# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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import os
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional, Union

import torch
import torch.nn as nn

from ...generation import (
    GenerateDecoderOnlyOutput,
    GenerationConfig,
    GenerationMixin,
    GenerationMode,
)
from ...generation.logits_process import LogitsProcessorList
from ...generation.stopping_criteria import MaxLengthCriteria, StoppingCriteriaList
from ...generation.utils import GenerateNonBeamOutput
from ...utils import logging


if TYPE_CHECKING:
    from ...generation.streamers import BaseStreamer


logger = logging.get_logger(__name__)


@dataclass
class CsmGenerateOutput(GenerateDecoderOnlyOutput):
    """
    Outputs of CsmForConditionalGeneration.generate.

    Args:
        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
            if all batches finished early due to the `eos_token_id`.
        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`):
            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
        logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
            Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
        past_key_values (`Cache`, *optional*, returned when `use_cache=True`):
            Returns the model cache, used to speed up decoding. Different models have a different cache format, check
        audio (`list(torch.FloatTensor)` of length `batch_size`):
            The generated audio.
    """

    audio: Optional[list[torch.Tensor]] = None


class CsmGenerationMixin(GenerationMixin):
    def _get_stopping_criteria(
        self,
        *args,
        **kwargs,
    ) -> StoppingCriteriaList:
        criteria = super()._get_stopping_criteria(*args, **kwargs)

        kept_criteria = StoppingCriteriaList()
        for criterion in criteria:
            if not isinstance(criterion, MaxLengthCriteria):
                logger.warning(
                    f"Csm does not support {criterion.__class__.__name__} stopping criteria, it will be ignored."
                )
            else:
                kept_criteria.append(criterion)
        return kept_criteria

    def _prepare_generation_config(
        self, generation_config: Optional[GenerationConfig], use_model_defaults: Optional[bool] = None, **kwargs: Any
    ) -> tuple[GenerationConfig, dict]:
        """
        This method overrides [~generation.utils.GenerationMixin._prepare_generation_config].
        It ensures that the depth decoder generation config is initialized and that passed args as depth_decoder_* are properly handled.
        """
        # extract depth decoder kwargs and remove them from the main kwargs
        depth_decoder_kwargs = {
            k[len("depth_decoder_") :]: v for k, v in kwargs.items() if k.startswith("depth_decoder_")
        }

        # remove the depth decoder keys from the original kwargs
        kwargs = {k: v for k, v in kwargs.items() if not k.startswith("depth_decoder_")}

        # initialize the generation config
        generation_config, model_kwargs = super()._prepare_generation_config(
            generation_config, use_model_defaults, **kwargs
        )
        self.depth_decoder.generation_config.update(**depth_decoder_kwargs)

        # ensure the depth decoder generation config is valid
        depth_decoder_min_new_tokens = getattr(self.depth_decoder.generation_config, "min_new_tokens") or (
            self.config.num_codebooks - 1
        )
        depth_decoder_max_new_tokens = getattr(self.depth_decoder.generation_config, "max_new_tokens") or (
            self.config.num_codebooks - 1
        )

        if {depth_decoder_min_new_tokens, depth_decoder_max_new_tokens} != {self.config.num_codebooks - 1}:
            raise ValueError(
                f"depth_decoder_generation_config's min_new_tokens ({depth_decoder_min_new_tokens}) and max_new_tokens ({depth_decoder_max_new_tokens}) must be equal to self.config.num_codebooks - 1 ({self.config.num_codebooks - 1})"
            )
        elif self.depth_decoder.generation_config.return_dict_in_generate:
            logger.warning(
                "depth_decoder_generation_config.return_dict_in_generate is set to True, but this will be ignored as the depth decoder model does not return a dictionary in generate"
            )
            self.depth_decoder.generation_config.return_dict_in_generate = False

        self.depth_decoder.generation_config.min_new_tokens = depth_decoder_min_new_tokens
        self.depth_decoder.generation_config.max_new_tokens = depth_decoder_max_new_tokens

