# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

from typing import List, Optional
import torch
from torch.nn import functional as F

from detectron2.structures import BoxMode, Instances

from ..structures import (
    DensePoseDataRelative,
    DensePoseList,
    DensePoseOutput,
    resample_output_to_bbox,
)


class DensePoseBaseSampler:
    """
    Base DensePose sampler to produce DensePose data from DensePose predictions.
    Samples for each class are drawn according to some distribution over all pixels estimated
    to belong to that class.
    """

    def __init__(self, count_per_class: int = 8):
        """
        Constructor

        Args:
          count_per_class (int): the sampler produces at most `count_per_class`
              samples for each category
        """
        self.count_per_class = count_per_class

    def __call__(self, instances: Instances) -> DensePoseList:
        """
        Convert DensePose predictions (an instance of `DensePoseOutput`)
        into DensePose annotations data (an instance of `DensePoseList`)
        """
        boxes_xyxy_abs = instances.pred_boxes.tensor.clone().cpu()
        boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
        dp_datas = []
        for i, box_xywh in enumerate(boxes_xywh_abs):
            labels_i, result_i = resample_output_to_bbox(
                instances.pred_densepose[i], box_xywh, self._confidence_channels()
            )
            annotation_i = self._sample(labels_i.cpu(), result_i.cpu(), box_xywh)
            annotation_i[DensePoseDataRelative.S_KEY] = self._resample_mask(
                instances.pred_densepose[i]
            )

            dp_datas.append(DensePoseDataRelative(annotation_i))
        # create densepose annotations on CPU
        dp_list = DensePoseList(dp_datas, boxes_xyxy_abs, instances.image_size)
        return dp_list

    def _sample(
        self, labels: torch.Tensor, dp_result: torch.Tensor, bbox_xywh: List[int]
    ) -> DensePoseDataRelative:
        """
        Sample DensPoseDataRelative from estimation results
        """
        annotation = {
            DensePoseDataRelative.X_KEY: [],
            DensePoseDataRelative.Y_KEY: [],
            DensePoseDataRelative.U_KEY: [],
            DensePoseDataRelative.V_KEY: [],
            DensePoseDataRelative.I_KEY: [],
        }
        x0, y0, _, _ = bbox_xywh
        n, h, w = dp_result.shape
        for part_id in range(1, DensePoseDataRelative.N_PART_LABELS + 1):
            # indices - tuple of 3 1D tensors of size k
            # 0: index along the first dimension N
            # 1: index along H dimension
            # 2: index along W dimension
            indices = torch.nonzero(labels.expand(n, h, w) == part_id, as_tuple=True)
            # values - an array of size [n, k]
            # n: number of channels (U, V, confidences)
            # k: number of points labeled with part_id
            values = dp_result[indices].view(n, -1)
            k = values.shape[1]
            count = min(self.count_per_class, k)
            if count <= 0:
                continue
            index_sample = self._produce_index_sample(values, count)
            sampled_values = values[:, index_sample]
            sampled_y = indices[1][index_sample] + 0.5
            sampled_x = indices[2][index_sample] + 0.5
            # prepare / normalize data
            x = (sampled_x / w * 256.0).cpu().tolist()
            y = (sampled_y / h * 256.0).cpu().tolist()
            u = sampled_values[0].clamp(0, 1).cpu().tolist()
            v = sampled_values[1].clamp(0, 1).cpu().tolist()
            fine_segm_labels = [part_id] * count
            # extend annotations
            annotation[DensePoseDataRelative.X_KEY].extend(x)
            annotation[DensePoseDataRelative.Y_KEY].extend(y)
            annotation[DensePoseDataRelative.U_KEY].extend(u)
            annotation[DensePoseDataRelative.V_KEY].extend(v)
            annotation[DensePoseDataRelative.I_KEY].extend(fine_segm_labels)
        return annotation

    def _confidence_channels(self) -> Optional[List[str]]:
        """
        Confedence channels to be used for sampling (to be overridden in children)
        """
        return None

    def _produce_index_sample(self, values: torch.Tensor, count: int):
        """
        Abstract method to produce a sample of indices to select data
        To be implemented in descendants

        Args:
            values (torch.Tensor): an array of size [n, k] that contains
                estimated values (U, V, confidences);
                n: number of channels (U, V, confidences)
                k: number of points labeled with part_id
            count (int): number of samples to produce, should be positive and <= k
:w

        Return:
            list(int): indices of values (along axis 1) selected as a sample
        """
        raise NotImplementedError

    def _resample_mask(self, output: DensePoseOutput) -> torch.Tensor:
        """
        Convert output mask tensors into the annotation mask tensor of size
        (256, 256)
        """
        sz = DensePoseDataRelative.MASK_SIZE
        S = (
            F.interpolate(output.S, (sz, sz), mode="bilinear", align_corners=False)
            .argmax(dim=1)
            .long()
        )
        I = (
            (
                F.interpolate(output.I, (sz, sz), mode="bilinear", align_corners=False).argmax(
                    dim=1
                )
                * (S > 0).long()
            )
            .squeeze()
            .cpu()
        )
        # Map fine segmentation results to coarse segmentation ground truth
        # TODO: extract this into separate classes
        # coarse segmentation: 1 = Torso, 2 = Right Hand, 3 = Left Hand,
        # 4 = Left Foot, 5 = Right Foot, 6 = Upper Leg Right, 7 = Upper Leg Left,
        # 8 = Lower Leg Right, 9 = Lower Leg Left, 10 = Upper Arm Left,
        # 11 = Upper Arm Right, 12 = Lower Arm Left, 13 = Lower Arm Right,
        # 14 = Head
        # fine segmentation: 1, 2 = Torso, 3 = Right Hand, 4 = Left Hand,
        # 5 = Left Foot, 6 = Right Foot, 7, 9 = Upper Leg Right,
        # 8, 10 = Upper Leg Left, 11, 13 = Lower Leg Right,
        # 12, 14 = Lower Leg Left, 15, 17 = Upper Arm Left,
        # 16, 18 = Upper Arm Right, 19, 21 = Lower Arm Left,
        # 20, 22 = Lower Arm Right, 23, 24 = Head
        FINE_TO_COARSE_SEGMENTATION = {
            1: 1,
            2: 1,
            3: 2,
            4: 3,
            5: 4,
            6: 5,
            7: 6,
            8: 7,
            9: 6,
            10: 7,
            11: 8,
            12: 9,
            13: 8,
            14: 9,
            15: 10,
            16: 11,
            17: 10,
            18: 11,
            19: 12,
            20: 13,
            21: 12,
            22: 13,
            23: 14,
            24: 14,
        }
        mask = torch.zeros((sz, sz), dtype=torch.int64, device=torch.device("cpu"))
        for i in range(DensePoseDataRelative.N_PART_LABELS):
            mask[I == i + 1] = FINE_TO_COARSE_SEGMENTATION[i + 1]
        return mask