





'''simple docstring'''







class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					int					,				__a     :					Union[str, Any]					):
								# we need a list not a string, so do something to change the type
								_a								= arr.split(","					)




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


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







'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXForCausalLM',
					    'GPTNeoXForQuestionAnswering',
					    'GPTNeoXForSequenceClassification',
					    'GPTNeoXForTokenClassification',
					    'GPTNeoXLayer',
					    'GPTNeoXModel',
					    'GPTNeoXPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

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