# coding=utf-8
# Copyright 2019 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Various reversible ops for the glow generative model."""

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

import functools
import numpy as np
import scipy
from tensor2tensor.layers import common_layers
from tensor2tensor.layers import common_video
import tensorflow as tf
import tensorflow_probability as tfp

arg_scope = tf.contrib.framework.arg_scope
add_arg_scope = tf.contrib.framework.add_arg_scope


def linear_interpolate(tensor1, tensor2, coeffs):
  """Linearly interpolate between two tensors at coeff.

  Args:
    tensor1: 4-D Tensor, shape=(NHWC)
    tensor2: 4-D Tensor, shape=(NHWC)
    coeffs: list of floats.
  Returns:
    interp_latents: 5-D Tensor, with interp_latents[i] representing
                    interpolations at coeffs[i].
                    shape=(len(coeffs), NHWC)
  """
  interp_tensors = []
  for coeff in coeffs:
    interp_tensor = tensor1 + coeff * (tensor2 - tensor1)
    interp_tensors.append(interp_tensor)
  return tf.concat(interp_tensors, axis=0)


def linear_interpolate_rank(tensor1, tensor2, coeffs, rank=1):
  """Linearly interpolate channel at "rank" between two tensors.

  The channels are ranked according to their L2 norm between tensor1[channel]
  and tensor2[channel].

  Args:
    tensor1: 4-D Tensor, NHWC
    tensor2: 4-D Tensor, NHWC
    coeffs: list of floats.
    rank: integer.
  Returns:
    interp_latents: list of interpolated 4-D Tensors, shape=(NHWC)
  """
  # sum across space, max across channels.
  _, _, _, num_channels = common_layers.shape_list(tensor1)
  diff_sq_sum = tf.reduce_sum((tensor1 - tensor2)**2, axis=(0, 1, 2))
  _, feature_ranks = tf.math.top_k(diff_sq_sum, k=rank)
  feature_rank = feature_ranks[-1]
  channel_inds = tf.range(num_channels, dtype=tf.int32)
  channel_mask = tf.equal(channel_inds, feature_rank)
  ones_t = tf.ones(num_channels, dtype=tf.float32)
  zeros_t = tf.zeros(num_channels, dtype=tf.float32)

  interp_tensors = []
  for coeff in coeffs:
    curr_coeff = tf.where(channel_mask, coeff * ones_t, zeros_t)
    interp_tensor = tensor1 + curr_coeff * (tensor2 - tensor1)
    interp_tensors.append(interp_tensor)
  return tf.concat(interp_tensors, axis=0)


def postprocess(x, n_bits_x=8):
  """Converts x from [-0.5, 0.5], to [0, 255].

  Args:
    x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
    n_bits_x: Number of bits representing each pixel of the output.
              Defaults to 8, to default to 256 possible values.
  Returns:
    x: 3-D or 4-D Tensor representing images or videos.
  """
  x = tf.where(tf.is_finite(x), x, tf.ones_like(x))
  x = tf.clip_by_value(x, -0.5, 0.5)
  x += 0.5
  x = x * 2**n_bits_x
  return tf.cast(tf.clip_by_value(x, 0, 255), dtype=tf.uint8)


class TemperedNormal(tfp.distributions.Normal):
  """Normal distribution with temperature T."""

  def __init__(self, loc, scale, temperature=1.0):
    self.temperature = temperature
    new_scale = scale * self.temperature
    tfp.distributions.Normal.__init__(self, loc=loc, scale=new_scale)

  def sample(self, sample_shape=(), seed=None, name="sample"):
    if self.temperature == 0.0:
      if not sample_shape:
        return self.loc
      loc = tf.expand_dims(self.loc, axis=0)
      return tf.tile(loc, (sample_shape[0], 1, 1))
    return super(TemperedNormal, self).sample(
        sample_shape=sample_shape, seed=seed, name=name)


def default_initializer(std=0.05):
  return tf.random_normal_initializer(0., std)


def get_eps(dist, x):
  """Z = (X - mu) / sigma."""
  return (x - dist.loc) / dist.scale


def set_eps(dist, eps):
  """Z = eps * sigma + mu."""
  return eps * dist.scale + dist.loc


@add_arg_scope
def assign(w, initial_value):
  w = w.assign(initial_value)
  with tf.control_dependencies([w]):
    return w


def get_cond_latents_at_level(cond_latents, level, hparams):
  """Returns a single or list of conditional latents at level 'level'."""
  if cond_latents:
    if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
      return [cond_latent[level] for cond_latent in cond_latents]
    elif hparams.latent_dist_encoder in ["pointwise", "conv_lstm"]:
      return cond_latents[level]


def check_cond_latents(cond_latents, hparams):
  """Shape checking for cond_latents."""
  if cond_latents is None:
    return
  if not isinstance(cond_latents[0], list):
    cond_latents = [cond_latents]
  exp_num_latents = hparams.num_cond_latents
  if hparams.latent_dist_encoder == "conv_net":
    exp_num_latents += int(hparams.cond_first_frame)
  if len(cond_latents) != exp_num_latents:
    raise ValueError("Expected number of cond_latents: %d, got %d" %
                     (exp_num_latents, len(cond_latents)))
  for cond_latent in cond_latents:
    if len(cond_latent) != hparams.n_levels - 1:
      raise ValueError("Expected level_latents to be %d, got %d" %
                       (hparams.n_levels - 1, len(cond_latent)))


@add_arg_scope
def get_variable_ddi(name, shape, initial_value, dtype=tf.float32, init=False,
                     trainable=True):
  """Wrapper for data-dependent initialization."""
  # If init is a tf bool: w is assigned dynamically at runtime.
  # If init is a python bool: then w is determined during graph construction.
  w = tf.get_variable(name, shape, dtype, None, trainable=trainable)
  if isinstance(init, bool):
    if init:
      return assign(w, initial_value)
    return w
  else:
    return tf.cond(init, lambda: assign(w, initial_value), lambda: w)


@add_arg_scope
def get_dropout(x, rate=0.0, init=True):
  """Dropout x with dropout_rate = rate.

  Apply zero dropout during init or prediction time.

  Args:
    x: 4-D Tensor, shape=(NHWC).
    rate: Dropout rate.
    init: Initialization.
  Returns:
    x: activations after dropout.
  """
  if init or rate == 0:
    return x
  return tf.layers.dropout(x, rate=rate, training=True)


@add_arg_scope
def actnorm_3d(name, x, logscale_factor=3.):
  """Applies actnorm to each time-step independently.

