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


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	Optional[Any]							     =		{
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""gpt_neox_japanese"""

  def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[Any]=3_20_00     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=25_60     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=32     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=4     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=1.00     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=1_00_00     ,							__SCREAMING_SNAKE_CASE :      str=20_48     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-5     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=3_19_96     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=3_19_99     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.0     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  max_position_embeddings
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_multiple_size
   __a       =  hidden_act
   __a       =  rotary_pct
   __a       =  rotary_emb_base
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  use_cache
   __a       =  attention_dropout
   __a       =  hidden_dropout



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


if __name__ == "__main__":
  print(f'''{solution() = }''')


