




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


lowerCAmelCase_						=			['gpt2']
lowerCAmelCase_						=			'gpt2'

if is_tf_available():
 class 						_A (  tf.Module				):






   """simple docstring"""




   def __init__(  self		:     Optional[Any]				,						_A		:     Optional[Any]				)		->   str:






        """simple docstring"""







        super().__init__()
        lowercase :       Dict       =			tokenizer
        lowercase :       List[Any]       =			AutoConfig.from_pretrained(__A				)
        lowercase :       int       =			TFGPTaLMHeadModel.from_config(__A				)






   @tf.function(input_signature=(tf.TensorSpec((None,)				,						tf.string				,						name='''text'''				),)				)
   def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   Union[str, Any]:






        """simple docstring"""







        lowercase :       Union[str, Any]       =			self.tokenizer(__A				)
        lowercase :       Optional[Any]       =			tokenized["""input_ids"""].to_tensor()

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

        lowercase :       Dict       =			self.model(input_ids=__A				,						attention_mask=__A				)["""logits"""]

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






  """simple docstring"""




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






       """simple docstring"""







       super().setUp()

       lowercase :       List[str]       =			[GPTaTokenizer.from_pretrained(__A				) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       lowercase :       Tuple       =			[TFGPTaTokenizer.from_pretrained(__A				) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers				) == len(self.tf_tokenizers				)

       lowercase :       Optional[int]       =			[
           """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""",
       ]
       lowercase :       Union[str, Any]       =			list(zip(self.test_sentences				,						self.test_sentences[::-1]				)				)






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       for tokenizer, tf_tokenizer in zip(self.tokenizers				,						self.tf_tokenizers				):
            for test_inputs in self.test_sentences:
                 lowercase :       Tuple       =			tokenizer([test_inputs]				,						return_tensors='''tf'''				)
                 lowercase :       List[Any]       =			tf_tokenizer([test_inputs]				)

                 for key in python_outputs.keys():
                      # convert them to numpy to avoid messing with ragged tensors
                      lowercase :       Union[str, Any]       =			python_outputs[key].numpy()
                      lowercase :       Tuple       =			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		:     int				)		->   str:






       """simple docstring"""







       for tf_tokenizer in self.tf_tokenizers:
            lowercase :       str       =			tf.function(__A				)
            for test_inputs in self.test_sentences:
                 lowercase :       List[str]       =			tf.constant(__A				)
                 lowercase :       str       =			compiled_tokenizer(__A				)
                 lowercase :       Optional[Any]       =			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				)		->   Optional[int]:






       """simple docstring"""







       for tf_tokenizer in self.tf_tokenizers:
            lowercase :       Optional[Any]       =			ModelToSave(tokenizer=__A				)
            lowercase :       Optional[int]       =			tf.convert_to_tensor([self.test_sentences[0]]				)
            lowercase :       Dict       =			model.serving(__A				)  # Build model with some sample inputs
            with TemporaryDirectory() as tempdir:
                 lowercase :       Optional[int]       =			Path(__A				) / """saved.model"""
                 tf.saved_model.save(__A				,						__A				,						signatures={'''serving_default''': model.serving}				)
                 lowercase :       Dict       =			tf.saved_model.load(__A				)
            lowercase :       int       =			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		:     Union[str, Any]				)		->   Dict:






       """simple docstring"""







       for tf_tokenizer in self.tf_tokenizers:
            lowercase :       Union[str, Any]       =			tf.convert_to_tensor([self.test_sentences[0]]				)
            lowercase :       Optional[Any]       =			tf_tokenizer(__A				)  # Build model with some sample inputs

            lowercase :       Any       =			tf_tokenizer.get_config()
            lowercase :       str       =			TFGPTaTokenizer.from_config(__A				)
            lowercase :       Optional[Any]       =			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		:     Optional[int]				)		->   Any:






       """simple docstring"""







       for tf_tokenizer in self.tf_tokenizers:
            # for the test to run
            lowercase :       Dict       =			123_123

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

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

                 assert out_length == max_length





import os
from collections.abc import Iterator






def  snake_case(    __magic_name__ = "."   )			->							Iterator[str]:





     '''simple docstring'''


     for dir_path, dir_names, filenames in os.walk(__magic_name__   ):
          lowercase :       Tuple       =			[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(__magic_name__   )[1] in (".py", ".ipynb"):
                    yield os.path.join(__magic_name__			,		__magic_name__   ).lstrip('''./'''   )






def  snake_case(    __magic_name__   )			->							Dict:





     '''simple docstring'''


     return F"""{i * '  '}*""" if i else "\n##"






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Dict       =			old_path.split(os.sep   )
     for i, new_part in enumerate(new_path.split(os.sep   )   ):
          if (i + 1 > len(__magic_name__   ) or old_parts[i] != new_part) and new_part:
               print(F"""{md_prefix(__magic_name__   )} {new_part.replace('_'			,		' '   ).title()}"""   )
     return new_path






def  snake_case(    __magic_name__ = "."   )			->							None:





     '''simple docstring'''


     lowercase :       str       =			''''''
     for filepath in sorted(good_file_paths(__magic_name__   )   ):
          lowercase      ,	lowercase :       Optional[int]       =			os.path.split(__magic_name__   )
          if filepath != old_path:
               lowercase :       str       =			print_path(__magic_name__			,		__magic_name__   )
          lowercase :       Optional[int]       =			(filepath.count(os.sep   ) + 1) if filepath else 0
          lowercase :       Optional[Any]       =			F"""{filepath}/{filename}""".replace(''' '''			,		'''%20'''   )
          lowercase :       List[str]       =			os.path.splitext(filename.replace('''_'''			,		''' '''   ).title()   )[0]
          print(F"""{md_prefix(__magic_name__   )} [{filename}]({url})"""   )


if __name__ == "__main__":
 print_directory_md('.')