


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


snake_case    :   str	    =					logging.get_logger(__name__)

snake_case    :   Optional[Any]	    =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

snake_case    :   List[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'''
        ),
    },
}

snake_case    :   Dict	    =					{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

snake_case    :   int	    =					'''▁'''







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__				=	['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
       a						:str										=	AddedToken(_lowerCamelCase ,  lstrip=_lowerCamelCase ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) else mask_token

       a						:List[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 ,  )

       a						:Optional[Any]										=	vocab_file
       a						:Dict										=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(str(_lowerCamelCase   )   )

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

       a						:Union[str, Any]										=	len(self.sp_model   ) - 1
       a						:str										=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




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

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a						:Optional[int]										=	[self.cls_token_id]
       a						:int										=	[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




 def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Any										=	[self.sep_token_id]
       a						:Dict										=	[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   ):
       return len(self.sp_model   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	{self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab




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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       a						:Tuple										=	self.sp_model.PieceToId(_lowerCamelCase   )

       return spm_id if spm_id else self.unk_token_id




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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Dict										=	[]
       a						:int										=	''''''
       a						: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
                   a						:str										=	True
                   a						:Dict										=	[]
             else:
                   current_sub_tokens.append(_lowerCamelCase   )
                   a						:Any										=	False
       out_string += self.sp_model.decode(_lowerCamelCase   )
       return out_string.strip()




 def __getstate__(		self   ):
       a						:Optional[Any]										=	self.__dict__.copy()
       a						:Optional[int]										=	None
       return state




 def __setstate__(		self ,  _lowerCamelCase   ):
       a						:int										=	d

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

       a						:Dict										=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       if not os.path.isdir(_lowerCamelCase   ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
             return
       a						:Tuple										=	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:
                   a						:Optional[Any]										=	self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase   )

       return (out_vocab_file,)









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





