


'''simple docstring'''







class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							arr.split(',' )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							[int(self.array[0] )] * len(self.array )
            _lowerCAmelCase     :				Tuple       =							[int(self.array[0] )] * len(self.array )
            for i in range(1   ,						len(self.array ) ):
                   _lowerCAmelCase     :				Any       =							max(
                       int(self.array[i] ) + sum_value[i - 1]   ,						int(self.array[i] ) )
                   _lowerCAmelCase     :				Any       =							max(sum_value[i]   ,						rear[i - 1] )
            return rear[len(self.array ) - 1]


if __name__ == "__main__":
       lowerCAmelCase     :  Dict											=    input("""please input some numbers:""")
       lowerCAmelCase     :  Union[str, Any]											=    SubArray(whole_array)
       lowerCAmelCase     :  Optional[int]											=    array.solve_sub_array()
       print(("""the results is:""", re))





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

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

