

"""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 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = "cpu"    ,						SCREAMING_SNAKE_CASE__ = "openai/clip-vit-large-patch14"		)   ->					int:
         lowerCamelCase__						=			device
         lowerCamelCase__						=			CLIPTokenizerFast.from_pretrained(_lowercase		)
         lowerCamelCase__						=			[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
         lowerCamelCase__						=			[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
         lowerCamelCase__						=			torchvision.transforms.Normalize(self.image_mean    ,						self.image_std		)
         lowerCamelCase__						=			torchvision.transforms.Resize(224		)
         lowerCamelCase__						=			torchvision.transforms.CenterCrop(224		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			self.resize(_lowercase		)
         lowerCamelCase__						=			self.center_crop(_lowercase		)
         lowerCamelCase__						=			self.normalize(_lowercase		)
         return images





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




class 		_A       (			nn.Module					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=0.01    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__="image"    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=False    ,						)   ->					List[Any]:
         super().__init__()
         lowerCamelCase__						=			None
         lowerCamelCase__						=			device if device else get_device()
         if vqgan:
            lowerCamelCase__						=			vqgan
         else:
            lowerCamelCase__						=			load_vqgan(self.device    ,						conf_path=_lowercase    ,						ckpt_path=_lowercase		)
         self.vqgan.eval()
         if clip:
            lowerCamelCase__						=			clip
         else:
            lowerCamelCase__						=			CLIPModel.from_pretrained("openai/clip-vit-base-patch32"		)
         self.clip.to(self.device		)
         lowerCamelCase__						=			ProcessorGradientFlow(device=self.device		)

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=True		)   ->					Dict:
         lowerCamelCase__						=			[]
         if output_path is None:
            lowerCamelCase__						=			"""./animation.gif"""
         if input_path is None:
            lowerCamelCase__						=			self.save_path
         lowerCamelCase__						=			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?)"		)
         lowerCamelCase__						=			total_duration / len(_lowercase		)
         lowerCamelCase__						=			[frame_duration] * len(_lowercase		)
         if extend_frames:
            lowerCamelCase__						=			1.5
            lowerCamelCase__						=			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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None		)   ->					Optional[int]:

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.latent.detach().requires_grad_()
         lowerCamelCase__						=			base_latent + transform_vector
         if self.quantize:
            lowerCamelCase__						=			self.vqgan.quantize(_lowercase		)
         else:
            lowerCamelCase__						=			trans_latent
         return self.vqgan.decode(_lowercase		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					Tuple:
         lowerCamelCase__						=			self.clip_preprocessor(text=_lowercase    ,						images=_lowercase    ,						return_tensors="pt"    ,						padding=_lowercase		)
         lowerCamelCase__						=			self.clip(**_lowercase		)
         lowerCamelCase__						=			clip_outputs.logits_per_image
         if weights is not None:
            lowerCamelCase__						=			similarity_logits * weights
         return similarity_logits.sum()





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			self._get_clip_similarity(pos_prompts["prompts"]    ,						_lowercase    ,						weights=(1 / pos_prompts["weights"])		)
         if neg_prompts:
            lowerCamelCase__						=			self._get_clip_similarity(neg_prompts["prompts"]    ,						_lowercase    ,						weights=neg_prompts["weights"]		)
         else:
            lowerCamelCase__						=			torch.tensor([1]    ,						device=self.device		)
         lowerCamelCase__						=			-torch.log(_lowercase		) + torch.log(_lowercase		)
         return loss





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         lowerCamelCase__						=			torch.randn_like(self.latent    ,						requires_grad=_lowercase    ,						device=self.device		)
         lowerCamelCase__						=			torch.optim.Adam([vector]    ,						lr=self.lr		)

         for i in range(self.iterations		):
            optim.zero_grad()
            lowerCamelCase__						=			self._add_vector(_lowercase		)
            lowerCamelCase__						=			loop_post_process(_lowercase		)
            lowerCamelCase__						=			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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         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:
            lowerCamelCase__						=			Image.open(_lowercase		)
            lowerCamelCase__						=			image.resize((256, 256)		)
            wandb.log("Original Image"    ,						wandb.Image(_lowercase		)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=None    ,						)   ->					List[Any]:

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

         lowerCamelCase__						=			self.vqgan.decode(self.latent		)[0]
         if show_intermediate:
            print("Original Image"		)
            show_pil(custom_to_pil(_lowercase		)		)

         lowerCamelCase__						=			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 copy

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


SCREAMING_SNAKE_CASE_     :			Dict	  =			logging.get_logger(__name__)


class 		_A       (			__a					):
      __a								=	'encoder-decoder'
      __a								=	True





      def __init__(  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         super().__init__(**SCREAMING_SNAKE_CASE__		)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         lowerCamelCase__						=			kwargs.pop("encoder"		)
         lowerCamelCase__						=			encoder_config.pop("model_type"		)
         lowerCamelCase__						=			kwargs.pop("decoder"		)
         lowerCamelCase__						=			decoder_config.pop("model_type"		)

         from ..auto.configuration_auto import AutoConfig

         lowerCamelCase__						=			AutoConfig.for_model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			AutoConfig.for_model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			True





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					PretrainedConfig:
         logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
         lowerCamelCase__						=			True
         lowerCamelCase__						=			True

         return cls(encoder=encoder_config.to_dict()    ,						decoder=decoder_config.to_dict()    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			copy.deepcopy(self.__dict__		)
         lowerCamelCase__						=			self.encoder.to_dict()
         lowerCamelCase__						=			self.decoder.to_dict()
         lowerCamelCase__						=			self.__class__.model_type
         return output




