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

"""Language model agent.

Agent outputs code in a sequence just like a language model. Can be trained
as a language model or using RL, or a combination of the two.
"""

from collections import namedtuple
from math import exp
from math import log
import time

from absl import logging
import numpy as np
from six.moves import xrange
import tensorflow as tf

from common import rollout as rollout_lib  # brain coder
from common import utils  # brain coder
from single_task import misc  # brain coder

# Experiments in the ICLR 2018 paper used reduce_sum instead of reduce_mean for
# some losses. We make all loses be batch_size independent, and multiply the
# changed losses by 64, which was the fixed batch_size when the experiments
# where run. The loss hyperparameters still match what is reported in the paper.
MAGIC_LOSS_MULTIPLIER = 64

def rshift_time(tensor_2d, fill=misc.BF_EOS_INT):
    """Right shifts a 2D tensor along the time dimension (axis-1)."""
    dim_0 = tf.shape(tensor_2d)[0]
    fill_tensor = tf.fill([dim_0, 1], fill)
    return tf.concat([fill_tensor, tensor_2d[:, :-1]], axis=1)

def join(a, b):
    # Concat a and b along 0-th dim.
    if a is None or len(a) == 0:  # pylint: disable=g-explicit-length-test
        return b
    if b is None or len(b) == 0:  # pylint: disable=g-explicit-length-test
        return a
    return np.concatenate((a, b))

def make_optimizer(kind, lr):
    if kind == 'sgd':
        return tf.train.GradientDescentOptimizer(lr)
    elif kind == 'adam':
        return tf.train.AdamOptimizer(lr)
    elif kind == 'rmsprop':
        return tf.train.RMSPropOptimizer(learning_rate=lr, decay=0.99)
    else:
        raise ValueError('Optimizer type "%s" not recognized.' % kind)

class LinearWrapper(tf.contrib.rnn.RNNCell):
    """RNNCell wrapper that adds a linear layer to the output."""

    def __init__(self, cell, output_size, dtype=tf.float32, suppress_index=None):
        self.cell = cell
        self._output_size = output_size
        self._dtype = dtype
        self._suppress_index = suppress_index
        self.smallest_float = -2.4e38

    def __call__(self, inputs, state, scope=None):
        with tf.variable_scope(type(self).__name__):
            outputs, state = self.cell(inputs, state, scope=scope)
            logits = tf.matmul(
                outputs,
                tf.get_variable('w_output',
                                [self.cell.output_size, self.output_size],
                                dtype=self._dtype))
            if self._suppress_index is not None:
                # Replace the target index with -inf, so that it never gets selected.
                batch_size = tf.shape(logits)[0]
                logits = tf.concat(
                    [logits[:, :self._suppress_index],
                     tf.fill([batch_size, 1], self.smallest_float),
                     logits[:, self._suppress_index + 1:]],
                    axis=1)

        return logits, state

    @property
    def output_size(self):
        return self._output_size

    @property
    def state_size(self):
        return self.cell.state_size

    def zero_state(self, batch_size, dtype):
        return self.cell.zero_state(batch_size, dtype)

UpdateStepResult = namedtuple(
    'UpdateStepResult',
    ['global_step', 'global_npe', 'summaries_list', 'gradients_dict'])

class AttrDict(dict):
    """Dict with attributes as keys.

    https://stackoverflow.com/a/14620633
    """

    def __init__(self, *args, **kwargs):
        super(AttrDict, self).__init__(*args, **kwargs)
        self.__dict__ = self

class LMAgent(object):
    """Language model agent."""
    action_space = misc.bf_num_tokens()
    observation_space = misc.bf_num_tokens()

    def __init__(self, global_config, task_id=0,
                 logging_file=None,
                 experience_replay_file=None,
                 global_best_reward_fn=None,
                 found_solution_op=None,
                 assign_code_solution_fn=None,
                 program_count=None,
                 do_iw_summaries=False,
                 stop_on_success=True,
                 dtype=tf.float32,
                 verbose_level=0,
                 is_local=True):
        self.config = config = global_config.agent
        self.logging_file = logging_file
        self.experience_replay_file = experience_replay_file
        self.task_id = task_id
        self.verbose_level = verbose_level
        self.global_best_reward_fn = global_best_reward_fn
        self.found_solution_op = found_solution_op
        self.assign_code_solution_fn = assign_code_solution_fn
        self.parent_scope_name = tf.get_variable_scope().name
        self.dtype = dtype
        self.allow_eos_token = config.eos_token
        self.stop_on_success = stop_on_success
        self.pi_loss_hparam = config.pi_loss_hparam
        self.vf_loss_hparam = config.vf_loss_hparam
        self.is_local = is_local

        self.top_reward = 0.0
        self.embeddings_trainable = True

        self.no_op = tf.no_op()

        self.learning_rate = tf.constant(
            config.lr, dtype=dtype, name='learning_rate')
        self.initializer = tf.contrib.layers.variance_scaling_initializer(
            factor=config.param_init_factor,
            mode='FAN_AVG',
            uniform=True,
            dtype=dtype)  # TF's default initializer.
        tf.get_variable_scope().set_initializer(self.initializer)

        self.a2c = config.ema_baseline_decay == 0
        if not self.a2c:
            logging.info('Using exponential moving average REINFORCE baselines.')
            self.ema_baseline_decay = config.ema_baseline_decay
            self.ema_by_len = [0.0] * global_config.timestep_limit
        else:
            logging.info('Using advantage (a2c) with learned value function.')
            self.ema_baseline_decay = 0.0
            self.ema_by_len = None

        # Top-k
        if config.topk and config.topk_loss_hparam:
            self.topk_loss_hparam = config.topk_loss_hparam
            self.topk_batch_size = config.topk_batch_size
            if self.topk_batch_size <= 0:
                raise ValueError('topk_batch_size must be a positive integer. Got %s',
                                 self.topk_batch_size)
            self.top_episodes = utils.MaxUniquePriorityQueue(config.topk)
            logging.info('Made max-priorty-queue with capacity %d',
                         self.top_episodes.capacity)
        else:
            self.top_episodes = None
            self.topk_loss_hparam = 0.0
            logging.info('No max-priorty-queue')

        # Experience replay.
        self.replay_temperature = config.replay_temperature
        self.num_replay_per_batch = int(global_config.batch_size * config.alpha)
        self.num_on_policy_per_batch = (
                global_config.batch_size - self.num_replay_per_batch)
        self.replay_alpha = (
                self.num_replay_per_batch / float(global_config.batch_size))
        logging.info('num_replay_per_batch: %d', self.num_replay_per_batch)
        logging.info('num_on_policy_per_batch: %d', self.num_on_policy_per_batch)
        logging.info('replay_alpha: %s', self.replay_alpha)
        if self.num_replay_per_batch > 0:
            # Train with off-policy episodes from replay buffer.
            start_time = time.time()
            self.experience_replay = utils.RouletteWheel(
                unique_mode=True, save_file=experience_replay_file)
            logging.info('Took %s sec to load replay buffer from disk.',
                         int(time.time() - start_time))
            logging.info('Replay buffer file location: "%s"',
                         self.experience_replay.save_file)
        else:
            # Only train on-policy.
            self.experience_replay = None

        if program_count is not None:
            self.program_count = program_count
            self.program_count_add_ph = tf.placeholder(
                tf.int64, [], 'program_count_add_ph')
            self.program_count_add_op = self.program_count.assign_add(
                self.program_count_add_ph)

        ################################
        # RL policy and value networks #
        ################################
        batch_size = global_config.batch_size
        logging.info('batch_size: %d', batch_size)

        self.policy_cell = LinearWrapper(
            tf.contrib.rnn.MultiRNNCell(
                [tf.contrib.rnn.BasicLSTMCell(cell_size)
                 for cell_size in config.policy_lstm_sizes]),
            self.action_space,
            dtype=dtype,
            suppress_index=None if self.allow_eos_token else misc.BF_EOS_INT)
        self.value_cell = LinearWrapper(
            tf.contrib.rnn.MultiRNNCell(
                [tf.contrib.rnn.BasicLSTMCell(cell_size)
                 for cell_size in config.value_lstm_sizes]),
            1,
            dtype=dtype)

        obs_embedding_scope = 'obs_embed'
        with tf.variable_scope(
                obs_embedding_scope,
                initializer=tf.random_uniform_initializer(minval=-1.0, maxval=1.0)):
            obs_embeddings = tf.get_variable(
                'embeddings',
                [self.observation_space, config.obs_embedding_size],
                dtype=dtype, trainable=self.embeddings_trainable)
            self.obs_embeddings = obs_embeddings

        ################################
        # RL policy and value networks #
        ################################

        initial_state = tf.fill([batch_size], misc.BF_EOS_INT)

        def loop_fn(loop_time, cell_output, cell_state, loop_state):
            """Function called by tf.nn.raw_rnn to instantiate body of the while_loop.

            See https://www.tensorflow.org/api_docs/python/tf/nn/raw_rnn for more
            information.

            When time is 0, and cell_output, cell_state, loop_state are all None,
            `loop_fn` will create the initial input, internal cell state, and loop
            state. When time > 0, `loop_fn` will operate on previous cell output,
            state, and loop state.

            Args:
              loop_time: A scalar tensor holding the current timestep (zero based
                  counting).
              cell_output: Output of the raw_rnn cell at the current timestep.
              cell_state: Cell internal state at the current timestep.
              loop_state: Additional loop state. These tensors were returned by the
                  previous call to `loop_fn`.

            Returns:
              elements_finished: Bool tensor of shape [batch_size] which marks each
                  sequence in the batch as being finished or not finished.
              next_input: A tensor containing input to be fed into the cell at the
                  next timestep.
              next_cell_state: Cell internal state to be fed into the cell at the
                  next timestep.
              emit_output: Tensor to be added to the TensorArray returned by raw_rnn
                  as output from the while_loop.
              next_loop_state: Additional loop state. These tensors will be fed back
                  into the next call to `loop_fn` as `loop_state`.
            """
            if cell_output is None:  # 0th time step.
                next_cell_state = self.policy_cell.zero_state(batch_size, dtype)
                elements_finished = tf.zeros([batch_size], tf.bool)
                output_lengths = tf.ones([batch_size], dtype=tf.int32)
                next_input = tf.gather(obs_embeddings, initial_state)
                emit_output = None
                next_loop_state = (
                    tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True),
                    output_lengths,
                    elements_finished
                )
            else:
                scaled_logits = cell_output * config.softmax_tr  # Scale temperature.
                prev_chosen, prev_output_lengths, prev_elements_finished = loop_state
                next_cell_state = cell_state
                chosen_outputs = tf.to_int32(tf.where(
                    tf.logical_not(prev_elements_finished),
                    tf.multinomial(logits=scaled_logits, num_samples=1)[:, 0],
                    tf.zeros([batch_size], dtype=tf.int64)))
                elements_finished = tf.logical_or(
                    tf.equal(chosen_outputs, misc.BF_EOS_INT),
                    loop_time >= global_config.timestep_limit)
                output_lengths = tf.where(
                    elements_finished,
                    prev_output_lengths,
                    # length includes EOS token. empty seq has len 1.
                    tf.tile(tf.expand_dims(loop_time + 1, 0), [batch_size])
                )
                next_input = tf.gather(obs_embeddings, chosen_outputs)
                emit_output = scaled_logits
                next_loop_state = (prev_chosen.write(loop_time - 1, chosen_outputs),
                                   output_lengths,
                                   tf.logical_or(prev_elements_finished,
                                                 elements_finished))
            return (elements_finished, next_input, next_cell_state, emit_output,
                    next_loop_state)

        with tf.variable_scope('policy'):
            (decoder_outputs_ta,
             _,  # decoder_state
             (sampled_output_ta, output_lengths, _)) = tf.nn.raw_rnn(
                cell=self.policy_cell,
                loop_fn=loop_fn)
        policy_logits = tf.transpose(decoder_outputs_ta.stack(), (1, 0, 2),
                                     name='policy_logits')
        sampled_tokens = tf.transpose(sampled_output_ta.stack(), (1, 0),
                                      name='sampled_tokens')
        # Add SOS to beginning of the sequence.
        rshift_sampled_tokens = rshift_time(sampled_tokens, fill=misc.BF_EOS_INT)

        # Initial state is 0, 2nd state is first token.
        # Note: If value of last state is computed, this will be used as bootstrap.
        if self.a2c:
            with tf.variable_scope('value'):
                value_output, _ = tf.nn.dynamic_rnn(
                    self.value_cell,
                    tf.gather(obs_embeddings, rshift_sampled_tokens),
                    sequence_length=output_lengths,
                    dtype=dtype)
            value = tf.squeeze(value_output, axis=[2])
        else:
            value = tf.zeros([], dtype=dtype)

        # for sampling actions from the agent, and which told tensors for doing
        # gradient updates on the agent.
        self.sampled_batch = AttrDict(
            logits=policy_logits,
            value=value,
            tokens=sampled_tokens,
            episode_lengths=output_lengths,
            probs=tf.nn.softmax(policy_logits),
            log_probs=tf.nn.log_softmax(policy_logits))

        # adjusted_lengths can be less than the full length of each episode.
        # Use this to train on only part of an episode (starting from t=0).
        self.adjusted_lengths = tf.placeholder(
            tf.int32, [None], name='adjusted_lengths')
        self.policy_multipliers = tf.placeholder(
            dtype,
            [None, None],
            name='policy_multipliers')
        # Empirical value, i.e. discounted sum of observed future rewards from each
        # time step in the episode.
        self.empirical_values = tf.placeholder(
            dtype,
            [None, None],
            name='empirical_values')

        # Off-policy training. Just add supervised loss to the RL loss.
        self.off_policy_targets = tf.placeholder(
            tf.int32,
            [None, None],
            name='off_policy_targets')
        self.off_policy_target_lengths = tf.placeholder(
            tf.int32, [None], name='off_policy_target_lengths')

        self.actions = tf.placeholder(tf.int32, [None, None], name='actions')
        # Add SOS to beginning of the sequence.
        inputs = rshift_time(self.actions, fill=misc.BF_EOS_INT)
        with tf.variable_scope('policy', reuse=True):
            logits, _ = tf.nn.dynamic_rnn(
                self.policy_cell, tf.gather(obs_embeddings, inputs),
                sequence_length=self.adjusted_lengths,
                dtype=dtype)

        if self.a2c:
            with tf.variable_scope('value', reuse=True):
                value_output, _ = tf.nn.dynamic_rnn(
                    self.value_cell,
                    tf.gather(obs_embeddings, inputs),
                    sequence_length=self.adjusted_lengths,
                    dtype=dtype)
            value2 = tf.squeeze(value_output, axis=[2])
        else:
            value2 = tf.zeros([], dtype=dtype)

        self.given_batch = AttrDict(
            logits=logits,
            value=value2,
            tokens=sampled_tokens,
            episode_lengths=self.adjusted_lengths,
            probs=tf.nn.softmax(logits),
            log_probs=tf.nn.log_softmax(logits))

        # Episode masks.
        max_episode_length = tf.shape(self.actions)[1]
        # range_row shape: [1, max_episode_length]
        range_row = tf.expand_dims(tf.range(max_episode_length), 0)
        episode_masks = tf.cast(
            tf.less(range_row, tf.expand_dims(self.given_batch.episode_lengths, 1)),
            dtype=dtype)
        episode_masks_3d = tf.expand_dims(episode_masks, 2)

        # Length adjusted episodes.
        self.a_probs = a_probs = self.given_batch.probs * episode_masks_3d
        self.a_log_probs = a_log_probs = (
                self.given_batch.log_probs * episode_masks_3d)
        self.a_value = a_value = self.given_batch.value * episode_masks
        self.a_policy_multipliers = a_policy_multipliers = (
                self.policy_multipliers * episode_masks)
        if self.a2c:
            self.a_empirical_values = a_empirical_values = (
                    self.empirical_values * episode_masks)