  There are a total of 2*n_channels*n_steps parameters learnt.

  Args:
    name: variable scope.
    x: 5-D Tensor, (NTHWC)
    logscale_factor: Increases the learning rate of the scale by
                     logscale_factor.
  Returns:
    x: 5-D Tensor, (NTHWC) with the per-timestep, per-channel normalization.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    x = tf.unstack(x, axis=1)
    x_normed = []
    for ind, x_step in enumerate(x):
      x_step, _ = actnorm("actnorm_%d" % ind, x_step,
                          logscale_factor=logscale_factor)
      x_normed.append(x_step)
    return tf.stack(x_normed, axis=1), None


@add_arg_scope
def actnorm(name, x, logscale_factor=3., reverse=False, init=False,
            trainable=True):
  """x_{ij} = s x x_{ij} + b. Per-channel scaling and bias.

  If init is set to True, the scaling and bias are initialized such
  that the mean and variance of the output activations of the first minibatch
  are zero and one respectively.

  Args:
    name: variable scope.
    x: input
    logscale_factor: Used in actnorm_scale. Optimizes f(ls*s') instead of f(s)
                     where s' = s / ls. Helps in faster convergence.
    reverse: forward or reverse operation.
    init: Whether or not to do data-dependent initialization.
    trainable:

  Returns:
    x: output after adding bias and scaling.
    objective: log(sum(s))
  """
  var_arg_scope = arg_scope([get_variable_ddi], trainable=trainable)
  var_scope = tf.variable_scope(name, reuse=tf.AUTO_REUSE)

  with var_scope, var_arg_scope:
    if not reverse:
      x = actnorm_center(name + "_center", x, reverse, init=init)
      x, objective = actnorm_scale(
          name + "_scale", x, logscale_factor=logscale_factor,
          reverse=reverse, init=init)
    else:
      x, objective = actnorm_scale(
          name + "_scale", x, logscale_factor=logscale_factor,
          reverse=reverse, init=init)
      x = actnorm_center(name + "_center", x, reverse, init=init)
    return x, objective


@add_arg_scope
def actnorm_center(name, x, reverse=False, init=False):
  """Add a bias to x.

  Initialize such that the output of the first minibatch is zero centered
  per channel.

  Args:
    name: scope
    x: 2-D or 4-D Tensor.
    reverse: Forward or backward operation.
    init: data-dependent initialization.

  Returns:
    x_center: (x + b), if reverse is True and (x - b) otherwise.
  """
  shape = common_layers.shape_list(x)
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    assert len(shape) == 2 or len(shape) == 4
    if len(shape) == 2:
      x_mean = tf.reduce_mean(x, [0], keepdims=True)
      b = get_variable_ddi("b", (1, shape[1]), initial_value=-x_mean,
                           init=init)
    elif len(shape) == 4:
      x_mean = tf.reduce_mean(x, [0, 1, 2], keepdims=True)
      b = get_variable_ddi(
          "b", (1, 1, 1, shape[3]), initial_value=-x_mean, init=init)

    if not reverse:
      x += b
    else:
      x -= b
    return x


@add_arg_scope
def actnorm_scale(name, x, logscale_factor=3., reverse=False, init=False):
  """Per-channel scaling of x."""
  x_shape = common_layers.shape_list(x)
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):

    # Variance initialization logic.
    assert len(x_shape) == 2 or len(x_shape) == 4
    if len(x_shape) == 2:
      x_var = tf.reduce_mean(x**2, [0], keepdims=True)
      logdet_factor = 1
      var_shape = (1, x_shape[1])
    elif len(x_shape) == 4:
      x_var = tf.reduce_mean(x**2, [0, 1, 2], keepdims=True)
      logdet_factor = x_shape[1]*x_shape[2]
      var_shape = (1, 1, 1, x_shape[3])

    init_value = tf.log(1.0 / (tf.sqrt(x_var) + 1e-6)) / logscale_factor
    logs = get_variable_ddi("logs", var_shape, initial_value=init_value,
                            init=init)
    logs = logs * logscale_factor

    # Function and reverse function.
    if not reverse:
      x = x * tf.exp(logs)
    else:
      x = x * tf.exp(-logs)

    # Objective calculation, h * w * sum(log|s|)
    dlogdet = tf.reduce_sum(logs) * logdet_factor
    if reverse:
      dlogdet *= -1
    return x, dlogdet


@add_arg_scope
def invertible_1x1_conv(name, x, reverse=False):
  """1X1 convolution on x.

  The 1X1 convolution is parametrized as P*L*(U + sign(s)*exp(log(s))) where
  1. P is a permutation matrix.
  2. L is a lower triangular matrix with diagonal entries unity.
  3. U is a upper triangular matrix where the diagonal entries zero.
  4. s is a vector.

  sign(s) and P are fixed and the remaining are optimized. P, L, U and s are
  initialized by the PLU decomposition of a random rotation matrix.

  Args:
    name: scope
    x: Input Tensor.
    reverse: whether the pass is from z -> x or x -> z.

  Returns:
    x_conv: x after a 1X1 convolution is applied on x.
    objective: sum(log(s))
  """
  _, height, width, channels = common_layers.shape_list(x)
  w_shape = [channels, channels]

  # Random rotation-matrix Q
  random_matrix = np.random.rand(channels, channels)
  np_w = scipy.linalg.qr(random_matrix)[0].astype("float32")

  # Initialize P,L,U and s from the LU decomposition of a random rotation matrix
  np_p, np_l, np_u = scipy.linalg.lu(np_w)
  np_s = np.diag(np_u)
  np_sign_s = np.sign(np_s)
  np_log_s = np.log(np.abs(np_s))
  np_u = np.triu(np_u, k=1)

  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    p = tf.get_variable("P", initializer=np_p, trainable=False)
    l = tf.get_variable("L", initializer=np_l)
    sign_s = tf.get_variable(
        "sign_S", initializer=np_sign_s, trainable=False)
    log_s = tf.get_variable("log_S", initializer=np_log_s)
    u = tf.get_variable("U", initializer=np_u)

