




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  (	UpperCAmelCase,	UpperCAmelCase = False       )  ->Optional[int]:






					"""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  lowerCAmelCase  (	UpperCAmelCase = None,	UpperCAmelCase = None       )  ->Union[str, Any]:






					"""simple docstring"""






					if rng_type == RNGType.TORCH:
										__magic_name__					:							Optional[Any]		   =							torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										__magic_name__					:							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."
										__magic_name__					:							Dict		   =							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__					:							int		   =							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__					:							Tuple		   =							torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										__magic_name__					:							Union[str, Any]		   =							generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					__magic_name__					:							Union[str, Any]		   =							AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										__magic_name__					:							Tuple		   =							rng_state.to(xm.xla_device()       )
										xm.collective_broadcast([rng_state]       )
										xm.mark_step()
										__magic_name__					:							List[str]		   =							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__					:							Union[str, Any]		   =							rng_state.to(state.device       )
										torch.distributed.broadcast(a_,	0       )
										__magic_name__					:							Tuple		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase = None       )  ->str:






					"""simple docstring"""






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





"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		assert (
		    isinstance(a_,						a_			) and mask > 0
		), F"""mask needs to be positive integer, your input {mask}"""
		lowerCamelCase     :			Tuple		=    []
		lowerCamelCase     :			Tuple		=    mask

		while submask:
				all_submasks.append(a_			)
				lowerCamelCase     :			Optional[int]		=    (submask - 1) & mask

		return all_submasks


if __name__ == "__main__":
					import doctest

					doctest.testmod()






