
'''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


a__						:      Dict   				=				logging.get_logger(__name__)

a__						:      List[str]   				=				{"vocab_file": "sentencepiece.bpe.model"}

a__						:      List[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"
        ),
    },
}

a__						:      Any   				=				{
    "moussaKam/mbarthez": 1_0_2_4,
    "moussaKam/barthez": 1_0_2_4,
    "moussaKam/barthez-orangesum-title": 1_0_2_4,
}

a__						:      List[Any]   				=				"▁"


class        UpperCamelCase__     (			__lowerCamelCase):
 UpperCAmelCase__			:			Optional[Any]   =    VOCAB_FILES_NAMES
 UpperCAmelCase__			:			Dict   =    PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__			:			int   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__			:			Optional[Any]   =    ['input_ids', 'attention_mask']






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







       '''simple docstring'''



       __A						=	AddedToken(UpperCamelCase_		, lstrip=UpperCamelCase_		, rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_		, UpperCamelCase_	) else mask_token

       __A						=	{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=UpperCamelCase_		, eos_token=UpperCamelCase_		, unk_token=UpperCamelCase_		, sep_token=UpperCamelCase_		, cls_token=UpperCamelCase_		, pad_token=UpperCamelCase_		, mask_token=UpperCamelCase_		, sp_model_kwargs=self.sp_model_kwargs		, **UpperCamelCase_		, )

       __A						=	vocab_file
       __A						=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(UpperCamelCase_	)	)

       __A						=	{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

       __A						=	len(self.sp_model	) - 1
       __A						=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






 def 		lowercase_  (	self     :Dict		, _A     :List[int]		, _A     :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]
       __A						=	[self.cls_token_id]
       __A						=	[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






 def 		lowercase_  (	self     :Dict		, _A     :List[int]		, _A     :Optional[List[int]] = None		, _A     :bool = False	) ->	Optional[Any]:







       '''simple docstring'''



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

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






 def 		lowercase_  (	self     :int		, _A     :List[int]		, _A     :Optional[List[int]] = None	) ->	str:







       '''simple docstring'''



       __A						=	[self.sep_token_id]
       __A						=	[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 		lowercase_  (	self     :str	) ->	List[str]:







       '''simple docstring'''



       return len(self.sp_model	)






 def 		lowercase_  (	self     :Optional[int]	) ->	Any:







       '''simple docstring'''



       __A						=	{self.convert_ids_to_tokens(UpperCamelCase_	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab






 def 		lowercase_  (	self     :Dict		, _A     :str	) ->	int:







       '''simple docstring'''



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






 def 		lowercase_  (	self     :int		, _A     :Optional[int]	) ->	Dict:







       '''simple docstring'''



       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       __A						=	self.sp_model.PieceToId(UpperCamelCase_	)

       return spm_id if spm_id else self.unk_token_id






 def 		lowercase_  (	self     :str		, _A     :int	) ->	Optional[Any]:







       '''simple docstring'''



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






 def 		lowercase_  (	self     :Dict		, _A     :int	) ->	str:







       '''simple docstring'''



       __A						=	[]
       __A						=	''
       __A						=	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(UpperCamelCase_	) + token
                   __A						=	True
                   __A						=	[]
             else:
                   current_sub_tokens.append(UpperCamelCase_	)
                   __A						=	False
       out_string += self.sp_model.decode(UpperCamelCase_	)
       return out_string.strip()






 def __getstate__(	self     :str	) ->	int:







       '''simple docstring'''



       __A						=	self.__dict__.copy()
       __A						=	None
       return state






 def __setstate__(	self     :Optional[int]		, _A     :List[Any]	) ->	List[Any]:







       '''simple docstring'''



       __A						=	d

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

       __A						=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(self.vocab_file	)






 def 		lowercase_  (	self     :Tuple		, _A     :str		, _A     :Optional[str] = None	) ->	Any:







       '''simple docstring'''



       if not os.path.isdir(UpperCamelCase_	):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
             return
       __A						=	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_	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file		, UpperCamelCase_	)
       elif not os.path.isfile(self.vocab_file	):
             with open(UpperCamelCase_		, 'wb'	) as fi:
                   __A						=	self.sp_model.serialized_model_proto()
                   fi.write(UpperCamelCase_	)

       return (out_vocab_file,)












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizer
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizerFast
      UpperCAmelCase__      :	Tuple    		=   True
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""   )

             __lowerCamelCase   =						tokenizer.encode("""sequence builders"""    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.encode("""multi-sequence build"""    ,       add_special_tokens=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,       UpperCamelCase_   )

             assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
             assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
                 tokenizer.sep_token_id
             ]

