text: import logging
  import datetime
  import pathlib
  import os
  import numpy as np
  import pandas as pd
  from typing import Any, Dict, List
  import time
  
  from base import BaseModel
  
  logging.basicConfig()
  logging.root.setLevel(logging.INFO)
  logger = logging.getLogger("datamodeler")
  
  import hydra
  from omegaconf import DictConfig
  
  from model_loader import available_models
  
  ## Add a local simulator in a `sim` folder to validate data-driven model
  ## Example: Moab from a Microsoft Bonsai
  ## from sim.moab.moab_main import SimulatorSession,  env_setup
  
  dir_path = os.path.dirname(os.path.realpath(__file__))
  env_name = "DDM"
  log_path = "logs"
  
  
  class Simulator(BaseModel):
      def __init__(
          self,
          model,
          states: List[str],
          actions: List[str],
          configs: List[str],
          log_file: str = None,
          diff_state: bool = False,
          sim_orig=None,
      ):
          self.dd_model = model
          self.features = states + actions + configs
          self.labels = states
          self.config_keys = configs
          self.state_keys = states
          self.action_keys = actions
          self.sim_orig = sim_orig  # include simulator function if comparing to simulator
          self.diff_state = diff_state
          if log_file == "enable":
              current_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
              log_file = os.path.join(
                  log_path, current_time + "_" + env_name + "_log.csv"
              )
              log_file2 = os.path.join(
                  log_path, current_time + "_" + "SIMDATA" + "_log.csv"
              )
              logs_directory = pathlib.Path(log_file).parent.absolute()
              if not pathlib.Path(logs_directory).exists():
                  print(
                      "Directory does not exist at {0}, creating now...".format(
                          str(logs_directory)
                      )
                  )
                  logs_directory.mkdir(parents=True, exist_ok=True)
          else:
              log_file2 = None
          self.log_file = log_file
          self.log_file2 = log_file2
  
      def episode_start(self, config: Dict[str, Any] = None):
          if config:
              self.config = config
          else:
              # configs randomized here. Need to decide a single place for configs
              # range either in main.py or in simulator configs
              self.config = {j: np.random.uniform(-1, 1) for j in self.config_keys}
          if self.sim_orig:
              # Assign same state as would be used by simulator
              self.sim_orig.episode_start(config)
              _fullstate = self.sim_orig.get_state()
              # idempotent dict comprehension for sims with prefix in configurations
              self.state = {
                  j: _fullstate[l]
                  for j in self.state_keys
                  for l in _fullstate.keys()
                  if l in j
              }
          else:
              # randomized state here need to be changed to appropriate ranges of each state
              # see Table of All Data (TOAD) from Discovery Session
              self.state = {j: np.random.uniform(-0.1, 0.1) for j in self.state_keys}
  
      def episode_step(self, action: Dict[str, int]):
  
          input_list = [
              list(
                  self.state.values()
              ),  # replace by self.sim_orig.state.values() for per iteration
              list(self.config.values()),
              list(action.values()),
          ]
  
          input_array = [item for subl in input_list for item in subl]
          X = np.array(input_array).reshape(1, -1)
          if self.diff_state:
              preds = np.array(list(self.state.values())) + self.dd_model.predict(
                  X
              )  # compensating for output being delta state st+1-st
              # preds = np.array(list(simstate))+self.dd_model.predict(X) # if doing per iteration prediction of delta state st+1-st
          else:
              preds = self.dd_model.predict(X)  # absolute prediction
          self.state = dict(zip(self.features, preds.reshape(preds.shape[1]).tolist()))
          return self.state
  
      def get_state(self):
          return self.state
  
      def get_sim_state(self):
          if self.sim_orig:
              _fullstate = self.sim_orig.get_state()
              # idempotent dict comprehension for sims with prefix in configurations
              return {
                  j: _fullstate[l]
                  for j in self.state_keys
                  for l in _fullstate.keys()
                  if l in j
              }
          else:
              return self.state
  
