



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def snake_case__  (			)		->							int:
		UpperCAmelCase_						=				HfArgumentParser(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				parser.parse_args_into_dataclasses()[0]
		UpperCAmelCase_						=				TensorFlowBenchmark(args=__SCREAMING_SNAKE_CASE )
		try:
				UpperCAmelCase_						=				parser.parse_args_into_dataclasses()[0]
		except ValueError as e:
				UpperCAmelCase_						=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
				UpperCAmelCase_						=				" ".join(str(__SCREAMING_SNAKE_CASE ).split(" " )[:-1] )
				UpperCAmelCase_						=				""
				UpperCAmelCase_						=				eval(str(__SCREAMING_SNAKE_CASE ).split(" " )[-1] )
				UpperCAmelCase_						=				[]
				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(__SCREAMING_SNAKE_CASE )
				if len(__SCREAMING_SNAKE_CASE ) > 0:
						UpperCAmelCase_						=				full_error_msg + begin_error_msg + str(__SCREAMING_SNAKE_CASE )
				raise ValueError(__SCREAMING_SNAKE_CASE )
		benchmark.run()


if __name__ == "__main__":
			main()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

