



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


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase = None   ,				__UpperCAmelCase = None      ):
		# Get the proper rng state
		if rng_type == RNGType.TORCH:
				_lowercase				: str     =   torch.get_rng_state()
		elif rng_type == RNGType.CUDA:
				_lowercase				: Tuple     =   torch.cuda.get_rng_state()
		elif rng_type == RNGType.XLA:
				assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
				_lowercase				: List[str]     =   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."
				_lowercase				: Optional[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."
				_lowercase				: str     =   torch.xpu.get_rng_state()
		elif rng_type == RNGType.GENERATOR:
				assert generator is not None, "Need a generator to synchronize its seed."
				_lowercase				: str     =   generator.get_state()

		# Broadcast the rng state from device 0 to other devices
		_lowercase				: Optional[Any]     =   AcceleratorState()
		if state.distributed_type == DistributedType.TPU:
				_lowercase				: Tuple     =   rng_state.to(xm.xla_device()      )
				xm.collective_broadcast([rng_state]      )
				xm.mark_step()
				_lowercase				: Tuple     =   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
		):
				_lowercase				: List[str]     =   rng_state.to(state.device      )
				torch.distributed.broadcast(__UpperCAmelCase   ,				0      )
				_lowercase				: int     =   rng_state.cpu()
		elif state.distributed_type == DistributedType.MULTI_CPU:
				torch.distributed.broadcast(__UpperCAmelCase   ,				0      )

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


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase = None      ):
		for rng_type in rng_types:
				synchronize_rng_state(RNGType(__UpperCAmelCase      )   ,				generator=__UpperCAmelCase      )








from .prime_check import Test

Test()

