# 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Any, Optional, Union

from torch import Tensor
from typing_extensions import Literal

from torchmetrics.classification.base import _ClassificationTaskWrapper
from torchmetrics.classification.precision_recall_curve import (
    BinaryPrecisionRecallCurve,
    MulticlassPrecisionRecallCurve,
    MultilabelPrecisionRecallCurve,
)
from torchmetrics.functional.classification.precision_fixed_recall import _precision_at_recall
from torchmetrics.functional.classification.recall_fixed_precision import (
    _binary_recall_at_fixed_precision_arg_validation,
    _binary_recall_at_fixed_precision_compute,
    _multiclass_recall_at_fixed_precision_arg_compute,
    _multiclass_recall_at_fixed_precision_arg_validation,
    _multilabel_recall_at_fixed_precision_arg_compute,
    _multilabel_recall_at_fixed_precision_arg_validation,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.enums import ClassificationTask
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = [
        "BinaryPrecisionAtFixedRecall.plot",
        "MulticlassPrecisionAtFixedRecall.plot",
        "MultilabelPrecisionAtFixedRecall.plot",
    ]


class BinaryPrecisionAtFixedRecall(BinaryPrecisionRecallCurve):
    r"""Compute the highest possible precision value given the minimum recall thresholds provided.

    This is done by first calculating the precision-recall curve for different thresholds and the find the precision for
    a given recall level.

    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.

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

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

    - ``precision`` (:class:`~torch.Tensor`): A scalar tensor with the maximum precision for the given recall level
    - ``threshold`` (:class:`~torch.Tensor`): A scalar tensor with the corresponding threshold level

    .. note::
       The implementation both supports calculating the metric in a non-binned but accurate version and a
       binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None``
       will activate the non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting
       the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory
       of size :math:`\mathcal{O}(n_{thresholds})` (constant memory).

    Args:
        min_recall: float value specifying minimum recall threshold.
        thresholds:
            Can be one of:

            - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the 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 torch import tensor
        >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall
        >>> preds = tensor([0, 0.5, 0.7, 0.8])
        >>> target = tensor([0, 1, 1, 0])
        >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5, thresholds=None)
        >>> metric(preds, target)
        (tensor(0.6667), tensor(0.5000))
        >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5, thresholds=5)
        >>> metric(preds, target)
        (tensor(0.6667), tensor(0.5000))

    """

    is_differentiable: bool = False
    higher_is_better: Optional[bool] = None
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0

    def __init__(
        self,
        min_recall: float,
        thresholds: Optional[Union[int, list[float], Tensor]] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(thresholds, ignore_index, validate_args=False, **kwargs)
        if validate_args:
            _binary_recall_at_fixed_precision_arg_validation(min_recall, thresholds, ignore_index)
        self.validate_args = validate_args
        self.min_recall = min_recall

    def compute(self) -> tuple[Tensor, Tensor]:  # type: ignore[override]
        """Compute metric."""
        state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
        return _binary_recall_at_fixed_precision_compute(
            state, self.thresholds, self.min_recall, reduce_fn=_precision_at_recall
        )

    def plot(  # type: ignore[override]
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 object and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> from torch import rand, randint
            >>> # Example plotting a single value
            >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall
            >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5)
            >>> metric.update(rand(10), randint(2,(10,)))
            >>> fig_, ax_ = metric.plot()  # the returned plot only shows the maximum recall value by default

        .. plot::
            :scale: 75

            >>> from torch import rand, randint
            >>> # Example plotting multiple values
            >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall
            >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5)
            >>> values = [ ]
            >>> for _ in range(10):
            ...     # we index by 0 such that only the maximum recall value is plotted
            ...     values.append(metric(rand(10), randint(2,(10,)))[0])
            >>> fig_, ax_ = metric.plot(values)

        """
        val = val or self.compute()[0]  # by default we select the maximum recall value to plot
        return self._plot(val, ax)


class MulticlassPrecisionAtFixedRecall(MulticlassPrecisionRecallCurve):
    r"""Compute the highest possible precision value given the minimum recall thresholds provided.

