# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):


import functools
import tensorflow as tf
import tensorflow.contrib.layers as contrib_layers
from ldif.util.file_util import log
#
# 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
#
#     https://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.
# Lint as: python3
"""Utilities for building neural networks."""


# LDIF is an internal package, should be imported last.
# pylint: disable=g-bad-import-order
# pylint: enable=g-bad-import-order


def twin_inference(inference_fun, observation, element_count,
                   flat_element_length, model_config):
  """Predicts both explicit and implicit parameters for each shape element."""
  element_embedding_length = model_config.hparams.ips
  # if element_embedding_length == 0:
  # raise ValueError(
  # 'Twin Inference networks requires a nonzero imlicit parameter count.')
  remaining_length = flat_element_length - element_embedding_length
  if remaining_length <= 0:

# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):


# Path: ldif/util/net_util.py
import functools
import tensorflow as tf
import tensorflow.contrib.layers as contrib_layers
from ldif.util.file_util import log

#
# 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
#
#     https://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.
# Lint as: python3
"""Utilities for building neural networks."""


# LDIF is an internal package, should be imported last.
# pylint: disable=g-bad-import-order
# pylint: enable=g-bad-import-order


def twin_inference(inference_fun, observation, element_count,
                   flat_element_length, model_config):
  """Predicts both explicit and implicit parameters for each shape element."""
  element_embedding_length = model_config.hparams.ips
  # if element_embedding_length == 0:
  # raise ValueError(
  # 'Twin Inference networks requires a nonzero imlicit parameter count.')
  remaining_length = flat_element_length - element_embedding_length
  if remaining_length <= 0:

    log.warning('Using less-tested option: single-tower in twin-tower.')