import torch
import config
import torch.nn as nn

from metrics import AverageMeter


class Trainer:

    def __init__(self, model, log_interval, eval_internal, epochs, 
                optimizer, lr_scheduler, model_dir):
        self.model = model
        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        self.epochs = epochs
        self.log_interval = log_interval
        self.eval_interval = eval_internal
        self.model_dir = model_dir
        self.evaluator = Evaluator(self.model)


    def train(self, train_loader, valid_loader, result_dict, fold):
        for epoch in range(self.epochs):
            result_dict["epoch"] = epoch
            result_dict = self._train_loop_for_one_epoch(
                epoch=epoch,
                model=self.model,
                train_loader=train_loader,
                valid_loader=valid_loader,
                optimizer=self.optimizer,
                lr_scheduler=self.lr_scheduler,
                result_dict=result_dict
            )
        
        print(f"Saving model in {self.model_dir}....")
        torch.save(self.model.state_dict(), f'{self.model_dir}/model-fold-{fold}_dict')
        
        return result_dict

    def _train_loop_for_one_epoch(self, epoch, train_loader, valid_loader, result_dict):
        self.model.train()
        losses = AverageMeter()
        for batch_idx, batch in enumerate(train_loader):
            input_ids, = batch["input_ids"].to(config.DEVICE)
            attention_mask = batch["attention_mask"].to(config.DEVICE)
            token_type_ids = batch["token_type_ids"].to(config.DEVICE)
            label = batch["label"].to(config.DEVICE)
            model = model.to(config.DEVICE)

            loss = self._train_loop_for_one_step(
                input_ids,
                attention_mask,
                token_type_ids,
                label
            )
            losses.update(loss.item(), input_ids.size(0))

            if batch_idx % self.log_interval == 0:
                print(f"Epoch={epoch}, Avg Loss={losses.avg}, Batch Idx={batch_idx}")

            if batch_idx % self.eval_interval == 0:
                result_dict = self.evaluator.evaluate(
                    valid_loader=valid_loader,
                    result_dict=result_dict,
                    epoch=epoch
                )
            
            result_dict["train_loss"].append(losses.avg)
            print("--------Training Results Summary------")
            print(f"Epoch: {epoch}, train_loss: {losses.avg}")
        return result_dict
    
    def _train_loop_for_one_step(self, input_ids, attention_mask, token_type_ids, label):
        self.optimizer.zero_grad()
        outputs = self.model(input_ids, attention_mask, token_type_ids, label)
        loss, logits = outputs[:2]
        loss.backward()
        self.optimizer.step()
        return loss



class Evaluator:

    def __init__(self, model):
        self.model = model

    def evaluate(self, epoch, valid_loader, result_dict):
        self.model.eval()
        losses = AverageMeter()
        with torch.no_grad():
            for batch_idx, batch in enumerate(valid_loader):
                input_ids, = batch["input_ids"].to(config.DEVICE)
                attention_mask = batch["attention_mask"].to(config.DEVICE)
                token_type_ids = batch["token_type_ids"].to(config.DEVICE)
                model = model.to(config.DEVICE)

                loss = self._eval_loop_for_one_step(
                    input_ids,
                    attention_mask,
                    token_type_ids
                )
                losses.update(loss.item(), input_ids.size(0))
                print("----------Validation Results Summary---------")
                print(f"Epoch: {epoch}, valid_loss: {losses.avg}")
                result_dict["valid_loss"].append(losses.avg)

        return result_dict

    def _eval_loop_for_one_step(self, input_ids, attention_mask, token_type_ids):
        outputs = self.model(input_ids, attention_mask, token_type_ids)
        loss, logits = outputs[:2]
        return loss
67b5072c21002a1d188bb52b49741a49763ce92e
<mask token>


