from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

from tensorflow.contrib import legacy_seq2seq

from lib.metrics import masked_mse_loss, masked_mae_loss, masked_rmse_loss
from model.dcrnn_cell import DCGRUCell


class DCRNNModel(object):
    def __init__(self, is_training, batch_size, scaler, adj_mx, **model_kwargs):
        # Scaler for data normalization.
        self._scaler = scaler

        # Train and loss
        self._loss = None
        self._mae = None
        self._train_op = None

        # Learning rate.
        self._lr = tf.get_variable('learning_rate', shape=(), initializer=tf.constant_initializer(0.01),
                                   trainable=False)
        self._new_lr = tf.placeholder(tf.float32, shape=(), name='new_learning_rate')
        self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')

        max_diffusion_step = int(model_kwargs.get('max_diffusion_step', 2))
        cl_decay_steps = int(model_kwargs.get('cl_decay_steps', 1000))
        filter_type = model_kwargs.get('filter_type', 'laplacian')
        horizon = int(model_kwargs.get('horizon', 1))
        max_grad_norm = float(model_kwargs.get('max_grad_norm', 5.0))
        num_nodes = int(model_kwargs.get('num_nodes', 1))
        num_rnn_layers = int(model_kwargs.get('num_rnn_layers', 1))
        rnn_units = int(model_kwargs.get('rnn_units'))
        seq_len = int(model_kwargs.get('seq_len'))
        use_curriculum_learning = bool(model_kwargs.get('use_curriculum_learning', False))
        input_dim = int(model_kwargs.get('input_dim', 1))
        output_dim = int(model_kwargs.get('output_dim', 1))
        aux_dim = input_dim - output_dim

        # Input (batch_size, timesteps, num_sensor, input_dim)
        self._inputs = tf.placeholder(tf.float32, shape=(batch_size, seq_len, num_nodes, input_dim), name='inputs')
        # Labels: (batch_size, timesteps, num_sensor, input_dim), same format with input except the temporal dimension.
        self._labels = tf.placeholder(tf.float32, shape=(batch_size, horizon, num_nodes, input_dim), name='labels')

        GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * input_dim))

        cell = DCGRUCell(rnn_units, adj_mx, max_diffusion_step=max_diffusion_step, num_nodes=num_nodes,
                         filter_type=filter_type)
        cell_with_projection = DCGRUCell(rnn_units, adj_mx, max_diffusion_step=max_diffusion_step, num_nodes=num_nodes,
                                         num_proj=output_dim, filter_type=filter_type)
        encoding_cells = [cell] * num_rnn_layers
        decoding_cells = [cell] * (num_rnn_layers - 1) + [cell_with_projection]
        encoding_cells = tf.contrib.rnn.MultiRNNCell(encoding_cells, state_is_tuple=True)
        decoding_cells = tf.contrib.rnn.MultiRNNCell(decoding_cells, state_is_tuple=True)

        global_step = tf.train.get_or_create_global_step()
        # Outputs: (batch_size, timesteps, num_nodes, output_dim)
        with tf.variable_scope('DCRNN_SEQ'):
            inputs = tf.unstack(tf.reshape(self._inputs, (batch_size, seq_len, num_nodes * input_dim)), axis=1)
            labels = tf.unstack(
                tf.reshape(self._labels[..., :output_dim], (batch_size, horizon, num_nodes * output_dim)), axis=1)
            if aux_dim > 0:
                aux_info = tf.unstack(self._labels[..., output_dim:], axis=1)
                aux_info.insert(0, None)
            labels.insert(0, GO_SYMBOL)

            def _loop_function(prev, i):
                if is_training:
                    # Return either the model's prediction or the previous ground truth in training.
                    if use_curriculum_learning:
                        c = tf.random_uniform((), minval=0, maxval=1.)
                        threshold = self._compute_sampling_threshold(global_step, cl_decay_steps)
                        result = tf.cond(tf.less(c, threshold), lambda: labels[i], lambda: prev)
                    else:
                        result = labels[i]
                else:
                    # Return the prediction of the model in testing.
                    result = prev
                if aux_dim > 0:
                    result = tf.reshape(result, (batch_size, num_nodes, output_dim))
                    result = tf.concat([result, aux_info[i]], axis=-1)
                    result = tf.reshape(result, (batch_size, num_nodes * input_dim))
                return result

            _, enc_state = tf.contrib.rnn.static_rnn(encoding_cells, inputs, dtype=tf.float32)
            outputs, final_state = legacy_seq2seq.rnn_decoder(labels, enc_state, decoding_cells,
                                                              loop_function=_loop_function)

        # Project the output to output_dim.
        outputs = tf.stack(outputs[:-1], axis=1)
        self._outputs = tf.reshape(outputs, (batch_size, horizon, num_nodes, output_dim), name='outputs')

        preds = self._outputs
        labels = self._labels[..., :output_dim]

        null_val = 0.
        self._mae = masked_mae_loss(self._scaler, null_val)(preds=preds, labels=labels)
        self._loss = masked_mae_loss(self._scaler, null_val)(preds=preds, labels=labels)
        if is_training:
            optimizer = tf.train.AdamOptimizer(self._lr)
            tvars = tf.trainable_variables()
            grads = tf.gradients(self._loss, tvars)
            grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
            self._train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step, name='train_op')

        self._merged = tf.summary.merge_all()

    @staticmethod
    def _compute_sampling_threshold(global_step, k):
        """
        Computes the sampling probability for scheduled sampling using inverse sigmoid.
        :param global_step:
        :param k:
        :return:
        """
        return tf.cast(k / (k + tf.exp(global_step / k)), tf.float32)

    @staticmethod
    def run_epoch_generator(sess, model, data_generator, return_output=False, train_op=None, writer=None):
        losses = []
        maes = []
        outputs = []

        fetches = {
            'mae': model.mae,
            'loss': model.loss,
            'global_step': tf.train.get_or_create_global_step()
        }
        if train_op:
            fetches.update({
                'train_op': train_op,
            })
            merged = model.merged
            if merged is not None:
                fetches.update({'merged': merged})

        if return_output:
            fetches.update({
                'outputs': model.outputs
            })

        for _, (x, y) in enumerate(data_generator):
            feed_dict = {
                model.inputs: x,
                model.labels: y,
            }

            vals = sess.run(fetches, feed_dict=feed_dict)

            losses.append(vals['loss'])
            maes.append(vals['mae'])
            if writer is not None and 'merged' in vals:
                writer.add_summary(vals['merged'], global_step=vals['global_step'])
            if return_output:
                outputs.append(vals['outputs'])

        results = {
            'loss': np.mean(losses),
            'mae': np.mean(maes)
        }
        if return_output:
            results['outputs'] = outputs
        return results

    def get_lr(self, sess):
        return np.asscalar(sess.run(self._lr))

    def set_lr(self, sess, lr):
        sess.run(self._lr_update, feed_dict={
            self._new_lr: lr
        })

    @property
    def inputs(self):
        return self._inputs

    @property
    def labels(self):
        return self._labels

    @property
    def loss(self):
        return self._loss

    @property
    def lr(self):
        return self._lr

    @property
    def mae(self):
        return self._mae

    @property
    def merged(self):
        return self._merged

    @property
    def outputs(self):
        return self._outputs

    @property
    def train_op(self):
        return self._train_op
