'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:	Optional[Any]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	List[Any]    ={'vocab_file': 'sentencepiece.model'}

SCREAMING_SNAKE_CASE_:	List[Any]    ={
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

SCREAMING_SNAKE_CASE_:	Tuple    ={
    'google/rembert': 2_56,
}




class 			__A (					UpperCamelCase__      ):
    a__			:		Any			  =    VOCAB_FILES_NAMES
    a__			:		Tuple			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		Optional[int]			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(self       :       Union[str, Any]  ,     __a       :       Tuple  ,     __a       :       Union[str, Any]=False  ,     __a       :       Union[str, Any]=True  ,     __a       :       Optional[Any]=True  ,     __a       :       str="[CLS]"  ,     __a       :       Optional[Any]="[SEP]"  ,     __a       :       List[str]="[UNK]"  ,     __a       :       Optional[int]="[SEP]"  ,     __a       :       List[Any]="[PAD]"  ,     __a       :       int="[CLS]"  ,     __a       :       Dict="[MASK]"  ,     **__a       :       Optional[int]  ,     ):
         super().__init__(
             do_lower_case=__a  ,     remove_space=__a  ,     keep_accents=__a  ,     bos_token=__a  ,     eos_token=__a  ,     unk_token=__a  ,     sep_token=__a  ,     pad_token=__a  ,     cls_token=__a  ,     mask_token=__a  ,     **__a  ,     )

         UpperCAmelCase_			       =  do_lower_case
         UpperCAmelCase_			       =  remove_space
         UpperCAmelCase_			       =  keep_accents
         UpperCAmelCase_			       =  vocab_file

         UpperCAmelCase_			       =  spm.SentencePieceProcessor()
         self.sp_model.Load(__a					)



    @property
    def    _lowercase		(self       :       Any					):
         return len(self.sp_model					)



    def    _lowercase		(self       :       List[Any]					):
         UpperCAmelCase_			       =  {self.convert_ids_to_tokens(__a					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab



    def __getstate__(self       :       Dict					):
         UpperCAmelCase_			       =  self.__dict__.copy()
         UpperCAmelCase_			       =  None
         return state



    def __setstate__(self       :       str  ,     __a       :       Optional[Any]					):
         UpperCAmelCase_			       =  d
         UpperCAmelCase_			       =  spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file					)



    def    _lowercase		(self       :       Dict  ,     __a       :       Union[str, Any]  ,     __a       :       int=False					):
         UpperCAmelCase_			       =  self.sp_model.EncodeAsPieces(__a					)
         return pieces



    def    _lowercase		(self       :       str  ,     __a       :       Optional[int]					):
         return self.sp_model.PieceToId(__a					)



    def    _lowercase		(self       :       int  ,     __a       :       Optional[int]					):
         return self.sp_model.IdToPiece(__a					)



    def    _lowercase		(self       :       Dict  ,     __a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.sp_model.decode_pieces(__a					)
         return out_string



    def    _lowercase		(self       :       int  ,     __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 cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep



    def    _lowercase		(self       :       List[Any]  ,     __a       :       List[int]  ,     __a       :       Optional[List[int]] = None  ,     __a       :       bool = False					):

         if already_has_special_tokens:
              if token_ids_a is not None:
                   raise ValueError(
                       "You should not supply a second sequence if the provided sequence of "
                       "ids is already formatted with special tokens for the model."					)
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
              return [1] + ([0] * len(__a					)) + [1] + ([0] * len(__a					)) + [1]
         return [1] + ([0] * len(__a					)) + [1]



    def    _lowercase		(self       :       Optional[int]  ,     __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					) * [0] + len(token_ids_a + sep					) * [1]






    def    _lowercase		(self       :       Tuple  ,     __a       :       str  ,     __a       :       Optional[str] = None					):
         if not os.path.isdir(__a					):
              logger.error("Vocabulary path ({}) should be a directory".format(__a					)					)
              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					):
              copyfile(self.vocab_file  ,     __a					)

         return (out_vocab_file,)





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