        # pi_loss is scalar
        acs_onehot = tf.one_hot(self.actions, self.action_space, dtype=dtype)
        self.acs_onehot = acs_onehot
        chosen_masked_log_probs = acs_onehot * a_log_probs
        pi_target = tf.expand_dims(a_policy_multipliers, -1)
        pi_loss_per_step = chosen_masked_log_probs * pi_target  # Maximize.
        self.pi_loss = pi_loss = (
                -tf.reduce_mean(tf.reduce_sum(pi_loss_per_step, axis=[1, 2]), axis=0)
                * MAGIC_LOSS_MULTIPLIER)  # Minimize.
        assert len(self.pi_loss.shape) == 0  # pylint: disable=g-explicit-length-test

        # shape: [batch_size, time]
        self.chosen_log_probs = tf.reduce_sum(chosen_masked_log_probs, axis=2)
        self.chosen_probs = tf.reduce_sum(acs_onehot * a_probs, axis=2)

        # loss of value function
        if self.a2c:
            vf_loss_per_step = tf.square(a_value - a_empirical_values)
            self.vf_loss = vf_loss = (
                    tf.reduce_mean(tf.reduce_sum(vf_loss_per_step, axis=1), axis=0)
                    * MAGIC_LOSS_MULTIPLIER)  # Minimize.
            assert len(self.vf_loss.shape) == 0  # pylint: disable=g-explicit-length-test
        else:
            self.vf_loss = vf_loss = 0.0

        # Maximize entropy regularizer
        self.entropy = entropy = (
                -tf.reduce_mean(
                    tf.reduce_sum(a_probs * a_log_probs, axis=[1, 2]), axis=0)
                * MAGIC_LOSS_MULTIPLIER)  # Maximize
        self.negentropy = -entropy  # Minimize negentropy.
        assert len(self.negentropy.shape) == 0  # pylint: disable=g-explicit-length-test

        # off-policy loss
        self.offp_switch = tf.placeholder(dtype, [], name='offp_switch')
        if self.top_episodes is not None:
            # Add SOS to beginning of the sequence.
            offp_inputs = tf.gather(obs_embeddings,
                                    rshift_time(self.off_policy_targets,
                                                fill=misc.BF_EOS_INT))
            with tf.variable_scope('policy', reuse=True):
                offp_logits, _ = tf.nn.dynamic_rnn(
                    self.policy_cell, offp_inputs, self.off_policy_target_lengths,
                    dtype=dtype)  # shape: [batch_size, time, action_space]
            topk_loss_per_step = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=self.off_policy_targets,
                logits=offp_logits,
                name='topk_loss_per_logit')
            # Take mean over batch dimension so that the loss multiplier strength is
            # independent of batch size. Sum over time dimension.
            topk_loss = tf.reduce_mean(
                tf.reduce_sum(topk_loss_per_step, axis=1), axis=0)
            assert len(topk_loss.shape) == 0  # pylint: disable=g-explicit-length-test
            self.topk_loss = topk_loss * self.offp_switch
            logging.info('Including off policy loss.')
        else:
            self.topk_loss = topk_loss = 0.0

        self.entropy_hparam = tf.constant(
            config.entropy_beta, dtype=dtype, name='entropy_beta')

        self.pi_loss_term = pi_loss * self.pi_loss_hparam
        self.vf_loss_term = vf_loss * self.vf_loss_hparam
        self.entropy_loss_term = self.negentropy * self.entropy_hparam
        self.topk_loss_term = self.topk_loss_hparam * topk_loss
        self.loss = (
                self.pi_loss_term
                + self.vf_loss_term
                + self.entropy_loss_term
                + self.topk_loss_term)

        params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                   tf.get_variable_scope().name)
        self.trainable_variables = params
        self.sync_variables = self.trainable_variables
        non_embedding_params = [p for p in params
                                if obs_embedding_scope not in p.name]
        self.non_embedding_params = non_embedding_params
        self.params = params

        if config.regularizer:
            logging.info('Adding L2 regularizer with scale %.2f.',
                         config.regularizer)
            self.regularizer = config.regularizer * sum(
                tf.nn.l2_loss(w) for w in non_embedding_params)
            self.loss += self.regularizer
        else:
            logging.info('Skipping regularizer.')
            self.regularizer = 0.0

        # Only build gradients graph for local model.
        if self.is_local:
            unclipped_grads = tf.gradients(self.loss, params)
            self.dense_unclipped_grads = [
                tf.convert_to_tensor(g) for g in unclipped_grads]
            self.grads, self.global_grad_norm = tf.clip_by_global_norm(
                unclipped_grads, config.grad_clip_threshold)
            self.gradients_dict = dict(zip(params, self.grads))
            self.optimizer = make_optimizer(config.optimizer, self.learning_rate)
            self.all_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                                   tf.get_variable_scope().name)

        self.do_iw_summaries = do_iw_summaries
        if self.do_iw_summaries:
            b = None
            self.log_iw_replay_ph = tf.placeholder(tf.float32, [b],
                                                   'log_iw_replay_ph')
            self.log_iw_policy_ph = tf.placeholder(tf.float32, [b],
                                                   'log_iw_policy_ph')
            self.log_prob_replay_ph = tf.placeholder(tf.float32, [b],
                                                     'log_prob_replay_ph')
            self.log_prob_policy_ph = tf.placeholder(tf.float32, [b],
                                                     'log_prob_policy_ph')
            self.log_norm_replay_weights_ph = tf.placeholder(
                tf.float32, [b], 'log_norm_replay_weights_ph')
            self.iw_summary_op = tf.summary.merge([
                tf.summary.histogram('is/log_iw_replay', self.log_iw_replay_ph),
                tf.summary.histogram('is/log_iw_policy', self.log_iw_policy_ph),
                tf.summary.histogram('is/log_prob_replay', self.log_prob_replay_ph),
                tf.summary.histogram('is/log_prob_policy', self.log_prob_policy_ph),
                tf.summary.histogram(
                    'is/log_norm_replay_weights', self.log_norm_replay_weights_ph),
            ])

    def make_summary_ops(self):
        """Construct summary ops for the model."""
        # size = number of timesteps across entire batch. Number normalized by size
        # will not be affected by the amount of padding at the ends of sequences
        # in the batch.
        size = tf.cast(
            tf.reduce_sum(self.given_batch.episode_lengths), dtype=self.dtype)
        offp_size = tf.cast(tf.reduce_sum(self.off_policy_target_lengths),
                            dtype=self.dtype)
        scope_prefix = self.parent_scope_name

        def _remove_prefix(prefix, name):
            assert name.startswith(prefix)
            return name[len(prefix):]

        # RL summaries.
        self.rl_summary_op = tf.summary.merge(
            [tf.summary.scalar('model/policy_loss', self.pi_loss / size),
             tf.summary.scalar('model/value_loss', self.vf_loss / size),
             tf.summary.scalar('model/topk_loss', self.topk_loss / offp_size),
             tf.summary.scalar('model/entropy', self.entropy / size),
             tf.summary.scalar('model/loss', self.loss / size),
             tf.summary.scalar('model/grad_norm',
                               tf.global_norm(self.grads)),
             tf.summary.scalar('model/unclipped_grad_norm', self.global_grad_norm),
             tf.summary.scalar('model/non_embedding_var_norm',
                               tf.global_norm(self.non_embedding_params)),
             tf.summary.scalar('hparams/entropy_beta', self.entropy_hparam),
             tf.summary.scalar('hparams/topk_loss_hparam', self.topk_loss_hparam),
             tf.summary.scalar('hparams/learning_rate', self.learning_rate),
             tf.summary.scalar('model/trainable_var_norm',
                               tf.global_norm(self.trainable_variables)),
             tf.summary.scalar('loss/loss', self.loss),
             tf.summary.scalar('loss/entropy', self.entropy_loss_term),
             tf.summary.scalar('loss/vf', self.vf_loss_term),
             tf.summary.scalar('loss/policy', self.pi_loss_term),
             tf.summary.scalar('loss/offp', self.topk_loss_term)] +
            [tf.summary.scalar(
                'param_norms/' + _remove_prefix(scope_prefix + '/', p.name),
                tf.norm(p))
                for p in self.params] +
            [tf.summary.scalar(
                'grad_norms/' + _remove_prefix(scope_prefix + '/', p.name),
                tf.norm(g))
                for p, g in zip(self.params, self.grads)] +
            [tf.summary.scalar(
                'unclipped_grad_norms/' + _remove_prefix(scope_prefix + '/',
                                                         p.name),
                tf.norm(g))
                for p, g in zip(self.params, self.dense_unclipped_grads)])

        self.text_summary_placeholder = tf.placeholder(tf.string, shape=[])
        self.rl_text_summary_op = tf.summary.text('rl',
                                                  self.text_summary_placeholder)

    def _rl_text_summary(self, session, step, npe, tot_r, num_steps,
                         input_case, code_output, code, reason):
        """Logs summary about a single episode and creates a text_summary for TB.

