# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.

from typing import Any, Dict, Iterable, List, Optional, Tuple

import torch
from torch.nn import functional as F

from .file_utils import ModelOutput
from .generation_beam_search import BeamScorer, BeamSearchScorer
from .generation_logits_process import (
    LogitsProcessorList,
    MinLengthLogitsProcessor,
    NoBadWordsLogitsProcessor,
    NoRepeatNGramLogitsProcessor,
    RepetitionPenaltyLogitsProcessor,
    TemperatureLogitsWarper,
    TopKLogitsWarper,
    TopPLogitsWarper,
)
from .utils import logging


logger = logging.get_logger(__name__)


class GenerationMixin:
    """
    A class containing all of the functions supporting generation, to be used as a mixin in
    :class:`~transformers.PreTrainedModel`.
    """

    def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> Dict[str, Any]:
        """
        Implement in subclasses of :class:`~transformers.PreTrainedModel` for custom behavior to prepare inputs in the
        generate method.
        """
        return {"input_ids": input_ids}

    def adjust_logits_during_generation(self, logits: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
        """
        Implement in subclasses of :class:`~transformers.PreTrainedModel` for custom behavior to adjust the logits in
        the generate method.
        """
        return logits

    def _prepare_input_ids_for_generation(self, bos_token_id: int) -> torch.LongTensor:
        if bos_token_id is None:
            raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
        return torch.ones((1, 1), dtype=torch.long, device=self.device) * bos_token_id

    def _prepare_attention_mask_for_generation(
        self, input_ids: torch.Tensor, pad_token_id: int, eos_token_id: int
    ) -> torch.LongTensor:
        is_pad_token_in_inputs_ids = (pad_token_id is not None) and (pad_token_id in input_ids)
        is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (
            (eos_token_id is not None) and (pad_token_id != eos_token_id)
        )
        if is_pad_token_in_inputs_ids and is_pad_token_not_equal_to_eos_token_id:
            return input_ids.ne(pad_token_id).long()
        return input_ids.new_ones(input_ids.shape)

    def _prepare_encoder_decoder_kwargs_for_generation(
        self, input_ids: torch.LongTensor, model_kwargs
    ) -> Dict[str, Any]:
        # retrieve encoder hidden states
        encoder = self.get_encoder()
        encoder_kwargs = {
            argument: value for argument, value in model_kwargs.items() if not argument.startswith("decoder_")
        }
        model_kwargs["encoder_outputs"]: ModelOutput = encoder(input_ids, return_dict=True, **encoder_kwargs)
        return model_kwargs

    def _prepare_decoder_input_ids_for_generation(
        self, input_ids: torch.LongTensor, decoder_start_token_id: int = None, bos_token_id: int = None, **model_kwargs
    ) -> torch.LongTensor:

        if "decoder_input_ids" in model_kwargs:
            return model_kwargs["decoder_input_ids"]

        decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
        decoder_input_ids = (
            torch.ones((input_ids.shape[0], 1), dtype=input_ids.dtype, device=input_ids.device)
            * decoder_start_token_id
        )
        return decoder_input_ids

    def _get_pad_token_id(self, pad_token_id: int = None, eos_token_id: int = None) -> int:
        if pad_token_id is None and eos_token_id is not None:
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
            pad_token_id = eos_token_id
        return pad_token_id

    def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
        decoder_start_token_id = (
            decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
        )
        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id

        if decoder_start_token_id is not None:
            return decoder_start_token_id
        elif (
            hasattr(self.config, "decoder")
            and hasattr(self.config.decoder, "decoder_start_token_id")
            and self.config.decoder.decoder_start_token_id is not None
        ):
            return self.config.decoder.decoder_start_token_id
        elif bos_token_id is not None:
            return bos_token_id
        elif (
            hasattr(self.config, "decoder")
            and hasattr(self.config.decoder, "bos_token_id")
            and self.config.decoder.bos_token_id is not None
        ):
            return self.config.decoder.bos_token_id
        raise ValueError(
            "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
        )

