# coding=utf-8
# Copyright 2023 The Suno AI Authors and 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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"""BARK model generation configuration"""

import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class BarkSemanticGenerationConfig(GenerationConfig):
    model_type = "semantic"

    def __init__(
        self,
        eos_token_id=10_000,
        renormalize_logits=True,
        max_new_tokens=768,
        output_scores=False,
        return_dict_in_generate=False,
        output_hidden_states=False,
        output_attentions=False,
        temperature=1.0,
        do_sample=False,
        text_encoding_offset=10_048,
        text_pad_token=129_595,
        semantic_infer_token=129_599,
        semantic_vocab_size=10_000,
        max_input_semantic_length=256,
        semantic_rate_hz=49.9,
        min_eos_p=None,
        **kwargs,
    ):
        """Class that holds a generation configuration for [`BarkSemanticModel`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            eos_token_id (`int`, *optional*, defaults to 10_000):
                The id of the *end-of-sequence* token.
            renormalize_logits (`bool`, *optional*, defaults to `True`):
                Whether to renormalize the logits after applying all the logits processors (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 break the normalization.
            max_new_tokens (`int`, *optional*, defaults to 768):
                The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
            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.
            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_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.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to modulate the next token probabilities.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            text_encoding_offset (`int`, *optional*, defaults to 10_048):
                Text encoding offset.
            text_pad_token (`int`, *optional*, defaults to 129_595):
                Text pad token.
            semantic_infer_token (`int`, *optional*, defaults to 129_599):
                Semantic infer token.
            semantic_vocab_size (`int`, *optional*, defaults to 10_000):
                Semantic vocab size.
            max_input_semantic_length (`int`, *optional*, defaults to 256):
                Max length of semantic input vector.
            semantic_rate_hz (`float`, *optional*, defaults to 49.9):
                Semantic rate in Hertz.
            min_eos_p (`float`, *optional*):
                Minimum threshold of the probability of the EOS token for it to be sampled. This is an early stopping
                strategy to mitigate potential unwanted generations at the end of a prompt. The original implementation
                suggests a default value of 0.2.
        """
        super().__init__(
            temperature=temperature,
            do_sample=do_sample,
            eos_token_id=eos_token_id,
            renormalize_logits=renormalize_logits,
            max_new_tokens=max_new_tokens,
            output_scores=output_scores,
            return_dict_in_generate=return_dict_in_generate,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            **kwargs,
        )

        self.text_encoding_offset = text_encoding_offset
        self.text_pad_token = text_pad_token
        self.semantic_pad_token = eos_token_id
        self.semantic_infer_token = semantic_infer_token
        self.semantic_vocab_size = semantic_vocab_size
        self.max_input_semantic_length = max_input_semantic_length
        self.semantic_rate_hz = semantic_rate_hz
        self.min_eos_p = min_eos_p


class BarkCoarseGenerationConfig(GenerationConfig):
    model_type = "coarse_acoustics"

    def __init__(
        self,
        renormalize_logits=True,
        output_scores=False,
        return_dict_in_generate=False,
        output_hidden_states=False,
        output_attentions=False,
        temperature=1.0,
        do_sample=False,
        coarse_semantic_pad_token=12_048,
        coarse_rate_hz=75,
        n_coarse_codebooks=2,
        coarse_infer_token=12_050,
        max_coarse_input_length=256,
        max_coarse_history: int = 630,
        sliding_window_len: int = 60,
        **kwargs,
    ):
        """Class that holds a generation configuration for [`BarkCoarseModel`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            renormalize_logits (`bool`, *optional*, defaults to `True`):
                Whether to renormalize the logits after applying all the logits processors (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 break the normalization.
            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.
            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_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.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to modulate the next token probabilities.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
                Coarse semantic pad token.
            coarse_rate_hz (`int`, *optional*, defaults to 75):
                Coarse rate in Hertz.
            n_coarse_codebooks (`int`, *optional*, defaults to 2):
                Number of coarse codebooks.
            coarse_infer_token (`int`, *optional*, defaults to 12_050):
                Coarse infer token.
            max_coarse_input_length (`int`, *optional*, defaults to 256):
                Max length of input coarse vector.
            max_coarse_history (`int`, *optional*, defaults to 630):
                Max length of the output of the coarse acoustics model used in the fine generation step.
            sliding_window_len (`int`, *optional*, defaults to 60):
                The coarse generation step uses a sliding window to generate raw audio.
        """
        super().__init__(
            temperature=temperature,
            do_sample=do_sample,
            renormalize_logits=renormalize_logits,
            output_scores=output_scores,
            return_dict_in_generate=return_dict_in_generate,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            **kwargs,
        )

