Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
async_setup_entry | (hass, config_entry, async_add_entities) | Set up the Demo config entry. | Set up the Demo config entry. | async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the Demo config entry."""
await async_setup_platform(hass, {}, async_add_entities) | [
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DemoSwitch.__init__ | (self, unique_id, name, state, icon, assumed, device_class=None) | Initialize the Demo switch. | Initialize the Demo switch. | def __init__(self, unique_id, name, state, icon, assumed, device_class=None):
"""Initialize the Demo switch."""
self._unique_id = unique_id
self._name = name or DEVICE_DEFAULT_NAME
self._state = state
self._icon = icon
self._assumed = assumed
self._device_class = ... | [
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DemoSwitch.device_info | (self) | Return device info. | Return device info. | def device_info(self):
"""Return device info."""
return {
"identifiers": {
# Serial numbers are unique identifiers within a specific domain
(DOMAIN, self.unique_id)
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"name": self.name,
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DemoSwitch.unique_id | (self) | Return the unique id. | Return the unique id. | def unique_id(self):
"""Return the unique id."""
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DemoSwitch.should_poll | (self) | No polling needed for a demo switch. | No polling needed for a demo switch. | def should_poll(self):
"""No polling needed for a demo switch."""
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DemoSwitch.name | (self) | Return the name of the device if any. | Return the name of the device if any. | def name(self):
"""Return the name of the device if any."""
return self._name | [
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DemoSwitch.icon | (self) | Return the icon to use for device if any. | Return the icon to use for device if any. | def icon(self):
"""Return the icon to use for device if any."""
return self._icon | [
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DemoSwitch.assumed_state | (self) | Return if the state is based on assumptions. | Return if the state is based on assumptions. | def assumed_state(self):
"""Return if the state is based on assumptions."""
return self._assumed | [
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DemoSwitch.current_power_w | (self) | Return the current power usage in W. | Return the current power usage in W. | def current_power_w(self):
"""Return the current power usage in W."""
if self._state:
return 100 | [
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DemoSwitch.today_energy_kwh | (self) | Return the today total energy usage in kWh. | Return the today total energy usage in kWh. | def today_energy_kwh(self):
"""Return the today total energy usage in kWh."""
return 15 | [
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DemoSwitch.is_on | (self) | Return true if switch is on. | Return true if switch is on. | def is_on(self):
"""Return true if switch is on."""
return self._state | [
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DemoSwitch.device_class | (self) | Return device of entity. | Return device of entity. | def device_class(self):
"""Return device of entity."""
return self._device_class | [
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DemoSwitch.turn_on | (self, **kwargs) | Turn the switch on. | Turn the switch on. | def turn_on(self, **kwargs):
"""Turn the switch on."""
self._state = True
self.schedule_update_ha_state() | [
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DemoSwitch.turn_off | (self, **kwargs) | Turn the device off. | Turn the device off. | def turn_off(self, **kwargs):
"""Turn the device off."""
self._state = False
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setup | (hass, config) | Set up the Goalfeed component. | Set up the Goalfeed component. | def setup(hass, config):
"""Set up the Goalfeed component."""
conf = config[DOMAIN]
username = conf.get(CONF_USERNAME)
password = conf.get(CONF_PASSWORD)
def goal_handler(data):
"""Handle goal events."""
goal = json.loads(json.loads(data))
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prune_columns | (file_schema: Schema, expected_schema: Schema) |
Given two Iceberg schema's returns a list of column_names for all id's in the
file schema that are projected in the expected schema
Parameters
----------
file_schema : iceberg.api.Schema
An Iceberg schema of the file being read
expected_schema : iceberg.api.Schema
An Iceberg sc... |
Given two Iceberg schema's returns a list of column_names for all id's in the
file schema that are projected in the expected schema | def prune_columns(file_schema: Schema, expected_schema: Schema) -> List[str]:
"""
Given two Iceberg schema's returns a list of column_names for all id's in the
file schema that are projected in the expected schema
Parameters
----------
file_schema : iceberg.api.Schema
An Iceberg schema ... | [
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init_deepspeed | (trainer, num_training_steps, resume_from_checkpoint=None) |
Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.
If ``resume_from_checkpoint`` was passed then an attempt to resume from a previously saved checkpoint will be made.
Args:
trainer: Trainer object
num_training_steps: per single gpu
resume_... |
Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args. | def init_deepspeed(trainer, num_training_steps, resume_from_checkpoint=None):
"""
Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.
If ``resume_from_checkpoint`` was passed then an attempt to resume from a previously saved checkpoint will be made.
Args:
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WandbCallback.setup | (self, args, state, model, **kwargs) |
Setup the optional Weights & Biases (`wandb`) integration.
One can subclass and override this method to customize the setup if needed. Find more information `here
<https://docs.wandb.ai/integrations/huggingface>`__. You can also override the following environment variables:
Environmen... |
Setup the optional Weights & Biases (`wandb`) integration. | def setup(self, args, state, model, **kwargs):
"""
Setup the optional Weights & Biases (`wandb`) integration.
One can subclass and override this method to customize the setup if needed. Find more information `here
<https://docs.wandb.ai/integrations/huggingface>`__. You can also overrid... | [
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CometCallback.setup | (self, args, state, model) |
Setup the optional Comet.ml integration.
Environment:
COMET_MODE (:obj:`str`, `optional`):
"OFFLINE", "ONLINE", or "DISABLED"
COMET_PROJECT_NAME (:obj:`str`, `optional`):
Comet.ml project name for experiments
COMET_OFFLINE_DIRECTORY (... |
Setup the optional Comet.ml integration. | def setup(self, args, state, model):
"""
Setup the optional Comet.ml integration.
Environment:
COMET_MODE (:obj:`str`, `optional`):
"OFFLINE", "ONLINE", or "DISABLED"
COMET_PROJECT_NAME (:obj:`str`, `optional`):
Comet.ml project name for e... | [
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MLflowCallback.setup | (self, args, state, model) |
Setup the optional MLflow integration.
Environment:
HF_MLFLOW_LOG_ARTIFACTS (:obj:`str`, `optional`):
Whether to use MLflow .log_artifact() facility to log artifacts.
This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or `... |
Setup the optional MLflow integration. | def setup(self, args, state, model):
"""
Setup the optional MLflow integration.
Environment:
HF_MLFLOW_LOG_ARTIFACTS (:obj:`str`, `optional`):
Whether to use MLflow .log_artifact() facility to log artifacts.
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test_get_device_detects_none | (hass, mock_openzwave) | Test device returns none. | Test device returns none. | def test_get_device_detects_none(hass, mock_openzwave):
"""Test device returns none."""
node = MockNode()
value = MockValue(data=0, node=node)
values = MockEntityValues(primary=value, node=node)
device = cover.get_device(hass=hass, node=node, values=values, node_config={})
assert device is None | [
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test_get_device_detects_rollershutter | (hass, mock_openzwave) | Test device returns rollershutter. | Test device returns rollershutter. | def test_get_device_detects_rollershutter(hass, mock_openzwave):
"""Test device returns rollershutter."""
hass.data[const.DATA_NETWORK] = MagicMock()
node = MockNode()
value = MockValue(
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test_get_device_detects_garagedoor_switch | (hass, mock_openzwave) | Test device returns garage door. | Test device returns garage door. | def test_get_device_detects_garagedoor_switch(hass, mock_openzwave):
"""Test device returns garage door."""
node = MockNode()
value = MockValue(
data=False, node=node, command_class=const.COMMAND_CLASS_SWITCH_BINARY
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values = MockEntityValues(primary=value, node=node)
device = cover.ge... | [
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test_get_device_detects_garagedoor_barrier | (hass, mock_openzwave) | Test device returns garage door. | Test device returns garage door. | def test_get_device_detects_garagedoor_barrier(hass, mock_openzwave):
"""Test device returns garage door."""
node = MockNode()
value = MockValue(
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test_roller_no_position_workaround | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_roller_no_position_workaround(hass, mock_openzwave):
"""Test position changed."""
hass.data[const.DATA_NETWORK] = MagicMock()
node = MockNode(manufacturer_id="0047", product_type="5a52")
value = MockValue(
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test_roller_value_changed | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_roller_value_changed(hass, mock_openzwave):
"""Test position changed."""
hass.data[const.DATA_NETWORK] = MagicMock()
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value = MockValue(
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test_roller_commands | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_roller_commands(hass, mock_openzwave):
"""Test position changed."""
mock_network = hass.data[const.DATA_NETWORK] = MagicMock()
node = MockNode()
value = MockValue(
data=50, node=node, command_class=const.COMMAND_CLASS_SWITCH_MULTILEVEL
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test_roller_invert_percent | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_roller_invert_percent(hass, mock_openzwave):
"""Test position changed."""
mock_network = hass.data[const.DATA_NETWORK] = MagicMock()
node = MockNode()
value = MockValue(
data=50, node=node, command_class=const.COMMAND_CLASS_SWITCH_MULTILEVEL
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open_value = MockValue(data=False, ... | [
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test_roller_reverse_open_close | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_roller_reverse_open_close(hass, mock_openzwave):
"""Test position changed."""
