||ne.length===0)return super._encode_text(ne);let ue=super._encode_text(aa+ne.replaceAll(aa," "));return ue.length>1&&ue[0]===aa&&this.special_tokens.includes(ue[1])&&(ue=ue.slice(1)),ue}}class Yn extends Re{}class Ya extends Re{}class Za extends Re{}class Ye extends Re{}class _t extends Re{}class Ct extends Re{}class gn extends Re{}class or extends Re{}class Ea extends Re{}function ra(Ee,F,ne,ue){if(!("language_codes"in Ee)||!Array.isArray(Ee.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Ee)||!(Ee.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Ee)||typeof Ee.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ge=ue.src_lang,xe=ue.tgt_lang;if(!Ee.language_codes.includes(xe))throw new Error(`Target language code "${xe}" is not valid. Must be one of: {${Ee.language_codes.join(", ")}}`);if(ge!==void 0){if(!Ee.language_codes.includes(ge))throw new Error(`Source language code "${ge}" is not valid. Must be one of: {${Ee.language_codes.join(", ")}}`);for(const Ne of Ee.post_processor.config.single)if("SpecialToken"in Ne&&Ee.languageRegex.test(Ne.SpecialToken.id)){Ne.SpecialToken.id=Ee.lang_to_token(ge);break}}return ue.forced_bos_token_id=Ee.model.convert_tokens_to_ids([Ee.lang_to_token(xe)])[0],Ee._call(F,ne)}class Mr extends Re{constructor(F,ne){super(F,ne),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)),this.lang_to_token=ue=>ue}_build_translation_inputs(F,ne,ue){return ra(this,F,ne,ue)}}class Gn extends Re{constructor(F,ne){super(F,ne),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)).map(ue=>ue.slice(2,-2)),this.lang_to_token=ue=>`__${ue}__`}_build_translation_inputs(F,ne,ue){return ra(this,F,ne,ue)}}class ia extends Re{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(F,{return_timestamps:ne=!1,return_language:ue=!1,time_precision:ge=null,force_full_sequences:xe=!0}={}){if(ge===null)throw Error("Must specify time_precision");let Ne=null;const rt=ne==="word";function pt(){return{language:Ne,timestamp:[null,null],text:""}}const ct=[];let gt=pt(),Ft=0;const pn=this.timestamp_begin,Sn=pn+1500;let hn=[],da=[],fa=!1,Sa=null;const oa=new Set(this.all_special_ids);for(const wn of F){const Rn=wn.tokens,Oa=rt?wn.token_timestamps:null;let Ja=null,fr=pn;if("stride"in wn){const[va,on,Nn]=wn.stride;if(Ft-=on,Sa=va-Nn,on&&(fr=on/ge+pn),Nn)for(let Zn=Rn.length-1;Zn>=0;--Zn){const pa=Number(Rn[Zn]);if(pa>=pn){if(Ja!==null&&(pa-pn)*ge<Sa)break;Ja=pa}}}let ba=[],Ca=[];for(let va=0;va<Rn.length;++va){const on=Number(Rn[va]);if(oa.has(on)){const Nn=this.decode([on]),Zn=g.WHISPER_LANGUAGE_MAPPING.get(Nn.slice(2,-2));if(Zn!==void 0){if(Ne!==null&&Zn!==Ne&&!ne){hn.push(ba);const pa=this.findLongestCommonSequence(hn)[0],kr=this.decode(pa);gt.text=kr,ct.push(gt),hn=[],ba=[],gt=pt()}Ne=gt.language=Zn}}else if(on>=pn&&on<=Sn){const Nn=(on-pn)*ge+Ft,Zn=(0,c.round)(Nn,2);if(Ja!==null&&on>=Ja)fa=!0;else if(fa||hn.length>0&&on<fr)fa=!1;else if(gt.timestamp[0]===null)gt.timestamp[0]=Zn;else if(Zn!==gt.timestamp[0]){gt.timestamp[1]=Zn,hn.push(ba),rt&&da.push(Ca);const[pa,kr]=this.findLongestCommonSequence(hn,da),Ss=this.decode(pa);gt.text=Ss,rt&&(gt.words=this.collateWordTimestamps(pa,kr,Ne)),ct.push(gt),hn=[],ba=[],da=[],Ca=[],gt=pt()}}else if(ba.push(on),rt){let Nn=(0,c.round)(Oa[va]+Ft,2),Zn;if(va+1<Oa.length){Zn=(0,c.round)(Oa[va+1]+Ft,2);const pa=this.decode([on]);x.test(pa)&&(Zn=(0,c.round)(Math.min(Nn+ge,Zn),2))}else Zn=null;Ca.push([Nn,Zn])}}if("stride"in wn){const[va,on,Nn]=wn.stride;Ft+=va-Nn}ba.length>0?