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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
WaterHeaterEntity.state_attributes | (self) | Return the optional state attributes. | Return the optional state attributes. | def state_attributes(self):
"""Return the optional state attributes."""
data = {
ATTR_CURRENT_TEMPERATURE: show_temp(
self.hass,
self.current_temperature,
self.temperature_unit,
self.precision,
),
ATTR_TE... | [
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WaterHeaterEntity.temperature_unit | (self) | Return the unit of measurement used by the platform. | Return the unit of measurement used by the platform. | def temperature_unit(self):
"""Return the unit of measurement used by the platform."""
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WaterHeaterEntity.current_operation | (self) | Return current operation ie. eco, electric, performance, ... | Return current operation ie. eco, electric, performance, ... | def current_operation(self):
"""Return current operation ie. eco, electric, performance, ..."""
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WaterHeaterEntity.operation_list | (self) | Return the list of available operation modes. | Return the list of available operation modes. | def operation_list(self):
"""Return the list of available operation modes."""
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WaterHeaterEntity.current_temperature | (self) | Return the current temperature. | Return the current temperature. | def current_temperature(self):
"""Return the current temperature."""
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WaterHeaterEntity.target_temperature | (self) | Return the temperature we try to reach. | Return the temperature we try to reach. | def target_temperature(self):
"""Return the temperature we try to reach."""
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226,
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WaterHeaterEntity.target_temperature_high | (self) | Return the highbound target temperature we try to reach. | Return the highbound target temperature we try to reach. | def target_temperature_high(self):
"""Return the highbound target temperature we try to reach."""
return None | [
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WaterHeaterEntity.target_temperature_low | (self) | Return the lowbound target temperature we try to reach. | Return the lowbound target temperature we try to reach. | def target_temperature_low(self):
"""Return the lowbound target temperature we try to reach."""
return None | [
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WaterHeaterEntity.is_away_mode_on | (self) | Return true if away mode is on. | Return true if away mode is on. | def is_away_mode_on(self):
"""Return true if away mode is on."""
return None | [
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WaterHeaterEntity.set_temperature | (self, **kwargs) | Set new target temperature. | Set new target temperature. | def set_temperature(self, **kwargs):
"""Set new target temperature."""
raise NotImplementedError() | [
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WaterHeaterEntity.async_set_temperature | (self, **kwargs) | Set new target temperature. | Set new target temperature. | async def async_set_temperature(self, **kwargs):
"""Set new target temperature."""
await self.hass.async_add_executor_job(
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WaterHeaterEntity.set_operation_mode | (self, operation_mode) | Set new target operation mode. | Set new target operation mode. | def set_operation_mode(self, operation_mode):
"""Set new target operation mode."""
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WaterHeaterEntity.async_set_operation_mode | (self, operation_mode) | Set new target operation mode. | Set new target operation mode. | async def async_set_operation_mode(self, operation_mode):
"""Set new target operation mode."""
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WaterHeaterEntity.turn_away_mode_on | (self) | Turn away mode on. | Turn away mode on. | def turn_away_mode_on(self):
"""Turn away mode on."""
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WaterHeaterEntity.async_turn_away_mode_on | (self) | Turn away mode on. | Turn away mode on. | async def async_turn_away_mode_on(self):
"""Turn away mode on."""
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WaterHeaterEntity.turn_away_mode_off | (self) | Turn away mode off. | Turn away mode off. | def turn_away_mode_off(self):
"""Turn away mode off."""
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WaterHeaterEntity.async_turn_away_mode_off | (self) | Turn away mode off. | Turn away mode off. | async def async_turn_away_mode_off(self):
"""Turn away mode off."""
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WaterHeaterEntity.supported_features | (self) | Return the list of supported features. | Return the list of supported features. | def supported_features(self):
"""Return the list of supported features."""
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WaterHeaterEntity.min_temp | (self) | Return the minimum temperature. | Return the minimum temperature. | def min_temp(self):
"""Return the minimum temperature."""
return convert_temperature(
DEFAULT_MIN_TEMP, TEMP_FAHRENHEIT, self.temperature_unit
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WaterHeaterEntity.max_temp | (self) | Return the maximum temperature. | Return the maximum temperature. | def max_temp(self):
"""Return the maximum temperature."""
