import inspect
import warnings
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union

import torch
import torch.distributed as dist
from torch import nn

from transformers.generation_beam_constraints import Constraint, DisjunctiveConstraint, PhrasalConstraint
from transformers.generation_beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
from transformers.generation_logits_process import (
    EncoderNoRepeatNGramLogitsProcessor,
    ExponentialDecayLengthPenalty,
    ForcedBOSTokenLogitsProcessor,
    ForcedEOSTokenLogitsProcessor,
    HammingDiversityLogitsProcessor,
    InfNanRemoveLogitsProcessor,
    LogitNormalization,
    LogitsProcessorList,
    MinLengthLogitsProcessor,
    NoBadWordsLogitsProcessor,
    NoRepeatNGramLogitsProcessor,
    PrefixConstrainedLogitsProcessor,
    RepetitionPenaltyLogitsProcessor,
    TemperatureLogitsWarper,
    TopKLogitsWarper,
    TopPLogitsWarper,
    TypicalLogitsWarper,
)
from transformers.generation_stopping_criteria import (
    MaxLengthCriteria,
    MaxTimeCriteria,
    StoppingCriteria,
    StoppingCriteriaList,
    validate_stopping_criteria,
)
from transformers.pytorch_utils import torch_int_div
from transformers.utils import ModelOutput

from transformers.generation_utils import (
    SampleOutput,
    BeamSearchOutput,
    BeamSampleOutput,
    GreedySearchOutput, GreedySearchDecoderOnlyOutput, SampleDecoderOnlyOutput, GreedySearchEncoderDecoderOutput,
    BeamSearchDecoderOnlyOutput, BeamSearchEncoderDecoderOutput, BeamSampleDecoderOnlyOutput,
    BeamSampleEncoderDecoderOutput, SampleEncoderDecoderOutput,
)
from utils import get_jump_chunks
from torch.nn.utils.rnn import pad_sequence

class GenerationMixin:
    """
    A class containing all functions for auto-regressive text generation, to be used as a mixin in [`PreTrainedModel`].

    The class exposes [`~generation_utils.GenerationMixin.generate`], which can be used for:
        - *greedy decoding* by calling [`~generation_utils.GenerationMixin.greedy_search`] if `num_beams=1` and
          `do_sample=False`.
        - *multinomial sampling* by calling [`~generation_utils.GenerationMixin.sample`] if `num_beams=1` and
          `do_sample=True`.
        - *beam-search decoding* by calling [`~generation_utils.GenerationMixin.beam_search`] if `num_beams>1` and
          `do_sample=False`.
        - *beam-search multinomial sampling* by calling [`~generation_utils.GenerationMixin.beam_sample`] if
          `num_beams>1` and `do_sample=True`.
        - *diverse beam-search decoding* by calling [`~generation_utils.GenerationMixin.group_beam_search`], if
          `num_beams>1` and `num_beam_groups>1`.
        - *constrained beam-search decoding* by calling [`~generation_utils.GenerationMixin.constrained_beam_search`],
          if `constraints!=None` or `force_words_ids!=None`.
    """

    def _prepare_model_inputs(
        self,
        inputs: Optional[torch.Tensor] = None,
        bos_token_id: Optional[int] = None,
        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
    ) -> Tuple[torch.Tensor, Optional[str], Dict[str, torch.Tensor]]:
        """
        This function extracts the model-specific `inputs` for generation.
        """
        # 1. retrieve all kwargs that are non-None or non-model input related.
        # some encoder-decoder models have different names for model and encoder
        if (
            self.config.is_encoder_decoder
            and hasattr(self, "encoder")
            and self.encoder.main_input_name != self.main_input_name
        ):
            input_name = self.encoder.main_input_name
        else:
            input_name = self.main_input_name

        model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None or k != input_name}

        # 2. check whether model_input_name is passed as kwarg
        # if yes and `inputs` is None use kwarg inputs
        inputs_kwarg = model_kwargs.pop(input_name, None)
        if inputs_kwarg is not None and inputs is not None:
            raise ValueError(
                f"`inputs`: {inputs}` were passed alongside "
                f"{input_name} which is not allowed."
                f"Make sure to either pass {inputs} or {input_name}=..."
            )
        elif inputs_kwarg is not None:
            inputs = inputs_kwarg

        # 3. models with `input_ids` can also make use of `inputs_embeds`
        if self._can_retrieve_inputs_from_name(inputs, "inputs_embeds", model_kwargs):
            inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"

        # 4. Only encoder-decoder models can have non `input_ids` input format
        if not self.config.is_encoder_decoder and input_name != "input_ids":
            raise ValueError(
                f"If {input_name} is passed as model-specific keyword "
                "input then model has to be an encoder-decoder and not a "
                f"{self.__class__.__name__}."
            )

        # 5. if `inputs` is still None, try to create `input_ids` from BOS token
        if inputs is None:
            inputs = self._prepare_input_ids_for_generation(bos_token_id, model_kwargs.get("encoder_outputs"))

        return inputs, input_name, model_kwargs

    def _can_retrieve_inputs_from_name(
        self, inputs: Optional[torch.Tensor], name: str, model_kwargs: Dict[str, torch.Tensor]
    ) -> torch.Tensor:
        """
        If `inputs` is None and `name` is in both forward function and keyword arguments, then inputs can be retrieved
        from name
        """
        can_retrieve_inputs = model_kwargs.get(name, None) is not None and name in set(
            inspect.signature(self.forward).parameters.keys()
        )

        if can_retrieve_inputs and inputs is not None:
            raise ValueError(f"Cannot only pass one of {name} and {self.main_input_name}")

        return can_retrieve_inputs

    def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> Dict[str, Any]:
        """
        Implement in subclasses of [`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 [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
        """
        return logits

    def _prepare_input_ids_for_generation(
        self, bos_token_id: Optional[int], encoder_outputs: Optional[ModelOutput]
    ) -> torch.LongTensor:
        if self.config.is_encoder_decoder and encoder_outputs is not None:
            # make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
            shape = encoder_outputs.last_hidden_state.size()[:-1]
            return torch.ones(shape, dtype=torch.long, device=self.device) * -100

        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,
        inputs: torch.Tensor,
        pad_token_id: int,
        eos_token_id: int,
    ) -> torch.LongTensor:
        is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [torch.int, torch.long]
        is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
        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)
        )
        # Check if input is input_ids and padded -> only then is attention_mask defined
        if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
            return inputs.ne(pad_token_id).long()
        else:
            return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device)

    def _prepare_encoder_decoder_kwargs_for_generation(
        self, inputs_tensor: torch.Tensor, model_kwargs, model_input_name: Optional[str] = None
    ) -> Dict[str, Any]:
        # 1. get encoder
        encoder = self.get_encoder()

        # 2. prepare encoder args and encoder kwargs from model kwargs
        irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
        encoder_kwargs = {
            argument: value
            for argument, value in model_kwargs.items()
            if not any(argument.startswith(p) for p in irrelevant_prefix)
        }
        print('encoder_kwargs:', encoder_kwargs)

        # 3. make sure that encoder returns `ModelOutput`
        model_input_name = model_input_name if model_input_name is not None else self.main_input_name
        encoder_kwargs["return_dict"] = True
        encoder_kwargs[model_input_name] = inputs_tensor
        model_kwargs["encoder_outputs"]: ModelOutput = encoder(**encoder_kwargs)

        return model_kwargs

    def _prepare_decoder_input_ids_for_generation(
        self,
        batch_size: int,
        decoder_start_token_id: int = None,
        bos_token_id: int = None,
        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        device: torch.device = None,
    ) -> torch.LongTensor:

        if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
            return model_kwargs.pop("decoder_input_ids")
        else:
            decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
            if device is None:
                device = self.device
            return torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_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: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[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:
            if encoder_outputs is None:
                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
            encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
                0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
            )
            model_kwargs["encoder_outputs"] = encoder_outputs
        return input_ids, model_kwargs

