



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


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''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 	__snake_case	(     _lowercase):
  snake_case__    :       int        =						"gpt_neox"




  def __init__(			self					:						Any						, __lowerCAmelCase					:						Any=5_0_4_3_2						, __lowerCAmelCase					:						List[str]=6_1_4_4						, __lowerCAmelCase					:						str=4_4						, __lowerCAmelCase					:						Any=6_4						, __lowerCAmelCase					:						Optional[int]=2_4_5_7_6						, __lowerCAmelCase					:						Union[str, Any]="gelu"						, __lowerCAmelCase					:						Dict=0.25						, __lowerCAmelCase					:						Any=1_0_0_0_0						, __lowerCAmelCase					:						str=0.0						, __lowerCAmelCase					:						int=0.0						, __lowerCAmelCase					:						Optional[int]=0.1						, __lowerCAmelCase					:						Optional[Any]=2_0_4_8						, __lowerCAmelCase					:						Any=0.02						, __lowerCAmelCase					:						List[Any]=1E-5						, __lowerCAmelCase					:						Optional[Any]=True						, __lowerCAmelCase					:						Union[str, Any]=0						, __lowerCAmelCase					:						Any=2						, __lowerCAmelCase					:						Optional[Any]=False						, __lowerCAmelCase					:						Tuple=True						, __lowerCAmelCase					:						Union[str, Any]=None						, **__lowerCAmelCase					:						List[Any]						, ):






         """simple docstring"""






         super().__init__(bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, **__lowerCAmelCase		)
         _lowerCamelCase						:    List[str]         =			vocab_size
         _lowerCamelCase						:    Tuple         =			max_position_embeddings
         _lowerCamelCase						:    Union[str, Any]         =			hidden_size
         _lowerCamelCase						:    Any         =			num_hidden_layers
         _lowerCamelCase						:    Optional[int]         =			num_attention_heads
         _lowerCamelCase						:    int         =			intermediate_size
         _lowerCamelCase						:    List[str]         =			hidden_act
         _lowerCamelCase						:    int         =			rotary_pct
         _lowerCamelCase						:    List[str]         =			rotary_emb_base
         _lowerCamelCase						:    Union[str, Any]         =			attention_dropout
         _lowerCamelCase						:    str         =			hidden_dropout
         _lowerCamelCase						:    Any         =			classifier_dropout
         _lowerCamelCase						:    List[Any]         =			initializer_range
         _lowerCamelCase						:    Optional[Any]         =			layer_norm_eps
         _lowerCamelCase						:    str         =			use_cache
         _lowerCamelCase						:    Optional[int]         =			tie_word_embeddings
         _lowerCamelCase						:    List[str]         =			use_parallel_residual
         _lowerCamelCase						:    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 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         if self.rope_scaling is None:
                return

         if not isinstance(self.rope_scaling						, __lowerCAmelCase		) 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						:    int         =			self.rope_scaling.get('''type'''						, __lowerCAmelCase		)
         _lowerCamelCase						:    Tuple         =			self.rope_scaling.get('''factor'''						, __lowerCAmelCase		)
         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(__lowerCAmelCase						, __lowerCAmelCase		) 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 math






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return math.sqrt(A_						) * math.sqrt(A_						) == num






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			0
       _lowerCamelCase						:    Tuple         =			n
       while left <= right:
              _lowerCamelCase						:    List[str]         =			(left + right) // 2
              if mid**2 == n:
                     return True
              elif mid**2 > n:
                     _lowerCamelCase						:    int         =			mid - 1
              else:
                     _lowerCamelCase						:    Optional[Any]         =			mid + 1
       return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