(hn.push(ba),rt&&da.push(Ca)):hn.every(va=>va.length===0)&&(gt=pt(),hn=[],ba=[],da=[],Ca=[])}if(hn.length>0){if(xe&&ne)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[wn,Rn]=this.findLongestCommonSequence(hn,da),Oa=this.decode(wn);gt.text=Oa,rt&&(gt.words=this.collateWordTimestamps(wn,Rn,Ne)),ct.push(gt)}let Mn=Object.create(null);const wr=ct.map(wn=>wn.text).join("");if(ne||ue){for(let wn=0;wn<ct.length;++wn){const Rn=ct[wn];ne||delete Rn.timestamp,ue||delete Rn.language}if(rt){const wn=[];for(const Rn of ct)for(const Oa of Rn.words)wn.push(Oa);Mn={chunks:wn}}else Mn={chunks:ct}}return[wr,Mn]}findLongestCommonSequence(F,ne=null){let ue=F[0],ge=ue.length,xe=[];const Ne=Array.isArray(ne)&&ne.length>0;let rt=Ne?[]:null,pt=Ne?ne[0]:null;for(let ct=1;ct<F.length;++ct){const gt=F[ct];let Ft=0,pn=[ge,ge,0,0];const un=gt.length;for(let Mn=1;Mn<ge+un;++Mn){const wr=Math.max(0,ge-Mn),wn=Math.min(ge,ge+un-Mn),Rn=ue.slice(wr,wn),Oa=Math.max(0,Mn-ge),Ja=Math.min(un,Mn),fr=gt.slice(Oa,Ja);if(Rn.length!==fr.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let ba;Ne?ba=Rn.filter((on,Nn)=>on===fr[Nn]&&pt[wr+Nn]<=ne[ct][Oa+Nn]).length:ba=Rn.filter((on,Nn)=>on===fr[Nn]).length;const Ca=Mn/1e4,va=ba/Mn+Ca;ba>1&&va>Ft&&(Ft=va,pn=[wr,wn,Oa,Ja])}const[Sn,hn,da,fa]=pn,Sa=Math.floor((hn+Sn)/2),oa=Math.floor((fa+da)/2);xe.push(...ue.slice(0,Sa)),ue=gt.slice(oa),ge=ue.length,Ne&&(rt.push(...pt.slice(0,Sa)),pt=ne[ct].slice(oa))}return xe.push(...ue),Ne?(rt.push(...pt),[xe,rt]):[xe,[]]}collateWordTimestamps(F,ne,ue){const[ge,xe,Ne]=this.combineTokensIntoWords(F,ue),rt=[];for(let pt=0;pt<ge.length;++pt){const ct=Ne[pt];rt.push({text:ge[pt],timestamp:[ne[ct.at(0)][0],ne[ct.at(-1)][1]]})}return rt}combineTokensIntoWords(F,ne,ue=`"'“¡¿([{-`,ge=`"'.。,，!！?？:：”)]}、`){ne=ne??"english";let xe,Ne,rt;return["chinese","japanese","thai","lao","myanmar"].includes(ne)?[xe,Ne,rt]=this.splitTokensOnUnicode(F):[xe,Ne,rt]=this.splitTokensOnSpaces(F),this.mergePunctuations(xe,Ne,rt,ue,ge)}decode(F,ne){let ue;return ne!=null&&ne.decode_with_timestamps?(F instanceof d.Tensor&&(F=$(F)),ue=this.decodeWithTimestamps(F,ne)):ue=super.decode(F,ne),ue}decodeWithTimestamps(F,ne){const ue=(ne==null?void 0:ne.time_precision)??.02,ge=Array.from(this.all_special_ids).at(-1)+1;let xe=[[]];for(let Ne of F)if(Ne=Number(Ne),Ne>=ge){const rt=((Ne-ge)*ue).toFixed(2);xe.push(`<|${rt}|>`),xe.push([])}else xe[xe.length-1].push(Ne);return xe=xe.map(Ne=>typeof Ne=="string"?Ne:super.decode(Ne,ne)),xe.join("")}splitTokensOnUnicode(F){const ne=this.decode(F,{decode_with_timestamps:!0}),ue="�",ge=[],xe=[],Ne=[];let rt=[],pt=[],ct=0;for(let gt=0;gt<F.length;++gt){const Ft=F[gt];rt.push(Ft),pt.push(gt);const pn=this.decode(rt,{decode_with_timestamps:!0});(!pn.includes(ue)||ne[ct+pn.indexOf(ue)]===ue)&&(ge.push(pn),xe.push(rt),Ne.push(pt),rt=[],pt=[],ct+=pn.length)}return[ge,xe,Ne]}splitTokensOnSpaces(F){const[ne,ue,ge]=this.splitTokensOnUnicode(F),xe=[],Ne=[],rt=[],pt=new RegExp(`^[${T}]$`,"gu");for(let ct=0;ct<ne.length;++ct){const gt=ne[ct],Ft=ue[ct],pn=ge[ct],un=Ft[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),Sn=gt.startsWith(" "),hn=gt.trim(),da=pt.test(hn);if(un||Sn||da||xe.length===0)xe.push(gt),Ne.push(Ft),rt.push(pn);else{const fa=xe.length-1;xe[fa]+=gt,Ne[fa].push(...Ft),rt[fa].push(...pn)}}return[xe,Ne,rt]}mergePunctuations(F,ne,ue,ge,xe){const Ne=structuredClone(F),rt=structuredClone(ne),pt=structuredClone(ue);let ct=Ne.length-2,gt=Ne.length-1;for(;ct>=0;)Ne[ct].startsWith(" ")&&ge.includes(Ne[ct].trim())?