Python collections.Sized() Examples

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def to_players(self, *players):
		"""
		Set the destination of the chat message.

		:param players: Player instance(s) or player login string(s). Can be a list, or a single entry.
		:return: Self reference.
		:rtype: pyplanet.contrib.chat.query.ChatQuery
		"""
		# Unpack list in unpacked list if given.
		if len(players) == 1 and isinstance(players[0], collections.Iterable):
			players = players[0]

		# Replace logins.
		if isinstance(players, Player):
			self._logins = set()
			self._logins.add(players.login)
		elif isinstance(players, str):
			self._logins = set()
			self._logins.add(players)
		elif isinstance(players, collections.Iterable) and isinstance(players, collections.Sized):
			self._logins = set()
			self.add_to(players)
		return self 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_abc_registry(self):
        d = dict(a=1)

        self.assertIsInstance(d.viewkeys(), collections.KeysView)
        self.assertIsInstance(d.viewkeys(), collections.MappingView)
        self.assertIsInstance(d.viewkeys(), collections.Set)
        self.assertIsInstance(d.viewkeys(), collections.Sized)
        self.assertIsInstance(d.viewkeys(), collections.Iterable)
        self.assertIsInstance(d.viewkeys(), collections.Container)

        self.assertIsInstance(d.viewvalues(), collections.ValuesView)
        self.assertIsInstance(d.viewvalues(), collections.MappingView)
        self.assertIsInstance(d.viewvalues(), collections.Sized)

        self.assertIsInstance(d.viewitems(), collections.ItemsView)
        self.assertIsInstance(d.viewitems(), collections.MappingView)
        self.assertIsInstance(d.viewitems(), collections.Set)
        self.assertIsInstance(d.viewitems(), collections.Sized)
        self.assertIsInstance(d.viewitems(), collections.Iterable)
        self.assertIsInstance(d.viewitems(), collections.Container) 

def set_marker(self, marker):
        if (isinstance(marker, np.ndarray) and marker.ndim == 2 and
                marker.shape[1] == 2):
            self._marker_function = self._set_vertices
        elif (isinstance(marker, Sized) and len(marker) in (2, 3) and
                marker[1] in (0, 1, 2, 3)):
            self._marker_function = self._set_tuple_marker
        elif (not isinstance(marker, (np.ndarray, list)) and
              marker in self.markers):
            self._marker_function = getattr(
                self, '_set_' + self.markers[marker])
        elif isinstance(marker, six.string_types) and is_math_text(marker):
            self._marker_function = self._set_mathtext_path
        elif isinstance(marker, Path):
            self._marker_function = self._set_path_marker
        else:
            try:
                Path(marker)
                self._marker_function = self._set_vertices
            except ValueError:
                raise ValueError('Unrecognized marker style'
                                 ' {0}'.format(marker))

        self._marker = marker
        self._recache() 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def _sequential(function: Callable, *iterables: Iterable, **kwargs: Any) -> Generator:
    """Returns a generator for a sequential map with a progress bar.

    Arguments:
        function(Callable): The function to apply to each element of the given Iterables.
        iterables(Tuple[Iterable]): One or more Iterables containing the data to be mapped.

    Returns:
        A generator which will apply the function to each element of the given Iterables
        sequentially in order with a progress bar.
    """

    # Determine length of tqdm (equal to length of shortest iterable)
    length = min(len(iterable) for iterable in iterables if isinstance(iterable, Sized))

    # Create sequential generator
    for item in tqdm(map(function, *iterables), total=length, **kwargs):
        yield item 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def map(
            self,
            func, iterable,
            chunksize=1, sort=True, total=None, desc='', callback=None,
            use_tqdm=True, update_interval=0.1, update_iters=1, **kwargs
    ):

        if total is None and isinstance(iterable, collections.Sized):
            total = len(iterable)
        if use_tqdm:
            pbar = tqdm_pbar(total=total, **kwargs)
            with pbar:
                return super().map(func, iterable, chunksize, sort, callback=self._wrap_callback(
                    callback, pbar, desc, update_interval=update_interval, update_iters=update_iters
                ))
        else:
            return super().map(func, iterable, chunksize, sort, callback=callback) 

def test_Sized(self):
        non_samples = [None, 42, 3.14, 1j,
                       (lambda: (yield))(),
                       (x for x in []),
                       ]
        for x in non_samples:
            self.assertNotIsInstance(x, Sized)
            self.assertFalse(issubclass(type(x), Sized), repr(type(x)))
        samples = [str(),
                   tuple(), list(), set(), frozenset(), dict(),
                   dict().keys(), dict().items(), dict().values(),
                   ]
        for x in samples:
            self.assertIsInstance(x, Sized)
            self.assertTrue(issubclass(type(x), Sized), repr(type(x)))
        self.validate_abstract_methods(Sized, '__len__')
        self.validate_isinstance(Sized, '__len__') 

def __init__(self, criterion_fns, submodules, collect_fn=None, reduce_method="mean"):
        super(GradAccumulator, self).__init__()
        assert isinstance(submodules, (Sized, Iterable)), "invalid submodules"
        if isinstance(criterion_fns, (Sized, Iterable)):
            assert len(submodules) == len(criterion_fns)
            assert all([isinstance(submodule, nn.Module) for submodule in submodules])
            assert all([isinstance(criterion_fn, nn.Module) for criterion_fn in criterion_fns])
        elif isinstance(criterion_fns, nn.Module):
            criterion_fns = [criterion_fns for _ in range(len(submodules))]
        elif criterion_fns is None:
            criterion_fns = [criterion_fns for _ in range(len(submodules))]
        else:
            raise ValueError("invalid criterion function")
        assert reduce_method in ("mean", "sum", None)

        self.submodules = nn.ModuleList(submodules)
        self.criterion_fns = nn.ModuleList(criterion_fns)
        self.method = reduce_method
        self.grad_buffer = None
        self.func = GradAccumulatorFunction.apply
        self.collect_fn = collect_fn 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def assertNotEmpty(self, container, msg=None):
    """Assert that an object has non-zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if not len(container):
      self.fail('{!r} has length of 0.'.format(container), msg) 

def assertEmpty(self, container, msg=None):
    """Assert that an object has zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if len(container):
      self.fail('{!r} has length of {}.'.format(container, len(container)), msg) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def _loop(self, output, element, context):
		var = element.attrib['var']
		expr = element.attrib['expr']
		items = expr(context)
		if not isinstance(items, abc.Iterable):
			raise TypeError('Can not iterate over: %s' % items)
		elif not isinstance(items, abc.Sized):
			# cast to list to ensure we have a len()
			items = list(items)

		# set "loop"
		outer = context.get('loop')
		if isinstance(outer, TemplateLoopState):
			loop = TemplateLoopState(len(items), outer)
		else:
			loop = TemplateLoopState(len(items), None)
		context['loop'] = loop

		# do the iterations
		myiter = MovingWindowIter(items)
		for i, items in enumerate(myiter):
			loop._update(i, myiter)
			self._set(context, var, items[1]) # set var
			self.__call__(output, element, context) # recurs

		# restore "loop"
		context['loop'] = outer 

def assertBigQuerySchemaEqual(self, a, b):
        """
        Assert that the given BigQuery schemas are equivalent.

        BigQuery schemas consist of lists whose order doesn't matter, dicts,
        and privimites.
        """
        # allow python to have the first pass at deciding whether two objects
        # are equal. If they aren't, apply less strict logic (e.g., allow lists
        # of differing orders to be equal).
        if a == b:
            return
        else:
            self.assertEquals(type(a), type(b))
            if isinstance(a, collections.Sized) \
                    and isinstance(b, collections.Sized):
                self.assertEquals(len(a), len(b))
            if isinstance(a, list) and isinstance(b, list):
                name_ordered_a = sorted(a, key=lambda x: x['name'])
                name_ordered_b = sorted(b, key=lambda x: x['name'])
                for x, y in zip(name_ordered_a, name_ordered_b):
                    self.assertBigQuerySchemaEqual(x, y)
            elif isinstance(a, dict):
                for k in a:
                    self.assertIn(k, b)
                    self.assertBigQuerySchemaEqual(a[k], b[k])
            else:
                self.assertEquals(a, b) 

def assertNotEmpty(self, container, msg=None):
    """Assert that an object has non-zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if not len(container):
      self.fail('{!r} has length of 0.'.format(container), msg) 

def assertEmpty(self, container, msg=None):
    """Assert that an object has zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if len(container):
      self.fail('{!r} has length of {}.'.format(container, len(container)), msg) 

