# 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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from typing import Any, Optional

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.classification.hinge import (
    _binary_confusion_matrix_format,
    _binary_hinge_loss_arg_validation,
    _binary_hinge_loss_tensor_validation,
    _binary_hinge_loss_update,
    _hinge_loss_compute,
    _multiclass_confusion_matrix_format,
    _multiclass_hinge_loss_arg_validation,
    _multiclass_hinge_loss_tensor_validation,
    _multiclass_hinge_loss_update,
)
from torchmetrics.metric import Metric


class BinaryHingeLoss(Metric):
    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.

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(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`` (:class:`~torch.Tensor`): An int tensor of shape ``(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.

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

    As output to ``forward`` and ``compute`` the metric returns the following output:

    - ``bhl`` (:class:`~torch.Tensor`): A tensor containing the hinge loss.

    Args:
        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.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torchmetrics.classification import BinaryHingeLoss
        >>> preds = torch.tensor([0.25, 0.25, 0.55, 0.75, 0.75])
        >>> target = torch.tensor([0, 0, 1, 1, 1])
        >>> bhl = BinaryHingeLoss()
        >>> bhl(preds, target)
        tensor(0.6900)
        >>> bhl = BinaryHingeLoss(squared=True)
        >>> bhl(preds, target)
        tensor(0.6905)
    """
    is_differentiable: bool = True
    higher_is_better: bool = False
    full_state_update: bool = False

    def __init__(
        self,
        squared: bool = False,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        if validate_args:
            _binary_hinge_loss_arg_validation(squared, ignore_index)
        self.validate_args = validate_args
        self.squared = squared
        self.ignore_index = ignore_index

        self.add_state("measures", default=torch.tensor(0.0), dist_reduce_fx="sum")
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
        if self.validate_args:
            _binary_hinge_loss_tensor_validation(preds, target, self.ignore_index)
        preds, target = _binary_confusion_matrix_format(
            preds, target, threshold=0.0, ignore_index=self.ignore_index, convert_to_labels=False
        )
        measures, total = _binary_hinge_loss_update(preds, target, self.squared)
        self.measures += measures
        self.total += total

    def compute(self) -> Tensor:
        return _hinge_loss_compute(self.measures, self.total)


class MulticlassHingeLoss(Metric):
    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.

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(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`` (:class:`~torch.Tensor`): An int tensor of shape ``(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).

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

    As output to ``forward`` and ``compute`` the metric returns the following output:

    - ``mchl`` (:class:`~torch.Tensor`): A tensor containing the multi-class hinge loss.

    Args:
        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.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torchmetrics.classification import MulticlassHingeLoss
        >>> 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])
        >>> mchl = MulticlassHingeLoss(num_classes=3)
        >>> mchl(preds, target)
        tensor(0.9125)
        >>> mchl = MulticlassHingeLoss(num_classes=3, squared=True)
        >>> mchl(preds, target)
        tensor(1.1131)
        >>> mchl = MulticlassHingeLoss(num_classes=3, multiclass_mode='one-vs-all')
        >>> mchl(preds, target)
        tensor([0.8750, 1.1250, 1.1000])
    """
    is_differentiable: bool = True
    higher_is_better: bool = False
    full_state_update: bool = False

    def __init__(
        self,
        num_classes: int,
        squared: bool = False,
        multiclass_mode: Literal["crammer-singer", "one-vs-all"] = "crammer-singer",
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        if validate_args:
            _multiclass_hinge_loss_arg_validation(num_classes, squared, multiclass_mode, ignore_index)
        self.validate_args = validate_args
        self.num_classes = num_classes
        self.squared = squared
        self.multiclass_mode = multiclass_mode
        self.ignore_index = ignore_index

        self.add_state(
            "measures",
            default=torch.tensor(0.0)
            if self.multiclass_mode == "crammer-singer"
            else torch.zeros(
                num_classes,
            ),
            dist_reduce_fx="sum",
        )
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
        if self.validate_args:
            _multiclass_hinge_loss_tensor_validation(preds, target, self.num_classes, self.ignore_index)
        preds, target = _multiclass_confusion_matrix_format(preds, target, self.ignore_index, convert_to_labels=False)
        measures, total = _multiclass_hinge_loss_update(preds, target, self.squared, self.multiclass_mode)
        self.measures += measures
        self.total += total

    def compute(self) -> Tensor:
        return _hinge_loss_compute(self.measures, self.total)


class HingeLoss:
    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
    :mod:`BinaryHingeLoss` and :mod:`MulticlassHingeLoss` 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 = HingeLoss(task="binary")
        >>> hinge(preds, target)
        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 = HingeLoss(task="multiclass", num_classes=3)
        >>> hinge(preds, target)
        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 = HingeLoss(task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
        >>> hinge(preds, target)
        tensor([1.3743, 1.1945, 1.2359])
    """

    def __new__(
        cls,
        task: Literal["binary", "multiclass"],
        num_classes: Optional[int] = None,
        squared: bool = False,
        multiclass_mode: Optional[Literal["crammer-singer", "one-vs-all"]] = "crammer-singer",
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> Metric:
        kwargs.update(dict(ignore_index=ignore_index, validate_args=validate_args))
        if task == "binary":
            return BinaryHingeLoss(squared, **kwargs)
        if task == "multiclass":
            assert isinstance(num_classes, int)
            return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
        raise ValueError(
            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
        )