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",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=e,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.min_score=pP(this.scores)[0],this.unk_score=this.min_score-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new lP,this.trie.extend(this.vocab),this.fuse_unk=!0}populate_nodes(t){let e=t.chars,r=1,s=0;for(;s<e.length;){let n=!1,o=[],a=e.slice(s).join(""),i=this.trie.common_prefix_search(a);for(let l of i){o.push(l);let c=this.tokens_to_ids.get(l),u=this.scores[c],f=sC(l);t.insert(s,f,u,c),!n&&f===r&&(n=!0)}n||t.insert(s,r,this.unk_score,this.unk_token_id),s+=r}}tokenize(t){let e=new uP(t,this.bos_token_id,this.eos_token_id);return this.populate_nodes(e),e.tokens()}encode(t){let e=[];for(let r of t){let s=this.tokenize(r);e.push(...s)}return e}},xE=fP,dP=class{constructor(t=(r,s)=>r>s,e=1/0){this._heap=[],this._comparator=t,this._max_size=e}get size(){return this._heap.length}is_empty(){return 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}},mP=dP,_P=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()}},hP=_P,gP=class extends rc{constructor(t){super(t),this.tokens_to_ids=Af(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 hP(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 mP((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}},yE=gP,wP=class extends rc{constructor(t,e){super(t);let r=t.vocab;this.tokens_to_ids=Af(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}},xP=wP;function yP(t,e){switch(t.type){case"WordPiece":return new gE(t);case"Unigram":return new xE(t,e.eos_token);case"BPE":return new yE(t);default:if(t.vocab)return Array.isArray(t.vocab)?new xE(t,e.eos_token):Object.hasOwn(t,"continuing_subword_prefix")&&Object.hasOwn(t,"unk_token")?Object.hasOwn(t,"merges")?new yE(t):new gE(t):new xP(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 bP=yP,vP=class extends co{constructor(t){super(),this.config=t}_call(t,...e){return this.post_process(t,...e)}},uo=vP,kP=class extends uo{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}}},EP=kP,AP=class extends uo{post_process(t,e=null){return{tokens:t,tokens_pair:e}}},TP=AP,MP=class extends uo{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}}},SP=MP,OP=class extends uo{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}}},IP=OP,CP=class extends uo{constructor(t){super(t),this.processors=(t.processors??[]).map(e=>ME(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}},PP=CP;function LP(t){if(t===null)return null;switch(t.type){case"TemplateProcessing":return new EP(t);case"ByteLevel":return new TP(t);case"BertProcessing":return new SP(t);case"RobertaProcessing":return new IP(t);case"Sequence":return new PP(t);default:throw new Error(`Unknown PostProcessor type: ${t.type}`)}}var ME=LP,NP=class extends co{constructor(t){super(),this.config=t,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets="trim_offsets"in t?t.trim_offsets:!1}_call(t){return this.decode(t)}decode(t){return this.decode_chain(t).join("")}},Ft=NP,zP=class extends Ft{constructor(t){super(t),this.byte_decoder=QI,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(t){let e=t.join(""),r=new Uint8Array([...e].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(r)}decode_chain(t){let e=[],r=[];for(let s of t)this.added_tokens.find(n=>n.content===s)!==void 0?(r.length>0&&(e.push(this.convert_tokens_to_string(r)),r=[]),e.push(s)):r.push(s);return r.length>0&&e.push(this.convert_tokens_to_string(r)),e}},RP=zP,$P=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=Ef(e)),e})}},DP=$P,BP=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}},UP=BP,FP=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?"":" "))}},GP=FP,jP=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=Ef(s).replaceAll(this.word_delimiter_token," ").trim()),s}decode_chain(t){return[this.convert_tokens_to_string(t)]}},qP=jP,WP=class extends Ft{constructor(t){super(t),this.decoders=(t.decoders??[]).map(e=>SE(e))}decode_chain(t){return this.decoders.reduce((e,r)=>r.decode_chain(e),t)}},VP=WP,HP=class extends Ft{decode_chain(t){let e=ec(this.config.pattern),r=this.config.content??"";return e===null?t:t.map(s=>s.replaceAll(e,r))}},XP=HP,KP=class extends Ft{decode_chain(t){return[t.join("")]}},YP=KP,QP=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)})}},JP=QP,ZP=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}},eL=ZP;function tL(t){if(t===null)return null;switch(t.type){case"ByteLevel":return new RP(t);case"WordPiece":return new DP(t);case"Metaspace":return new UP(t);case"BPEDecoder":return new GP(t);case"CTC":return new qP(t);case"Sequence":return new VP(t);case"Replace":return new XP(t);case"Fuse":return new YP(t);case"Strip":return new JP(t);case"ByteFallback":return new eL(t);default:throw new Error(`Unknown Decoder type: ${t.type}`)}}var SE=tL,rL=class{constructor(t,e){let r=hE(t,"Tokenizer",["model","decoder","post_processor","pre_tokenizer","normalizer"]);if(r)throw new Error(r);let s=hE(e,"Config");if(s)throw new Error(s);this.tokenizer=t,this.config=e,this.normalizer=kE(this.tokenizer.normalizer),this.pre_tokenizer=EE(this.tokenizer.pre_tokenizer),this.model=bP(this.tokenizer.model,this.config),this.post_processor=ME(this.tokenizer.post_processor),this.decoder=SE(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 KI(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 mE(n),this.splitter_normalized=new mE(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||!rC(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=Ef(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=nC(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}},OE=rL;var U=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 IE(t){return/\w/.test(t)}function po(t){return/[0-9]/.test(t)}function CE(t){return/\s/.test(t)}var 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je}evaluateUnaryExpression(t,e){let r=this.evaluate(t.argument,e);if(t.operator.value==="not")return new pe(!r.value);throw new SyntaxError(`Unknown operator: ${t.operator.value}`)}evaluateTernaryExpression(t,e){return this.evaluate(t.condition,e).__bool__().value?this.evaluate(t.trueExpr,e):this.evaluate(t.falseExpr,e)}evalProgram(t,e){return this.evaluateBlock(t.body,e)}evaluateBlock(t,e){let r="";for(let s of t){let n=this.evaluate(s,e);n.type!=="NullValue"&&n.type!=="UndefinedValue"&&(r+=n.toString())}return new te(r)}evaluateIdentifier(t,e){return e.lookupVariable(t.value)}evaluateCallExpression(t,e){let[r,s]=this.evaluateArguments(t.args,e);s.size>0&&r.push(new mo(s));let n=this.evaluate(t.callee,e);if(n.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${n.type}`);return n.value(r,e)}evaluateSliceExpression(t,e,r){if(!(t instanceof ye||t instanceof te))throw new Error("Slice object must be an array or string");let s=this.evaluate(e.start,r),n=this.evaluate(e.stop,r),o=this.evaluate(e.step,r);if(!(s instanceof _e||s instanceof je))throw new Error("Slice start must be numeric or undefined");if(!(n instanceof _e||n instanceof je))throw new Error("Slice stop must be numeric or undefined");if(!(o instanceof _e||o instanceof je))throw new Error("Slice step must be numeric or undefined");return t instanceof ye?new ye(zE(t.value,s.value,n.value,o.value)):new te(zE(Array.from(t.value),s.value,n.value,o.value).join(""))}evaluateMemberExpression(t,e){let r=this.evaluate(t.object,e),s;if(t.computed){if(t.property.type==="SliceExpression")return this.evaluateSliceExpression(r,t.property,e);s=this.evaluate(t.property,e)}else s=new te(t.property.value);let n;if(r instanceof ct){if(!(s instanceof te))throw new Error(`Cannot access property with non-string: got ${s.type}`);n=r.value.get(s.value)??r.builtins.get(s.value)}else if(r instanceof ye||r instanceof te)if(s instanceof _e)n=r.value.at(s.value),r instanceof te&&(n=new te(r.value.at(s.value)));else if(s instanceof te)n=r.builtins.get(s.value);else throw new Error(`Cannot access property with non-string/non-number: got ${s.type}`);else{if(!(s instanceof te))throw new Error(`Cannot access property with non-string: got ${s.type}`);n=r.builtins.get(s.value)}return n instanceof cr?n:new je}evaluateSet(t,e){let r=t.value?this.evaluate(t.value,e):this.evaluateBlock(t.body,e);if(t.assignee.type==="Identifier"){let s=t.assignee.value;e.setVariable(s,r)}else if(t.assignee.type==="TupleLiteral"){let s=t.assignee;if(!(r instanceof ye))throw new Error(`Cannot unpack non-iterable type in set: ${r.type}`);let n=r.value;if(n.length!==s.value.length)throw new Error(`Too ${s.value.length>n.length?"few":"many"} items to unpack in set`);for(let o=0;o<s.value.length;++o){let a=s.value[o];if(a.type!=="Identifier")throw new Error(`Cannot unpack to non-identifier in set: ${a.type}`);e.setVariable(a.value,n[o])}}else if(t.assignee.type==="MemberExpression"){let s=t.assignee,n=this.evaluate(s.object,e);if(!(n instanceof ct))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 Ve}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 gs(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 ct))throw new Error(`Expected iterable or object type in for loop: got ${n.type}`);n instanceof ct&&(n=n.keys());let o=[],a=[];for(let c=0;c<n.value.length;++c){let u=new gs(r),f=n.value[c],m;if(t.loopvar.type==="Identifier")m=_=>_.setVariable(t.loopvar.value,f);else if(t.loopvar.type==="TupleLiteral"){let _=t.loopvar;if(f.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${f.type}`);let w=f;if(_.value.length!==w.value.length)throw new Error(`Too ${_.value.length>w.value.length?"few":"many"} items to unpack`);m=x=>{for(let E=0;E<_.value.length;++E){if(_.value[E].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${_.value[E].type}`);x.setVariable(_.value[E].value,w.value[E])}}}else throw new Error(`Invalid loop variable(s): ${t.loopvar.type}`);s&&(m(u),!this.evaluate(s,u).__bool__().value)||(o.push(f),a.push(m))}let i="",l=!0;for(let c=0;c<o.length;++c){let u=new Map([["index",new _e(c+1)],["index0",new _e(c)],["revindex",new _e(o.length-c)],["revindex0",new _e(o.length-c-1)],["first",new pe(c===0)],["last",new pe(c===o.length-1)],["length",new _e(o.length)],["previtem",c>0?o[c-1]:new je],["nextitem",c<o.length-1?o[c+1]:new je]]);r.setVariable("loop",new ct(u)),a[c](r);try{let f=this.evaluateBlock(t.body,r);i+=f.value}catch(f){if(f instanceof $E)continue;if(f instanceof RE)break;throw f}l=!1}if(l){let c=this.evaluateBlock(t.defaultBlock,r);i+=c.value}return new te(i)}evaluateMacro(t,e){return e.setVariable(t.name.value,new qe((r,s)=>{let n=new gs(s);r=r.slice();let o;r.at(-1)?.type==="KeywordArgumentsValue"&&(o=r.pop());for(let a=0;a<t.args.length;++a){let i=t.args[a],l=r[a];if(i.type==="Identifier"){let c=i;if(!l)throw new Error(`Missing positional argument: ${c.value}`);n.setVariable(c.value,l)}else if(i.type==="KeywordArgumentExpression"){let c=i,u=l??o?.value.get(c.key.value)??this.evaluate(c.value,n);n.setVariable(c.key.value,u)}else throw new Error(`Unknown argument type: ${i.type}`)}return this.evaluateBlock(t.body,n)})),new Ve}evaluateCallStatement(t,e){let r=new qe((i,l)=>{let c=new gs(l);if(t.callerArgs)for(let u=0;u<t.callerArgs.length;++u){let f=t.callerArgs[u];if(f.type!=="Identifier")throw new Error(`Caller parameter must be an identifier, got 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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?Hf(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()}},SA=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=Xf();return this.tensorTrackersById.set(t,new Qf(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=Xf(),a=new Yf({sessionId:t,context:n,tensor:e,dataType:r,shape:s});return this.tensorTrackersById.set(o,new Qf(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 Yf({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)}},f2=(...t)=>new SA(...t)}),d2={};To(d2,{WebNNBackend:()=>m2});var bo,OA,m2,CN=be(()=>{"use strict";fn(),Os(),ON(),IN(),p2(),bo=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),OA=(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])},m2=class{constructor(t){this.tensorManager=f2(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,u2(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=>OA(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=bo.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=bo.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 c2(r,e)}}registerMLTensor(t,e,r,s){let n=bo.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=nd(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}}} ${a?"(Note: it was int64 data type and registered to int32 as workaround)":""}`),s.constant(n,u)}registerGraphInput(t){this.temporaryGraphInputs.push(t)}registerGraphOutput(t){this.temporaryGraphOutputs.push(t)}isGraphInput(t,e){let r=this.sessionGraphInputs.get(t);return r?r.includes(e):!1}isGraphOutput(t,e){let r=this.sessionGraphOutputs.get(t);return r?r.includes(e):!1}isGraphInputOutputTypeSupported(t,e,r=!0){let s=bo.get(Es(e)),n=this.mlOpSupportLimitsBySessionId.get(t);return typeof s>"u"?!1:r?!!n?.input.dataTypes.includes(s):!!n?.output.dataTypes.includes(s)}flush(){}}}),IA,gd,wd,Fr,CA,Jf,bc,xd,yd,Zf,bd,vd,kd,_2=be(()=>{"use strict";Ss(),MN(),SN(),fn(),Os(),dd(),l2(),IA=(t,e)=>{De()._OrtInit(t,e)!==0&&Pe("Can't initialize onnxruntime.")},gd=async t=>{IA(t.wasm.numThreads,yc(t.logLevel))},wd=async(t,e)=>{De().asyncInit?.();let r=t.webgpu.adapter;if(e==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");if(r){if(typeof r.limits!="object"||typeof r.features!="object"||typeof r.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let s=t.webgpu.powerPreference;if(s!==void 0&&s!=="low-power"&&s!=="high-performance")throw new Error(`Invalid powerPreference setting: "${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(CN(),gc(d2)).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,CA=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&&Pe("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)}},Jf=(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&&Pe("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)}},bc=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 ${t.byteLength}.`);return e.HEAPU8.set(t,r),[r,t.byteLength]},xd=async(t,e)=>{let r,s,n=De();Array.isArray(t)?[r,s]=t:t.buffer===n.HEAPU8.buffer?[r,s]=[t.byteOffset,t.byteLength]:[r,s]=bc(t);let o=0,a=0,i=0,l=[],c=[],u=[];try{if([a,l]=await i2(e),e?.externalData&&n.mountExternalData){let M=[];for(let I of e.externalData){let O=typeof I=="string"?I:I.path;M.push(hd(typeof I=="string"?I:I.data).then(v=>{n.mountExternalData(O,v)}))}await Promise.all(M)}for(let M of e?.executionProviders??[])if((typeof M=="string"?M:M.name)==="webnn"){if(n.shouldTransferToMLTensor=!1,typeof M!="string"){let I=M,O=I?.context,v=I?.gpuDevice,$=I?.deviceType,G=I?.powerPreference;O?n.currentContext=O:v?n.currentContext=await n.webnnCreateMLContext(v):n.currentContext=await n.webnnCreateMLContext({deviceType:$,powerPreference:G})}else n.currentContext=await n.webnnCreateMLContext();break}o=await n._OrtCreateSession(r,s,a),n.webgpuOnCreateSession?.(o),o===0&&Pe("Can't create a session."),n.jsepOnCreateSession?.(),n.currentContext&&(n.webnnRegisterMLContext(o,n.currentContext),n.currentContext=void 0,n.shouldTransferToMLTensor=!0);let[f,m]=CA(o),_=!!e?.enableGraphCapture,w=[],x=[],E=[],b=[],A=[];for(let M=0;M<f;M++){let[I,O,v]=Jf(o,M);I===0&&Pe("Can't get an input name."),c.push(I);let $=n.UTF8ToString(I);w.push($),E.push(O===0?{name:$,isTensor:!1}:{name:$,isTensor:!0,type:hc(O),shape:v})}for(let M=0;M<m;M++){let[I,O,v]=Jf(o,M+f);I===0&&Pe("Can't get an output name."),u.push(I);let $=n.UTF8ToString(I);x.push($),b.push(O===0?{name:$,isTensor:!1}:{name:$,isTensor:!0,type:hc(O),shape:v});{if(_&&e?.preferredOutputLocation===void 0){A.push("gpu-buffer");continue}let G=typeof e?.preferredOutputLocation=="string"?e.preferredOutputLocation:e?.preferredOutputLocation?.[$]??"cpu",F=n.webnnIsGraphOutput;if(G==="cpu"&&F&&F(o,$)){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}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);A.push(G)}}let S=null;return A.some(M=>M==="gpu-buffer"||M==="ml-tensor"||M==="ml-tensor-cpu-output")&&(i=n._OrtCreateBinding(o),i===0&&Pe("Can't create IO binding."),S={handle:i,outputPreferredLocations:A,outputPreferredLocationsEncoded:A.map(M=>M==="ml-tensor-cpu-output"?"ml-tensor":M).map(M=>sd(M))}),Fr.set(o,[o,c,u,S,_,!1]),[o,w,x,E,b]}catch(f){throw c.forEach(m=>n._OrtFree(m)),u.forEach(m=>n._OrtFree(m)),i!==0&&n._OrtReleaseBinding(i)!==0&&Pe("Can't release IO binding."),o!==0&&n._OrtReleaseSession(o)!==0&&Pe("Can't release session."),f}finally{n._free(r),a!==0&&n._OrtReleaseSessionOptions(a)!==0&&Pe("Can't release session options."),l.forEach(f=>n._free(f)),n.unmountExternalData?.()}},yd=t=>{let e=De(),r=Fr.get(t);if(!r)throw new Error(`cannot release session. invalid session id: ${t}`);let[s,n,o,a,i]=r;a&&(i&&e._OrtClearBoundOutputs(a.handle)!==0&&Pe("Can't clear bound 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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=_,Td(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(Td(x.data,i))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(a!==void 0)return Td(a,i);throw new Error("Input data provided is not supported - aborted tensor creation")},z2=(t,e)=>{let{width:r,height:s,download:n,dispose:o}=e,a=[1,s,r,4];return new xt({location:"texture",type:"float32",texture:t,dims:a,download:n,dispose:o})},R2=(t,e)=>{let{dataType:r,dims:s,download:n,dispose:o}=e;return new xt({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:s,download:n,dispose:o})},$2=(t,e)=>{let{dataType:r,dims:s,download:n,dispose:o}=e;return new xt({location:"ml-tensor",type:r??"float32",mlTensor:t,dims:s,download:n,dispose:o})},D2=(t,e,r)=>new xt({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]});var Is=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array],["int4",Uint8Array],["uint4",Uint8Array]]),Mo=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]),B2=!1,U2=()=>{if(!B2){B2=!0;let t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=globalThis.Float16Array,s=typeof r<"u"&&r.from;t&&(Is.set("int64",BigInt64Array),Mo.set(BigInt64Array,"int64")),e&&(Is.set("uint64",BigUint64Array),Mo.set(BigUint64Array,"uint64")),s?(Is.set("float16",r),Mo.set(r,"float16")):Is.set("float16",Uint16Array)}};var F2=t=>{let e=1;for(let r=0;r<t.length;r++){let s=t[r];if(typeof s!="number"||!Number.isSafeInteger(s))throw new TypeError(`dims[${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},G2=(t,e)=>{switch(t.location){case"cpu":return new xt(t.type,t.data,e);case"cpu-pinned":return new xt({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new xt({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new xt({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});case"ml-tensor":return new xt({location:"ml-tensor",mlTensor:t.mlTensor,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};var xt=class{constructor(e,r,s){U2();let n,o;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,n=e.type,o=e.dims,e.location){case"cpu-pinned":{let i=Is.get(n);if(!i)throw new TypeError(`unsupported type "${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=Is.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=Mo.get(e.constructor);if(c===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);n=c,i=e}if(l===void 0)l=[i.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");o=l,this.cpuData=i,this.dataLocation="cpu"}let a=F2(o);if(this.cpuData&&a!==this.cpuData.length&&!((n==="uint4"||n==="int4")&&Math.ceil(a/2)===this.cpuData.length))throw new Error(`Tensor's size(${a}) does not match data length(${this.cpuData.length}).`);this.type=n,this.dims=o,this.size=a}static async fromImage(e,r){return N2(e,r)}static fromTexture(e,r){return z2(e,r)}static fromGpuBuffer(e,r){return R2(e,r)}static fromMLTensor(e,r){return $2(e,r)}static fromPinnedBuffer(e,r,s){return D2(e,r,s)}toDataURL(e){return P2(this,e)}toImageData(e){return L2(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 G2(this,e)}};var vc=xt;var RN=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 V2(t){return t<=Ot.DEBUG?0:t<=Ot.INFO?2:t<=Ot.WARNING||t<=Ot.ERROR?3:4}var $N={0:"verbose",1:"info",2:"warning",3:"error",4:"fatal"},qt=[],Md,mn,j2=Symbol.for("onnxruntime");if(j2 in globalThis)mn=globalThis[j2];else if(ie.IS_NODE_ENV){switch(mn=Nf,process.platform){case"win32":qt.push("dml");break;case"linux":process.arch==="x64"&&qt.push("cuda");break;case"darwin":qt.push("coreml");break}qt.push("webgpu"),qt.push("cpu"),Md=["cpu"]}else mn=Ed,ie.IS_WEBNN_AVAILABLE&&qt.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),ie.IS_WEBGPU_AVAILABLE&&qt.push("webgpu"),qt.push("wasm"),Md=["wasm"];var DN=mn.InferenceSession;function H2(t=null){if(!t)return Md;switch(t){case"auto":return qt;case"gpu":return qt.filter(e=>["webgpu","cuda","dml","webnn-gpu"].includes(e))}if(qt.includes(t))return[RN[t]??t];throw new Error(`Unsupported device: "${t}". Should be one of: ${qt.join(", ")}.`)}var q2=Promise.resolve(),dn=null;async function BN(){if(dn)return dn;if(!(me.useWasmCache&&typeof st?.wasm?.wasmPaths=="object"&&st?.wasm?.wasmPaths?.wasm&&st?.wasm?.wasmPaths?.mjs)){if(ie.IS_DENO_WEB_RUNTIME)throw new Error("env.useWasmCache=false is not supported in Deno's web runtime. Remove the useWasmCache override.");return dn=Promise.resolve(),dn}return dn=(async()=>{let e=st.wasm.wasmPaths,r=!1;await Promise.all([e.wasm&&!Mf(e.wasm)?(async()=>{try{let s=await S2(Sf(e.wasm));s&&(st.wasm.wasmBinary=s,r=!0)}catch(s){ee.warn("Failed to pre-load WASM binary:",s)}})():Promise.resolve(),e.mjs&&!Mf(e.mjs)?(async()=>{try{let s=await O2(Sf(e.mjs));s&&(st.wasm.wasmPaths.mjs=s)}catch(s){ee.warn("Failed to pre-load WASM factory:",s)}})():Promise.resolve()]),r||(st.wasm.wasmPaths.mjs=e.mjs)})(),dn}async function kc(t,e,r){await BN();let s=V2(me.logLevel??Ot.WARNING),n=()=>DN.create(t,{logSeverityLevel:s,...e}),o=await(ie.IS_WEB_ENV?q2=q2.then(n):n());return o.config=r,o}var W2=Promise.resolve();async function Ec(t,e){let r=()=>t.run(e);return ie.IS_WEB_ENV?W2=W2.then(r):r()}function Ac(t){return t instanceof mn.Tensor}var st=mn?.env;function So(){return st?.wasm?.proxy}if(st){let t=function(e){let r=V2(e);st.logLevel=$N[r]};if(st.wasm){if(!(typeof ServiceWorkerGlobalScope<"u"&&self instanceof ServiceWorkerGlobalScope)&&st.versions?.web&&!st.wasm.wasmPaths){let e=`/assets/${st.versions.web}/dist/`;st.wasm.wasmPaths=ie.IS_SAFARI?{mjs:`${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`}}st.wasm.proxy=!1}st.webgpu&&(st.webgpu.powerPreference="high-performance"),t(me.logLevel??Ot.WARNING),me.backends.onnx={...st,setLogLevel:t}}var Wr=async(t,e,r)=>{let s=await kc(new Uint8Array(t),e);return(async n=>{let o=So(),a=Object.fromEntries(Object.entries(n).map(([l,c])=>[l,(o?c.clone():c).ort_tensor])),i=await Ec(s,a);return Array.isArray(r)?r.map(l=>new z(i[l])):new z(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 X2=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"}),Sd=ie.IS_NODE_ENV?"cpu":"wasm";function Tc(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 (${Sd}).`),Sd):Sd}var Q2=(function(){let t;return async function(){if(t===void 0)if(!ie.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}})(),yt=Object.freeze({auto:"auto",fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),K2=yt.fp32,Y2=Object.freeze({[X2.wasm]:yt.q8}),Vr=Object.freeze({[yt.fp32]:"",[yt.fp16]:"_fp16",[yt.int8]:"_int8",[yt.uint8]:"_uint8",[yt.q8]:"_quantized",[yt.q4]:"_q4",[yt.q4f16]:"_q4f16",[yt.bnb4]:"_bnb4"});function Mc(t,e,r,{configDtype:s=null,warn:n}={}){let o,a=!1;t&&typeof t!="string"?t.hasOwnProperty(e)?o=t[e]:(o=null,a=!0):o=t;let i;if(o===yt.auto){if(s){let l=typeof s=="string"?s:s?.[e];if(l&&l!==yt.auto&&yt.hasOwnProperty(l))return l}i=Y2[r]??K2}else o&&yt.hasOwnProperty(o)?i=o:i=Y2[r]??K2;return a&&n&&n(`dtype not specified for "${e}". 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this.clone().round_()}mean(e=null,r=!1){return Ic(this,e,r)}min(e=null,r=!1){if(e===null){let a=wo(this.data)[0];return new t(this.type,[a],[])}let[s,n,o]=Oo((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=Ce(this.data)[0];return new t(this.type,[a],[])}let[s,n,o]=Oo((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=wo(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=Ce(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=Od(s),c=Od(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: ${e}`);let r,s=["int64","uint64"].includes(this.type),n=["int64","uint64"].includes(e);if(s&&!n)r=Number;else if(!s&&n)["float16","float32","float64"].includes(this.type)?r=o=>BigInt(Math.floor(o)):r=BigInt;else if(this.type==="float16"&&e=="float32"&&this.data instanceof Uint16Array)return new t(e,aA(this.data),this.dims);return new t(e,kr[e].from(this.data,r),this.dims)}};function UN(t,e){let 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Specify `power` to fix this issue.");if(!u)throw new Error("`preemphasis_htk_flavor=false` is not currently supported.");if(a){let D=Math.floor(r/2);switch(i){case"reflect":{t=rz(t,D,D);break}case"constant":{let Z=new t.constructor(t.length+2*D);Z.set(t,D),t=Z;break}default:throw new Error(`pad_mode="${i}" not implemented yet.`)}}let G=Math.floor(1+Math.floor((t.length-r)/s));S!==null&&G<S&&(G=S);let F=l?Math.floor(n/2)+1:n,K=G,W=G;M!==null&&(M>G?I&&(W=M):W=K=M);let Q=new cc(n),B=new Float64Array(n),P=new Float64Array(Q.outputBufferSize),re=new Float32Array(F*W);for(let D=0;D<K;++D){let Z=D*s,xe=Math.min(t.length-Z,r);xe!==r&&B.fill(0,0,r);for(let ue=0;ue<xe;++ue)B[ue]=t[Z+ue];if(A){let ue=0;for(let ot=0;ot<xe;++ot)ue+=B[ot];let We=ue/xe;for(let ot=0;ot<xe;++ot)B[ot]-=We}if(c!==null){for(let ue=xe-1;ue>=1;--ue)B[ue]-=c*B[ue-1];B[0]*=1-c}for(let ue=0;ue<e.length;++ue)B[ue]*=e[ue];Q.realTransform(P,B);for(let ue=0;ue<F;++ue){let We=ue<<1;re[ue*W+D]=P[We]**2+P[We+1]**2}}if(o!==null&&o!==2){let D=o/2;for(let Z=0;Z<re.length;++Z)re[Z]**=D}let Y=f.length,L=await eT(new z("float32",f.flat(),[Y,F]),new z("float32",re,[F,W]));O&&(L=L.transpose(1,0));let R=L.data;for(let D=0;D<R.length;++D)R[D]=v+Math.max(m,R[D]);if(o!==null&&_!==null){let D=Math.min(R.length,K*Y);switch(_){case"log":for(let Z=0;Z<D;++Z)R[Z]=Math.log(R[Z]);break;case"log10":for(let Z=0;Z<D;++Z)R[Z]=Math.log10(R[Z]);break;case"log10_max_norm":{for(let ue=0;ue<D;++ue)R[ue]=Math.log10(R[ue]);let xe=(w??Ce(R)[0])-8;for(let ue=0;ue<D;++ue)R[ue]=(Math.max(R[ue],xe)+4)/4;break}case"dB":if(o===1)sz(R,x,E,b);else if(o===2)nz(R,x,E,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 L}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=pT(o);break;case"hamming":a=QN(o);break;case"povey":a=pT(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 oz(t,e){let r=t.reduce((o,a)=>o+a.length,0),s=new ArrayBuffer(44),n=new DataView(s);return Lc(n,0,"RIFF"),n.setUint32(4,36+r*4,!0),Lc(n,8,"WAVE"),Lc(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),Lc(n,36,"data"),n.setUint32(40,r*4,!0),new Blob([s,...t.map(o=>o.buffer)],{type:"audio/wav"})}function Lc(t,e,r){for(let s=0;s<r.length;++s)t.setUint8(e+s,r.charCodeAt(s))}var No=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]),oz(e,this.sampling_rate)}async save(e){return Pc(e,this.toBlob())}};var Cm=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=nt(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 ft(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){Le(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 zo=class extends Ee{async _call(e){Le(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 z("float32",e,s)}}};var Pm=class extends Ee{async _call(e){Le(e,"ChatterboxFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new z("float32",e,r)}}};var Lm=class extends Ee{constructor(e){super(e),this.mel_filters=nt(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=nt(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(xs.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 ft(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 Le(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 az=1e-5,Ro=class extends Ee{constructor(e){super(e),this.config.mel_filters??=nt(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 ft(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){Le(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(_)+az);for(let E=0;E<s;++E){let b=E*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 z("int64",u,[1,o])}}};var Nm=class extends Ro{_apply_dither(e){let r=this.config.dither??0;if(r<=0)return e;let s=new Nr(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){Le(e,"CohereAsrFeatureExtractor");let r=new Float64Array(e);return this._apply_dither(r),super._call(r)}};var $o=class extends zo{};var zm=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=nt(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 ft(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(Le(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=Xe([1,a.dims[0]],!0);return{input_features:a.unsqueeze_(0),input_features_mask:i}}};var Rm=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=nt(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){Le(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 ft(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 $m=class extends Ee{async _call(e){Le(e,"MoonshineFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new z("float32",e,r)}}};var Do=class extends Ee{async _call(e){Le(e,"PyAnnoteFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,1,e.length];return{input_values:new z("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]=Ce(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 Dm=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=nt(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),ft(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}={}){Le(e,"SeamlessM4TFeatureExtractor");let a=await this._extract_fbank_features(e,this.config.max_length);if(n){let[w,x]=a.dims,E=a.data;for(let b=0;b<x;++b){let A=0;for(let O=0;O<w;++O)A+=E[O*x+b];let S=A/w,M=0;for(let O=0;O<w;++O)M+=(E[O*x+b]-S)**2;M/=w-1;let I=Math.sqrt(M+1e-7);for(let O=0;O<w;++O){let v=O*x+b;E[v]=(E[v]-S)/I}}}let i;if(r){let[w,x]=a.dims,E=a.data,b=w%s;if(b>0){let A=new Float32Array(x*(w+b));A.set(E),A.fill(this.config.padding_value,E.length);let S=w+b;a=new z(a.type,A,[S,x]),o&&(i=new z("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 E=i.data;for(let b=1,A=0;b<l;b+=u,++A)x[A]=E[b]}else x.fill(1n);_.attention_mask=new z("int64",x,[1,w])}return _}};var Bm=class extends $o{};var Um=class extends Ee{};var Fm=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){Le(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 z("float32",r,s),attention_mask:new z("int64",new BigInt64Array(r.length).fill(1n),s)}}};var Gm=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=nt(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),ft(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){Le(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 jm=class extends Ee{constructor(e){super(e),this.config.mel_filters??=nt(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 ft(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 Le(e,"VoxtralRealtimeFeatureExtractor"),{input_features:(await this._extract_fbank_features(e,{center:r})).unsqueeze_(0)}}};var qm=class extends Ee{constructor(e){super(e),this.config.mel_filters??=nt(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 ft(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}={}){Le(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 ut(e,Lo,!0,r),n=s.feature_extractor_type,o=Bo[n];if(!o)throw new Error(`Unknown feature_extractor_type: '${n}'. Please report this at ${Hr}.`);return new o(s)}};var Wm=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e,r=null){let s=this.tokenizer(e),n=r?await this.feature_extractor(r):{};return{...s,...n}}};var iz=new Set(["ja","zh"]),Vm=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!0;get_decoder_prompt_ids(e="en"){let r=["\u2581","<|startofcontext|>","<|startoftranscript|>","<|emo:undefined|>",`<|${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=iz.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 Nc={};var Ps,_T,Xr;if(ie.IS_WEB_ENV)Ps=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this environment.");return new self.OffscreenCanvas(t,e)},Xr=self.createImageBitmap,_T=self.ImageData;else if(Nc)Xr=async t=>{let r=(await t.metadata()).channels,{data:s,info:n}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),o=new Qe(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 lz={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},cz=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]),Qe=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=Ps(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=Nc(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=lz[s]??s,o=bf(e),a=bf(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=Ps(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=Ps(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=Ps(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=Ps(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 z("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=Ps(e.width,e.height),s=new _T(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=cz.get(r)??"image/png",n=await this.toBlob(s);return Pc(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 Nc(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}},uz=Qe.read.bind(Qe);function hT(t,e,r=0,s=null){let n=t/e,o=nA(n)*e;return s!==null&&o>s&&(o=Math.floor(n)*e),o<r&&(o=Math.ceil(n)*e),o}function gT([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}function Hm([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 E=_[x],b=[],A;if(s){A=E.sigmoid().data;for(let S=0;S<A.length;++S)A[S]>e&&b.push(S)}else{let S=Ce(E.data)[1];if(S===l-1||(A=Re(E.data),A[S]<e))continue;b.push(S)}for(let S of b){let 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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 dz(t,e,r,s,n,o=null,a=null){let[i,l]=a??t[0].dims,c=new z("int32",new Int32Array(i*l),[i,l]),u=[];if(a!==null)for(let x=0;x<t.length;++x)t[x]=Id(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 E=e[x],b=t[x].data;for(let A=0;A<b.length;++A)b[A]*=E,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 E=r[x],[b,A]=fz(f,t,x,s,n);if(b){++_;for(let S of A)w[S]=_;u.push({id:_,label_id:E,score:e[x]})}}return[c,u]}function gn(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 ${Math.max(t,e)/Math.min(t,e)}`);let a=Math.round(t/r)*r,i=Math.round(e/r)*r;if(o*a*i>n){let l=Math.sqrt(o*t*e/n);a=Math.max(r,Math.floor(t/l/r)*r),i=Math.max(r,Math.floor(e/l/r)*r)}else if(o*a*i<s){let l=Math.sqrt(s/(o*t*e));a=Math.ceil(t*l/r)*r,i=Math.ceil(e*l/r)*r}return[i,a]}function Rc(t,e=.5,r=.5,s=.8,n=null,o=null){n===null&&(ee.warn("`label_ids_to_fuse` unset. No instance will be fused."),n=new Set);let a=t.class_queries_logits??t.logits,l=(t.masks_queries_logits??t.pred_masks).sigmoid(),[c,u,f]=a.dims;if(f-=1,o!==null&&o.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let m=[];for(let _=0;_<c;++_){let w=o!==null?o[_]:null,x=a[_],E=l[_],[b,A,S]=pz(x,E,e,f);if(S.length===0){let[O,v]=w??E.dims.slice(-2),$=new z("int32",new Int32Array(O*v).fill(-1),[O,v]);m.push({segmentation:$,segments_info:[]});continue}let[M,I]=dz(b,A,S,r,s,n,w);m.push({segmentation:M,segments_info:I})}return m}function $c(t,e=.5,r=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}var X=class extends Ke{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=wo(s.data)[0],a=Ce(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 E=(x+w)*u,b=x*l;for(let A=0;A<l;++A){let S=(E+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,E=l-1;for(let b=0;b<f;++b){let A=b*u,S=on(b,x)*l;for(let M=0;M<u;++M){if(b<i&&M<l)continue;let I=(A+M)*c,O=(S+on(M,E))*c;for(let v=0;v<c;++v)m[I+v]=e[O+v]}}}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]=gT([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=hT(c*n,this.config.ensure_multiple_of),i=hT(u*s,this.config.ensure_multiple_of)}return[i,l]}else{if(this.size_divisibility!==void 0)return gT([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 E=0;E<e.channels;++E)u[x+E]=(u[x+E]-_[E])/w[E]}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 z("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:Ct(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 ut(e,Er,!0,r);return new this(s)}};var vn={};_s(vn,{BeitFeatureExtractor:()=>Xm,BitImageProcessor:()=>Km,CHMv2ImageProcessor:()=>Qm,CLIPFeatureExtractor:()=>Jm,CLIPImageProcessor:()=>Dc,ChineseCLIPFeatureExtractor:()=>Ym,ConvNextFeatureExtractor:()=>Zm,ConvNextImageProcessor:()=>Bc,DINOv3ViTImageProcessor:()=>r_,DPTFeatureExtractor:()=>n_,DPTImageProcessor:()=>Gc,DeiTFeatureExtractor:()=>e_,DeiTImageProcessor:()=>Uc,DetrFeatureExtractor:()=>t_,DetrImageProcessor:()=>Fc,DonutFeatureExtractor:()=>s_,DonutImageProcessor:()=>wn,EfficientNetImageProcessor:()=>o_,GLPNFeatureExtractor:()=>l_,Gemma3ImageProcessor:()=>a_,Glm46VImageProcessor:()=>i_,GroundingDinoImageProcessor:()=>c_,Idefics3ImageProcessor:()=>jc,ImageFeatureExtractor:()=>X,ImageProcessor:()=>X,JinaCLIPImageProcessor:()=>p_,Lfm2VlImageProcessor:()=>f_,LlavaOnevisionImageProcessor:()=>d_,Mask2FormerImageProcessor:()=>__,MaskFormerFeatureExtractor:()=>m_,MaskFormerImageProcessor:()=>xn,MobileNetV1FeatureExtractor:()=>h_,MobileNetV1ImageProcessor:()=>qc,MobileNetV2FeatureExtractor:()=>g_,MobileNetV2ImageProcessor:()=>Wc,MobileNetV3FeatureExtractor:()=>w_,MobileNetV3ImageProcessor:()=>Vc,MobileNetV4FeatureExtractor:()=>x_,MobileNetV4ImageProcessor:()=>Hc,MobileViTFeatureExtractor:()=>y_,MobileViTImageProcessor:()=>Xc,NougatImageProcessor:()=>b_,OwlViTFeatureExtractor:()=>v_,OwlViTImageProcessor:()=>yn,Owlv2ImageProcessor:()=>k_,Phi3VImageProcessor:()=>T_,PixtralImageProcessor:()=>M_,PvtImageProcessor:()=>S_,Qwen2VLImageProcessor:()=>Uo,RTDetrImageProcessor:()=>O_,Sam2ImageProcessor:()=>Fo,Sam3ImageProcessor:()=>Fo,SamImageProcessor:()=>Fo,SapiensFeatureExtractor:()=>I_,SapiensImageProcessor:()=>Kc,SegformerFeatureExtractor:()=>C_,SegformerImageProcessor:()=>Yc,SiglipImageProcessor:()=>P_,SmolVLMImageProcessor:()=>jc,Swin2SRImageProcessor:()=>L_,VLMImageProcessor:()=>u_,ViTFeatureExtractor:()=>N_,ViTImageProcessor:()=>Qc,VitMatteImageProcessor:()=>z_,VitPoseImageProcessor:()=>R_,YolosFeatureExtractor:()=>$_,YolosImageProcessor:()=>Jc});var Xm=class extends X{};var Km=class extends X{};var Ym=class extends X{};var Qm=class extends X{};var Dc=class extends X{},Jm=class extends Dc{};var Bc=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}},Zm=class extends Bc{};var Uc=class extends X{},e_=class extends Uc{};var Fc=class extends X{async _call(e){let r=await super._call(e),s=[r.pixel_values.dims[0],64,64],n=Xe(s,1n);return{...r,pixel_mask:n}}post_process_object_detection(...e){return Kr(...e)}post_process_panoptic_segmentation(...e){return Rc(...e)}post_process_instance_segmentation(...e){return $c(...e)}},t_=class extends Fc{};var r_=class extends X{};var wn=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})}},s_=class extends wn{};var Gc=class extends X{},n_=class extends Gc{};var o_=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 a_=class extends X{};var Uo=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 gn(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 z("int64",[u,m,_],[1,3]);return{pixel_values:w,image_grid_thw:x,original_sizes:n,reshaped_input_sizes:o}}};var i_=class extends Uo{get_resize_output_image_size(e,r){let s=this.patch_size*this.merge_size,n=this.config.temporal_patch_size??2;return gn(e.height,e.width,s,this.min_pixels,this.max_pixels,n)}};var l_=class extends X{};var c_=class extends X{async _call(e){let r=await super._call(e),s=r.pixel_values.dims,n=Ze([s[0],s[2],s[3]]);return{...r,pixel_mask:n}}};var jc=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(v,$)=>{let G=this.get_resize_for_vision_encoder(v.pixel_values,S),F=await vt(v.pixel_values,{size:[G.height,G.width]}),{frames:K,num_splits_h:W,num_splits_w:Q}=await this.split_image(F,this.max_image_size);return I[$]=W,O[$]=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,E;if(u===1)x=o[0].unsqueeze_(0),E=Xe([u,f,_,w],!0);else{let b=Math.max(...o.map(M=>M.dims.at(0)));E=Xe([u,b,_,w],!0);let A=E.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],Xe([b-I,m,_,w],0)],0);let O=M*S+I*_*w,v=(M+1)*S;A.fill(!1,O,v)}}x=Ct(o,0)}return{pixel_values:x,pixel_attention_mask:E,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 E,b,A,S;w===l-1?(b=a-u,S=a):(b=w*u,S=(w+1)*u),x===c-1?(E=i-f,A=i):(E=x*f,A=(x+1)*f);let O=await Oc(e,[b,E],[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 u_=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 p_=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 wT(t,e){return Math.round(t/e)*e}function mz(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 _z(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 hz(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 E=0;E<i;++E){let b=w+(x*i+E)*l;for(let A=0;A<e;++A){let S=(x*e+A)*o+E*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 z("float32",u,[r,a*i,l])}function gz(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 z("float32",a,[1,e,s])}return{padded:o,mask:new z("int64",n,[e])}}var f_=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,wT(e,s)),o=Math.max(this.encoder_patch_size,wT(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=_z(this.min_tiles,this.max_tiles),[n,o]=mz(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,E=_.unsqueeze_(0),b=this.encoder_patch_size*this.downsample_factor,A=b**2,[S,M]=gn(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,v=1,$=this._is_image_too_large(w,x),G=this.do_image_splitting&&!(this.min_tiles===1&&this.max_tiles===1);if($&&G){let{grid_width:F,grid_height:K,target_width:W,target_height:Q}=this._get_grid_layout(w,x);O=K,v=F;let B=await vt(E,{size:[Q,W]});I=[];for(let P=0;P<K;++P)for(let re=0;re<F;++re){let Y=P*this.tile_size,L=re*this.tile_size;I.push(B.slice(null,null,[Y,Y+this.tile_size],[L,L+this.tile_size]))}this.use_thumbnail&&F*K!==1&&I.push(await vt(E,{size:[M,S]}))}else I=[await vt(E,{size:[M,S]})];for(let F of I){let[,,K,W]=F.dims,Q=hz(F,this.encoder_patch_size),{padded:B,mask:P}=gz(Q,this.max_num_patches);n.push(B),o.push(P),a.push([Math.floor(K/this.encoder_patch_size),Math.floor(W/this.encoder_patch_size)])}i.push(O),l.push(v),c.push([M,S])}}let u={pixel_values:ve(n,0),pixel_attention_mask:Ct(o,0),spatial_shapes:new z("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 d_=class extends X{};var xn=class extends X{post_process_panoptic_segmentation(...e){return Rc(...e)}post_process_instance_segmentation(...e){return $c(...e)}},m_=class extends xn{};var __=class extends xn{};var qc=class extends X{},h_=class extends qc{};var Wc=class extends X{},g_=class extends Wc{};var Vc=class extends X{},w_=class extends Vc{};var Hc=class extends X{},x_=class extends Hc{};var Xc=class extends X{},y_=class extends Xc{};var b_=class extends wn{};var yn=class extends X{post_process_object_detection(...e){return Kr(...e)}},v_=class extends yn{};var k_=class extends yn{};var $t=336,wz=[2,3],{ceil:E_,floor:bn,sqrt:A_}=Math,T_=class extends X{constructor(e){super({...e,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=e.num_crops}calc_num_image_tokens_from_image_size(e,r){let{num_img_tokens:s}=this.config;return bn((bn(r/$t)*bn(e/$t)+1)*s+1+(bn(r/$t)+1)*A_(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=$t*E_(o/$t),l=$t*E_(a/$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||A_(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 E=[],b=A_(r),A=bn(w/b),S=bn(_/b);for(let I=0;I<b;++I)for(let O=0;O<b;++O){let v,$,G,F;I===b-1?($=_-S,F=_):($=I*S,F=(I+1)*S),O===b-1?(v=w-A,G=w):(v=O*A,G=(O+1)*A);let Q=await Oc(m,[$,v],[F,G],wz);E.push(Q)}let M=await vt(ve(E,0),{size:[$t,$t],mode:"bicubic"});i.push(ve([x,M],0))}else i.push(x)}let l=Ct(i,0),c=a.map(m=>m.map(_=>$t*E_(_/$t))),u=new z("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 M_=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 S_=class extends X{};var O_=class extends X{post_process_object_detection(...e){return Kr(...e)}};var Fo=class extends X{reshape_input_points(e,r,s,n=!1){e=structuredClone(e);let o=vf(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 z("float32",Float32Array.from(e.flat(1/0)),o)}add_input_labels(e,r){let s=vf(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 z("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 z("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 Kc=class extends X{post_process_semantic_segmentation(...e){return zc(...e)}},I_=class extends Kc{};var Yc=class extends X{post_process_semantic_segmentation(...e){return zc(...e)}},C_=class extends Yc{};var P_=class extends X{};var L_=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 Qc=class extends X{},N_=class extends Qc{};var z_=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:Ct(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 R_=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],E=[],b=[],A=[],S=x.at(-2)/l,M=x.at(-1)/i;for(let I=0;I<f.length;++I){let[O,v]=[0,0],$=0,G=-1/0,F=f[I];for(let W=0;W<F.length;++W){let Q=F[W];for(let B=0;B<Q.length;++B){let P=Q[B];$+=P,G=Math.max(G,P),O+=(B+.5)*P,v+=W*P}}if(s!=null&&G<s)continue;let K=[S*O/$,M*v/$];E.push(K),A.push(I),b.push(G)}_.push({bbox:x,scores:b,labels:A,keypoints:E})}c.push(_)}return c}};var Jc=class extends X{post_process_object_detection(...e){return Kr(...e)}},$_=class extends Jc{};var Ae=class{static async from_pretrained(e,r={}){let s=await ut(e,Er,!0,r),n=s.image_processor_type??s.feature_extractor_type,o=vn[n?.replace(/Fast$/,"")];return o||(n!==void 0&&ee.warn(`Image processor type '${n}' not found, assuming base ImageProcessor. Please report this at ${Hr}.`),o=X),new o(s)}};var D_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;constructor(e,r,s){super(e,r,s);let{tasks_answer_post_processing_type:n,task_prompts_without_inputs:o,task_prompts_with_input:a}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(n??{})),this.task_prompts_without_inputs=new Map(Object.entries(o??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);let r=[];for(let s of e)if(this.task_prompts_without_inputs.has(s))r.push(this.task_prompts_without_inputs.get(s));else{for(let[n,o]of this.task_prompts_with_input)if(s.includes(n)){r.push(o.replaceAll("{input}",s).replaceAll(n,""));break}r.length!==e.length&&r.push(s)}return r}post_process_generation(e,r,s){let n=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("<s>","").replaceAll("</s>","");let o;switch(n){case"pure_text":o=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":let a=n==="ocr"?"quad_boxes":"bboxes",i=e.matchAll(this.regexes[a]),l=[],c=[];for(let[u,f,...m]of i)l.push(f?f.trim():l.at(-1)??""),c.push(m.map((_,w)=>(Number(_)+.5)/this.size_per_bin*s[w%2]));o={labels:l,[a]:c};break;default:throw new Error(`Task "${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 B_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;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 U_=class extends se{static image_processor_class=Ae;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 Ls=class extends se{static image_processor_class=Ae;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 F_=class extends Ls{static image_token="<|image|>"};var G_=class extends se{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 z("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 xz(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 j_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;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=>Hm(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 E=f[w],b=[],A=[],S=[];for(let M=0;M<c;++M){let I=E[M];if(I<=s)continue;let O=m[w][M],v=u[w][M];b.push(I),S.push(O);let $=xz(v.gt(n),r[w]);A.push($)}_.push({scores:b,boxes:S,labels:this.batch_decode(A)})}return _}};function yz(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)+`${s}`,a}function bz(t,e,r,s){return`${e}${s}`+r.repeat(t)+`${e}`}function vz(t,e,r,s,n,o){return t===0&&e===0?bz(r,s,n,o):yz(r,t,e,s,n,o)}var Zc=class extends se{static image_processor_class=Ae;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(dE(m,this.image_token));let x=_.map((A,S)=>vz(A,w[S],i,this.fake_image_token,this.image_token,this.global_img_token)),E=m.split(this.image_token);if(E.length===0)throw new Error("The image token should be present in the text.");let b=E[0];for(let A=0;A<x.length;++A)b+=x[A]+E[A+1];c.push(b)}return{...this.tokenizer(c),...n}}};var q_=class extends se{static image_processor_class=Ae;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(E=>E.images).flatMap(E=>E.images).map(E=>Qe.read(E)));let n=this.tokenizer,o=n.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0,chat_template:s}),a=E=>n.encode(E,{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 (${l})`);let[c,u,f]=n.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]),m=a(i[0]),_=new Array(m.length).fill(!1);for(let E=1;E<i.length;++E){let b=new Array(this.num_image_tokens).fill(c),A=a(i[E]);m=gt(m,[u],b,[f],A);let S=new Array(this.num_image_tokens).fill(!0);_=gt(_,[!1],S,[!1],new Array(A.length).fill(!1))}let w=[1,m.length],x={input_ids:new z("int64",m,w),attention_mask:new z("int64",new Array(m.length).fill(1),w),images_seq_mask:new z("bool",_,w),images_emb_mask:new z("bool",new Array(l*this.num_image_tokens).fill(!0),[1,l,this.num_image_tokens])};if(r&&r.length>0){let E=await this.image_processor(r);return E.pixel_values.unsqueeze_(0),{...x,...E}}return x}};var W_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;async _call(e=null,r=null,s={}){if(!e&&!r)throw new Error("Either text or images must be provided");let n=e?this.tokenizer(e,s):{},o=r?await this.image_processor(r,s):{};return{...n,...o}}};var V_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;async _call(e,r=null,s={}){let{image_rows:n,image_cols:o,image_sizes:a,...i}=await this.image_processor(e,{...s,return_row_col_info:!0});if(r){let l=this.config.image_token??"<image>",{tile_size:c=512,downsample_factor:u=2,encoder_patch_size:f=16,use_thumbnail:m=!0}=this.image_processor.config,_=S=>Math.ceil(Math.floor(S/f)/u),w=_(c)**2,x=this.config.image_start_token??"<|image_start|>",E=this.config.image_end_token??"<|image_end|>",b=this.config.image_thumbnail??"<|img_thumbnail|>";Array.isArray(r)||(r=[r]);let A=0;r=r.map(S=>{let M=S.split(l);return M[0]+M.slice(1).map(I=>{let O=A++,[v,$]=a[O],G=n[O],F=o[O],K=_(v)*_($),W=x;if(G>1||F>1){let Q=l.repeat(w);for(let B=0;B<G;++B)for(let P=0;P<F;++P)W+=`<|img_row_${B+1}_col_${P+1}|>`+Q;m&&(W+=b+l.repeat(K))}else W+=l.repeat(K);return W+E+I}).join("")})}return{...i,...r?this.tokenizer(r,s):{}}}};var H_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;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 xT={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]},X_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;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(!xT.hasOwnProperty(r))throw new Error(`Format ${r} is not supported.`);let[s,n]=xT[r],o=this[s].bind(this),[a,i]=e.dims,l=[],c=[],u=e.tolist();for(let m=0;m<a;++m){let _=u[m],w=[],x=[];for(let b=1;b<i;++b){let[A,S]=Ce(Re(_[b]));if(x.push(A),S==n)break;w.push(S)}let E=x.length>0?x.reduce((b,A)=>b*A,1):0;c.push(w),l.push(E)}return[o(c),l]}char_decode(e){return this.char_tokenizer.batch_decode(e).map(r=>r.replaceAll(" ",""))}bpe_decode(e){return this.bpe_tokenizer.batch_decode(e)}wp_decode(e){return this.wp_tokenizer.batch_decode(e).map(r=>r.replaceAll(" ",""))}batch_decode([e,r,s]){let[n,o]=this._decode_helper(e,"char"),[a,i]=this._decode_helper(r,"bpe"),[l,c]=this._decode_helper(s,"wp"),u=[],f=[];for(let m=0;m<n.length;++m){let[_,w]=Ce([o[m],i[m],c[m]]);u.push([n[m],a[m],l[m]][w]),f.push(_)}return{generated_text:u,scores:f,char_preds:n,bpe_preds:a,wp_preds:l}}static async from_pretrained(...e){let r=await super.from_pretrained(...e),s=await ne.from_pretrained("Xenova/gpt2"),n=await ne.from_pretrained("Xenova/bert-base-uncased");return r.components={image_processor:r.image_processor,char_tokenizer:r.tokenizer,bpe_tokenizer:s,wp_tokenizer:n},r}async _call(e,r=null){let s=await this.image_processor(e);return r&&(s.labels=this.tokenizer(r).input_ids),s}};var K_=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var Y_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae};var kn="<image>";function kz(t,e,r,s,n){return`${s.repeat(r*n)}${e}${t}
`}var Q_=class extends se{static tokenizer_class=ne;static image_processor_class=Ae;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(kn))?a=r.map(c=>{let u=c.replaceAll(kn,kn.repeat(o)),f=u.lastIndexOf(kn),m=f===-1?0:f+kn.length;return u.slice(0,m)+n+u.slice(m)+`
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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 E=0;E<r.length;++E){let b=l.repeat(x),A=b+c,S=b+u,M=A.repeat(w-1)+S;r[E]=r[E].replace(l,M)}}let o=r?this.tokenizer(r,s):{};return{...n,...o}}};var eh=class extends se{static feature_extractor_class=Do;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 Go=class extends Ls{};var th=class extends Go{};var jo=class extends se{static image_processor_class=Ae;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 eu=class extends jo{},rh=class extends eu{};var sh=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var nh=class extends se{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 tu="[AUDIO]",Az="[BEGIN_AUDIO]",Tz=375;function Mz(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 oh=class extends se{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(tu))throw new Error(`The input text does not contain the audio token ${tu}.`);Array.isArray(r)||(r=[r]);let a=e.split(tu),i=a.length-1;if(i!==r.length)throw new Error(`The number of audio inputs (${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=>Mz(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){_+=Az;for(let x=0;x<u[w];++x)_+=tu.repeat(Tz);_+=a[w+1]}e=_}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};var bT=32,ah=6,ru=8,Sz=10,Oz=32,ih=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;static uses_processor_config=!1;get num_mel_frames_first_audio_chunk(){return(ah+1)*ru}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 ru*e+r}get num_right_pad_tokens(){return ah+1+Sz}get audio_length_per_tok(){return ru}get raw_audio_length_per_tok(){return ru*this.feature_extractor.config.hop_length}async _call(e,{is_streaming:r=!1,is_first_audio_chunk:s=!0}={}){if(Le(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=bT*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+(bT+ah),c=new BigInt64Array(l).fill(BigInt(Oz));return c[0]=1n,{input_ids:new z("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 lh=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var ch=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var uh=class extends se{static tokenizer_class=ne;static feature_extractor_class=Ge;async _call(e){return await this.feature_extractor(e)}};var nu=class{static async from_pretrained(e,r={}){let s=await ut(e,Er,!0,r),{image_processor_type:n,feature_extractor_type:o,processor_class:a}=s;if(a&&su[a])return su[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=vn[n.replace(/Fast$/,"")];if(!c)throw new Error(`Unknown image_processor_type: '${n}'.`);i.image_processor=new c(s)}if(o){let c=vn[o];if(c)i.image_processor=new c(s);else{let u=Bo[o];if(!u)throw new Error(`Unknown feature_extractor_type: '${o}'.`);i.feature_extractor=new u(s)}}let l={};return new se(l,i,null)}};async function Iz(t,e){return await ut(t,"config.json",!0,e)}function En(t){let e={},r={};switch(t.model_type){case"llava":case"paligemma":case"gemma3":case"florence2":case"llava_onevision":case"idefics3":case"granite_speech":case"ultravox":case"voxtral":case"voxtral_realtime":case"smolvlm":case"gemma3n":case"lfm2_vl":case"chatterbox":case"lighton_ocr":case"glm_ocr":case"mistral3":case"qwen2_5_vl":case"qwen3_vl":case"qwen3_vl_moe":r=En(t.text_config);break;case"moondream1":r=En(t.phi_config);break;case"musicgen":r=En(t.decoder);break;case"multi_modality":r=En(t.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":case"modernbert-decoder":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"gpt_oss":case"llama":case"llama4_text":case"nanochat":case"apertus":case"arcee":case"afmoe":case"lfm2":case"lfm2_moe":case"smollm3":case"olmo":case"olmo2":case"olmo3":case"mobilellm":case"granite":case"granitemoehybrid":case"cohere":case"cohere2":case"mistral":case"voxtral_realtime_text":case"voxtral_realtime_encoder":case"starcoder2":case"qwen2":case"qwen2_moe":case"qwen2_vl":case"qwen2_vl_text":case"qwen2_5_vl_text":case"qwen3_moe":case"qwen3_vl_text":case"qwen3_vl_moe_text":case"phi":case"phi3":case"phi3_v":case"llava_qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads",e.dim_kv="head_dim";break;case"qwen3":case"solar_open":case"glm_ocr_text":case"gemma":case"gemma2":case"vaultgemma":case"gemma3_text":case"gemma3n_text":case"glm":case"helium":case"ernie4_5":case"hunyuan_v1_dense":case"falcon_h1":case"nemotron_h":case"ministral":case"ministral3":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"exaone":e.num_heads="num_key_value_heads",e.num_layers="num_layers",e.dim_kv="head_dim",e.num_attention_heads="num_attention_heads";break;case"youtu":case"deepseek_v3":case"glm_moe_dsa":case"mistral4":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="qk_head_dim",e.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"lite-whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"moonshine":e.num_decoder_layers="decoder_num_hidden_layers",e.num_decoder_heads="decoder_num_key_value_heads",e.num_encoder_layers="encoder_num_hidden_layers",e.num_encoder_heads="encoder_num_key_value_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"cohere_asr":e.num_decoder_layers="num_hidden_layers",e.num_decoder_heads="num_key_value_heads",e.decoder_hidden_size="hidden_size",e.decoder_dim_kv="head_dim";let{num_hidden_layers:n,num_attention_heads:o,hidden_size:a}=t.encoder_config;r={num_encoder_layers:n,num_encoder_heads:o,encoder_hidden_size:a,encoder_dim_kv:t.head_dim};break;case"vision-encoder-decoder":let i=En(t.decoder),l="num_decoder_layers"in i,c=Ye(t,["model_type","is_encoder_decoder"]);return l?(c.num_decoder_layers=i.num_decoder_layers,c.num_decoder_heads=i.num_decoder_heads,c.decoder_hidden_size=i.decoder_hidden_size,c.num_encoder_layers=i.num_encoder_layers,c.num_encoder_heads=i.num_encoder_heads,c.encoder_hidden_size=i.encoder_hidden_size):(c.num_layers=i.num_layers,c.num_heads=i.num_heads,c.hidden_size=i.hidden_size),c}let s={...r,...Ye(t,["model_type","multi_query","is_encoder_decoder"])};for(let n in e)s[n]=t[e[n]];return s}function Ns(t,e){t instanceof An||(t=new An(t));let r=e?.batch_size??1;if(["lfm2","lfm2_moe"].includes(t.model_type)){let s=e?.prefix??"past_key_values",n=s==="present"?"present":"past",o={},{layer_types:a,num_attention_heads:i,num_key_value_heads:l,hidden_size:c,conv_L_cache:u}=t,f=c/i;for(let m=0;m<a.length;++m)if(a[m]==="full_attention")for(let _ of["key","value"])o[`${s}.${m}.${_}`]=[r,l,0,f];else if(a[m]==="conv")o[`${n}_conv.${m}`]=[r,c,u];else 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s={...dr[H.ImageTextToText].text_only_sessions};return r||(s.vision_encoder="vision_encoder"),t.is_encoder_decoder&&(s.model="encoder_model"),s},cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[H.AudioTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[H.AudioTextToText].text_only_sessions};return r||(s.audio_encoder="audio_encoder"),s},cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[H.ImageAudioTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[H.ImageAudioTextToText].text_only_sessions};return r||(s.audio_encoder="audio_encoder",s.vision_encoder="vision_encoder"),s},optional_configs:{generation_config:"generation_config.json"}},[H.Phi3V]:{sessions:()=>({prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[H.MultiModality]:{sessions:()=>({prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[H.AutoEncoder]:{sessions:()=>({encoder_model:"encoder_model",decoder_model:"decoder_model"})},[H.Supertonic]:{sessions:()=>({text_encoder:"text_encoder",latent_denoiser:"latent_denoiser",voice_decoder:"voice_decoder"})},[H.Chatterbox]:{sessions:()=>({embed_tokens:"embed_tokens",speech_encoder:"speech_encoder",model:"language_model",conditional_decoder:"conditional_decoder"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[H.VoxtralRealtime]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[H.VoxtralRealtime].text_only_sessions};return r||(s.audio_encoder="audio_encoder"),s},cache_sessions:{decoder_model_merged:!0,audio_encoder:!0},optional_configs:{generation_config:"generation_config.json"}},default:{sessions:(t,e)=>({model:e.model_file_name??"model"})}};function gh(t){return dr[t]?.text_only_sessions??null}function Vo(t,e,r={}){let s=dr[t]??dr.default;return{sessions:s.sessions(e,r,r.textOnly??!1),cache_sessions:s.cache_sessions,optional_configs:s.optional_configs}}function On(t,{warn:e=!0}={}){let r=t.architectures||[];for(let s of r){let n=rr.get(s);if(n!==void 0)return n}if(t.model_type){let s=rr.get(t.model_type);if(s!==void 0)return s;for(let n of Object.values($s))if(n.has(t.model_type)){let o=rr.get(n.get(t.model_type));if(o!==void 0)return o}}if(e){let s=r.length>0?r.join(", "):"(none)";ee.warn(`[resolve_model_type] Architecture(s) not found in MODEL_TYPE_MAPPING: [${s}] for model type '${t.model_type}'. Falling back to EncoderOnly (single model.onnx file). 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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 z)){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=wh(i)}return l.decoder_attention_mask=Cc(i),{input_ids:i,model_inputs:l}}async 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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 W=this.getAttentions(S);for(let Q in W)Q in M||(M[Q]=[]),M[Q].push(W[Q])}else this._return_dict_in_generate_keys&&Object.assign(I,Ye(S,this._return_dict_in_generate_keys));let $=S.logits.slice(null,-1,null).to("float32"),G=_(A,$),F=[];for(let W=0;W<G.dims.at(0);++W){let Q=G[W],B=await E(Q);for(let[P,re]of B){let Y=BigInt(P);b[W]+=re,A[W].push(Y),F.push([Y]);break}}if(o&&o.put(F),w(A).every(W=>W))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),v=new z("int64",A.flat(),[A.length,A[0].length]);if(r.return_dict_in_generate)return{sequences:v,past_key_values:O,...M,...I};for(let $ of Object.values(S))$.location==="gpu-buffer"&&$.dispose();return v}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 Wo(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 z(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,Ye(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 xu(t,e){let{encoder_outputs:r,input_ids:s,decoder_input_ids:n,decoder_attention_mask:o,...a}=e;if(!r){let i=Ye(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=Ye(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 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y{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","audio_values","exaggeration","audio_features","audio_tokens","speaker_embeddings","speaker_features","past_key_values"];main_input_name="input_ids";_return_dict_in_generate_keys=["audio_tokens","speaker_embeddings","speaker_features"]},sa=class extends ku{async encode_speech(e){return fe(this.sessions.speech_encoder,{audio_values:e})}async forward({input_ids:e=null,attention_mask:r=null,audio_values:s=null,exaggeration:n=null,position_ids:o=null,inputs_embeds:a=null,past_key_values:i=null,generation_config:l=null,logits_processor:c=null,audio_features:u=null,audio_tokens:f=null,speaker_embeddings:m=null,speaker_features:_=null,...w}){let x;if(!a){let b=this.sessions.embed_tokens.inputNames,A={input_ids:e};if(b.includes("exaggeration")){if(!(n instanceof z)){let S=e.dims[0];if(n==null)n=Xe([S],.5);else if(typeof n=="number")n=Xe([S],n);else if(Array.isArray(n))n=new z("float32",n,[S]);else throw new 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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 Oa=class extends y{},$w=class extends Oa{},Dw=class extends Oa{};var Ia=class extends y{},Bw=class extends Ia{},Uw=class extends Ia{};var Ru=class extends y{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]},Et=class extends Ru{_merge_input_ids_with_image_features(e){let r=e.image_features.dims.at(-1),s=e.image_features.view(-1,r);return Pn({image_token_id:this.config.image_token_index??this.config.image_token_id,...e,image_features:s})}},Fw=class extends Et{},Gw=class extends Et{};var $u=class extends y{},jw=class extends $u{},Du=class extends Et{},qw=class extends Du{};var Bu=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"]},Dn=class extends Bu{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 fe(this.sessions.vision_encoder,{pixel_values:s});({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)}_merge_input_ids_with_image_features(e){let r=e.image_features.dims.at(-1),s=e.image_features.view(-1,r);return Pn({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 vu({audio_token_id:this.config.audio_token_id,...e,audio_features:s})}},Ww=class extends Dn{};var Ca=class extends y{},Vw=class extends Ca{},Hw=class extends Ca{};var Pa=class extends y{},Xw=class extends Pa{},Kw=class extends Pa{};var Uu=class extends y{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]},Bn=class extends Uu{image_grid_thw_name="grid_thw";_get_text_only_rope_index(e,r){if(r){let{data:s,dims:n}=yh(r),o=BigInt64Array.from({length:3*s.length},(i,l)=>s[l%s.length]),a=Array.from({length:n[0]},(i,l)=>Ce(s.subarray(n[1]*l,n[1]*(l+1)))[0]+1n+BigInt(n[1]));return[new z("int64",o,[3,...n]),new z("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 z("int64",o,[3,...e.dims]),Ld([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,E=m,b=_;for(let A=0;A<f.length;++A){let S=c.findIndex((Z,xe)=>xe>x&&Z==a),M=c.findIndex((Z,xe)=>xe>x&&Z==i),I=E>0&&S!==-1?S:c.length+1,O=b>0&&M!==-1?M:c.length+1,v,$,G,F;I<O?([$,G,F]=r[o.image_index],++o.image_index,--E,v=I):([$,G,F]=s[o.video_index],++o.video_index,--b,v=O);let[K,W,Q]=[Number($),Math.floor(Number(G)/n),Math.floor(Number(F)/n)],B=v-x,P=w.length>0?Ce(w.at(-1))[0]+1:0;w.push(Array.from({length:3*B},(Z,xe)=>P+xe%B));let re=B+P,Y=K*W*Q,L=Array.from({length:Y},(Z,xe)=>re+Math.floor(xe/(W*Q))),R=Array.from({length:Y},(Z,xe)=>re+Math.floor(xe/Q)%W),D=Array.from({length:Y},(Z,xe)=>re+xe%Q);w.push([L,R,D].flat()),x=v+Y}if(x<c.length){let A=w.length>0?Ce(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=Cc(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),E=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(E,l[w],c,w);_.push(Ce(b)[0]+1-i[w].length)}return[new z("int64",c.flat(1/0),[3,e.dims[0],e.dims[1]]),new z("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 Pn({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 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forward(e){!e.image_embeddings||!e.image_positional_embeddings?e={...e,...await this.get_image_embeddings(e)}:e={...e},e.input_labels??=Ze(e.input_points.dims.slice(0,-1));let r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(r.input_points=e.input_points),e.input_labels&&(r.input_labels=e.input_labels),e.input_boxes&&(r.input_boxes=e.input_boxes),await fe(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new sp(await super._call(e))}};var op=class extends $e{constructor({iou_scores:e,pred_masks:r,object_score_logits:s}){super(),this.iou_scores=e,this.pred_masks=r,this.object_score_logits=s}},ap=class extends y{},ji=class extends ap{async get_image_embeddings({pixel_values:e}){return await Vt(this,{pixel_values:e})}async forward(e){let{num_feature_levels:r}=this.config.vision_config;if(Array.from({length:r},(a,i)=>`image_embeddings.${i}`).some(a=>!e[a])?e={...e,...await 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generate_speech(e,r,{threshold:s=.5,minlenratio:n=0,maxlenratio:o=20,vocoder:a=null}={}){let i={input_ids:e},{encoder_outputs:l,encoder_attention_mask:c}=await Vt(this,i),u=l.dims[1]/this.config.reduction_factor,f=Math.floor(u*o),m=Math.floor(u*n),_=this.config.num_mel_bins,w=[],x=null,E=null,b=0;for(;;){++b;let M=xh(!!E),I;E?I=E.output_sequence_out:I=new z("float32",new Float32Array(_),[1,1,_]);let O={use_cache_branch:M,output_sequence:I,encoder_attention_mask:c,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(O,x),E=await fe(this.sessions.decoder_model_merged,O),x=this.getPastKeyValues(E,x);let{prob:v,spectrum:$}=E;if(w.push($),b>=m&&(Array.from(v.data).filter(G=>G>=s).length>0||b>=f))break}let A=ve(w),{waveform:S}=await fe(a.sessions.model,{spectrogram:A});return{spectrogram:A,waveform:S}}},$b=class extends y{main_input_name="spectrogram"};var Xs=class extends y{},Db=class extends Xs{},Bb=class extends Xs{async _call(e){return new ge(await 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y{},W1=class extends il{},V1=class extends il{async _call(e){return new bp(await super._call(e))}},bp=class extends $e{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}};var ll=class extends y{},H1=class extends ll{},X1=class extends ll{};var Wz=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"]]),Vz=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"]]),Hz=new Map([["mimi","MimiModel"],["dac","DacModel"],["snac","SnacModel"]]),Xz=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"]]),RT=new Map([["speecht5","SpeechT5ForSpeechToText"],["whisper","WhisperForConditionalGeneration"],["lite-whisper","LiteWhisperForConditionalGeneration"],["moonshine","MoonshineForConditionalGeneration"],["cohere_asr","CohereAsrForConditionalGeneration"]]),$T=new Map([["speecht5","SpeechT5ForTextToSpeech"]]),DT=new Map([["vits","VitsModel"],["musicgen","MusicgenForConditionalGeneration"],["supertonic","SupertonicForConditionalGeneration"]]),BT=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"]]),UT=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"]]),FT=new Map([["t5","T5ForConditionalGeneration"],["longt5","LongT5ForConditionalGeneration"],["mt5","MT5ForConditionalGeneration"],["bart","BartForConditionalGeneration"],["mbart","MBartForConditionalGeneration"],["marian","MarianMTModel"],["m2m_100","M2M100ForConditionalGeneration"],["blenderbot","BlenderbotForConditionalGeneration"],["blenderbot-small","BlenderbotSmallForConditionalGeneration"]]),GT=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"],["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"]]),Kz=new Map([["multi_modality","MultiModalityCausalLM"]]),jT=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"]]),qT=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"]]),WT=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"]]),VT=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"],["mistral3","Mistral3ForConditionalGeneration"],["lighton_ocr","LightOnOcrForConditionalGeneration"],["glm_ocr","GlmOcrForConditionalGeneration"]]),HT=new Map([["granite_speech","GraniteSpeechForConditionalGeneration"],["ultravox","UltravoxModel"],["voxtral","VoxtralForConditionalGeneration"],["voxtral_realtime","VoxtralRealtimeForConditionalGeneration"]]),Yz=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"]]),XT=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"]]),KT=new Map([["detr","DetrForObjectDetection"],["rt_detr","RTDetrForObjectDetection"],["rt_detr_v2","RTDetrV2ForObjectDetection"],["rf_detr","RFDetrForObjectDetection"],["d_fine","DFineForObjectDetection"],["table-transformer","TableTransformerForObjectDetection"],["yolos","YolosForObjectDetection"]]),YT=new Map([["owlvit","OwlViTForObjectDetection"],["owlv2","Owlv2ForObjectDetection"],["grounding-dino","GroundingDinoForObjectDetection"]]),eo=new Map([["detr","DetrForSegmentation"],["clipseg","CLIPSegForImageSegmentation"]]),QT=new Map([["segformer","SegformerForSemanticSegmentation"],["sapiens","SapiensForSemanticSegmentation"],["swin","SwinForSemanticSegmentation"],["mobilenet_v1","MobileNetV1ForSemanticSegmentation"],["mobilenet_v2","MobileNetV2ForSemanticSegmentation"],["mobilenet_v3","MobileNetV3ForSemanticSegmentation"],["mobilenet_v4","MobileNetV4ForSemanticSegmentation"]]),JT=new Map([["detr","DetrForSegmentation"],["maskformer","MaskFormerForInstanceSegmentation"]]),ZT=new Map([["sam","SamModel"],["sam2","Sam2Model"],["edgetam","EdgeTamModel"],["sam3_tracker","Sam3TrackerModel"]]),eM=new Map([["wav2vec2","Wav2Vec2ForCTC"],["wav2vec2-bert","Wav2Vec2BertForCTC"],["unispeech","UniSpeechForCTC"],["unispeech-sat","UniSpeechSatForCTC"],["wavlm","WavLMForCTC"],["hubert","HubertForCTC"],["parakeet_ctc","ParakeetForCTC"]]),tM=new