from __future__ import print_function

import argparse

import pdb

import os

import math

# internal imports

from utils.file_utils import save_pkl, load_pkl

from utils.utils import *

from utils.core_utils import train

from utils.core_utils_mtl import train as train_mtl

from datasets.dataset_generic import Generic_WSI_Classification_Dataset, Generic_MIL_Dataset

from datasets.dataset_mtl import Generic_WSI_MTL_Dataset, Generic_MIL_MTL_Dataset

# pytorch imports

import torch

from torch.utils.data import DataLoader, sampler

import torch.nn as nn

import torch.nn.functional as F

import pandas as pd

import numpy as np

# Rejection grade:

# binary classifier:

# class 0 - low grade

# class 1 - high grade

#-------------------------------

def main_grade(args):

    print("-----------------------------------------")

    print(" Grade Net (single task binary classifier")

    print("-----------------------------------------")

    # create results directory if necessary

    if not os.path.isdir(args.results_dir):

        os.mkdir(args.results_dir)

    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

    all_test_auc = []

    all_val_auc = []

    all_test_acc = []

    all_val_acc = []

    folds = np.arange(start, end)

    for i in folds:

        seed_torch(args.seed)

        train_dataset, val_dataset, test_dataset = dataset.return_splits(from_id=False,

                csv_path='{}/splits_{}.csv'.format(args.split_dir, i))

        datasets = (train_dataset, val_dataset, test_dataset)

        results, test_auc, val_auc, test_acc, val_acc  = train(datasets, i, args)

        all_test_auc.append(test_auc)

        all_val_auc.append(val_auc)

        all_test_acc.append(test_acc)

        all_val_acc.append(val_acc)

        #write results to pkl

        filename = os.path.join(args.results_dir, 'split_{}_results.pkl'.format(i))

        save_pkl(filename, results)

    final_df = pd.DataFrame({'folds': folds, 'test_auc': all_test_auc,

        'val_auc': all_val_auc, 'test_acc': all_test_acc, 'val_acc' : all_val_acc})

    if len(folds) != args.k:

        save_name = 'summary_partial_{}_{}.csv'.format(start, end)

    else:

        save_name = 'summary.csv'

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

# Multi task classifier for EMB evaluation:

# consist of 3 simultaneous tasks:

# task1: cellular vs non-cellular

# task2: antibody vs non-antibody

# task3: quilty lesion vs no quilty lesion

#-------------------------------------------

def main_mtl(args):

    print("----------------------------------------")

    print(" EMB assessment - multi task classifier ")

    print("----------------------------------------")

   # create results directory if necessary

    if not os.path.isdir(args.results_dir):

        os.mkdir(args.results_dir)

    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

# arrays to collect scores -- replace by generic one when refactoring

    all_task1_test_auc = []

    all_task1_val_auc  = []

    all_task1_test_acc = []

    all_task1_val_acc  = []

    all_task2_test_auc = []

    all_task2_val_auc  = []

    all_task2_test_acc = []

    all_task2_val_acc  = []

    all_task3_test_auc = []

    all_task3_val_auc  = []

    all_task3_test_acc = []

    all_task3_val_acc  = []

    folds = np.arange(start, end)

    for i in folds:

        seed_torch(args.seed)

        train_dataset, val_dataset, test_dataset = dataset.return_splits(from_id=False,

                csv_path='{}/splits_{}.csv'.format(args.split_dir, i))

        print('training: {}, validation: {}, testing: {}'.format(len(train_dataset), len(val_dataset), len(test_dataset)))

        datasets = (train_dataset, val_dataset, test_dataset)

        results, \

        task1_test_auc, task1_val_auc, task1_test_acc, task1_val_acc, \

        task2_test_auc, task2_val_auc, task2_test_acc, task2_val_acc, \

        task3_test_auc, task3_val_auc, task3_test_acc, task3_val_acc = train_mtl(datasets, i, args)

        all_task1_test_auc.append(task1_test_auc)

        all_task1_val_auc.append( task1_val_auc )

        all_task1_test_acc.append(task1_test_acc)

        all_task1_val_acc.append( task1_val_acc )

        all_task2_test_auc.append(task2_test_auc)

        all_task2_val_auc.append( task2_val_auc )

        all_task2_test_acc.append(task2_test_acc)

        all_task2_val_acc.append( task2_val_acc )

        all_task3_test_auc.append(task3_test_auc)

        all_task3_val_auc.append( task3_val_auc )

        all_task3_test_acc.append(task3_test_acc)

        all_task3_val_acc.append( task3_val_acc )

        #write results to pkl

        filename = os.path.join(args.results_dir, 'split_{}_results.pkl'.format(i))

        save_pkl(filename, results)

    final_df = pd.DataFrame({'folds': folds,

        'task1_test_auc': all_task1_test_auc, 'task1_val_auc': all_task1_val_auc,

        'task1_test_acc': all_task1_test_acc, 'task1_val_acc': all_task1_val_acc,

        'task2_test_auc': all_task2_test_auc, 'task2_val_auc': all_task2_val_auc,

        'task2_test_acc': all_task2_test_acc, 'task2_val_acc': all_task2_val_acc,

        'task3_test_auc': all_task3_test_auc, 'task3_val_auc': all_task3_val_auc,

        'task3_test_acc': all_task3_test_acc, 'task3_val_acc': all_task3_val_acc})

    if len(folds) != args.k:

        save_name = 'summary_partial_{}_{}.csv'.format(start, end)

    else:

        save_name = 'summary.csv'

