from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__      						=  "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

SCREAMING_SNAKE_CASE__      						=  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__      						=  "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"



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



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										return datasets.MetricInfo(
										    description=_DESCRIPTION		,    citation=_CITATION		,    inputs_description=_KWARGS_DESCRIPTION		,    features=datasets.Features(self._get_feature_types()    )		,    reference_urls=[
										        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
										    ]		,    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.config_name == "multilist":
																return {
																    "predictions": datasets.Sequence(datasets.Value('float'    )    ),
																    "references": datasets.Sequence(datasets.Value('float'    )    ),
																}
										else:
																return {
																    "predictions": datasets.Value('float'    ),
																    "references": datasets.Value('float'    ),
																}

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase=None		,    lowerCAmelCase="uniform_average"		,    lowerCAmelCase=True    ):






										"""simple docstring"""

										snake_case								=			mean_squared_error(
										    __lowercase		,    __lowercase		,    sample_weight=__lowercase		,    multioutput=__lowercase		,    squared=__lowercase    )

										return {"mse": mse}



"""simple docstring"""
import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			['a', 'b', 'c']

										# Defaults to last layer if both are None
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['c']    )
										self.assertEqual(lowerCAmelCase		,    [2]    )

										# Out indices set to match out features
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(['a', 'c']		,    lowerCAmelCase		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['a', 'c']    )
										self.assertEqual(lowerCAmelCase		,    [0, 2]    )

										# Out features set to match out indices
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(lowerCAmelCase		,    [0, 2]		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['a', 'c']    )
										self.assertEqual(lowerCAmelCase		,    [0, 2]    )

										# Out features selected from negative indices
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(lowerCAmelCase		,    [-3, -1]		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['a', 'c']    )
										self.assertEqual(lowerCAmelCase		,    [-3, -1]    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0, 1)		,    lowerCAmelCase    )

										# Out features must be a list
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(('a', 'b')		,    (0, 1)		,    ['a', 'b']    )

										# Out features must be a subset of stage names
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0, 1)		,    ['a']    )

										# Out indices must be a list or tuple
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(lowerCAmelCase		,    0		,    ['a', 'b']    )

										# Out indices must be a subset of stage names
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(lowerCAmelCase		,    (0, 1)		,    ['a']    )

										# Out features and out indices must be the same length
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0,)		,    ['a', 'b', 'c']    )

										# Out features should match out indices
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0, 2)		,    ['a', 'b', 'c']    )

										# Out features and out indices should be in order
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['b', 'a']		,    (0, 1)		,    ['a', 'b']    )

										# Check passes with valid inputs
										verify_out_features_out_indices(['a', 'b', 'd']		,    (0, 1, -1)		,    ['a', 'b', 'c', 'd']    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			BackboneMixin()

										snake_case								=			['a', 'b', 'c']
										snake_case								=			['a', 'c']
										snake_case								=			[0, 2]

										# Check that the output features and indices are set correctly
										self.assertEqual(backbone.out_features		,    ['a', 'c']    )
										self.assertEqual(backbone.out_indices		,    [0, 2]    )

										# Check out features and indices are updated correctly
										snake_case								=			['a', 'b']
										self.assertEqual(backbone.out_features		,    ['a', 'b']    )
										self.assertEqual(backbone.out_indices		,    [0, 1]    )

										snake_case								=			[-3, -1]
										self.assertEqual(backbone.out_features		,    ['a', 'c']    )
										self.assertEqual(backbone.out_indices		,    [-3, -1]    )


