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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Audio Spectogram Transformer (AST) model configuration"""

from huggingface_hub.dataclasses import strict

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


@auto_docstring(checkpoint="MIT/ast-finetuned-audioset-10-10-0.4593")
@strict
class ASTConfig(PreTrainedConfig):
    r"""
    frequency_stride (`int`, *optional*, defaults to 10):
        Frequency stride to use when patchifying the spectrograms.
    time_stride (`int`, *optional*, defaults to 10):
        Temporal stride to use when patchifying the spectrograms.
    max_length (`int`, *optional*, defaults to 1024):
        Temporal dimension of the spectrograms.

    Example:

    ```python
    >>> from transformers import ASTConfig, ASTModel

    >>> # Initializing a AST MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> configuration = ASTConfig()

    >>> # Initializing a model (with random weights) from the MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> model = ASTModel(configuration)

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

    model_type = "audio-spectrogram-transformer"

    hidden_size: int = 768
    num_hidden_layers: int = 12
    num_attention_heads: int = 12
    intermediate_size: int = 3072
    hidden_act: str = "gelu"
    hidden_dropout_prob: float | int = 0.0
    attention_probs_dropout_prob: float | int = 0.0
    initializer_range: float = 0.02
    layer_norm_eps: float = 1e-12
    patch_size: int | list[int] | tuple[int, int] = 16
    qkv_bias: bool = True
    frequency_stride: int = 10
    time_stride: int = 10
    max_length: int = 1024
    num_mel_bins: int = 128


__all__ = ["ASTConfig"]
