





"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : List[str]	    =   (DPMSolverSinglestepScheduler,)
      lowercase_      : List[str]	    =   (("""num_inference_steps""", 25),)

      def 					lowerCamelCase_			(     self					,      **snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.00_01,
            '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(**snake_case_  )
        return config

      def 					lowerCamelCase_			(     self					,      snake_case_=0					,      **snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  dict(self.forward_default_kwargs  )
        A_  :       int										=  kwargs.pop('num_inference_steps'					,      snake_case_  )
        A_  :       Optional[int]										=  self.dummy_sample
        A_  :       Tuple										=  0.1 * sample
        A_  :       Optional[Any]										=  [residual + 0.2, residual + 0.15, residual + 0.10]

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(snake_case_  )
            A_  :       List[str]										=  scheduler_class.from_pretrained(snake_case_  )
            new_scheduler.set_timesteps(snake_case_  )
            # copy over dummy past residuals
            A_  :       int										=  dummy_past_residuals[: new_scheduler.config.solver_order]

          A_						,       A_  :       Tuple										=  sample, sample
          for t in range(snake_case_					,      time_step + scheduler.config.solver_order + 1  ):
            A_  :       Union[str, Any]										=  scheduler.step(snake_case_					,      snake_case_					,      snake_case_					,      **snake_case_  ).prev_sample
            A_  :       str										=  new_scheduler.step(snake_case_					,      snake_case_					,      snake_case_					,      **snake_case_  ).prev_sample

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self					,      snake_case_=0					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Optional[int]										=  dict(self.forward_default_kwargs  )
        A_  :       str										=  kwargs.pop('num_inference_steps'					,      snake_case_  )
        A_  :       str										=  self.dummy_sample
        A_  :       Optional[Any]										=  0.1 * sample
        A_  :       int										=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
          A_  :       Dict										=  self.get_scheduler_config()
          A_  :       Dict										=  scheduler_class(**snake_case_  )
          scheduler.set_timesteps(snake_case_  )

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

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

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

          A_  :       Any										=  scheduler.step(snake_case_					,      snake_case_					,      snake_case_					,      **snake_case_  ).prev_sample
          A_  :       Dict										=  new_scheduler.step(snake_case_					,      snake_case_					,      snake_case_					,      **snake_case_  ).prev_sample

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

      def 					lowerCamelCase_			(     self					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        if scheduler is None:
          A_  :       str										=  self.scheduler_classes[0]
          A_  :       Dict										=  self.get_scheduler_config(**snake_case_  )
          A_  :       int										=  scheduler_class(**snake_case_  )

        A_  :       Dict										=  self.scheduler_classes[0]
        A_  :       Optional[Any]										=  self.get_scheduler_config(**snake_case_  )
        A_  :       int										=  scheduler_class(**snake_case_  )

        A_  :       Optional[Any]										=  1_0
        A_  :       Any										=  self.dummy_model()
        A_  :       Optional[int]										=  self.dummy_sample_deter
        scheduler.set_timesteps(snake_case_  )

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

        return sample

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
        A_  :       Any										=  5_0
        A_  :       Union[str, Any]										=  self.dummy_model()
        A_  :       Tuple										=  self.dummy_sample_deter
        scheduler.set_timesteps(snake_case_  )

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

        A_  :       Any										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.25_74  ) < 1E-3

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
        A_  :       str										=  self.full_loop(scheduler=snake_case_  )
        A_  :       List[str]										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.27_91  ) < 1E-3

        A_  :       Union[str, Any]										=  DEISMultistepScheduler.from_config(scheduler.config  )
        A_  :       Union[str, Any]										=  DPMSolverMultistepScheduler.from_config(scheduler.config  )
        A_  :       Optional[Any]										=  UniPCMultistepScheduler.from_config(scheduler.config  )
        A_  :       Union[str, Any]										=  DPMSolverSinglestepScheduler.from_config(scheduler.config  )

        A_  :       Tuple										=  self.full_loop(scheduler=snake_case_  )
        A_  :       Dict										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.27_91  ) < 1E-3

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.check_over_configs(thresholding=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=snake_case_					,      prediction_type=snake_case_					,      sample_max_value=snake_case_					,      algorithm_type='dpmsolver++'					,      solver_order=snake_case_					,      solver_type=snake_case_					,      )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self  ):

        """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=snake_case_					,      solver_type=snake_case_					,      prediction_type=snake_case_					,      algorithm_type=snake_case_					,      )
                A_  :       Optional[Any]										=  self.full_loop(
                    solver_order=snake_case_					,      solver_type=snake_case_					,      prediction_type=snake_case_					,      algorithm_type=snake_case_					,      )
                assert not torch.isnan(snake_case_  ).any(), "Samples have nan numbers"

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.check_over_configs(lower_order_final=snake_case_  )
        self.check_over_configs(lower_order_final=snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.check_over_configs(variance_type=snake_case_  )
        self.check_over_configs(variance_type='learned_range'  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        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=snake_case_					,      time_step=0  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  self.full_loop()
        A_  :       str										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.27_91  ) < 1E-3

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  self.full_loop(use_karras_sigmas=snake_case_  )
        A_  :       int										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.22_48  ) < 1E-3

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       str										=  self.full_loop(prediction_type='v_prediction'  )
        A_  :       Optional[int]										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.14_53  ) < 1E-3

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.full_loop(prediction_type='v_prediction'					,      use_karras_sigmas=snake_case_  )
        A_  :       int										=  torch.mean(torch.abs(snake_case_  )  )

        assert abs(result_mean.item() - 0.06_49  ) < 1E-3

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.scheduler_classes[0]
        A_  :       Optional[int]										=  self.get_scheduler_config(thresholding=snake_case_					,      dynamic_thresholding_ratio=0  )
        A_  :       int										=  scheduler_class(**snake_case_  )

        A_  :       str										=  1_0
        A_  :       int										=  self.dummy_model()
        A_  :       str										=  self.dummy_sample_deter.half()
        scheduler.set_timesteps(snake_case_  )

        for i, t in enumerate(scheduler.timesteps  ):
          A_  :       Tuple										=  model(snake_case_					,      snake_case_  )
          A_  :       Tuple										=  scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        assert sample.dtype == torch.floataa






"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result