# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from collections.abc import Sequence
from typing import Any, List, Optional, Union

from torch import Tensor

from torchmetrics.functional.clustering.homogeneity_completeness_v_measure import (
    completeness_score,
    homogeneity_score,
    v_measure_score,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = ["HomogeneityScore.plot", "CompletenessScore.plot", "VMeasureScore.plot"]


class HomogeneityScore(Metric):
    r"""Compute `Homogeneity Score`_.

    The homogeneity score is a metric to measure the homogeneity of a clustering. A clustering result satisfies
    homogeneity if all of its clusters contain only data points which are members of a single class. The metric is not
    symmetric, therefore swapping ``preds`` and ``target`` yields a different score.

    This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not
    be available in practice since clustering in generally is used for unsupervised learning.

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

    - ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
    - ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels

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

    - ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score

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

    Example:
        >>> import torch
        >>> from torchmetrics.clustering import HomogeneityScore
        >>> preds = torch.tensor([2, 1, 0, 1, 0])
        >>> target = torch.tensor([0, 2, 1, 1, 0])
        >>> metric = HomogeneityScore()
        >>> metric(preds, target)
        tensor(0.4744)

    """

    is_differentiable: bool = True
    higher_is_better: bool = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    preds: List[Tensor]
    target: List[Tensor]

    def __init__(self, **kwargs: Any) -> None:
        super().__init__(**kwargs)

        self.add_state("preds", default=[], dist_reduce_fx="cat")
        self.add_state("target", default=[], dist_reduce_fx="cat")

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        self.preds.append(preds)
        self.target.append(target)

    def compute(self) -> Tensor:
        """Compute rand score over state."""
        return homogeneity_score(dim_zero_cat(self.preds), dim_zero_cat(self.target))

    def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
        """Plot a single or multiple values from the metric.

        Args:
            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
                If no value is provided, will automatically call `metric.compute` and plot that result.
            ax: An matplotlib axis object. If provided will add plot to that axis

        Returns:
            Figure and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> # Example plotting a single value
            >>> import torch
            >>> from torchmetrics.clustering import HomogeneityScore
            >>> metric = HomogeneityScore()
            >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
            >>> fig_, ax_ = metric.plot(metric.compute())

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.clustering import HomogeneityScore
            >>> metric = HomogeneityScore()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)


class CompletenessScore(Metric):
    r"""Compute `Completeness Score`_.

    A clustering result satisfies completeness if all the data points that are members of a given class are elements of
    the same cluster. The metric is not symmetric, therefore swapping ``preds`` and ``target`` yields a different score.

    This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not
    be available in practice since clustering in generally is used for unsupervised learning.

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

    - ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
    - ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels

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

    - ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score

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

    Example:
        >>> import torch
        >>> from torchmetrics.clustering import CompletenessScore
        >>> preds = torch.tensor([2, 1, 0, 1, 0])
        >>> target = torch.tensor([0, 2, 1, 1, 0])
        >>> metric = CompletenessScore()
        >>> metric(preds, target)
        tensor(0.4744)

    """

    is_differentiable: bool = True
    higher_is_better: bool = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    preds: List[Tensor]
    target: List[Tensor]

    def __init__(self, **kwargs: Any) -> None:
        super().__init__(**kwargs)

        self.add_state("preds", default=[], dist_reduce_fx="cat")
        self.add_state("target", default=[], dist_reduce_fx="cat")

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        self.preds.append(preds)
        self.target.append(target)

    def compute(self) -> Tensor:
        """Compute rand score over state."""
        return completeness_score(dim_zero_cat(self.preds), dim_zero_cat(self.target))

    def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
        """Plot a single or multiple values from the metric.

        Args:
            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
                If no value is provided, will automatically call `metric.compute` and plot that result.
            ax: An matplotlib axis object. If provided will add plot to that axis

        Returns:
            Figure and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> # Example plotting a single value
            >>> import torch
            >>> from torchmetrics.clustering import CompletenessScore
            >>> metric = CompletenessScore()
            >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
            >>> fig_, ax_ = metric.plot(metric.compute())

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.clustering import CompletenessScore
            >>> metric = CompletenessScore()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)


class VMeasureScore(Metric):
    r"""Compute `V-Measure Score`_.

    The V-measure is the harmonic mean between homogeneity and completeness:

    .. math::
        v = \frac{(1 + \beta) * homogeneity * completeness}{\beta * homogeneity + completeness}

    where :math:`\beta` is a weight parameter that defines the weight of homogeneity in the harmonic mean, with the
    default value :math:`\beta=1`. The V-measure is symmetric, which means that swapping ``preds`` and ``target`` does
    not change the score.

    This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not
    be available in practice since clustering in generally is used for unsupervised learning.

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

    - ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
    - ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels

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

    - ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score

    Args:
        beta: Weight parameter that defines the weight of homogeneity in the harmonic mean
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example::
        >>> import torch
        >>> from torchmetrics.clustering import VMeasureScore
        >>> preds = torch.tensor([2, 1, 0, 1, 0])
        >>> target = torch.tensor([0, 2, 1, 1, 0])
        >>> metric = VMeasureScore(beta=2.0)
        >>> metric(preds, target)
        tensor(0.4744)

    """

    is_differentiable: bool = True
    higher_is_better: bool = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    preds: List[Tensor]
    target: List[Tensor]

    def __init__(self, beta: float = 1.0, **kwargs: Any) -> None:
        super().__init__(**kwargs)
        if not (isinstance(beta, float) and beta > 0):
            raise ValueError(f"Argument `beta` should be a positive float. Got {beta}.")
        self.beta = beta

        self.add_state("preds", default=[], dist_reduce_fx="cat")
        self.add_state("target", default=[], dist_reduce_fx="cat")

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        self.preds.append(preds)
        self.target.append(target)

    def compute(self) -> Tensor:
        """Compute rand score over state."""
        return v_measure_score(dim_zero_cat(self.preds), dim_zero_cat(self.target), beta=self.beta)

    def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
        """Plot a single or multiple values from the metric.

        Args:
            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
                If no value is provided, will automatically call `metric.compute` and plot that result.
            ax: An matplotlib axis object. If provided will add plot to that axis

        Returns:
            Figure and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> # Example plotting a single value
            >>> import torch
            >>> from torchmetrics.clustering import VMeasureScore
            >>> metric = VMeasureScore()
            >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
            >>> fig_, ax_ = metric.plot(metric.compute())

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.clustering import VMeasureScore
            >>> metric = VMeasureScore()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)