

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      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     bool = 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      a__	(						UpperCAmelCase				:     Optional[RNGType] = None							,					UpperCAmelCase				:     Optional[torch.Generator] = None     )    ->		Dict:
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    UpperCAmelCase			:				Dict										=					torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    UpperCAmelCase			:				Union[str, Any]										=					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			:				List[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			:				Any										=					generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  UpperCAmelCase			:				Any										=					AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    UpperCAmelCase			:				List[str]										=					rng_state.to(xm.xla_device()     )
    xm.collective_broadcast([rng_state]     )
    xm.mark_step()
    UpperCAmelCase			:				Optional[int]										=					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			:				Optional[int]										=					rng_state.to(state.device     )
    torch.distributed.broadcast(UpperCAmelCase							,					0     )
    UpperCAmelCase			:				List[Any]										=					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      a__	(						UpperCAmelCase				:     List[Union[str, RNGType]]							,					UpperCAmelCase				:     Optional[torch.Generator] = None     )    ->		Tuple:
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(UpperCAmelCase     )							,					generator=UpperCAmelCase     )








def      a__	(						UpperCAmelCase				:     list     )    ->		list:
  UpperCAmelCase			:				Tuple										=					len(UpperCAmelCase     )

  # Set the gap value to be decreased by a factor of 1.3
  # after each iteration
  UpperCAmelCase			:				Optional[Any]										=					1.3

  # Continue sorting until the gap is 1
  while gap > 1:
    # Decrease the gap value
    UpperCAmelCase			:				int										=					int(gap / shrink     )

    # Sort the elements using insertion sort
    for i in range(UpperCAmelCase							,					len(UpperCAmelCase     )     ):
      UpperCAmelCase			:				Union[str, Any]										=					collection[i]
      UpperCAmelCase			:				List[Any]										=					i
      while j >= gap and collection[j - gap] > temp:
        UpperCAmelCase			:				int										=					collection[j - gap]
        j -= gap
      UpperCAmelCase			:				List[Any]										=					temp

  return collection


if __name__ == "__main__":
     import doctest

     doctest.testmod()





