
import numpy as np
import keras
from keras.layers import *
from keras.optimizers import Adam
from keras.models import load_model


class ReplayBuffer():
    def __init__(self, max_size, input_dims):
        self.mem_size = max_size
        self.mem_counter = 0
        self.state_memory = np.zeros((self.mem_size, *input_dims))
        self.action_memory = np.zeros(self.mem_size, dtype=np.int32)
        self.reward_memory = np.zeros(self.mem_size)
        self.terminal_memory = np.zeros(self.mem_size, dtype=np.int32)

    def store_transition(self, state, action, reward, done):
        index = self.mem_counter % self.mem_size
        self.state_memory[index] = state
        self.reward_memory[index] = reward
        self.action_memory[index] = action
        self.terminal_memory[index] = 1 - int(done)
        self.mem_counter += 1

    def sample_buffer(self, batch_size):
        max_mem = min(self.mem_counter, self.mem_size)
        batch = np.random.choice(max_mem, batch_size, replace=False)
        states = self.state_memory[batch]
        states_ = self.state_memory[(batch + 1) % self.state_memory.shape[0]]
        rewards = self.reward_memory[batch]
        actions = self.action_memory[batch]
        terminal = self.terminal_memory[batch]
        return states, actions, rewards, states_, terminal


def build_dqn(lr, n_actions, input_dims, fc1_dims, fc2_dims):
    model = keras.Sequential([
        Dense(fc1_dims, activation="relu"),
        Dense(fc2_dims, activation="relu"),
        Dense(n_actions, activation=None)
    ])
    opt = Adam(learning_rate=lr)
    model.compile(optimizer=opt, loss="mean_squared_error")
    return model


class Agent:
    def __init__(self, lr, gamma, n_actions, epsilon, batch_size, input_dims, epsilon_dec=1e-3, epsilon_end=0.01, mem_size=int(1e6), fname="dqn_model.h5"):
        self.action_space = [i for i in range(n_actions)]
        self.gamma = gamma
        self.epsilon = epsilon
        self.batch_size = batch_size
        self.model_file = fname
        self.epsilon = epsilon
        self.epsilon_min = epsilon_end
        self.epsilon_dec = epsilon_dec
        self.q_eval = build_dqn(lr, n_actions, input_dims, 256, 256)
        self.memory = ReplayBuffer(mem_size, input_dims)

    def store_transition(self, state, action, reward, done):
        self.memory.store_transition(state, action, reward, done)

    def choose_action(self,observation):
        if np.random.random() < self.epsilon:
            action = np.random.choice(self.action_space)
        else:
            state = np.array([observation])
            actions = self.q_eval.predict(state)
            action = np.argmax(actions)
        return action
    def learn(self):
        if self.memory.mem_counter < self.batch_size:
            return
        states,actions,rewards,states_,dones = self.memory.sample_buffer(self.batch_size)
        current_qs = self.q_eval.predict(states)
        next_qs = self.q_eval.predict(states_)

        q_target = np.copy(current_qs)
        batch_index = np.arange(self.batch_size,dtype=np.int32)
        q_target[batch_index,actions] = rewards + self.gamma * np.max(next_qs,axis=1) * dones
        self.q_eval.train_on_batch(states,q_target)
        self.epsilon = self.epsilon - self.epsilon_dec if self.epsilon > self.epsilon_min else self.epsilon_min

    def save_model(self):
        self.q_eval.save(self.model_file)
    def load_model(self):
        self.q_eval = load_model(self.model_file)
