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def class_logit(layer, label):
"""Like channel, but for softmax layers.
Args:
layer: A layer name string.
label: Either a string (refering to a label in model.labels) or an int
label position.
Returns:
Objective maximizing a logit.
"""
def inner(T):
if isinstance(label, int):
cla... |
def as_objective(obj):
"""Convert obj into Objective class.
Strings of the form "layer:n" become the Objective channel(layer, n).
Objectives are returned unchanged.
Args:
obj: string or Objective.
Returns:
Objective
"""
if isinstance(obj, Objective):
return obj
elif callable(obj):
ret... |
def _constrain_L2_grad(op, grad):
"""Gradient for constrained optimization on an L2 unit ball.
This function projects the gradient onto the ball if you are on the boundary
(or outside!), but leaves it untouched if you are inside the ball.
Args:
op: the tensorflow op we're computing the gradient for.
g... |
def unit_ball_L2(shape):
"""A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
return constrain_L2(x) |
def unit_ball_L_inf(shape, precondition=True):
"""A tensorflow variable tranfomed to be constrained in a L_inf unit ball.
Note that this code also preconditions the gradient to go in the L_inf
direction of steepest descent.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they a... |
def render_vis(model, objective_f, param_f=None, optimizer=None,
transforms=None, thresholds=(512,), print_objectives=None,
verbose=True, relu_gradient_override=True, use_fixed_seed=False):
"""Flexible optimization-base feature vis.
There's a lot of ways one might wish to customize ot... |
def make_vis_T(model, objective_f, param_f=None, optimizer=None,
transforms=None, relu_gradient_override=False):
"""Even more flexible optimization-base feature vis.
This function is the inner core of render_vis(), and can be used
when render_vis() isn't flexible enough. Unfortunately, it's a bit ... |
def grid(metadata, layout, params):
"""
layout: numpy arrays x, y
metadata: user-defined numpy arrays with metadata
n_layer: number of cells in the layer (squared)
n_tile: number of cells in the tile (squared)
"""
x = layout["x"]
y = layout["y"]
x_min = np.min(x)
x_max = np.max(x)
y_min = np.min(y... |
def write_grid_local(tiles, params):
"""
Write a file for each tile
"""
# TODO: this isn't being used right now, will need to be
# ported to gfile if we want to keep it
for ti,tj,tile in enumerate_tiles(tiles):
filename = "{directory}/{name}/tile_{n_layer}_{n_tile}_{ti}_{tj}".format(ti=ti, tj=tj, **para... |
def enumerate_tiles(tiles):
"""
Convenience
"""
enumerated = []
for key in tiles.keys():
enumerated.append((key[0], key[1], tiles[key]))
return enumerated |
def _load_img(handle, target_dtype=np.float32, size=None, **kwargs):
"""Load image file as numpy array."""
image_pil = PIL.Image.open(handle, **kwargs)
# resize the image to the requested size, if one was specified
if size is not None:
if len(size) > 2:
size = size[:2]
... |
def _load_text(handle, split=False, encoding="utf-8"):
"""Load and decode a string."""
string = handle.read().decode(encoding)
return string.splitlines() if split else string |
def _load_graphdef_protobuf(handle, **kwargs):
"""Load GraphDef from a binary proto file."""
# as_graph_def
graph_def = tf.GraphDef.FromString(handle.read())
# check if this is a lucid-saved model
# metadata = modelzoo.util.extract_metadata(graph_def)
# if metadata is not None:
# url = ha... |
def load(url_or_handle, cache=None, **kwargs):
"""Load a file.
File format is inferred from url. File retrieval strategy is inferred from
URL. Returned object type is inferred from url extension.
Args:
url_or_handle: a (reachable) URL, or an already open file handle
Raises:
RuntimeErr... |
def crop_or_pad_to(height, width):
"""Ensures the specified spatial shape by either padding or cropping.
Meant to be used as a last transform for architectures insisting on a specific
spatial shape of their inputs.