    @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

    def _reorder_cache(self, past, beam_idx):
        raise NotImplementedError(
            f"Make sure that a `_reorder_cache` function is correctly implemented in {self.__class__.__module__} to enable beam search for {self.__class__}"
        )

    def _get_logits_warper(
        self,
        top_k: Optional[int] = None,
        top_p: Optional[float] = None,
        typical_p: Optional[float] = None,
        temperature: Optional[float] = None,
        num_beams: Optional[int] = None,
        renormalize_logits: Optional[bool] = None,
    ) -> LogitsProcessorList:
        """
        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`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
        typical_p = typical_p if typical_p is not None else self.config.typical_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 temperature is not None and temperature != 1.0:
            warpers.append(TemperatureLogitsWarper(temperature))
        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 typical_p is not None and typical_p < 1.0:
            warpers.append(TypicalLogitsWarper(mass=typical_p, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
        # `LogitNormalization` should always be the last logit processor, when present
        if renormalize_logits is True:
            warpers.append(LogitNormalization())
        return warpers

    def _get_logits_processor(
        self,
        repetition_penalty: float,
        no_repeat_ngram_size: int,
        encoder_no_repeat_ngram_size: int,
        input_ids_seq_length: int,
        encoder_input_ids: torch.LongTensor,
        bad_words_ids: List[List[int]],
        min_length: int,
        max_length: int,
        eos_token_id: int,
        forced_bos_token_id: int,
        forced_eos_token_id: int,
        prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]],
        num_beams: int,
        num_beam_groups: int,
        diversity_penalty: float,
        remove_invalid_values: bool,
        exponential_decay_length_penalty: Tuple,
        logits_processor: Optional[LogitsProcessorList],
        renormalize_logits: Optional[bool],
    ) -> LogitsProcessorList:
        """
        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
        instances used to modify the scores of the language model head.
        """
        processors = LogitsProcessorList()

        # 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
        )
        encoder_no_repeat_ngram_size = (
            encoder_no_repeat_ngram_size
            if encoder_no_repeat_ngram_size is not None
            else self.config.encoder_no_repeat_ngram_size
        )
        bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
        diversity_penalty = diversity_penalty if diversity_penalty is not None else self.config.diversity_penalty
        forced_bos_token_id = (
            forced_bos_token_id if forced_bos_token_id is not None else self.config.forced_bos_token_id
        )
        forced_eos_token_id = (
            forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id
        )
        remove_invalid_values = (
            remove_invalid_values if remove_invalid_values is not None else self.config.remove_invalid_values
        )
        exponential_decay_length_penalty = (
            exponential_decay_length_penalty
            if exponential_decay_length_penalty is not None
            else self.config.exponential_decay_length_penalty
        )
        # instantiate processors list

        # 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 diversity_penalty is not None and diversity_penalty > 0.0:
            processors.append(
                HammingDiversityLogitsProcessor(
                    diversity_penalty=diversity_penalty, num_beams=num_beams, num_beam_groups=num_beam_groups
                )
            )
        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 encoder_no_repeat_ngram_size is not None and encoder_no_repeat_ngram_size > 0:
            if self.config.is_encoder_decoder:
                processors.append(EncoderNoRepeatNGramLogitsProcessor(encoder_no_repeat_ngram_size, encoder_input_ids))
            else:
                raise ValueError(
                    "It's impossible to use `encoder_no_repeat_ngram_size` with decoder-only architecture"
                )
        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 > 0:
            processors.append(MinLengthLogitsProcessor(min_length, eos_token_id))
        if prefix_allowed_tokens_fn is not None:
            processors.append(PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, num_beams // num_beam_groups))
        if forced_bos_token_id is not None:
            processors.append(ForcedBOSTokenLogitsProcessor(forced_bos_token_id))
        if forced_eos_token_id is not None:
            processors.append(ForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id))
        if remove_invalid_values is True:
            processors.append(InfNanRemoveLogitsProcessor())
        if exponential_decay_length_penalty is not None:
            processors.append(
                ExponentialDecayLengthPenalty(exponential_decay_length_penalty, eos_token_id, input_ids_seq_length)
            )
        processors = self._merge_criteria_processor_list(processors, logits_processor)
        # `LogitNormalization` should always be the last logit processor, when present
        if renormalize_logits is True:
            processors.append(LogitNormalization())
        return processors

    def _get_stopping_criteria(
        self, max_length: Optional[int], max_time: Optional[float], stopping_criteria: Optional[StoppingCriteriaList]
    ) -> StoppingCriteriaList:
        criteria = StoppingCriteriaList()
        if max_length is not None:
            criteria.append(MaxLengthCriteria(max_length=max_length))
        if max_time is not None:
            criteria.append(MaxTimeCriteria(max_time=max_time))
        criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)
        return criteria

    def _merge_criteria_processor_list(
        self,
        default_list: Union[LogitsProcessorList, StoppingCriteriaList],
        custom_list: Union[LogitsProcessorList, StoppingCriteriaList],
    ) -> Union[LogitsProcessorList, StoppingCriteriaList]:
        if len(custom_list) == 0:
            return default_list
        for default in default_list:
            for custom in custom_list:
                if type(custom) is type(default):
                    object_type = "stopping criteria" if isinstance(custom, StoppingCriteria) else "logits processor"
                    raise ValueError(
                        f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to `generate`, "
                        f"but it has already been created with the values {default}. {default} has been created by passing the "
                        "corresponding arguments to generate or by the model's config default values. "
                        f"If you just want to change the default values of {object_type} consider passing them as arguments "
                        f"to `generate` instead of using a custom {object_type}."
                    )
        default_list.extend(custom_list)
        return default_list

    def compute_transition_beam_scores(
        self,
        sequences: torch.Tensor,
        scores: Tuple[torch.Tensor],
        beam_indices: torch.Tensor,
        eos_token_id: int = None,
    ):
        """compute the transition probabilities of sequences given generation
        scores and beam indices"""

        # reshape scores as [vocab_size * batch_size, # generation steps]
        # with batch_size being 2 * vocab_size and # generation steps being
        # seq_len - input_length
        scores = torch.stack(scores).reshape(len(scores), -1).transpose(0, 1)

        # start of generated tokens
        cut_idx = sequences.shape[-1] - scores.shape[-1]
        # adjust for beam indices
        beam_sequence_indices = torch.tensor(beam_indices, device=sequences.device) * self.config.vocab_size
        # compute real indices
        indices = sequences[:, cut_idx:] + beam_sequence_indices
        # gather scores and run
        transition_scores = scores.gather(0, indices)
        # make sure that if EOS token was used before length of sequence `sequence.shape[-1]`
        # get first occurence of EOS token
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id

        if eos_token_id is not None:
            is_eos_token_id = sequences[:, cut_idx:] == eos_token_id
            # make sure first eos token still contributes to transition probs
            is_eos_token_id[:, -1] = False
            is_eos_token_id = is_eos_token_id.roll(1, -1)
            # all indices after eos shoud be masked
            zero_transition_prob_mask = is_eos_token_id.cumsum(-1).bool()
            # zero out padded probs
            transition_scores.masked_fill_(zero_transition_prob_mask, 0.0)

        return transition_scores

    # ADDED FRED
    def remove_subsets(self, l):
        #l = [[1, 2, 4, 8], [1, 2, 4, 5, 6], [1, 2, 3], [2, 3, 21], [1, 2, 3, 4], [1, 2, 3, 4, 5, 6, 7]]
        l2 = l[:]
        for m in l:
            for n in l:
                if set(m).issubset(set(n)) and m != n:
                    l2.remove(m)
                    break
        return l2

