text: #!/usr/bin/env python3
  
  """
  Demonstrates how to:
      * use the MAML wrapper for fast-adaptation,
      * use the benchmark interface to load Omniglot, and
      * sample tasks and split them in adaptation and evaluation sets.
  """
  
  import argparse
  import wandb
  import random
  import numpy as np
  import torch
  import random
  import numpy as np
  import torch
  import learn2learn as l2l
  from torch import nn, optim
  
  
  
  def main(
          ways=5,
          shots=1,
          meta_lr=0.003,
          fast_lr=0.5,
          meta_batch_size=32,
          adaptation_steps=1,
          num_iterations=60000,
          cuda=True,
          seed=42,
  ):
      random.seed(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      device = torch.device('cpu')
      if cuda:
          torch.cuda.manual_seed(seed)
          device = torch.device('cuda')
  
      # Load train/validation/test tasksets using the benchmark interface
      tasksets = l2l.vision.benchmarks.get_tasksets('omniglot',
                                                    train_ways=ways,
                                                    train_samples=2 * shots,
                                                    test_ways=ways,
                                                    test_samples=2 * shots,
                                                    num_tasks=20000,
                                                    root='~/data',
                                                    )
  
      # Create model
      model = l2l.vision.models.OmniglotFC(28 ** 2, ways)
      model.to(device)
      maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
      opt = optim.Adam(maml.parameters(), meta_lr)
      loss = nn.CrossEntropyLoss(reduction='mean')
  
      for iteration in range(num_iterations):
          opt.zero_grad()
          meta_train_error = 0.0
          meta_train_accuracy = 0.0
          meta_valid_error = 0.0
          meta_valid_accuracy = 0.0
          for task in range(meta_batch_size):
              # Compute meta-training loss
              learner = maml.clone()
              batch = tasksets.train.sample()
              evaluation_error, evaluation_accuracy = fast_adapt(batch,
                                                                 learner,
                                                                 loss,
                                                                 adaptation_steps,
                                                                 shots,
                                                                 ways,
                                                                 device)
              evaluation_error.backward()
              meta_train_error += evaluation_error.item()
              meta_train_accuracy += evaluation_accuracy.item()
  
              # Compute meta-validation loss
              learner = maml.clone()
              batch = tasksets.validation.sample()
              evaluation_error, evaluation_accuracy = fast_adapt(batch,
                                                                 learner,
                                                                 loss,
                                                                 adaptation_steps,
                                                                 shots,
                                                                 ways,
                                                                 device)
              meta_valid_error += evaluation_error.item()
              meta_valid_accuracy += evaluation_accuracy.item()
  
          # Print some metrics
          print('\n')
          print('Iteration', iteration)
          print('Meta Train Error', meta_train_error / meta_batch_size)
          print('Meta Train Accuracy', meta_train_accuracy / meta_batch_size)
          print('Meta Valid Error', meta_valid_error / meta_batch_size)
          print('Meta Valid Accuracy', meta_valid_accuracy / meta_batch_size)
  
          # Average the accumulated gradients and optimize
          for p in maml.parameters():
              p.grad.data.mul_(1.0 / meta_batch_size)
          opt.step()
  
      meta_test_error = 0.0
      meta_test_accuracy = 0.0
      for task in range(meta_batch_size):
          # Compute meta-testing loss
          learner = maml.clone()
          batch = tasksets.test.sample()
          evaluation_error, evaluation_accuracy = fast_adapt(batch,
                                                             learner,
                                                             loss,
                                                             adaptation_steps,
                                                             shots,
                                                             ways,
                                                             device)
          meta_test_error += evaluation_error.item()
          meta_test_accuracy += evaluation_accuracy.item()
      print('Meta Test Error', meta_test_error / meta_batch_size)
      print('Meta Test Accuracy', meta_test_accuracy / meta_batch_size)
  
  
  
  if __name__ == '__main__':
  
      print(args)
      # main()
  