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The following inputs will be ignored: "${s.join(", ")}".`)}return s}(e,t);try{let t=await e.run(s);return t=y(t),t}catch(e){throw console.error(`An error occurred during model execution: "${e}".`),console.error("Inputs given to model:",s),e}}function y(e){for(let t in e)e[t]instanceof u?e[t]=new i.Tensor(e[t]):"object"==typeof e[t]&&y(e[t]);return e}function F(e){if(e instanceof i.Tensor)return e;if(0===e.length)throw Error("items must be non-empty");if(Array.isArray(e[0])){if(e.some((t=>t.length!==e[0].length)))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new i.Tensor("int64",BigInt64Array.from(e.flat().map((e=>BigInt(e)))),[e.length,e[0].length])}return new i.Tensor("int64",BigInt64Array.from(e.map((e=>BigInt(e)))),[1,e.length])}function C(e,t){let s=e.config.pad_token_id??null,o=e.config.eos_token_id??null;(0,n.isIntegralNumber)(o)&&(o=[o]);let r=-1!==t.indexOf(s),a=null===o||!o.includes(s);if(r&&a){let e=BigInt64Array.from(t.data.map((e=>e!=s)));return new i.Tensor("int64",e,t.dims)}return(0,i.ones_like)(t)}function P(e,t,s){if(!e.inputNames.includes("position_ids"))return;const o=new BigInt64Array(t.attention_mask.data.length);for(let e=0;e0&&o.push(new a.NoRepeatNGramLogitsProcessor(e.no_repeat_ngram_size)),null!==e.bad_words_ids&&o.push(new a.NoBadWordsLogitsProcessor(e.bad_words_ids,e.eos_token_id)),null!==e.min_length&&null!==e.eos_token_id&&e.min_length>0&&o.push(new a.MinLengthLogitsProcessor(e.min_length,e.eos_token_id)),null!==e.min_new_tokens&&null!==e.eos_token_id&&e.min_new_tokens>0&&o.push(new a.MinNewTokensLengthLogitsProcessor(t,e.min_new_tokens,e.eos_token_id)),null!==e.forced_bos_token_id&&o.push(new a.ForcedBOSTokenLogitsProcessor(e.forced_bos_token_id)),null!==e.forced_eos_token_id&&o.push(new a.ForcedEOSTokenLogitsProcessor(e.max_length,e.forced_eos_token_id)),null!==e.begin_suppress_tokens){let s=t>1||null===e.forced_bos_token_id?t:t+1;null!==e.forced_decoder_ids&&(s+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),o.push(new a.SuppressTokensAtBeginLogitsProcessor(e.begin_suppress_tokens,s))}return null!==e.forced_decoder_ids&&o.push(new a.ForceTokensLogitsProcessor(e.forced_decoder_ids)),null!==s&&o.extend(s),o}_get_generation_config(e){let t=new a.GenerationConfig(this.config);return"generation_config"in this&&Object.assign(t,this.generation_config),null!==e&&Object.assign(t,e),t}async generate(e,t=null,s=null,{inputs_attention_mask:o=null}={}){if(!this.can_generate){let e=`The current model class (${k.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const t=this.config.model_type,s=Br.get(t)??zr.get(t)??vr.get(t)??Dr.get(t);throw s&&(e+=` Please use the following class instead: '${s[0]}'`),Error(e)}if(!(e instanceof i.Tensor||(0,n.isTypedArray)(e)||Array.isArray(e)))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${e.constructor.name}".`);let r;if(this.config.is_encoder_decoder)r=0;else if(r=e instanceof i.Tensor?e.dims.at(-1):e.length,0===r)throw Error("Must supply a non-empty array of input token ids.");t=this._get_generation_config(t),s=s??new a.LogitsProcessorList,s=this._get_logits_processor(t,r,s);let l=t.eos_token_id;null===l||Array.isArray(l)||(l=[l]);let c=1;const d=c+(t.max_new_tokens??1/0),u=Number.isInteger(t.max_length)&&null===(t.max_new_tokens??null);let h=a.Sampler.getSampler(t),p=this.getStartBeams(e,t,c,o);for(;p.some((e=>!e.done))&&c=t.max_length){o.done=!0,e.push(o);continue}let n=await this.runBeam(o);t.output_attentions&&this.addAttentionsToBeam(o,n),t.output_scores;let r=n.logits.slice(null,-1,null);s(o.output_token_ids,r);let a=h(r);for(let[t,s]of a){let n={...o};this.updateBeam(n,t),n.score+=s,l&&l.includes(t)&&(n.done=!0),e.push(n)}}++c,e=this.groupBeams(e).map((e=>e.sort(((e,t)=>t.score-e.score)).slice(0,t.num_beams))),p=e.flat(),t.callback_function&&t.callback_function(p)}const _=this.groupBeams(p),m=e=>_.map((s=>t.num_return_sequences>1?s.slice(0,t.num_return_sequences).map((t=>t[e])):[s[0][e]])).flat(),f=m("output_token_ids");if(t.return_dict_in_generate){return{sequences:f,decoder_attentions:m("decoder_attentions"),cross_attentions:m("cross_attentions")}}return f}addAttentionsToBeam(e,t){if(this.config.is_encoder_decoder){if(!t.cross_attentions||0===t.cross_attentions.length)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(t.cross_attentions)}if(!t.decoder_attentions||0===t.decoder_attentions.length)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(t.decoder_attentions)}groupBeams(e){const t=Object.create(null);for(const s of e)void 0===t[s.id]?t[s.id]=[s]:t[s.id].push(s);return Object.values(t)}getPastKeyValues(e,t){const s=Object.create(null);for(const o in e)if(o.startsWith("present")){let n=o.replace("present","past_key_values");t&&o.includes("encoder")?s[n]=t[n]:s[n]=e[o]}return s}getAttentions(e){const t=Object.create(null);for(const s of["cross_attentions","decoder_attentions"]){const o=[];for(const t in e)if(t.startsWith(s)){o[t.split(".").pop()]=e[t]}t[s]=o}return t}addPastKeyValues(e,t){if(t)Object.assign(e,t);else{const t=1;if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??1)){let s=[t,this.num_encoder_heads,0,this.encoder_dim_kv],o=[t,this.num_decoder_heads,0,this.decoder_dim_kv];for(let t=0;t{let o=Array.from({length:this.config.decoder_layers},((t,s)=>(0,i.cat)(e.map((e=>e[s])),2))),n=(0,i.stack)(t.map((([e,t])=>s?o[e].slice(null,t,null,[0,s]):o[e].slice(null,t))));n=n.transpose(1,0,2,3);let[a,l]=(0,i.std_mean)(n,-2,0,!0),d=n.clone();for(let e=0;es[t+1]-s[t])),c=(0,n.mergeArrays)([1],l).map((e=>!!e)),u=[];for(let e=0;ee*t),1);e.input_labels=new i.Tensor("int64",new BigInt64Array(s).fill(1n),t)}return await x(this.prompt_encoder_mask_decoder,{input_points:e.input_points,input_labels:e.input_labels,image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings})}async _call(e){return new un(await super._call(e))}}class un extends V{constructor({iou_scores:e,pred_masks:t}){super(),this.iou_scores=e,this.pred_masks=t}}class hn extends N{}class pn extends hn{}class _n extends hn{constructor(e,t,s,o){super(e,t),this.decoder_merged_session=s,this.generation_config=o,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class mn extends N{}class fn extends mn{}class gn extends mn{constructor(e,t,s,o){super(e,t),this.decoder_merged_session=s,this.generation_config=o,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Mn extends N{}class wn extends Mn{}class Tn extends Mn{async _call(e){return new za(await super._call(e))}}class kn extends Mn{async _call(e){return new va(await super._call(e))}}class bn extends Mn{async _call(e){return new Aa(await super._call(e))}}class xn extends N{}class yn extends xn{}class Fn extends xn{async _call(e){return new za(await super._call(e))}}class Cn extends xn{async _call(e){return new va(await super._call(e))}}class Pn extends N{}class vn extends Pn{}class Sn extends Pn{async _call(e){return new za(await super._call(e))}}class An extends Pn{async _call(e){return new va(await super._call(e))}}class Ln extends Pn{async _call(e){return new Aa(await super._call(e))}}class En extends N{}class zn extends En{}class Bn extends En{async _call(e){return new za(await super._call(e))}}class In extends En{async _call(e){return new va(await super._call(e))}}class On extends N{}class Dn extends Mn{}class Nn extends Mn{async _call(e){return new za(await super._call(e))}}class Vn extends Mn{async _call(e){return new va(await super._call(e))}}class qn extends N{}class jn extends qn{}class Rn extends qn{async _call(e){return new za(await super._call(e))}}class Gn extends qn{async _call(e){return new va(await super._call(e))}}class Wn extends qn{async _call(e){return new Sa(await super._call(e))}}class $n extends qn{async _call(e){return new Aa(await super._call(e))}}class Un extends N{}class Xn extends Un{}class Qn extends Un{}class Hn extends Un{constructor(e,t,s,o){super(e,t),this.decoder_merged_session=s,this.generation_config=o,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.hidden_size/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.hidden_size/this.num_encoder_heads}async generate_speech(e,t,{threshold:s=.5,minlenratio:o=0,maxlenratio:n=20,vocoder:r=null}={}){const a={input_ids:e},{encoder_outputs:l,encoder_attention_mask:c}=await z(this,a),d=l.dims[1]/this.config.reduction_factor,u=Math.floor(d*n),h=Math.floor(d*o),p=this.config.num_mel_bins;let _=[],m=null,f=null,g=0;for(;;){++g;const e=v(!!f);let o;o=f?f.output_sequence_out:new i.Tensor("float32",new Float32Array(p),[1,1,p]);let n={use_cache_branch:e,output_sequence:o,encoder_attention_mask:c,speaker_embeddings:t,encoder_hidden_states:l};this.addPastKeyValues(n,m),f=await x(this.decoder_merged_session,n),m=this.getPastKeyValues(f,m);const{prob:r,spectrum:a}=f;if(_.push(a),g>=h&&(Array.from(r.data).filter((e=>e>=s)).length>0||g>=u))break}const M=(0,i.cat)(_),{waveform:w}=await x(r.session,{spectrogram:M});return{spectrogram:M,waveform:w}}}class Yn extends N{main_input_name="spectrogram"}class Jn extends N{constructor(e,t,s){super(e,t),this.generation_config=s,this.config.pad_token_id=this.config.eos_token_id,this.num_encoder_layers=this.num_decoder_layers=this.config.decoder_layers,this.num_encoder_heads=this.num_decoder_heads=this.config.decoder_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads}}class Zn extends Jn{}class Kn extends N{constructor(e,t,s){super(e,t),this.generation_config=s,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class er extends Kn{}class tr extends Kn{}class sr extends N{constructor(e,t,s){super(e,t),this.generation_config=s,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class or extends sr{}class nr extends sr{}class rr extends N{constructor(e,t,s){super(e,t),this.generation_config=s,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class ar extends rr{}class ir extends rr{}class lr extends N{}class cr extends lr{}class dr extends lr{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class ur extends lr{static async from_pretrained(e,t={}){return t.model_file_name??