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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
model = GPT2LMHeadModel.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)

inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute and &quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
generation_output = model.generate(**inputs, return_dict_in_generate=<span class="hljs-literal">True</span>, output_scores=<span class="hljs-literal">True</span>)`}}),lt=new i0({props:{code:"generation_output[:2]",highlighted:'generation_output[:<span class="hljs-number">2</span>]'}}),dt=new Ae({}),pt=new v({props:{name:"class transformers.generation_utils.GreedySearchDecoderOnlyOutput",anchor:"transformers.generation_utils.GreedySearchDecoderOnlyOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],parametersDescription:[{anchor:"transformers.generation_utils.GreedySearchDecoderOnlyOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
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Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for
each generated token), with each tensor of shape <code>(batch_size, config.vocab_size)</code>.`,name:"scores"},{anchor:"transformers.generation_utils.GreedySearchDecoderOnlyOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for
each generated token), with each tensor of shape <code>(batch_size, config.vocab_size)</code>.`,name:"scores"},{anchor:"transformers.generation_utils.GreedySearchEncoderDecoderOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.`,name:"encoder_attentions"},{anchor:"transformers.generation_utils.GreedySearchEncoderDecoderOutput.encoder_hidden_states",description:`<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>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.`,name:"decoder_attentions"},{anchor:"transformers.generation_utils.GreedySearchEncoderDecoderOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.`,name:"cross_attentions"},{anchor:"transformers.generation_utils.GreedySearchEncoderDecoderOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(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>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_utils.py#L151"}}),ft=new v({props:{name:"class transformers.generation_flax_utils.FlaxGreedySearchOutput",anchor:"transformers.generation_flax_utils.FlaxGreedySearchOutput",parameters:[{name:"sequences",val:": ndarray = None"}],parametersDescription:[{anchor:"transformers.generation_flax_utils.FlaxGreedySearchOutput.sequences",description:`<strong>sequences</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, max_length)</code>) &#x2014;
The generated sequences.`,name:"sequences"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_utils.py#L51"}}),gt=new v({props:{name:"replace",anchor:"transformers.generation_flax_utils.FlaxGreedySearchOutput.replace",parameters:[{name:"**updates",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/flax/struct.py#L108"}}),ht=new Ae({}),ut=new v({props:{name:"class transformers.generation_utils.SampleDecoderOnlyOutput",anchor:"transformers.generation_utils.SampleDecoderOnlyOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],parametersDescription:[{anchor:"transformers.generation_utils.SampleDecoderOnlyOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) &#x2014;
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.SampleDecoderOnlyOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for
each generated token), with each tensor of shape <code>(batch_size*num_return_sequences, config.vocab_size)</code>.`,name:"scores"},{anchor:"transformers.generation_utils.SampleDecoderOnlyOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
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Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for
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Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape
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Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_utils.py#L222"}}),bt=new v({props:{name:"class transformers.generation_flax_utils.FlaxSampleOutput",anchor:"transformers.generation_flax_utils.FlaxSampleOutput",parameters:[{name:"sequences",val:": ndarray = None"}],parametersDescription:[{anchor:"transformers.generation_flax_utils.FlaxSampleOutput.sequences",description:`<strong>sequences</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, max_length)</code>) &#x2014;
The generated sequences.`,name:"sequences"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_utils.py#L65"}}),vt=new v({props:{name:"replace",anchor:"transformers.generation_flax_utils.FlaxSampleOutput.replace",parameters:[{name:"**updates",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/flax/struct.py#L108"}}),$t=new Ae({}),Tt=new v({props:{name:"class transformers.generation_utils.BeamSearchDecoderOnlyOutput",anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[torch.LongTensor] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],parametersDescription:[{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) &#x2014;
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for each generated token),
with each tensor of shape <code>(batch_size*num_beams*num_return_sequences, config.vocab_size)</code>.`,name:"scores"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam indices of generated token id at each generation step. <code>torch.LongTensor</code> of shape
<code>(batch_size*num_return_sequences, input_ids.shape[-1])</code>.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.`,name:"attentions"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(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>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)</code>.`,name:"hidden_states"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_utils.py#L265"}}),yt=new v({props:{name:"class transformers.generation_utils.BeamSearchEncoderDecoderOutput",anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[torch.LongTensor] = None"},{name:"encoder_attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"encoder_hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"decoder_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"cross_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"decoder_hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],parametersDescription:[{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) &#x2014;
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for each generated token),
with each tensor of shape <code>(batch_size*num_beams, config.vocab_size)</code>.`,name:"scores"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam indices of generated token id at each generation step. <code>torch.LongTensor</code> of shape
<code>(batch_size*num_return_sequences, max_length-1)</code>.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.attentions",description:"<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;",name:"attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.`,name:"encoder_attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_hidden_states",description:`<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>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)</code>.`,name:"encoder_hidden_states"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_attentions",description:`<strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, num_heads, generated_length, sequence_length)</code>.`,name:"decoder_attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.`,name:"cross_attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(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>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_utils.py#L300"}}),kt=new Ae({}),xt=new v({props:{name:"class transformers.generation_utils.BeamSampleDecoderOnlyOutput",anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[torch.LongTensor] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],parametersDescription:[{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) &#x2014;
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_return_sequence)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for each generated token),
with each tensor of shape <code>(batch_size*num_beams*num_return_sequences, config.vocab_size)</code>.`,name:"scores"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam indices of generated token id at each generation step. <code>torch.LongTensor</code> of shape
<code>(batch_size*num_return_sequences, input_ids.shape[-1])</code>.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.`,name:"attentions"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(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>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, generated_length, hidden_size)</code>.`,name:"hidden_states"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_utils.py#L351"}}),wt=new v({props:{name:"class transformers.generation_utils.BeamSampleEncoderDecoderOutput",anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[torch.LongTensor] = None"},{name:"encoder_attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"encoder_hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"decoder_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"cross_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"decoder_hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],parametersDescription:[{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_beams, sequence_length)</code>) &#x2014;
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_return_sequence)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of <code>torch.FloatTensor</code> with up to <code>max_new_tokens</code> elements (one element for each generated token),
with each tensor of shape <code>(batch_size*num_beams, config.vocab_size)</code>).`,name:"scores"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>torch.LongTensor</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) &#x2014;
Beam indices of generated token id at each generation step. <code>torch.LongTensor</code> of shape
<code>(batch_size*num_return_sequences, max_length-1)</code>.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.`,name:"encoder_attentions"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_hidden_states",description:`<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>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_beams, sequence_length, hidden_size)</code>.`,name:"encoder_hidden_states"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_attentions",description:`<strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.`,name:"decoder_attentions"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.`,name:"cross_attentions"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(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>) &#x2014;
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_utils.py#L386"}}),Lt=new Ae({}),Et=new v({props:{name:"class transformers.LogitsProcessor",anchor:"transformers.LogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L51"}}),Pt=new v({props:{name:"__call__",anchor:"transformers.LogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],parametersDescription:[{anchor:"transformers.LogitsProcessor.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.LogitsProcessor.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L54",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Ft=new v({props:{name:"class transformers.LogitsProcessorList",anchor:"transformers.LogitsProcessorList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L73"}}),Dt=new v({props:{name:"__call__",anchor:"transformers.LogitsProcessorList.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.LogitsProcessorList.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.LogitsProcessorList.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L80",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),St=new v({props:{name:"class transformers.LogitsWarper",anchor:"transformers.LogitsWarper",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L62"}}),zt=new v({props:{name:"__call__",anchor:"transformers.LogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],parametersDescription:[{anchor:"transformers.LogitsWarper.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.LogitsWarper.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L65",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Ot=new v({props:{name:"class transformers.MinLengthLogitsProcessor",anchor:"transformers.MinLengthLogitsProcessor",parameters:[{name:"min_length",val:": int"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.MinLengthLogitsProcessor.min_length",description:`<strong>min_length</strong> (<code>int</code>) &#x2014;
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float(&quot;Inf&quot;)</code>.`,name:"min_length"},{anchor:"transformers.MinLengthLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L96"}}),qt=new v({props:{name:"__call__",anchor:"transformers.MinLengthLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L117"}}),Bt=new v({props:{name:"class transformers.TemperatureLogitsWarper",anchor:"transformers.TemperatureLogitsWarper",parameters:[{name:"temperature",val:": float"}],parametersDescription:[{anchor:"transformers.TemperatureLogitsWarper.temperature",description:`<strong>temperature</strong> (<code>float</code>) &#x2014;
The value used to module the logits distribution.`,name:"temperature"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L124"}}),It=new v({props:{name:"__call__",anchor:"transformers.TemperatureLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L139"}}),At=new v({props:{name:"class transformers.RepetitionPenaltyLogitsProcessor",anchor:"transformers.RepetitionPenaltyLogitsProcessor",parameters:[{name:"penalty",val:": float"}],parametersDescription:[{anchor:"transformers.RepetitionPenaltyLogitsProcessor.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>) &#x2014;
