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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
AbstractAutoCompressionModule.post_compress_finetuning_epochs | (cls, compress_algorithm_name: str) |
The epochs in post-compress finetuning process.
Parameters
----------
compress_algorithm_name: str
The name of pruner and quantizer, i.e. 'level', 'l1', 'qat'.
Returns
-------
int
The finetuning epoch number.
|
The epochs in post-compress finetuning process. | def post_compress_finetuning_epochs(cls, compress_algorithm_name: str) -> int:
"""
The epochs in post-compress finetuning process.
Parameters
----------
compress_algorithm_name: str
The name of pruner and quantizer, i.e. 'level', 'l1', 'qat'.
Returns
... | [
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StreamOutput.__init__ | (self, stream, timeout: int = 300) | Initialize a stream output. | Initialize a stream output. | def __init__(self, stream, timeout: int = 300) -> None:
"""Initialize a stream output."""
self.idle = False
self.timeout = timeout
self._stream = stream
self._cursor = None
self._event = asyncio.Event()
self._segments = deque(maxlen=MAX_SEGMENTS)
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StreamOutput.name | (self) | Return provider name. | Return provider name. | def name(self) -> str:
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StreamOutput.format | (self) | Return container format. | Return container format. | def format(self) -> str:
"""Return container format."""
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StreamOutput.audio_codecs | (self) | Return desired audio codecs. | Return desired audio codecs. | def audio_codecs(self) -> str:
"""Return desired audio codecs."""
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StreamOutput.video_codecs | (self) | Return desired video codecs. | Return desired video codecs. | def video_codecs(self) -> tuple:
"""Return desired video codecs."""
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StreamOutput.container_options | (self) | Return Callable which takes a sequence number and returns container options. | Return Callable which takes a sequence number and returns container options. | def container_options(self) -> Callable[[int], dict]:
"""Return Callable which takes a sequence number and returns container options."""
return None | [
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StreamOutput.segments | (self) | Return current sequence from segments. | Return current sequence from segments. | def segments(self) -> List[int]:
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StreamOutput.target_duration | (self) | Return the max duration of any given segment in seconds. | Return the max duration of any given segment in seconds. | def target_duration(self) -> int:
"""Return the max duration of any given segment in seconds."""
segment_length = len(self._segments)
if not segment_length:
return 1
durations = [s.duration for s in self._segments]
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StreamOutput.get_segment | (self, sequence: int = None) | Retrieve a specific segment, or the whole list. | Retrieve a specific segment, or the whole list. | def get_segment(self, sequence: int = None) -> Any:
"""Retrieve a specific segment, or the whole list."""
self.idle = False
# Reset idle timeout
if self._unsub is not None:
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self._unsub = async_call_later(self._stream.hass, self.timeout, self._timeout)
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StreamOutput.recv | (self) | Wait for and retrieve the latest segment. | Wait for and retrieve the latest segment. | async def recv(self) -> Segment:
"""Wait for and retrieve the latest segment."""
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StreamOutput.put | (self, segment: Segment) | Store output. | Store output. | def put(self, segment: Segment) -> None:
"""Store output."""
# Start idle timeout when we start receiving data
if self._unsub is None:
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138,
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StreamOutput._timeout | (self, _now=None) | Handle stream timeout. | Handle stream timeout. | def _timeout(self, _now=None):
"""Handle stream timeout."""
self._unsub = None
if self._stream.keepalive:
self.idle = True
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StreamOutput.cleanup | (self) | Handle cleanup. | Handle cleanup. | def cleanup(self):
"""Handle cleanup."""
self._segments = deque(maxlen=MAX_SEGMENTS)
self._stream.remove_provider(self) | [
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StreamView.get | (self, request, token, sequence=None) | Start a GET request. | Start a GET request. | async def get(self, request, token, sequence=None):
"""Start a GET request."""
hass = request.app["hass"]
stream = next(
(
s
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StreamView.handle | (self, request, stream, sequence) | Handle the stream request. | Handle the stream request. | async def handle(self, request, stream, sequence):
"""Handle the stream request."""