        self.coarse_semantic_pad_token = coarse_semantic_pad_token
        self.coarse_rate_hz = coarse_rate_hz
        self.n_coarse_codebooks = n_coarse_codebooks
        self.coarse_infer_token = coarse_infer_token
        self.max_coarse_input_length = max_coarse_input_length
        self.max_coarse_history = max_coarse_history
        self.sliding_window_len = sliding_window_len


class BarkFineGenerationConfig(GenerationConfig):
    model_type = "fine_acoustics"

    def __init__(
        self,
        temperature=1.0,
        max_fine_history_length=512,
        max_fine_input_length=1024,
        n_fine_codebooks=8,
        **kwargs,
    ):
        """Class that holds a generation configuration for [`BarkFineModel`].

        [`BarkFineModel`] is an autoencoder model, so should not usually be used for generation. However, under the
        hood, it uses `temperature` when used by [`BarkModel`]

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            temperature (`float`, *optional*):
                The value used to modulate the next token probabilities.
            max_fine_history_length (`int`, *optional*, defaults to 512):
                Max length of the fine history vector.
            max_fine_input_length (`int`, *optional*, defaults to 1024):
                Max length of fine input vector.
            n_fine_codebooks (`int`, *optional*, defaults to 8):
                Number of codebooks used.
        """
        super().__init__(temperature=temperature)

        self.max_fine_history_length = max_fine_history_length
        self.max_fine_input_length = max_fine_input_length
        self.n_fine_codebooks = n_fine_codebooks

    def validate(self, **kwargs):
        """
        Overrides GenerationConfig.validate because BarkFineGenerationConfig don't use any parameters outside
        temperature.
        """
        pass


class BarkGenerationConfig(GenerationConfig):
    model_type = "bark"
    is_composition = True

    # TODO (joao): nested from_dict

    def __init__(
        self,
        semantic_config: Dict = None,
        coarse_acoustics_config: Dict = None,
        fine_acoustics_config: Dict = None,
        sample_rate=24_000,
        codebook_size=1024,
        **kwargs,
    ):
        """Class that holds a generation configuration for [`BarkModel`].

        The [`BarkModel`] does not have a `generate` method, but uses this class to generate speeches with a nested
        [`BarkGenerationConfig`] which uses [`BarkSemanticGenerationConfig`], [`BarkCoarseGenerationConfig`],
        [`BarkFineGenerationConfig`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            semantic_config (`Dict`, *optional*):
                Semantic generation configuration.
            coarse_acoustics_config (`Dict`, *optional*):
                Coarse generation configuration.
            fine_acoustics_config (`Dict`, *optional*):
                Fine generation configuration.
            sample_rate (`int`, *optional*, defaults to 24_000):
                Sample rate.
            codebook_size (`int`, *optional*, defaults to 1024):
                Vector length for each codebook.
        """
        if semantic_config is None:
            semantic_config = {}
            logger.info("semantic_config is None. initializing the semantic model with default values.")

        if coarse_acoustics_config is None:
            coarse_acoustics_config = {}
            logger.info("coarse_acoustics_config is None. initializing the coarse model with default values.")

        if fine_acoustics_config is None:
            fine_acoustics_config = {}
            logger.info("fine_acoustics_config is None. initializing the fine model with default values.")

        self.semantic_config = BarkSemanticGenerationConfig(**semantic_config)
        self.coarse_acoustics_config = BarkCoarseGenerationConfig(**coarse_acoustics_config)
        self.fine_acoustics_config = BarkFineGenerationConfig(**fine_acoustics_config)

        self.sample_rate = sample_rate
        self.codebook_size = codebook_size

    @classmethod
    def from_sub_model_configs(
        cls,
        semantic_config: BarkSemanticGenerationConfig,
        coarse_acoustics_config: BarkCoarseGenerationConfig,
        fine_acoustics_config: BarkFineGenerationConfig,
        **kwargs,
    ):
        r"""
        Instantiate a [`BarkGenerationConfig`] (or a derived class) from bark sub-models generation configuration.

        Returns:
            [`BarkGenerationConfig`]: An instance of a configuration object
        """
        return cls(
            semantic_config=semantic_config.to_dict(),
            coarse_acoustics_config=coarse_acoustics_config.to_dict(),
            fine_acoustics_config=fine_acoustics_config.to_dict(),
            **kwargs,
        )

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)

        output["semantic_config"] = self.semantic_config.to_dict()
        output["coarse_acoustics_config"] = self.coarse_acoustics_config.to_dict()
        output["fine_acoustics_config"] = self.fine_acoustics_config.to_dict()

        output["model_type"] = self.__class__.model_type
        return output
