text: """ Code for the MAML algorithm and network definitions. """
  from __future__ import print_function
  import numpy as np
  import sys
  import tensorflow as tf
  try:
      import special_grads
  except KeyError as e:
      print('WARN: Cannot define MaxPoolGrad, likely already defined for this version of tensorflow: %s' % e,
            file=sys.stderr)
  
  from tensorflow.python.platform import flags
  from utils import mse, xent, conv_block, normalize
  
  FLAGS = flags.FLAGS
  
  class MAML:
      def __init__(self, dim_input=1, dim_output=1, test_num_updates=5):
          """ must call construct_model() after initializing MAML! """
          self.dim_input = dim_input
          self.dim_output = dim_output
          self.update_lr = FLAGS.update_lr
          self.meta_lr = tf.placeholder_with_default(FLAGS.meta_lr, ())
          self.classification = False
          self.test_num_updates = test_num_updates
          if FLAGS.datasource == 'sinusoid':
              self.dim_hidden = [40, 40]
              self.loss_func = mse
              self.forward = self.forward_fc
              self.construct_weights = self.construct_fc_weights
          elif FLAGS.datasource == 'omniglot' or FLAGS.datasource == 'miniimagenet':
              self.loss_func = xent
              self.classification = True
              if FLAGS.conv:
                  self.dim_hidden = FLAGS.num_filters
                  self.forward = self.forward_conv
                  self.construct_weights = self.construct_conv_weights
              else:
                  self.dim_hidden = [256, 128, 64, 64]
                  self.forward=self.forward_fc
                  self.construct_weights = self.construct_fc_weights
              if FLAGS.datasource == 'miniimagenet':
                  self.channels = 3
              else:
                  self.channels = 1
              self.img_size = int(np.sqrt(self.dim_input/self.channels))
          else:
              raise ValueError('Unrecognized data source.')
  
      def construct_model(self, input_tensors=None, prefix='metatrain_'):
          # a: training data for inner gradient, b: test data for meta gradient
          if input_tensors is None:
              self.inputa = tf.placeholder(tf.float32)
              self.inputb = tf.placeholder(tf.float32)
              self.labela = tf.placeholder(tf.float32)
              self.labelb = tf.placeholder(tf.float32)
          else:
              self.inputa = input_tensors['inputa']
              self.inputb = input_tensors['inputb']
              self.labela = input_tensors['labela']
              self.labelb = input_tensors['labelb']
  
          with tf.variable_scope('model', reuse=None) as training_scope:
              if 'weights' in dir(self):
                  training_scope.reuse_variables()
                  weights = self.weights
              else:
                  # Define the weights
                  self.weights = weights = self.construct_weights()
                  self.task_weights = weights_task = self.construct_weights()
  
              # outputbs[i] and lossesb[i] is the output and loss after i+1 gradient updates
              lossesa, outputas, lossesb, outputbs = [], [], [], []
              accuraciesa, accuraciesb = [], []
              num_updates = max(self.test_num_updates, FLAGS.num_updates)
              outputbs = [[]]*num_updates
              lossesb = [[]]*num_updates
              accuraciesb = [[]]*num_updates
  
              def task_metalearn(inp, reuse=True):
                  """ Perform gradient descent for one task in the meta-batch. """
                  inputa, inputb, labela, labelb = inp
                  task_outputbs, task_lossesb = [], []
  
                  if self.classification:
                      task_accuraciesb = []
  
                  task_outputa = self.forward(inputa, weights, reuse=reuse)  # only reuse on the first iter
                  task_lossa = self.loss_func(task_outputa, labela)
  
                  grads = tf.gradients(task_lossa, list(weights.values()))
                  if FLAGS.stop_grad:
                      grads = [tf.stop_gradient(grad) for grad in grads]
                  gradients = dict(zip(weights.keys(), grads))
                  fast_weights = dict(zip(weights.keys(), [weights[key] - self.update_lr*gradients[key] for key in weights.keys()]))
                  output = self.forward(inputb, fast_weights, reuse=True) + self.forward(input_b, task_weights)
                  task_outputbs.append(output)
                  task_lossesb.append(self.loss_func(output, labelb))
  
                  for j in range(num_updates - 1):
                      loss = self.loss_func(self.forward(inputa, fast_weights, reuse=True), labela)
                      grads = tf.gradients(loss, list(fast_weights.values()))
                      if FLAGS.stop_grad:
                          grads = [tf.stop_gradient(grad) for grad in grads]
                      gradients = dict(zip(fast_weights.keys(), grads))
                      fast_weights = dict(zip(fast_weights.keys(), [fast_weights[key] - self.update_lr*gradients[key] for key in fast_weights.keys()]))
                      output = self.forward(inputb, fast_weights, reuse=True) + self.forward(input_b, task_weights)
                      task_outputbs.append(output)
                      task_lossesb.append(self.loss_func(output, labelb))
  
                  task_output = [task_outputa, task_outputbs, task_lossa, task_lossesb]
  
                  if self.classification:
                      task_accuracya = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputa), 1), tf.argmax(labela, 1))
                      for j in range(num_updates):
                          task_accuraciesb.append(tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputbs[j]), 1), tf.argmax(labelb, 1)))
                      task_output.extend([task_accuracya, task_accuraciesb])
  
                  return task_output
  
              if FLAGS.norm is not 'None':
                  # to initialize the batch norm vars, might want to combine this, and not run idx 0 twice.
                  unused = task_metalearn((self.inputa[0], self.inputb[0], self.labela[0], self.labelb[0]), False)
  
              out_dtype = [tf.float32, [tf.float32]*num_updates, tf.float32, [tf.float32]*num_updates]
              if self.classification:
                  out_dtype.extend([tf.float32, [tf.float32]*num_updates])
              result = tf.map_fn(task_metalearn, elems=(self.inputa, self.inputb, self.labela, self.labelb), dtype=out_dtype, parallel_iterations=FLAGS.meta_batch_size)
              if self.classification:
                  outputas, outputbs, lossesa, lossesb, accuraciesa, accuraciesb = result
              else:
                  outputas, outputbs, lossesa, lossesb  = result
  
          ## Performance & Optimization
          if 'train' in prefix:
              self.total_loss1 = total_loss1 = tf.reduce_sum(lossesa) / tf.to_float(FLAGS.meta_batch_size)
              self.total_losses2 = total_losses2 = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
              # after the map_fn
              self.outputas, self.outputbs = outputas, outputbs
              if self.classification:
                  self.total_accuracy1 = total_accuracy1 = tf.reduce_sum(accuraciesa) / tf.to_float(FLAGS.meta_batch_size)
                  self.total_accuracies2 = total_accuracies2 = [tf.reduce_sum(accuraciesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
              self.pretrain_op = tf.train.AdamOptimizer(self.meta_lr).minimize(total_loss1)
  
              if FLAGS.metatrain_iterations > 0:
                  optimizer = tf.train.AdamOptimizer(self.meta_lr)
                  self.gvs = gvs = optimizer.compute_gradients(self.total_losses2[FLAGS.num_updates-1])
                  if FLAGS.datasource == 'miniimagenet':
                      gvs = [(tf.clip_by_value(grad, -10, 10), var) for grad, var in gvs]
                  self.metatrain_op = optimizer.apply_gradients(gvs)
          else:
              self.metaval_total_loss1 = total_loss1 = tf.reduce_sum(lossesa) / tf.to_float(FLAGS.meta_batch_size)
              self.metaval_total_losses2 = total_losses2 = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
              if self.classification:
                  self.metaval_total_accuracy1 = total_accuracy1 = tf.reduce_sum(accuraciesa) / tf.to_float(FLAGS.meta_batch_size)
                  self.metaval_total_accuracies2 = total_accuracies2 =[tf.reduce_sum(accuraciesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
  
          ## Summaries
          tf.summary.scalar(prefix+'Pre-update loss', total_loss1)
          if self.classification:
              tf.summary.scalar(prefix+'Pre-update accuracy', total_accuracy1)
  
          for j in range(num_updates):
              tf.summary.scalar(prefix+'Post-update loss, step ' + str(j+1), total_losses2[j])
              if self.classification:
                  tf.summary.scalar(prefix+'Post-update accuracy, step ' + str(j+1), total_accuracies2[j])
  