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WaterHeaterDevice.__init_subclass__ | (cls, **kwargs) | Print deprecation warning. | Print deprecation warning. | def __init_subclass__(cls, **kwargs):
"""Print deprecation warning."""
super().__init_subclass__(**kwargs)
_LOGGER.warning(
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SklearnTopicModels.__init__ | (self, n_topics=50, estimator='LDA') |
n_topics is the desired number of topics
To use Latent Semantic Analysis, set estimator to 'LSA',
To use Non-Negative Matrix Factorization, set estimator to 'NMF',
otherwise, defaults to Latent Dirichlet Allocation ('LDA').
|
n_topics is the desired number of topics
To use Latent Semantic Analysis, set estimator to 'LSA',
To use Non-Negative Matrix Factorization, set estimator to 'NMF',
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SklearnTopicModels.get_topics | (self, n=25) |
n is the number of top terms to show for each topic
|
n is the number of top terms to show for each topic
| def get_topics(self, n=25):
"""
n is the number of top terms to show for each topic
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GensimTopicModels.__init__ | (self, n_topics=50, estimator='LDA') |
n_topics is the desired number of topics
To use Latent Semantic Analysis, set estimator to 'LSA'
otherwise defaults to Latent Dirichlet Allocation.
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n_topics is the desired number of topics | def __init__(self, n_topics=50, estimator='LDA'):
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convert_pytorch_checkpoint_to_tf | (model: BertModel, ckpt_dir: str, model_name: str) |
Args:
model: BertModel Pytorch model instance to be converted
ckpt_dir: Tensorflow model directory
model_name: model name
Currently supported HF models:
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- N BertForMaskedLM
- N BertForPreTraining
- N BertForMultipleChoice
- N BertF... |
Args:
model: BertModel Pytorch model instance to be converted
ckpt_dir: Tensorflow model directory
model_name: model name | def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name: str):
"""
Args:
model: BertModel Pytorch model instance to be converted
ckpt_dir: Tensorflow model directory
model_name: model name
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TFRobertaEmbeddings.create_position_ids_from_input_ids | (self, input_ids) |
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
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Args:
input_ids: tf.Tensor
Returns: tf.Tensor
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
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"""
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input_ids: tf.Tensor
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TFRobertaEmbeddings.call | (self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False) |
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
Applies embedding based on inputs tensor. | def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
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Applies embedding based on inputs tensor.
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async_setup_reload_service | (hass) | Create the reload service for the template domain. | Create the reload service for the template domain. | async def async_setup_reload_service(hass):
"""Create the reload service for the template domain."""
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T5Tokenizer.get_special_tokens_mask | (
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) |
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`,... |
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method. | def get_special_tokens_mask(
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) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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T5Tokenizer._add_eos_if_not_present | (self, token_ids: List[int]) | Do not add eos again if user already added it. | Do not add eos again if user already added it. | def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
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T5Tokenizer.create_token_type_ids_from_sequences | (
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
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Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
use of token type ids, therefore a list of zeros is returned. | def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
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T5Tokenizer.build_inputs_with_special_tokens | (
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A sequence has the following format:
- single sequence: ``X </s>``
- pair of sequences: ``A </s> B </s>``
Args:
token_ids_0 (:obj:... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A sequence has the following format: | def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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T5Tokenizer._tokenize | (self, text, sample=False) | Take as input a string and return a list of strings (tokens) for words/sub-words | Take as input a string and return a list of strings (tokens) for words/sub-words | def _tokenize(self, text, sample=False):
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T5Tokenizer._convert_token_to_id | (self, token) | Converts a token (str) in an id using the vocab. | Converts a token (str) in an id using the vocab. | def _convert_token_to_id(self, token):
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T5Tokenizer._convert_id_to_token | (self, index) | Converts an index (integer) in a token (str) using the vocab. | Converts an index (integer) in a token (str) using the vocab. | def _convert_id_to_token(self, index):
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T5Tokenizer.convert_tokens_to_string | (self, tokens) | Converts a sequence of tokens (string) in a single string. | Converts a sequence of tokens (string) in a single string. | def convert_tokens_to_string(self, tokens):
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setup_scanner | (hass, config: dict, see, discovery_info=None) | Validate the configuration and return a TrackR scanner. | Validate the configuration and return a TrackR scanner. | def setup_scanner(hass, config: dict, see, discovery_info=None):
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TrackRDeviceScanner(hass, config, see)
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TrackRDeviceScanner.__init__ | (self, hass, config: dict, see) | Initialize the TrackR device scanner. | Initialize the TrackR device scanner. | def __init__(self, hass, config: dict, see) -> None:
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self.hass = hass
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TrackRDeviceScanner._update_info | (self, now=None) | Update the device info. | Update the device info. | def _update_info(self, now=None) -> None:
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async_describe_on_off_states | (
hass: HomeAssistantType, registry: GroupIntegrationRegistry
) | Describe group on off states. | Describe group on off states. | def async_describe_on_off_states(
hass: HomeAssistantType, registry: GroupIntegrationRegistry
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async_setup_entry | (
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hass: HomeAssistantType, config_entry: ConfigEntry, async_add_entities
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"""Set up discovered binary sensors."""
