





'''simple docstring'''






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

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


if is_sentencepiece_available():
      from .tokenization_barthez import BarthezTokenizer
else:
      __lowerCamelCase						: Tuple		=   None

__lowerCamelCase						: List[Any]		=   logging.get_logger(__name__)

__lowerCamelCase						: List[str]		=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Optional[Any]		=   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase						: Any		=   {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__lowerCamelCase						: Any		=   '▁'







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]
  lowerCAmelCase_					=				BarthezTokenizer


  def __init__(				self    :					str							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					int=None							,	UpperCamelCase__    :					Optional[Any]="<s>"							,	UpperCamelCase__    :					List[str]="</s>"							,	UpperCamelCase__    :					str="</s>"							,	UpperCamelCase__    :					Optional[int]="<s>"							,	UpperCamelCase__    :					Optional[Any]="<unk>"							,	UpperCamelCase__    :					Optional[Any]="<pad>"							,	UpperCamelCase__    :					Optional[Any]="<mask>"							,	**UpperCamelCase__    :					str							,	)     ->						List[str]:
    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else mask_token

    super().__init__(
        UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	bos_token=UpperCamelCase__							,	eos_token=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=vocab_file
    _UpperCamelCase       			=False if not self.vocab_file else True


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:

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


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[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]




  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    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(UpperCamelCase__  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    _UpperCamelCase       			=os.path.join(
        UpperCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

    if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase__  ):
      copyfile(self.vocab_file							,	UpperCamelCase__  )

    return (out_vocab_file,)













'''simple docstring'''






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)







class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				None




@experimental
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  return _map_with_joblib(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE   ) else len(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[]  # We organize the splits ourselve (contiguous splits)
  for index in range(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) // num_proc
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) % num_proc
    _UpperCamelCase       			=div * index + min(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

  if len(__SCREAMING_SNAKE_CASE   ) != sum(len(i[1]   ) for i in split_kwds   ):
    raise ValueError(
        f'''Error dividing inputs iterable among processes. '''
        f'''Total number of objects {len(__SCREAMING_SNAKE_CASE   )}, '''
        f'''length: {sum(len(i[1]   ) for i in split_kwds   )}'''   )

  logger.info(
      f'''Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}'''   )
  _UpperCamelCase			,     _UpperCamelCase       			=None, None
  if not disable_tqdm:
    _UpperCamelCase			,     _UpperCamelCase       			=(RLock(),), tqdm.set_lock
  with Pool(__SCREAMING_SNAKE_CASE  ,      initargs=__SCREAMING_SNAKE_CASE  ,      initializer=__SCREAMING_SNAKE_CASE   ) as pool:
    _UpperCamelCase       			=pool.map(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  logger.info(f'''Finished {num_proc} processes'''   )
  _UpperCamelCase       			=[obj for proc_res in mapped for obj in proc_res]
  logger.info(f'''Unpacked {len(__SCREAMING_SNAKE_CASE   )} objects'''   )

  return mapped


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name  ,      n_jobs=__SCREAMING_SNAKE_CASE   ):
    return joblib.Parallel()(
        joblib.delayed(__SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _UpperCamelCase       			=None