    @staticmethod
    def _expand_inputs_for_generation(
        input_ids: torch.LongTensor,
        expand_size: int = 1,
        is_encoder_decoder: bool = False,
        attention_mask: torch.LongTensor = None,
        encoder_outputs: ModelOutput = None,
        **model_kwargs
    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
        expanded_return_idx = (
            torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
        )
        input_ids = input_ids.index_select(0, expanded_return_idx)

        if "token_type_ids" in model_kwargs:
            token_type_ids = model_kwargs["token_type_ids"]
            model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx)

        if attention_mask is not None:
            model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)

        if is_encoder_decoder:
            assert encoder_outputs is not None
            encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
                0, expanded_return_idx
            )
            model_kwargs["encoder_outputs"] = encoder_outputs
        return input_ids, model_kwargs

    @staticmethod
    def _init_sequence_length_for_generation(
        input_ids: torch.LongTensor, max_length: int
    ) -> Tuple[torch.Tensor, torch.Tensor, int]:
        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
        sequence_lengths = input_ids.new(input_ids.shape[0]).fill_(max_length)

        cur_len = input_ids.shape[-1]
        return sequence_lengths, unfinished_sequences, cur_len

    @staticmethod
    def _update_seq_length_for_generation(
        sequence_lengths: torch.LongTensor,
        unfinished_sequences: torch.LongTensor,
        cur_len: int,
        is_eos_in_next_token: torch.BoolTensor,
    ) -> Tuple[torch.LongTensor, torch.LongTensor]:
        # check if sentence is not finished yet
        is_sent_unfinished = unfinished_sequences.mul(is_eos_in_next_token.long()).bool()

        # update sentence length
        sequence_lengths = sequence_lengths.masked_fill(is_sent_unfinished, cur_len)
        unfinished_sequences = unfinished_sequences.mul((~is_eos_in_next_token).long())
        return sequence_lengths, unfinished_sequences

    @staticmethod
    def _update_model_kwargs_for_generation(
        outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
    ) -> Dict[str, Any]:
        # update past
        if "past_key_values" in outputs:
            model_kwargs["past"] = outputs.past_key_values
        elif "mems" in outputs:
            model_kwargs["past"] = outputs.mems
        elif "past_buckets_states" in outputs:
            model_kwargs["past"] = outputs.past_buckets_states
        else:
            model_kwargs["past"] = None

        # update token_type_ids with last value
        if "token_type_ids" in model_kwargs:
            token_type_ids = model_kwargs["token_type_ids"]
            model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)

        # update attention mask
        if not is_encoder_decoder:
            if "attention_mask" in model_kwargs:
                attention_mask = model_kwargs["attention_mask"]
                model_kwargs["attention_mask"] = torch.cat(
                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
                )

        return model_kwargs

    @staticmethod
    def _reorder_cache(past: Tuple[torch.Tensor], beam_idx: torch.Tensor) -> Tuple[torch.Tensor]:
        """
        This function is used to re-order the :obj:`past_key_values` or :obj:`mems` cache if
        :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
        called. This is required to match :obj:`past_key_values` or :obj:`mems` with the correct beam_idx at every
        generation step.

        For custom re-ordering of :obj:`past_key_values` or :obj:`mems`, the function should be implemented in
        subclasses of :class:`~transformers.PreTrainedModel`.
        """
        return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)

    def _get_logits_warper(
        self, top_k: int = None, top_p: float = None, temperature: float = None, num_beams: int = None
    ) -> LogitsProcessorList:
        """
        This class returns a :obj:`~transformers.LogitsProcessorList` list object that contains all relevant
        :obj:`~transformers.LogitsWarper` instances used for multinomial sampling.
        """

        # init warp parameters
        top_k = top_k if top_k is not None else self.config.top_k
        top_p = top_p if top_p is not None else self.config.top_p
        temperature = temperature if temperature is not None else self.config.temperature
        # instantiate warpers list
        warpers = LogitsProcessorList()

