





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					(DDPMScheduler,)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    **A			:   Tuple		):
         __snake_case:						Union[str, Any]			  =						{
             """num_train_timesteps""": 1_000,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """variance_type""": """fixed_small""",
             """clip_sample""": True,
         }

         config.update(**A		)
         return config




       def 							UpperCAmelCase__			(  self			:   Dict		):
         for timesteps in [1, 5, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         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=A							,    beta_end=A		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=A		)




       def 							UpperCAmelCase__			(  self			:   str		):
         for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=A		)




       def 							UpperCAmelCase__			(  self			:   int		):
         for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=A		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         self.check_over_configs(thresholding=A		)
         for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=A							,    prediction_type=A							,    sample_max_value=A							,    )




       def 							UpperCAmelCase__			(  self			:   Any		):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         for t in [0, 500, 999]:
           self.check_over_forward(time_step=A		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Tuple			  =						self.scheduler_classes[0]
         __snake_case:						Optional[Any]			  =						self.get_scheduler_config()
         __snake_case:						Optional[Any]			  =						scheduler_class(**A		)

         assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						self.scheduler_classes[0]
         __snake_case:						str			  =						self.get_scheduler_config()
         __snake_case:						Dict			  =						scheduler_class(**A		)

         __snake_case:						str			  =						len(A		)

         __snake_case:						Dict			  =						self.dummy_model()
         __snake_case:						Tuple			  =						self.dummy_sample_deter
         __snake_case:						Optional[Any]			  =						torch.manual_seed(0		)

         for t in reversed(range(A		)		):
           # 1. predict noise residual
           __snake_case:						str			  =						model(A							,    A		)

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

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           __snake_case:						Optional[int]			  =						pred_prev_sample

         __snake_case:						List[Any]			  =						torch.sum(torch.abs(A		)		)
         __snake_case:						str			  =						torch.mean(torch.abs(A		)		)

         assert abs(result_sum.item() - 258.9606		) < 1E-2
         assert abs(result_mean.item() - 0.3372		) < 1E-3




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Dict			  =						self.scheduler_classes[0]
         __snake_case:						Dict			  =						self.get_scheduler_config(prediction_type="""v_prediction"""		)
         __snake_case:						List[str]			  =						scheduler_class(**A		)

         __snake_case:						List[str]			  =						len(A		)

         __snake_case:						Tuple			  =						self.dummy_model()
         __snake_case:						Dict			  =						self.dummy_sample_deter
         __snake_case:						Optional[int]			  =						torch.manual_seed(0		)

         for t in reversed(range(A		)		):
           # 1. predict noise residual
           __snake_case:						Any			  =						model(A							,    A		)

           # 2. predict previous mean of sample x_t-1
           __snake_case:						int			  =						scheduler.step(A							,    A							,    A							,    generator=A		).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           __snake_case:						str			  =						pred_prev_sample

         __snake_case:						int			  =						torch.sum(torch.abs(A		)		)
         __snake_case:						Optional[Any]			  =						torch.mean(torch.abs(A		)		)

         assert abs(result_sum.item() - 202.0296		) < 1E-2
         assert abs(result_mean.item() - 0.2631		) < 1E-3




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						List[Any]			  =						self.scheduler_classes[0]
         __snake_case:						Optional[Any]			  =						self.get_scheduler_config()
         __snake_case:						str			  =						scheduler_class(**A		)

         __snake_case:						List[str]			  =						[100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=A		)

         __snake_case:						List[str]			  =						scheduler.timesteps

         for i, timestep in enumerate(A		):
           if i == len(A		) - 1:
             __snake_case:						Dict			  =						-1
           else:
             __snake_case:						Optional[Any]			  =						timesteps[i + 1]

           __snake_case:						Union[str, Any]			  =						scheduler.previous_timestep(A		)
           __snake_case:						Tuple			  =						prev_t.item()

           self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[Any]			  =						self.scheduler_classes[0]
         __snake_case:						Optional[int]			  =						self.get_scheduler_config()
         __snake_case:						str			  =						scheduler_class(**A		)

         __snake_case:						Optional[int]			  =						[100, 87, 50, 51, 0]

         with self.assertRaises(A							,    msg="""`custom_timesteps` must be in descending order."""		):
           scheduler.set_timesteps(timesteps=A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Any			  =						self.scheduler_classes[0]
         __snake_case:						str			  =						self.get_scheduler_config()
         __snake_case:						str			  =						scheduler_class(**A		)

         __snake_case:						List[Any]			  =						[100, 87, 50, 1, 0]
         __snake_case:						List[str]			  =						len(A		)

         with self.assertRaises(A							,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""		):
           scheduler.set_timesteps(num_inference_steps=A							,    timesteps=A		)






       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Tuple			  =						self.scheduler_classes[0]
         __snake_case:						List[str]			  =						self.get_scheduler_config()
         __snake_case:						List[str]			  =						scheduler_class(**A		)

         __snake_case:						Dict			  =						[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             A							,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""							,    ):
           scheduler.set_timesteps(timesteps=A		)













import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)






