

"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			Tuple	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Union[str, 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 		_A       (			__lowercase					):
      __a								=	'gpt_neox'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=50432    ,						SCREAMING_SNAKE_CASE__=6144    ,						SCREAMING_SNAKE_CASE__=44    ,						SCREAMING_SNAKE_CASE__=64    ,						SCREAMING_SNAKE_CASE__=24576    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.25    ,						SCREAMING_SNAKE_CASE__=10000    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=2048    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1e-5    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					str:
         super().__init__(bos_token_id=__A    ,						eos_token_id=__A    ,						**__A		)
         lowerCamelCase__						=			vocab_size
         lowerCamelCase__						=			max_position_embeddings
         lowerCamelCase__						=			hidden_size
         lowerCamelCase__						=			num_hidden_layers
         lowerCamelCase__						=			num_attention_heads
         lowerCamelCase__						=			intermediate_size
         lowerCamelCase__						=			hidden_act
         lowerCamelCase__						=			rotary_pct
         lowerCamelCase__						=			rotary_emb_base
         lowerCamelCase__						=			attention_dropout
         lowerCamelCase__						=			hidden_dropout
         lowerCamelCase__						=			classifier_dropout
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			layer_norm_eps
         lowerCamelCase__						=			use_cache
         lowerCamelCase__						=			tie_word_embeddings
         lowerCamelCase__						=			use_parallel_residual
         lowerCamelCase__						=			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 					_lowerCamelCase  (  self		)   ->					int:
         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}'		)
         lowerCamelCase__						=			self.rope_scaling.get("type"    ,						__A		)
         lowerCamelCase__						=			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}'		)







"""simple docstring"""




import numpy as np



def 	UpperCAmelCase__   (				A__   )      ->      np.array:




   """simple docstring"""
   return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()




