import torch
import torchvision

import torch.nn.functional as F
from torch import nn, Tensor

from torchvision.ops import boxes as box_ops

from torchvision.ops import roi_align

from . import _utils as det_utils

from typing import Optional, List, Dict, Tuple


def fastrcnn_loss(class_logits, box_regression, labels, regression_targets):
    # type: (Tensor, Tensor, List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor]
    """
    Computes the loss for Faster R-CNN.

    Args:
        class_logits (Tensor)
        box_regression (Tensor)
        labels (list[BoxList])
        regression_targets (Tensor)

    Returns:
        classification_loss (Tensor)
        box_loss (Tensor)
    """

    labels = torch.cat(labels, dim=0)
    regression_targets = torch.cat(regression_targets, dim=0)

    classification_loss = F.cross_entropy(class_logits, labels)

    # get indices that correspond to the regression targets for
    # the corresponding ground truth labels, to be used with
    # advanced indexing
    sampled_pos_inds_subset = torch.where(labels > 0)[0]
    labels_pos = labels[sampled_pos_inds_subset]
    N, num_classes = class_logits.shape
    box_regression = box_regression.reshape(N, box_regression.size(-1) // 4, 4)

    box_loss = det_utils.smooth_l1_loss(
        box_regression[sampled_pos_inds_subset, labels_pos],
        regression_targets[sampled_pos_inds_subset],
        beta=1 / 9,
        size_average=False,
    )
    box_loss = box_loss / labels.numel()

    return classification_loss, box_loss


def maskrcnn_inference(x, labels):
    # type: (Tensor, List[Tensor]) -> List[Tensor]
    """
    From the results of the CNN, post process the masks
    by taking the mask corresponding to the class with max
    probability (which are of fixed size and directly output
    by the CNN) and return the masks in the mask field of the BoxList.

    Args:
        x (Tensor): the mask logits
        labels (list[BoxList]): bounding boxes that are used as
            reference, one for ech image

    Returns:
        results (list[BoxList]): one BoxList for each image, containing
            the extra field mask
    """
    mask_prob = x.sigmoid()

    # select masks corresponding to the predicted classes
    num_masks = x.shape[0]
    boxes_per_image = [label.shape[0] for label in labels]
    labels = torch.cat(labels)
    index = torch.arange(num_masks, device=labels.device)
    mask_prob = mask_prob[index, labels][:, None]
    mask_prob = mask_prob.split(boxes_per_image, dim=0)

    return mask_prob


def project_masks_on_boxes(gt_masks, boxes, matched_idxs, M):
    # type: (Tensor, Tensor, Tensor, int) -> Tensor
    """
    Given segmentation masks and the bounding boxes corresponding
    to the location of the masks in the image, this function
    crops and resizes the masks in the position defined by the
    boxes. This prepares the masks for them to be fed to the
    loss computation as the targets.
    """
    matched_idxs = matched_idxs.to(boxes)
    rois = torch.cat([matched_idxs[:, None], boxes], dim=1)
    gt_masks = gt_masks[:, None].to(rois)
    return roi_align(gt_masks, rois, (M, M), 1.)[:, 0]


def maskrcnn_loss(mask_logits, proposals, gt_masks, gt_labels, mask_matched_idxs):
    # type: (Tensor, List[Tensor], List[Tensor], List[Tensor], List[Tensor]) -> Tensor
    """
    Args:
        proposals (list[BoxList])
        mask_logits (Tensor)
        targets (list[BoxList])

    Return:
        mask_loss (Tensor): scalar tensor containing the loss
    """

    discretization_size = mask_logits.shape[-1]
    labels = [gt_label[idxs] for gt_label, idxs in zip(gt_labels, mask_matched_idxs)]
    mask_targets = [
        project_masks_on_boxes(m, p, i, discretization_size)
        for m, p, i in zip(gt_masks, proposals, mask_matched_idxs)
    ]

    labels = torch.cat(labels, dim=0)
    mask_targets = torch.cat(mask_targets, dim=0)

