'''simple docstring'''

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =0
        # max greyscale value for #FFFFFF
        _UpperCamelCase  :str       =int(self.get_greyscale(255					,      255					,      255     )     )

        if not self.min_threshold < threshold < self.max_threshold:
             _UpperCamelCase  :Union[str, Any]       =f'''Factor value should be from 0 to {self.max_threshold}'''
             raise ValueError(lowerCAmelCase__     )

        _UpperCamelCase  :str       =input_img
        _UpperCamelCase  :Dict       =threshold
        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        _UpperCamelCase  :List[str]       =[
            [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
        ]
        _UpperCamelCase  :Optional[Any]       =np.ones((self.width, self.height, 3)					,      np.uinta     ) * 255



   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     float:
        """simple docstring"""





        return 0.114 * blue + 0.587 * green + 0.2126 * red




   def  _UpperCamelCase		(    self     )				->     None:
        """simple docstring"""





        for y in range(self.height     ):
             for x in range(self.width     ):
                  _UpperCamelCase  :str       =int(self.get_greyscale(*self.input_img[y][x]     )     )
                  if self.threshold > greyscale + self.error_table[y][x]:
                       _UpperCamelCase  :Any       =(0, 0, 0)
                       _UpperCamelCase  :Tuple       =greyscale + self.error_table[x][y]
                  else:
                       _UpperCamelCase  :Optional[Any]       =(255, 255, 255)
                       _UpperCamelCase  :int       =greyscale + self.error_table[x][y] - 255
                  self.error_table[y][x + 1] += int(8 / 32 * current_error     )
                  self.error_table[y][x + 2] += int(4 / 32 * current_error     )
                  self.error_table[y + 1][x] += int(8 / 32 * current_error     )
                  self.error_table[y + 1][x + 1] += int(4 / 32 * current_error     )
                  self.error_table[y + 1][x + 2] += int(2 / 32 * current_error     )
                  self.error_table[y + 1][x - 1] += int(4 / 32 * current_error     )
                  self.error_table[y + 1][x - 2] += int(2 / 32 * current_error     )


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      _lowerCamelCase :				Optional[Any]														= [
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 1_26, 1_30, 1_40)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                f"Original image with dithering threshold: {burkes.threshold}",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()
'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase :				Union[str, Any]														= """\
@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}
}
"""

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


_lowerCamelCase :				Union[str, 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    lowerCamelCase__  (   datasets.Metric     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """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_absolute_error.html"""
            ]					,      )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}