





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  lowerCAmelCase							:







  def __init__(      self  :List[Any]   ,							_lowercase  :str = "cpu"   ,							_lowercase  :str = "openai/clip-vit-large-patch14" ):






     '''simple docstring'''





     lowercase__			       =  device
     lowercase__			       =  CLIPTokenizerFast.from_pretrained(_lowercase )
     lowercase__			       =  [0.48145466, 0.4578275, 0.40821073]
     lowercase__			       =  [0.26862954, 0.26130258, 0.27577711]
     lowercase__			       =  torchvision.transforms.Normalize(self.image_mean   ,							self.image_std )
     lowercase__			       =  torchvision.transforms.Resize(2_24 )
     lowercase__			       =  torchvision.transforms.CenterCrop(2_24 )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.resize(_lowercase )
     lowercase__			       =  self.center_crop(_lowercase )
     lowercase__			       =  self.normalize(_lowercase )
     return images







  def __call__(      self  :List[Any]   ,							_lowercase  :Optional[int]=None   ,							_lowercase  :Optional[int]=None   ,							**_lowercase  :List[Any] ):






     '''simple docstring'''





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





class  lowerCAmelCase							(       nn.Module ):







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






     '''simple docstring'''





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

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







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :Union[str, Any]=None   ,							_lowercase  :Optional[Any]=None   ,							_lowercase  :List[str]=5   ,							_lowercase  :str=True ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[Any]=None   ,							_lowercase  :Dict=None ):






     '''simple docstring'''





     if not (path or img):
        raise ValueError("Input either path or tensor" )
     if img is not None:
        raise NotImplementedError
     lowercase__			       =  preprocess(Image.open(_lowercase )   ,							target_image_size=2_56 ).to(self.device )
     lowercase__			       =  preprocess_vqgan(_lowercase )
     lowercase__       ,							*lowercase__			       =  self.vqgan.encode(_lowercase )
     return z







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :str ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Any   ,							_lowercase  :Optional[int]   ,							_lowercase  :Union[str, Any]=None ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :int   ,							_lowercase  :Dict   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Tuple   ,							_lowercase  :Any ):






     '''simple docstring'''





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

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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :Any   ,							_lowercase  :List[str] ):






     '''simple docstring'''





     wandb.init(reinit=_lowercase   ,							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:
        lowercase__			       =  Image.open(_lowercase )
        lowercase__			       =  image.resize((2_56, 2_56) )
        wandb.log("Original Image"   ,							wandb.Image(_lowercase ) )







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :List[str] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Dict   ,							_lowercase  :Tuple=None   ,							_lowercase  :List[Any]=None   ,							_lowercase  :Optional[int]=True   ,							_lowercase  :str=False   ,							_lowercase  :Dict=True   ,							_lowercase  :int=True   ,							_lowercase  :List[str]=None   ,							):






     '''simple docstring'''





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

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

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

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













from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def _A       (   ):
   lowercase__			       =  HfArgumentParser(__magic_name__	)
   lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   lowercase__			       =  TensorFlowBenchmark(args=__magic_name__	)
   try:
      lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      lowercase__			       =  "Arg --no_{0} is no longer used, please use --no-{0} instead."
      lowercase__			       =  " ".join(str(__magic_name__	).split(" "	)[:-1]	)
      lowercase__			       =  ""
      lowercase__			       =  eval(str(__magic_name__	).split(" "	)[-1]	)
      lowercase__			       =  []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]	)
         else:
            wrong_args.append(__magic_name__	)
      if len(__magic_name__	) > 0:
         lowercase__			       =  full_error_msg + begin_error_msg + str(__magic_name__	)
      raise ValueError(__magic_name__	)
   benchmark.run()


if __name__ == "__main__":
  main()






