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* Licensed under the MIT License.
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this._heap[0]=e,this._sift_down(),t}_parent(e){return(e+1>>>1)-1}_left(e){return(e<<1)+1}_right(e){return e+1<<1}_greater(e,t){return this._comparator(this._heap[e],this._heap[t])}_swap(e,t){let n=this._heap[e];this._heap[e]=this._heap[t],this._heap[t]=n}_sift_up(){this._sift_up_from(this.size-1)}_sift_up_from(e){for(;e>0&&this._greater(e,this._parent(e));)this._swap(e,this._parent(e)),e=this._parent(e)}_sift_down(){let e=0;for(;this._left(e)<this.size&&this._greater(this._left(e),e)||this._right(e)<this.size&&this._greater(this._right(e),e);){let t=this._right(e)<this.size&&this._greater(this._right(e),this._left(e))?this._right(e):this._left(e);this._swap(e,t),e=t}}_smallest(){return 2**Math.floor(Math.log2(this.size))-1}},$a=class{constructor(e){this.capacity=e,this.cache=/* @__PURE__ */ new Map}get(e){if(!this.cache.has(e))return;let t=this.cache.get(e);return 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io{constructor(e){super(e),this.sep=e.sep,this.cls=e.cls}post_process(e,t,n=!0){n&&(e=sa([this.cls[0]],e,[this.sep[0]]));let r=Array(e.length).fill(0);if(t){let i=n?[this.sep[0]]:[],a=n?[this.sep[0]]:[];e=sa(e,i,t,a),r=sa(r,Array(t.length+i.length+a.length).fill(1))}return{tokens:e,token_type_ids:r}}},lo=class extends io{constructor(e){super(e),this.processors=(e.processors??[]).map(e=>fo(e))}post_process(e,t=null,n=!0){let r={tokens:e,tokens_pair:t};for(let e of this.processors)r=e.post_process(r.tokens,r.tokens_pair,n);return r}};function uo(e){if(e===null)return null;switch(e.type){case`TemplateProcessing`:return new ao(e);case`ByteLevel`:return new oo(e);case`BertProcessing`:return new so(e);case`RobertaProcessing`:return new co(e);case`Sequence`:return new lo(e);default:throw Error(`Unknown PostProcessor type: ${e.type}`)}}var fo=uo,po=class extends pa{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=`trim_offsets`in 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instanceof ServiceWorkerGlobalScope)&&Gl.versions?.web&&!Gl.wasm.wasmPaths){let e=`https://cdn.jsdelivr.net/npm/onnxruntime-web@${Gl.versions.web}/dist/`,t=`.asyncify`;j.IS_SAFARI_BELOW_26&&!j.IS_WEBGPU_AVAILABLE&&(t=``),Gl.wasm.wasmPaths={mjs:`${e}ort-wasm-simd-threaded${t}.mjs`,wasm:`${e}ort-wasm-simd-threaded${t}.wasm`}}Gl.wasm.proxy=!1}Gl.webgpu&&(Gl.webgpu.powerPreference=`high-performance`),e(M.logLevel??Qr.WARNING),M.backends.onnx={...Gl,setLogLevel:e}}var ql=async(e,t,n)=>{let r=await Vl(new Uint8Array(e),t);return(async e=>{let t=Kl(),i=Object.fromEntries(Object.entries(e).map(([e,n])=>[e,(t?n.clone():n).ort_tensor])),a=await Ul(r,i);return Array.isArray(n)?n.map(e=>new U(a[e])):new U(a[n])})},Jl=class{static session_options={};static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||=ql([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,`y`),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||=ql([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,`y`),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||=ql([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,`y`),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||=ql([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,`c`),this._matmul}static get stft(){return this._stft||=ql([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,`o`),this._stft}static get rfft(){return this._rfft||=ql([8,9,58,93,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,19,10,1,120,18,14,10,12,8,1,18,8,10,0,10,0,10,2,8,1,90,11,10,1,97,18,6,10,4,8,7,18,0,98,19,10,1,121,18,14,10,12,8,1,18,8,10,0,10,0,10,2,8,2,66,2,16,20],this.session_options,`y`),this._rfft}static get top_k(){return this._top_k||=ql([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,[`v`,`i`]),this._top_k}static get slice(){return this._slice||=ql([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,`y`),this._slice}},Yl=Object.freeze({auto:`auto`,gpu:`gpu`,cpu:`cpu`,wasm:`wasm`,webgpu:`webgpu`,cuda:`cuda`,dml:`dml`,coreml:`coreml`,webnn:`webnn`,"webnn-npu":`webnn-npu`,"webnn-gpu":`webnn-gpu`,"webnn-cpu":`webnn-cpu`}),Xl=j.IS_NODE_ENV?`cpu`:`wasm`;function Zl(e,t,{warn:n}={}){return e?typeof e==`string`?e:e.hasOwnProperty(t)?e[t]:(n&&n(`device not 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Yd{constructor(e){super(e),this.mel_filters=ff(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=ff(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=vf(this.config.fft_window_size,`hann`)}async _get_input_mel(e,t,n,r){let i,a=e.length-t;if(a>0){if(n===`rand_trunc`){let n=Math.floor(Cc.random()*(a+1));e=e.subarray(n,n+t),i=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw Error(`Truncation strategy "${n}" not implemented`)}else{if(a<0){let n=new Float64Array(t);if(n.set(e),r===`repeat`)for(let r=e.length;r<t;r+=e.length)n.set(e.subarray(0,Math.min(e.length,t-r)),r);else if(r===`repeatpad`)for(let t=e.length;t<-a;t+=e.length)n.set(e,t);e=n}if(n===`fusion`)throw Error(`Truncation strategy "${n}" not 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t=this.config.preemphasis;e=new Float64Array(e);for(let n=e.length-1;n>=1;--n)e[n]-=t*e[n-1];return await _f(e,this.window,this.window.length,this.config.hop_length,{fft_length:this.config.n_fft,power:2,mel_filters:this.config.mel_filters,log_mel:`log`,mel_floor:-1/0,pad_mode:`constant`,center:!0,transpose:!0,mel_offset:2**-24})}async _call(e){Xd(e,`ParakeetFeatureExtractor`);let t=await this._extract_fbank_features(e),n=Math.floor((e.length+Math.floor(this.config.n_fft/2)*2-this.config.n_fft)/this.config.hop_length),r=t.data;r.fill(0,n*t.dims[1]);let[i,a]=t.dims,o=new Float64Array(a),s=new Float64Array(a);for(let e=0;e<n;++e){let t=e*a;for(let e=0;e<a;++e){let n=r[t+e];o[e]+=n,s[e]+=n*n}}let c=n>1?n-1:1;for(let e=0;e<a;++e){let t=o[e]/n,i=(s[e]-n*t*t)/c,l=1/(Math.sqrt(i)+Ef);for(let i=0;i<n;++i){let n=i*a+e;r[n]=(r[n]-t)*l}}let l=new BigInt64Array(i);return l.fill(1n,0,n),{input_features:t.unsqueeze_(0),attention_mask:new U(`int64`,l,[1,i])}}},Of=class extends Df{_apply_dither(e){let 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this._extract_fbank_features(e,this.config.max_length),o=Eu([1,a.dims[0]],!0);return{input_features:a.unsqueeze_(0),input_features_mask:o}}},jf=class extends Af{async _extract_fbank_features(e,t){let{frame_length:n,hop_length:r,fft_length:i}=this.config,a=Math.floor(n/2),o=Math.floor((e.length+a-(n+1))/r)+1;return _f(e,this.window,n,r,{fft_length:i,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:o})}async _call(e,t={}){Xd(e,`Gemma4AudioFeatureExtractor`);let n=e.length,r=await super._call(e,t),{input_features:i}=r,[,a,o]=i.dims,{frame_length:s,hop_length:c}=this.config,l=Math.floor(s/2),u=s+1,d=new Uint8Array(n+l+(t.pad_to_multiple_of??128));d.fill(1,l,l+n);let f=new Uint8Array(a);for(let e=0;e<a;++e)f[e]=+!!d[e*c+u-1];let p=i.data;for(let e=0;e<a;++e)f[e]||p.fill(0,e*o,(e+1)*o);return r.input_features_mask=new U(`bool`,f,[1,a]),r}},Mf=class extends Yd{constructor(e){super(e);let{n_fft:t,win_length:n,n_mels:r,sample_rate:i}=e.melspec_kwargs;this.mel_filters=ff(Math.floor(1+t/2),r,0,i/2,i,null,`htk`);let a=vf(n,`hann`);this.window=new Float64Array(t);let o=Math.floor((t-n)/2);this.window.set(a,o)}async _call(e){Xd(e,`GraniteSpeechFeatureExtractor`);let{n_fft:t,hop_length:n,n_mels:r}=this.config.melspec_kwargs,i=Math.floor(e.length/n)+1,a=i-i%2;return{input_features:(await _f(e,this.window,t,n,{power:2,mel_filters:this.mel_filters,log_mel:`log10_max_norm`,transpose:!0,max_num_frames:a,do_pad:!1})).view(-1,2*r).unsqueeze_(0)}}},Nf=class extends Yd{async _call(e){Xd(e,`MoonshineFeatureExtractor`),e instanceof Float64Array&&(e=new Float32Array(e));let t=[1,e.length];return{input_values:new U(`float32`,e,t)}}},Pf=class extends Yd{async _call(e){Xd(e,`PyAnnoteFeatureExtractor`),e instanceof Float64Array&&(e=new Float32Array(e));let t=[1,1,e.length];return{input_values:new U(`float32`,e,t)}}samples_to_frames(e){return(e-this.config.offset)/this.config.step}post_process_speaker_diarization(e,t){let n=t/this.samples_to_frames(t)/this.config.sampling_rate,r=[];for(let t of e.tolist()){let e=[],i=-1;for(let n=0;n<t.length;++n){let[r,a]=hl(ll(t[n])),[o,s]=[n,n+1];a===i?(e.at(-1).end=s,e.at(-1).score+=r):(i=a,e.push({id:a,start:o,end:s,score:r}))}r.push(e.map(({id:e,start:t,end:r,score:i})=>({id:e,start:t*n,end:r*n,confidence:i/(r-t)})))}return r}},Ff=class extends Yd{constructor(e){super(e);let t=this.config.sampling_rate,n=ff(257,this.config.num_mel_bins,20,Math.floor(t/2),t,null,`kaldi`,!0);this.mel_filters=n,this.window=vf(400,`povey`,{periodic:!1})}async _extract_fbank_features(e,t){return e=e.map(e=>e*32768),_f(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:`log`,mel_floor:1.192092955078125e-7,remove_dc_offset:!0,max_num_frames:t,transpose:!0})}async _call(e,{padding:t=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:r=!0,return_attention_mask:i=!0}={}){Xd(e,`SeamlessM4TFeatureExtractor`);let a=await this._extract_fbank_features(e,this.config.max_length);if(r){let[e,t]=a.dims,n=a.data;for(let r=0;r<t;++r){let i=0;for(let a=0;a<e;++a)i+=n[a*t+r];let a=i/e,o=0;for(let i=0;i<e;++i)o+=(n[i*t+r]-a)**2;o/=e-1;let s=Math.sqrt(o+1e-7);for(let i=0;i<e;++i){let e=i*t+r;n[e]=(n[e]-a)/s}}}let o;if(t){let[e,t]=a.dims,r=a.data,s=e%n;if(s>0){let n=new Float32Array(t*(e+s));n.set(r),n.fill(this.config.padding_value,r.length);let c=e+s;a=new U(a.type,n,[c,t]),i&&(o=new U(`int64`,new BigInt64Array(c),[1,c]),o.data.fill(1n,0,e))}}let[s,c]=a.dims,l=this.config.stride;if(s%l!==0)throw Error(`The number of frames (${s}) must be a multiple of the stride (${l}).`);let u=a.view(1,Math.floor(s/l),c*l),d={input_features:u};if(i){let e=u.dims[1],t=new BigInt64Array(e);if(o){let e=o.data;for(let n=1,r=0;n<s;n+=l,++r)t[r]=e[n]}else t.fill(1n);d.attention_mask=new U(`int64`,t,[1,e])}return d}},If=class extends kf{},Lf=class extends Yd{},Rf=class extends Yd{_zero_mean_unit_var_norm(e){let t=e.reduce((e,t)=>e+t,0)/e.length,n=e.reduce((e,n)=>e+(n-t)**2,0)/e.length;return e.map(e=>(e-t)/Math.sqrt(n+1e-7))}async _call(e){Xd(e,`Wav2Vec2FeatureExtractor`),e instanceof Float64Array&&(e=new Float32Array(e));let t=e;this.config.do_normalize&&(t=this._zero_mean_unit_var_norm(t));let n=[1,t.length];return{input_values:new U(`float32`,t,n),attention_mask:new U(`int64`,new BigInt64Array(t.length).fill(1n),n)}}},zf=class extends Yd{constructor(e){super(e);let t=this.config.sampling_rate,n=ff(257,this.config.num_mel_bins,20,Math.floor(t/2),t,null,`kaldi`,!0);this.mel_filters=n,this.window=vf(400,`hamming`,{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(e){return e=e.map(e=>e*32768),_f(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:`log`,mel_floor:1.192092955078125e-7,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(e){Xd(e,`WeSpeakerFeatureExtractor`);let t=(await this._extract_fbank_features(e)).unsqueeze_(0);if(this.config.fbank_centering_span===null){let e=t.mean(1).data,n=t.data,[r,i,a]=t.dims;for(let t=0;t<r;++t){let r=t*i*a,o=t*a;for(let t=0;t<i;++t){let i=r+t*a;for(let t=0;t<a;++t)n[i+t]-=e[o+t]}}}return{input_features:t}}},Bf=class extends Yd{constructor(e){super(e),this.config.mel_filters??=ff(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,`slaney`,`slaney`),this.window=vf(this.config.n_fft,`hann`)}async _extract_fbank_features(e,{center:t=!0}={}){let{n_fft:n,hop_length:r,mel_filters:i,global_log_mel_max:a}=this.config,o=Math.floor(t?e.length/r:(e.length-n)/r);return await _f(e,this.window,n,r,{power:2,mel_filters:i,log_mel:`log10_max_norm`,max_log_mel:a,center:t,max_num_frames:o,do_pad:!1})}async _call(e,{center:t=!0}={}){return Xd(e,`VoxtralRealtimeFeatureExtractor`),{input_features:(await this._extract_fbank_features(e,{center:t})).unsqueeze_(0)}}},Vf=class extends Yd{constructor(e){super(e),this.config.mel_filters??=ff(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,`slaney`,`slaney`),this.window=vf(this.config.n_fft,`hann`)}async _extract_fbank_features(e){return await _f(e,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(e.length/this.config.hop_length),this.config.nb_max_frames)})}async _call(e,{max_length:t=null}={}){Xd(e,`WhisperFeatureExtractor`);let n,r=t??this.config.n_samples;return e.length>r?(e.length>this.config.n_samples&&N.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`."),n=e.slice(0,r)):(n=new Float32Array(r),n.set(e)),{input_features:(await this._extract_fbank_features(n)).unsqueeze_(0)}}},Hf=class{static async from_pretrained(e,t={}){let n=await ol(e,Wd,!0,t),r=n.feature_extractor_type,i=Zd[r];if(!i)throw Error(`Unknown feature_extractor_type: '${r}'. Please report this at ${Ud}.`);return new i(n)}},Uf=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;async _call(e,t=null){let n=this.tokenizer(e),r=t?await this.feature_extractor(t):{};return{...n,...r}}},Wf=/* @__PURE__ */ new Set([`ja`,`zh`]),Gf=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;static uses_processor_config=!0;get_decoder_prompt_ids(e=`en`){let t=[`▁`,`<|startofcontext|>`,`<|startoftranscript|>`,`<|emo:undefined|>`,`<|${e}|>`,`<|${e}|>`,`<|pnc|>`,`<|noitn|>`,`<|notimestamp|>`,`<|nodiarize|>`];return this.tokenizer.convert_tokens_to_ids(t)}static join_chunks(e,t=`en`){let n=e.filter(e=>e&&e.trim());if(n.length===0)return``;let r=Wf.has(t)?``:` `;return[n[0].trimEnd(),...n.slice(1).map(e=>e.trim())].join(r)}async _call(e){return await this.feature_extractor(e)}},Kf={},qf,Jf,Yf;if(j.IS_WEB_ENV)qf=(e,t)=>{if(!self.OffscreenCanvas)throw Error(`OffscreenCanvas not supported by this environment.`);return new self.OffscreenCanvas(e,t)},Yf=self.createImageBitmap,Jf=self.ImageData;else if(Kf)Yf=async e=>{let t=(await e.metadata()).channels,{data:n,info:r}=await e.rotate().raw().toBuffer({resolveWithObject:!0}),i=new Qf(new Uint8ClampedArray(n),r.width,r.height,r.channels);return t!==void 0&&t!==r.channels&&i.convert(t),i};else throw Error(`Unable to load image processing library.`);var Xf={0:`nearest`,1:`lanczos`,2:`bilinear`,3:`bicubic`,4:`box`,5:`hamming`},Zf=/* @__PURE__ */ new Map([[`png`,`image/png`],[`jpg`,`image/jpeg`],[`jpeg`,`image/jpeg`],[`gif`,`image/gif`]]),Qf=class e{constructor(e,t,n,r){this.data=e,this.width=t,this.height=n,this.channels=r}get size(){return[this.width,this.height]}static async read(t){if(t instanceof e)return t;if(typeof t==`string`||t instanceof URL)return await this.fromURL(t);if(t instanceof Blob)return await this.fromBlob(t);if(typeof HTMLCanvasElement<`u`&&t instanceof HTMLCanvasElement||typeof OffscreenCanvas<`u`&&t instanceof OffscreenCanvas)return this.fromCanvas(t);throw Error(`Unsupported input type: ${typeof t}`)}static fromCanvas(t){if(!j.IS_WEB_ENV)throw Error(`fromCanvas() is only supported in browser environments.`);let n=t.getContext(`2d`).getImageData(0,0,t.width,t.height).data;return new e(n,t.width,t.height,4)}static async fromURL(e){let t=await Zc(e);if(t.status!==200)throw Error(`Unable to read image from "${e}" (${t.status} ${t.statusText})`);let n=await t.blob();return this.fromBlob(n)}static async fromBlob(e){if(j.IS_WEB_ENV){let t=await Yf(e),n=qf(t.width,t.height).getContext(`2d`);return n.drawImage(t,0,0),new this(n.getImageData(0,0,t.width,t.height).data,t.width,t.height,4)}{let t=Kf(await e.arrayBuffer());return await Yf(t)}}static fromTensor(t,n=`CHW`){if(t.dims.length!==3)throw Error(`Tensor should have 3 dimensions, but has ${t.dims.length} dimensions.`);if(n===`CHW`)t=t.permute(1,2,0);else if(n!==`HWC`)throw Error(`Unsupported channel format: ${n}`);if(!