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i=0,l=null;for(let c of this.end_nodes[e]){let u=c.backtrace_score+a.score;(l===null||u>i)&&(l=c.clone(),i=u)}if(l!==null)a.prev=l,a.backtrace_score=i;else return[]}++e}let r=[],n=this.begin_nodes[t][0].prev;if(n===null)return[];let o=n.clone();for(;o.prev!==null;)r.push(o.clone()),o=o.clone().prev.clone();return r.reverse(),r}piece(t){return this.chars.slice(t.pos,t.pos+t.length).join("")}tokens(){return this.viterbi().map(e=>this.piece(e))}token_ids(){return this.viterbi().map(e=>e.token_id)}},_P=mP;function hP(t){if(t.length===0)throw new Error("Array must not be empty");let e=t[0],r=0;for(let s=1;s<t.length;++s)t[s]<e&&(e=t[s],r=s);return[e,r]}var gP=class extends ic{constructor(t,e){super(t);let r=t.vocab.length;this.vocab=new Array(r),this.scores=new Array(r);for(let s=0;s<r;++s)[this.vocab[s],this.scores[s]]=t.vocab[s];this.unk_token_id=t.unk_id,this.unk_token=this.vocab[t.unk_id],this.tokens_to_ids=new Map(this.vocab.map((s,n)=>[s,n])),this.bos_token=" 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this.size===0}peek(){return this._heap[0]}push(...t){return this.extend(t)}extend(t){for(let e of t)if(this.size<this._max_size)this._heap.push(e),this._sift_up();else{let r=this._smallest();this._comparator(e,this._heap[r])&&(this._heap[r]=e,this._sift_up_from(r))}return this.size}pop(){let t=this.peek(),e=this.size-1;return e>0&&this._swap(0,e),this._heap.pop(),this._sift_down(),t}replace(t){let e=this.peek();return this._heap[0]=t,this._sift_down(),e}_parent(t){return(t+1>>>1)-1}_left(t){return(t<<1)+1}_right(t){return t+1<<1}_greater(t,e){return this._comparator(this._heap[t],this._heap[e])}_swap(t,e){let r=this._heap[t];this._heap[t]=this._heap[e],this._heap[e]=r}_sift_up(){this._sift_up_from(this.size-1)}_sift_up_from(t){for(;t>0&&this._greater(t,this._parent(t));)this._swap(t,this._parent(t)),t=this._parent(t)}_sift_down(){let t=0;for(;this._left(t)<this.size&&this._greater(this._left(t),t)||this._right(t)<this.size&&this._greater(this._right(t),t);){let e=this._right(t)<this.size&&this._greater(this._right(t),this._left(t))?this._right(t):this._left(t);this._swap(t,e),t=e}}_smallest(){return 2**Math.floor(Math.log2(this.size))-1}},xP=wP,yP=class{constructor(t){this.capacity=t,this.cache=new Map}get(t){if(!this.cache.has(t))return;let e=this.cache.get(t);return this.cache.delete(t),this.cache.set(t,e),e}put(t,e){this.cache.has(t)&&this.cache.delete(t),this.cache.set(t,e),this.cache.size>this.capacity&&this.cache.delete(this.cache.keys().next().value)}clear(){this.cache.clear()}},bP=yP,vP=class extends ic{constructor(t){super(t),this.tokens_to_ids=Pf(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(let[r,s]of this.tokens_to_ids)this.vocab[s]=r;let e=Array.isArray(t.merges[0]);this.merges=e?t.merges:t.merges.map(r=>r.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((r,s)=>[JSON.stringify(r),s])),this.end_of_word_suffix=t.end_of_word_suffix,this.continuing_subword_suffix=t.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new bP(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(t){if(t.length===0)return[];let e=this.cache.get(t);if(e!==void 0)return e;let r=Array.from(t);this.end_of_word_suffix&&(r[r.length-1]+=this.end_of_word_suffix);let s=[];if(r.length>1){let n=new xP((i,l)=>i.score<l.score),o={token:r[0],bias:0,prev:null,next:null},a=o;for(let i=1;i<r.length;++i){let l={bias:i/r.length,token:r[i],prev:a,next:null};a.next=l,this.add_node(n,a),a=l}for(;!n.is_empty();){let i=n.pop();if(i.deleted||!i.next||i.next.deleted)continue;if(i.deleted=!0,i.next.deleted=!0,i.prev){let c={...i.prev};i.prev.deleted=!0,i.prev=c,c.prev?c.prev.next=c:o=c}let l={token:i.token+i.next.token,bias:i.bias,prev:i.prev,next:i.next.next};l.prev?(l.prev.next=l,this.add_node(n,l.prev)):o=l,l.next&&(l.next.prev=l,this.add_node(n,l))}for(let i=o;i!==null;i=i.next)s.push(i.token)}else s=r;if(this.continuing_subword_suffix)for(let n=0;n<s.length-1;++n)s[n]+=this.continuing_subword_suffix;return t.length<this.max_length_to_cache&&this.cache.put(t,s),s}add_node(t,e){let r=this.bpe_ranks.get(JSON.stringify([e.token,e.next.token]));r!==void 0&&(e.score=r+e.bias,t.push(e))}encode(t){let e=[];for(let r of t){if(this.ignore_merges&&this.tokens_to_ids.has(r)){e.push(r);continue}let s=this.bpe(r);for(let n of s)if(this.tokens_to_ids.has(n))e.push(n);else if(this.byte_fallback){let o=Array.from(this.text_encoder.encode(n)).map(a=>`<0x${a.toString(16).toUpperCase().padStart(2,"0")}>`);o.every(a=>this.tokens_to_ids.has(a))?e.push(...o):this.unk_token!=null&&e.push(this.unk_token)}else this.unk_token!=null&&e.push(this.unk_token)}return e}},ME=vP,kP=class extends ic{constructor(t,e){super(t);let r=t.vocab;this.tokens_to_ids=Pf(e.target_lang?r[e.target_lang]:r),this.bos_token=e.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=e.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=e.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=e.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(let[s,n]of this.tokens_to_ids)this.vocab[n]=s}encode(t){return t}},EP=kP;function AP(t,e){switch(t.type){case"WordPiece":return new EE(t);case"Unigram":return new TE(t,e.eos_token);case"BPE":return new ME(t);default:if(t.vocab)return Array.isArray(t.vocab)?new TE(t,e.eos_token):Object.hasOwn(t,"continuing_subword_prefix")&&Object.hasOwn(t,"unk_token")?Object.hasOwn(t,"merges")?new ME(t):new EE(t):new EP(t,{target_lang:e.target_lang,bos_token:e.bos_token,eos_token:e.eos_token,pad_token:e.pad_token,unk_token:e.unk_token});throw new Error(`Unknown TokenizerModel type: ${t?.type}`)}}var TP=AP,MP=class extends uo{constructor(t){super(),this.config=t}_call(t,...e){return this.post_process(t,...e)}},po=MP,SP=class extends po{post_process(t,e=null,r=!0){let s=e===null?this.config.single:this.config.pair,n=[],o=[];for(let a of s)"SpecialToken"in a?r&&(n.push(a.SpecialToken.id),o.push(a.SpecialToken.type_id)):"Sequence"in a&&(a.Sequence.id==="A"?(n=Kt(n,t),o=Kt(o,new Array(t.length).fill(a.Sequence.type_id))):a.Sequence.id==="B"&&(n=Kt(n,e),o=Kt(o,new Array(e.length).fill(a.Sequence.type_id))));return{tokens:n,token_type_ids:o}}},OP=SP,IP=class extends po{post_process(t,e=null){return{tokens:t,tokens_pair:e}}},CP=IP,PP=class extends po{constructor(t){super(t),this.sep=t.sep,this.cls=t.cls}post_process(t,e=null,r=!0){r&&(t=Kt([this.cls[0]],t,[this.sep[0]]));let s=new Array(t.length).fill(0);if(e){let n=[],o=r?[this.sep[0]]:[];t=Kt(t,n,e,o),s=Kt(s,new Array(e.length+n.length+o.length).fill(1))}return{tokens:t,token_type_ids:s}}},LP=PP,zP=class extends po{constructor(t){super(t),this.sep=t.sep,this.cls=t.cls}post_process(t,e,r=!0){r&&(t=Kt([this.cls[0]],t,[this.sep[0]]));let s=new Array(t.length).fill(0);if(e){let n=r?[this.sep[0]]:[],o=r?[this.sep[0]]:[];t=Kt(t,n,e,o),s=Kt(s,new Array(e.length+n.length+o.length).fill(1))}return{tokens:t,token_type_ids:s}}},NP=zP,RP=class extends po{constructor(t){super(t),this.processors=(t.processors??[]).map(e=>zE(e))}post_process(t,e=null,r=!0){let s={tokens:t,tokens_pair:e};for(let n of this.processors)s=n.post_process(s.tokens,s.tokens_pair,r);return s}},$P=RP;function DP(t){if(t===null)return null;switch(t.type){case"TemplateProcessing":return new OP(t);case"ByteLevel":return new CP(t);case"BertProcessing":return new LP(t);case"RobertaProcessing":return new NP(t);case"Sequence":return new $P(t);default:throw new Error(`Unknown PostProcessor type: ${t.type}`)}}var zE=DP,BP=class extends uo{constructor(t){super(),this.config=t,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets="trim_offsets"in t?t.trim_offsets:!1}_call(t){return this.decode(t)}decode(t){return this.decode_chain(t).join("")}},Ft=BP,UP=class extends Ft{constructor(t){super(t),this.byte_decoder=rC,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(t){let e=t.join(""),r=new Uint8Array([...e].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(r)}decode_chain(t){let e=[],r=[];for(let s of t)this.added_tokens.find(n=>n.content===s)!==void 0?(r.length>0&&(e.push(this.convert_tokens_to_string(r)),r=[]),e.push(s)):r.push(s);return r.length>0&&e.push(this.convert_tokens_to_string(r)),e}},FP=UP,GP=class extends Ft{constructor(t){super(t),this.cleanup=t.cleanup}decode_chain(t){return t.map((e,r)=>{if(r!==0){let s=this.config.prefix;s&&e.startsWith(s)?e=e.replace(s,""):e=" "+e}return this.cleanup&&(e=Cf(e)),e})}},jP=GP,qP=class extends Ft{constructor(t){super(t),this.replacement=t.replacement??"\u2581"}decode_chain(t){let e=[];for(let r=0;r<t.length;++r){let s=t[r].replaceAll(this.replacement," ");r==0&&s.startsWith(" ")&&(s=s.substring(1)),e.push(s)}return e}},WP=qP,VP=class extends Ft{constructor(t){super(t),this.suffix=t.suffix??""}decode_chain(t){return t.map((e,r)=>e.replaceAll(this.suffix,r===t.length-1?"":" "))}},HP=VP,XP=class extends Ft{constructor(t){super(t),this.pad_token=t.pad_token??"",this.word_delimiter_token=t.word_delimiter_token??"",this.cleanup=t.cleanup}convert_tokens_to_string(t){if(t.length===0)return"";let e=[t[0]];for(let n=1;n<t.length;++n)t[n]!==e.at(-1)&&e.push(t[n]);let s=e.filter(n=>n!==this.pad_token).join("");return this.cleanup&&(s=Cf(s).replaceAll(this.word_delimiter_token," ").trim()),s}decode_chain(t){return[this.convert_tokens_to_string(t)]}},KP=XP,YP=class extends Ft{constructor(t){super(t),this.decoders=(t.decoders??[]).map(e=>NE(e))}decode_chain(t){return this.decoders.reduce((e,r)=>r.decode_chain(e),t)}},QP=YP,JP=class extends Ft{decode_chain(t){let e=oc(this.config.pattern),r=this.config.content??"";return e===null?t:t.map(s=>s.replaceAll(e,r))}},ZP=JP,eL=class extends Ft{decode_chain(t){return[t.join("")]}},tL=eL,rL=class extends Ft{constructor(t){super(t),this.content=t.content??"",this.start=t.start??0,this.stop=t.stop??0}decode_chain(t){return t.map(e=>{let r=0;for(let n=0;n<this.start&&e[n]===this.content;++n){r=n+1;continue}let s=e.length;for(let n=0;n<this.stop;++n){let o=e.length-n-1;if(e[o]===this.content){s=o;continue}else break}return e.slice(r,s)})}},sL=rL,nL=class extends Ft{constructor(t){super(t),this.text_decoder=new TextDecoder}decode_chain(t){let e=[],r=[];for(let s of t){let n=null;if(s.length===6&&s.startsWith("<0x")&&s.endsWith(">")){let o=parseInt(s.slice(3,5),16);isNaN(o)||(n=o)}if(n!==null)r.push(n);else{if(r.length>0){let o=this.text_decoder.decode(Uint8Array.from(r));e.push(o),r=[]}e.push(s)}}if(r.length>0){let s=this.text_decoder.decode(Uint8Array.from(r));e.push(s),r=[]}return e}},oL=nL;function aL(t){if(t===null)return null;switch(t.type){case"ByteLevel":return new FP(t);case"WordPiece":return new jP(t);case"Metaspace":return new WP(t);case"BPEDecoder":return new HP(t);case"CTC":return new KP(t);case"Sequence":return new QP(t);case"Replace":return new ZP(t);case"Fuse":return new tL(t);case"Strip":return new sL(t);case"ByteFallback":return new oL(t);default:throw new Error(`Unknown Decoder type: ${t.type}`)}}var NE=aL,iL=class{constructor(t,e){let r=kE(t,"Tokenizer",["model","decoder","post_processor","pre_tokenizer","normalizer"]);if(r)throw new Error(r);let s=kE(e,"Config");if(s)throw new Error(s);this.tokenizer=t,this.config=e,this.normalizer=IE(this.tokenizer.normalizer),this.pre_tokenizer=CE(this.tokenizer.pre_tokenizer),this.model=TP(this.tokenizer.model,this.config),this.post_processor=zE(this.tokenizer.post_processor),this.decoder=NE(this.tokenizer.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];let n=[],o=[];this.added_tokens_map=new Map;for(let a of this.tokenizer.added_tokens){let i=new eC(a);if(this.added_tokens.push(i),this.model.tokens_to_ids.set(i.content,i.id),this.model.vocab[i.id]=i.content,i.special&&(this.special_tokens.push(i.content),this.all_special_ids.push(i.id)),this.added_tokens_map.set(i.content,i),i.normalized&&this.normalizer!==null){let l=this.normalizer(i.content);o.push(l),this.added_tokens_map.set(l,i)}else n.push(i.content)}(this.config.additional_special_tokens??[]).forEach(a=>{this.special_tokens.includes(a)||this.special_tokens.push(a)}),this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.splitter_unnormalized=new bE(n),this.splitter_normalized=new bE(o),this.remove_space=this.config.remove_space,this.clean_up_tokenization_spaces=this.config.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=this.config.do_lowercase_and_remove_accent??!1}encode(t,{text_pair:e=null,add_special_tokens:r=!0,return_token_type_ids:s=null}={}){let{tokens:n,token_type_ids:o}=this.tokenize_helper(t,{text_pair:e,add_special_tokens:r}),a=n.map(l=>this.added_tokens_map.get(l)?.id??this.model.tokens_to_ids.get(l)??this.model.unk_token_id),i={ids:a,tokens:n,attention_mask:new Array(a.length).fill(1)};return s&&o&&(i.token_type_ids=o),i}decode(t,e={}){if(!Array.isArray(t)||t.length===0||!iC(t[0]))throw Error("token_ids must be a non-empty array of integers.");let r=t.map(n=>this.model.vocab[Number(n)]??this.model.unk_token);e.skip_special_tokens&&(r=r.filter(n=>!this.special_tokens.includes(n)));let s=this.decoder?this.decoder(r):r.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(s=s.replaceAll(this.decoder.end_of_word_suffix," "),e.skip_special_tokens&&(s=s.trim())),(e.clean_up_tokenization_spaces??this.clean_up_tokenization_spaces)&&(s=Cf(s)),s}tokenize(t,{text_pair:e=null,add_special_tokens:r=!1}={}){return this.tokenize_helper(t,{text_pair:e,add_special_tokens:r}).tokens}encode_text(t){if(t===null)return null;let e=this.splitter_unnormalized.split(t);return e.forEach((r,s)=>{let n=this.added_tokens_map.get(r);n&&(n.lstrip&&s>0&&(e[s-1]=e[s-1].trimEnd()),n.rstrip&&s<e.length-1&&(e[s+1]=e[s+1].trimStart()))}),e.flatMap((r,s)=>{if(r.length===0)return[];if(this.added_tokens_map.has(r))return[r];if(this.remove_space===!0&&(r=r.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(r=cC(r)),this.normalizer!==null&&(r=this.normalizer(r)),r.length===0)return[];let n=this.splitter_normalized.split(r);return n.forEach((o,a)=>{let i=this.added_tokens_map.get(o);i&&(i.lstrip&&a>0&&(n[a-1]=n[a-1].trimEnd()),i.rstrip&&a<n.length-1&&(n[a+1]=n[a+1].trimStart()))}),n.flatMap(o=>{if(o.length===0)return[];if(this.added_tokens_map.has(o))return[o];let a=this.pre_tokenizer!==null?this.pre_tokenizer(o,{section_index:s}):[o];return this.model(a)})})}tokenize_helper(t,{text_pair:e=null,add_special_tokens:r=!0}){let s=this.encode_text(t),n=this.encode_text(e||null);return this.post_processor?this.post_processor(s,n,r):{tokens:Kt(s??[],n??[])}}token_to_id(t){return this.model.tokens_to_ids.get(t)}id_to_token(t){return this.model.vocab[t]}get_added_tokens_decoder(){let t=new Map;for(let e of this.added_tokens)t.set(e.id,e);return t}get_vocab(t=!0){let e=new Map;for(let r=0;r<this.model.vocab.length;++r){let s=this.model.vocab[r];(t||!this.added_tokens_map.has(s))&&e.set(s,r)}return e}},RE=iL;var D=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Comment:"Comment"}),Gt=class{constructor(t,e){this.value=t,this.type=e}};function $E(t){return/\w/.test(t)}function fo(t){return/[0-9]/.test(t)}function DE(t){return/\s/.test(t)}var 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if(t.assignee.type==="MemberExpression"){let s=t.assignee,n=this.evaluate(s.object,e);if(!(n instanceof pt))throw new Error("Cannot assign to member of non-object");if(s.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");n.value.set(s.property.value,r)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(t.assignee)}`);return new Xe}evaluateIf(t,e){let r=this.evaluate(t.test,e);return this.evaluateBlock(r.__bool__().value?t.body:t.alternate,e)}evaluateFor(t,e){let r=new ws(e),s,n;if(t.iterable.type==="SelectExpression"){let c=t.iterable;n=this.evaluate(c.lhs,r),s=c.test}else n=this.evaluate(t.iterable,r);if(!(n instanceof ye||n instanceof pt))throw new Error(`Expected iterable or object type in for loop: got ${n.type}`);n instanceof pt&&(n=n.keys());let o=[],a=[];for(let c=0;c<n.value.length;++c){let u=new ws(r),f=n.value[c],m;if(t.loopvar.type==="Identifier")m=_=>_.setVariable(t.loopvar.value,f);else 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Releasing tensor."),this.releaseTensor()}this.activeUpload?this.activeUpload.set(e):this.activeUpload=new Uint8Array(e)}async download(t){if(this.activeUpload){let e=this.wrapper?.isDataConverted?Zf(this.activeUpload,this.wrapper?.type):this.activeUpload;if(t){t instanceof ArrayBuffer?new Uint8Array(t).set(e):new Uint8Array(t.buffer,t.byteOffset,t.byteLength).set(e);return}else return e.buffer}if(!this.wrapper)throw new Error("Tensor has not been created.");return t?this.wrapper.read(t):this.wrapper.read()}},NA=class{constructor(t){this.backend=t,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}getMLContext(t){let e=this.backend.getMLContext(t);if(!e)throw new Error("MLContext not found for session.");return e}getMLOpSupportLimits(t){return this.backend.getMLOpSupportLimits(t)}reserveTensorId(){let t=ed();return this.tensorTrackersById.set(t,new sd(this)),t}releaseTensorId(t){let e=this.tensorTrackersById.get(t);e&&(this.tensorTrackersById.delete(t),e.tensorWrapper&&this.releaseTensor(e.tensorWrapper))}async ensureTensor(t,e,r,s,n){bt("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${r}, shape: ${s}, copyOld: ${n}}`);let o=this.tensorTrackersById.get(e);if(!o)throw new Error("Tensor not found.");return o.ensureTensor(t,r,s,n)}upload(t,e){let r=this.tensorTrackersById.get(t);if(!r)throw new Error("Tensor not found.");r.upload(e)}async download(t,e){bt("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${t}, dstBuffer: ${e?.byteLength}}`);let r=this.tensorTrackersById.get(t);if(!r)throw new Error("Tensor not found.");return r.download(e)}releaseTensorsForSession(t){for(let e of this.freeTensors)e.sessionId===t&&e.destroy();this.freeTensors=this.freeTensors.filter(e=>e.sessionId!==t)}registerTensor(t,e,r,s){let n=this.getMLContext(t),o=ed(),a=new rd({sessionId:t,context:n,tensor:e,dataType:r,shape:s});return this.tensorTrackersById.set(o,new sd(this,a)),this.externalTensors.add(a),o}async getCachedTensor(t,e,r,s,n,o,a){let i=this.getMLContext(t);for(let[c,u]of this.freeTensors.entries())if(u.canReuseTensor(i,e,r)){bt("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, ${a?`fallbackDataType: ${a},`:""} shape: ${r}`);let f=this.freeTensors.splice(c,1)[0];return f.sessionId=t,f}bt("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, ${a?`fallbackDataType: ${a},`:""} shape: ${r}}`);let l=await i.createTensor({dataType:a??e,shape:r,dimensions:r,usage:s,writable:n,readable:o});return new rd({sessionId:t,context:i,tensor:l,dataType:e,shape:r,fallbackDataType:a})}releaseTensor(t){this.externalTensors.has(t)&&this.externalTensors.delete(t),this.freeTensors.push(t)}},x2=(...t)=>new NA(...t)}),y2={};So(y2,{WebNNBackend:()=>b2});var ko,RA,b2,Rz=be(()=>{"use strict";dn(),Is(),zz(),Nz(),w2(),ko=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),RA=(t,e)=>{if(t===e)return!0;if(t===void 0||e===void 0)return!1;let r=Object.keys(t).sort(),s=Object.keys(e).sort();return r.length===s.length&&r.every((n,o)=>n===s[o]&&t[n]===e[n])},b2=class{constructor(t){this.tensorManager=x2(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],this.sessionGraphInputs=new Map,this.sessionGraphOutputs=new Map,this.temporaryGraphInputs=[],this.temporaryGraphOutputs=[],this.temporarySessionTensorIds=new Map,this.mlOpSupportLimitsBySessionId=new Map,g2(t.logLevel,!!t.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(t){bt("verbose",()=>`[WebNN] onRunStart {sessionId: ${t}}`),this.activeSessionId=t}onRunEnd(t){bt("verbose",()=>`[WebNN] onRunEnd {sessionId: ${t}}`);let e=this.temporarySessionTensorIds.get(t);if(e){for(let r of e)bt("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${r}}`),this.tensorManager.releaseTensorId(r);this.temporarySessionTensorIds.delete(t),this.activeSessionId=void 0}}async createMLContext(t){if(t instanceof GPUDevice){let r=this.mlContextCache.findIndex(s=>s.gpuDevice===t);if(r!==-1)return this.mlContextCache[r].mlContext;{let s=await navigator.ml.createContext(t);return this.mlContextCache.push({gpuDevice:t,mlContext:s}),s}}else if(t===void 0){let r=this.mlContextCache.findIndex(s=>s.options===void 0&&s.gpuDevice===void 0);if(r!==-1)return this.mlContextCache[r].mlContext;{let s=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:s}),s}}let e=this.mlContextCache.findIndex(r=>RA(r.options,t));if(e!==-1)return this.mlContextCache[e].mlContext;{let r=await navigator.ml.createContext(t);return this.mlContextCache.push({options:t,mlContext:r}),r}}registerMLContext(t,e){this.mlContextBySessionId.set(t,e);let r=this.sessionIdsByMLContext.get(e);r||(r=new Set,this.sessionIdsByMLContext.set(e,r)),r.add(t),this.mlOpSupportLimitsBySessionId.has(t)||this.mlOpSupportLimitsBySessionId.set(t,e.opSupportLimits()),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(t,this.temporaryGraphInputs),this.temporaryGraphInputs=[]),this.temporaryGraphOutputs.length>0&&(this.sessionGraphOutputs.set(t,this.temporaryGraphOutputs),this.temporaryGraphOutputs=[])}onReleaseSession(t){this.sessionGraphInputs.delete(t),this.sessionGraphOutputs.delete(t);let e=this.mlContextBySessionId.get(t);if(!e)return;this.tensorManager.releaseTensorsForSession(t),this.mlContextBySessionId.delete(t),this.mlOpSupportLimitsBySessionId.delete(t);let r=this.sessionIdsByMLContext.get(e);if(r.delete(t),r.size===0){this.sessionIdsByMLContext.delete(e);let s=this.mlContextCache.findIndex(n=>n.mlContext===e);s!==-1&&this.mlContextCache.splice(s,1)}}getMLContext(t){return this.mlContextBySessionId.get(t)}getMLOpSupportLimits(t){return this.mlOpSupportLimitsBySessionId.get(t)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(t){bt("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t)}async ensureTensor(t,e,r,s,n){let o=ko.get(r);if(!o)throw new Error(`Unsupported ONNX data type: ${r}`);return this.tensorManager.ensureTensor(t??this.currentSessionId,e,o,s,n)}async createTemporaryTensor(t,e,r){bt("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${e}, shape: ${r}}`);let s=ko.get(e);if(!s)throw new Error(`Unsupported ONNX data type: ${e}`);let n=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(t,n,s,r,!1);let o=this.temporarySessionTensorIds.get(t);return o?o.push(n):this.temporarySessionTensorIds.set(t,[n]),n}uploadTensor(t,e){if(!De().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");bt("verbose",()=>`[WebNN] uploadTensor {tensorId: ${t}, data: ${e.byteLength}}`),this.tensorManager.upload(t,e)}async downloadTensor(t,e){return this.tensorManager.download(t,e)}createMLTensorDownloader(t,e){return async()=>{let r=await this.tensorManager.download(t);return h2(r,e)}}registerMLTensor(t,e,r,s){let n=ko.get(r);if(!n)throw new Error(`Unsupported ONNX data type: ${r}`);let o=this.tensorManager.registerTensor(t,e,n,s);return bt("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${s}} -> {tensorId: ${o}}`),o}registerMLConstant(t,e,r,s,n,o,a=!1){if(!o)throw new Error("External mounted files are not available.");let i=t;t.startsWith("./")&&(i=t.substring(2));let l=o.get(i);if(!l)throw new Error(`File with name ${i} not found in preloaded files.`);if(e+r>l.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let c=l.slice(e,e+r).buffer,u;switch(n.dataType){case"float32":u=new Float32Array(c);break;case"float16":u=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(c):new Uint16Array(c);break;case"int32":u=new Int32Array(c);break;case"uint32":u=new Uint32Array(c);break;case"int64":if(a){let f=ud(new Uint8Array(c),"int64");u=new Int32Array(f.buffer),n.dataType="int32"}else u=new BigInt64Array(c);break;case"uint64":u=new BigUint64Array(c);break;case"int8":u=new Int8Array(c);break;case"int4":case"uint4":case"uint8":u=new Uint8Array(c);break;default:throw new Error(`Unsupported data type: ${n.dataType} in creating WebNN Constant from external data.`)}return bt("verbose",()=>`[WebNN] registerMLConstant {dataType: ${n.dataType}, shape: ${n.shape}}} ${a?"(Note: it was int64 data type and registered to int32 as workaround)":""}`),s.constant(n,u)}registerGraphInput(t){this.temporaryGraphInputs.push(t)}registerGraphOutput(t){this.temporaryGraphOutputs.push(t)}isGraphInput(t,e){let r=this.sessionGraphInputs.get(t);return r?r.includes(e):!1}isGraphOutput(t,e){let r=this.sessionGraphOutputs.get(t);return r?r.includes(e):!1}isGraphInputOutputTypeSupported(t,e,r=!0){let s=ko.get(As(e)),n=this.mlOpSupportLimitsBySessionId.get(t);return typeof s>"u"?!1:r?!!n?.input.dataTypes.includes(s):!!n?.output.dataTypes.includes(s)}flush(){}}}),$A,kd,Ed,Fr,DA,nd,Tc,Ad,Td,od,Md,Sd,Od,v2=be(()=>{"use strict";Os(),Pz(),Lz(),dn(),Is(),xd(),_2(),$A=(t,e)=>{De()._OrtInit(t,e)!==0&&Le("Can't initialize onnxruntime.")},kd=async t=>{$A(t.wasm.numThreads,Ac(t.logLevel))},Ed=async(t,e)=>{De().asyncInit?.();let r=t.webgpu.adapter;if(e==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");if(r){if(typeof r.limits!="object"||typeof r.features!="object"||typeof r.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let s=t.webgpu.powerPreference;if(s!==void 0&&s!=="low-power"&&s!=="high-performance")throw new Error(`Invalid powerPreference setting: "${s}"`);let n=t.webgpu.forceFallbackAdapter;if(n!==void 0&&typeof n!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${n}"`);if(r=await navigator.gpu.requestAdapter({powerPreference:s,forceFallbackAdapter:n}),!r)throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.')}}if(e==="webnn"&&(typeof navigator>"u"||!navigator.ml))throw new Error("WebNN is not supported in current environment");if(e==="webgpu"&&De().webgpuInit(s=>{t.webgpu.device=s}),e==="webnn"){let s=new(Rz(),vc(y2)).WebNNBackend(t);De().webnnInit([s,()=>s.reserveTensorId(),n=>s.releaseTensorId(n),async(n,o,a,i,l)=>s.ensureTensor(n,o,a,i,l),(n,o)=>{s.uploadTensor(n,o)},async(n,o)=>s.downloadTensor(n,o),(n,o)=>s.registerMLContext(n,o),!!t.trace])}},Fr=new Map,DA=t=>{let e=De(),r=e.stackSave();try{let s=e.PTR_SIZE,n=e.stackAlloc(2*s);e._OrtGetInputOutputCount(t,n,n+s)!==0&&Le("Can't get session input/output count.");let o=s===4?"i32":"i64";return[Number(e.getValue(n,o)),Number(e.getValue(n+s,o))]}finally{e.stackRestore(r)}},nd=(t,e)=>{let r=De(),s=r.stackSave(),n=0;try{let o=r.PTR_SIZE,a=r.stackAlloc(2*o);r._OrtGetInputOutputMetadata(t,e,a,a+o)!==0&&Le("Can't get session input/output metadata.");let i=Number(r.getValue(a,"*"));n=Number(r.getValue(a+o,"*"));let l=r.HEAP32[n/4];if(l===0)return[i,0];let c=r.HEAPU32[n/4+1],u=[];for(let f=0;f<c;f++){let m=Number(r.getValue(n+8+f*o,"*"));u.push(m!==0?r.UTF8ToString(m):Number(r.getValue(n+8+(f+c)*o,"*")))}return[i,l,u]}finally{r.stackRestore(s),n!==0&&r._OrtFree(n)}},Tc=t=>{let e=De(),r=e._malloc(t.byteLength);if(r===0)throw new Error(`Can't create a session. failed to allocate a buffer of size ${t.byteLength}.`);return e.HEAPU8.set(t,r),[r,t.byteLength]},Ad=async(t,e)=>{let r,s,n=De();Array.isArray(t)?[r,s]=t:t.buffer===n.HEAPU8.buffer?[r,s]=[t.byteOffset,t.byteLength]:[r,s]=Tc(t);let o=0,a=0,i=0,l=[],c=[],u=[];try{if([a,l]=await m2(e),e?.externalData&&n.mountExternalData){let M=[];for(let I of e.externalData){let O=typeof I=="string"?I:I.path;M.push(vd(typeof I=="string"?I:I.data).then(k=>{n.mountExternalData(O,k)}))}await Promise.all(M)}for(let M of e?.executionProviders??[])if((typeof M=="string"?M:M.name)==="webnn"){if(n.shouldTransferToMLTensor=!1,typeof M!="string"){let I=M,O=I?.context,k=I?.gpuDevice,R=I?.deviceType,G=I?.powerPreference;O?n.currentContext=O:k?n.currentContext=await n.webnnCreateMLContext(k):n.currentContext=await n.webnnCreateMLContext({deviceType:R,powerPreference:G})}else n.currentContext=await n.webnnCreateMLContext();break}o=await n._OrtCreateSession(r,s,a),n.webgpuOnCreateSession?.(o),o===0&&Le("Can't create a session."),n.jsepOnCreateSession?.(),n.currentContext&&(n.webnnRegisterMLContext(o,n.currentContext),n.currentContext=void 0,n.shouldTransferToMLTensor=!0);let[f,m]=DA(o),_=!!e?.enableGraphCapture,w=[],x=[],v=[],b=[],A=[];for(let M=0;M<f;M++){let[I,O,k]=nd(o,M);I===0&&Le("Can't get an input name."),c.push(I);let R=n.UTF8ToString(I);w.push(R),v.push(O===0?{name:R,isTensor:!1}:{name:R,isTensor:!0,type:bc(O),shape:k})}for(let M=0;M<m;M++){let[I,O,k]=nd(o,M+f);I===0&&Le("Can't get an output name."),u.push(I);let R=n.UTF8ToString(I);x.push(R),b.push(O===0?{name:R,isTensor:!1}:{name:R,isTensor:!0,type:bc(O),shape:k});{if(_&&e?.preferredOutputLocation===void 0){A.push("gpu-buffer");continue}let G=typeof e?.preferredOutputLocation=="string"?e.preferredOutputLocation:e?.preferredOutputLocation?.[R]??"cpu",F=n.webnnIsGraphOutput;if(G==="cpu"&&F&&F(o,R)){A.push("ml-tensor-cpu-output");continue}if(G!=="cpu"&&G!=="cpu-pinned"&&G!=="gpu-buffer"&&G!=="ml-tensor")throw new Error(`Not supported preferred output location: ${G}.`);if(_&&G!=="gpu-buffer")throw new Error(`Not supported preferred output location: ${G}. 