

'''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 					A__      :



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





           '''simple docstring'''
           A_						=      device
           A_						=      CLIPTokenizerFast.from_pretrained(UpperCamelCase__       )
           A_						=      [0.48145466, 0.4578275, 0.40821073]
           A_						=      [0.26862954, 0.26130258, 0.27577711]
           A_						=      torchvision.transforms.Normalize(self.image_mean		,				self.image_std       )
           A_						=      torchvision.transforms.Resize(224       )
           A_						=      torchvision.transforms.CenterCrop(224       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           A_						=      self.resize(UpperCamelCase__       )
           A_						=      self.center_crop(UpperCamelCase__       )
           A_						=      self.normalize(UpperCamelCase__       )
           return images







    def __call__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           A_						=      self.tokenizer(text=UpperCamelCase__		,				**UpperCamelCase__       )
           A_						=      self.preprocess_img(UpperCamelCase__       )
           A_						=      {key: value.to(self.device       ) for (key, value) in encoding.items()}
           return encoding


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__=10		,				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__()
           A_						=      None
           A_						=      device if device else get_device()
           if vqgan:
                  A_						=      vqgan
           else:
                  A_						=      load_vqgan(self.device		,				conf_path=UpperCamelCase__		,				ckpt_path=UpperCamelCase__       )
           self.vqgan.eval()
           if clip:
                  A_						=      clip
           else:
                  A_						=      CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""       )
           self.clip.to(self.device       )
           A_						=      ProcessorGradientFlow(device=self.device       )

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



    def 					snake_case_			(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=5		,				UpperCamelCase__=True       )						->      List[str]:





           '''simple docstring'''
           A_						=      []
           if output_path is None:
                  A_						=      """./animation.gif"""
           if input_path is None:
                  A_						=      self.save_path
           A_						=      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?)"""       )
           A_						=      total_duration / len(UpperCamelCase__       )
           A_						=      [frame_duration] * len(UpperCamelCase__       )
           if extend_frames:
                  A_						=      1.5
                  A_						=      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 					snake_case_			(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None       )						->      Optional[int]:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      self.latent.detach().requires_grad_()
           A_						=      base_latent + transform_vector
           if self.quantize:
                  A_					,			*A_						=      self.vqgan.quantize(UpperCamelCase__       )
           else:
                  A_						=      trans_latent
           return self.vqgan.decode(UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      self.clip_preprocessor(text=UpperCamelCase__		,				images=UpperCamelCase__		,				return_tensors="""pt"""		,				padding=UpperCamelCase__       )
           A_						=      self.clip(**UpperCamelCase__       )
           A_						=      clip_outputs.logits_per_image
           if weights is not None:
                  A_						=      similarity_logits * weights
           return similarity_logits.sum()



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      self._get_clip_similarity(pos_prompts["""prompts"""]		,				UpperCamelCase__		,				weights=(1 / pos_prompts["""weights"""])       )
           if neg_prompts:
                  A_						=      self._get_clip_similarity(neg_prompts["""prompts"""]		,				UpperCamelCase__		,				weights=neg_prompts["""weights"""]       )
           else:
                  A_						=      torch.tensor([1]		,				device=self.device       )
           A_						=      -torch.log(UpperCamelCase__       ) + torch.log(UpperCamelCase__       )
           return loss



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      torch.randn_like(self.latent		,				requires_grad=UpperCamelCase__		,				device=self.device       )
           A_						=      torch.optim.Adam([vector]		,				lr=self.lr       )

           for i in range(self.iterations       ):
                  optim.zero_grad()
                  A_						=      self._add_vector(UpperCamelCase__       )
                  A_						=      loop_post_process(UpperCamelCase__       )
                  A_						=      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 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''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:
                  A_						=      Image.open(UpperCamelCase__       )
                  A_						=      image.resize((256, 256)       )
                  wandb.log("""Original Image"""		,				wandb.Image(UpperCamelCase__       )       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[Any]:





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







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=True		,				UpperCamelCase__=False		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=None		,				)						->      Tuple:





           '''simple docstring'''
           if image_path:
                  A_						=      self._get_latent(UpperCamelCase__       )
           else:
                  A_						=      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."
           A_						=      self.process_prompts(UpperCamelCase__       )
           A_						=      self.process_prompts(UpperCamelCase__       )
           if save_final and save_path is None:
                  A_						=      os.path.join("""./outputs/"""		,				"""_""".join(pos_prompts["""prompts"""]       )       )
           if not os.path.exists(UpperCamelCase__       ):
                  os.makedirs(UpperCamelCase__       )
           else:
                  A_						=      save_path + """_""" + get_timestamp()
                  os.makedirs(UpperCamelCase__       )
           A_						=      save_path

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

           A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(a - b				) for a, b in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			None:
       if point:
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     for item in point:
                            if not isinstance(UpperCAmelCase__,       (int, float)				):
                                   A_						=      (
                                       """Expected a list of numbers as input, found """
                                       F'''{type(UpperCAmelCase__				).__name__}'''
                                   )
                                   raise TypeError(UpperCAmelCase__				)
              else:
                     A_						=      F'''Expected a list of numbers as input, found {type(UpperCAmelCase__				).__name__}'''
                     raise TypeError(UpperCAmelCase__				)
       else:
              raise ValueError("""Missing an input"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(x - y				) for x, y in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
