





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					(UniPCMultistepScheduler,)
       lowerCAmelCase__						=					(("""num_inference_steps""", 25),)




       def 							UpperCAmelCase__			(  self			:   Any							,    **A			:   Optional[int]		):
         __snake_case:						int			  =						{
             """num_train_timesteps""": 1_000,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
             """solver_type""": """bh2""",
         }

         config.update(**A		)
         return config




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   Optional[Any]=0							,    **A			:   List[str]		):
         __snake_case:						Dict			  =						dict(self.forward_default_kwargs		)
         __snake_case:						Tuple			  =						kwargs.pop("""num_inference_steps"""							,    A		)
         __snake_case:						Optional[Any]			  =						self.dummy_sample
         __snake_case:						List[Any]			  =						0.1 * sample
         __snake_case:						List[Any]			  =						[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           __snake_case:						str			  =						self.get_scheduler_config(**A		)
           __snake_case:						Union[str, Any]			  =						scheduler_class(**A		)
           scheduler.set_timesteps(A		)
           # copy over dummy past residuals
           __snake_case:						Union[str, Any]			  =						dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A		)
             __snake_case:						Union[str, Any]			  =						scheduler_class.from_pretrained(A		)
             new_scheduler.set_timesteps(A		)
             # copy over dummy past residuals
             __snake_case:						str			  =						dummy_past_residuals[: new_scheduler.config.solver_order]

           __snake_case  ,       __snake_case:						int			  =						sample, sample
           for t in range(A							,    time_step + scheduler.config.solver_order + 1		):
             __snake_case:						List[str]			  =						scheduler.step(A							,    A							,    A							,    **A		).prev_sample
             __snake_case:						str			  =						new_scheduler.step(A							,    A							,    A							,    **A		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"




       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Dict=0							,    **A			:   str		):
         __snake_case:						Tuple			  =						dict(self.forward_default_kwargs		)
         __snake_case:						Optional[Any]			  =						kwargs.pop("""num_inference_steps"""							,    A		)
         __snake_case:						Optional[Any]			  =						self.dummy_sample
         __snake_case:						Any			  =						0.1 * sample
         __snake_case:						str			  =						[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           __snake_case:						List[Any]			  =						self.get_scheduler_config()
           __snake_case:						Tuple			  =						scheduler_class(**A		)
           scheduler.set_timesteps(A		)

           # copy over dummy past residuals (must be after setting timesteps)
           __snake_case:						int			  =						dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A		)
             __snake_case:						List[str]			  =						scheduler_class.from_pretrained(A		)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(A		)

             # copy over dummy past residual (must be after setting timesteps)
             __snake_case:						Any			  =						dummy_past_residuals[: new_scheduler.config.solver_order]

           __snake_case:						int			  =						scheduler.step(A							,    A							,    A							,    **A		).prev_sample
           __snake_case:						int			  =						new_scheduler.step(A							,    A							,    A							,    **A		).prev_sample

           assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   str=None							,    **A			:   Any		):
         if scheduler is None:
           __snake_case:						Any			  =						self.scheduler_classes[0]
           __snake_case:						Any			  =						self.get_scheduler_config(**A		)
           __snake_case:						Optional[Any]			  =						scheduler_class(**A		)

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

         __snake_case:						Optional[int]			  =						10
         __snake_case:						List[Any]			  =						self.dummy_model()
         __snake_case:						Tuple			  =						self.dummy_sample_deter
         scheduler.set_timesteps(A		)

         for i, t in enumerate(scheduler.timesteps		):
           __snake_case:						Tuple			  =						model(A							,    A		)
           __snake_case:						List[str]			  =						scheduler.step(A							,    A							,    A		).prev_sample

         return sample




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						int			  =						dict(self.forward_default_kwargs		)

         __snake_case:						Union[str, Any]			  =						kwargs.pop("""num_inference_steps"""							,    A		)

         for scheduler_class in self.scheduler_classes:
           __snake_case:						Optional[Any]			  =						self.get_scheduler_config()
           __snake_case:						Tuple			  =						scheduler_class(**A		)

