# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Collection, Iterator
from typing import Optional, cast

import torch
from torch import Tensor

from torchmetrics.utilities import rank_zero_warn

_Color = tuple[int, int]  # A (category_id, instance_id) tuple that uniquely identifies a panoptic segment.


def _nested_tuple(nested_list: list) -> tuple:
    """Construct a nested tuple from a nested list.

    Args:
        nested_list: The nested list to convert to a nested tuple.

    Returns:
        A nested tuple with the same content.

    """
    return tuple(map(_nested_tuple, nested_list)) if isinstance(nested_list, list) else nested_list


def _to_tuple(t: Tensor) -> tuple:
    """Convert a tensor into a nested tuple.

    Args:
        t: The tensor to convert.

    Returns:
        A nested tuple with the same content.

    """
    return _nested_tuple(t.tolist())


def _get_color_areas(inputs: Tensor) -> dict[tuple, Tensor]:
    """Measure the size of each instance.

    Args:
        inputs: the input tensor containing the colored pixels.

    Returns:
        A dictionary specifying the `(category_id, instance_id)` and the corresponding number of occurrences.

    """
    unique_keys, unique_keys_area = torch.unique(inputs, dim=0, return_counts=True)
    # dictionary indexed by color tuples
    return dict(zip(_to_tuple(unique_keys), unique_keys_area))


def _parse_categories(things: Collection[int], stuffs: Collection[int]) -> tuple[set[int], set[int]]:
    """Parse and validate metrics arguments for `things` and `stuff`.

    Args:
        things: All possible IDs for things categories.
        stuffs: All possible IDs for stuff categories.

    Returns:
        things_parsed: A set of unique category IDs for the things categories.
        stuffs_parsed: A set of unique category IDs for the stuffs categories.

    """
    things_parsed = set(things)
    if len(things_parsed) < len(things):
        rank_zero_warn("The provided `things` categories contained duplicates, which have been removed.", UserWarning)
    stuffs_parsed = set(stuffs)
    if len(stuffs_parsed) < len(stuffs):
        rank_zero_warn("The provided `stuffs` categories contained duplicates, which have been removed.", UserWarning)
    if not all(isinstance(val, int) for val in things_parsed):
        raise TypeError(f"Expected argument `things` to contain `int` categories, but got {things}")
    if not all(isinstance(val, int) for val in stuffs_parsed):
        raise TypeError(f"Expected argument `stuffs` to contain `int` categories, but got {stuffs}")
    if things_parsed & stuffs_parsed:
        raise ValueError(
            f"Expected arguments `things` and `stuffs` to have distinct keys, but got {things} and {stuffs}"
        )
    if not (things_parsed | stuffs_parsed):
        raise ValueError("At least one of `things` and `stuffs` must be non-empty.")
    return things_parsed, stuffs_parsed


def _validate_inputs(preds: Tensor, target: torch.Tensor) -> None:
    """Validate the shapes of prediction and target tensors.

    Args:
        preds: the prediction tensor
        target: the target tensor

    """
    if not isinstance(preds, Tensor):
        raise TypeError(f"Expected argument `preds` to be of type `torch.Tensor`, but got {type(preds)}")
    if not isinstance(target, Tensor):
        raise TypeError(f"Expected argument `target` to be of type `torch.Tensor`, but got {type(target)}")
    if preds.shape != target.shape:
        raise ValueError(
            f"Expected argument `preds` and `target` to have the same shape, but got {preds.shape} and {target.shape}"
        )
    if preds.dim() < 3:
        raise ValueError(
            "Expected argument `preds` to have at least one spatial dimension (B, *spatial_dims, 2), "
            f"got {preds.shape}"
        )
    if preds.shape[-1] != 2:
        raise ValueError(
            "Expected argument `preds` to have exactly 2 channels in the last dimension (category, instance), "
            f"got {preds.shape} instead"
        )


def _get_void_color(things: set[int], stuffs: set[int]) -> tuple[int, int]:
    """Get an unused color ID.

    Args:
        things: The set of category IDs for things.
        stuffs: The set of category IDs for stuffs.

    Returns:
        A new color ID that does not belong to things nor stuffs.

    """
    unused_category_id = 1 + max([0, *list(things), *list(stuffs)])
    return unused_category_id, 0


def _get_category_id_to_continuous_id(things: set[int], stuffs: set[int]) -> dict[int, int]:
    """Convert original IDs to continuous IDs.

    Args:
        things: All unique IDs for things classes.
        stuffs: All unique IDs for stuff classes.

    Returns:
        A mapping from the original category IDs to continuous IDs (i.e., 0, 1, 2, ...).