(Ne[gt]=Ne[ct]+Ne[gt],rt[gt]=(0,l.mergeArrays)(rt[ct],rt[gt]),pt[gt]=(0,l.mergeArrays)(pt[ct],pt[gt]),Ne[ct]="",rt[ct]=[],pt[ct]=[]):gt=ct,--ct;for(ct=0,gt=1;gt<Ne.length;)!Ne[ct].endsWith(" ")&&xe.includes(Ne[gt])?(Ne[ct]+=Ne[gt],rt[ct]=(0,l.mergeArrays)(rt[ct],rt[gt]),pt[ct]=(0,l.mergeArrays)(pt[ct],pt[gt]),Ne[gt]="",rt[gt]=[],pt[gt]=[]):ct=gt,++gt;return[Ne.filter(Ft=>Ft),rt.filter(Ft=>Ft.length>0),pt.filter(Ft=>Ft.length>0)]}}class dr extends Re{}class Ur extends Re{}class ii extends Re{}class ja extends Re{constructor(F,ne){super(F,ne),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ue=>this.languageRegex.test(ue)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. 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${E.type}`);switch(E.dims[2]){case 1:case 2:case 3:case 4:return new M(E.data,E.dims[1],E.dims[0],E.dims[2]);default:throw new Error(`Unsupported number of channels: ${E.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const E=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let S=0,D=0;S<this.data.length;S+=this.channels){const A=this.data[S],C=this.data[S+1],T=this.data[S+2];E[D++]=Math.round(.2989*A+.587*C+.114*T)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(E,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const E=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let S=0,D=0;S<this.data.length;++S)E[D++]=this.data[S],E[D++]=this.data[S],E[D++]=this.data[S];break;case 4:for(let S=0,D=0;S<this.data.length;S+=4)E[D++]=this.data[S],E[D++]=this.data[S+1],E[D++]=this.data[S+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(E,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const E=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let S=0,D=0;S<this.data.length;++S)E[D++]=this.data[S],E[D++]=this.data[S],E[D++]=this.data[S],E[D++]=255;break;case 3:for(let S=0,D=0;S<this.data.length;S+=3)E[D++]=this.data[S],E[D++]=this.data[S+1],E[D++]=this.data[S+2],E[D++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(E,this.width,this.height,4)}putAlpha(E){if(E.width!==this.width||E.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${E.width}x${E.height}`);if(E.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${E.channels}`);const S=this.data,D=E.data,A=this.width*this.height;if(this.channels===3){const C=new Uint8ClampedArray(A*4);for(let T=0,x=0,O=0;T<A;++T)C[O++]=S[x++],C[O++]=S[x++],C[O++]=S[x++],C[O++]=D[T];return this._update(C,this.width,this.height,4)}else if(this.channels===4){for(let C=0;C<A;++C)S[4*C+3]=D[C];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(E,S,{resample:D=2}={}){if(this.width===E&&this.height===S)return this;let A=_[D]??D;const C=(0,i.isNullishDimension)(E),T=(0,i.isNullishDimension)(S);if(C&&T)return this;if(C?E=S/this.height*this.width:T&&(S=E/this.width*this.height),h){const x=this.channels,O=this.toCanvas(),N=f(E,S).getContext("2d");return N.drawImage(O,0,0,E,S),new M(N.getImageData(0,0,E,S).data,E,S,4).convert(x)}else{let x=this.toSharp();switch(A){case"box":case"hamming":(A==="box"||A==="hamming")&&(console.warn(`Resampling method ${A} is not yet supported. Using bilinear instead.