import{S as rm,i as lm,s as dm,e as a,k as p,w as k,t as l,M as im,c as s,d as t,m,a as r,x as T,h as d,b as h,G as e,g as _,y as v,q as M,o as w,B as F,v as cm,L as Se}from"../../chunks/vendor-hf-doc-builder.js";import{T as Dn}from"../../chunks/Tip-hf-doc-builder.js";import{D as E}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Je}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as xe}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as je}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function pm($){let i,y,f,u,b;return u=new Je({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> BlenderbotSmallConfig, BlenderbotSmallModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlenderbotSmall facebook/blenderbot_small-90M style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BlenderbotSmallConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the facebook/blenderbot_small-90M style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlenderbotSmallModel(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(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function mm($){let i,y,f,u,b;return{c(){i=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),u=l("Module"),b=l(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=s(c,"CODE",{});var S=r(f);u=d(S,"Module"),S.forEach(t),b=d(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(n,c){_(n,i,c),e(i,y),e(i,f),e(f,u),e(i,b)},d(n){n&&t(i)}}}function hm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, BlenderbotSmallModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlenderbotSmallModel.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Studies have been shown that owning a dog is good for you&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_inputs = tokenizer(<span class="hljs-string">&quot;Studies show that&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">512</span>]`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function um($){let i,y,f,u,b;return{c(){i=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),u=l("Module"),b=l(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=s(c,"CODE",{});var S=r(f);u=d(S,"Module"),S.forEach(t),b=d(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(n,c){_(n,i,c),e(i,y),e(i,f),e(f,u),e(i,b)},d(n){n&&t(i)}}}function fm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, BlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>mname = <span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(mname)
<span class="hljs-meta">&gt;&gt;&gt; </span>UTTERANCE = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Human: &quot;</span>, UTTERANCE)
Human:  My friends are cool but they eat too many carbs.

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([UTTERANCE], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>reply_ids = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Bot: &quot;</span>, tokenizer.batch_decode(reply_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>])
Bot:  what kind of carbs do they eat? i don<span class="hljs-string">&#x27;t know much about carbs.

&gt;&gt;&gt; REPLY = &quot;I&#x27;</span>m <span class="hljs-keyword">not</span> sure<span class="hljs-string">&quot;
&gt;&gt;&gt; print(&quot;</span>Human: <span class="hljs-string">&quot;, REPLY)
Human: I&#x27;m not sure

&gt;&gt;&gt; NEXT_UTTERANCE = (
...     &quot;</span>My friends are cool but they eat too many carbs.__end__ __start__what kind of carbs do they eat? <span class="hljs-string">&quot;
...     &quot;</span>i don<span class="hljs-string">&#x27;t know much about carbs__end__ &quot;
...     &quot;__start__ I&#x27;</span>m <span class="hljs-keyword">not</span> sure.<span class="hljs-string">&quot;
... )
&gt;&gt;&gt; inputs = tokenizer([NEXT_UTTERANCE], return_tensors=&quot;</span>pt<span class="hljs-string">&quot;)
&gt;&gt;&gt; next_reply_ids = model.generate(**inputs)
&gt;&gt;&gt; print(&quot;</span>Bot: <span class="hljs-string">&quot;, tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
Bot:  they eat a lot of carbs. carbs are high in fat, protein, and fats.</span>`}}),{c(){i=a("p"),y=l("Conversation example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Conversation example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function _m($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, BlenderbotSmallForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlenderbotSmallForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>, add_cross_attention=<span class="hljs-literal">False</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">assert</span> model.config.is_decoder, <span class="hljs-string">f&quot;<span class="hljs-subst">{model.__class__}</span> has to be configured as a decoder.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>expected_shape = [<span class="hljs-number">1</span>, inputs.input_ids.shape[-<span class="hljs-number">1</span>], model.config.vocab_size]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape) == expected_shape
<span class="hljs-literal">True</span>`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function gm($){let i,y,f,u,b,n,c,S,Re,Ce,J,Xe,K,z,Ae,P,Pe,Ye,R,Le,Oe,Y,He,De,L,Qe,Ee,I,ee,ke,H,U,q,Ke,te,oe,et,X,tt,ot,G,he,de,Ue,ue,D,nt,qe,V,at,ne,fe,ie,Z,Ge,C,st,ce;return{c(){i=a("p"),y=l("TensorFlow models and layers in "),f=a("code"),u=l("transformers"),b=l(" accept two formats as input:"),n=p(),c=a("ul"),S=a("li"),Re=l("having all inputs as keyword arguments (like PyTorch models), or"),Ce=p(),J=a("li"),Xe=l("having all inputs as a list, tuple or dict in the first positional argument."),K=p(),z=a("p"),Ae=l(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),P=a("code"),Pe=l("model.fit()"),Ye=l(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),R=a("code"),Le=l("model.fit()"),Oe=l(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a("code"),He=l("fit()"),De=l(" and "),L=a("code"),Qe=l("predict()"),Ee=l(`, such as when creating your own layers or models with
