





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		:







 '''simple docstring'''

 def __init__(self							:  List[Any]			,					UpperCAmelCase_							:  str = "cpu"			,					UpperCAmelCase_							:  str = "openai/clip-vit-large-patch14")			->None:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =device
   lowerCamelCase__: Optional[int]         =CLIPTokenizerFast.from_pretrained(UpperCAmelCase_)
   lowerCamelCase__: Any         =[0.4814_5466, 0.457_8275, 0.4082_1073]
   lowerCamelCase__: Union[str, Any]         =[0.2686_2954, 0.2613_0258, 0.2757_7711]
   lowerCamelCase__: Union[str, Any]         =torchvision.transforms.Normalize(self.image_mean			,					self.image_std)
   lowerCamelCase__: List[Any]         =torchvision.transforms.Resize(224)
   lowerCamelCase__: Dict         =torchvision.transforms.CenterCrop(224)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  str)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.resize(UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =self.center_crop(UpperCAmelCase_)
   lowerCamelCase__: Dict         =self.normalize(UpperCAmelCase_)
   return images







 def __call__(self							:  Any			,					UpperCAmelCase_							:  List[str]=None			,					UpperCAmelCase_							:  str=None			,					**UpperCAmelCase_							:  Tuple)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =self.tokenizer(text=UpperCAmelCase_			,					**UpperCAmelCase_)
   lowerCamelCase__: str         =self.preprocess_img(UpperCAmelCase_)
   lowerCamelCase__: str         ={key: value.to(self.device) for (key, value) in encoding.items()}
   return encoding



class   _SCREAMING_SNAKE_CASE		(      nn.Module       ):







 '''simple docstring'''

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



   '''simple docstring'''



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

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[Any]=None			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  List[Any]=5			,					UpperCAmelCase_							:  Union[str, Any]=True)			->Tuple:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  Any=None)			->Tuple:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  List[Any])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.latent.detach().requires_grad_()
   lowerCamelCase__: List[Any]         =base_latent + transform_vector
   if self.quantize:
     lowerCamelCase__    ,   *lowerCamelCase__: int         =self.vqgan.quantize(UpperCAmelCase_)
   else:
     lowerCamelCase__: str         =trans_latent
   return self.vqgan.decode(UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Union[str, Any]=None)			->Optional[int]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  int)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self._get_clip_similarity(pos_prompts["prompts"]			,					UpperCAmelCase_			,					weights=(1 / pos_prompts["weights"]))
   if neg_prompts:
     lowerCamelCase__: Tuple         =self._get_clip_similarity(neg_prompts["prompts"]			,					UpperCAmelCase_			,					weights=neg_prompts["weights"])
   else:
     lowerCamelCase__: List[Any]         =torch.tensor([1]			,					device=self.device)
   lowerCamelCase__: Union[str, Any]         =-torch.log(UpperCAmelCase_) + torch.log(UpperCAmelCase_)
   return loss

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Union[str, Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: Dict         =torch.randn_like(self.latent			,					requires_grad=UpperCAmelCase_			,					device=self.device)
   lowerCamelCase__: Any         =torch.optim.Adam([vector]			,					lr=self.lr)

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  List[Any])			->str:



   '''simple docstring'''



   wandb.init(reinit=UpperCAmelCase_			,					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__: Dict         =Image.open(UpperCAmelCase_)
     lowerCamelCase__: str         =image.resize((256, 256))
     wandb.log("Original Image"			,					wandb.Image(UpperCAmelCase_))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  Optional[int])			->int:



   '''simple docstring'''



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







 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  Any=True			,					UpperCAmelCase_							:  List[Any]=False			,					UpperCAmelCase_							:  Optional[int]=True			,					UpperCAmelCase_							:  Union[str, Any]=True			,					UpperCAmelCase_							:  Tuple=None			,					)			->List[str]:



   '''simple docstring'''



   if image_path:
     lowerCamelCase__: Any         =self._get_latent(UpperCAmelCase_)
   else:
     lowerCamelCase__: int         =torch.randn(self.latent_dim			,					device=self.device)
   if self.log:
     self._init_logging(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_)

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

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

   lowerCamelCase__: List[Any]         =loop_post_process(UpperCAmelCase_)
   for iter, transformed_img in enumerate(self._optimize_CLIP(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_)):
     if show_intermediate:
       show_pil(UpperCAmelCase_)
     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(UpperCAmelCase_)})
   if show_final:
     show_pil(UpperCAmelCase_)
   if save_final:
     transformed_img.save(os.path.join(self.save_path			,					F"""iter_{iter:03d}_final.png"""))




"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