def assertNotEmpty(self, container, msg=None):
    """Assert that an object has non-zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if not len(container):
      self.fail('{!r} has length of 0.'.format(container), msg) 

def assertEmpty(self, container, msg=None):
    """Assert that an object has zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if len(container):
      self.fail('{!r} has length of {}.'.format(container, len(container)), msg) 

def assertNotEmpty(self, container, msg=None):
    """Assert that an object has non-zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if not len(container):
      self.fail('{!r} has length of 0.'.format(container), msg) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def test_direct_subclassing(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C(B):
                pass
            self.assertTrue(issubclass(C, B))
            self.assertFalse(issubclass(int, C)) 

def test_registration(self):
        for B in Hashable, Iterable, Iterator, Sized, Container, Callable:
            class C:
                __metaclass__ = type
                __hash__ = None  # Make sure it isn't hashable by default
            self.assertFalse(issubclass(C, B), B.__name__)
            B.register(C)
            self.assertTrue(issubclass(C, B)) 

def test_parameter_grid():
    # Test basic properties of ParameterGrid.
    params1 = {"foo": [1, 2, 3]}
    grid1 = ParameterGrid(params1)
    assert_true(isinstance(grid1, Iterable))
    assert_true(isinstance(grid1, Sized))
    assert_equal(len(grid1), 3)
    assert_grid_iter_equals_getitem(grid1)

    params2 = {"foo": [4, 2],
               "bar": ["ham", "spam", "eggs"]}
    grid2 = ParameterGrid(params2)
    assert_equal(len(grid2), 6)

    # loop to assert we can iterate over the grid multiple times
    for i in xrange(2):
        # tuple + chain transforms {"a": 1, "b": 2} to ("a", 1, "b", 2)
        points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)
        assert_equal(points,
                     set(("bar", x, "foo", y)
                         for x, y in product(params2["bar"], params2["foo"])))

    assert_grid_iter_equals_getitem(grid2)

    # Special case: empty grid (useful to get default estimator settings)
    empty = ParameterGrid({})
    assert_equal(len(empty), 1)
    assert_equal(list(empty), [{}])
    assert_grid_iter_equals_getitem(empty)
    assert_raises(IndexError, lambda: empty[1])

    has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])
    assert_equal(len(has_empty), 4)
    assert_equal(list(has_empty), [{'C': 1}, {'C': 10}, {}, {'C': .5}])
    assert_grid_iter_equals_getitem(has_empty) 

def from_list(name, colors, N=256, gamma=1.0):
        """
        Make a linear segmented colormap with *name* from a sequence
        of *colors* which evenly transitions from colors[0] at val=0
        to colors[-1] at val=1.  *N* is the number of rgb quantization
        levels.
        Alternatively, a list of (value, color) tuples can be given
        to divide the range unevenly.
        """

        if not cbook.iterable(colors):
            raise ValueError('colors must be iterable')

        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
                and not isinstance(colors[0], six.string_types)):
            # List of value, color pairs
            vals, colors = zip(*colors)
        else:
            vals = np.linspace(0, 1, len(colors))

        cdict = dict(red=[], green=[], blue=[], alpha=[])
        for val, color in zip(vals, colors):
            r, g, b, a = colorConverter.to_rgba(color)
            cdict['red'].append((val, r, r))
            cdict['green'].append((val, g, g))
            cdict['blue'].append((val, b, b))
            cdict['alpha'].append((val, a, a))

        return LinearSegmentedColormap(name, cdict, N, gamma) 

def assertEmpty(self, container, msg=None):
    """Assert that an object has zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if len(container):
      self.fail('{!r} has length of {}.'.format(container, len(container)), msg) 

def assertNotEmpty(self, container, msg=None):
    """Assert that an object has non-zero length.

    Args:
      container: Anything that implements the collections.Sized interface.
      msg: Optional message to report on failure.
    """
    if not isinstance(container, collections.Sized):
      self.fail('Expected a Sized object, got: '
                '{!r}'.format(type(container).__name__), msg)

    # explicitly check the length since some Sized objects (e.g. numpy.ndarray)
    # have strange __nonzero__/__bool__ behavior.
    if not len(container):
      self.fail('{!r} has length of 0.'.format(container), msg)