'''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


__a:			Optional[int]	       =       ["""gpt2"""]
__a:			str	       =       """gpt2"""

if is_tf_available():



  class       UpperCAmelCase  (						tf.Module ):





         '''simple docstring'''



         def __init__(						self     ,       __lowerCAmelCase			)  ->  Any:
          super().__init__()
          lowercase__    :   Tuple						= tokenizer
          lowercase__    :   Tuple						= AutoConfig.from_pretrained(__lowerCAmelCase			)
          lowercase__    :   int						= TFGPTaLMHeadModel.from_config(__lowerCAmelCase			)


         @tf.function(input_signature=(tf.TensorSpec((None,)     ,       tf.string     ,       name='''text'''			),)			)
         def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
          lowercase__    :   Dict						= self.tokenizer(__lowerCAmelCase			)
          lowercase__    :   List[str]						= tokenized['''input_ids'''].to_tensor()

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

          lowercase__    :   int						= self.model(input_ids=__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase			)['''logits''']

          return outputs



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





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().setUp()

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

        lowercase__    :   str						= [
            '''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 _lowerCAmelCase(						self			)  ->  List[Any]:
        for tokenizer, tf_tokenizer in zip(self.tokenizers     ,       self.tf_tokenizers			):
         for test_inputs in self.test_sentences:
          lowercase__    :   str						= tokenizer([test_inputs]     ,       return_tensors='''tf'''			)
          lowercase__    :   Dict						= tf_tokenizer([test_inputs]			)

          for key in python_outputs.keys():
           # convert them to numpy to avoid messing with ragged tensors
           lowercase__    :   List[Any]						= python_outputs[key].numpy()
           lowercase__    :   List[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 _lowerCAmelCase(						self			)  ->  Optional[Any]:
        for tf_tokenizer in self.tf_tokenizers:
         lowercase__    :   Optional[Any]						= tf.function(__lowerCAmelCase			)
         for test_inputs in self.test_sentences:
          lowercase__    :   List[str]						= tf.constant(__lowerCAmelCase			)
          lowercase__    :   Optional[Any]						= compiled_tokenizer(__lowerCAmelCase			)
          lowercase__    :   Union[str, Any]						= tf_tokenizer(__lowerCAmelCase			)

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

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

         lowercase__    :   Dict						= tf_tokenizer.get_config()
         lowercase__    :   int						= TFGPTaTokenizer.from_config(__lowerCAmelCase			)
         lowercase__    :   Tuple						= model_from_config(__lowerCAmelCase			)

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


       @slow
       def _lowerCAmelCase(						self			)  ->  List[str]:
        for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         lowercase__    :   List[str]						= 123123

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

          lowercase__    :   List[str]						= out['''input_ids'''].numpy().shape[1]

          assert out_length == max_length


'''simple docstring'''

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,					[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,					['''default''', 0, 100 * 2**20, 900 * 2**20]					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config		,					'''IN_MEMORY_MAX_SIZE'''		,					UpperCAmelCase					)
 lowercase__    :   List[Any]						= datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  lowercase__    :   str						= dataset_size < in_memory_max_size
 else:
  lowercase__    :   Optional[int]						= False
 lowercase__    :   Optional[Any]						= is_small_dataset(UpperCAmelCase					)
 assert result == expected

