



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

import cva
_lowerCAmelCase    :Union[str, Any]     =					''
_lowerCAmelCase    :Union[str, Any]     =					''
_lowerCAmelCase    :int     =					''
_lowerCAmelCase    :Any     =					1  # (0 is vertical, 1 is horizontal)





def  lowerCamelCase_						():
    _UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    get_dataset(UpperCamelCase__     ,       UpperCamelCase__							)
    print('''Processing...'''							)
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    update_image_and_anno(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

    for index, image in enumerate(UpperCamelCase__							):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        _UpperCAmelCase							:  List[Any]											=    random_chars(32							)
        _UpperCAmelCase							:  Tuple											=    paths[index].split(os.sep							)[-1].rsplit('''.'''     ,       1							)[0]
        _UpperCAmelCase							:  Dict											=    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}'							)
        _UpperCAmelCase							:  List[str]											=    []
        for anno in new_annos[index]:
            _UpperCAmelCase							:  Dict											=    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							):
    _UpperCAmelCase							:  Union[str, Any]											=    []
    _UpperCAmelCase							:  str											=    []
    for label_file in glob.glob(os.path.join(UpperCamelCase__     ,       '''*.txt'''							)							):
        _UpperCAmelCase							:  List[Any]											=    label_file.split(os.sep							)[-1].rsplit('''.'''     ,       1							)[0]
        with open(UpperCamelCase__							) as in_file:
            _UpperCAmelCase							:  Tuple											=    in_file.readlines()
        _UpperCAmelCase							:  Optional[Any]											=    os.path.join(UpperCamelCase__     ,       F'{label_name}.jpg'							)

        _UpperCAmelCase							:  Any											=    []
        for obj_list in obj_lists:
            _UpperCAmelCase							:  List[Any]											=    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							):
    _UpperCAmelCase							:  Optional[Any]											=    []
    _UpperCAmelCase							:  str											=    []
    _UpperCAmelCase							:  str											=    []
    for idx in range(len(UpperCamelCase__							)							):
        _UpperCAmelCase							:  List[str]											=    []
        _UpperCAmelCase							:  Dict											=    img_list[idx]
        path_list.append(UpperCamelCase__							)
        _UpperCAmelCase							:  int											=    anno_list[idx]
        _UpperCAmelCase							:  Any											=    cva.imread(UpperCamelCase__							)
        if flip_type == 1:
            _UpperCAmelCase							:  Dict											=    cva.flip(UpperCamelCase__     ,       UpperCamelCase__							)
            for bbox in img_annos:
                _UpperCAmelCase							:  Optional[Any]											=    1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]							)
        elif flip_type == 0:
            _UpperCAmelCase							:  str											=    cva.flip(UpperCamelCase__     ,       UpperCamelCase__							)
            for bbox in img_annos:
                _UpperCAmelCase							:  Any											=    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							):
    assert number_char > 1, "The number of character should greater than 1"
    _UpperCAmelCase							:  Optional[Any]											=    ascii_lowercase + digits
    return "".join(random.choice(UpperCamelCase__							) for _ in range(UpperCamelCase__							)							)


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







"""simple docstring"""
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase    :int     =					['small', 'medium', 'large']

_lowerCAmelCase    :int     =					'lm_head.decoder.weight'
_lowerCAmelCase    :Dict     =					'lm_head.weight'





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    d.pop(UpperCamelCase__							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    torch.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					argparse.ArgumentParser()
  parser.add_argument('--dialogpt_path', default='.', type=str)
  _lowerCAmelCase    :str     =					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase    :Tuple     =					os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
    _lowerCAmelCase    :int     =					f"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )


