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test_config_platform_valid
(isfile_patch, loop)
Test a valid platform setup.
Test a valid platform setup.
def test_config_platform_valid(isfile_patch, loop): """Test a valid platform setup.""" files = {YAML_CONFIG_FILE: BASE_CONFIG + "light:\n platform: demo"} with patch_yaml_files(files): res = check_config.check(get_test_config_dir()) assert res["components"].keys() == {"homeassistant", "light"} assert res["components"]["light"] == [{"platform": "demo"}] assert res["except"] == {} assert res["secret_cache"] == {} assert res["secrets"] == {} assert len(res["yaml_files"]) == 1
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[ 49, 0 ]
[ 59, 42 ]
python
en
['en', 'da', 'en']
True
test_component_platform_not_found
(isfile_patch, loop)
Test errors if component or platform not found.
Test errors if component or platform not found.
def test_component_platform_not_found(isfile_patch, loop): """Test errors if component or platform not found.""" # Make sure they don't exist files = {YAML_CONFIG_FILE: BASE_CONFIG + "beer:"} with patch_yaml_files(files): res = check_config.check(get_test_config_dir()) assert res["components"].keys() == {"homeassistant"} assert res["except"] == { check_config.ERROR_STR: [ "Component error: beer - Integration 'beer' not found." ] } assert res["secret_cache"] == {} assert res["secrets"] == {} assert len(res["yaml_files"]) == 1 files = {YAML_CONFIG_FILE: BASE_CONFIG + "light:\n platform: beer"} with patch_yaml_files(files): res = check_config.check(get_test_config_dir()) assert res["components"].keys() == {"homeassistant", "light"} assert res["components"]["light"] == [] assert res["except"] == { check_config.ERROR_STR: [ "Platform error light.beer - Integration 'beer' not found." ] } assert res["secret_cache"] == {} assert res["secrets"] == {} assert len(res["yaml_files"]) == 1
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[ 63, 0 ]
[ 91, 42 ]
python
en
['en', 'en', 'en']
True
test_secrets
(isfile_patch, loop)
Test secrets config checking method.
Test secrets config checking method.
def test_secrets(isfile_patch, loop): """Test secrets config checking method.""" secrets_path = get_test_config_dir("secrets.yaml") files = { get_test_config_dir(YAML_CONFIG_FILE): BASE_CONFIG + ("http:\n cors_allowed_origins: !secret http_pw"), secrets_path: ("logger: debug\nhttp_pw: http://google.com"), } with patch_yaml_files(files): res = check_config.check(get_test_config_dir(), True) assert res["except"] == {} assert res["components"].keys() == {"homeassistant", "http"} assert res["components"]["http"] == { "cors_allowed_origins": ["http://google.com"], "ip_ban_enabled": True, "login_attempts_threshold": -1, "server_port": 8123, "ssl_profile": "modern", } assert res["secret_cache"] == {secrets_path: {"http_pw": "http://google.com"}} assert res["secrets"] == {"http_pw": "http://google.com"} assert normalize_yaml_files(res) == [ ".../configuration.yaml", ".../secrets.yaml", ]
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[ 95, 0 ]
[ 123, 9 ]
python
en
['en', 'da', 'en']
True
test_package_invalid
(isfile_patch, loop)
Test an invalid package.
Test an invalid package.
def test_package_invalid(isfile_patch, loop): """Test an invalid package.""" files = { YAML_CONFIG_FILE: BASE_CONFIG + (" packages:\n p1:\n" ' group: ["a"]') } with patch_yaml_files(files): res = check_config.check(get_test_config_dir()) assert res["except"].keys() == {"homeassistant.packages.p1.group"} assert res["except"]["homeassistant.packages.p1.group"][1] == {"group": ["a"]} assert len(res["except"]) == 1 assert res["components"].keys() == {"homeassistant"} assert len(res["components"]) == 1 assert res["secret_cache"] == {} assert res["secrets"] == {} assert len(res["yaml_files"]) == 1
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[ 127, 0 ]
[ 142, 42 ]
python
en
['en', 'en', 'en']
True
test_bootstrap_error
(loop)
Test a valid platform setup.
Test a valid platform setup.
def test_bootstrap_error(loop): """Test a valid platform setup.""" files = {YAML_CONFIG_FILE: BASE_CONFIG + "automation: !include no.yaml"} with patch_yaml_files(files): res = check_config.check(get_test_config_dir(YAML_CONFIG_FILE)) err = res["except"].pop(check_config.ERROR_STR) assert len(err) == 1 assert res["except"] == {} assert res["components"] == {} # No components, load failed assert res["secret_cache"] == {} assert res["secrets"] == {} assert res["yaml_files"] == {}
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[ 145, 0 ]
[ 156, 38 ]
python
en
['en', 'da', 'en']
True
launch_dashboard
(source_path: str, scenario: GlobalScenarios, epoch_num: int, prefix: str)
Launch streamlit dashboard. Args: source_path (str): The root path of the dumped snapshots data for the corresponding experiment. scenario (GlobalScenarios): Name of current scenario. epoch_num (int): Number of epochs. prefix (str): Prefix of data folders.
Launch streamlit dashboard.
def launch_dashboard(source_path: str, scenario: GlobalScenarios, epoch_num: int, prefix: str): """Launch streamlit dashboard. Args: source_path (str): The root path of the dumped snapshots data for the corresponding experiment. scenario (GlobalScenarios): Name of current scenario. epoch_num (int): Number of epochs. prefix (str): Prefix of data folders. """ vis_path = os.path.expanduser("~/.maro/vis/templates/visualization.py") os.system( f"streamlit run {vis_path} " f"-- --source_path {source_path} --scenario {scenario.value} --epoch_num {epoch_num} --prefix {prefix}" )
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[ 8, 0 ]
[ 21, 5 ]
python
de
['de', 'ha', 'en']
False
test_discovery
(hass: HomeAssistant)
Test setting up discovery.
Test setting up discovery.
async def test_discovery(hass: HomeAssistant): """Test setting up discovery.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == "form" assert result["step_id"] == "user" assert not result["errors"] with _patch_discovery(f"{MODULE_CONFIG_FLOW}.yeelight"): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == "form" assert result2["step_id"] == "pick_device" assert not result2["errors"] with patch(f"{MODULE}.async_setup", return_value=True) as mock_setup, patch( f"{MODULE}.async_setup_entry", return_value=True, ) as mock_setup_entry: result3 = await hass.config_entries.flow.async_configure( result["flow_id"], {CONF_DEVICE: ID} ) assert result3["type"] == "create_entry" assert result3["title"] == UNIQUE_NAME assert result3["data"] == {CONF_ID: ID} await hass.async_block_till_done() mock_setup.assert_called_once() mock_setup_entry.assert_called_once() # ignore configured devices result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == "form" assert result["step_id"] == "user" assert not result["errors"] with _patch_discovery(f"{MODULE_CONFIG_FLOW}.yeelight"): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == "abort" assert result2["reason"] == "no_devices_found"
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[ 44, 0 ]
[ 90, 50 ]
python
en
['en', 'en', 'en']
True
test_discovery_no_device
(hass: HomeAssistant)
Test discovery without device.
Test discovery without device.
async def test_discovery_no_device(hass: HomeAssistant): """Test discovery without device.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) with _patch_discovery(f"{MODULE_CONFIG_FLOW}.yeelight", no_device=True): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == "abort" assert result2["reason"] == "no_devices_found"
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[ 93, 0 ]
[ 106, 50 ]
python
en
['en', 'en', 'en']
True
test_import
(hass: HomeAssistant)
Test import from yaml.
Test import from yaml.
async def test_import(hass: HomeAssistant): """Test import from yaml.""" config = { CONF_NAME: DEFAULT_NAME, CONF_HOST: IP_ADDRESS, CONF_TRANSITION: DEFAULT_TRANSITION, CONF_MODE_MUSIC: DEFAULT_MODE_MUSIC, CONF_SAVE_ON_CHANGE: DEFAULT_SAVE_ON_CHANGE, CONF_NIGHTLIGHT_SWITCH_TYPE: NIGHTLIGHT_SWITCH_TYPE_LIGHT, } # Cannot connect mocked_bulb = _mocked_bulb(cannot_connect=True) with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=config ) type(mocked_bulb).get_capabilities.assert_called_once() type(mocked_bulb).get_properties.assert_called_once() assert result["type"] == "abort" assert result["reason"] == "cannot_connect" # Success mocked_bulb = _mocked_bulb() with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb), patch( f"{MODULE}.async_setup", return_value=True ) as mock_setup, patch( f"{MODULE}.async_setup_entry", return_value=True, ) as mock_setup_entry: result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=config ) type(mocked_bulb).get_capabilities.assert_called_once() assert result["type"] == "create_entry" assert result["title"] == DEFAULT_NAME assert result["data"] == { CONF_NAME: DEFAULT_NAME, CONF_HOST: IP_ADDRESS, CONF_TRANSITION: DEFAULT_TRANSITION, CONF_MODE_MUSIC: DEFAULT_MODE_MUSIC, CONF_SAVE_ON_CHANGE: DEFAULT_SAVE_ON_CHANGE, CONF_NIGHTLIGHT_SWITCH: True, } await hass.async_block_till_done() mock_setup.assert_called_once() mock_setup_entry.assert_called_once() # Duplicate mocked_bulb = _mocked_bulb() with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=config ) assert result["type"] == "abort" assert result["reason"] == "already_configured"
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[ 109, 0 ]
[ 164, 51 ]
python
en
['en', 'en', 'en']
True
test_manual
(hass: HomeAssistant)
Test manually setup.