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test_duplicate_error | (hass) | Test that errors are shown when duplicates are added. | Test that errors are shown when duplicates are added. | async def test_duplicate_error(hass):
"""Test that errors are shown when duplicates are added."""
conf = {CONF_ZIP_CODE: "12345"}
MockConfigEntry(domain=DOMAIN, unique_id="12345", data=conf).add_to_hass(hass)
result = await hass.config_entries.flow.async_init(
DOMAIN, context={"source": SOURCE... | [
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test_invalid_zip_code | (hass) | Test that an invalid ZIP code key throws an error. | Test that an invalid ZIP code key throws an error. | async def test_invalid_zip_code(hass):
"""Test that an invalid ZIP code key throws an error."""
conf = {CONF_ZIP_CODE: "abcde"}
result = await hass.config_entries.flow.async_init(
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assert result["type"] == data_entry_flow.RESULT_TYPE_FORM... | [
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test_show_form | (hass) | Test that the form is served with no input. | Test that the form is served with no input. | async def test_show_form(hass):
"""Test that the form is served with no input."""
result = await hass.config_entries.flow.async_init(
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test_step_user | (hass) | Test that the user step works (without MFA). | Test that the user step works (without MFA). | async def test_step_user(hass):
"""Test that the user step works (without MFA)."""
conf = {CONF_ZIP_CODE: "12345"}
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async_setup_entry | (
hass: HomeAssistant, entry: ConfigEntry, async_add_entities: Callable
) | Set up Guardian switches based on a config entry. | Set up Guardian switches based on a config entry. | async def async_setup_entry(
hass: HomeAssistant, entry: ConfigEntry, async_add_entities: Callable
) -> None:
"""Set up Guardian switches based on a config entry."""
platform = entity_platform.current_platform.get()
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ValveControllerSwitch.__init__ | (
self,
entry: ConfigEntry,
client: Client,
coordinators: Dict[str, DataUpdateCoordinator],
) | Initialize. | Initialize. | def __init__(
self,
entry: ConfigEntry,
client: Client,
coordinators: Dict[str, DataUpdateCoordinator],
):
"""Initialize."""
super().__init__(
entry, coordinators, "valve", "Valve Controller", None, "mdi:water"
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self._client = client
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ValveControllerSwitch.available | (self) | Return whether the entity is available. | Return whether the entity is available. | def available(self) -> bool:
"""Return whether the entity is available."""
return self.coordinators[API_VALVE_STATUS].last_update_success | [
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ValveControllerSwitch.is_on | (self) | Return True if the valve is open. | Return True if the valve is open. | def is_on(self) -> bool:
"""Return True if the valve is open."""
return self._is_on | [
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ValveControllerSwitch._async_continue_entity_setup | (self) | Register API interest (and related tasks) when the entity is added. | Register API interest (and related tasks) when the entity is added. | async def _async_continue_entity_setup(self):
"""Register API interest (and related tasks) when the entity is added."""
self.async_add_coordinator_update_listener(API_VALVE_STATUS) | [
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ValveControllerSwitch._async_update_from_latest_data | (self) | Update the entity. | Update the entity. | def _async_update_from_latest_data(self) -> None:
"""Update the entity."""
self._is_on = self.coordinators[API_VALVE_STATUS].data["state"] in (
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self._attrs.update(
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ValveControllerSwitch.async_disable_ap | (self) | Disable the device's onboard access point. | Disable the device's onboard access point. | async def async_disable_ap(self):
"""Disable the device's onboard access point."""
try:
async with self._client:
await self._client.wifi.disable_ap()
except GuardianError as err:
LOGGER.error("Error while disabling valve controller AP: %s", err) | [
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ValveControllerSwitch.async_enable_ap | (self) | Enable the device's onboard access point. | Enable the device's onboard access point. | async def async_enable_ap(self):
"""Enable the device's onboard access point."""
try:
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ValveControllerSwitch.async_pair_sensor | (self, *, uid) | Add a new paired sensor. | Add a new paired sensor. | async def async_pair_sensor(self, *, uid):
"""Add a new paired sensor."""
try:
async with self._client:
await self._client.sensor.pair_sensor(uid)
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ValveControllerSwitch.async_reboot | (self) | Reboot the device. | Reboot the device. | async def async_reboot(self):
"""Reboot the device."""
try:
async with self._client:
await self._client.system.reboot()
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LOGGER.error("Error while rebooting valve controller: %s", err) | [
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166,
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172,
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ValveControllerSwitch.async_reset_valve_diagnostics | (self) | Fully reset system motor diagnostics. | Fully reset system motor diagnostics. | async def async_reset_valve_diagnostics(self):
"""Fully reset system motor diagnostics."""
try:
async with self._client:
await self._client.valve.reset()
except GuardianError as err:
LOGGER.error("Error while resetting valve diagnostics: %s", err) | [
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ValveControllerSwitch.async_unpair_sensor | (self, *, uid) | Add a new paired sensor. | Add a new paired sensor. | async def async_unpair_sensor(self, *, uid):
"""Add a new paired sensor."""
