


'''simple docstring'''



import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class 	UpperCamelCase__:




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			int = "cpu"				,   lowerCAmelCase      :			Tuple = "openai/clip-vit-large-patch14"				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     device
										UpperCAmelCase					    =     CLIPTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     [0.48145466, 0.4578275, 0.40821073]
										UpperCAmelCase					    =     [0.26862954, 0.26130258, 0.27577711]
										UpperCAmelCase					    =     torchvision.transforms.Normalize(self.image_mean				,   self.image_std				)
										UpperCAmelCase					    =     torchvision.transforms.Resize(224				)
										UpperCAmelCase					    =     torchvision.transforms.CenterCrop(224				)




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Any				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     self.resize(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     self.center_crop(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     self.normalize(_SCREAMING_SNAKE_CASE				)
										return images





				def __call__(		self      :			Tuple				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			List[str]				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     self.tokenizer(text=_SCREAMING_SNAKE_CASE				,   **_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     self.preprocess_img(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     {key: value.to(self.device				) for (key, value) in encoding.items()}
										return encoding







class 	UpperCamelCase__(       nn.Module	):




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			Union[str, Any]=10				,   lowerCAmelCase      :			int=0.01				,   lowerCAmelCase      :			Any=None				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			Optional[Any]=None				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			Any=False				,   lowerCAmelCase      :			Optional[int]=True				,   lowerCAmelCase      :			Optional[int]="image"				,   lowerCAmelCase      :			List[str]=True				,   lowerCAmelCase      :			Optional[Any]=False				,   lowerCAmelCase      :			List[Any]=False				,   lowerCAmelCase      :			Dict=False				,   )->					None:

										"""simple docstring"""




										super().__init__()
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     device if device else get_device()
										if vqgan:
																UpperCAmelCase					    =     vqgan
										else:
																UpperCAmelCase					    =     load_vqgan(self.device				,   conf_path=_SCREAMING_SNAKE_CASE				,   ckpt_path=_SCREAMING_SNAKE_CASE				)
										self.vqgan.eval()
										if clip:
																UpperCAmelCase					    =     clip
										else:
																UpperCAmelCase					    =     CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''				)
										self.clip.to(self.device				)
										UpperCAmelCase					    =     ProcessorGradientFlow(device=self.device				)

										UpperCAmelCase					    =     iterations
										UpperCAmelCase					    =     lr
										UpperCAmelCase					    =     log
										UpperCAmelCase					    =     make_grid
										UpperCAmelCase					    =     return_val
										UpperCAmelCase					    =     quantize
										UpperCAmelCase					    =     self.vqgan.decoder.z_shape




				def 					a__(		self      :			int				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			List[str]=5				,   lowerCAmelCase      :			List[Any]=True				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										if output_path is None:
																UpperCAmelCase					    =     "./animation.gif"
										if input_path is None:
																UpperCAmelCase					    =     self.save_path
										UpperCAmelCase					    =     sorted(glob(input_path + '''/*'''				)				)
										if not len(_SCREAMING_SNAKE_CASE				):
																raise ValueError(
																    '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
																    ''' function?)'''				)
										if len(_SCREAMING_SNAKE_CASE				) == 1:
																print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''				)
										UpperCAmelCase					    =     total_duration / len(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     [frame_duration] * len(_SCREAMING_SNAKE_CASE				)
										if extend_frames:
																UpperCAmelCase					    =     1.5
																UpperCAmelCase					    =     3
										for file_name in paths:
																if file_name.endswith('''.png'''				):
																						images.append(imageio.imread(_SCREAMING_SNAKE_CASE				)				)
										imageio.mimsave(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   duration=_SCREAMING_SNAKE_CASE				)
										print(F"""gif saved to {output_path}"""				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			Optional[Any]=None				)->					Any:

										"""simple docstring"""




										if not (path or img):
																raise ValueError('''Input either path or tensor'''				)
										if img is not None:
																raise NotImplementedError
										UpperCAmelCase					    =     preprocess(Image.open(_SCREAMING_SNAKE_CASE				)				,   target_image_size=256				).to(self.device				)
										UpperCAmelCase					    =     preprocess_vqgan(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     self.vqgan.encode(_SCREAMING_SNAKE_CASE				)
										return z




