# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import List, Optional, Tuple, Union

import torch
from torch import Tensor, tensor
from typing_extensions import Literal

from torchmetrics.functional.classification.precision_recall_curve import (
    _binary_precision_recall_curve_arg_validation,
    _binary_precision_recall_curve_format,
    _binary_precision_recall_curve_tensor_validation,
    _binary_precision_recall_curve_update,
    _multiclass_precision_recall_curve_arg_validation,
    _multiclass_precision_recall_curve_format,
    _multiclass_precision_recall_curve_tensor_validation,
    _multiclass_precision_recall_curve_update,
    _multilabel_precision_recall_curve_arg_validation,
    _multilabel_precision_recall_curve_format,
    _multilabel_precision_recall_curve_tensor_validation,
    _multilabel_precision_recall_curve_update,
)
from torchmetrics.functional.classification.roc import (
    _binary_roc_compute,
    _multiclass_roc_compute,
    _multilabel_roc_compute,
)
from torchmetrics.utilities.compute import _auc_compute_without_check, _safe_divide
from torchmetrics.utilities.data import _bincount
from torchmetrics.utilities.prints import rank_zero_warn


def _reduce_auroc(
    fpr: Union[Tensor, List[Tensor]],
    tpr: Union[Tensor, List[Tensor]],
    average: Optional[Literal["macro", "weighted", "none"]] = "macro",
    weights: Optional[Tensor] = None,
) -> Tensor:
    """Utility function for reducing multiple average precision score into one number."""
    if isinstance(fpr, Tensor):
        res = _auc_compute_without_check(fpr, tpr, 1.0, axis=1)
    else:
        res = [_auc_compute_without_check(x, y, 1.0) for x, y in zip(fpr, tpr)]
        res = torch.stack(res)
    if average is None or average == "none":
        return res
    if torch.isnan(res).any():
        rank_zero_warn(
            f"Average precision score for one or more classes was `nan`. Ignoring these classes in {average}-average",
            UserWarning,
        )
    idx = ~torch.isnan(res)
    if average == "macro":
        return res[idx].mean()
    elif average == "weighted" and weights is not None:
        weights = _safe_divide(weights[idx], weights[idx].sum())
        return (res[idx] * weights).sum()
    else:
        raise ValueError("Received an incompatible combinations of inputs to make reduction.")


def _binary_auroc_arg_validation(
    max_fpr: Optional[float] = None,
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    ignore_index: Optional[int] = None,
) -> None:
    _binary_precision_recall_curve_arg_validation(thresholds, ignore_index)
    if max_fpr is not None and not isinstance(max_fpr, float) and 0 < max_fpr <= 1:
        raise ValueError(f"Arguments `max_fpr` should be a float in range (0, 1], but got: {max_fpr}")


def _binary_auroc_compute(
    state: Union[Tensor, Tuple[Tensor, Tensor]],
    thresholds: Optional[Tensor],
    max_fpr: Optional[float] = None,
    pos_label: int = 1,
) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor]]:
    fpr, tpr, _ = _binary_roc_compute(state, thresholds, pos_label)
    if max_fpr is None or max_fpr == 1:
        return _auc_compute_without_check(fpr, tpr, 1.0)

    _device = fpr.device if isinstance(fpr, Tensor) else fpr[0].device
    max_area: Tensor = tensor(max_fpr, device=_device)
    # Add a single point at max_fpr and interpolate its tpr value
    stop = torch.bucketize(max_area, fpr, out_int32=True, right=True)
    weight = (max_area - fpr[stop - 1]) / (fpr[stop] - fpr[stop - 1])
    interp_tpr: Tensor = torch.lerp(tpr[stop - 1], tpr[stop], weight)
    tpr = torch.cat([tpr[:stop], interp_tpr.view(1)])
    fpr = torch.cat([fpr[:stop], max_area.view(1)])

    # Compute partial AUC
    partial_auc = _auc_compute_without_check(fpr, tpr, 1.0)

    # McClish correction: standardize result to be 0.5 if non-discriminant and 1 if maximal
    min_area: Tensor = 0.5 * max_area**2
    return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))


def binary_auroc(
    preds: Tensor,
    target: Tensor,
    max_fpr: Optional[float] = None,
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tuple[Tensor, Tensor, Tensor]:
    r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for binary tasks. The AUROC
    score summarizes the ROC curve into an single number that describes the performance of a model for multiple
    thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
    corresponds to random guessing.

    Accepts the following input tensors:

    - ``preds`` (float tensor): ``(N, ...)``. Preds should be a tensor containing probabilities or logits for each
      observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
      sigmoid per element.
    - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.

    Additional dimension ``...`` will be flattened into the batch dimension.

    The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
    that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
    non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
    argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds})` (constant memory).

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``.
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        A single scalar with the auroc score

    Example:
        >>> from torchmetrics.functional.classification import binary_auroc
        >>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
        >>> target = torch.tensor([0, 1, 1, 0])
        >>> binary_auroc(preds, target, thresholds=None)
        tensor(0.5000)
        >>> binary_auroc(preds, target, thresholds=5)
        tensor(0.5000)
    """
    if validate_args:
        _binary_auroc_arg_validation(max_fpr, thresholds, ignore_index)
        _binary_precision_recall_curve_tensor_validation(preds, target, ignore_index)
    preds, target, thresholds = _binary_precision_recall_curve_format(preds, target, thresholds, ignore_index)
    state = _binary_precision_recall_curve_update(preds, target, thresholds)
    return _binary_auroc_compute(state, thresholds, max_fpr)


def _multiclass_auroc_arg_validation(
    num_classes: int,
    average: Optional[Literal["macro", "weighted", "none"]] = "macro",
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    ignore_index: Optional[int] = None,
) -> None:
    _multiclass_precision_recall_curve_arg_validation(num_classes, thresholds, ignore_index)
    allowed_average = ("macro", "weighted", "none", None)
    if average not in allowed_average:
        raise ValueError(f"Expected argument `average` to be one of {allowed_average} but got {average}")


def _multiclass_auroc_compute(
    state: Union[Tensor, Tuple[Tensor, Tensor]],
    num_classes: int,
    average: Optional[Literal["macro", "weighted", "none"]] = "macro",
    thresholds: Optional[Tensor] = None,
) -> Tensor:
    fpr, tpr, _ = _multiclass_roc_compute(state, num_classes, thresholds)
    return _reduce_auroc(
        fpr,
        tpr,
        average,
        weights=_bincount(state[1], minlength=num_classes).float() if thresholds is None else state[0][:, 1, :].sum(-1),
    )


def multiclass_auroc(
    preds: Tensor,
    target: Tensor,
    num_classes: int,
    average: Optional[Literal["macro", "weighted", "none"]] = "macro",
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multiclass tasks. The AUROC
    score summarizes the ROC curve into an single number that describes the performance of a model for multiple
    thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
    corresponds to random guessing.

    Accepts the following input tensors:

    - ``preds`` (float tensor): ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each
      observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
      softmax per sample.
    - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
      only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).

    Additional dimension ``...`` will be flattened into the batch dimension.

    The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
    that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
    non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
    argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_classes: Integer specifing the number of classes
        average:
            Defines the reduction that is applied over classes. Should be one of the following:

            - ``macro``: Calculate score for each class and average them
            - ``weighted``: Calculates score for each class and computes weighted average using their support
            - ``"none"`` or ``None``: Calculates score for each class and applies no reduction
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class.
        If `average="macro"|"weighted"` then a single scalar is returned.

    Example:
        >>> from torchmetrics.functional.classification import multiclass_auroc
        >>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
        ...                       [0.05, 0.75, 0.05, 0.05, 0.05],
        ...                       [0.05, 0.05, 0.75, 0.05, 0.05],
        ...                       [0.05, 0.05, 0.05, 0.75, 0.05]])
        >>> target = torch.tensor([0, 1, 3, 2])
        >>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=None)
        tensor(0.5333)
        >>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=None)
        tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])
        >>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=5)
        tensor(0.5333)
        >>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=5)
        tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])
    """
    if validate_args:
        _multiclass_auroc_arg_validation(num_classes, average, thresholds, ignore_index)
        _multiclass_precision_recall_curve_tensor_validation(preds, target, num_classes, ignore_index)
    preds, target, thresholds = _multiclass_precision_recall_curve_format(
        preds, target, num_classes, thresholds, ignore_index
    )
    state = _multiclass_precision_recall_curve_update(preds, target, num_classes, thresholds)
    return _multiclass_auroc_compute(state, num_classes, average, thresholds)


def _multilabel_auroc_arg_validation(
    num_labels: int,
    average: Optional[Literal["micro", "macro", "weighted", "none"]],
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    ignore_index: Optional[int] = None,
) -> None:
    _multilabel_precision_recall_curve_arg_validation(num_labels, thresholds, ignore_index)
    allowed_average = ("micro", "macro", "weighted", "none", None)
    if average not in allowed_average:
        raise ValueError(f"Expected argument `average` to be one of {allowed_average} but got {average}")


