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__    :		int,						UpperCamelCase__    :		Any = False       ):







				'''simple docstring'''







				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     lowerCamelCase_ (			UpperCamelCase__    :		Optional[Any] = None,						UpperCamelCase__    :		Optional[Any] = None       ):







				'''simple docstring'''







				if rng_type == RNGType.TORCH:
								UpperCamelCase__       						=							torch.get_rng_state()
				elif rng_type == RNGType.CUDA:
								UpperCamelCase__       						=							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__       						=							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__       						=							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__       						=							torch.xpu.get_rng_state()
				elif rng_type == RNGType.GENERATOR:
								assert generator is not None, "Need a generator to synchronize its seed."
								UpperCamelCase__       						=							generator.get_state()

				# Broadcast the rng state from device 0 to other devices
				UpperCamelCase__       						=							AcceleratorState()
				if state.distributed_type == DistributedType.TPU:
								UpperCamelCase__       						=							rng_state.to(xm.xla_device()       )
								xm.collective_broadcast([rng_state]       )
								xm.mark_step()
								UpperCamelCase__       						=							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__       						=							rng_state.to(state.device       )
								torch.distributed.broadcast(UpperCamelCase__,						0       )
								UpperCamelCase__       						=							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     lowerCamelCase_ (			UpperCamelCase__    :		Optional[Any],						UpperCamelCase__    :		int = None       ):







				'''simple docstring'''







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





def 				A__    (  lowerCamelCase = 20  )     ->		int:

				try:
								UpperCamelCase_:							int		=  int(lowerCamelCase  )
				except (TypeError, ValueError):
								raise TypeError("""Parameter n must be int or castable to int."""  )
				if n <= 0:
								raise ValueError("""Parameter n must be greater than or equal to one."""  )
				UpperCamelCase_:							Any		=  0
				while 1:
								i += n * (n - 1)
								UpperCamelCase_:							List[Any]		=  0
								for j in range(2   ,			lowerCamelCase  ):
												if i % j != 0:
																UpperCamelCase_:							Union[str, Any]		=  1
																break
								if nfound == 0:
												if i == 0:
																UpperCamelCase_:							List[str]		=  1
												return i
				return None


if __name__ == "__main__":
							print(F"""{solution() = }""")