"""
def inner(t_image):
return tf.image.resize_image_with_crop_or_pad(t_image... |
def _normalize_array(array, domain=(0, 1)):
"""Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults ... |
def _serialize_normalized_array(array, fmt='png', quality=70):
"""Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from... |
def serialize_array(array, domain=(0, 1), fmt='png', quality=70):
"""Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string ... |
def array_to_jsbuffer(array):
"""Serialize 1d NumPy array to JS TypedArray.
Data is serialized to base64-encoded string, which is much faster
and memory-efficient than json list serialization.
Args:
array: 1d NumPy array, dtype must be one of JS_ARRAY_TYPES.
Returns:
JS code that evaluates to a Typ... |
def _apply_flat(cls, f, acts):
"""Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged.
"""
orig_shape = acts.shape
acts_flat = acts.reshape([-1, acts.shape[-1]])
new_flat = f(acts... |
def set_style(self, input_feeds):
"""Set target style variables.
Expected usage:
style_loss = StyleLoss(style_layers)
...
init_op = tf.global_variables_initializer()
init_op.run()
feeds = {... session.run() 'feeds' argument that will make 'style_layers'
... |
def _image_url(array, fmt='png', mode="data", quality=90, domain=None):
"""Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image
"""
supported_modes = ("data")
if mode not in supported_mo... |
def image(array, domain=None, width=None, format='png', **kwargs):
"""Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor int... |
def images(arrays, labels=None, domain=None, w=None):
"""Display a list of images with optional labels.
Args:
arrays: A list of NumPy arrays representing images
labels: A list of strings to label each image.
Defaults to show index if None
domain: Domain of pixel values, inferred from min & max va... |
def show(thing, domain=(0, 1), **kwargs):
"""Display a nupmy array without having to specify what it represents.
This module will attempt to infer how to display your tensor based on its
rank, shape and dtype. rank 4 tensors will be displayed as image grids, rank
2 and 3 tensors as images.
"""
if isinstanc... |
def _strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def.
This is mostly a utility function for graph(), and also originates here:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
"""
strip_def = tf.Gr... |
def graph(graph_def, max_const_size=32):
"""Visualize a TensorFlow graph.
This function was originally found in this notebook (also Apache licensed):
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
"""
if hasattr(graph_def, 'as_graph_def'... |
def resize(image, target_size, **kwargs):
"""Resize an ndarray image of rank 3 or 4.
target_size can be a tuple `(width, height)` or scalar `width`."""
if isinstance(target_size, int):
target_size = (target_size, target_size)
if not isinstance(target_size, (list, tuple, np.ndarray)):
m... |
def composite(
background_image,
foreground_image,
foreground_width_ratio=0.25,
foreground_position=(0.0, 0.0),
):
"""Takes two images and composites them."""
if foreground_width_ratio <= 0:
return background_image
composite = background_image.copy()
width = int(foreground_widt... |
def lowres_tensor(shape, underlying_shape, offset=None, sd=None):
"""Produces a tensor paramaterized by a interpolated lower resolution tensor.
This is like what is done in a laplacian pyramid, but a bit more general. It
can be a powerful way to describe images.
Args:
shape: desired shape of resulting t... |
def create_session(target='', timeout_sec=10):
'''Create an intractive TensorFlow session.
Helper function that creates TF session that uses growing GPU memory
allocation and opration timeout. 'allow_growth' flag prevents TF
from allocating the whole GPU memory an once, which is useful
when having multiple p... |
def read(url, encoding=None, cache=None, mode="rb"):
"""Read from any URL.
Internally differentiates between URLs supported by tf.gfile, such as URLs
with the Google Cloud Storage scheme ('gs://...') or local paths, and HTTP
URLs. This way users don't need to know about the underlying fetch mechanism.
... |
def read_handle(url, cache=None, mode="rb"):
"""Read from any URL with a file handle.
