'''simple docstring'''






from __future__ import annotations

import sys





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int							):
     if n < 0:
          raise ValueError('Negative arguments are not supported'							)
     return _fib(__snake_case							)[0]





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int							):
     if n == 0:  # (F(0), F(1))
          return (0, 1)

     # F(2n) = F(n)[2F(n+1) − F(n)]
     # F(2n+1) = F(n+1)^2+F(n)^2
     _A		,					_A								=     _fib(n // 2							)
     _A								=     a * (b * 2 - a)
     _A								=     a * a + b * b
     return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
   _UpperCAmelCase						:    List[str]     					=					int(sys.argv[1])
   print(F'''fibonacci({n}) is {fibonacci(n)}''')




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Accelerator,   UpperCamelCase__			: torch.nn.Module,   UpperCamelCase__			: int,   UpperCamelCase__			: bool = True    )     -> str:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
        _A								=     enabled and accelerator.distributed_type != DistributedType.NO
        _A								=     0
        if self.enabled:
             _A								=     accelerator
             _A								=     model
             _A								=     local_sgd_steps
   def   __UpperCAmelCase      (			self			: Any    )     -> int:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



