# Copyright The Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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from typing import Optional

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.classification.confusion_matrix import (
    _binary_confusion_matrix_arg_validation,
    _binary_confusion_matrix_format,
    _binary_confusion_matrix_tensor_validation,
    _binary_confusion_matrix_update,
    _multiclass_confusion_matrix_arg_validation,
    _multiclass_confusion_matrix_format,
    _multiclass_confusion_matrix_tensor_validation,
    _multiclass_confusion_matrix_update,
)
from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel


def _cohen_kappa_reduce(confmat: Tensor, weights: Optional[Literal["linear", "quadratic", "none"]] = None) -> Tensor:
    """Reduce an un-normalized confusion matrix of shape (n_classes, n_classes) into the cohen kappa score."""
    confmat = confmat.float() if not confmat.is_floating_point() else confmat
    num_classes = confmat.shape[0]
    sum0 = confmat.sum(dim=0, keepdim=True)
    sum1 = confmat.sum(dim=1, keepdim=True)
    expected = sum1 @ sum0 / sum0.sum()  # outer product

    if weights is None or weights == "none":
        w_mat = torch.ones_like(confmat).flatten()
        w_mat[:: num_classes + 1] = 0
        w_mat = w_mat.reshape(num_classes, num_classes)
    elif weights in ("linear", "quadratic"):
        w_mat = torch.zeros_like(confmat)
        w_mat += torch.arange(num_classes, dtype=w_mat.dtype, device=w_mat.device)
        w_mat = torch.abs(w_mat - w_mat.T) if weights == "linear" else torch.pow(w_mat - w_mat.T, 2.0)
    else:
        raise ValueError(
            f"Received {weights} for argument ``weights`` but should be either None, 'linear' or 'quadratic'"
        )
    k = torch.sum(w_mat * confmat) / torch.sum(w_mat * expected)
    return 1 - k


def _binary_cohen_kappa_arg_validation(
    threshold: float = 0.5,
    ignore_index: Optional[int] = None,
    weights: Optional[Literal["linear", "quadratic", "none"]] = None,
) -> None:
    """Validate non tensor input.

    - ``threshold`` has to be a float in the [0,1] range
    - ``ignore_index`` has to be None or int
    - ``weights`` has to be "linear" | "quadratic" | "none" | None

    """
    _binary_confusion_matrix_arg_validation(threshold, ignore_index, normalize=None)
    allowed_weights = ("linear", "quadratic", "none", None)
    if weights not in allowed_weights:
        raise ValueError(f"Expected argument `weight` to be one of {allowed_weights}, but got {weights}.")


def binary_cohen_kappa(
    preds: Tensor,
    target: Tensor,
    threshold: float = 0.5,
    weights: Optional[Literal["linear", "quadratic", "none"]] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for binary tasks.

    .. math::
        \kappa = (p_o - p_e) / (1 - p_e)

    where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
    the expected agreement when both annotators assign labels randomly. Note that
    :math:`p_e` is estimated using a per-annotator empirical prior over the
    class labels.

    Accepts the following input tensors:

    - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside
      [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Additionally,
      we convert to int tensor with thresholding using the value in ``threshold``.
    - ``target`` (int tensor): ``(N, ...)``

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

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        threshold: Threshold for transforming probability to binary (0,1) predictions
        weights: Weighting type to calculate the score. Choose from:

            - ``None`` or ``'none'``: no weighting
            - ``'linear'``: linear weighting
            - ``'quadratic'``: quadratic weighting
        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 (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import binary_cohen_kappa
        >>> target = tensor([1, 1, 0, 0])
        >>> preds = tensor([0, 1, 0, 0])
        >>> binary_cohen_kappa(preds, target)
        tensor(0.5000)

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import binary_cohen_kappa
        >>> target = tensor([1, 1, 0, 0])
        >>> preds = tensor([0.35, 0.85, 0.48, 0.01])
        >>> binary_cohen_kappa(preds, target)
        tensor(0.5000)

    """
    if validate_args:
        _binary_cohen_kappa_arg_validation(threshold, ignore_index, weights)
        _binary_confusion_matrix_tensor_validation(preds, target, ignore_index)
    preds, target = _binary_confusion_matrix_format(preds, target, threshold, ignore_index)
    confmat = _binary_confusion_matrix_update(preds, target)
    return _cohen_kappa_reduce(confmat, weights)


def _multiclass_cohen_kappa_arg_validation(
    num_classes: int,
    ignore_index: Optional[int] = None,
    weights: Optional[Literal["linear", "quadratic", "none"]] = None,
) -> None:
    """Validate non tensor input.

