





'''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__							:							int =logging.get_logger(__name__)

a__							:							Any ={'''vocab_file''': '''sentencepiece.bpe.model'''}

a__							:							List[str] ={
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

a__							:							int ={
    '''camembert-base''': 512,
}

a__							:							List[Any] ='''▁'''

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					int					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					str					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					int					=["input_ids", "attention_mask"]






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

        __UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__A							,	eos_token=__A							,	unk_token=__A							,	sep_token=__A							,	cls_token=__A							,	pad_token=__A							,	mask_token=__A							,	additional_special_tokens=__A							,	sp_model_kwargs=self.sp_model_kwargs							,	**__A							,	)
        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(__A	)	)
        __UpperCamelCase       =  vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        __UpperCamelCase       =  {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
        __UpperCamelCase       =  len(self.fairseq_tokens_to_ids	)
        __UpperCamelCase       =  len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
        __UpperCamelCase       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):

        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        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None							,	__A       :							bool = False	):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__A							,	token_ids_a=__A							,	already_has_special_tokens=__A	)

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






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        __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]






 @property
 def        _lowerCamelCase					(       self       :							Any	):
        return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  {self.convert_ids_to_tokens(__A	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							str	):
        return self.sp_model.encode(__A							,	out_type=__A	)






 def        _lowerCamelCase					(       self       :							str							,	__A       :							List[str]	):
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(__A	) == 0:
               # Convert sentence piece unk token to fairseq unk token index
               return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(__A	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Union[str, Any]	):
        if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[str]	):
        __UpperCamelCase       =  []
        __UpperCamelCase       =  ''
        __UpperCamelCase       =  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(__A	) + token
                      __UpperCamelCase       =  True
                      __UpperCamelCase       =  []
               else:
                      current_sub_tokens.append(__A	)
                      __UpperCamelCase       =  False
        out_string += self.sp_model.decode(__A	)
        return out_string.strip()






 def __getstate__(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.__dict__.copy()
        __UpperCamelCase       =  None
        return state






 def __setstate__(       self       :							Tuple							,	__A       :							Tuple	):
        __UpperCamelCase       =  d

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

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






 def        _lowerCamelCase					(       self       :							int							,	__A       :							str							,	__A       :							Optional[str] = None	):
        if not os.path.isdir(__A	):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
               return
        __UpperCamelCase       =  os.path.join(
            __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

        if os.path.abspath(self.vocab_file	) != os.path.abspath(__A	) and os.path.isfile(self.vocab_file	):
               copyfile(self.vocab_file							,	__A	)
        elif not os.path.isfile(self.vocab_file	):
               with open(__A							,	'wb'	) as fi:
                      __UpperCamelCase       =  self.sp_model.serialized_model_proto()
                      fi.write(__A	)

        return (out_vocab_file,)







'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
