from math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()


import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)