



UpperCAmelCase_							:      Optional[Any]      =  tuple[float, float, float]
UpperCAmelCase_							:      Union[str, Any]      =  tuple[float, float, float]
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]    )->       Vectorad:





						"""simple docstring"""





						A__							=       end_pointa[0] - end_pointa[0]
						A__							=       end_pointa[1] - end_pointa[1]
						A__							=       end_pointa[2] - end_pointa[2]
						return (x, y, z)
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				List[str]    )->       Vectorad:





						"""simple docstring"""





						A__							=       ab[1] * ac[2] - ab[2] * ac[1]  # *i
						A__							=       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
						A__							=       ab[0] * ac[1] - ab[1] * ac[0]  # *k
						return (x, y, z)
def  UpperCamelCase						(       _A			:				str			,							_A			:				int    )->       bool:





						"""simple docstring"""





						return tuple(round(a__			,							a__    ) for x in vector    ) == (0, 0, 0)
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Dict			,							_A			:				Union[str, Any]			,							_A			:				Optional[int] = 10    )->       bool:





						"""simple docstring"""





						A__							=       create_vector(a__			,							a__    )
						A__							=       create_vector(a__			,							a__    )
						return is_zero_vector(get_ad_vectors_cross(a__			,							a__    )			,							a__    )










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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      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      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



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

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) 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}"""		)
							A__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							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(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





