from __future__ import print_function

import numpy as np

import argparse

import torch

import torch.nn as nn

import pdb

import os

import pandas as pd

from utils.utils import *

from math import floor

import matplotlib.pyplot as plt

from datasets.dataset_mtl_concat import Generic_MIL_MTL_Dataset, save_splits

import h5py

from utils.eval_utils_mtl_concat import *

# Training settings

parser = argparse.ArgumentParser(description='TOAD Evaluation Script')

parser.add_argument('--data_root_dir', type=str, help='data directory')

parser.add_argument('--results_dir', type=str, default='./results',

                    help='relative path to results folder, i.e. '+

                    'the directory containing models_exp_code relative to project root (default: ./results)')

parser.add_argument('--save_exp_code', type=str, default=None,

                    help='experiment code to save eval results')

parser.add_argument('--models_exp_code', type=str, default=None,

                    help='experiment code to load trained models (directory under results_dir containing model checkpoints')

parser.add_argument('--splits_dir', type=str, default=None,

                    help='splits directory, if using custom splits other than what matches the task (default: None)')

parser.add_argument('--drop_out', action='store_true', default=False, 

                    help='whether model uses dropout')

parser.add_argument('--k', type=int, default=1, help='number of folds (default: 1)')

parser.add_argument('--k_start', type=int, default=-1, help='start fold (default: -1, last fold)')

parser.add_argument('--k_end', type=int, default=-1, help='end fold (default: -1, first fold)')

parser.add_argument('--fold', type=int, default=-1, help='single fold to evaluate')

parser.add_argument('--micro_average', action='store_true', default=False, 

                    help='use micro_average instead of macro_avearge for multiclass AUC')

parser.add_argument('--split', type=str, choices=['train', 'val', 'test', 'all'], default='test')

parser.add_argument('--task', type=str, choices=['dummy_mtl_concat'])

args = parser.parse_args()

device=torch.device("cuda" if torch.cuda.is_available() else "cpu")

encoding_size = 1024

args.save_dir = os.path.join('./eval_results', 'EVAL_' + str(args.save_exp_code))

args.models_dir = os.path.join(args.results_dir, str(args.models_exp_code))

os.makedirs(args.save_dir, exist_ok=True)

if args.splits_dir is None:

    args.splits_dir = args.models_dir

assert os.path.isdir(args.models_dir)

assert os.path.isdir(args.splits_dir)

settings = {'task': args.task,

            'split': args.split,

            'save_dir': args.save_dir, 

            'models_dir': args.models_dir,

            'drop_out': args.drop_out,

            'micro_avg': args.micro_average}

with open(args.save_dir + '/eval_experiment_{}.txt'.format(args.save_exp_code), 'w') as f:

    print(settings, file=f)

f.close()

print(settings)

if args.task == 'dummy_mtl_concat':

    args.n_classes=18

    dataset = Generic_MIL_MTL_Dataset(csv_path = 'dataset_csv/dummy_dataset.csv',

                            data_dir= os.path.join(args.data_root_dir,'DATASET_DIR'),

                            shuffle = False, 

                            print_info = True,

                            label_dicts = [{'Lung':0, 'Breast':1, 'Colorectal':2, 'Ovarian':3, 

                                                                'Pancreatic':4, 'Adrenal':5, 

                                                                'Skin':6, 'Prostate':7, 'Renal':8, 'Bladder':9, 

                                                                'Esophagogastric':10,  'Thyroid':11,

                                                                'Head Neck':12,  'Glioma':13, 

                                                                'Germ Cell':14, 'Endometrial': 15, 'Cervix': 16, 'Liver': 17},

                                            {'Primary':0,  'Metastatic':1},

                                            {'F':0, 'M':1}],

                            label_cols = ['label', 'site', 'sex'],

                            patient_strat= False)

else:

    raise NotImplementedError

if args.k_start == -1:

    start = 0

else:

    start = args.k_start

if args.k_end == -1:

    end = args.k

else:

    end = args.k_end

if args.fold == -1:

    folds = range(start, end)

else:

    folds = range(args.fold, args.fold+1)

ckpt_paths = [os.path.join(args.models_dir, 's_{}_checkpoint.pt'.format(fold)) for fold in folds]

datasets_id = {'train': 0, 'val': 1, 'test': 2, 'all': -1}

if __name__ == "__main__":

    all_cls_auc = []

    all_cls_acc = []

    all_site_auc = []

    all_site_acc = []

    all_cls_top3_acc = []

    all_cls_top5_acc = []

    for ckpt_idx in range(len(ckpt_paths)):

        if datasets_id[args.split] < 0:

            split_dataset = dataset

            csv_path = None

        else:

            csv_path = '{}/splits_{}.csv'.format(args.splits_dir, folds[ckpt_idx])

            datasets = dataset.return_splits(from_id=False, csv_path=csv_path)

            split_dataset = datasets[datasets_id[args.split]]

        model, results_dict = eval(split_dataset, args, ckpt_paths[ckpt_idx])

        for cls_idx in range(len(results_dict['cls_aucs'])):

            print('class {} auc: {}'.format(cls_idx, results_dict['cls_aucs'][cls_idx]))

        all_cls_auc.append(results_dict['cls_auc'])

        all_cls_acc.append(1-results_dict['cls_test_error'])

        all_site_auc.append(results_dict['site_auc'])

        all_site_acc.append(1-results_dict['site_test_error'])

        all_cls_top3_acc.append(results_dict['top3_acc'])

        all_cls_top5_acc.append(results_dict['top5_acc'])

        df = results_dict['df']

        df.to_csv(os.path.join(args.save_dir, 'fold_{}.csv'.format(folds[ckpt_idx])), index=False)

    df_dict = {'folds': folds, 'cls_test_auc': all_cls_auc, 'cls_test_acc': all_cls_acc, 'cls_top3_acc': all_cls_top3_acc, 'cls_top5_acc': all_cls_top5_acc,

                'site_test_auc': all_site_auc, 'site_test_acc': all_site_acc}

    final_df = pd.DataFrame(df_dict)

    if len(folds) != args.k:

        save_name = 'summary_partial_{}_{}.csv'.format(folds[0], folds[-1])

    else:

        save_name = 'summary.csv'

    final_df.to_csv(os.path.join(args.save_dir, save_name))