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L144"}}),Ct=new v({props:{name:"__call__",anchor:"transformers.RepetitionPenaltyLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L160"}}),Wt=new v({props:{name:"class transformers.TopPLogitsWarper",anchor:"transformers.TopPLogitsWarper",parameters:[{name:"top_p",val:": float"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.TopPLogitsWarper.top_p",description:`<strong>top_p</strong> (<code>float</code>) &#x2014;
If set to &lt; 1, only the smallest set of most probable tokens with probabilities that add up to <code>top_p</code> or
higher are kept for generation.`,name:"top_p"},{anchor:"transformers.TopPLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TopPLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L170"}}),Nt=new v({props:{name:"__call__",anchor:"transformers.TopPLogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L193"}}),Vt=new v({props:{name:"class transformers.TopKLogitsWarper",anchor:"transformers.TopKLogitsWarper",parameters:[{name:"top_k",val:": int"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.TopKLogitsWarper.top_k",description:`<strong>top_k</strong> (<code>int</code>) &#x2014;
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.TopKLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TopKLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L209"}}),Mt=new v({props:{name:"__call__",anchor:"transformers.TopKLogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L230"}}),Gt=new v({props:{name:"class transformers.TypicalLogitsWarper",anchor:"transformers.TypicalLogitsWarper",parameters:[{name:"mass",val:": float = 0.9"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.TypicalLogitsWarper.mass",description:`<strong>mass</strong> (<code>float</code>) &#x2014;
Value of typical_p between 0 and 1 inclusive, defaults to 0.9.`,name:"mass"},{anchor:"transformers.TypicalLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TypicalLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L238"}}),Ht=new v({props:{name:"__call__",anchor:"transformers.TypicalLogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L261"}}),Rt=new v({props:{name:"class transformers.NoRepeatNGramLogitsProcessor",anchor:"transformers.NoRepeatNGramLogitsProcessor",parameters:[{name:"ngram_size",val:": int"}],parametersDescription:[{anchor:"transformers.NoRepeatNGramLogitsProcessor.ngram_size",description:`<strong>ngram_size</strong> (<code>int</code>) &#x2014;
All ngrams of size <code>ngram_size</code> can only occur once.`,name:"ngram_size"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L322"}}),Ut=new v({props:{name:"__call__",anchor:"transformers.NoRepeatNGramLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L337"}}),Yt=new v({props:{name:"class transformers.NoBadWordsLogitsProcessor",anchor:"transformers.NoBadWordsLogitsProcessor",parameters:[{name:"bad_words_ids",val:": typing.List[typing.List[int]]"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.NoBadWordsLogitsProcessor.bad_words_ids",description:`<strong>bad_words_ids</strong> (<code>List[List[int]]</code>) &#x2014;
List of list of token ids that are not allowed to be generated. In order to get the token ids of the words
that should not appear in the generated text, use <code>tokenizer(bad_words, add_prefix_space=True, add_special_tokens=False).input_ids</code>.`,name:"bad_words_ids"},{anchor:"transformers.NoBadWordsLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L389"}}),Xt=new v({props:{name:"__call__",anchor:"transformers.NoBadWordsLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L431"}}),Jt=new v({props:{name:"class transformers.PrefixConstrainedLogitsProcessor",anchor:"transformers.PrefixConstrainedLogitsProcessor",parameters:[{name:"prefix_allowed_tokens_fn",val:": typing.Callable[[int, torch.Tensor], typing.List[int]]"},{name:"num_beams",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L517"}}),Zt=new v({props:{name:"__call__",anchor:"transformers.PrefixConstrainedLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L534"}}),eo=new v({props:{name:"class transformers.HammingDiversityLogitsProcessor",anchor:"transformers.HammingDiversityLogitsProcessor",parameters:[{name:"diversity_penalty",val:": float"},{name:"num_beams",val:": int"},{name:"num_beam_groups",val:": int"}],parametersDescription:[{anchor:"transformers.HammingDiversityLogitsProcessor.diversity_penalty",description:`<strong>diversity_penalty</strong> (<code>float</code>) &#x2014;
This value is subtracted from a beam&#x2019;s score if it generates a token same as any beam from other group at a
particular time. Note that <code>diversity_penalty</code> is only effective if <code>group beam search</code> is enabled.`,name:"diversity_penalty"},{anchor:"transformers.HammingDiversityLogitsProcessor.num_beams",description:`<strong>num_beams</strong> (<code>int</code>) &#x2014;
Number of beams used for group beam search. See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more
details.`,name:"num_beams"},{anchor:"transformers.HammingDiversityLogitsProcessor.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>) &#x2014;
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L543"}}),to=new v({props:{name:"__call__",anchor:"transformers.HammingDiversityLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"current_tokens",val:": LongTensor"},{name:"beam_group_idx",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L574"}}),oo=new v({props:{name:"class transformers.ForcedBOSTokenLogitsProcessor",anchor:"transformers.ForcedBOSTokenLogitsProcessor",parameters:[{name:"bos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.ForcedBOSTokenLogitsProcessor.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>) &#x2014;
The id of the token to force as the first generated token.`,name:"bos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L603"}}),no=new v({props:{name:"__call__",anchor:"transformers.ForcedBOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L615"}}),so=new v({props:{name:"class transformers.ForcedEOSTokenLogitsProcessor",anchor:"transformers.ForcedEOSTokenLogitsProcessor",parameters:[{name:"max_length",val:": int"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.ForcedEOSTokenLogitsProcessor.max_length",description:`<strong>max_length</strong> (<code>int</code>) &#x2014;
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.ForcedEOSTokenLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the token to force as the last generated token when <code>max_length</code> is reached.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L624"}}),ao=new v({props:{name:"__call__",anchor:"transformers.ForcedEOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L639"}}),io=new v({props:{name:"class transformers.InfNanRemoveLogitsProcessor",anchor:"transformers.InfNanRemoveLogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L648"}}),co=new v({props:{name:"__call__",anchor:"transformers.InfNanRemoveLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_logits_process.py#L655"}}),lo=new v({props:{name:"class transformers.TFLogitsProcessor",anchor:"transformers.TFLogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L53"}}),po=new v({props:{name:"__call__",anchor:"transformers.TFLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],parametersDescription:[{anchor:"transformers.TFLogitsProcessor.__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/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.TFLogitsProcessor.__call__.scores",description:`<strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search.`,name:"scores"},{anchor:"transformers.TFLogitsProcessor.__call__.cur_len",description:`<strong>cur_len</strong> (<code>int</code>) &#x2014;
The current length of valid input sequence tokens. In the TF implementation, the input_ids&#x2019; sequence length
is the maximum length generate can produce, and we need to know which of its tokens are valid.
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"cur_len"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L56",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),mo=new v({props:{name:"class transformers.TFLogitsProcessorList",anchor:"transformers.TFLogitsProcessorList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L75"}}),fo=new v({props:{name:"__call__",anchor:"transformers.TFLogitsProcessorList.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFLogitsProcessorList.__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/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.TFLogitsProcessorList.__call__.scores",description:`<strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search.`,name:"scores"},{anchor:"transformers.TFLogitsProcessorList.__call__.cur_len",description:`<strong>cur_len</strong> (<code>int</code>) &#x2014;
The current length of valid input sequence tokens. In the TF implementation, the input_ids&#x2019; sequence length
is the maximum length generate can produce, and we need to know which of its tokens are valid.
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"cur_len"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L82",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),go=new v({props:{name:"class transformers.TFLogitsWarper",anchor:"transformers.TFLogitsWarper",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L64"}}),ho=new v({props:{name:"__call__",anchor:"transformers.TFLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],parametersDescription:[{anchor:"transformers.TFLogitsWarper.__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/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.TFLogitsWarper.__call__.scores",description:`<strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search.`,name:"scores"},{anchor:"transformers.TFLogitsWarper.__call__.cur_len",description:`<strong>cur_len</strong> (<code>int</code>) &#x2014;
The current length of valid input sequence tokens. In the TF implementation, the input_ids&#x2019; sequence length
is the maximum length generate can produce, and we need to know which of its tokens are valid.
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"cur_len"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L67",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),uo=new v({props:{name:"class transformers.TFTemperatureLogitsWarper",anchor:"transformers.TFTemperatureLogitsWarper",parameters:[{name:"temperature",val:": float"}],parametersDescription:[{anchor:"transformers.TFTemperatureLogitsWarper.temperature",description:`<strong>temperature</strong> (<code>float</code>) &#x2014;
The value used to module the logits distribution.`,name:"temperature"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L98"}}),_o=new v({props:{name:"__call__",anchor:"transformers.TFTemperatureLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L113"}}),bo=new v({props:{name:"class transformers.TFTopPLogitsWarper",anchor:"transformers.TFTopPLogitsWarper",parameters:[{name:"top_p",val:": float"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.TFTopPLogitsWarper.top_p",description:`<strong>top_p</strong> (<code>float</code>) &#x2014;
If set to &lt; 1, only the smallest set of most probable tokens with probabilities that add up to <code>top_p</code> or
higher are kept for generation.`,name:"top_p"},{anchor:"transformers.TFTopPLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TFTopPLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L147"}}),vo=new v({props:{name:"__call__",anchor:"transformers.TFTopPLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L169"}}),$o=new v({props:{name:"class transformers.TFTopKLogitsWarper",anchor:"transformers.TFTopKLogitsWarper",parameters:[{name:"top_k",val:": int"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.TFTopKLogitsWarper.top_k",description:`<strong>top_k</strong> (<code>int</code>) &#x2014;
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.TFTopKLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TFTopKLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L118"}}),To=new v({props:{name:"__call__",anchor:"transformers.TFTopKLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L139"}}),yo=new v({props:{name:"class transformers.TFMinLengthLogitsProcessor",anchor:"transformers.TFMinLengthLogitsProcessor",parameters:[{name:"min_length",val:": int"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.TFMinLengthLogitsProcessor.min_length",description:`<strong>min_length</strong> (<code>int</code>) &#x2014;
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float(&quot;Inf&quot;)</code>.`,name:"min_length"},{anchor:"transformers.TFMinLengthLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L201"}}),ko=new v({props:{name:"__call__",anchor:"transformers.TFMinLengthLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L227"}}),xo=new v({props:{name:"class transformers.TFNoBadWordsLogitsProcessor",anchor:"transformers.TFNoBadWordsLogitsProcessor",parameters:[{name:"bad_words_ids",val:": typing.List[typing.List[int]]"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.TFNoBadWordsLogitsProcessor.bad_words_ids",description:`<strong>bad_words_ids</strong> (<code>List[List[int]]</code>) &#x2014;
List of list of token ids that are not allowed to be generated. In order to get the tokens of the words
