




import functools
import operator

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


__UpperCamelCase					:       Dict    					=      logging.get_logger(__name__)

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







								'''simple docstring'''







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

								# ctc loss
								a										=							ctc_loss_reduction
								a										=							ctc_zero_infinity

								# sequence classification
								a										=							use_weighted_layer_sum
								a										=							classifier_proj_size





							@property
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







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







from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

