






'''simple docstring'''



import glob
import os
import random
from string import ascii_lowercase, digits

import cva
SCREAMING_SNAKE_CASE							=						''
SCREAMING_SNAKE_CASE							=						''
SCREAMING_SNAKE_CASE							=						''
SCREAMING_SNAKE_CASE							=						1  # (0 is vertical, 1 is horizontal)


def 				lowercase_					(     )							->   None:



 """simple docstring"""




 lowercase	,		lowercase       : int						=get_dataset(__A       ,   __A      )
 print('''Processing...'''      )
 lowercase	,		lowercase	,		lowercase       : int						=update_image_and_anno(__A       ,   __A       ,   __A      )

 for index, image in enumerate(__A      ):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  lowercase       : Any						=random_chars(3_2      )
  lowercase       : Optional[int]						=paths[index].split(os.sep      )[-1].rsplit('''.'''       ,   1      )[0]
  lowercase       : Optional[Any]						=F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
  cva.imwrite(F'/{file_root}.jpg'       ,   __A       ,   [cva.IMWRITE_JPEG_QUALITY, 8_5]      )
  print(F'Success {index+1}/{len(__A      )} with {file_name}'      )
  lowercase       : Optional[Any]						=[]
  for anno in new_annos[index]:
   lowercase       : Tuple						=F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
   annos_list.append(__A      )
  with open(F'/{file_root}.txt'       ,   '''w'''      ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list      )      )


def 				lowercase_					(     __A			:							str       ,   __A			:							str      )							->   tuple[list, list]:



 """simple docstring"""




 lowercase       : List[str]						=[]
 lowercase       : int						=[]
 for label_file in glob.glob(os.path.join(__A       ,   '''*.txt'''      )      ):
  lowercase       : Any						=label_file.split(os.sep      )[-1].rsplit('''.'''       ,   1      )[0]
  with open(__A      ) as in_file:
   lowercase       : Optional[int]						=in_file.readlines()
  lowercase       : int						=os.path.join(__A       ,   F'{label_name}.jpg'      )

  lowercase       : Optional[Any]						=[]
  for obj_list in obj_lists:
   lowercase       : Dict						=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(__A      )
  labels.append(__A      )
 return img_paths, labels


def 				lowercase_					(     __A			:							list       ,   __A			:							list       ,   __A			:							int = 1      )							->   tuple[list, list, list]:



 """simple docstring"""




 lowercase       : Optional[int]						=[]
 lowercase       : Optional[Any]						=[]
 lowercase       : List[str]						=[]
 for idx in range(len(__A      )      ):
  lowercase       : Optional[Any]						=[]
  lowercase       : Optional[Any]						=img_list[idx]
  path_list.append(__A      )
  lowercase       : Any						=anno_list[idx]
  lowercase       : Any						=cva.imread(__A      )
  if flip_type == 1:
   lowercase       : Dict						=cva.flip(__A       ,   __A      )
   for bbox in img_annos:
    lowercase       : Any						=1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]      )
  elif flip_type == 0:
   lowercase       : Dict						=cva.flip(__A       ,   __A      )
   for bbox in img_annos:
    lowercase       : Optional[int]						=1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]      )
  new_annos_lists.append(__A      )
  new_imgs_list.append(__A      )
 return new_imgs_list, new_annos_lists, path_list


def 				lowercase_					(     __A			:							int = 3_2      )							->   str:



 """simple docstring"""




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


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





"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {'vocab_file': 'sentencepiece.bpe.model'}

__SCREAMING_SNAKE_CASE							=     {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__SCREAMING_SNAKE_CASE							=     {
    'camembert-base': 512,
}

__SCREAMING_SNAKE_CASE							=     '▁'





class 				a__					(						A__       ):
     UpperCAmelCase__         =      VOCAB_FILES_NAMES
     UpperCAmelCase__         =      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__         =      ['''input_ids''', '''attention_mask''']



     def __init__(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :List[Any]="<s>"      ,  _lowerCamelCase :List[Any]="</s>"      ,  _lowerCamelCase :Optional[int]="</s>"      ,  _lowerCamelCase :List[Any]="<s>"      ,  _lowerCamelCase :List[Any]="<unk>"      ,  _lowerCamelCase :Union[str, Any]="<pad>"      ,  _lowerCamelCase :int="<mask>"      ,  _lowerCamelCase :Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,  _lowerCamelCase :Optional[Dict[str, Any]] = None      ,  **_lowerCamelCase :Optional[int]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      UpperCamelCase_     :					List[str]   	={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  additional_special_tokens=_lowerCamelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCamelCase      ,  )
      UpperCamelCase_     :					Optional[int]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(_lowerCamelCase   )   )
      UpperCamelCase_     :					str   	=vocab_file
      # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
      # sentencepiece vocabulary (this is the case for <s> and </s>
      UpperCamelCase_     :					Tuple   	={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      UpperCamelCase_     :					List[str]   	=len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					List[str]   	=len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					Any   	={v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase_     :					Any   	=[self.cls_token_id]
      UpperCamelCase_     :					str   	=[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase      ,  token_ids_a=_lowerCamelCase      ,  already_has_special_tokens=_lowerCamelCase   )

      if token_ids_a is None:
       return [1] + ([0] * len(_lowerCamelCase   )) + [1]
      return [1] + ([0] * len(_lowerCamelCase   )) + [1, 1] + ([0] * len(_lowerCamelCase   )) + [1]



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					Dict   	=[self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



     @property
     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	={self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return self.sp_model.encode(_lowerCamelCase      ,  out_type=_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      elif self.sp_model.PieceToId(_lowerCamelCase   ) == 0:
       # Convert sentence piece unk token to fairseq unk token index
       return self.unk_token_id
      return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset   )



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[]
      UpperCamelCase_     :					Optional[Any]   	=''
      UpperCamelCase_     :					Optional[Any]   	=False
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        if not prev_is_special:
         out_string += " "
        out_string += self.sp_model.decode(_lowerCamelCase   ) + token
        UpperCamelCase_     :					Union[str, Any]   	=True
        UpperCamelCase_     :					Optional[Any]   	=[]
       else:
        current_sub_tokens.append(_lowerCamelCase   )
        UpperCamelCase_     :					Any   	=False
      out_string += self.sp_model.decode(_lowerCamelCase   )
      return out_string.strip()



     def __getstate__(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.__dict__.copy()
      UpperCamelCase_     :					str   	=None
      return state



     def __setstate__(							self :Optional[Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=d

      # for backward compatibility
      if not hasattr(self      ,  'sp_model_kwargs'   ):
       UpperCamelCase_     :					Any   	={}

      UpperCamelCase_     :					List[Any]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      if not os.path.isdir(_lowerCamelCase   ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
       return
      UpperCamelCase_     :					Any   	=os.path.join(
          _lowerCamelCase      ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(_lowerCamelCase   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file      ,  _lowerCamelCase   )
      elif not os.path.isfile(self.vocab_file   ):
       with open(_lowerCamelCase      ,  'wb'   ) as fi:
        UpperCamelCase_     :					int   	=self.sp_model.serialized_model_proto()
        fi.write(_lowerCamelCase   )

      return (out_vocab_file,)
