
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   ,			lowerCamelCase = False  )     ->		Optional[Any]:
				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 = None   ,			lowerCamelCase = None  )     ->		Optional[Any]:
				# Get the proper rng state
				if rng_type == RNGType.TORCH:
								UpperCamelCase_:							Union[str, Any]		=  torch.get_rng_state()
				elif rng_type == RNGType.CUDA:
								UpperCamelCase_:							List[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_:							Union[str, Any]		=  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_:							Optional[int]		=  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_:							Union[str, Any]		=  AcceleratorState()
				if state.distributed_type == DistributedType.TPU:
								UpperCamelCase_:							List[Any]		=  rng_state.to(xm.xla_device()  )
								xm.collective_broadcast([rng_state]  )
								xm.mark_step()
								UpperCamelCase_:							Dict		=  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_:							int		=  rng_state.to(state.device  )
								torch.distributed.broadcast(lowerCamelCase   ,			0  )
								UpperCamelCase_:							Any		=  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   ,			lowerCamelCase = None  )     ->		Tuple:
				for rng_type in rng_types:
								synchronize_rng_state(RNGType(lowerCamelCase  )   ,			generator=lowerCamelCase  )








def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				for _ in range(
				    0   ,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
								for j in range(0   ,			no_of_elements - 1  ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							lowerCamelCase_  :       Union[str, Any]					      =     [int(x) for x in input().split()]  # inputing elements of the list in one line
							lowerCamelCase_  :       Union[str, Any]					      =     double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)






