import{S as il,i as cl,s as dl,e as o,k as d,w as v,t as n,M as pl,c as s,d as r,m as p,a as l,x as b,h as a,b as i,N as ml,G as e,g as m,y as M,q as T,o as w,B as y,v as hl,L as ll}from"../../chunks/vendor-hf-doc-builder.js";import{T as nl}from"../../chunks/Tip-hf-doc-builder.js";import{D as L}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Wn}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as $e}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as al}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function fl(Z){let f,k,u,g,$;return g=new Wn({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MgpstrConfig, MgpstrForSceneTextRecognition

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Mgpstr mgp-str-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MgpstrConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the mgp-str-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MgpstrForSceneTextRecognition(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){f=o("p"),k=n("Example:"),u=d(),v(g.$$.fragment)},l(h){f=s(h,"P",{});var _=l(f);k=a(_,"Example:"),_.forEach(r),u=p(h),b(g.$$.fragment,h)},m(h,_){m(h,f,_),e(f,k),m(h,u,_),M(g,h,_),$=!0},p:ll,i(h){$||(T(g.$$.fragment,h),$=!0)},o(h){w(g.$$.fragment,h),$=!1},d(h){h&&r(f),h&&r(u),y(g,h)}}}function gl(Z){let f,k,u,g,$;return{c(){f=o("p"),k=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),g=n("Module"),$=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(h){f=s(h,"P",{});var _=l(f);k=a(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(_,"CODE",{});var J=l(u);g=a(J,"Module"),J.forEach(r),$=a(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(r)},m(h,_){m(h,f,_),e(f,k),e(f,u),e(u,g),e(f,$)},d(h){h&&r(f)}}}function ul(Z){let f,k,u,g,$;return{c(){f=o("p"),k=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),g=n("Module"),$=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(h){f=s(h,"P",{});var _=l(f);k=a(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(_,"CODE",{});var J=l(u);g=a(J,"Module"),J.forEach(r),$=a(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(r)},m(h,_){m(h,f,_),e(f,k),e(f,u),e(u,g),e(f,$)},d(h){h&&r(f)}}}function _l(Z){let f,k,u,g,$;return g=new Wn({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMCglMEElMjAlMjAlMjAlMjBNZ3BzdHJQcm9jZXNzb3IlMkMlMEElMjAlMjAlMjAlMjBNZ3BzdHJGb3JTY2VuZVRleHRSZWNvZ25pdGlvbiUyQyUwQSklMEFpbXBvcnQlMjByZXF1ZXN0cyUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQSUwQSUyMyUyMGxvYWQlMjBpbWFnZSUyMGZyb20lMjB0aGUlMjBJSUlULTVrJTIwZGF0YXNldCUwQXVybCUyMCUzRCUyMCUyMmh0dHBzJTNBJTJGJTJGaS5wb3N0aW1nLmNjJTJGWkt3TGcyR3clMkYzNjctMTQucG5nJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpLmNvbnZlcnQoJTIyUkdCJTIyKSUwQSUwQXByb2Nlc3NvciUyMCUzRCUyME1ncHN0clByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyYWxpYmFiYS1kYW1vJTJGbWdwLXN0ci1iYXNlJTIyKSUwQXBpeGVsX3ZhbHVlcyUyMCUzRCUyMHByb2Nlc3NvcihpbWFnZXMlM0RpbWFnZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpLnBpeGVsX3ZhbHVlcyUwQSUwQW1vZGVsJTIwJTNEJTIwTWdwc3RyRm9yU2NlbmVUZXh0UmVjb2duaXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmFsaWJhYmEtZGFtbyUyRm1ncC1zdHItYmFzZSUyMiklMEElMEElMjMlMjBpbmZlcmVuY2UlMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwocGl4ZWxfdmFsdWVzKSUwQW91dF9zdHJzJTIwJTNEJTIwcHJvY2Vzc29yLmJhdGNoX2RlY29kZShvdXRwdXRzLmxvZ2l0cyklMEFvdXRfc3RycyU1QiUyMmdlbmVyYXRlZF90ZXh0JTIyJTVE",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span>    MgpstrProcessor,
<span class="hljs-meta">... </span>    MgpstrForSceneTextRecognition,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># load image from the IIIT-5k dataset</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://i.postimg.cc/ZKwLg2Gw/367-14.png&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = MgpstrProcessor.from_pretrained(<span class="hljs-string">&quot;alibaba-damo/mgp-str-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MgpstrForSceneTextRecognition.from_pretrained(<span class="hljs-string">&quot;alibaba-damo/mgp-str-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># inference</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(pixel_values)
