






'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			NestedDataStructureLike[PathLike]  ,						__SCREAMING_SNAKE_CASE      :			Optional[NamedSplit] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Features] = None  ,						__SCREAMING_SNAKE_CASE      :			str = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						**__SCREAMING_SNAKE_CASE      :			Any  ,						)			->			Union[str, Any]:
							super().__init__(
							    __SCREAMING_SNAKE_CASE  ,						split=__SCREAMING_SNAKE_CASE  ,						features=__SCREAMING_SNAKE_CASE  ,						cache_dir=__SCREAMING_SNAKE_CASE  ,						keep_in_memory=__SCREAMING_SNAKE_CASE  ,						streaming=__SCREAMING_SNAKE_CASE  ,						num_proc=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)
							a_		:  List[Any]											=      field
							a_		:  int											=      path_or_paths if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else {self.split: path_or_paths}
							a_		:  Optional[int]											=      Json(
							    cache_dir=__SCREAMING_SNAKE_CASE  ,						data_files=__SCREAMING_SNAKE_CASE  ,						features=__SCREAMING_SNAKE_CASE  ,						field=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Any:
							# Build iterable dataset
							if self.streaming:
											a_		:  List[Any]											=      self.builder.as_streaming_dataset(split=self.split					)
							# Build regular (map-style) dataset
							else:
											a_		:  int											=      None
											a_		:  List[str]											=      None
											a_		:  Dict											=      None
											a_		:  Optional[Any]											=      None

											self.builder.download_and_prepare(
											    download_config=__SCREAMING_SNAKE_CASE  ,						download_mode=__SCREAMING_SNAKE_CASE  ,						verification_mode=__SCREAMING_SNAKE_CASE  ,						base_path=__SCREAMING_SNAKE_CASE  ,						num_proc=self.num_proc  ,						)
											a_		:  Optional[int]											=      self.builder.as_dataset(
											    split=self.split  ,						verification_mode=__SCREAMING_SNAKE_CASE  ,						in_memory=self.keep_in_memory					)
							return dataset





class 	SCREAMING_SNAKE_CASE					:






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dataset  ,						__SCREAMING_SNAKE_CASE      :			Union[PathLike, BinaryIO]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						)			->			Optional[int]:
							if num_proc is not None and num_proc <= 0:
											raise ValueError(f'num_proc {num_proc} must be an integer > 0.'					)

							a_		:  int											=      dataset
							a_		:  List[str]											=      path_or_buf
							a_		:  List[Any]											=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							a_		:  Tuple											=      num_proc
							a_		:  int											=      '''utf-8'''
							a_		:  int											=      to_json_kwargs






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			int:
							a_		:  Any											=      self.to_json_kwargs.pop('''path_or_buf'''  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      self.to_json_kwargs.pop('''orient'''  ,						'''records'''					)
							a_		:  Tuple											=      self.to_json_kwargs.pop('''lines'''  ,						True if orient == '''records''' else False					)
							a_		:  str											=      self.to_json_kwargs.pop('''index'''  ,						False if orient in ['''split''', '''table'''] else True					)
							a_		:  Tuple											=      self.to_json_kwargs.pop('''compression'''  ,						__SCREAMING_SNAKE_CASE					)

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(f'`datasets` currently does not support {compression} compression'					)

							if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)					):
											with fsspec.open(self.path_or_buf  ,						'''wb'''  ,						compression=__SCREAMING_SNAKE_CASE					) as buffer:
															a_		:  int											=      self._write(file_obj=__SCREAMING_SNAKE_CASE  ,						orient=__SCREAMING_SNAKE_CASE  ,						lines=__SCREAMING_SNAKE_CASE  ,						index=__SCREAMING_SNAKE_CASE  ,						**self.to_json_kwargs					)
							else:
											if compression:
															raise NotImplementedError(
															    f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
															    ''' was passed. Please provide a local path instead.'''					)
											a_		:  str											=      self._write(
											    file_obj=self.path_or_buf  ,						orient=__SCREAMING_SNAKE_CASE  ,						lines=__SCREAMING_SNAKE_CASE  ,						index=__SCREAMING_SNAKE_CASE  ,						**self.to_json_kwargs					)
							return written






