
'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):







				def __init__(    self		,							A_		,							A_		,							A_		,							A_		,							A_		,							)->						str:







											'''simple docstring'''






											super().__init__()

											self.register_modules(vae=A_		,							text_encoder=A_		,							tokenizer=A_		,							unet=A_		,							scheduler=A_			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						str:







											'''simple docstring'''






											with torch.no_grad():
																		UpperCamelCase					       =			self.vae.encode(tfms.ToTensor()(A_			).unsqueeze(0			).to(self.device			) * 2 - 1			)
																		UpperCamelCase					       =			0.18_215 * latent.latent_dist.sample()
											return latent







				def 		UpperCAmelCase_  (    self		,							A_			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			(1 / 0.18_215) * latent
											with torch.no_grad():
																		UpperCamelCase					       =			self.vae.decode(A_			).sample
											UpperCamelCase					       =			(img / 2 + 0.5).clamp(0		,							1			)
											UpperCamelCase					       =			img.detach().cpu().permute(0		,							2		,							3		,							1			).numpy()
											UpperCamelCase					       =			(img * 255).round().astype('uint8'			)
											return Image.fromarray(img[0]			)







				def 		UpperCAmelCase_  (    self		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.tokenizer(
											    A_		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							truncation=A_		,							return_tensors='pt'		,							)

											UpperCamelCase					       =			self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

											UpperCamelCase					       =			self.tokenizer(
											    ''		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							truncation=A_		,							return_tensors='pt'		,							)

											UpperCamelCase					       =			self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

											return torch.cat([uncond_embedding, text_embedding]			)







				def __call__(    self		,							A_		,							A_		,							A_ = 0.3		,							A_ = 0.6		,							A_ = 0.5		,							A_ = 42		,							A_ = 50		,							A_ = 7.5		,							)->						Image.Image:







											'''simple docstring'''






											UpperCamelCase					       =			steps - int(kmin * steps			)
											UpperCamelCase					       =			steps - int(kmax * steps			)

											UpperCamelCase					       =			self.prep_text(A_			)

											self.scheduler.set_timesteps(A_			)

											UpperCamelCase       ,     UpperCamelCase					       =			img.size
											UpperCamelCase					       =			self.encode(A_			)

											torch.manual_seed(A_			)
											UpperCamelCase					       =			torch.randn(
											    (1, self.unet.config.in_channels, height // 8, width // 8)		,							).to(self.device			)

											UpperCamelCase					       =			self.scheduler.add_noise(
											    A_		,							A_		,							timesteps=self.scheduler.timesteps[tmax]		,							)

											UpperCamelCase					       =			torch.cat([latents] * 2			)

											UpperCamelCase					       =			self.scheduler.scale_model_input(A_		,							self.scheduler.timesteps[tmax]			)

											with torch.no_grad():
																		UpperCamelCase					       =			self.unet(
																		    A_		,							self.scheduler.timesteps[tmax]		,							encoder_hidden_states=A_		,							).sample

											UpperCamelCase       ,     UpperCamelCase					       =			pred.chunk(2			)
											UpperCamelCase					       =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

											UpperCamelCase					       =			self.scheduler.step(A_		,							self.scheduler.timesteps[tmax]		,							A_			).prev_sample

											for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
																		if i > tmax:
																									if i < tmin:  # layout generation phase
																																UpperCamelCase					       =			self.scheduler.add_noise(
																																    A_		,							A_		,							timesteps=A_		,							)

																																UpperCamelCase					       =			(mix_factor * latents) + (
																																    1 - mix_factor
																																) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																																UpperCamelCase					       =			torch.cat([input] * 2			)

																									else:  # content generation phase
																																UpperCamelCase					       =			torch.cat([latents] * 2			)

																									UpperCamelCase					       =			self.scheduler.scale_model_input(A_		,							A_			)

																									with torch.no_grad():
																																UpperCamelCase					       =			self.unet(
																																    A_		,							A_		,							encoder_hidden_states=A_		,							).sample

																									UpperCamelCase       ,     UpperCamelCase					       =			pred.chunk(2			)
																									UpperCamelCase					       =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

																									UpperCamelCase					       =			self.scheduler.step(A_		,							A_		,							A_			).prev_sample

											return self.decode(A_			)












"""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

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	k_size // 2
       UpperCAmelCase			,				UpperCAmelCase					=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase					=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
       )
       return g






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

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

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

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

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

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

       return destination






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     if destination[row, col] >= threshold_high:
                            UpperCAmelCase					=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCAmelCase					=	0
                     else:
                            UpperCAmelCase					=	weak






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       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],
                            ):
                                   UpperCAmelCase					=	strong
                            else:
                                   UpperCAmelCase					=	0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


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