






"""simple docstring"""







import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


_UpperCamelCase					      =  logging.get_logger(__name__)

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

_UpperCamelCase					      =  {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_UpperCamelCase					      =  {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

_UpperCamelCase					      =  """▁"""







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A     ,A="</s>"     ,A="<unk>"     ,A="<pad>"     ,A=100     ,A=None     ,A = None     ,A=True     ,**A     ,):
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
                UpperCAmelCase          =	[F'''<extra_id_{i}>''' for i in range(A )]
          elif extra_ids > 0 and additional_special_tokens is not None:
                # Check that we have the right number of extra_id special tokens
                UpperCAmelCase          =	len(set(filter(lambda A : bool("""extra_id""" in str(A ) )     ,A ) ) )
                if extra_tokens != extra_ids:
                      raise ValueError(
                          F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                          """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                          """ tokens""" )
          if legacy:
                logger.warning_once(
                    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565""" )

          UpperCAmelCase          =	legacy
          UpperCAmelCase          =	{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A     ,unk_token=A     ,pad_token=A     ,extra_ids=A     ,additional_special_tokens=A     ,sp_model_kwargs=self.sp_model_kwargs     ,legacy=A     ,**A     ,)

          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	extra_ids

          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(A )






    @staticmethod
    def     _UpperCamelCase							(			A     ,A     ,A ):
          if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                UpperCAmelCase          =	TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
                if init_max_model_length is not None and init_max_model_length != max_model_length:
                      return init_max_model_length
                elif init_max_model_length is None:
                      warnings.warn(
                          """This tokenizer was incorrectly instantiated with a model max length of"""
                          F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                          """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                          """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                          F''' {pretrained_model_name_or_path} automatically truncating your input to'''
                          F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                          F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                          """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                          """ instantiate this tokenizer with `model_max_length` set to your preferred value."""     ,A     ,)

          return max_model_length






    @property
    def     _UpperCamelCase							(			self ):
          return self.sp_model.get_piece_size() + self._extra_ids






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






    def     _UpperCamelCase							(			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 )

          # normal case: some special tokens
          if token_ids_a is None:
                return ([0] * len(A )) + [1]
          return ([0] * len(A )) + [1] + ([0] * len(A )) + [1]






    def     _UpperCamelCase							(			self ):
          return list(
              set(filter(lambda A : bool(re.search(r"""<extra_id_\d+>"""     ,A ) ) is not None     ,self.additional_special_tokens ) ) )






    def     _UpperCamelCase							(			self ):
          return [self._convert_token_to_id(A ) for token in self.get_sentinel_tokens()]






    def     _UpperCamelCase							(			self     ,A ):
          if len(A ) > 0 and token_ids[-1] == self.eos_token_id:
                warnings.warn(
                    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
                    """ eos tokens being added.""" )
                return token_ids
          else:
                return token_ids + [self.eos_token_id]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	[self.eos_token_id]

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






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	self._add_eos_if_not_present(A )
          if token_ids_a is None:
                return token_ids_a
          else:
                UpperCAmelCase          =	self._add_eos_if_not_present(A )
                return token_ids_a + token_ids_a






    def __getstate__(			self ):
          UpperCAmelCase          =	self.__dict__.copy()
          UpperCAmelCase          =	None
          return state






    def __setstate__(			self     ,A ):
          UpperCAmelCase          =	d

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

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






    def     _UpperCamelCase							(			self     ,A     ,**A ):
          # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
          # the beginning of the text
          if not self.legacy:
                UpperCAmelCase          =	SPIECE_UNDERLINE + text.replace(A     ,""" """ )
          return super().tokenize(A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,**A ):
          if not self.legacy:
                UpperCAmelCase          =	text.startswith(A )
                if is_first:
                      UpperCAmelCase          =	text[1:]

          UpperCAmelCase          =	self.sp_model.encode(A     ,out_type=A )

          if not self.legacy and not is_first and not text.startswith(""" """ ) and tokens[0].startswith(A ):
                UpperCAmelCase          =	([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
          return tokens






    def     _UpperCamelCase							(			self     ,A ):
          if token.startswith("""<extra_id_""" ):
                UpperCAmelCase          =	re.match(r"""<extra_id_(\d+)>"""     ,A )
                UpperCAmelCase          =	int(match.group(1 ) )
                return self.vocab_size - num - 1
          return self.sp_model.piece_to_id(A )






    def     _UpperCamelCase							(			self     ,A ):
          if index < self.sp_model.get_piece_size():
                UpperCAmelCase          =	self.sp_model.IdToPiece(A )
          else:
                UpperCAmelCase          =	F'''<extra_id_{self.vocab_size - 1 - index}>'''
          return token






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	[]
          UpperCAmelCase          =	""""""
          UpperCAmelCase          =	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
                      UpperCAmelCase          =	True
                      UpperCAmelCase          =	[]
                else:
                      current_sub_tokens.append(A )
                      UpperCAmelCase          =	False
          out_string += self.sp_model.decode(A )
          return out_string.strip()






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

          return (out_vocab_file,)











"""simple docstring"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if (voltage, current, resistance).count(0							) != 1:
            raise ValueError("""One and only one argument must be 0"""							)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""							)
      if voltage == 0:
            return {"voltage": float(current * resistance							)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



