



'''simple docstring'''





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


__a          =		logging.get_logger(__name__)

__a          =		{
    "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_       (       _a	):
    """simple docstring"""







    lowercase					=		"gpt_neox"




    def __init__(    self					:     int ,     snake_case_					:     Union[str, Any]=50_432 ,     snake_case_					:     Union[str, Any]=6_144 ,     snake_case_					:     Union[str, Any]=44 ,     snake_case_					:     Any=64 ,     snake_case_					:     Union[str, Any]=24_576 ,     snake_case_					:     List[Any]="gelu" ,     snake_case_					:     Any=0.25 ,     snake_case_					:     Dict=10_000 ,     snake_case_					:     List[Any]=0.0 ,     snake_case_					:     Tuple=0.0 ,     snake_case_					:     Optional[int]=0.1 ,     snake_case_					:     Any=2_048 ,     snake_case_					:     str=0.02 ,     snake_case_					:     Dict=1E-5 ,     snake_case_					:     Union[str, Any]=True ,     snake_case_					:     Dict=0 ,     snake_case_					:     Optional[Any]=2 ,     snake_case_					:     Dict=False ,     snake_case_					:     Optional[Any]=True ,     snake_case_					:     Optional[Any]=None ,     **snake_case_					:     List[Any] ,     ):
          super().__init__(bos_token_id=snake_case_ ,     eos_token_id=snake_case_ ,     **snake_case_						)
          snake_case__  :    Tuple    						=      vocab_size
          snake_case__  :    Union[str, Any]    						=      max_position_embeddings
          snake_case__  :    Optional[int]    						=      hidden_size
          snake_case__  :    List[Any]    						=      num_hidden_layers
          snake_case__  :    Union[str, Any]    						=      num_attention_heads
          snake_case__  :    List[Any]    						=      intermediate_size
          snake_case__  :    Tuple    						=      hidden_act
          snake_case__  :    str    						=      rotary_pct
          snake_case__  :    Tuple    						=      rotary_emb_base
          snake_case__  :    List[str]    						=      attention_dropout
          snake_case__  :    Tuple    						=      hidden_dropout
          snake_case__  :    Dict    						=      classifier_dropout
          snake_case__  :    Dict    						=      initializer_range
          snake_case__  :    Optional[int]    						=      layer_norm_eps
          snake_case__  :    Optional[int]    						=      use_cache
          snake_case__  :    Union[str, Any]    						=      tie_word_embeddings
          snake_case__  :    str    						=      use_parallel_residual
          snake_case__  :    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 lowerCamelCase       (    self					:     List[str]						):
          if self.rope_scaling is None:
                return

          if not isinstance(self.rope_scaling ,     snake_case_						) 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}"						)
          snake_case__  :    Optional[int]    						=      self.rope_scaling.get("""type""" ,     snake_case_						)
          snake_case__  :    int    						=      self.rope_scaling.get("""factor""" ,     snake_case_						)
          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(snake_case_ ,     snake_case_						) 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'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