the Keras `),I=a("code"),ee=l("Functional"),ke=l(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),H=p(),U=a("ul"),q=a("li"),Ke=l("a single Tensor with "),te=a("code"),oe=l("input_ids"),et=l(" only and nothing else: "),X=a("code"),tt=l("model(input_ids)"),ot=p(),G=a("li"),he=l(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),ne=a("code"),fe=l('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ie=p(),Z=a("p"),Ge=l(`Note that when creating models and layers with
`),C=a("a"),st=l("subclassing"),ce=l(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(B){i=s(B,"P",{});var j=r(i);y=d(j,"TensorFlow models and layers in "),f=s(j,"CODE",{});var Te=r(f);u=d(Te,"transformers"),Te.forEach(t),b=d(j," accept two formats as input:"),j.forEach(t),n=m(B),c=s(B,"UL",{});var ve=r(c);S=s(ve,"LI",{});var bt=r(S);Re=d(bt,"having all inputs as keyword arguments (like PyTorch models), or"),bt.forEach(t),Ce=m(ve),J=s(ve,"LI",{});var mt=r(J);Xe=d(mt,"having all inputs as a list, tuple or dict in the first positional argument."),mt.forEach(t),ve.forEach(t),K=m(B),z=s(B,"P",{});var x=r(z);Ae=d(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),P=s(x,"CODE",{});var _e=r(P);Pe=d(_e,"model.fit()"),_e.forEach(t),Ye=d(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),R=s(x,"CODE",{});var rt=r(R);Le=d(rt,"model.fit()"),rt.forEach(t),Oe=d(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s(x,"CODE",{});var Me=r(Y);He=d(Me,"fit()"),Me.forEach(t),De=d(x," and "),L=s(x,"CODE",{});var yt=r(L);Qe=d(yt,"predict()"),yt.forEach(t),Ee=d(x,`, such as when creating your own layers or models with
the Keras `),I=s(x,"CODE",{});var lt=r(I);ee=d(lt,"Functional"),lt.forEach(t),ke=d(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),H=m(B),U=s(B,"UL",{});var Q=r(U);q=s(Q,"LI",{});var ae=r(q);Ke=d(ae,"a single Tensor with "),te=s(ae,"CODE",{});var se=r(te);oe=d(se,"input_ids"),se.forEach(t),et=d(ae," only and nothing else: "),X=s(ae,"CODE",{});var dt=r(X);tt=d(dt,"model(input_ids)"),dt.forEach(t),ae.forEach(t),ot=m(Q),G=s(Q,"LI",{});var O=r(G);he=d(O,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),de=s(O,"CODE",{});var kt=r(de);Ue=d(kt,"model([input_ids, attention_mask])"),kt.forEach(t),ue=d(O," or "),D=s(O,"CODE",{});var Tt=r(D);nt=d(Tt,"model([input_ids, attention_mask, token_type_ids])"),Tt.forEach(t),O.forEach(t),qe=m(Q),V=s(Q,"LI",{});var we=r(V);at=d(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ne=s(we,"CODE",{});var Ie=r(ne);fe=d(Ie,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ie.forEach(t),we.forEach(t),Q.forEach(t),ie=m(B),Z=s(B,"P",{});var Fe=r(Z);Ge=d(Fe,`Note that when creating models and layers with
`),C=s(Fe,"A",{href:!0,rel:!0});var vt=r(C);st=d(vt,"subclassing"),vt.forEach(t),ce=d(Fe,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),Fe.forEach(t),this.h()},h(){h(C,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),h(C,"rel","nofollow")},m(B,j){_(B,i,j),e(i,y),e(i,f),e(f,u),e(i,b),_(B,n,j),_(B,c,j),e(c,S),e(S,Re),e(c,Ce),e(c,J),e(J,Xe),_(B,K,j),_(B,z,j),e(z,Ae),e(z,P),e(P,Pe),e(z,Ye),e(z,R),e(R,Le),e(z,Oe),e(z,Y),e(Y,He),e(z,De),e(z,L),e(L,Qe),e(z,Ee),e(z,I),e(I,ee),e(z,ke),_(B,H,j),_(B,U,j),e(U,q),e(q,Ke),e(q,te),e(te,oe),e(q,et),e(q,X),e(X,tt),e(U,ot),e(U,G),e(G,he),e(G,de),e(de,Ue),e(G,ue),e(G,D),e(D,nt),e(U,qe),e(U,V),e(V,at),e(V,ne),e(ne,fe),_(B,ie,j),_(B,Z,j),e(Z,Ge),e(Z,C),e(C,st),e(Z,ce)},d(B){B&&t(i),B&&t(n),B&&t(c),B&&t(K),B&&t(z),B&&t(H),B&&t(U),B&&t(ie),B&&t(Z)}}}function bm($){let i,y,f,u,b;return{c(){i=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),u=l("Module"),b=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.`)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=s(c,"CODE",{});var S=r(f);u=d(S,"Module"),S.forEach(t),b=d(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(n,c){_(n,i,c),e(i,y),e(i,f),e(f,u),e(i,b)},d(n){n&&t(i)}}}function ym($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, TFBlenderbotSmallModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlenderbotSmallModel.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function km($){let i,y,f,u,b,n,c,S,Re,Ce,J,Xe,K,z,Ae,P,Pe,Ye,R,Le,Oe,Y,He,De,L,Qe,Ee,I,ee,ke,H,U,q,Ke,te,oe,et,X,tt,ot,G,he,de,Ue,ue,D,nt,qe,V,at,ne,fe,ie,Z,Ge,C,st,ce;return{c(){i=a("p"),y=l("TensorFlow models and layers in "),f=a("code"),u=l("transformers"),b=l(" accept two formats as input:"),n=p(),c=a("ul"),S=a("li"),Re=l("having all inputs as keyword arguments (like PyTorch models), or"),Ce=p(),J=a("li"),Xe=l("having all inputs as a list, tuple or dict in the first positional argument."),K=p(),z=a("p"),Ae=l(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),P=a("code"),Pe=l("model.fit()"),Ye=l(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),R=a("code"),Le=l("model.fit()"),Oe=l(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a("code"),He=l("fit()"),De=l(" and "),L=a("code"),Qe=l("predict()"),Ee=l(`, such as when creating your own layers or models with
the Keras `),I=a("code"),ee=l("Functional"),ke=l(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),H=p(),U=a("ul"),q=a("li"),Ke=l("a single Tensor with "),te=a("code"),oe=l("input_ids"),et=l(" only and nothing else: "),X=a("code"),tt=l("model(input_ids)"),ot=p(),G=a("li"),he=l(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),de=a("code"),Ue=l("model([input_ids, attention_mask])"),ue=l(" or "),D=a("code"),nt=l("model([input_ids, attention_mask, token_type_ids])"),qe=p(),V=a("li"),at=l(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ne=a("code"),fe=l('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ie=p(),Z=a("p"),Ge=l(`Note that when creating models and layers with
