






'''simple docstring'''
import unittest

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

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:






  class        __SCREAMING_SNAKE_CASE :




   @staticmethod
   def        __magic_name__		( *__lowercase		:							Any   , **__lowercase		:							Optional[Any] )		->  Dict:
         pass






@is_pipeline_test
@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):
 snake_case_												=							MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




 def        __magic_name__		( self		:							Dict   , __lowercase		:							Any   , __lowercase		:							List[str]   , __lowercase		:							List[Any] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			str													=pipeline('''visual-question-answering'''   , model='''hf-internal-testing/tiny-vilt-random-vqa''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=[
           {
               '''image''': Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
               '''question''': '''How many cats are there?''',
           },
           {
               '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
               '''question''': '''How many cats are there?''',
           },
       ]
       return vqa_pipeline, examples




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							Optional[Any]   , __lowercase		:							Optional[Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=vqa_pipeline(__lowercase   , top_k=1 )
       self.assertEqual(
           __lowercase   , [
               [{'''score''': ANY(__lowercase ), '''answer''': ANY(__lowercase )}],
               [{'''score''': ANY(__lowercase ), '''answer''': ANY(__lowercase )}],
           ]   , )




 @require_torch
 def        __magic_name__		( self		:							List[str] )		->  int:
       SCREAMING_SNAKE_CASE__       :			str													=pipeline('''visual-question-answering'''   , model='''hf-internal-testing/tiny-vilt-random-vqa''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													='''./tests/fixtures/tests_samples/COCO/000000039769.png'''
       SCREAMING_SNAKE_CASE__       :			Any													='''How many cats are there?'''

       SCREAMING_SNAKE_CASE__       :			Any													=vqa_pipeline(image=__lowercase   , question='''How many cats are there?'''   , top_k=2 )
       self.assertEqual(
           __lowercase   , [{'''score''': ANY(__lowercase ), '''answer''': ANY(__lowercase )}, {'''score''': ANY(__lowercase ), '''answer''': ANY(__lowercase )}] )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=vqa_pipeline({'''image''': image, '''question''': question}   , top_k=2 )
       self.assertEqual(
           __lowercase   , [{'''score''': ANY(__lowercase ), '''answer''': ANY(__lowercase )}, {'''score''': ANY(__lowercase ), '''answer''': ANY(__lowercase )}] )




 @slow
 @require_torch
 def        __magic_name__		( self		:							Union[str, Any] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Dict													=pipeline('''visual-question-answering'''   , model='''dandelin/vilt-b32-finetuned-vqa''' )
       SCREAMING_SNAKE_CASE__       :			int													='''./tests/fixtures/tests_samples/COCO/000000039769.png'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''How many cats are there?'''

       SCREAMING_SNAKE_CASE__       :			Dict													=vqa_pipeline(image=__lowercase   , question=__lowercase   , top_k=2 )
       self.assertEqual(
           nested_simplify(__lowercase   , decimals=4 )   , [{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}] )

       SCREAMING_SNAKE_CASE__       :			str													=vqa_pipeline({'''image''': image, '''question''': question}   , top_k=2 )
       self.assertEqual(
           nested_simplify(__lowercase   , decimals=4 )   , [{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}] )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=vqa_pipeline(
           [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]   , top_k=2 )
       self.assertEqual(
           nested_simplify(__lowercase   , decimals=4 )   , [[{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}]] * 2   , )




 @require_tf
 @unittest.skip('''Visual question answering not implemented in TF''' )
 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[int]:
       pass







'''simple docstring'''
from scipy.stats import pearsonr

import datasets


a_					=	'\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_					=	'\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_					=	'\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        __SCREAMING_SNAKE_CASE (     datasets.Metric			):




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       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        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[Any]   , __lowercase		:							List[Any]   , __lowercase		:							str=False )		->  int:
       if return_pvalue:
             SCREAMING_SNAKE_CASE__       :			List[str]													=pearsonr(__lowercase   , __lowercase )
             return {"pearsonr": results[0], "p-value": results[1]}
       else:
             return {"pearsonr": float(pearsonr(__lowercase   , __lowercase )[0] )}