
'''simple docstring'''






from math import pi, sqrt, tan





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]):
				if side_length < 0:
								raise ValueError('surface_area_cube() only accepts non-negative values')
				return 6 * side_length**2





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Dict):
				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('surface_area_cuboid() only accepts non-negative values')
				return 2 * ((length * breadth) + (breadth * height) + (length * height))





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]):
				if radius < 0:
								raise ValueError('surface_area_sphere() only accepts non-negative values')
				return 4 * pi * radius**2





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple):
				if radius < 0:
								raise ValueError('surface_area_hemisphere() only accepts non-negative values')
				return 3 * pi * radius**2





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							str):
				if radius < 0 or height < 0:
								raise ValueError('surface_area_cone() only accepts non-negative values')
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							Optional[int]):
				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    'surface_area_conical_frustum() only accepts non-negative values')
				lowerCamelCase					:			Tuple									= (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							List[Any]):
				if radius < 0 or height < 0:
								raise ValueError('surface_area_cylinder() only accepts non-negative values')
				return 2 * pi * radius * (height + radius)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Optional[int]):
				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('surface_area_torus() only accepts non-negative values')
				if torus_radius < tube_radius:
								raise ValueError(
								    'surface_area_torus() does not support spindle or self intersecting tori')
				return 4 * pow(_lowerCAmelCase       ,							2) * torus_radius * tube_radius





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							List[str]):
				if length < 0 or width < 0:
								raise ValueError('area_rectangle() only accepts non-negative values')
				return length * width





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple):
				if side_length < 0:
								raise ValueError('area_square() only accepts non-negative values')
				return side_length**2





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Optional[int]):
				if base < 0 or height < 0:
								raise ValueError('area_triangle() only accepts non-negative values')
				return (base * height) / 2





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							Optional[int]):
				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('area_triangle_three_sides() only accepts non-negative values')
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('Given three sides do not form a triangle')
				lowerCamelCase					:			Optional[Any]									= (sidea + sidea + sidea) / 2
				lowerCamelCase					:			List[Any]									= sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea))
				return area





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							str):
				if base < 0 or height < 0:
								raise ValueError('area_parallelogram() only accepts non-negative values')
				return base * height





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any):
				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('area_trapezium() only accepts non-negative values')
				return 1 / 2 * (basea + basea) * height





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]):
				if radius < 0:
								raise ValueError('area_circle() only accepts non-negative values')
				return pi * radius**2





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Dict):
				if radius_x < 0 or radius_y < 0:
								raise ValueError('area_ellipse() only accepts non-negative values')
				return pi * radius_x * radius_y





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Dict):
				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('area_rhombus() only accepts non-negative values')
				return 1 / 2 * diagonal_a * diagonal_a





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Tuple):
				if not isinstance(_lowerCAmelCase       ,							_lowerCAmelCase) or sides < 3:
								raise ValueError(
								    'area_reg_polygon() only accepts integers greater than or \\nequal to three as number of sides')
				elif length < 0:
								raise ValueError(
								    'area_reg_polygon() only accepts non-negative values as \\nlength of a side')
				return (sides * length**2) / (4 * tan(pi / sides))
				return (sides * length**2) / (4 * tan(pi / sides))


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

			print('[DEMO] Areas of various geometric shapes: \n')
			print(f"""Rectangle: {area_rectangle(10, 20) = }""")
			print(f"""Square: {area_square(10) = }""")
			print(f"""Triangle: {area_triangle(10, 10) = }""")
			print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
			print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
			print(f"""Rhombus: {area_rhombus(10, 20) = }""")
			print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
			print(f"""Circle: {area_circle(20) = }""")
			print(f"""Ellipse: {area_ellipse(10, 20) = }""")
			print('\nSurface Areas of various geometric shapes: \n')
			print(f"""Cube: {surface_area_cube(20) = }""")
			print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
			print(f"""Sphere: {surface_area_sphere(20) = }""")
			print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
			print(f"""Cone: {surface_area_cone(10, 20) = }""")
			print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
			print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
			print(f"""Torus: {surface_area_torus(20, 10) = }""")
			print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
			print(f"""Square: {area_reg_polygon(4, 10) = }""")
			print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")