`),C=a("a"),st=l("subclassing"),ce=l(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(B){i=s(B,"P",{});var j=r(i);y=d(j,"TensorFlow models and layers in "),f=s(j,"CODE",{});var Te=r(f);u=d(Te,"transformers"),Te.forEach(t),b=d(j," accept two formats as input:"),j.forEach(t),n=m(B),c=s(B,"UL",{});var ve=r(c);S=s(ve,"LI",{});var bt=r(S);Re=d(bt,"having all inputs as keyword arguments (like PyTorch models), or"),bt.forEach(t),Ce=m(ve),J=s(ve,"LI",{});var mt=r(J);Xe=d(mt,"having all inputs as a list, tuple or dict in the first positional argument."),mt.forEach(t),ve.forEach(t),K=m(B),z=s(B,"P",{});var x=r(z);Ae=d(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),P=s(x,"CODE",{});var _e=r(P);Pe=d(_e,"model.fit()"),_e.forEach(t),Ye=d(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),R=s(x,"CODE",{});var rt=r(R);Le=d(rt,"model.fit()"),rt.forEach(t),Oe=d(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s(x,"CODE",{});var Me=r(Y);He=d(Me,"fit()"),Me.forEach(t),De=d(x," and "),L=s(x,"CODE",{});var yt=r(L);Qe=d(yt,"predict()"),yt.forEach(t),Ee=d(x,`, such as when creating your own layers or models with
the Keras `),I=s(x,"CODE",{});var lt=r(I);ee=d(lt,"Functional"),lt.forEach(t),ke=d(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),H=m(B),U=s(B,"UL",{});var Q=r(U);q=s(Q,"LI",{});var ae=r(q);Ke=d(ae,"a single Tensor with "),te=s(ae,"CODE",{});var se=r(te);oe=d(se,"input_ids"),se.forEach(t),et=d(ae," only and nothing else: "),X=s(ae,"CODE",{});var dt=r(X);tt=d(dt,"model(input_ids)"),dt.forEach(t),ae.forEach(t),ot=m(Q),G=s(Q,"LI",{});var O=r(G);he=d(O,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),de=s(O,"CODE",{});var kt=r(de);Ue=d(kt,"model([input_ids, attention_mask])"),kt.forEach(t),ue=d(O," or "),D=s(O,"CODE",{});var Tt=r(D);nt=d(Tt,"model([input_ids, attention_mask, token_type_ids])"),Tt.forEach(t),O.forEach(t),qe=m(Q),V=s(Q,"LI",{});var we=r(V);at=d(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ne=s(we,"CODE",{});var Ie=r(ne);fe=d(Ie,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Ie.forEach(t),we.forEach(t),Q.forEach(t),ie=m(B),Z=s(B,"P",{});var Fe=r(Z);Ge=d(Fe,`Note that when creating models and layers with
`),C=s(Fe,"A",{href:!0,rel:!0});var vt=r(C);st=d(vt,"subclassing"),vt.forEach(t),ce=d(Fe,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),Fe.forEach(t),this.h()},h(){h(C,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),h(C,"rel","nofollow")},m(B,j){_(B,i,j),e(i,y),e(i,f),e(f,u),e(i,b),_(B,n,j),_(B,c,j),e(c,S),e(S,Re),e(c,Ce),e(c,J),e(J,Xe),_(B,K,j),_(B,z,j),e(z,Ae),e(z,P),e(P,Pe),e(z,Ye),e(z,R),e(R,Le),e(z,Oe),e(z,Y),e(Y,He),e(z,De),e(z,L),e(L,Qe),e(z,Ee),e(z,I),e(I,ee),e(z,ke),_(B,H,j),_(B,U,j),e(U,q),e(q,Ke),e(q,te),e(te,oe),e(q,et),e(q,X),e(X,tt),e(U,ot),e(U,G),e(G,he),e(G,de),e(de,Ue),e(G,ue),e(G,D),e(D,nt),e(U,qe),e(U,V),e(V,at),e(V,ne),e(ne,fe),_(B,ie,j),_(B,Z,j),e(Z,Ge),e(Z,C),e(C,st),e(Z,ce)},d(B){B&&t(i),B&&t(n),B&&t(c),B&&t(K),B&&t(z),B&&t(H),B&&t(U),B&&t(ie),B&&t(Z)}}}function Tm($){let i,y,f,u,b;return{c(){i=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),u=l("Module"),b=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.`)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=s(c,"CODE",{});var S=r(f);u=d(S,"Module"),S.forEach(t),b=d(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(n,c){_(n,i,c),e(i,y),e(i,f),e(f,u),e(i,b)},d(n){n&&t(i)}}}function vm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, TFBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>mname = <span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(mname)

<span class="hljs-meta">&gt;&gt;&gt; </span>UTTERANCE = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Human: &quot;</span>, UTTERANCE)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([UTTERANCE], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>reply_ids = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Bot: &quot;</span>, tokenizer.batch_decode(reply_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>])
what kind of carbs do they eat? i don<span class="hljs-string">&#x27;t know much about carbs.

&gt;&gt;&gt; REPLY = &quot;I&#x27;</span>m <span class="hljs-keyword">not</span> sure<span class="hljs-string">&quot;
&gt;&gt;&gt; print(&quot;</span>Human: <span class="hljs-string">&quot;, REPLY)
&gt;&gt;&gt; NEXT_UTTERANCE = (
...     &quot;</span>My friends are cool but they eat too many carbs.&lt;/s&gt; <span class="hljs-string">&quot;
...     &quot;</span>&lt;s&gt;what kind of carbs do they eat? i don<span class="hljs-string">&#x27;t know much about carbs.&lt;/s&gt; &quot;
...     &quot;&lt;s&gt;I&#x27;</span>m <span class="hljs-keyword">not</span> sure.<span class="hljs-string">&quot;
... )

&gt;&gt;&gt; inputs = tokenizer([NEXT_UTTERANCE], return_tensors=&quot;</span>t<span class="hljs-string">f&quot;)
&gt;&gt;&gt; inputs.pop(&quot;</span>token_type_ids<span class="hljs-string">&quot;)
&gt;&gt;&gt; next_reply_ids = model.generate(**inputs)
&gt;&gt;&gt; print(&quot;</span>Bot: <span class="hljs-string">&quot;, tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])</span>`}}),{c(){i=a("p"),y=l("Conversation example::"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Conversation example::"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function Mm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, FlaxBlenderbotSmallModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallModel.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function wm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, max_length=<span class="hljs-number">1024</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>encoder_outputs = model.encode(**inputs)`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function Fm($){let i,y,f,u,b;return u=new Je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> jax.numpy <span class="hljs-keyword">as</span> jnp
