'''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 				lowercase_     :







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





     """simple docstring"""

     UpperCAmelCase_												=       device
     UpperCAmelCase_												=       CLIPTokenizerFast.from_pretrained(_UpperCamelCase					)
     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(2_2_4					)
     UpperCAmelCase_												=       torchvision.transforms.CenterCrop(2_2_4					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							int:





     """simple docstring"""

     UpperCAmelCase_												=       self.resize(_UpperCamelCase					)
     UpperCAmelCase_												=       self.center_crop(_UpperCamelCase					)
     UpperCAmelCase_												=       self.normalize(_UpperCamelCase					)
     return images







    def __call__(						self							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  **UpperCamelCase__					)					->							Optional[Any]:





     """simple docstring"""

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


class 				lowercase_     (  nn.Module    ):







    def __init__(						self							,  UpperCamelCase__=1_0							,  UpperCamelCase__=0.01							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=False							,  UpperCamelCase__=True							,  UpperCamelCase__="image"							,  UpperCamelCase__=True							,  UpperCamelCase__=False							,  UpperCamelCase__=False							,  UpperCamelCase__=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=_UpperCamelCase							,  ckpt_path=_UpperCamelCase					)
     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 	lowerCamelCase_     (						self							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=5							,  UpperCamelCase__=True					)					->							Tuple:





     """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(_UpperCamelCase					):
      raise ValueError(
          "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
          " function?)"					)
     if len(_UpperCamelCase					) == 1:
      print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"					)
     UpperCAmelCase_												=       total_duration / len(_UpperCamelCase					)
     UpperCAmelCase_												=       [frame_duration] * len(_UpperCamelCase					)
     if extend_frames:
      UpperCAmelCase_												=       1.5
      UpperCAmelCase_												=       3
     for file_name in paths:
      if file_name.endswith(".png"					):
       images.append(imageio.imread(_UpperCamelCase					)					)
     imageio.mimsave(_UpperCamelCase							,  _UpperCamelCase							,  duration=_UpperCamelCase					)
     print(F"""gif saved to {output_path}"""					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__=None							,  UpperCamelCase__=None					)					->							Union[str, 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(_UpperCamelCase					)							,  target_image_size=2_5_6					).to(self.device					)
     UpperCAmelCase_												=       preprocess_vqgan(_UpperCamelCase					)
     UpperCAmelCase_							,							*UpperCAmelCase_												=       self.vqgan.encode(_UpperCamelCase					)
     return z







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							int:





     """simple docstring"""

     UpperCAmelCase_												=       self.latent.detach().requires_grad_()
     UpperCAmelCase_												=       base_latent + transform_vector
     if self.quantize:
      UpperCAmelCase_							,							*UpperCAmelCase_												=       self.vqgan.quantize(_UpperCamelCase					)
     else:
      UpperCAmelCase_												=       trans_latent
     return self.vqgan.decode(_UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__							,  UpperCamelCase__=None					)					->							Optional[Any]:





     """simple docstring"""

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







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__							,  UpperCamelCase__					)					->							Any:





     """simple docstring"""

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







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__							,  UpperCamelCase__					)					->							List[Any]:





     """simple docstring"""

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

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







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__							,  UpperCamelCase__					)					->							str:





     """simple docstring"""

     wandb.init(reinit=_UpperCamelCase							,  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(_UpperCamelCase					)
      UpperCAmelCase_												=       image.resize((2_5_6, 2_5_6)					)
      wandb.log("Original Image"							,  wandb.Image(_UpperCamelCase					)					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							Union[str, Any]:





     """simple docstring"""

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







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__=None							,  UpperCamelCase__=None							,  UpperCamelCase__=True							,  UpperCamelCase__=False							,  UpperCamelCase__=True							,  UpperCamelCase__=True							,  UpperCamelCase__=None							,  )					->							List[Any]:





     """simple docstring"""

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

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

     UpperCAmelCase_												=       self.vqgan.decode(self.latent					)[0]
     if show_intermediate:
      print("Original Image"					)
      show_pil(custom_to_pil(_UpperCamelCase					)					)

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




'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       int		)					->	int:







     """simple docstring"""
     while second != 0:
          _A				=							first & second
          first ^= second
          _A				=							c << 1
     return first


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase						=					int(input("""Enter the first number: """).strip())
    lowerCAmelCase						=					int(input("""Enter the second number: """).strip())
    print(F'{add(first, second) = }')





