from functools import partial

import torch

import torch.nn as nn
import torch.nn.functional as F
from timm.models.vision_transformer import resize_pos_embed
from timm.models.layers import DropPath, to_2tuple, trunc_normal_

from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event
from lib.models.mamba_fetrack.utils import combine_tokens, recover_tokens


class BaseBackbone(nn.Module):
    def __init__(self):
        super().__init__()

        # for original ViT
        self.pos_embed = None
        self.pos_embed_event = PatchEmbed_event(in_chans=190, embed_dim=768, kernel_size=5, stride=1)
        self.img_size = [224, 224]
        self.patch_size = 16
        self.embed_dim = 384

        self.cat_mode = 'direct'

        self.pos_embed_z = None
        self.pos_embed_x = None
        # self.pos_embed_z_event = None
        # self.pos_embed_x_event = None

        self.template_segment_pos_embed = None
        self.search_segment_pos_embed = None

        self.return_inter = False
        self.return_stage = [2, 5, 8, 11]

        self.add_cls_token = False
        self.add_sep_seg = False

    def finetune_track(self, cfg, patch_start_index=1):

        search_size = to_2tuple(cfg.DATA.SEARCH.SIZE)
        template_size = to_2tuple(cfg.DATA.TEMPLATE.SIZE)
        new_patch_size = cfg.MODEL.BACKBONE.STRIDE

        self.cat_mode = cfg.MODEL.BACKBONE.CAT_MODE
        self.return_inter = cfg.MODEL.RETURN_INTER
        self.return_stage = cfg.MODEL.RETURN_STAGES
        self.add_sep_seg = cfg.MODEL.BACKBONE.SEP_SEG

        # resize patch embedding
        if new_patch_size != self.patch_size:
            print('Inconsistent Patch Size With The Pretrained Weights, Interpolate The Weight!')
            old_patch_embed = {}
            for name, param in self.patch_embed.named_parameters():
                if 'weight' in name:
                    param = nn.functional.interpolate(param, size=(new_patch_size, new_patch_size),
                                                      mode='bicubic', align_corners=False)
                    param = nn.Parameter(param)
                old_patch_embed[name] = param
            self.patch_embed = PatchEmbed(img_size=self.img_size, patch_size=new_patch_size, in_chans=3,
                                          embed_dim=self.embed_dim)
            self.patch_embed.proj.bias = old_patch_embed['proj.bias']
            self.patch_embed.proj.weight = old_patch_embed['proj.weight']

        # for patch embedding
        patch_pos_embed = self.pos_embed[:, patch_start_index:, :]
        patch_pos_embed = patch_pos_embed.transpose(1, 2)
        B, E, Q = patch_pos_embed.shape
        P_H, P_W = self.img_size[0] // self.patch_size, self.img_size[1] // self.patch_size
        patch_pos_embed = patch_pos_embed.view(B, E, P_H, P_W)

        # for search region
        H, W = search_size
        new_P_H, new_P_W = H // new_patch_size, W // new_patch_size
        search_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic',
                                                           align_corners=False)
        search_patch_pos_embed = search_patch_pos_embed.flatten(2).transpose(1, 2)

        # for template region
        H, W = template_size
        new_P_H, new_P_W = H // new_patch_size, W // new_patch_size
        template_patch_pos_embed = nn.functional.interpolate(patch_pos_embed, size=(new_P_H, new_P_W), mode='bicubic',
                                                             align_corners=False)
        template_patch_pos_embed = template_patch_pos_embed.flatten(2).transpose(1, 2)

        self.pos_embed_z = nn.Parameter(template_patch_pos_embed)
        self.pos_embed_x = nn.Parameter(search_patch_pos_embed)
        # self.pos_embed_z_event = nn.Parameter(template_patch_pos_embed)
        # self.pos_embed_x_event = nn.Parameter(search_patch_pos_embed)

        # for cls token (keep it but not used)
        if self.add_cls_token and patch_start_index > 0:
            cls_pos_embed = self.pos_embed[:, 0:1, :]
            self.cls_pos_embed = nn.Parameter(cls_pos_embed)

        # separate token and segment token
        if self.add_sep_seg:
            self.template_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
            self.template_segment_pos_embed = trunc_normal_(self.template_segment_pos_embed, std=.02)
            self.search_segment_pos_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
            self.search_segment_pos_embed = trunc_normal_(self.search_segment_pos_embed, std=.02)

        # self.cls_token = None
        # self.pos_embed = None

        if self.return_inter:
            for i_layer in self.return_stage:
                if i_layer != 11:
                    norm_layer = partial(nn.LayerNorm, eps=1e-6)
                    layer = norm_layer(self.embed_dim)
                    layer_name = f'norm{i_layer}'
                    self.add_module(layer_name, layer)

    def forward_features(self, z, x, event_z, event_x):
        # N, L = event_z.shape[2], event_z.shape[3]
        event_x = self.pos_embed_event(event_x)     # B 768 256
        event_z = self.pos_embed_event(event_z)
        event_x += self.pos_embed_x
        event_z += self.pos_embed_z
        B, H, W = x.shape[0], x.shape[2], x.shape[3]

        x = self.patch_embed(x)
        z = self.patch_embed(z)

        if self.add_cls_token:
            cls_tokens = self.cls_token.expand(B, -1, -1)
            cls_tokens = cls_tokens + self.cls_pos_embed

        z += self.pos_embed_z
        x += self.pos_embed_x

        if self.add_sep_seg:
            x += self.search_segment_pos_embed
            z += self.template_segment_pos_embed

        x = combine_tokens(z, x, mode=self.cat_mode)
        if self.add_cls_token:
            x = torch.cat([cls_tokens, x], dim=1)

        x = self.pos_drop(x)

        for i, blk in enumerate(self.blocks):
            x = blk(x)

        lens_z = self.pos_embed_z.shape[1]
        lens_x = self.pos_embed_x.shape[1]
        x = recover_tokens(x, lens_z, lens_x, mode=self.cat_mode)

        aux_dict = {"attn": None}
        return self.norm(x), aux_dict

    def forward(self, z, x, event_z, event_x, **kwargs):
        """
        Joint feature extraction and relation modeling for the basic ViT backbone.
        Args:
            z (torch.Tensor): template feature, [B, C, H_z, W_z]
            x (torch.Tensor): search region feature, [B, C, H_x, W_x]

        Returns:
            x (torch.Tensor): merged template and search region feature, [B, L_z+L_x, C]
            attn : None
        """
        x, aux_dict = self.forward_features(z, x, event_z, event_x)

        return x, aux_dict
