



"""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  _lowerCAmelCase     :





		"""simple docstring"""






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







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      device
								snake_case_ :			List[Any]            =      CLIPTokenizerFast.from_pretrained(_lowercase )
								snake_case_ :			Optional[int]            =      [0.4814_5466, 0.457_8275, 0.4082_1073]
								snake_case_ :			Union[str, Any]            =      [0.2686_2954, 0.2613_0258, 0.2757_7711]
								snake_case_ :			str            =      torchvision.transforms.Normalize(self.image_mean				,     self.image_std )
								snake_case_ :			Any            =      torchvision.transforms.Resize(2_2_4 )
								snake_case_ :			str            =      torchvision.transforms.CenterCrop(2_2_4 )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			int            =      self.resize(_lowercase )
								snake_case_ :			int            =      self.center_crop(_lowercase )
								snake_case_ :			Union[str, Any]            =      self.normalize(_lowercase )
								return images





		def __call__(       self				,     _lowercase=None				,     _lowercase=None				,     **_lowercase )			->    Tuple:







								'''simple docstring'''

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




class  _lowerCAmelCase     (					nn.Module		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase=1_0				,     _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:







								'''simple docstring'''

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

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





		def  UpperCAmelCase__				(       self				,     _lowercase=None				,     _lowercase=None				,     _lowercase=5				,     _lowercase=True )			->    int:







								'''simple docstring'''

								snake_case_ :			Tuple            =      []
								if output_path is None:
														snake_case_ :			List[Any]            =      """./animation.gif"""
								if input_path is None:
														snake_case_ :			Dict            =      self.save_path
								snake_case_ :			Optional[Any]            =      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?)""" )
								snake_case_ :			List[Any]            =      total_duration / len(_lowercase )
								snake_case_ :			List[str]            =      [frame_duration] * len(_lowercase )
								if extend_frames:
														snake_case_ :			Dict            =      1.5
														snake_case_ :			Union[str, Any]            =      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  UpperCAmelCase__				(       self				,     _lowercase=None				,     _lowercase=None )			->    Tuple:







								'''simple docstring'''

								if not (path or img):
														raise ValueError("""Input either path or tensor""" )
								if img is not None:
														raise NotImplementedError
								snake_case_ :			Any            =      preprocess(Image.open(_lowercase )				,     target_image_size=2_5_6 ).to(self.device )
								snake_case_ :			Dict            =      preprocess_vqgan(_lowercase )
								snake_case_      ,		*snake_case_ :			str            =      self.vqgan.encode(_lowercase )
								return z





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    List[str]:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase=None )			->    int:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      torch.randn_like(self.latent				,     requires_grad=_lowercase				,     device=self.device )
								snake_case_ :			Dict            =      torch.optim.Adam([vector]				,     lr=self.lr )

								for i in range(self.iterations ):
														optim.zero_grad()
														snake_case_ :			List[str]            =      self._add_vector(_lowercase )
														snake_case_ :			Union[str, Any]            =      loop_post_process(_lowercase )
														snake_case_ :			int            =      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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

								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:
														snake_case_ :			Tuple            =      Image.open(_lowercase )
														snake_case_ :			str            =      image.resize((2_5_6, 2_5_6) )
														wandb.log("""Original Image"""				,     wandb.Image(_lowercase ) )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Any:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=None				,     _lowercase=None				,     _lowercase=True				,     _lowercase=False				,     _lowercase=True				,     _lowercase=True				,     _lowercase=None				,     )			->    Union[str, Any]:







								'''simple docstring'''

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

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

								snake_case_ :			Tuple            =      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 argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase     :						str						       =  logging.getLogger(__name__)

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path'  ,     type=_UpperCAmelCase  ,     default='data/dump.txt'  ,     help='The path to the data.'			)
 parser.add_argument('--tokenizer_type'  ,     type=_UpperCAmelCase  ,     default='bert'  ,     choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name'  ,     type=_UpperCAmelCase  ,     default='bert-base-uncased'  ,     help='The tokenizer to use.'			)
 parser.add_argument('--dump_file'  ,     type=_UpperCAmelCase  ,     default='data/dump'  ,     help='The dump file prefix.'			)
 lowerCAmelCase    				= parser.parse_args()

 logger.info(F'Loading Tokenizer ({args.tokenizer_name})'			)
 if args.tokenizer_type == "bert":
  lowerCAmelCase    				= BertTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  lowerCAmelCase    				= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `<s>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  lowerCAmelCase    				= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(F'Loading text from {args.file_path}'			)
 with open(args.file_path  ,     'r'  ,     encoding='utf8'			) as fp:
  lowerCAmelCase    				= fp.readlines()

 logger.info('Start encoding'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples to process.'			)

 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 1_0000
 lowerCAmelCase    				= time.time()
 for text in data:
  lowerCAmelCase    				= F'{bos} {text.strip()} {sep}'
  lowerCAmelCase    				= tokenizer.encode(_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase			)
  rslt.append(_UpperCAmelCase			)

  iter += 1
  if iter % interval == 0:
   lowerCAmelCase    				= time.time()
   logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
   lowerCAmelCase    				= time.time()
 logger.info('Finished binarization'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples processed.'			)

 lowerCAmelCase    				= F'{args.dump_file}.{args.tokenizer_name}.pickle'
 lowerCAmelCase    				= tokenizer.vocab_size
 if vocab_size < (1 << 16):
  lowerCAmelCase    				= [np.uintaa(_UpperCAmelCase			) for d in rslt]
 else:
  lowerCAmelCase    				= [np.intaa(_UpperCAmelCase			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(F'Dump to {dp_file}'			)
 with open(_UpperCAmelCase  ,     'wb'			) as handle:
  pickle.dump(rslt_  ,     _UpperCAmelCase  ,     protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
       main()





