text: import ast
  
  import pickle
  from copy import deepcopy
  
  import math
  
  import pandas as pd
  from typing import List, Tuple, Union
  import numpy as np
  import random
  
  from rl_coach.core_types import Transition, Episode
  from rl_coach.filters.filter import InputFilter
  from rl_coach.logger import screen
  from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
  from rl_coach.utils import ReaderWriterLock, ProgressBar
  from rl_coach.core_types import CsvDataset
  
  
  class DeepRacerMemoryParameters(MemoryParameters):
      def __init__(self):
          super().__init__()
          self.max_size = (MemoryGranularity.Transitions, 1000000)
          self.n_step = -1
          self.train_to_eval_ratio = 1  # for OPE we'll want a value < 1
  
      @property
      def path(self):
          return 'markov.memories.deepracer_memory:DeepRacerMemory'
  
  
  class DeepRacerMemory(Memory):
      """
      A replay buffer that stores episodes of transitions. The additional structure allows performing various
      calculations of total return and other values that depend on the sequential behavior of the transitions
      in the episode.
      """
  
      def __init__(self, max_size: Tuple[MemoryGranularity, int] = (MemoryGranularity.Transitions, 1000000), n_step=-1,
                   train_to_eval_ratio: int = 1):
          """
          :param max_size: the maximum number of transitions or episodes to hold in the memory
          """
          super().__init__(max_size)
          self.n_step = n_step
          self._buffer = [Episode(n_step=self.n_step)]  # list of episodes
          self.transitions = []
          self._length = 1  # the episodic replay buffer starts with a single empty episode
          self._num_transitions = 0
          self._num_transitions_in_complete_episodes = 0
          self.last_training_set_episode_id = None  # used in batch-rl
          self.last_training_set_transition_id = None  # used in batch-rl
          self.train_to_eval_ratio = train_to_eval_ratio  # used in batch-rl
          self.evaluation_dataset_as_episodes = None
          self.evaluation_dataset_as_transitions = None
  
          self.frozen = False
  
      def length(self, lock: bool = False) -> int:
          """
          Get the number of episodes in the ER (even if they are not complete)
          """
          length = self._length
          if self._length is not 0 and self._buffer[-1].is_empty():
              length = self._length - 1
  
          return length
  
      def num_complete_episodes(self):
          """ Get the number of complete episodes in ER """
          length = self._length - 1
  
          return length
  
      def num_transitions(self):
          return self._num_transitions
  
      def num_transitions_in_complete_episodes(self):
          return self._num_transitions_in_complete_episodes
  
      def get_last_training_set_episode_id(self):
          return self.last_training_set_episode_id
  
      def sample(self, size: int, is_consecutive_transitions=False) -> List[Transition]:
          """
          Sample a batch of transitions from the replay buffer. If the requested size is larger than the number
          of samples available in the replay buffer then the batch will return empty.
          :param size: the size of the batch to sample
          :param is_consecutive_transitions: if set True, samples a batch of consecutive transitions.
          :return: a batch (list) of selected transitions from the replay buffer
          """
          if self.num_complete_episodes() >= 1:
              if is_consecutive_transitions:
                  episode_idx = np.random.randint(0, self.num_complete_episodes())
                  if self._buffer[episode_idx].length() <= size:
                      batch = self._buffer[episode_idx].transitions
                  else:
                      transition_idx = np.random.randint(size, self._buffer[episode_idx].length())
                      batch = self._buffer[episode_idx].transitions[transition_idx - size:transition_idx]
              else:
                  transitions_idx = np.random.randint(self.num_transitions_in_complete_episodes(), size=size)
                  batch = [self.transitions[i] for i in transitions_idx]
  
          else:
              raise ValueError("The episodic replay buffer cannot be sampled since there are no complete episodes yet. "
                               "There is currently 1 episodes with {} transitions".format(self._buffer[0].length()))
  
          return batch
  
      def get_episode_for_transition(self, transition: Transition) -> (int, Episode):
          """
          Get the episode from which that transition came from.
          :param transition: The transition to lookup the episode for
          :return: (Episode number, the episode) or (-1, None) if could not find a matching episode.
          """
  
          for i, episode in enumerate(self._buffer):
              if transition in episode.transitions:
                  return i, episode
          return -1, None
  
      def shuffle_episodes(self):
          """
          Shuffle all the complete episodes in the replay buffer, while deleting the last non-complete episode
          :return:
          """
          self.assert_not_frozen()
  
