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 __init__(self, model, data):
        # try and import pytorch
        global torch
        if torch is None:
            import torch
            if version.parse(torch.__version__) < version.parse("0.4"):
                warnings.warn("Your PyTorch version is older than 0.4 and not supported.")

        # check if we have multiple inputs
        self.multi_input = False
        if type(data) == list:
            self.multi_input = True
        if type(data) != list:
            data = [data]
        self.data = data
        self.layer = None
        self.input_handle = None
        self.interim = False
        self.interim_inputs_shape = None
        self.expected_value = None  # to keep the DeepExplainer base happy
        if type(model) == tuple:
            self.interim = True
            model, layer = model
            model = model.eval()
            self.layer = layer
            self.add_target_handle(self.layer)

            # if we are taking an interim layer, the 'data' is going to be the input
            # of the interim layer; we will capture this using a forward hook
            with torch.no_grad():
                _ = model(*data)
                interim_inputs = self.layer.target_input
                if type(interim_inputs) is tuple:
                    # this should always be true, but just to be safe
                    self.interim_inputs_shape = [i.shape for i in interim_inputs]
                else:
                    self.interim_inputs_shape = [interim_inputs.shape]
            self.target_handle.remove()
            del self.layer.target_input
        self.model = model.eval()

        self.multi_output = False
        self.num_outputs = 1
        with torch.no_grad():
            outputs = model(*data)

            # also get the device everything is running on
            self.device = outputs.device
            if outputs.shape[1] > 1:
                self.multi_output = True
                self.num_outputs = outputs.shape[1]
            self.expected_value = outputs.mean(0).cpu().numpy()