        Args:
          session: tf.Session instance.
          step: Global training step.
          npe: Number of programs executed so far.
          tot_r: Total reward.
          num_steps: Number of timesteps in the episode (i.e. code length).
          input_case: Inputs for test cases.
          code_output: Outputs produced by running the code on the inputs.
          code: String representation of the code.
          reason: Reason for the reward assigned by the task.

        Returns:
          Serialized text summary data for tensorboard.
        """
        if not input_case:
            input_case = ' '
        if not code_output:
            code_output = ' '
        if not code:
            code = ' '
        text = (
                'Tot R: **%.2f**;  Len: **%d**;  Reason: **%s**\n\n'
                'Input: **`%s`**; Output: **`%s`**\n\nCode: **`%s`**'
                % (tot_r, num_steps, reason, input_case, code_output, code))
        text_summary = session.run(self.rl_text_summary_op,
                                   {self.text_summary_placeholder: text})
        logging.info(
            'Step %d.\t NPE: %d\t Reason: %s.\t Tot R: %.2f.\t Length: %d. '
            '\tInput: %s \tOutput: %s \tProgram: %s',
            step, npe, reason, tot_r, num_steps, input_case,
            code_output, code)
        return text_summary

    def _rl_reward_summary(self, total_rewards):
        """Create summary ops that report on episode rewards.

        Creates summaries for average, median, max, and min rewards in the batch.

        Args:
          total_rewards: Tensor of shape [batch_size] containing the total reward
              from each episode in the batch.

        Returns:
          tf.Summary op.
        """
        tr = np.asarray(total_rewards)
        reward_summary = tf.Summary(value=[
            tf.Summary.Value(
                tag='reward/avg',
                simple_value=np.mean(tr)),
            tf.Summary.Value(
                tag='reward/med',
                simple_value=np.median(tr)),
            tf.Summary.Value(
                tag='reward/max',
                simple_value=np.max(tr)),
            tf.Summary.Value(
                tag='reward/min',
                simple_value=np.min(tr))])
        return reward_summary

    def _iw_summary(self, session, replay_iw, replay_log_probs,
                    norm_replay_weights, on_policy_iw,
                    on_policy_log_probs):
        """Compute summaries for importance weights at a given batch.

        Args:
          session: tf.Session instance.
          replay_iw: Importance weights for episodes from replay buffer.
          replay_log_probs: Total log probabilities of the replay episodes under the
              current policy.
          norm_replay_weights: Normalized replay weights, i.e. values in `replay_iw`
              divided by the total weight in the entire replay buffer. Note, this is
              also the probability of selecting each episode from the replay buffer
              (in a roulette wheel replay buffer).
          on_policy_iw: Importance weights for episodes sampled from the current
              policy.
          on_policy_log_probs: Total log probabilities of the on-policy episodes
              under the current policy.

        Returns:
          Serialized TF summaries. Use a summary writer to write these summaries to
          disk.
        """
        return session.run(
            self.iw_summary_op,
            {self.log_iw_replay_ph: np.log(replay_iw),
             self.log_iw_policy_ph: np.log(on_policy_iw),
             self.log_norm_replay_weights_ph: np.log(norm_replay_weights),
             self.log_prob_replay_ph: replay_log_probs,
             self.log_prob_policy_ph: on_policy_log_probs})

    def _compute_iw(self, policy_log_probs, replay_weights):
        """Compute importance weights for a batch of episodes.

        Arguments are iterables of length batch_size.

        Args:
          policy_log_probs: Log probability of each episode under the current
              policy.
          replay_weights: Weight of each episode in the replay buffer. 0 for
              episodes not sampled from the replay buffer (i.e. sampled from the
              policy).

        Returns:
          Numpy array of shape [batch_size] containing the importance weight for
          each episode in the batch.
        """
        log_total_replay_weight = log(self.experience_replay.total_weight)

        # importance weight
        # = 1 / [(1 - a) + a * exp(log(replay_weight / total_weight / p))]
        # = 1 / ((1-a) + a*q/p)
        a = float(self.replay_alpha)
        a_com = 1.0 - a  # compliment of a
        importance_weights = np.asarray(
            [1.0 / (a_com
                    + a * exp((log(replay_weight) - log_total_replay_weight)
                              - log_p))
             if replay_weight > 0 else 1.0 / a_com
             for log_p, replay_weight
             in zip(policy_log_probs, replay_weights)])
        return importance_weights

    def update_step(self, session, rl_batch, train_op, global_step_op,
                    return_gradients=False):
        """Perform gradient update on the model.

        Args:
          session: tf.Session instance.
          rl_batch: RLBatch instance from data.py. Use DataManager to create a
              RLBatch for each call to update_step. RLBatch contains a batch of
              tasks.
          train_op: A TF op which will perform the gradient update. LMAgent does not
              own its training op, so that trainers can do distributed training
              and construct a specialized training op.
          global_step_op: A TF op which will return the current global step when
              run (should not increment it).
          return_gradients: If True, the gradients will be saved and returned from
              this method call. This is useful for testing.

        Returns:
          Results from the update step in a UpdateStepResult namedtuple, including
          global step, global NPE, serialized summaries, and optionally gradients.
        """
        assert self.is_local

        # Do update for REINFORCE or REINFORCE + replay buffer.
        if self.experience_replay is None:
            # Train with on-policy REINFORCE.

            # Sample new programs from the policy.
            num_programs_from_policy = rl_batch.batch_size
            (batch_actions,
             batch_values,
             episode_lengths) = session.run(
                [self.sampled_batch.tokens, self.sampled_batch.value,
                 self.sampled_batch.episode_lengths])
            if episode_lengths.size == 0:
                # This should not happen.
                logging.warn(
                    'Shapes:\n'
                    'batch_actions.shape: %s\n'
                    'batch_values.shape: %s\n'
                    'episode_lengths.shape: %s\n',
                    batch_actions.shape, batch_values.shape, episode_lengths.shape)

            # Compute rewards.
            code_scores = compute_rewards(
                rl_batch, batch_actions, episode_lengths)
            code_strings = code_scores.code_strings
            batch_tot_r = code_scores.total_rewards
            test_cases = code_scores.test_cases
            code_outputs = code_scores.code_outputs
            reasons = code_scores.reasons

            # Process on-policy samples.
            batch_targets, batch_returns = process_episodes(
                code_scores.batch_rewards, episode_lengths, a2c=self.a2c,
                baselines=self.ema_by_len,
                batch_values=batch_values)
            batch_policy_multipliers = batch_targets
            batch_emp_values = batch_returns if self.a2c else [[]]
            adjusted_lengths = episode_lengths

            if self.top_episodes:
                assert len(self.top_episodes) > 0  # pylint: disable=g-explicit-length-test
                off_policy_targets = [
                    item for item, _
                    in self.top_episodes.random_sample(self.topk_batch_size)]
                off_policy_target_lengths = [len(t) for t in off_policy_targets]
                off_policy_targets = utils.stack_pad(off_policy_targets, pad_axes=0,
                                                     dtype=np.int32)
                offp_switch = 1
            else:
                off_policy_targets = [[0]]
                off_policy_target_lengths = [1]
                offp_switch = 0

            fetches = {
                'global_step': global_step_op,
                'program_count': self.program_count,
                'summaries': self.rl_summary_op,
                'train_op': train_op,
                'gradients': self.gradients_dict if return_gradients else self.no_op}
            fetched = session.run(
                fetches,
                {self.actions: batch_actions,
                 self.empirical_values: batch_emp_values,
                 self.policy_multipliers: batch_policy_multipliers,
                 self.adjusted_lengths: adjusted_lengths,
                 self.off_policy_targets: off_policy_targets,
                 self.off_policy_target_lengths: off_policy_target_lengths,
                 self.offp_switch: offp_switch})

            combined_adjusted_lengths = adjusted_lengths
            combined_returns = batch_returns
        else:
            # Train with REINFORCE + off-policy replay buffer by using importance
            # sampling.