<span class="hljs-meta">&gt;&gt;&gt; </span>out_strs = processor.batch_decode(outputs.logits)
<span class="hljs-meta">&gt;&gt;&gt; </span>out_strs[<span class="hljs-string">&quot;generated_text&quot;</span>]
<span class="hljs-string">&#x27;[&quot;ticket&quot;]&#x27;</span>`}}),{c(){f=o("p"),k=n("Example:"),u=d(),v(g.$$.fragment)},l(h){f=s(h,"P",{});var _=l(f);k=a(_,"Example:"),_.forEach(r),u=p(h),b(g.$$.fragment,h)},m(h,_){m(h,f,_),e(f,k),m(h,u,_),M(g,h,_),$=!0},p:ll,i(h){$||(T(g.$$.fragment,h),$=!0)},o(h){w(g.$$.fragment,h),$=!1},d(h){h&&r(f),h&&r(u),y(g,h)}}}function vl(Z){let f,k,u,g,$,h,_,J,oo,br,H,ne,Nt,ke,so,Vt,no,Mr,S,ao,xe,lo,io,Zt,co,po,Wt,mo,ho,nt,fo,go,Tr,at,uo,wr,lt,At,_o,yr,ae,An,$r,le,vo,it,bo,Mo,kr,ct,To,xr,ie,Q,wo,dt,yo,$o,Ee,ko,xo,Eo,X,Po,Pe,So,Ro,Se,jo,zo,Er,q,ce,Ft,Re,Co,Bt,Uo,Pr,je,pt,Io,Go,Sr,E,Jo,mt,No,Vo,ht,Zo,Wo,ft,Ao,Fo,gt,Bo,Lo,ut,Ho,Qo,Rr,_t,Lt,Xo,jr,ze,zr,Y,de,Ht,Ce,qo,Qt,Yo,Cr,z,Ue,Do,D,Oo,vt,Ko,es,Ie,ts,rs,os,O,ss,bt,ns,as,Mt,ls,is,cs,pe,Ur,K,me,Xt,Ge,ds,qt,ps,Ir,C,Je,ms,Yt,hs,fs,Ne,gs,Tt,us,_s,vs,wt,Ve,Gr,ee,he,Dt,Ze,bs,Ot,Ms,Jr,P,We,Ts,Kt,ws,ys,R,yt,$s,ks,er,xs,Es,$t,Ps,Ss,Ae,tr,Rs,js,zs,kt,Cs,Us,Is,fe,Fe,Gs,x,Js,Be,rr,Ns,Vs,Zs,or,Ws,As,sr,Fs,Bs,Le,nr,Ls,Hs,Qs,ar,Xs,qs,lr,Ys,Ds,Os,W,He,Ks,ir,en,tn,Qe,rn,xt,on,sn,Nr,te,ge,cr,Xe,nn,dr,an,Vr,N,qe,ln,Ye,cn,De,dn,pn,mn,A,Oe,hn,re,fn,Et,gn,un,pr,_n,vn,bn,ue,Zr,oe,_e,mr,Ke,Mn,hr,Tn,Wr,U,et,wn,fr,yn,$n,tt,kn,rt,xn,En,Pn,I,ot,Sn,se,Rn,Pt,jn,zn,gr,Cn,Un,In,ve,Gn,be,Ar;return h=new $e({}),ke=new $e({}),Re=new $e({}),ze=new Wn({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MgpstrProcessor, MgpstrForSceneTextRecognition
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = MgpstrProcessor.from_pretrained(<span class="hljs-string">&#x27;alibaba-damo/mgp-str-base&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MgpstrForSceneTextRecognition.from_pretrained(<span class="hljs-string">&#x27;alibaba-damo/mgp-str-base&#x27;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># load image from the IIIT-5k dataset</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://i.postimg.cc/ZKwLg2Gw/367-14.png&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(pixel_values)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_text = processor.batch_decode(outputs.logits)[<span class="hljs-string">&#x27;generated_text&#x27;</span>]`}}),Ce=new $e({}),Ue=new L({props:{name:"class transformers.MgpstrConfig",anchor:"transformers.MgpstrConfig",parameters:[{name:"image_size",val:" = [32, 128]"},{name:"patch_size",val:" = 4"},{name:"num_channels",val:" = 3"},{name:"max_token_length",val:" = 27"},{name:"num_character_labels",val:" = 38"},{name:"num_bpe_labels",val:" = 50257"},{name:"num_wordpiece_labels",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"mlp_ratio",val:" = 4.0"},{name:"qkv_bias",val:" = True"},{name:"distilled",val:" = False"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"drop_rate",val:" = 0.0"},{name:"attn_drop_rate",val:" = 0.0"},{name:"drop_path_rate",val:" = 0.0"},{name:"output_a3_attentions",val:" = False"},{name:"initializer_range",val:" = 0.02"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MgpstrConfig.image_size",description:`<strong>image_size</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[32, 128]</code>) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.MgpstrConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.MgpstrConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.MgpstrConfig.max_token_length",description:`<strong>max_token_length</strong> (<code>int</code>, <em>optional</em>, defaults to 27) &#x2014;
The max number of output tokens.`,name:"max_token_length"},{anchor:"transformers.MgpstrConfig.num_character_labels",description:`<strong>num_character_labels</strong> (<code>int</code>, <em>optional</em>, defaults to 38) &#x2014;