="audio_model",super.from_pretrained(e,t)}}class hr extends N{}class pr extends hr{async _call(e){return new Oa(await super._call(e))}}class _r extends N{}class mr extends _r{}class fr extends _r{}class gr extends _r{}class Mr extends N{constructor(e,t,s){super(e,t),this.generation_config=s,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class wr extends Mr{}class Tr extends Mr{}class kr extends N{}class br extends kr{}class xr extends kr{async _call(e){return new va(await super._call(e))}}class yr{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{quantized:t=!0,progress_callback:s=null,config:n=null,cache_dir:r=null,local_files_only:a=!1,revision:i="main",model_file_name:l=null}={}){let c={quantized:t,progress_callback:s,config:n,cache_dir:r,local_files_only:a,revision:i,model_file_name:l};if(n=await o.AutoConfig.from_pretrained(e,c),c.config||(c.config=n),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let t of this.MODEL_CLASS_MAPPINGS){const s=t.get(n.model_type);if(s)return await s[1].from_pretrained(e,c)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${n.model_type}", attempting to construct from base class.`),await N.from_pretrained(e,c);throw Error(`Unsupported model type: ${n.model_type}`)}}const Fr=new Map([["bert",["BertModel",R]],["nomic_bert",["NomicBertModel",Q]],["roformer",["RoFormerModel",Y]],["electra",["ElectraModel",le]],["esm",["EsmModel",Ne]],["convbert",["ConvBertModel",se]],["camembert",["CamembertModel",_e]],["deberta",["DebertaModel",Te]],["deberta-v2",["DebertaV2Model",Ce]],["mpnet",["MPNetModel",Qe]],["albert",["AlbertModel",rt]],["distilbert",["DistilBertModel",Ee]],["roberta",["RobertaModel",Bt]],["xlm",["XLMModel",qt]],["xlm-roberta",["XLMRobertaModel",Ut]],["clap",["ClapModel",cr]],["clip",["CLIPModel",rs]],["clipseg",["CLIPSegModel",ms]],["chinese_clip",["ChineseCLIPModel",ps]],["siglip",["SiglipModel",cs]],["mobilebert",["MobileBertModel",Ge]],["squeezebert",["SqueezeBertModel",et]],["wav2vec2",["Wav2Vec2Model",wn]],["wav2vec2-bert",["Wav2Vec2BertModel",zn]],["unispeech",["UniSpeechModel",yn]],["unispeech-sat",["UniSpeechSatModel",vn]],["hubert",["HubertModel",Dn]],["wavlm",["WavLMModel",jn]],["audio-spectrogram-transformer",["ASTModel",Zt]],["vits",["VitsModel",pr]],["detr",["DetrModel",Mo]],["table-transformer",["TableTransformerModel",yo]],["vit",["ViTModel",eo]],["mobilevit",["MobileViTModel",ro]],["owlvit",["OwlViTModel",lo]],["owlv2",["Owlv2Model",ho]],["beit",["BeitModel",mo]],["deit",["DeiTModel",vo]],["convnext",["ConvNextModel",Yo]],["convnextv2",["ConvNextV2Model",Ko]],["dinov2",["Dinov2Model",sn]],["resnet",["ResNetModel",Lo]],["swin",["SwinModel",Bo]],["swin2sr",["Swin2SRModel",Do]],["donut-swin",["DonutSwinModel",Qo]],["yolos",["YolosModel",rn]],["dpt",["DPTModel",qo]],["glpn",["GLPNModel",$o]],["hifigan",["SpeechT5HifiGan",Yn]],["efficientnet",["EfficientNetModel",br]]]),Cr=new Map([["t5",["T5Model",dt]],["longt5",["LongT5Model",pt]],["mt5",["MT5Model",ft]],["bart",["BartModel",wt]],["mbart",["MBartModel",xt]],["marian",["MarianModel",pn]],["whisper",["WhisperModel",ts]],["m2m_100",["M2M100Model",fn]],["blenderbot",["BlenderbotModel",vt]],["blenderbot-small",["BlenderbotSmallModel",Lt]]]),Pr=new Map([["bloom",["BloomModel",$s]],["gpt2",["GPT2Model",Ms]],["gptj",["GPTJModel",Ps]],["gpt_bigcode",["GPTBigCodeModel",As]],["gpt_neo",["GPTNeoModel",ks]],["gpt_neox",["GPTNeoXModel",ys]],["codegen",["CodeGenModel",zs]],["llama",["LlamaModel",Os]],["qwen2",["Qwen2Model",Vs]],["phi",["PhiModel",Rs]],["mpt",["MptModel",Qs]],["opt",["OPTModel",Js]],["mistral",["MistralModel",er]],["starcoder2",["Starcoder2Model",or]],["falcon",["FalconModel",ar]]]),vr=new Map([["speecht5",["SpeechT5ForSpeechToText",Qn]],["whisper",["WhisperForConditionalGeneration",ss]]]),Sr=new Map([["speecht5",["SpeechT5ForTextToSpeech",Hn]]]),Ar=new Map([["vits",["VitsModel",pr]]]),Lr=new Map([["bert",["BertForSequenceClassification",W]],["roformer",["RoFormerForSequenceClassification",Z]],["electra",["ElectraForSequenceClassification",de]],["esm",["EsmForSequenceClassification",qe]],["convbert",["ConvBertForSequenceClassification",ne]],["camembert",["CamembertForSequenceClassification",fe]],["deberta",["DebertaForSequenceClassification",be]],["deberta-v2",["DebertaV2ForSequenceClassification",ve]],["mpnet",["MPNetForSequenceClassification",Ye]],["albert",["AlbertForSequenceClassification",at]],["distilbert",["DistilBertForSequenceClassification",ze]],["roberta",["RobertaForSequenceClassification",Ot]],["xlm",["XLMForSequenceClassification",Rt]],["xlm-roberta",["XLMRobertaForSequenceClassification",Qt]],["bart",["BartForSequenceClassification",kt]],["mbart",["MBartForSequenceClassification",Ft]],["mobilebert",["MobileBertForSequenceClassification",$e]],["squeezebert",["SqueezeBertForSequenceClassification",st]]]),Er=new Map([["bert",["BertForTokenClassification",$]],["roformer",["RoFormerForTokenClassification",K]],["electra",["ElectraForTokenClassification",ue]],["esm",["EsmForTokenClassification",je]],["convbert",["ConvBertForTokenClassification",re]],["camembert",["CamembertForTokenClassification",ge]],["deberta",["DebertaForTokenClassification",xe]],["deberta-v2",["DebertaV2ForTokenClassification",Se]],["mpnet",["MPNetForTokenClassification",Je]],["distilbert",["DistilBertForTokenClassification",Be]],["roberta",["RobertaForTokenClassification",Dt]],["xlm",["XLMForTokenClassification",Gt]],["xlm-roberta",["XLMRobertaForTokenClassification",Ht]]]),zr=new Map([["t5",["T5ForConditionalGeneration",ut]],["longt5",["LongT5ForConditionalGeneration",_t]],["mt5",["MT5ForConditionalGeneration",gt]],["bart",["BartForConditionalGeneration",Tt]],["mbart",["MBartForConditionalGeneration",yt]],["marian",["MarianMTModel",_n]],["m2m_100",["M2M100ForConditionalGeneration",gn]],["blenderbot",["BlenderbotForConditionalGeneration",St]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Et]]]),Br=new Map([["bloom",["BloomForCausalLM",Us]],["gpt2",["GPT2LMHeadModel",ws]],["gptj",["GPTJForCausalLM",vs]],["gpt_bigcode",["GPTBigCodeForCausalLM",Ls]],["gpt_neo",["GPTNeoForCausalLM",bs]],["gpt_neox",["GPTNeoXForCausalLM",Fs]],["codegen",["CodeGenForCausalLM",Bs]],["llama",["LlamaForCausalLM",Ds]],["qwen2",["Qwen2ForCausalLM",qs]],["phi",["PhiForCausalLM",Gs]],["mpt",["MptForCausalLM",Hs]],["opt",["OPTForCausalLM",Zs]],["mbart",["MBartForCausalLM",Ct]],["mistral",["MistralForCausalLM",tr]],["starcoder2",["Starcoder2ForCausalLM",nr]],["falcon",["FalconForCausalLM",ir]],["trocr",["TrOCRForCausalLM",Zn]],["stablelm",["StableLmForCausalLM",Tr]]]),Ir=new Map([["bert",["BertForMaskedLM",G]],["roformer",["RoFormerForMaskedLM",J]],["electra",["ElectraForMaskedLM",ce]],["esm",["EsmForMaskedLM",Ve]],["convbert",["ConvBertForMaskedLM",oe]],["camembert",["CamembertForMaskedLM",me]],["deberta",["DebertaForMaskedLM",ke]],["deberta-v2",["DebertaV2ForMaskedLM",Pe]],["mpnet",["MPNetForMaskedLM",He]],["albert",["AlbertForMaskedLM",lt]],["distilbert",["DistilBertForMaskedLM",Oe]],["roberta",["RobertaForMaskedLM",It]],["xlm",["XLMWithLMHeadModel",jt]],["xlm-roberta",["XLMRobertaForMaskedLM",Xt]],["mobilebert",["MobileBertForMaskedLM",We]],["squeezebert",["SqueezeBertForMaskedLM",tt]]]),Or=new Map([["bert",["BertForQuestionAnswering",U]],["roformer",["RoFormerForQuestionAnswering",ee]],["electra",["ElectraForQuestionAnswering",he]],["convbert",["ConvBertForQuestionAnswering",ae]],["camembert",["CamembertForQuestionAnswering",Me]],["deberta",["DebertaForQuestionAnswering",ye]],["deberta-v2",["DebertaV2ForQuestionAnswering",Ae]],["mpnet",["MPNetForQuestionAnswering",Ze]],["albert",["AlbertForQuestionAnswering",it]],["distilbert",["DistilBertForQuestionAnswering",Ie]],["roberta",["RobertaForQuestionAnswering",Nt]],["xlm",["XLMForQuestionAnswering",Wt]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Yt]],["mobilebert",["MobileBertForQuestionAnswering",Ue]],["squeezebert",["SqueezeBertForQuestionAnswering",ot]]]),Dr=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",os]]]),Nr=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",os]]]),Vr=new Map([["vit",["ViTForImageClassification",to]],["mobilevit",["MobileViTForImageClassification",ao]],["beit",["BeitForImageClassification",fo]],["deit",["DeiTForImageClassification",So]],["convnext",["ConvNextForImageClassification",Jo]],["convnextv2",["ConvNextV2ForImageClassification",en]],["dinov2",["Dinov2ForImageClassification",on]],["resnet",["ResNetForImageClassification",Eo]],["swin",["SwinForImageClassification",Io]],["segformer",["SegformerForImageClassification",fr]],["efficientnet",["EfficientNetForImageClassification",xr]]]),qr=new Map([["detr",["DetrForObjectDetection",wo]],["table-transformer",["TableTransformerForObjectDetection",Fo]],["yolos",["YolosForObjectDetection",an]]]),jr=new Map([["owlvit",["OwlViTForObjectDetection",co]],["owlv2",["Owlv2ForObjectDetection",po]]]),Rr=new Map([["detr",["DetrForSegmentation",To]],["clipseg",["CLIPSegForImageSegmentation",fs]]]),Gr=new Map([["segformer",["SegformerForSemanticSegmentation",gr]]]),Wr=new Map([["sam",["SamModel",dn]]]),$r=new Map([["wav2vec2",["Wav2Vec2ForCTC",Tn]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Bn]],["unispeech",["UniSpeechForCTC",Fn]],["unispeech-sat",["UniSpeechSatForCTC",Sn]],["wavlm",["WavLMForCTC",Rn]],["hubert",["HubertForCTC",Nn]]]),Ur=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",kn]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",In]],["unispeech",["UniSpeechForSequenceClassification",Cn]],["unispeech-sat",["UniSpeechSatForSequenceClassification",An]],["wavlm",["WavLMForSequenceClassification",Gn]],["hubert",["HubertForSequenceClassification",Vn]],["audio-spectrogram-transformer",["ASTForAudioClassification",Kt]]]),Xr=new Map([["wavlm",["WavLMForXVector",Wn]]]),Qr=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Ln]],["wavlm",["WavLMForAudioFrameClassification",$n]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",bn]]]),Hr=new Map([["vitmatte",["VitMatteForImageMatting",oo]]]),Yr=new Map([["swin2sr",["Swin2SRForImageSuperResolution",No]]]),Jr=new Map([["dpt",["DPTForDepthEstimation",jo]],["depth_anything",["DepthAnythingForDepthEstimation",Go]],["glpn",["GLPNForDepthEstimation",Uo]]]),Zr=new Map([["clip",["CLIPVisionModelWithProjection",is]],["siglip",["SiglipVisionModel",us]]]),Kr=[[Fr,p],[Cr,_],[Pr,g],[Lr,p],[Er,p],[zr,m],[vr,m],[Br,g],[Ir,p],[Or,p],[Dr,f],[Vr,p],[Rr,p],[Gr,p],[Hr,p],[Yr,p],[Jr,p],[qr,p],[jr,p],[Wr,M],[$r,p],[Ur,p],[Sr,m],[Ar,p],[Xr,p],[Qr,p],[Zr,p]];for(const[e,t]of Kr)for(const[s,o]of e.values())w.set(s,t),k.set(o,s),T.set(s,o);const ea=[["CLIPTextModelWithProjection",as,p],["SiglipTextModel",ds,p],["ClapTextModelWithProjection",dr,p],["ClapAudioModelWithProjection",ur,p]];for(const[e,t,s]of