from collections import deque
import numpy as np


class MemoryBase:
    """
        An abstract class for memory
    """

    def push(self, *args, **kwargs):
        """
            abstract function that is for appending experience tuple to the memory
        """
        pass

    def sample(self, *args, **kwargs):
        """
            abstract function that is for getting samples from the memory
        """

    def reset(self, *args, **kwargs):
        """
            abstract function that is for resetting memory
        """
        pass


class EpisodicMemory(MemoryBase):

    def __init__(self, max_n_episodes, spec, gamma, max_traj_len=100000):
        self.max_n_episodes = max_n_episodes
        self.trajectories = deque(maxlen=max_n_episodes)

        self.spec = spec
        self.gamma = gamma
        self.max_traj_len = max_traj_len

    def len_trajectories(self):
        return np.array([traj.length for traj in self.trajectories])

    def reset(self):
        self.trajectories.clear()
2050243f0df17cc4821a0fbf155a080382c751e2
<mask token>


class EpisodicMemory(MemoryBase):

    def __init__(self, max_n_episodes, spec, gamma, max_traj_len=100000):
        self.max_n_episodes = max_n_episodes
        self.trajectories = deque(maxlen=max_n_episodes)
        self.spec = spec
        self.gamma = gamma
        self.max_traj_len = max_traj_len

    def len_trajectories(self):
        return np.array([traj.length for traj in self.trajectories])
    <mask token>

<mask token>


class MemoryBase:
    <mask token>
    <mask token>
    <mask token>
    <mask token>


class EpisodicMemory(MemoryBase):

    def __init__(self, max_n_episodes, spec, gamma, max_traj_len=100000):
        self.max_n_episodes = max_n_episodes
        self.trajectories = deque(maxlen=max_n_episodes)
        self.spec = spec
        self.gamma = gamma
        self.max_traj_len = max_traj_len

    def len_trajectories(self):
        return np.array([traj.length for traj in self.trajectories])

    def reset(self):
        self.trajectories.clear()

<mask token>


class MemoryBase:
    <mask token>

    def push(self, *args, **kwargs):
        """
            abstract function that is for appending experience tuple to the memory
        """
        pass

    def sample(self, *args, **kwargs):
        """
            abstract function that is for getting samples from the memory
        """

    def reset(self, *args, **kwargs):
        """
            abstract function that is for resetting memory
        """
        pass


class EpisodicMemory(MemoryBase):

    def __init__(self, max_n_episodes, spec, gamma, max_traj_len=100000):
        self.max_n_episodes = max_n_episodes
        self.trajectories = deque(maxlen=max_n_episodes)
        self.spec = spec
        self.gamma = gamma
        self.max_traj_len = max_traj_len

    def len_trajectories(self):
        return np.array([traj.length for traj in self.trajectories])

    def reset(self):
        self.trajectories.clear()

<mask token>


class MemoryBase:
    """
        An abstract class for memory
    """

    def push(self, *args, **kwargs):
        """
            abstract function that is for appending experience tuple to the memory
        """
        pass

    def sample(self, *args, **kwargs):
        """
            abstract function that is for getting samples from the memory
        """

    def reset(self, *args, **kwargs):
        """
            abstract function that is for resetting memory
        """
        pass


class EpisodicMemory(MemoryBase):

    def __init__(self, max_n_episodes, spec, gamma, max_traj_len=100000):
        self.max_n_episodes = max_n_episodes
        self.trajectories = deque(maxlen=max_n_episodes)
        self.spec = spec
        self.gamma = gamma
        self.max_traj_len = max_traj_len

    def len_trajectories(self):
        return np.array([traj.length for traj in self.trajectories])

    def reset(self):
        self.trajectories.clear()

from collections import deque
import numpy as np


class MemoryBase:
    """
        An abstract class for memory
    """

    def push(self, *args, **kwargs):
        """
            abstract function that is for appending experience tuple to the memory
        """
        pass

    def sample(self, *args, **kwargs):
        """
            abstract function that is for getting samples from the memory
        """

    def reset(self, *args, **kwargs):
        """
            abstract function that is for resetting memory
        """
        pass


class EpisodicMemory(MemoryBase):

    def __init__(self, max_n_episodes, spec, gamma, max_traj_len=100000):
        self.max_n_episodes = max_n_episodes
        self.trajectories = deque(maxlen=max_n_episodes)

        self.spec = spec
        self.gamma = gamma
        self.max_traj_len = max_traj_len

    def len_trajectories(self):
        return np.array([traj.length for traj in self.trajectories])

    def reset(self):
        self.trajectories.clear()