Python functools.partial() Examples

def multi_apply(func, *args, **kwargs):
    """Apply function to a list of arguments.

    Note:
        This function applies the ``func`` to multiple inputs and
            map the multiple outputs of the ``func`` into different
            list. Each list contains the same type of outputs corresponding
            to different inputs.

    Args:
        func (Function): A function that will be applied to a list of
            arguments

    Returns:
        tuple(list): A tuple containing multiple list, each list contains
            a kind of returned results by the function
    """
    pfunc = partial(func, **kwargs) if kwargs else func
    map_results = map(pfunc, *args)
    return tuple(map(list, zip(*map_results))) 

def load_plugins():
    here = os.path.abspath(os.path.dirname(__file__))
    get_path = partial(os.path.join, here)
    plugin_dir = get_path('plugins')

    plugin_base = PluginBase(
        package='wafw00f.plugins', searchpath=[plugin_dir]
    )
    plugin_source = plugin_base.make_plugin_source(
        searchpath=[plugin_dir], persist=True
    )

    plugin_dict = {}
    for plugin_name in plugin_source.list_plugins():
        plugin_dict[plugin_name] = plugin_source.load_plugin(plugin_name)

    return plugin_dict 

def tasks(args):
    list_clusters = clients.ecs.get_paginator("list_clusters")
    list_tasks = clients.ecs.get_paginator("list_tasks")

    def list_tasks_worker(worker_args):
        cluster, status = worker_args
        return cluster, status, list(paginate(list_tasks, cluster=cluster, desiredStatus=status))

    def describe_tasks_worker(t, cluster=None):
        return clients.ecs.describe_tasks(cluster=cluster, tasks=t)["tasks"] if t else []

    task_descs = []
    if args.clusters is None:
        args.clusters = [__name__.replace(".", "_")] if args.tasks else list(paginate(list_clusters))
    if args.tasks:
        task_descs = describe_tasks_worker(args.tasks, cluster=args.clusters[0])
    else:
        with ThreadPoolExecutor() as executor:
            for cluster, status, tasks in executor.map(list_tasks_worker, product(args.clusters, args.desired_status)):
                worker = partial(describe_tasks_worker, cluster=cluster)
                descs = executor.map(worker, (tasks[pos:pos + 100] for pos in range(0, len(tasks), 100)))
                task_descs += sum(descs, [])
    page_output(tabulate(task_descs, args)) 

def get_arg_serializer(arg_type):
    if isinstance(arg_type, GraphQLNonNull):
        return get_arg_serializer(arg_type.of_type)
    if isinstance(arg_type, GraphQLInputField):
        return get_arg_serializer(arg_type.type)
    if isinstance(arg_type, GraphQLInputObjectType):
        serializers = {k: get_arg_serializer(v) for k, v in arg_type.fields.items()}
        return lambda value: ObjectValueNode(
            fields=FrozenList(
                ObjectFieldNode(name=NameNode(value=k), value=serializers[k](v))
                for k, v in value.items()
            )
        )
    if isinstance(arg_type, GraphQLList):
        inner_serializer = get_arg_serializer(arg_type.of_type)
        return partial(serialize_list, inner_serializer)
    if isinstance(arg_type, GraphQLEnumType):
        return lambda value: EnumValueNode(value=arg_type.serialize(value))
    return lambda value: ast_from_value(arg_type.serialize(value), arg_type) 

def subscribe(self, channel, callback=None, priority=None):
        """Add the given callback at the given channel (if not present).

        If callback is None, return a partial suitable for decorating
        the callback.
        """
        if callback is None:
            return functools.partial(
                self.subscribe,
                channel,
                priority=priority,
            )

        ch_listeners = self.listeners.setdefault(channel, set())
        ch_listeners.add(callback)

        if priority is None:
            priority = getattr(callback, 'priority', 50)
        self._priorities[(channel, callback)] = priority 

def popup_choice(self, window, title, image, text, choices):
        popup = tk.Toplevel(window)
        popup.resizable(False, False)
        if MAIN_ICON != None:
            if os.path.splitext(MAIN_ICON)[1].lower() == ".gif":
                dummy = None
                #popup.call('wm', 'iconphoto', popup._w, tk.PhotoImage(file=MAIN_ICON))
            else:
                popup.iconbitmap(MAIN_ICON)
        popup.wm_title(title)
        popup.tkraise(window)
        