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document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},c=u=>typeof HTMLCanvasElement<"u"&&u instanceof HTMLCanvasElement||u instanceof OffscreenCanvas?u.getContext("2d"):null;if(r){let u=l();u.width=t.width,u.height=t.height;let f=c(u);if(f!=null){let m=t.height,_=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(m=e.resizedHeight,_=e.resizedWidth),e!==void 0){if(i=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");i.tensorFormat="RGBA",i.height=m,i.width=_}else i.tensorFormat="RGBA",i.height=m,i.width=_;f.drawImage(t,0,0),a=f.getImageData(0,0,_,m).data}else throw new Error("Can not access image data")}else if(s){let u,f;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(u=e.resizedHeight,f=e.resizedWidth):(u=t.height,f=t.width),e!==void 0&&(i=e),i.format="RGBA",i.height=u,i.width=f,e!==void 0){let m=l();m.width=f,m.height=u;let _=c(m);if(_!=null)_.putImageData(t,0,0),a=_.getImageData(0,0,f,u).data;else throw new Error("Can not access image data")}else a=t.data}else if(n){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");let u=l();u.width=t.width,u.height=t.height;let f=c(u);if(f!=null){let m=t.height,_=t.width;return f.drawImage(t,0,0,_,m),a=f.getImageData(0,0,_,m).data,i.height=m,i.width=_,Pd(a,i)}else throw new Error("Can not access image data")}else{if(o)return new Promise((u,f)=>{let m=l(),_=c(m);if(!t||!_)return f();let w=new Image;w.crossOrigin="Anonymous",w.src=t,w.onload=()=>{m.width=w.width,m.height=w.height,_.drawImage(w,0,0,m.width,m.height);let x=_.getImageData(0,0,m.width,m.height);i.height=m.height,i.width=m.width,u(Pd(x.data,i))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(a!==void 0)return Pd(a,i);throw new Error("Input data provided is not supported - aborted tensor creation")},G2=(t,e)=>{let{width:r,height:s,download:n,dispose:o}=e,a=[1,s,r,4];return new wt({location:"texture",type:"float32",texture:t,dims:a,download:n,dispose:o})},j2=(t,e)=>{let{dataType:r,dims:s,download:n,dispose:o}=e;return new wt({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:s,download:n,dispose:o})},q2=(t,e)=>{let{dataType:r,dims:s,download:n,dispose:o}=e;return new wt({location:"ml-tensor",type:r??"float32",mlTensor:t,dims:s,download:n,dispose:o})},W2=(t,e,r)=>new wt({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]});var Cs=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array],["int4",Uint8Array],["uint4",Uint8Array]]),Oo=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]),V2=!1,H2=()=>{if(!V2){V2=!0;let t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=globalThis.Float16Array,s=typeof r<"u"&&r.from;t&&(Cs.set("int64",BigInt64Array),Oo.set(BigInt64Array,"int64")),e&&(Cs.set("uint64",BigUint64Array),Oo.set(BigUint64Array,"uint64")),s?(Cs.set("float16",r),Oo.set(r,"float16")):Cs.set("float16",Uint16Array)}};var X2=t=>{let e=1;for(let r=0;r<t.length;r++){let s=t[r];if(typeof s!="number"||!Number.isSafeInteger(s))throw new TypeError(`dims[${r}] must be an integer, got: ${s}`);if(s<0)throw new RangeError(`dims[${r}] must be a non-negative integer, got: ${s}`);e*=s}return e},K2=(t,e)=>{switch(t.location){case"cpu":return new wt(t.type,t.data,e);case"cpu-pinned":return new wt({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new wt({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new wt({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});case"ml-tensor":return new wt({location:"ml-tensor",mlTensor:t.mlTensor,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};var wt=class{constructor(e,r,s){H2();let n,o;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,n=e.type,o=e.dims,e.location){case"cpu-pinned":{let i=Cs.get(n);if(!i)throw new TypeError(`unsupported type "${n}" to create tensor from pinned buffer`);if(!(e.data instanceof i))throw new TypeError(`buffer should be of type ${i.name}`);this.cpuData=e.data;break}case"texture":{if(n!=="float32")throw new TypeError(`unsupported type "${n}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(n!=="float32"&&n!=="float16"&&n!=="int32"&&n!=="int64"&&n!=="uint32"&&n!=="uint8"&&n!=="bool"&&n!=="uint4"&&n!=="int4")throw new TypeError(`unsupported type "${n}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}case"ml-tensor":{if(n!=="float32"&&n!=="float16"&&n!=="int32"&&n!=="int64"&&n!=="uint32"&&n!=="uint64"&&n!=="int8"&&n!=="uint8"&&n!=="bool"&&n!=="uint4"&&n!=="int4")throw new TypeError(`unsupported type "${n}" to create tensor from MLTensor`);this.mlTensorData=e.mlTensor,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let i,l;if(typeof e=="string")if(n=e,l=s,e==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");i=r}else{let c=Cs.get(e);if(c===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16"&&c===Uint16Array||e==="uint4"||e==="int4")throw new TypeError(`Creating a ${e} tensor from number array is not supported. Please use ${c.name} as data.`);e==="uint64"||e==="int64"?i=c.from(r,BigInt):i=c.from(r)}else if(r instanceof c)i=r;else if(r instanceof Uint8ClampedArray)if(e==="uint8")i=Uint8Array.from(r);else throw new TypeError("A Uint8ClampedArray tensor's data must be type of uint8");else if(e==="float16"&&r instanceof Uint16Array&&c!==Uint16Array)i=new globalThis.Float16Array(r.buffer,r.byteOffset,r.length);else throw new TypeError(`A ${n} tensor's data must be type of ${c}`)}else if(l=r,Array.isArray(e)){if(e.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");let c=typeof e[0];if(c==="string")n="string",i=e;else if(c==="boolean")n="bool",i=Uint8Array.from(e);else throw new TypeError(`Invalid element type of data array: ${c}.`)}else if(e instanceof Uint8ClampedArray)n="uint8",i=Uint8Array.from(e);else{let c=Oo.get(e.constructor);if(c===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);n=c,i=e}if(l===void 0)l=[i.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");o=l,this.cpuData=i,this.dataLocation="cpu"}let a=X2(o);if(this.cpuData&&a!==this.cpuData.length&&!((n==="uint4"||n==="int4")&&Math.ceil(a/2)===this.cpuData.length))throw new Error(`Tensor's size(${a}) does not match data length(${this.cpuData.length}).`);this.type=n,this.dims=o,this.size=a}static async fromImage(e,r){return F2(e,r)}static fromTexture(e,r){return G2(e,r)}static fromGpuBuffer(e,r){return j2(e,r)}static fromMLTensor(e,r){return q2(e,r)}static fromPinnedBuffer(e,r,s){return W2(e,r,s)}toDataURL(e){return B2(this,e)}toImageData(e){return U2(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}get mlTensor(){if(this.ensureValid(),!this.mlTensorData)throw new Error("The data is not stored as a WebNN MLTensor.");return this.mlTensorData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":case"ml-tensor":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;let r=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=r,e&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.mlTensorData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return K2(this,e)}};var Mc=wt;var Fz=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",coreml:"coreml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}});function Z2(t){return t<=It.DEBUG?0:t<=It.INFO?2:t<=It.WARNING||t<=It.ERROR?3:4}var Gz={0:"verbose",1:"info",2:"warning",3:"error",4:"fatal"},qt=[],Ld,_n,Y2=Symbol.for("onnxruntime");if(Y2 in globalThis)_n=globalThis[Y2];else if(ie.IS_NODE_ENV){switch(_n=Uf,process.platform){case"win32":qt.push("dml");break;case"linux":process.arch==="x64"&&qt.push("cuda");break;case"darwin":qt.push("coreml");break}qt.push("webgpu"),qt.push("cpu"),Ld=["cpu"]}else _n=Id,ie.IS_WEBNN_AVAILABLE&&qt.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),ie.IS_WEBGPU_AVAILABLE&&qt.push("webgpu"),qt.push("wasm"),Ld=["wasm"];var jz=_n.InferenceSession;function eT(t=null){if(!t)return Ld;switch(t){case"auto":return qt;case"gpu":return qt.filter(e=>["webgpu","cuda","dml","webnn-gpu"].includes(e))}if(qt.includes(t))return[Fz[t]??t];throw new Error(`Unsupported device: "${t}". Should be one of: ${qt.join(", ")}.`)}var Q2=Promise.resolve(),mn=null;async function qz(){if(mn)return mn;if(!(_e.useWasmCache&&typeof at?.wasm?.wasmPaths=="object"&&at?.wasm?.wasmPaths?.wasm&&at?.wasm?.wasmPaths?.mjs)){if(ie.IS_DENO_WEB_RUNTIME)throw new Error("env.useWasmCache=false is not supported in Deno's web runtime. Remove the useWasmCache override.");return mn=Promise.resolve(),mn}return mn=(async()=>{let e=at.wasm.wasmPaths,r=!1;await Promise.all([e.wasm&&!zf(e.wasm)?(async()=>{try{let s=await N2(Nf(e.wasm));s&&(at.wasm.wasmBinary=s,r=!0)}catch(s){ee.warn("Failed to pre-load WASM binary:",s)}})():Promise.resolve(),e.mjs&&!zf(e.mjs)?(async()=>{try{let s=await R2(Nf(e.mjs));s&&(at.wasm.wasmPaths.mjs=s)}catch(s){ee.warn("Failed to pre-load WASM factory:",s)}})():Promise.resolve()]),r||(at.wasm.wasmPaths.mjs=e.mjs)})(),mn}async function Sc(t,e,r){await qz();let s=Z2(_e.logLevel??It.WARNING),n=()=>jz.create(t,{logSeverityLevel:s,...e}),o=await(ie.IS_WEB_ENV?Q2=Q2.then(n):n());return o.config=r,o}var J2=Promise.resolve();async function Oc(t,e){let r=()=>t.run(e);return ie.IS_WEB_ENV?J2=J2.then(r):r()}function Ic(t){return t instanceof _n.Tensor}var at=_n?.env;function Io(){return at?.wasm?.proxy}if(at){let t=function(e){let r=Z2(e);at.logLevel=Gz[r]};if(at.wasm){if(!(typeof ServiceWorkerGlobalScope<"u"&&self instanceof ServiceWorkerGlobalScope)&&at.versions?.web&&!at.wasm.wasmPaths){let e=`/assets/${at.versions.web}/dist/`;at.wasm.wasmPaths=ie.IS_SAFARI?{mjs:`${e}ort-wasm-simd-threaded.mjs`,wasm:`${e}ort-wasm-simd-threaded.wasm`}:{mjs:`${e}ort-wasm-simd-threaded.asyncify.mjs`,wasm:`${e}ort-wasm-simd-threaded.asyncify.wasm`}}at.wasm.proxy=!1}at.webgpu&&(at.webgpu.powerPreference="high-performance"),t(_e.logLevel??It.WARNING),_e.backends.onnx={...at,setLogLevel:t}}var Wr=async(t,e,r)=>{let s=await Sc(new Uint8Array(t),e);return(async n=>{let o=Io(),a=Object.fromEntries(Object.entries(n).map(([l,c])=>[l,(o?c.clone():c).ort_tensor])),i=await Oc(s,a);return Array.isArray(r)?r.map(l=>new N(i[l])):new N(i[r])})},pr=class{static session_options={};static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=Wr([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Wr([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=Wr([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=Wr([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=Wr([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=Wr([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=Wr([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=Wr([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}};var tT=Object.freeze({auto:"auto",gpu:"gpu",cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",coreml:"coreml",webnn:"webnn","webnn-npu":"webnn-npu","webnn-gpu":"webnn-gpu","webnn-cpu":"webnn-cpu"}),zd=ie.IS_NODE_ENV?"cpu":"wasm";function Cc(t,e,{warn:r}={}){return t?typeof t=="string"?t:t.hasOwnProperty(e)?t[e]:(r&&r(`device not specified for "${e}". Using the default device (${zd}).`),zd):zd}var nT=(function(){let t;return async function(){if(t===void 0)if(!ie.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}})(),xt=Object.freeze({auto:"auto",fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),rT=xt.fp32,sT=Object.freeze({[tT.wasm]:xt.q8}),Vr=Object.freeze({[xt.fp32]:"",[xt.fp16]:"_fp16",[xt.int8]:"_int8",[xt.uint8]:"_uint8",[xt.q8]:"_quantized",[xt.q4]:"_q4",[xt.q4f16]:"_q4f16",[xt.bnb4]:"_bnb4"});function Pc(t,e,r,{configDtype:s=null,warn:n}={}){let o,a=!1;t&&typeof t!="string"?t.hasOwnProperty(e)?o=t[e]:(o=null,a=!0):o=t;let i;if(o===xt.auto){if(s){let l=typeof s=="string"?s:s?.[e];if(l&&l!==xt.auto&&xt.hasOwnProperty(l))return l}i=sT[r]??rT}else o&&xt.hasOwnProperty(o)?i=o:i=sT[r]??rT;return a&&n&&n(`dtype not specified for "${e}". 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this.dims=[...s,n.reduce((a,i)=>a*i,1),...o],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;n<e.length;++n)if(e[n]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=n}let s=this.data;if(r!==-1){let n=e.reduce((o,a,i)=>i!==r?o*a:o,1);e[r]=s.length/n}return new t(this.type,s,e)}neg_(){let e=this.data;for(let r=0;r<e.length;++r)e[r]=-e[r];return this}neg(){return this.clone().neg_()}gt(e){let r=new Uint8Array(this.data.length),s=this.data;for(let n=0;n<s.length;++n)r[n]=s[n]>e?1:0;return new t("bool",r,this.dims)}lt(e){let r=new Uint8Array(this.data.length),s=this.data;for(let n=0;n<s.length;++n)r[n]=s[n]<e?1:0;return new t("bool",r,this.dims)}clamp_(e,r){let s=this.data;for(let n=0;n<s.length;++n)s[n]=Math.min(Math.max(s[n],e),r);return this}clamp(e,r){return this.clone().clamp_(e,r)}round_(){let e=this.data;for(let r=0;r<e.length;++r)e[r]=Math.round(e[r]);return this}round(){return this.clone().round_()}mean(e=null,r=!1){return Nc(this,e,r)}min(e=null,r=!1){if(e===null){let a=yo(this.data)[0];return new t(this.type,[a],[])}let[s,n,o]=Co((a,i)=>Math.min(a,i),this,e,r,1/0);return new t(s,n,o)}max(e=null,r=!1){if(e===null){let a=Pe(this.data)[0];return new t(this.type,[a],[])}let[s,n,o]=Co((a,i)=>Math.max(a,i),this,e,r,-1/0);return new t(s,n,o)}argmin(e=null,r=!1){if(e!==null)throw new Error("`dim !== null` not yet implemented.");let s=yo(this.data)[1];return new t("int64",[BigInt(s)],[])}argmax(e=null,r=!1){if(e!==null)throw new Error("`dim !== null` not yet implemented.");let s=Pe(this.data)[1];return new t("int64",[BigInt(s)],[])}repeat(...e){if(e.length<this.dims.length)throw new Error(`Number of dimensions of repeat dims (${e.length}) cannot be smaller than number of dimensions of tensor (${this.dims.length})`);if(e.every(u=>u===1)){if(e.length===this.dims.length)return this.clone();let u=e.length-this.dims.length,f=Array(u).fill(1).concat(this.dims);return new t(this.type,this.data.slice(),f)}let r=e.length-this.dims.length,s=Array(r).fill(1).concat(this.dims),n=s.map((u,f)=>u*e[f]),o=n.reduce((u,f)=>u*f,1),a=this.data,i=new a.constructor(o),l=Nd(s),c=Nd(n);for(let u=0;u<o;++u){let f=u,m=0;for(let _=0;_<n.length;++_){let w=Math.floor(f/c[_]);f=f%c[_];let x=w%s[_];m+=x*l[_]}i[u]=a[m]}return new t(this.type,i,n)}tile(...e){if(e.length<this.dims.length){let r=this.dims.length-e.length;e=Array(r).fill(1).concat(e)}return this.repeat(...e)}to(e){if(this.type===e)return this;if(!kr.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);let r,s=["int64","uint64"].includes(this.type),n=["int64","uint64"].includes(e);if(s&&!n)r=Number;else if(!s&&n)["float16","float32","float64"].includes(this.type)?r=o=>BigInt(Math.floor(o)):r=BigInt;else if(this.type==="float16"&&e=="float32"&&this.data instanceof Uint16Array)return new t(e,dA(this.data),this.dims);return new t(e,kr[e].from(this.data,r),this.dims)}};function Wz(t,e){let r=t.length,s=e.reduce((o,a)=>o*a);if(r!==s)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let n=t;for(let o=e.length-1;o>=0;o--)n=n.reduce((a,i)=>{let l=a[a.length-1];return l.length<e[o]?l.push(i):a.push([i]),a},[[]]);return n[0]}function Vz(t,e){let[r,s]=iA(t.data,t.dims,e);return new N(t.type,r,s)}function Rd(t,[e,r],s="bilinear",n=!1){let o=t.dims.at(-3)??1,a=t.dims.at(-2),i=t.dims.at(-1),l=aA(t.data,[o,a,i],[e,r],s,n);return new N(t.type,l,[o,e,r])}async function vt(t,{size:e=null,mode:r="bilinear"}={}){if(t.dims.length!==4)throw new Error("`interpolate_4d` currently only supports 4D input.");if(!e)throw new Error("`interpolate_4d` requires a `size` argument.");let s;if(e.length===2)s=[...t.dims.slice(0,2),...e];else if(e.length===3)s=[t.dims[0],...e];else if(e.length===4)s=e;else throw new Error("`size` must be of length 2, 3, or 4.");let n;if(r==="nearest")n=await pr.nearest_interpolate_4d;else if(r==="bilinear")n=await pr.bilinear_interpolate_4d;else 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Specify `power` to fix this issue.");if(!u)throw new Error("`preemphasis_htk_flavor=false` is not currently supported.");if(a){let J=Math.floor(r/2);switch(i){case"reflect":{t=iN(t,J,J);break}case"constant":{let le=new t.constructor(t.length+2*J);le.set(t,J),t=le;break}case"semicausal":{let le=new t.constructor(t.length+J);le.set(t,J),t=le;break}default:throw new Error(`pad_mode="${i}" not implemented yet.`)}}let F=Math.floor(1+Math.floor((t.length-r)/s));S!==null&&F<S&&(F=S);let K=l?Math.floor(n/2)+1:n,V=F,Q=F;M!==null&&(M>F?I&&(Q=M):Q=V=M);let U=new mc(n),C=new Float64Array(n),se=new Float64Array(U.outputBufferSize),Y=new Float32Array(K*Q);for(let J=0;J<V;++J){let le=J*s,je=Math.min(t.length-le,r);je!==r&&C.fill(0,0,r);for(let ce=0;ce<je;++ce)C[ce]=t[le+ce];if(A){let ce=0;for(let qe=0;qe<je;++qe)ce+=C[qe];let At=ce/je;for(let qe=0;qe<je;++qe)C[qe]-=At}if(c!==null){for(let ce=je-1;ce>=1;--ce)C[ce]-=c*C[ce-1];C[0]*=1-c}for(let ce=0;ce<e.length;++ce)C[ce]*=e[ce];U.realTransform(se,C);for(let ce=0;ce<K;++ce){let At=ce<<1;Y[ce*Q+J]=se[At]**2+se[At+1]**2}}if(o!==null&&o!==2){let J=o/2;for(let le=0;le<Y.length;++le)Y[le]**=J}let L=f.length,$=await iT(new N("float32",f.flat(),[L,K]),new N("float32",Y,[K,Q]));O&&($=$.transpose(1,0));let B=$.data;if(R==="add")for(let J=0;J<B.length;++J)B[J]=k+B[J]+m;else for(let J=0;J<B.length;++J)B[J]=k+Math.max(m,B[J]);if(o!==null&&_!==null){let J=Math.min(B.length,V*L);switch(_){case"log":for(let le=0;le<J;++le)B[le]=Math.log(B[le]);break;case"log10":for(let le=0;le<J;++le)B[le]=Math.log10(B[le]);break;case"log10_max_norm":{for(let ce=0;ce<J;++ce)B[ce]=Math.log10(B[ce]);let je=(w??Pe(B)[0])-8;for(let ce=0;ce<J;++ce)B[ce]=(Math.max(B[ce],je)+4)/4;break}case"dB":if(o===1)lN(B,x,v,b);else if(o===2)cN(B,x,v,b);else throw new Error(`Cannot use log_mel option '${_}' with power ${o}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10', 'log10_max_norm', or 'dB'. Got '${_}'`)}}return $}function dt(t,e,{periodic:r=!0,frame_length:s=null,center:n=!0}={}){let o=r?t+1:t,a;switch(e){case"boxcar":a=new Float64Array(o).fill(1);break;case"hann":case"hann_window":a=hT(o);break;case"hamming":a=rN(o);break;case"povey":a=hT(o).map(c=>Math.pow(c,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(a=a.subarray(0,t)),s===null||t===s)return a;if(t>s)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${s})`);let i=new Float64Array(s),l=n?Math.floor((s-t)/2):0;return i.set(a,l),i}function uN(t,e){let r=t.reduce((o,a)=>o+a.length,0),s=new ArrayBuffer(44),n=new DataView(s);return Uc(n,0,"RIFF"),n.setUint32(4,36+r*4,!0),Uc(n,8,"WAVE"),Uc(n,12,"fmt "),n.setUint32(16,16,!0),n.setUint16(20,3,!0),n.setUint16(22,1,!0),n.setUint32(24,e,!0),n.setUint32(28,e*4,!0),n.setUint16(32,4,!0),n.setUint16(34,32,!0),Uc(n,36,"data"),n.setUint32(40,r*4,!0),new Blob([s,...t.map(o=>o.buffer)],{type:"audio/wav"})}function Uc(t,e,r){for(let s=0;s<r.length;++s)t.setUint8(e+s,r.charCodeAt(s))}var Ro=class{constructor(e,r){this.audio=e,this.sampling_rate=r}get data(){if(Array.isArray(this.audio)){if(this.audio.length===0)return new Float32Array(0);if(this.audio.length===1)return this.audio[0];let e=this.audio.reduce((n,o)=>n+o.length,0),r=new Float32Array(e),s=0;for(let n of this.audio)r.set(n,s),s+=n.length;return r}else return this.audio}toBlob(){let e=this.audio;return e instanceof Float32Array&&(e=[e]),uN(e,this.sampling_rate)}async save(e){return Bc(e,this.toBlob())}};var $m=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=it(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=dt(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,r){return nt(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){Ae(e,"ASTFeatureExtractor");let r=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){let s=this.std*2,n=r.data;for(let o=0;o<n.length;++o)n[o]=(n[o]-this.mean)/s}return{input_values:r.unsqueeze_(0)}}};var $o=class extends Ee{async _call(e){Ae(e,"EncodecFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=this.config.feature_size;if(e.length%r!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${r}).`);let s=[1,r,e.length/r];return{input_values:new N("float32",e,s)}}};var Dm=class extends Ee{async _call(e){Ae(e,"ChatterboxFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new N("float32",e,r)}}};var Bm=class extends Ee{constructor(e){super(e),this.mel_filters=it(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=it(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=dt(this.config.fft_window_size,"hann")}async _get_input_mel(e,r,s,n){let o,a=!1,i=e.length-r;if(i>0)if(s==="rand_trunc"){a=!0;let l=Math.floor(ys.random()*(i+1));e=e.subarray(l,l+r),o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${s}" not implemented`);else{if(i<0){let l=new Float64Array(r);if(l.set(e),n==="repeat")for(let c=e.length;c<r;c+=e.length)l.set(e.subarray(0,Math.min(e.length,r-c)),c);else if(n==="repeatpad")for(let c=e.length;c<-i;c+=e.length)l.set(e,c);e=l}if(s==="fusion")throw new Error(`Truncation strategy "${s}" not implemented`);o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}return o.unsqueeze_(0)}async _extract_fbank_features(e,r,s=null){return nt(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:r,log_mel:"dB",max_num_frames:s,do_pad:!1,transpose:!0})}async _call(e,{max_length:r=null}={}){return Ae(e,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(e,r??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}};var pN=1e-5,Do=class extends Ee{constructor(e){super(e),this.config.mel_filters??=it(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,this.config.sampling_rate/2,this.config.sampling_rate,"slaney","slaney");let r=dt(this.config.win_length,"hann",{periodic:!1});this.window=new Float64Array(this.config.n_fft);let s=Math.floor((this.config.n_fft-this.config.win_length)/2);this.window.set(r,s)}async _extract_fbank_features(e){let r=this.config.preemphasis;e=new Float64Array(e);for(let n=e.length-1;n>=1;--n)e[n]-=r*e[n-1];return await nt(e,this.window,this.window.length,this.config.hop_length,{fft_length:this.config.n_fft,power:2,mel_filters:this.config.mel_filters,log_mel:"log",mel_floor:-1/0,pad_mode:"constant",center:!0,transpose:!0,mel_offset:2**-24})}async _call(e){Ae(e,"ParakeetFeatureExtractor");let r=await this._extract_fbank_features(e),s=Math.floor((e.length+Math.floor(this.config.n_fft/2)*2-this.config.n_fft)/this.config.hop_length),n=r.data;n.fill(0,s*r.dims[1]);let[o,a]=r.dims,i=new Float64Array(a),l=new Float64Array(a);for(let f=0;f<s;++f){let m=f*a;for(let _=0;_<a;++_){let w=n[m+_];i[_]+=w,l[_]+=w*w}}let c=s>1?s-1:1;for(let f=0;f<a;++f){let m=i[f]/s,_=(l[f]-s*m*m)/c,x=1/(Math.sqrt(_)+pN);for(let v=0;v<s;++v){let b=v*a+f;n[b]=(n[b]-m)*x}}let u=new BigInt64Array(o);return u.fill(1n,0,s),{input_features:r.unsqueeze_(0),attention_mask:new N("int64",u,[1,o])}}};var Um=class extends Do{_apply_dither(e){let r=this.config.dither??0;if(r<=0)return e;let s=new zr(e.length);for(let n=0;n<e.length;++n)e[n]+=r*s.gauss();return e}split_audio(e){let r=this.config.max_audio_clip_s??35,s=this.config.overlap_chunk_second??5,n=this.config.min_energy_window_samples??1600,o=this.config.sampling_rate,a=Math.max(1,Math.round(r*o)),i=Math.max(1,Math.round(s*o));if(e.length<=a)return[e];let l=[],c=0,u=e.length;for(;c<u;){if(c+a>=u){l.push(e.slice(c,u));break}let f=Math.max(c,c+a-i),m=Math.min(c+a,u),_;m<=f?_=c+a:_=this._find_split_point_energy(e,f,m,n),_=Math.max(c+1,Math.min(_,u)),l.push(e.slice(c,_)),c=_}return l}_find_split_point_energy(e,r,s,n){let o=s-r;if(o<=n)return Math.floor((r+s)/2);let a=1/0,i=r,l=o-n;for(let c=0;c<=l;c+=n){let u=0;for(let f=0;f<n;++f){let m=e[r+c+f];u+=m*m}u=Math.sqrt(u/n),u<a&&(a=u,i=r+c)}return i}async _call(e){Ae(e,"CohereAsrFeatureExtractor");let r=new Float64Array(e);return this._apply_dither(r),super._call(r)}};var Bo=class extends $o{};var Uo=class extends Ee{constructor(e){super(e);let{fft_length:r,feature_size:s,min_frequency:n,max_frequency:o,sampling_rate:a,frame_length:i}=this.config,l=it(Math.floor(1+r/2),s,n,o,a,null,"htk",!1);this.mel_filters=l,this.window=dt(i,"hann")}async _extract_fbank_features(e,r){return nt(e,this.window,this.config.frame_length,this.config.hop_length,{fft_length:this.config.fft_length,center:!1,onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,remove_dc_offset:!1,transpose:!0})}async _call(e,{max_length:r=48e4,truncation:s=!0,padding:n=!0,pad_to_multiple_of:o=128}={}){if(Ae(e,"Gemma3nAudioFeatureExtractor"),s&&e.length>r&&(e=e.slice(0,r)),n&&e.length%o!==0){let l=o-e.length%o,c=new Float64Array(e.length+l);c.set(e),this.config.padding_value!==0&&c.fill(this.config.padding_value,e.length),e=c}let a=await this._extract_fbank_features(e,this.config.max_length),i=Ye([1,a.dims[0]],!0);return{input_features:a.unsqueeze_(0),input_features_mask:i}}};var Fo=class extends Uo{async _extract_fbank_features(e,r){let{frame_length:s,hop_length:n,fft_length:o}=this.config,a=Math.floor(s/2),i=Math.floor((e.length+a-(s+1))/n)+1;return nt(e,this.window,s,n,{fft_length:o,center:!0,pad_mode:"semicausal",onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,mel_floor_mode:"add",remove_dc_offset:!1,transpose:!0,max_num_frames:i})}async _call(e,r={}){Ae(e,"Gemma4AudioFeatureExtractor");let s=e.length,n=await super._call(e,r),{input_features:o}=n,[,a,i]=o.dims,{frame_length:l,hop_length:c}=this.config,u=Math.floor(l/2),f=l+1,m=new Uint8Array(s+u+(r.pad_to_multiple_of??128));m.fill(1,u,u+s);let _=new Uint8Array(a);for(let x=0;x<a;++x)_[x]=m[x*c+f-1]?1:0;let w=o.data;for(let x=0;x<a;++x)_[x]||w.fill(0,x*i,(x+1)*i);return n.input_features_mask=new N("bool",_,[1,a]),n}};var Fm=class extends Ee{constructor(e){super(e);let{n_fft:r,win_length:s,n_mels:n,sample_rate:o}=e.melspec_kwargs;this.mel_filters=it(Math.floor(1+r/2),n,0,o/2,o,null,"htk");let a=dt(s,"hann");this.window=new Float64Array(r);let i=Math.floor((r-s)/2);this.window.set(a,i)}async _call(e){Ae(e,"GraniteSpeechFeatureExtractor");let{n_fft:r,hop_length:s,n_mels:n}=this.config.melspec_kwargs,o=1+Math.floor((e.length-1)/s),a=o-o%2;return{input_features:(await nt(e,this.window,r,s,{power:2,mel_filters:this.mel_filters,log_mel:"log10_max_norm",transpose:!0,max_num_frames:a,do_pad:!1})).view(-1,2*n).unsqueeze_(0)}}};var Gm=class extends Ee{async _call(e){Ae(e,"MoonshineFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new N("float32",e,r)}}};var Go=class extends Ee{async _call(e){Ae(e,"PyAnnoteFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,1,e.length];return{input_values:new N("float32",e,r)}}samples_to_frames(e){return(e-this.config.offset)/this.config.step}post_process_speaker_diarization(e,r){let s=r/this.samples_to_frames(r)/this.config.sampling_rate,n=[];for(let o of e.tolist()){let a=[],i=-1;for(let l=0;l<o.length;++l){let c=Re(o[l]),[u,f]=Pe(c),[m,_]=[l,l+1];f!==i?(i=f,a.push({id:f,start:m,end:_,score:u})):(a.at(-1).end=_,a.at(-1).score+=u)}n.push(a.map(({id:l,start:c,end:u,score:f})=>({id:l,start:c*s,end:u*s,confidence:f/(u-c)})))}return n}};var jm=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=it(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=dt(400,"povey",{periodic:!1})}async _extract_fbank_features(e,r){return e=e.map(s=>s*32768),nt(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:s=2,do_normalize_per_mel_bins:n=!0,return_attention_mask:o=!0}={}){Ae(e,"SeamlessM4TFeatureExtractor");let a=await this._extract_fbank_features(e,this.config.max_length);if(n){let[w,x]=a.dims,v=a.data;for(let b=0;b<x;++b){let A=0;for(let O=0;O<w;++O)A+=v[O*x+b];let S=A/w,M=0;for(let O=0;O<w;++O)M+=(v[O*x+b]-S)**2;M/=w-1;let I=Math.sqrt(M+1e-7);for(let O=0;O<w;++O){let k=O*x+b;v[k]=(v[k]-S)/I}}}let i;if(r){let[w,x]=a.dims,v=a.data,b=w%s;if(b>0){let A=new Float32Array(x*(w+b));A.set(v),A.fill(this.config.padding_value,v.length);let S=w+b;a=new N(a.type,A,[S,x]),o&&(i=new N("int64",new BigInt64Array(S),[1,S]),i.data.fill(1n,0,w))}}let[l,c]=a.dims,u=this.config.stride;if(l%u!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${u}).`);let m=a.view(1,Math.floor(l/u),c*u),_={input_features:m};if(o){let w=m.dims[1],x=new BigInt64Array(w);if(i){let v=i.data;for(let b=1,A=0;b<l;b+=u,++A)x[A]=v[b]}else x.fill(1n);_.attention_mask=new N("int64",x,[1,w])}return _}};var qm=class extends Bo{};var Wm=class extends Ee{};var Vm=class extends Ee{_zero_mean_unit_var_norm(e){let s=e.reduce((o,a)=>o+a,0)/e.length,n=e.reduce((o,a)=>o+(a-s)**2,0)/e.length;return e.map(o=>(o-s)/Math.sqrt(n+1e-7))}async _call(e){Ae(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));let s=[1,r.length];return{input_values:new N("float32",r,s),attention_mask:new N("int64",new BigInt64Array(r.length).fill(1n),s)}}};var Hm=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=it(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=dt(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(e){return e=e.map(r=>r*32768),nt(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(e){Ae(e,"WeSpeakerFeatureExtractor");let r=(await this._extract_fbank_features(e)).unsqueeze_(0);if(this.config.fbank_centering_span===null){let s=r.mean(1).data,n=r.data,[o,a,i]=r.dims;for(let l=0;l<o;++l){let c=l*a*i,u=l*i;for(let f=0;f<a;++f){let m=c+f*i;for(let _=0;_<i;++_)n[m+_]-=s[u+_]}}}return{input_features:r}}};var Xm=class extends Ee{constructor(e){super(e),this.config.mel_filters??