import numpy as np

import torch

import torch.nn as nn

import torch.nn.functional as F

from models.model_attention_mil import MIL_Attention_fc_mtl

import pdb

import os

import pandas as pd

from utils.utils import *

from utils.core_utils import EarlyStopping,  Accuracy_Logger

from utils.file_utils import save_pkl, load_pkl

from sklearn.metrics import roc_auc_score, roc_curve, auc

import h5py

from models.resnet_custom import resnet50_baseline

import math

from sklearn.preprocessing import label_binarize

def initiate_model(args, ckpt_path=None):

    print('Init Model')

    model_dict = {"dropout": args.drop_out, 'n_classes': args.n_classes}

    if args.model_size is not None and args.model_type in ['clam', 'attention_mil', 'clam_simple']:

        model_dict.update({"size_arg": args.model_size})

    if args.model_type =='clam':

        raise NotImplementedError

    elif args.model_type =='clam_simple':

        raise NotImplementedError

    elif args.model_type == 'attention_mil':

        model = MIL_Attention_fc_mtl(**model_dict)

    else: # args.model_type == 'mil'

        raise NotImplementedError

    #model.relocate()

    print_network(model)

    if ckpt_path is not None:

        ckpt = torch.load(ckpt_path)

        ckpt_clean = {}

        for key in ckpt.keys():

            if 'instance_loss_fn' in key:

                continue

            ckpt_clean.update({key.replace('.module', ''):ckpt[key]})

        model.load_state_dict(ckpt_clean, strict=True)

    model.relocate()

    model.eval()

    return model

def eval(dataset, args, ckpt_path):

    model = initiate_model(args, ckpt_path)

    print('Init Loaders')

    loader = get_simple_loader(dataset, collate_fn='MIL_mtl')

    results_dict = summary(model, loader, args)

    print('test_error_task1: ', results_dict['test_error_task1'])

    print('auc_task1: ',        results_dict['auc_task1'])

    print('test_error_task2: ', results_dict['test_error_task2'])

    print('auc_task2: ',        results_dict['auc_task2'])

    print('test_error_task3: ', results_dict['test_error_task3'])

    print('auc_task3: ',        results_dict['auc_task3'])

    return model, results_dict

    # patient_results, test_error, auc, aucs, df

def infer(dataset, args, ckpt_path, class_labels, site_labels):

    model = initiate_model(args, ckpt_path)

    df = infer_dataset(model, dataset, args, class_labels, site_labels)

    return model, df

# Code taken from pytorch/examples for evaluating topk classification on on ImageNet

def accuracy(output, target, topk=(1,)):

    """Computes the accuracy over the k top predictions for the specified values of k"""

    with torch.no_grad():

        maxk = max(topk)

        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)

        pred = pred.t()

        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []

        for k in topk:

            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)

            res.append(correct_k.mul_(1.0 / batch_size))

        return res

def summary(model, loader, args):

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

    logger_task1 = Accuracy_Logger(n_classes=args.n_classes[0])

    logger_task2 = Accuracy_Logger(n_classes=args.n_classes[1])

    logger_task3 = Accuracy_Logger(n_classes=args.n_classes[2])

    model.eval()

    test_error_task1 = 0.

    test_loss_task1  = 0.

    test_error_task2 = 0.

    test_loss_task2  = 0.

    test_error_task3 = 0.

    test_loss_task3  = 0.

    all_probs_task1  = np.zeros((len(loader), args.n_classes[0]))

    all_labels_task1 = np.zeros(len(loader))

    all_probs_task2  = np.zeros((len(loader), args.n_classes[1]))

    all_labels_task2 = np.zeros(len(loader))

    all_probs_task3  = np.zeros((len(loader), args.n_classes[2]))

    all_labels_task3 = np.zeros(len(loader))

    if not args.patient_level:

        slide_ids = loader.dataset.slide_data['slide_id']

        patient_results = {}

        for batch_idx, (data, label_task1, label_task2, label_task3) in enumerate(loader):

            data =  data.to(device)     

            label_task1 = label_task1.to(device)

            label_task2 = label_task2.to(device)            

