import{S as dl,i as cl,s as hl,e as a,k as c,w as y,t as r,M as ml,c as s,d as n,m as h,a as o,x as b,h as l,b as u,G as t,g as f,y as J,q as M,o as U,B as k,v as ul,L as at}from"../../chunks/vendor-hf-doc-builder.js";import{T as pl}from"../../chunks/Tip-hf-doc-builder.js";import{D as Ue}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ke}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Q}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as nt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function fl(G){let p,T,m,g,j;return g=new ke({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> GPTSanJapaneseTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTSanJapaneseTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># You can confirm both \u6176\u5FDC and \u6176\u61C9 are encoded to 17750</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer(<span class="hljs-string">&quot;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002\u5B9F\u306F\u6176\u5FDC(\u6176\u61C9)\u5927\u5B66\u51FA\u8EAB&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
[<span class="hljs-number">35993</span>, <span class="hljs-number">35998</span>, <span class="hljs-number">34347</span>, <span class="hljs-number">31459</span>, <span class="hljs-number">30647</span>, <span class="hljs-number">31448</span>, <span class="hljs-number">25</span>, <span class="hljs-number">30659</span>, <span class="hljs-number">35729</span>, <span class="hljs-number">35676</span>, <span class="hljs-number">32417</span>, <span class="hljs-number">30647</span>, <span class="hljs-number">17750</span>, <span class="hljs-number">35589</span>, <span class="hljs-number">17750</span>, <span class="hljs-number">35590</span>, <span class="hljs-number">321</span>, <span class="hljs-number">1281</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Both \u6176\u5FDC and \u6176\u61C9 are decoded to \u6176\u5FDC</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(tokenizer(<span class="hljs-string">&quot;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002\u5B9F\u306F\u6176\u5FDC(\u6176\u61C9)\u5927\u5B66\u51FA\u8EAB&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-string">&#x27;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002\u5B9F\u306F\u6176\u5FDC(\u6176\u5FDC)\u5927\u5B66\u51FA\u8EAB&#x27;</span>`}}),{c(){p=a("p"),T=r("Example:"),m=c(),y(g.$$.fragment)},l(i){p=s(i,"P",{});var _=o(p);T=l(_,"Example:"),_.forEach(n),m=h(i),b(g.$$.fragment,i)},m(i,_){f(i,p,_),t(p,T),f(i,m,_),J(g,i,_),j=!0},p:at,i(i){j||(M(g.$$.fragment,i),j=!0)},o(i){U(g.$$.fragment,i),j=!1},d(i){i&&n(p),i&&n(m),k(g,i)}}}function gl(G){let p,T,m,g,j;return g=new ke({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> GPTSanJapaneseTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTSanJapaneseTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer(<span class="hljs-string">&quot;\u5B9F\u306F\u6176\u5FDC(\u6176\u61C9)\u5927\u5B66\u51FA\u8EAB&quot;</span>, prefix_text=<span class="hljs-string">&quot;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
[<span class="hljs-number">35993</span>, <span class="hljs-number">34347</span>, <span class="hljs-number">31459</span>, <span class="hljs-number">30647</span>, <span class="hljs-number">31448</span>, <span class="hljs-number">25</span>, <span class="hljs-number">30659</span>, <span class="hljs-number">35729</span>, <span class="hljs-number">35676</span>, <span class="hljs-number">35998</span>, <span class="hljs-number">32417</span>, <span class="hljs-number">30647</span>, <span class="hljs-number">17750</span>, <span class="hljs-number">35589</span>, <span class="hljs-number">17750</span>, <span class="hljs-number">35590</span>, <span class="hljs-number">321</span>, <span class="hljs-number">1281</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Mask for Prefix-LM inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer(<span class="hljs-string">&quot;\u5B9F\u306F\u6176\u5FDC(\u6176\u61C9)\u5927\u5B66\u51FA\u8EAB&quot;</span>, prefix_text=<span class="hljs-string">&quot;\u543E\u8F29\u306F\u732B\u3067\u3042\u308B\u{1F42F}\u3002&quot;</span>)[<span class="hljs-string">&quot;token_type_ids&quot;</span>]
[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]`}}),{c(){p=a("p"),T=r("Example for Prefix-LM:"),m=c(),y(g.$$.fragment)},l(i){p=s(i,"P",{});var _=o(p);T=l(_,"Example for Prefix-LM:"),_.forEach(n),m=h(i),b(g.$$.fragment,i)},m(i,_){f(i,p,_),t(p,T),f(i,m,_),J(g,i,_),j=!0},p:at,i(i){j||(M(g.$$.fragment,i),j=!0)},o(i){U(g.$$.fragment,i),j=!1},d(i){i&&n(p),i&&n(m),k(g,i)}}}function _l(G){let p,T,m,g,j;return g=new ke({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> GPTSanJapaneseTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTSanJapaneseTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer([[<span class="hljs-string">&quot;\u6B66\u7530\u4FE1\u7384&quot;</span>, <span class="hljs-string">&quot;\u306F\u3001&quot;</span>], [<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577&quot;</span>, <span class="hljs-string">&quot;\u306E\u914D\u4E0B\u306E\u3001&quot;</span>]], padding=<span class="hljs-literal">True</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
