

'''simple docstring'''




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       ( a		,						a = False )      ->	List[Any]:






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




def 						UpperCamelCase       ( a = None		,						a = None )      ->	List[Any]:






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

     # Broadcast the rng state from device 0 to other devices
     __magic_name__						   =				AcceleratorState()
     if state.distributed_type == DistributedType.TPU:
          __magic_name__						   =				rng_state.to(xm.xla_device() )
          xm.collective_broadcast([rng_state] )
          xm.mark_step()
          __magic_name__						   =				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
     ):
          __magic_name__						   =				rng_state.to(state.device )
          torch.distributed.broadcast(a_		,						0 )
          __magic_name__						   =				rng_state.cpu()
     elif state.distributed_type == DistributedType.MULTI_CPU:
          torch.distributed.broadcast(a_		,						0 )

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




def 						UpperCamelCase       ( a		,						a = None )      ->	int:






     '''simple docstring'''
     for rng_type in rng_types:
          synchronize_rng_state(RNGType(a_ )		,						generator=a_ )














from doctest import testmod
from math import sqrt
def      lowerCamelCase					(						a_	)	->			list:
							lowerCAmelCase_										=     []
							if num < 1:
														return facs
							facs.append(1	)
							if num == 1:
														return facs
							facs.append(a_	)
							for i in range(2 ,						int(sqrt(a_	)	) + 1	):
														if num % i == 0:  # If i is a factor of num
																					facs.append(a_	)
																					lowerCAmelCase_										=     num // i  # num//i is the other factor of num
																					if d != i:  # If d and i are distinct
																												facs.append(a_	)  # we have found another factor
							facs.sort()
							return facs


if __name__ == "__main__":
		testmod(name="""factors_of_a_number""", verbose=True)