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ValveControllerSwitch.async_upgrade_firmware | (self, *, url, port, filename) | Upgrade the device firmware. | Upgrade the device firmware. | async def async_upgrade_firmware(self, *, url, port, filename):
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try:
async with self._client:
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ValveControllerSwitch.async_turn_off | (self, **kwargs) | Turn the valve off (closed). | Turn the valve off (closed). | async def async_turn_off(self, **kwargs) -> None:
"""Turn the valve off (closed)."""
try:
async with self._client:
await self._client.valve.close()
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ValveControllerSwitch.async_turn_on | (self, **kwargs) | Turn the valve on (open). | Turn the valve on (open). | async def async_turn_on(self, **kwargs) -> None:
"""Turn the valve on (open)."""
try:
async with self._client:
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discover_mysensors_platform | (hass, hass_config, platform, new_devices) | Discover a MySensors platform. | Discover a MySensors platform. | def discover_mysensors_platform(hass, hass_config, platform, new_devices):
"""Discover a MySensors platform."""
task = hass.async_create_task(
discovery.async_load_platform(
hass,
platform,
DOMAIN,
{ATTR_DEVICES: new_devices, CONF_NAME: DOMAIN},
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default_schema | (gateway, child, value_type_name) | Return a default validation schema for value types. | Return a default validation schema for value types. | def default_schema(gateway, child, value_type_name):
"""Return a default validation schema for value types."""
schema = {value_type_name: cv.string}
return get_child_schema(gateway, child, value_type_name, schema) | [
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light_dimmer_schema | (gateway, child, value_type_name) | Return a validation schema for V_DIMMER. | Return a validation schema for V_DIMMER. | def light_dimmer_schema(gateway, child, value_type_name):
"""Return a validation schema for V_DIMMER."""
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light_percentage_schema | (gateway, child, value_type_name) | Return a validation schema for V_PERCENTAGE. | Return a validation schema for V_PERCENTAGE. | def light_percentage_schema(gateway, child, value_type_name):
"""Return a validation schema for V_PERCENTAGE."""
schema = {"V_PERCENTAGE": cv.string, "V_STATUS": cv.string}
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light_rgb_schema | (gateway, child, value_type_name) | Return a validation schema for V_RGB. | Return a validation schema for V_RGB. | def light_rgb_schema(gateway, child, value_type_name):
"""Return a validation schema for V_RGB."""
schema = {"V_RGB": cv.string, "V_STATUS": cv.string}
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light_rgbw_schema | (gateway, child, value_type_name) | Return a validation schema for V_RGBW. | Return a validation schema for V_RGBW. | def light_rgbw_schema(gateway, child, value_type_name):
"""Return a validation schema for V_RGBW."""
schema = {"V_RGBW": cv.string, "V_STATUS": cv.string}
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switch_ir_send_schema | (gateway, child, value_type_name) | Return a validation schema for V_IR_SEND. | Return a validation schema for V_IR_SEND. | def switch_ir_send_schema(gateway, child, value_type_name):
"""Return a validation schema for V_IR_SEND."""
schema = {"V_IR_SEND": cv.string, "V_LIGHT": cv.string}
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get_child_schema | (gateway, child, value_type_name, schema) | Return a child schema. | Return a child schema. | def get_child_schema(gateway, child, value_type_name, schema):
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set_req = gateway.const.SetReq
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schema = child_schema.extend(
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invalid_msg | (gateway, child, value_type_name) | Return a message for an invalid child during schema validation. | Return a message for an invalid child during schema validation. | def invalid_msg(gateway, child, value_type_name):
"""Return a message for an invalid child during schema validation."""
pres = gateway.const.Presentation
set_req = gateway.const.SetReq
return (
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validate_set_msg | (msg) | Validate a set message. | Validate a set message. | def validate_set_msg(msg):
"""Validate a set message."""
