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this.tokens_to_ids)this.vocab[X]=Q}encode(J){let Q=[];for(let X of J){let K=[...X];if(K.length>this.max_input_chars_per_word){Q.push(this.unk_token);continue}let Z=!1,H=0,Y=[];while(H<K.length){let $=K.length,V=null;while(H<$){let W=K.slice(H,$).join("");if(H>0)W=this.config.continuing_subword_prefix+W;if(this.tokens_to_ids.has(W)){V=W;break}--$}if(V===null){Z=!0;break}Y.push(V),H=$}if(Z)Q.push(this.unk_token);else Q.push(...Y)}return Q}},dY=I3,pY=class J{constructor(Q,X){this.is_leaf=Q,this.children=X}static default(){return new J(!1,new Map)}},v3=class{constructor(){this.root=pY.default()}extend(J){for(let Q of J)this.push(Q)}push(J){let Q=this.root;for(let X of J){let K=Q.children.get(X);if(K===void 0)K=pY.default(),Q.children.set(X,K);Q=K}Q.is_leaf=!0}*common_prefix_search(J){let Q=this.root;if(Q===void 0)return;let X="";for(let K of J){if(X+=K,Q=Q.children.get(K),Q===void 0)return;if(Q.is_leaf)yield X}}},y3=v3,QJ=class 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bilinear_interpolate_4d(){if(!this._bilinear_interpolate_4d)this._bilinear_interpolate_4d=S5([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");return this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){if(!this._bicubic_interpolate_4d)this._bicubic_interpolate_4d=S5([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");return this._bicubic_interpolate_4d}static get matmul(){if(!this._matmul)this._matmul=S5([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");return this._matmul}static get 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K=0;K<X.length;++K)J.setUint8(Q+K,X.charCodeAt(K))}var AO=class{constructor(J,Q){this.audio=J,this.sampling_rate=Q}get data(){if(Array.isArray(this.audio)){if(this.audio.length===0)return new Float32Array(0);if(this.audio.length===1)return this.audio[0];let J=this.audio.reduce((K,Z)=>K+Z.length,0),Q=new Float32Array(J),X=0;for(let K of this.audio)Q.set(K,X),X+=K.length;return Q}else return this.audio}toBlob(){let J=this.audio;if(J instanceof Float32Array)J=[J];return RO(J,this.sampling_rate)}async save(J){return CV(J,this.toBlob())}},PO=class extends V6{constructor(J){super(J);let Q=this.config.sampling_rate,X=o6(257,this.config.num_mel_bins,20,Math.floor(Q/2),Q,null,"kaldi",!0);this.mel_filters=X,this.window=q5(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(J,Q){return t6(J,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:0.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:0.0000001192092955078125,remove_dc_offset:!0,max_num_frames:Q,transpose:!0})}async _call(J){Z6(J,"ASTFeatureExtractor");let Q=await this._extract_fbank_features(J,this.config.max_length);if(this.config.do_normalize){let X=this.std*2,K=Q.data;for(let Z=0;Z<K.length;++Z)K[Z]=(K[Z]-this.mean)/X}return{input_values:Q.unsqueeze_(0)}}},PV=class extends V6{async _call(J){if(Z6(J,"EncodecFeatureExtractor"),J instanceof Float64Array)J=new Float32Array(J);let Q=this.config.feature_size;if(J.length%Q!==0)throw Error(`The length of the audio data must be a multiple of the number of channels (${Q}).`);let X=[1,Q,J.length/Q];return{input_values:new u("float32",J,X)}}},EO=class extends V6{async _call(J){if(Z6(J,"ChatterboxFeatureExtractor"),J instanceof Float64Array)J=new Float32Array(J);let Q=[1,J.length];return{input_values:new u("float32",J,Q)}}},wO=class extends V6{constructor(J){super(J);this.mel_filters=o6(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=o6(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=q5(this.config.fft_window_size,"hann")}async _get_input_mel(J,Q,X,K){let Z,H=!1,Y=J.length-Q;if(Y>0)if(X==="rand_trunc"){H=!0;let $=Math.floor(IJ.random()*(Y+1));J=J.subarray($,$+Q),Z=await this._extract_fbank_features(J,this.mel_filters_slaney,this.config.nb_max_samples)}else throw Error(`Truncation strategy "${X}" not implemented`);else{if(Y<0){let $=new Float64Array(Q);if($.set(J),K==="repeat")for(let V=J.length;V<Q;V+=J.length)$.set(J.subarray(0,Math.min(J.length,Q-V)),V);else if(K==="repeatpad")for(let V=J.length;V<-Y;V+=J.length)$.set(J,V);J=$}if(X==="fusion")throw Error(`Truncation strategy "${X}" not implemented`);Z=await this._extract_fbank_features(J,this.mel_filters_slaney,this.config.nb_max_samples)}return Z.unsqueeze_(0)}async _extract_fbank_features(J,Q,X=null){return t6(J,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:Q,log_mel:"dB",max_num_frames:X,do_pad:!1,transpose:!0})}async _call(J,{max_length:Q=null}={}){return Z6(J,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(J,Q??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}},TO=0.00001,EV=class extends V6{constructor(J){super(J);this.config.mel_filters??=o6(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,this.config.sampling_rate/2,this.config.sampling_rate,"slaney","slaney");let Q=q5(this.config.win_length,"hann",{periodic:!1});this.window=new Float64Array(this.config.n_fft);let X=Math.floor((this.config.n_fft-this.config.win_length)/2);this.window.set(Q,X)}async _extract_fbank_features(J){let Q=this.config.preemphasis;J=new Float64Array(J);for(let K=J.length-1;K>=1;--K)J[K]-=Q*J[K-1];return await t6(J,this.window,this.window.length,this.config.hop_length,{fft_length:this.config.n_fft,power:2,mel_filters:this.config.mel_filters,log_mel:"log",mel_floor:-1/0,pad_mode:"constant",center:!0,transpose:!0,mel_offset:0.00000005960464477539063})}async _call(J){Z6(J,"ParakeetFeatureExtractor");let Q=await this._extract_fbank_features(J),X=Math.floor((J.length+Math.floor(this.config.n_fft/2)*2-this.config.n_fft)/this.config.hop_length),K=Q.data;K.fill(0,X*Q.dims[1]);let[Z,H]=Q.dims,Y=new Float64Array(H),$=new Float64Array(H);for(let q=0;q<X;++q){let F=q*H;for(let N=0;N<H;++N){let j=K[F+N];Y[N]+=j,$[N]+=j*j}}let V=X>1?X-1:1;for(let q=0;q<H;++q){let F=Y[q]/X,N=($[q]-X*F*F)/V,B=1/(Math.sqrt(N)+TO);for(let D=0;D<X;++D){let L=D*H+q;K[L]=(K[L]-F)*B}}let W=new BigInt64Array(Z);return W.fill(1n,0,X),{input_features:Q.unsqueeze_(0),attention_mask:new u("int64",W,[1,Z])}}},IO=class extends EV{_apply_dither(J){let Q=this.config.dither??0;if(Q<=0)return J;let X=new V7(J.length);for(let K=0;K<J.length;++K)J[K]+=Q*X.gauss();return J}split_audio(J){let Q=this.config.max_audio_clip_s??35,X=this.config.overlap_chunk_second??5,K=this.config.min_energy_window_samples??1600,Z=this.config.sampling_rate,H=Math.max(1,Math.round(Q*Z)),Y=Math.max(1,Math.round(X*Z));if(J.length<=H)return[J];let $=[],V=0,W=J.length;while(V<W){if(V+H>=W){$.push(J.slice(V,W));break}let q=Math.max(V,V+H-Y),F=Math.min(V+H,W),N;if(F<=q)N=V+H;else N=this._find_split_point_energy(J,q,F,K);N=Math.max(V+1,Math.min(N,W)),$.push(J.slice(V,N)),V=N}return $}_find_split_point_energy(J,Q,X,K){let Z=X-Q;if(Z<=K)return Math.floor((Q+X)/2);let H=1/0,Y=Q,$=Z-K;for(let V=0;V<=$;V+=K){let W=0;for(let q=0;q<K;++q){let F=J[Q+V+q];W+=F*F}if(W=Math.sqrt(W/K),W<H)H=W,Y=Q+V}return Y}async _call(J){Z6(J,"CohereAsrFeatureExtractor");let Q=new Float64Array(J);return this._apply_dither(Q),super._call(Q)}},wV=class extends PV{},TV=class extends V6{constructor(J){super(J);let{fft_length:Q,feature_size:X,min_frequency:K,max_frequency:Z,sampling_rate:H,frame_length:Y}=this.config,$=o6(Math.floor(1+Q/2),X,K,Z,H,null,"htk",!1);this.mel_filters=$,this.window=q5(Y,"hann")}async _extract_fbank_features(J,Q){return t6(J,this.window,this.config.frame_length,this.config.hop_length,{fft_length:this.config.fft_length,center:!1,onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,remove_dc_offset:!1,transpose:!0})}async _call(J,{max_length:Q=480000,truncation:X=!0,padding:K=!0,pad_to_multiple_of:Z=128}={}){if(Z6(J,"Gemma3nAudioFeatureExtractor"),X&&J.length>Q)J=J.slice(0,Q);if(K&&J.length%Z!==0){let $=Z-J.length%Z,V=new Float64Array(J.length+$);if(V.set(J),this.config.padding_value!==0)V.fill(this.config.padding_value,J.length);J=V}let H=await this._extract_fbank_features(J,this.config.max_length),Y=$6([1,H.dims[0]],!0);return{input_features:H.unsqueeze_(0),input_features_mask:Y}}},IV=class extends TV{async _extract_fbank_features(J,Q){let{frame_length:X,hop_length:K,fft_length:Z}=this.config,H=Math.floor(X/2),Y=Math.floor((J.length+H-(X+1))/K)+1;return t6(J,this.window,X,K,{fft_length:Z,center:!0,pad_mode:"semicausal",onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,mel_floor_mode:"add",remove_dc_offset:!1,transpose:!0,max_num_frames:Y})}async _call(J,Q={}){Z6(J,"Gemma4AudioFeatureExtractor");let X=J.length,K=await super._call(J,Q),{input_features:Z}=K,[,H,Y]=Z.dims,{frame_length:$,hop_length:V}=this.config,W=Math.floor($/2),q=$+1,F=new Uint8Array(X+W+(Q.pad_to_multiple_of??128));F.fill(1,W,W+X);let N=new Uint8Array(H);for(let B=0;B<H;++B)N[B]=F[B*V+q-1]?1:0;let j=Z.data;for(let B=0;B<H;++B)if(!N[B])j.fill(0,B*Y,(B+1)*Y);return K.input_features_mask=new u("bool",N,[1,H]),K}},vO=class extends V6{constructor(J){super(J);let{n_fft:Q,win_length:X,n_mels:K,sample_rate:Z}=J.melspec_kwargs;this.mel_filters=o6(Math.floor(1+Q/2),K,0,Z/2,Z,null,"htk");let H=q5(X,"hann");this.window=new Float64Array(Q);let Y=Math.floor((Q-X)/2);this.window.set(H,Y)}async _call(J){Z6(J,"GraniteSpeechFeatureExtractor");let{n_fft:Q,hop_length:X,n_mels:K}=this.config.melspec_kwargs,Z=1+Math.floor((J.length-1)/X),H=Z-Z%2;return{input_features:(await t6(J,this.window,Q,X,{power:2,mel_filters:this.mel_filters,log_mel:"log10_max_norm",transpose:!0,max_num_frames:H,do_pad:!1})).view(-1,2*K).unsqueeze_(0)}}},yO=class extends V6{async _call(J){if(Z6(J,"MoonshineFeatureExtractor"),J instanceof Float64Array)J=new Float32Array(J);let Q=[1,J.length];return{input_values:new u("float32",J,Q)}}},vV=class extends V6{async _call(J){if(Z6(J,"PyAnnoteFeatureExtractor"),J instanceof Float64Array)J=new Float32Array(J);let Q=[1,1,J.length];return{input_values:new u("float32",J,Q)}}samples_to_frames(J){return(J-this.config.offset)/this.config.step}post_process_speaker_diarization(J,Q){let X=Q/this.samples_to_frames(Q)/this.config.sampling_rate,K=[];for(let Z of J.tolist()){let H=[],Y=-1;for(let $=0;$<Z.length;++$){let V=K6(Z[$]),[W,q]=c0(V),[F,N]=[$,$+1];if(q!==Y)Y=q,H.push({id:q,start:F,end:N,score:W});else H.at(-1).end=N,H.at(-1).score+=W}K.push(H.map(({id:$,start:V,end:W,score:q})=>({id:$,start:V*X,end:W*X,confidence:q/(W-V)})))}return K}},xO=class extends V6{constructor(J){super(J);let Q=this.config.sampling_rate,X=o6(257,this.config.num_mel_bins,20,Math.floor(Q/2),Q,null,"kaldi",!0);this.mel_filters=X,this.window=q5(400,"povey",{periodic:!1})}async _extract_fbank_features(J,Q){return J=J.map((X)=>X*32768),t6(J,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:0.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:0.0000001192092955078125,remove_dc_offset:!0,max_num_frames:Q,transpose:!0})}async _call(J,{padding:Q=!0,pad_to_multiple_of:X=2,do_normalize_per_mel_bins:K=!0,return_attention_mask:Z=!0}={}){Z6(J,"SeamlessM4TFeatureExtractor");let H=await this._extract_fbank_features(J,this.config.max_length);if(K){let[j,B]=H.dims,D=H.data;for(let L=0;L<B;++L){let M=0;for(let w=0;w<j;++w)M+=D[w*B+L];let R=M/j,E=0;for(let w=0;w<j;++w)E+=(D[w*B+L]-R)**2;E/=j-1;let P=Math.sqrt(E+0.0000001);for(let w=0;w<j;++w){let k=w*B+L;D[k]=(D[k]-R)/P}}}let Y;if(Q){let[j,B]=H.dims,D=H.data,L=j%X;if(L>0){let M=new Float32Array(B*(j+L));M.set(D),M.fill(this.config.padding_value,D.length);let R=j+L;if(H=new u(H.type,M,[R,B]),Z)Y=new u("int64",new BigInt64Array(R),[1,R]),Y.data.fill(1n,0,j)}}let[$,V]=H.dims,W=this.config.stride;if($%W!==0)throw Error(`The number of frames (${$}) must be a multiple of the stride (${W}).`);let F=H.view(1,Math.floor($/W),V*W),N={input_features:F};if(Z){let j=F.dims[1],B=new BigInt64Array(j);if(Y){let D=Y.data;for(let L=1,M=0;L<$;L+=W,++M)B[M]=D[L]}else B.fill(1n);N.attention_mask=new u("int64",B,[1,j])}return N}},kO=class extends wV{},hO=class extends V6{},bO=class extends V6{_zero_mean_unit_var_norm(J){let X=J.reduce((Z,H)=>Z+H,0)/J.length,K=J.reduce((Z,H)=>Z+(H-X)**2,0)/J.length;return J.map((Z)=>(Z-X)/Math.sqrt(K+0.0000001))}async _call(J){if(Z6(J,"Wav2Vec2FeatureExtractor"),J instanceof Float64Array)J=new Float32Array(J);let Q=J;if(this.config.do_normalize)Q=this._zero_mean_unit_var_norm(Q);let X=[1,Q.length];return{input_values:new u("float32",Q,X),attention_mask:new u("int64",new BigInt64Array(Q.length).fill(1n),X)}}},fO=class extends V6{constructor(J){super(J);let Q=this.config.sampling_rate,X=o6(257,this.config.num_mel_bins,20,Math.floor(Q/2),Q,null,"kaldi",!0);this.mel_filters=X,this.window=q5(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(J){return J=J.map((Q)=>Q*32768),t6(J,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:0.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:0.0000001192092955078125,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(J){Z6(J,"WeSpeakerFeatureExtractor");let Q=(await this._extract_fbank_features(J)).unsqueeze_(0);if(this.config.fbank_centering_span===null){let X=Q.mean(1).data,K=Q.data,[Z,H,Y]=Q.dims;for(let $=0;$<Z;++$){let V=$*H*Y,W=$*Y;for(let q=0;q<H;++q){let F=V+q*Y;for(let N=0;N<Y;++N)K[F+N]-=X[W+N]}}}return{input_features:Q}}},_O=class extends V6{constructor(J){super(J);this.config.mel_filters??=o6(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8000,this.config.sampling_rate,"slaney","slaney"),this.window=q5(this.config.n_fft,"hann")}async _extract_fbank_features(J,{center:Q=!0}={}){let{n_fft:X,hop_length:K,mel_filters:Z,global_log_mel_max:H}=this.config,Y=Q?Math.floor(J.length/K):Math.floor((J.length-X)/K);return await t6(J,this.window,X,K,{power:2,mel_filters:Z,log_mel:"log10_max_norm",max_log_mel:H,center:Q,max_num_frames:Y,do_pad:!1})}async _call(J,{center:Q=!0}={}){return Z6(J,"VoxtralRealtimeFeatureExtractor"),{input_features:(await this._extract_fbank_features(J,{center:Q})).unsqueeze_(0)}}},gO=class extends V6{constructor(J){super(J);this.config.mel_filters??=o6(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8000,this.config.sampling_rate,"slaney","slaney"),this.window=q5(this.config.n_fft,"hann")}async _extract_fbank_features(J){return await t6(J,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10_max_norm",max_num_frames:Math.min(Math.floor(J.length/this.config.hop_length),this.config.nb_max_frames)})}async _call(J,{max_length:Q=null}={}){Z6(J,"WhisperFeatureExtractor");let X,K=Q??this.config.n_samples;if(J.length>K){if(J.length>this.config.n_samples)$0.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`.");X=J.slice(0,K)}else X=new Float32Array(K),X.set(J);return{input_features:(await this._extract_fbank_features(X)).unsqueeze_(0)}}},E6=class{static async from_pretrained(J,Q={}){let X=await s6(J,_J,!0,Q),K=X.feature_extractor_type,Z=gJ[K];if(!Z)throw Error(`Unknown feature_extractor_type: '${K}'. Please report this at ${W7}.`);return new Z(X)}},cO=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;async _call(J,Q=null){let X=this.tokenizer(J),K=Q?await this.feature_extractor(Q):{};return{...X,...K}}},lO=new Set(["ja","zh"]),uO=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;static uses_processor_config=!0;get_decoder_prompt_ids(J="en"){let Q=["▁","<|startofcontext|>","<|startoftranscript|>","<|emo:undefined|>",`<|${J}|>`,`<|${J}|>`,"<|pnc|>","<|noitn|>","<|notimestamp|>","<|nodiarize|>"];return this.tokenizer.convert_tokens_to_ids(Q)}static join_chunks(J,Q="en"){let X=J.filter((H)=>H&&H.trim());if(X.length===0)return"";let K=lO.has(Q)?"":" ";return[X[0].trimEnd(),...X.slice(1).map((H)=>H.trim())].join(K)}async _call(J){return await this.feature_extractor(J)}},BJ={},l5,yV,L5;if(U0.IS_WEB_ENV)l5=(J,Q)=>{if(!self.OffscreenCanvas)throw Error("OffscreenCanvas not supported by this environment.");return new self.OffscreenCanvas(J,Q)},L5=self.createImageBitmap,yV=self.ImageData;else if(BJ)L5=async(J)=>{let X=(await J.metadata()).channels,{data:K,info:Z}=await J.rotate().raw().toBuffer({resolveWithObject:!0}),H=new h6(new Uint8ClampedArray(K),Z.width,Z.height,Z.channels);if(X!==void 0&&X!==Z.channels)H.convert(X);return H};else throw Error("Unable to load image processing library.");var mO={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},dO=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]),h6=class J{constructor(Q,X,K,Z){this.data=Q,this.width=X,this.height=K,this.channels=Z}get size(){return[this.width,this.height]}static async read(Q){if(Q instanceof J)return Q;else if(typeof Q==="string"||Q instanceof URL)return await this.fromURL(Q);else if(Q instanceof Blob)return await this.fromBlob(Q);else if(typeof HTMLCanvasElement<"u"&&Q instanceof HTMLCanvasElement||typeof OffscreenCanvas<"u"&&Q instanceof OffscreenCanvas)return this.fromCanvas(Q);else throw Error(`Unsupported input type: ${typeof Q}`)}static fromCanvas(Q){if(!U0.IS_WEB_ENV)throw Error("fromCanvas() is only supported in browser environments.");let K=Q.getContext("2d").getImageData(0,0,Q.width,Q.height).data;return new J(K,Q.width,Q.height,4)}static async fromURL(Q){let X=await e8(Q);if(X.status!==200)throw Error(`Unable to read image from "${Q}" (${X.status} ${X.statusText})`);let K=await X.blob();return this.fromBlob(K)}static async fromBlob(Q){if(U0.IS_WEB_ENV){let X=await L5(Q),K=l5(X.width,X.height).getContext("2d");return K.drawImage(X,0,0),new this(K.getImageData(0,0,X.width,X.height).data,X.width,X.height,4)}else{let X=BJ(await Q.arrayBuffer());return await L5(X)}}static fromTensor(Q,X="CHW"){if(Q.dims.length!==3)throw Error(`Tensor should have 3 dimensions, but has ${Q.dims.length} dimensions.`);if(X==="CHW")Q=Q.transpose(1,2,0);else if(X==="HWC");else throw Error(`Unsupported channel format: ${X}`);if(!(Q.data instanceof Uint8ClampedArray||Q.data instanceof Uint8Array))throw Error(`Unsupported tensor type: ${Q.type}`);switch(Q.dims[2]){case 1:case 2:case 3:case 4:return new J(Q.data,Q.dims[1],Q.dims[0],Q.dims[2]);default:throw Error(`Unsupported number of channels: ${Q.dims[2]}`)}}grayscale(){if(this.channels===1)return this;let Q=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let X=0,K=0;X<this.data.length;X+=this.channels){let Z=this.data[X],H=this.data[X+1],Y=this.data[X+2];Q[K++]=Math.round(0.2989*Z+0.587*H+0.114*Y)}break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(Q,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let Q=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let X=0,K=0;X<this.data.length;++X)Q[K++]=this.data[X],Q[K++]=this.data[X],Q[K++]=this.data[X];break;case 4:for(let X=0,K=0;X<this.data.length;X+=4)Q[K++]=this.data[X],Q[K++]=this.data[X+1],Q[K++]=this.data[X+2];break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(Q,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let Q=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let X=0,K=0;X<this.data.length;++X)Q[K++]=this.data[X],Q[K++]=this.data[X],Q[K++]=this.data[X],Q[K++]=255;break;case 3:for(let X=0,K=0;X<this.data.length;X+=3)Q[K++]=this.data[X],Q[K++]=this.data[X+1],Q[K++]=this.data[X+2],Q[K++]=255;break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(Q,this.width,this.height,4)}putAlpha(Q){if(Q.width!==this.width||Q.height!==this.height)throw Error(`Expected mask size to be ${this.width}x${this.height}, but got ${Q.width}x${Q.height}`);if(Q.channels!==1)throw Error(`Expected mask to have 1 channel, but got ${Q.channels}`);let X=this.data,K=Q.data,Z=this.width*this.height;if(this.channels===3){let H=new Uint8ClampedArray(Z*4);for(let Y=0,$=0,V=0;Y<Z;++Y)H[V++]=X[$++],H[V++]=X[$++],H[V++]=X[$++],H[V++]=K[Y];return this._update(H,this.width,this.height,4)}else if(this.channels===4){for(let H=0;H<Z;++H)X[4*H+3]=K[H];return this}throw Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(Q,X,{resample:K=2}={}){if(this.width===Q&&this.height===X)return this;let Z=mO[K]??K,H=gY(Q),Y=gY(X);if(H&&Y)return this;else if(H)Q=X/this.height*this.width;else if(Y)X=Q/this.width*this.height;if(U0.IS_WEB_ENV){let $=this.channels,V=this.toCanvas(),W=l5(Q,X).getContext("2d");return W.drawImage(V,0,0,Q,X),new J(W.getImageData(0,0,Q,X).data,Q,X,4).convert($)}else{let $=this.toSharp();switch(Z){case"box":case"hamming":if(Z==="box"||Z==="hamming")$0.warn(`Resampling method ${Z} is not yet supported. Using bilinear instead.`),Z="bilinear";case"nearest":case"bilinear":case"bicubic":$=$.affine([Q/this.width,0,0,X/this.height],{interpolator:Z});break;case"lanczos":$=$.resize({width:Q,height:X,fit:"fill",kernel:"lanczos3"});break;default:throw Error(`Resampling method ${Z} is not supported.