




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   (KDPMaDiscreteScheduler,)
 lowercase_       =   10



 def 				lowerCAmelCase_  (							self  :		Any    ,    **_lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_lowerCAmelCase						)
       return config



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,    [0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_lowerCAmelCase    ,    beta_end=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_lowerCAmelCase						)



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



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_     						=      self.get_scheduler_config(prediction_type='v_prediction'						)
       SCREAMING_SNAKE_CASE_     						=      scheduler_class(**_lowerCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       SCREAMING_SNAKE_CASE_     						=      self.dummy_model()
       SCREAMING_SNAKE_CASE_     						=      self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE_     						=      sample.to(_lowerCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             SCREAMING_SNAKE_CASE_     						=      scheduler.scale_model_input(_lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      scheduler.step(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      output.prev_sample

       SCREAMING_SNAKE_CASE_     						=      torch.sum(torch.abs(_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_     						=      torch.mean(torch.abs(_lowerCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_934E-07						) < 1E-2
             assert abs(result_mean.item() - 6.1_112E-10						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.693_428_650_170_972E-07						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3



 def 				lowerCAmelCase_  (							self  :		Any						):
       if torch_device == "mps":
             return
       SCREAMING_SNAKE_CASE_     						=      self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_     						=      self.get_scheduler_config()
       SCREAMING_SNAKE_CASE_     						=      scheduler_class(**_lowerCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       SCREAMING_SNAKE_CASE_     						=      self.dummy_model()
       SCREAMING_SNAKE_CASE_     						=      self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE_     						=      sample.to(_lowerCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             SCREAMING_SNAKE_CASE_     						=      scheduler.scale_model_input(_lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      scheduler.step(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      output.prev_sample

       SCREAMING_SNAKE_CASE_     						=      torch.sum(torch.abs(_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_     						=      torch.mean(torch.abs(_lowerCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3




 def 				lowerCAmelCase_  (							self  :		Tuple						):
       if torch_device == "mps":
             return
       SCREAMING_SNAKE_CASE_     						=      self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_     						=      self.get_scheduler_config()
       SCREAMING_SNAKE_CASE_     						=      scheduler_class(**_lowerCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps    ,    device=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      self.dummy_model()
       SCREAMING_SNAKE_CASE_     						=      self.dummy_sample_deter.to(_lowerCAmelCase						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             SCREAMING_SNAKE_CASE_     						=      scheduler.scale_model_input(_lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      scheduler.step(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      output.prev_sample

       SCREAMING_SNAKE_CASE_     						=      torch.sum(torch.abs(_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_     						=      torch.mean(torch.abs(_lowerCAmelCase						)						)

       if str(_lowerCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		int						):

       if k in (0.04, 0.06):
             SCREAMING_SNAKE_CASE_     						=      k
             SCREAMING_SNAKE_CASE_     						=      window_size
       else:
             raise ValueError('invalid k value'						)



 def __str__(							self  :		Tuple						):
       return str(self.k						)




 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      cva.imread(_lowerCAmelCase    ,    0						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      img.shape
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      img.copy()
       SCREAMING_SNAKE_CASE_     						=      cva.cvtColor(_lowerCAmelCase    ,    cva.COLOR_GRAY2RGB						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      np.gradient(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      dx**2
       SCREAMING_SNAKE_CASE_     						=      dy**2
       SCREAMING_SNAKE_CASE_     						=      dx * dy
       SCREAMING_SNAKE_CASE_     						=      0.04
       SCREAMING_SNAKE_CASE_     						=      self.window_size // 2
       for y in range(_lowerCAmelCase    ,    h - offset						):
             for x in range(_lowerCAmelCase    ,    w - offset						):
                   SCREAMING_SNAKE_CASE_     						=      ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_SNAKE_CASE_     						=      det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)    ,    0						)
                         color_img.itemset((y, x, 1)    ,    0						)
                         color_img.itemset((y, x, 2)    ,    255						)
       return color_img, corner_list


if __name__ == "__main__":
   lowerCamelCase__			:       Optional[int]         =							HarrisCorner(0.04, 3)
   lowerCamelCase__    ,							lowerCamelCase__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)