[[<span class="hljs-number">35993</span>, <span class="hljs-number">8640</span>, <span class="hljs-number">25948</span>, <span class="hljs-number">35998</span>, <span class="hljs-number">30647</span>, <span class="hljs-number">35675</span>, <span class="hljs-number">35999</span>, <span class="hljs-number">35999</span>], [<span class="hljs-number">35993</span>, <span class="hljs-number">10382</span>, <span class="hljs-number">9868</span>, <span class="hljs-number">35998</span>, <span class="hljs-number">30646</span>, <span class="hljs-number">9459</span>, <span class="hljs-number">30646</span>, <span class="hljs-number">35675</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Mask for Prefix-LM inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer([[<span class="hljs-string">&quot;\u6B66\u7530\u4FE1\u7384&quot;</span>, <span class="hljs-string">&quot;\u306F\u3001&quot;</span>], [<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577&quot;</span>, <span class="hljs-string">&quot;\u306E\u914D\u4E0B\u306E\u3001&quot;</span>]], padding=<span class="hljs-literal">True</span>)[<span class="hljs-string">&quot;token_type_ids&quot;</span>]
[[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>], [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Mask for padding</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer([[<span class="hljs-string">&quot;\u6B66\u7530\u4FE1\u7384&quot;</span>, <span class="hljs-string">&quot;\u306F\u3001&quot;</span>], [<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577&quot;</span>, <span class="hljs-string">&quot;\u306E\u914D\u4E0B\u306E\u3001&quot;</span>]], padding=<span class="hljs-literal">True</span>)[<span class="hljs-string">&quot;attention_mask&quot;</span>]
[[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>], [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]]`}}),{c(){p=a("p"),T=r("Example for batch encode:"),m=c(),y(g.$$.fragment)},l(i){p=s(i,"P",{});var _=o(p);T=l(_,"Example for batch encode:"),_.forEach(n),m=h(i),b(g.$$.fragment,i)},m(i,_){f(i,p,_),t(p,T),f(i,m,_),J(g,i,_),j=!0},p:at,i(i){j||(M(g.$$.fragment,i),j=!0)},o(i){U(g.$$.fragment,i),j=!1},d(i){i&&n(p),i&&n(m),k(g,i)}}}function Tl(G){let p,T,m,g,j;return g=new ke({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEdQVFNhbkphcGFuZXNlVG9rZW5pemVyJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwR1BUU2FuSmFwYW5lc2VUb2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMlRhbnJlaSUyRkdQVFNBTi1qYXBhbmVzZSUyMiklMEF4X3Rva2VuJTIwJTNEJTIwdG9rZW5pemVyKCUyMiVFRiVCRCVCMSVFRiVCRCVCMiVFRiVCRCVCMyVFRiVCRCVCNCUyMiklMEElMjMlMjBpbnB1dF9pZHMlM0ElMjAlMjAlMjAlMjAlMjAlMjAlN0MlMjBTT1QlMjAlN0MlMjBTRUclMjAlN0MlMjAlRUYlQkQlQjElMjAlN0MlMjAlRUYlQkQlQjIlMjAlN0MlMjAlRUYlQkQlQjMlMjAlN0MlMjAlRUYlQkQlQjQlMjAlN0MlMEElMjMlMjB0b2tlbl90eXBlX2lkcyUzQSUyMCU3QyUyMDElMjAlMjAlMjAlN0MlMjAwJTIwJTIwJTIwJTdDJTIwMCUyMCU3QyUyMDAlMjAlN0MlMjAwJTIwJTdDJTIwMCUyMCU3QyUwQSUwQXhfdG9rZW4lMjAlM0QlMjB0b2tlbml6ZXIoJTIyJTIyJTJDJTIwcHJlZml4X3RleHQlM0QlMjIlRUYlQkQlQjElRUYlQkQlQjIlRUYlQkQlQjMlRUYlQkQlQjQlMjIpJTBBJTIzJTIwaW5wdXRfaWRzJTNBJTIwJTIwJTIwJTIwJTIwJTIwJTdDJTIwU09UJTIwJTdDJTIwJUVGJUJEJUIxJTIwJTdDJTIwJUVGJUJEJUIyJTIwJTdDJTIwJUVGJUJEJUIzJTIwJTdDJTIwJUVGJUJEJUI0JTIwJTdDJTIwU0VHJTIwJTdDJTBBJTIzJTIwdG9rZW5fdHlwZV9pZHMlM0ElMjAlN0MlMjAxJTIwJTIwJTIwJTdDJTIwMSUyMCU3QyUyMDElMjAlN0MlMjAxJTIwJTdDJTIwMSUyMCU3QyUyMDAlMjAlMjAlN0MlMEElMEF4X3Rva2VuJTIwJTNEJTIwdG9rZW5pemVyKCUyMiVFRiVCRCVCMyVFRiVCRCVCNCUyMiUyQyUyMHByZWZpeF90ZXh0JTNEJTIyJUVGJUJEJUIxJUVGJUJEJUIyJTIyKSUwQSUyMyUyMGlucHV0X2lkcyUzQSUyMCUyMCUyMCUyMCUyMCUyMCU3QyUyMFNPVCUyMCU3QyUyMCVFRiVCRCVCMSUyMCU3QyUyMCVFRiVCRCVCMiUyMCU3QyUyMFNFRyUyMCU3QyUyMCVFRiVCRCVCMyUyMCU3QyUyMCVFRiVCRCVCNCUyMCU3QyUwQSUyMyUyMHRva2VuX3R5cGVfaWRzJTNBJTIwJTdDJTIwMSUyMCUyMCUyMCU3QyUyMDElMjAlN0MlMjAxJTIwJTdDJTIwMCUyMCUyMCUyMCU3QyUyMDAlMjAlN0MlMjAwJTIwJTdD",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPTSanJapaneseTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = GPTSanJapaneseTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>x_token = tokenizer(<span class="hljs-string">&quot;\uFF71\uFF72\uFF73\uFF74&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># input_ids:      | SOT | SEG | \uFF71 | \uFF72 | \uFF73 | \uFF74 |</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># token_type_ids: | 1   | 0   | 0 | 0 | 0 | 0 |</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>x_token = tokenizer(<span class="hljs-string">&quot;&quot;</span>, prefix_text=<span class="hljs-string">&quot;\uFF71\uFF72\uFF73\uFF74&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># input_ids:      | SOT | \uFF71 | \uFF72 | \uFF73 | \uFF74 | SEG |</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># token_type_ids: | 1   | 1 | 1 | 1 | 1 | 0  |</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>x_token = tokenizer(<span class="hljs-string">&quot;\uFF73\uFF74&quot;</span>, prefix_text=<span class="hljs-string">&quot;\uFF71\uFF72&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># input_ids:      | SOT | \uFF71 | \uFF72 | SEG | \uFF73 | \uFF74 |</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># token_type_ids: | 1   | 1 | 1 | 0   | 0 | 0 |</span>`}}),{c(){p=a("p"),T=r("Example:"),m=c(),y(g.