mock_network = hass.data[const.DATA_NETWORK] = MagicMock()
node = MockNode()
value = MockValue(
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test_switch_garage_value_changed | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_switch_garage_value_changed(hass, mock_openzwave):
"""Test position changed."""
node = MockNode()
value = MockValue(
data=False, node=node, command_class=const.COMMAND_CLASS_SWITCH_BINARY
)
values = MockEntityValues(primary=value, node=node)
device = cover.get_device(hass=hass, ... | [
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test_switch_garage_commands | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_switch_garage_commands(hass, mock_openzwave):
"""Test position changed."""
node = MockNode()
value = MockValue(
data=False, node=node, command_class=const.COMMAND_CLASS_SWITCH_BINARY
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values = MockEntityValues(primary=value, node=node)
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test_barrier_garage_value_changed | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_barrier_garage_value_changed(hass, mock_openzwave):
"""Test position changed."""
node = MockNode()
value = MockValue(
data="Closed", node=node, command_class=const.COMMAND_CLASS_BARRIER_OPERATOR
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test_barrier_garage_commands | (hass, mock_openzwave) | Test position changed. | Test position changed. | def test_barrier_garage_commands(hass, mock_openzwave):
"""Test position changed."""
node = MockNode()
value = MockValue(
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__git | (*opts) | Runs a git command and returns its output | Runs a git command and returns its output | def __git(*opts):
"""Runs a git command and returns its output"""
cmd = "git " + " ".join(list(opts))
ret = subprocess.check_output(cmd, shell=True)
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__gitdiff | (*opts) | Runs a git diff command with no pager set | Runs a git diff command with no pager set | def __gitdiff(*opts):
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branch | () | Returns the name of the current branch | Returns the name of the current branch | def branch():
"""Returns the name of the current branch"""
name = __git("rev-parse", "--abbrev-ref", "HEAD")
name = name.rstrip()
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uncommittedFiles | () |
Returns a list of all changed files that are not yet committed. This
means both untracked/unstaged as well as uncommitted files too.
|
Returns a list of all changed files that are not yet committed. This
means both untracked/unstaged as well as uncommitted files too.
| def uncommittedFiles():
"""
Returns a list of all changed files that are not yet committed. This
means both untracked/unstaged as well as uncommitted files too.
"""
files = __git("status", "-u", "-s")
ret = []
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changedFilesBetween | (b1, b2) | Returns a list of files changed between branches b1 and b2 | Returns a list of files changed between branches b1 and b2 | def changedFilesBetween(b1, b2):
"""Returns a list of files changed between branches b1 and b2"""
current = branch()
__git("checkout", "--quiet", b1)
__git("checkout", "--quiet", b2)
files = __gitdiff("--name-only", "--ignore-submodules", "%s...%s" %
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changesInFileBetween | (file, b1, b2, filter=None) | Filters the changed lines to a file between the branches b1 and b2 | Filters the changed lines to a file between the branches b1 and b2 | def changesInFileBetween(file, b1, b2, filter=None):
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current = branch()
__git("checkout", "--quiet", b1)
__git("checkout", "--quiet", b2)
diffs = __gitdiff("--ignore-submodules", "-w", "--minimal", "-U0",
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modifiedFiles | (filter=None) |
If inside a CI-env (ie. currentBranch=current-pr-branch and the env-var
PR_TARGET_BRANCH is defined), then lists out all files modified between
these 2 branches. Else, lists out all the uncommitted files in the current
branch.
Such utility function is helpful while putting checker scripts as part ... |
If inside a CI-env (ie. currentBranch=current-pr-branch and the env-var
PR_TARGET_BRANCH is defined), then lists out all files modified between
these 2 branches. Else, lists out all the uncommitted files in the current
branch. | def modifiedFiles(filter=None):
"""
If inside a CI-env (ie. currentBranch=current-pr-branch and the env-var
PR_TARGET_BRANCH is defined), then lists out all files modified between
these 2 branches. Else, lists out all the uncommitted files in the current
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listAllFilesInDir | (folder) | Utility function to list all files/subdirs in the input folder | Utility function to list all files/subdirs in the input folder | def listAllFilesInDir(folder):
"""Utility function to list all files/subdirs in the input folder"""
allFiles = []
for root, dirs, files in os.walk(folder):
for name in files:
allFiles.append(os.path.join(root, name))
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listFilesToCheck | (filesDirs, filter=None) |
Utility function to filter the input list of files/dirs based on the input
filter method and returns all the files that need to be checked
|
Utility function to filter the input list of files/dirs based on the input
filter method and returns all the files that need to be checked
| def listFilesToCheck(filesDirs, filter=None):
"""
Utility function to filter the input list of files/dirs based on the input
filter method and returns all the files that need to be checked
"""
allFiles = []
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activate | (hass, entity_id=ENTITY_MATCH_ALL) | Activate a scene. | Activate a scene. | def activate(hass, entity_id=ENTITY_MATCH_ALL):
"""Activate a scene."""
