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  !*** ./node_modules/onnxruntime-common/dist/esm/tensor-impl.js ***!

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  !*** ./src/generation/logits_process.js ***!

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s{constructor(e,t){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=t.length,t.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,t){for(let r=0;r<e.length;++r){const n=t[r].data;if(n[this.no_timestamps_token_id]=-1/0,e[r].length===this.begin_index-1){n.fill(-1/0),n[this.timestamp_begin]=0;continue}const s=e[r].slice(this.begin_index),a=s.length>=1&&s[s.length-1]>=this.timestamp_begin,i=s.length<2||s[s.length-2]>=this.timestamp_begin;if(a&&(i?n.subarray(this.timestamp_begin).fill(-1/0):n.subarray(0,this.eos_token_id).fill(-1/0)),e[r].length===this.begin_index&&null!==this.max_initial_timestamp_index){const e=this.timestamp_begin+this.max_initial_timestamp_index;n.subarray(e+1).fill(-1/0)}const 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  !*** ./src/generation/logits_sampler.js ***!

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  !*** ./src/generation/stopping_criteria.js ***!

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u.Tensor("bool",[e],[1])}async function I(e,t){let{encoder_outputs:r,input_ids:n,decoder_input_ids:o,...s}=t;if(!r){const n=(0,i.pick)(t,e.sessions.model.inputNames);r=(await B(e,n)).last_hidden_state}s.input_ids=o,s.encoder_hidden_states=r,e.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=t.attention_mask);return await N(e,s,!0)}async function B(e,t){const r=e.sessions.model,n=(0,i.pick)(t,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!n.inputs_embeds){if(!t.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");n.inputs_embeds=await e.encode_text({input_ids:t.input_ids})}return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new u.Tensor("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await A(r,n)}async function N(e,t,r=!1){const n=e.sessions[r?"decoder_model_merged":"model"],{past_key_values:o,...s}=t;n.inputNames.includes("use_cache_branch")&&(s.use_cache_branch=z(!!o)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=function(e,t=null){const{input_ids:r,inputs_embeds:n,attention_mask:o}=e,[s,a]=o.dims,i=new BigInt64Array(o.data.length);for(let e=0;e<s;++e){const t=e*a;let r=BigInt(0);for(let e=0;e<a;++e){const n=t+e;0n===o.data[n]?i[n]=BigInt(1):(i[n]=r,r+=o.data[n])}}let l=new u.Tensor("int64",i,o.dims);if(t){const e=-(r??n).dims.at(1);l=l.slice(null,[e,null])}return l}(s,o)),e.addPastKeyValues(s,o);const a=(0,i.pick)(s,n.inputNames);return await A(n,a)}async function O(e,{input_ids:t=null,attention_mask:r=null,pixel_values:n=null,position_ids:o=null,inputs_embeds:s=null,past_key_values:a=null,generation_config:i=null,logits_processor:l=null,...c}){if(!s)if(s=await e.encode_text({input_ids:t}),n&&1!==t.dims[1]){const o=await e.encode_image({pixel_values:n});({inputs_embeds:s,attention_mask:r}=e._merge_input_ids_with_image_features({image_features:o,inputs_embeds:s,input_ids:t,attention_mask:r}))}else if(a&&n&&1===t.dims[1]){const e=t.dims[1],n=Object.values(a)[0].dims.at(-2);r=(0,u.cat)([(0,u.ones)([t.dims[0],n]),r.slice(null,[r.dims[1]-e,r.dims[1]])],1)}return await N(e,{inputs_embeds:s,past_key_values:a,attention_mask:r,position_ids:o,generation_config:i,logits_processor:l},!0)}function D(e,t,r,n){if(r.past_key_values){const t=Object.values(r.past_key_values)[0].dims.at(-2),{input_ids:n,attention_mask:o}=r;if(o&&o.dims[1]>n.dims[1]);else if(t<n.dims[1])r.input_ids=n.slice(null,[t,null]);else if(null!=e.config.image_token_index&&n.data.some((t=>t==e.config.image_token_index))){const o=e.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const s=n.dims[1]-(t-o);r.input_ids=n.slice(null,[-s,null]),r.attention_mask=(0,u.ones)([1,t+s])}}return r}function V(e,t,r,n){return r.past_key_values&&(t=t.map((e=>[e.at(-1)]))),{...r,decoder_input_ids:L(t)}}function j(e,...t){return e.config.is_encoder_decoder?V(e,...t):D(e,...t)}class R extends a.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,t){super(),this.config=e,this.sessions=t;const r=v.get(this.constructor),n=P.get(r);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,n){case x:this.can_generate=!0,this._forward=N,this._prepare_inputs_for_generation=D;break;case T:case b:case F:this.can_generate=!0,this._forward=I,this._prepare_inputs_for_generation=V;break;case w:this._forward=I;break;case y:this.can_generate=!0,this._forward=O,this._prepare_inputs_for_generation=j;break;default:this._forward=B}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const e=[];for(const t of Object.values(this.sessions))t?.handler?.dispose&&e.push(t.handler.dispose());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:o=null,local_files_only:s=!1,revision:a="main",model_file_name:i=null,subfolder:c="onnx",device:d=null,dtype:u=null,use_external_data_format:p=null,session_options:h={}}={}){let m={progress_callback:t,config:r,cache_dir:o,local_files_only:s,revision:a,model_file_name:i,subfolder:c,device:d,dtype:u,use_external_data_format:p,session_options:h};const _=v.get(this),f=P.get(_);let g;if(r=m.config=await n.AutoConfig.from_pretrained(e,m),f===x)g=await Promise.all([S(e,{model:m.model_file_name??"model"},m),(0,l.getModelJSON)(e,"generation_config.json",!1,m)]);else if(f===T||f===b)g=await Promise.all([S(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},m),(0,l.getModelJSON)(e,"generation_config.json",!1,m)]);else if(f===k)g=await Promise.all([S(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},m)]);else if(f===w)g=await Promise.all([S(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},m)]);else if(f===y){const t={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};r.is_encoder_decoder&&(t.model="encoder_model"),g=await Promise.all([S(e,t,m),(0,l.getModelJSON)(e,"generation_config.json",!1,m)])}else f===F?g=await Promise.all([S(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},m),(0,l.getModelJSON)(e,"generation_config.json",!1,m)]):(f!==M&&console.warn(`Model type for '${_??r?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),g=await Promise.all([S(e,{model:m.model_file_name??"model"},m)]));return new this(r,...g)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const t=new c.LogitsProcessorList;return null!==e.temperature&&1!==e.temperature&&t.push(new c.TemperatureLogitsWarper(e.temperature)),null!==e.top_k&&0!==e.top_k&&t.push(new c.TopKLogitsWarper(e.top_k)),null!==e.top_p&&e.top_p<1&&t.push(new c.TopPLogitsWarper(e.top_p)),t}_get_logits_processor(e,t,r=null){const n=new c.LogitsProcessorList;if(null!==e.repetition_penalty&&1!==e.repetition_penalty&&n.push(new c.RepetitionPenaltyLogitsProcessor(e.repetition_penalty)),null!==e.no_repeat_ngram_size&&e.no_repeat_ngram_size>0&&n.push(new c.NoRepeatNGramLogitsProcessor(e.no_repeat_ngram_size)),null!==e.bad_words_ids&&n.push(new c.NoBadWordsLogitsProcessor(e.bad_words_ids,e.eos_token_id)),null!==e.min_length&&null!==e.eos_token_id&&e.min_length>0&&n.push(new c.MinLengthLogitsProcessor(e.min_length,e.eos_token_id)),null!==e.min_new_tokens&&null!==e.eos_token_id&&e.min_new_tokens>0&&n.push(new c.MinNewTokensLengthLogitsProcessor(t,e.min_new_tokens,e.eos_token_id)),null!==e.forced_bos_token_id&&n.push(new c.ForcedBOSTokenLogitsProcessor(e.forced_bos_token_id)),null!==e.forced_eos_token_id&&n.push(new c.ForcedEOSTokenLogitsProcessor(e.max_length,e.forced_eos_token_id)),null!==e.begin_suppress_tokens){const r=t>1||null===e.forced_bos_token_id?t:t+1;n.push(new c.SuppressTokensAtBeginLogitsProcessor(e.begin_suppress_tokens,r))}return null!==e.guidance_scale&&e.guidance_scale>1&&n.push(new c.ClassifierFreeGuidanceLogitsProcessor(e.guidance_scale)),null!==r&&n.extend(r),n}_prepare_generation_config(e,t,r=d.GenerationConfig){const n={...this.config};for(const e of["decoder","generator","text_config"])e in n&&Object.assign(n,n[e]);const o=new r(n);return"generation_config"in this&&Object.assign(o,this.generation_config),e&&Object.assign(o,e),t&&Object.assign(o,(0,i.pick)(t,Object.getOwnPropertyNames(o))),o}_get_stopping_criteria(e,t=null){const r=new h.StoppingCriteriaList;return null!==e.max_length&&r.push(new h.MaxLengthCriteria(e.max_length,this.config.max_position_embeddings??null)),null!==e.eos_token_id&&r.push(new h.EosTokenCriteria(e.eos_token_id)),t&&r.extend(t),r}_validate_model_class(){if(!this.can_generate){const e=[ii,di,ai,ti],t=v.get(this.constructor),r=new Set,n=this.config.model_type;for(const t of e){const e=t.get(n);e&&r.add(e[0])}let o=`The current model class (${t}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw r.size>0&&(o+=` Please use the following class instead: ${[...r].join(", ")}`),Error(o)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:t,model_inputs:r,is_encoder_decoder:n}){return r.past_key_values=this.getPastKeyValues(t,r.past_key_values),r.input_ids=new u.Tensor("int64",e.flat(),[e.length,1]),n||(r.attention_mask=(0,u.cat)([r.attention_mask,(0,u.ones)([r.attention_mask.dims[0],1])],1)),r.position_ids=null,r}_prepare_model_inputs({inputs:e,bos_token_id:t,model_kwargs:r}){const n=(0,i.pick)(r,this.forward_params),o=this.main_input_name;if(o in n){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else n[o]=e;return{inputs_tensor:n[o],model_inputs:n,model_input_name:o}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:t,model_input_name:r,generation_config:n}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!t.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:e,pixel_values:r,attention_mask:n,...o}=t,s=await this._prepare_inputs_embeds(t);t={...o,...(0,i.pick)(s,["inputs_embeds","attention_mask"])}}let{last_hidden_state:o}=await B(this,t);if(null!==n.guidance_scale&&n.guidance_scale>1)o=(0,u.cat)([o,(0,u.full_like)(o,0)],0),"attention_mask"in t&&(t.attention_mask=(0,u.cat)([t.attention_mask,(0,u.zeros_like)(t.attention_mask)],0));else if(t.decoder_input_ids){const e=L(t.decoder_input_ids).dims[0];if(e!==o.dims[0]){if(1!==o.dims[0])throw new Error(`The encoder outputs have a different batch size (${o.dims[0]}) than the decoder inputs (${e}).`);o=(0,u.cat)(Array.from({length:e},(()=>o)),0)}}return t.encoder_outputs=o,t}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:t,model_kwargs:r,decoder_start_token_id:n,bos_token_id:o,generation_config:s}){let{decoder_input_ids:a,...i}=r;if(a)Array.isArray(a[0])||(a=Array.from({length:e},(()=>a)));else if(n??=o,"musicgen"===this.config.model_type)a=Array.from({length:e*this.config.decoder.num_codebooks},(()=>[n]));else if(Array.isArray(n)){if(n.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${n.length}`);a=n}else a=Array.from({length:e},(()=>[n]));return a=L(a),r.decoder_attention_mask=(0,u.ones_like)(a),{input_ids:a,model_inputs:i}}async generate({inputs:e=null,generation_config:t=null,logits_processor:r=null,stopping_criteria:n=null,streamer:o=null,...s}){this._validate_model_class(),t=this._prepare_generation_config(t,s);let{inputs_tensor:a,model_inputs:i,model_input_name:l}=this._prepare_model_inputs({inputs:e,model_kwargs:s});const c=this.config.is_encoder_decoder;let d;c&&("encoder_outputs"in i||(i=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:a,model_inputs:i,model_input_name:l,generation_config:t}))),c?({input_ids:d,model_inputs:i}=this._prepare_decoder_input_ids_for_generation({batch_size:i[l].dims.at(0),model_input_name:l,model_kwargs:i,decoder_start_token_id:t.decoder_start_token_id,bos_token_id:t.bos_token_id,generation_config:t})):d=i[l];let p=d.dims.at(-1);null!==t.max_new_tokens&&(t.max_length=p+t.max_new_tokens);const h=this._get_logits_processor(t,p,r),_=this._get_stopping_criteria(t,n),f=i[l].dims.at(0),g=m.LogitsSampler.getSampler(t),M=new Array(f).fill(0),w=d.tolist();let T;o&&o.put(w);let b={};for(;;){if(i=this.prepare_inputs_for_generation(w,i,t),T=await this.forward(i),t.output_attentions&&t.return_dict_in_generate){const e=this.getAttentions(T);for(const t in e)t in b||(b[t]=[]),b[t].push(e[t])}const e=h(w,T.logits.slice(null,-1,null)),r=[];for(let t=0;t<e.dims.at(0);++t){const n=e[t],o=await g(n);for(const[e,n]of o){const o=BigInt(e);M[t]+=n,w[t].push(o),r.push([o]);break}}o&&o.put(r);if(_(w).every((e=>e)))break;i=this._update_model_kwargs_for_generation({generated_input_ids:r,outputs:T,model_inputs:i,is_encoder_decoder:c})}o&&o.end();const x=this.getPastKeyValues(T,i.past_key_values,!0),k=new u.Tensor("int64",w.flat(),[w.length,w[0].length]);if(t.return_dict_in_generate)return{sequences:k,past_key_values:x,...b};for(const e of Object.values(T))"gpu-buffer"===e.location&&e.dispose();return k}getPastKeyValues(e,t,r=!1){const n=Object.create(null);for(const o in e)if(o.startsWith("present")){const s=o.replace("present","past_key_values"),a=o.includes("encoder");if(n[s]=a&&t?t[s]:e[o],t&&(!a||r)){const e=t[s];"gpu-buffer"===e.location&&e.dispose()}}return n}getAttentions(e){const t={};for(const r of["cross_attentions","encoder_attentions","decoder_attentions"])for(const n in e)n.startsWith(r)&&(r in t||(t[r]=[]),t[r].push(e[n]));return t}addPastKeyValues(e,t){if(t)Object.assign(e,t);else{const t=this.custom_config.kv_cache_dtype??"float32",r="float16"===t?new Uint16Array:[],o=(0,n.getKeyValueShapes)(this.config);for(const n in o)e[n]=new u.Tensor(t,r,o[n])}}async encode_image({pixel_values:e}){const t=(await A(this.sessions.vision_encoder,{pixel_values:e})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${t.dims[1]}).`),this.config.num_image_tokens=t.dims[1]),t}async encode_text({input_ids:e}){return(await A(this.sessions.embed_tokens,{input_ids:e})).inputs_embeds}}class G{}class q extends G{constructor({last_hidden_state:e,hidden_states:t=null,attentions:r=null}){super(),this.last_hidden_state=e,this.hidden_states=t,this.attentions=r}}class $ extends R{}class W extends ${}class U extends ${async _call(e){return new cl(await super._call(e))}}class X extends ${async _call(e){return new al(await super._call(e))}}class Q extends ${async _call(e){return new ll(await super._call(e))}}class H extends ${async _call(e){return new dl(await super._call(e))}}class Y extends R{}class J extends Y{}class K extends R{}class Z extends K{}class ee extends K{async _call(e){return new cl(await super._call(e))}}class te extends K{async _call(e){return new al(await super._call(e))}}class re extends K{async _call(e){return new ll(await super._call(e))}}class ne extends K{async _call(e){return new dl(await super._call(e))}}class oe extends R{}class se extends oe{}class ae extends oe{async _call(e){return new cl(await super._call(e))}}class ie extends oe{async _call(e){return new al(await super._call(e))}}class le extends oe{async _call(e){return new ll(await super._call(e))}}class ce extends oe{async _call(e){return new dl(await super._call(e))}}class de extends R{}class ue extends de{}class pe extends de{async _call(e){return new cl(await super._call(e))}}class he extends de{async _call(e){return new al(await super._call(e))}}class me extends de{async _call(e){return new ll(await super._call(e))}}class _e extends de{async _call(e){return new dl(await super._call(e))}}class fe extends R{}class ge extends fe{}class Me extends fe{async _call(e){return new cl(await super._call(e))}}class we extends fe{async _call(e){return new al(await super._call(e))}}class Te extends fe{async _call(e){return new ll(await super._call(e))}}class be extends fe{async _call(e){return new dl(await super._call(e))}}class xe extends R{}class ke extends xe{}class ye extends xe{async _call(e){return new cl(await super._call(e))}}class Fe extends xe{async _call(e){return new al(await super._call(e))}}class Pe extends xe{async _call(e){return new ll(await super._call(e))}}class Ce extends xe{async _call(e){return new dl(await super._call(e))}}class ve extends R{}class Se extends ve{}class Ae extends ve{async _call(e){return new cl(await super._call(e))}}class Ee extends ve{async _call(e){return new al(await super._call(e))}}class Le extends ve{async _call(e){return new ll(await super._call(e))}}class ze extends ve{async _call(e){return new dl(await super._call(e))}}class Ie extends R{}class Be extends Ie{}class Ne extends Ie{async _call(e){return new al(await super._call(e))}}class Oe extends Ie{async _call(e){return new ll(await super._call(e))}}class De extends Ie{async _call(e){return new dl(await super._call(e))}}class Ve extends Ie{async _call(e){return new cl(await super._call(e))}}class je extends R{}class Re extends je{}class Ge extends je{async _call(e){return new cl(await super._call(e))}}class qe extends je{async _call(e){return new al(await super._call(e))}}class $e extends je{async _call(e){return new ll(await super._call(e))}}class We extends R{}class Ue extends We{}class Xe extends We{async _call(e){return new cl(await super._call(e))}}class Qe extends We{async _call(e){return new al(await super._call(e))}}class He extends We{async _call(e){return new dl(await super._call(e))}}class Ye extends R{}class Je extends Ye{}class Ke extends Ye{async _call(e){return new cl(await super._call(e))}}class Ze extends Ye{async _call(e){return new al(await super._call(e))}}class et extends Ye{async _call(e){return new ll(await super._call(e))}}class tt extends Ye{async _call(e){return new dl(await super._call(e))}}class rt extends R{}class nt extends rt{}class ot extends rt{async _call(e){return new cl(await super._call(e))}}class st extends rt{async _call(e){return new al(await super._call(e))}}class at extends rt{async _call(e){return new dl(await super._call(e))}}class it extends R{}class lt extends it{}class ct extends it{async _call(e){return new al(await super._call(e))}}class dt extends it{async _call(e){return new dl(await super._call(e))}}class ut extends it{async _call(e){return new cl(await super._call(e))}}class pt extends R{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,t,r){super(e,t),this.generation_config=r}}class ht extends pt{}class mt extends pt{}class _t extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class ft extends _t{}class gt extends _t{}class Mt extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class wt extends Mt{}class Tt extends Mt{}class bt extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class xt extends bt{}class kt extends bt{}class yt extends bt{async _call(e){return new al(await super._call(e))}}class Ft extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Pt extends Ft{}class Ct extends Ft{}class vt extends Ft{async _call(e){return new al(await super._call(e))}}class St extends Ft{}class At extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Et extends At{}class Lt extends At{}class zt extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class It extends zt{}class Bt extends zt{}class Nt extends R{}class Ot extends Nt{}class Dt extends Nt{async _call(e){return new cl(await super._call(e))}}class Vt extends Nt{async _call(e){return new al(await super._call(e))}}class jt extends Nt{async _call(e){return new ll(await super._call(e))}}class Rt extends Nt{async _call(e){return new dl(await super._call(e))}}class Gt extends R{}class qt extends Gt{}class $t extends Gt{async _call(e){return new cl(await super._call(e))}}class Wt extends Gt{async _call(e){return new al(await super._call(e))}}class Ut extends Gt{async _call(e){return new ll(await super._call(e))}}class Xt extends Gt{async _call(e){return new dl(await super._call(e))}}class Qt extends R{}class Ht extends Qt{}class Yt extends Qt{async _call(e){return new cl(await super._call(e))}}class Jt extends Qt{async _call(e){return new al(await super._call(e))}}class Kt extends Qt{async _call(e){return new ll(await super._call(e))}}class Zt extends Qt{async _call(e){return new dl(await super._call(e))}}class er extends R{}class tr extends er{}class rr extends er{}class nr extends R{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,t,r){super(e,t),this.generation_config=r}}class or extends nr{}class sr extends nr{_prepare_generation_config(e,t){return super._prepare_generation_config(e,t,f.WhisperGenerationConfig)}_retrieve_init_tokens(e){const t=[e.decoder_start_token_id];let r=e.language;const n=e.task;if(e.is_multilingual){r||(console.warn("No language specified - defaulting to English (en)."),r="en");const o=`<|${(0,g.whisper_language_to_code)(r)}|>`;t.push(e.lang_to_id[o]),t.push(e.task_to_id[n??"transcribe"])}else if(r||n)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&t.at(-1)!==e.no_timestamps_token_id?t.push(e.no_timestamps_token_id):e.return_timestamps&&t.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),t.pop()),t.filter((e=>null!=e))}async generate({inputs:e=null,generation_config:t=null,logits_processor:r=null,stopping_criteria:n=null,...o}){t=this._prepare_generation_config(t,o);const s=o.decoder_input_ids??this._retrieve_init_tokens(t);if(t.return_timestamps&&(r??=new c.LogitsProcessorList,r.push(new c.WhisperTimeStampLogitsProcessor(t,s))),t.begin_suppress_tokens&&(r??=new c.LogitsProcessorList,r.push(new c.SuppressTokensAtBeginLogitsProcessor(t.begin_suppress_tokens,s.length))),t.return_token_timestamps){if(!t.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");"translate"===t.task&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),t.output_attentions=!0,t.return_dict_in_generate=!0}const a=await super.generate({inputs:e,generation_config:t,logits_processor:r,decoder_input_ids:s,...o});return t.return_token_timestamps&&(a.token_timestamps=this._extract_token_timestamps(a,t.alignment_heads,t.num_frames)),a}_extract_token_timestamps(e,t,r=null,n=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");null==r&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let o=this.config.median_filter_width;void 0===o&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),o=7);const s=e.cross_attentions,a=Array.from({length:this.config.decoder_layers},((e,t)=>(0,u.cat)(s.map((e=>e[t])),2))),l=(0,u.stack)(t.map((([e,t])=>{if(e>=a.length)throw new Error(`Layer index ${e} is out of bounds for cross attentions (length ${a.length}).`);return r?a[e].slice(null,t,null,[0,r]):a[e].slice(null,t)}))).transpose(1,0,2,3),[c,d]=(0,u.std_mean)(l,-2,0,!0),h=l.clone();for(let e=0;e<h.dims[0];++e){const t=h[e];for(let r=0;r<t.dims[0];++r){const n=t[r],s=c[e][r][0].data,a=d[e][r][0].data;for(let e=0;e<n.dims[0];++e){let t=n[e].data;for(let e=0;e<t.length;++e)t[e]=(t[e]-a[e])/s[e];t.set((0,p.medianFilter)(t,o))}}}const m=[(0,u.mean)(h,1)],_=e.sequences.dims,f=new u.Tensor("float32",new Float32Array(_[0]*_[1]),_);for(let e=0;e<_[0];++e){const t=m[e].neg().squeeze_(0),[r,o]=(0,p.dynamic_time_warping)(t.tolist()),s=Array.from({length:r.length-1},((e,t)=>r[t+1]-r[t])),a=(0,i.mergeArrays)([1],s).map((e=>!!e)),l=[];for(let e=0;e<a.length;++e)a[e]&&l.push(o[e]*n);f[e].data.set(l,1)}return f}}class ar extends R{main_input_name="pixel_values";forward_params=["pixel_values","input_ids","encoder_hidden_states","past_key_values"];constructor(e,t,r){super(e,t),this.generation_config=r}}class ir extends R{forward_params=["input_ids","pixel_values","attention_mask","position_ids","past_key_values"];constructor(e,t,r){super(e,t),this.generation_config=r}}class lr extends ir{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:t,input_ids:r,attention_mask:n}){const o=this.config.image_token_index,s=r.tolist().map((e=>e.findIndex((e=>e==o)))),a=s.every((e=>-1===e)),i=s.every((e=>-1!==e));if(!a&&!i)throw new Error("Every input should contain either 0 or 1 image token.");if(a)return{inputs_embeds:e,attention_mask:n};const l=[],c=[];for(let r=0;r<s.length;++r){const o=s[r],a=e[r],i=t[r],d=n[r];l.push((0,u.cat)([a.slice([0,o]),i,a.slice([o+1,a.dims[0]])],0)),c.push((0,u.cat)([d.slice([0,o]),(0,u.ones)([i.dims[0]]),d.slice([o+1,d.dims[0]])],0))}return{inputs_embeds:(0,u.stack)(l,0),attention_mask:(0,u.stack)(c,0)}}}class cr extends lr{}class dr extends R{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds";constructor(e,t,r){super(e,t),this.generation_config=r}}class ur extends dr{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:t,input_ids:r,attention_mask:n}){return{inputs_embeds:(0,u.cat)([t,e],1),attention_mask:(0,u.cat)([(0,u.ones)(t.dims.slice(0,2)),n],1)}}async _prepare_inputs_embeds({input_ids:e,pixel_values:t,inputs_embeds:r,attention_mask:n}){if(!e&&!t)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let o,s;return e&&(o=await this.encode_text({input_ids:e})),t&&(s=await this.encode_image({pixel_values:t})),o&&s?