


'''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


__magic_name__			=      ['gpt2']
__magic_name__			=      'gpt2'

if is_tf_available():



     class 	__lowerCAmelCase		(							tf.Module    ):


       '''simple docstring'''







       def __init__(							self :       Any	,_a :       List[Any] ):

             '''simple docstring'''




             super().__init__()
             A_						:  Union[str, Any]          =				tokenizer
             A_						:  str          =				AutoConfig.from_pretrained(_a )
             A_						:  int          =				TFGPTaLMHeadModel.from_config(_a )






       @tf.function(input_signature=(tf.TensorSpec((None,)	,tf.string	,name="""text""" ),) )
       def        _a							(							self :       Tuple	,_a :       List[str] ):

             '''simple docstring'''




             A_						:  Union[str, Any]          =				self.tokenizer(_a )
             A_						:  str          =				tokenized["""input_ids"""].to_tensor()

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

             A_						:  int          =				self.model(input_ids=_a	,attention_mask=_a )["""logits"""]

             return outputs



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


  '''simple docstring'''







  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        super().setUp()

        A_						:  str          =				[GPTaTokenizer.from_pretrained(_a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
        A_						:  str          =				[TFGPTaTokenizer.from_pretrained(_a ) for checkpoint in TOKENIZER_CHECKPOINTS]
        assert len(self.tokenizers ) == len(self.tf_tokenizers )

        A_						:  Tuple          =				[
            """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_						:  Any          =				list(zip(self.test_sentences	,self.test_sentences[::-1] ) )






  def        _a							(							self :       Any ):

        '''simple docstring'''




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

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

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






  @slow
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        for tf_tokenizer in self.tf_tokenizers:
              A_						:  Any          =				tf.function(_a )
              for test_inputs in self.test_sentences:
                    A_						:  int          =				tf.constant(_a )
                    A_						:  Any          =				compiled_tokenizer(_a )
                    A_						:  Tuple          =				tf_tokenizer(_a )

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






  @slow
  def        _a							(							self :       Any ):

        '''simple docstring'''




        for tf_tokenizer in self.tf_tokenizers:
              A_						:  Tuple          =				ModelToSave(tokenizer=_a )
              A_						:  List[str]          =				tf.convert_to_tensor([self.test_sentences[0]] )
              A_						:  Any          =				model.serving(_a )  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                    A_						:  Dict          =				Path(_a ) / """saved.model"""
                    tf.saved_model.save(_a	,_a	,signatures={"""serving_default""": model.serving} )
                    A_						:  Dict          =				tf.saved_model.load(_a )
              A_						:  Union[str, Any]          =				loaded_model.signatures["""serving_default"""](_a )["""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        _a							(							self :       List[Any] ):

        '''simple docstring'''




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

              A_						:  Tuple          =				tf_tokenizer.get_config()
              A_						:  Tuple          =				TFGPTaTokenizer.from_config(_a )
              A_						:  Optional[int]          =				model_from_config(_a )

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






  @slow
  def        _a							(							self :       Any ):

        '''simple docstring'''




        for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              A_						:  Any          =				123123

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

                    A_						:  int          =				out["""input_ids"""].numpy().shape[1]

                    assert out_length == max_length




'''simple docstring'''


import baseaa




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return baseaa.aaaencode(string.encode("""utf-8"""))




def      lowerCamelCase					(      lowerCamelCase  :      bytes):
      return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")


if __name__ == "__main__":
     import doctest

     doctest.testmod()