(t.data instanceof Uint8ClampedArray||t.data instanceof Uint8Array))throw Error(`Unsupported tensor type: ${t.type}`);switch(t.dims[2]){case 1:case 2:case 3:case 4:return new e(t.data,t.dims[1],t.dims[0],t.dims[2]);default:throw Error(`Unsupported number of channels: ${t.dims[2]}`)}}grayscale(){if(this.channels===1)return this;let e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let t=0,n=0;t<this.data.length;t+=this.channels){let r=this.data[t],i=this.data[t+1],a=this.data[t+2];e[n++]=Math.round(.2989*r+.587*i+.114*a)}break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let t=0,n=0;t<this.data.length;++t)e[n++]=this.data[t],e[n++]=this.data[t],e[n++]=this.data[t];break;case 4:for(let t=0,n=0;t<this.data.length;t+=4)e[n++]=this.data[t],e[n++]=this.data[t+1],e[n++]=this.data[t+2];break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let t=0,n=0;t<this.data.length;++t)e[n++]=this.data[t],e[n++]=this.data[t],e[n++]=this.data[t],e[n++]=255;break;case 3:for(let t=0,n=0;t<this.data.length;t+=3)e[n++]=this.data[t],e[n++]=this.data[t+1],e[n++]=this.data[t+2],e[n++]=255;break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}putAlpha(e){if(e.width!==this.width||e.height!==this.height)throw Error(`Expected mask size to be ${this.width}x${this.height}, but got ${e.width}x${e.height}`);if(e.channels!==1)throw Error(`Expected mask to have 1 channel, but got ${e.channels}`);let t=this.data,n=e.data,r=this.width*this.height;if(this.channels===3){let e=new Uint8ClampedArray(r*4);for(let i=0,a=0,o=0;i<r;++i)e[o++]=t[a++],e[o++]=t[a++],e[o++]=t[a++],e[o++]=n[i];return this._update(e,this.width,this.height,4)}if(this.channels===4){for(let e=0;e<r;++e)t[4*e+3]=n[e];return this}throw Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(t,n,{resample:r=2}={}){if(this.width===t&&this.height===n)return this;let i=Xf[r]??r,a=ai(t),o=ai(n);if(a&&o)return this;if(a?t=n/this.height*this.width:o&&(n=t/this.width*this.height),j.IS_WEB_ENV){let r=this.channels,i=this.toCanvas(),a=qf(t,n).getContext(`2d`);return a.drawImage(i,0,0,t,n),new e(a.getImageData(0,0,t,n).data,t,n,4).convert(r)}{let e=this.toSharp();switch(i){case`box`:case`hamming`:(i===`box`||i===`hamming`)&&(N.warn(`Resampling method ${i} is not yet supported. Using bilinear instead.`),i=`bilinear`);case`nearest`:case`bilinear`:case`bicubic`:e=e.affine([t/this.width,0,0,n/this.height],{interpolator:i});break;case`lanczos`:e=e.resize({width:t,height:n,fit:`fill`,kernel:`lanczos3`});break;default:throw Error(`Resampling method ${i} is not supported.`)}return await Yf(e)}}async pad([t,n,r,i]){if(t=Math.max(t,0),n=Math.max(n,0),r=Math.max(r,0),i=Math.max(i,0),t===0&&n===0&&r===0&&i===0)return this;if(j.IS_WEB_ENV){let a=this.channels,o=this.toCanvas(),s=this.width+t+n,c=this.height+r+i,l=qf(s,c).getContext(`2d`);return l.drawImage(o,0,0,this.width,this.height,t,r,this.width,this.height),new e(l.getImageData(0,0,s,c).data,s,c,4).convert(a)}{let e=this.toSharp().extend({left:t,right:n,top:r,bottom:i});return await Yf(e)}}async crop([t,n,r,i]){if(t=Math.max(t,0),n=Math.max(n,0),r=Math.min(r,this.width-1),i=Math.min(i,this.height-1),t===0&&n===0&&r===this.width-1&&i===this.height-1)return this;let a=r-t+1,o=i-n+1;if(j.IS_WEB_ENV){let r=this.channels,i=this.toCanvas(),s=qf(a,o).getContext(`2d`);return s.drawImage(i,t,n,a,o,0,0,a,o),new e(s.getImageData(0,0,a,o).data,a,o,4).convert(r)}{let e=this.toSharp().extract({left:t,top:n,width:a,height:o});return await Yf(e)}}async center_crop(t,n){if(this.width===t&&this.height===n)return this;let r=(this.width-t)/2,i=(this.height-n)/2;if(j.IS_WEB_ENV){let a=this.channels,o=this.toCanvas(),s=qf(t,n).getContext(`2d`),c=0,l=0,u=0,d=0;return r>=0?c=r:u=-r,i>=0?l=i:d=-i,s.drawImage(o,c,l,t,n,u,d,t,n),new e(s.getImageData(0,0,t,n).data,t,n,4).convert(a)}{let e=this.toSharp();if(r>=0&&i>=0)e=e.extract({left:Math.floor(r),top:Math.floor(i),width:t,height:n});else if(r<=0&&i<=0){let a=Math.floor(-i),o=Math.floor(-r);e=e.extend({top:a,left:o,right:t-this.width-o,bottom:n-this.height-a})}else{let a=[0,0],o=0;i<0?(a[0]=Math.floor(-i),a[1]=n-this.height-a[0]):o=Math.floor(i);let s=[0,0],c=0;r<0?(s[0]=Math.floor(-r),s[1]=t-this.width-s[0]):c=Math.floor(r),e=e.extend({top:a[0],bottom:a[1],left:s[0],right:s[1]}).extract({left:c,top:o,width:t,height:n})}return await Yf(e)}}async toBlob(e=`image/png`,t=1){if(!j.IS_WEB_ENV)throw Error(`toBlob() is only supported in browser environments.`);return await this.toCanvas().convertToBlob({type:e,quality:t})}toTensor(e=`CHW`){let t=new U(`uint8`,new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!==`HWC`){if(e===`CHW`)t=t.permute(2,0,1);else throw Error(`Unsupported channel format: ${e}`)}return t}toCanvas(){if(!j.IS_WEB_ENV)throw Error(`toCanvas() is only supported in browser environments.`);let e=this.clone().rgba(),t=qf(e.width,e.height),n=new Jf(e.data,e.width,e.height);return t.getContext(`2d`).putImageData(n,0,0),t}split(){let{data:t,width:n,height:r,channels:i}=this,a=t.constructor,o=t.length/i,s=Array.from({length:i},()=>new a(o));for(let e=0;e<o;++e){let n=i*e;for(let r=0;r<i;++r)s[r][e]=t[n+r]}return s.map(t=>new e(t,n,r,1))}_update(e,t,n,r=null){return this.data=e,this.width=t,this.height=n,r!==null&&(this.channels=r),this}clone(){return new e(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(j.IS_WEB_ENV){if(j.IS_WEBWORKER_ENV)throw Error(`Unable to save an image from a Web Worker.`);let t=e.split(`.`).pop().toLowerCase(),n=Zf.get(t)??`image/png`;return $d(e,await this.toBlob(n))}if(j.IS_FS_AVAILABLE)await this.toSharp().toFile(e);else throw Error(`Unable to save the image because filesystem is disabled in this environment.`)}toSharp(){if(j.IS_WEB_ENV)throw Error(`toSharp() is only supported in server-side environments.`);return Kf(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}},$f=Qf.read.bind(Qf);function ep(e,t,n=0,r=null){let i=e/t,a=Sl(i)*t;return r!==null&&a>r&&(a=Math.floor(i)*t),a<n&&(a=Math.ceil(i)*t),a}function tp([e,t],n){return[Math.max(Math.floor(e/n),1)*n,Math.max(Math.floor(t/n),1)*n]}function np([e,t,n,r]){return[e-n/2,t-r/2,e+n/2,t+r/2]}function rp(e,t=.5,n=null,r=!1){let i=e.logits,a=e.pred_boxes,[o,s,c]=i.dims;if(n!==null&&n.length!==o)throw Error(`Make sure that you pass in as many target sizes as the batch dimension of the logits`);let l=[];for(let e=0;e<o;++e){let o=n===null?null:n[e],u={boxes:[],classes:[],scores:[]},d=i[e],f=a[e];for(let e=0;e<s;++e){let n=d[e],i=[],a;if(r){a=n.sigmoid().data;for(let e=0;e<a.length;++e)a[e]>t&&i.push(e)}else{let e=hl(n.data)[1];if(e===c-1||(a=ll(n.data),a[e]<t))continue;i.push(e)}for(let t of i){let n=f[e].data;n=np(n),o!==null&&(n=n.map((e,t)=>e*o[(t+1)%2])),u.boxes.push(n),u.classes.push(t),u.scores.push(a[t])}}l.push(u)}return l}function ip(e,t=null){let n=e.logits,r=n.dims[0];if(t!==null&&t.length!==r)throw Error(`Make sure that you pass in as many target sizes as the batch dimension of the logits`);let i=[];for(let e=0;e<r;++e){let r=t===null?null:t[e],a=n[e];r!==null&&(a=su(a,r,`bilinear`,!1));let[o,s]=r??a.dims.slice(-2),c=new U(`int32`,new Int32Array(o*s),[o,s]),l=a[0].data,u=c.data;for(let e=1;e<a.dims[0];++e){let t=a[e].data;for(let n=0;n<t.length;++n)t[n]>l[n]&&(l[n]=t[n],u[n]=e)}let d=Array(a.dims[0]);for(let e=0;e<u.length;++e){let t=u[e];d[t]=t}let f=d.filter(e=>e!==void 0);i.push({segmentation:c,labels:f})}return i}function ap(e,t,n,r){let i=[],a=[],o=[];for(let s=0;s<e.dims[0];++s){let c=e[s],l=t[s],u=hl(c.data)[1];if(u===r)continue;let d=ll(c.data)[u];d>n&&(i.push(l),a.push(d),o.push(u))}return[i,a,o]}function op(e,t,n,r=.5,i=.8){let a=[],o=0,s=0,c=t[n].data;for(let t=0;t<e.length;++t)e[t]===n&&(a.push(t),++o),c[t]>=r&&++s;let l=o>0&&s>0;return l&&=o/s>i,[l,a]}function sp(e,t,n,r,i,a=null,o=null){let[s,c]=o??e[0].dims,l=new U(`int32`,new Int32Array(s*c),[s,c]),u=[];if(o!==null)for(let t=0;t<e.length;++t)e[t]=su(e[t],o,`bilinear`,!1);let d=new Int32Array(e[0].data.length),f=new Float32Array(e[0].data.length);for(let n=0;n<e.length;++n){let r=t[n],i=e[n].data;for(let e=0;e<i.length;++e)i[e]*=r,i[e]>f[e]&&(d[e]=n,f[e]=i[e])}let p=0,m=l.data;for(let a=0;a<n.length;++a){let o=n[a],[s,c]=op(d,e,a,r,i);if(s){++p;for(let e of c)m[e]=p;u.push({id:p,label_id:o,score:t[a]})}}return[l,u]}function cp(e,t,n=28,r=3136,i=1003520,a=1){if(e<n||t<n){let r=Math.max(n/e,n/t);e=Math.round(e*r),t=Math.round(t*r)}if(Math.max(e,t)/Math.min(e,t)>200)throw Error(`absolute aspect ratio must be smaller than 200, got ${Math.max(e,t)/Math.min(e,t)}`);let o=Math.round(e/n)*n,s=Math.round(t/n)*n;if(a*o*s>i){let r=Math.sqrt(a*e*t/i);o=Math.max(n,Math.floor(e/r/n)*n),s=Math.max(n,Math.floor(t/r/n)*n)}else if(a*o*s<r){let i=Math.sqrt(r/(a*e*t));o=Math.ceil(e*i/n)*n,s=Math.ceil(t*i/n)*n}return[s,o]}function lp(e,t=.5,n=.5,r=.8,i=null,a=null){i===null&&(N.warn("`label_ids_to_fuse` unset. No instance will be fused."),i=/* @__PURE__ */ new Set);let o=e.class_queries_logits??e.logits,s=(e.masks_queries_logits??e.pred_masks).sigmoid(),[c,l,u]=o.dims;if(--u,a!==null&&a.length!==c)throw Error(`Make sure that you pass in as many target sizes as the batch dimension of the logits`);let d=[];for(let e=0;e<c;++e){let c=a===null?null:a[e],l=o[e],f=s[e],[p,m,h]=ap(l,f,t,u);if(h.length===0){let[e,t]=c??f.dims.slice(-2),n=new U(`int32`,new Int32Array(e*t).fill(-1),[e,t]);d.push({segmentation:n,segments_info:[]});continue}let[g,_]=sp(p,m,h,n,r,i,c);d.push({segmentation:g,segments_info:_})}return d}function up(e,t=.5,n=null){throw Error("`post_process_instance_segmentation` is not yet implemented.")}var q=class extends ti{constructor(e){super(),this.image_mean=e.image_mean??e.mean,this.image_std=e.image_std??e.std,this.resample=e.resample??2,this.do_rescale=e.do_rescale??!0,this.rescale_factor=e.rescale_factor??1/255,this.do_normalize=e.do_normalize,this.do_thumbnail=e.do_thumbnail,this.size=e.size??e.image_size,this.do_resize=e.do_resize??this.size!==void 0,this.size_divisibility=e.size_divisibility??e.size_divisor,this.do_center_crop=e.do_center_crop,this.crop_size=e.crop_size,this.do_convert_rgb=e.do_convert_rgb??!0,this.do_crop_margin=e.do_crop_margin,this.pad_size=e.pad_size,this.do_pad=e.do_pad,this.min_pixels=e.min_pixels,this.max_pixels=e.max_pixels,this.do_pad&&!this.pad_size&&!this.size_divisibility&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=e.do_flip_channel_order??!1,this.config=e}async thumbnail(e,t,n=2){let r=e.height,i=e.width,a=t.height,o=t.width,s=Math.min(r,a),c=Math.min(i,o);return s===r&&c===i?e:(r>i?c=Math.floor(i*s/r):i>r&&(s=Math.floor(r*c/i)),await e.resize(c,s,{resample:n}))}async crop_margin(e,t=200){let n=e.clone().grayscale(),r=ml(n.data)[0],i=hl(n.data)[0]-r;if(i===0)return e;let a=t/255,o=n.width,s=n.height,c=0,l=0,u=n.data;for(let e=0;e<n.height;++e){let t=e*n.width;for(let d=0;d<n.width;++d)(u[t+d]-r)/i<a&&(o=Math.min(o,d),s=Math.min(s,e),c=Math.max(c,d),l=Math.max(l,e))}return e=await e.crop([o,s,c,l]),e}pad_image(e,t,n,{mode:r=`constant`,center:i=!1,constant_values:a=0}={}){let[o,s,c]=t,l,u;if(typeof n==`number`?(l=n,u=n):n===`square`?l=u=Math.max(o,s):(l=n.width,u=n.height),l!==s||u!==o){let n=new Float32Array(l*u*c);if(Array.isArray(a))for(let e=0;e<n.length;++e)n[e]=a[e%c];else a!==0&&n.fill(a);let[d,f]=i?[Math.floor((l-s)/2),Math.floor((u-o)/2)]:[0,0];for(let t=0;t<o;++t){let r=(t+f)*l,i=t*s;for(let t=0;t<s;++t){let a=(r+t+d)*c,o=(i+t)*c;for(let t=0;t<c;++t)n[a+t]=e[o+t]}}if(r===`symmetric`){if(i)throw Error("`center` padding is not supported when `mode` is set to `symmetric`.");let t=o-1,r=s-1;for(let i=0;i<u;++i){let a=i*l,u=li(i,t)*s;for(let t=0;t<l;++t){if(i<o&&t<s)continue;let l=(a+t)*c,d=(u+li(t,r))*c;for(let t=0;t<c;++t)n[l+t]=e[d+t]}}}e=n,t=[u,l,c]}return[e,t]}rescale(e){for(let t=0;t<e.length;++t)e[t]=this.rescale_factor*e[t]}get_resize_output_image_size(e,t){let[n,r]=e.size,i,a;if(this.do_thumbnail){let{height:e,width:n}=t;i=Math.min(e,n)}else Number.isInteger(t)?(i=t,a=this.config.max_size??i):t!==void 0&&(i=t.shortest_edge,a=t.longest_edge);if(i!==void 0||a!==void 0){let e=i===void 0?1:Math.max(i/n,i/r),t=n*e,o=r*e,s=a===void 0?1:Math.min(a/t,a/o),c=Math.floor(Number((t*s).toFixed(2))),l=Math.floor(Number((o*s).toFixed(2)));return this.size_divisibility!==void 0&&([c,l]=tp([c,l],this.size_divisibility)),[c,l]}if(t!==void 0&&t.width!==void 0&&t.height!==void 0){let e=t.width,i=t.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let t=i/r,a=e/n;Math.abs(1-a)<Math.abs(1-t)?t=a:a=t,i=ep(t*r,this.config.ensure_multiple_of),e=ep(a*n,this.config.ensure_multiple_of)}return[e,i]}if(this.size_divisibility!==void 0)return tp([n,r],this.size_divisibility);throw Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(t)}`)}async resize(e){let[t,n]=this.get_resize_output_image_size(e,this.size);return await e.resize(t,n,{resample:this.resample})}async preprocess(e,{do_normalize:t=null,do_pad:n=null,do_convert_rgb:r=null,do_convert_grayscale:i=null,do_flip_channel_order:a=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));let[o,s]=e.size;if(r??this.do_convert_rgb?e=e.rgb():i&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let t,n;Number.isInteger(this.crop_size)?(t=this.crop_size,n=this.crop_size):(t=this.crop_size.width,n=this.crop_size.height),e=await e.center_crop(t,n)}let c=[e.height,e.width],l=Float32Array.from(e.data),u=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(l),t??this.do_normalize){let t=this.image_mean;Array.isArray(this.image_mean)||(t=Array(e.channels).fill(t));let n=this.image_std;if(Array.isArray(this.image_std)||(n=Array(e.channels).fill(n)),t.length!==e.channels||n.length!==e.channels)throw Error(`When set to arrays, the length of \`image_mean\` (${t.length}) and \`image_std\` (${n.length}) must match the number of channels in the image (${e.channels}).`);for(let r=0;r<l.length;r+=e.channels)for(let i=0;i<e.channels;++i)l[r+i]=(l[r+i]-t[i])/n[i]}if(n??this.do_pad){if(this.pad_size){let t=this.pad_image(l,[e.height,e.width,e.channels],this.pad_size);[l,u]=t}else if(this.size_divisibility){let e=Math.ceil(u[1]/this.size_divisibility)*this.size_divisibility,t=Math.ceil(u[0]/this.size_divisibility)*this.size_divisibility;[l,u]=this.pad_image(l,u,{width:e,height:t})}}if(a??this.do_flip_channel_order){if(u[2]!==3)throw Error(`Flipping channel order is only supported for RGB images.`);for(let e=0;e<l.length;e+=3){let t=l[e];l[e]=l[e+2],l[e+2]=t}}let d=new U(`float32`,l,u).permute(2,0,1);return{original_size:[s,o],reshaped_input_size:c,pixel_values:d}}async _call(e,...t){Array.isArray(e)||(e=[e]);let n=await Promise.all(e.map(e=>this.preprocess(e)));return{pixel_values:bu(n.map(e=>e.pixel_values),0),original_sizes:n.map(e=>e.original_size),reshaped_input_sizes:n.map(e=>e.reshaped_input_size)}}static async from_pretrained(e,t={}){let n=await ol(e,Gd,!0,t);return new this(n)}},dp={};Tr(dp,{BeitFeatureExtractor:()=>fp,BitImageProcessor:()=>pp,CHMv2ImageProcessor:()=>hp,CLIPFeatureExtractor:()=>_p,CLIPImageProcessor:()=>gp,ChineseCLIPFeatureExtractor:()=>mp,ConvNextFeatureExtractor:()=>yp,ConvNextImageProcessor:()=>vp,DINOv3ViTImageProcessor:()=>wp,DPTFeatureExtractor:()=>Op,DPTImageProcessor:()=>Dp,DeiTFeatureExtractor:()=>xp,DeiTImageProcessor:()=>bp,DetrFeatureExtractor:()=>Cp,DetrImageProcessor:()=>Sp,DonutFeatureExtractor:()=>Ep,DonutImageProcessor:()=>Tp,EfficientNetImageProcessor:()=>kp,GLPNFeatureExtractor:()=>Ip,Gemma3ImageProcessor:()=>Ap,Gemma4ImageProcessor:()=>Np,Glm46VImageProcessor:()=>Fp,GroundingDinoImageProcessor:()=>Lp,Idefics3ImageProcessor:()=>Rp,ImageFeatureExtractor:()=>q,ImageProcessor:()=>q,JinaCLIPImageProcessor:()=>Bp,Lfm2VlImageProcessor:()=>Kp,LlavaOnevisionImageProcessor:()=>qp,Mask2FormerImageProcessor:()=>Xp,MaskFormerFeatureExtractor:()=>Yp,MaskFormerImageProcessor:()=>Jp,MobileNetV1FeatureExtractor:()=>Qp,MobileNetV1ImageProcessor:()=>Zp,MobileNetV2FeatureExtractor:()=>em,MobileNetV2ImageProcessor:()=>$p,MobileNetV3FeatureExtractor:()=>nm,MobileNetV3ImageProcessor:()=>tm,MobileNetV4FeatureExtractor:()=>im,MobileNetV4ImageProcessor:()=>rm,MobileViTFeatureExtractor:()=>om,MobileViTImageProcessor:()=>am,NougatImageProcessor:()=>sm,OwlViTFeatureExtractor:()=>lm,OwlViTImageProcessor:()=>cm,Owlv2ImageProcessor:()=>um,Phi3VImageProcessor:()=>gm,PixtralImageProcessor:()=>_m,PvtImageProcessor:()=>vm,Qwen2VLImageProcessor:()=>Pp,RTDetrImageProcessor:()=>ym,Sam2ImageProcessor:()=>bm,Sam3ImageProcessor:()=>bm,SamImageProcessor:()=>bm,SapiensFeatureExtractor:()=>Sm,SapiensImageProcessor:()=>xm,SegformerFeatureExtractor:()=>wm,SegformerImageProcessor:()=>Cm,SiglipImageProcessor:()=>Tm,SmolVLMImageProcessor:()=>Rp,Swin2SRImageProcessor:()=>Em,VLMImageProcessor:()=>zp,ViTFeatureExtractor:()=>Om,ViTImageProcessor:()=>Dm,VitMatteImageProcessor:()=>km,VitPoseImageProcessor:()=>Am,YolosFeatureExtractor:()=>Mm,YolosImageProcessor:()=>jm});var fp=class extends q{},pp=class extends q{},mp=class extends q{},hp=class extends q{},gp=class extends q{},_p=class extends gp{},vp=class extends q{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){let t=this.size?.shortest_edge;if(t===void 0)throw Error(`Size dictionary must contain 'shortest_edge' key.