=it(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=dt(this.config.n_fft,"hann")}async _extract_fbank_features(e,{center:r=!0}={}){let{n_fft:s,hop_length:n,mel_filters:o,global_log_mel_max:a}=this.config,i=Math.floor(r?e.length/n:(e.length-s)/n);return await nt(e,this.window,s,n,{power:2,mel_filters:o,log_mel:"log10_max_norm",max_log_mel:a,center:r,max_num_frames:i,do_pad:!1})}async _call(e,{center:r=!0}={}){return Ae(e,"VoxtralRealtimeFeatureExtractor"),{input_features:(await this._extract_fbank_features(e,{center:r})).unsqueeze_(0)}}};var Km=class extends Ee{constructor(e){super(e),this.config.mel_filters??=it(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=dt(this.config.n_fft,"hann")}async _extract_fbank_features(e){return await nt(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10_max_norm",max_num_frames:Math.min(Math.floor(e.length/this.config.hop_length),this.config.nb_max_frames)})}async _call(e,{max_length:r=null}={}){Ae(e,"WhisperFeatureExtractor");let s,n=r??this.config.n_samples;return e.length>n?(e.length>this.config.n_samples&&ee.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),s=e.slice(0,n)):(s=new Float32Array(n),s.set(e)),{input_features:(await this._extract_fbank_features(s)).unsqueeze_(0)}}};var Ge=class{static async from_pretrained(e,r={}){let s=await st(e,No,!0,r),n=s.feature_extractor_type,o=jo[n];if(!o)throw new Error(`Unknown feature_extractor_type: '${n}'. Please report this at ${Hr}.`);return new o(s)}};var Ym=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e,r=null){let s=this.tokenizer(e),n=r?await this.feature_extractor(r):{};return{...s,...n}}};var fN=new Set(["ja","zh"]),Qm=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!0;get_decoder_prompt_ids(e="en"){let r=["\u2581","<|startofcontext|>","<|startoftranscript|>","<|emo:undefined|>",`<|${e}|>`,`<|${e}|>`,"<|pnc|>","<|noitn|>","<|notimestamp|>","<|nodiarize|>"];return this.tokenizer.convert_tokens_to_ids(r)}static join_chunks(e,r="en"){let s=e.filter(a=>a&&a.trim());if(s.length===0)return"";let n=fN.has(r)?"":" ";return[s[0].trimEnd(),...s.slice(1).map(a=>a.trim())].join(n)}async _call(e){return await this.feature_extractor(e)}};var Fc={};var Ls,yT,Xr;if(ie.IS_WEB_ENV)Ls=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this environment.");return new self.OffscreenCanvas(t,e)},Xr=self.createImageBitmap,yT=self.ImageData;else if(Fc)Xr=async t=>{let r=(await t.metadata()).channels,{data:s,info:n}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),o=new Je(new Uint8ClampedArray(s),n.width,n.height,n.channels);return r!==void 0&&r!==n.channels&&o.convert(r),o};else throw new Error("Unable to load image processing library.");var dN={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},mN=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]),Je=class t{constructor(e,r,s,n){this.data=e,this.width=r,this.height=s,this.channels=n}get size(){return[this.width,this.height]}static async read(e){if(e instanceof t)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);if(e instanceof Blob)return await this.fromBlob(e);if(typeof HTMLCanvasElement<"u"&&e instanceof HTMLCanvasElement||typeof OffscreenCanvas<"u"&&e instanceof OffscreenCanvas)return this.fromCanvas(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!ie.IS_WEB_ENV)throw new Error("fromCanvas() is only supported in browser environments.");let s=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new t(s,e.width,e.height,4)}static async fromURL(e){let r=await Rr(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);let s=await r.blob();return this.fromBlob(s)}static async fromBlob(e){if(ie.IS_WEB_ENV){let r=await Xr(e),s=Ls(r.width,r.height).getContext("2d");return s.drawImage(r,0,0),new this(s.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{let r=Fc(await e.arrayBuffer());return await Xr(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new t(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;let e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,s=0;r<this.data.length;r+=this.channels){let n=this.data[r],o=this.data[r+1],a=this.data[r+2];e[s++]=Math.round(.2989*n+.587*o+.114*a)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let r=0,s=0;r<this.data.length;++r)e[s++]=this.data[r],e[s++]=this.data[r],e[s++]=this.data[r];break;case 4:for(let r=0,s=0;r<this.data.length;r+=4)e[s++]=this.data[r],e[s++]=this.data[r+1],e[s++]=this.data[r+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let r=0,s=0;r<this.data.length;++r)e[s++]=this.data[r],e[s++]=this.data[r],e[s++]=this.data[r],e[s++]=255;break;case 3:for(let r=0,s=0;r<this.data.length;r+=3)e[s++]=this.data[r],e[s++]=this.data[r+1],e[s++]=this.data[r+2],e[s++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}putAlpha(e){if(e.width!==this.width||e.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${e.width}x${e.height}`);if(e.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${e.channels}`);let r=this.data,s=e.data,n=this.width*this.height;if(this.channels===3){let o=new Uint8ClampedArray(n*4);for(let a=0,i=0,l=0;a<n;++a)o[l++]=r[i++],o[l++]=r[i++],o[l++]=r[i++],o[l++]=s[a];return this._update(o,this.width,this.height,4)}else if(this.channels===4){for(let o=0;o<n;++o)r[4*o+3]=s[o];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(e,r,{resample:s=2}={}){if(this.width===e&&this.height===r)return this;let n=dN[s]??s,o=Sf(e),a=Sf(r);if(o&&a)return this;if(o?e=r/this.height*this.width:a&&(r=e/this.width*this.height),ie.IS_WEB_ENV){let i=this.channels,l=this.toCanvas(),c=Ls(e,r).getContext("2d");return c.drawImage(l,0,0,e,r),new t(c.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();switch(n){case"box":case"hamming":(n==="box"||n==="hamming")&&(ee.warn(`Resampling method ${n} is not yet supported. Using bilinear instead.`),n="bilinear");case"nearest":case"bilinear":case"bicubic":i=i.affine([e/this.width,0,0,r/this.height],{interpolator:n});break;case"lanczos":i=i.resize({width:e,height:r,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${n} is not supported.`)}return await Xr(i)}}async pad([e,r,s,n]){if(e=Math.max(e,0),r=Math.max(r,0),s=Math.max(s,0),n=Math.max(n,0),e===0&&r===0&&s===0&&n===0)return this;if(ie.IS_WEB_ENV){let o=this.channels,a=this.toCanvas(),i=this.width+e+r,l=this.height+s+n,c=Ls(i,l).getContext("2d");return c.drawImage(a,0,0,this.width,this.height,e,s,this.width,this.height),new t(c.getImageData(0,0,i,l).data,i,l,4).convert(o)}else{let o=this.toSharp().extend({left:e,right:r,top:s,bottom:n});return await Xr(o)}}async crop([e,r,s,n]){if(e=Math.max(e,0),r=Math.max(r,0),s=Math.min(s,this.width-1),n=Math.min(n,this.height-1),e===0&&r===0&&s===this.width-1&&n===this.height-1)return this;let o=s-e+1,a=n-r+1;if(ie.IS_WEB_ENV){let i=this.channels,l=this.toCanvas(),c=Ls(o,a).getContext("2d");return c.drawImage(l,e,r,o,a,0,0,o,a),new t(c.getImageData(0,0,o,a).data,o,a,4).convert(i)}else{let i=this.toSharp().extract({left:e,top:r,width:o,height:a});return await Xr(i)}}async center_crop(e,r){if(this.width===e&&this.height===r)return this;let s=(this.width-e)/2,n=(this.height-r)/2;if(ie.IS_WEB_ENV){let o=this.channels,a=this.toCanvas(),i=Ls(e,r).getContext("2d"),l=0,c=0,u=0,f=0;return s>=0?l=s:u=-s,n>=0?c=n:f=-n,i.drawImage(a,l,c,e,r,u,f,e,r),new t(i.getImageData(0,0,e,r).data,e,r,4).convert(o)}else{let o=this.toSharp();if(s>=0&&n>=0)o=o.extract({left:Math.floor(s),top:Math.floor(n),width:e,height:r});else if(s<=0&&n<=0){let a=Math.floor(-n),i=Math.floor(-s);o=o.extend({top:a,left:i,right:e-this.width-i,bottom:r-this.height-a})}else{let a=[0,0],i=0;n<0?(a[0]=Math.floor(-n),a[1]=r-this.height-a[0]):i=Math.floor(n);let l=[0,0],c=0;s<0?(l[0]=Math.floor(-s),l[1]=e-this.width-l[0]):c=Math.floor(s),o=o.extend({top:a[0],bottom:a[1],left:l[0],right:l[1]}).extract({left:c,top:i,width:e,height:r})}return await Xr(o)}}async toBlob(e="image/png",r=1){if(!ie.IS_WEB_ENV)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new N("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!ie.IS_WEB_ENV)throw new Error("toCanvas() is only supported in browser environments.");let e=this.clone().rgba(),r=Ls(e.width,e.height),s=new yT(e.data,e.width,e.height);return r.getContext("2d").putImageData(s,0,0),r}split(){let{data:e,width:r,height:s,channels:n}=this,o=e.constructor,a=e.length/n,i=Array.from({length:n},()=>new o(a));for(let l=0;l<a;++l){let c=n*l;for(let u=0;u<n;++u)i[u][l]=e[c+u]}return i.map(l=>new t(l,r,s,1))}_update(e,r,s,n=null){return this.data=e,this.width=r,this.height=s,n!==null&&(this.channels=n),this}clone(){return new t(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(ie.IS_WEB_ENV){if(ie.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");let r=e.split(".").pop().toLowerCase(),s=mN.get(r)??"image/png",n=await this.toBlob(s);return Bc(e,n)}else if(ie.IS_FS_AVAILABLE)await this.toSharp().toFile(e);else throw new Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(ie.IS_WEB_ENV)throw new Error("toSharp() is only supported in server-side environments.");return Fc(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}},_N=Je.read.bind(Je);function bT(t,e,r=0,s=null){let n=t/e,o=pA(n)*e;return s!==null&&o>s&&(o=Math.floor(n)*e),o<r&&(o=Math.ceil(n)*e),o}function vT([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}function Jm([t,e,r,s]){return[t-r/2,e-s/2,t+r/2,e+s/2]}function Kr(t,e=.5,r=null,s=!1){let n=t.logits,o=t.pred_boxes,[a,i,l]=n.dims;if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=[];for(let u=0;u<a;++u){let f=r!==null?r[u]:null,m={boxes:[],classes:[],scores:[]},_=n[u],w=o[u];for(let x=0;x<i;++x){let v=_[x],b=[],A;if(s){A=v.sigmoid().data;for(let S=0;S<A.length;++S)A[S]>e&&b.push(S)}else{let S=Pe(v.data)[1];if(S===l-1||(A=Re(v.data),A[S]<e))continue;b.push(S)}for(let S of b){let M=w[x].data;M=Jm(M),f!==null&&(M=M.map((I,O)=>I*f[(O+1)%2])),m.boxes.push(M),m.classes.push(S),m.scores.push(A[S])}}c.push(m)}return c}function Gc(t,e=null){let r=t.logits,s=r.dims[0];if(e!==null&&e.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let n=[];for(let o=0;o<s;++o){let a=e!==null?e[o]:null,i=r[o];a!==null&&(i=Rd(i,a,"bilinear",!1));let[l,c]=a??i.dims.slice(-2),u=new N("int32",new Int32Array(l*c),[l,c]),f=i[0].data,m=u.data;for(let x=1;x<i.dims[0];++x){let v=i[x].data;for(let b=0;b<v.length;++b)v[b]>f[b]&&(f[b]=v[b],m[b]=x)}let _=new Array(i.dims[0]);for(let x=0;x<m.length;++x){let v=m[x];_[v]=v}let w=_.filter(x=>x!==void 0);n.push({segmentation:u,labels:w})}return n}function hN(t,e,r,s){let n=[],o=[],a=[];for(let i=0;i<t.dims[0];++i){let l=t[i],c=e[i],u=Pe(l.data)[1];if(u===s)continue;let m=Re(l.data)[u];m>r&&(n.push(c),o.push(m),a.push(u))}return[n,o,a]}function gN(t,e,r,s=.5,n=.8){let o=[],a=0,i=0,l=e[r].data;for(let u=0;u<t.length;++u)t[u]===r&&(o.push(u),++a),l[u]>=s&&++i;let c=a>0&&i>0;return c&&(c=a/i>n),[c,o]}function wN(t,e,r,s,n,o=null,a=null){let[i,l]=a??t[0].dims,c=new N("int32",new Int32Array(i*l),[i,l]),u=[];if(a!==null)for(let x=0;x<t.length;++x)t[x]=Rd(t[x],a,"bilinear",!1);let f=new Int32Array(t[0].data.length),m=new Float32Array(t[0].data.length);for(let x=0;x<t.length;++x){let v=e[x],b=t[x].data;for(let A=0;A<b.length;++A)b[A]*=v,b[A]>m[A]&&(f[A]=x,m[A]=b[A])}let _=0,w=c.data;for(let x=0;x<r.length;++x){let v=r[x],[b,A]=gN(f,t,x,s,n);if(b){++_;for(let S of A)w[S]=_;u.push({id:_,label_id:v,score:e[x]})}}return[c,u]}function wn(t,e,r=28,s=3136,n=784*1280,o=1){if(t<r||e<r){let l=Math.max(r/t,r/e);t=Math.round(t*l),e=Math.round(e*l)}if(Math.max(t,e)/Math.min(t,e)>200)throw new Error(`absolute aspect ratio must be smaller than 200, got ${Math.max(t,e)/Math.min(t,e)}`);let a=Math.round(t/r)*r,i=Math.round(e/r)*r;if(o*a*i>n){let l=Math.sqrt(o*t*e/n);a=Math.max(r,Math.floor(t/l/r)*r),i=Math.max(r,Math.floor(e/l/r)*r)}else if(o*a*i<s){let l=Math.sqrt(s/(o*t*e));a=Math.ceil(t*l/r)*r,i=Math.ceil(e*l/r)*r}return[i,a]}function jc(t,e=.5,r=.5,s=.8,n=null,o=null){n===null&&(ee.warn("`label_ids_to_fuse` unset. No instance will be fused."),n=new Set);let a=t.class_queries_logits??t.logits,l=(t.masks_queries_logits??t.pred_masks).sigmoid(),[c,u,f]=a.dims;if(f-=1,o!==null&&o.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let m=[];for(let _=0;_<c;++_){let w=o!==null?o[_]:null,x=a[_],v=l[_],[b,A,S]=hN(x,v,e,f);if(S.length===0){let[O,k]=w??v.dims.slice(-2),R=new N("int32",new Int32Array(O*k).fill(-1),[O,k]);m.push({segmentation:R,segments_info:[]});continue}let[M,I]=wN(b,A,S,r,s,n,w);m.push({segmentation:M,segments_info:I})}return m}function qc(t,e=.5,r=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}var X=class extends We{constructor(e){super(),this.image_mean=e.image_mean??e.mean,this.image_std=e.image_std??e.std,this.resample=e.resample??2,this.do_rescale=e.do_rescale??!0,this.rescale_factor=e.rescale_factor??1/255,this.do_normalize=e.do_normalize,this.do_thumbnail=e.do_thumbnail,this.size=e.size??e.image_size,this.do_resize=e.do_resize??this.size!==void 0,this.size_divisibility=e.size_divisibility??e.size_divisor,this.do_center_crop=e.do_center_crop,this.crop_size=e.crop_size,this.do_convert_rgb=e.do_convert_rgb??!0,this.do_crop_margin=e.do_crop_margin,this.pad_size=e.pad_size,this.do_pad=e.do_pad,this.min_pixels=e.min_pixels,this.max_pixels=e.max_pixels,this.do_pad&&!this.pad_size&&!this.size_divisibility&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=e.do_flip_channel_order??!1,this.config=e}async thumbnail(e,r,s=2){let n=e.height,o=e.width,a=r.height,i=r.width,l=Math.min(n,a),c=Math.min(o,i);return l===n&&c===o?e:(n>o?c=Math.floor(o*l/n):o>n&&(l=Math.floor(n*c/o)),await e.resize(c,l,{resample:s}))}async crop_margin(e,r=200){let s=e.clone().grayscale(),n=yo(s.data)[0],a=Pe(s.data)[0]-n;if(a===0)return e;let i=r/255,l=s.width,c=s.height,u=0,f=0,m=s.data;for(let _=0;_<s.height;++_){let w=_*s.width;for(let x=0;x<s.width;++x)(m[w+x]-n)/a<i&&(l=Math.min(l,x),c=Math.min(c,_),u=Math.max(u,x),f=Math.max(f,_))}return e=await e.crop([l,c,u,f]),e}pad_image(e,r,s,{mode:n="constant",center:o=!1,constant_values:a=0}={}){let[i,l,c]=r,u,f;if(typeof s=="number"?(u=s,f=s):s==="square"?u=f=Math.max(i,l):(u=s.width,f=s.height),u!==l||f!==i){let m=new Float32Array(u*f*c);if(Array.isArray(a))for(let x=0;x<m.length;++x)m[x]=a[x%c];else a!==0&&m.fill(a);let[_,w]=o?[Math.floor((u-l)/2),Math.floor((f-i)/2)]:[0,0];for(let x=0;x<i;++x){let v=(x+w)*u,b=x*l;for(let A=0;A<l;++A){let S=(v+A+_)*c,M=(b+A)*c;for(let I=0;I<c;++I)m[S+I]=e[M+I]}}if(n==="symmetric"){if(o)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");let x=i-1,v=l-1;for(let b=0;b<f;++b){let A=b*u,S=an(b,x)*l;for(let M=0;M<u;++M){if(b<i&&M<l)continue;let I=(A+M)*c,O=(S+an(M,v))*c;for(let k=0;k<c;++k)m[I+k]=e[O+k]}}}e=m,r=[f,u,c]}return[e,r]}rescale(e){for(let r=0;r<e.length;++r)e[r]=this.rescale_factor*e[r]}get_resize_output_image_size(e,r){let[s,n]=e.size,o,a;if(this.do_thumbnail){let{height:i,width:l}=r;o=Math.min(i,l)}else Number.isInteger(r)?(o=r,a=this.config.max_size??o):r!==void 0&&(o=r.shortest_edge,a=r.longest_edge);if(o!==void 0||a!==void 0){let i=o===void 0?1:Math.max(o/s,o/n),l=s*i,c=n*i,u=a===void 0?1:Math.min(a/l,a/c),f=Math.floor(Number((l*u).toFixed(2))),m=Math.floor(Number((c*u).toFixed(2)));return this.size_divisibility!==void 0&&([f,m]=vT([f,m],this.size_divisibility)),[f,m]}else if(r!==void 0&&r.width!==void 0&&r.height!==void 0){let i=r.width,l=r.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let c=l/n,u=i/s;Math.abs(1-u)<Math.abs(1-c)?c=u:u=c,l=bT(c*n,this.config.ensure_multiple_of),i=bT(u*s,this.config.ensure_multiple_of)}return[i,l]}else{if(this.size_divisibility!==void 0)return vT([s,n],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){let[r,s]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,s,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:s=null,do_convert_rgb:n=null,do_convert_grayscale:o=null,do_flip_channel_order:a=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));let[i,l]=e.size;if(n??this.do_convert_rgb?e=e.rgb():o&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let _,w;Number.isInteger(this.crop_size)?(_=this.crop_size,w=this.crop_size):(_=this.crop_size.width,w=this.crop_size.height),e=await e.center_crop(_,w)}let c=[e.height,e.width],u=Float32Array.from(e.data),f=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(u),r??this.do_normalize){let _=this.image_mean;Array.isArray(this.image_mean)||(_=new Array(e.channels).fill(_));let w=this.image_std;if(Array.isArray(this.image_std)||(w=new Array(e.channels).fill(w)),_.length!==e.channels||w.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${_.length}) and \`image_std\` (${w.length}) must match the number of channels in the image (${e.channels}).`);for(let x=0;x<u.length;x+=e.channels)for(let v=0;v<e.channels;++v)u[x+v]=(u[x+v]-_[v])/w[v]}if(s??this.do_pad){if(this.pad_size)[u,f]=this.pad_image(u,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){let _=Math.ceil(f[1]/this.size_divisibility)*this.size_divisibility,w=Math.ceil(f[0]/this.size_divisibility)*this.size_divisibility;[u,f]=this.pad_image(u,f,{width:_,height:w})}}if(a??this.do_flip_channel_order){if(f[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let _=0;_<u.length;_+=3){let w=u[_];u[_]=u[_+2],u[_+2]=w}}let m=new N("float32",u,f).permute(2,0,1);return{original_size:[l,i],reshaped_input_size:c,pixel_values:m}}async _call(e,...r){Array.isArray(e)||(e=[e]);let s=await Promise.all(e.map(o=>this.preprocess(o)));return{pixel_values:yt(s.map(o=>o.pixel_values),0),original_sizes:s.map(o=>o.original_size),reshaped_input_sizes:s.map(o=>o.reshaped_input_size)}}static async from_pretrained(e,r={}){let s=await st(e,Er,!0,r);return new this(s)}};var kn={};hs(kn,{BeitFeatureExtractor:()=>Zm,BitImageProcessor:()=>e_,CHMv2ImageProcessor:()=>r_,CLIPFeatureExtractor:()=>s_,CLIPImageProcessor:()=>Wc,ChineseCLIPFeatureExtractor:()=>t_,ConvNextFeatureExtractor:()=>n_,ConvNextImageProcessor:()=>Vc,DINOv3ViTImageProcessor:()=>i_,DPTFeatureExtractor:()=>c_,DPTImageProcessor:()=>Kc,DeiTFeatureExtractor:()=>o_,DeiTImageProcessor:()=>Hc,DetrFeatureExtractor:()=>a_,DetrImageProcessor:()=>Xc,DonutFeatureExtractor:()=>l_,DonutImageProcessor:()=>xn,EfficientNetImageProcessor:()=>u_,GLPNFeatureExtractor:()=>d_,Gemma3ImageProcessor:()=>p_,Gemma4ImageProcessor:()=>qo,Glm46VImageProcessor:()=>f_,GroundingDinoImageProcessor:()=>m_,Idefics3ImageProcessor:()=>Yc,ImageFeatureExtractor:()=>X,ImageProcessor:()=>X,JinaCLIPImageProcessor:()=>h_,Lfm2VlImageProcessor:()=>g_,LlavaOnevisionImageProcessor:()=>w_,Mask2FormerImageProcessor:()=>y_,MaskFormerFeatureExtractor:()=>x_,MaskFormerImageProcessor:()=>yn,MobileNetV1FeatureExtractor:()=>b_,MobileNetV1ImageProcessor:()=>Qc,MobileNetV2FeatureExtractor:()=>v_,MobileNetV2ImageProcessor:()=>Jc,MobileNetV3FeatureExtractor:()=>k_,MobileNetV3ImageProcessor:()=>Zc,MobileNetV4FeatureExtractor:()=>E_,MobileNetV4ImageProcessor:()=>eu,MobileViTFeatureExtractor:()=>A_,MobileViTImageProcessor:()=>tu,NougatImageProcessor:()=>T_,OwlViTFeatureExtractor:()=>M_,OwlViTImageProcessor:()=>bn,Owlv2ImageProcessor:()=>S_,Phi3VImageProcessor:()=>C_,PixtralImageProcessor:()=>P_,PvtImageProcessor:()=>L_,Qwen2VLImageProcessor:()=>Wo,RTDetrImageProcessor:()=>z_,Sam2ImageProcessor:()=>Vo,Sam3ImageProcessor:()=>Vo,SamImageProcessor:()=>Vo,SapiensFeatureExtractor:()=>N_,SapiensImageProcessor:()=>ru,SegformerFeatureExtractor:()=>R_,SegformerImageProcessor:()=>su,SiglipImageProcessor:()=>$_,SmolVLMImageProcessor:()=>Yc,Swin2SRImageProcessor:()=>D_,VLMImageProcessor:()=>__,ViTFeatureExtractor:()=>B_,ViTImageProcessor:()=>nu,VitMatteImageProcessor:()=>U_,VitPoseImageProcessor:()=>F_,YolosFeatureExtractor:()=>G_,YolosImageProcessor:()=>ou});var Zm=class extends X{};var e_=class extends X{};var t_=class extends X{};var r_=class extends X{};var Wc=class extends X{},s_=class extends Wc{};var Vc=class extends X{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){let r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){let s=Math.floor(r/this.crop_pct),[n,o]=this.get_resize_output_image_size(e,{shortest_edge:s});e=await e.resize(n,o,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}},n_=class extends Vc{};var Hc=class extends X{},o_=class extends Hc{};var Xc=class extends X{async _call(e){let r=await super._call(e),s=[r.pixel_values.dims[0],64,64],n=Ye(s,1n);return{...r,pixel_mask:n}}post_process_object_detection(...e){return Kr(...e)}post_process_panoptic_segmentation(...e){return jc(...e)}post_process_instance_segmentation(...e){return qc(...e)}},a_=class extends Xc{};var i_=class extends X{};var xn=class extends X{pad_image(e,r,s,n={}){let[o,a,i]=r,l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(i).fill(l));let c=this.image_std;Array.isArray(c)||(c=new Array(i).fill(l));let u=l.map((f,m)=>-f/c[m]);return super.pad_image(e,r,s,{center:!0,constant_values:u,...n})}},l_=class extends xn{};var Kc=class extends X{},c_=class extends Kc{};var u_=class extends X{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}};var p_=class extends X{};function xN(t,e,r,s,n){let o=s*r**2,a=Math.sqrt(o/(t*e)),i=n*r,l=Math.floor(a*t/i)*i,c=Math.floor(a*e/i)*i;if(l===0&&c===0)throw new Error(`Attempting to resize to a 0 x 0 image. 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We{constructor(e){super(),this.config=e,this.patch_size=e.patch_size??16,this.max_soft_tokens=e.max_soft_tokens??280,this.pooling_kernel_size=e.pooling_kernel_size??3,this.resample=e.resample??3,this.rescale_factor=e.rescale_factor??1/255,this.do_rescale=e.do_rescale??!0,this.do_resize=e.do_resize??!0,this.do_convert_rgb=e.do_convert_rgb??!0}async _call(e){Array.isArray(e)||(e=[e]);let{patch_size:r,pooling_kernel_size:s}=this,n=this.max_soft_tokens*s**2,o=[],a=[],i=[];for(let l of e){if(this.do_convert_rgb&&(l=l.rgb()),this.do_resize){let[_,w]=xN(l.height,l.width,r,n,s);(_!==l.height||w!==l.width)&&(l=await l.resize(w,_,{resample:this.resample}))}let c=Float32Array.from(l.data);if(this.do_rescale)for(let _=0;_<c.length;++_)c[_]*=this.rescale_factor;let{patches:u,positions:f,num_soft_tokens:m}=yN(c,l.height,l.width,l.channels,r,n,s);o.push(u),a.push(f),i.push(m)}return{pixel_values:yt(o,0),image_position_ids:yt(a,0),num_soft_tokens_per_image:i}}};var Wo=class extends X{constructor(e){super(e),this.min_pixels=e.min_pixels??e.size?.shortest_edge,this.max_pixels=e.max_pixels??e.size?.longest_edge,this.patch_size=e.patch_size,this.merge_size=e.merge_size}get_resize_output_image_size(e,r){let s=this.patch_size*this.merge_size;return wn(e.height,e.width,s,this.min_pixels,this.max_pixels)}async _call(e,...r){let{pixel_values:s,original_sizes:n,reshaped_input_sizes:o}=await super._call(e,...r),a=s,{temporal_patch_size:i,merge_size:l,patch_size:c}=this.config;a.dims[0]===1&&(a=ve(Array.from({length:i},()=>a),0));let u=a.dims[0]/i,f=a.dims[1],m=Math.floor(a.dims[2]/c),_=Math.floor(a.dims[3]/c),w=a.view(u,i,f,Math.floor(m/l),l,c,Math.floor(_/l),l,c).permute(0,3,6,4,7,2,1,5,8).view(u*m*_,f*i*c*c),x=new N("int64",[u,m,_],[1,3]);return{pixel_values:w,image_grid_thw:x,original_sizes:n,reshaped_input_sizes:o}}};var f_=class extends Wo{get_resize_output_image_size(e,r){let s=this.patch_size*this.merge_size,n=this.config.temporal_patch_size??2;return wn(e.height,e.width,s,this.min_pixels,this.max_pixels,n)}};var d_=class extends X{};var m_=class extends X{async _call(e){let r=await super._call(e),s=r.pixel_values.dims,n=et([s[0],s[2],s[3]]);return{...r,pixel_mask:n}}};var Yc=class extends X{constructor(e){super(e),this.do_image_splitting=e.do_image_splitting??!0,this.max_image_size=e.max_image_size}get_resize_for_vision_encoder(e,r){let[s,n]=e.dims.slice(-2),o=n/s;return n>=s?(n=Math.ceil(n/r)*r,s=Math.floor(n/o),s=Math.ceil(s/r)*r):(s=Math.ceil(s/r)*r,n=Math.floor(s*o),n=Math.ceil(n/r)*r),{height:s,width:n}}async _call(e,{do_image_splitting:r=null,return_row_col_info:s=!1}={}){let n;if(!Array.isArray(e))n=[[e]];else{if(e.length===0||!e[0])throw new Error("No images provided.");Array.isArray(e[0])?n=e:n=[e]}let o=[],a=[],i=[],l=[],c=[];for(let b of n){let A=await Promise.all(b.map(I=>this.preprocess(I)));l.push(...A.map(I=>I.original_size)),c.push(...A.map(I=>I.reshaped_input_size)),A.forEach(I=>I.pixel_values.unsqueeze_(0));let{longest_edge:S}=this.max_image_size,M;if(r??this.do_image_splitting){let I=new Array(A.length),O=new Array(A.length);M=await Promise.all(A.map(async(k,R)=>{let G=this.get_resize_for_vision_encoder(k.pixel_values,S),F=await vt(k.pixel_values,{size:[G.height,G.width]}),{frames:K,num_splits_h:V,num_splits_w:Q}=await this.split_image(F,this.max_image_size);return I[R]=V,O[R]=Q,ve(K,0)})),a.push(I),i.push(O)}else{let I=[S,S];M=await Promise.all(A.map(O=>vt(O.pixel_values,{size:I}))),a.push(new Array(A.length).fill(0)),i.push(new Array(A.length).fill(0))}o.push(ve(M,0))}let u=o.length,[f,m,_,w]=o[0].dims,x,v;if(u===1)x=o[0].unsqueeze_(0),v=Ye([u,f,_,w],!0);else{let b=Math.max(...o.map(M=>M.dims.at(0)));v=Ye([u,b,_,w],!0);let A=v.data,S=b*_*w;for(let M=0;M<u;++M){let I=o[M].dims[0];if(I<b){o[M]=ve([o[M],Ye([b-I,m,_,w],0)],0);let O=M*S+I*_*w,k=(M+1)*S;A.fill(!1,O,k)}}x=yt(o,0)}return{pixel_values:x,pixel_attention_mask:v,original_sizes:l,reshaped_input_sizes:c,...s?{rows:a,cols:i}:{}}}async split_image(e,{longest_edge:r}){let s=r,n=r,o=[],[a,i]=e.dims.slice(-2),l=0,c=0;if(a>s||i>n){l=Math.ceil(a/s),c=Math.ceil(i/n);let u=Math.ceil(a/l),f=Math.ceil(i/c);for(let w=0;w<l;++w)for(let x=0;x<c;++x){let v,b,A,S;w===l-1?(b=a-u,S=a):(b=w*u,S=(w+1)*u),x===c-1?(v=i-f,A=i):(v=x*f,A=(x+1)*f);let O=await zc(e,[b,v],[S,A],[2,3]);o.push(O)}let m=s,_=n;(a!==m||i!==_)&&(e=await vt(e,{size:[m,_]}))}return o.push(e),{frames:o,num_splits_h:l,num_splits_w:c}}};var __=class extends X{constructor(e){super({do_pad:!0,pad_size:{width:e.image_size,height:e.image_size},...e}),this.constant_values=this.config.background_color.map(r=>r*this.rescale_factor)}pad_image(e,r,s,n){return super.pad_image(e,r,s,{constant_values:this.constant_values,center:!0,...n})}};var h_=class extends X{constructor(e){let{resize_mode:r,fill_color:s,interpolation:n,size:o,...a}=e,i=r==="squash"?{width:o,height:o}:r==="shortest"?{shortest_edge:o}:{longest_edge:o},l=n==="bicubic"?3:2;super({...a,size:i,resample:l,do_center_crop:!0,crop_size:o,do_normalize:!0})}};function kT(t,e){return Math.round(t/e)*e}function bN(t,e,r,s,n){let o=1/0,a=[1,1],i=r*s;for(let l of e){let c=Math.abs(t-l[0]/l[1]);c<o?(o=c,a=l):c===o&&i>.5*n*n*l[0]*l[1]&&(a=l)}return a}function vN(t,e){let r=[],s=new Set;for(let n=t;n<=e;++n)for(let o=1;o<=n;++o)for(let a=1;a<=n;++a){let i=o*a;if(i>=t&&i<=e){let l=o<<16|a;s.has(l)||(s.add(l),r.push([o,a]))}}return r.sort((n,o)=>n[0]*n[1]-o[0]*o[1])}function kN(t,e){let[r,s,n,o]=t.dims,a=Math.floor(n/e),i=Math.floor(o/e),l=e*e*s,c=t.data,u=new Float32Array(r*a*i*l),f=n*o;for(let m=0;m<r;++m){let _=m*s*f,w=m*a*i*l;for(let x=0;x<a;++x)for(let v=0;v<i;++v){let b=w+(x*i+v)*l;for(let A=0;A<e;++A){let S=(x*e+A)*o+v*e;for(let M=0;M<e;++M){let I=S+M;for(let O=0;O<s;++O)u[b++]=c[_+O*f+I]}}}}return new N("float32",u,[r,a*i,l])}function EN(t,e){let[,r,s]=t.dims,n=new BigInt64Array(e);n.fill(1n,0,r);let o=t;if(r<e){let a=new Float32Array(e*s);a.set(t.data),o=new N("float32",a,[1,e,s])}return{padded:o,mask:new N("int64",n,[e])}}var g_=class extends X{constructor(e){super(e),this.downsample_factor=e.downsample_factor??2,this.do_image_splitting=e.do_image_splitting??!0,this.min_tiles=e.min_tiles??2,this.max_tiles=e.max_tiles??10,this.use_thumbnail=e.use_thumbnail??!0,this.min_image_tokens=e.min_image_tokens??64,this.max_image_tokens=e.max_image_tokens??256,this.encoder_patch_size=e.encoder_patch_size??e.patch_size??16,this.tile_size=e.tile_size??512,this.max_pixels_tolerance=e.max_pixels_tolerance??2,this.return_row_col_info=e.return_row_col_info??!1;let r=this.max_image_tokens*this.downsample_factor**2,s=this.do_image_splitting?(this.tile_size/this.encoder_patch_size)**2:0;this.max_num_patches=Math.max(r,s)}_is_image_too_large(e,r){let s=this.encoder_patch_size*this.downsample_factor,n=Math.max(this.encoder_patch_size,kT(e,s)),o=Math.max(this.encoder_patch_size,kT(r,s));return n*o>this.max_image_tokens*(this.encoder_patch_size*this.downsample_factor)**2*this.max_pixels_tolerance}_get_grid_layout(e,r){let s=vN(this.min_tiles,this.max_tiles),[n,o]=bN(r/e,s,r,e,this.tile_size);return{grid_width:n,grid_height:o,target_width:this.tile_size*n,target_height:this.tile_size*o}}async _call(e,{return_row_col_info:r=null}={}){let s;Array.isArray(e)?Array.isArray(e[0])?s=e:s=[e]:s=[[e]];let n=[],o=[],a=[],i=[],l=[],c=[];for(let f of s){let m=await Promise.all(f.map(_=>this.preprocess(_,{do_pad:!1})));for(let{pixel_values:_}of m){let[,w,x]=_.dims,v=_.unsqueeze_(0),b=this.encoder_patch_size*this.downsample_factor,A=b**2,[S,M]=wn(Math.max(b,w),Math.max(b,x),b,this.min_image_tokens*A,this.max_image_tokens*A).map(F=>Math.max(b,F)),I,O=1,k=1,R=this._is_image_too_large(w,x),G=this.do_image_splitting&&!