from scipy.stats import pearsonr

import datasets


lowerCamelCase_    :		Dict							=	"""
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
"""


lowerCamelCase_    :		List[Any]							=	"""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


lowerCamelCase_    :		Union[str, Any]							=	"""
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,						_KWARGS_DESCRIPTION		)
class 						a__							(       datasets.Metric		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=False   )			->   Dict:
								if return_pvalue:
													__a		 =       pearsonr(UpperCAmelCase				,						UpperCAmelCase   )
													return {"pearsonr": results[0], "p-value": results[1]}
								else:
													return {"pearsonr": float(pearsonr(UpperCAmelCase				,						UpperCAmelCase   )[0]   )}





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   List[Any]:
								__a		 =       parent
								__a		 =       config_class
								__a		 =       has_text_modality
								__a		 =       kwargs
								__a		 =       common_properties



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.config_class(**self.inputs_dict   )
								__a		 =       (
								    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
													common_properties.extend(['vocab_size']   )

								# Test that config has the common properties as getters
								for prop in common_properties:
													self.parent.assertTrue(hasattr(UpperCAmelCase				,						UpperCAmelCase   )				,						msg=f'''`{prop}` does not exist'''   )

								# Test that config has the common properties as setter
								for idx, name in enumerate(UpperCAmelCase   ):
													try:
																		setattr(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )
																		self.parent.assertEqual(
																		    getattr(UpperCAmelCase				,						UpperCAmelCase   )				,						UpperCAmelCase				,						msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase				,						UpperCAmelCase   )}'''   )
													except NotImplementedError:
																		# Some models might not be able to implement setters for common_properties
																		# In that case, a NotImplementedError is raised
																		pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(UpperCAmelCase   ):
													try:
																		__a		 =       self.config_class(**{name: idx}   )
																		self.parent.assertEqual(
																		    getattr(UpperCAmelCase				,						UpperCAmelCase   )				,						UpperCAmelCase				,						msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase				,						UpperCAmelCase   )}'''   )
													except NotImplementedError:
																		# Some models might not be able to implement setters for common_properties
																		# In that case, a NotImplementedError is raised
																		pass



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.config_class(**self.inputs_dict   )
								__a		 =       json.loads(config.to_json_string()   )
								for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.config_class(**self.inputs_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
													__a		 =       os.path.join(UpperCAmelCase				,						'config.json'   )
													config_first.to_json_file(UpperCAmelCase   )
													__a		 =       self.config_class.from_json_file(UpperCAmelCase   )

								self.parent.assertEqual(config_second.to_dict()				,						config_first.to_dict()   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.config_class(**self.inputs_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(UpperCAmelCase   )
													__a		 =       self.config_class.from_pretrained(UpperCAmelCase   )

								self.parent.assertEqual(config_second.to_dict()				,						config_first.to_dict()   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.config_class(**self.inputs_dict   )

								__a		 =       'test'
								with tempfile.TemporaryDirectory() as tmpdirname:
													__a		 =       os.path.join(UpperCAmelCase				,						UpperCAmelCase   )
													config_first.save_pretrained(UpperCAmelCase   )
													__a		 =       self.config_class.from_pretrained(UpperCAmelCase				,						subfolder=UpperCAmelCase   )

								self.parent.assertEqual(config_second.to_dict()				,						config_first.to_dict()   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       self.config_class(**self.inputs_dict				,						num_labels=5   )
								self.parent.assertEqual(len(config.idalabel   )				,						5   )
								self.parent.assertEqual(len(config.labelaid   )				,						5   )

								__a		 =       3
								self.parent.assertEqual(len(config.idalabel   )				,						3   )
								self.parent.assertEqual(len(config.labelaid   )				,						3   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								if self.config_class.is_composition:
													return
								__a		 =       self.config_class()
								self.parent.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       copy.deepcopy(UpperCAmelCase   )
								__a		 =       self.config_class(**UpperCAmelCase   )
								__a		 =       []
								for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																		if not is_torch_available():
																							continue
																		else:
																							import torch

																							if config.torch_dtype != torch.floataa:
																												wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)   )
													elif getattr(UpperCAmelCase				,						UpperCAmelCase   ) != value:
																		wrong_values.append((key, getattr(UpperCAmelCase				,						UpperCAmelCase   ), value)   )

								if len(UpperCAmelCase   ) > 0:
													__a		 =       '\n'.join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]   )
													raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()




