

"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest





class 					snake_case			(       A_						):
				SCREAMING_SNAKE_CASE_     :   Optional[Any]        =						(DDIMScheduler,)
				SCREAMING_SNAKE_CASE_     :   Optional[Any]        =						(("""eta""", 0.0), ("""num_inference_steps""", 50))

				def 							lowercase_  (			self							:       List[Any]  ,						**UpperCamelCase__							:       str)->     List[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    {
						    '''num_train_timesteps''': 1_0_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''clip_sample''': True,
						}

						config.update(**UpperCamelCase__)
						return config

				def 							lowercase_  (			self							:       str  ,						**UpperCamelCase__							:       int)->     List[Any]:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    self.scheduler_classes[0]
						__lowerCAmelCase: Dict					   =    self.get_scheduler_config(**UpperCamelCase__)
						__lowerCAmelCase: List[str]					   =    scheduler_class(**UpperCamelCase__)

						__lowerCAmelCase: int					   =    1_0, 0.0

						__lowerCAmelCase: List[str]					   =    self.dummy_model()
						__lowerCAmelCase: int					   =    self.dummy_sample_deter

						scheduler.set_timesteps(UpperCamelCase__)

						for t in scheduler.timesteps:
								__lowerCAmelCase: Union[str, Any]					   =    model(UpperCamelCase__  ,						UpperCamelCase__)
								__lowerCAmelCase: List[Any]					   =    scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__).prev_sample

						return sample

				def 							lowercase_  (			self							:       Dict)->     int:



						'''simple docstring'''


						for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
								self.check_over_configs(num_train_timesteps=UpperCamelCase__)

				def 							lowercase_  (			self							:       List[Any])->     str:



						'''simple docstring'''


						for steps_offset in [0, 1]:
								self.check_over_configs(steps_offset=UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    self.scheduler_classes[0]
						__lowerCAmelCase: Tuple					   =    self.get_scheduler_config(steps_offset=1)
						__lowerCAmelCase: Union[str, Any]					   =    scheduler_class(**UpperCamelCase__)
						scheduler.set_timesteps(5)
						assert torch.equal(scheduler.timesteps  ,						torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]))

				def 							lowercase_  (			self							:       Optional[Any])->     str:



						'''simple docstring'''


						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,						[0.002, 0.02, 0.2, 2]):
								self.check_over_configs(beta_start=UpperCamelCase__  ,						beta_end=UpperCamelCase__)

				def 							lowercase_  (			self							:       Any)->     Any:



						'''simple docstring'''


						for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=UpperCamelCase__)

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



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       Optional[Any])->     Tuple:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       Any)->     Any:



						'''simple docstring'''


						for timestep_spacing in ["trailing", "leading"]:
								self.check_over_configs(timestep_spacing=UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str])->     List[Any]:



						'''simple docstring'''


						for rescale_betas_zero_snr in [True, False]:
								self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[Any])->     Dict:



						'''simple docstring'''


						self.check_over_configs(thresholding=UpperCamelCase__)
						for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(
										    thresholding=UpperCamelCase__  ,						prediction_type=UpperCamelCase__  ,						sample_max_value=UpperCamelCase__  ,						)

				def 							lowercase_  (			self							:       str)->     str:



						'''simple docstring'''


						for t in [1, 1_0, 4_9]:
								self.check_over_forward(time_step=UpperCamelCase__)

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



						'''simple docstring'''


						for t, num_inference_steps in zip([1, 1_0, 5_0]  ,						[1_0, 5_0, 5_0_0]):
								self.check_over_forward(time_step=UpperCamelCase__  ,						num_inference_steps=UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[Any])->     List[str]:



						'''simple docstring'''


						for t, eta in zip([1, 1_0, 4_9]  ,						[0.0, 0.5, 1.0]):
								self.check_over_forward(time_step=UpperCamelCase__  ,						eta=UpperCamelCase__)

				def 							lowercase_  (			self							:       List[Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: int					   =    self.scheduler_classes[0]
						__lowerCAmelCase: str					   =    self.get_scheduler_config()
						__lowerCAmelCase: int					   =    scheduler_class(**UpperCamelCase__)

						assert torch.sum(torch.abs(scheduler._get_variance(0  ,						0) - 0.0)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,						4_0_0) - 0.14771)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,						9_6_0) - 0.32460)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(0  ,						0) - 0.0)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,						4_8_6) - 0.00979)) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,						9_9_8) - 0.02)) < 1e-5

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



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    self.full_loop()

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

						assert abs(result_sum.item() - 172.0067) < 1e-2
						assert abs(result_mean.item() - 0.223967) < 1e-3

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



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.full_loop(prediction_type="v_prediction")

						__lowerCAmelCase: int					   =    torch.sum(torch.abs(UpperCamelCase__))
						__lowerCAmelCase: Union[str, Any]					   =    torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_sum.item() - 52.5302) < 1e-2
						assert abs(result_mean.item() - 0.0684) < 1e-3

				def 							lowercase_  (			self							:       Any)->     List[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    self.full_loop(set_alpha_to_one=UpperCamelCase__  ,						beta_start=0.01)
						__lowerCAmelCase: List[Any]					   =    torch.sum(torch.abs(UpperCamelCase__))
						__lowerCAmelCase: Any					   =    torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_sum.item() - 149.8295) < 1e-2
						assert abs(result_mean.item() - 0.1951) < 1e-3




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



						'''simple docstring'''


						__lowerCAmelCase: int					   =    self.full_loop(set_alpha_to_one=UpperCamelCase__  ,						beta_start=0.01)
						__lowerCAmelCase: Optional[int]					   =    torch.sum(torch.abs(UpperCamelCase__))
						__lowerCAmelCase: Dict					   =    torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_sum.item() - 149.0784) < 1e-2
						assert abs(result_mean.item() - 0.1941) < 1e-3





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[float, float]:
		assert (
		    c % greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) == 0
		)  # greatest_common_divisor(a,b) function implemented below
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # extended_gcd(a,b) function implemented below
		__lowerCAmelCase: Optional[int]					   =    c / d
		return (r * x, r * y)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 2				)			->			None:
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)): Optional[Any]					   =    diophantine(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # Initial value
		__lowerCAmelCase: Optional[Any]					   =    greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Any					   =    a // d
		__lowerCAmelCase: str					   =    b // d

		for i in range(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: Dict					   =    xa + i * q
				__lowerCAmelCase: List[str]					   =    ya - i * p
				print(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		if a < b:
				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    b, a

		while a % b != 0:
				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    b, a % b

		return b






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[int, int, int]:
		assert a >= 0 and b >= 0

		if b == 0:
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    a, 1, 0
		else:
				((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				a % b				)
				__lowerCAmelCase: Dict					   =    q
				__lowerCAmelCase: int					   =    p - q * (a // b)

		assert a % d == 0 and b % d == 0
		assert d == a * x + b * y

		return (d, x, y)


if __name__ == "__main__":
			from doctest import testmod

			testmod(name="diophantine", verbose=True)
			testmod(name="diophantine_all_soln", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)