devs = []
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HassAqualinkBinarySensor.name | (self) | Return the name of the binary sensor. | Return the name of the binary sensor. | def name(self) -> str:
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HassAqualinkBinarySensor.is_on | (self) | Return whether the binary sensor is on or not. | Return whether the binary sensor is on or not. | def is_on(self) -> bool:
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HassAqualinkBinarySensor.device_class | (self) | Return the class of the binary sensor. | Return the class of the binary sensor. | def device_class(self) -> str:
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test_cover | (hass, cover_data, sent_messages, cover_msg) | Test setting up config entry. | Test setting up config entry. | async def test_cover(hass, cover_data, sent_messages, cover_msg):
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test_barrier | (hass, cover_gdo_data, sent_messages, cover_gdo_msg) | Test setting up config entry. | Test setting up config entry. | async def test_barrier(hass, cover_gdo_data, sent_messages, cover_gdo_msg):
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_blink_startup_wrapper | (hass, entry) | Startup wrapper for blink. | Startup wrapper for blink. | def _blink_startup_wrapper(hass, entry):
"""Startup wrapper for blink."""
blink = Blink()
auth_data = deepcopy(dict(entry.data))
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_reauth_flow_wrapper | (hass, data) | Reauth flow wrapper. | Reauth flow wrapper. | def _reauth_flow_wrapper(hass, data):
"""Reauth flow wrapper."""
hass.add_job(
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DOMAIN, context={"source": "reauth"}, data=data
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persistent_notification.async_create(
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async_setup | (hass, config) | Set up a Blink component. | Set up a Blink component. | async def async_setup(hass, config):
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hass.data[DOMAIN] = {}
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async_migrate_entry | (hass, entry) | Handle migration of a previous version config entry. | Handle migration of a previous version config entry. | async def async_migrate_entry(hass, entry):
"""Handle migration of a previous version config entry."""
data = {**entry.data}
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async_setup_entry | (hass, entry) | Set up Blink via config entry. | Set up Blink via config entry. | async def async_setup_entry(hass, entry):
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async_unload_entry | (hass, entry) | Unload Blink entry. | Unload Blink entry. | async def async_unload_entry(hass, entry):
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unload_ok = all(
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async_handle_save_video_service | (hass, entry, call) | Handle save video service calls. | Handle save video service calls. | async def async_handle_save_video_service(hass, entry, call):
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camera_name = call.data[CONF_NAME]
video_path = call.data[CONF_FILENAME]
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async_register_built_in_panel | (
hass,
component_name,
sidebar_title=None,
sidebar_icon=None,
frontend_url_path=None,
config=None,
require_admin=False,
*,
update=False,
) | Register a built-in panel. | Register a built-in panel. | def async_register_built_in_panel(
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frontend_url_path=None,
config=None,
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async_remove_panel | (hass, frontend_url_path) | Remove a built-in panel. | Remove a built-in panel. | def async_remove_panel(hass, frontend_url_path):
"""Remove a built-in panel."""
panel = hass.data.get(DATA_PANELS, {}).pop(frontend_url_path, None)
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add_extra_js_url | (hass, url, es5=False) | Register extra js or module url to load. | Register extra js or module url to load. | def add_extra_js_url(hass, url, es5=False):
"""Register extra js or module url to load."""
key = DATA_EXTRA_JS_URL_ES5 if es5 else DATA_EXTRA_MODULE_URL
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... | [
214,
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] | [
220,
20
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add_manifest_json_key | (key, val) | Add a keyval to the manifest.json. | Add a keyval to the manifest.json. | def add_manifest_json_key(key, val):
"""Add a keyval to the manifest.json."""
