from glasflow import RealNVP
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import torch
import torch.nn as nn
from itertools import chain, permutations
from datetime import datetime
import pickle
from scipy.interpolate import RectBivariateSpline
from scipy.integrate import quad
import time
import shutil

import seaborn as sns
import os

from scipy.stats import norm

from nessai.flowsampler import FlowSampler
from nessai.model import Model
from nessai.utils import setup_logger
import corner

seed = 2
torch.manual_seed(seed)
np.random.seed(seed)
sns.set_context("notebook")
sns.set_palette("colorblind")
device = "cuda"
date_id = datetime.today().isoformat()
plot_path = '/data/www.astro/chrism/uroboros/{}'.format(date_id)
ns_path = './'
try:
    os.mkdir('{}'.format(plot_path))
except:
    print('unable to make output directory {}'.format(plot_path))
    exit(1)
shutil.copyfile('./uroboros.py', '{}/uroboros.txt'.format(plot_path))

n_test = 4    # number of individual test data samples
n_prior = 250  # number of samples used to represent the conditonal prior
n_max = 5     # max the number of measurements per sample
n_cos = 2     # the number of cosmological params
n_cc_out = 16  # the size of compressed prior
n_sigma = 0.025   # the scale of the distance noise
n_post = 3000   # the number of posterior samples used for plotting
lr = 1e-3     # the learning rate
run_id = 'new_s{}_nmax{}_ntest{}'.format(seed,n_max,n_test)


# define the Flow that will estimate the cosmological parameters conditional on new data and compressed prior information
flow = RealNVP(
    n_inputs=n_cos,                           # number of cosmological params
    n_transforms=5,
    n_conditional_inputs=n_cc_out+1,      # size of compressed prior plus size of measurement
    n_neurons=32,
    batch_norm_between_transforms=True,
)
flow.to(device)
print(f"Created flow and sent to {device}")

# the compression model that takes samples from the prior and compresses them
cc_model = nn.Sequential(
    nn.Linear((n_cos+1)*n_prior, 64),
    nn.ReLU(),
    nn.Linear(64, 64),
    nn.ReLU(),
    nn.Linear(64, 64),
    nn.ReLU(),
    nn.Linear(64, n_cc_out),
    nn.Sigmoid()
)
cc_model.to(device)

def dL_slow(Om,z,H0=1.0):
    """
    returns the luminosity distance given the Hubble constant, the matter energy
    density and the redshift
    This is a basic expansion of the true relation for low redshift.
    """
    f = lambda zp: (Om*(1+zp)**3 + (1.0 - Om))**(-0.5)
    y, err = quad(f,0,z)
    dL = y/H0
    return dL

def dL_fast(H0,Om,z,LUT=None):
    if LUT is None:
        values = np.zeros((Om.size, z.size))
        for i,a in enumerate(Om):
            for j,b in enumerate(z):
                values[i,j] = dL_slow(a,b)
        LUT = RectBivariateSpline(Om,z,values)
        return LUT
    else:
        return LUT.ev(Om,z)/H0

# TEST
LUT = dL_fast(1.0,np.linspace(0,1,100),np.linspace(0,3,100))
zvec = np.linspace(0,3,100)
plt.figure()
plt.plot(zvec,dL_fast(0.1,0.3,zvec,LUT=LUT))
plt.plot(zvec,dL_fast(0.2,0.6,zvec,LUT=LUT))
plt.plot(zvec,dL_fast(0.7,0.1,zvec,LUT=LUT))
plt.savefig('{}/test.png'.format(plot_path))
plt.close()


def dL(H0,Om,z):
    """ 
    returns the luminosity distance given the Hubble constant, the matter energy
    density and the redshift
    This is a basic expansion of the true relation for low redshift.
    """
    q0 = 1.0 - 0.5*Om
    dL = (1.0/H0)*(z + 0.5*(1.0-q0)*z**2)
    return dL

class GaussianModel(Model):
    """A simple two-dimensional Gaussian likelihood."""

