





import math

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


UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)

UpperCAmelCase     :     Tuple							=	{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[str]            =      """data2vec-audio"""







      def __init__(   self		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=7_6_8		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=3_0_7_2		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=1E-5		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,				lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)		,				lowerCAmelCase__=(1_0, 3, 3, 3, 3, 2, 2)		,				lowerCAmelCase__=False		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=1_9		,				lowerCAmelCase__=5		,				lowerCAmelCase__=0.05		,				lowerCAmelCase__=1_0		,				lowerCAmelCase__=2		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=1_0		,				lowerCAmelCase__=0		,				lowerCAmelCase__="sum"		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False		,				lowerCAmelCase__=2_5_6		,				lowerCAmelCase__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,				lowerCAmelCase__=(5, 3, 3, 1, 1)		,				lowerCAmelCase__=(1, 2, 3, 1, 1)		,				lowerCAmelCase__=5_1_2		,				lowerCAmelCase__=0		,				lowerCAmelCase__=1		,				lowerCAmelCase__=2		,				lowerCAmelCase__=False		,				lowerCAmelCase__=3		,				lowerCAmelCase__=2		,				lowerCAmelCase__=3		,				lowerCAmelCase__=None		,				**lowerCAmelCase__		,				)       ->						Any:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__		,				pad_token_id=lowerCAmelCase__		,				bos_token_id=lowerCAmelCase__		,				eos_token_id=lowerCAmelCase__							)
        a__     : Tuple  =hidden_size
        a__     : Tuple  =feat_extract_activation
        a__     : Optional[Any]  =list(lowerCAmelCase__							)
        a__     : Tuple  =list(lowerCAmelCase__							)
        a__     : Optional[Any]  =list(lowerCAmelCase__							)
        a__     : Dict  =conv_bias
        a__     : Tuple  =num_conv_pos_embeddings
        a__     : Optional[Any]  =num_conv_pos_embedding_groups
        a__     : Union[str, Any]  =conv_pos_kernel_size
        a__     : Any  =len(self.conv_dim							)
        a__     : Tuple  =num_hidden_layers
        a__     : Union[str, Any]  =intermediate_size
        a__     : Optional[Any]  =hidden_act
        a__     : Union[str, Any]  =num_attention_heads
        a__     : str  =hidden_dropout
        a__     : Optional[Any]  =attention_dropout
        a__     : List[str]  =activation_dropout
        a__     : Optional[Any]  =feat_proj_dropout
        a__     : Tuple  =final_dropout
        a__     : str  =layerdrop
        a__     : List[Any]  =layer_norm_eps
        a__     : Any  =initializer_range
        a__     : Any  =vocab_size
        a__     : Dict  =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
        a__     : List[str]  =mask_time_prob
        a__     : Dict  =mask_time_length
        a__     : str  =mask_time_min_masks
        a__     : str  =mask_feature_prob
        a__     : int  =mask_feature_length
        a__     : Any  =mask_feature_min_masks

        # ctc loss
        a__     : Optional[int]  =ctc_loss_reduction
        a__     : List[str]  =ctc_zero_infinity

        # adapter
        a__     : Union[str, Any]  =add_adapter
        a__     : int  =adapter_kernel_size
        a__     : Any  =adapter_stride
        a__     : str  =num_adapter_layers
        a__     : List[Any]  =output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        a__     : List[str]  =classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        a__     : Optional[Any]  =list(lowerCAmelCase__							)
        a__     : str  =list(lowerCAmelCase__							)
        a__     : int  =list(lowerCAmelCase__							)
        a__     : int  =xvector_output_dim







      @property
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        return math.prod(self.conv_stride							)













def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F"""{price_plus_tax(100, 0.2_5) = }""")
     print(F"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")






