



'''simple docstring'''




import unittest

from knapsack import knapsack as k







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					0
				A_     :						List[str]     =					[0]
				A_     :						str     =					[0]
				A_     :						Tuple     =					len(lowercase							)
				self.assertEqual(k.knapsack(lowercase	,				lowercase	,				lowercase	,				lowercase							)	,				0							)

				A_     :						Dict     =					[60]
				A_     :						Tuple     =					[10]
				A_     :						Dict     =					len(lowercase							)
				self.assertEqual(k.knapsack(lowercase	,				lowercase	,				lowercase	,				lowercase							)	,				0							)


	def    _a     (self							):
				A_     :						Tuple     =					3
				A_     :						Optional[Any]     =					[1, 2, 3]
				A_     :						Dict     =					[3, 2, 1]
				A_     :						Optional[int]     =					len(lowercase							)
				self.assertEqual(k.knapsack(lowercase	,				lowercase	,				lowercase	,				lowercase							)	,				5							)


	def    _a     (self							):
				A_     :						Optional[int]     =					50
				A_     :						str     =					[60, 100, 120]
				A_     :						Tuple     =					[10, 20, 30]
				A_     :						List[Any]     =					len(lowercase							)
				self.assertEqual(k.knapsack(lowercase	,				lowercase	,				lowercase	,				lowercase							)	,				220							)


if __name__ == "__main__":
						unittest.main()




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)