# Copyright The PyTorch Lightning team.
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# 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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# Unless required by applicable law or agreed to in writing, software
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from typing import Optional, Tuple

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

from torchmetrics.functional.classification.confusion_matrix import (
    _binary_confusion_matrix_format,
    _binary_confusion_matrix_tensor_validation,
    _multiclass_confusion_matrix_format,
    _multiclass_confusion_matrix_tensor_validation,
)
from torchmetrics.utilities.data import to_onehot


def _hinge_loss_compute(measure: Tensor, total: Tensor) -> Tensor:
    return measure / total


def _binary_hinge_loss_arg_validation(squared: bool, ignore_index: Optional[int] = None) -> None:
    if not isinstance(squared, bool):
        raise ValueError(f"Expected argument `squared` to be an bool but got {squared}")
    if ignore_index is not None and not isinstance(ignore_index, int):
        raise ValueError(f"Expected argument `ignore_index` to either be `None` or an integer, but got {ignore_index}")


def _binary_hinge_loss_tensor_validation(preds: Tensor, target: Tensor, ignore_index: Optional[int] = None) -> None:
    _binary_confusion_matrix_tensor_validation(preds, target, ignore_index)
    if not preds.is_floating_point():
        raise ValueError(
            "Expected argument `preds` to be floating tensor with probabilities/logits"
            f" but got tensor with dtype {preds.dtype}"
        )


def _binary_hinge_loss_update(
    preds: Tensor,
    target: Tensor,
    squared: bool,
) -> Tuple[Tensor, Tensor]:

    target = target.bool()
    margin = torch.zeros_like(preds)
    margin[target] = preds[target]
    margin[~target] = -preds[~target]

    measures = 1 - margin
    measures = torch.clamp(measures, 0)

    if squared:
        measures = measures.pow(2)

    total = tensor(target.shape[0], device=target.device)
    return measures.sum(dim=0), total


def binary_hinge_loss(
    preds: Tensor,
    target: Tensor,
    squared: bool = False,
    ignore_index: Optional[int] = None,
    validate_args: bool = False,
) -> Tensor:
    r"""Computes the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs) for binary tasks. It is
    defined as:

    .. math::
        \text{Hinge loss} = \max(0, 1 - y \times \hat{y})

    Where :math:`y \in {-1, 1}` is the target, and :math:`\hat{y} \in \mathbb{R}` is the prediction.

    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.

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        squared:
            If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
        ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Example:
        >>> from torchmetrics.functional.classification import binary_hinge_loss
        >>> preds = torch.tensor([0.25, 0.25, 0.55, 0.75, 0.75])
        >>> target = torch.tensor([0, 0, 1, 1, 1])
        >>> binary_hinge_loss(preds, target)
        tensor(0.6900)
        >>> binary_hinge_loss(preds, target, squared=True)
        tensor(0.6905)
    """
    if validate_args:
        _binary_hinge_loss_arg_validation(squared, ignore_index)
        _binary_hinge_loss_tensor_validation(preds, target, ignore_index)
    preds, target = _binary_confusion_matrix_format(
        preds, target, threshold=0.0, ignore_index=ignore_index, convert_to_labels=False
    )
    measures, total = _binary_hinge_loss_update(preds, target, squared)
    return _hinge_loss_compute(measures, total)


def _multiclass_hinge_loss_arg_validation(
    num_classes: int,
    squared: bool = False,
    multiclass_mode: Literal["crammer-singer", "one-vs-all"] = "crammer-singer",
    ignore_index: Optional[int] = None,
) -> None:
    _binary_hinge_loss_arg_validation(squared, ignore_index)
    if not isinstance(num_classes, int) or num_classes < 2:
        raise ValueError(f"Expected argument `num_classes` to be an integer larger than 1, but got {num_classes}")
    allowed_mm = ("crammer-singer", "one-vs-all")
    if multiclass_mode not in allowed_mm:
        raise ValueError(f"Expected argument `multiclass_mode` to be one of {allowed_mm}, but got {multiclass_mode}.")