`)}return await L5($)}}async pad([Q,X,K,Z]){if(Q=Math.max(Q,0),X=Math.max(X,0),K=Math.max(K,0),Z=Math.max(Z,0),Q===0&&X===0&&K===0&&Z===0)return this;if(U0.IS_WEB_ENV){let H=this.channels,Y=this.toCanvas(),$=this.width+Q+X,V=this.height+K+Z,W=l5($,V).getContext("2d");return W.drawImage(Y,0,0,this.width,this.height,Q,K,this.width,this.height),new J(W.getImageData(0,0,$,V).data,$,V,4).convert(H)}else{let H=this.toSharp().extend({left:Q,right:X,top:K,bottom:Z});return await L5(H)}}async crop([Q,X,K,Z]){if(Q=Math.max(Q,0),X=Math.max(X,0),K=Math.min(K,this.width-1),Z=Math.min(Z,this.height-1),Q===0&&X===0&&K===this.width-1&&Z===this.height-1)return this;let H=K-Q+1,Y=Z-X+1;if(U0.IS_WEB_ENV){let $=this.channels,V=this.toCanvas(),W=l5(H,Y).getContext("2d");return W.drawImage(V,Q,X,H,Y,0,0,H,Y),new J(W.getImageData(0,0,H,Y).data,H,Y,4).convert($)}else{let $=this.toSharp().extract({left:Q,top:X,width:H,height:Y});return await L5($)}}async center_crop(Q,X){if(this.width===Q&&this.height===X)return this;let K=(this.width-Q)/2,Z=(this.height-X)/2;if(U0.IS_WEB_ENV){let H=this.channels,Y=this.toCanvas(),$=l5(Q,X).getContext("2d"),V=0,W=0,q=0,F=0;if(K>=0)V=K;else q=-K;if(Z>=0)W=Z;else F=-Z;return $.drawImage(Y,V,W,Q,X,q,F,Q,X),new J($.getImageData(0,0,Q,X).data,Q,X,4).convert(H)}else{let H=this.toSharp();if(K>=0&&Z>=0)H=H.extract({left:Math.floor(K),top:Math.floor(Z),width:Q,height:X});else if(K<=0&&Z<=0){let Y=Math.floor(-Z),$=Math.floor(-K);H=H.extend({top:Y,left:$,right:Q-this.width-$,bottom:X-this.height-Y})}else{let Y=[0,0],$=0;if(Z<0)Y[0]=Math.floor(-Z),Y[1]=X-this.height-Y[0];else $=Math.floor(Z);let V=[0,0],W=0;if(K<0)V[0]=Math.floor(-K),V[1]=Q-this.width-V[0];else W=Math.floor(K);H=H.extend({top:Y[0],bottom:Y[1],left:V[0],right:V[1]}).extract({left:W,top:$,width:Q,height:X})}return await L5(H)}}async toBlob(Q="image/png",X=1){if(!U0.IS_WEB_ENV)throw Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:Q,quality:X})}toTensor(Q="CHW"){let X=new u("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(Q==="HWC");else if(Q==="CHW")X=X.permute(2,0,1);else throw Error(`Unsupported channel format: ${Q}`);return X}toCanvas(){if(!U0.IS_WEB_ENV)throw Error("toCanvas() is only supported in browser environments.");let Q=this.clone().rgba(),X=l5(Q.width,Q.height),K=new yV(Q.data,Q.width,Q.height);return X.getContext("2d").putImageData(K,0,0),X}split(){let{data:Q,width:X,height:K,channels:Z}=this,H=Q.constructor,Y=Q.length/Z,$=Array.from({length:Z},()=>new H(Y));for(let V=0;V<Y;++V){let W=Z*V;for(let q=0;q<Z;++q)$[q][V]=Q[W+q]}return $.map((V)=>new J(V,X,K,1))}_update(Q,X,K,Z=null){if(this.data=Q,this.width=X,this.height=K,Z!==null)this.channels=Z;return this}clone(){return new J(this.data.slice(),this.width,this.height,this.channels)}convert(Q){if(this.channels===Q)return this;switch(Q){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(Q){if(U0.IS_WEB_ENV){if(U0.IS_WEBWORKER_ENV)throw Error("Unable to save an image from a Web Worker.");let X=Q.split(".").pop().toLowerCase(),K=dO.get(X)??"image/png",Z=await this.toBlob(K);return CV(Q,Z)}else if(U0.IS_FS_AVAILABLE)await this.toSharp().toFile(Q);else throw Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(U0.IS_WEB_ENV)throw Error("toSharp() is only supported in server-side environments.");return BJ(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}},ZI=h6.read.bind(h6);function M$(J,Q,X=0,K=null){let Z=J/Q,H=pj(Z)*Q;if(K!==null&&H>K)H=Math.floor(Z)*Q;if(H<X)H=Math.ceil(Z)*Q;return H}function S$([J,Q],X){return[Math.max(Math.floor(J/X),1)*X,Math.max(Math.floor(Q/X),1)*X]}function xV([J,Q,X,K]){return[J-X/2,Q-K/2,J+X/2,Q+K/2]}function G7(J,Q=0.5,X=null,K=!1){let{logits:Z,pred_boxes:H}=J,[Y,$,V]=Z.dims;if(X!==null&&X.length!==Y)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let W=[];for(let q=0;q<Y;++q){let F=X!==null?X[q]:null,N={boxes:[],classes:[],scores:[]},j=Z[q],B=H[q];for(let D=0;D<$;++D){let L=j[D],M=[],R;if(K){R=L.sigmoid().data;for(let E=0;E<R.length;++E)if(R[E]>Q)M.push(E)}else{let E=c0(L.data)[1];if(E===V-1)continue;if(R=K6(L.data),R[E]<Q)continue;M.push(E)}for(let E of M){let P=B[D].data;if(P=xV(P),F!==null)P=P.map((w,k)=>w*F[(k+1)%2]);N.boxes.push(P),N.classes.push(E),N.scores.push(R[E])}}W.push(N)}return W}function kV(J,Q=null){let X=J.logits,K=X.dims[0];if(Q!==null&&Q.length!==K)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Z=[];for(let H=0;H<K;++H){let Y=Q!==null?Q[H]:null,$=X[H];if(Y!==null)$=qV($,Y,"bilinear",!1);let[V,W]=Y??$.dims.slice(-2),q=new u("int32",new Int32Array(V*W),[V,W]),F=$[0].data,N=q.data;for(let D=1;D<$.dims[0];++D){let L=$[D].data;for(let M=0;M<L.length;++M)if(L[M]>F[M])F[M]=L[M],N[M]=D}let j=Array($.dims[0]);for(let D=0;D<N.length;++D){let L=N[D];j[L]=L}let B=j.filter((D)=>D!==void 0);Z.push({segmentation:q,labels:B})}return Z}function pO(J,Q,X,K){let Z=[],H=[],Y=[];for(let $=0;$<J.dims[0];++$){let V=J[$],W=Q[$],q=c0(V.data)[1];if(q===K)continue;let N=K6(V.data)[q];if(N>X)Z.push(W),H.push(N),Y.push(q)}return[Z,H,Y]}function iO(J,Q,X,K=0.5,Z=0.8){let H=[],Y=0,$=0,V=Q[X].data;for(let q=0;q<J.length;++q){if(J[q]===X)H.push(q),++Y;if(V[q]>=K)++$}let W=Y>0&&$>0;if(W)W=Y/$>Z;return[W,H]}function rO(J,Q,X,K,Z,H=null,Y=null){let[$,V]=Y??J[0].dims,W=new u("int32",new Int32Array($*V),[$,V]),q=[];if(Y!==null)for(let D=0;D<J.length;++D)J[D]=qV(J[D],Y,"bilinear",!1);let F=new Int32Array(J[0].data.length),N=new Float32Array(J[0].data.length);for(let D=0;D<J.length;++D){let L=Q[D],M=J[D].data;for(let R=0;R<M.length;++R)if(M[R]*=L,M[R]>N[R])F[R]=D,N[R]=M[R]}let j=0,B=W.data;for(let D=0;D<X.length;++D){let L=X[D],[M,R]=iO(F,J,D,K,Z);if(!M)continue;++j;for(let E of R)B[E]=j;q.push({id:j,label_id:L,score:Q[D]})}return[W,q]}function cJ(J,Q,X=28,K=3136,Z=1003520,H=1){if(J<X||Q<X){let V=Math.max(X/J,X/Q);J=Math.round(J*V),Q=Math.round(Q*V)}if(Math.max(J,Q)/Math.min(J,Q)>200)throw Error(`absolute aspect ratio must be smaller than 200, got ${Math.max(J,Q)/Math.min(J,Q)}`);let Y=Math.round(J/X)*X,$=Math.round(Q/X)*X;if(H*Y*$>Z){let V=Math.sqrt(H*J*Q/Z);Y=Math.max(X,Math.floor(J/V/X)*X),$=Math.max(X,Math.floor(Q/V/X)*X)}else if(H*Y*$<K){let V=Math.sqrt(K/(H*J*Q));Y=Math.ceil(J*V/X)*X,$=Math.ceil(Q*V/X)*X}return[$,Y]}function hV(J,Q=0.5,X=0.5,K=0.8,Z=null,H=null){if(Z===null)$0.warn("`label_ids_to_fuse` unset. No instance will be fused."),Z=new Set;let Y=J.class_queries_logits??J.logits,V=(J.masks_queries_logits??J.pred_masks).sigmoid(),[W,q,F]=Y.dims;if(F-=1,H!==null&&H.length!==W)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let N=[];for(let j=0;j<W;++j){let B=H!==null?H[j]:null,D=Y[j],L=V[j],[M,R,E]=pO(D,L,Q,F);if(E.length===0){let[k,C]=B??L.dims.slice(-2),c=new u("int32",new Int32Array(k*C).fill(-1),[k,C]);N.push({segmentation:c,segments_info:[]});continue}let[P,w]=rO(M,R,E,X,K,Z,B);N.push({segmentation:P,segments_info:w})}return N}function bV(J,Q=0.5,X=null){throw Error("`post_process_instance_segmentation` is not yet implemented.")}var V0=class extends z6{constructor(J){super();if(this.image_mean=J.image_mean??J.mean,this.image_std=J.image_std??J.std,this.resample=J.resample??2,this.do_rescale=J.do_rescale??!0,this.rescale_factor=J.rescale_factor??0.00392156862745098,this.do_normalize=J.do_normalize,this.do_thumbnail=J.do_thumbnail,this.size=J.size??J.image_size,this.do_resize=J.do_resize??this.size!==void 0,this.size_divisibility=J.size_divisibility??J.size_divisor,this.do_center_crop=J.do_center_crop,this.crop_size=J.crop_size,this.do_convert_rgb=J.do_convert_rgb??!0,this.do_crop_margin=J.do_crop_margin,this.pad_size=J.pad_size,this.do_pad=J.do_pad,this.min_pixels=J.min_pixels,this.max_pixels=J.max_pixels,this.do_pad&&!this.pad_size&&!this.size_divisibility&&this.size&&this.size.width!==void 0&&this.size.height!==void 0)this.pad_size=this.size;this.do_flip_channel_order=J.do_flip_channel_order??!1,this.config=J}async thumbnail(J,Q,X=2){let{height:K,width:Z}=J,H=Q.height,Y=Q.width,$=Math.min(K,H),V=Math.min(Z,Y);if($===K&&V===Z)return J;if(K>Z)V=Math.floor(Z*$/K);else if(Z>K)$=Math.floor(K*V/Z);return await J.resize(V,$,{resample:X})}async crop_margin(J,Q=200){let X=J.clone().grayscale(),K=NJ(X.data)[0],H=c0(X.data)[0]-K;if(H===0)return J;let Y=Q/255,$=X.width,V=X.height,W=0,q=0,F=X.data;for(let N=0;N<X.height;++N){let j=N*X.width;for(let B=0;B<X.width;++B)if((F[j+B]-K)/H<Y)$=Math.min($,B),V=Math.min(V,N),W=Math.max(W,B),q=Math.max(q,N)}return J=await J.crop([$,V,W,q]),J}pad_image(J,Q,X,{mode:K="constant",center:Z=!1,constant_values:H=0}={}){let[Y,$,V]=Q,W,q;if(typeof X==="number")W=X,q=X;else if(X==="square")W=q=Math.max(Y,$);else W=X.width,q=X.height;if(W!==$||q!==Y){let F=new Float32Array(W*q*V);if(Array.isArray(H))for(let B=0;B<F.length;++B)F[B]=H[B%V];else if(H!==0)F.fill(H);let[N,j]=Z?[Math.floor((W-$)/2),Math.floor((q-Y)/2)]:[0,0];for(let B=0;B<Y;++B){let D=(B+j)*W,L=B*$;for(let M=0;M<$;++M){let R=(D+M+N)*V,E=(L+M)*V;for(let P=0;P<V;++P)F[R+P]=J[E+P]}}if(K==="symmetric"){if(Z)throw Error("`center` padding is not supported when `mode` is set to `symmetric`.");let B=Y-1,D=$-1;for(let L=0;L<q;++L){let M=L*W,R=n9(L,B)*$;for(let E=0;E<W;++E){if(L<Y&&E<$)continue;let P=(M+E)*V,w=(R+n9(E,D))*V;for(let k=0;k<V;++k)F[P+k]=J[w+k]}}}J=F,Q=[q,W,V]}return[J,Q]}rescale(J){for(let Q=0;Q<J.length;++Q)J[Q]=this.rescale_factor*J[Q]}get_resize_output_image_size(J,Q){let[X,K]=J.size,Z,H;if(this.do_thumbnail){let{height:Y,width:$}=Q;Z=Math.min(Y,$)}else if(Number.isInteger(Q))Z=Q,H=this.config.max_size??Z;else if(Q!==void 0)Z=Q.shortest_edge,H=Q.longest_edge;if(Z!==void 0||H!==void 0){let Y=Z===void 0?1:Math.max(Z/X,Z/K),$=X*Y,V=K*Y,W=H===void 0?1:Math.min(H/$,H/V),q=Math.floor(Number(($*W).toFixed(2))),F=Math.floor(Number((V*W).toFixed(2)));if(this.size_divisibility!==void 0)[q,F]=S$([q,F],this.size_divisibility);return[q,F]}else if(Q!==void 0&&Q.width!==void 0&&Q.height!==void 0){let{width:Y,height:$}=Q;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let V=$/K,W=Y/X;if(Math.abs(1-W)<Math.abs(1-V))V=W;else W=V;$=M$(V*K,this.config.ensure_multiple_of),Y=M$(W*X,this.config.ensure_multiple_of)}return[Y,$]}else if(this.size_divisibility!==void 0)return S$([X,K],this.size_divisibility);else throw Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(Q)}`)}async resize(J){let[Q,X]=this.get_resize_output_image_size(J,this.size);return await J.resize(Q,X,{resample:this.resample})}async preprocess(J,{do_normalize:Q=null,do_pad:X=null,do_convert_rgb:K=null,do_convert_grayscale:Z=null,do_flip_channel_order:H=null}={}){if(this.do_crop_margin)J=await this.crop_margin(J);let[Y,$]=J.size;if(K??this.do_convert_rgb)J=J.rgb();else if(Z)J=J.grayscale();if(this.do_resize)J=await this.resize(J);if(this.do_thumbnail)J=await this.thumbnail(J,this.size,this.resample);if(this.do_center_crop){let N,j;if(Number.isInteger(this.crop_size))N=this.crop_size,j=this.crop_size;else N=this.crop_size.width,j=this.crop_size.height;J=await J.center_crop(N,j)}let V=[J.height,J.width],W=Float32Array.from(J.data),q=[J.height,J.width,J.channels];if(this.do_rescale)this.rescale(W);if(Q??this.do_normalize){let N=this.image_mean;if(!Array.isArray(this.image_mean))N=Array(J.channels).fill(N);let j=this.image_std;if(!Array.isArray(this.image_std))j=Array(J.channels).fill(j);if(N.length!==J.channels||j.length!==J.channels)throw Error(`When set to arrays, the length of \`image_mean\` (${N.length}) and \`image_std\` (${j.length}) must match the number of channels in the image (${J.channels}).`);for(let B=0;B<W.length;B+=J.channels)for(let D=0;D<J.channels;++D)W[B+D]=(W[B+D]-N[D])/j[D]}if(X??this.do_pad){if(this.pad_size)[W,q]=this.pad_image(W,[J.height,J.width,J.channels],this.pad_size);else if(this.size_divisibility){let N=Math.ceil(q[1]/this.size_divisibility)*this.size_divisibility,j=Math.ceil(q[0]/this.size_divisibility)*this.size_divisibility;[W,q]=this.pad_image(W,q,{width:N,height:j})}}if(H??this.do_flip_channel_order){if(q[2]!==3)throw Error("Flipping channel order is only supported for RGB images.");for(let N=0;N<W.length;N+=3){let j=W[N];W[N]=W[N+2],W[N+2]=j}}let F=new u("float32",W,q).permute(2,0,1);return{original_size:[$,Y],reshaped_input_size:V,pixel_values:F}}async _call(J,...Q){if(!Array.isArray(J))J=[J];let X=await Promise.all(J.map((Z)=>this.preprocess(Z)));return{pixel_values:$5(X.map((Z)=>Z.pixel_values),0),original_sizes:X.map((Z)=>Z.original_size),reshaped_input_sizes:X.map((Z)=>Z.reshaped_input_size)}}static async from_pretrained(J,Q={}){let X=await s6(J,Q9,!0,Q);return new this(X)}},Q7={};U8(Q7,{BeitFeatureExtractor:()=>aO,BitImageProcessor:()=>oO,CHMv2ImageProcessor:()=>sO,CLIPFeatureExtractor:()=>tO,CLIPImageProcessor:()=>fV,ChineseCLIPFeatureExtractor:()=>nO,ConvNextFeatureExtractor:()=>eO,ConvNextImageProcessor:()=>_V,DINOv3ViTImageProcessor:()=>XB,DPTFeatureExtractor:()=>ZB,DPTImageProcessor:()=>lV,DeiTFeatureExtractor:()=>JB,DeiTImageProcessor:()=>gV,DetrFeatureExtractor:()=>QB,DetrImageProcessor:()=>cV,DonutFeatureExtractor:()=>KB,DonutImageProcessor:()=>lJ,EfficientNetImageProcessor:()=>HB,GLPNFeatureExtractor:()=>GB,Gemma3ImageProcessor:()=>YB,Gemma4ImageProcessor:()=>uV,Glm46VImageProcessor:()=>WB,GroundingDinoImageProcessor:()=>qB,Idefics3ImageProcessor:()=>L$,ImageFeatureExtractor:()=>V0,ImageProcessor:()=>V0,JinaCLIPImageProcessor:()=>UB,Lfm2VlImageProcessor:()=>BB,LlavaOnevisionImageProcessor:()=>DB,Mask2FormerImageProcessor:()=>SB,MaskFormerFeatureExtractor:()=>MB,MaskFormerImageProcessor:()=>uJ,MobileNetV1FeatureExtractor:()=>LB,MobileNetV1ImageProcessor:()=>dV,MobileNetV2FeatureExtractor:()=>CB,MobileNetV2ImageProcessor:()=>pV,MobileNetV3FeatureExtractor:()=>RB,MobileNetV3ImageProcessor:()=>iV,MobileNetV4FeatureExtractor:()=>AB,MobileNetV4ImageProcessor:()=>rV,MobileViTFeatureExtractor:()=>PB,MobileViTImageProcessor:()=>aV,NougatImageProcessor:()=>EB,OwlViTFeatureExtractor:()=>wB,OwlViTImageProcessor:()=>mJ,Owlv2ImageProcessor:()=>TB,Phi3VImageProcessor:()=>vB,PixtralImageProcessor:()=>yB,PvtImageProcessor:()=>xB,Qwen2VLImageProcessor:()=>mV,RTDetrImageProcessor:()=>kB,Sam2ImageProcessor:()=>VJ,Sam3ImageProcessor:()=>VJ,SamImageProcessor:()=>VJ,SapiensFeatureExtractor:()=>hB,SapiensImageProcessor:()=>oV,SegformerFeatureExtractor:()=>bB,SegformerImageProcessor:()=>nV,SiglipImageProcessor:()=>fB,SmolVLMImageProcessor:()=>L$,Swin2SRImageProcessor:()=>_B,VLMImageProcessor:()=>FB,ViTFeatureExtractor:()=>gB,ViTImageProcessor:()=>sV,VitMatteImageProcessor:()=>cB,VitPoseImageProcessor:()=>lB,YolosFeatureExtractor:()=>uB,YolosImageProcessor:()=>tV});var aO=class extends V0{},oO=class extends V0{},nO=class extends V0{},sO=class extends V0{},fV=class extends V0{},tO=class extends fV{},_V=class extends V0{constructor(J){super(J);this.crop_pct=this.config.crop_pct??0.875}async resize(J){let Q=this.size?.shortest_edge;if(Q===void 0)throw Error("Size dictionary must contain 'shortest_edge' key.");if(Q<384){let X=Math.floor(Q/this.crop_pct),[K,Z]=this.get_resize_output_image_size(J,{shortest_edge:X});J=await J.resize(K,Z,{resample:this.resample}),J=await J.center_crop(Q,Q)}else J=await J.resize(Q,Q,{resample:this.resample});return J}},eO=class extends _V{},gV=class extends V0{},JB=class extends gV{},cV=class extends V0{async _call(J){let Q=await super._call(J),X=[Q.pixel_values.dims[0],64,64],K=$6(X,1n);return{...Q,pixel_mask:K}}post_process_object_detection(...J){return G7(...J)}post_process_panoptic_segmentation(...J){return hV(...J)}post_process_instance_segmentation(...J){return bV(...J)}},QB=class extends cV{},XB=class extends V0{},lJ=class extends V0{pad_image(J,Q,X,K={}){let[Z,H,Y]=Q,$=this.image_mean;if(!Array.isArray(this.image_mean))$=Array(Y).fill($);let V=this.image_std;if(!Array.isArray(V))V=Array(Y).fill($);let W=$.map((q,F)=>-q/V[F]);return super.pad_image(J,Q,X,{center:!0,constant_values:W,...K})}},KB=class extends lJ{},lV=class extends V0{},ZB=class extends lV{},HB=class extends V0{constructor(J){super(J);if(this.include_top=this.config.include_top??!0,this.include_top)this.image_std=this.image_std.map((Q)=>Q*Q)}},YB=class extends V0{};function $B(J,Q,X,K,Z){let H=K*X**2,Y=Math.sqrt(H/(J*Q)),$=Z*X,V=Math.floor(Y*J/$)*$,W=Math.floor(Y*Q/$)*$;if(V===0&&W===0)throw Error(`Attempting to resize to a 0 x 0 image. Resized height should be divisible by \`pooling_kernel_size * patch_size\`=${$}.`);let q=Math.floor(K/Z**2)*$;if(V===0)V=$,W=Math.min(Math.floor(Q/J)*$,q);else if(W===0)W=$,V=Math.min(Math.floor(J/Q)*$,q);return[V,W]}function VB(J,Q,X,K,Z,H,Y){let $=Math.floor(Q/Z),V=Math.floor(X/Z),W=$*V,q=Z*Z*K,F=new Float32Array(H*q),N=0;for(let D=0;D<$;++D)for(let L=0;L<V;++L)for(let M=0;M<Z;++M){let R=(D*Z+M)*X*K+L*Z*K;for(let E=0;E<Z;++E){let P=R+E*K;for(let w=0;w<K;++w)F[N++]=J[P+w]}}let j=new BigInt64Array(H*2).fill(-1n),B=0;for(let D=0;D<$;++D)for(let L=0;L<V;++L)j[B++]=BigInt(L),j[B++]=BigInt(D);return{patches:new u("float32",F,[H,q]),positions:new u("int64",j,[H,2]),num_soft_tokens:Math.floor(W/Y**2)}}var uV=class extends z6{constructor(J){super();this.config=J,this.patch_size=J.patch_size??16,this.max_soft_tokens=J.max_soft_tokens??280,this.pooling_kernel_size=J.pooling_kernel_size??3,this.resample=J.resample??3,this.rescale_factor=J.rescale_factor??0.00392156862745098,this.do_rescale=J.do_rescale??!0,this.do_resize=J.do_resize??!0,this.do_convert_rgb=J.do_convert_rgb??!0}async _call(J){if(!Array.isArray(J))J=[J];let{patch_size:Q,pooling_kernel_size:X}=this,K=this.max_soft_tokens*X**2,Z=[],H=[],Y=[];for(let $ of J){if(this.do_convert_rgb)$=$.rgb();if(this.do_resize){let[N,j]=$B($.height,$.width,Q,K,X);if(N!==$.height||j!==$.width)$=await $.resize(j,N,{resample:this.resample})}let V=Float32Array.from($.data);if(this.do_rescale)for(let N=0;N<V.length;++N)V[N]*=this.rescale_factor;let{patches:W,positions:q,num_soft_tokens:F}=VB(V,$.height,$.width,$.channels,Q,K,X);Z.push(W),H.push(q),Y.push(F)}return{pixel_values:$5(Z,0),image_position_ids:$5(H,0),num_soft_tokens_per_image:Y}}},mV=class extends V0{constructor(J){super(J);this.min_pixels=J.min_pixels??J.size?.shortest_edge,this.max_pixels=J.max_pixels??J.size?.longest_edge,this.patch_size=J.patch_size,this.merge_size=J.merge_size}get_resize_output_image_size(J,Q){let X=this.patch_size*this.merge_size;return cJ(J.height,J.width,X,this.min_pixels,this.max_pixels)}async _call(J,...Q){let{pixel_values:X,original_sizes:K,reshaped_input_sizes:Z}=await super._call(J,...Q),H=X,{temporal_patch_size:Y,merge_size:$,patch_size:V}=this.config;if(H.dims[0]===1)H=h0(Array.from({length:Y},()=>H),0);let W=H.dims[0]/Y,q=H.dims[1],F=Math.floor(H.dims[2]/V),N=Math.floor(H.dims[3]/V),j=H.view(W,Y,q,Math.floor(F/$),$,V,Math.floor(N/$),$,V).permute(0,3,6,4,7,2,1,5,8).view(W*F*N,q*Y*V*V),B=new u("int64",[W,F,N],[1,3]);return{pixel_values:j,image_grid_thw:B,original_sizes:K,reshaped_input_sizes:Z}}},WB=class extends mV{get_resize_output_image_size(J,Q){let X=this.patch_size*this.merge_size,K=this.config.temporal_patch_size??2;return cJ(J.height,J.width,X,this.min_pixels,this.max_pixels,K)}},GB=class extends V0{},qB=class extends V0{async _call(J){let Q=await super._call(J),X=Q.pixel_values.dims,K=j6([X[0],X[2],X[3]]);return{...Q,pixel_mask:K}}},L$=class extends V0{constructor(J){super(J);this.do_image_splitting=J.do_image_splitting??!0,this.max_image_size=J.max_image_size}get_resize_for_vision_encoder(J,Q){let[X,K]=J.dims.slice(-2),Z=K/X;if(K>=X)K=Math.ceil(K/Q)*Q,X=Math.floor(K/Z),X=Math.ceil(X/Q)*Q;else X=Math.ceil(X/Q)*Q,K=Math.floor(X*Z),K=Math.ceil(K/Q)*Q;return{height:X,width:K}}async _call(J,{do_image_splitting:Q=null,return_row_col_info:X=!1}={}){let K;if(!Array.isArray(J))K=[[J]];else{if(J.length===0||!J[0])throw Error("No images provided.");if(!Array.isArray(J[0]))K=[J];else K=J}let Z=[],H=[],Y=[],$=[],V=[];for(let L of K){let M=await Promise.all(L.map((P)=>this.preprocess(P)));$.push(...M.map((P)=>P.original_size)),V.push(...M.map((P)=>P.reshaped_input_size)),M.forEach((P)=>P.pixel_values.unsqueeze_(0));let{longest_edge:R}=this.max_image_size,E;if(Q??this.do_image_splitting){let P=Array(M.length),w=Array(M.length);E=await Promise.all(M.map(async(k,C)=>{let c=this.get_resize_for_vision_encoder(k.pixel_values,R),g=await k6(k.pixel_values,{size:[c.height,c.width]}),{frames:d,num_splits_h:r,num_splits_w:i}=await this.split_image(g,this.max_image_size);return P[C]=r,w[C]=i,h0(d,0)})),H.push(P),Y.push(w)}else{let P=[R,R];E=await Promise.all(M.map((w)=>k6(w.pixel_values,{size:P}))),H.push(Array(M.length).fill(0)),Y.push(Array(M.length).fill(0))}Z.push(h0(E,0))}let W=Z.length,[q,F,N,j]=Z[0].dims,B,D;if(W===1)B=Z[0].unsqueeze_(0),D=$6([W,q,N,j],!0);else{let L=Math.max(...Z.map((E)=>E.dims.at(0)));D=$6([W,L,N,j],!0);let M=D.data,R=L*N*j;for(let E=0;E<W;++E){let P=Z[E].dims[0];if(P<L){Z[E]=h0([Z[E],$6([L-P,F,N,j],0)],0);let w=E*R+P*N*j,k=(E+1)*R;M.fill(!1,w,k)}}B=$5(Z,0)}return{pixel_values:B,pixel_attention_mask:D,original_sizes:$,reshaped_input_sizes:V,...X?{rows:H,cols:Y}:{}}}async split_image(J,{longest_edge:Q}){let X=Q,K=Q,Z=[],[H,Y]=J.dims.slice(-2),$=0,V=0;if(H>X||Y>K){$=Math.ceil(H/X),V=Math.ceil(Y/K);let W=Math.ceil(H/$),q=Math.ceil(Y/V);for(let j=0;j<$;++j)for(let B=0;B<V;++B){let D,L,M,R;if(j===$-1)L=H-W,R=H;else L=j*W,R=(j+1)*W;if(B===V-1)D=Y-q,M=Y;else D=B*q,M=(B+1)*q;let w=await FV(J,[L,D],[R,M],[2,3]);Z.push(w)}let F=X,N=K;if(H!==F||Y!