that should not appear in the generated text, use <code>tokenizer(bad_word, add_prefix_space=True).input_ids</code>.`,name:"bad_words_ids"},{anchor:"transformers.TFNoBadWordsLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L287"}}),wo=new v({props:{name:"__call__",anchor:"transformers.TFNoBadWordsLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L364"}}),Lo=new v({props:{name:"class transformers.TFNoRepeatNGramLogitsProcessor",anchor:"transformers.TFNoRepeatNGramLogitsProcessor",parameters:[{name:"ngram_size",val:": int"}],parametersDescription:[{anchor:"transformers.TFNoRepeatNGramLogitsProcessor.ngram_size",description:`<strong>ngram_size</strong> (<code>int</code>) &#x2014;
All ngrams of size <code>ngram_size</code> can only occur once.`,name:"ngram_size"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L385"}}),Po=new v({props:{name:"__call__",anchor:"transformers.TFNoRepeatNGramLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L424"}}),Fo=new v({props:{name:"class transformers.TFRepetitionPenaltyLogitsProcessor",anchor:"transformers.TFRepetitionPenaltyLogitsProcessor",parameters:[{name:"penalty",val:": float"}],parametersDescription:[{anchor:"transformers.TFRepetitionPenaltyLogitsProcessor.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>) &#x2014;
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L237"}}),Do=new v({props:{name:"__call__",anchor:"transformers.TFRepetitionPenaltyLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L279"}}),So=new v({props:{name:"class transformers.TFForcedBOSTokenLogitsProcessor",anchor:"transformers.TFForcedBOSTokenLogitsProcessor",parameters:[{name:"bos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.TFForcedBOSTokenLogitsProcessor.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>) &#x2014;
The id of the token to force as the first generated token.`,name:"bos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L446"}}),zo=new v({props:{name:"__call__",anchor:"transformers.TFForcedBOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L460"}}),Oo=new v({props:{name:"class transformers.TFForcedEOSTokenLogitsProcessor",anchor:"transformers.TFForcedEOSTokenLogitsProcessor",parameters:[{name:"max_length",val:": int"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.TFForcedEOSTokenLogitsProcessor.max_length",description:`<strong>max_length</strong> (<code>int</code>) &#x2014;
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.TFForcedEOSTokenLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the token to force as the last generated token when <code>max_length</code> is reached.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L476"}}),qo=new v({props:{name:"__call__",anchor:"transformers.TFForcedEOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_tf_logits_process.py#L493"}}),Bo=new v({props:{name:"class transformers.FlaxLogitsProcessor",anchor:"transformers.FlaxLogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L50"}}),Io=new v({props:{name:"__call__",anchor:"transformers.FlaxLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"}],parametersDescription:[{anchor:"transformers.FlaxLogitsProcessor.__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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxLogitsProcessor.__call__.scores",description:`<strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L53",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Ao=new v({props:{name:"class transformers.FlaxLogitsProcessorList",anchor:"transformers.FlaxLogitsProcessorList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L72"}}),Co=new v({props:{name:"__call__",anchor:"transformers.FlaxLogitsProcessorList.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxLogitsProcessorList.__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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxLogitsProcessorList.__call__.scores",description:`<strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L79",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Wo=new v({props:{name:"class transformers.FlaxLogitsWarper",anchor:"transformers.FlaxLogitsWarper",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L61"}}),No=new v({props:{name:"__call__",anchor:"transformers.FlaxLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"}],parametersDescription:[{anchor:"transformers.FlaxLogitsWarper.__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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.FlaxLogitsWarper.__call__.scores",description:`<strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs &#x2014;
Additional logits processor specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L64",returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Vo=new v({props:{name:"class transformers.FlaxTemperatureLogitsWarper",anchor:"transformers.FlaxTemperatureLogitsWarper",parameters:[{name:"temperature",val:": float"}],parametersDescription:[{anchor:"transformers.FlaxTemperatureLogitsWarper.temperature",description:`<strong>temperature</strong> (<code>float</code>) &#x2014;
The value used to module the logits distribution.`,name:"temperature"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L95"}}),Mo=new v({props:{name:"__call__",anchor:"transformers.FlaxTemperatureLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L110"}}),Go=new v({props:{name:"class transformers.FlaxTopPLogitsWarper",anchor:"transformers.FlaxTopPLogitsWarper",parameters:[{name:"top_p",val:": float"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.FlaxTopPLogitsWarper.top_p",description:`<strong>top_p</strong> (<code>float</code>) &#x2014;
If set to &lt; 1, only the smallest set of most probable tokens with probabilities that add up to <code>top_p</code> or
higher are kept for generation.`,name:"top_p"},{anchor:"transformers.FlaxTopPLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.FlaxTopPLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L115"}}),jo=new v({props:{name:"__call__",anchor:"transformers.FlaxTopPLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L137"}}),Ho=new v({props:{name:"class transformers.FlaxTopKLogitsWarper",anchor:"transformers.FlaxTopKLogitsWarper",parameters:[{name:"top_k",val:": int"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],parametersDescription:[{anchor:"transformers.FlaxTopKLogitsWarper.top_k",description:`<strong>top_k</strong> (<code>int</code>) &#x2014;
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.FlaxTopKLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float(&quot;Inf&quot;)</code>) &#x2014;
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.FlaxTopKLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L157"}}),Ro=new v({props:{name:"__call__",anchor:"transformers.FlaxTopKLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L178"}}),Ko=new v({props:{name:"class transformers.FlaxForcedBOSTokenLogitsProcessor",anchor:"transformers.FlaxForcedBOSTokenLogitsProcessor",parameters:[{name:"bos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.FlaxForcedBOSTokenLogitsProcessor.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>) &#x2014;
The id of the token to force as the first generated token.`,name:"bos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L193"}}),Uo=new v({props:{name:"__call__",anchor:"transformers.FlaxForcedBOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L205"}}),Yo=new v({props:{name:"class transformers.FlaxForcedEOSTokenLogitsProcessor",anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor",parameters:[{name:"max_length",val:": int"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor.max_length",description:`<strong>max_length</strong> (<code>int</code>) &#x2014;
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the token to force as the last generated token when <code>max_length</code> is reached.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L215"}}),Xo=new v({props:{name:"__call__",anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L230"}}),Jo=new v({props:{name:"class transformers.FlaxMinLengthLogitsProcessor",anchor:"transformers.FlaxMinLengthLogitsProcessor",parameters:[{name:"min_length",val:": int"},{name:"eos_token_id",val:": int"}],parametersDescription:[{anchor:"transformers.FlaxMinLengthLogitsProcessor.min_length",description:`<strong>min_length</strong> (<code>int</code>) &#x2014;
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float(&quot;Inf&quot;)</code>.`,name:"min_length"},{anchor:"transformers.FlaxMinLengthLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L240"}}),Qo=new v({props:{name:"__call__",anchor:"transformers.FlaxMinLengthLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_flax_logits_process.py#L261"}}),Zo=new Ae({}),en=new v({props:{name:"class transformers.StoppingCriteria",anchor:"transformers.StoppingCriteria",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L33"}}),rn=new v({props:{name:"__call__",anchor:"transformers.StoppingCriteria.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.StoppingCriteria.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.StoppingCriteria.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs &#x2014;
Additional stopping criteria specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L36",returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),tn=new v({props:{name:"class transformers.StoppingCriteriaList",anchor:"transformers.StoppingCriteriaList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L110"}}),on=new v({props:{name:"__call__",anchor:"transformers.StoppingCriteriaList.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.StoppingCriteriaList.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.StoppingCriteriaList.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs &#x2014;
Additional stopping criteria specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L111",returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),nn=new v({props:{name:"class transformers.MaxLengthCriteria",anchor:"transformers.MaxLengthCriteria",parameters:[{name:"max_length",val:": int"}],parametersDescription:[{anchor:"transformers.MaxLengthCriteria.max_length",description:`<strong>max_length</strong> (<code>int</code>) &#x2014;
The maximum length that the output sequence can have in number of tokens.`,name:"max_length"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L41"}}),an=new v({props:{name:"__call__",anchor:"transformers.MaxLengthCriteria.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MaxLengthCriteria.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.MaxLengthCriteria.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs &#x2014;
Additional stopping criteria specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L54",returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),cn=new v({props:{name:"class transformers.MaxTimeCriteria",anchor:"transformers.MaxTimeCriteria",parameters:[{name:"max_time",val:": float"},{name:"initial_timestamp",val:": typing.Optional[float] = None"}],parametersDescription:[{anchor:"transformers.MaxTimeCriteria.max_time",description:`<strong>max_time</strong> (<code>float</code>) &#x2014;
The maximum allowed time in seconds for the generation.`,name:"max_time"},{anchor:"transformers.MaxTimeCriteria.initial_time",description:`<strong>initial_time</strong> (<code>float</code>, <em>optional</em>, defaults to <code>time.time()</code>) &#x2014;
The start of the generation allowed time.`,name:"initial_time"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L88"}}),dn=new v({props:{name:"__call__",anchor:"transformers.MaxTimeCriteria.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MaxTimeCriteria.__call__.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_19907/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.MaxTimeCriteria.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) &#x2014;
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs &#x2014;
Additional stopping criteria specific kwargs.`,name:"scores"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_stopping_criteria.py#L105",returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),pn=new Ae({}),mn=new v({props:{name:"class transformers.Constraint",anchor:"transformers.Constraint",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L5"}}),jr=new qk({props:{anchor:"transformers.Constraint.example",$$slots:{default:[Bk]},$$scope:{ctx:xd}}}),fn=new v({props:{name:"advance",anchor:"transformers.Constraint.advance",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L48",returnDescription:`
<p>Must be a tensor of a list of indexable tokens, not some integer.</p>
`,returnType:`
<p>token_ids(<code>torch.tensor</code>)</p>
`}}),gn=new v({props:{name:"copy",anchor:"transformers.Constraint.copy",parameters:[{name:"stateful",val:" = False"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L113",returnDescription:`
<p>The same constraint as the one being called from.</p>
`,returnType:`
<p>constraint(<code>Constraint</code>)</p>
`}}),hn=new v({props:{name:"does_advance",anchor:"transformers.Constraint.does_advance",parameters:[{name:"token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L60"}}),un=new v({props:{name:"remaining",anchor:"transformers.Constraint.remaining",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L104"}}),bn=new v({props:{name:"reset",anchor:"transformers.Constraint.reset",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L94"}}),vn=new v({props:{name:"test",anchor:"transformers.Constraint.test",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L24"}}),$n=new v({props:{name:"update",anchor:"transformers.Constraint.update",parameters:[{name:"token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L69",returnDescription:`
<p>Whether this constraint has become one step closer to being fulfuilled.
completed(<code>bool</code>):
Whether this constraint has been completely fulfilled by this token being generated.
