import torch
import inspect
import warnings
import torch.nn as nn
from typing import Optional, Union, List, Callable
import torch.distributed as dist

from transformers import PreTrainedModel
from transformers.generation.streamers import BaseStreamer
from transformers.generation.utils import (
    GenerationConfig,
    GenerationMode,
    LogitsProcessorList,
    StoppingCriteriaList,
    GenerationMixin,
    GenerateEncoderDecoderOutput,
    GenerateDecoderOnlyOutput,
    GenerateNonBeamOutput,
    is_deepspeed_zero3_enabled,
    is_torchdynamo_compiling,
    NEED_SETUP_CACHE_CLASSES_MAPPING,
    QUANT_BACKEND_CLASSES_MAPPING,
    is_hqq_available,
    QuantizedCacheConfig,
    is_quanto_available,
    DynamicCache,
    EncoderDecoderCache,
    logging,
)

logger = logging.get_logger(__name__)


class GenerationWithCTC(GenerationMixin):

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        speech: Optional[torch.Tensor] = None,
        speech_lengths: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
        synced_gpus: Optional[bool] = None,
        assistant_model: Optional["PreTrainedModel"] = None,
        streamer: Optional["BaseStreamer"] = None,
        streamer_unit: Optional["BaseStreamer"] = None,
        streaming_unit_gen=False,
        negative_prompt_ids: Optional[torch.Tensor] = None,
        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ):

        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
        self._validate_model_class()
        tokenizer = kwargs.pop("tokenizer", None)  # Pull this out first, we only use it for stopping criteria
        generation_config, model_kwargs = self._prepare_generation_config(generation_config, **kwargs)
        self._validate_model_kwargs(model_kwargs.copy())
        self._validate_assistant(assistant_model)

        # 2. Set generation parameters if not already defined
        if synced_gpus is None:
            if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
                synced_gpus = True
            else:
                synced_gpus = False

        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()

        accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
        requires_attention_mask = "encoder_outputs" not in model_kwargs
        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None

        # 3. Define model inputs
        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
            inputs, generation_config.bos_token_id, model_kwargs
        )
        batch_size = inputs_tensor.shape[0]

        device = inputs_tensor.device
        self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)

        # decoder-only models must use left-padding for batched generation.
        if not self.config.is_encoder_decoder and not is_torchdynamo_compiling():
            # If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
            # Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
            if (
                generation_config._pad_token_tensor is not None
                and batch_size > 1
                and len(inputs_tensor.shape) == 2
                and torch.sum(inputs_tensor[:, -1] == generation_config._pad_token_tensor) > 0
            ):
                logger.warning(
                    "A decoder-only architecture is being used, but right-padding was detected! For correct "
                    "generation results, please set `padding_side='left'` when initializing the tokenizer."
                )

        # 4. Define other model kwargs
        # decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
        # generating the first new token or not, and we only want to use the embeddings for the first new token)
        if not self.config.is_encoder_decoder and model_input_name == "inputs_embeds":
            model_kwargs["use_cache"] = True
        else:
            model_kwargs["use_cache"] = generation_config.use_cache

        if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
            )

        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, generation_config
            )

        # 5. Prepare `input_ids` which will be used for auto-regressive generation
        if self.config.is_encoder_decoder:
            input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
                batch_size=batch_size,
                model_input_name=model_input_name,
                model_kwargs=model_kwargs,
                decoder_start_token_id=generation_config._decoder_start_token_tensor,
                device=inputs_tensor.device,
            )
        else:
            input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")

        if generation_config.token_healing:
            input_ids = self.heal_tokens(input_ids, tokenizer)

        if streamer is not None:
            streamer.put(input_ids.cpu())

        # 6. Prepare `max_length` depending on other stopping criteria.
        input_ids_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
        generation_config = self._prepare_generated_length(
            generation_config=generation_config,
            has_default_max_length=has_default_max_length,
            has_default_min_length=has_default_min_length,
            model_input_name=model_input_name,
            inputs_tensor=inputs_tensor,
            input_ids_length=input_ids_length,
        )

