


"""simple docstring"""







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


__lowercase    =				["""gpt2"""]
__lowercase    =				"""gpt2"""

if is_tf_available():






 class 						_A  (  tf.Module  ):







     """simple docstring"""




     def __init__( self    :   str    ,			__UpperCAmelCase    :   Tuple):
       super().__init__()
       a						:		int											=      tokenizer
       a						:		str											=      AutoConfig.from_pretrained(__UpperCAmelCase)
       a						:		Dict											=      TFGPTaLMHeadModel.from_config(__UpperCAmelCase)






     @tf.function(input_signature=(tf.TensorSpec((None,)    ,			tf.string    ,			name="text"),))
     def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   int):
       a						:		Optional[Any]											=      self.tokenizer(__UpperCAmelCase)
       a						:		int											=      tokenized["input_ids"].to_tensor()

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

       a						:		Any											=      self.model(input_ids=__UpperCAmelCase    ,			attention_mask=__UpperCAmelCase)["logits"]

       return outputs






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







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      super().setUp()

      a						:		int											=      [GPTaTokenizer.from_pretrained(__UpperCAmelCase) for checkpoint in (TOKENIZER_CHECKPOINTS)]
      a						:		Tuple											=      [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase) for checkpoint in TOKENIZER_CHECKPOINTS]
      assert len(self.tokenizers) == len(self.tf_tokenizers)

      a						:		Dict											=      [
          "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",
      ]
      a						:		int											=      list(zip(self.test_sentences    ,			self.test_sentences[::-1]))






    def      __snake_case							( self    :   Any):
      for tokenizer, tf_tokenizer in zip(self.tokenizers    ,			self.tf_tokenizers):
        for test_inputs in self.test_sentences:
          a						:		str											=      tokenizer([test_inputs]    ,			return_tensors="tf")
          a						:		Tuple											=      tf_tokenizer([test_inputs])

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

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






    @slow
    def      __snake_case							( self    :   Dict):
      for tf_tokenizer in self.tf_tokenizers:
        a						:		Optional[Any]											=      tf.function(__UpperCAmelCase)
        for test_inputs in self.test_sentences:
          a						:		Optional[Any]											=      tf.constant(__UpperCAmelCase)
          a						:		str											=      compiled_tokenizer(__UpperCAmelCase)
          a						:		str											=      tf_tokenizer(__UpperCAmelCase)

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






    @slow
    def      __snake_case							( self    :   Optional[int]):
      for tf_tokenizer in self.tf_tokenizers:
        a						:		Dict											=      ModelToSave(tokenizer=__UpperCAmelCase)
        a						:		Optional[int]											=      tf.convert_to_tensor([self.test_sentences[0]])
        a						:		int											=      model.serving(__UpperCAmelCase)  # Build model with some sample inputs
        with TemporaryDirectory() as tempdir:
          a						:		List[Any]											=      Path(__UpperCAmelCase) / "saved.model"
          tf.saved_model.save(__UpperCAmelCase    ,			__UpperCAmelCase    ,			signatures={"serving_default": model.serving})
          a						:		int											=      tf.saved_model.load(__UpperCAmelCase)
        a						:		Any											=      loaded_model.signatures["serving_default"](__UpperCAmelCase)["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      __snake_case							( self    :   Optional[Any]):
      for tf_tokenizer in self.tf_tokenizers:
        a						:		Optional[Any]											=      tf.convert_to_tensor([self.test_sentences[0]])
        a						:		Tuple											=      tf_tokenizer(__UpperCAmelCase)  # Build model with some sample inputs

        a						:		str											=      tf_tokenizer.get_config()
        a						:		Any											=      TFGPTaTokenizer.from_config(__UpperCAmelCase)
        a						:		Dict											=      model_from_config(__UpperCAmelCase)

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






    @slow
    def      __snake_case							( self    :   Union[str, Any]):
      for tf_tokenizer in self.tf_tokenizers:
        # for the test to run
        a						:		List[Any]											=      123123

        for max_length in [3, 5, 1024]:
          a						:		Optional[int]											=      tf.convert_to_tensor([self.test_sentences[0]])
          a						:		Optional[int]											=      tf_tokenizer(__UpperCAmelCase    ,			max_length=__UpperCAmelCase)

          a						:		Tuple											=      out["input_ids"].numpy().shape[1]

          assert out_length == max_length










"""simple docstring"""







def 					lowercase      (    A_  ,       A_	)->				float:
  '''simple docstring'''







  def get_matched_characters(A_  ,       A_	) -> str:
    a						:		Optional[int]											=      []
    a						:		List[Any]											=      min(len(_stra	)  ,       len(_stra	)	) // 2
    for i, l in enumerate(_stra	):
      a						:		int											=      int(max(0  ,       i - limit	)	)
      a						:		Optional[int]											=      int(min(i + limit + 1  ,       len(_stra	)	)	)
      if l in _stra[left:right]:
        matched.append(A_	)
        a						:		int											=      F'''{_stra[0:_stra.index(A_	)]} {_stra[_stra.index(A_	) + 1:]}'''

    return "".join(A_	)

  # matching characters
  a						:		Tuple											=      get_matched_characters(A_  ,       A_	)
  a						:		str											=      get_matched_characters(A_  ,       A_	)
  a						:		List[str]											=      len(A_	)

  # transposition
  a						:		Union[str, Any]											=      (
      len([(ca, ca) for ca, ca in zip(A_  ,       A_	) if ca != ca]	) // 2
  )

  if not match_count:
    a						:		Tuple											=      0.0
  else:
    a						:		List[str]											=      (
        1
        / 3
        * (
            match_count / len(A_	)
            + match_count / len(A_	)
            + (match_count - transpositions) / match_count
        )
    )

  # common prefix up to 4 characters
  a						:		Union[str, Any]											=      0
  for ca, ca in zip(stra[:4]  ,       stra[:4]	):
    if ca == ca:
      prefix_len += 1
    else:
      break

  return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler("""hello""", """world"""))






