code: 
  
  
  import random
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  
  from ..state import AcceleratorState
  from .constants import CUDA_DISTRIBUTED_TYPES
  from .dataclasses import DistributedType, RNGType
  from .imports import is_npu_available, is_tpu_available, is_xpu_available
  
  
  if is_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_ = False       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if device_specific:
            seed += AcceleratorState().process_index
       random.seed(UpperCamelCase_       )
       np.random.seed(UpperCamelCase_       )
       torch.manual_seed(UpperCamelCase_       )
       if is_xpu_available():
            torch.xpu.manual_seed_all(UpperCamelCase_       )
       elif is_npu_available():
            torch.npu.manual_seed_all(UpperCamelCase_       )
       else:
            torch.cuda.manual_seed_all(UpperCamelCase_       )
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
            xm.set_rng_state(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_ = None							,UpperCamelCase_ = None       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if rng_type == RNGType.TORCH:
            snake_case					=       torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
            snake_case					=       torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            snake_case					=       torch.tensor(xm.get_rng_state()       )
       elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            snake_case					=       torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            snake_case					=       torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            snake_case					=       generator.get_state()
  
       # Broadcast the rng state from device 0 to other devices
       snake_case					=       AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
            snake_case					=       rng_state.to(xm.xla_device()       )
            xm.collective_broadcast([rng_state]       )
            xm.mark_step()
            snake_case					=       rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
            snake_case					=       rng_state.to(state.device       )
            torch.distributed.broadcast(UpperCamelCase_							,0       )
            snake_case					=       rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(UpperCamelCase_							,0       )
  
       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
            torch.set_rng_state(UpperCamelCase_       )
       elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(UpperCamelCase_       )
       elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(UpperCamelCase_       )
       elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(UpperCamelCase_       )
       elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()       )
       elif rng_type == RNGType.GENERATOR:
            generator.set_state(UpperCamelCase_       )
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_ = None       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for rng_type in rng_types:
            synchronize_rng_state(RNGType(UpperCamelCase_       )							,generator=UpperCamelCase_       )
  
  
  
  
  
style_context: 
  
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __enter__( self					):
           if self.enabled:
                snake_case					=       self.model.no_sync()
                self.model_sync_obj.__enter__()
  
           return self
      def __exit__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           if self.enabled:
                # Average all models on exit
                self._sync_and_avg_model_params()
                self.model_sync_obj.__exit__(__snake_case    ,      __snake_case    ,      __snake_case					)
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = True					):
           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)'''					)
           snake_case					=       enabled and accelerator.distributed_type != DistributedType.NO
           snake_case					=       0
           if self.enabled:
                snake_case					=       accelerator
                snake_case					=       model
                snake_case					=       local_sgd_steps
      def 	a_    ( self					):
           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 	a_    ( self					):
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                for param in self.model.parameters():
                     snake_case					=       self.accelerator.reduce(param.data    ,      reduction='''mean'''					)
  
  
  
  
  