



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


A__  :  str								=					logging.get_logger(__name__)

A__  :  List[str]								=					{
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''speech_to_text'''
							_a			      = ['''past_key_values''']
							_a			      = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




							def __init__(       self     :       Union[str, Any]				,				A_     :       Optional[Any]=1_0_0_0_0				,				A_     :       Optional[int]=1_2				,				A_     :       int=2_0_4_8				,				A_     :       str=4				,				A_     :       Tuple=6				,				A_     :       Dict=2_0_4_8				,				A_     :       Optional[int]=4				,				A_     :       Any=0.0				,				A_     :       Dict=0.0				,				A_     :       Optional[Any]=True				,				A_     :       List[Any]=True				,				A_     :       Tuple="relu"				,				A_     :       List[str]=2_5_6				,				A_     :       Tuple=0.1				,				A_     :       Dict=0.0				,				A_     :       List[str]=0.0				,				A_     :       Optional[Any]=0.02				,				A_     :       Dict=2				,				A_     :       Tuple=True				,				A_     :       int=1				,				A_     :       Any=0				,				A_     :       List[Any]=2				,				A_     :       Optional[Any]=6_0_0_0				,				A_     :       Union[str, Any]=1_0_2_4				,				A_     :       List[str]=2				,				A_     :       List[Any]=(5, 5)				,				A_     :       Union[str, Any]=1_0_2_4				,				A_     :       Any=8_0				,				A_     :       Any=1				,				**A_     :       List[str]				,				):
									lowerCAmelCase_			:     Dict									= vocab_size
									lowerCAmelCase_			:     Optional[Any]									= d_model
									lowerCAmelCase_			:     List[str]									= encoder_ffn_dim
									lowerCAmelCase_			:     Optional[Any]									= encoder_layers
									lowerCAmelCase_			:     Any									= encoder_attention_heads
									lowerCAmelCase_			:     List[str]									= decoder_ffn_dim
									lowerCAmelCase_			:     int									= decoder_layers
									lowerCAmelCase_			:     Tuple									= decoder_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[Any]									= attention_dropout
									lowerCAmelCase_			:     List[Any]									= activation_dropout
									lowerCAmelCase_			:     List[str]									= activation_function
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= encoder_layerdrop
									lowerCAmelCase_			:     Union[str, Any]									= decoder_layerdrop
									lowerCAmelCase_			:     Union[str, Any]									= use_cache
									lowerCAmelCase_			:     Optional[int]									= encoder_layers
									lowerCAmelCase_			:     Union[str, Any]									= scale_embedding  # scale factor will be sqrt(d_model) if True
									lowerCAmelCase_			:     Optional[Any]									= max_source_positions
									lowerCAmelCase_			:     int									= max_target_positions
									lowerCAmelCase_			:     Tuple									= num_conv_layers
									lowerCAmelCase_			:     str									= list(A_)
									lowerCAmelCase_			:     int									= conv_channels
									lowerCAmelCase_			:     str									= input_feat_per_channel
									lowerCAmelCase_			:     Dict									= input_channels

									if len(self.conv_kernel_sizes) != self.num_conv_layers:
											raise ValueError(
											    '''Configuration for convolutional module is incorrect. '''
											    '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
											    F"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, """
											    F"""`config.num_conv_layers = {self.num_conv_layers}`.""")

									super().__init__(
									    pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				is_encoder_decoder=A_				,				decoder_start_token_id=A_				,				**A_				,				)










import string


def 			UpperCamelCase(  __UpperCamelCase							:		str ):
		for key in range(len(string.ascii_uppercase ) ):
				lowerCAmelCase_			:     List[Any]									= ''''''
				for symbol in message:
						if symbol in string.ascii_uppercase:
								lowerCAmelCase_			:     Optional[int]									= string.ascii_uppercase.find(__UpperCamelCase )
								lowerCAmelCase_			:     Optional[int]									= num - key
								if num < 0:
										lowerCAmelCase_			:     int									= num + len(string.ascii_uppercase )
								lowerCAmelCase_			:     int									= translated + string.ascii_uppercase[num]
						else:
								lowerCAmelCase_			:     Optional[int]									= translated + symbol
				print(f"""Decryption using Key #{key}: {translated}""" )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Dict									= input('''Encrypted message: ''' )
		lowerCAmelCase_			:     List[str]									= message.upper()
		decrypt(__UpperCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