        # use_dynamic_cache_by_default = False
        if "mamba" in self.__class__.__name__.lower():
            cache_name = "cache_params"
        else:
            cache_name = "past_key_values"
        if generation_config.cache_implementation is not None and (model_kwargs.get(cache_name) is not None):
            raise ValueError(
                f"Passing both `cache_implementation` (used to initialize certain caches) and `{cache_name}` (a "
                "Cache object) is unsupported. Please use only one of the two."
            )
        elif generation_config.cache_implementation is not None:
            if generation_config.cache_implementation in NEED_SETUP_CACHE_CLASSES_MAPPING:
                if generation_config.cache_implementation == "static" and not self._supports_static_cache:
                    raise ValueError(
                        "This model does not support `cache_implementation='static'`. Please check the following "
                        "issue: https://github.com/huggingface/transformers/issues/28981"
                    )
                model_kwargs[cache_name] = self._get_cache(
                    generation_config.cache_implementation,
                    getattr(generation_config, "num_beams", 1) * batch_size,
                    generation_config.max_length,
                    model_kwargs,
                )
            elif generation_config.cache_implementation == "quantized":
                if not self._supports_quantized_cache:
                    raise ValueError(
                        "This model does not support the quantized cache. If you want your model to support quantized "
                        "cache, please open an issue."
                    )

                cache_config = (
                    generation_config.cache_config
                    if generation_config.cache_config is not None
                    else QuantizedCacheConfig()
                )
                cache_class = QUANT_BACKEND_CLASSES_MAPPING[cache_config.backend]

                if cache_config.backend == "quanto" and not is_quanto_available():
                    raise ImportError(
                        "You need to install `quanto` in order to use KV cache quantization with quanto backend. "
                        "Please install it via  with `pip install quanto`"
                    )
                elif cache_config.backend == "HQQ" and not is_hqq_available():
                    raise ImportError(
                        "You need to install `HQQ` in order to use KV cache quantization with HQQ backend. "
                        "Please install it via  with `pip install hqq`"
                    )

                model_kwargs[cache_name] = cache_class(cache_config)
        # Use DynamicCache() instance by default. This will avoid back and forth from legacy format that
        # keeps copying the cache thus using much more memory
        elif generation_config.cache_implementation is None and self._supports_default_dynamic_cache():
            past = model_kwargs.get(cache_name, None)
            requires_cross_attention_cache = (
                self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
            )
            if past is None:
                model_kwargs[cache_name] = (
                    DynamicCache()
                    if not requires_cross_attention_cache
                    else EncoderDecoderCache(DynamicCache(), DynamicCache())
                )
                # use_dynamic_cache_by_default = True
            elif isinstance(past, tuple):
                model_kwargs[cache_name] = (
                    DynamicCache.from_legacy_cache(past)
                    if not requires_cross_attention_cache
                    else EncoderDecoderCache.from_legacy_cache(past)
                )
                # use_dynamic_cache_by_default = True
        self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)

        # 7. determine generation mode
        generation_mode = generation_config.get_generation_mode(assistant_model)

        if (streamer is not None or streamer_unit is not None) and (generation_config.num_beams > 1):
            raise ValueError(
                "`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
            )

        if self.device.type != input_ids.device.type:
            warnings.warn(
                "You are calling .generate() with the `input_ids` being on a device type different"
                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
                " Please make sure that you have put `input_ids` to the"
                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
                " running `.generate()`.",
                UserWarning,
            )

        # 8. prepare distribution pre_processing samplers
        prepared_logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_length,
            encoder_input_ids=inputs_tensor,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            logits_processor=logits_processor,
            device=inputs_tensor.device,
            model_kwargs=model_kwargs,
            negative_prompt_ids=negative_prompt_ids,
            negative_prompt_attention_mask=negative_prompt_attention_mask,
        )

        # 9. prepare stopping criteria
        prepared_stopping_criteria = self._get_stopping_criteria(
            generation_config=generation_config, stopping_criteria=stopping_criteria, tokenizer=tokenizer, **kwargs
        )
        # 10. go into different generation modes

        if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
            # 11. prepare logits warper
            prepared_logits_warper = (
                self._get_logits_warper(generation_config, device=input_ids.device)
                if generation_config.do_sample
                else None
            )

