




import unittest

from knapsack import knapsack as k




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					0
										SCREAMING_SNAKE_CASE           =					[0]
										SCREAMING_SNAKE_CASE           =					[0]
										SCREAMING_SNAKE_CASE           =					len(a			)
										self.assertEqual(k.knapsack(a       ,     a       ,     a       ,     a			)       ,     0			)

										SCREAMING_SNAKE_CASE           =					[60]
										SCREAMING_SNAKE_CASE           =					[10]
										SCREAMING_SNAKE_CASE           =					len(a			)
										self.assertEqual(k.knapsack(a       ,     a       ,     a       ,     a			)       ,     0			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					3
										SCREAMING_SNAKE_CASE           =					[1, 2, 3]
										SCREAMING_SNAKE_CASE           =					[3, 2, 1]
										SCREAMING_SNAKE_CASE           =					len(a			)
										self.assertEqual(k.knapsack(a       ,     a       ,     a       ,     a			)       ,     5			)







							def    _UpperCAmelCase     (					self       :	str			)			->					str:
										SCREAMING_SNAKE_CASE           =					50
										SCREAMING_SNAKE_CASE           =					[60, 100, 120]
										SCREAMING_SNAKE_CASE           =					[10, 20, 30]
										SCREAMING_SNAKE_CASE           =					len(a			)
										self.assertEqual(k.knapsack(a       ,     a       ,     a       ,     a			)       ,     220			)


if __name__ == "__main__":
		unittest.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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

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

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

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






