

"""simple docstring"""




__A	:       Union[str, Any]        =						{
    'A': ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
    'B': ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'),
    'C': ('ABCDEFGHIJKLM', 'ZNOPQRSTUVWXY'),
    'D': ('ABCDEFGHIJKLM', 'ZNOPQRSTUVWXY'),
    'E': ('ABCDEFGHIJKLM', 'YZNOPQRSTUVWX'),
    'F': ('ABCDEFGHIJKLM', 'YZNOPQRSTUVWX'),
    'G': ('ABCDEFGHIJKLM', 'XYZNOPQRSTUVW'),
    'H': ('ABCDEFGHIJKLM', 'XYZNOPQRSTUVW'),
    'I': ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'),
    'J': ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'),
    'K': ('ABCDEFGHIJKLM', 'VWXYZNOPQRSTU'),
    'L': ('ABCDEFGHIJKLM', 'VWXYZNOPQRSTU'),
    'M': ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'),
    'N': ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'),
    'O': ('ABCDEFGHIJKLM', 'TUVWXYZNOPQRS'),
    'P': ('ABCDEFGHIJKLM', 'TUVWXYZNOPQRS'),
    'Q': ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'),
    'R': ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'),
    'S': ('ABCDEFGHIJKLM', 'RSTUVWXYZNOPQ'),
    'T': ('ABCDEFGHIJKLM', 'RSTUVWXYZNOPQ'),
    'U': ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
    'V': ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'),
    'W': ('ABCDEFGHIJKLM', 'PQRSTUVWXYZNO'),
    'X': ('ABCDEFGHIJKLM', 'PQRSTUVWXYZNO'),
    'Y': ('ABCDEFGHIJKLM', 'OPQRSTUVWXYZN'),
    'Z': ('ABCDEFGHIJKLM', 'OPQRSTUVWXYZN'),
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					return [alphabet[char] for char in key.upper()]
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   ""
					A     =   0
					A     =   generate_table(lowercase__ )
					for char in words.upper():
										cipher += get_opponent(table[count]     ,				lowercase__ )
										A     =   (count + 1) % len(lowercase__ )
					return cipher
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return encrypt(lowercase__     ,				lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   0 if char in table[0] else 1
					A     =   table[row].index(lowercase__ )
					return row, col
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     ,       A     =   get_position(lowercase__     ,				char.upper() )
					if row == 1:
										return table[0][col]
					else:
										return table[1][col] if row == 0 else char


if __name__ == "__main__":
	import doctest

	doctest.testmod()  # Fist ensure that all our tests are passing...
	__A	:       List[str]        =						input('Enter key: ').strip()
	__A	:       str        =						input('Enter text to encrypt: ').strip()
	__A	:       List[Any]        =						encrypt(key, text)

	print(f"""Encrypted: {cipher_text}""")
	print(f"""Decrypted with key: {decrypt(key, cipher_text)}""")









"""simple docstring"""






import torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)

								else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."  )

								return d






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )  )

												output.sort(key=lambda UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]  )
																				temp.append((decoded, output[k][2] * output[k][3])  )

												final_outputs.append(UpperCAmelCase__  )

								return final_outputs
