






"""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_rembert import RemBertTokenizer
else:
					UpperCamelCase							       =    None

UpperCamelCase							       =    logging.get_logger(__name__)
UpperCamelCase							       =    {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase							       =    {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase							       =    {
    """google/rembert""": 256,
}

UpperCamelCase							       =    """▁"""




class    lowercase_    (__A     ):
						A__	:		Dict       			=				VOCAB_FILES_NAMES
						A__	:		Optional[int]       			=				PRETRAINED_VOCAB_FILES_MAP
						A__	:		int       			=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A__	:		List[str]       			=				RemBertTokenizer






						def __init__(    self     ,     a_=None     ,     a_=None     ,     a_=True     ,     a_=True     ,     a_=False     ,     a_="[CLS]"     ,     a_="[SEP]"     ,     a_="<unk>"     ,     a_="[SEP]"     ,     a_="<pad>"     ,     a_="[CLS]"     ,     a_="[MASK]"     ,     **a_     ,     )	->Any:







								'''simple docstring'''


								_a							=	AddedToken(UpperCamelCase__     ,     lstrip=UpperCamelCase__     ,     rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__     ,     UpperCamelCase__	) else mask_token

								super().__init__(
								    UpperCamelCase__     ,     tokenizer_file=UpperCamelCase__     ,     do_lower_case=UpperCamelCase__     ,     remove_space=UpperCamelCase__     ,     keep_accents=UpperCamelCase__     ,     bos_token=UpperCamelCase__     ,     eos_token=UpperCamelCase__     ,     unk_token=UpperCamelCase__     ,     sep_token=UpperCamelCase__     ,     pad_token=UpperCamelCase__     ,     cls_token=UpperCamelCase__     ,     mask_token=UpperCamelCase__     ,     **UpperCamelCase__     ,     )

								_a							=	do_lower_case
								_a							=	remove_space
								_a							=	keep_accents
								_a							=	vocab_file
								_a							=	False if not self.vocab_file else True






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None	)	->str:







								'''simple docstring'''


								_a							=	[self.sep_token_id]
								_a							=	[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 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None     ,     a_ = False	)	->Optional[int]:







								'''simple docstring'''


								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(UpperCamelCase__	)) + [1] + ([0] * len(UpperCamelCase__	)) + [1]
								return [1] + ([0] * len(UpperCamelCase__	)) + [1]






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None	)	->List[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	) * [0] + len(token_ids_a + sep	) * [1]






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None	)	->List[Any]:







								'''simple docstring'''


								if not os.path.isdir(UpperCamelCase__	):
										logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__	)	)
										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__	):
										copyfile(self.vocab_file     ,     UpperCamelCase__	)

								return (out_vocab_file,)










"""simple docstring"""




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def        lowerCAmelCase (      UpperCamelCase_:							Features					)       ->							Optional[int]:







  '''simple docstring'''







  _a							=	np.inf

  def set_batch_size(UpperCamelCase_:							FeatureType					) -> None:
    nonlocal batch_size
    if isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
    elif isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
    elif isinstance(UpperCamelCase_     , UpperCamelCase_					) and feature.dtype == "binary":
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

  _visit(UpperCamelCase_     , UpperCamelCase_					)

  return None if batch_size is np.inf else batch_size




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     a_     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = False     ,     a_ = False     ,     a_ = None     ,     **a_     ,     )	->Optional[int]:







        '''simple docstring'''


        super().__init__(
            a_     ,     split=a_     ,     features=a_     ,     cache_dir=a_     ,     keep_in_memory=a_     ,     streaming=a_     ,     num_proc=a_     ,     **a_     ,     )
        _a							=	path_or_paths if isinstance(a_     ,     a_	) else {self.split: path_or_paths}
        _a							=	_PACKAGED_DATASETS_MODULES["parquet"][1]
        _a							=	Parquet(
            cache_dir=a_     ,     data_files=a_     ,     features=a_     ,     hash=a_     ,     **a_     ,     )






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        if self.streaming:
          _a							=	self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
          _a							=	None
          _a							=	None
          _a							=	None
          _a							=	None

          self.builder.download_and_prepare(
              download_config=a_     ,     download_mode=a_     ,     verification_mode=a_     ,     base_path=a_     ,     num_proc=self.num_proc     ,     )
          _a							=	self.builder.as_dataset(
              split=self.split     ,     verification_mode=a_     ,     in_memory=self.keep_in_memory	)
        return dataset







class    lowercase_    :






      def __init__(    self     ,     a_     ,     a_     ,     a_ = None     ,     **a_     ,     )	->int:







        '''simple docstring'''


        _a							=	dataset
        _a							=	path_or_buf
        _a							=	batch_size or get_writer_batch_size(dataset.features	)
        _a							=	parquet_writer_kwargs






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf     ,     (str, bytes, os.PathLike)	):
          with open(self.path_or_buf     ,     "wb+"	) as buffer:
            _a							=	self._write(file_obj=a_     ,     batch_size=a_     ,     **self.parquet_writer_kwargs	)
        else:
          _a							=	self._write(file_obj=self.path_or_buf     ,     batch_size=a_     ,     **self.parquet_writer_kwargs	)
        return written






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     **a_	)	->int:







        '''simple docstring'''


        _a							=	0
        _a							=	parquet_writer_kwargs.pop("path_or_buf"     ,     a_	)
        _a							=	self.dataset.features.arrow_schema

        _a							=	pq.ParquetWriter(a_     ,     schema=a_     ,     **a_	)

        for offset in logging.tqdm(
            range(0     ,     len(self.dataset	)     ,     a_	)     ,     unit="ba"     ,     disable=not logging.is_progress_bar_enabled()     ,     desc="Creating parquet from Arrow format"     ,     ):
          _a							=	query_table(
              table=self.dataset._data     ,     key=slice(a_     ,     offset + batch_size	)     ,     indices=self.dataset._indices if self.dataset._indices is not None else None     ,     )
          writer.write_table(a_	)
          written += batch.nbytes
        writer.close()
        return written


