





"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[str]		     =      (DPMSolverSinglestepScheduler,)
     __snake_case   :     Union[str, Any]		     =      (("num_inference_steps", 25),)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,**lowerCamelCase__						:   Optional[int]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{
               """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     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   int=0						,**lowerCamelCase__						:   List[Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						dict(self.forward_default_kwargs	)
           SCREAMING_SNAKE_CASE		=						kwargs.pop("""num_inference_steps"""						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.dummy_sample
           SCREAMING_SNAKE_CASE		=						0.1 * sample
           SCREAMING_SNAKE_CASE		=						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						sample, sample
                 for t in range(lowerCamelCase__						,time_step + scheduler.config.solver_order + 1	):
                       SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	).prev_sample
                       SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




           '''simple docstring'''


           pass






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   List[Any]=0						,**lowerCamelCase__						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						dict(self.forward_default_kwargs	)
           SCREAMING_SNAKE_CASE		=						kwargs.pop("""num_inference_steps"""						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.dummy_sample
           SCREAMING_SNAKE_CASE		=						0.1 * sample
           SCREAMING_SNAKE_CASE		=						[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE		=						self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)
                 scheduler.set_timesteps(lowerCamelCase__	)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						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)
                       SCREAMING_SNAKE_CASE		=						dummy_past_residuals[: new_scheduler.config.solver_order]

                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	).prev_sample
                 SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Dict=None						,**lowerCamelCase__						:   int	)					->		List[str]:




           '''simple docstring'''


           if scheduler is None:
                 SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
                 SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(**lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						10
           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase__	)

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

           return sample






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
           SCREAMING_SNAKE_CASE		=						50
           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase__	)

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

           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		int:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
           SCREAMING_SNAKE_CASE		=						self.full_loop(scheduler=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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

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

           SCREAMING_SNAKE_CASE		=						self.full_loop(scheduler=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		str:




           '''simple docstring'''


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






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




           '''simple docstring'''


           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__						,)
                                   SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Optional[Any]:




           '''simple docstring'''


           self.check_over_configs(lower_order_final=lowerCamelCase__	)
           self.check_over_configs(lower_order_final=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           self.check_over_configs(lambda_min_clipped=-float("""inf"""	)	)
           self.check_over_configs(lambda_min_clipped=-5.1	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		str:




           '''simple docstring'''


           self.check_over_configs(variance_type=lowerCamelCase__	)
           self.check_over_configs(variance_type="""learned_range"""	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop()
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(use_karras_sigmas=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(prediction_type="""v_prediction"""	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(prediction_type="""v_prediction"""						,use_karras_sigmas=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(thresholding=lowerCamelCase__						,dynamic_thresholding_ratio=0	)
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						10
           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase__	)

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

           assert sample.dtype == torch.floataa









def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      if index == number_of_items:
            return 0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						knapsack(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						index + 1     )
      if weights[index] <= max_weight:
            SCREAMING_SNAKE_CASE		=						values[index] + knapsack(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						max_weight - weights[index]			,						index + 1     )
      return max(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


