






"""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


_UpperCamelCase		:  Any						=		logging.get_logger(__name__)

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

_UpperCamelCase		:  Dict						=		{
    '''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'''
        ),
    }
}

_UpperCamelCase		:  Any						=		{
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}

class        a     (   lowerCAmelCase__	):
    UpperCAmelCase_ :     Union[str, Any]	     =VOCAB_FILES_NAMES
    UpperCAmelCase_ :     Any	     =PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_ :     Tuple	     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_ :     int	     =["""input_ids""", """attention_mask"""]
    UpperCAmelCase_ :     List[int]	     =[]







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase="<unk>"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="<pad>"							,		_lowerCamelCase="[SEP]"							,		_lowerCamelCase="[MASK]"							,		_lowerCamelCase="[CLS]"							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      lowercase							    =       AddedToken(lowercase_							,		lstrip=lowercase_							,		rstrip=lowercase_       ) if isinstance(lowercase_							,		lowercase_       ) else bos_token
      lowercase							    =       AddedToken(lowercase_							,		lstrip=lowercase_							,		rstrip=lowercase_       ) if isinstance(lowercase_							,		lowercase_       ) else eos_token
      lowercase							    =       AddedToken(lowercase_							,		lstrip=lowercase_							,		rstrip=lowercase_       ) if isinstance(lowercase_							,		lowercase_       ) else unk_token
      lowercase							    =       AddedToken(lowercase_							,		lstrip=lowercase_							,		rstrip=lowercase_       ) if isinstance(lowercase_							,		lowercase_       ) else pad_token
      lowercase							    =       AddedToken(lowercase_							,		lstrip=lowercase_							,		rstrip=lowercase_       ) if isinstance(lowercase_							,		lowercase_       ) else cls_token
      lowercase							    =       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
      lowercase							    =       AddedToken(lowercase_							,		lstrip=lowercase_							,		rstrip=lowercase_       ) if isinstance(lowercase_							,		lowercase_       ) else mask_token

      lowercase							    =       {} 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_							,		)

      lowercase							    =       vocab_file

      lowercase							    =       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(lowercase_       )







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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       {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       ):
      lowercase							    =       self.__dict__.copy()
      lowercase							    =       None
      return state







    def __setstate__(			self							,		_lowerCamelCase       ):
      lowercase							    =       d

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

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







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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      return self.sp_model.piece_to_id(lowercase_       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       self.sp_model.IdToPiece(lowercase_       )
      return token







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       []
      lowercase							    =       ''''''
      lowercase							    =       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
          lowercase							    =       True
          lowercase							    =       []
        else:
          current_sub_tokens.append(lowercase_       )
          lowercase							    =       False
      out_string += self.sp_model.decode(lowercase_       )
      return out_string.strip()







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = False							,		_lowerCamelCase = None							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       kwargs.pop('use_source_tokenizer'							,		lowercase_       )

      lowercase							    =       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
      lowercase							    =       []
      lowercase							    =       []
      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_       )       )
            lowercase							    =       []
          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:
        lowercase							    =       re.sub(R' (\[(MASK|SEP)\])'							,		R'\1'							,		' '.join(lowercase_       )       )
      else:
        lowercase							    =       ''''''.join(lowercase_       )

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None       ):
      if not os.path.isdir(lowercase_       ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
        return
      lowercase							    =       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:
          lowercase							    =       self.sp_model.serialized_model_proto()
          fi.write(lowercase_       )

      return (out_vocab_file,)







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None       ):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase							    =       [self.cls_token_id]
      lowercase							    =       [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = 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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None       ):
      lowercase							    =       [self.sep_token_id]
      lowercase							    =       [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 functools import lru_cache




@lru_cache
def __lowercase			(							_a    ):
    if num < 0:
        raise ValueError('''Number should not be negative.'''    )

    return 1 if num in (0, 1) else num * factorial(num - 1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





