





"""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 		lowercase					:





      def __init__(       self      ,						lowercase = "cpu"      ,						lowercase = "openai/clip-vit-large-patch14" )		->     None:
            lowerCAmelCase  						=       device
            lowerCAmelCase  						=       CLIPTokenizerFast.from_pretrained(lowercase )
            lowerCAmelCase  						=       [0.48_145_466, 0.4_578_275, 0.40_821_073]
            lowerCAmelCase  						=       [0.26_862_954, 0.26_130_258, 0.27_577_711]
            lowerCAmelCase  						=       torchvision.transforms.Normalize(self.image_mean      ,						self.image_std )
            lowerCAmelCase  						=       torchvision.transforms.Resize(224 )
            lowerCAmelCase  						=       torchvision.transforms.CenterCrop(224 )





      def     _snake_case			(       self      ,						lowercase )		->     Dict:
            lowerCAmelCase  						=       self.resize(lowercase )
            lowerCAmelCase  						=       self.center_crop(lowercase )
            lowerCAmelCase  						=       self.normalize(lowercase )
            return images





      def __call__(       self      ,						lowercase=None      ,						lowercase=None      ,						**lowercase )		->     List[str]:
            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 		lowercase					(					nn.Module					):





      def __init__(       self      ,						lowercase=10      ,						lowercase=0.01      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=False      ,						lowercase=True      ,						lowercase="image"      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						)		->     None:
            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     _snake_case			(       self      ,						lowercase=None      ,						lowercase=None      ,						lowercase=5      ,						lowercase=True )		->     Optional[int]:
            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     _snake_case			(       self      ,						lowercase=None      ,						lowercase=None )		->     str:
            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						,		*lowerCAmelCase  						=       self.vqgan.encode(lowercase )
            return z





      def     _snake_case			(       self      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       self.latent.detach().requires_grad_()
            lowerCAmelCase  						=       base_latent + transform_vector
            if self.quantize:
                  lowerCAmelCase						,		*lowerCAmelCase  						=       self.vqgan.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       trans_latent
            return self.vqgan.decode(lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Optional[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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Dict:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[str]:
            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     _snake_case			(       self      ,						lowercase )		->     List[str]:
            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						,		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     _snake_case			(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=None      ,						lowercase=True      ,						lowercase=False      ,						lowercase=True      ,						lowercase=True      ,						lowercase=None      ,						)		->     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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__					=   "▁"

SCREAMING_SNAKE_CASE__					=   {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__					=   {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

SCREAMING_SNAKE_CASE__					=   {
    "google/pegasus-xsum": 512,
}


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES

      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']





      def __init__(       self      ,						lowercase      ,						lowercase="<pad>"      ,						lowercase="</s>"      ,						lowercase="<unk>"      ,						lowercase="<mask_2>"      ,						lowercase="<mask_1>"      ,						lowercase=None      ,						lowercase=103      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            lowerCAmelCase  						=       offset
            if additional_special_tokens is not None:
                  if not isinstance(lowercase      ,						lowercase ):
                        raise TypeError(
                            f'additional_special_tokens should be of type {type(lowercase )}, but is'
                            f' {type(lowercase )}' )

                  lowerCAmelCase  						=       (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f'<unk_{i}>' for i in range(len(lowercase )      ,						self.offset - 1 )
                  ]

                  if len(set(lowercase ) ) != len(lowercase ):
                        raise ValueError(
                            """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                            f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
                  lowerCAmelCase  						=       additional_special_tokens_extended
            else:
                  lowerCAmelCase  						=       [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f'<unk_{i}>' for i in range(2      ,						self.offset )]

            lowerCAmelCase  						=       {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=lowercase      ,						unk_token=lowercase      ,						mask_token=lowercase      ,						pad_token=lowercase      ,						mask_token_sent=lowercase      ,						offset=lowercase      ,						additional_special_tokens=lowercase      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**lowercase      ,						)
            lowerCAmelCase  						=       mask_token_sent
            lowerCAmelCase  						=       vocab_file
            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )

            # add special tokens to encoder dict
            lowerCAmelCase  						=       {
                0: self.pad_token,
                1: self.eos_token,
            }

            if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      } )

            if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,						self.offset - 1 )} )

            lowerCAmelCase  						=       {v: k for k, v in self.encoder.items()}





      @property
      def     _snake_case			(       self )		->     int:
            return len(self.sp_model ) + self.offset





      def     _snake_case			(       self )		->     Dict[str, int]:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





      def __getstate__(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            return state





      def __setstate__(       self      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       d

            # for backward compatibility
            if not hasattr(self      ,						"""sp_model_kwargs""" ):
                  lowerCAmelCase  						=       {}

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(self.vocab_file )





      def     _snake_case			(       self      ,						lowercase )		->     List[str]:
            return self.sp_model.encode(lowercase      ,						out_type=lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            if token in self.decoder:
                  return self.decoder[token]
            elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
            lowerCAmelCase  						=       self.sp_model.piece_to_id(lowercase )
            return sp_id + self.offset





      def     _snake_case			(       self      ,						lowercase )		->     str:
            if index in self.encoder:
                  return self.encoder[index]
            elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
            else:
                  lowerCAmelCase  						=       self.sp_model.IdToPiece(index - self.offset )
            return token





      def     _snake_case			(       self      ,						lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       """"""
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        out_string += self.sp_model.decode(lowercase ) + token
                        lowerCAmelCase  						=       []
                  else:
                        current_sub_tokens.append(lowercase )
            out_string += self.sp_model.decode(lowercase )
            return out_string.strip()





      def     _snake_case			(       self      ,						lowercase=False )		->     Tuple:
            return 1





      def     _snake_case			(       self      ,						lowercase )		->     Tuple:
            lowerCAmelCase  						=       set(self.all_special_ids )  # call it once instead of inside list comp
            all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

            return [1 if x in all_special_ids else 0 for x in seq]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = False )		->     List[int]:
            if already_has_special_tokens:
                  return self._special_token_mask(lowercase )
            elif token_ids_a is None:
                  return self._special_token_mask(lowercase ) + [1]
            else:
                  return self._special_token_mask(token_ids_a + token_ids_a ) + [1]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None )		->     List[int]:
            if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return token_ids_a + token_ids_a + [self.eos_token_id]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            if not os.path.isdir(lowercase ):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                  return
            lowerCAmelCase  						=       os.path.join(
                lowercase      ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

            if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
                  copyfile(self.vocab_file      ,						lowercase )
            elif not os.path.isfile(self.vocab_file ):
                  with open(lowercase      ,						"""wb""" ) as fi:
                        lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
                        fi.write(lowercase )

            return (out_vocab_file,)