`),A="bilinear");case"nearest":case"bilinear":case"bicubic":x=x.affine([E/this.width,0,0,S/this.height],{interpolator:A});break;case"lanczos":x=x.resize({width:E,height:S,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${A} is not supported.`)}return await g(x)}}async pad([E,S,D,A]){if(E=Math.max(E,0),S=Math.max(S,0),D=Math.max(D,0),A=Math.max(A,0),E===0&&S===0&&D===0&&A===0)return this;if(h){const C=this.channels,T=this.toCanvas(),x=this.width+E+S,O=this.height+D+A,N=f(x,O).getContext("2d");return N.drawImage(T,0,0,this.width,this.height,E,D,this.width,this.height),new M(N.getImageData(0,0,x,O).data,x,O,4).convert(C)}else{const C=this.toSharp().extend({left:E,right:S,top:D,bottom:A});return await g(C)}}async crop([E,S,D,A]){if(E=Math.max(E,0),S=Math.max(S,0),D=Math.min(D,this.width-1),A=Math.min(A,this.height-1),E===0&&S===0&&D===this.width-1&&A===this.height-1)return this;const C=D-E+1,T=A-S+1;if(h){const x=this.channels,O=this.toCanvas(),N=f(C,T).getContext("2d");return N.drawImage(O,E,S,C,T,0,0,C,T),new M(N.getImageData(0,0,C,T).data,C,T,4).convert(x)}else{const x=this.toSharp().extract({left:E,top:S,width:C,height:T});return await g(x)}}async center_crop(E,S){if(this.width===E&&this.height===S)return this;const D=(this.width-E)/2,A=(this.height-S)/2;if(h){const C=this.channels,T=this.toCanvas(),x=f(E,S).getContext("2d");let O=0,N=0,G=0,te=0;return D>=0?O=D:G=-D,A>=0?N=A:te=-A,x.drawImage(T,O,N,E,S,G,te,E,S),new M(x.getImageData(0,0,E,S).data,E,S,4).convert(C)}else{let C=this.toSharp();if(D>=0&&A>=0)C=C.extract({left:Math.floor(D),top:Math.floor(A),width:E,height:S});else if(D<=0&&A<=0){const T=Math.floor(-A),x=Math.floor(-D);C=C.extend({top:T,left:x,right:E-this.width-x,bottom:S-this.height-T})}else{let T=[0,0],x=0;A<0?(T[0]=Math.floor(-A),T[1]=S-this.height-T[0]):x=Math.floor(A);let O=[0,0],N=0;D<0?(O[0]=Math.floor(-D),O[1]=E-this.width-O[0]):N=Math.floor(D),C=C.extend({top:T[0],bottom:T[1],left:O[0],right:O[1]}).extract({left:N,top:x,width:E,height:S})}return await g(C)}}async toBlob(E="image/png",S=1){if(!h)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:E,quality:S})}toTensor(E="CHW"){let S=new c.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(E!=="HWC")if(E==="CHW")S=S.permute(2,0,1);else throw new Error(`Unsupported channel format: ${E}`);return S}toCanvas(){if(!h)throw new Error("toCanvas() is only supported in browser environments.");const E=this.clone().rgba(),S=f(E.width,E.height),D=new p(E.data,E.width,E.height);return S.getContext("2d").putImageData(D,0,0),S}split(){const{data:E,width:S,height:D,channels:A}=this,C=E.constructor,T=E.length/A,x=Array.from({length:A},()=>new C(T));for(let O=0;O<T;++O){const N=A*O;for(let G=0;G<A;++G)x[G][O]=E[N+G]}return x.map(O=>new M(O,S,D,1))}_update(E,S,D,A=null){return this.data=E,this.width=S,this.height=D,A!==null&&(this.channels=A),this}clone(){return new M(this.data.slice(),this.width,this.height,this.channels)}convert(E){if(this.channels===E)return this;switch(E){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(E){if(h){if(o.