          # unlike the standard usage of the EpisodicExperienceReplay, where we always leave an empty episode after
          # the last full one, so that new transitions will have where to be added, in this case we delibrately remove
          # that empty last episode, as we are about to shuffle the memory, and we don't want it to be shuffled in
          self.remove_last_episode(lock=False)
  
          random.shuffle(self._buffer)
          self.transitions = [t for e in self._buffer for t in e.transitions]
  
          # create a new Episode for the next transitions to be placed into
          self._buffer.append(Episode(n_step=self.n_step))
          self._length += 1
  
      def get_shuffled_training_data_generator(self, size: int) -> List[Transition]:
          """
          Get an generator for iterating through the shuffled replay buffer, for processing the data in epochs.
          If the requested size is larger than the number of samples available in the replay buffer then the batch will
          return empty. The last returned batch may be smaller than the size requested, to accommodate for all the
          transitions in the replay buffer.
  
          :param size: the size of the batch to return
          :return: a batch (list) of selected transitions from the replay buffer
          """
          shuffled_transition_indices = list(range(self.last_training_set_transition_id))
          random.shuffle(shuffled_transition_indices)
  
          # The last batch drawn will usually be < batch_size (=the size variable)
          for i in range(math.ceil(len(shuffled_transition_indices) / size)):
              sample_data = [self.transitions[j] for j in shuffled_transition_indices[i * size: (i + 1) * size]]
  
              yield sample_data
  
      def get_all_complete_episodes_transitions(self) -> List[Transition]:
          """
          Get all the transitions from all the complete episodes in the buffer
          :return: a list of transitions
          """
          return self.transitions[:self.num_transitions_in_complete_episodes()]
  
      def get_all_complete_episodes(self) -> List[Episode]:
          """
          Get all the transitions from all the complete episodes in the buffer
          :return: a list of transitions
          """
          return self.get_all_complete_episodes_from_to(0, self.num_complete_episodes())
  
      def get_all_complete_episodes_from_to(self, start_episode_id, end_episode_id) -> List[Episode]:
          """
          Get all the transitions from all the complete episodes in the buffer matching the given episode range
          :return: a list of transitions
          """
          return self._buffer[start_episode_id:end_episode_id]
  
      def _enforce_max_length(self) -> None:
          """
          Make sure that the size of the replay buffer does not pass the maximum size allowed.
          If it passes the max size, the oldest episode in the replay buffer will be removed.
          :return: None
          """
          granularity, size = self.max_size
          if granularity == MemoryGranularity.Transitions:
              while size != 0 and self.num_transitions() > size:
                  self.remove_first_episode(lock=False)
          elif granularity == MemoryGranularity.Episodes:
              while self.length() > size:
                  self.remove_first_episode(lock=False)
  
      def _update_episode(self, episode: Episode) -> None:
          episode.update_transitions_rewards_and_bootstrap_data()
  
      def verify_last_episode_is_closed(self) -> None:
          """
          Verify that there is no open episodes in the replay buffer
          :return: None
          """
          last_episode = self.get(-1, False)
          if last_episode and last_episode.length() > 0:
              self.close_last_episode(lock=False)
  
      def close_last_episode(self, lock=True) -> None:
          """
          Close the last episode in the replay buffer and open a new one
          :return: None
          """
  
          last_episode = self._buffer[-1]
  
          self._num_transitions_in_complete_episodes += last_episode.length()
          self._length += 1
  
          # create a new Episode for the next transitions to be placed into
          self._buffer.append(Episode(n_step=self.n_step))
  
          # if update episode adds to the buffer, a new Episode needs to be ready first
          # it would be better if this were less state full
          self._update_episode(last_episode)
  
          self._enforce_max_length()
  
      def store(self, transition: Transition) -> None:
          """
          Store a new transition in the memory. If the transition game_over flag is on, this closes the episode and
          creates a new empty episode.
          Warning! using the episodic memory by storing individual transitions instead of episodes will use the default
          Episode class parameters in order to create new episodes.
          :param transition: a transition to store
          :return: None
          """
          self.assert_not_frozen()
  
          # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.
          super().store(transition)
  
          if len(self._buffer) == 0:
              self._buffer.append(Episode(n_step=self.n_step))
          last_episode = self._buffer[-1]
          last_episode.insert(transition)
          self.transitions.append(transition)
          self._num_transitions += 1
          if transition.game_over:
              self.close_last_episode(False)
  
          self._enforce_max_length()
  
      def store_episode(self, episode: Episode, lock: bool = False) -> None:
          """
          Store a new episode in the memory.
          :param episode: the new episode to store
          :return: None
          """
          self.assert_not_frozen()
  
          # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.
          super().store_episode(episode)
  
          if self._buffer[-1].length() == 0:
              self._buffer[-1] = episode
          else:
              self._buffer.append(episode)
          self.transitions.extend(episode.transitions)
          self._num_transitions += episode.length()
          self.close_last_episode(False)
  
      def _remove_episode(self, episode_index: int) -> None:
          """
          Remove either the first or the last index
          :param episode_index: the index of the episode to remove (either 0 or -1)
          :return: None
          """
          self.assert_not_frozen()
          assert episode_index == 0 or episode_index == -1, "_remove_episode only supports removing the first or the last " \
                                                            "episode"
  
          if len(self._buffer) > 0:
              episode_length = self._buffer[episode_index].length()
              self._length -= 1
              self._num_transitions -= episode_length
              self._num_transitions_in_complete_episodes -= episode_length
              if episode_index == 0:
                  del self.transitions[:episode_length]
              else:  # episode_index = -1
                  del self.transitions[-episode_length:]
              del self._buffer[episode_index]
  
      def remove_first_episode(self, lock: bool = True) -> None:
          """
          Remove the first episode (even if it is not complete yet)
          :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class
                       locks and then calls store with lock = True
          :return: None
          """
          self._remove_episode(0)
  
      def remove_last_episode(self, lock: bool = True) -> None:
          """
          Remove the last episode (even if it is not complete yet)
          :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class
                       locks and then calls store with lock = True
          :return: None
          """
          self._remove_episode(-1)
  
      # for API compatibility
      def get(self, episode_index: int, lock: bool = True) -> Union[None, Episode]:
          """
          Returns the episode in the given index. If the episode does not exist, returns None instead.
          :param episode_index: the index of the episode to return
          :return: the corresponding episode
          """
          if self.length() == 0 or episode_index >= self.length():
              episode = None
          else:
              episode = self._buffer[episode_index]
  
          return episode
  
      def get_last_complete_episode(self) -> Union[None, Episode]:
          """
          Returns the last complete episode in the memory or None if there are no complete episodes
          :return: None or the last complete episode
          """
          last_complete_episode_index = self.num_complete_episodes() - 1
          episode = None
          if last_complete_episode_index >= 0:
              episode = self.get(last_complete_episode_index)
     
          return episode
  
      def clean(self) -> None:
          """
          Clean the memory by removing all the episodes
          :return: None
          """
          self.assert_not_frozen()
  
          self.transitions = []
          self._buffer = [Episode(n_step=self.n_step)]
          self._length = 1
          self._num_transitions = 0
          self._num_transitions_in_complete_episodes = 0
  
      def mean_reward(self) -> np.ndarray:
          """
          Get the mean reward in the replay buffer
          :return: the mean reward
          """
          mean = np.mean([transition.reward for transition in self.transitions])
  
          return mean
  
      def load_csv(self, csv_dataset: CsvDataset, input_filter: InputFilter) -> None:
          """
          Restore the replay buffer contents from a csv file.
          The csv file is assumed to include a list of transitions.
          :param csv_dataset: A construct which holds the dataset parameters
          :param input_filter: A filter used to filter the CSV data before feeding it to the memory.
          """
          self.assert_not_frozen()
  
          df = pd.read_csv(csv_dataset.filepath)
          if len(df) > self.max_size[1]:
              screen.warning("Warning! The number of transitions to load into the replay buffer ({}) is "
                             "bigger than the max size of the replay buffer ({}). The excessive transitions will "
                             "not be stored.".format(len(df), self.max_size[1]))
  
          episode_ids = df['episode_id'].unique()
          progress_bar = ProgressBar(len(episode_ids))
          state_columns = [col for col in df.columns if col.startswith('state_feature')]
  
          for e_id in episode_ids:
              progress_bar.update(e_id)
              df_episode_transitions = df[df['episode_id'] == e_id]
              input_filter.reset()
  