Use this to get a handle to a file rather than eagerly load the data:
```
with read_handle(url) as handle:
result = something.load(handle)
result.do_something()
```
When program execution leaves th... |
def local_cache_path(remote_url):
"""Returns the path that remote_url would be cached at locally."""
local_name = RESERVED_PATH_CHARS.sub("_", remote_url)
return os.path.join(gettempdir(), local_name) |
def cppn(
width,
batch=1,
num_output_channels=3,
num_hidden_channels=24,
num_layers=8,
activation_func=_composite_activation,
normalize=False,
):
"""Compositional Pattern Producing Network
Args:
width: width of resulting image, equals height
batch: batch dimension of out... |
def get_model(name):
"""Returns a model instance such as `model = vision_models.InceptionV1()`.
In the future may be expanded to filter by additional criteria, such as
architecture, dataset, and task the model was trained on.
Args:
name: The name of the model, as given by the class name in vision_... |
def activation_atlas(
model,
layer,
grid_size=10,
icon_size=96,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders an Activation Atlas of the given model's layer."""
activations = layer.activations[:number_activations, ...]
layout, =... |
def aligned_activation_atlas(
model1,
layer1,
model2,
layer2,
grid_size=10,
icon_size=80,
num_steps=1024,
whiten_layers=True,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders two aligned Activation Atlases of the given model... |
def _combine_activations(
layer1,
layer2,
activations1=None,
activations2=None,
mode=ActivationTranslation.BIDIRECTIONAL,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
):
"""Given two layers, combines their activations according to mode.
ActivationTranslation.ONE_TO_TWO:
Transla... |
def bin_laid_out_activations(layout, activations, grid_size, threshold=5):
"""Given a layout and activations, overlays a grid on the layout and returns
averaged activations for each grid cell. If a cell contains less than `threshold`
activations it will be discarded, so the number of returned data is variab... |
def load_graphdef(model_url, reset_device=True):
"""Load GraphDef from a binary proto file."""
graph_def = load(model_url)
if reset_device:
for n in graph_def.node:
n.device = ""
return graph_def |
def forget_xy(t):
"""Ignore sizes of dimensions (1, 2) of a 4d tensor in shape inference.
This allows using smaller input sizes, which create an invalid graph at higher
layers (for example because a spatial dimension becomes smaller than a conv
filter) when we only use early parts of it.
"""
shape = (t.sha... |
def frozen_default_graph_def(input_node_names, output_node_names):
"""Return frozen and simplified graph_def of default graph."""
sess = tf.get_default_session()
input_graph_def = tf.get_default_graph().as_graph_def()
pruned_graph = tf.graph_util.remove_training_nodes(
input_graph_def, protected_nodes=(... |
def infuse_metadata(graph_def, info):
"""Embed meta data as a string constant in a TF graph.
This function takes info, converts it into json, and embeds
it in graph_def as a constant op called `__lucid_metadata_json`.
"""
temp_graph = tf.Graph()
with temp_graph.as_default():
tf.constant(json.dumps(info... |
def extract_metadata(graph_def):
"""Attempt to extract meta data hidden in graph_def.
Looks for a `__lucid_metadata_json` constant string op.
If present, extract it's content and convert it from json to python.
If not, returns None.
"""
meta_matches = [n for n in graph_def.node if n.name==metadata_node_nam... |
def neighborhood(self, node, degree=4):
"""Am I really handcoding graph traversal please no"""
assert self.by_name[node.name] == node
already_visited = frontier = set([node.name])
for _ in range(degree):
neighbor_names = set()
for node_name in frontier:
outgoing = set(n.name for n in... |
async def _retrieve_messages_before_strategy(self, retrieve):
"""Retrieve messages using before parameter."""
before = self.before.id if self.before else None
data = await self.logs_from(self.channel.id, retrieve, before=before)
if len(data):
if self.limit is not None:
... |
async def _retrieve_messages_after_strategy(self, retrieve):
"""Retrieve messages using after parameter."""
after = self.after.id if self.after else None
data = await self.logs_from(self.channel.id, retrieve, after=after)
if len(data):
if self.limit is not None:
... |
async def _retrieve_messages_around_strategy(self, retrieve):
"""Retrieve messages using around parameter."""