        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
        # all samplers can be found in `generation_utils_samplers.py`
        if top_k is not None and top_k != 0:
            warpers.append(TopKLogitsWarper(top_k=top_k, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
        if top_p is not None and top_p < 1.0:
            warpers.append(TopPLogitsWarper(top_p=top_p, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
        if temperature is not None and temperature != 1.0:
            warpers.append(TemperatureLogitsWarper(temperature))
        return warpers

    def _get_logits_processor(
        self,
        repetition_penalty: float,
        no_repeat_ngram_size: int,
        bad_words_ids: List[List[int]],
        min_length: int,
        eos_token_id: int,
    ) -> LogitsProcessorList:
        """
        This class returns a :obj:`~transformers.LogitsProcessorList` list object that contains all relevant
        :obj:`~transformers.LogitsProcessor` instances used to modify the scores of the language model head.
        """

        # init warp parameters
        repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
        no_repeat_ngram_size = (
            no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
        )
        bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
        min_length = min_length if min_length is not None else self.config.min_length
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
        # instantiate processors list
        processors = LogitsProcessorList()

        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
        # all samplers can be found in `generation_utils_samplers.py`
        if repetition_penalty is not None and repetition_penalty != 1.0:
            processors.append(RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty))
        if no_repeat_ngram_size is not None and no_repeat_ngram_size > 0:
            processors.append(NoRepeatNGramLogitsProcessor(no_repeat_ngram_size))
        if bad_words_ids is not None:
            processors.append(NoBadWordsLogitsProcessor(bad_words_ids, eos_token_id))
        if min_length is not None and eos_token_id is not None and min_length > -1:
            processors.append(MinLengthLogitsProcessor(min_length, eos_token_id))
        return processors

    @torch.no_grad()
    def generate(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        max_length: Optional[int] = None,
        min_length: Optional[int] = None,
        do_sample: Optional[bool] = None,
        early_stopping: Optional[bool] = None,
        num_beams: Optional[int] = None,
        temperature: Optional[float] = None,
        top_k: Optional[int] = None,
        top_p: Optional[float] = None,
        repetition_penalty: Optional[float] = None,
        bad_words_ids: Optional[Iterable[int]] = None,
        bos_token_id: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        length_penalty: Optional[float] = None,
        no_repeat_ngram_size: Optional[int] = None,
        num_return_sequences: Optional[int] = None,
        decoder_start_token_id: Optional[int] = None,
        use_cache: Optional[bool] = None,
        **model_kwargs
    ) -> torch.LongTensor:
        r"""
        Generates sequences for models with a language modeling head. The method currently supports greedy decoding,
        multinomial sampling, beam-search decoding, and beam-search multinomial sampling.

        Apart from :obj:`input_ids` and :obj:`attention_mask`, all the arguments below will default to the value of the
        attribute of the same name inside the :class:`~transformers.PretrainedConfig` of the model. The default values
        indicated are the default values of those config.

        Most of these parameters are explained in more detail in `this blog post
        <https://huggingface.co/blog/how-to-generate>`__.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            min_length (:obj:`int`, `optional`, defaults to 10):
                The minimum length of the sequence to be generated.
            do_sample (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether to stop the beam search when at least ``num_beams`` sentences are finished per batch or not.
            num_beams (:obj:`int`, `optional`, defaults to 1):
                Number of beams for beam search. 1 means no beam search.
            temperature (:obj:`float`, `optional`, defaults tp 1.0):
                The value used to module the next token probabilities.
            top_k (:obj:`int`, `optional`, defaults to 50):
                The number of highest probability vocabulary tokens to keep for top-k-filtering.
            top_p (:obj:`float`, `optional`, defaults to 1.0):
                If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or
                higher are kept for generation.
            repetition_penalty (:obj:`float`, `optional`, defaults to 1.0):
                The parameter for repetition penalty. 1.0 means no penalty. See `this paper
                <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            bos_token_id (:obj:`int`, `optional`):
                The id of the `beginning-of-sequence` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            length_penalty (:obj:`float`, `optional`, defaults to 1.0):
                Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
                model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
                sequences.
            no_repeat_ngram_size (:obj:`int`, `optional`, defaults to 0):
                If set to int > 0, all ngrams of that size can only occur once.
            bad_words_ids(:obj:`List[List[int]]`, `optional`):
                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 :obj:`tokenizer(bad_word,
                add_prefix_space=True).input_ids`.
            num_return_sequences(:obj:`int`, `optional`, defaults to 1):
                The number of independently computed returned sequences for each element in the batch.
            attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Mask to avoid performing attention on padding token indices. Mask values are in ``[0, 1]``, 1 for
                tokens that are not masked, and 0 for masked tokens. If not provided, will default to a tensor the same
                shape as :obj:`input_ids` that masks the pad token. `What are attention masks?
                <../glossary.html#attention-mask>`__
            decoder_start_token_id (:obj:`int`, `optional`):
                If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
            use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not the model should use the past last key/values attentions (if applicable to the model) to
                speed up decoding.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If the
                model is an Encoder-Decoder model, encoder specific kwargs should not be prefixed and decoder specific
                kwargs should be prefixed with `decoder_`.

        Return:
            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
            sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all
            batches finished early due to the :obj:`eos_token_id`.

        Examples::

            >>> from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM

            >>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
            >>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
            >>> # do greedy decoding without providing a prompt
            >>> outputs = model.generate(max_length=40)
            >>> print("Generated:", tokenizer.decode(outputs[0], skip_special_tokens=True))

            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
            >>> document = (
            ... "at least two people were killed in a suspected bomb attack on a passenger bus "
            ... "in the strife-torn southern philippines on monday , the military said."
            ... )
            >>> # encode input contex
            >>> input_ids = tokenizer(document, return_tensors="pt").input_ids
            >>> # generate 3 independent sequences using beam search decoding (5 beams)
            >>> # with T5 encoder-decoder model conditioned on short news article.
            >>> outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3)
            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))

            >>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
            >>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
            >>> input_context = "The dog"
            >>> # encode input context
            >>> input_ids = tokenizer(input_context, return_tensors="pt").input_ids
            >>> # generate 3 candidates using sampling
            >>> outputs = model.generate(input_ids=input_ids, max_length=20, num_return_sequences=3, do_sample=True)
            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))

            >>> tokenizer = AutoTokenizer.from_pretrained("ctrl")
            >>> model = AutoModelForCausalLM.from_pretrained("ctrl")
            >>> # "Legal" is one of the control codes for ctrl
            >>> input_context = "Legal My neighbor is"
            >>> # encode input context
            >>> input_ids = tokenizer(input_context, return_tensors="pt").input_ids
            >>> outputs = model.generate(input_ids=input_ids, max_length=20, repetition_penalty=1.2)
            >>> print("Generated:", tokenizer.decode(outputs[0], skip_special_tokens=True))

            >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
            >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
            >>> input_context = "My cute dog"
            >>> # get tokens of words that should not be generated
            >>> bad_words_ids = [tokenizer(bad_word, add_prefix_space=True).input_ids for bad_word in ["idiot", "stupid", "shut up"]]
            >>> # encode input context
            >>> input_ids = tokenizer(input_context, return_tensors="pt").input_ids
            >>> # generate sequences without allowing bad_words to be generated
            >>> outputs = model.generate(input_ids=input_ids, max_length=20, do_sample=True, bad_words_ids=bad_words_ids)
            >>> print("Generated:", tokenizer.decode(outputs[0], skip_special_tokens=True))
        """

        # set init values
        num_beams = num_beams if num_beams is not None else self.config.num_beams
        max_length = max_length if max_length is not None else self.config.max_length
        do_sample = do_sample if do_sample is not None else self.config.do_sample
        num_return_sequences = (
            num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
        )

        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        if input_ids is None:
            # init `input_ids` with bos_token_id
            input_ids = self._prepare_input_ids_for_generation(bos_token_id)

        if model_kwargs.get("attention_mask", None) is None:
            # init `attention_mask` depending on `pad_token_id`
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                input_ids, pad_token_id, eos_token_id
            )

        # special case if pad_token_id is not defined
        if pad_token_id is None and eos_token_id is not None:
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
            pad_token_id = eos_token_id

        if self.config.is_encoder_decoder:
            # add encoder_outputs to model_kwargs
            model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, model_kwargs)

            # set input_ids as decoder_input_ids
            input_ids = self._prepare_decoder_input_ids_for_generation(
                input_ids, decoder_start_token_id=decoder_start_token_id, bos_token_id=bos_token_id, **model_kwargs
            )

            if "encoder_outputs" not in model_kwargs or not isinstance(model_kwargs["encoder_outputs"], ModelOutput):
                raise ValueError("Make sure that `model_kwargs` include `encoder_outputs` of type `ModelOutput`.")

