import{S as xi,i as Ii,s as Fi,e as n,k as c,w as T,t as a,M as qi,c as s,d as t,m as h,a as i,x as b,h as r,b as l,G as e,g as m,y as k,q as C,o as w,B as P,v as Ai,L as mr}from"../../chunks/vendor-hf-doc-builder.js";import{T as un}from"../../chunks/Tip-hf-doc-builder.js";import{D as ie}from"../../chunks/Docstring-hf-doc-builder.js";import{C as gr}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as $e}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as fr}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Si(E){let p,y,g,_,v;return _=new gr({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> GPTBigCodeConfig, GPTBigCodeModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a GPTBigCode configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = GPTBigCodeConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTBigCodeModel(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(){p=n("p"),y=a("Example:"),g=c(),T(_.$$.fragment)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Example:"),f.forEach(t),g=h(d),b(_.$$.fragment,d)},m(d,f){m(d,p,f),e(p,y),m(d,g,f),k(_,d,f),v=!0},p:mr,i(d){v||(C(_.$$.fragment,d),v=!0)},o(d){w(_.$$.fragment,d),v=!1},d(d){d&&t(p),d&&t(g),P(_,d)}}}function Ni(E){let p,y,g,_,v;return{c(){p=n("p"),y=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),_=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(f,"CODE",{});var B=i(g);_=r(B,"Module"),B.forEach(t),v=r(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(d,f){m(d,p,f),e(p,y),e(p,g),e(g,_),e(p,v)},d(d){d&&t(p)}}}function Oi(E){let p,y,g,_,v;return _=new gr({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, GPTBigCodeModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bigcode/gpt_bigcode-santacoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTBigCodeModel.from_pretrained(<span class="hljs-string">&quot;bigcode/gpt_bigcode-santacoder&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>last_hidden_states = outputs.last_hidden_state`}}),{c(){p=n("p"),y=a("Example:"),g=c(),T(_.$$.fragment)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Example:"),f.forEach(t),g=h(d),b(_.$$.fragment,d)},m(d,f){m(d,p,f),e(p,y),m(d,g,f),k(_,d,f),v=!0},p:mr,i(d){v||(C(_.$$.fragment,d),v=!0)},o(d){w(_.$$.fragment,d),v=!1},d(d){d&&t(p),d&&t(g),P(_,d)}}}function Wi(E){let p,y,g,_,v;return{c(){p=n("p"),y=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),_=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(f,"CODE",{});var B=i(g);_=r(B,"Module"),B.forEach(t),v=r(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(d,f){m(d,p,f),e(p,y),e(p,g),e(g,_),e(p,v)},d(d){d&&t(p)}}}function Ji(E){let p,y,g,_,v;return _=new gr({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, GPTBigCodeForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bigcode/gpt_bigcode-santacoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GPTBigCodeForCausalLM.from_pretrained(<span class="hljs-string">&quot;bigcode/gpt_bigcode-santacoder&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, labels=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){p=n("p"),y=a("Example:"),g=c(),T(_.$$.fragment)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Example:"),f.forEach(t),g=h(d),b(_.$$.fragment,d)},m(d,f){m(d,p,f),e(p,y),m(d,g,f),k(_,d,f),v=!0},p:mr,i(d){v||(C(_.$$.fragment,d),v=!0)},o(d){w(_.$$.fragment,d),v=!1},d(d){d&&t(p),d&&t(g),P(_,d)}}}function Li(E){let p,y,g,_,v;return{c(){p=n("p"),y=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),_=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(f,"CODE",{});var B=i(g);_=r(B,"Module"),B.forEach(t),v=r(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(d,f){m(d,p,f),e(p,y),e(p,g),e(g,_),e(p,v)},d(d){d&&t(p)}}}function Ui(E){let p,y,g,_,v;return{c(){p=n("p"),y=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),_=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){p=s(d,"P",{});var f=i(p);y=r(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(f,"CODE",{});var B=i(g);_=r(B,"Module"),B.forEach(t),v=r(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(d,f){m(d,p,f),e(p,y),e(p,g),e(g,_),e(p,v)},d(d){d&&t(p)}}}function ji(E){let p,y,g,_,v,d,f,B,fn,Bt,Z,de,No,Ge,mn,Oo,gn,Et,le,_n,Be,vn,yn,zt,po,Tn,xt,uo,fo,bn,Ee,kn,It,ce,Cn,ze,wn,Pn,Ft,R,he,Wo,xe,Mn,Jo,$n,qt,mo,Gn,At,M,Lo,Bn,En,V,zn,Uo,xn,In,jo,Fn,qn,An,Do,Sn,Nn,Zo,On,Wn,S,Jn,Ro,Ln,Un,Vo,jn,Dn,Ho,Zn,Rn,Qo,Vn,Hn,Yo,Qn,Yn,Xo,Xn,Kn,Ko,es,os,et,ts,ns,Ie,ss,ot,as,rs,St,Fe,is,qe,ds,Nt,H,pe,tt,Ae,ls,nt,cs,Ot,q,Se,hs,Q,ps,go,us,fs,Ne,ms,gs,_s,Y,vs,_o,ys,Ts,vo,bs,ks,Cs,ue,Wt,X,fe,st,Oe,ws,at,Ps,Jt,z,We,Ms,rt,$s,Gs,Je,Bs,yo,Es,zs,xs,Le,Is,Ue,Fs,qs,As,N,je,Ss,K,Ns,To,Os,Ws,it,Js,Ls,Us,me,js,ge,Lt,ee,_e,dt,De,Ds,lt,Zs,Ut,x,Ze,Rs,ct,Vs,Hs,Re,Qs,bo,Ys,Xs,Ks,Ve,ea,He,oa,ta,na,O,Qe,sa,oe,aa,ko,ra,ia,ht,da,la,ca,ve,ha,ye,jt,te,Te,pt,Ye,pa,ut,ua,Dt,G,Xe,fa,ft,ma,ga,Co,wo,_a,va,ya,A,Ta,mt,ba,ka,gt,Ca,wa,_t,Pa,Ma,vt,$a,Ga,Ba,Ke,Ea,Po,za,xa,Ia,eo,Fa,oo,qa,Aa,Sa,W,to,Na,ne,Oa,Mo,Wa,Ja,yt,La,Ua,ja,be,Zt,se,ke,Tt,no,Da,bt,Za,Rt,I,so,Ra,kt,Va,Ha,ao,Qa,$o,Ya,Xa,Ka,ro,er,io,or,tr,nr,J,lo,sr,ae,ar,Go,rr,ir,Ct,dr,lr,cr,Ce,Vt;return d=new $e({}),Ge=new $e({}),xe=new $e({}),Ae=new $e({}),Se=new ie({props:{name:"class transformers.GPTBigCodeConfig",anchor:"transformers.GPTBigCodeConfig",parameters:[{name:"vocab_size",val:" = 50257"},{name:"n_positions",val:" = 1024"},{name:"n_embd",val:" = 768"},{name:"n_layer",val:" = 12"},{name:"n_head",val:" = 12"},{name:"n_inner",val:" = None"},{name:"activation_function",val:" = 'gelu_pytorch_tanh'"},{name:"resid_pdrop",val:" = 0.1"},{name:"embd_pdrop",val:" = 0.1"},{name:"attn_pdrop",val:" = 0.1"},{name:"layer_norm_epsilon",val:" = 1e-05"},{name:"initializer_range",val:" = 0.02"},{name:"scale_attn_weights",val:" = True"},{name:"use_cache",val:" = True"},{name:"bos_token_id",val:" = 50256"},{name:"eos_token_id",val:" = 50256"},{name:"attention_softmax_in_fp32",val:" = True"},{name:"scale_attention_softmax_in_fp32",val:" = True"},{name:"multi_query",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GPTBigCodeConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 50257) &#x2014;
