


'''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   ,   __lowercase = "cpu"   ,   __lowercase = "openai/clip-vit-large-patch14"    ):

  """simple docstring"""







  __A	:		List[str]	  =      device
  __A	:		Optional[Any]	  =      CLIPTokenizerFast.from_pretrained(__lowercase    )
  __A	:		List[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]
  __A	:		str	  =      [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]
  __A	:		Any	  =      torchvision.transforms.Normalize(self.image_mean   ,   self.image_std    )
  __A	:		Dict	  =      torchvision.transforms.Resize(224    )
  __A	:		Tuple	  =      torchvision.transforms.CenterCrop(224    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      self.resize(__lowercase    )
  __A	:		List[Any]	  =      self.center_crop(__lowercase    )
  __A	:		List[str]	  =      self.normalize(__lowercase    )
  return images







 def __call__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.tokenizer(text=__lowercase   ,   **__lowercase    )
  __A	:		int	  =      self.preprocess_img(__lowercase    )
  __A	:		List[str]	  =      {key: value.to(self.device    ) for (key, value) in encoding.items()}
  return encoding


class     _lowerCamelCase       (     nn.Module					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase=10   ,   __lowercase=0.0_1   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=False   ,   __lowercase=True   ,   __lowercase="image"   ,   __lowercase=True   ,   __lowercase=False   ,   __lowercase=False   ,   __lowercase=False   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Tuple	  =      None
  __A	:		List[str]	  =      device if device else get_device()
  if vqgan:
   __A	:		List[str]	  =      vqgan
  else:
   __A	:		List[Any]	  =      load_vqgan(self.device   ,   conf_path=__lowercase   ,   ckpt_path=__lowercase    )
  self.vqgan.eval()
  if clip:
   __A	:		Union[str, Any]	  =      clip
  else:
   __A	:		List[str]	  =      CLIPModel.from_pretrained('openai/clip-vit-base-patch32'    )
  self.clip.to(self.device    )
  __A	:		int	  =      ProcessorGradientFlow(device=self.device    )

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


 def 			snake_case__ (   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=5   ,   __lowercase=True    ):

  """simple docstring"""







  __A	:		str	  =      []
  if output_path is None:
   __A	:		Tuple	  =      './animation.gif'
  if input_path is None:
   __A	:		Tuple	  =      self.save_path
  __A	:		Union[str, Any]	  =      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?)'    )
  __A	:		Optional[int]	  =      total_duration / len(__lowercase    )
  __A	:		List[str]	  =      [frame_duration] * len(__lowercase    )
  if extend_frames:
   __A	:		str	  =      1.5
   __A	:		Tuple	  =      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 			snake_case__ (   self   ,   __lowercase=None   ,   __lowercase=None    ):

  """simple docstring"""







  if not (path or img):
   raise ValueError('Input either path or tensor'    )
  if img is not None:
   raise NotImplementedError
  __A	:		int	  =      preprocess(Image.open(__lowercase    )   ,   target_image_size=256    ).to(self.device    )
  __A	:		Optional[Any]	  =      preprocess_vqgan(__lowercase    )
  __A  ,*__A	:		Optional[int]	  =      self.vqgan.encode(__lowercase    )
  return z


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[Any]	  =      self.latent.detach().requires_grad_()
  __A	:		Union[str, Any]	  =      base_latent + transform_vector
  if self.quantize:
   __A  ,*__A	:		Tuple	  =      self.vqgan.quantize(__lowercase    )
  else:
   __A	:		int	  =      trans_latent
  return self.vqgan.decode(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  __A	:		str	  =      self.clip_preprocessor(text=__lowercase   ,   images=__lowercase   ,   return_tensors='pt'   ,   padding=__lowercase    )
  __A	:		Union[str, Any]	  =      self.clip(**__lowercase    )
  __A	:		Union[str, Any]	  =      clip_outputs.logits_per_image
  if weights is not None:
   __A	:		int	  =      similarity_logits * weights
  return similarity_logits.sum()


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[Any]	  =      self._get_clip_similarity(pos_prompts['prompts']   ,   __lowercase   ,   weights=(1 / pos_prompts['weights'])    )
  if neg_prompts:
   __A	:		Tuple	  =      self._get_clip_similarity(neg_prompts['prompts']   ,   __lowercase   ,   weights=neg_prompts['weights']    )
  else:
   __A	:		Union[str, Any]	  =      torch.tensor([1]   ,   device=self.device    )
  __A	:		Dict	  =      -torch.log(__lowercase    ) + torch.log(__lowercase    )
  return loss


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      torch.randn_like(self.latent   ,   requires_grad=__lowercase   ,   device=self.device    )
  __A	:		Optional[int]	  =      torch.optim.Adam([vector]   ,   lr=self.lr    )

  for i in range(self.iterations    ):
   optim.zero_grad()
   __A	:		str	  =      self._add_vector(__lowercase    )
   __A	:		str	  =      loop_post_process(__lowercase    )
   __A	:		List[Any]	  =      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 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """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:
   __A	:		Any	  =      Image.open(__lowercase    )
   __A	:		Union[str, Any]	  =      image.resize((256, 256)    )
   wandb.log('Original Image'   ,   wandb.Image(__lowercase    )    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=True   ,   __lowercase=False   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=None   ,   ):

  """simple docstring"""







  if image_path:
   __A	:		Union[str, Any]	  =      self._get_latent(__lowercase    )
  else:
   __A	:		Optional[int]	  =      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."
  __A	:		Any	  =      self.process_prompts(__lowercase    )
  __A	:		int	  =      self.process_prompts(__lowercase    )
  if save_final and save_path is None:
   __A	:		Dict	  =      os.path.join('./outputs/'   ,   '_'.join(pos_prompts['prompts']    )    )
  if not os.path.exists(__lowercase    ):
   os.makedirs(__lowercase    )
  else:
   __A	:		Any	  =      save_path + '_' + get_timestamp()
   os.makedirs(__lowercase    )
  __A	:		Optional[int]	  =      save_path

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

  __A	:		Union[str, Any]	  =      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"""    )    )










'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if dst_width < 0 or dst_height < 0:
   raise ValueError('Destination width/height should be > 0'    )

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






