




import torch

from accelerate import Accelerator, DistributedType

class 			_lowerCamelCase    :

      """simple docstring"""

      def __enter__(  self       :			Optional[int]					):
          if self.enabled:
              __UpperCamelCase			=    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self







      def __exit__(  self       :			Union[str, Any]			,							snake_case       :			Dict			,							snake_case       :			Any			,							snake_case       :			Dict					):
          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       :			Tuple			,							snake_case       :			Accelerator			,							snake_case       :			torch.nn.Module			,							snake_case       :			int			,							snake_case       :			bool = 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)'''					)
          __UpperCamelCase			=    enabled and accelerator.distributed_type != DistributedType.NO
          __UpperCamelCase			=    0
          if self.enabled:
              __UpperCamelCase			=    accelerator
              __UpperCamelCase			=    model
              __UpperCamelCase			=    local_sgd_steps







      def    snake_case						(  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    snake_case						(  self       :			str					):
          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  __UpperCamelCase			=    self.accelerator.reduce(param.data			,							reduction='''mean'''					)







'''simple docstring'''


import io
import json
import subprocess


snake_case__	:     Any      =    [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

snake_case__	:     Tuple      =    8


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = {}
     for src, tgt in pairs:
          UpperCAmelCase_				:  str  = F'''{src}-{tgt}'''
          UpperCAmelCase_				:  List[str]  = F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
          UpperCAmelCase_				:  Any  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  Any  = F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
          UpperCAmelCase_				:  List[str]  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  str  = {'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
     return text


snake_case__	:     Union[str, Any]      =    get_all_data(pairs, n_objs)
snake_case__	:     Dict      =    '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
 snake_case__	:     List[str]      =    json.dump(text, f, indent=2, ensure_ascii=False)






