


'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 UpperCAmelCase_ :     Tuple								=      None


UpperCAmelCase_ :     Any								=      logging.get_logger(__name__)

UpperCAmelCase_ :     int								=      {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_ :     Optional[Any]								=      {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ :     Dict								=      {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple   							=						VOCAB_FILES_NAMES
 __lowercase				:		Optional[Any]   							=						PRETRAINED_VOCAB_FILES_MAP
 __lowercase				:		int   							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase				:		Optional[int]   							=						['''input_ids''', '''attention_mask''']
 __lowercase				:		Optional[int]   							=						TaTokenizer

 __lowercase				:		List[int]   							=						[]


 def __init__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase="</s>"      ,      __lowercase="<unk>"      ,      __lowercase="<pad>"      ,      __lowercase=100      ,      __lowercase=None      ,      **__lowercase      ,      ):

  """simple docstring"""







  if extra_ids > 0 and additional_special_tokens is None:
   __A  :		List[Any]       =			[F"""<extra_id_{i}>""" for i in range(__lowercase						)]
  elif extra_ids > 0 and additional_special_tokens is not None:
   # Check that we have the right number of extra special tokens
   __A  :		Union[str, Any]       =			len(set(filter(lambda __lowercase						: bool('extra_id_' in str(__lowercase						)						)      ,      __lowercase						)						)						)
   if extra_tokens != extra_ids:
    raise ValueError(
        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
        ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
        ' tokens'						)

  super().__init__(
      __lowercase      ,      tokenizer_file=__lowercase      ,      eos_token=__lowercase      ,      unk_token=__lowercase      ,      pad_token=__lowercase      ,      extra_ids=__lowercase      ,      additional_special_tokens=__lowercase      ,      **__lowercase      ,      )

  __A  :		List[str]       =			vocab_file
  __A  :		Any       =			False if not self.vocab_file else True
  __A  :		List[Any]       =			extra_ids


 @staticmethod
 def  snake_case__   (      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
   __A  :		str       =			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
   if init_max_model_length is not None and init_max_model_length != max_model_length:
    return init_max_model_length
   elif init_max_model_length is None:
    warnings.warn(
        'This tokenizer was incorrectly instantiated with a model max length of'
        F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
        ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
        ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
        F""" {pretrained_model_name_or_path} automatically truncating your input to"""
        F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
        F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
        ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
        ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,      __lowercase      ,      )

  return max_model_length


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  if not self.can_save_slow_tokenizer:
   raise ValueError(
       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
       'tokenizer.'						)

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

  if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowercase						):
   copyfile(self.vocab_file      ,      __lowercase						)
   logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

  return (out_vocab_file,)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		Optional[Any]       =			token_ids_a + [self.eos_token_id]
  if token_ids_a is None:
   return self.prefix_tokens + token_ids_a
  else:
   __A  :		List[str]       =			token_ids_a + [self.eos_token_id]
   return self.prefix_tokens + token_ids_a + token_ids_a


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		List[Any]       =			[self.eos_token_id]

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


 def  snake_case__   (      self						):

  """simple docstring"""







  return list(
      set(filter(lambda __lowercase						: bool(re.search(R'<extra_id_\d+>'      ,      __lowercase						)						) is not None      ,      self.additional_special_tokens						)						)						)







 def  snake_case__   (      self						):

  """simple docstring"""







  return [self.convert_tokens_to_ids(__lowercase						) for token in self.get_sentinel_tokens()]










'''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 ✅')






