
from sklearn.metrics import mean_absolute_error

import datasets


lowerCamelCase_  :       List[str]					      =     """\
@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_  :       Union[str, Any]					      =     """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCamelCase_  :       int					      =     """
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 					lowerCAmelCase__		(				self       :				Union[str, 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 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							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       :				List[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Dict  ,		snake_case_       :				Dict=None  ,		snake_case_       :				Optional[Any]="uniform_average"					):
							UpperCamelCase_:							List[str]		=  mean_absolute_error(snake_case_  ,		snake_case_  ,		sample_weight=snake_case_  ,		multioutput=snake_case_					)

							return {"mae": mae_score}









import math
import os

import cva
import numpy as np

__UpperCAmelCase      = 255.0





def 	snake_case_      (__A     :						float					,   __A     :						float     )			->	float:
   __lowerCAmelCase  :			Any					    =						np.mean((original - contrast) ** 2     )
   if mse == 0:
      return 1_0_0

   return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__A     )     )





def 	snake_case_      ()			->	None:
   __lowerCAmelCase  :			int					    =						os.path.dirname(os.path.realpath(__A     )     )
   # Loading images (original image and compressed image)
   __lowerCAmelCase  :			Tuple					    =						cva.imread(os.path.join(__A					,   """image_data/original_image.png"""     )     )
   __lowerCAmelCase  :			Dict					    =						cva.imread(os.path.join(__A					,   """image_data/compressed_image.png"""     )					,   1     )

   __lowerCAmelCase  :			Optional[int]					    =						cva.imread(os.path.join(__A					,   """image_data/PSNR-example-base.png"""     )     )
   __lowerCAmelCase  :			Optional[int]					    =						cva.imread(
       os.path.join(__A					,   """image_data/PSNR-example-comp-10.jpg"""     )					,   1     )

   # Value expected: 29.73dB
   print("""-- First Test --"""     )
   print(f'''PSNR value is {peak_signal_to_noise_ratio(__A					,   __A     )} dB'''     )

   # # Value expected: 31.53dB (Wikipedia Example)
   print("""\n-- Second Test --"""     )
   print(f'''PSNR value is {peak_signal_to_noise_ratio(__A					,   __A     )} dB'''     )


if __name__ == "__main__":
   main()


