Python contextlib.nullcontext方法代碼示例

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def __init__(self, fileobj, filename=None):
        super(tzfile, self).__init__()

        file_opened_here = False
        if isinstance(fileobj, string_types):
            self._filename = fileobj
            fileobj = open(fileobj, 'rb')
            file_opened_here = True
        elif filename is not None:
            self._filename = filename
        elif hasattr(fileobj, "name"):
            self._filename = fileobj.name
        else:
            self._filename = repr(fileobj)

        if fileobj is not None:
            if not file_opened_here:
                fileobj = _nullcontext(fileobj)

            with fileobj as file_stream:
                tzobj = self._read_tzfile(file_stream)

            self._set_tzdata(tzobj) 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def check_model_compatibility(self, backend_config, save, load):
        C = self.n_channels // self.groups
        old_model = {
            'avg_mean': numpy.random.uniform(
                -1, 1, (C,)).astype(self.dtype),
            'avg_projection': numpy.random.uniform(
                0.5, 1, (C, C)).astype(self.dtype),
            'N': numpy.array(0)
        }
        save(self.temp_file_path, old_model)

        model = links.DecorrelatedBatchNormalization(
            self.n_channels, groups=self.groups, dtype=self.dtype)
        model.to_device(backend_config.device)
        with (
                testing.assert_warns(UserWarning) if self.groups != 1
                else nullcontext()):
            load(self.temp_file_path, model)
        x = numpy.random.rand(5, self.n_channels, 2).astype(self.dtype)
        x = backend_config.get_array(x)
        with chainer.using_config('train', False):
            model(x)
        model(x) 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def __init__(
            self,
            mode: int,
            ctx: moderngl.Context,
            prog: moderngl.Program,
            dtype: np.dtype,
            draw_context: ContextManager = nullcontext(),
            capacity: int = 256,
            index_capacity: int = 512):
        self.mode = mode
        self.ctx = ctx
        self.prog = prog
        self.dtype = dtype_to_moderngl(dtype)
        self.allocs: Dict[int, Tuple[slice, np.ndarray]] = {}
        self.verts = MemoryBackedBuffer(ctx, capacity, dtype)
        self.indexes = IndexBuffer(ctx)
        self.draw_context = draw_context
        self.dirty = False 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def iteration(self,
                  data: Tuple[torch.Tensor, torch.Tensor]) -> Mapping[str, torch.Tensor]:
        input, target = data
        context = torch.cuda.amp.autocast if self._use_amp else contextlib.nullcontext
        with context():
            output = self.model(input)
            loss = self.loss_f(output, target)
        if self.is_train:
            self.optimizer.zero_grad()
            if self._use_amp:
                self.scaler.scale(loss).backward()
                self.scaler.step(self.optimizer)
                self.scaler.update()
            else:
                loss.backward()
                self.optimizer.step()
        return TensorMap(loss=loss, output=output) 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def get_psl_context_manager(self, side_effect_parameter):
        if side_effect_parameter is None:
            return nullcontext()

        if callable(side_effect_parameter):
            side_effect = side_effect_parameter
        else:
            side_effect = partial(
                self._mock_get_public_suffix,
                public_suffixes=[side_effect_parameter] if not isinstance(side_effect_parameter, list) else list(side_effect_parameter)
            )

        return mock.patch.object(psl, 'get_public_suffix', side_effect=side_effect) 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def get_distribution_scope(batch_size):
    if num_gpus() > 1:
        strategy = tf.distribute.MirroredStrategy()
        assert (
            batch_size % strategy.num_replicas_in_sync == 0
        ), f"Batch size {batch_size} cannot be divided onto {num_gpus()} GPUs"
        distribution_scope = strategy.scope
    else:
        if sys.version_info >= (3, 7):
            distribution_scope = contextlib.nullcontext
        else:
            distribution_scope = contextlib.suppress

    return distribution_scope() 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def _null_context() -> Iterator[None]:
  # TODO(b/154533346)
  # This should move to `contextlib.nullcontext` once TFF's minimum
  # Python version moves up to 3.7,
  yield None 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def forward(self, link, inputs, backend_config):
        x, = inputs
        with chainer.using_config('train', not self.test):
            y = link(x)
        return y,


# TODO(kataoka) Use `contextlib.nullcontext` if Python 3.7 or higher is assumed 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def nullcontext():
    yield 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def nullcontext():
        yield 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def __init__(self, length, multithread_safe=False, do_pickle=False):
        self._multithread_safe = multithread_safe
        self.length = length
        assert self.length > 0

        if self._multithread_safe:
            self.lock = threading.Lock()
        else:
            # Use contextlib.nullcontext() when Python 3.6 is dropped.
            self.lock = contextlib.suppress()

        self.data = [None for _ in range(self.length)] 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def nullcontext(obj):
        yield obj 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def nullcontext():
        yield None 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def nullcontext(enter_result=None):
    """Backport of the Python >=3.7 trivial context manager.

    See `the Python documentation
    <https://docs.python.org/3/library/contextlib.html#contextlib.nullcontext>`_
    for details.
    """
    try:
        yield enter_result
    finally:
        pass 

# 需要導入模塊: import contextlib [as 別名]
# 或者: from contextlib import nullcontext [as 別名]
def read_until_prompt(
        self,
        prompt: typing.Optional[ConvenientSearchString] = None,
        timeout: typing.Optional[float] = None,
    ) -> str:
        """
        Read until prompt is detected.

        Read from the channel until the configured prompt string is detected.
        All data captured up until the prompt is returned, decoded as UTF-8.
        If ``prompt`` is ``None``, the prompt which was set using
        :py:meth:`tbot.machine.channel.Channel.with_prompt` is used.

        :param ConvenientSearchString prompt: The prompt to read up to.  It
            must appear as the very last readable data in the channel's data
            stream.  See :ref:`channel_search_string` for more info about which
            types can be passed for this parameter.
        :param float timeout: Optional timeout.  If ``timeout`` is set and
            expires before the prompt was detected, ``read_until_prompt``
            raises an execption.
        :rtype: str
        :returns: UTF-8 decoded string of all bytes read up to the prompt.
        """
        ctx: typing.ContextManager[typing.Any]
        if prompt is not None:
            ctx = self.with_prompt(prompt)
        else:
            # contextlib.nullcontext() would be a better fit here but sadly it
            # is only available in 3.7+
            ctx = contextlib.ExitStack()

        buf = bytearray()

        with ctx:
            for new in self.read_iter(timeout=timeout):
                buf += new

                if isinstance(self.prompt, bytes):
                    if buf.endswith(self.prompt):
                        return (
                            buf[: -len(self.prompt)]
                            .decode("utf-8", errors="replace")
                            .replace("\r\n", "\n")
                            .replace("\n\r", "\n")
                        )
                elif isinstance(self.prompt, BoundedPattern):
                    match = self.prompt.pattern.search(buf)
                    if match is not None:
                        return (
                            buf[: match.span()[0]]
                            .decode("utf-8", errors="replace")
                            .replace("\r\n", "\n")
                            .replace("\n\r", "\n")
                        )

        raise RuntimeError("unreachable")

    # }}}

    # miscellaneous {{{