Test manually setup.
async def test_manual(hass: HomeAssistant): """Test manually setup.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == "form" assert result["step_id"] == "user" assert not result["errors"] # Cannot connect (timeout) mocked_bulb = _mocked_bulb(cannot_connect=True) with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {CONF_HOST: IP_ADDRESS} ) assert result2["type"] == "form" assert result2["step_id"] == "user" assert result2["errors"] == {"base": "cannot_connect"} # Cannot connect (error) type(mocked_bulb).get_capabilities = MagicMock(side_effect=OSError) with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb): result3 = await hass.config_entries.flow.async_configure( result["flow_id"], {CONF_HOST: IP_ADDRESS} ) assert result3["errors"] == {"base": "cannot_connect"} # Success mocked_bulb = _mocked_bulb() with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb), patch( f"{MODULE}.async_setup", return_value=True ), patch( f"{MODULE}.async_setup_entry", return_value=True, ): result4 = await hass.config_entries.flow.async_configure( result["flow_id"], {CONF_HOST: IP_ADDRESS} ) await hass.async_block_till_done() assert result4["type"] == "create_entry" assert result4["title"] == IP_ADDRESS assert result4["data"] == {CONF_HOST: IP_ADDRESS} # Duplicate result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) mocked_bulb = _mocked_bulb() with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {CONF_HOST: IP_ADDRESS} ) assert result2["type"] == "abort" assert result2["reason"] == "already_configured"
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[ 167, 0 ]
[ 220, 52 ]
python
en
['en', 'zu', 'en']
True
test_options
(hass: HomeAssistant)
Test options flow.
Test options flow.
async def test_options(hass: HomeAssistant): """Test options flow.""" config_entry = MockConfigEntry( domain=DOMAIN, data={CONF_HOST: IP_ADDRESS, CONF_NAME: NAME} ) config_entry.add_to_hass(hass) mocked_bulb = _mocked_bulb() with patch(f"{MODULE}.Bulb", return_value=mocked_bulb): assert await hass.config_entries.async_setup(config_entry.entry_id) await hass.async_block_till_done() config = { CONF_NAME: NAME, CONF_MODEL: "", CONF_TRANSITION: DEFAULT_TRANSITION, CONF_MODE_MUSIC: DEFAULT_MODE_MUSIC, CONF_SAVE_ON_CHANGE: DEFAULT_SAVE_ON_CHANGE, CONF_NIGHTLIGHT_SWITCH: DEFAULT_NIGHTLIGHT_SWITCH, } assert config_entry.options == config assert hass.states.get(f"light.{NAME}_nightlight") is None result = await hass.config_entries.options.async_init(config_entry.entry_id) assert result["type"] == "form" assert result["step_id"] == "init" config[CONF_NIGHTLIGHT_SWITCH] = True user_input = {**config} user_input.pop(CONF_NAME) with patch(f"{MODULE}.Bulb", return_value=mocked_bulb): result2 = await hass.config_entries.options.async_configure( result["flow_id"], user_input ) await hass.async_block_till_done() assert result2["type"] == "create_entry" assert result2["data"] == config assert result2["data"] == config_entry.options assert hass.states.get(f"light.{NAME}_nightlight") is not None
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[ 223, 0 ]
[ 261, 66 ]
python
en
['en', 'fr', 'en']
True
test_manual_no_capabilities
(hass: HomeAssistant)
Test manually setup without successful get_capabilities.
Test manually setup without successful get_capabilities.
async def test_manual_no_capabilities(hass: HomeAssistant): """Test manually setup without successful get_capabilities.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == "form" assert result["step_id"] == "user" assert not result["errors"] mocked_bulb = _mocked_bulb() type(mocked_bulb).get_capabilities = MagicMock(return_value=None) with patch(f"{MODULE_CONFIG_FLOW}.yeelight.Bulb", return_value=mocked_bulb), patch( f"{MODULE}.async_setup", return_value=True ), patch( f"{MODULE}.async_setup_entry", return_value=True, ): result = await hass.config_entries.flow.async_configure( result["flow_id"], {CONF_HOST: IP_ADDRESS} ) type(mocked_bulb).get_capabilities.assert_called_once() type(mocked_bulb).get_properties.assert_called_once() assert result["type"] == "create_entry" assert result["data"] == {CONF_HOST: IP_ADDRESS}
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[ 264, 0 ]
[ 287, 52 ]
python
en
['en', 'en', 'en']
True
test_sensor
(hass, config_entry, aioclient_mock)
Test that the sensor is setup.
Test that the sensor is setup.
async def test_sensor(hass, config_entry, aioclient_mock): """Test that the sensor is setup.""" await setup_integration(hass, config_entry, aioclient_mock) await refresh_data(hass, config_entry, aioclient_mock) meter = hass.states.get(TEST_ENTITY_ID) assert meter assert meter.state == "9751.212"
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[ 18, 0 ]
[ 25, 36 ]
python
en
['en', 'bg', 'en']
True
test_name
(hass, config_entry, aioclient_mock)
Test sensor name property.
Test sensor name property.
async def test_name(hass, config_entry, aioclient_mock): """Test sensor name property.""" await setup_integration(hass, config_entry, aioclient_mock) await refresh_data(hass, config_entry, aioclient_mock) meter = hass.states.get(TEST_ENTITY_ID) assert meter.name == f"{ELECTRIC_METER} 123456789"
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[ 28, 0 ]
[ 34, 54 ]
python
en
['en', 'da', 'en']
True
test_attributes
(hass, config_entry, aioclient_mock)
Test meter attributes.
Test meter attributes.
async def test_attributes(hass, config_entry, aioclient_mock): """Test meter attributes.""" await setup_integration(hass, config_entry, aioclient_mock) await refresh_data(hass, config_entry, aioclient_mock) meter = hass.states.get(TEST_ENTITY_ID) assert meter.attributes[METER_NUMBER] == "123456789" assert meter.attributes[ESIID] == "12345678901234567" assert meter.attributes[CONF_ADDRESS] == "123 MAIN ST"
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[ 37, 0 ]
[ 45, 58 ]
python
en
['sv', 'jv', 'en']
False
test_generic_entity_update_service
(hass, config_entry, aioclient_mock)
Test generic update entity service homeasasistant/update_entity.
Test generic update entity service homeasasistant/update_entity.
async def test_generic_entity_update_service(hass, config_entry, aioclient_mock): """Test generic update entity service homeasasistant/update_entity.""" await setup_integration(hass, config_entry, aioclient_mock) await async_setup_component(hass, HA_DOMAIN, {}) with patch("smart_meter_texas.Meter.read_meter") as updater: await hass.services.async_call( HA_DOMAIN, SERVICE_UPDATE_ENTITY, {ATTR_ENTITY_ID: TEST_ENTITY_ID}, blocking=True, ) await hass.async_block_till_done() updater.assert_called_once()
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[ 48, 0 ]
[ 60, 36 ]
python
en
['en', 'en', 'en']
True
TariffSelectorConfigFlow.async_step_user
(self, user_input=None)
Handle the initial step.
Handle the initial step.
async def async_step_user(self, user_input=None): """Handle the initial step.""" if user_input is not None: await self.async_set_unique_id(user_input[ATTR_TARIFF]) self._abort_if_unique_id_configured() return self.async_create_entry(title=user_input[CONF_NAME], data=user_input) return self.async_show_form(step_id="user", data_schema=UI_CONFIG_SCHEMA)
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[ 15, 4 ]
[ 22, 81 ]
python
en
['en', 'en', 'en']
True
TariffSelectorConfigFlow.async_step_import
(self, import_info)
Handle import from config file.
Handle import from config file.
async def async_step_import(self, import_info): """Handle import from config file.""" return await self.async_step_user(import_info)
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[ 24, 4 ]
[ 26, 54 ]
python
en
['en', 'en', 'en']
True
setup
(hass, config)
Set up the Route53 component.
Set up the Route53 component.
def setup(hass, config): """Set up the Route53 component.""" domain = config[DOMAIN][CONF_DOMAIN] records = config[DOMAIN][CONF_RECORDS] zone = config[DOMAIN][CONF_ZONE] aws_access_key_id = config[DOMAIN][CONF_ACCESS_KEY_ID] aws_secret_access_key = config[DOMAIN][CONF_SECRET_ACCESS_KEY] ttl = config[DOMAIN][CONF_TTL] def update_records_interval(now): """Set up recurring update.""" _update_route53( aws_access_key_id, aws_secret_access_key, zone, domain, records, ttl ) def update_records_service(now): """Set up service for manual trigger.""" _update_route53( aws_access_key_id, aws_secret_access_key, zone, domain, records, ttl ) track_time_interval(hass, update_records_interval, INTERVAL) hass.services.register(DOMAIN, "update_records", update_records_service) return True
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[ 41, 0 ]
[ 65, 15 ]
python
en
['en', 'en', 'en']
True
async_setup_platform
(hass, config, async_add_entities, discovery_info=None)
Set up the Microsoft Face identify platform.
Set up the Microsoft Face identify platform.
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None): """Set up the Microsoft Face identify platform.""" api = hass.data[DATA_MICROSOFT_FACE] face_group = config[CONF_GROUP] confidence = config[CONF_CONFIDENCE] entities = [] for camera in config[CONF_SOURCE]: entities.append( MicrosoftFaceIdentifyEntity( camera[CONF_ENTITY_ID], api, face_group, confidence, camera.get(CONF_NAME), ) ) async_add_entities(entities)
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[ 27, 0 ]
[ 45, 32 ]
python
en
['en', 'cs', 'en']
True
MicrosoftFaceIdentifyEntity.__init__
(self, camera_entity, api, face_group, confidence, name=None)
Initialize the Microsoft Face API.
Initialize the Microsoft Face API.
def __init__(self, camera_entity, api, face_group, confidence, name=None): """Initialize the Microsoft Face API.""" super().__init__() self._api = api self._camera = camera_entity self._confidence = confidence self._face_group = face_group if name: self._name = name else: self._name = f"MicrosoftFace {split_entity_id(camera_entity)[1]}"
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[ 51, 4 ]
[ 63, 77 ]
python
en
['en', 'sm', 'en']
True
MicrosoftFaceIdentifyEntity.confidence
(self)
Return minimum confidence for send events.