({inputs_embeds:r,attention_mask:n}=this._merge_input_ids_with_image_features({inputs_embeds:o,image_features:s,input_ids:e,attention_mask:n})):r=o||s,{inputs_embeds:r,attention_mask:n}}async forward({input_ids:e,pixel_values:t,attention_mask:r,decoder_input_ids:n,decoder_attention_mask:o,encoder_outputs:s,past_key_values:a,inputs_embeds:i,decoder_inputs_embeds:l}){if(i||({inputs_embeds:i,attention_mask:r}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:t,inputs_embeds:i,attention_mask:r})),!s){let{last_hidden_state:e}=await B(this,{inputs_embeds:i,attention_mask:r});s=e}if(!l){if(!n)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");l=await this.encode_text({input_ids:n})}const c={inputs_embeds:l,attention_mask:o,encoder_attention_mask:r,encoder_hidden_states:s,past_key_values:a};return await N(this,c,!0)}}class pr extends R{}class hr extends pr{}class mr extends pr{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class _r extends pr{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class fr extends pr{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class gr extends pr{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class Mr extends R{}class wr extends Mr{}class Tr extends Mr{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class br extends pr{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class xr extends R{}class kr extends xr{}class yr extends R{}class Fr extends yr{}class Pr extends yr{}class Cr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class vr extends Cr{}class Sr extends Cr{}class Ar extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Er extends Ar{}class Lr extends Ar{}class zr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Ir extends zr{}class Br extends zr{}class Nr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Or extends Nr{}class Dr extends Nr{}class Vr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class jr extends Vr{}class Rr extends Vr{}class Gr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class qr extends Gr{}class $r extends Gr{}class Wr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Ur extends Wr{}class Xr extends Wr{}class Qr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Hr extends Qr{}class Yr extends Qr{}class Jr extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Kr extends Jr{}class Zr extends Jr{}class en extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class tn extends en{}class rn extends en{}class nn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class on extends nn{}class sn extends nn{}class an extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class ln extends an{}class cn extends an{}class dn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class un extends dn{}class pn extends dn{}class hn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class mn extends hn{}class _n extends hn{}class fn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class gn extends fn{}class Mn extends fn{}class wn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Tn extends wn{}class bn extends wn{}class xn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class kn extends xn{}class yn extends xn{}class Fn extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Pn extends Fn{}class Cn extends Fn{}class vn extends R{}class Sn extends vn{}class An extends vn{async _call(e){return new al(await super._call(e))}}class En extends R{}class Ln extends En{}class zn extends En{async _call(e){return new al(await super._call(e))}}class In extends R{}class Bn extends In{}class Nn extends R{}class On extends Nn{}class Dn extends Nn{async _call(e){return new al(await super._call(e))}}class Vn extends R{}class jn extends Vn{}class Rn extends R{}class Gn extends Rn{}class qn extends Rn{async _call(e){return new al(await super._call(e))}}class $n extends R{}class Wn extends $n{async _call(e){return new hl(await super._call(e))}}class Un extends R{}class Xn extends Un{}class Qn extends Un{async _call(e){return new al(await super._call(e))}}class Hn extends R{}class Yn extends Hn{}class Jn extends Hn{async _call(e){return new al(await super._call(e))}}class Kn extends R{}class Zn extends Kn{}class eo extends Kn{}class to extends R{}class ro extends to{}class no extends to{}class oo extends R{}class so extends oo{}class ao extends oo{async _call(e){return new al(await super._call(e))}}class io extends R{}class lo extends io{}class co extends io{async _call(e){return new po(await super._call(e))}}class uo extends io{async _call(e){return new ho(await super._call(e))}}class po extends G{constructor({logits:e,pred_boxes:t}){super(),this.logits=e,this.pred_boxes=t}}class ho extends G{constructor({logits:e,pred_boxes:t,pred_masks:r}){super(),this.logits=e,this.pred_boxes=t,this.pred_masks=r}}class mo extends R{}class _o extends mo{}class fo extends mo{async _call(e){return new go(await super._call(e))}}class go extends G{constructor({logits:e,pred_boxes:t}){super(),this.logits=e,this.pred_boxes=t}}class Mo extends R{}class wo extends Mo{}class To extends Mo{async _call(e){return new bo(await super._call(e))}}class bo extends po{}class xo extends R{}class ko extends xo{}class yo extends xo{async _call(e){return new al(await super._call(e))}}class Fo extends R{}class Po extends Fo{}class Co extends Fo{async _call(e){return new al(await super._call(e))}}class vo extends R{}class So extends vo{}class Ao extends vo{async _call(e){return new al(await super._call(e))}}class Eo extends R{}class Lo extends Eo{}class zo extends Eo{async _call(e){return new al(await super._call(e))}}class Io extends R{}class Bo extends Io{}class No extends Io{}class Oo extends R{}class Do extends Oo{}class Vo extends Oo{}class jo extends R{}class Ro extends jo{}class Go extends R{}class qo extends Go{}class $o extends Go{}class Wo extends Go{}class Uo extends R{}class Xo extends Uo{}class Qo extends Uo{}class Ho extends R{}class Yo extends Ho{}class Jo extends Ho{}class Ko extends R{}class Zo extends Ko{}class es extends R{}class ts extends es{}class rs extends es{async _call(e){return new al(await super._call(e))}}class ns extends R{}class os extends ns{}class ss extends ns{async _call(e){return new al(await super._call(e))}}class as extends R{}class is extends as{}class ls extends as{async _call(e){return new al(await super._call(e))}}class cs extends R{}class ds extends cs{}class us extends cs{async _call(e){return new ps(await super._call(e))}}class ps extends G{constructor({logits:e,pred_boxes:t}){super(),this.logits=e,this.pred_boxes=t}}class hs extends R{}class ms extends hs{async get_image_embeddings({pixel_values:e}){return await B(this,{pixel_values:e})}async forward(e){if(e.image_embeddings&&e.image_positional_embeddings||(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels&&e.input_points){const t=e.input_points.dims.slice(0,-1),r=t.reduce(((e,t)=>e*t),1);e.input_labels=new u.Tensor("int64",new BigInt64Array(r).fill(1n),t)}const t={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(t.input_points=e.input_points),e.input_labels&&(t.input_labels=e.input_labels),e.input_boxes&&(t.input_boxes=e.input_boxes),await A(this.sessions.prompt_encoder_mask_decoder,t)}async _call(e){return new _s(await super._call(e))}}class _s extends G{constructor({iou_scores:e,pred_masks:t}){super(),this.iou_scores=e,this.pred_masks=t}}class fs extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class gs extends fs{}class Ms extends fs{}class ws extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class Ts extends ws{}class bs extends ws{}class xs extends R{}class ks extends xs{}class ys extends xs{async _call(e){return new ul(await super._call(e))}}class Fs extends xs{async _call(e){return new al(await super._call(e))}}class Ps extends xs{async _call(e){return new ll(await super._call(e))}}class Cs extends R{}class vs extends Cs{}class Ss extends Cs{async _call(e){return new ll(await super._call(e))}}class As extends R{}class Es extends As{}class Ls extends R{}class zs extends Ls{}class Is extends Ls{async _call(e){return new ul(await super._call(e))}}class Bs extends Ls{async _call(e){return new al(await super._call(e))}}class Ns extends R{}class Os extends Ns{}class Ds extends Ns{async _call(e){return new ul(await super._call(e))}}class Vs extends Ns{async _call(e){return new al(await super._call(e))}}class js extends Ns{async _call(e){return new ll(await super._call(e))}}class Rs extends R{}class Gs extends Rs{}class qs extends Rs{async _call(e){return new ul(await super._call(e))}}class $s extends Rs{async _call(e){return new al(await super._call(e))}}class Ws extends R{}class Us extends xs{}class Xs extends xs{async _call(e){return new ul(await super._call(e))}}class Qs extends xs{async _call(e){return new al(await super._call(e))}}class Hs extends R{}class Ys extends Hs{}class Js extends Hs{async _call(e){return new ul(await super._call(e))}}class Ks extends Hs{async _call(e){return new al(await super._call(e))}}class Zs extends Hs{async _call(e){return new il(await super._call(e))}}class ea extends Hs{async _call(e){return new ll(await super._call(e))}}class ta extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class ra extends ta{}class na extends ta{}class oa extends ta{async generate_speech(e,t,{threshold:r=.5,minlenratio:n=0,maxlenratio:o=20,vocoder:s=null}={}){const a={input_ids:e},{encoder_outputs:i,encoder_attention_mask:l}=await B(this,a),c=i.dims[1]/this.config.reduction_factor,d=Math.floor(c*o),p=Math.floor(c*n),h=this.config.num_mel_bins;let m=[],_=null,f=null,g=0;for(;;){++g;const e=z(!!f);let n;n=f?f.output_sequence_out:new u.Tensor("float32",new Float32Array(h),[1,1,h]);let o={use_cache_branch:e,output_sequence:n,encoder_attention_mask:l,speaker_embeddings:t,encoder_hidden_states:i};this.addPastKeyValues(o,_),f=await A(this.sessions.decoder_model_merged,o),_=this.getPastKeyValues(f,_);const{prob:s,spectrum:a}=f;if(m.push(a),g>=p&&(Array.from(s.data).filter((e=>e>=r)).length>0||g>=d))break}const M=(0,u.cat)(m),{waveform:w}=await A(s.sessions.model,{spectrogram:M});return{spectrogram:M,waveform:w}}}class sa extends R{main_input_name="spectrogram"}class aa extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class ia extends aa{}class la extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class ca extends la{}class da extends la{}class ua extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class pa extends ua{}class ha extends ua{}class ma extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class _a extends ma{}class fa extends ma{}class ga extends R{}class Ma extends ga{}class wa extends ga{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class Ta extends ga{static async from_pretrained(e,t={}){return t.model_file_name??="audio_model",super.from_pretrained(e,t)}}class ba extends R{}class xa extends ba{async _call(e){return new ml(await super._call(e))}}class ka extends R{}class ya extends ka{}class Fa extends ka{}class Pa extends ka{}class Ca extends R{constructor(e,t,r){super(e,t),this.generation_config=r}}class va extends Ca{}class Sa extends Ca{}class Aa extends R{}class Ea extends Aa{}class La extends Aa{async _call(e){return new al(await super._call(e))}}class za extends R{}class Ia extends za{}class Ba extends za{}class Na extends R{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,t,r){super(e,t),this.generation_config=r}_apply_and_filter_by_delay_pattern_mask(e){const[t,r]=e.dims,n=this.config.decoder.num_codebooks,o=r-n;let s=0;for(let t=0;t<e.size;++t){if(e.data[t]===this.config.decoder.pad_token_id)continue;const a=t%r-Math.floor(t/r)%n;a>0&&a<=o&&(e.data[s++]=e.data[t])}const a=Math.floor(t/n),i=s/(a*n);return new u.Tensor(e.type,e.data.slice(0,s),[a,n,i])}prepare_inputs_for_generation(e,t,r){let n=structuredClone(e);for(let e=0;e<n.length;++e)for(let t=0;t<n[e].length;++t)e%this.config.decoder.num_codebooks>=t&&(n[e][t]=BigInt(this.config.decoder.pad_token_id));null!==r.guidance_scale&&r.guidance_scale>1&&(n=n.concat(n));return super.prepare_inputs_for_generation(n,t,r)}async generate(e){const t=await super.generate(e),r=this._apply_and_filter_by_delay_pattern_mask(t).unsqueeze_(0),{audio_values:n}=await A(this.sessions.encodec_decode,{audio_codes:r});return n}}class Oa extends R{}class Da extends Oa{}class Va extends Oa{async _call(e){return new al(await super._call(e))}}class ja extends R{}class Ra extends ja{}class Ga extends ja{async _call(e){return new al(await super._call(e))}}class qa extends R{}class $a extends qa{}class Wa extends qa{async _call(e){return new al(await super._call(e))}}class Ua extends R{}class Xa extends Ua{}class Qa extends Ua{async _call(e){return new al(await super._call(e))}}class Ha extends R{}class Ya extends Ha{}class Ja{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:o=null,local_files_only:s=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:c=null,dtype:d=null,use_external_data_format:u=null,session_options:p={}}={}){const h={progress_callback:t,config:r,cache_dir:o,local_files_only:s,revision:a,model_file_name:i,subfolder:l,device:c,dtype:d,use_external_data_format:u,session_options:p};if(h.config=await n.AutoConfig.from_pretrained(e,h),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const t of this.MODEL_CLASS_MAPPINGS){const r=t.get(h.config.model_type);if(r)return await r[1].from_pretrained(e,h)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${h.config.model_type}", attempting to construct from base class.`),await R.from_pretrained(e,h);throw Error(`Unsupported model type: ${h.config.model_type}`)}}const Ka=new Map([["bert",["BertModel",W]],["nomic_bert",["NomicBertModel",J]],["roformer",["RoFormerModel",Z]],["electra",["ElectraModel",ue]],["esm",["EsmModel",Re]],["convbert",["ConvBertModel",se]],["camembert",["CamembertModel",ge]],["deberta",["DebertaModel",ke]],["deberta-v2",["DebertaV2Model",Se]],["mpnet",["MPNetModel",Je]],["albert",["AlbertModel",lt]],["distilbert",["DistilBertModel",Be]],["roberta",["RobertaModel",Ot]],["xlm",["XLMModel",qt]],["xlm-roberta",["XLMRobertaModel",Ht]],["clap",["ClapModel",Ma]],["clip",["CLIPModel",hr]],["clipseg",["CLIPSegModel",Fr]],["chinese_clip",["ChineseCLIPModel",kr]],["siglip",["SiglipModel",wr]],["mobilebert",["MobileBertModel",Ue]],["squeezebert",["SqueezeBertModel",nt]],["wav2vec2",["Wav2Vec2Model",ks]],["wav2vec2-bert",["Wav2Vec2BertModel",Gs]],["unispeech",["UniSpeechModel",zs]],["unispeech-sat",["UniSpeechSatModel",Os]],["hubert",["HubertModel",Us]],["wavlm",["WavLMModel",Ys]],["audio-spectrogram-transformer",["ASTModel",tr]],["vits",["VitsModel",xa]],["pyannote",["PyAnnoteModel",vs]],["wespeaker-resnet",["WeSpeakerResNetModel",Es]],["detr",["DetrModel",lo]],["rt_detr",["RTDetrModel",_o]],["table-transformer",["TableTransformerModel",wo]],["vit",["ViTModel",Sn]],["pvt",["PvtModel",Ln]],["vit_msn",["ViTMSNModel",On]],["vit_mae",["ViTMAEModel",Bn]],["groupvit",["GroupViTModel",jn]],["fastvit",["FastViTModel",Gn]],["mobilevit",["MobileViTModel",Xn]],["mobilevitv2",["MobileViTV2Model",Yn]],["owlvit",["OwlViTModel",Zn]],["owlv2",["Owlv2Model",ro]],["beit",["BeitModel",so]],["deit",["DeiTModel",ko]],["hiera",["HieraModel",Po]],["convnext",["ConvNextModel",ts]],["convnextv2",["ConvNextV2Model",os]],["dinov2",["Dinov2Model",is]],["resnet",["ResNetModel",So]],["swin",["SwinModel",Lo]],["swin2sr",["Swin2SRModel",Bo]],["donut-swin",["DonutSwinModel",Zo]],["yolos",["YolosModel",ds]],["dpt",["DPTModel",Do]],["glpn",["GLPNModel",Yo]],["hifigan",["SpeechT5HifiGan",sa]],["efficientnet",["EfficientNetModel",Ea]],["decision_transformer",["DecisionTransformerModel",Ya]],["mobilenet_v1",["MobileNetV1Model",Da]],["mobilenet_v2",["MobileNetV2Model",Ra]],["mobilenet_v3",["MobileNetV3Model",$a]],["mobilenet_v4",["MobileNetV4Model",Xa]],["maskformer",["MaskFormerModel",Xo]]]),Za=new Map([["t5",["T5Model",ht]],["longt5",["LongT5Model",ft]],["mt5",["MT5Model",wt]],["bart",["BartModel",xt]],["mbart",["MBartModel",Pt]],["marian",["MarianModel",gs]],["whisper",["WhisperModel",or]],["m2m_100",["M2M100Model",Ts]],["blenderbot",["BlenderbotModel",Et]],["blenderbot-small",["BlenderbotSmallModel",It]]]),ei=new Map([["bloom",["BloomModel",Tn]],["jais",["JAISModel",Er]],["gpt2",["GPT2Model",vr]],["gptj",["GPTJModel",jr]],["gpt_bigcode",["GPTBigCodeModel",qr]],["gpt_neo",["GPTNeoModel",Ir]],["gpt_neox",["GPTNeoXModel",Or]],["codegen",["CodeGenModel",Ur]],["llama",["LlamaModel",Hr]],["cohere",["CohereModel",Kr]],["gemma",["GemmaModel",tn]],["gemma2",["Gemma2Model",on]],["openelm",["OpenELMModel",ln]],["qwen2",["Qwen2Model",un]],["phi",["PhiModel",mn]],["phi3",["Phi3Model",gn]],["mpt",["MptModel",kn]],["opt",["OPTModel",Pn]],["mistral",["MistralModel",ca]],["starcoder2",["Starcoder2Model",pa]],["falcon",["FalconModel",_a]],["stablelm",["StableLmModel",va]]]),ti=new Map([["speecht5",["SpeechT5ForSpeechToText",na]],["whisper",["WhisperForConditionalGeneration",sr]]]),ri=new Map([["speecht5",["SpeechT5ForTextToSpeech",oa]]]),ni=new Map([["vits",["VitsModel",xa]],["musicgen",["MusicgenForConditionalGeneration",Na]]]),oi=new Map([["bert",["BertForSequenceClassification",X]],["roformer",["RoFormerForSequenceClassification",te]],["electra",["ElectraForSequenceClassification",he]],["esm",["EsmForSequenceClassification",qe]],["convbert",["ConvBertForSequenceClassification",ie]],["camembert",["CamembertForSequenceClassification",we]],["deberta",["DebertaForSequenceClassification",Fe]],["deberta-v2",["DebertaV2ForSequenceClassification",Ee]],["mpnet",["MPNetForSequenceClassification",Ze]],["albert",["AlbertForSequenceClassification",ct]],["distilbert",["DistilBertForSequenceClassification",Ne]],["roberta",["RobertaForSequenceClassification",Vt]],["xlm",["XLMForSequenceClassification",Wt]],["xlm-roberta",["XLMRobertaForSequenceClassification",Jt]],["bart",["BartForSequenceClassification",yt]],["mbart",["MBartForSequenceClassification",vt]],["mobilebert",["MobileBertForSequenceClassification",Qe]],["squeezebert",["SqueezeBertForSequenceClassification",st]]]),si=new Map([["bert",["BertForTokenClassification",Q]],["roformer",["RoFormerForTokenClassification",re]],["electra",["ElectraForTokenClassification",me]],["esm",["EsmForTokenClassification",$e]],["convbert",["ConvBertForTokenClassification",le]],["camembert",["CamembertForTokenClassification",Te]],["deberta",["DebertaForTokenClassification",Pe]],["deberta-v2",["DebertaV2ForTokenClassification",Le]],["mpnet",["MPNetForTokenClassification",et]],["distilbert",["DistilBertForTokenClassification",Oe]],["roberta",["RobertaForTokenClassification",jt]],["xlm",["XLMForTokenClassification",Ut]],["xlm-roberta",["XLMRobertaForTokenClassification",Kt]]]),ai=new Map([["t5",["T5ForConditionalGeneration",mt]],["longt5",["LongT5ForConditionalGeneration",gt]],["mt5",["MT5ForConditionalGeneration",Tt]],["bart",["BartForConditionalGeneration",kt]],["mbart",["MBartForConditionalGeneration",Ct]],["marian",["MarianMTModel",Ms]],["m2m_100",["M2M100ForConditionalGeneration",bs]],["blenderbot",["BlenderbotForConditionalGeneration",Lt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Bt]]]),ii=new Map([["bloom",["BloomForCausalLM",bn]],["gpt2",["GPT2LMHeadModel",Sr]],["jais",["JAISLMHeadModel",Lr]],["gptj",["GPTJForCausalLM",Rr]],["gpt_bigcode",["GPTBigCodeForCausalLM",$r]],["gpt_neo",["GPTNeoForCausalLM",Br]],["gpt_neox",["GPTNeoXForCausalLM",Dr]],["codegen",["CodeGenForCausalLM",Xr]],["llama",["LlamaForCausalLM",Yr]],["cohere",["CohereForCausalLM",Zr]],["gemma",["GemmaForCausalLM",rn]],["gemma2",["Gemma2ForCausalLM",sn]],["openelm",["OpenELMForCausalLM",cn]],["qwen2",["Qwen2ForCausalLM",pn]],["phi",["PhiForCausalLM",_n]],["phi3",["Phi3ForCausalLM",Mn]],["mpt",["MptForCausalLM",yn]],["opt",["OPTForCausalLM",Cn]],["mbart",["MBartForCausalLM",St]],["mistral",["MistralForCausalLM",da]],["starcoder2",["Starcoder2ForCausalLM",ha]],["falcon",["FalconForCausalLM",fa]],["trocr",["TrOCRForCausalLM",ia]],["stablelm",["StableLmForCausalLM",Sa]]]),li=new Map([["bert",["BertForMaskedLM",U]],["roformer",["RoFormerForMaskedLM",ee]],["electra",["ElectraForMaskedLM",pe]],["esm",["EsmForMaskedLM",Ge]],["convbert",["ConvBertForMaskedLM",ae]],["camembert",["CamembertForMaskedLM",Me]],["deberta",["DebertaForMaskedLM",ye]],["deberta-v2",["DebertaV2ForMaskedLM",Ae]],["mpnet",["MPNetForMaskedLM",Ke]],["albert",["AlbertForMaskedLM",ut]],["distilbert",["DistilBertForMaskedLM",Ve]],["roberta",["RobertaForMaskedLM",Dt]],["xlm",["XLMWithLMHeadModel",$t]],["xlm-roberta",["XLMRobertaForMaskedLM",Yt]],["mobilebert",["MobileBertForMaskedLM",Xe]],["squeezebert",["SqueezeBertForMaskedLM",ot]]]),ci=new Map([["bert",["BertForQuestionAnswering",H]],["roformer",["RoFormerForQuestionAnswering",ne]],["electra",["ElectraForQuestionAnswering",_e]],["convbert",["ConvBertForQuestionAnswering",ce]],["camembert",["CamembertForQuestionAnswering",be]],["deberta",["DebertaForQuestionAnswering",Ce]],["deberta-v2",["DebertaV2ForQuestionAnswering",ze]],["mpnet",["MPNetForQuestionAnswering",tt]],["albert",["AlbertForQuestionAnswering",dt]],["distilbert",["DistilBertForQuestionAnswering",De]],["roberta",["RobertaForQuestionAnswering",Rt]],["xlm",["XLMForQuestionAnswering",Xt]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Zt]],["mobilebert",["MobileBertForQuestionAnswering",He]],["squeezebert",["SqueezeBertForQuestionAnswering",at]]]),di=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ar]]]),ui=new Map([["llava",["LlavaForConditionalGeneration",lr]],["moondream1",["Moondream1ForConditionalGeneration",cr]],["florence2",["Florence2ForConditionalGeneration",ur]]]),pi=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ar]]]),hi=new Map([["vit",["ViTForImageClassification",An]],["pvt",["PvtForImageClassification",zn]],["vit_msn",["ViTMSNForImageClassification",Dn]],["fastvit",["FastViTForImageClassification",qn]],["mobilevit",["MobileViTForImageClassification",Qn]],["mobilevitv2",["MobileViTV2ForImageClassification",Jn]],["beit",["BeitForImageClassification",ao]],["deit",["DeiTForImageClassification",yo]],["hiera",["HieraForImageClassification",Co]],["convnext",["ConvNextForImageClassification",rs]],["convnextv2",["ConvNextV2ForImageClassification",ss]],["dinov2",["Dinov2ForImageClassification",ls]],["resnet",["ResNetForImageClassification",Ao]],["swin",["SwinForImageClassification",zo]],["segformer",["SegformerForImageClassification",Fa]],["efficientnet",["EfficientNetForImageClassification",La]],["mobilenet_v1",["MobileNetV1ForImageClassification",Va]],["mobilenet_v2",["MobileNetV2ForImageClassification",Ga]],["mobilenet_v3",["MobileNetV3ForImageClassification",Wa]],["mobilenet_v4",["MobileNetV4ForImageClassification",Qa]]]),mi=new Map([["detr",["DetrForObjectDetection",co]],["rt_detr",["RTDetrForObjectDetection",fo]],["table-transformer",["TableTransformerForObjectDetection",To]],["yolos",["YolosForObjectDetection",us]]]),_i=new Map([["owlvit",["OwlViTForObjectDetection",eo]],["owlv2",["Owlv2ForObjectDetection",no]]]),fi=new Map([["detr",["DetrForSegmentation",uo]],["clipseg",["CLIPSegForImageSegmentation",Pr]]]),gi=new Map([["segformer",["SegformerForSemanticSegmentation",Pa]],["sapiens",["SapiensForSemanticSegmentation",qo]]]),Mi=new Map([["detr",["DetrForSegmentation",uo]],["maskformer",["MaskFormerForInstanceSegmentation",Qo]]]),wi=new Map([["sam",["SamModel",ms]]]),Ti=new Map([["wav2vec2",["Wav2Vec2ForCTC",ys]],["wav2vec2-bert",["Wav2Vec2BertForCTC",qs]],["unispeech",["UniSpeechForCTC",Is]],["unispeech-sat",["UniSpeechSatForCTC",Ds]],["wavlm",["WavLMForCTC",Js]],["hubert",["HubertForCTC",Xs]]]),bi=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Fs]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",$s]],["unispeech",["UniSpeechForSequenceClassification",Bs]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Vs]],["wavlm",["WavLMForSequenceClassification",Ks]],["hubert",["HubertForSequenceClassification",Qs]],["audio-spectrogram-transformer",["ASTForAudioClassification",rr]]]),xi=new Map([["wavlm",["WavLMForXVector",Zs]]]),ki=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",js]],["wavlm",["WavLMForAudioFrameClassification",ea]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Ps]],["pyannote",["PyAnnoteForAudioFrameClassification",Ss]]]),yi=new Map([["vitmatte",["VitMatteForImageMatting",Wn]]]),Fi=new Map([["swin2sr",["Swin2SRForImageSuperResolution",No]]]),Pi=new Map([["dpt",["DPTForDepthEstimation",Vo]],["depth_anything",["DepthAnythingForDepthEstimation",Ro]],["glpn",["GLPNForDepthEstimation",Jo]],["sapiens",["SapiensForDepthEstimation",$o]]]),Ci=new Map([["sapiens",["SapiensForNormalEstimation",Wo]]]),vi=new Map([["clip",["CLIPVisionModelWithProjection",gr]],["siglip",["SiglipVisionModel",br]]]),Si=[[Ka,M],[Za,w],[ei,x],[oi,M],[si,M],[ai,T],[ti,T],[ii,x],[li,M],[ci,M],[di,b],[ui,y],[hi,M],[fi,M],[Mi,M],[gi,M],[yi,M],[Fi,M],[Pi,M],[Ci,M],[mi,M],[_i,M],[wi,k],[Ti,M],[bi,M],[ri,T],[ni,M],[xi,M],[ki,M],[vi,M]];for(const[e,t]of Si)for(const[r,n]of e.values())P.set(r,t),v.set(n,r),C.set(r,n);const Ai=[["MusicgenForConditionalGeneration",Na,F],["CLIPTextModelWithProjection",_r,M],["SiglipTextModel",Tr,M],["ClapTextModelWithProjection",wa,M],["ClapAudioModelWithProjection",Ta,M]];for(const[e,t,r]of Ai)P.set(e,r),v.set(t,e),C.set(e,t);class Ei extends Ja{static MODEL_CLASS_MAPPINGS=Si.map((e=>e[0]));static BASE_IF_FAIL=!0}class Li extends Ja{static MODEL_CLASS_MAPPINGS=[oi]}class zi extends Ja{static MODEL_CLASS_MAPPINGS=[si]}class Ii extends Ja{static MODEL_CLASS_MAPPINGS=[ai]}class Bi extends Ja{static MODEL_CLASS_MAPPINGS=[ti]}class Ni extends Ja{static MODEL_CLASS_MAPPINGS=[ri]}class Oi extends Ja{static MODEL_CLASS_MAPPINGS=[ni]}class Di extends Ja{static MODEL_CLASS_MAPPINGS=[ii]}class Vi extends Ja{static MODEL_CLASS_MAPPINGS=[li]}class ji extends Ja{static MODEL_CLASS_MAPPINGS=[ci]}class Ri extends Ja{static MODEL_CLASS_MAPPINGS=[di]}class Gi extends Ja{static MODEL_CLASS_MAPPINGS=[hi]}class qi extends Ja{static MODEL_CLASS_MAPPINGS=[fi]}class $i extends Ja{static MODEL_CLASS_MAPPINGS=[gi]}class Wi extends Ja{static MODEL_CLASS_MAPPINGS=[Mi]}class Ui extends Ja{static MODEL_CLASS_MAPPINGS=[mi]}class Xi extends Ja{static MODEL_CLASS_MAPPINGS=[_i]}class Qi extends Ja{static MODEL_CLASS_MAPPINGS=[wi]}class Hi extends Ja{static MODEL_CLASS_MAPPINGS=[Ti]}class Yi extends Ja{static MODEL_CLASS_MAPPINGS=[bi]}class Ji extends Ja{static MODEL_CLASS_MAPPINGS=[xi]}class Ki extends Ja{static MODEL_CLASS_MAPPINGS=[ki]}class Zi extends Ja{static MODEL_CLASS_MAPPINGS=[pi]}class el extends Ja{static MODEL_CLASS_MAPPINGS=[yi]}class tl extends Ja{static MODEL_CLASS_MAPPINGS=[Fi]}class rl extends Ja{static MODEL_CLASS_MAPPINGS=[Pi]}class nl extends Ja{static MODEL_CLASS_MAPPINGS=[Ci]}class ol extends Ja{static MODEL_CLASS_MAPPINGS=[vi]}class sl extends G{constructor({logits:e,past_key_values:t,encoder_outputs:r,decoder_attentions:n=null,cross_attentions:o=null}){super(),this.logits=e,this.past_key_values=t,this.encoder_outputs=r,this.decoder_attentions=n,this.cross_attentions=o}}class al extends G{constructor({logits:e}){super(),this.logits=e}}class il extends G{constructor({logits:e,embeddings:t}){super(),this.logits=e,this.embeddings=t}}class ll extends G{constructor({logits:e}){super(),this.logits=e}}class cl extends G{constructor({logits:e}){super(),this.logits=e}}class dl extends G{constructor({start_logits:e,end_logits:t}){super(),this.start_logits=e,this.end_logits=t}}class ul extends G{constructor({logits:e}){super(),this.logits=e}}class pl extends G{constructor({logits:e,past_key_values:t}){super(),this.logits=e,this.past_key_values=t}}class hl extends G{constructor({alphas:e}){super(),this.alphas=e}}class ml extends G{constructor({waveform:e,spectrogram:t}){super(),this.waveform=e,this.spectrogram=t}}},"./src/models/whisper/common_whisper.js":

