






__A  							=  [0.485, 0.456, 0.406]
__A  							=  [0.229, 0.224, 0.225]
__A  							=  [0.5, 0.5, 0.5]
__A  							=  [0.5, 0.5, 0.5]
__A  							=  [0.4814_5466, 0.457_8275, 0.4082_1073]
__A  							=  [0.2686_2954, 0.2613_0258, 0.2757_7711]












import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 							_A (    UpperCamelCase							):
    """simple docstring"""

    lowerCamelCase  :       str   =   (DPMSolverMultistepScheduler,)
    lowerCamelCase  :       List[str]   =   (('num_inference_steps', 25),)



    def _a       (							self			:    Dict   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Dict:
           __UpperCAmelCase    							={
               """num_train_timesteps""": 1000,
               """beta_start""": 0.0_001,
               """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""",
               """lower_order_final""": False,
               """lambda_min_clipped""": -float("""inf"""			),
               """variance_type""": None,
           }

           config.update(**__SCREAMING_SNAKE_CASE			)
           return config



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0   ,    **__SCREAMING_SNAKE_CASE			:    int			)			->       Dict:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=self.dummy_sample
           __UpperCAmelCase    							=0.1 * sample
           __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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



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



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

           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config()
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                  scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

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

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

                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                  __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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



    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    **__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Optional[Any]:
           if scheduler is None:
                  __UpperCAmelCase    							=self.scheduler_classes[0]
                  __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=10
           __UpperCAmelCase    							=self.dummy_model()
           __UpperCAmelCase    							=self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

           return sample



    def _a       (							self			:    Dict			)			->       Optional[int]:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)

           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)

           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config()
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

                  __UpperCAmelCase    							=self.dummy_sample
                  __UpperCAmelCase    							=0.1 * sample

                  if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                         scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                  elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                         __UpperCAmelCase    							=num_inference_steps

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

                  __UpperCAmelCase    							=scheduler.timesteps[5]
                  __UpperCAmelCase    							=scheduler.timesteps[6]

                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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



    def _a       (							self			:    List[Any]			)			->       Tuple:
           for timesteps in [25, 50, 100, 999, 1000]:
                  self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       int:
           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE			)
           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=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    sample_max_value=__SCREAMING_SNAKE_CASE   ,    algorithm_type="""dpmsolver++"""   ,    solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    )



    def _a       (							self			:    Union[str, Any]			)			->       List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       Tuple:
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                  for solver_type in ["midpoint", "heun"]:
                         for order in [1, 2, 3]:
                                for prediction_type in ["epsilon", "sample"]:
                                       if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                              if order == 3:
                                                     continue
                                       else:
                                              self.check_over_configs(
                                                  solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                       __UpperCAmelCase    							=self.full_loop(
                                           solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                       assert not torch.isnan(__SCREAMING_SNAKE_CASE			).any(), "Samples have nan numbers"



    def _a       (							self			:    Dict			)			->       str:
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
           self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
           self.check_over_configs(lambda_min_clipped=-5.1			)



    def _a       (							self			:    Any			)			->       List[str]:
           self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE			)
           self.check_over_configs(variance_type="""learned_range"""			)



    def _a       (							self			:    List[Any]			)			->       int:
           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                  self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE   ,    time_step=0			)



    def _a       (							self			:    Optional[int]			)			->       str:
           __UpperCAmelCase    							=self.full_loop()
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_301			) < 1e-3



    def _a       (							self			:    Union[str, Any]			)			->       str:
           __UpperCAmelCase    							=self.full_loop(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0.87   ,    sample_max_value=0.5			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 1.1_364			) < 1e-3



    def _a       (							self			:    List[Any]			)			->       List[Any]:
           __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.2_251			) < 1e-3



    def _a       (							self			:    int			)			->       Union[str, Any]:
           __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""   ,    use_karras_sigmas=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.2_096			) < 1e-3



    def _a       (							self			:    int			)			->       Optional[Any]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           __UpperCAmelCase    							=DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
           __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_301			) < 1e-3

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

           __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_301			) < 1e-3



    def _a       (							self			:    List[Any]			)			->       str:
           __UpperCAmelCase    							=self.scheduler_classes[0]
           __UpperCAmelCase    							=self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0			)
           __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=10
           __UpperCAmelCase    							=self.dummy_model()
           __UpperCAmelCase    							=self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

           assert sample.dtype == torch.floataa



    def _a       (							self			:    Optional[int]   ,    **__SCREAMING_SNAKE_CASE			:    Tuple			)			->       Optional[int]:
           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

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