Return minimum confidence for send events.
def confidence(self): """Return minimum confidence for send events.""" return self._confidence
[ "def", "confidence", "(", "self", ")", ":", "return", "self", ".", "_confidence" ]
[ 66, 4 ]
[ 68, 31 ]
python
en
['en', 'la', 'en']
True
MicrosoftFaceIdentifyEntity.camera_entity
(self)
Return camera entity id from process pictures.
Return camera entity id from process pictures.
def camera_entity(self): """Return camera entity id from process pictures.""" return self._camera
[ "def", "camera_entity", "(", "self", ")", ":", "return", "self", ".", "_camera" ]
[ 71, 4 ]
[ 73, 27 ]
python
en
['en', 'en', 'en']
True
MicrosoftFaceIdentifyEntity.name
(self)
Return the name of the entity.
Return the name of the entity.
def name(self): """Return the name of the entity.""" return self._name
[ "def", "name", "(", "self", ")", ":", "return", "self", ".", "_name" ]
[ 76, 4 ]
[ 78, 25 ]
python
en
['en', 'en', 'en']
True
MicrosoftFaceIdentifyEntity.async_process_image
(self, image)
Process image. This method is a coroutine.
Process image.
async def async_process_image(self, image): """Process image. This method is a coroutine. """ detect = [] try: face_data = await self._api.call_api("post", "detect", image, binary=True) if face_data: face_ids = [data["faceId"] for data in face_data] detect = await self._api.call_api( "post", "identify", {"faceIds": face_ids, "personGroupId": self._face_group}, ) except HomeAssistantError as err: _LOGGER.error("Can't process image on Microsoft face: %s", err) return # Parse data known_faces = [] total = 0 for face in detect: total += 1 if not face["candidates"]: continue data = face["candidates"][0] name = "" for s_name, s_id in self._api.store[self._face_group].items(): if data["personId"] == s_id: name = s_name break known_faces.append( {ATTR_NAME: name, ATTR_CONFIDENCE: data["confidence"] * 100} ) self.async_process_faces(known_faces, total)
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[ 80, 4 ]
[ 120, 52 ]
python
en
['en', 'ny', 'en']
False
NERTransformer.training_step
(self, batch, batch_num)
Compute loss and log.
Compute loss and log.
def training_step(self, batch, batch_num): "Compute loss and log." inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if self.config.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if self.config.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use token_type_ids outputs = self(**inputs) loss = outputs[0] # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]} return {"loss": loss}
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[ 46, 4 ]
[ 57, 29 ]
python
en
['en', 'ceb', 'en']
True
NERTransformer.prepare_data
(self)
Called to initialize data. Use the call to construct features
Called to initialize data. Use the call to construct features
def prepare_data(self): "Called to initialize data. Use the call to construct features" args = self.hparams for mode in ["train", "dev", "test"]: cached_features_file = self._feature_file(mode) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) examples = self.token_classification_task.read_examples_from_file(args.data_dir, mode) features = self.token_classification_task.convert_examples_to_features( examples, self.labels, args.max_seq_length, self.tokenizer, cls_token_at_end=bool(self.config.model_type in ["xlnet"]), cls_token=self.tokenizer.cls_token, cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0, sep_token=self.tokenizer.sep_token, sep_token_extra=False, pad_on_left=bool(self.config.model_type in ["xlnet"]), pad_token=self.tokenizer.pad_token_id, pad_token_segment_id=self.tokenizer.pad_token_type_id, pad_token_label_id=self.pad_token_label_id, ) logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file)
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[ 59, 4 ]
[ 86, 58 ]
python
en
['en', 'en', 'en']
True
NERTransformer.get_dataloader
(self, mode: int, batch_size: int, shuffle: bool = False)
Load datasets. Called after prepare data.
Load datasets. Called after prepare data.
def get_dataloader(self, mode: int, batch_size: int, shuffle: bool = False) -> DataLoader: "Load datasets. Called after prepare data." cached_features_file = self._feature_file(mode) logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) if features[0].token_type_ids is not None: all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) else: all_token_type_ids = torch.tensor([0 for f in features], dtype=torch.long) # HACK(we will not use this anymore soon) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) return DataLoader( TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_label_ids), batch_size=batch_size )
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[ 88, 4 ]
[ 103, 9 ]
python
en
['en', 'en', 'en']
True
NERTransformer._eval_end
(self, outputs)
Evaluation called for both Val and Test
Evaluation called for both Val and Test
def _eval_end(self, outputs): "Evaluation called for both Val and Test" val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean() preds = np.concatenate([x["pred"] for x in outputs], axis=0) preds = np.argmax(preds, axis=2) out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0) label_map = {i: label for i, label in enumerate(self.labels)} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] for i in range(out_label_ids.shape[0]): for j in range(out_label_ids.shape[1]): if out_label_ids[i, j] != self.pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) results = { "val_loss": val_loss_mean, "accuracy_score": accuracy_score(out_label_list, preds_list), "precision": precision_score(out_label_list, preds_list), "recall": recall_score(out_label_list, preds_list), "f1": f1_score(out_label_list, preds_list), } ret = {k: v for k, v in results.items()} ret["log"] = results return ret, preds_list, out_label_list
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[ 118, 4 ]
[ 145, 46 ]
python
en
['en', 'en', 'en']
True
nasbench_format_to_architecture_repr
(adjacency_matrix, labeling)
Computes a graph-invariance MD5 hash of the matrix and label pair. Imported from NAS-Bench-101 repo. Parameters ---------- adjacency_matrix : np.ndarray A 2D array of shape NxN, where N is the number of vertices. ``matrix[u][v]`` is 1 if there is a direct edge from `u` to `v`, otherwise it will be 0. labeling : list of str A list of str that starts with input and ends with output. The intermediate nodes are chosen from candidate operators. Returns ------- tuple and int and dict Converted number of vertices and architecture.
Computes a graph-invariance MD5 hash of the matrix and label pair. Imported from NAS-Bench-101 repo.
def nasbench_format_to_architecture_repr(adjacency_matrix, labeling): """ Computes a graph-invariance MD5 hash of the matrix and label pair. Imported from NAS-Bench-101 repo. Parameters ---------- adjacency_matrix : np.ndarray A 2D array of shape NxN, where N is the number of vertices. ``matrix[u][v]`` is 1 if there is a direct edge from `u` to `v`, otherwise it will be 0. labeling : list of str A list of str that starts with input and ends with output. The intermediate nodes are chosen from candidate operators. Returns ------- tuple and int and dict Converted number of vertices and architecture. """ num_vertices = adjacency_matrix.shape[0] assert len(labeling) == num_vertices architecture = {} for i in range(1, num_vertices - 1): architecture['op{}'.format(i)] = labeling[i] assert labeling[i] not in [INPUT, OUTPUT] for i in range(1, num_vertices): architecture['input{}'.format(i)] = [k for k in range(i) if adjacency_matrix[k, i]] return num_vertices, architecture
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[ 19, 0 ]
[ 47, 37 ]
python
en
['en', 'error', 'th']
False
infer_num_vertices
(architecture)
Infer number of vertices from an architecture dict. Parameters ---------- architecture : dict Architecture in NNI format. Returns ------- int Number of vertices.
Infer number of vertices from an architecture dict.
def infer_num_vertices(architecture): """ Infer number of vertices from an architecture dict. Parameters ---------- architecture : dict Architecture in NNI format. Returns ------- int Number of vertices. """ op_keys = set([k for k in architecture.keys() if k.startswith('op')]) intermediate_vertices = len(op_keys) assert op_keys == {'op{}'.format(i) for i in range(1, intermediate_vertices + 1)} return intermediate_vertices + 2
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[ 50, 0 ]
[ 67, 36 ]
python
en
['en', 'error', 'th']
False
hash_module
(architecture, vertices)
Computes a graph-invariance MD5 hash of the matrix and label pair. This snippet is modified from code in NAS-Bench-101 repo. Parameters ---------- matrix : np.ndarray Square upper-triangular adjacency matrix. labeling : list of int Labels of length equal to both dimensions of matrix. Returns ------- str MD5 hash of the matrix and labeling.
Computes a graph-invariance MD5 hash of the matrix and label pair. This snippet is modified from code in NAS-Bench-101 repo.
def hash_module(architecture, vertices): """ Computes a graph-invariance MD5 hash of the matrix and label pair. This snippet is modified from code in NAS-Bench-101 repo. Parameters ---------- matrix : np.ndarray Square upper-triangular adjacency matrix. labeling : list of int Labels of length equal to both dimensions of matrix. Returns ------- str MD5 hash of the matrix and labeling. """ labeling = _labeling_from_architecture(architecture, vertices) labeling = [LABEL2ID[t] for t in labeling] matrix = _adjancency_matrix_from_architecture(architecture, vertices) in_edges = np.sum(matrix, axis=0).tolist() out_edges = np.sum(matrix, axis=1).tolist() assert len(in_edges) == len(out_edges) == len(labeling) hashes = list(zip(out_edges, in_edges, labeling)) hashes = [hashlib.md5(str(h).encode('utf-8')).hexdigest() for h in hashes] # Computing this up to the diameter is probably sufficient but since the # operation is fast, it is okay to repeat more times. for _ in range(vertices): new_hashes = [] for v in range(vertices): in_neighbors = [hashes[w] for w in range(vertices) if matrix[w, v]] out_neighbors = [hashes[w] for w in range(vertices) if matrix[v, w]] new_hashes.append(hashlib.md5( (''.join(sorted(in_neighbors)) + '|' + ''.join(sorted(out_neighbors)) + '|' + hashes[v]).encode('utf-8')).hexdigest()) hashes = new_hashes fingerprint = hashlib.md5(str(sorted(hashes)).encode('utf-8')).hexdigest() return fingerprint
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[ 70, 0 ]
[ 110, 22 ]
python
en
['en', 'error', 'th']
False
ActiveConnection.__init__
(self, logger, hass, send_message, user, refresh_token)
Initialize an active connection.
