




"""simple docstring"""




def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Tuple:






					"""simple docstring"""






					if not isinstance(UpperCAmelCase,     UpperCAmelCase	):
										raise TypeError('''plaintext must be a string'''	)
					if not isinstance(UpperCAmelCase,     UpperCAmelCase	):
										raise TypeError('''key must be a string'''	)

					if not plaintext:
										raise ValueError('''plaintext is empty'''	)
					if not key:
										raise ValueError('''key is empty'''	)

					key += plaintext
					__magic_name__ :							int		       =							plaintext.lower()
					__magic_name__ :							List[Any]		       =							key.lower()
					__magic_name__ :							List[str]		       =							0
					__magic_name__ :							Dict		       =							0
					__magic_name__ :							List[Any]		       =							''''''
					while plaintext_iterator < len(UpperCAmelCase	):
										if (
										    ord(plaintext[plaintext_iterator]	) < 97
										    or ord(plaintext[plaintext_iterator]	) > 122
										):
															ciphertext += plaintext[plaintext_iterator]
															plaintext_iterator += 1
										elif ord(key[key_iterator]	) < 97 or ord(key[key_iterator]	) > 122:
															key_iterator += 1
										else:
															ciphertext += chr(
															    (
															        (ord(plaintext[plaintext_iterator]	) - 97 + ord(key[key_iterator]	))
															        - 97
															    )
															    % 26
															    + 97	)
															key_iterator += 1
															plaintext_iterator += 1
					return ciphertext







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Optional[Any]:






					"""simple docstring"""






					if not isinstance(UpperCAmelCase,     UpperCAmelCase	):
										raise TypeError('''ciphertext must be a string'''	)
					if not isinstance(UpperCAmelCase,     UpperCAmelCase	):
										raise TypeError('''key must be a string'''	)

					if not ciphertext:
										raise ValueError('''ciphertext is empty'''	)
					if not key:
										raise ValueError('''key is empty'''	)

					__magic_name__ :							Any		       =							key.lower()
					__magic_name__ :							int		       =							0
					__magic_name__ :							List[str]		       =							0
					__magic_name__ :							List[Any]		       =							''''''
					while ciphertext_iterator < len(UpperCAmelCase	):
										if (
										    ord(ciphertext[ciphertext_iterator]	) < 97
										    or ord(ciphertext[ciphertext_iterator]	) > 122
										):
															plaintext += ciphertext[ciphertext_iterator]
										else:
															plaintext += chr(
															    (ord(ciphertext[ciphertext_iterator]	) - ord(key[key_iterator]	)) % 26
															    + 97	)
															key += chr(
															    (ord(ciphertext[ciphertext_iterator]	) - ord(key[key_iterator]	)) % 26
															    + 97	)
															key_iterator += 1
										ciphertext_iterator += 1
					return plaintext


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				lowercase_		=							int(input('''Type 1 to encrypt or 2 to decrypt:'''))
				if operation == 1:
								lowercase_		=							input('''Typeplaintext to be encrypted:\n''')
								lowercase_		=							input('''Type the key:\n''')
								print(encrypt(plaintext, key))
				elif operation == 2:
								lowercase_		=							input('''Type the ciphertext to be decrypted:\n''')
								lowercase_		=							input('''Type the key:\n''')
								print(decrypt(ciphertext, key))
				decrypt('''jsqqs avvwo''', '''coffee''')









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]    ,					device=lowerCamelCase    ,					dtype=torch.float    ,					)
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]    ,					lowerCamelCase    ,					atol=1e-4						)						)



