"""Accuracy metric."""

import datasets
import numpy as np

from skimage.metrics import structural_similarity
from typing import Dict, Optional
import evaluate


_DESCRIPTION = """
Compute the mean Structural Similarity Index Measure (SSIM) between two images.

Please pay attention to the `data_range` parameter with floating-point images.

Notes:
-----

If `data_range` is not specified, the range is automatically guessed based on the image 
data type. However for floating-point image data, this estimate yields a result double
the value of the desired range, as the `dtype_range` in `skimage.util.dtype.py` has
defined intervals from -1 to +1. This yields an estimate of 2, instead of 1, which is
most oftenrequired when working with image data (as negative light intentsities are
nonsensical). In case of working with YCbCr-like color data, note that these ranges are
different per channel (Cb and Cr have double the range of Y), so one cannot calculate a
channel-averaged SSIM with a single call to this function, as identical ranges are
assumed for each channel. To match the implementation of Wang et al. [1]_, set
`gaussian_weights` to True, `sigma` to 1.5, `use_sample_covariance` to False, and
specify the `data_range` argument.
"""


_KWARGS_DESCRIPTION = """
Args:
    predictions (`list` of `np.array`): Predicted labels.
    references (`list` of `np.array`): Ground truth labels.
    sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
    ssim (`float`): Structural Similarity Index Measure. The SSIM values are in range
    (-1, 1], when pixels are non-negative.
Examples:
    Example 1-A simple example
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}
    Example 2-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


_CITATION = """
@article{boulogne2014scikit,
  title={Scikit-image: Image processing in Python},
  author={Boulogne, Fran{\c{c}}ois and Warner, Joshua D and Neil Yager, Emmanuelle},
  journal={J. PeerJ},
  volume={2},
  pages={453},
  year={2014}
}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class StructuralSimilarityIndexMeasure(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features({
                "predictions": datasets.Sequence(datasets.Array2D("float32")),
                "references": datasets.Sequence(datasets.Array2D("float32")),
            }),
            reference_urls=["https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html"],
        )

    def _compute(
        self, 
        predictions, 
        references, 
        win_size: Optional[int] = None, 
        gaussian_weights: Optional[bool] = False,
        data_range: Optional[float] = None, 
        multichannel: Optional[bool] = False,
        sample_weight=None,
        **kwargs
    ) -> Dict[str, float]:
        def func_ssim(args):
            return structural_similarity(
                *args, 
                win_size=win_size, 
                gaussian_weights=gaussian_weights, 
                data_range=data_range, 
                multichannel=multichannel
            )
            
        return {
            "ssim": np.average(
                list(map(func_ssim, zip(predictions, references))), 
                weights=sample_weight
            )
        }