import torch

from accelerate import Accelerator, DistributedType






class 				_SCREAMING_SNAKE_CASE:
   def __enter__(     self     )						->List[str]:
        if self.enabled:
             a	:     Any						      =    self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(_lowercase				,  _lowercase				,  _lowercase     )
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = True     )						->Union[str, Any]:
        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	:     List[Any]						      =    enabled and accelerator.distributed_type != DistributedType.NO
        a	:     Union[str, Any]						      =    0
        if self.enabled:
             a	:     Any						      =    accelerator
             a	:     Optional[Any]						      =    model
             a	:     Tuple						      =    local_sgd_steps
   def _lowercase(     self     )						->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 _lowercase(     self     )						->int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  a	:     Optional[int]						      =    self.accelerator.reduce(param.data				,  reduction='''mean'''     )
"""simple docstring"""







from __future__ import annotations


def 	a__   (		UpperCAmelCase__							:      list      )->						int | float:
						UpperCAmelCase__       						=		sorted(UpperCAmelCase__      )
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	a__   (		)->						Dict:
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()

