# import os, pickle, torch, random, argparse
# import yaml
# import numpy as np 
# from tqdm import tqdm 
# torch.manual_seed(1)
# np.random.seed(2)
# random.seed(1)
# from tdc import Oracle
# import sys
# # sys.path.append('../..')
# path_here = os.path.dirname(os.path.realpath(__file__))
# sys.path.append(path_here)
# sys.path.append('.')
# from main.optimizer import BaseOptimizer

from tdc import Oracle
oracle = Oracle('qed')
import tensorflow as tf

from utils.sparse_molecular_dataset import SparseMolecularDataset
from utils.trainer import Trainer
from utils.utils import *

from models.gan import GraphGANModel
from models import encoder_rgcn, decoder_adj, decoder_dot, decoder_rnn

from optimizers.gan import GraphGANOptimizer

# from rdkit import Chem
# smiles_list = [Chem.MolToSmiles(mol) for mol in mols]


batch_dim = 128
la = 1
dropout = 0
n_critic = 5
metric = 'validity,sas'
n_samples = 5000
z_dim = 8
epochs = 10
save_every = 1 # May lead to errors if left as None

data = SparseMolecularDataset()
data.load('data/gdb9_9nodes.sparsedataset')

steps = (len(data) // batch_dim)


def train_fetch_dict(i, steps, epoch, epochs, min_epochs, model, optimizer):
    a = [optimizer.train_step_G] if i % n_critic == 0 else [optimizer.train_step_D]
    b = [optimizer.train_step_V] if i % n_critic == 0 and la < 1 else []
    return a + b


def train_feed_dict(i, steps, epoch, epochs, min_epochs, model, optimizer, batch_dim):
    mols, _, _, a, x, _, _, _, _ = data.next_train_batch(batch_dim)
    embeddings = model.sample_z(batch_dim)

    if la < 1:

        if i % n_critic == 0:
            rewardR = reward(mols)

            n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax],
                               feed_dict={model.training: False, model.embeddings: embeddings})
            n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1)
            mols = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)]

            rewardF = reward(mols)

            feed_dict = {model.edges_labels: a,
                         model.nodes_labels: x,
                         model.embeddings: embeddings,
                         model.rewardR: rewardR,
                         model.rewardF: rewardF,
                         model.training: True,
                         model.dropout_rate: dropout,
                         optimizer.la: la if epoch > 0 else 1.0}

        else:
            feed_dict = {model.edges_labels: a,
                         model.nodes_labels: x,
                         model.embeddings: embeddings,
                         model.training: True,
                         model.dropout_rate: dropout,
                         optimizer.la: la if epoch > 0 else 1.0}
    else:
        feed_dict = {model.edges_labels: a,
                     model.nodes_labels: x,
                     model.embeddings: embeddings,
                     model.training: True,
                     model.dropout_rate: dropout,
                     optimizer.la: 1.0}

    return feed_dict


def eval_fetch_dict(i, epochs, min_epochs, model, optimizer):
    return {'loss D': optimizer.loss_D, 'loss G': optimizer.loss_G,
            'loss RL': optimizer.loss_RL, 'loss V': optimizer.loss_V,
            'la': optimizer.la}


def eval_feed_dict(i, epochs, min_epochs, model, optimizer, batch_dim):
    mols, _, _, a, x, _, _, _, _ = data.next_validation_batch()
    embeddings = model.sample_z(a.shape[0])

    rewardR = reward(mols)

    n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax],
                       feed_dict={model.training: False, model.embeddings: embeddings})
    n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1)
    mols = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)]

    rewardF = reward(mols)

    feed_dict = {model.edges_labels: a,
                 model.nodes_labels: x,
                 model.embeddings: embeddings,
                 model.rewardR: rewardR,
                 model.rewardF: rewardF,
                 model.training: False}
    return feed_dict


def test_fetch_dict(model, optimizer):
    return {'loss D': optimizer.loss_D, 'loss G': optimizer.loss_G,
            'loss RL': optimizer.loss_RL, 'loss V': optimizer.loss_V,
            'la': optimizer.la}