    # W = P * L * (U + sign_s * exp(log_s))
    l_mask = np.tril(np.ones([channels, channels], dtype=np.float32), -1)
    l = l * l_mask + tf.eye(channels, channels)
    u = u * np.transpose(l_mask) + tf.diag(sign_s * tf.exp(log_s))
    w = tf.matmul(p, tf.matmul(l, u))

    # If height or width cannot be statically determined then they end up as
    # tf.int32 tensors, which cannot be directly multiplied with a floating
    # point tensor without a cast.
    objective = tf.reduce_sum(log_s) * tf.cast(height * width, log_s.dtype)
    if not reverse:
      w = tf.reshape(w, [1, 1] + w_shape)
      x = tf.nn.conv2d(x, w, [1, 1, 1, 1], "SAME", data_format="NHWC")
    else:
      w_inv = tf.reshape(tf.linalg.inv(w), [1, 1]+w_shape)
      x = tf.nn.conv2d(
          x, w_inv, [1, 1, 1, 1], "SAME", data_format="NHWC")
      objective *= -1
  return x, objective


def add_edge_bias(x, filter_size):
  """Pad x and concatenates an edge bias across the depth of x.

  The edge bias can be thought of as a binary feature which is unity when
  the filter is being convolved over an edge and zero otherwise.

  Args:
    x: Input tensor, shape (NHWC)
    filter_size: filter_size to determine padding.
  Returns:
    x_pad: Input tensor, shape (NHW(c+1))
  """
  x_shape = common_layers.shape_list(x)
  if filter_size[0] == 1 and filter_size[1] == 1:
    return x
  a = (filter_size[0] - 1) // 2  # vertical padding size
  b = (filter_size[1] - 1) // 2  # horizontal padding size
  padding = [[0, 0], [a, a], [b, b], [0, 0]]
  x_bias = tf.zeros(x_shape[:-1] + [1])

  x = tf.pad(x, padding)
  x_pad = tf.pad(x_bias, padding, constant_values=1)
  return tf.concat([x, x_pad], axis=3)


def time_pad(x, filter_size, dilations):
  """Pad left across time and pad valid across the spatial components.

  Also concats a binary feature that indicates if a feature is padded or not.

  Args:
    x: 5-D Tensor, (NTHWC)
    filter_size: list of ints
    dilations: list of ints, dilations - 1 specifies the number of holes
               between two filter elements.
  Returns:
    x_pad: 5-D Tensor.
  """
  x_shape = common_layers.shape_list(x)
  if filter_size == [1, 1, 1]:
    return x
  _, h, w = filter_size
  eff_h = h + (h - 1)*(dilations[2] - 1)
  eff_w = w + (w - 1)*(dilations[3] - 1)
  a = (eff_h - 1) // 2  # vertical padding size
  b = (eff_w - 1) // 2  # horizontal padding size
  c = filter_size[0] - 1

  # pad across edges.
  padding = [[0, 0], [c, 0], [a, a], [b, b], [0, 0]]

  # concat a binary feature across channels to indicate a padding.
  # 1 indicates that the feature is a padding.
  x_bias = tf.zeros(x_shape[:-1] + [1])
  x_bias = tf.pad(x_bias, padding, constant_values=1)
  x_pad = tf.pad(x, padding)
  x_pad = tf.concat((x_bias, x_pad), axis=-1)
  return x_pad


@add_arg_scope
def conv(name, x, output_channels, filter_size=None, stride=None,
         logscale_factor=3.0, apply_actnorm=True, conv_init="default",
         dilations=None):
  """Convolutional layer with edge bias padding and optional actnorm.

  If x is 5-dimensional, actnorm is applied independently across every
  time-step.

  Args:
    name: variable scope.
    x: 4-D Tensor or 5-D Tensor of shape NHWC or NTHWC
    output_channels: Number of output channels.
    filter_size: list of ints, if None [3, 3] and [2, 3, 3] are defaults for
                 4-D and 5-D input tensors respectively.
    stride: list of ints, default stride: 1
    logscale_factor: see actnorm for parameter meaning.
    apply_actnorm: if apply_actnorm the activations of the first minibatch
                   have zero mean and unit variance. Else, there is no scaling
                   applied.
    conv_init: default or zeros. default is a normal distribution with 0.05 std.
    dilations: List of integers, apply dilations.
  Returns:
    x: actnorm(conv2d(x))
  Raises:
    ValueError: if init is set to "zeros" and apply_actnorm is set to True.
  """
  if conv_init == "zeros" and apply_actnorm:
    raise ValueError("apply_actnorm is unstable when init is set to zeros.")

  x_shape = common_layers.shape_list(x)
  is_2d = len(x_shape) == 4
  num_steps = x_shape[1]

  # set filter_size, stride and in_channels
  if is_2d:
    if filter_size is None:
      filter_size = [3, 3]
    if stride is None:
      stride = [1, 1]
    if dilations is None:
      dilations = [1, 1, 1, 1]
    actnorm_func = actnorm
    x = add_edge_bias(x, filter_size=filter_size)
    conv_filter = tf.nn.conv2d
  else:
    if filter_size is None:
      if num_steps == 1:
        filter_size = [1, 3, 3]
      else:
        filter_size = [2, 3, 3]
    if stride is None:
      stride = [1, 1, 1]
    if dilations is None:
      dilations = [1, 1, 1, 1, 1]
    actnorm_func = actnorm_3d
    x = time_pad(x, filter_size=filter_size, dilations=dilations)
    conv_filter = tf.nn.conv3d

  in_channels = common_layers.shape_list(x)[-1]
  filter_shape = filter_size + [in_channels, output_channels]
  stride_shape = [1] + stride + [1]

  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):

    if conv_init == "default":
      initializer = default_initializer()
    elif conv_init == "zeros":
      initializer = tf.zeros_initializer()

    w = tf.get_variable("W", filter_shape, tf.float32, initializer=initializer)
    x = conv_filter(x, w, stride_shape, padding="VALID", dilations=dilations)
    if apply_actnorm:
      x, _ = actnorm_func("actnorm", x, logscale_factor=logscale_factor)
    else:
      x += tf.get_variable("b", [1, 1, 1, output_channels],
                           initializer=tf.zeros_initializer())
      logs = tf.get_variable("logs", [1, output_channels],
                             initializer=tf.zeros_initializer())
      x *= tf.exp(logs * logscale_factor)
    return x


@add_arg_scope
def conv_block(name, x, mid_channels, dilations=None, activation="relu",
               dropout=0.0):
  """2 layer conv block used in the affine coupling layer.

