# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, ConvModule
from .multi_scale_deform_attn import MultiScaleDeformableAttention
from mmengine.model import (BaseModule, ModuleList, caffe2_xavier_init,
                            normal_init, xavier_init)
from torch import Tensor

from mmdet.models.layers.transformer.detr_layers import (DetrTransformerEncoder, DetrTransformerEncoderLayer)
from mmdet.utils import ConfigType, OptMultiConfig
from mmdet.models.task_modules.prior_generators import MlvlPointGenerator
from mmdet.models.layers.positional_encoding import SinePositionalEncoding

from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN

try:
    from fairscale.nn.checkpoint import checkpoint_wrapper
except Exception:
    checkpoint_wrapper = None

def get_embedding_weight(model, size):
    vocab_size = size
    indices = torch.arange(vocab_size).cuda().to(torch.int64)
    embeddings = model(indices)
    return embeddings

# @MODELS.register_module()
class MSDeformAttnPixelDecoder(BaseModule):
    """Pixel decoder with multi-scale deformable attention.

    Args:
        in_channels (list[int] | tuple[int]): Number of channels in the
            input feature maps.
        strides (list[int] | tuple[int]): Output strides of feature from
            backbone.
        feat_channels (int): Number of channels for feature.
        out_channels (int): Number of channels for output.
        num_outs (int): Number of output scales.
        norm_cfg (:obj:`ConfigDict` or dict): Config for normalization.
            Defaults to dict(type='GN', num_groups=32).
        act_cfg (:obj:`ConfigDict` or dict): Config for activation.
            Defaults to dict(type='ReLU').
        encoder (:obj:`ConfigDict` or dict): Config for transformer
            encoder. Defaults to None.
        positional_encoding (:obj:`ConfigDict` or dict): Config for
            transformer encoder position encoding. Defaults to
            dict(num_feats=128, normalize=True).
        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
            dict], optional): Initialization config dict. Defaults to None.
    """

    def __init__(self,
                 in_channels: Union[List[int],
                                    Tuple[int]] = [256, 512, 1024, 2048],
                 strides: Union[List[int], Tuple[int]] = [4, 8, 16, 32],
                 feat_channels: int = 256,
                 out_channels: int = 256,
                 num_outs: int = 3,
                 norm_cfg: ConfigType = dict(type='GN', num_groups=32),
                 act_cfg: ConfigType = dict(type='ReLU'),
                 encoder: ConfigType = None,
                 positional_encoding: ConfigType = dict(
                     num_feats=128, normalize=True),
                 init_cfg: OptMultiConfig = None) -> None:
        super().__init__(init_cfg=init_cfg)
        self.strides = strides
        self.num_input_levels = len(in_channels)
        self.num_encoder_levels = \
            encoder.layer_cfg.self_attn_cfg.num_levels
        assert self.num_encoder_levels >= 1, \
            'num_levels in attn_cfgs must be at least one'
        input_conv_list = []
        # from top to down (low to high resolution)
        for i in range(self.num_input_levels - 1,
                       self.num_input_levels - self.num_encoder_levels - 1,
                       -1):
            input_conv = ConvModule(
                in_channels[i],
                feat_channels,
                kernel_size=1,
                norm_cfg=norm_cfg,
                act_cfg=None,
                bias=True)
            input_conv_list.append(input_conv)
        self.input_convs = ModuleList(input_conv_list)

        self.encoder = Mask2FormerTransformerEncoder(**encoder)
        self.postional_encoding = SinePositionalEncoding(**positional_encoding)
        # high resolution to low resolution
        self.level_encoding = nn.Embedding(self.num_encoder_levels,
                                           feat_channels)

        # fpn-like structure
        self.lateral_convs = ModuleList()
        self.output_convs = ModuleList()
        self.use_bias = norm_cfg is None
        # from top to down (low to high resolution)
        # fpn for the rest features that didn't pass in encoder
        for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1,
                       -1):
            lateral_conv = ConvModule(
                in_channels[i],
                feat_channels,
                kernel_size=1,
                bias=self.use_bias,
                norm_cfg=norm_cfg,
                act_cfg=None)
            output_conv = ConvModule(
                feat_channels,
                feat_channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=self.use_bias,
                norm_cfg=norm_cfg,
                act_cfg=act_cfg)
            self.lateral_convs.append(lateral_conv)
            self.output_convs.append(output_conv)

        self.mask_feature = Conv2d(
            feat_channels, out_channels, kernel_size=1, stride=1, padding=0)

        self.num_outs = num_outs
        self.point_generator = MlvlPointGenerator(strides)

    def init_weights(self) -> None:
        """Initialize weights."""
        for i in range(0, self.num_encoder_levels):
            xavier_init(
                self.input_convs[i].conv,
                gain=1,
                bias=0,
                distribution='uniform')

        for i in range(0, self.num_input_levels - self.num_encoder_levels):
            caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
            caffe2_xavier_init(self.output_convs[i].conv, bias=0)

        caffe2_xavier_init(self.mask_feature, bias=0)

        normal_init(self.level_encoding, mean=0, std=1)
        for p in self.encoder.parameters():
            if p.dim() > 1:
                nn.init.xavier_normal_(p)

        # init_weights defined in MultiScaleDeformableAttention
        for m in self.encoder.layers.modules():
            if isinstance(m, MultiScaleDeformableAttention):
                m.init_weights()

    def forward(self, feats: List[Tensor]) -> Tuple[Tensor, Tensor]:
        """
        Args:
            feats (list[Tensor]): Feature maps of each level. Each has
                shape of (batch_size, c, h, w).