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Tuple					)			->			Optional[Any]:
							a_				,			a_				,			a_				,			a_				,			a_		:  str											=      args

							a_		:  Tuple											=      query_table(
							    table=self.dataset.data  ,						key=slice(__SCREAMING_SNAKE_CASE  ,						offset + self.batch_size					)  ,						indices=self.dataset._indices  ,						)
							a_		:  Tuple											=      batch.to_pandas().to_json(
							    path_or_buf=__SCREAMING_SNAKE_CASE  ,						orient=__SCREAMING_SNAKE_CASE  ,						lines=__SCREAMING_SNAKE_CASE  ,						index=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
							if not json_str.endswith('''\n'''					):
											json_str += "\n"
							return json_str.encode(self.encoding					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			BinaryIO  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						**__SCREAMING_SNAKE_CASE      :			int  ,						)			->			int:
							a_		:  Optional[int]											=      0

							if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0  ,						len(self.dataset					)  ,						self.batch_size					)  ,						unit='''ba'''  ,						disable=not logging.is_progress_bar_enabled()  ,						desc='''Creating json from Arrow format'''  ,						):
															a_		:  str											=      self._batch_json((offset, orient, lines, index, to_json_kwargs)					)
															written += file_obj.write(__SCREAMING_SNAKE_CASE					)
							else:
											a_				,			a_		:  Union[str, Any]											=      len(self.dataset					), self.batch_size
											with multiprocessing.Pool(self.num_proc					) as pool:
															for json_str in logging.tqdm(
															    pool.imap(
															        self._batch_json  ,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)]  ,						)  ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,						unit='''ba'''  ,						disable=not logging.is_progress_bar_enabled()  ,						desc='''Creating json from Arrow format'''  ,						):
																			written += file_obj.write(__SCREAMING_SNAKE_CASE					)

							return written












'''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


__lowerCAmelCase       		=      logging.get_logger(__name__)

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

__lowerCAmelCase       		=      {
    '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'
        ),
    },
}

__lowerCAmelCase       		=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__lowerCAmelCase       		=      '▁'





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]






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

							a_		:  List[str]											=      {} if sp_model_kwargs is None else sp_model_kwargs

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

							a_		:  Tuple											=      vocab_file
							a_		:  Optional[Any]											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE					)					)

							a_		:  Optional[Any]											=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							a_		:  Any											=      len(self.sp_model					) - 1
							a_		:  Optional[Any]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






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

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  List[str]											=      [self.cls_token_id]
							a_		:  Optional[Any]											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE  ,						token_ids_a=__SCREAMING_SNAKE_CASE  ,						already_has_special_tokens=__SCREAMING_SNAKE_CASE					)

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  Any											=      [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 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							return len(self.sp_model					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							a_		:  int											=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,						out_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							a_		:  Optional[int]											=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE					)

							return spm_id if spm_id else self.unk_token_id






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Dict											=      []
							a_		:  List[Any]											=      ''''''
							a_		:  Dict											=      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(__SCREAMING_SNAKE_CASE					) + token
															a_		:  Dict											=      True
															a_		:  Optional[Any]											=      []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					)
							return out_string.strip()






			def __getstate__( self      :			Dict					)			->			int:
							a_		:  Dict											=      self.__dict__.copy()
							a_		:  List[str]											=      None
							return state






			def __setstate__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							a_		:  Optional[Any]											=      d

							# for backward compatibility
							if not hasattr(self  ,						'''sp_model_kwargs'''					):
											a_		:  Union[str, Any]											=      {}

							a_		:  int											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Union[str, Any]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

							return (out_vocab_file,)




