from aeirl.baselines.common import explained_variance, zipsame, dataset
from aeirl.baselines import logger
import aeirl.baselines.common.tf_util as U
import tensorflow as tf, numpy as np
import time, os, sys
from aeirl.baselines.common import colorize
from mpi4py import MPI
from collections import deque
from aeirl.baselines.common.mpi_adam import MpiAdam
from aeirl.baselines.common.cg import cg
from contextlib import contextmanager
import pickle as pkl

# Sample one trajectory (until trajectory end)
def traj_episode_generator(pi, env, horizon, stochastic):
    t = 0
    ac = env.action_space.sample() # not used, just so we have the datatype
    new = True # marks if we're on first timestep of an episode

    ob = env.reset()
    cur_ep_ret = 0 # return in current episode
    cur_ep_len = 0 # len of current episode

    # Initialize history arrays
    obs = []; rews = []; news = []; acs = []

    while True:
        prevac = ac
        ac, vpred = pi.act(stochastic, ob)
        obs.append(ob)
        news.append(new)
        acs.append(ac)
        ob, rew, new, _ = env.step(ac)
        rews.append(rew)

        cur_ep_ret += rew
        cur_ep_len += 1
        if t > 0 and (new or t % horizon == 0):
            # convert list into numpy array
            obs = np.array(obs)
            rews = np.array(rews)
            news = np.array(news)
            acs = np.array(acs)
            yield {"ob":obs, "rew":rews, "new":news, "ac":acs,
                    "ep_ret":cur_ep_ret, "ep_len":cur_ep_len}
            ob = env.reset()
            cur_ep_ret = 0; cur_ep_len = 0; t = 0

            # Initialize history arrays
            obs = []; rews = []; news = []; acs = []
        t += 1

def traj_segment_generator(pi, env, horizon, stochastic):
    # Initialize state variables
    t = 0
    ac = env.action_space.sample()
    new = True
    rew = 0.0
    ob = env.reset()

    cur_ep_ret = 0
    cur_ep_len = 0
    ep_rets = []
    ep_lens = []

    # Initialize history arrays
    obs = np.array([ob for _ in range(horizon)])
    rews = np.zeros(horizon, 'float32')
    vpreds = np.zeros(horizon, 'float32')
    news = np.zeros(horizon, 'int32')
    acs = np.array([ac for _ in range(horizon)])
    prevacs = acs.copy()

    while True:
        prevac = ac
        ac, vpred = pi.act(stochastic, ob)
        # Slight weirdness here because we need value function at time T
        # before returning segment [0, T-1] so we get the correct
        # terminal value
        if t > 0 and t % horizon == 0:
            yield {"ob" : obs, "rew" : rews, "vpred" : vpreds, "new" : news,
                    "ac" : acs, "prevac" : prevacs, "nextvpred": vpred * (1 - new),
                    "ep_rets" : ep_rets, "ep_lens" : ep_lens}
            _, vpred = pi.act(stochastic, ob)            
            # Be careful!!! if you change the downstream algorithm to aggregate
            # several of these batches, then be sure to do a deepcopy
            ep_rets = []
            ep_lens = []
        i = t % horizon
        obs[i] = ob
        vpreds[i] = vpred
        news[i] = new
        acs[i] = ac
        prevacs[i] = prevac

        ob, rew, new, _ = env.step(ac)
        rews[i] = rew

        cur_ep_ret += rew
        cur_ep_len += 1
        if new:
            ep_rets.append(cur_ep_ret)
            ep_lens.append(cur_ep_len)
            cur_ep_ret = 0
            cur_ep_len = 0
            ob = env.reset()
        t += 1

def add_vtarg_and_adv(seg, gamma, lam):
    new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1
    vpred = np.append(seg["vpred"], seg["nextvpred"])
    T = len(seg["rew"])
    seg["adv"] = gaelam = np.empty(T, 'float32')
    rew = seg["rew"]
    lastgaelam = 0
    for t in reversed(range(T)):
        nonterminal = 1-new[t+1]
        delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t]
        gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
    seg["tdlamret"] = seg["adv"] + seg["vpred"]