reset (<code>bool</code>):
Whether this constraint has reset its progress by this token being generated.</p>
`,returnType:`
<p>stepped(<code>bool</code>)</p>
`}}),yn=new v({props:{name:"class transformers.PhrasalConstraint",anchor:"transformers.PhrasalConstraint",parameters:[{name:"token_ids",val:": typing.List[int]"}],parametersDescription:[{anchor:"transformers.PhrasalConstraint.token_ids",description:`<strong>token_ids</strong> (<code>List[int]</code>) &#x2014;
The id of the token that must be generated by the output.`,name:"token_ids"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L129"}}),kn=new v({props:{name:"class transformers.DisjunctiveConstraint",anchor:"transformers.DisjunctiveConstraint",parameters:[{name:"nested_token_ids",val:": typing.List[typing.List[int]]"}],parametersDescription:[{anchor:"transformers.DisjunctiveConstraint.nested_token_ids",description:"<strong>nested_token_ids</strong> (<code>List[List[int]]</code>) &#x2014; a list of words, where each word is a list of ids. This constraint",name:"nested_token_ids"},{anchor:"transformers.DisjunctiveConstraint.is",description:"<strong>is</strong> fulfilled by generating just one from the list of words. &#x2014;",name:"is"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L261"}}),wn=new v({props:{name:"class transformers.ConstraintListState",anchor:"transformers.ConstraintListState",parameters:[{name:"constraints",val:": typing.List[transformers.generation_beam_constraints.Constraint]"}],parametersDescription:[{anchor:"transformers.ConstraintListState.constraints",description:`<strong>constraints</strong> (<code>List[Constraint]</code>) &#x2014;
A list of <a href="/docs/transformers/pr_19907/en/internal/generation_utils#transformers.Constraint">Constraint</a> objects that must be fulfilled by the beam scorer.`,name:"constraints"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L350"}}),Ln=new v({props:{name:"advance",anchor:"transformers.ConstraintListState.advance",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L382"}}),En=new v({props:{name:"reset",anchor:"transformers.ConstraintListState.reset",parameters:[{name:"token_ids",val:": typing.Optional[typing.List[int]]"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_constraints.py#L417"}}),Pn=new Ae({}),Fn=new v({props:{name:"class transformers.BeamScorer",anchor:"transformers.BeamScorer",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L88"}}),Dn=new v({props:{name:"process",anchor:"transformers.BeamScorer.process",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BeamScorer.process.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_19907/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.BeamScorer.process.next_scores",description:`<strong>next_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) &#x2014;
Current scores of the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_scores"},{anchor:"transformers.BeamScorer.process.next_tokens",description:`<strong>next_tokens</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) &#x2014;
<code>input_ids</code> of the tokens corresponding to the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_tokens"},{anchor:"transformers.BeamScorer.process.next_indices",description:`<strong>next_indices</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) &#x2014;
Beam indices indicating to which beam hypothesis the <code>next_tokens</code> correspond.`,name:"next_indices"},{anchor:"transformers.BeamScorer.process.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.BeamScorer.process.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L94",returnDescription:`
<p>A dictionary composed of the fields as defined above:</p>
<ul>
<li><strong>next_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Updated scores of all
non-finished beams.</li>
<li><strong>next_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Next tokens to be added
to the non-finished beam_hypotheses.</li>
<li><strong>next_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Beam indices
indicating to which beam the next tokens shall be added.</li>
</ul>
`,returnType:`
<p><code>UserDict</code></p>
`}}),Sn=new v({props:{name:"finalize",anchor:"transformers.BeamScorer.finalize",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"max_length",val:": int"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BeamScorer.finalize.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_19907/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.BeamScorer.finalize.final_beam_scores",description:`<strong>final_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) &#x2014;
The final scores of all non-finished beams.`,name:"final_beam_scores"},{anchor:"transformers.BeamScorer.finalize.final_beam_tokens",description:`<strong>final_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) &#x2014;
The last tokens to be added to the non-finished beam_hypotheses.`,name:"final_beam_tokens"},{anchor:"transformers.BeamScorer.finalize.final_beam_indices",description:`<strong>final_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) &#x2014;
The beam indices indicating to which beam the <code>final_beam_tokens</code> shall be added.`,name:"final_beam_indices"},{anchor:"transformers.BeamScorer.finalize.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.BeamScorer.finalize.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L106",returnDescription:`
<p>The generated sequences.
The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter if all batches finished early
due to the <code>eos_token_id</code>.</p>
`,returnType:`
<p><code>torch.LongTensor</code> of shape <code>(batch_size * num_return_sequences, sequence_length)</code></p>
`}}),zn=new v({props:{name:"class transformers.BeamSearchScorer",anchor:"transformers.BeamSearchScorer",parameters:[{name:"batch_size",val:": int"},{name:"num_beams",val:": int"},{name:"device",val:": device"},{name:"length_penalty",val:": typing.Optional[float] = 1.0"},{name:"do_early_stopping",val:": typing.Optional[bool] = False"},{name:"num_beam_hyps_to_keep",val:": typing.Optional[int] = 1"},{name:"num_beam_groups",val:": typing.Optional[int] = 1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BeamSearchScorer.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) &#x2014;
Batch Size of <code>input_ids</code> for which standard beam search decoding is run in parallel.`,name:"batch_size"},{anchor:"transformers.BeamSearchScorer.max_length",description:`<strong>max_length</strong> (<code>int</code>) &#x2014;
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.BeamSearchScorer.num_beams",description:`<strong>num_beams</strong> (<code>int</code>) &#x2014;
Number of beams for beam search.`,name:"num_beams"},{anchor:"transformers.BeamSearchScorer.device",description:`<strong>device</strong> (<code>torch.device</code>) &#x2014;
Defines the device type (<em>e.g.</em>, <code>&quot;cpu&quot;</code> or <code>&quot;cuda&quot;</code>) on which this instance of <code>BeamSearchScorer</code> will be
allocated.`,name:"device"},{anchor:"transformers.BeamSearchScorer.length_penalty",description:`<strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), <code>length_penalty</code> &gt; 0.0 promotes longer sequences, while
<code>length_penalty</code> &lt; 0.0 encourages shorter sequences.`,name:"length_penalty"},{anchor:"transformers.BeamSearchScorer.do_early_stopping",description:`<strong>do_early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.`,name:"do_early_stopping"},{anchor:"transformers.BeamSearchScorer.num_beam_hyps_to_keep",description:`<strong>num_beam_hyps_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of beam hypotheses that shall be returned upon calling
<code>~transformer.BeamSearchScorer.finalize</code>.`,name:"num_beam_hyps_to_keep"},{anchor:"transformers.BeamSearchScorer.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>) &#x2014;
