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 _building_block_v2(inputs, filters, training, projection_shortcut, strides,
                       data_format, name):
  """A single block for ResNet v2, without a bottleneck.

  Batch normalization then ReLu then convolution as described by:
    Identity Mappings in Deep Residual Networks
    https://arxiv.org/pdf/1603.05027.pdf
    by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or [batch,
      height_in, width_in, channels] depending on data_format.
    filters: The number of filters for the convolutions.
    training: A Boolean for whether the model is in training or inference mode.
      Needed for batch normalization.
    projection_shortcut: The function to use for projection shortcuts (typically
      a 1x1 convolution when downsampling the input).
    strides: The block's stride. If greater than 1, this block will ultimately
      downsample the input.
    data_format: The input format ('channels_last' or 'channels_first').
    name: Block name.

  Returns:
    The output tensor of the block; shape should match inputs.
  """
  shortcut = inputs
  first_name = name + 'first'
  inputs = batch_norm(
      inputs, training, data_format, name=first_name + 'batch_norm')
  inputs = tf.nn.relu(inputs, name=first_name + 'relu')

  # The projection shortcut should come after the first batch norm and ReLU
  # since it performs a 1x1 convolution.
  if projection_shortcut is not None:
    shortcut = projection_shortcut(inputs, name=first_name + 'proj')

  second_name = name + 'second'
  inputs = conv2d_fixed_padding(
      inputs=inputs,
      filters=filters,
      kernel_size=3,
      strides=strides,
      data_format=data_format,
      name=second_name + 'input')

  inputs = batch_norm(
      inputs, training, data_format, name=second_name + 'batch_norm')
  inputs = tf.nn.relu(inputs, name=second_name + 'relu')

  third_name = name + 'third'
  inputs = conv2d_fixed_padding(
      inputs=inputs,
      filters=filters,
      kernel_size=3,
      strides=1,
      data_format=data_format,
      name=third_name + 'input')

  return inputs + shortcut