ea)w.set(e,s),k.set(t,e),T.set(e,t);class ta extends yr{static MODEL_CLASS_MAPPINGS=Kr.map((e=>e[0]));static BASE_IF_FAIL=!0}class sa extends yr{static MODEL_CLASS_MAPPINGS=[Lr]}class oa extends yr{static MODEL_CLASS_MAPPINGS=[Er]}class na extends yr{static MODEL_CLASS_MAPPINGS=[zr]}class ra extends yr{static MODEL_CLASS_MAPPINGS=[vr]}class aa extends yr{static MODEL_CLASS_MAPPINGS=[Sr]}class ia extends yr{static MODEL_CLASS_MAPPINGS=[Ar]}class la extends yr{static MODEL_CLASS_MAPPINGS=[Br]}class ca extends yr{static MODEL_CLASS_MAPPINGS=[Ir]}class da extends yr{static MODEL_CLASS_MAPPINGS=[Or]}class ua extends yr{static MODEL_CLASS_MAPPINGS=[Dr]}class ha extends yr{static MODEL_CLASS_MAPPINGS=[Vr]}class pa extends yr{static MODEL_CLASS_MAPPINGS=[Rr]}class _a extends yr{static MODEL_CLASS_MAPPINGS=[Gr]}class ma extends yr{static MODEL_CLASS_MAPPINGS=[qr]}class fa extends yr{static MODEL_CLASS_MAPPINGS=[jr]}class ga extends yr{static MODEL_CLASS_MAPPINGS=[Wr]}class Ma extends yr{static MODEL_CLASS_MAPPINGS=[$r]}class wa extends yr{static MODEL_CLASS_MAPPINGS=[Ur]}class Ta extends yr{static MODEL_CLASS_MAPPINGS=[Xr]}class ka extends yr{static MODEL_CLASS_MAPPINGS=[Qr]}class ba extends yr{static MODEL_CLASS_MAPPINGS=[Nr]}class xa extends yr{static MODEL_CLASS_MAPPINGS=[Hr]}class ya extends yr{static MODEL_CLASS_MAPPINGS=[Yr]}class Fa extends yr{static MODEL_CLASS_MAPPINGS=[Jr]}class Ca extends yr{static MODEL_CLASS_MAPPINGS=[Zr]}class Pa extends V{constructor({logits:e,past_key_values:t,encoder_outputs:s,decoder_attentions:o=null,cross_attentions:n=null}){super(),this.logits=e,this.past_key_values=t,this.encoder_outputs=s,this.decoder_attentions=o,this.cross_attentions=n}}class va extends V{constructor({logits:e}){super(),this.logits=e}}class Sa extends V{constructor({logits:e,embeddings:t}){super(),this.logits=e,this.embeddings=t}}class Aa extends V{constructor({logits:e}){super(),this.logits=e}}class La extends V{constructor({logits:e}){super(),this.logits=e}}class Ea extends V{constructor({start_logits:e,end_logits:t}){super(),this.start_logits=e,this.end_logits=t}}class za extends V{constructor({logits:e}){super(),this.logits=e}}class Ba extends V{constructor({logits:e,past_key_values:t}){super(),this.logits=e,this.past_key_values=t}}class Ia extends V{constructor({alphas:e}){super(),this.alphas=e}}class Oa extends V{constructor({waveform:e,spectrogram:t}){super(),this.waveform=e,this.spectrogram=t}}},"./src/pipelines.js": /*!**************************!*\ !*** ./src/pipelines.js ***! \**************************/(e,t,s)=>{s.r(t),s.d(t,{AudioClassificationPipeline:()=>C,AutomaticSpeechRecognitionPipeline:()=>v,DepthEstimationPipeline:()=>N,DocumentQuestionAnsweringPipeline:()=>I,FeatureExtractionPipeline:()=>y,FillMaskPipeline:()=>M,ImageClassificationPipeline:()=>A,ImageFeatureExtractionPipeline:()=>F,ImageSegmentationPipeline:()=>L,ImageToImagePipeline:()=>D,ImageToTextPipeline:()=>S,ObjectDetectionPipeline:()=>z,Pipeline:()=>_,QuestionAnsweringPipeline:()=>g,SummarizationPipeline:()=>T,Text2TextGenerationPipeline:()=>w,TextClassificationPipeline:()=>m,TextGenerationPipeline:()=>b,TextToAudioPipeline:()=>O,TokenClassificationPipeline:()=>f,TranslationPipeline:()=>k,ZeroShotAudioClassificationPipeline:()=>P,ZeroShotClassificationPipeline:()=>x,ZeroShotImageClassificationPipeline:()=>E,ZeroShotObjectDetectionPipeline:()=>B,pipeline:()=>j});var o=s(/*! ./tokenizers.js */"./src/tokenizers.js"),n=s(/*! ./models.js */"./src/models.js"),r=s(/*! ./processors.js */"./src/processors.js"),a=s(/*! ./utils/core.js */"./src/utils/core.js"),i=s(/*! ./utils/maths.js */"./src/utils/maths.js"),l=s(/*! ./utils/audio.js */"./src/utils/audio.js"),c=s(/*! ./utils/tensor.js */"./src/utils/tensor.js"),d=s(/*! ./utils/image.js */"./src/utils/image.js");async function u(e){return Array.isArray(e)||(e=[e]),await Promise.all(e.map((e=>d.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,l.read_audio)(e,t):e instanceof Float64Array?new Float32Array(e):e)))}function p(e,t){t&&(e=e.map((e=>0|e)));const[s,o,n,r]=e;return{xmin:s,ymin:o,xmax:n,ymax:r}}class _ extends a.Callable{constructor({task:e,model:t,tokenizer:s=null,processor:o=null}){super(),this.task=e,this.model=t,this.tokenizer=s,this.processor=o}async dispose(){await this.model.dispose()}}class m extends _{constructor(e){super(e)}async _call(e,{topk:t=1}={}){const s=this.tokenizer(e,{padding:!0,truncation:!0}),o=await this.model(s),n="multi_label_classification"===this.model.config.problem_type?e=>e.sigmoid().data:e=>(0,i.softmax)(e.data),r=this.model.config.id2label,a=[];for(const e of o.logits){const s=n(e),o=(0,i.getTopItems)(s,t).map((e=>({label:r[e[0]],score:e[1]})));1===t?a.push(...o):a.push(o)}return Array.isArray(e)||1===t?a:a[0]}}class f extends _{constructor(e){super(e)}async _call(e,{ignore_labels:t=["O"]}={}){const s=Array.isArray(e),o=this.tokenizer(s?e:[e],{padding:!0,truncation:!0}),n=(await this.model(o)).logits,r=this.model.config.id2label,a=[];for(let e=0;e[e,t])).filter((e=>e[1]>l)),d=Array.from((0,i.softmax)(n.end_logits[e].data)).map(((e,t)=>[e,t])).filter((e=>e[1]>l)),u=(0,a.product)(c,d).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{const t=[...r];return t[a]=e[0],{score:e[1],token:e[0],token_str:this.tokenizer.model.vocab[e[0]],sequence:this.tokenizer.decode(t,{skip_special_tokens:!0})}})))}return Array.isArray(e)?n:n[0]}}class w 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 s=this.model.config.task_specific_params;s&&s[this.task]&&s[this.task].prefix&&(e=e.map((e=>s[this.task].prefix+e)));const o=this.tokenizer,n={padding:!0,truncation:!0};let r;r=this instanceof k&&"_build_translation_inputs"in o?o._build_translation_inputs(e,n,t).input_ids:o(e,n).input_ids;const a=await this.model.generate(r,t);return o.batch_decode(a,{skip_special_tokens:!0}).map((e=>({[this._key]:e})))}}class T extends w{_key="summary_text";constructor(e){super(e)}}class k extends w{_key="translation_text";constructor(e){super(e)}}class b extends _{constructor(e){super(e)}async _call(e,t={}){const s=Array.isArray(e);s||(e=[e]);const o=t.add_special_tokens??!1;this.tokenizer.padding_side="left";const{input_ids:n,attention_mask:r}=this.tokenizer(e,{add_special_tokens:o,padding:!0,truncation:!0}),a=await this.model.generate(n,t,null,{inputs_attention_mask:r}),i=this.tokenizer.batch_decode(a,{skip_special_tokens:!0}),l=Array.from({length:e.length},(e=>[]));for(let t=0;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:s="This example is {}.",multi_label:o=!1}={}){const n=Array.isArray(e);n||(e=[e]),Array.isArray(t)||(t=[t]);const r=t.map((e=>s.replace("{}",e))),a=o||1===t.length,l=[];for(const s of e){const e=[];for(const t of r){const o=this.tokenizer(s,{text_pair:t,padding:!0,truncation:!0}),n=await this.model(o);a?e.push([n.logits.data[this.contradiction_id],n.logits.data[this.entailment_id]]):e.push(n.logits.data[this.entailment_id])}const o=(a?e.map((e=>(0,i.softmax)(e)[1])):(0,i.softmax)(e)).map(((e,t)=>[e,t])).sort(((e,t)=>t[0]-e[0]));l.push({sequence:s,labels:o.map((e=>t[e[1]])),scores:o.map((e=>e[0]))})}return n?l:l[0]}}class y extends _{constructor(e){super(e)}async _call(e,{pooling:t="none",normalize:s=!1}={}){const o=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(o);let r=n.last_hidden_state??n.logits;if("none"===t);else if("mean"===t)r=(0,c.mean_pooling)(r,o.attention_mask);else{if("cls"!==t)throw Error(`Pooling method '${t}' not supported.`);r=r.slice(null,0)}return s&&(r=r.normalize(2,-1)),r}}class F extends _{constructor(e){super(e)}async _call(e,{pool:t=null}={}){const s=await u(e),{pixel_values:o}=await this.processor(s),n=await this.model({pixel_values:o});let r;if(t){if(!("pooler_output"in n))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");r=n.pooler_output}else r=n.last_hidden_state??n.logits??n.image_embeds;return r}}class C extends _{constructor(e){super(e)}async _call(e,{topk:t=null}={}){const s=!Array.isArray(e),o=this.processor.feature_extractor.config.sampling_rate,n=await h(e,o),r=this.model.config.id2label,a=[];for(const e of n){const s=await this.processor(e),o=(await this.model(s)).logits[0],n=(0,i.getTopItems)((0,i.softmax)(o.data),t).map((e=>({label:r[e[0]],score:e[1]})));1===t?a.push(...n):a.push(n)}return s&&1!==t?a[0]:a}}class P extends _{constructor(e){super(e)}async _call(e,t,{hypothesis_template:s="This is a sound of {}."}={}){const o=!Array.isArray(e);o&&(e=[e]);const n=t.map((e=>s.replace("{}",e))),r=this.tokenizer(n,{padding:!0,truncation:!0}),a=this.processor.feature_extractor.config.sampling_rate,l=await h(e,a),c=[];for(const e of l){const s=await this.processor(e),o=await this.model({...r,...s}),n=(0,i.softmax)(o.logits_per_audio.data);c.push([...n].map(((e,s)=>({score:e,label:t[s]}))))}return o?c[0]:c}}class v 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 s=!Array.isArray(e);s&&(e=[e]);const o=this.processor.feature_extractor.config.sampling_rate,n=await h(e,o),r=[];for(const e of n){const t=await this.processor(e),s=(await this.model(t)).logits[0],o=[];for(const e of s)o.push((0,i.max)(e.data)[1]);const n=this.tokenizer.decode(o);r.push({text:n})}return s?r[0]:r}async _call_whisper(e,t={}){const s=t.return_timestamps??!1,o=t.chunk_length_s??0,n=t.chunk_callback??null,r=t.force_full_sequences??!1;let l=t.stride_length_s??null;"word"===s&&(t.return_token_timestamps=!0);const c=(0,a.pop)(t,"language",null),d=(0,a.pop)(t,"task",null);if(c||d||s){if(t.forced_decoder_ids)throw new Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");const e=this.tokenizer.get_decoder_prompt_ids({language:c,task:d,no_timestamps:!s});e.length>0&&(t.forced_decoder_ids=e)}const u=!Array.isArray(e);u&&(e=[e]);const p=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,_=this.processor.feature_extractor.config.hop_length,m=this.processor.feature_extractor.config.sampling_rate,f=await h(e,m),g=[];for(const e of f){let a=[];if(o>0){if(null===l)l=o/6;else if(o<=l)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const t=m*o,s=m*l,n=t-2*s;let r=0;for(;r=e.length;a.push({stride:[o.length,l?0:s,c?0:s],input_features:i.input_features,is_last:c}),r+=n}}else a=[{stride:[e.length,0,0],input_features:(await this.processor(e)).input_features,is_last:!0}];for(const e of a){t.num_frames=Math.floor(e.stride[0]/_);const o=await this.model.generate(e.input_features,t);"word"===s?(e.tokens=o.sequences[0],e.token_timestamps=o.token_timestamps.tolist()[0].map((e=>(0,i.round)(e,2)))):e.tokens=o[0],e.stride=e.stride.map((e=>e/m)),null!==n&&n(e)}const[c,d]=this.tokenizer._decode_asr(a,{time_precision:p,return_timestamps:s,force_full_sequences:r});g.push({text:c,...d})}return u?g[0]:g}}class S extends _{constructor(e){super(e)}async _call(e,t={}){const s=Array.isArray(e),o=await u(e),{pixel_values:n}=await this.processor(o),r=[];for(const e of n){e.dims=[1,...e.dims];const s=await this.model.generate(e,t),o=this.tokenizer.batch_decode(s,{skip_special_tokens:!0}).map((e=>({generated_text:e.trim()})));r.push(o)}return s?r:r[0]}}class A extends _{constructor(e){super(e)}async _call(e,{topk:t=1}={}){const s=Array.isArray(e),o=await u(e),{pixel_values:n}=await this.processor(o),r=await this.model({pixel_values:n}),a=this.model.config.id2label,l=[];for(const e of r.logits){const s=(0,i.getTopItems)((0,i.softmax)(e.data),t).map((e=>({label:a[e[0]],score:e[1]})));1===t?l.push(...s):l.push(s)}return s||1===t?l:l[0]}}class L 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:s=.5,overlap_mask_area_threshold:o=.8,label_ids_to_fuse:n=null,target_sizes:r=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 u(e),l=i.map((e=>[e.height,e.width])),{pixel_values:c,pixel_mask:h}=await this.processor(i),p=await this.model({pixel_values:c,pixel_mask:h});let _=null;if(null!==a)_=this.subtasks_mapping[a];else for(let[e,t]of Object.entries(this.subtasks_mapping))if(t in this.processor.feature_extractor){_=this.processor.feature_extractor[t].bind(this.processor.feature_extractor),a=e;break}const m=this.model.config.id2label,f=[];if("panoptic"===a||"instance"===a){const e=_(p,t,s,o,n,r??l)[0],a=e.segmentation;for(const t of e.segments_info){const e=new Uint8ClampedArray(a.data.length);for(let s=0;ss.replace("{}",e))),a=this.tokenizer(r,{padding:"siglip"!==this.model.config.model_type||"max_length",truncation:!0}),{pixel_values:l}=await this.processor(n),c=await this.model({...a,pixel_values:l}),d="siglip"===this.model.config.model_type?e=>e.sigmoid().data:e=>(0,i.softmax)(e.data),h=[];for(const e of c.logits_per_image){const s=[...d(e)].map(((e,s)=>({score:e,label:t[s]})));s.sort(((e,t)=>t.score-e.score)),h.push(s)}return o?h:h[0]}}class z extends _{constructor(e){super(e)}async _call(e,{threshold:t=.9,percentage:s=!1}={}){const o=Array.isArray(e);if(o&&1!==e.length)throw Error("Object detection pipeline currently only supports a batch size of 1.");const n=await u(e),r=s?null:n.map((e=>[e.height,e.width])),{pixel_values:a,pixel_mask:i}=await this.processor(n),l=await this.model({pixel_values:a,pixel_mask:i}),c=this.processor.feature_extractor.post_process_object_detection(l,t,r),d=this.model.config.id2label,h=c.map((e=>e.boxes.map(((t,o)=>({score:e.scores[o],label:d[e.classes[o]],box:p(t,!s)})))));return o?h:h[0]}}class B extends _{constructor(e){super(e)}async _call(e,t,{threshold:s=.1,topk:o=null,percentage:n=!1}={}){const r=Array.isArray(e),a=await u(e),i=this.tokenizer(t,{padding:!0,truncation:!0}),l=await this.processor(a),c=[];for(let e=0;e({score:_.scores[s],label:t[_.classes[s]],box:p(e,!n)}))).sort(((e,t)=>t.score-e.score));null!==o&&(m=m.slice(0,o)),c.push(m)}return r?c:c[0]}}class I extends _{constructor(e){super(e)}async _call(e,t,s={}){const o=(await u(e))[0],{pixel_values:n}=await this.processor(o),r=`${t}`,a=this.tokenizer(r,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,i=await this.model.generate(n,{...s,decoder_input_ids:a,max_length:this.model.config.decoder.max_position_embeddings}),l=this.tokenizer.batch_decode(i)[0].match(/(.*?)<\/s_answer>/);let c=null;return l&&l.length>=2&&(c=l[1].trim()),[{answer:c}]}}class O 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:s}=await 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o.reconstruction){const t=e.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");n.push(d.RawImage.fromTensor(t))}return n.length>1?n:n[0]}}class N extends _{constructor(e){super(e)}async _call(e){const t=await u(e),s=await this.processor(t),{predicted_depth:o}=await this.model(s),n=[];for(let e=0;e1?n:n[0]}}const 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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 p(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 _(e){return e.replace(/[\u0300-\u036f]/g,"")}const m="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",f=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class g{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 M extends o.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 w(e);case"Unigram":return new T(e,...t);case"BPE":return new x(e);default:if(e.vocab)return new y(e,...t);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){let t=this.encode(e);return this.fuse_unk&&(t=function(e,t,s){const o=[];let n=0;for(;nthis.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 w extends M{constructor(e){super(e),this.tokens_to_ids=u(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 s of e){const e=[...s];if(e.length>this.max_input_chars_per_word){t.push(this.unk_token);continue}let o=!1,n=0;const r=[];for(;n0&&(o=this.config.continuing_subword_prefix+o),this.tokens_to_ids.has(o)){s=o;break}--t}if(null===s){o=!0;break}r.push(s),n=t}o?t.push(this.unk_token):t.push(...r)}return t}}class T extends M{constructor(e,t){super(e);const s=e.vocab.length;this.vocab=new Array(s),this.scores=new Array(s);for(let t=0;t[e,t]))),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=t.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=(0,r.min)(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new i.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const t=e.sentence,s=t.length;let o=0;for(;o{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 s=0;for(let o=0;o<256;++o)e.includes(o)||(e.push(o),t.push(256+s),s+=1);const o=t.map((e=>String.fromCharCode(e)));return Object.fromEntries(e.map(((e,t)=>[e,o[t]])))})(),b=(0,o.reverseDictionary)(k);class x extends M{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=u(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.cache=new Map}bpe(e){if(0===e.length)return[];const t=this.cache.get(e);if(void 0!==t)return t;const s=Array.from(e);this.end_of_word_suffix&&(s[s.length-1]+=this.end_of_word_suffix);let o=[];if(s.length>1){const e=new i.PriorityQueue(((e,t)=>e.score`<0x${e.toString(16).toUpperCase().padStart(2,"0")}>`))):t.push(this.unk_token)}return t}}class y extends M{constructor(e,t){super(e),this.tokens_to_ids=u(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 F extends o.Callable{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"BertNormalizer":return new I(e);case"Precompiled":return new ae(e);case"Sequence":return new B(e);case"Replace":return new C(e);case"NFC":return new P(e);case"NFKC":return new v(e);case"NFKD":return new S(e);case"Strip":return new A(e);case"StripAccents":return new L(e);case"Lowercase":return new E(e);case"Prepend":return new z(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class C extends F{normalize(e){const t=d(this.config.pattern);return null===t?e:e.replaceAll(t,this.config.content)}}class P extends F{normalize(e){return e=e.normalize("NFC")}}class v extends F{normalize(e){return e=e.normalize("NFKC")}}class S extends F{normalize(e){return e=e.normalize("NFKD")}}class A extends F{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class L extends F{normalize(e){return e=_(e)}}class E extends F{normalize(e){return e=e.toLowerCase()}}class z extends F{normalize(e){return e=this.config.prepend+e}}class B extends F{constructor(e){super(e),this.normalizers=e.normalizers.map((e=>F.fromConfig(e)))}normalize(e){return this.normalizers.reduce(((e,t)=>t.normalize(e)),e)}}class I extends F{_tokenize_chinese_chars(e){const t=[];for(let s=0;s=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case"\t":case"\n":case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(e)}}_clean_text(e){const t=[];for(const s of e){const e=s.charCodeAt(0);0===e||65533===e||this._is_control(s)||(/^\s$/.test(s)?t.push(" "):t.push(s))}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 O extends o.Callable{static fromConfig(e){if(null===e)return null;switch(e.type){case"BertPreTokenizer":return new D(e);case"Sequence":return new ie(e);case"Whitespace":return new le(e);case"WhitespaceSplit":return new ce(e);case"Metaspace":return new ne(e);case"ByteLevel":return new N(e);case"Split":return new V(e);case"Punctuation":return new q(e);case"Digits":return new j(e);case"Replace":return new de(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,t){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,t){return(Array.isArray(e)?e.map((e=>this.pre_tokenize_text(e,t))):this.pre_tokenize_text(e,t)).flat()}_call(e,t){return this.pre_tokenize(e,t)}}class D extends O{constructor(e){super(),this.pattern=new RegExp(`[^\\s${m}]+|[${m}]`,"gu")}pre_tokenize_text(e,t){return e.trim().match(this.pattern)||[]}}class N extends O{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=k,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 V extends O{constructor(e){super(),this.config=e,this.pattern=d(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 s=[];let o=0;for(const n of e.matchAll(t)){const t=n[0];o0&&s.push(t),o=n.index+t.length}return oe.replaceAll(t,this.config.content)))}}class H extends X{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const t=[];let s=[];for(const o of e){let e=null;if(6===o.length&&o.startsWith("<0x")&&o.endsWith(">")){const t=parseInt(o.slice(3,5),16);isNaN(t)||(e=t)}if(null!==e)s.push(e);else{if(s.length>0){const e=this.text_decoder.decode(Uint8Array.from(s));t.push(e),s=[]}t.push(o)}}if(s.length>0){const e=this.text_decoder.decode(Uint8Array.from(s));t.push(e),s=[]}return t}}class Y extends X{decode_chain(e){return[e.join("")]}}class J extends X{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 s=0;s(0!==t&&(e=e.startsWith(this.config.prefix)?e.replace(this.config.prefix,""):" "+e),this.cleanup&&(e=p(e)),e)))}}class K extends X{constructor(e){super(e),this.byte_decoder=b,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(""),s=new Uint8Array([...t].map((e=>this.byte_decoder[e])));return this.text_decoder.decode(s)}decode_chain(e){const t=[];let s=[];for(const o of e)void 0!==this.added_tokens.find((e=>e.content===o))?(s.length>0&&(t.push(this.convert_tokens_to_string(s)),s=[]),t.push(o)):s.push(o);return s.length>0&&t.push(this.convert_tokens_to_string(s)),t}}class ee extends X{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 s=1;se!==this.pad_token)).join("");return this.cleanup&&(s=p(s).replaceAll(this.word_delimiter_token," ").trim()),s}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class te extends X{constructor(e){super(e),this.decoders=e.decoders.map((e=>X.fromConfig(e)))}decode_chain(e){return this.decoders.reduce(((e,t)=>t.decode_chain(e)),e)}}class se extends X{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map(((t,s)=>t.replaceAll(this.suffix,s===e.length-1?"":" ")))}}class oe extends X{decode_chain(e){let t="";for(let s=1;se.normalize("NFKC"))).join("～")}else e=e.normalize("NFKC");return e}}class ie extends O{constructor(e){super(),this.tokenizers=e.pretokenizers.map((e=>O.fromConfig(e)))}pre_tokenize_text(e,t){return this.tokenizers.reduce(((e,s)=>s.pre_tokenize(e,t)),[e])}}class le extends O{constructor(e){super()}pre_tokenize_text(e,t){return e.match(/\w+|[^\w\s]+/g)||[]}}class ce extends O{constructor(e){super()}pre_tokenize_text(e,t){return function(e){return e.match(/\S+/g)||[]}(e)}}class de extends O{constructor(e){super(),this.config=e,this.pattern=d(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 ue=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function he(e,t,s,n){for(const r of Object.keys(e)){const a=t-e[r].length,i=s(r),l=new Array(a).fill(i);e[r]="right"===n?(0,o.mergeArrays)(e[r],l):(0,o.mergeArrays)(l,e[r])}}function pe(e,t){for(const s of Object.keys(e))e[s].length=t}class _e extends o.Callable{return_token_type_ids=!1;_default_chat_template="{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}";constructor(e,t){super(),this._tokenizer_config=t,this.normalizer=F.fromConfig(e.normalizer),this.pre_tokenizer=O.fromConfig(e.pre_tokenizer),this.model=M.fromConfig(e.model,t),this.post_processor=R.fromConfig(e.post_processor),this.decoder=X.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const t of e.added_tokens){const e=new g(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.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,this.padding_side="right",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:s}of this.chat_template){if("string"!=typeof t||"string"!=typeof s)throw new Error('Chat template must be a list of objects with "name" and "template" properties');e[t]=s}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:s=null,cache_dir:o=null,local_files_only:n=!1,revision:r="main",legacy:a=null}={}){return new this(...await c(e,{progress_callback:t,config:s,cache_dir:o,local_files_only:n,revision:r,legacy:a}))}_call(e,{text_pair:t=null,add_special_tokens:s=!0,padding:o=!1,truncation:n=null,max_length:i=null,return_tensor:l=!0}={}){const c=Array.isArray(e);let d;if(c){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");d=e.map(((e,o)=>this._encode_plus(e,t[o],{add_special_tokens:s})))}else d=e.map((e=>this._encode_plus(e,null,{add_special_tokens:s})))}else{if(null===e)throw Error("text may not be null");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).");d=[this._encode_plus(e,t,{add_special_tokens:s})]}if(null===i?i="max_length"===o?this.model_max_length:(0,r.max)(d.map((e=>e.input_ids.length)))[0]:n||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."),i=Math.min(i,this.model_max_length),o||n)for(let e=0;ei?n&&pe(d[e],i):o&&he(d[e],i,(e=>"input_ids"===e?this.pad_token_id:0),this.padding_side));const u={};if(l){if((!o||!n)&&d.some((e=>{for(const t of Object.keys(e))if(e[t].length!==d[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=[d.length,d[0].input_ids.length];for(const t of Object.keys(d[0]))u[t]=new a.Tensor("int64",BigInt64Array.from(d.flatMap((e=>e[t])).map(BigInt)),e)}else{for(const e of Object.keys(d[0]))u[e]=d.map((t=>t[e]));if(!c)for(const e of Object.keys(u))u[e]=u[e][0]}return u}_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 _(e.toLowerCase())}(e)),null!==this.normalizer&&(e=this.normalizer(e)),0===e.length)return[];const s=null!==this.pre_tokenizer?this.pre_tokenizer(e,{section_index:t}):[e];return this.model(s)}})).flat();return t}_encode_plus(e,t=null,{add_special_tokens:s=!0}={}){const n=this._encode_text(e),r=this._encode_text(t),a=this.post_processor?this.post_processor(n,r,{add_special_tokens:s}):{tokens:(0,o.mergeArrays)(n??[],r??[])},i=this.model.convert_tokens_to_ids(a.tokens),l={input_ids:i,attention_mask:new Array(i.length).fill(1)};return this.return_token_type_ids&&a.token_type_ids&&(l.token_type_ids=a.token_type_ids),l}encode(e,t=null,{add_special_tokens:s=!0}={}){const{input_ids:o}=this._encode_plus(e,t,{add_special_tokens:s});return o}batch_decode(e,t={}){return e instanceof a.Tensor&&(e=e.tolist()),e.map((e=>this.decode(e,t)))}decode(e,t={}){if(e instanceof a.Tensor&&(e=h(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:s=null}){let o=this.model.convert_ids_to_tokens(e);t&&(o=o.filter((e=>!this.special_tokens.includes(e))));let n=this.decoder?this.decoder(o):o.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(n=n.replaceAll(this.decoder.end_of_word_suffix," "),t&&(n=n.trim())),(s??this.clean_up_tokenization_spaces)&&(n=p(n)),n}get default_chat_template(){return this._warned_about_chat_template||(console.warn("No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."),this._warned_about_chat_template=!0),this._default_chat_template}apply_chat_template(e,{chat_template:t=null,add_generation_prompt:s=!1,tokenize:o=!0,padding:n=!1,truncation:r=!1,max_length:a=null,return_tensor:i=!0,tokenizer_kwargs:c={},...d}={}){if(this.chat_template&&"object"==typeof this.chat_template||null===this.chat_template&&this.default_chat_template&&"object"==typeof this.default_chat_template){const e=this.chat_template??this.default_chat_template;if(null!==t&&Object.hasOwn(e,t))t=e[t];else if(null===t&&"default"in e)t=e.default;else if(null===t)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 ${Object.keys(e).sort()}.`)}else t??=this.chat_template??this.default_chat_template;if("string"!=typeof t)throw Error("chat_template must be a string, but got "+typeof t);let u=this._compiled_template_cache.get(t);void 0===u&&(u=new l.Template(t),this._compiled_template_cache.set(t,u));const h=Object.create(null);for(const e of ue){const t=this.getToken(e);t&&(h[e]=t)}const p=u.render({messages:e,add_generation_prompt:s,...h,...d});return o?this._call(p,{add_special_tokens:!1,padding:n,truncation:r,max_length:a,return_tensor:i,...c}).input_ids:p}}class me extends _e{return_token_type_ids=!0}class fe extends _e{return_token_type_ids=!0}class ge extends _e{return_token_type_ids=!0}class Me extends _e{return_token_type_ids=!0}class we extends _e{return_token_type_ids=!0}class Te extends _e{return_token_type_ids=!0}class ke extends _e{return_token_type_ids=!0}class be extends _e{return_token_type_ids=!0}class xe extends _e{return_token_type_ids=!0}class ye extends _e{}class Fe extends _e{}class Ce extends _e{return_token_type_ids=!0;constructor(e,t){super(e,t),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Pe extends _e{return_token_type_ids=!0}class ve extends _e{}class Se extends _e{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}'}class Ae extends _e{}class Le extends _e{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))),this.lang_to_token=e=>e}_build_translation_inputs(e,t,s){return Ue(this,e,t,s)}}class Ee extends Le{}class ze extends _e{}class Be extends Se{constructor(e,t){const s=".,!?…。，、।۔،",o=e.pre_tokenizer?.pretokenizers[0]?.pattern;o&&o.Regex===` ?[^(\\s|[${s}])]+`&&(o.Regex=` ?[^\\s${s}]+`),super(e,t)}}const Ie="▁";class Oe extends _e{_default_chat_template="{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\n' + system_message + '\n<>\n\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<>\n' + content.strip() + '\n<>\n\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}";DEFAULT_SYSTEM_PROMPT="You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.";constructor(e,t){super(e,t),this.use_default_system_prompt=t.use_default_system_prompt??!1,this.legacy=t.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new ne({replacement:Ie,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(Ie+e.replaceAll(Ie," "));return t.length>1&&t[0]===Ie&&this.special_tokens.includes(t[1])&&(t=t.slice(1)),t}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll("\n","\\n").replaceAll("'","\\'"))}}class De extends Oe{}class Ne extends _e{}class Ve extends _e{}class qe extends _e{}class je extends _e{}class Re extends _e{}class Ge extends _e{}class We extends _e{_default_chat_template="{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\n' + message['content'] | trim + '\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\n'}}{% endif %}"}class $e extends _e{}function Ue(e,t,s,o){if(!("language_codes"in e)||!Array.isArray(e.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in e&&e.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in e)||"function"!=typeof e.lang_to_token)throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const n=o.src_lang,r=o.tgt_lang;if(!e.language_codes.includes(r))throw new Error(`Target language code "${r}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);if(void 0!==n){if(!e.language_codes.includes(n))throw new Error(`Source language code "${n}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);for(const t of e.post_processor.config.single)if("SpecialToken"in t&&e.languageRegex.test(t.SpecialToken.id)){t.SpecialToken.id=e.lang_to_token(n);break}}return o.forced_bos_token_id=e.model.convert_tokens_to_ids([e.lang_to_token(r)])[0],e._call(t,s)}class Xe extends _e{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))),this.lang_to_token=e=>e}_build_translation_inputs(e,t,s){return Ue(this,e,t,s)}}class Qe extends _e{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,s){return Ue(this,e,t,s)}}const He=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],Ye=new Map(He),Je=new Map([...He.map((([e,t])=>[t,e])),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class Ze extends _e{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}';_decode_asr(e,{return_timestamps:t=!1,return_language:s=!1,time_precision:o=null,force_full_sequences:n=!0}={}){if(null===o)throw Error("Must specify time_precision");let a=null;const i="word"===t;function l(){return{language:a,timestamp:[null,null],text:""}}const c=[];let d=l(),u=0;const h=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let p=[],_=[],m=!1,f=null;const g=new Set(this.all_special_ids);for(const s of e){const e=s.tokens,n=i?s.token_timestamps:null;let M=null,w=h;if("stride"in s){const[t,n,r]=s.stride;if(u-=n,f=t-r,n&&(w=n/o+h),r)for(let t=e.length-1;t>=0;--t){const s=e[t];if(s>=h){if(null!==M&&(s-h)*o=h){const e=(f-h)*o+u,t=(0,r.round)(e,2);if(null!==M&&f>=M)m=!0;else if(m||p.length>0&&f0?(p.push(T),i&&_.push(k)):p.every((e=>0===e.length))&&(d=l(),p=[],T=[],_=[],k=[])}if(p.length>0){if(n&&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,s]=this.findLongestCommonSequence(p,_),o=this.decode(e);d.text=o,i&&(d.words=this.collateWordTimestamps(e,s,a)),c.push(d)}let M=Object.create(null);const w=c.map((e=>e.text)).join("");if(t||s){for(let e=0;e0;let a=r?[]:null,i=r?t[0]:null;for(let l=1;le===p[t])).length,m=_/e+t;_>1&&m>d&&(d=m,u=[n,r,i,l])}const[p,_,m,f]=u,g=Math.floor((_+p)/2),M=Math.floor((f+m)/2);n.push(...s.slice(0,g)),s=c.slice(M),o=s.length,r&&(a.push(...i.slice(0,g)),i=t[l].slice(M))}return n.push(...s),r?(a.push(...i),[n,a]):[n,[]]}collateWordTimestamps(e,t,s){const[o,n,r]=this.combineTokensIntoWords(e,s),a=[];for(let e=0;e=o){const e=(0,r.round)((t-o)*s,2);n.push(`<|${e}|>`),n.push([])}else n[n.length-1].push(t);return n=n.map((e=>"string"==typeof e?e:super.decode(e,t))),n.join("")}splitTokensOnUnicode(e){const t=this.decode(e,{decode_with_timestamps:!0}),s=[],o=[],n=[];let r=[],a=[],i=0;for(let l=0;l=this.model.tokens_to_ids.get("<|endoftext|>"),h=l.startsWith(" "),p=l.trim(),_=i.test(p);if(u||h||_||0===n.length)n.push(l),r.push(c),a.push(d);else{const e=n.length-1;n[e]+=l,r[e].push(...c),a[e].push(...d)}}return[n,r,a]}mergePunctuations(e,t,s,n,r){const a=structuredClone(e),i=structuredClone(t),l=structuredClone(s);let c=a.length-2,d=a.length-1;for(;c>=0;)a[c].startsWith(" ")&&n.includes(a[c].trim())?(a[d]=a[c]+a[d],i[d]=(0,o.mergeArrays)(i[c],i[d]),l[d]=(0,o.mergeArrays)(l[c],l[d]),a[c]="",i[c]=[],l[c]=[]):d=c,--c;for(c=0,d=1;de)),i.filter((e=>e.length>0)),l.filter((e=>e.length>0))]}get_decoder_prompt_ids({language:e=null,task:t=null,no_timestamps:s=!0}={}){const o=[];if(e){e=e.toLowerCase();let t=Je.get(e);if(void 0===t){if(!Ye.has(e)){const t=2===e.length?Ye.keys():Ye.values();throw new Error(`Language "${e}" is not supported. Must be one of: ${JSON.stringify(t)}`)}t=e}const s=this.model.tokens_to_ids.get(`<|${t}|>`);if(void 0===s)throw new Error(`Unable to find language "${t}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);o.push(s)}else o.push(null);if(t){if("transcribe"!==(t=t.toLowerCase())&&"translate"!==t)throw new Error(`Task "${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 https://github.com/xenova/transformers.js/issues/new/choose.`);o.push(e)}else o.push(null);if(s){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 https://github.com/xenova/transformers.js/issues/new/choose.');o.push(e)}return o.map(((e,t)=>[t+1,e])).filter((e=>null!==e[1]))}}class Ke extends _e{}class et extends _e{}class tt extends _e{}class st extends _e{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,...s]=e.trim().split(this.languageRegex);if(0===s.length)return super._encode_text(t);if(2===s.length){const[e,t]=s;return this.supported_language_codes.includes(e)||console.warn(`Unsupported language code "${e}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,o.mergeArrays)([e],super._encode_text(t))}}}class ot extends _e{}class nt extends _e{_default_chat_template="{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}"}class rt extends nt{}class at extends _e{}class it extends _e{}class lt extends _e{constructor(e,t){super(e,t),this.decoder=new oe({})}}class ct extends _e{}class dt{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:ve,DistilBertTokenizer:ye,CamembertTokenizer:Fe,DebertaTokenizer:we,DebertaV2Tokenizer:Te,BertTokenizer:me,HerbertTokenizer:ke,ConvBertTokenizer:be,RoFormerTokenizer:xe,XLMTokenizer:Ce,ElectraTokenizer:Pe,MobileBertTokenizer:ge,SqueezeBertTokenizer:Me,AlbertTokenizer:fe,GPT2Tokenizer:Se,BartTokenizer:Ae,MBartTokenizer:Le,MBart50Tokenizer:Ee,RobertaTokenizer:ze,WhisperTokenizer:Ze,CodeGenTokenizer:Ke,CLIPTokenizer:et,SiglipTokenizer:tt,MarianTokenizer:st,BloomTokenizer:Be,NllbTokenizer:Xe,M2M100Tokenizer:Qe,LlamaTokenizer:Oe,CodeLlamaTokenizer:De,XLMRobertaTokenizer:Ne,MPNetTokenizer:Ve,FalconTokenizer:qe,GPTNeoXTokenizer:je,EsmTokenizer:Re,Wav2Vec2CTCTokenizer:ot,BlenderbotTokenizer:nt,BlenderbotSmallTokenizer:rt,SpeechT5Tokenizer:at,NougatTokenizer:it,VitsTokenizer:lt,Qwen2Tokenizer:Ge,GemmaTokenizer:We,Grok1Tokenizer:$e,CohereTokenizer:ct,PreTrainedTokenizer:_e};static async from_pretrained(e,{quantized:t=!0,progress_callback:s=null,config:o=null,cache_dir:n=null,local_files_only:r=!1,revision:a="main",legacy:i=null}={}){const[l,d]=await c(e,{quantized:t,progress_callback:s,config:o,cache_dir:n,local_files_only:r,revision:a,legacy:i}),u=d.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[u];return h||(console.warn(`Unknown tokenizer class "${u}", attempting to construct from base class.`),h=_e),new h(l,d)}}},"./src/transformers.js": /*!*****************************!*\ !*** ./src/transformers.js ***! 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Unexpected trace[${a}, ${l}]. 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n=o("./src/transformers.js"),r=n.ASTFeatureExtractor,a=n.ASTForAudioClassification,i=n.ASTModel,l=n.ASTPreTrainedModel,c=n.AlbertForMaskedLM,d=n.AlbertForQuestionAnswering,u=n.AlbertForSequenceClassification,h=n.AlbertModel,p=n.AlbertPreTrainedModel,_=n.AlbertTokenizer,m=n.AudioClassificationPipeline,f=n.AutoConfig,g=n.AutoModel,M=n.AutoModelForAudioClassification,w=n.AutoModelForAudioFrameClassification,T=n.AutoModelForCTC,k=n.AutoModelForCausalLM,b=n.AutoModelForDepthEstimation,x=n.AutoModelForDocumentQuestionAnswering,y=n.AutoModelForImageClassification,F=n.AutoModelForImageFeatureExtraction,C=n.AutoModelForImageMatting,P=n.AutoModelForImageSegmentation,v=n.AutoModelForImageToImage,S=n.AutoModelForMaskGeneration,A=n.AutoModelForMaskedLM,L=n.AutoModelForObjectDetection,E=n.AutoModelForQuestionAnswering,z=n.AutoModelForSemanticSegmentation,B=n.AutoModelForSeq2SeqLM,I=n.AutoModelForSequenceClassification,O=n.AutoModelForSpeechSeq2Seq,D=n.AutoModelForTextToSpectrogram,N=n.AutoModelForTextToWaveform,V=n.AutoModelForTokenClassification,q=n.AutoModelForVision2Seq,j=n.AutoModelForXVector,R=n.AutoModelForZeroShotObjectDetection,G=n.AutoProcessor,W=n.AutoTokenizer,$=n.AutomaticSpeechRecognitionPipeline,U=n.BartForConditionalGeneration,X=n.BartForSequenceClassification,Q=n.BartModel,H=n.BartPretrainedModel,Y=n.BartTokenizer,J=n.BaseModelOutput,Z=n.BeitFeatureExtractor,K=n.BeitForImageClassification,ee=n.BeitModel,te=n.BeitPreTrainedModel,se=n.BertForMaskedLM,oe=n.BertForQuestionAnswering,ne=n.BertForSequenceClassification,re=n.BertForTokenClassification,ae=n.BertModel,ie=n.BertPreTrainedModel,le=n.BertTokenizer,ce=n.BitImageProcessor,de=n.BlenderbotForConditionalGeneration,ue=n.BlenderbotModel,he=n.BlenderbotPreTrainedModel,pe=n.BlenderbotSmallForConditionalGeneration,_e=n.BlenderbotSmallModel,me=n.BlenderbotSmallPreTrainedModel,fe=n.BlenderbotSmallTokenizer,ge=n.BlenderbotTokenizer,Me=n.BloomForCausalLM,we=n.BloomModel,Te=n.BloomPreTrainedModel,ke=n.BloomTokenizer,be=n.CLIPFeatureExtractor,xe=n.CLIPModel,ye=n.CLIPPreTrainedModel,Fe=n.CLIPSegForImageSegmentation,Ce=n.CLIPSegModel,Pe=n.CLIPSegPreTrainedModel,ve=n.CLIPTextModelWithProjection,Se=n.CLIPTokenizer,Ae=n.CLIPVisionModelWithProjection,Le=n.CamembertForMaskedLM,Ee=n.CamembertForQuestionAnswering,ze=n.CamembertForSequenceClassification,Be=n.CamembertForTokenClassification,Ie=n.CamembertModel,Oe=n.CamembertPreTrainedModel,De=n.CamembertTokenizer,Ne=n.CausalLMOutput,Ve=n.CausalLMOutputWithPast,qe=n.ChineseCLIPFeatureExtractor,je=n.ChineseCLIPModel,Re=n.ChineseCLIPPreTrainedModel,Ge=n.ClapAudioModelWithProjection,We=n.ClapFeatureExtractor,$e=n.ClapModel,Ue=n.ClapPreTrainedModel,Xe=n.ClapTextModelWithProjection,Qe=n.CodeGenForCausalLM,He=n.CodeGenModel,Ye=n.CodeGenPreTrainedModel,Je=n.CodeGenTokenizer,Ze=n.CodeLlamaTokenizer,Ke=n.CohereTokenizer,et=n.ConvBertForMaskedLM,tt=n.ConvBertForQuestionAnswering,st=n.ConvBertForSequenceClassification,ot=n.ConvBertForTokenClassification,nt=n.ConvBertModel,rt=n.ConvBertPreTrainedModel,at=n.ConvBertTokenizer,it=n.ConvNextFeatureExtractor,lt=n.ConvNextForImageClassification,ct=n.ConvNextImageProcessor,dt=n.ConvNextModel,ut=n.ConvNextPreTrainedModel,ht=n.ConvNextV2ForImageClassification,pt=n.ConvNextV2Model,_t=n.ConvNextV2PreTrainedModel,mt=n.DPTFeatureExtractor,ft=n.DPTForDepthEstimation,gt=n.DPTImageProcessor,Mt=n.DPTModel,wt=n.DPTPreTrainedModel,Tt=n.DebertaForMaskedLM,kt=n.DebertaForQuestionAnswering,bt=n.DebertaForSequenceClassification,xt=n.DebertaForTokenClassification,yt=n.DebertaModel,Ft=n.DebertaPreTrainedModel,Ct=n.DebertaTokenizer,Pt=n.DebertaV2ForMaskedLM,vt=n.DebertaV2ForQuestionAnswering,St=n.DebertaV2ForSequenceClassification,At=n.DebertaV2ForTokenClassification,Lt=n.DebertaV2Model,Et=n.DebertaV2PreTrainedModel,zt=n.DebertaV2Tokenizer,Bt=n.DeiTFeatureExtractor,It=n.DeiTForImageClassification,Ot=n.DeiTModel,Dt=n.DeiTPreTrainedModel,Nt=n.DepthAnythingForDepthEstimation,Vt=n.DepthAnythingPreTrainedModel,qt=n.DepthEstimationPipeline,jt=n.DetrFeatureExtractor,Rt=n.DetrForObjectDetection,Gt=n.DetrForSegmentation,Wt=n.DetrModel,$t=n.DetrObjectDetectionOutput,Ut=n.DetrPreTrainedModel,Xt=n.DetrSegmentationOutput,Qt=n.Dinov2ForImageClassification,Ht=n.Dinov2Model,Yt=n.Dinov2PreTrainedModel,Jt=n.DistilBertForMaskedLM,Zt=n.DistilBertForQuestionAnswering,Kt=n.DistilBertForSequenceClassification,es=n.DistilBertForTokenClassification,ts=n.DistilBertModel,ss=n.DistilBertPreTrainedModel,os=n.DistilBertTokenizer,ns=n.DocumentQuestionAnsweringPipeline,rs=n.DonutFeatureExtractor,as=n.DonutSwinModel,is=n.DonutSwinPreTrainedModel,ls=n.EfficientNetForImageClassification,cs=n.EfficientNetImageProcessor,ds=n.EfficientNetModel,us=n.EfficientNetPreTrainedModel,hs=n.ElectraForMaskedLM,ps=n.ElectraForQuestionAnswering,_s=n.ElectraForSequenceClassification,ms=n.ElectraForTokenClassification,fs=n.ElectraModel,gs=n.ElectraPreTrainedModel,Ms=n.ElectraTokenizer,ws=n.EsmForMaskedLM,Ts=n.EsmForSequenceClassification,ks=n.EsmForTokenClassification,bs=n.EsmModel,xs=n.EsmPreTrainedModel,ys=n.EsmTokenizer,Fs=n.FFT,Cs=n.FalconForCausalLM,Ps=n.FalconModel,vs=n.FalconPreTrainedModel,Ss=n.