






"""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		:						Dict							,						UpperCAmelCase__		:						str = "cpu"							,						UpperCAmelCase__		:						str = "openai/clip-vit-large-patch14"							)					->       None:
   _a  :       List[Any]             =	device
   _a  :       str             =	CLIPTokenizerFast.from_pretrained(__lowerCAmelCase							)
   _a  :       List[str]             =	[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
   _a  :       str             =	[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
   _a  :       Union[str, Any]             =	torchvision.transforms.Normalize(self.image_mean							,						self.image_std							)
   _a  :       Union[str, Any]             =	torchvision.transforms.Resize(224							)
   _a  :       int             =	torchvision.transforms.CenterCrop(224							)


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Optional[int]							)					->       Optional[int]:
   _a  :       Union[str, Any]             =	self.resize(__lowerCAmelCase							)
   _a  :       Tuple             =	self.center_crop(__lowerCAmelCase							)
   _a  :       Union[str, Any]             =	self.normalize(__lowerCAmelCase							)
   return images







 def __call__(     self		:						Dict							,						UpperCAmelCase__		:						Tuple=None							,						UpperCAmelCase__		:						Any=None							,						**UpperCAmelCase__		:						Dict							)					->       Optional[int]:
   _a  :       Optional[int]             =	self.tokenizer(text=__lowerCAmelCase							,						**__lowerCAmelCase							)
   _a  :       Dict             =	self.preprocess_img(__lowerCAmelCase							)
   _a  :       Optional[Any]             =	{key: value.to(self.device							) for (key, value) in encoding.items()}
   return encoding







class        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						Dict							,						UpperCAmelCase__		:						Any=10							,						UpperCAmelCase__		:						Union[str, Any]=0.0_1							,						UpperCAmelCase__		:						Tuple=None							,						UpperCAmelCase__		:						int=None							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						str=None							,						UpperCAmelCase__		:						str=False							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						List[Any]="image"							,						UpperCAmelCase__		:						List[Any]=True							,						UpperCAmelCase__		:						List[Any]=False							,						UpperCAmelCase__		:						int=False							,						UpperCAmelCase__		:						int=False							,						)					->       None:
   super().__init__()
   _a  :       Dict             =	None
   _a  :       Optional[int]             =	device if device else get_device()
   if vqgan:
     _a  :       List[Any]             =	vqgan
   else:
     _a  :       Tuple             =	load_vqgan(self.device							,						conf_path=__lowerCAmelCase							,						ckpt_path=__lowerCAmelCase							)
   self.vqgan.eval()
   if clip:
     _a  :       Tuple             =	clip
   else:
     _a  :       Union[str, Any]             =	CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""							)
   self.clip.to(self.device							)
   _a  :       Any             =	ProcessorGradientFlow(device=self.device							)

   _a  :       Dict             =	iterations
   _a  :       Tuple             =	lr
   _a  :       List[str]             =	log
   _a  :       Any             =	make_grid
   _a  :       int             =	return_val
   _a  :       str             =	quantize
   _a  :       int             =	self.vqgan.decoder.z_shape


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						int=None							,						UpperCAmelCase__		:						Optional[Any]=None							,						UpperCAmelCase__		:						List[Any]=5							,						UpperCAmelCase__		:						Optional[int]=True							)					->       Optional[int]:
   _a  :       str             =	[]
   if output_path is None:
     _a  :       Union[str, Any]             =	"""./animation.gif"""
   if input_path is None:
     _a  :       str             =	self.save_path
   _a  :       Optional[int]             =	sorted(glob(input_path + """/*"""							)							)
   if not len(__lowerCAmelCase							):
     raise ValueError(
         """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
         """ function?)"""							)
   if len(__lowerCAmelCase							) == 1:
     print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""							)
   _a  :       Union[str, Any]             =	total_duration / len(__lowerCAmelCase							)
   _a  :       List[Any]             =	[frame_duration] * len(__lowerCAmelCase							)
   if extend_frames:
     _a  :       Tuple             =	1.5
     _a  :       List[Any]             =	3
   for file_name in paths:
     if file_name.endswith(""".png"""							):
       images.append(imageio.imread(__lowerCAmelCase							)							)
   imageio.mimsave(__lowerCAmelCase							,						__lowerCAmelCase							,						duration=__lowerCAmelCase							)
   print(f"""gif saved to {output_path}"""							)


 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						Optional[Any]=None							,						UpperCAmelCase__		:						Any=None							)					->       List[Any]:






   if not (path or img):
     raise ValueError("""Input either path or tensor"""							)
   if img is not None:
     raise NotImplementedError
   _a  :       Optional[Any]             =	preprocess(Image.open(__lowerCAmelCase							)							,						target_image_size=256							).to(self.device							)
   _a  :       str             =	preprocess_vqgan(__lowerCAmelCase							)
   _a      ,  *_a  :       List[Any]             =	self.vqgan.encode(__lowerCAmelCase							)
   return z


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Any							)					->       Tuple:
   _a  :       Tuple             =	self.latent.detach().requires_grad_()
   _a  :       Union[str, Any]             =	base_latent + transform_vector
   if self.quantize:
     _a      ,  *_a  :       Dict             =	self.vqgan.quantize(__lowerCAmelCase							)
   else:
     _a  :       str             =	trans_latent
   return self.vqgan.decode(__lowerCAmelCase							)


