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"""CLAP model configuration"""

from ...configuration_utils import PreTrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class ClapTextConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the CLAP
    [calp-hsat-fused](https://huggingface.co/laion/clap-hsat-fused) architecture.

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the CLAP model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ClapTextModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"relu"`,
            `"relu"`, `"silu"` and `"relu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`ClapTextModel`].
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        projection_dim (`int`, *optional*, defaults to 512)
            Dimension of the projection head of the `ClapTextModelWithProjection`.

    Examples:

    ```python
    >>> from transformers import ClapTextConfig, ClapTextModel

    >>> # Initializing a CLAP text configuration
    >>> configuration = ClapTextConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = ClapTextModel(configuration)

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

    model_type = "clap_text_model"
    base_config_key = "text_config"

    def __init__(
        self,
        vocab_size=50265,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=514,
        type_vocab_size=1,
        initializer_factor=1.0,
        layer_norm_eps=1e-12,
        projection_dim=512,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        projection_hidden_act="relu",
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_factor = initializer_factor
        self.layer_norm_eps = layer_norm_eps
        self.projection_hidden_act = projection_hidden_act
        self.projection_dim = projection_dim


class ClapAudioConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
    CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP
    [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture.

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        window_size (`int`, *optional*, defaults to 8):
            Image size of the spectrogram
        num_mel_bins (`int`, *optional*, defaults to 64):
            Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class.
        spec_size (`int`, *optional*, defaults to 256):
            Desired input size of the spectrogram that the model supports. It can be different from the output of the
            `ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size`
            of the audio models.
        hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        patch_size (`int`, *optional*, defaults to 4):
            Patch size for the audio spectrogram
        patch_stride (`list`, *optional*, defaults to `[4, 4]`):
            Patch stride for the audio spectrogram
        num_classes (`int`, *optional*, defaults to 527):
            Number of classes used for the head training
        hidden_size (`int`, *optional*, defaults to 768):
            Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's
            output,which is sent to the projection MLP layer.
        projection_dim (`int`, *optional*, defaults to 512):
            Hidden size of the projection layer.
        depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`):
            Depths used for the Swin Layers of the audio model
        num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
            Number of attention heads used for the Swin Layers of the audio model
        enable_fusion (`bool`, *optional*, defaults to `False`):
            Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the
            best results.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the encoder.
        fusion_type (`[type]`, *optional*):
            Fusion type used for the patch fusion.
        patch_embed_input_channels (`int`, *optional*, defaults to 1):
            Number of channels used for the input spectrogram
        flatten_patch_embeds (`bool`, *optional*, defaults to `True`):
            Whether or not to flatten the patch embeddings
        patch_embeds_hidden_size (`int`, *optional*, defaults to 96):
            Hidden size of the patch embeddings. It is used as the number of output channels.
        enable_patch_layer_norm (`bool`, *optional*, defaults to `True`):
            Whether or not to enable layer normalization for the patch embeddings
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            Drop path rate for the patch fusion
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to add a bias to the query, key, value projections.
        mlp_ratio (`float`, *optional*, defaults to 4.0):
            Ratio of the mlp hidden dim to embedding dim.
        aff_block_r (`int`, *optional*, defaults to 4):
            downsize_ratio used in the AudioFF block
        num_hidden_layers (`int`, *optional*, defaults to 4):
            Number of hidden layers in the Transformer encoder.
        projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        layer_norm_eps (`[type]`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from transformers import ClapAudioConfig, ClapAudioModel

    >>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration
    >>> configuration = ClapAudioConfig()

    >>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration
    >>> model = ClapAudioModel(configuration)