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, max_length=<span class="hljs-number">1024</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>encoder_outputs = model.encode(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_start_token_id = model.config.decoder_start_token_id
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_input_ids = jnp.ones((inputs.input_ids.shape[<span class="hljs-number">0</span>], <span class="hljs-number">1</span>), dtype=<span class="hljs-string">&quot;i4&quot;</span>) * decoder_start_token_id

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.decode(decoder_input_ids, encoder_outputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_decoder_hidden_states = outputs.last_hidden_state`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function Bm($){let i,y,f,u,b;return{c(){i=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),f=a("code"),u=l("Module"),b=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.`)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),f=s(c,"CODE",{});var S=r(f);u=d(S,"Module"),S.forEach(t),b=d(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(n,c){_(n,i,c),e(i,y),e(i,f),e(f,u),e(i,b)},d(n){n&&t(i)}}}function $m($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>ARTICLE_TO_SUMMARIZE = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=<span class="hljs-number">1024</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Generate Summary</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary_ids = model.generate(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>]).sequences
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.batch_decode(summary_ids, skip_special_tokens=<span class="hljs-literal">True</span>, clean_up_tokenization_spaces=<span class="hljs-literal">False</span>))`}}),{c(){i=a("p"),y=l("Summarization example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Summarization example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function zm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>TXT = <span class="hljs-string">&quot;My friends are &lt;mask&gt; but they eat too many carbs.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer([TXT], return_tensors=<span class="hljs-string">&quot;np&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(input_ids).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>masked_index = (input_ids[<span class="hljs-number">0</span>] == tokenizer.mask_token_id).nonzero().item()
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = jax.nn.softmax(logits[<span class="hljs-number">0</span>, masked_index], axis=<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>values, predictions = jax.lax.top_k(probs)

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(predictions).split()`}}),{c(){i=a("p"),y=l("Mask filling example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Mask filling example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function xm($){let i,y,f,u,b;return u=new Je({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> AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, max_length=<span class="hljs-number">1024</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>encoder_outputs = model.encode(**inputs)`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function jm($){let i,y,f,u,b;return u=new Je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> jax.numpy <span class="hljs-keyword">as</span> jnp
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/blenderbot_small-90M&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My friends are cool but they eat too many carbs.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, max_length=<span class="hljs-number">1024</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>encoder_outputs = model.encode(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_start_token_id = model.config.decoder_start_token_id
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_input_ids = jnp.ones((inputs.input_ids.shape[<span class="hljs-number">0</span>], <span class="hljs-number">1</span>), dtype=<span class="hljs-string">&quot;i4&quot;</span>) * decoder_start_token_id

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.decode(decoder_input_ids, encoder_outputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){i=a("p"),y=l("Example:"),f=p(),k(u.$$.fragment)},l(n){i=s(n,"P",{});var c=r(i);y=d(c,"Example:"),c.forEach(t),f=m(n),T(u.$$.fragment,n)},m(n,c){_(n,i,c),e(i,y),_(n,f,c),v(u,n,c),b=!0},p:Se,i(n){b||(M(u.$$.fragment,n),b=!0)},o(n){w(u.$$.fragment,n),b=!1},d(n){n&&t(i),n&&t(f),F(u,n)}}}function Sm($){let i,y,f,u,b,n,c,S,Re,Ce,J,Xe,K,z,Ae,P,Pe,Ye,R,Le,Oe,Y,He,De,L,Qe,Ee,I,ee,ke,H,U,q,Ke,te,oe,et,X,tt,ot,G,he,de,Ue,ue,D,nt,qe,V,at,ne,fe,ie,Z,Ge,C,st,ce,B,j,Te,ve,bt,mt,x,_e,rt,Me,yt,lt,Q,ae,se,dt,O,kt,Tt,we,Ie,Fe,vt,xa,Qn,Gr,qs,Mt,Vt,ja,qo,Ir,Sa,Zr,Gs,Be,Go,Wr,wt,Nr,Kn,Vr,Rr,Io,Xr,Ar,Pr,Ft,Yr,ea,Lr,Or,ta,Hr,Dr,Qr,Rt,Is,Bt,Xt,Ja,Zo,Kr,Ca,el,Zs,A,Wo,tl,Ea,ol,nl,No,al,oa,sl,rl,ll,ht,Vo,dl,Ua,il,cl,qa,pl,ml,At,Ro,hl,$t,ul,Ga,fl,_l,Ia,gl,bl,yl,ut,Xo,kl,na,Tl,aa,vl,Ml,Za,wl,Fl,sa,Ao,Ws,zt,Pt,Wa,Po,Bl,Na,$l,Ns,it,Yo,zl,Lo,xl,Va,jl,Sl,Jl,Yt,Oo,Cl,Ra,El,Vs,xt,Lt,Xa,Ho,Ul,Aa,ql,Rs,$e,Do,Gl,Qo,Il,ra,Zl,Wl,Nl,Ko,Vl,en,Rl,Xl,Al,Ze,tn,Pl,jt,Yl,la,Ll,Ol,Pa,Hl,Dl,Ql,Ot,Kl,Ht,Xs,St,Dt,Ya,on,ed,La,td,As,ze,nn,od,an,nd,da,ad,sd,rd,sn,ld,rn,dd,id,cd,We,ln,pd,Jt,md,ia,hd,ud,Oa,fd,_d,gd,Qt,bd,Kt,Ps,Ct,eo,Ha,dn,yd,Da,kd,Ys,Et,cn,Td,to,pn,vd,oo,Ls,Ut,no,Qa,mn,Md,Ka,wd,Os,pe,hn,Fd,un,Bd,ca,$d,zd,xd,fn,jd,_n,Sd,Jd,Cd,ao,Ed,Ne,gn,Ud,qt,qd,pa,Gd,Id,es,Zd,Wd,Nd,so,Vd,ro,Hs,Gt,lo,ts,bn,Rd,os,Xd,Ds,me,yn,Ad,kn,Pd,ma,Yd,Ld,Od,Tn,Hd,vn,Dd,Qd,Kd,io,ei,Ve,Mn,ti,It,oi,ha,ni,ai,ns,si,ri,li,co,di,po,Qs,Zt,mo,as,wn,ii,ss,ci,Ks,W,Fn,pi,Bn,mi,ua,hi,ui,fi,$n,_i,zn,gi,bi,yi,rs,ki,Ti,ct,ls,xn,vi,Mi,ds,jn,wi,Fi,is,Sn,Bi,$i,cs,Jn,zi,xi,ho,Cn,ji,uo,Si,fo,En,Ji,_o,Ci,go,Un,Ei,bo,er,Wt,yo,ps,qn,Ui,ms,qi,tr,N,Gn,Gi,In,Ii,fa,Zi,Wi,Ni,Zn,Vi,Wn,Ri,Xi,Ai,hs,Pi,Yi,pt,us,Nn,Li,Oi,fs,Vn,Hi,Di,_s,Rn,Qi,Ki,gs,Xn,ec,tc,ge,An,oc,Nt,nc,bs,ac,sc,ys,rc,lc,dc,ko,ic,To,cc,vo,pc,Mo,Pn,mc,wo,hc,Fo,Yn,uc,Bo,or;return