



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       A_   (						__a  :					int			,		__a  :					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       A_   (						__a  :					Optional[RNGType] = None			,		__a  :					Optional[torch.Generator] = None		):

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

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

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






UpperCAmelCase							=       (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
UpperCAmelCase							=       (-153, -1, 0, 1.2, 200, "A", [], {}, None)

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Initialization of sum and number of digits.
				a__					   =		0
				a__					   =		0
				a__					   =		n
				# Calculation of digits of the number
				while temp > 0:
								number_of_digits += 1
								temp //= 10
				# Dividing number into separate digits and find Armstrong number
				a__					   =		n
				while temp > 0:
								a__					   =		temp % 10
								total += rem**number_of_digits
								temp //= 10
				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Init a "histogram" of the digits
				a__					   =		[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
				a__					   =		0
				a__					   =		0
				a__					   =		n
				while temp > 0:
								a__   ,      a__					   =		divmod(__a			,		10		)
								digit_histogram[rem] += 1
								digit_total += 1

				for cnt, i in zip(__a			,		range(len(__a		)		)		):
								total += cnt * i**digit_total

				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False
				a__					   =		len(str(__a		)		)  # the power that all digits will be raised to
				# check if sum of each digit multiplied expo times is equal to number
				return n == sum(int(__a		) ** expo for i in str(__a		)		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		int(input("""Enter an integer to see if it is an Armstrong number: """		).strip()		)
				print(F'''{num} is {"" if armstrong_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if narcissistic_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if pluperfect_number(__a		) else "not "}an Armstrong number.'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

