






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

import datasets


__UpperCAmelCase     							=						'\\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'


__UpperCAmelCase     							=						'\\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'


__UpperCAmelCase     							=						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   lowerCamelCase				(datasets.Metric      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							int:
							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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				Any						=						0.0
							for i, j in zip(_UpperCamelCase    ,		_UpperCamelCase       ):
												n_correct += 1.0 if math_equivalence.is_equiv(_UpperCamelCase    ,		_UpperCamelCase       ) else 0.0
							UpperCAmelCase_   :				Tuple						=						n_correct / len(_UpperCamelCase       )
							return {
							    "accuracy": accuracy,
							}














import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[Any]:
							UpperCAmelCase_   :				List[str]						=						hf_hub_download(
							    repo_id='nateraw/video-demo'    ,		filename='archery.mp4'    ,		repo_type='dataset'       )
							UpperCAmelCase_   :				str						=						VideoClassificationPipeline(model=_UpperCamelCase    ,		image_processor=_UpperCamelCase    ,		top_k=2       )
							UpperCAmelCase_   :				List[str]						=						[
							    example_video_filepath,
							    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
							]
							return video_classifier, examples



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Dict:
							for example in examples:
												UpperCAmelCase_   :				str						=						video_classifier(_UpperCamelCase       )

												self.assertEqual(
												    _UpperCamelCase    ,		[
												        {'score': ANY(_UpperCamelCase       ), 'label': ANY(_UpperCamelCase       )},
												        {'score': ANY(_UpperCamelCase       ), 'label': ANY(_UpperCamelCase       )},
												    ]    ,		)



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
							UpperCAmelCase_   :				Optional[Any]						=						VideoMAEFeatureExtractor(
							    size={'shortest_edge': 1_0}    ,		crop_size={'height': 1_0, 'width': 1_0}       )
							UpperCAmelCase_   :				str						=						pipeline(
							    'video-classification'    ,		model=_UpperCamelCase    ,		feature_extractor=_UpperCamelCase    ,		frame_sampling_rate=4       )

							UpperCAmelCase_   :				Any						=						hf_hub_download(repo_id='nateraw/video-demo'    ,		filename='archery.mp4'    ,		repo_type='dataset'       )
							UpperCAmelCase_   :				List[str]						=						video_classifier(_UpperCamelCase    ,		top_k=2       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase    ,		decimals=4       )    ,		[{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}]    ,		)

							UpperCAmelCase_   :				Tuple						=						video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]    ,		top_k=2    ,		)
							self.assertEqual(
							    nested_simplify(_UpperCamelCase    ,		decimals=4       )    ,		[
							        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
							        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
							    ]    ,		)



		@require_tf
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass






