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			(     snake_case__ :					Union[str, Any] ,  snake_case__ :					Optional[int] = False			)			->  Dict:
  
  
  
  
  
  
     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			(     snake_case__ :					Dict = None ,  snake_case__ :					Dict = None			)			->  Optional[int]:
  
  
  
  
  
  
     if rng_type == RNGType.TORCH:
        UpperCamelCase					:						str					=			torch.get_rng_state()
     elif rng_type == RNGType.CUDA:
        UpperCamelCase					:						List[str]					=			torch.cuda.get_rng_state()
     elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        UpperCamelCase					:						str					=			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."
        UpperCamelCase					:						Optional[Any]					=			torch.npu.get_rng_state()
     elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        UpperCamelCase					:						Any					=			torch.xpu.get_rng_state()
     elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        UpperCamelCase					:						Dict					=			generator.get_state()
  
     # Broadcast the rng state from device 0 to other devices
     UpperCamelCase					:						List[Any]					=			AcceleratorState()
     if state.distributed_type == DistributedType.TPU:
        UpperCamelCase					:						Union[str, Any]					=			rng_state.to(xm.xla_device()			)
        xm.collective_broadcast([rng_state]			)
        xm.mark_step()
        UpperCamelCase					:						Tuple					=			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
     ):
        UpperCamelCase					:						Tuple					=			rng_state.to(state.device			)
        torch.distributed.broadcast(UpperCamelCase_ ,  0			)
        UpperCamelCase					:						int					=			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			(     snake_case__ :					int ,  snake_case__ :					List[Any] = None			)			->  Union[str, Any]:
  
  
  
  
  
  
     for rng_type in rng_types:
        synchronize_rng_state(RNGType(UpperCamelCase_			) ,  generator=UpperCamelCase_			)
  
  
  
  
style_context: 
  
  
  
  import json
  import logging
  import os
  import subprocess
  from argparse import ArgumentParser
  
  
  __UpperCAmelCase      = logging.getLogger(__name__)
  def 	UpperCamelCase			(     )			->  List[str]:
     UpperCamelCase					:						Union[str, Any]					=			ArgumentParser()
     UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			parser.parse_known_args()
     for arg in unknown:
        if arg.startswith(('-', '--')			):
           parser.add_argument(arg.split('='			)[0]			)
  
     return parser.parse_args()
  def 	UpperCamelCase			(     )			->  Optional[Any]:
     UpperCamelCase					:						int					=			parse_args()
     UpperCamelCase					:						Dict					=			8888
     UpperCamelCase					:						Union[str, Any]					=			int(os.environ['SM_NUM_GPUS']			)
     UpperCamelCase					:						Optional[Any]					=			json.loads(os.environ['SM_HOSTS']			)
     UpperCamelCase					:						Dict					=			len(snake_case__			)
     UpperCamelCase					:						int					=			os.environ['SM_CURRENT_HOST']
     UpperCamelCase					:						List[str]					=			hosts.index(snake_case__			)
     UpperCamelCase					:						Optional[Any]					=			'INFO'
  
     if num_nodes > 1:
        UpperCamelCase					:						List[Any]					=			F"""python -m torch.distributed.launch \
                  --nnodes={num_nodes}  \
                  --node_rank={rank}  \
                  --nproc_per_node={num_gpus}  \
                  --master_addr={hosts[0]}  \
                  --master_port={port} \
                  ./run_glue.py \
                  {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     else:
        UpperCamelCase					:						Any					=			F"""python -m torch.distributed.launch \
              --nproc_per_node={num_gpus}  \
              ./run_glue.py \
              {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     try:
        subprocess.run(snake_case__ ,  shell=snake_case__			)
     except Exception as e:
        logger.info(snake_case__			)
  
  
  if __name__ == "__main__":
      main()
  
  
  
  