



"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> 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       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> dict[str, str]:






	"""simple docstring"""





	A__											=					[chr(i + 65     ) for i in range(26     )]
	# Remove duplicate characters from key
	A__											=					remove_duplicates(key.upper()     )
	A__											=					len(UpperCAmelCase_     )
	# First fill cipher with key characters
	A__											=					{alphabet[i]: char for i, char in enumerate(UpperCAmelCase_     )}
	# Then map remaining characters in alphabet to
	# the alphabet from the beginning
	for i in range(len(UpperCAmelCase_     ),	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       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       dict[str, str]     )	-> str:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       dict[str, str]     )	-> str:






	"""simple docstring"""





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

def       _lowerCamelCase				(							)	-> None:






	"""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(UpperCAmelCase_     )
	print(func(UpperCAmelCase_,	UpperCAmelCase_     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()









"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False						,	)      ->	Union[str, Any]:
				super().__init__()

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					nn.Embedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					False

				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)

				A__											=					TaConfig(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	d_model=SCREAMING_SNAKE_CASE__						,	num_heads=SCREAMING_SNAKE_CASE__						,	d_kv=SCREAMING_SNAKE_CASE__						,	d_ff=SCREAMING_SNAKE_CASE__						,	dropout_rate=SCREAMING_SNAKE_CASE__						,	feed_forward_proj=SCREAMING_SNAKE_CASE__						,	is_decoder=SCREAMING_SNAKE_CASE__						,	is_encoder_decoder=SCREAMING_SNAKE_CASE__						,	)

				A__											=					nn.ModuleList()
				for lyr_num in range(SCREAMING_SNAKE_CASE__		):
					A__											=					TaBlock(SCREAMING_SNAKE_CASE__		)
					self.encoders.append(SCREAMING_SNAKE_CASE__		)

				A__											=					TaLayerNorm(SCREAMING_SNAKE_CASE__		)
				A__											=					nn.Dropout(p=SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					self.token_embedder(SCREAMING_SNAKE_CASE__		)

				A__											=					encoder_input_tokens.shape[1]
				A__											=					torch.arange(SCREAMING_SNAKE_CASE__						,	device=encoder_input_tokens.device		)
				x += self.position_encoding(SCREAMING_SNAKE_CASE__		)

				A__											=					self.dropout_pre(SCREAMING_SNAKE_CASE__		)

				# inverted the attention mask
				A__											=					encoder_input_tokens.size()
				A__											=					self.get_extended_attention_mask(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				for lyr in self.encoders:
					A__											=					lyr(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)[0]
				A__											=					self.layer_norm(SCREAMING_SNAKE_CASE__		)

				return self.dropout_post(SCREAMING_SNAKE_CASE__		), encoder_inputs_mask




