# 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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from typing import Any, Optional

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
from torchmetrics.metric import Metric


class BinaryHammingDistance(BinaryStatScores):
    r"""Computes the average `Hamming distance`_ (also known as Hamming loss) for binary tasks:

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

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

    - ``preds`` (:class:`~torch.Tensor`): An int 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, ...)``.


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

    - ``bhd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` arguments:

        - If ``multidim_average`` is set to ``global``, the metric returns a scalar value.
        - If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a
          scalar value per sample.

    Args:
        threshold: Threshold for transforming probability to binary {0,1} predictions
        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        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.

    Example (preds is int tensor):
        >>> from torchmetrics.classification import BinaryHammingDistance
        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
        >>> preds = torch.tensor([0, 0, 1, 1, 0, 1])
        >>> metric = BinaryHammingDistance()
        >>> metric(preds, target)
        tensor(0.3333)

    Example (preds is float tensor):
        >>> from torchmetrics.classification import BinaryHammingDistance
        >>> 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 = BinaryHammingDistance()
        >>> metric(preds, target)
        tensor(0.3333)

    Example (multidim tensors):
        >>> from torchmetrics.classification import BinaryHammingDistance
        >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
        >>> preds = torch.tensor(
        ...     [
        ...         [[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
        ...         [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]],
        ...     ]
        ... )
        >>> metric = BinaryHammingDistance(multidim_average='samplewise')
        >>> metric(preds, target)
        tensor([0.6667, 0.8333])
    """

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

    def compute(self) -> Tensor:
        tp, fp, tn, fn = self._final_state()
        return _hamming_distance_reduce(tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average)


class MulticlassHammingDistance(MulticlassStatScores):
    r"""Computes the average `Hamming distance`_ (also known as Hamming loss) for multiclass tasks:

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

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

    - ``preds`` (:class:`~torch.Tensor`): An 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, ...)``.


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

    - ``mchd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
      ``multidim_average`` arguments:

        - If ``multidim_average`` is set to ``global``:

          - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
          - If ``average=None/'none'``, the shape will be ``(C,)``

        - If ``multidim_average`` is set to ``samplewise``:

          - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
          - If ``average=None/'none'``, the shape will be ``(N, C)``

    Args:
        num_classes: Integer specifing the number of classes
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum statistics over all labels
            - ``macro``: Calculate statistics for each label and average them
            - ``weighted``: Calculates statistics for each label and computes weighted average using their support
            - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction
        top_k:
            Number of highest probability or logit score predictions considered to find the correct label.
            Only works when ``preds`` contain probabilities/logits.
        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        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.

    Example (preds is int tensor):
        >>> from torchmetrics.classification import MulticlassHammingDistance
        >>> target = torch.tensor([2, 1, 0, 0])
        >>> preds = torch.tensor([2, 1, 0, 1])
        >>> metric = MulticlassHammingDistance(num_classes=3)
        >>> metric(preds, target)
        tensor(0.1667)
        >>> mchd = MulticlassHammingDistance(num_classes=3, average=None)
        >>> mchd(preds, target)
        tensor([0.5000, 0.0000, 0.0000])

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MulticlassHammingDistance
        >>> 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 = MulticlassHammingDistance(num_classes=3)
        >>> metric(preds, target)
        tensor(0.1667)
        >>> mchd = MulticlassHammingDistance(num_classes=3, average=None)
        >>> mchd(preds, target)
        tensor([0.5000, 0.0000, 0.0000])

    Example (multidim tensors):
        >>> from torchmetrics.classification import MulticlassHammingDistance
        >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
        >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
        >>> metric = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise')
        >>> metric(preds, target)
        tensor([0.5000, 0.7222])
        >>> mchd = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise', average=None)
        >>> mchd(preds, target)
        tensor([[0.0000, 1.0000, 0.5000],
                [1.0000, 0.6667, 0.5000]])
    """

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

    def compute(self) -> Tensor:
        tp, fp, tn, fn = self._final_state()
        return _hamming_distance_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)


class MultilabelHammingDistance(MultilabelStatScores):
    r"""Computes the average `Hamming distance`_ (also known as Hamming loss) for multilabel tasks:

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

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

    - ``preds`` (:class:`~torch.Tensor`): An int tensor 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, ...)``.


