



'''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 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase = "cpu"  ,__UpperCAmelCase = "openai/clip-vit-large-patch14"				)   ->		None:
      lowerCAmelCase__			:  Optional[Any]											= device
      lowerCAmelCase__			:  str											= CLIPTokenizerFast.from_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__			:  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]
      lowerCAmelCase__			:  Tuple											= [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]
      lowerCAmelCase__			:  List[Any]											= torchvision.transforms.Normalize(self.image_mean  ,self.image_std				)
      lowerCAmelCase__			:  Any											= torchvision.transforms.Resize(224				)
      lowerCAmelCase__			:  List[Any]											= torchvision.transforms.CenterCrop(224				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[str]:
      lowerCAmelCase__			:  List[str]											= self.resize(__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= self.center_crop(__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= self.normalize(__UpperCAmelCase				)
      return images






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







class 	lowerCAmelCase_(    nn.Module					):
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase=10  ,__UpperCAmelCase=0.0_1  ,__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:
      super().__init__()
      lowerCAmelCase__			:  int											= None
      lowerCAmelCase__			:  List[str]											= device if device else get_device()
      if vqgan:
          lowerCAmelCase__			:  Dict											= vqgan
      else:
          lowerCAmelCase__			:  str											= load_vqgan(self.device  ,conf_path=__UpperCAmelCase  ,ckpt_path=__UpperCAmelCase				)
      self.vqgan.eval()
      if clip:
          lowerCAmelCase__			:  List[Any]											= clip
      else:
          lowerCAmelCase__			:  Any											= CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""				)
      self.clip.to(self.device				)
      lowerCAmelCase__			:  Optional[Any]											= ProcessorGradientFlow(device=self.device				)

      lowerCAmelCase__			:  Any											= iterations
      lowerCAmelCase__			:  Any											= lr
      lowerCAmelCase__			:  List[str]											= log
      lowerCAmelCase__			:  Union[str, Any]											= make_grid
      lowerCAmelCase__			:  List[Any]											= return_val
      lowerCAmelCase__			:  str											= quantize
      lowerCAmelCase__			:  List[str]											= self.vqgan.decoder.z_shape




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=5  ,__UpperCAmelCase=True				)   ->		List[str]:
      lowerCAmelCase__			:  Optional[Any]											= []
      if output_path is None:
          lowerCAmelCase__			:  int											= """./animation.gif"""
      if input_path is None:
          lowerCAmelCase__			:  List[Any]											= self.save_path
      lowerCAmelCase__			:  Optional[Any]											= 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?)"""				)
      lowerCAmelCase__			:  str											= total_duration / len(__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= [frame_duration] * len(__UpperCAmelCase				)
      if extend_frames:
          lowerCAmelCase__			:  Tuple											= 1.5
          lowerCAmelCase__			:  Tuple											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None				)   ->		int:
      if not (path or img):
          raise ValueError("""Input either path or tensor"""				)
      if img is not None:
          raise NotImplementedError
      lowerCAmelCase__			:  List[str]											= preprocess(Image.open(__UpperCAmelCase				)  ,target_image_size=256				).to(self.device				)
      lowerCAmelCase__			:  Optional[int]											= preprocess_vqgan(__UpperCAmelCase				)
      lowerCAmelCase__				,       *lowerCAmelCase__			:  Any											= self.vqgan.encode(__UpperCAmelCase				)
      return z




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		int:
      lowerCAmelCase__			:  int											= self.latent.detach().requires_grad_()
      lowerCAmelCase__			:  Dict											= base_latent + transform_vector
      if self.quantize:
          lowerCAmelCase__				,       *lowerCAmelCase__			:  List[str]											= self.vqgan.quantize(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  str											= trans_latent
      return self.vqgan.decode(__UpperCAmelCase				)




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




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




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Any:
      lowerCAmelCase__			:  List[str]											= torch.randn_like(self.latent  ,requires_grad=__UpperCAmelCase  ,device=self.device				)
      lowerCAmelCase__			:  Dict											= torch.optim.Adam([vector]  ,lr=self.lr				)

      for i in range(self.iterations				):
          optim.zero_grad()
          lowerCAmelCase__			:  Optional[int]											= self._add_vector(__UpperCAmelCase				)
          lowerCAmelCase__			:  int											= loop_post_process(__UpperCAmelCase				)
          lowerCAmelCase__			:  Union[str, Any]											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		str:
      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:
          lowerCAmelCase__			:  Any											= Image.open(__UpperCAmelCase				)
          lowerCAmelCase__			:  List[str]											= image.resize((256, 256)				)
          wandb.log("""Original Image"""  ,wandb.Image(__UpperCAmelCase				)				)




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






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,)   ->		Optional[int]:
      if image_path:
          lowerCAmelCase__			:  Optional[int]											= self._get_latent(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  List[str]											= 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."
      lowerCAmelCase__			:  int											= self.process_prompts(__UpperCAmelCase				)
      lowerCAmelCase__			:  Dict											= self.process_prompts(__UpperCAmelCase				)
      if save_final and save_path is None:
          lowerCAmelCase__			:  Union[str, Any]											= os.path.join("""./outputs/"""  ,"""_""".join(pos_prompts["""prompts"""]				)				)
      if not os.path.exists(__UpperCAmelCase				):
          os.makedirs(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  List[Any]											= save_path + """_""" + get_timestamp()
          os.makedirs(__UpperCAmelCase				)
      lowerCAmelCase__			:  Optional[int]											= save_path

      lowerCAmelCase__			:  Optional[Any]											= self.vqgan.decode(self.latent				)[0]
      if show_intermediate:
          print("""Original Image"""				)
          show_pil(custom_to_pil(__UpperCAmelCase				)				)

      lowerCAmelCase__			:  Any											= 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase = 1000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= -1
    lowerCAmelCase__			:  Optional[Any]											= 0
    for a in range(1							,   n // 3      ):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        lowerCAmelCase__			:  Optional[Any]											= (n * n - 2 * a * n) // (2 * n - 2 * a)
        lowerCAmelCase__			:  Tuple											= n - a - b
        if c * c == (a * a + b * b):
            lowerCAmelCase__			:  int											= a * b * c
            if candidate >= product:
                lowerCAmelCase__			:  Any											= candidate
    return product


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