`);if(t<384){let n=Math.floor(t/this.crop_pct),[r,i]=this.get_resize_output_image_size(e,{shortest_edge:n});e=await e.resize(r,i,{resample:this.resample}),e=await e.center_crop(t,t)}else e=await e.resize(t,t,{resample:this.resample});return e}},yp=class extends vp{},bp=class extends q{},xp=class extends bp{},Sp=class extends q{async _call(e){let t=await super._call(e),n=Eu([t.pixel_values.dims[0],64,64],1n);return{...t,pixel_mask:n}}post_process_object_detection(...e){return rp(...e)}post_process_panoptic_segmentation(...e){return lp(...e)}post_process_instance_segmentation(...e){return up(...e)}},Cp=class extends Sp{},wp=class extends q{},Tp=class extends q{pad_image(e,t,n,r={}){let[i,a,o]=t,s=this.image_mean;Array.isArray(this.image_mean)||(s=Array(o).fill(s));let c=this.image_std;Array.isArray(c)||(c=Array(o).fill(s));let l=s.map((e,t)=>-e/c[t]);return super.pad_image(e,t,n,{center:!0,constant_values:l,...r})}},Ep=class extends Tp{},Dp=class extends q{},Op=class extends Dp{},kp=class extends q{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(e=>e*e))}},Ap=class extends q{};function jp(e,t,n,r,i){let a=r*n**2,o=Math.sqrt(a/(e*t)),s=i*n,c=Math.floor(o*e/s)*s,l=Math.floor(o*t/s)*s;if(c===0&&l===0)throw Error(`Attempting to resize to a 0 x 0 image. Resized height should be divisible by \`pooling_kernel_size * patch_size\`=${s}.`);let u=Math.floor(r/i**2)*s;return c===0?(c=s,l=Math.min(Math.floor(t/e)*s,u)):l===0&&(l=s,c=Math.min(Math.floor(e/t)*s,u)),[c,l]}function Mp(e,t,n,r,i,a,o){let s=Math.floor(t/i),c=Math.floor(n/i),l=s*c,u=i*i*r,d=new Float32Array(a*u),f=0;for(let t=0;t<s;++t)for(let a=0;a<c;++a)for(let o=0;o<i;++o){let s=(t*i+o)*n*r+a*i*r;for(let t=0;t<i;++t){let n=s+t*r;for(let t=0;t<r;++t)d[f++]=e[n+t]}}let p=new BigInt64Array(a*2).fill(-1n),m=0;for(let e=0;e<s;++e)for(let t=0;t<c;++t)p[m++]=BigInt(t),p[m++]=BigInt(e);return{patches:new U(`float32`,d,[a,u]),positions:new U(`int64`,p,[a,2]),num_soft_tokens:Math.floor(l/o**2)}}var Np=class extends ti{constructor(e){super(),this.config=e,this.patch_size=e.patch_size??16,this.max_soft_tokens=e.max_soft_tokens??280,this.pooling_kernel_size=e.pooling_kernel_size??3,this.resample=e.resample??3,this.rescale_factor=e.rescale_factor??1/255,this.do_rescale=e.do_rescale??!0,this.do_resize=e.do_resize??!0,this.do_convert_rgb=e.do_convert_rgb??!0}async _call(e){Array.isArray(e)||(e=[e]);let{patch_size:t,pooling_kernel_size:n}=this,r=this.max_soft_tokens*n**2,i=[],a=[],o=[];for(let s of e){if(this.do_convert_rgb&&(s=s.rgb()),this.do_resize){let[e,i]=jp(s.height,s.width,t,r,n);(e!==s.height||i!==s.width)&&(s=await s.resize(i,e,{resample:this.resample}))}let e=Float32Array.from(s.data);if(this.do_rescale)for(let t=0;t<e.length;++t)e[t]*=this.rescale_factor;let{patches:c,positions:l,num_soft_tokens:u}=Mp(e,s.height,s.width,s.channels,t,r,n);i.push(c),a.push(l),o.push(u)}return{pixel_values:bu(i,0),image_position_ids:bu(a,0),num_soft_tokens_per_image:o}}},Pp=class extends q{constructor(e){super(e),this.min_pixels=e.min_pixels??e.size?.shortest_edge,this.max_pixels=e.max_pixels??e.size?.longest_edge,this.patch_size=e.patch_size,this.merge_size=e.merge_size}get_resize_output_image_size(e,t){let n=this.patch_size*this.merge_size;return cp(e.height,e.width,n,this.min_pixels,this.max_pixels)}async _call(e,...t){let{pixel_values:n,original_sizes:r,reshaped_input_sizes:i}=await super._call(e,...t),a=n,{temporal_patch_size:o,merge_size:s,patch_size:c}=this.config;a.dims[0]===1&&(a=yu(Array.from({length:o},()=>a),0));let l=a.dims[0]/o,u=a.dims[1],d=Math.floor(a.dims[2]/c),f=Math.floor(a.dims[3]/c);return{pixel_values:a.view(l,o,u,Math.floor(d/s),s,c,Math.floor(f/s),s,c).permute(0,3,6,4,7,2,1,5,8).view(l*d*f,u*o*c*c),image_grid_thw:new U(`int64`,[l,d,f],[1,3]),original_sizes:r,reshaped_input_sizes:i}}},Fp=class extends Pp{get_resize_output_image_size(e,t){let n=this.patch_size*this.merge_size,r=this.config.temporal_patch_size??2;return cp(e.height,e.width,n,this.min_pixels,this.max_pixels,r)}},Ip=class extends q{},Lp=class extends q{async _call(e){let t=await super._call(e),n=t.pixel_values.dims,r=Ou([n[0],n[2],n[3]]);return{...t,pixel_mask:r}}},Rp=class extends q{constructor(e){super(e),this.do_image_splitting=e.do_image_splitting??!0,this.max_image_size=e.max_image_size}get_resize_for_vision_encoder(e,t){let[n,r]=e.dims.slice(-2),i=r/n;return r>=n?(r=Math.ceil(r/t)*t,n=Math.floor(r/i),n=Math.ceil(n/t)*t):(n=Math.ceil(n/t)*t,r=Math.floor(n*i),r=Math.ceil(r/t)*t),{height:n,width:r}}async _call(e,{do_image_splitting:t=null,return_row_col_info:n=!1}={}){let r;if(!Array.isArray(e))r=[[e]];else{if(e.length===0||!e[0])throw Error(`No images provided.`);r=Array.isArray(e[0])?e:[e]}let i=[],a=[],o=[],s=[],c=[];for(let e of r){let n=await Promise.all(e.map(e=>this.preprocess(e)));s.push(...n.map(e=>e.original_size)),c.push(...n.map(e=>e.reshaped_input_size)),n.forEach(e=>e.pixel_values.unsqueeze_(0));let{longest_edge:r}=this.max_image_size,l;if(t??this.do_image_splitting){let e=Array(n.length),t=Array(n.length);l=await Promise.all(n.map(async(n,i)=>{let a=this.get_resize_for_vision_encoder(n.pixel_values,r),o=await cu(n.pixel_values,{size:[a.height,a.width]}),{frames:s,num_splits_h:c,num_splits_w:l}=await this.split_image(o,this.max_image_size);return e[i]=c,t[i]=l,yu(s,0)})),a.push(e),o.push(t)}else{let e=[r,r];l=await Promise.all(n.map(t=>cu(t.pixel_values,{size:e}))),a.push(Array(n.length).fill(0)),o.push(Array(n.length).fill(0))}i.push(yu(l,0))}let l=i.length,[u,d,f,p]=i[0].dims,m,h;if(l===1)m=i[0].unsqueeze_(0),h=Eu([l,u,f,p],!0);else{let e=Math.max(...i.map(e=>e.dims.at(0)));h=Eu([l,e,f,p],!0);let t=h.data,n=e*f*p;for(let r=0;r<l;++r){let a=i[r].dims[0];if(a<e){i[r]=yu([i[r],Eu([e-a,d,f,p],0)],0);let o=r*n+a*f*p,s=(r+1)*n;t.fill(!1,o,s)}}m=bu(i,0)}return{pixel_values:m,pixel_attention_mask:h,original_sizes:s,reshaped_input_sizes:c,...n?{rows:a,cols:o}:{}}}async split_image(e,{longest_edge:t}){let n=t,r=t,i=[],[a,o]=e.dims.slice(-2),s=0,c=0;if(a>n||o>r){s=Math.ceil(a/n),c=Math.ceil(o/r);let t=Math.ceil(a/s),l=Math.ceil(o/c);for(let n=0;n<s;++n)for(let r=0;r<c;++r){let u,d,f,p;n===s-1?(d=a-t,p=a):(d=n*t,p=(n+1)*t),r===c-1?(u=o-l,f=o):(u=r*l,f=(r+1)*l);let m=await pu(e,[d,u],[p,f],[2,3]);i.push(m)}let u=n,d=r;(a!==u||o!==d)&&(e=await cu(e,{size:[u,d]}))}return i.push(e),{frames:i,num_splits_h:s,num_splits_w:c}}},zp=class extends q{constructor(e){super({do_pad:!0,pad_size:{width:e.image_size,height:e.image_size},...e}),this.constant_values=this.config.background_color.map(e=>e*this.rescale_factor)}pad_image(e,t,n,r){return super.pad_image(e,t,n,{constant_values:this.constant_values,center:!0,...r})}},Bp=class extends q{constructor(e){let{resize_mode:t,fill_color:n,interpolation:r,size:i,...a}=e,o=t===`squash`?{width:i,height:i}:t===`shortest`?{shortest_edge:i}:{longest_edge:i},s=r===`bicubic`?3:2;super({...a,size:o,resample:s,do_center_crop:!0,crop_size:i,do_normalize:!0})}};function Vp(e,t){return Math.round(e/t)*t}function Hp(e,t,n,r,i){let a=1/0,o=[1,1],s=n*r;for(let n of t){let t=Math.abs(e-n[0]/n[1]);t<a?(a=t,o=n):t===a&&s>.5*i*i*n[0]*n[1]&&(o=n)}return o}function Up(e,t){let n=[],r=/* @__PURE__ */ new Set;for(let i=e;i<=t;++i)for(let a=1;a<=i;++a)for(let o=1;o<=i;++o){let i=a*o;if(i>=e&&i<=t){let e=a<<16|o;r.has(e)||(r.add(e),n.push([a,o]))}}return n.sort((e,t)=>e[0]*e[1]-t[0]*t[1])}function Wp(e,t){let[n,r,i,a]=e.dims,o=Math.floor(i/t),s=Math.floor(a/t),c=t*t*r,l=e.data,u=new Float32Array(n*o*s*c),d=i*a;for(let e=0;e<n;++e){let n=e*r*d,i=e*o*s*c;for(let e=0;e<o;++e)for(let o=0;o<s;++o){let f=i+(e*s+o)*c;for(let i=0;i<t;++i){let s=(e*t+i)*a+o*t;for(let e=0;e<t;++e){let t=s+e;for(let e=0;e<r;++e)u[f++]=l[n+e*d+t]}}}}return new U(`float32`,u,[n,o*s,c])}function Gp(e,t){let[,n,r]=e.dims,i=new BigInt64Array(t);i.fill(1n,0,n);let a=e;if(n<t){let n=new Float32Array(t*r);n.set(e.data),a=new U(`float32`,n,[1,t,r])}return{padded:a,mask:new U(`int64`,i,[t])}}var Kp=class extends q{constructor(e){super(e),this.downsample_factor=e.downsample_factor??2,this.do_image_splitting=e.do_image_splitting??!0,this.min_tiles=e.min_tiles??2,this.max_tiles=e.max_tiles??10,this.use_thumbnail=e.use_thumbnail??!0,this.min_image_tokens=e.min_image_tokens??64,this.max_image_tokens=e.max_image_tokens??256,this.encoder_patch_size=e.encoder_patch_size??e.patch_size??16,this.tile_size=e.tile_size??512,this.max_pixels_tolerance=e.max_pixels_tolerance??2,this.return_row_col_info=e.return_row_col_info??!1;let t=this.max_image_tokens*this.downsample_factor**2,n=this.do_image_splitting?(this.tile_size/this.encoder_patch_size)**2:0;this.max_num_patches=Math.max(t,n)}_is_image_too_large(e,t){let n=this.encoder_patch_size*this.downsample_factor;return Math.max(this.encoder_patch_size,Vp(e,n))*Math.max(this.encoder_patch_size,Vp(t,n))>this.max_image_tokens*(this.encoder_patch_size*this.downsample_factor)**2*this.max_pixels_tolerance}_get_grid_layout(e,t){let n=Up(this.min_tiles,this.max_tiles),[r,i]=Hp(t/e,n,t,e,this.tile_size);return{grid_width:r,grid_height:i,target_width:this.tile_size*r,target_height:this.tile_size*i}}async _call(e,{return_row_col_info:t=null}={}){let n;n=Array.isArray(e)?Array.isArray(e[0])?e:[e]:[[e]];let r=[],i=[],a=[],o=[],s=[],c=[];for(let e of n){let t=await Promise.all(e.map(e=>this.preprocess(e,{do_pad:!1})));for(let{pixel_values:e}of t){let[,t,n]=e.dims,l=e.unsqueeze_(0),u=this.encoder_patch_size*this.downsample_factor,d=u**2,[f,p]=cp(Math.max(u,t),Math.max(u,n),u,this.min_image_tokens*d,this.max_image_tokens*d).map(e=>Math.max(u,e)),m,h=1,g=1,_=this._is_image_too_large(t,n),v=this.do_image_splitting&&(this.min_tiles!==1||this.max_tiles!==1);if(_&&v){let{grid_width:e,grid_height:r,target_width:i,target_height:a}=this._get_grid_layout(t,n);h=r,g=e;let o=await cu(l,{size:[a,i]});m=[];for(let t=0;t<r;++t)for(let n=0;n<e;++n){let e=t*this.tile_size,r=n*this.tile_size;m.push(o.slice(null,null,[e,e+this.tile_size],[r,r+this.tile_size]))}this.use_thumbnail&&e*r!==1&&m.push(await cu(l,{size:[p,f]}))}else m=[await cu(l,{size:[p,f]})];for(let e of m){let[,,t,n]=e.dims,{padded:o,mask:s}=Gp(Wp(e,this.encoder_patch_size),this.max_num_patches);r.push(o),i.push(s),a.push([Math.floor(t/this.encoder_patch_size),Math.floor(n/this.encoder_patch_size)])}o.push(h),s.push(g),c.push([p,f])}}let l={pixel_values:yu(r,0),pixel_attention_mask:bu(i,0),spatial_shapes:new U(`int64`,BigInt64Array.from(a.flat(),BigInt),[a.length,2])};return(t??this.return_row_col_info)&&(l.image_rows=o,l.image_cols=s,l.image_sizes=c),l}},qp=class extends q{},Jp=class extends q{post_process_panoptic_segmentation(...e){return lp(...e)}post_process_instance_segmentation(...e){return up(...e)}},Yp=class extends Jp{},Xp=class extends Jp{},Zp=class extends q{},Qp=class extends Zp{},$p=class extends q{},em=class extends $p{},tm=class extends q{},nm=class extends tm{},rm=class extends q{},im=class extends rm{},am=class extends q{},om=class extends am{},sm=class extends Tp{},cm=class extends q{post_process_object_detection(...e){return rp(...e)}},lm=class extends cm{},um=class extends cm{},dm=336,fm=[2,3],{ceil:pm,floor:mm,sqrt:hm}=Math,gm=class extends q{constructor(e){super({...e,do_normalize:!0,do_pad:!0,pad_size:`custom`,do_convert_rgb:!0,do_resize:!0}),this._num_crops=e.num_crops}calc_num_image_tokens_from_image_size(e,t){let{num_img_tokens:n}=this.config;return mm((mm(t/dm)*mm(e/dm)+1)*n+1+(mm(t/dm)+1)*hm(n))}get_resize_output_image_size(e,t){let n=this._num_crops,[r,i]=e.size,a=r/i,o=1;for(;o*Math.ceil(o/a)<=n;)o+=1;--o;let s=Math.floor(o*336);return[s,Math.floor(s/a)]}pad_image(e,t,n,r={}){let[i,a]=t,o=dm*pm(i/dm),s=dm*pm(a/dm),c=[1,1,1].map((e,t)=>(e-this.image_mean[t])/this.image_std[t]);return super.pad_image(e,t,{width:s,height:o},{center:!0,constant_values:c,...r})}async _call(e,{num_crops:t=null}={}){if(this._num_crops=t??=this.config.num_crops,t<4||hm(t)%1!=0)throw Error(`num_crops must be a square number >= 4`);Array.isArray(e)||(e=[e]);let n=e.length,r=await Promise.all(e.map(e=>this.preprocess(e))),i=r.map(e=>e.original_size),a=r.map(e=>e.reshaped_input_size),o=[];for(let{pixel_values:e}of r){e.unsqueeze_(0);let[n,r]=e.dims.slice(-2),i=await cu(e,{size:[dm,dm],mode:`bicubic`});if(t>0){let a=[],s=hm(t),c=mm(r/s),l=mm(n/s);for(let t=0;t<s;++t)for(let i=0;i<s;++i){let o,u,d,f;t===s-1?(u=n-l,f=n):(u=t*l,f=(t+1)*l),i===s-1?(o=r-c,d=r):(o=i*c,d=(i+1)*c);let p=await pu(e,[u,o],[f,d],fm);a.push(p)}let u=await cu(yu(a,0),{size:[dm,dm],mode:`bicubic`});o.push(yu([i,u],0))}else o.push(i)}let s=bu(o,0),c=a.map(e=>e.map(e=>dm*pm(e/dm)));return{pixel_values:s,original_sizes:i,reshaped_input_sizes:a,image_sizes:new U(`int64`,c.flat(),[n,2]),num_img_tokens:c.map(([e,t])=>this.calc_num_image_tokens_from_image_size(t,e))}}},_m=class extends q{get_resize_output_image_size(e,t){let{longest_edge:n}=t;if(n===void 0)throw Error(`size must contain 'longest_edge'`);let[r,i]=e.size,a=Math.max(r,i)/n,o=r,s=i;a>1&&(o=Math.floor(r/a),s=Math.floor(i/a));let{patch_size:c,spatial_merge_size:l}=this.config;if(!l)throw Error(`config must contain 'spatial_merge_size'`);let u=c*l,d=Math.floor((o-1)/u)+1,f=Math.floor((s-1)/u)+1;return[d*u,f*u]}},vm=class extends q{},ym=class extends q{post_process_object_detection(...e){return rp(...e)}},bm=class extends q{reshape_input_points(e,t,n,r=!1){e=structuredClone(e);let i=oi(e);if(i.length===3)r||(i=[1,...i]),e=[e];else if(i.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 r=0;r<e.length;++r){let[i,a]=t[r],[o,s]=n[r],c=[s/a,o/i];for(let t=0;t<e[r].length;++t)for(let n=0;n<e[r][t].length;++n)for(let i=0;i<e[r][t][n].length;++i)e[r][t][n][i]*=c[i%2]}return new U(`float32`,Float32Array.from(e.flat(1/0)),i)}add_input_labels(e,t){let n=oi(e);if(n.length===2)n=[1,...n],e=[e];else if(n.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(n.some((e,n)=>e!==t.dims[n]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new U(`int64`,e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:t=null,input_labels:n=null,input_boxes:r=null}={}){let i=await super._call(e);if(t&&(i.input_points=this.reshape_input_points(t,i.original_sizes,i.reshaped_input_sizes)),n){if(!i.