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

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.classification import BinaryConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_reduce
from torchmetrics.metric import Metric


class BinaryMatthewsCorrCoef(BinaryConfusionMatrix):
    r"""Calculates `Matthews correlation coefficient`_ for binary tasks. This metric measures the general
    correlation or quality of a classification.

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

    - ``preds`` (:class:`~torch.Tensor`): A int tensor or float tensor of shape ``(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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``

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

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

    - ``bmcc`` (:class:`~torch.Tensor`): A tensor containing the Binary Matthews Correlation Coefficient.

    Args:
        threshold: Threshold for transforming probability to binary (0,1) predictions
        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 torchmetrics.classification import BinaryMatthewsCorrCoef
        >>> target = torch.tensor([1, 1, 0, 0])
        >>> preds = torch.tensor([0, 1, 0, 0])
        >>> metric = BinaryMatthewsCorrCoef()
        >>> metric(preds, target)
        tensor(0.5774)

    Example (preds is float tensor):
        >>> from torchmetrics.classification import BinaryMatthewsCorrCoef
        >>> target = torch.tensor([1, 1, 0, 0])
        >>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01])
        >>> metric = BinaryMatthewsCorrCoef()
        >>> metric(preds, target)
        tensor(0.5774)
    """

    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False

    def __init__(
        self,
        threshold: float = 0.5,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(threshold, ignore_index, normalize=None, validate_args=validate_args, **kwargs)

    def compute(self) -> Tensor:
        return _matthews_corrcoef_reduce(self.confmat)


class MulticlassMatthewsCorrCoef(MulticlassConfusionMatrix):
    r"""Calculates `Matthews correlation coefficient`_ for multiclass tasks. This metric measures the general
    correlation or quality of a classification.

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

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

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

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

    - ``mcmcc`` (:class:`~torch.Tensor`): A tensor containing the Multi-class Matthews Correlation Coefficient.

    Args:
        num_classes: Integer specifing the number of classes
        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 torchmetrics.classification import MulticlassMatthewsCorrCoef
        >>> target = torch.tensor([2, 1, 0, 0])
        >>> preds = torch.tensor([2, 1, 0, 1])
        >>> metric = MulticlassMatthewsCorrCoef(num_classes=3)
        >>> metric(preds, target)
        tensor(0.7000)

    Example (pred is float tensor):
        >>> from torchmetrics.classification import MulticlassMatthewsCorrCoef
        >>> target = torch.tensor([2, 1, 0, 0])
        >>> preds = torch.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],
        ... ])
        >>> metric = MulticlassMatthewsCorrCoef(num_classes=3)
        >>> metric(preds, target)
        tensor(0.7000)
    """

    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False

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

    def compute(self) -> Tensor:
        return _matthews_corrcoef_reduce(self.confmat)


class MultilabelMatthewsCorrCoef(MultilabelConfusionMatrix):
    r"""Calculates `Matthews correlation coefficient`_ for multilabel tasks. This metric measures the general
    correlation or quality of a classification.

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

    - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, C, ...)``. 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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``

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

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

    - ``mlmcc`` (:class:`~torch.Tensor`): A tensor containing the Multi-label Matthews Correlation Coefficient.

    Args:
        num_classes: Integer specifing the number of labels
        threshold: Threshold for transforming probability to binary (0,1) predictions
        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 torchmetrics.classification import MultilabelMatthewsCorrCoef
        >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
        >>> metric = MultilabelMatthewsCorrCoef(num_labels=3)
        >>> metric(preds, target)
        tensor(0.3333)

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MultilabelMatthewsCorrCoef
        >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
        >>> metric = MultilabelMatthewsCorrCoef(num_labels=3)
        >>> metric(preds, target)
        tensor(0.3333)
    """

    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False

    def __init__(
        self,
        num_labels: int,
        threshold: float = 0.5,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(num_labels, threshold, ignore_index, normalize=None, validate_args=validate_args, **kwargs)

    def compute(self) -> Tensor:
        return _matthews_corrcoef_reduce(self.confmat)


class MatthewsCorrCoef:
    r"""Calculates `Matthews correlation coefficient`_ . This metric measures the general correlation or quality of
    a classification.

    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
    :mod:`BinaryMatthewsCorrCoef`, :mod:`MulticlassMatthewsCorrCoef` and :mod:`MultilabelMatthewsCorrCoef` for
    the specific details of each argument influence and examples.

    Legacy Example:
        >>> target = torch.tensor([1, 1, 0, 0])
        >>> preds = torch.tensor([0, 1, 0, 0])
        >>> matthews_corrcoef = MatthewsCorrCoef(task='binary')
        >>> matthews_corrcoef(preds, target)
        tensor(0.5774)
    """

    def __new__(
        cls,
        task: Literal["binary", "multiclass", "multilabel"] = None,
        threshold: float = 0.5,
        num_classes: Optional[int] = None,
        num_labels: Optional[int] = None,
        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 BinaryMatthewsCorrCoef(threshold, **kwargs)
        if task == "multiclass":
            assert isinstance(num_classes, int)
            return MulticlassMatthewsCorrCoef(num_classes, **kwargs)
        if task == "multilabel":
            assert isinstance(num_labels, int)
            return MultilabelMatthewsCorrCoef(num_labels, threshold, **kwargs)
        raise ValueError(
            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
        )