==N)J=await k6(J,{size:[F,N]})}return Z.push(J),{frames:Z,num_splits_h:$,num_splits_w:V}}},FB=class extends V0{constructor(J){super({do_pad:!0,pad_size:{width:J.image_size,height:J.image_size},...J});this.constant_values=this.config.background_color.map((Q)=>Q*this.rescale_factor)}pad_image(J,Q,X,K){return super.pad_image(J,Q,X,{constant_values:this.constant_values,center:!0,...K})}},UB=class extends V0{constructor(J){let{resize_mode:Q,fill_color:X,interpolation:K,size:Z,...H}=J,Y=Q==="squash"?{width:Z,height:Z}:Q==="shortest"?{shortest_edge:Z}:{longest_edge:Z},$=K==="bicubic"?3:2;super({...H,size:Y,resample:$,do_center_crop:!0,crop_size:Z,do_normalize:!0})}};function C$(J,Q){return Math.round(J/Q)*Q}function NB(J,Q,X,K,Z){let H=1/0,Y=[1,1],$=X*K;for(let V of Q){let W=Math.abs(J-V[0]/V[1]);if(W<H)H=W,Y=V;else if(W===H&&$>0.5*Z*Z*V[0]*V[1])Y=V}return Y}function jB(J,Q){let X=[],K=new Set;for(let Z=J;Z<=Q;++Z)for(let H=1;H<=Z;++H)for(let Y=1;Y<=Z;++Y){let $=H*Y;if($>=J&&$<=Q){let V=H<<16|Y;if(!K.has(V))K.add(V),X.push([H,Y])}}return X.sort((Z,H)=>Z[0]*Z[1]-H[0]*H[1])}function zB(J,Q){let[X,K,Z,H]=J.dims,Y=Math.floor(Z/Q),$=Math.floor(H/Q),V=Q*Q*K,W=J.data,q=new Float32Array(X*Y*$*V),F=Z*H;for(let N=0;N<X;++N){let j=N*K*F,B=N*Y*$*V;for(let D=0;D<Y;++D)for(let L=0;L<$;++L){let M=B+(D*$+L)*V;for(let R=0;R<Q;++R){let E=(D*Q+R)*H+L*Q;for(let P=0;P<Q;++P){let w=E+P;for(let k=0;k<K;++k)q[M++]=W[j+k*F+w]}}}}return new u("float32",q,[X,Y*$,V])}function OB(J,Q){let[,X,K]=J.dims,Z=new BigInt64Array(Q);Z.fill(1n,0,X);let H=J;if(X<Q){let Y=new Float32Array(Q*K);Y.set(J.data),H=new u("float32",Y,[1,Q,K])}return{padded:H,mask:new u("int64",Z,[Q])}}var BB=class extends V0{constructor(J){super(J);this.downsample_factor=J.downsample_factor??2,this.do_image_splitting=J.do_image_splitting??!0,this.min_tiles=J.min_tiles??2,this.max_tiles=J.max_tiles??10,this.use_thumbnail=J.use_thumbnail??!0,this.min_image_tokens=J.min_image_tokens??64,this.max_image_tokens=J.max_image_tokens??256,this.encoder_patch_size=J.encoder_patch_size??J.patch_size??16,this.tile_size=J.tile_size??512,this.max_pixels_tolerance=J.max_pixels_tolerance??2,this.return_row_col_info=J.return_row_col_info??!1;let Q=this.max_image_tokens*this.downsample_factor**2,X=this.do_image_splitting?(this.tile_size/this.encoder_patch_size)**2:0;this.max_num_patches=Math.max(Q,X)}_is_image_too_large(J,Q){let X=this.encoder_patch_size*this.downsample_factor,K=Math.max(this.encoder_patch_size,C$(J,X)),Z=Math.max(this.encoder_patch_size,C$(Q,X));return K*Z>this.max_image_tokens*(this.encoder_patch_size*this.downsample_factor)**2*this.max_pixels_tolerance}_get_grid_layout(J,Q){let X=jB(this.min_tiles,this.max_tiles),[K,Z]=NB(Q/J,X,Q,J,this.tile_size);return{grid_width:K,grid_height:Z,target_width:this.tile_size*K,target_height:this.tile_size*Z}}async _call(J,{return_row_col_info:Q=null}={}){let X;if(!Array.isArray(J))X=[[J]];else if(!Array.isArray(J[0]))X=[J];else X=J;let K=[],Z=[],H=[],Y=[],$=[],V=[];for(let q of X){let F=await Promise.all(q.map((N)=>this.preprocess(N,{do_pad:!1})));for(let{pixel_values:N}of F){let[,j,B]=N.dims,D=N.unsqueeze_(0),L=this.encoder_patch_size*this.downsample_factor,M=L**2,[R,E]=cJ(Math.max(L,j),Math.max(L,B),L,this.min_image_tokens*M,this.max_image_tokens*M).map((g)=>Math.max(L,g)),P,w=1,k=1,C=this._is_image_too_large(j,B),c=this.do_image_splitting&&!(this.min_tiles===1&&this.max_tiles===1);if(C&&c){let{grid_width:g,grid_height:d,target_width:r,target_height:i}=this._get_grid_layout(j,B);w=d,k=g;let p=await k6(D,{size:[i,r]});P=[];for(let f=0;f<d;++f)for(let v=0;v<g;++v){let o=f*this.tile_size,Q0=v*this.tile_size;P.push(p.slice(null,null,[o,o+this.tile_size],[Q0,Q0+this.tile_size]))}if(this.use_thumbnail&&g*d!==1)P.push(await k6(D,{size:[E,R]}))}else P=[await k6(D,{size:[E,R]})];for(let g of P){let[,,d,r]=g.dims,i=zB(g,this.encoder_patch_size),{padded:p,mask:f}=OB(i,this.max_num_patches);K.push(p),Z.push(f),H.push([Math.floor(d/this.encoder_patch_size),Math.floor(r/this.encoder_patch_size)])}Y.push(w),$.push(k),V.push([E,R])}}let W={pixel_values:h0(K,0),pixel_attention_mask:$5(Z,0),spatial_shapes:new u("int64",BigInt64Array.from(H.flat(),BigInt),[H.length,2])};if(Q??this.return_row_col_info)W.image_rows=Y,W.image_cols=$,W.image_sizes=V;return W}},DB=class extends V0{},uJ=class extends V0{post_process_panoptic_segmentation(...J){return hV(...J)}post_process_instance_segmentation(...J){return bV(...J)}},MB=class extends uJ{},SB=class extends uJ{},dV=class extends V0{},LB=class extends dV{},pV=class extends V0{},CB=class extends pV{},iV=class extends V0{},RB=class extends iV{},rV=class extends V0{},AB=class extends rV{},aV=class extends V0{},PB=class extends aV{},EB=class extends lJ{},mJ=class extends V0{post_process_object_detection(...J){return G7(...J)}},wB=class extends mJ{},TB=class extends mJ{},M6=336,IB=[2,3],{ceil:YJ,floor:K8,sqrt:$J}=Math,vB=class extends V0{constructor(J){super({...J,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0});this._num_crops=J.num_crops}calc_num_image_tokens_from_image_size(J,Q){let{num_img_tokens:X}=this.config;return K8((K8(Q/M6)*K8(J/M6)+1)*X+1+(K8(Q/M6)+1)*$J(X))}get_resize_output_image_size(J,Q){let X=this._num_crops,[K,Z]=J.size,H=K/Z,Y=1;while(Y*Math.ceil(Y/H)<=X)Y+=1;Y-=1;let $=Math.floor(Y*336),V=Math.floor($/H);return[$,V]}pad_image(J,Q,X,K={}){let[Z,H]=Q,Y=M6*YJ(Z/M6),$=M6*YJ(H/M6),V=[1,1,1].map((W,q)=>(W-this.image_mean[q])/this.image_std[q]);return super.pad_image(J,Q,{width:$,height:Y},{center:!0,constant_values:V,...K})}async _call(J,{num_crops:Q=null}={}){if(this._num_crops=Q??=this.config.num_crops,Q<4||$J(Q)%1!==0)throw Error("num_crops must be a square number >= 4");if(!Array.isArray(J))J=[J];let X=J.length,K=await Promise.all(J.map((F)=>this.preprocess(F))),Z=K.map((F)=>F.original_size),H=K.map((F)=>F.reshaped_input_size),Y=[];for(let{pixel_values:F}of K){F.unsqueeze_(0);let[N,j]=F.dims.slice(-2),B=await k6(F,{size:[M6,M6],mode:"bicubic"});if(Q>0){let D=[],L=$J(Q),M=K8(j/L),R=K8(N/L);for(let P=0;P<L;++P)for(let w=0;w<L;++w){let k,C,c,g;if(P===L-1)C=N-R,g=N;else C=P*R,g=(P+1)*R;if(w===L-1)k=j-M,c=j;else k=w*M,c=(w+1)*M;let i=await FV(F,[C,k],[g,c],IB);D.push(i)}let E=await k6(h0(D,0),{size:[M6,M6],mode:"bicubic"});Y.push(h0([B,E],0))}else Y.push(B)}let $=$5(Y,0),V=H.map((F)=>F.map((N)=>M6*YJ(N/M6))),W=new u("int64",V.flat(),[X,2]),q=V.map(([F,N])=>this.calc_num_image_tokens_from_image_size(N,F));return{pixel_values:$,original_sizes:Z,reshaped_input_sizes:H,image_sizes:W,num_img_tokens:q}}},yB=class extends V0{get_resize_output_image_size(J,Q){let{longest_edge:X}=Q;if(X===void 0)throw Error("size must contain 'longest_edge'");let[K,Z]=J.size,H=Math.max(K,Z)/X,Y=K,$=Z;if(H>1)Y=Math.floor(K/H),$=Math.floor(Z/H);let{patch_size:V,spatial_merge_size:W}=this.config;if(!W)throw Error("config must contain 'spatial_merge_size'");let q=V*W,F=Math.floor((Y-1)/q)+1,N=Math.floor(($-1)/q)+1;return[F*q,N*q]}},xB=class extends V0{},kB=class extends V0{post_process_object_detection(...J){return G7(...J)}},VJ=class extends V0{reshape_input_points(J,Q,X,K=!1){J=structuredClone(J);let Z=cY(J);if(Z.length===3){if(!K)Z=[1,...Z];J=[J]}else if(Z.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let H=0;H<J.length;++H){let[Y,$]=Q[H],[V,W]=X[H],q=[W/$,V/Y];for(let F=0;F<J[H].length;++F)for(let N=0;N<J[H][F].length;++N)for(let j=0;j<J[H][F][N].length;++j)J[H][F][N][j]*=q[j%2]}return new u("float32",Float32Array.from(J.flat(1/0)),Z)}add_input_labels(J,Q){let X=cY(J);if(X.length===2)X=[1,...X],J=[J];else if(X.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(X.some((K,Z)=>K!==Q.dims[Z]))throw Error(`The first ${X.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new u("int64",J.flat(1/0).map(BigInt),X)}async _call(J,{input_points:Q=null,input_labels:X=null,input_boxes:K=null}={}){let Z=await super._call(J);if(Q)Z.input_points=this.reshape_input_points(Q,Z.original_sizes,Z.reshaped_input_sizes);if(X){if(!Z.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");Z.input_labels=this.add_input_labels(X,Z.input_points)}if(K)Z.input_boxes=this.reshape_input_points(K,Z.original_sizes,Z.reshaped_input_sizes,!0);return Z}async post_process_masks(J,Q,X,{mask_threshold:K=0,binarize:Z=!0,pad_size:H=null}={}){let Y=[];H=H??this.pad_size??this.size;let $=[H.height,H.width];for(let V=0;V<Q.length;++V){let W=Q[V],q=X[V],F=await k6(J[V],{mode:"bilinear",size:$});if(F=F.slice(null,null,[0,q[0]],[0,q[1]]),F=await k6(F,{mode:"bilinear",size:W}),Z){let N=F.data,j=new Uint8Array(N.length);for(let B=0;B<N.length;++B)if(N[B]>K)j[B]=1;F=new u("bool",j,F.dims)}Y.push(F)}return Y}generate_crop_boxes(J,Q,{crop_n_layers:X=0,overlap_ratio:K=0.3413333333333333,points_per_crop:Z=32,crop_n_points_downscale_factor:H=1}={}){}},oV=class extends V0{post_process_semantic_segmentation(...J){return kV(...J)}},hB=class extends oV{},nV=class extends V0{post_process_semantic_segmentation(...J){return kV(...J)}},bB=class extends nV{},fB=class extends V0{},_B=class extends V0{pad_image(J,Q,X,K={}){let[Z,H,Y]=Q;return super.pad_image(J,Q,{width:H+(X-H%X)%X,height:Z+(X-Z%X)%X},{mode:"symmetric",center:!1,constant_values:-1,...K})}},sV=class extends V0{},gB=class extends sV{},cB=class extends V0{async _call(J,Q){if(!Array.isArray(J))J=[J];if(!Array.isArray(Q))Q=[Q];let X=await Promise.all(J.map((H)=>this.preprocess(H))),K=await Promise.all(Q.map((H)=>this.preprocess(H,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:$5(X.map((H,Y)=>h0([H.pixel_values,K[Y].pixel_values],0)),0),original_sizes:X.map((H)=>H.original_size),reshaped_input_sizes:X.map((H)=>H.reshaped_input_size)}}},lB=class extends V0{post_process_pose_estimation(J,Q,{threshold:X=null}={}){let K=J.tolist(),[Z,H,Y,$]=J.dims,V=[];for(let W=0;W<Z;++W){let q=K[W],F=Q[W],N=[];for(let j=0;j<F.length;++j){let B=F[j],D=[],L=[],M=[],R=B.at(-2)/$,E=B.at(-1)/Y;for(let P=0;P<q.length;++P){let[w,k]=[0,0],C=0,c=-1/0,g=q[P];for(let r=0;r<g.length;++r){let i=g[r];for(let p=0;p<i.length;++p){let f=i[p];C+=f,c=Math.max(c,f),w+=(p+0.5)*f,k+=r*f}}if(X!=null&&c<X)continue;let d=[R*w/C,E*k/C];D.push(d),M.push(P),L.push(c)}N.push({bbox:B,scores:L,labels:M,keypoints:D})}V.push(N)}return V}},tV=class extends V0{post_process_object_detection(...J){return G7(...J)}},uB=class extends tV{},W6=class{static async from_pretrained(J,Q={}){let X=await s6(J,Q9,!0,Q),K=X.image_processor_type??X.feature_extractor_type,Z=Q7[K?.replace(/Fast$/,"")];if(!Z){if(K!==void 0)$0.warn(`Image processor type '${K}' not found, assuming base ImageProcessor. Please report this at ${W7}.`);Z=V0}return new Z(X)}},mB=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;constructor(J,Q,X){super(J,Q,X);let{tasks_answer_post_processing_type:K,task_prompts_without_inputs:Z,task_prompts_with_input:H}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(K??{})),this.task_prompts_without_inputs=new Map(Object.entries(Z??{})),this.task_prompts_with_input=new Map(Object.entries(H??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1000}construct_prompts(J){if(typeof J==="string")J=[J];let Q=[];for(let X of J)if(this.task_prompts_without_inputs.has(X))Q.push(this.task_prompts_without_inputs.get(X));else{for(let[K,Z]of this.task_prompts_with_input)if(X.includes(K)){Q.push(Z.replaceAll("{input}",X).replaceAll(K,""));break}if(Q.length!==J.length)Q.push(X)}return Q}post_process_generation(J,Q,X){let K=this.tasks_answer_post_processing_type.get(Q)??"pure_text";J=J.replaceAll("<s>","").replaceAll("</s>","");let Z;switch(K){case"pure_text":Z=J;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":let H=K==="ocr"?"quad_boxes":"bboxes",Y=J.matchAll(this.regexes[H]),$=[],V=[];for(let[W,q,...F]of Y)$.push(q?q.trim():$.at(-1)??""),V.push(F.map((N,j)=>(Number(N)+0.5)/this.size_per_bin*X[j%2]));Z={labels:$,[H]:V};break;default:throw Error(`Task "${Q}" (of type "${K}") not yet implemented.`)}return{[Q]:Z}}async _call(J,Q=null,X={}){if(!J&&!Q)throw Error("Either text or images must be provided");let K=await this.image_processor(J,X),Z=Q?this.tokenizer(this.construct_prompts(Q),X):{};return{...K,...Z}}},dB=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(J,Q,X){super(J,Q,X);this.image_seq_length=this.config.image_seq_length;let{boi_token:K,image_token:Z,eoi_token:H}=this.tokenizer.config;this.boi_token=K,this.image_token=Z,this.eoi_token=H;let Y=Z.repeat(this.image_seq_length);this.full_image_sequence=`

${K}${Y}${H}

`}async _call(J,Q=null,X={}){if(typeof J==="string")J=[J];let K;if(Q)K=await this.image_processor(Q,X),J=J.map((H)=>H.replaceAll(this.boi_token,this.full_image_sequence));return{...this.tokenizer(J,X),...K}}},pB=class extends R0{static image_processor_class=W6;static feature_extractor_class=E6;static tokenizer_class=C0;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(J,Q,X){super(J,Q,X);this.audio_seq_length=this.config.audio_seq_length,this.image_seq_length=this.config.image_seq_length;let{audio_token_id:K,boa_token:Z,audio_token:H,eoa_token:Y,image_token_id:$,boi_token:V,image_token:W,eoi_token:q}=this.tokenizer.config;this.audio_token_id=K,this.boa_token=Z,this.audio_token=H;let F=H.repeat(this.audio_seq_length);this.full_audio_sequence=`

${Z}${F}${Y}

`,this.image_token_id=$,this.boi_token=V,this.image_token=W;let N=W.repeat(this.image_seq_length);this.full_image_sequence=`

${V}${N}${q}

`}async _call(J,Q=null,X=null,K={}){if(typeof J==="string")J=[J];let Z;if(X)Z=await this.feature_extractor(X,K),J=J.map(($)=>$.replaceAll(this.audio_token,this.full_audio_sequence));let H;if(Q)H=await this.image_processor(Q,K),J=J.map(($)=>$.replaceAll(this.image_token,this.full_image_sequence));return{...this.tokenizer(J,K),...H,...Z}}},iB=class extends R0{static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(J,Q,X){super(J,Q,X);this.audio_ms_per_token=this.config.audio_ms_per_token??40,this.audio_seq_length=this.config.audio_seq_length??750,this.image_seq_length=this.config.image_seq_length??280;let{audio_token:K,boa_token:Z,eoa_token:H,image_token:Y,boi_token:$,eoi_token:V}=this.tokenizer.config;this.audio_token=K,this.boa_token=Z,this.eoa_token=H,this.image_token=Y,this.boi_token=$,this.eoi_token=V}static async from_pretrained(J,Q={}){let[X,K,Z]=await Promise.all([s6(J,SV,!0,Q),C0.from_pretrained(J,Q),yJ(J,LV,!1,Q)]),H={tokenizer:K};if(X.image_processor)H.image_processor=new uV(X.image_processor);if(X.feature_extractor)H.feature_extractor=new IV(X.feature_extractor);return new this(X,H,Z)}_compute_audio_num_tokens(J,Q){let X=Math.round(Q*20/1000),K=Math.round(Q*10/1000),Z=Math.floor(X/2),H=Math.floor((J+Z-X-1)/K)+1;if(H<=0)return 0;for(let Y=0;Y<2;++Y)H=Math.floor((H-1)/2)+1;return Math.min(H,this.audio_seq_length)}async _call(J,Q=null,X=null,K={}){if(typeof J==="string")J=[J];let Z;if(Q){Z=await this.image_processor(Q,K);let Y=Z.num_soft_tokens_per_image,$=0;J=J.map((V)=>V.replaceAll(this.image_token,()=>`

${this.boi_token}${this.image_token.repeat(Y[$++])}${this.eoi_token}

`))}let H;if(X){let Y=Array.isArray(X)?X:[X];H=await this.feature_extractor(Y[0],K);let $=this.feature_extractor.config.sampling_rate??16000,V=0;J=J.map((W)=>W.replaceAll(this.audio_token,()=>`

${this.boa_token}${this.audio_token.repeat(this._compute_audio_num_tokens(Y[V++].length,$))}${this.eoa_token}

`))}return{...this.tokenizer(J,K),...Z,...H}}},dJ=class extends R0{static image_processor_class=W6;static tokenizer_class=C0;static image_token="<|image_pad|>";async _call(J,Q=null,...X){if(!Array.isArray(J))J=[J];let K,Z;if(Q)K=await this.image_processor(Q),Z=K.image_grid_thw;if(Z){let Y=this.image_processor.config.merge_size**2,$=0,V=this.constructor.image_token,W=Z.tolist();J=J.map((q)=>{while(q.includes(V)){let F=Number(W[$++].reduce((N,j)=>N*j,1n));q=q.replace(V,"<|placeholder|>".repeat(Math.floor(F/Y)))}return q.replaceAll("<|placeholder|>",V)})}return{...this.tokenizer(J),...K}}},rB=class extends dJ{static image_token="<|image|>"},aB=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;static uses_processor_config=!0;_get_num_audio_features(J){let{hop_length:Q}=this.feature_extractor.config.melspec_kwargs,{projector_window_size:X,projector_downsample_rate:K}=this.feature_extractor.config,Z=Math.floor(X/K),H=Math.floor(J/Q)+1,Y=Math.floor(H/2);return Math.ceil(Y/X)*Z}async _call(J,Q=null,X={}){if(Array.isArray(J))throw Error("Batched inputs are not supported yet.");let K={};if(Q){let{input_features:H}=await this.feature_extractor(Q);K.input_features=H;let Y=this._get_num_audio_features(Q.length),$=new Uint8Array(Y).fill(1);K.input_features_mask=new u("bool",$,[1,Y]);let V=this.config.audio_token??"<|audio|>";if(!J.includes(V))throw Error(`The input text does not contain the audio token ${V}.`);J=J.replaceAll(V,V.repeat(Y))}return{...this.tokenizer(J,{add_special_tokens:!1,...X}),...K}}};function oB(J,Q){let K=J.dims.at(-1)-1,Z=J.tolist();Z.fill(!1,0,1),Z.fill(!1,K);let H=Q.tolist();return Z.map((Y,$)=>Y?$:null).filter((Y)=>Y!==null).map((Y)=>H[Y])}var nB=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;async _call(J,Q,X={}){let K=J?await this.image_processor(J,X):{};return{...Q?this.tokenizer(Q,X):{},...K}}post_process_grounded_object_detection(J,Q,{box_threshold:X=0.25,text_threshold:K=0.25,target_sizes:Z=null}={}){let{logits:H,pred_boxes:Y}=J,$=H.dims[0];if(Z!==null&&Z.length!==$)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let V=H.dims.at(1),W=H.sigmoid(),q=W.max(-1).tolist(),F=Y.tolist().map((j)=>j.map((B)=>xV(B))),N=[];for(let j=0;j<$;++j){let B=Z!==null?Z[j]:null;if(B!==null)F[j]=F[j].map((E)=>E.map((P,w)=>P*B[(w+1)%2]));let D=q[j],L=[],M=[],R=[];for(let E=0;E<V;++E){let P=D[E];if(P<=X)continue;let w=F[j][E],k=W[j][E];L.push(P),R.push(w);let C=oB(k.gt(K),Q[j]);M.push(C)}N.push({scores:L,boxes:R,labels:this.batch_decode(M)})}return N}};function sB(J,Q,X,K,Z,H){let Y="";for(let $=0;$<Q;++$){for(let V=0;V<X;++V)Y+=K+`<row_${$+1}_col_${V+1}>`+Z.repeat(J);Y+=`
`}return Y+=`
${K}${H}`+Z.repeat(J)+`${K}`,Y}function tB(J,Q,X,K){return`${Q}${K}`+X.repeat(J)+`${Q}`}function eB(J,Q,X,K,Z,H){if(J===0&&Q===0)return tB(X,K,Z,H);return sB(X,J,Q,K,Z,H)}var R$=class extends R0{static image_processor_class=W6;static tokenizer_class=C0;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(J,Q=null,X={}){X.return_row_col_info??=!0;let K;if(Q)K=await this.image_processor(Q,X);if(!Array.isArray(J))J=[J];let Z=K.rows??[Array(J.length).fill(0)],H=K.cols??[Array(J.length).fill(0)],Y=this.config.image_seq_len,$=[],V=[];for(let q=0;q<J.length;++q){let F=J[q],N=Z[q],j=H[q];$.push(zU(F,this.image_token));let B=N.map((M,R)=>eB(M,j[R],Y,this.fake_image_token,this.image_token,this.global_img_token)),D=F.split(this.image_token);if(D.length===0)throw Error("The image token should be present in the text.");let L=D[0];for(let M=0;M<B.length;++M)L+=B[M]+D[M+1];V.push(L)}return{...this.tokenizer(V),...K}}},JD=class extends R0{static image_processor_class=W6;static tokenizer_class=C0;static uses_processor_config=!0;constructor(J,Q,X){super(J,Q,X);this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(J,{images:Q=null,chat_template:X="default"}={}){if(!Q)Q=await Promise.all(J.filter((D)=>D.images).flatMap((D)=>D.images).map((D)=>h6.read(D)));else if(!Array.isArray(Q))Q=[Q];let K=this.tokenizer,Z=K.apply_chat_template(J,{tokenize:!1,add_generation_prompt:!0,chat_template:X}),H=(D)=>K.encode(D,{add_special_tokens:!1}),Y=Z.split(this.image_tag),$=Y.length-1;if(Q.length!==$)throw Error(`Number of images provided (${Q.length}) does not match number of "${this.image_tag}" image tags (${$})`);let[V,W,q]=K.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]),F=H(Y[0]),N=Array(F.length).fill(!1);for(let D=1;D<Y.length;++D){let L=Array(this.num_image_tokens).fill(V),M=H(Y[D]);F=a6(F,[W],L,[q],M);let R=Array(this.num_image_tokens).fill(!0);N=a6(N,[!1],R,[!1],Array(M.length).fill(!1))}let j=[1,F.length],B={input_ids:new u("int64",F,j),attention_mask:new u("int64",Array(F.length).fill(1),j),images_seq_mask:new u("bool",N,j),images_emb_mask:new u("bool",Array($*this.num_image_tokens).fill(!0),[1,$,this.num_image_tokens])};if(Q&&Q.length>0){let D=await this.image_processor(Q);return D.pixel_values.unsqueeze_(0),{...B,...D}}return B}},QD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;async _call(J=null,Q=null,X={}){if(!J&&!Q)throw Error("Either text or images must be provided");let K=J?this.tokenizer(J,X):{},Z=Q?await this.image_processor(Q,X):{};return{...K,...Z}}},XD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;async _call(J,Q=null,X={}){let{image_rows:K,image_cols:Z,image_sizes:H,...Y}=await this.image_processor(J,{...X,return_row_col_info:!0});if(Q){let $=this.config.image_token??"<image>",{tile_size:V=512,downsample_factor:W=2,encoder_patch_size:q=16,use_thumbnail:F=!0}=this.image_processor.config,N=(R)=>Math.ceil(Math.floor(R/q)/W),j=N(V)**2,B=this.config.image_start_token??"<|image_start|>",D=this.config.image_end_token??"<|image_end|>",L=this.config.image_thumbnail??"<|img_thumbnail|>";if(!Array.isArray(Q))Q=[Q];let M=0;Q=Q.map((R)=>{let E=R.split($);return E[0]+E.slice(1).map((P)=>{let w=M++,[k,C]=H[w],c=K[w],g=Z[w],d=N(k)*N(C),r=B;if(c>1||g>1){let i=$.repeat(j);for(let p=0;p<c;++p)for(let f=0;f<g;++f)r+=`<|img_row_${p+1}_col_${f+1}|>`+i;if(F)r+=L+$.repeat(d)}else r+=$.repeat(d);return r+D+P}).join("")})}return{...Y,...Q?this.tokenizer(Q,X):{}}}},KD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;static uses_processor_config=!0;async _call(J,Q=null,X={}){let K=await this.image_processor(J,X);if(Q){let[H,Y]=K.pixel_values.dims.slice(-2),{image_token:$,patch_size:V,num_additional_image_tokens:W}=this.config,q=Math.floor(H/V)*Math.floor(Y/V)+W;if(Q=structuredClone(Q),!Array.isArray(Q))Q=[Q];for(let F=0;F<Q.length;++F)Q[F]=Q[F].replace($,$.repeat(q))}let Z=Q?this.tokenizer(Q,X):{};return{...K,...Z}}},A$={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]},ZD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(J,Q){if(!A$.hasOwnProperty(Q))throw Error(`Format ${Q} is not supported.`);let[X,K]=A$[Q],Z=this[X].bind(this),[H,Y]=J.dims,$=[],V=[],W=J.tolist();for(let F=0;F<H;++F){let N=W[F],j=[],B=[];for(let L=1;L<Y;++L){let[M,R]=c0(K6(N[L]));if(B.push(M),R==K)break;j.push(R)}let D=B.length>0?B.reduce((L,M)=>L*M,1):0;V.push(j),$.push(D)}return[Z(V),$]}char_decode(J){return this.char_tokenizer.batch_decode(J).map((Q)=>Q.replaceAll(" ",""))}bpe_decode(J){return this.bpe_tokenizer.batch_decode(J)}wp_decode(J){return this.wp_tokenizer.batch_decode(J).map((Q)=>Q.replaceAll(" ",""))}batch_decode([J,Q,X]){let[K,Z]=this._decode_helper(J,"char"),[H,Y]=this._decode_helper(Q,"bpe"),[$,V]=this._decode_helper(X,"wp"),W=[],q=[];for(let F=0;F<K.length;++F){let[N,j]=c0([Z[F],Y[F],V[F]]);W.push([K[F],H[F],$[F]][j]),q.push(N)}return{generated_text:W,scores:q,char_preds:K,bpe_preds:H,wp_preds:$}}static async from_pretrained(...J){let Q=await super.from_pretrained(...J),X=await C0.from_pretrained("Xenova/gpt2"),K=await C0.from_pretrained("Xenova/bert-base-uncased");return Q.components={image_processor:Q.image_processor,char_tokenizer:Q.tokenizer,bpe_tokenizer:X,wp_tokenizer:K},Q}async _call(J,Q=null){let X=await this.image_processor(J);if(Q)X.labels=this.tokenizer(Q).input_ids;return X}},HD=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;async _call(J){return await this.feature_extractor(J)}},YD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6},Z8="<image>";function $D(J,Q,X,K,Z){return`${K.repeat(X*Z)}${Q}${J}
