# Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings

import torch
import torch.nn as nn
from mmcv.cnn import xavier_init
from mmcv.cnn.bricks.registry import TRANSFORMER_LAYER_SEQUENCE
from mmcv.cnn.bricks.transformer import TransformerLayerSequence
from mmdet.models.utils.builder import TRANSFORMER
from mmdet.models.utils.transformer import Transformer
from torch.nn.init import normal_

try:
    from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
except ImportError:
    warnings.warn(
        '`MultiScaleDeformableAttention` in MMCV has been moved to '
        '`mmcv.ops.multi_scale_deform_attn`, please update your MMCV')
    from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention

from .fp16_dattn import MultiScaleDeformableAttentionFp16


def inverse_sigmoid(x, eps=1e-5):
    """Inverse function of sigmoid.
    Args:
        x (Tensor): The tensor to do the
            inverse.
        eps (float): EPS avoid numerical
            overflow. Defaults 1e-5.
    Returns:
        Tensor: The x has passed the inverse
            function of sigmoid, has same
            shape with input.
    """
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)


@TRANSFORMER_LAYER_SEQUENCE.register_module()
class DeformableDetrTransformerDecoder_(TransformerLayerSequence):
    """Implements the decoder in DETR transformer.
    Args:
        return_intermediate (bool): Whether to return intermediate outputs.
        coder_norm_cfg (dict): Config of last normalization layer. Default：
            `LN`.
    """

    def __init__(self, *args,
                 return_intermediate=False, coord_dim=2, kp_coord_dim=2, **kwargs):

        super(DeformableDetrTransformerDecoder_, self).__init__(*args, **kwargs)
        self.return_intermediate = return_intermediate
        self.coord_dim = coord_dim
        self.kp_coord_dim = kp_coord_dim

    def forward(self,
                query,
                *args,
                reference_points=None,
                valid_ratios=None,
                reg_branches=None,
                **kwargs):
        """Forward function for `TransformerDecoder`.
        Args:
            query (Tensor): Input query with shape
                `(num_query, bs, embed_dims)`.
            reference_points (Tensor): The reference
                points of offset. has shape
                (bs, num_query, 4) when as_two_stage,
                otherwise has shape ((bs, num_query, 2).
            valid_ratios (Tensor): The radios of valid
                points on the feature map, has shape
                (bs, num_levels, 2)
            reg_branch: (obj:`nn.ModuleList`): Used for
                refining the regression results. Only would
                be passed when with_box_refine is True,
                otherwise would be passed a `None`.
        Returns:
            Tensor: Results with shape [1, num_query, bs, embed_dims] when
                return_intermediate is `False`, otherwise it has shape
                [num_layers, num_query, bs, embed_dims].
        """
        output = query
        intermediate = []
        intermediate_reference_points = []
        for lid, layer in enumerate(self.layers):

            reference_points_input = \
                reference_points[:, :, None, :self.kp_coord_dim] * \
                valid_ratios[:, None, :, :self.kp_coord_dim]
            # if reference_points.shape[-1] == 3 and self.kp_coord_dim==2:
            output = layer(
                output,
                *args,
                reference_points=reference_points_input[..., :self.kp_coord_dim],
                **kwargs)
            output = output.permute(1, 0, 2)

            if reg_branches is not None:
                tmp = reg_branches[lid](output)
                new_reference_points = tmp
                new_reference_points[..., :self.kp_coord_dim] = tmp[
                                                                ..., :self.kp_coord_dim] + inverse_sigmoid(
                    reference_points)
                new_reference_points = new_reference_points.sigmoid()
                if reference_points.shape[-1] == 3 and self.kp_coord_dim == 2:
                    reference_points[..., -1] = tmp[..., -1].sigmoid().detach()
                reference_points[..., :self.coord_dim] = new_reference_points.detach()

            output = output.permute(1, 0, 2)
            if self.return_intermediate:
                intermediate.append(output)
                intermediate_reference_points.append(reference_points)

        if self.return_intermediate:
            return torch.stack(intermediate), torch.stack(
                intermediate_reference_points)

        return output, reference_points


@TRANSFORMER.register_module()
class DeformableDetrTransformer_(Transformer):
    """Implements the DeformableDETR transformer.
    Args:
        as_two_stage (bool): Generate query from encoder features.
            Default: False.
        num_feature_levels (int): Number of feature maps from FPN:
            Default: 4.
        two_stage_num_proposals (int): Number of proposals when set
            `as_two_stage` as True. Default: 300.
    """

