


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 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__ = "cpu",	__magic_name__ = "openai/clip-vit-large-patch14"							)				->   None:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   device
       UpperCamelCase__							:       Optional[int]      =   CLIPTokenizerFast.from_pretrained(__A							)
       UpperCamelCase__							:       int      =   [0.4814_5466, 0.457_8275, 0.4082_1073]
       UpperCamelCase__							:       int      =   [0.2686_2954, 0.2613_0258, 0.2757_7711]
       UpperCamelCase__							:       int      =   torchvision.transforms.Normalize(self.image_mean,	self.image_std							)
       UpperCamelCase__							:       Any      =   torchvision.transforms.Resize(224							)
       UpperCamelCase__							:       Any      =   torchvision.transforms.CenterCrop(224							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.resize(__A							)
       UpperCamelCase__							:       Optional[int]      =   self.center_crop(__A							)
       UpperCamelCase__							:       Optional[Any]      =   self.normalize(__A							)
       return images




   def __call__(					self,	__magic_name__=None,	__magic_name__=None,	**__magic_name__							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   self.tokenizer(text=__A,	**__A							)
       UpperCamelCase__							:       Optional[int]      =   self.preprocess_img(__A							)
       UpperCamelCase__							:       Tuple      =   {key: value.to(self.device							) for (key, value) in encoding.items()}
       return encoding






class 					lowercase__ (			nn.Module       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__=10,	__magic_name__=0.01,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__=False,	__magic_name__=True,	__magic_name__="image",	__magic_name__=True,	__magic_name__=False,	__magic_name__=False,	__magic_name__=False,	)				->   None:






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

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




   def    UpperCamelCase__						(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=5,	__magic_name__=True							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   []
       if output_path is None:
           UpperCamelCase__							:       str      =   '''./animation.gif'''
       if input_path is None:
           UpperCamelCase__							:       Union[str, Any]      =   self.save_path
       UpperCamelCase__							:       Optional[int]      =   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?)'''							)
       UpperCamelCase__							:       Tuple      =   total_duration / len(__A							)
       UpperCamelCase__							:       Optional[Any]      =   [frame_duration] * len(__A							)
       if extend_frames:
           UpperCamelCase__							:       Any      =   1.5
           UpperCamelCase__							:       List[str]      =   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    UpperCamelCase__						(					self,	__magic_name__=None,	__magic_name__=None							)				->   str:






       """simple docstring"""



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




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Any:






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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__=None							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.clip_preprocessor(text=__A,	images=__A,	return_tensors='''pt''',	padding=__A							)
       UpperCamelCase__							:       int      =   self.clip(**__A							)
       UpperCamelCase__							:       Tuple      =   clip_outputs.logits_per_image
       if weights is not None:
           UpperCamelCase__							:       List[Any]      =   similarity_logits * weights
       return similarity_logits.sum()




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   List[str]:






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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Dict:






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

       for i in range(self.iterations							):
           optim.zero_grad()
           UpperCamelCase__							:       List[str]      =   self._add_vector(__A							)
           UpperCamelCase__							:       int      =   loop_post_process(__A							)
           UpperCamelCase__							:       Tuple      =   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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Union[str, 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:
           UpperCamelCase__							:       Optional[Any]      =   Image.open(__A							)
           UpperCamelCase__							:       Union[str, Any]      =   image.resize((256, 256)							)
           wandb.log('''Original Image''',	wandb.Image(__A							)							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""



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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=None,	__magic_name__=None,	__magic_name__=True,	__magic_name__=False,	__magic_name__=True,	__magic_name__=True,	__magic_name__=None,	)				->   Tuple:






       """simple docstring"""



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

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

       UpperCamelCase__							:       List[Any]      =   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"							)							)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	"upernet"




   def __init__(					self,	__magic_name__=None,	__magic_name__=512,	__magic_name__=0.02,	__magic_name__=[1, 2, 3, 6],	__magic_name__=True,	__magic_name__=0.4,	__magic_name__=384,	__magic_name__=256,	__magic_name__=1,	__magic_name__=False,	__magic_name__=255,	**__magic_name__,	)				->   Dict:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
           UpperCamelCase__							:       str      =   CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
       elif isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Tuple      =   backbone_config.get('''model_type'''							)
           UpperCamelCase__							:       List[str]      =   CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__							:       Optional[int]      =   config_class.from_dict(__magic_name__							)

       UpperCamelCase__							:       Dict      =   backbone_config
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       int      =   initializer_range
       UpperCamelCase__							:       Dict      =   pool_scales
       UpperCamelCase__							:       Tuple      =   use_auxiliary_head
       UpperCamelCase__							:       Tuple      =   auxiliary_loss_weight
       UpperCamelCase__							:       Optional[int]      =   auxiliary_in_channels
       UpperCamelCase__							:       Optional[Any]      =   auxiliary_channels
       UpperCamelCase__							:       Any      =   auxiliary_num_convs
       UpperCamelCase__							:       Union[str, Any]      =   auxiliary_concat_input
       UpperCamelCase__							:       List[str]      =   loss_ignore_index




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       int      =   self.backbone_config.to_dict()
       UpperCamelCase__							:       Any      =   self.__class__.model_type
       return output

