@huggingface/transformers/dist/transformers.node.min.mjs

State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!

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No instance will be fused."),n=new Set);const i=e.class_queries_logits??e.logits,l=(e.masks_queries_logits??e.pred_masks).sigmoid();let[c,d,u]=i.dims;if(u-=1,null!==a&&a.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let _=[];for(let e=0;e<c;++e){let c=null!==a?a[e]:null,d=i[e],m=l[e],[h,f,M]=p(d,m,t,u);if(0===M.length){let[e,t]=c??m.dims.slice(-2),s=new r.Tensor("int32",new Int32Array(e*t).fill(-1),[e,t]);_.push({segmentation:s,segments_info:[]});continue}let[w,T]=g(h,f,M,s,o,n,c);_.push({segmentation:w,segments_info:T})}return _}function M(e,t=.5,s=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class w extends o.Callable{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??void 0!==this.size,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.do_pad&&!this.pad_size&&this.size&&void 0!==this.size.width&&void 0!==this.size.height&&(this.pad_size=this.size),this.do_flip_channel_order=e.do_flip_channel_order??!1,this.config=e}async thumbnail(e,t,s=2){const o=e.height,r=e.width,n=t.height,a=t.width;let i=Math.min(o,n),l=Math.min(r,a);return i===o&&l===r?e:(o>r?l=Math.floor(r*i/o):r>o&&(i=Math.floor(o*l/r)),await e.resize(l,i,{resample:s}))}async crop_margin(e,t=200){const s=e.clone().grayscale(),o=(0,n.min)(s.data)[0],r=(0,n.max)(s.data)[0]-o;if(0===r)return e;const a=t/255;let i=s.width,l=s.height,c=0,d=0;const u=s.data;for(let e=0;e<s.height;++e){const t=e*s.width;for(let n=0;n<s.width;++n)(u[t+n]-o)/r<a&&(i=Math.min(i,n),l=Math.min(l,e),c=Math.max(c,n),d=Math.max(d,e))}return e=await e.crop([i,l,c,d])}pad_image(e,t,s,{mode:o="constant",center:r=!1,constant_values:n=0}={}){const[i,l,c]=t;let d,u;if("number"==typeof s?(d=s,u=s):"square"===s?d=u=Math.max(i,l):(d=s.width,u=s.height),d!==l||u!==i){const s=new Float32Array(d*u*c);if(Array.isArray(n))for(let e=0;e<s.length;++e)s[e]=n[e%c];else 0!==n&&s.fill(n);const[_,m]=r?[Math.floor((d-l)/2),Math.floor((u-i)/2)]:[0,0];for(let t=0;t<i;++t){const o=(t+m)*d,r=t*l;for(let t=0;t<l;++t){const n=(o+t+_)*c,a=(r+t)*c;for(let t=0;t<c;++t)s[n+t]=e[a+t]}}if("symmetric"===o){if(r)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const t=i-1,o=l-1;for(let r=0;r<u;++r){const n=r*d,u=(0,a.calculateReflectOffset)(r,t)*l;for(let t=0;t<d;++t){if(r<i&&t<l)continue;const d=(n+t)*c,_=(u+(0,a.calculateReflectOffset)(t,o))*c;for(let t=0;t<c;++t)s[d+t]=e[_+t]}}}e=s,t=[u,d,c]}return[e,t]}rescale(e){for(let t=0;t<e.length;++t)e[t]=this.rescale_factor*e[t]}get_resize_output_image_size(e,t){const[s,o]=e.size;let r,n;if(this.do_thumbnail){const{height:e,width:s}=t;r=Math.min(e,s)}else Number.isInteger(t)?(r=t,n=this.config.max_size??r):void 0!==t&&(r=t.shortest_edge,n=t.longest_edge);if(void 0!==r||void 0!==n){const e=void 0===r?1:Math.max(r/s,r/o),t=s*e,a=o*e,i=void 0===n?1:Math.min(n/t,n/a);let l=Math.floor(Number((t*i).toFixed(2))),c=Math.floor(Number((a*i).toFixed(2)));return void 0!==this.size_divisibility&&([l,c]=d([l,c],this.size_divisibility)),[l,c]}if(void 0!==t&&void 0!==t.width&&void 0!==t.height){let e=t.width,r=t.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let t=r/o,n=e/s;Math.abs(1-n)<Math.abs(1-t)?t=n:n=t,r=c(t*o,this.config.ensure_multiple_of),e=c(n*s,this.config.ensure_multiple_of)}return[e,r]}if(void 0!==this.size_divisibility)return d([s,o],this.size_divisibility);if(void 0!==t.min_pixels&&void 0!