



"""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__   :




      """simple docstring"""




      def __init__(      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str = "cpu"    ,			SCREAMING_SNAKE_CASE_				:      str = "openai/clip-vit-large-patch14"       ):
        lowerCAmelCase_   :				List[Any]								= device
        lowerCAmelCase_   :				str								= CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Any								= [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
        lowerCAmelCase_   :				List[Any]								= [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
        lowerCAmelCase_   :				Dict								= torchvision.transforms.Normalize(self.image_mean    ,			self.image_std       )
        lowerCAmelCase_   :				Union[str, Any]								= torchvision.transforms.Resize(2_2_4       )
        lowerCAmelCase_   :				List[str]								= torchvision.transforms.CenterCrop(2_2_4       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				str								= self.resize(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				int								= self.center_crop(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[int]								= self.normalize(SCREAMING_SNAKE_CASE_       )
        return images





      def __call__(      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=None    ,			**SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				List[str]								= self.tokenizer(text=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Any								= self.preprocess_img(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= {key: value.to(self.device       ) for (key, value) in encoding.items()}
        return encoding







class       UpperCamelCase__   (	nn.Module  ):




      """simple docstring"""




      def __init__(      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int=1_0    ,			SCREAMING_SNAKE_CASE_				:      int=0.01    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Any=None    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=False    ,			SCREAMING_SNAKE_CASE_				:      int=True    ,			SCREAMING_SNAKE_CASE_				:      List[str]="image"    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True    ,			SCREAMING_SNAKE_CASE_				:      Dict=False    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=False    ,			):
        super().__init__()
        lowerCAmelCase_   :				str								= None
        lowerCAmelCase_   :				int								= device if device else get_device()
        if vqgan:
          lowerCAmelCase_   :				Optional[Any]								= vqgan
        else:
          lowerCAmelCase_   :				Dict								= load_vqgan(self.device    ,			conf_path=SCREAMING_SNAKE_CASE_    ,			ckpt_path=SCREAMING_SNAKE_CASE_       )
        self.vqgan.eval()
        if clip:
          lowerCAmelCase_   :				Union[str, Any]								= clip
        else:
          lowerCAmelCase_   :				List[str]								= CLIPModel.from_pretrained('openai/clip-vit-base-patch32'       )
        self.clip.to(self.device       )
        lowerCAmelCase_   :				int								= ProcessorGradientFlow(device=self.device       )

        lowerCAmelCase_   :				int								= iterations
        lowerCAmelCase_   :				List[str]								= lr
        lowerCAmelCase_   :				List[Any]								= log
        lowerCAmelCase_   :				List[Any]								= make_grid
        lowerCAmelCase_   :				Optional[Any]								= return_val
        lowerCAmelCase_   :				List[Any]								= quantize
        lowerCAmelCase_   :				Optional[Any]								= self.vqgan.decoder.z_shape



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Dict=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=5    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True       ):
        lowerCAmelCase_   :				Any								= []
        if output_path is None:
          lowerCAmelCase_   :				str								= './animation.gif'
        if input_path is None:
          lowerCAmelCase_   :				Any								= self.save_path
        lowerCAmelCase_   :				int								= 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?)'       )
        lowerCAmelCase_   :				List[Any]								= total_duration / len(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= [frame_duration] * len(SCREAMING_SNAKE_CASE_       )
        if extend_frames:
          lowerCAmelCase_   :				List[str]								= 1.5
          lowerCAmelCase_   :				List[Any]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      Any=None    ,			SCREAMING_SNAKE_CASE_				:      Dict=None       ):
        if not (path or img):
          raise ValueError('Input either path or tensor'       )
        if img is not None:
          raise NotImplementedError
        lowerCAmelCase_   :				Any								= preprocess(Image.open(SCREAMING_SNAKE_CASE_       )    ,			target_image_size=2_5_6       ).to(self.device       )
        lowerCAmelCase_   :				Dict								= preprocess_vqgan(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_						,*lowerCAmelCase_   :				List[str]								= self.vqgan.encode(SCREAMING_SNAKE_CASE_       )
        return z



