



import os
import re
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


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

lowercase__					:						Dict												=			{'''vocab_file''': '''spiece.model'''}

lowercase__					:						Optional[int]												=			{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowercase__					:						int												=			{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    str	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    Any	      =	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    int	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    Any	      =	["""input_ids""", """attention_mask"""]
 _lowerCAmelCase     :    List[int]	      =	[]

 def __init__(  self	: Optional[Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: List[Any]="<unk>"						,  lowercase_	: List[str]="<s>"						,  lowercase_	: int="</s>"						,  lowercase_	: List[Any]="<pad>"						,  lowercase_	: List[str]="[SEP]"						,  lowercase_	: Union[str, Any]="[MASK]"						,  lowercase_	: Optional[Any]="[CLS]"						,  lowercase_	: Optional[Dict[str, Any]] = None						,  **lowercase_	: Optional[Any]						,  ):
     snake_case_				:  Union[str, Any]       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else bos_token
     snake_case_				:  str       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else eos_token
     snake_case_				:  Dict       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else unk_token
     snake_case_				:  List[str]       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else pad_token
     snake_case_				:  Optional[Any]       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else cls_token
     snake_case_				:  List[Any]       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else sep_token

     # Mask token behave like a normal word, i.e. include the space before it
     snake_case_				:  Dict       =			AddedToken(lowercase_						,  lstrip=lowercase_						,  rstrip=lowercase_       ) if isinstance(lowercase_						,  lowercase_       ) else mask_token

     snake_case_				:  Any       =			{} if sp_model_kwargs is None else sp_model_kwargs

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

     snake_case_				:  List[str]       =			vocab_file

     snake_case_				:  Dict       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(lowercase_       )

 @property
 def 		_snake_case      (  self	: List[str]       ):
     return self.sp_model.get_piece_size()

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[Any]       =			{self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab

 def __getstate__(  self	: List[str]       ):
     snake_case_				:  Optional[int]       =			self.__dict__.copy()
     snake_case_				:  Optional[int]       =			None
     return state

 def __setstate__(  self	: Dict						,  lowercase_	: Tuple       ):
     snake_case_				:  Any       =			d

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

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

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: str       ):
     return self.sp_model.encode(lowercase_						,  out_type=lowercase_       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: Union[str, Any]       ):
     return self.sp_model.piece_to_id(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: Optional[Any]       ):
     snake_case_				:  Dict       =			self.sp_model.IdToPiece(lowercase_       )
     return token

 def 		_snake_case      (  self	: Any						,  lowercase_	: List[Any]       ):
     snake_case_				:  Any       =			[]
     snake_case_				:  Optional[int]       =			''''''
     snake_case_				:  Dict       =			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(lowercase_       ) + token
             snake_case_				:  Union[str, Any]       =			True
             snake_case_				:  List[str]       =			[]
         else:
             current_sub_tokens.append(lowercase_       )
             snake_case_				:  Tuple       =			False
     out_string += self.sp_model.decode(lowercase_       )
     return out_string.strip()

 def 		_snake_case      (  self	: int						,  lowercase_	: List[int]						,  lowercase_	: bool = False						,  lowercase_	: bool = None						,  lowercase_	: bool = True						,  **lowercase_	: Tuple						,  ):
     snake_case_				:  Optional[int]       =			kwargs.pop('''use_source_tokenizer'''						,  lowercase_       )

     snake_case_				:  List[str]       =			self.convert_ids_to_tokens(lowercase_						,  skip_special_tokens=lowercase_       )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     snake_case_				:  Dict       =			[]
     snake_case_				:  Any       =			[]
     for token in filtered_tokens:
         if skip_special_tokens and token in self.all_special_ids:
             continue
         if token in self.added_tokens_encoder:
             if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(lowercase_       )       )
                 snake_case_				:  Optional[int]       =			[]
             sub_texts.append(lowercase_       )
         else:
             current_sub_text.append(lowercase_       )
     if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(lowercase_       )       )

     # Mimic the behavior of the Rust tokenizer:
     # No space before [MASK] and [SEP]
     if spaces_between_special_tokens:
         snake_case_				:  List[Any]       =			re.sub(r''' (\[(MASK|SEP)\])'''						,  r'''\1'''						,  ''' '''.join(lowercase_       )       )
     else:
         snake_case_				:  int       =			''''''.join(lowercase_       )

     snake_case_				:  Optional[int]       =			(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
         snake_case_				:  Optional[int]       =			self.clean_up_tokenization(lowercase_       )
         return clean_text
     else:
         return text

 def 		_snake_case      (  self	: int						,  lowercase_	: str						,  lowercase_	: Optional[str] = None       ):
     if not os.path.isdir(lowercase_       ):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
         return
     snake_case_				:  Optional[int]       =			os.path.join(
         lowercase_						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

     return (out_vocab_file,)

 def 		_snake_case      (  self	: int						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None       ):
     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     snake_case_				:  Optional[int]       =			[self.cls_token_id]
     snake_case_				:  List[Any]       =			[self.sep_token_id]
     return cls + token_ids_a + sep + token_ids_a + sep

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None						,  lowercase_	: bool = False       ):
     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_						,  token_ids_a=lowercase_						,  already_has_special_tokens=lowercase_       )

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






 def 		_snake_case      (  self	: str						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None       ):
     snake_case_				:  Union[str, Any]       =			[self.sep_token_id]
     snake_case_				:  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       ) * [0] + len(token_ids_a + sep       ) * [1]










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

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





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 def 		_snake_case      (  self	: Any       ):
     if self.task == "multiple-choice":
         snake_case_				:  List[Any]       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         snake_case_				:  Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )





