def secure_filename(filename):
    r"""Borrowed from :mod:`werkzeug.utils`, under the BSD 3-clause license.

    Pass it a filename and it will return a secure version of it.  This
    filename can then safely be stored on a regular file system and passed
    to :func:`os.path.join`.  The filename returned is an ASCII only string
    for maximum portability.

    On windows systems the function also makes sure that the file is not
    named after one of the special device files.

    >>> secure_filename("My cool movie.mov")
    'My_cool_movie.mov'
    >>> secure_filename("../../../etc/passwd")
    'etc_passwd'
    >>> secure_filename(u'i contain cool \xfcml\xe4uts.txt')
    'i_contain_cool_umlauts.txt'

    The function might return an empty filename.  It's your responsibility
    to ensure that the filename is unique and that you generate random
    filename if the function returned an empty one.

    :param filename: the filename to secure
    """
    _filename_ascii_strip_re = re.compile(r'[^A-Za-z0-9_.-]')
    _windows_device_files = ('CON', 'AUX', 'COM1', 'COM2', 'COM3', 'COM4',
                             'LPT1', 'LPT2', 'LPT3', 'PRN', 'NUL')
    if isinstance(filename, text_type):
        from unicodedata import normalize
        filename = normalize('NFKD', filename).encode('ascii', 'ignore')
        if not PY2:
            filename = filename.decode('ascii')
    for sep in os.path.sep, os.path.altsep:
        if sep:
            filename = filename.replace(sep, ' ')
    filename = str(_filename_ascii_strip_re.sub('', '_'.join(
                   filename.split()))).strip('._')

    # on nt a couple of special files are present in each folder.  We
    # have to ensure that the target file is not such a filename.  In
    # this case we prepend an underline
    if os.name == 'nt' and filename and \
       filename.split('.')[0].upper() in _windows_device_files:
        filename = '_' + filename

    return filename
def shap_values(self, X, ranked_outputs=None, output_rank_order="max", check_additivity=False):

        # X ~ self.model_input
        # X_data ~ self.data

        # check if we have multiple inputs
        if not self.multi_input:
            assert type(X) != list, "Expected a single tensor model input!"
            X = [X]
        else:
            assert type(X) == list, "Expected a list of model inputs!"

        X = [x.detach().to(self.device) for x in X]

        if ranked_outputs is not None and self.multi_output:
            with torch.no_grad():
                model_output_values = self.model(*X)
            # rank and determine the model outputs that we will explain
            if output_rank_order == "max":
                _, model_output_ranks = torch.sort(model_output_values, descending=True)
            elif output_rank_order == "min":
                _, model_output_ranks = torch.sort(model_output_values, descending=False)
            elif output_rank_order == "max_abs":
                _, model_output_ranks = torch.sort(torch.abs(model_output_values), descending=True)
            else:
                assert False, "output_rank_order must be max, min, or max_abs!"
            model_output_ranks = model_output_ranks[:, :ranked_outputs]
        else:
            model_output_ranks = (torch.ones((X[0].shape[0], self.num_outputs)).int() *
                                  torch.arange(0, self.num_outputs).int())

        # add the gradient handles
        handles = self.add_handles(self.model, add_interim_values, deeplift_grad)
        if self.interim:
            self.add_target_handle(self.layer)

        # compute the attributions
        output_phis = []
        for i in range(model_output_ranks.shape[1]):
            phis = []
            if self.interim:
                for k in range(len(self.interim_inputs_shape)):
                    phis.append(np.zeros((X[0].shape[0], ) + self.interim_inputs_shape[k][1: ]))
            else:
                for k in range(len(X)):
                    phis.append(np.zeros(X[k].shape))
            for j in range(X[0].shape[0]):
                # tile the inputs to line up with the background data samples
                tiled_X = [X[l][j:j + 1].repeat(
                                   (self.data[l].shape[0],) + tuple([1 for k in range(len(X[l].shape) - 1)])) for l
                           in range(len(X))]
                joint_x = [torch.cat((tiled_X[l], self.data[l]), dim=0) for l in range(len(X))]
                # run attribution computation graph
                feature_ind = model_output_ranks[j, i]
                sample_phis = self.gradient(feature_ind, joint_x)
                # assign the attributions to the right part of the output arrays
                if self.interim:
                    sample_phis, output = sample_phis
                    x, data = [], []
                    for k in range(len(output)):
                        x_temp, data_temp = np.split(output[k], 2)
                        x.append(x_temp)
                        data.append(data_temp)
                    for l in range(len(self.interim_inputs_shape)):
                        phis[l][j] = (sample_phis[l][self.data[l].shape[0]:] * (x[l] - data[l])).mean(0)
                else:
                    for l in range(len(X)):
                        phis[l][j] = (torch.from_numpy(sample_phis[l][self.data[l].shape[0]:]).to(self.device) * (X[l][j: j + 1] - self.data[l])).cpu().detach().numpy().mean(0)
            output_phis.append(phis[0] if not self.multi_input else phis)
        # cleanup; remove all gradient handles
        for handle in handles:
            handle.remove()
        self.remove_attributes(self.model)
        if self.interim:
            self.target_handle.remove()

        if not self.multi_output:
            return output_phis[0]
        elif ranked_outputs is not None:
            return output_phis, model_output_ranks
        else:
            return output_phis