




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_camembert import CamembertTokenizer
else:
	_lowerCamelCase   :	str       =    None


_lowerCamelCase   :	Tuple       =    logging.get_logger(__name__)

_lowerCamelCase   :	Tuple       =    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase   :	Union[str, Any]       =    {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

_lowerCamelCase   :	Any       =    {
    "camembert-base": 5_1_2,
}

_lowerCamelCase   :	str       =    "▁"
class 							__snake_case	(_a		):
				lowerCAmelCase__      						=  VOCAB_FILES_NAMES
				lowerCAmelCase__      						=  PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__      						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__      						=  ["input_ids", "attention_mask"]
				lowerCAmelCase__      						=  CamembertTokenizer



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





								'''simple docstring'''




								_lowerCAmelCase     :     Optional[int]              =   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		,						sep_token=_UpperCAmelCase		,						cls_token=_UpperCAmelCase		,						unk_token=_UpperCAmelCase		,						pad_token=_UpperCAmelCase		,						mask_token=_UpperCAmelCase		,						additional_special_tokens=_UpperCAmelCase		,						**_UpperCAmelCase		,						)

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



				def    SCREAMING_SNAKE_CASE							(   self   :		Union[str, Any]		,						_UpperCAmelCase   :		List[int]		,						_UpperCAmelCase   :		Optional[List[int]] = None   )	->     Tuple:





								'''simple docstring'''




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



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





								'''simple docstring'''




								_lowerCAmelCase     :     str              =   [self.sep_token_id]
								_lowerCAmelCase     :     List[Any]              =   [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    SCREAMING_SNAKE_CASE							(   self   :		List[str]		,						_UpperCAmelCase   :		str		,						_UpperCAmelCase   :		Optional[str] = None   )	->     List[str]:





								'''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(_UpperCAmelCase   ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
												return
								_lowerCAmelCase     :     int              =   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,)







import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__UpperCamelCase   :			Optional[int]          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Union[str, Any]						, *__magic_name__		:int						, **__magic_name__		:str							):







								'''simple docstring'''







								warnings.warn(
								    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
								    """ Please use LayoutLMv2ImageProcessor instead."""						, __magic_name__						, )
								super().__init__(*__magic_name__						, **__magic_name__							)