`}var VD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;static uses_processor_config=!1;async _call(J,Q=null,X={}){if(!Q)$0.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),Q="";if(!Array.isArray(J))J=[J];if(!Array.isArray(Q))Q=[Q];let K=this.tokenizer.bos_token,Z=this.image_processor.config.image_seq_length,H;if(Q.some((V)=>V.includes(Z8)))H=Q.map((V)=>{let W=V.replaceAll(Z8,Z8.repeat(Z)),q=W.lastIndexOf(Z8),F=q===-1?0:q+Z8.length;return W.slice(0,F)+K+W.slice(F)+`
`});else $0.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),H=Q.map((V)=>$D(V,K,Z,Z8,J.length));let Y=this.tokenizer(H,X);return{...await this.image_processor(J,X),...Y}}},P$="<|image|>",WD=/<\|image_\d+\|>/g,GD=class extends R0{static image_processor_class=W6;static tokenizer_class=C0;async _call(J,Q=null,{padding:X=!0,truncation:K=!0,num_crops:Z=null}={}){if(!Array.isArray(J))J=[J];let H,Y;if(Q){Y=await this.image_processor(Q,{num_crops:Z});let{num_img_tokens:$}=Y,V=J.map((q,F)=>q.split(WD).join(P$.repeat($[F])));H=this.tokenizer(V,{padding:X,truncation:K});let W=this.tokenizer._tokenizer.token_to_id(P$);H.input_ids.map_((q)=>q==W?-q:q)}else H=this.tokenizer(J);return{...H,...Y}}},qD=class extends R0{static tokenizer_class=C0;static image_processor_class=W6;static uses_processor_config=!0;async _call(J,Q=null,X={}){let K=await this.image_processor(J,X);if(Q){let[H,Y]=K.pixel_values.dims.slice(-2),{image_token:$,image_break_token:V,image_end_token:W,patch_size:q,spatial_merge_size:F}=this.config,N=q*F,j=Math.floor(H/N),B=Math.floor(Y/N);if(Q=structuredClone(Q),!Array.isArray(Q))Q=[Q];for(let D=0;D<Q.length;++D){let L=$.repeat(B),M=L+V,R=L+W,E=M.repeat(j-1)+R;Q[D]=Q[D].replace($,E)}}let Z=Q?this.tokenizer(Q,X):{};return{...K,...Z}}},FD=class extends R0{static feature_extractor_class=vV;async _call(J){return await this.feature_extractor(J)}post_process_speaker_diarization(...J){return this.feature_extractor.post_process_speaker_diarization(...J)}get sampling_rate(){return this.feature_extractor.config.sampling_rate}},eV=class extends dJ{},UD=class extends eV{},JW=class extends R0{static image_processor_class=W6;async _call(...J){return await this.image_processor(...J)}post_process_masks(...J){return this.image_processor.post_process_masks(...J)}reshape_input_points(...J){return this.image_processor.reshape_input_points(...J)}},QW=class extends JW{},ND=class extends QW{},jD=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;async _call(J){return await this.feature_extractor(J)}},zD=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;static uses_processor_config=!0;async _call(J,Q=null,X={}){if(Array.isArray(J))throw Error("Batched inputs are not supported yet.");let K={};if(Q){let H=Q.length,{input_features:Y}=await this.feature_extractor(Q,{...X,max_length:H}),$=Math.round(H/this.config.encoder_ds_factor+0.0001),V=1+Math.ceil($/this.config.stack_factor);K.audio_token_len=[V],K.audio_values=Y;let W=this.config.audio_placeholder;if(!J.includes(W))throw Error(`The input text does not contain the image token ${W}.`);J=J.replaceAll(W,W.repeat(V))}return{...this.tokenizer(J,{add_special_tokens:!1,...X}),...K}}},u9="[AUDIO]",OD="[BEGIN_AUDIO]",BD=375;function DD(J,Q){let X=[];for(let K=0;K<J.length;K+=Q)X.push(J.subarray(K,Math.min(K+Q,J.length)));return X}var MD=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;static uses_processor_config=!1;async _call(J,Q=null,X={}){if(Array.isArray(J))throw Error("Batched inputs are not supported yet.");let K={};if(Q){if(!J.includes(u9))throw Error(`The input text does not contain the audio token ${u9}.`);if(!Array.isArray(Q))Q=[Q];let H=J.split(u9),Y=H.length-1;if(Y!==Q.length)throw Error(`The number of audio inputs (${Q.length}) does not match the number of audio tokens in the text (${Y}).`);let $=this.feature_extractor.config.n_samples,V=Q.map((j)=>DD(j,$)),W=V.map((j)=>j.length),q=V.flat(),F=(await Promise.all(q.map((j)=>this.feature_extractor(j,X)))).map((j)=>j.input_features);K.audio_values=F.length>1?h0(F,0):F[0];let N=H[0];for(let j=0;j<W.length;++j){N+=OD;for(let B=0;B<W[j];++B)N+=u9.repeat(BD);N+=H[j+1]}J=N}return{...this.tokenizer(J,{add_special_tokens:!1,...X}),...K}}},E$=32,WJ=6,m9=8,SD=10,LD=32,CD=class extends R0{static tokenizer_class=C0;static feature_extractor_class=E6;static uses_processor_config=!1;get num_mel_frames_first_audio_chunk(){return(WJ+1)*m9}get num_samples_first_audio_chunk(){let{hop_length:J,n_fft:Q}=this.feature_extractor.config;return(this.num_mel_frames_first_audio_chunk-1)*J+Math.floor(Q/2)}get num_samples_per_audio_chunk(){let{hop_length:J,n_fft:Q}=this.feature_extractor.config;return m9*J+Q}get num_right_pad_tokens(){return WJ+1+SD}get audio_length_per_tok(){return m9}get raw_audio_length_per_tok(){return m9*this.feature_extractor.config.hop_length}async _call(J,{is_streaming:Q=!1,is_first_audio_chunk:X=!0}={}){if(Z6(J,"VoxtralRealtimeProcessor"),!Q&&!X)throw Error("In non-streaming mode (`is_streaming=false`), `is_first_audio_chunk` must be `true`.");if(X)if(Q){let K=E$*this.raw_audio_length_per_tok,Z=new 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YW=class{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;streamer=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(J){Object.assign(this,e0(J,Object.getOwnPropertyNames(this)))}},F7=class extends z6{_call(J,Q){throw Error("StoppingCriteria needs to be subclassed")}},$W=class J extends z6{constructor(){super();this.criteria=[]}push(Q){this.criteria.push(Q)}extend(Q){if(Q instanceof J)Q=Q.criteria;else if(Q instanceof F7)Q=[Q];this.criteria.push(...Q)}_call(Q,X){let K=Array(Q.length).fill(!1);for(let Z of this.criteria){let H=Z(Q,X);for(let Y=0;Y<K.length;++Y)K[Y]||=H[Y]}return K}[Symbol.iterator](){return this.criteria.values()}},aD=class extends F7{constructor(J,Q=null){super();this.max_length=J,this.max_position_embeddings=Q}_call(J){return J.map((Q)=>Q.length>=this.max_length)}},oD=class extends F7{constructor(J){super();if(!Array.isArray(J))J=[J];this.eos_token_id=J}_call(J,Q){return J.map((X)=>{let K=X.at(-1);return this.eos_token_id.some((Z)=>K==Z)})}};var U7=class extends z6{constructor(J){super();this.generation_config=J}async _call(J){return this.sample(J)}async sample(J){throw Error("sample should be implemented in subclasses.")}getLogits(J,Q){let X=J.dims.at(-1),K=J.data;if(Q===-1)K=K.slice(-X);else{let Z=Q*X;K=K.slice(Z,Z+X)}return K}randomSelect(J){return Rj(J)}static getSampler(J){if(J.do_sample)return new sD(J);else if(J.num_beams>1)return new tD(J);else{if(J.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${J.num_return_sequences}.`);return new nD(J)}}},nD=class extends U7{async sample(J){let Q=c0(J.data)[1];return[[BigInt(Q),0]]}},sD=class extends U7{async sample(J){let Q=J.dims.at(-1);if(this.generation_config.top_k>0)Q=Math.min(this.generation_config.top_k,Q);let[X,K]=await z8(J,Q),Z=K6(X.data);return Array.from({length:this.generation_config.num_beams},()=>{let H=this.randomSelect(Z);return[K.data[H],Math.log(Z[H])]})}},tD=class extends U7{async sample(J){let Q=J.dims.at(-1);if(this.generation_config.top_k>0)Q=Math.min(this.generation_config.top_k,Q);let[X,K]=await z8(J,Q),Z=K6(X.data);return Array.from({length:this.generation_config.num_beams},(H,Y)=>{return[K.data[Y],Math.log(Z[Y])]})}},eD=class{constructor(J){if(!J)return;for(let Q in J){if(Q in this)throw TypeError(`Key "${Q}" conflicts with an existing property on DynamicCache`);let X=J[Q];if(!(X instanceof u))throw TypeError(`Expected a Tensor for key "${Q}", got ${typeof X}`);this[Q]=X}}get_seq_length(){let J=this;if(Object.keys(J).length===0)return 0;for(let Q in J)if(Q.startsWith("past_key_values."))return J[Q].dims.at(-2);throw Error("Unable to determine sequence length from the cache.")}update(J){for(let Q in J){let X=this[Q],K=J[Q];if(X&&X!==K&&X.location==="gpu-buffer")X.dispose();this[Q]=K}}async dispose(){let J=[];for(let Q of Object.values(this))if(Q.location==="gpu-buffer")J.push(Q.dispose());await Promise.all(J)}},pJ=eD,m={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,DecoderOnlyWithoutHead:5,MaskGeneration:6,ImageTextToText:7,Musicgen:8,MultiModality:9,Phi3V:10,AudioTextToText:11,AutoEncoder:12,ImageAudioTextToText:13,Supertonic:14,Chatterbox:15,VoxtralRealtime:16},H5={[m.DecoderOnly]:{sessions:(J,Q)=>({model:Q.model_file_name??"model"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[m.DecoderOnlyWithoutHead]:{sessions:(J,Q)=>({model:Q.model_file_name??"model"})},[m.Seq2Seq]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[m.Vision2Seq]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[m.Musicgen]:{sessions:()=>({model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[m.EncoderDecoder]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0}},[m.MaskGeneration]:{sessions:()=>({model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"})},[m.ImageTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(J,Q,X)=>{let K={...H5[m.ImageTextToText].text_only_sessions};if(!X)K.vision_encoder="vision_encoder";if(J.is_encoder_decoder)K.model="encoder_model";return K},cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[m.AudioTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(J,Q,X)=>{let K={...H5[m.AudioTextToText].text_only_sessions};if(!X)K.audio_encoder="audio_encoder";return K},cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[m.ImageAudioTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(J,Q,X)=>{let K={...H5[m.ImageAudioTextToText].text_only_sessions};if(!X)K.audio_encoder="audio_encoder",K.vision_encoder="vision_encoder";return K},optional_configs:{generation_config:"generation_config.json"}},[m.Phi3V]:{sessions:()=>({prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[m.MultiModality]:{sessions:()=>({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"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[m.AutoEncoder]:{sessions:()=>({encoder_model:"encoder_model",decoder_model:"decoder_model"})},[m.Supertonic]:{sessions:()=>({text_encoder:"text_encoder",latent_denoiser:"latent_denoiser",voice_decoder:"voice_decoder"})},[m.Chatterbox]:{sessions:()=>({embed_tokens:"embed_tokens",speech_encoder:"speech_encoder",model:"language_model",conditional_decoder:"conditional_decoder"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[m.VoxtralRealtime]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(J,Q,X)=>{let K={...H5[m.VoxtralRealtime].text_only_sessions};if(!X)K.audio_encoder="audio_encoder";return K},cache_sessions:{decoder_model_merged:!0,audio_encoder:!0},optional_configs:{generation_config:"generation_config.json"}},default:{sessions:(J,Q)=>({model:Q.model_file_name??"model"})}};function JM(J){return H5[J]?.text_only_sessions??null}function QM(J,Q,X={}){let K=H5[J]??H5.default;return{sessions:K.sessions(Q,X,X.textOnly??!1),cache_sessions:K.cache_sessions,optional_configs:K.optional_configs}}function VW(J,{warn:Q=!0}={}){let X=J.architectures||[];for(let K of X){let Z=Y5.get(K);if(Z!==void 0)return Z}if(J.model_type){let K=Y5.get(J.model_type);if(K!==void 0)return K;for(let Z of Object.values(Y8))if(Z.has(J.model_type)){let H=Y5.get(Z.get(J.model_type));if(H!==void 0)return H}}if(Q){let K=X.length>0?X.join(", "):"(none)";$0.warn(`[resolve_model_type] Architecture(s) not found in MODEL_TYPE_MAPPING: [${K}] for model type '${J.model_type}'. Falling back to EncoderOnly (single model.onnx file). If you encounter issues, please report at: ${W7}`)}return m.EncoderOnly}function WW(J,{config:Q=null,cache_dir:X=null,local_files_only:K=!1,revision:Z="main"}={}){if(Q!==null)return X9.from_pretrained(J,{config:Q,cache_dir:X,local_files_only:K,revision:Z});let H=JSON.stringify([J,X,K,Z]);return s$(H,()=>X9.from_pretrained(J,{config:Q,cache_dir:X,local_files_only:K,revision:Z}))}async function GW(J,{config:Q=null,dtype:X=null,device:K=null,model_file_name:Z=null}={}){Q=await WW(J,{config:Q});let H=["config.json"],Y=Q["transformers.js_config"]??{},$=Y.use_external_data_format,V="onnx",W=K??Y.device,q=X??Y.dtype,F=VW(Q),N=(D,L=null)=>{L=L??D;let M=WV(W,D),R=GV(q,D,M),E=J7[R]??"",P=`${L}${E}.onnx`,w=V?`${V}/${P}`:P;H.push(w);let k=XW($,P,D);for(let C of KW(P,k)){let c=V?`${V}/${C}`:C;H.push(c)}},{sessions:j,optional_configs:B}=QM(F,Q,{model_file_name:Z});for(let[D,L]of Object.entries(j))N(D,L);if(B)for(let D of Object.values(B))H.push(D);return H}var Y8=null;function XM(J){Y8=J}function SJ(J){if(J instanceof u)return J;if(J.length===0)throw Error("items must be non-empty");if(Array.isArray(J[0])){if(J.some((Q)=>Q.length!==J[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new u("int64",BigInt64Array.from(J.flat().map((Q)=>BigInt(Q))),[J.length,J[0].length])}else return new u("int64",BigInt64Array.from(J.map((Q)=>BigInt(Q))),[1,J.length])}function qW(J){return new u("bool",[J],[1])}var w$={[m.DecoderOnly]:{can_generate:!0,forward:V5,prepare_inputs:t8},[m.DecoderOnlyWithoutHead]:{can_generate:!1,forward:V5,prepare_inputs:t8},[m.Seq2Seq]:{can_generate:!0,forward:d9,prepare_inputs:X7},[m.Vision2Seq]:{can_generate:!0,forward:d9,prepare_inputs:X7},[m.Musicgen]:{can_generate:!0,forward:d9},[m.EncoderDecoder]:{can_generate:!1,forward:d9},[m.ImageTextToText]:{can_generate:!0,forward:YM,prepare_inputs:p9},[m.AudioTextToText]:{can_generate:!0,forward:HM,prepare_inputs:p9},[m.ImageAudioTextToText]:{can_generate:!0,prepare_inputs:p9},[m.Phi3V]:{can_generate:!0,prepare_inputs:p9},[m.MultiModality]:{can_generate:!0},[m.AutoEncoder]:{can_generate:!1,forward:KM},[m.Chatterbox]:{can_generate:!0,forward:R5},[m.VoxtralRealtime]:{can_generate:!0,prepare_inputs:t8},default:{can_generate:!1,forward:R5}};function T$(J,Q){let X=Y5.get(J),K=!1,Z=Q?.architectures?.[0];if(Z&&Z!==J&&J?.endsWith("ForCausalLM")&&Z.endsWith("ForConditionalGeneration")){let $=Y5.get(Z);if($!==void 0)X=$,K=!0}let H=w$[X]??w$.default,Y=H5[X]??H5.default;return{typeConfig:{...H,...Y},textOnly:K,modelType:X}}var Y5=new Map,iJ=new Map,F8=new Map,T=class extends z6{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];_return_dict_in_generate_keys=null;constructor(J,Q,X){super();this.config=J,this.sessions=Q,this.configs=X;let K=F8.get(this.constructor),{typeConfig:Z}=T$(K,J);if(this.can_generate=Z.can_generate,this._forward=Z.forward,this._prepare_inputs_for_generation=Z.prepare_inputs,this.can_generate)this.forward_params.push("past_key_values");this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){let J=[];for(let Q of Object.values(this.sessions))J.push(Q.release?.());return await Promise.all(J)}static async from_pretrained(J,{progress_callback:Q=null,config:X=null,cache_dir:K=null,local_files_only:Z=!1,revision:H="main",model_file_name:Y=null,subfolder:$="onnx",device:V=null,dtype:W=null,use_external_data_format:q=null,session_options:F={}}={}){let N={progress_callback:Q,config:X,cache_dir:K,local_files_only:Z,revision:H,model_file_name:Y,subfolder:$,device:V,dtype:W,use_external_data_format:q,session_options:F},j=F8.get(this);X=N.config=await X9.from_pretrained(J,N);let{typeConfig:B,textOnly:D,modelType:L}=T$(j,X);if(L===void 0){let P=j??X?.model_type;if(P!=="custom")$0.warn(`Model type for '${P}' not found, assuming encoder-only architecture. Please report this at ${W7}.`)}if(Q&&!(Q instanceof FJ)){let P={};try{let w=await GW(J,{config:X,dtype:W,device:V,model_file_name:Y});(await Promise.all(w.map((C)=>H9(J,C,N)))).forEach((C,c)=>{if(C.exists){let g=w[c]==="config.json";P[w[c]]={loaded:g?C.size??0:0,total:C.size??0}}})}catch(w){$0.warn(`Unable to fetch model file metadata for total progress tracking: ${w}`)}if(Object.keys(P).length>0)N.progress_callback=new FJ(Q,P)}let M=B.sessions(X,N,D),R=[xD(J,M,N,B.cache_sessions)];if(B.optional_configs)R.push(VM(J,B.optional_configs,N));let E=await Promise.all(R);return new this(X,...E)}async _call(J){return await this.forward(J)}async forward(J){return await this._forward(this,J)}get generation_config(){return this.configs?.generation_config??null}_get_logits_processor(J,Q,X=null){let K=new MJ;if(J.repetition_penalty!==null&&J.repetition_penalty!==1)K.push(new uD(J.repetition_penalty));if(J.no_repeat_ngram_size!==null&&J.no_repeat_ngram_size>0)K.push(new lD(J.no_repeat_ngram_size));if(J.bad_words_ids!==null)K.push(new pD(J.bad_words_ids,J.eos_token_id));if(J.min_length!==null&&J.eos_token_id!==null&&J.min_length>0)K.push(new mD(J.min_length,J.eos_token_id));if(J.min_new_tokens!==null&&J.eos_token_id!==null&&J.min_new_tokens>0)K.push(new dD(Q,J.min_new_tokens,J.eos_token_id));if(J.forced_bos_token_id!==null)K.push(new fD(J.forced_bos_token_id));if(J.forced_eos_token_id!==null)K.push(new _D(J.max_length,J.forced_eos_token_id));if(J.suppress_tokens!==null)K.push(new gD(J.suppress_tokens));if(J.begin_suppress_tokens!==null){let Z=Q>1||J.forced_bos_token_id===null?Q:Q+1;K.push(new HW(J.begin_suppress_tokens,Z))}if(J.guidance_scale!==null&&J.guidance_scale>1)K.push(new iD(J.guidance_scale));if(J.temperature===0&&J.do_sample)$0.warn("`do_sample` changed to false because `temperature: 0` implies greedy sampling (always selecting the most likely token), which is incompatible with `do_sample: true`."),J.do_sample=!1;if(J.do_sample){if(J.temperature!==null&&J.temperature!==1)K.push(new rD(J.temperature))}if(X!==null)K.extend(X);return K}_prepare_generation_config(J,Q,X=YW){let K={...this.config};for(let H of["decoder","generator","text_config"])if(H in K)Object.assign(K,K[H]);let Z=new X(K);if(Object.assign(Z,this.generation_config??{}),J)Object.assign(Z,J);if(Q)Object.assign(Z,e0(Q,Object.getOwnPropertyNames(Z)));return Z}_get_stopping_criteria(J,Q=null){let X=new $W;if(J.max_length!==null)X.push(new aD(J.max_length,this.config.max_position_embeddings??null));if(J.eos_token_id!==null)X.push(new oD(J.eos_token_id));if(Q)X.extend(Q);return X}_validate_model_class(){if(!this.can_generate){let J=[Y8.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,Y8.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,Y8.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,Y8.