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const S=E.split(".").pop().toLowerCase(),D=w.get(S)??"image/png",A=await this.toBlob(D);(0,i.saveBlob)(E,A)}else{if(o.apis.IS_FS_AVAILABLE)return await this.toSharp().toFile(E);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(h)throw new Error("toSharp() is only supported in server-side environments.");return d(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}const $=M.read.bind(M)},"./src/utils/maths.js":(t,a,n)=>{n.r(a),n.d(a,{FFT:()=>$,bankers_round:()=>S,cos_sim:()=>f,dot:()=>d,dynamic_time_warping:()=>D,interpolate_data:()=>i,log_softmax:()=>c,magnitude:()=>p,max:()=>h,medianFilter:()=>L,min:()=>g,permute_data:()=>l,round:()=>E,softmax:()=>o});function i(A,[C,T,x],[O,N],G="bilinear",te=!1){const se=N/x,Q=O/T,le=new A.constructor(O*N*C),re=T*x,H=O*N;for(let ee=0;ee<O;++ee)for(let oe=0;oe<N;++oe){const ve=ee*N+oe,U=(oe+.5)/se-.5,j=(ee+.5)/Q-.5;let z=Math.floor(U),V=Math.floor(j);const ie=Math.min(z+1,x-1),Me=Math.min(V+1,T-1);z=Math.max(z,0),V=Math.max(V,0);const ce=U-z,ye=j-V,Be=(1-ce)*(1-ye),Ke=ce*(1-ye),be=(1-ce)*ye,X=ce*ye,de=V*x,J=Me*x,Ie=de+z,Oe=de+ie,We=J+z,Le=J+ie;for(let Ve=0;Ve<C;++Ve){const et=Ve*re;le[Ve*H+ve]=Be*A[et+Ie]+Ke*A[et+Oe]+be*A[et+We]+X*A[et+Le]}}return le}function l(A,C,T){const x=new Array(T.length),O=new Array(T.length);for(let te=T.length-1,se=1;te>=0;--te)O[te]=se,x[te]=C[T[te]],se*=x[te];const N=T.map((te,se)=>O[T.indexOf(se)]),G=new A.constructor(A.length);for(let te=0;te<A.length;++te){let se=0;for(let Q=C.length-1,le=te;Q>=0;--Q)se+=le%C[Q]*N[Q],le=Math.floor(le/C[Q]);G[se]=A[te]}return[G,x]}function o(A){const C=h(A)[0],T=A.map(N=>Math.exp(N-C)),x=T.reduce((N,G)=>N+G,0);return T.map(N=>N/x)}function c(A){const C=h(A)[0];let T=0;for(let N=0;N<A.length;++N)T+=Math.exp(A[N]-C);const x=Math.log(T);return A.map(N=>N-C-x)}function d(A,C){let T=0;for(let x=0;x<A.length;++x)T+=A[x]*C[x];return T}function f(A,C){const T=d(A,C),x=p(A),O=p(C);return T/(x*O)}function p(A){return Math.sqrt(A.reduce((C,T)=>C+T*T,0))}function g(A){if(A.length===0)throw Error("Array must not be empty");let C=A[0],T=0;for(let x=1;x<A.length;++x)A[x]<C&&(C=A[x],T=x);return[C,T]}function h(A){if(A.length===0)throw Error("Array must not be empty");let C=A[0],T=0;for(let x=1;x<A.length;++x)A[x]>C&&(C=A[x],T=x);return[C,T]}function _(A){return A>0&&(A&A-1)===0}class w{constructor(C){if(this.size=C|0,this.size<=1||!_(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=C<<1,this.table=new Float64Array(this.size*2);for(let x=0;x<this.table.length;x+=2){const O=Math.PI*x/this.size;this.table[x]=Math.cos(O),this.table[x+1]=-Math.sin(O)}let T=0;for(let x=1;this.size>x;x<<=1)++T;this._width=T%2===0?T-1:T,this._bitrev=new Int32Array(1<<this._width);for(let x=0;x<this._bitrev.length;++x){this._bitrev[x]=0;for(let O=0;O<this._width;O+=2){const N=this._width-O-2;this._bitrev[x]|=(x>>>O&3)<<N}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(C,T){const x=T||new Array(C.length>>>1);for(let O=0;O<C.length;O+=2)x[O>>>1]=C[O];return x}toComplexArray(C,T){const x=T||this.createComplexArray();for(let O=0;O<x.length;O+=2)x[O]=C[O>>>1],x[O+1]=0;return x}transform(C,T){if(C===T)throw new Error("Input and output buffers must be different");this._transform4(C,T,1)}realTransform(C,T){if(C===T)throw new Error("Input and output buffers must be different");this._realTransform4(C,T,1)}inverseTransform(C,T){if(C===T)throw new Error("Input and output buffers must be different");this._transform4(C,T,-1);for(let x=0;x<C.length;++x)C[x]/=this.size}_transform4(C,T,x){const O=this._csize;let G=1<<this._width,te=O/G<<1,se,Q;const le=this._bitrev;if(te===4)for(se=0,Q=0;se<O;se+=te,++Q){const H=le[Q];this._singleTransform2(T,C,se,H,G)}else for(se=0,Q=0;se<O;se+=te,++Q){const