import numpy as np

import torch

import torch.nn as nn

import torch.nn.functional as F

from models.model_mil import MIL_fc, MIL_fc_mc

from models.model_clam import CLAM_SB, CLAM_MB

import pdb

import os

import pandas as pd

from utils.utils import *

from utils.core_utils import Accuracy_Logger

from sklearn.metrics import roc_auc_score, roc_curve, auc

from sklearn.preprocessing import label_binarize

import matplotlib.pyplot as plt

def initiate_model(args, ckpt_path):

    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_sb', 'clam_mb']:

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

    if args.model_type =='clam_sb':

        model = CLAM_SB(**model_dict)

    elif args.model_type =='clam_mb':

        model = CLAM_MB(**model_dict)

    else: # args.model_type == 'mil'

        if args.n_classes > 2:

            model = MIL_fc_mc(**model_dict)

        else:

            model = MIL_fc(**model_dict)

    print_network(model)

    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)

    patient_results, test_error, auc, df, _ = summary(model, loader, args)

    print('test_error: ', test_error)

    print('auc: ', auc)

    return model, patient_results, test_error, auc, df

def summary(model, loader, args):

    acc_logger = Accuracy_Logger(n_classes=args.n_classes)

    model.eval()

    test_loss = 0.

    test_error = 0.

    all_probs = np.zeros((len(loader), args.n_classes))

    all_labels = np.zeros(len(loader))

    all_preds = np.zeros(len(loader))

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

    patient_results = {}

    for batch_idx, (data, label) in enumerate(loader):

        data, label = data.to(device), label.to(device)

        slide_id = slide_ids.iloc[batch_idx]

        with torch.no_grad():

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

        acc_logger.log(Y_hat, label)

        probs = Y_prob.cpu().numpy()

        all_probs[batch_idx] = probs

        all_labels[batch_idx] = label.item()

        all_preds[batch_idx] = Y_hat.item()

        patient_results.update({slide_id: {'slide_id': np.array(slide_id), 'prob': probs, 'label': label.item()}})

        error = calculate_error(Y_hat, label)

        test_error += error

    del data

    test_error /= len(loader)

    aucs = []

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

        auc_score = -1

    else: 

        if args.n_classes == 2:

            auc_score = roc_auc_score(all_labels, all_probs[:, 1])

        else:

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

            for class_idx in range(args.n_classes):

                if class_idx in all_labels:

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

                    aucs.append(auc(fpr, tpr))

                else:

                    aucs.append(float('nan'))

            if args.micro_average:

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

                fpr, tpr, _ = roc_curve(binary_labels.ravel(), all_probs.ravel())

                auc_score = auc(fpr, tpr)

            else:

                auc_score = np.nanmean(np.array(aucs))

    results_dict = {'slide_id': slide_ids, 'Y': all_labels, 'Y_hat': all_preds}

    for c in range(args.n_classes):

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

    df = pd.DataFrame(results_dict)

    return patient_results, test_error, auc_score, df, acc_logger