$$.fragment)},l(i){p=s(i,"P",{});var _=o(p);T=l(_,"Example:"),_.forEach(n),m=h(i),b(g.$$.fragment,i)},m(i,_){f(i,p,_),t(p,T),f(i,m,_),J(g,i,_),j=!0},p:at,i(i){j||(M(g.$$.fragment,i),j=!0)},o(i){U(g.$$.fragment,i),j=!1},d(i){i&&n(p),i&&n(m),k(g,i)}}}function yl(G){let p,T,m,g,j;return{c(){p=a("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a("code"),g=r("Module"),j=r(`
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(i){p=s(i,"P",{});var _=o(p);T=l(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=s(_,"CODE",{});var P=o(m);g=l(P,"Module"),P.forEach(n),j=l(_,`
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(n)},m(i,_){f(i,p,_),t(p,T),t(p,m),t(m,g),t(p,j)},d(i){i&&n(p)}}}function bl(G){let p,T,m,g,j;return{c(){p=a("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a("code"),g=r("Module"),j=r(`
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(i){p=s(i,"P",{});var _=o(p);T=l(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=s(_,"CODE",{});var P=o(m);g=l(P,"Module"),P.forEach(n),j=l(_,`
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(n)},m(i,_){f(i,p,_),t(p,T),t(p,m),t(m,g),t(p,j)},d(i){i&&n(p)}}}function Jl(G){let p,T;return p=new ke({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> AutoModel, AutoTokenizer, trainer_utils

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>).to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>x_token = tokenizer(<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577\u306F\u3001&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>trainer_utils.set_seed(<span class="hljs-number">30</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = x_token.input_ids.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen_token = model.generate(input_ids, max_new_tokens=<span class="hljs-number">50</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(gen_token[<span class="hljs-number">0</span>])
<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577\u306F\u3001\u653F\u6CBB\u30FB\u8ECD\u4E8B\u306E\u4E2D\u67A2\u307E\u3067\u638C\u63E1\u3057\u305F\u653F\u6CBB\u5BB6\u3067\u3042\u308A\u3001\u65E5\u672C\u53F2\u4E0A\u985E\u3092\u898B\u306A\u3044\u9A5A\u7570\u7684\u306A\u8ECD\u4E8B\u4FB5\u653B\u3092\u7D9A\u3051...&quot;</span>`}}),{c(){y(p.$$.fragment)},l(m){b(p.$$.fragment,m)},m(m,g){J(p,m,g),T=!0},p:at,i(m){T||(M(p.$$.fragment,m),T=!0)},o(m){U(p.$$.fragment,m),T=!1},d(m){k(p,m)}}}function Ml(G){let p,T;return p=new ke({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> AutoModel, AutoTokenizer, trainer_utils

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>).to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>x_token = tokenizer(<span class="hljs-string">&quot;&quot;</span>, prefix_text=<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577\u306F\u3001&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>trainer_utils.set_seed(<span class="hljs-number">30</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = x_token.input_ids.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>token_type_ids = x_token.token_type_ids.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen_token = model.generate(input_ids, token_type_ids=token_type_ids, max_new_tokens=<span class="hljs-number">50</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(gen_token[<span class="hljs-number">0</span>])
<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577\u306F\u3001\u653F\u6CBB\u30FB\u5916\u4EA4\u3067\u6570\u3005\u306E\u6226\u679C\u3092\u4E0A\u3052\u308B\u304C\u30011568\u5E74\u304B\u3089\u306F\u3001\u3044\u308F\u3086\u308B\u672C\u80FD\u5BFA\u306E\u5909\u3067\u7D30\u5DDD\u6674\u5143\u306B\u6697\u6BBA\u3055\u308C\u308B...&quot;</span>`}}),{c(){y(p.$$.fragment)},l(m){b(p.$$.fragment,m)},m(m,g){J(p,m,g),T=!0},p:at,i(m){T||(M(p.$$.fragment,m),T=!0)},o(m){U(p.