# Copyright The PyTorch Lightning team.
#
# 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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#
#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Optional, Union

import torch
from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.nominal.pearson import (
    _pearsons_contingency_coefficient_compute,
    _pearsons_contingency_coefficient_update,
)
from torchmetrics.functional.nominal.utils import _nominal_input_validation
from torchmetrics.metric import Metric


class PearsonsContingencyCoefficient(Metric):
    r"""Compute `Pearson's Contingency Coefficient`_ statistic measuring the association between two categorical
    (nominal) data series.

    .. math::
        Pearson = \sqrt{\frac{\chi^2 / n}{1 + \chi^2 / n}}

    where

    .. math::
        \chi^2 = \sum_{i,j} \ frac{\left(n_{ij} - \frac{n_{i.} n_{.j}}{n}\right)^2}{\frac{n_{i.} n_{.j}}{n}}

    where :math:`n_{ij}` denotes the number of times the values :math:`(A_i, B_j)` are observed
    with :math:`A_i, B_j` represent frequencies of values in ``preds`` and ``target``, respectively.

    Pearson's Contingency Coefficient is a symmetric coefficient, i.e.
    :math:`Pearson(preds, target) = Pearson(target, preds)`.

    The output values lies in [0, 1] with 1 meaning the perfect association.

    Args:
        num_classes: Integer specifing the number of classes
        nan_strategy: Indication of whether to replace or drop ``NaN`` values
        nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'``
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Returns:
        Pearson's Contingency Coefficient statistic

    Raises:
        ValueError:
            If `nan_strategy` is not one of `'replace'` and `'drop'`
        ValueError:
            If `nan_strategy` is equal to `'replace'` and `nan_replace_value` is not an `int` or `float`

    Example:
        >>> from torchmetrics import PearsonsContingencyCoefficient
        >>> _ = torch.manual_seed(42)
        >>> preds = torch.randint(0, 4, (100,))
        >>> target = torch.round(preds + torch.randn(100)).clamp(0, 4)
        >>> pearsons_contingency_coefficient = PearsonsContingencyCoefficient(num_classes=5)
        >>> pearsons_contingency_coefficient(preds, target)
        tensor(0.6948)
    """

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

    def __init__(
        self,
        num_classes: int,
        nan_strategy: Literal["replace", "drop"] = "replace",
        nan_replace_value: Optional[Union[int, float]] = 0.0,
        **kwargs: Any,
    ):
        super().__init__(**kwargs)
        self.num_classes = num_classes

        _nominal_input_validation(nan_strategy, nan_replace_value)
        self.nan_strategy = nan_strategy
        self.nan_replace_value = nan_replace_value

        self.add_state("confmat", torch.zeros(num_classes, num_classes), dist_reduce_fx="sum")

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

        Args:
            preds: 1D or 2D tensor of categorical (nominal) data:

                - 1D shape: (batch_size,)
                - 2D shape: (batch_size, num_classes)

            target: 1D or 2D tensor of categorical (nominal) data:

                - 1D shape: (batch_size,)
                - 2D shape: (batch_size, num_classes)
        """
        confmat = _pearsons_contingency_coefficient_update(
            preds, target, self.num_classes, self.nan_strategy, self.nan_replace_value
        )
        self.confmat += confmat

    def compute(self) -> Tensor:
        """Computer Pearson's Contingency Coefficient statistic."""
        return _pearsons_contingency_coefficient_compute(self.confmat)