

import unittest

from knapsack import knapsack as k



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Dict        =		0
						A__							:    List[str]        =		[0]
						A__							:    Any        =		[0]
						A__							:    int        =		len(A__			)
						self.assertEqual(k.knapsack(A__    ,       A__    ,       A__    ,       A__			)    ,       0			)

						A__							:    List[str]        =		[60]
						A__							:    List[str]        =		[10]
						A__							:    List[str]        =		len(A__			)
						self.assertEqual(k.knapsack(A__    ,       A__    ,       A__    ,       A__			)    ,       0			)






					def   __A			( self			):
						A__							:    Dict        =		3
						A__							:    int        =		[1, 2, 3]
						A__							:    str        =		[3, 2, 1]
						A__							:    List[Any]        =		len(A__			)
						self.assertEqual(k.knapsack(A__    ,       A__    ,       A__    ,       A__			)    ,       5			)






					def   __A			( self			):
						A__							:    int        =		50
						A__							:    Any        =		[60, 100, 120]
						A__							:    Optional[int]        =		[10, 20, 30]
						A__							:    Tuple        =		len(A__			)
						self.assertEqual(k.knapsack(A__    ,       A__    ,       A__    ,       A__			)    ,       220			)


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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_       : Optional[int]         =			{'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : str         =			[
	    'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'IBertForMaskedLM',
	    'IBertForMultipleChoice',
	    'IBertForQuestionAnswering',
	    'IBertForSequenceClassification',
	    'IBertForTokenClassification',
	    'IBertModel',
	    'IBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

	A_       : Union[str, Any]         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