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

    model_type = "clap_audio_model"
    base_config_key = "audio_config"

    def __init__(
        self,
        window_size=8,
        num_mel_bins=64,
        spec_size=256,
        hidden_act="gelu",
        patch_size=4,
        patch_stride=[4, 4],
        num_classes=527,
        hidden_size=768,
        projection_dim=512,
        depths=[2, 2, 6, 2],
        num_attention_heads=[4, 8, 16, 32],
        enable_fusion=False,
        hidden_dropout_prob=0.1,
        fusion_type=None,
        patch_embed_input_channels=1,
        flatten_patch_embeds=True,
        patch_embeds_hidden_size=96,
        enable_patch_layer_norm=True,
        drop_path_rate=0.0,
        attention_probs_dropout_prob=0.0,
        qkv_bias=True,
        mlp_ratio=4.0,
        aff_block_r=4,
        num_hidden_layers=4,
        projection_hidden_act="relu",
        layer_norm_eps=1e-5,
        initializer_factor=1.0,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.window_size = window_size
        self.num_mel_bins = num_mel_bins
        self.spec_size = spec_size
        self.patch_size = patch_size
        self.patch_stride = patch_stride
        self.num_classes = num_classes
        self.hidden_size = hidden_size
        self.depths = depths
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.window_size = window_size
        self.enable_fusion = enable_fusion
        self.fusion_type = fusion_type
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.projection_dim = projection_dim
        self.flatten_patch_embeds = flatten_patch_embeds
        self.patch_embeds_hidden_size = patch_embeds_hidden_size
        self.enable_patch_layer_norm = enable_patch_layer_norm
        self.drop_path_rate = drop_path_rate
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.qkv_bias = qkv_bias
        self.mlp_ratio = mlp_ratio
        self.patch_embed_input_channels = patch_embed_input_channels
        self.aff_block_r = aff_block_r
        self.layer_norm_eps = layer_norm_eps
        self.initializer_factor = initializer_factor
        self.projection_hidden_act = projection_hidden_act


class ClapConfig(PreTrainedConfig):
    r"""
    [`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate
    a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the CLAP
    [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture.

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.

    Args:
        text_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`ClapTextConfig`].
        audio_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`ClapAudioConfig`].
        logit_scale_init_value (`float`, *optional*, defaults to 14.29):
            The initial value of the *logit_scale* parameter. Default is used as per the original CLAP implementation.
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and audio projection layers.
        projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
            Activation function for the projection layers.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            Factor to scale the initialization of the model weights.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import ClapConfig, ClapModel

    >>> # Initializing a ClapConfig with laion-ai/base style configuration
    >>> configuration = ClapConfig()

    >>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration
    >>> model = ClapModel(configuration)

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

    >>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig
    >>> from transformers import ClapTextConfig, ClapAudioConfig

    >>> # Initializing a ClapText and ClapAudioConfig configuration
    >>> config_text = ClapTextConfig()
    >>> config_audio = ClapAudioConfig()

    >>> config = ClapConfig(text_config=config_text, audio_config=config_audio)
    ```"""

    model_type = "clap"
    sub_configs = {"text_config": ClapTextConfig, "audio_config": ClapAudioConfig}

    def __init__(
        self,
        text_config=None,
        audio_config=None,
        logit_scale_init_value=(1 / 0.07),
        projection_dim=512,
        projection_hidden_act="relu",
        initializer_factor=1.0,
        **kwargs,
    ):
        if text_config is None:
            text_config = ClapTextConfig()
            logger.info("`text_config` is `None`. initializing the `ClapTextConfig` with default values.")
        elif isinstance(text_config, dict):
            text_config = ClapTextConfig(**text_config)

        if audio_config is None:
            audio_config = ClapAudioConfig()
            logger.info("`audio_config` is `None`. initializing the `ClapAudioConfig` with default values.")
        elif isinstance(audio_config, dict):
            audio_config = ClapAudioConfig(**audio_config)

        self.text_config = text_config
        self.audio_config = audio_config

        self.text_config.projection_dim = projection_dim
        self.audio_config.projection_dim = projection_dim

        self.text_config.projection_hidden_act = projection_hidden_act
        self.audio_config.projection_hidden_act = projection_hidden_act

        self.projection_dim = projection_dim
        self.projection_hidden_act = projection_hidden_act
        self.hidden_size = self.text_config.hidden_size

        self.logit_scale_init_value = logit_scale_init_value
        self.initializer_factor = initializer_factor
        self.num_hidden_layers = self.text_config.num_hidden_layers + len(self.audio_config.depths)
        super().__init__(**kwargs)


__all__ = ["ClapAudioConfig", "ClapConfig", "ClapTextConfig"]
