





"""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        __UpperCamelCase				:






									@staticmethod
									def     __a   (   *lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> Any:
										pass






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Image					) ->str:






	'''simple docstring'''






	a      :		str	=				hashlib.mda(image.tobytes()					)
	return m.hexdigest()[:10]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Image					) ->Dict:






	'''simple docstring'''






	a      :		Dict	=				np.array(_lowerCamelCase					)
	a      :		Union[str, Any]	=				npimg.shape
	return {"hash": hashimage(_lowerCamelCase					), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class        __UpperCamelCase				(						unittest.TestCase						):
							lowerCamelCase   :					Optional[Any]      					=dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
							lowerCamelCase   :					Union[str, Any]      					=dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])						)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Union[str, Any]	=				MaskGenerationPipeline(model=a__      ,					image_processor=a__      )
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Any:
								pass






							@require_tf
							@unittest.skip("Image segmentation not implemented in TF"      )
							def     __a   (   self      )	-> Tuple:
								pass






							@slow
							@require_torch
							def     __a   (   self      )	-> Optional[int]:
								a      :		Dict	=				pipeline("mask-generation"      ,					model="facebook/sam-vit-huge"      )

								a      :		Optional[int]	=				image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"      ,					points_per_batch=256      )

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

								# fmt: off
								self.assertEqual(
								    nested_simplify(a__      ,					decimals=4      )      ,					[
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_444},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_967},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_909},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_879},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_834},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_716},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_612},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_599},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_552},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_532},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_516},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_499},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_483},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_464},
								        {"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": 0.9_408},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_335},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_326},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_262},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_999},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_986},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_984},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_873},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_871}
								    ]      ,					)
								# fmt: on






							@require_torch
							@slow
							def     __a   (   self      )	-> Optional[int]:
								a      :		Optional[int]	=				"""facebook/sam-vit-huge"""
								a      :		Any	=				pipeline("mask-generation"      ,					model=a__      )

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

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

								self.assertEqual(
								    nested_simplify(a__      ,					decimals=4      )      ,					[
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_444},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_210},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
								    ]      ,					)



"""simple docstring"""




from scipy.stats import pearsonr

import datasets


_a      :		str           =   '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


_a      :		List[str]           =   '\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


_a      :		List[Any]           =   '\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,	_KWARGS_DESCRIPTION		)
class 						__A							( datasets.Metric		):






     def     __A  (		self  ):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
               {
                   """predictions""": datasets.Value("""float"""  ),
                   """references""": datasets.Value("""float"""  ),
               }  )				,     reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]				,     )






     def     __A  (		self				,     a__				,     a__				,     a__=False  ):
       if return_pvalue:
         _lowerCAmelCase						:       List[Any]  			=     pearsonr(a__				,     a__  )
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(a__				,     a__  )[0]  )}




