text: from collections import defaultdict
  import random
  from random import choice
  from time import sleep
  import time
  
  import click
  import numpy as np
  import tensorflow as tf
  from tqdm import tqdm
  
  from energypy import alpha, checkpoint, json_util, init
  from energypy import alpha, memory, policy, qfunc, random_policy, target, utils
  from energypy.sampling import sample_random, sample_test, sample_train
  from energypy.train import train
  
  
  def main(
      hyp,
      paths,
      counters,
      env,
      buffer,
      nets,
      writers,
      optimizers,
      transition_logger,
      rewards
  ):
      if 'seed' not in hyp.keys():
          hyp['seed'] = choice(range(int(1e4)))
  
      utils.set_seeds(hyp['seed'])
  
      json_util.save(hyp, paths['run'] / 'hyperparameters.json')
  
      if not buffer.full:
          sample_random(
              env,
              buffer,
              hyp,
              writers,
              counters,
              rewards,
              transition_logger,
          )
          memory.save(buffer, paths['run'] / 'random.pkl')
          memory.save(buffer, paths['experiment'] / 'random.pkl')
  
      rewards = defaultdict(list)
      for _ in range(int(hyp['n-episodes'])):
          if counters['train-episodes'] % hyp['test-every'] == 0:
              test_rewards = sample_test(
                  env,
                  buffer,
                  nets['actor'],
                  hyp,
                  writers,
                  counters,
                  rewards,
                  transition_logger
              )
  
              checkpoint.save(
                  hyp,
                  nets,
                  optimizers,
                  buffer,
                  episode=counters['test-episodes'],
                  rewards=rewards,
                  counters=counters,
                  paths=paths
              )
  
          train_rewards = sample_train(
              env,
              buffer,
              nets['actor'],
              hyp,
              writers,
              counters,
              rewards,
              transition_logger
          )
  
          train_steps = len(train_rewards) * hyp.get('episode_length', 48)
  
          print(f'training \n step {counters["train-steps"]:6.0f}, {train_steps} steps')
          for _ in tqdm(range(train_steps)):
              train(
                  buffer.sample(hyp['batch-size']),
                  nets['actor'],
                  [nets['online-1'], nets['online-2']],
                  [nets['target-1'], nets['target-2']],
                  nets['alpha'],
                  writers['train'],
                  optimizers,
                  counters,
                  hyp
              )
          utils.print_counters(counters)
  
      if counters['train-episodes'] % hyp['test-every'] == 0:
          test_rewards = sample_test(
              env,
              buffer,
              nets['actor'],
              hyp,
              writers,
              counters,
              rewards,
              transition_logger
          )
  
          checkpoint.save(
              hyp,
              nets,
              optimizers,
              buffer,
              episode=counters['test-episodes'],
              rewards=rewards,
              counters=counters,
              paths=paths
          )
  
  
  @click.command()
  @click.argument("experiment-json", nargs=1)
  @click.option("-n", "--run-name", default=None)
  @click.option("-b", "--buffer", nargs=1, default="new")
  @click.option("-s", "--seed", nargs=1, default=None)
  @click.option("-c", "--checkpoint_path", nargs=1, default=None)
  def cli(experiment_json, run_name, buffer, seed, checkpoint_path):
  
      print('cli')
      print('------')
      print(experiment_json, run_name, buffer)
      print('')
  
      hyp = json_util.load(experiment_json)
      hyp['buffer'] = buffer
  
      if run_name:
          hyp['run-name'] = run_name
  
      print('params')
      print('------')
      print(hyp)
      print('')
      sleep(2)
  
      if checkpoint_path:
          print(f'checkpointing from {checkpoint_path}')
          print('')
          main(**init.init_checkpoint(checkpoint_path))
  
      else:
          print(f'starting so fresh, so clean')
          print('')
          main(**init.init_fresh(hyp))
  
  
  if __name__ == '__main__':
      cli()
  