if self.around:
around = self.around.id if self.around else None
data = await self.logs_from(self.channel.id, retrieve, around=around)
self.around = None
... |
async def _retrieve_guilds_before_strategy(self, retrieve):
"""Retrieve guilds using before parameter."""
before = self.before.id if self.before else None
data = await self.get_guilds(retrieve, before=before)
if len(data):
if self.limit is not None:
self.limit... |
async def _retrieve_guilds_after_strategy(self, retrieve):
"""Retrieve guilds using after parameter."""
after = self.after.id if self.after else None
data = await self.get_guilds(retrieve, after=after)
if len(data):
if self.limit is not None:
self.limit -= ret... |
async def fetch_invite(self, *, with_counts=True):
"""|coro|
Retrieves an :class:`Invite` from a invite URL or ID.
This is the same as :meth:`Client.get_invite`; the invite
code is abstracted away.
Parameters
-----------
with_counts: :class:`bool`
Wh... |
def from_hsv(cls, h, s, v):
"""Constructs a :class:`Colour` from an HSV tuple."""
rgb = colorsys.hsv_to_rgb(h, s, v)
return cls.from_rgb(*(int(x * 255) for x in rgb)) |
def description(self):
""":class:`str`: Returns the cog's description, typically the cleaned docstring."""
try:
return self.__cog_cleaned_doc__
except AttributeError:
self.__cog_cleaned_doc__ = cleaned = inspect.getdoc(self)
return cleaned |
def walk_commands(self):
"""An iterator that recursively walks through this cog's commands and subcommands."""
from .core import GroupMixin
for command in self.__cog_commands__:
if command.parent is None:
yield command
if isinstance(command, GroupMixin... |
def get_listeners(self):
"""Returns a :class:`list` of (name, function) listener pairs that are defined in this cog."""
return [(name, getattr(self, method_name)) for name, method_name in self.__cog_listeners__] |
def listener(cls, name=None):
"""A decorator that marks a function as a listener.
This is the cog equivalent of :meth:`.Bot.listen`.
Parameters
------------
name: :class:`str`
The name of the event being listened to. If not provided, it
defaults to the f... |
def set_footer(self, *, text=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the footer for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
text: :class:`str`
The footer text.
icon_url:... |
def set_author(self, *, name, url=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the author for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the author.
... |
def add_field(self, *, name, value, inline=True):
"""Adds a field to the embed object.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the field.
value: :class:`str`
... |
def set_field_at(self, index, *, name, value, inline=True):
"""Modifies a field to the embed object.
The index must point to a valid pre-existing field.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
index: :... |
def to_dict(self):
"""Converts this embed object into a dict."""
# add in the raw data into the dict
result = {
key[1:]: getattr(self, key)
for key in self.__slots__
if key[0] == '_' and hasattr(self, key)
}
# deal with basic convenience wrap... |
def avatar_url_as(self, *, format=None, static_format='webp', size=1024):
"""Returns a friendly URL version of the avatar the user has.
If the user does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'webp', 'jpeg', 'jpg', 'png' o... |
def mentioned_in(self, message):
"""Checks if the user is mentioned in the specified message.
Parameters
-----------
message: :class:`Message`
The message to check if you're mentioned in.
"""
if message.mention_everyone:
return True
for ... |
def friends(self):
r"""Returns a :class:`list` of :class:`User`\s that the user is friends with.
.. note::
This only applies to non-bot accounts.
"""
return [r.user for r in self._relationships.values() if r.type is RelationshipType.friend] |
def blocked(self):
r"""Returns a :class:`list` of :class:`User`\s that the user has blocked.
.. note::
This only applies to non-bot accounts.
"""
return [r.user for r in self._relationships.values() if r.type is RelationshipType.blocked] |
async def edit(self, **fields):
"""|coro|
Edits the current profile of the client.
