




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class        __snake_case  (_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     KandinskyVaaControlnetImgaImgPipeline
				lowerCAmelCase__				=     ["image_embeds", "negative_image_embeds", "image", "hint"]
				lowerCAmelCase__				=     ["image_embeds", "negative_image_embeds", "image", "hint"]
				lowerCAmelCase__				=     [
				    "generator",
				    "height",
				    "width",
				    "strength",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				lowerCAmelCase__				=     False



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




								return 32



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								return 32



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Any:





								'''simple docstring'''




								return self.time_input_dim



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    List[Any]:





								'''simple docstring'''




								return self.time_input_dim * 4



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




								return 100



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Union[str, Any]:





								'''simple docstring'''




								torch.manual_seed(0      )

								_lowerCAmelCase    :    int										=      {
								    """in_channels""": 8,
								    # Out channels is double in channels because predicts mean and variance
								    """out_channels""": 8,
								    """addition_embed_type""": """image_hint""",
								    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
								    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
								    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
								    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    """layers_per_block""": 1,
								    """encoder_hid_dim""": self.text_embedder_hidden_size,
								    """encoder_hid_dim_type""": """image_proj""",
								    """cross_attention_dim""": self.cross_attention_dim,
								    """attention_head_dim""": 4,
								    """resnet_time_scale_shift""": """scale_shift""",
								    """class_embed_type""": None,
								}

								_lowerCAmelCase    :    Union[str, Any]										=      UNetaDConditionModel(**_UpperCAmelCase      )
								return model



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								return {
								    "block_out_channels": [32, 32, 64, 64],
								    "down_block_types": [
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "AttnDownEncoderBlock2D",
								    ],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "vq_embed_dim": 4,
								}



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    List[str]:





								'''simple docstring'''




								torch.manual_seed(0      )
								_lowerCAmelCase    :    Optional[Any]										=      VQModel(**self.dummy_movq_kwargs      )
								return model



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.dummy_unet
								_lowerCAmelCase    :    Optional[int]										=      self.dummy_movq

								_lowerCAmelCase    :    int										=      {
								    """num_train_timesteps""": 1000,
								    """beta_schedule""": """linear""",
								    """beta_start""": 0.00085,
								    """beta_end""": 0.012,
								    """clip_sample""": False,
								    """set_alpha_to_one""": False,
								    """steps_offset""": 0,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								}

								_lowerCAmelCase    :    Optional[int]										=      DDIMScheduler(**_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      {
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int=0      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(seed + 1      )      ).to(
								    _UpperCAmelCase      )
								# create init_image
								_lowerCAmelCase    :    Any										=      floats_tensor((1, 3, 64, 64)		,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      image.cpu().permute(0		,	2		,	3		,	1      )[0]
								_lowerCAmelCase    :    Any										=      Image.fromarray(np.uinta(_UpperCAmelCase      )      ).convert("""RGB"""      ).resize((256, 256)      )
								# create hint
								_lowerCAmelCase    :    Tuple										=      floats_tensor((1, 3, 64, 64)		,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )

								if str(_UpperCAmelCase      ).startswith("""mps"""      ):
												_lowerCAmelCase    :    List[str]										=      torch.manual_seed(_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    Optional[int]										=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      {
								    """image""": init_image,
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """hint""": hint,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """num_inference_steps""": 10,
								    """guidance_scale""": 7.0,
								    """strength""": 0.2,
								    """output_type""": """np""",
								}
								return inputs



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      """cpu"""

								_lowerCAmelCase    :    Any										=      self.get_dummy_components()

								_lowerCAmelCase    :    Optional[int]										=      self.pipeline_class(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      pipe.to(_UpperCAmelCase      )

								pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

								_lowerCAmelCase    :    List[str]										=      pipe(**self.get_dummy_inputs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      output.images

								_lowerCAmelCase    :    Tuple										=      pipe(
								    **self.get_dummy_inputs(_UpperCAmelCase      )		,	return_dict=_UpperCAmelCase		,	)[0]

								_lowerCAmelCase    :    str										=      image[0, -3:, -3:, -1]
								_lowerCAmelCase    :    Optional[int]										=      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								_lowerCAmelCase    :    Union[str, Any]										=      np.array(
								    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]      )
								assert (
								    np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
								), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
								), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""      )

								_lowerCAmelCase    :    Tuple										=      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
								_lowerCAmelCase    :    Optional[int]										=      init_image.resize((512, 512)      )

								_lowerCAmelCase    :    List[str]										=      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/hint_image_cat.png"""      )
								_lowerCAmelCase    :    str										=      torch.from_numpy(np.array(_UpperCAmelCase      )      ).float() / 255.0
								_lowerCAmelCase    :    Tuple										=      hint.permute(2		,	0		,	1      ).unsqueeze(0      )

								_lowerCAmelCase    :    Any										=      """A robot, 4k photo"""

								_lowerCAmelCase    :    int										=      KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""		,	torch_dtype=torch.floataa      )
								pipe_prior.to(_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-controlnet-depth"""		,	torch_dtype=torch.floataa      )
								_lowerCAmelCase    :    Any										=      pipeline.to(_UpperCAmelCase      )

								pipeline.set_progress_bar_config(disable=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      torch.Generator(device="""cpu"""      ).manual_seed(0      )

								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      pipe_prior(
								    _UpperCAmelCase		,	image=_UpperCAmelCase		,	strength=0.85		,	generator=_UpperCAmelCase		,	negative_prompt=""""""		,	).to_tuple()

								_lowerCAmelCase    :    List[str]										=      pipeline(
								    image=_UpperCAmelCase		,	image_embeds=_UpperCAmelCase		,	negative_image_embeds=_UpperCAmelCase		,	hint=_UpperCAmelCase		,	generator=_UpperCAmelCase		,	num_inference_steps=100		,	height=512		,	width=512		,	strength=0.5		,	output_type="""np"""		,	)

								_lowerCAmelCase    :    str										=      output.images[0]

								assert image.shape == (512, 512, 3)

								assert_mean_pixel_difference(_UpperCAmelCase		,	_UpperCAmelCase      )







import cva
import numpy as np
class        __snake_case  :



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		int      )  ->    int:





								'''simple docstring'''




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



				def __str__(      self      :		int      )  ->    str:





								'''simple docstring'''




								return str(self.k      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		str      )  ->    tuple[cva.Mat, list[list[int]]]:





								'''simple docstring'''




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

																_lowerCAmelCase    :    Dict										=      (wxx * wyy) - (wxy**2)
																_lowerCAmelCase    :    Union[str, Any]										=      wxx + wyy
																_lowerCAmelCase    :    int										=      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      :  int      						=						HarrisCorner(0.0_4, 3)
	_lowerCamelCase						, _lowerCamelCase      :  Any      						=						edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)

