def get_bower_package(self, url, dest=None, version=None,
                          process_deps=True):
        dest = dest or Path(".") / "assets"
        parsed_url = urlparse(url)
        if parsed_url.scheme == "git" or parsed_url.path.endswith(".git"):
            if parsed_url.netloc == "github.com":
                user, repo = parsed_url.path[1:-4].split("/")
                response = get(github_api_uri +
                               "/repos/{}/{}/tags".format(user, repo))
                tags = response.json()
                target = None
                if not len(tags):
                    click.secho("fatal: no tags exist for {}/{}".format(
                        user, repo), fg="red")
                    raise InvalidPackageError
                if version is None:
                    target = tags[0]
                else:
                    for tag in tags:
                        if semver.match(tag["name"],
                                        Bower.clean_semver(version)):
                            target = tag
                            break
                if not target:
                    click.secho(
                        "fatal: failed to find matching tag for "
                        "{user}/{repo} {version}".format(user, repo, version),
                        fg="red")
                    raise VersionNotFoundError
                click.secho("installing {}/{}#{}".format(
                    user, repo, tags[0]["name"]), fg="green")
                return self.get_bower_package(
                    url=target["zipball_url"],
                    dest=dest,
                    version=version)
            raise NotImplementedError
            click.echo("git clone {url}".format(url=url))
            cmd = envoy.run('git clone {url} "{dest}"'.format(
                url=url, dest=dest))
        elif parsed_url.scheme in ("http", "https"):
            zip_dest = download_file(url, dest=self.temp_dir,
                                     label="{dest_basename}",
                                     expected_extension="zip")
            with zipfile.ZipFile(zip_dest, "r") as pkg:
                return self.extract_bower_zipfile(pkg, dest,
                                                  expected_version=version)
                # pkg.extractall(str(dest))
        else:
            click.secho("protocol currently unsupported :(")
            sys.exit(1)
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