/*!**********************************************!*\

  !*** ./src/models/whisper/common_whisper.js ***!

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  !*** ./src/models/whisper/generation_whisper.js ***!

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/*!*****************************!*\

  !*** ./src/ops/registry.js ***!

  \*****************************/(e,t,r)=>{r.r(t),r.d(t,{TensorOpRegistry:()=>a});var n=r(/*! ../backends/onnx.js */"./src/backends/onnx.js"),o=r(/*! ../utils/tensor.js */"./src/utils/tensor.js");const s=async(e,t,r)=>{const s=await(0,n.createInferenceSession)(new Uint8Array(e),t);return async e=>{const t=Object.fromEntries(Object.entries(e).map((([e,t])=>[e,t.ort_tensor]))),n=await s.run(t);return Array.isArray(r)?r.map((e=>new o.Tensor(n[e]))):new o.Tensor(n[r])}};class a{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=s([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=s([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=s([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=s([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=s([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=s([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}}},"./src/pipelines.js":

/*!**************************!*\

  !*** ./src/pipelines.js ***!

  \**************************/(e,t,r)=>{r.r(t),r.d(t,{AudioClassificationPipeline:()=>v,AutomaticSpeechRecognitionPipeline:()=>A,DepthEstimationPipeline:()=>j,DocumentQuestionAnsweringPipeline:()=>O,FeatureExtractionPipeline:()=>P,FillMaskPipeline:()=>w,ImageClassificationPipeline:()=>L,ImageFeatureExtractionPipeline:()=>C,ImageSegmentationPipeline:()=>z,ImageToImagePipeline:()=>V,ImageToTextPipeline:()=>E,ObjectDetectionPipeline:()=>B,Pipeline:()=>_,QuestionAnsweringPipeline:()=>M,SummarizationPipeline:()=>b,Text2TextGenerationPipeline:()=>T,TextClassificationPipeline:()=>f,TextGenerationPipeline:()=>y,TextToAudioPipeline:()=>D,TokenClassificationPipeline:()=>g,TranslationPipeline:()=>x,ZeroShotAudioClassificationPipeline:()=>S,ZeroShotClassificationPipeline:()=>F,ZeroShotImageClassificationPipeline:()=>I,ZeroShotObjectDetectionPipeline:()=>N,pipeline:()=>q});var n=r(/*! ./tokenizers.js */"./src/tokenizers.js"),o=r(/*! ./models.js */"./src/models.js"),s=r(/*! ./processors.js */"./src/processors.js"),a=r(/*! ./utils/generic.js */"./src/utils/generic.js"),i=r(/*! ./utils/core.js */"./src/utils/core.js"),l=r(/*! ./utils/maths.js */"./src/utils/maths.js"),c=r(/*! ./utils/audio.js */"./src/utils/audio.js"),d=r(/*! ./utils/tensor.js */"./src/utils/tensor.js"),u=r(/*! ./utils/image.js */"./src/utils/image.js");async function p(e){return Array.isArray(e)||(e=[e]),await Promise.all(e.map((e=>u.RawImage.read(e))))}async function h(e,t){return Array.isArray(e)||(e=[e]),await Promise.all(e.map((e=>"string"==typeof e||e instanceof URL?(0,c.read_audio)(e,t):e instanceof Float64Array?new Float32Array(e):e)))}function m(e,t){t&&(e=e.map((e=>0|e)));const[r,n,o,s]=e;return{xmin:r,ymin:n,xmax:o,ymax:s}}class _ extends a.Callable{constructor({task:e,model:t,tokenizer:r=null,processor:n=null}){super(),this.task=e,this.model=t,this.tokenizer=r,this.processor=n}async dispose(){await this.model.dispose()}}class f extends _{constructor(e){super(e)}async _call(e,{top_k:t=1}={}){const r=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(r),o="multi_label_classification"===this.model.config.problem_type?e=>e.sigmoid():e=>new d.Tensor("float32",(0,l.softmax)(e.data),e.dims),s=this.model.config.id2label,a=[];for(const e of n.logits){const r=o(e),n=await(0,d.topk)(r,t),i=n[0].tolist(),l=n[1].tolist().map(((e,t)=>({label:s?s[e]:`LABEL_${e}`,score:i[t]})));1===t?a.push(...l):a.push(l)}return Array.isArray(e)||1===t?a:a[0]}}class g extends _{constructor(e){super(e)}async _call(e,{ignore_labels:t=["O"]}={}){const r=Array.isArray(e),n=this.tokenizer(r?e:[e],{padding:!0,truncation:!0}),o=(await this.model(n)).logits,s=this.model.config.id2label,a=[];for(let e=0;e<o.dims[0];++e){const r=n.input_ids[e],i=o[e],c=[];for(let e=0;e<i.dims[0];++e){const n=i[e],o=(0,l.max)(n.data)[1],a=s?s[o]:`LABEL_${o}`;if(t.includes(a))continue;const d=this.tokenizer.decode([r[e].item()],{skip_special_tokens:!0});if(""===d)continue;const u=(0,l.softmax)(n.data);c.push({entity:a,score:u[o],index:e,word:d})}a.push(c)}return r?a:a[0]}}class M extends _{constructor(e){super(e)}async _call(e,t,{top_k:r=1}={}){const n=this.tokenizer(e,{text_pair:t,padding:!0,truncation:!0}),{start_logits:o,end_logits:s}=await this.model(n),a=n.input_ids.tolist(),c=n.attention_mask.tolist(),d=this.tokenizer.all_special_ids,u=[];for(let e=0;e<o.dims[0];++e){const t=a[e],n=t.findIndex((e=>e==this.tokenizer.sep_token_id)),p=(c[e].map(((e,r)=>1==e&&(0===r||r>n&&-1===d.findIndex((e=>e==t[r]))))),o[e].tolist()),h=s[e].tolist();for(let r=1;r<p.length;++r)(0==c[e]||r<=n||-1!==d.findIndex((e=>e==t[r])))&&(p[r]=-1/0,h[r]=-1/0);const m=(0,l.softmax)(p).map(((e,t)=>[e,t])),_=(0,l.softmax)(h).map(((e,t)=>[e,t]));m[0][0]=0,_[0][0]=0;const f=(0,i.product)(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]));for(let e=0;e<Math.min(f.length,r);++e){const[r,n,o]=f[e],s=t.slice(r,n+1),a=this.tokenizer.decode(s,{skip_special_tokens:!0});u.push({answer:a,score:o})}}return 1===r?u[0]:u}}class w extends _{constructor(e){super(e)}async _call(e,{top_k:t=5}={}){const r=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:n}=await this.model(r),o=[],s=r.input_ids.tolist();for(let e=0;e<s.length;++e){const r=s[e],a=r.findIndex((e=>e==this.tokenizer.mask_token_id));if(-1===a)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const i=n[e][a],c=await(0,d.topk)(new d.Tensor("float32",(0,l.softmax)(i.data),i.dims),t),u=c[0].tolist(),p=c[1].tolist();o.push(p.map(((e,t)=>{const n=r.slice();return n[a]=e,{score:u[t],token:Number(e),token_str:this.tokenizer.model.vocab[e],sequence:this.tokenizer.decode(n,{skip_special_tokens:!0})}})))}return Array.isArray(e)?o:o[0]}}class T extends _{_key="generated_text";constructor(e){super(e)}async _call(e,t={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map((e=>this.model.config.prefix+e)));const r=this.model.config.task_specific_params;r&&r[this.task]&&r[this.task].prefix&&(e=e.map((e=>r[this.task].prefix+e)));const n=this.tokenizer,o={padding:!0,truncation:!0};let s;s=this instanceof x&&"_build_translation_inputs"in n?n._build_translation_inputs(e,o,t):n(e,o);const a=await this.model.generate({...s,...t});return n.batch_decode(a,{skip_special_tokens:!0}).map((e=>({[this._key]:e})))}}class b extends T{_key="summary_text";constructor(e){super(e)}}class x extends T{_key="translation_text";constructor(e){super(e)}}function k(e){return Array.isArray(e)&&e.every((e=>"role"in e&&"content"in e))}class y extends _{constructor(e){super(e)}async _call(e,t={}){let r,n=!1,o=!1;if("string"==typeof e)r=e=[e];else if(Array.isArray(e)&&e.every((e=>"string"==typeof e)))n=!0,r=e;else{if(k(e))e=[e];else{if(!Array.isArray(e)||!e.every(k))throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");n=!0}o=!0,r=e.map((e=>this.tokenizer.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0})))}const s=t.add_special_tokens??!1,a=!o&&(t.return_full_text??!0);this.tokenizer.padding_side="left";const i=this.tokenizer(r,{add_special_tokens:s,padding:!0,truncation:!0}),l=await this.model.generate({...i,...t}),c=this.tokenizer.batch_decode(l,{skip_special_tokens:!0});let d;!a&&i.input_ids.dims.at(-1)>0&&(d=this.tokenizer.batch_decode(i.input_ids,{skip_special_tokens:!0}).map((e=>e.length)));const u=Array.from({length:e.length},(e=>[]));for(let t=0;t<c.length;++t){const r=Math.floor(t/l.dims[0]*e.length);d&&(c[t]=c[t].slice(d[r])),u[r].push({generated_text:o?[...e[r],{role:"assistant",content:c[t]}]:c[t]})}return n||1!==u.length?u:u[0]}}class F extends _{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,void 0===this.entailment_id&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,void 0===this.contradiction_id&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,t,{hypothesis_template:r="This example is {}.",multi_label:n=!1}={}){const o=Array.isArray(e);o||(e=[e]),Array.isArray(t)||(t=[t]);const s=t.map((e=>r.replace("{}",e))),a=n||1===t.length,i=[];for(const r of e){const e=[];for(const t of s){const n=this.tokenizer(r,{text_pair:t,padding:!0,truncation:!0}),o=await this.model(n);a?e.push([o.logits.data[this.contradiction_id],o.logits.data[this.entailment_id]]):e.push(o.logits.data[this.entailment_id])}const n=(a?e.map((e=>(0,l.softmax)(e)[1])):(0,l.softmax)(e)).map(((e,t)=>[e,t])).sort(((e,t)=>t[0]-e[0]));i.push({sequence:r,labels:n.map((e=>t[e[1]])),scores:n.map((e=>e[0]))})}return o?i:i[0]}}class P extends _{constructor(e){super(e)}async _call(e,{pooling:t="none",normalize:r=!1,quantize:n=!1,precision:o="binary"}={}){const s=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(s);let i=a.last_hidden_state??a.logits??a.token_embeddings;if("none"===t);else if("mean"===t)i=(0,d.mean_pooling)(i,s.attention_mask);else{if("cls"!==t)throw Error(`Pooling method '${t}' not supported.`);i=i.slice(null,0)}return r&&(i=i.normalize(2,-1)),n&&(i=(0,d.quantize_embeddings)(i,o)),i}}class C extends _{constructor(e){super(e)}async _call(e,{pool:t=null}={}){const r=await p(e),{pixel_values:n}=await this.processor(r),o=await this.model({pixel_values:n});let s;if(t){if(!("pooler_output"in o))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");s=o.pooler_output}else s=o.last_hidden_state??o.logits??o.image_embeds;return s}}class v extends _{constructor(e){super(e)}async _call(e,{top_k:t=5}={}){const r=this.processor.feature_extractor.config.sampling_rate,n=await h(e,r),o=this.model.config.id2label,s=[];for(const e of n){const r=await this.processor(e),n=(await this.model(r)).logits[0],a=await(0,d.topk)(new d.Tensor("float32",(0,l.softmax)(n.data),n.dims),t),i=a[0].tolist(),c=a[1].tolist().map(((e,t)=>({label:o?o[e]:`LABEL_${e}`,score:i[t]})));s.push(c)}return Array.isArray(e)?s:s[0]}}class S extends _{constructor(e){super(e)}async _call(e,t,{hypothesis_template:r="This is a sound of {}."}={}){const n=!Array.isArray(e);n&&(e=[e]);const o=t.map((e=>r.replace("{}",e))),s=this.tokenizer(o,{padding:!0,truncation:!0}),a=this.processor.feature_extractor.config.sampling_rate,i=await h(e,a),c=[];for(const e of i){const r=await this.processor(e),n=await this.model({...s,...r}),o=(0,l.softmax)(n.logits_per_audio.data);c.push([...o].map(((e,r)=>({score:e,label:t[r]}))))}return n?c[0]:c}}class A extends _{constructor(e){super(e)}async _call(e,t={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,t);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,t);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,t){t.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),t.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const r=!Array.isArray(e);r&&(e=[e]);const n=this.processor.feature_extractor.config.sampling_rate,o=await h(e,n),s=[];for(const e of o){const t=await this.processor(e),r=(await this.model(t)).logits[0],n=[];for(const e of r)n.push((0,l.max)(e.data)[1]);const o=this.tokenizer.decode(n);s.push({text:o})}return r?s[0]:s}async _call_whisper(e,t){const r=t.return_timestamps??!1,n=t.chunk_length_s??0,o=t.force_full_sequences??!1;let s=t.stride_length_s??null;const a={...t};"word"===r&&(a.return_token_timestamps=!0,a.return_timestamps=!1);const i=!Array.isArray(e);i&&(e=[e]);const c=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,d=this.processor.feature_extractor.config.hop_length,u=this.processor.feature_extractor.config.sampling_rate,p=await h(e,u),m=[];for(const e of p){let t=[];if(n>0){if(null===s)s=n/6;else if(n<=s)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const r=u*n,o=u*s,a=r-2*o;let i=0;for(;;){const n=i+r,s=e.subarray(i,n),l=await this.processor(s),c=0===i,d=n>=e.length;if(t.push({stride:[s.length,c?0:o,d?0:o],input_features:l.input_features,is_last:d}),d)break;i+=a}}else t=[{stride:[e.length,0,0],input_features:(await this.processor(e)).input_features,is_last:!0}];for(const e of t){a.num_frames=Math.floor(e.stride[0]/d);const t=await this.model.generate({inputs:e.input_features,...a});"word"===r?(e.tokens=t.sequences.tolist()[0],e.token_timestamps=t.token_timestamps.tolist()[0].map((e=>(0,l.round)(e,2)))):e.tokens=t[0].tolist(),e.stride=e.stride.map((e=>e/u))}const[i,p]=this.tokenizer._decode_asr(t,{time_precision:c,return_timestamps:r,force_full_sequences:o});m.push({text:i,...p})}return i?m[0]:m}}class E extends _{constructor(e){super(e)}async _call(e,t={}){const r=Array.isArray(e),n=await p(e),{pixel_values:o}=await this.processor(n),s=[];for(const e of o){e.dims=[1,...e.dims];const r=await this.model.generate({inputs:e,...t}),n=this.tokenizer.batch_decode(r,{skip_special_tokens:!0}).map((e=>({generated_text:e.trim()})));s.push(n)}return r?s:s[0]}}class L extends _{constructor(e){super(e)}async _call(e,{top_k:t=5}={}){const r=await p(e),{pixel_values:n}=await this.processor(r),o=await this.model({pixel_values:n}),s=this.model.config.id2label,a=[];for(const e of o.logits){const r=await(0,d.topk)(new d.Tensor("float32",(0,l.softmax)(e.data),e.dims),t),n=r[0].tolist(),o=r[1].tolist().map(((e,t)=>({label:s?s[e]:`LABEL_${e}`,score:n[t]})));a.push(o)}return Array.isArray(e)?a:a[0]}}class z extends _{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:t=.5,mask_threshold:r=.5,overlap_mask_area_threshold:n=.8,label_ids_to_fuse:o=null,target_sizes:s=null,subtask:a=null}={}){if(Array.isArray(e)&&1!==e.length)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const i=await p(e),l=i.map((e=>[e.height,e.width])),{pixel_values:c,pixel_mask:d}=await this.processor(i),h=await this.model({pixel_values:c,pixel_mask:d});let m=null;if(null!==a)m=this.subtasks_mapping[a];else for(let[e,t]of Object.entries(this.subtasks_mapping))if(t in this.processor.feature_extractor){m=this.processor.feature_extractor[t].bind(this.processor.feature_extractor),a=e;break}const _=this.model.config.id2label,f=[];if("panoptic"===a||"instance"===a){const e=m(h,t,r,n,o,s??l)[0],a=e.segmentation;for(const t of e.segments_info){const e=new Uint8ClampedArray(a.data.length);for(let r=0;r<a.data.length;++r)a.data[r]===t.id&&(e[r]=255);const r=new u.RawImage(e,a.dims[1],a.dims[0],1);f.push({score:t.score,label:_[t.label_id],mask:r})}}else{if("semantic"!==a)throw Error(`Subtask ${a} not supported.`);{const{segmentation:e,labels:t}=m(h,s??l)[0];for(const r of t){const t=new Uint8ClampedArray(e.data.length);for(let n=0;n<e.data.length;++n)e.data[n]===r&&(t[n]=255);const n=new u.RawImage(t,e.dims[1],e.dims[0],1);f.push({score:null,label:_[r],mask:n})}}}return f}}class I extends _{constructor(e){super(e)}async _call(e,t,{hypothesis_template:r="This is a photo of {}"}={}){const n=Array.isArray(e),o=await p(e),s=t.map((e=>r.replace("{}",e))),a=this.tokenizer(s,{padding:"siglip"!==this.model.config.model_type||"max_length",truncation:!0}),{pixel_values:i}=await this.processor(o),c=await this.model({...a,pixel_values:i}),d="siglip"===this.model.config.model_type?e=>e.sigmoid().data:e=>(0,l.softmax)(e.data),u=[];for(const e of c.logits_per_image){const r=[...d(e)].map(((e,r)=>({score:e,label:t[r]})));r.sort(((e,t)=>t.score-e.score)),u.push(r)}return n?u:u[0]}}class B extends _{constructor(e){super(e)}async _call(e,{threshold:t=.9,percentage:r=!1}={}){const n=Array.isArray(e);if(n&&1!==e.length)throw Error("Object detection pipeline currently only supports a batch size of 1.");const o=await p(e),s=r?null:o.map((e=>[e.height,e.width])),{pixel_values:a,pixel_mask:i}=await this.processor(o),l=await this.model({pixel_values:a,pixel_mask:i}),c=this.processor.feature_extractor.post_process_object_detection(l,t,s),d=this.model.config.id2label,u=c.map((e=>e.boxes.map(((t,n)=>({score:e.scores[n],label:d[e.classes[n]],box:m(t,!r)})))));return n?u:u[0]}}class N extends _{constructor(e){super(e)}async _call(e,t,{threshold:r=.1,top_k:n=null,percentage:o=!1}={}){const s=Array.isArray(e),a=await p(e),i=this.tokenizer(t,{padding:!0,truncation:!0}),l=await this.processor(a),c=[];for(let e=0;e<a.length;++e){const s=a[e],d=o?null:[[s.height,s.width]],u=l.pixel_values[e].unsqueeze_(0),p=await this.model({...i,pixel_values:u}),h=this.processor.feature_extractor.post_process_object_detection(p,r,d,!0)[0];let _=h.boxes.map(((e,r)=>({score:h.scores[r],label:t[h.classes[r]],box:m(e,!o)}))).sort(((e,t)=>t.score-e.score));null!==n&&(_=_.slice(0,n)),c.push(_)}return s?c:c[0]}}class O extends _{constructor(e){super(e)}async _call(e,t,r={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class D extends _{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _call(e,{speaker_embeddings:t=null}={}){return this.processor?this._call_text_to_spectrogram(e,{speaker_embeddings:t}):this._call_text_to_waveform(e)}async _call_text_to_waveform(e){const t=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:r}=await this.model(t),n=this.model.config.sampling_rate;return{audio:r.data,sampling_rate:n}}async _call_text_to_spectrogram(e,{speaker_embeddings:t}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await o.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),("string"==typeof t||t instanceof URL)&&(t=new Float32Array(await(await fetch(t)).arrayBuffer())),t instanceof Float32Array)t=new d.Tensor("float32",t,[1,t.length]);else if(!(t instanceof d.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:r}=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:n}=await this.model.generate_speech(r,t,{vocoder:this.vocoder}),s=this.processor.feature_extractor.config.sampling_rate;return{audio:n.data,sampling_rate:s}}}class V extends _{constructor(e){super(e)}async _call(e){const t=await p(e),r=await this.processor(t),n=await this.model(r),o=[];for(const e of n.reconstruction){const t=e.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");o.push(u.RawImage.fromTensor(t))}return o.length>1?o:o[0]}}class j extends _{constructor(e){super(e)}async _call(e){const t=await p(e),r=await this.processor(t),{predicted_depth:n}=await this.model(r),o=[];for(let e=0;e<t.length;++e){const r=(0,d.interpolate)(n[e],t[e].size.reverse(),"bilinear",!1),s=r.mul_(255/(0,l.max)(r.data)[0]).to("uint8");o.push({predicted_depth:n[e],depth:u.RawImage.fromTensor(s)})}return o.length>1?o:o[0]}}const R=Object.freeze({"text-classification":{tokenizer:n.AutoTokenizer,pipeline:f,model:o.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:n.AutoTokenizer,pipeline:g,model:o.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:n.AutoTokenizer,pipeline:M,model:o.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:n.AutoTokenizer,pipeline:w,model:o.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:n.AutoTokenizer,pipeline:b,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:n.AutoTokenizer,pipeline:x,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:n.AutoTokenizer,pipeline:T,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:n.AutoTokenizer,pipeline:y,model:o.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:n.AutoTokenizer,pipeline:F,model:o.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:v,model:o.AutoModelForAudioClassification,processor:s.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:n.AutoTokenizer,pipeline:S,model:o.AutoModel,processor:s.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:n.AutoTokenizer,pipeline:A,model:[o.AutoModelForSpeechSeq2Seq,o.AutoModelForCTC],processor:s.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:n.AutoTokenizer,pipeline:D,model:[o.AutoModelForTextToWaveform,o.AutoModelForTextToSpectrogram],processor:[s.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:n.AutoTokenizer,pipeline:E,model:o.AutoModelForVision2Seq,processor:s.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:L,model:o.AutoModelForImageClassification,processor:s.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:z,model:[o.AutoModelForImageSegmentation,o.AutoModelForSemanticSegmentation,o.AutoModelForUniversalSegmentation],processor:s.