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

            # 13. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)
            if streaming_unit_gen:
                return self._sample_streaming_unit(
                    input_ids,
                    logits_processor=prepared_logits_processor,
                    logits_warper=prepared_logits_warper,
                    stopping_criteria=prepared_stopping_criteria,
                    generation_config=generation_config,
                    synced_gpus=synced_gpus,
                    streamer=streamer,
                    streamer_unit=streamer_unit,
                    **model_kwargs,
                )
            else:
                return self._sample(
                    input_ids,
                    logits_processor=prepared_logits_processor,
                    logits_warper=prepared_logits_warper,
                    stopping_criteria=prepared_stopping_criteria,
                    generation_config=generation_config,
                    synced_gpus=synced_gpus,
                    streamer=streamer,
                    **model_kwargs,
                )
        else:
            raise NotImplementedError

    def _sample(
        self,
        input_ids: torch.Tensor,
        logits_processor: LogitsProcessorList,
        stopping_criteria: StoppingCriteriaList,
        generation_config: GenerationConfig,
        synced_gpus: bool,
        streamer: Optional["BaseStreamer"],
        logits_warper: Optional[LogitsProcessorList],
        **model_kwargs,
    ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
        # init values
        pad_token_id = generation_config._pad_token_tensor
        output_attentions = generation_config.output_attentions
        output_hidden_states = generation_config.output_hidden_states
        output_scores = generation_config.output_scores
        output_logits = generation_config.output_logits
        return_dict_in_generate = generation_config.return_dict_in_generate
        has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
        do_sample = generation_config.do_sample
        if do_sample is True and not isinstance(logits_warper, LogitsProcessorList):
            raise ValueError(
                "`do_sample` is set to `True`, `logits_warper` must be a `LogitsProcessorList` instance (it is "
                f"{logits_warper})."
            )

        # init attention / hidden states / scores tuples
        # scores = () if (return_dict_in_generate and output_scores) else None
        # raw_logits = () if (return_dict_in_generate and output_logits) 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

        scores: tuple = ()
        raw_logits: tuple = ()
        decoder_attentions: tuple = ()
        cross_attentions: tuple = ()
        decoder_hidden_states: tuple = ()

        # 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
        batch_size = input_ids.shape[0]
        this_peer_finished = False
        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
        model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)

        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
            # prepare model inputs
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # prepare variable output controls (note: some models won't accept all output controls)
            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})

            # forward pass to get next token
            outputs = self(**model_inputs, return_dict=True)

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

            # Clone is needed to avoid keeping a hanging ref to outputs.logits which may be
            # very large for first iteration (the clone itself is always small)
            next_token_logits = outputs.logits[:, -1, :].clone()

            # pre-process distribution
            next_token_scores = logits_processor(input_ids, next_token_logits)
            if do_sample and logits_warper is not None:
                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_logits:
                    raw_logits += (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,)
                    )

            # token selection
            if do_sample:
                probs = nn.functional.softmax(next_token_scores, dim=-1)
                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
            else:
                next_tokens = torch.argmax(next_token_scores, dim=-1)

            # finished sentences should have their next token be a padding token
            if has_eos_stopping_criteria:
                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)
            if streamer is not None:
                streamer.put(next_tokens.cpu())
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs,
                model_kwargs,
                is_encoder_decoder=self.config.is_encoder_decoder,
            )

            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
            this_peer_finished = bool(int(unfinished_sequences.max()) == 0)

            # This is needed to properly delete outputs.logits which may be very large for first iteration
            # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
            del outputs

        if streamer is not None:
            streamer.end()

        if return_dict_in_generate:
            if self.config.is_encoder_decoder:
                return GenerateEncoderDecoderOutput(
                    sequences=input_ids,
                    scores=scores,
                    logits=raw_logits,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                    past_key_values=model_kwargs.get("past_key_values"),
                )
            else:
                return GenerateDecoderOnlyOutput(
                    sequences=input_ids,
                    scores=scores,
                    logits=raw_logits,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                    past_key_values=model_kwargs.get("past_key_values"),
                )
        else:
            return input_ids

    def _sample_streaming_unit(
        self,
        input_ids: torch.Tensor,
        logits_processor: LogitsProcessorList,
        stopping_criteria: StoppingCriteriaList,
        generation_config: GenerationConfig,
        synced_gpus: bool,
        streamer: Optional["BaseStreamer"],
        streamer_unit: Optional["BaseStreamer"],
        logits_warper: Optional[LogitsProcessorList],
        **model_kwargs,
    ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
        # init values
        pad_token_id = generation_config._pad_token_tensor
        output_attentions = generation_config.output_attentions
        output_hidden_states = generation_config.output_hidden_states
        output_scores = generation_config.output_scores
        output_logits = generation_config.output_logits
        return_dict_in_generate = generation_config.return_dict_in_generate
        has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
        do_sample = generation_config.do_sample
        if do_sample is True and not isinstance(logits_warper, LogitsProcessorList):
            raise ValueError(
                "`do_sample` is set to `True`, `logits_warper` must be a `LogitsProcessorList` instance (it is "
                f"{logits_warper})."
            )

