


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


lowerCAmelCase       =							logging.get_logger(__name__)

lowerCAmelCase       =							{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase       =							{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase       =							{
    'camembert-base': 512,
}

lowerCAmelCase       =							'▁'


class        _a							(			UpperCamelCase__			):
							_lowercase	:			List[Any]        = VOCAB_FILES_NAMES
							_lowercase	:			List[Any]        = PRETRAINED_VOCAB_FILES_MAP
							_lowercase	:			Union[str, Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase	:			Optional[Any]        = ['''input_ids''', '''attention_mask''']

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





									"""simple docstring"""







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

									lowercase__            =      {} 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_    ,						additional_special_tokens=UpperCamelCase_    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**UpperCamelCase_    ,						)
									lowercase__            =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(str(UpperCamelCase_							)							)
									lowercase__            =      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>
									lowercase__            =      {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
									lowercase__            =      len(self.fairseq_tokens_to_ids							)
									lowercase__            =      len(self.sp_model							) + len(self.fairseq_tokens_to_ids							)
									lowercase__            =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	List[int]    ,						UpperCamelCase_:	Optional[List[int]] = None							)			->   List[int]:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	List[int]    ,						UpperCamelCase_:	Optional[List[int]] = None    ,						UpperCamelCase_:	bool = False							)			->   List[int]:





									"""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       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	List[int]    ,						UpperCamelCase_:	Optional[List[int]] = None							)			->   List[int]:





									"""simple docstring"""







									lowercase__            =      [self.sep_token_id]
									lowercase__            =      [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:	Union[str, Any]							)			->   List[Any]:





									"""simple docstring"""







									return len(self.fairseq_tokens_to_ids							) + len(self.sp_model							)

							def       lowerCamelCase_ (							self:	List[str]							)			->   int:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	str							)			->   List[str]:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Optional[Any]							)			->   Optional[Any]:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	List[str]							)			->   Dict:





									"""simple docstring"""







									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:	List[str]    ,						UpperCamelCase_:	Optional[int]							)			->   List[Any]:





									"""simple docstring"""







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

							def __getstate__(							self:	List[str]							)			->   Union[str, Any]:





									"""simple docstring"""







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

							def __setstate__(							self:	Any    ,						UpperCamelCase_:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      d

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

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


							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Optional[str] = None							)			->   Tuple[str]:





									"""simple docstring"""







									if not os.path.isdir(UpperCamelCase_							):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
											return
									lowercase__            =      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:
													lowercase__            =      self.sp_model.serialized_model_proto()
													fi.write(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 				lowercase__   (   __lowercase						:  Features     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  np.inf

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

       _visit(__lowercase				,  __lowercase     )

       return None if batch_size is np.inf else batch_size

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							NestedDataStructureLike[PathLike]							,	__A       :							Optional[NamedSplit] = None							,	__A       :							Optional[Features] = None							,	__A       :							str = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        super().__init__(
            __A							,	split=__A							,	features=__A							,	cache_dir=__A							,	keep_in_memory=__A							,	streaming=__A							,	num_proc=__A							,	**__A							,	)
        __UpperCamelCase       =  path_or_paths if isinstance(__A							,	__A	) else {self.split: path_or_paths}
        __UpperCamelCase       =  _PACKAGED_DATASETS_MODULES['parquet'][1]
        __UpperCamelCase       =  Parquet(
            cache_dir=__A							,	data_files=__A							,	features=__A							,	hash=__A							,	**__A							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Build iterable dataset
        if self.streaming:
               __UpperCamelCase       =  self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None

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






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							Dataset							,	__A       :							Union[PathLike, BinaryIO]							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  path_or_buf
        __UpperCamelCase       =  batch_size or get_writer_batch_size(dataset.features	)
        __UpperCamelCase       =  parquet_writer_kwargs






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  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:
                      __UpperCamelCase       =  self._write(file_obj=__A							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        else:
               __UpperCamelCase       =  self._write(file_obj=self.path_or_buf							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        return written






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							BinaryIO							,	__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  0
        __UpperCamelCase       =  parquet_writer_kwargs.pop('path_or_buf'							,	__A	)
        __UpperCamelCase       =  self.dataset.features.arrow_schema

        __UpperCamelCase       =  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'							,	):
               __UpperCamelCase       =  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