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:n.AutoTokenizer,pipeline:I,model:o.AutoModel,processor:s.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:B,model:o.AutoModelForObjectDetection,processor:s.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:n.AutoTokenizer,pipeline:N,model:o.AutoModelForZeroShotObjectDetection,processor:s.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:n.AutoTokenizer,pipeline:O,model:o.AutoModelForDocumentQuestionAnswering,processor:s.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:V,model:o.AutoModelForImageToImage,processor:s.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:j,model:o.AutoModelForDepthEstimation,processor:s.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:n.AutoTokenizer,pipeline:P,model:o.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:s.AutoProcessor,pipeline:C,model:[o.AutoModelForImageFeatureExtraction,o.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),G=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function q(e,t=null,{progress_callback:r=null,config:n=null,cache_dir:o=null,local_files_only:s=!1,revision:a="main",device:l=null,dtype:c=null,model_file_name:d=null,session_options:u={}}={}){e=G[e]??e;const p=R[e.split("_",1)[0]];if(!p)throw Error(`Unsupported pipeline: ${e}. 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w{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&void 0!==this.size.width&&void 0!==this.size.height&&(this.pad_size=this.size),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(e,t,r=2){const n=e.height,o=e.width,s=t.height,a=t.width;let i=Math.min(n,s),l=Math.min(o,a);return i===n&&l===o?e:(n>o?l=Math.floor(o*i/n):o>n&&(i=Math.floor(n*l/o)),await e.resize(l,i,{resample:r}))}async crop_margin(e,t=200){const r=e.clone().grayscale(),n=(0,a.min)(r.data)[0],o=(0,a.max)(r.data)[0]-n;if(0===o)return e;const s=t/255;let i=r.width,l=r.height,c=0,d=0;const u=r.data;for(let e=0;e<r.height;++e){const t=e*r.width;for(let a=0;a<r.width;++a)(u[t+a]-n)/o<s&&(i=Math.min(i,a),l=Math.min(l,e),c=Math.max(c,a),d=Math.max(d,e))}return e=await e.crop([i,l,c,d])}pad_image(e,t,r,{mode:n="constant",center:s=!1,constant_values:a=0}={}){const[i,l,c]=t;let d,u;if("number"==typeof r?(d=r,u=r):(d=r.width,u=r.height),d!==l||u!==i){const r=new Float32Array(d*u*c);if(Array.isArray(a))for(let e=0;e<r.length;++e)r[e]=a[e%c];else 0!==a&&r.fill(a);const[p,h]=s?[Math.floor((d-l)/2),Math.floor((u-i)/2)]:[0,0];for(let t=0;t<i;++t){const n=(t+h)*d,o=t*l;for(let t=0;t<l;++t){const s=(n+t+p)*c,a=(o+t)*c;for(let t=0;t<c;++t)r[s+t]=e[a+t]}}if("symmetric"===n){if(s)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const t=i-1,n=l-1;for(let s=0;s<u;++s){const a=s*d,u=(0,o.calculateReflectOffset)(s,t)*l;for(let t=0;t<d;++t){if(s<i&&t<l)continue;const d=(a+t)*c,p=(u+(0,o.calculateReflectOffset)(t,n))*c;for(let t=0;t<c;++t)r[d+t]=e[p+t]}}}e=r,t=[u,d,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){const[r,n]=e.size;let o,s;if(this.do_thumbnail){const{height:e,width:r}=t;o=Math.min(e,r)}else Number.isInteger(t)?(o=t,s=this.config.max_size??o):void 0!==t&&(o=t.shortest_edge,s=t.longest_edge);if(void 0!==o||void 0!==s){const e=void 0===o?1:Math.max(o/r,o/n),t=r*e,a=n*e,i=void 0===s?1:Math.min(s/t,s/a);let l=Math.floor(Number((t*i).toFixed(2))),c=Math.floor(Number((a*i).toFixed(2)));return void 0!==this.size_divisibility&&([l,c]=M([l,c],this.size_divisibility)),[l,c]}if(void 0!==t&&void 0!==t.width&&void 0!==t.height){let e=t.width,o=t.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let t=o/n,s=e/r;Math.abs(1-s)<Math.abs(1-t)?t=s:s=t,o=g(t*n,this.config.ensure_multiple_of),e=g(s*r,this.config.ensure_multiple_of)}return[e,o]}if(void 0!==this.size_divisibility)return M([r,n],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(t)}`)}async resize(e){const[t,r]=this.get_resize_output_image_size(e,this.size);return await e.resize(t,r,{resample:this.resample})}async preprocess(e,{do_normalize:t=null,do_pad:r=null,do_convert_rgb:n=null,do_convert_grayscale:o=null,do_flip_channel_order:s=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[a,l]=e.size;if(n??this.do_convert_rgb?e=e.rgb():o&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let t,r;Number.isInteger(this.crop_size)?(t=this.crop_size,r=this.crop_size):(t=this.crop_size.width,r=this.crop_size.height),e=await e.center_crop(t,r)}const c=[e.height,e.width];let d=Float32Array.from(e.data),u=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(d),t??this.do_normalize){let t=this.image_mean;Array.isArray(this.image_mean)||(t=new Array(e.channels).fill(t));let r=this.image_std;if(Array.isArray(this.image_std)||(r=new Array(e.channels).fill(t)),t.length!==e.channels||r.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${t.length}) and \`image_std\` (${r.length}) must match the number of channels in the image (${e.channels}).`);for(let n=0;n<d.length;n+=e.channels)for(let o=0;o<e.channels;++o)d[n+o]=(d[n+o]-t[o])/r[o]}if(r??this.do_pad)if(this.pad_size){const t=this.pad_image(d,[e.height,e.width,e.channels],this.pad_size);[d,u]=t}else if(this.size_divisibility){const[e,t]=M([u[1],u[0]],this.size_divisibility);[d,u]=this.pad_image(d,u,{width:e,height:t})}if(s??this.do_flip_channel_order){if(3!==u[2])throw new Error("Flipping channel order is only supported for RGB images.");for(let e=0;e<d.length;e+=3){const t=d[e];d[e]=d[e+2],d[e+2]=t}}return{original_size:[l,a],reshaped_input_size:c,pixel_values:new i.Tensor("float32",d,u).permute(2,0,1)}}async _call(e,...t){Array.isArray(e)||(e=[e]);const r=await Promise.all(e.map((e=>this.preprocess(e))));return{pixel_values:(0,i.stack)(r.map((e=>e.pixel_values)),0),original_sizes:r.map((e=>e.original_size)),reshaped_input_sizes:r.map((e=>e.reshaped_input_size))}}}class b extends T{post_process_semantic_segmentation(...e){return u(...e)}}class x extends T{post_process_semantic_segmentation(...e){return u(...e)}}class k extends T{}class y extends T{}class F extends y{}class P extends T{}class C extends T{}class v extends T{}class S extends v{}class A extends T{}class E extends T{}class L extends T{constructor(e){super(e),this.crop_pct=this.config.crop_pct??.875}async resize(e){const t=this.size?.shortest_edge;if(void 0===t)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(t<384){const r=Math.floor(t/this.crop_pct),[n,o]=this.get_resize_output_image_size(e,{shortest_edge:r});e=await e.resize(n,o,{resample:this.resample}),e=await e.center_crop(t,t)}else e=await e.resize(t,t,{resample:this.resample});return e}}class z extends L{}class I extends T{}class B extends T{}class N extends T{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)))}}class O extends T{}class D extends T{}class V extends T{}class j extends T{}class R extends T{}class G extends R{}class q extends T{post_process_object_detection(...e){return d(...e)}}class $ extends q{}class W extends T{post_process_object_detection(...e){return d(...e)}}class U extends T{}class X extends T{}class Q extends T{pad_image(e,t,r,n={}){const[o,s,a]=t;let i=this.image_mean;Array.isArray(this.image_mean)||(i=new Array(a).fill(i));let l=this.image_std;Array.isArray(l)||(l=new Array(a).fill(i));const c=i.map(((e,t)=>-e/l[t]));return super.pad_image(e,t,r,{center:!0,constant_values:c,...n})}}class H extends Q{}class Y extends T{async _call(e){const t=await super._call(e),r=[t.pixel_values.dims[0],64,64],n=(0,i.full)(r,1n);return{...t,pixel_mask:n}}post_process_object_detection(...e){return d(...e)}post_process_panoptic_segmentation(...e){return _(...e)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class J extends T{post_process_panoptic_segmentation(...e){return _(...e)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class K extends T{post_process_object_detection(...e){return d(...e)}}class Z extends T{reshape_input_points(e,t,r,n=!1){e=structuredClone(e);let s=(0,o.calculateDimensions)(e);if(3===s.length)n||(s=[1,...s]),e=[e];else if(4!==s.length)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let n=0;n<e.length;++n){let o=t[n],s=r[n],a=[s[0]/o[0],s[1]/o[1]];for(let t=0;t<e[n].length;++t)for(let r=0;r<e[n][t].length;++r)for(let o=0;o<e[n][t][r].length;++o)e[n][t][r][o]*=a[o%2]}return new i.Tensor("float32",Float32Array.from(e.flat(1/0)),s)}add_input_labels(e,t){let r=(0,o.calculateDimensions)(e);if(2===r.length)r=[1,...r],e=[e];else if(3!==r.length)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(r.some(((e,r)=>e!==t.dims[r])))throw Error(`The first ${r.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new i.Tensor("int64",e.flat(1/0).map(BigInt),r)}async _call(e,{input_points:t=null,input_labels:r=null,input_boxes:n=null}={}){const o=await super._call(e);if(t&&(o.input_points=this.reshape_input_points(t,o.original_sizes,o.reshaped_input_sizes)),r){if(!o.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");o.input_labels=this.add_input_labels(r,o.input_points)}return n&&(o.input_boxes=this.reshape_input_points(n,o.original_sizes,o.reshaped_input_sizes,!0)),o}async post_process_masks(e,t,r,{mask_threshold:n=0,binarize:o=!0,pad_size:s=null}={}){const a=[],l=[(s=s??this.pad_size).height,s.width];for(let s=0;s<t.length;++s){const c=t[s],d=r[s];let u=await(0,i.interpolate_4d)(e[s],{mode:"bilinear",size:l});if(u=u.slice(null,null,[0,d[0]],[0,d[1]]),u=await(0,i.interpolate_4d)(u,{mode:"bilinear",size:c}),o){const e=u.data,t=new Uint8Array(e.length);for(let r=0;r<e.length;++r)e[r]>n&&(t[r]=1);u=new i.Tensor("bool",t,u.dims)}a.push(u)}return a}generate_crop_boxes(e,t,{crop_n_layers:r=0,overlap_ratio:n=512/1500,points_per_crop:o=32,crop_n_points_downscale_factor:s=1}={}){}}class ee extends T{pad_image(e,t,r,n={}){const[o,s,a]=t;return super.pad_image(e,t,{width:s+(r-s%r)%r,height:o+(r-o%r)%r},{mode:"symmetric",center:!1,constant_values:-1,...n})}}class te extends T{async _call(e,t){Array.isArray(e)||(e=[e]),Array.isArray(t)||(t=[t]);const r=await Promise.all(e.map((e=>this.preprocess(e)))),n=await Promise.all(t.map((e=>this.preprocess(e,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0}))));return{pixel_values:(0,i.stack)(r.map(((e,t)=>(0,i.cat)([e.pixel_values,n[t].pixel_values],0))),0),original_sizes:r.map((e=>e.original_size)),reshaped_input_sizes:r.map((e=>e.reshaped_input_size))}}}class re extends w{constructor(e){super(e),this.config.mel_filters??=(0,l.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=(0,l.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(e){const t=await(0,l.spectrogram)(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),r=t.data,n=(0,a.max)(r)[0];for(let e=0;e<r.length;++e)r[e]=(Math.max(r[e],n-8)+4)/4;return t}async _call(e){let t;f(e,"WhisperFeatureExtractor"),e.length>this.config.n_samples?(console.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`."),t=e.slice(0,this.config.n_samples)):(t=new Float32Array(this.config.n_samples),t.set(e));return{input_features:(await this._extract_fbank_features(t)).unsqueeze_(0)}}}class ne extends w{_zero_mean_unit_var_norm(e){const t=e.reduce(((e,t)=>e+t),0)/e.length,r=e.reduce(((e,r)=>e+(r-t)**2),0)/e.length;return e.map((e=>(e-t)/Math.sqrt(r+1e-7)))}async _call(e){f(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let t=e;this.config.do_normalize&&(t=this._zero_mean_unit_var_norm(t));const r=[1,t.length];return{input_values:new i.Tensor("float32",t,r),attention_mask:new i.Tensor("int64",new BigInt64Array(t.length).fill(1n),r)}}}class oe extends w{constructor(e){super(e);const t=this.config.sampling_rate,r=(0,l.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e<r.length;++e)r[e].push(0);this.mel_filters=r,this.window=(0,l.window_function)(400,"povey",{periodic:!1})}async _extract_fbank_features(e,t){return e=e.map((e=>32768*e)),(0,l.spectrogram)(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:r=2,do_normalize_per_mel_bins:n=!0,return_attention_mask:o=!0}={}){f(e,"SeamlessM4TFeatureExtractor");let s,a=await this._extract_fbank_features(e,this.config.max_length);if(n){const[e,t]=a.dims,r=a.data;for(let n=0;n<t;++n){let o=0;for(let s=0;s<e;++s)o+=r[s*t+n];const s=o/e;let a=0;for(let o=0;o<e;++o)a+=(r[o*t+n]-s)**2;a/=e-1;const i=Math.sqrt(a+1e-7);for(let o=0;o<e;++o){const e=o*t+n;r[e]=(r[e]-s)/i}}}if(t){const[e,t]=a.dims,n=a.data,l=e%r;if(l>0){const r=new Float32Array(t*(e+l));r.set(n),r.fill(this.config.padding_value,n.length);const c=e+l;a=new i.Tensor(a.type,r,[c,t]),o&&(s=new i.Tensor("int64",new BigInt64Array(c),[1,c]),s.data.fill(1n,0,e))}}const[l,c]=a.dims,d=this.config.stride;if(0!==l%d)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${d}).`);const u=a.view(1,Math.floor(l/d),c*d),p={input_features:u};if(o){const e=u.dims[1],t=new BigInt64Array(e);if(s){const e=s.data;for(let r=1,n=0;r<l;r+=d,++n)t[n]=e[r]}else t.fill(1n);p.attention_mask=new i.Tensor("int64",t,[1,e])}return p}}class se extends w{constructor(e){super(e);const t=this.config.sampling_rate,r=(0,l.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e<r.length;++e)r[e].push(0);this.mel_filters=r,this.window=(0,l.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,t){return(0,l.spectrogram)(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1.192092955078125e-7,remove_dc_offset:!0,max_num_frames:t,transpose:!0})}async _call(e){f(e,"ASTFeatureExtractor");const t=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const e=2*this.std,r=t.data;for(let t=0;t<r.length;++t)r[t]=(r[t]-this.mean)/e}return{input_values:t.unsqueeze_(0)}}}class ae extends w{constructor(e){super(e),this.mel_filters=(0,l.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,l.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,l.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(e,t,r,n){let o,s=!1;const a=e.length-t;if(a>0){if("rand_trunc"!==r)throw new Error(`Truncation strategy "${r}" not implemented`);{s=!0;const r=Math.floor(Math.random()*(a+1));e=e.subarray(r,r+t),o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}}else{if(a<0){let r=new Float64Array(t);if(r.set(e),"repeat"===n)for(let n=e.length;n<t;n+=e.length)r.set(e.subarray(0,Math.min(e.length,t-n)),n);else if("repeatpad"===n)for(let t=e.length;t<-a;t+=e.length)r.set(e,t);e=r}if("fusion"===r)throw new Error(`Truncation strategy "${r}" not implemented`);o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}return o.unsqueeze_(0)}async _extract_fbank_features(e,t,r=null){return(0,l.spectrogram)(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:t,log_mel:"dB",max_num_frames:r,do_pad:!1,transpose:!0})}async _call(e,{max_length:t=null}={}){f(e,"ClapFeatureExtractor");return{input_features:(await this._get_input_mel(e,t??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}class ie extends w{async _call(e){f(e,"PyAnnoteFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));const t=[1,1,e.length];return{input_values:new i.Tensor("float32",e,t)}}samples_to_frames(e){return(e-this.config.offset)/this.config.step}post_process_speaker_diarization(e,t){const r=t/this.samples_to_frames(t)/this.config.sampling_rate,n=[];for(const t of e.tolist()){const e=[];let o=-1;for(let r=0;r<t.length;++r){const n=(0,a.softmax)(t[r]),[s,i]=(0,a.max)(n),[l,c]=[r,r+1];i!==o?(o=i,e.push({id:i,start:l,end:c,score:s})):(e.at(-1).end=c,e.at(-1).score+=s)}n.push(e.map((({id:e,start:t,end:n,score:o})=>({id:e,start:t*r,end:n*r,confidence:o/(n-t)}))))}return n}}class le extends w{constructor(e){super(e);const t=this.config.sampling_rate,r=(0,l.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e<r.length;++e)r[e].push(0);this.mel_filters=r,this.window=(0,l.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(e){return e=e.map((e=>32768*e)),(0,l.spectrogram)(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){f(e,"WeSpeakerFeatureExtractor");const t=(await this._extract_fbank_features(e)).unsqueeze_(0);if(null===this.config.fbank_centering_span){const e=t.mean(1).data,r=t.data,[n,o,s]=t.dims;for(let t=0;t<n;++t){const n=t*o*s,a=t*s;for(let t=0;t<o;++t){const o=n+t*s;for(let t=0;t<s;++t)r[o+t]-=e[a+t]}}}return{input_features:t}}}class ce extends w{}class de extends n.Callable{constructor(e){super(),this.feature_extractor=e}async _call(e,...t){return await this.feature_extractor(e,...t)}}class ue extends de{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class pe extends de{async _call(e){return await this.feature_extractor(e)}}class he extends de{async _call(e){return await this.feature_extractor(e)}}class me extends de{async _call(e){return await this.feature_extractor(e)}post_process_speaker_diarization(...e){return this.feature_extractor.post_process_speaker_diarization(...e)}}class _e extends de{async _call(e){return await this.feature_extractor(e)}}class fe extends de{}class ge extends de{constructor(e){super(e);const{tasks_answer_post_processing_type:t,task_prompts_without_inputs:r,task_prompts_with_input:n}=e.config;this.tasks_answer_post_processing_type=new Map(Object.entries(t??{})),this.task_prompts_without_inputs=new Map(Object.entries(r??{})),this.task_prompts_with_input=new Map(Object.entries(n??{})),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){"string"==typeof e&&(e=[e]);const t=[];for(const r of e)if(this.task_prompts_without_inputs.has(r))t.push(this.task_prompts_without_inputs.get(r));else{for(const[e,n]of this.task_prompts_with_input)if(r.includes(e)){t.push(n.replaceAll("{input}",r).replaceAll(e,""));break}t.length!==e.length&&t.push(r)}return t}post_process_generation(e,t,r){const n=this.tasks_answer_post_processing_type.get(t)??"pure_text";let o;switch(e=e.replaceAll("<s>","").replaceAll("</s>",""),n){case"pure_text":o=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const s="ocr"===n?"quad_boxes":"bboxes",a=e.matchAll(this.regexes[s]),i=[],l=[];for(const[e,t,...n]of a)i.push(t?t.trim():i.at(-1)??""),l.push(n.map(((e,t)=>(Number(e)+.5)/this.size_per_bin*r[t%2])));o={labels:i,[s]:l};break;default:throw new Error(`Task "${t}" (of type "${n}") not yet implemented.`)}return{[t]:o}}}class Me{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:T,WhisperFeatureExtractor:re,ViTFeatureExtractor:I,MobileViTFeatureExtractor:R,MobileViTImageProcessor:G,MobileNetV1FeatureExtractor:O,MobileNetV2FeatureExtractor:D,MobileNetV3FeatureExtractor:V,MobileNetV4FeatureExtractor:j,OwlViTFeatureExtractor:q,Owlv2ImageProcessor:$,CLIPFeatureExtractor:v,CLIPImageProcessor:S,Florence2Processor:ge,ChineseCLIPFeatureExtractor:A,SiglipImageProcessor:E,ConvNextFeatureExtractor:L,ConvNextImageProcessor:z,SegformerFeatureExtractor:x,SapiensFeatureExtractor:b,BitImageProcessor:P,DPTImageProcessor:F,DPTFeatureExtractor:y,PvtImageProcessor:k,GLPNFeatureExtractor:C,BeitFeatureExtractor:X,DeiTFeatureExtractor:U,DetrFeatureExtractor:Y,RTDetrImageProcessor:W,MaskFormerFeatureExtractor:J,YolosFeatureExtractor:K,DonutFeatureExtractor:Q,NougatImageProcessor:H,EfficientNetImageProcessor:N,ViTImageProcessor:B,VitMatteImageProcessor:te,SamImageProcessor:Z,Swin2SRImageProcessor:ee,Wav2Vec2FeatureExtractor:ne,SeamlessM4TFeatureExtractor:oe,SpeechT5FeatureExtractor:ce,ASTFeatureExtractor:se,ClapFeatureExtractor:ae,PyAnnoteFeatureExtractor:ie,WeSpeakerFeatureExtractor:le};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:pe,Wav2Vec2ProcessorWithLM:he,PyAnnoteProcessor:me,SamProcessor:ue,SpeechT5Processor:_e,OwlViTProcessor:fe,Florence2Processor:ge};static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:n=null,local_files_only:o=!1,revision:a="main"}={}){let i=r??await(0,s.getModelJSON)(e,"preprocessor_config.json",!0,{progress_callback:t,config:r,cache_dir:n,local_files_only:o,revision:a}),l=i.feature_extractor_type??i.image_processor_type,c=this.FEATURE_EXTRACTOR_CLASS_MAPPING[l];if(!c){if(void 0===i.size)throw new Error(`Unknown Feature Extractor type: ${l}`);console.warn(`Feature extractor type "${l}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),c=T}return new(this.PROCESSOR_CLASS_MAPPING[i.processor_class]??de)(new c(i))}}},"./src/tokenizers.js":