    def __init__(self,
                 as_two_stage=False,
                 num_feature_levels=1,
                 two_stage_num_proposals=300,
                 coord_dim=2,
                 **kwargs):
        super(DeformableDetrTransformer_, self).__init__(**kwargs)
        self.as_two_stage = as_two_stage
        self.num_feature_levels = num_feature_levels
        self.two_stage_num_proposals = two_stage_num_proposals
        self.embed_dims = self.encoder.embed_dims
        self.coord_dim = coord_dim
        self.init_layers()

    def init_layers(self):
        """Initialize layers of the DeformableDetrTransformer."""
        self.level_embeds = nn.Parameter(
            torch.Tensor(self.num_feature_levels, self.embed_dims))

        if self.as_two_stage:
            self.enc_output = nn.Linear(self.embed_dims, self.embed_dims)
            self.enc_output_norm = nn.LayerNorm(self.embed_dims)
            self.pos_trans = nn.Linear(self.embed_dims * 2,
                                       self.embed_dims * 2)
            self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2)
        else:
            self.reference_points_embed = nn.Linear(self.embed_dims, self.coord_dim)

    def init_weights(self):
        """Initialize the transformer weights."""
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)
        for m in self.modules():
            if isinstance(m, MultiScaleDeformableAttention):
                m.init_weights()
            elif isinstance(m, MultiScaleDeformableAttentionFp16):
                m.init_weights()
        if not self.as_two_stage:
            xavier_init(self.reference_points_embed, distribution='uniform', bias=0.)
        normal_(self.level_embeds)

    @staticmethod
    def get_reference_points(spatial_shapes, valid_ratios, device):
        """Get the reference points used in decoder.
        Args:
            spatial_shapes (Tensor): The shape of all
                feature maps, has shape (num_level, 2).
            valid_ratios (Tensor): The radios of valid
                points on the feature map, has shape
                (bs, num_levels, 2)
            device (obj:`device`): The device where
                reference_points should be.
        Returns:
            Tensor: reference points used in decoder, has \
                shape (bs, num_keys, num_levels, 2).
        """
        reference_points_list = []
        for lvl, (H, W) in enumerate(spatial_shapes):
            #  TODO  check this 0.5
            ref_y, ref_x = torch.meshgrid(
                torch.linspace(
                    0.5, H - 0.5, H, dtype=torch.float32, device=device),
                torch.linspace(
                    0.5, W - 0.5, W, dtype=torch.float32, device=device))
            ref_y = ref_y.reshape(-1)[None] / (
                    valid_ratios[:, None, lvl, 1] * H)
            ref_x = ref_x.reshape(-1)[None] / (
                    valid_ratios[:, None, lvl, 0] * W)
            ref = torch.stack((ref_x, ref_y), -1)
            reference_points_list.append(ref)
        reference_points = torch.cat(reference_points_list, 1)
        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
        return reference_points

    def get_valid_ratio(self, mask):
        """Get the valid radios of feature maps of all  level."""
        _, H, W = mask.shape
        valid_H = torch.sum(~mask[:, :, 0], 1)
        valid_W = torch.sum(~mask[:, 0, :], 1)
        valid_ratio_h = valid_H.float() / H
        valid_ratio_w = valid_W.float() / W
        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
        return valid_ratio

    def get_proposal_pos_embed(self,
                               proposals,
                               num_pos_feats=128,
                               temperature=10000):
        """Get the position embedding of proposal."""
        scale = 2 * math.pi
        dim_t = torch.arange(
            num_pos_feats, dtype=torch.float32, device=proposals.device)
        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
        # N, L, 4
        proposals = proposals.sigmoid() * scale
        # N, L, 4, 128
        pos = proposals[:, :, :, None] / dim_t
        # N, L, 4, 64, 2
        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()),
                          dim=4).flatten(2)
        return pos

