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from datetime import datetime

import numpy as np
from keras import Input
import keras.backend as K
from keras.callbacks import TensorBoard
from keras.engine import Model
from keras.layers import LSTM, TimeDistributed, Dense, Reshape, Flatten, LeakyReLU, Lambda
from keras.optimizers import Adam, sgd

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import torch
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F

import torch.optim as optim

import torchvision
import torchvision.transforms as transforms

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import roslib #; roslib.load_manifest('sr_example')
import rospy
from geometry_msgs.msg import Twist
from std_msgs.msg import Float64, String

rospy.init_node('turtlebot_controller', anonymous=True)

def move(dist, angle):

	pub = rospy.Publisher('/turtle1/cmd_vel', Twist, queue_size=10)

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import torch
import torch.nn as nn
import torch.nn.functional as F

import torchvision

from torch.autograd import Variable
from torchvision.transforms import CenterCrop, ToTensor, Compose, Lambda, Resize, Grayscale
from torchvision.datasets import coco

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import torch, os, sys

from torch import nn
import torch.nn.functional as F
import torch.distributions as ds

from math import sqrt, ceil

import layers, util

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import torch
from torch import nn

import torch.distributions as dist

## REINFORCE

adjacencies, num_edges, targets = load_data(...)
opt = ...

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# -- assignment 1 --
import numpy as np
from urllib import request
import gzip
import pickle
import os

def load_synth(num_train=60_000, num_val=10_000, seed=0):
    """
    Load some very basic synthetic data that should be easy to classify. Two features, so that we can plot the

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package org.submassive;

import java.io.BufferedInputStream;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;

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import wget, os, gzip, pickle, random, re, sys

IMDB_URL = 'http://dlvu.github.io/data/imdb.{}.pkl.gz'
IMDB_FILE = 'imdb.{}.pkl.gz'

PAD, START, END, UNK = '.pad', '.start', '.end', '.unk'

def load_imdb(final=False, val=5000, seed=0, voc=None, char=False):

    cst = 'char' if char else 'word'