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L120"}}),In=new v({props:{name:"process",anchor:"transformers.BeamSearchScorer.process",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"beam_indices",val:": typing.Optional[torch.LongTensor] = None"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L208"}}),An=new v({props:{name:"finalize",anchor:"transformers.BeamSearchScorer.finalize",parameters:[{name:"input_ids",val:": LongTensor"},{name:"final_beam_scores",val:": FloatTensor"},{name:"final_beam_tokens",val:": LongTensor"},{name:"final_beam_indices",val:": LongTensor"},{name:"max_length",val:": int"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"beam_indices",val:": typing.Optional[torch.LongTensor] = None"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L302"}}),Cn=new v({props:{name:"class transformers.ConstrainedBeamSearchScorer",anchor:"transformers.ConstrainedBeamSearchScorer",parameters:[{name:"batch_size",val:": int"},{name:"num_beams",val:": int"},{name:"constraints",val:": typing.List[transformers.generation_beam_constraints.Constraint]"},{name:"device",val:": device"},{name:"length_penalty",val:": typing.Optional[float] = 1.0"},{name:"do_early_stopping",val:": typing.Optional[bool] = False"},{name:"num_beam_hyps_to_keep",val:": typing.Optional[int] = 1"},{name:"num_beam_groups",val:": typing.Optional[int] = 1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ConstrainedBeamSearchScorer.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) &#x2014;
Batch Size of <code>input_ids</code> for which standard beam search decoding is run in parallel.`,name:"batch_size"},{anchor:"transformers.ConstrainedBeamSearchScorer.max_length",description:`<strong>max_length</strong> (<code>int</code>) &#x2014;
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.ConstrainedBeamSearchScorer.num_beams",description:`<strong>num_beams</strong> (<code>int</code>) &#x2014;
Number of beams for beam search.`,name:"num_beams"},{anchor:"transformers.ConstrainedBeamSearchScorer.constraints",description:`<strong>constraints</strong> (<code>List[Constraint]</code>) &#x2014;
A list of positive constraints represented as <code>Constraint</code> objects that must be fulfilled in the generation
output. For more information, the documentation of <a href="/docs/transformers/pr_19907/en/internal/generation_utils#transformers.Constraint">Constraint</a> should be read.`,name:"constraints"},{anchor:"transformers.ConstrainedBeamSearchScorer.device",description:`<strong>device</strong> (<code>torch.device</code>) &#x2014;
Defines the device type (<em>e.g.</em>, <code>&quot;cpu&quot;</code> or <code>&quot;cuda&quot;</code>) on which this instance of <code>BeamSearchScorer</code> will be
allocated.`,name:"device"},{anchor:"transformers.ConstrainedBeamSearchScorer.length_penalty",description:`<strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), <code>length_penalty</code> &gt; 0.0 promotes longer sequences, while
<code>length_penalty</code> &lt; 0.0 encourages shorter sequences.`,name:"length_penalty"},{anchor:"transformers.ConstrainedBeamSearchScorer.do_early_stopping",description:`<strong>do_early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.`,name:"do_early_stopping"},{anchor:"transformers.ConstrainedBeamSearchScorer.num_beam_hyps_to_keep",description:`<strong>num_beam_hyps_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of beam hypotheses that shall be returned upon calling
<code>~transformer.BeamSearchScorer.finalize</code>.`,name:"num_beam_hyps_to_keep"},{anchor:"transformers.ConstrainedBeamSearchScorer.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>) &#x2014;
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/generation_beam_search.py#L390"}}),Wn=new v({props:{name:"process",anchor:"transformers.ConstrainedBeamSearchScorer.process",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"scores_for_all_vocab",val:": FloatTensor"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"transformers.ConstrainedBeamSearchScorer.process.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_19907/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_19907/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_19907/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.ConstrainedBeamSearchScorer.process.next_scores",description:`<strong>next_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) &#x2014;
Current scores of the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_scores"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.next_tokens",description:`<strong>next_tokens</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) &#x2014;
<code>input_ids</code> of the tokens corresponding to the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_tokens"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.next_indices",description:`<strong>next_indices</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) &#x2014;
Beam indices indicating to which beam hypothesis the <code>next_tokens</code> correspond.`,name:"next_indices"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.scores_for_all_vocab",description:`<strong>scores_for_all_vocab</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) &#x2014;
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<ul>
<li>
<p><strong>next_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Updated scores of
all
non-finished beams.</p>
</li>
<li>
<p><strong>next_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Next tokens to be
added
to the non-finished beam_hypotheses.</p>
</li>
<li>
<p><strong>next_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Beam indices
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</li>
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encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),oT.forEach(t),lm.forEach(t),Gd=l(e),R=n(e,"DIV",{class:!0});var $i=s(R);g(ft.$$.fragment,$i),Ng=l($i),Hc=n($i,"P",{});var nT=s(Hc);Vg=i(nT,"Flax Base class for outputs of decoder-only generation models using greedy search."),nT.forEach(t),Mg=l($i),$r=n($i,"DIV",{class:!0});var dm=s($r);g(gt.$$.fragment,dm),Gg=l(dm),Rc=n(dm,"P",{});var sT=s(Rc);jg=i(sT,"\u201CReturns a new object replacing the specified fields with new values."),sT.forEach(t),dm.forEach(t),$i.forEach(t),jd=l(e),Je=n(e,"H3",{class:!0});var pm=s(Je);Tr=n(pm,"A",{id:!0,class:!0,href:!0});var aT=s(Tr);Kc=n(aT,"SPAN",{});var iT=s(Kc);g(ht.$$.fragment,iT),iT.forEach(t),aT.forEach(t),Hg=l(pm),Uc=n(pm,"SPAN",{});var cT=s(Uc);Rg=i(cT,"SampleOutput"),cT.forEach(t),pm.forEach(t),Hd=l(e),Qe=n(e,"DIV",{class:!0});var mm=s(Qe);g(ut.$$.fragment,mm),Kg=l(mm),Yc=n(mm,"P",{});var lT=s(Yc);Ug=i(lT,"Base class for outputs of decoder-only generation models using sampling."),lT.forEach(t),mm.forEach(t),Rd=l(e),Ze=n(e,"DIV",{class:!0});var fm=s(Ze);g(_t.$$.fragment,fm),Yg=l(fm),Xc=n(fm,"P",{});var dT=s(Xc);Xg=i(dT,`Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),dT.forEach(t),fm.forEach(t),Kd=l(e),K=n(e,"DIV",{class:!0});var Ti=s(K);g(bt.$$.fragment,Ti),Jg=l(Ti),Jc=n(Ti,"P",{});var pT=s(Jc);Qg=i(pT,"Flax Base class for outputs of decoder-only generation models using sampling."),pT.forEach(t),Zg=l(Ti),yr=n(Ti,"DIV",{class:!0});var gm=s(yr);g(vt.$$.fragment,gm),eh=l(gm),Qc=n(gm,"P",{});var mT=s(Qc);rh=i(mT,"\u201CReturns a new object replacing the specified fields with new values."),mT.forEach(t),gm.forEach(t),Ti.forEach(t),Ud=l(e),er=n(e,"H3",{class:!0});var hm=s(er);kr=n(hm,"A",{id:!0,class:!0,href:!0});var fT=s(kr);Zc=n(fT,"SPAN",{});var gT=s(Zc);g($t.$$.fragment,gT),gT.forEach(t),fT.forEach(t),th=l(hm),el=n(hm,"SPAN",{});var hT=s(el);oh=i(hT,"BeamSearchOutput"),hT.forEach(t),hm.forEach(t),Yd=l(e),rr=n(e,"DIV",{class:!0});var um=s(rr);g(Tt.$$.fragment,um),nh=l(um),rl=n(um,"P",{});var uT=s(rl);sh=i(uT,"Base class for outputs of decoder-only generation models using beam search."),uT.forEach(t),um.forEach(t),Xd=l(e),tr=n(e,"DIV",{class:!0});var _m=s(tr);g(yt.$$.fragment,_m),ah=l(_m),tl=n(_m,"P",{});var _T=s(tl);ih=i(_T,`Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),_T.forEach(t),_m.forEach(t),Jd=l(e),or=n(e,"H3",{class:!0});var bm=s(or);xr=n(bm,"A",{id:!0,class:!0,href:!0});var bT=s(xr);ol=n(bT,"SPAN",{});var vT=s(ol);g(kt.$$.fragment,vT),vT.forEach(t),bT.forEach(t),ch=l(bm),nl=n(bm,"SPAN",{});var $T=s(nl);lh=i($T,"BeamSampleOutput"),$T.forEach(t),bm.forEach(t),Qd=l(e),nr=n(e,"DIV",{class:!0});var vm=s(nr);g(xt.$$.fragment,vm),dh=l(vm),sl=n(vm,"P",{});var TT=s(sl);ph=i(TT,"Base class for outputs of decoder-only generation models using beam sample."),TT.forEach(t),vm.forEach(t),Zd=l(e),sr=n(e,"DIV",{class:!0});var $m=s(sr);g(wt.$$.fragment,$m),mh=l($m),al=n($m,"P",{});var yT=s(al);fh=i(yT,`Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),yT.forEach(t),$m.forEach(t),ep=l(e),ar=n(e,"H2",{class:!0});var Tm=s(ar);wr=n(Tm,"A",{id:!0,class:!0,href:!0});var kT=s(wr);il=n(kT,"SPAN",{});var xT=s(il);g(Lt.$$.fragment,xT),xT.forEach(t),kT.forEach(t),gh=l(Tm),cl=n(Tm,"SPAN",{});var wT=s(cl);hh=i(wT,"LogitsProcessor"),wT.forEach(t),Tm.forEach(t),rp=l(e),Lr=n(e,"P",{});var ym=s(Lr);uh=i(ym,"A "),fs=n(ym,"A",{href:!0});var LT=s(fs);_h=i(LT,"LogitsProcessor"),LT.forEach(t),bh=i(ym,` can be used to modify the prediction scores of a language model head for
