




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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
     __a			=					self.resize(UpperCamelCase      )
     __a			=					self.center_crop(UpperCamelCase      )
     __a			=					self.normalize(UpperCamelCase      )
     return images







   def __call__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							**UpperCamelCase      )				->    List[str]:
     __a			=					self.tokenizer(text=UpperCamelCase      ,							**UpperCamelCase      )
     __a			=					self.preprocess_img(UpperCamelCase      )
     __a			=					{key: value.to(self.device      ) for (key, value) in encoding.items()}
     return encoding





class    __lowercase    (  nn.Module ):







   def __init__(	self      ,							UpperCamelCase=10      ,							UpperCamelCase=0.01      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							UpperCamelCase=True      ,							UpperCamelCase="image"      ,							UpperCamelCase=True      ,							UpperCamelCase=False      ,							UpperCamelCase=False      ,							UpperCamelCase=False      ,							)				->    None:
     super().__init__()
     __a			=					None
     __a			=					device if device else get_device()
     if vqgan:
       __a			=					vqgan
     else:
       __a			=					load_vqgan(self.device      ,							conf_path=UpperCamelCase      ,							ckpt_path=UpperCamelCase      )
     self.vqgan.eval()
     if clip:
       __a			=					clip
     else:
       __a			=					CLIPModel.from_pretrained('openai/clip-vit-base-patch32'      )
     self.clip.to(self.device      )
     __a			=					ProcessorGradientFlow(device=self.device      )

     __a			=					iterations
     __a			=					lr
     __a			=					log
     __a			=					make_grid
     __a			=					return_val
     __a			=					quantize
     __a			=					self.vqgan.decoder.z_shape







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=5      ,							UpperCamelCase=True      )				->    List[str]:
     __a			=					[]
     if output_path is None:
       __a			=					'./animation.gif'
     if input_path is None:
       __a			=					self.save_path
     __a			=					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?)'      )
     __a			=					total_duration / len(UpperCamelCase      )
     __a			=					[frame_duration] * len(UpperCamelCase      )
     if extend_frames:
       __a			=					1.5
       __a			=					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 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      )				->    Any:
     if not (path or img):
       raise ValueError('Input either path or tensor'      )
     if img is not None:
       raise NotImplementedError
     __a			=					preprocess(Image.open(UpperCamelCase      )      ,							target_image_size=256      ).to(self.device      )
     __a			=					preprocess_vqgan(UpperCamelCase      )
     __a	,       *__a			=					self.vqgan.encode(UpperCamelCase      )
     return z







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    List[str]:
     __a			=					self.clip_preprocessor(text=UpperCamelCase      ,							images=UpperCamelCase      ,							return_tensors='pt'      ,							padding=UpperCamelCase      )
     __a			=					self.clip(**UpperCamelCase      )
     __a			=					clip_outputs.logits_per_image
     if weights is not None:
       __a			=					similarity_logits * weights
     return similarity_logits.sum()







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Any:
     __a			=					self._get_clip_similarity(pos_prompts['prompts']      ,							UpperCamelCase      ,							weights=(1 / pos_prompts['weights'])      )
     if neg_prompts:
       __a			=					self._get_clip_similarity(neg_prompts['prompts']      ,							UpperCamelCase      ,							weights=neg_prompts['weights']      )
     else:
       __a			=					torch.tensor([1]      ,							device=self.device      )
     __a			=					-torch.log(UpperCamelCase      ) + torch.log(UpperCamelCase      )
     return loss







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					torch.randn_like(self.latent      ,							requires_grad=UpperCamelCase      ,							device=self.device      )
     __a			=					torch.optim.Adam([vector]      ,							lr=self.lr      )

     for i in range(self.iterations      ):
       optim.zero_grad()
       __a			=					self._add_vector(UpperCamelCase      )
       __a			=					loop_post_process(UpperCamelCase      )
       __a			=					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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    str:
     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:
       __a			=					Image.open(UpperCamelCase      )
       __a			=					image.resize((256, 256)      )
       wandb.log('Original Image'      ,							wandb.Image(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     if not prompts:
       return []
     __a			=					[]
     __a			=					[]
     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					[prompt.strip() for prompt in prompts.split('|'      )]
     for prompt in prompts:
       if isinstance(UpperCamelCase      ,							(tuple, list)      ):
         __a			=					prompt[0]
         __a			=					float(prompt[1]      )
       elif ":" in prompt:
         __a	,       __a			=					prompt.split(':'      )
         __a			=					float(UpperCamelCase      )
       else:
         __a			=					prompt
         __a			=					1.0
       processed_prompts.append(UpperCamelCase      )
       weights.append(UpperCamelCase      )
     return {
         "prompts": processed_prompts,
         "weights": torch.tensor(UpperCamelCase      ,							device=self.device      ),
     }







