




"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase__			(     snake_case_	):
			a__       :					str	    =   (DEISMultistepScheduler,)
			a__       :					Dict	    =   (('''num_inference_steps''', 25),)


			def 							lowerCamelCase_ (   self ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[Any]    	= {
					    """num_train_timesteps""": 1_000,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					    """solver_order""": 2,
					}

					config.update(**UpperCAmelCase__							)
					return config


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE=0 ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[Any]    	= dict(self.forward_default_kwargs							)
					snake_case						:  Union[str, Any]    	= kwargs.pop("num_inference_steps" ,       UpperCAmelCase__							)
					snake_case						:  Union[str, Any]    	= self.dummy_sample
					snake_case						:  List[str]    	= 0.1 * sample
					snake_case						:  Optional[int]    	= [residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
							snake_case						:  Optional[Any]    	= self.get_scheduler_config(**UpperCAmelCase__							)
							snake_case						:  Tuple    	= scheduler_class(**UpperCAmelCase__							)
							scheduler.set_timesteps(UpperCAmelCase__							)
							# copy over dummy past residuals
							snake_case						:  int    	= dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(UpperCAmelCase__							)
									snake_case						:  Union[str, Any]    	= scheduler_class.from_pretrained(UpperCAmelCase__							)
									new_scheduler.set_timesteps(UpperCAmelCase__							)
									# copy over dummy past residuals
									snake_case						:  Tuple    	= dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case						:  List[Any]    	= sample, sample
							for t in range(UpperCAmelCase__ ,       time_step + scheduler.config.solver_order + 1							):
									snake_case						:  str    	= scheduler.step(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__ ,       **UpperCAmelCase__							).prev_sample
									snake_case						:  int    	= new_scheduler.step(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__ ,       **UpperCAmelCase__							).prev_sample

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


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE=0 ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  str    	= dict(self.forward_default_kwargs							)
					snake_case						:  Optional[int]    	= kwargs.pop("num_inference_steps" ,       UpperCAmelCase__							)
					snake_case						:  Optional[int]    	= self.dummy_sample
					snake_case						:  Dict    	= 0.1 * sample
					snake_case						:  int    	= [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						:  Optional[Any]    	= scheduler_class(**UpperCAmelCase__							)
							scheduler.set_timesteps(UpperCAmelCase__							)

							# copy over dummy past residuals (must be after setting timesteps)
							snake_case						:  Union[str, Any]    	= dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(UpperCAmelCase__							)
									snake_case						:  Tuple    	= scheduler_class.from_pretrained(UpperCAmelCase__							)
									# copy over dummy past residuals
									new_scheduler.set_timesteps(UpperCAmelCase__							)

									# 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(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__ ,       **UpperCAmelCase__							).prev_sample
							snake_case						:  Tuple    	= new_scheduler.step(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__ ,       **UpperCAmelCase__							).prev_sample

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


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE=None ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if scheduler is None:
							snake_case						:  Dict    	= self.scheduler_classes[0]
							snake_case						:  Union[str, Any]    	= self.get_scheduler_config(**UpperCAmelCase__							)
							snake_case						:  Dict    	= scheduler_class(**UpperCAmelCase__							)

					snake_case						:  Union[str, Any]    	= self.scheduler_classes[0]
					snake_case						:  List[str]    	= self.get_scheduler_config(**UpperCAmelCase__							)
					snake_case						:  int    	= scheduler_class(**UpperCAmelCase__							)

					snake_case						:  str    	= 10
					snake_case						:  Optional[Any]    	= self.dummy_model()
					snake_case						:  List[Any]    	= self.dummy_sample_deter
					scheduler.set_timesteps(UpperCAmelCase__							)

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

					return sample


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  str    	= dict(self.forward_default_kwargs							)

					snake_case						:  Tuple    	= kwargs.pop("num_inference_steps" ,       UpperCAmelCase__							)

					for scheduler_class in self.scheduler_classes:
							snake_case						:  Optional[int]    	= self.get_scheduler_config()
							snake_case						:  Union[str, Any]    	= scheduler_class(**UpperCAmelCase__							)

							snake_case						:  int    	= self.dummy_sample
							snake_case						:  List[str]    	= 0.1 * sample

							if num_inference_steps is not None and hasattr(UpperCAmelCase__ ,       "set_timesteps"							):
									scheduler.set_timesteps(UpperCAmelCase__							)
							elif num_inference_steps is not None and not hasattr(UpperCAmelCase__ ,       "set_timesteps"							):
									snake_case						:  int    	= num_inference_steps

