"""simple docstring"""


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


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """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				(		__a   ):
    _snake_case     :  int     							=							'gpt_neox'


    def __init__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  Dict=5_0432   ,				__lowerCAmelCase	:  Tuple=6144   ,				__lowerCAmelCase	:  int=44   ,				__lowerCAmelCase	:  int=64   ,				__lowerCAmelCase	:  Optional[Any]=2_4576   ,				__lowerCAmelCase	:  List[str]="gelu"   ,				__lowerCAmelCase	:  int=0.25   ,				__lowerCAmelCase	:  int=1_0000   ,				__lowerCAmelCase	:  Optional[int]=0.0   ,				__lowerCAmelCase	:  List[Any]=0.0   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  Tuple=2048   ,				__lowerCAmelCase	:  str=0.02   ,				__lowerCAmelCase	:  Any=1e-5   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				__lowerCAmelCase	:  Any=0   ,				__lowerCAmelCase	:  int=2   ,				__lowerCAmelCase	:  Tuple=False   ,				__lowerCAmelCase	:  str=True   ,				__lowerCAmelCase	:  Any=None   ,				**__lowerCAmelCase	:  Dict   ,				):
      super().__init__(bos_token_id=A__   ,				eos_token_id=A__   ,				**A__					)
      _UpperCAmelCase						 =      vocab_size
      _UpperCAmelCase						 =      max_position_embeddings
      _UpperCAmelCase						 =      hidden_size
      _UpperCAmelCase						 =      num_hidden_layers
      _UpperCAmelCase						 =      num_attention_heads
      _UpperCAmelCase						 =      intermediate_size
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      rotary_pct
      _UpperCAmelCase						 =      rotary_emb_base
      _UpperCAmelCase						 =      attention_dropout
      _UpperCAmelCase						 =      hidden_dropout
      _UpperCAmelCase						 =      classifier_dropout
      _UpperCAmelCase						 =      initializer_range
      _UpperCAmelCase						 =      layer_norm_eps
      _UpperCAmelCase						 =      use_cache
      _UpperCAmelCase						 =      tie_word_embeddings
      _UpperCAmelCase						 =      use_parallel_residual
      _UpperCAmelCase						 =      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	:  Optional[Any]					):
      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}'''					)
      _UpperCAmelCase						 =      self.rope_scaling.get("""type"""   ,				A__					)
      _UpperCAmelCase						 =      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"""


from __future__ import annotations

from random import choice






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return choice(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      random_pivot(lowercase		)

  # partition based on pivot
  # linear time
  _UpperCAmelCase						 =      [e for e in lst if e < pivot]
  _UpperCAmelCase						 =      [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(lowercase		) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(lowercase		) < k - 1:
    return kth_number(lowercase   ,k - len(lowercase		) - 1		)
  # pivot is in elements smaller than k
  else:
    return kth_number(lowercase   ,lowercase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
