
'''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


__lowerCAmelCase         =    ["""gpt2"""]
__lowerCAmelCase         =    """gpt2"""

if is_tf_available():







  class UpperCAmelCase__	( tf.Module	):





    """simple docstring"""

    def __init__(		self      :     Optional[int]							,_a      :     List[str]       ):


       '''simple docstring'''






       super().__init__()
       _a				:	List[str]			=   tokenizer
       _a				:	Optional[Any]			=   AutoConfig.from_pretrained(_a       )
       _a				:	Dict			=   TFGPTaLMHeadModel.from_config(_a       )


    @tf.function(input_signature=(tf.TensorSpec((None,)							,tf.string							,name='text'       ),)       )
    def   __lowercase		(		self      :     str							,_a      :     Tuple       ):


       '''simple docstring'''






       _a				:	Optional[int]			=   self.tokenizer(_a       )
       _a				:	Optional[int]			=   tokenized['input_ids'].to_tensor()

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

       _a				:	List[Any]			=   self.model(input_ids=_a							,attention_mask=_a       )['logits']

       return outputs







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





  """simple docstring"""

  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     super().setUp()

     _a				:	int			=   [GPTaTokenizer.from_pretrained(_a       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
     _a				:	Optional[int]			=   [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				:	Dict			=   list(zip(self.test_sentences							,self.test_sentences[::-1]       )       )


  def   __lowercase		(		self      :     List[str]       ):


     '''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				:	Union[str, Any]			=   tf_tokenizer([test_inputs]       )

           for key in python_outputs.keys():
              # convert them to numpy to avoid messing with ragged tensors
              _a				:	Any			=   python_outputs[key].numpy()
              _a				:	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   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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

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


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


     '''simple docstring'''






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


     '''simple docstring'''






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

        _a				:	Optional[Any]			=   tf_tokenizer.get_config()
        _a				:	Optional[Any]			=   TFGPTaTokenizer.from_config(_a       )
        _a				:	str			=   model_from_config(_a       )

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


  @slow
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     for tf_tokenizer in self.tf_tokenizers:
        # for the test to run
        _a				:	Any			=   12_3123

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

           _a				:	int			=   out['input_ids'].numpy().shape[1]

           assert out_length == max_length





'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



