






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class  __lowercase (		__A					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			(DDIMScheduler,)
     SCREAMING_SNAKE_CASE				  =			(("eta", 0.0), ("num_inference_steps", 50))




     def 						lowerCAmelCase_       (			self    :     int	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         __A             =		{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**UpperCamelCase_			)
         return config




     def 						lowerCAmelCase_       (			self    :     int	,		**UpperCamelCase_    :     Any			):


         """simple docstring"""



         __A             =		self.scheduler_classes[0]
         __A             =		self.get_scheduler_config(**UpperCamelCase_			)
         __A             =		scheduler_class(**UpperCamelCase_			)

         __A             =		10, 0.0

         __A             =		self.dummy_model()
         __A             =		self.dummy_sample_deter

         scheduler.set_timesteps(UpperCamelCase_			)

         for t in scheduler.timesteps:
             __A             =		model(UpperCamelCase_	,		UpperCamelCase_			)
             __A             =		scheduler.step(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			).prev_sample

         return sample




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         for timesteps in [100, 500, 1_000]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



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

         __A             =		self.scheduler_classes[0]
         __A             =		self.get_scheduler_config(steps_offset=1			)
         __A             =		scheduler_class(**UpperCamelCase_			)
         scheduler.set_timesteps(5			)
         assert torch.equal(scheduler.timesteps	,		torch.LongTensor([801, 601, 401, 201, 1]			)			)




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """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 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     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 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         for t in [1, 10, 49]:
             self.check_over_forward(time_step=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         for t, num_inference_steps in zip([1, 10, 50]	,		[10, 50, 500]			):
             self.check_over_forward(time_step=UpperCamelCase_	,		num_inference_steps=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         for t, eta in zip([1, 10, 49]	,		[0.0, 0.5, 1.0]			):
             self.check_over_forward(time_step=UpperCamelCase_	,		eta=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Dict			):


         """simple docstring"""



         __A             =		self.scheduler_classes[0]
         __A             =		self.get_scheduler_config()
         __A             =		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(420	,		400			) - 0.14771			)			) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980	,		960			) - 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(487	,		486			) - 0.00979			)			) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999	,		998			) - 0.02			)			) < 1e-5




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		self.full_loop()

         __A             =		torch.sum(torch.abs(UpperCamelCase_			)			)
         __A             =		torch.mean(torch.abs(UpperCamelCase_			)			)

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




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         __A             =		self.full_loop(prediction_type="""v_prediction"""			)

         __A             =		torch.sum(torch.abs(UpperCamelCase_			)			)
         __A             =		torch.mean(torch.abs(UpperCamelCase_			)			)

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




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __A             =		self.full_loop(set_alpha_to_one=UpperCamelCase_	,		beta_start=0.01			)
         __A             =		torch.sum(torch.abs(UpperCamelCase_			)			)
         __A             =		torch.mean(torch.abs(UpperCamelCase_			)			)

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





     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		self.full_loop(set_alpha_to_one=UpperCamelCase_	,		beta_start=0.01			)
         __A             =		torch.sum(torch.abs(UpperCamelCase_			)			)
         __A             =		torch.mean(torch.abs(UpperCamelCase_			)			)

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












from ..utils import DummyObject, requires_backends


__a :    int								=				None



def      _SCREAMING_SNAKE_CASE  (	*__lowercase				:     Any     ,			**__lowercase				:     Optional[int]      )  -> Tuple:





    """simple docstring"""

    requires_backends(__lowercase     ,			["""sentencepiece""", """tokenizers"""]      )




