import pickle, tensorflow as tf, numpy as np
import matplotlib.pyplot as plt
from tensorflow.python.framework import ops

def create_placeholders(n_x, n_y):
    """
    Creates the placeholders for the tensorflow session.
    
    Arguments:
    n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)
    n_y -- scalar, number of classes (from 0 to 5, so -> 6)
    
    Returns:
    X -- placeholder for the data input, of shape [n_x, None] and dtype "float"
    Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"
    
    Tips:
    - You will use None because it let's us be flexible on the number of examples you will for the placeholders.
      In fact, the number of examples during test/train is different.
    """

    X = tf.placeholder(tf.float32, shape = (n_x, None))
    Y = tf.placeholder(tf.float32, shape = (n_y, None))
    
    return X, Y



def initialize_parameters():
    """
    Initializes parameters to build a neural network with tensorflow. The shapes are:
                        W1 : [25, 12288]
                        b1 : [25, 1]
                        W2 : [12, 25]
                        b2 : [12, 1]
                        W3 : [6, 12]
                        b3 : [6, 1]
    
    Returns:
    parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3
    """
        
    W1 = tf.get_variable("W1", [25,376], initializer = tf.contrib.layers.xavier_initializer())
    b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer())
    W2 = tf.get_variable("W2", [12, 25], initializer = tf.contrib.layers.xavier_initializer())
    b2 = tf.get_variable("b2", [12, 1], initializer = tf.zeros_initializer())
    W3 = tf.get_variable("W3", [17, 12], initializer = tf.contrib.layers.xavier_initializer())
    b3 = tf.get_variable("b3", [17,1], initializer = tf.zeros_initializer())

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2,
                  "W3": W3,
                  "b3": b3}
    
    return parameters


def forward_propagation(X, parameters):
    """
    Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
    
    Arguments:
    X -- input dataset placeholder, of shape (input size, number of examples)
    parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"
                  the shapes are given in initialize_parameters

    Returns:
    Z3 -- the output of the last LINEAR unit
    """
    
    # Retrieve the parameters from the dictionary "parameters" 
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    W3 = parameters['W3']
    b3 = parameters['b3']
    
    Z1 = tf.add(tf.matmul(W1, tf.cast(X, tf.float32)), b1)                                             # Z1 = np.dot(W1, X) + b1
    A1 = tf.nn.relu(Z1)                                             # A1 = relu(Z1)
    Z2 = tf.add(tf.matmul(W2, A1), b2)                                              # Z2 = np.dot(W2, a1) + b2
    A2 = tf.nn.relu(Z2)                                              # A2 = relu(Z2)
    Z3 = tf.add(tf.matmul(W3, A2), b3)                                               # Z3 = np.dot(W3,Z2) + b3
    
    return Z3


def compute_cost(Z3, Y):
    """
    Computes the cost
    
    Arguments:
    Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)
    Y -- "true" labels vector placeholder, same shape as Z3
    
    Returns:
    cost - Tensor of the cost function
    """
    
    
    cost = tf.reduce_mean(tf.square(Y-Z3))
    
    return cost


def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
          num_epochs = 1500, minibatch_size = 32, print_cost = True):
    """
    Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
    
    Arguments:
    X_train -- training set, of shape (input size = 12288, number of training examples = 1080)
    Y_train -- test set, of shape (output size = 6, number of training examples = 1080)
    X_test -- training set, of shape (input size = 12288, number of training examples = 120)
    Y_test -- test set, of shape (output size = 6, number of test examples = 120)
    learning_rate -- learning rate of the optimization
    num_epochs -- number of epochs of the optimization loop
    minibatch_size -- size of a minibatch
    print_cost -- True to print the cost every 100 epochs
    
    Returns:
    parameters -- parameters learnt by the model. They can then be used to predict.
    """
    
    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
    (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)
    n_y = Y_train.shape[0]                            # n_y : output size
    costs = []                                        # To keep track of the cost
    
    # Create Placeholders of shape (n_x, n_y)
    X, Y = create_placeholders(n_x, n_y)

    # Initialize parameters
    parameters = initialize_parameters()
    
    # Forward propagation: Build the forward propagation in the tensorflow graph
    Z3 = forward_propagation(X, parameters)
    
    # Cost function: Add cost function to tensorflow graph
    cost = compute_cost(Z3, Y)
    
    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
    
    # Initialize all the variables
    init = tf.global_variables_initializer()

    # Start the session to compute the tensorflow graph
    with tf.Session() as sess:
        
        # Run the initialization
        sess.run(init)
        
        # Do the training loop
        for epoch in range(num_epochs):

            epoch_cost = 0.                       # Defines a cost related to an epoch
            _, epoch_cost = sess.run([optimizer, cost], feed_dict={X: X_train, Y: Y_train})

            # Print the cost every epoch
            if print_cost == True and epoch % 100 == 0:
                print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
            if print_cost == True and epoch % 5 == 0:
                costs.append(epoch_cost)
                
        # plot the cost
        plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per tens)')
        plt.title("Learning rate =" + str(learning_rate))
        plt.show()

        # lets save the parameters in a variable
        parameters = sess.run(parameters)
        print ("Parameters have been trained!")

        # Calculate the correct predictions
        correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))

        # Calculate accuracy on the test set
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
        print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))
        
        return parameters



def build_policy():

    expert = pickle.load( open( "expert_data.pkl", "rb" ) )
 
    X = expert['observations']
    Y = expert['actions']
    Y = [i[0] for i in Y]

    X = np.array(X)
    Y = np.array(Y)

    print (X.shape)
    print (Y.shape)

    X_train = X[:4000]
    Y_train = Y[:4000]
 
    X_test = X[4000:5000]
    Y_test = Y[4000:5000]

    parameters = model(X_train.T, Y_train.T, X_test.T, Y_test.T)

    with open('bc.pkl', 'wb') as f:
      pickle.dump(parameters, f, pickle.HIGHEST_PROTOCOL)

def run_policy():
    
    with tf.Session():

        import gym
        env = gym.make('Humanoid-v1')
        max_steps = env.spec.timestep_limit

        parameters = pickle.load( open( "bc.pkl", "rb" ) )
        render = True

        returns = []
        observations = []
        actions = []
        for i in range(20):
            print('iter', i)
            obs = env.reset()
            done = False
            totalr = 0.
            steps = 0
            while not done:
                action = forward_propagation(obs[None, :].T, parameters)
                observations.append(obs)
                actions.append(action)
                print(action.eval())
                obs, r, done, _ = env.step(action.eval())
                totalr += r
                steps += 1
                if render:
                    env.render()
                if steps % 100 == 0: print("%i/%i"%(steps, max_steps))
                if steps >= max_steps:
                    break
            returns.append(totalr)

        print('returns', returns)
        print('mean return', np.mean(returns))
        print('std of return', np.std(returns))


if __name__ == "__main__":
    build_policy()
    run_policy()
