import os
import shutil
import ffmpeg
from datetime import datetime
from pathlib import Path
import numpy as np
import cv2
import torch
import spaces
from diffusers import AutoencoderKL, DDIMScheduler
from einops import repeat
from omegaconf import OmegaConf
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection

from src.models.pose_guider import PoseGuider
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d import UNet3DConditionModel
from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
from src.utils.util import get_fps, read_frames, save_videos_grid

from src.utils.mp_utils  import LMKExtractor
from src.utils.draw_util import FaceMeshVisualizer
from src.utils.pose_util import project_points_with_trans, matrix_to_euler_and_translation, euler_and_translation_to_matrix
from src.audio2vid import smooth_pose_seq
from src.utils.crop_face_single import crop_face

# @spaces.GPU(duration=150)
def video2video(ref_img, source_video, size=512, steps=25, length=150, seed=42):
    cfg = 3.5
    
    config = OmegaConf.load('./configs/prompts/animation_facereenac.yaml')

    if config.weight_dtype == "fp16":
        weight_dtype = torch.float16
    else:
        weight_dtype = torch.float32

    vae = AutoencoderKL.from_pretrained(
        config.pretrained_vae_path,
    ).to("cuda", dtype=weight_dtype)

    reference_unet = UNet2DConditionModel.from_pretrained(
        config.pretrained_base_model_path,
        subfolder="unet",
    ).to(dtype=weight_dtype, device="cuda")

    inference_config_path = config.inference_config
    infer_config = OmegaConf.load(inference_config_path)
    denoising_unet = UNet3DConditionModel.from_pretrained_2d(
        config.pretrained_base_model_path,
        config.motion_module_path,
        subfolder="unet",
        unet_additional_kwargs=infer_config.unet_additional_kwargs,
    ).to(dtype=weight_dtype, device="cuda")

    pose_guider = PoseGuider(noise_latent_channels=320, use_ca=True).to(device="cuda", dtype=weight_dtype) # not use cross attention

    image_enc = CLIPVisionModelWithProjection.from_pretrained(
        config.image_encoder_path
    ).to(dtype=weight_dtype, device="cuda")

    sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
    scheduler = DDIMScheduler(**sched_kwargs)

    generator = torch.manual_seed(seed)

    width, height = size, size

    # load pretrained weights
    denoising_unet.load_state_dict(
        torch.load(config.denoising_unet_path, map_location="cpu"),
        strict=False,
    )
    reference_unet.load_state_dict(
        torch.load(config.reference_unet_path, map_location="cpu"),
    )
    pose_guider.load_state_dict(
        torch.load(config.pose_guider_path, map_location="cpu"),
    )

    pipe = Pose2VideoPipeline(
        vae=vae,
        image_encoder=image_enc,
        reference_unet=reference_unet,
        denoising_unet=denoising_unet,
        pose_guider=pose_guider,
        scheduler=scheduler,
    )
    pipe = pipe.to("cuda", dtype=weight_dtype)

    date_str = datetime.now().strftime("%Y%m%d")
    time_str = datetime.now().strftime("%H%M")
    save_dir_name = f"{time_str}--seed_{seed}-{size}x{size}"

    save_dir = Path(f"output/{date_str}/{save_dir_name}")
    save_dir.mkdir(exist_ok=True, parents=True)


    lmk_extractor = LMKExtractor()
    vis = FaceMeshVisualizer(forehead_edge=False)
    
    ref_image_np = cv2.cvtColor(ref_img, cv2.COLOR_RGB2BGR)
    ref_image_np = crop_face(ref_image_np, lmk_extractor)
    if ref_image_np is None:
        return None, Image.fromarray(ref_img)
    
    ref_image_np = cv2.resize(ref_image_np, (size, size))
    ref_image_pil = Image.fromarray(cv2.cvtColor(ref_image_np, cv2.COLOR_BGR2RGB))
    
    face_result = lmk_extractor(ref_image_np)
    if face_result is None: 
        return None, ref_image_pil
    
    lmks = face_result['lmks'].astype(np.float32)
    ref_pose = vis.draw_landmarks((ref_image_np.shape[1], ref_image_np.shape[0]), lmks, normed=True)

