





"""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 	__lowercase  (     snake_case_						:						Any       ,snake_case_						:						List[str] = False		)     ->List[str]:



				'''simple docstring'''







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







def 	__lowercase  (     snake_case_						:						int = None       ,snake_case_						:						Any = None		)     ->Dict:



				'''simple docstring'''







				if rng_type == RNGType.TORCH:
								__A					:		int						=  torch.get_rng_state()
				elif rng_type == RNGType.CUDA:
								__A					:		Optional[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."
								__A					:		Dict						=  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					:		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."
								__A					:		Any						=  torch.xpu.get_rng_state()
				elif rng_type == RNGType.GENERATOR:
								assert generator is not None, "Need a generator to synchronize its seed."
								__A					:		Tuple						=  generator.get_state()

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

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







def 	__lowercase  (     snake_case_						:						str       ,snake_case_						:						Tuple = None		)     ->Tuple:



				'''simple docstring'''







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











"""simple docstring"""



import decimal







def 	__lowercase  (     )     ->int:



    '''simple docstring'''







    __A					:		List[str]						=  0
    __A					:		Tuple						=  decimal.Context(prec=105		)
    for i in range(2       ,100		):
        __A					:		int						=  decimal.Decimal(snake_case_		)
        __A					:		str						=  number.sqrt(snake_case_		)
        if len(str(snake_case_		)		) > 1:
            answer += int(str(snake_case_		)[0]		)
            __A					:		Optional[Any]						=  str(snake_case_		)[2:101]
            answer += sum(int(snake_case_		) for x in sqrt_number_str		)
    return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f'''{solution() = }''')