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");i.input_labels=this.add_input_labels(n,i.input_points)}return r&&(i.input_boxes=this.reshape_input_points(r,i.original_sizes,i.reshaped_input_sizes,!0)),i}async post_process_masks(e,t,n,{mask_threshold:r=0,binarize:i=!0,pad_size:a=null}={}){let o=[];a=a??this.pad_size??this.size;let s=[a.height,a.width];for(let a=0;a<t.length;++a){let c=t[a],l=n[a],u=await cu(e[a],{mode:`bilinear`,size:s});if(u=u.slice(null,null,[0,l[0]],[0,l[1]]),u=await cu(u,{mode:`bilinear`,size:c}),i){let e=u.data,t=new Uint8Array(e.length);for(let n=0;n<e.length;++n)e[n]>r&&(t[n]=1);u=new U(`bool`,t,u.dims)}o.push(u)}return o}generate_crop_boxes(e,t,{crop_n_layers:n=0,overlap_ratio:r=512/1500,points_per_crop:i=32,crop_n_points_downscale_factor:a=1}={}){}},xm=class extends q{post_process_semantic_segmentation(...e){return ip(...e)}},Sm=class extends xm{},Cm=class extends q{post_process_semantic_segmentation(...e){return ip(...e)}},wm=class extends Cm{},Tm=class extends q{},Em=class extends q{pad_image(e,t,n,r={}){let[i,a,o]=t;return super.pad_image(e,t,{width:a+(n-a%n)%n,height:i+(n-i%n)%n},{mode:`symmetric`,center:!1,constant_values:-1,...r})}},Dm=class extends q{},Om=class extends Dm{},km=class extends q{async _call(e,t){Array.isArray(e)||(e=[e]),Array.isArray(t)||(t=[t]);let n=await Promise.all(e.map(e=>this.preprocess(e))),r=await Promise.all(t.map(e=>this.preprocess(e,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:bu(n.map((e,t)=>yu([e.pixel_values,r[t].pixel_values],0)),0),original_sizes:n.map(e=>e.original_size),reshaped_input_sizes:n.map(e=>e.reshaped_input_size)}}},Am=class extends q{post_process_pose_estimation(e,t,{threshold:n=null}={}){let r=e.tolist(),[i,a,o,s]=e.dims,c=[];for(let e=0;e<i;++e){let i=r[e],a=t[e],l=[];for(let e=0;e<a.length;++e){let t=a[e],r=[],c=[],u=[],d=t.at(-2)/s,f=t.at(-1)/o;for(let e=0;e<i.length;++e){let[t,a]=[0,0],o=0,s=-1/0,l=i[e];for(let e=0;e<l.length;++e){let n=l[e];for(let r=0;r<n.length;++r){let i=n[r];o+=i,s=Math.max(s,i),t+=(r+.5)*i,a+=e*i}}if(n!=null&&s<n)continue;let p=[d*t/o,f*a/o];r.push(p),u.push(e),c.push(s)}l.push({bbox:t,scores:c,labels:u,keypoints:r})}c.push(l)}return c}},jm=class extends q{post_process_object_detection(...e){return rp(...e)}},Mm=class extends jm{},Nm=class{static async from_pretrained(e,t={}){let n=await ol(e,Gd,!0,t),r=n.image_processor_type??n.feature_extractor_type,i=dp[r?.replace(/Fast$/,``)];return i||=(r!==void 0&&N.warn(`Image processor type '${r}' not found, assuming base ImageProcessor. Please report this at ${Ud}.`),q),new i(n)}},Pm=class extends K{static tokenizer_class=G;static image_processor_class=Nm;constructor(e,t,n){super(e,t,n);let{tasks_answer_post_processing_type:r,task_prompts_without_inputs:i,task_prompts_with_input:a}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(r??{})),this.task_prompts_without_inputs=new Map(Object.entries(i??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e==`string`&&(e=[e]);let t=[];for(let n of e)if(this.task_prompts_without_inputs.has(n))t.push(this.task_prompts_without_inputs.get(n));else{for(let[e,r]of this.task_prompts_with_input)if(n.includes(e)){t.push(r.replaceAll(`{input}`,n).replaceAll(e,``));break}t.length!==e.length&&t.push(n)}return t}post_process_generation(e,t,n){let r=this.tasks_answer_post_processing_type.get(t)??`pure_text`;e=e.replaceAll(`<s>`,``).replaceAll(`</s>`,``);let i;switch(r){case`pure_text`:i=e;break;case`description_with_bboxes`:case`bboxes`:case`phrase_grounding`:case`ocr`:let a=r===`ocr`?`quad_boxes`:`bboxes`,o=e.matchAll(this.regexes[a]),s=[],c=[];for(let[e,t,...r]of o)s.push(t?t.trim():s.at(-1)??``),c.push(r.map((e,t)=>(Number(e)+.5)/this.size_per_bin*n[t%2]));i={labels:s,[a]:c};break;default:throw Error(`Task "${t}" (of type "${r}") not yet implemented.`)}return{[t]:i}}async _call(e,t=null,n={}){if(!e&&!t)throw Error(`Either text or images must be provided`);let r=await this.image_processor(e,n),i=t?this.tokenizer(this.construct_prompts(t),n):{};return{...r,...i}}},Fm=class extends K{static tokenizer_class=G;static image_processor_class=Nm;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,t,n){super(e,t,n),this.image_seq_length=this.config.image_seq_length;let{boi_token:r,image_token:i,eoi_token:a}=this.tokenizer.config;this.boi_token=r,this.image_token=i,this.eoi_token=a;let o=i.repeat(this.image_seq_length);this.full_image_sequence=`

${r}${o}${a}

`}async _call(e,t=null,n={}){typeof e==`string`&&(e=[e]);let r;return t&&(r=await this.image_processor(t,n),e=e.map(e=>e.replaceAll(this.boi_token,this.full_image_sequence))),{...this.tokenizer(e,n),...r}}},Im=class extends K{static image_processor_class=Nm;static feature_extractor_class=Hf;static tokenizer_class=G;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,t,n){super(e,t,n),this.audio_seq_length=this.config.audio_seq_length,this.image_seq_length=this.config.image_seq_length;let{audio_token_id:r,boa_token:i,audio_token:a,eoa_token:o,image_token_id:s,boi_token:c,image_token:l,eoi_token:u}=this.tokenizer.config;this.audio_token_id=r,this.boa_token=i,this.audio_token=a;let d=a.repeat(this.audio_seq_length);this.full_audio_sequence=`

${i}${d}${o}

`,this.image_token_id=s,this.boi_token=c,this.image_token=l;let f=l.repeat(this.image_seq_length);this.full_image_sequence=`

${c}${f}${u}

`}async _call(e,t=null,n=null,r={}){typeof e==`string`&&(e=[e]);let i;n&&(i=await this.feature_extractor(n,r),e=e.map(e=>e.replaceAll(this.audio_token,this.full_audio_sequence)));let a;return t&&(a=await this.image_processor(t,r),e=e.map(e=>e.replaceAll(this.image_token,this.full_image_sequence))),{...this.tokenizer(e,r),...a,...i}}},Lm=class extends K{static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,t,n){super(e,t,n),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:r,boa_token:i,eoa_token:a,image_token:o,boi_token:s,eoi_token:c}=this.tokenizer.config;this.audio_token=r,this.boa_token=i,this.eoa_token=a,this.image_token=o,this.boi_token=s,this.eoi_token=c}static async from_pretrained(e,t={}){let[n,r,i]=await Promise.all([ol(e,Kd,!0,t),G.from_pretrained(e,t),al(e,qd,!1,t)]),a={tokenizer:r};return n.image_processor&&(a.image_processor=new Np(n.image_processor)),n.feature_extractor&&(a.feature_extractor=new jf(n.feature_extractor)),new this(n,a,i)}_compute_audio_num_tokens(e,t){let n=Math.round(t*20/1e3),r=Math.round(t*10/1e3),i=Math.floor(n/2),a=Math.floor((e+i-n-1)/r)+1;if(a<=0)return 0;for(let e=0;e<2;++e)a=Math.floor((a-1)/2)+1;return Math.min(a,this.audio_seq_length)}async _call(e,t=null,n=null,r={}){typeof e==`string`&&(e=[e]);let i;if(t){i=await this.image_processor(t,r);let n=i.num_soft_tokens_per_image,a=0;e=e.map(e=>e.replaceAll(this.image_token,()=>`

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

`))}let a;if(n){let t=Array.isArray(n)?n:[n];a=await this.feature_extractor(t[0],r);let i=this.feature_extractor.config.sampling_rate??16e3,o=0;e=e.map(e=>e.replaceAll(this.audio_token,()=>`

${this.boa_token}${this.audio_token.repeat(this._compute_audio_num_tokens(t[o++].length,i))}${this.eoa_token}

`))}return{...this.tokenizer(e,r),...i,...a}}},Rm=class extends K{static image_processor_class=Nm;static tokenizer_class=G;static image_token=`<|image_pad|>`;async _call(e,t=null,...n){Array.isArray(e)||(e=[e]);let r,i;if(t&&(r=await this.image_processor(t),i=r.image_grid_thw),i){let t=this.image_processor.config.merge_size**2,n=0,r=this.constructor.image_token,a=i.tolist();e=e.map(e=>{for(;e.includes(r);){let i=Number(a[n++].reduce((e,t)=>e*t,1n));e=e.replace(r,`<|placeholder|>`.repeat(Math.floor(i/t)))}return e.replaceAll(`<|placeholder|>`,r)})}return{...this.tokenizer(e),...r}}},zm=class extends Rm{static image_token=`<|image|>`},Bm=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;static uses_processor_config=!0;_get_num_audio_features(e){let{hop_length:t}=this.feature_extractor.config.melspec_kwargs,{projector_window_size:n,projector_downsample_rate:r}=this.feature_extractor.config,i=Math.floor(n/r),a=Math.floor(e/t)+1,o=Math.floor(a/2);return Math.ceil(o/n)*i}async _call(e,t=null,n={}){if(Array.isArray(e))throw Error(`Batched inputs are not supported yet.`);let r={};if(t){let{input_features:n}=await this.feature_extractor(t);r.input_features=n;let i=this._get_num_audio_features(t.length);r.input_features_mask=new U(`bool`,new Uint8Array(i).fill(1),[1,i]);let a=this.config.audio_token??`<|audio|>`;if(!e.includes(a))throw Error(`The input text does not contain the audio token ${a}.`);e=e.replaceAll(a,a.repeat(i))}return{...this.tokenizer(e,{add_special_tokens:!1,...n}),...r}}};function Vm(e,t){let n=e.dims.at(-1)-1,r=e.tolist();r.fill(!1,0,1),r.fill(!1,n);let i=t.tolist();return r.map((e,t)=>e?t:null).filter(e=>e!==null).map(e=>i[e])}var Hm=class extends K{static tokenizer_class=G;static image_processor_class=Nm;async _call(e,t,n={}){let r=e?await this.image_processor(e,n):{};return{...t?this.tokenizer(t,n):{},...r}}post_process_grounded_object_detection(e,t,{box_threshold:n=.25,text_threshold:r=.25,target_sizes:i=null}={}){let{logits:a,pred_boxes:o}=e,s=a.dims[0];if(i!==null&&i.length!==s)throw Error(`Make sure that you pass in as many target sizes as the batch dimension of the logits`);let c=a.dims.at(1),l=a.sigmoid(),u=l.max(-1).tolist(),d=o.tolist().map(e=>e.map(e=>np(e))),f=[];for(let e=0;e<s;++e){let a=i===null?null:i[e];a!==null&&(d[e]=d[e].map(e=>e.map((e,t)=>e*a[(t+1)%2])));let o=u[e],s=[],p=[],m=[];for(let i=0;i<c;++i){let a=o[i];if(a<=n)continue;let c=d[e][i],u=l[e][i];s.push(a),m.push(c);let f=Vm(u.gt(r),t[e]);p.push(f)}f.push({scores:s,boxes:m,labels:this.batch_decode(p)})}return f}};function Um(e,t,n,r,i,a){let o=``;for(let a=0;a<t;++a){for(let t=0;t<n;++t)o+=r+`<row_${a+1}_col_${t+1}>`+i.repeat(e);o+=`
`}return o+=`
${r}${a}`+i.repeat(e)+`${r}`,o}function Wm(e,t,n,r){return`${t}${r}`+n.repeat(e)+`${t}`}function Gm(e,t,n,r,i,a){return e===0&&t===0?Wm(n,r,i,a):Um(n,e,t,r,i,a)}var Km=class extends K{static image_processor_class=Nm;static tokenizer_class=G;static uses_processor_config=!0;fake_image_token=`<fake_token_around_image>`;image_token=`<image>`;global_img_token=`<global-img>`;async _call(e,t=null,n={}){n.return_row_col_info??=!0;let r;t&&(r=await this.image_processor(t,n)),Array.isArray(e)||(e=[e]);let i=r.rows??[Array(e.length).fill(0)],a=r.cols??[Array(e.length).fill(0)],o=this.config.image_seq_len,s=[],c=[];for(let t=0;t<e.length;++t){let n=e[t],r=i[t],l=a[t];s.push(di(n,this.image_token));let u=r.map((e,t)=>Gm(e,l[t],o,this.fake_image_token,this.image_token,this.global_img_token)),d=n.split(this.image_token);if(d.length===0)throw Error(`The image token should be present in the text.`);let f=d[0];for(let e=0;e<u.length;++e)f+=u[e]+d[e+1];c.push(f)}return{...this.tokenizer(c),...r}}},qm=class extends K{static image_processor_class=Nm;static tokenizer_class=G;static uses_processor_config=!0;constructor(e,t,n){super(e,t,n),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(e,{images:t=null,chat_template:n=`default`}={}){t?Array.isArray(t)||(t=[t]):t=await Promise.all(e.filter(e=>e.images).flatMap(e=>e.images).map(e=>Qf.read(e)));let r=this.tokenizer,i=r.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0,chat_template:n}),a=e=>r.encode(e,{add_special_tokens:!1}),o=i.split(this.image_tag),s=o.length-1;if(t.length!==s)throw Error(`Number of images provided (${t.length}) does not match number of "${this.image_tag}" image tags (${s})`);let[c,l,u]=r.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]),d=a(o[0]),f=Array(d.length).fill(!1);for(let e=1;e<o.length;++e){let t=Array(this.num_image_tokens).fill(c),n=a(o[e]);d=si(d,[l],t,[u],n);let r=Array(this.num_image_tokens).fill(!0);f=si(f,[!1],r,[!1],Array(n.length).fill(!1))}let p=[1,d.length],m={input_ids:new U(`int64`,d,p),attention_mask:new U(`int64`,Array(d.length).fill(1),p),images_seq_mask:new U(`bool`,f,p),images_emb_mask:new U(`bool`,Array(s*this.num_image_tokens).fill(!0),[1,s,this.num_image_tokens])};if(t&&t.length>0){let e=await this.image_processor(t);return e.pixel_values.unsqueeze_(0),{...m,...e}}return m}},Jm=class extends K{static tokenizer_class=G;static image_processor_class=Nm;async _call(e=null,t=null,n={}){if(!e&&!t)throw Error(`Either text or images must be provided`);let r=e?this.tokenizer(e,n):{},i=t?await this.image_processor(t,n):{};return{...r,...i}}},Ym=class extends K{static tokenizer_class=G;static image_processor_class=Nm;async _call(e,t=null,n={}){let{image_rows:r,image_cols:i,image_sizes:a,...o}=await this.image_processor(e,{...n,return_row_col_info:!0});if(t){let e=this.config.image_token??`<image>`,{tile_size:n=512,downsample_factor:o=2,encoder_patch_size:s=16,use_thumbnail:c=!0}=this.image_processor.config,l=e=>Math.ceil(Math.floor(e/s)/o),u=l(n)**2,d=this.config.image_start_token??`<|image_start|>`,f=this.config.image_end_token??`<|image_end|>`,p=this.config.image_thumbnail??`<|img_thumbnail|>`;Array.isArray(t)||(t=[t]);let m=0;t=t.map(t=>{let n=t.split(e);return n[0]+n.slice(1).map(t=>{let n=m++,[o,s]=a[n],h=r[n],g=i[n],_=l(o)*l(s),v=d;if(h>1||g>1){let t=e.repeat(u);for(let e=0;e<h;++e)for(let n=0;n<g;++n)v+=`<|img_row_${e+1}_col_${n+1}|>`+t;c&&(v+=p+e.repeat(_))}else v+=e.repeat(_);return v+f+t}).join(``)})}return{...o,...t?this.tokenizer(t,n):{}}}},Xm=class extends K{static tokenizer_class=G;static image_processor_class=Nm;static uses_processor_config=!0;async _call(e,t=null,n={}){let r=await this.image_processor(e,n);if(t){let[e,n]=r.pixel_values.dims.slice(-2),{image_token:i,patch_size:a,num_additional_image_tokens:o}=this.config,s=Math.floor(e/a)*Math.floor(n/a)+o;t=structuredClone(t),Array.isArray(t)||(t=[t]);for(let e=0;e<t.length;++e)t[e]=t[e].replace(i,i.repeat(s))}let i=t?this.tokenizer(t,n):{};return{...r,...i}}},Zm={char:[`char_decode`,1],bpe:[`bpe_decode`,2],wp:[`wp_decode`,102]},Qm=class extends K{static tokenizer_class=G;static image_processor_class=Nm;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(e,t){if(!Zm.hasOwnProperty(t))throw Error(`Format ${t} is not supported.`);let[n,r]=Zm[t],i=this[n].bind(this),[a,o]=e.dims,s=[],c=[],l=e.tolist();for(let e=0;e<a;++e){let t=l[e],n=[],i=[];for(let e=1;e<o;++e){let[a,o]=hl(ll(t[e]));if(i.push(a),o==r)break;n.push(o)}let a=i.length>0?i.reduce((e,t)=>e*t,1):0;c.push(n),s.push(a)}return[i(c),s]}char_decode(e){return this.char_tokenizer.batch_decode(e).map(e=>e.replaceAll(` `,``))}bpe_decode(e){return this.bpe_tokenizer.batch_decode(e)}wp_decode(e){return this.wp_tokenizer.batch_decode(e).map(e=>e.replaceAll(` `,``))}batch_decode([e,t,n]){let[r,i]=this._decode_helper(e,`char`),[a,o]=this._decode_helper(t,`bpe`),[s,c]=this._decode_helper(n,`wp`),l=[],u=[];for(let e=0;e<r.length;++e){let[t,n]=hl([i[e],o[e],c[e]]);l.push([r[e],a[e],s[e]][n]),u.push(t)}return{generated_text:l,scores:u,char_preds:r,bpe_preds:a,wp_preds:s}}static async from_pretrained(...e){let t=await super.from_pretrained(...e),n=await G.from_pretrained(`Xenova/gpt2`),r=await G.from_pretrained(`Xenova/bert-base-uncased`);return t.components={image_processor:t.image_processor,char_tokenizer:t.tokenizer,bpe_tokenizer:n,wp_tokenizer:r},t}async _call(e,t=null){let n=await this.image_processor(e);return t&&(n.labels=this.tokenizer(t).input_ids),n}},$m=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;async _call(e){return await this.feature_extractor(e)}},eh=class extends K{static tokenizer_class=G;static image_processor_class=Nm},th=`<image>`;function nh(e,t,n,r,i){return`${r.repeat(n*i)}${t}${e}
`}var rh=class extends K{static tokenizer_class=G;static image_processor_class=Nm;static uses_processor_config=!1;async _call(e,t=null,n={}){t||=(N.warn(`You are using PaliGemma without a text prefix. It will perform as a picture-captioning model.`),``),Array.isArray(e)||(e=[e]),Array.isArray(t)||(t=[t]);let r=this.tokenizer.bos_token,i=this.image_processor.config.image_seq_length,a;t.some(e=>e.includes(th))?a=t.map(e=>{let t=e.replaceAll(th,th.repeat(i)),n=t.lastIndexOf(th),a=n===-1?0:n+th.length;return t.slice(0,a)+r+t.slice(a)+`