(this.min_tiles===1&&this.max_tiles===1);if(R&&G){let{grid_width:F,grid_height:K,target_width:V,target_height:Q}=this._get_grid_layout(w,x);O=K,k=F;let U=await vt(v,{size:[Q,V]});I=[];for(let C=0;C<K;++C)for(let se=0;se<F;++se){let Y=C*this.tile_size,L=se*this.tile_size;I.push(U.slice(null,null,[Y,Y+this.tile_size],[L,L+this.tile_size]))}this.use_thumbnail&&F*K!==1&&I.push(await vt(v,{size:[M,S]}))}else I=[await vt(v,{size:[M,S]})];for(let F of I){let[,,K,V]=F.dims,Q=kN(F,this.encoder_patch_size),{padded:U,mask:C}=EN(Q,this.max_num_patches);n.push(U),o.push(C),a.push([Math.floor(K/this.encoder_patch_size),Math.floor(V/this.encoder_patch_size)])}i.push(O),l.push(k),c.push([M,S])}}let u={pixel_values:ve(n,0),pixel_attention_mask:yt(o,0),spatial_shapes:new N("int64",BigInt64Array.from(a.flat(),BigInt),[a.length,2])};return(r??this.return_row_col_info)&&(u.image_rows=i,u.image_cols=l,u.image_sizes=c),u}};var w_=class extends X{};var yn=class extends X{post_process_panoptic_segmentation(...e){return jc(...e)}post_process_instance_segmentation(...e){return qc(...e)}},x_=class extends yn{};var y_=class extends yn{};var Qc=class extends X{},b_=class extends Qc{};var Jc=class extends X{},v_=class extends Jc{};var Zc=class extends X{},k_=class extends Zc{};var eu=class extends X{},E_=class extends eu{};var tu=class extends X{},A_=class extends tu{};var T_=class extends xn{};var bn=class extends X{post_process_object_detection(...e){return Kr(...e)}},M_=class extends bn{};var S_=class extends bn{};var $t=336,AN=[2,3],{ceil:O_,floor:vn,sqrt:I_}=Math,C_=class extends X{constructor(e){super({...e,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=e.num_crops}calc_num_image_tokens_from_image_size(e,r){let{num_img_tokens:s}=this.config;return vn((vn(r/$t)*vn(e/$t)+1)*s+1+(vn(r/$t)+1)*I_(s))}get_resize_output_image_size(e,r){let s=this._num_crops,[n,o]=e.size,a=n/o,i=1;for(;i*Math.ceil(i/a)<=s;)i+=1;i-=1;let l=Math.floor(i*336),c=Math.floor(l/a);return[l,c]}pad_image(e,r,s,n={}){let[o,a]=r,i=$t*O_(o/$t),l=$t*O_(a/$t),c=[1,1,1].map((u,f)=>(u-this.image_mean[f])/this.image_std[f]);return super.pad_image(e,r,{width:l,height:i},{center:!0,constant_values:c,...n})}async _call(e,{num_crops:r=null}={}){if(this._num_crops=r??=this.config.num_crops,r<4||I_(r)%1!==0)throw new Error("num_crops must be a square number >= 4");Array.isArray(e)||(e=[e]);let s=e.length,n=await Promise.all(e.map(m=>this.preprocess(m))),o=n.map(m=>m.original_size),a=n.map(m=>m.reshaped_input_size),i=[];for(let{pixel_values:m}of n){m.unsqueeze_(0);let[_,w]=m.dims.slice(-2),x=await vt(m,{size:[$t,$t],mode:"bicubic"});if(r>0){let v=[],b=I_(r),A=vn(w/b),S=vn(_/b);for(let I=0;I<b;++I)for(let O=0;O<b;++O){let k,R,G,F;I===b-1?(R=_-S,F=_):(R=I*S,F=(I+1)*S),O===b-1?(k=w-A,G=w):(k=O*A,G=(O+1)*A);let Q=await zc(m,[R,k],[F,G],AN);v.push(Q)}let M=await vt(ve(v,0),{size:[$t,$t],mode:"bicubic"});i.push(ve([x,M],0))}else i.push(x)}let l=yt(i,0),c=a.map(m=>m.map(_=>$t*O_(_/$t))),u=new N("int64",c.flat(),[s,2]),f=c.map(([m,_])=>this.calc_num_image_tokens_from_image_size(_,m));return{pixel_values:l,original_sizes:o,reshaped_input_sizes:a,image_sizes:u,num_img_tokens:f}}};var P_=class extends X{get_resize_output_image_size(e,r){let{longest_edge:s}=r;if(s===void 0)throw new Error("size must contain 'longest_edge'");let[n,o]=e.size,a=Math.max(n,o)/s,i=n,l=o;a>1&&(i=Math.floor(n/a),l=Math.floor(o/a));let{patch_size:c,spatial_merge_size:u}=this.config;if(!u)throw new Error("config must contain 'spatial_merge_size'");let f=c*u,m=Math.floor((i-1)/f)+1,_=Math.floor((l-1)/f)+1;return[m*f,_*f]}};var L_=class extends X{};var z_=class extends X{post_process_object_detection(...e){return Kr(...e)}};var Vo=class extends X{reshape_input_points(e,r,s,n=!1){e=structuredClone(e);let o=Of(e);if(o.length===3)n||(o=[1,...o]),e=[e];else if(o.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let a=0;a<e.length;++a){let[i,l]=r[a],[c,u]=s[a],f=[u/l,c/i];for(let m=0;m<e[a].length;++m)for(let _=0;_<e[a][m].length;++_)for(let w=0;w<e[a][m][_].length;++w)e[a][m][_][w]*=f[w%2]}return new N("float32",Float32Array.from(e.flat(1/0)),o)}add_input_labels(e,r){let s=Of(e);if(s.length===2)s=[1,...s],e=[e];else if(s.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(s.some((n,o)=>n!==r.dims[o]))throw Error(`The first ${s.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new N("int64",e.flat(1/0).map(BigInt),s)}async _call(e,{input_points:r=null,input_labels:s=null,input_boxes:n=null}={}){let o=await super._call(e);if(r&&(o.input_points=this.reshape_input_points(r,o.original_sizes,o.reshaped_input_sizes)),s){if(!o.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");o.input_labels=this.add_input_labels(s,o.input_points)}return n&&(o.input_boxes=this.reshape_input_points(n,o.original_sizes,o.reshaped_input_sizes,!0)),o}async post_process_masks(e,r,s,{mask_threshold:n=0,binarize:o=!0,pad_size:a=null}={}){let i=[];a=a??this.pad_size??this.size;let l=[a.height,a.width];for(let c=0;c<r.length;++c){let u=r[c],f=s[c],m=await vt(e[c],{mode:"bilinear",size:l});if(m=m.slice(null,null,[0,f[0]],[0,f[1]]),m=await vt(m,{mode:"bilinear",size:u}),o){let _=m.data,w=new Uint8Array(_.length);for(let x=0;x<_.length;++x)_[x]>n&&(w[x]=1);m=new N("bool",w,m.dims)}i.push(m)}return i}generate_crop_boxes(e,r,{crop_n_layers:s=0,overlap_ratio:n=512/1500,points_per_crop:o=32,crop_n_points_downscale_factor:a=1}={}){}};var ru=class extends X{post_process_semantic_segmentation(...e){return Gc(...e)}},N_=class extends ru{};var su=class extends X{post_process_semantic_segmentation(...e){return Gc(...e)}},R_=class extends su{};var $_=class extends X{};var D_=class extends X{pad_image(e,r,s,n={}){let[o,a,i]=r;return super.pad_image(e,r,{width:a+(s-a%s)%s,height:o+(s-o%s)%s},{mode:"symmetric",center:!1,constant_values:-1,...n})}};var nu=class extends X{},B_=class extends nu{};var U_=class extends X{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);let s=await Promise.all(e.map(a=>this.preprocess(a))),n=await Promise.all(r.map(a=>this.preprocess(a,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:yt(s.map((a,i)=>ve([a.pixel_values,n[i].pixel_values],0)),0),original_sizes:s.map(a=>a.original_size),reshaped_input_sizes:s.map(a=>a.reshaped_input_size)}}};var F_=class extends X{post_process_pose_estimation(e,r,{threshold:s=null}={}){let n=e.tolist(),[o,a,i,l]=e.dims,c=[];for(let u=0;u<o;++u){let f=n[u],m=r[u],_=[];for(let w=0;w<m.length;++w){let x=m[w],v=[],b=[],A=[],S=x.at(-2)/l,M=x.at(-1)/i;for(let I=0;I<f.length;++I){let[O,k]=[0,0],R=0,G=-1/0,F=f[I];for(let V=0;V<F.length;++V){let Q=F[V];for(let U=0;U<Q.length;++U){let C=Q[U];R+=C,G=Math.max(G,C),O+=(U+.5)*C,k+=V*C}}if(s!=null&&G<s)continue;let K=[S*O/R,M*k/R];v.push(K),A.push(I),b.push(G)}_.push({bbox:x,scores:b,labels:A,keypoints:v})}c.push(_)}return c}};var ou=class extends X{post_process_object_detection(...e){return Kr(...e)}},G_=class extends ou{};var Te=class{static async from_pretrained(e,r={}){let s=await st(e,Er,!0,r),n=s.image_processor_type??s.feature_extractor_type,o=kn[n?.replace(/Fast$/,"")];return o||(n!==void 0&&ee.warn(`Image processor type '${n}' not found, assuming base ImageProcessor. Please report this at ${Hr}.`),o=X),new o(s)}};var j_=class extends re{static tokenizer_class=ne;static image_processor_class=Te;constructor(e,r,s){super(e,r,s);let{tasks_answer_post_processing_type:n,task_prompts_without_inputs:o,task_prompts_with_input:a}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(n??{})),this.task_prompts_without_inputs=new Map(Object.entries(o??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);let r=[];for(let s of e)if(this.task_prompts_without_inputs.has(s))r.push(this.task_prompts_without_inputs.get(s));else{for(let[n,o]of this.task_prompts_with_input)if(s.includes(n)){r.push(o.replaceAll("{input}",s).replaceAll(n,""));break}r.length!==e.length&&r.push(s)}return r}post_process_generation(e,r,s){let n=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("<s>","").replaceAll("</s>","");let o;switch(n){case"pure_text":o=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":let a=n==="ocr"?"quad_boxes":"bboxes",i=e.matchAll(this.regexes[a]),l=[],c=[];for(let[u,f,...m]of i)l.push(f?f.trim():l.at(-1)??""),c.push(m.map((_,w)=>(Number(_)+.5)/this.size_per_bin*s[w%2]));o={labels:l,[a]:c};break;default:throw new Error(`Task "${r}" (of type "${n}") not yet implemented.`)}return{[r]:o}}async _call(e,r=null,s={}){if(!e&&!r)throw new Error("Either text or images must be provided");let n=await this.image_processor(e,s),o=r?this.tokenizer(this.construct_prompts(r),s):{};return{...n,...o}}};var q_=class extends re{static tokenizer_class=ne;static image_processor_class=Te;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,r,s){super(e,r,s),this.image_seq_length=this.config.image_seq_length;let{boi_token:n,image_token:o,eoi_token:a}=this.tokenizer.config;this.boi_token=n,this.image_token=o,this.eoi_token=a;let i=o.repeat(this.image_seq_length);this.full_image_sequence=`

${n}${i}${a}

`}async _call(e,r=null,s={}){typeof e=="string"&&(e=[e]);let n;return r&&(n=await this.image_processor(r,s),e=e.map(a=>a.replaceAll(this.boi_token,this.full_image_sequence))),{...this.tokenizer(e,s),...n}}};var W_=class extends re{static image_processor_class=Te;static feature_extractor_class=Ge;static tokenizer_class=ne;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,r,s){super(e,r,s),this.audio_seq_length=this.config.audio_seq_length,this.image_seq_length=this.config.image_seq_length;let{audio_token_id:n,boa_token:o,audio_token:a,eoa_token:i,image_token_id:l,boi_token:c,image_token:u,eoi_token:f}=this.tokenizer.config;this.audio_token_id=n,this.boa_token=o,this.audio_token=a;let m=a.repeat(this.audio_seq_length);this.full_audio_sequence=`

${o}${m}${i}

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

${c}${_}${f}

`}async _call(e,r=null,s=null,n={}){typeof e=="string"&&(e=[e]);let o;s&&(o=await this.feature_extractor(s,n),e=e.map(l=>l.replaceAll(this.audio_token,this.full_audio_sequence)));let a;return r&&(a=await this.image_processor(r,n),e=e.map(l=>l.replaceAll(this.image_token,this.full_image_sequence))),{...this.tokenizer(e,n),...a,...o}}};var V_=class extends re{static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,r,s){super(e,r,s),this.audio_ms_per_token=this.config.audio_ms_per_token??40,this.audio_seq_length=this.config.audio_seq_length??750,this.image_seq_length=this.config.image_seq_length??280;let{audio_token:n,boa_token:o,eoa_token:a,image_token:i,boi_token:l,eoi_token:c}=this.tokenizer.config;this.audio_token=n,this.boa_token=o,this.eoa_token=a,this.image_token=i,this.boi_token=l,this.eoi_token=c}static async from_pretrained(e,r={}){let[s,n,o]=await Promise.all([st(e,$c,!0,r),ne.from_pretrained(e,r),xo(e,Dc,!1,r)]),a={tokenizer:n};return s.image_processor&&(a.image_processor=new qo(s.image_processor)),s.feature_extractor&&(a.feature_extractor=new Fo(s.feature_extractor)),new this(s,a,o)}_compute_audio_num_tokens(e,r){let s=Math.round(r*20/1e3),n=Math.round(r*10/1e3),o=Math.floor(s/2),a=Math.floor((e+o-s-1)/n)+1;if(a<=0)return 0;for(let i=0;i<2;++i)a=Math.floor((a-1)/2)+1;return Math.min(a,this.audio_seq_length)}async _call(e,r=null,s=null,n={}){typeof e=="string"&&(e=[e]);let o;if(r){o=await this.image_processor(r,n);let i=o.num_soft_tokens_per_image,l=0;e=e.map(c=>c.replaceAll(this.image_token,()=>`

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

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

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

`))}return{...this.tokenizer(e,n),...o,...a}}};var zs=class extends re{static image_processor_class=Te;static tokenizer_class=ne;static image_token="<|image_pad|>";async _call(e,r=null,...s){Array.isArray(e)||(e=[e]);let n,o;if(r&&(n=await this.image_processor(r),o=n.image_grid_thw),o){let i=this.image_processor.config.merge_size**2,l=0,c=this.constructor.image_token,u=o.tolist();e=e.map(f=>{for(;f.includes(c);){let m=Number(u[l++].reduce((_,w)=>_*w,1n));f=f.replace(c,"<|placeholder|>".repeat(Math.floor(m/i)))}return f.replaceAll("<|placeholder|>",c)})}return{...this.tokenizer(e),...n}}};var H_=class extends zs{static image_token="<|image|>"};var X_=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!0;_get_num_audio_features(e){let{hop_length:r}=this.feature_extractor.config.melspec_kwargs,{projector_window_size:s,projector_downsample_rate:n}=this.feature_extractor.config,o=Math.floor(s/n),a=Math.floor(e/r)+1,i=Math.floor(a/2);return Math.ceil(i/s)*o}async _call(e,r=null,s={}){if(Array.isArray(e))throw new Error("Batched inputs are not supported yet.");let n={};if(r){let{input_features:a}=await this.feature_extractor(r);n.input_features=a;let i=this._get_num_audio_features(r.length),l=new Uint8Array(i).fill(1);n.input_features_mask=new N("bool",l,[1,i]);let c=this.config.audio_token??"<|audio|>";if(!e.includes(c))throw new Error(`The input text does not contain the audio token ${c}.`);e=e.replaceAll(c,c.repeat(i))}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};function TN(t,e){let s=t.dims.at(-1)-1,n=t.tolist();n.fill(!1,0,1),n.fill(!1,s);let o=e.tolist();return n.map((a,i)=>a?i:null).filter(a=>a!==null).map(a=>o[a])}var K_=class extends re{static tokenizer_class=ne;static image_processor_class=Te;async _call(e,r,s={}){let n=e?await this.image_processor(e,s):{};return{...r?this.tokenizer(r,s):{},...n}}post_process_grounded_object_detection(e,r,{box_threshold:s=.25,text_threshold:n=.25,target_sizes:o=null}={}){let{logits:a,pred_boxes:i}=e,l=a.dims[0];if(o!==null&&o.length!==l)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=a.dims.at(1),u=a.sigmoid(),f=u.max(-1).tolist(),m=i.tolist().map(w=>w.map(x=>Jm(x))),_=[];for(let w=0;w<l;++w){let x=o!==null?o[w]:null;x!==null&&(m[w]=m[w].map(M=>M.map((I,O)=>I*x[(O+1)%2])));let v=f[w],b=[],A=[],S=[];for(let M=0;M<c;++M){let I=v[M];if(I<=s)continue;let O=m[w][M],k=u[w][M];b.push(I),S.push(O);let R=TN(k.gt(n),r[w]);A.push(R)}_.push({scores:b,boxes:S,labels:this.batch_decode(A)})}return _}};function MN(t,e,r,s,n,o){let a="";for(let i=0;i<e;++i){for(let l=0;l<r;++l)a+=s+`<row_${i+1}_col_${l+1}>`+n.repeat(t);a+=`
`}return a+=`
${s}${o}`+n.repeat(t)+`${s}`,a}function SN(t,e,r,s){return`${e}${s}`+r.repeat(t)+`${e}`}function ON(t,e,r,s,n,o){return t===0&&e===0?SN(r,s,n,o):MN(r,t,e,s,n,o)}var au=class extends re{static image_processor_class=Te;static tokenizer_class=ne;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(e,r=null,s={}){s.return_row_col_info??=!0;let n;r&&(n=await this.image_processor(r,s)),Array.isArray(e)||(e=[e]);let o=n.rows??[new Array(e.length).fill(0)],a=n.cols??[new Array(e.length).fill(0)],i=this.config.image_seq_len,l=[],c=[];for(let f=0;f<e.length;++f){let m=e[f],_=o[f],w=a[f];l.push(yE(m,this.image_token));let x=_.map((A,S)=>ON(A,w[S],i,this.fake_image_token,this.image_token,this.global_img_token)),v=m.split(this.image_token);if(v.length===0)throw new Error("The image token should be present in the text.");let b=v[0];for(let A=0;A<x.length;++A)b+=x[A]+v[A+1];c.push(b)}return{...this.tokenizer(c),...n}}};var Y_=class extends re{static image_processor_class=Te;static tokenizer_class=ne;static uses_processor_config=!0;constructor(e,r,s){super(e,r,s),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(e,{images:r=null,chat_template:s="default"}={}){r?Array.isArray(r)||(r=[r]):r=await Promise.all(e.filter(v=>v.images).flatMap(v=>v.images).map(v=>Je.read(v)));let n=this.tokenizer,o=n.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0,chat_template:s}),a=v=>n.encode(v,{add_special_tokens:!1}),i=o.split(this.image_tag),l=i.length-1;if(r.length!==l)throw new Error(`Number of images provided (${r.length}) does not match number of "${this.image_tag}" image tags (${l})`);let[c,u,f]=n.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]),m=a(i[0]),_=new Array(m.length).fill(!1);for(let v=1;v<i.length;++v){let b=new Array(this.num_image_tokens).fill(c),A=a(i[v]);m=ht(m,[u],b,[f],A);let S=new Array(this.num_image_tokens).fill(!0);_=ht(_,[!1],S,[!1],new Array(A.length).fill(!1))}let w=[1,m.length],x={input_ids:new N("int64",m,w),attention_mask:new N("int64",new Array(m.length).fill(1),w),images_seq_mask:new N("bool",_,w),images_emb_mask:new N("bool",new Array(l*this.num_image_tokens).fill(!0),[1,l,this.num_image_tokens])};if(r&&r.length>0){let v=await this.image_processor(r);return v.pixel_values.unsqueeze_(0),{...x,...v}}return x}};var Q_=class extends re{static tokenizer_class=ne;static image_processor_class=Te;async _call(e=null,r=null,s={}){if(!e&&!r)throw new Error("Either text or images must be provided");let n=e?this.tokenizer(e,s):{},o=r?await this.image_processor(r,s):{};return{...n,...o}}};var J_=class extends re{static tokenizer_class=ne;static image_processor_class=Te;async _call(e,r=null,s={}){let{image_rows:n,image_cols:o,image_sizes:a,...i}=await this.image_processor(e,{...s,return_row_col_info:!0});if(r){let l=this.config.image_token??"<image>",{tile_size:c=512,downsample_factor:u=2,encoder_patch_size:f=16,use_thumbnail:m=!0}=this.image_processor.config,_=S=>Math.ceil(Math.floor(S/f)/u),w=_(c)**2,x=this.config.image_start_token??"<|image_start|>",v=this.config.image_end_token??"<|image_end|>",b=this.config.image_thumbnail??"<|img_thumbnail|>";Array.isArray(r)||(r=[r]);let A=0;r=r.map(S=>{let M=S.split(l);return M[0]+M.slice(1).map(I=>{let O=A++,[k,R]=a[O],G=n[O],F=o[O],K=_(k)*_(R),V=x;if(G>1||F>1){let Q=l.repeat(w);for(let U=0;U<G;++U)for(let C=0;C<F;++C)V+=`<|img_row_${U+1}_col_${C+1}|>`+Q;m&&(V+=b+l.repeat(K))}else V+=l.repeat(K);return V+v+I}).join("")})}return{...i,...r?this.tokenizer(r,s):{}}}};var Z_=class extends re{static tokenizer_class=ne;static image_processor_class=Te;static uses_processor_config=!0;async _call(e,r=null,s={}){let n=await this.image_processor(e,s);if(r){let[a,i]=n.pixel_values.dims.slice(-2),{image_token:l,patch_size:c,num_additional_image_tokens:u}=this.config,f=Math.floor(a/c)*Math.floor(i/c)+u;r=structuredClone(r),Array.isArray(r)||(r=[r]);for(let m=0;m<r.length;++m)r[m]=r[m].replace(l,l.repeat(f))}let o=r?this.tokenizer(r,s):{};return{...n,...o}}};var ET={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]},eh=class extends re{static tokenizer_class=ne;static image_processor_class=Te;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(e,r){if(!ET.hasOwnProperty(r))throw new Error(`Format ${r} is not supported.`);let[s,n]=ET[r],o=this[s].bind(this),[a,i]=e.dims,l=[],c=[],u=e.tolist();for(let m=0;m<a;++m){let _=u[m],w=[],x=[];for(let b=1;b<i;++b){let[A,S]=Pe(Re(_[b]));if(x.push(A),S==n)break;w.push(S)}let v=x.length>0?x.reduce((b,A)=>b*A,1):0;c.push(w),l.push(v)}return[o(c),l]}char_decode(e){return this.char_tokenizer.batch_decode(e).map(r=>r.replaceAll(" ",""))}bpe_decode(e){return this.bpe_tokenizer.batch_decode(e)}wp_decode(e){return this.wp_tokenizer.batch_decode(e).map(r=>r.replaceAll(" ",""))}batch_decode([e,r,s]){let[n,o]=this._decode_helper(e,"char"),[a,i]=this._decode_helper(r,"bpe"),[l,c]=this._decode_helper(s,"wp"),u=[],f=[];for(let m=0;m<n.length;++m){let[_,w]=Pe([o[m],i[m],c[m]]);u.push([n[m],a[m],l[m]][w]),f.push(_)}return{generated_text:u,scores:f,char_preds:n,bpe_preds:a,wp_preds:l}}static async from_pretrained(...e){let r=await super.from_pretrained(...e),s=await ne.from_pretrained("Xenova/gpt2"),n=await ne.from_pretrained("Xenova/bert-base-uncased");return r.components={image_processor:r.image_processor,char_tokenizer:r.tokenizer,bpe_tokenizer:s,wp_tokenizer:n},r}async _call(e,r=null){let s=await this.image_processor(e);return r&&(s.labels=this.tokenizer(r).input_ids),s}};var th=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var rh=class extends re{static tokenizer_class=ne;static image_processor_class=Te};var En="<image>";function IN(t,e,r,s,n){return`${s.repeat(r*n)}${e}${t}
`}var sh=class extends re{static tokenizer_class=ne;static image_processor_class=Te;static uses_processor_config=!1;async _call(e,r=null,s={}){r||(ee.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),r=""),Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);let n=this.tokenizer.bos_token,o=this.image_processor.config.image_seq_length,a;r.some(c=>c.includes(En))?a=r.map(c=>{let u=c.replaceAll(En,En.repeat(o)),f=u.lastIndexOf(En),m=f===-1?0:f+En.length;return u.slice(0,m)+n+u.slice(m)+`
`}):(ee.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),a=r.map(c=>IN(c,n,o,En,e.length)));let i=this.tokenizer(a,s);return{...await this.image_processor(e,s),...i}}};var AT="<|image|>",CN=/<\|image_\d+\|>/g,nh=class extends re{static image_processor_class=Te;static tokenizer_class=ne;async _call(e,r=null,{padding:s=!0,truncation:n=!0,num_crops:o=null}={}){Array.isArray(e)||(e=[e]);let a,i;if(r){i=await this.image_processor(r,{num_crops:o});let{num_img_tokens:l}=i,c=e.map((f,m)=>f.split(CN).join(AT.repeat(l[m])));a=this.tokenizer(c,{padding:s,truncation:n});let u=this.tokenizer._tokenizer.token_to_id(AT);a.input_ids.map_(f=>f==u?-f:f)}else a=this.tokenizer(e);return{...a,...i}}};var oh=class extends re{static tokenizer_class=ne;static image_processor_class=Te;static uses_processor_config=!0;async _call(e,r=null,s={}){let n=await this.image_processor(e,s);if(r){let[a,i]=n.pixel_values.dims.slice(-2),{image_token:l,image_break_token:c,image_end_token:u,patch_size:f,spatial_merge_size:m}=this.config,_=f*m,w=Math.floor(a/_),x=Math.floor(i/_);r=structuredClone(r),Array.isArray(r)||(r=[r]);for(let v=0;v<r.length;++v){let b=l.repeat(x),A=b+c,S=b+u,M=A.repeat(w-1)+S;r[v]=r[v].replace(l,M)}}let o=r?this.tokenizer(r,s):{};return{...n,...o}}};var ah=class extends re{static feature_extractor_class=Go;async _call(e){return await this.feature_extractor(e)}post_process_speaker_diarization(...e){return this.feature_extractor.post_process_speaker_diarization(...e)}get sampling_rate(){return this.feature_extractor.config.sampling_rate}};var Ho=class extends zs{};var ih=class extends Ho{};var Xo=class extends re{static image_processor_class=Te;async _call(...e){return await this.image_processor(...e)}post_process_masks(...e){return this.image_processor.post_process_masks(...e)}reshape_input_points(...e){return this.image_processor.reshape_input_points(...e)}};var iu=class extends Xo{},lh=class extends iu{};var ch=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var uh=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!0;async _call(e,r=null,s={}){if(Array.isArray(e))throw new Error("Batched inputs are not supported yet.");let n={};if(r){let a=r.length,{input_features:i}=await this.feature_extractor(r,{...s,max_length:a}),l=Math.round(a/this.config.encoder_ds_factor+1e-4),c=1+Math.ceil(l/this.config.stack_factor);n.audio_token_len=[c],n.audio_values=i;let u=this.config.audio_placeholder;if(!e.includes(u))throw new Error(`The input text does not contain the image token ${u}.`);e=e.replaceAll(u,u.repeat(c))}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};var lu="[AUDIO]",PN="[BEGIN_AUDIO]",LN=375;function zN(t,e){let r=[];for(let s=0;s<t.length;s+=e)r.push(t.subarray(s,Math.min(s+e,t.length)));return r}var ph=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!1;async _call(e,r=null,s={}){if(Array.isArray(e))throw new Error("Batched inputs are not supported yet.");let n={};if(r){if(!e.includes(lu))throw new Error(`The input text does not contain the audio token ${lu}.`);Array.isArray(r)||(r=[r]);let a=e.split(lu),i=a.length-1;if(i!==r.length)throw new Error(`The number of audio inputs (${r.length}) does not match the number of audio tokens in the text (${i}).`);let l=this.feature_extractor.config.n_samples,c=r.map(w=>zN(w,l)),u=c.map(w=>w.length),f=c.flat(),m=(await Promise.all(f.map(w=>this.feature_extractor(w,s)))).map(w=>w.input_features);n.audio_values=m.length>1?ve(m,0):m[0];let _=a[0];for(let w=0;w<u.length;++w){_+=PN;for(let x=0;x<u[w];++x)_+=lu.repeat(LN);_+=a[w+1]}e=_}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};var TT=32,fh=6,cu=8,NN=10,RN=32,dh=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!1;get num_mel_frames_first_audio_chunk(){return(fh+1)*cu}get num_samples_first_audio_chunk(){let{hop_length:e,n_fft:r}=this.feature_extractor.config;return(this.num_mel_frames_first_audio_chunk-1)*e+Math.floor(r/2)}get num_samples_per_audio_chunk(){let{hop_length:e,n_fft:r}=this.feature_extractor.config;return cu*e+r}get num_right_pad_tokens(){return fh+1+NN}get audio_length_per_tok(){return cu}get raw_audio_length_per_tok(){return cu*this.feature_extractor.config.hop_length}async _call(e,{is_streaming:r=!1,is_first_audio_chunk:s=!0}={}){if(Ae(e,"VoxtralRealtimeProcessor"),!r&&!s)throw new Error("In non-streaming mode (`is_streaming=false`), `is_first_audio_chunk` must be `true`.");if(s)if(r){let n=TT*this.raw_audio_length_per_tok,o=new Float32Array(n+e.length);o.set(e,n);let a=await this.feature_extractor(o,{center:!0}),l=1+(TT+fh),c=new BigInt64Array(l).fill(BigInt(RN));return c[0]=1n,{input_ids:new N("int64",c,[1,l]),...a}}else{let n=this.num_right_pad_tokens*this.raw_audio_length_per_tok,o=new Float32Array(e.length+n);return o.set(e),await this.feature_extractor(o,{center:!0})}else return await this.feature_extractor(e,{center:!1})}};var mh=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var _h=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var hh=class extends re{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var pu=class{static async from_pretrained(e,r={}){let s=await st(e,Er,!0,r),{image_processor_type:n,feature_extractor_type:o,processor_class:a}=s;if(a&&uu[a])return uu[a].from_pretrained(e,r);if(!n&&!o)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");let i={};if(n){let c=kn[n.replace(/Fast$/,"")];if(!c)throw new Error(`Unknown image_processor_type: '${n}'.`);i.image_processor=new c(s)}if(o){let c=kn[o];if(c)i.image_processor=new c(s);else{let u=jo[o];if(!u)throw new Error(`Unknown feature_extractor_type: '${o}'.`);i.feature_extractor=new u(s)}}let l={};return new re(l,i,null)}};async function $N(t,e){return await st(t,"config.json",!0,e)}function An(t){let 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Sn=class{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;streamer=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,Qe(e,Object.getOwnPropertyNames(this)))}};var Yr=class 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H={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,DecoderOnlyWithoutHead:5,MaskGeneration:6,ImageTextToText:7,Musicgen:8,MultiModality:9,Phi3V:10,AudioTextToText:11,AutoEncoder:12,ImageAudioTextToText:13,Supertonic:14,Chatterbox:15,VoxtralRealtime:16},dr={[H.DecoderOnly]:{sessions:(t,e)=>({model:e.model_file_name??"model"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[H.DecoderOnlyWithoutHead]:{sessions:(t,e)=>({model:e.model_file_name??"model"})},[H.Seq2Seq]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[H.Vision2Seq]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[H.Musicgen]:{sessions:()=>({model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[H.EncoderDecoder]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0}},[H.MaskGeneration]:{sessions:()=>({model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"})},[H.ImageTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let 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Falling back to EncoderOnly (single model.onnx file). If you encounter issues, please report at: ${Hr}`)}return H.EncoderOnly}function Jo(t,{config:e=null,cache_dir:r=null,local_files_only:s=!1,revision:n="main"}={}){if(e!==null)return Wt.from_pretrained(t,{config:e,cache_dir:r,local_files_only:s,revision:n});let o=JSON.stringify([t,r,s,n]);return pc(o,()=>Wt.from_pretrained(t,{config:e,cache_dir:r,local_files_only:s,revision:n}))}async function Cn(t,{config:e=null,dtype:r=null,device:s=null,model_file_name:n=null}={}){e=await Jo(t,{config:e});let o=["config.json"],a=e["transformers.js_config"]??{},i=a.use_external_data_format,l="onnx",c=s??a.device,u=r??a.dtype,f=In(e),m=(x,v=null)=>{v=v??x;let b=Cc(c,x),A=Pc(u,x,b),S=Vr[A]??"",M=`${v}${S}.onnx`,I=l?`${l}/${M}`:M;o.push(I);let O=gh(i,M,x);for(let k of wh(M,O)){let R=l?`${l}/${k}`:k;o.push(R)}},{sessions:_,optional_configs:w}=Qo(f,e,{model_file_name:n});for(let[x,v]of Object.entries(_))m(x,v);if(w)for(let x of Object.values(w))o.push(x);return o}var Ds=null;function RT(t){Ds=t}function Eh(t){if(t instanceof N)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[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 N("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new N("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Ah(t){return new N("bool",[t],[1])}var zT={[H.DecoderOnly]:{can_generate:!0,forward:Pt,prepare_inputs:Pn},[H.DecoderOnlyWithoutHead]:{can_generate:!1,forward:Pt,prepare_inputs:Pn},[H.Seq2Seq]:{can_generate:!0,forward:Tu,prepare_inputs:Zo},[H.Vision2Seq]:{can_generate:!0,forward:Tu,prepare_inputs:Zo},[H.Musicgen]:{can_generate:!0,forward:Tu},[H.EncoderDecoder]:{can_generate:!1,forward:Tu},[H.ImageTextToText]:{can_generate:!0,forward:jN,prepare_inputs:Mu},[H.AudioTextToText]:{can_generate:!0,forward:GN,prepare_inputs:Mu},[H.ImageAudioTextToText]:{can_generate:!0,prepare_inputs:Mu},[H.Phi3V]:{can_generate:!0,prepare_inputs:Mu},[H.MultiModality]:{can_generate:!0},[H.AutoEncoder]:{can_generate:!1,forward:FN},[H.Chatterbox]:{can_generate:!0,forward:Vt},[H.VoxtralRealtime]:{can_generate:!0,prepare_inputs:Pn},default:{can_generate:!1,forward:Vt}};function NT(t,e){let r=rr.get(t),s=!1,n=e?.architectures?.