def learn(env, policy_func, *,
        timesteps_per_batch, # what to train on
        max_kl, cg_iters,
        gamma, lam, # advantage estimation
        entcoeff=0.0,
        cg_damping=1e-2,
        vf_stepsize=3e-4,
        vf_iters =3,
        max_timesteps=0, max_episodes=0, max_iters=0,  # time constraint
        callback=None,
        sample_stochastic=True, task="train",
        ckpt_dir=None, save_per_iter=100,
        load_model_path=None, task_name=None,
        max_sample_traj=1500
        ):
    nworkers = MPI.COMM_WORLD.Get_size()
    rank = MPI.COMM_WORLD.Get_rank()
    np.set_printoptions(precision=3)    
    # Setup losses and stuff
    # ----------------------------------------
    ob_space = env.observation_space
    ac_space = env.action_space
    pi = policy_func("pi", ob_space, ac_space)
    oldpi = policy_func("oldpi", ob_space, ac_space)
    atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
    ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return

    ob = U.get_placeholder_cached(name="ob")
    ac = pi.pdtype.sample_placeholder([None])

    kloldnew = oldpi.pd.kl(pi.pd)
    ent = pi.pd.entropy()
    meankl = U.mean(kloldnew)
    meanent = U.mean(ent)
    entbonus = entcoeff * meanent

    vferr = U.mean(tf.square(pi.vpred - ret))

    ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold
    surrgain = U.mean(ratio * atarg)

    optimgain = surrgain + entbonus
    losses = [optimgain, meankl, entbonus, surrgain, meanent]
    loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]

    dist = meankl

    all_var_list = pi.get_trainable_variables()
    var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
    vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
    vfadam = MpiAdam(vf_var_list)

    get_flat = U.GetFlat(var_list)
    set_from_flat = U.SetFromFlat(var_list)
    klgrads = tf.gradients(dist, var_list)
    flat_tangent = tf.placeholder(dtype=tf.float32, shape=[None], name="flat_tan")
    shapes = [var.get_shape().as_list() for var in var_list]
    start = 0
    tangents = []
    for shape in shapes:
        sz = U.intprod(shape)
        tangents.append(tf.reshape(flat_tangent[start:start+sz], shape))
        start += sz
    gvp = tf.add_n([U.sum(g*tangent) for (g, tangent) in zipsame(klgrads, tangents)]) #pylint: disable=E1111
    fvp = U.flatgrad(gvp, var_list)

    assign_old_eq_new = U.function([],[], updates=[tf.assign(oldv, newv)
        for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables())])
    compute_losses = U.function([ob, ac, atarg], losses)
    compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)])
    compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
    compute_vflossandgrad = U.function([ob, ret], U.flatgrad(vferr, vf_var_list))

    @contextmanager
    def timed(msg):
        if rank == 0:
            print(colorize(msg, color='magenta'))
            tstart = time.time()
            yield
            print(colorize("done in %.3f seconds"%(time.time() - tstart), color='magenta'))
        else:
            yield
    
    def allmean(x):
        assert isinstance(x, np.ndarray)
        out = np.empty_like(x)
        MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
        out /= nworkers
        return out

    U.initialize()
    th_init = get_flat()
    MPI.COMM_WORLD.Bcast(th_init, root=0)
    set_from_flat(th_init)
    vfadam.sync()
    print("Init param sum", th_init.sum(), flush=True)

    # Prepare for rollouts
    # ----------------------------------------
    seg_gen = traj_segment_generator(pi, env, timesteps_per_batch, stochastic=True)
    traj_gen =  traj_episode_generator(pi, env, timesteps_per_batch, stochastic=sample_stochastic)

    episodes_so_far = 0
    timesteps_so_far = 0
    iters_so_far = 0
    tstart = time.time()
    lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
    rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards

    assert sum([max_iters>0, max_timesteps>0, max_episodes>0])==1
    if task == 'sample_trajectory':
        # not elegant, i know :(
        sample_trajectory(load_model_path, max_sample_traj, traj_gen, task_name, sample_stochastic)
        sys.exit()

    while True:        
        if callback: callback(locals(), globals())
        if max_timesteps and timesteps_so_far >= max_timesteps:
            break
        elif max_episodes and episodes_so_far >= max_episodes:
            break
        elif max_iters and iters_so_far >= max_iters:
            break
        logger.log("********** Iteration %i ************"%iters_so_far)
        # Save model
        if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
            U.save_state(os.path.join(ckpt_dir, task_name), counter=iters_so_far)

        with timed("sampling"):
            seg = seg_gen.__next__()
        add_vtarg_and_adv(seg, gamma, lam)

        # ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
        ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
        vpredbefore = seg["vpred"] # predicted value function before udpate
        atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate

        if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
        if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy

        args = seg["ob"], seg["ac"], atarg
        fvpargs = [arr[::5] for arr in args]
        def fisher_vector_product(p):
            return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p

        assign_old_eq_new() # set old parameter values to new parameter values
        with timed("computegrad"):
            *lossbefore, g = compute_lossandgrad(*args)
        lossbefore = allmean(np.array(lossbefore))
        g = allmean(g)
        if np.allclose(g, 0):
            logger.log("Got zero gradient. not updating")
        else:
            with timed("cg"):
                stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank==0)
            assert np.isfinite(stepdir).all()
            shs = .5*stepdir.dot(fisher_vector_product(stepdir))
            lm = np.sqrt(shs / max_kl)
            # logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
            fullstep = stepdir / lm
            expectedimprove = g.dot(fullstep)
            surrbefore = lossbefore[0]
            stepsize = 1.0
            thbefore = get_flat()
            for _ in range(10):
                thnew = thbefore + fullstep * stepsize
                set_from_flat(thnew)
                meanlosses = surr, kl, *_ = allmean(np.array(compute_losses(*args)))
                improve = surr - surrbefore
                logger.log("Expected: %.3f Actual: %.3f"%(expectedimprove, improve))
                if not np.isfinite(meanlosses).all():
                    logger.log("Got non-finite value of losses -- bad!")
                elif kl > max_kl * 1.5:
                    logger.log("violated KL constraint. shrinking step.")
                elif improve < 0:
                    logger.log("surrogate didn't improve. shrinking step.")
                else:
                    logger.log("Stepsize OK!")
                    break
                stepsize *= .5
            else:
                logger.log("couldn't compute a good step")
                set_from_flat(thbefore)
            if nworkers > 1 and iters_so_far % 20 == 0:
                paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples
                assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:])

        for (lossname, lossval) in zip(loss_names, meanlosses):
            logger.record_tabular(lossname, lossval)

        with timed("vf"):

            for _ in range(vf_iters):
                for (mbob, mbret) in dataset.iterbatches((seg["ob"], seg["tdlamret"]), 
                include_final_partial_batch=False, batch_size=64):
                    g = allmean(compute_vflossandgrad(mbob, mbret))
                    vfadam.update(g, vf_stepsize)

        logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))

        lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
        listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
        lens, rews = map(flatten_lists, zip(*listoflrpairs))
        lenbuffer.extend(lens)
        rewbuffer.extend(rews)

        logger.record_tabular("EpLenMean", np.mean(lenbuffer))
        logger.record_tabular("EpRewMean", np.mean(rewbuffer))
        logger.record_tabular("EpThisIter", len(lens))
        episodes_so_far += len(lens)
        timesteps_so_far += sum(lens)
        iters_so_far += 1

        logger.record_tabular("EpisodesSoFar", episodes_so_far)
        logger.record_tabular("TimestepsSoFar", timesteps_so_far)
        logger.record_tabular("TimeElapsed", time.time() - tstart)

        if rank==0:
            logger.dump_tabular()

def sample_trajectory(load_model_path, max_sample_traj, traj_gen, task_name, sample_stochastic):

    assert load_model_path is not None
    U.load_state(load_model_path)
    sample_trajs = []
    for iters_so_far in range(max_sample_traj):
        logger.log("********** Iteration %i ************"%iters_so_far)
        traj = traj_gen.__next__()
        ob, new, ep_ret, ac, rew, ep_len = traj['ob'], traj['new'], traj['ep_ret'], traj['ac'], traj['rew'], traj['ep_len']
        logger.record_tabular("ep_ret", ep_ret)
        logger.record_tabular("ep_len", ep_len)
        logger.record_tabular("immediate reward", np.mean(rew))
        if MPI.COMM_WORLD.Get_rank()==0:
            logger.dump_tabular()
        traj_data = {"ob":ob, "ac":ac, "rew": rew, "ep_ret":ep_ret}
        sample_trajs.append(traj_data)

    sample_ep_rets = [traj["ep_ret"] for traj in sample_trajs]
    logger.log("Average total return: %f"%(sum(sample_ep_rets)/len(sample_ep_rets)))
    if sample_stochastic:
        task_name = 'stochastic.' + task_name
    else:
        task_name = 'deterministic.' + task_name
    pkl.dump(sample_trajs, open(task_name+".pkl", "wb"))

def flatten_lists(listoflists):
    return [el for list_ in listoflists for el in list_]
