


"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       (DDIMScheduler,)
		A         =       (("eta", 0.0), ("num_inference_steps", 50))
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  {
					    '''num_train_timesteps''': 10_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''clip_sample''': True,
					}

					config.update(**SCREAMING_SNAKE_CASE__      )
					return config
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  self.scheduler_classes[0]
					_UpperCamelCase				:Tuple				    =  self.get_scheduler_config(**SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Any				    =  scheduler_class(**SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Dict				    =  10, 0.0

					_UpperCamelCase				:List[Any]				    =  self.dummy_model()
					_UpperCamelCase				:Optional[Any]				    =  self.dummy_sample_deter

					scheduler.set_timesteps(SCREAMING_SNAKE_CASE__      )

					for t in scheduler.timesteps:
								_UpperCamelCase				:Union[str, Any]				    =  model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
								_UpperCamelCase				:Tuple				    =  scheduler.step(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ).prev_sample

					return sample
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					for timesteps in [1_00, 5_00, 10_00]:
								self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



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

					_UpperCamelCase				:Tuple				    =  self.scheduler_classes[0]
					_UpperCamelCase				:List[Any]				    =  self.get_scheduler_config(steps_offset=1      )
					_UpperCamelCase				:Optional[int]				    =  scheduler_class(**SCREAMING_SNAKE_CASE__      )
					scheduler.set_timesteps(5      )
					assert torch.equal(scheduler.timesteps				,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,							[0.0_0_2, 0.0_2, 0.2, 2]      ):
								self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__				,							beta_end=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					for timestep_spacing in ["trailing", "leading"]:
								self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					for rescale_betas_zero_snr in [True, False]:
								self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__      )
					for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(
											    thresholding=SCREAMING_SNAKE_CASE__				,							prediction_type=SCREAMING_SNAKE_CASE__				,							sample_max_value=SCREAMING_SNAKE_CASE__				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					for t in [1, 10, 49]:
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for t, num_inference_steps in zip([1, 10, 50]				,							[10, 50, 5_00]      ):
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__				,							num_inference_steps=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					for t, eta in zip([1, 10, 49]				,							[0.0, 0.5, 1.0]      ):
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__				,							eta=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  self.scheduler_classes[0]
					_UpperCamelCase				:Tuple				    =  self.get_scheduler_config()
					_UpperCamelCase				:Union[str, Any]				    =  scheduler_class(**SCREAMING_SNAKE_CASE__      )

					assert torch.sum(torch.abs(scheduler._get_variance(0				,							0      ) - 0.0      )      ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_20				,							4_00      ) - 0.1_4_7_7_1      )      ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80				,							9_60      ) - 0.3_2_4_6_0      )      ) < 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_87				,							4_86      ) - 0.0_0_9_7_9      )      ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99				,							9_98      ) - 0.0_2      )      ) < 1E-5
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.full_loop()

					_UpperCamelCase				:Optional[int]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:Dict				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
					assert abs(result_mean.item() - 0.2_2_3_9_6_7      ) < 1E-3
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  self.full_loop(prediction_type='''v_prediction'''      )

					_UpperCamelCase				:Optional[int]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:int				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					assert abs(result_sum.item() - 52.53_02      ) < 1E-2
					assert abs(result_mean.item() - 0.0_6_8_4      ) < 1E-3
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							beta_start=0.0_1      )
					_UpperCamelCase				:Optional[Any]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:int				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
					assert abs(result_mean.item() - 0.1_9_5_1      ) < 1E-3





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

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							beta_start=0.0_1      )
					_UpperCamelCase				:List[str]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:Any				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
					assert abs(result_mean.item() - 0.1_9_4_1      ) < 1E-3





"""simple docstring"""






from __future__ import annotations

from enum import Enum






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       24
		A         =       21
		A         =       18
		A         =       15
		A         =       12
		A         =       9
		A         =       6
		A         =       3
		A         =       2
		A         =       1
		A         =       -1
		A         =       -2
		A         =       -3
		A         =       -6
		A         =       -9
		A         =       -12
		A         =       -15
		A         =       -18
		A         =       -21
		A         =       -24






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       8
		A         =       7
		A         =       6
		A         =       5
		A         =       4
		A         =       3
		A         =       2
		A         =       1





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				)				->     float:
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Tuple				    =  SIUnit[known_prefix.lower()]
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Optional[int]				    =  SIUnit[unknown_prefix.lower()]
			_UpperCamelCase				:float				    =  known_amount * (
			    10 ** (known_prefix.value - unknown_prefix.value)
			)
			return unknown_amount





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				)				->     float:
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Any				    =  BinaryUnit[known_prefix.lower()]
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Dict				    =  BinaryUnit[unknown_prefix.lower()]
			_UpperCamelCase				:float				    =  known_amount * (
			    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
			)
			return unknown_amount


if __name__ == "__main__":
					import doctest

					doctest.testmod()