MANIFEST_JSON[key] = val | [
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_frontend_root | (dev_repo_path) | Return root path to the frontend files. | Return root path to the frontend files. | def _frontend_root(dev_repo_path):
"""Return root path to the frontend files."""
if dev_repo_path is not None:
return pathlib.Path(dev_repo_path) / "hass_frontend"
# Keep import here so that we can import frontend without installing reqs
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async_setup | (hass, config) | Set up the serving of the frontend. | Set up the serving of the frontend. | async def async_setup(hass, config):
"""Set up the serving of the frontend."""
await async_setup_frontend_storage(hass)
hass.components.websocket_api.async_register_command(websocket_get_panels)
hass.components.websocket_api.async_register_command(websocket_get_themes)
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_async_setup_themes | (hass, themes) | Set up themes data and services. | Set up themes data and services. | async def _async_setup_themes(hass, themes):
"""Set up themes data and services."""
hass.data[DATA_THEMES] = themes or {}
store = hass.data[DATA_THEMES_STORE] = hass.helpers.storage.Store(
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websocket_get_panels | (hass, connection, msg) | Handle get panels command. | Handle get panels command. | def websocket_get_panels(hass, connection, msg):
"""Handle get panels command."""
user_is_admin = connection.user.is_admin
panels = {
panel_key: panel.to_response()
for panel_key, panel in connection.hass.data[DATA_PANELS].items()
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websocket_get_themes | (hass, connection, msg) | Handle get themes command. | Handle get themes command. | def websocket_get_themes(hass, connection, msg):
"""Handle get themes command."""
if hass.config.safe_mode:
connection.send_message(
websocket_api.result_message(
msg["id"],
{
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websocket_get_translations | (hass, connection, msg) | Handle get translations command. | Handle get translations command. | async def websocket_get_translations(hass, connection, msg):
"""Handle get translations command."""
resources = await async_get_translations(
hass,
msg["language"],
msg["category"],
msg.get("integration"),
msg.get("config_flow"),
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websocket_get_version | (hass, connection, msg) | Handle get version command. | Handle get version command. | async def websocket_get_version(hass, connection, msg):
"""Handle get version command."""
integration = await async_get_integration(hass, "frontend")
frontend = None
for req in integration.requirements:
if req.startswith("home-assistant-frontend=="):
frontend = req.split("==", 1)[1... | [
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Panel.__init__ | (
self,
component_name,
sidebar_title,
sidebar_icon,
frontend_url_path,
config,
require_admin,
) | Initialize a built-in panel. | Initialize a built-in panel. | def __init__(
self,
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sidebar_title,
sidebar_icon,
frontend_url_path,
config,
require_admin,
):
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self.component_name = component_name
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Panel.to_response | (self) | Panel as dictionary. | Panel as dictionary. | def to_response(self):
"""Panel as dictionary."""
return {
"component_name": self.component_name,
"icon": self.sidebar_icon,
"title": self.sidebar_title,
"config": self.config,
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IndexView.__init__ | (self, repo_path, hass) | Initialize the frontend view. | Initialize the frontend view. | def __init__(self, repo_path, hass):
"""Initialize the frontend view."""
super().__init__(name="frontend:index")
self.repo_path = repo_path
self.hass = hass
self._template_cache = None | [
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IndexView.canonical | (self) | Return resource's canonical path. | Return resource's canonical path. | def canonical(self) -> str:
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IndexView._route | (self) | Return the index route. | Return the index route. | def _route(self):
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IndexView.url_for | (self, **kwargs: str) | Construct url for resource with additional params. | Construct url for resource with additional params. | def url_for(self, **kwargs: str) -> URL:
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IndexView.resolve | (
self, request: web.Request
) | Resolve resource.
Return (UrlMappingMatchInfo, allowed_methods) pair.
| Resolve resource. | async def resolve(
self, request: web.Request
) -> Tuple[Optional[web_urldispatcher.UrlMappingMatchInfo], Set[str]]:
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Return (UrlMappingMatchInfo, allowed_methods) pair.
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IndexView.add_prefix | (self, prefix: str) | Add a prefix to processed URLs.