            # Sample new programs from the policy.
            # Note: batch size is constant. A full batch will be sampled, but not all
            # programs will be executed and added to the replay buffer. Those which
            # are not executed will be discarded and not counted.
            batch_actions, batch_values, episode_lengths, log_probs = session.run(
                [self.sampled_batch.tokens, self.sampled_batch.value,
                 self.sampled_batch.episode_lengths, self.sampled_batch.log_probs])
            if episode_lengths.size == 0:
                # This should not happen.
                logging.warn(
                    'Shapes:\n'
                    'batch_actions.shape: %s\n'
                    'batch_values.shape: %s\n'
                    'episode_lengths.shape: %s\n',
                    batch_actions.shape, batch_values.shape, episode_lengths.shape)

            # Sample from experince replay buffer
            empty_replay_buffer = (
                self.experience_replay.is_empty()
                if self.experience_replay is not None else True)
            num_programs_from_replay_buff = (
                self.num_replay_per_batch if not empty_replay_buffer else 0)
            num_programs_from_policy = (
                    rl_batch.batch_size - num_programs_from_replay_buff)
            if (not empty_replay_buffer) and num_programs_from_replay_buff:
                result = self.experience_replay.sample_many(
                    num_programs_from_replay_buff)
                experience_samples, replay_weights = zip(*result)
                (replay_actions,
                 replay_rewards,
                 _,  # log probs
                 replay_adjusted_lengths) = zip(*experience_samples)

                replay_batch_actions = utils.stack_pad(replay_actions, pad_axes=0,
                                                       dtype=np.int32)

                # compute log probs for replay samples under current policy
                all_replay_log_probs, = session.run(
                    [self.given_batch.log_probs],
                    {self.actions: replay_batch_actions,
                     self.adjusted_lengths: replay_adjusted_lengths})
                replay_log_probs = [
                    np.choose(replay_actions[i], all_replay_log_probs[i, :l].T).sum()
                    for i, l in enumerate(replay_adjusted_lengths)]
            else:
                # Replay buffer is empty. Do not sample from it.
                replay_actions = None
                replay_policy_multipliers = None
                replay_adjusted_lengths = None
                replay_log_probs = None
                replay_weights = None
                replay_returns = None
                on_policy_weights = [0] * num_programs_from_replay_buff

            assert not self.a2c  # TODO(danabo): Support A2C with importance sampling.

            # Compute rewards.
            code_scores = compute_rewards(
                rl_batch, batch_actions, episode_lengths,
                batch_size=num_programs_from_policy)
            code_strings = code_scores.code_strings
            batch_tot_r = code_scores.total_rewards
            test_cases = code_scores.test_cases
            code_outputs = code_scores.code_outputs
            reasons = code_scores.reasons

            # Process on-policy samples.
            p = num_programs_from_policy
            batch_targets, batch_returns = process_episodes(
                code_scores.batch_rewards, episode_lengths[:p], a2c=False,
                baselines=self.ema_by_len)
            batch_policy_multipliers = batch_targets
            batch_emp_values = [[]]
            on_policy_returns = batch_returns

            # Process off-policy samples.
            if (not empty_replay_buffer) and num_programs_from_replay_buff:
                offp_batch_rewards = [
                    [0.0] * (l - 1) + [r]
                    for l, r in zip(replay_adjusted_lengths, replay_rewards)]
                assert len(offp_batch_rewards) == num_programs_from_replay_buff
                assert len(replay_adjusted_lengths) == num_programs_from_replay_buff
                replay_batch_targets, replay_returns = process_episodes(
                    offp_batch_rewards, replay_adjusted_lengths, a2c=False,
                    baselines=self.ema_by_len)
                # Convert 2D array back into ragged 2D list.
                replay_policy_multipliers = [
                    replay_batch_targets[i, :l]
                    for i, l
                    in enumerate(
                        replay_adjusted_lengths[:num_programs_from_replay_buff])]

            adjusted_lengths = episode_lengths[:num_programs_from_policy]

            if self.top_episodes:
                assert len(self.top_episodes) > 0  # pylint: disable=g-explicit-length-test
                off_policy_targets = [
                    item for item, _
                    in self.top_episodes.random_sample(self.topk_batch_size)]
                off_policy_target_lengths = [len(t) for t in off_policy_targets]
                off_policy_targets = utils.stack_pad(off_policy_targets, pad_axes=0,
                                                     dtype=np.int32)
                offp_switch = 1
            else:
                off_policy_targets = [[0]]
                off_policy_target_lengths = [1]
                offp_switch = 0

            # On-policy episodes.
            if num_programs_from_policy:
                separate_actions = [
                    batch_actions[i, :l]
                    for i, l in enumerate(adjusted_lengths)]
                chosen_log_probs = [
                    np.choose(separate_actions[i], log_probs[i, :l].T)
                    for i, l in enumerate(adjusted_lengths)]
                new_experiences = [
                    (separate_actions[i],
                     batch_tot_r[i],
                     chosen_log_probs[i].sum(), l)
                    for i, l in enumerate(adjusted_lengths)]
                on_policy_policy_multipliers = [
                    batch_policy_multipliers[i, :l]
                    for i, l in enumerate(adjusted_lengths)]
                (on_policy_actions,
                 _,  # rewards
                 on_policy_log_probs,
                 on_policy_adjusted_lengths) = zip(*new_experiences)
            else:
                new_experiences = []
                on_policy_policy_multipliers = []
                on_policy_actions = []
                on_policy_log_probs = []
                on_policy_adjusted_lengths = []

            if (not empty_replay_buffer) and num_programs_from_replay_buff:
                # Look for new experiences in replay buffer. Assign weight if an episode
                # is in the buffer.
                on_policy_weights = [0] * num_programs_from_policy
                for i, cs in enumerate(code_strings):
                    if self.experience_replay.has_key(cs):
                        on_policy_weights[i] = self.experience_replay.get_weight(cs)

            # Randomly select on-policy or off policy episodes to train on.
            combined_actions = join(replay_actions, on_policy_actions)
            combined_policy_multipliers = join(
                replay_policy_multipliers, on_policy_policy_multipliers)
            combined_adjusted_lengths = join(
                replay_adjusted_lengths, on_policy_adjusted_lengths)
            combined_returns = join(replay_returns, on_policy_returns)
            combined_actions = utils.stack_pad(combined_actions, pad_axes=0)
            combined_policy_multipliers = utils.stack_pad(combined_policy_multipliers,
                                                          pad_axes=0)
            # P
            combined_on_policy_log_probs = join(replay_log_probs, on_policy_log_probs)
            # Q
            # Assume weight is zero for all sequences sampled from the policy.
            combined_q_weights = join(replay_weights, on_policy_weights)

            # Importance adjustment. Naive formulation:
            # E_{x~p}[f(x)] ~= 1/N sum_{x~p}(f(x)) ~= 1/N sum_{x~q}(f(x) * p(x)/q(x)).
            # p(x) is the policy, and q(x) is the off-policy distribution, i.e. replay
            # buffer distribution. Importance weight w(x) = p(x) / q(x).