            label_task3 = label_task3.to(device)

            slide_id = slide_ids.iloc[batch_idx]

            with torch.no_grad():

                model_results_dict = model(data)

            logits_task1, Y_prob_task1, Y_hat_task1  = model_results_dict['logits_task1'], model_results_dict['Y_prob_task1'], model_results_dict['Y_hat_task1']

            logits_task2, Y_prob_task2, Y_hat_task2  = model_results_dict['logits_task2'], model_results_dict['Y_prob_task2'], model_results_dict['Y_hat_task2']

            logits_task3, Y_prob_task3, Y_hat_task3  = model_results_dict['logits_task3'], model_results_dict['Y_prob_task3'], model_results_dict['Y_hat_task3']

            del model_results_dict

            logger_task1.log(Y_hat_task1, label_task1)

            logger_task2.log(Y_hat_task2, label_task2)

            logger_task3.log(Y_hat_task3, label_task3)

            probs_task1 = Y_prob_task1.cpu().numpy()

            all_probs_task1[batch_idx] = probs_task1

            all_labels_task1[batch_idx] = label_task1.item()

            probs_task2 = Y_prob_task2.cpu().numpy()

            all_probs_task2[batch_idx] = probs_task2

            all_labels_task2[batch_idx] = label_task2.item()

            probs_task3 = Y_prob_task3.cpu().numpy()

            all_probs_task3[batch_idx] = probs_task3

            all_labels_task3[batch_idx] = label_task3.item()

            patient_results.update({slide_id: {'slide_id': np.array(slide_id),

                    'prob_task1': probs_task1, 'label_task1': label_task1.item(),

                    'prob_task2': probs_task2, 'label_task2': label_task2.item(),

                    'prob_task3': probs_task3, 'label_task3': label_task3.item() }})

            error_task1 = calculate_error(Y_hat_task1, label_task1)

            test_error_task1 += error_task1

            error_task2 = calculate_error(Y_hat_task2, label_task2)

            test_error_task2 += error_task2

            error_task3 = calculate_error(Y_hat_task3, label_task3)

            test_error_task3 += error_task3

    else:

        case_ids = loader.dataset.slide_data['case_id']

        patient_results = {}

        for batch_idx, (data, label_task1, label_task2, label_task3) in enumerate(loader):

            data =  data.to(device)

            label_task1 = label_task1.to(device)

            label_task2 = label_task2.to(device)

            label_task3 = label_task3.to(device)

            case_id = case_ids.iloc[batch_idx]

            with torch.no_grad():

                model_results_dict = model(data)

            logits_task1, Y_prob_task1, Y_hat_task1  = model_results_dict['logits_task1'], model_results_dict['Y_prob_task1'], model_results_dict['Y_hat_task1']

            logits_task2, Y_prob_task2, Y_hat_task2  = model_results_dict['logits_task2'], model_results_dict['Y_prob_task2'], model_results_dict['Y_hat_task2']

            logits_task3, Y_prob_task3, Y_hat_task3  = model_results_dict['logits_task3'], model_results_dict['Y_prob_task3'], model_results_dict['Y_hat_task3']

            del model_results_dict

            logger_task1.log(Y_hat_task1, label_task1)

            logger_task2.log(Y_hat_task2, label_task2)

            logger_task3.log(Y_hat_task3, label_task3)

            probs_task1 = Y_prob_task1.cpu().numpy()

            all_probs_task1[batch_idx] = probs_task1

            all_labels_task1[batch_idx] = label_task1.item()

            probs_task2 = Y_prob_task2.cpu().numpy()

            all_probs_task2[batch_idx] = probs_task2

            all_labels_task2[batch_idx] = label_task2.item()

            probs_task3 = Y_prob_task3.cpu().numpy()

            all_probs_task3[batch_idx] = probs_task3

            all_labels_task3[batch_idx] = label_task3.item()

            patient_results.update({case_id: {'case_id': np.array(case_id),

                                    'prob_task1': probs_task1, 'label_task1': label_task1.item(),

                                    'prob_task2': probs_task2, 'label_task2': label_task2.item(),

                                    'prob_task3': probs_task3, 'label_task3': label_task3.item() }})

