


'''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 :       List[Any]	,_a :       str = "cpu"	,_a :       str = "openai/clip-vit-large-patch14" ):

        '''simple docstring'''




        A_						:  Any          =				device
        A_						:  Any          =				CLIPTokenizerFast.from_pretrained(__A )
        A_						:  Any          =				[0.48145466, 0.4578275, 0.40821073]
        A_						:  Union[str, Any]          =				[0.26862954, 0.26130258, 0.27577711]
        A_						:  Tuple          =				torchvision.transforms.Normalize(self.image_mean	,self.image_std )
        A_						:  Any          =				torchvision.transforms.Resize(224 )
        A_						:  Tuple          =				torchvision.transforms.CenterCrop(224 )






  def        _a							(							self :       Any	,_a :       List[Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.resize(__A )
        A_						:  Optional[Any]          =				self.center_crop(__A )
        A_						:  List[Any]          =				self.normalize(__A )
        return images






  def __call__(							self :       Optional[Any]	,_a :       Dict=None	,_a :       Optional[Any]=None	,**_a :       Tuple ):

        '''simple docstring'''




        A_						:  str          =				self.tokenizer(text=__A	,**__A )
        A_						:  Tuple          =				self.preprocess_img(__A )
        A_						:  str          =				{key: value.to(self.device ) for (key, value) in encoding.items()}
        return encoding







class 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Union[str, Any]=10	,_a :       Tuple=0.01	,_a :       List[str]=None	,_a :       int=None	,_a :       Optional[int]=None	,_a :       List[Any]=None	,_a :       Optional[Any]=None	,_a :       Any=None	,_a :       List[str]=False	,_a :       Optional[int]=True	,_a :       Dict="image"	,_a :       int=True	,_a :       Optional[Any]=False	,_a :       Dict=False	,_a :       str=False	,):

        '''simple docstring'''




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

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






  def        _a							(							self :       Tuple	,_a :       str=None	,_a :       int=None	,_a :       Union[str, Any]=5	,_a :       List[str]=True ):

        '''simple docstring'''




        A_						:  int          =				[]
        if output_path is None:
              A_						:  Optional[int]          =				"""./animation.gif"""
        if input_path is None:
              A_						:  Union[str, Any]          =				self.save_path
        A_						:  Tuple          =				sorted(glob(input_path + """/*""" ) )
        if not len(__A ):
              raise ValueError(
                  """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
                  """ function?)""" )
        if len(__A ) == 1:
              print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""" )
        A_						:  Dict          =				total_duration / len(__A )
        A_						:  List[str]          =				[frame_duration] * len(__A )
        if extend_frames:
              A_						:  Optional[int]          =				1.5
              A_						:  Optional[Any]          =				3
        for file_name in paths:
              if file_name.endswith(""".png""" ):
                    images.append(imageio.imread(__A ) )
        imageio.mimsave(__A	,__A	,duration=__A )
        print(f'gif saved to {output_path}' )






  def        _a							(							self :       Dict	,_a :       int=None	,_a :       Optional[Any]=None ):

        '''simple docstring'''




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






  def        _a							(							self :       Any	,_a :       List[str] ):

        '''simple docstring'''




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






  def        _a							(							self :       Any	,_a :       Optional[Any]	,_a :       Tuple	,_a :       Optional[Any]=None ):

        '''simple docstring'''




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






  def        _a							(							self :       Dict	,_a :       Optional[Any]	,_a :       Dict	,_a :       List[Any] ):

        '''simple docstring'''




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






  def        _a							(							self :       Dict	,_a :       str	,_a :       int	,_a :       List[str] ):

        '''simple docstring'''




        A_						:  List[Any]          =				torch.randn_like(self.latent	,requires_grad=__A	,device=self.device )
        A_						:  List[str]          =				torch.optim.Adam([vector]	,lr=self.lr )

        for i in range(self.iterations ):
              optim.zero_grad()
              A_						:  Tuple          =				self._add_vector(__A )
              A_						:  Union[str, Any]          =				loop_post_process(__A )
              A_						:  List[Any]          =				self._get_CLIP_loss(__A	,__A	,__A )
              print("""CLIP loss"""	,__A )
              if self.log:
                    wandb.log({"""CLIP Loss""": clip_loss} )
              clip_loss.backward(retain_graph=__A )
              optim.step()
              if self.return_val == "image":
                    yield custom_to_pil(transformed_img[0] )
              else:
                    yield vector






  def        _a							(							self :       List[str]	,_a :       List[Any]	,_a :       Optional[Any]	,_a :       Any ):

        '''simple docstring'''




        wandb.init(reinit=__A	,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(__A )
              A_						:  Optional[int]          =				image.resize((256, 256) )
              wandb.log("""Original Image"""	,wandb.Image(__A ) )






  def        _a							(							self :       Union[str, Any]	,_a :       Optional[Any] ):