            # Instead of sampling from the replay buffer only, we sample from a
            # mixture distribution of the policy and replay buffer.
            # We are sampling from the mixture a*q(x) + (1-a)*p(x), where 0 <= a <= 1.
            # Thus the importance weight w(x) = p(x) / (a*q(x) + (1-a)*p(x))
            # = 1 / ((1-a) + a*q(x)/p(x)) where q(x) is 0 for x sampled from the
            #                             policy.
            # Note: a = self.replay_alpha
            if empty_replay_buffer:
                # The replay buffer is empty.
                # Do no gradient update this step. The replay buffer will have stuff in
                # it next time.
                combined_policy_multipliers *= 0
            elif not num_programs_from_replay_buff:
                combined_policy_multipliers = np.ones([len(combined_actions), 1],
                                                      dtype=np.float32)
            else:
                # If a < 1 compute importance weights
                # importance weight
                # = 1 / [(1 - a) + a * exp(log(replay_weight / total_weight / p))]
                # = 1 / ((1-a) + a*q/p)
                importance_weights = self._compute_iw(combined_on_policy_log_probs,
                                                      combined_q_weights)
                if self.config.iw_normalize:
                    importance_weights *= (
                            float(rl_batch.batch_size) / importance_weights.sum())
                combined_policy_multipliers *= importance_weights.reshape(-1, 1)

            # Train on replay batch, top-k MLE.
            assert self.program_count is not None
            fetches = {
                'global_step': global_step_op,
                'program_count': self.program_count,
                'summaries': self.rl_summary_op,
                'train_op': train_op,
                'gradients': self.gradients_dict if return_gradients else self.no_op}
            fetched = session.run(
                fetches,
                {self.actions: combined_actions,
                 self.empirical_values: [[]],  # replay_emp_values,
                 self.policy_multipliers: combined_policy_multipliers,
                 self.adjusted_lengths: combined_adjusted_lengths,
                 self.off_policy_targets: off_policy_targets,
                 self.off_policy_target_lengths: off_policy_target_lengths,
                 self.offp_switch: offp_switch})

            # Add to experience replay buffer.
            self.experience_replay.add_many(
                objs=new_experiences,
                weights=[exp(r / self.replay_temperature) for r in batch_tot_r],
                keys=code_strings)

        # Update program count.
        session.run(
            [self.program_count_add_op],
            {self.program_count_add_ph: num_programs_from_policy})

        # Update EMA baselines on the mini-batch which we just did traning on.
        if not self.a2c:
            for i in xrange(rl_batch.batch_size):
                episode_length = combined_adjusted_lengths[i]
                empirical_returns = combined_returns[i, :episode_length]
                for j in xrange(episode_length):
                    # Update ema_baselines in place.
                    self.ema_by_len[j] = (
                            self.ema_baseline_decay * self.ema_by_len[j]
                            + (1 - self.ema_baseline_decay) * empirical_returns[j])

        global_step = fetched['global_step']
        global_npe = fetched['program_count']
        core_summaries = fetched['summaries']
        summaries_list = [core_summaries]

        if num_programs_from_policy:
            s_i = 0
            text_summary = self._rl_text_summary(
                session,
                global_step,
                global_npe,
                batch_tot_r[s_i],
                episode_lengths[s_i], test_cases[s_i],
                code_outputs[s_i], code_strings[s_i], reasons[s_i])
            reward_summary = self._rl_reward_summary(batch_tot_r)

            is_best = False
            if self.global_best_reward_fn:
                # Save best reward.
                best_reward = np.max(batch_tot_r)
                is_best = self.global_best_reward_fn(session, best_reward)

            if self.found_solution_op is not None and 'correct' in reasons:
                session.run(self.found_solution_op)

                # Save program to disk for record keeping.
                if self.stop_on_success:
                    solutions = [
                        {'code': code_strings[i], 'reward': batch_tot_r[i],
                         'npe': global_npe}
                        for i in xrange(len(reasons)) if reasons[i] == 'correct']
                elif is_best:
                    solutions = [
                        {'code': code_strings[np.argmax(batch_tot_r)],
                         'reward': np.max(batch_tot_r),
                         'npe': global_npe}]
                else:
                    solutions = []
                if solutions:
                    if self.assign_code_solution_fn:
                        self.assign_code_solution_fn(session, solutions[0]['code'])
                    with tf.gfile.FastGFile(self.logging_file, 'a') as writer:
                        for solution_dict in solutions:
                            writer.write(str(solution_dict) + '\n')

            max_i = np.argmax(batch_tot_r)
            max_tot_r = batch_tot_r[max_i]
            if max_tot_r >= self.top_reward:
                if max_tot_r >= self.top_reward:
                    self.top_reward = max_tot_r
                logging.info('Top code: r=%.2f, \t%s', max_tot_r, code_strings[max_i])
            if self.top_episodes is not None:
                self.top_episodes.push(
                    max_tot_r, tuple(batch_actions[max_i, :episode_lengths[max_i]]))

            summaries_list += [text_summary, reward_summary]

            if self.do_iw_summaries and not empty_replay_buffer:
                # prob of replay samples under replay buffer sampling.
                norm_replay_weights = [
                    w / self.experience_replay.total_weight
                    for w in replay_weights]
                replay_iw = self._compute_iw(replay_log_probs, replay_weights)
                on_policy_iw = self._compute_iw(on_policy_log_probs, on_policy_weights)
                summaries_list.append(
                    self._iw_summary(
                        session, replay_iw, replay_log_probs, norm_replay_weights,
                        on_policy_iw, on_policy_log_probs))

        return UpdateStepResult(
            global_step=global_step,
            global_npe=global_npe,
            summaries_list=summaries_list,
            gradients_dict=fetched['gradients'])

def io_to_text(io_case, io_type):
    if isinstance(io_case, misc.IOTuple):
        # If there are many strings, join them with ','.
        return ','.join([io_to_text(e, io_type) for e in io_case])
    if io_type == misc.IOType.string:
        # There is one string. Return it.
        return misc.tokens_to_text(io_case)
    if (io_type == misc.IOType.integer
            or io_type == misc.IOType.boolean):
        if len(io_case) == 1:
            return str(io_case[0])
        return str(io_case)

CodeScoreInfo = namedtuple(
    'CodeScoreInfo',
    ['code_strings', 'batch_rewards', 'total_rewards', 'test_cases',
     'code_outputs', 'reasons'])

def compute_rewards(rl_batch, batch_actions, episode_lengths, batch_size=None):
    """Compute rewards for each episode in the batch.

    Args:
      rl_batch: A data.RLBatch instance. This holds information about the task
          each episode is solving, and a reward function for each episode.
      batch_actions: Contains batch of episodes. Each sequence of actions will be
          converted into a BF program and then scored. A numpy array of shape
          [batch_size, max_sequence_length].
      episode_lengths: The sequence length of each episode in the batch. Iterable
          of length batch_size.
      batch_size: (optional) number of programs to score. Use this to limit the
          number of programs executed from this batch. For example, when doing
          importance sampling some of the on-policy episodes will be discarded
          and they should not be executed. `batch_size` can be less than or equal
          to the size of the input batch.

    Returns:
      CodeScoreInfo namedtuple instance. This holds not just the computed rewards,
      but additional information computed during code execution which can be used
      for debugging and monitoring. this includes: BF code strings, test cases
      the code was executed on, code outputs from those test cases, and reasons
      for success or failure.
    """
    code_strings = [
        ''.join([misc.bf_int2char(a) for a in action_sequence[:l]])
        for action_sequence, l in zip(batch_actions, episode_lengths)]
    if batch_size is None:
        batch_size = len(code_strings)
    else:
        assert batch_size <= len(code_strings)
        code_strings = code_strings[:batch_size]

    if isinstance(rl_batch.reward_fns, (list, tuple)):
        # reward_fns is a list of functions, same length as code_strings.
        assert len(rl_batch.reward_fns) >= batch_size
        r_fn_results = [
            rl_batch.reward_fns[i](code_strings[i]) for i in xrange(batch_size)]
    else:
        # reward_fns is allowed to be one function which processes a batch of code
        # strings. This is useful for efficiency and batch level computation.
        r_fn_results = rl_batch.reward_fns(code_strings)

    # Expecting that r_fn returns a list of rewards. Length of list equals
    # length of the code string (including EOS char).

    batch_rewards = [r.episode_rewards for r in r_fn_results]
    total_rewards = [sum(b) for b in batch_rewards]
    test_cases = [io_to_text(r.input_case, r.input_type) for r in r_fn_results]
    code_outputs = [io_to_text(r.code_output, r.output_type)
                    for r in r_fn_results]
    reasons = [r.reason for r in r_fn_results]
    return CodeScoreInfo(
        code_strings=code_strings,
        batch_rewards=batch_rewards,
        total_rewards=total_rewards,
        test_cases=test_cases,
        code_outputs=code_outputs,
        reasons=reasons)

def process_episodes(
        batch_rewards, episode_lengths, a2c=False, baselines=None,
        batch_values=None):
    """Compute REINFORCE targets.