H=le[Q];this._singleTransform4(T,C,se,H,G,x)}const re=this.table;for(G>>=2;G>=2;G>>=2){te=O/G<<1;const H=te>>>2;for(se=0;se<O;se+=te){const ee=se+H-1;for(let oe=se,ve=0;oe<ee;oe+=2,ve+=G){const U=oe,j=U+H,z=j+H,V=z+H,ie=C[U],Me=C[U+1],ce=C[j],ye=C[j+1],Be=C[z],Ke=C[z+1],be=C[V],X=C[V+1],de=re[ve],J=x*re[ve+1],Ie=ce*de-ye*J,Oe=ce*J+ye*de,We=re[2*ve],Le=x*re[2*ve+1],Ve=Be*We-Ke*Le,et=Be*Le+Ke*We,at=re[3*ve],dt=x*re[3*ve+1],ft=be*at-X*dt,Qe=be*dt+X*at,ut=ie+Ve,bt=Me+et,Mt=ie-Ve,yt=Me-et,lt=Ie+ft,He=Oe+Qe,It=x*(Ie-ft),tn=x*(Oe-Qe);C[U]=ut+lt,C[U+1]=bt+He,C[j]=Mt+tn,C[j+1]=yt-It,C[z]=ut-lt,C[z+1]=bt-He,C[V]=Mt-tn,C[V+1]=yt+It}}}}_singleTransform2(C,T,x,O,N){const G=C[O],te=C[O+1],se=C[O+N],Q=C[O+N+1];T[x]=G+se,T[x+1]=te+Q,T[x+2]=G-se,T[x+3]=te-Q}_singleTransform4(C,T,x,O,N,G){const te=N*2,se=N*3,Q=C[O],le=C[O+1],re=C[O+N],H=C[O+N+1],ee=C[O+te],oe=C[O+te+1],ve=C[O+se],U=C[O+se+1],j=Q+ee,z=le+oe,V=Q-ee,ie=le-oe,Me=re+ve,ce=H+U,ye=G*(re-ve),Be=G*(H-U);T[x]=j+Me,T[x+1]=z+ce,T[x+2]=V+Be,T[x+3]=ie-ye,T[x+4]=j-Me,T[x+5]=z-ce,T[x+6]=V-Be,T[x+7]=ie+ye}_realTransform4(C,T,x){const O=this._csize;let G=1<<this._width,te=O/G<<1,se,Q;const le=this._bitrev;if(te===4)for(se=0,Q=0;se<O;se+=te,++Q){const ee=le[Q];this._singleRealTransform2(T,C,se,ee>>>1,G>>>1)}else for(se=0,Q=0;se<O;se+=te,++Q){const ee=le[Q];this._singleRealTransform4(T,C,se,ee>>>1,G>>>1,x)}const re=this.table;for(G>>=2;G>=2;G>>=2){te=O/G<<1;const ee=te>>>1,oe=ee>>>1,ve=oe>>>1;for(se=0;se<O;se+=te)for(let U=0,j=0;U<=ve;U+=2,j+=G){const z=se+U,V=z+oe,ie=V+oe,Me=ie+oe,ce=C[z],ye=C[z+1],Be=C[V],Ke=C[V+1],be=C[ie],X=C[ie+1],de=C[Me],J=C[Me+1],Ie=ce,Oe=ye,We=re[j],Le=x*re[j+1],Ve=Be*We-Ke*Le,et=Be*Le+Ke*We,at=re[2*j],dt=x*re[2*j+1],ft=be*at-X*dt,Qe=be*dt+X*at,ut=re[3*j],bt=x*re[3*j+1],Mt=de*ut-J*bt,yt=de*bt+J*ut,lt=Ie+ft,He=Oe+Qe,It=Ie-ft,tn=Oe-Qe,xn=Ve+Mt,_n=et+yt,sa=x*(Ve-Mt),Ln=x*(et-yt);if(C[z]=lt+xn,C[z+1]=He+_n,C[V]=It+Ln,C[V+1]=tn-sa,U===0){C[ie]=lt-xn,C[ie+1]=He-_n;continue}if(U===ve)continue;const Nt=se+oe-U,Xa=se+ee-U;C[Nt]=It-x*Ln,C[Nt+1]=-tn-x*sa,C[Xa]=lt-x*xn,C[Xa+1]=-He+x*_n}}const H=O>>>1;for(let ee=2;ee<H;ee+=2)C[O-ee]=C[ee],C[O-ee+1]=-C[ee+1]}_singleRealTransform2(C,T,x,O,N){const G=C[O],te=C[O+N];T[x]=G+te,T[x+1]=0,T[x+2]=G-te,T[x+3]=0}_singleRealTransform4(C,T,x,O,N,G){const te=N*2,se=N*3,Q=C[O],le=C[O+N],re=C[O+te],H=C[O+se],ee=Q+re,oe=Q-re,ve=le+H,U=G*(le-H);T[x]=ee+ve,T[x+1]=0,T[x+2]=oe,T[x+3]=-U,T[x+4]=ee-ve,T[x+5]=0,T[x+6]=oe,T[x+7]=U}}class M{constructor(C){const T=2*(C-1),x=2*(2*C-1),O=2**Math.ceil(Math.log2(x));this.bufferSize=O,this._a=T;const N=new Float64Array(x),G=new Float64Array(O);this._chirpBuffer=new Float64Array(O),this._buffer1=new Float64Array(O),this._buffer2=new Float64Array(O),this._outBuffer1=new Float64Array(O),this._outBuffer2=new Float64Array(O);const te=-2*Math.PI/C,se=Math.cos(te),Q=Math.sin(te);for(let le=0;le<x>>1;++le){const re=(le+1-C)**2/2,H=Math.sqrt(se**2+Q**2)**re,ee=re*Math.atan2(Q,se),oe=2*le;N[oe]=H*Math.cos(ee),N[oe+1]=H*Math.sin(ee),G[oe]=N[oe],G[oe+1]=-N[oe+1]}this._slicedChirpBuffer=N.subarray(T,x),this._f=new w(O>>1),this._f.transform(this._chirpBuffer,G)}_transform(C,T,x){const O=this._buffer1,N=this._buffer2,G=this._outBuffer1,te=this._outBuffer2,se=this._chirpBuffer,Q=this._slicedChirpBuffer,le=this._a;if(x)for(let re=0;re<Q.length;re+=2){const H=re+1,ee=re>>1,oe=T[ee];O[re]=oe*Q[re],O[H]=oe*Q[H]}else for(let re=0;re<Q.length;re+=2){const H=re+1;O[re]=T[re]*Q[re]-T[H]*Q[H],O[H]=T[re]*Q[H]+T[H]*Q[re]}this._f.transform(G,O);for(let re=0;re<se.length;re+=2){const H=re+1;N[re]=G[re]*se[re]-G[H]*se[H],N[H]=G[re]*se[H]+G[H]*se[re]}this._f.inverseTransform(te,N);for(let re=0;re<te.length;re+=2){const H=te[re+le],ee=te[re+le+1],oe=Q[re],ve=Q[re+1];C[re]=H*oe-ee*ve,C[re+1]=H*ve+ee*oe}}transform(C,T){this._transform(C,T,!1)}realTransform(C,T){this._transform(C,T,!0)}}class ${constructor(C){this.fft_length=C,this.isPowerOfTwo=_(C),this.isPowerOfTwo?