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				str								= self.latent.detach().requires_grad_()
        lowerCAmelCase_   :				Optional[int]								= base_latent + transform_vector
        if self.quantize:
          lowerCAmelCase_						,*lowerCAmelCase_   :				Optional[int]								= self.vqgan.quantize(SCREAMING_SNAKE_CASE_       )
        else:
          lowerCAmelCase_   :				List[Any]								= trans_latent
        return self.vqgan.decode(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      str=None       ):
        lowerCAmelCase_   :				Optional[int]								= self.clip_preprocessor(text=SCREAMING_SNAKE_CASE_    ,			images=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'    ,			padding=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[str]								= self.clip(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= clip_outputs.logits_per_image
        if weights is not None:
          lowerCAmelCase_   :				Tuple								= similarity_logits * weights
        return similarity_logits.sum()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				Tuple								= self._get_clip_similarity(pos_prompts['prompts']    ,			SCREAMING_SNAKE_CASE_    ,			weights=(1 / pos_prompts['weights'])       )
        if neg_prompts:
          lowerCAmelCase_   :				List[Any]								= self._get_clip_similarity(neg_prompts['prompts']    ,			SCREAMING_SNAKE_CASE_    ,			weights=neg_prompts['weights']       )
        else:
          lowerCAmelCase_   :				Optional[Any]								= torch.tensor([1]    ,			device=self.device       )
        lowerCAmelCase_   :				Any								= -torch.log(SCREAMING_SNAKE_CASE_       ) + torch.log(SCREAMING_SNAKE_CASE_       )
        return loss



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				Tuple								= torch.randn_like(self.latent    ,			requires_grad=SCREAMING_SNAKE_CASE_    ,			device=self.device       )
        lowerCAmelCase_   :				List[str]								= torch.optim.Adam([vector]    ,			lr=self.lr       )

        for i in range(self.iterations       ):
          optim.zero_grad()
          lowerCAmelCase_   :				Optional[int]								= self._add_vector(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				List[Any]								= loop_post_process(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        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:
          lowerCAmelCase_   :				str								= Image.open(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				List[Any]								= image.resize((2_5_6, 2_5_6)       )
          wandb.log('Original Image'    ,			wandb.Image(SCREAMING_SNAKE_CASE_       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        if not prompts:
          return []
        lowerCAmelCase_   :				List[Any]								= []
        lowerCAmelCase_   :				Tuple								= []
        if isinstance(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
          lowerCAmelCase_   :				List[str]								= [prompt.strip() for prompt in prompts.split('|'       )]
        for prompt in prompts:
          if isinstance(SCREAMING_SNAKE_CASE_    ,			(tuple, list)       ):
            lowerCAmelCase_   :				List[Any]								= prompt[0]
            lowerCAmelCase_   :				int								= float(prompt[1]       )
          elif ":" in prompt:
            lowerCAmelCase_						,lowerCAmelCase_   :				List[Any]								= prompt.split(':'       )
            lowerCAmelCase_   :				List[str]								= float(SCREAMING_SNAKE_CASE_       )
          else:
            lowerCAmelCase_   :				Any								= prompt
            lowerCAmelCase_   :				List[Any]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Dict=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=True    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=False    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      List[str]=True    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=None    ,			):
        if image_path:
          lowerCAmelCase_   :				Any								= self._get_latent(SCREAMING_SNAKE_CASE_       )
        else:
          lowerCAmelCase_   :				Optional[Any]								= 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."
        lowerCAmelCase_   :				List[str]								= self.process_prompts(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= self.process_prompts(SCREAMING_SNAKE_CASE_       )
        if save_final and save_path is None:
          lowerCAmelCase_   :				Tuple								= os.path.join('./outputs/'    ,			'_'.join(pos_prompts['prompts']       )       )
        if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
          os.makedirs(SCREAMING_SNAKE_CASE_       )
        else:
          lowerCAmelCase_   :				Any								= save_path + '_' + get_timestamp()
          os.makedirs(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				int								= save_path

        lowerCAmelCase_   :				List[str]								= self.vqgan.decode(self.latent       )[0]
        if show_intermediate:
          print('Original Image'       )
          show_pil(custom_to_pil(SCREAMING_SNAKE_CASE_       )       )

        lowerCAmelCase_   :				List[Any]								= 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"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = 100						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= (n * (n + 1) // 2) ** 2
  lowerCAmelCase_   :				Optional[int]								= n * (n + 1) * (2 * n + 1) // 6
  return sum_cubes - sum_squares


if __name__ == "__main__":
     print(f'{solution() = }')





