






import math

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


lowerCamelCase     :							Any    =logging.get_logger(__name__)

lowerCamelCase     :							str    ={
    '''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       __a  (   A__	):
 _lowerCAmelCase	:	int				=			'''data2vec-audio'''







 def __init__(		self :       int							,       SCREAMING_SNAKE_CASE :       Optional[int]=32							,       SCREAMING_SNAKE_CASE :       int=7_68							,       SCREAMING_SNAKE_CASE :       Any=12							,       SCREAMING_SNAKE_CASE :       Tuple=12							,       SCREAMING_SNAKE_CASE :       Optional[Any]=30_72							,       SCREAMING_SNAKE_CASE :       str="gelu"							,       SCREAMING_SNAKE_CASE :       List[Any]=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Optional[Any]=0.1							,       SCREAMING_SNAKE_CASE :       Dict=0.0							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       List[Any]=0.1							,       SCREAMING_SNAKE_CASE :       List[str]=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[Any]="gelu"							,       SCREAMING_SNAKE_CASE :       Dict=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)							,       SCREAMING_SNAKE_CASE :       Any=(5, 2, 2, 2, 2, 2, 2)							,       SCREAMING_SNAKE_CASE :       List[Any]=(10, 3, 3, 3, 3, 2, 2)							,       SCREAMING_SNAKE_CASE :       List[str]=False							,       SCREAMING_SNAKE_CASE :       List[str]=16							,       SCREAMING_SNAKE_CASE :       Any=19							,       SCREAMING_SNAKE_CASE :       Optional[Any]=5							,       SCREAMING_SNAKE_CASE :       List[str]=0.0_5							,       SCREAMING_SNAKE_CASE :       Tuple=10							,       SCREAMING_SNAKE_CASE :       Optional[Any]=2							,       SCREAMING_SNAKE_CASE :       int=0.0							,       SCREAMING_SNAKE_CASE :       str=10							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=0							,       SCREAMING_SNAKE_CASE :       Union[str, Any]="sum"							,       SCREAMING_SNAKE_CASE :       Optional[int]=False							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       SCREAMING_SNAKE_CASE :       Dict=2_56							,       SCREAMING_SNAKE_CASE :       List[Any]=(5_12, 5_12, 5_12, 5_12, 15_00)							,       SCREAMING_SNAKE_CASE :       Any=(5, 3, 3, 1, 1)							,       SCREAMING_SNAKE_CASE :       List[str]=(1, 2, 3, 1, 1)							,       SCREAMING_SNAKE_CASE :       Dict=5_12							,       SCREAMING_SNAKE_CASE :       Optional[int]=0							,       SCREAMING_SNAKE_CASE :       Tuple=1							,       SCREAMING_SNAKE_CASE :       Dict=2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=False							,       SCREAMING_SNAKE_CASE :       Optional[int]=3							,       SCREAMING_SNAKE_CASE :       Optional[Any]=2							,       SCREAMING_SNAKE_CASE :       Any=3							,       SCREAMING_SNAKE_CASE :       List[Any]=None							,       **SCREAMING_SNAKE_CASE :       str							,       ):


      '''simple docstring'''






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

      # ctc loss
      UpperCamelCase__      :       Union[str, Any]	    =  ctc_loss_reduction
      UpperCamelCase__      :       Any	    =  ctc_zero_infinity

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

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      UpperCamelCase__      :       str	    =  classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      UpperCamelCase__      :       List[Any]	    =  list(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[str]	    =  list(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       str	    =  list(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  xvector_output_dim







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return math.prod(self.conv_stride						)











def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Any:
     # "extended trapezoidal rule"
     # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
     UpperCamelCase__      :       int	    =  (boundary[1] - boundary[0]) / steps
     UpperCamelCase__      :       Optional[Any]	    =  boundary[0]
     UpperCamelCase__      :       List[Any]	    =  boundary[1]
     UpperCamelCase__      :       List[Any]	    =  make_points(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )
     UpperCamelCase__      :       int	    =  0.0
     y += (h / 2.0) * f(__lowerCAmelCase      )
     for i in x_i:
          # print(i)
          y += h * f(__lowerCAmelCase      )
     y += (h / 2.0) * f(__lowerCAmelCase      )
     return y







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[str]:
     UpperCamelCase__      :       Optional[int]	    =  a + h
     while x < (b - h):
          yield x
          UpperCamelCase__      :       Union[str, Any]	    =  x + h







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Any:  # enter your function here
     UpperCamelCase__      :       Dict	    =  (x - 0) * (x - 0)
     return y







def 		SCREAMING_SNAKE_CASE							( )			->     Dict:
     UpperCamelCase__      :       List[Any]	    =  0.0  # Lower bound of integration
     UpperCamelCase__      :       Tuple	    =  1.0  # Upper bound of integration
     UpperCamelCase__      :       Any	    =  1_0.0  # define number of steps or resolution
     UpperCamelCase__      :       List[str]	    =  [a, b]  # define boundary of integration
     UpperCamelCase__      :       Any	    =  method_a(__lowerCAmelCase ,   __lowerCAmelCase      )
     print(f'y = {y}'      )


if __name__ == "__main__":
 main()