
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					   =logging.get_logger(__name__)

_lowerCamelCase					   ={"""vocab_file""": """spiece.model"""}

_lowerCamelCase					   ={
    """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					   ={
    """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__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    int           =			VOCAB_FILES_NAMES
      _UpperCAmelCase				:    int           =			PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase				:    Optional[int]           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase				:    Tuple           =			["""input_ids""", """attention_mask"""]
      _UpperCAmelCase				:    List[int]           =			[]
      def __init__(      self    ,  __magic_name__    ,  __magic_name__="<unk>"    ,  __magic_name__="<s>"    ,  __magic_name__="</s>"    ,  __magic_name__="<pad>"    ,  __magic_name__="[SEP]"    ,  __magic_name__="[MASK]"    ,  __magic_name__="[CLS]"    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Tuple							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else bos_token
       lowerCamelCase		:	Optional[int]							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else eos_token
       lowerCamelCase		:	str							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else unk_token
       lowerCamelCase		:	int							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else pad_token
       lowerCamelCase		:	Optional[Any]							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else cls_token
       lowerCamelCase		:	Any							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else sep_token

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

       lowerCamelCase		:	List[Any]							=      {} if sp_model_kwargs is None else sp_model_kwargs

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

       lowerCamelCase		:	Any							=      vocab_file

       lowerCamelCase		:	List[str]							=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.sp_model.get_piece_size()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      {self.convert_ids_to_tokens(__magic_name__      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab
      def __getstate__(      self      ):
       lowerCamelCase		:	str							=      self.__dict__.copy()
       lowerCamelCase		:	Optional[int]							=      None
       return state
      def __setstate__(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      d

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

       lowerCamelCase		:	Optional[Any]							=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       return self.sp_model.encode(__magic_name__    ,  out_type=__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       return self.sp_model.piece_to_id(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	List[str]							=      self.sp_model.IdToPiece(__magic_name__      )
       return token
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Any							=      []
       lowerCamelCase		:	Dict							=      """"""
       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(__magic_name__      ) + token
         lowerCamelCase		:	Union[str, Any]							=      True
         lowerCamelCase		:	List[str]							=      []
        else:
         current_sub_tokens.append(__magic_name__      )
         lowerCamelCase		:	Dict							=      False
       out_string += self.sp_model.decode(__magic_name__      )
       return out_string.strip()
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  __magic_name__ = True    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Optional[int]							=      kwargs.pop("""use_source_tokenizer"""    ,  __magic_name__      )

       lowerCamelCase		:	Any							=      self.convert_ids_to_tokens(__magic_name__    ,  skip_special_tokens=__magic_name__      )

       # 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		:	Any							=      []
       lowerCamelCase		:	Tuple							=      []
       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(__magic_name__      )      )
          lowerCamelCase		:	str							=      []
         sub_texts.append(__magic_name__      )
        else:
         current_sub_text.append(__magic_name__      )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(__magic_name__      )      )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
        lowerCamelCase		:	Union[str, Any]							=      re.sub(r""" (\[(MASK|SEP)\])"""    ,  r"""\1"""    ,  """ """.join(__magic_name__      )      )
       else:
        lowerCamelCase		:	int							=      """""".join(__magic_name__      )

       lowerCamelCase		:	int							=      (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        lowerCamelCase		:	Optional[int]							=      self.clean_up_tokenization(__magic_name__      )
        return clean_text
       else:
        return text
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       if not os.path.isdir(__magic_name__      ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
        return
       lowerCamelCase		:	List[str]							=      os.path.join(
           __magic_name__    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

       return (out_vocab_file,)
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowerCamelCase		:	int							=      [self.cls_token_id]
       lowerCamelCase		:	Optional[int]							=      [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = False      ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__magic_name__    ,  token_ids_a=__magic_name__    ,  already_has_special_tokens=__magic_name__      )

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






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       lowerCamelCase		:	str							=      [self.sep_token_id]
       lowerCamelCase		:	Any							=      [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]






_lowerCamelCase					   ={
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355818,
}





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
  lowerCamelCase		:	Dict							=      (
      F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Valid values are: {", ".join(lowerCamelCase							)}'''
  )
  raise ValueError(lowerCamelCase							)
 return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