        # determine generation mode
        is_greedy_gen_mode = (num_beams == 1) and do_sample is False
        is_sample_gen_mode = (num_beams == 1) and do_sample is True
        is_beam_gen_mode = (num_beams > 1) and do_sample is False
        is_beam_sample_gen_mode = (num_beams > 1) and do_sample is True

        # set model_kwargs
        model_kwargs["use_cache"] = use_cache

        # get distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            repetition_penalty=repetition_penalty,
            no_repeat_ngram_size=no_repeat_ngram_size,
            bad_words_ids=bad_words_ids,
            min_length=min_length,
            eos_token_id=eos_token_id,
        )

        if is_greedy_gen_mode:
            if num_return_sequences > 1:
                raise ValueError(
                    f"num_return_sequences has to be 1, but is {num_return_sequences} when doing greedy search."
                )

            # greedy search
            return self.greedy_search(
                input_ids,
                logits_processor=logits_processor,
                max_length=max_length,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                **model_kwargs,
            )

        elif is_sample_gen_mode:
            # get probability distribution warper
            logits_warper = self._get_logits_warper(
                top_k=top_k, top_p=top_p, temperature=temperature, num_beams=num_beams
            )

            # expand input_ids with `num_return_sequences` additional sequences per batch
            input_ids, model_kwargs = self._expand_inputs_for_generation(
                input_ids,
                expand_size=num_return_sequences,
                is_encoder_decoder=self.config.is_encoder_decoder,
                **model_kwargs,
            )

            # sample
            return self.sample(
                input_ids,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                max_length=max_length,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                **model_kwargs,
            )

        elif is_beam_gen_mode:
            batch_size = input_ids.shape[0]

            length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
            early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping

            if num_return_sequences > num_beams:
                raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")

            beam_scorer = BeamSearchScorer(
                batch_size=batch_size,
                max_length=max_length,
                num_beams=num_beams,
                device=self.device,
                length_penalty=length_penalty,
                do_early_stopping=early_stopping,
                num_beam_hyps_to_keep=num_return_sequences,
            )
            # interleave with `num_beams`
            input_ids, model_kwargs = self._expand_inputs_for_generation(
                input_ids, expand_size=num_beams, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
            )
            return self.beam_search(
                input_ids,
                beam_scorer,
                logits_processor=logits_processor,
                max_length=max_length,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                **model_kwargs,
            )

        elif is_beam_sample_gen_mode:
            logits_warper = self._get_logits_warper(
                top_k=top_k, top_p=top_p, temperature=temperature, num_beams=num_beams
            )

            batch_size = input_ids.shape[0] * num_return_sequences

            length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
            beam_scorer = BeamSearchScorer(
                batch_size=batch_size,
                max_length=max_length,
                num_beams=num_beams,
                device=self.device,
                length_penalty=length_penalty,
                do_early_stopping=early_stopping,
            )

            # interleave with `num_beams * num_return_sequences`
            input_ids, model_kwargs = self._expand_inputs_for_generation(
                input_ids,
                expand_size=num_beams * num_return_sequences,
                is_encoder_decoder=self.config.is_encoder_decoder,
                **model_kwargs,
            )

            return self.beam_sample(
                input_ids,
                beam_scorer,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                max_length=max_length,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                **model_kwargs,
            )

    def greedy_search(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        **model_kwargs
    ):
        r"""
        Generates sequences for models with a language modeling head using greedy decoding.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            logits_processor (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling
                head applied at each generation step.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            model_kwargs:
                Additional model specific keyword arguments will be forwarded to the :obj:`forward` function of the
                model. If model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.

        Return:
            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
            sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all
            batches finished early due to the :obj:`eos_token_id`.