Initialize an active connection.
def __init__(self, logger, hass, send_message, user, refresh_token): """Initialize an active connection.""" self.logger = logger self.hass = hass self.send_message = send_message self.user = user if refresh_token: self.refresh_token_id = refresh_token.id else: self.refresh_token_id = None self.subscriptions: Dict[Hashable, Callable[[], Any]] = {} self.last_id = 0
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[ 17, 4 ]
[ 29, 24 ]
python
en
['en', 'en', 'en']
True
ActiveConnection.context
(self, msg)
Return a context.
Return a context.
def context(self, msg): """Return a context.""" user = self.user if user is None: return Context() return Context(user_id=user.id)
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[ 31, 4 ]
[ 36, 39 ]
python
ca
['es', 'ca', 'en']
False
ActiveConnection.send_result
(self, msg_id: int, result: Optional[Any] = None)
Send a result message.
Send a result message.
def send_result(self, msg_id: int, result: Optional[Any] = None) -> None: """Send a result message.""" self.send_message(messages.result_message(msg_id, result))
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[ 39, 4 ]
[ 41, 66 ]
python
en
['en', 'de', 'en']
True
ActiveConnection.send_big_result
(self, msg_id, result)
Send a result message that would be expensive to JSON serialize.
Send a result message that would be expensive to JSON serialize.
async def send_big_result(self, msg_id, result): """Send a result message that would be expensive to JSON serialize.""" content = await self.hass.async_add_executor_job( const.JSON_DUMP, messages.result_message(msg_id, result) ) self.send_message(content)
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[ 43, 4 ]
[ 48, 34 ]
python
en
['en', 'en', 'en']
True
ActiveConnection.send_error
(self, msg_id: int, code: str, message: str)
Send a error message.
Send a error message.
def send_error(self, msg_id: int, code: str, message: str) -> None: """Send a error message.""" self.send_message(messages.error_message(msg_id, code, message))
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[ 51, 4 ]
[ 53, 72 ]
python
en
['ro', 'lb', 'en']
False
ActiveConnection.async_handle
(self, msg)
Handle a single incoming message.
Handle a single incoming message.
def async_handle(self, msg): """Handle a single incoming message.""" handlers = self.hass.data[const.DOMAIN] try: msg = messages.MINIMAL_MESSAGE_SCHEMA(msg) cur_id = msg["id"] except vol.Invalid: self.logger.error("Received invalid command", msg) self.send_message( messages.error_message( msg.get("id"), const.ERR_INVALID_FORMAT, "Message incorrectly formatted.", ) ) return if cur_id <= self.last_id: self.send_message( messages.error_message( cur_id, const.ERR_ID_REUSE, "Identifier values have to increase." ) ) return if msg["type"] not in handlers: self.logger.error("Received invalid command: {}".format(msg["type"])) self.send_message( messages.error_message( cur_id, const.ERR_UNKNOWN_COMMAND, "Unknown command." ) ) return handler, schema = handlers[msg["type"]] try: handler(self.hass, self, schema(msg)) except Exception as err: # pylint: disable=broad-except self.async_handle_exception(msg, err) self.last_id = cur_id
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[ 56, 4 ]
[ 98, 29 ]
python
en
['en', 'en', 'en']
True
ActiveConnection.async_close
(self)
Close down connection.
Close down connection.
def async_close(self): """Close down connection.""" for unsub in self.subscriptions.values(): unsub()
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[ 101, 4 ]
[ 104, 19 ]
python
en
['en', 'en', 'en']
True
ActiveConnection.async_handle_exception
(self, msg, err)
Handle an exception while processing a handler.
Handle an exception while processing a handler.
def async_handle_exception(self, msg, err): """Handle an exception while processing a handler.""" log_handler = self.logger.error if isinstance(err, Unauthorized): code = const.ERR_UNAUTHORIZED err_message = "Unauthorized" elif isinstance(err, vol.Invalid): code = const.ERR_INVALID_FORMAT err_message = vol.humanize.humanize_error(msg, err) elif isinstance(err, asyncio.TimeoutError): code = const.ERR_TIMEOUT err_message = "Timeout" elif isinstance(err, HomeAssistantError): code = const.ERR_UNKNOWN_ERROR err_message = str(err) else: code = const.ERR_UNKNOWN_ERROR err_message = "Unknown error" log_handler = self.logger.exception log_handler("Error handling message: %s", err_message) self.send_message(messages.error_message(msg["id"], code, err_message))
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[ 107, 4 ]
[ 130, 79 ]
python
en
['en', 'en', 'en']
True
async_setup
(hass, config)
Set up the config component.
Set up the config component.
async def async_setup(hass, config): """Set up the config component.""" hass.components.frontend.async_register_built_in_panel( "config", "config", "hass:cog", require_admin=True ) async def setup_panel(panel_name): """Set up a panel.""" panel = importlib.import_module(f".{panel_name}", __name__) if not panel: return success = await panel.async_setup(hass) if success: key = f"{DOMAIN}.{panel_name}" hass.bus.async_fire(EVENT_COMPONENT_LOADED, {ATTR_COMPONENT: key}) @callback def component_loaded(event): """Respond to components being loaded.""" panel_name = event.data.get(ATTR_COMPONENT) if panel_name in ON_DEMAND: hass.async_create_task(setup_panel(panel_name)) hass.bus.async_listen(EVENT_COMPONENT_LOADED, component_loaded) tasks = [setup_panel(panel_name) for panel_name in SECTIONS] for panel_name in ON_DEMAND: if panel_name in hass.config.components: tasks.append(setup_panel(panel_name)) if tasks: await asyncio.wait(tasks) return True
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[ 39, 0 ]
[ 76, 15 ]
python
en
['en', 'en', 'en']
True
_read
(path)
Read YAML helper.
Read YAML helper.
def _read(path): """Read YAML helper.""" if not os.path.isfile(path): return None return load_yaml(path)
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[ 246, 0 ]
[ 251, 26 ]
python
en
['en', 'ku', 'en']
True
_write
(path, data)
Write YAML helper.
Write YAML helper.
def _write(path, data): """Write YAML helper.""" # Do it before opening file. If dump causes error it will now not # truncate the file. data = dump(data) with open(path, "w", encoding="utf-8") as outfile: outfile.write(data)
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[ 254, 0 ]
[ 260, 27 ]
python
en
['en', 'ku', 'tr']
False
BaseEditConfigView.__init__
( self, component, config_type, path, key_schema, data_schema, *, post_write_hook=None, data_validator=None, )
Initialize a config view.
Initialize a config view.
def __init__( self, component, config_type, path, key_schema, data_schema, *, post_write_hook=None, data_validator=None, ): """Initialize a config view.""" self.url = f"/api/config/{component}/{config_type}/{{config_key}}" self.name = f"api:config:{component}:{config_type}" self.path = path self.key_schema = key_schema self.data_schema = data_schema self.post_write_hook = post_write_hook self.data_validator = data_validator self.mutation_lock = asyncio.Lock()
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[ 82, 4 ]
[ 101, 43 ]
python
en
['en', 'en', 'en']
True
BaseEditConfigView._empty_config
(self)
Empty config if file not found.
Empty config if file not found.
def _empty_config(self): """Empty config if file not found.""" raise NotImplementedError
[ "def", "_empty_config", "(", "self", ")", ":", "raise", "NotImplementedError" ]
[ 103, 4 ]
[ 105, 33 ]
python
en
['en', 'en', 'en']
True
BaseEditConfigView._delete_value
(self, hass, data, config_key)
Delete value.
Delete value.
def _delete_value(self, hass, data, config_key): """Delete value.""" raise NotImplementedError
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[ 115, 4 ]
[ 117, 33 ]
python
en
['en', 'it', 'en']
False
BaseEditConfigView.get
(self, request, config_key)
Fetch device specific config.
Fetch device specific config.
async def get(self, request, config_key): """Fetch device specific config.""" hass = request.app["hass"] async with self.mutation_lock: current = await self.read_config(hass) value = self._get_value(hass, current, config_key) if value is None: return self.json_message("Resource not found", HTTP_NOT_FOUND) return self.json(value)
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[ 119, 4 ]
[ 129, 31 ]
python
en
['en', 'en', 'en']
True
BaseEditConfigView.post
(self, request, config_key)
Validate config and return results.
Validate config and return results.
async def post(self, request, config_key): """Validate config and return results.""" try: data = await request.json() except ValueError: return self.json_message("Invalid JSON specified", HTTP_BAD_REQUEST) try: self.key_schema(config_key) except vol.Invalid as err: return self.json_message(f"Key malformed: {err}", HTTP_BAD_REQUEST) hass = request.app["hass"] try: # We just validate, we don't store that data because # we don't want to store the defaults. if self.data_validator: await self.data_validator(hass, data) else: self.data_schema(data) except (vol.Invalid, HomeAssistantError) as err: return self.json_message(f"Message malformed: {err}", HTTP_BAD_REQUEST) path = hass.config.path(self.path) async with self.mutation_lock: current = await self.read_config(hass) self._write_value(hass, current, config_key, data) await hass.async_add_executor_job(_write, path, current) if self.post_write_hook is not None: hass.async_create_task( self.post_write_hook(ACTION_CREATE_UPDATE, config_key) ) return self.json({"result": "ok"})
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[ 131, 4 ]
[ 168, 42 ]
python
en
['en', 'en', 'en']
True
BaseEditConfigView.delete
(self, request, config_key)
Remove an entry.