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a>.`,name:"vocab_size"},{anchor:"transformers.GPTBigCodeConfig.n_positions",description:`<strong>n_positions</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#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:"n_positions"},{anchor:"transformers.GPTBigCodeConfig.n_embd",description:`<strong>n_embd</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the embeddings and hidden states.`,name:"n_embd"},{anchor:"transformers.GPTBigCodeConfig.n_layer",description:`<strong>n_layer</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"n_layer"},{anchor:"transformers.GPTBigCodeConfig.n_head",description:`<strong>n_head</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"n_head"},{anchor:"transformers.GPTBigCodeConfig.n_inner",description:`<strong>n_inner</strong> (<code>int</code>, <em>optional</em>, defaults to None) &#x2014;
Dimensionality of the inner feed-forward layers. <code>None</code> will set it to 4 times n_embd`,name:"n_inner"},{anchor:"transformers.GPTBigCodeConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;gelu_pytorch_tanh&quot;</code>) &#x2014;
Activation function, to be selected in the list <code>[&quot;relu&quot;, &quot;silu&quot;, &quot;gelu&quot;, &quot;tanh&quot;, &quot;gelu_new&quot;, &quot;gelu_pytorch_tanh&quot;]</code>.`,name:"activation_function"},{anchor:"transformers.GPTBigCodeConfig.resid_pdrop",description:`<strong>resid_pdrop</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:"resid_pdrop"},{anchor:"transformers.GPTBigCodeConfig.embd_pdrop",description:`<strong>embd_pdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the embeddings.`,name:"embd_pdrop"},{anchor:"transformers.GPTBigCodeConfig.attn_pdrop",description:`<strong>attn_pdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention.`,name:"attn_pdrop"},{anchor:"transformers.GPTBigCodeConfig.layer_norm_epsilon",description:`<strong>layer_norm_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon to use in the layer normalization layers.`,name:"layer_norm_epsilon"},{anchor:"transformers.GPTBigCodeConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.GPTBigCodeConfig.scale_attn_weights",description:`<strong>scale_attn_weights</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Scale attention weights by dividing by sqrt(hidden_size)..`,name:"scale_attn_weights"},{anchor:"transformers.GPTBigCodeConfig.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.GPTBigCodeConfig.attention_softmax_in_fp32",description:`<strong>attention_softmax_in_fp32</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to call the fused softmax in float32.`,name:"attention_softmax_in_fp32"},{anchor:"transformers.GPTBigCodeConfig.scale_attention_softmax_in_fp32",description:`<strong>scale_attention_softmax_in_fp32</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to scale the attention softmax in float32.`,name:"scale_attention_softmax_in_fp32"},{anchor:"transformers.GPTBigCodeConfig.attention_type",description:`<strong>attention_type</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use Multi-Query Attion (<code>True</code>) or Multi-Head Attention (<code>False</code>).`,name:"attention_type"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py#L28"}}),ue=new fr({props:{anchor:"transformers.GPTBigCodeConfig.example",$$slots:{default:[Si]},$$scope:{ctx:E}}}),Oe=new $e({}),We=new ie({props:{name:"class transformers.GPTBigCodeModel",anchor:"transformers.GPTBigCodeModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.GPTBigCodeModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L502"}}),je=new ie({props:{name:"forward",anchor:"transformers.GPTBigCodeModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.Tensor]] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = 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.GPTBigCodeModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>) &#x2014;
<code>input_ids_length</code> = <code>sequence_length</code> if <code>past_key_values</code> is <code>None</code> else
<code>past_key_values[0][0].shape[-2]</code> (<code>sequence_length</code> of input past key value states). Indices of input
sequence tokens in the vocabulary.</p>
<p>If <code>past_key_values</code> is used, only <code>input_ids</code> that do not have their past calculated should be passed as
<code>input_ids</code>.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.32.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.32.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.32.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.GPTBigCodeModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[torch.Tensor]</code> of length <code>config.n_layers</code>) &#x2014;
