# Code adapted from https://github.com/JunyaoHu/common_metrics_on_video_quality
import numpy as np
import torch
from tqdm import tqdm

def trans(x):
    # if greyscale images add channel
    if x.shape[-3] == 1:
        x = x.repeat(1, 1, 3, 1, 1)

    # permute BTCHW -> BCTHW
    x = x.permute(0, 2, 1, 3, 4)

    return x

def calculate_fvd(videos1, videos2, device="cuda", method='styleganv'):

    if method == 'styleganv':
        from .fvd.styleganv.fvd import get_fvd_feats, frechet_distance, load_i3d_pretrained
    elif method == 'videogpt':
        from .fvd.videogpt.fvd import load_i3d_pretrained
        from .fvd.videogpt.fvd import get_fvd_logits as get_fvd_feats
        from .fvd.videogpt.fvd import frechet_distance


    # videos [batch_size, timestamps, channel, h, w]

    assert videos1.shape == videos2.shape

    i3d = load_i3d_pretrained(device=device)
    fvd_results = []

    # support grayscale input, if grayscale -> channel*3
    # BTCHW -> BCTHW
    # videos -> [batch_size, channel, timestamps, h, w]

    videos1 = trans(videos1)
    videos2 = trans(videos2)

    # fvd_results = {}

    # for calculate FVD, each clip_timestamp must >= 10
    for clip_timestamp in tqdm(range(10, videos1.shape[-3]+1)):
        # print("clip_timestamp", clip_timestamp)
        # get a video clip
        # videos_clip [batch_size, channel, timestamps[:clip], h, w]
        videos_clip1 = videos1[:, :, : clip_timestamp]
        videos_clip2 = videos2[:, :, : clip_timestamp]

        # get FVD features
        feats1 = get_fvd_feats(videos_clip1, i3d=i3d, device=device)
        feats2 = get_fvd_feats(videos_clip2, i3d=i3d, device=device)

        # calculate FVD when timestamps[:clip]
        fvd_results.append(frechet_distance(feats1, feats2))


    return fvd_results[-1] # only the last step

# test code / using example

def main():
    NUMBER_OF_VIDEOS = 8
    VIDEO_LENGTH = 50
    CHANNEL = 3
    SIZE = 64
    videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
    videos2 = torch.ones(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
    device = torch.device("cuda")
    # device = torch.device("cpu")

    import json
    result = calculate_fvd(videos1, videos2, device, method='videogpt')
    print(json.dumps(result, indent=4))

    result = calculate_fvd(videos1, videos2, device, method='styleganv')
    print(json.dumps(result, indent=4))

if __name__ == "__main__":
    main()