


import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       __UpperCamelCase       :       Union[str, Any]						       =				None


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Dict						       =				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__UpperCamelCase       :       Optional[Any]						       =				{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCamelCase       :       List[Any]						       =				{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

class       __magic_name__	(     _a):
     A:		Union[str, Any]			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		List[str]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Tuple			=      ["""input_ids""", """attention_mask"""]
     A:		List[str]			=      TaTokenizer

     A:		List[int]			=      []


     def __init__(      self							:     Optional[int]							,	lowerCamelCase__							:     List[str]=None							,	lowerCamelCase__							:     str=None							,	lowerCamelCase__							:     Union[str, Any]="</s>"							,	lowerCamelCase__							:     str="<unk>"							,	lowerCamelCase__							:     Tuple="<pad>"							,	lowerCamelCase__							:     List[str]=100							,	lowerCamelCase__							:     Union[str, Any]=None							,	**lowerCamelCase__							:     Dict							,	)  ->      Dict:





      '''simple docstring'''






      if extra_ids > 0 and additional_special_tokens is None:
       UpperCamelCase__					:    Optional[Any]          =   [F"<extra_id_{i}>" for i in range(snake_case_			)]
      elif extra_ids > 0 and additional_special_tokens is not None:
       # Check that we have the right number of extra special tokens
       UpperCamelCase__					:    Dict          =   len(set(filter(lambda lowerCamelCase__			: bool('''extra_id_''' in str(snake_case_			)			)							,	snake_case_			)			)			)
       if extra_tokens != extra_ids:
        raise ValueError(
            F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
            ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
            ''' tokens'''			)

      super().__init__(
          snake_case_							,	tokenizer_file=snake_case_							,	eos_token=snake_case_							,	unk_token=snake_case_							,	pad_token=snake_case_							,	extra_ids=snake_case_							,	additional_special_tokens=snake_case_							,	**snake_case_							,	)

      UpperCamelCase__					:    Optional[Any]          =   vocab_file
      UpperCamelCase__					:    Any          =   False if not self.vocab_file else True
      UpperCamelCase__					:    Tuple          =   extra_ids


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[Any]			)  ->      int:





      '''simple docstring'''






      if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
       UpperCamelCase__					:    List[str]          =   TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
       if init_max_model_length is not None and init_max_model_length != max_model_length:
        return init_max_model_length
       elif init_max_model_length is None:
        warnings.warn(
            '''This tokenizer was incorrectly instantiated with a model max length of'''
            F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
            ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
            ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
            F" {pretrained_model_name_or_path} automatically truncating your input to"
            F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
            F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
            ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
            ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''							,	snake_case_							,	)

      return max_model_length


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     int = None			)  ->      Tuple[str]:





      '''simple docstring'''






      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(snake_case_			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    Tuple          =   os.path.join(
          snake_case_							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(snake_case_			):
       copyfile(self.vocab_file							,	snake_case_			)
       logger.info(F"Copy vocab file to {out_vocab_file}"			)

      return (out_vocab_file,)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Dict = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return self.prefix_tokens + token_ids_a
      else:
       UpperCamelCase__					:    Any          =   token_ids_a + [self.eos_token_id]
       return self.prefix_tokens + token_ids_a + token_ids_a


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Union[str, Any] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [self.eos_token_id]

      if token_ids_a is None:
       return len(token_ids_a + eos			) * [0]
      return len(token_ids_a + eos + token_ids_a + eos			) * [0]


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





      '''simple docstring'''






      return list(
          set(filter(lambda lowerCamelCase__			: bool(re.search(r'''<extra_id_\d+>'''							,	snake_case_			)			) is not None							,	self.additional_special_tokens			)			)			)


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





      '''simple docstring'''






      return [self.convert_tokens_to_ids(snake_case_			) for token in self.get_sentinel_tokens()]






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


__UpperCamelCase       :       Tuple						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Union[str, Any]						       =				{"vocab_file": "sentencepiece.bpe.model"}

__UpperCamelCase       :       List[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"
        ),
    },
}

__UpperCamelCase       :       int						       =				{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__UpperCamelCase       :       int						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      VOCAB_FILES_NAMES
     A:		Optional[int]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		str			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Union[str, Any]			=      ["input_ids", "attention_mask"]


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





      '''simple docstring'''






      UpperCamelCase__					:    str          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      UpperCamelCase__					:    Optional[int]          =   {} if sp_model_kwargs is None else sp_model_kwargs

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

      UpperCamelCase__					:    Any          =   vocab_file
      UpperCamelCase__					:    str          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(str(lowerCamelCase__			)			)

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

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


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     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]
      UpperCamelCase__					:    List[str]          =   [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							:     Dict							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None							,	lowerCamelCase__							:     bool = False			)  ->      List[int]:





      '''simple docstring'''






      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowerCamelCase__							,	token_ids_a=lowerCamelCase__							,	already_has_special_tokens=lowerCamelCase__			)

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


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





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [self.sep_token_id]
      UpperCamelCase__					:    List[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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      return len(self.sp_model			)


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





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   {self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab


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





      '''simple docstring'''






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


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





      '''simple docstring'''






      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      UpperCamelCase__					:    List[str]          =   self.sp_model.PieceToId(lowerCamelCase__			)

      return spm_id if spm_id else self.unk_token_id


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     int			)  ->      List[str]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   []
      UpperCamelCase__					:    Any          =   ''''''
      UpperCamelCase__					:    str          =   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(lowerCamelCase__			) + token
        UpperCamelCase__					:    str          =   True
        UpperCamelCase__					:    Tuple          =   []
       else:
        current_sub_tokens.append(lowerCamelCase__			)
        UpperCamelCase__					:    Any          =   False
      out_string += self.sp_model.decode(lowerCamelCase__			)
      return out_string.strip()


     def __getstate__(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.__dict__.copy()
      UpperCamelCase__					:    int          =   None
      return state


     def __setstate__(      self							:     Tuple							,	lowerCamelCase__							:     Any			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   d

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

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


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    Any          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

      return (out_vocab_file,)


