code: 
  
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  class 						A__      (					A__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      @classmethod
      def 	a_    ( cls					):
           return cls
  
  
  
  
      @slow
      @require_torch_gpu
      def 	a_    ( self					):
           snake_case					=       self.get_auto_remove_tmp_dir()
           snake_case					=       F'''\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        '''.split()
  
           # we want this test to run even if there is only one GPU, but if there are more we use them all
           snake_case					=       get_gpu_count()
           snake_case					=       F'''\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        '''.split()
           snake_case					=       [sys.executable] + distributed_args + args
           execute_subprocess_async(__snake_case    ,      env=self.get_env()					)
  
           snake_case					=       os.path.join(__snake_case    ,      '''test_bleu.json'''					)
           snake_case					=       load_json(__snake_case					)
           # print(metrics)
           self.assertGreaterEqual(metrics['''bleu''']    ,      2_5					)
  
  
  
  
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'''					)
  
  
  
  
  