`}):(N.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."),a=t.map(t=>nh(t,r,i,th,e.length)));let o=this.tokenizer(a,n);return{...await this.image_processor(e,n),...o}}},ih=`<|image|>`,ah=/<\|image_\d+\|>/g,oh=class extends K{static image_processor_class=Nm;static tokenizer_class=G;async _call(e,t=null,{padding:n=!0,truncation:r=!0,num_crops:i=null}={}){Array.isArray(e)||(e=[e]);let a,o;if(t){o=await this.image_processor(t,{num_crops:i});let{num_img_tokens:s}=o,c=e.map((e,t)=>e.split(ah).join(ih.repeat(s[t])));a=this.tokenizer(c,{padding:n,truncation:r});let l=this.tokenizer._tokenizer.token_to_id(ih);a.input_ids.map_(e=>e==l?-e:e)}else a=this.tokenizer(e);return{...a,...o}}},sh=class extends K{static tokenizer_class=G;static image_processor_class=Nm;static uses_processor_config=!0;async _call(e,t=null,n={}){let r=await this.image_processor(e,n);if(t){let[e,n]=r.pixel_values.dims.slice(-2),{image_token:i,image_break_token:a,image_end_token:o,patch_size:s,spatial_merge_size:c}=this.config,l=s*c,u=Math.floor(e/l),d=Math.floor(n/l);t=structuredClone(t),Array.isArray(t)||(t=[t]);for(let e=0;e<t.length;++e){let n=i.repeat(d),r=n+a,s=n+o,c=r.repeat(u-1)+s;t[e]=t[e].replace(i,c)}}let i=t?this.tokenizer(t,n):{};return{...r,...i}}},ch=class extends K{static feature_extractor_class=Pf;async _call(e){return await this.feature_extractor(e)}post_process_speaker_diarization(...e){return this.feature_extractor.post_process_speaker_diarization(...e)}get sampling_rate(){return this.feature_extractor.config.sampling_rate}},lh=class extends Rm{},uh=class extends lh{},dh=class extends K{static image_processor_class=Nm;async _call(...e){return await this.image_processor(...e)}post_process_masks(...e){return this.image_processor.post_process_masks(...e)}reshape_input_points(...e){return this.image_processor.reshape_input_points(...e)}},fh=class extends dh{},ph=class extends fh{},mh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;async _call(e){return await this.feature_extractor(e)}},hh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;static uses_processor_config=!0;async _call(e,t=null,n={}){if(Array.isArray(e))throw Error(`Batched inputs are not supported yet.`);let r={};if(t){let i=t.length,{input_features:a}=await this.feature_extractor(t,{...n,max_length:i}),o=Math.round(i/this.config.encoder_ds_factor+1e-4),s=1+Math.ceil(o/this.config.stack_factor);r.audio_token_len=[s],r.audio_values=a;let c=this.config.audio_placeholder;if(!e.includes(c))throw Error(`The input text does not contain the image token ${c}.`);e=e.replaceAll(c,c.repeat(s))}return{...this.tokenizer(e,{add_special_tokens:!1,...n}),...r}}},gh=`[AUDIO]`,_h=`[BEGIN_AUDIO]`,vh=375;function yh(e,t){let n=[];for(let r=0;r<e.length;r+=t)n.push(e.subarray(r,Math.min(r+t,e.length)));return n}var bh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;static uses_processor_config=!1;async _call(e,t=null,n={}){if(Array.isArray(e))throw Error(`Batched inputs are not supported yet.`);let r={};if(t){if(!e.includes(gh))throw Error(`The input text does not contain the audio token ${gh}.`);Array.isArray(t)||(t=[t]);let i=e.split(gh),a=i.length-1;if(a!==t.length)throw Error(`The number of audio inputs (${t.length}) does not match the number of audio tokens in the text (${a}).`);let o=this.feature_extractor.config.n_samples,s=t.map(e=>yh(e,o)),c=s.map(e=>e.length),l=s.flat(),u=(await Promise.all(l.map(e=>this.feature_extractor(e,n)))).map(e=>e.input_features);r.audio_values=u.length>1?yu(u,0):u[0];let d=i[0];for(let e=0;e<c.length;++e){d+=_h;for(let t=0;t<c[e];++t)d+=gh.repeat(vh);d+=i[e+1]}e=d}return{...this.tokenizer(e,{add_special_tokens:!1,...n}),...r}}},xh=32,Sh=6,Ch=8,wh=10,Th=32,Eh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;static uses_processor_config=!1;get num_mel_frames_first_audio_chunk(){return(Sh+1)*Ch}get num_samples_first_audio_chunk(){let{hop_length:e,n_fft:t}=this.feature_extractor.config;return(this.num_mel_frames_first_audio_chunk-1)*e+Math.floor(t/2)}get num_samples_per_audio_chunk(){let{hop_length:e,n_fft:t}=this.feature_extractor.config;return Ch*e+t}get num_right_pad_tokens(){return Sh+1+wh}get audio_length_per_tok(){return Ch}get raw_audio_length_per_tok(){return Ch*this.feature_extractor.config.hop_length}async _call(e,{is_streaming:t=!1,is_first_audio_chunk:n=!0}={}){if(Xd(e,`VoxtralRealtimeProcessor`),!t&&!n)throw Error("In non-streaming mode (`is_streaming=false`), `is_first_audio_chunk` must be `true`.");if(n){if(t){let t=xh*this.raw_audio_length_per_tok,n=new Float32Array(t+e.length);n.set(e,t);let r=await this.feature_extractor(n,{center:!0}),i=1+(xh+Sh),a=new BigInt64Array(i).fill(BigInt(Th));return a[0]=1n,{input_ids:new U(`int64`,a,[1,i]),...r}}{let t=this.num_right_pad_tokens*this.raw_audio_length_per_tok,n=new Float32Array(e.length+t);return n.set(e),await this.feature_extractor(n,{center:!0})}}return await this.feature_extractor(e,{center:!1})}},Dh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;async _call(e){return await this.feature_extractor(e)}},Oh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;async _call(e){return await this.feature_extractor(e)}},kh=class extends K{static tokenizer_class=G;static feature_extractor_class=Hf;async _call(e){return await this.feature_extractor(e)}},Ah=class{static async from_pretrained(e,t={}){let n=await ol(e,Gd,!0,t),{image_processor_type:r,feature_extractor_type:i,processor_class:a}=n;if(a&&Jd[a])return Jd[a].from_pretrained(e,t);if(!r&&!i)throw Error("No `image_processor_type` or `feature_extractor_type` found in the config.");let o={};if(r){let e=dp[r.replace(/Fast$/,``)];if(!e)throw Error(`Unknown image_processor_type: '${r}'.`);o.image_processor=new e(n)}if(i){let e=dp[i];if(e)o.image_processor=new e(n);else{let e=Zd[i];if(!e)throw Error(`Unknown feature_extractor_type: '${i}'.`);o.feature_extractor=new e(n)}}return new K({},o,null)}};async function jh(e,t){return await ol(e,`config.json`,!0,t)}function Mh(e){let 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Make sure to either pass {inputs} or {input_name}=...")}else r[i]=e;return{inputs_tensor:r[i],model_inputs:r,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:t,model_input_name:n,generation_config:r}){if(this.sessions.model.inputNames.includes(`inputs_embeds`)&&!t.inputs_embeds&&`_prepare_inputs_embeds`in this){let{input_ids:e,pixel_values:n,attention_mask:r,...i}=t,a=await this._prepare_inputs_embeds(t);t={...i,...ui(a,[`inputs_embeds`,`attention_mask`])}}let{last_hidden_state:i}=await Wg(this,t);if(r.guidance_scale!==null&&r.guidance_scale>1)i=yu([i,Du(i,0)],0),`attention_mask`in t&&(t.attention_mask=yu([t.attention_mask,ju(t.attention_mask)],0));else if(t.decoder_input_ids){let e=Ig(t.decoder_input_ids).dims[0];if(e!==i.dims[0]){if(i.dims[0]!==1)throw Error(`The encoder outputs have a different batch size (${i.dims[0]}) than the decoder inputs (${e}).`);i=yu(Array.from({length:e},()=>i),0)}}return t.encoder_outputs=i,t}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:t,model_kwargs:n,decoder_start_token_id:r,bos_token_id:i,generation_config:a}){let{decoder_input_ids:o,...s}=n;if(!(o instanceof U)){if(o)Array.isArray(o[0])||(o=Array.from({length:e},()=>o));else if(r??=i,this.config.model_type===`musicgen`)o=Array.from({length:e*this.config.decoder.num_codebooks},()=>[r]);else if(Array.isArray(r)){if(r.length!==e)throw Error(`\`decoder_start_token_id\` expected to have length ${e} but got ${r.length}`);o=r}else o=Array.from({length:e},()=>[r]);o=Ig(o)}return s.decoder_attention_mask=ku(o),{input_ids:o,model_inputs:s}}async generate({inputs:e=null,generation_config:t=null,logits_processor:n=null,stopping_criteria:r=null,streamer:i=null,...a}){this._validate_model_class(),t=this._prepare_generation_config(t,a);let{inputs_tensor:o,model_inputs:s,model_input_name:c}=this._prepare_model_inputs({inputs:e,model_kwargs:a}),l=this.config.is_encoder_decoder;l&&(`encoder_outputs`in s||(s=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:o,model_inputs:s,model_input_name:c,generation_config:t})));let u;l?{input_ids:u,model_inputs:s}=this._prepare_decoder_input_ids_for_generation({batch_size:s[c].dims.at(0),model_input_name:c,model_kwargs:s,decoder_start_token_id:t.decoder_start_token_id,bos_token_id:t.bos_token_id,generation_config:t}):u=s[c];let d=u.dims.at(-1);t.max_new_tokens!==null&&(t.max_length=d+t.max_new_tokens);let f=this._get_logits_processor(t,d,n),p=this._get_stopping_criteria(t,r),m=s[c].dims.at(0),h=Cg.getSampler(t),g=Array(m).fill(0),_=u.tolist();i&&i.put(_);let v,y={},b={};for(;;){if(s=this.prepare_inputs_for_generation(_,s,t),v=await this.forward(s),t.return_dict_in_generate){if(t.output_attentions){let e=qg(v);for(let t in e)t in y||(y[t]=[]),y[t].push(e[t])}else this._return_dict_in_generate_keys&&Object.assign(b,ui(v,this._return_dict_in_generate_keys))}let e=f(_,v.logits.slice(null,-1,null).to(`float32`)),n=[];for(let t=0;t<e.dims.at(0);++t){let r=e[t],i=await h(r);for(let[e,r]of i){let i=BigInt(e);g[t]+=r,_[t].push(i),n.push([i]);break}}if(i&&i.put(n),p(_).every(e=>e))break;s=this._update_model_kwargs_for_generation({generated_input_ids:n,outputs:v,model_inputs:s,is_encoder_decoder:l})}i&&i.end();let x=new U(`int64`,_.flat(),[_.length,_[0].length]),ee=Kg(v,s.past_key_values),S=new Set(Object.values(ee));for(let e of Object.values(v))e.location===`gpu-buffer`&&!S.has(e)&&e.dispose();return`past_key_values`in a||t.return_dict_in_generate||await ee.dispose(),t.return_dict_in_generate?{sequences:x,past_key_values:ee,...y,...b}:x}async _encode_input(e,t,n){if(!Object.hasOwn(this.sessions,e))throw Error(`Model does not have a ${e} session.`);let r=this.sessions[e];return(await J(r,ui(t,r.inputNames)))[n]}async encode_image(e){return this._encode_input(`vision_encoder`,e,`image_features`)}async encode_text(e){return this._encode_input(`embed_tokens`,e,`inputs_embeds`)}async encode_audio(e){return this._encode_input(`audio_encoder`,e,`audio_features`)}};async function Ug(e,t){let{encoder_outputs:n,input_ids:r,decoder_input_ids:i,decoder_attention_mask:a,...o}=t;return n||=(await Wg(e,ui(t,e.sessions.model.inputNames))).last_hidden_state,o.input_ids=i,o.encoder_hidden_states=n,e.sessions.decoder_model_merged.inputNames.includes(`encoder_attention_mask`)&&(o.encoder_attention_mask=t.attention_mask),a&&!o.attention_mask&&(o.attention_mask=a),await Zg(e,o,!0)}async function Wg(e,t){let n=e.sessions.model,r=ui(t,n.inputNames);if(n.inputNames.includes(`inputs_embeds`)&&!r.inputs_embeds){if(!t.input_ids)throw Error("Both 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forward({input_ids:e=null,attention_mask:t=null,pixel_values:n=null,input_features:r=null,input_features_mask:i=null,position_ids:a=null,inputs_embeds:o=null,per_layer_inputs:s=null,past_key_values:c=null,generation_config:l=null,logits_processor:u=null,num_logits_to_keep:d=null,...f}){if((!o||!s)&&({inputs_embeds:o,per_layer_inputs:s}=await J(this.sessions.embed_tokens,{input_ids:e}),e.dims[1]!==1)){if(n){let{image_features:r}=await this._encode_vision({pixel_values:n,...f});({inputs_embeds:o,attention_mask:t}=this._merge_input_ids_with_image_features({image_features:r,inputs_embeds:o,input_ids:e,attention_mask:t}))}if(r){let{audio_features:n}=await J(this.sessions.audio_encoder,{input_features:r,input_features_mask:i});({inputs_embeds:o,attention_mask:t}=this._merge_input_ids_with_audio_features({audio_features:n,inputs_embeds:o,input_ids:e,attention_mask:t}))}}return await 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ax{},sx=class extends Z{},cx=class extends sx{},lx=class extends sx{},ux=class extends Z{},dx=class extends ux{},fx=class extends ux{},px=class extends Z{forward_params=[`input_ids`,`attention_mask`,`position_ids`,`past_key_values`,`pixel_values`,`image_grid_thw`]},mx=class extends px{image_grid_thw_name=`grid_thw`;_get_text_only_rope_index(e,t){if(t){let{data:e,dims:n}=t_(t),r=BigInt64Array.from({length:3*e.length},(t,n)=>e[n%e.length]),i=Array.from({length:n[0]},(t,r)=>hl(e.subarray(n[1]*r,n[1]*(r+1)))[0]+1n+BigInt(n[1]));return[new U(`int64`,r,[3,...n]),new U(`int64`,i,[i.length,1])]}{let[t,n]=e.dims;return[new U(`int64`,BigInt64Array.from({length:3*t*n},(e,r)=>BigInt(Math.floor(r%n/t))),[3,...e.dims]),Au([t,1])]}}_reorder_and_write_positions(e,t,n,r){let i=e.reduce((e,t)=>e+t.length,0),a=Array(i),o=0;for(let t=0;t<3;++t)for(let n of e){let e=n.length/3;for(let r=t*e;r<(t+1)*e;++r)a[o++]=n[r]}let s=0;for(let e=0;e<t.length;++e)if(t[e]==1){for(let t=0;t<3;++t)n[t][r][e]=a[t*i/3+s];++s}return a}_get_multimodal_rope_positions({filtered_ids:e,image_grid_thw_list:t,video_grid_thw_list:n,spatial_merge_size:r,state:i}){let{image_token_id:a,video_token_id:o,vision_start_token_id:s}=this.config,c=e,l=c.reduce((e,t,n)=>(t==s&&e.push(n),e),[]).map(e=>c[e+1]),u=l.filter(e=>e==a).length,d=l.filter(e=>e==o).length,f=[],p=0,m=u,h=d;for(let e=0;e<l.length;++e){let e=c.findIndex((e,t)=>t>p&&e==a),s=c.findIndex((e,t)=>t>p&&e==o),l=m>0&&e!==-1?e:c.length+1,u=h>0&&s!==-1?s:c.length+1,d,g,_,v;l<u?([g,_,v]=t[i.image_index],++i.image_index,--m,d=l):([g,_,v]=n[i.video_index],++i.video_index,--h,d=u);let[y,b,x]=[Number(g),Math.floor(Number(_)/r),Math.floor(Number(v)/r)],ee=d-p,S=f.length>0?hl(f.at(-1))[0]+1:0;f.push(Array.from({length:3*ee},(e,t)=>S+t%ee));let te=ee+S,C=y*b*x,w=Array.from({length:C},(e,t)=>te+Math.floor(t/(b*x))),ne=Array.from({length:C},(e,t)=>te+Math.floor(t/x)%b),re=Array.from({length:C},(e,t)=>te+t%x);f.push([w,ne,re].flat()),p=d+C}if(p<c.length){let e=f.length>0?hl(f.at(-1))[0]+1:0,t=c.length-p;f.push(Array.from({length:3*t},(n,r)=>e+r%t))}return f}get_rope_index(e,t,n,r){let{vision_config:i}=this.config,a=i.spatial_merge_size??2;if(t||n){let i=e.tolist();r||=ku(e);let o=r.tolist(),s=Array.from({length:3},()=>Array.from({length:e.dims[0]},()=>Array.from({length:e.dims[1]},()=>0))),c=t?t.tolist():[],l=n?n.tolist():[],u={image_index:0,video_index:0},d=[];for(let e=0;e<i.length;++e){let t=i[e].filter((t,n)=>o[e][n]==1),n=this._get_multimodal_rope_positions({filtered_ids:t,image_grid_thw_list:c,video_grid_thw_list:l,spatial_merge_size:a,state:u}),r=this._reorder_and_write_positions(n,o[e],s,e);d.push(hl(r)[0]+1-i[e].length)}return[new U(`int64`,s.flat(1/0),[3,e.dims[0],e.dims[1]]),new U(`int64`,d,[d.length,1])]}return this._get_text_only_rope_index(e,r)}async encode_image({pixel_values:e,image_grid_thw:t}){return(await J(this.sessions.vision_encoder,{pixel_values:e,[this.image_grid_thw_name]:t})).image_features}_merge_input_ids_with_image_features(e){return s_({image_token_id:this.config.image_token_id,...e})}prepare_inputs_for_generation(e,t,n){if(Xg(this,t,1n),!t.attention_mask||t.position_ids||!(this.sessions.decoder_model_merged??this.sessions.model).inputNames.includes(`position_ids`))return t;if(!t.past_key_values)[t.position_ids,t.rope_deltas]=this.get_rope_index(t.input_ids,t.image_grid_thw,t.video_grid_thw,t.attention_mask);else{t.pixel_values=null;let e=t.past_key_values.get_seq_length();if(e<t.input_ids.dims[1]){let[n,r]=this.get_rope_index(t.input_ids,t.image_grid_thw,t.video_grid_thw,t.attention_mask);t.rope_deltas=r,t.position_ids=n.slice(null,null,[e,null]),t.input_ids=t.input_ids.slice(null,[e,null])}else{t.rope_deltas||([,t.rope_deltas]=this.get_rope_index(t.input_ids,t.image_grid_thw,t.video_grid_thw,t.attention_mask));let n=BigInt(e),r=t.rope_deltas.map(e=>n+e);t.position_ids=bu([r,r,r],0)}}return t}},hx=class extends mx{},gx=class extends mx{image_grid_thw_name=`image_grid_thw`},_x=class extends hx{image_grid_thw_name=`image_grid_thw`},vx=class extends gx{get_vision_position_ids(e,t,n,r){let i=Math.floor(t[0]/n),a=Math.floor(t[1]/r),o=Math.floor(t[2]/r),s=a*o*i,c=Array.from({length:s},()=>e),l=Array.from({length:s},(t,n)=>e+Math.floor(n/(o*i))),u=Array.from({length:s},(t,n)=>e+n%o);return[...c,...l,...u]}_get_multimodal_rope_positions({filtered_ids:e,image_grid_thw_list:t,video_grid_thw_list:n,spatial_merge_size:r,state:i}){let{image_token_id:a}=this.config,o=[],s=0,c=+(e[0]==a);for(let t=1;t<=e.length;++t){let n=t<e.length?+(e[t]==a):-1;n!