





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__A				:    Tuple       =   logging.get_logger(__name__)

enable_full_determinism()

class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			UNetaDModel
	UpperCAmelCase__					=			'''sample'''
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				4
				UpperCAmelCase   		=				3
				UpperCAmelCase   		=				(3_2, 3_2)

				UpperCAmelCase   		=				floats_tensor((batch_size, num_channels) + sizes		).to(_A		)
				UpperCAmelCase   		=				torch.tensor([1_0]		).to(_A		)

				return {"sample": noise, "timestep": time_step}
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return (3, 3_2, 3_2)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return (3, 3_2, 3_2)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				{
				    '''block_out_channels''': (3_2, 6_4),
				    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
				    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
				    '''attention_head_dim''': 3,
				    '''out_channels''': 3,
				    '''in_channels''': 3,
				    '''layers_per_block''': 2,
				    '''sample_size''': 3_2,
				}
				UpperCAmelCase   		=				self.dummy_input
				return init_dict, inputs_dict

class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			UNetaDModel
	UpperCAmelCase__					=			'''sample'''
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				4
				UpperCAmelCase   		=				4
				UpperCAmelCase   		=				(3_2, 3_2)

				UpperCAmelCase   		=				floats_tensor((batch_size, num_channels) + sizes		).to(_A		)
				UpperCAmelCase   		=				torch.tensor([1_0]		).to(_A		)

				return {"sample": noise, "timestep": time_step}
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return (4, 3_2, 3_2)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return (4, 3_2, 3_2)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				{
				    '''sample_size''': 3_2,
				    '''in_channels''': 4,
				    '''out_channels''': 4,
				    '''layers_per_block''': 2,
				    '''block_out_channels''': (3_2, 6_4),
				    '''attention_head_dim''': 3_2,
				    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
				    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
				}
				UpperCAmelCase   		=				self.dummy_input
				return init_dict, inputs_dict
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							,					output_loading_info=_A		)

				self.assertIsNotNone(_A		)
				self.assertEqual(len(loading_info['''missing_keys''']		)							,					0		)

				model.to(_A		)
				UpperCAmelCase   		=				model(**self.dummy_input		).sample

				assert image is not None, "Make sure output is not None"
	@unittest.skipIf(torch_device != '''cuda'''							,					'''This test is supposed to run on GPU'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							,					output_loading_info=_A		)
				model.to(_A		)
				UpperCAmelCase   		=				model(**self.dummy_input		).sample

				assert image is not None, "Make sure output is not None"
	@unittest.skipIf(torch_device != '''cuda'''							,					'''This test is supposed to run on GPU'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							,					output_loading_info=_A		)
				model_accelerate.to(_A		)
				model_accelerate.eval()

				UpperCAmelCase   		=				torch.randn(
				    1							,					model_accelerate.config.in_channels							,					model_accelerate.config.sample_size							,					model_accelerate.config.sample_size							,					generator=torch.manual_seed(0		)							,					)
				UpperCAmelCase   		=				noise.to(_A		)
				UpperCAmelCase   		=				torch.tensor([1_0] * noise.shape[0]		).to(_A		)

				UpperCAmelCase   		=				model_accelerate(_A							,					_A		)['''sample''']

				# two models don't need to stay in the device at the same time
				del model_accelerate
				torch.cuda.empty_cache()
				gc.collect()

				UpperCAmelCase		,			UpperCAmelCase   		=				UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''							,					output_loading_info=_A							,					low_cpu_mem_usage=_A		)
				model_normal_load.to(_A		)
				model_normal_load.eval()
				UpperCAmelCase   		=				model_normal_load(_A							,					_A		)['''sample''']

				assert torch_all_close(_A							,					_A							,					rtol=1E-3		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		)
				model.eval()
				model.to(_A		)