        Examples::

            >>> from transformers import (
            ... AutoTokenizer,
            ... AutoModelForCausalLM,
            ... LogitsProcessorList,
            ... MinLengthLogitsProcessor,
            ... )

            >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
            >>> model = AutoModelForCausalLM.from_pretrained("gpt2")

            >>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
            >>> model.config.pad_token_id = model.config.eos_token_id

            >>> input_prompt = "Today is a beautiful day, and"
            >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids

            >>> # instantiate logits processors
            >>> logits_processor = LogitsProcessorList([
            ...     MinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
            ... ])

            >>> outputs = model.greedy_search(input_ids, logits_processor=logits_processor)

            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
        """

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        max_length = max_length if max_length is not None else self.config.max_length
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id

        # init sequence length tensors
        sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation(
            input_ids, max_length
        )

        while cur_len < max_length:
            # prepare model inputs
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # forward pass to get next token
            outputs = self(**model_inputs, return_dict=True)
            next_token_logits = outputs.logits[:, -1, :]

            # pre-process distribution
            scores = logits_processor(input_ids, next_token_logits)

            # argmax
            next_tokens = torch.argmax(scores, dim=-1)

            # add code that transfomers next_tokens to tokens_to_add
            if eos_token_id is not None:
                assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
                next_tokens = next_tokens * unfinished_sequences + (pad_token_id) * (1 - unfinished_sequences)

            # add token and increase length by one
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)

            # update sequence length
            if eos_token_id is not None:
                sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation(
                    sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id
                )

            # update model kwargs
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )

            # stop when there is a </s> in each sentence, or if we exceed the maximul length
            if unfinished_sequences.max() == 0:
                break

            # increase cur_len
            cur_len = cur_len + 1

        return input_ids

    def sample(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        logits_warper: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        **model_kwargs
    ):
        r"""
        Generates sequences for models with a language modeling head using multinomial sampling.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            logits_processor (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling
                head applied at each generation step.
            logits_warper (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsWarper` used to warp the prediction score distribution of the language
                modeling head applied before multinomial sampling at each generation step.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If
                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.

        Return:
            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
            sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all
            batches finished early due to the :obj:`eos_token_id`.

        Examples::

            >>> from transformers import (
            ...    AutoTokenizer,
            ...    AutoModelForCausalLM,
            ...    LogitsProcessorList,
            ...    MinLengthLogitsProcessor,
            ...    TopKLogitsWarper,
            ...    TemperatureLogitsWarper,
            ... )

            >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
            >>> model = AutoModelForCausalLM.from_pretrained("gpt2")

            >>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
            >>> model.config.pad_token_id = model.config.eos_token_id

            >>> input_prompt = "Today is a beautiful day, and"
            >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids

            >>> # instantiate logits processors
            >>> logits_processor = LogitsProcessorList([
            ...     MinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
            ... ])
            >>> # instantiate logits processors
            >>> logits_warper = LogitsProcessorList([
            ...     TopKLogitsWarper(50),
            ...     TemperatureLogitsWarper(0.7),
            ... ])

            >>> outputs = model.sample(input_ids, logits_processor=logits_processor, logits_warper=logits_warper)

            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
        """

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
        max_length = max_length if max_length is not None else self.config.max_length
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id

        # init sequence length tensors
        sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation(
            input_ids, max_length
        )

        # auto-regressive generation
        while cur_len < max_length:
            # prepare model inputs
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # forward pass to get next token
            outputs = self(**model_inputs, return_dict=True)
            next_token_logits = outputs.logits[:, -1, :]

            # pre-process distribution
            scores = logits_processor(input_ids, next_token_logits)
            scores = logits_warper(input_ids, scores)

            # sample
            probs = F.softmax(scores, dim=-1)
            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)

            # add code that transfomers next_tokens to tokens_to_add
            if eos_token_id is not None:
                assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
                next_tokens = next_tokens * unfinished_sequences + (pad_token_id) * (1 - unfinished_sequences)

            # add token and increase length by one
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
            cur_len = cur_len + 1

            # update sequence length
            if eos_token_id is not None:
                sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation(
                    sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id
                )

            # stop when there is a </s> in each sentence, or if we exceed the maximul length
            if unfinished_sequences.max() == 0:
                break

            # update model kwargs
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )

        return input_ids

    def beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        **model_kwargs
    ):
        r"""
        Generates sequences for models with a language modeling head using beam search decoding.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            beam_scorer (:obj:`BeamScorer`):
                An derived instance of :class:`~transformers.BeamScorer` that defines how beam hypotheses are
                constructed, stored and sorted during generation. For more information, the documentation of
                :class:`~transformers.BeamScorer` should be read.
            logits_processor (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling
                head applied at each generation step.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If
                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.