Required for subapplications support.
| Add a prefix to processed URLs. | def add_prefix(self, prefix: str) -> None:
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IndexView.get_info | (self) | Return a dict with additional info useful for introspection. | Return a dict with additional info useful for introspection. | def get_info(self):
"""Return a dict with additional info useful for introspection."""
return {"panels": list(self.hass.data[DATA_PANELS])} | [
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IndexView.freeze | (self) | Freeze the resource. | Freeze the resource. | def freeze(self) -> None:
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IndexView.raw_match | (self, path: str) | Perform a raw match against path. | Perform a raw match against path. | def raw_match(self, path: str) -> bool:
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IndexView.get_template | (self) | Get template. | Get template. | def get_template(self):
"""Get template."""
tpl = self._template_cache
if tpl is None:
with open(str(_frontend_root(self.repo_path) / "index.html")) as file:
tpl = jinja2.Template(file.read())
# Cache template if not running from repository
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IndexView.get | (self, request: web.Request) | Serve the index page for panel pages. | Serve the index page for panel pages. | async def get(self, request: web.Request) -> web.Response:
"""Serve the index page for panel pages."""
hass = request.app["hass"]
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IndexView.__len__ | (self) | Return length of resource. | Return length of resource. | def __len__(self) -> int:
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IndexView.__iter__ | (self) | Iterate over routes. | Iterate over routes. | def __iter__(self):
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ManifestJSONView.get | (self, request) | Return the manifest.json. | Return the manifest.json. | def get(self, request): # pylint: disable=no-self-use
"""Return the manifest.json."""
msg = json.dumps(MANIFEST_JSON, sort_keys=True)
return web.Response(text=msg, content_type="application/manifest+json") | [
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async_setup | (hass: HomeAssistant, config: dict) | Set up the JuiceNet component. | Set up the JuiceNet component. | async def async_setup(hass: HomeAssistant, config: dict):
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conf = config.get(DOMAIN)
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async_setup_entry | (hass: HomeAssistant, entry: ConfigEntry) | Set up JuiceNet from a config entry. | Set up JuiceNet from a config entry. | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):
"""Set up JuiceNet from a config entry."""
config = entry.data
session = async_get_clientsession(hass)
access_token = config[CONF_ACCESS_TOKEN]
api = Api(access_token, session)
juicenet = JuiceNetApi(api)
try:
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async_unload_entry | (hass: HomeAssistant, entry: ConfigEntry) | Unload a config entry. | Unload a config entry. | async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry):
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unload_ok = all(
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test_is_invalid_auth_code | () | Test for invalid auth. | Test for invalid auth. | async def test_is_invalid_auth_code():
"""Test for invalid auth."""
assert util.is_invalid_auth_code(HTTP_UNAUTHORIZED) is True
assert util.is_invalid_auth_code(HTTP_FORBIDDEN) is True
assert util.is_invalid_auth_code(HTTP_NOT_FOUND) is False | [
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test_percent_conv | () | Test percentage conversion. | Test percentage conversion. | async def test_percent_conv():
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assert util.percent_conv(0.12) == 12.0
assert util.percent_conv(0.123) == 12.3 | [
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async_reload | (hass: HomeAssistantType, integration_name: str) | Register notify services for an integration. | Register notify services for an integration. | async def async_reload(hass: HomeAssistantType, integration_name: str) -> None:
"""Register notify services for an integration."""
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tasks = [
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async_reset_platform | (hass: HomeAssistantType, integration_name: str) | Unregister notify services for an integration. | Unregister notify services for an integration. | async def async_reset_platform(hass: HomeAssistantType, integration_name: str) -> None:
"""Unregister notify services for an integration."""
if not _async_integration_has_notify_services(hass, integration_name):
return
tasks = [
notify_service.async_unregister_services()
for notify_... | [
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_async_integration_has_notify_services | (
hass: HomeAssistantType, integration_name: str
) | Determine if an integration has notify services registered. | Determine if an integration has notify services registered. | def _async_integration_has_notify_services(
hass: HomeAssistantType, integration_name: str
) -> bool:
"""Determine if an integration has notify services registered."""
if (
NOTIFY_SERVICES not in hass.data
or integration_name not in hass.data[NOTIFY_SERVICES]
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return False
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async_setup | (hass, config) | Set up the notify services. | Set up the notify services. | async def async_setup(hass, config):
"""Set up the notify services."""
hass.data.setdefault(NOTIFY_SERVICES, {})
async def persistent_notification(service: ServiceCall) -> None:
"""Send notification via the built-in persistsent_notify integration."""
