




"""simple docstring"""







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


__A   :  List[str]					=      logging.get_logger(__name__)

__A   :  List[Any]					=      {
    '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       lowerCAmelCase__       (			__A    ):


				"""simple docstring"""





				__UpperCAmelCase :     List[str]       =      "gpt_neox"



				def __init__(     self					:	Optional[int]    ,   lowercase__					:	Dict=5_0_4_3_2    ,   lowercase__					:	Tuple=6_1_4_4    ,   lowercase__					:	Optional[int]=4_4    ,   lowercase__					:	Optional[Any]=6_4    ,   lowercase__					:	int=2_4_5_7_6    ,   lowercase__					:	int="gelu"    ,   lowercase__					:	Optional[int]=0.2_5    ,   lowercase__					:	List[str]=1_0_0_0_0    ,   lowercase__					:	Dict=0.0    ,   lowercase__					:	Dict=0.0    ,   lowercase__					:	Dict=0.1    ,   lowercase__					:	Optional[Any]=2_0_4_8    ,   lowercase__					:	int=0.0_2    ,   lowercase__					:	str=1e-5    ,   lowercase__					:	Union[str, Any]=True    ,   lowercase__					:	List[Any]=0    ,   lowercase__					:	Union[str, Any]=2    ,   lowercase__					:	Optional[Any]=False    ,   lowercase__					:	Optional[int]=True    ,   lowercase__					:	Any=None    ,   **lowercase__					:	int    ,   ):
						super().__init__(bos_token_id=lowercase__    ,   eos_token_id=lowercase__    ,   **lowercase__						)
						__lowercase				:		Optional[int]												=				vocab_size
						__lowercase				:		Tuple												=				max_position_embeddings
						__lowercase				:		Optional[Any]												=				hidden_size
						__lowercase				:		Any												=				num_hidden_layers
						__lowercase				:		Optional[Any]												=				num_attention_heads
						__lowercase				:		List[str]												=				intermediate_size
						__lowercase				:		Tuple												=				hidden_act
						__lowercase				:		Optional[Any]												=				rotary_pct
						__lowercase				:		int												=				rotary_emb_base
						__lowercase				:		Optional[Any]												=				attention_dropout
						__lowercase				:		List[Any]												=				hidden_dropout
						__lowercase				:		int												=				classifier_dropout
						__lowercase				:		Dict												=				initializer_range
						__lowercase				:		Optional[Any]												=				layer_norm_eps
						__lowercase				:		Optional[Any]												=				use_cache
						__lowercase				:		int												=				tie_word_embeddings
						__lowercase				:		Any												=				use_parallel_residual
						__lowercase				:		Optional[int]												=				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       snake_case    (     self					:	Optional[int]						):





						if self.rope_scaling is None:
								return

						if not isinstance(self.rope_scaling    ,   lowercase__						) 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}'						)
						__lowercase				:		Union[str, Any]												=				self.rope_scaling.get("type"    ,   lowercase__						)
						__lowercase				:		List[Any]												=				self.rope_scaling.get("factor"    ,   lowercase__						)
						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(lowercase__    ,   lowercase__						) or rope_scaling_factor <= 1.0:
								raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)







"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if a < 0:
				raise ValueError("Input value must be a positive integer"				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				raise TypeError("Input value must be a 'int' type"				)
		return bin(_lowerCamelCase				).count("1"				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