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES].filter(Boolean),Q=F8.get(this.constructor),X=new Set,K=this.config.model_type;for(let H of J){let Y=H?.get(K);if(Y)X.add(Y)}let Z=`The current model class (${Q}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;if(X.size>0)Z+=` Please use the following class instead: ${[...X].join(", ")}`;throw Error(Z)}}prepare_inputs_for_generation(...J){if(!this._prepare_inputs_for_generation)throw Error("prepare_inputs_for_generation is not implemented for this model.");return this._prepare_inputs_for_generation(this,...J)}_update_model_kwargs_for_generation({generated_input_ids:J,outputs:Q,model_inputs:X,is_encoder_decoder:K}){if(X.past_key_values=LJ(Q,X.past_key_values),X.input_ids=new u("int64",J.flat(),[J.length,1]),!K)X.attention_mask=h0([X.attention_mask,j6([X.attention_mask.dims[0],1])],1);else if("decoder_attention_mask"in X)X.decoder_attention_mask=h0([X.decoder_attention_mask,j6([X.decoder_attention_mask.dims[0],1])],1);return X.position_ids=null,X}_prepare_model_inputs({inputs:J,bos_token_id:Q,model_kwargs:X}){let K=e0(X,this.forward_params),Z=this.main_input_name;if(Z in K){if(J)throw Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else K[Z]=J;return{inputs_tensor:K[Z],model_inputs:K,model_input_name:Z}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:J,model_inputs:Q,model_input_name:X,generation_config:K}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!Q.inputs_embeds&&"_prepare_inputs_embeds"in this){let{input_ids:H,pixel_values:Y,attention_mask:$,...V}=Q,W=await this._prepare_inputs_embeds(Q);Q={...V,...e0(W,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Z}=await R5(this,Q);if(K.guidance_scale!==null&&K.guidance_scale>1){if(Z=h0([Z,zJ(Z,0)],0),"attention_mask"in Q)Q.attention_mask=h0([Q.attention_mask,jV(Q.attention_mask)],0)}else if(Q.decoder_input_ids){let H=SJ(Q.decoder_input_ids).dims[0];if(H!==Z.dims[0]){if(Z.dims[0]!==1)throw Error(`The encoder outputs have a different batch size (${Z.dims[0]}) than the decoder inputs (${H}).`);Z=h0(Array.from({length:H},()=>Z),0)}}return Q.encoder_outputs=Z,Q}_prepare_decoder_input_ids_for_generation({batch_size:J,model_input_name:Q,model_kwargs:X,decoder_start_token_id:K,bos_token_id:Z,generation_config:H}){let{decoder_input_ids:Y,...$}=X;if(!(Y instanceof u)){if(!Y)if(K??=Z,this.config.model_type==="musicgen")Y=Array.from({length:J*this.config.decoder.num_codebooks},()=>[K]);else if(Array.isArray(K)){if(K.length!==J)throw Error(`\`decoder_start_token_id\` expcted to have length ${J} but got ${K.length}`);Y=K}else Y=Array.from({length:J},()=>[K]);else if(!Array.isArray(Y[0]))Y=Array.from({length:J},()=>Y);Y=SJ(Y)}return $.decoder_attention_mask=UV(Y),{input_ids:Y,model_inputs:$}}async generate({inputs:J=null,generation_config:Q=null,logits_processor:X=null,stopping_criteria:K=null,streamer:Z=null,...H}){this._validate_model_class(),Q=this._prepare_generation_config(Q,H);let{inputs_tensor:Y,model_inputs:$,model_input_name:V}=this._prepare_model_inputs({inputs:J,model_kwargs:H}),W=this.config.is_encoder_decoder;if(!W);else if(!("encoder_outputs"in $))$=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Y,model_inputs:$,model_input_name:V,generation_config:Q});let q;if(W)({input_ids:q,model_inputs:$}=this._prepare_decoder_input_ids_for_generation({batch_size:$[V].dims.at(0),model_input_name:V,model_kwargs:$,decoder_start_token_id:Q.decoder_start_token_id,bos_token_id:Q.bos_token_id,generation_config:Q}));else q=$[V];let F=q.dims.at(-1);if(Q.max_new_tokens!==null)Q.max_length=F+Q.max_new_tokens;let N=this._get_logits_processor(Q,F,X),j=this._get_stopping_criteria(Q,K),B=$[V].dims.at(0),D=U7.getSampler(Q),L=Array(B).fill(0),M=q.tolist();if(Z)Z.put(M);let R,E={},P={};while(!0){if($=this.prepare_inputs_for_generation(M,$,Q),R=await this.forward($),Q.return_dict_in_generate){if(Q.output_attentions){let p=ZM(R);for(let f in p){if(!(f in E))E[f]=[];E[f].push(p[f])}}else if(this._return_dict_in_generate_keys)Object.assign(P,e0(R,this._return_dict_in_generate_keys))}let g=R.logits.slice(null,-1,null).to("float32"),d=N(M,g),r=[];for(let p=0;p<d.dims.at(0);++p){let f=d[p],v=await D(f);for(let[o,Q0]of v){let x=BigInt(o);L[p]+=Q0,M[p].push(x),r.push([x]);break}}if(Z)Z.put(r);if(j(M).every((p)=>p))break;$=this._update_model_kwargs_for_generation({generated_input_ids:r,outputs:R,model_inputs:$,is_encoder_decoder:W})}if(Z)Z.end();let w=new u("int64",M.flat(),[M.length,M[0].length]),k=LJ(R,$.past_key_values),C=new Set(Object.values(k));for(let g of Object.values(R))if(g.location==="gpu-buffer"&&!C.has(g))g.dispose();if(!(("past_key_values"in H)||Q.return_dict_in_generate))await k.dispose();if(Q.return_dict_in_generate)return{sequences:w,past_key_values:k,...E,...P};return w}async _encode_input(J,Q,X){if(!Object.hasOwn(this.sessions,J))throw Error(`Model does not have a ${J} session.`);let K=this.sessions[J];return(await O0(K,e0(Q,K.inputNames)))[X]}async encode_image(J){return this._encode_input("vision_encoder",J,"image_features")}async encode_text(J){return this._encode_input("embed_tokens",J,"inputs_embeds")}async encode_audio(J){return this._encode_input("audio_encoder",J,"audio_features")}};async function d9(J,Q){let{encoder_outputs:X,input_ids:K,decoder_input_ids:Z,decoder_attention_mask:H,...Y}=Q;if(!X){let $=e0(Q,J.sessions.model.inputNames);X=(await R5(J,$)).last_hidden_state}if(Y.input_ids=Z,Y.encoder_hidden_states=X,J.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask"))Y.encoder_attention_mask=Q.attention_mask;if(H&&!Y.attention_mask)Y.attention_mask=H;return await V5(J,Y,!0)}async function R5(J,Q){let X=J.sessions.model,K=e0(Q,X.inputNames);if(X.inputNames.includes("inputs_embeds")&&!K.inputs_embeds){if(!Q.input_ids)throw Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");K.inputs_embeds=await J.encode_text({input_ids:Q.input_ids})}if(X.inputNames.includes("token_type_ids")&&!K.token_type_ids){if(!K.input_ids)throw Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");K.token_type_ids=jV(K.input_ids)}if(X.inputNames.includes("pixel_mask")&&!K.pixel_mask){if(!K.pixel_values)throw Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");let Z=K.pixel_values.dims;K.pixel_mask=j6([Z[0],Z[2],Z[3]])}return await O0(X,K)}async function KM(J,Q){let X=await J.encode(Q);return await J.decode(X)}function LJ(J,Q){let X=Object.create(null);for(let K in J)if(K.startsWith("present")){let Z=K.replace("present_ssm","past_ssm").replace("present_conv","past_conv").replace("present_recurrent","past_recurrent").replace("present","past_key_values");if(K.includes("encoder")&&Q)X[Z]=Q[Z];else X[Z]=J[K]}if(Q)return Q.update(X),Q;return new pJ(X)}function ZM(J){let Q={};for(let X of["cross_attentions","encoder_attentions","decoder_attentions"])for(let K in J)if(K.startsWith(X)){if(!(X in Q))Q[X]=[];Q[X].push(J[K])}return Q}function FW(J,Q){return J.map((X)=>{if(typeof X==="number")return X;return Q[X]??0})}function rJ(J,Q,X){if(X&&Object.keys(X).length>0)return Object.assign(Q,X),X;let K=J.sessions.decoder_model_merged??J.sessions.model,Z=(Q[J.main_input_name]??Q.attention_mask)?.dims?.[0]??1,H=q7(J.config),Y=J.config?.normalized_config?.num_heads,$={batch_size:Z};if(typeof Y==="number")$["batch_size x num_heads"]=Z*Y;let V=Object.create(null);for(let W of K.inputMetadata){if(!H.has(W.name))continue;let q=FW(W.shape,$),F=q.reduce((B,D)=>B*D,1),N=J9[W.type],j=new u(W.type,new N(F),q);Q[W.name]=j,V[W.name]=j}if(X)return X.update(V),X;return new pJ(V)}async function V5(J,Q,X=!1){let K=J.sessions[X?"decoder_model_merged":"model"],{past_key_values:Z,...H}=Q;if(K.inputNames.includes("use_cache_branch"))H.use_cache_branch=qW(Z!=null&&Object.keys(Z).length>0);if(K.inputNames.includes("position_ids")&&H.attention_mask&&!H.position_ids){let $=["paligemma","gemma3_text","gemma3"].includes(J.config.model_type)?1:0;H.position_ids=$M(H,Z,$)}if(K.inputNames.includes("num_logits_to_keep")&&!H.num_logits_to_keep)H.num_logits_to_keep=new u("int64",[0n],[]);rJ(J,H,Z);let Y=e0(H,K.inputNames);return await O0(K,Y)}async function UW(J,{encode_function:Q,merge_function:X,modality_input_names:K,modality_output_name:Z,input_ids:H=null,attention_mask:Y=null,position_ids:$=null,inputs_embeds:V=null,past_key_values:W=null,generation_config:q=null,logits_processor:F=null,...N}){if(!V){V=await J.encode_text({input_ids:H,...N});let B=e0(N,K);if(Object.keys(B).length>0){if(H.dims[1]!==1){let D=await Q({...B,...N});({inputs_embeds:V,attention_mask:Y}=X({[Z]:D,inputs_embeds:V,input_ids:H,attention_mask:Y}))}else if(W&&H.dims[1]===1){let D=H.dims[1],L=W.get_seq_length();Y=h0([j6([H.dims[0],L]),Y.slice(null,[Y.dims[1]-D,Y.dims[1]])],1)}}}if(!$){if(["qwen2_vl","qwen2_vl_text","qwen2_5_vl","qwen2_5_vl_text","qwen3_vl","qwen3_vl_text","qwen3_vl_moe","qwen3_vl_moe_text","qwen3_5","qwen3_5_text","qwen3_5_moe","qwen3_5_moe_text","glm_ocr","glm_ocr_text"].includes(J.config.model_type)){let{image_grid_thw:B,video_grid_thw:D}=N;[$]=J.get_rope_index(H,B,D,Y)}}return await 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V}function t8(J,Q,X,K){let Z=X.past_key_values?X.past_key_values.get_seq_length():0;if((J.sessions.decoder_model_merged??J.sessions.model)?.inputNames.includes("num_logits_to_keep")&&!X.num_logits_to_keep)X.num_logits_to_keep=new u("int64",[1n],[]);if(!X.attention_mask){let Y;for(let $ of["input_ids","inputs_embeds","position_ids"])if(X[$]){Y=X[$].dims;break}if(!Y)throw Error("attention_mask is not provided, and unable to infer its shape from model inputs.");X.attention_mask=j6([Y[0],Z+Y[1]])}if(X.past_key_values){let{input_ids:Y,attention_mask:$}=X;if($&&$.dims[1]>Y.dims[1]);else if(Z<Y.dims[1])X.input_ids=Y.slice(null,[Z,null])}return X}function X7(J,Q,X,K){if(X.past_key_values)Q=Q.map((Z)=>[Z.at(-1)]);return{...X,decoder_input_ids:SJ(Q)}}function p9(J,...Q){if(J.config.is_encoder_decoder)return X7(J,...Q);else return t8(J,...Q)}function jW({modality_token_id:J,inputs_embeds:Q,modality_features:X,input_ids:K,attention_mask:Z}){let 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extends AQ{},EL=class extends AQ{},PQ=class extends T{},wL=class extends PQ{},TL=class extends PQ{},bW=class extends T{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]},W5=class extends bW{_merge_input_ids_with_image_features(J){let Q=J.image_features.dims.at(-1),X=J.image_features.view(-1,Q);return aJ({image_token_id:this.config.image_token_index??this.config.image_token_id,...J,image_features:X})}},IL=class extends W5{},vL=class extends W5{},fW=class extends T{},yL=class extends fW{},_W=class extends W5{},xL=class extends _W{},gW=class extends T{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","input_features","input_features_mask","past_key_values"]},B7=class extends gW{async forward({input_ids:J=null,attention_mask:Q=null,pixel_values:X=null,input_features:K=null,input_features_mask:Z=null,position_ids:H=null,inputs_embeds:Y=null,per_layer_inputs:$=null,past_key_values:V=null,generation_config:W=null,logits_processor:q=null,...F}){if(!Y||!$){if({inputs_embeds:Y,per_layer_inputs:$}=await O0(this.sessions.embed_tokens,{input_ids:J}),J.dims[1]!==1){if(X){let{image_features:j}=await this._encode_vision({pixel_values:X,...F});({inputs_embeds:Y,attention_mask:Q}=this._merge_input_ids_with_image_features({image_features:j,inputs_embeds:Y,input_ids:J,attention_mask:Q}))}if(K){let{audio_features:j}=await O0(this.sessions.audio_encoder,{input_features:K,input_features_mask:Z});({inputs_embeds:Y,attention_mask:Q}=this._merge_input_ids_with_audio_features({audio_features:j,inputs_embeds:Y,input_ids:J,attention_mask:Q}))}}}return await V5(this,{inputs_embeds:Y,per_layer_inputs:$,past_key_values:V,attention_mask:Q,position_ids:H,generation_config:W,logits_processor:q},!0)}_encode_vision(J){return O0(this.sessions.vision_encoder,{pixel_values:J.pixel_values})}_merge_input_ids_with_image_features(J){let Q=J.image_features.dims.at(-1),X=J.image_features.view(-1,Q);return aJ({image_token_id:this.config.image_token_id,...J,image_features:X})}_merge_input_ids_with_audio_features(J){let Q=J.audio_features.dims.at(-1),X=J.audio_features.view(-1,Q);return zW({audio_token_id:this.config.audio_token_id,...J,audio_features:X})}},kL=class extends B7{},EQ=class extends B7{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","image_position_ids","input_features","input_features_mask","past_key_values"];_encode_vision(J){return O0(this.sessions.vision_encoder,{pixel_values:J.pixel_values,pixel_position_ids:J.image_position_ids})}},hL=class extends EQ{},wQ=class extends T{},bL=class extends wQ{},fL=class extends wQ{},TQ=class extends T{},_L=class extends TQ{},gL=class extends TQ{},cW=class extends T{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]},IQ=class extends cW{image_grid_thw_name="grid_thw";_get_text_only_rope_index(J,Q){if(Q){let{data:X,dims:K}=NW(Q),Z=BigInt64Array.from({length:3*X.length},(Y,$)=>X[$%X.length]),H=Array.from({length:K[0]},(Y,$)=>c0(X.subarray(K[1]*$,K[1]*($+1)))[0]+1n+BigInt(K[1]));return[new u("int64",Z,[3,...K]),new u("int64",H,[H.length,1])]}else{let[X,K]=J.dims,Z=BigInt64Array.from({length:3*X*K},(H,Y)=>BigInt(Math.floor(Y%K/X)));return[new u("int64",Z,[3,...J.dims]),NV([X,1])]}}_reorder_and_write_positions(J,Q,X,K){let Z=J.reduce((V,W)=>V+W.length,0),H=Array(Z),Y=0;for(let V=0;V<3;++V)for(let W of J){let q=W.length/3;for(let F=V*q;F<(V+1)*q;++F)H[Y++]=W[F]}let $=0;for(let V=0;V<Q.length;++V)if(Q[V]==1){for(let W=0;W<3;++W)X[W][K][V]=H[W*Z/3+$];++$}return H}_get_multimodal_rope_positions({filtered_ids:J,image_grid_thw_list:Q,video_grid_thw_list:X,spatial_merge_size:K,state:Z}){let{image_token_id:H,video_token_id:Y,vision_start_token_id:$}=this.config,V=J,q=V.reduce((M,R,E)=>{if(R==$)M.push(E);return M},[]).map((M)=>V[M+1]),F=q.filter((M)=>M==H).length,N=q.filter((M)=>M==Y).length,j=[],B=0,D=F,L=N;for(let M=0;M<q.length;++M){let R=V.findIndex((_,a)=>a>B&&_==H),E=V.findIndex((_,a)=>a>B&&_==Y),P=D>0&&R!==-1?R:V.length+1,w=L>0&&E!==-1?E:V.length+1,k,C,c,g;if(P<w)[C,c,g]=Q[Z.image_index],++Z.image_index,--D,k=P;else[C,c,g]=X[Z.video_index],++Z.video_index,--L,k=w;let[d,r,i]=[Number(C),Math.floor(Number(c)/K),Math.floor(Number(g)/K)],p=k-B,f=j.length>0?c0(j.at(-1))[0]+1:0;j.push(Array.from({length:3*p},(_,a)=>f+a%p));let v=p+f,o=d*r*i,Q0=Array.from({length:o},(_,a)=>v+Math.floor(a/(r*i))),x=Array.from({length:o},(_,a)=>v+Math.floor(a/i)%r),h=Array.from({length:o},(_,a)=>v+a%i);j.push([Q0,x,h].flat()),B=k+o}if(B<V.length){let M=j.length>0?c0(j.at(-1))[0]+1:0,R=V.length-B;j.push(Array.from({length:3*R},(E,P)=>M+P%R))}return j}get_rope_index(J,Q,X,K){let{vision_config:Z}=this.config,H=Z.spatial_merge_size??2;if(Q||X){let Y=J.tolist();if(!K)K=UV(J);let $=K.tolist(),V=Array.from({length:3},()=>Array.from({length:J.dims[0]},()=>Array.from({length:J.dims[1]},()=>0))),W=Q?Q.tolist():[],q=X?X.tolist():[],F={image_index:0,video_index:0},N=[];for(let j=0;j<Y.length;++j){let B=Y[j].filter((M,R)=>$[j][R]==1),D=this._get_multimodal_rope_positions({filtered_ids:B,image_grid_thw_list:W,video_grid_thw_list:q,spatial_merge_size:H,state:F}),L=this._reorder_and_write_positions(D,$[j],V,j);N.push(c0(L)[0]+1-Y[j].length)}return[new u("int64",V.flat(1/0),[3,J.dims[0],J.dims[1]]),new u("int64",N,[N.length,1])]}else return this._get_text_only_rope_index(J,K)}async encode_image({pixel_values:J,image_grid_thw:Q}){return(await O0(this.sessions.vision_encoder,{pixel_values:J,[this.image_grid_thw_name]:Q})).image_features}_merge_input_ids_with_image_features(J){return aJ({image_token_id:this.config.image_token_id,...J})}prepare_inputs_for_generation(J,Q,X){if(!Q.attention_mask||Q.position_ids)return Q;if(!(this.sessions.decoder_model_merged??this.sessions.model).inputNames.includes("position_ids"))return Q;if(!Q.past_key_values)[Q.position_ids,Q.rope_deltas]=this.get_rope_index(Q.input_ids,Q.image_grid_thw,Q.video_grid_thw,Q.attention_mask);else{Q.pixel_values=null;let Z=Q.past_key_values.get_seq_length();if(Z<Q.input_ids.dims[1]){let[H,Y]=this.get_rope_index(Q.input_ids,Q.image_grid_thw,Q.video_grid_thw,Q.attention_mask);Q.rope_deltas=Y,Q.position_ids=H.slice(null,null,[Z,null]),Q.input_ids=Q.input_ids.slice(null,[Z,null])}else{if(!Q.rope_deltas)[,Q.rope_deltas]=this.get_rope_index(Q.input_ids,Q.image_grid_thw,Q.video_grid_thw,Q.attention_mask);let H=BigInt(Z),Y=Q.rope_deltas.map(($)=>H+$);Q.position_ids=$5([Y,Y,Y],0)}}return Q}},lW=class extends IQ{},vQ=class extends IQ{image_grid_thw_name="image_grid_thw"},uW=class extends lW{image_grid_thw_name="image_grid_thw"},cL=class extends vQ{get_vision_position_ids(J,Q,X,K){let Z=Math.floor(Q[0]/X),H=Math.floor(Q[1]/K),Y=Math.floor(Q[2]/K),$=H*Y*Z,V=Array.from({length:$},()=>J),W=Array.from({length:$},(F,N)=>J+Math.floor(N/(Y*Z))),q=Array.from({length:$},(F,N)=>J+N%Y);return[...V,...W,...q]}_get_multimodal_rope_positions({filtered_ids:J,image_grid_thw_list:Q,video_grid_thw_list:X,spatial_merge_size:K,state:Z}){let{image_token_id:H}=this.config,Y=[],$=0,V=J[0]==H?1:0;for(let F=1;F<=J.length;++F){let N=F<J.length?J[F]==H?1:0:-1;if(N!==V)Y.push([V,$,F]),$=F,V=N}let W=0,q=[];for(let[F,N,j]of Y)if(F===0){let B=j-N;q.push(Array.from({length:3*B},(D,L)=>W+L%B)),W+=B}else{let B=Q[Z.image_index++].map(Number),D=B[0];q.push(this.get_vision_position_ids(W,B,D,K)),W+=Math.max(B[1],B[2])/K}return q}},yQ=class extends T{},lL=class extends yQ{},uL=class extends yQ{},xQ=class extends T{},mL=class extends xQ{},dL=class extends xQ{},kQ=class extends T{},pL=class extends kQ{},iL=class extends kQ{},hQ=class extends T{},rL=class extends hQ{},aL=class extends hQ{},bQ=class extends T{},oL=class extends bQ{},nL=class extends bQ{},fQ=class extends T{},sL=class extends fQ{},tL=class extends fQ{},_Q=class extends T{},eL=class extends _Q{},JC=class extends _Q{},gQ=class extends T{},QC=class extends gQ{},XC=class extends gQ{},cQ=class extends T{},KC=class extends cQ{},ZC=class extends cQ{},mW=class extends T{forward_params=["input_ids","attention_mask","position_ids","audio_values","past_key_values"]},lQ=class extends mW{_merge_input_ids_with_audio_features(J){let Q=J.audio_features.dims.at(-1),X=J.audio_features.view(-1,Q);return zW({audio_token_id:this.config.ignore_index??this.config.audio_token_id??this.config.audio_token_index,...J,audio_features:X})}},HC=class extends 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W5{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"]},pQ=class extends T{},LC=class extends pQ{},CC=class extends pQ{async _call(J){return new Z0(await super._call(J))}},iQ=class extends T{},RC=class extends iQ{},AC=class extends iQ{},D7=class extends T{},PC=class extends D7{async forward(J){let Q=!J.input_ids,X=!J.pixel_values;if(Q&&X)throw Error("Either `input_ids` or `pixel_values` should be provided.");if(Q)J.input_ids=j6([J.pixel_values.dims[0],1]);if(X){let{image_size:V}=this.config.vision_config;J.pixel_values=$6([0,3,V,V],0)}let{text_embeddings:K,image_embeddings:Z,l2norm_text_embeddings:H,l2norm_image_embeddings:Y}=await super.forward(J),$={};if(!Q)$.text_embeddings=K,$.l2norm_text_embeddings=H;if(!X)$.image_embeddings=Z,$.l2norm_image_embeddings=Y;return $}},rW=class extends D7{static async from_pretrained(J,Q={}){return super.from_pretrained(J,{...Q,model_file_name:Q.model_file_name??"text_model"})}},EC=class 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T{},oC=class extends oW{},nW=class extends T{},nC=class extends nW{},sW=class extends r0{constructor({char_logits:J,bpe_logits:Q,wp_logits:X}){super();this.char_logits=J,this.bpe_logits=Q,this.wp_logits=X}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}},tW=class extends T{},sC=class extends tW{async _call(J){return new sW(await super._call(J))}},eW=class extends r0{constructor({audio_codes:J}){super();this.audio_codes=J}},JG=class extends r0{constructor({audio_values:J}){super();this.audio_values=J}},M7=class extends T{main_input_name="input_values";forward_params=["input_values"]},tC=class extends M7{async encode(J){return new eW(await O0(this.sessions.encoder_model,J))}async decode(J){return new JG(await O0(this.sessions.decoder_model,J))}},QG=class extends M7{static async from_pretrained(J,Q={}){return super.from_pretrained(J,{...Q,model_file_name:Q.model_file_name??"encoder_model"})}},XG=class extends M7{static async from_pretrained(J,Q={}){return 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R7{},DR=class extends R7{async _call(J){return new Z0(await super._call(J))}},MR=class extends R7{},KX=class extends T{},SR=class extends KX{},LR=class extends KX{async _call(J){return new Z0(await super._call(J))}},ZX=class extends T{},CR=class extends ZX{},RR=class extends ZX{async _call(J){return new Z0(await super._call(J))}},F9=class extends T{},AR=class extends F9{},PR=class extends F9{async _call(J){return new a0(await super._call(J))}},ER=class extends F9{async _call(J){return new Z0(await super._call(J))}},wR=class extends F9{async _call(J){return new m0(await super._call(J))}},HX=class extends T{},TR=class extends HX{},IR=class extends HX{},YX=class extends T{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]},vR=class extends YX{},yR=class extends YX{},R8=class extends T{},xR=class extends R8{},kR=class extends R8{async _call(J){return new a0(await super._call(J))}},hR=class extends R8{async _call(J){return new Z0(await super._call(J))}},bR=class extends R8{async _call(J){return new m0(await super._call(J))}},fR=class extends R8{async _call(J){return new G6(await super._call(J))}},$X=class extends T{},_R=class extends $X{},gR=class extends $X{},VX=class extends T{},cR=class extends VX{},lR=class extends VX{},KG=class extends T{},uR=class extends KG{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...J){super(...J);this._generation_mode="text"}async forward(J){let Q=this._generation_mode??"text",X;if(Q==="text"||!J.past_key_values){let $=this.sessions.prepare_inputs_embeds,V=e0(J,$.inputNames);X=await O0($,V)}else{let $=this.sessions.gen_img_embeds,V=e0({image_ids:J.input_ids},$.inputNames);X=await O0($,V)}let K={...J,...X},Z=await V5(this,K),H=this.sessions[Q==="text"?"lm_head":"gen_head"];if(!H)throw Error(`Unable to find "${H}" generation head`);let Y=await O0(H,e0(Z,H.inputNames));return{...X,...Z,...Y}}prepare_inputs_for_generation(J,Q,X){let K=!!Q.past_key_values;if(X.guidance_scale!