generation.`),ym.forEach(t),tp=l(e),U=n(e,"DIV",{class:!0});var yi=s(U);g(Et.$$.fragment,yi),vh=l(yi),ll=n(yi,"P",{});var ET=s(ll);$h=i(ET,"Abstract base class for all logit processors that can be applied during generation."),ET.forEach(t),Th=l(yi),Er=n(yi,"DIV",{class:!0});var km=s(Er);g(Pt.$$.fragment,km),yh=l(km),dl=n(km,"P",{});var PT=s(dl);kh=i(PT,"Torch method for processing logits."),PT.forEach(t),km.forEach(t),yi.forEach(t),op=l(e),Y=n(e,"DIV",{class:!0});var ki=s(Y);g(Ft.$$.fragment,ki),xh=l(ki),E=n(ki,"P",{});var B=s(E);wh=i(B,"This class can be used to create a list of "),gs=n(B,"A",{href:!0});var FT=s(gs);Lh=i(FT,"LogitsProcessor"),FT.forEach(t),Eh=i(B," or "),hs=n(B,"A",{href:!0});var DT=s(hs);Ph=i(DT,"LogitsWarper"),DT.forEach(t),Fh=i(B,` to subsequently process a
`),pl=n(B,"CODE",{});var ST=s(pl);Dh=i(ST,"scores"),ST.forEach(t),Sh=i(B," input tensor. This class inherits from list and adds a specific "),ml=n(B,"EM",{});var zT=s(ml);fl=n(zT,"STRONG",{});var OT=s(fl);zh=i(OT,"call"),OT.forEach(t),zT.forEach(t),Oh=i(B,` method to apply each
`),us=n(B,"A",{href:!0});var qT=s(us);qh=i(qT,"LogitsProcessor"),qT.forEach(t),Bh=i(B," or "),_s=n(B,"A",{href:!0});var BT=s(_s);Ih=i(BT,"LogitsWarper"),BT.forEach(t),Ah=i(B," to the inputs."),B.forEach(t),Ch=l(ki),bs=n(ki,"DIV",{class:!0});var IT=s(bs);g(Dt.$$.fragment,IT),IT.forEach(t),ki.forEach(t),np=l(e),X=n(e,"DIV",{class:!0});var xi=s(X);g(St.$$.fragment,xi),Wh=l(xi),gl=n(xi,"P",{});var AT=s(gl);Nh=i(AT,"Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),AT.forEach(t),Vh=l(xi),Pr=n(xi,"DIV",{class:!0});var xm=s(Pr);g(zt.$$.fragment,xm),Mh=l(xm),hl=n(xm,"P",{});var CT=s(hl);Gh=i(CT,"Torch method for warping logits."),CT.forEach(t),xm.forEach(t),xi.forEach(t),sp=l(e),J=n(e,"DIV",{class:!0});var wi=s(J);g(Ot.$$.fragment,wi),jh=l(wi),vs=n(wi,"P",{});var I$=s(vs);$s=n(I$,"A",{href:!0});var WT=s($s);Hh=i(WT,"LogitsProcessor"),WT.forEach(t),Rh=i(I$," enforcing a min-length by setting EOS probability to 0."),I$.forEach(t),Kh=l(wi),Ts=n(wi,"DIV",{class:!0});var NT=s(Ts);g(qt.$$.fragment,NT),NT.forEach(t),wi.forEach(t),ap=l(e),Q=n(e,"DIV",{class:!0});var Li=s(Q);g(Bt.$$.fragment,Li),Uh=l(Li),ys=n(Li,"P",{});var A$=s(ys);ks=n(A$,"A",{href:!0});var VT=s(ks);Yh=i(VT,"LogitsWarper"),VT.forEach(t),Xh=i(A$," for temperature (exponential scaling output probability distribution)."),A$.forEach(t),Jh=l(Li),xs=n(Li,"DIV",{class:!0});var MT=s(xs);g(It.$$.fragment,MT),MT.forEach(t),Li.forEach(t),ip=l(e),Z=n(e,"DIV",{class:!0});var Ei=s(Z);g(At.$$.fragment,Ei),Qh=l(Ei),ws=n(Ei,"P",{});var C$=s(ws);Ls=n(C$,"A",{href:!0});var GT=s(Ls);Zh=i(GT,"LogitsProcessor"),GT.forEach(t),eu=i(C$," enforcing an exponential penalty on repeated sequences."),C$.forEach(t),ru=l(Ei),Es=n(Ei,"DIV",{class:!0});var jT=s(Es);g(Ct.$$.fragment,jT),jT.forEach(t),Ei.forEach(t),cp=l(e),ee=n(e,"DIV",{class:!0});var Pi=s(ee);g(Wt.$$.fragment,Pi),tu=l(Pi),Ps=n(Pi,"P",{});var W$=s(Ps);Fs=n(W$,"A",{href:!0});var HT=s(Fs);ou=i(HT,"LogitsWarper"),HT.forEach(t),nu=i(W$," that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off."),W$.forEach(t),su=l(Pi),Ds=n(Pi,"DIV",{class:!0});var RT=s(Ds);g(Nt.$$.fragment,RT),RT.forEach(t),Pi.forEach(t),lp=l(e),re=n(e,"DIV",{class:!0});var Fi=s(re);g(Vt.$$.fragment,Fi),au=l(Fi),Ss=n(Fi,"P",{});var N$=s(Ss);zs=n(N$,"A",{href:!0});var KT=s(zs);iu=i(KT,"LogitsWarper"),KT.forEach(t),cu=i(N$," that performs top-k, i.e. restricting to the k highest probability elements."),N$.forEach(t),lu=l(Fi),Os=n(Fi,"DIV",{class:!0});var UT=s(Os);g(Mt.$$.fragment,UT),UT.forEach(t),Fi.forEach(t),dp=l(e),te=n(e,"DIV",{class:!0});var Di=s(te);g(Gt.$$.fragment,Di),du=l(Di),Fr=n(Di,"P",{});var _d=s(Fr);qs=n(_d,"A",{href:!0});var YT=s(qs);pu=i(YT,"LogitsWarper"),YT.forEach(t),mu=i(_d," that performs typical decoding. See "),jt=n(_d,"A",{href:!0,rel:!0});var XT=s(jt);fu=i(XT,`Typical Decoding for Natural Language
Generation`),XT.forEach(t),gu=i(_d," for more information."),_d.forEach(t),hu=l(Di),Bs=n(Di,"DIV",{class:!0});var JT=s(Bs);g(Ht.$$.fragment,JT),JT.forEach(t),Di.forEach(t),pp=l(e),oe=n(e,"DIV",{class:!0});var Si=s(oe);g(Rt.$$.fragment,Si),uu=l(Si),Dr=n(Si,"P",{});var bd=s(Dr);Is=n(bd,"A",{href:!0});var QT=s(Is);_u=i(QT,"LogitsProcessor"),QT.forEach(t),bu=i(bd,` that enforces no repetition of n-grams. See
`),Kt=n(bd,"A",{href:!0,rel:!0});var ZT=s(Kt);vu=i(ZT,"Fairseq"),ZT.forEach(t),$u=i(bd,"."),bd.forEach(t),Tu=l(Si),As=n(Si,"DIV",{class:!0});var e2=s(As);g(Ut.$$.fragment,e2),e2.forEach(t),Si.forEach(t),mp=l(e),ne=n(e,"DIV",{class:!0});var zi=s(ne);g(Yt.$$.fragment,zi),yu=l(zi),Cs=n(zi,"P",{});var V$=s(Cs);Ws=n(V$,"A",{href:!0});var r2=s(Ws);ku=i(r2,"LogitsProcessor"),r2.forEach(t),xu=i(V$," that enforces that specified sequences will never be sampled."),V$.forEach(t),wu=l(zi),Ns=n(zi,"DIV",{class:!0});var t2=s(Ns);g(Xt.$$.fragment,t2),t2.forEach(t),zi.forEach(t),fp=l(e),se=n(e,"DIV",{class:!0});var Oi=s(se);g(Jt.$$.fragment,Oi),Lu=l(Oi),Sr=n(Oi,"P",{});var vd=s(Sr);Vs=n(vd,"A",{href:!0});var o2=s(Vs);Eu=i(o2,"LogitsProcessor"),o2.forEach(t),Pu=i(vd,` that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See `),Qt=n(vd,"A",{href:!0,rel:!0});var n2=s(Qt);Fu=i(n2,"Autoregressive Entity Retrieval"),n2.forEach(t),Du=i(vd," for more information."),vd.forEach(t),Su=l(Oi),Ms=n(Oi,"DIV",{class:!0});var s2=s(Ms);g(Zt.$$.fragment,s2),s2.forEach(t),Oi.forEach(t),gp=l(e),ae=n(e,"DIV",{class:!0});var qi=s(ae);g(eo.$$.fragment,qi),zu=l(qi),Ne=n(qi,"P",{});var Kn=s(Ne);Gs=n(Kn,"A",{href:!0});var a2=s(Gs);Ou=i(a2,"LogitsProcessor"),a2.forEach(t),qu=i(Kn,` that enforces diverse beam search. Note that this logits processor is only effective for
`),js=n(Kn,"A",{href:!0});var i2=s(js);Bu=i(i2,"PreTrainedModel.group_beam_search()"),i2.forEach(t),Iu=i(Kn,". See "),ro=n(Kn,"A",{href:!0,rel:!0});var c2=s(ro);Au=i(c2,`Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models`),c2.forEach(t),Cu=i(Kn," for more details."),Kn.forEach(t),Wu=l(qi),Hs=n(qi,"DIV",{class:!0});var l2=s(Hs);g(to.$$.fragment,l2),l2.forEach(t),qi.forEach(t),hp=l(e),ie=n(e,"DIV",{class:!0});var Bi=s(ie);g(oo.$$.fragment,Bi),Nu=l(Bi),Rs=n(Bi,"P",{});var M$=s(Rs);Ks=n(M$,"A",{href:!0});var d2=s(Ks);Vu=i(d2,"LogitsProcessor"),d2.forEach(t),Mu=i(M$," that enforces the specified token as the first generated token."),M$.forEach(t),Gu=l(Bi),Us=n(Bi,"DIV",{class:!0});var p2=s(Us);g(no.$$.fragment,p2),p2.forEach(t),Bi.forEach(t),up=l(e),ce=n(e,"DIV",{class:!0});var Ii=s(ce);g(so.$$.fragment,Ii),ju=l(Ii),zr=n(Ii,"P",{});var $d=s(zr);Ys=n($d,"A",{href:!0});var m2=s(Ys);Hu=i(m2,"LogitsProcessor"),m2.forEach(t),Ru=i($d," that enforces the specified token as the last generated token when "),ul=n($d,"CODE",{});var f2=s(ul);Ku=i(f2,"max_length"),f2.forEach(t),Uu=i($d," is reached."),$d.forEach(t),Yu=l(Ii),Xs=n(Ii,"DIV",{class:!0});var g2=s(Xs);g(ao.$$.fragment,g2),g2.forEach(t),Ii.forEach(t),_p=l(e),le=n(e,"DIV",{class:!0});var Ai=s(le);g(io.$$.fragment,Ai),Xu=l(Ai),G=n(Ai,"P",{});var ur=s(G);Js=n(ur,"A",{href:!0});var h2=s(Js);Ju=i(h2,"LogitsProcessor"),h2.forEach(t),Qu=i(ur," that removes all "),_l=n(ur,"CODE",{});var u2=s(_l);Zu=i(u2,"nan"),u2.forEach(t),e_=i(ur," and "),bl=n(ur,"CODE",{});var _2=s(bl);r_=i(_2,"inf"),_2.forEach(t),t_=i(ur,` values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method. `),vl=n(ur,"CODE",{});var b2=s(vl);o_=i(b2,"max_length"),b2.forEach(t),n_=i(ur,` is
reached.`),ur.forEach(t),s_=l(Ai),Qs=n(Ai,"DIV",{class:!0});var v2=s(Qs);g(co.$$.fragment,v2),v2.forEach(t),Ai.forEach(t),bp=l(e),de=n(e,"DIV",{class:!0});var Ci=s(de);g(lo.$$.fragment,Ci),a_=l(Ci),$l=n(Ci,"P",{});var $2=s($l);i_=i($2,"Abstract base class for all logit processors that can be applied during generation."),$2.forEach(t),c_=l(Ci),Or=n(Ci,"DIV",{class:!0});var wm=s(Or);g(po.$$.fragment,wm),l_=l(wm),Tl=n(wm,"P",{});var T2=s(Tl);d_=i(T2,"TF method for processing logits."),T2.forEach(t),wm.forEach(t),Ci.forEach(t),vp=l(e),pe=n(e,"DIV",{class:!0});var Wi=s(pe);g(mo.$$.fragment,Wi),p_=l(Wi),A=n(Wi,"P",{});var He=s(A);m_=i(He,"This class can be used to create a list of "),Zs=n(He,"A",{href:!0});var y2=s(Zs);f_=i(y2,"TFLogitsProcessor"),y2.forEach(t),g_=i(He," to subsequently process a "),yl=n(He,"CODE",{});var k2=s(yl);h_=i(k2,"scores"),k2.forEach(t),u_=i(He,` input tensor.