def test_feed_dict(model, optimizer, batch_dim):
    mols, _, _, a, x, _, _, _, _ = data.next_test_batch()
    embeddings = model.sample_z(a.shape[0])

    rewardR = reward(mols)

    n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax],
                       feed_dict={model.training: False, model.embeddings: embeddings})
    n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1)
    mols = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)]

    rewardF = reward(mols)

    feed_dict = {model.edges_labels: a,
                 model.nodes_labels: x,
                 model.embeddings: embeddings,
                 model.rewardR: rewardR,
                 model.rewardF: rewardF,
                 model.training: False}
    return feed_dict



def reward(mols):
    rr = 1.
    for m in ('logp,sas,qed,unique' if metric == 'all' else metric).split(','):
        if m == 'np':
            rr *= MolecularMetrics.natural_product_scores(mols, norm=True)
        elif m == 'logp':
            rr *= MolecularMetrics.water_octanol_partition_coefficient_scores(mols, norm=True)
        elif m == 'sas':
            rr *= MolecularMetrics.synthetic_accessibility_score_scores(mols, norm=True)
        elif m == 'qed':
            rr *= MolecularMetrics.quantitative_estimation_druglikeness_scores(mols, norm=True)
        elif m == 'novelty':
            rr *= MolecularMetrics.novel_scores(mols, data)
        elif m == 'dc':
            rr *= MolecularMetrics.drugcandidate_scores(mols, data)
        elif m == 'unique':
            rr *= MolecularMetrics.unique_scores(mols)
        elif m == 'diversity':
            rr *= MolecularMetrics.diversity_scores(mols, data)
        elif m == 'validity':
            rr *= MolecularMetrics.valid_scores(mols)
        else:
            raise RuntimeError('{} is not defined as a metric'.format(m))

    return rr.reshape(-1, 1)


def _eval_update(i, epochs, min_epochs, model, optimizer, batch_dim, eval_batch):
    mols = samples(data, model, session, model.sample_z(n_samples), sample=True)
    m0, m1 = all_scores(mols, data, norm=True)
    m0 = {k: np.array(v)[np.nonzero(v)].mean() for k, v in m0.items()}
    m0.update(m1)
    return m0


def _test_update(model, optimizer, batch_dim, test_batch):
    mols = samples(data, model, session, model.sample_z(n_samples), sample=True)
    m0, m1 = all_scores(mols, data, norm=True)
    m0 = {k: np.array(v)[np.nonzero(v)].mean() for k, v in m0.items()}
    m0.update(m1)
    return m0


model = GraphGANModel(data.vertexes, data.bond_num_types, data.atom_num_types, z_dim,
                      decoder_units=(128, 256, 512), discriminator_units=((128, 64), 128, (128, 64)),
                      decoder=decoder_adj, discriminator=encoder_rgcn,
                      soft_gumbel_softmax=False, hard_gumbel_softmax=False, batch_discriminator=False)
optimizer = GraphGANOptimizer(model, learning_rate=1e-3, feature_matching=False)
session = tf.Session()
session.run(tf.global_variables_initializer())
trainer = Trainer(model, optimizer, session)
print('Parameters: {}'.format(np.sum([np.prod(e.shape) for e in session.run(tf.trainable_variables())])))




# class MolGAN_Optimizer(BaseOptimizer):

#     def __init__(self, args=None):
#         super().__init__(args)
#         self.model_name = "MolGAN"

#     def _optimize(self, oracle, config):
#         self.oracle.assign_evaluator(oracle)


trainer.train(batch_dim=batch_dim,
              epochs=epochs,
              steps=steps,
              train_fetch_dict=train_fetch_dict,
              train_feed_dict=train_feed_dict,
              eval_fetch_dict=eval_fetch_dict,
              eval_feed_dict=eval_feed_dict,
              test_fetch_dict=test_fetch_dict,
              test_feed_dict=test_feed_dict,
              save_every=save_every,
              directory='save', # here users need to first create and then specify a folder where to save the model
              _eval_update=_eval_update,
              _test_update=_test_update)








