


'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class 						_lowerCamelCase					:




 '''simple docstring'''
 __lowercase				:		str    							=						field(
     metadata={'''help''': '''The output directory where the model will be written.'''}    ,		)
 __lowercase				:		str    							=						field(
     metadata={
         '''help''': (
             '''The encoder model checkpoint for weights initialization.'''
             '''Don\'t set if you want to train an encoder model from scratch.'''
         )
     }    ,		)
 __lowercase				:		str    							=						field(
     metadata={
         '''help''': (
             '''The decoder model checkpoint for weights initialization.'''
             '''Don\'t set if you want to train a decoder model from scratch.'''
         )
     }    ,		)
 __lowercase				:		Optional[str]    							=						field(
     default=snake_case_    ,		metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}				)
 __lowercase				:		Optional[str]    							=						field(
     default=snake_case_    ,		metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}				)


def 			_lowercase       (				):
 __A  :		Dict       =			HfArgumentParser((ModelArguments,)			)
 ((__A)     ,)  :		Optional[Any]       =			parser.parse_args_into_dataclasses()

 # Load pretrained model and tokenizer

 # Use explicit specified encoder config
 if model_args.encoder_config_name:
  __A  :		Dict       =			AutoConfig.from_pretrained(model_args.encoder_config_name			)
 # Use pretrained encoder model's config
 else:
  __A  :		Optional[Any]       =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path			)

 # Use explicit specified decoder config
 if model_args.decoder_config_name:
  __A  :		Dict       =			AutoConfig.from_pretrained(model_args.decoder_config_name			)
 # Use pretrained decoder model's config
 else:
  __A  :		Tuple       =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path			)

 # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
 __A  :		Dict       =			True
 __A  :		int       =			True

 __A  :		str       =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
     encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,				decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,				encoder_config=UpperCamelCase__,				decoder_config=UpperCamelCase__,				)

 # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
 __A  :		List[str]       =			decoder_config.decoder_start_token_id
 __A  :		Tuple       =			decoder_config.pad_token_id
 if decoder_start_token_id is None:
  __A  :		List[str]       =			decoder_config.bos_token_id
 if pad_token_id is None:
  __A  :		Optional[int]       =			decoder_config.eos_token_id

 # This is necessary to make Flax's generate() work
 __A  :		Optional[int]       =			decoder_config.eos_token_id
 __A  :		str       =			decoder_start_token_id
 __A  :		Union[str, Any]       =			pad_token_id

 __A  :		Any       =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path			)

 __A  :		List[str]       =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path			)
 __A  :		Union[str, Any]       =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id			)

 model.save_pretrained(model_args.output_dir			)
 image_processor.save_pretrained(model_args.output_dir			)
 tokenizer.save_pretrained(model_args.output_dir			)


if __name__ == "__main__":
 main()










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

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


def 			_lowercase       (				):
 __A     ,__A  :		Optional[int]       =			get_dataset(UpperCamelCase__,				UpperCamelCase__			)
 print('Processing...'			)
 __A     ,__A     ,__A  :		Any       =			update_image_and_anno(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

  __A  :		Optional[int]       =			[]
  for obj_list in obj_lists:
   __A  :		str       =			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 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					list,				UpperCamelCase__       :					int = 1			):
 __A  :		int       =			[]
 __A  :		Optional[Any]       =			[]
 __A  :		str       =			[]
 for idx in range(len(UpperCamelCase__			)			):
  __A  :		List[Any]       =			[]
  __A  :		List[str]       =			img_list[idx]
  path_list.append(UpperCamelCase__			)
  __A  :		Optional[Any]       =			anno_list[idx]
  __A  :		Union[str, Any]       =			cva.imread(UpperCamelCase__			)
  if flip_type == 1:
   __A  :		int       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Union[str, Any]       =			1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
  elif flip_type == 0:
   __A  :		Tuple       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Dict       =			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 			_lowercase       (				UpperCamelCase__       :					int = 32			):
 assert number_char > 1, "The number of character should greater than 1"
 __A  :		int       =			ascii_lowercase + digits
 return "".join(random.choice(UpperCamelCase__			) for _ in range(UpperCamelCase__			)			)


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






