


'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase    :     List[str]					=						{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     List[str]					=						["""DebertaTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Tuple					=						[
           """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DebertaForMaskedLM""",
           """DebertaForQuestionAnswering""",
           """DebertaForSequenceClassification""",
           """DebertaForTokenClassification""",
           """DebertaModel""",
           """DebertaPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Any					=						[
           """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFDebertaForMaskedLM""",
           """TFDebertaForQuestionAnswering""",
           """TFDebertaForSequenceClassification""",
           """TFDebertaForTokenClassification""",
           """TFDebertaModel""",
           """TFDebertaPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase    :     Optional[int]					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''simple docstring'''







import unittest

from knapsack import greedy_knapsack as kp
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       [10, 20, 30, 40, 50, 60]
            _lowerCAmelCase    :							Union[str, Any]   			=       [2, 4, 6, 8, 10, 12]
            _lowerCAmelCase    :							Dict   			=       100
            self.assertEqual(kp.calc_profit(snake_case__      ,	snake_case__      ,	snake_case__   )      ,	210   )







     def a	( self   ):

            '''simple docstring'''

            self.assertRaisesRegex(snake_case__      ,	'max_weight must greater than zero.'   )







     def a	( self   ):

            '''simple docstring'''

            self.assertRaisesRegex(snake_case__      ,	'Weight can not be negative.'   )







     def a	( self   ):

            '''simple docstring'''

            self.assertRaisesRegex(snake_case__      ,	'Profit can not be negative.'   )







     def a	( self   ):

            '''simple docstring'''

            self.assertRaisesRegex(snake_case__      ,	'max_weight must greater than zero.'   )







     def a	( self   ):

            '''simple docstring'''

            self.assertRaisesRegex(
                snake_case__      ,	'The length of profit and weight must be same.'   )


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