$$.fragment,m),T=!1},d(m){k(p,m)}}}function Ul(G){let p,T;return p=new ke({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUyQyUyMEF1dG9Ub2tlbml6ZXIlMkMlMjB0cmFpbmVyX3V0aWxzJTBBJTBBZGV2aWNlJTIwJTNEJTIwJTIyY3VkYSUyMiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMjJUYW5yZWklMkZHUFRTQU4tamFwYW5lc2UlMjIpLnRvKGRldmljZSklMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJUYW5yZWklMkZHUFRTQU4tamFwYW5lc2UlMjIpJTBBbWFza2VkX3NlbnRlbmNlJTIwJTNEJTIwJTIyJUU2JUFEJUE2JUU3JTk0JUIwJUU0JUJGJUExJUU3JThFJTg0JUUzJTgxJUFGJUUzJTgwJTgxJTNDJTdDaW5wdXRtYXNrJTdDJTNFJUU2JTk5JTgyJUU0JUJCJUEzJUUzJTgzJTk1JUUzJTgyJUExJUUzJTgzJUIzJUUzJTgxJUFBJUUzJTgyJTg5JUUzJTgxJTlDJUUzJTgxJUIyJUU2JThBJUJDJUUzJTgxJTk1JUUzJTgxJTg4JTNDJTdDaW5wdXRtYXNrJTdDJTNFJUUzJTgxJThEJUUzJTgxJTlGJUUzJTgxJTg0JUU1JTkwJThEJUU1JUIwJTg2JUUzJTgxJUFFJUU0JUI4JTgwJUU0JUJBJUJBJUUzJTgwJTgyJTIyJTBBeF90b2tlbiUyMCUzRCUyMHRva2VuaXplciglMjIlMjIlMkMlMjBwcmVmaXhfdGV4dCUzRG1hc2tlZF9zZW50ZW5jZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBdHJhaW5lcl91dGlscy5zZXRfc2VlZCgzMCklMEFpbnB1dF9pZHMlMjAlM0QlMjB4X3Rva2VuLmlucHV0X2lkcy50byhkZXZpY2UpJTBBdG9rZW5fdHlwZV9pZHMlMjAlM0QlMjB4X3Rva2VuLnRva2VuX3R5cGVfaWRzLnRvKGRldmljZSklMEFvdXRfbG1fdG9rZW4lMjAlM0QlMjBtb2RlbC5nZW5lcmF0ZShpbnB1dF9pZHMlMkMlMjB0b2tlbl90eXBlX2lkcyUzRHRva2VuX3R5cGVfaWRzJTJDJTIwbWF4X25ld190b2tlbnMlM0Q1MCklMEFvdXRfbWxtX3Rva2VuJTIwJTNEJTIwbW9kZWwoaW5wdXRfaWRzJTJDJTIwdG9rZW5fdHlwZV9pZHMlM0R0b2tlbl90eXBlX2lkcykubG9naXRzLmFyZ21heChheGlzJTNELTEpJTBBdG9rZW5pemVyLmRlY29kZShvdXRfbWxtX3Rva2VuJTVCMCU1RCklMEElMEF0b2tlbml6ZXIuZGVjb2RlKG91dF9sbV90b2tlbiU1QjAlNUQlNUJpbnB1dF9pZHMuc2hhcGUlNUIxJTVEJTIwJTNBJTVEKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel, AutoTokenizer, trainer_utils

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>).to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>masked_sentence = <span class="hljs-string">&quot;\u6B66\u7530\u4FE1\u7384\u306F\u3001&lt;|inputmask|&gt;\u6642\u4EE3\u30D5\u30A1\u30F3\u306A\u3089\u305C\u3072\u62BC\u3055\u3048&lt;|inputmask|&gt;\u304D\u305F\u3044\u540D\u5C06\u306E\u4E00\u4EBA\u3002&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>x_token = tokenizer(<span class="hljs-string">&quot;&quot;</span>, prefix_text=masked_sentence, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>trainer_utils.set_seed(<span class="hljs-number">30</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = x_token.input_ids.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>token_type_ids = x_token.token_type_ids.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>out_lm_token = model.generate(input_ids, token_type_ids=token_type_ids, max_new_tokens=<span class="hljs-number">50</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>out_mlm_token = model(input_ids, token_type_ids=token_type_ids).logits.argmax(axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(out_mlm_token[<span class="hljs-number">0</span>])
<span class="hljs-string">&quot;\u6B66\u7530\u4FE1\u7384\u306F\u3001\u6226\u56FD\u6642\u4EE3\u30D5\u30A1\u30F3\u306A\u3089\u305C\u3072\u62BC\u3055\u3048\u3066\u304A\u304D\u305F\u3044\u540D\u5C06\u306E\u4E00\u4EBA\u3002&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(out_lm_token[<span class="hljs-number">0</span>][input_ids.shape[<span class="hljs-number">1</span>] :])
<span class="hljs-string">&quot;\u6B66\u7530\u6C0F\u306E\u4E09\u4EE3\u306B\u6E21\u3063\u305F\u6B66\u7530\u5BB6\u306E\u3072\u3068\u308A\\n\u7532\u6590\u5E02\u306B\u4F4F\u3080\u3001\u65E5\u672C\u53F2\u4E0A\u6700\u5927\u306E\u6226\u56FD\u5927\u540D\u3002...&quot;</span>`}}),{c(){y(p.$$.fragment)},l(m){b(p.$$.fragment,m)},m(m,g){J(p,m,g),T=!0},p:at,i(m){T||(M(p.$$.fragment,m),T=!0)},o(m){U(p.$$.fragment,m),T=!1},d(m){k(p,m)}}}function kl(G){let p,T,m,g,j,i,_,P,ya,Vn,A,ne,vt,je,ba,wt,Ja,xn,st,Ma,$n,ot,Ua,En,q,ae,St,ve,ka,Gt,ja,Cn,se,va,Vt,wa,Sa,Pn,we,Nn,Z,oe,xt,Se,Ga,$t,Va,Fn,rt,xa,Rn,B,re,Et,Ge,$a,Ct,Ea,In,w,Ca,Pt,Pa,Na,Nt,Fa,Ra,Ft,Ia,za,Rt,Qa,Aa,It,qa,Za,zt,Ba,Oa,Qt,Wa,Xa,At,Da,Ya,zn,lt,La,Qn,it,Ha,An,pt,Ka,qn,dt,qt,Zt,Bt,es,Zn,ct,Ot,Wt,Xt,ts,Bn,ht,Dt,Yt,Lt,ns,On,O,le,Ht,Ve,as,Kt,ss,Wn,ie,os,en,rs,ls,Xn,W,pe,tn,xe,is,nn,ps,Dn,N,$e,ds,X,cs,mt,hs,ms,Ee,us,fs,gs,D,_s,ut,Ts,ys,ft,bs,Js,Yn,Y,de,an,Ce,Ms,sn,Us,Ln,S,Pe,ks,on,js,vs,L,rn,ws,Ss,ln,Gs,Vs,Ne,xs,Fe,$s,Es,Cs,ce,Ps,he,Ns,me,Fs,ue,Re,Rs,pn,Is,zs,F,Ie,Qs,dn,As,qs,fe,Hn,H,ge,cn,ze,Zs,hn,Bs,Kn,E,Qe,Os,mn,Ws,Xs,Ae,Ds,qe,Ys,Ls,Hs,Ze,Ks,Be,eo,to,no,R,Oe,ao,K,so,gt,oo,ro,un,lo,io,po,_e,ea,ee,Te,fn,We,co,gn,ho,ta,C,Xe,mo,_n,uo,fo,De,go,Ye,_o,To,yo,Le,bo,He,Jo,Mo,Uo,v,Ke,ko,te,jo,_t,vo,wo,Tn,So,Go,Vo,ye,xo,yn,$o,Eo,bn,Co,Po,be,No,Jn,Fo,Ro,Je,Io,Mn,zo,Qo,Me,na;return i=new Q({}),je=new Q({}),ve=new Q({}),we=new ke({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> AutoModel, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;Tanrei/GPTSAN-japanese&quot;</span>).cuda()
<span class="hljs-meta">&gt;&gt;&gt; </span>x_tok = tokenizer(<span class="hljs-string">&quot;\u306F\u3001&quot;</span>, prefix_text=<span class="hljs-string">&quot;\u7E54\u7530\u4FE1\u9577&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tok = model.generate(x_tok.input_ids.cuda(), token_type_ids=x_tok.token_type_ids.cuda(), max_new_tokens=<span class="hljs-number">20</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(gen_tok[<span class="hljs-number">0</span>])
