





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
     import tensorflow as tf

if is_keras_nlp_available():
     from transformers.models.gpta import TFGPTaTokenizer


__A             =       ["gpt2"]
__A             =       "gpt2"

if is_tf_available():



     class      _SCREAMING_SNAKE_CASE		(			tf.Module					):







      '''simple docstring'''

      def __init__(self       :     Optional[int] ,				UpperCAmelCase_       :     List[Any]) ->int:



        '''simple docstring'''



        super().__init__()
        lowerCamelCase__:   Any    						=tokenizer
        lowerCamelCase__:   Union[str, Any]    						=AutoConfig.from_pretrained(lowercase__)
        lowerCamelCase__:   Union[str, Any]    						=TFGPTaLMHeadModel.from_config(lowercase__)







      @tf.function(input_signature=(tf.TensorSpec((None,) ,				tf.string ,				name="text"),))
      def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple ,				UpperCAmelCase_       :     Optional[Any]) ->int:



        '''simple docstring'''



        lowerCamelCase__:   str    						=self.tokenizer(lowercase__)
        lowerCamelCase__:   str    						=tokenized["""input_ids"""].to_tensor()

        lowerCamelCase__:   Optional[int]    						=tf.cast(input_ids_dense > 0 ,				tf.intaa)
        # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

        lowerCamelCase__:   str    						=self.model(input_ids=lowercase__ ,				attention_mask=lowercase__)["""logits"""]

        return outputs



@require_tf
@require_keras_nlp
class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Optional[int]:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:   List[Any]    						=[GPTaTokenizer.from_pretrained(lowercase__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   lowerCamelCase__:   Optional[Any]    						=[TFGPTaTokenizer.from_pretrained(lowercase__) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers) == len(self.tf_tokenizers)

   lowerCamelCase__:   Any    						=[
       """This is a straightforward English test sentence.""",
       """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
       """Now we're going to add some Chinese: 一 二 三 一二三""",
       """And some much more rare Chinese: 齉 堃 齉堃""",
       """Je vais aussi écrire en français pour tester les accents""",
       """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
   ]
   lowerCamelCase__:   Union[str, Any]    						=list(zip(self.test_sentences ,				self.test_sentences[::-1]))

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Optional[int]:



   '''simple docstring'''



   for tokenizer, tf_tokenizer in zip(self.tokenizers ,				self.tf_tokenizers):
     for test_inputs in self.test_sentences:
       lowerCamelCase__:   List[str]    						=tokenizer([test_inputs] ,				return_tensors="tf")
       lowerCamelCase__:   Tuple    						=tf_tokenizer([test_inputs])

       for key in python_outputs.keys():
         # convert them to numpy to avoid messing with ragged tensors
         lowerCamelCase__:   Dict    						=python_outputs[key].numpy()
         lowerCamelCase__:   Dict    						=tf_outputs[key].numpy()

         self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
         self.assertTrue(tf.reduce_all(tf.cast(lowercase__ ,				tf.intaa) == tf_outputs_values))

 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->int:



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     lowerCamelCase__:   int    						=tf.function(lowercase__)
     for test_inputs in self.test_sentences:
       lowerCamelCase__:   int    						=tf.constant(lowercase__)
       lowerCamelCase__:   str    						=compiled_tokenizer(lowercase__)
       lowerCamelCase__:   Dict    						=tf_tokenizer(lowercase__)

       for key in eager_outputs.keys():
         self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))

 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Dict:



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     lowerCamelCase__:   Optional[int]    						=ModelToSave(tokenizer=lowercase__)
     lowerCamelCase__:   Optional[Any]    						=tf.convert_to_tensor([self.test_sentences[0]])
     lowerCamelCase__:   Tuple    						=model.serving(lowercase__)  # Build model with some sample inputs
     with TemporaryDirectory() as tempdir:
       lowerCamelCase__:   int    						=Path(lowercase__) / """saved.model"""
       tf.saved_model.save(lowercase__ ,				lowercase__ ,				signatures={"serving_default": model.serving})
       lowerCamelCase__:   Tuple    						=tf.saved_model.load(lowercase__)
     lowerCamelCase__:   List[Any]    						=loaded_model.signatures["""serving_default"""](lowercase__)["""output_0"""]
     # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
     self.assertTrue(tf.reduce_all(out == loaded_output))

 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->List[Any]:



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     lowerCamelCase__:   List[str]    						=tf.convert_to_tensor([self.test_sentences[0]])
     lowerCamelCase__:   List[Any]    						=tf_tokenizer(lowercase__)  # Build model with some sample inputs

     lowerCamelCase__:   Any    						=tf_tokenizer.get_config()
     lowerCamelCase__:   List[Any]    						=TFGPTaTokenizer.from_config(lowercase__)
     lowerCamelCase__:   List[str]    						=model_from_config(lowercase__)

     for key in from_config_output.keys():
       self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))







 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->str:



   '''simple docstring'''



   for tf_tokenizer in self.tf_tokenizers:
     # for the test to run
     lowerCamelCase__:   Tuple    						=123_123

     for max_length in [3, 5, 1_024]:
       lowerCamelCase__:   Tuple    						=tf.convert_to_tensor([self.test_sentences[0]])
       lowerCamelCase__:   List[Any]    						=tf_tokenizer(lowercase__ ,				max_length=lowercase__)

       lowerCamelCase__:   Union[str, Any]    						=out["""input_ids"""].numpy().shape[1]

       assert out_length == max_length












def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=""
  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_				( __a	)  ->	dict[str, str]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=[chr(i + 65	) for i in range(26	)]
  # Remove duplicate characters from key
  lowerCamelCase__:   Union[str, Any]    						=remove_duplicates(key.upper()	)
  lowerCamelCase__:   List[Any]    						=len(__a	)
  # First fill cipher with key characters
  lowerCamelCase__:   Optional[Any]    						={alphabet[i]: char for i, char in enumerate(__a	)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(__a	)			,					26	):
    lowerCamelCase__:   Optional[int]    						=alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      lowerCamelCase__:   List[Any]    						=alphabet[i - offset]
    lowerCamelCase__:   str    						=char
  return cipher_alphabet





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





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





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						={v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( )  ->	None:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=input("Enter message to encode or decode: "	).strip()
  lowerCamelCase__:   Dict    						=input("Enter keyword: "	).strip()
  lowerCamelCase__:   Any    						=input("Encipher or decipher? E/D:"	).strip()[0].lower()
  try:
    lowerCamelCase__:   Union[str, Any]    						={"e": encipher, "d": decipher}[option]
  except KeyError:
    raise KeyError("invalid input option"	)
  lowerCamelCase__:   List[Any]    						=create_cipher_map(__a	)
  print(func(__a			,					__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