            error_task1 = calculate_error(Y_hat_task1, label_task1)

            test_error_task1 += error_task1

            error_task2 = calculate_error(Y_hat_task2, label_task2)

            test_error_task2 += error_task2

            error_task3 = calculate_error(Y_hat_task3, label_task3)

            test_error_task3 += error_task3

    test_error_task1 /= len(loader)

    test_error_task2 /= len(loader)

    test_error_task3 /= len(loader)

    all_preds_task1 = np.argmax(all_probs_task1, axis=1)

    all_preds_task2 = np.argmax(all_probs_task2, axis=1)

    all_preds_task3 = np.argmax(all_probs_task3, axis=1)

    #if args.n_classes > 2:

    #    acc1, acc3 = accuracy(torch.from_numpy(all_cls_probs), torch.from_numpy(all_cls_labels), topk=(1, 3))

    #    print('top1 acc: {:.3f}, top3 acc: {:.3f}'.format(acc1.item(), acc3.item()))

    # IF MORE THAN BINARY CLASSIFICATION

    #if len(np.unique(all_labels_task1)) == 1:

    #    auc_task1 = -1

    #    aucs_task1 = []

    # else:

    #     if args.n_classes[0] == 2:

    #         auc_task1 = roc_auc_score(all_labels_task1, all_probs_task1[:, 1])

    #         aucs_task1 = []

    #     else:

    #         aucs_task1 = []

    #         binary_labels = label_binarize(all_labels_task1, classes=[i for i in range(args.n_classes[0])])

    #         for class_idx in range(args.n_classes[0[]]):

    #             if class_idx in all_labels_task1:

    #                 fpr, tpr, _ = roc_curve(binary_labels[:, class_idx], all_probs_task1[:, class_idx])

    #                 aucs_task1.append(auc(fpr, tpr))

    #             else:

    #                 aucs_task1.append(float('nan'))

    #         if args.micro_average:

    #             binary_labels = label_binarize(all_labels_task1, classes=[i for i in range(args.n_classes[0])])

    #             fpr, tpr, _ = roc_curve(binary_labels.ravel(), all_probs_task1.ravel())

    #             auc_task1 = auc(fpr, tpr)

    #         else:

    #             auc_task1 = np.nanmean(np.array(aucs_task1))

    # ASSUME BINARY CLASSIFICATION

    if len(np.unique(all_labels_task1)) == 1:

        auc_task1 = -1

    else:

        auc_task1 = roc_auc_score(all_labels_task1, all_probs_task1[:, 1])

    if len(np.unique(all_labels_task2)) == 1:

        auc_task2 = -1

    else:

        auc_task2 = roc_auc_score(all_labels_task2, all_probs_task2[:, 1])

    if len(np.unique(all_labels_task3)) == 1:

        auc_task3 = -1

    else:

        auc_task3 = roc_auc_score(all_labels_task3, all_probs_task3[:, 1])

    if not args.patient_level:

        results_dict = {'slide_id': slide_ids,

            'Y_task1': all_labels_task1, 'Y_hat_task1': all_preds_task1,

            'Y_task2': all_labels_task2, 'Y_hat_task2': all_preds_task2,

            'Y_task3': all_labels_task3, 'Y_hat_task3': all_preds_task3}

    else:

        results_dict = {'case_id': case_ids,

                        'Y_task1': all_labels_task1, 'Y_hat_task1': all_preds_task1,

                        'Y_task2': all_labels_task2, 'Y_hat_task2': all_preds_task2,

                        'Y_task3': all_labels_task3, 'Y_hat_task3': all_preds_task3}

    results_dict.update({'p0_task1': all_probs_task1[:,0]})

    results_dict.update({'p1_task1': all_probs_task1[:,1]})

    results_dict.update({'p0_task2': all_probs_task2[:,0]})

    results_dict.update({'p1_task2': all_probs_task2[:,1]})

    results_dict.update({'p0_task3': all_probs_task3[:,0]})

    results_dict.update({'p1_task3': all_probs_task3[:,1]})

