# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# limitations under the License.
from typing import Optional

import torch
import torch.nn as nn
from torch import Tensor

from ..utils import is_accelerate_available, is_scipy_available, is_vision_available, requires_backends


if is_accelerate_available():
    from accelerate import PartialState
    from accelerate.utils import reduce

if is_scipy_available():
    from scipy.optimize import linear_sum_assignment


if is_vision_available():
    from transformers.image_transforms import center_to_corners_format


def dice_loss(inputs, targets, num_boxes):
    """
    Compute the DICE loss, similar to generalized IOU for masks

    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: A float tensor with the same shape as inputs. Stores the binary
                 classification label for each element in inputs (0 for the negative class and 1 for the positive
                 class).
    """
    inputs = inputs.sigmoid()
    inputs = inputs.flatten(1)
    numerator = 2 * (inputs * targets).sum(1)
    denominator = inputs.sum(-1) + targets.sum(-1)
    loss = 1 - (numerator + 1) / (denominator + 1)
    return loss.sum() / num_boxes


def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
    """
    Loss used in RetinaNet for dense detection: https://huggingface.co/papers/1708.02002.

    Args:
        inputs (`torch.FloatTensor` of arbitrary shape):
            The predictions for each example.
        targets (`torch.FloatTensor` with the same shape as `inputs`)
            A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class
            and 1 for the positive class).
        alpha (`float`, *optional*, defaults to `0.25`):
            Optional weighting factor in the range (0,1) to balance positive vs. negative examples.
        gamma (`int`, *optional*, defaults to `2`):
            Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.

    Returns:
        Loss tensor
    """
    prob = inputs.sigmoid()
    ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
    # add modulating factor
    p_t = prob * targets + (1 - prob) * (1 - targets)
    loss = ce_loss * ((1 - p_t) ** gamma)

    if alpha >= 0:
        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
        loss = alpha_t * loss

    return loss.mean(1).sum() / num_boxes


# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
class ImageLoss(nn.Module):
    """
    This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1)
    we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair
    of matched ground-truth / prediction (supervise class and box).

    A note on the `num_classes` argument (copied from original repo in detr.py): "the naming of the `num_classes`
    parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is
    the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to
    be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2
    (`max_obj_id` + 1). For more details on this, check the following discussion
    https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223"


    Args:
        matcher (`DetrHungarianMatcher`):
            Module able to compute a matching between targets and proposals.
        num_classes (`int`):
            Number of object categories, omitting the special no-object category.
        eos_coef (`float`):
            Relative classification weight applied to the no-object category.
        losses (`list[str]`):
            List of all the losses to be applied. See `get_loss` for a list of all available losses.
    """

    def __init__(self, matcher, num_classes, eos_coef, losses):
        super().__init__()
        self.matcher = matcher
        self.num_classes = num_classes
        self.eos_coef = eos_coef
        self.losses = losses
        empty_weight = torch.ones(self.num_classes + 1)
        empty_weight[-1] = self.eos_coef
        self.register_buffer("empty_weight", empty_weight)

    # removed logging parameter, which was part of the original implementation
    def loss_labels(self, outputs, targets, indices, num_boxes):
        """
        Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim
        [nb_target_boxes]
        """
        if "logits" not in outputs:
            raise KeyError("No logits were found in the outputs")
        source_logits = outputs["logits"]

        idx = self._get_source_permutation_idx(indices)
        target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
        target_classes = torch.full(
            source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
        )
        target_classes[idx] = target_classes_o

        loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight)
        losses = {"loss_ce": loss_ce}

        return losses

    @torch.no_grad()
    def loss_cardinality(self, outputs, targets, indices, num_boxes):
        """
        Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.

        This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
        """
        logits = outputs["logits"]
        device = logits.device
        target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
        # Count the number of predictions that are NOT "no-object" (which is the last class)
        card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1)
        card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float())
        losses = {"cardinality_error": card_err}
        return losses

    def loss_boxes(self, outputs, targets, indices, num_boxes):
        """
        Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.

        Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
        are expected in format (center_x, center_y, w, h), normalized by the image size.
        """
        if "pred_boxes" not in outputs:
            raise KeyError("No predicted boxes found in outputs")
        idx = self._get_source_permutation_idx(indices)
        source_boxes = outputs["pred_boxes"][idx]
        target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)

        loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none")

        losses = {}
        losses["loss_bbox"] = loss_bbox.sum() / num_boxes

        loss_giou = 1 - torch.diag(
            generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes))
        )
        losses["loss_giou"] = loss_giou.sum() / num_boxes
        return losses

    def loss_masks(self, outputs, targets, indices, num_boxes):
        """
        Compute the losses related to the masks: the focal loss and the dice loss.

        Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
        """
        if "pred_masks" not in outputs:
            raise KeyError("No predicted masks found in outputs")

        source_idx = self._get_source_permutation_idx(indices)
        target_idx = self._get_target_permutation_idx(indices)
        source_masks = outputs["pred_masks"]
        source_masks = source_masks[source_idx]
        masks = [t["masks"] for t in targets]
        # TODO use valid to mask invalid areas due to padding in loss
        target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
        target_masks = target_masks.to(source_masks)
        target_masks = target_masks[target_idx]

        # upsample predictions to the target size
        source_masks = nn.functional.interpolate(
            source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
        )
        source_masks = source_masks[:, 0].flatten(1)

        target_masks = target_masks.flatten(1)
        target_masks = target_masks.view(source_masks.shape)
        losses = {
            "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
            "loss_dice": dice_loss(source_masks, target_masks, num_boxes),
        }
        return losses

    def _get_source_permutation_idx(self, indices):
        # permute predictions following indices
        batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
        source_idx = torch.cat([source for (source, _) in indices])
        return batch_idx, source_idx

    def _get_target_permutation_idx(self, indices):
        # permute targets following indices
        batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
        target_idx = torch.cat([target for (_, target) in indices])
        return batch_idx, target_idx

    def get_loss(self, loss, outputs, targets, indices, num_boxes):
        loss_map = {
            "labels": self.loss_labels,
            "cardinality": self.loss_cardinality,
            "boxes": self.loss_boxes,
            "masks": self.loss_masks,
        }
        if loss not in loss_map:
            raise ValueError(f"Loss {loss} not supported")
        return loss_map[loss](outputs, targets, indices, num_boxes)

    def forward(self, outputs, targets):
        """
        This performs the loss computation.

        Args:
             outputs (`dict`, *optional*):
                Dictionary of tensors, see the output specification of the model for the format.
             targets (`list[dict]`, *optional*):
                List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
                losses applied, see each loss' doc.
        """
        outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"}

        # Retrieve the matching between the outputs of the last layer and the targets
        indices = self.matcher(outputs_without_aux, targets)

        # Compute the average number of target boxes across all nodes, for normalization purposes
        num_boxes = sum(len(t["class_labels"]) for t in targets)
        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
        world_size = 1
        if is_accelerate_available():
            if PartialState._shared_state != {}:
                num_boxes = reduce(num_boxes)
                world_size = PartialState().num_processes
        num_boxes = torch.clamp(num_boxes / world_size, min=1).item()

        # Compute all the requested losses
        losses = {}
        for loss in self.losses:
            losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))

        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
        if "auxiliary_outputs" in outputs:
            for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
                indices = self.matcher(auxiliary_outputs, targets)
                for loss in self.losses:
                    if loss == "masks":
                        # Intermediate masks losses are too costly to compute, we ignore them.
                        continue
                    l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes)
                    l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
                    losses.update(l_dict)

        return losses


# taken from https://github.com/facebookresearch/detr/blob/master/models/matcher.py
class HungarianMatcher(nn.Module):
    """
    This class computes an assignment between the targets and the predictions of the network.

    For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
    predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are
    un-matched (and thus treated as non-objects).