    # ADDED FRED
    @torch.no_grad()
    def cs_generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        contexts:List[str]=None, #input data
        model_input:Dict=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,
        typical_p: Optional[float] = None,
        repetition_penalty: Optional[float] = None,
        bad_words_ids: Optional[Iterable[int]] = None,
        force_words_ids: Optional[Union[Iterable[int], Iterable[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,
        encoder_no_repeat_ngram_size: Optional[int] = None,
        num_return_sequences: Optional[int] = None,
        max_time: Optional[float] = None,
        max_new_tokens: Optional[int] = None,
        decoder_start_token_id: Optional[int] = None,
        use_cache: Optional[bool] = None,
        num_beam_groups: Optional[int] = None,
        diversity_penalty: Optional[float] = None,
        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
        logits_processor: Optional[LogitsProcessorList] = LogitsProcessorList(),
        renormalize_logits: Optional[bool] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = StoppingCriteriaList(),
        constraints: Optional[List[Constraint]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        forced_bos_token_id: Optional[int] = None,
        forced_eos_token_id: Optional[int] = None,
        remove_invalid_values: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        exponential_decay_length_penalty: Optional[Tuple[Union[int, float]]] = None,
        use_kg:bool=False, #added
        relation_mapper_builder=None,
        tokenizer=None,
        max_neig_per_concept=1, #it slows down quite a lot
        **model_kwargs,
    ) -> Union[GreedySearchOutput, SampleOutput, BeamSearchOutput, BeamSampleOutput, torch.LongTensor]:
        # print(model_input)
        input_ids = model_input['input_ids']
        if "input_commonsense_relations" in model_input:
            # print(model_input['input_commonsense_relations'].sum())
            model_kwargs["relation_inputs"] = model_input.get("input_commonsense_relations").to(input_ids.device)
        if use_kg:
            all_constraints = []
            print('contexts:', contexts[:3])
            for context in contexts:
                constraints = []
                print('+++++++')
                concepts_from_context = relation_mapper_builder.get_concepts_from_context(context=context,
                                                                                               clear_common_wds=True, alignment=1)
                print('concepts_from_context:', concepts_from_context)
                useful_concepts = [relation_mapper_builder.swow_knowledge.get_related_concepts(concept) for concept in
                                   concepts_from_context]
                if not useful_concepts:
                    useful_concepts = [relation_mapper_builder.knowledge.get_related_concepts(concept) for concept in concepts_from_context]
                useful_concepts = [[f'{phrase}' for phrase in concepts] for concepts in useful_concepts]  # add spaces
                # useful_concepts = [[phrase for phrase in concepts if len(phrase.split(' ')) == 1] for concepts in useful_concepts]
                # useful_concepts = list(itertools.chain.from_iterable(useful_concepts))
                # print('useful_concepts:', useful_concepts[:5])
                print('-------')
                print('useful_concepts:', useful_concepts)
                if concepts_from_context and useful_concepts:
                    for context_concept, neighbour_concepts in zip(concepts_from_context, useful_concepts):
                        print('neighbour:', neighbour_concepts[:5])
                        # flexible_words = self.most_similar_words(context_concept, neighbour_concepts) # limit the upperbound
                        # flexible_words = [word for word in flexible_words if word not in context_concept] # remove input concepts
                        flexible_words = [word for word in neighbour_concepts if
                                          word not in context_concept]  # remove input concepts
                        print('flexible_words:', flexible_words[:5])
                        if not flexible_words:
                            continue
                        flexible_words_ids: List[List[int]] = tokenizer(flexible_words, add_special_tokens=False).input_ids #add_prefix_space=True,
                        flexible_words_ids = self.remove_subsets(flexible_words_ids)
                        # add_prefix_space=True
                        # flexible_words_ids = [x for x in flexible_words_ids if len(x) == 1] # problem with subsets
                        flexible_words_ids = flexible_words_ids[:max_neig_per_concept]
                        #print('flexible_words_ids:', flexible_words_ids[:3])
                        constraint = DisjunctiveConstraint(flexible_words_ids)
                        constraints.append(constraint)
                all_constraints.extend(constraints)
        else:
            all_constraints = None

        generated_answers_encoded = self.generate(input_ids=input_ids,
                                                   #attention_mask=model_input["attention_mask"].to(input_ids.device),
                                                   constraints=all_constraints,
                                                   min_length=min_length,
                                                   #max_length=max_length,
                                                   do_sample=do_sample,
                                                   early_stopping=early_stopping,
                                                   #num_beams=num_beams,
                                                   temperature=temperature,
                                                   top_k=top_k,
                                                   top_p=top_p,
                                                   # eos_token_id=tokenizer.eos_token_id,
                                                   no_repeat_ngram_size=no_repeat_ngram_size,
                                                   num_return_sequences=num_return_sequences,
                                                   return_dict_in_generate=return_dict_in_generate,
                                                   output_attentions=output_attentions,
                                                   output_scores=output_scores,
                                                   **model_kwargs,
        )
        return generated_answers_encoded

    # ADDED FRED
    @torch.no_grad()
    def cs_simple_generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        neighbours_contexts:List[List[str]]=None, #input data
        model_input:Dict=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,
        typical_p: Optional[float] = None,
        repetition_penalty: Optional[float] = None,
        bad_words_ids: Optional[Iterable[int]] = None,
        force_words_ids: Optional[Union[Iterable[int], Iterable[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,
        encoder_no_repeat_ngram_size: Optional[int] = None,
        num_return_sequences: Optional[int] = None,
        max_time: Optional[float] = None,
        max_new_tokens: Optional[int] = None,
        decoder_start_token_id: Optional[int] = None,
        use_cache: Optional[bool] = None,
        num_beam_groups: Optional[int] = None,
        diversity_penalty: Optional[float] = None,
        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
        logits_processor: Optional[LogitsProcessorList] = LogitsProcessorList(),
        renormalize_logits: Optional[bool] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = StoppingCriteriaList(),
        constraints: Optional[List[Constraint]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        forced_bos_token_id: Optional[int] = None,
        forced_eos_token_id: Optional[int] = None,
        remove_invalid_values: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        exponential_decay_length_penalty: Optional[Tuple[Union[int, float]]] = None,
        use_kg:bool=False, #added
        relation_mapper_builder=None,
        tokenizer=None,
        max_concepts=2, #it slows down quite a lot
        **model_kwargs,
    ) -> Union[GreedySearchOutput, SampleOutput, BeamSearchOutput, BeamSampleOutput, torch.LongTensor]:
        # print(model_input)
        input_ids = model_input['input_ids']
        if use_kg:
            all_constraints = []
            for context_neighbours in neighbours_contexts:
                # context_neighbours is a collection of concepts
                # lets create sub collections of concepts
                context_neighbours = [f' {concept}' for concept in context_neighbours if len(concept) > 3]
                n_size_chuncks = len(context_neighbours) // max_concepts
                n_size_chuncks = n_size_chuncks if n_size_chuncks > 0 else 1
                sub_concepts_collection = list(get_jump_chunks(context_neighbours, jump=n_size_chuncks))
                constraints = []
                for sub_concepts in sub_concepts_collection[:max_concepts]:
                    flexible_words_ids: List[List[int]] = tokenizer(sub_concepts, add_special_tokens=False).input_ids #add_prefix_space=True,
                    #flexible_words_ids = self.remove_subsets(flexible_words_ids)
                    flexible_words_ids = [[word_ids[0]] for word_ids in flexible_words_ids]
                    disjunctive_set = list(map(list, set(map(frozenset, flexible_words_ids))))

                    # add_prefix_space=True
                    # flexible_words_ids = [x for x in flexible_words_ids if len(x) == 1] # problem with subsets
                    #flexible_words_ids = flexible_words_ids[:max_neig_per_concept]
                    #print('flexible_words_ids:', flexible_words_ids[:3])
                    if not any(disjunctive_set):
                        continue
                    constraint = DisjunctiveConstraint(disjunctive_set)
                    constraints.append(constraint)
                if not any(constraints):
                    constraints=None
                all_constraints.append(constraints)
        else:
            all_constraints = None
        if not all_constraints:
            all_constraints = None

        generated_answers_encoded = []
        #print('all_constraints:', all_constraints)
        for i, contraints in enumerate(all_constraints):
            #print('contraints.token_ids:', [x.token_ids for x in contraints])
            if "input_commonsense_relations" in model_input:
                # print(model_input['input_commonsense_relations'].sum())
                model_kwargs["relation_inputs"] = model_input.get("input_commonsense_relations")[i].unsqueeze(0).to(input_ids.device)
            #print('model_kwargs.get("attention_mask"):', model_kwargs.get("attention_mask"))
            model_kwargs["attention_mask"] = model_input.get("attention_mask")[i].unsqueeze(0).to(input_ids.device)
            gen = self.generate(input_ids=input_ids[i].unsqueeze(0),
                               constraints=contraints,
                               min_length=min_length,
                               #max_length=max_length,
                               do_sample=do_sample,
                               early_stopping=early_stopping,
                               #num_beams=num_beams,
                               temperature=temperature,
                               top_k=top_k,
                               top_p=top_p,
                               # eos_token_id=tokenizer.eos_token_id,
                               no_repeat_ngram_size=no_repeat_ngram_size,
                               num_return_sequences=num_return_sequences,
                               return_dict_in_generate=return_dict_in_generate,
                               output_attentions=output_attentions,
                               output_scores=output_scores,
                               **model_kwargs)
            #print('[gen]:', gen)
            #print(tokenizer.batch_decode(gen))
            generated_answers_encoded.append(gen[0].detach().cpu())
        #torch.LongTensor(generated_answers_encoded)
        #print('generated_answers_encoded:', generated_answers_encoded)
        return torch.LongTensor(pad_sequence(generated_answers_encoded, batch_first=True, padding_value=tokenizer.pad_token_id)).to(input_ids.device)

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = 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,
        typical_p: Optional[float] = None,
        repetition_penalty: Optional[float] = None,
        bad_words_ids: Optional[Iterable[int]] = None,
        force_words_ids: Optional[Union[Iterable[int], Iterable[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,
        encoder_no_repeat_ngram_size: Optional[int] = None,
        num_return_sequences: Optional[int] = None,
        max_time: Optional[float] = None,
        max_new_tokens: Optional[int] = None,
        decoder_start_token_id: Optional[int] = None,
        use_cache: Optional[bool] = None,
        num_beam_groups: Optional[int] = None,
        diversity_penalty: Optional[float] = None,
        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
        logits_processor: Optional[LogitsProcessorList] = LogitsProcessorList(),
        renormalize_logits: Optional[bool] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = StoppingCriteriaList(),
        constraints: Optional[List[Constraint]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        forced_bos_token_id: Optional[int] = None,
        forced_eos_token_id: Optional[int] = None,
        remove_invalid_values: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        exponential_decay_length_penalty: Optional[Tuple[Union[int, float]]] = None,
        **model_kwargs,
    ) -> Union[GreedySearchOutput, SampleOutput, BeamSearchOutput, BeamSampleOutput, torch.LongTensor]:
        r"""

        Generates sequences of token ids for models with a language modeling head. The method supports the following
        generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:

            - *greedy decoding* by calling [`~generation_utils.GenerationMixin.greedy_search`] if `num_beams=1` and
              `do_sample=False`.
            - *multinomial sampling* by calling [`~generation_utils.GenerationMixin.sample`] if `num_beams=1` and
              `do_sample=True`.
            - *beam-search decoding* by calling [`~generation_utils.GenerationMixin.beam_search`] if `num_beams>1` and
              `do_sample=False`.
            - *beam-search multinomial sampling* by calling [`~generation_utils.GenerationMixin.beam_sample`] if
              `num_beams>1` and `do_sample=True`.
            - *diverse beam-search decoding* by calling [`~generation_utils.GenerationMixin.group_beam_search`], if
              `num_beams>1` and `num_beam_groups>1`.
            - *constrained beam-search decoding* by calling
              [`~generation_utils.GenerationMixin.constrained_beam_search`], if `constraints!=None` or
              `force_words_ids!=None`.

        <Tip warning={true}>

        Apart from `inputs`, all the arguments below will default to the value of the attribute of the same name as
        defined in the model's config (`config.json`) which in turn defaults to the
        [`~modeling_utils.PretrainedConfig`] of the model.

        </Tip>

        Most of these parameters are explained in more detail in [this blog
        post](https://huggingface.co/blog/how-to-generate).

        Parameters:
            inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
                The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
                method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
                should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
                `input_ids`, `input_values`, `input_features`, or `pixel_values`.
            max_length (`int`, *optional*, defaults to `model.config.max_length`):
                The maximum length of the sequence to be generated.
            max_new_tokens (`int`, *optional*, defaults to None):
                The maximum numbers of tokens to generate, ignore the current number of tokens. Use either
                `max_new_tokens` or `max_length` but not both, they serve the same purpose.
            min_length (`int`, *optional*, defaults to 10):
                The minimum length of the sequence to be generated.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            early_stopping (`bool`, *optional*, defaults to `False`):
                Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
            num_beams (`int`, *optional*, defaults to 1):
                Number of beams for beam search. 1 means no beam search.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to module the next token probabilities.
            top_k (`int`, *optional*, defaults to 50):
                The number of highest probability vocabulary tokens to keep for top-k-filtering.
            top_p (`float`, *optional*, defaults to 1.0):
                If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher
                are kept for generation.
            repetition_penalty (`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 (`int`, *optional*):
                The id of the *padding* token.
            bos_token_id (`int`, *optional*):
                The id of the *beginning-of-sequence* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            length_penalty (`float`, *optional*, defaults to 1.0):
                 Exponential penalty to the length. 1.0 means that the beam score is penalized by the sequence length.
                 0.0 means no penalty. Set to values < 0.0 in order to encourage the model to generate longer
                 sequences, to a value > 0.0 in order to encourage the model to produce shorter sequences.
            no_repeat_ngram_size (`int`, *optional*, defaults to 0):
                If set to int > 0, all ngrams of that size can only occur once.
            encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0):
                If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the
                `decoder_input_ids`.
            bad_words_ids(`List[List[int]]`, *optional*):
                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 `tokenizer(bad_words, add_prefix_space=True,
                add_special_tokens=False).input_ids`.
            force_words_ids(`List[List[int]]` or `List[List[List[int]]]`, *optional*):
                List of token ids that must be generated. If given a `List[List[int]]`, this is treated as a simple
                list of words that must be included, the opposite to `bad_words_ids`. If given `List[List[List[int]]]`,
                this triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081),
                where one can allow different forms of each word.
            num_return_sequences(`int`, *optional*, defaults to 1):
                The number of independently computed returned sequences for each element in the batch.
            max_time(`float`, *optional*, defaults to None):
                The maximum amount of time you allow the computation to run for in seconds. generation will still
                finish the current pass after allocated time has been passed.
            attention_mask (`torch.LongTensor` of shape `(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 `input_ids` that masks the pad token. [What are attention masks?](../glossary#attention-mask)
            decoder_start_token_id (`int`, *optional*):
                If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
            use_cache: (`bool`, *optional*, defaults to `True`):
                Whether or not the model should use the past last key/values attentions (if applicable to the model) to
                speed up decoding.
            num_beam_groups (`int`, *optional*, defaults to 1):
                Number of groups to divide `num_beams` into in order to ensure diversity among different groups of
                beams. [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
            diversity_penalty (`float`, *optional*, defaults to 0.0):
                This value is subtracted from a beam's score if it generates a token same as any beam from other group
                at a particular time. Note that `diversity_penalty` is only effective if `group beam search` is
                enabled.
            prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
                If provided, this function constraints the beam search to allowed tokens only at each step. If not
                provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
                `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
                on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
                for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
                Retrieval](https://arxiv.org/abs/2010.00904).
            logits_processor (`LogitsProcessorList`, *optional*):
                 Custom logits processors that complement the default logits processors built from arguments and a
                 model's config. If a logit processor is passed that is already created with the arguments or a model's
                 config an error is thrown. This feature is intended for advanced users.
            renormalize_logits: (`bool`, *optional*, defaults to `False`):
                Whether to renormalize the logits after applying all the logits processors or warpers (including the
                custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
                score logits are normalized but some logit processors or warpers break the normalization.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                 Custom stopping criteria that complement the default stopping criteria built from arguments and a
                 model's config. If a stopping criteria is passed that is already created with the arguments or a
                 model's config an error is thrown. This feature is intended for advanced users.
            constraints (`List[Constraint]`, *optional*):
                 Custom constraints that can be added to the generation to ensure that the output will contain the use
                 of certain tokens as defined by `Constraint` objects, in the most sensible way possible.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            forced_bos_token_id (`int`, *optional*):
                The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
                for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
                the target language token.
            forced_eos_token_id (`int`, *optional*):
                The id of the token to force as the last generated token when `max_length` is reached.
            remove_invalid_values (`bool`, *optional*):
                Whether to remove possible *nan* and *inf* outputs of the model to prevent the generation method to
                crash. Note that using `remove_invalid_values` can slow down generation.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            exponential_decay_length_penalty (`tuple(int, float)`, *optional*):
                This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been
                generated. The tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates
                where penalty starts and `decay_factor` represents the factor of exponential decay

            model_kwargs:
                Additional model specific kwargs will be forwarded to the `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:
            [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
            or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.