The number of classes for character head .`,name:"num_character_labels"},{anchor:"transformers.MgpstrConfig.num_bpe_labels",description:`<strong>num_bpe_labels</strong> (<code>int</code>, <em>optional</em>, defaults to 50257) &#x2014;
The number of classes for bpe head .`,name:"num_bpe_labels"},{anchor:"transformers.MgpstrConfig.num_wordpiece_labels",description:`<strong>num_wordpiece_labels</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
The number of classes for wordpiece head .`,name:"num_wordpiece_labels"},{anchor:"transformers.MgpstrConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
The embedding dimension.`,name:"hidden_size"},{anchor:"transformers.MgpstrConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.MgpstrConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.MgpstrConfig.mlp_ratio",description:`<strong>mlp_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
The ratio of mlp hidden dim to embedding dim.`,name:"mlp_ratio"},{anchor:"transformers.MgpstrConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.MgpstrConfig.distilled",description:`<strong>distilled</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Model includes a distillation token and head as in DeiT models.`,name:"distilled"},{anchor:"transformers.MgpstrConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.MgpstrConfig.drop_rate",description:`<strong>drop_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder.`,name:"drop_rate"},{anchor:"transformers.MgpstrConfig.attn_drop_rate",description:`<strong>attn_drop_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attn_drop_rate"},{anchor:"transformers.MgpstrConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The stochastic depth rate.`,name:"drop_path_rate"},{anchor:"transformers.MgpstrConfig.output_a3_attentions",description:`<strong>output_a3_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should returns A^3 module attentions.`,name:"output_a3_attentions"},{anchor:"transformers.MgpstrConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mgp_str/configuration_mgp_str.py#L28"}}),pe=new al({props:{anchor:"transformers.MgpstrConfig.example",$$slots:{default:[fl]},$$scope:{ctx:Z}}}),Ge=new $e({}),Je=new L({props:{name:"class transformers.MgpstrTokenizer",anchor:"transformers.MgpstrTokenizer",parameters:[{name:"vocab_file",val:""},{name:"unk_token",val:" = '[GO]'"},{name:"bos_token",val:" = '[GO]'"},{name:"eos_token",val:" = '[s]'"},{name:"pad_token",val:" = '[GO]'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MgpstrTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.MgpstrTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[GO]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.MgpstrTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[GO]&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.MgpstrTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[s]&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.MgpstrTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, , defaults to <code>&quot;[GO]&quot;</code>) &#x2014;
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.`,name:"pad_token"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mgp_str/tokenization_mgp_str.py#L38"}}),Ve=new L({props:{name:"save_vocabulary",anchor:"transformers.MgpstrTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mgp_str/tokenization_mgp_str.py#L99"}}),Ze=new $e({}),We=new L({props:{name:"class transformers.MgpstrProcessor",anchor:"transformers.MgpstrProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MgpstrProcessor.image_processor",description:`<strong>image_processor</strong> (<code>ViTImageProcessor</code>) &#x2014;
An instance of <code>ViTImageProcessor</code>. The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.MgpstrProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mgp-str#transformers.MgpstrTokenizer">MgpstrTokenizer</a>) &#x2014;
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<p>Dictionary of all the outputs of the decoded results.
