"""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							(       UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							bool = False    )				->     Optional[int]:

	'''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     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[RNGType] = None     ,				UpperCAmelCase_       :							Optional[torch.Generator] = None    )				->     Any:

	'''simple docstring'''





	if rng_type == RNGType.TORCH:
		__snake_case     :				Tuple 						=					torch.get_rng_state()
	elif rng_type == RNGType.CUDA:
		__snake_case     :				Union[str, 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."
		__snake_case     :				int 						=					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."
		__snake_case     :				List[str] 						=					torch.npu.get_rng_state()
	elif rng_type == RNGType.XPU:
		assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
		__snake_case     :				int 						=					torch.xpu.get_rng_state()
	elif rng_type == RNGType.GENERATOR:
		assert generator is not None, "Need a generator to synchronize its seed."
		__snake_case     :				int 						=					generator.get_state()

	# Broadcast the rng state from device 0 to other devices
	__snake_case     :				List[Any] 						=					AcceleratorState()
	if state.distributed_type == DistributedType.TPU:
		__snake_case     :				Dict 						=					rng_state.to(xm.xla_device()    )
		xm.collective_broadcast([rng_state]    )
		xm.mark_step()
		__snake_case     :				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
	):
		__snake_case     :				Tuple 						=					rng_state.to(state.device    )
		torch.distributed.broadcast(UpperCAmelCase_     ,				0    )
		__snake_case     :				str 						=					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     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Union[str, RNGType]]     ,				UpperCAmelCase_       :							Optional[torch.Generator] = None    )				->     Dict:

	'''simple docstring'''





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






"""simple docstring"""







def     __UpperCAmelCase							(       UpperCAmelCase_       :							int = 1_00    )				->     int:

	'''simple docstring'''





	__snake_case     :				Any 						=					1
	__snake_case     :				Tuple 						=					0
	for i in range(1     ,				num + 1    ):
		fact *= i

	for j in str(UpperCAmelCase_    ):
		result += int(UpperCAmelCase_    )

	return result


if __name__ == "__main__":
	print(solution(int(input("Enter the Number: ").strip())))





