def extract_bower_zipfile(self, zip_file, dest, expected_version=None):
        bower_json = None
        root = None
        deps_installed = []
        for info in zip_file.infolist():
            if PurePath(info.filename).name == "bower.json":
                with zip_file.open(info) as f:
                    bower_json = json.load(f)
                    root = str(PurePath(info.filename).parent)
                break
        version = bower_json["version"]
        if expected_version is not None:
            expected_version = Bower.clean_semver(expected_version)
            if not semver.match(version, expected_version):
                click.secho("error: versions do not match ({} =/= {})".format(
                    version, expected_version))
                raise InvalidPackageError
        if "dependencies" in bower_json:
            for package, version in bower_json["dependencies"].items():
                url = Bower.get_package_url(package)
                deps_installed.extend(self.get_bower_package(
                    url, dest=dest, version=version))
        ignore_patterns = [GitIgnorePattern(ig) for ig in bower_json["ignore"]]
        path_spec = PathSpec(ignore_patterns)
        namelist = [path for path in zip_file.namelist()
                    if PurePath(path).parts[0] == root]
        ignored = list(path_spec.match_files(namelist))
        for path in namelist:
            dest_path = PurePath(
                bower_json["name"],
                *PurePath(path).parts[1:])

            if path in ignored:
                continue

            for path in ignored:
                for parent in PurePath(path):
                    if parent in ignored:
                        continue

            if path.endswith("/"):
                if list(path_spec.match_files([str(dest_path)])):
                    ignored.append(PurePath(path))
                elif not (dest / dest_path).is_dir():
                    (dest / dest_path).mkdir(parents=True)
            else:
                target_path = dest / dest_path.parent / dest_path.name
                source = zip_file.open(path)
                target = target_path.open("wb")
                with source, target:
                    shutil.copyfileobj(source, target)
        deps_installed.append((bower_json["name"], bower_json["version"]))
        return deps_installed
def run_model(prior, images, model, disc_reg_weight):
  """Run the model with new data and samples.

  Args:
    prior: the noise source as the generator input.
    images: images sampled from dataset.
    model: a GAN model defined in gan.py.
    disc_reg_weight: regularisation weight for discrmininator gradients.

  Returns:
    debug_ops: statistics from the model, see gan.py for more detials.
    disc_grads: discriminator gradients.
    gen_grads: generator gradients.
  """
  generator_inputs = prior.sample(FLAGS.batch_size)
  model_output = model.connect(images, generator_inputs)
  optimization_components = model_output.optimization_components

  disc_grads = tf.gradients(
      optimization_components['disc'].loss,
      optimization_components['disc'].vars)

  gen_grads = tf.gradients(
      optimization_components['gen'].loss,
      optimization_components['gen'].vars)

  if disc_reg_weight > 0.0:
    reg_grads = _compute_reg_grads(gen_grads,
                                   optimization_components['disc'].vars)
    disc_grads = _acc_grads(disc_grads, disc_reg_weight, reg_grads)

  debug_ops = model_output.debug_ops

  return debug_ops, disc_grads, gen_grads