data = {}
if entity_id:
data[ATTR_ENTITY_ID] = entity_id
hass.services.call(DOMAIN, SERVICE_TURN_ON, data) | [
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dispatcher_connect | (
hass: HomeAssistantType, signal: str, target: Callable[..., None]
) | Connect a callable function to a signal. | Connect a callable function to a signal. | def dispatcher_connect(
hass: HomeAssistantType, signal: str, target: Callable[..., None]
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async_unsub = run_callback_threadsafe(
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async_dispatcher_connect | (
hass: HomeAssistantType, signal: str, target: Callable[..., Any]
) | Connect a callable function to a signal.
This method must be run in the event loop.
| Connect a callable function to a signal. | def async_dispatcher_connect(
hass: HomeAssistantType, signal: str, target: Callable[..., Any]
) -> Callable[[], None]:
"""Connect a callable function to a signal.
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dispatcher_send | (hass: HomeAssistantType, signal: str, *args: Any) | Send signal and data. | Send signal and data. | def dispatcher_send(hass: HomeAssistantType, signal: str, *args: Any) -> None:
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async_dispatcher_send | (hass: HomeAssistantType, signal: str, *args: Any) | Send signal and data.
This method must be run in the event loop.
| Send signal and data. | def async_dispatcher_send(hass: HomeAssistantType, signal: str, *args: Any) -> None:
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setup_platform | (hass, config, add_entities, discovery_info=None) | Set up the Zestimate sensor. | Set up the Zestimate sensor. | def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the Zestimate sensor."""
name = config.get(CONF_NAME)
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ZestimateDataSensor.__init__ | (self, name, params) | Initialize the sensor. | Initialize the sensor. | def __init__(self, name, params):
"""Initialize the sensor."""
self._name = name
self.params = params
self.data = None
self.address = None
self._state = None | [
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ZestimateDataSensor.unique_id | (self) | Return the ZPID. | Return the ZPID. | def unique_id(self):
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ZestimateDataSensor.name | (self) | Return the name of the sensor. | Return the name of the sensor. | def name(self):
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ZestimateDataSensor.state | (self) | Return the state of the sensor. | Return the state of the sensor. | def state(self):
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ZestimateDataSensor.device_state_attributes | (self) | Return the state attributes. | Return the state attributes. | def device_state_attributes(self):
"""Return the state attributes."""
attributes = {}
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attributes = self.data
attributes["address"] = self.address
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ZestimateDataSensor.icon | (self) | Icon to use in the frontend, if any. | Icon to use in the frontend, if any. | def icon(self):
"""Icon to use in the frontend, if any."""
return ICON | [
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ZestimateDataSensor.update | (self) | Get the latest data and update the states. | Get the latest data and update the states. | def update(self):
"""Get the latest data and update the states."""
try:
response = requests.get(_RESOURCE, params=self.params, timeout=5)
data = response.content.decode("utf-8")
data_dict = xmltodict.parse(data).get(ZESTIMATE)
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"\... | [
102,
4
] | [
134,
74
] | python | en | ['en', 'en', 'en'] | True |
tile | (x, count, dim=0) |
Tiles x on dimension dim count times.
|
Tiles x on dimension dim count times.
| def tile(x, count, dim=0):
"""
Tiles x on dimension dim count times.
"""
perm = list(range(len(x.size())))
if dim != 0:
perm[0], perm[dim] = perm[dim], perm[0]
x = x.permute(perm).contiguous()
out_size = list(x.size())
out_size[0] *= count
batch = x.size(0)
x = x.view... | [
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TransformerDecoder.forward | (
self,
input_ids,
encoder_hidden_states=None,
state=None,
attention_mask=None,
memory_lengths=None,
step=None,
cache=None,
encoder_attention_mask=None,
) |
See :obj:`onmt.modules.RNNDecoderBase.forward()`
memory_bank = encoder_hidden_states
|
See :obj:`onmt.modules.RNNDecoderBase.forward()`
memory_bank = encoder_hidden_states
| def forward(
self,
input_ids,
encoder_hidden_states=None,
state=None,
attention_mask=None,
memory_lengths=None,
step=None,
cache=None,
encoder_attention_mask=None,
):
"""
See :obj:`onmt.modules.RNNDecoderBase.forward()`
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] | [
250,
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] | python | en | ['en', 'error', 'th'] | False |
TransformerDecoder.init_decoder_state | (self, src, memory_bank, with_cache=False) | Init decoder state | Init decoder state | def init_decoder_state(self, src, memory_bank, with_cache=False):
""" Init decoder state """
state = TransformerDecoderState(src)
if with_cache:
state._init_cache(memory_bank, self.num_layers)
return state | [
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TransformerDecoderLayer.forward | (
self,
inputs,
memory_bank,
src_pad_mask,
tgt_pad_mask,
previous_input=None,
layer_cache=None,
step=None,
) |
Args:
inputs (`FloatTensor`): `[batch_size x 1 x model_dim]`
memory_bank (`FloatTensor`): `[batch_size x src_len x model_dim]`
src_pad_mask (`LongTensor`): `[batch_size x 1 x src_len]`
tgt_pad_mask (`LongTensor`): `[batch_size x 1 x 1]`
Returns:
... |
Args:
inputs (`FloatTensor`): `[batch_size x 1 x model_dim]`
memory_bank (`FloatTensor`): `[batch_size x src_len x model_dim]`
src_pad_mask (`LongTensor`): `[batch_size x 1 x src_len]`
tgt_pad_mask (`LongTensor`): `[batch_size x 1 x 1]` | def forward(
self,
inputs,
memory_bank,
src_pad_mask,
tgt_pad_mask,
previous_input=None,
layer_cache=None,
step=None,
):
"""
Args:
inputs (`FloatTensor`): `[batch_size x 1 x model_dim]`
memory_bank (`FloatTensor`... | [
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314,
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368,
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TransformerDecoderLayer._get_attn_subsequent_mask | (self, size) |
Get an attention mask to avoid using the subsequent info.
Args:
size: int
Returns:
(`LongTensor`):
* subsequent_mask `[1 x size x size]`
|
Get an attention mask to avoid using the subsequent info. | def _get_attn_subsequent_mask(self, size):
"""
Get an attention mask to avoid using the subsequent info.
Args:
size: int
Returns:
(`LongTensor`):
* subsequent_mask `[1 x size x size]`
"""
attn_shape = (1, size, size)
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MultiHeadedAttention.forward | (
self,
key,
value,
query,
mask=None,
layer_cache=None,
type=None,
predefined_graph_1=None,
) |
Compute the context vector and the attention vectors.
Args:
key (`FloatTensor`): set of `key_len`
key vectors `[batch, key_len, dim]`
value (`FloatTensor`): set of `key_len`
value vectors `[batch, key_len, dim]`
query (`FloatTensor`): se... |
Compute the context vector and the attention vectors. | def forward(
self,
key,
value,
query,
mask=None,
layer_cache=None,
type=None,
predefined_graph_1=None,
):
"""
Compute the context vector and the attention vectors.
Args:
key (`FloatTensor`): set of `key_len`
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448,
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560,
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DecoderState.detach | (self) | Need to document this | Need to document this | def detach(self):
""" Need to document this """
self.hidden = tuple([_.detach() for _ in self.hidden])
self.input_feed = self.input_feed.detach() | [
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DecoderState.beam_update | (self, idx, positions, beam_size) | Need to document this | Need to document this | def beam_update(self, idx, positions, beam_size):
""" Need to document this """
for e in self._all:
sizes = e.size()
br = sizes[1]
if len(sizes) == 3:
sent_states = e.view(sizes[0], beam_size, br // beam_size, sizes[2])[:, :, idx]
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TransformerDecoderState.__init__ | (self, src) |
Args:
src (FloatTensor): a sequence of source words tensors
with optional feature tensors, of size (len x batch).
|
Args:
src (FloatTensor): a sequence of source words tensors
with optional feature tensors, of size (len x batch).
| def __init__(self, src):
"""
Args:
src (FloatTensor): a sequence of source words tensors
with optional feature tensors, of size (len x batch).
"""
self.src = src
self.previous_input = None
self.previous_layer_inputs = None
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TransformerDecoderState._all | (self) |
Contains attributes that need to be updated in self.beam_update().
|
Contains attributes that need to be updated in self.beam_update().
| def _all(self):
"""
Contains attributes that need to be updated in self.beam_update().
