






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_		:		Union[str, Any]		     =			logging.get_logger(__name__)

A_		:		Union[str, Any]		     =			{'vocab_file': 'sentencepiece.bpe.model'}

A_		:		Union[str, 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'
        ),
    },
}

A_		:		Union[str, Any]		     =			{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

A_		:		str		     =			'▁'




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									=VOCAB_FILES_NAMES
       a    :		str									=PRETRAINED_VOCAB_FILES_MAP
       a    :		Tuple									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		str									=['''input_ids''', '''attention_mask''']





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

         UpperCamelCase_: str   				=    {} 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_: Optional[int]   				=    vocab_file
         UpperCamelCase_: int   				=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(_lowerCamelCase							)							)

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

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False							):
         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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         UpperCamelCase_: List[Any]   				=    [self.sep_token_id]
         UpperCamelCase_: Optional[int]   				=    [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       _a   ( self							):
         return len(self.sp_model							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    {self.convert_ids_to_tokens(_lowerCamelCase							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.sp_model.encode(_lowerCamelCase  ,      out_type=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         UpperCamelCase_: Dict   				=    self.sp_model.PieceToId(_lowerCamelCase							)

         return spm_id if spm_id else self.unk_token_id





       def       _a   ( self  ,      _lowerCamelCase							):
         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    []
         UpperCamelCase_: Optional[Any]   				=    ''
         UpperCamelCase_: List[Any]   				=    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_: Union[str, Any]   				=    True
             UpperCamelCase_: Tuple   				=    []
           else:
             current_sub_tokens.append(_lowerCamelCase							)
             UpperCamelCase_: List[str]   				=    False
         out_string += self.sp_model.decode(_lowerCamelCase							)
         return out_string.strip()





       def __getstate__( self							):
         UpperCamelCase_: Any   				=    self.__dict__.copy()
         UpperCamelCase_: str   				=    None
         return state





       def __setstate__( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Tuple   				=    d

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

         UpperCamelCase_: int   				=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(self.vocab_file							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_: List[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,)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )