

'''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


__snake_case			:     Union[str, Any]										=      ['gpt2']
__snake_case			:     List[str]										=      'gpt2'

if is_tf_available():



		class lowerCamelCase			(  tf.Module						):



					'''simple docstring'''






					def __init__(				self	:						int		,      lowerCAmelCase_	:						str			)      ->		List[Any]:





											'''simple docstring'''
											super().__init__()
											A__					:       Any     =tokenizer
											A__					:       Tuple     =AutoConfig.from_pretrained(lowerCAmelCase_			)
											A__					:       Any     =TFGPTaLMHeadModel.from_config(lowerCAmelCase_			)



					@tf.function(input_signature=(tf.TensorSpec((None,)		,      tf.string		,      name="""text"""			),)			)
					def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						Dict			)      ->		Dict:





											'''simple docstring'''
											A__					:       Dict     =self.tokenizer(lowerCAmelCase_			)
											A__					:       Optional[Any]     =tokenized["""input_ids"""].to_tensor()

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

											A__					:       Optional[int]     =self.model(input_ids=lowerCAmelCase_		,      attention_mask=lowerCAmelCase_			)["""logits"""]

											return outputs



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



			'''simple docstring'''






			def lowercase__						(				self	:						Any			)      ->		List[Any]:





									'''simple docstring'''
									super().setUp()

									A__					:       Optional[Any]     =[GPTaTokenizer.from_pretrained(lowerCAmelCase_			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
									A__					:       Optional[int]     =[TFGPTaTokenizer.from_pretrained(lowerCAmelCase_			) 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__					:       Optional[int]     =list(zip(self.test_sentences		,      self.test_sentences[::-1]			)			)


			def lowercase__						(				self	:						Dict			)      ->		List[str]:





									'''simple docstring'''
									for tokenizer, tf_tokenizer in zip(self.tokenizers		,      self.tf_tokenizers			):
															for test_inputs in self.test_sentences:
																					A__					:       Any     =tokenizer([test_inputs]		,      return_tensors="""tf"""			)
																					A__					:       str     =tf_tokenizer([test_inputs]			)

																					for key in python_outputs.keys():
																											# convert them to numpy to avoid messing with ragged tensors
																											A__					:       Optional[int]     =python_outputs[key].numpy()
																											A__					:       Union[str, Any]     =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 lowercase__						(				self	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									for tf_tokenizer in self.tf_tokenizers:
															A__					:       Optional[int]     =tf.function(lowerCAmelCase_			)
															for test_inputs in self.test_sentences:
																					A__					:       List[str]     =tf.constant(lowerCAmelCase_			)
																					A__					:       Union[str, Any]     =compiled_tokenizer(lowerCAmelCase_			)
																					A__					:       Dict     =tf_tokenizer(lowerCAmelCase_			)

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


			@slow
			def lowercase__						(				self	:						Any			)      ->		Any:





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





									'''simple docstring'''
									for tf_tokenizer in self.tf_tokenizers:
															A__					:       Optional[Any]     =tf.convert_to_tensor([self.test_sentences[0]]			)
															A__					:       Dict     =tf_tokenizer(lowerCAmelCase_			)  # Build model with some sample inputs

															A__					:       List[Any]     =tf_tokenizer.get_config()
															A__					:       str     =TFGPTaTokenizer.from_config(lowerCAmelCase_			)
															A__					:       Any     =model_from_config(lowerCAmelCase_			)

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



			@slow
			def lowercase__						(				self	:						Tuple			)      ->		Union[str, Any]:





									'''simple docstring'''
									for tf_tokenizer in self.tf_tokenizers:
															# for the test to run
															A__					:       List[str]     =12_31_23

															for max_length in [3, 5, 10_24]:
																					A__					:       List[str]     =tf.convert_to_tensor([self.test_sentences[0]]			)
																					A__					:       str     =tf_tokenizer(lowerCAmelCase_		,      max_length=lowerCAmelCase_			)

																					A__					:       int     =out["""input_ids"""].numpy().shape[1]

																					assert out_length == max_length




'''simple docstring'''



def 						__lowerCamelCase	(       __snake_case					:		int	)	->							bool:






						"""simple docstring"""





						if p < 2:
												raise ValueError("""p should not be less than 2!"""	)
						elif p == 2:
												return True

						A__					:       Any     =4
						A__					:       int     =(1 << p) - 1
						for _ in range(p - 2	):
												A__					:       Dict     =((s * s) - 2) % m
						return s == 0


if __name__ == "__main__":
		print(lucas_lehmer_test(7))
		print(lucas_lehmer_test(11))

