



'''simple docstring'''





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =(LMSDiscreteScheduler,)
  a_							 =10




  def 						_lowercase					(			self				:     List[str]      ,     **_a				:     Optional[Any]       ) -> Dict:
    __lowerCamelCase	:	str   	=			{
        'num_train_timesteps': 1100,
        'beta_start': 0.0001,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
    }

    config.update(**_a       )
    return config




  def 						_lowercase					(			self				:     Dict       ) -> List[Any]:
    for timesteps in [10, 50, 100, 1000]:
      self.check_over_configs(num_train_timesteps=_a       )




  def 						_lowercase					(			self				:     Optional[int]       ) -> Tuple:
    for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]      ,     [0.0002, 0.002, 0.02]       ):
      self.check_over_configs(beta_start=_a      ,     beta_end=_a       )




  def 						_lowercase					(			self				:     Any       ) -> Tuple:
    for schedule in ["linear", "scaled_linear"]:
      self.check_over_configs(beta_schedule=_a       )




  def 						_lowercase					(			self				:     Tuple       ) -> Optional[Any]:
    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=_a       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[str]:
    for t in [0, 500, 800]:
      self.check_over_forward(time_step=_a       )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    __lowerCamelCase	:	str   	=			self.scheduler_classes[0]
    __lowerCamelCase	:	Optional[Any]   	=			self.get_scheduler_config()
    __lowerCamelCase	:	Tuple   	=			scheduler_class(**_a       )

    scheduler.set_timesteps(self.num_inference_steps       )

    __lowerCamelCase	:	Dict   	=			self.dummy_model()
    __lowerCamelCase	:	Optional[int]   	=			self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps       ):
      __lowerCamelCase	:	Dict   	=			scheduler.scale_model_input(_a      ,     _a       )

      __lowerCamelCase	:	int   	=			model(_a      ,     _a       )

      __lowerCamelCase	:	Union[str, Any]   	=			scheduler.step(_a      ,     _a      ,     _a       )
      __lowerCamelCase	:	Tuple   	=			output.prev_sample

    __lowerCamelCase	:	List[Any]   	=			torch.sum(torch.abs(_a       )       )
    __lowerCamelCase	:	List[Any]   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_sum.item() - 1006.388       ) < 1e-2
    assert abs(result_mean.item() - 1.31       ) < 1e-3




  def 						_lowercase					(			self				:     Optional[int]       ) -> Optional[int]:
    __lowerCamelCase	:	str   	=			self.scheduler_classes[0]
    __lowerCamelCase	:	List[str]   	=			self.get_scheduler_config(prediction_type='v_prediction'       )
    __lowerCamelCase	:	int   	=			scheduler_class(**_a       )

    scheduler.set_timesteps(self.num_inference_steps       )

    __lowerCamelCase	:	Any   	=			self.dummy_model()
    __lowerCamelCase	:	Optional[Any]   	=			self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps       ):
      __lowerCamelCase	:	Dict   	=			scheduler.scale_model_input(_a      ,     _a       )

      __lowerCamelCase	:	Optional[Any]   	=			model(_a      ,     _a       )

      __lowerCamelCase	:	int   	=			scheduler.step(_a      ,     _a      ,     _a       )
      __lowerCamelCase	:	Tuple   	=			output.prev_sample

    __lowerCamelCase	:	Optional[Any]   	=			torch.sum(torch.abs(_a       )       )
    __lowerCamelCase	:	Union[str, Any]   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_sum.item() - 0.0017       ) < 1e-2
    assert abs(result_mean.item() - 2.2_676e-06       ) < 1e-3




  def 						_lowercase					(			self				:     int       ) -> Any:
    __lowerCamelCase	:	List[str]   	=			self.scheduler_classes[0]
    __lowerCamelCase	:	List[str]   	=			self.get_scheduler_config()
    __lowerCamelCase	:	Union[str, Any]   	=			scheduler_class(**_a       )

    scheduler.set_timesteps(self.num_inference_steps      ,     device=_a       )

    __lowerCamelCase	:	Optional[int]   	=			self.dummy_model()
    __lowerCamelCase	:	str   	=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    __lowerCamelCase	:	Any   	=			sample.to(_a       )

    for i, t in enumerate(scheduler.timesteps       ):
      __lowerCamelCase	:	Tuple   	=			scheduler.scale_model_input(_a      ,     _a       )

      __lowerCamelCase	:	Optional[int]   	=			model(_a      ,     _a       )

      __lowerCamelCase	:	Optional[int]   	=			scheduler.step(_a      ,     _a      ,     _a       )
      __lowerCamelCase	:	Optional[Any]   	=			output.prev_sample

    __lowerCamelCase	:	Dict   	=			torch.sum(torch.abs(_a       )       )
    __lowerCamelCase	:	List[str]   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_sum.item() - 1006.388       ) < 1e-2
    assert abs(result_mean.item() - 1.31       ) < 1e-3




  def 						_lowercase					(			self				:     Optional[int]       ) -> Dict:
    __lowerCamelCase	:	Optional[Any]   	=			self.scheduler_classes[0]
    __lowerCamelCase	:	List[str]   	=			self.get_scheduler_config()
    __lowerCamelCase	:	str   	=			scheduler_class(**_a      ,     use_karras_sigmas=_a       )

    scheduler.set_timesteps(self.num_inference_steps      ,     device=_a       )

    __lowerCamelCase	:	str   	=			self.dummy_model()
    __lowerCamelCase	:	Dict   	=			self.dummy_sample_deter.to(_a       ) * scheduler.init_noise_sigma
    __lowerCamelCase	:	List[str]   	=			sample.to(_a       )

    for t in scheduler.timesteps:
      __lowerCamelCase	:	Tuple   	=			scheduler.scale_model_input(_a      ,     _a       )

      __lowerCamelCase	:	Optional[Any]   	=			model(_a      ,     _a       )

      __lowerCamelCase	:	Tuple   	=			scheduler.step(_a      ,     _a      ,     _a       )
      __lowerCamelCase	:	List[Any]   	=			output.prev_sample

    __lowerCamelCase	:	Tuple   	=			torch.sum(torch.abs(_a       )       )
    __lowerCamelCase	:	Tuple   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_sum.item() - 3812.9927       ) < 2e-2
    assert abs(result_mean.item() - 4.9648       ) < 1e-3











'''simple docstring'''





import random






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     float      ,     _a				:     float       ) -> None:
    __lowerCamelCase	:	Optional[int]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y




  def 						_lowercase					(			self				:     Tuple       ) -> bool:
    return (self.x**2 + self.y**2) <= 1




  @classmethod
  def 						_lowercase					(			cls				:     List[Any]       ) -> Optional[int]:
    return cls(x=random.random()      ,     y=random.random()       )




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

  __lowerCamelCase	:	Optional[int]   	=			0
  for _ in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			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

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






