def extract_bower_zipfile(self, zip_file, dest, expected_version=None):
        bower_json = None
        root = None
        deps_installed = []
        for info in zip_file.infolist():
            if PurePath(info.filename).name == "bower.json":
                with zip_file.open(info) as f:
                    bower_json = json.load(f)
                    root = str(PurePath(info.filename).parent)
                break
        version = bower_json["version"]
        if expected_version is not None:
            expected_version = Bower.clean_semver(expected_version)
            if not semver.match(version, expected_version):
                click.secho("error: versions do not match ({} =/= {})".format(
                    version, expected_version))
                raise InvalidPackageError
        if "dependencies" in bower_json:
            for package, version in bower_json["dependencies"].items():
                url = Bower.get_package_url(package)
                deps_installed.extend(self.get_bower_package(
                    url, dest=dest, version=version))
        ignore_patterns = [GitIgnorePattern(ig) for ig in bower_json["ignore"]]
        path_spec = PathSpec(ignore_patterns)
        namelist = [path for path in zip_file.namelist()
                    if PurePath(path).parts[0] == root]
        ignored = list(path_spec.match_files(namelist))
        for path in namelist:
            dest_path = PurePath(
                bower_json["name"],
                *PurePath(path).parts[1:])

            if path in ignored:
                continue

            for path in ignored:
                for parent in PurePath(path):
                    if parent in ignored:
                        continue

            if path.endswith("/"):
                if list(path_spec.match_files([str(dest_path)])):
                    ignored.append(PurePath(path))
                elif not (dest / dest_path).is_dir():
                    (dest / dest_path).mkdir(parents=True)
            else:
                target_path = dest / dest_path.parent / dest_path.name
                source = zip_file.open(path)
                target = target_path.open("wb")
                with source, target:
                    shutil.copyfileobj(source, target)
        deps_installed.append((bower_json["name"], bower_json["version"]))
        return deps_installed
def __init__(self,
               resnet_size,
               bottleneck,
               num_classes,
               num_filters,
               kernel_size,
               conv_stride,
               first_pool_size,
               first_pool_stride,
               block_sizes,
               block_strides,
               resnet_version=DEFAULT_VERSION,
               data_format=None,
               dtype=DEFAULT_DTYPE):
    """Creates a model for classifying an image.

    Args:
      resnet_size: A single integer for the size of the ResNet model.
      bottleneck: Use regular blocks or bottleneck blocks.
      num_classes: The number of classes used as labels.
      num_filters: The number of filters to use for the first block layer of the
        model. This number is then doubled for each subsequent block layer.
      kernel_size: The kernel size to use for convolution.
      conv_stride: stride size for the initial convolutional layer
      first_pool_size: Pool size to be used for the first pooling layer. If
        none, the first pooling layer is skipped.
      first_pool_stride: stride size for the first pooling layer. Not used if
        first_pool_size is None.
      block_sizes: A list containing n values, where n is the number of sets of
        block layers desired. Each value should be the number of blocks in the
        i-th set.
      block_strides: List of integers representing the desired stride size for
        each of the sets of block layers. Should be same length as block_sizes.
      resnet_version: Integer representing which version of the ResNet network
        to use. See README for details. Valid values: [1, 2]
      data_format: Input format ('channels_last', 'channels_first', or None). If
        set to None, the format is dependent on whether a GPU is available.
      dtype: The TensorFlow dtype to use for calculations. If not specified
        tf.float32 is used.

    Raises:
      ValueError: if invalid version is selected.
    """
    self.resnet_size = resnet_size

    if not data_format:
      data_format = ('channels_first'
                     if tf.test.is_built_with_cuda() else 'channels_last')

    self.resnet_version = resnet_version
    if resnet_version not in (1, 2):
      raise ValueError(
          'Resnet version should be 1 or 2. See README for citations.')

    self.bottleneck = bottleneck
    self.block_fn = _building_block_v2

    if dtype not in ALLOWED_TYPES:
      raise ValueError('dtype must be one of: {}'.format(ALLOWED_TYPES))

    self.data_format = data_format
    self.num_classes = num_classes
    self.num_filters = num_filters
    self.kernel_size = kernel_size
    self.conv_stride = conv_stride
    self.first_pool_size = first_pool_size
    self.first_pool_stride = first_pool_stride
    self.block_sizes = block_sizes
    self.block_strides = block_strides
    self.dtype = dtype
    self.pre_activation = resnet_version == 2