==c&&(o.push([c,s,t]),s=t,c=n)}let l=0,u=[];for(let[e,n,a]of o)if(e===0){let e=a-n;u.push(Array.from({length:3*e},(t,n)=>l+n%e)),l+=e}else{let e=t[i.image_index++].map(Number),n=e[0];u.push(this.get_vision_position_ids(l,e,n,r)),l+=Math.max(e[1],e[2])/r}return u}},yx=class extends Z{},bx=class extends yx{},xx=class extends yx{},Sx=class extends Z{},Cx=class extends Sx{},wx=class extends Sx{},Tx=class extends Z{},Ex=class extends Tx{},Dx=class extends Tx{},Ox=class extends Z{},kx=class extends Ox{},Ax=class extends Ox{},jx=class extends Z{},Mx=class extends jx{},Nx=class extends jx{},Px=class extends Z{},Fx=class extends Px{},Ix=class extends Px{},Lx=class extends Z{},Rx=class extends Lx{},zx=class extends Lx{},Bx=class extends Z{},Vx=class extends Bx{},Hx=class extends Bx{},Ux=class extends Z{},Wx=class extends Ux{},Gx=class extends Ux{},Kx=class extends Z{forward_params=[`input_ids`,`attention_mask`,`position_ids`,`audio_values`,`past_key_values`]},qx=class extends Kx{_merge_input_ids_with_audio_features(e){let t=e.audio_features.dims.at(-1),n=e.audio_features.view(-1,t);return c_({audio_token_id:this.config.ignore_index??this.config.audio_token_id??this.config.audio_token_index,...e,audio_features:n})}},Jx=class extends qx{forward_params=[`input_ids`,`attention_mask`,`input_features`,`past_key_values`]},Yx=class extends Z{},Xx=class extends Yx{},Zx=class extends Z{},Qx=class extends Zx{},$x=class extends Z{},eS=class extends $x{},tS=class extends $x{},nS=class extends Z{},rS=class extends nS{},iS=class extends nS{async _call(e){return new Y(await super._call(e))}},aS=class extends Z{},oS=class extends aS{},sS=class extends aS{forward_params=[`input_ids`,`attention_mask`,`token_type_ids`,`past_key_values`];prepare_inputs_for_generation(e,t,n){let r=r_(this,e,t,n);return r.token_type_ids=(r.past_key_values?ju:ku)(r.input_ids),r}},cS=class extends Z{},lS=class extends cS{},uS=class extends cS{async _call(e){return new Xh(await super._call(e))}},dS=class extends cS{async _call(e){return new Y(await super._call(e))}},fS=class extends cS{async _call(e){return new qh(await super._call(e))}},pS=class extends Z{},mS=class extends cS{},hS=class extends cS{async _call(e){return new Xh(await super._call(e))}},gS=class extends cS{async _call(e){return new Y(await super._call(e))}},_S=class extends Z{},vS=class extends _S{},yS=class extends _S{},bS=class extends Yb{forward_params=[`input_ids`,`attention_mask`,`pixel_values`,`pixel_attention_mask`,`position_ids`,`past_key_values`]},xS=class extends 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Q={MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES:jk,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES:Mk,MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES:kk,MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES:Ak,MODEL_FOR_MASKED_LM_MAPPING_NAMES:Ik,MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES:Lk,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:Hk,MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES:Gk,MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES:Kk,MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES:qk,MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES:Uk,MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES:Wk,MODEL_FOR_MASK_GENERATION_MAPPING_NAMES:Jk,MODEL_FOR_CTC_MAPPING_NAMES:Yk,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES:Xk,MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES:Zk,MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES:Qk,MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES:Vk,MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES:$k,MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES:tA,MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES:nA,MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES:rA,MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES:iA,MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES:aA,MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES:zk,MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES:Bk,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:Nk,MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES:Ok,MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:Pk,MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES:Rk};Fg(Q);var 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MODEL_CLASS_MAPPINGS=[Q.MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES]},zA=class extends ${static MODEL_CLASS_MAPPINGS=[Q.MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES]},BA=class extends ${static MODEL_CLASS_MAPPINGS=[Q.MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES]};async function VA(e){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>Qf.read(e)))}async function HA(e,t){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>typeof e==`string`||e instanceof URL?tf(e,t):e instanceof Float64Array?new Float32Array(e):e))}function UA(e,t){t&&(e=e.map(e=>e|0));let[n,r,i,a]=e;return{xmin:n,ymin:r,xmax:i,ymax:a}}var WA=class extends ti{constructor({task:e,model:t,tokenizer:n=null,processor:r=null}){super(),this.task=e,this.model=t,this.tokenizer=n,this.processor=r}async dispose(){await this.model.dispose()}},GA=class extends WA{async _call(e,{top_k:t=1}={}){let n=this.tokenizer(e,{padding:!0,truncation:!0}),r=await this.model(n),{problem_type:i,id2label:a}=this.model.config,o=i===`multi_label_classification`?e=>e.sigmoid():e=>new U(`float32`,ll(e.data),e.dims),s=[];for(let e of r.logits){let n=await du(o(e),t),r=n[0].tolist(),i=n[1].tolist().map((e,t)=>({label:a?a[e]:`LABEL_${e}`,score:r[t]}));t===1?s.push(...i):s.push(i)}return Array.isArray(e)||t===1?s:s[0]}},KA=class extends WA{async _call(e,{ignore_labels:t=[`O`],aggregation_strategy:n=`none`}={}){if(n!==`none`&&n!==`simple`)throw Error(`Invalid aggregation_strategy: "${n}". Must be one of "none" or "simple".`);let r=Array.isArray(e),i=this.tokenizer(r?e:[e],{padding:!0,truncation:!0}),a=(await this.model(i)).logits,o=this.model.config.id2label,s=[];for(let e=0;e<a.dims[0];++e){let r=i.input_ids[e].tolist(),c=a[e],l=[];for(let e=0;e<c.dims[0];++e){let n=c[e],i=hl(n.data)[1],a=o?o[i]:`LABEL_${i}`;if(t.includes(a))continue;let s=this.tokenizer.decode([r[e]],{skip_special_tokens:!0});if(s===``)continue;let u=ll(n.data);l.push({entity:a,score:u[i],index:e,word:s})}s.push(n===`simple`?JA(l,r,this.tokenizer):l)}return r?s:s[0]}};function qA(e){let t=e[0];return e[1]===`-`&&(t===`B`||t===`I`||t===`E`||t===`S`)?[t,e.slice(2)]:[`I`,e]}function JA(e,t,n){let r=[],i=null;for(let t=0;t<e.length;++t){let[n,a]=qA(e[t].entity);i===a&&n!==`B`&&n!==`S`?(r[r.length-1].end=t+1,n===`E`&&(i=null)):(r.push({tag:a,start:t,end:t+1}),i=n===`S`?null:a)}return r.map(({tag:r,start:i,end:a})=>{let o=0,s=[];for(let n=i;n<a;++n)o+=e[n].score,s.push(t[e[n].index]);return{entity_group:r,score:o/(a-i),word:n.decode(s,{skip_special_tokens:!0})}})}var YA=class extends WA{async _call(e,t,{top_k:n=1}={}){let r=this.tokenizer(e,{text_pair:t,padding:!0,truncation:!0}),i=Array.isArray(e),{start_logits:a,end_logits:o}=await this.model(r),s=r.input_ids.tolist(),c=r.attention_mask.tolist(),{all_special_ids:l,sep_token_id:u}=this.tokenizer,d=[];for(let e=0;e<a.dims[0];++e){let t=s[e],r=t.findIndex(e=>e==u),i=a[e].tolist(),f=o[e].tolist();for(let n=1;n<i.length;++n)(c[e]==0||n<=r||l.findIndex(e=>e==t[n])!==-1)&&(i[n]=-1/0,f[n]=-1/0);let p=ll(i).map((e,t)=>[e,t]),m=ll(f).map((e,t)=>[e,t]);p[0][0]=0,m[0][0]=0;let h=ci(p,m).filter(e=>e[0][1]<=e[1][1]).map(e=>[e[0][1],e[1][1],e[0][0]*e[1][0]]).sort((e,t)=>t[2]-e[2]),g=[];for(let e=0;e<Math.min(h.length,n);++e){let[n,r,i]=h[e],a=t.slice(n,r+1),o=this.tokenizer.decode(a,{skip_special_tokens:!0});g.push({answer:o,score:i})}n===1?d.push(...g):d.push(g)}return i?d:d[0]}},XA=class extends WA{async _call(e,{top_k:t=5}={}){let{mask_token_id:n,mask_token:r}=this.tokenizer,i=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:a}=await this.model(i),o=[],s=i.input_ids.tolist();for(let e=0;e<s.length;++e){let i=s[e],c=i.findIndex(e=>e==n);if(c===-1)throw Error(`Mask token (${r}) not found in text.`);let l=a[e][c],u=await du(new U(`float32`,ll(l.data),l.dims),t),d=u[0].tolist(),f=u[1].tolist();o.push(f.map((e,t)=>{let n=i.slice();return n[c]=e,{score:d[t],token:Number(e),token_str:this.tokenizer.decode([e]),sequence:this.tokenizer.decode(n,{skip_special_tokens:!0})}}))}return Array.isArray(e)?o:o[0]}},ZA=class extends WA{_default_generation_config={max_new_tokens:256};_key=`generated_text`;async _call(e,t={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(e=>this.model.config.prefix+e));let n=this.model.config.task_specific_params;n&&n[this.task]&&n[this.task].prefix&&(e=e.map(e=>n[this.task].prefix+e));let r=this.tokenizer,i={padding:!0,truncation:!0},a;a=this.task===`translation`&&`_build_translation_inputs`in r?r._build_translation_inputs(e,i,t):r(e,i);let o=await this.model.generate({...a,...this._default_generation_config,...t});return r.batch_decode(o,{skip_special_tokens:!0}).map(e=>({[this._key]:e}))}},QA=class extends ZA{_key=`summary_text`},$A=class extends ZA{_key=`translation_text`};function ej(e){return Array.isArray(e)&&e.every(e=>`role`in e&&`content`in e)}var tj=class extends WA{_default_generation_config={max_new_tokens:256};async _call(e,t={}){let{add_special_tokens:n,return_full_text:r,tools:i,documents:a,chat_template:o,tokenizer_encode_kwargs:s,...c}=t,l=!1,u=!1,d=n??(this.tokenizer.add_bos_token||this.tokenizer.add_eos_token)??!1,f=s,p;if(typeof e==`string`)p=e=[e];else if(Array.isArray(e)&&e.every(e=>typeof e==`string`))l=!0,p=e;else{if(ej(e))e=[e];else if(Array.isArray(e)&&e.every(ej))l=!0;else throw Error(`Input must be a string, an array of strings, a Chat, or an array of Chats`);u=!0;let t={tokenize:!1,add_generation_prompt:!0,...ui({tools:i,documents:a,chat_template:o},[`tools`,`documents`,`chat_template`]),...f};p=e.map(e=>this.tokenizer.apply_chat_template(e,t)),d=!1,f=void 0}let m=u?!1:r??!0;this.tokenizer.padding_side=`left`;let h=this.tokenizer(p,{add_special_tokens:d,padding:!0,truncation:!0,...f}),g=await this.model.generate({...h,...this._default_generation_config,...c}),_=this.tokenizer.batch_decode(g,{skip_special_tokens:!0}),v;!m&&h.input_ids.dims.at(-1)>0&&(v=this.tokenizer.batch_decode(h.input_ids,{skip_special_tokens:!0}).map(e=>e.length));let y=Array.from({length:e.length},e=>[]);for(let t=0;t<_.length;++t){let n=Math.floor(t/g.dims[0]*e.length);v&&(_[t]=_[t].slice(v[n])),y[n].push({generated_text:u?[...e[n],{role:`assistant`,content:_[t]}]:_[t]})}return!l&&y.length===1?y[0]:y}},nj=class extends WA{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([e,t])=>[e.toLowerCase(),t])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(N.warn(`Could not find 'entailment' in label2id mapping. Using 2 as entailment_id.`),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(N.warn(`Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id.`),this.contradiction_id=0)}async _call(e,t,{hypothesis_template:n=`This example is {}.`,multi_label:r=!1}={}){let i=Array.isArray(e);i||(e=[e]),Array.isArray(t)||(t=[t]);let a=t.map(e=>n.replace(`{}`,e)),o=r||t.length===1,s=[];for(let n of e){let e=[];for(let t of a){let r=this.tokenizer(n,{text_pair:t,padding:!0,truncation:!0}),i=await this.model(r);o?e.push([i.logits.data[this.contradiction_id],i.logits.data[this.entailment_id]]):e.push(i.logits.data[this.entailment_id])}let r=(o?e.map(e=>ll(e)[1]):ll(e)).map((e,t)=>[e,t]).sort((e,t)=>t[0]-e[0]);s.push({sequence:n,labels:r.map(e=>t[e[1]]),scores:r.map(e=>e[0])})}return i?s:s[0]}},rj=class extends WA{async _call(e,{top_k:t=5}={}){let n=this.processor.feature_extractor.config.sampling_rate,r=await HA(e,n),i=this.model.config.id2label,a=[];for(let e of r){let n=await this.processor(e),r=(await this.model(n)).logits[0],o=await du(new U(`float32`,ll(r.data),r.dims),t),s=o[0].tolist(),c=o[1].tolist().map((e,t)=>({label:i?i[e]:`LABEL_${e}`,score:s[t]}));a.push(c)}return Array.isArray(e)?a:a[0]}},ij=class extends WA{async _call(e,t,{hypothesis_template:n=`This is a sound of {}.`}={}){let r=!Array.isArray(e);r&&(e=[e]);let i=t.map(e=>n.replace(`{}`,e)),a=this.tokenizer(i,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,s=await HA(e,o),c=[];for(let e of s){let n=await this.processor(e),r=ll((await this.model({...a,...n})).logits_per_audio.data);c.push([...r].map((e,n)=>({score:e,label:t[n]})))}return r?c[0]:c}},aj=class extends WA{_default_generation_config={};async _call(e,t={}){switch(t={...this._default_generation_config,...t},this.model.config.model_type){case`whisper`:case`lite-whisper`:return this._call_whisper(e,t);case`wav2vec2`:case`wav2vec2-bert`:case`unispeech`:case`unispeech-sat`:case`hubert`:case`parakeet_ctc`:return this._call_wav2vec2(e,t);case`moonshine`:return this._call_moonshine(e,t);case`cohere_asr`:return this._call_cohere_asr(e,t);default:throw Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,t){t.language&&N.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),t.task&&N.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let n=!Array.isArray(e),r=n?[e]:e,i=this.processor.feature_extractor.config.sampling_rate,a=await HA(r,i),o=[];for(let e of a){let t=await this.processor(e),n=(await this.model(t)).logits[0],r=[];for(let e of n)r.push(hl(e.data)[1]);let i=this.tokenizer.decode(r,{skip_special_tokens:!0}).trim();o.push({text:i})}return n?o[0]:o}async _call_whisper(e,t){let n=t.return_timestamps??!1,r=t.chunk_length_s??0,i=t.force_full_sequences??!1,a=t.stride_length_s??null,o={...t};n===`word`&&(o.return_token_timestamps=!0,o.return_timestamps=!0);let s=!Array.isArray(e),c=s?[e]:e,l=this.processor.feature_extractor.config,u=l.chunk_length/this.model.config.max_source_positions,d=l.hop_length,f=l.sampling_rate,p=await HA(c,f),m=[];for(let e of p){let t=[];if(r>0){if(a===null)a=r/6;else if(r<=a)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let n=f*r,i=f*a,o=n-2*i,s=0;for(;;){let r=s+n,a=e.subarray(s,r),c=await this.processor(a),l=s===0,u=r>=e.length;if(t.push({stride:[a.length,l?0:i,u?0:i],input_features:c.input_features,is_last:u}),u)break;s+=o}}else t=[{stride:[e.length,0,0],input_features:(await this.processor(e)).input_features,is_last:!0}];for(let e of t){o.num_frames=Math.floor(e.stride[0]/d);let t=await this.model.generate({inputs:e.input_features,...o});if(n===`word`){let n=t.sequences.tolist()[0],r=t.token_timestamps.tolist()[0],i=this.tokenizer.timestamp_begin,a=Math.max(n.findIndex(e=>Number(e)>=i),0);e.tokens=n.slice(a),e.token_timestamps=r.slice(a).map(e=>xl(e,2))}else e.tokens=t[0].tolist();e.stride=e.stride.map(e=>e/f)}let[s,c]=this.tokenizer._decode_asr(t,{time_precision:u,return_timestamps:n,force_full_sequences:i});m.push({text:s,...c})}return s?m[0]:m}async _call_moonshine(e,t){let n=!Array.isArray(e),r=n?[e]:e,i=this.processor.feature_extractor.config.sampling_rate,a=await HA(r,i),o=[];for(let e of a){let n=await this.processor(e),r=Math.floor(e.length/i)*6,a=await this.model.generate({max_new_tokens:r,...t,...n}),s=this.tokenizer.batch_decode(a,{skip_special_tokens:!0})[0];o.push({text:s})}return n?o[0]:o}async _call_cohere_asr(e,t){let n=!Array.isArray(e),r=n?[e]:e,i=this.processor.feature_extractor,a=i.config.sampling_rate,o=await HA(r,a),s=t.language??`en`,c=this.processor.get_decoder_prompt_ids(s),l=[];for(let e of o){let n=i.split_audio(e),r=[];for(let e of n){let n=await this.processor(e),i=await this.model.generate({...n,decoder_input_ids:c,...t}),a=this.tokenizer.decode(i[0].tolist(),{skip_special_tokens:!0}).trim();r.push(a)}let a=this.processor.constructor.join_chunks(r,s);l.push({text:a})}return n?l[0]:l}},oj=class extends WA{DEFAULT_VOCODER_ID=`Xenova/speecht5_hifigan`;constructor(e){super(e),this.vocoder=e.vocoder??null}async _prepare_speaker_embeddings(e,t){if((typeof e==`string`||e instanceof URL)&&(e=new Float32Array(await(await M.fetch(e)).arrayBuffer())),e instanceof Float32Array)e=new U(`float32`,e,[e.length]);else if(!(e instanceof U))throw Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");if(t>1){if(e.dims[0]===1)e=e.repeat(t,1);else if(e.dims[0]!==t)throw Error(`Expected speaker embeddings batch size to be 1 or ${t}, but got ${e.dims[0]}.`)}return e}_postprocess_waveform(e,t,n,r=null){let i=t.data,[a,o]=t.dims,s=r?r.data:null,c=[];for(let e=0;e<a;++e){let t=s?Math.min(Math.ceil(s[e]),o):o,r=e*o;c.push(new xf(i.slice(r,r+t),n))}return Array.isArray(e)?c:c[0]}async _call(e,t){return this.processor?this._call_text_to_spectrogram(e,t):this.model.config.model_type===`supertonic`?this._call_supertonic(e,t):this._call_text_to_waveform(e)}async _call_supertonic(e,{speaker_embeddings:t,num_inference_steps:n,speed:r}){if(!t)throw Error(`Speaker embeddings must be provided for Supertonic models.