





def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  List[Any]	)				->    Any:
   if mass < 0:
      raise ValueError('''The mass of a body cannot be negative'''	)
   return 0.5 * mass * abs(lowercase__	) * abs(lowercase__	)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)








import functools
import operator

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


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    str							=   'sew-d'


      def __init__(self	:				str    ,      a__	:				List[Any]=32    ,      a__	:				Optional[int]=768    ,      a__	:				Union[str, Any]=12    ,      a__	:				Tuple=12    ,      a__	:				int=3072    ,      a__	:				List[Any]=2    ,      a__	:				Dict=512    ,      a__	:				Dict=256    ,      a__	:				List[str]=True    ,      a__	:				List[str]=True    ,      a__	:				str=("p2c", "c2p")    ,      a__	:				str="layer_norm"    ,      a__	:				str="gelu_python"    ,      a__	:				List[Any]=0.1    ,      a__	:				Dict=0.1    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				Dict=0.1    ,      a__	:				int=0.0_2    ,      a__	:				str=1E-7    ,      a__	:				Union[str, Any]=1E-5    ,      a__	:				Union[str, Any]="group"    ,      a__	:				Optional[Any]="gelu"    ,      a__	:				List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,      a__	:				Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,      a__	:				Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,      a__	:				List[str]=False    ,      a__	:				Optional[Any]=128    ,      a__	:				Union[str, Any]=16    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=0.0_5    ,      a__	:				Tuple=10    ,      a__	:				str=2    ,      a__	:				int=0.0    ,      a__	:				Optional[Any]=10    ,      a__	:				List[Any]=0    ,      a__	:				Optional[int]="mean"    ,      a__	:				List[str]=False    ,      a__	:				Tuple=False    ,      a__	:				Optional[int]=256    ,      a__	:				str=0    ,      a__	:				List[Any]=1    ,      a__	:				int=2    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         super().__init__(**a__    ,      pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__				)
         __snake_case								=       hidden_size
         __snake_case								=       feat_extract_norm
         __snake_case								=       feat_extract_activation
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       conv_bias
         __snake_case								=       num_conv_pos_embeddings
         __snake_case								=       num_conv_pos_embedding_groups
         __snake_case								=       len(self.conv_dim				)
         __snake_case								=       num_hidden_layers
         __snake_case								=       intermediate_size
         __snake_case								=       squeeze_factor
         __snake_case								=       max_position_embeddings
         __snake_case								=       position_buckets
         __snake_case								=       share_att_key
         __snake_case								=       relative_attention
         __snake_case								=       norm_rel_ebd
         __snake_case								=       list(a__				)
         __snake_case								=       hidden_act
         __snake_case								=       num_attention_heads
         __snake_case								=       hidden_dropout
         __snake_case								=       attention_dropout
         __snake_case								=       activation_dropout
         __snake_case								=       feat_proj_dropout
         __snake_case								=       final_dropout
         __snake_case								=       layer_norm_eps
         __snake_case								=       feature_layer_norm_eps
         __snake_case								=       initializer_range
         __snake_case								=       vocab_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)`,'''
                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				)}`."""				)

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

         # ctc loss
         __snake_case								=       ctc_loss_reduction
         __snake_case								=       ctc_zero_infinity

         # sequence classification
         __snake_case								=       use_weighted_layer_sum
         __snake_case								=       classifier_proj_size


      @property
      def        a			(self	:				List[Any]				):





         """simple docstring"""



         return functools.reduce(operator.mul    ,      self.conv_stride    ,      1				)

