'''simple docstring'''





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class        __A      ( UpperCamelCase__	):
    a__       :						Any          =  (UniPCMultistepScheduler,)
    a__       :						Tuple          =  (("""num_inference_steps""", 25),)



    def 					_lowercase						(self    :    str   ,			**__a    :    Tuple ):
         UpperCAmelCase_           =   {
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
             "solver_type": "bh2",
         }

         config.update(**__a )
         return config



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Dict=0   ,			**__a    :    Dict ):
         UpperCAmelCase_           =   dict(self.forward_default_kwargs )
         UpperCAmelCase_           =   kwargs.pop("num_inference_steps"   ,			__a )
         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(**__a )
              UpperCAmelCase_           =   scheduler_class(**__a )
              scheduler.set_timesteps(__a )
              # copy over dummy past residuals
              UpperCAmelCase_           =   dummy_past_residuals[: scheduler.config.solver_order]

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

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

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



    def 					_lowercase						(self    :    Any   ,			__a    :    Optional[int]=0   ,			**__a    :    Dict ):
         UpperCAmelCase_           =   dict(self.forward_default_kwargs )
         UpperCAmelCase_           =   kwargs.pop("num_inference_steps"   ,			__a )
         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(**__a )
              scheduler.set_timesteps(__a )

              # 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(__a )
                   UpperCAmelCase_           =   scheduler_class.from_pretrained(__a )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(__a )

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

              UpperCAmelCase_           =   scheduler.step(__a   ,			__a   ,			__a   ,			**__a ).prev_sample
              UpperCAmelCase_           =   new_scheduler.step(__a   ,			__a   ,			__a   ,			**__a ).prev_sample

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



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Union[str, Any]=None   ,			**__a    :    Optional[int] ):
         if scheduler is None:
              UpperCAmelCase_           =   self.scheduler_classes[0]
              UpperCAmelCase_           =   self.get_scheduler_config(**__a )
              UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config(**__a )
         UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   10
         UpperCAmelCase_           =   self.dummy_model()
         UpperCAmelCase_           =   self.dummy_sample_deter
         scheduler.set_timesteps(__a )

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

         return sample



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   dict(self.forward_default_kwargs )

         UpperCAmelCase_           =   kwargs.pop("num_inference_steps"   ,			__a )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_           =   self.get_scheduler_config()
              UpperCAmelCase_           =   scheduler_class(**__a )

              UpperCAmelCase_           =   self.dummy_sample
              UpperCAmelCase_           =   0.1 * sample

              if num_inference_steps is not None and hasattr(__a   ,			"set_timesteps" ):
                   scheduler.set_timesteps(__a )
              elif num_inference_steps is not None and not hasattr(__a   ,			"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(__a   ,			__a   ,			__a   ,			**__a ).prev_sample
              UpperCAmelCase_           =   scheduler.step(__a   ,			__a   ,			__a   ,			**__a ).prev_sample

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



    def 					_lowercase						(self    :    List[Any] ):
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         UpperCAmelCase_           =   UniPCMultistepScheduler(**self.get_scheduler_config() )
         UpperCAmelCase_           =   self.full_loop(scheduler=__a )
         UpperCAmelCase_           =   torch.mean(torch.abs(__a ) )

         assert abs(result_mean.item() - 0.24_64 ) < 1E-3

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

         UpperCAmelCase_           =   self.full_loop(scheduler=__a )
         UpperCAmelCase_           =   torch.mean(torch.abs(__a ) )

         assert abs(result_mean.item() - 0.24_64 ) < 1E-3



    def 					_lowercase						(self    :    Union[str, Any] ):
         for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=__a )



    def 					_lowercase						(self    :    Optional[int] ):
         self.check_over_configs(thresholding=__a )
         for order in [1, 2, 3]:
              for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 thresholding=__a   ,			prediction_type=__a   ,			sample_max_value=__a   ,			solver_order=__a   ,			solver_type=__a   ,			)



    def 					_lowercase						(self    :    str ):
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__a )



    def 					_lowercase						(self    :    List[Any] ):
         for solver_type in ["bh1", "bh2"]:
              for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=__a   ,			solver_type=__a   ,			prediction_type=__a   ,			)
                        UpperCAmelCase_           =   self.full_loop(
                            solver_order=__a   ,			solver_type=__a   ,			prediction_type=__a   ,			)
                        assert not torch.isnan(__a ).any(), "Samples have nan numbers"



    def 					_lowercase						(self    :    int ):
         self.check_over_configs(lower_order_final=__a )
         self.check_over_configs(lower_order_final=__a )



    def 					_lowercase						(self    :    Any ):
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=__a   ,			time_step=0 )



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.full_loop()
         UpperCAmelCase_           =   torch.mean(torch.abs(__a ) )

         assert abs(result_mean.item() - 0.24_64 ) < 1E-3



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.full_loop(prediction_type="v_prediction" )
         UpperCAmelCase_           =   torch.mean(torch.abs(__a ) )

         assert abs(result_mean.item() - 0.10_14 ) < 1E-3



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config(thresholding=__a   ,			dynamic_thresholding_ratio=0 )
         UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   10
         UpperCAmelCase_           =   self.dummy_model()
         UpperCAmelCase_           =   self.dummy_sample_deter.half()
         scheduler.set_timesteps(__a )

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

         assert sample.dtype == torch.floataa






    def 					_lowercase						(self    :    Dict   ,			**__a    :    Optional[int] ):
         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_           =   self.get_scheduler_config(**__a )
              UpperCAmelCase_           =   scheduler_class(**__a )

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











from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





