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


__lowerCamelCase    :				str							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{'''vocab_file''': '''spiece.model'''}

__lowerCamelCase    :				str							     =	{
    '''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'''
        ),
    }
}

__lowerCamelCase    :				List[str]							     =	{
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							[]




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       Optional[int]="<unk>" ,			_lowercase    :       List[Any]="<s>" ,			_lowercase    :       Dict="</s>" ,			_lowercase    :       int="<pad>" ,			_lowercase    :       str="[SEP]" ,			_lowercase    :       List[str]="[MASK]" ,			_lowercase    :       Dict="[CLS]" ,			_lowercase    :       Optional[Dict[str, Any]] = None ,			**_lowercase    :       Optional[int] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else bos_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else eos_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else unk_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else pad_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else cls_token
     SCREAMING_SNAKE_CASE__    							=   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
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else mask_token

     SCREAMING_SNAKE_CASE__    							=   {} 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 ,			)

     SCREAMING_SNAKE_CASE__    							=   vocab_file

     SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(_lowercase   )




   @property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     return self.sp_model.get_piece_size()




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {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    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.__dict__.copy()
     SCREAMING_SNAKE_CASE__    							=   None
     return state




   def __setstate__(					self    :       List[str] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   d

     # for backward compatibility
     if not hasattr(self ,			"""sp_model_kwargs"""   ):
       SCREAMING_SNAKE_CASE__    							=   {}

     SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return self.sp_model.encode(_lowercase ,			out_type=_lowercase   )




   def 			__a		(					self    :       Any ,			_lowercase    :       str   ):



     """simple docstring"""






     return self.sp_model.piece_to_id(_lowercase   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.sp_model.IdToPiece(_lowercase   )
     return token




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     SCREAMING_SNAKE_CASE__    							=   """"""
     SCREAMING_SNAKE_CASE__    							=   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
         SCREAMING_SNAKE_CASE__    							=   True
         SCREAMING_SNAKE_CASE__    							=   []
       else:
         current_sub_tokens.append(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   False
     out_string += self.sp_model.decode(_lowercase   )
     return out_string.strip()




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[int] ,			_lowercase    :       bool = False ,			_lowercase    :       bool = None ,			_lowercase    :       bool = True ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""use_source_tokenizer""" ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   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
     SCREAMING_SNAKE_CASE__    							=   []
     SCREAMING_SNAKE_CASE__    							=   []
     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   )   )
           SCREAMING_SNAKE_CASE__    							=   []
         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:
       SCREAMING_SNAKE_CASE__    							=   re.sub(R""" (\[(MASK|SEP)\])""" ,			R"""\1""" ,			""" """.join(_lowercase   )   )
     else:
       SCREAMING_SNAKE_CASE__    							=   """""".join(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
       SCREAMING_SNAKE_CASE__    							=   self.clean_up_tokenization(_lowercase   )
       return clean_text
     else:
       return text




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if not os.path.isdir(_lowercase   ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
       return
     SCREAMING_SNAKE_CASE__    							=   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:
         SCREAMING_SNAKE_CASE__    							=   self.sp_model.serialized_model_proto()
         fi.write(_lowercase   )

     return (out_vocab_file,)




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



     """simple docstring"""






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




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None ,			_lowercase    :       bool = False   ):



     """simple docstring"""






     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 			__a		(					self    :       List[str] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
     SCREAMING_SNAKE_CASE__    							=   [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]

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     if self.label_column not in features:
       raise ValueError(f"""Column {self.label_column} is not present in features."""   )
     if not isinstance(features[self.label_column] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
