




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      lowerCAmelCase_     (					lowerCamelCase:    int       ,			lowerCamelCase:    bool = False  )   ->     List[str]:


      '''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      lowerCAmelCase_     (					lowerCamelCase:    Optional[RNGType] = None       ,			lowerCamelCase:    Optional[torch.Generator] = None  )   ->     Dict:


      '''simple docstring'''




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

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


      '''simple docstring'''




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





def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if right <= left:
            return
      lowerCamelCase__ 					=			lowerCamelCase__ 					=			left
      lowerCamelCase__ 					=			right
      lowerCamelCase__ 					=			sorting[left]
      while i <= b:
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[a]
                  a += 1
                  i += 1
            elif sorting[i] > pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[b]
                  b -= 1
            else:
                  i += 1
      quick_sort_apartition(lowerCamelCase       ,			lowerCamelCase       ,			a - 1  )
      quick_sort_apartition(lowerCamelCase       ,			b + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if left < right:
            lowerCamelCase__ 					=			lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			pivot_index - 1  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			pivot_index + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			sorting[right]
      lowerCamelCase__ 					=			left
      for i in range(lowerCamelCase       ,			lowerCamelCase  ):
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[store_index]
                  store_index += 1
      lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[store_index], sorting[right]
      return store_index

def      lowerCAmelCase_     (					lowerCamelCase:    list  )   ->     list:


      '''simple docstring'''




      if len(lowerCamelCase  ) <= 1:
            return sorting
      return (
          three_way_radix_quicksort([i for i in sorting if i < sorting[0]]  )
          + [i for i in sorting if i == sorting[0]]
          + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]  )
      )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				str				      =[int(item) for item in user_input.split(""",""")]
      quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
      print(unsorted)