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inputs.`),He.forEach(t),T_=l(Wi),ra=n(Wi,"DIV",{class:!0});var E2=s(ra);g(fo.$$.fragment,E2),E2.forEach(t),Wi.forEach(t),$p=l(e),me=n(e,"DIV",{class:!0});var Ni=s(me);g(go.$$.fragment,Ni),y_=l(Ni),wl=n(Ni,"P",{});var P2=s(wl);k_=i(P2,"Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),P2.forEach(t),x_=l(Ni),qr=n(Ni,"DIV",{class:!0});var Lm=s(qr);g(ho.$$.fragment,Lm),w_=l(Lm),Ll=n(Lm,"P",{});var F2=s(Ll);L_=i(F2,"TF method for warping logits."),F2.forEach(t),Lm.forEach(t),Ni.forEach(t),Tp=l(e),fe=n(e,"DIV",{class:!0});var Vi=s(fe);g(uo.$$.fragment,Vi),E_=l(Vi),ta=n(Vi,"P",{});var G$=s(ta);oa=n(G$,"A",{href:!0});var D2=s(oa);P_=i(D2,"TFLogitsWarper"),D2.forEach(t),F_=i(G$," for temperature (exponential scaling output probability distribution)."),G$.forEach(t),D_=l(Vi),na=n(Vi,"DIV",{class:!0});var S2=s(na);g(_o.$$.fragment,S2),S2.forEach(t),Vi.forEach(t),yp=l(e),ge=n(e,"DIV",{class:!0});var Mi=s(ge);g(bo.$$.fragment,Mi),S_=l(Mi),sa=n(Mi,"P",{});var j$=s(sa);aa=n(j$,"A",{href:!0});var z2=s(aa);z_=i(z2,"TFLogitsWarper"),z2.forEach(t),O_=i(j$," that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off."),j$.forEach(t),q_=l(Mi),ia=n(Mi,"DIV",{class:!0});var O2=s(ia);g(vo.$$.fragment,O2),O2.forEach(t),Mi.forEach(t),kp=l(e),he=n(e,"DIV",{class:!0});var Gi=s(he);g($o.$$.fragment,Gi),B_=l(Gi),ca=n(Gi,"P",{});var H$=s(ca);la=n(H$,"A",{href:!0});var q2=s(la);I_=i(q2,"TFLogitsWarper"),q2.forEach(t),A_=i(H$," that performs top-k, i.e. restricting to the k highest probability elements."),H$.forEach(t),C_=l(Gi),da=n(Gi,"DIV",{class:!0});var B2=s(da);g(To.$$.fragment,B2),B2.forEach(t),Gi.forEach(t),xp=l(e),ue=n(e,"DIV",{class:!0});var ji=s(ue);g(yo.$$.fragment,ji),W_=l(ji),pa=n(ji,"P",{});var R$=s(pa);ma=n(R$,"A",{href:!0});var I2=s(ma);N_=i(I2,"TFLogitsProcessor"),I2.forEach(t),V_=i(R$," enforcing a min-length by setting EOS probability to 0."),R$.forEach(t),M_=l(ji),fa=n(ji,"DIV",{class:!0});var A2=s(fa);g(ko.$$.fragment,A2),A2.forEach(t),ji.forEach(t),wp=l(e),_e=n(e,"DIV",{class:!0});var Hi=s(_e);g(xo.$$.fragment,Hi),G_=l(Hi),ga=n(Hi,"P",{});var K$=s(ga);ha=n(K$,"A",{href:!0});var C2=s(ha);j_=i(C2,"TFLogitsProcessor"),C2.forEach(t),H_=i(K$," that enforces that specified sequences will never be sampled."),K$.forEach(t),R_=l(Hi),ua=n(Hi,"DIV",{class:!0});var W2=s(ua);g(wo.$$.fragment,W2),W2.forEach(t),Hi.forEach(t),Lp=l(e),be=n(e,"DIV",{class:!0});var Ri=s(be);g(Lo.$$.fragment,Ri),K_=l(Ri),Br=n(Ri,"P",{});var Td=s(Br);_a=n(Td,"A",{href:!0});var N2=s(_a);U_=i(N2,"TFLogitsProcessor"),N2.forEach(t),Y_=i(Td,` that enforces no repetition of n-grams. See
`),Eo=n(Td,"A",{href:!0,rel:!0});var V2=s(Eo);X_=i(V2,"Fairseq"),V2.forEach(t),J_=i(Td,"."),Td.forEach(t),Q_=l(Ri),ba=n(Ri,"DIV",{class:!0});var M2=s(ba);g(Po.$$.fragment,M2),M2.forEach(t),Ri.forEach(t),Ep=l(e),ve=n(e,"DIV",{class:!0});var Ki=s(ve);g(Fo.$$.fragment,Ki),Z_=l(Ki),va=n(Ki,"P",{});var U$=s(va);$a=n(U$,"A",{href:!0});var G2=s($a);eb=i(G2,"TFLogitsProcessor"),G2.forEach(t),rb=i(U$," enforcing an exponential penalty on repeated sequences."),U$.forEach(t),tb=l(Ki),Ta=n(Ki,"DIV",{class:!0});var j2=s(Ta);g(Do.$$.fragment,j2),j2.forEach(t),Ki.forEach(t),Pp=l(e),$e=n(e,"DIV",{class:!0});var Ui=s($e);g(So.$$.fragment,Ui),ob=l(Ui),ya=n(Ui,"P",{});var Y$=s(ya);ka=n(Y$,"A",{href:!0});var H2=s(ka);nb=i(H2,"TFLogitsProcessor"),H2.forEach(t),sb=i(Y$," that enforces the specified token as the first generated token."),Y$.forEach(t),ab=l(Ui),xa=n(Ui,"DIV",{class:!0});var R2=s(xa);g(zo.$$.fragment,R2),R2.forEach(t),Ui.forEach(t),Fp=l(e),Te=n(e,"DIV",{class:!0});var Yi=s(Te);g(Oo.$$.fragment,Yi),ib=l(Yi),Ir=n(Yi,"P",{});var yd=s(Ir);wa=n(yd,"A",{href:!0});var K2=s(wa);cb=i(K2,"TFLogitsProcessor"),K2.forEach(t),lb=i(yd," that enforces the specified token as the last generated token when "),El=n(yd,"CODE",{});var U2=s(El);db=i(U2,"max_length"),U2.forEach(t),pb=i(yd," is reached."),yd.forEach(t),mb=l(Yi),La=n(Yi,"DIV",{class:!0});var Y2=s(La);g(qo.$$.fragment,Y2),Y2.forEach(t),Yi.forEach(t),Dp=l(e),ye=n(e,"DIV",{class:!0});var Xi=s(ye);g(Bo.$$.fragment,Xi),fb=l(Xi),Pl=n(Xi,"P",{});var X2=s(Pl);gb=i(X2,"Abstract base class for all logit processors that can be applied during generation."),X2.forEach(t),hb=l(Xi),Ar=n(Xi,"DIV",{class:!0});var Em=s(Ar);g(Io.$$.fragment,Em),ub=l(Em),Fl=n(Em,"P",{});var J2=s(Fl);_b=i(J2,"Flax method for processing logits."),J2.forEach(t),Em.forEach(t),Xi.forEach(t),Sp=l(e),ke=n(e,"DIV",{class:!0});var Ji=s(ke);g(Ao.$$.fragment,Ji),bb=l(Ji),P=n(Ji,"P",{});var I=s(P);vb=i(I,"This class can be used to create a list of "),Ea=n(I,"A",{href:!0});var Q2=s(Ea);$b=i(Q2,"FlaxLogitsProcessor"),Q2.forEach(t),Tb=i(I," or "),Pa=n(I,"A",{href:!0});var Z2=s(Pa);yb=i(Z2,"FlaxLogitsWarper"),Z2.forEach(t),kb=i(I,` to subsequently process
a `),Dl=n(I,"CODE",{});var ey=s(Dl);xb=i(ey,"scores"),ey.forEach(t),wb=i(I," input tensor. This class inherits from list and adds a specific "),Sl=n(I,"EM",{});var ry=s(Sl);zl=n(ry,"STRONG",{});var ty=s(zl);Lb=i(ty,"call"),ty.forEach(t),ry.forEach(t),Eb=i(I,` method to apply each
`),Fa=n(I,"A",{href:!0});var oy=s(Fa);Pb=i(oy,"FlaxLogitsProcessor"),oy.forEach(t),Fb=i(I," or "),Da=n(I,"A",{href:!0});var ny=s(Da);Db=i(ny,"FlaxLogitsWarper"),ny.forEach(t),Sb=i(I," to the inputs."),I.forEach(t),zb=l(Ji),Sa=n(Ji,"DIV",{class:!0});var sy=s(Sa);g(Co.$$.fragment,sy),sy.forEach(t),Ji.forEach(t),zp=l(e),xe=n(e,"DIV",{class:!0});var Qi=s(xe);g(Wo.$$.fragment,Qi),Ob=l(Qi),Ol=n(Qi,"P",{});var ay=s(Ol);qb=i(ay,"Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),ay.forEach(t),Bb=l(Qi),Cr=n(Qi,"DIV",{class:!0});var Pm=s(Cr);g(No.$$.fragment,Pm),Ib=l(Pm),ql=n(Pm,"P",{});var iy=s(ql);Ab=i(iy,"Flax method for warping logits."),iy.forEach(t),Pm.forEach(t),Qi.forEach(t),Op=l(e),we=n(e,"DIV",{class:!0});var Zi=s(we);g(Vo.$$.fragment,Zi),Cb=l(Zi),za=n(Zi,"P",{});var X$=s(za);Oa=n(X$,"A",{href:!0});var cy=s(Oa);Wb=i(cy,"FlaxLogitsWarper"),cy.forEach(t),Nb=i(X$," for temperature (exponential scaling output probability distribution)."),X$.forEach(t),Vb=l(Zi),qa=n(Zi,"DIV",{class:!0});var ly=s(qa);g(Mo.$$.fragment,ly),ly.forEach(t),Zi.forEach(t),qp=l(e),Le=n(e,"DIV",{class:!0});var ec=s(Le);g(Go.$$.fragment,ec),Mb=l(ec),Ba=n(ec,"P",{});var J$=s(Ba);Ia=n(J$,"A",{href:!0});var dy=s(Ia);Gb=i(dy,"FlaxLogitsWarper"),dy.forEach(t),jb=i(J$," that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off."),J$.forEach(t),Hb=l(ec),Aa=n(ec,"DIV",{class:!0});var py=s(Aa);g(jo.$$.fragment,py),py.forEach(t),ec.forEach(t),Bp=l(e),Ee=n(e,"DIV",{class:!0});var rc=s(Ee);g(Ho.$$.fragment,rc),Rb=l(rc),Ca=n(rc,"P",{});var Q$=s(Ca);Wa=n(Q$,"A",{href:!0});var my=s(Wa);Kb=i(my,"FlaxLogitsWarper"),my.forEach(t),Ub=i(Q$," that performs top-k, i.e. restricting to the k highest probability elements."),Q$.forEach(t),Yb=l(rc),Na=n(rc,"DIV",{class:!0});var fy=s(Na);g(Ro.$$.fragment,fy),fy.forEach(t),rc.forEach(t),Ip=l(e),Pe=n(e,"DIV",{class:!0});var tc=s(Pe);g(Ko.$$.fragment,tc),Xb=l(tc),Va=n(tc,"P",{});var Z$=s(Va);Ma=n(Z$,"A",{href:!0});var gy=s(Ma);Jb=i(gy,"FlaxLogitsProcessor"),gy.forEach(t),Qb=i(Z$," that enforces the specified token as the first generated token."),Z$.forEach(t),Zb=l(tc),Ga=n(tc,"DIV",{class:!0});var hy=s(Ga);g(Uo.$$.fragment,hy),hy.forEach(t),tc.forEach(t),Ap=l(e),Fe=n(e,"DIV",{class:!0});var oc=s(Fe);g(Yo.