    """
    # things metrics are stored with a continuous id in [0, len(things)[,
    thing_id_to_continuous_id = {thing_id: idx for idx, thing_id in enumerate(sorted(things))}
    # stuff metrics are stored with a continuous id in [len(things), len(things) + len(stuffs)[
    stuff_id_to_continuous_id = {stuff_id: idx + len(things) for idx, stuff_id in enumerate(sorted(stuffs))}
    cat_id_to_continuous_id = {}
    cat_id_to_continuous_id.update(thing_id_to_continuous_id)
    cat_id_to_continuous_id.update(stuff_id_to_continuous_id)
    return cat_id_to_continuous_id


def _isin(arr: Tensor, values: list) -> Tensor:
    """Check if all values of an arr are in another array. Implementation of torch.isin to support pre 0.10 version.

    Args:
        arr: the torch tensor to check for availabilities
        values: the values to search the tensor for.

    Returns:
        a bool tensor of the same shape as :param:`arr` indicating for each
        position whether the element of the tensor is in :param:`values`

    """
    return (arr[..., None] == arr.new(values)).any(-1)


def _prepocess_inputs(
    things: set[int],
    stuffs: set[int],
    inputs: Tensor,
    void_color: tuple[int, int],
    allow_unknown_category: bool,
) -> Tensor:
    """Preprocesses an input tensor for metric calculation.

    NOTE: The input tensor is assumed to have dimension ordering (B, spatial_dim0, ..., spatial_dim_N, 2).
    Spelled out explicitly, this means (B, num_points, 2) for point clouds, (B, H, W, 2) for images, and so on.

    Args:
        things: All category IDs for things classes.
        stuffs: All category IDs for stuff classes.
        inputs: The input tensor.
        void_color: An additional color that is masked out during metrics calculation.
        allow_unknown_category: If true, unknown category IDs are mapped to "void".
            Otherwise, an exception is raised if they occur.

    Returns:
        The preprocessed input tensor flattened along the spatial dimensions.

    """
    # flatten the spatial dimensions of the input tensor, e.g., (B, H, W, C) -> (B, H*W, C).
    out = inputs.detach().clone()
    out = torch.flatten(out, 1, -2)
    mask_stuffs = _isin(out[:, :, 0], list(stuffs))
    mask_things = _isin(out[:, :, 0], list(things))
    # reset instance IDs of stuffs
    mask_stuffs_instance = torch.stack([torch.zeros_like(mask_stuffs), mask_stuffs], dim=-1)
    out[mask_stuffs_instance] = 0
    if not allow_unknown_category and not torch.all(mask_things | mask_stuffs):
        raise ValueError(f"Unknown categories found: {out[~(mask_things | mask_stuffs)]}")
    # set unknown categories to void color
    out[~(mask_things | mask_stuffs)] = out.new(void_color)
    return out


def _calculate_iou(
    pred_color: _Color,
    target_color: _Color,
    pred_areas: dict[_Color, Tensor],
    target_areas: dict[_Color, Tensor],
    intersection_areas: dict[tuple[_Color, _Color], Tensor],
    void_color: _Color,
) -> Tensor:
    """Helper function that calculates the IoU from precomputed areas of segments and their intersections.

    Args:
        pred_color: The `(category_id, instance_id)`, or "color", of a predicted segment that is being matched with a
            target segment.
        target_color: The `(category_id, instance_id)`, or "color", of a ground truth segment that is being matched
            with a predicted segment.
        pred_areas: Mapping from colors of the predicted segments to their extents.
        target_areas: Mapping from colors of the ground truth segments to their extents.
        intersection_areas: Mapping from tuples of `(pred_color, target_color)` to their extent.
        void_color: An additional color that is masked out during metrics calculation.

    Returns:
        The calculated IoU as a torch.Tensor containing a single scalar value.

    """
    if pred_color[0] != target_color[0]:
        raise ValueError(
            "Attempting to compute IoU on segments with different category ID: "
            f"pred {pred_color[0]}, target {target_color[0]}"
        )
    if pred_color == void_color:
        raise ValueError("Attempting to compute IoU on a void segment.")
    intersection = intersection_areas[(pred_color, target_color)]
    pred_area = pred_areas[pred_color]
    target_area = target_areas[target_color]
    pred_void_area = intersection_areas.get((pred_color, void_color), 0)
    void_target_area = intersection_areas.get((void_color, target_color), 0)
    union = pred_area - pred_void_area + target_area - void_target_area - intersection
    return intersection / union


def _filter_false_negatives(
    target_areas: dict[_Color, Tensor],
    target_segment_matched: set[_Color],
    intersection_areas: dict[tuple[_Color, _Color], Tensor],
    void_color: tuple[int, int],
) -> Iterator[int]:
    """Filter false negative segments and yield their category IDs.

    False negatives occur when a ground truth segment is not matched with a prediction.
    Areas that are mostly void in the prediction are ignored.

    Args:
        target_areas: Mapping from colors of the ground truth segments to their extents.
        target_segment_matched: Set of ground truth segments that have been matched to a prediction.
        intersection_areas: Mapping from tuples of `(pred_color, target_color)` to their extent.
        void_color: An additional color that is masked out during metrics calculation.

    Yields:
        Category IDs of segments that account for false negatives.

    """
    false_negative_colors = set(target_areas) - target_segment_matched
    false_negative_colors.discard(void_color)
    for target_color in false_negative_colors:
        void_target_area = intersection_areas.get((void_color, target_color), 0)
        if void_target_area / target_areas[target_color] <= 0.5:
            yield target_color[0]


def _filter_false_positives(
    pred_areas: dict[_Color, Tensor],
    pred_segment_matched: set[_Color],
    intersection_areas: dict[tuple[_Color, _Color], Tensor],
    void_color: tuple[int, int],
) -> Iterator[int]:
    """Filter false positive segments and yield their category IDs.

    False positives occur when a predicted segment is not matched with a corresponding target one.
    Areas that are mostly void in the target are ignored.

    Args:
        pred_areas: Mapping from colors of the predicted segments to their extents.
        pred_segment_matched: Set of predicted segments that have been matched to a ground truth.
        intersection_areas: Mapping from tuples of `(pred_color, target_color)` to their extent.
        void_color: An additional color that is masked out during metrics calculation.

    Yields:
        Category IDs of segments that account for false positives.

    """
    false_positive_colors = set(pred_areas) - pred_segment_matched
    false_positive_colors.discard(void_color)
    for pred_color in false_positive_colors:
        pred_void_area = intersection_areas.get((pred_color, void_color), 0)
        if pred_void_area / pred_areas[pred_color] <= 0.5:
            yield pred_color[0]


def _panoptic_quality_update_sample(
    flatten_preds: Tensor,
    flatten_target: Tensor,
    cat_id_to_continuous_id: dict[int, int],
    void_color: tuple[int, int],
    stuffs_modified_metric: Optional[set[int]] = None,
) -> tuple[Tensor, Tensor, Tensor, Tensor]:
    """Calculate stat scores required to compute the metric **for a single sample**.

    Computed scores: iou sum, true positives, false positives, false negatives.

    NOTE: For the modified PQ case, this implementation uses the `true_positives` output tensor to aggregate the actual
        TPs for things classes, but the number of target segments for stuff classes.
        The `iou_sum` output tensor, instead, aggregates the IoU values at different thresholds (i.e., 0.5 for things
        and 0 for stuffs).
        This allows seamlessly using the same `.compute()` method for both PQ variants.

    Args:
        flatten_preds: A flattened prediction tensor referring to a single sample, shape (num_points, 2).
        flatten_target: A flattened target tensor referring to a single sample, shape (num_points, 2).
        cat_id_to_continuous_id: Mapping from original category IDs to continuous IDs
        void_color: an additional, unused color.
        stuffs_modified_metric: Set of stuff category IDs for which the PQ metric is computed using the "modified"
            formula. If not specified, the original formula is used for all categories.

    Returns:
        - IOU Sum
        - True positives
        - False positives
        - False negatives.

    """
    stuffs_modified_metric = stuffs_modified_metric or set()
    device = flatten_preds.device
    num_categories = len(cat_id_to_continuous_id)
    iou_sum = torch.zeros(num_categories, dtype=torch.double, device=device)
    true_positives = torch.zeros(num_categories, dtype=torch.int, device=device)
    false_positives = torch.zeros(num_categories, dtype=torch.int, device=device)
    false_negatives = torch.zeros(num_categories, dtype=torch.int, device=device)

    # calculate the area of each prediction, ground truth and pairwise intersection.
    # NOTE: mypy needs `cast()` because the annotation for `_get_color_areas` is too generic.
    pred_areas = cast(dict[_Color, Tensor], _get_color_areas(flatten_preds))
    target_areas = cast(dict[_Color, Tensor], _get_color_areas(flatten_target))
    # intersection matrix of shape [num_pixels, 2, 2]
    intersection_matrix = torch.transpose(torch.stack((flatten_preds, flatten_target), -1), -1, -2)
    intersection_areas = cast(dict[tuple[_Color, _Color], Tensor], _get_color_areas(intersection_matrix))

