from __future__ import print_function

import numpy as np

import torch_geometric

import argparse

import pdb

import os

import math

import sys

from timeit import default_timer as timer

import numpy as np

import pandas as pd

### Internal Imports

from datasets.dataset_survival import Generic_WSI_Survival_Dataset, Generic_MIL_Survival_Dataset

from utils.file_utils import save_pkl, load_pkl

from utils.core_utils import train

from utils.utils import get_custom_exp_code

### PyTorch Imports

import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.utils.data import DataLoader, sampler

def main(args):

    #### Create Results Directory

    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

    latest_val_cindex = []

    folds = np.arange(start, end)

    ### Start 5-Fold CV Evaluation.

    for i in folds:

        start = timer()

        seed_torch(args.seed)

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

        if os.path.isfile(results_pkl_path) and (not args.overwrite):

            print("Skipping Split %d" % i)

            continue

        ### Gets the Train + Val Dataset Loader.

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

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

        train_dataset.set_split_id(split_id=i)

        val_dataset.set_split_id(split_id=i)

        #pdb.set_trace()

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

        datasets = (train_dataset, val_dataset)

        ### Specify the input dimension size if using genomic features.

        if 'omic' in args.mode or args.mode == 'cluster' or args.mode == 'graph' or args.mode == 'pyramid':

            args.omic_input_dim = train_dataset.genomic_features.shape[1]

            print("Genomic Dimension", args.omic_input_dim)

        elif 'coattn' in args.mode:

            args.omic_sizes = train_dataset.omic_sizes

            print('Genomic Dimensions', args.omic_sizes)

        else:

            args.omic_input_dim = 0

        ### Run Train-Val on Survival Task.

        if args.task_type == 'survival':

            val_latest, cindex_latest = train(datasets, i, args)

            latest_val_cindex.append(cindex_latest)

        ### Write Results for Each Split to PKL

        save_pkl(results_pkl_path, val_latest)

        end = timer()

        print('Fold %d Time: %f seconds' % (i, end - start))

    ### Finish 5-Fold CV Evaluation.

    if args.task_type == 'survival':

        results_latest_df = pd.DataFrame({'folds': folds, 'val_cindex': latest_val_cindex})

    if len(folds) != args.k:

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

    else:

        save_name = 'summary.csv'

    results_latest_df.to_csv(os.path.join(args.results_dir, 'summary_latest.csv'))

### Training settings

parser = argparse.ArgumentParser(description='Configurations for Survival Analysis on TCGA Data.')

### Checkpoint + Misc. Pathing Parameters

parser.add_argument('--data_root_dir',   type=str, default='path/to/data_root_dir', help='Data directory to WSI features (extracted via CLAM')

parser.add_argument('--seed',            type=int, default=1, help='Random seed for reproducible experiment (default: 1)')

parser.add_argument('--k',               type=int, default=5, help='Number of folds (default: 5)')

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',     type=str, default='./results_new', help='Results directory (Default: ./results)')

parser.add_argument('--which_splits',    type=str, default='5foldcv', help='Which splits folder to use in ./splits/ (Default: ./splits/5foldcv')

parser.add_argument('--split_dir',       type=str, default='tcga_blca', help='Which cancer type within ./splits/<which_splits> to use for training. Used synonymously for "task" (Default: tcga_blca_100)')

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

parser.add_argument('--overwrite',       action='store_true', default=False, help='Whether or not to overwrite experiments (if already ran)')

### Model Parameters.

parser.add_argument('--model_type',      type=str, default='mcat', help='Type of model (Default: mcat)')

parser.add_argument('--mode',            type=str, choices=['omic', 'path', 'pathomic', 'pathomic_fast', 'cluster', 'coattn'], default='coattn', help='Specifies which modalities to use / collate function in dataloader.')

parser.add_argument('--fusion',          type=str, choices=['None', 'concat', 'bilinear'], default='None', help='Type of fusion. (Default: concat).')

parser.add_argument('--apply_sig',       action='store_true', default=False, help='Use genomic features as signature embeddings.')

parser.add_argument('--apply_sigfeats',  action='store_true', default=False, help='Use genomic features as tabular features.')