        def run_end(func):
            popup.destroy()
            if func != None:
                func()

        picture_label = tk.Label(popup, image=image)
        picture_label.photo = image
        picture_label.grid(column=0, row=0, rowspan=2, padx=10, pady=10)
        tk.Label(popup, text=text, justify=tk.CENTER).grid(column=1, row=0, columnspan=len(choices), padx=10, pady=10)
        for idx, choice in enumerate(choices):
            run_end_func = partial(run_end, choice[1])
            tk.Button(popup, text=choice[0], command=run_end_func).grid(column=1 + idx, row=1, padx=10, pady=10)
        popup.wait_visibility()
        popup.grab_set()
        popup.wait_window() 

def fprop(self, x, **kwargs):
        del kwargs
        my_conv = functools.partial(tf.layers.conv2d,
                                    kernel_size=3,
                                    strides=2,
                                    padding='valid',
                                    activation=tf.nn.relu,
                                    kernel_initializer=HeReLuNormalInitializer)
        my_dense = functools.partial(
            tf.layers.dense, kernel_initializer=HeReLuNormalInitializer)

        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            for depth in [96, 256, 384, 384, 256]:
                x = my_conv(x, depth)
            y = tf.layers.flatten(x)
            y = my_dense(y, 4096, tf.nn.relu)
            y = fc7 = my_dense(y, 4096, tf.nn.relu)
            y = my_dense(y, 1000)
            return {'fc7': fc7,
                    self.O_LOGITS: y,
                    self.O_PROBS: tf.nn.softmax(logits=y)} 

def run(self):
        self.monitor.start()
        notifier = poll.Poll.for_events(
            (self.monitor, 'r'), (self._stop_event.source, 'r'))
        while True:
            for file_descriptor, event in eintr_retry_call(notifier.poll):
                if file_descriptor == self._stop_event.source.fileno():
                    # in case of a stop event, close our pipe side, and
                    # return from the thread
                    self._stop_event.source.close()
                    return
                elif file_descriptor == self.monitor.fileno() and event == 'r':
                    read_device = partial(eintr_retry_call, self.monitor.poll, timeout=0)
                    for device in iter(read_device, None):
                        self._callback(device)
                else:
                    raise EnvironmentError('Observed monitor hung up') 

def _create_evaluation_spec(params, audio_adapter, audio_path):
    """ Setup eval spec evaluating ever n seconds

    :param params: TF params to build spec from.
    :returns: Built evaluation spec.
    """
    input_fn = partial(
        get_validation_dataset,
        params,
        audio_adapter,
        audio_path)
    evaluation_spec = tf.estimator.EvalSpec(
        input_fn=input_fn,
        steps=None,
        throttle_secs=params['throttle_secs'])
    return evaluation_spec 

def __init__(self, learning_rate, max_iteration_steps, seed=None):
        """Initializes a `Generator` that builds `SimpleCNNs`.

        Args:
          learning_rate: The float learning rate to use.
          max_iteration_steps: The number of steps per iteration.
          seed: The random seed.

        Returns:
          An instance of `Generator`.
        """
        self._seed = seed
        self._cnn_builder_fn = functools.partial(
            SimpleCNNBuilder,
            learning_rate=learning_rate,
            max_iteration_steps=max_iteration_steps) 

def __init__(self, input_shape, num_hidden,
                 h2h_kernel=(3, 3), h2h_dilate=(1, 1),
                 i2h_kernel=(3, 3), i2h_stride=(1, 1),
                 i2h_pad=(1, 1), i2h_dilate=(1, 1),
                 i2h_weight_initializer=None, h2h_weight_initializer=None,
                 i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
                 activation=functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2),
                 prefix='ConvRNN_', params=None, conv_layout='NCHW'):
        super(ConvRNNCell, self).__init__(input_shape=input_shape, num_hidden=num_hidden,
                                          h2h_kernel=h2h_kernel, h2h_dilate=h2h_dilate,
                                          i2h_kernel=i2h_kernel, i2h_stride=i2h_stride,
                                          i2h_pad=i2h_pad, i2h_dilate=i2h_dilate,
                                          i2h_weight_initializer=i2h_weight_initializer,
                                          h2h_weight_initializer=h2h_weight_initializer,
                                          i2h_bias_initializer=i2h_bias_initializer,
                                          h2h_bias_initializer=h2h_bias_initializer,
                                          activation=activation, prefix=prefix,
                                          params=params, conv_layout=conv_layout) 