`);let{sampling_rate:i,style_dim:a}=this.model.config,o=this.tokenizer(e,{padding:!0,truncation:!0}),s=o.input_ids.dims[0];t=await this._prepare_speaker_embeddings(t,s),t=t.view(s,-1,a);let{waveform:c,durations:l}=await this.model.generate_speech({...o,style:t,num_inference_steps:n,speed:r});return this._postprocess_waveform(e,c,i,l)}async _call_text_to_waveform(e){let t=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:n}=await this.model(t),r=this.model.config.sampling_rate;return this._postprocess_waveform(e,n,r)}async _call_text_to_spectrogram(e,{speaker_embeddings:t}){this.vocoder||=(N.info(`No vocoder specified, using default HifiGan vocoder.`),await uA.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:`fp32`}));let{input_ids:n}=this.tokenizer(e,{padding:!0,truncation:!0}),r=n.dims[0];t=await this._prepare_speaker_embeddings(t,r),t=t.view(r,-1);let{waveform:i}=await this.model.generate_speech(n,t,{vocoder:this.vocoder}),a=this.processor.feature_extractor.config.sampling_rate;return this._postprocess_waveform(e,i,a)}},sj=class extends WA{async _call(e,t={}){let n=Array.isArray(e),r=await VA(e),{pixel_values:i}=await this.processor(r),a=[];for(let e of i){e.dims=[1,...e.dims];let n=await this.model.generate({inputs:e,...t}),r=this.tokenizer.batch_decode(n,{skip_special_tokens:!0}).map(e=>({generated_text:e.trim()}));a.push(r)}return n?a:a[0]}},cj=class extends WA{async _call(e,{top_k:t=5}={}){let n=await VA(e),{pixel_values:r}=await this.processor(n),i=await this.model({pixel_values:r}),{id2label:a}=this.model.config,o=[];for(let e of i.logits){let n=await du(new U(`float32`,ll(e.data),e.dims),t),r=n[0].tolist(),i=n[1].tolist().map((e,t)=>({label:a?a[e]:`LABEL_${e}`,score:r[t]}));o.push(i)}return Array.isArray(e)?o:o[0]}},lj={panoptic:`post_process_panoptic_segmentation`,instance:`post_process_instance_segmentation`,semantic:`post_process_semantic_segmentation`},uj=class extends WA{async _call(e,{threshold:t=.5,mask_threshold:n=.5,overlap_mask_area_threshold:r=.8,label_ids_to_fuse:i=null,target_sizes:a=null,subtask:o=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error(`Image segmentation pipeline currently only supports a batch size of 1.`);let s=await VA(e),c=s.map(e=>[e.height,e.width]),l=await this.processor(s),{inputNames:u,outputNames:d}=this.model.sessions.model;if(!u.includes(`pixel_values`)){if(u.length!==1)throw Error(`Expected a single input name, but got ${u.length} inputs: ${u}.`);let e=u[0];if(e in l)throw Error(`Input name ${e} already exists in the inputs.`);l[e]=l.pixel_values}let f=await this.model(l),p=null;if(o!==null)p=lj[o];else if(this.processor.image_processor){for(let[e,t]of Object.entries(lj))if(t in this.processor.image_processor){p=this.processor.image_processor[t].bind(this.processor.image_processor),o=e;break}}let m=this.model.config.id2label,h=[];if(!o){let e=f[d[0]];for(let t=0;t<c.length;++t){let n=c[t],r=e[t];r.data.some(e=>e<-1e-5||e>1.00001)&&r.sigmoid_();let i=await Qf.fromTensor(r.mul_(255).to(`uint8`)).resize(n[1],n[0]);h.push({label:null,score:null,mask:i})}}else if(o===`panoptic`||o===`instance`){let e=p(f,t,n,r,i,a??c)[0],o=e.segmentation;for(let t of e.segments_info){let e=new Uint8ClampedArray(o.data.length);for(let n=0;n<o.data.length;++n)o.data[n]===t.id&&(e[n]=255);let n=new Qf(e,o.dims[1],o.dims[0],1);h.push({score:t.score,label:m[t.label_id],mask:n})}}else if(o===`semantic`){let{segmentation:e,labels:t}=p(f,a??c)[0];for(let n of t){let t=new Uint8ClampedArray(e.data.length);for(let r=0;r<e.data.length;++r)e.data[r]===n&&(t[r]=255);let r=new Qf(t,e.dims[1],e.dims[0],1);h.push({score:null,label:m[n],mask:r})}}else throw Error(`Subtask ${o} not supported.`);return h}},dj=class extends uj{async _call(e,t={}){let n=await VA(e),r=await super._call(e,t),i=n.map((e,t)=>{let n=e.clone();return n.putAlpha(r[t].mask),n});return Array.isArray(e)?i:i[0]}},fj=class extends WA{async _call(e,t,{hypothesis_template:n=`This is a photo of {}`}={}){let r=Array.isArray(e),i=await VA(e),a=t.map(e=>n.replace(`{}`,e)),o=this.tokenizer(a,{padding:this.model.config.model_type!==`siglip`||`max_length`,truncation:!0}),{pixel_values:s}=await this.processor(i),c=await this.model({...o,pixel_values:s}),l=this.model.config.model_type===`siglip`?e=>e.sigmoid().data:e=>ll(e.data),u=[];for(let e of c.logits_per_image){let n=[...l(e)].map((e,n)=>({score:e,label:t[n]}));n.sort((e,t)=>t.score-e.score),u.push(n)}return r?u:u[0]}},pj=class extends WA{async _call(e,{threshold:t=.9,percentage:n=!1}={}){let r=Array.isArray(e);if(r&&e.length!==1)throw Error(`Object detection pipeline currently only supports a batch size of 1.`);let i=await VA(e),a=n?null:i.map(e=>[e.height,e.width]),{pixel_values:o,pixel_mask:s}=await this.processor(i),c=await this.model({pixel_values:o,pixel_mask:s}),l=this.processor.image_processor.post_process_object_detection(c,t,a),{id2label:u}=this.model.config,d=l.map(e=>e.boxes.map((t,r)=>({score:e.scores[r],label:u[e.classes[r]],box:UA(t,!n)})));return r?d:d[0]}},mj=class extends WA{async _call(e,t,{threshold:n=.1,top_k:r=null,percentage:i=!1}={}){let a=Array.isArray(e),o=await VA(e),s=this.tokenizer(t,{padding:!0,truncation:!0}),c=await this.processor(o),l=[];for(let e=0;e<o.length;++e){let a=o[e],u=i?null:[[a.height,a.width]],d=c.pixel_values[e].unsqueeze_(0),f=await this.model({...s,pixel_values:d}),p;if(`post_process_grounded_object_detection`in this.processor){let 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AlbertForMaskedLM,m_ as AlbertForQuestionAnswering,p_ as AlbertForSequenceClassification,f_ as AlbertModel,d_ as AlbertPreTrainedModel,Gu as AlbertTokenizer,v_ as ApertusForCausalLM,__ as ApertusModel,g_ as ApertusPreTrainedModel,w_ as ArceeForCausalLM,C_ as ArceeModel,S_ as ArceePreTrainedModel,rj as AudioClassificationPipeline,Ih as AutoConfig,Hf as AutoFeatureExtractor,Nm as AutoImageProcessor,uA as AutoModel,kA as AutoModelForAudioClassification,jA as AutoModelForAudioFrameClassification,BA as AutoModelForAudioTextToText,OA as AutoModelForCTC,_A as AutoModelForCausalLM,FA as AutoModelForDepthEstimation,MA as AutoModelForDocumentQuestionAnswering,xA as AutoModelForImageClassification,RA as AutoModelForImageFeatureExtraction,NA as AutoModelForImageMatting,SA as AutoModelForImageSegmentation,zA as AutoModelForImageTextToText,PA as AutoModelForImageToImage,DA as AutoModelForMaskGeneration,vA as AutoModelForMaskedLM,IA as AutoModelForNormalEstimation,TA as AutoModelForObjectDetection,LA as AutoModelForPoseEstimation,yA as AutoModelForQuestionAnswering,CA as AutoModelForSemanticSegmentation,pA as AutoModelForSeq2SeqLM,dA as AutoModelForSequenceClassification,mA as AutoModelForSpeechSeq2Seq,hA as AutoModelForTextToSpectrogram,gA as AutoModelForTextToWaveform,fA as AutoModelForTokenClassification,wA as AutoModelForUniversalSegmentation,bA as AutoModelForVision2Seq,AA as AutoModelForXVector,EA as AutoModelForZeroShotObjectDetection,Ah as AutoProcessor,G as AutoTokenizer,aj as AutomaticSpeechRecognitionPipeline,dj as BackgroundRemovalPipeline,A_ as BartForConditionalGeneration,j_ as BartForSequenceClassification,k_ as BartModel,O_ as BartPretrainedModel,Ku as BartTokenizer,Kh as BaseModelOutput,Dj as BaseStreamer,fp as BeitFeatureExtractor,P_ as BeitForImageClassification,N_ as BeitModel,M_ as BeitPreTrainedModel,L_ as BertForMaskedLM,B_ as BertForQuestionAnswering,R_ as BertForSequenceClassification,z_ as BertForTokenClassification,I_ as BertModel,F_ as BertPreTrainedModel,qu as BertTokenizer,pp as BitImageProcessor,U_ as BlenderbotForConditionalGeneration,H_ as BlenderbotModel,V_ as BlenderbotPreTrainedModel,K_ as BlenderbotSmallForConditionalGeneration,G_ as BlenderbotSmallModel,W_ as BlenderbotSmallPreTrainedModel,Ju as BlenderbotSmallTokenizer,Yu as BlenderbotTokenizer,Y_ as BloomForCausalLM,J_ as BloomModel,q_ as BloomPreTrainedModel,Xu as BloomTokenizer,lv as CHMv2ForDepthEstimation,hp as CHMv2ImageProcessor,cv as CHMv2PreTrainedModel,_p as CLIPFeatureExtractor,gp as CLIPImageProcessor,hv as CLIPModel,mv as CLIPPreTrainedModel,Sv as CLIPSegForImageSegmentation,xv as CLIPSegModel,bv as CLIPSegPreTrainedModel,gv as CLIPTextModel,_v as CLIPTextModelWithProjection,Qu as CLIPTokenizer,vv as CLIPVisionModel,yv as CLIPVisionModelWithProjection,Q_ as CamembertForMaskedLM,tv as CamembertForQuestionAnswering,$_ as CamembertForSequenceClassification,ev as CamembertForTokenClassification,Z_ as CamembertModel,X_ as CamembertPreTrainedModel,Zu as CamembertTokenizer,Xh as CausalLMOutput,Zh as CausalLMOutputWithPast,wf as ChatterboxFeatureExtractor,av as ChatterboxModel,iv as ChatterboxPreTrainedModel,Uf as ChatterboxProcessor,mp as ChineseCLIPFeatureExtractor,sv as ChineseCLIPModel,ov as ChineseCLIPPreTrainedModel,pv as ClapAudioModelWithProjection,Tf as ClapFeatureExtractor,dv as ClapModel,uv as ClapPreTrainedModel,fv as ClapTextModelWithProjection,pg as ClassifierFreeGuidanceLogitsProcessor,Tv as CodeGenForCausalLM,wv as CodeGenModel,Cv as CodeGenPreTrainedModel,ed as CodeGenTokenizer,$u as CodeLlamaTokenizer,jv as Cohere2ForCausalLM,Av as Cohere2Model,kv as Cohere2PreTrainedModel,Of as CohereAsrFeatureExtractor,Pv as CohereAsrForConditionalGeneration,Nv as CohereAsrModel,Mv as CohereAsrPreTrainedModel,Gf as CohereAsrProcessor,nd as CohereAsrTokenizer,Ov as CohereForCausalLM,Dv as CohereModel,Ev as CoherePreTrainedModel,td as CohereTokenizer,Lv as ConvBertForMaskedLM,Bv as ConvBertForQuestionAnswering,Rv as ConvBertForSequenceClassification,zv as ConvBertForTokenClassification,Iv as ConvBertModel,Fv as ConvBertPreTrainedModel,rd as ConvBertTokenizer,yp as ConvNextFeatureExtractor,Uv as ConvNextForImageClassification,vp as ConvNextImageProcessor,Hv as ConvNextModel,Vv as ConvNextPreTrainedModel,Kv as ConvNextV2ForImageClassification,Gv as ConvNextV2Model,Wv as ConvNextV2PreTrainedModel,$v as DFineForObjectDetection,Qv as DFineModel,Zv as DFinePreTrainedModel,Ky as DINOv3ConvNextModel,Gy as DINOv3ConvNextPreTrainedModel,wp as DINOv3ViTImageProcessor,Jy as DINOv3ViTModel,qy as DINOv3ViTPreTrainedModel,Op as DPTFeatureExtractor,ab as DPTForDepthEstimation,Dp as DPTImageProcessor,ib as DPTModel,rb as DPTPreTrainedModel,ay as DacDecoderModel,ty as DacDecoderOutput,iy as DacEncoderModel,ey as DacEncoderOutput,kf as DacFeatureExtractor,ry as DacModel,ny as DacPreTrainedModel,cy as DebertaForMaskedLM,dy as DebertaForQuestionAnswering,ly as DebertaForSequenceClassification,uy as DebertaForTokenClassification,sy as DebertaModel,oy as DebertaPreTrainedModel,ad as DebertaTokenizer,by as DebertaV2ForMaskedLM,Cy as DebertaV2ForQuestionAnswering,xy as DebertaV2ForSequenceClassification,Sy as DebertaV2ForTokenClassification,yy as DebertaV2Model,vy as DebertaV2PreTrainedModel,id as DebertaV2Tokenizer,Ty as DecisionTransformerModel,wy as DecisionTransformerPreTrainedModel,my as DeepseekV3ForCausalLM,py as DeepseekV3Model,fy as DeepseekV3PreTrainedModel,_y as DeepseekV4ForCausalLM,gy as DeepseekV4Model,hy as DeepseekV4PreTrainedModel,xp as DeiTFeatureExtractor,Oy as DeiTForImageClassification,bp as DeiTImageProcessor,Dy as DeiTModel,Ey as DeiTPreTrainedModel,Ay as DepthAnythingForDepthEstimation,ky as DepthAnythingPreTrainedModel,_j as DepthEstimationPipeline,My as DepthProForDepthEstimation,jy as DepthProPreTrainedModel,Cp as DetrFeatureExtractor,Fy as DetrForObjectDetection,Iy as DetrForSegmentation,Sp as DetrImageProcessor,Py as DetrModel,Ly as DetrObjectDetectionOutput,Ny as DetrPreTrainedModel,Ry as DetrSegmentationOutput,Vy as Dinov2ForImageClassification,By as Dinov2Model,zy as Dinov2PreTrainedModel,Wy as Dinov2WithRegistersForImageClassification,Uy as Dinov2WithRegistersModel,Hy as Dinov2WithRegistersPreTrainedModel,eb as DistilBertForMaskedLM,$y as DistilBertForQuestionAnswering,Zy as DistilBertForSequenceClassification,Qy as DistilBertForTokenClassification,Xy as DistilBertModel,Yy as DistilBertPreTrainedModel,od as DistilBertTokenizer,hj as DocumentQuestionAnsweringPipeline,Ep as DonutFeatureExtractor,Tp as DonutImageProcessor,nb as DonutSwinModel,tb as DonutSwinPreTrainedModel,Dg as DynamicCache,KE as EdgeTamModel,cb as EfficientNetForImageClassification,kp as EfficientNetImageProcessor,sb as EfficientNetModel,ob as EfficientNetPreTrainedModel,db as ElectraForMaskedLM,mb as ElectraForQuestionAnswering,fb as ElectraForSequenceClassification,pb as ElectraForTokenClassification,ub as ElectraModel,lb as ElectraPreTrainedModel,sd as ElectraTokenizer,Cf as EncodecFeatureExtractor,xg as EosTokenCriteria,_b as Ernie4_5ForCausalLM,gb as Ernie4_5Model,hb as Ernie4_5PretrainedModel,bb as EsmForMaskedLM,xb as EsmForSequenceClassification,Sb as EsmForTokenClassification,yb as EsmModel,vb as EsmPreTrainedModel,cd as EsmTokenizer,Tb as EuroBertForMaskedLM,Eb as EuroBertForSequenceClassification,Db as EuroBertForTokenClassification,wb as EuroBertModel,Cb as EuroBertPreTrainedModel,Ab as ExaoneForCausalLM,kb as ExaoneModel,Ob as ExaonePreTrainedModel,Nb as FalconForCausalLM,Ib as FalconH1ForCausalLM,Fb as FalconH1Model,Pb as FalconH1PreTrainedModel,Mb as FalconModel,jb as FalconPreTrainedModel,ld as FalconTokenizer,zb as FastViTForImageClassification,Rb as FastViTModel,Lb as FastViTPreTrainedModel,vj as FeatureExtractionPipeline,Yd as FeatureExtractor,XA as FillMaskPipeline,Vb as Florence2ForConditionalGeneration,Bb as Florence2PreTrainedModel,Pm as Florence2Processor,rg as ForcedBOSTokenLogitsProcessor,ig as ForcedEOSTokenLogitsProcessor,Ip as GLPNFeatureExtractor,xx as GLPNForDepthEstimation,bx as GLPNModel,yx as GLPNPreTrainedModel,Ix as GPT2LMHeadModel,Fx as GPT2Model,Px as GPT2PreTrainedModel,fd as GPT2Tokenizer,wx as GPTBigCodeForCausalLM,Cx as GPTBigCodeModel,Sx as GPTBigCodePreTrainedModel,zx as GPTJForCausalLM,Rx as GPTJModel,Lx as GPTJPreTrainedModel,Dx as GPTNeoForCausalLM,Ex as GPTNeoModel,Tx as GPTNeoPreTrainedModel,Ax as GPTNeoXForCausalLM,kx as GPTNeoXModel,Ox as GPTNeoXPreTrainedModel,dd as GPTNeoXTokenizer,qb as Gemma2ForCausalLM,Kb as Gemma2Model,Gb as Gemma2PreTrainedModel,tx as Gemma3ForCausalLM,ex as Gemma3ForConditionalGeneration,Ap as Gemma3ImageProcessor,$b as Gemma3Model,Qb as Gemma3PreTrainedModel,Fm as Gemma3Processor,Af as Gemma3nAudioFeatureExtractor,ix as Gemma3nForCausalLM,rx as Gemma3nForConditionalGeneration,nx as Gemma3nPreTrainedModel,Im as Gemma3nProcessor,jf as Gemma4AudioFeatureExtractor,ox as Gemma4ForCausalLM,ax as Gemma4ForConditionalGeneration,Np as Gemma4ImageProcessor,Lm as Gemma4Processor,Wb as GemmaForCausalLM,Ub as GemmaModel,Hb as GemmaPreTrainedModel,ud as GemmaTokenizer,_g as GenerationConfig,Fp as Glm46VImageProcessor,zm as Glm46VProcessor,lx as GlmForCausalLM,cx as GlmModel,fx as GlmMoeDsaForCausalLM,dx as GlmMoeDsaModel,ux as GlmMoeDsaPreTrainedModel,vx as GlmOcrForConditionalGeneration,sx as GlmPreTrainedModel,Nx as GptOssForCausalLM,Mx as GptOssModel,jx as GptOssPreTrainedModel,Hx as GraniteForCausalLM,Vx as GraniteModel,Gx as GraniteMoeHybridForCausalLM,Wx as GraniteMoeHybridModel,Ux as GraniteMoeHybridPreTrainedModel,Bx as GranitePreTrainedModel,Mf as GraniteSpeechFeatureExtractor,Jx as GraniteSpeechForConditionalGeneration,Bm as GraniteSpeechProcessor,Xx as GroundingDinoForObjectDetection,Lp as GroundingDinoImageProcessor,Yx as GroundingDinoPreTrainedModel,Hm as GroundingDinoProcessor,Qx as GroupViTModel,Zx as GroupViTPreTrainedModel,tS as HeliumForCausalLM,eS as HeliumModel,$x as HeliumPreTrainedModel,pd as HerbertTokenizer,iS as HieraForImageClassification,rS as HieraModel,nS as HieraPreTrainedModel,sS as HrmTextForCausalLM,oS as HrmTextModel,aS as HrmTextPreTrainedModel,hS as HubertForCTC,gS as HubertForSequenceClassification,mS as HubertModel,pS as HubertPreTrainedModel,yS as HunYuanDenseV1ForCausalLM,vS as HunYuanDenseV1Model,_S as HunYuanDenseV1PreTrainedModel,CS