      ### Network construction functions (fc networks and conv networks)
      def construct_fc_weights(self):
          weights = {}
          weights['w1'] = tf.Variable(tf.truncated_normal([self.dim_input, self.dim_hidden[0]], stddev=0.01))
          weights['b1'] = tf.Variable(tf.zeros([self.dim_hidden[0]]))
          for i in range(1,len(self.dim_hidden)):
              weights['w'+str(i+1)] = tf.Variable(tf.truncated_normal([self.dim_hidden[i-1], self.dim_hidden[i]], stddev=0.01))
              weights['b'+str(i+1)] = tf.Variable(tf.zeros([self.dim_hidden[i]]))
          weights['w'+str(len(self.dim_hidden)+1)] = tf.Variable(tf.truncated_normal([self.dim_hidden[-1], self.dim_output], stddev=0.01))
          weights['b'+str(len(self.dim_hidden)+1)] = tf.Variable(tf.zeros([self.dim_output]))
          return weights
  
      def forward_fc(self, inp, weights, reuse=False):
          hidden = normalize(tf.matmul(inp, weights['w1']) + weights['b1'], activation=tf.nn.relu, reuse=reuse, scope='0')
          for i in range(1,len(self.dim_hidden)):
              hidden = normalize(tf.matmul(hidden, weights['w'+str(i+1)]) + weights['b'+str(i+1)], activation=tf.nn.relu, reuse=reuse, scope=str(i+1))
          return tf.matmul(hidden, weights['w'+str(len(self.dim_hidden)+1)]) + weights['b'+str(len(self.dim_hidden)+1)]
  
      def construct_conv_weights(self):
          weights = {}
  
          dtype = tf.float32
          conv_initializer =  tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
          fc_initializer =  tf.contrib.layers.xavier_initializer(dtype=dtype)
          k = 3
  
          weights['conv1'] = tf.get_variable('conv1', [k, k, self.channels, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
          weights['b1'] = tf.Variable(tf.zeros([self.dim_hidden]))
          weights['conv2'] = tf.get_variable('conv2', [k, k, self.dim_hidden, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
          weights['b2'] = tf.Variable(tf.zeros([self.dim_hidden]))
          weights['conv3'] = tf.get_variable('conv3', [k, k, self.dim_hidden, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
          weights['b3'] = tf.Variable(tf.zeros([self.dim_hidden]))
          weights['conv4'] = tf.get_variable('conv4', [k, k, self.dim_hidden, self.dim_hidden], initializer=conv_initializer, dtype=dtype)
          weights['b4'] = tf.Variable(tf.zeros([self.dim_hidden]))
          if FLAGS.datasource == 'miniimagenet':
              # assumes max pooling
              weights['w5'] = tf.get_variable('w5', [self.dim_hidden*5*5, self.dim_output], initializer=fc_initializer)
              weights['b5'] = tf.Variable(tf.zeros([self.dim_output]), name='b5')
          else:
              weights['w5'] = tf.Variable(tf.random_normal([self.dim_hidden, self.dim_output]), name='w5')
              weights['b5'] = tf.Variable(tf.zeros([self.dim_output]), name='b5')
          return weights
  
      def forward_conv(self, inp, weights, reuse=False, scope=''):
          # reuse is for the normalization parameters.
          channels = self.channels
          inp = tf.reshape(inp, [-1, self.img_size, self.img_size, channels])
  
          hidden1 = conv_block(inp, weights['conv1'], weights['b1'], reuse, scope+'0')
          hidden2 = conv_block(hidden1, weights['conv2'], weights['b2'], reuse, scope+'1')
          hidden3 = conv_block(hidden2, weights['conv3'], weights['b3'], reuse, scope+'2')
          hidden4 = conv_block(hidden3, weights['conv4'], weights['b4'], reuse, scope+'3')
          if FLAGS.datasource == 'miniimagenet':
              # last hidden layer is 6x6x64-ish, reshape to a vector
              hidden4 = tf.reshape(hidden4, [-1, np.prod([int(dim) for dim in hidden4.get_shape()[1:]])])
          else:
              hidden4 = tf.reduce_mean(hidden4, [1, 2])
  
          return tf.matmul(hidden4, weights['w5']) + weights['b5']
  
  
  