$$.fragment,oc),ev=l(oc),Wr=n(oc,"P",{});var kd=s(Wr);ja=n(kd,"A",{href:!0});var uy=s(ja);rv=i(uy,"FlaxLogitsProcessor"),uy.forEach(t),tv=i(kd," that enforces the specified token as the last generated token when "),Bl=n(kd,"CODE",{});var _y=s(Bl);ov=i(_y,"max_length"),_y.forEach(t),nv=i(kd," is reached."),kd.forEach(t),sv=l(oc),Ha=n(oc,"DIV",{class:!0});var by=s(Ha);g(Xo.$$.fragment,by),by.forEach(t),oc.forEach(t),Cp=l(e),De=n(e,"DIV",{class:!0});var nc=s(De);g(Jo.$$.fragment,nc),av=l(nc),Ra=n(nc,"P",{});var e0=s(Ra);Ka=n(e0,"A",{href:!0});var vy=s(Ka);iv=i(vy,"FlaxLogitsProcessor"),vy.forEach(t),cv=i(e0," enforcing a min-length by setting EOS probability to 0."),e0.forEach(t),lv=l(nc),Ua=n(nc,"DIV",{class:!0});var $y=s(Ua);g(Qo.$$.fragment,$y),$y.forEach(t),nc.forEach(t),Wp=l(e),ir=n(e,"H2",{class:!0});var Fm=s(ir);Nr=n(Fm,"A",{id:!0,class:!0,href:!0});var Ty=s(Nr);Il=n(Ty,"SPAN",{});var yy=s(Il);g(Zo.$$.fragment,yy),yy.forEach(t),Ty.forEach(t),dv=l(Fm),Al=n(Fm,"SPAN",{});var ky=s(Al);pv=i(ky,"StoppingCriteria"),ky.forEach(t),Fm.forEach(t),Np=l(e),Vr=n(e,"P",{});var Dm=s(Vr);mv=i(Dm,"A "),Ya=n(Dm,"A",{href:!0});var xy=s(Ya);fv=i(xy,"StoppingCriteria"),xy.forEach(t),gv=i(Dm," can be used to change when to stop generation (other than EOS token)."),Dm.forEach(t),Vp=l(e),Se=n(e,"DIV",{class:!0});var sc=s(Se);g(en.$$.fragment,sc),hv=l(sc),Cl=n(sc,"P",{});var wy=s(Cl);uv=i(wy,"Abstract base class for all stopping criteria that can be applied during generation."),wy.forEach(t),_v=l(sc),Xa=n(sc,"DIV",{class:!0});var Ly=s(Xa);g(rn.$$.fragment,Ly),Ly.forEach(t),sc.forEach(t),Mp=l(e),cr=n(e,"DIV",{class:!0});var Sm=s(cr);g(tn.$$.fragment,Sm),bv=l(Sm),Ja=n(Sm,"DIV",{class:!0});var Ey=s(Ja);g(on.$$.fragment,Ey),Ey.forEach(t),Sm.forEach(t),Gp=l(e),ze=n(e,"DIV",{class:!0});var ac=s(ze);g(nn.$$.fragment,ac),vv=l(ac),sn=n(ac,"P",{});var zm=s(sn);$v=i(zm,"This class can be used to stop generation whenever the full generated number of tokens exceeds "),Wl=n(zm,"CODE",{});var Py=s(Wl);Tv=i(Py,"max_length"),Py.forEach(t),yv=i(zm,`. Keep
in mind for decoder-only type of transformers, this will include the initial prompted tokens.`),zm.forEach(t),kv=l(ac),Qa=n(ac,"DIV",{class:!0});var Fy=s(Qa);g(an.$$.fragment,Fy),Fy.forEach(t),ac.forEach(t),jp=l(e),Oe=n(e,"DIV",{class:!0});var ic=s(Oe);g(cn.$$.fragment,ic),xv=l(ic),ln=n(ic,"P",{});var Om=s(ln);wv=i(Om,`This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the
time will start being counted when you initialize this function. You can override this by passing an
`),Nl=n(Om,"CODE",{});var Dy=s(Nl);Lv=i(Dy,"initial_time"),Dy.forEach(t),Ev=i(Om,"."),Om.forEach(t),Pv=l(ic),Za=n(ic,"DIV",{class:!0});var Sy=s(Za);g(dn.$$.fragment,Sy),Sy.forEach(t),ic.forEach(t),Hp=l(e),lr=n(e,"H2",{class:!0});var qm=s(lr);Mr=n(qm,"A",{id:!0,class:!0,href:!0});var zy=s(Mr);Vl=n(zy,"SPAN",{});var Oy=s(Vl);g(pn.$$.fragment,Oy),Oy.forEach(t),zy.forEach(t),Fv=l(qm),Ml=n(qm,"SPAN",{});var qy=s(Ml);Dv=i(qy,"Constraints"),qy.forEach(t),qm.forEach(t),Rp=l(e),Gr=n(e,"P",{});var Bm=s(Gr);Sv=i(Bm,"A "),ei=n(Bm,"A",{href:!0});var By=s(ei);zv=i(By,"Constraint"),By.forEach(t),Ov=i(Bm," can be used to force the generation to include specific tokens or sequences in the output."),Bm.forEach(t),Kp=l(e),$=n(e,"DIV",{class:!0});var T=s($);g(mn.$$.fragment,T),qv=l(T),Gl=n(T,"P",{});var Iy=s(Gl);Bv=i(Iy,`Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.`),Iy.forEach(t),Iv=l(T),jl=n(T,"P",{});var Ay=s(jl);Av=i(Ay,"All classes that inherit Constraint must follow the requirement that"),Ay.forEach(t),Cv=l(T),g(jr.$$.fragment,T),Wv=l(T),Hl=n(T,"P",{});var Cy=s(Hl);Nv=i(Cy,"will always terminate (halt)."),Cy.forEach(t),Vv=l(T),Hr=n(T,"DIV",{class:!0});var Im=s(Hr);g(fn.$$.fragment,Im),Mv=l(Im),Rl=n(Im,"P",{});var Wy=s(Rl);Gv=i(Wy,"When called, returns the token that would take this constraint one step closer to being fulfilled."),Wy.forEach(t),Im.forEach(t),jv=l(T),Rr=n(T,"DIV",{class:!0});var Am=s(Rr);g(gn.$$.fragment,Am),Hv=l(Am),Kl=n(Am,"P",{});var Ny=s(Kl);Rv=i(Ny,"Creates a new instance of this constraint."),Ny.forEach(t),Am.forEach(t),Kv=l(T),Kr=n(T,"DIV",{class:!0});var Cm=s(Kr);g(hn.$$.fragment,Cm),Uv=l(Cm),Ul=n(Cm,"P",{});var Vy=s(Ul);Yv=i(Vy,"Reads in a token and returns whether it creates progress."),Vy.forEach(t),Cm.forEach(t),Xv=l(T),Ur=n(T,"DIV",{class:!0});var Wm=s(Ur);g(un.$$.fragment,Wm),Jv=l(Wm),_n=n(Wm,"P",{});var Nm=s(_n);Qv=i(Nm,"Returns the number of remaining steps of "),Yl=n(Nm,"CODE",{});var My=s(Yl);Zv=i(My,"advance()"),My.forEach(t),e1=i(Nm," in order to complete this constraint."),Nm.forEach(t),Wm.forEach(t),r1=l(T),Yr=n(T,"DIV",{class:!0});var Vm=s(Yr);g(bn.$$.fragment,Vm),t1=l(Vm),Xl=n(Vm,"P",{});var Gy=s(Xl);o1=i(Gy,`Resets the state of this constraint to its initialization. We would call this in cases where the fulfillment of
a constraint is abrupted by an unwanted token.`),Gy.forEach(t),Vm.forEach(t),n1=l(T),Xr=n(T,"DIV",{class:!0});var Mm=s(Xr);g(vn.$$.fragment,Mm),s1=l(Mm),Jl=n(Mm,"P",{});var jy=s(Jl);a1=i(jy,"Tests whether this constraint has been properly defined."),jy.forEach(t),Mm.forEach(t),i1=l(T),Ve=n(T,"DIV",{class:!0});var cc=s(Ve);g($n.$$.fragment,cc),c1=l(cc),Tn=n(cc,"P",{});var Gm=s(Tn);l1=i(Gm,`Reads in a token and returns booleans that indicate the progress made by it. This function will update the
state of this object unlikes `),Ql=n(Gm,"CODE",{});var Hy=s(Ql);d1=i(Hy,"does_advance(self, token_id: int)"),Hy.forEach(t),p1=i(Gm,"."),Gm.forEach(t),m1=l(cc),Zl=n(cc,"P",{});var Ry=s(Zl);f1=i(Ry,`This isn\u2019t to test whether a certain token will advance the progress; it\u2019s to update its state as if it has
been generated. This becomes important if token_id != desired token (refer to else statement in
PhrasalConstraint)`),Ry.forEach(t),cc.forEach(t),T.forEach(t),Up=l(e),dr=n(e,"DIV",{class:!0});var jm=s(dr);g(yn.$$.fragment,jm),g1=l(jm),ri=n(jm,"P",{});var r0=s(ri);ti=n(r0,"A",{href:!0});var Ky=s(ti);h1=i(Ky,"Constraint"),Ky.forEach(t),u1=i(r0," enforcing that an ordered sequence of tokens is included in the output."),r0.forEach(t),jm.forEach(t),Yp=l(e),pr=n(e,"DIV",{class:!0});var Hm=s(pr);g(kn.$$.fragment,Hm),_1=l(Hm),xn=n(Hm,"P",{});var Rm=s(xn);b1=i(Rm,"A special "),oi=n(Rm,"A",{href:!0});var Uy=s(oi);v1=i(Uy,"Constraint"),Uy.forEach(t),$1=i(Rm," that is fulfilled by fulfilling just one of several constraints."),Rm.forEach(t),Hm.forEach(t),Xp=l(e),C=n(e,"DIV",{class:!0});var tt=s(C);g(wn.$$.fragment,tt),T1=l(tt),ed=n(tt,"P",{});var Yy=s(ed);y1=i(Yy,"A class for beam scorers to track its progress through a list of constraints."),Yy.forEach(t),k1=l(tt),D=n(tt,"DIV",{class:!0});var j=s(D);g(Ln.$$.fragment,j),x1=l(j),rd=n(j,"P",{});var Xy=s(rd);w1=i(Xy,`The list of tokens to generate such that we can make progress.
By \u201Clist\u201D we don\u2019t mean the list of token that will fully fulfill a constraint.`),Xy.forEach(t),L1=l(j),mr=n(j,"P",{});var lc=s(mr);E1=i(lc,"Given constraints "),td=n(lc,"CODE",{});var Jy=s(td);P1=i(Jy,"c_i = {t_ij | j == # of tokens}"),Jy.forEach(t),F1=i(lc,`, If we\u2019re not in the middle of progressing through a
specific constraint `),od=n(lc,"CODE",{});var Qy=s(od);D1=i(Qy,"c_i"),Qy.forEach(t),S1=i(lc,", we return:"),lc.forEach(t),z1=l(j),nd=n(j,"P",{});var Zy=s(nd);sd=n(Zy,"CODE",{});var ek=s(sd);O1=i(ek,"[t_k1 for k in indices of unfulfilled constraints]"),ek.forEach(t),Zy.forEach(t),q1=l(j),qe=n(j,"P",{});var ot=s(qe);B1=i(ot,`If we are in the middle of a constraint, then we return:
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