
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class        _UpperCamelCase				(					a__  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = (DDIMParallelScheduler,)
			__UpperCamelCase      :						int					      = (("""eta""", 0.0), ("""num_inference_steps""", 50))

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       List[str]      ):
							UpperCamelCase_:							str	=						{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**lowercase__      )
							return config

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	**snake_case_ :       str      ):
							UpperCamelCase_:							List[Any]	=						self.scheduler_classes[0]
							UpperCamelCase_:							List[Any]	=						self.get_scheduler_config(**lowercase__      )
							UpperCamelCase_:							str	=						scheduler_class(**lowercase__      )

							UpperCamelCase_:							Union[str, Any]	=						10, 0.0

							UpperCamelCase_:							List[str]	=						self.dummy_model()
							UpperCamelCase_:							Tuple	=						self.dummy_sample_deter

							scheduler.set_timesteps(lowercase__      )

							for t in scheduler.timesteps:
											UpperCamelCase_:							Union[str, Any]	=						model(lowercase__						,	lowercase__      )
											UpperCamelCase_:							Any	=						scheduler.step(lowercase__						,	lowercase__						,	lowercase__						,	lowercase__      ).prev_sample

							return sample

			def       lowerCAmelCase__	(       self :       List[Any]      ):





							for timesteps in [100, 500, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase__      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):





							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=lowercase__      )

							UpperCamelCase_:							int	=						self.scheduler_classes[0]
							UpperCamelCase_:							int	=						self.get_scheduler_config(steps_offset=1      )
							UpperCamelCase_:							Optional[int]	=						scheduler_class(**lowercase__      )
							scheduler.set_timesteps(5      )
							assert torch.equal(scheduler.timesteps						,	torch.LongTensor([801, 601, 401, 201, 1]      )      )

			def       lowerCAmelCase__	(       self :       List[str]      ):





							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,	[0.002, 0.02, 0.2, 2]      ):
											self.check_over_configs(beta_start=lowercase__						,	beta_end=lowercase__      )

			def       lowerCAmelCase__	(       self :       str      ):





							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowercase__      )

			def       lowerCAmelCase__	(       self :       List[str]      ):





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

			def       lowerCAmelCase__	(       self :       List[str]      ):





							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowercase__      )

			def       lowerCAmelCase__	(       self :       int      ):





							for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=lowercase__      )

			def       lowerCAmelCase__	(       self :       Any      ):





							for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=lowercase__      )

			def       lowerCAmelCase__	(       self :       Any      ):
							self.check_over_configs(thresholding=lowercase__      )
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowercase__						,	prediction_type=lowercase__						,	sample_max_value=lowercase__						,	)

			def       lowerCAmelCase__	(       self :       int      ):





							for t in [1, 10, 49]:
											self.check_over_forward(time_step=lowercase__      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):





							for t, num_inference_steps in zip([1, 10, 50]						,	[10, 50, 500]      ):
											self.check_over_forward(time_step=lowercase__						,	num_inference_steps=lowercase__      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):





							for t, eta in zip([1, 10, 49]						,	[0.0, 0.5, 1.0]      ):
											self.check_over_forward(time_step=lowercase__						,	eta=lowercase__      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							List[Any]	=						self.scheduler_classes[0]
							UpperCamelCase_:							List[Any]	=						self.get_scheduler_config()
							UpperCamelCase_:							Union[str, Any]	=						scheduler_class(**lowercase__      )

							assert torch.sum(torch.abs(scheduler._get_variance(0						,	0      ) - 0.0      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420						,	400      ) - 0.1_4771      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980						,	960      ) - 0.3_2460      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0						,	0      ) - 0.0      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487						,	486      ) - 0.0_0979      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999						,	998      ) - 0.02      )      ) < 1e-5

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Any	=						self.scheduler_classes[0]
							UpperCamelCase_:							Optional[int]	=						self.get_scheduler_config()
							UpperCamelCase_:							Optional[int]	=						scheduler_class(**lowercase__      )

							UpperCamelCase_:							Tuple	=						10, 0.0
							scheduler.set_timesteps(lowercase__      )

							UpperCamelCase_:							Tuple	=						self.dummy_model()
							UpperCamelCase_:							Any	=						self.dummy_sample_deter
							UpperCamelCase_:							Tuple	=						self.dummy_sample_deter + 0.1
							UpperCamelCase_:							Optional[Any]	=						self.dummy_sample_deter - 0.1

							UpperCamelCase_:							Optional[int]	=						samplea.shape[0]
							UpperCamelCase_:							List[str]	=						torch.stack([samplea, samplea, samplea]						,	dim=0      )
							UpperCamelCase_:							Optional[Any]	=						torch.arange(lowercase__      )[0:3, None].repeat(1						,	lowercase__      )

							UpperCamelCase_:							List[str]	=						model(samples.flatten(0						,	1      )						,	timesteps.flatten(0						,	1      )      )
							UpperCamelCase_:							Dict	=						scheduler.batch_step_no_noise(lowercase__						,	timesteps.flatten(0						,	1      )						,	samples.flatten(0						,	1      )						,	lowercase__      )

							UpperCamelCase_:							List[Any]	=						torch.sum(torch.abs(lowercase__      )      )
							UpperCamelCase_:							Optional[int]	=						torch.mean(torch.abs(lowercase__      )      )

							assert abs(result_sum.item() - 1147.7904      ) < 1e-2
							assert abs(result_mean.item() - 0.4982      ) < 1e-3

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							List[Any]	=						self.full_loop()

							UpperCamelCase_:							int	=						torch.sum(torch.abs(lowercase__      )      )
							UpperCamelCase_:							int	=						torch.mean(torch.abs(lowercase__      )      )

							assert abs(result_sum.item() - 172.0067      ) < 1e-2
							assert abs(result_mean.item() - 0.22_3967      ) < 1e-3

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						self.full_loop(prediction_type="""v_prediction"""      )

							UpperCamelCase_:							int	=						torch.sum(torch.abs(lowercase__      )      )
							UpperCamelCase_:							Optional[Any]	=						torch.mean(torch.abs(lowercase__      )      )

							assert abs(result_sum.item() - 52.5302      ) < 1e-2
							assert abs(result_mean.item() - 0.0684      ) < 1e-3

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Tuple	=						self.full_loop(set_alpha_to_one=lowercase__						,	beta_start=0.01      )
							UpperCamelCase_:							str	=						torch.sum(torch.abs(lowercase__      )      )
							UpperCamelCase_:							Optional[Any]	=						torch.mean(torch.abs(lowercase__      )      )

							assert abs(result_sum.item() - 149.8295      ) < 1e-2
							assert abs(result_mean.item() - 0.1951      ) < 1e-3




			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Optional[Any]	=						self.full_loop(set_alpha_to_one=lowercase__						,	beta_start=0.01      )
							UpperCamelCase_:							Dict	=						torch.sum(torch.abs(lowercase__      )      )
							UpperCamelCase_:							Optional[Any]	=						torch.mean(torch.abs(lowercase__      )      )

							assert abs(result_sum.item() - 149.0784      ) < 1e-2
							assert abs(result_mean.item() - 0.1941      ) < 1e-3








import cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

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




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

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							Dict	=						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[Any]         =  HarrisCorner(0.04, 3)
							lowerCamelCase_   ,       lowerCamelCase_						: Any         =  edge_detect.detect("""path_to_image""")
							cva.imwrite("""detect.png""", color_img)






