from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  CustomTokenizer
     pass
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  UpperCamelCase				:

     '''simple docstring'''

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






           """simple docstring"""
           _lowerCamelCase			    =				device
           _lowerCamelCase			    =				CLIPTokenizerFast.from_pretrained(A_						)
           _lowerCamelCase			    =				[0.48145466, 0.4578275, 0.40821073]
           _lowerCamelCase			    =				[0.26862954, 0.26130258, 0.27577711]
           _lowerCamelCase			    =				torchvision.transforms.Normalize(self.image_mean				,				self.image_std						)
           _lowerCamelCase			    =				torchvision.transforms.Resize(2_24						)
           _lowerCamelCase			    =				torchvision.transforms.CenterCrop(2_24						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.resize(A_						)
           _lowerCamelCase			    =				self.center_crop(A_						)
           _lowerCamelCase			    =				self.normalize(A_						)
           return images





     def __call__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(text=A_				,				**A_						)
           _lowerCamelCase			    =				self.preprocess_img(A_						)
           _lowerCamelCase			    =				{key: value.to(self.device						) for (key, value) in encoding.items()}
           return encoding





class  UpperCamelCase				( nn.Module ):

     '''simple docstring'''

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






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				device if device else get_device()
           if vqgan:
                 _lowerCamelCase			    =				vqgan
           else:
                 _lowerCamelCase			    =				load_vqgan(self.device				,				conf_path=A_				,				ckpt_path=A_						)
           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 		UpperCamelCase_						(					self				,				A_=None				,				A_=None				,				A_=5				,				A_=True						) ->				Any:






           """simple docstring"""
           _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(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?)'''						)
           _lowerCamelCase			    =				total_duration / len(A_						)
           _lowerCamelCase			    =				[frame_duration] * len(A_						)
           if extend_frames:
                 _lowerCamelCase			    =				1.5
                 _lowerCamelCase			    =				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				,				A_=None				,				A_=None						) ->				Union[str, Any]:






           """simple docstring"""
           if not (path or img):
                 raise ValueError('''Input either path or tensor'''						)
           if img is not None:
                 raise NotImplementedError
           _lowerCamelCase			    =				preprocess(Image.open(A_						)				,				target_image_size=2_56						).to(self.device						)
           _lowerCamelCase			    =				preprocess_vqgan(A_						)
           _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.encode(A_						)
           return z



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.latent.detach().requires_grad_()
           _lowerCamelCase			    =				base_latent + transform_vector
           if self.quantize:
                 _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.quantize(A_						)
           else:
                 _lowerCamelCase			    =				trans_latent
           return self.vqgan.decode(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_=None						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.clip_preprocessor(text=A_				,				images=A_				,				return_tensors='''pt'''				,				padding=A_						)
           _lowerCamelCase			    =				self.clip(**A_						)
           _lowerCamelCase			    =				clip_outputs.logits_per_image
           if weights is not None:
                 _lowerCamelCase			    =				similarity_logits * weights
           return similarity_logits.sum()



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self._get_clip_similarity(pos_prompts['''prompts''']				,				A_				,				weights=(1 / pos_prompts['''weights'''])						)
           if neg_prompts:
                 _lowerCamelCase			    =				self._get_clip_similarity(neg_prompts['''prompts''']				,				A_				,				weights=neg_prompts['''weights''']						)
           else:
                 _lowerCamelCase			    =				torch.tensor([1]				,				device=self.device						)
           _lowerCamelCase			    =				-torch.log(A_						) + torch.log(A_						)
           return loss



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				torch.randn_like(self.latent				,				requires_grad=A_				,				device=self.device						)
           _lowerCamelCase			    =				torch.optim.Adam([vector]				,				lr=self.lr						)

           for i in range(self.iterations						):
                 optim.zero_grad()
                 _lowerCamelCase			    =				self._add_vector(A_						)
                 _lowerCamelCase			    =				loop_post_process(A_						)
                 _lowerCamelCase			    =				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				,				A_				,				A_				,				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:
                 _lowerCamelCase			    =				Image.open(A_						)
                 _lowerCamelCase			    =				image.resize((2_56, 2_56)						)
                 wandb.log('''Original Image'''				,				wandb.Image(A_						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






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





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=True				,				A_=False				,				A_=True				,				A_=True				,				A_=None				,				) ->				str:






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

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

           _lowerCamelCase			    =				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'						)						)