==null&&X.guidance_scale>1)if(K)Q.input_ids=h0([Q.input_ids,Q.input_ids],0);else Q.input_ids=h0([Q.input_ids,zJ(Q.input_ids,BigInt(X.pad_token_id))],0),Q.attention_mask=h0([Q.attention_mask,zJ(Q.attention_mask,0n)],0);if(K||!Q.pixel_values)Q.pixel_values=$6([0,0,3,384,384],1);if(K)Q.images_seq_mask=new u("bool",[,].fill(!0).fill(!1,0,1),[1,1]),Q.images_emb_mask=new u("bool",[].fill(!1),[1,1,0]);return Q}async generate(J){return this._generation_mode="text",super.generate(J)}async generate_images(J){this._generation_mode="image";let Q=(J.inputs??J[this.main_input_name]).dims[1],K=(await super.generate(J)).slice(null,[Q,null]),Z=this.sessions.image_decode,{decoded_image:H}=await O0(Z,{generated_tokens:K}),Y=H.add_(1).mul_(127.5).clamp_(0,255).to("uint8"),$=[];for(let V of Y){let W=h6.fromTensor(V);$.push(W)}return $}},WX=class extends T{},mR=class extends WX{},dR=class extends WX{},ZG=class extends T{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(J){let[Q,X]=J.dims,K=this.config.decoder.num_codebooks,Z=X-K,H=0;for(let V=0;V<J.size;++V){if(J.data[V]==this.config.decoder.pad_token_id)continue;let W=V%X,q=Math.floor(V/X)%K,F=W-q;if(F>0&&F<=Z)J.data[H++]=J.data[V]}let Y=Math.floor(Q/K),$=H/(Y*K);return new u(J.type,J.data.slice(0,H),[Y,K,$])}prepare_inputs_for_generation(J,Q,X){let K=BigInt(this.config.decoder.pad_token_id),Z=structuredClone(J);for(let H=0;H<Z.length;++H)for(let Y=0;Y<Z[H].length;++Y)if(H%this.config.decoder.num_codebooks>=Y)Z[H][Y]=K;if(X.guidance_scale!==null&&X.guidance_scale>1)Z=Z.concat(Z);return X7(this,Z,Q,X)}async generate(J){let Q=await super.generate(J),X=this._apply_and_filter_by_delay_pattern_mask(Q).unsqueeze_(0),{audio_values:K}=await O0(this.sessions.encodec_decode,{audio_codes:X});return K}},GX=class extends T{},pR=class extends GX{},iR=class extends GX{},qX=class extends T{},rR=class extends qX{},aR=class extends qX{},A8=class extends T{},oR=class extends A8{},nR=class extends A8{async _call(J){return new a0(await super._call(J))}},sR=class extends A8{async _call(J){return new Z0(await super._call(J))}},tR=class extends A8{async _call(J){return new m0(await super._call(J))}},eR=class extends A8{async _call(J){return new G6(await super._call(J))}},HG=class extends T{},JA=class extends HG{},FX=class extends T{},QA=class extends FX{},XA=class extends FX{},UX=class extends T{},KA=class extends UX{},ZA=class extends UX{},NX=class extends T{},HA=class extends NX{},YA=class extends NX{},jX=class extends T{},$A=class extends jX{},VA=class extends jX{},zX=class extends T{},WA=class extends zX{},GA=class extends zX{async _call(J){return new Z0(await super._call(J))}},OX=class extends T{},qA=class extends OX{},FA=class extends OX{},BX=class extends T{},UA=class extends BX{},NA=class extends BX{},DX=class extends T{},jA=class extends DX{},zA=class extends DX{},MX=class extends T{},OA=class extends MX{},BA=class extends MX{},DA=class extends W5{},YG=class extends T{},MA=class extends YG{async _call(J){return new m5(await super._call(J))}},SX=class extends T{},SA=class extends SX{},LA=class extends SX{},LX=class extends T{},CA=class extends LX{},RA=class extends LX{},CX=class extends T{},AA=class extends CX{},PA=class extends CX{},RX=class extends T{},EA=class extends RX{},wA=class extends RX{},$G=class extends T{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"]},VG=class extends $G{async forward({input_ids:J=null,attention_mask:Q=null,pixel_values:X=null,image_sizes:K=null,position_ids:Z=null,inputs_embeds:H=null,past_key_values:Y=null,generation_config:$=null,logits_processor:V=null,...W}){if(!H){let F;if(X&&J.dims[1]!==1){if(!K)throw Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:F}=await O0(this.sessions.vision_encoder,{pixel_values:X,image_sizes:K}))}else{let N=this.config.normalized_config.hidden_size;F=new u("float32",[],[0,N])}({inputs_embeds:H}=await O0(this.sessions.prepare_inputs_embeds,{input_ids:J,image_features:F}))}return await V5(this,{inputs_embeds:H,past_key_values:Y,attention_mask:Q,position_ids:Z,generation_config:$,logits_processor:V},!1)}},AX=class extends T{},TA=class extends AX{},IA=class extends AX{async _call(J){return new Z0(await super._call(J))}},PX=class extends T{},vA=class extends PX{},yA=class extends PX{async _call(J){return new m0(await super._call(J))}},EX=class extends T{},xA=class extends EX{},kA=class extends EX{},wX=class extends T{},hA=class extends wX{},bA=class extends wX{},TX=class extends T{},fA=class extends TX{},_A=class extends TX{},IX=class extends T{},gA=class extends IX{},cA=class extends IX{},vX=class extends T{},lA=class extends vX{},uA=class extends vX{},yX=class extends vQ{},WG=class extends uW{},mA=class extends yX{},dA=class extends WG{},xX=class extends yX{},GG=class extends xX{},pA=class extends xX{},iA=class extends GG{},kX=class extends T{},rA=class extends kX{},aA=class extends kX{async _call(J){return new Z0(await super._call(J))}},hX=class extends T{},oA=class extends hX{},nA=class extends hX{async _call(J){return new qG(await super._call(J))}},qG=class extends $9{},P8=class extends T{},sA=class extends P8{},tA=class extends P8{async _call(J){return new a0(await super._call(J))}},eA=class extends P8{async _call(J){return new Z0(await super._call(J))}},JP=class extends P8{async _call(J){return new m0(await super._call(J))}},QP=class extends P8{async _call(J){return new G6(await super._call(J))}},E8=class extends T{},XP=class extends E8{},KP=class extends E8{async _call(J){return new a0(await super._call(J))}},ZP=class extends E8{async _call(J){return new Z0(await super._call(J))}},HP=class extends E8{async _call(J){return new m0(await super._call(J))}},YP=class extends E8{async _call(J){return new G6(await super._call(J))}},bX=class extends T{},$P=class extends bX{},VP=class extends bX{async _call(J){return new FG(await super._call(J))}},FG=class extends $9{},UG=class extends r0{constructor({iou_scores:J,pred_masks:Q}){super();this.iou_scores=J,this.pred_masks=Q}},NG=class extends T{},WP=class extends NG{async get_image_embeddings({pixel_values:J}){return await R5(this,{pixel_values:J})}async forward(J){if(!J.image_embeddings||!J.image_positional_embeddings)J={...J,...await this.get_image_embeddings(J)};else J={...J};J.input_labels??=j6(J.input_points.dims.slice(0,-1));let Q={image_embeddings:J.image_embeddings,image_positional_embeddings:J.image_positional_embeddings};if(J.input_points)Q.input_points=J.input_points;if(J.input_labels)Q.input_labels=J.input_labels;if(J.input_boxes)Q.input_boxes=J.input_boxes;return await O0(this.sessions.prompt_encoder_mask_decoder,Q)}async _call(J){return new UG(await super._call(J))}},jG=class extends r0{constructor({iou_scores:J,pred_masks:Q,object_score_logits:X}){super();this.iou_scores=J,this.pred_masks=Q,this.object_score_logits=X}},zG=class extends T{},fX=class extends zG{async get_image_embeddings({pixel_values:J}){return await R5(this,{pixel_values:J})}async forward(J){let{num_feature_levels:Q}=this.config.vision_config;if(Array.from({length:Q},(H,Y)=>`image_embeddings.${Y}`).some((H)=>!J[H]))J={...J,...await this.get_image_embeddings(J)};else J={...J};if(J.input_points){if(J.input_boxes&&J.input_boxes.dims[1]!==1)throw Error("When both `input_points` and `input_boxes` are provided, the number of boxes per image must be 1.");let H=J.input_points.dims;J.input_labels??=j6(H.slice(0,-1)),J.input_boxes??=$6([H[0],0,4],0)}else if(J.input_boxes){let H=J.input_boxes.dims;J.input_labels=$6([H[0],H[1],0],-1n),J.input_points=$6([H[0],1,0,2],0)}else throw Error("At least one of `input_points` or `input_boxes` must be provided.");let K=this.sessions.prompt_encoder_mask_decoder,Z=e0(J,K.inputNames);return await O0(K,Z)}async _call(J){return new jG(await super._call(J))}},GP=class extends fX{},qP=class extends fX{},A7=class extends T{},FP=class extends A7{},UP=class extends A7{},NP=class extends A7{},P7=class extends T{},jP=class extends P7{},zP=class extends P7{},OP=class extends P7{},_X=class extends T{},BP=class extends _X{},OG=class extends _X{static async from_pretrained(J,Q={}){return super.from_pretrained(J,{...Q,model_file_name:Q.model_file_name??"text_model"})}},DP=class extends d5{static async from_pretrained(J,Q={}){return super.from_pretrained(J,{...Q,model_file_name:Q.model_file_name??"vision_model"})}},gX=class extends T{},MP=class extends gX{},SP=class extends gX{},LP=class extends iW{},E7=class extends T{main_input_name="input_values";forward_params=["input_values"]},CP=class extends E7{async encode(J){return await O0(this.sessions.encoder_model,J)}async decode(J){return await O0(this.sessions.decoder_model,J)}},BG=class extends E7{static async from_pretrained(J,Q={}){return super.from_pretrained(J,{...Q,model_file_name:Q.model_file_name??"encoder_model"})}},DG=class extends E7{static async from_pretrained(J,Q={}){return super.from_pretrained(J,{...Q,model_file_name:Q.model_file_name??"decoder_model"})}},cX=class extends T{},RP=class extends cX{},AP=class extends cX{},w7=class extends T{},PP=class extends w7{},EP=class extends w7{},wP=class extends w7{async generate_speech(J,Q,{threshold:X=0.5,minlenratio:K=0,maxlenratio:Z=20,vocoder:H=null}={}){let Y={input_ids:J},{encoder_outputs:$,encoder_attention_mask:V}=await R5(this,Y),W=$.dims[1]/this.config.reduction_factor,q=Math.floor(W*Z),F=Math.floor(W*K),N=this.config.num_mel_bins,j=[],B=null,D=null,L=0;while(!0){++L;let E=qW(!!D),P;if(D)P=D.output_sequence_out;else P=new u("float32",new Float32Array(N),[1,1,N]);let w={use_cache_branch:E,output_sequence:P,encoder_attention_mask:V,speaker_embeddings:Q,encoder_hidden_states:$};rJ(this,w,B),D=await O0(this.sessions.decoder_model_merged,w),B=LJ(D,B);let{prob:k,spectrum:C}=D;if(j.push(C),L>=F&&(Array.from(k.data).filter((c)=>c>=X).length>0||L>=q))break}let M=h0(j),{waveform:R}=await O0(H.sessions.model,{spectrogram:M});return{spectrogram:M,waveform:R}}},TP=class extends T{main_input_name="spectrogram"},U9=class extends T{},IP=class extends U9{},vP=class extends U9{async _call(J){return new a0(await super._call(J))}},yP=class extends U9{async _call(J){return new Z0(await super._call(J))}},xP=class extends U9{async _call(J){return new G6(await super._call(J))}},lX=class extends T{},kP=class extends lX{},hP=class extends lX{},uX=class extends T{},bP=class extends uX{},fP=class extends uX{},MG=class extends T{},_P=class extends MG{},SG=class extends T{},LG=class extends SG{async generate_speech({input_ids:J,attention_mask:Q,style:X,num_inference_steps:K=5,speed:Z=1.05}){let{sampling_rate:H,chunk_compress_factor:Y,base_chunk_size:$,latent_dim:V}=this.config,{last_hidden_state:W,durations:q}=await O0(this.sessions.text_encoder,{input_ids:J,attention_mask:Q,style:X}),F=q.div(Z).mul_(H),N=$*Y,j=F.data,B=Int32Array.from(j,(g)=>Math.ceil(g/N)),D=Math.max(...B),L=J.dims[0],M=new BigInt64Array(L*D);for(let g=0;g<L;++g)M.fill(1n,g*D,g*D+B[g]);let R=new u("int64",M,[L,D]),E=V*Y,P=E*D,w=Wz([L,E,D]),k=w.data;for(let g=0;g<L;++g){if(B[g]===D)continue;for(let d=0;d<E;++d)k.fill(0,g*P+d*D+B[g],g*P+(d+1)*D)}let C=$6([L],K);for(let g=0;g<K;++g){let d=$6([L],g);({denoised_latents:w}=await O0(this.sessions.latent_denoiser,{style:X,noisy_latents:w,latent_mask:R,encoder_outputs:W,attention_mask:Q,timestep:d,num_inference_steps:C}))}let{waveform:c}=await O0(this.sessions.voice_decoder,{latents:w});return{waveform:c,durations:F}}},T7=class extends T{},gP=class extends T7{},cP=class extends T7{async _call(J){return new Z0(await super._call(J))}},lP=class extends T7{},mX=class extends T{},uP=class extends mX{},mP=class extends mX{},dX=class extends T{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]},dP=class extends dX{},pP=class extends dX{},pX=class extends T{},iP=class extends pX{},rP=class extends pX{async _call(J){return new CG(await super._call(J))}},CG=class extends jQ{},RG=class extends T{},aP=class extends RG{},I7=class extends T{},oP=class extends I7{},nP=class extends I7{async _call(J){return new m5(await super._call(J))}},sP=class extends I7{async _call(J){return new Z0(await super._call(J))}},N9=class extends T{},tP=class extends N9{},eP=class extends N9{async _call(J){return new m5(await super._call(J))}},JE=class extends N9{async _call(J){return new Z0(await super._call(J))}},QE=class extends N9{async _call(J){return new m0(await super._call(J))}},iX=class extends T{},XE=class extends iX{},KE=class extends iX{},ZE=class extends T{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]},rX=class extends T{},HE=class extends rX{},YE=class extends rX{async _call(J){return new Z0(await super._call(J))}},AG=class extends T{},$E=class extends AG{},aX=class extends T{},VE=class extends aX{},WE=class extends aX{async _call(J){return new Z0(await super._call(J))}},PG=class extends T{},GE=class extends PG{async _call(J){return new hD(await super._call(J))}},EG=class extends T{},qE=class extends EG{},wG=class extends r0{constructor({waveform:J,spectrogram:Q}){super();this.waveform=J,this.spectrogram=Q}},TG=class extends T{},FE=class extends TG{async _call(J){return new wG(await super._call(J))}},UE=class extends lQ{},v$=2,NE=1,GJ=new WeakMap;function jE(J,Q){let{text_config:X,audio_config:K}=J.config,Z=J.sessions.audio_encoder,{num_mel_bins:H,hidden_size:Y}=K,$=H+Y,V=new pJ,W=q7(K),q={batch_size:1},F="float32";for(let D of Z.inputMetadata){if(D.name==="past_padding_cache"){F=D.type;continue}if(!W.has(D.name))continue;let L=FW(D.shape,q),M=L.reduce((E,P)=>E*P,1),R=J9[D.type];V[D.name]=new u(D.type,new R(M),L)}let N=J9[F],j=new u(F,new N($*v$),[1,$,v$]),B=Q[Symbol.asyncIterator]?.()??Q[Symbol.iterator]?.();if(!B)throw Error("input_features must be iterable or async iterable");return{encoder_session:Z,enc_kv_cache:V,enc_padding_cache:j,enc_past_seq_len:0,audio_embed_queue:[],audio_embed_total_tokens:0,audio_queue_offset:0,audio_consumed:0,stream_exhausted:!1,chunks_iter:B,text_hidden_size:X.hidden_size}}async function zE(J,Q){let X=Q.dims[2],K=Math.floor((NE+X-3)/2)+1,Z=new u("int64",BigInt64Array.from({length:K},(q,F)=>BigInt(J.enc_past_seq_len+F)),[1,K]),H=J.enc_past_seq_len+K,Y=j6([1,H]),{audio_embeds:$,present_padding_cache:V,...W}=await O0(J.encoder_session,{input_features:Q,attention_mask:Y,position_ids:Z,past_padding_cache:J.enc_padding_cache,...J.enc_kv_cache});if(J.enc_padding_cache.location==="gpu-buffer")J.enc_padding_cache.dispose();J.enc_padding_cache=V;for(let q in W)if(q.startsWith("present.")){let F=q.replace("present","past_key_values"),N=J.enc_kv_cache[F];if(N?.location==="gpu-buffer")N.dispose();J.enc_kv_cache[F]=W[q]}return J.enc_past_seq_len=H,$}async function OE(J,Q){while(J.audio_embed_total_tokens<Q&&!J.stream_exhausted){let X=await J.chunks_iter.next();if(X.done){J.stream_exhausted=!0;break}let K=await zE(J,X.value);J.audio_embed_queue.push({data:K.data,tokens:K.dims[1]}),J.audio_embed_total_tokens+=K.dims[1]}}function BE(J,Q,X){if(J.audio_embed_queue.length===0)return;let K=Q.data,Z=0,H=X;while(H>0&&J.audio_embed_queue.length>0){let Y=J.audio_embed_queue[0],$=Y.tokens-J.audio_queue_offset,V=Math.min(H,$),W=J.audio_queue_offset*J.text_hidden_size;for(let q=0;q<V*J.text_hidden_size;++q)K[Z*J.text_hidden_size+q]+=Y.data[W+q];if(Z+=V,H-=V,J.audio_queue_offset+=V,J.audio_queue_offset>=Y.tokens)J.audio_embed_queue.shift(),J.audio_queue_offset=0}J.audio_consumed+=X-H}var DE=class extends F7{constructor(J){super();this._s=J}_call(J){let Q=this._s.stream_exhausted&&this._s.audio_embed_queue.length===0;return J.map(()=>Q)}},IG=class extends T{forward_params=["input_ids","attention_mask","position_ids","past_key_values"]},vG=class extends IG{async forward({input_ids:J,past_key_values:Q,...X}){let K=J.dims[1],Z=GJ.get(this);if(Z)await OE(Z,Z.audio_consumed+K);let{inputs_embeds:H}=await O0(this.sessions.embed_tokens,{input_ids:J});if(Z)BE(Z,H,K);let Y={inputs_embeds:H,...X};rJ(this,Y,Q);let $=this.sessions.decoder_model_merged,V=e0(Y,$.inputNames);return await O0($,V)}async generate({input_features:J,stopping_criteria:Q,...X}){if(!J)throw Error("input_features (generator/iterable) must be provided");let K=jE(this,J);GJ.set(this,K);let Z=new $W;if(Z.push(new DE(K)),Q)Z.extend(Q);try{return await super.generate({...X,stopping_criteria:Z})}finally{K.enc_kv_cache.dispose(),GJ.delete(this)}}},v7=class extends T{},ME=class extends v7{},SE=class extends v7{async _call(J){return new m5(await super._call(J))}},LE=class extends v7{async _call(J){return new Z0(await super._call(J))}},yG=class extends r0{constructor({logits:J,embeddings:Q}){super();this.logits=J,this.embeddings=Q}},w8=class extends T{},CE=class extends w8{},RE=class extends w8{async _call(J){return new m5(await super._call(J))}},AE=class extends w8{async _call(J){return new Z0(await super._call(J))}},PE=class extends w8{async _call(J){return new yG(await super._call(J))}},EE=class extends w8{async _call(J){return new m0(await super._call(J))}},xG=class extends T{},wE=class extends xG{},TE=class extends YW{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1},oX=class extends T{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]},IE=class extends oX{},kG=class extends oX{_prepare_generation_config(J,Q){return super._prepare_generation_config(J,Q,TE)}_retrieve_init_tokens(J){let Q=[J.decoder_start_token_id],X=J.language,K=J.task;if(J.is_multilingual){if(!X)$0.warn("No language specified - defaulting to English (en)."),X="en";let H=`<|${ZO(X)}|>`;Q.push(J.lang_to_id[H]),Q.push(J.task_to_id[K??"transcribe"])}else if(X||K)throw 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.");if(!J.return_timestamps&&J.no_timestamps_token_id&&Q.at(-1)!==J.no_timestamps_token_id)Q.push(J.no_timestamps_token_id);else if(J.return_timestamps&&Q.at(-1)===J.no_timestamps_token_id)$0.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),Q.pop();return Q.filter((Z)=>Z!=null)}async generate({inputs:J=null,generation_config:Q=null,logits_processor:X=null,stopping_criteria:K=null,...Z}){Q=this._prepare_generation_config(Q,Z);let H=Z.decoder_input_ids instanceof u?bJ(Z.decoder_input_ids):Z.decoder_input_ids??this._retrieve_init_tokens(Q);if(Q.return_timestamps)X??=new MJ,X.push(new cD(Q,H));if(Q.begin_suppress_tokens)X??=new MJ,X.push(new HW(Q.begin_suppress_tokens,H.length));if(Q.return_token_timestamps){if(!Q.alignment_heads)throw 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.");if(Q.task==="translate")$0.warn("Token-level timestamps may not be reliable for task 'translate'.");Q.output_attentions=!0,Q.return_dict_in_generate=!0}if(Q.return_timestamps&&!Z.max_new_tokens)return this._generate_with_seek({inputs:J,generation_config:Q,logits_processor:X,init_tokens:H,kwargs:Z});let Y=await super.generate({inputs:J,generation_config:Q,logits_processor:X,decoder_input_ids:H,...Z});if(Q.return_token_timestamps)Y.token_timestamps=this._extract_token_timestamps(Y,Q.alignment_heads,Q.num_frames,0.02,H.length);return Y}async _generate_with_seek({inputs:J,generation_config:Q,logits_processor:X,init_tokens:K,kwargs:Z}){let H=Q.no_timestamps_token_id+1,Y=Array.isArray(Q.eos_token_id)?Q.eos_token_id[0]:Q.eos_token_id,$=Q.return_token_timestamps,V=J,W=V.dims[2],q=2,N=2*this.config.max_source_positions,j=0,B=[],D=[];while(j<W){let M=Math.min(j+N,W),R=V.slice(null,null,[j,M]),E,P=R.dims[2];if(P<N){let o=V.dims[1],Q0=new Float32Array(o*N),x=R.data;for(let h=0;h<o;++h)Q0.set(x.subarray(h*P,(h+1)*P),h*N);E=new u("float32",Q0,[1,o,N])}else E=R;if(X){for(let o of X)if("begin_index"in o)o.begin_index=K.length}let w=await super.generate({inputs:E,generation_config:Q,logits_processor:X,decoder_input_ids:K,...Z}),C=($?w.sequences:w)[0].tolist().map(Number).slice(K.length),c;if($){w.token_timestamps=this._extract_token_timestamps(w,Q.alignment_heads,Math.floor((M-j)/2),0.02,K.length);let o=j/2*0.02;c=w.token_timestamps[0].tolist().slice(K.length).map((Q0)=>Q0+o)}if(C.length>0&&C.at(-1)===Y)C.pop();if(C.length===0)break;let g=C.map((o)=>o>=H),d=C.length>=2&&g[C.length-1]&&!g[C.length-2],r=[];for(let o=0;o<C.length-1;++o)if(g[o]&&g[o+1])r.push(o+1);let i,p=C.length;if(r.length>0)if(d)i=M-j;else{let o=r.at(-1);i=(C[o-1]-H)*2,p=o}else i=M-j;let f=Math.floor(j/2),v=H+1500;for(let o=0;o<p;++o)if(C[o]>=H)C[o]=Math.min(C[o]+f,v);if(B.push(...C.slice(0,p)),c)D.push(...c.slice(0,p));j+=i}B.push(Y);let L=[...K,...B];if($){let M=new u("int64",L.map(BigInt),[1,L.length]),R=[...Array(K.length).fill(0),...D,0],E=new u("float32",new Float32Array(R),[1,R.length]);return{sequences:M,token_timestamps:E}}return new u("int64",L.map(BigInt),[1,L.length])}_extract_token_timestamps(J,Q,X=null,K=0.02,Z=0){if(!J.cross_attentions)throw Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");if(X==null)$0.