


'''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:
 UpperCAmelCase_      :		Any          =		None

UpperCAmelCase_      :		str          =		logging.get_logger(__name__)

UpperCAmelCase_      :		Dict          =		{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_      :		Optional[int]          =		{
    "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"
        ),
    },
}

UpperCAmelCase_      :		Union[str, Any]          =		{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCAmelCase_      :		Any          =		"▁"







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[str]							  =		VOCAB_FILES_NAMES
 __lowerCAmelCase						: Dict							  =		PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase						: Dict							  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase						: List[Any]							  =		["""input_ids""", """attention_mask"""]
 __lowerCAmelCase						: Tuple							  =		BarthezTokenizer




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

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

    _a					:  str						=  vocab_file
    _a					:  Union[str, Any]						=  False if not self.vocab_file else True




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[int]:

    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _a					:  Any						=  [self.cls_token_id]
    _a					:  Optional[int]						=  [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[int]:
    _a					:  str						=  [self.sep_token_id]
    _a					:  int						=  [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		,      lowerCamelCase_		,      lowerCamelCase_ = 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(lowerCamelCase_      ):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _a					:  Optional[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_      ):
       copyfile(self.vocab_file		,      lowerCamelCase_      )

    return (out_vocab_file,)






'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_      :		Tuple          =		logging.get_logger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    None:
    warnings.warn(
        'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
        ' Please use PerceiverImageProcessor instead.'		,      lowerCamelCase_		,      )
    super().__init__(*lowerCamelCase_		,      **lowerCamelCase_      )