Remove an entry.
async def delete(self, request, config_key): """Remove an entry.""" hass = request.app["hass"] async with self.mutation_lock: current = await self.read_config(hass) value = self._get_value(hass, current, config_key) path = hass.config.path(self.path) if value is None: return self.json_message("Resource not found", HTTP_NOT_FOUND) self._delete_value(hass, current, config_key) await hass.async_add_executor_job(_write, path, current) if self.post_write_hook is not None: hass.async_create_task(self.post_write_hook(ACTION_DELETE, config_key)) return self.json({"result": "ok"})
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[ 170, 4 ]
[ 187, 42 ]
python
en
['br', 'en', 'en']
True
BaseEditConfigView.read_config
(self, hass)
Read the config.
Read the config.
async def read_config(self, hass): """Read the config.""" current = await hass.async_add_executor_job(_read, hass.config.path(self.path)) if not current: current = self._empty_config() return current
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[ 189, 4 ]
[ 194, 22 ]
python
en
['en', 'en', 'en']
True
EditKeyBasedConfigView._empty_config
(self)
Return an empty config.
Return an empty config.
def _empty_config(self): """Return an empty config.""" return {}
[ "def", "_empty_config", "(", "self", ")", ":", "return", "{", "}" ]
[ 200, 4 ]
[ 202, 17 ]
python
en
['en', 'cy', 'en']
True
EditKeyBasedConfigView._delete_value
(self, hass, data, config_key)
Delete value.
Delete value.
def _delete_value(self, hass, data, config_key): """Delete value.""" return data.pop(config_key)
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[ 212, 4 ]
[ 214, 35 ]
python
en
['en', 'it', 'en']
False
EditIdBasedConfigView._empty_config
(self)
Return an empty config.
Return an empty config.
def _empty_config(self): """Return an empty config.""" return []
[ "def", "_empty_config", "(", "self", ")", ":", "return", "[", "]" ]
[ 220, 4 ]
[ 222, 17 ]
python
en
['en', 'cy', 'en']
True
EditIdBasedConfigView._delete_value
(self, hass, data, config_key)
Delete value.
Delete value.
def _delete_value(self, hass, data, config_key): """Delete value.""" index = next( idx for idx, val in enumerate(data) if val.get(CONF_ID) == config_key ) data.pop(index)
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[ 238, 4 ]
[ 243, 23 ]
python
en
['en', 'it', 'en']
False
T5TokenizerFast.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:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
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 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:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. """ token_ids_0 = token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return self.prefix_tokens + token_ids_0 else: token_ids_1 = token_ids_1 + [self.eos_token_id] return self.prefix_tokens + token_ids_0 + token_ids_1
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[ 155, 4 ]
[ 179, 65 ]
python
en
['en', 'error', 'th']
False
T5TokenizerFast.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 use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of zeros.
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 use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of zeros. """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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[ 181, 4 ]
[ 201, 63 ]
python
en
['en', 'error', 'th']
False
async_setup_entry
(hass: HomeAssistantType, config_entry, async_add_entities)
Set up Xbox Live friends.
Set up Xbox Live friends.
async def async_setup_entry(hass: HomeAssistantType, config_entry, async_add_entities): """Set up Xbox Live friends.""" coordinator: XboxUpdateCoordinator = hass.data[DOMAIN][config_entry.entry_id][ "coordinator" ] update_friends = partial(async_update_friends, coordinator, {}, async_add_entities) unsub = coordinator.async_add_listener(update_friends) hass.data[DOMAIN][config_entry.entry_id]["binary_sensor_unsub"] = unsub update_friends()
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[ 18, 0 ]
[ 28, 20 ]
python
en
['en', 'fr', 'en']
True
async_update_friends
( coordinator: XboxUpdateCoordinator, current: Dict[str, List[XboxBinarySensorEntity]], async_add_entities, )
Update friends.
Update friends.
def async_update_friends( coordinator: XboxUpdateCoordinator, current: Dict[str, List[XboxBinarySensorEntity]], async_add_entities, ) -> None: """Update friends.""" new_ids = set(coordinator.data.presence) current_ids = set(current) # Process new favorites, add them to Home Assistant new_entities = [] for xuid in new_ids - current_ids: current[xuid] = [ XboxBinarySensorEntity(coordinator, xuid, attribute) for attribute in PRESENCE_ATTRIBUTES ] new_entities = new_entities + current[xuid] if new_entities: async_add_entities(new_entities) # Process deleted favorites, remove them from Home Assistant for xuid in current_ids - new_ids: coordinator.hass.async_create_task( async_remove_entities(xuid, coordinator, current) )
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[ 44, 0 ]
[ 69, 9 ]
python
en
['en', 'co', 'en']
False
async_remove_entities
( xuid: str, coordinator: XboxUpdateCoordinator, current: Dict[str, XboxBinarySensorEntity], )
Remove friend sensors from Home Assistant.
Remove friend sensors from Home Assistant.
async def async_remove_entities( xuid: str, coordinator: XboxUpdateCoordinator, current: Dict[str, XboxBinarySensorEntity], ) -> None: """Remove friend sensors from Home Assistant.""" registry = await async_get_entity_registry(coordinator.hass) entities = current[xuid] for entity in entities: if entity.entity_id in registry.entities: registry.async_remove(entity.entity_id) del current[xuid]
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[ 72, 0 ]
[ 83, 21 ]
python
en
['en', 'en', 'en']
True
XboxBinarySensorEntity.is_on
(self)
Return the status of the requested attribute.
Return the status of the requested attribute.
def is_on(self) -> bool: """Return the status of the requested attribute.""" if not self.coordinator.last_update_success: return False return getattr(self.data, self.attribute, False)
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[ 35, 4 ]
[ 40, 56 ]
python
en
['en', 'en', 'en']
True
conv3x3
(in_planes, out_planes, stride=1)
3x3 convolution with padding
3x3 convolution with padding
def conv3x3(in_planes, out_planes, stride=1): " 3x3 convolution with padding " return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
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[ 14, 0 ]
[ 16, 96 ]
python
en
['en', 'ja', 'en']
True
_CrossNeuronBlockInternal.forward
(self, x)
:param x: (bt, c, h, w) :return:
:param x: (bt, c, h, w) :return:
def forward(self, x): ''' :param x: (bt, c, h, w) :return: ''' bt, c, h, w = x.shape residual = x x_v = self.conv_in(x) # b x c x h x w x_m = x_v.mean(3).mean(2).unsqueeze(2) # bt x c x 1 score = -(x_m - x_m.permute(0, 2, 1).contiguous())**2 # bt x c x c # score = torch.bmm(x_v.transpose(1, 2).contiguous(), x_v) # x_v = F.dropout(x_v, 0.1, self.training) # score.masked_fill_(self.mask.unsqueeze(0).expand_as(score).type_as(score).eq(0), -np.inf) attn = F.softmax(score, dim=2) # bt x c x c out = self.conv_out(torch.bmm(attn, x_v.view(bt, c, -1)).view(bt, c, x_v.shape[2], x_v.shape[3])) return F.relu(residual + out)
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[ 58, 4 ]
[ 74, 37 ]
python
en
['en', 'error', 'th']
False
_CrossNeuronBlock.forward
(self, x)
:param x: (bt, c, h, w) :return:
:param x: (bt, c, h, w) :return:
def forward(self, x): ''' :param x: (bt, c, h, w) :return: ''' bt, c, h, w = x.shape residual = x x_stretch = x.view(bt, c, h * w) if self.spatial_height == h and self.spatial_width == w: x_stacked = x_stretch # (b) x c x (h * w) x_stacked = x_stacked.view(bt * self.nblocks_channel, c // self.nblocks_channel, -1) x_v = x_stacked.permute(0, 2, 1).contiguous() # (b) x (h * w) x c if self.enc_dec: x_v = self.fc_in(x_v) # (b) x c x (h * w) # x_m = self.global_context(x_v) # import pdb; pdb.set_trace() # x_m = self.global_context(x_v) # b x 1 x c x_m = x_v.mean(1).unsqueeze(1) # b x 1 x c score = -(x_m - x_m.permute(0, 2, 1).contiguous())**2 # (b * h * w) x c x c # score = -torch.abs(x_m - x_m.permute(0, 2, 1).contiguous()) # (b * h * w) x c x c # score = torch.bmm(x_v.transpose(1, 2).contiguous(), x_v) # x_v = F.dropout(x_v, 0.1, self.training) # score.masked_fill_(self.mask.unsqueeze(0).expand_as(score).type_as(score).eq(0), -np.inf) attn = F.softmax(score, dim=1) # (b * h * w) x c x c # attn = F.dropout(attn, 0.2, self.training) if self.communication: if self.enc_dec: out = self.bn(self.fc_out(torch.bmm(x_v, attn))) # (b) x (h * w) x c else: out = self.bn(torch.bmm(x_v, attn)) # (b) x (h * w) x c else: out = self.bn(self.fc_out(x_v)) # (b) x (h * w) x c out = out.permute(0, 2, 1).contiguous().view(bt, c, h, w) return F.relu(residual + out) else: x = F.interpolate(x, (self.spatial_height, self.spatial_width)) x_stretch = x.view(bt, c, self.spatial_height * self.spatial_width) x_stretch = x.view(bt * self.nblocks_channel, c // self.nblocks_channel, self.spatial_height * self.spatial_width) x_stacked = x_stretch # (b) x c x (h * w) x_v = x_stacked.permute(0, 2, 1).contiguous() # (b) x (h * w) x c if self.enc_dec: x_v = self.fc_in(x_v) # (b) x (h * w) x c # x_m = self.global_context(x_v) # x_m = self.global_context(x_v) # b x 1 x c x_m = x_v.mean(1).unsqueeze(1) # (b) x 1 x c score = -(x_m - x_m.permute(0, 2, 1).contiguous())**2 # (b * h * w) x c x c # score = -torch.abs(x_m - x_m.permute(0, 2, 1).contiguous()) # (b * h * w) x c x c # score = torch.bmm(x_v.transpose(1, 2).contiguous(), x_v) # x_v = F.dropout(x_v, 0.1, self.training) # score.masked_fill_(self.mask.unsqueeze(0).expand_as(score).type_as(score).eq(0), -np.inf) attn = F.softmax(score, dim=1) # (b * h * w) x c x c # attn = F.dropout(attn, 0.2, self.training) if self.communication: if self.enc_dec: out = self.bn(self.fc_out(torch.bmm(x_v, attn))) # (b) x (h * w) x c else: out = self.bn(torch.bmm(x_v, attn)) # (b) x (h * w) x c else: out = self.bn(self.fc_out(x_v)) # (b) x (h * w) x c out = out.permute(0, 2, 1).contiguous().view(bt, c, self.spatial_height, self.spatial_width) out = F.interpolate(out, (h, w)) return F.relu(residual + out)
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[ 147, 4 ]
[ 217, 41 ]
python
en
['en', 'error', 'th']
False
create_parameter_id
()
Create an id Returns ------- int parameter id
Create an id
def create_parameter_id(): """Create an id Returns ------- int parameter id """ global _next_parameter_id _next_parameter_id += 1 return _next_parameter_id - 1
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[ 30, 0 ]
[ 40, 33 ]
python
en
['en', 'gd', 'en']
True
create_bracket_parameter_id
(brackets_id, brackets_curr_decay, increased_id=-1)
Create a full id for a specific bracket's hyperparameter configuration Parameters ---------- brackets_id: string brackets id brackets_curr_decay: brackets curr decay increased_id: int increased id Returns ------- int params id
Create a full id for a specific bracket's hyperparameter configuration
def create_bracket_parameter_id(brackets_id, brackets_curr_decay, increased_id=-1): """Create a full id for a specific bracket's hyperparameter configuration Parameters ---------- brackets_id: string brackets id brackets_curr_decay: brackets curr decay increased_id: int increased id Returns ------- int params id """ if increased_id == -1: increased_id = str(create_parameter_id()) params_id = '_'.join([brackets_id, str(brackets_curr_decay), increased_id]) return params_id
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[ 43, 0 ]
[ 65, 20 ]
python
en
['en', 'en', 'en']
True
json2parameter
(ss_spec, random_state)
Randomly generate values for hyperparameters from hyperparameter space i.e., x. Parameters ---------- ss_spec: hyperparameter space random_state: random operator to generate random values Returns ------- Parameter: Parameters in this experiment
Randomly generate values for hyperparameters from hyperparameter space i.e., x.
def json2parameter(ss_spec, random_state): """Randomly generate values for hyperparameters from hyperparameter space i.e., x. Parameters ---------- ss_spec: hyperparameter space random_state: random operator to generate random values Returns ------- Parameter: Parameters in this experiment """ if isinstance(ss_spec, dict): if NodeType.TYPE in ss_spec.keys(): _type = ss_spec[NodeType.TYPE] _value = ss_spec[NodeType.VALUE] if _type == 'choice': _index = random_state.randint(len(_value)) chosen_params = json2parameter(ss_spec[NodeType.VALUE][_index], random_state) else: chosen_params = getattr(parameter_expressions, _type)(*(_value + [random_state])) else: chosen_params = dict() for key in ss_spec.keys(): chosen_params[key] = json2parameter(ss_spec[key], random_state) elif isinstance(ss_spec, list): chosen_params = list() for _, subspec in enumerate(ss_spec): chosen_params.append(json2parameter(subspec, random_state)) else: chosen_params = copy.deepcopy(ss_spec) return chosen_params
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[ 68, 0 ]
[ 102, 24 ]
python
en
['en', 'en', 'en']
True
Bracket.is_completed
(self)
check whether this bracket has sent out all the hyperparameter configurations
check whether this bracket has sent out all the hyperparameter configurations
def is_completed(self): """check whether this bracket has sent out all the hyperparameter configurations""" return self.no_more_trial
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[ 141, 4 ]
[ 143, 33 ]
python
en
['en', 'en', 'en']
True
Bracket.get_n_r
(self)
return the values of n and r for the next round
return the values of n and r for the next round
def get_n_r(self): """return the values of n and r for the next round""" return math.floor(self.n / self.eta ** self.i + _epsilon), math.floor(self.r * self.eta ** self.i + _epsilon)
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[ 145, 4 ]
[ 147, 117 ]
python
en
['en', 'en', 'en']
True
Bracket.increase_i
(self)
i means the ith round. Increase i by 1
i means the ith round. Increase i by 1
def increase_i(self): """i means the ith round. Increase i by 1""" self.i += 1 if self.i > self.s: self.no_more_trial = True
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[ 149, 4 ]
[ 153, 37 ]
python
en
['en', 'en', 'en']
True
Bracket.set_config_perf
(self, i, parameter_id, seq, value)
update trial's latest result with its sequence number, e.g., epoch number or batch number Parameters ---------- i: int the ith round parameter_id: int the id of the trial/parameter seq: int sequence number, e.g., epoch number or batch number value: int latest result with sequence number seq Returns ------- None
update trial's latest result with its sequence number, e.g., epoch number or batch number
def set_config_perf(self, i, parameter_id, seq, value): """update trial's latest result with its sequence number, e.g., epoch number or batch number Parameters ---------- i: int the ith round parameter_id: int the id of the trial/parameter seq: int sequence number, e.g., epoch number or batch number value: int latest result with sequence number seq Returns ------- None """ if parameter_id in self.configs_perf[i]: if self.configs_perf[i][parameter_id][0] < seq: self.configs_perf[i][parameter_id] = [seq, value] else: self.configs_perf[i][parameter_id] = [seq, value]
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[ 155, 4 ]
[ 177, 61 ]
python
en
['en', 'en', 'en']
True
Bracket.inform_trial_end
(self, i)
If the trial is finished and the corresponding round (i.e., i) has all its trials finished, it will choose the top k trials for the next round (i.e., i+1) Parameters ---------- i: int the ith round
If the trial is finished and the corresponding round (i.e., i) has all its trials finished, it will choose the top k trials for the next round (i.e., i+1)
def inform_trial_end(self, i): """If the trial is finished and the corresponding round (i.e., i) has all its trials finished, it will choose the top k trials for the next round (i.e., i+1) Parameters ---------- i: int the ith round """ global _KEY self.num_finished_configs[i] += 1 _logger.debug('bracket id: %d, round: %d %d, finished: %d, all: %d', self.bracket_id, self.i, i, self.num_finished_configs[i], self.num_configs_to_run[i]) if self.num_finished_configs[i] >= self.num_configs_to_run[i] \ and self.no_more_trial is False: # choose candidate configs from finished configs to run in the next round assert self.i == i + 1 this_round_perf = self.configs_perf[i] if self.optimize_mode is OptimizeMode.Maximize: sorted_perf = sorted(this_round_perf.items(), key=lambda kv: kv[1][1], reverse=True) # reverse else: sorted_perf = sorted(this_round_perf.items(), key=lambda kv: kv[1][1]) _logger.debug('bracket %s next round %s, sorted hyper configs: %s', self.bracket_id, self.i, sorted_perf) next_n, next_r = self.get_n_r() _logger.debug('bracket %s next round %s, next_n=%d, next_r=%d', self.bracket_id, self.i, next_n, next_r) hyper_configs = dict() for k in range(next_n): params_id = sorted_perf[k][0] params = self.hyper_configs[i][params_id] params[_KEY] = next_r # modify r # generate new id increased_id = params_id.split('_')[-1] new_id = create_bracket_parameter_id(self.bracket_id, self.i, increased_id) hyper_configs[new_id] = params self._record_hyper_configs(hyper_configs) return [[key, value] for key, value in hyper_configs.items()] return None
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[ 179, 4 ]
[ 215, 19 ]
python
en
['en', 'en', 'en']
True
Bracket.get_hyperparameter_configurations
(self, num, r, searchspace_json, random_state)
Randomly generate num hyperparameter configurations from search space Parameters ---------- num: int the number of hyperparameter configurations Returns ------- list a list of hyperparameter configurations. Format: [[key1, value1], [key2, value2], ...]
Randomly generate num hyperparameter configurations from search space
def get_hyperparameter_configurations(self, num, r, searchspace_json, random_state): """Randomly generate num hyperparameter configurations from search space Parameters ---------- num: int the number of hyperparameter configurations Returns ------- list a list of hyperparameter configurations. Format: [[key1, value1], [key2, value2], ...] """ global _KEY assert self.i == 0 hyperparameter_configs = dict() for _ in range(num): params_id = create_bracket_parameter_id(self.bracket_id, self.i) params = json2parameter(searchspace_json, random_state) params[_KEY] = r hyperparameter_configs[params_id] = params self._record_hyper_configs(hyperparameter_configs) return [[key, value] for key, value in hyperparameter_configs.items()]
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[ 217, 4 ]
[ 239, 78 ]
python
en
['en', 'en', 'en']
True
Bracket._record_hyper_configs
(self, hyper_configs)
after generating one round of hyperconfigs, this function records the generated hyperconfigs, creates a dict to record the performance when those hyperconifgs are running, set the number of finished configs in this round to be 0, and increase the round number. Parameters ---------- hyper_configs: list the generated hyperconfigs
after generating one round of hyperconfigs, this function records the generated hyperconfigs, creates a dict to record the performance when those hyperconifgs are running, set the number of finished configs in this round to be 0, and increase the round number.
