





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 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      List[str]				,			a__       :      str = "cpu"				,			a__       :      str = "openai/clip-vit-large-patch14"      ):





         """simple docstring"""



         __snake_case							       =   device
         __snake_case							       =   CLIPTokenizerFast.from_pretrained(a__      )
         __snake_case							       =   [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]
         __snake_case							       =   [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]
         __snake_case							       =   torchvision.transforms.Normalize(self.image_mean				,			self.image_std      )
         __snake_case							       =   torchvision.transforms.Resize(224      )
         __snake_case							       =   torchvision.transforms.CenterCrop(224      )


      def    a (self       :      Optional[Any]				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.resize(a__      )
         __snake_case							       =   self.center_crop(a__      )
         __snake_case							       =   self.normalize(a__      )
         return images


      def __call__(self       :      Union[str, Any]				,			a__       :      Tuple=None				,			a__       :      Tuple=None				,			**a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer(text=a__				,			**a__      )
         __snake_case							       =   self.preprocess_img(a__      )
         __snake_case							       =   {key: value.to(self.device      ) for (key, value) in encoding.items()}
         return encoding






class 		SCREAMING_SNAKE_CASE__				(       nn.Module  ):


      def __init__(self       :      Optional[int]				,			a__       :      Dict=10				,			a__       :      Dict=0.0_1				,			a__       :      Union[str, Any]=None				,			a__       :      Any=None				,			a__       :      Optional[Any]=None				,			a__       :      Dict=None				,			a__       :      Union[str, Any]=None				,			a__       :      List[str]=None				,			a__       :      Union[str, Any]=False				,			a__       :      Dict=True				,			a__       :      Dict="image"				,			a__       :      Optional[Any]=True				,			a__       :      List[str]=False				,			a__       :      Optional[Any]=False				,			a__       :      List[str]=False				,			):





         """simple docstring"""



         super().__init__()
         __snake_case							       =   None
         __snake_case							       =   device if device else get_device()
         if vqgan:
            __snake_case							       =   vqgan
         else:
            __snake_case							       =   load_vqgan(self.device				,			conf_path=a__				,			ckpt_path=a__      )
         self.vqgan.eval()
         if clip:
            __snake_case							       =   clip
         else:
            __snake_case							       =   CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''      )
         self.clip.to(self.device      )
         __snake_case							       =   ProcessorGradientFlow(device=self.device      )

         __snake_case							       =   iterations
         __snake_case							       =   lr
         __snake_case							       =   log
         __snake_case							       =   make_grid
         __snake_case							       =   return_val
         __snake_case							       =   quantize
         __snake_case							       =   self.vqgan.decoder.z_shape


      def    a (self       :      str				,			a__       :      Optional[int]=None				,			a__       :      Tuple=None				,			a__       :      str=5				,			a__       :      int=True      ):





         """simple docstring"""



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





         """simple docstring"""



         if not (path or img):
            raise ValueError('''Input either path or tensor'''      )
         if img is not None:
            raise NotImplementedError
         __snake_case							       =   preprocess(Image.open(a__      )				,			target_image_size=256      ).to(self.device      )
         __snake_case							       =   preprocess_vqgan(a__      )
         __snake_case       ,	*__snake_case							       =   self.vqgan.encode(a__      )
         return z


      def    a (self       :      List[str]				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.latent.detach().requires_grad_()
         __snake_case							       =   base_latent + transform_vector
         if self.quantize:
            __snake_case       ,	*__snake_case							       =   self.vqgan.quantize(a__      )
         else:
            __snake_case							       =   trans_latent
         return self.vqgan.decode(a__      )


      def    a (self       :      int				,			a__       :      Dict				,			a__       :      str				,			a__       :      str=None      ):





         """simple docstring"""



         __snake_case							       =   self.clip_preprocessor(text=a__				,			images=a__				,			return_tensors='''pt'''				,			padding=a__      )
         __snake_case							       =   self.clip(**a__      )
         __snake_case							       =   clip_outputs.logits_per_image
         if weights is not None:
            __snake_case							       =   similarity_logits * weights
         return similarity_logits.sum()


      def    a (self       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      Union[str, Any]				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   self._get_clip_similarity(pos_prompts['''prompts''']				,			a__				,			weights=(1 / pos_prompts['''weights'''])      )
         if neg_prompts:
            __snake_case							       =   self._get_clip_similarity(neg_prompts['''prompts''']				,			a__				,			weights=neg_prompts['''weights''']      )
         else:
            __snake_case							       =   torch.tensor([1]				,			device=self.device      )
         __snake_case							       =   -torch.log(a__      ) + torch.log(a__      )
         return loss


      def    a (self       :      Any				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   torch.randn_like(self.latent				,			requires_grad=a__				,			device=self.device      )
         __snake_case							       =   torch.optim.Adam([vector]				,			lr=self.lr      )

         for i in range(self.iterations      ):
            optim.zero_grad()
            __snake_case							       =   self._add_vector(a__      )
            __snake_case							       =   loop_post_process(a__      )
            __snake_case							       =   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       :      int				,			a__       :      Optional[Any]				,			a__       :      Optional[int]				,			a__       :      List[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:
            __snake_case							       =   Image.open(a__      )
            __snake_case							       =   image.resize((256, 256)      )
            wandb.log('''Original Image'''				,			wandb.Image(a__      )      )


      def    a (self       :      Optional[int]				,			a__       :      int      ):





         """simple docstring"""



         if not prompts:
            return []
         __snake_case							       =   []
         __snake_case							       =   []
         if isinstance(a__				,			a__      ):
            __snake_case							       =   [prompt.strip() for prompt in prompts.split('''|'''      )]
         for prompt in prompts:
            if isinstance(a__				,			(tuple, list)      ):
               __snake_case							       =   prompt[0]
               __snake_case							       =   float(prompt[1]      )
            elif ":" in prompt:
               __snake_case       ,	__snake_case							       =   prompt.split(''':'''      )
               __snake_case							       =   float(a__      )
            else:
               __snake_case							       =   prompt
               __snake_case							       =   1.0
            processed_prompts.append(a__      )
            weights.append(a__      )
         return {
             "prompts": processed_prompts,
             "weights": torch.tensor(a__				,			device=self.device      ),
         }


      def    a (self       :      Optional[Any]				,			a__       :      Union[str, Any]				,			a__       :      Any=None				,			a__       :      List[Any]=None				,			a__       :      Optional[int]=True				,			a__       :      Any=False				,			a__       :      Any=True				,			a__       :      int=True				,			a__       :      List[str]=None				,			):





         """simple docstring"""



         if image_path:
            __snake_case							       =   self._get_latent(a__      )
         else:
            __snake_case							       =   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."
         __snake_case							       =   self.process_prompts(a__      )
         __snake_case							       =   self.process_prompts(a__      )
         if save_final and save_path is None:
            __snake_case							       =   os.path.join('''./outputs/'''				,			'''_'''.join(pos_prompts['''prompts''']      )      )
         if not os.path.exists(a__      ):
            os.makedirs(a__      )
         else:
            __snake_case							       =   save_path + '''_''' + get_timestamp()
            os.makedirs(a__      )
         __snake_case							       =   save_path

         __snake_case							       =   self.vqgan.decode(self.latent      )[0]
         if show_intermediate:
            print('''Original Image'''      )
            show_pil(custom_to_pil(a__      )      )

         __snake_case							       =   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"""      )      )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