              if len(df_episode_transitions) < 2:
                  # we have to have at least 2 rows in each episode for creating a transition
                  continue
  
              episode = Episode()
              transitions = []
              for (_, current_transition), (_, next_transition) in zip(df_episode_transitions[:-1].iterrows(),
                                                                       df_episode_transitions[1:].iterrows()):
                  state = np.array([current_transition[col] for col in state_columns])
                  next_state = np.array([next_transition[col] for col in state_columns])
  
                  transitions.append(
                      Transition(state={'observation': state},
                                 action=int(current_transition['action']), reward=current_transition['reward'],
                                 next_state={'observation': next_state}, game_over=False,
                                 info={'all_action_probabilities':
                                 ast.literal_eval(current_transition['all_action_probabilities'])}),
                                  )
  
              transitions = input_filter.filter(transitions, deep_copy=False)
              for t in transitions:
                  episode.insert(t)
  
              # Set the last transition to end the episode
              if csv_dataset.is_episodic:
                  episode.get_last_transition().game_over = True
  
              self.store_episode(episode)
  
          # close the progress bar
          progress_bar.update(len(episode_ids))
          progress_bar.close()
  
      def freeze(self):
          """
          Freezing the replay buffer does not allow any new transitions to be added to the memory.
          Useful when working with a dataset (e.g. batch-rl or imitation learning).
          :return: None
          """
          self.frozen = True
  
      def assert_not_frozen(self):
          """
          Check that the memory is not frozen, and can be changed.
          :return:
          """
          assert self.frozen is False, "Memory is frozen, and cannot be changed."
  
      def prepare_evaluation_dataset(self):
          """
          Gather the memory content that will be used for off-policy evaluation in episodes and transitions format
          :return:
          """
          self._split_training_and_evaluation_datasets()
          self.evaluation_dataset_as_episodes = deepcopy(
              self.get_all_complete_episodes_from_to(self.get_last_training_set_episode_id() + 1,
                                                         self.num_complete_episodes()))
  
          if len(self.evaluation_dataset_as_episodes) == 0:
              raise ValueError('train_to_eval_ratio is too high causing the evaluation set to be empty. '
                               'Consider decreasing its value.')
  
          self.evaluation_dataset_as_transitions = [t for e in self.evaluation_dataset_as_episodes
                                                    for t in e.transitions]
  
      def _split_training_and_evaluation_datasets(self):
          """
          If the data in the buffer was not split to training and evaluation yet, split it accordingly.
          :return: None
          """
  
          if self.last_training_set_transition_id is None:
              if self.train_to_eval_ratio < 0 or self.train_to_eval_ratio >= 1:
                  raise ValueError('train_to_eval_ratio should be in the (0, 1] range.')
  
              transition = self.transitions[round(self.train_to_eval_ratio * self.num_transitions_in_complete_episodes())]
              episode_num, episode = self.get_episode_for_transition(transition)
              self.last_training_set_episode_id = episode_num
              self.last_training_set_transition_id = \
                  len([t for e in self.get_all_complete_episodes_from_to(0, self.last_training_set_episode_id + 1) for t in e])
  
      def save(self, file_path: str) -> None:
          """
          Save the replay buffer contents to a pickle file
          :param file_path: the path to the file that will be used to store the pickled transitions
          """
          with open(file_path, 'wb') as file:
              pickle.dump(self.get_all_complete_episodes(), file)
  
      def load_pickled(self, file_path: str) -> None:
          """
          Restore the replay buffer contents from a pickle file.
          The pickle file is assumed to include a list of transitions.
          :param file_path: The path to a pickle file to restore
          """
          self.assert_not_frozen()
  
          with open(file_path, 'rb') as file:
              episodes = pickle.load(file)
              num_transitions = sum([len(e.transitions) for e in episodes])
              if num_transitions > self.max_size[1]:
                  screen.warning("Warning! The number of transition to load into the replay buffer ({}) is "
                                 "bigger than the max size of the replay buffer ({}). The excessive transitions will "
                                 "not be stored.".format(num_transitions, self.max_size[1]))
  
              progress_bar = ProgressBar(len(episodes))
              for episode_idx, episode in enumerate(episodes):
                  self.store_episode(episode)
  
                  # print progress
                  progress_bar.update(episode_idx)
  
              progress_bar.close()
  