        # Monkey patch the get_generation_mode method to support CSM model
        original_get_generation_mode = generation_config.get_generation_mode

        def patched_get_generation_mode(assistant_model=None):
            generation_mode = original_get_generation_mode(assistant_model)
            if generation_mode not in [GenerationMode.GREEDY_SEARCH, GenerationMode.SAMPLE]:
                raise ValueError(
                    f"Generation mode {generation_mode} is not supported for CSM model. Please set generation parameters to use greedy or sampling generation."
                )

            return generation_mode

        generation_config.get_generation_mode = patched_get_generation_mode

        return generation_config, model_kwargs

    def _sample(
        self,
        input_ids: torch.LongTensor,
        logits_processor: LogitsProcessorList,
        stopping_criteria: StoppingCriteriaList,
        generation_config: GenerationConfig,
        synced_gpus: bool = False,
        streamer: Optional["BaseStreamer"] = None,
        **model_kwargs,
    ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
        """
        This method overrides [~generation.utils.GenerationMixin._sample].
        To ease maintenance, modifications are marked with the comment "Csm specific".

        Indeed, Csm model requires a custom generation sampling step:
        1. Infer the backbone model to sample the first codebook token
        2. Call generate on the depth decoder with the first codebook token as input_ids to sample the next codebook tokens
        3. Use these generated codebook tokens as input_ids to sample the next first codebook token using the backbone model
        4. Repeat until stopping criteria is met

        Csm supports two stopping criteria:
        - stop when the generated sequence is at max_length
        - stop when all the generated codebook tokens are the codebook_eos_token_id
        """
        # init values
        # *************** Csm specific ***************
        pad_token_id = self.config.codebook_pad_token_id
        has_eos_stopping_criteria = generation_config._eos_token_tensor is not None
        # ============================================
        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
        do_sample = generation_config.do_sample

        # 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
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # keep track of which sequences are already finished
        batch_size, cur_len = input_ids.shape[:2]
        this_peer_finished = False
        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
        model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)

        # *************** Csm specific ***************
        if input_ids.ndim == 2 and model_kwargs.get("inputs_embeds") is None:
            # in the case where the passed input_ids correspond to text tokens, i.e. don't have a third dimension for codebook ids,
            # we need to remove the input length to the MaxLengthCriteria stopping criteria has such input are not returned
            for criterion in stopping_criteria:
                if isinstance(criterion, MaxLengthCriteria):
                    criterion.max_length -= cur_len
        # ============================================

        model_forward = self.__call__
        compile_forward = self._valid_auto_compile_criteria(model_kwargs, generation_config)
        if compile_forward:
            os.environ["TOKENIZERS_PARALLELISM"] = "0"
            model_forward = self.get_compiled_call(generation_config.compile_config)

        is_prefill = True
        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 {})
            # *************** Csm specific ***************
            model_inputs.update({"output_hidden_states": True})
            # ============================================

            if is_prefill:
                outputs = self(**model_inputs, return_dict=True)
                is_prefill = False
            else:
                outputs = model_forward(**model_inputs, return_dict=True)

            # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs,
                model_kwargs,
            )
            if synced_gpus and this_peer_finished:
                continue

            # 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().float()
            next_token_logits = next_token_logits.to(input_ids.device)

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

            # 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.attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (outputs.hidden_states,)

            # token selection
            if do_sample:
                probs = nn.functional.softmax(next_token_scores, dim=-1)
                # TODO (joao): this OP throws "skipping cudagraphs due to ['incompatible ops']", find solution
                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
            else:
                next_tokens = torch.argmax(next_token_scores, dim=-1)