/*!***************************!*\

  !*** ./src/tokenizers.js ***!

  \***************************/(e,t,r)=>{r.r(t),r.d(t,{AlbertTokenizer:()=>xe,AutoTokenizer:()=>ht,BartTokenizer:()=>Ne,BertTokenizer:()=>be,BlenderbotSmallTokenizer:()=>lt,BlenderbotTokenizer:()=>it,BloomTokenizer:()=>je,CLIPTokenizer:()=>nt,CamembertTokenizer:()=>Ee,CodeGenTokenizer:()=>rt,CodeLlamaTokenizer:()=>qe,CohereTokenizer:()=>pt,ConvBertTokenizer:()=>ve,DebertaTokenizer:()=>Fe,DebertaV2Tokenizer:()=>Pe,DistilBertTokenizer:()=>Ae,ElectraTokenizer:()=>ze,EsmTokenizer:()=>Qe,FalconTokenizer:()=>Ue,GPT2Tokenizer:()=>Be,GPTNeoXTokenizer:()=>Xe,GemmaTokenizer:()=>Ye,Grok1Tokenizer:()=>Je,HerbertTokenizer:()=>Ce,LlamaTokenizer:()=>Ge,M2M100Tokenizer:()=>et,MBart50Tokenizer:()=>De,MBartTokenizer:()=>Oe,MPNetTokenizer:()=>We,MarianTokenizer:()=>st,MobileBertTokenizer:()=>ke,NllbTokenizer:()=>Ze,NougatTokenizer:()=>dt,PreTrainedTokenizer:()=>Te,Qwen2Tokenizer:()=>He,RoFormerTokenizer:()=>Se,RobertaTokenizer:()=>Ve,SiglipTokenizer:()=>ot,SpeechT5Tokenizer:()=>ct,SqueezeBertTokenizer:()=>ye,T5Tokenizer:()=>Ie,TokenizerModel:()=>k,VitsTokenizer:()=>ut,Wav2Vec2CTCTokenizer:()=>at,WhisperTokenizer:()=>tt,XLMRobertaTokenizer:()=>$e,XLMTokenizer:()=>Le,is_chinese_char:()=>M});var n=r(/*! ./utils/generic.js */"./src/utils/generic.js"),o=r(/*! ./utils/core.js */"./src/utils/core.js"),s=r(/*! ./utils/hub.js */"./src/utils/hub.js"),a=r(/*! ./utils/maths.js */"./src/utils/maths.js"),i=r(/*! ./utils/tensor.js */"./src/utils/tensor.js"),l=r(/*! ./utils/data-structures.js */"./src/utils/data-structures.js"),c=r(/*! @huggingface/jinja */"./node_modules/@huggingface/jinja/dist/index.js"),d=r(/*! ./models/whisper/common_whisper.js */"./src/models/whisper/common_whisper.js"),u=r(/*! ./utils/constants.js */"./src/utils/constants.js");async function p(e,t){const r=await Promise.all([(0,s.getModelJSON)(e,"tokenizer.json",!0,t),(0,s.getModelJSON)(e,"tokenizer_config.json",!0,t)]);return null!==t.legacy&&(r[1].legacy=t.legacy),r}function h(e,t=!0){if(void 0!==e.Regex){let t=e.Regex.replace(/\\([#&~])/g,"$1");for(const[e,r]of b)t=t.replaceAll(e,r);return new RegExp(t,"gu")}if(void 0!==e.String){const r=(0,o.escapeRegExp)(e.String);return new RegExp(t?r:`(${r})`,"gu")}return console.warn("Unknown pattern type:",e),null}function m(e){return new Map(Object.entries(e))}function _(e){const t=e.dims;switch(t.length){case 1:return e.tolist();case 2:if(1!==t[0])throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return e.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${t.length}.`)}}function f(e){return e.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function g(e){return e.replace(/\p{M}/gu,"")}function M(e){return e>=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}const w="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",T=new RegExp(`^[${w}]+$`,"gu"),b=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class x{constructor(e){this.content=e.content,this.id=e.id,this.single_word=e.single_word??!1,this.lstrip=e.lstrip??!1,this.rstrip=e.rstrip??!1,this.special=e.special??!1,this.normalized=e.normalized??null}}class k extends n.Callable{constructor(e){super(),this.config=e,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(e,...t){switch(e.type){case"WordPiece":return new y(e);case"Unigram":return new F(e,...t);case"BPE":return new v(e);default:if(e.vocab)return new S(e,...t);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){return e=this.encode(e),this.fuse_unk&&(e=function(e,t,r){const n=[];let o=0;for(;o<e.length;)if(n.push(e[o]),(t.get(e[o])??r)===r)for(;++o<e.length&&(t.get(e[o])??r)===r;)t.get(n.at(-1))!==r&&(n[n.length-1]+=e[o]);else++o;return n}(e,this.tokens_to_ids,this.unk_token_id)),e}encode(e){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(e){return e.map((e=>this.tokens_to_ids.get(e)??this.unk_token_id))}convert_ids_to_tokens(e){return e.map((e=>this.vocab[e]??this.unk_token))}}class y extends k{constructor(e){super(e),this.tokens_to_ids=m(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[e,t]of this.tokens_to_ids)this.vocab[t]=e}encode(e){const t=[];for(const r of e){const e=[...r];if(e.length>this.max_input_chars_per_word){t.push(this.unk_token);continue}let n=!1,o=0;const s=[];for(;o<e.length;){let t=e.length,r=null;for(;o<t;){let n=e.slice(o,t).join("");if(o>0&&(n=this.config.continuing_subword_prefix+n),this.tokens_to_ids.has(n)){r=n;break}--t}if(null===r){n=!0;break}s.push(r),o=t}n?t.push(this.unk_token):t.push(...s)}return t}}class F extends k{constructor(e,t){super(e);const r=e.vocab.length;this.vocab=new Array(r),this.scores=new Array(r);for(let t=0;t<r;++t){const r=e.vocab[t];this.vocab[t]=r[0],this.scores[t]=r[1]}this.unk_token_id=e.unk_id,this.unk_token=this.vocab[e.unk_id],this.tokens_to_ids=new Map(this.vocab.map(((e,t)=>[e,t]))),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,a.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new l.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const t=e.chars;let r=0;for(;r<t.length;){let n=!1;const s=[],a=t.slice(r).join(""),i=this.trie.commonPrefixSearch(a);for(const t of i){s.push(t);const a=this.tokens_to_ids.get(t),i=this.scores[a],l=(0,o.len)(t);e.insert(r,l,i,a),n||1!==l||(n=!0)}n||e.insert(r,1,this.unk_score,this.unk_token_id),r+=1}}tokenize(e){const t=new l.TokenLattice(e,this.bos_token_id,this.eos_token_id);return this.populateNodes(t),t.tokens()}encode(e){const t=[];for(const r of e){const e=this.tokenize(r);t.push(...e)}return t}}const P=(()=>{const e=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},((e,t)=>t+"!".charCodeAt(0))),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},((e,t)=>t+"¡".charCodeAt(0))),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},((e,t)=>t+"®".charCodeAt(0)))],t=e.slice();let r=0;for(let n=0;n<256;++n)e.includes(n)||(e.push(n),t.push(256+r),r+=1);const n=t.map((e=>String.fromCharCode(e)));return Object.fromEntries(e.map(((e,t)=>[e,n[t]])))})(),C=(0,o.reverseDictionary)(P);class v extends k{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=m(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[e,t]of this.tokens_to_ids)this.vocab[t]=e;this.bpe_ranks=new Map(e.merges.map(((e,t)=>[e,t]))),this.merges=e.merges.map((e=>e.split(this.BPE_SPLIT_TOKEN))),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(e){if(0===e.length)return[];const t=this.cache.get(e);if(void 0!==t)return t;const r=Array.from(e);this.end_of_word_suffix&&(r[r.length-1]+=this.end_of_word_suffix);let n=[];if(r.length>1){const e=new l.PriorityQueue(((e,t)=>e.score<t.score));let t={token:r[0],bias:0,prev:null,next:null},o=t;for(let t=1;t<r.length;++t){const n={bias:t/r.length,token:r[t],prev:o,next:null};o.next=n,this._add_node(e,o),o=n}for(;!e.isEmpty();){const r=e.pop();if(r.deleted||!r.next||r.next.deleted)continue;if(r.deleted=!0,r.next.deleted=!0,r.prev){const e={...r.prev};r.prev.deleted=!0,r.prev=e,e.prev?e.prev.next=e:t=e}const n={token:r.token+r.next.token,bias:r.bias,prev:r.prev,next:r.next.next};n.prev?(n.prev.next=n,this._add_node(e,n.prev)):t=n,n.next&&(n.next.prev=n,this._add_node(e,n))}for(let e=t;null!==e;e=e.next)n.push(e.token)}else n=r;if(this.continuing_subword_suffix)for(let e=0;e<n.length-1;++e)n[e]+=this.continuing_subword_suffix;return this.cache.set(e,n),n}_add_node(e,t){const r=this.bpe_ranks.get(t.token+this.BPE_SPLIT_TOKEN+t.next.token);void 0!==r&&(t.score=r+t.bias,e.push(t))}encode(e){const t=[];for(const r of e){if(this.ignore_merges&&this.tokens_to_ids.has(r)){t.push(r);continue}const e=this.bpe(r);for(const r of e)if(this.tokens_to_ids.has(r))t.push(r);else if(this.byte_fallback){const e=Array.from(this.text_encoder.encode(r)).map((e=>`<0x${e.toString(16).toUpperCase().padStart(2,"0")}>`));e.every((e=>this.tokens_to_ids.has(e)))?t.push(...e):t.push(this.unk_token)}else t.push(this.unk_token)}return t}}class S extends k{constructor(e,t){super(e),this.tokens_to_ids=m(t.target_lang?e.vocab[t.target_lang]:e.vocab),this.bos_token=t.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=t.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=t.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[e,t]of this.tokens_to_ids)this.vocab[t]=e}encode(e){return e}}class A extends n.Callable{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"BertNormalizer":return new j(e);case"Precompiled":return new pe(e);case"Sequence":return new V(e);case"Replace":return new E(e);case"NFC":return new L(e);case"NFKC":return new z(e);case"NFKD":return new I(e);case"Strip":return new B(e);case"StripAccents":return new N(e);case"Lowercase":return new O(e);case"Prepend":return new D(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class E extends A{normalize(e){const t=h(this.config.pattern);return null===t?e:e.replaceAll(t,this.config.content)}}class L extends A{normalize(e){return e=e.normalize("NFC")}}class z extends A{normalize(e){return e=e.normalize("NFKC")}}class I extends A{normalize(e){return e=e.normalize("NFKD")}}class B extends A{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class N extends A{normalize(e){return e=g(e)}}class O extends A{normalize(e){return e=e.toLowerCase()}}class D extends A{normalize(e){return e=this.config.prepend+e}}class V extends A{constructor(e){super(e),this.normalizers=e.normalizers.map((e=>A.fromConfig(e)))}normalize(e){return this.normalizers.reduce(((e,t)=>t.normalize(e)),e)}}class j extends A{_tokenize_chinese_chars(e){const t=[];for(let r=0;r<e.length;++r){const n=e[r];M(n.charCodeAt(0))?(t.push(" "),t.push(n),t.push(" ")):t.push(n)}return t.join("")}stripAccents(e){return e.normalize("NFD").replace(/\p{Mn}/gu,"")}_is_control(e){switch(e){case"\t":case"\n":case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(e)}}_clean_text(e){const t=[];for(const r of e){const e=r.charCodeAt(0);0===e||65533===e||this._is_control(r)||(/^\s$/.test(r)?t.push(" "):t.push(r))}return t.join("")}normalize(e){return this.config.clean_text&&(e=this._clean_text(e)),this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),!1!==this.config.strip_accents&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class R extends n.Callable{static fromConfig(e){if(null===e)return null;switch(e.type){case"BertPreTokenizer":return new G(e);case"Sequence":return new he(e);case"Whitespace":return new me(e);case"WhitespaceSplit":return new _e(e);case"Metaspace":return new de(e);case"ByteLevel":return new q(e);case"Split":return new $(e);case"Punctuation":return new W(e);case"Digits":return new U(e);case"Replace":return new fe(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,t){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,t){return(Array.isArray(e)?e.map((e=>this.pre_tokenize_text(e,t))):this.pre_tokenize_text(e,t)).flat()}_call(e,t){return this.pre_tokenize(e,t)}}class G extends R{constructor(e){super(),this.pattern=new RegExp(`[^\\s${w}]+|[${w}]`,"gu")}pre_tokenize_text(e,t){return e.trim().match(this.pattern)||[]}}class q extends R{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=P,this.text_encoder=new TextEncoder}pre_tokenize_text(e,t){this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e);return(this.use_regex?e.match(this.pattern)||[]:[e]).map((e=>Array.from(this.text_encoder.encode(e),(e=>this.byte_encoder[e])).join("")))}}class $ extends R{constructor(e){super(),this.config=e,this.pattern=h(this.config.pattern,this.config.invert)}pre_tokenize_text(e,t){return null===this.pattern?[]:this.config.invert?e.match(this.pattern)||[]:function(e,t){const r=[];let n=0;for(const o of e.matchAll(t)){const t=o[0];n<o.index&&r.push(e.slice(n,o.index)),t.length>0&&r.push(t),n=o.index+t.length}return n<e.length&&r.push(e.slice(n)),r}(e,this.pattern)}}class W extends R{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${w}]+|[${w}]+`,"gu")}pre_tokenize_text(e,t){return e.match(this.pattern)||[]}}class U extends R{constructor(e){super(),this.config=e;const t="[^\\d]+|\\d"+(this.config.individual_digits?"":"+");this.pattern=new RegExp(t,"gu")}pre_tokenize_text(e,t){return e.match(this.pattern)||[]}}class X extends n.Callable{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"TemplateProcessing":return new Y(e);case"ByteLevel":return new J(e);case"RobertaProcessing":return new H(e);case"BertProcessing":return new Q(e);case"Sequence":return new K(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...t){throw Error("post_process should be implemented in subclass.")}_call(e,...t){return this.post_process(e,...t)}}class Q extends X{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,t=null,{add_special_tokens:r=!0}={}){r&&(e=(0,o.mergeArrays)([this.cls],e,[this.sep]));let n=new Array(e.length).fill(0);if(null!==t){const s=r&&this instanceof H?[this.sep]:[],a=r?[this.sep]:[];e=(0,o.mergeArrays)(e,s,t,a),n=(0,o.mergeArrays)(n,new Array(t.length+s.length+a.length).fill(1))}return{tokens:e,token_type_ids:n}}}class H extends Q{}class Y extends X{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,t=null,{add_special_tokens:r=!0}={}){const n=null===t?this.single:this.pair;let s=[],a=[];for(const i of n)"SpecialToken"in i?r&&(s.push(i.SpecialToken.id),a.push(i.SpecialToken.type_id)):"Sequence"in i&&("A"===i.Sequence.id?(s=(0,o.mergeArrays)(s,e),a=(0,o.mergeArrays)(a,new Array(e.length).fill(i.Sequence.type_id))):"B"===i.Sequence.id&&(s=(0,o.mergeArrays)(s,t),a=(0,o.mergeArrays)(a,new Array(t.length).fill(i.Sequence.type_id))));return{tokens:s,token_type_ids:a}}}class J extends X{post_process(e,t=null){return t&&(e=(0,o.mergeArrays)(e,t)),{tokens:e}}}class K extends X{constructor(e){super(e),this.processors=e.processors.map((e=>X.fromConfig(e)))}post_process(e,t=null,r={}){let n;for(const o of this.processors)if(o instanceof J){if(e=o.post_process(e).tokens,t){t=o.post_process(t).tokens}}else{const s=o.post_process(e,t,r);e=s.tokens,n=s.token_type_ids}return{tokens:e,token_type_ids:n}}}class Z extends n.Callable{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(null===e)return null;switch(e.type){case"WordPiece":return new oe(e);case"Metaspace":return new ue(e);case"ByteLevel":return new se(e);case"Replace":return new ee(e);case"ByteFallback":return new te(e);case"Fuse":return new re(e);case"Strip":return new ne(e);case"Sequence":return new ie(e);case"CTC":return new ae(e);case"BPEDecoder":return new le(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class ee extends Z{decode_chain(e){const t=h(this.config.pattern);return null===t?e:e.map((e=>e.replaceAll(t,this.config.content)))}}class te extends Z{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const t=[];let r=[];for(const n of e){let e=null;if(6===n.length&&n.startsWith("<0x")&&n.endsWith(">")){const t=parseInt(n.slice(3,5),16);isNaN(t)||(e=t)}if(null!==e)r.push(e);else{if(r.length>0){const e=this.text_decoder.decode(Uint8Array.from(r));t.push(e),r=[]}t.push(n)}}if(r.length>0){const e=this.text_decoder.decode(Uint8Array.from(r));t.push(e),r=[]}return t}}class re extends Z{decode_chain(e){return[e.join("")]}}class ne extends Z{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map((e=>{let t=0;for(let r=0;r<this.start&&e[r]===this.content;++r)t=r+1;let r=e.length;for(let t=0;t<this.stop;++t){const n=e.length-t-1;if(e[n]!==this.content)break;r=n}return e.slice(t,r)}))}}class oe extends Z{constructor(e){super(e),this.cleanup=e.cleanup}decode_chain(e){return e.map(((e,t)=>(0!==t&&(e=e.startsWith(this.config.prefix)?e.replace(this.config.prefix,""):" "+e),this.cleanup&&(e=f(e)),e)))}}class se extends Z{constructor(e){super(e),this.byte_decoder=C,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const t=e.join(""),r=new Uint8Array([...t].map((e=>this.byte_decoder[e])));return this.text_decoder.decode(r)}decode_chain(e){const t=[];let r=[];for(const n of e)void 0!==this.added_tokens.find((e=>e.content===n))?(r.length>0&&(t.push(this.convert_tokens_to_string(r)),r=[]),t.push(n)):r.push(n);return r.length>0&&t.push(this.convert_tokens_to_string(r)),t}}class ae extends Z{constructor(e){super(e),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(e){if(0===e.length)return"";const t=[e[0]];for(let r=1;r<e.length;++r)e[r]!==t.at(-1)&&t.push(e[r]);let r=t.filter((e=>e!==this.pad_token)).join("");return this.cleanup&&(r=f(r).replaceAll(this.word_delimiter_token," ").trim()),r}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class ie extends Z{constructor(e){super(e),this.decoders=e.decoders.map((e=>Z.fromConfig(e)))}decode_chain(e){return this.decoders.reduce(((e,t)=>t.decode_chain(e)),e)}}class le extends Z{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map(((t,r)=>t.replaceAll(this.suffix,r===e.length-1?"":" ")))}}class ce extends Z{decode_chain(e){let t="";for(let r=1;r<e.length;r+=2)t+=e[r];return[t]}}class de extends R{constructor(e){super(),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement,this.strRep=e.str_rep||this.replacement,this.prepend_scheme=e.prepend_scheme??"always"}pre_tokenize_text(e,{section_index:t}={}){let r=e.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!r.startsWith(this.replacement)&&("always"===this.prepend_scheme||"first"===this.prepend_scheme&&0===t)&&(r=this.strRep+r),[r]}}class ue extends Z{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){const t=[];for(let r=0;r<e.length;++r){let n=e[r].replaceAll(this.replacement," ");this.addPrefixSpace&&0==r&&n.startsWith(" ")&&(n=n.substring(1)),t.push(n)}return t}}class pe extends A{constructor(e){super(e),this.charsmap=e.precompiled_charsmap}normalize(e){if((e=(e=e.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,"")).replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," ")).includes("～")){const t=e.split("～");e=t.map((e=>e.normalize("NFKC"))).join("～")}else e=e.normalize("NFKC");return e}}class he extends R{constructor(e){super(),this.tokenizers=e.pretokenizers.map((e=>R.fromConfig(e)))}pre_tokenize_text(e,t){return this.tokenizers.reduce(((e,r)=>r.pre_tokenize(e,t)),[e])}}class me extends R{constructor(e){super()}pre_tokenize_text(e,t){return e.match(/\w+|[^\w\s]+/g)||[]}}class _e extends R{constructor(e){super()}pre_tokenize_text(e,t){return function(e){return e.match(/\S+/g)||[]}(e)}}class fe extends R{constructor(e){super(),this.config=e,this.pattern=h(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,t){return null===this.pattern?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const ge=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Me(e,t,r,n){for(const s of Object.keys(e)){const a=t-e[s].length,i=r(s),l=new Array(a).fill(i);e[s]="right"===n?(0,o.mergeArrays)(e[s],l):(0,o.mergeArrays)(l,e[s])}}function we(e,t){for(const r of Object.keys(e))e[r].length=t}class Te extends n.Callable{return_token_type_ids=!1;padding_side="right";constructor(e,t){super(),this._tokenizer_config=t,this.normalizer=A.fromConfig(e.normalizer),this.pre_tokenizer=R.fromConfig(e.pre_tokenizer),this.model=k.fromConfig(e.model,t),this.post_processor=X.fromConfig(e.post_processor),this.decoder=Z.