generated_text (<code>List[str]</code>): The final results after fusion of char, bpe, and wp. scores
(<code>List[float]</code>): The final scores after fusion of char, bpe, and wp. char_preds (<code>List[str]</code>): The list
of character decoded sentences. bpe_preds (<code>List[str]</code>): The list of bpe decoded sentences. wp_preds
(<code>List[str]</code>): The list of wp decoded sentences.</p>
`,returnType:`
<p><code>Dict[str, any]</code></p>
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Initializing with a config file does not load the weights associated with the model, only the
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<p>A <code>transformers.models.mgp_str.modeling_mgp_str.MgpstrModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>&lt;class 'transformers.models.mgp_str.configuration_mgp_str.MgpstrConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>tuple(torch.FloatTensor)</code> of shape <code>(batch_size, config.num_character_labels)</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of character of shape <code>(batch_size, config.max_token_length, config.num_character_labels)</code>, + one for the output of bpe of shape <code>(batch_size, config.max_token_length, config.num_bpe_labels)</code>, + one for the output of wordpiece of shape <code>(batch_size, config.max_token_length, config.num_wordpiece_labels)</code>) .</p>
<p>Classification scores (before SoftMax) of character, bpe and wordpiece.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, config.max_token_length, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>a3_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_a3_attentions=True</code> is passed or when <code>config.output_a3_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the attention of character, + one for the attention of bpe<code>, + one for the attention of wordpiece) of shape </code>(batch_size, config.max_token_length, sequence_length)\`.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.mgp_str.modeling_mgp_str.MgpstrModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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The three types of predictions are fused to give the final prediction result.`),Sr=d(),E=o("p"),Jo=n("The "),mt=o("a"),No=n("ViTImageProcessor"),Vo=n(` class is responsible for preprocessing the input image and
`),ht=o("a"),Zo=n("MgpstrTokenizer"),Wo=n(` decodes the generated character tokens to the target string. The
`),ft=o("a"),Ao=n("MgpstrProcessor"),Fo=n(" wraps "),gt=o("a"),Bo=n("ViTImageProcessor"),Lo=n(" and "),ut=o("a"),Ho=n("MgpstrTokenizer"),Qo=n(`
into a single instance to both extract the input features and decode the predicted token ids.`),Rr=d(),_t=o("ul"),Lt=o("li"),Xo=n("Step-by-step Optical Character Recognition (OCR)"),jr=d(),v(ze.$$.fragment),zr=d(),Y=o("h2"),de=o("a"),Ht=o("span"),v(Ce.$$.fragment),qo=d(),Qt=o("span"),Yo=n("MgpstrConfig"),Cr=d(),z=o("div"),v(Ue.$$.fragment),Do=d(),D=o("p"),Oo=n("This is the configuration class to store the configuration of an "),vt=o("a"),Ko=n("MgpstrModel"),es=n(`. It is used to instantiate an
MGP-STR model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MGP-STR