            # *************** Csm specific ***************
            # infer the depth decoder
            first_codebook_ids = next_tokens[:, None]
            # adds place holder in position 0 that will be replaced by the backbone_last_hidden_state
            depth_decoder_input_ids = nn.functional.pad(first_codebook_ids, (1, 0), value=0)
            backbone_last_hidden_state = outputs.hidden_states[-1][:, -1, :]

            depth_decoder_outputs = self.depth_decoder.generate(
                input_ids=depth_decoder_input_ids, backbone_last_hidden_state=backbone_last_hidden_state.clone()
            )
            codebook_ids = (
                depth_decoder_outputs
                if isinstance(depth_decoder_outputs, torch.Tensor)
                else depth_decoder_outputs.sequences
            )
            # remove the place holder in position 0
            codebook_ids = codebook_ids[:, 1:]
            next_tokens = codebook_ids

            # finished sentences should have their next token be a padding token
            if has_eos_stopping_criteria:
                next_tokens = next_tokens * unfinished_sequences.unsqueeze(-1) + pad_token_id * (
                    1 - unfinished_sequences.unsqueeze(-1)
                )

            # update generated ids, model inputs, and length for next step
            if input_ids.ndim == 2:
                input_ids = next_tokens[:, None, :]
            else:
                input_ids = torch.cat([input_ids, next_tokens[:, None, :]], dim=1)
            # ============================================

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

            # *************** Csm specific ***************
            # for the eos stopping criteria, is it expected that the eos token is the same for each codebook !!!!
            unfinished_sequences = unfinished_sequences & ~(
                input_ids[:, -1, :-1] == self.config.codebook_eos_token_id
            ).all(-1)
            # ============================================
            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
            this_peer_finished = unfinished_sequences.max() == 0
            cur_len += 1

            # 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

            # *************** Csm specific ***************
            del depth_decoder_outputs
            # ============================================

        if streamer is not None:
            streamer.end()

        if return_dict_in_generate:
            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 generate(
        self,
        input_ids: Optional[torch.Tensor] = None,
        input_values: Optional[torch.Tensor] = None,
        input_values_cutoffs: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        synced_gpus: Optional[bool] = None,
        streamer: Optional["BaseStreamer"] = None,
        output_audio: Optional[bool] = False,
        **kwargs,
    ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
        r"""
        This method overrides [`~generation.utils.GenerationMixin.generate`] to match the specifics of the Csm model.
        Indeed, Csm model requires a custom generation sampling step:
        1. Infer the backbone model to sample the first codebook token
        2. Call generate on the depth decoder with the first codebook token as `input_ids` to sample the next codebook tokens
        3. Use these generated codebook tokens as `input_ids` to sample the next first codebook token using the backbone model
        4. Repeat until stopping criteria is met

        <Tip warning={true}>

        Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
        model's default generation configuration. You can override any `generation_config` by passing the corresponding
        parameters to generate(), e.g. `.generate(inputs, do_sample=True)`.
        </Tip>

        Parameters:
            inputs_ids (`torch.Tensor` of shape (batch_size, seq_length), *optional*):
                The sequence used as a prompt for the backbone model.
            input_values (`torch.Tensor` of shape (batch_size, channels, max_concatenated_audio_length), *optional*):
                The batched audio input values, where each batch entry contains the concatenation of all audio segments for that entry.
                These values will be encoded into codebook tokens using the codec model and merged with the text input ids provided in `input_ids`.
            input_values_cutoffs (`torch.Tensor` of shape (batch_size, max_num_audio), *optional*):
                Specify the end positions of audio segments within each batch entry, relative to the concatenated audio input.
                If a batch entry has fewer segments than the maximum, it is padded with -1. For example, in a batch of 2 sequences
                where the first contains 2 audio segments of length l1, and the second contains 1 audio segment of length l2,
                the input_values_cutoffs would be: [[l1, 2 * l1], [l2, -1]].
            generation_config ([`~generation.GenerationConfig`], *optional*):
                The generation configuration to be used as base parametrization for the generation call. `**kwargs`
                passed to generate matching the attributes of `generation_config` will override them. If
                `generation_config` is not provided, the default will be used, which has the following loading
                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
                default values, whose documentation should be checked to parameterize generation.
            logits_processor (`LogitsProcessorList`, *optional*):
                Custom logits processors that complement the default logits processors built from arguments and
                generation config. If a logit processor is passed that is already created with the arguments or a
                generation config an error is thrown. This feature is intended for advanced users.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                Custom stopping criteria that complements the default stopping criteria built from arguments and a
                generation config. If a stopping criteria is passed that is already created with the arguments or a
                generation config an error is thrown. If your stopping criteria depends on the `scores` input, make
                sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is
                intended for advanced users.
            synced_gpus (`bool`, *optional*):
                Whether to continue running the while loop until max_length. Unless overridden, this flag will be set
                to `True` if using `FullyShardedDataParallel` or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid
                deadlocking if one GPU finishes generating before other GPUs. Otherwise, defaults to `False`.
            streamer (`BaseStreamer`, *optional*):
                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
            output_audio (`bool`, *optional*):
                Whether to return the generated audio.
            kwargs (`dict[str, Any]`, *optional*):
                Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be
                forwarded to the `forward` function of the model. Depth decoder specific kwargs should be prefixed with *depth_decoder_*.