        Returns:
            tuple: A tuple containing the following:

                - mask_feature (Tensor): shape (batch_size, c, h, w).
                - multi_scale_features (list[Tensor]): Multi scale \
                        features, each in shape (batch_size, c, h, w).
        """
        # generate padding mask for each level, for each image
        batch_size = feats[0].shape[0]
        encoder_input_list = []
        padding_mask_list = []
        level_positional_encoding_list = []
        spatial_shapes = []
        reference_points_list = []
        for i in range(self.num_encoder_levels):
            level_idx = self.num_input_levels - i - 1
            feat = feats[level_idx]
            feat_projected = self.input_convs[i](feat)
            feat_hw = torch._shape_as_tensor(feat)[2:].to(feat.device)

            # no padding
            padding_mask_resized = feat.new_zeros(
                (batch_size, ) + feat.shape[-2:], dtype=torch.bool)
            pos_embed = self.postional_encoding(padding_mask_resized)
            # level_embed = self.level_encoding.weight[i]
            level_embed = get_embedding_weight(self.level_encoding, self.num_encoder_levels)[i]
            level_pos_embed = level_embed.view(1, -1, 1, 1) + pos_embed
            # (h_i * w_i, 2)
            reference_points = self.point_generator.single_level_grid_priors(
                feat.shape[-2:], level_idx, device=feat.device)
            # normalize
            feat_wh = feat_hw.unsqueeze(0).flip(dims=[0, 1])
            factor = feat_wh * self.strides[level_idx]
            reference_points = reference_points / factor

            # shape (batch_size, c, h_i, w_i) -> (h_i * w_i, batch_size, c)
            feat_projected = feat_projected.flatten(2).permute(0, 2, 1)
            level_pos_embed = level_pos_embed.flatten(2).permute(0, 2, 1)
            padding_mask_resized = padding_mask_resized.flatten(1)

            encoder_input_list.append(feat_projected)
            padding_mask_list.append(padding_mask_resized)
            level_positional_encoding_list.append(level_pos_embed)
            spatial_shapes.append(feat_hw)
            reference_points_list.append(reference_points)
        # shape (batch_size, total_num_queries),
        # total_num_queries=sum([., h_i * w_i,.])
        padding_masks = torch.cat(padding_mask_list, dim=1)
        # shape (total_num_queries, batch_size, c)
        encoder_inputs = torch.cat(encoder_input_list, dim=1)
        level_positional_encodings = torch.cat(
            level_positional_encoding_list, dim=1)
        # shape (num_encoder_levels, 2), from low
        # resolution to high resolution
        num_queries_per_level = [e[0] * e[1] for e in spatial_shapes]
        spatial_shapes = torch.cat(spatial_shapes).view(-1, 2)
        # shape (0, h_0*w_0, h_0*w_0+h_1*w_1, ...)
        level_start_index = torch.cat((spatial_shapes.new_zeros(
            (1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
        reference_points = torch.cat(reference_points_list, dim=0)
        reference_points = reference_points[None, :, None].repeat(
            batch_size, 1, self.num_encoder_levels, 1)
        valid_radios = reference_points.new_ones(
            (batch_size, self.num_encoder_levels, 2))
        # shape (num_total_queries, batch_size, c)
        memory = self.encoder(
            query=encoder_inputs.to(torch.bfloat16),
            query_pos=level_positional_encodings.to(torch.bfloat16),
            key_padding_mask=padding_masks,
            spatial_shapes=spatial_shapes,
            reference_points=reference_points.to(torch.bfloat16),
            level_start_index=level_start_index,
            valid_ratios=valid_radios)
        # (batch_size, c, num_total_queries)
        memory = memory.permute(0, 2, 1)

        # from low resolution to high resolution
        outs = torch.split(memory, num_queries_per_level, dim=-1)
        outs = [
            x.reshape(batch_size, -1, spatial_shapes[i][0],
                      spatial_shapes[i][1]) for i, x in enumerate(outs)
        ]

        for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1,
                       -1):
            x = feats[i]
            cur_feat = self.lateral_convs[i](x)
            y = cur_feat + F.interpolate(
                outs[-1],
                size=cur_feat.shape[-2:],
                mode='bilinear',
                align_corners=False)
            y = self.output_convs[i](y)
            outs.append(y)
        multi_scale_features = outs[:self.num_outs]

        mask_feature = self.mask_feature(outs[-1])
        return mask_feature, multi_scale_features

class DeformableDetrTransformerEncoderLayer(DetrTransformerEncoderLayer):
    """Encoder layer of Deformable DETR."""

    def _init_layers(self) -> None:
        """Initialize self_attn, ffn, and norms."""
        self.self_attn = MultiScaleDeformableAttention(**self.self_attn_cfg)
        self.embed_dims = self.self_attn.embed_dims
        self.ffn = FFN(**self.ffn_cfg)
        norms_list = [
            build_norm_layer(self.norm_cfg, self.embed_dims)[1]
            for _ in range(2)
        ]
        self.norms = ModuleList(norms_list)

class DeformableDetrTransformerEncoder(DetrTransformerEncoder):
    """Transformer encoder of Deformable DETR."""

    def _init_layers(self) -> None:
        """Initialize encoder layers."""
        self.layers = ModuleList([
            DeformableDetrTransformerEncoderLayer(**self.layer_cfg)
            for _ in range(self.num_layers)
        ])
        self.embed_dims = self.layers[0].embed_dims

    def forward(self, query: Tensor, query_pos: Tensor,
                key_padding_mask: Tensor, spatial_shapes: Tensor,
                level_start_index: Tensor, valid_ratios: Tensor,
                **kwargs) -> Tensor:
        """Forward function of Transformer encoder.

        Args:
            query (Tensor): The input query, has shape (bs, num_queries, dim).
            query_pos (Tensor): The positional encoding for query, has shape
                (bs, num_queries, dim).
            key_padding_mask (Tensor): The `key_padding_mask` of `self_attn`
                input. ByteTensor, has shape (bs, num_queries).
            spatial_shapes (Tensor): Spatial shapes of features in all levels,
                has shape (num_levels, 2), last dimension represents (h, w).
            level_start_index (Tensor): The start index of each level.
                A tensor has shape (num_levels, ) and can be represented
                as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
            valid_ratios (Tensor): The ratios of the valid width and the valid
                height relative to the width and the height of features in all
                levels, has shape (bs, num_levels, 2).

        Returns:
            Tensor: Output queries of Transformer encoder, which is also
            called 'encoder output embeddings' or 'memory', has shape
            (bs, num_queries, dim)
        """
        reference_points = self.get_encoder_reference_points(
            spatial_shapes, valid_ratios, device=query.device)
        for layer in self.layers:
            query = layer(
                query=query,
                query_pos=query_pos,
                key_padding_mask=key_padding_mask,
                spatial_shapes=spatial_shapes,
                level_start_index=level_start_index,
                valid_ratios=valid_ratios,
                reference_points=reference_points,
                **kwargs)
        return query

    @staticmethod
    def get_encoder_reference_points(
            spatial_shapes: Tensor, valid_ratios: Tensor,
            device: Union[torch.device, str]) -> Tensor:
        """Get the reference points used in encoder.

        Args:
            spatial_shapes (Tensor): Spatial shapes of features in all levels,
                has shape (num_levels, 2), last dimension represents (h, w).
            valid_ratios (Tensor): The ratios of the valid width and the valid
                height relative to the width and the height of features in all
                levels, has shape (bs, num_levels, 2).
            device (obj:`device` or str): The device acquired by the
                `reference_points`.

        Returns:
            Tensor: Reference points used in decoder, has shape (bs, length,
            num_levels, 2).
        """

        reference_points_list = []
        for lvl, (H, W) in enumerate(spatial_shapes):
            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)
        # [bs, sum(hw), num_level, 2]
        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
        return reference_points

class Mask2FormerTransformerEncoder(DeformableDetrTransformerEncoder):
    """Encoder in PixelDecoder of Mask2Former."""

    def forward(self, query: Tensor, query_pos: Tensor,
                key_padding_mask: Tensor, spatial_shapes: Tensor,
                level_start_index: Tensor, valid_ratios: Tensor,
                reference_points: Tensor, **kwargs) -> Tensor:
        """Forward function of Transformer encoder.

        Args:
            query (Tensor): The input query, has shape (bs, num_queries, dim).
            query_pos (Tensor): The positional encoding for query, has shape
                (bs, num_queries, dim). If not None, it will be added to the
                `query` before forward function. Defaults to None.
            key_padding_mask (Tensor): The `key_padding_mask` of `self_attn`
                input. ByteTensor, has shape (bs, num_queries).
            spatial_shapes (Tensor): Spatial shapes of features in all levels,
                has shape (num_levels, 2), last dimension represents (h, w).
            level_start_index (Tensor): The start index of each level.
                A tensor has shape (num_levels, ) and can be represented
                as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
            valid_ratios (Tensor): The ratios of the valid width and the valid
                height relative to the width and the height of features in all
                levels, has shape (bs, num_levels, 2).
            reference_points (Tensor): The initial reference, has shape
                (bs, num_queries, 2) with the last dimension arranged
                as (cx, cy).

        Returns:
            Tensor: Output queries of Transformer encoder, which is also
            called 'encoder output embeddings' or 'memory', has shape
            (bs, num_queries, dim)
        """
        for layer in self.layers:
            query = layer(
                query=query,
                query_pos=query_pos,
                key_padding_mask=key_padding_mask,
                spatial_shapes=spatial_shapes,
                level_start_index=level_start_index,
                valid_ratios=valid_ratios,
                reference_points=reference_points,
                **kwargs)
        return query