






import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class   lowerCamelCase				(datasets.BuilderConfig      ):







		'''simple docstring'''



		_snake_case   :   Optional[datasets.Features]							=  None





class   lowerCamelCase				(datasets.ArrowBasedBuilder      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  PandasConfig



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return datasets.DatasetInfo(features=self.config.features       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Optional[Any]:
							if not self.config.data_files:
												raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"       )
							UpperCAmelCase_   :				List[str]						=						dl_manager.download_and_extract(self.config.data_files       )
							if isinstance(_UpperCamelCase    ,		(str, list, tuple)       ):
												UpperCAmelCase_   :				Union[str, Any]						=						data_files
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				Optional[int]						=						[files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												UpperCAmelCase_   :				List[str]						=						[dl_manager.iter_files(_UpperCamelCase       ) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,		gen_kwargs={'files': files}       )]
							UpperCAmelCase_   :				str						=						[]
							for split_name, files in data_files.items():
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				Union[str, Any]						=						[files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												UpperCAmelCase_   :				List[str]						=						[dl_manager.iter_files(_UpperCamelCase       ) for file in files]
												splits.append(datasets.SplitGenerator(name=_UpperCamelCase    ,		gen_kwargs={'files': files}       )       )
							return splits



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							pa.Table:
							if self.config.features is not None:
												# more expensive cast to support nested features with keys in a different order
												# allows str <-> int/float or str to Audio for example
												UpperCAmelCase_   :				Optional[Any]						=						table_cast(_UpperCamelCase    ,		self.config.features.arrow_schema       )
							return pa_table



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							for i, file in enumerate(itertools.chain.from_iterable(_UpperCamelCase       )       ):
												with open(_UpperCamelCase    ,		'rb'       ) as f:
																	UpperCAmelCase_   :				List[str]						=						pa.Table.from_pandas(pd.read_pickle(_UpperCamelCase       )       )
																	yield i, self._cast_table(_UpperCamelCase       )














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_barthez import BarthezTokenizer
else:
		__UpperCAmelCase     							=						None

__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    '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'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCAmelCase     							=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__UpperCAmelCase     							=						'▁'



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Dict							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Dict							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   Union[str, Any]							=  BarthezTokenizer



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

							super().__init__(
							    _UpperCamelCase    ,		tokenizer_file=_UpperCamelCase    ,		bos_token=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		sep_token=_UpperCamelCase    ,		cls_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		mask_token=_UpperCamelCase    ,		**_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Tuple						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						False if not self.vocab_file else True



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:

							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_   :				Union[str, Any]						=						[self.cls_token_id]
							UpperCAmelCase_   :				Optional[Any]						=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Any						=						[self.sep_token_id]
							UpperCAmelCase_   :				List[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]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
												    'tokenizer.'       )

							if not os.path.isdir(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				List[Any]						=						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,)