        Return:
            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
            sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all
            batches finished early due to the :obj:`eos_token_id`.

        Examples::

            >>> from transformers import (
            ...    AutoTokenizer,
            ...    AutoModelForSeq2SeqLM,
            ...    LogitsProcessorList,
            ...    MinLengthLogitsProcessor,
            ...    BeamSearchScorer,
            ... )
            >>> import torch

            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

            >>> encoder_input_str = "translate English to German: How old are you?"
            >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids


            >>> # lets run beam search using 3 beams
            >>> num_beams = 3
            >>> # define decoder start token ids
            >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
            >>> input_ids = input_ids * model.config.decoder_start_token_id

            >>> # add encoder_outputs to model keyword arguments
            >>> model_kwargs = {
            ...     "encoder_outputs": model.get_encoder()(encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True)
            ... }

            >>> # instantiate beam scorer
            >>> beam_scorer = BeamSearchScorer(
            ...     batch_size=1,
            ...     max_length=model.config.max_length,
            ...     num_beams=num_beams,
            ...     device=model.device,
            ... )

            >>> # instantiate logits processors
            >>> logits_processor = LogitsProcessorList([
            ...     MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
            ... ])

            >>> outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)

            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
        """

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        max_length = max_length if max_length is not None else self.config.max_length
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id

        batch_size = len(beam_scorer._beam_hyps)
        num_beams = beam_scorer.num_beams

        batch_beam_size, cur_len = input_ids.shape

        assert (
            num_beams * batch_size == batch_beam_size
        ), "Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."

        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
        beam_scores[:, 1:] = -1e9
        beam_scores = beam_scores.view((batch_size * num_beams,))

        while cur_len < max_length:
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            outputs = self(**model_inputs, return_dict=True)
            next_token_logits = outputs.logits[:, -1, :]

            # adjust tokens for Bart, *e.g.*
            next_token_logits = self.adjust_logits_during_generation(
                next_token_logits, cur_len=cur_len, max_length=max_length
            )

            next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)

            next_token_scores = logits_processor(input_ids, next_token_scores)
            next_token_scores = next_token_scores + beam_scores[:, None].expand_as(next_token_scores)
            # reshape for beam search
            vocab_size = next_token_scores.shape[-1]
            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)

            next_token_scores, next_tokens = torch.topk(
                next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
            )

            next_indices = next_tokens // vocab_size
            next_tokens = next_tokens % vocab_size

            # stateless
            beam_outputs = beam_scorer.process(
                input_ids,
                next_token_scores,
                next_tokens,
                next_indices,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
            )
            beam_scores = beam_outputs["next_beam_scores"]
            beam_next_tokens = beam_outputs["next_beam_tokens"]
            beam_idx = beam_outputs["next_beam_indices"]

            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
            cur_len = cur_len + 1

            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            if model_kwargs["past"] is not None:
                model_kwargs["past"] = self._reorder_cache(model_kwargs["past"], beam_idx)

            if beam_scorer.is_done:
                break

        decoded = beam_scorer.finalize(
            input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id
        )

        return decoded

    def beam_sample(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        logits_warper: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        **model_kwargs
    ):
        r"""
        Generates sequences for models with a language modeling head using beam search with multinomial sampling.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            beam_scorer (:obj:`BeamScorer`):
                A derived instance of :class:`~transformers.BeamScorer` that defines how beam hypotheses are
                constructed, stored and sorted during generation. For more information, the documentation of
                :class:`~transformers.BeamScorer` should be read.
            logits_processor (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling
                head applied at each generation step.
            logits_warper (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsWarper` used to warp the prediction score distribution of the language
                modeling head applied before multinomial sampling at each generation step.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If
                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.

        Return:
            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
            sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all
            batches finished early due to the :obj:`eos_token_id`.