				UpperCAmelCase   		=				torch.randn(
				    1							,					model.config.in_channels							,					model.config.sample_size							,					model.config.sample_size							,					generator=torch.manual_seed(0		)							,					)
				UpperCAmelCase   		=				noise.to(_A		)
				UpperCAmelCase   		=				torch.tensor([1_0] * noise.shape[0]		).to(_A		)

				with torch.no_grad():
							UpperCAmelCase   		=				model(_A							,					_A		).sample

				UpperCAmelCase   		=				output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				UpperCAmelCase   		=				torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]		)
				# fmt: on

				self.assertTrue(torch_all_close(_A							,					_A							,					rtol=1E-3		)		)

class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			UNetaDModel
	UpperCAmelCase__					=			'''sample'''
	@property
	def      _lowercase				(    self							,					_A=(3_2, 3_2)		):






				'''simple docstring'''







				UpperCAmelCase   		=				4
				UpperCAmelCase   		=				3

				UpperCAmelCase   		=				floats_tensor((batch_size, num_channels) + sizes		).to(_A		)
				UpperCAmelCase   		=				torch.tensor(batch_size * [1_0]		).to(dtype=torch.intaa							,					device=_A		)

				return {"sample": noise, "timestep": time_step}
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return (3, 3_2, 3_2)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return (3, 3_2, 3_2)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				{
				    '''block_out_channels''': [3_2, 6_4, 6_4, 6_4],
				    '''in_channels''': 3,
				    '''layers_per_block''': 1,
				    '''out_channels''': 3,
				    '''time_embedding_type''': '''fourier''',
				    '''norm_eps''': 1E-6,
				    '''mid_block_scale_factor''': math.sqrt(2.0		),
				    '''norm_num_groups''': None,
				    '''down_block_types''': [
				        '''SkipDownBlock2D''',
				        '''AttnSkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				    ],
				    '''up_block_types''': [
				        '''SkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				        '''AttnSkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				    ],
				}
				UpperCAmelCase   		=				self.dummy_input
				return init_dict, inputs_dict
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''							,					output_loading_info=_A		)
				self.assertIsNotNone(_A		)
				self.assertEqual(len(loading_info['''missing_keys''']		)							,					0		)

				model.to(_A		)
				UpperCAmelCase   		=				self.dummy_input
				UpperCAmelCase   		=				floats_tensor((4, 3) + (2_5_6, 2_5_6)		).to(_A		)
				UpperCAmelCase   		=				noise
				UpperCAmelCase   		=				model(**_A		)

				assert image is not None, "Make sure output is not None"
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''		)
				model.to(_A		)

				UpperCAmelCase   		=				4
				UpperCAmelCase   		=				3
				UpperCAmelCase   		=				(2_5_6, 2_5_6)

				UpperCAmelCase   		=				torch.ones((batch_size, num_channels) + sizes		).to(_A		)
				UpperCAmelCase   		=				torch.tensor(batch_size * [1E-4]		).to(_A		)

				with torch.no_grad():
							UpperCAmelCase   		=				model(_A							,					_A		).sample

				UpperCAmelCase   		=				output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				UpperCAmelCase   		=				torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]		)
				# fmt: on

				self.assertTrue(torch_all_close(_A							,					_A							,					rtol=1E-2		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''		)
				model.to(_A		)

				UpperCAmelCase   		=				4
				UpperCAmelCase   		=				3
				UpperCAmelCase   		=				(3_2, 3_2)

				UpperCAmelCase   		=				torch.ones((batch_size, num_channels) + sizes		).to(_A		)
				UpperCAmelCase   		=				torch.tensor(batch_size * [1E-4]		).to(_A		)

				with torch.no_grad():
							UpperCAmelCase   		=				model(_A							,					_A		).sample

				UpperCAmelCase   		=				output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				UpperCAmelCase   		=				torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]		)
				# fmt: on

				self.assertTrue(torch_all_close(_A							,					_A							,					rtol=1E-2		)		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				pass










import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				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)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