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase=False      ,							UpperCamelCase=True      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							)				->    Any:
     if image_path:
       __a			=					self._get_latent(UpperCamelCase      )
     else:
       __a			=					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."
     __a			=					self.process_prompts(UpperCamelCase      )
     __a			=					self.process_prompts(UpperCamelCase      )
     if save_final and save_path is None:
       __a			=					os.path.join('./outputs/'      ,							'_'.join(pos_prompts['prompts']      )      )
     if not os.path.exists(UpperCamelCase      ):
       os.makedirs(UpperCamelCase      )
     else:
       __a			=					save_path + '_' + get_timestamp()
       os.makedirs(UpperCamelCase      )
     __a			=					save_path

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

     __a			=					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 argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  if (
      (cp >= 0X4e00 and cp <= 0X9fff)
      or (cp >= 0X3400 and cp <= 0X4dbf)  #
      or (cp >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # word like '180' or '身高' or '神'
  for char in word:
    __a			=					ord(a_			)
    if not _is_chinese_char(a_			):
      return 0
  return 1


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

  for token in tokens:
    __a			=					len(a_			) > 1 and is_chinese(a_			)
    if chinese_word:
      word_set.add(a_			)
  __a			=					list(a_			)
  return word_list


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  if not chinese_word_set:
    return bert_tokens
  __a			=					max([len(a_			) for w in chinese_word_set]			)

  __a			=					bert_tokens
  __a	,       __a			=					0, len(a_			)
  while start < end:
    __a			=					True
    if is_chinese(bert_word[start]			):
      __a			=					min(end - start				,					a_			)
      for i in range(a_				,					1				,					-1			):
        __a			=					''.join(bert_word[start : start + i]			)
        if whole_word in chinese_word_set:
          for j in range(start + 1				,					start + i			):
            __a			=					'##' + bert_word[j]
          __a			=					start + i
          __a			=					False
          break
    if single_word:
      start += 1
  return bert_word


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

  __a			=					[]
  for i in range(0				,					len(a_			)				,					100			):
    __a			=					bert_tokenizer(lines[i : i + 100]				,					add_special_tokens=a_				,					truncation=a_				,					max_length=512			)
    bert_res.extend(res['input_ids']			)
  assert len(a_			) == len(a_			)

  __a			=					[]
  for input_ids, chinese_word in zip(a_				,					a_			):
    __a			=					[]
    for id in input_ids:
      __a			=					bert_tokenizer._convert_id_to_token(a_			)
      input_tokens.append(a_			)
    __a			=					add_sub_symbol(a_				,					a_			)
    __a			=					[]
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(a_			):
      if token[:2] == "##":
        __a			=					token[2:]
        # save chinese tokens' pos
        if len(a_			) == 1 and _is_chinese_char(ord(a_			)			):
          ref_id.append(a_			)
    ref_ids.append(a_			)

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name				,					'r'				,					encoding='utf-8'			) as f:
    __a			=					f.readlines()
  __a			=					[line.strip() for line in data if len(a_			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  __a			=					LTP(args.ltp			)  # faster in GPU device
  __a			=					BertTokenizer.from_pretrained(args.bert			)

  __a			=					prepare_ref(a_				,					a_				,					a_			)

  with open(args.save_path				,					'w'				,					encoding='utf-8'			) as f:
    __a			=					[json.dumps(a_			) + '\n' for ref in ref_ids]
    f.writelines(a_			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser(description="prepare_chinese_ref")
  parser.add_argument(
      "--file_name",
      type=str,
      default="./resources/chinese-demo.txt",
      help="file need process, same as training data in lm",
  )
  parser.add_argument(
      "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
  )
  parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
  parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)



