


from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class       __magic_name__	(     __lowercase):
     A:		Optional[int]			=      '''philschmid/bart-large-cnn-samsum'''
     A:		Optional[int]			=      (
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     A:		Optional[Any]			=      '''summarizer'''
     A:		str			=      AutoTokenizer
     A:		Tuple			=      AutoModelForSeqaSeqLM

     A:		List[str]			=      ['''text''']
     A:		Dict			=      ['''text''']


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





      '''simple docstring'''






      return self.pre_processor(__a							,	return_tensors='''pt'''							,	truncation=__a			)


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





      '''simple docstring'''






      return self.model.generate(**__a			)[0]


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





      '''simple docstring'''






      return self.pre_processor.decode(__a							,	skip_special_tokens=__a							,	clean_up_tokenization_spaces=__a			)






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,)


