




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		:	List[str]      ,						_A		:	str = "cpu"      ,						_A		:	str = "openai/clip-vit-large-patch14"      )		->    str:






       """simple docstring"""







       lowercase					:   Optional[int]								= device
       lowercase					:   Any								= CLIPTokenizerFast.from_pretrained(a__      )
       lowercase					:   str								= [0.48_145_466, 0.4_578_275, 0.40_821_073]
       lowercase					:   Optional[int]								= [0.26_862_954, 0.26_130_258, 0.27_577_711]
       lowercase					:   Optional[Any]								= torchvision.transforms.Normalize(self.image_mean      ,						self.image_std      )
       lowercase					:   Union[str, Any]								= torchvision.transforms.Resize(224      )
       lowercase					:   List[Any]								= torchvision.transforms.CenterCrop(224      )






  def 					__a			(  self		:	Optional[Any]      ,						_A		:	str      )		->    Union[str, Any]:






       """simple docstring"""







       lowercase					:   Dict								= self.resize(a__      )
       lowercase					:   Optional[Any]								= self.center_crop(a__      )
       lowercase					:   List[str]								= self.normalize(a__      )
       return images






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






       """simple docstring"""







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


class 						_A					(  nn.Module      ):






  def __init__(  self		:	Optional[int]      ,						_A		:	Dict=10      ,						_A		:	Dict=0.01      ,						_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      ,						)		->    Any:






       """simple docstring"""







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

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






  def 					__a			(  self		:	str      ,						_A		:	Optional[int]=None      ,						_A		:	Tuple=None      ,						_A		:	str=5      ,						_A		:	int=True      )		->    str:






       """simple docstring"""







       lowercase					:   Any								= []
       if output_path is None:
            lowercase					:   int								= '''./animation.gif'''
       if input_path is None:
            lowercase					:   List[Any]								= self.save_path
       lowercase					:   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?)'''      )
       lowercase					:   List[Any]								= total_duration / len(a__      )
       lowercase					:   int								= [frame_duration] * len(a__      )
       if extend_frames:
            lowercase					:   Dict								= 1.5
            lowercase					:   Any								= 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      )		->    Tuple:






       """simple docstring"""







       if not (path or img):
            raise ValueError('''Input either path or tensor'''      )
       if img is not None:
            raise NotImplementedError
       lowercase					:   int								= preprocess(Image.open(a__      )      ,						target_image_size=256      ).to(self.device      )
       lowercase					:   Union[str, Any]								= preprocess_vqgan(a__      )
       lowercase			,       *lowercase					:   Tuple								= self.vqgan.encode(a__      )
       return z






  def 					__a			(  self		:	List[str]      ,						_A		:	int      )		->    Dict:






       """simple docstring"""







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






  def 					__a			(  self		:	int      ,						_A		:	Dict      ,						_A		:	str      ,						_A		:	str=None      )		->    Tuple:






       """simple docstring"""







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






  def 					__a			(  self		:	Dict      ,						_A		:	Union[str, Any]      ,						_A		:	Union[str, Any]      ,						_A		:	int      )		->    List[Any]:






       """simple docstring"""







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






  def 					__a			(  self		:	Any      ,						_A		:	Optional[Any]      ,						_A		:	List[str]      ,						_A		:	Tuple      )		->    List[Any]:






       """simple docstring"""







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

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






  def 					__a			(  self		:	Optional[int]      ,						_A		:	int      )		->    List[str]:






       """simple docstring"""







       if not prompts:
            return []
       lowercase					:   str								= []
       lowercase					:   Optional[int]								= []
       if isinstance(a__      ,						a__      ):
            lowercase					:   Optional[int]								= [prompt.strip() for prompt in prompts.split('''|'''      )]
       for prompt in prompts:
            if isinstance(a__      ,						(tuple, list)      ):
                 lowercase					:   Any								= prompt[0]
                 lowercase					:   List[Any]								= float(prompt[1]      )
            elif ":" in prompt:
                 lowercase			,       lowercase					:   List[Any]								= prompt.split(''':'''      )
                 lowercase					:   int								= float(a__      )
            else:
                 lowercase					:   int								= prompt
                 lowercase					:   int								= 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      ,						)		->    Any:






       """simple docstring"""







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

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

       lowercase					:   Optional[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 os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_				=			get_tests_dir('fixtures/test_sentencepiece_bpe.model')

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    BartphoTokenizer
      A_						:				List[str]							=    False
      A_						:				Optional[Any]							=    True


      def    a (self       :      Tuple      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
         __snake_case							       =   dict(zip(a__				,			range(len(a__      )      )      )      )
         __snake_case							       =   {'''unk_token''': '''<unk>'''}

         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''monolingual_vocab_file''']      )
         with open(self.monolingual_vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as fp:
            for token in vocab_tokens:
               fp.write(f"""{token} {vocab_tokens[token]}\n"""      )

         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         tokenizer.save_pretrained(self.tmpdirname      )


      def    a (self       :      str				,			**a__       :      str      ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return BartphoTokenizer.from_pretrained(self.tmpdirname				,			**a__      )


      def    a (self       :      str				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''This is a<unk><unk> test'''
         return input_text, output_text


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''▁This ▁is ▁a ▁l à ▁t est'''.split()
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )

         __snake_case							       =   tokens + [tokenizer.unk_token]
         __snake_case							       =   [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