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
<code>past_key_values</code> output below). Can be used to speed up sequential decoding. The <code>input_ids</code> which have
their past given to this model should not be passed as <code>input_ids</code> as they have already been computed.`,name:"past_key_values"},{anchor:"transformers.GPTBigCodeModel.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>If <code>past_key_values</code> is used, <code>attention_mask</code> needs to contain the masking strategy that was used for
<code>past_key_values</code>. In other words, the <code>attention_mask</code> always has to have the length:
<code>len(past_key_values) + len(input_ids)</code></p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GPTBigCodeModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.GPTBigCodeModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.Tensor</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>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GPTBigCodeModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</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.GPTBigCodeModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
<code>past_key_values</code>).`,name:"inputs_embeds"},{anchor:"transformers.GPTBigCodeModel.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.GPTBigCodeModel.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.GPTBigCodeModel.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.GPTBigCodeModel.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L531",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</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/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig"
>GPTBigCodeConfig</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 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 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(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> and <code>config.add_cross_attention=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>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),me=new un({props:{$$slots:{default:[Ni]},$$scope:{ctx:E}}}),ge=new fr({props:{anchor:"transformers.GPTBigCodeModel.forward.example",$$slots:{default:[Oi]},$$scope:{ctx:E}}}),De=new $e({}),Ze=new ie({props:{name:"class transformers.GPTBigCodeForCausalLM",anchor:"transformers.GPTBigCodeForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.GPTBigCodeForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L723"}}),Qe=new ie({props:{name:"forward",anchor:"transformers.GPTBigCodeForCausalLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = 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.GPTBigCodeForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>) &#x2014;
<code>input_ids_length</code> = <code>sequence_length</code> if <code>past_key_values</code> is <code>None</code> else
<code>past_key_values[0][0].shape[-2]</code> (<code>sequence_length</code> of input past key value states). Indices of input
sequence tokens in the vocabulary.</p>
<p>If <code>past_key_values</code> is used, only <code>input_ids</code> that do not have their past calculated should be passed as
<code>input_ids</code>.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.32.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.32.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.32.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.GPTBigCodeForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[torch.Tensor]</code> of length <code>config.n_layers</code>) &#x2014;
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
<code>past_key_values</code> output below). Can be used to speed up sequential decoding. The <code>input_ids</code> which have
their past given to this model should not be passed as <code>input_ids</code> as they have already been computed.`,name:"past_key_values"},{anchor:"transformers.GPTBigCodeForCausalLM.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>If <code>past_key_values</code> is used, <code>attention_mask</code> needs to contain the masking strategy that was used for
<code>past_key_values</code>. In other words, the <code>attention_mask</code> always has to have the length:
<code>len(past_key_values) + len(input_ids)</code></p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GPTBigCodeForCausalLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.GPTBigCodeForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.Tensor</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>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GPTBigCodeForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</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.GPTBigCodeForCausalLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
<code>past_key_values</code>).`,name:"inputs_embeds"},{anchor:"transformers.GPTBigCodeForCausalLM.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.GPTBigCodeForCausalLM.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.GPTBigCodeForCausalLM.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.GPTBigCodeForCausalLM.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/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.GPTBigCodeForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for language modeling. Note that the labels <strong>are shifted</strong> inside the model, i.e. you can set