    - ``num_classes`` has to be a int larger than 1
    - ``ignore_index`` has to be None or int
    - ``weights`` has to be "linear" | "quadratic" | "none" | None

    """
    _multiclass_confusion_matrix_arg_validation(num_classes, ignore_index, normalize=None)
    allowed_weights = ("linear", "quadratic", "none", None)
    if weights not in allowed_weights:
        raise ValueError(f"Expected argument `weight` to be one of {allowed_weights}, but got {weights}.")


def multiclass_cohen_kappa(
    preds: Tensor,
    target: Tensor,
    num_classes: int,
    weights: Optional[Literal["linear", "quadratic", "none"]] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for multiclass tasks.

    .. math::
        \kappa = (p_o - p_e) / (1 - p_e)

    where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
    the expected agreement when both annotators assign labels randomly. Note that
    :math:`p_e` is estimated using a per-annotator empirical prior over the
    class labels.

    Accepts the following input tensors:

    - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point
      we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into
      an int tensor.
    - ``target`` (int tensor): ``(N, ...)``

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

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_classes: Integer specifying the number of classes
        weights: Weighting type to calculate the score. Choose from:

            - ``None`` or ``'none'``: no weighting
            - ``'linear'``: linear weighting
            - ``'quadratic'``: quadratic weighting


        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 (pred is integer tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import multiclass_cohen_kappa
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([2, 1, 0, 1])
        >>> multiclass_cohen_kappa(preds, target, num_classes=3)
        tensor(0.6364)

    Example (pred is float tensor):
        >>> from torchmetrics.functional.classification import multiclass_cohen_kappa
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([[0.16, 0.26, 0.58],
        ...                 [0.22, 0.61, 0.17],
        ...                 [0.71, 0.09, 0.20],
        ...                 [0.05, 0.82, 0.13]])
        >>> multiclass_cohen_kappa(preds, target, num_classes=3)
        tensor(0.6364)

    """
    if validate_args:
        _multiclass_cohen_kappa_arg_validation(num_classes, ignore_index, weights)
        _multiclass_confusion_matrix_tensor_validation(preds, target, num_classes, ignore_index)
    preds, target = _multiclass_confusion_matrix_format(preds, target, ignore_index)
    confmat = _multiclass_confusion_matrix_update(preds, target, num_classes)
    return _cohen_kappa_reduce(confmat, weights)


def cohen_kappa(
    preds: Tensor,
    target: Tensor,
    task: Literal["binary", "multiclass"],
    threshold: float = 0.5,
    num_classes: Optional[int] = None,
    weights: Optional[Literal["linear", "quadratic", "none"]] = None,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Calculate `Cohen's kappa score`_ that measures inter-annotator agreement. It is defined as.

    .. math::
        \kappa = (p_o - p_e) / (1 - p_e)

    where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
    the expected agreement when both annotators assign labels randomly. Note that
    :math:`p_e` is estimated using a per-annotator empirical prior over the
    class labels.

    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:`~torchmetrics.functional.classification.binary_cohen_kappa` and
    :func:`~torchmetrics.functional.classification.multiclass_cohen_kappa` for the specific details of
    each argument influence and examples.

    Legacy Example:
        >>> from torch import tensor
        >>> target = tensor([1, 1, 0, 0])
        >>> preds = tensor([0, 1, 0, 0])
        >>> cohen_kappa(preds, target, task="multiclass", num_classes=2)
        tensor(0.5000)

    """
    task = ClassificationTaskNoMultilabel.from_str(task)
    if task == ClassificationTaskNoMultilabel.BINARY:
        return binary_cohen_kappa(preds, target, threshold, weights, ignore_index, validate_args)
    if task == ClassificationTaskNoMultilabel.MULTICLASS:
        if not isinstance(num_classes, int):
            raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
        return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
    raise ValueError(f"Not handled value: {task}")