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S)a�26        Generates sequences for models with a language modeling head using beam search decoding.27 28        Parameters:29 30            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):31                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty32                :obj:`torch.LongTensor` of shape :obj:`(1,)`.33            beam_scorer (:obj:`BeamScorer`):34                An derived instance of :class:`~transformers.BeamScorer` that defines how beam hypotheses are35                constructed, stored and sorted during generation. For more information, the documentation of36                :class:`~transformers.BeamScorer` should be read.37            logits_processor (:obj:`LogitsProcessorList`, `optional`):38                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from39                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling40                head applied at each generation step.41            max_length (:obj:`int`, `optional`, defaults to 20):42                The maximum length of the sequence to be generated.43            pad_token_id (:obj:`int`, `optional`):44                The id of the `padding` token.45            eos_token_id (:obj:`int`, `optional`):46                The id of the `end-of-sequence` token.47            output_attentions (:obj:`bool`, `optional`, defaults to `False`):48                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under49                returned tensors for more details.50            output_hidden_states (:obj:`bool`, `optional`, defaults to `False`):51                Whether or not to return trhe hidden states of all layers. See ``hidden_states`` under returned tensors52                for more details.53            output_scores (:obj:`bool`, `optional`, defaults to `False`):54                Whether or not to return the prediction scores. See ``scores`` under returned tensors for more details.55            return_dict_in_generate (:obj:`bool`, `optional`, defaults to `False`):56                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.57            model_kwargs:58                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If59                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.60 61        Return:62            :class:`~transformers.generation_utilsBeamSearchDecoderOnlyOutput`,63            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` or obj:`torch.LongTensor`: A64            :obj:`torch.LongTensor` containing the generated tokens (default behaviour) or a65            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput` if66            ``model.config.is_encoder_decoder=False`` and ``return_dict_in_generate=True`` or a67            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` if68            ``model.config.is_encoder_decoder=True``.69 70 71        Examples::72 73            >>> from transformers import (74            ...    AutoTokenizer,75            ...    AutoModelForSeq2SeqLM,76            ...    LogitsProcessorList,77            ...    MinLengthLogitsProcessor,78            ...    BeamSearchScorer,79            ... )80            >>> import torch81 82            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")83            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")84 85            >>> encoder_input_str = "translate English to German: How old are you?"86            >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids87 88 89            >>> # lets run beam search using 3 beams90            >>> num_beams = 391            >>> # define decoder start token ids92            >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)93            >>> input_ids = input_ids * model.config.decoder_start_token_id94 95            >>> # add encoder_outputs to model keyword arguments96            >>> model_kwargs = {97            ...     "encoder_outputs": model.get_encoder()(encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True)98            ... }99 100            >>> # instantiate beam scorer101            >>> beam_scorer = BeamSearchScorer(102            ...     batch_size=1,103            ...     max_length=model.config.max_length,104            ...     num_beams=num_beams,105            ...     device=model.device,106            ... )107 108            >>> # instantiate logits processors109            >>> logits_processor = LogitsProcessorList([110            ...     MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),111            ... ])112 113            >>> outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)114 115            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))116        N��encoder_outputs�117attentions�
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S)$a�147        Generates sequences for models with a language modeling head using beam search decoding.148 149        Parameters:150 151            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):152                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty153                :obj:`torch.LongTensor` of shape :obj:`(1,)`.154            beam_scorer (:obj:`BeamScorer`):155                An derived instance of :class:`~transformers.BeamScorer` that defines how beam hypotheses are156                constructed, stored and sorted during generation. For more information, the documentation of157                :class:`~transformers.BeamScorer` should be read.158            logits_processor (:obj:`LogitsProcessorList`, `optional`):159                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from160                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling161                head applied at each generation step.162            max_length (:obj:`int`, `optional`, defaults to 20):163                The maximum length of the sequence to be generated.164            pad_token_id (:obj:`int`, `optional`):165                The id of the `padding` token.166            eos_token_id (:obj:`int`, `optional`):167                The id of the `end-of-sequence` token.168            output_attentions (:obj:`bool`, `optional`, defaults to `False`):169                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under170                returned tensors for more details.171            output_hidden_states (:obj:`bool`, `optional`, defaults to `False`):172                Whether or not to return trhe hidden states of all layers. See ``hidden_states`` under returned tensors173                for more details.174            output_scores (:obj:`bool`, `optional`, defaults to `False`):175                Whether or not to return the prediction scores. See ``scores`` under returned tensors for more details.176            return_dict_in_generate (:obj:`bool`, `optional`, defaults to `False`):177                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.178            model_kwargs:179                Additional model specific kwargs that will be forwarded to the :obj:`forward` function of the model. If180                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.181 182        Return:183            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput`,184            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` or obj:`torch.LongTensor`: A185            :obj:`torch.LongTensor` containing the generated tokens (default behaviour) or a186            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput` if187            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput` if188            ``model.config.is_encoder_decoder=False`` and ``return_dict_in_generate=True`` or a189            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` if190            ``model.config.is_encoder_decoder=True``.191 192        Examples::193 194            >>> from transformers import (195            ...    AutoTokenizer,196            ...    AutoModelForSeq2SeqLM,197            ...    LogitsProcessorList,198            ...    MinLengthLogitsProcessor,199            ...    HammingDiversityLogitsProcessor,200            ...    BeamSearchScorer,201            ... )202            >>> import torch203 204            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")205            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")206 207            >>> encoder_input_str = "translate English to German: How old are you?"208            >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids209 210 211            >>> # lets run diverse beam search using 6 beams212            >>> num_beams = 6213            >>> # define decoder start token ids214            >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)215            >>> input_ids = input_ids * model.config.decoder_start_token_id216 217            >>> # add encoder_outputs to model keyword arguments218            >>> model_kwargs = {219            ...     "encoder_outputs": model.get_encoder()(encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True)220            ... }221 222            >>> # instantiate beam scorer223            >>> beam_scorer = BeamSearchScorer(224            ...     batch_size=1,225            ...     max_length=model.config.max_length,226            ...     num_beams=num_beams,227            ...     device=model.device,228            ...     num_beam_groups=3229            ... )230 231            >>> # instantiate logits processors232            >>> logits_processor = LogitsProcessorList([233            ...     HammingDiversityLogitsProcessor(5.5, num_beams=6, num_beam_groups=3),234            ...     MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),235            ... ])236 237            >>> outputs = model.group_beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)238 239            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))240        Nrrrr z)Batch dimension of `input_ids` should be z	, but is �
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