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 testWithoutMomentum(self, dtype):
    with self.session(use_gpu=True):
      var0 = variables.Variable([1.0, 2.0], dtype=dtype)
      var1 = variables.Variable([3.0, 4.0], dtype=dtype)
      grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
      grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
      opt = rmsprop.RMSPropOptimizer(
          learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0)
      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
      variables.global_variables_initializer().run()

      rms0 = opt.get_slot(var0, "rms")
      self.assertIsNotNone(rms0)
      rms1 = opt.get_slot(var1, "rms")
      self.assertIsNotNone(rms1)
      mom0 = opt.get_slot(var0, "momentum")
      self.assertIsNotNone(mom0)
      mom1 = opt.get_slot(var1, "momentum")
      self.assertIsNotNone(mom1)

      # Fetch params to validate initial values
      self.assertAllClose([1.0, 2.0], var0.eval())
      self.assertAllClose([3.0, 4.0], var1.eval())
      # Step 1: the rms accumulators where 1. So we should see a normal
      # update: v -= grad * learning_rate
      update.run()
      # Check the root mean square accumulators.
      self.assertAllCloseAccordingToType(
          np.array([0.901, 0.901]), rms0.eval())
      self.assertAllCloseAccordingToType(
          np.array([0.90001, 0.90001]), rms1.eval())
      # Check the parameters.
      self.assertAllCloseAccordingToType(
          np.array([
              1.0 - (0.1 * 2.0 / math.sqrt(0.901)),
              2.0 - (0.1 * 2.0 / math.sqrt(0.901))
          ]), var0.eval())
      self.assertAllCloseAccordingToType(
          np.array([
              3.0 - (0.01 * 2.0 / math.sqrt(0.90001)),
              4.0 - (0.01 * 2.0 / math.sqrt(0.90001))
          ]), var1.eval())
      # Step 2: the root mean square accumulators contain the previous update.
      update.run()
      # Check the rms accumulators.
      self.assertAllCloseAccordingToType(
          np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval())
      self.assertAllCloseAccordingToType(
          np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval())
      # Check the parameters.
      self.assertAllCloseAccordingToType(
          np.array([
              1.0 - (0.1 * 2.0 / math.sqrt(0.901)) -
              (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001)),
              2.0 - (0.1 * 2.0 / math.sqrt(0.901)) -
              (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001))
          ]), var0.eval())
      self.assertAllCloseAccordingToType(
          np.array([
              3.0 - (0.01 * 2.0 / math.sqrt(0.90001)) -
              (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5)),
              4.0 - (0.01 * 2.0 / math.sqrt(0.90001)) -
              (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5))
          ]), var1.eval())