
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 				_lowerCamelCase      (       ):



				'''simple docstring'''







				A  :	Optional[int]				=					HfArgumentParser(lowerCamelCase_		)
				A  :	str				=					parser.parse_args_into_dataclasses()[0]
				A  :	List[str]				=					TensorFlowBenchmark(args=lowerCamelCase_		)
				try:
								A  :	List[str]				=					parser.parse_args_into_dataclasses()[0]
				except ValueError as e:
								A  :	Any				=					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
								A  :	Optional[Any]				=					''' '''.join(str(lowerCamelCase_		).split(''' '''		)[:-1]		)
								A  :	Dict				=					''''''
								A  :	Any				=					eval(str(lowerCamelCase_		).split(''' '''		)[-1]		)
								A  :	int				=					[]
								for arg in depreciated_args:
												# arg[2:] removes '--'
												if arg[2:] in TensorFlowBenchmark.deprecated_args:
																# arg[5:] removes '--no_'
																full_error_msg += arg_error_msg.format(arg[5:]		)
												else:
																wrong_args.append(lowerCamelCase_		)
								if len(lowerCamelCase_		) > 0:
												A  :	int				=					full_error_msg + begin_error_msg + str(lowerCamelCase_		)
								raise ValueError(lowerCamelCase_		)
				benchmark.run()


if __name__ == "__main__":
					main()






"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,     expected_slice.numpy()    ,     atol=1e-4      )      )