    This is done by first calculating the precision-recall curve for different thresholds and the find the precision for
    a given recall level.

    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).

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

    As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:

    - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the
      given recall level per class
    - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold
      level per class

    .. note::
       The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
       that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the
       non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
       argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
       size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).

    Args:
        num_classes: Integer specifying the number of classes
        min_recall: float value specifying minimum recall threshold.
        thresholds:
            Can be one of:

            - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the 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 torch import tensor
        >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall
        >>> preds = tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
        ...                 [0.05, 0.75, 0.05, 0.05, 0.05],
        ...                 [0.05, 0.05, 0.75, 0.05, 0.05],
        ...                 [0.05, 0.05, 0.05, 0.75, 0.05]])
        >>> target = tensor([0, 1, 3, 2])
        >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=5, min_recall=0.5, thresholds=None)
        >>> metric(preds, target)  # doctest: +NORMALIZE_WHITESPACE
        (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]),
         tensor([0.7500, 0.7500, 0.0500, 0.0500, nan]))
        >>> mcrafp = MulticlassPrecisionAtFixedRecall(num_classes=5, min_recall=0.5, thresholds=5)
        >>> mcrafp(preds, target)  # doctest: +NORMALIZE_WHITESPACE
        (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]),
         tensor([0.7500, 0.7500, 0.0000, 0.0000, nan]))

    """

    is_differentiable: bool = False
    higher_is_better: Optional[bool] = None
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    plot_legend_name: str = "Class"

    def __init__(
        self,
        num_classes: int,
        min_recall: float,
        thresholds: Optional[Union[int, list[float], Tensor]] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            num_classes=num_classes, thresholds=thresholds, ignore_index=ignore_index, validate_args=False, **kwargs
        )
        if validate_args:
            _multiclass_recall_at_fixed_precision_arg_validation(num_classes, min_recall, thresholds, ignore_index)
        self.validate_args = validate_args
        self.min_recall = min_recall

    def compute(self) -> tuple[Tensor, Tensor]:  # type: ignore[override]
        """Compute metric."""
        state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
        return _multiclass_recall_at_fixed_precision_arg_compute(
            state, self.num_classes, self.thresholds, self.min_recall, reduce_fn=_precision_at_recall
        )

    def plot(  # type: ignore[override]
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 object and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> from torch import rand, randint
            >>> # Example plotting a single value per class
            >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall
            >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=3, min_recall=0.5)
            >>> metric.update(rand(20, 3).softmax(dim=-1), randint(3, (20,)))
            >>> fig_, ax_ = metric.plot()  # the returned plot only shows the maximum recall value by default

        .. plot::
            :scale: 75

            >>> from torch import rand, randint
            >>> # Example plotting a multiple values per class
            >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall
            >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=3, min_recall=0.5)
            >>> values = []
            >>> for _ in range(20):
            ...     # we index by 0 such that only the maximum recall value is plotted
            ...     values.append(metric(rand(20, 3).softmax(dim=-1), randint(3, (20,)))[0])
            >>> fig_, ax_ = metric.plot(values)

        """
        val = val or self.compute()[0]  # by default we select the maximum recall value to plot
        return self._plot(val, ax)


class MultilabelPrecisionAtFixedRecall(MultilabelPrecisionRecallCurve):
    r"""Compute the highest possible precision value given the minimum recall thresholds provided.

    This is done by first calculating the precision-recall curve for different thresholds and the find the precision for
    a given recall level.

    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 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.

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

    As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:

    - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the
      given recall level per class
    - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold
      level per class

    .. note::
       The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
       that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the
       non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
       argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
       size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).