if not validate_node(msg.gateway, msg.node_id):
return {}
child = msg.gateway.sensors[msg.node_id].children[msg.child_id]
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validate_node | (gateway, node_id) | Validate a node. | Validate a node. | def validate_node(gateway, node_id):
"""Validate a node."""
if gateway.sensors[node_id].sketch_name is None:
_LOGGER.debug("Node %s is missing sketch name", node_id)
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validate_child | (gateway, node_id, child, value_type=None) | Validate a child. | Validate a child. | def validate_child(gateway, node_id, child, value_type=None):
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validated = defaultdict(list)
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read_init | () | Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects. | Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects. | def read_init():
""" Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects. """
with open(os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f:
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create_dummy_object | (name, backend_name) | Create the code for the dummy object corresponding to `name`. | Create the code for the dummy object corresponding to `name`. | def create_dummy_object(name, backend_name):
""" Create the code for the dummy object corresponding to `name`."""
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create_dummy_files | () | Create the content of the dummy files. | Create the content of the dummy files. | def create_dummy_files():
""" Create the content of the dummy files. """
backend_specific_objects = read_init()
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check_dummies | (overwrite=False) | Check if the dummy files are up to date and maybe `overwrite` with the right content. | Check if the dummy files are up to date and maybe `overwrite` with the right content. | def check_dummies(overwrite=False):
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async_setup | (hass: HomeAssistant, config: dict) | Set up the NEW_NAME component. | Set up the NEW_NAME component. | async def async_setup(hass: HomeAssistant, config: dict):
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hass.data[DOMAIN] = {}
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async_setup_entry | (hass: HomeAssistant, entry: ConfigEntry) | Set up NEW_NAME from a config entry. | Set up NEW_NAME from a config entry. | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):
"""Set up NEW_NAME from a config entry."""
implementation = (
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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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MsgDispatcher.handle_initialize | (self, data) | Data is search space
| Data is search space
| def handle_initialize(self, data):
"""Data is search space
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self.tuner.update_search_space(data)
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85,
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89,
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MsgDispatcher.send_trial_callback | (self, id_, params) | For tuner to issue trial config when the config is generated
| For tuner to issue trial config when the config is generated
| def send_trial_callback(self, id_, params):
"""For tuner to issue trial config when the config is generated
"""
send(CommandType.NewTrialJob, _pack_parameter(id_, params)) | [
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MsgDispatcher.handle_import_data | (self, data) | Import additional data for tuning
data: a list of dictionaries, each of which has at least two keys, 'parameter' and 'value'
| Import additional data for tuning
data: a list of dictionaries, each of which has at least two keys, 'parameter' and 'value'
| def handle_import_data(self, data):
"""Import additional data for tuning
data: a list of dictionaries, each of which has at least two keys, 'parameter' and 'value'
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MsgDispatcher.handle_report_metric_data | (self, data) |
data: a dict received from nni_manager, which contains:
- 'parameter_id': id of the trial
- 'value': metric value reported by nni.report_final_result()
- 'type': report type, support {'FINAL', 'PERIODICAL'}
|
data: a dict received from nni_manager, which contains:
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- 'type': report type, support {'FINAL', 'PERIODICAL'}
| def handle_report_metric_data(self, data):
"""
data: a dict received from nni_manager, which contains:
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- 'value': metric value reported by nni.report_final_result()
- 'type': report type, support {'FINAL', 'PERIODICAL'}
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MsgDispatcher.handle_trial_end | (self, data) |
data: 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 generated and returned by tuner
|
data: 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 generated and returned by tuner
| def handle_trial_end(self, data):
"""
data: 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 generated and returned by tuner
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MsgDispatcher._handle_final_metric_data | (self, data) | Call tuner to process final results
| Call tuner to process final results
| def _handle_final_metric_data(self, data):
"""Call tuner to process final results
"""
id_ = data['parameter_id']
value = data['value']
if id_ is None or id_ in _customized_parameter_ids:
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MsgDispatcher._handle_intermediate_metric_data | (self, data) | Call assessor to process intermediate results
| Call assessor to process intermediate results
| def _handle_intermediate_metric_data(self, data):
"""Call assessor to process intermediate results
"""
if data['type'] != MetricType.PERIODICAL:
return
if self.assessor is None:
return
trial_job_id = data['trial_job_id']
if trial_job_id in _ended_... | [
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MsgDispatcher._earlystop_notify_tuner | (self, data) | Send last intermediate result as final result to tuner in case the
trial is early stopped.
| Send last intermediate result as final result to tuner in case the
trial is early stopped.
| def _earlystop_notify_tuner(self, data):
"""Send last intermediate result as final result to tuner in case the
trial is early stopped.
