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 add_interim_values(module, input, output):
    """The forward hook used to save interim tensors, detached
    from the graph. Used to calculate the multipliers
    """
    try:
        del module.x
    except AttributeError:
        pass
    try:
        del module.y
    except AttributeError:
        pass
    module_type = module.__class__.__name__
    if module_type in op_handler:
        func_name = op_handler[module_type].__name__
        # First, check for cases where we don't need to save the x and y tensors
        if func_name == 'passthrough':
            pass
        else:
            # check only the 0th input varies
            for i in range(len(input)):
                if i != 0 and type(output) is tuple:
                    assert input[i] == output[i], "Only the 0th input may vary!"
            # if a new method is added, it must be added here too. This ensures tensors
            # are only saved if necessary
            if func_name in ['maxpool', 'nonlinear_1d']:
                # only save tensors if necessary
                if type(input) is tuple:
                    setattr(module, 'x', torch.nn.Parameter(input[0].detach()))
                else:
                    setattr(module, 'x', torch.nn.Parameter(input.detach()))
                if type(output) is tuple:
                    setattr(module, 'y', torch.nn.Parameter(output[0].detach()))
                else:
                    setattr(module, 'y', torch.nn.Parameter(output.detach()))
            if module_type in failure_case_modules:
                input[0].register_hook(deeplift_tensor_grad)