    # torch.mean (in binary_cross_entropy_with_logits) doesn't
    # accept empty tensors, so handle it separately
    if mask_targets.numel() == 0:
        return mask_logits.sum() * 0

    mask_loss = F.binary_cross_entropy_with_logits(
        mask_logits[torch.arange(labels.shape[0], device=labels.device), labels], mask_targets
    )
    return mask_loss


def keypoints_to_heatmap(keypoints, rois, heatmap_size):
    # type: (Tensor, Tensor, int) -> Tuple[Tensor, Tensor]
    offset_x = rois[:, 0]
    offset_y = rois[:, 1]
    scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
    scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])

    offset_x = offset_x[:, None]
    offset_y = offset_y[:, None]
    scale_x = scale_x[:, None]
    scale_y = scale_y[:, None]

    x = keypoints[..., 0]
    y = keypoints[..., 1]

    x_boundary_inds = x == rois[:, 2][:, None]
    y_boundary_inds = y == rois[:, 3][:, None]

    x = (x - offset_x) * scale_x
    x = x.floor().long()
    y = (y - offset_y) * scale_y
    y = y.floor().long()

    x[x_boundary_inds] = heatmap_size - 1
    y[y_boundary_inds] = heatmap_size - 1

    valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
    vis = keypoints[..., 2] > 0
    valid = (valid_loc & vis).long()

    lin_ind = y * heatmap_size + x
    heatmaps = lin_ind * valid

    return heatmaps, valid


def _onnx_heatmaps_to_keypoints(maps, maps_i, roi_map_width, roi_map_height,
                                widths_i, heights_i, offset_x_i, offset_y_i):
    num_keypoints = torch.scalar_tensor(maps.size(1), dtype=torch.int64)

    width_correction = widths_i / roi_map_width
    height_correction = heights_i / roi_map_height

    roi_map = F.interpolate(
        maps_i[:, None], size=(int(roi_map_height), int(roi_map_width)), mode='bicubic', align_corners=False)[:, 0]

    w = torch.scalar_tensor(roi_map.size(2), dtype=torch.int64)
    pos = roi_map.reshape(num_keypoints, -1).argmax(dim=1)

    x_int = (pos % w)
    y_int = ((pos - x_int) // w)

    x = (torch.tensor(0.5, dtype=torch.float32) + x_int.to(dtype=torch.float32)) * \
        width_correction.to(dtype=torch.float32)
    y = (torch.tensor(0.5, dtype=torch.float32) + y_int.to(dtype=torch.float32)) * \
        height_correction.to(dtype=torch.float32)

    xy_preds_i_0 = x + offset_x_i.to(dtype=torch.float32)
    xy_preds_i_1 = y + offset_y_i.to(dtype=torch.float32)
    xy_preds_i_2 = torch.ones(xy_preds_i_1.shape, dtype=torch.float32)
    xy_preds_i = torch.stack([xy_preds_i_0.to(dtype=torch.float32),
                              xy_preds_i_1.to(dtype=torch.float32),
                              xy_preds_i_2.to(dtype=torch.float32)], 0)

    # TODO: simplify when indexing without rank will be supported by ONNX
    base = num_keypoints * num_keypoints + num_keypoints + 1
    ind = torch.arange(num_keypoints)
    ind = ind.to(dtype=torch.int64) * base
    end_scores_i = roi_map.index_select(1, y_int.to(dtype=torch.int64)) \
        .index_select(2, x_int.to(dtype=torch.int64)).view(-1).index_select(0, ind.to(dtype=torch.int64))

    return xy_preds_i, end_scores_i


@torch.jit._script_if_tracing
def _onnx_heatmaps_to_keypoints_loop(maps, rois, widths_ceil, heights_ceil,
                                     widths, heights, offset_x, offset_y, num_keypoints):
    xy_preds = torch.zeros((0, 3, int(num_keypoints)), dtype=torch.float32, device=maps.device)
    end_scores = torch.zeros((0, int(num_keypoints)), dtype=torch.float32, device=maps.device)

    for i in range(int(rois.size(0))):
        xy_preds_i, end_scores_i = _onnx_heatmaps_to_keypoints(maps, maps[i],
                                                               widths_ceil[i], heights_ceil[i],
                                                               widths[i], heights[i],
                                                               offset_x[i], offset_y[i])
        xy_preds = torch.cat((xy_preds.to(dtype=torch.float32),
                              xy_preds_i.unsqueeze(0).to(dtype=torch.float32)), 0)
        end_scores = torch.cat((end_scores.to(dtype=torch.float32),
                                end_scores_i.to(dtype=torch.float32).unsqueeze(0)), 0)
    return xy_preds, end_scores


def heatmaps_to_keypoints(maps, rois):
    """Extract predicted keypoint locations from heatmaps. Output has shape
    (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob)
    for each keypoint.
    """
    # This function converts a discrete image coordinate in a HEATMAP_SIZE x
    # HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain
    # consistency with keypoints_to_heatmap_labels by using the conversion from
    # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a
    # continuous coordinate.
    offset_x = rois[:, 0]
    offset_y = rois[:, 1]

    widths = rois[:, 2] - rois[:, 0]
    heights = rois[:, 3] - rois[:, 1]
    widths = widths.clamp(min=1)
    heights = heights.clamp(min=1)
    widths_ceil = widths.ceil()
    heights_ceil = heights.ceil()

