






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 							_UpperCamelCase			(a_			):
      snake_case_  =							(DPMSolverSinglestepScheduler,)
      snake_case_  =							(("""num_inference_steps""", 25),)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Optional[Any]:
           __lowerCAmelCase								=					{
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_0_0_1,
               "beta_end": 0.0_2,
               "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(**__UpperCamelCase	)
           return config


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=0					,						**__UpperCamelCase	)->	Any:
           __lowerCAmelCase								=					dict(self.forward_default_kwargs	)
           __lowerCAmelCase								=					kwargs.pop("num_inference_steps"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					self.dummy_sample
           __lowerCAmelCase								=					0.1 * sample
           __lowerCAmelCase								=					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           pass


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=0					,						**__UpperCamelCase	)->	Optional[int]:
           __lowerCAmelCase								=					dict(self.forward_default_kwargs	)
           __lowerCAmelCase								=					kwargs.pop("num_inference_steps"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					self.dummy_sample
           __lowerCAmelCase								=					0.1 * sample
           __lowerCAmelCase								=					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase								=					self.get_scheduler_config()
                __lowerCAmelCase								=					scheduler_class(**__UpperCamelCase	)
                scheduler.set_timesteps(__UpperCamelCase	)

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

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

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

                __lowerCAmelCase								=					scheduler.step(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase	).prev_sample
                __lowerCAmelCase								=					new_scheduler.step(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase	).prev_sample

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=None					,						**__UpperCamelCase	)->	Optional[int]:
           if scheduler is None:
                __lowerCAmelCase								=					self.scheduler_classes[0]
                __lowerCAmelCase								=					self.get_scheduler_config(**__UpperCamelCase	)
                __lowerCAmelCase								=					scheduler_class(**__UpperCamelCase	)

           __lowerCAmelCase								=					self.scheduler_classes[0]
           __lowerCAmelCase								=					self.get_scheduler_config(**__UpperCamelCase	)
           __lowerCAmelCase								=					scheduler_class(**__UpperCamelCase	)

           __lowerCAmelCase								=					1_0
           __lowerCAmelCase								=					self.dummy_model()
           __lowerCAmelCase								=					self.dummy_sample_deter
           scheduler.set_timesteps(__UpperCamelCase	)

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

           return sample


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
           __lowerCAmelCase								=					5_0
           __lowerCAmelCase								=					self.dummy_model()
           __lowerCAmelCase								=					self.dummy_sample_deter
           scheduler.set_timesteps(__UpperCamelCase	)

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

           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.2_5_7_4	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           __lowerCAmelCase								=					DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
           __lowerCAmelCase								=					self.full_loop(scheduler=__UpperCamelCase	)
           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.2_7_9_1	) < 1e-3

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

           __lowerCAmelCase								=					self.full_loop(scheduler=__UpperCamelCase	)
           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.2_7_9_1	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           self.check_over_configs(thresholding=__UpperCamelCase	)
           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=__UpperCamelCase					,						prediction_type=__UpperCamelCase					,						sample_max_value=__UpperCamelCase					,						algorithm_type="dpmsolver++"					,						solver_order=__UpperCamelCase					,						solver_type=__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self	)->	List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	str:
           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=__UpperCamelCase					,						solver_type=__UpperCamelCase					,						prediction_type=__UpperCamelCase					,						algorithm_type=__UpperCamelCase					,						)
                               __lowerCAmelCase								=					self.full_loop(
                                   solver_order=__UpperCamelCase					,						solver_type=__UpperCamelCase					,						prediction_type=__UpperCamelCase					,						algorithm_type=__UpperCamelCase					,						)
                               assert not torch.isnan(__UpperCamelCase	).any(), "Samples have nan numbers"


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           self.check_over_configs(lower_order_final=__UpperCamelCase	)
           self.check_over_configs(lower_order_final=__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           self.check_over_configs(lambda_min_clipped=-float("inf"	)	)
           self.check_over_configs(lambda_min_clipped=-5.1	)


      def  __UpperCAmelCase		(	self	)->	List[str]:
           self.check_over_configs(variance_type=__UpperCamelCase	)
           self.check_over_configs(variance_type="learned_range"	)


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                self.check_over_forward(num_inference_steps=__UpperCamelCase					,						time_step=0	)


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					self.full_loop()
           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.2_7_9_1	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					self.full_loop(use_karras_sigmas=__UpperCamelCase	)
           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.2_2_4_8	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.full_loop(prediction_type="v_prediction"	)
           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.1_4_5_3	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           __lowerCAmelCase								=					self.full_loop(prediction_type="v_prediction"					,						use_karras_sigmas=__UpperCamelCase	)
           __lowerCAmelCase								=					torch.mean(torch.abs(__UpperCamelCase	)	)

           assert abs(result_mean.item() - 0.0_6_4_9	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					self.scheduler_classes[0]
           __lowerCAmelCase								=					self.get_scheduler_config(thresholding=__UpperCamelCase					,						dynamic_thresholding_ratio=0	)
           __lowerCAmelCase								=					scheduler_class(**__UpperCamelCase	)

           __lowerCAmelCase								=					1_0
           __lowerCAmelCase								=					self.dummy_model()
           __lowerCAmelCase								=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(__UpperCamelCase	)

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

           assert sample.dtype == torch.floataa














from __future__ import annotations





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
     __lowerCAmelCase								=					[v / w for v, w in zip(__snake_case					, __snake_case       )]
     index.sort(key=lambda __snake_case       : ratio[i]					, reverse=__snake_case       )

     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					[0] * len(__snake_case       )
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase								=					1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase								=					capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