If a bot account is used then a password field is optional,
otherwise it is required.
Note
-----
To upload an avatar, a :term:`py:bytes-like object` must be passed in that
... |
async def create_group(self, *recipients):
r"""|coro|
Creates a group direct message with the recipients
provided. These recipients must be have a relationship
of type :attr:`RelationshipType.friend`.
.. note::
This only applies to non-bot accounts.
Parame... |
async def edit_settings(self, **kwargs):
"""|coro|
Edits the client user's settings.
.. note::
This only applies to non-bot accounts.
Parameters
-------
afk_timeout: :class:`int`
How long (in seconds) the user needs to be AFK until Discord
... |
async def create_dm(self):
"""Creates a :class:`DMChannel` with this user.
This should be rarely called, as this is done transparently for most
people.
"""
found = self.dm_channel
if found is not None:
return found
state = self._state
data = ... |
async def mutual_friends(self):
"""|coro|
Gets all mutual friends of this user.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to get mutual friends of this user.
HTTPException
Getting ... |
def is_friend(self):
""":class:`bool`: Checks if the user is your friend.
.. note::
This only applies to non-bot accounts.
"""
r = self.relationship
if r is None:
return False
return r.type is RelationshipType.friend |
def is_blocked(self):
""":class:`bool`: Checks if the user is blocked.
.. note::
This only applies to non-bot accounts.
"""
r = self.relationship
if r is None:
return False
return r.type is RelationshipType.blocked |
async def block(self):
"""|coro|
Blocks the user.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to block this user.
HTTPException
Blocking the user failed.
"""
await s... |
async def send_friend_request(self):
"""|coro|
Sends the user a friend request.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to send a friend request to the user.
HTTPException
Sendin... |
async def profile(self):
"""|coro|
Gets the user's profile.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to fetch profiles.
HTTPException
Fetching the profile failed.
Returns... |
def time_snowflake(datetime_obj, high=False):
"""Returns a numeric snowflake pretending to be created at the given date.
When using as the lower end of a range, use time_snowflake(high=False) - 1 to be inclusive, high=True to be exclusive
When using as the higher end of a range, use time_snowflake(high=Tru... |
def get(iterable, **attrs):
r"""A helper that returns the first element in the iterable that meets
all the traits passed in ``attrs``. This is an alternative for
:func:`discord.utils.find`.
When multiple attributes are specified, they are checked using
logical AND, not logical OR. Meaning they have... |
def _string_width(string, *, _IS_ASCII=_IS_ASCII):
"""Returns string's width."""
match = _IS_ASCII.match(string)
if match:
return match.endpos
UNICODE_WIDE_CHAR_TYPE = 'WFA'
width = 0
func = unicodedata.east_asian_width
for char in string:
width += 2 if func(char) in UNICODE... |
def resolve_invite(invite):
"""
Resolves an invite from a :class:`Invite`, URL or ID
Parameters
-----------
invite: Union[:class:`Invite`, :class:`Object`, :class:`str`]
The invite.
Returns
--------
:class:`str`
The invite code.
"""
from .invite import Invite #... |
def escape_markdown(text, *, as_needed=False, ignore_links=True):
r"""A helper function that escapes Discord's markdown.
Parameters
-----------
text: :class:`str`
The text to escape markdown from.
as_needed: :class:`bool`
Whether to escape the markdown characters as needed. This
... |
async def add(ctx, left: int, right: int):
"""Adds two numbers together."""
await ctx.send(left + right) |
async def roll(ctx, dice: str):
"""Rolls a dice in NdN format."""
try:
rolls, limit = map(int, dice.split('d'))
except Exception:
await ctx.send('Format has to be in NdN!')
return
result = ', '.join(str(random.randint(1, limit)) for r in range(rolls))
await ctx.send(result) |
async def repeat(ctx, times: int, content='repeating...'):
"""Repeats a message multiple times."""