(this.fft=new w(C),this.outputBufferSize=2*C):(this.fft=new 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-  !*** ./src/transformers.js ***!
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TNeoXModel;v.GPTNeoXPreTrainedModel;v.GPTNeoXTokenizer;v.Gemma2ForCausalLM;v.Gemma2Model;v.Gemma2PreTrainedModel;v.Gemma3ForCausalLM;v.Gemma3Model;v.Gemma3PreTrainedModel;v.GemmaForCausalLM;v.GemmaModel;v.GemmaPreTrainedModel;v.GemmaTokenizer;v.GlmForCausalLM;v.GlmModel;v.GlmPreTrainedModel;v.GraniteForCausalLM;v.GraniteModel;v.GranitePreTrainedModel;v.Grok1Tokenizer;v.GroundingDinoForObjectDetection;v.GroundingDinoImageProcessor;v.GroundingDinoPreTrainedModel;v.GroundingDinoProcessor;v.GroupViTModel;v.GroupViTPreTrainedModel;v.HeliumForCausalLM;v.HeliumModel;v.HeliumPreTrainedModel;v.HerbertTokenizer;v.HieraForImageClassification;v.HieraModel;v.HieraPreTrainedModel;v.HubertForCTC;v.HubertForSequenceClassification;v.HubertModel;v.HubertPreTrainedModel;v.IJepaForImageClassification;v.IJepaModel;v.IJepaPreTrainedModel;v.Idefics3ForConditionalGeneration;v.Idefics3ImageProcessor;v.Idefics3PreTrainedModel;v.Idefics3Processor;v.ImageClassificationPipeline;v.ImageFeatureExtractionPipeline;v.ImageFeatureExtractor;v.ImageMattingOutput;v.ImageProcessor;v.ImageSegmentationPipeline;v.ImageToImagePipeline;v.ImageToTextPipeline;v.InterruptableStoppingCriteria;v.JAISLMHeadModel;v.JAISModel;v.JAISPreTrainedModel;v.JinaCLIPImageProcessor;v.JinaCLIPModel;v.JinaCLIPPreTrainedModel;v.JinaCLIPProcessor;v.JinaCLIPTextModel;v.JinaCLIPVisionModel;v.LiteWhisperForConditionalGeneration;v.LlamaForCausalLM;v.LlamaModel;v.LlamaPreTrainedModel;v.LlamaTokenizer;v.LlavaForConditionalGeneration;v.LlavaOnevisionForConditionalGeneration;v.LlavaOnevisionImageProcessor;v.LlavaPreTrainedModel;v.LogitsProcessor;v.LogitsProcessorList;v.LogitsWarper;v.LongT5ForConditionalGeneration;v.LongT5Model;v.LongT5PreTrainedModel;v.M2M100ForConditionalGeneration;v.M2M100Model;v.M2M100PreTrainedModel;v.M2M100Tokenizer;v.MBart50Tokenizer;v.MBartForCausalLM;v.MBartForConditionalGeneration;v.MBartForSequenceClassification;v.MBartModel;v.MBartPreTrainedModel;v.MBartTokenizer;v.MPNetForMaskedLM;v.MPNetForQuestionAnswering;v.MPNetForSequenceClassification;v.MPNetForTokenClassification;v.MPNetModel;v.MPNetPreTrainedModel;v.MPNetTokenizer;v.MT5ForConditionalGeneration;v.MT5Model;v.MT5PreTrainedModel;v.MarianMTModel;v.MarianModel;v.MarianPreTrainedModel;v.MarianTokenizer;v.Mask2FormerImageProcessor;v.MaskFormerFeatureExtractor;v.MaskFormerForInstanceSegmentation;v.MaskFormerImageProcessor;v.MaskFormerModel;v.MaskFormerPreTrainedModel;v.MaskedLMOutput;v.MaxLengthCriteria;v.Metric3DForDepthEstimation;v.Metric3DPreTrainedModel;v.Metric3Dv2ForDepthEstimation;v.Metric3Dv2PreTrainedModel;v.MgpstrForSceneTextRecognition;v.MgpstrModelOutput;v.MgpstrPreTrainedModel;v.MgpstrProcessor;v.MgpstrTokenizer;v.MimiDecoderModel;v.