


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							:       List[Any]										=	logging.get_logger(__name__)

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

snake_case							:       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'''
        ),
    },
}

snake_case							:       List[str]										=	{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

snake_case							:       Optional[Any]										=	'''▁'''







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		:List[str]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase   ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else mask_token

       a		: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   ,    )

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

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

       a		:Dict									=			len(self.sp_model       ) - 1
       a		:int									=			{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		:Union[str, Any]									=			[self.cls_token_id]
       a		:Optional[Any]									=			[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		:Dict									=			[self.sep_token_id]
       a		: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 			SCREAMING_SNAKE_CASE__							(						self       ):
       return len(self.sp_model       )




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




 def __getstate__(						self       ):
       a		:Tuple									=			self.__dict__.copy()
       a		:Union[str, Any]									=			None
       return state




 def __setstate__(						self   ,    _lowerCamelCase       ):
       a		:Optional[Any]									=			d

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

       a		:List[str]									=			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[int]									=			self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase       )

       return (out_vocab_file,)









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			self.base_model(
           input_ids=_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    encoder_hidden_states=_lowerCamelCase   ,    encoder_attention_mask=_lowerCamelCase   ,    output_attentions=_lowerCamelCase   ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,    return_dict=_lowerCamelCase   ,    )

       if self.has_pre_transformation:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			self.transformation_pre(_lowerCamelCase       )

             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )
       else:
             a		:List[str]									=			self.transformation(outputs.last_hidden_state       )
             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )





