






"""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 _lowerCAmelCase						(    tf.Module       ):







									def __init__(    self   ,				lowerCAmelCase_							)		->							int:
													super().__init__()
													_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tokenizer
													_SCREAMING_SNAKE_CASE   :		str       		=    AutoConfig.from_pretrained(lowerCAmelCase_							)
													_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    TFGPTaLMHeadModel.from_config(lowerCAmelCase_							)







									@tf.function(input_signature=(tf.TensorSpec((None,)   ,				tf.string   ,				name='text'							),)							)
									def 			A      (    self   ,				lowerCAmelCase_							)		->							Optional[Any]:
													_SCREAMING_SNAKE_CASE   :		int       		=    self.tokenizer(lowerCAmelCase_							)
													_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tokenized['input_ids'].to_tensor()

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

													_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model(input_ids=lowerCAmelCase_   ,				attention_mask=lowerCAmelCase_							)['logits']

													return outputs


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







						def 			A      (    self							)		->							List[Any]:
										super().setUp()

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

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







						def 			A      (    self							)		->							List[str]:
										for tokenizer, tf_tokenizer in zip(self.tokenizers   ,				self.tf_tokenizers							):
														for test_inputs in self.test_sentences:
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    tokenizer([test_inputs]   ,				return_tensors='tf'							)
																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    tf_tokenizer([test_inputs]							)

																		for key in python_outputs.keys():
																						# convert them to numpy to avoid messing with ragged tensors
																						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    python_outputs[key].numpy()
																						_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				tf.intaa							) == tf_outputs_values							)							)







						@slow
						def 			A      (    self							)		->							Tuple:
										for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.function(lowerCAmelCase_							)
														for test_inputs in self.test_sentences:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    tf.constant(lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    compiled_tokenizer(lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		str       		=    tf_tokenizer(lowerCAmelCase_							)

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







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

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf_tokenizer.get_config()
														_SCREAMING_SNAKE_CASE   :		int       		=    TFGPTaTokenizer.from_config(lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    model_from_config(lowerCAmelCase_							)

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







						@slow
						def 			A      (    self							)		->							Optional[Any]:
										for tf_tokenizer in self.tf_tokenizers:
														# for the test to run
														_SCREAMING_SNAKE_CASE   :		str       		=    1_2_3_1_2_3

														for max_length in [3, 5, 1_0_2_4]:
																		_SCREAMING_SNAKE_CASE   :		Any       		=    tf.convert_to_tensor([self.test_sentences[0]]							)
																		_SCREAMING_SNAKE_CASE   :		int       		=    tf_tokenizer(lowerCAmelCase_   ,				max_length=lowerCAmelCase_							)

																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    out['input_ids'].numpy().shape[1]

																		assert out_length == max_length









"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return numa ^ numa < 0


if __name__ == "__main__":
			import doctest

			doctest.testmod()

