text: """
  Example template for defining a system
  """
  import os
  from collections import OrderedDict
  import torch.nn as nn
  from torchvision.datasets import MNIST
  import torchvision.transforms as transforms
  import torch
  import torch.nn.functional as F
  from test_tube import HyperOptArgumentParser
  from torch import optim
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  
  import pytorch_lightning as pl
  from pytorch_lightning.root_module.root_module import LightningModule
  
  
  class LightningTemplateModel(LightningModule):
      """
      Sample model to show how to define a template
      """
  
      def __init__(self, hparams):
          """
          Pass in parsed HyperOptArgumentParser to the model
          :param hparams:
          """
          # init superclass
          super(LightningTemplateModel, self).__init__()
          self.hparams = hparams
  
          self.batch_size = hparams.batch_size
  
          # if you specify an example input, the summary will show input/output for each layer
          self.example_input_array = torch.rand(5, 28 * 28)
  
          # build model
          self.__build_model()
  
      # ---------------------
      # MODEL SETUP
      # ---------------------
      def __build_model(self):
          """
          Layout model
          :return:
          """
          self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
                                out_features=self.hparams.hidden_dim)
          self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
          self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
  
          self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
                                out_features=self.hparams.out_features)
  
      # ---------------------
      # TRAINING
      # ---------------------
      def forward(self, x):
          """
          No special modification required for lightning, define as you normally would
          :param x:
          :return:
          """
  
          x = self.c_d1(x)
          x = torch.tanh(x)
          x = self.c_d1_bn(x)
          x = self.c_d1_drop(x)
  
          x = self.c_d2(x)
          logits = F.log_softmax(x, dim=1)
  
          return logits
  
      def loss(self, labels, logits):
          nll = F.nll_loss(logits, labels)
          return nll
  
      def training_step(self, data_batch, batch_i):
          """
          Lightning calls this inside the training loop
          :param data_batch:
          :return:
          """
          # forward pass
          x, y = data_batch
          x = x.view(x.size(0), -1)
  
          y_hat = self.forward(x)
  
          # calculate loss
          loss_val = self.loss(y, y_hat)
  
          # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
          if self.trainer.use_dp:
              loss_val = loss_val.unsqueeze(0)
  
          output = OrderedDict({
              'loss': loss_val
          })
  
          # can also return just a scalar instead of a dict (return loss_val)
          return output
  
      def validation_step(self, data_batch, batch_i):
          """
          Lightning calls this inside the validation loop
          :param data_batch:
          :return:
          """
          x, y = data_batch
          x = x.view(x.size(0), -1)
          y_hat = self.forward(x)
  
          loss_val = self.loss(y, y_hat)
  
          # acc
          labels_hat = torch.argmax(y_hat, dim=1)
          val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
          val_acc = torch.tensor(val_acc)
  
          if self.on_gpu:
              val_acc = val_acc.cuda(loss_val.device.index)
  
          # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
          if self.trainer.use_dp:
              loss_val = loss_val.unsqueeze(0)
              val_acc = val_acc.unsqueeze(0)
  
          output = OrderedDict({
              'val_loss': loss_val,
              'val_acc': val_acc,
          })
  
          # can also return just a scalar instead of a dict (return loss_val)
          return output
  
      def validation_end(self, outputs):
          """
          Called at the end of validation to aggregate outputs
          :param outputs: list of individual outputs of each validation step
          :return:
          """
          # if returned a scalar from validation_step, outputs is a list of tensor scalars
          # we return just the average in this case (if we want)
          # return torch.stack(outputs).mean()
  
          val_loss_mean = 0
          val_acc_mean = 0
          for output in outputs:
              val_loss = output['val_loss']
  
              # reduce manually when using dp
              if self.trainer.use_dp:
                  val_loss = torch.mean(val_loss)
              val_loss_mean += val_loss
  
              # reduce manually when using dp
              val_acc = output['val_acc']
              if self.trainer.use_dp:
                  val_acc = torch.mean(val_acc)
  
              val_acc_mean += val_acc
  
          val_loss_mean /= len(outputs)
          val_acc_mean /= len(outputs)
          tqdm_dic = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}
          return tqdm_dic
  
      # ---------------------
      # TRAINING SETUP
      # ---------------------
      def configure_optimizers(self):
          """
          return whatever optimizers we want here
          :return: list of optimizers
          """
          optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
          scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
          return [optimizer], [scheduler]
  
      def __dataloader(self, train):
          # init data generators
          transform = transforms.Compose([transforms.ToTensor(),
                                          transforms.Normalize((0.5,), (1.0,))])
          dataset = MNIST(root=self.hparams.data_root, train=train,
                          transform=transform, download=True)
  
          # when using multi-node (ddp) we need to add the datasampler
          train_sampler = None
          batch_size = self.hparams.batch_size
  
          if self.use_ddp:
              train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
              batch_size = batch_size // self.trainer.world_size  # scale batch size
  
          should_shuffle = train_sampler is None
          loader = DataLoader(
              dataset=dataset,
              batch_size=batch_size,
              shuffle=should_shuffle,
              sampler=train_sampler
          )
  
          return loader
  
      @pl.data_loader
      def tng_dataloader(self):
          print('tng data loader called')
          return self.__dataloader(train=True)
  
      @pl.data_loader
      def val_dataloader(self):
          print('val data loader called')
          return self.__dataloader(train=False)
  
      @pl.data_loader
      def test_dataloader(self):
          print('test data loader called')
          return self.__dataloader(train=False)
  
      @staticmethod
      def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
          """
          Parameters you define here will be available to your model through self.hparams
          :param parent_parser:
          :param root_dir:
          :return:
          """
          parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
  
          # param overwrites
          # parser.set_defaults(gradient_clip=5.0)
  
          # network params
          parser.add_argument('--in_features', default=28 * 28, type=int)
          parser.add_argument('--out_features', default=10, type=int)
          # use 500 for CPU, 50000 for GPU to see speed difference
          parser.add_argument('--hidden_dim', default=50000, type=int)
          parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
  
          # data
          parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
  
          # training params (opt)
          parser.opt_list('--learning_rate', default=0.001 * 8, type=float,
                          options=[0.0001, 0.0005, 0.001, 0.005],
                          tunable=False)
          parser.opt_list('--optimizer_name', default='adam', type=str,
                          options=['adam'], tunable=False)
  
          # if using 2 nodes with 4 gpus each the batch size here
          #  (256) will be 256 / (2*8) = 16 per gpu
          parser.opt_list('--batch_size', default=256 * 8, type=int,
                          options=[32, 64, 128, 256], tunable=False,
                          help='batch size will be divided over all gpus being used across all nodes')
          return parser
  