'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	(DPMSolverSinglestepScheduler,)
					_lowerCAmelCase			     =	(('''num_inference_steps''', 25),)
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lambda_min_clipped': -float('inf'      ),
									    'variance_type': None,
									}

									config.update(**A      )
									return config
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			Optional[Any]									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			Dict									= 0.1 * sample
									lowerCamelCase					:			Dict									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Dict									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )
													# copy over dummy past residuals
													lowerCamelCase					:			str									= dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			List[Any]									= scheduler_class.from_pretrained(A      )
																	new_scheduler.set_timesteps(A      )
																	# copy over dummy past residuals
																	lowerCamelCase					:			Optional[int]									= dummy_past_residuals[: new_scheduler.config.solver_order]

													lowerCamelCase			, lowerCamelCase					:			Optional[int]									= sample, sample
													for t in range(A,    time_step + scheduler.config.solver_order + 1      ):
																	lowerCamelCase					:			Dict									= scheduler.step(A,    A,    A,    **A      ).prev_sample
																	lowerCamelCase					:			Optional[int]									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

																	assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			str									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			List[str]									= 0.1 * sample
									lowerCamelCase					:			List[str]									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Tuple									= self.get_scheduler_config()
													lowerCamelCase					:			Optional[Any]									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )

													# copy over dummy past residuals (must be after setting timesteps)
													lowerCamelCase					:			Any									= dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			Tuple									= scheduler_class.from_pretrained(A      )
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(A      )

																	# copy over dummy past residual (must be after setting timesteps)
																	lowerCamelCase					:			List[Any]									= dummy_past_residuals[: new_scheduler.config.solver_order]

													lowerCamelCase					:			int									= scheduler.step(A,    A,    A,    **A      ).prev_sample
													lowerCamelCase					:			Dict									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

													assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self,    A=None,    **A      ):

									"""simple docstring"""


									if scheduler is None:
													lowerCamelCase					:			Any									= self.scheduler_classes[0]
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
									lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			Any									= 10
									lowerCamelCase					:			Dict									= self.dummy_model()
									lowerCamelCase					:			Any									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			Dict									= model(A,    A      )
													lowerCamelCase					:			List[str]									= scheduler.step(A,    A,    A      ).prev_sample

									return sample
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			Dict									= 50
									lowerCamelCase					:			Tuple									= self.dummy_model()
									lowerCamelCase					:			Optional[int]									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]      ):
													lowerCamelCase					:			Any									= model(A,    A      )
													lowerCamelCase					:			Optional[int]									= scheduler.step(A,    A,    A      ).prev_sample

									lowerCamelCase					:			Any									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2574      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3

									lowerCamelCase					:			Dict									= DEISMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Any									= UniPCMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[Any]									= DPMSolverSinglestepScheduler.from_config(scheduler.config      )

									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(thresholding=A      )
									for order in [1, 2, 3]:
													for solver_type in ["midpoint", "heun"]:
																	for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    thresholding=A,    prediction_type=A,    sample_max_value=A,    algorithm_type='dpmsolver++',    solver_order=A,    solver_type=A,    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																	for order in [1, 2, 3]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									lowerCamelCase					:			Optional[Any]									= self.full_loop(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									assert not torch.isnan(A      ).any(), "Samples have nan numbers"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lower_order_final=A      )
									self.check_over_configs(lower_order_final=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
									self.check_over_configs(lambda_min_clipped=-5.1      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(variance_type=A      )
									self.check_over_configs(variance_type='learned_range'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=A,    time_step=0      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop()
									lowerCamelCase					:			str									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop(use_karras_sigmas=A      )
									lowerCamelCase					:			Tuple									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2248      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction'      )
									lowerCamelCase					:			Dict									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.1453      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction',    use_karras_sigmas=A      )
									lowerCamelCase					:			Optional[Any]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.0649      ) < 1e-3




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Dict									= self.get_scheduler_config(thresholding=A,    dynamic_thresholding_ratio=0      )
									lowerCamelCase					:			str									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= 10
									lowerCamelCase					:			List[str]									= self.dummy_model()
									lowerCamelCase					:			int									= self.dummy_sample_deter.half()
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			str									= model(A,    A      )
													lowerCamelCase					:			Tuple									= scheduler.step(A,    A,    A      ).prev_sample

									assert sample.dtype == torch.floataa