def _multiclass_hinge_loss_tensor_validation(
    preds: Tensor, target: Tensor, num_classes: int, ignore_index: Optional[int] = None
) -> None:
    _multiclass_confusion_matrix_tensor_validation(preds, target, num_classes, ignore_index)
    if not preds.is_floating_point():
        raise ValueError(
            "Expected argument `preds` to be floating tensor with probabilities/logits"
            f" but got tensor with dtype {preds.dtype}"
        )


def _multiclass_hinge_loss_update(
    preds: Tensor,
    target: Tensor,
    squared: bool,
    multiclass_mode: Literal["crammer-singer", "one-vs-all"] = "crammer-singer",
) -> Tuple[Tensor, Tensor]:
    if not torch.all((0 <= preds) * (preds <= 1)):
        preds = preds.softmax(1)

    target = to_onehot(target, max(2, preds.shape[1])).bool()
    if multiclass_mode == "crammer-singer":
        margin = preds[target]
        margin -= torch.max(preds[~target].view(preds.shape[0], -1), dim=1)[0]
    else:
        target = target.bool()
        margin = torch.zeros_like(preds)
        margin[target] = preds[target]
        margin[~target] = -preds[~target]

    measures = 1 - margin
    measures = torch.clamp(measures, 0)

    if squared:
        measures = measures.pow(2)

    total = tensor(target.shape[0], device=target.device)
    return measures.sum(dim=0), total


def multiclass_hinge_loss(
    preds: Tensor,
    target: Tensor,
    num_classes: int,
    squared: bool = False,
    multiclass_mode: Literal["crammer-singer", "one-vs-all"] = "crammer-singer",
    ignore_index: Optional[int] = None,
    validate_args: bool = False,
) -> Tensor:
    r"""Computes the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs) for multiclass tasks.

    The metric can be computed in two ways. Either, the definition by Crammer and Singer is used:

    .. math::
        \text{Hinge loss} = \max\left(0, 1 - \hat{y}_y + \max_{i \ne y} (\hat{y}_i)\right)

    Where :math:`y \in {0, ..., \mathrm{C}}` is the target class (where :math:`\mathrm{C}` is the number of classes),
    and :math:`\hat{y} \in \mathbb{R}^\mathrm{C}` is the predicted output per class. Alternatively, the metric can
    also be computed in one-vs-all approach, where each class is valued against all other classes in a binary fashion.

    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.

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_classes: Integer specifing the number of classes
        squared:
            If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
        multiclass_mode:
            Determines how to compute the metric
        ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Example:
        >>> from torchmetrics.functional.classification import multiclass_hinge_loss
        >>> preds = torch.tensor([[0.25, 0.20, 0.55],
        ...                       [0.55, 0.05, 0.40],
        ...                       [0.10, 0.30, 0.60],
        ...                       [0.90, 0.05, 0.05]])
        >>> target = torch.tensor([0, 1, 2, 0])
        >>> multiclass_hinge_loss(preds, target, num_classes=3)
        tensor(0.9125)
        >>> multiclass_hinge_loss(preds, target, num_classes=3, squared=True)
        tensor(1.1131)
        >>> multiclass_hinge_loss(preds, target, num_classes=3, multiclass_mode='one-vs-all')
        tensor([0.8750, 1.1250, 1.1000])
    """
    if validate_args:
        _multiclass_hinge_loss_arg_validation(num_classes, squared, multiclass_mode, ignore_index)
        _multiclass_hinge_loss_tensor_validation(preds, target, num_classes, ignore_index)
    preds, target = _multiclass_confusion_matrix_format(preds, target, ignore_index, convert_to_labels=False)
    measures, total = _multiclass_hinge_loss_update(preds, target, squared, multiclass_mode)
    return _hinge_loss_compute(measures, total)


def hinge_loss(
    preds: Tensor,
    target: Tensor,
    task: Literal["binary", "multiclass"],
    num_classes: Optional[int] = None,
    squared: bool = False,
    multiclass_mode: Literal["crammer-singer", "one-vs-all"] = "crammer-singer",
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Computes the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs).

    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'`` or ``'multiclass'``. See the documentation of
    :func:`binary_hinge_loss` and :func:`multiclass_hinge_loss` for the specific details of
    each argument influence and examples.

    Legacy Example:
        >>> import torch
        >>> target = torch.tensor([0, 1, 1])
        >>> preds = torch.tensor([0.5, 0.7, 0.1])
        >>> hinge_loss(preds, target, task="binary")
        tensor(0.9000)

        >>> target = torch.tensor([0, 1, 2])
        >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
        >>> hinge_loss(preds, target, task="multiclass", num_classes=3)
        tensor(1.5551)

        >>> target = torch.tensor([0, 1, 2])
        >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
        >>> hinge_loss(preds, target, task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
        tensor([1.3743, 1.1945, 1.2359])
    """
    if task == "binary":
        return binary_hinge_loss(preds, target, squared, ignore_index, validate_args)
    if task == "multiclass":
        assert isinstance(num_classes, int)
        return multiclass_hinge_loss(preds, target, num_classes, squared, multiclass_mode, ignore_index, validate_args)
    raise ValueError(f"Expected argument `task` to either be `'binary'` or `'multilabel'` but got {task}")