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 testSparse(self, dtype, param_value):
    (learning_rate, decay, momentum, epsilon, centered, _) = tuple(
        param_value)
    with self.session(use_gpu=True):
      # Initialize variables for numpy implementation.
      var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
      grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype)
      var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
      grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype)

      var0 = variables.Variable(var0_np)
      var1 = variables.Variable(var1_np)
      grads0_np_indices = np.array([0], dtype=np.int32)
      grads0 = ops.IndexedSlices(
          constant_op.constant(grads0_np),
          constant_op.constant(grads0_np_indices), constant_op.constant([1]))
      grads1_np_indices = np.array([1], dtype=np.int32)
      grads1 = ops.IndexedSlices(
          constant_op.constant(grads1_np),
          constant_op.constant(grads1_np_indices), constant_op.constant([1]))
      opt = rmsprop.RMSPropOptimizer(
          learning_rate=learning_rate,
          decay=decay,
          momentum=momentum,
          epsilon=epsilon,
          centered=centered)
      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
      variables.global_variables_initializer().run()

      mg0 = opt.get_slot(var0, "mg")
      self.assertEqual(mg0 is not None, centered)
      mg1 = opt.get_slot(var1, "mg")
      self.assertEqual(mg1 is not None, centered)
      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)

      mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
      mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
      rms0_np = np.array([epsilon, epsilon], dtype=dtype.as_numpy_dtype)
      rms1_np = np.array([epsilon, epsilon], dtype=dtype.as_numpy_dtype)
      mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
      mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)

      # Fetch params to validate initial values
      self.assertAllClose([1.0, 2.0], var0.eval())
      self.assertAllClose([3.0, 4.0], var1.eval())

      # Run 4 steps of RMSProp
      for _ in range(4):
        update.run()

        var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy(
            var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np,
            learning_rate, decay, momentum, centered)
        var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy(
            var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np,
            learning_rate, decay, momentum, centered)

        # Validate updated params
        if centered:
          self.assertAllCloseAccordingToType(mg0_np, mg0.eval())
          self.assertAllCloseAccordingToType(mg1_np, mg1.eval())
        self.assertAllCloseAccordingToType(rms0_np, rms0.eval())
        self.assertAllCloseAccordingToType(rms1_np, rms1.eval())
        self.assertAllCloseAccordingToType(mom0_np, mom0.eval())
        self.assertAllCloseAccordingToType(mom1_np, mom1.eval())
        self.assertAllCloseAccordingToType(var0_np, var0.eval())
        self.assertAllCloseAccordingToType(var1_np, var1.eval())