





'''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


lowerCAmelCase       		=      logging.get_logger(__name__)

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

lowerCAmelCase       		=      {
    """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
lowerCAmelCase       		=      {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

lowerCAmelCase       		=      """▁"""
class 		lowerCamelCase  (      _A       ):
 snake_case_      =   VOCAB_FILES_NAMES
 snake_case_      =   PRETRAINED_VOCAB_FILES_MAP
 snake_case_      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 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_		,      ):







   if extra_ids > 0 and additional_special_tokens is None:
     lowerCAmelCase     :    List[str]						=   [F'''<extra_id_{i}>''' for i in range(lowercase_	)]
   elif extra_ids > 0 and additional_special_tokens is not None:
     # Check that we have the right number of extra_id special tokens
     lowerCAmelCase     :    Optional[Any]						=   len(set(filter(lambda a_	: bool("extra_id" in str(lowercase_	)	)		,      lowercase_	)	)	)
     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"	)

   lowerCAmelCase     :    str						=   legacy
   lowerCAmelCase     :    Tuple						=   {} if sp_model_kwargs is None else sp_model_kwargs

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

   lowerCAmelCase     :    Optional[Any]						=   vocab_file
   lowerCAmelCase     :    Any						=   extra_ids

   lowerCAmelCase     :    Union[str, Any]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
   self.sp_model.Load(lowercase_	)



 @staticmethod
 def   _lowerCamelCase    (   a_		,      a_		,      a_	):







   if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
     lowerCAmelCase     :    int						=   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."		,      lowercase_		,      )

   return max_model_length



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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    List[Any]						=   {self.convert_ids_to_tokens(lowercase_	): i for i in range(self.vocab_size	)}
   vocab.update(self.added_tokens_encoder	)
   return vocab



 def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = 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_	)

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



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



 def   _lowerCamelCase    (   self	):
   return [self._convert_token_to_id(lowercase_	) for token in self.get_sentinel_tokens()]



 def   _lowerCamelCase    (   self		,      a_	):







   if len(lowercase_	) > 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   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
   lowerCAmelCase     :    List[str]						=   [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   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
   lowerCAmelCase     :    Dict						=   self._add_eos_if_not_present(lowercase_	)
   if token_ids_a is None:
     return token_ids_a
   else:
     lowerCAmelCase     :    List[Any]						=   self._add_eos_if_not_present(lowercase_	)
     return token_ids_a + token_ids_a



 def __getstate__(   self	):
   lowerCAmelCase     :    Optional[Any]						=   self.__dict__.copy()
   lowerCAmelCase     :    Optional[int]						=   None
   return state



 def __setstate__(   self		,      a_	):
   lowerCAmelCase     :    Union[str, Any]						=   d

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

   lowerCAmelCase     :    List[str]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
   self.sp_model.Load(self.vocab_file	)



 def   _lowerCamelCase    (   self		,      a_		,      **a_	):







   if not self.legacy:
     lowerCAmelCase     :    str						=   SPIECE_UNDERLINE + text.replace(lowercase_		,      " "	)
   return super().tokenize(lowercase_		,      **lowercase_	)



 def   _lowerCamelCase    (   self		,      a_		,      **a_	):







   if not self.legacy:
     lowerCAmelCase     :    Optional[Any]						=   text.startswith(lowercase_	)
     if is_first:
       lowerCAmelCase     :    Union[str, Any]						=   text[1:]

   lowerCAmelCase     :    List[Any]						=   self.sp_model.encode(lowercase_		,      out_type=lowercase_	)

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



 def   _lowerCamelCase    (   self		,      a_	):







   if token.startswith("<extra_id_"	):
     lowerCAmelCase     :    Optional[Any]						=   re.match(r"<extra_id_(\d+)>"		,      lowercase_	)
     lowerCAmelCase     :    Any						=   int(match.group(1	)	)
     return self.vocab_size - num - 1
   return self.sp_model.piece_to_id(lowercase_	)



 def   _lowerCamelCase    (   self		,      a_	):







   if index < self.sp_model.get_piece_size():
     lowerCAmelCase     :    List[str]						=   self.sp_model.IdToPiece(lowercase_	)
   else:
     lowerCAmelCase     :    str						=   F'''<extra_id_{self.vocab_size - 1 - index}>'''
   return token



 def   _lowerCamelCase    (   self		,      a_	):
   lowerCAmelCase     :    str						=   []
   lowerCAmelCase     :    List[str]						=   ""
   lowerCAmelCase     :    List[Any]						=   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
       lowerCAmelCase     :    List[str]						=   True
       lowerCAmelCase     :    Optional[Any]						=   []
     else:
       current_sub_tokens.append(lowercase_	)
       lowerCAmelCase     :    List[Any]						=   False
   out_string += self.sp_model.decode(lowercase_	)
   return out_string.strip()





 def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):







   if not os.path.isdir(lowercase_	):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
     return
   lowerCAmelCase     :    str						=   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:
       lowerCAmelCase     :    str						=   self.sp_model.serialized_model_proto()
       fi.write(lowercase_	)

   return (out_vocab_file,)










'''simple docstring'''






import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowerCAmelCase       		=      """\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


lowerCAmelCase       		=      """\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


lowerCAmelCase       		=      R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION       )
class 		lowerCamelCase  (      datasets.Metric       ):



	def   _lowerCamelCase    (   self	):
			return datasets.MetricInfo(
			    description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(
			        {
			            "predictions": datasets.Value("string"	),
			            "references": datasets.Value("string"	),
			        }	)		,      homepage="https://github.com/hendrycks/math"		,      codebase_urls=["https://github.com/hendrycks/math"]		,      )





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   0.0
			for i, j in zip(a_		,      a_	):
					n_correct += 1.0 if math_equivalence.is_equiv(a_		,      a_	) else 0.0
			lowerCAmelCase     :    List[str]						=   n_correct / len(a_	)
			return {
			    "accuracy": accuracy,
			}