def _record_hyper_configs(self, hyper_configs): """after generating one round of hyperconfigs, this function records the generated hyperconfigs, creates a dict to record the performance when those hyperconifgs are running, set the number of finished configs in this round to be 0, and increase the round number. Parameters ---------- hyper_configs: list the generated hyperconfigs """ self.hyper_configs.append(hyper_configs) self.configs_perf.append(dict()) self.num_finished_configs.append(0) self.num_configs_to_run.append(len(hyper_configs)) self.increase_i()
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[ 241, 4 ]
[ 255, 25 ]
python
en
['en', 'en', 'en']
True
Hyperband.__init__
(self, R=60, eta=3, optimize_mode='maximize', exec_mode='parallelism')
B = (s_max + 1)R
B = (s_max + 1)R
def __init__(self, R=60, eta=3, optimize_mode='maximize', exec_mode='parallelism'): """B = (s_max + 1)R""" super(Hyperband, self).__init__() self.R = R self.eta = eta self.brackets = dict() # dict of Bracket self.generated_hyper_configs = [] # all the configs waiting for run self.completed_hyper_configs = [] # all the completed configs self.s_max = math.floor(math.log(self.R, self.eta) + _epsilon) self.curr_s = self.s_max self.curr_hb = 0 self.exec_mode = exec_mode self.curr_bracket_id = None self.searchspace_json = None self.random_state = None self.optimize_mode = OptimizeMode(optimize_mode) # This is for the case that nnimanager requests trial config, but tuner cannot provide immediately. # In this case, tuner increases self.credit to issue a trial config sometime later. self.credit = 0 # record the latest parameter_id of the trial job trial_job_id. # if there is no running parameter_id, self.job_id_para_id_map[trial_job_id] == None # new trial job is added to this dict and finished trial job is removed from it. self.job_id_para_id_map = dict()
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[ 283, 4 ]
[ 308, 40 ]
python
en
['en', 'id', 'ur']
False
Hyperband.handle_initialize
(self, data)
callback for initializing the advisor Parameters ---------- data: dict search space
callback for initializing the advisor Parameters ---------- data: dict search space
def handle_initialize(self, data): """callback for initializing the advisor Parameters ---------- data: dict search space """ self.handle_update_search_space(data) send(CommandType.Initialized, '')
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[ 310, 4 ]
[ 318, 41 ]
python
en
['en', 'zu', 'en']
True
Hyperband.handle_request_trial_jobs
(self, data)
Parameters ---------- data: int number of trial jobs
Parameters ---------- data: int number of trial jobs
def handle_request_trial_jobs(self, data): """ Parameters ---------- data: int number of trial jobs """ self.credit += data for _ in range(self.credit): self._request_one_trial_job()
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[ 320, 4 ]
[ 330, 41 ]
python
en
['en', 'error', 'th']
False
Hyperband._get_one_trial_job
(self)
get one trial job, i.e., one hyperparameter configuration.
get one trial job, i.e., one hyperparameter configuration.
def _get_one_trial_job(self): """get one trial job, i.e., one hyperparameter configuration.""" if not self.generated_hyper_configs: if self.exec_mode == 'parallelism' or \ (self.exec_mode == 'serial' and (self.curr_bracket_id is None or self.brackets[self.curr_bracket_id].is_completed())): if self.curr_s < 0: self.curr_s = self.s_max self.curr_hb += 1 _logger.debug('create a new bracket, self.curr_hb=%d, self.curr_s=%d', self.curr_hb, self.curr_s) self.curr_bracket_id = '{}-{}'.format(self.curr_hb, self.curr_s) self.brackets[self.curr_bracket_id] = Bracket(self.curr_bracket_id, self.curr_s, self.s_max, self.eta, self.R, self.optimize_mode) next_n, next_r = self.brackets[self.curr_bracket_id].get_n_r() _logger.debug('new bracket, next_n=%d, next_r=%d', next_n, next_r) assert self.searchspace_json is not None and self.random_state is not None generated_hyper_configs = self.brackets[self.curr_bracket_id].get_hyperparameter_configurations(next_n, next_r, self.searchspace_json, self.random_state) self.generated_hyper_configs = generated_hyper_configs.copy() self.curr_s -= 1 else: ret = { 'parameter_id': '-1_0_0', 'parameter_source': 'algorithm', 'parameters': '' } send(CommandType.NoMoreTrialJobs, json_tricks.dumps(ret)) return None assert self.generated_hyper_configs params = self.generated_hyper_configs.pop(0) ret = { 'parameter_id': params[0], 'parameter_source': 'algorithm', 'parameters': params[1] } return ret
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[ 338, 4 ]
[ 373, 18 ]
python
en
['en', 'pt', 'en']
True
Hyperband.handle_update_search_space
(self, data)
data: JSON object, which is search space
data: JSON object, which is search space
def handle_update_search_space(self, data): """data: JSON object, which is search space """ self.searchspace_json = data self.random_state = np.random.RandomState()
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[ 375, 4 ]
[ 379, 51 ]
python
en
['en', 'en', 'en']
True
Hyperband._handle_trial_end
(self, parameter_id)
Parameters ---------- parameter_id: parameter id of the finished config
Parameters ---------- parameter_id: parameter id of the finished config
def _handle_trial_end(self, parameter_id): """ Parameters ---------- parameter_id: parameter id of the finished config """ bracket_id, i, _ = parameter_id.split('_') hyper_configs = self.brackets[bracket_id].inform_trial_end(int(i)) if hyper_configs is not None: _logger.debug('bracket %s next round %s, hyper_configs: %s', bracket_id, i, hyper_configs) self.generated_hyper_configs = self.generated_hyper_configs + hyper_configs for _ in range(self.credit): self._request_one_trial_job()
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[ 381, 4 ]
[ 393, 41 ]
python
en
['en', 'error', 'th']
False
Hyperband.handle_trial_end
(self, data)
Parameters ---------- data: dict() it has three keys: trial_job_id, event, hyper_params trial_job_id: the id generated by training service event: the job's state hyper_params: the hyperparameters (a string) generated and returned by tuner
Parameters ---------- data: dict() it has three keys: trial_job_id, event, hyper_params trial_job_id: the id generated by training service event: the job's state hyper_params: the hyperparameters (a string) generated and returned by tuner
def handle_trial_end(self, data): """ Parameters ---------- data: dict() it has three keys: trial_job_id, event, hyper_params trial_job_id: the id generated by training service event: the job's state hyper_params: the hyperparameters (a string) generated and returned by tuner """ hyper_params = json_tricks.loads(data['hyper_params']) self._handle_trial_end(hyper_params['parameter_id']) if data['trial_job_id'] in self.job_id_para_id_map: del self.job_id_para_id_map[data['trial_job_id']]
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[ 395, 4 ]
[ 408, 61 ]
python
en
['en', 'error', 'th']
False
Hyperband.handle_report_metric_data
(self, data)
Parameters ---------- data: it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'. Raises ------ ValueError Data type not supported
Parameters ---------- data: it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
def handle_report_metric_data(self, data): """ Parameters ---------- data: it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'. Raises ------ ValueError Data type not supported """ if 'value' in data: data['value'] = json_tricks.loads(data['value']) # multiphase? need to check if data['type'] == MetricType.REQUEST_PARAMETER: assert multi_phase_enabled() assert data['trial_job_id'] is not None assert data['parameter_index'] is not None assert data['trial_job_id'] in self.job_id_para_id_map self._handle_trial_end(self.job_id_para_id_map[data['trial_job_id']]) ret = self._get_one_trial_job() if data['trial_job_id'] is not None: ret['trial_job_id'] = data['trial_job_id'] if data['parameter_index'] is not None: ret['parameter_index'] = data['parameter_index'] self.job_id_para_id_map[data['trial_job_id']] = ret['parameter_id'] send(CommandType.SendTrialJobParameter, json_tricks.dumps(ret)) else: value = extract_scalar_reward(data['value']) bracket_id, i, _ = data['parameter_id'].split('_') # add <trial_job_id, parameter_id> to self.job_id_para_id_map here, # because when the first parameter_id is created, trial_job_id is not known yet. if data['trial_job_id'] in self.job_id_para_id_map: assert self.job_id_para_id_map[data['trial_job_id']] == data['parameter_id'] else: self.job_id_para_id_map[data['trial_job_id']] = data['parameter_id'] if data['type'] == MetricType.FINAL: # sys.maxsize indicates this value is from FINAL metric data, because data['sequence'] from FINAL metric # and PERIODICAL metric are independent, thus, not comparable. self.brackets[bracket_id].set_config_perf(int(i), data['parameter_id'], sys.maxsize, value) self.completed_hyper_configs.append(data) elif data['type'] == MetricType.PERIODICAL: self.brackets[bracket_id].set_config_perf(int(i), data['parameter_id'], data['sequence'], value) else: raise ValueError('Data type not supported: {}'.format(data['type']))
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[ 410, 4 ]
[ 457, 84 ]
python
en
['en', 'error', 'th']
False
async_setup_entry
( hass: HomeAssistantType, entry: ConfigEntry, async_add_entities )
Set up device tracker for Freebox component.
Set up device tracker for Freebox component.
async def async_setup_entry( hass: HomeAssistantType, entry: ConfigEntry, async_add_entities ) -> None: """Set up device tracker for Freebox component.""" router = hass.data[DOMAIN][entry.unique_id] tracked = set() @callback def update_router(): """Update the values of the router.""" add_entities(router, async_add_entities, tracked) router.listeners.append( async_dispatcher_connect(hass, router.signal_device_new, update_router) ) update_router()
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[ 16, 0 ]
[ 32, 19 ]
python
en
['en', 'en', 'en']
True
add_entities
(router, async_add_entities, tracked)
Add new tracker entities from the router.
Add new tracker entities from the router.
def add_entities(router, async_add_entities, tracked): """Add new tracker entities from the router.""" new_tracked = [] for mac, device in router.devices.items(): if mac in tracked: continue new_tracked.append(FreeboxDevice(router, device)) tracked.add(mac) if new_tracked: async_add_entities(new_tracked, True)
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[ 36, 0 ]
[ 48, 45 ]
python
en
['en', 'en', 'en']
True
icon_for_freebox_device
(device)
Return a device icon from its type.