n=new xe({}),H=new xe({}),Me=new xe({}),qo=new xe({}),Go=new E({props:{name:"class transformers.BlenderbotSmallConfig",anchor:"transformers.BlenderbotSmallConfig",parameters:[{name:"vocab_size",val:" = 50265"},{name:"max_position_embeddings",val:" = 512"},{name:"encoder_layers",val:" = 8"},{name:"encoder_ffn_dim",val:" = 2048"},{name:"encoder_attention_heads",val:" = 16"},{name:"decoder_layers",val:" = 8"},{name:"decoder_ffn_dim",val:" = 2048"},{name:"decoder_attention_heads",val:" = 16"},{name:"encoder_layerdrop",val:" = 0.0"},{name:"decoder_layerdrop",val:" = 0.0"},{name:"use_cache",val:" = True"},{name:"is_encoder_decoder",val:" = True"},{name:"activation_function",val:" = 'gelu'"},{name:"d_model",val:" = 512"},{name:"dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.0"},{name:"activation_dropout",val:" = 0.0"},{name:"init_std",val:" = 0.02"},{name:"decoder_start_token_id",val:" = 1"},{name:"scale_embedding",val:" = False"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"forced_eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlenderbotSmallConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 50265) &#x2014;
Vocabulary size of the BlenderbotSmall 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/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> or <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a>.`,name:"vocab_size"},{anchor:"transformers.BlenderbotSmallConfig.d_model",description:`<strong>d_model</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the layers and the pooler layer.`,name:"d_model"},{anchor:"transformers.BlenderbotSmallConfig.encoder_layers",description:`<strong>encoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of encoder layers.`,name:"encoder_layers"},{anchor:"transformers.BlenderbotSmallConfig.decoder_layers",description:`<strong>decoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of decoder layers.`,name:"decoder_layers"},{anchor:"transformers.BlenderbotSmallConfig.encoder_attention_heads",description:`<strong>encoder_attention_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:"encoder_attention_heads"},{anchor:"transformers.BlenderbotSmallConfig.decoder_attention_heads",description:`<strong>decoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer decoder.`,name:"decoder_attention_heads"},{anchor:"transformers.BlenderbotSmallConfig.decoder_ffn_dim",description:`<strong>decoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"decoder_ffn_dim"},{anchor:"transformers.BlenderbotSmallConfig.encoder_ffn_dim",description:`<strong>encoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"encoder_ffn_dim"},{anchor:"transformers.BlenderbotSmallConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation_function"},{anchor:"transformers.BlenderbotSmallConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.BlenderbotSmallConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.BlenderbotSmallConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.BlenderbotSmallConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.BlenderbotSmallConfig.init_std",description:`<strong>init_std</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:"init_std"},{anchor:"transformers.BlenderbotSmallConfig.encoder_layerdrop",description:`<strong>encoder_layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The LayerDrop probability for the encoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
for more details.`,name:"encoder_layerdrop"},{anchor:"transformers.BlenderbotSmallConfig.decoder_layerdrop",description:`<strong>decoder_layerdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The LayerDrop probability for the decoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
for more details.`,name:"decoder_layerdrop"},{anchor:"transformers.BlenderbotSmallConfig.scale_embedding",description:`<strong>scale_embedding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Scale embeddings by diving by sqrt(d_model).`,name:"scale_embedding"},{anchor:"transformers.BlenderbotSmallConfig.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"},{anchor:"transformers.BlenderbotSmallConfig.forced_eos_token_id",description:`<strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The id of the token to force as the last generated token when <code>max_length</code> is reached. Usually set to
<code>eos_token_id</code>.`,name:"forced_eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py#L36"}}),Rt=new je({props:{anchor:"transformers.BlenderbotSmallConfig.example",$$slots:{default:[pm]},$$scope:{ctx:$}}}),Zo=new xe({}),Wo=new E({props:{name:"class transformers.BlenderbotSmallTokenizer",anchor:"transformers.BlenderbotSmallTokenizer",parameters:[{name:"vocab_file",val:""},{name:"merges_file",val:""},{name:"bos_token",val:" = '__start__'"},{name:"eos_token",val:" = '__end__'"},{name:"unk_token",val:" = '__unk__'"},{name:"pad_token",val:" = '__null__'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlenderbotSmallTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.BlenderbotSmallTokenizer.merges_file",description:`<strong>merges_file</strong> (<code>str</code>) &#x2014;
Path to the merges file.`,name:"merges_file"},{anchor:"transformers.BlenderbotSmallTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;__start__&quot;</code>) &#x2014;
The beginning of sentence token.`,name:"bos_token"},{anchor:"transformers.BlenderbotSmallTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;__end__&quot;</code>) &#x2014;
The end of sentence token.`,name:"eos_token"},{anchor:"transformers.BlenderbotSmallTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;__unk__&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.BlenderbotSmallTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;__pad__&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.