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

    - ``mlhd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
      ``multidim_average`` arguments:

        - If ``multidim_average`` is set to ``global``:

          - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
          - If ``average=None/'none'``, the shape will be ``(C,)``

        - If ``multidim_average`` is set to ``samplewise``:

          - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
          - If ``average=None/'none'``, the shape will be ``(N, C)``

    Args:
        num_labels: Integer specifing the number of labels
        threshold: Threshold for transforming probability to binary (0,1) predictions
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum statistics over all labels
            - ``macro``: Calculate statistics for each label and average them
            - ``weighted``: Calculates statistics for each label and computes weighted average using their support
            - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction

        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        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.

    Example (preds is int tensor):
        >>> from torchmetrics.classification import MultilabelHammingDistance
        >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
        >>> metric = MultilabelHammingDistance(num_labels=3)
        >>> metric(preds, target)
        tensor(0.3333)
        >>> mlhd = MultilabelHammingDistance(num_labels=3, average=None)
        >>> mlhd(preds, target)
        tensor([0.0000, 0.5000, 0.5000])

    Example (preds is float tensor):
        >>> from torchmetrics.classification import MultilabelHammingDistance
        >>> 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 = MultilabelHammingDistance(num_labels=3)
        >>> metric(preds, target)
        tensor(0.3333)
        >>> mlhd = MultilabelHammingDistance(num_labels=3, average=None)
        >>> mlhd(preds, target)
        tensor([0.0000, 0.5000, 0.5000])

    Example (multidim tensors):
        >>> from torchmetrics.classification import MultilabelHammingDistance
        >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
        >>> preds = torch.tensor(
        ...     [
        ...         [[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
        ...         [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]],
        ...     ]
        ... )
        >>> metric = MultilabelHammingDistance(num_labels=3, multidim_average='samplewise')
        >>> metric(preds, target)
        tensor([0.6667, 0.8333])
        >>> mlhd = MultilabelHammingDistance(num_labels=3, multidim_average='samplewise', average=None)
        >>> mlhd(preds, target)
        tensor([[0.5000, 0.5000, 1.0000],
                [1.0000, 1.0000, 0.5000]])
    """

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

    def compute(self) -> Tensor:
        tp, fp, tn, fn = self._final_state()
        return _hamming_distance_reduce(
            tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average, multilabel=True
        )


class HammingDistance:
    r"""Computes the average `Hamming distance`_ (also known as Hamming loss):

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

    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:`BinaryHammingDistance`, :mod:`MulticlassHammingDistance` and :mod:`MultilabelHammingDistance` for the
    specific details of each argument influence and examples.

    Legacy Example:
        >>> target = torch.tensor([[0, 1], [1, 1]])
        >>> preds = torch.tensor([[0, 1], [0, 1]])
        >>> hamming_distance = HammingDistance(task="multilabel", num_labels=2)
        >>> hamming_distance(preds, target)
        tensor(0.2500)
    """

    def __new__(
        cls,
        task: Literal["binary", "multiclass", "multilabel"],
        threshold: float = 0.5,
        num_classes: Optional[int] = None,
        num_labels: Optional[int] = None,
        average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
        multidim_average: Optional[Literal["global", "samplewise"]] = "global",
        top_k: Optional[int] = 1,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> Metric:

        assert multidim_average is not None
        kwargs.update(dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args))
        if task == "binary":
            return BinaryHammingDistance(threshold, **kwargs)
        if task == "multiclass":
            assert isinstance(num_classes, int)
            assert isinstance(top_k, int)
            return MulticlassHammingDistance(num_classes, top_k, average, **kwargs)
        if task == "multilabel":
            assert isinstance(num_labels, int)
            return MultilabelHammingDistance(num_labels, threshold, average, **kwargs)
        raise ValueError(
            f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
        )