Return a device icon from its type.
def icon_for_freebox_device(device) -> str: """Return a device icon from its type.""" return DEVICE_ICONS.get(device["host_type"], "mdi:help-network")
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[ 144, 0 ]
[ 146, 68 ]
python
en
['en', 'en', 'en']
True
FreeboxDevice.__init__
(self, router: FreeboxRouter, device: Dict[str, any])
Initialize a Freebox device.
Initialize a Freebox device.
def __init__(self, router: FreeboxRouter, device: Dict[str, any]) -> None: """Initialize a Freebox device.""" self._router = router self._name = device["primary_name"].strip() or DEFAULT_DEVICE_NAME self._mac = device["l2ident"]["id"] self._manufacturer = device["vendor_name"] self._icon = icon_for_freebox_device(device) self._active = False self._attrs = {}
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[ 54, 4 ]
[ 62, 24 ]
python
en
['es', 'pl', 'en']
False
FreeboxDevice.async_update_state
(self)
Update the Freebox device.
Update the Freebox device.
def async_update_state(self) -> None: """Update the Freebox device.""" device = self._router.devices[self._mac] self._active = device["active"] if device.get("attrs") is None: # device self._attrs = { "last_time_reachable": datetime.fromtimestamp( device["last_time_reachable"] ), "last_time_activity": datetime.fromtimestamp(device["last_activity"]), } else: # router self._attrs = device["attrs"]
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[ 65, 4 ]
[ 79, 41 ]
python
en
['en', 'en', 'en']
True
FreeboxDevice.unique_id
(self)
Return a unique ID.
Return a unique ID.
def unique_id(self) -> str: """Return a unique ID.""" return self._mac
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[ 82, 4 ]
[ 84, 24 ]
python
ca
['fr', 'ca', 'en']
False
FreeboxDevice.name
(self)
Return the name.
Return the name.
def name(self) -> str: """Return the name.""" return self._name
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[ 87, 4 ]
[ 89, 25 ]
python
en
['en', 'ig', 'en']
True
FreeboxDevice.is_connected
(self)
Return true if the device is connected to the network.
Return true if the device is connected to the network.
def is_connected(self): """Return true if the device is connected to the network.""" return self._active
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[ 92, 4 ]
[ 94, 27 ]
python
en
['en', 'en', 'en']
True
FreeboxDevice.source_type
(self)
Return the source type.
Return the source type.
def source_type(self) -> str: """Return the source type.""" return SOURCE_TYPE_ROUTER
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[ 97, 4 ]
[ 99, 33 ]
python
en
['en', 'en', 'en']
True
FreeboxDevice.icon
(self)
Return the icon.
Return the icon.
def icon(self) -> str: """Return the icon.""" return self._icon
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[ 102, 4 ]
[ 104, 25 ]
python
en
['en', 'sr', 'en']
True
FreeboxDevice.device_state_attributes
(self)
Return the attributes.
Return the attributes.
def device_state_attributes(self) -> Dict[str, any]: """Return the attributes.""" return self._attrs
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[ 107, 4 ]
[ 109, 26 ]
python
en
['en', 'la', 'en']
True
FreeboxDevice.device_info
(self)
Return the device information.
Return the device information.
def device_info(self) -> Dict[str, any]: """Return the device information.""" return { "connections": {(CONNECTION_NETWORK_MAC, self._mac)}, "identifiers": {(DOMAIN, self.unique_id)}, "name": self.name, "manufacturer": self._manufacturer, }
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[ 112, 4 ]
[ 119, 9 ]
python
en
['en', 'en', 'en']
True
FreeboxDevice.should_poll
(self)
No polling needed.
No polling needed.
def should_poll(self) -> bool: """No polling needed.""" return False
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[ 122, 4 ]
[ 124, 20 ]
python
en
['en', 'en', 'en']
True
FreeboxDevice.async_on_demand_update
(self)
Update state.
Update state.
def async_on_demand_update(self): """Update state.""" self.async_update_state() self.async_write_ha_state()
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[ 127, 4 ]
[ 130, 35 ]
python
en
['en', 'co', 'en']
False
FreeboxDevice.async_added_to_hass
(self)
Register state update callback.
Register state update callback.
async def async_added_to_hass(self): """Register state update callback.""" self.async_update_state() self.async_on_remove( async_dispatcher_connect( self.hass, self._router.signal_device_update, self.async_on_demand_update, ) )
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[ 132, 4 ]
[ 141, 9 ]
python
en
['en', 'co', 'en']
True
nested_value
(ndict, *keys)
Return a nested dict value or None if it doesn't exist.
Return a nested dict value or None if it doesn't exist.
def nested_value(ndict, *keys): """Return a nested dict value or None if it doesn't exist.""" if len(keys) == 0: return ndict key = keys[0] if not isinstance(ndict, dict) or key not in ndict: return None return nested_value(ndict[key], *keys[1:])
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[ 137, 0 ]
[ 144, 46 ]
python
en
['en', 'en', 'en']
True
test_setup
(hass)
Test that devices are reported correctly.
Test that devices are reported correctly.
async def test_setup(hass): """Test that devices are reported correctly.""" with patch( "sense_energy.SenseLink", return_value=Mock(start=AsyncMock(), close=AsyncMock()), ): assert await async_setup_component(hass, DOMAIN, CONFIG) is True
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[ 147, 0 ]
[ 153, 72 ]
python
en
['en', 'en', 'en']
True
test_float
(hass)
Test a configuration using a simple float.
Test a configuration using a simple float.
async def test_float(hass): """Test a configuration using a simple float.""" config = CONFIG_SWITCH[DOMAIN][CONF_ENTITIES] assert await async_setup_component( hass, SWITCH_DOMAIN, {SWITCH_DOMAIN: {"platform": "demo"}}, ) with patch( "sense_energy.SenseLink", return_value=Mock(start=AsyncMock(), close=AsyncMock()), ): assert await async_setup_component(hass, DOMAIN, CONFIG_SWITCH) is True await hass.async_block_till_done() await emulated_kasa.validate_configs(hass, config) # Turn switch on await hass.services.async_call( SWITCH_DOMAIN, SERVICE_TURN_ON, {ATTR_ENTITY_ID: ENTITY_SWITCH}, blocking=True ) switch = hass.states.get(ENTITY_SWITCH) assert switch.state == STATE_ON plug_it = emulated_kasa.get_plug_devices(hass, config) plug = next(plug_it).generate_response() assert nested_value(plug, "system", "get_sysinfo", "alias") == ENTITY_SWITCH_NAME power = nested_value(plug, "emeter", "get_realtime", "power") assert math.isclose(power, ENTITY_SWITCH_POWER) # Turn off await hass.services.async_call( SWITCH_DOMAIN, SERVICE_TURN_OFF, {ATTR_ENTITY_ID: ENTITY_SWITCH}, blocking=True ) plug_it = emulated_kasa.get_plug_devices(hass, config) plug = next(plug_it).generate_response() assert nested_value(plug, "system", "get_sysinfo", "alias") == ENTITY_SWITCH_NAME power = nested_value(plug, "emeter", "get_realtime", "power") assert math.isclose(power, 0)
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[ 156, 0 ]
[ 196, 33 ]
python
en
['en', 'en', 'en']
True
test_switch_power
(hass)
Test a configuration using a simple float.
Test a configuration using a simple float.
async def test_switch_power(hass): """Test a configuration using a simple float.""" config = CONFIG_SWITCH_NO_POWER[DOMAIN][CONF_ENTITIES] assert await async_setup_component( hass, SWITCH_DOMAIN, {SWITCH_DOMAIN: {"platform": "demo"}}, ) with patch( "sense_energy.SenseLink", return_value=Mock(start=AsyncMock(), close=AsyncMock()), ): assert await async_setup_component(hass, DOMAIN, CONFIG_SWITCH_NO_POWER) is True await hass.async_block_till_done() await emulated_kasa.validate_configs(hass, config) # Turn switch on await hass.services.async_call( SWITCH_DOMAIN, SERVICE_TURN_ON, {ATTR_ENTITY_ID: ENTITY_SWITCH}, blocking=True ) switch = hass.states.get(ENTITY_SWITCH) assert switch.state == STATE_ON power = switch.attributes[ATTR_CURRENT_POWER_W] assert power == 100 assert switch.name == "AC" plug_it = emulated_kasa.get_plug_devices(hass, config) plug = next(plug_it).generate_response() assert nested_value(plug, "system", "get_sysinfo", "alias") == "AC" power = nested_value(plug, "emeter", "get_realtime", "power") assert math.isclose(power, power) hass.states.async_set( ENTITY_SWITCH, STATE_ON, attributes={ATTR_CURRENT_POWER_W: 120, ATTR_FRIENDLY_NAME: "AC"}, ) plug_it = emulated_kasa.get_plug_devices(hass, config) plug = next(plug_it).generate_response() assert nested_value(plug, "system", "get_sysinfo", "alias") == "AC" power = nested_value(plug, "emeter", "get_realtime", "power") assert math.isclose(power, 120) # Turn off await hass.services.async_call( SWITCH_DOMAIN, SERVICE_TURN_OFF, {ATTR_ENTITY_ID: ENTITY_SWITCH}, blocking=True ) plug_it = emulated_kasa.get_plug_devices(hass, config) plug = next(plug_it).generate_response() assert nested_value(plug, "system", "get_sysinfo", "alias") == "AC" power = nested_value(plug, "emeter", "get_realtime", "power") assert math.isclose(power, 0)
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[ 199, 0 ]
[ 255, 33 ]
python
en
['en', 'en', 'en']
True