





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase  :   List[Any]						      =get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

__lowercase  :   Any						      =25_0004
__lowercase  :   Optional[int]						      =25_0020

@require_sentencepiece
@require_tokenizers
class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =MBartaaTokenizer
      _snake_case							     =MBartaaTokenizerFast
      _snake_case							     =True
      _snake_case							     =True



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[int]:
         '''simple docstring'''

         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_											=MBartaaTokenizer(_lowerCAmelCase		,  src_lang="en_XX"		,  tgt_lang="ro_RO"		,  keep_accents=_lowerCAmelCase       )
         tokenizer.save_pretrained(self.tmpdirname       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											="<s>"
         UpperCAmelCase_											=0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase       )		,  _lowerCAmelCase       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase       )		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]		,  "<s>"       )
         self.assertEqual(vocab_keys[1]		,  "<pad>"       )
         self.assertEqual(vocab_keys[-1]		,  "<mask>"       )
         self.assertEqual(len(_lowerCAmelCase       )		,  1054       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    str:
         '''simple docstring'''

         self.assertEqual(self.get_tokenizer().vocab_size		,  1054       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=MBartaaTokenizer(_lowerCAmelCase		,  src_lang="en_XX"		,  tgt_lang="ro_RO"		,  keep_accents=_lowerCAmelCase       )

         UpperCAmelCase_											=tokenizer.tokenize("This is a test"       )
         self.assertListEqual(_lowerCAmelCase		,  ["▁This", "▁is", "▁a", "▁t", "est"]       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )		,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,  )

         UpperCAmelCase_											=tokenizer.tokenize("I was born in 92000, and this is falsé."       )
         self.assertListEqual(
             _lowerCAmelCase		,  [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]		,  )
         UpperCAmelCase_											=tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )
         self.assertListEqual(
             _lowerCAmelCase		,  [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]		,  )

         UpperCAmelCase_											=tokenizer.convert_ids_to_tokens(_lowerCAmelCase       )
         self.assertListEqual(
             _lowerCAmelCase		,  [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]		,  )



      @slow
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											={"input_ids": [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCAmelCase		,  model_name="facebook/mbart-large-50"		,  revision="d3913889c59cd5c9e456b269c376325eabad57e2"		,  )






      def 	lowerCAmelCase__    (	self:						Tuple       )						->    str:
         '''simple docstring'''

         if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

         UpperCAmelCase_											=(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
               UpperCAmelCase_											=self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase		,  **_lowerCAmelCase       )
               UpperCAmelCase_											=self.tokenizer_class.from_pretrained(_lowerCAmelCase		,  **_lowerCAmelCase       )

               UpperCAmelCase_											=tempfile.mkdtemp()

               UpperCAmelCase_											=tokenizer_r.save_pretrained(_lowerCAmelCase       )
               UpperCAmelCase_											=tokenizer_p.save_pretrained(_lowerCAmelCase       )

               # Checks it save with the same files + the tokenizer.json file for the fast one
               self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )
               UpperCAmelCase_											=tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f       )
               self.assertSequenceEqual(_lowerCAmelCase		,  _lowerCAmelCase       )

               # Checks everything loads correctly in the same way
               UpperCAmelCase_											=tokenizer_r.from_pretrained(_lowerCAmelCase       )
               UpperCAmelCase_											=tokenizer_p.from_pretrained(_lowerCAmelCase       )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_lowerCAmelCase		,  _lowerCAmelCase       )       )
                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

               shutil.rmtree(_lowerCAmelCase       )

               # Save tokenizer rust, legacy_format=True
               UpperCAmelCase_											=tempfile.mkdtemp()

               UpperCAmelCase_											=tokenizer_r.save_pretrained(_lowerCAmelCase		,  legacy_format=_lowerCAmelCase       )
               UpperCAmelCase_											=tokenizer_p.save_pretrained(_lowerCAmelCase       )

               # Checks it save with the same files
               self.assertSequenceEqual(_lowerCAmelCase		,  _lowerCAmelCase       )

               # Checks everything loads correctly in the same way
               UpperCAmelCase_											=tokenizer_r.from_pretrained(_lowerCAmelCase       )
               UpperCAmelCase_											=tokenizer_p.from_pretrained(_lowerCAmelCase       )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_lowerCAmelCase		,  _lowerCAmelCase       )       )

               shutil.rmtree(_lowerCAmelCase       )

               # Save tokenizer rust, legacy_format=False
               UpperCAmelCase_											=tempfile.mkdtemp()

               UpperCAmelCase_											=tokenizer_r.save_pretrained(_lowerCAmelCase		,  legacy_format=_lowerCAmelCase       )
               UpperCAmelCase_											=tokenizer_p.save_pretrained(_lowerCAmelCase       )

               # Checks it saved the tokenizer.json file
               self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )

               # Checks everything loads correctly in the same way
               UpperCAmelCase_											=tokenizer_r.from_pretrained(_lowerCAmelCase       )
               UpperCAmelCase_											=tokenizer_p.from_pretrained(_lowerCAmelCase       )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_lowerCAmelCase		,  _lowerCAmelCase       )       )

               shutil.rmtree(_lowerCAmelCase       )

