



"""simple docstring"""


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	(DPMSolverSDEScheduler,)
			UpperCamelCase__			      =	10



			def  lowercase__						(		self     :    str				,   **__UpperCamelCase     :    List[Any]   )->Tuple:
							_UpperCAmelCase					=    {
							    "num_train_timesteps": 1_1_0_0,
							    "beta_start": 0.0_0_0_1,
							    "beta_end": 0.0_2,
							    "beta_schedule": "linear",
							    "noise_sampler_seed": 0,
							}

							config.update(**__A   )
							return config



			def  lowercase__						(		self     :    List[str]   )->Tuple:
							for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=__A   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,   [0.0_0_0_2, 0.0_0_2, 0.0_2]   ):
											self.check_over_configs(beta_start=__A				,   beta_end=__A   )



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



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



			def  lowercase__						(		self     :    Tuple   )->Dict:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config()
							_UpperCAmelCase					=    scheduler_class(**__A   )

							scheduler.set_timesteps(self.num_inference_steps   )

							_UpperCAmelCase					=    self.dummy_model()
							_UpperCAmelCase					=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCAmelCase					=    sample.to(__A   )

							for i, t in enumerate(scheduler.timesteps   ):
											_UpperCAmelCase					=    scheduler.scale_model_input(__A				,   __A   )

											_UpperCAmelCase					=    model(__A				,   __A   )

											_UpperCAmelCase					=    scheduler.step(__A				,   __A				,   __A   )
											_UpperCAmelCase					=    output.prev_sample

							_UpperCAmelCase					=    torch.sum(torch.abs(__A   )   )
							_UpperCAmelCase					=    torch.mean(torch.abs(__A   )   )

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5   ) < 1e-2
											assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7   ) < 1e-3
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6   ) < 1e-2
											assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2   ) < 1e-3
							else:
											assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1e-2
											assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1e-3



			def  lowercase__						(		self     :    int   )->Any:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config(prediction_type='''v_prediction'''   )
							_UpperCAmelCase					=    scheduler_class(**__A   )

							scheduler.set_timesteps(self.num_inference_steps   )

							_UpperCAmelCase					=    self.dummy_model()
							_UpperCAmelCase					=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCAmelCase					=    sample.to(__A   )

							for i, t in enumerate(scheduler.timesteps   ):
											_UpperCAmelCase					=    scheduler.scale_model_input(__A				,   __A   )

											_UpperCAmelCase					=    model(__A				,   __A   )

											_UpperCAmelCase					=    scheduler.step(__A				,   __A				,   __A   )
											_UpperCAmelCase					=    output.prev_sample

							_UpperCAmelCase					=    torch.sum(torch.abs(__A   )   )
							_UpperCAmelCase					=    torch.mean(torch.abs(__A   )   )

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3   ) < 1e-2
											assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4   ) < 1e-3
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3   ) < 1e-2
											assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7   ) < 1e-3
							else:
											assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5   ) < 1e-2
											assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1   ) < 1e-3



			def  lowercase__						(		self     :    Union[str, Any]   )->Any:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config()
							_UpperCAmelCase					=    scheduler_class(**__A   )

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

							_UpperCAmelCase					=    self.dummy_model()
							_UpperCAmelCase					=    self.dummy_sample_deter.to(__A   ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											_UpperCAmelCase					=    scheduler.scale_model_input(__A				,   __A   )

											_UpperCAmelCase					=    model(__A				,   __A   )

											_UpperCAmelCase					=    scheduler.step(__A				,   __A				,   __A   )
											_UpperCAmelCase					=    output.prev_sample

							_UpperCAmelCase					=    torch.sum(torch.abs(__A   )   )
							_UpperCAmelCase					=    torch.mean(torch.abs(__A   )   )

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8   ) < 1e-2
											assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5   ) < 1e-3
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2   ) < 1e-2
											assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1   ) < 1e-3
							else:
											assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1e-2
											assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1e-3



			def  lowercase__						(		self     :    Any   )->Tuple:
							_UpperCAmelCase					=    self.scheduler_classes[0]
							_UpperCAmelCase					=    self.get_scheduler_config()
							_UpperCAmelCase					=    scheduler_class(**__A				,   use_karras_sigmas=__A   )

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

							_UpperCAmelCase					=    self.dummy_model()
							_UpperCAmelCase					=    self.dummy_sample_deter.to(__A   ) * scheduler.init_noise_sigma
							_UpperCAmelCase					=    sample.to(__A   )

							for t in scheduler.timesteps:
											_UpperCAmelCase					=    scheduler.scale_model_input(__A				,   __A   )

											_UpperCAmelCase					=    model(__A				,   __A   )

											_UpperCAmelCase					=    scheduler.step(__A				,   __A				,   __A   )
											_UpperCAmelCase					=    output.prev_sample

							_UpperCAmelCase					=    torch.sum(torch.abs(__A   )   )
							_UpperCAmelCase					=    torch.mean(torch.abs(__A   )   )

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8   ) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5   ) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2
							else:
											assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2   ) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2








"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def is_in_circle(_SCREAMING_SNAKE_CASE   :						float					,     _SCREAMING_SNAKE_CASE   :						float				) -> bool:
								_UpperCAmelCase					=    sqrt((x**2) + (y**2)				)
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_UpperCAmelCase					=    mean(
				    int(is_in_circle(uniform(-1.0					,     1.0				)					,     uniform(-1.0					,     1.0				)				)				)
				    for _ in range(_SCREAMING_SNAKE_CASE				)				)
				# The ratio of the area for circle to square is pi/4.
				_UpperCAmelCase					=    proportion * 4
				print(f'The estimated value of pi is {pi_estimate}'				)
				print(f'The numpy value of pi is {pi}'				)
				print(f'The total error is {abs(pi - pi_estimate				)}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Callable[[float], float]					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0					,     ):






				'''simple docstring'''



				return mean(
				    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)				) for _ in range(_SCREAMING_SNAKE_CASE				)				) * (max_value - min_value)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0				):






				'''simple docstring'''



				def identity_function(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return x

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    (max_value * max_value - min_value * min_value) / 2

				print('''******************'''				)
				print(f'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {expected_value}'				)
				print(f'Total error is {abs(estimated_value - expected_value				)}'				)
				print('''******************'''				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def function_to_integrate(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return sqrt(4.0 - x * x				)

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     0.0					,     2.0				)

				print('''******************'''				)
				print('''Estimating pi using area_under_curve_estimator'''				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {pi}'				)
				print(f'Total error is {abs(estimated_value - pi				)}'				)
				print('''******************'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