def _multilabel_auroc_compute(
    state: Union[Tensor, Tuple[Tensor, Tensor]],
    num_labels: int,
    average: Optional[Literal["micro", "macro", "weighted", "none"]],
    thresholds: Optional[Tensor],
    ignore_index: Optional[int] = None,
) -> Union[Tuple[Tensor, Tensor, Tensor], Tensor]:
    if average == "micro":
        if isinstance(state, Tensor) and thresholds is not None:
            return _binary_auroc_compute(state.sum(1), thresholds, max_fpr=None)
        else:
            preds = state[0].flatten()
            target = state[1].flatten()
            if ignore_index is not None:
                idx = target == ignore_index
                preds = preds[~idx]
                target = target[~idx]
            return _binary_auroc_compute((preds, target), thresholds, max_fpr=None)

    else:
        fpr, tpr, _ = _multilabel_roc_compute(state, num_labels, thresholds, ignore_index)
        return _reduce_auroc(
            fpr,
            tpr,
            average,
            weights=(state[1] == 1).sum(dim=0).float() if thresholds is None else state[0][:, 1, :].sum(-1),
        )


def multilabel_auroc(
    preds: Tensor,
    target: Tensor,
    num_labels: int,
    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
    r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multilabel tasks. The AUROC
    score summarizes the ROC curve into an single number that describes the performance of a model for multiple
    thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
    corresponds to random guessing.

    Accepts the following input tensors:

    - ``preds`` (float tensor): ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each
      observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
      sigmoid per element.
    - ``target`` (int tensor): ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore
      only contain {0,1} values (except if `ignore_index` is specified).

    Additional dimension ``...`` will be flattened into the batch dimension.

    The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
    that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
    non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
    argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_labels: Integer specifing the number of labels
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum score over all labels
            - ``macro``: Calculate score for each label and average them
            - ``weighted``: Calculates score for each label and computes weighted average using their support
            - ``"none"`` or ``None``: Calculates score for each label and applies no reduction
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class.
        If `average="micro|macro"|"weighted"` then a single scalar is returned.

    Example:
        >>> from torchmetrics.functional.classification import multilabel_auroc
        >>> preds = torch.tensor([[0.75, 0.05, 0.35],
        ...                       [0.45, 0.75, 0.05],
        ...                       [0.05, 0.55, 0.75],
        ...                       [0.05, 0.65, 0.05]])
        >>> target = torch.tensor([[1, 0, 1],
        ...                        [0, 0, 0],
        ...                        [0, 1, 1],
        ...                        [1, 1, 1]])
        >>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=None)
        tensor(0.6528)
        >>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=None)
        tensor([0.6250, 0.5000, 0.8333])
        >>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=5)
        tensor(0.6528)
        >>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=5)
        tensor([0.6250, 0.5000, 0.8333])
    """
    if validate_args:
        _multilabel_auroc_arg_validation(num_labels, average, thresholds, ignore_index)
        _multilabel_precision_recall_curve_tensor_validation(preds, target, num_labels, ignore_index)
    preds, target, thresholds = _multilabel_precision_recall_curve_format(
        preds, target, num_labels, thresholds, ignore_index
    )
    state = _multilabel_precision_recall_curve_update(preds, target, num_labels, thresholds)
    return _multilabel_auroc_compute(state, num_labels, average, thresholds, ignore_index)


def auroc(
    preds: Tensor,
    target: Tensor,
    task: Literal["binary", "multiclass", "multilabel"],
    thresholds: Optional[Union[int, List[float], Tensor]] = None,
    num_classes: Optional[int] = None,
    num_labels: Optional[int] = None,
    average: Optional[Literal["macro", "weighted", "none"]] = "macro",
    max_fpr: Optional[float] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
    r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_). The AUROC score summarizes the
    ROC curve into an single number that describes the performance of a model for multiple thresholds at the same
    time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.

    This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
    :func:`binary_auroc`, :func:`multiclass_auroc` and :func:`multilabel_auroc` for the specific details of
    each argument influence and examples.

    Legacy Example:
        >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
        >>> target = torch.tensor([0, 0, 1, 1, 1])
        >>> auroc(preds, target, task='binary')
        tensor(0.5000)

        >>> preds = torch.tensor([[0.90, 0.05, 0.05],
        ...                       [0.05, 0.90, 0.05],
        ...                       [0.05, 0.05, 0.90],
        ...                       [0.85, 0.05, 0.10],
        ...                       [0.10, 0.10, 0.80]])
        >>> target = torch.tensor([0, 1, 1, 2, 2])
        >>> auroc(preds, target, task='multiclass', num_classes=3)
        tensor(0.7778)
    """
    if task == "binary":
        return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
    if task == "multiclass":
        assert isinstance(num_classes, int)
        return multiclass_auroc(preds, target, num_classes, average, thresholds, ignore_index, validate_args)
    if task == "multilabel":
        assert isinstance(num_labels, int)
        return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
    raise ValueError(
        f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
    )