				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			str				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.latent.detach().requires_grad_()
										UpperCAmelCase					    =     base_latent + transform_vector
										if self.quantize:
																UpperCAmelCase					    =     self.vqgan.quantize(_SCREAMING_SNAKE_CASE				)
										else:
																UpperCAmelCase					    =     trans_latent
										return self.vqgan.decode(_SCREAMING_SNAKE_CASE				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Union[str, Any]=None				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     self.clip_preprocessor(text=_SCREAMING_SNAKE_CASE				,   images=_SCREAMING_SNAKE_CASE				,   return_tensors='''pt'''				,   padding=_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     self.clip(**_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     clip_outputs.logits_per_image
										if weights is not None:
																UpperCAmelCase					    =     similarity_logits * weights
										return similarity_logits.sum()




				def 					a__(		self      :			int				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._get_clip_similarity(pos_prompts['''prompts''']				,   _SCREAMING_SNAKE_CASE				,   weights=(1 / pos_prompts['''weights'''])				)
										if neg_prompts:
																UpperCAmelCase					    =     self._get_clip_similarity(neg_prompts['''prompts''']				,   _SCREAMING_SNAKE_CASE				,   weights=neg_prompts['''weights''']				)
										else:
																UpperCAmelCase					    =     torch.tensor([1]				,   device=self.device				)
										UpperCAmelCase					    =     -torch.log(_SCREAMING_SNAKE_CASE				) + torch.log(_SCREAMING_SNAKE_CASE				)
										return loss




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			List[str]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.randn_like(self.latent				,   requires_grad=_SCREAMING_SNAKE_CASE				,   device=self.device				)
										UpperCAmelCase					    =     torch.optim.Adam([vector]				,   lr=self.lr				)

										for i in range(self.iterations				):
																optim.zero_grad()
																UpperCAmelCase					    =     self._add_vector(_SCREAMING_SNAKE_CASE				)
																UpperCAmelCase					    =     loop_post_process(_SCREAMING_SNAKE_CASE				)
																UpperCAmelCase					    =     self._get_CLIP_loss(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				)
																print('''CLIP loss'''				,   _SCREAMING_SNAKE_CASE				)
																if self.log:
																						wandb.log({'''CLIP Loss''': clip_loss}				)
																clip_loss.backward(retain_graph=_SCREAMING_SNAKE_CASE				)
																optim.step()
																if self.return_val == "image":
																						yield custom_to_pil(transformed_img[0]				)
																else:
																						yield vector




				def 					a__(		self      :			int				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										wandb.init(reinit=_SCREAMING_SNAKE_CASE				,   project='''face-editor'''				)
										wandb.config.update({'''Positive Prompts''': positive_prompts}				)
										wandb.config.update({'''Negative Prompts''': negative_prompts}				)
										wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}				)
										if image_path:
																UpperCAmelCase					    =     Image.open(_SCREAMING_SNAKE_CASE				)
																UpperCAmelCase					    =     image.resize((256, 256)				)
																wandb.log('''Original Image'''				,   wandb.Image(_SCREAMING_SNAKE_CASE				)				)




				def 					a__(		self      :			int				,   lowerCAmelCase      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										if not prompts:
																return []
										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []
										if isinstance(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				):
																UpperCAmelCase					    =     [prompt.strip() for prompt in prompts.split('''|'''				)]
										for prompt in prompts:
																if isinstance(_SCREAMING_SNAKE_CASE				,   (tuple, list)				):
																						UpperCAmelCase					    =     prompt[0]
																						UpperCAmelCase					    =     float(prompt[1]				)
																elif ":" in prompt:
																						UpperCAmelCase					    =     prompt.split(''':'''				)
																						UpperCAmelCase					    =     float(_SCREAMING_SNAKE_CASE				)
																else:
																						UpperCAmelCase					    =     prompt
																						UpperCAmelCase					    =     1.0
																processed_prompts.append(_SCREAMING_SNAKE_CASE				)
																weights.append(_SCREAMING_SNAKE_CASE				)
										return {
										    "prompts": processed_prompts,
										    "weights": torch.tensor(_SCREAMING_SNAKE_CASE				,   device=self.device				),
										}





				def 					a__(		self      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			str=None				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			Union[str, Any]=True				,   lowerCAmelCase      :			Optional[int]=False				,   lowerCAmelCase      :			Union[str, Any]=True				,   lowerCAmelCase      :			Union[str, Any]=True				,   lowerCAmelCase      :			Optional[Any]=None				,   )->					Optional[Any]:

										"""simple docstring"""




										if image_path:
																UpperCAmelCase					    =     self._get_latent(_SCREAMING_SNAKE_CASE				)
										else:
																UpperCAmelCase					    =     torch.randn(self.latent_dim				,   device=self.device				)
										if self.log:
																self._init_logging(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				)

										assert pos_prompts, "You must provide at least one positive prompt."
										UpperCAmelCase					    =     self.process_prompts(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     self.process_prompts(_SCREAMING_SNAKE_CASE				)
										if save_final and save_path is None:
																UpperCAmelCase					    =     os.path.join('''./outputs/'''				,   '''_'''.join(pos_prompts['''prompts''']				)				)
										if not os.path.exists(_SCREAMING_SNAKE_CASE				):
																os.makedirs(_SCREAMING_SNAKE_CASE				)
										else:
																UpperCAmelCase					    =     save_path + "_" + get_timestamp()
																os.makedirs(_SCREAMING_SNAKE_CASE				)
										UpperCAmelCase					    =     save_path

										UpperCAmelCase					    =     self.vqgan.decode(self.latent				)[0]
										if show_intermediate:
																print('''Original Image'''				)
																show_pil(custom_to_pil(_SCREAMING_SNAKE_CASE				)				)

										UpperCAmelCase					    =     loop_post_process(_SCREAMING_SNAKE_CASE				)
										for iter, transformed_img in enumerate(self._optimize_CLIP(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				)				):
																if show_intermediate:
																						show_pil(_SCREAMING_SNAKE_CASE				)
																if save_intermediate:
																						transformed_img.save(os.path.join(self.save_path				,   F"""iter_{iter:03d}.png"""				)				)
																if self.log:
																						wandb.log({'''Image''': wandb.Image(_SCREAMING_SNAKE_CASE				)}				)
										if show_final:
																show_pil(_SCREAMING_SNAKE_CASE				)
										if save_final:
																transformed_img.save(os.path.join(self.save_path				,   F"""iter_{iter:03d}_final.png"""				)				)









'''simple docstring'''



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






						if (ksize % 2) == 0:
												UpperCAmelCase					    =     ksize + 1
						UpperCAmelCase					    =     np.zeros((ksize, ksize)				,       dtype=np.floataa					)

						# each value
						for y in range(A					):
												for x in range(A					):
																		# distance from center
																		UpperCAmelCase					    =     x - ksize // 2
																		UpperCAmelCase					    =     y - ksize // 2

																		# degree to radiant
																		UpperCAmelCase					    =     theta / 1_80 * np.pi
																		UpperCAmelCase					    =     np.cos(_theta					)
																		UpperCAmelCase					    =     np.sin(_theta					)

																		# get kernel x
																		UpperCAmelCase					    =     cos_theta * px + sin_theta * py

																		# get kernel y
																		UpperCAmelCase					    =     -sin_theta * px + cos_theta * py

																		# fill kernel
																		UpperCAmelCase					    =     np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

						return gabor


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# read original image
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

	# Apply multiple Kernel to detect edges
	_lowercase      :      List[str]            =					np.zeros(gray.shape[:2])
	for theta in [0, 30, 60, 90, 120, 150]:
		_lowercase      :      List[Any]            =					gabor_filter_kernel(10, 8, theta, 10, 0, 0)
		out += filteraD(gray, CV_8UC3, kernel_aa)
	_lowercase      :      Optional[int]            =					out / out.max() * 255
	_lowercase      :      Optional[int]            =					out.astype(np.uinta)

	imshow("""Original""", gray)
	imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

	waitKey(0)





