




import functools
import operator

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


lowerCamelCase							:						int					       =  logging.get_logger(__name__)

lowerCamelCase							:						Union[str, Any]					       =  {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		'''sew'''

 def __init__(							self   :    int		,				A_   :    Optional[Any]=32		,				A_   :    str=768		,				A_   :    Any=12		,				A_   :    Optional[Any]=12		,				A_   :    str=3072		,				A_   :    Union[str, Any]=2		,				A_   :    Union[str, Any]="gelu"		,				A_   :    Dict=0.1		,				A_   :    Optional[int]=0.1		,				A_   :    Optional[int]=0.1		,				A_   :    List[str]=0.0		,				A_   :    List[str]=0.1		,				A_   :    int=0.1		,				A_   :    Any=0.02		,				A_   :    Tuple=1E-5		,				A_   :    Optional[Any]="group"		,				A_   :    Union[str, Any]="gelu"		,				A_   :    List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				A_   :    Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				A_   :    List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				A_   :    str=False		,				A_   :    int=128		,				A_   :    Optional[Any]=16		,				A_   :    List[Any]=True		,				A_   :    List[str]=0.05		,				A_   :    List[str]=10		,				A_   :    int=2		,				A_   :    Union[str, Any]=0.0		,				A_   :    List[Any]=10		,				A_   :    Dict=0		,				A_   :    List[str]="mean"		,				A_   :    Optional[Any]=False		,				A_   :    Union[str, Any]=False		,				A_   :    Optional[int]=256		,				A_   :    Optional[Any]=0		,				A_   :    List[Any]=1		,				A_   :    Optional[int]=2		,				**A_   :    Tuple		,				)  ->      List[Any]:

       """simple docstring"""






       super().__init__(**A_		,				pad_token_id=A_		,				bos_token_id=A_		,				eos_token_id=A_    )
       lowerCamelCase_							=       hidden_size
       lowerCamelCase_							=       feat_extract_norm
       lowerCamelCase_							=       feat_extract_activation
       lowerCamelCase_							=       list(A_    )
       lowerCamelCase_							=       list(A_    )
       lowerCamelCase_							=       list(A_    )
       lowerCamelCase_							=       conv_bias
       lowerCamelCase_							=       num_conv_pos_embeddings
       lowerCamelCase_							=       num_conv_pos_embedding_groups
       lowerCamelCase_							=       len(self.conv_dim    )
       lowerCamelCase_							=       num_hidden_layers
       lowerCamelCase_							=       intermediate_size
       lowerCamelCase_							=       squeeze_factor
       lowerCamelCase_							=       hidden_act
       lowerCamelCase_							=       num_attention_heads
       lowerCamelCase_							=       hidden_dropout
       lowerCamelCase_							=       attention_dropout
       lowerCamelCase_							=       activation_dropout
       lowerCamelCase_							=       feat_proj_dropout
       lowerCamelCase_							=       final_dropout
       lowerCamelCase_							=       layerdrop
       lowerCamelCase_							=       layer_norm_eps
       lowerCamelCase_							=       initializer_range
       lowerCamelCase_							=       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
       lowerCamelCase_							=       apply_spec_augment
       lowerCamelCase_							=       mask_time_prob
       lowerCamelCase_							=       mask_time_length
       lowerCamelCase_							=       mask_time_min_masks
       lowerCamelCase_							=       mask_feature_prob
       lowerCamelCase_							=       mask_feature_length
       lowerCamelCase_							=       mask_feature_min_masks

       # ctc loss
       lowerCamelCase_							=       ctc_loss_reduction
       lowerCamelCase_							=       ctc_zero_infinity

       # sequence classification
       lowerCamelCase_							=       use_weighted_layer_sum
       lowerCamelCase_							=       classifier_proj_size





 @property
 def 							a__					(							self   :    Union[str, Any]    )  ->      Any:

       """simple docstring"""






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









def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