as IJepaForImageClassification,SS as IJepaModel,xS as IJepaPreTrainedModel,bS as Idefics3ForConditionalGeneration,Rp as Idefics3ImageProcessor,Rp as SmolVLMImageProcessor,Km as Idefics3Processor,Km as SmolVLMProcessor,cj as ImageClassificationPipeline,yj as ImageFeatureExtractionPipeline,q as ImageFeatureExtractor,q as ImageProcessor,$h as ImageMattingOutput,uj as ImageSegmentationPipeline,gj as ImageToImagePipeline,sj as ImageToTextPipeline,Sg as InterruptableStoppingCriteria,ES as JAISLMHeadModel,TS as JAISModel,wS as JAISPreTrainedModel,Bp as JinaCLIPImageProcessor,OS as JinaCLIPModel,DS as JinaCLIPPreTrainedModel,Jm as JinaCLIPProcessor,kS as JinaCLIPTextModel,AS as JinaCLIPVisionModel,NS as Lfm2ForCausalLM,MS as Lfm2Model,LS as Lfm2MoeForCausalLM,IS as Lfm2MoeModel,FS as Lfm2MoePreTrainedModel,jS as Lfm2PreTrainedModel,RS as Lfm2VlForConditionalGeneration,Kp as Lfm2VlImageProcessor,Ym as Lfm2VlProcessor,PS as LightOnOcrForConditionalGeneration,tk as LiteWhisperForConditionalGeneration,US as Llama4ForCausalLM,HS as Llama4PreTrainedModel,VS as LlamaForCausalLM,BS as LlamaModel,zS as LlamaPreTrainedModel,md as LlamaTokenizer,Yb as LlavaForConditionalGeneration,Yb as LlavaOnevisionForConditionalGeneration,qp as LlavaOnevisionImageProcessor,Jb as LlavaPreTrainedModel,Xm as LlavaProcessor,Zb as LlavaQwen2ForCausalLM,Qr as LogLevel,eg as LogitsProcessor,ng as LogitsProcessorList,tg as LogitsWarper,KS as LongT5ForConditionalGeneration,GS as LongT5Model,WS as LongT5PreTrainedModel,YS as M2M100ForConditionalGeneration,JS as M2M100Model,qS as M2M100PreTrainedModel,hd as M2M100Tokenizer,vd as MBart50Tokenizer,oC as MBartForCausalLM,iC as MBartForConditionalGeneration,aC as MBartForSequenceClassification,rC as MBartModel,nC as MBartPreTrainedModel,_d as MBartTokenizer,Sw as MPNetForMaskedLM,Tw as MPNetForQuestionAnswering,Cw as MPNetForSequenceClassification,ww as MPNetForTokenClassification,xw as MPNetModel,bw as MPNetPreTrainedModel,xd as MPNetTokenizer,jw as MT5ForConditionalGeneration,Aw as MT5Model,kw as MT5PreTrainedModel,QS as MarianMTModel,ZS as MarianModel,XS as MarianPreTrainedModel,gd as MarianTokenizer,Xp as Mask2FormerImageProcessor,Yp as MaskFormerFeatureExtractor,tC as MaskFormerForInstanceSegmentation,Jp as MaskFormerImageProcessor,eC as MaskFormerModel,$S as MaskFormerPreTrainedModel,Jh as MaskedLMOutput,bg as MaxLengthCriteria,cC as Metric3DForDepthEstimation,sC as Metric3DPreTrainedModel,uC as Metric3Dv2ForDepthEstimation,lC as Metric3Dv2PreTrainedModel,pC as MgpstrForSceneTextRecognition,dC as MgpstrModelOutput,fC as MgpstrPreTrainedModel,Qm as MgpstrProcessor,yd as MgpstrTokenizer,yC as MimiDecoderModel,hC as MimiDecoderOutput,vC as MimiEncoderModel,mC as MimiEncoderOutput,_C as MimiModel,gC as MimiPreTrainedModel,ug as MinLengthLogitsProcessor,dg as MinNewTokensLengthLogitsProcessor,TC as Ministral3ForCausalLM,wC as Ministral3Model,CC as Ministral3PreTrainedModel,SC as MinistralForCausalLM,xC as MinistralModel,bC as MinistralPreTrainedModel,kC as Mistral3ForConditionalGeneration,MC as Mistral4ForCausalLM,jC as Mistral4Model,AC as Mistral4PreTrainedModel,OC as MistralForCausalLM,DC as MistralModel,EC as MistralPreTrainedModel,FC as MobileBertForMaskedLM,LC as MobileBertForQuestionAnswering,IC as MobileBertForSequenceClassification,PC as MobileBertModel,NC as MobileBertPreTrainedModel,bd as MobileBertTokenizer,BC as MobileLLMForCausalLM,zC as MobileLLMModel,RC as MobileLLMPreTrainedModel,Qp as MobileNetV1FeatureExtractor,UC as MobileNetV1ForImageClassification,WC as MobileNetV1ForSemanticSegmentation,Zp as MobileNetV1ImageProcessor,HC as MobileNetV1Model,VC as MobileNetV1PreTrainedModel,em as MobileNetV2FeatureExtractor,qC as MobileNetV2ForImageClassification,JC as MobileNetV2ForSemanticSegmentation,$p as MobileNetV2ImageProcessor,KC as MobileNetV2Model,GC as MobileNetV2PreTrainedModel,nm as MobileNetV3FeatureExtractor,ZC as MobileNetV3ForImageClassification,QC as MobileNetV3ForSemanticSegmentation,tm as MobileNetV3ImageProcessor,XC as MobileNetV3Model,YC as MobileNetV3PreTrainedModel,im as MobileNetV4FeatureExtractor,tw as MobileNetV4ForImageClassification,nw as MobileNetV4ForSemanticSegmentation,rm as MobileNetV4ImageProcessor,ew as MobileNetV4Model,$C as MobileNetV4PreTrainedModel,om as MobileViTFeatureExtractor,aw as MobileViTForImageClassification,am as MobileViTImageProcessor,iw as MobileViTModel,rw as MobileViTPreTrainedModel,cw as MobileViTV2ForImageClassification,sw as MobileViTV2Model,ow as MobileViTV2PreTrainedModel,Pc as ModelFileNotFoundError,Gh as ModelOutput,Gj as ModelRegistry,gw as ModernBertDecoderForCausalLM,hw as ModernBertDecoderModel,mw as ModernBertDecoderPreTrainedModel,dw as ModernBertForMaskedLM,fw as ModernBertForSequenceClassification,pw as ModernBertForTokenClassification,uw as ModernBertModel,lw as ModernBertPreTrainedModel,Xb as Moondream1ForConditionalGeneration,Nf as MoonshineFeatureExtractor,yw as MoonshineForConditionalGeneration,vw as MoonshineModel,_w as MoonshinePreTrainedModel,$m as MoonshineProcessor,Ow as MptForCausalLM,Dw as MptModel,Ew as MptPreTrainedModel,Nw as MultiModalityCausalLM,Mw as MultiModalityPreTrainedModel,Iw as MusicgenForCausalLM,Lw as MusicgenForConditionalGeneration,Fw as MusicgenModel,Pw as MusicgenPreTrainedModel,Bw as NanoChatForCausalLM,zw as NanoChatModel,Rw as NanoChatPreTrainedModel,Uw as NemotronHForCausalLM,Hw as NemotronHModel,Vw as NemotronHPreTrainedModel,Kw as NeoBertForMaskedLM,Yw as NeoBertForQuestionAnswering,qw as NeoBertForSequenceClassification,Jw as NeoBertForTokenClassification,Gw as NeoBertModel,Ww as NeoBertPreTrainedModel,Sd as NllbTokenizer,fg as NoBadWordsLogitsProcessor,cg as NoRepeatNGramLogitsProcessor,Zw as NomicBertModel,Xw as NomicBertPreTrainedModel,sm as NougatImageProcessor,Cd as NougatTokenizer,vT as OPTForCausalLM,_T as OPTModel,gT as OPTPreTrainedModel,pj as ObjectDetectionPipeline,rT as Olmo2ForCausalLM,nT as Olmo2Model,tT as Olmo2PreTrainedModel,oT as Olmo3ForCausalLM,aT as Olmo3Model,iT as Olmo3PreTrainedModel,eT as OlmoForCausalLM,lT as OlmoHybridForCausalLM,cT as OlmoHybridModel,sT as OlmoHybridPreTrainedModel,$w as OlmoModel,Qw as OlmoPreTrainedModel,fT as OpenAIPrivacyFilterForTokenClassification,dT as OpenAIPrivacyFilterModel,uT as OpenAIPrivacyFilterPreTrainedModel,hT as OpenELMForCausalLM,mT as OpenELMModel,pT as OpenELMPreTrainedModel,lm as OwlViTFeatureExtractor,wT as OwlViTForObjectDetection,cm as OwlViTImageProcessor,CT as OwlViTModel,ST as OwlViTPreTrainedModel,eh as OwlViTProcessor,xT as Owlv2ForObjectDetection,um as Owlv2ImageProcessor,bT as Owlv2Model,yT as Owlv2PreTrainedModel,TT as PaliGemmaForConditionalGeneration,rh as PaliGemmaProcessor,Df as ParakeetFeatureExtractor,DT as ParakeetForCTC,ET as ParakeetPreTrainedModel,AT as PatchTSMixerForPrediction,kT as PatchTSMixerModel,OT as PatchTSMixerPreTrainedModel,NT as PatchTSTForPrediction,MT as PatchTSTModel,jT as PatchTSTPreTrainedModel,zT as Phi3ForCausalLM,RT as Phi3Model,LT as Phi3PreTrainedModel,VT as Phi3VForCausalLM,gm as Phi3VImageProcessor,BT as Phi3VPreTrainedModel,oh as Phi3VProcessor,IT as PhiForCausalLM,FT as PhiModel,PT as PhiPreTrainedModel,_m as PixtralImageProcessor,sh as PixtralProcessor,Z as PreTrainedModel,W as PreTrainedTokenizer,W as TokenizersBackend,Fh as PretrainedConfig,K as Processor,WT as PvtForImageClassification,vm as PvtImageProcessor,UT as PvtModel,HT as PvtPreTrainedModel,Pf as PyAnnoteFeatureExtractor,qT as PyAnnoteForAudioFrameClassification,KT as PyAnnoteModel,GT as PyAnnotePreTrainedModel,ch as PyAnnoteProcessor,Yh as QuestionAnsweringModelOutput,YA as QuestionAnsweringPipeline,XT as Qwen2ForCausalLM,YT as Qwen2Model,$T as Qwen2MoeForCausalLM,QT as Qwen2MoeModel,ZT as Qwen2MoePreTrainedModel,JT as Qwen2PreTrainedModel,wd as Qwen2Tokenizer,hx as Qwen2VLForCausalLM,mx as Qwen2VLForConditionalGeneration,Pp as Qwen2VLImageProcessor,px as Qwen2VLPreTrainedModel,Rm as Qwen2VLProcessor,_x as Qwen2_5_VLForCausalLM,gx as Qwen2_5_VLForConditionalGeneration,lh as Qwen2_5_VLProcessor,nE as Qwen3ForCausalLM,tE as Qwen3Model,aE as Qwen3MoeForCausalLM,iE as Qwen3MoeModel,rE as Qwen3MoePreTrainedModel,cE as Qwen3NextForCausalLM,sE as Qwen3NextModel,oE as Qwen3NextPreTrainedModel,eE as Qwen3PreTrainedModel,uE as Qwen3VLForCausalLM,lE as Qwen3VLForConditionalGeneration,fE as Qwen3VLMoeForCausalLM,dE as Qwen3VLMoeForConditionalGeneration,uh as Qwen3VLProcessor,mE as Qwen3_5ForCausalLM,pE as Qwen3_5ForConditionalGeneration,gE as Qwen3_5MoeForCausalLM,hE as Qwen3_5MoeForConditionalGeneration,SE as RFDetrForObjectDetection,xE as RFDetrModel,CE as RFDetrObjectDetectionOutput,bE as RFDetrPreTrainedModel,Yv as RTDetrForObjectDetection,ym as RTDetrImageProcessor,Jv as RTDetrModel,Xv as RTDetrObjectDetectionOutput,qv as RTDetrPreTrainedModel,RE as RTDetrV2ForObjectDetection,LE as RTDetrV2Model,zE as RTDetrV2ObjectDetectionOutput,IE as RTDetrV2PreTrainedModel,xf as RawAudio,Qf as RawImage,Mj as RawVideo,jj as RawVideoFrame,lg as RepetitionPenaltyLogitsProcessor,yE as ResNetForImageClassification,vE as ResNetModel,_E as ResNetPreTrainedModel,ME as RoFormerForMaskedLM,FE as RoFormerForQuestionAnswering,NE as RoFormerForSequenceClassification,PE as RoFormerForTokenClassification,jE as RoFormerModel,AE as RoFormerPreTrainedModel,Ed as RoFormerTokenizer,EE as RobertaForMaskedLM,kE as RobertaForQuestionAnswering,DE as RobertaForSequenceClassification,OE as RobertaForTokenClassification,TE as RobertaModel,wE as RobertaPreTrainedModel,Td as RobertaTokenizer,bm as Sam2ImageProcessor,bm as Sam3ImageProcessor,bm as SamImageProcessor,UE as Sam2ImageSegmentationOutput,GE as Sam2Model,WE as Sam2PreTrainedModel,fh as Sam2Processor,ph as Sam2VideoProcessor,qE as Sam3TrackerModel,BE as SamImageSegmentationOutput,HE as SamModel,VE as SamPreTrainedModel,dh as SamProcessor,Sm as SapiensFeatureExtractor,XE as SapiensForDepthEstimation,ZE as SapiensForNormalEstimation,YE as SapiensForSemanticSegmentation,xm as SapiensImageProcessor,JE as SapiensPreTrainedModel,Ff as SeamlessM4TFeatureExtractor,wm as SegformerFeatureExtractor,eD as SegformerForImageClassification,tD as SegformerForSemanticSegmentation,Cm as SegformerImageProcessor,$E as SegformerModel,QE as SegformerPreTrainedModel,Qh as Seq2SeqLMOutput,Y as SequenceClassifierOutput,Tm as SiglipImageProcessor,rD as SiglipModel,nD as SiglipPreTrainedModel,iD as SiglipTextModel,Dd as SiglipTokenizer,aD as SiglipVisionModel,cD as SmolLM3ForCausalLM,sD as SmolLM3Model,oD as SmolLM3PreTrainedModel,lD as SmolVLMForConditionalGeneration,pD as SnacDecoderModel,fD as SnacEncoderModel,If as SnacFeatureExtractor,dD as SnacModel,uD as SnacPreTrainedModel,gD as SolarOpenForCausalLM,hD as SolarOpenModel,mD as SolarOpenPreTrainedModel,Lf as SpeechT5FeatureExtractor,yD as SpeechT5ForSpeechToText,bD as SpeechT5ForTextToSpeech,xD as SpeechT5HifiGan,vD as SpeechT5Model,_D as SpeechT5PreTrainedModel,mh as SpeechT5Processor,Od as SpeechT5Tokenizer,wD as SqueezeBertForMaskedLM,ED as SqueezeBertForQuestionAnswering,TD as SqueezeBertForSequenceClassification,CD as SqueezeBertModel,SD as SqueezeBertPreTrainedModel,kd as SqueezeBertTokenizer,kD as StableLmForCausalLM,OD as StableLmModel,DD as StableLmPreTrainedModel,MD as Starcoder2ForCausalLM,jD as Starcoder2Model,AD as Starcoder2PreTrainedModel,vg as StoppingCriteria,yg as StoppingCriteriaList,PD as StyleTextToSpeech2Model,ND as StyleTextToSpeech2PreTrainedModel,QA as SummarizationPipeline,ID as SupertonicForConditionalGeneration,FD as SupertonicPreTrainedModel,og as SuppressTokensAtBeginLogitsProcessor,ag as SuppressTokensLogitsProcessor,UD as Swin2SRForImageSuperResolution,Em as Swin2SRImageProcessor,HD as Swin2SRModel,VD as Swin2SRPreTrainedModel,zD as SwinForImageClassification,BD as SwinForSemanticSegmentation,RD as SwinModel,LD as SwinPreTrainedModel,KD as T5ForConditionalGeneration,GD as T5Model,WD as T5PreTrainedModel,Ad as T5Tokenizer,YD as TableTransformerForObjectDetection,JD as TableTransformerModel,XD as TableTransformerObjectDetectionOutput,qD as TableTransformerPreTrainedModel,mg as TemperatureLogitsWarper,U as Tensor,ZA as Text2TextGenerationPipeline,GA as TextClassificationPipeline,tj as TextGenerationPipeline,kj as TextStreamer,oj as TextToAudioPipeline,KA as TokenClassificationPipeline,qh as TokenClassifierOutput,gg as TopKLogitsWarper,hg as TopPLogitsWarper,QD as TrOCRForCausalLM,ZD as TrOCRPreTrainedModel,$A as TranslationPipeline,qx as UltravoxModel,Kx as UltravoxPreTrainedModel,hh as UltravoxProcessor,tO as UniSpeechForCTC,nO as UniSpeechForSequenceClassification,eO as UniSpeechModel,$D as UniSpeechPreTrainedModel,sO as UniSpeechSatForAudioFrameClassification,aO as UniSpeechSatForCTC,oO as UniSpeechSatForSequenceClassification,iO as UniSpeechSatModel,rO as UniSpeechSatPreTrainedModel,qm as VLChatProcessor,zp as VLMImageProcessor,uO as VaultGemmaForCausalLM,lO as VaultGemmaModel,cO as VaultGemmaPreTrainedModel,Om as ViTFeatureExtractor,mO as ViTForImageClassification,Dm as ViTImageProcessor,gO as ViTMAEModel,hO as ViTMAEPreTrainedModel,yO as ViTMSNForImageClassification,vO as ViTMSNModel,_O as ViTMSNPreTrainedModel,pO as ViTModel,fO as ViTPreTrainedModel,dO as VisionEncoderDecoderModel,xO as VitMatteForImageMatting,km as VitMatteImageProcessor,bO as VitMattePreTrainedModel,CO as VitPoseForPoseEstimation,Am as VitPoseImageProcessor,SO as VitPosePreTrainedModel,EO as VitsModel,wO as VitsModelOutput,TO as VitsPreTrainedModel,Md as VitsTokenizer,DO as VoxtralForConditionalGeneration,bh as VoxtralProcessor,Bf as VoxtralRealtimeFeatureExtractor,LO as VoxtralRealtimeForConditionalGeneration,IO as VoxtralRealtimePreTrainedModel,Eh as VoxtralRealtimeProcessor,BO as Wav2Vec2BertForCTC,VO as Wav2Vec2BertForSequenceClassification,zO as Wav2Vec2BertModel,RO as Wav2Vec2BertPreTrainedModel,Nd as Wav2Vec2CTCTokenizer,Rf as Wav2Vec2FeatureExtractor,fS as Wav2Vec2ForAudioFrameClassification,uS as Wav2Vec2ForCTC,dS as Wav2Vec2ForSequenceClassification,lS as Wav2Vec2Model,cS as Wav2Vec2PreTrainedModel,Dh as Wav2Vec2Processor,Oh as Wav2Vec2ProcessorWithLM,JO as WavLMForAudioFrameClassification,GO as WavLMForCTC,KO as WavLMForSequenceClassification,qO as WavLMForXVector,WO as WavLMModel,UO as WavLMPreTrainedModel,zf as WeSpeakerFeatureExtractor,XO as WeSpeakerResNetModel,YO as WeSpeakerResNetPreTrainedModel,Vf as WhisperFeatureExtractor,ek as WhisperForConditionalGeneration,$O as WhisperModel,QO as WhisperPreTrainedModel,kh as WhisperProcessor,Aj as WhisperTextStreamer,sg as WhisperTimeStampLogitsProcessor,Bd as WhisperTokenizer,sk as XLMForQuestionAnswering,ak as XLMForSequenceClassification,ok as XLMForTokenClassification,rk as XLMModel,nk as XLMPreTrainedModel,uk as XLMRobertaForMaskedLM,pk as XLMRobertaForQuestionAnswering,dk as XLMRobertaForSequenceClassification,fk as XLMRobertaForTokenClassification,lk as XLMRobertaModel,ck as XLMRobertaPreTrainedModel,Vd as XLMRobertaTokenizer,Hd as XLMTokenizer,ik as XLMWithLMHeadModel,HO as XVectorOutput,Mm as YolosFeatureExtractor,gk as YolosForObjectDetection,jm as YolosImageProcessor,hk as YolosModel,_k as YolosObjectDetectionOutput,mk as YolosPreTrainedModel,bk as YoutuForCausalLM,yk as YoutuModel,vk as YoutuPreTrainedModel,Ck as ZayaForCausalLM,Sk as ZayaModel,xk as ZayaPreTrainedModel,ij as ZeroShotAudioClassificationPipeline,nj as ZeroShotClassificationPipeline,fj as ZeroShotImageClassificationPipeline,mj as ZeroShotObjectDetectionPipeline,yu as cat,fl as cos_sim,dl as dot,M as env,Eu as full,Du as full_like,su as interpolate,cu as interpolate_4d,hu as layer_norm,ef as load_audio,$f as load_image,Nj as load_video,ul as log_softmax,lu as matmul,Cu as mean,mu as mean_pooling,Ou as ones,ku as ones_like,ou as permute,Tj as pipeline,Pu as quantize_embeddings,Mu as rand,Nu as randn,Cc as random,tf as read_audio,uu as rfft,pu as slice,ll as softmax,bu as stack,Su as std_mean,du as topk,Au as zeros,ju as zeros_like};