"""
if self.previous_input is not None and self.previous_layer_inputs is not None:
return (self.previous_input, self.previous_layer_inputs, self.src)
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TransformerDecoderState.repeat_beam_size_times | (self, beam_size) | Repeat beam_size times along batch dimension. | Repeat beam_size times along batch dimension. | def repeat_beam_size_times(self, beam_size):
""" Repeat beam_size times along batch dimension. """
self.src = self.src.data.repeat(1, beam_size, 1) | [
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GNMTGlobalScorer.score | (self, beam, logprobs) |
Rescores a prediction based on penalty functions
|
Rescores a prediction based on penalty functions
| def score(self, beam, logprobs):
"""
Rescores a prediction based on penalty functions
"""
normalized_probs = self.length_penalty(beam, logprobs, self.alpha)
return normalized_probs | [
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PenaltyBuilder.length_wu | (self, beam, logprobs, alpha=0.0) |
NMT length re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`.
|
NMT length re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`.
| def length_wu(self, beam, logprobs, alpha=0.0):
"""
NMT length re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`.
"""
modifier = ((5 + len(beam.next_ys)) ** alpha) / ((5 + 1) ** alpha)
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PenaltyBuilder.length_average | (self, beam, logprobs, alpha=0.0) |
Returns the average probability of tokens in a sequence.
|
Returns the average probability of tokens in a sequence.
| def length_average(self, beam, logprobs, alpha=0.0):
"""
Returns the average probability of tokens in a sequence.
"""
return logprobs / len(beam.next_ys) | [
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PenaltyBuilder.length_none | (self, beam, logprobs, alpha=0.0, beta=0.0) |
Returns unmodified scores.
|
Returns unmodified scores.
| def length_none(self, beam, logprobs, alpha=0.0, beta=0.0):
"""
Returns unmodified scores.
"""
return logprobs | [
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Translator.translate | (self, batch, step, attn_debug=False) | Generates summaries from one batch of data. | Generates summaries from one batch of data. | def translate(self, batch, step, attn_debug=False):
"""Generates summaries from one batch of data."""
self.model.eval()
with torch.no_grad():
batch_data = self.translate_batch(batch)
translations = self.from_batch(batch_data)
return translations | [
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Translator.translate_batch | (self, batch, fast=False) |
Translate a batch of sentences.
Mostly a wrapper around :obj:`Beam`.
Args:
batch (:obj:`Batch`): a batch from a dataset object
fast (bool): enables fast beam search (may not support all features)
|
Translate a batch of sentences. | def translate_batch(self, batch, fast=False):
"""
Translate a batch of sentences.
Mostly a wrapper around :obj:`Beam`.
Args:
batch (:obj:`Batch`): a batch from a dataset object
fast (bool): enables fast beam search (may not support all features)
"""
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Translator._fast_translate_batch | (self, batch, max_length, min_length=0) | Beam Search using the encoder inputs contained in `batch`. | Beam Search using the encoder inputs contained in `batch`. | def _fast_translate_batch(self, batch, max_length, min_length=0):
"""Beam Search using the encoder inputs contained in `batch`."""
# The batch object is funny
# Instead of just looking at the size of the arguments we encapsulate
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# Where is it defined?
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get_service | (hass, config, discovery_info=None) | Get the MessageBird notification service. | Get the MessageBird notification service. | def get_service(hass, config, discovery_info=None):
"""Get the MessageBird notification service."""
client = messagebird.Client(config[CONF_API_KEY])
try:
# validates the api key
client.balance()
except messagebird.client.ErrorException:
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MessageBirdNotificationService.__init__ | (self, sender, client) | Initialize the service. | Initialize the service. | def __init__(self, sender, client):
"""Initialize the service."""
self.sender = sender
self.client = client | [
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MessageBirdNotificationService.send_message | (self, message=None, **kwargs) | Send a message to a specified target. | Send a message to a specified target. | def send_message(self, message=None, **kwargs):
"""Send a message to a specified target."""
targets = kwargs.get(ATTR_TARGET)
if not targets:
_LOGGER.error("No target specified")
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TestTTSMaryTTSPlatform.setup_method | (self) | Set up things to be run when tests are started. | Set up things to be run when tests are started. | def setup_method(self):
"""Set up things to be run when tests are started."""
self.hass = get_test_home_assistant()
asyncio.run_coroutine_threadsafe(
async_process_ha_core_config(
self.hass, {"internal_url": "http://example.local:8123"}
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TestTTSMaryTTSPlatform.teardown_method | (self) | Stop everything that was started. | Stop everything that was started. | def teardown_method(self):
"""Stop everything that was started."""
