import os
import torch
import numpy as np

from engine.solver import Trainer
from Utils.metric_utils import visualization
from Data.build_dataloader import build_dataloader
from Data.build_dataloader import build_dataloader_cond
from Utils.io_utils import load_yaml_config, instantiate_from_config
from Models.interpretable_diffusion.model_utils import unnormalize_to_zero_to_one
from src.evaluation import evaluate_data


cfg = {
    'method_list': '[MDD,ACD,SD,KD,ED,DTW]',
    'dataset_name': 'stock_10',
    'model': 'PreDiffusion',
    'no_cuda': False,
    'cuda_device': 0,
    'result_path': './result/'
}
ori_data = np.load('./toy_exp/samples/stock_ground_truth_24_train.npy')
gen_data = np.load('./toy_exp/ddpm_fake_stock_10.npy')
ori_data = ori_data[0:2, :, :]
gen_data = gen_data[0:2, :, :]
evaluate_data(cfg, ori_data, gen_data)