           __snake_case:						int			  =						self.dummy_sample
           __snake_case:						str			  =						0.1 * sample

           if num_inference_steps is not None and hasattr(A							,    """set_timesteps"""		):
             scheduler.set_timesteps(A		)
           elif num_inference_steps is not None and not hasattr(A							,    """set_timesteps"""		):
             __snake_case:						Union[str, Any]			  =						num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           __snake_case:						Any			  =						[residual + 0.2, residual + 0.15, residual + 0.10]
           __snake_case:						str			  =						dummy_past_residuals[: scheduler.config.solver_order]

           __snake_case:						Dict			  =						scheduler.timesteps[5]
           __snake_case:						Union[str, Any]			  =						scheduler.timesteps[6]

           __snake_case:						Dict			  =						scheduler.step(A							,    A							,    A							,    **A		).prev_sample
           __snake_case:						Optional[Any]			  =						scheduler.step(A							,    A							,    A							,    **A		).prev_sample

           self.assertEqual(output_a.shape							,    sample.shape		)
           self.assertEqual(output_a.shape							,    output_a.shape		)




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         __snake_case:						str			  =						UniPCMultistepScheduler(**self.get_scheduler_config()		)
         __snake_case:						str			  =						self.full_loop(scheduler=A		)
         __snake_case:						int			  =						torch.mean(torch.abs(A		)		)

         assert abs(result_mean.item() - 0.2464		) < 1E-3

         __snake_case:						Optional[int]			  =						DPMSolverSinglestepScheduler.from_config(scheduler.config		)
         __snake_case:						Dict			  =						DEISMultistepScheduler.from_config(scheduler.config		)
         __snake_case:						Optional[Any]			  =						DPMSolverMultistepScheduler.from_config(scheduler.config		)
         __snake_case:						Tuple			  =						UniPCMultistepScheduler.from_config(scheduler.config		)

         __snake_case:						Optional[int]			  =						self.full_loop(scheduler=A		)
         __snake_case:						Dict			  =						torch.mean(torch.abs(A		)		)

         assert abs(result_mean.item() - 0.2464		) < 1E-3




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         self.check_over_configs(thresholding=A		)
         for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=A							,    prediction_type=A							,    sample_max_value=A							,    solver_order=A							,    solver_type=A							,    )




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




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
             for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(
                   solver_order=A							,    solver_type=A							,    prediction_type=A							,    )
               __snake_case:						List[str]			  =						self.full_loop(
                   solver_order=A							,    solver_type=A							,    prediction_type=A							,    )
               assert not torch.isnan(A		).any(), "Samples have nan numbers"




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         self.check_over_configs(lower_order_final=A		)
         self.check_over_configs(lower_order_final=A		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=A							,    time_step=0		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						str			  =						self.full_loop()
         __snake_case:						Dict			  =						torch.mean(torch.abs(A		)		)

         assert abs(result_mean.item() - 0.2464		) < 1E-3




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						List[Any]			  =						self.full_loop(prediction_type="""v_prediction"""		)
         __snake_case:						List[str]			  =						torch.mean(torch.abs(A		)		)

         assert abs(result_mean.item() - 0.1014		) < 1E-3




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

         __snake_case:						Optional[Any]			  =						10
         __snake_case:						Union[str, Any]			  =						self.dummy_model()
         __snake_case:						int			  =						self.dummy_sample_deter.half()
         scheduler.set_timesteps(A		)

         for i, t in enumerate(scheduler.timesteps		):
           __snake_case:						List[str]			  =						model(A							,    A		)
           __snake_case:						Union[str, Any]			  =						scheduler.step(A							,    A							,    A		).prev_sample

         assert sample.dtype == torch.floataa






       def 							UpperCAmelCase__			(  self			:   List[str]							,    **A			:   List[str]		):
         for scheduler_class in self.scheduler_classes:
           __snake_case:						str			  =						self.get_scheduler_config(**A		)
           __snake_case:						Optional[int]			  =						scheduler_class(**A		)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
           assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps













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."
)