default_tts = self.hass.config.path(tts.DEFAULT_CACHE_DIR)
if os.path.isdir(default_tts):
shutil.rmtree(default_tts)
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TestTTSMaryTTSPlatform.test_setup_component | (self) | Test setup component. | Test setup component. | def test_setup_component(self):
"""Test setup component."""
config = {tts.DOMAIN: {"platform": "marytts"}}
with assert_setup_component(1, tts.DOMAIN):
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"tts",
".",
"DOMAIN",
")",
":",
"setup_component",
"(",... | [
51,
4
] | [
56,
58
] | python | en | ['en', 'da', 'en'] | True |
TestTTSMaryTTSPlatform.test_service_say | (self) | Test service call say. | Test service call say. | def test_service_say(self):
"""Test service call say."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {tts.DOMAIN: {"platform": "marytts"}}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
with pa... | [
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... | [
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] | [
85,
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TestTTSMaryTTSPlatform.test_service_say_with_effect | (self) | Test service call say with effects. | Test service call say with effects. | def test_service_say_with_effect(self):
"""Test service call say with effects."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {
tts.DOMAIN: {"platform": "marytts", "effect": {"Volume": "amount:2.0;"}}
}
with assert_setup_component(1, tts.... | [
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] | [
116,
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] | python | en | ['en', 'en', 'en'] | True |
TestTTSMaryTTSPlatform.test_service_say_http_error | (self) | Test service call say. | Test service call say. | def test_service_say_http_error(self):
"""Test service call say."""
calls = mock_service(self.hass, DOMAIN_MP, SERVICE_PLAY_MEDIA)
config = {tts.DOMAIN: {"platform": "marytts"}}
with assert_setup_component(1, tts.DOMAIN):
setup_component(self.hass, tts.DOMAIN, config)
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get_checkpoint_callback | (output_dir, metric) | Saves the best model by validation EM score. | Saves the best model by validation EM score. | def get_checkpoint_callback(output_dir, metric):
"""Saves the best model by validation EM score."""
if metric == "rouge2":
exp = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
exp = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
exp = "{val_avg_em:.4f}-{step_c... | [
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22,
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] | [
42,
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] | python | en | ['en', 'en', 'en'] | True |
flow_handler | (hass) | Return a registered config flow. | Return a registered config flow. | def flow_handler(hass):
"""Return a registered config flow."""
mock_platform(hass, f"{TEST_DOMAIN}.config_flow")
class TestFlowHandler(config_entry_oauth2_flow.AbstractOAuth2FlowHandler):
"""Test flow handler."""
DOMAIN = TEST_DOMAIN
@property
def logger(self) -> logging.... | [
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"\"\"\"Test flow handler.\"\"\"",
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19,
0
] | [
35,
29
] | python | en | ['en', 'da', 'en'] | True |
test_setup_provide_implementation | (hass) | Test that we provide implementations. | Test that we provide implementations. | async def test_setup_provide_implementation(hass):
"""Test that we provide implementations."""
account_link.async_setup(hass)
with patch(
"homeassistant.components.cloud.account_link._get_services",
return_value=[
{"service": "test", "min_version": "0.1.0"},
{"servic... | [
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":... | [
38,
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] | [
66,
48
] | python | en | ['en', 'en', 'en'] | True |
test_get_services_cached | (hass) | Test that we cache services. | Test that we cache services. | async def test_get_services_cached(hass):
"""Test that we cache services."""
hass.data["cloud"] = None
services = 1
with patch.object(account_link, "CACHE_TIMEOUT", 0), patch(
"hass_nabucasa.account_link.async_fetch_available_services",
side_effect=lambda _: services,
) as mock_fet... | [
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... | [
69,
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] | [
95,
58
] | python | en | ['en', 'en', 'en'] | True |
test_get_services_error | (hass) | Test that we cache services. | Test that we cache services. | async def test_get_services_error(hass):
"""Test that we cache services."""
hass.data["cloud"] = None
with patch.object(account_link, "CACHE_TIMEOUT", 0), patch(
"hass_nabucasa.account_link.async_fetch_available_services",
side_effect=asyncio.TimeoutError,
):
assert await accoun... | [
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98,
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107,
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test_implementation | (hass, flow_handler) | Test Cloud OAuth2 implementation. | Test Cloud OAuth2 implementation. | async def test_implementation(hass, flow_handler):
"""Test Cloud OAuth2 implementation."""
hass.data["cloud"] = None
impl = account_link.CloudOAuth2Implementation(hass, "test")
assert impl.name == "Home Assistant Cloud"
assert impl.domain == "cloud"
flow_handler.async_register_implementation(h... | [
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".... | [
110,
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] | [
169,
5
] | python | en | ['nl', 'fr', 'en'] | False |
normalize | (inputs,
epsilon=1e-8,
scope="ln") | Applies layer normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse ... | Applies layer normalization. | def normalize(inputs,
epsilon=1e-8,
scope="ln"):
'''Applies layer normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
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multihead_attention | (queries,
keys,
scope="multihead_attention",
num_units=None,
num_heads=4,
dropout_rate=0,
is_training=True,
causality=False) | Applies multihead attention.