    def __init__(self,d):
        # Names of parameters to sample
        self.nmeas = d.size
        self.dvec = d
        self.LUT = dL_fast(1.0,np.linspace(0,1,100),np.linspace(0,3,100))
        print('dvec {}'.format(self.dvec))
        self.names = ["H0", "Om"]
        self.bounds = {"H0": [0.2, 1], "Om": [0, 1]}
        for i in range(self.nmeas):
            self.names.append('z{}'.format(i))
            self.bounds['z{}'.format(i)] = [0,3]

    def log_prior(self, x):
        """
        Returns log of prior given a live point assuming uniform
        priors on each parameter.
        """
        # Check if values are in bounds, returns True/False
        # Then take the log to get 0/-inf and make sure the dtype is float
        log_p = np.log(self.in_bounds(x), dtype="float")
        # Iterate through each parameter (x and y)
        # since the live points are a structured array we can
        # get each value using just the name
        #for n in self.names:
        log_p -= np.log(self.bounds["H0"][1] - self.bounds["H0"][0])                  # uniform prior
        log_p -= np.log(self.bounds["Om"][1] - self.bounds["Om"][0])                  # uniform prior
        for k in self.names[2:]:   # a prior for each measurement
            log_p += np.log(3.0) - 3.0*np.log(self.bounds[k][1]) + 2.0*np.log(x[k])   # p(z) ~ z^2 prior
        return log_p

    def log_likelihood(self, x):
        """
        Returns log likelihood of given live point assuming a Gaussian
        likelihood.
        """
        log_l = 0.0
        for d,z in zip(self.dvec,self.names[2:]):
            d0 = dL_fast(x["H0"],x["Om"],x[z],LUT=self.LUT)
            log_l += norm.logpdf(d0,loc=d,scale=n_sigma)
        return log_l

def make_data(n_data,n_prior=100,n_max=1,n_post=None,flow=None,cc_model=None,meas=None,LUT=None):
    """
    function to make training data
    n_data = number of training samples to make
    n_prior = the number of samples to use from the prior before compression 
    n_post = the number of posterior samples 
    n_max = the max number of measurements per training data sample
    meas = the distance measurements
    """
    Omega = None
    z = None

    # generate measured data if none has been supplied
    if meas is None:
        H0 = (0.2 + (0.8*torch.rand(size=(n_data,1)))).to(device)    # the true H0 value (bs,1)
        Om = (torch.rand(size=(n_data,1))).to(device)    # the true Om value (bs,1)
        Omega = (torch.concatenate((H0,Om),axis=1)).to(device)
        z = 3*(torch.rand(size=(n_data,n_max))**(1.0/3.0)).to(device)             # redshift (bs,n_max)
        n = (n_sigma*torch.randn(size=(n_data,n_max))).to(device)                                         # noise on distance (bs,n_max)
        meas = dL_fast(torch.tile(H0,(1,n_max)).flatten().cpu(),torch.tile(Om,(1,n_max)).flatten().cpu(),z.flatten().cpu(),LUT=LUT).reshape(n_data,n_max).to(device) + n  # distance (bs,n_max)
        meas = meas.reshape(n_data,n_max).to(device)
        meas = meas.type(torch.cuda.FloatTensor)

    # initialise the prior samples tensor
    # there should be a small set of prior samples (and log probs) for each measurement and for each sample 
    prior_label = torch.zeros(n_data,n_prior,n_cos+1,n_max).to(device)   # initialise the prior label tensor
    #post_label = None
    #if n_post is not None:
    #post_label = torch.zeros(n_data,n_post,n_cos+1,n_max).to(device)   # initialise the posterior label tensor

    # PRIOR 1 - for 1st signals sample from uniform prior
    prior_label[:,:,0,0] = (0.2 + 0.8*torch.rand(size=(n_data,n_prior))).to(device)
    prior_label[:,:,1,0] = torch.rand(size=(n_data,n_prior)).to(device)
    prior_label[:,:,2,0] = -1.0*np.log(0.8)*torch.ones(size=(n_data,n_prior)).to(device)

    flow.eval()

    for i in range(n_max-1):
        c_prior = cc_model(prior_label[:,:,:,i].flatten(1,2)).detach()
        test_cond = torch.cat((meas[:,i].reshape(n_data,1).detach(),c_prior),dim=1).to(device)
        test_cond = test_cond.tile(n_prior,1).to(device)        # has shape (n_data*n_prior,n_cc+n_meas)
        with torch.no_grad():
            prior_samples = flow.sample(n_data*n_prior,conditional=test_cond).to(device)   # output shape should be (n_data*n_prior,n_cos)
            prior_logprob = flow.log_prob(prior_samples,conditional=test_cond).to(device)    # output shape should be (n_data*n_prior)
        prior_label[:,:,0,i+1] = prior_samples[:,0].reshape(n_prior,n_data).transpose(1,0).to(device)
        prior_label[:,:,1,i+1] = prior_samples[:,1].reshape(n_prior,n_data).transpose(1,0).to(device)
        prior_label[:,:,2,i+1] = prior_logprob.reshape(n_prior,n_data).transpose(1,0).to(device)