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

							snake_case						:  Dict    	= scheduler.timesteps[5]
							snake_case						:  str    	= scheduler.timesteps[6]

							snake_case						:  Optional[Any]    	= scheduler.step(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__ ,       **UpperCAmelCase__							).prev_sample
							snake_case						:  int    	= scheduler.step(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__ ,       **UpperCAmelCase__							).prev_sample

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


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  List[Any]    	= DEISMultistepScheduler(**self.get_scheduler_config()							)
					snake_case						:  Dict    	= self.full_loop(scheduler=UpperCAmelCase__							)
					snake_case						:  Tuple    	= torch.mean(torch.abs(UpperCAmelCase__							)							)

					assert abs(result_mean.item() - 0.2_39_16							) < 1E-3

					snake_case						:  Optional[int]    	= DPMSolverSinglestepScheduler.from_config(scheduler.config							)
					snake_case						:  Dict    	= DPMSolverMultistepScheduler.from_config(scheduler.config							)
					snake_case						:  Optional[Any]    	= UniPCMultistepScheduler.from_config(scheduler.config							)
					snake_case						:  List[str]    	= DEISMultistepScheduler.from_config(scheduler.config							)

					snake_case						:  List[Any]    	= self.full_loop(scheduler=UpperCAmelCase__							)
					snake_case						:  Tuple    	= torch.mean(torch.abs(UpperCAmelCase__							)							)

					assert abs(result_mean.item() - 0.2_39_16							) < 1E-3


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					for timesteps in [25, 50, 100, 999, 1_000]:
							self.check_over_configs(num_train_timesteps=UpperCAmelCase__							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					self.check_over_configs(thresholding=UpperCAmelCase__							)
					for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    thresholding=UpperCAmelCase__ ,       prediction_type=UpperCAmelCase__ ,       sample_max_value=UpperCAmelCase__ ,       algorithm_type="deis" ,       solver_order=UpperCAmelCase__ ,       solver_type=UpperCAmelCase__ ,       )


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
									for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    solver_order=UpperCAmelCase__ ,       solver_type=UpperCAmelCase__ ,       prediction_type=UpperCAmelCase__ ,       algorithm_type=UpperCAmelCase__ ,       )
													snake_case						:  int    	= self.full_loop(
													    solver_order=UpperCAmelCase__ ,       solver_type=UpperCAmelCase__ ,       prediction_type=UpperCAmelCase__ ,       algorithm_type=UpperCAmelCase__ ,       )
													assert not torch.isnan(UpperCAmelCase__							).any(), "Samples have nan numbers"


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					self.check_over_configs(lower_order_final=UpperCAmelCase__							)
					self.check_over_configs(lower_order_final=UpperCAmelCase__							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
							self.check_over_forward(num_inference_steps=UpperCAmelCase__ ,       time_step=0							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]    	= self.full_loop()
					snake_case						:  Optional[Any]    	= torch.mean(torch.abs(UpperCAmelCase__							)							)

					assert abs(result_mean.item() - 0.2_39_16							) < 1E-3


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  List[Any]    	= self.full_loop(prediction_type="v_prediction"							)
					snake_case						:  Any    	= torch.mean(torch.abs(UpperCAmelCase__							)							)

					assert abs(result_mean.item() - 0.0_91							) < 1E-3



			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Tuple    	= self.scheduler_classes[0]
					snake_case						:  Any    	= self.get_scheduler_config(thresholding=UpperCAmelCase__ ,       dynamic_thresholding_ratio=0							)
					snake_case						:  Tuple    	= scheduler_class(**UpperCAmelCase__							)

					snake_case						:  int    	= 10
					snake_case						:  Dict    	= self.dummy_model()
					snake_case						:  List[Any]    	= self.dummy_sample_deter.half()
					scheduler.set_timesteps(UpperCAmelCase__							)

					for i, t in enumerate(scheduler.timesteps							):
							snake_case						:  List[str]    	= model(UpperCAmelCase__ ,       UpperCAmelCase__							)
							snake_case						:  List[Any]    	= scheduler.step(UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__							).prev_sample

					assert sample.dtype == torch.floataa









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


 def __str__(     self		:						Dict							)					->       str:
   return str(self.k							)







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	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__":
 _snake_case          =   HarrisCorner(0.04, 3)
 _snake_case      , _snake_case          =   edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)

