




import functools
import operator

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


__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				List[str]       			=		{
    '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     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					"""sew"""





				def __init__(      self				:     int					,      __lowerCamelCase				:     str=32					,      __lowerCamelCase				:     List[Any]=7_68					,      __lowerCamelCase				:     Tuple=12					,      __lowerCamelCase				:     Any=12					,      __lowerCamelCase				:     Optional[int]=30_72					,      __lowerCamelCase				:     Dict=2					,      __lowerCamelCase				:     List[Any]="gelu"					,      __lowerCamelCase				:     str=0.1					,      __lowerCamelCase				:     Optional[int]=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     int=0.0					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     List[Any]=0.02					,      __lowerCamelCase				:     str=1e-5					,      __lowerCamelCase				:     Optional[Any]="group"					,      __lowerCamelCase				:     Any="gelu"					,      __lowerCamelCase				:     Tuple=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)					,      __lowerCamelCase				:     Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,      __lowerCamelCase				:     int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,      __lowerCamelCase				:     str=False					,      __lowerCamelCase				:     Dict=1_28					,      __lowerCamelCase				:     Optional[Any]=16					,      __lowerCamelCase				:     Optional[int]=True					,      __lowerCamelCase				:     Any=0.05					,      __lowerCamelCase				:     List[str]=10					,      __lowerCamelCase				:     Union[str, Any]=2					,      __lowerCamelCase				:     List[str]=0.0					,      __lowerCamelCase				:     Dict=10					,      __lowerCamelCase				:     List[str]=0					,      __lowerCamelCase				:     int="mean"					,      __lowerCamelCase				:     int=False					,      __lowerCamelCase				:     Dict=False					,      __lowerCamelCase				:     Dict=2_56					,      __lowerCamelCase				:     Optional[int]=0					,      __lowerCamelCase				:     Any=1					,      __lowerCamelCase				:     int=2					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     Dict:
							super().__init__(**__lowerCamelCase					,      pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase							)
							a    =			hidden_size
							a    =			feat_extract_norm
							a    =			feat_extract_activation
							a    =			list(__lowerCamelCase							)
							a    =			list(__lowerCamelCase							)
							a    =			list(__lowerCamelCase							)
							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 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Optional[int]:
							return functools.reduce(operator.mul					,      self.conv_stride					,      1							)












def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			[chr(i + 65   ) for i in range(26   )]
			# Remove duplicate characters from key
			a    =			remove_duplicates(key.upper()   )
			a    =			len(A   )
			# First fill cipher with key characters
			a    =			{alphabet[i]: char for i, char in enumerate(A   )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(A   ),  26   ):
						a    =			alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									a    =			alphabet[i - offset]
						a    =			char
			return cipher_alphabet




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			return "".join(cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			a    =			{v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			input("Enter message to encode or decode: "   ).strip()
			a    =			input("Enter keyword: "   ).strip()
			a    =			input("Encipher or decipher? E/D:"   ).strip()[0].lower()
			try:
						a    =			{"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"   )
			a    =			create_cipher_map(A   )
			print(func(A,  A   )   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