class Trainer:
    <mask token>
    <mask token>

    def _train_loop_for_one_epoch(self, epoch, train_loader, valid_loader,
        result_dict):
        self.model.train()
        losses = AverageMeter()
        for batch_idx, batch in enumerate(train_loader):
            input_ids, = batch['input_ids'].to(config.DEVICE)
            attention_mask = batch['attention_mask'].to(config.DEVICE)
            token_type_ids = batch['token_type_ids'].to(config.DEVICE)
            label = batch['label'].to(config.DEVICE)
            model = model.to(config.DEVICE)
            loss = self._train_loop_for_one_step(input_ids, attention_mask,
                token_type_ids, label)
            losses.update(loss.item(), input_ids.size(0))
            if batch_idx % self.log_interval == 0:
                print(
                    f'Epoch={epoch}, Avg Loss={losses.avg}, Batch Idx={batch_idx}'
                    )
            if batch_idx % self.eval_interval == 0:
                result_dict = self.evaluator.evaluate(valid_loader=
                    valid_loader, result_dict=result_dict, epoch=epoch)
            result_dict['train_loss'].append(losses.avg)
            print('--------Training Results Summary------')
            print(f'Epoch: {epoch}, train_loss: {losses.avg}')
        return result_dict
    <mask token>


class Evaluator:

    def __init__(self, model):
        self.model = model

    def evaluate(self, epoch, valid_loader, result_dict):
        self.model.eval()
        losses = AverageMeter()
        with torch.no_grad():
            for batch_idx, batch in enumerate(valid_loader):
                input_ids, = batch['input_ids'].to(config.DEVICE)
                attention_mask = batch['attention_mask'].to(config.DEVICE)
                token_type_ids = batch['token_type_ids'].to(config.DEVICE)
                model = model.to(config.DEVICE)
                loss = self._eval_loop_for_one_step(input_ids,
                    attention_mask, token_type_ids)
                losses.update(loss.item(), input_ids.size(0))
                print('----------Validation Results Summary---------')
                print(f'Epoch: {epoch}, valid_loss: {losses.avg}')
                result_dict['valid_loss'].append(losses.avg)
        return result_dict

    def _eval_loop_for_one_step(self, input_ids, attention_mask, token_type_ids
        ):
        outputs = self.model(input_ids, attention_mask, token_type_ids)
        loss, logits = outputs[:2]
        return loss

<mask token>


class Trainer:
    <mask token>

    def train(self, train_loader, valid_loader, result_dict, fold):
        for epoch in range(self.epochs):
            result_dict['epoch'] = epoch
            result_dict = self._train_loop_for_one_epoch(epoch=epoch, model
                =self.model, train_loader=train_loader, valid_loader=
                valid_loader, optimizer=self.optimizer, lr_scheduler=self.
                lr_scheduler, result_dict=result_dict)
        print(f'Saving model in {self.model_dir}....')
        torch.save(self.model.state_dict(),
            f'{self.model_dir}/model-fold-{fold}_dict')
        return result_dict

    def _train_loop_for_one_epoch(self, epoch, train_loader, valid_loader,
        result_dict):
        self.model.train()
        losses = AverageMeter()
        for batch_idx, batch in enumerate(train_loader):
            input_ids, = batch['input_ids'].to(config.DEVICE)
            attention_mask = batch['attention_mask'].to(config.DEVICE)
            token_type_ids = batch['token_type_ids'].to(config.DEVICE)
            label = batch['label'].to(config.DEVICE)
            model = model.to(config.DEVICE)
            loss = self._train_loop_for_one_step(input_ids, attention_mask,
                token_type_ids, label)
            losses.update(loss.item(), input_ids.size(0))
            if batch_idx % self.log_interval == 0:
                print(
                    f'Epoch={epoch}, Avg Loss={losses.avg}, Batch Idx={batch_idx}'
                    )
            if batch_idx % self.eval_interval == 0:
                result_dict = self.evaluator.evaluate(valid_loader=
                    valid_loader, result_dict=result_dict, epoch=epoch)
            result_dict['train_loss'].append(losses.avg)
            print('--------Training Results Summary------')
            print(f'Epoch: {epoch}, train_loss: {losses.avg}')
        return result_dict
    <mask token>