      def get_init_sim_actions(self, _init_actions=None):
          # If simulator exists, get initial actions from the sim
          if self.sim_orig:
              _fullstate = self.sim_orig.get_state()
              _init_actions = {
                  j: _fullstate[l]
                  for j in self.action_keys
                  for l in _fullstate.keys()
                  if l in j
              }
          if not _init_actions:
              # If simulator is unavailable or does not have initial actions, assign
              # random actions, with range same as actuator limits. See TOAD from discovery
              _init_actions = {j: np.random.uniform(-1, 1) for j in self.action_keys}
          return _init_actions
  
      def test_policies(self, policy, action):
          if policy == "random":
              return {j: np.random.uniform(-1, 1) for j in self.action_keys}
          elif policy == "zeros":
              return {j: 0 for j in self.action_keys}
          else:  # coasting is default policy - no change in actions
              return action
          # TO DO: Add benchmark policy or other case specific scenarios
  
      def halted(self):
          pass
  
      def log_iterations(
          self, state, action, fname: str, episode: int = 0, iteration: int = 1
      ):
          """Log iterations during training to a CSV.
  
          Parameters
          ----------
          state : Dict
          action : Dict
          episode : int, optional
          iteration : int, optional
          """
          config = self.config
          data = {**state, **action, **config}
          data["episode"] = episode
          data["iteration"] = iteration
          log_df = pd.DataFrame(data, index=[0])
  
          if os.path.exists(fname):
              log_df.to_csv(path_or_buf=fname, mode="a", header=False, index=False)
          else:
              log_df.to_csv(path_or_buf=fname, mode="w", header=True, index=False)
  
  
  def test_sim_model(
      num_episodes: int = 100,
      num_iterations: int = 250,
      log_iterations: bool = True,
      sim: Simulator = None,
  ):
      """Test a policy using random actions over a fixed number of episodes
  
      Parameters
      ----------
      num_episodes : int, optional
          number of iterations to run, by default 10
      """
      for episode in range(num_episodes):
          iteration = 0
          terminal = False
          sim.episode_start()
          ddm_state = sim.get_state()
          sim_state = sim.get_sim_state()
          # it is important to know initial actions for evolution of the dynamics
          # action = random_action()
  
          action = sim.get_init_sim_actions()
          if log_iterations:
              sim.log_iterations(ddm_state, action, sim.log_file, episode, iteration)
              if sim.sim_orig:
                  sim.log_iterations(sim_state, action, sim.log_file2, episode, iteration)
          while not terminal:
              action = sim.test_policies("random", action)
              # sim iteration
              sim.episode_step(action)
              ddm_state = sim.get_state()
              if sim.sim_orig:
                  sim.sim_orig.episode_step(action)
                  sim_state = sim.get_sim_state()
              iteration += 1
              if log_iterations:
                  sim.log_iterations(ddm_state, action, sim.log_file, episode, iteration)
                  if sim.sim_orig:
                      sim.log_iterations(
                          sim_state, action, sim.log_file2, episode, iteration
                      )
              print(f"Running iteration #{iteration} for episode #{episode}")
              print(f"Observations for Sim: {sim_state}")
              print(f"Observations for Data: {ddm_state}")
              # Add additional terminal conditions if required. Here only time-out is used.
              terminal = iteration >= num_iterations
  
      return sim
  
  
  @hydra.main(config_path="conf", config_name="config")
  def main(cfg: DictConfig):
  
      save_path = cfg["model"]["saver"][0]["filename"]
      if cfg["data"]["full_or_relative"] == "relative":
          save_path = os.path.join(dir_path, save_path)
      model_name = cfg["model"]["name"]
      states = cfg["simulator"]["states"]
      actions = cfg["simulator"]["actions"]
      configs = cfg["simulator"]["configs"]
      policy = cfg["simulator"]["policy"]
      logflag = cfg["simulator"]["logging"]
      scale_data = cfg["model"]["build_params"][7]["scale_data"]
      diff_state = cfg["data"]["diff_state"]
  
      logger.info(f"Training with a new {policy} policy")
  
      ddModel = available_models[model_name]
      model = ddModel()
  
      model.build_model(**cfg["model"]["build_params"])
      model.load_model(filename=save_path, scale_data=scale_data)
  
      # Grab standardized way to interact with sim API
      sim = Simulator(model, states, actions, configs, logflag, diff_state)
  
      test_sim_model(2, 250, logflag, sim)
  
      return sim
  
  
  if __name__ == "__main__":
  
      main()
  