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from typing import Any

from torch import Tensor, tensor

from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _PYSTOI_AVAILABLE

__doctest_requires__ = {"ShortTimeObjectiveIntelligibility": ["pystoi"]}


class ShortTimeObjectiveIntelligibility(Metric):
    r"""Calculates STOI (Short-Time Objective Intelligibility) metric for evaluating speech signals. Intelligibility
    measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive
    noise, single-/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-
    measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative
    to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in
    the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech
    intelligibility. Description taken from  `Cees Taal's website`_ and for further defails see `STOI ref1`_ and
    `STOI ref2`_.

    This metric is a wrapper for the `pystoi package`_. As the implementation backend implementation only supports
    calculations on CPU, all input will automatically be moved to CPU to perform the metric calculation before being
    moved back to the original device.

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

    - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
    - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``

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

    - ``stoi`` (:class:`~torch.Tensor`): float scalar tensor

    .. note:: using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
        torchmetrics[audio]`` or ``pip install pystoi``.

    Args:
        fs: sampling frequency (Hz)
        extended: whether to use the extended STOI described in `STOI ref3`_.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Raises:
        ModuleNotFoundError:
            If ``pystoi`` package is not installed

    Example:
        >>> from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
        >>> import torch
        >>> g = torch.manual_seed(1)
        >>> preds = torch.randn(8000)
        >>> target = torch.randn(8000)
        >>> stoi = ShortTimeObjectiveIntelligibility(8000, False)
        >>> stoi(preds, target)
        tensor(-0.0100)
    """
    sum_stoi: Tensor
    total: Tensor
    full_state_update: bool = False
    is_differentiable: bool = False
    higher_is_better: bool = True

    def __init__(
        self,
        fs: int,
        extended: bool = False,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        if not _PYSTOI_AVAILABLE:
            raise ModuleNotFoundError(
                "STOI metric requires that `pystoi` is installed."
                " Either install as `pip install torchmetrics[audio]` or `pip install pystoi`."
            )
        self.fs = fs
        self.extended = extended

        self.add_state("sum_stoi", default=tensor(0.0), dist_reduce_fx="sum")
        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        stoi_batch = short_time_objective_intelligibility(preds, target, self.fs, self.extended, False).to(
            self.sum_stoi.device
        )

        self.sum_stoi += stoi_batch.sum()
        self.total += stoi_batch.numel()

    def compute(self) -> Tensor:
        """Computes metric."""
        return self.sum_stoi / self.total