    REINFORCE here takes the form:
    grad_t = grad[log(pi(a_t|c_t))*target_t]
    where c_t is context: i.e. RNN state or environment state (or both).

    Two types of targets are supported:
    1) Advantage actor critic (a2c).
    2) Vanilla REINFORCE with baseline.

    Args:
      batch_rewards: Rewards received in each episode in the batch. A numpy array
          of shape [batch_size, max_sequence_length]. Note, these are per-timestep
          rewards, not total reward.
      episode_lengths: Length of each episode. An iterable of length batch_size.
      a2c: A bool. Whether to compute a2c targets (True) or vanilla targets
          (False).
      baselines: If a2c is False, provide baselines for each timestep. This is a
          list (or indexable container) of length max_time. Note: baselines are
          shared across all episodes, which is why there is no batch dimension.
          It is up to the caller to update baselines accordingly.
      batch_values: If a2c is True, provide values computed by a value estimator.
          A numpy array of shape [batch_size, max_sequence_length].

    Returns:
      batch_targets: REINFORCE targets for each episode and timestep. A numpy
          array of shape [batch_size, max_sequence_length].
      batch_returns: Returns computed for each episode and timestep. This is for
          reference, and is not used in the REINFORCE gradient update (but was
          used to compute the targets). A numpy array of shape
          [batch_size, max_sequence_length].
    """
    num_programs = len(batch_rewards)
    assert num_programs <= len(episode_lengths)
    batch_returns = [None] * num_programs
    batch_targets = [None] * num_programs
    for i in xrange(num_programs):
        episode_length = episode_lengths[i]
        assert len(batch_rewards[i]) == episode_length
        # Compute target for each timestep.
        # If we are computing A2C:
        #    target_t = advantage_t = R_t - V(c_t)
        #    where V(c_t) is a learned value function (provided as `values`).
        # Otherwise:
        #    target_t = R_t - baselines[t]
        #    where `baselines` are provided.
        # In practice we use a more generalized formulation of advantage. See docs
        # for `discounted_advantage_and_rewards`.
        if a2c:
            # Compute advantage.
            assert batch_values is not None
            episode_values = batch_values[i, :episode_length]
            episode_rewards = batch_rewards[i]
            emp_val, gen_adv = rollout_lib.discounted_advantage_and_rewards(
                episode_rewards, episode_values, gamma=1.0, lambda_=1.0)
            batch_returns[i] = emp_val
            batch_targets[i] = gen_adv
        else:
            # Compute return for each timestep. See section 3 of
            # https://arxiv.org/pdf/1602.01783.pdf
            assert baselines is not None
            empirical_returns = rollout_lib.discount(batch_rewards[i], gamma=1.0)
            targets = [None] * episode_length
            for j in xrange(episode_length):
                targets[j] = empirical_returns[j] - baselines[j]
            batch_returns[i] = empirical_returns
            batch_targets[i] = targets
    batch_returns = utils.stack_pad(batch_returns, 0)
    if num_programs:
        batch_targets = utils.stack_pad(batch_targets, 0)
    else:
        batch_targets = np.array([], dtype=np.float32)

    return (batch_targets, batch_returns)


import torch
import time
import numpy as np
import random
import os

from util.config import cfg
cfg.task = 'test'
from util.log import logger
import util.utils as utils
import util.eval as eval

def init():
    global result_dir
    result_dir = os.path.join(cfg.exp_path, 'result', cfg.split)
    backup_dir = os.path.join(result_dir, 'backup_files')
    os.makedirs(backup_dir, exist_ok=True)
    os.makedirs(os.path.join(result_dir, 'predicted_masks'), exist_ok=True)
    os.system('cp test.py {}'.format(backup_dir))
    os.system('cp {} {}'.format(cfg.model_dir, backup_dir))
    os.system('cp {} {}'.format(cfg.dataset_dir, backup_dir))
    os.system('cp {} {}'.format(cfg.config, backup_dir))

    global semantic_label_idx
    semantic_label_idx = list(range(1, 29))

    logger.info(cfg)

    random.seed(cfg.test_seed)
    np.random.seed(cfg.test_seed)
    torch.manual_seed(cfg.test_seed)
    torch.cuda.manual_seed_all(cfg.test_seed)

def test(model, model_fn, data_name, epoch):
    logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')

    if cfg.dataset == 'scannetv2' or cfg.dataset == 'multiscan':
        if data_name == 'scannet' or data_name == 'multiscan':
            from data.scannetv2_inst import Dataset
            dataset = Dataset(test=True)
            dataset.testLoader()
        else:
            print("Error: no data loader - " + data_name)
            exit(0)
    dataloader = dataset.test_data_loader

    with torch.no_grad():
        model = model.eval()

        total_end1 = 0.
        matches = {}
        for i, batch in enumerate(dataloader):

            # inference
            start1 = time.time()
            preds = model_fn(batch, model, epoch)
            end1 = time.time() - start1

            # decode results for evaluation
            N = batch['feats'].shape[0]
            test_scene_name = dataset.test_file_names[int(batch['id'][0])].split('/')[-1].split('_inst_nostuff.pth')[0]
            semantic_scores = preds['semantic']  # (N, nClass=20) float32, cuda
            semantic_pred = semantic_scores.max(1)[1]  # (N) long, cuda
            pt_offsets = preds['pt_offsets']    # (N, 3), float32, cuda
            if (epoch > cfg.prepare_epochs):
                scores = preds['score']   # (nProposal, 1) float, cuda
                scores_pred = torch.sigmoid(scores.view(-1))

                proposals_idx, proposals_offset, mask_scores = preds['proposals']
                # proposals_idx: (sumNPoint, 2), int, cpu, [:, 0] for cluster_id, [:, 1] for corresponding point idxs in N
                # proposals_offset: (nProposal + 1), int, cpu
                proposals_pred = torch.zeros((proposals_offset.shape[0] - 1, N), dtype=torch.int, device=scores_pred.device)
                # (nProposal, N), int, cuda

                # outlier filtering
                test_mask_score_thre = getattr(cfg, 'test_mask_score_thre', -0.5)
                _mask = mask_scores.squeeze(1) > test_mask_score_thre
                proposals_pred[proposals_idx[_mask][:, 0].long(), proposals_idx[_mask][:, 1].long()] = 1

                semantic_id = torch.tensor(semantic_label_idx, device=scores_pred.device) \
                    [semantic_pred[proposals_idx[:, 1][proposals_offset[:-1].long()].long()]] # (nProposal), long
                # semantic_id_idx = semantic_pred[proposals_idx[:, 1][proposals_offset[:-1].long()].long()]

                # score threshold
                score_mask = (scores_pred > cfg.TEST_SCORE_THRESH)
                scores_pred = scores_pred[score_mask]
                proposals_pred = proposals_pred[score_mask]
                semantic_id = semantic_id[score_mask]
                # semantic_id_idx = semantic_id_idx[score_mask]

                # npoint threshold
                proposals_pointnum = proposals_pred.sum(1)
                npoint_mask = (proposals_pointnum >= cfg.TEST_NPOINT_THRESH)
                scores_pred = scores_pred[npoint_mask]
                proposals_pred = proposals_pred[npoint_mask]
                semantic_id = semantic_id[npoint_mask]

