def download_file(url, dest=None, chunk_size=1024, replace="ask",
                  label="Downloading {dest_basename} ({size:.2f}MB)",
                  expected_extension=None):
    """Download a file from a given URL and display progress.

    :param dest: If the destination exists and is a directory, the filename
        will be guessed from the Content-Disposition header. If the destination
        is an existing file, the user will either be prompted to overwrite, or
        the file will be replaced (depending on the value of **replace**). If
        the destination does not exist, it will be used as the filename.
    :param int chunk_size: bytes read in at a time.
    :param replace: If `False`, an existing destination file will not be
        overwritten.
    :param label: a string which is formatted and displayed as the progress bar
        label. Variables provided include *dest_basename*, *dest*, and *size*.
    :param expected_extension: if set, the filename will be sanitized to ensure
        it has the given extension. The extension should not start with a dot
        (`.`).
    """
    dest = Path(dest or url.split("/")[-1])
    response = get(url, stream=True)
    if (dest.exists()
            and dest.is_dir()
            and "Content-Disposition" in response.headers):
        content_disposition = rfc6266.parse_requests_response(response)
        if expected_extension is not None:
            filename = content_disposition.filename_sanitized(
                expected_extension)
        filename = secure_filename(filename)
        dest = dest / filename
    if dest.exists() and not dest.is_dir():
        if (replace is False
                or replace == "ask"
                and not click.confirm("Replace {}?".format(dest))):
            return str(dest)
    size = int(response.headers.get("content-length", 0))
    label = label.format(dest=dest, dest_basename=dest.name,
                         size=size/1024.0/1024)
    with click.open_file(str(dest), "wb") as f:
        content_iter = response.iter_content(chunk_size=chunk_size)
        with click.progressbar(content_iter, length=size/1024,
                               label=label) as bar:
            for chunk in bar:
                if chunk:
                    f.write(chunk)
                    f.flush()
    return str(dest)
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())