

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  _snake_case		(snake_case_					:     Optional[Any]    ,       snake_case_					:     List[Any] = False  )							->		Optional[int]:
  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  _snake_case		(snake_case_					:     List[str] = None    ,       snake_case_					:     Optional[Any] = None  )							->		Tuple:
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    __UpperCAmelCase	:  Any		=     torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    __UpperCAmelCase	:  int		=     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	:  Optional[Any]		=     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	:  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."
    __UpperCAmelCase	:  Dict		=     torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    __UpperCAmelCase	:  List[str]		=     generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  __UpperCAmelCase	:  str		=     AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    __UpperCAmelCase	:  int		=     rng_state.to(xm.xla_device()  )
    xm.collective_broadcast([rng_state]  )
    xm.mark_step()
    __UpperCAmelCase	:  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
  ):
    __UpperCAmelCase	:  Dict		=     rng_state.to(state.device  )
    torch.distributed.broadcast(a_    ,       0  )
    __UpperCAmelCase	:  Dict		=     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  _snake_case		(snake_case_					:     int    ,       snake_case_					:     List[str] = None  )							->		Union[str, Any]:
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(a_  )    ,       generator=a_  )













import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return (self.x**2 + self.y**2) <= 1







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

									return cls(x=random.random()				,    y=random.random()       )






def 				A	(			a_   )	->		float:
					if number_of_simulations < 1:
										raise ValueError('At least one simulation is necessary to estimate PI.'   )

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=Point.random_unit_square()

										if random_point.is_in_unit_circle():
															number_in_unit_circle += 1

					return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							A_			:Optional[int]	    =		'''Please enter the desired number of Monte Carlo simulations: '''
							A_			:int	    =		estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")

