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""" UniSpeech model configuration"""

import functools
import operator

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


logger = logging.get_logger(__name__)

UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}


class UniSpeechConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`UniSpeechModel`]. It is used to instantiate an
    UniSpeech 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 UniSpeech
    [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) 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 32):
            Vocabulary size of the UniSpeech model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`UniSpeechModel`]. Vocabulary size of the model. Defines the
            different tokens that can be represented by the *inputs_ids* passed to the forward method of
            [`UniSpeechModel`].
        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" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        activation_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for activations inside the fully connected layer.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        final_dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for the final projection layer of [`UniSpeechForCTC`].
        layerdrop (`float`, *optional*, defaults to 0.1):
            The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
            details.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        feat_extract_norm (`str`, *optional*, defaults to `"group"`):
            The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
            normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
            convolutional layers.
        feat_proj_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for output of the feature encoder.
        feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the 1D convolutional layers of the feature
            extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
        feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for quantized feature encoder states.
        conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(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 encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
        conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
            A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
            of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
        conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
            A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
            length of *conv_kernel* defines the number of convolutional layers and has to match the length of
            *conv_dim*.
        conv_bias (`bool`, *optional*, defaults to `False`):
            Whether the 1D convolutional layers have a bias.
        num_conv_pos_embeddings (`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 (`int`, *optional*, defaults to 16):
            Number of groups of 1D convolutional positional embeddings layer.
        do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
            Whether to 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.
        apply_spec_augment (`bool`, *optional*, defaults to `True`):
            Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
            [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
            Recognition](https://arxiv.org/abs/1904.08779).
        mask_time_prob (`float`, *optional*, defaults to 0.05):
            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
            procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
            reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
            masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
            actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
        mask_time_length (`int`, *optional*, defaults to 10):
            Length of vector span along the time axis.
        mask_time_min_masks (`int`, *optional*, defaults to 2),:
            The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
            irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
            mask_time_min_masks''
        mask_feature_prob (`float`, *optional*, defaults to 0.0):
            Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
            masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
            the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
            span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
            may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
            True`.
        mask_feature_length (`int`, *optional*, defaults to 10):
            Length of vector span along the feature axis.
        mask_feature_min_masks (`int`, *optional*, defaults to 0),:
            The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
            step, irrespectively of `mask_feature_prob`. Only relevant if
            ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
        num_codevectors_per_group (`int`, *optional*, defaults to 320):
            Number of entries in each quantization codebook (group).
        num_codevector_groups (`int`, *optional*, defaults to 2):
            Number of codevector groups for product codevector quantization.
        contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
            The temperature *kappa* in the contrastive loss.
        feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
        num_negatives (`int`, *optional*, defaults to 100):
            Number of negative samples for the contrastive loss.
        codevector_dim (`int`, *optional*, defaults to 256):
            Dimensionality of the quantized feature vectors.
        proj_codevector_dim (`int`, *optional*, defaults to 256):
            Dimensionality of the final projection of both the quantized and the transformer features.
        diversity_loss_weight (`int`, *optional*, defaults to 0.1):
            The weight of the codebook diversity loss component.
        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 [`UniSpeechForCTC`].
        ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
            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 [`UniSpeechForCTC`].
        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
            instance of [`UniSpeechForSequenceClassification`].
        classifier_proj_size (`int`, *optional*, defaults to 256):
            Dimensionality of the projection before token mean-pooling for classification.
        replace_prob (`float`, *optional*, defaults to 0.5):
            Propability that transformer feature is replaced by quantized feature for pretraining.

    Example:

    ```python
    >>> from transformers import UniSpeechConfig, UniSpeechModel

    >>> # Initializing a UniSpeech facebook/unispeech-base-960h style configuration
    >>> configuration = UniSpeechConfig()

    >>> # Initializing a model (with random weights) from the facebook/unispeech-base-960h style configuration
    >>> model = UniSpeechModel(configuration)

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

    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=0.1,
        activation_dropout=0.1,
        attention_dropout=0.1,
        feat_proj_dropout=0.0,
        feat_quantizer_dropout=0.0,
        final_dropout=0.1,
        layerdrop=0.1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        feat_extract_norm="group",
        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,
        apply_spec_augment=True,
        mask_time_prob=0.05,
        mask_time_length=10,
        mask_time_min_masks=2,
        mask_feature_prob=0.0,
        mask_feature_length=10,
        mask_feature_min_masks=0,
        num_codevectors_per_group=320,
        num_codevector_groups=2,
        contrastive_logits_temperature=0.1,
        num_negatives=100,
        codevector_dim=256,
        proj_codevector_dim=256,
        diversity_loss_weight=0.1,
        ctc_loss_reduction="mean",
        ctc_zero_infinity=False,
        use_weighted_layer_sum=False,
        classifier_proj_size=256,
        num_ctc_classes=80,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        replace_prob=0.5,
        **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_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 = hidden_dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.feat_proj_dropout = feat_proj_dropout
        self.final_dropout = final_dropout
        self.layerdrop = layerdrop
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        self.num_ctc_classes = num_ctc_classes
        self.vocab_size = vocab_size
        self.do_stable_layer_norm = do_stable_layer_norm
        self.use_weighted_layer_sum = use_weighted_layer_sum
        self.classifier_proj_size = classifier_proj_size

        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)`, but is `len(config.conv_dim) ="
                f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
                f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
            )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        self.apply_spec_augment = apply_spec_augment
        self.mask_time_prob = mask_time_prob
        self.mask_time_length = mask_time_length
        self.mask_time_min_masks = mask_time_min_masks
        self.mask_feature_prob = mask_feature_prob
        self.mask_feature_length = mask_feature_length
        self.mask_feature_min_masks = mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        self.num_codevectors_per_group = num_codevectors_per_group
        self.num_codevector_groups = num_codevector_groups
        self.contrastive_logits_temperature = contrastive_logits_temperature
        self.feat_quantizer_dropout = feat_quantizer_dropout
        self.num_negatives = num_negatives
        self.codevector_dim = codevector_dim
        self.proj_codevector_dim = proj_codevector_dim
        self.diversity_loss_weight = diversity_loss_weight

        # ctc loss
        self.ctc_loss_reduction = ctc_loss_reduction
        self.ctc_zero_infinity = ctc_zero_infinity

        # pretraining loss
        self.replace_prob = replace_prob

    @property
    def inputs_to_logits_ratio(self):
        return functools.reduce(operator.mul, self.conv_stride, 1)
