"""simple docstring"""





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

import datasets


_UpperCamelCase     :		int											=					"\\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     :		str											=					"\\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     :		List[str]											=					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   UpperCAmelCase_			(					datasets.Metric):


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             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	,    a	,    a							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			0.0
             for i, j in zip(a	,    a							):
                    n_correct += 1.0 if math_equivalence.is_equiv(a	,    a							) else 0.0
             lowercase__    :	List[str]     					=			n_correct / len(a							)
             return {
                 "accuracy": accuracy,
             }


"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	List[Any]     					=			image_processing(a	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Any     					=			image_processing(a	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Tuple     					=			image_processing(a	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