        # init attention / hidden states / scores tuples
        # scores = () if (return_dict_in_generate and output_scores) else None
        # raw_logits = () if (return_dict_in_generate and output_logits) 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: tuple = () if (return_dict_in_generate and output_hidden_states) else None

        scores: tuple = ()
        raw_logits: tuple = ()
        decoder_attentions: tuple = ()
        cross_attentions: tuple = ()
        decoder_hidden_states: tuple = ()

        # 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
        batch_size = input_ids.shape[0]
        this_peer_finished = False
        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
        model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)

        generated_units = torch.tensor([])
        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
            # prepare model inputs
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # prepare variable output controls (note: some models won't accept all output controls)
            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})

            # forward pass to get next token
            outputs = self(**model_inputs, return_dict=True)

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

            # Clone is needed to avoid keeping a hanging ref to outputs.logits
            # which may be very large for first iteration (the clone itself is always small)
            next_token_logits = outputs.logits[:, -1, :].clone()

            # pre-process distribution
            next_token_scores = logits_processor(input_ids, next_token_logits)
            if do_sample and logits_warper is not None:
                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 and scores is not None and next_token_scores is not None:
                    scores += (next_token_scores,)
                if output_logits and raw_logits is not None and next_token_logits is not None:
                    raw_logits += (next_token_logits,)
                if output_attentions and decoder_attentions is not None:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder and cross_attentions is not None:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states and decoder_hidden_states is not None:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,) if self.config.is_encoder_decoder else (outputs.hidden_states,)
                    )

            # token selection
            if do_sample:
                probs = nn.functional.softmax(next_token_scores, dim=-1)
                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
            else:
                next_tokens = torch.argmax(next_token_scores, dim=-1)

            # speechgen
            hidden_states = torch.cat(
                [decoder_hidden_states[0][-1][:, -1:, :]]
                + [decoder_hidden_states[i][-1] for i in range(1, len(decoder_hidden_states))],
                dim=1,
            )
            ctc_pred = self.speech_generator.predict(hidden_states.squeeze(0))
            cur_units = ctc_postprocess(ctc_pred, blank=self.model.config.unit_vocab_size)

            # finished sentences should have their next token be a padding token
            if has_eos_stopping_criteria:
                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)
            if streamer is not None:
                streamer.put(next_tokens.cpu())
            if streamer_unit is not None:
                for i in range(len(generated_units), len(cur_units)):
                    streamer_unit.put(cur_units[i].unsqueeze(0))
            generated_units = cur_units
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs,
                model_kwargs,
                is_encoder_decoder=self.config.is_encoder_decoder,
            )

            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
            this_peer_finished = bool(int(unfinished_sequences.max()) == 0)

            # This is needed to properly delete outputs.logits which may be very large for first iteration
            # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
            del outputs

        if streamer is not None:
            streamer.end()

        if return_dict_in_generate:
            if self.config.is_encoder_decoder:
                return GenerateEncoderDecoderOutput(
                    sequences=input_ids,
                    scores=scores,
                    logits=raw_logits,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                    past_key_values=model_kwargs.get("past_key_values"),
                )
            else:
                return GenerateDecoderOnlyOutput(
                    sequences=input_ids,
                    scores=scores,
                    logits=raw_logits,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                    past_key_values=model_kwargs.get("past_key_values"),
                )
        else:
            return input_ids


def ctc_postprocess(tokens, blank):
    _toks = tokens.squeeze(0).tolist()
    deduplicated_toks = [v for i, v in enumerate(_toks) if i == 0 or v != _toks[i - 1]]
    hyp = torch.tensor([v for v in deduplicated_toks if v != blank])
    return hyp