<span class="hljs-string">&#x27;\u7E54\u7530\u4FE1\u9577\u306F\u30012004\u5E74\u306B\u300E\u6226\u56FDBASARA\u300F\u306E\u305F\u3081\u306B\u3001\u8C4A\u81E3\u79C0\u5409&#x27;</span>`}}),Se=new Q({}),Ge=new Q({}),Ve=new Q({}),xe=new Q({}),$e=new Ue({props:{name:"class transformers.GPTSanJapaneseConfig",anchor:"transformers.GPTSanJapaneseConfig",parameters:[{name:"vocab_size",val:" = 36000"},{name:"max_position_embeddings",val:" = 1280"},{name:"d_model",val:" = 1024"},{name:"d_ff",val:" = 8192"},{name:"d_ext",val:" = 4096"},{name:"d_spout",val:" = 128"},{name:"num_switch_layers",val:" = 10"},{name:"num_ext_layers",val:" = 0"},{name:"num_heads",val:" = 16"},{name:"num_experts",val:" = 16"},{name:"expert_capacity",val:" = 128"},{name:"dropout_rate",val:" = 0.0"},{name:"layer_norm_epsilon",val:" = 1e-05"},{name:"router_bias",val:" = False"},{name:"router_jitter_noise",val:" = 0.0"},{name:"router_dtype",val:" = 'float32'"},{name:"router_ignore_padding_tokens",val:" = False"},{name:"output_hidden_states",val:" = False"},{name:"output_attentions",val:" = False"},{name:"initializer_factor",val:" = 0.002"},{name:"output_router_logits",val:" = False"},{name:"use_cache",val:" = True"},{name:"separator_token_id",val:" = 35998"},{name:"pad_token_id",val:" = 35995"},{name:"eos_token_id",val:" = 35999"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GPTSanJapaneseConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 36000) &#x2014;
Vocabulary size of the GPTSANJapanese model. Defines the number of different tokens that can be represented
by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseModel">GPTSanJapaneseModel</a>.`,name:"vocab_size"},{anchor:"transformers.GPTSanJapaneseConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 1280) &#x2014;
The maximum sequence length that this model might ever be used with. Defaults set this to 1280.`,name:"max_position_embeddings"},{anchor:"transformers.GPTSanJapaneseConfig.d_model",description:`<strong>d_model</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Size of the encoder layers and the pooler layer.`,name:"d_model"},{anchor:"transformers.GPTSanJapaneseConfig.d_ff",description:`<strong>d_ff</strong> (<code>int</code>, <em>optional</em>, defaults to 8192) &#x2014;
Size of the intermediate feed forward layer in each <code>SwitchTransformersBlock</code>.`,name:"d_ff"},{anchor:"transformers.GPTSanJapaneseConfig.d_ext",description:`<strong>d_ext</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Size of the intermediate feed forward layer in each Extra-layers.`,name:"d_ext"},{anchor:"transformers.GPTSanJapaneseConfig.d_spout",description:`<strong>d_spout</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Size of the <code>spout</code> vector.`,name:"d_spout"},{anchor:"transformers.GPTSanJapaneseConfig.num_switch_layers",description:`<strong>num_switch_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
Number of layers in the Switch Transformer layer.`,name:"num_switch_layers"},{anchor:"transformers.GPTSanJapaneseConfig.num_ext_layers",description:`<strong>num_ext_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Number of layers in the Extra-layers.`,name:"num_ext_layers"},{anchor:"transformers.GPTSanJapaneseConfig.num_heads",description:`<strong>num_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_heads"},{anchor:"transformers.GPTSanJapaneseConfig.num_experts",description:`<strong>num_experts</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of experts for each SwitchTransformer layer.`,name:"num_experts"},{anchor:"transformers.GPTSanJapaneseConfig.expert_capacity",description:`<strong>expert_capacity</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Number of tokens that can be stored in each expert. If set to 1, the model will behave like a regular
Transformer.`,name:"expert_capacity"},{anchor:"transformers.GPTSanJapaneseConfig.dropout_rate",description:`<strong>dropout_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The ratio for all dropout layers.`,name:"dropout_rate"},{anchor:"transformers.GPTSanJapaneseConfig.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.GPTSanJapaneseConfig.router_bias",description:`<strong>router_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to add a bias to the router.`,name:"router_bias"},{anchor:"transformers.GPTSanJapaneseConfig.router_jitter_noise",description:`<strong>router_jitter_noise</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Amount of noise to add to the router. Set it to 0.0 during prediction or set small value (usually 1e-2)
during training.`,name:"router_jitter_noise"},{anchor:"transformers.GPTSanJapaneseConfig.router_dtype",description:`<strong>router_dtype</strong> (<code>str</code>, <em>optional</em>, default to <code>&quot;float32&quot;</code>) &#x2014;
The <code>dtype</code> used for the routers. It is preferable to keep the <code>dtype</code> to <code>&quot;float32&quot;</code> as specified in the
