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# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
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""" Wav2Vec2 model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class Wav2Vec2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.Wav2Vec2Model`. It is used to
    instantiate an Wav2Vec2 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 Wav2Vec2
    `facebook/wav2vec2-base-960h <https://huggingface.co/facebook/wav2vec2-base-960h>`__ architecture.

    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.


    Args:
        vocab_size (:obj:`int`, `optional`, defaults to 32):
            Vocabulary size of the Wav2Vec2 model. Defines the number of different tokens that can be represented by
            the :obj:`inputs_ids` passed when calling :class:`~transformers.Wav2Vec2Model` or
            :class:`~transformers.TFWav2Vec2Model`. Vocabulary size of the model. Defines the different tokens that can
            be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.Wav2Vec2Model`.
        hidden_size (:obj:`int`, `optional`, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string,
            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        feat_extract_norm (:obj:`str`, `optional`, defaults to :obj:`"group"`):
            The norm to be applied to 1D convolutional layers in feature extractor. One of :obj:`"group"` for group
            normalization of only the first 1D convolutional layer or :obj:`"layer"` for layer normalization of all 1D
            convolutional layers.
        feat_extract_dropout (:obj:`float`, `optional`, defaults to 0.0):
            The dropout probabilitiy for all 1D convolutional layers in feature extractor.
        feat_extract_activation (:obj:`str, `optional`, defaults to :obj:`"gelu"`):
            The non-linear activation function (function or string) in the 1D convolutional layers of the feature
            extractor. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
        conv_dim (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(512, 512, 512, 512, 512, 512, 512)`):
            A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
            feature extractor. The length of `conv_dim` defines the number of 1D convolutional layers.
        conv_stride (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 2, 2, 2, 2, 2, 2)`):
            A tuple of integers defining the stride of each 1D convolutional layer in the feature extractor. The length
            of `conv_stride` defines the number of convolutional layers and has to match the the length of `conv_dim`.
        conv_kernel (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(10, 3, 3, 3, 3, 3, 3)`):
            A tuple of integers defining the kernel size of each 1D convolutional layer in the feature extractor. The
            length of `conv_kernel` defines the number of convolutional layers and has to match the the length of
            `conv_dim`.
        conv_bias (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether the 1D convolutional layers have a bias.
        num_conv_pos_embeddings (:obj:`int`, `optional`, defaults to 128):
            Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
            embeddings layer.
        num_conv_pos_embedding_groups (:obj:`int`, `optional`, defaults to 16):
            Number of groups of 1D convolutional positional embeddings layer.
        do_stable_layer_norm (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether do apply `stable` layer norm architecture of the Transformer encoder. ``do_stable_layer_norm is
            True`` corresponds to applying layer norm before the attention layer, whereas ``do_stable_layer_norm is
            False`` corresponds to applying layer norm after the attention layer.

    Example::

        >>> from transformers import Wav2Vec2Model, Wav2Vec2Config

        >>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration
        >>> configuration = Wav2Vec2Config()

        >>> # Initializing a model from the facebook/wav2vec2-base-960h style configuration
        >>> model = Wav2Vec2Model(configuration)

        >>> # Accessing the model configuration
        >>> configuration = model.config
    """
    model_type = "wav2vec2"

    def __init__(
        self,
        vocab_size=32,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,  # TODO(PVP) this is most likely not correctly set yet - correct when adding train
        attention_probs_dropout_prob=0.1,  # TODO(PVP) this is most likely not correctly set yet - correct when adding train
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        feat_extract_norm="group",
        feat_extract_dropout=0.0,
        feat_extract_activation="gelu",
        conv_dim=(512, 512, 512, 512, 512, 512, 512),
        conv_stride=(5, 2, 2, 2, 2, 2, 2),
        conv_kernel=(10, 3, 3, 3, 3, 2, 2),
        conv_bias=False,
        num_conv_pos_embeddings=128,
        num_conv_pos_embedding_groups=16,
        do_stable_layer_norm=False,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        **kwargs
    ):
        super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
        self.hidden_size = hidden_size
        self.feat_extract_norm = feat_extract_norm
        self.feat_extract_dropout = feat_extract_dropout
        self.feat_extract_activation = feat_extract_activation
        self.conv_dim = list(conv_dim)
        self.conv_stride = list(conv_stride)
        self.conv_kernel = list(conv_kernel)
        self.conv_bias = conv_bias
        self.num_conv_pos_embeddings = num_conv_pos_embeddings
        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
        self.num_feat_extract_layers = len(self.conv_dim)
        self.num_hidden_layers = num_hidden_layers
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.num_attention_heads = num_attention_heads
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        self.vocab_size = vocab_size
        self.do_stable_layer_norm = do_stable_layer_norm

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                "Configuration for convolutional layers is incorrect."
                "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"
                f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`."
            )