Args:
queries: A 3d tensor with shape of [N, T_q, C_q].
keys: A 3d tensor with shape of [N, T_k, C_k].
num_units: A cdscalar. Attention size.
dropout_rate: A floating point number.
is_training: Boolean. Controller of mechanism for dropout.
causality:... | Applies multihead attention. | def multihead_attention(queries,
keys,
scope="multihead_attention",
num_units=None,
num_heads=4,
dropout_rate=0,
is_training=True,
causality=False):
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positional_encoding | (inputs,
num_units=None,
zero_pad=True,
scale=True,
scope="positional_encoding",
reuse=None) |
Return positinal embedding.
|
Return positinal embedding.
| def positional_encoding(inputs,
num_units=None,
zero_pad=True,
scale=True,
scope="positional_encoding",
reuse=None):
'''
Return positinal embedding.
'''
Shape = tf.shape(inputs)
N ... | [
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166,
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204,
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] | python | en | ['en', 'error', 'th'] | False |
feedforward | (inputs,
num_units,
scope="multihead_attention") | Point-wise feed forward net.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3d tensor with the ... | Point-wise feed forward net. | def feedforward(inputs,
num_units,
scope="multihead_attention"):
'''Point-wise feed forward net.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to r... | [
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239,
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] | python | en | ['en', 'en', 'en'] | True |
setup_platform | (hass, config, add_entities, discovery_info=None) | Find and return switches controlled by shell commands. | Find and return switches controlled by shell commands. | def setup_platform(hass, config, add_entities, discovery_info=None):
"""Find and return switches controlled by shell commands."""
setup_reload_service(hass, DOMAIN, PLATFORMS)
devices = config.get(CONF_SWITCHES, {})
switches = []
for object_id, device_config in devices.items():
value_temp... | [
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","... | [
42,
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] | [
73,
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] | python | en | ['en', 'en', 'en'] | True |
CommandSwitch.__init__ | (
self,
hass,
object_id,
friendly_name,
command_on,
command_off,
command_state,
value_template,
timeout,
) | Initialize the switch. | Initialize the switch. | def __init__(
self,
hass,
object_id,
friendly_name,
command_on,
command_off,
command_state,
value_template,
timeout,
):
"""Initialize the switch."""
self._hass = hass
self.entity_id = ENTITY_ID_FORMAT.format(object_id)
... | [
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"entity_... | [
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] | [
99,
31
] | python | en | ['en', 'en', 'en'] | True |
CommandSwitch._switch | (self, command) | Execute the actual commands. | Execute the actual commands. | def _switch(self, command):
"""Execute the actual commands."""
_LOGGER.info("Running command: %s", command)
success = call_shell_with_timeout(command, self._timeout) == 0
if not success:
_LOGGER.error("Command failed: %s", command)
return success | [
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] | [
110,
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] | python | en | ['en', 'en', 'en'] | True |
CommandSwitch._query_state_value | (self, command) | Execute state command for return value. | Execute state command for return value. | def _query_state_value(self, command):
"""Execute state command for return value."""
_LOGGER.info("Running state value command: %s", command)
return check_output_or_log(command, self._timeout) | [
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115,
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CommandSwitch._query_state_code | (self, command) | Execute state command for return code. | Execute state command for return code. | def _query_state_code(self, command):
"""Execute state command for return code."""
_LOGGER.info("Running state code command: %s", command)
return (
call_shell_with_timeout(command, self._timeout, log_return_code=False) == 0
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CommandSwitch.should_poll | (self) | Only poll if we have state command. | Only poll if we have state command. | def should_poll(self):
"""Only poll if we have state command."""
return self._command_state is not None | [
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CommandSwitch.name | (self) | Return the name of the switch. | Return the name of the switch. | def name(self):
"""Return the name of the switch."""
return self._name | [
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CommandSwitch.is_on | (self) | Return true if device is on. | Return true if device is on. | def is_on(self):
"""Return true if device is on."""
return self._state | [
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