<em>selective precision</em> discussion in <a href="https://arxiv.org/abs/2101.03961" rel="nofollow">the paper</a>.`,name:"router_dtype"},{anchor:"transformers.GPTSanJapaneseConfig.router_ignore_padding_tokens",description:`<strong>router_ignore_padding_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to ignore padding tokens when routing.`,name:"router_ignore_padding_tokens"},{anchor:"transformers.GPTSanJapaneseConfig.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, default to <code>False</code>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.GPTSanJapaneseConfig.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to return the attentions tensors of all attention layers.`,name:"output_attentions"},{anchor:"transformers.GPTSanJapaneseConfig.initializer_factor",description:`<strong>initializer_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.002) &#x2014;
A factor for initializing all weight matrices.`,name:"initializer_factor"},{anchor:"transformers.GPTSanJapaneseConfig.output_router_logits",description:`<strong>output_router_logits</strong> (<code>bool</code>, <em>optional</em>, default to <code>False</code>) &#x2014;
Whether or not to return the router logits of all experts.`,name:"output_router_logits"},{anchor:"transformers.GPTSanJapaneseConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models)`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/configuration_gptsan_japanese.py#L29"}}),Ce=new Q({}),Pe=new Ue({props:{name:"class transformers.GPTSanJapaneseTokenizer",anchor:"transformers.GPTSanJapaneseTokenizer",parameters:[{name:"vocab_file",val:""},{name:"emoji_file",val:""},{name:"unk_token",val:" = '<|nottoken|>'"},{name:"pad_token",val:" = '<|separator|>'"},{name:"bos_token",val:" = '<|startoftext|>'"},{name:"eos_token",val:" = '<|endoftext|>'"},{name:"sep_token",val:" = '<|segmenter|>'"},{name:"do_clean_text",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GPTSanJapaneseTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.GPTSanJapaneseTokenizer.emoji_file",description:`<strong>emoji_file</strong> (<code>str</code>) &#x2014;
File containing the emoji.`,name:"emoji_file"},{anchor:"transformers.GPTSanJapaneseTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|nottoken|&gt;&quot;</code>) &#x2014;
The token used for unknown charactor`,name:"unk_token"},{anchor:"transformers.GPTSanJapaneseTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|separator|&gt;&quot;</code>) &#x2014;
The token used for padding`,name:"pad_token"},{anchor:"transformers.GPTSanJapaneseTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|startoftext|&gt;&quot;&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.GPTSanJapaneseTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.GPTSanJapaneseTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|segmenter|&gt;&quot;</code>) &#x2014;
A special token to separate token to prefix part and general input part.`,name:"sep_token"},{anchor:"transformers.GPTSanJapaneseTokenizer.do_clean_text",description:`<strong>do_clean_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to clean text for URL, EMAIL, TEL, Japanese DATE and Japanese PRICE.`,name:"do_clean_text"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py#L74"}}),ce=new nt({props:{anchor:"transformers.GPTSanJapaneseTokenizer.example",$$slots:{default:[fl]},$$scope:{ctx:G}}}),he=new nt({props:{anchor:"transformers.GPTSanJapaneseTokenizer.example-2",$$slots:{default:[gl]},$$scope:{ctx:G}}}),me=new nt({props:{anchor:"transformers.GPTSanJapaneseTokenizer.example-3",$$slots:{default:[_l]},$$scope:{ctx:G}}}),Re=new Ue({props:{name:"convert_tokens_to_string",anchor:"transformers.GPTSanJapaneseTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py#L218"}}),Ie=new Ue({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.GPTSanJapaneseTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py#L301"}}),fe=new nt({props:{anchor:"transformers.GPTSanJapaneseTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Tl]},$$scope:{ctx:G}}}),ze=new Q({}),Qe=new Ue({props:{name:"class transformers.GPTSanJapaneseModel",anchor:"transformers.GPTSanJapaneseModel",parameters:[{name:"config",val:": GPTSanJapaneseConfig"}],parametersDescription:[{anchor:"transformers.GPTSanJapaneseModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py#L868"}}),Oe=new Ue({props:{name:"forward",anchor:"transformers.GPTSanJapaneseModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"spout",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = False"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"output_router_logits",val:": typing.Optional[bool] = None"},{name:"num_precontext",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.GPTSanJapaneseModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. GPTSAN-japanese is a model that generates sentence
continuations or predicts tokens at mask positions. Special tokens required for inputs to the model are
automatically appended.`,name:"input_ids"},{anchor:"transformers.GPTSanJapaneseModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GPTSanJapaneseModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
An input that masks the Prefix part in the Prefix-LM input. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>prefix</strong> input,</li>
