




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 		__A   (      unittest.TestCase      ):

						@slow
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""     ,			return_dict=__snake_case			).to(__snake_case			)
										__magic_name__						:						Any										=AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										__magic_name__						:						Dict										=tokenizer("""Hello there"""     ,			return_tensors="""pt"""			).input_ids
										__magic_name__						:						Tuple										=tokenizer("""Hi I am"""     ,			return_tensors="""pt"""			).input_ids

										__magic_name__						:						List[Any]										=model(input_ids.to(__snake_case			)     ,			labels=labels.to(__snake_case			)			).loss
										__magic_name__						:						List[Any]										=-(labels.shape[-1] * loss.item())

										__magic_name__						:						Optional[int]										=-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)





'''simple docstring'''




from functools import lru_cache






@lru_cache
def 							snake_case__				(     _A:   int					)       ->      int:

		'''simple docstring'''


		if num < 0:
				raise ValueError("""Number should not be negative."""					)

		return 1 if num in (0, 1) else num * factorial(num - 1					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