  Args:
    name: variable scope.
    x: 4-D or 5-D Tensor.
    mid_channels: Output channels of the second layer.
    dilations: Optional, list of integers.
    activation: relu or gatu.
      If relu, the second layer is relu(W*x)
      If gatu, the second layer is tanh(W1*x) * sigmoid(W2*x)
    dropout: Dropout probability.
  Returns:
    x: 4-D Tensor: Output activations.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):

    x_shape = common_layers.shape_list(x)
    is_2d = len(x_shape) == 4
    num_steps = x_shape[1]
    if is_2d:
      first_filter = [3, 3]
      second_filter = [1, 1]
    else:
      # special case when number of steps equal 1 to avoid
      # padding.
      if num_steps == 1:
        first_filter = [1, 3, 3]
      else:
        first_filter = [2, 3, 3]
      second_filter = [1, 1, 1]

    # Edge Padding + conv2d + actnorm + relu:
    # [output: 512 channels]
    x = conv("1_1", x, output_channels=mid_channels, filter_size=first_filter,
             dilations=dilations)
    x = tf.nn.relu(x)
    x = get_dropout(x, rate=dropout)

    # Padding + conv2d + actnorm + activation.
    # [input, output: 512 channels]
    if activation == "relu":
      x = conv("1_2", x, output_channels=mid_channels,
               filter_size=second_filter, dilations=dilations)
      x = tf.nn.relu(x)
    elif activation == "gatu":
      # x = tanh(w1*x) * sigm(w2*x)
      x_tanh = conv("1_tanh", x, output_channels=mid_channels,
                    filter_size=second_filter, dilations=dilations)
      x_sigm = conv("1_sigm", x, output_channels=mid_channels,
                    filter_size=second_filter, dilations=dilations)
      x = tf.nn.tanh(x_tanh) * tf.nn.sigmoid(x_sigm)

    x = get_dropout(x, rate=dropout)
    return x


def dilated_conv_stack(name, x, mid_channels, output_channels,
                       dilation_rates, activation="relu",
                       dropout=0.0):
  """Dilated convolutional stack.

  Features at different rates are computed independently using a 3 layer
  convolutional stack and added.

  Args:
    name: variable scope.
    x: 5-D Tensor.
    mid_channels: Number of output channels of the first layer in the conv
                  stack.
    output_channels: Number of output channels of the last layer.
    dilation_rates: A list of dilation rates.
    activation: Can be either "relu" or "gatu"
    dropout: dropout.
  Returns:
    output: 5-D Tensor.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    output = 0.0
    for dil_ind, dil_rate in enumerate(dilation_rates):
      # TODO(mechcoder) try (concat across channels + 1x1) modulo memory issues.
      curr_out = conv_stack("dil_%d" % dil_ind, x, mid_channels=mid_channels,
                            output_channels=output_channels, dilations=dil_rate,
                            activation=activation, dropout=dropout)
      output += curr_out
    return output


@add_arg_scope
def conv_stack(name, x, mid_channels, output_channels, dilations=None,
               activation="relu", dropout=0.0):
  """3-layer convolutional stack.

  Args:
    name: variable scope.
    x: 5-D Tensor.
    mid_channels: Number of output channels of the first layer.
    output_channels: Number of output channels.
    dilations: Dilations to apply in the first 3x3 layer and the last 3x3 layer.
               By default, apply no dilations.
    activation: relu or gatu.
      If relu, the second layer is relu(W*x)
      If gatu, the second layer is tanh(W1*x) * sigmoid(W2*x)
    dropout: float, 0.0
  Returns:
    output: output of 3 layer conv network.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):

    x = conv_block("conv_block", x, mid_channels=mid_channels,
                   dilations=dilations, activation=activation,
                   dropout=dropout)

    # Final layer.
    x = conv("zeros", x, apply_actnorm=False, conv_init="zeros",
             output_channels=output_channels, dilations=dilations)
  return x


@add_arg_scope
def additive_coupling(name, x, mid_channels=512, reverse=False,
                      activation="relu", dropout=0.0):
  """Reversible additive coupling layer.

  Args:
    name: variable scope.
    x: 4-D Tensor, shape=(NHWC).
    mid_channels: number of channels in the coupling layer.
    reverse: Forward or reverse operation.
    activation: "relu" or "gatu"
    dropout: default, 0.0
  Returns:
    output: 4-D Tensor, shape=(NHWC)
    objective: 0.0
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    output_channels = common_layers.shape_list(x)[-1] // 2
    x1, x2 = tf.split(x, num_or_size_splits=2, axis=-1)

    z1 = x1
    shift = conv_stack("nn", x1, mid_channels, output_channels=output_channels,
                       activation=activation, dropout=dropout)

    if not reverse:
      z2 = x2 + shift
    else:
      z2 = x2 - shift
    return tf.concat([z1, z2], axis=3), 0.0


@add_arg_scope
def affine_coupling(name, x, mid_channels=512, activation="relu",
                    reverse=False, dropout=0.0):
  """Reversible affine coupling layer.

  Args:
    name: variable scope.
    x: 4-D Tensor.
    mid_channels: number of channels in the coupling layer.
    activation: Can be either "relu" or "gatu".
    reverse: Forward or reverse operation.
    dropout: default, 0.0
  Returns:
    output: x shifted and scaled by an affine transformation.
    objective: log-determinant of the jacobian
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    x_shape = common_layers.shape_list(x)
    x1, x2 = tf.split(x, num_or_size_splits=2, axis=-1)

    # scale, shift = NN(x1)
    # If reverse:
    # z2 = scale * (x2 + shift)
    # Else:
    # z2 = (x2 / scale) - shift
    z1 = x1
    log_scale_and_shift = conv_stack(
        "nn", x1, mid_channels, x_shape[-1], activation=activation,
        dropout=dropout)
    shift = log_scale_and_shift[:, :, :, 0::2]
    scale = tf.nn.sigmoid(log_scale_and_shift[:, :, :, 1::2] + 2.0)
    if not reverse:
      z2 = (x2 + shift) * scale
    else:
      z2 = x2 / scale - shift

    objective = tf.reduce_sum(tf.log(scale), axis=[1, 2, 3])
    if reverse:
      objective *= -1
    return tf.concat([z1, z2], axis=3), objective


@add_arg_scope
def squeeze(name, x, factor=2, reverse=True):
  """Block-wise spatial squeezing of x to increase the number of channels.