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

parser.add_argument('--model_size_wsi',  type=str, default='small', help='Network size of AMIL model')

parser.add_argument('--model_size_omic', type=str, default='small', help='Network size of SNN model')

parser.add_argument('--n_classes', type=int, default=4)

### PORPOISE

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

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

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

parser.add_argument('--scale_dim1', type=int, default=8)

parser.add_argument('--scale_dim2', type=int, default=8)

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

parser.add_argument('--dropinput', type=float, default=0.0)

parser.add_argument('--path_input_dim', type=int, default=1024)

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

### Optimizer Parameters + Survival Loss Function

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

parser.add_argument('--batch_size',      type=int, default=1, help='Batch Size (Default: 1, due to varying bag sizes)')

parser.add_argument('--gc',              type=int, default=32, help='Gradient Accumulation Step.')

parser.add_argument('--max_epochs',      type=int, default=20, help='Maximum number of epochs to train (default: 20)')

parser.add_argument('--lr',              type=float, default=2e-4, help='Learning rate (default: 0.0001)')

parser.add_argument('--bag_loss',        type=str, choices=['svm', 'ce', 'ce_surv', 'nll_surv'], default='nll_surv', help='slide-level classification loss function (default: ce)')

parser.add_argument('--label_frac',      type=float, default=1.0, help='fraction of training labels (default: 1.0)')

parser.add_argument('--reg',             type=float, default=1e-5, help='L2-regularization weight decay (default: 1e-5)')

parser.add_argument('--alpha_surv',      type=float, default=0.0, help='How much to weigh uncensored patients')

parser.add_argument('--reg_type',        type=str, choices=['None', 'omic', 'pathomic'], default='None', help='Which network submodules to apply L1-Regularization (default: None)')

parser.add_argument('--lambda_reg',      type=float, default=1e-5, help='L1-Regularization Strength (Default 1e-4)')

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

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

### CLAM-Specific Parameters

parser.add_argument('--bag_weight',      type=float, default=0.7, help='clam: weight coefficient for bag-level loss (default: 0.7)')

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

args = parser.parse_args()

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

### Creates Experiment Code from argparse + Folder Name to Save Results

args = get_custom_exp_code(args)

args.task = '_'.join(args.split_dir.split('_')[:2]) + '_survival'

print("Experiment Name:", args.exp_code)

### Sets Seed for reproducible experiments.

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,

            'bag_loss': args.bag_loss,

            #'bag_weight': args.bag_weight,

            'seed': args.seed,

            'model_type': args.model_type,

            'model_size_wsi': args.model_size_wsi,

            'model_size_omic': args.model_size_omic,

            "use_drop_out": args.drop_out,

            'weighted_sample': args.weighted_sample,

            'gc': args.gc,

            'opt': args.opt}

print('\nLoad Dataset')

if 'survival' in args.task:

    study = '_'.join(args.task.split('_')[:2])

    if study == 'tcga_kirc' or study == 'tcga_kirp':

        combined_study = 'tcga_kidney'

    elif study == 'tcga_luad' or study == 'tcga_lusc':

        combined_study = 'tcga_lung'

    else:

        combined_study = study

    study_dir = '%s_20x_features' % combined_study

    dataset = Generic_MIL_Survival_Dataset(csv_path = './%s/%s_all_clean.csv.zip' % (args.dataset_path, study),

                                           mode = args.mode,

                                           apply_sig = args.apply_sig,

                                           data_dir= os.path.join(args.data_root_dir, study_dir),

                                           shuffle = False, 

                                           seed = args.seed, 

                                           print_info = True,

                                           patient_strat= False,

                                           n_bins=4,

                                           label_col = 'survival_months',

                                           ignore=[])

else:

    raise NotImplementedError

if isinstance(dataset, Generic_MIL_Survival_Dataset):

    args.task_type = 'survival'

else:

    raise NotImplementedError

### Creates results_dir Directory.

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

    os.mkdir(args.results_dir)

### Appends to the results_dir path: 1) which splits were used for training (e.g. - 5foldcv), and then 2) the parameter code and 3) experiment code

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

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

    os.makedirs(args.results_dir)

if ('summary_latest.csv' in os.listdir(args.results_dir)) and (not args.overwrite):

    print("Exp Code <%s> already exists! Exiting script." % args.exp_code)

    sys.exit()

### Sets the absolute path of split_dir

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

print("split_dir", 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__":

    start = timer()

    results = main(args)

    end = timer()

    print("finished!")

    print("end script")

    print('Script Time: %f seconds' % (end - start))