


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")




def 			_a			(		_lowercase      :			Union[str, Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    test_file.split(os.path.sep						)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
              F'{test_file} instead.'						)
     __UpperCAmelCase			:				Any													=    components[-1]
     if not test_fn.endswith('''py'''						):
          raise ValueError(F'`test_file` should be a python file. Got {test_fn} instead.'						)
     if not test_fn.startswith('''test_modeling_'''						):
          raise ValueError(
              F'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'						)

     __UpperCAmelCase			:				Optional[Any]													=    components[:-1] + [test_fn.replace('''.py'''    ,      ''''''						)]
     __UpperCAmelCase			:				int													=    '''.'''.join(_lowercase						)

     return test_module_path




def 			_a			(		_lowercase      :			Dict						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[Any]													=    get_module_path(_lowercase						)
     __UpperCAmelCase			:				str													=    importlib.import_module(_lowercase						)

     return test_module




def 			_a			(		_lowercase      :			Optional[int]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    []
     __UpperCAmelCase			:				int													=    get_test_module(_lowercase						)
     for attr in dir(_lowercase						):
          if attr.endswith('''ModelTester'''						):
               tester_classes.append(getattr(_lowercase    ,      _lowercase						)						)

    # sort with class names
     return sorted(_lowercase    ,      key=lambda _lowercase						: x.__name__						)




def 			_a			(		_lowercase      :			Union[str, Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[Any]													=    []
     __UpperCAmelCase			:				int													=    get_test_module(_lowercase						)
     for attr in dir(_lowercase						):
          __UpperCAmelCase			:				Optional[int]													=    getattr(_lowercase    ,      _lowercase						)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          __UpperCAmelCase			:				Optional[Any]													=    getattr(_lowercase    ,      '''all_model_classes'''    ,      []						)
          if len(_lowercase						) > 0:
               test_classes.append(_lowercase						)

    # sort with class names
     return sorted(_lowercase    ,      key=lambda _lowercase						: x.__name__						)




def 			_a			(		_lowercase      :			Optional[int]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    get_test_classes(_lowercase						)
     __UpperCAmelCase			:				Tuple													=    set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes						)

     # sort with class names
     return sorted(_lowercase    ,      key=lambda _lowercase						: x.__name__						)




def 			_a			(		_lowercase      :			Optional[int]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    test_class()
     if hasattr(_lowercase    ,      '''setUp'''						):
          test.setUp()

     __UpperCAmelCase			:				Optional[Any]													=    None
     if hasattr(_lowercase    ,      '''model_tester'''						):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               __UpperCAmelCase			:				List[Any]													=    test.model_tester.__class__

     return model_tester




def 			_a			(		_lowercase      :			Dict    ,      _lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    get_test_classes(_lowercase						)

     __UpperCAmelCase			:				Union[str, Any]													=    []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(_lowercase						)

    # sort with class names
     return sorted(_lowercase    ,      key=lambda _lowercase						: x.__name__						)




def 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    get_test_classes_for_model(_lowercase    ,      _lowercase						)

     __UpperCAmelCase			:				Tuple													=    []
     for test_class in test_classes:
          __UpperCAmelCase			:				str													=    get_model_tester_from_test_class(_lowercase						)
          if tester_class is not None:
               tester_classes.append(_lowercase						)

    # sort with class names
     return sorted(_lowercase    ,      key=lambda _lowercase						: x.__name__						)




def 			_a			(		_lowercase      :			Optional[int]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    get_test_classes(_lowercase						)
     __UpperCAmelCase			:				str													=    {test_class: get_model_tester_from_test_class(_lowercase						) for test_class in test_classes}
     return test_tester_mapping




def 			_a			(		_lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    get_model_classes(_lowercase						)
     __UpperCAmelCase			:				Optional[Any]													=    {
         model_class: get_test_classes_for_model(_lowercase    ,      _lowercase						) for model_class in model_classes
     }
     return model_test_mapping




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    get_model_classes(_lowercase						)
     __UpperCAmelCase			:				Optional[int]													=    {
         model_class: get_tester_classes_for_model(_lowercase    ,      _lowercase						) for model_class in model_classes
     }
     return model_to_tester_mapping




def 			_a			(		_lowercase      :			List[Any]						):





     '''simple docstring'''






     if isinstance(_lowercase    ,      _lowercase						):
          return o
     elif isinstance(_lowercase    ,      _lowercase						):
          return o.__name__
     elif isinstance(_lowercase    ,      (list, tuple)						):
          return [to_json(_lowercase						) for x in o]
     elif isinstance(_lowercase    ,      _lowercase						):
          return {to_json(_lowercase						): to_json(_lowercase						) for k, v in o.items()}
     else:
          return o



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\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"


__UpperCAmelCase    :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"


__UpperCAmelCase    :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 ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, 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 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}