  Args:
    name: Used for variable scoping.
    x: 4-D Tensor of shape (batch_size X H X W X C)
    factor: Factor by which the spatial dimensions should be squeezed.
    reverse: Squueze or unsqueeze operation.

  Returns:
    x: 4-D Tensor of shape (batch_size X (H//factor) X (W//factor) X
       (cXfactor^2). If reverse is True, then it is factor = (1 / factor)
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    shape = common_layers.shape_list(x)
    if factor == 1:
      return x
    height = int(shape[1])
    width = int(shape[2])
    n_channels = int(shape[3])

    if not reverse:
      assert height % factor == 0 and width % factor == 0
      x = tf.reshape(x, [-1, height//factor, factor,
                         width//factor, factor, n_channels])
      x = tf.transpose(x, [0, 1, 3, 5, 2, 4])
      x = tf.reshape(x, [-1, height//factor, width //
                         factor, n_channels*factor*factor])
    else:
      x = tf.reshape(
          x, (-1, height, width, int(n_channels/factor**2), factor, factor))
      x = tf.transpose(x, [0, 1, 4, 2, 5, 3])
      x = tf.reshape(x, (-1, int(height*factor),
                         int(width*factor), int(n_channels/factor**2)))
    return x


def get_dilation_rates(hparams, width):
  """Get a list of valid dilation rates.

  Args:
    hparams: HParams.
    width: spatial dimension. Ensures that the effective filter size is
           not larger than the spatial dimension.
  Returns:
    allowed_dilations: A list of dilation rates.
  """
  # dil_rate=1 means no dilation.
  allowed_dilations = [[1]*5]
  apply_dilations = hparams.get("latent_apply_dilations", False)
  dilation_rates = hparams.get("latent_dilation_rates", [1, 3])
  if apply_dilations:
    for rate in dilation_rates:
      # k + (k - 1) * rate but k is harcoded to be 3 everywhere.
      filter_size = 3 + 2 * rate
      if filter_size <= width:
        curr_dilation = [1, 1, rate+1, rate+1, 1]
        allowed_dilations.append(curr_dilation)
  return allowed_dilations


@add_arg_scope
def temporal_latent_to_dist(name, x, hparams, output_channels=None):
  """Network that maps a time-indexed list of 3-D latents to a gaussian.

  Args:
    name: variable scope.
    x: List of 4-D Tensors indexed by time, (NHWC)
    hparams: tf.contrib.training.Hparams.
    output_channels: int, Number of channels of the output gaussian mean.
  Returns:
    dist: tfp.distributions.Normal
  """
  _, _, width, _, res_channels = common_layers.shape_list(x)
  if output_channels is None:
    output_channels = res_channels
  dilation_rates = get_dilation_rates(hparams, width)
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    h = x
    for i in range(hparams.latent_encoder_depth):
      if hparams.latent_apply_dilations:
        h2 = dilated_conv_stack("dil_latent_3d_res_%d" % i, h,
                                mid_channels=hparams.latent_encoder_width,
                                output_channels=res_channels,
                                dilation_rates=dilation_rates,
                                activation=hparams.latent_activation,
                                dropout=hparams.latent_dropout)
      else:
        h2 = conv_stack("latent_3d_res_%d" % i, h,
                        mid_channels=hparams.latent_encoder_width,
                        output_channels=res_channels,
                        activation=hparams.latent_activation,
                        dropout=hparams.latent_dropout)
      h += h2

    # take last activation that should capture all context since padding is
    # on left.
    h = h[:, -1, :, :, :]
    h = conv("res_final", h, apply_actnorm=False, conv_init="zeros",
             output_channels=2*output_channels, filter_size=[1, 1])
    mean, log_scale = h[:, :, :, 0::2], h[:, :, :, 1::2]
  return tfp.distributions.Normal(mean, tf.exp(log_scale))


@add_arg_scope
def single_conv_dist(name, x, output_channels=None):
  """A 3x3 convolution mapping x to a standard normal distribution at init.

  Args:
    name: variable scope.
    x: 4-D Tensor.
    output_channels: number of channels of the mean and std.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    x_shape = common_layers.shape_list(x)
    if output_channels is None:
      output_channels = x_shape[-1]
    mean_log_scale = conv("conv2d", x, output_channels=2*output_channels,
                          conv_init="zeros", apply_actnorm=False)
    mean = mean_log_scale[:, :, :, 0::2]
    log_scale = mean_log_scale[:, :, :, 1::2]
    return tf.distributions.Normal(mean, tf.exp(log_scale))


@add_arg_scope
def latent_to_dist(name, x, hparams, output_channels=None):
  """Map latent to the mean and log-scale of a Gaussian.

  Args:
    name: variable scope.
    x: 4-D Tensor of shape (NHWC)
    hparams: HParams.
      latent_architecture - can be "single_conv", "glow_nn" or "glow_resnet",
                            default = single_conv
      latent_encoder_depth - int, depth of architecture, valid if
                             latent_architecture is "glow_nn" or "glow_resnet".
      latent_pre_output_channels - 512, valid only when latent_architecture
                                   is "glow_nn".
      latent_encoder_width - 512, maximum width of the network
    output_channels: int, number of output channels of the mean (and std).
                     if not provided, set it to be the output channels of x.
  Returns:
    dist: instance of tfp.distributions.Normal
  Raises:
    ValueError: If architecture not in ["single_conv", "glow_nn"]
  """
  architecture = hparams.get("latent_architecture", "single_conv")
  depth = hparams.get("latent_encoder_depth", 1)
  pre_output_channels = hparams.get("latent_pre_output_channels", 512)
  width = hparams.get("latent_encoder_width", 512)

