
'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_snake_case					: List[Any]     			=					logging.get_logger(__name__)  # pylint: disable=invalid-name

_snake_case					: Optional[Any]     			=					"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def       _a		(						_SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=8	):
						_SCREAMING_SNAKE_CASE					=			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_SCREAMING_SNAKE_CASE					=			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Optional[int]=512						,      _SCREAMING_SNAKE_CASE		: List[Any]=512	):
						_SCREAMING_SNAKE_CASE					=			pil_image.resize((w, h)						,      resample=Image.BICUBIC						,      reducing_gap=1	)
						_SCREAMING_SNAKE_CASE					=			np.array(pil_image.convert("RGB"	)	)
						_SCREAMING_SNAKE_CASE					=			arr.astype(np.floataa	) / 127.5 - 1
						_SCREAMING_SNAKE_CASE					=			np.transpose(_SCREAMING_SNAKE_CASE						,      [2, 0, 1]	)
						_SCREAMING_SNAKE_CASE					=			torch.from_numpy(_SCREAMING_SNAKE_CASE	).unsqueeze(0	)
						return image




class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    ):
								super().__init__()

								self.register_modules(
								    unet=UpperCamelCase       ,    scheduler=UpperCamelCase       ,    movq=UpperCamelCase       ,    )
								_SCREAMING_SNAKE_CASE					=			2 ** (len(self.movq.config.block_out_channels   ) - 1)




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								# get the original timestep using init_timestep
								_SCREAMING_SNAKE_CASE					=			min(int(num_inference_steps * strength   )       ,    UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			max(num_inference_steps - init_timestep       ,    0   )
								_SCREAMING_SNAKE_CASE					=			self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None   ):
								if not isinstance(UpperCamelCase       ,    (torch.Tensor, PIL.Image.Image, list)   ):
														raise ValueError(
														    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCamelCase   )}'   )

								_SCREAMING_SNAKE_CASE					=			image.to(device=UpperCamelCase       ,    dtype=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			batch_size * num_images_per_prompt

								if image.shape[1] == 4:
														_SCREAMING_SNAKE_CASE					=			image

								else:
														if isinstance(UpperCamelCase       ,    UpperCamelCase   ) and len(UpperCamelCase   ) != batch_size:
																				raise ValueError(
																				    F'You have passed a list of generators of length {len(UpperCamelCase   )}, but requested an effective batch'
																				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

														elif isinstance(UpperCamelCase       ,    UpperCamelCase   ):
																				_SCREAMING_SNAKE_CASE					=			[
																				    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(UpperCamelCase   )
																				]
																				_SCREAMING_SNAKE_CASE					=			torch.cat(UpperCamelCase       ,    dim=0   )
														else:
																				_SCREAMING_SNAKE_CASE					=			self.movq.encode(UpperCamelCase   ).latent_dist.sample(UpperCamelCase   )

														_SCREAMING_SNAKE_CASE					=			self.movq.config.scaling_factor * init_latents

								_SCREAMING_SNAKE_CASE					=			torch.cat([init_latents]       ,    dim=0   )

								_SCREAMING_SNAKE_CASE					=			init_latents.shape
								_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=UpperCamelCase       ,    dtype=UpperCamelCase   )

								# get latents
								_SCREAMING_SNAKE_CASE					=			self.scheduler.add_noise(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			init_latents

								return latents




		def      lowercase							(					self       ,    UpperCamelCase=0   ):
								if is_accelerate_available():
														from accelerate import cpu_offload
								else:
														raise ImportError("Please install accelerate via `pip install accelerate`"   )

								_SCREAMING_SNAKE_CASE					=			torch.device(F'cuda:{gpu_id}'   )

								_SCREAMING_SNAKE_CASE					=			[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																				cpu_offload(UpperCamelCase       ,    UpperCamelCase   )




		def      lowercase							(					self       ,    UpperCamelCase=0   ):
								if is_accelerate_available() and is_accelerate_version(">="       ,    "0.17.0.dev0"   ):
														from accelerate import cpu_offload_with_hook
								else:
														raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."   )

								_SCREAMING_SNAKE_CASE					=			torch.device(F'cuda:{gpu_id}'   )

								if self.device.type != "cpu":
														self.to("cpu"       ,    silence_dtype_warnings=UpperCamelCase   )
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

								_SCREAMING_SNAKE_CASE					=			None
								for cpu_offloaded_model in [self.unet, self.movq]:
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			cpu_offload_with_hook(UpperCamelCase       ,    UpperCamelCase       ,    prev_module_hook=UpperCamelCase   )

								# We'll offload the last model manually.
								_SCREAMING_SNAKE_CASE					=			hook




		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def      lowercase							(					self   ):
								if not hasattr(self.unet       ,    "_hf_hook"   ):
														return self.device
								for module in self.unet.modules():
														if (
														    hasattr(UpperCamelCase       ,    "_hf_hook"   )
														    and hasattr(module._hf_hook       ,    "execution_device"   )
														    and module._hf_hook.execution_device is not None
														):
																				return torch.device(module._hf_hook.execution_device   )
								return self.device