MimiDecoderOutput;v.MimiEncoderModel;v.MimiEncoderOutput;v.MimiModel;v.MimiPreTrainedModel;v.MinLengthLogitsProcessor;v.MinNewTokensLengthLogitsProcessor;v.MistralForCausalLM;v.MistralModel;v.MistralPreTrainedModel;v.MobileBertForMaskedLM;v.MobileBertForQuestionAnswering;v.MobileBertForSequenceClassification;v.MobileBertModel;v.MobileBertPreTrainedModel;v.MobileBertTokenizer;v.MobileLLMForCausalLM;v.MobileLLMModel;v.MobileLLMPreTrainedModel;v.MobileNetV1FeatureExtractor;v.MobileNetV1ForImageClassification;v.MobileNetV1ForSemanticSegmentation;v.MobileNetV1ImageProcessor;v.MobileNetV1Model;v.MobileNetV1PreTrainedModel;v.MobileNetV2FeatureExtractor;v.MobileNetV2ForImageClassification;v.MobileNetV2ForSemanticSegmentation;v.MobileNetV2ImageProcessor;v.MobileNetV2Model;v.MobileNetV2PreTrainedModel;v.MobileNetV3FeatureExtractor;v.MobileNetV3ForImageClassification;v.MobileNetV3ForSemanticSegmentation;v.MobileNetV3ImageProcessor;v.MobileNetV3Model;v.MobileNetV3PreTrainedModel;v.MobileNetV4FeatureExtractor;v.MobileNetV4ForImageClassification;v.MobileNetV4ForSemanticSegmentation;v.MobileNetV4ImageProcessor;v.MobileNetV4Model;v.MobileNetV4PreTrainedModel;v.MobileViTFeatureExtractor;v.MobileViTForImageClassification;v.MobileViTImageProcessor;v.MobileViTModel;v.MobileViTPreTrainedModel;v.MobileViTV2ForImageClassification;v.MobileViTV2Model;v.MobileViTV2PreTrainedModel;v.ModelOutput;v.ModernBertForMaskedLM;v.ModernBertForSequenceClassification;v.ModernBertForTokenClassification;v.ModernBertModel;v.ModernBertPreTrainedModel;v.Moondream1ForConditionalGeneration;v.MoonshineFeatureExtractor;v.MoonshineForConditionalGeneration;v.MoonshineModel;v.MoonshinePreTrainedModel;v.MoonshineProcessor;v.MptForCausalLM;v.MptModel;v.MptPreTrainedModel;v.MultiModalityCausalLM;v.MultiModalityPreTrainedModel;v.MusicgenForCausalLM;v.MusicgenForConditionalGeneration;v.MusicgenModel;v.MusicgenPreTrainedModel;v.NllbTokenizer;v.NoBadWordsLogitsProcessor;v.NoRepeatNGramLogitsProcessor;v.NomicBertModel;v.NomicBertPreTrainedModel;v.NougatImageProcessor;v.NougatTokenizer;v.OPTForCausalLM;v.OPTModel;v.OPTPreTrainedModel;v.ObjectDetectionPipeline;v.Olmo2ForCausalLM;v.Olmo2Model;v.Olmo2PreTrainedModel;v.OlmoForCausalLM;v.OlmoModel;v.OlmoPreTrainedModel;v.OpenELMForCausalLM;v.OpenELMModel;v.OpenELMPreTrained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m2=v.cat;v.cos_sim;v.dot;v.dynamic_time_warping;v.env;v.full;v.full_like;v.getKeyValueShapes;v.hamming;v.hanning;v.interpolate;v.interpolate_4d;v.interpolate_data;v.is_chinese_char;v.layer_norm;v.load_image;v.load_video;v.log_softmax;v.magnitude;v.matmul;v.max;v.mean;v.mean_pooling;v.medianFilter;v.mel_filter_bank;v.min;v.ones;v.ones_like;v.permute;v.permute_data;v.pipeline;v.quantize_embeddings;v.rand;v.read_audio;v.rfft;v.round;v.slice;v.softmax;v.spectrogram;v.stack;v.std_mean;v.topk;v.window_function;v.zeros;v.zeros_like;const qv="onnx-community/Qwen3-Embedding-0.6B-ONNX";let Yh=null,Zh=null;function _2(t,a){const n=new Float32Array(Array.from(a.data,Number)),i=new h2("float32",n,a.dims),[l,o,c]=t.dims;if(i.slice([0,l],[o-1,o]).sum().data[0]===l)return t.slice([0,l],[o-1,o],[0,c]).squeeze(1);{const p=i.sum(1).sub(1),g=[];for(let h=0;h<l;++h){const _=p.data[h],w=t.slice([h,h+1],[Number(_),Number(_)+1],[0,c]);g.push(w)}return m2(g,0).squeeze(1)}}async function LT(t){return 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