

import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

UpperCamelCase__					: List[str]								=      [[12, 10], [3, 9]]
UpperCamelCase__					: List[Any]								=      [[3, 4], [7, 4]]
UpperCamelCase__					: str								=      [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
UpperCamelCase__					: Dict								=      [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
UpperCamelCase__					: Union[str, Any]								=      [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
UpperCamelCase__					: Tuple								=      [1]
UpperCamelCase__					: Optional[Any]								=      [2]

UpperCamelCase__					: Union[str, Any]								=      logging.getLogger()
UpperCamelCase__					: List[str]								=      logging.DEBUG
UpperCamelCase__					: Union[str, Any]								=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('mat1', 'mat2')	,				[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]    )
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Optional[int]    ):





    """simple docstring"""


    if (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2):
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            logger.info(f"""\n\t{test_addition.__name__} returned integer"""    )
            matop.add(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
    elif (np.array(_SCREAMING_SNAKE_CASE    )).shape == (np.array(_SCREAMING_SNAKE_CASE    )).shape:
        logger.info(f"""\n\t{test_addition.__name__} with same matrix dims"""    )
        SCREAMING_SNAKE_CASE_													=  (np.array(_SCREAMING_SNAKE_CASE    ) + np.array(_SCREAMING_SNAKE_CASE    )).tolist()
        SCREAMING_SNAKE_CASE_													=  matop.add(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
        assert theo == act
    else:
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            logger.info(f"""\n\t{test_addition.__name__} with different matrix dims"""    )
            matop.add(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('mat1', 'mat2')	,				[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]    )
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Tuple    ):





    """simple docstring"""


    if (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2):
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            logger.info(f"""\n\t{test_subtraction.__name__} returned integer"""    )
            matop.subtract(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
    elif (np.array(_SCREAMING_SNAKE_CASE    )).shape == (np.array(_SCREAMING_SNAKE_CASE    )).shape:
        logger.info(f"""\n\t{test_subtraction.__name__} with same matrix dims"""    )
        SCREAMING_SNAKE_CASE_													=  (np.array(_SCREAMING_SNAKE_CASE    ) - np.array(_SCREAMING_SNAKE_CASE    )).tolist()
        SCREAMING_SNAKE_CASE_													=  matop.subtract(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
        assert theo == act
    else:
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            logger.info(f"""\n\t{test_subtraction.__name__} with different matrix dims"""    )
            assert matop.subtract(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('mat1', 'mat2')	,				[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]    )
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						Any    ):





    """simple docstring"""


    if (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2):
        logger.info(f"""\n\t{test_multiplication.__name__} returned integer"""    )
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            matop.add(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
    elif (np.array(_SCREAMING_SNAKE_CASE    )).shape == (np.array(_SCREAMING_SNAKE_CASE    )).shape:
        logger.info(f"""\n\t{test_multiplication.__name__} meets dim requirements"""    )
        SCREAMING_SNAKE_CASE_													=  (np.matmul(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )).tolist()
        SCREAMING_SNAKE_CASE_													=  matop.multiply(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
        assert theo == act
    else:
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            logger.info(
                f"""\n\t{test_multiplication.__name__} does not meet dim requirements"""    )
            assert matop.subtract(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )


@pytest.mark.mat_ops
def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  (3.5 * np.array(_SCREAMING_SNAKE_CASE    )).tolist()
    SCREAMING_SNAKE_CASE_													=  matop.scalar_multiply(_SCREAMING_SNAKE_CASE	,				3.5    )
    assert theo == act


@pytest.mark.mat_ops
def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  (np.identity(5    )).tolist()
    SCREAMING_SNAKE_CASE_													=  matop.identity(5    )
    assert theo == act





@pytest.mark.mat_ops
@pytest.mark.parametrize('mat'	,				[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]    )
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if (np.array(_SCREAMING_SNAKE_CASE    )).shape < (2, 2):
        with pytest.raises(_SCREAMING_SNAKE_CASE    ):
            logger.info(f"""\n\t{test_transpose.__name__} returned integer"""    )
            matop.transpose(_SCREAMING_SNAKE_CASE    )
    else:
        SCREAMING_SNAKE_CASE_													=  (np.transpose(_SCREAMING_SNAKE_CASE    )).tolist()
        SCREAMING_SNAKE_CASE_													=  matop.transpose(_SCREAMING_SNAKE_CASE	,				return_map=_SCREAMING_SNAKE_CASE    )
        assert theo == act





from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase__					: Union[str, Any]								=      "\\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"

UpperCamelCase__					: Optional[Any]								=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


UpperCamelCase__					: Tuple								=      "\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\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"



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

  def   lowerCAmelCase__       (   self):
      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   lowerCAmelCase__       (   self):
      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   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A="uniform_average"):
      SCREAMING_SNAKE_CASE_													=  mean_absolute_error(_A  ,						_A  ,						sample_weight=_A  ,						multioutput=_A)

      return {"mae": mae_score}