FalconTokenizer,As=n.FeatureExtractionPipeline,Ls=n.FeatureExtractor,Es=n.FillMaskPipeline,zs=n.GLPNFeatureExtractor,Bs=n.GLPNForDepthEstimation,Is=n.GLPNModel,Os=n.GLPNPreTrainedModel,Ds=n.GPT2LMHeadModel,Ns=n.GPT2Model,Vs=n.GPT2PreTrainedModel,qs=n.GPT2Tokenizer,js=n.GPTBigCodeForCausalLM,Rs=n.GPTBigCodeModel,Gs=n.GPTBigCodePreTrainedModel,Ws=n.GPTJForCausalLM,$s=n.GPTJModel,Us=n.GPTJPreTrainedModel,Xs=n.GPTNeoForCausalLM,Qs=n.GPTNeoModel,Hs=n.GPTNeoPreTrainedModel,Ys=n.GPTNeoXForCausalLM,Js=n.GPTNeoXModel,Zs=n.GPTNeoXPreTrainedModel,Ks=n.GPTNeoXTokenizer,eo=n.GemmaTokenizer,to=n.Grok1Tokenizer,so=n.HerbertTokenizer,oo=n.HubertForCTC,no=n.HubertForSequenceClassification,ro=n.HubertModel,ao=n.HubertPreTrainedModel,io=n.ImageClassificationPipeline,lo=n.ImageFeatureExtractionPipeline,co=n.ImageFeatureExtractor,uo=n.ImageMattingOutput,ho=n.ImageSegmentationPipeline,po=n.ImageToImagePipeline,_o=n.ImageToTextPipeline,mo=n.LlamaForCausalLM,fo=n.LlamaModel,go=n.LlamaPreTrainedModel,Mo=n.LlamaTokenizer,wo=n.LongT5ForConditionalGeneration,To=n.LongT5Model,ko=n.LongT5PreTrainedModel,bo=n.M2M100ForConditionalGeneration,xo=n.M2M100Model,yo=n.M2M100PreTrainedModel,Fo=n.M2M100Tokenizer,Co=n.MBart50Tokenizer,Po=n.MBartForCausalLM,vo=n.MBartForConditionalGeneration,So=n.MBartForSequenceClassification,Ao=n.MBartModel,Lo=n.MBartPreTrainedModel,Eo=n.MBartTokenizer,zo=n.MPNetForMaskedLM,Bo=n.MPNetForQuestionAnswering,Io=n.MPNetForSequenceClassification,Oo=n.MPNetForTokenClassification,Do=n.MPNetModel,No=n.MPNetPreTrainedModel,Vo=n.MPNetTokenizer,qo=n.MT5ForConditionalGeneration,jo=n.MT5Model,Ro=n.MT5PreTrainedModel,Go=n.MarianMTModel,Wo=n.MarianModel,$o=n.MarianPreTrainedModel,Uo=n.MarianTokenizer,Xo=n.MaskedLMOutput,Qo=n.MistralForCausalLM,Ho=n.MistralModel,Yo=n.MistralPreTrainedModel,Jo=n.MobileBertForMaskedLM,Zo=n.MobileBertForQuestionAnswering,Ko=n.MobileBertForSequenceClassification,en=n.MobileBertModel,tn=n.MobileBertPreTrainedModel,sn=n.MobileBertTokenizer,on=n.MobileViTFeatureExtractor,nn=n.MobileViTForImageClassification,rn=n.MobileViTModel,an=n.MobileViTPreTrainedModel,ln=n.ModelOutput,cn=n.MptForCausalLM,dn=n.MptModel,un=n.MptPreTrainedModel,hn=n.NllbTokenizer,pn=n.NomicBertModel,_n=n.NomicBertPreTrainedModel,mn=n.NougatImageProcessor,fn=n.NougatTokenizer,gn=n.OPTForCausalLM,Mn=n.OPTModel,wn=n.OPTPreTrainedModel,Tn=n.ObjectDetectionPipeline,kn=n.OwlViTFeatureExtractor,bn=n.OwlViTForObjectDetection,xn=n.OwlViTModel,yn=n.OwlViTPreTrainedModel,Fn=n.OwlViTProcessor,Cn=n.Owlv2ForObjectDetection,Pn=n.Owlv2ImageProcessor,vn=n.Owlv2Model,Sn=n.Owlv2PreTrainedModel,An=n.PhiForCausalLM,Ln=n.PhiModel,En=n.PhiPreTrainedModel,zn=n.Pipeline,Bn=n.PreTrainedModel,In=n.PreTrainedTokenizer,On=n.PretrainedConfig,Dn=n.PretrainedMixin,Nn=n.Processor,Vn=n.QuestionAnsweringModelOutput,qn=n.QuestionAnsweringPipeline,jn=n.Qwen2ForCausalLM,Rn=n.Qwen2Model,Gn=n.Qwen2PreTrainedModel,Wn=n.Qwen2Tokenizer,$n=n.RawImage,Un=n.ResNetForImageClassification,Xn=n.ResNetModel,Qn=n.ResNetPreTrainedModel,Hn=n.RoFormerForMaskedLM,Yn=n.RoFormerForQuestionAnswering,Jn=n.RoFormerForSequenceClassification,Zn=n.RoFormerForTokenClassification,Kn=n.RoFormerModel,er=n.RoFormerPreTrainedModel,tr=n.RoFormerTokenizer,sr=n.RobertaForMaskedLM,or=n.RobertaForQuestionAnswering,nr=n.RobertaForSequenceClassification,rr=n.RobertaForTokenClassification,ar=n.RobertaModel,ir=n.RobertaPreTrainedModel,lr=n.RobertaTokenizer,cr=n.SamImageProcessor,dr=n.SamImageSegmentationOutput,ur=n.SamModel,hr=n.SamPreTrainedModel,pr=n.SamProcessor,_r=n.SeamlessM4TFeatureExtractor,mr=n.SegformerFeatureExtractor,fr=n.SegformerForImageClassification,gr=n.SegformerForSemanticSegmentation,Mr=n.SegformerModel,wr=n.SegformerPreTrainedModel,Tr=n.Seq2SeqLMOutput,kr=n.SequenceClassifierOutput,br=n.SiglipImageProcessor,xr=n.SiglipModel,yr=n.SiglipPreTrainedModel,Fr=n.SiglipTextModel,Cr=n.SiglipTokenizer,Pr=n.SiglipVisionModel,vr=n.SpeechT5FeatureExtractor,Sr=n.SpeechT5ForSpeechToText,Ar=n.SpeechT5ForTextToSpeech,Lr=n.SpeechT5HifiGan,Er=n.SpeechT5Model,zr=n.SpeechT5PreTrainedModel,Br=n.SpeechT5Processor,Ir=n.SpeechT5Tokenizer,Or=n.SqueezeBertForMaskedLM,Dr=n.SqueezeBertForQuestionAnswering,Nr=n.SqueezeBertForSequenceClassification,Vr=n.SqueezeBertModel,qr=n.SqueezeBertPreTrainedModel,jr=n.SqueezeBertTokenizer,Rr=n.StableLmForCausalLM,Gr=n.StableLmModel,Wr=n.StableLmPreTrainedModel,$r=n.Starcoder2ForCausalLM,Ur=n.Starcoder2Model,Xr=n.Starcoder2PreTrainedModel,Qr=n.SummarizationPipeline,Hr=n.Swin2SRForImageSuperResolution,Yr=n.Swin2SRImageProcessor,Jr=n.Swin2SRModel,Zr=n.Swin2SRPreTrainedModel,Kr=n.SwinForImageClassification,ea=n.SwinModel,ta=n.SwinPreTrainedModel,sa=n.T5ForConditionalGeneration,oa=n.T5Model,na=n.T5PreTrainedModel,ra=n.T5Tokenizer,aa=n.TableTransformerForObjectDetection,ia=n.TableTransformerModel,la=n.TableTransformerObjectDetectionOutput,ca=n.TableTransformerPreTrainedModel,da=n.Tensor,ua=n.Text2TextGenerationPipeline,ha=n.TextClassificationPipeline,pa=n.TextGenerationPipeline,_a=n.TextToAudioPipeline,ma=n.TokenClassificationPipeline,fa=n.TokenClassifierOutput,ga=n.TokenizerModel,Ma=n.TrOCRForCausalLM,wa=n.TrOCRPreTrainedModel,Ta=n.TranslationPipeline,ka=n.UniSpeechForCTC,ba=n.UniSpeechForSequenceClassification,xa=n.UniSpeechModel,ya=n.UniSpeechPreTrainedModel,Fa=n.UniSpeechSatForAudioFrameClassification,Ca=n.UniSpeechSatForCTC,Pa=n.UniSpeechSatForSequenceClassification,va=n.UniSpeechSatModel,Sa=n.UniSpeechSatPreTrainedModel,Aa=n.ViTFeatureExtractor,La=n.ViTForImageClassification,Ea=n.ViTImageProcessor,za=n.ViTModel,Ba=n.ViTPreTrainedModel,Ia=n.VisionEncoderDecoderModel,Oa=n.VitMatteForImageMatting,Da=n.VitMatteImageProcessor,Na=n.VitMattePreTrainedModel,Va=n.VitsModel,qa=n.VitsModelOutput,ja=n.VitsPreTrainedModel,Ra=n.VitsTokenizer,Ga=n.Wav2Vec2BertForCTC,Wa=n.Wav2Vec2BertForSequenceClassification,$a=n.Wav2Vec2BertModel,Ua=n.Wav2Vec2BertPreTrainedModel,Xa=n.Wav2Vec2CTCTokenizer,Qa=n.Wav2Vec2FeatureExtractor,Ha=n.Wav2Vec2ForAudioFrameClassification,Ya=n.Wav2Vec2ForCTC,Ja=n.Wav2Vec2ForSequenceClassification,Za=n.Wav2Vec2Model,Ka=n.Wav2Vec2PreTrainedModel,ei=n.Wav2Vec2ProcessorWithLM,ti=n.WavLMForAudioFrameClassification,si=n.WavLMForCTC,oi=n.WavLMForSequenceClassification,ni=n.WavLMForXVector,ri=n.WavLMModel,ai=n.WavLMPreTrainedModel,ii=n.WhisperFeatureExtractor,li=n.WhisperForConditionalGeneration,ci=n.WhisperModel,di=n.WhisperPreTrainedModel,ui=n.WhisperProcessor,hi=n.WhisperTokenizer,pi=n.XLMForQuestionAnswering,_i=n.XLMForSequenceClassification,mi=n.XLMForTokenClassification,fi=n.XLMModel,gi=n.XLMPreTrainedModel,Mi=n.XLMRobertaForMaskedLM,wi=n.XLMRobertaForQuestionAnswering,Ti=n.XLMRobertaForSequenceClassification,ki=n.XLMRobertaForTokenClassification,bi=n.XLMRobertaModel,xi=n.XLMRobertaPreTrainedModel,yi=n.XLMRobertaTokenizer,Fi=n.XLMTokenizer,Ci=n.XLMWithLMHeadModel,Pi=n.XVectorOutput,vi=n.YolosFeatureExtractor,Si=n.YolosForObjectDetection,Ai=n.YolosModel,Li=n.YolosObjectDetectionOutput,Ei=n.YolosPreTrainedModel,zi=n.ZeroShotAudioClassificationPipeline,Bi=n.ZeroShotClassificationPipeline,Ii=n.ZeroShotImageClassificationPipeline,Oi=n.ZeroShotObjectDetectionPipeline,Di=n.bankers_round,Ni=n.cat,Vi=n.cos_sim,qi=n.dot,ji=n.dynamicTimeWarping,Ri=n.env,Gi=n.getTopItems,Wi=n.hanning,$i=n.interpolate,Ui=n.interpolate_data,Xi=n.layer_norm,Qi=n.log_softmax,Hi=n.magnitude,Yi=n.max,Ji=n.mean,Zi=n.mean_pooling,Ki=n.medianFilter,el=n.mel_filter_bank,tl=n.min,sl=n.ones,ol=n.ones_like,nl=n.permute,rl=n.permute_data,al=n.pipeline,il=n.read_audio,ll=n.round,cl=n.softmax,dl=n.spectrogram,ul=n.stack,hl=n.std_mean,pl=n.window_function;export{r as ASTFeatureExtractor,a as ASTForAudioClassification,i as ASTModel,l as ASTPreTrainedModel,c as AlbertForMaskedLM,d as AlbertForQuestionAnswering,u as AlbertForSequenceClassification,h as AlbertModel,p as AlbertPreTrainedModel,_ as AlbertTokenizer,m as AudioClassificationPipeline,f as AutoConfig,g as AutoModel,M as AutoModelForAudioClassification,w as AutoModelForAudioFrameClassification,T as AutoModelForCTC,k as AutoModelForCausalLM,b as AutoModelForDepthEstimation,x as AutoModelForDocumentQuestionAnswering,y as AutoModelForImageClassification,F as AutoModelForImageFeatureExtraction,C as AutoModelForImageMatting,P as AutoModelForImageSegmentation,v as AutoModelForImageToImage,S as AutoModelForMaskGeneration,A as AutoModelForMaskedLM,L as AutoModelForObjectDetection,E as AutoModelForQuestionAnswering,z as AutoModelForSemanticSegmentation,B as AutoModelForSeq2SeqLM,I as AutoModelForSequenceClassification,O as AutoModelForSpeechSeq2Seq,D as AutoModelForTextToSpectrogram,N as AutoModelForTextToWaveform,V as AutoModelForTokenClassification,q as AutoModelForVision2Seq,j as AutoModelForXVector,R as AutoModelForZeroShotObjectDetection,G as AutoProcessor,W as AutoTokenizer,$ as AutomaticSpeechRecognitionPipeline,U as BartForConditionalGeneration,X as BartForSequenceClassification,Q as BartModel,H as BartPretrainedModel,Y as BartTokenizer,J as BaseModelOutput,Z as BeitFeatureExtractor,K as BeitForImageClassification,ee as BeitModel,te as BeitPreTrainedModel,se as BertForMaskedLM,oe as BertForQuestionAnswering,ne as BertForSequenceClassification,re as BertForTokenClassification,ae as BertModel,ie as BertPreTrainedModel,le as BertTokenizer,ce as BitImageProcessor,de as BlenderbotForConditionalGeneration,ue as BlenderbotModel,he as BlenderbotPreTrainedModel,pe as BlenderbotSmallForConditionalGeneration,_e as BlenderbotSmallModel,me as BlenderbotSmallPreTrainedModel,fe as BlenderbotSmallTokenizer,ge as BlenderbotTokenizer,Me as BloomForCausalLM,we as BloomModel,Te as BloomPreTrainedModel,ke as BloomTokenizer,be as CLIPFeatureExtractor,xe as CLIPModel,ye as CLIPPreTrainedModel,Fe as CLIPSegForImageSegmentation,Ce as CLIPSegModel,Pe as CLIPSegPreTrainedModel,ve as CLIPTextModelWithProjection,Se as CLIPTokenizer,Ae as CLIPVisionModelWithProjection,Le as CamembertForMaskedLM,Ee as CamembertForQuestionAnswering,ze as CamembertForSequenceClassification,Be as CamembertForTokenClassification,Ie as CamembertModel,Oe as CamembertPreTrainedModel,De as CamembertTokenizer,Ne as CausalLMOutput,Ve as CausalLMOutputWithPast,qe as ChineseCLIPFeatureExtractor,je as ChineseCLIPModel,Re as ChineseCLIPPreTrainedModel,Ge as ClapAudioModelWithProjection,We as ClapFeatureExtractor,$e as ClapModel,Ue as ClapPreTrainedModel,Xe as ClapTextModelWithProjection,Qe as CodeGenForCausalLM,He as CodeGenModel,Ye