





class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      a__						:    List[Any]   		=	arr.split(","   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





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


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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      Tuple         =    [
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

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