    post_label = None
    if n_post is not None:

        post_label = torch.zeros(n_data,n_post,n_cos+1,n_max).to(device)   # initialise the posterior label tensor

        for i in range(n_max):
            # POST 1 - compress the uniform prior and add the 1st meas condition
            c_prior = cc_model(prior_label[:,:,:,i].flatten(1,2)).detach()
            test_cond = torch.cat((meas[:,i].reshape(n_data,1).detach(),c_prior),dim=1).to(device)
            test_cond = test_cond.tile(n_post,1).to(device)        # has shape (n_data*n_prior,n_cc+n_meas)
            with torch.no_grad():
                prior_samples = flow.sample(n_data*n_post,conditional=test_cond).to(device)   # output shape should be (n_data*n_post,n_cos)
                prior_logprob = flow.log_prob(prior_samples,conditional=test_cond).to(device)    # output shape should be (n_data*n_post)
            post_label[:,:,0,i] = prior_samples[:,0].reshape(n_post,n_data).transpose(1,0).to(device)
            post_label[:,:,1,i] = prior_samples[:,1].reshape(n_post,n_data).transpose(1,0).to(device)
            post_label[:,:,2,i] = prior_logprob.reshape(n_post,n_data).transpose(1,0).to(device)

    return Omega, meas, prior_label, z, post_label

# save or load the true params and measurements
try:
    os.mkdir('{}/{}'.format(ns_path,run_id))
except:
    pass
parfile = '{}/{}/testdata_{}.dat'.format(ns_path,run_id,run_id)
if os.path.isfile(parfile)==False:

    # generate test data
    LUT = dL_fast(1.0,np.linspace(0,1,100),np.linspace(0,3,100))
    data_test_tensor, d_test_tensor, prior_test_tensor, test_z, _ = make_data(n_data=n_test,n_max=n_max,n_prior=n_prior,flow=flow,cc_model=cc_model,LUT=LUT)
    test_n_sig = torch.ones(n_test)*n_max
 
    with open(parfile, 'wb') as f:  # Python 3: open(..., 'wb')
        pickle.dump([data_test_tensor.cpu().numpy(), d_test_tensor.cpu().numpy(), test_z.cpu().numpy()], f)
    print('saved par file {}'.format(parfile))
else:
    with open(parfile, 'rb') as f:  # Python 3: open(..., 'rb')
        data_test_tensor, d_test_tensor, test_z = pickle.load(f)
    data_test_tensor = torch.tensor(data_test_tensor).to(device)
    d_test_tensor = torch.tensor(d_test_tensor).to(device)
    test_z = torch.tensor(test_z).to(device)
    test_n_sig = torch.ones(n_test)*n_max
    print('read in par file {}'.format(parfile))

# The FlowSampler object is used to managed the sampling. Keyword arguments
# are passed to the nested sampling.
j = 0
ns_samples = []
for x,d in zip(data_test_tensor.cpu().numpy(),d_test_tensor.cpu().numpy()):
    for i in range(n_max):
        if os.path.isfile("{}/{}/ns_{}_{}_{}.dat".format(ns_path,run_id,run_id,i,j))==False:
            print('starting ns')
            output = "{}/{}/ns_{}_{}_{}/".format(ns_path,run_id,run_id,i,j)
            logger = setup_logger(output=output)
            print(n_max,d[:i+1])
            fs = FlowSampler(GaussianModel(d[:i+1]), output=output, resume=False, seed=1234)
            fs.run()
            temp_ns_samples = []
            for s in fs.posterior_samples:
               temp_ns_samples.append([s[q] for q in np.arange(3)])
            temp_ns_samples = np.array(temp_ns_samples,dtype=np.float64)
            ns_samples.append(temp_ns_samples)
            print(temp_ns_samples.shape)
            corner.corner(temp_ns_samples[:,:2],labels=["H0","Om"],truths=[x[0],x[1]])
            plt.show()

            # save nested sampling samples
            print('saving ns')
            with open("{}/{}/ns_{}_{}_{}.dat".format(ns_path,run_id,run_id,i,j), "wb") as f:
                temp_ns_samples.tofile(f)

        else:
            fn = '{}/{}/ns_{}_{}_{}.dat'.format(ns_path,run_id,run_id,i,j)
            print('loading ns file {}'.format(fn))
            with open("{}/{}/ns_{}_{}_{}.dat".format(ns_path,run_id,run_id,i,j), "rb") as f:
                ns_samples.append(np.fromfile(f).reshape(-1,n_cos+1).astype(np.float64))

