

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__       :







  """simple docstring"""




  def __init__(			self    : List[str]				,				UpperCAmelCase__    : str = "cpu"				,				UpperCAmelCase__    : str = "openai/clip-vit-large-patch14"		)					->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   device
        SCREAMING_SNAKE_CASE :      List[Any]       =   CLIPTokenizerFast.from_pretrained(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Tuple       =   [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
        SCREAMING_SNAKE_CASE :      Optional[int]       =   [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
        SCREAMING_SNAKE_CASE :      Any       =   torchvision.transforms.Normalize(self.image_mean				,				self.image_std		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   torchvision.transforms.Resize(2_2_4		)
        SCREAMING_SNAKE_CASE :      Dict       =   torchvision.transforms.CenterCrop(2_2_4		)


  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : Any		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.resize(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   self.center_crop(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   self.normalize(UpperCAmelCase__		)
        return images




  def __call__(			self    : List[Any]				,				UpperCAmelCase__    : str=None				,				UpperCAmelCase__    : List[str]=None				,				**UpperCAmelCase__    : Dict		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.tokenizer(text=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   self.preprocess_img(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   {key: value.to(self.device		) for (key, value) in encoding.items()}
        return encoding







class        a__       (   nn.Module       ):







  """simple docstring"""




  def __init__(			self    : int				,				UpperCAmelCase__    : int=1_0				,				UpperCAmelCase__    : int=0.01				,				UpperCAmelCase__    : str=None				,				UpperCAmelCase__    : List[Any]=None				,				UpperCAmelCase__    : List[Any]=None				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : Optional[Any]=None				,				UpperCAmelCase__    : Union[str, Any]=None				,				UpperCAmelCase__    : str=False				,				UpperCAmelCase__    : Union[str, Any]=True				,				UpperCAmelCase__    : Any="image"				,				UpperCAmelCase__    : List[str]=True				,				UpperCAmelCase__    : int=False				,				UpperCAmelCase__    : str=False				,				UpperCAmelCase__    : Any=False				,				)					->None:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE :      Tuple       =   None
        SCREAMING_SNAKE_CASE :      Tuple       =   device if device else get_device()
        if vqgan:
              SCREAMING_SNAKE_CASE :      List[Any]       =   vqgan
        else:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   load_vqgan(self.device				,				conf_path=UpperCAmelCase__				,				ckpt_path=UpperCAmelCase__		)
        self.vqgan.eval()
        if clip:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   clip
        else:
              SCREAMING_SNAKE_CASE :      Dict       =   CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""		)
        self.clip.to(self.device		)
        SCREAMING_SNAKE_CASE :      int       =   ProcessorGradientFlow(device=self.device		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   iterations
        SCREAMING_SNAKE_CASE :      Optional[int]       =   lr
        SCREAMING_SNAKE_CASE :      int       =   log
        SCREAMING_SNAKE_CASE :      Tuple       =   make_grid
        SCREAMING_SNAKE_CASE :      int       =   return_val
        SCREAMING_SNAKE_CASE :      Dict       =   quantize
        SCREAMING_SNAKE_CASE :      Tuple       =   self.vqgan.decoder.z_shape


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : List[Any]=5				,				UpperCAmelCase__    : List[str]=True		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   []
        if output_path is None:
              SCREAMING_SNAKE_CASE :      int       =   """./animation.gif"""
        if input_path is None:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.save_path
        SCREAMING_SNAKE_CASE :      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?)"""		)
        SCREAMING_SNAKE_CASE :      Any       =   total_duration / len(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   [frame_duration] * len(UpperCAmelCase__		)
        if extend_frames:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1.5
              SCREAMING_SNAKE_CASE :      Dict       =   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  _lowercase      (			self    : Any				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : List[str]=None		)					->int:
        """simple docstring"""







        if not (path or img):
              raise ValueError("""Input either path or tensor"""		)
        if img is not None:
              raise NotImplementedError
        SCREAMING_SNAKE_CASE :      Optional[int]       =   preprocess(Image.open(UpperCAmelCase__		)				,				target_image_size=2_5_6		).to(self.device		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   preprocess_vqgan(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE     ,	*SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.vqgan.encode(UpperCAmelCase__		)
        return z


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : List[str]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.latent.detach().requires_grad_()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   base_latent + transform_vector
        if self.quantize:
              SCREAMING_SNAKE_CASE     ,	*SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.vqgan.quantize(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE :      Any       =   trans_latent
        return self.vqgan.decode(UpperCAmelCase__		)


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : List[Any]=None		)					->Tuple:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Dict		)					->str:
        """simple docstring"""







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


  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Any		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   torch.randn_like(self.latent				,				requires_grad=UpperCAmelCase__				,				device=self.device		)
        SCREAMING_SNAKE_CASE :      str       =   torch.optim.Adam([vector]				,				lr=self.lr		)

        for i in range(self.iterations		):
              optim.zero_grad()
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self._add_vector(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Optional[int]       =   loop_post_process(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Tuple       =   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  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Dict		)					->Optional[Any]:
        """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:
              SCREAMING_SNAKE_CASE :      Any       =   Image.open(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   image.resize((2_5_6, 2_5_6)		)
              wandb.log("""Original Image"""				,				wandb.Image(UpperCAmelCase__		)		)


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : Any		)					->Any:
        """simple docstring"""







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




  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : Union[str, Any]=None				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Optional[int]=False				,				UpperCAmelCase__    : int=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Optional[Any]=None				,				)					->List[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.vqgan.decode(self.latent		)[0]
        if show_intermediate:
              print("""Original Image"""		)
              show_pil(custom_to_pil(UpperCAmelCase__		)		)

        SCREAMING_SNAKE_CASE :      str       =   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"		)		)







def 					__lowercase      (     _A						,    _A			)     ->						int:
      assert x is not None
      assert y is not None

      SCREAMING_SNAKE_CASE :      int       =   len(_A			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   len(_A			)

      # declaring the array for storing the dp values
      SCREAMING_SNAKE_CASE :      Tuple       =   [[0] * (n + 1) for _ in range(m + 1			)]  # noqa: E741

      for i in range(1						,    m + 1			):
            for j in range(1						,    n + 1			):
                  SCREAMING_SNAKE_CASE :      List[Any]       =   1 if x[i - 1] == y[j - 1] else 0

                  SCREAMING_SNAKE_CASE :      int       =   max(l[i - 1][j]						,    l[i][j - 1]						,    l[i - 1][j - 1] + match			)

      SCREAMING_SNAKE_CASE :      int       =   """"""
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      int       =   m, n
      while i > 0 and j > 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   1 if x[i - 1] == y[j - 1] else 0

            if l[i][j] == l[i - 1][j - 1] + match:
                  if match == 1:
                        SCREAMING_SNAKE_CASE :      List[Any]       =   x[i - 1] + seq
                  i -= 1
                  j -= 1
            elif l[i][j] == l[i - 1][j]:
                  i -= 1
            else:
                  j -= 1

      return l[m][n], seq


if __name__ == "__main__":
  UpperCAmelCase__ :     str				    =    """AGGTAB"""
  UpperCAmelCase__ :     Optional[int]				    =    """GXTXAYB"""
  UpperCAmelCase__ :     Tuple				    =    4
  UpperCAmelCase__ :     Dict				    =    """GTAB"""

  UpperCAmelCase__    ,   UpperCAmelCase__ :     Optional[int]				    =    longest_common_subsequence(a, b)
  print("""len =""", ln, """, sub-sequence =""", subseq)
  import doctest

  doctest.testmod()