<li>0 for tokens that are <strong>not-prefix</strong> input.</li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.GPTSanJapaneseModel.forward.spout",description:`<strong>spout</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, config.d_spout)</code>) &#x2014;
This vector is transformed through an 8-layer FFN and can be used instead of <code>past_key_values</code>.`,name:"spout"},{anchor:"transformers.GPTSanJapaneseModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.GPTSanJapaneseModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:`,name:"head_mask"},{anchor:"transformers.GPTSanJapaneseModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.GPTSanJapaneseModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.GPTSanJapaneseModel.forward.decoder_inputs_embeds",description:`<strong>decoder_inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, target_sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>decoder_input_ids</code> you can choose to directly pass an embedded
representation. If <code>past_key_values</code> is used, optionally only the last <code>decoder_inputs_embeds</code> have to be
input (see <code>past_key_values</code>). This is useful if you want more control over how to convert
<code>decoder_input_ids</code> indices into associated vectors than the model&#x2019;s internal embedding lookup matrix.`,name:"decoder_inputs_embeds"},{anchor:"transformers.GPTSanJapaneseModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.GPTSanJapaneseModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.GPTSanJapaneseModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.GPTSanJapaneseModel.forward.router_logits",description:`<strong>router_logits</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_router_logits=True</code> is passed or when <code>config.add_router_probs=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, sequence_length, num_experts)</code>.
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.`,name:"router_logits"},{anchor:"transformers.GPTSanJapaneseModel.forward.num_precontext",description:`<strong>num_precontext</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,1)</code>) &#x2014;
length of <code>hybrid</code> input tokens in the input. Tokens up to this length refer to both front and back like
BERT, tokens after that refer only to front like GPT. see also:
<a href="https://github.com/tanreinama/GPTSAN/blob/main/report/model.md" rel="nofollow">https://github.com/tanreinama/GPTSAN/blob/main/report/model.md</a>`,name:"num_precontext"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py#L902",returnDescription:`
<p><code>MoEModelOutputWithPastAndCrossAttentions</code> or <code>tuple</code> if <code>return_dict</code> returns
MoEModelOutputWithPastAndCrossAttentions insted of tuple</p>
`}}),_e=new pl({props:{$$slots:{default:[yl]},$$scope:{ctx:G}}}),We=new Q({}),Xe=new Ue({props:{name:"class transformers.GPTSanJapaneseForConditionalGeneration",anchor:"transformers.GPTSanJapaneseForConditionalGeneration",parameters:[{name:"config",val:": GPTSanJapaneseConfig"}],parametersDescription:[{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py#L1113"}}),Ke=new Ue({props:{name:"forward",anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"spout",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = False"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"output_router_logits",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. GPTSAN-japanese is a model that generates sentence
continuations or predicts tokens at mask positions. Special tokens required for inputs to the model are
automatically appended.`,name:"input_ids"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
An input that masks the Prefix part in the Prefix-LM input. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>prefix</strong> input,</li>
<li>0 for tokens that are <strong>not-prefix</strong> input.</li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.spout",description:`<strong>spout</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, config.d_spout)</code>) &#x2014;
This vector is transformed through an 8-layer FFN and can be used instead of <code>past_key_values</code>.`,name:"spout"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:`,name:"head_mask"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
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Optionally, instead of passing <code>decoder_input_ids</code> you can choose to directly pass an embedded
representation. If <code>past_key_values</code> is used, optionally only the last <code>decoder_inputs_embeds</code> have to be
input (see <code>past_key_values</code>). This is useful if you want more control over how to convert
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Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, sequence_length, num_experts)</code>.