    # select intersection of things of same category with iou > 0.5
    pred_segment_matched = set()
    target_segment_matched = set()
    for pred_color, target_color in intersection_areas:
        # test only non void, matching category
        if target_color == void_color:
            continue
        if pred_color[0] != target_color[0]:
            continue
        iou = _calculate_iou(pred_color, target_color, pred_areas, target_areas, intersection_areas, void_color)
        continuous_id = cat_id_to_continuous_id[target_color[0]]
        if target_color[0] not in stuffs_modified_metric and iou > 0.5:
            pred_segment_matched.add(pred_color)
            target_segment_matched.add(target_color)
            iou_sum[continuous_id] += iou
            true_positives[continuous_id] += 1
        elif target_color[0] in stuffs_modified_metric and iou > 0:
            iou_sum[continuous_id] += iou

    for cat_id in _filter_false_negatives(target_areas, target_segment_matched, intersection_areas, void_color):
        if cat_id not in stuffs_modified_metric:
            continuous_id = cat_id_to_continuous_id[cat_id]
            false_negatives[continuous_id] += 1

    for cat_id in _filter_false_positives(pred_areas, pred_segment_matched, intersection_areas, void_color):
        if cat_id not in stuffs_modified_metric:
            continuous_id = cat_id_to_continuous_id[cat_id]
            false_positives[continuous_id] += 1

    for cat_id, _ in target_areas:
        if cat_id in stuffs_modified_metric:
            continuous_id = cat_id_to_continuous_id[cat_id]
            true_positives[continuous_id] += 1

    return iou_sum, true_positives, false_positives, false_negatives


def _panoptic_quality_update(
    flatten_preds: Tensor,
    flatten_target: Tensor,
    cat_id_to_continuous_id: dict[int, int],
    void_color: tuple[int, int],
    modified_metric_stuffs: Optional[set[int]] = None,
) -> tuple[Tensor, Tensor, Tensor, Tensor]:
    """Calculate stat scores required to compute the metric for a full batch.

    Computed scores: iou sum, true positives, false positives, false negatives.

    Args:
        flatten_preds: A flattened prediction tensor, shape (B, num_points, 2).
        flatten_target: A flattened target tensor, shape (B, num_points, 2).
        cat_id_to_continuous_id: Mapping from original category IDs to continuous IDs.
        void_color: an additional, unused color.
        modified_metric_stuffs: Set of stuff category IDs for which the PQ metric is computed using the "modified"
            formula. If not specified, the original formula is used for all categories.

    Returns:
        - IOU Sum
        - True positives
        - False positives
        - False negatives

    """
    device = flatten_preds.device
    num_categories = len(cat_id_to_continuous_id)
    iou_sum = torch.zeros(num_categories, dtype=torch.double, device=device)
    true_positives = torch.zeros(num_categories, dtype=torch.int, device=device)
    false_positives = torch.zeros(num_categories, dtype=torch.int, device=device)
    false_negatives = torch.zeros(num_categories, dtype=torch.int, device=device)

    # Loop over each sample independently: segments must not be matched across frames.
    for flatten_preds_single, flatten_target_single in zip(flatten_preds, flatten_target):
        result = _panoptic_quality_update_sample(
            flatten_preds_single,
            flatten_target_single,
            cat_id_to_continuous_id,
            void_color,
            stuffs_modified_metric=modified_metric_stuffs,
        )
        iou_sum += result[0]
        true_positives += result[1]
        false_positives += result[2]
        false_negatives += result[3]

    return iou_sum, true_positives, false_positives, false_negatives


def _panoptic_quality_compute(
    iou_sum: Tensor,
    true_positives: Tensor,
    false_positives: Tensor,
    false_negatives: Tensor,
) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
    """Compute the final panoptic quality from interim values.

    Args:
        iou_sum: the iou sum from the update step
        true_positives: the TP value from the update step
        false_positives: the FP value from the update step
        false_negatives: the FN value from the update step

    Returns:
        A tuple containing the per-class panoptic, segmentation and recognition quality followed by the averages

    """
    # compute segmentation and recognition quality (per-class)
    sq: Tensor = torch.where(true_positives > 0.0, iou_sum / true_positives, 0.0)
    denominator: Tensor = true_positives + 0.5 * false_positives + 0.5 * false_negatives
    rq: Tensor = torch.where(denominator > 0.0, true_positives / denominator, 0.0)
    # compute per-class panoptic quality
    pq: Tensor = sq * rq
    # compute averages
    pq_avg: Tensor = torch.mean(pq[denominator > 0])
    sq_avg: Tensor = torch.mean(sq[denominator > 0])
    rq_avg: Tensor = torch.mean(rq[denominator > 0])
    return pq, sq, rq, pq_avg, sq_avg, rq_avg