    j += 1

# restructure the ns samples
k = 0
min_len = 1e16
for i in range(n_test):
    for j in range(n_max):
        if ns_samples[k].shape[0] < min_len:
            min_len = ns_samples[k].shape[0]
        k += 1
ns_min = min(min_len,n_post)
temp_ns_samples = np.zeros((n_test,n_max,ns_min,n_cos+1))
k = 0
for i in range(n_test):
    for j in range(n_max):
        temp_ns_samples[i,j,:,:] = ns_samples[k][:ns_min,:]
        k += 1
ns_samples = temp_ns_samples
print(ns_samples.shape)


optimiser = torch.optim.Adam(chain(flow.parameters(),cc_model.parameters()),lr=lr)
LUT = dL_fast(1.0,np.linspace(0,1,100),np.linspace(0,3,100))
iterations = 10000
batch_size = 1024
n_loss_avg = 1000
loss = dict(train=[], val=[])
train_loss_smooth = []
val_loss_smooth = []
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimiser, iterations, eta_min=1e-6, last_epoch=-1)
current_n_max = 1
sub_batch = batch_size

for i in range(iterations+1):

    flow.eval()
    cc_model.eval()
    data_train_tensor, dist_train_tensor, priors_train_tensor, _, _ = make_data(n_data=sub_batch,n_max=1,n_prior=n_prior,flow=flow,cc_model=cc_model,LUT=LUT)
    train_cond = torch.cat((dist_train_tensor[:,-1].reshape(-1,1),cc_model(priors_train_tensor[:,:,:,-1].flatten(1,2))),dim=1).to(device)
    for k in range(2,current_n_max):
        temp_data_train_tensor, temp_dist_train_tensor, temp_priors_train_tensor, _, _ = make_data(n_data=sub_batch,n_max=k,n_prior=n_prior,flow=flow,cc_model=cc_model,LUT=LUT)
        temp_train_cond = torch.cat((temp_dist_train_tensor[:,-1].reshape(-1,1),cc_model(temp_priors_train_tensor[:,:,:,-1].flatten(1,2))),dim=1).to(device)
        data_train_tensor = torch.cat((data_train_tensor,temp_data_train_tensor),dim=0)
        train_cond = torch.cat((train_cond,temp_train_cond),dim=0)
    flow.train()
    cc_model.train()
    optimiser.zero_grad()
    _loss = -flow.log_prob(data_train_tensor, conditional=train_cond).mean()      # compute the loss
    _loss.backward()
    optimiser.step()
    scheduler.step()
    train_loss = _loss.item()
    loss["train"].append(train_loss)
    train_loss_smooth.append(np.median(loss["train"][max(i-n_loss_avg,0):]))

    flow.eval()
    cc_model.eval()
    with torch.no_grad():
        data_val_tensor, dist_val_tensor, priors_val_tensor, _, _ = make_data(n_data=sub_batch,n_max=1,n_prior=n_prior,flow=flow,cc_model=cc_model,LUT=LUT)
        val_cond = torch.cat((dist_val_tensor[:,-1].reshape(-1,1),cc_model(priors_val_tensor[:,:,:,-1].flatten(1,2))),dim=1).to(device)
    for k in range(2,current_n_max):
        with torch.no_grad():
            temp_data_val_tensor, temp_dist_val_tensor, temp_priors_val_tensor, _, _ = make_data(n_data=sub_batch,n_max=k,n_prior=n_prior,flow=flow,cc_model=cc_model,LUT=LUT)
            temp_val_cond = torch.cat((temp_dist_val_tensor[:,-1].reshape(-1,1),cc_model(temp_priors_val_tensor[:,:,:,-1].flatten(1,2))),dim=1).to(device)
        data_val_tensor = torch.cat((data_val_tensor,temp_data_val_tensor),dim=0)
        val_cond = torch.cat((val_cond,temp_val_cond),dim=0)
    with torch.no_grad():
        _loss = -flow.log_prob(data_val_tensor, conditional=val_cond).mean().item()
    val_loss = _loss
    loss["val"].append(val_loss)
    val_loss_smooth.append(np.median(loss["val"][max(i-n_loss_avg,0):]))

