



'''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 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    bool = False       ):






	'''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       (				lowerCamelCase :    Optional[RNGType] = None					,		lowerCamelCase :    Optional[torch.Generator] = None       ):






	'''simple docstring'''




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

	# Broadcast the rng state from device 0 to other devices
	__lowerCAmelCase       				=	AcceleratorState()
	if state.distributed_type == DistributedType.TPU:
		__lowerCAmelCase       				=	rng_state.to(xm.xla_device()       )
		xm.collective_broadcast([rng_state]       )
		xm.mark_step()
		__lowerCAmelCase       				=	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
	):
		__lowerCAmelCase       				=	rng_state.to(state.device       )
		torch.distributed.broadcast(__a					,		0       )
		__lowerCAmelCase       				=	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       (				lowerCamelCase :    List[Union[str, RNGType]]					,		lowerCamelCase :    Optional[torch.Generator] = None       ):






	'''simple docstring'''




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




import math
from decimal import Decimal, getcontext

def       A_   (						__a  :					int = 4_000_000		):

				"""simple docstring"""

				try:
								a__					   =		int(__a		)
				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."""		)
				a__					   =		100
				a__					   =		(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

				a__					   =		(math.floor(math.log(n * (phi + 2)			,		__a		) - 1		) // 3) * 3 + 2
				a__					   =		Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
				a__					   =		num // 2
				return int(__a		)


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