    Args:
        num_labels: Integer specifying the number of labels
        min_recall: float value specifying minimum recall threshold.
        thresholds:
            Can be one of:

            - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the 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 torch import tensor
        >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall
        >>> preds = tensor([[0.75, 0.05, 0.35],
        ...                 [0.45, 0.75, 0.05],
        ...                 [0.05, 0.55, 0.75],
        ...                 [0.05, 0.65, 0.05]])
        >>> target = tensor([[1, 0, 1],
        ...                  [0, 0, 0],
        ...                  [0, 1, 1],
        ...                  [1, 1, 1]])
        >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=None)
        >>> metric(preds, target)
        (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5500, 0.3500]))
        >>> mlrafp = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=5)
        >>> mlrafp(preds, target)
        (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5000, 0.2500]))

    """

    is_differentiable: bool = False
    higher_is_better: Optional[bool] = None
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    plot_legend_name: str = "Label"

    def __init__(
        self,
        num_labels: int,
        min_recall: float,
        thresholds: Optional[Union[int, list[float], Tensor]] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            num_labels=num_labels, thresholds=thresholds, ignore_index=ignore_index, validate_args=False, **kwargs
        )
        if validate_args:
            _multilabel_recall_at_fixed_precision_arg_validation(num_labels, min_recall, thresholds, ignore_index)
        self.validate_args = validate_args
        self.min_recall = min_recall

    def compute(self) -> tuple[Tensor, Tensor]:  # type: ignore[override]
        """Compute metric."""
        state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
        return _multilabel_recall_at_fixed_precision_arg_compute(
            state, self.num_labels, self.thresholds, self.ignore_index, self.min_recall, reduce_fn=_precision_at_recall
        )

    def plot(  # type: ignore[override]
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 object and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> from torch import rand, randint
            >>> # Example plotting a single value
            >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall
            >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5)
            >>> metric.update(rand(20, 3), randint(2, (20, 3)))
            >>> fig_, ax_ = metric.plot()  # the returned plot only shows the maximum recall value by default

        .. plot::
            :scale: 75

            >>> from torch import rand, randint
            >>> # Example plotting multiple values
            >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall
            >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5)
            >>> values = [ ]
            >>> for _ in range(10):
            ...     # we index by 0 such that only the maximum recall value is plotted
            ...     values.append(metric(rand(20, 3), randint(2, (20, 3)))[0])
            >>> fig_, ax_ = metric.plot(values)

        """
        val = val or self.compute()[0]  # by default we select the maximum recall value to plot
        return self._plot(val, ax)


class PrecisionAtFixedRecall(_ClassificationTaskWrapper):
    r"""Compute the highest possible recall value given the minimum precision thresholds provided.

    This is done by first calculating the precision-recall curve for different thresholds and the find the recall for
    a given precision level.

    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
    :class:`~torchmetrics.classification.BinaryPrecisionAtFixedRecall`,
    :class:`~torchmetrics.classification.MulticlassPrecisionAtFixedRecall` and
    :class:`~torchmetrics.classification.MultilabelPrecisionAtFixedRecall` for the specific details of each argument
    influence and examples.

    """

    def __new__(  # type: ignore[misc]
        cls: type["PrecisionAtFixedRecall"],
        task: Literal["binary", "multiclass", "multilabel"],
        min_recall: float,
        thresholds: Optional[Union[int, list[float], Tensor]] = None,
        num_classes: Optional[int] = None,
        num_labels: Optional[int] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> Metric:
        """Initialize task metric."""
        task = ClassificationTask.from_str(task)
        if task == ClassificationTask.BINARY:
            return BinaryPrecisionAtFixedRecall(min_recall, thresholds, ignore_index, validate_args, **kwargs)
        if task == ClassificationTask.MULTICLASS:
            if not isinstance(num_classes, int):
                raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
            return MulticlassPrecisionAtFixedRecall(
                num_classes, min_recall, thresholds, ignore_index, validate_args, **kwargs
            )
        if task == ClassificationTask.MULTILABEL:
            if not isinstance(num_labels, int):
                raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
            return MultilabelPrecisionAtFixedRecall(
                num_labels, min_recall, thresholds, ignore_index, validate_args, **kwargs
            )
        raise ValueError(f"Task {task} not supported!")
