



"""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


A:     Optional[int]										=	logging.get_logger(__name__)

A:     Optional[Any]										=	{"vocab_file": "sentencepiece.bpe.model"}

A:     str										=	{
    "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"
        ),
    },
}

A:     Optional[Any]										=	{
    "moussaKam/mbarthez": 1_0_2_4,
    "moussaKam/barthez": 1_0_2_4,
    "moussaKam/barthez-orangesum-title": 1_0_2_4,
}

A:     str										=	"▁"



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Dict		=		VOCAB_FILES_NAMES
   __lowerCAmelCase						:     Optional[int]		=		PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase						:     str		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase						:     List[Any]		=		['input_ids', 'attention_mask']

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="<s>"   ,  _SCREAMING_SNAKE_CASE="</s>"   ,  _SCREAMING_SNAKE_CASE="</s>"   ,  _SCREAMING_SNAKE_CASE="<s>"   ,  _SCREAMING_SNAKE_CASE="<unk>"   ,  _SCREAMING_SNAKE_CASE="<pad>"   ,  _SCREAMING_SNAKE_CASE="<mask>"   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else mask_token

    UpperCAmelCase			:      Tuple			=			{} 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   ,  )

    UpperCAmelCase			:      Union[str, Any]			=			vocab_file
    UpperCAmelCase			:      Optional[Any]			=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
    self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)

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

    UpperCAmelCase			:      Dict			=			len(self.sp_model		) - 1
    UpperCAmelCase			:      Dict			=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    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			:      Dict			=			[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = False		)						->   List[int]:





    '''simple docstring'''




    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   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			[self.sep_token_id]
    UpperCAmelCase			:      str			=			[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		)						->   Tuple:





    '''simple docstring'''




    return len(self.sp_model		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			{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   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    UpperCAmelCase			:      str			=			self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

    return spm_id if spm_id else self.unk_token_id

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[int]:





    '''simple docstring'''




    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   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			[]
    UpperCAmelCase			:      Optional[Any]			=			""""""
    UpperCAmelCase			:      Tuple			=			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
      UpperCAmelCase			:      int			=			True
      UpperCAmelCase			:      List[str]			=			[]
     else:
      current_sub_tokens.append(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Tuple			=			False
    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		)
    return out_string.strip()

   def __getstate__(     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			self.__dict__.copy()
    UpperCAmelCase			:      Optional[Any]			=			None
    return state

   def __setstate__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			d

    # for backward compatibility
    if not hasattr(self   ,  """sp_model_kwargs"""		):
     UpperCAmelCase			:      Any			=			{}

    UpperCAmelCase			:      Optional[Any]			=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
    self.sp_model.Load(self.vocab_file		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   Tuple[str]:





    '''simple docstring'''




    if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
     return
    UpperCAmelCase			:      Tuple			=			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:
      UpperCAmelCase			:      Union[str, Any]			=			self.sp_model.serialized_model_proto()
      fi.write(_SCREAMING_SNAKE_CASE		)

    return (out_vocab_file,)





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,	[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]		):
 UpperCAmelCase			:      List[Any]			=			tmp_path_factory.mktemp("""dset_infos_dir"""		)
 if "full:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""		)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write(""""""		)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / """dataset_infos.json"""    ,	"""w"""		) as f:
   f.write("""{\"default\": {\"dataset_size\": 42}}"""		)
 UpperCAmelCase			:      str			=			DatasetInfosDict.from_directory(UpperCamelCase		)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,	[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	DatasetInfo		):
 UpperCAmelCase			:      List[Any]			=			str(UpperCamelCase		)
 dataset_info.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			DatasetInfo.from_directory(UpperCamelCase		)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(UpperCamelCase    ,	"""dataset_info.json"""		)		)

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo(
     description="""foo"""    ,	citation="""bar"""    ,	homepage="""https://foo.bar"""    ,	license="""CC0"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	post_processed={}    ,	supervised_keys=()    ,	task_templates=[]    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train""", """num_examples""": 42}]    ,	download_checksums={}    ,	download_size=1337    ,	post_processing_size=442    ,	dataset_size=1234    ,	size_in_bytes=1337 + 442 + 1234    ,	)
 UpperCAmelCase			:      str			=			dataset_info._to_yaml_dict()
 assert sorted(UpperCamelCase		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]    ,	(list, dict, int, str)		)
 UpperCAmelCase			:      Union[str, Any]			=			yaml.safe_dump(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			yaml.safe_load(UpperCamelCase		)
 assert dataset_info_yaml_dict == reloaded

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo()
 UpperCAmelCase			:      Union[str, Any]			=			dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,	[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}		),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}		),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	)
            }		),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42		),
                """v2""": DatasetInfo(dataset_size=1337		),
            }		),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	DatasetInfosDict		):
 UpperCAmelCase			:      List[str]			=			str(UpperCamelCase		)
 dataset_infos_dict.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			DatasetInfosDict.from_directory(UpperCamelCase		)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  UpperCAmelCase			:      Union[str, Any]			=			config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  UpperCAmelCase			:      Optional[Any]			=			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(UpperCamelCase    ,	"""README.md"""		)		)
