


"""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


UpperCAmelCase      		=			['''gpt2''']
UpperCAmelCase      		=			'''gpt2'''

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






        def __init__(   self  :    List[Any]  ,     snake_case__  :    int				):

               '''simple docstring'''






               super().__init__()
               lowercase		:List[str]       							=				tokenizer
               lowercase		:Optional[int]       							=				AutoConfig.from_pretrained(snake_case__				)
               lowercase		:Dict       							=				TFGPTaLMHeadModel.from_config(snake_case__				)







        @tf.function(input_signature=(tf.TensorSpec((None,)  ,     tf.string  ,     name='''text'''				),)				)
        def  __snake_case						(   self  :    Any  ,     snake_case__  :    List[str]				):

               '''simple docstring'''






               lowercase		:Any       							=				self.tokenizer(snake_case__				)
               lowercase		:Union[str, Any]       							=				tokenized['''input_ids'''].to_tensor()

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

               lowercase		:Tuple       							=				self.model(input_ids=snake_case__  ,     attention_mask=snake_case__				)['''logits''']

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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              super().setUp()

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

              lowercase		:Optional[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''',
              ]
              lowercase		:Any       							=				list(zip(self.test_sentences  ,     self.test_sentences[::-1]				)				)






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






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

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

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






       @slow
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              for tf_tokenizer in self.tf_tokenizers:
                     lowercase		:Any       							=				tf.function(snake_case__				)
                     for test_inputs in self.test_sentences:
                            lowercase		:Optional[Any]       							=				tf.constant(snake_case__				)
                            lowercase		:Optional[Any]       							=				compiled_tokenizer(snake_case__				)
                            lowercase		:Optional[Any]       							=				tf_tokenizer(snake_case__				)

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






       @slow
       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






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

              '''simple docstring'''






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

                     lowercase		:Optional[Any]       							=				tf_tokenizer.get_config()
                     lowercase		:List[str]       							=				TFGPTaTokenizer.from_config(snake_case__				)
                     lowercase		:List[str]       							=				model_from_config(snake_case__				)

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







       @slow
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              for tf_tokenizer in self.tf_tokenizers:
                     # for the test to run
                     lowercase		:Dict       							=				1_2_3_1_2_3

                     for max_length in [3, 5, 1_0_2_4]:
                            lowercase		:List[Any]       							=				tf.convert_to_tensor([self.test_sentences[0]]				)
                            lowercase		:Union[str, Any]       							=				tf_tokenizer(snake_case__  ,     max_length=snake_case__				)

                            lowercase		:Any       							=				out['''input_ids'''].numpy().shape[1]

                            assert out_length == max_length





"""simple docstring"""



def       lowerCamelCase					(a_						:int)     ->							str:
       if number > 0:
              raise ValueError('''input must be a negative integer''')
       lowercase		:Any       							=				len(bin(a_)[3:])
       lowercase		:Any       							=				bin(abs(a_) - (1 << binary_number_length))[3:]
       lowercase		:Tuple       							=				(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(a_))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
 import doctest

 doctest.testmod()

