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"""Parakeet model configuration."""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="nvidia/parakeet-ctc-1.1b")
@strict
class ParakeetEncoderConfig(PreTrainedConfig):
    r"""
    convolution_bias (`bool`, *optional*, defaults to `True`):
        Whether to use bias in convolutions of the conformer's convolution module.
    conv_kernel_size (`int`, *optional*, defaults to 9):
        The kernel size of the convolution layers in the Conformer block.
    subsampling_factor (`int`, *optional*, defaults to 8):
        The factor by which the input sequence is subsampled.
    subsampling_conv_channels (`int`, *optional*, defaults to 256):
        The number of channels in the subsampling convolution layers.
    num_mel_bins (`int`, *optional*, defaults to 80):
        Number of mel features.
    subsampling_conv_kernel_size (`int`, *optional*, defaults to 3):
        The kernel size of the subsampling convolution layers.
    subsampling_conv_stride (`int`, *optional*, defaults to 2):
        The stride of the subsampling convolution layers.
    dropout_positions (`float`, *optional*, defaults to 0.0):
        The dropout ratio for the positions in the input sequence.
    scale_input (`bool`, *optional*, defaults to `True`):
        Whether to scale the input embeddings.

    Example:
        ```python
        >>> from transformers import ParakeetEncoderModel, ParakeetEncoderConfig

        >>> # Initializing a `ParakeetEncoder` configuration
        >>> configuration = ParakeetEncoderConfig()

        >>> # Initializing a model from the configuration
        >>> model = ParakeetEncoderModel(configuration)

        >>> # Accessing the model configuration
        >>> configuration = model.config
        ```

    This configuration class is based on the ParakeetEncoder architecture from NVIDIA NeMo. You can find more details
    and pre-trained models at [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b).
    """

    model_type = "parakeet_encoder"
    keys_to_ignore_at_inference = ["past_key_values"]

    hidden_size: int = 1024
    num_hidden_layers: int = 24
    num_attention_heads: int = 8
    intermediate_size: int = 4096
    hidden_act: str = "silu"
    attention_bias: bool = True
    convolution_bias: bool = True
    conv_kernel_size: int = 9
    subsampling_factor: int = 8
    subsampling_conv_channels: int = 256
    num_mel_bins: int = 80
    subsampling_conv_kernel_size: int = 3
    subsampling_conv_stride: int = 2
    dropout: float | int = 0.1
    dropout_positions: float | int = 0.0
    layerdrop: float | int = 0.1
    activation_dropout: float | int = 0.1
    attention_dropout: float | int = 0.1
    max_position_embeddings: int = 5000
    scale_input: bool = True
    initializer_range: float = 0.02

    def __post_init__(self, **kwargs):
        self.num_key_value_heads = self.num_attention_heads
        super().__post_init__(**kwargs)


@auto_docstring(checkpoint="nvidia/parakeet-ctc-1.1b")
@strict
class ParakeetCTCConfig(PreTrainedConfig):
    r"""
    ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
        Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
        instance of [`ParakeetForCTC`].
    ctc_zero_infinity (`bool`, *optional*, defaults to `True`):
        Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
        occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
        of [`ParakeetForCTC`].
    encoder_config (`Union[dict, ParakeetEncoderConfig]`, *optional*):
        The config object or dictionary of the encoder.

    Example:

    ```python
    >>> from transformers import ParakeetForCTC, ParakeetCTCConfig
    >>> # Initializing a Parakeet configuration
    >>> configuration = ParakeetCTCConfig()
    >>> # Initializing a model from the configuration
    >>> model = ParakeetForCTC(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "parakeet_ctc"
    sub_configs = {"encoder_config": ParakeetEncoderConfig}

    vocab_size: int = 1025
    ctc_loss_reduction: str = "mean"
    ctc_zero_infinity: bool = True
    encoder_config: dict | PreTrainedConfig | None = None
    pad_token_id: int | None = 1024

    def __post_init__(self, **kwargs):
        if isinstance(self.encoder_config, dict):
            self.encoder_config = ParakeetEncoderConfig(**self.encoder_config)
        elif self.encoder_config is None:
            self.encoder_config = ParakeetEncoderConfig()
        self.initializer_range = self.encoder_config.initializer_range
        super().__post_init__(**kwargs)


__all__ = ["ParakeetCTCConfig", "ParakeetEncoderConfig"]