		@torch.no_grad()
		@replace_example_docstring(UpperCamelCase   )
		def __call__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase = 512       ,    UpperCamelCase = 512       ,    UpperCamelCase = 100       ,    UpperCamelCase = 4.0       ,    UpperCamelCase = 0.3       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								_SCREAMING_SNAKE_CASE					=			self._execution_device

								_SCREAMING_SNAKE_CASE					=			guidance_scale > 1.0

								if isinstance(UpperCamelCase       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			torch.cat(UpperCamelCase       ,    dim=0   )
								if isinstance(UpperCamelCase       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			torch.cat(UpperCamelCase       ,    dim=0   )
								if isinstance(UpperCamelCase       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			torch.cat(UpperCamelCase       ,    dim=0   )

								_SCREAMING_SNAKE_CASE					=			image_embeds.shape[0]

								if do_classifier_free_guidance:
														_SCREAMING_SNAKE_CASE					=			image_embeds.repeat_interleave(UpperCamelCase       ,    dim=0   )
														_SCREAMING_SNAKE_CASE					=			negative_image_embeds.repeat_interleave(UpperCamelCase       ,    dim=0   )
														_SCREAMING_SNAKE_CASE					=			hint.repeat_interleave(UpperCamelCase       ,    dim=0   )

								_SCREAMING_SNAKE_CASE					=			torch.cat([negative_image_embeds, image_embeds]       ,    dim=0   ).to(dtype=self.unet.dtype       ,    device=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			torch.cat([hint, hint]       ,    dim=0   ).to(dtype=self.unet.dtype       ,    device=UpperCamelCase   )

								if not isinstance(UpperCamelCase       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			[image]
								if not all(isinstance(UpperCamelCase       ,    (PIL.Image.Image, torch.Tensor)   ) for i in image   ):
														raise ValueError(
														    F'Input is in incorrect format: {[type(UpperCamelCase   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'   )

								_SCREAMING_SNAKE_CASE					=			torch.cat([prepare_image(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ) for i in image]       ,    dim=0   )
								_SCREAMING_SNAKE_CASE					=			image.to(dtype=image_embeds.dtype       ,    device=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			self.movq.encode(UpperCamelCase   )["latents"]
								_SCREAMING_SNAKE_CASE					=			latents.repeat_interleave(UpperCamelCase       ,    dim=0   )
								self.scheduler.set_timesteps(UpperCamelCase       ,    device=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.get_timesteps(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			timesteps[:1].repeat(batch_size * num_images_per_prompt   )
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			downscale_height_and_width(UpperCamelCase       ,    UpperCamelCase       ,    self.movq_scale_factor   )
								_SCREAMING_SNAKE_CASE					=			self.prepare_latents(
								    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    image_embeds.dtype       ,    UpperCamelCase       ,    UpperCamelCase   )
								for i, t in enumerate(self.progress_bar(UpperCamelCase   )   ):
														# expand the latents if we are doing classifier free guidance
														_SCREAMING_SNAKE_CASE					=			torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

														_SCREAMING_SNAKE_CASE					=			{"image_embeds": image_embeds, "hint": hint}
														_SCREAMING_SNAKE_CASE					=			self.unet(
														    sample=UpperCamelCase       ,    timestep=UpperCamelCase       ,    encoder_hidden_states=UpperCamelCase       ,    added_cond_kwargs=UpperCamelCase       ,    return_dict=UpperCamelCase       ,    )[0]

														if do_classifier_free_guidance:
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			noise_pred.split(latents.shape[1]       ,    dim=1   )
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			noise_pred.chunk(2   )
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			variance_pred.chunk(2   )
																				_SCREAMING_SNAKE_CASE					=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																				_SCREAMING_SNAKE_CASE					=			torch.cat([noise_pred, variance_pred_text]       ,    dim=1   )

														if not (
														    hasattr(self.scheduler.config       ,    "variance_type"   )
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			noise_pred.split(latents.shape[1]       ,    dim=1   )

														# compute the previous noisy sample x_t -> x_t-1

														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(
														    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase       ,    )[0]

								# post-processing
								_SCREAMING_SNAKE_CASE					=			self.movq.decode(UpperCamelCase       ,    force_not_quantize=UpperCamelCase   )["sample"]

								if output_type not in ["pt", "np", "pil"]:
														raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

								if output_type in ["np", "pil"]:
														_SCREAMING_SNAKE_CASE					=			image * 0.5 + 0.5
														_SCREAMING_SNAKE_CASE					=			image.clamp(0       ,    1   )
														_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).float().numpy()

								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )

'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)