


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_						(						_UpperCAmelCase       :int     ,	_UpperCAmelCase       :bool = False							)  ->							Tuple:
     '''simple docstring'''


     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_						(						_UpperCAmelCase       :Optional[RNGType] = None     ,	_UpperCAmelCase       :Optional[torch.Generator] = None							)  ->							List[str]:
     '''simple docstring'''


     if rng_type == RNGType.TORCH:
          A_					      =					torch.get_rng_state()
     elif rng_type == RNGType.CUDA:
          A_					      =					torch.cuda.get_rng_state()
     elif rng_type == RNGType.XLA:
          assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
          A_					      =					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."
          A_					      =					torch.npu.get_rng_state()
     elif rng_type == RNGType.XPU:
          assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
          A_					      =					torch.xpu.get_rng_state()
     elif rng_type == RNGType.GENERATOR:
          assert generator is not None, "Need a generator to synchronize its seed."
          A_					      =					generator.get_state()

     # Broadcast the rng state from device 0 to other devices
     A_					      =					AcceleratorState()
     if state.distributed_type == DistributedType.TPU:
          A_					      =					rng_state.to(xm.xla_device()							)
          xm.collective_broadcast([rng_state]							)
          xm.mark_step()
          A_					      =					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
     ):
          A_					      =					rng_state.to(state.device							)
          torch.distributed.broadcast(_UpperCAmelCase     ,	0							)
          A_					      =					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_						(						_UpperCAmelCase       :List[Union[str, RNGType]]     ,	_UpperCAmelCase       :Optional[torch.Generator] = None							)  ->							List[str]:
     '''simple docstring'''


     for rng_type in rng_types:
          synchronize_rng_state(RNGType(_UpperCAmelCase							)     ,	generator=_UpperCAmelCase							)










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



