"""simple docstring"""





class   UpperCAmelCase_			:


      def __init__(     self	,    a							) ->      Tuple:
             # we need a list not a string, so do something to change the type
             lowercase__    :	int     					=			arr.split(','							)


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


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


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

       _UpperCamelCase     :		List[str]											=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