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const t of e.added_tokens){const e=new x(t);this.added_tokens.push(e),this.model.tokens_to_ids.set(e.content,e.id),this.model.vocab[e.id]=e.content,e.special&&(this.special_tokens.push(e.content),this.all_special_ids.push(e.id))}if(this.additional_special_tokens=t.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort(((e,t)=>t.content.length-e.content.length)).map((e=>`${e.lstrip?"\\s*":""}(${(0,o.escapeRegExp)(e.content)})${e.rstrip?"\\s*":""}`)).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=t.model_max_length,this.remove_space=t.remove_space,this.clean_up_tokenization_spaces=t.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=t.do_lowercase_and_remove_accent??!1,t.padding_side&&(this.padding_side=t.padding_side),this.legacy=!1,this.chat_template=t.chat_template??null,Array.isArray(this.chat_template)){const e=Object.create(null);for(const{name:t,template:r}of this.chat_template){if("string"!=typeof t||"string"!=typeof r)throw new Error('Chat template must be a list of objects with "name" and "template" properties');e[t]=r}this.chat_template=e}this._compiled_template_cache=new Map}getToken(...e){for(const t of e){const e=this._tokenizer_config[t];if(e){if("object"==typeof e){if("AddedToken"===e.__type)return e.content;throw Error(`Unknown token: ${e}`)}return e}}return null}static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:n=null,local_files_only:o=!1,revision:s="main",legacy:a=null}={}){return new this(...await p(e,{progress_callback:t,config:r,cache_dir:n,local_files_only:o,revision:s,legacy:a}))}_call(e,{text_pair:t=null,add_special_tokens:r=!0,padding:n=!1,truncation:o=null,max_length:s=null,return_tensor:l=!0,return_token_type_ids:c=null}={}){const d=Array.isArray(e);let u;if(d){if(0===e.length)throw Error("text array must be non-empty");if(null!==t){if(!Array.isArray(t))throw Error("text_pair must also be an array");if(e.length!==t.length)throw Error("text and text_pair must have the same length");u=e.map(((e,n)=>this._encode_plus(e,{text_pair:t[n],add_special_tokens:r,return_token_type_ids:c})))}else u=e.map((e=>this._encode_plus(e,{add_special_tokens:r,return_token_type_ids:c})))}else{if(null==e)throw Error("text may not be null or undefined");if(Array.isArray(t))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");u=[this._encode_plus(e,{text_pair:t,add_special_tokens:r,return_token_type_ids:c})]}if(null===s?s="max_length"===n?this.model_max_length:(0,a.max)(u.map((e=>e.input_ids.length)))[0]:o||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),s=Math.min(s,this.model_max_length??1/0),n||o)for(let e=0;e<u.length;++e)u[e].input_ids.length!==s&&(u[e].input_ids.length>s?o&&we(u[e],s):n&&Me(u[e],s,(e=>"input_ids"===e?this.pad_token_id:0),this.padding_side));const p={};if(l){if((!n||!o)&&u.some((e=>{for(const t of Object.keys(e))if(e[t].length!==u[0][t]?.length)return!0;return!1})))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const e=[u.length,u[0].input_ids.length];for(const t of Object.keys(u[0]))p[t]=new i.Tensor("int64",BigInt64Array.from(u.flatMap((e=>e[t])).map(BigInt)),e)}else{for(const e of Object.keys(u[0]))p[e]=u.map((t=>t[e]));if(!d)for(const e of Object.keys(p))p[e]=p[e][0]}return p}_encode_text(e){if(null===e)return null;const t=(this.added_tokens_regex?e.split(this.added_tokens_regex).filter((e=>e)):[e]).map(((e,t)=>{if(void 0!==this.added_tokens.find((t=>t.content===e)))return e;{if(!0===this.remove_space&&(e=e.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(e=function(e){return g(e.toLowerCase())}(e)),null!==this.normalizer&&(e=this.normalizer(e)),0===e.length)return[];const r=null!==this.pre_tokenizer?this.pre_tokenizer(e,{section_index:t}):[e];return this.model(r)}})).flat();return t}_encode_plus(e,{text_pair:t=null,add_special_tokens:r=!0,return_token_type_ids:n=null}={}){const{tokens:o,token_type_ids:s}=this._tokenize_helper(e,{pair:t,add_special_tokens:r}),a=this.model.convert_tokens_to_ids(o),i={input_ids:a,attention_mask:new Array(a.length).fill(1)};return(n??this.return_token_type_ids)&&s&&(i.token_type_ids=s),i}_tokenize_helper(e,{pair:t=null,add_special_tokens:r=!1}={}){const n=this._encode_text(e),s=this._encode_text(t);return this.post_processor?this.post_processor(n,s,{add_special_tokens:r}):{tokens:(0,o.mergeArrays)(n??[],s??[])}}tokenize(e,{pair:t=null,add_special_tokens:r=!1}={}){return this._tokenize_helper(e,{pair:t,add_special_tokens:r}).tokens}encode(e,{text_pair:t=null,add_special_tokens:r=!0,return_token_type_ids:n=null}={}){return this._encode_plus(e,{text_pair:t,add_special_tokens:r,return_token_type_ids:n}).input_ids}batch_decode(e,t={}){return e instanceof i.Tensor&&(e=e.tolist()),e.map((e=>this.decode(e,t)))}decode(e,t={}){if(e instanceof i.Tensor&&(e=_(e)),!Array.isArray(e)||0===e.length||!(0,o.isIntegralNumber)(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,t)}decode_single(e,{skip_special_tokens:t=!1,clean_up_tokenization_spaces:r=null}){let n=this.model.convert_ids_to_tokens(e);t&&(n=n.filter((e=>!this.special_tokens.includes(e))));let o=this.decoder?this.decoder(n):n.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(o=o.replaceAll(this.decoder.end_of_word_suffix," "),t&&(o=o.trim())),(r??this.clean_up_tokenization_spaces)&&(o=f(o)),o}get_chat_template({chat_template:e=null,tools:t=null}={}){if(this.chat_template&&"object"==typeof this.chat_template){const r=this.chat_template;if(null!==e&&Object.hasOwn(r,e))e=r[e];else if(null===e)if(null!==t&&"tool_use"in r)e=r.tool_use;else{if(!("default"in r))throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(r).sort()}.`);e=r.default}}else if(null===e){if(!this.chat_template)throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");e=this.chat_template}return e}apply_chat_template(e,{tools:t=null,documents:r=null,chat_template:n=null,add_generation_prompt:o=!1,tokenize:s=!0,padding:a=!1,truncation:i=!1,max_length:l=null,return_tensor:d=!0,return_dict:u=!1,tokenizer_kwargs:p={},...h}={}){if("string"!=typeof(n=this.get_chat_template({chat_template:n,tools:t})))throw Error("chat_template must be a string, but got "+typeof n);let m=this._compiled_template_cache.get(n);void 0===m&&(m=new c.Template(n),this._compiled_template_cache.set(n,m));const _=Object.create(null);for(const e of ge){const t=this.getToken(e);t&&(_[e]=t)}const f=m.render({messages:e,add_generation_prompt:o,tools:t,documents:r,..._,...h});if(s){const e=this._call(f,{add_special_tokens:!1,padding:a,truncation:i,max_length:l,return_tensor:d,...p});return u?e:e.input_ids}return f}}class be extends Te{return_token_type_ids=!0}class xe extends Te{return_token_type_ids=!0}class ke extends Te{return_token_type_ids=!0}class ye extends Te{return_token_type_ids=!0}class Fe extends Te{return_token_type_ids=!0}class Pe extends Te{return_token_type_ids=!0}class Ce extends Te{return_token_type_ids=!0}class ve extends Te{return_token_type_ids=!0}class Se extends Te{return_token_type_ids=!0}class Ae extends Te{}class Ee extends Te{}class Le extends Te{return_token_type_ids=!0;constructor(e,t){super(e,t),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class ze extends Te{return_token_type_ids=!0}class Ie extends Te{}class Be extends Te{}class Ne extends Te{}class Oe extends Te{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))),this.lang_to_token=e=>e}_build_translation_inputs(e,t,r){return Ke(this,e,t,r)}}class De extends Oe{}class Ve extends Te{}class je extends Te{constructor(e,t){const r=".,!?…。，、।۔،",n=e.pre_tokenizer?.pretokenizers[0]?.pattern;n&&n.Regex===` ?[^(\\s|[${r}])]+`&&(n.Regex=` ?[^\\s${r}]+`),super(e,t)}}const Re="▁";class Ge extends Te{padding_side="left";constructor(e,t){super(e,t),this.legacy=t.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new de({replacement:Re,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(e){if(null===e)return null;if(this.legacy||0===e.length)return super._encode_text(e);let t=super._encode_text(Re+e.replaceAll(Re," "));return t.length>1&&t[0]===Re&&this.special_tokens.includes(t[1])&&(t=t.slice(1)),t}}class qe extends Te{}class $e extends Te{}class We extends Te{}class Ue extends Te{}class Xe extends Te{}class Qe extends Te{}class He extends Te{}class Ye extends Te{}class Je extends Te{}function Ke(e,t,r,n){if(!("language_codes"in e)||!Array.isArray(e.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in e&&e.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in e)||"function"!=typeof e.lang_to_token)throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const o=n.src_lang,s=n.tgt_lang;if(!e.language_codes.includes(s))throw new Error(`Target language code "${s}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);if(void 0!==o){if(!e.language_codes.includes(o))throw new Error(`Source language code "${o}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);for(const t of e.post_processor.config.single)if("SpecialToken"in t&&e.languageRegex.test(t.SpecialToken.id)){t.SpecialToken.id=e.lang_to_token(o);break}}return n.forced_bos_token_id=e.model.convert_tokens_to_ids([e.lang_to_token(s)])[0],e._call(t,r)}class Ze extends Te{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))),this.lang_to_token=e=>e}_build_translation_inputs(e,t,r){return Ke(this,e,t,r)}}class et extends Te{constructor(e,t){super(e,t),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))).map((e=>e.slice(2,-2))),this.lang_to_token=e=>`__${e}__`}_build_translation_inputs(e,t,r){return Ke(this,e,t,r)}}class tt extends Te{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(e,{return_timestamps:t=!1,return_language:r=!1,time_precision:n=null,force_full_sequences:o=!0}={}){if(null===n)throw Error("Must specify time_precision");let s=null;const i="word"===t;function l(){return{language:s,timestamp:[null,null],text:""}}const c=[];let u=l(),p=0;const h=this.timestamp_begin;let m=[],_=[],f=!1,g=null;const M=new Set(this.all_special_ids);for(const r of e){const e=r.tokens,o=i?r.token_timestamps:null;let w=null,b=h;if("stride"in r){const[t,o,s]=r.stride;if(p-=o,g=t-s,o&&(b=o/n+h),s)for(let t=e.length-1;t>=0;--t){const r=Number(e[t]);if(r>=h){if(null!==w&&(r-h)*n<g)break;w=r}}}let x=[],k=[];for(let r=0;r<e.length;++r){const g=Number(e[r]);if(M.has(g)){const e=this.decode([g]),r=d.WHISPER_LANGUAGE_MAPPING.get(e.slice(2,-2));if(void 0!==r){if(null!==s&&r!==s&&!t){m.push(x);const e=this.findLongestCommonSequence(m)[0],t=this.decode(e);u.text=t,c.push(u),m=[],x=[],u=l()}s=u.language=r}}else if(g>=h){const e=(g-h)*n+p,t=(0,a.round)(e,2);if(null!==w&&g>=w)f=!0;else if(f||m.length>0&&g<b)f=!1;else if(null===u.timestamp[0])u.timestamp[0]=t;else if(t===u.timestamp[0]);else{u.timestamp[1]=t,m.push(x),i&&_.push(k);const[e,r]=this.findLongestCommonSequence(m,_),n=this.decode(e);u.text=n,i&&(u.words=this.collateWordTimestamps(e,r,s)),c.push(u),m=[],x=[],_=[],k=[],u=l()}}else if(x.push(g),i){let e,t=(0,a.round)(o[r]+p,2);if(r+1<o.length){e=(0,a.round)(o[r+1]+p,2);const s=this.decode([g]);T.test(s)&&(e=(0,a.round)(Math.min(t+n,e),2))}else e=null;k.push([t,e])}}if("stride"in r){const[e,t,n]=r.stride;p+=e-n}x.length>0?(m.push(x),i&&_.push(k)):m.every((e=>0===e.length))&&(u=l(),m=[],x=[],_=[],k=[])}if(m.length>0){if(o&&t)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[e,r]=this.findLongestCommonSequence(m,_),n=this.decode(e);u.text=n,i&&(u.words=this.collateWordTimestamps(e,r,s)),c.push(u)}let w=Object.create(null);const b=c.map((e=>e.text)).join("");if(t||r){for(let e=0;e<c.length;++e){const n=c[e];t||delete n.timestamp,r||delete n.language}if(i){const e=[];for(const t of c)for(const r of t.words)e.push(r);w={chunks:e}}else w={chunks:c}}return[b,w]}findLongestCommonSequence(e,t=null){let r=e[0],n=r.length,o=[];const s=Array.isArray(t)&&t.length>0;let a=s?[]:null,i=s?t[0]:null;for(let l=1;l<e.length;++l){const c=e[l];let d=0,u=[n,n,0,0];const p=c.length;for(let e=1;e<n+p;++e){const o=Math.max(0,n-e),a=Math.min(n,n+p-e),h=r.slice(o,a),m=Math.max(0,e-n),_=Math.min(p,e),f=c.slice(m,_);if(h.length!==f.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let g;g=s?h.filter(((e,r)=>e===f[r]&&i[o+r]<=t[l][m+r])).length:h.filter(((e,t)=>e===f[t])).length;const M=g/e+e/1e4;g>1&&M>d&&(d=M,u=[o,a,m,_])}const[h,m,_,f]=u,g=Math.floor((m+h)/2),M=Math.floor((f+_)/2);o.push(...r.slice(0,g)),r=c.slice(M),n=r.length,s&&(a.push(...i.slice(0,g)),i=t[l].slice(M))}return o.push(...r),s?(a.push(...i),[o,a]):[o,[]]}collateWordTimestamps(e,t,r){const[n,o,s]=this.combineTokensIntoWords(e,r),a=[];for(let e=0;e<n.length;++e){const r=s[e];a.push({text:n[e],timestamp:[t[r.at(0)][0],t[r.at(-1)][1]]})}return a}combineTokensIntoWords(e,t,r="\"'“¡¿([{-",n="\"'.。,，!！?？:：”)]}、"){let o,s,a;return["chinese","japanese","thai","lao","myanmar"].includes(t=t??"english")?[o,s,a]=this.splitTokensOnUnicode(e):[o,s,a]=this.splitTokensOnSpaces(e),this.mergePunctuations(o,s,a,r,n)}decode(e,t){let r;return t?.decode_with_timestamps?(e instanceof i.Tensor&&(e=_(e)),r=this.decodeWithTimestamps(e,t)):r=super.decode(e,t),r}decodeWithTimestamps(e,t){const r=t?.time_precision??.02,n=Array.from(this.all_special_ids).at(-1)+1;let o=[[]];for(let t of e)if(t=Number(t),t>=n){const e=((t-n)*r).toFixed(2);o.push(`<|${e}|>`),o.push([])}else o[o.length-1].push(t);return o=o.map((e=>"string"==typeof e?e:super.decode(e,t))),o.join("")}splitTokensOnUnicode(e){const t=this.decode(e,{decode_with_timestamps:!0}),r=[],n=[],o=[];let s=[],a=[],i=0;for(let l=0;l<e.length;++l){const c=e[l];s.push(c),a.push(l);const d=this.decode(s,{decode_with_timestamps:!0});d.includes("�")&&"�"!==t[i+d.indexOf("�")]||(r.push(d),n.push(s),o.push(a),s=[],a=[],i+=d.length)}return[r,n,o]}splitTokensOnSpaces(e){const[t,r,n]=this.splitTokensOnUnicode(e),o=[],s=[],a=[],i=new RegExp(`^[${w}]$`,"gu");for(let e=0;e<t.length;++e){const l=t[e],c=r[e],d=n[e],u=c[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),p=l.startsWith(" "),h=l.trim(),m=i.test(h);if(u||p||m||0===o.length)o.push(l),s.push(c),a.push(d);else{const e=o.length-1;o[e]+=l,s[e].push(...c),a[e].push(...d)}}return[o,s,a]}mergePunctuations(e,t,r,n,s){const a=structuredClone(e),i=structuredClone(t),l=structuredClone(r);let c=a.length-2,d=a.length-1;for(;c>=0;)a[c].startsWith(" ")&&n.includes(a[c].trim())?(a[d]=a[c]+a[d],i[d]=(0,o.mergeArrays)(i[c],i[d]),l[d]=(0,o.mergeArrays)(l[c],l[d]),a[c]="",i[c]=[],l[c]=[]):d=c,--c;for(c=0,d=1;d<a.length;)!a[c].endsWith(" ")&&s.includes(a[d])?(a[c]+=a[d],i[c]=(0,o.mergeArrays)(i[c],i[d]),l[c]=(0,o.mergeArrays)(l[c],l[d]),a[d]="",i[d]=[],l[d]=[]):c=d,++d;return[a.filter((e=>e)),i.filter((e=>e.length>0)),l.filter((e=>e.length>0))]}get_decoder_prompt_ids({language:e=null,task:t=null,no_timestamps:r=!0}={}){const n=[];if(e){const t=(0,d.whisper_language_to_code)(e),r=this.model.tokens_to_ids.get(`<|${t}|>`);if(void 0===r)throw new Error(`Unable to find language "${t}" in model vocabulary. Please report this issue at ${u.GITHUB_ISSUE_URL}.`);n.push(r)}else n.push(null);if(t){if("transcribe"!==(t=t.toLowerCase())&&"translate"!==t)throw new Error(`Task "${t}" is not supported. Must be one of: ["transcribe", "translate"]`);const e=this.model.tokens_to_ids.get(`<|${t}|>`);if(void 0===e)throw new Error(`Unable to find task "${t}" in model vocabulary. Please report this issue at ${u.GITHUB_ISSUE_URL}.`);n.push(e)}else n.push(null);if(r){const e=this.model.tokens_to_ids.get("<|notimestamps|>");if(void 0===e)throw new Error(`Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at ${u.GITHUB_ISSUE_URL}.`);n.push(e)}return n.map(((e,t)=>[t+1,e])).filter((e=>null!==e[1]))}}class rt extends Te{}class nt extends Te{}class ot extends Te{}class st extends Te{constructor(e,t){super(e,t),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter((e=>this.languageRegex.test(e))),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(null===e)return null;const[t,...r]=e.trim().split(this.languageRegex);if(0===r.length)return super._encode_text(t);if(2===r.length){const[e,t]=r;return this.supported_language_codes.includes(e)||console.warn(`Unsupported language code "${e}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,o.mergeArrays)([e],super._encode_text(t))}}}class at extends Te{}class it extends Te{}class lt extends Te{}class ct extends Te{}class dt extends Te{}class ut extends Te{constructor(e,t){super(e,t),this.decoder=new ce({})}}class pt extends Te{}class ht{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:Ie,DistilBertTokenizer:Ae,CamembertTokenizer:Ee,DebertaTokenizer:Fe,DebertaV2Tokenizer:Pe,BertTokenizer:be,HerbertTokenizer:Ce,ConvBertTokenizer:ve,RoFormerTokenizer:Se,XLMTokenizer:Le,ElectraTokenizer:ze,MobileBertTokenizer:ke,SqueezeBertTokenizer:ye,AlbertTokenizer:xe,GPT2Tokenizer:Be,BartTokenizer:Ne,MBartTokenizer:Oe,MBart50Tokenizer:De,RobertaTokenizer:Ve,WhisperTokenizer:tt,CodeGenTokenizer:rt,CLIPTokenizer:nt,SiglipTokenizer:ot,MarianTokenizer:st,BloomTokenizer:je,NllbTokenizer:Ze,M2M100Tokenizer:et,LlamaTokenizer:Ge,CodeLlamaTokenizer:qe,XLMRobertaTokenizer:$e,MPNetTokenizer:We,FalconTokenizer:Ue,GPTNeoXTokenizer:Xe,EsmTokenizer:Qe,Wav2Vec2CTCTokenizer:at,BlenderbotTokenizer:it,BlenderbotSmallTokenizer:lt,SpeechT5Tokenizer:ct,NougatTokenizer:dt,VitsTokenizer:ut,Qwen2Tokenizer:He,GemmaTokenizer:Ye,Grok1Tokenizer:Je,CohereTokenizer:pt,PreTrainedTokenizer:Te};static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:n=null,local_files_only:o=!1,revision:s="main",legacy:a=null}={}){const[i,l]=await p(e,{progress_callback:t,config:r,cache_dir:n,local_files_only:o,revision:s,legacy:a}),c=l.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let d=this.TOKENIZER_CLASS_MAPPING[c];return d||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),d=Te),new d(i,l)}}},"./src/utils/audio.js":

/*!****************************!*\

  !*** ./src/utils/audio.js ***!

  \****************************/(e,t,r)=>{r.r(t),r.d(t,{hamming:()=>d,hanning:()=>c,mel_filter_bank:()=>_,read_audio:()=>i,spectrogram:()=>g,window_function:()=>M});var n=r(/*! ./hub.js */"./src/utils/hub.js"),o=r(/*! ./maths.js */"./src/utils/maths.js"),s=r(/*! ./core.js */"./src/utils/core.js"),a=r(/*! ./tensor.js */"./src/utils/tensor.js");async function i(e,t){if("undefined"==typeof AudioContext)throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. 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Got '${_}'`)}}return B}function M(e,t,{periodic:r=!0,frame_length:n=null,center:o=!0}={}){const s=r?e+1:e;let a;switch(t){case"boxcar":a=new Float64Array(s).fill(1);break;case"hann":case"hann_window":a=c(s);break;case"hamming":a=d(s);break;case"povey":a=c(s).map((e=>Math.pow(e,.85)));break;default:throw new Error(`Unknown window type ${t}.`)}if(r&&(a=a.subarray(0,e)),null===n)return a;if(e>n)throw new Error(`Length of the window (${e}) may not be larger than frame_length (${n})`);return a}},"./src/utils/constants.js":

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  !*** ./src/utils/constants.js ***!

  \********************************/(e,t,r)=>{r.r(t),r.d(t,{GITHUB_ISSUE_URL:()=>n});const n="https://github.com/xenova/transformers.js/issues/new/choose"},"./src/utils/core.js":

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  !*** ./src/utils/core.js ***!

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  !*** ./src/utils/image.js ***!