        Return:
            [`CsmGenerateOutput`] or `torch.LongTensor` or `list[torch.FloatTensor]`: A [`CsmGenerateOutput`]
            (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.LongTensor` when `output_audio=False`
            or a `list[torch.FloatTensor]` otherwise.

        Example:

        ```python
        >>> from transformers import CsmProcessor, CsmForConditionalGeneration
        >>> from datasets import load_dataset, Audio

        >>> model_id = "sesame/csm-1b"
        >>> torch_device = "cuda" if torch.cuda.is_available() else "cpu"

        >>> processor = AutoProcessor.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
        >>> # ensure the audio is 24kHz
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=24000))

        >>> conversation = []
        >>> # prepare a conversation with text and corresponding audio
        >>> for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
        ...     conversation.append(
        ...         {
        ...             "role": f"{speaker_id}",
        ...             "content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
        ...         }
        ...     )

        >>> # text prompt
        >>> conversation.append({"role": f"{ds[4]['speaker_id']}", "content": [{"type": "text", "text": ds[4]["text"]}]})

        >>> inputs = processor.apply_chat_template(
        ...     conversation,
        ...     tokenize=True,
        ...     return_dict=True,
        ... ).to(torch_device)

        >>> model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
        >>> audio = model.generate(**inputs, output_audio=True)
        >>> processor.save_audio(audio, "output.wav")
        ```
        """
        generate_output = super().generate(
            input_ids=input_ids,
            input_values=input_values,
            input_values_cutoffs=input_values_cutoffs,
            generation_config=generation_config,
            logits_processor=logits_processor,
            stopping_criteria=stopping_criteria,
            synced_gpus=synced_gpus,
            streamer=streamer,
            **kwargs,
        )

        generate_returned_dict = not isinstance(generate_output, torch.Tensor)
        audio = None
        if output_audio:
            generated_audio_codes = generate_output.sequences if generate_returned_dict else generate_output

            # infer the codec model
            audio = []
            with torch.no_grad():
                # =======================================
                # TODO: @eustlb, this should be batched !!!
                # but requires making sure batched inference of the codec model works as intended
                for audio_codes_batch in generated_audio_codes:
                    eos_idxs = (audio_codes_batch == self.config.codebook_eos_token_id).all(dim=-1).nonzero()
                    if eos_idxs.numel() != 0:
                        cutoff_idx = eos_idxs.min()
                    else:
                        cutoff_idx = audio_codes_batch.shape[0]

                    audio_codes_batch = audio_codes_batch[:cutoff_idx]
                    codec_decode_output = self.codec_model.decode(audio_codes_batch.transpose(0, 1).unsqueeze(0))
                    audio.append(codec_decode_output.audio_values[0, 0])
                # =======================================

        if generate_returned_dict:
            return CsmGenerateOutput(audio=audio, **generate_output)
        elif output_audio:
            return audio
        else:
            return generate_output