[0];if(n&&n!==t&&t?.endsWith("ForCausalLM")&&n.endsWith("ForConditionalGeneration")){let i=rr.get(n);i!==void 0&&(r=i,s=!0)}let o=zT[r]??zT.default,a=dr[r]??dr.default;return{typeConfig:{...o,...a},textOnly:s,modelType:r}}var rr=new Map,Su=new Map,Bs=new Map,y=class extends We{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];_return_dict_in_generate_keys=null;constructor(e,r,s){super(),this.config=e,this.sessions=r,this.configs=s;let n=Bs.get(this.constructor),{typeConfig:o}=NT(n,e);this.can_generate=o.can_generate,this._forward=o.forward,this._prepare_inputs_for_generation=o.prepare_inputs,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){let e=[];for(let r of Object.values(this.sessions))e.push(r.release?.());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:o=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:c=null,dtype:u=null,use_external_data_format:f=null,session_options:m={}}={}){let _={progress_callback:r,config:s,cache_dir:n,local_files_only:o,revision:a,model_file_name:i,subfolder:l,device:c,dtype:u,use_external_data_format:f,session_options:m},w=Bs.get(this);s=_.config=await Wt.from_pretrained(e,_);let{typeConfig:x,textOnly:v,modelType:b}=NT(w,s);if(b===void 0){let I=w??s?.model_type;I!=="custom"&&ee.warn(`Model type for '${I}' not found, assuming encoder-only architecture. Please report this at ${Hr}.`)}if(r&&!(r instanceof gs)){let I={};try{let O=await Cn(e,{config:s,dtype:u,device:c,model_file_name:i});(await Promise.all(O.map(R=>Ct(e,R,_)))).forEach((R,G)=>{if(R.exists){let F=O[G]==="config.json";I[O[G]]={loaded:F?R.size??0:0,total:R.size??0}}})}catch(O){ee.warn(`Unable to fetch model file metadata for total progress tracking: ${O}`)}Object.keys(I).length>0&&(_.progress_callback=new gs(r,I))}let A=x.sessions(s,_,v),S=[OT(e,A,_,x.cache_sessions)];x.optional_configs&&S.push(WN(e,x.optional_configs,_));let M=await Promise.all(S);return new this(s,...M)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}get generation_config(){return this.configs?.generation_config??null}_get_logits_processor(e,r,s=null){let n=new Rs;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&n.push(new wu(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&n.push(new gu(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&n.push(new bu(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&n.push(new xu(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&n.push(new yu(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&n.push(new du(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&n.push(new mu(e.max_length,e.forced_eos_token_id)),e.suppress_tokens!==null&&n.push(new _u(e.suppress_tokens)),e.begin_suppress_tokens!==null){let o=r>1||e.forced_bos_token_id===null?r:r+1;n.push(new Mn(e.begin_suppress_tokens,o))}return e.guidance_scale!==null&&e.guidance_scale>1&&n.push(new vu(e.guidance_scale)),e.temperature===0&&e.do_sample&&(ee.warn("`do_sample` changed to false because `temperature: 0` implies greedy sampling (always selecting the most likely token), which is incompatible with `do_sample: true`."),e.do_sample=!1),e.do_sample&&e.temperature!==null&&e.temperature!==1&&n.push(new ku(e.temperature)),s!==null&&n.extend(s),n}_prepare_generation_config(e,r,s=Sn){let n={...this.config};for(let a of["decoder","generator","text_config"])a in n&&Object.assign(n,n[a]);let o=new s(n);return Object.assign(o,this.generation_config??{}),e&&Object.assign(o,e),r&&Object.assign(o,Qe(r,Object.getOwnPropertyNames(o))),o}_get_stopping_criteria(e,r=null){let s=new On;return e.max_length!==null&&s.push(new Eu(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&s.push(new Au(e.eos_token_id)),r&&s.extend(r),s}_validate_model_class(){if(!this.can_generate){let e=[Ds.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,Ds.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,Ds.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,Ds.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES].filter(Boolean),r=Bs.get(this.constructor),s=new Set,n=this.config.model_type;for(let a of e){let i=a?.get(n);i&&s.add(i)}let o=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw s.size>0&&(o+=` Please use the following class instead: ${[...s].join(", ")}`),Error(o)}}prepare_inputs_for_generation(...e){if(!this._prepare_inputs_for_generation)throw new Error("prepare_inputs_for_generation is not implemented for this model.");return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:s,is_encoder_decoder:n}){return s.past_key_values=this.getPastKeyValues(r,s.past_key_values),s.input_ids=new N("int64",e.flat(),[e.length,1]),n?"decoder_attention_mask"in s&&(s.decoder_attention_mask=ve([s.decoder_attention_mask,et([s.decoder_attention_mask.dims[0],1])],1)):s.attention_mask=ve([s.attention_mask,et([s.attention_mask.dims[0],1])],1),s.position_ids=null,s}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:s}){let n=Qe(s,this.forward_params),o=this.main_input_name;if(o in n){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else n[o]=e;return{inputs_tensor:n[o],model_inputs:n,model_input_name:o}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:r,model_input_name:s,generation_config:n}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!r.inputs_embeds&&"_prepare_inputs_embeds"in this){let{input_ids:a,pixel_values:i,attention_mask:l,...c}=r,u=await this._prepare_inputs_embeds(r);r={...c,...Qe(u,["inputs_embeds","attention_mask"])}}let{last_hidden_state:o}=await Vt(this,r);if(n.guidance_scale!==null&&n.guidance_scale>1)o=ve([o,Po(o,0)],0),"attention_mask"in r&&(r.attention_mask=ve([r.attention_mask,Ud(r.attention_mask)],0));else if(r.decoder_input_ids){let a=Eh(r.decoder_input_ids).dims[0];if(a!==o.dims[0]){if(o.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${o.dims[0]}) than the decoder inputs (${a}).`);o=ve(Array.from({length:a},()=>o),0)}}return r.encoder_outputs=o,r}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:r,model_kwargs:s,decoder_start_token_id:n,bos_token_id:o,generation_config:a}){let{decoder_input_ids:i,...l}=s;if(!(i instanceof N)){if(i)Array.isArray(i[0])||(i=Array.from({length:e},()=>i));else if(n??=o,this.config.model_type==="musicgen")i=Array.from({length:e*this.config.decoder.num_codebooks},()=>[n]);else if(Array.isArray(n)){if(n.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${n.length}`);i=n}else i=Array.from({length:e},()=>[n]);i=Eh(i)}return l.decoder_attention_mask=Rc(i),{input_ids:i,model_inputs:l}}async generate({inputs:e=null,generation_config:r=null,logits_processor:s=null,stopping_criteria:n=null,streamer:o=null,...a}){this._validate_model_class(),r=this._prepare_generation_config(r,a);let{inputs_tensor:i,model_inputs:l,model_input_name:c}=this._prepare_model_inputs({inputs:e,model_kwargs:a}),u=this.config.is_encoder_decoder;u&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:i,model_inputs:l,model_input_name:c,generation_config:r})));let f;u?{input_ids:f,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[c].dims.at(0),model_input_name:c,model_kwargs:l,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):f=l[c];let m=f.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=m+r.max_new_tokens);let _=this._get_logits_processor(r,m,s),w=this._get_stopping_criteria(r,n),x=l[c].dims.at(0),v=$s.getSampler(r),b=new Array(x).fill(0),A=f.tolist();o&&o.put(A);let S,M={},I={};for(;;){if(l=this.prepare_inputs_for_generation(A,l,r),S=await this.forward(l),r.return_dict_in_generate)if(r.output_attentions){let V=this.getAttentions(S);for(let Q in V)Q in M||(M[Q]=[]),M[Q].push(V[Q])}else this._return_dict_in_generate_keys&&Object.assign(I,Qe(S,this._return_dict_in_generate_keys));let R=S.logits.slice(null,-1,null).to("float32"),G=_(A,R),F=[];for(let V=0;V<G.dims.at(0);++V){let Q=G[V],U=await v(Q);for(let[C,se]of U){let Y=BigInt(C);b[V]+=se,A[V].push(Y),F.push([Y]);break}}if(o&&o.put(F),w(A).every(V=>V))break;l=this._update_model_kwargs_for_generation({generated_input_ids:F,outputs:S,model_inputs:l,is_encoder_decoder:u})}o&&o.end();let O=this.getPastKeyValues(S,l.past_key_values,!0),k=new N("int64",A.flat(),[A.length,A[0].length]);if(r.return_dict_in_generate)return{sequences:k,past_key_values:O,...M,...I};for(let R of Object.values(S))R.location==="gpu-buffer"&&R.dispose();return k}getPastKeyValues(e,r,s=!1){let n=Object.create(null);for(let o in e)if(o.startsWith("present")){let a=o.replace("present_ssm","past_ssm").replace("present_conv","past_conv").replace("present_recurrent","past_recurrent").replace("present","past_key_values"),i=o.includes("encoder");if(i&&r?n[a]=r[a]:n[a]=e[o],r&&(!i||s)){let l=r[a];l.location==="gpu-buffer"&&l.dispose()}}return new Yo(n)}getAttentions(e){let r={};for(let s of["cross_attentions","encoder_attentions","decoder_attentions"])for(let n in e)n.startsWith(s)&&(s in r||(r[s]=[]),r[s].push(e[n]));return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);else{let s=this.sessions.decoder_model_merged??this.sessions.model,n=(e[this.main_input_name]??e.attention_mask)?.dims?.[0]??1,o=s?.config?.kv_cache_dtype??"float32",a=o==="float16"?kr.float16:kr.float32,i=Ns(this.config,{batch_size:n});for(let l in i){let c=i[l].reduce((u,f)=>u*f,1);e[l]=new N(o,new a(c),i[l])}}}async _encode_input(e,r,s){if(!Object.hasOwn(this.sessions,e))throw new Error(`Model does not have a ${e} session.`);let n=this.sessions[e];return(await fe(n,Qe(r,n.inputNames)))[s]}async encode_image(e){return this._encode_input("vision_encoder",e,"image_features")}async encode_text(e){return this._encode_input("embed_tokens",e,"inputs_embeds")}async encode_audio(e){return this._encode_input("audio_encoder",e,"audio_features")}};async function Tu(t,e){let{encoder_outputs:r,input_ids:s,decoder_input_ids:n,decoder_attention_mask:o,...a}=e;if(!r){let i=Qe(e,t.sessions.model.inputNames);r=(await Vt(t,i)).last_hidden_state}return a.input_ids=n,a.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(a.encoder_attention_mask=e.attention_mask),o&&!a.attention_mask&&(a.attention_mask=o),await Pt(t,a,!0)}async function Vt(t,e){let r=t.sessions.model,s=Qe(e,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!s.inputs_embeds){if(!e.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");s.inputs_embeds=await t.encode_text({input_ids:e.input_ids})}if(r.inputNames.includes("token_type_ids")&&!s.token_type_ids){if(!s.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");s.token_type_ids=Ud(s.input_ids)}if(r.inputNames.includes("pixel_mask")&&!s.pixel_mask){if(!s.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");let n=s.pixel_values.dims;s.pixel_mask=et([n[0],n[2],n[3]])}return await fe(r,s)}async function FN(t,e){let r=await t.encode(e);return await t.decode(r)}async function Pt(t,e,r=!1){let s=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:n,...o}=e;if(s.inputNames.includes("use_cache_branch")&&(o.use_cache_branch=Ah(!!n)),s.inputNames.includes("position_ids")&&o.attention_mask&&!o.position_ids){let i=["paligemma","gemma3_text","gemma3"].includes(t.config.model_type)?1:0;o.position_ids=qN(o,n,i)}s.inputNames.includes("num_logits_to_keep")&&!o.num_logits_to_keep&&(o.num_logits_to_keep=new N("int64",[0n],[])),t.addPastKeyValues(o,n);let a=Qe(o,s.inputNames);return await fe(s,a)}async function $T(t,{encode_function:e,merge_function:r,modality_input_names:s,modality_output_name:n,input_ids:o=null,attention_mask:a=null,position_ids:i=null,inputs_embeds:l=null,past_key_values:c=null,generation_config:u=null,logits_processor:f=null,...m}){if(!l){l=await t.encode_text({input_ids:o,...m});let w=Qe(m,s);if(Object.keys(w).length>0){if(o.dims[1]!==1){let x=await e({...w,...m});({inputs_embeds:l,attention_mask:a}=r({[n]:x,inputs_embeds:l,input_ids:o,attention_mask:a}))}else if(c&&o.dims[1]===1){let x=o.dims[1],v=c.get_seq_length();a=ve([et([o.dims[0],v]),a.slice(null,[a.dims[1]-x,a.dims[1]])],1)}}}if(!i&&["qwen2_vl","qwen2_vl_text","qwen2_5_vl","qwen2_5_vl_text","qwen3_vl","qwen3_vl_text","qwen3_vl_moe","qwen3_vl_moe_text","qwen3_5","qwen3_5_text","qwen3_5_moe","qwen3_5_moe_text","glm_ocr","glm_ocr_text"].includes(t.config.model_type)){let{image_grid_thw:w,video_grid_thw:x}=m;[i]=t.get_rope_index(o,w,x,a)}return await Pt(t,{inputs_embeds:l,past_key_values:c,attention_mask:a,position_ids:i,generation_config:u,logits_processor:f},!0)}async function GN(t,e){return await $T(t,{...e,modality_input_names:["audio_values","input_features"],modality_output_name:"audio_features",encode_function:t.encode_audio.bind(t),merge_function:t._merge_input_ids_with_audio_features.bind(t)})}async function jN(t,e){return await $T(t,{...e,modality_input_names:["pixel_values"],modality_output_name:"image_features",encode_function:t.encode_image.bind(t),merge_function:t._merge_input_ids_with_image_features.bind(t)})}function Th(t,e=0){let[r,s]=t.dims,n=t.data,o=new BigInt64Array(n.length);for(let a=0;a<r;++a){let i=a*s,l=BigInt(e);for(let c=0;c<s;++c){let u=i+c;n[u]===0n?o[u]=BigInt(1):(o[u]=l,l+=n[u])}}return{data:o,dims:t.dims}}function qN(t,e=null,r=0){let{input_ids:s,inputs_embeds:n,attention_mask:o}=t,{data:a,dims:i}=Th(o,r),l=new N("int64",a,i);if(e){let c=-(s??n).dims.at(1);l=l.slice(null,[c,null])}return l}function Pn(t,e,r,s){let n=r.past_key_values?r.past_key_values.get_seq_length():0;if((t.sessions.decoder_model_merged??t.sessions.model)?.inputNames.includes("num_logits_to_keep")&&!r.num_logits_to_keep&&(r.num_logits_to_keep=new N("int64",[1n],[])),!r.attention_mask){let a;for(let i of["input_ids","inputs_embeds","position_ids"])if(r[i]){a=r[i].dims;break}if(!a)throw new Error("attention_mask is not 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_prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:s,attention_mask:n}){if(!e&&!r)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let o,a;return e&&(o=await this.encode_text({input_ids:e})),r&&(a=await this.encode_image({pixel_values:r})),o&&a?{inputs_embeds:s,attention_mask:n}=this._merge_input_ids_with_image_features({inputs_embeds:o,image_features:a,input_ids:e,attention_mask:n}):s=o||a,{inputs_embeds:s,attention_mask:n}}async forward({input_ids:e,pixel_values:r,attention_mask:s,decoder_input_ids:n,decoder_attention_mask:o,encoder_outputs:a,past_key_values:i,inputs_embeds:l,decoder_inputs_embeds:c}){if(l||({inputs_embeds:l,attention_mask:s}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:l,attention_mask:s})),!a){let{last_hidden_state:f}=await Vt(this,{inputs_embeds:l,attention_mask:s});a=f}if(!c){if(!n)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");c=await this.encode_text({input_ids:n})}return await Pt(this,{inputs_embeds:c,attention_mask:o,encoder_attention_mask:s,encoder_hidden_states:a,past_key_values:i},!0)}};var za=class extends y{},jw=class extends za{},qw=class extends za{};var Na=class extends y{},Ww=class extends Na{},Vw=class extends Na{};var ju=class extends y{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]},Et=class extends ju{_merge_input_ids_with_image_features(e){let r=e.image_features.dims.at(-1),s=e.image_features.view(-1,r);return Ln({image_token_id:this.config.image_token_index??this.config.image_token_id,...e,image_features:s})}},Hw=class extends Et{},Xw=class extends Et{};var qu=class extends y{},Kw=class extends qu{},Wu=class extends Et{},Yw=class extends Wu{};var Vu=class extends y{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","input_features","input_features_mask","past_key_values"]},ns=class extends Vu{async forward({input_ids:e=null,attention_mask:r=null,pixel_values:s=null,input_features:n=null,input_features_mask:o=null,position_ids:a=null,inputs_embeds:i=null,per_layer_inputs:l=null,past_key_values:c=null,generation_config:u=null,logits_processor:f=null,...m}){if((!i||!l)&&({inputs_embeds:i,per_layer_inputs:l}=await fe(this.sessions.embed_tokens,{input_ids:e}),e.dims[1]!==1)){if(s){let{image_features:w}=await this._encode_vision({pixel_values:s,...m});({inputs_embeds:i,attention_mask:r}=this._merge_input_ids_with_image_features({image_features:w,inputs_embeds:i,input_ids:e,attention_mask:r}))}if(n){let{audio_features:w}=await fe(this.sessions.audio_encoder,{input_features:n,input_features_mask:o});({inputs_embeds:i,attention_mask:r}=this._merge_input_ids_with_audio_features({audio_features:w,inputs_embeds:i,input_ids:e,attention_mask:r}))}}return await Pt(this,{inputs_embeds:i,per_layer_inputs:l,past_key_values:c,attention_mask:r,position_ids:a,generation_config:u,logits_processor:f},!0)}_encode_vision(e){return fe(this.sessions.vision_encoder,{pixel_values:e.pixel_values})}_merge_input_ids_with_image_features(e){let r=e.image_features.dims.at(-1),s=e.image_features.view(-1,r);return Ln({image_token_id:this.config.image_token_id,...e,image_features:s})}_merge_input_ids_with_audio_features(e){let r=e.audio_features.dims.at(-1),s=e.audio_features.view(-1,r);return Ou({audio_token_id:this.config.audio_token_id,...e,audio_features:s})}},Qw=class extends ns{};var Bn=class extends ns{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","image_position_ids","input_features","input_features_mask","past_key_values"];_encode_vision(e){return fe(this.sessions.vision_encoder,{pixel_values:e.pixel_values,pixel_position_ids:e.image_position_ids})}},Jw=class extends Bn{};var Ra=class extends y{},Zw=class extends Ra{},ex=class extends Ra{};var $a=class extends y{},tx=class extends $a{},rx=class extends $a{};var Hu=class extends y{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]},Un=class extends Hu{image_grid_thw_name="grid_thw";_get_text_only_rope_index(e,r){if(r){let{data:s,dims:n}=Th(r),o=BigInt64Array.from({length:3*s.length},(i,l)=>s[l%s.length]),a=Array.from({length:n[0]},(i,l)=>Pe(s.subarray(n[1]*l,n[1]*(l+1)))[0]+1n+BigInt(n[1]));return[new N("int64",o,[3,...n]),new N("int64",a,[a.length,1])]}else{let[s,n]=e.dims,o=BigInt64Array.from({length:3*s*n},(a,i)=>BigInt(Math.floor(i%n/s)));return[new N("int64",o,[3,...e.dims]),Bd([s,1])]}}_reorder_and_write_positions(e,r,s,n){let o=e.reduce((c,u)=>c+u.length,0),a=new Array(o),i=0;for(let c=0;c<3;++c)for(let u of e){let f=u.length/3;for(let m=c*f;m<(c+1)*f;++m)a[i++]=u[m]}let l=0;for(let c=0;c<r.length;++c)if(r[c]==1){for(let u=0;u<3;++u)s[u][n][c]=a[u*o/3+l];++l}return a}_get_multimodal_rope_positions({filtered_ids:e,image_grid_thw_list:r,video_grid_thw_list:s,spatial_merge_size:n,state:o}){let{image_token_id:a,video_token_id:i,vision_start_token_id:l}=this.config,c=e,f=c.reduce((A,S,M)=>(S==l&&A.push(M),A),[]).map(A=>c[A+1]),m=f.filter(A=>A==a).length,_=f.filter(A=>A==i).length,w=[],x=0,v=m,b=_;for(let A=0;A<f.length;++A){let S=c.findIndex((J,le)=>le>x&&J==a),M=c.findIndex((J,le)=>le>x&&J==i),I=v>0&&S!==-1?S:c.length+1,O=b>0&&M!==-1?M:c.length+1,k,R,G,F;I<O?([R,G,F]=r[o.image_index],++o.image_index,--v,k=I):([R,G,F]=s[o.video_index],++o.video_index,--b,k=O);let[K,V,Q]=[Number(R),Math.floor(Number(G)/n),Math.floor(Number(F)/n)],U=k-x,C=w.length>0?Pe(w.at(-1))[0]+1:0;w.push(Array.from({length:3*U},(J,le)=>C+le%U));let se=U+C,Y=K*V*Q,L=Array.from({length:Y},(J,le)=>se+Math.floor(le/(V*Q))),$=Array.from({length:Y},(J,le)=>se+Math.floor(le/Q)%V),B=Array.from({length:Y},(J,le)=>se+le%Q);w.push([L,$,B].flat()),x=k+Y}if(x<c.length){let A=w.length>0?Pe(w.at(-1))[0]+1:0,S=c.length-x;w.push(Array.from({length:3*S},(M,I)=>A+I%S))}return w}get_rope_index(e,r,s,n){let{vision_config:o}=this.config,a=o.spatial_merge_size??2;if(r||s){let i=e.tolist();n||(n=Rc(e));let l=n.tolist(),c=Array.from({length:3},()=>Array.from({length:e.dims[0]},()=>Array.from({length:e.dims[1]},()=>0))),u=r?r.tolist():[],f=s?s.tolist():[],m={image_index:0,video_index:0},_=[];for(let w=0;w<i.length;++w){let x=i[w].filter((A,S)=>l[w][S]==1),v=this._get_multimodal_rope_positions({filtered_ids:x,image_grid_thw_list:u,video_grid_thw_list:f,spatial_merge_size:a,state:m}),b=this._reorder_and_write_positions(v,l[w],c,w);_.push(Pe(b)[0]+1-i[w].length)}return[new N("int64",c.flat(1/0),[3,e.dims[0],e.dims[1]]),new N("int64",_,[_.length,1])]}else return this._get_text_only_rope_index(e,n)}async encode_image({pixel_values:e,image_grid_thw:r}){return(await fe(this.sessions.vision_encoder,{pixel_values:e,[this.image_grid_thw_name]:r})).image_features}_merge_input_ids_with_image_features(e){return Ln({image_token_id:this.config.image_token_id,...e})}prepare_inputs_for_generation(e,r,s){if(!r.attention_mask||r.position_ids||!(this.sessions.decoder_model_merged??this.sessions.model).inputNames.includes("position_ids"))return r;if(!r.past_key_values)[r.position_ids,r.rope_deltas]=this.get_rope_index(r.input_ids,r.image_grid_thw,r.video_grid_thw,r.attention_mask);else{r.pixel_values=null;let o=r.past_key_values.get_seq_length();if(o<r.input_ids.dims[1]){let[a,i]=this.get_rope_index(r.input_ids,r.image_grid_thw,r.video_grid_thw,r.attention_mask);r.rope_deltas=i,r.position_ids=a.slice(null,null,[o,null]),r.input_ids=r.input_ids.slice(null,[o,null])}else{r.rope_deltas||([,r.rope_deltas]=this.get_rope_index(r.input_ids,r.image_grid_thw,r.video_grid_thw,r.attention_mask));let a=BigInt(o),i=r.rope_deltas.map(l=>a+l);r.position_ids=yt([i,i,i],0)}}return r}},Da=class extends Un{};var js=class extends Un{image_grid_thw_name="image_grid_thw"},Ba=class extends Da{image_grid_thw_name="image_grid_thw"};var sx=class extends js{get_vision_position_ids(e,r,s,n){let o=Math.floor(r[0]/s),a=Math.floor(r[1]/n),i=Math.floor(r[2]/n),l=a*i*o,c=Array.from({length:l},()=>e),u=Array.from({length:l},(m,_)=>e+Math.floor(_/(i*o))),f=Array.from({length:l},(m,_)=>e+_%i);return[...c,...u,...f]}_get_multimodal_rope_positions({filtered_ids:e,image_grid_thw_list:r,video_grid_thw_list:s,spatial_merge_size:n,state:o}){let{image_token_id:a}=this.config,i=[],l=0,c=e[0]==a?1:0;for(let m=1;m<=e.length;++m){let _=m<e.length?e[m]==a?1:0:-1;_!==c&&(i.push([c,l,m]),l=m,c=_)}let u=0,f=[];for(let[m,_,w]of i)if(m===0){let x=w-_;f.push(Array.from({length:3*x},(v,b)=>u+b%x)),u+=x}else{let x=r[o.image_index++].map(Number),v=x[0];f.push(this.get_vision_position_ids(u,x,v,n)),u+=Math.max(x[1],x[2])/n}return f}};var Ua=class extends y{},nx=class extends Ua{},ox=class extends Ua{};var Fa=class extends y{},ax=class extends Fa{},ix=class extends Fa{};var Ga=class extends y{},lx=class extends Ga{},cx=class extends Ga{};var ja=class extends y{},ux=class extends ja{},px=class extends ja{};var qa=class extends y{},fx=class extends qa{},dx=class extends qa{};var Wa=class extends y{},mx=class extends Wa{},_x=class extends Wa{};var Va=class extends y{},hx=class extends Va{},gx=class extends Va{};var Ha=class extends y{},wx=class extends Ha{},xx=class extends Ha{};var Xa=class extends y{},yx=class extends Xa{},bx=class extends Xa{};var Xu=class extends y{forward_params=["input_ids","attention_mask","position_ids","audio_values","past_key_values"]},qs=class extends Xu{_merge_input_ids_with_audio_features(e){let r=e.audio_features.dims.at(-1),s=e.audio_features.view(-1,r);return Ou({audio_token_id:this.config.ignore_index??this.config.audio_token_id??this.config.audio_token_index,...e,audio_features:s})}};var vx=class extends qs{forward_params=["input_ids","attention_mask","input_features","past_key_values"]};var Ku=class extends y{},kx=class extends Ku{};var Yu=class extends y{},Ex=class extends Yu{};var Ka=class extends y{},Ax=class extends Ka{},Tx=class extends Ka{};var Ya=class extends y{},Mx=class extends Ya{},Sx=class extends Ya{async _call(e){return new q(await super._call(e))}};var sr=class extends y{},Ox=class extends sr{},Ix=class extends sr{async _call(e){return new kt(await super._call(e))}},Cx=class extends sr{async _call(e){return new q(await super._call(e))}},Px=class extends sr{async _call(e){return new ge(await super._call(e))}};var Lx=class extends y{},zx=class extends sr{},Nx=class extends sr{async _call(e){return new kt(await super._call(e))}},Rx=class extends sr{async _call(e){return new q(await super._call(e))}};var Qa=class extends y{},$x=class extends Qa{},Dx=class extends Qa{};var Bx=class extends Et{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"]};var Ja=class extends y{},Ux=class extends Ja{},Fx=class extends Ja{async _call(e){return new q(await super._call(e))}};var Za=class extends y{},Gx=class extends Za{},jx=class extends Za{};var Fn=class extends y{},qx=class extends Fn{async forward(e){let r=!e.input_ids,s=!e.pixel_values;if(r&&s)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(r&&(e.input_ids=et([e.pixel_values.dims[0],1])),s){let{image_size:c}=this.config.vision_config;e.pixel_values=Ye([0,3,c,c],0)}let{text_embeddings:n,image_embeddings:o,l2norm_text_embeddings:a,l2norm_image_embeddings:i}=await super.forward(e),l={};return r||(l.text_embeddings=n,l.l2norm_text_embeddings=a),s||(l.image_embeddings=o,l.l2norm_image_embeddings=i),l}},ei=class extends Fn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"text_model"})}},Wx=class extends Fn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"vision_model"})}};var ti=class extends y{},Vx=class extends ti{},Hx=class extends ti{};var Xx=class extends Et{};var ri=class extends y{},Kx=class extends ri{},Yx=class extends ri{};var Qx=class extends Et{forward_params=["input_ids","attention_mask","pixel_values","pixel_attention_mask","spatial_shapes","position_ids","past_key_values"]};var si=class extends y{},Jx=class extends si{},Zx=class extends si{};var Qu=class extends y{},ey=class extends Qu{};var ni=class extends y{},ty=class extends ni{},ry=class extends ni{};var oi=class extends y{},sy=class extends oi{},ny=class extends oi{};var ai=class extends y{},oy=class extends ai{},ay=class extends ai{};var ii=class extends y{},iy=class extends ii{},ly=class extends ii{};var Ws=class extends y{},cy=class extends Ws{},uy=class extends Ws{},py=class extends Ws{async _call(e){return new q(await super._call(e))}},fy=class extends Ws{};var Ju=class extends y{},dy=class extends Ju{};var Zu=class extends y{},my=class extends Zu{};var ep=class extends $e{constructor({char_logits:e,bpe_logits:r,wp_logits:s}){super(),this.char_logits=e,this.bpe_logits=r,this.wp_logits=s}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}},tp=class extends y{},_y=class extends tp{async _call(e){return new ep(await super._call(e))}};var rp=class extends $e{constructor({audio_codes:e}){super(),this.audio_codes=e}},sp=class extends $e{constructor({audio_values:e}){super(),this.audio_values=e}},Gn=class extends y{main_input_name="input_values";forward_params=["input_values"]},hy=class extends Gn{async encode(e){return new rp(await fe(this.sessions.encoder_model,e))}async decode(e){return new sp(await fe(this.sessions.decoder_model,e))}},li=class extends Gn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"encoder_model"})}},ci=class extends Gn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"decoder_model"})}};var ui=class extends y{},gy=class extends ui{},wy=class extends ui{};var pi=class extends y{},xy=class extends pi{},yy=class extends pi{};var Vs=class extends y{},by=class extends Vs{},vy=class extends Vs{async _call(e){return new we(await super._call(e))}},ky=class extends Vs{async _call(e){return new q(await super._call(e))}},Ey=class extends Vs{async _call(e){return new Me(await super._call(e))}};var fi=class extends y{},Ay=class extends fi{},Ty=class extends fi{};var jn=class extends y{},My=class extends jn{},Sy=class extends jn{async _call(e){return new q(await super._call(e))}},Oy=class extends jn{};var qn=class extends y{},Iy=class extends qn{},Cy=class extends qn{async _call(e){return new q(await super._call(e))}},Py=class extends qn{};var Wn=class extends y{},Ly=class extends Wn{},zy=class extends Wn{async _call(e){return new q(await super._call(e))}},Ny=class extends Wn{};var Vn=class extends y{},Ry=class extends Vn{},$y=class extends Vn{async _call(e){return new q(await super._call(e))}},Dy=class extends Vn{};var di=class extends y{},By=class extends di{},Uy=class extends di{async _call(e){return new q(await super._call(e))}};var mi=class extends y{},Fy=class extends mi{},Gy=class extends mi{async _call(e){return new q(await super._call(e))}};var Hs=class extends y{},jy=class extends Hs{},qy=class extends Hs{async _call(e){return new we(await super._call(e))}},Wy=class extends Hs{async _call(e){return new q(await super._call(e))}},Vy=class extends Hs{async _call(e){return new ge(await super._call(e))}};var _i=class extends y{},Hy=class extends _i{},Xy=class extends _i{};var hi=class extends y{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]},Ky=class extends hi{},Yy=class extends hi{};var os=class extends y{},Qy=class extends os{},Jy=class extends os{async _call(e){return new we(await super._call(e))}},Zy=class extends os{async _call(e){return new q(await super._call(e))}},e0=class extends os{async _call(e){return new ge(await super._call(e))}},t0=class extends os{async _call(e){return new Me(await super._call(e))}};var gi=class extends y{},r0=class extends gi{},s0=class extends gi{};var wi=class extends y{},n0=class extends wi{},o0=class extends wi{};var np=class extends y{},a0=class extends np{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...e){super(...e),this._generation_mode="text"}async forward(e){let r=this._generation_mode??"text",s;if(r==="text"||!e.past_key_values){let l=this.sessions.prepare_inputs_embeds,c=Qe(e,l.inputNames);s=await fe(l,c)}else{let l=this.sessions.gen_img_embeds,c=Qe({image_ids:e.input_ids},l.inputNames);s=await fe(l,c)}let n={...e,...s},o=await Pt(this,n),a=this.sessions[r==="text"?"lm_head":"gen_head"];if(!a)throw new Error(`Unable to find "${a}" generation head`);let i=await fe(a,Qe(o,a.inputNames));return{...s,...o,...i}}prepare_inputs_for_generation(e,r,s){let n=!!r.past_key_values;return s.guidance_scale!