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Optional[Any]=None							)					->       Optional[Any]:
   _a  :       List[Any]             =	self.clip_preprocessor(text=__lowerCAmelCase							,						images=__lowerCAmelCase							,						return_tensors="""pt"""							,						padding=__lowerCAmelCase							)
   _a  :       Optional[Any]             =	self.clip(**__lowerCAmelCase							)
   _a  :       Optional[int]             =	clip_outputs.logits_per_image
   if weights is not None:
     _a  :       Union[str, Any]             =	similarity_logits * weights
   return similarity_logits.sum()


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Tuple             =	self._get_clip_similarity(pos_prompts["""prompts"""]							,						__lowerCAmelCase							,						weights=(1 / pos_prompts["""weights"""])							)
   if neg_prompts:
     _a  :       Any             =	self._get_clip_similarity(neg_prompts["""prompts"""]							,						__lowerCAmelCase							,						weights=neg_prompts["""weights"""]							)
   else:
     _a  :       Optional[int]             =	torch.tensor([1]							,						device=self.device							)
   _a  :       List[Any]             =	-torch.log(__lowerCAmelCase							) + torch.log(__lowerCAmelCase							)
   return loss


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						Tuple							)					->       str:
   _a  :       Tuple             =	torch.randn_like(self.latent							,						requires_grad=__lowerCAmelCase							,						device=self.device							)
   _a  :       Any             =	torch.optim.Adam([vector]							,						lr=self.lr							)

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


 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						str							)					->       Dict:
   wandb.init(reinit=__lowerCAmelCase							,						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:
     _a  :       Tuple             =	Image.open(__lowerCAmelCase							)
     _a  :       Any             =	image.resize((256, 256)							)
     wandb.log("""Original Image"""							,						wandb.Image(__lowerCAmelCase							)							)


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						Tuple							)					->       Optional[int]:






   if not prompts:
     return []
   _a  :       Optional[Any]             =	[]
   _a  :       List[str]             =	[]
   if isinstance(__lowerCAmelCase							,						__lowerCAmelCase							):
     _a  :       List[Any]             =	[prompt.strip() for prompt in prompts.split("""|"""							)]
   for prompt in prompts:
     if isinstance(__lowerCAmelCase							,						(tuple, list)							):
       _a  :       Optional[int]             =	prompt[0]
       _a  :       Optional[int]             =	float(prompt[1]							)
     elif ":" in prompt:
       _a      ,  _a  :       Optional[Any]             =	prompt.split(""":"""							)
       _a  :       Any             =	float(__lowerCAmelCase							)
     else:
       _a  :       Any             =	prompt
       _a  :       Dict             =	1.0
     processed_prompts.append(__lowerCAmelCase							)
     weights.append(__lowerCAmelCase							)
   return {
       "prompts": processed_prompts,
       "weights": torch.tensor(__lowerCAmelCase							,						device=self.device							),
   }







 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Any=None							,						UpperCAmelCase__		:						List[Any]=None							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						Dict=False							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						Dict=None							,						)					->       Dict:






   if image_path:
     _a  :       List[str]             =	self._get_latent(__lowerCAmelCase							)
   else:
     _a  :       List[Any]             =	torch.randn(self.latent_dim							,						device=self.device							)
   if self.log:
     self._init_logging(__lowerCAmelCase							,						__lowerCAmelCase							,						__lowerCAmelCase							)

   assert pos_prompts, "You must provide at least one positive prompt."
   _a  :       List[Any]             =	self.process_prompts(__lowerCAmelCase							)
   _a  :       Union[str, Any]             =	self.process_prompts(__lowerCAmelCase							)
   if save_final and save_path is None:
     _a  :       Any             =	os.path.join("""./outputs/"""							,						"""_""".join(pos_prompts["""prompts"""]							)							)
   if not os.path.exists(__lowerCAmelCase							):
     os.makedirs(__lowerCAmelCase							)
   else:
     _a  :       Dict             =	save_path + """_""" + get_timestamp()
     os.makedirs(__lowerCAmelCase							)
   _a  :       int             =	save_path

   _a  :       List[Any]             =	self.vqgan.decode(self.latent							)[0]
   if show_intermediate:
     print("""Original Image"""							)
     show_pil(custom_to_pil(__lowerCAmelCase							)							)

   _a  :       Tuple             =	loop_post_process(__lowerCAmelCase							)
   for iter, transformed_img in enumerate(self._optimize_CLIP(__lowerCAmelCase							,						__lowerCAmelCase							,						__lowerCAmelCase							)							):
     if show_intermediate:
       show_pil(__lowerCAmelCase							)
     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(__lowerCAmelCase							)}							)
   if show_final:
     show_pil(__lowerCAmelCase							)
   if save_final:
     transformed_img.save(os.path.join(self.save_path							,						f"""iter_{iter:03d}_final.png"""							)							)








from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
					A    :					List[str] =      input('''Enter image url: ''').strip()
					print(F'''Downloading image from {url} ...''')
					A    :					Any =      BeautifulSoup(requests.get(url).content, '''html.parser''')
					# The image URL is in the content field of the first meta tag with property og:image
					A    :					List[Any] =      soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
					A    :					Dict =      requests.get(image_url).content
					A    :					Tuple =      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
					with open(file_name, '''wb''') as fp:
										fp.write(image_data)
					print(F'''Done. Image saved to disk as {file_name}.''')