  \****************************/(e,t,r)=>{r.r(t),r.d(t,{RawImage:()=>m});var n=r(/*! ./hub.js */"./src/utils/hub.js"),o=r(/*! ../env.js */"./src/env.js"),s=r(/*! ./tensor.js */"./src/utils/tensor.js"),a=r(/*! sharp */"?2b25");const i="undefined"!=typeof self,l=i&&"DedicatedWorkerGlobalScope"===self.constructor.name;let c,d,u;if(i)c=(e,t)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(e,t)},u=self.createImageBitmap,d=self.ImageData;else{if(!a)throw new Error("Unable to load image processing library.");u=async e=>{const t=(await e.metadata()).channels,{data:r,info:n}=await e.rotate().raw().toBuffer({resolveWithObject:!0}),o=new m(new Uint8ClampedArray(r),n.width,n.height,n.channels);return void 0!==t&&t!==n.channels&&o.convert(t),o}}const p={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},h=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class m{constructor(e,t,r,n){this.data=e,this.width=t,this.height=r,this.channels=n}get size(){return[this.width,this.height]}static async read(e){if(e instanceof m)return e;if("string"==typeof e||e instanceof URL)return await this.fromURL(e);throw new Error("Unsupported input type: "+typeof e)}static fromCanvas(e){if(!i)throw new Error("fromCanvas() is only supported in browser environments.");const t=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new m(t,e.width,e.height,4)}static async fromURL(e){const t=await(0,n.getFile)(e);if(200!==t.status)throw new Error(`Unable to read image from "${e}" (${t.status} ${t.statusText})`);const r=await t.blob();return this.fromBlob(r)}static async fromBlob(e){if(i){const t=await u(e),r=c(t.width,t.height).getContext("2d");return r.drawImage(t,0,0),new this(r.getImageData(0,0,t.width,t.height).data,t.width,t.height,4)}{const t=a(await e.arrayBuffer());return await u(t)}}static fromTensor(e,t="CHW"){if(3!==e.dims.length)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if("CHW"===t)e=e.transpose(1,2,0);else if("HWC"!==t)throw new Error(`Unsupported channel format: ${t}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new m(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(1===this.channels)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let t=0,r=0;t<this.data.length;t+=this.channels){const n=this.data[t],o=this.data[t+1],s=this.data[t+2];e[r++]=Math.round(.2989*n+.587*o+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(3===this.channels)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let t=0,r=0;t<this.data.length;++t)e[r++]=this.data[t],e[r++]=this.data[t],e[r++]=this.data[t];break;case 4:for(let t=0,r=0;t<this.data.length;t+=4)e[r++]=this.data[t],e[r++]=this.data[t+1],e[r++]=this.data[t+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(4===this.channels)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let t=0,r=0;t<this.data.length;++t)e[r++]=this.data[t],e[r++]=this.data[t],e[r++]=this.data[t],e[r++]=255;break;case 3:for(let t=0,r=0;t<this.data.length;t+=3)e[r++]=this.data[t],e[r++]=this.data[t+1],e[r++]=this.data[t+2],e[r++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,t,{resample:r=2}={}){let n=p[r]??r;if(i){const r=this.channels,n=this.toCanvas(),o=c(e,t).getContext("2d");o.drawImage(n,0,0,e,t);return new m(o.getImageData(0,0,e,t).data,e,t,4).convert(r)}{let r=this.toSharp();switch(n){case"box":case"hamming":"box"!==n&&"hamming"!==n||(console.warn(`Resampling method ${n} is not yet supported. Using bilinear instead.`),n="bilinear");case"nearest":case"bilinear":case"bicubic":r=r.affine([e/this.width,0,0,t/this.height],{interpolator:n});break;case"lanczos":r=r.resize({width:e,height:t,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${n} is not supported.`)}return await u(r)}}async pad([e,t,r,n]){if(e=Math.max(e,0),t=Math.max(t,0),r=Math.max(r,0),n=Math.max(n,0),0===e&&0===t&&0===r&&0===n)return this;if(i){const o=this.channels,s=this.toCanvas(),a=this.width+e+t,i=this.height+r+n,l=c(a,i).getContext("2d");l.drawImage(s,0,0,this.width,this.height,e,r,a,i);return new m(l.getImageData(0,0,a,i).data,a,i,4).convert(o)}{const o=this.toSharp().extend({left:e,right:t,top:r,bottom:n});return await u(o)}}async crop([e,t,r,n]){if(e=Math.max(e,0),t=Math.max(t,0),r=Math.min(r,this.width-1),n=Math.min(n,this.height-1),0===e&&0===t&&r===this.width-1&&n===this.height-1)return this;const o=r-e+1,s=n-t+1;if(i){const r=this.channels,n=this.toCanvas(),a=c(o,s).getContext("2d");a.drawImage(n,e,t,o,s,0,0,o,s);return new m(a.getImageData(0,0,o,s).data,o,s,4).convert(r)}{const r=this.toSharp().extract({left:e,top:t,width:o,height:s});return await u(r)}}async center_crop(e,t){if(this.width===e&&this.height===t)return this;const r=(this.width-e)/2,n=(this.height-t)/2;if(i){const o=this.channels,s=this.toCanvas(),a=c(e,t).getContext("2d");let i=0,l=0,d=0,u=0;r>=0?i=r:d=-r,n>=0?l=n:u=-n,a.drawImage(s,i,l,e,t,d,u,e,t);return new m(a.getImageData(0,0,e,t).data,e,t,4).convert(o)}{let o=this.toSharp();if(r>=0&&n>=0)o=o.extract({left:Math.floor(r),top:Math.floor(n),width:e,height:t});else if(r<=0&&n<=0){const s=Math.floor(-n),a=Math.floor(-r);o=o.extend({top:s,left:a,right:e-this.width-a,bottom:t-this.height-s})}else{let s=[0,0],a=0;n<0?(s[0]=Math.floor(-n),s[1]=t-this.height-s[0]):a=Math.floor(n);let i=[0,0],l=0;r<0?(i[0]=Math.floor(-r),i[1]=e-this.width-i[0]):l=Math.floor(r),o=o.extend({top:s[0],bottom:s[1],left:i[0],right:i[1]}).extract({left:l,top:a,width:e,height:t})}return await u(o)}}async toBlob(e="image/png",t=1){if(!i)throw new Error("toBlob() is only supported in browser environments.");const r=this.toCanvas();return await r.convertToBlob({type:e,quality:t})}toTensor(e="CHW"){let t=new s.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if("HWC"===e);else{if("CHW"!==e)throw new Error(`Unsupported channel format: ${e}`);t=t.permute(2,0,1)}return t}toCanvas(){if(!i)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),t=c(e.width,e.height),r=new d(e.data,e.width,e.height);return t.getContext("2d").putImageData(r,0,0),t}_update(e,t,r,n=null){return this.data=e,this.width=t,this.height=r,null!==n&&(this.channels=n),this}clone(){return new m(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(!i){if(o.env.useFS){const t=this.toSharp();return await t.toFile(e)}throw new Error("Unable to save the image because filesystem is disabled in this environment.")}{if(l)throw new Error("Unable to save an image from a Web Worker.");const t=e.split(".").pop().toLowerCase(),r=h.get(t)??"image/png",n=await this.toBlob(r),o=URL.createObjectURL(n),s=document.createElement("a");s.href=o,s.download=e,s.click(),s.remove()}}toSharp(){if(i)throw new Error("toSharp() is only supported in server-side environments.");return a(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}},"./src/utils/maths.js":

/*!****************************!*\

  !*** ./src/utils/maths.js ***!

  \****************************/(e,t,r)=>{function n(e,[t,r,n],[o,s],a="bilinear",i=!1){const l=s/n,c=o/r,d=new e.constructor(o*s*t),u=r*n,p=o*s;for(let a=0;a<o;++a)for(let o=0;o<s;++o){const i=a*s+o,h=(o+.5)/l-.5,m=(a+.5)/c-.5;let _=Math.floor(h),f=Math.floor(m);const g=Math.min(_+1,n-1),M=Math.min(f+1,r-1);_=Math.max(_,0),f=Math.max(f,0);const w=h-_,T=m-f,b=(1-w)*(1-T),x=w*(1-T),k=(1-w)*T,y=w*T,F=f*n,P=M*n,C=F+_,v=F+g,S=P+_,A=P+g;for(let r=0;r<t;++r){const t=r*u;d[r*p+i]=b*e[t+C]+x*e[t+v]+k*e[t+S]+y*e[t+A]}}return d}function o(e,t,r){const n=new Array(r.length),o=new Array(r.length);for(let e=r.length-1,s=1;e>=0;--e)o[e]=s,n[e]=t[r[e]],s*=n[e];const s=r.map(((e,t)=>o[r.indexOf(t)])),a=new e.constructor(e.length);for(let r=0;r<e.length;++r){let n=0;for(let e=t.length-1,o=r;e>=0;--e)n+=o%t[e]*s[e],o=Math.floor(o/t[e]);a[n]=e[r]}return[a,n]}function s(e){const t=u(e)[0],r=e.map((e=>Math.exp(e-t))),n=r.reduce(((e,t)=>e+t),0);return r.map((e=>e/n))}function a(e){const t=u(e)[0];let r=0;for(let n=0;n<e.length;++n)r+=Math.exp(e[n]-t);const n=Math.log(r);return e.map((e=>e-t-n))}function i(e,t){let r=0;for(let n=0;n<e.length;++n)r+=e[n]*t[n];return r}function l(e,t){return i(e,t)/(c(e)*c(t))}function c(e){return Math.sqrt(e.reduce(((e,t)=>e+t*t),0))}function d(e){if(0===e.length)throw Error("Array must not be empty");let t=e[0],r=0;for(let n=1;n<e.length;++n)e[n]<t&&(t=e[n],r=n);return[t,r]}function u(e){if(0===e.length)throw Error("Array must not be empty");let t=e[0],r=0;for(let n=1;n<e.length;++n)e[n]>t&&(t=e[n],r=n);return[Number(t),r]}function p(e){return e>0&&!(e&e-1)}r.r(t),r.d(t,{FFT:()=>_,bankers_round:()=>M,cos_sim:()=>l,dot:()=>i,dynamic_time_warping:()=>w,interpolate_data:()=>n,log_softmax:()=>a,magnitude:()=>c,max:()=>u,medianFilter:()=>f,min:()=>d,permute_data:()=>o,round:()=>g,softmax:()=>s});class h{constructor(e){if(this.size=0|e,this.size<=1||!p(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=e<<1,this.table=new Float64Array(2*this.size);for(let e=0;e<this.table.length;e+=2){const t=Math.PI*e/this.size;this.table[e]=Math.cos(t),this.table[e+1]=-Math.sin(t)}let t=0;for(let e=1;this.size>e;e<<=1)++t;this._width=t%2==0?t-1:t,this._bitrev=new Int32Array(1<<this._width);for(let e=0;e<this._bitrev.length;++e){this._bitrev[e]=0;for(let t=0;t<this._width;t+=2){const r=this._width-t-2;this._bitrev[e]|=(e>>>t&3)<<r}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(e,t){const r=t||new Array(e.length>>>1);for(let t=0;t<e.length;t+=2)r[t>>>1]=e[t];return r}toComplexArray(e,t){const r=t||this.createComplexArray();for(let t=0;t<r.length;t+=2)r[t]=e[t>>>1],r[t+1]=0;return r}transform(e,t){if(e===t)throw new Error("Input and output buffers must be different");this._transform4(e,t,1)}realTransform(e,t){if(e===t)throw new Error("Input and output buffers must be different");this._realTransform4(e,t,1)}inverseTransform(e,t){if(e===t)throw new Error("Input and output buffers must be different");this._transform4(e,t,-1);for(let t=0;t<e.length;++t)e[t]/=this.size}_transform4(e,t,r){const n=this._csize;let o,s,a=1<<this._width,i=n/a<<1;const l=this._bitrev;if(4===i)for(o=0,s=0;o<n;o+=i,++s){const r=l[s];this._singleTransform2(t,e,o,r,a)}else for(o=0,s=0;o<n;o+=i,++s){const n=l[s];this._singleTransform4(t,e,o,n,a,r)}const c=this.table;for(a>>=2;a>=2;a>>=2){i=n/a<<1;const t=i>>>2;for(o=0;o<n;o+=i){const n=o+t-1;for(let s=o,i=0;s<n;s+=2,i+=a){const n=s,o=n+t,a=o+t,l=a+t,d=e[n],u=e[n+1],p=e[o],h=e[o+1],m=e[a],_=e[a+1],f=e[l],g=e[l+1],M=c[i],w=r*c[i+1],T=p*M-h*w,b=p*w+h*M,x=c[2*i],k=r*c[2*i+1],y=m*x-_*k,F=m*k+_*x,P=c[3*i],C=r*c[3*i+1],v=f*P-g*C,S=f*C+g*P,A=d+y,E=u+F,L=d-y,z=u-F,I=T+v,B=b+S,N=r*(T-v),O=r*(b-S);e[n]=A+I,e[n+1]=E+B,e[o]=L+O,e[o+1]=z-N,e[a]=A-I,e[a+1]=E-B,e[l]=L-O,e[l+1]=z+N}}}}_singleTransform2(e,t,r,n,o){const s=e[n],a=e[n+1],i=e[n+o],l=e[n+o+1];t[r]=s+i,t[r+1]=a+l,t[r+2]=s-i,t[r+3]=a-l}_singleTransform4(e,t,r,n,o,s){const a=2*o,i=3*o,l=e[n],c=e[n+1],d=e[n+o],u=e[n+o+1],p=e[n+a],h=e[n+a+1],m=e[n+i],_=e[n+i+1],f=l+p,g=c+h,M=l-p,w=c-h,T=d+m,b=u+_,x=s*(d-m),k=s*(u-_);t[r]=f+T,t[r+1]=g+b,t[r+2]=M+k,t[r+3]=w-x,t[r+4]=f-T,t[r+5]=g-b,t[r+6]=M-k,t[r+7]=w+x}_realTransform4(e,t,r){const n=this._csize;let o,s,a=1<<this._width,i=n/a<<1;const l=this._bitrev;if(4===i)for(o=0,s=0;o<n;o+=i,++s){const r=l[s];this._singleRealTransform2(t,e,o,r>>>1,a>>>1)}else for(o=0,s=0;o<n;o+=i,++s){const n=l[s];this._singleRealTransform4(t,e,o,n>>>1,a>>>1,r)}const c=this.table;for(a>>=2;a>=2;a>>=2){i=n/a<<1;const t=i>>>1,s=t>>>1,l=s>>>1;for(o=0;o<n;o+=i)for(let n=0,i=0;n<=l;n+=2,i+=a){const a=o+n,d=a+s,u=d+s,p=u+s,h=e[a],m=e[a+1],_=e[d],f=e[d+1],g=e[u],M=e[u+1],w=e[p],T=e[p+1],b=h,x=m,k=c[i],y=r*c[i+1],F=_*k-f*y,P=_*y+f*k,C=c[2*i],v=r*c[2*i+1],S=g*C-M*v,A=g*v+M*C,E=c[3*i],L=r*c[3*i+1],z=w*E-T*L,I=w*L+T*E,B=b+S,N=x+A,O=b-S,D=x-A,V=F+z,j=P+I,R=r*(F-z),G=r*(P-I);if(e[a]=B+V,e[a+1]=N+j,e[d]=O+G,e[d+1]=D-R,0===n){e[u]=B-V,e[u+1]=N-j;continue}if(n===l)continue;const q=o+s-n,$=o+t-n;e[q]=O-r*G,e[q+1]=-D-r*R,e[$]=B-r*V,e[$+1]=r*j-N}}const d=n>>>1;for(let t=2;t<d;t+=2)e[n-t]=e[t],e[n-t+1]=-e[t+1]}_singleRealTransform2(e,t,r,n,o){const s=e[n],a=e[n+o];t[r]=s+a,t[r+1]=0,t[r+2]=s-a,t[r+3]=0}_singleRealTransform4(e,t,r,n,o,s){const a=2*o,i=3*o,l=e[n],c=e[n+o],d=e[n+a],u=e[n+i],p=l+d,h=l-d,m=c+u,_=s*(c-u);t[r]=p+m,t[r+1]=0,t[r+2]=h,t[r+3]=-_,t[r+4]=p-m,t[r+5]=0,t[r+6]=h,t[r+7]=_}}class m{constructor(e){const t=2*(e-1),r=2*(2*e-1),n=2**Math.ceil(Math.log2(r));this.bufferSize=n,this._a=t;const o=new Float64Array(r),s=new Float64Array(n);this._chirpBuffer=new Float64Array(n),this._buffer1=new Float64Array(n),this._buffer2=new Float64Array(n),this._outBuffer1=new Float64Array(n),this._outBuffer2=new Float64Array(n);const a=-2*Math.PI/e,i=Math.cos(a),l=Math.sin(a);for(let t=0;t<r>>1;++t){const r=(t+1-e)**2/2,n=Math.sqrt(i**2+l**2)**r,a=r*Math.atan2(l,i),c=2*t;o[c]=n*Math.cos(a),o[c+1]=n*Math.sin(a),s[c]=o[c],s[c+1]=-o[c+1]}this._slicedChirpBuffer=o.subarray(t,r),this._f=new h(n>>1),this._f.transform(this._chirpBuffer,s)}_transform(e,t,r){const n=this._buffer1,o=this._buffer2,s=this._outBuffer1,a=this._outBuffer2,i=this._chirpBuffer,l=this._slicedChirpBuffer,c=this._a;if(r)for(let e=0;e<l.length;e+=2){const r=e+1,o=t[e>>1];n[e]=o*l[e],n[r]=o*l[r]}else for(let e=0;e<l.length;e+=2){const r=e+1;n[e]=t[e]*l[e]-t[r]*l[r],n[r]=t[e]*l[r]+t[r]*l[e]}this._f.transform(s,n);for(let e=0;e<i.length;e+=2){const t=e+1;o[e]=s[e]*i[e]-s[t]*i[t],o[t]=s[e]*i[t]+s[t]*i[e]}this._f.inverseTransform(a,o);for(let t=0;t<a.length;t+=2){const r=a[t+c],n=a[t+c+1],o=l[t],s=l[t+1];e[t]=r*o-n*s,e[t+1]=r*s+n*o}}transform(e,t){this._transform(e,t,!1)}realTransform(e,t){this._transform(e,t,!0)}}class _{constructor(e){this.fft_length=e,this.isPowerOfTwo=p(e),this.isPowerOfTwo?(this.fft=new h(e),this.outputBufferSize=2*e):(this.fft=new m(e),this.outputBufferSize=this.fft.bufferSize)}realTransform(e,t){this.fft.realTransform(e,t)}transform(e,t){this.fft.transform(e,t)}}function f(e,t){if(t%2==0||t<=0)throw new Error("Window size must be a positive odd number");const r=new e.constructor(e.length),n=new e.constructor(t),o=Math.floor(t/2);for(let t=0;t<e.length;++t){let s=0;for(let r=-o;r<=o;++r){let o=t+r;o<0?o=Math.abs(o):o>=e.length&&(o=2*(e.length-1)-o),n[s++]=e[o]}n.sort(),r[t]=n[o]}return r}function g(e,t){const r=Math.pow(10,t);return Math.round(e*r)/r}function M(e){const t=Math.round(e);return Math.abs(e)%1==.5?t%2==0?t:t-1:t}function w(e){const t=e.length,r=e[0].length,n=[t+1,r+1],o=Array.from({length:n[0]},(()=>Array(n[1]).fill(1/0)));o[0][0]=0;const s=Array.from({length:n[0]},(()=>Array(n[1]).fill(-1)));for(let t=1;t<n[1];++t)for(let r=1;r<n[0];++r){const n=o[r-1][t-1],a=o[r-1][t],i=o[r][t-1];let l,c;n<a&&n<i?(l=n,c=0):a<n&&a<i?(l=a,c=1):(l=i,c=2),o[r][t]=e[r-1][t-1]+l,s[r][t]=c}for(let e=0;e<n[1];++e)s[0][e]=2;for(let e=0;e<n[0];++e)s[e][0]=1;let a=t,i=r,l=[],c=[];for(;a>0||i>0;)switch(l.push(a-1),c.push(i-1),s[a][i]){case 0:--a,--i;break;case 1:--a;break;case 2:--i;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${a}, ${i}]. Please file a bug report.`)}return l.reverse(),c.reverse(),[l,c]}},"./src/utils/tensor.js":

/*!*****************************!*\

  !*** ./src/utils/tensor.js ***!

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/*!*****************************!*\

  !*** ./src/transformers.js ***!