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.`,name:"router_logits"},{anchor:"transformers.GPTSanJapaneseForConditionalGeneration.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
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labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py#L1124",returnDescription:`
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input_ids:      | SOT | SEG | \uFF71 | \uFF72 | \uFF73 | \uFF74 |
token_type_ids: | 1   | 0   | 0 | 0 | 0 | 0 |
prefix_lm_mask:
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input_ids:      | SOT | \uFF71 | \uFF72 | SEG | \uFF73 | \uFF74 |
token_type_ids: | 1   | 1 | 1 | 0   | 0 | 0 |
prefix_lm_mask:
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input_ids:      | SOT | SEG | \uFF71 | \uFF72 | \uFF73 | \uFF74 |
token_type_ids: | 1   | 0   | 0 | 0 | 0 | 0 |
prefix_lm_mask:
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SEG | 1 1 0 0 0 0 |
\uFF71   | 1 1 1 0 0 0 |
\uFF72   | 1 1 1 1 0 0 |
\uFF73   | 1 1 1 1 1 0 |
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token_type_ids: | 1   | 1 | 1 | 1 | 1 | 0  |
prefix_lm_mask:
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\uFF71   | 1 1 1 1 1 0 |
\uFF72   | 1 1 1 1 1 0 |
\uFF73   | 1 1 1 1 1 0 |
\uFF74   | 1 1 1 1 1 0 |
SEG | 1 1 1 1 1 1 |`),Tr.forEach(n),_r.forEach(n),gr.forEach(n),fr.forEach(n),Bn=h(e),ht=s(e,"BLOCKQUOTE",{});var yr=o(ht);Dt=s(yr,"BLOCKQUOTE",{});var br=o(Dt);Yt=s(br,"BLOCKQUOTE",{});var Jr=o(Yt);Lt=s(Jr,"P",{});var Mr=o(Lt);ns=l(Mr,`x_token = tokenizer(\u201C\uFF73\uFF74\u201D, prefix_text=\u201C\uFF71\uFF72\u201D)
input_ids:      | SOT | \uFF71 | \uFF72 | SEG | \uFF73 | \uFF74 |
token_type_ids: | 1   | 1 | 1 | 0   | 0 | 0 |
prefix_lm_mask:
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\uFF72   | 1 1 1 0 0 0 |
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\uFF74   | 1 1 1 1 1 1 |`),Mr.forEach(n),Jr.forEach(n),br.forEach(n),yr.forEach(n),On=h(e),O=s(e,"H3",{class:!0});var la=o(O);le=s(la,"A",{id:!0,class:!0,href:!0});var Ur=o(le);Ht=s(Ur,"SPAN",{});var kr=o(Ht);b(Ve.$$.fragment,kr),kr.forEach(n),Ur.forEach(n),as=h(la),Kt=s(la,"SPAN",{});var jr=o(Kt);ss=l(jr,"Spout Vector"),jr.forEach(n),la.forEach(n),Wn=h(e),ie=s(e,"P",{});var ia=o(ie);os=l(ia,`A Spout Vector is a special vector for controlling text generation.
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configuration with the defaults will yield a similar configuration to that of the GPTSANJapanese
`),Ee=s(yt,"A",{href:!0,rel:!0});var xr=o(Ee);us=l(xr,"Tanrei/GPTSAN-japanese"),xr.forEach(n),fs=l(yt," architecture."),yt.forEach(n),gs=h(Tt),D=s(Tt,"P",{});var bt=o(D);_s=l(bt,"Configuration objects inherit from "),ut=s(bt,"A",{href:!0});var $r=o(ut);Ts=l($r,"PretrainedConfig"),$r.forEach(n),ys=l(bt,` and can be used to control the model outputs. Read the
documentation from `),ft=s(bt,"A",{href:!0});var Er=o(ft);bs=l(Er,"PretrainedConfig"),Er.forEach(n),Js=l(bt," for more information."),bt.forEach(n),Tt.forEach(n),Yn=h(e),Y=s(e,"H2",{class:!0});var da=o(Y);de=s(da,"A",{id:!0,class:!0,href:!0});var Cr=o(de);an=s(Cr,"SPAN",{});var Pr=o(an);b(Ce.$$.fragment,Pr),Pr.forEach(n),Cr.forEach(n),Ms=h(da),sn=s(da,"SPAN",{});var Nr=o(sn);Us=l(Nr,"GPTSanJapaneseTokenizer"),Nr.forEach(n),da.forEach(n),Ln=h(e),S=s(e,"DIV",{class:!0});var $=o(S);b(Pe.$$.fragment,$),ks=h($),on=s($,"P",{});var Fr=o(on);js=l(Fr,"This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications"),Fr.forEach(n),vs=h($),L=s($,"UL",{});var Jt=o(L);rn=s(Jt,"LI",{});var Rr=o(rn);ws=l(Rr,"Decoding byte0~byte255 tokens correctly"),Rr.forEach(n),Ss=h(Jt),ln=s(Jt,"LI",{});var Ir=o(ln);Gs=l(Ir,"Added bagofword token handling"),Ir.forEach(n),Vs=h(Jt),Ne=s(Jt,"LI",{});var ca=o(Ne);xs=l(ca,`Return token_type_ids for Prefix-LM model
The bagofword token represents a repetition of the previous token and is converted to 3 consecutive tokens when
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