def __init__(self, input_shape, num_hidden,
                 h2h_kernel=(3, 3), h2h_dilate=(1, 1),
                 i2h_kernel=(3, 3), i2h_stride=(1, 1),
                 i2h_pad=(1, 1), i2h_dilate=(1, 1),
                 i2h_weight_initializer=None, h2h_weight_initializer=None,
                 i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
                 activation=functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2),
                 prefix='ConvLSTM_', params=None,
                 conv_layout='NCHW'):
        super(ConvLSTMCell, self).__init__(input_shape=input_shape, num_hidden=num_hidden,
                                           h2h_kernel=h2h_kernel, h2h_dilate=h2h_dilate,
                                           i2h_kernel=i2h_kernel, i2h_stride=i2h_stride,
                                           i2h_pad=i2h_pad, i2h_dilate=i2h_dilate,
                                           i2h_weight_initializer=i2h_weight_initializer,
                                           h2h_weight_initializer=h2h_weight_initializer,
                                           i2h_bias_initializer=i2h_bias_initializer,
                                           h2h_bias_initializer=h2h_bias_initializer,
                                           activation=activation, prefix=prefix,
                                           params=params, conv_layout=conv_layout) 

def __init__(self, input_shape, num_hidden,
                 h2h_kernel=(3, 3), h2h_dilate=(1, 1),
                 i2h_kernel=(3, 3), i2h_stride=(1, 1),
                 i2h_pad=(1, 1), i2h_dilate=(1, 1),
                 i2h_weight_initializer=None, h2h_weight_initializer=None,
                 i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
                 activation=functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2),
                 prefix='ConvGRU_', params=None, conv_layout='NCHW'):
        super(ConvGRUCell, self).__init__(input_shape=input_shape, num_hidden=num_hidden,
                                          h2h_kernel=h2h_kernel, h2h_dilate=h2h_dilate,
                                          i2h_kernel=i2h_kernel, i2h_stride=i2h_stride,
                                          i2h_pad=i2h_pad, i2h_dilate=i2h_dilate,
                                          i2h_weight_initializer=i2h_weight_initializer,
                                          h2h_weight_initializer=h2h_weight_initializer,
                                          i2h_bias_initializer=i2h_bias_initializer,
                                          h2h_bias_initializer=h2h_bias_initializer,
                                          activation=activation, prefix=prefix,
                                          params=params, conv_layout=conv_layout) 

def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir) 

def mmd_loss(source_samples, target_samples, weight, scope=None):
  """Adds a similarity loss term, the MMD between two representations.

  This Maximum Mean Discrepancy (MMD) loss is calculated with a number of
  different Gaussian kernels.

  Args:
    source_samples: a tensor of shape [num_samples, num_features].
    target_samples: a tensor of shape [num_samples, num_features].
    weight: the weight of the MMD loss.
    scope: optional name scope for summary tags.

  Returns:
    a scalar tensor representing the MMD loss value.
  """
  sigmas = [
      1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 5, 10, 15, 20, 25, 30, 35, 100,
      1e3, 1e4, 1e5, 1e6
  ]
  gaussian_kernel = partial(
      utils.gaussian_kernel_matrix, sigmas=tf.constant(sigmas))

  loss_value = maximum_mean_discrepancy(
      source_samples, target_samples, kernel=gaussian_kernel)
  loss_value = tf.maximum(1e-4, loss_value) * weight
  assert_op = tf.Assert(tf.is_finite(loss_value), [loss_value])
  with tf.control_dependencies([assert_op]):
    tag = 'MMD Loss'
    if scope:
      tag = scope + tag
    tf.summary.scalar(tag, loss_value)
    tf.losses.add_loss(loss_value)

