





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							(      lowerCamelCase:  int   ,							lowerCamelCase:  bool = False					)      ->		Dict:







      '''simple docstring'''






      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(lowerCamelCase					)
      np.random.seed(lowerCamelCase					)
      torch.manual_seed(lowerCamelCase					)
      if is_xpu_available():
            torch.xpu.manual_seed_all(lowerCamelCase					)
      elif is_npu_available():
            torch.npu.manual_seed_all(lowerCamelCase					)
      else:
            torch.cuda.manual_seed_all(lowerCamelCase					)
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(lowerCamelCase					)



def _a							(      lowerCamelCase:  Optional[RNGType] = None   ,							lowerCamelCase:  Optional[torch.Generator] = None					)      ->		Optional[int]:







      '''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(lowerCamelCase   ,							0					)
            __A			  =							rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCamelCase   ,							0					)

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(lowerCamelCase					)
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(lowerCamelCase					)
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(lowerCamelCase					)
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(lowerCamelCase					)
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()					)
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(lowerCamelCase					)







def _a							(      lowerCamelCase:  List[Union[str, RNGType]]   ,							lowerCamelCase:  Optional[torch.Generator] = None					)      ->		List[Any]:







      '''simple docstring'''






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












def _a							(      lowerCamelCase:  list					)      ->		list:







      '''simple docstring'''






      __A			  =							len(lowerCamelCase					)
      for cycle_start in range(0   ,							array_len - 1					):
            __A			  =							array[cycle_start]

            __A			  =							cycle_start
            for i in range(cycle_start + 1   ,							lowerCamelCase					):
                  if array[i] < item:
                        pos += 1

            if pos == cycle_start:
                  continue

            while item == array[pos]:
                  pos += 1

            __A  ,		__A			  =							item, array[pos]
            while pos != cycle_start:
                  __A			  =							cycle_start
                  for i in range(cycle_start + 1   ,							lowerCamelCase					):
                        if array[i] < item:
                              pos += 1

                  while item == array[pos]:
                        pos += 1

                  __A  ,		__A			  =							item, array[pos]

      return array


if __name__ == "__main__":
       assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





