




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      :						Dict							=  logging.get_logger(__name__)

_lowerCamelCase      :						Dict							=  {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCamelCase      :						Any							=  {
    """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      :						List[Any]							=  {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

_lowerCamelCase      :						Union[str, Any]							=  """▁"""


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = VOCAB_FILES_NAMES
    UpperCAmelCase__				    = PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__				    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__				    = ['''input_ids''', '''attention_mask''']


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





           '''simple docstring'''

           A__ 					=       AddedToken(UpperCAmelCase__   ,					lstrip=UpperCAmelCase__   ,					rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) else mask_token

           A__ 					=       {} 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__   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**UpperCAmelCase__   ,					)

           A__ 					=       vocab_file
           A__ 					=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(UpperCAmelCase__))

           A__ 					=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					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]
           A__ 					=       [self.cls_token_id]
           A__ 					=       [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


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





           '''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 + sep + token_ids_a + sep) * [0]


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Any:





           '''simple docstring'''

           return len(self.sp_model)


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





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      str)		->List[str]:





           '''simple docstring'''

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


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





           '''simple docstring'''

           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           A__ 					=       self.sp_model.PieceToId(UpperCAmelCase__)

           return spm_id if spm_id else self.unk_token_id


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      List[str])		->Optional[Any]:





           '''simple docstring'''

           if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      int)		->Union[str, Any]:





           '''simple docstring'''

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


    def __getstate__(     self :      Optional[Any])		->Optional[Any]:





           '''simple docstring'''

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


    def __setstate__(     self :      List[Any]   ,					UpperCAmelCase__ :      int)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       d

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

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





    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					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
           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__) 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:
                         A__ 					=       self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)








'''simple docstring'''



from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCamelCase_    	=				logging.get_logger(__name__)


UpperCamelCase_    	=				TypeVar("DatasetType", Dataset, IterableDataset)




def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[List[float]] = None      ,__UpperCamelCase:					Optional[int] = None      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					Literal["first_exhausted", "all_exhausted"] = "first_exhausted"      ,):

      """simple docstring"""






      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError('Unable to interleave an empty list of datasets.'    )
      for i, dataset in enumerate(__UpperCamelCase    ):
            if not isinstance(__UpperCamelCase      ,(Dataset, IterableDataset)    ):
                  if isinstance(__UpperCamelCase      ,(DatasetDict, IterableDatasetDict)    ):
                        if not dataset:
                              raise ValueError(
                                  f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                                  'is an empty dataset dictionary.'    )
                        raise ValueError(
                            f"Dataset at position {i} has at least one split: {list(__UpperCamelCase    )}\n"
                            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase    )    )}']"    )
                  raise ValueError(
                      f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase    ).__name__}."    )
            if i == 0:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						(
                      (Dataset, IterableDataset) if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
                  raise ValueError(
                      f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."    )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."    )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,stopping_strategy=__UpperCamelCase    )
      else:
            return _interleave_iterable_datasets(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,stopping_strategy=__UpperCamelCase    )





def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					int = 0      ,):

      """simple docstring"""






      if not dsets:
            raise ValueError('Unable to concatenate an empty list of datasets.'    )
      for i, dataset in enumerate(__UpperCamelCase    ):
            if not isinstance(__UpperCamelCase      ,(Dataset, IterableDataset)    ):
                  if isinstance(__UpperCamelCase      ,(DatasetDict, IterableDatasetDict)    ):
                        if not dataset:
                              raise ValueError(
                                  f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                                  'is an empty dataset dictionary.'    )
                        raise ValueError(
                            f"Dataset at position {i} has at least one split: {list(__UpperCamelCase    )}\n"
                            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase    )    )}']"    )
                  raise ValueError(
                      f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase    ).__name__}."    )
            if i == 0:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						(
                      (Dataset, IterableDataset) if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
                  raise ValueError(
                      f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."    )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,axis=__UpperCamelCase    )
      else:
            return _concatenate_iterable_datasets(__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,axis=__UpperCamelCase    )