        Examples::

            >>> from transformers import (
            ...     AutoTokenizer,
            ...     AutoModelForSeq2SeqLM,
            ...     LogitsProcessorList,
            ...     MinLengthLogitsProcessor,
            ...     TopKLogitsWarper,
            ...     TemperatureLogitsWarper,
            ...     BeamSearchScorer,
            ... )
            >>> import torch

            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

            >>> encoder_input_str = "translate English to German: How old are you?"
            >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids

            >>> # lets run beam search using 3 beams
            >>> num_beams = 3
            >>> # define decoder start token ids
            >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
            >>> input_ids = input_ids * model.config.decoder_start_token_id

            >>> # add encoder_outputs to model keyword arguments
            >>> model_kwargs = {
            ...     "encoder_outputs": model.get_encoder()(encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True)
            ... }

            >>> # instantiate beam scorer
            >>> beam_scorer = BeamSearchScorer(
            ...     batch_size=1,
            ...     max_length=model.config.max_length,
            ...     num_beams=num_beams,
            ...     device=model.device,
            ... )

            >>> # instantiate logits processors
            >>> logits_processor = LogitsProcessorList([
            ...     MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id)
            ... ])
            >>> # instantiate logits processors
            >>> logits_warper = LogitsProcessorList([
            ...     TopKLogitsWarper(50),
            ...     TemperatureLogitsWarper(0.7),
            ... ])

            >>> outputs = model.beam_sample(
            ...     input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
            ... )

            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
        """

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        max_length = max_length if max_length is not None else self.config.max_length
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id

        batch_size = len(beam_scorer._beam_hyps)
        num_beams = beam_scorer.num_beams

        batch_beam_size, cur_len = input_ids.shape

        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
        beam_scores = beam_scores.view((batch_size * num_beams,))

        while cur_len < max_length:
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            outputs = self(**model_inputs, return_dict=True)
            next_token_logits = outputs.logits[:, -1, :]

            # adjust token scores (a no-op by default)
            next_token_logits = self.adjust_logits_during_generation(
                next_token_logits, cur_len=cur_len, max_length=max_length
            )

            next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)

            next_token_scores = logits_processor(input_ids, next_token_scores)
            next_token_scores = next_token_scores + beam_scores[:, None].expand_as(next_token_scores)
            next_token_scores = logits_warper(input_ids, next_token_scores)

            # reshape for beam search
            vocab_size = next_token_scores.shape[-1]
            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)

            probs = F.softmax(next_token_scores, dim=-1)
            next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)
            next_token_scores = torch.gather(next_token_scores, -1, next_tokens)

            next_token_scores, _indices = torch.sort(next_token_scores, descending=True, dim=1)
            next_tokens = torch.gather(next_tokens, -1, _indices)

            next_indices = next_tokens // vocab_size
            next_tokens = next_tokens % vocab_size

            # stateless
            beam_outputs = beam_scorer.process(
                input_ids,
                next_token_scores,
                next_tokens,
                next_indices,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
            )
            beam_scores = beam_outputs["next_beam_scores"]
            beam_next_tokens = beam_outputs["next_beam_tokens"]
            beam_idx = beam_outputs["next_beam_indices"]

            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
            cur_len = cur_len + 1

            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            if model_kwargs["past"] is not None:
                model_kwargs["past"] = self._reorder_cache(model_kwargs["past"], beam_idx)

            if beam_scorer.is_done:
                break

        decoded = beam_scorer.finalize(
            input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id
        )

        return decoded


def top_k_top_p_filtering(
    logits: torch.FloatTensor,
    top_k: int = 0,
    top_p: float = 1.0,
    filter_value: float = -float("Inf"),
    min_tokens_to_keep: int = 1,
) -> torch.FloatTensor:
    """
    Filter a distribution of logits using top-k and/or nucleus (top-p) filtering

    Args:
        logits: logits distribution shape (batch size, vocabulary size)
        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
        Make sure we keep at least min_tokens_to_keep per batch example in the output
    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
    """
    if top_k > 0:
        logits = TopKLogitsWarper(top_k=top_k, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
            None, logits
        )

    if 0 <= top_p <= 1.0:
        logits = TopPLogitsWarper(top_p=top_p, min_tokens_to_keep=min_tokens_to_keep)(None, logits)

    return logits