                If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
                [`~utils.ModelOutput`] types are:

                    - [`~generation_utils.GreedySearchDecoderOnlyOutput`],
                    - [`~generation_utils.SampleDecoderOnlyOutput`],
                    - [`~generation_utils.BeamSearchDecoderOnlyOutput`],
                    - [`~generation_utils.BeamSampleDecoderOnlyOutput`]

                If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
                [`~utils.ModelOutput`] types are:

                    - [`~generation_utils.GreedySearchEncoderDecoderOutput`],
                    - [`~generation_utils.SampleEncoderDecoderOutput`],
                    - [`~generation_utils.BeamSearchEncoderDecoderOutput`],
                    - [`~generation_utils.BeamSampleEncoderDecoderOutput`]

        Examples:

        Greedy Decoding:

        ```python
        >>> from transformers import AutoTokenizer, AutoModelForCausalLM

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

        >>> prompt = "Today I believe we can finally"
        >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids

        >>> # generate up to 30 tokens
        >>> outputs = model.generate(input_ids, do_sample=False, max_length=30)
        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Today I believe we can finally get to the point where we can make a difference in the lives of the people of the United States of America.\n']
        ```

        Multinomial Sampling:

        ```python
        >>> from transformers import AutoTokenizer, AutoModelForCausalLM
        >>> import torch

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

        >>> prompt = "Today I believe we can finally"
        >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids

        >>> # sample up to 30 tokens
        >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
        >>> outputs = model.generate(input_ids, do_sample=True, max_length=30)
        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Today I believe we can finally get rid of discrimination," said Rep. Mark Pocan (D-Wis.).\n\n"Just look at the']
        ```

        Beam-search decoding:

        ```python
        >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

        >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
        >>> model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")

        >>> sentence = "Paris is one of the densest populated areas in Europe."
        >>> input_ids = tokenizer(sentence, return_tensors="pt").input_ids

        >>> outputs = model.generate(input_ids)
        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Paris ist eines der dichtesten besiedelten Gebiete Europas.']
        ```"""
        # 1. Set generation parameters if not already defined
        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
        num_beams = num_beams if num_beams is not None else self.config.num_beams
        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
        num_beam_groups = num_beam_groups if num_beam_groups is not None else self.config.num_beam_groups
        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
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id

        if eos_token_id is None and hasattr(self.config, "decoder"):
            eos_token_id = self.config.decoder.eos_token_id

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

        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        # 2. Define model inputs
        # inputs_tensor has to be defined
        # model_input_name is defined if model-specific keyword input is passed
        # otherwise model_input_name is None
        # all model-specific keyword inputs are removed from `model_kwargs`
        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(inputs, bos_token_id, model_kwargs)
        batch_size = inputs_tensor.shape[0]

        # 3. Define other model kwargs
        model_kwargs["output_attentions"] = output_attentions
        model_kwargs["output_hidden_states"] = output_hidden_states
        model_kwargs["use_cache"] = use_cache

        accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
        requires_attention_mask = "encoder_outputs" not in model_kwargs

        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                inputs_tensor, pad_token_id, eos_token_id
            )

        if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
            # if model is encoder decoder encoder_outputs are created
            # and added to `model_kwargs`
            model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
                inputs_tensor, model_kwargs, model_input_name
            )

        # 4. Prepare `input_ids` which will be used for auto-regressive generation
        if self.config.is_encoder_decoder:
            input_ids = self._prepare_decoder_input_ids_for_generation(
                batch_size,
                decoder_start_token_id=decoder_start_token_id,
                bos_token_id=bos_token_id,
                model_kwargs=model_kwargs,
                device=inputs_tensor.device,
            )
        else:
            # if decoder-only then inputs_tensor has to be `input_ids`
            input_ids = inputs_tensor

        input_ids_seq_length = input_ids.shape[-1]

        # 5. Prepare `max_length` depending on other stopping criteria
        # if `max_new_tokens` is passed, but not `max_length` -> set `max_length = max_new_tokens`
        if max_length is None and max_new_tokens is not None:
            max_length = max_new_tokens + input_ids_seq_length
        elif max_length is not None and max_new_tokens is not None:
            # Both are set, this is odd, raise a warning
            warnings.warn(
                "Both `max_length` and `max_new_tokens` have been set "
                f"but they serve the same purpose. `max_length` {max_length} "
                f"will take priority over `max_new_tokens` {max_new_tokens}.",
                UserWarning,
            )
        # default to config if still None
        max_length = max_length if max_length is not None else self.config.max_length
        min_length = min_length if min_length is not None else self.config.min_length

        if min_length is not None and min_length > max_length:
            raise ValueError(
                f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
                f"length ({max_length})"
            )
        if input_ids_seq_length >= max_length:
            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
            print(
                f"Input length of {input_ids_string} is {input_ids_seq_length}, but ``max_length`` is set to {max_length}. "
                "This can lead to unexpected behavior. You should consider increasing ``config.max_length`` or ``max_length``."
            )

        # 6. determine generation mode
        is_constraint_gen_mode = constraints is not None or force_words_ids is not None
        is_greedy_gen_mode = (
            (num_beams == 1) and (num_beam_groups == 1) and do_sample is False and not is_constraint_gen_mode
        )
        is_sample_gen_mode = (
            (num_beams == 1) and (num_beam_groups == 1) and do_sample is True and not is_constraint_gen_mode
        )
        is_beam_gen_mode = (
            (num_beams > 1) and (num_beam_groups == 1) and do_sample is False and not is_constraint_gen_mode
        )
        is_beam_sample_gen_mode = (
            (num_beams > 1) and (num_beam_groups == 1) and do_sample is True and not is_constraint_gen_mode
        )
        is_group_beam_gen_mode = (num_beams > 1) and (num_beam_groups > 1) and not is_constraint_gen_mode

        if num_beam_groups > num_beams:
            raise ValueError("`num_beam_groups` has to be smaller or equal to `num_beams`")
        if is_group_beam_gen_mode and do_sample is True:
            raise ValueError(
                "Diverse beam search cannot be used in sampling mode. Make sure that `do_sample` is set to `False`."
            )

        # 7. prepare distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            repetition_penalty=repetition_penalty,
            no_repeat_ngram_size=no_repeat_ngram_size,
            encoder_no_repeat_ngram_size=encoder_no_repeat_ngram_size,
            input_ids_seq_length=input_ids_seq_length,
            encoder_input_ids=inputs_tensor,
            bad_words_ids=bad_words_ids,
            min_length=min_length,
            max_length=max_length,
            eos_token_id=eos_token_id,
            forced_bos_token_id=forced_bos_token_id,
            forced_eos_token_id=forced_eos_token_id,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            num_beams=num_beams,
            num_beam_groups=num_beam_groups,
            diversity_penalty=diversity_penalty,
            remove_invalid_values=remove_invalid_values,
            exponential_decay_length_penalty=exponential_decay_length_penalty,
            logits_processor=logits_processor,
            renormalize_logits=renormalize_logits,
        )

        # 8. prepare stopping criteria
        stopping_criteria = self._get_stopping_criteria(
            max_length=max_length, max_time=max_time, stopping_criteria=stopping_criteria
        )

        # 9. go into different generation modes
        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."
                )

            # 10. run greedy search
            return self.greedy_search(
                input_ids,
                logits_processor=logits_processor,
                stopping_criteria=stopping_criteria,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                synced_gpus=synced_gpus,
                **model_kwargs,
            )

        elif is_sample_gen_mode:
            # 10. prepare logits warper
            logits_warper = self._get_logits_warper(
                top_k=top_k,
                top_p=top_p,
                typical_p=typical_p,
                temperature=temperature,
                num_beams=num_beams,
                renormalize_logits=renormalize_logits,
            )

            # 11. 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,
            )

            # 12. run sample
            return self.sample(
                input_ids,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                stopping_criteria=stopping_criteria,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                synced_gpus=synced_gpus,
                **model_kwargs,
            )

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

            if stopping_criteria.max_length is None:
                raise ValueError("`max_length` needs to be a stopping_criteria for now.")