                # nms (no need)
                if getattr(cfg, 'using_NMS', False):
                    if semantic_id.shape[0] == 0:
                        pick_idxs = np.empty(0)
                    else:
                        proposals_pred_f = proposals_pred.float()  # (nProposal, N), float, cuda
                        intersection = torch.mm(proposals_pred_f, proposals_pred_f.t())  # (nProposal, nProposal), float, cuda
                        proposals_pointnum = proposals_pred_f.sum(1)  # (nProposal), float, cuda
                        proposals_pn_h = proposals_pointnum.unsqueeze(-1).repeat(1, proposals_pointnum.shape[0])
                        proposals_pn_v = proposals_pointnum.unsqueeze(0).repeat(proposals_pointnum.shape[0], 1)
                        cross_ious = intersection / (proposals_pn_h + proposals_pn_v - intersection)
                        pick_idxs = non_max_suppression(cross_ious.cpu().numpy(), scores_pred.cpu().numpy(), cfg.TEST_NMS_THRESH)
                        # int, (nCluster, N)
                    clusters = proposals_pred[pick_idxs]
                    cluster_scores = scores_pred[pick_idxs]
                    cluster_semantic_id = semantic_id[pick_idxs]
                else:
                    clusters = proposals_pred
                    cluster_scores = scores_pred
                    cluster_semantic_id = semantic_id

                nclusters = clusters.shape[0]

                # prepare for evaluation
                if cfg.eval:
                    pred_info = {}
                    pred_info['conf'] = cluster_scores.cpu().numpy()
                    pred_info['label_id'] = cluster_semantic_id.cpu().numpy()
                    pred_info['mask'] = clusters.cpu().numpy()
                    gt_file = os.path.join(cfg.data_root, cfg.dataset, cfg.split + '_gt', test_scene_name + '.txt')
                    gt2pred, pred2gt = eval.assign_instances_for_scan(test_scene_name, pred_info, gt_file)

                    matches[test_scene_name] = {}
                    matches[test_scene_name]['gt'] = gt2pred
                    matches[test_scene_name]['pred'] = pred2gt

                    if cfg.split == 'val':
                        matches[test_scene_name]['seg_gt'] = batch['labels']
                        matches[test_scene_name]['seg_pred'] = semantic_pred

            # save files
            if cfg.save_semantic:
                os.makedirs(os.path.join(result_dir, 'semantic'), exist_ok=True)
                semantic_np = semantic_pred.cpu().numpy()
                np.save(os.path.join(result_dir, 'semantic', test_scene_name + '.npy'), semantic_np)

            if cfg.save_pt_offsets:
                os.makedirs(os.path.join(result_dir, 'coords_offsets'), exist_ok=True)
                pt_offsets_np = pt_offsets.cpu().numpy()
                coords_np = batch['locs_float'].numpy()
                coords_offsets = np.concatenate((coords_np, pt_offsets_np), 1)   # (N, 6)
                np.save(os.path.join(result_dir, 'coords_offsets', test_scene_name + '.npy'), coords_offsets)

            if(epoch > cfg.prepare_epochs and cfg.save_instance):
                f = open(os.path.join(result_dir, test_scene_name + '.txt'), 'w')
                for proposal_id in range(nclusters):
                    clusters_i = clusters[proposal_id].cpu().numpy()  # (N)
                    semantic_label = np.argmax(np.bincount(semantic_pred[np.where(clusters_i == 1)[0]].cpu()))
                    score = cluster_scores[proposal_id]
                    f.write('predicted_masks/{}_{:03d}.txt {} {:.4f}'.format( \
                        test_scene_name, proposal_id, semantic_label_idx[semantic_label], score))
                    if proposal_id < nclusters - 1:
                        f.write('\n')
                    np.savetxt(os.path.join(result_dir, 'predicted_masks', test_scene_name + '_%03d.txt' % (proposal_id)), clusters_i, fmt='%d')
                f.close()

            logger.info("instance iter: {}/{} point_num: {} ncluster: {} inference time: {:.2f}s".format( \
                batch['id'][0] + 1, len(dataset.test_files), N, nclusters, end1))
            total_end1 += end1

        # evaluation
        if cfg.eval:
            ap_scores = eval.evaluate_matches(matches)
            avgs = eval.compute_averages(ap_scores)
            eval.print_results(avgs)

        logger.info("whole set inference time: {:.2f}s, latency per frame: {:.2f}ms".format(total_end1, total_end1 / len(dataloader) * 1000))

        # evaluate semantic segmantation accuracy and mIoU
        if cfg.split == 'val':
            seg_accuracy = evaluate_semantic_segmantation_accuracy(matches)
            logger.info("semantic_segmantation_accuracy: {:.4f}".format(seg_accuracy))
            miou = evaluate_semantic_segmantation_miou(matches)
            logger.info("semantic_segmantation_mIoU: {:.4f}".format(miou))

def evaluate_semantic_segmantation_accuracy(matches):
    seg_gt_list = []
    seg_pred_list = []
    for k, v in matches.items():
        seg_gt_list.append(v['seg_gt'])
        seg_pred_list.append(v['seg_pred'])
    seg_gt_all = torch.cat(seg_gt_list, dim=0).cuda()
    seg_pred_all = torch.cat(seg_pred_list, dim=0).cuda()
    assert seg_gt_all.shape == seg_pred_all.shape
    correct = (seg_gt_all[seg_gt_all != -100] == seg_pred_all[seg_gt_all != -100]).sum()
    whole = (seg_gt_all != -100).sum()
    seg_accuracy = correct.float() / whole.float()
    return seg_accuracy

def evaluate_semantic_segmantation_miou(matches):
    seg_gt_list = []
    seg_pred_list = []
    for k, v in matches.items():
        seg_gt_list.append(v['seg_gt'])
        seg_pred_list.append(v['seg_pred'])
    seg_gt_all = torch.cat(seg_gt_list, dim=0).cuda()
    seg_pred_all = torch.cat(seg_pred_list, dim=0).cuda()
    assert seg_gt_all.shape == seg_pred_all.shape
    iou_list = []
    for _index in seg_gt_all.unique():
        if _index != -100:
            intersection = ((seg_gt_all == _index) &  (seg_pred_all == _index)).sum()
            union = ((seg_gt_all == _index) | (seg_pred_all == _index)).sum()
            iou = intersection.float() / union
            iou_list.append(iou)
    iou_tensor = torch.tensor(iou_list)
    miou = iou_tensor.mean()
    return miou

def non_max_suppression(ious, scores, threshold):
    ixs = scores.argsort()[::-1]
    pick = []
    while len(ixs) > 0:
        i = ixs[0]
        pick.append(i)
        iou = ious[i, ixs[1:]]
        remove_ixs = np.where(iou > threshold)[0] + 1
        ixs = np.delete(ixs, remove_ixs)
        ixs = np.delete(ixs, 0)
    return np.array(pick, dtype=np.int32)

if __name__ == '__main__':
    init()

    exp_name = cfg.config.split('/')[-1][:-5]
    model_name = exp_name.split('_')[0]
    data_name = exp_name.split('_')[-1]

    logger.info('=> creating model ...')
    logger.info('Classes: {}'.format(cfg.classes))

    if model_name == 'hais':
        from model.hais.hais import HAIS as Network
        from model.hais.hais import model_fn_decorator

    else:
        print("Error: no model version " + model_name)
        exit(0)
    model = Network(cfg)

    use_cuda = torch.cuda.is_available()
    logger.info('cuda available: {}'.format(use_cuda))
    assert use_cuda
    model = model.cuda()

    logger.info('#classifier parameters (model): {}'.format(sum([x.nelement() for x in model.parameters()])))
    model_fn = model_fn_decorator(test=True)

    # load model
    utils.checkpoint_restore(cfg, model, None, cfg.exp_path, cfg.config.split('/')[-1][:-5],
        use_cuda, cfg.test_epoch, dist=False, f=cfg.pretrain)
    # resume from the latest epoch, or specify the epoch to restore

    # evaluate
    test(model, model_fn, data_name, cfg.test_epoch)