
'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


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

_UpperCAmelCase   :      Optional[int] 						=  """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_UpperCAmelCase   :      List[Any] 						=  """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""

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





		"""simple docstring"""





		def _lowercase  (    self     )				->    Optional[Any]:
				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_absolute_error.html'''
				    ]  ,					)
		def _lowercase  (    self     )				->    Tuple:
				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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case=None  ,					_snake_case="uniform_average"     )				->    Dict:
				_UpperCamelCase       : Optional[Any]    =      mean_absolute_error(_snake_case  ,					_snake_case  ,					sample_weight=_snake_case  ,					multioutput=_snake_case     )

				return {"mae": mae_score}







'''simple docstring'''

import unittest

from transformers.image_processing_utils import get_size_dict

class 			UpperCAmelCase				(					unittest.TestCase ):





		"""simple docstring"""





		def _lowercase  (    self     )				->    Dict:
				# Test a dict with the wrong keys raises an error
				_UpperCamelCase       : List[Any]    =      {'''wrong_key''': 224}
				with self.assertRaises(_snake_case     ):
						get_size_dict(_snake_case     )

				_UpperCamelCase       : Dict    =      {'''height''': 224}
				with self.assertRaises(_snake_case     ):
						get_size_dict(_snake_case     )

				_UpperCamelCase       : str    =      {'''width''': 224, '''shortest_edge''': 224}
				with self.assertRaises(_snake_case     ):
						get_size_dict(_snake_case     )

				# Test a dict with the correct keys is returned as is
				_UpperCamelCase       : Any    =      {'''height''': 224, '''width''': 224}
				_UpperCamelCase       : Optional[int]    =      get_size_dict(_snake_case     )
				self.assertEqual(_snake_case  ,					_snake_case     )

				_UpperCamelCase       : Optional[Any]    =      {'''shortest_edge''': 224}
				_UpperCamelCase       : int    =      get_size_dict(_snake_case     )
				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224}     )

				_UpperCamelCase       : int    =      {'''longest_edge''': 224, '''shortest_edge''': 224}
				_UpperCamelCase       : Tuple    =      get_size_dict(_snake_case     )
				self.assertEqual(_snake_case  ,					{'''longest_edge''': 224, '''shortest_edge''': 224}     )

				# Test a single int value which  represents (size, size)
				_UpperCamelCase       : Any    =      get_size_dict(224     )
				self.assertEqual(_snake_case  ,					{'''height''': 224, '''width''': 224}     )

				# Test a single int value which represents the shortest edge
				_UpperCamelCase       : List[Any]    =      get_size_dict(224  ,					default_to_square=_snake_case     )
				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224}     )

				# Test a tuple of ints which represents (height, width)
				_UpperCamelCase       : Any    =      get_size_dict((150, 200)     )
				self.assertEqual(_snake_case  ,					{'''height''': 150, '''width''': 200}     )

				# Test a tuple of ints which represents (width, height)
				_UpperCamelCase       : List[str]    =      get_size_dict((150, 200)  ,					height_width_order=_snake_case     )
				self.assertEqual(_snake_case  ,					{'''height''': 200, '''width''': 150}     )

				# Test an int representing the shortest edge and max_size which represents the longest edge
				_UpperCamelCase       : Any    =      get_size_dict(224  ,					max_size=256  ,					default_to_square=_snake_case     )
				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224, '''longest_edge''': 256}     )

				# Test int with default_to_square=True and max_size fails
				with self.assertRaises(_snake_case     ):
						get_size_dict(224  ,					max_size=256  ,					default_to_square=_snake_case     )