class Evaluator:

    def __init__(self, model):
        self.model = model

    def evaluate(self, epoch, valid_loader, result_dict):
        self.model.eval()
        losses = AverageMeter()
        with torch.no_grad():
            for batch_idx, batch in enumerate(valid_loader):
                input_ids, = batch['input_ids'].to(config.DEVICE)
                attention_mask = batch['attention_mask'].to(config.DEVICE)
                token_type_ids = batch['token_type_ids'].to(config.DEVICE)
                model = model.to(config.DEVICE)
                loss = self._eval_loop_for_one_step(input_ids,
                    attention_mask, token_type_ids)
                losses.update(loss.item(), input_ids.size(0))
                print('----------Validation Results Summary---------')
                print(f'Epoch: {epoch}, valid_loss: {losses.avg}')
                result_dict['valid_loss'].append(losses.avg)
        return result_dict

    def _eval_loop_for_one_step(self, input_ids, attention_mask, token_type_ids
        ):
        outputs = self.model(input_ids, attention_mask, token_type_ids)
        loss, logits = outputs[:2]
        return loss

<mask token>


class Trainer:

    def __init__(self, model, log_interval, eval_internal, epochs,
        optimizer, lr_scheduler, model_dir):
        self.model = model
        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        self.epochs = epochs
        self.log_interval = log_interval
        self.eval_interval = eval_internal
        self.model_dir = model_dir
        self.evaluator = Evaluator(self.model)

    def train(self, train_loader, valid_loader, result_dict, fold):
        for epoch in range(self.epochs):
            result_dict['epoch'] = epoch
            result_dict = self._train_loop_for_one_epoch(epoch=epoch, model
                =self.model, train_loader=train_loader, valid_loader=
                valid_loader, optimizer=self.optimizer, lr_scheduler=self.
                lr_scheduler, result_dict=result_dict)
        print(f'Saving model in {self.model_dir}....')
        torch.save(self.model.state_dict(),
            f'{self.model_dir}/model-fold-{fold}_dict')
        return result_dict

    def _train_loop_for_one_epoch(self, epoch, train_loader, valid_loader,
        result_dict):
        self.model.train()
        losses = AverageMeter()
        for batch_idx, batch in enumerate(train_loader):
            input_ids, = batch['input_ids'].to(config.DEVICE)
            attention_mask = batch['attention_mask'].to(config.DEVICE)
            token_type_ids = batch['token_type_ids'].to(config.DEVICE)
            label = batch['label'].to(config.DEVICE)
            model = model.to(config.DEVICE)
            loss = self._train_loop_for_one_step(input_ids, attention_mask,
                token_type_ids, label)
            losses.update(loss.item(), input_ids.size(0))
            if batch_idx % self.log_interval == 0:
                print(
                    f'Epoch={epoch}, Avg Loss={losses.avg}, Batch Idx={batch_idx}'
                    )
            if batch_idx % self.eval_interval == 0:
                result_dict = self.evaluator.evaluate(valid_loader=
                    valid_loader, result_dict=result_dict, epoch=epoch)
            result_dict['train_loss'].append(losses.avg)
            print('--------Training Results Summary------')
            print(f'Epoch: {epoch}, train_loss: {losses.avg}')
        return result_dict
    <mask token>


class Evaluator:

    def __init__(self, model):
        self.model = model

    def evaluate(self, epoch, valid_loader, result_dict):
        self.model.eval()
        losses = AverageMeter()
        with torch.no_grad():
            for batch_idx, batch in enumerate(valid_loader):
                input_ids, = batch['input_ids'].to(config.DEVICE)
                attention_mask = batch['attention_mask'].to(config.DEVICE)
                token_type_ids = batch['token_type_ids'].to(config.DEVICE)
                model = model.to(config.DEVICE)
                loss = self._eval_loop_for_one_step(input_ids,
                    attention_mask, token_type_ids)
                losses.update(loss.item(), input_ids.size(0))
                print('----------Validation Results Summary---------')
                print(f'Epoch: {epoch}, valid_loss: {losses.avg}')
                result_dict['valid_loss'].append(losses.avg)
        return result_dict