@require_torch
@require_sentencepiece
@require_tokenizers
class      A       (	unittest.TestCase						):
      _snake_case							     ='''facebook/mbart-large-50-one-to-many-mmt'''
      _snake_case							     =[
          ''' UN Chief Says There Is No Military Solution in Syria''',
          ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
      ]
      _snake_case							     =[
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
          '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
          ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
          ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
      ]
      _snake_case							     =[EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name		,  src_lang="en_XX"		,  tgt_lang="ro_RO"       )
         UpperCAmelCase_											=1
         return cls



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    str:
         '''simple docstring'''

         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]		,  25_0001       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]		,  25_0004       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]		,  25_0020       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]		,  25_0038       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         self.assertIn(_lowerCAmelCase		,  self.tokenizer.all_special_ids       )
         UpperCAmelCase_											=[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
         UpperCAmelCase_											=self.tokenizer.decode(_lowerCAmelCase		,  skip_special_tokens=_lowerCAmelCase       )
         UpperCAmelCase_											=self.tokenizer.decode(generated_ids[1:]		,  skip_special_tokens=_lowerCAmelCase       )
         self.assertEqual(_lowerCAmelCase		,  _lowerCAmelCase       )
         self.assertNotIn(self.tokenizer.eos_token		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=["this is gunna be a long sentence " * 20]
         assert isinstance(src_text[0]		,  _lowerCAmelCase       )
         UpperCAmelCase_											=10
         UpperCAmelCase_											=self.tokenizer(_lowerCAmelCase		,  max_length=_lowerCAmelCase		,  truncation=_lowerCAmelCase       ).input_ids[0]
         self.assertEqual(ids[0]		,  _lowerCAmelCase       )
         self.assertEqual(ids[-1]		,  2       )
         self.assertEqual(len(_lowerCAmelCase       )		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Dict       )						->    List[Any]:
         '''simple docstring'''

         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )		,  [25_0053, 25_0001]       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=tempfile.mkdtemp()
         UpperCAmelCase_											=self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(_lowerCAmelCase       )
         UpperCAmelCase_											=MBartaaTokenizer.from_pretrained(_lowerCAmelCase       )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,  _lowerCAmelCase       )



      @require_torch
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.tokenizer(self.src_text		,  text_target=self.tgt_text		,  padding=_lowerCAmelCase		,  return_tensors="pt"       )
         UpperCAmelCase_											=shift_tokens_right(batch["labels"]		,  self.tokenizer.pad_token_id       )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



      @require_torch
      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.tokenizer(
             self.src_text		,  text_target=self.tgt_text		,  padding=_lowerCAmelCase		,  truncation=_lowerCAmelCase		,  max_length=len(self.expected_src_tokens       )		,  return_tensors="pt"		,  )
         UpperCAmelCase_											=shift_tokens_right(batch["labels"]		,  self.tokenizer.pad_token_id       )

         self.assertIsInstance(_lowerCAmelCase		,  _lowerCAmelCase       )

         self.assertEqual((2, 14)		,  batch.input_ids.shape       )
         self.assertEqual((2, 14)		,  batch.attention_mask.shape       )
         UpperCAmelCase_											=batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens		,  _lowerCAmelCase       )
         self.assertEqual(2		,  batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens		,  [EN_CODE]       )
         self.assertEqual(self.tokenizer.suffix_tokens		,  [self.tokenizer.eos_token_id]       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.tokenizer(self.src_text		,  padding=_lowerCAmelCase		,  truncation=_lowerCAmelCase		,  max_length=3		,  return_tensors="pt"       )
         UpperCAmelCase_											=self.tokenizer(
             text_target=self.tgt_text		,  padding=_lowerCAmelCase		,  truncation=_lowerCAmelCase		,  max_length=10		,  return_tensors="pt"       )
         UpperCAmelCase_											=targets["input_ids"]
         UpperCAmelCase_											=shift_tokens_right(_lowerCAmelCase		,  self.tokenizer.pad_token_id       )

         self.assertEqual(batch.input_ids.shape[1]		,  3       )
         self.assertEqual(batch.decoder_input_ids.shape[1]		,  10       )






      @require_torch
      def 	lowerCAmelCase__    (	self:						Any       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.tokenizer._build_translation_inputs(
             "A test"		,  return_tensors="pt"		,  src_lang="en_XX"		,  tgt_lang="ar_AR"       )

         self.assertEqual(
             nested_simplify(_lowerCAmelCase       )		,  {
                 # en_XX, A, test, EOS
                 "input_ids": [[25_0004, 62, 3034, 2]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 25_0001,
             }		,  )











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

import datasets


__lowercase  :   int						      ="""\
@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}
}
"""


__lowercase  :   List[Any]						      ="""\
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.
"""


__lowercase  :   str						      =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      A       (	datasets.Metric						):



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         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:						Any		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=0.0
         for i, j in zip(_lowerCAmelCase		,  _lowerCAmelCase       ):
            n_correct += 1.0 if math_equivalence.is_equiv(_lowerCAmelCase		,  _lowerCAmelCase       ) else 0.0
         UpperCAmelCase_											=n_correct / len(_lowerCAmelCase       )
         return {
             "accuracy": accuracy,
         }