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

# Training settings

parser = argparse.ArgumentParser(description='Configurations for WSI Training')

parser.add_argument('--data_root_dir', type=str, default='/media/fedshyvana/ssd1',

                    help='data directory')

parser.add_argument('--max_epochs', type=int, default=200,

                    help='maximum number of epochs to train (default: 200)')

parser.add_argument('--lr', type=float, default=1e-4,

                    help='learning rate (default: 0.0001)')

parser.add_argument('--label_frac', type=float, default=1.0,

                    help='fraction of training labels (default: 1.0)')

parser.add_argument('--bag_weight', type=float, default=0.7,

                    help='clam: weight coefficient for bag-level loss (default: 0.7)')

parser.add_argument('--reg', type=float, default=1e-5,

                    help='weight decay (default: 1e-5)')

parser.add_argument('--seed', type=int, default=1,

                    help='random seed for reproducible experiment (default: 1)')

parser.add_argument('--k', type=int, default=10, help='number of folds (default: 10)')

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('--results_dir', default='./results', help='results directory (default: ./results)')

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

                    help='manually specify the set of splits to use, '

                    +'instead of infering from the task and label_frac argument (default: None)')

parser.add_argument('--log_data', action='store_true', default=False, help='log data using tensorboard')

parser.add_argument('--testing', action='store_true', default=False, help='debugging tool')

parser.add_argument('--subtyping', action='store_true', default=False, help='subtyping problem')

parser.add_argument('--early_stopping', action='store_true', default=False, help='enable early stopping')

parser.add_argument('--opt', type=str, choices = ['adam', 'sgd'], default='adam')

parser.add_argument('--drop_out', action='store_true', default=False, help='enabel dropout (p=0.25)')

parser.add_argument('--inst_loss', type=str, choices=['svm', 'ce', None], default=None,

                     help='instance-level clustering loss function (default: None)')

parser.add_argument('--bag_loss', type=str, choices=['svm', 'ce'], default='ce',

                     help='slide-level classification loss function (default: ce)')

parser.add_argument('--model_type', type=str, choices=['clam', 'mil', 'clam_simple', 'attention_mil', 'histogram_mil'], default='attention_mil', help='type of model (default: attention_mil)')

parser.add_argument('--exp_code', type=str, help='experiment code for saving results')

parser.add_argument('--weighted_sample', action='store_true', default=False, help='enable weighted sampling')

parser.add_argument('--model_size', type=str, choices=['small', 'big'], default='big', help='size of model')

parser.add_argument('--mtl', action='store_true', default=False, help='flag to enable multi-task problem')

parser.add_argument('--task', type=str, choices=['cardiac-grade','cardiac-mtl'])

args = parser.parse_args()

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

def seed_torch(seed=7):

    import random

    random.seed(seed)

    os.environ['PYTHONHASHSEED'] = str(seed)

    np.random.seed(seed)

    torch.manual_seed(seed)

    if device.type == 'cuda':

        torch.cuda.manual_seed(seed)

        torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.

    torch.backends.cudnn.benchmark = False

    torch.backends.cudnn.deterministic = True

seed_torch(args.seed)

encoding_size = 1024

settings = {'num_splits': args.k,

            'k_start': args.k_start,

            'k_end': args.k_end,

            'task': args.task,

            'max_epochs': args.max_epochs,

            'results_dir': args.results_dir,

            'lr': args.lr,

            'experiment': args.exp_code,

            'reg': args.reg,

            'label_frac': args.label_frac,

            'inst_loss': args.inst_loss,

            'bag_loss': args.bag_loss,

            'bag_weight': args.bag_weight,

            'seed': args.seed,

            'model_type': args.model_type,

            'model_size': args.model_size,

            "use_drop_out": args.drop_out,

            'weighted_sample': args.weighted_sample,

            'opt': args.opt}

print('\nLoad Dataset')

if args.task == 'cardiac-grade':

    args.n_classes=2

    dataset = Generic_MIL_Dataset(csv_path = 'dataset_csv/CardiacDummy_Grade.csv',

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

                            shuffle = False,

                            seed = args.seed,

                            print_info = True,

                            label_dict = {'low':0, 'high':1},

                            label_cols=['label_grade'],

                            patient_strat=False,

                            ignore=[])

elif args.task == 'cardiac-mtl':

    args.n_classes=[2,2,2]  

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

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

                            shuffle = False,

                            seed = args.seed,

                            print_info = True,

                            label_dicts = [{'no_cell':0, 'cell':1},

                                            {'no_amr':0, 'amr':1},

                                            {'no_quilty':0, 'quilty':1}],

                            label_cols=['label_cell','label_amr','label_quilty'],

                            patient_strat=False,

                            ignore=[])

else:

    raise NotImplementedError

if not os.path.isdir(args.results_dir):

    os.mkdir(args.results_dir)

args.results_dir = os.path.join(args.results_dir, str(args.exp_code) + '_s{}'.format(args.seed))

if not os.path.isdir(args.results_dir):

    os.mkdir(args.results_dir)

if args.split_dir is None:

    args.split_dir = os.path.join('splits', args.task+'_{}'.format(int(args.label_frac*100)))

else:

    args.split_dir = os.path.join('splits', args.split_dir)

assert os.path.isdir(args.split_dir)

settings.update({'split_dir': args.split_dir})

with open(args.results_dir + '/experiment_{}.txt'.format(args.exp_code), 'w') as f:

    print(settings, file=f)

f.close()

print("################# Settings ###################")

for key, val in settings.items():

    print("{}:  {}".format(key, val))

if __name__ == "__main__":

    if args.mtl:

        results = main_mtl(args)

    else:

        results = main_grade(args)

    print("finished!")

    print("end script")