**kwargs &#x2014;
Additional keyword arguments passed along to <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>`,name:"pad_token"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py#L69"}}),Vo=new E({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BlenderbotSmallTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallTokenizer.build_inputs_with_special_tokens.token_ids_0",description:"<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014; The first tokenized sequence.",name:"token_ids_0"},{anchor:"transformers.BlenderbotSmallTokenizer.build_inputs_with_special_tokens.token_ids_1",description:"<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014; The second tokenized sequence.",name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L3237",returnDescription:`
<p>The model input with special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Ro=new E({props:{name:"get_special_tokens_mask",anchor:"transformers.BlenderbotSmallTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List"},{name:"token_ids_1",val:": typing.Optional[typing.List] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of ids of the first sequence.`,name:"token_ids_0"},{anchor:"transformers.BlenderbotSmallTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
List of ids of the second sequence.`,name:"token_ids_1"},{anchor:"transformers.BlenderbotSmallTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils.py#L852",returnDescription:`
<p>1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p>A list of integers in the range [0, 1]</p>
`}}),Xo=new E({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BlenderbotSmallTokenizer.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"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:"<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014; The first tokenized sequence.",name:"token_ids_0"},{anchor:"transformers.BlenderbotSmallTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:"<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014; The second tokenized sequence.",name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L3217",returnDescription:`
<p>The token type ids.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Ao=new E({props:{name:"save_vocabulary",anchor:"transformers.BlenderbotSmallTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py#L211"}}),Po=new xe({}),Yo=new E({props:{name:"class transformers.BlenderbotSmallTokenizerFast",anchor:"transformers.BlenderbotSmallTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"merges_file",val:" = None"},{name:"unk_token",val:" = '<|endoftext|>'"},{name:"bos_token",val:" = '<|endoftext|>'"},{name:"eos_token",val:" = '<|endoftext|>'"},{name:"add_prefix_space",val:" = False"},{name:"trim_offsets",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlenderbotSmallTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py#L52"}}),Oo=new E({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BlenderbotSmallTokenizerFast.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"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.BlenderbotSmallTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py#L98",returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Ho=new xe({}),Do=new E({props:{name:"class transformers.BlenderbotSmallModel",anchor:"transformers.BlenderbotSmallModel",parameters:[{name:"config",val:": BlenderbotSmallConfig"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</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/blenderbot_small/modeling_blenderbot_small.py#L1099"}}),tn=new E({props:{name:"forward",anchor:"transformers.BlenderbotSmallModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_outputs",val:": typing.Union[typing.Tuple, transformers.modeling_outputs.BaseModelOutput, NoneType] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = 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"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallModel.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. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BlenderbotSmallModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</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.BlenderbotSmallModel.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>BlenderbotSmall uses the <code>bos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.BlenderbotSmallModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.BlenderbotSmallModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BlenderbotSmallModel.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.BlenderbotSmallModel.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.BlenderbotSmallModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.BlenderbotSmallModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential 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>. inputs_embeds (<code>torch.FloatTensor</code> of shape
<code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>): 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:"past_key_values"},{anchor:"transformers.BlenderbotSmallModel.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.</p>
<p>If <code>decoder_input_ids</code> and <code>decoder_inputs_embeds</code> are both unset, <code>decoder_inputs_embeds</code> takes the value
of <code>inputs_embeds</code>.`,name:"decoder_inputs_embeds"},{anchor:"transformers.BlenderbotSmallModel.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.BlenderbotSmallModel.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.BlenderbotSmallModel.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.BlenderbotSmallModel.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py#L1128",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput"
>transformers.modeling_outputs.Seq2SeqModelOutput</a> 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 (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput"
>transformers.modeling_outputs.Seq2SeqModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ot=new Dn({props:{$$slots:{default:[mm]},$$scope:{ctx:$}}}),Ht=new je({props:{anchor:"transformers.BlenderbotSmallModel.forward.example",$$slots:{default:[hm]},$$scope:{ctx:$}}}),on=new xe({}),nn=new E({props:{name:"class transformers.BlenderbotSmallForConditionalGeneration",anchor:"transformers.BlenderbotSmallForConditionalGeneration",parameters:[{name:"config",val:": BlenderbotSmallConfig"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</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/blenderbot_small/modeling_blenderbot_small.py#L1227"}}),ln=new E({props:{name:"forward",anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_outputs",val:": typing.Union[typing.Tuple, transformers.modeling_outputs.BaseModelOutput, NoneType] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = 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"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallForConditionalGeneration.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. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</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.BlenderbotSmallForConditionalGeneration.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>BlenderbotSmall uses the <code>bos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential 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>. inputs_embeds (<code>torch.FloatTensor</code> of shape
<code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>): 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:"past_key_values"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.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.</p>
<p>If <code>decoder_input_ids</code> and <code>decoder_inputs_embeds</code> are both unset, <code>decoder_inputs_embeds</code> takes the value
of <code>inputs_embeds</code>.`,name:"decoder_inputs_embeds"},{anchor:"transformers.BlenderbotSmallForConditionalGeneration.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.BlenderbotSmallForConditionalGeneration.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.BlenderbotSmallForConditionalGeneration.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.BlenderbotSmallForConditionalGeneration.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.BlenderbotSmallForConditionalGeneration.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py#L1267",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput"
>transformers.modeling_outputs.Seq2SeqLMOutput</a> 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 (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_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 decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_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 encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput"
>transformers.modeling_outputs.Seq2SeqLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qt=new Dn({props:{$$slots:{default:[um]},$$scope:{ctx:$}}}),Kt=new je({props:{anchor:"transformers.BlenderbotSmallForConditionalGeneration.forward.example",$$slots:{default:[fm]},$$scope:{ctx:$}}}),dn=new xe({}),cn=new E({props:{name:"class transformers.BlenderbotSmallForCausalLM",anchor:"transformers.BlenderbotSmallForCausalLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py#L1404"}}),pn=new E({props:{name:"forward",anchor:"transformers.BlenderbotSmallForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = 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"}],parametersDescription:[{anchor:"transformers.BlenderbotSmallForCausalLM.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. Padding will be ignored by default should you
provide it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BlenderbotSmallForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</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.BlenderbotSmallForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.BlenderbotSmallForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_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 the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:`,name:"encoder_attention_mask"},{anchor:"transformers.BlenderbotSmallForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.BlenderbotSmallForCausalLM.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.BlenderbotSmallForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) &#x2014;
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of
shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential 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.BlenderbotSmallForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.BlenderbotSmallForCausalLM.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>).</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"use_cache"},{anchor:"transformers.BlenderbotSmallForCausalLM.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.BlenderbotSmallForCausalLM.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.BlenderbotSmallForCausalLM.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py#L1437",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> 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 (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</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, num_heads, 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>cross_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, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),oo=new je({props:{anchor:"transformers.BlenderbotSmallForCausalLM.forward.example",$$slots:{default:[_m]},$$scope:{ctx:$}}}),mn=new xe({}),hn=new E({props:{name:"class transformers.TFBlenderbotSmallModel",anchor:"transformers.TFBlenderbotSmallModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFBlenderbotSmallModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</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.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py#L1130"}}),ao=new Dn({props:{$$slots:{default:[gm]},$$scope:{ctx:$}}}),gn=new E({props:{name:"call",anchor:"transformers.TFBlenderbotSmallModel.call",parameters:[{name:"input_ids",val:": tf.Tensor | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"decoder_input_ids",val:": tf.Tensor | None = None"},{name:"decoder_attention_mask",val:": tf.Tensor | None = None"},{name:"decoder_position_ids",val:": tf.Tensor | None = None"},{name:"head_mask",val:": tf.Tensor | None = None"},{name:"decoder_head_mask",val:": tf.Tensor | None = None"},{name:"cross_attn_head_mask",val:": tf.Tensor | None = None"},{name:"encoder_outputs",val:": Optional[Union[Tuple, TFBaseModelOutput]] = None"},{name:"past_key_values",val:": List[tf.Tensor] | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"decoder_inputs_embeds",val:": tf.Tensor | None = None"},{name:"use_cache",val:": Optional[bool] = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": Optional[bool] = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFBlenderbotSmallModel.call.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFBlenderbotSmallModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>tf.Tensor</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.TFBlenderbotSmallModel.call.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>BlenderbotSmall uses the <code>bos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.TFBlenderbotSmallModel.call.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.`,name:"decoder_attention_mask"},{anchor:"transformers.TFBlenderbotSmallModel.call.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.TFBlenderbotSmallModel.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFBlenderbotSmallModel.call.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.TFBlenderbotSmallModel.call.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.TFBlenderbotSmallModel.call.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tf.FloatTensor</code>, <em>optional</em>) &#x2014;
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape <code>(batch_size, sequence_length, hidden_size)</code> is a sequence of`,name:"encoder_outputs"},{anchor:"transformers.TFBlenderbotSmallModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
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.TFBlenderbotSmallModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#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>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"},{anchor:"transformers.TFBlenderbotSmallModel.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFBlenderbotSmallModel.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFBlenderbotSmallModel.call.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. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFBlenderbotSmallModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py#L1142",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),so=new Dn({props:{$$slots:{default:[bm]},$$scope:{ctx:$}}}),ro=new je({props:{anchor:"transformers.TFBlenderbotSmallModel.call.example",$$slots:{default:[ym]},$$scope:{ctx:$}}}),bn=new xe({}),yn=new E({props:{name:"class transformers.TFBlenderbotSmallForConditionalGeneration",anchor:"transformers.TFBlenderbotSmallForConditionalGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</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.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py#L1235"}}),io=new Dn({props:{$$slots:{default:[km]},$$scope:{ctx:$}}}),Mn=new E({props:{name:"call",anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call",parameters:[{name:"input_ids",val:": tf.Tensor | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"decoder_input_ids",val:": tf.Tensor | None = None"},{name:"decoder_attention_mask",val:": tf.Tensor | None = None"},{name:"decoder_position_ids",val:": tf.Tensor | None = None"},{name:"head_mask",val:": tf.Tensor | None = None"},{name:"decoder_head_mask",val:": tf.Tensor | None = None"},{name:"cross_attn_head_mask",val:": tf.Tensor | None = None"},{name:"encoder_outputs",val:": Optional[TFBaseModelOutput] = None"},{name:"past_key_values",val:": List[tf.Tensor] | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"decoder_inputs_embeds",val:": tf.Tensor | None = None"},{name:"use_cache",val:": Optional[bool] = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.attention_mask",description:`<strong>attention_mask</strong> (<code>tf.Tensor</code> of shape <code>({0})</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.TFBlenderbotSmallForConditionalGeneration.call.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>BlenderbotSmall uses the <code>bos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.`,name:"decoder_attention_mask"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>tf.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tf.FloatTensor</code>, <em>optional</em>) &#x2014;
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape <code>(batch_size, sequence_length, hidden_size)</code> is a sequence of`,name:"encoder_outputs"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
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.TFBlenderbotSmallForConditionalGeneration.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#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>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.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. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.labels",description:`<strong>labels</strong> (<code>tf.tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py#L1273",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqLMOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) \u2014 Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"
>transformers.modeling_tf_outputs.TFSeq2SeqLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),co=new Dn({props:{$$slots:{default:[Tm]},$$scope:{ctx:$}}}),po=new je({props:{anchor:"transformers.TFBlenderbotSmallForConditionalGeneration.call.example",$$slots:{default:[vm]},$$scope:{ctx:$}}}),wn=new xe({}),Fn=new E({props:{name:"class transformers.FlaxBlenderbotSmallModel",anchor:"transformers.FlaxBlenderbotSmallModel",parameters:[{name:"config",val:": BlenderbotSmallConfig"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</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.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBlenderbotSmallModel.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L1213"}}),Cn=new E({props:{name:"__call__",anchor:"transformers.FlaxBlenderbotSmallModel.__call__",parameters:[{name:"input_ids",val:": Array"},{name:"attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"decoder_input_ids",val:": typing.Optional[jax.Array] = None"},{name:"decoder_attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"position_ids",val:": typing.Optional[jax.Array] = None"},{name:"decoder_position_ids",val:": typing.Optional[jax.Array] = 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:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L1149",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput</a> 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 (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),uo=new je({props:{anchor:"transformers.FlaxBlenderbotSmallModel.__call__.example",$$slots:{default:[Mm]},$$scope:{ctx:$}}}),En=new E({props:{name:"encode",anchor:"transformers.FlaxBlenderbotSmallModel.encode",parameters:[{name:"input_ids",val:": Array"},{name:"attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"position_ids",val:": typing.Optional[jax.Array] = 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:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallModel.encode.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxBlenderbotSmallModel.encode.attention_mask",description:`<strong>attention_mask</strong> (<code>jnp.ndarray</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.FlaxBlenderbotSmallModel.encode.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBlenderbotSmallModel.encode.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.FlaxBlenderbotSmallModel.encode.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.FlaxBlenderbotSmallModel.encode.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L970",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput"
>transformers.modeling_flax_outputs.FlaxBaseModelOutput</a> 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.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, 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>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput"