    df = pd.DataFrame(results_dict)

    if args.patient_level:

        df = df.drop_duplicates(subset=['case_id'])

    inference_results = {'patient_results': patient_results,

                        'test_error_task1': test_error_task1, 'auc_task1': auc_task1,

                        'test_error_task2': test_error_task2, 'auc_task2': auc_task2,

                        'test_error_task3': test_error_task3, 'auc_task3': auc_task3,

                        'loggers': (logger_task1, logger_task2, logger_task3), 'df':df}

    return inference_results

def infer_dataset(model, dataset, args, class_labels, site_labels, k=3):

    model.eval()

    all_probs_cls = np.zeros((len(dataset), k))

    all_probs_site = np.zeros((len(dataset),2))

    all_preds_cls = np.zeros((len(dataset), k))

    all_preds_cls_str = np.full((len(dataset), k), ' ', dtype=object)

    all_preds_site = np.full((len(dataset)), ' ', dtype=object)

    slide_ids = dataset.slide_data

    for batch_idx, data in enumerate(dataset):

        data = data.to(device)

        with torch.no_grad():

            results_dict = model(data)

        Y_prob, Y_hat = results_dict['Y_prob'], results_dict['Y_hat']

        site_prob, site_hat = results_dict['site_prob'], results_dict['site_hat']

        del results_dict

        probs, ids = torch.topk(Y_prob, k)

        probs = probs.cpu().numpy()

        site_prob = site_prob.cpu().numpy()

        ids = ids.cpu().numpy()

        all_probs_cls[batch_idx] = probs

        all_preds_cls[batch_idx] = ids

        all_preds_cls_str[batch_idx] = np.array(class_labels)[ids]

        all_probs_site[batch_idx] = site_prob

        all_preds_site[batch_idx] = np.array(site_labels)[site_hat.item()]

    del data

    results_dict = {'slide_id': slide_ids}

    for c in range(k):

        results_dict.update({'Pred_{}'.format(c): all_preds_cls_str[:, c]})

        results_dict.update({'p_{}'.format(c): all_probs_cls[:, c]})

    results_dict.update({'Site_Pred': all_preds_site, 'Site_p': all_probs_site[:, 1]})

    df = pd.DataFrame(results_dict)

    return df

# def infer_dataset(model, dataset, args, class_labels, k=3):

#     model.eval()

#     all_probs = np.zeros((len(dataset), args.n_classes))

#     all_preds = np.zeros(len(dataset))

#     all_str_preds = np.full(len(dataset), ' ', dtype=object)

#     slide_ids = dataset.slide_data

#     for batch_idx, data in enumerate(dataset):

#         data = data.to(device)

#         with torch.no_grad():

#             logits, Y_prob, Y_hat, _, results_dict = model(data)

#         probs = Y_prob.cpu().numpy()

#         all_probs[batch_idx] = probs

#         all_preds[batch_idx] = Y_hat.item()

#         all_str_preds[batch_idx] = class_labels[Y_hat.item()]

#     del data

#     results_dict = {'slide_id': slide_ids, 'Prediction': all_str_preds, 'Y_hat': all_preds}

#     for c in range(args.n_classes):

#         results_dict.update({'p_{}_{}'.format(c, class_labels[c]): all_probs[:,c]})

#     df = pd.DataFrame(results_dict)

#     return df

def compute_features(dataset, args, ckpt_path, save_dir, model=None, feature_dim=512):

    if model is None:

        model = initiate_model(args, ckpt_path)

    names = dataset.get_list(np.arange(len(dataset))).values

    file_path = os.path.join(save_dir, 'features.h5')

    initialize_features_hdf5_file(file_path, len(dataset), feature_dim=feature_dim, names=names)

    for i in range(len(dataset)):

        print("Progress: {}/{}".format(i, len(dataset)))

        save_features(dataset, i, model, args, file_path)

def save_features(dataset, idx, model, args, save_file_path):

    name = dataset.get_list(idx)

    print(name)

    features, label_task1, label_task2, label_task3 = dataset[idx]

    features = features.to(device)

    with torch.no_grad():

        results_dict = model(features, return_features=True)

