code: 
  
  
  '''simple docstring'''
  
  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 _SCREAMING_SNAKE_CASE   (A						,				A = False ) -> int:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if device_specific:
                seed += AcceleratorState().process_index
         random.seed(lowercase__ )
         np.random.seed(lowercase__ )
         torch.manual_seed(lowercase__ )
         if is_xpu_available():
                torch.xpu.manual_seed_all(lowercase__ )
         elif is_npu_available():
                torch.npu.manual_seed_all(lowercase__ )
         else:
                torch.cuda.manual_seed_all(lowercase__ )
         # ^^ safe to call this function even if cuda is not available
         if is_tpu_available():
                xm.set_rng_state(lowercase__ )
  
  def _SCREAMING_SNAKE_CASE   (A = None						,				A = None ) -> Union[str, Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if rng_type == RNGType.TORCH:
                lowercase__       =				torch.get_rng_state()
         elif rng_type == RNGType.CUDA:
                lowercase__       =				torch.cuda.get_rng_state()
         elif rng_type == RNGType.XLA:
                assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
                lowercase__       =				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."
                lowercase__       =				torch.npu.get_rng_state()
         elif rng_type == RNGType.XPU:
                assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
                lowercase__       =				torch.xpu.get_rng_state()
         elif rng_type == RNGType.GENERATOR:
                assert generator is not None, "Need a generator to synchronize its seed."
                lowercase__       =				generator.get_state()
  
         # Broadcast the rng state from device 0 to other devices
         lowercase__       =				AcceleratorState()
         if state.distributed_type == DistributedType.TPU:
                lowercase__       =				rng_state.to(xm.xla_device() )
                xm.collective_broadcast([rng_state] )
                xm.mark_step()
                lowercase__       =				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
         ):
                lowercase__       =				rng_state.to(state.device )
                torch.distributed.broadcast(lowercase__						,				0 )
                lowercase__       =				rng_state.cpu()
         elif state.distributed_type == DistributedType.MULTI_CPU:
                torch.distributed.broadcast(lowercase__						,				0 )
  
         # Set the broadcast rng state
         if rng_type == RNGType.TORCH:
                torch.set_rng_state(lowercase__ )
         elif rng_type == RNGType.CUDA:
                torch.cuda.set_rng_state(lowercase__ )
         elif rng_type == RNGType.NPU:
                torch.npu.set_rng_state(lowercase__ )
         elif rng_type == RNGType.XPU:
                torch.xpu.set_rng_state(lowercase__ )
         elif rng_type == RNGType.XLA:
                xm.set_rng_state(rng_state.item() )
         elif rng_type == RNGType.GENERATOR:
                generator.set_state(lowercase__ )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A = None ) -> Union[str, Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for rng_type in rng_types:
                synchronize_rng_state(RNGType(lowercase__ )						,				generator=lowercase__ )
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __enter__(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      lowercase__       =				self.model.no_sync()
                      self.model_sync_obj.__enter__()
  
               return self
  
  
  
        def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               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					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               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)'''				)
               lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
               lowercase__       =				0
               if self.enabled:
                      lowercase__       =				accelerator
                      lowercase__       =				model
                      lowercase__       =				local_sgd_steps
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               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					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.accelerator.wait_for_everyone()
               with self.accelerator.autocast():
                      for param in self.model.parameters():
                             lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)
  
  
  
  
  