
"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       (DPMSolverMultistepScheduler,)
    __lowercase				:			Dict	=       (("""num_inference_steps""", 2_5),)
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Optional[int]            = {
          "num_train_timesteps": 10_00,
          "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",
          "lower_order_final": False,
          "lambda_min_clipped": -float("inf"  ),
          "variance_type": None,
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self    ,     snake_case_=0    ,     **snake_case_  ):
      _snake_case  :    Optional[Any]            = dict(self.forward_default_kwargs  )
      _snake_case  :    List[str]            = kwargs.pop("num_inference_steps"    ,     snake_case_  )
      _snake_case  :    List[Any]            = self.dummy_sample
      _snake_case  :    List[Any]            = 0.1 * sample
      _snake_case  :    Union[str, Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

        _snake_case      ,       _snake_case  :    str            = sample, sample
        for t in range(snake_case_    ,     time_step + scheduler.config.solver_order + 1  ):
          _snake_case  :    Optional[int]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample
          _snake_case  :    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  ):
      pass
    def        lowerCamelCase__						(     self    ,     snake_case_=0    ,     **snake_case_  ):
      _snake_case  :    Optional[int]            = dict(self.forward_default_kwargs  )
      _snake_case  :    Any            = kwargs.pop("num_inference_steps"    ,     snake_case_  )
      _snake_case  :    Tuple            = self.dummy_sample
      _snake_case  :    Dict            = 0.1 * sample
      _snake_case  :    Optional[Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    List[str]            = self.get_scheduler_config()
        _snake_case  :    str            = scheduler_class(**snake_case_  )
        scheduler.set_timesteps(snake_case_  )

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

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(snake_case_  )
          _snake_case  :    Optional[int]            = 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)
          _snake_case  :    List[str]            = dummy_past_residuals[: new_scheduler.config.solver_order]

        _snake_case  :    List[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample
        _snake_case  :    Optional[int]            = 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_  ):
      if scheduler is None:
        _snake_case  :    Tuple            = self.scheduler_classes[0]
        _snake_case  :    Tuple            = self.get_scheduler_config(**snake_case_  )
        _snake_case  :    int            = scheduler_class(**snake_case_  )

      _snake_case  :    Tuple            = 10
      _snake_case  :    Union[str, Any]            = self.dummy_model()
      _snake_case  :    Dict            = self.dummy_sample_deter
      scheduler.set_timesteps(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    int            = model(snake_case_    ,     snake_case_  )
        _snake_case  :    str            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  ).prev_sample

      return sample
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = dict(self.forward_default_kwargs  )

      _snake_case  :    int            = kwargs.pop("num_inference_steps"    ,     snake_case_  )

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    Optional[int]            = self.get_scheduler_config()
        _snake_case  :    Tuple            = scheduler_class(**snake_case_  )

        _snake_case  :    List[str]            = self.dummy_sample
        _snake_case  :    Union[str, Any]            = 0.1 * sample

        if num_inference_steps is not None and hasattr(snake_case_    ,     "set_timesteps"  ):
          scheduler.set_timesteps(snake_case_  )
        elif num_inference_steps is not None and not hasattr(snake_case_    ,     "set_timesteps"  ):
          _snake_case  :    Tuple            = num_inference_steps

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

        _snake_case  :    Optional[Any]            = scheduler.timesteps[5]
        _snake_case  :    str            = scheduler.timesteps[6]

        _snake_case  :    str            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample
        _snake_case  :    Any            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample

        self.assertEqual(output_a.shape    ,     sample.shape  )
        self.assertEqual(output_a.shape    ,     output_a.shape  )
    def        lowerCamelCase__						(     self  ):
      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      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  ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      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=snake_case_    ,     solver_type=snake_case_    ,     prediction_type=snake_case_    ,     algorithm_type=snake_case_    ,     )
              _snake_case  :    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  ):
      self.check_over_configs(lower_order_final=snake_case_  )
      self.check_over_configs(lower_order_final=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(lambda_min_clipped=-float("inf"  )  )
      self.check_over_configs(lambda_min_clipped=-5.1  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(variance_type=snake_case_  )
      self.check_over_configs(variance_type="learned_range"  )
    def        lowerCamelCase__						(     self  ):
      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
        self.check_over_forward(num_inference_steps=snake_case_    ,     time_step=0  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.full_loop()
      _snake_case  :    List[str]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.full_loop(thresholding=snake_case_    ,     dynamic_thresholding_ratio=0.87    ,     sample_max_value=0.5  )
      _snake_case  :    Optional[int]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 1.1364  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.full_loop(prediction_type="v_prediction"  )
      _snake_case  :    Union[str, Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.2251  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.full_loop(prediction_type="v_prediction"    ,     use_karras_sigmas=snake_case_  )
      _snake_case  :    List[Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.2096  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      _snake_case  :    Union[str, Any]            = DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
      _snake_case  :    Dict            = self.full_loop(scheduler=snake_case_  )
      _snake_case  :    List[str]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3

      _snake_case  :    Optional[Any]            = DPMSolverSinglestepScheduler.from_config(scheduler.config  )
      _snake_case  :    Dict            = UniPCMultistepScheduler.from_config(scheduler.config  )
      _snake_case  :    List[Any]            = DEISMultistepScheduler.from_config(scheduler.config  )
      _snake_case  :    Tuple            = DPMSolverMultistepScheduler.from_config(scheduler.config  )

      _snake_case  :    Dict            = self.full_loop(scheduler=snake_case_  )
      _snake_case  :    Optional[Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.scheduler_classes[0]
      _snake_case  :    Tuple            = self.get_scheduler_config(thresholding=snake_case_    ,     dynamic_thresholding_ratio=0  )
      _snake_case  :    Any            = scheduler_class(**snake_case_  )

      _snake_case  :    int            = 10
      _snake_case  :    int            = self.dummy_model()
      _snake_case  :    str            = self.dummy_sample_deter.half()
      scheduler.set_timesteps(snake_case_  )

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

      assert sample.dtype == torch.floataa


    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      for scheduler_class in self.scheduler_classes:
        _snake_case  :    Union[str, Any]            = self.get_scheduler_config(**snake_case_  )
        _snake_case  :    Any            = scheduler_class(**snake_case_  )

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




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
