"""simple docstring"""




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


_a   :	Optional[int] 					=     logging.get_logger(__name__)

_a   :	Optional[Any] 					=     {'vocab_file': 'spiece.model'}

_a   :	Union[str, Any] 					=     {
    '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'
        ),
    }
}

_a   :	Union[str, Any] 					=     {
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    str	      =	VOCAB_FILES_NAMES
     _UpperCamelCase							:    int	      =	PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:    int	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:    str	      =	["input_ids", "attention_mask"]
     _UpperCamelCase							:    List[int]	      =	[]






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

       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase		: Union[str, Any]						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else mask_token

       _lowerCAmelCase		: int						    =  {} if sp_model_kwargs is None else sp_model_kwargs

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

       _lowerCAmelCase		: Optional[Any]						    =  vocab_file

       _lowerCAmelCase		: Dict						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(a__						)






     @property
     def 					__A						(	self						):
       return self.sp_model.get_piece_size()






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  {self.convert_ids_to_tokens(a__						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab






     def __getstate__(	self						):
       _lowerCAmelCase		: int						    =  self.__dict__.copy()
       _lowerCAmelCase		: str						    =  None
       return state






     def __setstate__(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  d

       # for backward compatibility
       if not hasattr(self       ,  """sp_model_kwargs"""						):
         _lowerCAmelCase		: Optional[Any]						    =  {}

       _lowerCAmelCase		: Any						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)






     def 					__A						(	self       ,  a__						):
       return self.sp_model.encode(a__       ,  out_type=a__						)






     def 					__A						(	self       ,  a__						):
       return self.sp_model.piece_to_id(a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Any						    =  self.sp_model.IdToPiece(a__						)
       return token






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  []
       _lowerCAmelCase		: List[Any]						    =  """"""
       _lowerCAmelCase		: int						    =  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(a__						) + token
           _lowerCAmelCase		: List[Any]						    =  True
           _lowerCAmelCase		: int						    =  []
         else:
           current_sub_tokens.append(a__						)
           _lowerCAmelCase		: List[str]						    =  False
       out_string += self.sp_model.decode(a__						)
       return out_string.strip()






     def 					__A						(	self       ,  a__       ,  a__ = False       ,  a__ = None       ,  a__ = True       ,  **a__       ,  ):
       _lowerCAmelCase		: Any						    =  kwargs.pop("""use_source_tokenizer"""       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  self.convert_ids_to_tokens(a__       ,  skip_special_tokens=a__						)

       # 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
       _lowerCAmelCase		: Optional[int]						    =  []
       _lowerCAmelCase		: Optional[int]						    =  []
       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(a__						)						)
             _lowerCAmelCase		: str						    =  []
           sub_texts.append(a__						)
         else:
           current_sub_text.append(a__						)
       if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(a__						)						)

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
         _lowerCAmelCase		: Tuple						    =  re.sub(r""" (\[(MASK|SEP)\])"""       ,  r"""\1"""       ,  """ """.join(a__						)						)
       else:
         _lowerCAmelCase		: Tuple						    =  """""".join(a__						)

       _lowerCAmelCase		: Optional[Any]						    =  (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
         _lowerCAmelCase		: Any						    =  self.clean_up_tokenization(a__						)
         return clean_text
       else:
         return text






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       if not os.path.isdir(a__						):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
         return
       _lowerCAmelCase		: Any						    =  os.path.join(
           a__       ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						) and os.path.isfile(self.vocab_file						):
         copyfile(self.vocab_file       ,  a__						)
       elif not os.path.isfile(self.vocab_file						):
         with open(a__       ,  """wb"""						) as fi:
           _lowerCAmelCase		: Tuple						    =  self.sp_model.serialized_model_proto()
           fi.write(a__						)

       return (out_vocab_file,)






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _lowerCAmelCase		: Dict						    =  [self.cls_token_id]
       _lowerCAmelCase		: Dict						    =  [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = False						):
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=a__       ,  token_ids_a=a__       ,  already_has_special_tokens=a__						)

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






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       _lowerCAmelCase		: int						    =  [self.sep_token_id]
       _lowerCAmelCase		: 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"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  *a__       ,  **a__						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""       ,  a__       ,  )
       super().__init__(*a__       ,  **a__						)