==t.max_pixels){const{min_pixels:e,max_pixels:r}=t;return function(e,t,s=28,o=3136,r=1003520){if(e<s||t<s)throw new Error(`height:${e} or width:${t} must be larger than factor:${s}`);if(Math.max(e,t)/Math.min(e,t)>200)throw new Error("absolute aspect ratio must be smaller than 200, got "+Math.max(e,t)/Math.min(e,t));let n=Math.round(e/s)*s,a=Math.round(t/s)*s;if(n*a>r){const o=Math.sqrt(e*t/r);n=Math.floor(e/o/s)*s,a=Math.floor(t/o/s)*s}else if(n*a<o){const r=Math.sqrt(o/(e*t));n=Math.ceil(e*r/s)*s,a=Math.ceil(t*r/s)*s}return[n,a]}(o,s,this.config.patch_size*this.config.merge_size,e,r)}throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(t)}`)}async resize(e){const[t,s]=this.get_resize_output_image_size(e,this.size);return await e.resize(t,s,{resample:this.resample})}async preprocess(e,{do_normalize:t=null,do_pad:s=null,do_convert_rgb:o=null,do_convert_grayscale:n=null,do_flip_channel_order:a=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[i,l]=e.size;if(o??this.do_convert_rgb?e=e.rgb():n&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let t,s;Number.isInteger(this.crop_size)?(t=this.crop_size,s=this.crop_size):(t=this.crop_size.width,s=this.crop_size.height),e=await e.center_crop(t,s)}const c=[e.height,e.width];let u=Float32Array.from(e.data),_=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(u),t??this.do_normalize){let t=this.image_mean;Array.isArray(this.image_mean)||(t=new Array(e.channels).fill(t));let s=this.image_std;if(Array.isArray(this.image_std)||(s=new Array(e.channels).fill(t)),t.length!==e.channels||s.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${t.length}) and \`image_std\` (${s.length}) must match the number of channels in the image (${e.channels}).`);for(let o=0;o<u.length;o+=e.channels)for(let r=0;r<e.channels;++r)u[o+r]=(u[o+r]-t[r])/s[r]}if(s??this.do_pad)if(this.pad_size){const t=this.pad_image(u,[e.height,e.width,e.channels],this.pad_size);[u,_]=t}else if(this.size_divisibility){const[e,t]=d([_[1],_[0]],this.size_divisibility);[u,_]=this.pad_image(u,_,{width:e,height:t})}if(a??this.do_flip_channel_order){if(3!==_[2])throw new Error("Flipping channel order is only supported for RGB images.");for(let e=0;e<u.length;e+=3){const t=u[e];u[e]=u[e+2],u[e+2]=t}}return{original_size:[l,i],reshaped_input_size:c,pixel_values:new r.Tensor("float32",u,_).permute(2,0,1)}}async _call(e,...t){Array.isArray(e)||(e=[e]);const s=await Promise.all(e.map((e=>this.preprocess(e))));return{pixel_values:(0,r.stack)(s.map((e=>e.pixel_values)),0),original_sizes:s.map((e=>e.original_size)),reshaped_input_sizes:s.map((e=>e.reshaped_input_size))}}static async from_pretrained(e,t){return new this(await(0,i.getModelJSON)(e,l.IMAGE_PROCESSOR_NAME,!0,t))}}},"./src/base/processing_utils.js":(e,t,s)=>{s.r(t),s.d(t,{Processor:()=>a});var o=s("./src/utils/constants.js"),r=s("./src/utils/generic.js"),n=s("./src/utils/hub.js");class a extends r.Callable{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;constructor(e,t){super(),this.config=e,this.components=t}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(e,t={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(e,{tokenize:!1,...t})}batch_decode(...e){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...e)}decode(...e){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...e)}async _call(e,...t){for(const s of[this.image_processor,this.feature_extractor,this.tokenizer])if(s)return s(e,...t);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(e,t){const[s,r]=await Promise.all([this.uses_processor_config?(0,n.getModelJSON)(e,o.PROCESSOR_NAME,!0,t):{},Promise.all(this.classes.filter((e=>e in this)).map((async s=>{const o=await this[s].from_pretrained(e,t);return[s.replace(/_class$/,""),o]}))).then(Object.fromEntries)]);return new this(s,r)}}},"./src/configs.js":(e,t,s)=>{s.r(t),s.d(t,{AutoConfig:()=>l,PretrainedConfig:()=>i,getKeyValueShapes:()=>a});var o=s("./src/utils/core.js"),r=s("./src/utils/hub.js");function n(e){const t={};let s={};switch(e.model_type){case"llava":case"paligemma":case"gemma3":case"florence2":case"llava_onevision":case"idefics3":case"ultravox":case"smolvlm":s=n(e.text_config);break;case"moondream1":s=n(e.phi_config);break;case"musicgen":s=n(e.decoder);break;case"multi_modality":s=n(e.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":t.num_heads="n_head",t.num_layers="n_layer",t.