            # 10. prepare beam search scorer
            beam_scorer = BeamSearchScorer(
                batch_size=batch_size,
                num_beams=num_beams,
                device=inputs_tensor.device,
                length_penalty=length_penalty,
                do_early_stopping=early_stopping,
                num_beam_hyps_to_keep=num_return_sequences,
            )
            # 11. interleave input_ids with `num_beams` additional sequences per batch
            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
            )
            # 12. run beam search
            return self.beam_search(
                input_ids,
                beam_scorer,
                logits_processor=logits_processor,
                stopping_criteria=stopping_criteria,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                synced_gpus=synced_gpus,
                **model_kwargs,
            )

        elif is_beam_sample_gen_mode:
            # 10. prepare logits warper
            logits_warper = self._get_logits_warper(
                top_k=top_k,
                top_p=top_p,
                typical_p=typical_p,
                temperature=temperature,
                num_beams=num_beams,
                renormalize_logits=renormalize_logits,
            )

            if stopping_criteria.max_length is None:
                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
            # 11. prepare beam search scorer
            beam_scorer = BeamSearchScorer(
                batch_size=batch_size * num_return_sequences,
                num_beams=num_beams,
                device=inputs_tensor.device,
                length_penalty=length_penalty,
                do_early_stopping=early_stopping,
            )

            # 12. interleave input_ids with `num_beams` additional sequences per batch
            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,
            )

            # 13. run beam sample
            return self.beam_sample(
                input_ids,
                beam_scorer,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                stopping_criteria=stopping_criteria,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                synced_gpus=synced_gpus,
                **model_kwargs,
            )

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

            if num_beams % num_beam_groups != 0:
                raise ValueError("`num_beams` should be divisible by `num_beam_groups` for group beam search.")

            if stopping_criteria.max_length is None:
                raise ValueError("`max_length` needs to be a stopping_criteria for now.")

            # 10. prepare beam search scorer
            beam_scorer = BeamSearchScorer(
                batch_size=batch_size,
                num_beams=num_beams,
                max_length=stopping_criteria.max_length,
                device=inputs_tensor.device,
                length_penalty=length_penalty,
                do_early_stopping=early_stopping,
                num_beam_hyps_to_keep=num_return_sequences,
                num_beam_groups=num_beam_groups,
            )
            # 11. interleave input_ids with `num_beams` additional sequences per batch
            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
            )
            # 12. run beam search
            return self.group_beam_search(
                input_ids,
                beam_scorer,
                logits_processor=logits_processor,
                stopping_criteria=stopping_criteria,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                synced_gpus=synced_gpus,
                **model_kwargs,
            )

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

            if stopping_criteria.max_length is None:
                raise ValueError("`max_length` needs to be a stopping_criteria for now.")

            if num_beams <= 1:
                raise ValueError("`num_beams` needs to be greater than 1 for constrained genertation.")

            if do_sample:
                raise ValueError("`do_sample` needs to be false for constrained generation.")

            if num_beam_groups is not None and num_beam_groups > 1:
                raise ValueError("`num_beam_groups` not supported yet for constrained generation.")

            final_constraints = []
            if constraints is not None:
                final_constraints = constraints

            if force_words_ids is not None:

                def typeerror():
                    raise ValueError(
                        "`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
                        f"of positive integers, but is {force_words_ids}."
                    )

                if not isinstance(force_words_ids, list) or len(force_words_ids) == 0:
                    typeerror()

                for word_ids in force_words_ids:
                    if isinstance(word_ids[0], list):
                        if not isinstance(word_ids, list) or len(word_ids) == 0:
                            typeerror()
                        if any(not isinstance(token_ids, list) for token_ids in word_ids):
                            typeerror()
                        if any(
                            any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
                            for token_ids in word_ids
                        ):
                            typeerror()

                        constraint = DisjunctiveConstraint(word_ids)
                    else:
                        if not isinstance(word_ids, list) or len(word_ids) == 0:
                            typeerror()
                        if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
                            typeerror()

                        constraint = PhrasalConstraint(word_ids)
                    final_constraints.append(constraint)

            # 10. prepare beam search scorer
            constrained_beam_scorer = ConstrainedBeamSearchScorer(
                constraints=final_constraints,
                batch_size=batch_size,
                num_beams=num_beams,
                device=inputs_tensor.device,
                length_penalty=length_penalty,
                do_early_stopping=early_stopping,
                num_beam_hyps_to_keep=num_return_sequences,
            )
            # 11. interleave input_ids with `num_beams` additional sequences per batch
            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
            )
            # 12. run beam search
            return self.constrained_beam_search(
                input_ids,
                constrained_beam_scorer=constrained_beam_scorer,
                logits_processor=logits_processor,
                stopping_criteria=stopping_criteria,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                synced_gpus=synced_gpus,
                **model_kwargs,
            )

    def greedy_search(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        **model_kwargs,
    ) -> Union[GreedySearchOutput, torch.LongTensor]:
        r"""
        Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be
        used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:

            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                The sequence used as a prompt for the generation.
            logits_processor (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.

            max_length (`int`, *optional*, defaults to 20):
                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
                tokens. The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            model_kwargs:
                Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
                If model is an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`~generation_utils.GreedySearchDecoderOnlyOutput`], [`~generation_utils.GreedySearchEncoderDecoderOutput`]
            or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation_utils.GreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation_utils.GreedySearchEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.

        Examples:

        ```python
        >>> from transformers import (
        ...     AutoTokenizer,
        ...     AutoModelForCausalLM,
        ...     LogitsProcessorList,
        ...     MinLengthLogitsProcessor,
        ...     StoppingCriteriaList,
        ...     MaxLengthCriteria,
        ... )

        >>> 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 = "It might be possible to"
        >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids

        >>> # instantiate logits processors
        >>> logits_processor = LogitsProcessorList(
        ...     [
        ...         MinLengthLogitsProcessor(10, eos_token_id=model.config.eos_token_id),
        ...     ]
        ... )
        >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])

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

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ["It might be possible to get a better understanding of the nature of the problem, but it's not"]
        ```"""
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, 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
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        # keep track of which sequences are already finished
        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
        cur_len = input_ids.shape[-1]

        this_peer_finished = False  # used by synced_gpus only
        while True:

            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            # 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,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_logits,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

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

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

            # finished sentences should have their next token be a padding token
            if eos_token_id is not None:
                if pad_token_id is None:
                    raise ValueError("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)

            # update generated ids, model inputs, and length for next step
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            cur_len = cur_len + 1

            # if eos_token was found in one sentence, set sentence to finished
            if eos_token_id is not None:
                unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long())

            # stop when each sentence is finished, or if we exceed the maximum length
            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        if return_dict_in_generate:
            if self.config.is_encoder_decoder:
                return GreedySearchEncoderDecoderOutput(
                    sequences=input_ids,
                    scores=scores,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return GreedySearchDecoderOnlyOutput(
                    sequences=input_ids,
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return input_ids

    def sample(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        logits_warper: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        **model_kwargs,
    ) -> Union[SampleOutput, torch.LongTensor]:
        r"""
        Generates sequences of token ids for models with a language modeling head using **multinomial sampling** and
        can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:

            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                The sequence used as a prompt for the generation.
            logits_processor (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.
            logits_warper (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
                to warp the prediction score distribution of the language modeling head applied before multinomial
                sampling at each generation step.
            max_length (`int`, *optional*, defaults to 20):
                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
                tokens. The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            model_kwargs:
                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
                an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`~generation_utils.SampleDecoderOnlyOutput`], [`~generation_utils.SampleEncoderDecoderOutput`] or
            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation_utils.SampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation_utils.SampleEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.

