import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import pandas as pd
import logging

from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from sklearn.neighbors import LocalOutlierFactor
from losses import loss_map
from utils.functions import save_model, restore_model
from utils.metrics import F_measure

class DeepUnkManager:
    
    def __init__(self, args, data, model, logger_name = 'Detection'):
        
        self.logger = logging.getLogger(logger_name)

        self.set_model_optimizer(args, data, model)

        self.data = data 
        self.train_dataloader = data.dataloader.train_labeled_loader
        self.eval_dataloader = data.dataloader.eval_loader 
        self.test_dataloader = data.dataloader.test_loader

        self.loss_fct = loss_map[args.loss_fct]

        if args.train:
            self.best_features = None

        else:
            restore_model(self.model, args.model_output_dir)
            self.best_features = np.load(os.path.join(args.method_output_dir, 'features.npy'))

    def set_model_optimizer(self, args, data, model):
        
        self.model = model.set_model(args, '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

    def train(self, args, data):     

        best_model = None
        best_eval_score = 0
        wait = 0
        
        for epoch in trange(int(args.num_train_epochs), desc="Epoch"):

            self.model.train()
            tr_loss = 0
            nb_tr_examples, nb_tr_steps = 0, 0
            
            for step, batch in enumerate(tqdm(self.train_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(True):
                    
                    loss = self.model(input_ids, segment_ids, input_mask, label_ids, mode='train', loss_fct = self.loss_fct)

                    self.optimizer.zero_grad()
                    loss.backward()
                    self.optimizer.step()
                    self.scheduler.step()
                    tr_loss += loss.item()
                    
                    nb_tr_examples += input_ids.size(0)
                    nb_tr_steps += 1

            loss = tr_loss / nb_tr_steps
            y_true, y_pred = self.get_outputs(args, data, mode = 'eval')
            eval_score = round(accuracy_score(y_true, y_pred) * 100, 2)
            
            eval_results = {
                'train_loss': loss,
                'eval_score': eval_score,
                'best_eval_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_eval_score = eval_score
                best_model = copy.deepcopy(self.model)
                wait = 0

            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 classify_lof(self, args, data, preds, train_feats, pred_feats):
        
        lof = LocalOutlierFactor(n_neighbors=args.n_neighbors, contamination = args.contamination, novelty=True, n_jobs=-1)
        lof.fit(train_feats)
        y_pred_lof = pd.Series(lof.predict(pred_feats))
        preds[y_pred_lof[y_pred_lof == -1].index] = data.unseen_label_id

        return preds

    def get_outputs(self, args, data, mode, get_feats = False, train_feats = None):
        
        if mode == 'train':
            dataloader = self.train_dataloader
        elif 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, data.num_labels)).to(self.device)
        total_features = torch.empty((0,args.feat_dim)).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):

                pooled_output, 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, pooled_output))

        if get_feats:
            feats = total_features.cpu().numpy()
            return feats 
        else:
            total_probs, y_pred = total_logits.max(dim = 1)
            y_pred = y_pred.cpu().numpy()
            y_true = total_labels.cpu().numpy()
            
            if train_feats is not None:
                feats = total_features.cpu().numpy()
                y_pred = self.classify_lof(args, data, y_pred, train_feats, feats)
            
            return y_true, y_pred

    def test(self, args, data, show=False):
        
        train_feats = self.get_outputs(args, data, mode = 'train', get_feats = True)
        y_true, y_pred = self.get_outputs(args, data, mode = 'test', train_feats = train_feats)

        cm = confusion_matrix(y_true, y_pred)
        test_results = F_measure(cm)

        acc = round(accuracy_score(y_true, y_pred) * 100, 2)
        test_results['Acc'] = acc
        
        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