    Args:
        class_cost:
            The relative weight of the classification error in the matching cost.
        bbox_cost:
            The relative weight of the L1 error of the bounding box coordinates in the matching cost.
        giou_cost:
            The relative weight of the giou loss of the bounding box in the matching cost.
    """

    def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1):
        super().__init__()
        requires_backends(self, ["scipy"])

        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
            raise ValueError("All costs of the Matcher can't be 0")

    @torch.no_grad()
    def forward(self, outputs, targets):
        """
        Args:
            outputs (`dict`):
                A dictionary that contains at least these entries:
                * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
                * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates.
            targets (`list[dict]`):
                A list of targets (len(targets) = batch_size), where each target is a dict containing:
                * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of
                  ground-truth
                 objects in the target) containing the class labels
                * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates.

        Returns:
            `list[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
            - index_i is the indices of the selected predictions (in order)
            - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        """
        batch_size, num_queries = outputs["logits"].shape[:2]

        # We flatten to compute the cost matrices in a batch
        out_prob = outputs["logits"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]
        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]

        # Also concat the target labels and boxes
        target_ids = torch.cat([v["class_labels"] for v in targets])
        target_bbox = torch.cat([v["boxes"] for v in targets])

        # Compute the classification cost. Contrary to the loss, we don't use the NLL,
        # but approximate it in 1 - proba[target class].
        # The 1 is a constant that doesn't change the matching, it can be omitted.
        class_cost = -out_prob[:, target_ids]

        # Compute the L1 cost between boxes
        bbox_cost = torch.cdist(out_bbox, target_bbox, p=1)

        # Compute the giou cost between boxes
        giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox))

        # Final cost matrix
        cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost
        cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu()

        sizes = [len(v["boxes"]) for v in targets]
        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]


# below: bounding box utilities taken from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py


def _upcast(t: Tensor) -> Tensor:
    # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
    if t.is_floating_point():
        return t if t.dtype in (torch.float32, torch.float64) else t.float()
    else:
        return t if t.dtype in (torch.int32, torch.int64) else t.int()


def box_area(boxes: Tensor) -> Tensor:
    """
    Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.

    Args:
        boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
            Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
            < x2` and `0 <= y1 < y2`.

    Returns:
        `torch.FloatTensor`: a tensor containing the area for each box.
    """
    boxes = _upcast(boxes)
    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])


# modified from torchvision to also return the union
def box_iou(boxes1, boxes2):
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)

    left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
    right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]

    width_height = (right_bottom - left_top).clamp(min=0)  # [N,M,2]
    inter = width_height[:, :, 0] * width_height[:, :, 1]  # [N,M]

    union = area1[:, None] + area2 - inter

    iou = inter / union
    return iou, union


def generalized_box_iou(boxes1, boxes2):
    """
    Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.

    Returns:
        `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
    """
    # degenerate boxes gives inf / nan results
    # so do an early check
    if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
        raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}")
    if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
        raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}")
    iou, union = box_iou(boxes1, boxes2)

    top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2])
    bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

    width_height = (bottom_right - top_left).clamp(min=0)  # [N,M,2]
    area = width_height[:, :, 0] * width_height[:, :, 1]

    return iou - (area - union) / area


# below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306
def _max_by_axis(the_list):
    # type: (list[list[int]]) -> list[int]
    maxes = the_list[0]
    for sublist in the_list[1:]:
        for index, item in enumerate(sublist):
            maxes[index] = max(maxes[index], item)
    return maxes


class NestedTensor:
    def __init__(self, tensors, mask: Optional[Tensor]):
        self.tensors = tensors
        self.mask = mask

    def to(self, device):
        cast_tensor = self.tensors.to(device)
        mask = self.mask
        if mask is not None:
            cast_mask = mask.to(device)
        else:
            cast_mask = None
        return NestedTensor(cast_tensor, cast_mask)

    def decompose(self):
        return self.tensors, self.mask

    def __repr__(self):
        return str(self.tensors)


def nested_tensor_from_tensor_list(tensor_list: list[Tensor]):
    if tensor_list[0].ndim == 3:
        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
        batch_shape = [len(tensor_list)] + max_size
        batch_size, num_channels, height, width = batch_shape
        dtype = tensor_list[0].dtype
        device = tensor_list[0].device
        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
        mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device)
        for img, pad_img, m in zip(tensor_list, tensor, mask):
            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
            m[: img.shape[1], : img.shape[2]] = False
    else:
        raise ValueError("Only 3-dimensional tensors are supported")
    return NestedTensor(tensor, mask)


# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
@torch.jit.unused
def _set_aux_loss(outputs_class, outputs_coord):
    # this is a workaround to make torchscript happy, as torchscript
    # doesn't support dictionary with non-homogeneous values, such
    # as a dict having both a Tensor and a list.
    return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]


def ForSegmentationLoss(
    logits, labels, device, pred_boxes, pred_masks, config, outputs_class=None, outputs_coord=None, **kwargs
):
    # First: create the matcher
    matcher = HungarianMatcher(class_cost=config.class_cost, bbox_cost=config.bbox_cost, giou_cost=config.giou_cost)
    # Second: create the criterion
    losses = ["labels", "boxes", "cardinality", "masks"]
    criterion = ImageLoss(
        matcher=matcher,
        num_classes=config.num_labels,
        eos_coef=config.eos_coefficient,
        losses=losses,
    )
    criterion.to(device)
    # Third: compute the losses, based on outputs and labels
    outputs_loss = {}
    outputs_loss["logits"] = logits
    outputs_loss["pred_boxes"] = pred_boxes
    outputs_loss["pred_masks"] = pred_masks

    auxiliary_outputs = None
    if config.auxiliary_loss:
        auxiliary_outputs = _set_aux_loss(outputs_class, outputs_coord)
        outputs_loss["auxiliary_outputs"] = auxiliary_outputs

    loss_dict = criterion(outputs_loss, labels)
    # Fourth: compute total loss, as a weighted sum of the various losses
    weight_dict = {"loss_ce": 1, "loss_bbox": config.bbox_loss_coefficient}
    weight_dict["loss_giou"] = config.giou_loss_coefficient
    weight_dict["loss_mask"] = config.mask_loss_coefficient
    weight_dict["loss_dice"] = config.dice_loss_coefficient
    if config.auxiliary_loss:
        aux_weight_dict = {}
        for i in range(config.decoder_layers - 1):
            aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
        weight_dict.update(aux_weight_dict)
    loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict if k in weight_dict)
    return loss, loss_dict, auxiliary_outputs


def ForObjectDetectionLoss(
    logits, labels, device, pred_boxes, config, outputs_class=None, outputs_coord=None, **kwargs
):
    # First: create the matcher
    matcher = HungarianMatcher(class_cost=config.class_cost, bbox_cost=config.bbox_cost, giou_cost=config.giou_cost)
    # Second: create the criterion
    losses = ["labels", "boxes", "cardinality"]
    criterion = ImageLoss(
        matcher=matcher,
        num_classes=config.num_labels,
        eos_coef=config.eos_coefficient,
        losses=losses,
    )
    criterion.to(device)
    # Third: compute the losses, based on outputs and labels
    outputs_loss = {}
    auxiliary_outputs = None
    outputs_loss["logits"] = logits
    outputs_loss["pred_boxes"] = pred_boxes
    if config.auxiliary_loss:
        auxiliary_outputs = _set_aux_loss(outputs_class, outputs_coord)
        outputs_loss["auxiliary_outputs"] = auxiliary_outputs

    loss_dict = criterion(outputs_loss, labels)
    # Fourth: compute total loss, as a weighted sum of the various losses
    weight_dict = {"loss_ce": 1, "loss_bbox": config.bbox_loss_coefficient}
    weight_dict["loss_giou"] = config.giou_loss_coefficient
    if config.auxiliary_loss:
        aux_weight_dict = {}
        for i in range(config.decoder_layers - 1):
            aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
        weight_dict.update(aux_weight_dict)
    loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict if k in weight_dict)
    return loss, loss_dict, auxiliary_outputs