




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		:				Union[str, Any]       			=		['gpt2']
__lowerCAmelCase		:				Optional[Any]       			=		'gpt2'

if is_tf_available():




		class     snake_case__							(tf.Module   ):
						"""simple docstring"""



						def __init__(      self				:     str					,      __lowerCamelCase				:     Dict							)					->     List[str]:
									super().__init__()
									a    =			tokenizer
									a    =			AutoConfig.from_pretrained(__lowerCamelCase							)
									a    =			TFGPTaLMHeadModel.from_config(__lowerCamelCase							)





						@tf.function(input_signature=(tf.TensorSpec((None,)					,      tf.string					,      name="text"							),)							)
						def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     List[Any]							)					->     str:
									a    =			self.tokenizer(__lowerCamelCase							)
									a    =			tokenized["input_ids"].to_tensor()

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

									a    =			self.model(input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase							)["logits"]

									return outputs




@require_tf
@require_keras_nlp
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Any:
							super().setUp()

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

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





				def 							__UpperCAmelCase							(      self				:     int							)					->     Tuple:
							for tokenizer, tf_tokenizer in zip(self.tokenizers					,      self.tf_tokenizers							):
										for test_inputs in self.test_sentences:
													a    =			tokenizer([test_inputs]					,      return_tensors="tf"							)
													a    =			tf_tokenizer([test_inputs]							)

													for key in python_outputs.keys():
																# convert them to numpy to avoid messing with ragged tensors
																a    =			python_outputs[key].numpy()
																a    =			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 							__UpperCAmelCase							(      self				:     List[str]							)					->     int:
							for tf_tokenizer in self.tf_tokenizers:
										a    =			tf.function(__lowerCamelCase							)
										for test_inputs in self.test_sentences:
													a    =			tf.constant(__lowerCamelCase							)
													a    =			compiled_tokenizer(__lowerCamelCase							)
													a    =			tf_tokenizer(__lowerCamelCase							)

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





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

										a    =			tf_tokenizer.get_config()
										a    =			TFGPTaTokenizer.from_config(__lowerCamelCase							)
										a    =			model_from_config(__lowerCamelCase							)

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





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							for tf_tokenizer in self.tf_tokenizers:
										# for the test to run
										a    =			12_31_23

										for max_length in [3, 5, 10_24]:
													a    =			tf.convert_to_tensor([self.test_sentences[0]]							)
													a    =			tf_tokenizer(__lowerCamelCase					,      max_length=__lowerCamelCase							)

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

													assert out_length == max_length












import numpy as np




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return 1 / (1 + np.exp(-vector   ))




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return vector * sigmoid(1.7_02 * vector   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






