

'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCamelCase :      Tuple					 =     ""
lowerCamelCase :      Union[str, Any]					 =     ""
lowerCamelCase :      Dict					 =     ""
lowerCamelCase :      str					 =     1  # (0 is vertical, 1 is horizontal)


def        _lowerCAmelCase					(						)      ->			None:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_dataset(_UpperCamelCase							, _UpperCamelCase				)
     print('Processing...'				)
     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =update_image_and_anno(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)

     for index, image in enumerate(_UpperCamelCase				):
          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          _SCREAMING_SNAKE_CASE							    =random_chars(32				)
          _SCREAMING_SNAKE_CASE							    =paths[index].split(os.sep				)[-1].rsplit('.'							, 1				)[0]
          _SCREAMING_SNAKE_CASE							    =f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
          cva.imwrite(f"/{file_root}.jpg"							, _UpperCamelCase							, [cva.IMWRITE_JPEG_QUALITY, 85]				)
          print(f"Success {index+1}/{len(_UpperCamelCase				)} with {file_name}"				)
          _SCREAMING_SNAKE_CASE							    =[]
          for anno in new_annos[index]:
               _SCREAMING_SNAKE_CASE							    =f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
               annos_list.append(_UpperCamelCase				)
          with open(f"/{file_root}.txt"							, 'w'				) as outfile:
               outfile.write('\n'.join(line for line in annos_list				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str				)      ->			tuple[list, list]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =[]
     for label_file in glob.glob(os.path.join(_UpperCamelCase							, '*.txt'				)				):
          _SCREAMING_SNAKE_CASE							    =label_file.split(os.sep				)[-1].rsplit('.'							, 1				)[0]
          with open(_UpperCamelCase				) as in_file:
               _SCREAMING_SNAKE_CASE							    =in_file.readlines()
          _SCREAMING_SNAKE_CASE							    =os.path.join(_UpperCamelCase							, f"{label_name}.jpg"				)

          _SCREAMING_SNAKE_CASE							    =[]
          for obj_list in obj_lists:
               _SCREAMING_SNAKE_CASE							    =obj_list.rstrip('\n'				).split(' '				)
               boxes.append(
                   [
                       int(obj[0]				),
                       float(obj[1]				),
                       float(obj[2]				),
                       float(obj[3]				),
                       float(obj[4]				),
                   ]				)
          if not boxes:
               continue
          img_paths.append(_UpperCamelCase				)
          labels.append(_UpperCamelCase				)
     return img_paths, labels


def        _lowerCAmelCase					(						_UpperCamelCase						:     list							, _UpperCamelCase						:     list							, _UpperCamelCase						:     int = 1				)      ->			tuple[list, list, list]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =[]
     for idx in range(len(_UpperCamelCase				)				):
          _SCREAMING_SNAKE_CASE							    =[]
          _SCREAMING_SNAKE_CASE							    =img_list[idx]
          path_list.append(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =anno_list[idx]
          _SCREAMING_SNAKE_CASE							    =cva.imread(_UpperCamelCase				)
          if flip_type == 1:
               _SCREAMING_SNAKE_CASE							    =cva.flip(_UpperCamelCase							, _UpperCamelCase				)
               for bbox in img_annos:
                    _SCREAMING_SNAKE_CASE							    =1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
          elif flip_type == 0:
               _SCREAMING_SNAKE_CASE							    =cva.flip(_UpperCamelCase							, _UpperCamelCase				)
               for bbox in img_annos:
                    _SCREAMING_SNAKE_CASE							    =1 - bbox[2]
                    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]				)
          new_annos_lists.append(_UpperCamelCase				)
          new_imgs_list.append(_UpperCamelCase				)
     return new_imgs_list, new_annos_lists, path_list


def        _lowerCAmelCase					(						_UpperCamelCase						:     int = 32				)      ->			str:
     """simple docstring"""




     assert number_char > 1, "The number of character should greater than 1"
     _SCREAMING_SNAKE_CASE							    =ascii_lowercase + digits
     return "".join(random.choice(_UpperCamelCase				) for _ in range(_UpperCamelCase				)				)


if __name__ == "__main__":
     main()
     print("DONE ✅")



'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