==null&&s.guidance_scale>1&&(n?r.input_ids=ve([r.input_ids,r.input_ids],0):(r.input_ids=ve([r.input_ids,Po(r.input_ids,BigInt(s.pad_token_id))],0),r.attention_mask=ve([r.attention_mask,Po(r.attention_mask,0n)],0))),(n||!r.pixel_values)&&(r.pixel_values=Ye([0,0,3,384,384],1)),n&&(r.images_seq_mask=new N("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),r.images_emb_mask=new N("bool",new Array(0).fill(!1),[1,1,0])),r}async generate(e){return this._generation_mode="text",super.generate(e)}async generate_images(e){this._generation_mode="image";let r=(e.inputs??e[this.main_input_name]).dims[1],n=(await super.generate(e)).slice(null,[r,null]),o=this.sessions.image_decode,{decoded_image:a}=await fe(o,{generated_tokens:n}),i=a.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),l=[];for(let c of i){let u=Je.fromTensor(c);l.push(u)}return l}};var xi=class extends y{},i0=class extends xi{},l0=class extends xi{},yi=class extends y{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(e){let[r,s]=e.dims,n=this.config.decoder.num_codebooks,o=s-n,a=0;for(let c=0;c<e.size;++c){if(e.data[c]==this.config.decoder.pad_token_id)continue;let u=c%s,f=Math.floor(c/s)%n,m=u-f;m>0&&m<=o&&(e.data[a++]=e.data[c])}let i=Math.floor(r/n),l=a/(i*n);return new N(e.type,e.data.slice(0,a),[i,n,l])}prepare_inputs_for_generation(e,r,s){let n=BigInt(this.config.decoder.pad_token_id),o=structuredClone(e);for(let a=0;a<o.length;++a)for(let i=0;i<o[a].length;++i)a%this.config.decoder.num_codebooks>=i&&(o[a][i]=n);return s.guidance_scale!==null&&s.guidance_scale>1&&(o=o.concat(o)),Zo(this,o,r,s)}async generate(e){let r=await super.generate(e),s=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:n}=await fe(this.sessions.encodec_decode,{audio_codes:s});return n}};var bi=class extends y{},c0=class extends 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Jn{async _call(e){return new q(await super._call(e))}},r1=class extends Jn{};var nl=class extends y{},s1=class extends nl{},n1=class extends nl{};var ol=class extends y{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]},o1=class extends ol{},a1=class extends ol{};var al=class extends y{},i1=class extends al{},l1=class extends al{async _call(e){return new hp(await super._call(e))}},hp=class extends Dn{};var gp=class extends y{},c1=class extends gp{};var Zn=class extends y{},u1=class extends Zn{},p1=class extends Zn{async _call(e){return new kt(await super._call(e))}},f1=class extends Zn{async _call(e){return new q(await super._call(e))}};var Ys=class extends y{},d1=class extends Ys{},m1=class extends Ys{async _call(e){return new kt(await super._call(e))}},_1=class extends Ys{async _call(e){return new q(await super._call(e))}},h1=class extends Ys{async _call(e){return new ge(await super._call(e))}};var il=class extends y{},g1=class extends il{},w1=class extends il{};var x1=class extends y{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]};var ll=class extends y{},y1=class extends ll{},b1=class extends ll{async _call(e){return new q(await super._call(e))}};var wp=class extends y{},v1=class extends wp{};var cl=class extends y{},k1=class extends cl{},E1=class extends cl{async _call(e){return new q(await super._call(e))}};var xp=class extends y{},A1=class extends xp{async _call(e){return new fu(await super._call(e))}};var yp=class extends y{},T1=class extends yp{};var bp=class extends $e{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}},vp=class extends y{},M1=class extends vp{async _call(e){return new bp(await super._call(e))}};var S1=class extends qs{};var UT=2,HN=1,O1=new WeakMap;function XN(t,e){let{text_config:r,audio_config:s}=t.config,n=t.sessions.audio_encoder,{num_mel_bins:o,hidden_size:a}=s,i=o+a,l=new Yo,c=n?.config?.kv_cache_dtype??"float32",u=c==="float16"?kr.float16:kr.float32,f=Ns(s,{batch_size:1});for(let w in f){let x=f[w].reduce((v,b)=>v*b,1);l[w]=new N(c,new u(x),f[w])}let m=new N(c,new u(i*UT),[1,i,UT]),_=e[Symbol.asyncIterator]?.()??e[Symbol.iterator]?.();if(!_)throw new Error("input_features must be iterable or async iterable");return{encoder_session:n,enc_kv_cache:l,enc_padding_cache:m,enc_past_seq_len:0,audio_embed_queue:[],audio_embed_total_tokens:0,audio_queue_offset:0,audio_consumed:0,stream_exhausted:!1,chunks_iter:_,text_hidden_size:r.hidden_size}}async function KN(t,e){let r=e.dims[2],s=Math.floor((HN+r-3)/2)+1,n=new N("int64",BigInt64Array.from({length:s},(u,f)=>BigInt(t.enc_past_seq_len+f)),[1,s]),o=t.enc_past_seq_len+s,a=et([1,o]),{audio_embeds:i,present_padding_cache:l,...c}=await fe(t.encoder_session,{input_features:e,attention_mask:a,position_ids:n,past_padding_cache:t.enc_padding_cache,...t.enc_kv_cache});t.enc_padding_cache.location==="gpu-buffer"&&t.enc_padding_cache.dispose(),t.enc_padding_cache=l;for(let u in c)if(u.startsWith("present.")){let f=u.replace("present","past_key_values"),m=t.enc_kv_cache[f];m?.location==="gpu-buffer"&&m.dispose(),t.enc_kv_cache[f]=c[u]}return t.enc_past_seq_len=o,i}async function YN(t,e){for(;t.audio_embed_total_tokens<e&&!t.stream_exhausted;){let r=await t.chunks_iter.next();if(r.done){t.stream_exhausted=!0;break}let s=await KN(t,r.value);t.audio_embed_queue.push({data:s.data,tokens:s.dims[1]}),t.audio_embed_total_tokens+=s.dims[1]}}function QN(t,e,r){if(t.audio_embed_queue.length===0)return;let s=e.data,n=0,o=r;for(;o>0&&t.audio_embed_queue.length>0;){let a=t.audio_embed_queue[0],i=a.tokens-t.audio_queue_offset,l=Math.min(o,i),c=t.audio_queue_offset*t.text_hidden_size;for(let u=0;u<l*t.text_hidden_size;++u)s[n*t.text_hidden_size+u]+=a.data[c+u];n+=l,o-=l,t.audio_queue_offset+=l,t.audio_queue_offset>=a.tokens&&(t.audio_embed_queue.shift(),t.audio_queue_offset=0)}t.audio_consumed+=r-o}var I1=class extends Yr{constructor(e){super(),this._s=e}_call(e){let r=this._s.stream_exhausted&&this._s.audio_embed_queue.length===0;return e.map(()=>r)}},kp=class extends y{forward_params=["input_ids","attention_mask","position_ids","past_key_values"]},ul=class extends kp{async forward({input_ids:e,past_key_values:r,...s}){let n=e.dims[1],o=O1.get(this);o&&await YN(o,o.audio_consumed+n);let{inputs_embeds:a}=await fe(this.sessions.embed_tokens,{input_ids:e});o&&QN(o,a,n);let i={inputs_embeds:a,...s};this.addPastKeyValues(i,r);let l=this.sessions.decoder_model_merged,c=Qe(i,l.inputNames);return await fe(l,c)}async generate({input_features:e,stopping_criteria:r,...s}){if(!e)throw new Error("input_features (generator/iterable) must be provided");let n=XN(this,e);O1.set(this,n);let o=new On;o.push(new I1(n)),r&&o.extend(r);try{return await super.generate({...s,stopping_criteria:o})}finally{n.enc_kv_cache.dispose(),O1.delete(this)}}};var eo=class extends y{},C1=class extends eo{},P1=class extends eo{async _call(e){return new kt(await super._call(e))}},L1=class extends eo{async _call(e){return new q(await super._call(e))}};var Ep=class extends $e{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}},cs=class extends y{},z1=class extends cs{},N1=class extends cs{async _call(e){return new kt(await super._call(e))}},R1=class extends cs{async _call(e){return new q(await super._call(e))}},$1=class extends cs{async _call(e){return new Ep(await super._call(e))}},D1=class extends cs{async _call(e){return new ge(await super._call(e))}};var Ap=class extends y{},B1=class extends Ap{};var Tp=class extends Sn{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1};var pl=class extends y{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]},U1=class extends pl{},Mp=class extends pl{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,Tp)}_retrieve_init_tokens(e){let r=[e.decoder_start_token_id],s=e.language,n=e.task;if(e.is_multilingual){s||(ee.warn("No language specified - defaulting to English (en)."),s="en");let a=`<|${fT(s)}|>`;r.push(e.lang_to_id[a]),r.push(e.task_to_id[n??"transcribe"])}else if(s||n)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(ee.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(o=>o!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:s=null,stopping_criteria:n=null,...o}){r=this._prepare_generation_config(r,o);let a=o.decoder_input_ids??this._retrieve_init_tokens(r);if(r.return_timestamps&&(s??=new Rs,s.push(new hu(r,a))),r.begin_suppress_tokens&&(s??=new Rs,s.push(new Mn(r.begin_suppress_tokens,a.length))),r.return_token_timestamps){if(!r.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");r.task==="translate"&&ee.warn("Token-level timestamps may not be reliable for task 'translate'."),r.output_attentions=!0,r.return_dict_in_generate=!0}if(r.return_timestamps&&!o.max_new_tokens)return this._generate_with_seek({inputs:e,generation_config:r,logits_processor:s,init_tokens:a,kwargs:o});let i=await super.generate({inputs:e,generation_config:r,logits_processor:s,decoder_input_ids:a,...o});return r.return_token_timestamps&&(i.token_timestamps=this._extract_token_timestamps(i,r.alignment_heads,r.num_frames,.02,a.length)),i}async _generate_with_seek({inputs:e,generation_config:r,logits_processor:s,init_tokens:n,kwargs:o}){let a=r.no_timestamps_token_id+1,i=Array.isArray(r.eos_token_id)?r.eos_token_id[0]:r.eos_token_id,l=r.return_token_timestamps,c=e,u=c.dims[2],f=2,m=this.config.max_source_positions,_=f*m,w=0,x=[],v=[];for(;w<u;){let A=Math.min(w+_,u),S=c.slice(null,null,[w,A]),M,I=S.dims[2];if(I<_){let Y=c.dims[1],L=new Float32Array(Y*_),$=S.data;for(let B=0;B<Y;++B)L.set($.subarray(B*I,(B+1)*I),B*_);M=new N("float32",L,[1,Y,_])}else M=S;if(s)for(let Y of s)"begin_index"in Y&&(Y.begin_index=n.length);let O=await super.generate({inputs:M,generation_config:r,logits_processor:s,decoder_input_ids:n,...o}),R=(l?O.sequences:O)[0].tolist().map(Number).slice(n.length),G;if(l){O.token_timestamps=this._extract_token_timestamps(O,r.alignment_heads,Math.floor((A-w)/f),.02,n.length);let Y=w/f*.02;G=O.token_timestamps[0].tolist().slice(n.length).map(L=>L+Y)}if(R.length>0&&R.at(-1)===i&&R.pop(),R.length===0)break;let F=R.map(Y=>Y>=a),K=R.length>=2&&F[R.length-1]&&!F[R.length-2],V=[];for(let Y=0;Y<R.length-1;++Y)F[Y]&&F[Y+1]&&V.push(Y+1);let Q,U=R.length;if(V.length>0)if(K)Q=A-w;else{let Y=V.at(-1);Q=(R[Y-1]-a)*f,U=Y}else Q=A-w;let C=Math.floor(w/f),se=a+1500;for(let Y=0;Y<U;++Y)R[Y]>=a&&(R[Y]=Math.min(R[Y]+C,se));x.push(...R.slice(0,U)),G&&v.push(...G.slice(0,U)),w+=Q}x.push(i);let b=[...n,...x];if(l){let A=new N("int64",b.map(BigInt),[1,b.length]),S=[...new Array(n.length).fill(0),...v,0],M=new N("float32",new Float32Array(S),[1,S.length]);return{sequences:A,token_timestamps:M}}return new N("int64",b.map(BigInt),[1,b.length])}_extract_token_timestamps(e,r,s=null,n=.02,o=0){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");s==null&&ee.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let a=this.config.median_filter_width;a===void 0&&(ee.warn("Model config has no `median_filter_width`, using default value of 7."),a=7);let i=e.cross_attentions,l=Array.from({length:this.config.decoder_layers},(b,A)=>ve(i.map(S=>S[A]),2)),c=yt(r.map(([b,A])=>{if(b>=l.length)throw new Error(`Layer index ${b} is out of bounds for cross attentions (length ${l.length}).`);return s?l[b].slice(null,A,null,[0,s]):l[b].slice(null,A)})).transpose(1,0,2,3),[u,f]=$d(c,-2,0,!0),m=c.clone();for(let b=0;b<m.dims[0];++b){let A=m[b];for(let S=0;S<A.dims[0];++S){let M=A[S],I=u[b][S][0].data,O=f[b][S][0].data;for(let k=0;k<M.dims[0];++k){let R=M[k].data;for(let G=0;G<R.length;++G)R[G]=(R[G]-O[G])/I[G];R.set(uA(R,a))}}}let _=o>0?m.slice(null,null,[o,m.dims[2]],null):m,w=[Nc(_,1)],x=e.sequences.dims,v=new N("float32",new Float32Array(x[0]*x[1]),x);for(let b=0;b<x[0];++b){let A=w[b].neg().squeeze_(0),[S,M]=fA(A.tolist()),I=Array.from({length:S.length-1},(G,F)=>S[F+1]-S[F]),O=ht([1],I).map(G=>!!G),k=[];for(let G=0;G<O.length;++G)O[G]&&k.push(M[G]*n);let R=new Array(o).fill(0);R.push(...k),k.length>0&&R.push(k.at(-1)),v[b].data.set(R)}return v}},F1=class extends Mp{};var us=class extends y{},G1=class extends us{},j1=class extends us{async _call(e){return new we(await super._call(e))}},q1=class extends us{async _call(e){return new q(await super._call(e))}},W1=class extends us{async _call(e){return new ge(await super._call(e))}},V1=class extends us{async _call(e){return new Me(await super._call(e))}};var ps=class extends y{},H1=class extends ps{},X1=class extends ps{async _call(e){return new we(await super._call(e))}},K1=class extends ps{async _call(e){return new q(await super._call(e))}},Y1=class extends ps{async _call(e){return new ge(await super._call(e))}},Q1=class extends ps{async _call(e){return new Me(await super._call(e))}};var fl=class extends y{},J1=class extends fl{},Z1=class extends fl{async _call(e){return new Sp(await super._call(e))}},Sp=class extends $e{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}};var dl=class extends y{},ev=class extends dl{},tv=class extends dl{};var JN=new Map([["bert","BertModel"],["eurobert","EuroBertModel"],["neobert","NeoBertModel"],["modernbert","ModernBertModel"],["nomic_bert","NomicBertModel"],["roformer","RoFormerModel"],["electra","ElectraModel"],["esm","EsmModel"],["convbert","ConvBertModel"],["camembert","CamembertModel"],["deberta","DebertaModel"],["deberta-v2","DebertaV2Model"],["mpnet","MPNetModel"],["albert","AlbertModel"],["distilbert","DistilBertModel"],["roberta","RobertaModel"],["xlm","XLMModel"],["xlm-roberta","XLMRobertaModel"],["clap","ClapModel"],["clip","CLIPModel"],["clipseg","CLIPSegModel"],["chinese_clip","ChineseCLIPModel"],["siglip","SiglipModel"],["jina_clip","JinaCLIPModel"],["mobilebert","MobileBertModel"],["squeezebert","SqueezeBertModel"],["wav2vec2","Wav2Vec2Model"],["wav2vec2-bert","Wav2Vec2BertModel"],["unispeech","UniSpeechModel"],["unispeech-sat","UniSpeechSatModel"],["hubert","HubertModel"],["wavlm","WavLMModel"],["audio-spectrogram-transformer","ASTModel"],["vits","VitsModel"],["pyannote","PyAnnoteModel"],["wespeaker-resnet","WeSpeakerResNetModel"],["detr","DetrModel"],["rt_detr","RTDetrModel"],["rt_detr_v2","RTDetrV2Model"],["rf_detr","RFDetrModel"],["d_fine","DFineModel"],["table-transformer","TableTransformerModel"],["vit","ViTModel"],["ijepa","IJepaModel"],["pvt","PvtModel"],["vit_msn","ViTMSNModel"],["vit_mae","ViTMAEModel"],["groupvit","GroupViTModel"],["fastvit","FastViTModel"],["mobilevit","MobileViTModel"],["mobilevitv2","MobileViTV2Model"],["owlvit","OwlViTModel"],["owlv2","Owlv2Model"],["beit","BeitModel"],["deit","DeiTModel"],["hiera","HieraModel"],["convnext","ConvNextModel"],["convnextv2","ConvNextV2Model"],["dinov2","Dinov2Model"],["dinov2_with_registers","Dinov2WithRegistersModel"],["dinov3_vit","DINOv3ViTModel"],["dinov3_convnext","DINOv3ConvNextModel"],["resnet","ResNetModel"],["swin","SwinModel"],["swin2sr","Swin2SRModel"],["donut-swin","DonutSwinModel"],["yolos","YolosModel"],["dpt","DPTModel"],["glpn","GLPNModel"],["hifigan","SpeechT5HifiGan"],["efficientnet","EfficientNetModel"],["decision_transformer","DecisionTransformerModel"],["patchtst","PatchTSTModel"],["patchtsmixer","PatchTSMixerModel"],["mobilenet_v1","MobileNetV1Model"],["mobilenet_v2","MobileNetV2Model"],["mobilenet_v3","MobileNetV3Model"],["mobilenet_v4","MobileNetV4Model"],["maskformer","MaskFormerModel"],["mgp-str","MgpstrForSceneTextRecognition"],["style_text_to_speech_2","StyleTextToSpeech2Model"]]),ZN=new Map([["t5","T5Model"],["longt5","LongT5Model"],["mt5","MT5Model"],["bart","BartModel"],["mbart","MBartModel"],["marian","MarianModel"],["whisper","WhisperModel"],["cohere_asr","CohereAsrModel"],["m2m_100","M2M100Model"],["blenderbot","BlenderbotModel"],["blenderbot-small","BlenderbotSmallModel"]]),e3=new Map([["mimi","MimiModel"],["dac","DacModel"],["snac","SnacModel"]]),t3=new Map([["bloom","BloomModel"],["jais","JAISModel"],["gpt2","GPT2Model"],["gpt_oss","GptOssModel"],["gptj","GPTJModel"],["gpt_bigcode","GPTBigCodeModel"],["gpt_neo","GPTNeoModel"],["gpt_neox","GPTNeoXModel"],["codegen","CodeGenModel"],["llama","LlamaModel"],["apertus","ApertusModel"],["nanochat","NanoChatModel"],["arcee","ArceeModel"],["afmoe","AfmoeModel"],["lfm2","Lfm2Model"],["lfm2_moe","Lfm2MoeModel"],["smollm3","SmolLM3Model"],["exaone","ExaoneModel"],["olmo","OlmoModel"],["olmo2","Olmo2Model"],["olmo3","Olmo3Model"],["olmo_hybrid","OlmoHybridModel"],["mobilellm","MobileLLMModel"],["granite","GraniteModel"],["granitemoehybrid","GraniteMoeHybridModel"],["cohere","CohereModel"],["cohere2","Cohere2Model"],["gemma","GemmaModel"],["gemma2","Gemma2Model"],["vaultgemma","VaultGemmaModel"],["gemma3_text","Gemma3Model"],["helium","HeliumModel"],["glm","GlmModel"],["glm_moe_dsa","GlmMoeDsaModel"],["openelm","OpenELMModel"],["qwen2","Qwen2Model"],["qwen2_moe","Qwen2MoeModel"],["qwen3","Qwen3Model"],["qwen3_moe","Qwen3MoeModel"],["qwen3_next","Qwen3NextModel"],["phi","PhiModel"],["phi3","Phi3Model"],["mpt","MptModel"],["opt","OPTModel"],["mistral","MistralModel"],["mistral4","Mistral4Model"],["ministral","MinistralModel"],["ministral3","Ministral3Model"],["ernie4_5","Ernie4_5ForCausalLM"],["starcoder2","Starcoder2Model"],["deepseek_v3","DeepseekV3Model"],["falcon","FalconModel"],["falcon_h1","FalconH1Model"],["nemotron_h","NemotronHModel"],["solar_open","SolarOpenModel"],["stablelm","StableLmModel"],["modernbert-decoder","ModernBertDecoderModel"],["hunyuan_v1_dense","HunYuanDenseV1Model"],["youtu","YoutuModel"]]),FT=new Map([["speecht5","SpeechT5ForSpeechToText"],["whisper","WhisperForConditionalGeneration"],["lite-whisper","LiteWhisperForConditionalGeneration"],["moonshine","MoonshineForConditionalGeneration"],["cohere_asr","CohereAsrForConditionalGeneration"]]),GT=new Map([["speecht5","SpeechT5ForTextToSpeech"]]),jT=new Map([["vits","VitsModel"],["musicgen","MusicgenForConditionalGeneration"],["supertonic","SupertonicForConditionalGeneration"]]),qT=new Map([["bert","BertForSequenceClassification"],["eurobert","EuroBertForSequenceClassification"],["neobert","NeoBertForSequenceClassification"],["modernbert","ModernBertForSequenceClassification"],["roformer","RoFormerForSequenceClassification"],["electra","ElectraForSequenceClassification"],["esm","EsmForSequenceClassification"],["convbert","ConvBertForSequenceClassification"],["camembert","CamembertForSequenceClassification"],["deberta","DebertaForSequenceClassification"],["deberta-v2","DebertaV2ForSequenceClassification"],["mpnet","MPNetForSequenceClassification"],["albert","AlbertForSequenceClassification"],["distilbert","DistilBertForSequenceClassification"],["roberta","RobertaForSequenceClassification"],["xlm","XLMForSequenceClassification"],["xlm-roberta","XLMRobertaForSequenceClassification"],["bart","BartForSequenceClassification"],["mbart","MBartForSequenceClassification"],["mobilebert","MobileBertForSequenceClassification"],["squeezebert","SqueezeBertForSequenceClassification"]]),WT=new Map([["bert","BertForTokenClassification"],["eurobert","EuroBertForTokenClassification"],["neobert","NeoBertForTokenClassification"],["modernbert","ModernBertForTokenClassification"],["roformer","RoFormerForTokenClassification"],["electra","ElectraForTokenClassification"],["esm","EsmForTokenClassification"],["convbert","ConvBertForTokenClassification"],["camembert","CamembertForTokenClassification"],["deberta","DebertaForTokenClassification"],["deberta-v2","DebertaV2ForTokenClassification"],["mpnet","MPNetForTokenClassification"],["distilbert","DistilBertForTokenClassification"],["roberta","RobertaForTokenClassification"],["xlm","XLMForTokenClassification"],["xlm-roberta","XLMRobertaForTokenClassification"]]),VT=new Map([["t5","T5ForConditionalGeneration"],["longt5","LongT5ForConditionalGeneration"],["mt5","MT5ForConditionalGeneration"],["bart","BartForConditionalGeneration"],["mbart","MBartForConditionalGeneration"],["marian","MarianMTModel"],["m2m_100","M2M100ForConditionalGeneration"],["blenderbot","BlenderbotForConditionalGeneration"],["blenderbot-small","BlenderbotSmallForConditionalGeneration"]]),HT=new Map([["bloom","BloomForCausalLM"],["gpt2","GPT2LMHeadModel"],["gpt_oss","GptOssForCausalLM"],["jais","JAISLMHeadModel"],["gptj","GPTJForCausalLM"],["gpt_bigcode","GPTBigCodeForCausalLM"],["gpt_neo","GPTNeoForCausalLM"],["gpt_neox","GPTNeoXForCausalLM"],["codegen","CodeGenForCausalLM"],["llama","LlamaForCausalLM"],["nanochat","NanoChatForCausalLM"],["apertus","ApertusForCausalLM"],["llama4_text","Llama4ForCausalLM"],["arcee","ArceeForCausalLM"],["afmoe","AfmoeForCausalLM"],["lfm2","Lfm2ForCausalLM"],["lfm2_moe","Lfm2MoeForCausalLM"],["smollm3","SmolLM3ForCausalLM"],["exaone","ExaoneForCausalLM"],["olmo","OlmoForCausalLM"],["olmo2","Olmo2ForCausalLM"],["olmo3","Olmo3ForCausalLM"],["olmo_hybrid","OlmoHybridForCausalLM"],["mobilellm","MobileLLMForCausalLM"],["granite","GraniteForCausalLM"],["granitemoehybrid","GraniteMoeHybridForCausalLM"],["cohere","CohereForCausalLM"],["cohere2","Cohere2ForCausalLM"],["gemma","GemmaForCausalLM"],["gemma2","Gemma2ForCausalLM"],["vaultgemma","VaultGemmaForCausalLM"],["gemma3_text","Gemma3ForCausalLM"],["gemma3","Gemma3ForCausalLM"],["helium","HeliumForCausalLM"],["glm","GlmForCausalLM"],["glm_moe_dsa","GlmMoeDsaForCausalLM"],["openelm","OpenELMForCausalLM"],["qwen2","Qwen2ForCausalLM"],["qwen2_moe","Qwen2MoeForCausalLM"],["qwen3","Qwen3ForCausalLM"],["qwen3_moe","Qwen3MoeForCausalLM"],["qwen3_next","Qwen3NextForCausalLM"],["qwen2_vl","Qwen2VLForCausalLM"],["qwen2_5_vl","Qwen2_5_VLForCausalLM"],["qwen3_vl","Qwen3VLForCausalLM"],["qwen3_vl_moe","Qwen3VLMoeForCausalLM"],["qwen3_5","Qwen3_5ForCausalLM"],["qwen3_5_text","Qwen3_5ForCausalLM"],["qwen3_5_moe","Qwen3_5MoeForCausalLM"],["gemma3n","Gemma3nForCausalLM"],["gemma4","Gemma4ForCausalLM"],["phi","PhiForCausalLM"],["phi3","Phi3ForCausalLM"],["mpt","MptForCausalLM"],["opt","OPTForCausalLM"],["mbart","MBartForCausalLM"],["mistral","MistralForCausalLM"],["mistral4","Mistral4ForCausalLM"],["ministral","MinistralForCausalLM"],["ministral3","Ministral3ForCausalLM"],["ernie4_5","Ernie4_5ForCausalLM"],["starcoder2","Starcoder2ForCausalLM"],["deepseek_v3","DeepseekV3ForCausalLM"],["falcon","FalconForCausalLM"],["falcon_h1","FalconH1ForCausalLM"],["nemotron_h","NemotronHForCausalLM"],["trocr","TrOCRForCausalLM"],["solar_open","SolarOpenForCausalLM"],["stablelm","StableLmForCausalLM"],["modernbert-decoder","ModernBertDecoderForCausalLM"],["hunyuan_v1_dense","HunYuanDenseV1ForCausalLM"],["youtu","YoutuForCausalLM"],["phi3_v","Phi3VForCausalLM"]]),r3=new Map([["multi_modality","MultiModalityCausalLM"]]),XT=new Map([["bert","BertForMaskedLM"],["eurobert","EuroBertForMaskedLM"],["neobert","NeoBertForMaskedLM"],["modernbert","ModernBertForMaskedLM"],["roformer","RoFormerForMaskedLM"],["electra","ElectraForMaskedLM"],["esm","EsmForMaskedLM"],["convbert","ConvBertForMaskedLM"],["camembert","CamembertForMaskedLM"],["deberta","DebertaForMaskedLM"],["deberta-v2","DebertaV2ForMaskedLM"],["mpnet","MPNetForMaskedLM"],["albert","AlbertForMaskedLM"],["distilbert","DistilBertForMaskedLM"],["roberta","RobertaForMaskedLM"],["xlm","XLMWithLMHeadModel"],["xlm-roberta","XLMRobertaForMaskedLM"],["mobilebert","MobileBertForMaskedLM"],["squeezebert","SqueezeBertForMaskedLM"]]),KT=new Map([["bert","BertForQuestionAnswering"],["neobert","NeoBertForQuestionAnswering"],["roformer","RoFormerForQuestionAnswering"],["electra","ElectraForQuestionAnswering"],["convbert","ConvBertForQuestionAnswering"],["camembert","CamembertForQuestionAnswering"],["deberta","DebertaForQuestionAnswering"],["deberta-v2","DebertaV2ForQuestionAnswering"],["mpnet","MPNetForQuestionAnswering"],["albert","AlbertForQuestionAnswering"],["distilbert","DistilBertForQuestionAnswering"],["roberta","RobertaForQuestionAnswering"],["xlm","XLMForQuestionAnswering"],["xlm-roberta","XLMRobertaForQuestionAnswering"],["mobilebert","MobileBertForQuestionAnswering"],["squeezebert","SqueezeBertForQuestionAnswering"]]),YT=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"]]),QT=new Map([["llava","LlavaForConditionalGeneration"],["llava_onevision","LlavaOnevisionForConditionalGeneration"],["moondream1","Moondream1ForConditionalGeneration"],["florence2","Florence2ForConditionalGeneration"],["qwen2_vl","Qwen2VLForConditionalGeneration"],["qwen2_5_vl","Qwen2_5_VLForConditionalGeneration"],["qwen3_vl","Qwen3VLForConditionalGeneration"],["qwen3_vl_moe","Qwen3VLMoeForConditionalGeneration"],["qwen3_5","Qwen3_5ForConditionalGeneration"],["qwen3_5_moe","Qwen3_5MoeForConditionalGeneration"],["lfm2_vl","Lfm2VlForConditionalGeneration"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"],["paligemma","PaliGemmaForConditionalGeneration"],["llava_qwen2","LlavaQwen2ForCausalLM"],["gemma3","Gemma3ForConditionalGeneration"],["gemma3n","Gemma3nForConditionalGeneration"],["gemma4","Gemma4ForConditionalGeneration"],["mistral3","Mistral3ForConditionalGeneration"],["lighton_ocr","LightOnOcrForConditionalGeneration"],["glm_ocr","GlmOcrForConditionalGeneration"]]),JT=new Map([["granite_speech","GraniteSpeechForConditionalGeneration"],["ultravox","UltravoxModel"],["voxtral","VoxtralForConditionalGeneration"],["voxtral_realtime","VoxtralRealtimeForConditionalGeneration"]]),s3=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"]]),ZT=new Map([["vit","ViTForImageClassification"],["ijepa","IJepaForImageClassification"],["pvt","PvtForImageClassification"],["vit_msn","ViTMSNForImageClassification"],["fastvit","FastViTForImageClassification"],["mobilevit","MobileViTForImageClassification"],["mobilevitv2","MobileViTV2ForImageClassification"],["beit","BeitForImageClassification"],["deit","DeiTForImageClassification"],["hiera","HieraForImageClassification"],["convnext","ConvNextForImageClassification"],["convnextv2","ConvNextV2ForImageClassification"],["dinov2","Dinov2ForImageClassification"],["dinov2_with_registers","Dinov2WithRegistersForImageClassification"],["resnet","ResNetForImageClassification"],["swin","SwinForImageClassification"],["segformer","SegformerForImageClassification"],["efficientnet","EfficientNetForImageClassification"],["mobilenet_v1","MobileNetV1ForImageClassification"],["mobilenet_v2","MobileNetV2ForImageClassification"],["mobilenet_v3","MobileNetV3ForImageClassification"],["mobilenet_v4","MobileNetV4ForImageClassification"]]),eM=new Map([["detr","DetrForObjectDetection"],["rt_detr","RTDetrForObjectDetection"],["rt_detr_v2","RTDetrV2ForObjectDetection"],["rf_detr","RFDetrForObjectDetection"],["d_fine","DFineForObjectDetection"],["table-transformer","TableTransformerForObjectDetection"],["yolos","YolosForObjectDetection"]]),tM=new Map([["owlvit","OwlViTForObjectDetection"],["owlv2","Owlv2ForObjectDetection"],["grounding-dino","GroundingDinoForObjectDetection"]]),to=new Map([["detr","DetrForSegmentation"],["clipseg","CLIPSegForImageSegmentation"]]),rM=new Map([["segformer","SegformerForSemanticSegmentation"],["sapiens","SapiensForSemanticSegmentation"],["swin","SwinForSemanticSegmentation"],["mobilenet_v1","MobileNetV1ForSemanticSegmentation"],["mobilenet_v2","MobileNetV2ForSemanticSegmentation"],["mobilenet_v3","MobileNetV3ForSemanticSegmentation"],["mobilenet_v4","MobileNetV4ForSemanticSegmentation"]]),sM=new Map([["detr","DetrForSegmentation"],["maskformer","MaskFormerForInstanceSegmentation"]]),nM=new Map([["sam","SamModel"],["sam2","Sam2Model"],["edgetam","EdgeTamModel"],["sam3_tracker","Sam3TrackerModel"]]),oM=new Map([["wav2vec2","Wav2Vec2ForCTC"],["wav2vec2-bert","Wav2Vec2BertForCTC"],["unispeech","UniSpeechForCTC"],["unispeech-sat","UniSpeechSatForCTC"],["wavlm","WavLMForCTC"],["hubert","HubertForCTC"],["parakeet_ctc","ParakeetForCTC"]]),aM=new