  return loss_value 

def _build_randomization_function_dict(self, env):
    func_dict = {}
    func_dict["mass"] = functools.partial(
        self._randomize_masses, minitaur=env.minitaur)
    func_dict["inertia"] = functools.partial(
        self._randomize_inertia, minitaur=env.minitaur)
    func_dict["latency"] = functools.partial(
        self._randomize_latency, minitaur=env.minitaur)
    func_dict["joint friction"] = functools.partial(
        self._randomize_joint_friction, minitaur=env.minitaur)
    func_dict["motor friction"] = functools.partial(
        self._randomize_motor_friction, minitaur=env.minitaur)
    func_dict["restitution"] = functools.partial(
        self._randomize_contact_restitution, minitaur=env.minitaur)
    func_dict["lateral friction"] = functools.partial(
        self._randomize_contact_friction, minitaur=env.minitaur)
    func_dict["battery"] = functools.partial(
        self._randomize_battery_level, minitaur=env.minitaur)
    func_dict["motor strength"] = functools.partial(
        self._randomize_motor_strength, minitaur=env.minitaur)
    # Settinmg control step needs access to the environment.
    func_dict["control step"] = functools.partial(
        self._randomize_control_step, env=env)
    return func_dict 

def step(self, action, blocking=True):
    """Step the environment.

    Args:
      action: The action to apply to the environment.
      blocking: Whether to wait for the result.

    Returns:
      Transition tuple when blocking, otherwise callable that returns the
      transition tuple.
    """
    self._conn.send((self._ACTION, action))
    if blocking:
      return self._receive(self._TRANSITION)
    else:
      return functools.partial(self._receive, self._TRANSITION) 

def load_textset(n_jobs, use_gpu, pin_memory, corpus, text):

    # Text tokenizer
    tokenizer = load_text_encoder(**text)
    # Dataset
    tr_set, dv_set, tr_loader_bs, dv_loader_bs, data_msg = create_textset(
        tokenizer, **corpus)
    collect_tr = partial(collect_text_batch, mode='train')
    collect_dv = partial(collect_text_batch, mode='dev')
    # Dataloader (Text data stored in RAM, no need num_workers)
    tr_set = DataLoader(tr_set, batch_size=tr_loader_bs, shuffle=True, drop_last=True, collate_fn=collect_tr,
                        num_workers=0, pin_memory=use_gpu)
    dv_set = DataLoader(dv_set, batch_size=dv_loader_bs, shuffle=False, drop_last=False, collate_fn=collect_dv,
                        num_workers=0, pin_memory=pin_memory)

    # Messages to show
    data_msg.append('I/O spec.  | Token type = {}\t| Vocab size = {}'
                    .format(tokenizer.token_type, tokenizer.vocab_size))

    return tr_set, dv_set, tokenizer.vocab_size, tokenizer, data_msg 

def set_alert_callback(self, callback):
        """
        Args:
            callback (func): called when alert popup
        
        Example of callback:

            def callback(session):
                session.alert.accept()
        """
        if callable(callback):
            self.http.alert_callback = functools.partial(callback, self)
        else:
            self.http.alert_callback = None

    #Not working
    #def get_clipboard(self):
    #    return self.http.post("/wda/getPasteboard").value

    # Not working
    #def siri_activate(self, text):
    #    self.http.post("/wda/siri/activate", {"text": text}) 

def grep(args):
    if args.context:
        args.before_context = args.after_context = args.context
    if not args.end_time:
        args.end_time = Timestamp("-0s")
    query = clients.logs.start_query(logGroupName=args.log_group,
                                     startTime=int(timestamp(args.start_time) * 1000),
                                     endTime=int(timestamp(args.end_time) * 1000),
                                     queryString=args.query)
    seen_results = {}
    print_with_context = partial(print_log_event_with_context, before=args.before_context, after=args.after_context)
    try:
        with ThreadPoolExecutor() as executor:
            while True:
                res = clients.logs.get_query_results(queryId=query["queryId"])
                log_record_pointers = []
                for record in res["results"]:
                    event = {r["field"]: r["value"] for r in record}
                    event_hash = hashlib.sha256(json.dumps(event, sort_keys=True).encode()).hexdigest()[:32]
                    if event_hash in seen_results:
                        continue
                    if "@ptr" in event and (args.before_context or args.after_context):
                        log_record_pointers.append(event["@ptr"])
                    else:
                        print_log_event(event)
                    seen_results[event_hash] = event
                if log_record_pointers:
                    executor.map(print_with_context, log_record_pointers)
                if res["status"] == "Complete":
                    break
                elif res["status"] in {"Failed", "Cancelled"}:
                    raise AegeaException("Query status: {}".format(res["status"]))
                time.sleep(1)
    finally:
        try:
            clients.logs.stop_query(queryId=query["queryId"])
        except clients.logs.exceptions.InvalidParameterException:
            pass
    logger.debug("Query %s: %s", query["queryId"], res["statistics"])
    return SystemExit(os.EX_OK if seen_results else os.EX_DATAERR) 