  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    x_shape = common_layers.shape_list(x)
    if output_channels is None:
      output_channels = x_shape[-1]
    if architecture == "single_conv":
      return single_conv_dist("single_conv", x, output_channels)
    if architecture == "glow_nn":
      mean_log_scale = x
      for layer in range(1, depth + 1):
        mid_channels = pre_output_channels // 2**(depth - layer)
        mean_log_scale = conv_block("glow_nn_%d" % layer, mean_log_scale,
                                    mid_channels=mid_channels)
      mean_log_scale = conv("glow_nn_zeros", mean_log_scale,
                            filter_size=[3, 3], stride=[1, 1],
                            output_channels=2*output_channels,
                            apply_actnorm=False, conv_init="zeros")
    elif architecture == "glow_resnet":
      h = x
      for layer in range(depth):
        h3 = conv_stack("latent_resnet_%d" % layer, h,
                        mid_channels=width, output_channels=x_shape[-1],
                        dropout=hparams.coupling_dropout)
        h += h3
      mean_log_scale = conv("glow_res_final", h, conv_init="zeros",
                            output_channels=2*output_channels,
                            apply_actnorm=False)
    else:
      raise ValueError("expected architecture to be single_conv or glow_nn "
                       "got %s" % architecture)

    mean = mean_log_scale[:, :, :, 0::2]
    log_scale = mean_log_scale[:, :, :, 1::2]
    return tfp.distributions.Normal(mean, tf.exp(log_scale))


@add_arg_scope
def noise_op(latents, hparams):
  """Adds isotropic gaussian-noise to each latent.

  Args:
    latents: 4-D or 5-D tensor, shape=(NTHWC) or (NHWC).
    hparams: HParams.
  Returns:
    latents: latents with isotropic gaussian noise appended.
  """
  if hparams.latent_noise == 0 or hparams.mode != tf.estimator.ModeKeys.TRAIN:
    return latents
  latent_shape = common_layers.shape_list(latents)
  return latents + tf.random_normal(latent_shape, stddev=hparams.latent_noise)


@add_arg_scope
def merge_level_and_latent_dist(level_dist, latent_dist,
                                merge_std="prev_level"):
  """Merge level_dist and latent_dist.

  new_dist ~ N(level_dist.mean + latent_dis.mean, std) where std is determined
  according to merge_std.

  Args:
    level_dist: instance of tfp.distributions.Normal
    latent_dist: instance of tfp.distributions.Normal
    merge_std: can be "prev_level", "prev_step" or "normal".
  Returns:
    merged_dist: instance of tfp.distributions.Normal
  """
  level_mean, level_std = level_dist.loc, level_dist.scale
  latent_mean, latent_std = latent_dist.loc, latent_dist.scale
  new_mean = level_mean + latent_mean
  if merge_std == "normal":
    z_shape = common_layers.shape_list(latent_mean)
    log_scale = tf.get_variable(
        "merge_std", shape=z_shape, dtype=tf.float32,
        initializer=tf.zeros_initializer(), trainable=False)
    scale = tf.exp(log_scale * 3.0)
  elif merge_std == "prev_level":
    scale = level_std
  elif merge_std == "prev_step":
    scale = latent_std
  return tfp.distributions.Normal(loc=new_mean, scale=scale)


@add_arg_scope
def level_cond_prior(prior_dist, z, latent, hparams, state):
  """Returns a conditional prior for each level.

  Args:
    prior_dist: Distribution conditioned on the previous levels.
    z: Tensor, output of the previous levels.
    latent: Tensor or a list of tensors to condition the latent_distribution.
    hparams: next_frame_glow hparams.
    state: Current LSTM state. Used only if hparams.latent_dist_encoder is
           a lstm.
  Raises:
    ValueError: If hparams.latent_dist_encoder is "pointwise" and if the shape
                of latent is different from z.
  """
  latent_dist_encoder = hparams.get("latent_dist_encoder", None)
  latent_skip = hparams.get("latent_skip", False)
  if latent_dist_encoder == "pointwise":
    last_latent = latent
    merge_std = hparams.level_scale
    latent_shape = common_layers.shape_list(latent)
    z_shape = common_layers.shape_list(z)
    if latent_shape != z_shape:
      raise ValueError("Expected latent_shape to be %s, got %s" %
                       (latent_shape, z_shape))
    latent_dist = scale_gaussian_prior(
        "latent_prior", latent, logscale_factor=3.0)
    cond_dist = merge_level_and_latent_dist(prior_dist, latent_dist,
                                            merge_std=merge_std)

  elif latent_dist_encoder == "conv_net":
    output_channels = common_layers.shape_list(z)[-1]
    last_latent = latent[-1]
    latent_stack = tf.concat([prior_dist.loc] + latent, axis=-1)
    latent_stack = noise_op(latent_stack, hparams)
    cond_dist = latent_to_dist(
        "latent_stack", latent_stack, hparams=hparams,
        output_channels=output_channels)

  elif latent_dist_encoder == "conv3d_net":
    last_latent = latent[-1]
    output_channels = common_layers.shape_list(last_latent)[-1]
    num_steps = len(latent)

    # Stack across time.
    cond_latents = tf.stack(latent, axis=1)

    # Concat latents from previous levels across channels.
    prev_latents = tf.tile(tf.expand_dims(prior_dist.loc, axis=1),
                           [1, num_steps, 1, 1, 1])
    cond_latents = tf.concat((cond_latents, prev_latents), axis=-1)
    cond_latents = noise_op(cond_latents, hparams)
    cond_dist = temporal_latent_to_dist(
        "latent_stack", cond_latents, hparams, output_channels=output_channels)

  elif latent_dist_encoder == "conv_lstm":
    last_latent = latent
    output_channels = common_layers.shape_list(z)[-1]
    latent_stack = tf.concat((prior_dist.loc, latent), axis=-1)
    latent_stack = noise_op(latent_stack, hparams)
    _, state = common_video.conv_lstm_2d(
        latent_stack, state, hparams.latent_encoder_width, kernel_size=3,
        name="conv_lstm")

    cond_dist = single_conv_dist(
        "state_to_dist", state.h, output_channels=output_channels)
  if latent_skip:
    new_mean = cond_dist.loc + last_latent
    cond_dist = tfp.distributions.Normal(new_mean, cond_dist.scale)
  return cond_dist.loc, cond_dist.scale, state


@add_arg_scope
def compute_prior(name, z, latent, hparams, condition=False, state=None,
                  temperature=1.0):
  """Distribution on z_t conditioned on z_{t-1} and latent.