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BaseNotificationService.send_message | (self, message, **kwargs) | Send a message.
kwargs can contain ATTR_TITLE to specify a title.
| Send a message. | def send_message(self, message, **kwargs):
"""Send a message.
kwargs can contain ATTR_TITLE to specify a title.
"""
raise NotImplementedError() | [
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BaseNotificationService.async_send_message | (self, message: Any, **kwargs: Any) | Send a message.
kwargs can contain ATTR_TITLE to specify a title.
| Send a message. | async def async_send_message(self, message: Any, **kwargs: Any) -> None:
"""Send a message.
kwargs can contain ATTR_TITLE to specify a title.
"""
await self.hass.async_add_executor_job(partial(self.send_message, message, **kwargs)) | [
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BaseNotificationService._async_notify_message_service | (self, service: ServiceCall) | Handle sending notification message service calls. | Handle sending notification message service calls. | async def _async_notify_message_service(self, service: ServiceCall) -> None:
"""Handle sending notification message service calls."""
kwargs = {}
message = service.data[ATTR_MESSAGE]
title = service.data.get(ATTR_TITLE)
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title.hass = self.hass
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BaseNotificationService.async_setup | (
self,
hass: HomeAssistantType,
service_name: str,
target_service_name_prefix: str,
) | Store the data for the notify service. | Store the data for the notify service. | async def async_setup(
self,
hass: HomeAssistantType,
service_name: str,
target_service_name_prefix: str,
) -> None:
"""Store the data for the notify service."""
# pylint: disable=attribute-defined-outside-init
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BaseNotificationService.async_register_services | (self) | Create or update the notify services. | Create or update the notify services. | async def async_register_services(self) -> None:
"""Create or update the notify services."""
assert self.hass
if hasattr(self, "targets"):
stale_targets = set(self._registered_targets)
# pylint: disable=no-member
for name, target in self.targets.items(): # ... | [
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BaseNotificationService.async_unregister_services | (self) | Unregister the notify services. | Unregister the notify services. | async def async_unregister_services(self) -> None:
"""Unregister the notify services."""
assert self.hass
if self._registered_targets:
remove_targets = set(self._registered_targets)
for remove_target_name in remove_targets:
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NetAdaptPruner.validate_config | (self, model, config_list) |
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
|
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
| def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
"""
if self._base_algo == 'level':
schema = CompressorSchema([{
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134,
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NetAdaptPruner._update_config_list | (self, config_list, op_name, sparsity) |
update sparsity of op_name in config_list
|
update sparsity of op_name in config_list
| def _update_config_list(self, config_list, op_name, sparsity):
'''
update sparsity of op_name in config_list
'''
config_list_updated = copy.deepcopy(config_list)
for idx, item in enumerate(config_list):
if op_name in item['op_names']:
config_list_upda... | [
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NetAdaptPruner._get_op_num_weights_remained | (self, op_name, module) |
Get the number of weights remained after channel pruning with current sparsity
Returns
-------
int
remained number of weights of the op
|
Get the number of weights remained after channel pruning with current sparsity | def _get_op_num_weights_remained(self, op_name, module):
'''
Get the number of weights remained after channel pruning with current sparsity
Returns
-------
int
remained number of weights of the op
'''
# if op is wrapped by the pruner
for wrap... | [
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NetAdaptPruner._calc_num_related_weights | (self, op_name) |
Calculate total number weights of the op and the next op, applicable only for models without dependencies among ops
Parameters
----------
op_name : str
Returns
-------
int
total number of all the realted (current and the next) op weights
|
Calculate total number weights of the op and the next op, applicable only for models without dependencies among ops | def _calc_num_related_weights(self, op_name):
'''
Calculate total number weights of the op and the next op, applicable only for models without dependencies among ops
Parameters
----------
op_name : str
Returns
-------
int
total number of all ... | [
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NetAdaptPruner.compress | (self) |
Compress the model.
Returns
-------
torch.nn.Module
model with specified modules compressed.
|
Compress the model. | def compress(self):
"""
Compress the model.
Returns
-------
torch.nn.Module
model with specified modules compressed.
"""
_logger.info('Starting NetAdapt Compression...')
pruning_iteration = 0
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