



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       =						['''gpt2''']
__A       =						'''gpt2'''

if is_tf_available():






	class 		lowercase						(			tf.Module):






		"""simple docstring"""


		def __init__(     self     :						Optional[int]						,  __UpperCAmelCase     :						List[Any]					)      ->			List[Any]:
								super().__init__()
								UpperCAmelCase_=       tokenizer
								UpperCAmelCase_=       AutoConfig.from_pretrained(__UpperCAmelCase					)
								UpperCAmelCase_=       TFGPTaLMHeadModel.from_config(__UpperCAmelCase					)




		@tf.function(input_signature=(tf.TensorSpec((None,)						,  tf.string						,  name="""text"""					),)					)
		def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			str:
								UpperCAmelCase_=       self.tokenizer(__UpperCAmelCase					)
								UpperCAmelCase_=       tokenized["""input_ids"""].to_tensor()

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

								UpperCAmelCase_=       self.model(input_ids=__UpperCAmelCase						,  attention_mask=__UpperCAmelCase					)["""logits"""]

								return outputs






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






	"""simple docstring"""


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

							UpperCAmelCase_=       [GPTaTokenizer.from_pretrained(__UpperCAmelCase					) for checkpoint in (TOKENIZER_CHECKPOINTS)]
							UpperCAmelCase_=       [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase					) for checkpoint in TOKENIZER_CHECKPOINTS]
							assert len(self.tokenizers					) == len(self.tf_tokenizers					)

							UpperCAmelCase_=       [
							    """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""",
							]
							UpperCAmelCase_=       list(zip(self.test_sentences						,  self.test_sentences[::-1]					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			List[Any]:
							for tokenizer, tf_tokenizer in zip(self.tokenizers						,  self.tf_tokenizers					):
													for test_inputs in self.test_sentences:
																			UpperCAmelCase_=       tokenizer([test_inputs]						,  return_tensors="""tf"""					)
																			UpperCAmelCase_=       tf_tokenizer([test_inputs]					)

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

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




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[Any]:
							for tf_tokenizer in self.tf_tokenizers:
													UpperCAmelCase_=       tf.function(__UpperCAmelCase					)
													for test_inputs in self.test_sentences:
																			UpperCAmelCase_=       tf.constant(__UpperCAmelCase					)
																			UpperCAmelCase_=       compiled_tokenizer(__UpperCAmelCase					)
																			UpperCAmelCase_=       tf_tokenizer(__UpperCAmelCase					)

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




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

													UpperCAmelCase_=       tf_tokenizer.get_config()
													UpperCAmelCase_=       TFGPTaTokenizer.from_config(__UpperCAmelCase					)
													UpperCAmelCase_=       model_from_config(__UpperCAmelCase					)

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




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			str:
							for tf_tokenizer in self.tf_tokenizers:
													# for the test to run
													UpperCAmelCase_=       123_123

													for max_length in [3, 5, 1_024]:
																			UpperCAmelCase_=       tf.convert_to_tensor([self.test_sentences[0]]					)
																			UpperCAmelCase_=       tf_tokenizer(__UpperCAmelCase						,  max_length=__UpperCAmelCase					)

																			UpperCAmelCase_=       out["""input_ids"""].numpy().shape[1]

																			assert out_length == max_length









import pytest

import datasets


# Import fixture modules as plugins
__A       =						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Any    )				->			Tuple:






						'''simple docstring'''



						for item in items:
												if any(marker in item.keywords for marker in ["""integration""", """unit"""]    ):
																		continue
												item.add_marker(pytest.mark.unit    )



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Optional[Any]:






						'''simple docstring'''



						config.addinivalue_line("""markers"""      ,"""torchaudio_latest: mark test to run with torchaudio>=0.12"""    )



@pytest.fixture(autouse=lowerCAmelCase_    )
def 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       tmp_path_factory.getbasetemp() / """cache"""
						UpperCAmelCase_=       test_hf_cache_home / """datasets"""
						UpperCAmelCase_=       test_hf_cache_home / """metrics"""
						UpperCAmelCase_=       test_hf_cache_home / """modules"""
						monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""      ,str(lowerCAmelCase_    )    )
						monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""      ,str(lowerCAmelCase_    )    )
						monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""      ,str(lowerCAmelCase_    )    )
						UpperCAmelCase_=       test_hf_datasets_cache / """downloads"""
						monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""      ,str(lowerCAmelCase_    )    )
						UpperCAmelCase_=       test_hf_datasets_cache / """downloads""" / """extracted"""
						monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""      ,str(lowerCAmelCase_    )    )



@pytest.fixture(autouse=lowerCAmelCase_      ,scope="""session"""    )
def 	__a					(   )				->			Optional[int]:






						'''simple docstring'''



						datasets.disable_progress_bar()



@pytest.fixture(autouse=lowerCAmelCase_    )
def 	__a					(   lowerCAmelCase_       : int    )				->			str:






						'''simple docstring'''



						monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""      ,lowerCAmelCase_    )



@pytest.fixture
def 	__a					(   lowerCAmelCase_       : List[str]    )				->			Union[str, Any]:






						'''simple docstring'''



						monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""      ,lowerCAmelCase_    )




