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


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class    A_ (       _A ):
      _SCREAMING_SNAKE_CASE    				=			"""open-llama"""

      def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=10_00_00     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=40_96     ,							__SCREAMING_SNAKE_CASE :      Dict=1_10_08     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=32     ,							__SCREAMING_SNAKE_CASE :      Dict=32     ,							__SCREAMING_SNAKE_CASE :      str="silu"     ,							__SCREAMING_SNAKE_CASE :      str=20_48     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.02     ,							__SCREAMING_SNAKE_CASE :      str=1E-6     ,							__SCREAMING_SNAKE_CASE :      int=True     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      int=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=False     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=True     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
       __a       =  vocab_size
       __a       =  max_position_embeddings
       __a       =  hidden_size
       __a       =  intermediate_size
       __a       =  num_hidden_layers
       __a       =  num_attention_heads
       __a       =  hidden_act
       __a       =  initializer_range
       __a       =  rms_norm_eps
       __a       =  use_cache
       __a       =  kwargs.pop(
           "use_memorry_efficient_attention"     ,							__SCREAMING_SNAKE_CASE       )
       __a       =  hidden_dropout_prob
       __a       =  attention_dropout_prob
       __a       =  use_stable_embedding
       __a       =  shared_input_output_embedding
       __a       =  rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)

      def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
       if self.rope_scaling is None:
        return

       if not isinstance(self.rope_scaling     ,							__SCREAMING_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}"""       )
       __a       =  self.rope_scaling.get("type"     ,							__SCREAMING_SNAKE_CASE       )
       __a       =  self.rope_scaling.get("factor"     ,							__SCREAMING_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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_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}"""       )



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


