


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			:						Any        =  'gpt2'

if is_tf_available():



	class 				A       (			tf.Module				):
						'''simple docstring'''






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




								"""simple docstring"""


								super().__init__()
								lowercase__	     =						tokenizer
								lowercase__	     =						AutoConfig.from_pretrained(_UpperCAmelCase						)
								lowercase__	     =						TFGPTaLMHeadModel.from_config(_UpperCAmelCase						)


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




								"""simple docstring"""


								lowercase__	     =						self.tokenizer(_UpperCAmelCase						)
								lowercase__	     =						tokenized["""input_ids"""].to_tensor()

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

								lowercase__	     =						self.model(input_ids=_UpperCAmelCase ,   attention_mask=_UpperCAmelCase						)["""logits"""]

								return outputs



@require_tf
@require_keras_nlp
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






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




							"""simple docstring"""


							super().setUp()

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

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

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Union[str, Any]:




							"""simple docstring"""


							for tokenizer, tf_tokenizer in zip(self.tokenizers ,   self.tf_tokenizers						):
									for test_inputs in self.test_sentences:
											lowercase__	     =						tokenizer([test_inputs] ,   return_tensors="""tf"""						)
											lowercase__	     =						tf_tokenizer([test_inputs]						)

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




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						tf.function(_UpperCAmelCase						)
									for test_inputs in self.test_sentences:
											lowercase__	     =						tf.constant(_UpperCAmelCase						)
											lowercase__	     =						compiled_tokenizer(_UpperCAmelCase						)
											lowercase__	     =						tf_tokenizer(_UpperCAmelCase						)

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

					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  int:




							"""simple docstring"""


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




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						tf.convert_to_tensor([self.test_sentences[0]]						)
									lowercase__	     =						tf_tokenizer(_UpperCAmelCase						)  # Build model with some sample inputs

									lowercase__	     =						tf_tokenizer.get_config()
									lowercase__	     =						TFGPTaTokenizer.from_config(_UpperCAmelCase						)
									lowercase__	     =						model_from_config(_UpperCAmelCase						)

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


					@slow
					def     lowerCamelCase__	(self       :		List[str]						)		->  str:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									lowercase__	     =						12_3123

									for max_length in [3, 5, 1024]:
											lowercase__	     =						tf.convert_to_tensor([self.test_sentences[0]]						)
											lowercase__	     =						tf_tokenizer(_UpperCAmelCase ,   max_length=_UpperCAmelCase						)

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

											assert out_length == max_length





"""simple docstring"""

def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return x if y == 0 else greatest_common_divisor(UpperCamelCase__					, x % y			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return (x * y) // greatest_common_divisor(UpperCamelCase__					, UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int = 20			):






 """simple docstring"""







 __lowercase						=						1
 for i in range(1					, n + 1			):
  __lowercase						=						lcm(UpperCamelCase__					, UpperCamelCase__			)
 return g


if __name__ == "__main__":
       print(f"""{solution() = }""")