<code>labels = input_ids</code> Indices are selected in <code>[-100, 0, ..., config.vocab_size]</code> All labels set to <code>-100</code>
are ignored (masked), the loss is only computed for labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L777",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/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/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig"
>GPTBigCodeConfig</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/v4.32.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ve=new un({props:{$$slots:{default:[Wi]},$$scope:{ctx:E}}}),ye=new fr({props:{anchor:"transformers.GPTBigCodeForCausalLM.forward.example",$$slots:{default:[Ji]},$$scope:{ctx:E}}}),Ye=new $e({}),Xe=new ie({props:{name:"class transformers.GPTBigCodeForSequenceClassification",anchor:"transformers.GPTBigCodeForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.GPTBigCodeForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</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/v4.32.0/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/v4.32.0/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L876"}}),to=new ie({props:{name:"forward",anchor:"transformers.GPTBigCodeForSequenceClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = 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.GPTBigCodeForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>) &#x2014;
<code>input_ids_length</code> = <code>sequence_length</code> if <code>past_key_values</code> is <code>None</code> else
<code>past_key_values[0][0].shape[-2]</code> (<code>sequence_length</code> of input past key value states). Indices of input
sequence tokens in the vocabulary.</p>
<p>If <code>past_key_values</code> is used, only <code>input_ids</code> that do not have their past calculated should be passed as
<code>input_ids</code>.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.32.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.32.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.32.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.GPTBigCodeForSequenceClassification.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[torch.Tensor]</code> of length <code>config.n_layers</code>) &#x2014;
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
<code>past_key_values</code> output below). Can be used to speed up sequential decoding. The <code>input_ids</code> which have
their past given to this model should not be passed as <code>input_ids</code> as they have already been computed.`,name:"past_key_values"},{anchor:"transformers.GPTBigCodeForSequenceClassification.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>If <code>past_key_values</code> is used, <code>attention_mask</code> needs to contain the masking strategy that was used for
<code>past_key_values</code>. In other words, the <code>attention_mask</code> always has to have the length:
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<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GPTBigCodeForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.GPTBigCodeForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.Tensor</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>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GPTBigCodeForSequenceClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</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.GPTBigCodeForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
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Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L886"}}),be=new un({props:{$$slots:{default:[Li]},$$scope:{ctx:E}}}),no=new $e({}),so=new ie({props:{name:"class transformers.GPTBigCodeForTokenClassification",anchor:"transformers.GPTBigCodeForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.GPTBigCodeForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
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<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p>If <code>past_key_values</code> is used, <code>attention_mask</code> needs to contain the masking strategy that was used for
<code>past_key_values</code>. In other words, the <code>attention_mask</code> always has to have the length:
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<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GPTBigCodeForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, input_ids_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.GPTBigCodeForTokenClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.Tensor</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>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GPTBigCodeForTokenClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
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<li>1 indicates the head is <strong>not masked</strong>,</li>
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model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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etc.)`),sn.forEach(t),Ks=h(U),Ve=s(U,"P",{});var an=i(Ve);ea=r(an,"This model is also a PyTorch "),He=s(an,"A",{href:!0,rel:!0});var di=i(He);oa=r(di,"torch.nn.Module"),di.forEach(t),ta=r(an,` subclass.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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