        '''simple docstring'''




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






  def        _a							(							self :       List[str]	,_a :       str	,_a :       Any=None	,_a :       List[Any]=None	,_a :       Any=True	,_a :       str=False	,_a :       int=True	,_a :       Tuple=True	,_a :       Union[str, Any]=None	,):

        '''simple docstring'''




        if image_path:
              A_						:  List[Any]          =				self._get_latent(__A )
        else:
              A_						:  Tuple          =				torch.randn(self.latent_dim	,device=self.device )
        if self.log:
              self._init_logging(__A	,__A	,__A )

        assert pos_prompts, "You must provide at least one positive prompt."
        A_						:  List[Any]          =				self.process_prompts(__A )
        A_						:  str          =				self.process_prompts(__A )
        if save_final and save_path is None:
              A_						:  List[str]          =				os.path.join("""./outputs/"""	,"""_""".join(pos_prompts["""prompts"""] ) )
        if not os.path.exists(__A ):
              os.makedirs(__A )
        else:
              A_						:  int          =				save_path + """_""" + get_timestamp()
              os.makedirs(__A )
        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(__A ) )

        A_						:  List[str]          =				loop_post_process(__A )
        for iter, transformed_img in enumerate(self._optimize_CLIP(__A	,__A	,__A ) ):
              if show_intermediate:
                    show_pil(__A )
              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(__A )} )
        if show_final:
              show_pil(__A )
        if save_final:
              transformed_img.save(os.path.join(self.save_path	,f'iter_{iter:03d}_final.png' ) )




'''simple docstring'''


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__magic_name__			=      {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}



class 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       Dict ):

        '''simple docstring'''




        super().__init__()
        A_						:  List[str]          =				torchvision.models.resnetaaa(pretrained=_a )
        A_						:  int          =				list(model.children() )[:-2]
        A_						:  int          =				nn.Sequential(*_a )
        A_						:  Optional[int]          =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






  def        _a							(							self :       str	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				self.pool(self.model(_a ) )
        A_						:  Any          =				torch.flatten(_a	,start_dim=2 )
        A_						:  str          =				out.transpose(1	,2 ).contiguous()
        return out  # BxNx2048







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Optional[Any]	,_a :       Optional[Any]	,_a :       Dict	,_a :       Dict	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Dict          =				[json.loads(_a ) for l in open(_a )]
        A_						:  Optional[int]          =				os.path.dirname(_a )
        A_						:  Optional[Any]          =				tokenizer
        A_						:  Optional[Any]          =				labels
        A_						:  List[Any]          =				len(_a )
        A_						:  str          =				max_seq_length

        A_						:  str          =				transforms






  def __len__(							self :       str ):

        '''simple docstring'''




        return len(self.data )






  def __getitem__(							self :       Tuple	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]	,add_special_tokens=_a ) )
        A_   ,					A_   ,					A_						:  Dict          =				sentence[0], sentence[1:-1], sentence[-1]
        A_						:  Optional[int]          =				sentence[: self.max_seq_length]

        A_						:  Any          =				torch.zeros(self.n_classes )
        A_						:  Tuple          =				1

        A_						:  Optional[Any]          =				Image.open(os.path.join(self.data_dir	,self.data[index]["""img"""] ) ).convert("""RGB""" )
        A_						:  Union[str, Any]          =				self.transforms(_a )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				Counter()
        for row in self.data:
              label_freqs.update(row["""label"""] )
        return label_freqs







def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  List[Any]          =				[len(row["""sentence"""]) for row in batch]
      A_   ,					A_						:  Dict          =				len(lowerCamelCase), max(lowerCamelCase)

      A_						:  Optional[int]          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)
      A_						:  Tuple          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase							,						lowerCamelCase)):
            A_						:  str          =				input_row["""sentence"""]
            A_						:  Tuple          =				1

      A_						:  int          =				torch.stack([row["""image"""] for row in batch])
      A_						:  str          =				torch.stack([row["""label"""] for row in batch])
      A_						:  List[Any]          =				torch.stack([row["""image_start_token"""] for row in batch])
      A_						:  Tuple          =				torch.stack([row["""image_end_token"""] for row in batch])

      return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def      lowerCamelCase					(      ):
      return [
          "Crime",
          "Drama",
          "Thriller",
          "Action",
          "Comedy",
          "Romance",
          "Documentary",
          "Short",
          "Mystery",
          "History",
          "Family",
          "Adventure",
          "Fantasy",
          "Sci-Fi",
          "Western",
          "Horror",
          "Sport",
          "War",
          "Music",
          "Musical",
          "Animation",
          "Biography",
          "Film-Noir",
      ]






def      lowerCamelCase					(      ):
      return transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,						),
          ])