for i in range(times):
await ctx.send(content) |
async def join(self, ctx, *, channel: discord.VoiceChannel):
"""Joins a voice channel"""
if ctx.voice_client is not None:
return await ctx.voice_client.move_to(channel)
await channel.connect() |
async def play(self, ctx, *, query):
"""Plays a file from the local filesystem"""
source = discord.PCMVolumeTransformer(discord.FFmpegPCMAudio(query))
ctx.voice_client.play(source, after=lambda e: print('Player error: %s' % e) if e else None)
await ctx.send('Now playing: {}'.format(que... |
async def stream(self, ctx, *, url):
"""Streams from a url (same as yt, but doesn't predownload)"""
async with ctx.typing():
player = await YTDLSource.from_url(url, loop=self.bot.loop, stream=True)
ctx.voice_client.play(player, after=lambda e: print('Player error: %s' % e) if e ... |
async def volume(self, ctx, volume: int):
"""Changes the player's volume"""
if ctx.voice_client is None:
return await ctx.send("Not connected to a voice channel.")
ctx.voice_client.source.volume = volume / 100
await ctx.send("Changed volume to {}%".format(volume)) |
def duration(self):
"""Queries the duration of the call.
If the call has not ended then the current duration will
be returned.
Returns
---------
datetime.timedelta
The timedelta object representing the duration.
"""
if self.ended_timestamp is... |
def connected(self):
"""A property that returns the :class:`list` of :class:`User` that are currently in this call."""
ret = [u for u in self.channel.recipients if self.voice_state_for(u) is not None]
me = self.channel.me
if self.voice_state_for(me) is not None:
ret.append(me... |
def partial(cls, id, token, *, adapter):
"""Creates a partial :class:`Webhook`.
A partial webhook is just a webhook object with an ID and a token.
Parameters
-----------
id: :class:`int`
The ID of the webhook.
token: :class:`str`
The authenticati... |
def from_url(cls, url, *, adapter):
"""Creates a partial :class:`Webhook` from a webhook URL.
Parameters
------------
url: :class:`str`
The URL of the webhook.
adapter: :class:`WebhookAdapter`
The webhook adapter to use when sending requests. This is
... |
def channel(self):
"""Optional[:class:`TextChannel`]: The text channel this webhook belongs to.
If this is a partial webhook, then this will always return ``None``.
"""
guild = self.guild
return guild and guild.get_channel(self.channel_id) |
def avatar_url_as(self, *, format=None, size=1024):
"""Returns a friendly URL version of the avatar the webhook has.
If the webhook does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'jpeg', 'jpg', or 'png'.
The size must... |
def edit(self, **kwargs):
"""|maybecoro|
Edits this Webhook.
If the webhook is constructed with a :class:`RequestsWebhookAdapter` then this is
not a coroutine.
Parameters
-------------
name: Optional[:class:`str`]
The webhook's new default name.
... |
def send(self, content=None, *, wait=False, username=None, avatar_url=None, tts=False,
file=None, files=None, embed=None, embeds=None):
"""|maybecoro|
Sends a message using the webhook.
If the webhook is constructed with a :class:`RequestsWebhookAdapter` the... |
def users(self, limit=None, after=None):
"""Returns an :class:`AsyncIterator` representing the users that have reacted to the message.
The ``after`` parameter must represent a member
and meet the :class:`abc.Snowflake` abc.
Examples
---------
Usage ::
# I ... |
def members(self):
"""Returns a :class:`list` of :class:`Member` that can see this channel."""
return [m for m in self.guild.members if self.permissions_for(m).read_messages] |
async def delete_messages(self, messages):
"""|coro|
Deletes a list of messages. This is similar to :meth:`Message.delete`
except it bulk deletes multiple messages.
As a special case, if the number of messages is 0, then nothing
is done. If the number of messages is 1 then sing... |
async def purge(self, *, limit=100, check=None, before=None, after=None, around=None, oldest_first=False, bulk=True):
"""|coro|
Purges a list of messages that meet the criteria given by the predicate
``check``. If a ``check`` is not provided then all messages are deleted
without discrim... |
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