import numpy as np


class ReplayBuffer(object):
    def __init__(self, state_dim, action_dim, max_size=int(1e6)):
        self.max_size = max_size
        self.ptr = 0
        self.size = 0

        self.state = np.zeros((max_size, state_dim))
        self.action = np.zeros((max_size, action_dim))
        self.next_state = np.zeros((max_size, state_dim))
        self.reward = np.zeros((max_size, 1))
        self.not_done = np.zeros((max_size, 1))

    def add(self, state, action, next_state, reward, done):
        self.state[self.ptr] = state
        self.action[self.ptr] = action
        self.next_state[self.ptr] = next_state
        self.reward[self.ptr] = reward
        self.not_done[self.ptr] = 1.0 - done

        self.ptr = (self.ptr + 1) % self.max_size
        self.size = min(self.size + 1, self.max_size)

    def sample(self, batch_size):
        ind = np.random.randint(0, self.size, size=batch_size)

        return (
            self.state[ind],
            self.action[ind],
            self.next_state[ind],
            self.reward[ind],
            self.not_done[ind],
        )

    def convert_D4RL(self, dataset):
        self.state = dataset["observations"]
        self.action = dataset["actions"]
        self.next_state = dataset["next_observations"]
        self.reward = dataset["rewards"].reshape(-1, 1)
        self.not_done = 1.0 - dataset["terminals"].reshape(-1, 1)
        self.size = self.state.shape[0]

    def normalize_states(self, eps=1e-3):
        mean = self.state.mean(0, keepdims=True)
        std = self.state.std(0, keepdims=True) + eps
        self.state = (self.state - mean) / std
        self.next_state = (self.next_state - mean) / std
        return mean, std
