






"""simple docstring"""
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


_lowerCAmelCase       =				get_tests_dir("fixtures/test_sentencepiece.model")

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

_lowerCAmelCase       =				250_004
_lowerCAmelCase       =				250_020



@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					MBartaaTokenizer
	_A							:					List[Any] 			=					MBartaaTokenizerFast
	_A							:					Any 			=					True
	_A							:					List[Any] 			=					True





	def  lowerCamelCase(self				):
		super().setUp()

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





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = """<s>"""
		A_		:    Tuple						     = 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				):
		A_		:    Optional[Any]						     = 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				):
		self.assertEqual(self.get_tokenizer().vocab_size  ,  1054				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = MBartaaTokenizer(lowerCAmelCase_  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""  ,  keep_accents=lowerCAmelCase_				)

		A_		:    Dict						     = 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]]  ,  )

		A_		:    Union[str, Any]						     = 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""", """."""]  ,  )
		A_		:    Optional[int]						     = 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]
		    ]  ,  )

		A_		:    Union[str, Any]						     = 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				):
		# fmt: off
		A_		:    int						     = {"""input_ids""": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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				):
		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

		A_		:    Optional[int]						     = (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})"""				):
				A_		:    Tuple						     = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    int						     = self.tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)

				A_		:    Dict						     = tempfile.mkdtemp()

				A_		:    Union[str, Any]						     = tokenizer_r.save_pretrained(lowerCAmelCase_				)
				A_		:    Dict						     = 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				)				)
				A_		:    List[Any]						     = 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
				A_		:    Tuple						     = tokenizer_r.from_pretrained(lowerCAmelCase_				)
				A_		:    Optional[int]						     = 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
				A_		:    int						     = tempfile.mkdtemp()

				A_		:    Dict						     = tokenizer_r.save_pretrained(lowerCAmelCase_  ,  legacy_format=lowerCAmelCase_				)
				A_		:    List[Any]						     = tokenizer_p.save_pretrained(lowerCAmelCase_				)

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

				# Checks everything loads correctly in the same way
				A_		:    Optional[int]						     = tokenizer_r.from_pretrained(lowerCAmelCase_				)
				A_		:    Dict						     = 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
				A_		:    Tuple						     = tempfile.mkdtemp()

				A_		:    Optional[Any]						     = tokenizer_r.save_pretrained(lowerCAmelCase_  ,  legacy_format=lowerCAmelCase_				)
				A_		:    Dict						     = 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
				A_		:    int						     = tokenizer_r.from_pretrained(lowerCAmelCase_				)
				A_		:    Union[str, Any]						     = 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   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Dict 			=					"""facebook/mbart-large-50-one-to-many-mmt"""
	_A							:					Optional[Any] 			=					[
	    """ 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.""",
	]
	_A							:					List[str] 			=					[
	    """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.""",
	]
	_A							:					Tuple 			=					[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]





	@classmethod
	def  lowerCamelCase(cls				):
		A_		:    MBartaaTokenizer						     = MBartaaTokenizer.from_pretrained(
		    cls.checkpoint_name  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""				)
		A_		:    Any						     = 1
		return cls





	def  lowerCamelCase(self				):
		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]  ,  250001				)
		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]  ,  250004				)
		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]  ,  250020				)
		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]  ,  250038				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
		self.assertListEqual(self.expected_src_tokens  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.assertIn(lowerCAmelCase_  ,  self.tokenizer.all_special_ids				)
		A_		:    List[str]						     = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
		A_		:    str						     = self.tokenizer.decode(lowerCAmelCase_  ,  skip_special_tokens=lowerCAmelCase_				)
		A_		:    List[Any]						     = self.tokenizer.decode(generated_ids[1:]  ,  skip_special_tokens=lowerCAmelCase_				)
		self.assertEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)
		self.assertNotIn(self.tokenizer.eos_token  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = ["""this is gunna be a long sentence """ * 20]
		assert isinstance(src_text[0]  ,  lowerCAmelCase_				)
		A_		:    List[Any]						     = 10
		A_		:    List[str]						     = 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				):
		self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]				)  ,  [250053, 250001]				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = tempfile.mkdtemp()
		A_		:    Dict						     = self.tokenizer.fairseq_tokens_to_ids
		self.tokenizer.save_pretrained(lowerCAmelCase_				)
		A_		:    Tuple						     = MBartaaTokenizer.from_pretrained(lowerCAmelCase_				)
		self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.tokenizer(self.src_text  ,  text_target=self.tgt_text  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
		A_		:    Dict						     = 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				):
		A_		:    str						     = self.tokenizer(
		    self.src_text  ,  text_target=self.tgt_text  ,  padding=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  max_length=len(self.expected_src_tokens				)  ,  return_tensors="""pt"""  ,  )
		A_		:    str						     = 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				)
		A_		:    Tuple						     = 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				):
		A_		:    Union[str, Any]						     = self.tokenizer(self.src_text  ,  padding=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  max_length=3  ,  return_tensors="""pt"""				)
		A_		:    List[str]						     = self.tokenizer(
		    text_target=self.tgt_text  ,  padding=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  max_length=10  ,  return_tensors="""pt"""				)
		A_		:    Union[str, Any]						     = targets["""input_ids"""]
		A_		:    List[Any]						     = 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				):
		A_		:    List[str]						     = 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""": [[250004, 62, 3034, 2]],
		        """attention_mask""": [[1, 1, 1, 1]],
		        # ar_AR
		        """forced_bos_token_id""": 250001,
		    }  ,  )









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

import datasets


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


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


_lowerCAmelCase       =				r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"



@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION    )
class   SCREAMING_SNAKE_CASE	(					datasets.Metric    ):





	"""simple docstring"""




	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  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = 0.0
		for i, j in zip(lowerCAmelCase_  ,  lowerCAmelCase_				):
			n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_  ,  lowerCAmelCase_				) else 0.0
		A_		:    List[str]						     = n_correct / len(lowerCAmelCase_				)
		return {
		    "accuracy": accuracy,
		}