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 H=this.config.median_filter_width;if(H===void 0)$0.warn("Model config has no `median_filter_width`, using default value of 7."),H=7;let Y=J.cross_attentions,$=Array.from({length:this.config.decoder_layers},(L,M)=>h0(Y.map((R)=>R[M]),2)),V=$5(Q.map(([L,M])=>{if(L>=$.length)throw Error(`Layer index ${L} is out of bounds for cross attentions (length ${$.length}).`);return X?$[L].slice(null,M,null,[0,X]):$[L].slice(null,M)})).transpose(1,0,2,3),[W,q]=Vz(V,-2,0,!0),F=V.clone();for(let L=0;L<F.dims[0];++L){let M=F[L];for(let R=0;R<M.dims[0];++R){let E=M[R],P=W[L][R][0].data,w=q[L][R][0].data;for(let k=0;k<E.dims[0];++k){let C=E[k].data;for(let c=0;c<C.length;++c)C[c]=(C[c]-w[c])/P[c];C.set(dj(C,H))}}}let N=Z>0?F.slice(null,null,[Z,F.dims[2]],null):F,j=[kJ(N,1)],B=J.sequences.dims,D=new u("float32",new Float32Array(B[0]*B[1]),B);for(let L=0;L<B[0];++L){let M=j[L].neg().squeeze_(0),[R,E]=ij(M.tolist()),P=Array.from({length:R.length-1},(c,g)=>R[g+1]-R[g]),w=a6([1],P).map((c)=>!!c),k=[];for(let c=0;c<w.length;++c)if(w[c])k.push(E[c]*K);let C=Array(Z).fill(0);if(C.push(...k),k.length>0)C.push(k.at(-1));D[L].data.set(C)}return D}},vE=class extends kG{},T8=class extends T{},yE=class extends T8{},xE=class extends T8{async _call(J){return new a0(await super._call(J))}},kE=class extends T8{async _call(J){return new Z0(await super._call(J))}},hE=class extends T8{async _call(J){return new m0(await super._call(J))}},bE=class extends T8{async _call(J){return new G6(await super._call(J))}},I8=class extends T{},fE=class extends I8{},_E=class extends I8{async _call(J){return new a0(await super._call(J))}},gE=class extends I8{async _call(J){return new Z0(await super._call(J))}},cE=class extends I8{async _call(J){return new m0(await super._call(J))}},lE=class extends I8{async _call(J){return new G6(await super._call(J))}},nX=class extends T{},uE=class extends nX{},mE=class extends nX{async _call(J){return new hG(await super._call(J))}},hG=class extends r0{constructor({logits:J,pred_boxes:Q}){super();this.logits=J,this.pred_boxes=Q}},sX=class extends T{},dE=class extends sX{},pE=class extends sX{},iE=new Map([["bert","BertModel"],["eurobert","EuroBertModel"],["neobert","NeoBertModel"],["modernbert","ModernBertModel"],["nomic_bert","NomicBertModel"],["roformer","RoFormerModel"],["electra","ElectraModel"],["esm","EsmModel"],["convbert","ConvBertModel"],["camembert","CamembertModel"],["deberta","DebertaModel"],["deberta-v2","DebertaV2Model"],["mpnet","MPNetModel"],["albert","AlbertModel"],["distilbert","DistilBertModel"],["roberta","RobertaModel"],["xlm","XLMModel"],["xlm-roberta","XLMRobertaModel"],["clap","ClapModel"],["clip","CLIPModel"],["clipseg","CLIPSegModel"],["chinese_clip","ChineseCLIPModel"],["siglip","SiglipModel"],["jina_clip","JinaCLIPModel"],["mobilebert","MobileBertModel"],["squeezebert","SqueezeBertModel"],["wav2vec2","Wav2Vec2Model"],["wav2vec2-bert","Wav2Vec2BertModel"],["unispeech","UniSpeechModel"],["unispeech-sat","UniSpeechSatModel"],["hubert","HubertModel"],["wavlm","WavLMModel"],["audio-spectrogram-transformer","ASTModel"],["vits","VitsModel"],["pyannote","PyAnnoteModel"],["wespeaker-resnet","WeSpeakerResNetModel"],["detr","DetrModel"],["rt_detr","RTDetrModel"],["rt_detr_v2","RTDetrV2Model"],["rf_detr","RFDetrModel"],["d_fine","DFineModel"],["table-transformer","TableTransformerModel"],["vit","ViTModel"],["ijepa","IJepaModel"],["pvt","PvtModel"],["vit_msn","ViTMSNModel"],["vit_mae","ViTMAEModel"],["groupvit","GroupViTModel"],["fastvit","FastViTModel"],["mobilevit","MobileViTModel"],["mobilevitv2","MobileViTV2Model"],["owlvit","OwlViTModel"],["owlv2","Owlv2Model"],["beit","BeitModel"],["deit","DeiTModel"],["hiera","HieraModel"],["convnext","ConvNextModel"],["convnextv2","ConvNextV2Model"],["dinov2","Dinov2Model"],["dinov2_with_registers","Dinov2WithRegistersModel"],["dinov3_vit","DINOv3ViTModel"],["dinov3_convnext","DINOv3ConvNextModel"],["resnet","ResNetModel"],["swin","SwinModel"],["swin2sr","Swin2SRModel"],["donut-swin","DonutSwinModel"],["yolos","YolosModel"],["dpt","DPTModel"],["glpn","GLPNModel"],["hifigan","SpeechT5HifiGan"],["efficientnet","EfficientNetModel"],["decision_transformer","DecisionTransformerModel"],["patchtst","PatchTSTModel"],["patchtsmixer","PatchTSMixerModel"],["mobilenet_v1","MobileNetV1Model"],["mobilenet_v2","MobileNetV2Model"],["mobilenet_v3","MobileNetV3Model"],["mobilenet_v4","MobileNetV4Model"],["maskformer","MaskFormerModel"],["mgp-str","MgpstrForSceneTextRecognition"],["style_text_to_speech_2","StyleTextToSpeech2Model"],["openai_privacy_filter","OpenAIPrivacyFilterModel"]]),rE=new Map([["t5","T5Model"],["longt5","LongT5Model"],["mt5","MT5Model"],["bart","BartModel"],["mbart","MBartModel"],["marian","MarianModel"],["whisper","WhisperModel"],["cohere_asr","CohereAsrModel"],["m2m_100","M2M100Model"],["blenderbot","BlenderbotModel"],["blenderbot-small","BlenderbotSmallModel"]]),aE=new Map([["mimi","MimiModel"],["dac","DacModel"],["snac","SnacModel"]]),oE=new Map([["bloom","BloomModel"],["jais","JAISModel"],["gpt2","GPT2Model"],["gpt_oss","GptOssModel"],["gptj","GPTJModel"],["gpt_bigcode","GPTBigCodeModel"],["gpt_neo","GPTNeoModel"],["gpt_neox","GPTNeoXModel"],["codegen","CodeGenModel"],["llama","LlamaModel"],["apertus","ApertusModel"],["nanochat","NanoChatModel"],["arcee","ArceeModel"],["afmoe","AfmoeModel"],["lfm2","Lfm2Model"],["lfm2_moe","Lfm2MoeModel"],["smollm3","SmolLM3Model"],["exaone","ExaoneModel"],["olmo","OlmoModel"],["olmo2","Olmo2Model"],["olmo3","Olmo3Model"],["olmo_hybrid","OlmoHybridModel"],["mobilellm","MobileLLMModel"],["granite","GraniteModel"],["granitemoehybrid","GraniteMoeHybridModel"],["cohere","CohereModel"],["cohere2","Cohere2Model"],["gemma","GemmaModel"],["gemma2","Gemma2Model"],["vaultgemma","VaultGemmaModel"],["gemma3_text","Gemma3Model"],["helium","HeliumModel"],["glm","GlmModel"],["glm_moe_dsa","GlmMoeDsaModel"],["openelm","OpenELMModel"],["qwen2","Qwen2Model"],["qwen2_moe","Qwen2MoeModel"],["qwen3","Qwen3Model"],["qwen3_moe","Qwen3MoeModel"],["qwen3_next","Qwen3NextModel"],["phi","PhiModel"],["phi3","Phi3Model"],["mpt","MptModel"],["opt","OPTModel"],["mistral","MistralModel"],["mistral4","Mistral4Model"],["ministral","MinistralModel"],["ministral3","Ministral3Model"],["ernie4_5","Ernie4_5ForCausalLM"],["starcoder2","Starcoder2Model"],["deepseek_v3","DeepseekV3Model"],["falcon","FalconModel"],["falcon_h1","FalconH1Model"],["nemotron_h","NemotronHModel"],["solar_open","SolarOpenModel"],["stablelm","StableLmModel"],["modernbert-decoder","ModernBertDecoderModel"],["hunyuan_v1_dense","HunYuanDenseV1Model"],["youtu","YoutuModel"]]),bG=new Map([["speecht5","SpeechT5ForSpeechToText"],["whisper","WhisperForConditionalGeneration"],["lite-whisper","LiteWhisperForConditionalGeneration"],["moonshine","MoonshineForConditionalGeneration"],["cohere_asr","CohereAsrForConditionalGeneration"]]),fG=new Map([["speecht5","SpeechT5ForTextToSpeech"]]),_G=new Map([["vits","VitsModel"],["musicgen","MusicgenForConditionalGeneration"],["supertonic","SupertonicForConditionalGeneration"]]),gG=new Map([["bert","BertForSequenceClassification"],["eurobert","EuroBertForSequenceClassification"],["neobert","NeoBertForSequenceClassification"],["modernbert","ModernBertForSequenceClassification"],["roformer","RoFormerForSequenceClassification"],["electra","ElectraForSequenceClassification"],["esm","EsmForSequenceClassification"],["convbert","ConvBertForSequenceClassification"],["camembert","CamembertForSequenceClassification"],["deberta","DebertaForSequenceClassification"],["deberta-v2","DebertaV2ForSequenceClassification"],["mpnet","MPNetForSequenceClassification"],["albert","AlbertForSequenceClassification"],["distilbert","DistilBertForSequenceClassification"],["roberta","RobertaForSequenceClassification"],["xlm","XLMForSequenceClassification"],["xlm-roberta","XLMRobertaForSequenceClassification"],["bart","BartForSequenceClassification"],["mbart","MBartForSequenceClassification"],["mobilebert","MobileBertForSequenceClassification"],["squeezebert","SqueezeBertForSequenceClassification"]]),cG=new Map([["bert","BertForTokenClassification"],["eurobert","EuroBertForTokenClassification"],["neobert","NeoBertForTokenClassification"],["modernbert","ModernBertForTokenClassification"],["roformer","RoFormerForTokenClassification"],["electra","ElectraForTokenClassification"],["esm","EsmForTokenClassification"],["convbert","ConvBertForTokenClassification"],["camembert","CamembertForTokenClassification"],["deberta","DebertaForTokenClassification"],["deberta-v2","DebertaV2ForTokenClassification"],["mpnet","MPNetForTokenClassification"],["distilbert","DistilBertForTokenClassification"],["roberta","RobertaForTokenClassification"],["xlm","XLMForTokenClassification"],["xlm-roberta","XLMRobertaForTokenClassification"],["openai_privacy_filter","OpenAIPrivacyFilterForTokenClassification"]]),lG=new Map([["t5","T5ForConditionalGeneration"],["longt5","LongT5ForConditionalGeneration"],["mt5","MT5ForConditionalGeneration"],["bart","BartForConditionalGeneration"],["mbart","MBartForConditionalGeneration"],["marian","MarianMTModel"],["m2m_100","M2M100ForConditionalGeneration"],["blenderbot","BlenderbotForConditionalGeneration"],["blenderbot-small","BlenderbotSmallForConditionalGeneration"]]),uG=new Map([["bloom","BloomForCausalLM"],["gpt2","GPT2LMHeadModel"],["gpt_oss","GptOssForCausalLM"],["jais","JAISLMHeadModel"],["gptj","GPTJForCausalLM"],["gpt_bigcode","GPTBigCodeForCausalLM"],["gpt_neo","GPTNeoForCausalLM"],["gpt_neox","GPTNeoXForCausalLM"],["codegen","CodeGenForCausalLM"],["llama","LlamaForCausalLM"],["nanochat","NanoChatForCausalLM"],["apertus","ApertusForCausalLM"],["llama4_text","Llama4ForCausalLM"],["arcee","ArceeForCausalLM"],["afmoe","AfmoeForCausalLM"],["lfm2","Lfm2ForCausalLM"],["lfm2_moe","Lfm2MoeForCausalLM"],["smollm3","SmolLM3ForCausalLM"],["exaone","ExaoneForCausalLM"],["olmo","OlmoForCausalLM"],["olmo2","Olmo2ForCausalLM"],["olmo3","Olmo3ForCausalLM"],["olmo_hybrid","OlmoHybridForCausalLM"],["mobilellm","MobileLLMForCausalLM"],["granite","GraniteForCausalLM"],["granitemoehybrid","GraniteMoeHybridForCausalLM"],["cohere","CohereForCausalLM"],["cohere2","Cohere2ForCausalLM"],["gemma","GemmaForCausalLM"],["gemma2","Gemma2ForCausalLM"],["vaultgemma","VaultGemmaForCausalLM"],["gemma3_text","Gemma3ForCausalLM"],["gemma3","Gemma3ForCausalLM"],["helium","HeliumForCausalLM"],["glm","GlmForCausalLM"],["glm_moe_dsa","GlmMoeDsaForCausalLM"],["openelm","OpenELMForCausalLM"],["qwen2","Qwen2ForCausalLM"],["qwen2_moe","Qwen2MoeForCausalLM"],["qwen3","Qwen3ForCausalLM"],["qwen3_moe","Qwen3MoeForCausalLM"],["qwen3_next","Qwen3NextForCausalLM"],["qwen2_vl","Qwen2VLForCausalLM"],["qwen2_5_vl","Qwen2_5_VLForCausalLM"],["qwen3_vl","Qwen3VLForCausalLM"],["qwen3_vl_moe","Qwen3VLMoeForCausalLM"],["qwen3_5","Qwen3_5ForCausalLM"],["qwen3_5_text","Qwen3_5ForCausalLM"],["qwen3_5_moe","Qwen3_5MoeForCausalLM"],["gemma3n","Gemma3nForCausalLM"],["gemma4","Gemma4ForCausalLM"],["phi","PhiForCausalLM"],["phi3","Phi3ForCausalLM"],["mpt","MptForCausalLM"],["opt","OPTForCausalLM"],["mbart","MBartForCausalLM"],["mistral","MistralForCausalLM"],["mistral4","Mistral4ForCausalLM"],["ministral","MinistralForCausalLM"],["ministral3","Ministral3ForCausalLM"],["ernie4_5","Ernie4_5ForCausalLM"],["starcoder2","Starcoder2ForCausalLM"],["deepseek_v3","DeepseekV3ForCausalLM"],["falcon","FalconForCausalLM"],["falcon_h1","FalconH1ForCausalLM"],["nemotron_h","NemotronHForCausalLM"],["trocr","TrOCRForCausalLM"],["solar_open","SolarOpenForCausalLM"],["stablelm","StableLmForCausalLM"],["modernbert-decoder","ModernBertDecoderForCausalLM"],["hunyuan_v1_dense","HunYuanDenseV1ForCausalLM"],["youtu","YoutuForCausalLM"],["phi3_v","Phi3VForCausalLM"]]),nE=new Map([["multi_modality","MultiModalityCausalLM"]]),mG=new Map([["bert","BertForMaskedLM"],["eurobert","EuroBertForMaskedLM"],["neobert","NeoBertForMaskedLM"],["modernbert","ModernBertForMaskedLM"],["roformer","RoFormerForMaskedLM"],["electra","ElectraForMaskedLM"],["esm","EsmForMaskedLM"],["convbert","ConvBertForMaskedLM"],["camembert","CamembertForMaskedLM"],["deberta","DebertaForMaskedLM"],["deberta-v2","DebertaV2ForMaskedLM"],["mpnet","MPNetForMaskedLM"],["albert","AlbertForMaskedLM"],["distilbert","DistilBertForMaskedLM"],["roberta","RobertaForMaskedLM"],["xlm","XLMWithLMHeadModel"],["xlm-roberta","XLMRobertaForMaskedLM"],["mobilebert","MobileBertForMaskedLM"],["squeezebert","SqueezeBertForMaskedLM"]]),dG=new Map([["bert","BertForQuestionAnswering"],["neobert","NeoBertForQuestionAnswering"],["roformer","RoFormerForQuestionAnswering"],["electra","ElectraForQuestionAnswering"],["convbert","ConvBertForQuestionAnswering"],["camembert","CamembertForQuestionAnswering"],["deberta","DebertaForQuestionAnswering"],["deberta-v2","DebertaV2ForQuestionAnswering"],["mpnet","MPNetForQuestionAnswering"],["albert","AlbertForQuestionAnswering"],["distilbert","DistilBertForQuestionAnswering"],["roberta","RobertaForQuestionAnswering"],["xlm","XLMForQuestionAnswering"],["xlm-roberta","XLMRobertaForQuestionAnswering"],["mobilebert","MobileBertForQuestionAnswering"],["squeezebert","SqueezeBertForQuestionAnswering"]]),pG=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"]]),iG=new Map([["llava","LlavaForConditionalGeneration"],["llava_onevision","LlavaOnevisionForConditionalGeneration"],["moondream1","Moondream1ForConditionalGeneration"],["florence2","Florence2ForConditionalGeneration"],["qwen2_vl","Qwen2VLForConditionalGeneration"],["qwen2_5_vl","Qwen2_5_VLForConditionalGeneration"],["qwen3_vl","Qwen3VLForConditionalGeneration"],["qwen3_vl_moe","Qwen3VLMoeForConditionalGeneration"],["qwen3_5","Qwen3_5ForConditionalGeneration"],["qwen3_5_moe","Qwen3_5MoeForConditionalGeneration"],["lfm2_vl","Lfm2VlForConditionalGeneration"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"],["paligemma","PaliGemmaForConditionalGeneration"],["llava_qwen2","LlavaQwen2ForCausalLM"],["gemma3","Gemma3ForConditionalGeneration"],["gemma3n","Gemma3nForConditionalGeneration"],["gemma4","Gemma4ForConditionalGeneration"],["mistral3","Mistral3ForConditionalGeneration"],["lighton_ocr","LightOnOcrForConditionalGeneration"],["glm_ocr","GlmOcrForConditionalGeneration"]]),rG=new Map([["granite_speech","GraniteSpeechForConditionalGeneration"],["ultravox","UltravoxModel"],["voxtral","VoxtralForConditionalGeneration"],["voxtral_realtime","VoxtralRealtimeForConditionalGeneration"]]),sE=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"]]),aG=new Map([["vit","ViTForImageClassification"],["ijepa","IJepaForImageClassification"],["pvt","PvtForImageClassification"],["vit_msn","ViTMSNForImageClassification"],["fastvit","FastViTForImageClassification"],["mobilevit","MobileViTForImageClassification"],["mobilevitv2","MobileViTV2ForImageClassification"],["beit","BeitForImageClassification"],["deit","DeiTForImageClassification"],["hiera","HieraForImageClassification"],["convnext","ConvNextForImageClassification"],["convnextv2","ConvNextV2ForImageClassification"],["dinov2","Dinov2ForImageClassification"],["dinov2_with_registers","Dinov2WithRegistersForImageClassification"],["resnet","ResNetForImageClassification"],["swin","SwinForImageClassification"],["segformer","SegformerForImageClassification"],["efficientnet","EfficientNetForImageClassification"],["mobilenet_v1","MobileNetV1ForImageClassification"],["mobilenet_v2","MobileNetV2ForImageClassification"],["mobilenet_v3","MobileNetV3ForImageClassification"],["mobilenet_v4","MobileNetV4ForImageClassification"]]),oG=new Map([["detr","DetrForObjectDetection"],["rt_detr","RTDetrForObjectDetection"],["rt_detr_v2","RTDetrV2ForObjectDetection"],["rf_detr","RFDetrForObjectDetection"],["d_fine","DFineForObjectDetection"],["table-transformer","TableTransformerForObjectDetection"],["yolos","YolosForObjectDetection"]]),nG=new Map([["owlvit","OwlViTForObjectDetection"],["owlv2","Owlv2ForObjectDetection"],["grounding-dino","GroundingDinoForObjectDetection"]]),$8=new Map([["detr","DetrForSegmentation"],["clipseg","CLIPSegForImageSegmentation"]]),sG=new Map([["segformer","SegformerForSemanticSegmentation"],["sapiens","SapiensForSemanticSegmentation"],["swin","SwinForSemanticSegmentation"],["mobilenet_v1","MobileNetV1ForSemanticSegmentation"],["mobilenet_v2","MobileNetV2ForSemanticSegmentation"],["mobilenet_v3","MobileNetV3ForSemanticSegmentation"],["mobilenet_v4","MobileNetV4ForSemanticSegmentation"]]),tG=new Map([["detr","DetrForSegmentation"],["maskformer","MaskFormerForInstanceSegmentation"]]),eG=new Map([["sam","SamModel"],["sam2","Sam2Model"],["edgetam","EdgeTamModel"],["sam3_tracker","Sam3TrackerModel"]]),Jq=new Map([["wav2vec2","Wav2Vec2ForCTC"],["wav2vec2-bert","Wav2Vec2BertForCTC"],["unispeech","UniSpeechForCTC"],["unispeech-sat","UniSpeechSatForCTC"],["wavlm","WavLMForCTC"],["hubert","HubertForCTC"],["parakeet_ctc","ParakeetForCTC"]]),Qq=new Map([["wav2vec2","Wav2Vec2ForSequenceClassification"],["wav2vec2-bert","Wav2Vec2BertForSequenceClassification"],["unispeech","UniSpeechForSequenceClassification"],["unispeech-sat","UniSpeechSatForSequenceClassification"],["wavlm","WavLMForSequenceClassification"],["hubert","HubertForSequenceClassification"],["audio-spectrogram-transformer","ASTForAudioClassification"]]),Xq=new Map([["wavlm","WavLMForXVector"]]),Kq=new Map([["unispeech-sat","UniSpeechSatForAudioFrameClassification"],["wavlm","WavLMForAudioFrameClassification"],["wav2vec2","Wav2Vec2ForAudioFrameClassification"],["pyannote","PyAnnoteForAudioFrameClassification"]]),Zq=new Map([["vitmatte","VitMatteForImageMatting"]]),tE=new Map([["patchtst","PatchTSTForPrediction"],["patchtsmixer","PatchTSMixerForPrediction"]]),Hq=new Map([["swin2sr","Swin2SRForImageSuperResolution"]]),Yq=new Map([["chmv2","CHMv2ForDepthEstimation"],["dpt","DPTForDepthEstimation"],["depth_anything","DepthAnythingForDepthEstimation"],["glpn","GLPNForDepthEstimation"],["sapiens","SapiensForDepthEstimation"],["depth_pro","DepthProForDepthEstimation"],["metric3d","Metric3DForDepthEstimation"],["metric3dv2","Metric3Dv2ForDepthEstimation"]]),$q=new Map([["sapiens","SapiensForNormalEstimation"]]),Vq=new Map([["vitpose","VitPoseForPoseEstimation"]]),Wq=new Map([["clip","CLIPVisionModelWithProjection"],["siglip","SiglipVisionModel"],["jina_clip","JinaCLIPVisionModel"]]),Gq=[[iE,m.EncoderOnly],[rE,m.EncoderDecoder],[oE,m.DecoderOnlyWithoutHead],[aE,m.AutoEncoder],[gG,m.EncoderOnly],[cG,m.EncoderOnly],[lG,m.Seq2Seq],[bG,m.Seq2Seq],[uG,m.DecoderOnly],[nE,m.MultiModality],[mG,m.EncoderOnly],[dG,m.EncoderOnly],[pG,m.Vision2Seq],[iG,m.ImageTextToText],[rG,m.AudioTextToText],[aG,m.EncoderOnly],[$8,m.EncoderOnly],[tG,m.EncoderOnly],[sG,m.EncoderOnly],[Zq,m.EncoderOnly],[tE,m.EncoderOnly],[Hq,m.EncoderOnly],[Yq,m.EncoderOnly],[$q,m.EncoderOnly],[Vq,m.EncoderOnly],[oG,m.EncoderOnly],[nG,m.EncoderOnly],[eG,m.MaskGeneration],[Jq,m.EncoderOnly],[Qq,m.EncoderOnly],[fG,m.Seq2Seq],[_G,m.EncoderOnly],[Xq,m.EncoderOnly],[Kq,m.EncoderOnly],[Wq,m.EncoderOnly]];for(let[J,Q]of Gq)for(let X of J.values()){Y5.set(X,Q);let K=oJ[X];F8.set(K,X),iJ.set(X,K)}var eE=[["MusicgenForConditionalGeneration",ZG,m.Musicgen],["Phi3VForCausalLM",VG,m.Phi3V],["CLIPTextModelWithProjection",CW,m.EncoderOnly],["SiglipTextModel",OG,m.EncoderOnly],["JinaCLIPTextModel",rW,m.EncoderOnly],["ClapTextModelWithProjection",SW,m.EncoderOnly],["ClapAudioModelWithProjection",LW,m.EncoderOnly],["DacEncoderModel",PW,m.EncoderOnly],["DacDecoderModel",EW,m.EncoderOnly],["MimiEncoderModel",QG,m.EncoderOnly],["MimiDecoderModel",XG,m.EncoderOnly],["SnacEncoderModel",BG,m.EncoderOnly],["SnacDecoderModel",DG,m.EncoderOnly],["Gemma3nForConditionalGeneration",B7,m.ImageAudioTextToText],["Gemma4ForConditionalGeneration",EQ,m.ImageAudioTextToText],["SupertonicForConditionalGeneration",LG,m.Supertonic],["ChatterboxModel",BW,m.Chatterbox],["VoxtralRealtimeForConditionalGeneration",vG,m.VoxtralRealtime]];for(let[J,Q,X]of eE)Y5.set(J,X),F8.set(Q,J),iJ.set(J,Q);var qq=new Map([["modnet",$8],["birefnet",$8],["isnet",$8],["ben",$8]]);for(let[J,Q]of qq.entries())Q.set(J,"PreTrainedModel"),Y5.set(J,m.EncoderOnly),iJ.set(J,T);var Jw=new Set(qq.keys());Y5.set("PreTrainedModel",m.EncoderOnly);F8.set(T,"PreTrainedModel");var A0={MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES:gG,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES:cG,MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES:fG,MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES:_G,MODEL_FOR_MASKED_LM_MAPPING_NAMES:mG,MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES:dG,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:aG,MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES:$8,MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES:sG,MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES:tG,MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES:oG,MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES:nG,MODEL_FOR_MASK_GENERATION_MAPPING_NAMES:eG,MODEL_FOR_CTC_MAPPING_NAMES:Jq,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES:Qq,MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES:Xq,MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES:Kq,MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES:sE,MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES:Zq,MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES:Hq,MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES:Yq,MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES:$q,MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES:Vq,MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES:Wq,MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES:iG,MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES:rG,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:lG,MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES:bG,MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:uG,MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES:pG};XM(A0);var P0=class{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static supports(J){if(!this.MODEL_CLASS_MAPPINGS)return!1;for(let Q of this.MODEL_CLASS_MAPPINGS)if(Q.has(J))return!0;return this.BASE_IF_FAIL}static async from_pretrained(J,{progress_callback:Q=null,config:X=null,cache_dir:K=null,local_files_only:Z=!1,revision:H="main",model_file_name:Y=null,subfolder:$="onnx",device:V=null,dtype:W=null,use_external_data_format:q=null,session_options:F={}}={}){let N={progress_callback:Q,config:X,cache_dir:K,local_files_only:Z,revision:H,model_file_name:Y,subfolder:$,device:V,dtype:W,use_external_data_format:q,session_options:F};if(N.config=await X9.from_pretrained(J,N),!this.MODEL_CLASS_MAPPINGS)throw Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);let{model_type:j}=N.config;for(let B of this.MODEL_CLASS_MAPPINGS){let D=B.get(j);if(!D){for(let L of B.values())if(L[0]===j){D=L;break}if(!D)continue}return await oJ[D].from_pretrained(J,N)}if(this.BASE_IF_FAIL){if(!Jw.has(j))$0.warn(`Unknown model class "${j}", attempting to construct from base class.`);return await T.from_pretrained(J,N)}else throw Error(`Unsupported model type: ${j}`)}},n8=class extends P0{static MODEL_CLASS_MAPPINGS=Gq.map((J)=>J[0]);static BASE_IF_FAIL=!0},y$=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]},Qw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]},qJ=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]},Xw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]},Kw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]},Zw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES]},Hw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES]},Yw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_MASKED_LM_MAPPING_NAMES]},$w=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]},Vw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]},Ww=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]},x$=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]},k$=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES]},h$=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES]},Gw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]},qw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES]},HI=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_MASK_GENERATION_MAPPING_NAMES]},Fw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_CTC_MAPPING_NAMES]},Uw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]},YI=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES]},$I=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES]},Nw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]},VI=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES]},jw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES]},zw=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES]},WI=class extends P0{static MODEL_CLASS_MAPPINGS=[A0.MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES]},GI=class extends P0{static 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_call(J,{top_k:Q=1}={}){let X=this.tokenizer(J,{padding:!0,truncation:!0}),K=await this.model(X),{problem_type:Z,id2label:H}=this.model.config,Y=Z==="multi_label_classification"?(V)=>V.sigmoid():(V)=>new u("float32",K6(V.data),V.dims),$=[];for(let V of K.logits){let W=Y(V),q=await z8(W,Q),F=q[0].tolist(),j=q[1].tolist().map((B,D)=>({label:H?H[B]:`LABEL_${B}`,score:F[D]}));if(Q===1)$.push(...j);else $.push(j)}return Array.isArray(J)||Q===1?$:$[0]}},Dw=class extends g0{async _call(J,{ignore_labels:Q=["O"],aggregation_strategy:X="none"}={}){if(X!=="none"&&X!=="simple")throw Error(`Invalid aggregation_strategy: "${X}". 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K=this.tokenizer,Z={padding:!0,truncation:!0},H;if(this.task==="translation"&&"_build_translation_inputs"in K)H=K._build_translation_inputs(J,Z,Q);else H=K(J,Z);let Y=await this.model.generate({...H,...this._default_generation_config,...Q});return K.batch_decode(Y,{skip_special_tokens:!0}).map(($)=>({[this._key]:$}))}},Rw=class extends tX{_key="summary_text"},Aw=class extends tX{_key="translation_text"};function b$(J){return Array.isArray(J)&&J.every((Q)=>("role"in Q)&&("content"in Q))}var Pw=class extends g0{_default_generation_config={max_new_tokens:256};async _call(J,Q={}){let{add_special_tokens:X,return_full_text:K,tools:Z,documents:H,chat_template:Y,tokenizer_encode_kwargs:$,...V}=Q,W=!1,q=!1,F=X??(this.tokenizer.add_bos_token||this.tokenizer.add_eos_token)??!1,N=$,j;if(typeof J==="string")j=J=[J];else if(Array.isArray(J)&&J.every((P)=>typeof P==="string"))W=!0,j=J;else{if(b$(J))J=[J];else if(Array.isArray(J)&&J.every(b$))W=!0;else throw Error("Input must be a string, an array of 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g0{constructor(J){super(J);if(this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([Q,X])=>[Q.toLowerCase(),X])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0)$0.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2;if(this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0)$0.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0}async _call(J,Q,{hypothesis_template:X="This example is {}.",multi_label:K=!1}={}){let Z=Array.isArray(J);if(!Z)J=[J];if(!Array.isArray(Q))Q=[Q];let H=Q.map((V)=>X.replace("{}",V)),Y=K||Q.length===1,$=[];for(let V of J){let W=[];for(let N of H){let j=this.tokenizer(V,{text_pair:N,padding:!0,truncation:!0}),B=await this.model(j);if(Y)W.push([B.logits.data[this.contradiction_id],B.logits.data[this.entailment_id]]);else W.push(B.logits.data[this.entailment_id])}let F=(Y?W.map((N)=>K6(N)[1]):K6(W)).map((N,j)=>[N,j]).sort((N,j)=>j[0]-N[0]);$.push({sequence:V,labels:F.map((N)=>Q[N[1]]),scores:F.map((N)=>N[0])})}return Z?$:$[0]}},ww=class extends g0{async _call(J,{top_k:Q=5}={}){let X=this.processor.feature_extractor.config.sampling_rate,K=await V8(J,X),Z=this.model.config.id2label,H=[];for(let Y of K){let $=await this.processor(Y),W=(await this.model($)).logits[0],q=await z8(new u("float32",K6(W.data),W.dims),Q),F=q[0].tolist(),j=q[1].tolist().map((B,D)=>({label:Z?Z[B]:`LABEL_${B}`,score:F[D]}));H.push(j)}return Array.isArray(J)?H:H[0]}},Tw=class extends g0{async _call(J,Q,{hypothesis_template:X="This is a sound of {}."}={}){let K=!Array.isArray(J);if(K)J=[J];let Z=Q.map((W)=>X.replace("{}",W)),H=this.tokenizer(Z,{padding:!0,truncation:!0}),Y=this.processor.feature_extractor.config.sampling_rate,$=await V8(J,Y),V=[];for(let W of $){let q=await this.processor(W),F=await this.model({...H,...q}),N=K6(F.logits_per_audio.data);V.push([...N].map((j,B)=>({score:j,label:Q[B]})))}return K?V[0]:V}},Iw=class extends g0{_default_generation_config={};async _call(J,Q={}){switch(Q={...this._default_generation_config,...Q},this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(J,Q);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":case"parakeet_ctc":return this._call_wav2vec2(J,Q);case"moonshine":return this._call_moonshine(J,Q);case"cohere_asr":return this._call_cohere_asr(J,Q);default:throw Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(J,Q){if(Q.language)$0.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".');if(Q.task)$0.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let X=!Array.isArray(J),K=X?[J]:J,Z=this.processor.feature_extractor.config.sampling_rate,H=await V8(K,Z),Y=[];for(let $ of H){let V=await this.processor($),q=(await this.model(V)).logits[0],F=[];for(let j of q)F.push(c0(j.data)[1]);let N=this.tokenizer.decode(F,{skip_special_tokens:!0}).trim();Y.push({text:N})}return X?Y[0]:Y}async _call_whisper(J,Q){let X=Q.return_timestamps??!1,K=Q.chunk_length_s??0,Z=Q.force_full_sequences??!1,H=Q.stride_length_s??null,Y={...Q};if(X==="word")Y.return_token_timestamps=!0,Y.return_timestamps=!0;let $=!Array.isArray(J),V=$?[J]:J,W=this.processor.feature_extractor.config,q=W.chunk_length/this.model.config.max_source_positions,F=W.hop_length,N=W.sampling_rate,j=await V8(V,N),B=[];for(let D of j){let L=[];if(K>0){if(H===null)H=K/6;else if(K<=H)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let E=N*K,P=N*H,w=E-2*P,k=0;while(!0){let C=k+E,c=D.subarray(k,C),g=await this.processor(c),d=k===0,r=C>=D.length;if(L.push({stride:[c.length,d?0:P,r?0:P],input_features:g.input_features,is_last:r}),r)break;k+=w}}else L=[{stride:[D.length,0,0],input_features:(await this.processor(D)).input_features,is_last:!0}];for(let E of L){Y.num_frames=Math.floor(E.stride[0]/F);let P=await this.model.generate({inputs:E.input_features,...Y});if(X==="word"){let w=P.sequences.tolist()[0],k=P.token_timestamps.tolist()[0],C=this.tokenizer.timestamp_begin,c=Math.max(w.findIndex((g)=>Number(g)>=C),0);E.tokens=w.slice(c),E.token_timestamps=k.slice(c).map((g)=>o8(g,2))}else E.tokens=P[0].tolist();E.stride=E.stride.map((w)=>w/N)}let[M,R]=this.tokenizer._decode_asr(L,{time_precision:q,return_timestamps:X,force_full_sequences:Z});B.push({text:M,...R})}return $?B[0]:B}async _call_moonshine(J,Q){let X=!Array.isArray(J),K=X?[J]:J,Z=this.processor.feature_extractor.config.sampling_rate,H=await V8(K,Z),Y=[];for(let $ of H){let V=await this.processor($),W=Math.floor($.length/Z)*6,q=await this.model.generate({max_new_tokens:W,...Q,...V}),F=this.processor.batch_decode(q,{skip_special_tokens:!0})[0];Y.push({text:F})}return X?Y[0]:Y}async _call_cohere_asr(J,Q){let X=!Array.isArray(J),K=X?[J]:J,Z=this.processor.feature_extractor,H=Z.config.sampling_rate,Y=await V8(K,H),$=Q.language??"en",V=this.processor.get_decoder_prompt_ids($),W=[];for(let q of Y){let F=Z.split_audio(q),N=[];for(let B of F){let D=await this.processor(B),L=await this.model.generate({...D,decoder_input_ids:V,...Q}),M=this.tokenizer.decode(L[0].tolist(),{skip_special_tokens:!0}).trim();N.push(M)}let j=this.processor.constructor.join_chunks(N,$);W.push({text:j})}return X?W[0]:W}},vw=class extends g0{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(J){super(J);this.vocoder=J.vocoder??null}async _prepare_speaker_embeddings(J,Q){if(typeof J==="string"||J instanceof URL)J=new Float32Array(await(await G0.fetch(J)).arrayBuffer());if(J instanceof Float32Array)J=new u("float32",J,[J.length]);else if(!(J instanceof u))throw Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");if(Q>1){if(J.dims[0]===1)J=J.repeat(Q,1);else if(J.dims[0]!==Q)throw Error(`Expected speaker embeddings batch size to be 1 or ${Q}, but got ${J.dims[0]}.`)}return J}_postprocess_waveform(J,Q,X,K=null){let Z=Q.data,[H,Y]=Q.dims,$=K?K.data:null,V=[];for(let W=0;W<H;++W){let q=$?Math.min(Math.ceil($[W]),Y):Y,F=W*Y;V.push(new AO(Z.slice(F,F+q),X))}return Array.isArray(J)?V:V[0]}async _call(J,Q){if(this.processor)return this._call_text_to_spectrogram(J,Q);else if(this.model.config.model_type==="supertonic")return this._call_supertonic(J,Q);else return this._call_text_to_waveform(J)}async _call_supertonic(J,{speaker_embeddings:Q,num_inference_steps:X,speed:K}){if(!Q)throw Error("Speaker embeddings must be provided for Supertonic models.");let{sampling_rate:Z,style_dim:H}=this.model.config,Y=this.tokenizer(J,{padding:!0,truncation:!0}),$=Y.input_ids.dims[0];Q=await this._prepare_speaker_embeddings(Q,$),Q=Q.view($,-1,H);let{waveform:V,durations:W}=await this.model.generate_speech({...Y,style:Q,num_inference_steps:X,speed:K});return this._postprocess_waveform(J,V,Z,W)}async _call_text_to_waveform(J){let Q=this.tokenizer(J,{padding:!0,truncation:!0}),{waveform:X}=await this.model(Q),K=this.model.config.sampling_rate;return this._postprocess_waveform(J,X,K)}async _call_text_to_spectrogram(J,{speaker_embeddings:Q}){if(!this.vocoder)$0.info("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await n8.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"});let{input_ids:X}=this.tokenizer(J,{padding:!0,truncation:!0}),K=X.dims[0];Q=await this._prepare_speaker_embeddings(Q,K),Q=Q.view(K,-1);let{waveform:Z}=await this.model.generate_speech(X,Q,{vocoder:this.vocoder}),H=this.processor.feature_extractor.config.sampling_rate;return this._postprocess_waveform(J,Z,H)}},yw=class extends g0{async _call(J,Q={}){let X=Array.isArray(J),K=await c6(J),{pixel_values:Z}=await this.processor(K),H=[];for(let Y of Z){Y.dims=[1,...Y.dims];let $=await this.model.generate({inputs:Y,...Q}),V=this.tokenizer.batch_decode($,{skip_special_tokens:!0}).map((W)=>({generated_text:W.trim()}));H.push(V)}return X?H:H[0]}},xw=class extends g0{async _call(J,{top_k:Q=5}={}){let X=await c6(J),{pixel_values:K}=await this.processor(X),Z=await this.model({pixel_values:K}),{id2label:H}=this.model.config,Y=[];for(let $ of Z.logits){let V=await z8(new u("float32",K6($.data),$.dims),Q),W=V[0].tolist(),F=V[1].tolist().map((N,j)=>({label:H?H[N]:`LABEL_${N}`,score:W[j]}));Y.push(F)}return Array.isArray(J)?Y:Y[0]}},f$={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"},Fq=class extends g0{async _call(J,{threshold:Q=0.5,mask_threshold:X=0.5,overlap_mask_area_threshold:K=0.8,label_ids_to_fuse:Z=null,target_sizes:H=null,subtask:Y=null}={}){if(Array.isArray(J)&&J.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");let V=await c6(J),W=V.map((M)=>[M.height,M.width]),q=await this.processor(V),{inputNames:F,outputNames:N}=this.model.sessions.model;if(!F.includes("pixel_values")){if(F.length!==1)throw Error(`Expected a single input name, but got ${F.length} inputs: ${F}.`);let M=F[0];if(M in q)throw Error(`Input name ${M} already exists in the inputs.`);q[M]=q.pixel_values}let j=await this.model(q),B=null;if(Y!==null)B=f$[Y];else if(this.processor.image_processor){for(let[M,R]of Object.entries(f$))if(R in this.processor.image_processor){B=this.processor.image_processor[R].bind(this.processor.image_processor),Y=M;break}}let D=this.model.config.id2label,L=[];if(!Y){let R=j[N[0]];for(let E=0;E<W.length;++E){let P=W[E],w=R[E];if(w.data.some((C)=>C<-0.00001||C>1.00001))w.sigmoid_();let k=await h6.fromTensor(w.mul_(255).to("uint8")).resize(P[1],P[0]);L.push({label:null,score:null,mask:k})}}else if(Y==="panoptic"||Y==="instance"){let M=B(j,Q,X,K,Z,H??W)[0],R=M.segmentation;for(let E of M.segments_info){let P=new Uint8ClampedArray(R.data.length);for(let k=0;k<R.data.length;++k)if(R.data[k]===E.id)P[k]=255;let w=new h6(P,R.dims[1],R.dims[0],1);L.push({score:E.score,label:D[E.label_id],mask:w})}}else if(Y==="semantic"){let{segmentation:M,labels:R}=B(j,H??W)[0];for(let E of R){let P=new Uint8ClampedArray(M.data.length);for(let k=0;k<M.data.length;++k)if(M.data[k]===E)P[k]=255;let w=new h6(P,M.dims[1],M.dims[0],1);L.push({score:null,label:D[E],mask:w})}}else throw Error(`Subtask ${Y} not supported.`);return L}},kw=class extends Fq{async _call(J,Q={}){let X=await c6(J),K=await super._call(J,Q),Z=X.map((H,Y)=>{let $=H.clone();return $.putAlpha(K[Y].mask),$});return Array.isArray(J)?Z:Z[0]}},hw=class extends g0{async _call(J,Q,{hypothesis_template:X="This is a photo of {}"}={}){let K=Array.isArray(J),Z=await c6(J),H=Q.map((F)=>X.replace("{}",F)),Y=this.tokenizer(H,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:$}=await this.processor(Z),V=await this.model({...Y,pixel_values:$}),W=this.model.config.model_type==="siglip"?(F)=>F.sigmoid().data:(F)=>K6(F.data),q=[];for(let F of V.logits_per_image){let j=[...W(F)].map((B,D)=>({score:B,label:Q[D]}));j.sort((B,D)=>D.score-B.score),q.push(j)}return K?q:q[0]}},bw=class extends g0{async _call(J,{threshold:Q=0.9,percentage:X=!1}={}){let K=Array.isArray(J);if(K&&J.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");let Z=await c6(J),H=X?null:Z.map((N)=>[N.height,N.width]),{pixel_values:Y,pixel_mask:$}=await this.processor(Z),V=await this.model({pixel_values:Y,pixel_mask:$}),W=this.processor.image_processor.post_process_object_detection(V,Q,H),{id2label:q}=this.model.config,F=W.map((N)=>N.boxes.map((j,B)=>({score:N.scores[B],label:q[N.classes[B]],box:CJ(j,!X)})));return K?F:F[0]}},fw=class extends g0{async _call(J,Q,{threshold:X=0.1,top_k:K=null,percentage:Z=!1}={}){let H=Array.isArray(J),Y=await c6(J),$=this.tokenizer(Q,{padding:!0,truncation:!0}),V=await this.processor(Y),W=[];for(let q=0;q<Y.length;++q){let F=Y[q],N=Z?null:[[F.height,F.width]],j=V.pixel_values[q].unsqueeze_(0),B=await this.model({...$,pixel_values:j}),D;if("post_process_grounded_object_detection"in this.processor){let L=this.processor.post_process_grounded_object_detection(B,$.input_ids,{box_threshold:X,text_threshold:X,target_sizes:N})[0];D=L.boxes.map((M,R)=>({score:L.scores[R],label:L.labels[R],box:CJ(M,!Z)}))}else{let L=this.processor.image_processor.post_process_object_detection(B,X,N,!0)[0];D=L.boxes.map((M,R)=>({score:L.scores[R],label:Q[L.classes[R]],box:CJ(M,!Z)}))}if(D.sort((L,M)=>M.score-L.score),K!==null)D=D.slice(0,K);W.push(D)}return H?W:W[0]}},_w=class extends g0{_default_generation_config={max_new_tokens:256};async _call(J,Q,X={}){if(Array.isArray(J)){if(J.length!==1)throw Error("Document Question Answering pipeline currently only supports a batch size of 1.");J=J[0]}let K=(await c6(J))[0],{pixel_values:Z}=await this.processor(K),H=`<s_docvqa><s_question>${Q}</s_question><s_answer>`,Y=this.tokenizer(H,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,$=await this.model.generate({inputs:Z,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Y,...this._default_generation_config,...X}),W=this.tokenizer.batch_decode($)[0].match(/<s_answer>(.*?)<\/s_answer>/),q=null;if(W&&W.length>=2)q=W[1].trim();return[{answer:q}]}},gw=class extends g0{async _call(J){let Q=await c6(J),X=await this.processor(Q),K=await this.model(X),Z=[];for(let H of K.reconstruction){let Y=H.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Z.push(h6.fromTensor(Y))}return Array.isArray(J)?Z:Z[0]}},cw=class extends g0{async _call(J){let Q=await c6(J),X=await this.processor(Q),{predicted_depth:K}=await this.model(X),Z=[];for(let H=0;H<Q.length;++H){let Y=K[H],[$,V]=Y.dims.slice(-2),[W,q]=Q[H].size,F=(await k6(Y.view(1,1,$,V),{size:[q,W],mode:"bilinear"})).view(q,W),N=F.min().item(),j=F.max().item(),B=F.sub(N).div_(j-N).mul_(255).to("uint8").unsqueeze(0),D=h6.fromTensor(B);Z.push({predicted_depth:F,depth:D})}return Array.isArray(J)?Z:Z[0]}},lw=class extends g0{async _call(J,{pooling:Q="none",normalize:X=!1,quantize:K=!1,precision:Z="binary"}={}){let H=this.tokenizer(J,{padding:!0,truncation:!0}),Y=await this.model(H),$=Y.last_hidden_state??Y.logits??Y.token_embeddings;switch(Q){case"none":break;case"mean":$=$z($,H.attention_mask);break;case"first_token":case"cls":$=$.slice(null,0);break;case"last_token":case"eos":$=$.slice(null,-1);break;default:throw Error(`Pooling method '${Q}' not supported.`)}if(X)$=$.normalize(2,-1);if(K)$=Gz($,Z);return $}},uw=class extends g0{async _call(J,{pool:Q=null}={}){let X=await c6(J),{pixel_values:K}=await this.processor(X),Z=await this.model({pixel_values:K}),H;if(Q){if(!("pooler_output"in Z))throw Error("No pooled output was returned. 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