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 maxpool(module, grad_input, grad_output):
    pool_to_unpool = {
        'MaxPool1d': torch.nn.functional.max_unpool1d,
        'MaxPool2d': torch.nn.functional.max_unpool2d,
        'MaxPool3d': torch.nn.functional.max_unpool3d
    }
    pool_to_function = {
        'MaxPool1d': torch.nn.functional.max_pool1d,
        'MaxPool2d': torch.nn.functional.max_pool2d,
        'MaxPool3d': torch.nn.functional.max_pool3d
    }
    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # we also need to check if the output is a tuple
    y, ref_output = torch.chunk(module.y, 2)
    cross_max = torch.max(y, ref_output)
    diffs = torch.cat([cross_max - ref_output, y - cross_max], 0)

    # all of this just to unpool the outputs
    with torch.no_grad():
        _, indices = pool_to_function[module.__class__.__name__](
            module.x, module.kernel_size, module.stride, module.padding,
            module.dilation, module.ceil_mode, True)
        xmax_pos, rmax_pos = torch.chunk(pool_to_unpool[module.__class__.__name__](
            grad_output[0] * diffs, indices, module.kernel_size, module.stride,
            module.padding, list(module.x.shape)), 2)
    org_input_shape = grad_input[0].shape  # for the maxpool 1d
    grad_input = [None for _ in grad_input]
    grad_input[0] = torch.where(torch.abs(delta_in) < 1e-7, torch.zeros_like(delta_in),
                           (xmax_pos + rmax_pos) / delta_in).repeat(dup0)
    if module.__class__.__name__ == 'MaxPool1d':
        complex_module_gradients.append(grad_input[0])
        # the grad input that is returned doesn't matter, since it will immediately be
        # be overridden by the grad in the complex_module_gradient
        grad_input[0] = torch.ones(org_input_shape)
    return tuple(grad_input)