    source_images = read_frames(source_video)
    src_fps = get_fps(source_video)
    pose_transform = transforms.Compose(
        [transforms.Resize((height, width)), transforms.ToTensor()]
    )
    
    step = 1
    if src_fps == 60:
        src_fps = 30
        step = 2
    
    pose_trans_list = []
    verts_list = []
    bs_list = []
    src_tensor_list = []
    args_L = len(source_images) if length==0 or length*step > len(source_images) else length*step
    args_L = min(args_L, 300*step)
    for src_image_pil in source_images[: args_L: step]:
        src_tensor_list.append(pose_transform(src_image_pil))
        src_img_np = cv2.cvtColor(np.array(src_image_pil), cv2.COLOR_RGB2BGR)
        frame_height, frame_width, _ = src_img_np.shape
        src_img_result = lmk_extractor(src_img_np)
        if src_img_result is None:
            break
        pose_trans_list.append(src_img_result['trans_mat'])
        verts_list.append(src_img_result['lmks3d'])
        bs_list.append(src_img_result['bs'])

    
    # pose_arr = np.array(pose_trans_list)
    trans_mat_arr = np.array(pose_trans_list)
    verts_arr = np.array(verts_list)
    bs_arr = np.array(bs_list)
    min_bs_idx = np.argmin(bs_arr.sum(1))
    
    # compute delta pose
    pose_arr = np.zeros([trans_mat_arr.shape[0], 6])

    for i in range(pose_arr.shape[0]):
        euler_angles, translation_vector = matrix_to_euler_and_translation(trans_mat_arr[i]) # real pose of source
        pose_arr[i, :3] =  euler_angles
        pose_arr[i, 3:6] =  translation_vector
    
    init_tran_vec = face_result['trans_mat'][:3, 3] # init translation of tgt
    pose_arr[:, 3:6] = pose_arr[:, 3:6] - pose_arr[0, 3:6] + init_tran_vec # (relative translation of source) + (init translation of tgt)

    pose_arr_smooth = smooth_pose_seq(pose_arr, window_size=3)
    pose_mat_smooth = [euler_and_translation_to_matrix(pose_arr_smooth[i][:3], pose_arr_smooth[i][3:6]) for i in range(pose_arr_smooth.shape[0])]    
    pose_mat_smooth = np.array(pose_mat_smooth)   

    # face retarget
    verts_arr = verts_arr - verts_arr[min_bs_idx] + face_result['lmks3d']
    # project 3D mesh to 2D landmark
    projected_vertices = project_points_with_trans(verts_arr, pose_mat_smooth, [frame_height, frame_width])
    
    pose_list = []
    for i, verts in enumerate(projected_vertices):
        lmk_img = vis.draw_landmarks((frame_width, frame_height), verts, normed=False)
        pose_image_np = cv2.resize(lmk_img,  (width, height))
        pose_list.append(pose_image_np)
    
    pose_list = np.array(pose_list)
    
    video_length = len(pose_list)

    video = pipe(
        ref_image_pil,
        pose_list,
        ref_pose,
        width,
        height,
        video_length,
        steps,
        cfg,
        generator=generator,
    ).videos

    save_path = f"{save_dir}/{size}x{size}_{time_str}_noaudio.mp4"
    save_videos_grid(
        video,
        save_path,
        n_rows=1,
        fps=src_fps,
    )
    
    audio_output = f'{save_dir}/audio_from_video.aac'
    # extract audio
    try:
        ffmpeg.input(source_video).output(audio_output, acodec='copy').run()
        # merge audio and video
        stream = ffmpeg.input(save_path)
        audio = ffmpeg.input(audio_output)
        ffmpeg.output(stream.video, audio.audio, save_path.replace('_noaudio.mp4', '.mp4'), vcodec='copy', acodec='aac', shortest=None).run()
    
        os.remove(save_path)
        os.remove(audio_output)
    except:
        shutil.move(
            save_path,
            save_path.replace('_noaudio.mp4', '.mp4')
        )
    
    return save_path.replace('_noaudio.mp4', '.mp4'), ref_image_pil