SCREAMING_SNAKE_CASE :	int							     =		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def   __A		(			):



 """simple docstring"""


 __a       =  input("Enter message: "					)
 __a       =  input("Enter key [alphanumeric]: "					)
 __a       =  input("Encrypt/Decrypt [e/d]: "					)

 if mode.lower().startswith("e"					):
  __a       =  "encrypt"
  __a       =  encrypt_message(__lowerCAmelCase							,				__lowerCAmelCase					)
 elif mode.lower().startswith("d"					):
  __a       =  "decrypt"
  __a       =  decrypt_message(__lowerCAmelCase							,				__lowerCAmelCase					)

 print(f"""\n{mode.title()}ed message:"""					)
 print(__lowerCAmelCase					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 return translate_message(__lowerCAmelCase							,				__lowerCAmelCase							,				"encrypt"					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 return translate_message(__lowerCAmelCase							,				__lowerCAmelCase							,				"decrypt"					)



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  []
 __a       =  0
 __a       =  key.upper()

 for symbol in message:
  __a       =  LETTERS.find(symbol.upper()					)
  if num != -1:
   if mode == "encrypt":
    num += LETTERS.find(key[key_index]					)
   elif mode == "decrypt":
    num -= LETTERS.find(key[key_index]					)

   num %= len(__lowerCAmelCase					)

   if symbol.isupper():
    translated.append(LETTERS[num]					)
   elif symbol.islower():
    translated.append(LETTERS[num].lower()					)

   key_index += 1
   if key_index == len(__lowerCAmelCase					):
    __a       =  0
  else:
   translated.append(__lowerCAmelCase					)
 return "".join(__lowerCAmelCase					)


if __name__ == "__main__":
  main()



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