Map([["wav2vec2","Wav2Vec2ForSequenceClassification"],["wav2vec2-bert","Wav2Vec2BertForSequenceClassification"],["unispeech","UniSpeechForSequenceClassification"],["unispeech-sat","UniSpeechSatForSequenceClassification"],["wavlm","WavLMForSequenceClassification"],["hubert","HubertForSequenceClassification"],["audio-spectrogram-transformer","ASTForAudioClassification"]]),iM=new Map([["wavlm","WavLMForXVector"]]),lM=new Map([["unispeech-sat","UniSpeechSatForAudioFrameClassification"],["wavlm","WavLMForAudioFrameClassification"],["wav2vec2","Wav2Vec2ForAudioFrameClassification"],["pyannote","PyAnnoteForAudioFrameClassification"]]),cM=new Map([["vitmatte","VitMatteForImageMatting"]]),n3=new Map([["patchtst","PatchTSTForPrediction"],["patchtsmixer","PatchTSMixerForPrediction"]]),uM=new Map([["swin2sr","Swin2SRForImageSuperResolution"]]),pM=new Map([["chmv2","CHMv2ForDepthEstimation"],["dpt","DPTForDepthEstimation"],["depth_anything","DepthAnythingForDepthEstimation"],["glpn","GLPNForDepthEstimation"],["sapiens","SapiensForDepthEstimation"],["depth_pro","DepthProForDepthEstimation"],["metric3d","Metric3DForDepthEstimation"],["metric3dv2","Metric3Dv2ForDepthEstimation"]]),fM=new Map([["sapiens","SapiensForNormalEstimation"]]),dM=new Map([["vitpose","VitPoseForPoseEstimation"]]),mM=new Map([["clip","CLIPVisionModelWithProjection"],["siglip","SiglipVisionModel"],["jina_clip","JinaCLIPVisionModel"]]),rv=[[JN,H.EncoderOnly],[ZN,H.EncoderDecoder],[t3,H.DecoderOnlyWithoutHead],[e3,H.AutoEncoder],[qT,H.EncoderOnly],[WT,H.EncoderOnly],[VT,H.Seq2Seq],[FT,H.Seq2Seq],[HT,H.DecoderOnly],[r3,H.MultiModality],[XT,H.EncoderOnly],[KT,H.EncoderOnly],[YT,H.Vision2Seq],[QT,H.ImageTextToText],[JT,H.AudioTextToText],[ZT,H.EncoderOnly],[to,H.EncoderOnly],[sM,H.EncoderOnly],[rM,H.EncoderOnly],[cM,H.EncoderOnly],[n3,H.EncoderOnly],[uM,H.EncoderOnly],[pM,H.EncoderOnly],[fM,H.EncoderOnly],[dM,H.EncoderOnly],[eM,H.EncoderOnly],[tM,H.EncoderOnly],[nM,H.MaskGeneration],[oM,H.EncoderOnly],[aM,H.EncoderOnly],[GT,H.Seq2Seq],[jT,H.EncoderOnly],[iM,H.EncoderOnly],[lM,H.EncoderOnly],[mM,H.EncoderOnly]];for(let[t,e]of rv)for(let r of t.values()){rr.set(r,e);let s=ml[r];Bs.set(s,r),Su.set(r,s)}var o3=[["MusicgenForConditionalGeneration",yi,H.Musicgen],["Phi3VForCausalLM",Ni,H.Phi3V],["CLIPTextModelWithProjection",pa,H.EncoderOnly],["SiglipTextModel",Yi,H.EncoderOnly],["JinaCLIPTextModel",ei,H.EncoderOnly],["ClapTextModelWithProjection",ca,H.EncoderOnly],["ClapAudioModelWithProjection",ua,H.EncoderOnly],["DacEncoderModel",ba,H.EncoderOnly],["DacDecoderModel",va,H.EncoderOnly],["MimiEncoderModel",li,H.EncoderOnly],["MimiDecoderModel",ci,H.EncoderOnly],["SnacEncoderModel",Ji,H.EncoderOnly],["SnacDecoderModel",Zi,H.EncoderOnly],["Gemma3nForConditionalGeneration",ns,H.ImageAudioTextToText],["Gemma4ForConditionalGeneration",Bn,H.ImageAudioTextToText],["SupertonicForConditionalGeneration",sl,H.Supertonic],["ChatterboxModel",la,H.Chatterbox],["VoxtralRealtimeForConditionalGeneration",ul,H.VoxtralRealtime]];for(let[t,e,r]of o3)rr.set(t,r),Bs.set(e,t),Su.set(t,e);var _M=new Map([["modnet",to],["birefnet",to],["isnet",to],["ben",to]]);for(let[t,e]of _M.entries())e.set(t,"PreTrainedModel"),rr.set(t,H.EncoderOnly),Su.set(t,y);var hM=new Set(_M.keys());rr.set("PreTrainedModel",H.EncoderOnly);Bs.set(y,"PreTrainedModel");var ze={MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES:qT,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES:WT,MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES:GT,MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES:jT,MODEL_FOR_MASKED_LM_MAPPING_NAMES:XT,MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES:KT,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:ZT,MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES:to,MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES:rM,MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES:sM,MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES:eM,MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES:tM,MODEL_FOR_MASK_GENERATION_MAPPING_NAMES:nM,MODEL_FOR_CTC_MAPPING_NAMES:oM,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES:aM,MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES:iM,MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES:lM,MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES:s3,MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES:cM,MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES:uM,MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES:pM,MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES:fM,MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES:dM,MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES:mM,MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES:QT,MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES:JT,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:VT,MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES:FT,MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:HT,MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES:YT};RT(ze);var Ce=class{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static supports(e){if(!this.MODEL_CLASS_MAPPINGS)return!1;for(let r of this.MODEL_CLASS_MAPPINGS)if(r.has(e))return!0;return this.BASE_IF_FAIL}static async from_pretrained(e,{progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:o=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:c=null,dtype:u=null,use_external_data_format:f=null,session_options:m={}}={}){let _={progress_callback:r,config:s,cache_dir:n,local_files_only:o,revision:a,model_file_name:i,subfolder:l,device:c,dtype:u,use_external_data_format:f,session_options:m};if(_.config=await Wt.from_pretrained(e,_),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);let{model_type:w}=_.config;for(let x of this.MODEL_CLASS_MAPPINGS){let v=x.get(w);if(!v){for(let b of x.values())if(b[0]===w){v=b;break}if(!v)continue}return await ml[v].from_pretrained(e,_)}if(this.BASE_IF_FAIL)return hM.has(w)||ee.warn(`Unknown model class "${w}", attempting to construct from base class.`),await y.from_pretrained(e,_);throw Error(`Unsupported model type: ${w}`)}},Ar=class extends Ce{static MODEL_CLASS_MAPPINGS=rv.map(e=>e[0]);static BASE_IF_FAIL=!0},_l=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]},Op=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]},ro=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]},Ip=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]},Cp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]},Pp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES]},Lp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES]},zp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_MASKED_LM_MAPPING_NAMES]},Np=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]},Rp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]},$p=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]},hl=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]},gl=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES]},wl=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES]},Dp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]},Bp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES]},gM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_MASK_GENERATION_MAPPING_NAMES]},Up=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_CTC_MAPPING_NAMES]},Fp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]},wM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES]},xM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES]},Gp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]},yM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES]},jp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES]},qp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES]},bM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES]},vM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES]},Wp=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES]},kM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES]},EM=class extends Ce{static MODEL_CLASS_MAPPINGS=[ze.MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES]};async function tt(t){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(e=>Je.read(e)))}async function Tr(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?Rm(r,e):r instanceof Float64Array?new Float32Array(r):r))}function xl(t,e){e&&(t=t.map(a=>a|0));let[r,s,n,o]=t;return{xmin:r,ymin:s,xmax:n,ymax:o}}var me=class extends We{constructor({task:e,model:r,tokenizer:s=null,processor:n=null}){super(),this.task=e,this.model=r,this.tokenizer=s,this.processor=n}async dispose(){await this.model.dispose()}};var yl=class extends me{async _call(e,{top_k:r=1}={}){let s=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(s),{problem_type:o,id2label:a}=this.model.config,i=o==="multi_label_classification"?c=>c.sigmoid():c=>new N("float32",Re(c.data),c.dims),l=[];for(let c of n.logits){let u=i(c),f=await tr(u,r),m=f[0].tolist(),w=f[1].tolist().map((x,v)=>({label:a?a[x]:`LABEL_${x}`,score:m[v]}));r===1?l.push(...w):l.push(w)}return Array.isArray(e)||r===1?l:l[0]}};var bl=class extends me{async _call(e,{ignore_labels:r=["O"]}={}){let s=Array.isArray(e),n=this.tokenizer(s?e:[e],{padding:!0,truncation:!0}),a=(await this.model(n)).logits,i=this.model.config.id2label,l=[];for(let c=0;c<a.dims[0];++c){let u=n.input_ids[c],f=a[c],m=[];for(let _=0;_<f.dims[0];++_){let w=f[_],x=Pe(w.data)[1],v=i?i[x]:`LABEL_${x}`;if(r.includes(v))continue;let b=this.tokenizer.decode([u[_].item()],{skip_special_tokens:!0});if(b==="")continue;let A=Re(w.data);m.push({entity:v,score:A[x],index:_,word:b})}l.push(m)}return s?l:l[0]}};var vl=class extends me{async _call(e,r,{top_k:s=1}={}){let n=this.tokenizer(e,{text_pair:r,padding:!0,truncation:!0}),o=Array.isArray(e),{start_logits:a,end_logits:i}=await this.model(n),l=n.input_ids.tolist(),c=n.attention_mask.tolist(),{all_special_ids:u,sep_token_id:f}=this.tokenizer,m=[];for(let _=0;_<a.dims[0];++_){let w=l[_],x=w.findIndex(O=>O==f),v=a[_].tolist(),b=i[_].tolist();for(let O=1;O<v.length;++O)(c[_]==0||O<=x||u.findIndex(k=>k==w[O])!==-1)&&(v[O]=-1/0,b[O]=-1/0);let A=Re(v).map((O,k)=>[O,k]),S=Re(b).map((O,k)=>[O,k]);A[0][0]=0,S[0][0]=0;let M=xE(A,S).filter(O=>O[0][1]<=O[1][1]).map(O=>[O[0][1],O[1][1],O[0][0]*O[1][0]]).sort((O,k)=>k[2]-O[2]),I=[];for(let O=0;O<Math.min(M.length,s);++O){let[k,R,G]=M[O],F=w.slice(k,R+1),K=this.tokenizer.decode(F,{skip_special_tokens:!0});I.push({answer:K,score:G})}s===1?m.push(...I):m.push(I)}return o?m:m[0]}};var kl=class extends me{async _call(e,{top_k:r=5}={}){let{mask_token_id:s,mask_token:n}=this.tokenizer,o=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:a}=await this.model(o),i=[],l=o.input_ids.tolist();for(let c=0;c<l.length;++c){let u=l[c],f=u.findIndex(v=>v==s);if(f===-1)throw Error(`Mask token (${n}) not found in text.`);let m=a[c][f],_=await tr(new N("float32",Re(m.data),m.dims),r),w=_[0].tolist(),x=_[1].tolist();i.push(x.map((v,b)=>{let A=u.slice();return A[f]=v,{score:w[b],token:Number(v),token_str:this.tokenizer.decode([v]),sequence:this.tokenizer.decode(A,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}};var Mr=class extends me{_key="generated_text";async _call(e,r={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(l=>this.model.config.prefix+l));let s=this.model.config.task_specific_params;s&&s[this.task]&&s[this.task].prefix&&(e=e.map(l=>s[this.task].prefix+l));let n=this.tokenizer,o={padding:!0,truncation:!0},a;this.task==="translation"&&"_build_translation_inputs"in n?a=n._build_translation_inputs(e,o,r):a=n(e,o);let i=await this.model.generate({...a,...r});return n.batch_decode(i,{skip_special_tokens:!0}).map(l=>({[this._key]:l}))}};var El=class extends Mr{_key="summary_text"};var Al=class extends Mr{_key="translation_text"};function AM(t){return Array.isArray(t)&&t.every(e=>"role"in e&&"content"in e)}var Tl=class extends me{async _call(e,r={}){let s=!1,n=!1,o=r.add_special_tokens??(this.tokenizer.add_bos_token||this.tokenizer.add_eos_token)??!1,a=r.tokenizer_encode_kwargs,i;if(typeof e=="string")i=e=[e];else if(Array.isArray(e)&&e.every(w=>typeof w=="string"))s=!0,i=e;else{if(AM(e))e=[e];else if(Array.isArray(e)&&e.every(AM))s=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");n=!0,i=e.map(w=>this.tokenizer.apply_chat_template(w,{tokenize:!1,add_generation_prompt:!0,...a})),o=!1,a=void 0}let l=n?!1:r.return_full_text??!0;this.tokenizer.padding_side="left";let c=this.tokenizer(i,{add_special_tokens:o,padding:!0,truncation:!0,...a}),u=await this.model.generate({...c,...r}),f=this.tokenizer.batch_decode(u,{skip_special_tokens:!0}),m;!l&&c.input_ids.dims.at(-1)>0&&(m=this.tokenizer.batch_decode(c.input_ids,{skip_special_tokens:!0}).map(w=>w.length));let _=Array.from({length:e.length},w=>[]);for(let w=0;w<f.length;++w){let x=Math.floor(w/u.dims[0]*e.length);m&&(f[w]=f[w].slice(m[x])),_[x].push({generated_text:n?[...e[x],{role:"assistant",content:f[w]}]:f[w]})}return!s&&_.length===1?_[0]:_}};var Ml=class extends me{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([r,s])=>[r.toLowerCase(),s])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(ee.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(ee.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,r,{hypothesis_template:s="This example is {}.",multi_label:n=!1}={}){let o=Array.isArray(e);o||(e=[e]),Array.isArray(r)||(r=[r]);let a=r.map(c=>s.replace("{}",c)),i=n||r.length===1,l=[];for(let c of e){let u=[];for(let _ of a){let w=this.tokenizer(c,{text_pair:_,padding:!0,truncation:!0}),x=await this.model(w);i?u.push([x.logits.data[this.contradiction_id],x.logits.data[this.entailment_id]]):u.push(x.logits.data[this.entailment_id])}let m=(i?u.map(_=>Re(_)[1]):Re(u)).map((_,w)=>[_,w]).sort((_,w)=>w[0]-_[0]);l.push({sequence:c,labels:m.map(_=>r[_[1]]),scores:m.map(_=>_[0])})}return o?l:l[0]}};var Sl=class extends me{async _call(e,{top_k:r=5}={}){let s=this.processor.feature_extractor.config.sampling_rate,n=await Tr(e,s),o=this.model.config.id2label,a=[];for(let i of n){let l=await this.processor(i),u=(await this.model(l)).logits[0],f=await tr(new N("float32",Re(u.data),u.dims),r),m=f[0].tolist(),w=f[1].tolist().map((x,v)=>({label:o?o[x]:`LABEL_${x}`,score:m[v]}));a.push(w)}return Array.isArray(e)?a:a[0]}};var Ol=class extends me{async _call(e,r,{hypothesis_template:s="This is a sound of {}."}={}){let n=!Array.isArray(e);n&&(e=[e]);let o=r.map(u=>s.replace("{}",u)),a=this.tokenizer(o,{padding:!0,truncation:!0}),i=this.processor.feature_extractor.config.sampling_rate,l=await Tr(e,i),c=[];for(let u of l){let f=await this.processor(u),m=await this.model({...a,...f}),_=Re(m.logits_per_audio.data);c.push([..._].map((w,x)=>({score:w,label:r[x]})))}return n?c[0]:c}};var Il=class extends me{async _call(e,r={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(e,r);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":case"parakeet_ctc":return this._call_wav2vec2(e,r);case"moonshine":return this._call_moonshine(e,r);case"cohere_asr":return this._call_cohere_asr(e,r);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,r){r.language&&ee.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),r.task&&ee.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let s=!Array.isArray(e),n=s?[e]:e,o=this.processor.feature_extractor.config.sampling_rate,a=await Tr(n,o),i=[];for(let l of a){let c=await this.processor(l),f=(await this.model(c)).logits[0],m=[];for(let w of f)m.push(Pe(w.data)[1]);let _=this.tokenizer.decode(m,{skip_special_tokens:!0}).trim();i.push({text:_})}return s?i[0]:i}async _call_whisper(e,r){let s=r.return_timestamps??!1,n=r.chunk_length_s??0,o=r.force_full_sequences??!1,a=r.stride_length_s??null,i={...r};s==="word"&&(i.return_token_timestamps=!0,i.return_timestamps=!0);let l=!Array.isArray(e),c=l?[e]:e,u=this.processor.feature_extractor.config,f=u.chunk_length/this.model.config.max_source_positions,m=u.hop_length,_=u.sampling_rate,w=await Tr(c,_),x=[];for(let v of w){let b=[];if(n>0){if(a===null)a=n/6;else if(n<=a)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let M=_*n,I=_*a,O=M-2*I,k=0;for(;;){let R=k+M,G=v.subarray(k,R),F=await this.processor(G),K=k===0,V=R>=v.length;if(b.push({stride:[G.length,K?0:I,V?0:I],input_features:F.input_features,is_last:V}),V)break;k+=O}}else b=[{stride:[v.length,0,0],input_features:(await this.processor(v)).input_features,is_last:!0}];for(let M of b){i.num_frames=Math.floor(M.stride[0]/m);let I=await this.model.generate({inputs:M.input_features,...i});if(s==="word"){let O=I.sequences.tolist()[0],k=I.token_timestamps.tolist()[0],R=this.tokenizer.timestamp_begin,G=Math.max(O.findIndex(F=>Number(F)>=R),0);M.tokens=O.slice(G),M.token_timestamps=k.slice(G).map(F=>bs(F,2))}else M.tokens=I[0].tolist();M.stride=M.stride.map(O=>O/_)}let[A,S]=this.tokenizer._decode_asr(b,{time_precision:f,return_timestamps:s,force_full_sequences:o});x.push({text:A,...S})}return l?x[0]:x}async _call_moonshine(e,r){let s=!Array.isArray(e),n=s?[e]:e,o=this.processor.feature_extractor.config.sampling_rate,a=await Tr(n,o),i=[];for(let l of a){let c=await this.processor(l),u=Math.floor(l.length/o)*6,f=await this.model.generate({max_new_tokens:u,...r,...c}),m=this.processor.batch_decode(f,{skip_special_tokens:!0})[0];i.push({text:m})}return s?i[0]:i}async _call_cohere_asr(e,r){let s=!Array.isArray(e),n=s?[e]:e,o=this.processor.feature_extractor,a=o.config.sampling_rate,i=await Tr(n,a),l=r.language??"en",c=this.processor.get_decoder_prompt_ids(l),u=[];for(let f of i){let m=o.split_audio(f),_=[];for(let x of m){let v=await this.processor(x),b=await this.model.generate({...v,decoder_input_ids:c,...r}),A=this.tokenizer.decode(b[0].tolist(),{skip_special_tokens:!0}).trim();_.push(A)}let w=this.processor.constructor.join_chunks(_,l);u.push({text:w})}return s?u[0]:u}};var Cl=class extends me{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _prepare_speaker_embeddings(e,r){if((typeof e=="string"||e instanceof URL)&&(e=new Float32Array(await(await _e.fetch(e)).arrayBuffer())),e instanceof Float32Array)e=new N("float32",e,[e.length]);else if(!(e instanceof N))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");if(r>1){if(e.dims[0]===1)e=e.repeat(r,1);else if(e.dims[0]!==r)throw new Error(`Expected speaker embeddings batch size to be 1 or ${r}, but got ${e.dims[0]}.`)}return e}_postprocess_waveform(e,r,s,n=null){let o=r.data,[a,i]=r.dims,l=n?n.data:null,c=[];for(let u=0;u<a;++u){let f=l?Math.min(Math.ceil(l[u]),i):i,m=u*i;c.push(new Ro(o.slice(m,m+f),s))}return Array.isArray(e)?c:c[0]}async _call(e,r){return this.processor?this._call_text_to_spectrogram(e,r):this.model.config.model_type==="supertonic"?this._call_supertonic(e,r):this._call_text_to_waveform(e)}async _call_supertonic(e,{speaker_embeddings:r,num_inference_steps:s,speed:n}){if(!r)throw new Error("Speaker embeddings must be provided for Supertonic models.");let{sampling_rate:o,style_dim:a}=this.model.config,i=this.tokenizer(e,{padding:!0,truncation:!0}),l=i.input_ids.dims[0];r=await this._prepare_speaker_embeddings(r,l),r=r.view(l,-1,a);let{waveform:c,durations:u}=await this.model.generate_speech({...i,style:r,num_inference_steps:s,speed:n});return this._postprocess_waveform(e,c,o,u)}async _call_text_to_waveform(e){let r=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:s}=await this.model(r),n=this.model.config.sampling_rate;return this._postprocess_waveform(e,s,n)}async _call_text_to_spectrogram(e,{speaker_embeddings:r}){this.vocoder||(ee.info("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await Ar.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"}));let{input_ids:s}=this.tokenizer(e,{padding:!0,truncation:!0}),n=s.dims[0];r=await this._prepare_speaker_embeddings(r,n),r=r.view(n,-1);let{waveform:o}=await this.model.generate_speech(s,r,{vocoder:this.vocoder}),a=this.processor.feature_extractor.config.sampling_rate;return this._postprocess_waveform(e,o,a)}};var Pl=class extends me{async _call(e,r={}){let s=Array.isArray(e),n=await tt(e),{pixel_values:o}=await this.processor(n),a=[];for(let i of o){i.dims=[1,...i.dims];let l=await this.model.generate({inputs:i,...r}),c=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(u=>({generated_text:u.trim()}));a.push(c)}return s?a:a[0]}};var Ll=class extends me{async _call(e,{top_k:r=5}={}){let s=await tt(e),{pixel_values:n}=await this.processor(s),o=await this.model({pixel_values:n}),{id2label:a}=this.model.config,i=[];for(let l of o.logits){let c=await tr(new N("float32",Re(l.data),l.dims),r),u=c[0].tolist(),m=c[1].tolist().map((_,w)=>({label:a?a[_]:`LABEL_${_}`,score:u[w]}));i.push(m)}return Array.isArray(e)?i:i[0]}};var TM={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"},Qs=class extends me{async _call(e,{threshold:r=.5,mask_threshold:s=.5,overlap_mask_area_threshold:n=.8,label_ids_to_fuse:o=null,target_sizes:a=null,subtask:i=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");let c=await tt(e),u=c.map(A=>[A.height,A.width]),f=await this.processor(c),{inputNames:m,outputNames:_}=this.model.sessions.model;if(!m.includes("pixel_values")){if(m.length!==1)throw Error(`Expected a single input name, but got ${m.length} inputs: ${m}.`);let A=m[0];if(A in f)throw Error(`Input name ${A} already exists in the inputs.`);f[A]=f.pixel_values}let w=await this.model(f),x=null;if(i!==null)x=TM[i];else if(this.processor.image_processor){for(let[A,S]of Object.entries(TM))if(S in this.processor.image_processor){x=this.processor.image_processor[S].bind(this.processor.image_processor),i=A;break}}let v=this.model.config.id2label,b=[];if(i)if(i==="panoptic"||i==="instance"){let A=x(w,r,s,n,o,a??u)[0],S=A.segmentation;for(let M of A.segments_info){let I=new Uint8ClampedArray(S.data.length);for(let k=0;k<S.data.length;++k)S.data[k]===M.id&&(I[k]=255);let O=new Je(I,S.dims[1],S.dims[0],1);b.push({score:M.score,label:v[M.label_id],mask:O})}}else if(i==="semantic"){let{segmentation:A,labels:S}=x(w,a??u)[0];for(let M of S){let I=new Uint8ClampedArray(A.data.length);for(let k=0;k<A.data.length;++k)A.data[k]===M&&(I[k]=255);let O=new Je(I,A.dims[1],A.dims[0],1);b.push({score:null,label:v[M],mask:O})}}else throw Error(`Subtask ${i} not supported.`);else{let S=w[_[0]];for(let M=0;M<u.length;++M){let I=u[M],O=S[M];O.data.some(R=>R<-1e-5||R>1+1e-5)&&O.sigmoid_();let k=await Je.fromTensor(O.mul_(255).to("uint8")).resize(I[1],I[0]);b.push({label:null,score:null,mask:k})}}return b}};var zl=class extends Qs{async _call(e,r={}){let s=await tt(e),n=await super._call(e,r),o=s.map((a,i)=>{let l=a.clone();return l.putAlpha(n[i].mask),l});return Array.isArray(e)?o:o[0]}};var Nl=class extends me{async _call(e,r,{hypothesis_template:s="This is a photo of {}"}={}){let n=Array.isArray(e),o=await tt(e),a=r.map(m=>s.replace("{}",m)),i=this.tokenizer(a,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(o),c=await this.model({...i,pixel_values:l}),u=this.model.config.model_type==="siglip"?m=>m.sigmoid().data:m=>Re(m.data),f=[];for(let m of c.logits_per_image){let w=[...u(m)].map((x,v)=>({score:x,label:r[v]}));w.sort((x,v)=>v.score-x.score),f.push(w)}return n?f:f[0]}};var Rl=class extends me{async _call(e,{threshold:r=.9,percentage:s=!1}={}){let n=Array.isArray(e);if(n&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");let o=await tt(e),a=s?null:o.map(_=>[_.height,_.width]),{pixel_values:i,pixel_mask:l}=await this.processor(o),c=await this.model({pixel_values:i,pixel_mask:l}),u=this.processor.image_processor.post_process_object_detection(c,r,a),{id2label:f}=this.model.config,m=u.map(_=>_.boxes.map((w,x)=>({score:_.scores[x],label:f[_.classes[x]],box:xl(w,!s)})));return n?m:m[0]}};var $l=class extends me{async _call(e,r,{threshold:s=.1,top_k:n=null,percentage:o=!1}={}){let a=Array.isArray(e),i=await tt(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),c=await this.processor(i),u=[];for(let f=0;f<i.length;++f){let m=i[f],_=o?null:[[m.height,m.width]],w=c.pixel_values[f].unsqueeze_(0),x=await this.model({...l,pixel_values:w}),v;if("post_process_grounded_object_detection"in this.processor){let b=this.processor.post_process_grounded_object_detection(x,l.input_ids,{box_threshold:s,text_threshold:s,target_sizes:_})[0];v=b.boxes.map((A,S)=>({score:b.scores[S],label:b.labels[S],box:xl(A,!o)}))}else{let b=this.processor.image_processor.post_process_object_detection(x,s,_,!0)[0];v=b.boxes.map((A,S)=>({score:b.scores[S],label:r[b.classes[S]],box:xl(A,!o)}))}v.sort((b,A)=>A.score-b.score),n!==null&&(v=v.slice(0,n)),u.push(v)}return a?u:u[0]}};var Dl=class extends me{async _call(e,r,s={}){if(Array.isArray(e)){if(e.length!==1)throw Error("Document Question Answering pipeline currently only supports a batch size of 1.");e=e[0]}let n=(await tt(e))[0],{pixel_values:o}=await this.processor(n),a=`<s_docvqa><s_question>${r}</s_question><s_answer>`,i=this.tokenizer(a,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,l=await this.model.generate({inputs:o,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:i,...s}),u=this.tokenizer.batch_decode(l)[0].match(/<s_answer>(.*?)<\/s_answer>/),f=null;return u&&u.length>=2&&(f=u[1].trim()),[{answer:f}]}};var Bl=class extends me{async _call(e){let r=await tt(e),s=await this.processor(r),n=await this.model(s),o=[];for(let a of n.reconstruction){let i=a.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");o.push(Je.fromTensor(i))}return Array.isArray(e)?o:o[0]}};var Ul=class extends me{async _call(e){let r=await tt(e),s=await this.processor(r),{predicted_depth:n}=await this.model(s),o=[];for(let a=0;a<r.length;++a){let i=n[a],[l,c]=i.dims.slice(-2),[u,f]=r[a].size,m=(await vt(i.view(1,1,l,c),{size:[f,u],mode:"bilinear"})).view(f,u),_=m.min().item(),w=m.max().item(),x=m.sub(_).div_(w-_).mul_(255).to("uint8").unsqueeze(0),v=Je.fromTensor(x);o.push({predicted_depth:m,depth:v})}return Array.isArray(e)?o:o[0]}};var Fl=class extends me{async _call(e,{pooling:r="none",normalize:s=!1,quantize:n=!1,precision:o="binary"}={}){let a=this.tokenizer(e,{padding:!0,truncation:!0}),i=await this.model(a),l=i.last_hidden_state??i.logits??i.token_embeddings;switch(r){case"none":break;case"mean":l=lT(l,a.attention_mask);break;case"first_token":case"cls":l=l.slice(null,0);break;case"last_token":case"eos":l=l.slice(null,-1);break;default:throw Error(`Pooling method '${r}' not supported.`)}return s&&(l=l.normalize(2,-1)),n&&(l=uT(l,o)),l}};var Gl=class extends me{async _call(e,{pool:r=null}={}){let s=await tt(e),{pixel_values:n}=await this.processor(s),o=await this.model({pixel_values:n}),a;if(r){if(!("pooler_output"in o))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");a=o.pooler_output}else a=o.last_hidden_state??o.logits??o.image_embeds;return a}};var so=Object.freeze({"text-classification":{pipeline:yl,model:_l,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{pipeline:bl,model:Op,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{pipeline:vl,model:Np,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{pipeline:kl,model:zp,default:{model:"onnx-community/ettin-encoder-32m-ONNX",dtype:"fp32"},type:"text"},summarization:{pipeline:El,model:ro,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{pipeline:Al,model:ro,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{pipeline:Mr,model:ro,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{pipeline:Tl,model:Lp,default:{model:"onnx-community/Qwen3-0.6B-ONNX",dtype:"q4"},type:"text"},"zero-shot-classification":{pipeline:Ml,model:_l,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Sl,model:Fp,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{pipeline:Ol,model:Ar,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{pipeline:Il,model:[Ip,Up],default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{pipeline:Cl,model:[Pp,Cp],default:{model:"onnx-community/Supertonic-TTS-ONNX",dtype:"fp32"},type:"text"},"image-to-text":{pipeline:Pl,model:Rp,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Ll,model:$p,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Qs,model:[hl,gl,wl],default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:zl,model:[hl,gl,wl],default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{pipeline:Nl,model:Ar,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Rl,model:Dp,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{pipeline:$l,model:Bp,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{pipeline:Dl,model:Gp,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Bl,model:jp,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Ul,model:qp,default:{model:"onnx-community/depth-anything-v2-small"},type:"image"},"feature-extraction":{pipeline:Fl,model:Ar,default:{model:"onnx-community/all-MiniLM-L6-v2-ONNX",dtype:"fp32"},type:"text"},"image-feature-extraction":{pipeline:Gl,model:[Wp,Ar],default:{model:"onnx-community/dinov3-vits16-pretrain-lvd1689m-ONNX",dtype:"fp32"},type:"image"}}),Vp=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function Hp(t){if(!t)throw new Error("modelId is required");return(await Ct(t,Er,{})).exists?