"""ECE metric file."""

from __future__ import annotations

from typing import TYPE_CHECKING

import datasets
import evaluate
from torch import LongTensor, Tensor
from torchmetrics.functional.classification.calibration_error import (
    binary_calibration_error,
    multiclass_calibration_error,
)

if TYPE_CHECKING:
    from collections.abc import Iterable

_CITATION = """\
@InProceedings{huggingface:ece,
title = {Expected calibration error (ECE)},
authors={Nathan Fradet},
year={2023}
}
"""

_DESCRIPTION = """\
This metrics computes the expected calibration error (ECE).
It directly calls the torchmetrics package:
https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
"""


_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predictions to score. They must have a shape (N,C,...) if
        multiclass, or (N,...) if binary.
    references: list of reference for each prediction, with a shape (N,...).
Returns:
    ece: expected calibration error
Examples:
    >>> ece = evaluate.load("Natooz/ece")
    >>> results = ece.compute(
    ...     references=np.array([[0.25, 0.20, 0.55],
    ...                          [0.55, 0.05, 0.40],
    ...                          [0.10, 0.30, 0.60],
    ...                          [0.90, 0.05, 0.05]]),
    ...     predictions=np.array([0, 1, 2, 0]),
    ...     num_classes=3,
    ...     n_bins=3,
    ...     norm="l1",
    ... )
    >>> print(results)
    {'ece': 0.2000}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class ECE(evaluate.Metric):
    """
    Module for the BinaryCalibrationError (ECE) metric of the torchmetrics package.

    https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html.
    """

    def _info(self) -> evaluate.MetricInfo:
        """
        Return the module info.

        :return: module info.
        """
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features(
                {
                    "predictions": datasets.Sequence(datasets.Value("float32")),
                    "references": datasets.Value("int64"),
                }
            ),
            # Homepage of the module for documentation
            homepage="https://huggingface.co/spaces/Natooz/ece",
            # Additional links to the codebase or references
            codebase_urls=[
                "https://github.com/Lightning-AI/torchmetrics/blob/v0.11.4/src/torchmetrics/classification/calibration_error.py"
            ],
            reference_urls=[
                "https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html"
            ],
        )

    def _compute(
        self,
        predictions: Iterable[float] | None = None,
        references: Iterable[int] | None = None,
        **kwargs
    ) -> dict[str, float]:
        """
        Return the Expected Calibration Error (ECE).

        See the torchmetrics documentation for more information on the method.
        https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
            predictions: (N,C,...) if multiclass or (N,...) if binary
            references: (N,...).

        If "num_classes" is not provided in a multiclass setting, the number maximum
        label index will be used as "num_classes".
        """
        # Convert the input
        predictions = Tensor(predictions)
        references = LongTensor(references)

        # Determine number of classes / binary or multiclass
        error_msg = (
            "Expected to have predictions with shape (N,C,...) for multiclass or "
            "(N,...) for binary, and references with shape (N,...), but got "
            f"{predictions.shape} and {references.shape}"
        )
        binary = True
        if predictions.dim() == references.dim() + 1:  # multiclass
            binary = False
            if "num_classes" not in kwargs:
                kwargs["num_classes"] = int(predictions.shape[1])
        elif predictions.dim() == references.dim() and "num_classes" in kwargs:
            raise ValueError(
                "You gave the num_classes argument, with predictions and references "
                "having the same number of dimensions. " + error_msg
            )
        elif predictions.dim() != references.dim():
            raise ValueError("Bad input shape. " + error_msg)

        # Compute the calibration
        if binary:
            ece = binary_calibration_error(predictions, references, **kwargs)
        else:
            ece = multiclass_calibration_error(predictions, references, **kwargs)
        return {
            "ece": float(ece),
        }