        Examples:

        ```python
        >>> from transformers import (
        ...     AutoTokenizer,
        ...     AutoModelForCausalLM,
        ...     LogitsProcessorList,
        ...     MinLengthLogitsProcessor,
        ...     TopKLogitsWarper,
        ...     TemperatureLogitsWarper,
        ...     StoppingCriteriaList,
        ...     MaxLengthCriteria,
        ... )
        >>> import torch

        >>> 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),
        ...     ]
        ... )

        >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])

        >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
        >>> outputs = model.sample(
        ...     input_ids,
        ...     logits_processor=logits_processor,
        ...     logits_warper=logits_warper,
        ...     stopping_criteria=stopping_criteria,
        ... )

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Today is a beautiful day, and a wonderful day.\n\nI was lucky enough to meet the']
        ```"""

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
        logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
        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
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        # keep track of which sequences are already finished
        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
        cur_len = input_ids.shape[-1]

        this_peer_finished = False  # used by synced_gpus only
        # auto-regressive generation
        while True:

            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            # 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,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]

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

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # sample
            probs = nn.functional.softmax(next_token_scores, dim=-1)
            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)

            # finished sentences should have their next token be a padding token
            if eos_token_id is not None:
                if pad_token_id is None:
                    raise ValueError("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)

            # update generated ids, model inputs, and length for next step
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            cur_len = cur_len + 1

            # if eos_token was found in one sentence, set sentence to finished
            if eos_token_id is not None:
                unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long())

            # stop when each sentence is finished, or if we exceed the maximum length
            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        if return_dict_in_generate:
            if self.config.is_encoder_decoder:
                return SampleEncoderDecoderOutput(
                    sequences=input_ids,
                    scores=scores,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return SampleDecoderOnlyOutput(
                    sequences=input_ids,
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return input_ids

    def beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        **model_kwargs,
    ) -> Union[BeamSearchOutput, torch.LongTensor]:
        r"""
        Generates sequences of token ids for models with a language modeling head using **beam search decoding** and
        can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:

            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                The sequence used as a prompt for the generation.
            beam_scorer (`BeamScorer`):
                An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
                sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
            logits_processor (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.
            max_length (`int`, *optional*, defaults to 20):
                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
                tokens. The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            model_kwargs:
                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
                an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`generation_utilsBeamSearchDecoderOnlyOutput`], [`~generation_utils.BeamSearchEncoderDecoderOutput`] or
            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation_utils.BeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation_utils.BeamSearchEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.


        Examples:

        ```python
        >>> 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,
        ...     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)

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Wie alt bist du?']
        ```"""
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
        if len(stopping_criteria) == 0:
            warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
        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
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

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

        batch_beam_size, cur_len = input_ids.shape

        if num_beams * batch_size != batch_beam_size:
            raise ValueError(
                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
            )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        beam_indices = (
            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
        )
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        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,))

        this_peer_finished = False  # used by synced_gpus only
        while True:

            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]
            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
            next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
            next_token_scores = nn.functional.log_softmax(
                next_token_logits, dim=-1
            )  # (batch_size * num_beams, vocab_size)

            #Normal execution
            next_token_scores_processed = logits_processor(input_ids, next_token_scores)
            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores_processed,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # 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 = torch_int_div(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)

            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 return_dict_in_generate and output_scores:
                beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))

            # increase cur_len
            cur_len = cur_len + 1

            if beam_scorer.is_done or stopping_criteria(input_ids, scores):
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        sequence_outputs = beam_scorer.finalize(
            input_ids,
            beam_scores,
            next_tokens,
            next_indices,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            max_length=stopping_criteria.max_length,
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"] = None
            else:
                num_return_sequences = beam_scorer.num_beam_hyps_to_keep
                # return only as many indices as sequences
                beam_indices = tuple(
                    (beam_indices[i * num_beams : i * num_beams + num_return_sequences] for i in range(batch_size))
                )
                beam_indices = sum(beam_indices, ())

            if self.config.is_encoder_decoder:
                return BeamSearchEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    beam_indices=beam_indices,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSearchDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    beam_indices=beam_indices,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]

    def beam_sample(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        logits_warper: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        **model_kwargs,
    ) -> Union[BeamSampleOutput, torch.LongTensor]:
        r"""
        Generates sequences of token ids for models with a language modeling head using **beam search multinomial
        sampling** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:

            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                The sequence used as a prompt for the generation.
            beam_scorer (`BeamScorer`):
                A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
                sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
            logits_processor (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.
            logits_warper (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
                to warp the prediction score distribution of the language modeling head applied before multinomial
                sampling at each generation step.
            max_length (`int`, *optional*, defaults to 20):
                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
                tokens. The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            model_kwargs:
                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
                an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`~generation_utils.BeamSampleDecoderOnlyOutput`], [`~generation_utils.BeamSampleEncoderDecoderOutput`] or
            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation_utils.BeamSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation_utils.BeamSampleEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.

        Examples:

        ```python
        >>> 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
        ... )

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Wie alt bist du?']
        ```"""
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, 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
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

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

        batch_beam_size, cur_len = input_ids.shape

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        beam_indices = (
            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
        )
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

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

        this_peer_finished = False  # used by synced_gpus only
        while True:

            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]

            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
            next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
            next_token_scores = nn.functional.log_softmax(
                next_token_logits, dim=-1
            )  # (batch_size * num_beams, vocab_size)

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

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (logits_warper(input_ids, next_token_scores_processed),)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # 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 = nn.functional.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 = torch_int_div(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)

            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 return_dict_in_generate and output_scores:
                beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))

            # increase cur_len
            cur_len = cur_len + 1

            if beam_scorer.is_done or stopping_criteria(input_ids, scores):
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        sequence_outputs = beam_scorer.finalize(
            input_ids,
            beam_scores,
            next_tokens,
            next_indices,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            max_length=stopping_criteria.max_length,
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"] = None
            else:
                num_return_sequences = beam_scorer.num_beam_hyps_to_keep
                # return only as many indices as sequences
                beam_indices = tuple(
                    (beam_indices[i * num_beams : i * num_beams + num_return_sequences] for i in range(batch_size))
                )
                beam_indices = sum(beam_indices, ())

            if self.config.is_encoder_decoder:
                return BeamSampleEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    beam_indices=beam_indices,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSampleDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    beam_indices=beam_indices,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]

    def group_beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: Optional[bool] = False,
        **model_kwargs,
    ):
        r"""
        Generates sequences of token ids for models with a language modeling head using **diverse beam search
        decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:

            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                The sequence used as a prompt for the generation.
            beam_scorer (`BeamScorer`):
                An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
                sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
            logits_processor (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.
            max_length (`int`, *optional*, defaults to 20):
                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
                tokens. The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)

            model_kwargs:
                Additional model specific kwargs that will be forwarded to the `forward` function of the model. If
                model is an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`~generation_utils.BeamSearchDecoderOnlyOutput`], [`~generation_utils.BeamSearchEncoderDecoderOutput`] or
            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation_utils.BeamSearchDecoderOnlyOutput`] if [`~generation_utils.BeamSearchDecoderOnlyOutput`] if
            `model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
            [`~generation_utils.BeamSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.