Map([["wav2vec2","Wav2Vec2ForSequenceClassification"],["wav2vec2-bert","Wav2Vec2BertForSequenceClassification"],["unispeech","UniSpeechForSequenceClassification"],["unispeech-sat","UniSpeechSatForSequenceClassification"],["wavlm","WavLMForSequenceClassification"],["hubert","HubertForSequenceClassification"],["audio-spectrogram-transformer","ASTForAudioClassification"]]),rM=new Map([["wavlm","WavLMForXVector"]]),sM=new Map([["unispeech-sat","UniSpeechSatForAudioFrameClassification"],["wavlm","WavLMForAudioFrameClassification"],["wav2vec2","Wav2Vec2ForAudioFrameClassification"],["pyannote","PyAnnoteForAudioFrameClassification"]]),nM=new Map([["vitmatte","VitMatteForImageMatting"]]),Qz=new Map([["patchtst","PatchTSTForPrediction"],["patchtsmixer","PatchTSMixerForPrediction"]]),oM=new Map([["swin2sr","Swin2SRForImageSuperResolution"]]),aM=new Map([["chmv2","CHMv2ForDepthEstimation"],["dpt","DPTForDepthEstimation"],["depth_anything","DepthAnythingForDepthEstimation"],["glpn","GLPNForDepthEstimation"],["sapiens","SapiensForDepthEstimation"],["depth_pro","DepthProForDepthEstimation"],["metric3d","Metric3DForDepthEstimation"],["metric3dv2","Metric3Dv2ForDepthEstimation"]]),iM=new Map([["sapiens","SapiensForNormalEstimation"]]),lM=new Map([["vitpose","VitPoseForPoseEstimation"]]),cM=new Map([["clip","CLIPVisionModelWithProjection"],["siglip","SiglipVisionModel"],["jina_clip","JinaCLIPVisionModel"]]),K1=[[Wz,H.EncoderOnly],[Vz,H.EncoderDecoder],[Xz,H.DecoderOnlyWithoutHead],[Hz,H.AutoEncoder],[BT,H.EncoderOnly],[UT,H.EncoderOnly],[FT,H.Seq2Seq],[RT,H.Seq2Seq],[GT,H.DecoderOnly],[Kz,H.MultiModality],[jT,H.EncoderOnly],[qT,H.EncoderOnly],[WT,H.Vision2Seq],[VT,H.ImageTextToText],[HT,H.AudioTextToText],[XT,H.EncoderOnly],[eo,H.EncoderOnly],[JT,H.EncoderOnly],[QT,H.EncoderOnly],[nM,H.EncoderOnly],[Qz,H.EncoderOnly],[oM,H.EncoderOnly],[aM,H.EncoderOnly],[iM,H.EncoderOnly],[lM,H.EncoderOnly],[KT,H.EncoderOnly],[YT,H.EncoderOnly],[ZT,H.MaskGeneration],[eM,H.EncoderOnly],[tM,H.EncoderOnly],[$T,H.Seq2Seq],[DT,H.EncoderOnly],[rM,H.EncoderOnly],[sM,H.EncoderOnly],[cM,H.EncoderOnly]];for(let[t,e]of K1)for(let r of t.values()){rr.set(r,e);let s=cl[r];Ds.set(s,r),bu.set(r,s)}var Jz=[["MusicgenForConditionalGeneration",_i,H.Musicgen],["Phi3VForCausalLM",Ii,H.Phi3V],["CLIPTextModelWithProjection",aa,H.EncoderOnly],["SiglipTextModel",Wi,H.EncoderOnly],["JinaCLIPTextModel",Ka,H.EncoderOnly],["ClapTextModelWithProjection",na,H.EncoderOnly],["ClapAudioModelWithProjection",oa,H.EncoderOnly],["DacEncoderModel",ha,H.EncoderOnly],["DacDecoderModel",ga,H.EncoderOnly],["MimiEncoderModel",si,H.EncoderOnly],["MimiDecoderModel",ni,H.EncoderOnly],["SnacEncoderModel",Hi,H.EncoderOnly],["SnacDecoderModel",Xi,H.EncoderOnly],["Gemma3nForConditionalGeneration",Dn,H.ImageAudioTextToText],["SupertonicForConditionalGeneration",Ji,H.Supertonic],["ChatterboxModel",sa,H.Chatterbox],["VoxtralRealtimeForConditionalGeneration",ol,H.VoxtralRealtime]];for(let[t,e,r]of Jz)rr.set(t,r),Ds.set(e,t),bu.set(t,e);var uM=new Map([["modnet",eo],["birefnet",eo],["isnet",eo],["ben",eo]]);for(let[t,e]of uM.entries())e.set(t,"PreTrainedModel"),rr.set(t,H.EncoderOnly),bu.set(t,y);var pM=new Set(uM.keys());rr.set("PreTrainedModel",H.EncoderOnly);Ds.set(y,"PreTrainedModel");var Ne={MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES:BT,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES:UT,MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES:$T,MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES:DT,MODEL_FOR_MASKED_LM_MAPPING_NAMES:jT,MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES:qT,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:XT,MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES:eo,MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES:QT,MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES:JT,MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES:KT,MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES:YT,MODEL_FOR_MASK_GENERATION_MAPPING_NAMES:ZT,MODEL_FOR_CTC_MAPPING_NAMES:eM,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES:tM,MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES:rM,MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES:sM,MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES:Yz,MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES:nM,MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES:oM,MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES:aM,MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES:iM,MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES:lM,MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES:cM,MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES:VT,MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES:HT,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:FT,MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES:RT,MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:GT,MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES:WT};CT(Ne);var Ie=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 E=x.get(w);if(!E){for(let b of x.values())if(b[0]===w){E=b;break}if(!E)continue}return await cl[E].from_pretrained(e,_)}if(this.BASE_IF_FAIL)return pM.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 Ie{static MODEL_CLASS_MAPPINGS=K1.map(e=>e[0]);static BASE_IF_FAIL=!0},ul=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]},vp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]},to=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]},kp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]},Ep=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]},Ap=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES]},Tp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES]},Mp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_MASKED_LM_MAPPING_NAMES]},Sp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]},Op=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]},Ip=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]},pl=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]},fl=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES]},dl=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES]},Cp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]},Pp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES]},fM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_MASK_GENERATION_MAPPING_NAMES]},Lp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_CTC_MAPPING_NAMES]},Np=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]},dM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES]},mM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES]},zp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]},_M=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES]},Rp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES]},$p=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES]},hM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES]},gM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES]},Dp=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES]},wM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES]},xM=class extends Ie{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES]};async function et(t){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(e=>Qe.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?Im(r,e):r instanceof Float64Array?new Float32Array(r):r))}function ml(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 de=class extends Ke{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 _l=class extends de{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 z("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,E)=>({label:a?a[x]:`LABEL_${x}`,score:m[E]}));r===1?l.push(...w):l.push(w)}return Array.isArray(e)||r===1?l:l[0]}};var hl=class extends de{async _call(e,{ignore_labels:r=["O"]}={}){let s=Array.isArray(e),n=this.tokenizer(s?e:[e],{padding:!0,truncation:!0}),a=(await this.model(n)).logits,i=this.model.config.id2label,l=[];for(let c=0;c<a.dims[0];++c){let u=n.input_ids[c],f=a[c],m=[];for(let _=0;_<f.dims[0];++_){let w=f[_],x=Ce(w.data)[1],E=i?i[x]:`LABEL_${x}`;if(r.includes(E))continue;let b=this.tokenizer.decode([u[_].item()],{skip_special_tokens:!0});if(b==="")continue;let A=Re(w.data);m.push({entity:E,score:A[x],index:_,word:b})}l.push(m)}return s?l:l[0]}};var gl=class extends de{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),E=a[_].tolist(),b=i[_].tolist();for(let O=1;O<E.length;++O)(c[_]==0||O<=x||u.findIndex(v=>v==w[O])!==-1)&&(E[O]=-1/0,b[O]=-1/0);let A=Re(E).map((O,v)=>[O,v]),S=Re(b).map((O,v)=>[O,v]);A[0][0]=0,S[0][0]=0;let M=fE(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,v)=>v[2]-O[2]),I=[];for(let O=0;O<Math.min(M.length,s);++O){let[v,$,G]=M[O],F=w.slice(v,$+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 wl=class extends de{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(E=>E==s);if(f===-1)throw Error(`Mask token (${n}) not found in text.`);let m=a[c][f],_=await tr(new z("float32",Re(m.data),m.dims),r),w=_[0].tolist(),x=_[1].tolist();i.push(x.map((E,b)=>{let A=u.slice();return A[f]=E,{score:w[b],token:Number(E),token_str:this.tokenizer.decode([E]),sequence:this.tokenizer.decode(A,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}};var Mr=class extends de{_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 xl=class extends Mr{_key="summary_text"};var yl=class extends Mr{_key="translation_text"};function yM(t){return Array.isArray(t)&&t.every(e=>"role"in e&&"content"in e)}var bl=class extends de{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(yM(e))e=[e];else if(Array.isArray(e)&&e.every(yM))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 vl=class extends de{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 kl=class extends de{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 z("float32",Re(u.data),u.dims),r),m=f[0].tolist(),w=f[1].tolist().map((x,E)=>({label:o?o[x]:`LABEL_${x}`,score:m[E]}));a.push(w)}return Array.isArray(e)?a:a[0]}};var El=class extends de{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 Al=class extends de{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(Ce(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 E 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,v=0;for(;;){let $=v+M,G=E.subarray(v,$),F=await this.processor(G),K=v===0,W=$>=E.length;if(b.push({stride:[G.length,K?0:I,W?0:I],input_features:F.input_features,is_last:W}),W)break;v+=O}}else b=[{stride:[E.length,0,0],input_features:(await this.processor(E)).input_features,is_last:!0}];for(let M of b){i.num_frames=Math.floor(M.stride[0]/m);let I=await this.model.generate({inputs:M.input_features,...i});if(s==="word"){let O=I.sequences.tolist()[0],v=I.token_timestamps.tolist()[0],$=this.tokenizer.timestamp_begin,G=Math.max(O.findIndex(F=>Number(F)>=$),0);M.tokens=O.slice(G),M.token_timestamps=v.slice(G).map(F=>ys(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 E=await this.processor(x),b=await this.model.generate({...E,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 Tl=class extends de{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 me.fetch(e)).arrayBuffer())),e instanceof Float32Array)e=new z("float32",e,[e.length]);else if(!(e instanceof z))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 No(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 Ml=class extends de{async _call(e,r={}){let s=Array.isArray(e),n=await et(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 Sl=class extends de{async _call(e,{top_k:r=5}={}){let s=await et(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 z("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 bM={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"},Ys=class extends de{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 et(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=bM[i];else if(this.processor.image_processor){for(let[A,S]of Object.entries(bM))if(S in this.processor.image_processor){x=this.processor.image_processor[S].bind(this.processor.image_processor),i=A;break}}let E=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 v=0;v<S.data.length;++v)S.data[v]===M.id&&(I[v]=255);let O=new Qe(I,S.dims[1],S.dims[0],1);b.push({score:M.score,label:E[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 v=0;v<A.data.length;++v)A.data[v]===M&&(I[v]=255);let O=new Qe(I,A.dims[1],A.dims[0],1);b.push({score:null,label:E[M],mask:O})}}else throw Error(`Subtask ${i} not supported.`);else{let S=w[_[0]];for(let M=0;M<u.length;++M){let I=u[M],O=S[M];O.data.some($=>$<-1e-5||$>1+1e-5)&&O.sigmoid_();let v=await Qe.fromTensor(O.mul_(255).to("uint8")).resize(I[1],I[0]);b.push({label:null,score:null,mask:v})}}return b}};var Ol=class extends Ys{async _call(e,r={}){let s=await et(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 Il=class extends de{async _call(e,r,{hypothesis_template:s="This is a photo of {}"}={}){let n=Array.isArray(e),o=await et(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,E)=>({score:x,label:r[E]}));w.sort((x,E)=>E.score-x.score),f.push(w)}return n?f:f[0]}};var Cl=class extends de{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 et(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:ml(w,!s)})));return n?m:m[0]}};var Pl=class extends de{async _call(e,r,{threshold:s=.1,top_k:n=null,percentage:o=!1}={}){let a=Array.isArray(e),i=await et(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}),E;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];E=b.boxes.map((A,S)=>({score:b.scores[S],label:b.labels[S],box:ml(A,!o)}))}else{let b=this.processor.image_processor.post_process_object_detection(x,s,_,!0)[0];E=b.boxes.map((A,S)=>({score:b.scores[S],label:r[b.classes[S]],box:ml(A,!o)}))}E.sort((b,A)=>A.score-b.score),n!==null&&(E=E.slice(0,n)),u.push(E)}return a?u:u[0]}};var Ll=class extends de{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 et(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 Nl=class extends de{async _call(e){let r=await et(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(Qe.fromTensor(i))}return Array.isArray(e)?o:o[0]}};var zl=class extends de{async _call(e){let r=await et(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),E=Qe.fromTensor(x);o.push({predicted_depth:m,depth:E})}return Array.isArray(e)?o:o[0]}};var Rl=class extends de{async 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Dl=class{constructor(e,r){this.image=e,this.timestamp=r}},Fp=class{constructor(e,r){e.length>0&&e[0]instanceof Qe&&(e=e.map((s,n)=>new Dl(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 e3(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 me.fetch(n.src)).blob();n.src=URL.createObjectURL(m),await new Promise(_=>n.onloadedmetadata=_)}let o=n.duration,a,i;e!