





from PIL import Image


def 	UpperCamelCase__(       UpperCamelCase__			:       Image ,     UpperCamelCase__			:       float  )->Image:

       def brightness(UpperCamelCase__			:       int  ) -> float:
              return 1_28 + level + (c - 1_28)

       if not -255.0 <= level <= 255.0:
              raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''  )
       return img.point(UpperCamelCase__  )


if __name__ == "__main__":
       # Load image
       with Image.open('image_data/lena.jpg') as img:
              # Change brightness to 100
              a__:      List[Any]									=		change_brightness(img, 100)
              brigt_img.save('image_data/lena_brightness.png', format='png')












import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''prediction_type''': '''epsilon''',
            '''thresholding''': False,
            '''sample_max_value''': 1.0,
            '''algorithm_type''': '''dpmsolver++''',
            '''solver_type''': '''midpoint''',
            '''lambda_min_clipped''': -float('''inf''' ),
            '''variance_type''': None,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )
               # copy over dummy past residuals
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						scheduler_class.from_pretrained(__lowerCamelCase )
                      new_scheduler.set_timesteps(__lowerCamelCase )
                      # copy over dummy past residuals
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

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



 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						scheduler_class.from_pretrained(__lowerCamelCase )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__lowerCamelCase )

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

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

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



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:] ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2574 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3

        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,algorithm_type='''dpmsolver++''',solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,)



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



 def 				UpperCamelCase			(							self ):
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    A__											=						self.full_loop(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
        self.check_over_configs(lambda_min_clipped=-5.1 )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



 def 				UpperCamelCase			(							self ):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=__lowerCamelCase,time_step=0 )



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2248 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.1453 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.0649 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        assert sample.dtype == torch.floataa





