
"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (UnCLIPScheduler,)





	def   __lowerCAmelCase (						self    :str		, **lowerCamelCase_    :Optional[Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= {
			    '''num_train_timesteps''': 10_00,
			    '''variance_type''': '''fixed_small_log''',
			    '''clip_sample''': True,
			    '''clip_sample_range''': 1.0,
			    '''prediction_type''': '''epsilon''',
			}

			config.update(**lowerCamelCase_					)
			return config





	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			for timesteps in [1, 5, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :str					)							->			Optional[int]:







			'''simple docstring'''




			for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			List[Any]:







			'''simple docstring'''




			for clip_sample_range in [1, 5, 10, 20]:
					self.check_over_configs(clip_sample_range=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			List[str]:







			'''simple docstring'''




			for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			for time_step in [0, 5_00, 9_99]:
					for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
							if prev_timestep is not None and prev_timestep >= time_step:
									continue

							self.check_over_forward(time_step=lowerCamelCase_		, prev_timestep=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_scheduler_config(variance_type='''fixed_small_log'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= scheduler_class(**lowerCamelCase_					)

			assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0000E-10					)					) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_5_4_9_6_2_5					)					) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.9_9_9_4_9_8_7					)					) < 1E-5





	def   __lowerCAmelCase (						self    :Any					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.get_scheduler_config(variance_type='''learned_range'''					)
			SCREAMING_SNAKE_CASE       :     Dict				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0.5

			assert scheduler._get_variance(1		, predicted_variance=lowerCamelCase_					) - -1_0.1_7_1_2_7_9_0 < 1E-5
			assert scheduler._get_variance(4_87		, predicted_variance=lowerCamelCase_					) - -5.7_9_9_8_0_5_2 < 1E-5
			assert scheduler._get_variance(9_99		, predicted_variance=lowerCamelCase_					) - -0.0_0_1_0_0_1_1 < 1E-5





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     int				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Tuple				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= scheduler.timesteps

			SCREAMING_SNAKE_CASE       :     str				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     List[str]				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.manual_seed(0					)

			for i, t in enumerate(lowerCamelCase_					):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_		, lowerCamelCase_					)

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE       :     Tuple				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, generator=lowerCamelCase_					).prev_sample

					SCREAMING_SNAKE_CASE       :     int				= pred_prev_sample

			SCREAMING_SNAKE_CASE       :     Any				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5					) < 1E-2
			assert abs(result_mean.item() - 0.3_2_8_4_7_4_3					) < 1E-3





	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Dict				= scheduler_class(**lowerCamelCase_					)

			scheduler.set_timesteps(25					)

			SCREAMING_SNAKE_CASE       :     Any				= scheduler.timesteps

			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     int				= torch.manual_seed(0					)

			for i, t in enumerate(lowerCamelCase_					):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, lowerCamelCase_					)

					if i + 1 == timesteps.shape[0]:
							SCREAMING_SNAKE_CASE       :     int				= None
					else:
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.step(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, prev_timestep=lowerCamelCase_		, generator=lowerCamelCase_					).prev_sample

					SCREAMING_SNAKE_CASE       :     Any				= pred_prev_sample

			SCREAMING_SNAKE_CASE       :     Tuple				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Any				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3					) < 1E-2
			assert abs(result_mean.item() - 0.3_3_6_2_0_3_8					) < 1E-3





	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			pass







	def   __lowerCAmelCase (						self    :Optional[int]					)							->			int:







			'''simple docstring'''




			pass







"""simple docstring"""






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def __A						(   a_ :							float		,		a_ :							float		,		a_ :							bool = False     )->							list[float]:




		'''simple docstring'''






		if radian_mode:
				return [magnitude * cos(a_     ), magnitude * sin(a_     )]
		return [magnitude * cos(radians(a_     )     ), magnitude * sin(radians(a_     )     )]





def __A						(   a_ :							NDArray[floataa]		,		a_ :							NDArray[floataa]		,		a_ :							float = 10**-1     )->							bool:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     NDArray[floataa]				= cross(a_		,		a_     )
		SCREAMING_SNAKE_CASE       :     float				= sum(a_     )
		return abs(a_     ) < eps


if __name__ == "__main__":
					# Test to check if it works
					lowerCamelCase__ :       Optional[Any]						     =			array(
					    [
					        polar_force(7_1_8.4, 180 - 30),
					        polar_force(8_7_9.5_4, 45),
					        polar_force(100, -90),
					    ]
					)

					lowerCamelCase__ :       NDArray[floataa]						     =			array([[0, 0], [0, 0], [0, 0]])

					assert in_static_equilibrium(forces, location)

					# Problem 1 in image_data/2D_problems.jpg
					lowerCamelCase__ :       Union[str, Any]						     =			array(
					    [
					        polar_force(30 * 9.8_1, 15),
					        polar_force(215, 180 - 45),
					        polar_force(264, 90 - 30),
					    ]
					)

					lowerCamelCase__ :       Any						     =			array([[0, 0], [0, 0], [0, 0]])

					assert in_static_equilibrium(forces, location)

					# Problem in image_data/2D_problems_1.jpg
					lowerCamelCase__ :       Union[str, Any]						     =			array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

					lowerCamelCase__ :       Optional[int]						     =			array([[0, 0], [6, 0], [10, 0], [12, 0]])

					assert in_static_equilibrium(forces, location)

					import doctest

					doctest.testmod()





