import torch
import logging
import copy
import torch.nn.functional as F

from tqdm import trange, tqdm
from sklearn.metrics import confusion_matrix

from losses import loss_map
from utils.metrics import clustering_score
from utils.functions import restore_model, save_model

class MCLManager:
    
    def __init__(self, args, data, model, logger_name = 'Discovery'):

        self.logger = logging.getLogger(logger_name)
        self.num_labels = data.num_labels   
        loader = data.dataloader
        self.train_dataloader, self.eval_dataloader, self.test_dataloader = \
            loader.train_outputs['loader'], loader.eval_outputs['loader'], loader.test_outputs['loader']

        backbone = args.backbone
        args.backbone = backbone
        self.model = model.set_model(args, data, 'bert')
        self.optimizer , self.scheduler = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \
        args.num_train_epochs, args.lr, args.warmup_proportion)
        self.device = model.device

        self.loss_fct = loss_map[args.loss_fct]

        if not args.train:
            self.model = restore_model(self.model, args.model_output_dir)

    def train(self, args, data): 

        best_model = None
        wait = 0
        best_eval_score = 0

        for epoch in trange(int(args.num_train_epochs), desc="Epoch"):  
            
            tr_loss = 0
            nb_tr_examples, nb_tr_steps = 0, 0
            self.model.train()

            for batch in tqdm(self.train_dataloader, desc="Training(All)"):

                batch = tuple(t.to(self.device) for t in batch)
                input_ids, input_mask, segment_ids, label_ids = batch
                loss = self.model(input_ids, segment_ids, input_mask, label_ids, mode = 'train', loss_fct = self.loss_fct)

                loss.backward()
                tr_loss += loss.item() 
                nb_tr_examples += input_ids.size(0)
                nb_tr_steps += 1

                self.optimizer.step()
                self.scheduler.step()
                self.optimizer.zero_grad()
            
            tr_loss = tr_loss / nb_tr_steps

            y_true, y_pred = self.get_outputs(args, mode = 'eval')
            eval_score = clustering_score(y_true, y_pred)['NMI']
            eval_results = {
                'train_loss': tr_loss,
                'eval_score': eval_score,
                'best_score': best_eval_score,
            }

            self.logger.info("***** Epoch: %s: Eval results *****", str(epoch + 1))
            for key in sorted(eval_results.keys()):
                self.logger.info("  %s = %s", key, str(eval_results[key]))

            if eval_score > best_eval_score:

                best_model = copy.deepcopy(self.model)
                wait = 0
                best_eval_score = eval_score

            elif eval_score > 0:

                wait += 1
                if wait >= args.wait_patient:
                    break

        self.model = best_model
        
        if args.save_model:
            save_model(self.model, args.model_output_dir)

    def get_outputs(self, args, mode = 'eval', get_feats = False):
        
        if mode == 'eval':
            dataloader = self.eval_dataloader
        elif mode == 'test':
            dataloader = self.test_dataloader

        self.model.eval()
        total_labels = torch.empty(0, dtype=torch.long).to(self.device)
        total_logits = torch.empty((0, args.num_labels)).to(self.device)
        total_features = torch.empty((0, args.feat_dim)).to(self.device)
        total_preds = torch.empty(0, dtype=torch.long).to(self.device)

        for batch in tqdm(dataloader, desc="Iteration"):
            batch = tuple(t.to(self.device) for t in batch)
            input_ids, input_mask, segment_ids, label_ids = batch

            with torch.set_grad_enabled(False):

                features, logits = self.model(input_ids, segment_ids, input_mask)  
                total_labels = torch.cat((total_labels, label_ids))
                total_logits = torch.cat((total_logits, logits))
                total_features = torch.cat((total_features, features))

        if get_feats:

            feats = total_features.cpu().numpy()
            return feats
        
        else:

            total_probs = F.softmax(total_logits.detach(), dim = 1)
            total_maxprobs, total_preds = total_probs.max(dim = 1)

            y_true = total_labels.cpu().numpy()
            y_pred = total_preds.cpu().numpy()

            return y_true, y_pred

    def test(self, args, data):

        y_true, y_pred = self.get_outputs(args, mode = 'test')
        test_results = clustering_score(y_true, y_pred)

        cm = confusion_matrix(y_true, y_pred)
        
        self.logger.info
        self.logger.info("***** Test: Confusion Matrix *****")
        self.logger.info("%s", str(cm))
        self.logger.info("***** Test results *****")
        
        for key in sorted(test_results.keys()):
            self.logger.info("  %s = %s", key, str(test_results[key]))

        test_results['y_true'] = y_true
        test_results['y_pred'] = y_pred
        
        return test_results