def add_time_bound_args(p, snap=0):
    t = partial(Timestamp, snap=snap)
    p.add_argument("--start-time", type=t, default=Timestamp("-7d", snap=snap), help=Timestamp.__doc__, metavar="START")
    p.add_argument("--end-time", type=t, help=Timestamp.__doc__, metavar="END") 

def file_sha256(path):
    """Get the hex digest of the given file"""
    sha256 = hashlib.sha256()
    with open(path, "rb") as f:
        map(sha256.update, iter(partial(f.read, BLOCK_SIZE), ""))
    return sha256.hexdigest() 

def file_sha256(path):
    """Get the hex digest of the given file"""
    sha256 = hashlib.sha256()
    with open(path, "rb") as f:
        map(sha256.update, iter(partial(f.read, BLOCK_SIZE), ""))
    return sha256.hexdigest() 

def main(loop):
    # Create a shared event
    event = asyncio.Event()
    print('event start state: {}'.format(event.is_set()))

    loop.call_later(
        0.1, functools.partial(set_event, event)
    )

    await asyncio.wait([coro1(event), coro2(event)])
    print('event end state: {}'.format(event.is_set())) 

def setUp(self):
        super(TestFastFeatureAdversaries, self).setUp()

        def make_imagenet_cnn(input_shape=(None, 224, 224, 3)):
            """
            Similar CNN to AlexNet.
            """

            class ModelImageNetCNN(Model):
                def __init__(self, scope, nb_classes=1000, **kwargs):
                    del kwargs
                    Model.__init__(self, scope, nb_classes, locals())

                def fprop(self, x, **kwargs):
                    del kwargs
                    my_conv = functools.partial(tf.layers.conv2d,
                                                kernel_size=3,
                                                strides=2,
                                                padding='valid',
                                                activation=tf.nn.relu,
                                                kernel_initializer=HeReLuNormalInitializer)
                    my_dense = functools.partial(tf.layers.dense,
                                                 kernel_initializer=HeReLuNormalInitializer)
                    with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
                        for depth in [96, 256, 384, 384, 256]:
                            x = my_conv(x, depth)
                        y = tf.layers.flatten(x)
                        y = my_dense(y, 4096, tf.nn.relu)
                        y = fc7 = my_dense(y, 4096, tf.nn.relu)
                        y = my_dense(y, 1000)
                        return {'fc7': fc7,
                                self.O_LOGITS: y,
                                self.O_PROBS: tf.nn.softmax(logits=y)}

            return ModelImageNetCNN('imagenet')

        self.input_shape = [10, 224, 224, 3]
        self.sess = tf.Session()
        self.model = make_imagenet_cnn(self.input_shape)
        self.attack = FastFeatureAdversaries(self.model) 

def fprop(self, x, **kwargs):
        del kwargs
        my_dense = functools.partial(
            tf.layers.dense, kernel_initializer=HeReLuNormalInitializer)
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            y = tf.layers.flatten(x)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            logits = my_dense(y, self.nb_classes)
            return {self.O_LOGITS: logits,
                    self.O_PROBS: tf.nn.softmax(logits=logits)} 

def fprop(self, x, **kwargs):
        del kwargs
        my_dense = functools.partial(
            tf.layers.dense, kernel_initializer=HeReLuNormalInitializer)
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            y = tf.layers.flatten(x)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            logits = my_dense(y, self.nb_classes)
            return {self.O_LOGITS: logits,
                    self.O_PROBS: tf.nn.softmax(logits=logits)} 

def _get_pipe2_implementation():
    """Find the appropriate implementation for ``pipe2``.

Return a function implementing ``pipe2``."""
    if hasattr(os, 'pipe2'):
        return os.pipe2 # pylint: disable=no-member
    else:
        try:
            libc = load_ctypes_library("libc", SIGNATURES, ERROR_CHECKERS)
            return (partial(_pipe2_ctypes, libc)
                    if hasattr(libc, 'pipe2') else
                    _pipe2_by_pipe)
        except ImportError:
            return _pipe2_by_pipe 

def add_tokens(*tokens):
    return partial(op_add_tokens, tokens) 

def remove_tokens(*tokens):
    return partial(op_remove_tokens, tokens)