[Er]:[]}async function Sr(t,{config:e=null,dtype:r=null,device:s=null,model_file_name:n=null,include_tokenizer:o=!0,include_processor:a=!0}={}){let i=await Cn(t,{config:e,dtype:r,device:s,model_file_name:n});if(o){let l=await hn(t);i.push(...l)}if(a){let l=await Hp(t);i.push(...l)}return i}async function Or(t,e,r={}){t=Vp[t]??t;let s=so[t];if(!s)throw new Error(`Unsupported pipeline task: ${t}. 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jl=class{constructor(e,r){this.image=e,this.timestamp=r}},Xp=class{constructor(e,r){e.length>0&&e[0]instanceof Je&&(e=e.map((s,n)=>new jl(s,(n+1)/(e.length+1)*r))),this.frames=e,this.duration=r}get width(){return this.frames[0].image.width}get height(){return this.frames[0].image.height}get fps(){return this.frames.length/this.duration}};async function i3(t,{num_frames:e=null,fps:r=null}={}){if(!ie.IS_BROWSER_ENV)throw new Error("`load_video` is currently only supported in browser environments.");if(e==null&&r==null)throw new Error("Either num_frames or fps must be provided.");let s=[],n=document.createElement("video");if(n.crossOrigin="anonymous",n.muted=!0,typeof t=="string")n.src=t;else if(t instanceof Blob)n.src=URL.createObjectURL(t);else if(t instanceof HTMLVideoElement)n.src=t.src;else throw new Error("Invalid URL or video element provided.");if(await new Promise(f=>n.onloadedmetadata=f),n.seekable.start(0)===n.seekable.end(0)){let m=await(await 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Zt(r?.cache_dir);if(!s)return!1;let{localPath:n,proposedCacheKey:o}=$r(t,e,r,s);return!!await Dr(s,n,o)}async function IM(t,e={}){if(!t)throw new Error("modelId is required");if(!await OM(t,"config.json",e))return!1;let r=await Sr(t,e);return(await Kp(t,r,e)).allCached}async function CM(t,e={}){if(!t)throw new Error("modelId is required");let r=await Sr(t,e);return await Kp(t,r,e)}async function PM(t,e,r={}){if(!t)throw new Error("task is required");if(!e)throw new Error("modelId is required");if(!await OM(e,"config.json",r))return!1;let s=await Or(t,e,r);return(await Kp(e,s,r)).allCached}async function LM(t,e,r={}){if(!t)throw new Error("task is required");if(!e)throw new Error("modelId is required");let s=await Or(t,e,r);return await Kp(e,s,r)}async function zM(t,e,r={}){let s=await Zt(r?.cache_dir);if(!s)return{filesDeleted:0,filesCached:0,files:e.map(o=>({file:o,deleted:!1,wasCached:!1}))};if(!s.delete)throw new Error("Cache does not support delete operation");let n=await 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Promise.all(c.map(async x=>{let v=`${a}/${x}${_}.onnx`;return(await Ct(t,v,u)).exists}));return{dtype:m,available:w.every(Boolean)}}))).filter(m=>m.available).map(m=>m.dtype)}var ov=class{static async get_files(e,r={}){return Sr(e,r)}static async get_pipeline_files(e,r,s={}){return Or(e,r,s)}static async get_model_files(e,r={}){return Cn(e,r)}static async get_tokenizer_files(e){return hn(e)}static async get_processor_files(e){return Hp(e)}static async get_available_dtypes(e,r={}){return $M(e,r)}static async is_cached(e,r={}){return IM(e,r)}static async is_cached_files(e,r={}){return CM(e,r)}static async is_pipeline_cached(e,r,s={}){return PM(e,r,s)}static async is_pipeline_cached_files(e,r,s={}){return LM(e,r,s)}static async get_file_metadata(e,r,s={}){return Ct(e,r,s)}static async clear_cache(e,r={}){return NM(e,r)}static async clear_pipeline_cache(e,r,s={}){return RM(e,r,s)}};export{$m as ASTFeatureExtractor,Dh as ASTForAudioClassification,$h as ASTModel,sa as ASTPreTrainedModel,zh as AfmoeForCausalLM,Lh as AfmoeModel,ta as AfmoePreTrainedModel,Ih as AlbertForMaskedLM,Oh as AlbertForQuestionAnswering,Sh as AlbertForSequenceClassification,Mh as AlbertModel,Us as AlbertPreTrainedModel,jd as AlbertTokenizer,Ph as ApertusForCausalLM,Ch as ApertusModel,ea as ApertusPreTrainedModel,Rh as ArceeForCausalLM,Nh as ArceeModel,ra as ArceePreTrainedModel,Sl as AudioClassificationPipeline,Wt as AutoConfig,Ge as AutoFeatureExtractor,Te as AutoImageProcessor,Ar as AutoModel,Fp as AutoModelForAudioClassification,xM as AutoModelForAudioFrameClassification,EM as AutoModelForAudioTextToText,Up as AutoModelForCTC,Lp as AutoModelForCausalLM,qp as AutoModelForDepthEstimation,Gp as AutoModelForDocumentQuestionAnswering,$p as AutoModelForImageClassification,Wp as AutoModelForImageFeatureExtraction,yM as AutoModelForImageMatting,hl as AutoModelForImageSegmentation,kM as AutoModelForImageTextToText,jp as AutoModelForImageToImage,gM as AutoModelForMaskGeneration,zp as AutoModelForMaskedLM,bM as AutoModelForNormalEstimation,Dp as AutoModelForObjectDetection,vM as AutoModelForPoseEstimation,Np as AutoModelForQuestionAnswering,gl as AutoModelForSemanticSegmentation,ro as AutoModelForSeq2SeqLM,_l as AutoModelForSequenceClassification,Ip as AutoModelForSpeechSeq2Seq,Cp as AutoModelForTextToSpectrogram,Pp as AutoModelForTextToWaveform,Op as AutoModelForTokenClassification,wl as AutoModelForUniversalSegmentation,Rp as AutoModelForVision2Seq,wM as AutoModelForXVector,Bp as AutoModelForZeroShotObjectDetection,pu as AutoProcessor,ne as AutoTokenizer,Il as AutomaticSpeechRecognitionPipeline,zl as BackgroundRemovalPipeline,Uh as BartForConditionalGeneration,Fh as BartForSequenceClassification,Bh as BartModel,zn as BartPretrainedModel,qd as BartTokenizer,sv as BaseStreamer,Zm as BeitFeatureExtractor,jh as BeitForImageClassification,Gh as BeitModel,na as BeitPreTrainedModel,Wh as BertForMaskedLM,Xh as BertForQuestionAnswering,Vh as BertForSequenceClassification,Hh as BertForTokenClassification,qh as BertModel,Qr as BertPreTrainedModel,Wd as BertTokenizer,e_ as BitImageProcessor,Yh as BlenderbotForConditionalGeneration,Kh as BlenderbotModel,oa as BlenderbotPreTrainedModel,Jh as BlenderbotSmallForConditionalGeneration,Qh as BlenderbotSmallModel,aa as BlenderbotSmallPreTrainedModel,Vd as BlenderbotSmallTokenizer,Hd as BlenderbotTokenizer,eg as BloomForCausalLM,Zh as BloomModel,ia as BloomPreTrainedModel,Xd as BloomTokenizer,ig as CHMv2ForDepthEstimation,r_ as CHMv2ImageProcessor,Pu as CHMv2PreTrainedModel,s_ as CLIPFeatureExtractor,Wc as CLIPImageProcessor,cg as CLIPModel,mr as CLIPPreTrainedModel,mg as CLIPSegForImageSegmentation,dg as CLIPSegModel,fa as CLIPSegPreTrainedModel,ug as CLIPTextModel,pa as CLIPTextModelWithProjection,Yd as CLIPTokenizer,pg as CLIPVisionModel,fg as CLIPVisionModelWithProjection,rg as CamembertForMaskedLM,og as CamembertForQuestionAnswering,sg as CamembertForSequenceClassification,ng as CamembertForTokenClassification,tg as CamembertModel,Jr as CamembertPreTrainedModel,Kd as CamembertTokenizer,Dm as ChatterboxFeatureExtractor,la as ChatterboxModel,Iu as ChatterboxPreTrainedModel,Ym as ChatterboxProcessor,t_ as ChineseCLIPFeatureExtractor,ag as ChineseCLIPModel,Cu as ChineseCLIPPreTrainedModel,ua as ClapAudioModelWithProjection,Bm as ClapFeatureExtractor,lg as ClapModel,Nn as ClapPreTrainedModel,ca as ClapTextModelWithProjection,vu as ClassifierFreeGuidanceLogitsProcessor,hg as CodeGenForCausalLM,_g as CodeGenModel,da as CodeGenPreTrainedModel,Jd as CodeGenTokenizer,Qd as CodeLlamaTokenizer,yg as Cohere2ForCausalLM,xg as Cohere2Model,_a as Cohere2PreTrainedModel,Um as CohereAsrFeatureExtractor,vg as CohereAsrForConditionalGeneration,bg as CohereAsrModel,ha as CohereAsrPreTrainedModel,Qm as CohereAsrProcessor,em as CohereAsrTokenizer,wg as CohereForCausalLM,gg as CohereModel,ma as CoherePreTrainedModel,Zd as CohereTokenizer,Eg as ConvBertForMaskedLM,Mg as ConvBertForQuestionAnswering,Ag as ConvBertForSequenceClassification,Tg as ConvBertForTokenClassification,kg as ConvBertModel,Zr as ConvBertPreTrainedModel,tm as ConvBertTokenizer,n_ as ConvNextFeatureExtractor,Og as ConvNextForImageClassification,Vc as ConvNextImageProcessor,Sg as ConvNextModel,ga as ConvNextPreTrainedModel,Cg as ConvNextV2ForImageClassification,Ig as ConvNextV2Model,wa as ConvNextV2PreTrainedModel,Ng as DFineForObjectDetection,zg as DFineModel,ya as DFinePreTrainedModel,iw as DINOv3ConvNextModel,Bu as DINOv3ConvNextPreTrainedModel,i_ as DINOv3ViTImageProcessor,lw as DINOv3ViTModel,Uu as DINOv3ViTPreTrainedModel,c_ as DPTFeatureExtractor,hw as DPTForDepthEstimation,Kc as DPTImageProcessor,_w as DPTModel,Ma as DPTPreTrainedModel,va as DacDecoderModel,zu as DacDecoderOutput,ba as DacEncoderModel,Lu as DacEncoderOutput,Bo as DacFeatureExtractor,Rg as DacModel,Rn as DacPreTrainedModel,Dg as DebertaForMaskedLM,Fg as DebertaForQuestionAnswering,Bg as DebertaForSequenceClassification,Ug as DebertaForTokenClassification,$g as DebertaModel,es as DebertaPreTrainedModel,sm as DebertaTokenizer,Wg as DebertaV2ForMaskedLM,Xg as DebertaV2ForQuestionAnswering,Vg as DebertaV2ForSequenceClassification,Hg as DebertaV2ForTokenClassification,qg as DebertaV2Model,ts as DebertaV2PreTrainedModel,rm as DebertaV2Tokenizer,Kg as DecisionTransformerModel,Nu as DecisionTransformerPreTrainedModel,jg as DeepseekV3ForCausalLM,Gg as DeepseekV3Model,ka as DeepseekV3PreTrainedModel,o_ as DeiTFeatureExtractor,Qg as DeiTForImageClassification,Hc as DeiTImageProcessor,Yg as DeiTModel,Ea as DeiTPreTrainedModel,Jg as DepthAnythingForDepthEstimation,Ru as DepthAnythingPreTrainedModel,Ul as DepthEstimationPipeline,Zg as DepthProForDepthEstimation,$u as DepthProPreTrainedModel,a_ as DetrFeatureExtractor,tw as DetrForObjectDetection,rw as DetrForSegmentation,Xc as DetrImageProcessor,ew as DetrModel,Dn as DetrObjectDetectionOutput,$n as DetrPreTrainedModel,Du as DetrSegmentationOutput,nw as Dinov2ForImageClassification,sw as Dinov2Model,Aa as Dinov2PreTrainedModel,aw as Dinov2WithRegistersForImageClassification,ow as Dinov2WithRegistersModel,Ta as Dinov2WithRegistersPreTrainedModel,dw as DistilBertForMaskedLM,fw as DistilBertForQuestionAnswering,uw as DistilBertForSequenceClassification,pw as DistilBertForTokenClassification,cw as DistilBertModel,rs as DistilBertPreTrainedModel,nm as DistilBertTokenizer,Dl as DocumentQuestionAnsweringPipeline,l_ as DonutFeatureExtractor,xn as DonutImageProcessor,mw as DonutSwinModel,Fu as DonutSwinPreTrainedModel,Yo as DynamicCache,Tb as EdgeTamModel,ww as EfficientNetForImageClassification,u_ as EfficientNetImageProcessor,gw as EfficientNetModel,Sa as EfficientNetPreTrainedModel,yw as ElectraForMaskedLM,kw as ElectraForQuestionAnswering,bw as ElectraForSequenceClassification,vw as ElectraForTokenClassification,xw as ElectraModel,ss as ElectraPreTrainedModel,om as ElectraTokenizer,$o as EncodecFeatureExtractor,Au as EosTokenCriteria,Aw as Ernie4_5ForCausalLM,Ew as Ernie4_5Model,Oa as Ernie4_5PretrainedModel,Mw as EsmForMaskedLM,Sw as EsmForSequenceClassification,Ow as EsmForTokenClassification,Tw as EsmModel,Fs as EsmPreTrainedModel,am as EsmTokenizer,Cw as EuroBertForMaskedLM,Pw as EuroBertForSequenceClassification,Lw as EuroBertForTokenClassification,Iw as EuroBertModel,Gs as EuroBertPreTrainedModel,Nw as ExaoneForCausalLM,zw as ExaoneModel,Ia as ExaonePreTrainedModel,$w as FalconForCausalLM,Bw as FalconH1ForCausalLM,Dw as FalconH1Model,Pa as FalconH1PreTrainedModel,Rw as FalconModel,Ca as FalconPreTrainedModel,im as FalconTokenizer,Fw as FastViTForImageClassification,Uw as FastViTModel,La as FastViTPreTrainedModel,Fl as FeatureExtractionPipeline,Ee as FeatureExtractor,kl as FillMaskPipeline,Gw as Florence2ForConditionalGeneration,Gu as Florence2PreTrainedModel,j_ as Florence2Processor,du as ForcedBOSTokenLogitsProcessor,mu as ForcedEOSTokenLogitsProcessor,d_ as GLPNFeatureExtractor,ox as GLPNForDepthEstimation,nx as GLPNModel,Ua as GLPNPreTrainedModel,_x as GPT2LMHeadModel,mx as GPT2Model,Wa as GPT2PreTrainedModel,um as GPT2Tokenizer,ix as GPTBigCodeForCausalLM,ax as GPTBigCodeModel,Fa as GPTBigCodePreTrainedModel,gx as GPTJForCausalLM,hx as GPTJModel,Va as GPTJPreTrainedModel,cx as GPTNeoForCausalLM,lx as GPTNeoModel,Ga as GPTNeoPreTrainedModel,px as GPTNeoXForCausalLM,ux as GPTNeoXModel,ja as GPTNeoXPreTrainedModel,cm as GPTNeoXTokenizer,Vw as Gemma2ForCausalLM,Ww as Gemma2Model,Na as Gemma2PreTrainedModel,Yw as Gemma3ForCausalLM,Wu as Gemma3ForConditionalGeneration,p_ as Gemma3ImageProcessor,Kw as Gemma3Model,qu as Gemma3PreTrainedModel,q_ as Gemma3Processor,Uo as Gemma3nAudioFeatureExtractor,Qw as Gemma3nForCausalLM,ns as Gemma3nForConditionalGeneration,Vu as Gemma3nPreTrainedModel,W_ as Gemma3nProcessor,Fo as Gemma4AudioFeatureExtractor,Jw as Gemma4ForCausalLM,Bn as Gemma4ForConditionalGeneration,qo as Gemma4ImageProcessor,V_ as Gemma4Processor,qw as GemmaForCausalLM,jw as GemmaModel,za as GemmaPreTrainedModel,lm as GemmaTokenizer,f_ as Glm46VImageProcessor,H_ as Glm46VProcessor,ex as GlmForCausalLM,Zw as GlmModel,rx as GlmMoeDsaForCausalLM,tx as GlmMoeDsaModel,$a as GlmMoeDsaPreTrainedModel,sx as GlmOcrForConditionalGeneration,Ra as GlmPreTrainedModel,dx as GptOssForCausalLM,fx as GptOssModel,qa as GptOssPreTrainedModel,xx as GraniteForCausalLM,wx as GraniteModel,bx as GraniteMoeHybridForCausalLM,yx as GraniteMoeHybridModel,Xa as GraniteMoeHybridPreTrainedModel,Ha as GranitePreTrainedModel,Fm as GraniteSpeechFeatureExtractor,vx as GraniteSpeechForConditionalGeneration,X_ as GraniteSpeechProcessor,kx as GroundingDinoForObjectDetection,m_ as GroundingDinoImageProcessor,Ku as GroundingDinoPreTrainedModel,K_ as GroundingDinoProcessor,Ex as GroupViTModel,Yu as GroupViTPreTrainedModel,Tx as HeliumForCausalLM,Ax as HeliumModel,Ka as HeliumPreTrainedModel,pm as HerbertTokenizer,Sx as HieraForImageClassification,Mx as HieraModel,Ya as HieraPreTrainedModel,Nx as HubertForCTC,Rx as HubertForSequenceClassification,zx as HubertModel,Lx as HubertPreTrainedModel,Dx as HunYuanDenseV1ForCausalLM,$x as HunYuanDenseV1Model,Qa as HunYuanDenseV1PreTrainedModel,Fx as IJepaForImageClassification,Ux as IJepaModel,Ja as IJepaPreTrainedModel,Bx as Idefics3ForConditionalGeneration,Yc as Idefics3ImageProcessor,au as Idefics3Processor,Ll as ImageClassificationPipeline,Gl as ImageFeatureExtractionPipeline,X as ImageFeatureExtractor,X as ImageProcessor,Qs as ImageSegmentationPipeline,Bl as ImageToImagePipeline,Pl as ImageToTextPipeline,LT as InterruptableStoppingCriteria,jx as JAISLMHeadModel,Gx as JAISModel,Za as JAISPreTrainedModel,h_ as JinaCLIPImageProcessor,qx as JinaCLIPModel,Fn as JinaCLIPPreTrainedModel,Q_ as JinaCLIPProcessor,ei as JinaCLIPTextModel,Wx as JinaCLIPVisionModel,Hx as Lfm2ForCausalLM,Vx as Lfm2Model,Yx as Lfm2MoeForCausalLM,Kx as Lfm2MoeModel,ri as Lfm2MoePreTrainedModel,ti as Lfm2PreTrainedModel,Qx as Lfm2VlForConditionalGeneration,g_ as Lfm2VlImageProcessor,J_ as Lfm2VlProcessor,Xx as LightOnOcrForConditionalGeneration,F1 as LiteWhisperForConditionalGeneration,ey as Llama4ForCausalLM,Qu as Llama4PreTrainedModel,Zx as LlamaForCausalLM,Jx as LlamaModel,si as LlamaPreTrainedModel,fm as LlamaTokenizer,Et as LlavaForConditionalGeneration,Et as LlavaOnevisionForConditionalGeneration,w_ as LlavaOnevisionImageProcessor,ju as LlavaPreTrainedModel,Z_ as LlavaProcessor,Xw as LlavaQwen2ForCausalLM,It as LogLevel,Dt as LogitsProcessor,Rs as LogitsProcessorList,Ko as LogitsWarper,ry as LongT5ForConditionalGeneration,ty as LongT5Model,ni as LongT5PreTrainedModel,ny as M2M100ForConditionalGeneration,sy as M2M100Model,oi as M2M100PreTrainedModel,dm as M2M100Tokenizer,_m as MBart50Tokenizer,fy as MBartForCausalLM,uy as MBartForConditionalGeneration,py as MBartForSequenceClassification,cy as MBartModel,Ws as MBartPreTrainedModel,Lo as MBartTokenizer,Jy as MPNetForMaskedLM,t0 as MPNetForQuestionAnswering,Zy as MPNetForSequenceClassification,e0 as MPNetForTokenClassification,Qy as MPNetModel,os as MPNetPreTrainedModel,wm as MPNetTokenizer,o0 as MT5ForConditionalGeneration,n0 as MT5Model,wi as MT5PreTrainedModel,ay as MarianMTModel,oy as MarianModel,ai as MarianPreTrainedModel,mm as MarianTokenizer,y_ as Mask2FormerImageProcessor,x_ as MaskFormerFeatureExtractor,ly as MaskFormerForInstanceSegmentation,yn as MaskFormerImageProcessor,iy as MaskFormerModel,ii as MaskFormerPreTrainedModel,Eu as MaxLengthCriteria,dy as Metric3DForDepthEstimation,Ju as Metric3DPreTrainedModel,my as Metric3Dv2ForDepthEstimation,Zu as Metric3Dv2PreTrainedModel,_y as MgpstrForSceneTextRecognition,ep as MgpstrModelOutput,tp as MgpstrPreTrainedModel,eh as MgpstrProcessor,hm as MgpstrTokenizer,ci as MimiDecoderModel,sp as MimiDecoderOutput,li as MimiEncoderModel,rp as MimiEncoderOutput,hy as MimiModel,Gn as MimiPreTrainedModel,xu as MinLengthLogitsProcessor,yu as MinNewTokensLengthLogitsProcessor,yy as Mistral4ForCausalLM,xy as Mistral4Model,pi as Mistral4PreTrainedModel,wy as MistralForCausalLM,gy as MistralModel,ui as MistralPreTrainedModel,vy as MobileBertForMaskedLM,Ey as MobileBertForQuestionAnswering,ky as MobileBertForSequenceClassification,by as MobileBertModel,Vs as MobileBertPreTrainedModel,gm as MobileBertTokenizer,Ty as MobileLLMForCausalLM,Ay as MobileLLMModel,fi as MobileLLMPreTrainedModel,b_ as MobileNetV1FeatureExtractor,Sy as MobileNetV1ForImageClassification,Oy as MobileNetV1ForSemanticSegmentation,Qc as MobileNetV1ImageProcessor,My as MobileNetV1Model,jn as MobileNetV1PreTrainedModel,v_ as MobileNetV2FeatureExtractor,Cy as MobileNetV2ForImageClassification,Py as MobileNetV2ForSemanticSegmentation,Jc as MobileNetV2ImageProcessor,Iy as MobileNetV2Model,qn as MobileNetV2PreTrainedModel,k_ as MobileNetV3FeatureExtractor,zy as MobileNetV3ForImageClassification,Ny as MobileNetV3ForSemanticSegmentation,Zc as MobileNetV3ImageProcessor,Ly as MobileNetV3Model,Wn as MobileNetV3PreTrainedModel,E_ as MobileNetV4FeatureExtractor,$y as MobileNetV4ForImageClassification,Dy as MobileNetV4ForSemanticSegmentation,eu as MobileNetV4ImageProcessor,Ry as MobileNetV4Model,Vn as MobileNetV4PreTrainedModel,A_ as MobileViTFeatureExtractor,Uy as MobileViTForImageClassification,tu as MobileViTImageProcessor,By as MobileViTModel,di as MobileViTPreTrainedModel,Gy as MobileViTV2ForImageClassification,Fy as MobileViTV2Model,mi as MobileViTV2PreTrainedModel,ov as ModelRegistry,Xy as ModernBertDecoderForCausalLM,Hy as ModernBertDecoderModel,_i as ModernBertDecoderPreTrainedModel,qy as ModernBertForMaskedLM,Wy as ModernBertForSequenceClassification,Vy as ModernBertForTokenClassification,jy as ModernBertModel,Hs as ModernBertPreTrainedModel,Hw as Moondream1ForConditionalGeneration,Gm as MoonshineFeatureExtractor,Yy as MoonshineForConditionalGeneration,Ky as MoonshineModel,hi as MoonshinePreTrainedModel,th as MoonshineProcessor,s0 as MptForCausalLM,r0 as MptModel,gi as MptPreTrainedModel,a0 as MultiModalityCausalLM,np as MultiModalityPreTrainedModel,l0 as MusicgenForCausalLM,yi as MusicgenForConditionalGeneration,i0 as MusicgenModel,xi as MusicgenPreTrainedModel,u0 as NanoChatForCausalLM,c0 as NanoChatModel,bi as NanoChatPreTrainedModel,f0 as NemotronHForCausalLM,p0 as NemotronHModel,vi as NemotronHPreTrainedModel,m0 as NeoBertForMaskedLM,g0 as NeoBertForQuestionAnswering,_0 as NeoBertForSequenceClassification,h0 as NeoBertForTokenClassification,d0 as NeoBertModel,as as NeoBertPreTrainedModel,xm as NllbTokenizer,bu as NoBadWordsLogitsProcessor,gu as NoRepeatNGramLogitsProcessor,w0 as NomicBertModel,op as NomicBertPreTrainedModel,T_ as NougatImageProcessor,ym as NougatTokenizer,I0 as OPTForCausalLM,O0 as OPTModel,Si as OPTPreTrainedModel,Rl as ObjectDetectionPipeline,v0 as Olmo2ForCausalLM,b0 as Olmo2Model,Ei as Olmo2PreTrainedModel,E0 as Olmo3ForCausalLM,k0 as Olmo3Model,Ai as Olmo3PreTrainedModel,y0 as OlmoForCausalLM,T0 as OlmoHybridForCausalLM,A0 as OlmoHybridModel,Ti as OlmoHybridPreTrainedModel,x0 as OlmoModel,ki as OlmoPreTrainedModel,S0 as OpenELMForCausalLM,M0 as OpenELMModel,Mi as OpenELMPreTrainedModel,M_ as OwlViTFeatureExtractor,z0 as OwlViTForObjectDetection,bn as OwlViTImageProcessor,L0 as OwlViTModel,Ii as OwlViTPreTrainedModel,rh as OwlViTProcessor,P0 as Owlv2ForObjectDetection,S_ as Owlv2ImageProcessor,C0 as Owlv2Model,Oi as Owlv2PreTrainedModel,N0 as PaliGemmaForConditionalGeneration,sh as PaliGemmaProcessor,Do as ParakeetFeatureExtractor,R0 as ParakeetForCTC,ap as ParakeetPreTrainedModel,D0 as PatchTSMixerForPrediction,$0 as PatchTSMixerModel,Ci as PatchTSMixerPreTrainedModel,U0 as PatchTSTForPrediction,B0 as PatchTSTModel,Pi as PatchTSTPreTrainedModel,q0 as Phi3ForCausalLM,j0 as Phi3Model,zi as Phi3PreTrainedModel,Ni as Phi3VForCausalLM,C_ as Phi3VImageProcessor,ip as Phi3VPreTrainedModel,nh as Phi3VProcessor,G0 as PhiForCausalLM,F0 as PhiModel,Li as PhiPreTrainedModel,P_ as PixtralImageProcessor,oh as PixtralProcessor,y as PreTrainedModel,W as PreTrainedTokenizer,Tn as PretrainedConfig,re as Processor,V0 as PvtForImageClassification,L_ as PvtImageProcessor,W0 as PvtModel,Ri as PvtPreTrainedModel,Go as PyAnnoteFeatureExtractor,X0 as PyAnnoteForAudioFrameClassification,H0 as PyAnnoteModel,$i as PyAnnotePreTrainedModel,ah as PyAnnoteProcessor,vl as QuestionAnsweringPipeline,Y0 as Qwen2ForCausalLM,K0 as Qwen2Model,J0 as Qwen2MoeForCausalLM,Q0 as Qwen2MoeModel,Bi as Qwen2MoePreTrainedModel,Di as Qwen2PreTrainedModel,bm as Qwen2Tokenizer,Da as Qwen2VLForCausalLM,Un as Qwen2VLForConditionalGeneration,Wo as Qwen2VLImageProcessor,Hu as Qwen2VLPreTrainedModel,zs as Qwen2VLProcessor,Ba as Qwen2_5_VLForCausalLM,js as Qwen2_5_VLForConditionalGeneration,Ho as Qwen2_5_VLProcessor,eb as Qwen3ForCausalLM,Z0 as Qwen3Model,rb as Qwen3MoeForCausalLM,tb as Qwen3MoeModel,Fi as Qwen3MoePreTrainedModel,nb as Qwen3NextForCausalLM,sb as Qwen3NextModel,Gi as Qwen3NextPreTrainedModel,Ui as Qwen3PreTrainedModel,ji as Qwen3VLForCausalLM,Xs as Qwen3VLForConditionalGeneration,ab as Qwen3VLMoeForCausalLM,ob as Qwen3VLMoeForConditionalGeneration,ih as Qwen3VLProcessor,qi as Qwen3_5ForCausalLM,Hn as Qwen3_5ForConditionalGeneration,lb as Qwen3_5MoeForCausalLM,ib as Qwen3_5MoeForConditionalGeneration,fb as RFDetrForObjectDetection,pb as RFDetrModel,lp as RFDetrObjectDetectionOutput,Vi as RFDetrPreTrainedModel,Lg as RTDetrForObjectDetection,z_ as RTDetrImageProcessor,Pg as RTDetrModel,_r as RTDetrObjectDetectionOutput,xa as RTDetrPreTrainedModel,Eb as RTDetrV2ForObjectDetection,kb as RTDetrV2Model,cp as RTDetrV2ObjectDetectionOutput,Hi as RTDetrV2PreTrainedModel,Ro as RawAudio,Je as RawImage,Xp as RawVideo,jl as RawVideoFrame,wu as RepetitionPenaltyLogitsProcessor,ub as ResNetForImageClassification,cb as ResNetModel,Wi as ResNetPreTrainedModel,xb as RoFormerForMaskedLM,vb as RoFormerForQuestionAnswering,yb as RoFormerForSequenceClassification,bb as RoFormerForTokenClassification,wb as RoFormerModel,ls as RoFormerPreTrainedModel,km as RoFormerTokenizer,mb as RobertaForMaskedLM,gb as RobertaForQuestionAnswering,_b as RobertaForSequenceClassification,hb as RobertaForTokenClassification,db as RobertaModel,is as RobertaPreTrainedModel,vm as RobertaTokenizer,Vo as Sam2ImageProcessor,fp as Sam2ImageSegmentationOutput,Xi as Sam2Model,dp as Sam2PreTrainedModel,iu as Sam2Processor,lh as Sam2VideoProcessor,Vo as Sam3ImageProcessor,Mb as Sam3TrackerModel,Vo as SamImageProcessor,up as SamImageSegmentationOutput,Ab as SamModel,pp as SamPreTrainedModel,Xo as SamProcessor,N_ as SapiensFeatureExtractor,Ob as SapiensForDepthEstimation,Ib as SapiensForNormalEstimation,Sb as SapiensForSemanticSegmentation,ru as SapiensImageProcessor,Xn as SapiensPreTrainedModel,jm as SeamlessM4TFeatureExtractor,R_ as SegformerFeatureExtractor,Pb as SegformerForImageClassification,Lb as SegformerForSemanticSegmentation,su as SegformerImageProcessor,Cb as SegformerModel,Kn as SegformerPreTrainedModel,$_ as SiglipImageProcessor,zb as SiglipModel,Ki as SiglipPreTrainedModel,Yi as SiglipTextModel,Em as SiglipTokenizer,Nb as SiglipVisionModel,$b as SmolLM3ForCausalLM,Rb as SmolLM3Model,Qi as SmolLM3PreTrainedModel,Yc as SmolVLMImageProcessor,au as SmolVLMProcessor,Zi as SnacDecoderModel,Ji as SnacEncoderModel,qm as SnacFeatureExtractor,Db as SnacModel,Yn as SnacPreTrainedModel,Ub as SolarOpenForCausalLM,Bb as SolarOpenModel,el as SolarOpenPreTrainedModel,Wm as SpeechT5FeatureExtractor,Gb as SpeechT5ForSpeechToText,jb as SpeechT5ForTextToSpeech,qb as SpeechT5HifiGan,Fb as SpeechT5Model,Qn as SpeechT5PreTrainedModel,ch as SpeechT5Processor,Am as SpeechT5Tokenizer,Vb as SqueezeBertForMaskedLM,Xb as SqueezeBertForQuestionAnswering,Hb as SqueezeBertForSequenceClassification,Wb as SqueezeBertModel,Ks as SqueezeBertPreTrainedModel,Tm as SqueezeBertTokenizer,Yb as StableLmForCausalLM,Kb as StableLmModel,tl as StableLmPreTrainedModel,Jb as Starcoder2ForCausalLM,Qb as Starcoder2Model,rl as Starcoder2PreTrainedModel,Yr as StoppingCriteria,On as StoppingCriteriaList,Zb as StyleTextToSpeech2Model,mp as StyleTextToSpeech2PreTrainedModel,El as SummarizationPipeline,sl as SupertonicForConditionalGeneration,_p as SupertonicPreTrainedModel,Mn as SuppressTokensAtBeginLogitsProcessor,_u as SuppressTokensLogitsProcessor,n1 as Swin2SRForImageSuperResolution,D_ as Swin2SRImageProcessor,s1 as Swin2SRModel,nl as Swin2SRPreTrainedModel,t1 as SwinForImageClassification,r1 as SwinForSemanticSegmentation,e1 as SwinModel,Jn as SwinPreTrainedModel,a1 as T5ForConditionalGeneration,o1 as T5Model,ol as T5PreTrainedModel,Mm as T5Tokenizer,l1 as TableTransformerForObjectDetection,i1 as TableTransformerModel,hp as TableTransformerObjectDetectionOutput,al as TableTransformerPreTrainedModel,ku as TemperatureLogitsWarper,N as Tensor,Mr as Text2TextGenerationPipeline,yl as TextClassificationPipeline,Tl as TextGenerationPipeline,nv as TextStreamer,Cl as TextToAudioPipeline,bl as TokenClassificationPipeline,W as TokenizersBackend,PT as TopKLogitsWarper,CT as TopPLogitsWarper,c1 as TrOCRForCausalLM,gp as TrOCRPreTrainedModel,Al as TranslationPipeline,qs as UltravoxModel,Xu as UltravoxPreTrainedModel,uh as UltravoxProcessor,p1 as UniSpeechForCTC,f1 as UniSpeechForSequenceClassification,u1 as UniSpeechModel,Zn as UniSpeechPreTrainedModel,h1 as UniSpeechSatForAudioFrameClassification,m1 as UniSpeechSatForCTC,_1 as UniSpeechSatForSequenceClassification,d1 as UniSpeechSatModel,Ys as UniSpeechSatPreTrainedModel,Y_ as VLChatProcessor,__ as VLMImageProcessor,w1 as VaultGemmaForCausalLM,g1 as VaultGemmaModel,il as VaultGemmaPreTrainedModel,B_ as ViTFeatureExtractor,b1 as ViTForImageClassification,nu as ViTImageProcessor,v1 as ViTMAEModel,wp as ViTMAEPreTrainedModel,E1 as ViTMSNForImageClassification,k1 as ViTMSNModel,cl as ViTMSNPreTrainedModel,y1 as ViTModel,ll as ViTPreTrainedModel,x1 as VisionEncoderDecoderModel,A1 as VitMatteForImageMatting,U_ as VitMatteImageProcessor,xp as VitMattePreTrainedModel,T1 as VitPoseForPoseEstimation,F_ as VitPoseImageProcessor,yp as VitPosePreTrainedModel,M1 as VitsModel,bp as VitsModelOutput,vp as VitsPreTrainedModel,Om as VitsTokenizer,S1 as VoxtralForConditionalGeneration,ph as VoxtralProcessor,Xm as VoxtralRealtimeFeatureExtractor,ul as VoxtralRealtimeForConditionalGeneration,kp as VoxtralRealtimePreTrainedModel,dh as VoxtralRealtimeProcessor,P1 as Wav2Vec2BertForCTC,L1 as Wav2Vec2BertForSequenceClassification,C1 as Wav2Vec2BertModel,eo as Wav2Vec2BertPreTrainedModel,Im as Wav2Vec2CTCTokenizer,Vm as Wav2Vec2FeatureExtractor,Px as Wav2Vec2ForAudioFrameClassification,Ix as Wav2Vec2ForCTC,Cx as Wav2Vec2ForSequenceClassification,Ox as Wav2Vec2Model,sr as Wav2Vec2PreTrainedModel,mh as Wav2Vec2Processor,_h as Wav2Vec2ProcessorWithLM,D1 as WavLMForAudioFrameClassification,N1 as WavLMForCTC,R1 as WavLMForSequenceClassification,$1 as WavLMForXVector,z1 as WavLMModel,cs as WavLMPreTrainedModel,Hm as WeSpeakerFeatureExtractor,B1 as WeSpeakerResNetModel,Ap as WeSpeakerResNetPreTrainedModel,Km as WhisperFeatureExtractor,Mp as WhisperForConditionalGeneration,U1 as WhisperModel,pl as WhisperPreTrainedModel,hh as WhisperProcessor,SM as WhisperTextStreamer,hu as WhisperTimeStampLogitsProcessor,Cm as WhisperTokenizer,V1 as XLMForQuestionAnswering,q1 as XLMForSequenceClassification,W1 as XLMForTokenClassification,G1 as XLMModel,us as XLMPreTrainedModel,X1 as XLMRobertaForMaskedLM,Q1 as XLMRobertaForQuestionAnswering,K1 as XLMRobertaForSequenceClassification,Y1 as XLMRobertaForTokenClassification,H1 as XLMRobertaModel,ps as XLMRobertaPreTrainedModel,Pm as XLMRobertaTokenizer,Lm as XLMTokenizer,j1 as XLMWithLMHeadModel,Ep as XVectorOutput,G_ as YolosFeatureExtractor,Z1 as YolosForObjectDetection,ou as YolosImageProcessor,J1 as YolosModel,Sp as YolosObjectDetectionOutput,fl as YolosPreTrainedModel,tv as YoutuForCausalLM,ev as YoutuModel,dl as YoutuPreTrainedModel,Ol as ZeroShotAudioClassificationPipeline,Ml as ZeroShotClassificationPipeline,Nl as ZeroShotImageClassificationPipeline,$l as ZeroShotObjectDetectionPipeline,ve as cat,uz as cos_sim,lA as dot,_e as env,Ye as full,Po as full_like,Rd as interpolate,vt as interpolate_4d,ND as layer_norm,_N as load_image,i3 as load_video,Bf as log_softmax,iT as matmul,Nc as mean,lT as mean_pooling,et as ones,Rc as ones_like,Vz as permute,Lce as pipeline,uT as quantize_embeddings,RD as rand,cT as randn,ys as random,Rm as read_audio,zD as rfft,zc as slice,Re as softmax,yt as stack,$d as std_mean,tr as topk,Bd as zeros,Ud as zeros_like};