text: """Simple Iterative Training Strategy."""
  
  import os
  import numpy as np
  
  from .strategy import BaseStrategy
  from l2o import deserialize
  
  
  class SimpleStrategy(BaseStrategy):
      """Simple Iterative Training Strategy.
  
      Parameters
      ----------
      learner : train.OptimizerTraining
          Optimizer training wrapper.
      problems : problems.ProblemSpec[]
          List of problem specifications to train on.
  
      Keyword Args
      ------------
      validation_problems : problems.ProblemSpec[] or None.
          List of problems to validate with. If None, validates on the training
          problem set.
      validation_seed : int
          Seed for optimizee initialization during validation
      directory : str
          Directory to save weights and other data to
      name : str
          Strategy name.
      num_periods : int
          Number of periods to train for
      unroll_len : integer_schedule
          Specifies unroll length for each period.
      depth : integer_schedule
          Specifies number of outer steps per outer epoch for each period
          (number of outer steps before resetting training problem)
      epochs : integer_schedule
          Specifies number of outer epochs to run for each period.
      annealing_schedule : float_schedule
          Specifies p_teacher for each period.
      validation_epochs : int
          Number of outer epochs during validation.
      validation_depth : int
          Depth during validation.
      validation_unroll : int
          Unroll length to use for validation.
      warmup : integer_schedule
          Number of iterations for warmup; if 0, no warmup is applied.
      warmup_rate : float_schedule
          SGD Learning rate during warmup period.
      validation_warmup : int
          Number of iterations for warmup during validation.
      validation_warmup_rate : float
          SGD learning rate during warmup for validation.
      """
  
      metadata_columns = {
          "period": int,
      }
      hyperparameter_columns = {
          "warmup": int,
          "warmup_rate": float,
          "p_teacher": float,
          "depth": int,
          "unroll_len": int,
          "epochs": int,
      }
  
      def __init__(
              self, *args, num_periods=100, unroll_len=200, epochs=1,
              depth=1, annealing_schedule=0.1, validation_epochs=None,
              validation_depth=None, validation_unroll=None, warmup=0,
              warmup_rate=0.01, validation_warmup=0, validation_warmup_rate=0.01,
              name="SimpleStrategy", **kwargs):
  
          self.num_periods = num_periods
  
          def _default(val, default):
              return val if val is not None else default
  
          self.validation_epochs = _default(validation_epochs, epochs)
          self.validation_depth = _default(validation_depth, depth)
          self.validation_unroll = _default(validation_unroll, unroll_len)
  
          self.epochs = deserialize.integer_schedule(epochs, name="epochs")
          self.depth = deserialize.integer_schedule(depth, name="depth")
          self.unroll_len = deserialize.integer_schedule(
              unroll_len, name="unroll")
          self.annealing_schedule = deserialize.float_schedule(
              annealing_schedule, name="annealing")
  
          self.warmup_schedule = deserialize.integer_schedule(
              warmup, name="warmup")
          self.warmup_rate_schedule = deserialize.float_schedule(
              warmup_rate, name="warmup_rate")
          self.validation_warmup = validation_warmup
          self.validation_warmup_rate = validation_warmup_rate
  
          super().__init__(*args, name=name, **kwargs)
  
      def _path(self, period=0, dtype="checkpoint", file="test"):
          """Get file path for the given metadata."""
          return self._base_path("period_{:n}".format(period), dtype, file=file)
  
      def _resume(self):
          """Resume current optimization."""
          self.period = int(self.summary["period"].max() + 1)
  
      def _start(self):
          """Start new optimization."""
          self.period = 0
  
      def train(self):
          """Start or resume training."""
          if self.period > 0:
              self._load_network(period=self.period - 1)
  
          while self.period < self.num_periods:
  
              train_args = {
                  "unroll_len": self.unroll_len(self.period),
                  "p_teacher": self.annealing_schedule(self.period),
                  "warmup": self.warmup_schedule(self.period),
                  "warmup_rate": self.warmup_rate_schedule(self.period),
                  "depth": self.depth(self.period),
                  "epochs": self.epochs(self.period)
              }
              validation_args = {
                  "unroll_len": self.validation_unroll,
                  "p_teacher": 0,
                  "warmup": self.validation_warmup,
                  "warmup_rate": self.validation_warmup_rate,
                  "depth": self.validation_depth,
                  "epochs": self.validation_epochs
              }
              metadata = {"period": self.period}
  
              hypers = ", ".join([
                  "{}={}".format(k, train_args[k])
                  for k in self.hyperparameter_columns])
              print("\nPeriod {}: {}".format(self.period, hypers))
  
              self._training_period(train_args, validation_args, metadata)
              self.period += 1
  