from __future__ import print_function, division
import os
import sys
import time
import argparse
import warnings
import torch
import pickle
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from torch.utils.data import Dataset, DataLoader, TensorDataset
from torchvision import transforms, utils
from models.modeling import PATHOLOGICAL_CLASSFIER, CONFIGS

device = "cuda" if torch.cuda.is_available() else "cpu"
def load_weights(model, weight_path):
    print("Loading PATHOLOGICAL_CLASSFIER...",weight_path)
    loadnet = torch.load(weight_path,map_location=device)
    if "model_state_dict" in loadnet:
        keyname = "model_state_dict"
    else:
        keyname = "model_state_dict"
    model.load_state_dict(loadnet[keyname], strict=True)
    return model

class MyDataset(Dataset):
    def __init__(self, root_path):
        m_data = []
        img_pkl_file_path = os.path.join(root_path, "img_feature")
        txt_pkl_file_path = os.path.join(root_path, "txt_feature")
        target_pkl_file_path = os.path.join(root_path, "target")
        for file in os.listdir(img_pkl_file_path):

            img_pkl_file = os.path.join(img_pkl_file_path, file)
            txt_pkl_file = os.path.join(txt_pkl_file_path, file)
            target_pkl_file = os.path.join(target_pkl_file_path, file)
            with open(img_pkl_file, "rb") as img_f:
                img_load_dict = pickle.load(img_f)
                m_input_img = img_load_dict["img_feature"]
            with open(txt_pkl_file, "rb") as txt_f:
                txt_load_dict = pickle.load(txt_f)
                m_input_txt = txt_load_dict["txt_feature"]
            with open(target_pkl_file, "rb") as target_f:
                target_load_dict = pickle.load(target_f)
                m_output_os = target_load_dict["target_os"]
                m_output_dfs = target_load_dict["target_dfs"]
            m_data.append((m_input_img, m_input_txt, m_output_os, m_output_dfs,file))
        self.m_data = m_data
    def __getitem__(self, idx):
        inp_i, inp_txt, oup_os, oup_dfs,f_name = self.m_data[idx]
        return inp_i, inp_txt, oup_os, oup_dfs,f_name
    def __len__(self):
        return len(self.m_data)

def valid(args):
    torch.manual_seed(0)
    num_classes = 2
    config = CONFIGS["PATHOLOGICAL_CLASSFIER"]
    model = PATHOLOGICAL_CLASSFIER(config, num_classes=num_classes, vis=True, mm=True)

    model_path = '/your/trained/model/path/'
    p_c_model = load_weights(model, model_path)

    p_c_model.to(device)
    test_dataset = MyDataset("/your/dataset/path/" )
    test_loader = DataLoader(test_dataset, batch_size=1)

        # #----- Test ------
    print("--------Start testing-------")
    p_c_model.eval()

    valid_1_acc = 0
    valid_1_total = 0
    valid_1_cnt = 0

    valid_2_acc = 0
    valid_2_total = 0
    valid_2_cnt = 0
    valid_total_cnt=0

    target_cnt_0=0
    target_cnt_1=0
    with torch.no_grad():
        for imgs, txt, target_1, target_2,file_name in test_loader:    
            output_1, output_2, = model(imgs.to(device), txt.to(device))

            out_1_list_prob = (torch.softmax(output_1.squeeze(1), axis=-1).cpu().numpy().tolist())

            out_1_list = (torch.argmax(output_1.squeeze(1), axis=-1).cpu().numpy().tolist())
            target_1_list = target_1.tolist()

            out_2_list = (torch.argmax(output_2.squeeze(1), axis=-1).cpu().numpy().tolist())
            target_2_list = target_2.tolist()

            valid_1_total += len(out_1_list)
            valid_2_total += len(out_2_list)

            for i in range(len(out_1_list)):
                if out_1_list[i] == target_1_list[i]:
                    valid_1_cnt += 1
                if out_2_list[i] == target_2_list[i]:
                    valid_2_cnt += 1
                if out_1_list[i] == target_1_list[i] and out_2_list[i] == target_2_list[i]:
                    valid_total_cnt+=1

        valid_1_acc = valid_1_cnt / valid_1_total
        valid_2_acc = valid_2_cnt / valid_2_total
        valid_total_acc =valid_total_cnt/valid_1_total

        print(valid_1_acc,valid_1_total, valid_2_acc,valid_2_total,valid_total_acc,valid_total_cnt)
        print("="*100)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="")
    args = parser.parse_args()
    valid(args)