# NEEDS TO BE FINISHED
# TODO CHECK ABOUT IMAGE DIMENSIONS
# TODO ENSURE ITERATION WORKS
import glob
import nibabel as nib
import numpy as np
import random
import torch
from torch.utils.data import Dataset
import pandas as pd 
from torch.utils.data import DataLoader



'''
Prepares CustomDatasets for training, validating, and testing CNN
'''
def prepare_datasets(mri_dir, xls_file, val_split=0.2, seed=50):

    rndm = random.Random(seed)
    xls_data = pd.read_csv(xls_file).set_index('Image Data ID')
    raw_data = glob.glob(mri_dir + "*")
    AD_list = []
    NL_list = []

    # TODO Check that image is in CSV?
    for image in raw_data:
        if "NL" in image:
            NL_list.append(image)
        elif "AD" in image:
            AD_list.append(image)

    rndm.shuffle(AD_list)
    rndm.shuffle(NL_list)

    train_list, val_list, test_list = get_train_val_test(AD_list, NL_list, val_split)

    rndm.shuffle(train_list)
    rndm.shuffle(val_list)
    rndm.shuffle(test_list)

    train_dataset = ADNIDataset(train_list, xls_data)
    val_dataset = ADNIDataset(val_list, xls_data)
    test_dataset = ADNIDataset(test_list, xls_data)

    return train_dataset, val_dataset, test_dataset

    # TODO  Normalize data? Later add / Exctract clinical data? Which data?

'''
Returns train_list, val_list and test_list in format [(image, id), ...] each
'''
def get_train_val_test(AD_list, NL_list, val_split):

    train_list, val_list, test_list = [], [], []

    num_test_ad = int(len(AD_list) * val_split)
    num_test_nl = int(len(NL_list) * val_split)

    num_val_ad = int((len(AD_list) - num_test_ad) * val_split)
    num_val_nl = int((len(NL_list) - num_test_nl) * val_split)

    # Sets up ADs
    for image in AD_list[0:num_val_ad]:
        val_list.append((image, 1))

    for image in AD_list[num_val_ad:num_test_ad]:
        test_list.append((image, 1))

    for image in AD_list[num_test_ad:]:
        train_list.append((image, 1))

    # Sets up NLs
    for image in NL_list[0:num_val_nl]:
        val_list.append((image, 0))

    for image in NL_list[num_val_nl:num_test_nl]:
        test_list.append((image, 0))

    for image in NL_list[num_test_nl:]:
        train_list.append((image, 0))

    return train_list, val_list, test_list


class ADNIDataset(Dataset):
    def __init__(self, mri, xls: pd.DataFrame):
        self.mri_data = mri        # DATA IS A LIST WITH TUPLES (image_dir, class_id)
        self.xls_data = xls


    def __len__(self):
        return len(self.mri_data)
    
    def _xls_to_tensor(self, xls_data: pd.Series):
        #Get used data

        #data = xls_data.loc[['Sex', 'Age (current)', 'PTID', 'DXCONFID (1=uncertain, 2= mild, 3= moderate, 4=high confidence)', 'Alz_csf']]
        data = xls_data.loc[['Sex', 'Age (current)']]
        
        data.replace({'M': 0, 'F': 1}, inplace=True)
        

        #Convert to tensor
        xls_tensor = torch.tensor(data.values.astype(float))
        
        return xls_tensor

    def __getitem__(self, idx):     # RETURNS TUPLE WITH IMAGE AND CLASS_ID, BASED ON INDEX IDX
        mri_path, class_id = self.mri_data[idx]
        mri = nib.load(mri_path)
        mri_data = mri.get_fdata()

        xls = self.xls_data.iloc[idx]

        #Convert xls data to tensor
        xls_tensor = self._xls_to_tensor(xls)
        mri_tensor = torch.from_numpy(mri_data).unsqueeze(0)
        
        class_id = torch.tensor([class_id])
        #Convert to one-hot and squeeze
        class_id = torch.nn.functional.one_hot(class_id, num_classes=2).squeeze(0)
        
        #Convert to float
        mri_tensor = mri_tensor.float()
        xls_tensor = xls_tensor.float()
        class_id = class_id.float()

        return (mri_tensor, xls_tensor), class_id
    
    
def initalize_dataloaders(training_data, val_data, test_data, cuda_device=torch.device('cuda:0'), batch_size=64):
    train_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True)
    test_dataloader = DataLoader(test_data, batch_size=(batch_size // 4), shuffle=True)
    val_dataloader = DataLoader(val_data, batch_size=batch_size, shuffle=True)
    return train_dataloader, val_dataloader, test_dataloader