  Args:
    name: variable scope.
    z: 4-D Tensor.
    latent: optional,
            if hparams.latent_dist_encoder == "pointwise", this is a list
            of 4-D Tensors of length hparams.num_cond_latents.
            else, this is just a 4-D Tensor
            The first-three dimensions of the latent should be the same as z.
    hparams: next_frame_glow_hparams.
    condition: Whether or not to condition the distribution on latent.
    state: tf.nn.rnn_cell.LSTMStateTuple.
           the current state of a LSTM used to model the distribution. Used
           only if hparams.latent_dist_encoder = "conv_lstm".
    temperature: float, temperature with which to sample from the Gaussian.
  Returns:
    prior_dist: instance of tfp.distributions.Normal
    state: Returns updated state.
  Raises:
    ValueError: If hparams.latent_dist_encoder is "pointwise" and if the shape
                of latent is different from z.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    if isinstance(condition, bool):
      condition = tf.constant(condition, dtype=tf.bool)
    prior_dist = single_conv_dist("level_prior", z)
    prior_mean, prior_scale = prior_dist.loc, prior_dist.scale

    if latent is None:
      mean, scale = prior_mean, prior_scale
    else:
      cond_mean, cond_scale, state = level_cond_prior(
          prior_dist, z, latent, hparams, state)
      mean, scale = tf.cond(
          condition, lambda: (cond_mean, cond_scale),
          lambda: (prior_mean, prior_scale))
    dist = TemperedNormal(mean, scale, temperature)
    return dist, state


@add_arg_scope
def split(name, x, reverse=False, eps=None, eps_std=None, cond_latents=None,
          hparams=None, state=None, condition=False, temperature=1.0):
  """Splits / concatenates x into x1 and x2 across number of channels.

  For the forward pass, x2 is assumed be gaussian,
  i.e P(x2 | x1) ~ N(mu, sigma) where mu and sigma are the outputs of
  a network conditioned on x1 and optionally on cond_latents.
  For the reverse pass, x2 is determined from mu(x1) and sigma(x1).
  This is deterministic/stochastic depending on whether eps is provided.

  Args:
    name: variable scope.
    x: 4-D Tensor, shape (NHWC).
    reverse: Forward or reverse pass.
    eps: If eps is provided, x2 is set to be mu(x1) + eps * sigma(x1).
    eps_std: Sample x2 with the provided eps_std.
    cond_latents: optionally condition x2 on cond_latents.
    hparams: next_frame_glow hparams.
    state: tf.nn.rnn_cell.LSTMStateTuple.. Current state of the LSTM over z_2.
           Used only when hparams.latent_dist_encoder == "conv_lstm"
    condition: bool, Whether or not to condition the distribution on
               cond_latents.
    temperature: Temperature with which to sample from the gaussian.

  Returns:
    If reverse:
      x: 4-D Tensor, concats input and x2 across channels.
      x2: 4-D Tensor, a sample from N(mu(x1), sigma(x1))
    Else:
      x1: 4-D Tensor, Output of the split operation.
      logpb: log-probability of x2 belonging to mu(x1), sigma(x1)
      eps: 4-D Tensor, (x2 - mu(x1)) / sigma(x1)
      x2: 4-D Tensor, Latent representation at the current level.
    state: Current LSTM state.
           4-D Tensor, only if hparams.latent_dist_encoder is set to conv_lstm.
  Raises:
    ValueError: If latent is provided and shape is not equal to NHW(C/2)
                where (NHWC) is the size of x.
  """
  # TODO(mechcoder) Change the return type to be a dict.
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    if not reverse:
      x1, x2 = tf.split(x, num_or_size_splits=2, axis=-1)

      # objective: P(x2|x1) ~N(x2 ; NN(x1))
      prior_dist, state = compute_prior(
          "prior_on_z2", x1, cond_latents, hparams, condition, state=state)
      logpb = tf.reduce_sum(prior_dist.log_prob(x2), axis=[1, 2, 3])
      eps = get_eps(prior_dist, x2)
      return x1, logpb, eps, x2, state
    else:
      prior_dist, state = compute_prior(
          "prior_on_z2", x, cond_latents, hparams, condition, state=state,
          temperature=temperature)
      if eps is not None:
        x2 = set_eps(prior_dist, eps)
      elif eps_std is not None:
        x2 = eps_std * tf.random_normal(common_layers.shape_list(x))
      else:
        x2 = prior_dist.sample()
      return tf.concat([x, x2], 3), x2, state


@add_arg_scope
def revnet_step(name, x, hparams, reverse=True):
  """One step of glow generative flow.

  Actnorm + invertible 1X1 conv + affine_coupling.

  Args:
    name: used for variable scope.
    x: input
    hparams: coupling_width is the only hparam that is being used in
             this function.
    reverse: forward or reverse pass.
  Returns:
    z: Output of one step of reversible flow.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    if hparams.coupling == "additive":
      coupling_layer = functools.partial(
          additive_coupling, name="additive", reverse=reverse,
          mid_channels=hparams.coupling_width,
          activation=hparams.activation, dropout=hparams.coupling_dropout)
    else:
      coupling_layer = functools.partial(
          affine_coupling, name="affine", reverse=reverse,
          mid_channels=hparams.coupling_width,
          activation=hparams.activation, dropout=hparams.coupling_dropout)
    ops = [
        functools.partial(actnorm, name="actnorm", reverse=reverse),
        functools.partial(invertible_1x1_conv, name="invertible",
                          reverse=reverse), coupling_layer]

    if reverse:
      ops = ops[::-1]

    objective = 0.0
    for op in ops:
      x, curr_obj = op(x=x)
      objective += curr_obj
    return x, objective


def revnet(name, x, hparams, reverse=True):
  """'hparams.depth' steps of generative flow.

  Args:
    name: variable scope for the revnet block.
    x: 4-D Tensor, shape=(NHWC).
    hparams: HParams.
    reverse: bool, forward or backward pass.
  Returns:
    x: 4-D Tensor, shape=(NHWC).
    objective: float.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    steps = np.arange(hparams.depth)
    if reverse:
      steps = steps[::-1]

    objective = 0.0
    for step in steps:
      x, curr_obj = revnet_step(
          "revnet_step_%d" % step, x, hparams, reverse=reverse)
      objective += curr_obj
    return x, objective


@add_arg_scope
def scale_gaussian_prior(name, z, logscale_factor=3.0, trainable=True):
  """Returns N(s^i * z^i, std^i) where s^i and std^i are pre-component.

  s^i is a learnable parameter with identity initialization.
  std^i is optionally learnable with identity initialization.

  Args:
    name: variable scope.
    z: input_tensor
    logscale_factor: equivalent to scaling up the learning_rate by a factor
                     of logscale_factor.
    trainable: Whether or not std^i is learnt.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    z_shape = common_layers.shape_list(z)
    latent_multiplier = tf.get_variable(
        "latent_multiplier", shape=z_shape, dtype=tf.float32,
        initializer=tf.ones_initializer())
    log_scale = tf.get_variable(
        "log_scale_latent", shape=z_shape, dtype=tf.float32,
        initializer=tf.zeros_initializer(), trainable=trainable)
    log_scale = log_scale * logscale_factor
    return tfp.distributions.Normal(
        loc=latent_multiplier * z, scale=tf.exp(log_scale))


@add_arg_scope
def top_prior(name, z_shape, learn_prior="normal", temperature=1.0):
  """Unconditional prior distribution.

  Args:
    name: variable scope
    z_shape: Shape of the mean / scale of the prior distribution.
    learn_prior: Possible options are "normal" and "single_conv".
                 If set to "single_conv", the gaussian is parametrized by a
                 single convolutional layer whose input are an array of zeros
                 and initialized such that the mean and std are zero and one.
                 If set to "normal", the prior is just a Gaussian with zero
                 mean and unit variance.
    temperature: Temperature with which to sample from the Gaussian.
  Returns:
    objective: 1-D Tensor shape=(batch_size,) summed across spatial components.
  Raises:
    ValueError: If learn_prior not in "normal" or "single_conv"
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    h = tf.zeros(z_shape, dtype=tf.float32)
    if learn_prior == "normal":
      prior_dist = tfp.distributions.Normal(h, tf.exp(h))
    elif learn_prior == "single_conv":
      prior_dist = single_conv_dist("top_learn_prior", h)
    else:
      raise ValueError("Expected learn_prior to be normal or single_conv "
                       "got %s" % learn_prior)
    return TemperedNormal(prior_dist.loc, prior_dist.scale, temperature)


def uniform_binning_correction(x, n_bits=8):
  """Replaces x^i with q^i(x) = U(x, x + 1.0 / 256.0).

  Args:
    x: 4-D Tensor of shape (NHWC)
    n_bits: optional.
  Returns:
    x: x ~ U(x, x + 1.0 / 256)
    objective: Equivalent to -q(x)*log(q(x)).
  """
  n_bins = 2**n_bits
  batch_size, height, width, n_channels = common_layers.shape_list(x)
  hwc = float(height * width * n_channels)

  x = x + tf.random_uniform(
      shape=(batch_size, height, width, n_channels),
      minval=0.0, maxval=1.0/n_bins)
  objective = -np.log(n_bins) * hwc * tf.ones(batch_size)
  return x, objective


@add_arg_scope
def encoder_decoder(name, x, hparams, eps=None, reverse=False,
                    cond_latents=None, condition=False, states=None,
                    temperature=1.0):
  """Glow encoder-decoder. n_levels of (Squeeze + Flow + Split.) operations.

  Args:
    name: variable scope.
    x: 4-D Tensor, shape=(NHWC).
    hparams: HParams.
    eps: Stores (glow(x) - mu) / sigma during the forward pass.
         Used only to test if the network is reversible.
    reverse: Forward or reverse pass.
    cond_latents: list of lists of tensors.
                  outer length equals hparams.num_cond_latents
                  innter length equals hparams.num_levels - 1.
    condition: If set to True, condition the encoder/decoder on cond_latents.
    states: LSTM states, used only if hparams.latent_dist_encoder is set
            to "conv_lstm.
    temperature: Temperature set during sampling.
  Returns:
    x: If reverse, decoded image, else the encoded glow latent representation.
    objective: log-likelihood.
    eps: list of tensors, shape=(num_levels-1).
         Stores (glow(x) - mu_level(x)) / sigma_level(x)) for each level.
    all_latents: list of tensors, shape=(num_levels-1).
                 Latent representatios for each level.
    new_states: list of tensors, shape=(num_levels-1).
                useful only if hparams.latent_dist_encoder="conv_lstm", returns
                the current state of each level.
  """
  # TODO(mechcoder) Change return_type to a dict to be backward compatible.
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):

    if states and len(states) != hparams.n_levels - 1:
      raise ValueError("Expected length of states to be %d, got %d" %
                       (hparams.n_levels - 1, len(states)))
    if states is None:
      states = [None] * (hparams.n_levels - 1)
    if eps and len(eps) != hparams.n_levels - 1:
      raise ValueError("Expected length of eps to be %d, got %d" %
                       (hparams.n_levels - 1, len(eps)))
    if eps is None:
      eps = [None] * (hparams.n_levels - 1)
    check_cond_latents(cond_latents, hparams)

    objective = 0.0
    all_eps = []
    all_latents = []
    new_states = []

    if not reverse:
      # Squeeze + Flow + Split
      for level in range(hparams.n_levels):
        x = squeeze("squeeze_%d" % level, x, factor=2, reverse=False)

        x, obj = revnet("revnet_%d" % level, x, hparams, reverse=False)
        objective += obj

        if level < hparams.n_levels - 1:
          curr_cond_latents = get_cond_latents_at_level(
              cond_latents, level, hparams)
          x, obj, eps, z, state = split("split_%d" % level, x, reverse=False,
                                        cond_latents=curr_cond_latents,
                                        condition=condition,
                                        hparams=hparams, state=states[level])
          objective += obj
          all_eps.append(eps)
          all_latents.append(z)
          new_states.append(state)

      return x, objective, all_eps, all_latents, new_states

    else:
      for level in reversed(range(hparams.n_levels)):
        if level < hparams.n_levels - 1:

          curr_cond_latents = get_cond_latents_at_level(
              cond_latents, level, hparams)

          x, latent, state = split("split_%d" % level, x, eps=eps[level],
                                   reverse=True, cond_latents=curr_cond_latents,
                                   condition=condition, hparams=hparams,
                                   state=states[level],
                                   temperature=temperature)
          new_states.append(state)
          all_latents.append(latent)

        x, obj = revnet(
            "revnet_%d" % level, x, hparams=hparams, reverse=True)
        objective += obj
        x = squeeze("squeeze_%d" % level, x, reverse=True)
      return x, objective, all_latents[::-1], new_states[::-1]