=null?(a=e,i=e===1?0:o/(e-1)):(i=1/r,a=Math.floor(o/i));let l=[];for(let f=0;f<a;++f)l.push(e===1?o/2:f*i);let c=document.createElement("canvas");c.width=n.videoWidth,c.height=n.videoHeight;let u=c.getContext("2d",{willReadFrequently:!0});for(let f of l){n.currentTime=f,await new Promise(x=>{n.onseeked=x}),u.drawImage(n,0,0,c.width,c.height);let m=u.getImageData(0,0,c.width,c.height),_=new Qe(m.data,c.width,c.height,4),w=new Dl(_,f);s.push(w)}return n.remove(),new Fp(s,o)}async function Gp(t,e,r={}){let s=await Zt(r?.cache_dir);if(!s)return{allCached:!1,files:e.map(a=>({file:a,cached:!1}))};let n=await Promise.all(e.map(async o=>{let{localPath:a,proposedCacheKey:i}=$r(t,o,r,s),l=await Dr(s,a,i);return{file:o,cached:!!l}}));return{allCached:n.every(o=>o.cached),files:n}}async function EM(t,e,r={}){let s=await Zt(r?.cache_dir);if(!s)return!1;let{localPath:n,proposedCacheKey:o}=$r(t,e,r,s);return!!await Dr(s,n,o)}async function AM(t,e={}){if(!t)throw new Error("modelId is required");if(!await EM(t,"config.json",e))return!1;let r=await Sr(t,e);return(await Gp(t,r,e)).allCached}async function TM(t,e={}){if(!t)throw new Error("modelId is required");let r=await Sr(t,e);return await Gp(t,r,e)}async function MM(t,e,r={}){if(!t)throw new Error("task is required");if(!e)throw new Error("modelId is required");if(!await EM(e,"config.json",r))return!1;let s=await Or(t,e,r);return(await Gp(e,s,r)).allCached}async function SM(t,e,r={}){if(!t)throw new Error("task is required");if(!e)throw new Error("modelId is required");let s=await Or(t,e,r);return await Gp(e,s,r)}async function OM(t,e,r={}){let s=await Zt(r?.cache_dir);if(!s)return{filesDeleted:0,filesCached:0,files:e.map(o=>({file:o,deleted:!1,wasCached:!1}))};if(!s.delete)throw new Error("Cache does not support delete operation");let n=await Promise.all(e.map(async o=>{let{localPath:a,proposedCacheKey:i}=$r(t,o,r,s),c=!!await Dr(s,a,i),u=!1;if(c){let f=await s.delete(i),m=!f&&i!==a?await s.delete(a):!1;u=f||m}return{file:o,deleted:u,wasCached:c}}));return{filesDeleted:n.filter(o=>o.deleted).length,filesCached:n.filter(o=>o.wasCached).length,files:n}}async function IM(t,e={}){if(!t)throw new Error("modelId is required");let r=await Sr(t,e);return await OM(t,r,e)}async function CM(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 OM(e,s,r)}var t3=Object.keys(Vr);async function PM(t,{config:e=null,model_file_name:r=null,revision:s="main",cache_dir:n=null,local_files_only:o=!1}={}){e=await Ho(t,{config:e,cache_dir:n,local_files_only:o,revision:s});let a="onnx",i=On(e),{sessions:l}=Vo(i,e,{model_file_name:r}),c=Object.values(l),u={revision:s,cache_dir:n,local_files_only:o};return(await Promise.all(t3.map(async m=>{let _=Vr[m]??"",w=await Promise.all(c.map(async x=>{let E=`${a}/${x}${_}.onnx`;return(await It(t,E,u)).exists}));return{dtype:m,available:w.every(Boolean)}}))).filter(m=>m.available).map(m=>m.dtype)}var J1=class{static async get_files(e,r={}){return Sr(e,r)}static async get_pipeline_files(e,r,s={}){return Or(e,r,s)}static async get_model_files(e,r={}){return In(e,r)}static async get_tokenizer_files(e){return _n(e)}static async get_processor_files(e){return Up(e)}static async get_available_dtypes(e,r={}){return PM(e,r)}static async is_cached(e,r={}){return AM(e,r)}static async is_cached_files(e,r={}){return TM(e,r)}static async is_pipeline_cached(e,r,s={}){return MM(e,r,s)}static async is_pipeline_cached_files(e,r,s={}){return SM(e,r,s)}static async get_file_metadata(e,r,s={}){return It(e,r,s)}static async clear_cache(e,r={}){return IM(e,r)}static async clear_pipeline_cache(e,r,s={}){return CM(e,r,s)}};export{Cm as ASTFeatureExtractor,Ph as ASTForAudioClassification,Ch as ASTModel,Jo as ASTPreTrainedModel,Sh as AfmoeForCausalLM,Mh as AfmoeModel,Yo as AfmoePreTrainedModel,Eh as AlbertForMaskedLM,kh as AlbertForQuestionAnswering,vh as AlbertForSequenceClassification,bh as AlbertModel,Bs as AlbertPreTrainedModel,$d as AlbertTokenizer,Th as ApertusForCausalLM,Ah as ApertusModel,Ko as ApertusPreTrainedModel,Ih as ArceeForCausalLM,Oh as ArceeModel,Qo as ArceePreTrainedModel,kl as AudioClassificationPipeline,Wt as AutoConfig,Ge as AutoFeatureExtractor,Ae as AutoImageProcessor,Ar as AutoModel,Np as AutoModelForAudioClassification,mM as AutoModelForAudioFrameClassification,xM as AutoModelForAudioTextToText,Lp as AutoModelForCTC,Tp as AutoModelForCausalLM,$p as AutoModelForDepthEstimation,zp as AutoModelForDocumentQuestionAnswering,Ip as AutoModelForImageClassification,Dp as AutoModelForImageFeatureExtraction,_M as AutoModelForImageMatting,pl as AutoModelForImageSegmentation,wM as AutoModelForImageTextToText,Rp as AutoModelForImageToImage,fM as AutoModelForMaskGeneration,Mp as AutoModelForMaskedLM,hM as AutoModelForNormalEstimation,Cp as AutoModelForObjectDetection,gM as AutoModelForPoseEstimation,Sp as AutoModelForQuestionAnswering,fl as AutoModelForSemanticSegmentation,to as AutoModelForSeq2SeqLM,ul as AutoModelForSequenceClassification,kp as AutoModelForSpeechSeq2Seq,Ep as AutoModelForTextToSpectrogram,Ap as AutoModelForTextToWaveform,vp as AutoModelForTokenClassification,dl as AutoModelForUniversalSegmentation,Op as AutoModelForVision2Seq,dM as AutoModelForXVector,Pp as AutoModelForZeroShotObjectDetection,nu as AutoProcessor,ne as AutoTokenizer,Al as AutomaticSpeechRecognitionPipeline,Ol as BackgroundRemovalPipeline,Nh as BartForConditionalGeneration,zh as BartForSequenceClassification,Lh as BartModel,Ln as BartPretrainedModel,Dd as BartTokenizer,Y1 as BaseStreamer,Xm as BeitFeatureExtractor,$h as BeitForImageClassification,Rh as BeitModel,Zo as BeitPreTrainedModel,Bh as BertForMaskedLM,Gh as BertForQuestionAnswering,Uh as BertForSequenceClassification,Fh as BertForTokenClassification,Dh as BertModel,Qr as BertPreTrainedModel,Bd as BertTokenizer,Km as BitImageProcessor,qh as BlenderbotForConditionalGeneration,jh as BlenderbotModel,ea as BlenderbotPreTrainedModel,Vh as BlenderbotSmallForConditionalGeneration,Wh as BlenderbotSmallModel,ta as BlenderbotSmallPreTrainedModel,Ud as BlenderbotSmallTokenizer,Fd as BlenderbotTokenizer,Xh as BloomForCausalLM,Hh as BloomModel,ra as BloomPreTrainedModel,Gd as BloomTokenizer,tg as CHMv2ForDepthEstimation,Qm as CHMv2ImageProcessor,Au as CHMv2PreTrainedModel,Jm as CLIPFeatureExtractor,Dc as CLIPImageProcessor,sg as CLIPModel,mr as CLIPPreTrainedModel,lg as CLIPSegForImageSegmentation,ig as CLIPSegModel,ia as CLIPSegPreTrainedModel,ng as CLIPTextModel,aa as CLIPTextModelWithProjection,qd as CLIPTokenizer,og as CLIPVisionModel,ag as CLIPVisionModelWithProjection,Yh as CamembertForMaskedLM,Zh as CamembertForQuestionAnswering,Qh as CamembertForSequenceClassification,Jh as CamembertForTokenClassification,Kh as CamembertModel,Jr as CamembertPreTrainedModel,jd as CamembertTokenizer,Pm as ChatterboxFeatureExtractor,sa as ChatterboxModel,ku as ChatterboxPreTrainedModel,Wm as ChatterboxProcessor,Ym as ChineseCLIPFeatureExtractor,eg as ChineseCLIPModel,Eu as ChineseCLIPPreTrainedModel,oa as ClapAudioModelWithProjection,Lm as ClapFeatureExtractor,rg as ClapModel,Nn as ClapPreTrainedModel,na as ClapTextModelWithProjection,_u as ClassifierFreeGuidanceLogitsProcessor,ug as CodeGenForCausalLM,cg as CodeGenModel,la as CodeGenPreTrainedModel,Vd as CodeGenTokenizer,Wd as CodeLlamaTokenizer,mg as Cohere2ForCausalLM,dg as Cohere2Model,ua as Cohere2PreTrainedModel,Nm as CohereAsrFeatureExtractor,hg as CohereAsrForConditionalGeneration,_g as CohereAsrModel,pa as CohereAsrPreTrainedModel,Vm as CohereAsrProcessor,Xd as CohereAsrTokenizer,fg as CohereForCausalLM,pg as CohereModel,ca as CoherePreTrainedModel,Hd as CohereTokenizer,wg as ConvBertForMaskedLM,bg as ConvBertForQuestionAnswering,xg as ConvBertForSequenceClassification,yg as ConvBertForTokenClassification,gg as ConvBertModel,Zr as ConvBertPreTrainedModel,Kd as ConvBertTokenizer,Zm as ConvNextFeatureExtractor,kg as ConvNextForImageClassification,Bc as ConvNextImageProcessor,vg as ConvNextModel,fa as ConvNextPreTrainedModel,Ag as ConvNextV2ForImageClassification,Eg as ConvNextV2Model,da as ConvNextV2PreTrainedModel,Og as DFineForObjectDetection,Sg as DFineModel,_a as DFinePreTrainedModel,tw as DINOv3ConvNextModel,Pu as DINOv3ConvNextPreTrainedModel,r_ as DINOv3ViTImageProcessor,rw as DINOv3ViTModel,Lu as DINOv3ViTPreTrainedModel,n_ as DPTFeatureExtractor,uw as DPTForDepthEstimation,Gc as DPTImageProcessor,cw as DPTModel,va as DPTPreTrainedModel,ga as DacDecoderModel,Mu as DacDecoderOutput,ha as DacEncoderModel,Tu as DacEncoderOutput,$o as DacFeatureExtractor,Ig as DacModel,zn as DacPreTrainedModel,Pg as DebertaForMaskedLM,zg as DebertaForQuestionAnswering,Lg as DebertaForSequenceClassification,Ng as DebertaForTokenClassification,Cg as DebertaModel,es as DebertaPreTrainedModel,Qd as DebertaTokenizer,Bg as DebertaV2ForMaskedLM,Gg as DebertaV2ForQuestionAnswering,Ug as DebertaV2ForSequenceClassification,Fg as DebertaV2ForTokenClassification,Dg as DebertaV2Model,ts as DebertaV2PreTrainedModel,Yd as DebertaV2Tokenizer,jg as DecisionTransformerModel,Su as DecisionTransformerPreTrainedModel,$g as DeepseekV3ForCausalLM,Rg as DeepseekV3Model,wa as DeepseekV3PreTrainedModel,e_ as DeiTFeatureExtractor,Wg as DeiTForImageClassification,Uc as DeiTImageProcessor,qg as DeiTModel,xa as DeiTPreTrainedModel,Vg as DepthAnythingForDepthEstimation,Ou as DepthAnythingPreTrainedModel,zl as DepthEstimationPipeline,Hg as DepthProForDepthEstimation,Iu as DepthProPreTrainedModel,t_ as DetrFeatureExtractor,Kg as DetrForObjectDetection,Yg as DetrForSegmentation,Fc as DetrImageProcessor,Xg as DetrModel,$n as DetrObjectDetectionOutput,Rn as DetrPreTrainedModel,Cu as DetrSegmentationOutput,Jg as Dinov2ForImageClassification,Qg as Dinov2Model,ya as Dinov2PreTrainedModel,ew as Dinov2WithRegistersForImageClassification,Zg as Dinov2WithRegistersModel,ba as Dinov2WithRegistersPreTrainedModel,iw as DistilBertForMaskedLM,aw as DistilBertForQuestionAnswering,nw as DistilBertForSequenceClassification,ow as DistilBertForTokenClassification,sw as DistilBertModel,rs as DistilBertPreTrainedModel,Jd as DistilBertTokenizer,Ll as DocumentQuestionAnsweringPipeline,s_ as DonutFeatureExtractor,wn as DonutImageProcessor,lw as DonutSwinModel,Nu as DonutSwinPreTrainedModel,Wo as DynamicCache,xb as EdgeTamModel,fw as EfficientNetForImageClassification,o_ as EfficientNetImageProcessor,pw as EfficientNetModel,ka as EfficientNetPreTrainedModel,mw as ElectraForMaskedLM,gw as ElectraForQuestionAnswering,_w as ElectraForSequenceClassification,hw as ElectraForTokenClassification,dw as ElectraModel,ss as ElectraPreTrainedModel,Zd as ElectraTokenizer,zo as EncodecFeatureExtractor,wu as EosTokenCriteria,xw as Ernie4_5ForCausalLM,ww as Ernie4_5Model,Ea as Ernie4_5PretrainedModel,bw as EsmForMaskedLM,vw as EsmForSequenceClassification,kw as EsmForTokenClassification,yw as EsmModel,Us as EsmPreTrainedModel,em as EsmTokenizer,Aw as EuroBertForMaskedLM,Tw as EuroBertForSequenceClassification,Mw as EuroBertForTokenClassification,Ew as EuroBertModel,Fs as EuroBertPreTrainedModel,Ow as ExaoneForCausalLM,Sw as ExaoneModel,Aa as ExaonePreTrainedModel,Cw as FalconForCausalLM,Lw as FalconH1ForCausalLM,Pw as FalconH1Model,Ma as FalconH1PreTrainedModel,Iw as FalconModel,Ta as FalconPreTrainedModel,tm as FalconTokenizer,zw as FastViTForImageClassification,Nw as FastViTModel,Sa as FastViTPreTrainedModel,Rl as FeatureExtractionPipeline,Ee as FeatureExtractor,wl as FillMaskPipeline,Rw as Florence2ForConditionalGeneration,zu as Florence2PreTrainedModel,D_ as Florence2Processor,au as ForcedBOSTokenLogitsProcessor,iu as ForcedEOSTokenLogitsProcessor,l_ as GLPNFeatureExtractor,Jw as GLPNForDepthEstimation,Qw as GLPNModel,za as GLPNPreTrainedModel,lx as GPT2LMHeadModel,ix as GPT2Model,Ua as GPT2PreTrainedModel,nm as GPT2Tokenizer,ex as GPTBigCodeForCausalLM,Zw as GPTBigCodeModel,Ra as GPTBigCodePreTrainedModel,ux as GPTJForCausalLM,cx as GPTJModel,Fa as GPTJPreTrainedModel,rx as GPTNeoForCausalLM,tx as GPTNeoModel,$a as GPTNeoPreTrainedModel,nx as GPTNeoXForCausalLM,sx as GPTNeoXModel,Da as GPTNeoXPreTrainedModel,sm as GPTNeoXTokenizer,Uw as Gemma2ForCausalLM,Bw as Gemma2Model,Ia as Gemma2PreTrainedModel,qw as Gemma3ForCausalLM,Du as Gemma3ForConditionalGeneration,a_ as Gemma3ImageProcessor,jw as Gemma3Model,$u as Gemma3PreTrainedModel,B_ as Gemma3Processor,zm as Gemma3nAudioFeatureExtractor,Ww as Gemma3nForCausalLM,Dn as Gemma3nForConditionalGeneration,Bu as Gemma3nPreTrainedModel,U_ as Gemma3nProcessor,Dw as GemmaForCausalLM,$w as GemmaModel,Oa as GemmaPreTrainedModel,rm as GemmaTokenizer,i_ as Glm46VImageProcessor,F_ as Glm46VProcessor,Hw as GlmForCausalLM,Vw as GlmModel,Kw as GlmMoeDsaForCausalLM,Xw as GlmMoeDsaModel,Pa as GlmMoeDsaPreTrainedModel,Yw as GlmOcrForConditionalGeneration,Ca as GlmPreTrainedModel,ax as GptOssForCausalLM,ox as GptOssModel,Ba as GptOssPreTrainedModel,fx as GraniteForCausalLM,px as GraniteModel,mx as GraniteMoeHybridForCausalLM,dx as GraniteMoeHybridModel,ja as GraniteMoeHybridPreTrainedModel,Ga as GranitePreTrainedModel,Rm as GraniteSpeechFeatureExtractor,_x as GraniteSpeechForConditionalGeneration,G_ as GraniteSpeechProcessor,hx as GroundingDinoForObjectDetection,c_ as GroundingDinoImageProcessor,Gu as GroundingDinoPreTrainedModel,j_ as GroundingDinoProcessor,gx as GroupViTModel,ju as GroupViTPreTrainedModel,xx as HeliumForCausalLM,wx as HeliumModel,qa as HeliumPreTrainedModel,om as HerbertTokenizer,bx as HieraForImageClassification,yx as HieraModel,Wa as HieraPreTrainedModel,Sx as HubertForCTC,Ox as HubertForSequenceClassification,Mx as HubertModel,Tx as HubertPreTrainedModel,Cx as HunYuanDenseV1ForCausalLM,Ix as HunYuanDenseV1Model,Va as HunYuanDenseV1PreTrainedModel,Nx as IJepaForImageClassification,Lx as IJepaModel,Ha as IJepaPreTrainedModel,Px as Idefics3ForConditionalGeneration,jc as Idefics3ImageProcessor,Zc as Idefics3Processor,Sl as ImageClassificationPipeline,$l as ImageFeatureExtractionPipeline,X as ImageFeatureExtractor,X as ImageProcessor,Ys as ImageSegmentationPipeline,Nl as ImageToImagePipeline,Ml as ImageToTextPipeline,ST as InterruptableStoppingCriteria,Rx as JAISLMHeadModel,zx as JAISModel,Xa as JAISPreTrainedModel,p_ as JinaCLIPImageProcessor,$x as JinaCLIPModel,Un as JinaCLIPPreTrainedModel,W_ as JinaCLIPProcessor,Ka as JinaCLIPTextModel,Dx as JinaCLIPVisionModel,Ux as Lfm2ForCausalLM,Bx as Lfm2Model,jx as Lfm2MoeForCausalLM,Gx as