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"""Musicgen Melody model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig, SubConfigSpec
from ...utils import auto_docstring, logging
from ..auto.configuration_auto import AutoConfig


logger = logging.get_logger(__name__)


@auto_docstring(checkpoint="facebook/musicgen-melody")
@strict
class MusicgenMelodyDecoderConfig(PreTrainedConfig):
    r"""
    audio_channels (`int`, *optional*, defaults to 1):
        Number of audio channels used by the model (either mono or stereo). Stereo models generate a separate
        audio stream for the left/right output channels. Mono models generate a single audio stream output.
    """

    model_type = "musicgen_melody_decoder"
    base_config_key = "decoder_config"
    keys_to_ignore_at_inference = ["past_key_values"]

    vocab_size: int = 2048
    max_position_embeddings: int = 2048
    num_hidden_layers: int = 24
    ffn_dim: int = 4096
    num_attention_heads: int = 16
    layerdrop: float | int = 0.0
    use_cache: bool = True
    activation_function: str = "gelu"
    hidden_size: int = 1024
    dropout: float | int = 0.1
    attention_dropout: float | int = 0.0
    activation_dropout: float | int = 0.0
    initializer_factor: float = 0.02
    scale_embedding: bool = False
    num_codebooks: int = 4
    audio_channels: int = 1
    pad_token_id: int | None = 2048
    bos_token_id: int | None = 2048
    eos_token_id: int | list[int] | None = None
    tie_word_embeddings: bool = False
    is_decoder: bool = False
    add_cross_attention: bool = False

    def validate_token_ids(self):
        # override: MusicGenMelody init an embedding layer with `vocab_size+1`
        text_config = self.get_text_config(decoder=True)
        vocab_size = getattr(text_config, "vocab_size", None)
        if vocab_size is not None:
            # Check for all special tokens, e..g. pad_token_id, image_token_id, audio_token_id
            for name in text_config:
                value = getattr(text_config, name)
                if name.endswith("_token_id") and isinstance(value, int) and not 0 <= value <= vocab_size:
                    # Can't be an exception until we can load configs that fail validation: several configs on the Hub
                    # store invalid special tokens, e.g. `pad_token_id=-1`
                    logger.warning_once(
                        f"Model config: {name} must be `None` or an integer within the vocabulary (between 0 "
                        f"and {vocab_size}), got {value}. This may result in unexpected behavior."
                    )

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        if self.audio_channels not in [1, 2]:
            raise ValueError(f"Expected 1 (mono) or 2 (stereo) audio channels, got {self.audio_channels} channels.")


@auto_docstring(checkpoint="facebook/musicgen-melody")
@strict
class MusicgenMelodyConfig(PreTrainedConfig):
    r"""
    text_encoder (`Union[dict, `PretrainedConfig`]`):
        An instance of a configuration object that defines the text encoder config.
    audio_encoder (`Union[dict, `PretrainedConfig`]`):
        An instance of a configuration object that defines the audio encoder config.
    decoder (`Union[dict, `PretrainedConfig`]`):
        An instance of a configuration object that defines the decoder config.
    num_chroma (`int`, *optional*, defaults to 12):
        Number of chroma bins to use.
    chroma_length (`int`, *optional*, defaults to 235):
        Maximum chroma duration if audio is used to condition the model. Corresponds to the maximum duration used during training.

    Example:

    ```python
    >>> from transformers import (
    ...     MusicgenMelodyConfig,
    ...     MusicgenMelodyDecoderConfig,
    ...     T5Config,
    ...     EncodecConfig,
    ...     MusicgenMelodyForConditionalGeneration,
    ... )

    >>> # Initializing text encoder, audio encoder, and decoder model configurations
    >>> text_encoder_config = T5Config()
    >>> audio_encoder_config = EncodecConfig()
    >>> decoder_config = MusicgenMelodyDecoderConfig()

    >>> configuration = MusicgenMelodyConfig(
    ...     text_encoder=text_encoder_config, audio_encoder=audio_encoder_config, decoder=decoder_config
    ... )

    >>> # Initializing a MusicgenMelodyForConditionalGeneration (with random weights) from the facebook/musicgen-melody style configuration
    >>> model = MusicgenMelodyForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    >>> config_text_encoder = model.config.text_encoder
    >>> config_audio_encoder = model.config.audio_encoder
    >>> config_decoder = model.config.decoder

    >>> # Saving the model, including its configuration
    >>> model.save_pretrained("musicgen_melody-model")

    >>> # loading model and config from pretrained folder
    >>> musicgen_melody_config = MusicgenMelodyConfig.from_pretrained("musicgen_melody-model")
    >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("musicgen_melody-model", config=musicgen_melody_config)
    ```"""

    model_type = "musicgen_melody"
    sub_configs_defaults = {
        "text_encoder": SubConfigSpec(config_class=AutoConfig, model_type="t5"),
        "audio_encoder": SubConfigSpec(config_class=AutoConfig, model_type="encodec"),
        "decoder": SubConfigSpec(config_class=MusicgenMelodyDecoderConfig),
    }

    text_encoder: dict | PreTrainedConfig | None = None
    audio_encoder: dict | PreTrainedConfig | None = None
    decoder: dict | PreTrainedConfig | None = None
    num_chroma: int = 12
    chroma_length: int = 235
    initializer_factor: float = 0.02

    def __post_init__(self, **kwargs):
        super().__post_init__(**kwargs)
        self.is_encoder_decoder = True

    @property
    # This is a property because you might want to change the codec model on the fly
    def sampling_rate(self):
        return self.audio_encoder.sampling_rate


__all__ = ["MusicgenMelodyConfig", "MusicgenMelodyDecoderConfig"]