"""
_logger.debug('Early stop notify tuner data: [%s]', data)
data['type'] = MetricType.FINAL
if multi_thread_enabled():
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async_setup_platform | (hass, config, async_add_entities, discovery_info=None) | Set up a Logi Circle Camera. Obsolete. | Set up a Logi Circle Camera. Obsolete. | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up a Logi Circle Camera. Obsolete."""
_LOGGER.warning("Logi Circle no longer works with camera platform configuration") | [
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async_setup_entry | (hass, entry, async_add_entities) | Set up a Logi Circle Camera based on a config entry. | Set up a Logi Circle Camera based on a config entry. | async def async_setup_entry(hass, entry, async_add_entities):
"""Set up a Logi Circle Camera based on a config entry."""
devices = await hass.data[LOGI_CIRCLE_DOMAIN].cameras
ffmpeg = hass.data[DATA_FFMPEG]
cameras = [LogiCam(device, entry, ffmpeg) for device in devices]
async_add_entities(cameras... | [
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LogiCam.__init__ | (self, camera, device_info, ffmpeg) | Initialize Logi Circle camera. | Initialize Logi Circle camera. | def __init__(self, camera, device_info, ffmpeg):
"""Initialize Logi Circle camera."""
super().__init__()
self._camera = camera
self._name = self._camera.name
self._id = self._camera.mac_address
self._has_battery = self._camera.supports_feature("battery_level")
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LogiCam.async_added_to_hass | (self) | Connect camera methods to signals. | Connect camera methods to signals. | async def async_added_to_hass(self):
"""Connect camera methods to signals."""
def _dispatch_proxy(method):
"""Expand parameters & filter entity IDs."""
async def _call(params):
entity_ids = params.get(ATTR_ENTITY_ID)
filtered_params = {
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LogiCam.async_will_remove_from_hass | (self) | Disconnect dispatcher listeners when removed. | Disconnect dispatcher listeners when removed. | async def async_will_remove_from_hass(self):
"""Disconnect dispatcher listeners when removed."""
for detach in self._listeners:
detach() | [
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LogiCam.unique_id | (self) | Return a unique ID. | Return a unique ID. | def unique_id(self):
"""Return a unique ID."""
return self._id | [
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LogiCam.name | (self) | Return the name of this camera. | Return the name of this camera. | def name(self):
"""Return the name of this camera."""
return self._name | [
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LogiCam.supported_features | (self) | Logi Circle camera's support turning on and off ("soft" switch). | Logi Circle camera's support turning on and off ("soft" switch). | def supported_features(self):
"""Logi Circle camera's support turning on and off ("soft" switch)."""
return SUPPORT_ON_OFF | [
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LogiCam.device_info | (self) | Return information about the device. | Return information about the device. | def device_info(self):
"""Return information about the device."""
return {
"name": self._camera.name,
"identifiers": {(LOGI_CIRCLE_DOMAIN, self._camera.id)},
"model": self._camera.model_name,
"sw_version": self._camera.firmware,
"manufacturer":... | [
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LogiCam.device_state_attributes | (self) | Return the state attributes. | Return the state attributes. | def device_state_attributes(self):
"""Return the state attributes."""
state = {
ATTR_ATTRIBUTION: ATTRIBUTION,
"battery_saving_mode": (
STATE_ON if self._camera.battery_saving else STATE_OFF
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LogiCam.async_camera_image | (self) | Return a still image from the camera. | Return a still image from the camera. | async def async_camera_image(self):
"""Return a still image from the camera."""
return await self._camera.live_stream.download_jpeg() | [
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LogiCam.async_turn_off | (self) | Disable streaming mode for this camera. | Disable streaming mode for this camera. | async def async_turn_off(self):
"""Disable streaming mode for this camera."""
await self._camera.set_config("streaming", False) | [
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LogiCam.async_turn_on | (self) | Enable streaming mode for this camera. | Enable streaming mode for this camera. | async def async_turn_on(self):
"""Enable streaming mode for this camera."""
