
class 				lowercase					:



		def __init__( self				,      A_       )  ->					Dict:
				"""simple docstring"""
				# we need a list not a string, so do something to change the type
				UpperCamelCase		=     arr.split(','       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     [int(self.array[0]       )] * len(self.array       )
				UpperCamelCase		=     [int(self.array[0]       )] * len(self.array       )
				for i in range(1				,      len(self.array       )       ):
						UpperCamelCase		=     max(
						    int(self.array[i]       ) + sum_value[i - 1]				,      int(self.array[i]       )       )
						UpperCamelCase		=     max(sum_value[i]				,      rear[i - 1]       )
				return rear[len(self.array       ) - 1]


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


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase					: Union[str, Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: List[str]      =       {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				"gpt_neox"



		def __init__( self				,      A_=50_432				,      A_=6_144				,      A_=44				,      A_=64				,      A_=24_576				,      A_="gelu"				,      A_=0.25				,      A_=10_000				,      A_=0.0				,      A_=0.0				,      A_=0.1				,      A_=2_048				,      A_=0.02				,      A_=1e-5				,      A_=True				,      A_=0				,      A_=2				,      A_=False				,      A_=True				,      A_=None				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				super().__init__(bos_token_id=A_				,      eos_token_id=A_				,      **A_       )
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     rotary_pct
				UpperCamelCase		=     rotary_emb_base
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     hidden_dropout
				UpperCamelCase		=     classifier_dropout
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     tie_word_embeddings
				UpperCamelCase		=     use_parallel_residual
				UpperCamelCase		=     rope_scaling
				self._rope_scaling_validation()

				if self.hidden_size % self.num_attention_heads != 0:
						raise ValueError(
						    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				if self.rope_scaling is None:
						return

				if not isinstance(self.rope_scaling				,      A_       ) or len(self.rope_scaling       ) != 2:
						raise ValueError(
						    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
						    F'''got {self.rope_scaling}'''       )
				UpperCamelCase		=     self.rope_scaling.get('type'				,      A_       )
				UpperCamelCase		=     self.rope_scaling.get('factor'				,      A_       )
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''       )
				if rope_scaling_factor is None or not isinstance(A_				,      A_       ) or rope_scaling_factor <= 1.0:
						raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )
