




import functools
import operator

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


a													=			logging.get_logger(__name__)

a													=			{
    "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       _A				(		__lowercase     ):
  __a					    =       """wav2vec2"""






  def __init__(    self						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-5						,		_SCREAMING_SNAKE_CASE="group"						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)						,		_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)						,		_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=128						,		_SCREAMING_SNAKE_CASE=16						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=0.05						,		_SCREAMING_SNAKE_CASE=10						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=10						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=320						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=100						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE="sum"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1500)						,		_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)						,		_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE						,		):
        super().__init__(**_SCREAMING_SNAKE_CASE						,		pad_token_id=_SCREAMING_SNAKE_CASE						,		bos_token_id=_SCREAMING_SNAKE_CASE						,		eos_token_id=_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			hidden_size
        _UpperCAmelCase      =			feat_extract_norm
        _UpperCAmelCase      =			feat_extract_activation
        _UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			conv_bias
        _UpperCAmelCase      =			num_conv_pos_embeddings
        _UpperCAmelCase      =			num_conv_pos_embedding_groups
        _UpperCAmelCase      =			len(self.conv_dim			)
        _UpperCAmelCase      =			num_hidden_layers
        _UpperCAmelCase      =			intermediate_size
        _UpperCAmelCase      =			hidden_act
        _UpperCAmelCase      =			num_attention_heads
        _UpperCAmelCase      =			hidden_dropout
        _UpperCAmelCase      =			attention_dropout
        _UpperCAmelCase      =			activation_dropout
        _UpperCAmelCase      =			feat_proj_dropout
        _UpperCAmelCase      =			final_dropout
        _UpperCAmelCase      =			layerdrop
        _UpperCAmelCase      =			layer_norm_eps
        _UpperCAmelCase      =			initializer_range
        _UpperCAmelCase      =			vocab_size
        _UpperCAmelCase      =			do_stable_layer_norm
        _UpperCAmelCase      =			use_weighted_layer_sum

        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
        _UpperCAmelCase      =			apply_spec_augment
        _UpperCAmelCase      =			mask_time_prob
        _UpperCAmelCase      =			mask_time_length
        _UpperCAmelCase      =			mask_time_min_masks
        _UpperCAmelCase      =			mask_feature_prob
        _UpperCAmelCase      =			mask_feature_length
        _UpperCAmelCase      =			mask_feature_min_masks

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

        # ctc loss
        _UpperCAmelCase      =			ctc_loss_reduction
        _UpperCAmelCase      =			ctc_zero_infinity

        # adapter
        _UpperCAmelCase      =			add_adapter
        _UpperCAmelCase      =			adapter_kernel_size
        _UpperCAmelCase      =			adapter_stride
        _UpperCAmelCase      =			num_adapter_layers
        _UpperCAmelCase      =			output_hidden_size or hidden_size
        _UpperCAmelCase      =			adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        _UpperCAmelCase      =			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        _UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
        _UpperCAmelCase      =			xvector_output_dim






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





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * power_factor




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()