





import unittest

from knapsack import knapsack as k




class        _a    (    unittest.TestCase):

      """simple docstring"""


      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     0
          UpperCamelCase__:	Optional[int]					=     [0]
          UpperCamelCase__:	Dict					=     [0]
          UpperCamelCase__:	str					=     len(__lowerCamelCase					)
          self.assertEqual(k.knapsack(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)	,    0					)

          UpperCamelCase__:	Tuple					=     [60]
          UpperCamelCase__:	List[Any]					=     [10]
          UpperCamelCase__:	Any					=     len(__lowerCamelCase					)
          self.assertEqual(k.knapsack(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)	,    0					)






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     3
          UpperCamelCase__:	Any					=     [1, 2, 3]
          UpperCamelCase__:	Dict					=     [3, 2, 1]
          UpperCamelCase__:	List[str]					=     len(__lowerCamelCase					)
          self.assertEqual(k.knapsack(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)	,    5					)






      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     50
          UpperCamelCase__:	Optional[Any]					=     [60, 100, 120]
          UpperCamelCase__:	Optional[Any]					=     [10, 20, 30]
          UpperCamelCase__:	Any					=     len(__lowerCamelCase					)
          self.assertEqual(k.knapsack(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)	,    220					)


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_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[Any]  	=	[
       '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RobertaPreLayerNormForCausalLM''',
       '''RobertaPreLayerNormForMaskedLM''',
       '''RobertaPreLayerNormForMultipleChoice''',
       '''RobertaPreLayerNormForQuestionAnswering''',
       '''RobertaPreLayerNormForSequenceClassification''',
       '''RobertaPreLayerNormForTokenClassification''',
       '''RobertaPreLayerNormModel''',
       '''RobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple  	=	[
       '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRobertaPreLayerNormForCausalLM''',
       '''TFRobertaPreLayerNormForMaskedLM''',
       '''TFRobertaPreLayerNormForMultipleChoice''',
       '''TFRobertaPreLayerNormForQuestionAnswering''',
       '''TFRobertaPreLayerNormForSequenceClassification''',
       '''TFRobertaPreLayerNormForTokenClassification''',
       '''TFRobertaPreLayerNormMainLayer''',
       '''TFRobertaPreLayerNormModel''',
       '''TFRobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''FlaxRobertaPreLayerNormForCausalLM''',
       '''FlaxRobertaPreLayerNormForMaskedLM''',
       '''FlaxRobertaPreLayerNormForMultipleChoice''',
       '''FlaxRobertaPreLayerNormForQuestionAnswering''',
       '''FlaxRobertaPreLayerNormForSequenceClassification''',
       '''FlaxRobertaPreLayerNormForTokenClassification''',
       '''FlaxRobertaPreLayerNormModel''',
       '''FlaxRobertaPreLayerNormPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roberta_prelayernorm import (
       ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
       RobertaPreLayerNormConfig,
       RobertaPreLayerNormOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaPreLayerNormForCausalLM,
          RobertaPreLayerNormForMaskedLM,
          RobertaPreLayerNormForMultipleChoice,
          RobertaPreLayerNormForQuestionAnswering,
          RobertaPreLayerNormForSequenceClassification,
          RobertaPreLayerNormForTokenClassification,
          RobertaPreLayerNormModel,
          RobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta_prelayernorm import (
          TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaPreLayerNormForCausalLM,
          TFRobertaPreLayerNormForMaskedLM,
          TFRobertaPreLayerNormForMultipleChoice,
          TFRobertaPreLayerNormForQuestionAnswering,
          TFRobertaPreLayerNormForSequenceClassification,
          TFRobertaPreLayerNormForTokenClassification,
          TFRobertaPreLayerNormMainLayer,
          TFRobertaPreLayerNormModel,
          TFRobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormPreTrainedModel,
      )

else:
   import sys

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

