import h5py
from diffusion_model.stable_diffusion import diffusion
import torch
import numpy as np
import os
from PIL import Image
from pycocotools.coco import COCO
from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
    CenterCrop
from open_clip.transform import ResizeLongest, _convert_to_rgb
from torchvision import transforms
import matplotlib.pyplot as plt
import cv2
from functools import reduce
import torch.nn.functional as F

def build_DINOv2():
    model_name='dinov2_vitb14_reg'
    hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dinov2_main'
    try:
        vfm = torch.hub.load(hub_path, model_name, source='local').half()
    except Exception as e:
        raise RuntimeError(f"Failed to load DINOv2 model '{model_name}': {e}")
    return vfm

def visualize_sd_dino_att(
    ori_img, self_att, sim_dino_soft, sim_dino_refined, 
    token_choosen, filename, attn_map_hw=(64, 64), vis_hw=(512, 512)
):
    """
    可视化SD传播对DINO自相关的细化效果, 4列分别为:
    1. 原图带token
    2. SD自注意力传播
    3. DINO自相关
    4. 细化后的DINO自相关
    """
    # 1. 原图
    # 支持PIL.Image、np.ndarray、tensor三种输入
    if isinstance(ori_img, torch.Tensor):
        img = ori_img
        if img.ndim == 4:  # [1,3,H,W] -> [3,H,W]
            img = img[0]
        img = img.cpu().numpy()
        img = np.transpose(img, (1, 2, 0))  # [H, W, 3]
        img = (img * 255).clip(0, 255).astype(np.uint8)
    elif isinstance(ori_img, Image.Image):
        img = np.array(ori_img)
        if img.dtype != np.uint8:
            img = (img * 255).clip(0, 255).astype(np.uint8)
    elif isinstance(ori_img, np.ndarray):
        img = ori_img
        if img.dtype != np.uint8:
            img = (img * 255).clip(0, 255).astype(np.uint8)
    else:
        raise ValueError("ori_img should be torch.Tensor, PIL.Image, or np.ndarray")
    if img.shape[2] == 4:  # RGBA to RGB
        img = img[:, :, :3]
    img_resized = cv2.resize(img, vis_hw, interpolation=cv2.INTER_LINEAR)

    # 2. token坐标
    h_attn, w_attn = attn_map_hw
    row, col = token_choosen
    y_vis = int((row + 0.5) * vis_hw[0] / h_attn)
    x_vis = int((col + 0.5) * vis_hw[1] / w_attn)
    img_with_dot = img_resized.copy()
    cv2.circle(img_with_dot, (x_vis, y_vis), radius=7, color=(255,0,0), thickness=-1)  # 红点

    # 3. SD自注意力传播
    att_map_sd = self_att[row * w_attn + col].to(torch.float32).detach().cpu().numpy().reshape(h_attn, w_attn)
    att_map_sd = (att_map_sd - att_map_sd.min()) / (att_map_sd.max() - att_map_sd.min() + 1e-8)
    att_map_sd_up = cv2.resize(att_map_sd, vis_hw, interpolation=cv2.INTER_LINEAR)

    # 4. DINO自相关（传播前）
    att_map_dino = sim_dino_soft[row * w_attn + col].detach().cpu().numpy().reshape(h_attn, w_attn)
    att_map_dino = (att_map_dino - att_map_dino.min()) / (att_map_dino.max() - att_map_dino.min() + 1e-8)
    att_map_dino = att_map_dino.astype(np.float32) 
    att_map_dino_up = cv2.resize(att_map_dino, vis_hw, interpolation=cv2.INTER_LINEAR)

    # 5. DINO传播后
    att_map_dino_ref = sim_dino_refined[row * w_attn + col].detach().cpu().numpy().reshape(h_attn, w_attn)
    att_map_dino_ref = (att_map_dino_ref - att_map_dino_ref.min()) / (att_map_dino_ref.max() - att_map_dino_ref.min() + 1e-8)
    att_map_dino_ref = att_map_dino_ref.astype(np.float32) 
    att_map_dino_ref_up = cv2.resize(att_map_dino_ref, vis_hw, interpolation=cv2.INTER_LINEAR)

    # 6. 绘图
    fig, axs = plt.subplots(1, 4, figsize=(18, 5))

    axs[0].imshow(img_with_dot)
    axs[0].set_title("Original (with token)")
    axs[0].axis('off')

    axs[1].imshow(att_map_sd_up, cmap='jet')
    axs[1].set_title(f'SD propagation (token {token_choosen})')
    axs[1].axis('off')

    axs[2].imshow(att_map_dino_up, cmap='jet')
    axs[2].set_title(f'DINO sim (pre-propagate)')
    axs[2].axis('off')

    axs[3].imshow(att_map_dino_ref_up, cmap='jet')
    axs[3].set_title(f'DINO sim (post-propagate)')
    axs[3].axis('off')

    plt.tight_layout()
    plt.savefig(filename, dpi=200, bbox_inches='tight')
    plt.close()

attention_layers_to_use= [-4, -6]
sd_version='v2.1'
time_step=45
device="cuda:2"
dino=build_DINOv2().to(device)
image_root='/mnt/SSD8T/home/wjj/dataset/standard_coco/train2017'
cache=h5py.File("/mnt/SSD8T/home/wjj/code/DeCLIP/sd_self_attn_cache/sd_self_attn_coco.h5", 'r', swmr=True)
image_file=os.listdir(image_root)[5]
cache_attn=torch.from_numpy(cache[image_file][()]).to(device)

image_name=os.path.join(image_root, image_file)
# image = Image.open('demo_images/horses.jpg')
image = Image.open(image_name)
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]

normalize = Normalize(mean=mean, std=std)
DINO_transform=transforms.Compose([
                                ResizeLongest(490, fill=0),
                                _convert_to_rgb,
                                ToTensor(),
                                normalize])
img_transform=transforms.Compose([ResizeLongest(560, fill=0)])
dino_img=DINO_transform(image).unsqueeze(0).to(torch.float16).to(device)

# dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
dino_feats = F.normalize(dino_feats_raw, dim=2)
sim_dino = torch.einsum('bic,bjc->bij', dino_feats, dino_feats).squeeze(0)
self_att=cache_attn.to(sim_dino.dtype)
refined_sim_dino = self_att @ sim_dino @ self_att.transpose(0, 1)
# alpha = 0.1  # 你可以把它设置成任意[0,1]的数，逐渐试
# sim_dino_mixed = (1 - alpha) * sim_dino_softmax + alpha * sim_dino_refined

output_dir = "sd_vis"
if not os.path.exists(output_dir):
    os.mkdir(output_dir)

token_choosen=(15, 15)
vis_img = img_transform(image)
visualize_sd_dino_att(
    ori_img=vis_img,                # [1,3,H,W]，归一化 0-1 float
    self_att=self_att,             # (4096,4096)
    sim_dino_soft=sim_dino,   # (4096,4096)
    sim_dino_refined=refined_sim_dino, # (4096,4096)
    token_choosen=token_choosen,
    filename=os.path.join(output_dir, f"vis.png"),
    attn_map_hw=(35, 35),
    vis_hw=(560, 560)
)


# visualize_sd_self_att(sd_img, self_att, (30,30), os.path.join(output_dir,"vis.png"),)