    def _eval_loop_for_one_step(self, input_ids, attention_mask, token_type_ids
        ):
        outputs = self.model(input_ids, attention_mask, token_type_ids)
        loss, logits = outputs[:2]
        return loss

<mask token>


class Trainer:

    def __init__(self, model, log_interval, eval_internal, epochs,
        optimizer, lr_scheduler, model_dir):
        self.model = model
        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        self.epochs = epochs
        self.log_interval = log_interval
        self.eval_interval = eval_internal
        self.model_dir = model_dir
        self.evaluator = Evaluator(self.model)

    def train(self, train_loader, valid_loader, result_dict, fold):
        for epoch in range(self.epochs):
            result_dict['epoch'] = epoch
            result_dict = self._train_loop_for_one_epoch(epoch=epoch, model
                =self.model, train_loader=train_loader, valid_loader=
                valid_loader, optimizer=self.optimizer, lr_scheduler=self.
                lr_scheduler, result_dict=result_dict)
        print(f'Saving model in {self.model_dir}....')
        torch.save(self.model.state_dict(),
            f'{self.model_dir}/model-fold-{fold}_dict')
        return result_dict

    def _train_loop_for_one_epoch(self, epoch, train_loader, valid_loader,
        result_dict):
        self.model.train()
        losses = AverageMeter()
        for batch_idx, batch in enumerate(train_loader):
            input_ids, = batch['input_ids'].to(config.DEVICE)
            attention_mask = batch['attention_mask'].to(config.DEVICE)
            token_type_ids = batch['token_type_ids'].to(config.DEVICE)
            label = batch['label'].to(config.DEVICE)
            model = model.to(config.DEVICE)
            loss = self._train_loop_for_one_step(input_ids, attention_mask,
                token_type_ids, label)
            losses.update(loss.item(), input_ids.size(0))
            if batch_idx % self.log_interval == 0:
                print(
                    f'Epoch={epoch}, Avg Loss={losses.avg}, Batch Idx={batch_idx}'
                    )
            if batch_idx % self.eval_interval == 0:
                result_dict = self.evaluator.evaluate(valid_loader=
                    valid_loader, result_dict=result_dict, epoch=epoch)
            result_dict['train_loss'].append(losses.avg)
            print('--------Training Results Summary------')
            print(f'Epoch: {epoch}, train_loss: {losses.avg}')
        return result_dict

    def _train_loop_for_one_step(self, input_ids, attention_mask,
        token_type_ids, label):
        self.optimizer.zero_grad()
        outputs = self.model(input_ids, attention_mask, token_type_ids, label)
        loss, logits = outputs[:2]
        loss.backward()
        self.optimizer.step()
        return loss


class Evaluator:

    def __init__(self, model):
        self.model = model

    def evaluate(self, epoch, valid_loader, result_dict):
        self.model.eval()
        losses = AverageMeter()
        with torch.no_grad():
            for batch_idx, batch in enumerate(valid_loader):
                input_ids, = batch['input_ids'].to(config.DEVICE)
                attention_mask = batch['attention_mask'].to(config.DEVICE)
                token_type_ids = batch['token_type_ids'].to(config.DEVICE)
                model = model.to(config.DEVICE)
                loss = self._eval_loop_for_one_step(input_ids,
                    attention_mask, token_type_ids)
                losses.update(loss.item(), input_ids.size(0))
                print('----------Validation Results Summary---------')
                print(f'Epoch: {epoch}, valid_loss: {losses.avg}')
                result_dict['valid_loss'].append(losses.avg)
        return result_dict