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LogiCam.should_poll | (self) | Update the image periodically. | Update the image periodically. | def should_poll(self):
"""Update the image periodically."""
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LogiCam.set_config | (self, mode, value) | Set an configuration property for the target camera. | Set an configuration property for the target camera. | async def set_config(self, mode, value):
"""Set an configuration property for the target camera."""
if mode == LED_MODE_KEY:
await self._camera.set_config("led", value)
if mode == RECORDING_MODE_KEY:
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LogiCam.download_livestream | (self, filename, duration) | Download a recording from the camera's livestream. | Download a recording from the camera's livestream. | async def download_livestream(self, filename, duration):
"""Download a recording from the camera's livestream."""
# Render filename from template.
filename.hass = self.hass
stream_file = filename.async_render(variables={ATTR_ENTITY_ID: self.entity_id})
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LogiCam.livestream_snapshot | (self, filename) | Download a still frame from the camera's livestream. | Download a still frame from the camera's livestream. | async def livestream_snapshot(self, filename):
"""Download a still frame from the camera's livestream."""
# Render filename from template.
filename.hass = self.hass
snapshot_file = filename.async_render(
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# Respect ... | [
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LogiCam.async_update | (self) | Update camera entity and refresh attributes. | Update camera entity and refresh attributes. | async def async_update(self):
"""Update camera entity and refresh attributes."""
await self._camera.update() | [
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setup_platform | (hass, config, add_devices, discovery_info=None) | Set up the Sterling Bank sensor platform. | Set up the Sterling Bank sensor platform. | def setup_platform(hass, config, add_devices, discovery_info=None):
"""Set up the Sterling Bank sensor platform."""
sensors = []
for account in config[CONF_ACCOUNTS]:
try:
starling_account = StarlingAccount(
account[CONF_ACCESS_TOKEN], sandbox=account[CONF_SANDBOX]
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StarlingBalanceSensor.__init__ | (self, starling_account, account_name, balance_data_type) | Initialize the sensor. | Initialize the sensor. | def __init__(self, starling_account, account_name, balance_data_type):
"""Initialize the sensor."""
self._starling_account = starling_account
self._balance_data_type = balance_data_type
self._state = None
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StarlingBalanceSensor.name | (self) | Return the name of the sensor. | Return the name of the sensor. | def name(self):
"""Return the name of the sensor."""
return "{} {}".format(
self._account_name, self._balance_data_type.replace("_", " ").capitalize()
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StarlingBalanceSensor.state | (self) | Return the state of the sensor. | Return the state of the sensor. | def state(self):
"""Return the state of the sensor."""
return self._state | [
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StarlingBalanceSensor.unit_of_measurement | (self) | Return the unit of measurement. | Return the unit of measurement. | def unit_of_measurement(self):
"""Return the unit of measurement."""
return self._starling_account.currency | [
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StarlingBalanceSensor.icon | (self) | Return the entity icon. | Return the entity icon. | def icon(self):
"""Return the entity icon."""
return ICON | [
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StarlingBalanceSensor.update | (self) | Fetch new state data for the sensor. | Fetch new state data for the sensor. | def update(self):
"""Fetch new state data for the sensor."""
self._starling_account.update_balance_data()
if self._balance_data_type == "cleared_balance":
self._state = self._starling_account.cleared_balance / 100
elif self._balance_data_type == "effective_balance":
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set_seed | (seed: int) |
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if
installed).
Args:
seed (:obj:`int`): The seed to set.
|
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if
installed). | def set_seed(seed: int):
"""
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if
installed).
Args:
seed (:obj:`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.ma... | [
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default_compute_objective | (metrics: Dict[str, float]) |
The default objective to maximize/minimize when doing an hyperparameter search. It is the evaluation loss if no
metrics are provided to the :class:`~transformers.Trainer`, the sum of all metrics otherwise.
Args:
metrics (:obj:`Dict[str, float]`): The metrics returned by the evaluate method.
R... |
The default objective to maximize/minimize when doing an hyperparameter search. It is the evaluation loss if no
metrics are provided to the :class:`~transformers.Trainer`, the sum of all metrics otherwise. | def default_compute_objective(metrics: Dict[str, float]) -> float:
"""
The default objective to maximize/minimize when doing an hyperparameter search. It is the evaluation loss if no
metrics are provided to the :class:`~transformers.Trainer`, the sum of all metrics otherwise.
