





import unittest

from knapsack import greedy_knapsack as kp


class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   Optional[Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






					A__  				=       [10, 20, 30, 40, 50, 60]
					A__  				=       [2, 4, 6, 8, 10, 12]
					A__  				=       1_00
					self.assertEqual(kp.calc_profit(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )							,     2_10       )






			def        a_	(      self       :   Tuple       )     ->     Optional[Any]:







					"""simple docstring"""






					self.assertRaisesRegex(__lowerCAmelCase							,     """max_weight must greater than zero."""       )






			def        a_	(      self       :   str       )     ->     List[str]:







					"""simple docstring"""






					self.assertRaisesRegex(__lowerCAmelCase							,     """Weight can not be negative."""       )






			def        a_	(      self       :   Dict       )     ->     Tuple:







					"""simple docstring"""






					self.assertRaisesRegex(__lowerCAmelCase							,     """Profit can not be negative."""       )






			def        a_	(      self       :   Optional[Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






					self.assertRaisesRegex(__lowerCAmelCase							,     """max_weight must greater than zero."""       )







			def        a_	(      self       :   List[Any]       )     ->     str:







					"""simple docstring"""






					self.assertRaisesRegex(
					    __lowerCAmelCase							,     """The length of profit and weight must be same."""       )


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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

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



