

class       __snake_case  :

  def __init__(					self						,		_A):
      # we need a list not a string, so do something to change the type
      SCREAMING_SNAKE_CASE_									=						arr.split(',')


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


if __name__ == "__main__":
       UpperCamelCase__  :      Tuple			=    input("please input some numbers:")
       UpperCamelCase__  :      Dict			=    SubArray(whole_array)
       UpperCamelCase__  :      Dict			=    array.solve_sub_array()
       print(("the results is:", re))





from typing import TYPE_CHECKING

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


UpperCamelCase__  :      Any			=    {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Optional[int]			=    ["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      str			=    [
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__  :      Dict			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


