

'''simple docstring'''



import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
   from PIL import Image
else:

   class       SCREAMING_SNAKE_CASE       :






         '''simple docstring'''






         @staticmethod
         def 						__UpperCAmelCase      (       *snake_case			:		Optional[Any]  ,       **snake_case			:		str ):



             """simple docstring"""





             pass

def 	lowerCamelCase__							(   a__)		->     List[Any]:



    """simple docstring"""







    _snake_case			:			int				       =		hashlib.mda(image.tobytes())
    return m.hexdigest()[:1_0]

def 	lowerCamelCase__							(   a__)		->     List[Any]:



    """simple docstring"""







    _snake_case			:			str				       =		np.array(snake_case__)
    _snake_case			:			int				       =		npimg.shape
    return {"hash": hashimage(snake_case__), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class       SCREAMING_SNAKE_CASE       (				unittest.TestCase       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					Tuple     =					dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
      SCREAMING_SNAKE_CASE__			:					int     =					dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )




      def 						__UpperCAmelCase      (       self			:		List[Any]  ,       snake_case			:		Union[str, Any]  ,       snake_case			:		List[Any]  ,       snake_case			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			Tuple				       =		MaskGenerationPipeline(model=lowerCAmelCase_  ,       image_processor=lowerCAmelCase_ )
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]




      def 						__UpperCAmelCase      (       self			:		Union[str, Any]  ,       snake_case			:		List[Any]  ,       snake_case			:		List[str] ):



          """simple docstring"""





          pass




      @require_tf
      @unittest.skip('Image segmentation not implemented in TF' )
      def 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          pass




      @slow
      @require_torch
      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			Dict				       =		pipeline('mask-generation'  ,       model='facebook/sam-vit-huge' )

          _snake_case			:			Optional[Any]				       =		image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'  ,       points_per_batch=256 )

          # Shortening by hashing
          _snake_case			:			Tuple				       =		[]
          for i, o in enumerate(outputs['masks'] ):
              new_outupt += [{"mask": mask_to_test_readable(lowerCAmelCase_ ), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(lowerCAmelCase_  ,       decimals=4 )  ,       [
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
                  {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
                  {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
                  {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
                  {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
                  {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
                  {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
                  {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
                  {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
                  {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
                  {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
                  {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
                  {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
                  {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
                  {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
              ]  ,       )
          # fmt: on




      @require_torch
      @slow
      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			Union[str, Any]				       =		"""facebook/sam-vit-huge"""
          _snake_case			:			List[str]				       =		pipeline('mask-generation'  ,       model=lowerCAmelCase_ )

          _snake_case			:			Optional[int]				       =		image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'  ,       pred_iou_thresh=1  ,       points_per_batch=256 )

          # Shortening by hashing
          _snake_case			:			Dict				       =		[]
          for i, o in enumerate(outputs['masks'] ):
              new_outupt += [{"mask": mask_to_test_readable(lowerCAmelCase_ ), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(lowerCAmelCase_  ,       decimals=4 )  ,       [
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
              ]  ,       )











"""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,
		}

