


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


__UpperCamelCase    :						Any     =							['gpt2']
__UpperCamelCase    :						Union[str, Any]     =							'gpt2'

if is_tf_available():



    class 							_UpperCamelCase (						tf.Module						):

       '''simple docstring'''







       def __init__(  self  : Tuple       ,  _lowerCamelCase  : Optional[Any]							):






             '''simple docstring'''



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


       @tf.function(input_signature=(tf.TensorSpec((None,)       ,  tf.string       ,  name="""text"""							),)							)
       def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : int							):






             '''simple docstring'''



             __lowerCamelCase     :      int       						=						self.tokenizer(_lowerCamelCase							)
             __lowerCamelCase     :      Optional[int]       						=						tokenized["""input_ids"""].to_tensor()

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

             __lowerCamelCase     :      List[str]       						=						self.model(input_ids=_lowerCamelCase       ,  attention_mask=_lowerCamelCase							)["""logits"""]

             return outputs



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

   '''simple docstring'''







   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         super().setUp()

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

         __lowerCamelCase     :      List[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     :      List[str]       						=						list(zip(self.test_sentences       ,  self.test_sentences[::-1]							)							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,  self.tf_tokenizers							):
               for test_inputs in self.test_sentences:
                     __lowerCamelCase     :      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     :      Tuple       						=						python_outputs[key].numpy()
                           __lowerCamelCase     :      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(_lowerCamelCase       ,  tf.intaa							) == tf_outputs_values							)							)

   @slow
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         for tf_tokenizer in self.tf_tokenizers:
               __lowerCamelCase     :      List[Any]       						=						tf.function(_lowerCamelCase							)
               for test_inputs in self.test_sentences:
                     __lowerCamelCase     :      Tuple       						=						tf.constant(_lowerCamelCase							)
                     __lowerCamelCase     :      Union[str, Any]       						=						compiled_tokenizer(_lowerCamelCase							)
                     __lowerCamelCase     :      Tuple       						=						tf_tokenizer(_lowerCamelCase							)

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

   @slow
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



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






         '''simple docstring'''



         for tf_tokenizer in self.tf_tokenizers:
               __lowerCamelCase     :      str       						=						tf.convert_to_tensor([self.test_sentences[0]]							)
               __lowerCamelCase     :      Dict       						=						tf_tokenizer(_lowerCamelCase							)  # Build model with some sample inputs

               __lowerCamelCase     :      Optional[int]       						=						tf_tokenizer.get_config()
               __lowerCamelCase     :      Union[str, Any]       						=						TFGPTaTokenizer.from_config(_lowerCamelCase							)
               __lowerCamelCase     :      Union[str, Any]       						=						model_from_config(_lowerCamelCase							)

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


   @slow
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         for tf_tokenizer in self.tf_tokenizers:
               # for the test to run
               __lowerCamelCase     :      Union[str, Any]       						=						1_2_3_1_2_3

               for max_length in [3, 5, 1_0_2_4]:
                     __lowerCamelCase     :      int       						=						tf.convert_to_tensor([self.test_sentences[0]]							)
                     __lowerCamelCase     :      Dict       						=						tf_tokenizer(_lowerCamelCase       ,  max_length=_lowerCamelCase							)

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

                     assert out_length == max_length







from typing import Any





def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      if not input_list:
            return []
      __lowerCamelCase     :      int       						=						[input_list.count(UpperCAmelCase      ) for value in input_list]
      __lowerCamelCase     :      Tuple       						=						max(UpperCAmelCase      )  # Gets the maximum count in the input list.
      # Gets values of modes
      return sorted({input_list[i] for i, value in enumerate(UpperCAmelCase      ) if value == y}      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