Args:
metrics (:ob... | [
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is_main_process | (local_rank) |
Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
`local_rank`.
|
Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
`local_rank`.
| def is_main_process(local_rank):
"""
Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
`local_rank`.
"""
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
return xm.get_ordinal() == 0
return local_rank... | [
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total_processes_number | (local_rank) |
Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
|
Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
| def total_processes_number(local_rank):
"""
Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
"""
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
return xm.xrt_world_size()
elif is_sagemaker_distributed_available():
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speed_metrics | (split, start_time, num_samples=None) |
Measure and return speed performance metrics.
This function requires a time snapshot `start_time` before the operation to be measured starts and this function
should be run immediately after the operation to be measured has completed.
Args:
- split: name to prefix metric (like train, eval, test.... |
Measure and return speed performance metrics. | def speed_metrics(split, start_time, num_samples=None):
"""
Measure and return speed performance metrics.
This function requires a time snapshot `start_time` before the operation to be measured starts and this function
should be run immediately after the operation to be measured has completed.
Arg... | [
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227,
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245,
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denumpify_detensorize | (metrics) |
Recursively calls `.item()` on the element of the dictionary passed
|
Recursively calls `.item()` on the element of the dictionary passed
| def denumpify_detensorize(metrics):
"""
Recursively calls `.item()` on the element of the dictionary passed
"""
if isinstance(metrics, (list, tuple)):
return type(metrics)(denumpify_detensorize(m) for m in metrics)
elif isinstance(metrics, dict):
return type(metrics)({k: denumpify_de... | [
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] | python | en | ['en', 'error', 'th'] | False |
TrainerMemoryTracker.derive_stage | (self) | derives the stage/caller name automatically | derives the stage/caller name automatically | def derive_stage(self):
""" derives the stage/caller name automatically """
caller = inspect.currentframe().f_back.f_back.f_code.co_name
if caller in self.stages:
return self.stages[caller]
else:
raise ValueError(
f"was called from {caller}, but on... | [
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314,
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] | [
322,
13
] | python | en | ['en', 'en', 'en'] | True |
TrainerMemoryTracker.cpu_mem_used | (self) | get resident set size memory for the current process | get resident set size memory for the current process | def cpu_mem_used(self):
""" get resident set size memory for the current process """
return self.process.memory_info().rss | [
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324,
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] | [
326,
45
] | python | en | ['en', 'en', 'en'] | True |
TrainerMemoryTracker.start | (self) | start tracking for the caller's stage | start tracking for the caller's stage | def start(self):
""" start tracking for the caller's stage """
if self.skip_memory_metrics:
return
stage = self.derive_stage()
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
r... | [
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TrainerMemoryTracker.stop | (self, stage) | stop tracking for the passed stage | stop tracking for the passed stage | def stop(self, stage):
""" stop tracking for the passed stage """
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
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# this sends a signal to peak_monitor_func to complete its loop
se... | [
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TrainerMemoryTracker.update_metrics | (self, stage, metrics) | stop tracking for the passed stage | stop tracking for the passed stage | def update_metrics(self, stage, metrics):
""" stop tracking for the passed stage """
if self.skip_memory_metrics:
return
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
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430,
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] | python | en | ['en', 'en', 'en'] | True |
TrainerMemoryTracker.stop_and_update_metrics | (self, metrics=None) | combine stop + update in one call for simpler code | combine stop + update in one call for simpler code | def stop_and_update_metrics(self, metrics=None):
""" combine stop + update in one call for simpler code """
if self.skip_memory_metrics:
return
stage = self.derive_stage()
self.stop(stage)
# init doesn't have metrics to update so we just save that data for later sta... | [
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test_doorsense | (hass) | Test creation of a lock with doorsense and bridge. | Test creation of a lock with doorsense and bridge. | async def test_doorsense(hass):
"""Test creation of a lock with doorsense and bridge."""
lock_one = await _mock_lock_from_fixture(
hass, "get_lock.online_with_doorsense.json"
)
await _create_august_with_devices(hass, [lock_one])
binary_sensor_online_with_doorsense_name = hass.states.get(
... | [
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"bina... | [
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