`),Ie=o("a"),ts=n("alibaba-damo/mgp-str-base"),rs=n(" architecture."),os=d(),O=o("p"),ss=n("Configuration objects inherit from "),bt=o("a"),ns=n("PretrainedConfig"),as=n(` and can be used to control the model outputs. Read the
documentation from `),Mt=o("a"),ls=n("PretrainedConfig"),is=n(" for more information."),cs=d(),v(pe.$$.fragment),Ur=d(),K=o("h2"),me=o("a"),Xt=o("span"),v(Ge.$$.fragment),ds=d(),qt=o("span"),ps=n("MgpstrTokenizer"),Ir=d(),C=o("div"),v(Je.$$.fragment),ms=d(),Yt=o("p"),hs=n("Construct a MGP-STR char tokenizer."),fs=d(),Ne=o("p"),gs=n("This tokenizer inherits from "),Tt=o("a"),us=n("PreTrainedTokenizer"),_s=n(` which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`),vs=d(),wt=o("div"),v(Ve.$$.fragment),Gr=d(),ee=o("h2"),he=o("a"),Dt=o("span"),v(Ze.$$.fragment),bs=d(),Ot=o("span"),Ms=n("MgpstrProcessor"),Jr=d(),P=o("div"),v(We.$$.fragment),Ts=d(),Kt=o("p"),ws=n("Constructs a MGP-STR processor which wraps an image processor and MGP-STR tokenizers into a single"),ys=d(),R=o("p"),yt=o("a"),$s=n("MgpstrProcessor"),ks=n(" offers all the functionalities of "),er=o("code"),xs=n("ViTImageProcessor"),Es=n("] and "),$t=o("a"),Ps=n("MgpstrTokenizer"),Ss=n(`. See the
`),Ae=o("a"),tr=o("strong"),Rs=n("call"),js=n("()"),zs=n(" and "),kt=o("a"),Cs=n("batch_decode()"),Us=n(" for more information."),Is=d(),fe=o("div"),v(Fe.$$.fragment),Gs=d(),x=o("p"),Js=n(`When used in normal mode, this method forwards all its arguments to ViTImageProcessor\u2019s
`),Be=o("a"),rr=o("strong"),Ns=n("call"),Vs=n("()"),Zs=n(" and returns its output. This method also forwards the "),or=o("code"),Ws=n("text"),As=n(" and "),sr=o("code"),Fs=n("kwargs"),Bs=n(`
arguments to MgpstrTokenizer\u2019s `),Le=o("a"),nr=o("strong"),Ls=n("call"),Hs=n("()"),Qs=n(" if "),ar=o("code"),Xs=n("text"),qs=n(" is not "),lr=o("code"),Ys=n("None"),Ds=n(` to encode the text. Please
refer to the doctsring of the above methods for more information.`),Os=d(),W=o("div"),v(He.$$.fragment),Ks=d(),ir=o("p"),en=n("Convert a list of lists of token ids into a list of strings by calling decode."),tn=d(),Qe=o("p"),rn=n("This method forwards all its arguments to PreTrainedTokenizer\u2019s "),xt=o("a"),on=n("batch_decode()"),sn=n(`. Please
refer to the docstring of this method for more information.`),Nr=d(),te=o("h2"),ge=o("a"),cr=o("span"),v(Xe.$$.fragment),nn=d(),dr=o("span"),an=n("MgpstrModel"),Vr=d(),N=o("div"),v(qe.$$.fragment),ln=d(),Ye=o("p"),cn=n(`The bare MGP-STR Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch `),De=o("a"),dn=n("torch.nn.Module"),pn=n(` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),mn=d(),A=o("div"),v(Oe.$$.fragment),hn=d(),re=o("p"),fn=n("The "),Et=o("a"),gn=n("MgpstrModel"),un=n(" forward method, overrides the "),pr=o("code"),_n=n("__call__"),vn=n(" special method."),bn=d(),v(ue.$$.fragment),Zr=d(),oe=o("h2"),_e=o("a"),mr=o("span"),v(Ke.$$.fragment),Mn=d(),hr=o("span"),Tn=n("MgpstrForSceneTextRecognition"),Wr=d(),U=o("div"),v(et.$$.fragment),wn=d(),fr=o("p"),yn=n(`MGP-STR Model transformer with three classification heads on top (three A^3 modules and three linear layer on top