    def forward(self,
                mlvl_feats,
                mlvl_masks,
                query_embed,
                mlvl_pos_embeds,
                reg_branches=None,
                cls_branches=None,
                **kwargs):
        """Forward function for `Transformer`.
        Args:
            mlvl_feats (list(Tensor)): Input queries from
                different level. Each element has shape
                [bs, embed_dims, h, w].
            mlvl_masks (list(Tensor)): The key_padding_mask from
                different level used for encoder and decoder,
                each element has shape  [bs, h, w].
            query_embed (Tensor): The query embedding for decoder,
                with shape [num_query, c].
            mlvl_pos_embeds (list(Tensor)): The positional encoding
                of feats from different level, has the shape
                 [bs, embed_dims, h, w].
            reg_branches (obj:`nn.ModuleList`): Regression heads for
                feature maps from each decoder layer. Only would
                be passed when
                `with_box_refine` is True. Default to None.
            cls_branches (obj:`nn.ModuleList`): Classification heads
                for feature maps from each decoder layer. Only would
                 be passed when `as_two_stage`
                 is True. Default to None.
        Returns:
            tuple[Tensor]: results of decoder containing the following tensor.
                - inter_states: Outputs from decoder. If
                    return_intermediate_dec is True output has shape \
                      (num_dec_layers, bs, num_query, embed_dims), else has \
                      shape (1, bs, num_query, embed_dims).
                - init_reference_out: The initial value of reference \
                    points, has shape (bs, num_queries, 4).
                - inter_references_out: The internal value of reference \
                    points in decoder, has shape \
                    (num_dec_layers, bs,num_query, embed_dims)
                - enc_outputs_class: The classification score of \
                    proposals generated from \
                    encoder's feature maps, has shape \
                    (batch, h*w, num_classes). \
                    Only would be returned when `as_two_stage` is True, \
                    otherwise None.
                - enc_outputs_coord_unact: The regression results \
                    generated from encoder's feature maps., has shape \
                    (batch, h*w, 4). Only would \
                    be returned when `as_two_stage` is True, \
                    otherwise None.
        """
        assert self.as_two_stage or query_embed is not None

        feat_flatten = []
        mask_flatten = []
        lvl_pos_embed_flatten = []
        spatial_shapes = []
        for lvl, (feat, mask, pos_embed) in enumerate(
                zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)):
            bs, c, h, w = feat.shape
            spatial_shape = (h, w)
            spatial_shapes.append(spatial_shape)
            feat = feat.flatten(2).transpose(1, 2)
            mask = mask.flatten(1)
            pos_embed = pos_embed.flatten(2).transpose(1, 2)
            lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1)
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            feat_flatten.append(feat)
            mask_flatten.append(mask)
        feat_flatten = torch.cat(feat_flatten, 1)
        mask_flatten = torch.cat(mask_flatten, 1)
        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
        spatial_shapes = torch.as_tensor(
            spatial_shapes, dtype=torch.long, device=feat_flatten.device)
        level_start_index = torch.cat((spatial_shapes.new_zeros(
            (1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
        valid_ratios = torch.stack(
            [self.get_valid_ratio(m) for m in mlvl_masks], 1)

        # reference_points = \
        #     self.get_reference_points(spatial_shapes,
        #                               valid_ratios,
        #                               device=feat.device)

        feat_flatten = feat_flatten.permute(1, 0, 2)  # (H*W, bs, embed_dims)
        # lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(
        #     1, 0, 2)  # (H*W, bs, embed_dims)
        # memory = self.encoder(
        #     query=feat_flatten,
        #     key=None,
        #     value=None,
        #     query_pos=lvl_pos_embed_flatten,
        #     query_key_padding_mask=mask_flatten,
        #     spatial_shapes=spatial_shapes,
        #     reference_points=reference_points,
        #     level_start_index=level_start_index,
        #     valid_ratios=valid_ratios,
        #     **kwargs)

        memory = feat_flatten.permute(1, 0, 2)
        bs, _, c = memory.shape

        query_pos, query = torch.split(query_embed, c, dim=-1)
        reference_points = self.reference_points_embed(query_pos).sigmoid()
        init_reference_out = reference_points

        # decoder
        query = query.permute(1, 0, 2)
        memory = memory.permute(1, 0, 2)
        query_pos = query_pos.permute(1, 0, 2)
        inter_states, inter_references = self.decoder(
            query=query,
            key=None,
            value=memory,
            query_pos=query_pos,
            key_padding_mask=mask_flatten,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            level_start_index=level_start_index,
            valid_ratios=valid_ratios,
            reg_branches=reg_branches,
            **kwargs)

        inter_references_out = inter_references
        return inter_states, init_reference_out, inter_references_out
