






"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= (DDPMScheduler,)






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		Any:
      lowerCamelCase_         =						{
          'num_train_timesteps': 1000,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**__SCREAMING_SNAKE_CASE					)
      return config






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[Any]:
      for timesteps in [1, 5, 100, 1000]:
        self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Union[str, Any]:
      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]       ,						[0.002, 0.02, 0.2, 2]					):
        self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE       ,						beta_end=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							int					)     ->		Optional[int]:
      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							str					)     ->		List[str]:
      for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Any:
      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							int					)     ->		Any:
      self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE					)
      for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(
              thresholding=__SCREAMING_SNAKE_CASE       ,						prediction_type=__SCREAMING_SNAKE_CASE       ,						sample_max_value=__SCREAMING_SNAKE_CASE       ,						)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		int:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[Any]:
      for t in [0, 500, 999]:
        self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Any:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.00_979					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1e-5






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Dict:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.dummy_model()
      lowerCamelCase_         =						self.dummy_sample_deter
      lowerCamelCase_         =						torch.manual_seed(0					)

      for t in reversed(range(__SCREAMING_SNAKE_CASE					)					):
        # 1. predict noise residual
        lowerCamelCase_         =						model(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

        # 2. predict previous mean of sample x_t-1
        lowerCamelCase_         =						scheduler.step(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE					).prev_sample

        # if t > 0:
        #     noise = self.dummy_sample_deter
        #     variance = scheduler.get_variance(t) ** (0.5) * noise
        #
        # sample = pred_prev_sample + variance
        lowerCamelCase_         =						pred_prev_sample

      lowerCamelCase_         =						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE					)					)

      assert abs(result_sum.item() - 258.9_606					) < 1e-2
      assert abs(result_mean.item() - 0.3_372					) < 1e-3






    def    UpperCamelCase       (       self     :							Dict					)     ->		List[str]:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config(prediction_type='v_prediction'					)
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.dummy_model()
      lowerCamelCase_         =						self.dummy_sample_deter
      lowerCamelCase_         =						torch.manual_seed(0					)

      for t in reversed(range(__SCREAMING_SNAKE_CASE					)					):
        # 1. predict noise residual
        lowerCamelCase_         =						model(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

        # 2. predict previous mean of sample x_t-1
        lowerCamelCase_         =						scheduler.step(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE					).prev_sample

        # if t > 0:
        #     noise = self.dummy_sample_deter
        #     variance = scheduler.get_variance(t) ** (0.5) * noise
        #
        # sample = pred_prev_sample + variance
        lowerCamelCase_         =						pred_prev_sample

      lowerCamelCase_         =						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE					)					)

      assert abs(result_sum.item() - 202.0_296					) < 1e-2
      assert abs(result_mean.item() - 0.2_631					) < 1e-3






    def    UpperCamelCase       (       self     :							str					)     ->		str:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						[100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						scheduler.timesteps

      for i, timestep in enumerate(__SCREAMING_SNAKE_CASE					):
        if i == len(__SCREAMING_SNAKE_CASE					) - 1:
          lowerCamelCase_         =						-1
        else:
          lowerCamelCase_         =						timesteps[i + 1]

        lowerCamelCase_         =						scheduler.previous_timestep(__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						prev_t.item()

        self.assertEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		int:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						[100, 87, 50, 51, 0]

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ,						msg='`custom_timesteps` must be in descending order.'					):
        scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Optional[int]:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						[100, 87, 50, 1, 0]
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ,						msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'					):
        scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE       ,						timesteps=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          __SCREAMING_SNAKE_CASE       ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,						):
        scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE					)












"""simple docstring"""





from cva import destroyAllWindows, imread, imshow, waitKey







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Dict:
  # getting number of pixels in the image
  lowerCamelCase_     ,	lowerCamelCase_         =						img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_lowerCamelCase ):
    for j in range(_lowerCamelCase ):
      lowerCamelCase_         =						[255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						imread('''image_data/lena.jpg''', 1)

  # convert to its negative
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						convert_to_negative(img)

  # show result image
  imshow('''negative of original image''', img)
  waitKey(0)
  destroyAllWindows()