Lfm2MoeModel,Qa as Lfm2MoePreTrainedModel,Ya as Lfm2PreTrainedModel,qx as Lfm2VlForConditionalGeneration,f_ as Lfm2VlImageProcessor,V_ as Lfm2VlProcessor,Fx as LightOnOcrForConditionalGeneration,N1 as LiteWhisperForConditionalGeneration,Hx as Llama4ForCausalLM,qu as Llama4PreTrainedModel,Vx as LlamaForCausalLM,Wx as LlamaModel,Ja as LlamaPreTrainedModel,am as LlamaTokenizer,Et as LlavaForConditionalGeneration,Et as LlavaOnevisionForConditionalGeneration,d_ as LlavaOnevisionImageProcessor,Ru as LlavaPreTrainedModel,H_ as LlavaProcessor,Gw as LlavaQwen2ForCausalLM,Ot as LogLevel,Dt as LogitsProcessor,zs as LogitsProcessorList,qo as LogitsWarper,Kx as LongT5ForConditionalGeneration,Xx as LongT5Model,Za as LongT5PreTrainedModel,Qx as M2M100ForConditionalGeneration,Yx as M2M100Model,ei as M2M100PreTrainedModel,im as M2M100Tokenizer,cm as MBart50Tokenizer,oy as MBartForCausalLM,sy as MBartForConditionalGeneration,ny as MBartForSequenceClassification,ry as MBartModel,qs as MBartPreTrainedModel,Co as MBartTokenizer,Wy as MPNetForMaskedLM,Xy as MPNetForQuestionAnswering,Vy as MPNetForSequenceClassification,Hy as MPNetForTokenClassification,qy as MPNetModel,ns as MPNetPreTrainedModel,fm as MPNetTokenizer,Jy as MT5ForConditionalGeneration,Qy as MT5Model,di as MT5PreTrainedModel,Zx as MarianMTModel,Jx as MarianModel,ti as MarianPreTrainedModel,lm as MarianTokenizer,__ as Mask2FormerImageProcessor,m_ as MaskFormerFeatureExtractor,ty as MaskFormerForInstanceSegmentation,xn as MaskFormerImageProcessor,ey as MaskFormerModel,ri as MaskFormerPreTrainedModel,gu as MaxLengthCriteria,ay as Metric3DForDepthEstimation,Wu as Metric3DPreTrainedModel,iy as Metric3Dv2ForDepthEstimation,Vu as Metric3Dv2PreTrainedModel,ly as MgpstrForSceneTextRecognition,Hu as MgpstrModelOutput,Xu as MgpstrPreTrainedModel,X_ as MgpstrProcessor,um as MgpstrTokenizer,ni as MimiDecoderModel,Yu as MimiDecoderOutput,si as MimiEncoderModel,Ku as MimiEncoderOutput,cy as MimiModel,Fn as MimiPreTrainedModel,fu as MinLengthLogitsProcessor,du as MinNewTokensLengthLogitsProcessor,dy as Mistral4ForCausalLM,fy as Mistral4Model,ai as Mistral4PreTrainedModel,py as MistralForCausalLM,uy as MistralModel,oi as MistralPreTrainedModel,_y as MobileBertForMaskedLM,gy as MobileBertForQuestionAnswering,hy as MobileBertForSequenceClassification,my as MobileBertModel,Ws as MobileBertPreTrainedModel,pm as MobileBertTokenizer,xy as MobileLLMForCausalLM,wy as MobileLLMModel,ii as MobileLLMPreTrainedModel,h_ as MobileNetV1FeatureExtractor,by as MobileNetV1ForImageClassification,vy as MobileNetV1ForSemanticSegmentation,qc as MobileNetV1ImageProcessor,yy as MobileNetV1Model,Gn as MobileNetV1PreTrainedModel,g_ as MobileNetV2FeatureExtractor,Ey as MobileNetV2ForImageClassification,Ay as MobileNetV2ForSemanticSegmentation,Wc as MobileNetV2ImageProcessor,ky as MobileNetV2Model,jn as MobileNetV2PreTrainedModel,w_ as MobileNetV3FeatureExtractor,My as MobileNetV3ForImageClassification,Sy as MobileNetV3ForSemanticSegmentation,Vc as MobileNetV3ImageProcessor,Ty as MobileNetV3Model,qn as MobileNetV3PreTrainedModel,x_ as MobileNetV4FeatureExtractor,Iy as MobileNetV4ForImageClassification,Cy as MobileNetV4ForSemanticSegmentation,Hc as MobileNetV4ImageProcessor,Oy as MobileNetV4Model,Wn as MobileNetV4PreTrainedModel,y_ as MobileViTFeatureExtractor,Ly as MobileViTForImageClassification,Xc as MobileViTImageProcessor,Py as MobileViTModel,li as MobileViTPreTrainedModel,zy as MobileViTV2ForImageClassification,Ny as MobileViTV2Model,ci as MobileViTV2PreTrainedModel,J1 as ModelRegistry,Fy as ModernBertDecoderForCausalLM,Uy as ModernBertDecoderModel,ui as ModernBertDecoderPreTrainedModel,$y as ModernBertForMaskedLM,Dy as ModernBertForSequenceClassification,By as ModernBertForTokenClassification,Ry as ModernBertModel,Vs as ModernBertPreTrainedModel,Fw as Moondream1ForConditionalGeneration,$m as MoonshineFeatureExtractor,jy as MoonshineForConditionalGeneration,Gy as MoonshineModel,pi as MoonshinePreTrainedModel,K_ as MoonshineProcessor,Yy as MptForCausalLM,Ky as MptModel,fi as MptPreTrainedModel,Zy as MultiModalityCausalLM,Qu as MultiModalityPreTrainedModel,t0 as MusicgenForCausalLM,_i as MusicgenForConditionalGeneration,e0 as MusicgenModel,mi as MusicgenPreTrainedModel,s0 as NanoChatForCausalLM,r0 as NanoChatModel,hi as NanoChatPreTrainedModel,o0 as NemotronHForCausalLM,n0 as NemotronHModel,gi as NemotronHPreTrainedModel,i0 as NeoBertForMaskedLM,u0 as NeoBertForQuestionAnswering,l0 as NeoBertForSequenceClassification,c0 as NeoBertForTokenClassification,a0 as NeoBertModel,os as NeoBertPreTrainedModel,dm as NllbTokenizer,mu as NoBadWordsLogitsProcessor,uu as NoRepeatNGramLogitsProcessor,p0 as NomicBertModel,Ju as NomicBertPreTrainedModel,b_ as NougatImageProcessor,mm as NougatTokenizer,k0 as OPTForCausalLM,v0 as OPTModel,ki as OPTPreTrainedModel,Cl as ObjectDetectionPipeline,_0 as Olmo2ForCausalLM,m0 as Olmo2Model,xi as Olmo2PreTrainedModel,g0 as Olmo3ForCausalLM,h0 as Olmo3Model,yi as Olmo3PreTrainedModel,d0 as OlmoForCausalLM,x0 as OlmoHybridForCausalLM,w0 as OlmoHybridModel,bi as OlmoHybridPreTrainedModel,f0 as OlmoModel,wi as OlmoPreTrainedModel,b0 as OpenELMForCausalLM,y0 as OpenELMModel,vi as OpenELMPreTrainedModel,v_ as OwlViTFeatureExtractor,M0 as OwlViTForObjectDetection,yn as OwlViTImageProcessor,T0 as OwlViTModel,Ai as OwlViTPreTrainedModel,Y_ as OwlViTProcessor,A0 as Owlv2ForObjectDetection,k_ as Owlv2ImageProcessor,E0 as Owlv2Model,Ei as Owlv2PreTrainedModel,S0 as PaliGemmaForConditionalGeneration,Q_ as PaliGemmaProcessor,Ro as ParakeetFeatureExtractor,O0 as ParakeetForCTC,Zu as ParakeetPreTrainedModel,C0 as PatchTSMixerForPrediction,I0 as PatchTSMixerModel,Ti as PatchTSMixerPreTrainedModel,L0 as PatchTSTForPrediction,P0 as PatchTSTModel,Mi as PatchTSTPreTrainedModel,$0 as Phi3ForCausalLM,R0 as Phi3Model,Oi as Phi3PreTrainedModel,Ii as Phi3VForCausalLM,T_ as Phi3VImageProcessor,ep as Phi3VPreTrainedModel,J_ as Phi3VProcessor,z0 as PhiForCausalLM,N0 as PhiModel,Si as PhiPreTrainedModel,M_ as PixtralImageProcessor,Z_ as PixtralProcessor,y as PreTrainedModel,V as PreTrainedTokenizer,An as PretrainedConfig,se as Processor,B0 as PvtForImageClassification,S_ as PvtImageProcessor,D0 as PvtModel,Ci as PvtPreTrainedModel,Do as PyAnnoteFeatureExtractor,F0 as PyAnnoteForAudioFrameClassification,U0 as PyAnnoteModel,Pi as PyAnnotePreTrainedModel,eh as PyAnnoteProcessor,gl as QuestionAnsweringPipeline,j0 as Qwen2ForCausalLM,G0 as Qwen2Model,W0 as Qwen2MoeForCausalLM,q0 as Qwen2MoeModel,Ni as Qwen2MoePreTrainedModel,Li as Qwen2PreTrainedModel,_m as Qwen2Tokenizer,La as Qwen2VLForCausalLM,Bn as Qwen2VLForConditionalGeneration,Uo as Qwen2VLImageProcessor,Uu as Qwen2VLPreTrainedModel,Ls as Qwen2VLProcessor,Na as Qwen2_5_VLForCausalLM,Gs as Qwen2_5_VLForConditionalGeneration,Go as Qwen2_5_VLProcessor,H0 as Qwen3ForCausalLM,V0 as Qwen3Model,K0 as Qwen3MoeForCausalLM,X0 as Qwen3MoeModel,Ri as Qwen3MoePreTrainedModel,Q0 as Qwen3NextForCausalLM,Y0 as Qwen3NextModel,$i as Qwen3NextPreTrainedModel,zi as Qwen3PreTrainedModel,Di as Qwen3VLForCausalLM,Hs as Qwen3VLForConditionalGeneration,Z0 as Qwen3VLMoeForCausalLM,J0 as Qwen3VLMoeForConditionalGeneration,th as Qwen3VLProcessor,Bi as Qwen3_5ForCausalLM,Vn as Qwen3_5ForConditionalGeneration,tb as Qwen3_5MoeForCausalLM,eb as Qwen3_5MoeForConditionalGeneration,ob as RFDetrForObjectDetection,nb as RFDetrModel,tp as RFDetrObjectDetectionOutput,Fi as RFDetrPreTrainedModel,Mg as RTDetrForObjectDetection,O_ as RTDetrImageProcessor,Tg as RTDetrModel,_r as RTDetrObjectDetectionOutput,ma as RTDetrPreTrainedModel,gb as RTDetrV2ForObjectDetection,hb as RTDetrV2Model,rp as RTDetrV2ObjectDetectionOutput,Gi as RTDetrV2PreTrainedModel,No as RawAudio,Qe as RawImage,Fp as RawVideo,Dl as RawVideoFrame,pu as RepetitionPenaltyLogitsProcessor,sb as ResNetForImageClassification,rb as ResNetModel,Ui as ResNetPreTrainedModel,fb as RoFormerForMaskedLM,_b as RoFormerForQuestionAnswering,db as RoFormerForSequenceClassification,mb as RoFormerForTokenClassification,pb as RoFormerModel,is as RoFormerPreTrainedModel,gm as RoFormerTokenizer,ib as RobertaForMaskedLM,ub as RobertaForQuestionAnswering,lb as RobertaForSequenceClassification,cb as RobertaForTokenClassification,ab as RobertaModel,as as RobertaPreTrainedModel,hm as RobertaTokenizer,Fo as Sam2ImageProcessor,op as Sam2ImageSegmentationOutput,ji as Sam2Model,ap as Sam2PreTrainedModel,eu as Sam2Processor,rh as Sam2VideoProcessor,Fo as Sam3ImageProcessor,yb as Sam3TrackerModel,Fo as SamImageProcessor,sp as SamImageSegmentationOutput,wb as SamModel,np as SamPreTrainedModel,jo as SamProcessor,I_ as SapiensFeatureExtractor,vb as SapiensForDepthEstimation,kb as SapiensForNormalEstimation,bb as SapiensForSemanticSegmentation,Kc as SapiensImageProcessor,Hn as SapiensPreTrainedModel,Dm as SeamlessM4TFeatureExtractor,C_ as SegformerFeatureExtractor,Ab as SegformerForImageClassification,Tb as SegformerForSemanticSegmentation,Yc as SegformerImageProcessor,Eb as SegformerModel,Xn as SegformerPreTrainedModel,P_ as SiglipImageProcessor,Mb as SiglipModel,qi as SiglipPreTrainedModel,Wi as SiglipTextModel,wm as SiglipTokenizer,Sb as SiglipVisionModel,Ib as SmolLM3ForCausalLM,Ob as SmolLM3Model,Vi as SmolLM3PreTrainedModel,jc as SmolVLMImageProcessor,Zc as SmolVLMProcessor,Xi as SnacDecoderModel,Hi as SnacEncoderModel,Bm as SnacFeatureExtractor,Cb as SnacModel,Kn as SnacPreTrainedModel,Lb as SolarOpenForCausalLM,Pb as SolarOpenModel,Ki as SolarOpenPreTrainedModel,Um as SpeechT5FeatureExtractor,zb as SpeechT5ForSpeechToText,Rb as SpeechT5ForTextToSpeech,$b as SpeechT5HifiGan,Nb as SpeechT5Model,Yn as SpeechT5PreTrainedModel,sh as SpeechT5Processor,xm as SpeechT5Tokenizer,Bb as SqueezeBertForMaskedLM,Fb as SqueezeBertForQuestionAnswering,Ub as SqueezeBertForSequenceClassification,Db as SqueezeBertModel,Xs as SqueezeBertPreTrainedModel,ym as SqueezeBertTokenizer,jb as StableLmForCausalLM,Gb as StableLmModel,Yi as StableLmPreTrainedModel,Wb as Starcoder2ForCausalLM,qb as Starcoder2Model,Qi as Starcoder2PreTrainedModel,Yr as StoppingCriteria,Sn as StoppingCriteriaList,Vb as StyleTextToSpeech2Model,ip as StyleTextToSpeech2PreTrainedModel,xl as SummarizationPipeline,Ji as SupertonicForConditionalGeneration,lp as SupertonicPreTrainedModel,Tn as SuppressTokensAtBeginLogitsProcessor,lu as SuppressTokensLogitsProcessor,Qb as Swin2SRForImageSuperResolution,L_ as Swin2SRImageProcessor,Yb as Swin2SRModel,Zi as Swin2SRPreTrainedModel,Xb as SwinForImageClassification,Kb as SwinForSemanticSegmentation,Hb as SwinModel,Qn as SwinPreTrainedModel,Zb as T5ForConditionalGeneration,Jb as T5Model,el as T5PreTrainedModel,bm as T5Tokenizer,t1 as TableTransformerForObjectDetection,e1 as TableTransformerModel,cp as TableTransformerObjectDetectionOutput,tl as TableTransformerPreTrainedModel,hu as TemperatureLogitsWarper,z as Tensor,Mr as Text2TextGenerationPipeline,_l as TextClassificationPipeline,bl as TextGenerationPipeline,Q1 as TextStreamer,Tl as TextToAudioPipeline,hl as TokenClassificationPipeline,V as TokenizersBackend,MT as TopKLogitsWarper,TT as TopPLogitsWarper,r1 as TrOCRForCausalLM,up as TrOCRPreTrainedModel,yl as TranslationPipeline,js as UltravoxModel,Fu as UltravoxPreTrainedModel,nh as UltravoxProcessor,n1 as UniSpeechForCTC,o1 as UniSpeechForSequenceClassification,s1 as UniSpeechModel,Jn as UniSpeechPreTrainedModel,c1 as UniSpeechSatForAudioFrameClassification,i1 as UniSpeechSatForCTC,l1 as UniSpeechSatForSequenceClassification,a1 as UniSpeechSatModel,Ks as UniSpeechSatPreTrainedModel,q_ as VLChatProcessor,u_ as VLMImageProcessor,p1 as VaultGemmaForCausalLM,u1 as VaultGemmaModel,rl as VaultGemmaPreTrainedModel,N_ as ViTFeatureExtractor,m1 as ViTForImageClassification,Qc as ViTImageProcessor,_1 as ViTMAEModel,pp as ViTMAEPreTrainedModel,g1 as ViTMSNForImageClassification,h1 as ViTMSNModel,nl as ViTMSNPreTrainedModel,d1 as ViTModel,sl as ViTPreTrainedModel,f1 as VisionEncoderDecoderModel,w1 as VitMatteForImageMatting,z_ as VitMatteImageProcessor,fp as VitMattePreTrainedModel,x1 as VitPoseForPoseEstimation,R_ as VitPoseImageProcessor,dp as VitPosePreTrainedModel,y1 as VitsModel,mp as VitsModelOutput,_p as VitsPreTrainedModel,km as VitsTokenizer,b1 as VoxtralForConditionalGeneration,oh as VoxtralProcessor,jm as VoxtralRealtimeFeatureExtractor,ol as VoxtralRealtimeForConditionalGeneration,hp as VoxtralRealtimePreTrainedModel,ih as VoxtralRealtimeProcessor,A1 as Wav2Vec2BertForCTC,T1 as Wav2Vec2BertForSequenceClassification,E1 as Wav2Vec2BertModel,Zn as Wav2Vec2BertPreTrainedModel,Em as Wav2Vec2CTCTokenizer,Fm as Wav2Vec2FeatureExtractor,Ax as Wav2Vec2ForAudioFrameClassification,kx as Wav2Vec2ForCTC,Ex as Wav2Vec2ForSequenceClassification,vx as Wav2Vec2Model,sr as Wav2Vec2PreTrainedModel,lh as Wav2Vec2Processor,ch as Wav2Vec2ProcessorWithLM,C1 as WavLMForAudioFrameClassification,S1 as WavLMForCTC,O1 as WavLMForSequenceClassification,I1 as WavLMForXVector,M1 as WavLMModel,ls as WavLMPreTrainedModel,Gm as WeSpeakerFeatureExtractor,P1 as WeSpeakerResNetModel,wp as WeSpeakerResNetPreTrainedModel,qm as WhisperFeatureExtractor,yp as WhisperForConditionalGeneration,L1 as WhisperModel,al as WhisperPreTrainedModel,uh as WhisperProcessor,kM as WhisperTextStreamer,cu as WhisperTimeStampLogitsProcessor,Am as WhisperTokenizer,B1 as XLMForQuestionAnswering,$1 as XLMForSequenceClassification,D1 as XLMForTokenClassification,z1 as XLMModel,cs as XLMPreTrainedModel,F1 as XLMRobertaForMaskedLM,q1 as XLMRobertaForQuestionAnswering,G1 as XLMRobertaForSequenceClassification,j1 as XLMRobertaForTokenClassification,U1 as XLMRobertaModel,us as XLMRobertaPreTrainedModel,Tm as XLMRobertaTokenizer,Mm as XLMTokenizer,R1 as XLMWithLMHeadModel,gp as XVectorOutput,$_ as YolosFeatureExtractor,V1 as YolosForObjectDetection,Jc as YolosImageProcessor,W1 as YolosModel,bp as YolosObjectDetectionOutput,il as YolosPreTrainedModel,X1 as YoutuForCausalLM,H1 as YoutuModel,ll as YoutuPreTrainedModel,El as ZeroShotAudioClassificationPipeline,vl as ZeroShotClassificationPipeline,Il as ZeroShotImageClassificationPipeline,Pl as ZeroShotObjectDetectionPipeline,ve as cat,oN as cos_sim,tA as dot,me as env,Xe as full,Io as full_like,Id as interpolate,vt as interpolate_4d,SD as layer_norm,uz as load_image,e3 as load_video,Lf as log_softmax,eT as matmul,Ic as mean,tT as mean_pooling,Ze as ones,Cc as ones_like,FN as permute,rce as pipeline,sT as quantize_embeddings,OD as rand,rT as randn,xs as random,Im as read_audio,MD as rfft,Oc as slice,Re as softmax,Ct as stack,Cd as std_mean,tr as topk,Ld as zeros,Nd as zeros_like};