    num_keypoints = maps.shape[1]

    if torchvision._is_tracing():
        xy_preds, end_scores = _onnx_heatmaps_to_keypoints_loop(maps, rois,
                                                                widths_ceil, heights_ceil, widths, heights,
                                                                offset_x, offset_y,
                                                                torch.scalar_tensor(num_keypoints, dtype=torch.int64))
        return xy_preds.permute(0, 2, 1), end_scores

    xy_preds = torch.zeros((len(rois), 3, num_keypoints), dtype=torch.float32, device=maps.device)
    end_scores = torch.zeros((len(rois), num_keypoints), dtype=torch.float32, device=maps.device)
    for i in range(len(rois)):
        roi_map_width = int(widths_ceil[i].item())
        roi_map_height = int(heights_ceil[i].item())
        width_correction = widths[i] / roi_map_width
        height_correction = heights[i] / roi_map_height
        roi_map = F.interpolate(
            maps[i][:, None], size=(roi_map_height, roi_map_width), mode='bicubic', align_corners=False)[:, 0]
        # roi_map_probs = scores_to_probs(roi_map.copy())
        w = roi_map.shape[2]
        pos = roi_map.reshape(num_keypoints, -1).argmax(dim=1)

        x_int = pos % w
        y_int = (pos - x_int) // w
        # assert (roi_map_probs[k, y_int, x_int] ==
        #         roi_map_probs[k, :, :].max())
        x = (x_int.float() + 0.5) * width_correction
        y = (y_int.float() + 0.5) * height_correction
        xy_preds[i, 0, :] = x + offset_x[i]
        xy_preds[i, 1, :] = y + offset_y[i]
        xy_preds[i, 2, :] = 1
        end_scores[i, :] = roi_map[torch.arange(num_keypoints), y_int, x_int]

    return xy_preds.permute(0, 2, 1), end_scores


def keypointrcnn_loss(keypoint_logits, proposals, gt_keypoints, keypoint_matched_idxs):
    # type: (Tensor, List[Tensor], List[Tensor], List[Tensor]) -> Tensor
    N, K, H, W = keypoint_logits.shape
    assert H == W
    discretization_size = H
    heatmaps = []
    valid = []
    for proposals_per_image, gt_kp_in_image, midx in zip(proposals, gt_keypoints, keypoint_matched_idxs):
        kp = gt_kp_in_image[midx]
        heatmaps_per_image, valid_per_image = keypoints_to_heatmap(
            kp, proposals_per_image, discretization_size
        )
        heatmaps.append(heatmaps_per_image.view(-1))
        valid.append(valid_per_image.view(-1))

    keypoint_targets = torch.cat(heatmaps, dim=0)
    valid = torch.cat(valid, dim=0).to(dtype=torch.uint8)
    valid = torch.where(valid)[0]

    # torch.mean (in binary_cross_entropy_with_logits) does'nt
    # accept empty tensors, so handle it sepaartely
    if keypoint_targets.numel() == 0 or len(valid) == 0:
        return keypoint_logits.sum() * 0

    keypoint_logits = keypoint_logits.view(N * K, H * W)

    keypoint_loss = F.cross_entropy(keypoint_logits[valid], keypoint_targets[valid])
    return keypoint_loss


def keypointrcnn_inference(x, boxes):
    # type: (Tensor, List[Tensor]) -> Tuple[List[Tensor], List[Tensor]]
    kp_probs = []
    kp_scores = []

    boxes_per_image = [box.size(0) for box in boxes]
    x2 = x.split(boxes_per_image, dim=0)

    for xx, bb in zip(x2, boxes):
        kp_prob, scores = heatmaps_to_keypoints(xx, bb)
        kp_probs.append(kp_prob)
        kp_scores.append(scores)

    return kp_probs, kp_scores


def _onnx_expand_boxes(boxes, scale):
    # type: (Tensor, float) -> Tensor
    w_half = (boxes[:, 2] - boxes[:, 0]) * .5
    h_half = (boxes[:, 3] - boxes[:, 1]) * .5
    x_c = (boxes[:, 2] + boxes[:, 0]) * .5
    y_c = (boxes[:, 3] + boxes[:, 1]) * .5

    w_half = w_half.to(dtype=torch.float32) * scale
    h_half = h_half.to(dtype=torch.float32) * scale

    boxes_exp0 = x_c - w_half
    boxes_exp1 = y_c - h_half
    boxes_exp2 = x_c + w_half
    boxes_exp3 = y_c + h_half
    boxes_exp = torch.stack((boxes_exp0, boxes_exp1, boxes_exp2, boxes_exp3), 1)
    return boxes_exp