



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_			)








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()