>transformers.modeling_flax_outputs.FlaxBaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_o=new je({props:{anchor:"transformers.FlaxBlenderbotSmallModel.encode.example",$$slots:{default:[wm]},$$scope:{ctx:$}}}),Un=new E({props:{name:"decode",anchor:"transformers.FlaxBlenderbotSmallModel.decode",parameters:[{name:"decoder_input_ids",val:""},{name:"encoder_outputs",val:""},{name:"encoder_attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"decoder_attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"decoder_position_ids",val:": typing.Optional[jax.Array] = None"},{name:"past_key_values",val:": dict = 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:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallModel.decode.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>For translation and summarization training, <code>decoder_input_ids</code> should be provided. If no
<code>decoder_input_ids</code> is provided, the model will create this tensor by shifting the <code>input_ids</code> to the right
for denoising pre-training following the paper.`,name:"decoder_input_ids"},{anchor:"transformers.FlaxBlenderbotSmallModel.decode.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(jnp.ndarray)</code>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.FlaxBlenderbotSmallModel.decode.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>jnp.ndarray</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:"encoder_attention_mask"},{anchor:"transformers.FlaxBlenderbotSmallModel.decode.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>If you want to change padding behavior, you should modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the
paper</a> for more information on the default strategy.`,name:"decoder_attention_mask"},{anchor:"transformers.FlaxBlenderbotSmallModel.decode.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.FlaxBlenderbotSmallModel.decode.past_key_values",description:`<strong>past_key_values</strong> (<code>Dict[str, np.ndarray]</code>, <em>optional</em>, returned by <code>init_cache</code> or when passing previous <code>past_key_values</code>) &#x2014;
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape <em>[batch_size, max_length]</em>.`,name:"past_key_values"},{anchor:"transformers.FlaxBlenderbotSmallModel.decode.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.FlaxBlenderbotSmallModel.decode.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.FlaxBlenderbotSmallModel.decode.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L1033",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions</a> 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.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, 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>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),bo=new je({props:{anchor:"transformers.FlaxBlenderbotSmallModel.decode.example",$$slots:{default:[Fm]},$$scope:{ctx:$}}}),qn=new xe({}),Gn=new E({props:{name:"class transformers.FlaxBlenderbotSmallForConditionalGeneration",anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration",parameters:[{name:"config",val:": BlenderbotSmallConfig"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</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.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L1299"}}),An=new E({props:{name:"__call__",anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__",parameters:[{name:"input_ids",val:": Array"},{name:"attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"decoder_input_ids",val:": typing.Optional[jax.Array] = None"},{name:"decoder_attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"position_ids",val:": typing.Optional[jax.Array] = None"},{name:"decoder_position_ids",val:": typing.Optional[jax.Array] = 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:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>jnp.ndarray</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.FlaxBlenderbotSmallForConditionalGeneration.__call__.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>For translation and summarization training, <code>decoder_input_ids</code> should be provided. If no
<code>decoder_input_ids</code> is provided, the model will create this tensor by shifting the <code>input_ids</code> to the right
for denoising pre-training following the paper.`,name:"decoder_input_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>If you want to change padding behavior, you should modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the
paper</a> for more information on the default strategy.`,name:"decoder_attention_mask"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.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.FlaxBlenderbotSmallForConditionalGeneration.__call__.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.FlaxBlenderbotSmallForConditionalGeneration.__call__.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L1149",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput</a> 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 (<a
  href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig"
>BlenderbotSmallConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"
>transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ko=new Dn({props:{$$slots:{default:[Bm]},$$scope:{ctx:$}}}),To=new je({props:{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.example",$$slots:{default:[$m]},$$scope:{ctx:$}}}),vo=new je({props:{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.__call__.example-2",$$slots:{default:[zm]},$$scope:{ctx:$}}}),Pn=new E({props:{name:"encode",anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.encode",parameters:[{name:"input_ids",val:": Array"},{name:"attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"position_ids",val:": typing.Optional[jax.Array] = 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:"train",val:": bool = False"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.encode.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.encode.attention_mask",description:`<strong>attention_mask</strong> (<code>jnp.ndarray</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.FlaxBlenderbotSmallForConditionalGeneration.encode.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.encode.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.FlaxBlenderbotSmallForConditionalGeneration.encode.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.FlaxBlenderbotSmallForConditionalGeneration.encode.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L970",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput"
>transformers.modeling_flax_outputs.FlaxBaseModelOutput</a> 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.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, 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>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput"
>transformers.modeling_flax_outputs.FlaxBaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),wo=new je({props:{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.encode.example",$$slots:{default:[xm]},$$scope:{ctx:$}}}),Yn=new E({props:{name:"decode",anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode",parameters:[{name:"decoder_input_ids",val:""},{name:"encoder_outputs",val:""},{name:"encoder_attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"decoder_attention_mask",val:": typing.Optional[jax.Array] = None"},{name:"decoder_position_ids",val:": typing.Optional[jax.Array] = None"},{name:"past_key_values",val:": dict = 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:"deterministic",val:": bool = True"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"}],parametersDescription:[{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>For translation and summarization training, <code>decoder_input_ids</code> should be provided. If no
<code>decoder_input_ids</code> is provided, the model will create this tensor by shifting the <code>input_ids</code> to the right
for denoising pre-training following the paper.`,name:"decoder_input_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(jnp.ndarray)</code>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>jnp.ndarray</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:"encoder_attention_mask"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>If you want to change padding behavior, you should modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the
paper</a> for more information on the default strategy.`,name:"decoder_attention_mask"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.decoder_position_ids",description:`<strong>decoder_position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"decoder_position_ids"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.past_key_values",description:`<strong>past_key_values</strong> (<code>Dict[str, np.ndarray]</code>, <em>optional</em>, returned by <code>init_cache</code> or when passing previous <code>past_key_values</code>) &#x2014;
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape <em>[batch_size, max_length]</em>.`,name:"past_key_values"},{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.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.FlaxBlenderbotSmallForConditionalGeneration.decode.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.FlaxBlenderbotSmallForConditionalGeneration.decode.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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py#L1303",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> 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.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, 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>cross_attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Bo=new je({props:{anchor:"transformers.FlaxBlenderbotSmallForConditionalGeneration.decode.example",$$slots:{default:[jm]},$$scope:{ctx:$}}}),{c(){i=a("meta"),y=p(),f=a("h1"),u=a("a"),b=a("span"),k(n.$$.fragment),c=p(),S=a("span"),Re=l("Blenderbot Small"),Ce=p(),J=a("p"),Xe=l("Note that "),K=a("a"),z=l("BlenderbotSmallModel"),Ae=l(` and
`),P=a("a"),Pe=l("BlenderbotSmallForConditionalGeneration"),Ye=l(` are only used in combination with the checkpoint
`),R=a("a"),Le=l("facebook/blenderbot-90M"),Oe=l(`. Larger Blenderbot checkpoints should
instead be used with `),Y=a("a"),He=l("BlenderbotModel"),De=l(` and
`),L=a("a"),Qe=l("BlenderbotForConditionalGeneration"),Ee=p(),I=a("h2"),ee=a("a"),ke=a("span"),k(H.$$.fragment),U=p(),q=a("span"),Ke=l("Overview"),te=p(),oe=a("p"),et=l("The Blender chatbot model was proposed in "),X=a("a"),tt=l("Recipes for building an open-domain chatbot"),ot=l(` Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
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