    def _eval_loop_for_one_step(self, input_ids, attention_mask, token_type_ids
        ):
        outputs = self.model(input_ids, attention_mask, token_type_ids)
        loss, logits = outputs[:2]
        return loss

import torch
import config
import torch.nn as nn

from metrics import AverageMeter


class Trainer:

    def __init__(self, model, log_interval, eval_internal, epochs, 
                optimizer, lr_scheduler, model_dir):
        self.model = model
        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        self.epochs = epochs
        self.log_interval = log_interval
        self.eval_interval = eval_internal
        self.model_dir = model_dir
        self.evaluator = Evaluator(self.model)


    def train(self, train_loader, valid_loader, result_dict, fold):
        for epoch in range(self.epochs):
            result_dict["epoch"] = epoch
            result_dict = self._train_loop_for_one_epoch(
                epoch=epoch,
                model=self.model,
                train_loader=train_loader,
                valid_loader=valid_loader,
                optimizer=self.optimizer,
                lr_scheduler=self.lr_scheduler,
                result_dict=result_dict
            )
        
        print(f"Saving model in {self.model_dir}....")
        torch.save(self.model.state_dict(), f'{self.model_dir}/model-fold-{fold}_dict')
        
        return result_dict

    def _train_loop_for_one_epoch(self, epoch, train_loader, valid_loader, result_dict):
        self.model.train()
        losses = AverageMeter()
        for batch_idx, batch in enumerate(train_loader):
            input_ids, = batch["input_ids"].to(config.DEVICE)
            attention_mask = batch["attention_mask"].to(config.DEVICE)
            token_type_ids = batch["token_type_ids"].to(config.DEVICE)
            label = batch["label"].to(config.DEVICE)
            model = model.to(config.DEVICE)

            loss = self._train_loop_for_one_step(
                input_ids,
                attention_mask,
                token_type_ids,
                label
            )
            losses.update(loss.item(), input_ids.size(0))

            if batch_idx % self.log_interval == 0:
                print(f"Epoch={epoch}, Avg Loss={losses.avg}, Batch Idx={batch_idx}")

            if batch_idx % self.eval_interval == 0:
                result_dict = self.evaluator.evaluate(
                    valid_loader=valid_loader,
                    result_dict=result_dict,
                    epoch=epoch
                )
            
            result_dict["train_loss"].append(losses.avg)
            print("--------Training Results Summary------")
            print(f"Epoch: {epoch}, train_loss: {losses.avg}")
        return result_dict
    
    def _train_loop_for_one_step(self, input_ids, attention_mask, token_type_ids, label):
        self.optimizer.zero_grad()
        outputs = self.model(input_ids, attention_mask, token_type_ids, label)
        loss, logits = outputs[:2]
        loss.backward()
        self.optimizer.step()
        return loss



class Evaluator:

    def __init__(self, model):
        self.model = model

    def evaluate(self, epoch, valid_loader, result_dict):
        self.model.eval()
        losses = AverageMeter()
        with torch.no_grad():
            for batch_idx, batch in enumerate(valid_loader):
                input_ids, = batch["input_ids"].to(config.DEVICE)
                attention_mask = batch["attention_mask"].to(config.DEVICE)
                token_type_ids = batch["token_type_ids"].to(config.DEVICE)
                model = model.to(config.DEVICE)

                loss = self._eval_loop_for_one_step(
                    input_ids,
                    attention_mask,
                    token_type_ids
                )
                losses.update(loss.item(), input_ids.size(0))
                print("----------Validation Results Summary---------")
                print(f"Epoch: {epoch}, valid_loss: {losses.avg}")
                result_dict["valid_loss"].append(losses.avg)

        return result_dict

    def _eval_loop_for_one_step(self, input_ids, attention_mask, token_type_ids):
        outputs = self.model(input_ids, attention_mask, token_type_ids)
        loss, logits = outputs[:2]
        return loss