    if not i % 500 and i>0:
        my_lr = scheduler.get_last_lr()[0]
        print(f"Epoch {i} - train: {loss['train'][-1]:.3f}, val: {loss['val'][-1]:.3f}, lr: {my_lr:.3e}, n_max: {current_n_max}, sub_batch: {sub_batch}")
        fig, ax = plt.subplots(n_test,n_max+1, figsize=(3*n_max, 16), dpi=100)
        loss_fig, loss_ax = plt.subplots(1, 1, figsize=(8, 8), dpi=100)
        j = 0
        print(d_test_tensor.device)
        for x,d,nd in zip(data_test_tensor,d_test_tensor,test_n_sig.cpu().numpy().astype(int)):
            _, _, old_samples, _, samples = make_data(1,n_prior=n_prior,n_max=nd,n_post=n_post,flow=flow,cc_model=cc_model,meas=d.reshape(1,-1),LUT=LUT)
            # reshape and plot
            samples = torch.permute(samples,(0,3,1,2))[0,:,:,:].cpu().numpy().reshape(nd,n_post,n_cos+1)
            old_samples = torch.permute(old_samples,(0,3,1,2))[0,:,:,:].cpu().numpy().reshape(nd,n_prior,n_cos+1)
            x = x.cpu().numpy()     
            ax[j,0].plot(old_samples[0,:,0],old_samples[0,:,1],'xc',markersize=10)
            for k in range(1,nd):
                prior_s = old_samples[k,:,:].reshape(n_prior,n_cos+1)
                post_s = samples[k-1,:,:].reshape(n_post,n_cos+1)
                ax[j,k].plot(post_s[:,0],post_s[:,1],'.b',markersize=1)
                ax[j,k].plot(ns_samples[j,k-1,:,0],ns_samples[j,k-1,:,1],'.r',markersize=1)
                ax[j,k].set_xlim([0.2,1.0])
                ax[j,k].set_ylim([0.0,1.0])
                ax[j,k].plot(x[0],x[1],'xk',label='truth',markersize=10)
            ax[j,nd].plot(x[0],x[1],'xk',label='truth')
            ax[j,nd].set_xlim([0.2,1.0])
            ax[j,nd].set_ylim([0.0,1.0])
            ax[j,nd].legend(loc='upper right')

            # loop over multiple different orders of the measurements
            #if n_max<=4:
            #    it = permutations(d)
            #else:
            it = iter([d[np.random.permutation(n_max)] for i in range(50)])
            new_samples = []
            new_n_post = n_post // 50
            for new_d in it:
                _, _, _, _, temp_samples = make_data(1,n_prior=n_prior,n_max=nd,n_post=new_n_post,flow=flow,cc_model=cc_model,meas=new_d.reshape(1,-1),LUT=LUT)
                new_samples.append(torch.permute(temp_samples,(0,3,1,2))[0,-1,:,:].cpu().numpy().reshape(new_n_post,n_cos+1))
            new_samples = np.array(new_samples).reshape(-1,n_cos+1)
            ax[j,nd].plot(samples[-1,:,0],samples[-1,:,1],'.b',markersize=1)
            ax[j,nd].plot(new_samples[:,0],new_samples[:,1],'.g',markersize=1)
            ax[j,nd].plot(ns_samples[j,-1,:,0],ns_samples[j,-1,:,1],'.r',markersize=1)

            kw = {"plot_datapoints": False, "plot_density": False, "levels": [0.5,0.99]}
            ax2 = corner.corner(ns_samples[j,-1,:,:2],bins=20,labels=["H0","Om"],truths=[x[0],x[1]],color='r', quantiles=None,**kw) #,'levels=[0.5,0.9]','plot_datapoints=False','smooth=0.02'})
            corner.corner(samples[nd-1,:,:2],bins=20,fig=ax2,color='b',**kw)
            corner.corner(new_samples[:,:2],bins=20,fig=ax2,color='g',**kw)
            ax2.savefig('{}/training_corner_{}_{}.png'.format(plot_path,i,j))
            j += 1
        fig.savefig('{}/training_{}.png'.format(plot_path,i))

        loss_ax.semilogx(train_loss_smooth, alpha=0.5, label="Train")
        loss_ax.semilogx(val_loss_smooth, alpha=0.5, label="Val.")
        loss_ax.set_ylim(-3, 0)
        loss_ax.set_xlabel("Epoch")
        loss_ax.set_ylabel("Loss")
        loss_ax.legend()
        loss_ax.grid('on')
        loss_fig.savefig('{}/loss.png'.format(plot_path))
        plt.close('all')


    if i>0 and not i % 500 and current_n_max<=n_max:
         current_n_max += 1
         sub_batch = batch_size // current_n_max
         print('updated current nmax to {} and sub batch size to {}'.format(current_n_max,sub_batch))
    #if i>500 and i<1000:
    #     current_n_max = 2
    #elif i>=1000 and i<1500:
    #     current_n_max = 3
    #elif i>=1500:
    #     current_n_max = 4

flow.eval()
print("Finished training")