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":t.num_heads="num_attention_heads",t.num_layers="num_hidden_layers",t.hidden_size="hidden_size";break;case"llama":case"olmo":case"olmo2":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":case"phi":case"phi3":case"phi3_v":t.num_heads="num_key_value_heads",t.num_layers="num_hidden_layers",t.hidden_size="hidden_size",t.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":case"gemma3_text":case"glm":case"helium":t.num_heads="num_key_value_heads",t.num_layers="num_hidden_layers",t.dim_kv="head_dim";break;case"openelm":t.num_heads="num_kv_heads",t.num_layers="num_transformer_layers",t.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":t.num_heads="num_heads",t.num_layers="num_layers",t.hidden_size="hidden_size";break;case"bloom":t.num_heads="n_head",t.num_layers="n_layer",t.hidden_size="hidden_size";break;case"mpt":t.num_heads="n_heads",t.num_layers="n_layers",t.hidden_size="d_model";break;case"exaone":t.num_heads="num_key_value_heads",t.num_layers="num_layers",t.dim_kv="head_dim",t.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":t.num_decoder_layers="num_decoder_layers",t.num_decoder_heads="num_heads",t.decoder_dim_kv="d_kv",t.num_encoder_layers="num_layers",t.num_encoder_heads="num_heads",t.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":t.num_decoder_layers="decoder_layers",t.num_decoder_heads="decoder_attention_heads",t.decoder_hidden_size="d_model",t.num_encoder_layers="encoder_layers",t.num_encoder_heads="encoder_attention_heads",t.encoder_hidden_size="d_model";break;case"speecht5":t.num_decoder_layers="decoder_layers",t.num_decoder_heads="decoder_attention_heads",t.decoder_hidden_size="hidden_size",t.num_encoder_layers="encoder_layers",t.num_encoder_heads="encoder_attention_heads",t.encoder_hidden_size="hidden_size";break;case"trocr":t.num_encoder_layers=t.num_decoder_layers="decoder_layers",t.num_encoder_heads=t.num_decoder_heads="decoder_attention_heads",t.encoder_hidden_size=t.decoder_hidden_size="d_model";break;case"musicgen_decoder":t.num_encoder_layers=t.num_decoder_layers="num_hidden_layers",t.num_encoder_heads=t.num_decoder_heads="num_attention_heads",t.encoder_hidden_size=t.decoder_hidden_size="hidden_size";break;case"moonshine":t.num_decoder_layers="decoder_num_hidden_layers",t.num_decoder_heads="decoder_num_key_value_heads",t.num_encoder_layers="encoder_num_hidden_layers",t.num_encoder_heads="encoder_num_key_value_heads",t.encoder_hidden_size=t.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const r=n(e.decoder),a="num_decoder_layers"in r,i=(0,o.pick)(e,["model_type","is_encoder_decoder"]);return a?(i.num_decoder_layers=r.num_decoder_layers,i.num_decoder_heads=r.num_decoder_heads,i.decoder_hidden_size=r.decoder_hidden_size,i.num_encoder_layers=r.num_encoder_layers,i.num_encoder_heads=r.num_encoder_heads,i.encoder_hidden_size=r.encoder_hidden_size):(i.num_layers=r.num_layers,i.num_heads=r.num_heads,i.hidden_size=r.hidden_size),i}const r={...s,...(0,o.pick)(e,["model_type","multi_query","is_encoder_decoder"])};for(const s in t)r[s]=e[t[s]];return r}function a(e,{prefix:t="past_key_values",batch_size:s=1}={}){const o={},r=e.normalized_config;if(r.is_encoder_decoder&&"num_encoder_heads"in r&&"num_decoder_heads"in r){const e=r.encoder_dim_kv??r.encoder_hidden_size/r.num_encoder_heads,n=r.decoder_dim_kv??r.decoder_hidden_size/r.num_decoder_heads,a=[s,r.num_encoder_heads,0,e],i=[s,r.num_decoder_heads,0,n];for(let e=0;e<r.num_decoder_layers;++e)o[`${t}.${e}.encoder.key`]=a,o[`${t}.${e}.encoder.value`]=a,o[`${t}.${e}.decoder.key`]=i,o[`${t}.${e}.decoder.value`]=i}else{const e=r.num_heads,n=r.num_layers,a=r.dim_kv??r.hidden_size/(r.num_attention_heads??e);if("falcon"===r.model_type){const r=[s*e,0,a];for(let e=0;e<n;++e)o[`${t}.${e}.key`]=r,o[`${t}.${e}.value`]=r}else if(r.multi_query){const r=[s*e,0,2*a];for(let e=0;e<n;++e)o[`${t}.${e}.key_value`]=r}else if("bloom"===r.model_type){const r=[s*e,a,0],i=[s*e,0,a];for(let e=0;e<n;++e)o[`${t}.${e}.key`]=r,o[`