# 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.
# 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.
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from typing import Any, List

from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.image.sam import _sam_compute, _sam_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat


class SpectralAngleMapper(Metric):
    """The metric `Spectral Angle Mapper`_ determines the spectral similarity between image spectra and reference
    spectra by calculating the angle between the spectra, where small angles between indicate high similarity and
    high angles indicate low similarity.

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

    - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)``
    - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)``

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

    - ``sam`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SAM value
      over sample else returns tensor of shape ``(N,)`` with SAM values per sample

    Args:
        reduction: a method to reduce metric score over labels.

            - ``'elementwise_mean'``: takes the mean (default)
            - ``'sum'``: takes the sum
            - ``'none'`` or ``None``: no reduction will be applied

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

    Return:
        Tensor with SpectralAngleMapper score

    Example:
        >>> import torch
        >>> from torchmetrics import SpectralAngleMapper
        >>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42))
        >>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123))
        >>> sam = SpectralAngleMapper()
        >>> sam(preds, target)
        tensor(0.5943)
    """

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

    preds: List[Tensor]
    target: List[Tensor]

    def __init__(
        self,
        reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean",
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        rank_zero_warn(
            "Metric `SpectralAngleMapper` will save all targets and predictions in the buffer."
            " For large datasets, this may lead to a large memory footprint."
        )

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

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

    def compute(self) -> Tensor:
        """Computes spectra over state."""
        preds = dim_zero_cat(self.preds)
        target = dim_zero_cat(self.target)
        return _sam_compute(preds, target, self.reduction)