        Examples:

        ```python
        >>> from transformers import (
        ...     AutoTokenizer,
        ...     AutoModelForSeq2SeqLM,
        ...     LogitsProcessorList,
        ...     MinLengthLogitsProcessor,
        ...     HammingDiversityLogitsProcessor,
        ...     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 diverse beam search using 6 beams
        >>> num_beams = 6
        >>> # 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,
        ...     num_beam_groups=3,
        ... )

        >>> # instantiate logits processors
        >>> logits_processor = LogitsProcessorList(
        ...     [
        ...         HammingDiversityLogitsProcessor(5.5, num_beams=6, num_beam_groups=3),
        ...         MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
        ...     ]
        ... )

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

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Wie alt bist du?']
        ```"""
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, 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
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        batch_size = len(beam_scorer._beam_hyps)
        num_beams = beam_scorer.num_beams
        num_beam_groups = beam_scorer.num_beam_groups
        num_sub_beams = num_beams // num_beam_groups
        device = input_ids.device

        batch_beam_size, cur_len = input_ids.shape

        if return_dict_in_generate and output_scores:
            beam_indices = [tuple(() for _ in range(num_sub_beams * batch_size)) for _ in range(num_beam_groups)]
        else:
            beam_indices = None

        if num_beams * batch_size != batch_beam_size:
            raise ValueError(
                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
            )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
        # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in
        # the same group don't produce same tokens everytime.
        beam_scores[:, ::num_sub_beams] = 0
        beam_scores = beam_scores.view((batch_size * num_beams,))

        this_peer_finished = False  # used by synced_gpus only
        while True:

            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            # predicted tokens in cur_len step
            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)

            # indices which will form the beams in the next time step
            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)

            # do one decoder step on all beams of all sentences in batch
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            if output_scores:
                processed_score = torch.zeros_like(outputs.logits[:, -1, :])

            for beam_group_idx in range(num_beam_groups):
                group_start_idx = beam_group_idx * num_sub_beams
                group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
                group_size = group_end_idx - group_start_idx

                # indices of beams of current group among all sentences in batch
                batch_group_indices = []

                for batch_idx in range(batch_size):
                    batch_group_indices.extend(
                        [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
                    )
                group_input_ids = input_ids[batch_group_indices]

                # select outputs of beams of current group only
                next_token_logits = outputs.logits[batch_group_indices, -1, :]

                # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
                # cannot be generated both before and after the `nn.functional.log_softmax` operation.
                next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
                next_token_scores = nn.functional.log_softmax(
                    next_token_logits, dim=-1
                )  # (batch_size * group_size, vocab_size)
                vocab_size = next_token_scores.shape[-1]

                next_token_scores_processed = logits_processor(
                    group_input_ids, next_token_scores, current_tokens=current_tokens, beam_group_idx=beam_group_idx
                )
                next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
                next_token_scores = next_token_scores.expand_as(next_token_scores_processed)

                if output_scores:
                    processed_score[batch_group_indices] = next_token_scores_processed

                # reshape for beam search
                next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)

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

                next_indices = torch_int_div(next_tokens, vocab_size)
                next_tokens = next_tokens % vocab_size

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

                if return_dict_in_generate and output_scores:
                    beam_indices[beam_group_idx] = tuple(
                        beam_indices[beam_group_idx][beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices[0]))
                    )

                input_ids[batch_group_indices] = group_input_ids[beam_idx]
                group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
                current_tokens[batch_group_indices] = group_input_ids[:, -1]

                # (beam_idx // group_size) -> batch_idx
                # (beam_idx % group_size) -> offset of idx inside the group
                reordering_indices[batch_group_indices] = (
                    num_beams * torch_int_div(beam_idx, group_size) + group_start_idx + (beam_idx % group_size)
                )

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (processed_score,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-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"], reordering_indices)

            # increase cur_len
            cur_len = cur_len + 1

            if beam_scorer.is_done or stopping_criteria(input_ids, scores):
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        sequence_outputs = beam_scorer.finalize(
            input_ids,
            beam_scores,
            next_tokens,
            next_indices,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            max_length=stopping_criteria.max_length,
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"] = None
            else:
                beam_indices = sum(beam_indices, ())
                num_return_sequences = beam_scorer.num_beam_hyps_to_keep
                # return only as many indices as sequences
                beam_indices = tuple(
                    (beam_indices[i * num_beams : i * num_beams + num_return_sequences] for i in range(batch_size))
                )
                beam_indices = sum(beam_indices, ())

            if self.config.is_encoder_decoder:
                return BeamSearchEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    beam_indices=beam_indices,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSearchDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]

    def constrained_beam_search(
        self,
        input_ids: torch.LongTensor,
        constrained_beam_scorer: ConstrainedBeamSearchScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: Optional[bool] = None,
        **model_kwargs,
    ) -> Union[BeamSearchOutput, torch.LongTensor]:

        r"""
        Generates sequences of token ids for models with a language modeling head using **constrained beam search
        decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                The sequence used as a prompt for the generation.
            constrained_beam_scorer (`ConstrainedBeamSearchScorer`):
                A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
                sorted during generation, while satisfying a list of positive constraints. For more information, the
                documentation of [`ConstrainedBeamSearchScorer`] should be read.
            logits_processor (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.
            logits_warper (`LogitsProcessorList`, *optional*):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
                to warp the prediction score distribution of the language modeling head applied before multinomial
                sampling at each generation step.
            max_length (`int`, *optional*, defaults to 20):
                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
                tokens. The maximum length of the sequence to be generated.
            pad_token_id (`int`, *optional*):
                The id of the *padding* token.
            eos_token_id (`int`, *optional*):
                The id of the *end-of-sequence* token.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            model_kwargs:
                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
                an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`generation_utilsBeamSearchDecoderOnlyOutput`], [`~generation_utils.BeamSearchEncoderDecoderOutput`] or
            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation_utils.BeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation_utils.BeamSearchEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.


        Examples:

        ```python
        >>> from transformers import (
        ...     AutoTokenizer,
        ...     AutoModelForSeq2SeqLM,
        ...     LogitsProcessorList,
        ...     MinLengthLogitsProcessor,
        ...     ConstrainedBeamSearchScorer,
        ...     PhrasalConstraint,
        ... )
        >>> 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
        ...     )
        ... }

        >>> constraint_str = "Sie"
        >>> constraint_token_ids = tokenizer.encode(constraint_str)[:-1]  # slice to remove eos token
        >>> constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]


        >>> # instantiate beam scorer
        >>> beam_scorer = ConstrainedBeamSearchScorer(
        ...     batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints
        ... )

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

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

        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
        ['Wie alt sind Sie?']
        ```"""
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
        if len(stopping_criteria) == 0:
            warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
        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
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

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

        batch_beam_size, cur_len = input_ids.shape

        if num_beams * batch_size != batch_beam_size:
            raise ValueError(
                f"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,))

        this_peer_finished = False  # used by synced_gpus only
        while True:

            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]
            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
            next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
            next_token_scores = nn.functional.log_softmax(
                next_token_logits, dim=-1
            )  # (batch_size * num_beams, vocab_size)

            next_token_scores_processed = logits_processor(input_ids, next_token_scores)

            scores_for_all_vocab = next_token_scores_processed.clone()

            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # 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).long()
            next_tokens = next_tokens % vocab_size

            # stateless
            beam_outputs = constrained_beam_scorer.process(
                input_ids,
                next_token_scores,
                next_tokens,
                next_indices,
                scores_for_all_vocab,
                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)
            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)

            # increase cur_len
            cur_len = cur_len + 1

            if constrained_beam_scorer.is_done or stopping_criteria(input_ids, scores):
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        sequence_outputs = constrained_beam_scorer.finalize(
            input_ids,
            beam_scores,
            next_tokens,
            next_indices,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            max_length=stopping_criteria.max_length,
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"] = None
            if self.config.is_encoder_decoder:
                return BeamSearchEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSearchDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]


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)
        top_k (`int`, *optional*, defaults to 0):
            If > 0, only keep the top k tokens with highest probability (top-k filtering)
        top_p (`float`, *optional*, defaults to 1.0):
            If < 1.0, only 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)
        min_tokens_to_keep (`int`, *optional*, defaults to 1):
            Minimumber of tokens we 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