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taForTokenClassification:()=>i.RobertaForTokenClassification,RobertaModel:()=>i.RobertaModel,RobertaPreTrainedModel:()=>i.RobertaPreTrainedModel,RobertaTokenizer:()=>l.RobertaTokenizer,SamImageProcessor:()=>c.SamImageProcessor,SamImageSegmentationOutput:()=>i.SamImageSegmentationOutput,SamModel:()=>i.SamModel,SamPreTrainedModel:()=>i.SamPreTrainedModel,SamProcessor:()=>c.SamProcessor,SapiensFeatureExtractor:()=>c.SapiensFeatureExtractor,SapiensForDepthEstimation:()=>i.SapiensForDepthEstimation,SapiensForNormalEstimation:()=>i.SapiensForNormalEstimation,SapiensForSemanticSegmentation:()=>i.SapiensForSemanticSegmentation,SapiensPreTrainedModel:()=>i.SapiensPreTrainedModel,SeamlessM4TFeatureExtractor:()=>c.SeamlessM4TFeatureExtractor,SegformerFeatureExtractor:()=>c.SegformerFeatureExtractor,SegformerForImageClassification:()=>i.SegformerForImageClassification,SegformerForSemanticSegmentation:()=>i.SegformerForSemanticSegmentation,SegformerModel:()=>i.SegformerModel,SegformerPreTrainedModel:()=>i.SegformerPreTrainedModel,Seq2SeqLMOutput:()=>i.Seq2SeqLMOutput,SequenceClassifierOutput:()=>i.SequenceClassifierOutput,SiglipImageProcessor:()=>c.SiglipImageProcessor,SiglipModel:()=>i.SiglipModel,SiglipPreTrainedModel:()=>i.SiglipPreTrainedModel,SiglipTextModel:()=>i.SiglipTextModel,SiglipTokenizer:()=>l.SiglipTokenizer,SiglipVisionModel:()=>i.SiglipVisionModel,SpeechT5FeatureExtractor:()=>c.SpeechT5FeatureExtractor,SpeechT5ForSpeechToText:()=>i.SpeechT5ForSpeechToText,SpeechT5ForTextToSpeech:()=>i.SpeechT5ForTextToSpeech,SpeechT5HifiGan:()=>i.SpeechT5HifiGan,SpeechT5Model:()=>i.SpeechT5Model,SpeechT5PreTrainedModel:()=>i.SpeechT5PreTrainedModel,SpeechT5Processor:()=>c.SpeechT5Processor,SpeechT5Tokenizer:()=>l.SpeechT5Tokenizer,SqueezeBertForMaskedLM:()=>i.SqueezeBertForMaskedLM,SqueezeBertForQuestionAnswering:()=>i.SqueezeBertForQuestionAnswering,SqueezeBertForSequenceClassification:()=>i.SqueezeBertForSequenceClassification,SqueezeBertModel:()=>i.SqueezeBertModel,SqueezeBertPreTrainedModel:()=>i.SqueezeBertPreTrainedModel,SqueezeBertTokenizer:()=>l.SqueezeBertTokenizer,StableLmForCausalLM:()=>i.StableLmForCausalLM,StableLmModel:()=>i.StableLmModel,StableLmPreTrainedModel:()=>i.StableLmPreTrainedModel,Starcoder2ForCausalLM:()=>i.Starcoder2ForCausalLM,Starcoder2Model:()=>i.Starcoder2Model,Starcoder2PreTrainedModel:()=>i.Starcoder2PreTrainedModel,StoppingCriteria:()=>f.StoppingCriteria,StoppingCriteriaList:()=>f.StoppingCriteriaList,SummarizationPipeline:()=>a.SummarizationPipeline,Swin2SRForImageSuperResolution:()=>i.Swin2SRForImageSuperResolution,Swin2SRImageProcessor:()=>c.Swin2SRImageProcessor,Swin2SRModel:()=>i.Swin2SRModel,Swin2SRPreTrainedModel:()=>i.Swin2SRPreTrainedModel,SwinForImageClassification:()=>i.SwinForImageClassification,SwinModel:()=>i.SwinModel,SwinPreTrainedModel:()=>i.SwinPreTrainedModel,T5ForConditionalGeneration:()=>i.T5ForConditionalGeneration,T5Model:()=>i.T5Model,T5PreTrainedModel:()=>i.T5PreTrainedModel,T5Tokenizer:()=>l.T5Tokenizer,TableTransformerForObjectDetection:()=>i.TableTransformerForObjectDetection,TableTransformerModel:()=>i.TableTransformerModel,TableTransformerObjectDetectionOutput:()=>i.TableTransformerObjectDetectionOutput,TableTransformerPreTrainedModel:()=>i.TableTransformerPreTrainedModel,Tensor:()=>h.Tensor,Text2TextGenerationPipeline:()=>a.Text2TextGenerationPipeline,TextClassificationPipeline:()=>a.TextClassificationPipeline,TextGenerationPipeline:()=>a.TextGenerationPipeline,TextStreamer:()=>_.TextStreamer,TextToAudioPipeline:()=>a.TextToAudioPipeline,TokenClassificationPipeline:()=>a.TokenClassificationPipeline,TokenClassifierOutput:()=>i.TokenClassifierOutput,TokenizerModel:()=>l.TokenizerModel,TrOCRForCausalLM:()=>i.TrOCRForCausalLM,TrOCRPreTrainedModel:()=>i.TrOCRPreTrainedModel,TranslationPipeline:()=>a.TranslationPipeline,UniSpeechForCTC:()=>i.UniSpeechForCTC,UniSpeechForSequenceClassification:()=>i.UniSpeechForSequenceClassification,UniSpeechModel:()=>i.UniSpeechModel,UniSpeechPreTrainedModel:()=>i.UniSpeechPreTrainedModel,UniSpeechSatForAudioFrameClassification:()=>i.UniSpeechSatForAudioFrameClassification,UniSpeechSatForCTC:()=>i.UniSpeechSatForCTC,UniSpeechSatForSequenceClassification:()=>i.UniSpeechSatForSequenceClassification,UniSpeechSatModel:()=>i.UniSpeechSatModel,UniSpeechSatPreTrainedModel:()=>i.UniSpeechSatPreTrainedModel,ViTFeatureExtractor:()=>c.ViTFeatureExtractor,ViTForImageClassification:()=>i.ViTForImageClassification,ViTImageProcessor:()=>c.ViTImageProcessor,ViTMAEModel:()=>i.ViTMAEModel,ViTMAEPreTrainedModel:()=>i.ViTMAEPreTrainedModel,ViTMSNForImageClassification:()=>i.ViTMSNForImageClassification,ViTMSNModel:()=>i.ViTMSNModel,ViTMSNPreTrainedModel:()=>i.ViTMSNPreTrainedModel,ViTModel:()=>i.ViTModel,ViTPreTrainedModel:()=>i.ViTPreTrainedModel,VisionEncoderDecoderModel:()=>i.VisionEncoderDecoderModel,VitMatteForImageMatting:()=>i.VitMatteForImageMatting,VitMatteImageProcessor:()=>c.VitMatteImageProcessor,VitMattePreTrainedModel:()=>i.VitMattePre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otImageClassificationPipeline,Gc=o.ZeroShotObjectDetectionPipeline,qc=o.bankers_round,$c=o.cat,Wc=o.cos_sim,Uc=o.dot,Xc=o.dynamic_time_warping,Qc=o.env,Hc=o.full,Yc=o.full_like,Jc=o.getKeyValueShapes,Kc=o.hamming,Zc=o.hanning,ed=o.interpolate,td=o.interpolate_4d,rd=o.interpolate_data,nd=o.is_chinese_char,od=o.layer_norm,sd=o.log_softmax,ad=o.magnitude,id=o.matmul,ld=o.max,cd=o.mean,dd=o.mean_pooling,ud=o.medianFilter,pd=o.mel_filter_bank,hd=o.min,md=o.ones,_d=o.ones_like,fd=o.permute,gd=o.permute_data,Md=o.pipeline,wd=o.quantize_embeddings,Td=o.read_audio,bd=o.rfft,xd=o.round,kd=o.softmax,yd=o.spectrogram,Fd=o.stack,Pd=o.std_mean,Cd=o.topk,vd=o.window_function,Sd=o.zeros,Ad=o.zeros_like;export{g as ASTFeatureExtractor,M as ASTForAudioClassification,w as ASTModel,T as ASTPreTrainedModel,b as AlbertForMaskedLM,x as AlbertForQuestionAnswering,k as AlbertForSequenceClassification,y as AlbertModel,F as AlbertPreTrainedModel,P as AlbertTokenizer,C as AudioClassificationPipeline,v as AutoConfig,S as AutoModel,A as AutoModelForAudioClassification,E as AutoModelForAudioFrameClassification,L as AutoModelForCTC,z as AutoModelForCausalLM,I as AutoModelForDepthEstimation,B as AutoModelForDocumentQuestionAnswering,N as AutoModelForImageClassification,O as AutoModelForImageFeatureExtraction,D as AutoModelForImageMatting,V as AutoModelForImageSegmentation,j as AutoModelForImageToImage,R as AutoModelForMaskGeneration,G as AutoModelForMaskedLM,q as AutoModelForNormalEstimation,$ as AutoModelForObjectDetection,W as AutoModelForQuestionAnswering,U as AutoModelForSemanticSegmentation,X as AutoModelForSeq2SeqLM,Q as AutoModelForSequenceClassification,H as AutoModelForSpeechSeq2Seq,Y as AutoModelForTextToSpectrogram,J as AutoModelForTextToWaveform,K as AutoModelForTokenClassification,Z as AutoModelForUniversalSegmentation,ee as AutoModelForVision2Seq,te as AutoModelForXVector,re as AutoModelForZeroShotObjectDetection,ne as AutoProcessor,oe as AutoTokenizer,se as AutomaticSpeechRecognitionPipeline,ae as BartForConditionalGeneration,ie as BartForSequenceClassification,le as BartModel,ce as BartPretrainedModel,de as BartTokenizer,ue as BaseModelOutput,pe as BaseStreamer,he as BeitFeatureExtractor,me as BeitForImageClassification,_e as BeitModel,fe as BeitPreTrainedModel,ge as BertForMaskedLM,Me as BertForQuestionAnswering,we as BertForSequenceClassification,Te as BertForTokenClassification,be as BertModel,xe as BertPreTrainedModel,ke as BertTokenizer,ye as BitImageProcessor,Fe as BlenderbotForConditionalGeneration,Pe as BlenderbotModel,Ce as BlenderbotPreTrainedModel,ve as BlenderbotSmallForConditionalGeneration,Se as BlenderbotSmallModel,Ae as BlenderbotSmallPreTrainedModel,Ee as BlenderbotSmallTokenizer,Le as BlenderbotTokenizer,ze as BloomForCausalLM,Ie as BloomModel,Be as BloomPreTrainedModel,Ne as BloomTokenizer,Oe as CLIPFeatureExtractor,De as CLIPImageProcessor,Ve as CLIPModel,je as CLIPPreTrainedModel,Re as CLIPSegForImageSegmentation,Ge as CLIPSegModel,qe as CLIPSegPreTrainedModel,$e as CLIPTextModel,We as CLIPTextModelWithProjection,Ue as CLIPTokenizer,Xe as CLIPVisionModel,Qe as CLIPVisionModelWithProjection,He as CamembertForMaskedLM,Ye as CamembertForQuestionAnswering,Je as CamembertForSequenceClassification,Ke as CamembertForTokenClassification,Ze as CamembertModel,et as CamembertPreTrainedModel,tt as CamembertTokenizer,rt as CausalLMOutput,nt as CausalLMOutputWithPast,ot as ChineseCLIPFeatureExtractor,st as ChineseCLIPModel,at as ChineseCLIPPreTrainedModel,it as ClapAudioModelWithProjection,lt as ClapFeatureExtractor,ct as ClapModel,dt as ClapPreTrainedModel,ut as ClapTextModelWithProjection,pt as CodeGenForCausalLM,ht as CodeGenModel,mt as CodeGenPreTrainedModel,_t as CodeGenTokenizer,ft as CodeLlamaTokenizer,gt as CohereForCausalLM,Mt as CohereModel,wt as CoherePreTrainedModel,Tt as CohereTokenizer,bt as ConvBertForMaskedLM,xt as ConvBertForQuestionAnswering,kt as ConvBertForSequenceClassification,yt as ConvBertForTokenClassification,Ft as ConvBertModel,Pt as ConvBertPreTrainedModel,Ct as ConvBertTokenizer,vt as ConvNextFeatureExtractor,St as ConvNextForImageClassification,At as ConvNextImageProcessor,Et as ConvNextModel,Lt as ConvNextPreTrainedModel,zt as ConvNextV2ForImageClassification,It as ConvNextV2Model,Bt as ConvNextV2PreTrainedModel,Nt as DPTFeatureExtractor,Ot as DPTForDepthEstimation,Dt as DPTImageProcessor,Vt as DPTModel,jt as DPTPreTrainedModel,Rt as DebertaForMaskedLM,Gt as DebertaForQuestionAnswering,qt as DebertaForSequenceClassification,$t as DebertaForTokenClassification,Wt as DebertaModel,Ut as DebertaPreTrainedModel,Xt as DebertaTokenizer,Qt as DebertaV2ForMaskedLM,Ht as DebertaV2ForQuestionAnswering,Yt as DebertaV2ForSequenceClassification,Jt as DebertaV2ForTokenClassification,Kt as DebertaV2Model,Zt as DebertaV2PreTrainedModel,er as DebertaV2Tokenizer,tr as DecisionTransformerModel,rr as DecisionTransformerPreTrainedModel,nr as DeiTFeatureExtractor,or as DeiTForImageClassification,sr as DeiTModel,ar as DeiTPreTrainedModel,ir as DepthAnythingForDepthEstimation,lr as DepthAnythingPreTrainedModel,cr as DepthEstimationPipeline,dr as DetrFeatureExtractor,ur as DetrForObjectDetection,pr as DetrForSegmentation,hr as DetrModel,mr as DetrObjectDetectionOutput,_r as DetrPreTrainedModel,fr as DetrSegmentationOutput,gr as Dinov2ForImageClassification,Mr as Dinov2Model,wr as Dinov2PreTrainedModel,Tr as DistilBertForMaskedLM,br as DistilBertForQuestionAnswering,xr as DistilBertForSequenceClassification,kr as DistilBertForTokenClassification,yr as DistilBertModel,Fr as DistilBertPreTrainedModel,Pr as DistilBertTokenizer,Cr as DocumentQuestionAnsweringPipeline,vr as DonutFeatureExtractor,Sr as DonutSwinModel,Ar as DonutSwinPreTrainedModel,Er as EfficientNetForImageClassification,Lr as EfficientNetImageProcessor,zr as EfficientNetModel,Ir as EfficientNetPreTrainedModel,Br as ElectraForMaskedLM,Nr as ElectraForQuestionAnswering,Or as ElectraForSequenceClassification,Dr as ElectraForTokenClassification,Vr as ElectraModel,jr as ElectraPreTrainedModel,Rr as ElectraTokenizer,Gr as EosTokenCriteria,qr as EsmForMaskedLM,$r as EsmForSequenceClassification,Wr as EsmForTokenClassification,Ur as EsmModel,Xr as EsmPreTrainedModel,Qr as EsmTokenizer,Hr as FFT,Yr as FalconForCausalLM,Jr as FalconModel,Kr as FalconPreTrainedModel,Zr as FalconTokenizer,en as FastViTForImageClassification,tn as FastViTModel,rn as FastViTPreTrainedModel,nn as FeatureExtractionPipeline,on as FeatureExtractor,sn as FillMaskPipeline,an as Florence2ForConditionalGeneration,ln as Florence2PreTrainedModel,cn as Florence2Processor,dn as GLPNFeatureExtractor,un as GLPNForDepthEstimation,pn as GLPNModel,hn as GLPNPreTrainedModel,mn as GPT2LMHeadModel,_n as GPT2Model,fn as GPT2PreTrainedModel,gn as GPT2Tokenizer,Mn as GPTBigCodeForCausalLM,wn as GPTBigCodeModel,Tn as GPTBigCodePreTrainedModel,bn as GPTJForCausalLM,xn as GPTJModel,kn as GPTJPreTrainedModel,yn as GPTNeoForCausalLM,Fn as GPTNeoModel,Pn as GPTNeoPreTrainedModel,Cn as GPTNeoXForCausalLM,vn as GPTNeoXModel,Sn as GPTNeoXPreTrainedModel,An as GPTNeoXTokenizer,En as Gemma2ForCausalLM,Ln as Gemma2Model,zn as Gemma2PreTrainedModel,In as GemmaForCausalLM,Bn as GemmaModel,Nn as GemmaPreTrainedModel,On as GemmaTokenizer,Dn as Grok1Tokenizer,Vn as GroupViTModel,jn as GroupViTPreTrainedModel,Rn as HerbertTokenizer,Gn as HieraForImageClassification,qn as HieraModel,$n as HieraPreTrainedModel,Wn as HubertForCTC,Un as HubertForSequenceClassification,Xn as HubertModel,Qn as HubertPreTrainedModel,Hn as ImageClassificationPipeline,Yn as ImageFeatureExtractionPipeline,Jn as ImageFeatureExtractor,Kn as ImageMattingOutput,Zn as ImageSegmentationPipeline,eo as ImageToImagePipeline,to as ImageToTextPipeline,ro as InterruptableStoppingCriteria,no as JAISLMHeadModel,oo as JAISModel,so as JAISPreTrainedModel,ao as LlamaForCausalLM,io as LlamaModel,lo as LlamaPreTrainedModel,co as LlamaTokenizer,uo as LlavaForConditionalGeneration,po as LlavaPreTrainedModel,ho as LongT5ForConditionalGeneration,mo as LongT5Model,_o as LongT5PreTrainedModel,fo as M2M100ForConditionalGeneration,go as M2M100Model,Mo as M2M100PreTrainedModel,wo as M2M100Tokenizer,To as MBart50Tokenizer,bo as MBartForCausalLM,xo as MBartForConditionalGeneration,ko as MBartForSequenceClassification,yo as MBartModel,Fo as MBartPreTrainedModel,Po as MBartTokenizer,Co as MPNetForMaskedLM,vo as MPNetForQuestionAnswering,So as MPNetForSequenceClassification,Ao as MPNetForTokenClassification,Eo as MPNetModel,Lo as MPNetPreTrainedModel,zo as MPNetTokenizer,Io as MT5ForConditionalGeneration,Bo as MT5Model,No as MT5PreTrainedModel,Oo as MarianMTModel,Do as MarianModel,Vo as MarianPreTrainedModel,jo as MarianTokenizer,Ro as MaskFormerFeatureExtractor,Go as MaskFormerForInstanceSegmentation,qo as MaskFormerModel,$o as MaskFormerPreTrainedModel,Wo as MaskedLMOutput,Uo as MaxLengthCriteria,Xo as MistralForCausalLM,Qo as MistralModel,Ho as MistralPreTrainedModel,Yo as MobileBertForMaskedLM,Jo as MobileBertForQuestionAnswering,Ko as MobileBertForSequenceClassification,Zo as MobileBertModel,es as MobileBertPreTrainedModel,ts as MobileBertTokenizer,rs as MobileNetV1FeatureExtractor,ns as MobileNetV1ForImageClassification,os as MobileNetV1Model,ss as MobileNetV1PreTrainedModel,as as MobileNetV2FeatureExtractor,is as MobileNetV2ForImageClassification,ls as MobileNetV2Model,cs as MobileNetV2PreTrainedModel,ds as MobileNetV3FeatureExtractor,us as MobileNetV3ForImageClassification,ps as MobileNetV3Model,hs as MobileNetV3PreTrainedModel,ms as MobileNetV4FeatureExtractor,_s as MobileNetV4ForImageClassification,fs as MobileNetV4Model,gs as MobileNetV4PreTrainedModel,Ms as MobileViTFeatureExtractor,ws as MobileViTForImageClassification,Ts as MobileViTImageProcessor,bs as MobileViTModel,xs as MobileViTPreTrainedModel,ks as MobileViTV2ForImageClassification,ys as MobileViTV2Model,Fs as MobileViTV2PreTrainedModel,Ps as ModelOutput,Cs as Moondream1ForConditionalGeneration,vs as MptForCausalLM,Ss as MptModel,As as MptPreTrainedModel,Es as MusicgenForCausalLM,Ls as MusicgenForConditionalGeneration,zs as MusicgenModel,Is as MusicgenPreTrainedModel,Bs as NllbTokenizer,Ns as NomicBertModel,Os as NomicBertPreTrainedModel,Ds as NougatImageProcessor,Vs as NougatTokenizer,js as OPTForCausalLM,Rs as OPTModel,Gs as OPTPreTrainedModel,qs as ObjectDetectionPipeline,$s as OpenELMForCausalLM,Ws as OpenELMModel,Us as OpenELMPreTrainedModel,Xs as OwlViTFeatureExtractor,Qs as OwlViTForObjectDetection,Hs as OwlViTModel,Ys as OwlViTPreTrainedModel,Js as OwlViTProcessor,Ks as Owlv2ForObjectDetection,Zs as Owlv2ImageProcessor,ea as Owlv2Model,ta as Owlv2PreTrainedModel,ra as Phi3ForCausalLM,na as Phi3Model,oa as Phi3PreTrainedModel,sa as PhiForCausalLM,aa as PhiModel,ia as PhiPreTrainedModel,la as Pipeline,ca as PreTrainedModel,da as PreTrainedTokenizer,ua as PretrainedConfig,pa as PretrainedMixin,ha as Processor,ma as PvtForImageClassification,_a as PvtImageProcessor,fa as PvtModel,ga as PvtPreTrainedModel,Ma as PyAnnoteFeatureExtractor,wa as PyAnnoteForAudioFrameClassification,Ta as PyAnnoteModel,ba as PyAnnotePreTrainedModel,xa as PyAnnoteProcessor,ka as QuestionAnsweringModelOutput,ya as QuestionAnsweringPipeline,Fa as Qwen2ForCausalLM,Pa as Qwen2Model,Ca as Qwen2PreTrainedModel,va as Qwen2Tokenizer,Sa as RTDetrForObjectDetection,Aa as RTDetrImageProcessor,Ea as RTDetrModel,La as RTDetrObjectDetectionOutput,za as RTDetrPreTrainedModel,Ia as RawImage,Ba as ResNetForImageClassification,Na as ResNetModel,Oa as ResNetPreTrainedModel,Da as RoFormerForMaskedLM,Va as RoFormerForQuestionAnswering,ja as RoFormerForSequenceClassification,Ra as RoFormerForTokenClassification,Ga as RoFormerModel,qa as RoFormerPreTrainedModel,$a as RoFormerTokenizer,Wa as RobertaForMaskedLM,Ua as RobertaForQuestionAnswering,Xa as RobertaForSequenceClassification,Qa as RobertaForTokenClassification,Ha as RobertaModel,Ya as RobertaPreTrainedModel,Ja as RobertaTokenizer,Ka as SamImageProcessor,Za as SamImageSegmentationOutput,ei as SamModel,ti as SamPreTrainedModel,ri as SamProcessor,ni as SapiensFeatureExtractor,oi as SapiensForDepthEstimation,si as SapiensForNormalEstimation,ai as SapiensForSemanticSegmentation,ii as SapiensPreTrainedModel,li as SeamlessM4TFeatureExtractor,ci as SegformerFeatureExtractor,di as SegformerForImageClassification,ui as SegformerForSemanticSegmentation,pi as SegformerModel,hi as SegformerPreTrainedModel,mi as Seq2SeqLMOutput,_i as SequenceClassifierOutput,fi as SiglipImageProcessor,gi as SiglipModel,Mi as SiglipPreTrainedModel,wi as SiglipTextModel,Ti as SiglipTokenizer,bi as SiglipVisionModel,xi as SpeechT5FeatureExtractor,ki as SpeechT5ForSpeechToText,yi as SpeechT5ForTextToSpeech,Fi as SpeechT5HifiGan,Pi as SpeechT5Model,Ci as SpeechT5PreTrainedModel,vi as SpeechT5Processor,Si as SpeechT5Tokenizer,Ai as SqueezeBertForMaskedLM,Ei as SqueezeBertForQuestionAnswering,Li as SqueezeBertForSequenceClassification,zi as SqueezeBertModel,Ii as SqueezeBertPreTrainedModel,Bi as SqueezeBertTokenizer,Ni as StableLmForCausalLM,Oi as StableLmModel,Di as StableLmPreTrainedModel,Vi as Starcoder2ForCausalLM,ji as Starcoder2Model,Ri as Starcoder2PreTrainedModel,Gi as StoppingCriteria,qi as StoppingCriteriaList,$i as SummarizationPipeline,Wi as Swin2SRForImageSuperResolution,Ui as Swin2SRImageProcessor,Xi as Swin2SRModel,Qi as Swin2SRPreTrainedModel,Hi as SwinForImageClassification,Yi as SwinModel,Ji as SwinPreTrainedModel,Ki as T5ForConditionalGeneration,Zi as T5Model,el as T5PreTrainedModel,tl as T5Tokenizer,rl as TableTransformerForObjectDetection,nl as TableTransformerModel,ol as TableTransformerObjectDetectionOutput,sl as TableTransformerPreTrainedModel,al as Tensor,il as Text2TextGenerationPipeline,ll as TextClassificationPipeline,cl as TextGenerationPipeline,dl as TextStreamer,ul as TextToAudioPipeline,pl as TokenClassificationPipeline,hl as TokenClassifierOutput,ml as TokenizerModel,_l as TrOCRForCausalLM,fl as TrOCRPreTrainedModel,gl as TranslationPipeline,Ml as UniSpeechForCTC,wl as UniSpeechForSequenceClassification,Tl as UniSpeechModel,bl as UniSpeechPreTrainedModel,xl as UniSpeechSatForAudioFrameClassification,kl as UniSpeechSatForCTC,yl as UniSpeechSatForSequenceClassification,Fl as UniSpeechSatModel,Pl as UniSpeechSatPreTrainedModel,Cl as ViTFeatureExtractor,vl as ViTForImageClassification,Sl as ViTImageProcessor,Al as ViTMAEModel,El as ViTMAEPreTrainedModel,Ll as ViTMSNForImageClassification,zl as ViTMSNModel,Il as ViTMSNPreTrainedModel,Bl as ViTModel,Nl as ViTPreTrainedModel,Ol as VisionEncoderDecoderModel,Dl as VitMatteForImageMatting,Vl as VitMatteImageProcessor,jl as VitMattePreTrainedModel,Rl as VitsModel,Gl as VitsModelOutput,ql as VitsPreTrainedModel,$l as VitsTokenizer,Wl as Wav2Vec2BertForCTC,Ul as Wav2Vec2BertForSequenceClassification,Xl as Wav2Vec2BertModel,Ql as Wav2Vec2BertPreTrainedModel,Hl as Wav2Vec2CTCTokenizer,Yl as Wav2Vec2FeatureExtractor,Jl as Wav2Vec2ForAudioFrameClassification,Kl as Wav2Vec2ForCTC,Zl as Wav2Vec2ForSequenceClassification,ec as Wav2Vec2Model,tc as Wav2Vec2PreTrainedModel,rc as Wav2Vec2ProcessorWithLM,nc as WavLMForAudioFrameClassification,oc as WavLMForCTC,sc as WavLMForSequenceClassification,ac as WavLMForXVector,ic as WavLMModel,lc as WavLMPreTrainedModel,cc as WeSpeakerFeatureExtractor,dc as WeSpeakerResNetModel,uc as WeSpeakerResNetPreTrainedModel,pc as WhisperFeatureExtractor,hc as WhisperForConditionalGeneration,mc as WhisperModel,_c as WhisperPreTrainedModel,fc as WhisperProcessor,gc as WhisperTextStreamer,Mc as WhisperTokenizer,wc as XLMForQuestionAnswering,Tc as XLMForSequenceClassification,bc as XLMForTokenClassification,xc as XLMModel,kc as XLMPreTrainedModel,yc as XLMRobertaForMaskedLM,Fc as XLMRobertaForQuestionAnswering,Pc as XLMRobertaForSequenceClassification,Cc as XLMRobertaForTokenClassification,vc as XLMRobertaModel,Sc as XLMRobertaPreTrainedModel,Ac as XLMRobertaTokenizer,Ec as XLMTokenizer,Lc as XLMWithLMHeadModel,zc as XVectorOutput,Ic as YolosFeatureExtractor,Bc as YolosForObjectDetection,Nc as YolosModel,Oc as YolosObjectDetectionOutput,Dc as YolosPreTrainedModel,Vc as ZeroShotAudioClassificationPipeline,jc as ZeroShotClassificationPipeline,Rc as ZeroShotImageClassificationPipeline,Gc as ZeroShotObjectDetectionPipeline,qc as bankers_round,$c as cat,Wc as cos_sim,Uc as dot,Xc as dynamic_time_warping,Qc as env,Hc as full,Yc as full_like,Jc as getKeyValueShapes,Kc as hamming,Zc as hanning,ed as interpolate,td as interpolate_4d,rd as interpolate_data,nd as is_chinese_char,od as layer_norm,sd as log_softmax,ad as magnitude,id as matmul,ld as max,cd as mean,dd as mean_pooling,ud as medianFilter,pd as mel_filter_bank,hd as min,md as ones,_d as ones_like,fd as permute,gd as permute_data,Md as pipeline,wd as quantize_embeddings,Td as read_audio,bd as rfft,xd as round,kd as softmax,yd as spectrogram,Fd as stack,Pd as std_mean,Cd as topk,vd as window_function,Sd as zeros,Ad as zeros_like};

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