        Y_prob_task1, Y_hat_task1 = results_dict['Y_prob_task1'], results_dict['Y_hat_task1']

        Y_prob_task2, Y_hat_task2 = results_dict['Y_prob_task2'], results_dict['Y_hat_task2']

        Y_prob_task3, Y_hat_task3 = results_dict['Y_prob_task3'], results_dict['Y_hat_task3']

        feat_task1 = results_dict['features'][0]

        feat_task2 = results_dict['features'][1]

        feat_task3 = results_dict['features'][2]

    del results_dict

    del features

    Y_hat_task1  = Y_hat_task1.item()

    Y_prob_task1 = Y_prob_task1.view(-1).cpu().numpy()

    Y_hat_task2  = Y_hat_task2.item()

    Y_prob_task2 = Y_prob_task2.view(-1).cpu().numpy()

    Y_hat_task3  = Y_hat_task3.item()

    Y_prob_task3 = Y_prob_task3.view(-1).cpu().numpy()

    feat_task1 = feat_task1.view(1, -1).cpu().numpy()

    feat_task2 = feat_task2.view(1, -1).cpu().numpy()

    feat_task3 = feat_task3.view(1, -1).cpu().numpy()

    with h5py.File(save_file_path, 'r+') as file:

        print('label_task1', label_task1)

        file['features_task1'][idx, :] = feat_task1

        file['features_task2'][idx, :] = feat_task2

        file['features_task3'][idx, :] = feat_task3

        file['label_task1'][idx] = label_task1

        file['Y_hat_task1'][idx] = Y_hat_task1

        file['Y_prob_task1'][idx] = Y_prob_task1[1]

        file['label_task2'][idx] = label_task2

        file['Y_hat_task2'][idx] = Y_hat_task2

        file['Y_prob_task2'][idx] = Y_prob_task2[1]

        file['label_task3'][idx] = label_task3

        file['Y_hat_task3'][idx] = Y_hat_task3

        file['Y_prob_task3'][idx] = Y_prob_task3[1]

def initialize_features_hdf5_file(file_path, length, feature_dim=512, names = None):

    file = h5py.File(file_path, "w")

    dset = file.create_dataset('features_task1',

                                shape=(length, feature_dim), chunks=(1, feature_dim), dtype=np.float32)

    dset = file.create_dataset('features_task2',

                                shape=(length, feature_dim), chunks=(1, feature_dim), dtype=np.float32)

    dset = file.create_dataset('features_task3',

                                shape=(length, feature_dim), chunks=(1, feature_dim), dtype=np.float32)

    # if names is not None:

    #     names = np.array(names, dtype='S')

    #     dset.attrs['names'] = names

    if names is not None:

        dt = h5py.string_dtype()

        label_dset = file.create_dataset('names', shape=(length, ), chunks=(1, ), dtype=dt)

        file['names'][:] = names

    label_dset = file.create_dataset('label_task1', shape=(length, ), chunks=(1, ), dtype=np.int32)

    pred_dset = file.create_dataset( 'Y_hat_task1', shape=(length, ), chunks=(1, ), dtype=np.int32)

    prob_dset = file.create_dataset( 'Y_prob_task1', shape=(length, ), chunks=(1, ), dtype=np.float32)

    label_dset = file.create_dataset('label_task2', shape=(length, ), chunks=(1, ), dtype=np.int32)

    pred_dset = file.create_dataset( 'Y_hat_task2', shape=(length, ), chunks=(1, ), dtype=np.int32)

    prob_dset = file.create_dataset( 'Y_prob_task2', shape=(length, ), chunks=(1, ), dtype=np.float32)

    label_dset = file.create_dataset('label_task3', shape=(length, ), chunks=(1, ), dtype=np.int32)

    pred_dset = file.create_dataset( 'Y_hat_task3', shape=(length, ), chunks=(1, ), dtype=np.int32)

    prob_dset = file.create_dataset( 'Y_prob_task3', shape=(length, ), chunks=(1, ), dtype=np.float32)

    file.close()

    return file_path