of the transformer encoder output) for scene text recognition (STR) .`),$n=d(),tt=o("p"),kn=n("This model is a PyTorch "),rt=o("a"),xn=n("torch.nn.Module"),En=n(` subclass. Use it
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`),ht=s(G,"A",{href:!0});var da=l(ht);Zo=a(da,"MgpstrTokenizer"),da.forEach(r),Wo=a(G,` decodes the generated character tokens to the target string. The
`),ft=s(G,"A",{href:!0});var pa=l(ft);Ao=a(pa,"MgpstrProcessor"),pa.forEach(r),Fo=a(G," wraps "),gt=s(G,"A",{href:!0});var ma=l(gt);Bo=a(ma,"ViTImageProcessor"),ma.forEach(r),Lo=a(G," and "),ut=s(G,"A",{href:!0});var ha=l(ut);Ho=a(ha,"MgpstrTokenizer"),ha.forEach(r),Qo=a(G,`
into a single instance to both extract the input features and decode the predicted token ids.`),G.forEach(r),Rr=p(t),_t=s(t,"UL",{});var fa=l(_t);Lt=s(fa,"LI",{});var ga=l(Lt);Xo=a(ga,"Step-by-step Optical Character Recognition (OCR)"),ga.forEach(r),fa.forEach(r),jr=p(t),b(ze.$$.fragment,t),zr=p(t),Y=s(t,"H2",{class:!0});var Qr=l(Y);de=s(Qr,"A",{id:!0,class:!0,href:!0});var ua=l(de);Ht=s(ua,"SPAN",{});var _a=l(Ht);b(Ce.$$.fragment,_a),_a.forEach(r),ua.forEach(r),qo=p(Qr),Qt=s(Qr,"SPAN",{});var va=l(Qt);Yo=a(va,"MgpstrConfig"),va.forEach(r),Qr.forEach(r),Cr=p(t),z=s(t,"DIV",{class:!0});var Me=l(z);b(Ue.$$.fragment,Me),Do=p(Me),D=s(Me,"P",{});var jt=l(D);Oo=a(jt,"This is the configuration class to store the configuration of an "),vt=s(jt,"A",{href:!0});var ba=l(vt);Ko=a(ba,"MgpstrModel"),ba.forEach(r),es=a(jt,`. It is used to instantiate an
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`),Ie=s(jt,"A",{href:!0,rel:!0});var Ma=l(Ie);ts=a(Ma,"alibaba-damo/mgp-str-base"),Ma.forEach(r),rs=a(jt," architecture."),jt.forEach(r),os=p(Me),O=s(Me,"P",{});var zt=l(O);ss=a(zt,"Configuration objects inherit from "),bt=s(zt,"A",{href:!0});var Ta=l(bt);ns=a(Ta,"PretrainedConfig"),Ta.forEach(r),as=a(zt,` and can be used to control the model outputs. Read the
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`),Ae=s(V,"A",{href:!0});var Nn=l(Ae);tr=s(Nn,"STRONG",{});var Ga=l(tr);Rs=a(Ga,"call"),Ga.forEach(r),js=a(Nn,"()"),Nn.forEach(r),zs=a(V," and "),kt=s(V,"A",{href:!0});var Ja=l(kt);Cs=a(Ja,"batch_decode()"),Ja.forEach(r),Us=a(V," for more information."),V.forEach(r),Is=p(B),fe=s(B,"DIV",{class:!0});var Dr=l(fe);b(Fe.$$.fragment,Dr),Gs=p(Dr),x=s(Dr,"P",{});var j=l(x);Js=a(j,`When used in normal mode, this method forwards all its arguments to ViTImageProcessor\u2019s
`),Be=s(j,"A",{href:!0});var Vn=l(Be);rr=s(Vn,"STRONG",{});var Na=l(rr);Ns=a(Na,"call"),Na.forEach(r),Vs=a(Vn,"()"),Vn.forEach(r),Zs=a(j," and returns its output. This method also forwards the "),or=s(j,"CODE",{});var Va=l(or);Ws=a(Va,"text"),Va.forEach(r),As=a(j," and "),sr=s(j,"CODE",{});var Za=l(sr);Fs=a(Za,"kwargs"),Za.forEach(r),Bs=a(j,`
arguments to MgpstrTokenizer\u2019s `),Le=s(j,"A",{href:!0});var Zn=l(Le);nr=s(Zn,"STRONG",{});var Wa=l(nr);Ls=a(Wa,"call"),Wa.forEach(r),Hs=a(Zn,"()"),Zn.forEach(r),Qs=a(j," if "),ar=s(j,"CODE",{});var Aa=l(ar);Xs=a(Aa,"text"),Aa.forEach(r),qs=a(j," is not "),lr=s(j,"CODE",{});var Fa=l(lr);Ys=a(Fa,"None"),Fa.forEach(r),Ds=a(j,` to encode the text. Please
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