as CodeGenPreTrainedModel,Je as CodeGenTokenizer,Ze as CodeLlamaTokenizer,Ke as CohereTokenizer,et as ConvBertForMaskedLM,tt as ConvBertForQuestionAnswering,st as ConvBertForSequenceClassification,ot as ConvBertForTokenClassification,nt as ConvBertModel,rt as ConvBertPreTrainedModel,at as ConvBertTokenizer,it as ConvNextFeatureExtractor,lt as ConvNextForImageClassification,ct as ConvNextImageProcessor,dt as ConvNextModel,ut as ConvNextPreTrainedModel,ht as ConvNextV2ForImageClassification,pt as ConvNextV2Model,_t as ConvNextV2PreTrainedModel,mt as DPTFeatureExtractor,ft as DPTForDepthEstimation,gt as DPTImageProcessor,Mt as DPTModel,wt as DPTPreTrainedModel,Tt as DebertaForMaskedLM,kt as DebertaForQuestionAnswering,bt as DebertaForSequenceClassification,xt as DebertaForTokenClassification,yt as DebertaModel,Ft as DebertaPreTrainedModel,Ct as DebertaTokenizer,Pt as DebertaV2ForMaskedLM,vt as DebertaV2ForQuestionAnswering,St as DebertaV2ForSequenceClassification,At as DebertaV2ForTokenClassification,Lt as DebertaV2Model,Et as DebertaV2PreTrainedModel,zt as DebertaV2Tokenizer,Bt as DeiTFeatureExtractor,It as DeiTForImageClassification,Ot as DeiTModel,Dt as DeiTPreTrainedModel,Nt as DepthAnythingForDepthEstimation,Vt as DepthAnythingPreTrainedModel,qt as DepthEstimationPipeline,jt as DetrFeatureExtractor,Rt as DetrForObjectDetection,Gt as DetrForSegmentation,Wt as DetrModel,$t as DetrObjectDetectionOutput,Ut as DetrPreTrainedModel,Xt as DetrSegmentationOutput,Qt as Dinov2ForImageClassification,Ht as Dinov2Model,Yt as Dinov2PreTrainedModel,Jt as DistilBertForMaskedLM,Zt as DistilBertForQuestionAnswering,Kt as DistilBertForSequenceClassification,es as DistilBertForTokenClassification,ts as DistilBertModel,ss as DistilBertPreTrainedModel,os as DistilBertTokenizer,ns as DocumentQuestionAnsweringPipeline,rs as DonutFeatureExtractor,as as DonutSwinModel,is as DonutSwinPreTrainedModel,ls as EfficientNetForImageClassification,cs as EfficientNetImageProcessor,ds as EfficientNetModel,us as EfficientNetPreTrainedModel,hs as ElectraForMaskedLM,ps as ElectraForQuestionAnswering,_s as ElectraForSequenceClassification,ms as ElectraForTokenClassification,fs as ElectraModel,gs as ElectraPreTrainedModel,Ms as ElectraTokenizer,ws as EsmForMaskedLM,Ts as EsmForSequenceClassification,ks as EsmForTokenClassification,bs as EsmModel,xs as EsmPreTrainedModel,ys as EsmTokenizer,Fs as FFT,Cs as FalconForCausalLM,Ps as FalconModel,vs as FalconPreTrainedModel,Ss as FalconTokenizer,As as FeatureExtractionPipeline,Ls as FeatureExtractor,Es as FillMaskPipeline,zs as GLPNFeatureExtractor,Bs as GLPNForDepthEstimation,Is as GLPNModel,Os as GLPNPreTrainedModel,Ds as GPT2LMHeadModel,Ns as GPT2Model,Vs as GPT2PreTrainedModel,qs as GPT2Tokenizer,js as GPTBigCodeForCausalLM,Rs as GPTBigCodeModel,Gs as GPTBigCodePreTrainedModel,Ws as GPTJForCausalLM,$s as GPTJModel,Us as GPTJPreTrainedModel,Xs as GPTNeoForCausalLM,Qs as GPTNeoModel,Hs as GPTNeoPreTrainedModel,Ys as GPTNeoXForCausalLM,Js as GPTNeoXModel,Zs as GPTNeoXPreTrainedModel,Ks as GPTNeoXTokenizer,eo as GemmaTokenizer,to as Grok1Tokenizer,so as HerbertTokenizer,oo as HubertForCTC,no as HubertForSequenceClassification,ro as HubertModel,ao as HubertPreTrainedModel,io as ImageClassificationPipeline,lo as ImageFeatureExtractionPipeline,co as ImageFeatureExtractor,uo as ImageMattingOutput,ho as ImageSegmentationPipeline,po as ImageToImagePipeline,_o as ImageToTextPipeline,mo as LlamaForCausalLM,fo as LlamaModel,go as LlamaPreTrainedModel,Mo as LlamaTokenizer,wo as LongT5ForConditionalGeneration,To as LongT5Model,ko as LongT5PreTrainedModel,bo as M2M100ForConditionalGeneration,xo as M2M100Model,yo as M2M100PreTrainedModel,Fo as M2M100Tokenizer,Co as MBart50Tokenizer,Po as MBartForCausalLM,vo as MBartForConditionalGeneration,So as MBartForSequenceClassification,Ao as MBartModel,Lo as MBartPreTrainedModel,Eo as MBartTokenizer,zo as MPNetForMaskedLM,Bo as MPNetForQuestionAnswering,Io as MPNetForSequenceClassification,Oo as MPNetForTokenClassification,Do as MPNetModel,No as MPNetPreTrainedModel,Vo as MPNetTokenizer,qo as MT5ForConditionalGeneration,jo as MT5Model,Ro as MT5PreTrainedModel,Go as MarianMTModel,Wo as MarianModel,$o as MarianPreTrainedModel,Uo as MarianTokenizer,Xo as MaskedLMOutput,Qo as MistralForCausalLM,Ho as MistralModel,Yo as MistralPreTrainedModel,Jo as MobileBertForMaskedLM,Zo as MobileBertForQuestionAnswering,Ko as MobileBertForSequenceClassification,en as MobileBertModel,tn as MobileBertPreTrainedModel,sn as MobileBertTokenizer,on as MobileViTFeatureExtractor,nn as MobileViTForImageClassification,rn as MobileViTModel,an as MobileViTPreTrainedModel,ln as ModelOutput,cn as MptForCausalLM,dn as MptModel,un as MptPreTrainedModel,hn as NllbTokenizer,pn as NomicBertModel,_n as NomicBertPreTrainedModel,mn as NougatImageProcessor,fn as NougatTokenizer,gn as OPTForCausalLM,Mn as OPTModel,wn as OPTPreTrainedModel,Tn as ObjectDetectionPipeline,kn as OwlViTFeatureExtractor,bn as OwlViTForObjectDetection,xn as OwlViTModel,yn as OwlViTPreTrainedModel,Fn as OwlViTProcessor,Cn as Owlv2ForObjectDetection,Pn as Owlv2ImageProcessor,vn as Owlv2Model,Sn as Owlv2PreTrainedModel,An as PhiForCausalLM,Ln as PhiModel,En as PhiPreTrainedModel,zn as Pipeline,Bn as PreTrainedModel,In as PreTrainedTokenizer,On as PretrainedConfig,Dn as PretrainedMixin,Nn as Processor,Vn as QuestionAnsweringModelOutput,qn as QuestionAnsweringPipeline,jn as Qwen2ForCausalLM,Rn as Qwen2Model,Gn as Qwen2PreTrainedModel,Wn as Qwen2Tokenizer,$n as RawImage,Un as ResNetForImageClassification,Xn as ResNetModel,Qn as ResNetPreTrainedModel,Hn as RoFormerForMaskedLM,Yn as RoFormerForQuestionAnswering,Jn as RoFormerForSequenceClassification,Zn as RoFormerForTokenClassification,Kn as RoFormerModel,er as RoFormerPreTrainedModel,tr as RoFormerTokenizer,sr as RobertaForMaskedLM,or as RobertaForQuestionAnswering,nr as RobertaForSequenceClassification,rr as RobertaForTokenClassification,ar as RobertaModel,ir as RobertaPreTrainedModel,lr as RobertaTokenizer,cr as SamImageProcessor,dr as SamImageSegmentationOutput,ur as SamModel,hr as SamPreTrainedModel,pr as SamProcessor,_r as SeamlessM4TFeatureExtractor,mr as SegformerFeatureExtractor,fr as SegformerForImageClassification,gr as SegformerForSemanticSegmentation,Mr as SegformerModel,wr as SegformerPreTrainedModel,Tr as Seq2SeqLMOutput,kr as SequenceClassifierOutput,br as SiglipImageProcessor,xr as SiglipModel,yr as SiglipPreTrainedModel,Fr as SiglipTextModel,Cr as SiglipTokenizer,Pr as SiglipVisionModel,vr as SpeechT5FeatureExtractor,Sr as SpeechT5ForSpeechToText,Ar as SpeechT5ForTextToSpeech,Lr as SpeechT5HifiGan,Er as SpeechT5Model,zr as SpeechT5PreTrainedModel,Br as SpeechT5Processor,Ir as SpeechT5Tokenizer,Or as SqueezeBertForMaskedLM,Dr as SqueezeBertForQuestionAnswering,Nr as SqueezeBertForSequenceClassification,Vr as SqueezeBertModel,qr as SqueezeBertPreTrainedModel,jr as SqueezeBertTokenizer,Rr as StableLmForCausalLM,Gr as StableLmModel,Wr as StableLmPreTrainedModel,$r as Starcoder2ForCausalLM,Ur as Starcoder2Model,Xr as Starcoder2PreTrainedModel,Qr as SummarizationPipeline,Hr as Swin2SRForImageSuperResolution,Yr as Swin2SRImageProcessor,Jr as Swin2SRModel,Zr as Swin2SRPreTrainedModel,Kr as SwinForImageClassification,ea as SwinModel,ta as SwinPreTrainedModel,sa as T5ForConditionalGeneration,oa as T5Model,na as T5PreTrainedModel,ra as T5Tokenizer,aa as TableTransformerForObjectDetection,ia as TableTransformerModel,la as TableTransformerObjectDetectionOutput,ca as TableTransformerPreTrainedModel,da as Tensor,ua as Text2TextGenerationPipeline,ha as TextClassificationPipeline,pa as TextGenerationPipeline,_a as TextToAudioPipeline,ma as TokenClassificationPipeline,fa as TokenClassifierOutput,ga as TokenizerModel,Ma as TrOCRForCausalLM,wa as TrOCRPreTrainedModel,Ta as TranslationPipeline,ka as UniSpeechForCTC,ba as UniSpeechForSequenceClassification,xa as UniSpeechModel,ya as UniSpeechPreTrainedModel,Fa as UniSpeechSatForAudioFrameClassification,Ca as UniSpeechSatForCTC,Pa as UniSpeechSatForSequenceClassification,va as UniSpeechSatModel,Sa as UniSpeechSatPreTrainedModel,Aa as ViTFeatureExtractor,La as ViTForImageClassification,Ea as ViTImageProcessor,za as ViTModel,Ba as ViTPreTrainedModel,Ia as VisionEncoderDecoderModel,Oa as VitMatteForImageMatting,Da as VitMatteImageProcessor,Na as VitMattePreTrainedModel,Va as VitsModel,qa as VitsModelOutput,ja as VitsPreTrainedModel,Ra as VitsTokenizer,Ga as Wav2Vec2BertForCTC,Wa as Wav2Vec2BertForSequenceClassification,$a as Wav2Vec2BertModel,Ua as Wav2Vec2BertPreTrainedModel,Xa as Wav2Vec2CTCTokenizer,Qa as Wav2Vec2FeatureExtractor,Ha as Wav2Vec2ForAudioFrameClassification,Ya as Wav2Vec2ForCTC,Ja as Wav2Vec2ForSequenceClassification,Za as Wav2Vec2Model,Ka as Wav2Vec2PreTrainedModel,ei as Wav2Vec2ProcessorWithLM,ti as WavLMForAudioFrameClassification,si as WavLMForCTC,oi as WavLMForSequenceClassification,ni as WavLMForXVector,ri as WavLMModel,ai as WavLMPreTrainedModel,ii as WhisperFeatureExtractor,li as WhisperForConditionalGeneration,ci as WhisperModel,di as WhisperPreTrainedModel,ui as WhisperProcessor,hi as WhisperTokenizer,pi as XLMForQuestionAnswering,_i as XLMForSequenceClassification,mi as XLMForTokenClassification,fi as XLMModel,gi as XLMPreTrainedModel,Mi as XLMRobertaForMaskedLM,wi as XLMRobertaForQuestionAnswering,Ti as XLMRobertaForSequenceClassification,ki as XLMRobertaForTokenClassification,bi as XLMRobertaModel,xi as XLMRobertaPreTrainedModel,yi as XLMRobertaTokenizer,Fi as XLMTokenizer,Ci as XLMWithLMHeadModel,Pi as XVectorOutput,vi as YolosFeatureExtractor,Si as YolosForObjectDetection,Ai as YolosModel,Li as YolosObjectDetectionOutput,Ei as YolosPreTrainedModel,zi as ZeroShotAudioClassificationPipeline,Bi as ZeroShotClassificationPipeline,Ii as ZeroShotImageClassificationPipeline,Oi as ZeroShotObjectDetectionPipeline,Di as bankers_round,Ni as cat,Vi as cos_sim,qi as dot,ji as dynamicTimeWarping,Ri as env,Gi as getTopItems,Wi as hanning,$i as interpolate,Ui as interpolate_data,Xi as layer_norm,Qi as log_softmax,Hi as magnitude,Yi as max,Ji as mean,Zi as mean_pooling,Ki as medianFilter,el as mel_filter_bank,tl as min,sl as ones,ol as ones_like,nl as permute,rl as permute_data,al as pipeline,il as read_audio,ll as round,cl as softmax,dl as spectrogram,ul as stack,hl as std_mean,pl as window_function}; //# sourceMappingURL=transformers.min.js.map