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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Unauthorized.__init__ | (
self,
context: Optional["Context"] = None,
user_id: Optional[str] = None,
entity_id: Optional[str] = None,
config_entry_id: Optional[str] = None,
perm_category: Optional[str] = None,
permission: Optional[str] = None,
) | Unauthorized error. | Unauthorized error. | def __init__(
self,
context: Optional["Context"] = None,
user_id: Optional[str] = None,
entity_id: Optional[str] = None,
config_entry_id: Optional[str] = None,
perm_category: Optional[str] = None,
permission: Optional[str] = None,
) -> None:
"""Unautho... | [
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42,
4
] | [
64,
36
] | python | de | ['de', 'sr', 'it'] | False |
ServiceNotFound.__init__ | (self, domain: str, service: str) | Initialize error. | Initialize error. | def __init__(self, domain: str, service: str) -> None:
"""Initialize error."""
super().__init__(self, f"Service {domain}.{service} not found")
self.domain = domain
self.service = service | [
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ServiceNotFound.__str__ | (self) | Return string representation. | Return string representation. | def __str__(self) -> str:
"""Return string representation."""
return f"Unable to find service {self.domain}/{self.service}" | [
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run | (args) | Handle keyring script. | Handle keyring script. | def run(args):
"""Handle keyring script."""
parser = argparse.ArgumentParser(
description=(
"Modify Home Assistant secrets in the default keyring. "
"Use the secrets in configuration files with: "
"!secret <name>"
)
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websocket_client | (hass, hass_ws_client) | Create a websocket client. | Create a websocket client. | async def websocket_client(hass, hass_ws_client):
"""Create a websocket client."""
return await hass_ws_client(hass) | [
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no_auth_websocket_client | (hass, aiohttp_client) | Websocket connection that requires authentication. | Websocket connection that requires authentication. | async def no_auth_websocket_client(hass, aiohttp_client):
"""Websocket connection that requires authentication."""
assert await async_setup_component(hass, "websocket_api", {})
await hass.async_block_till_done()
client = await aiohttp_client(hass.http.app)
ws = await client.ws_connect(URL)
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setup_platform | (hass, config, add_entities, discovery_info=None) | Set up the Deutsche Bahn Sensor. | Set up the Deutsche Bahn Sensor. | def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the Deutsche Bahn Sensor."""
start = config.get(CONF_START)
destination = config[CONF_DESTINATION]
offset = config[CONF_OFFSET]
only_direct = config[CONF_ONLY_DIRECT]
add_entities([DeutscheBahnSensor(start, destinati... | [
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39,
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DeutscheBahnSensor.__init__ | (self, start, goal, offset, only_direct) | Initialize the sensor. | Initialize the sensor. | def __init__(self, start, goal, offset, only_direct):
"""Initialize the sensor."""
self._name = f"{start} to {goal}"
self.data = SchieneData(start, goal, offset, only_direct)
self._state = None | [
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DeutscheBahnSensor.name | (self) | Return the name of the sensor. | Return the name of the sensor. | def name(self):
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DeutscheBahnSensor.icon | (self) | Return the icon for the frontend. | Return the icon for the frontend. | def icon(self):
"""Return the icon for the frontend."""
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DeutscheBahnSensor.state | (self) | Return the departure time of the next train. | Return the departure time of the next train. | def state(self):
"""Return the departure time of the next train."""
return self._state | [
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DeutscheBahnSensor.device_state_attributes | (self) | Return the state attributes. | Return the state attributes. | def device_state_attributes(self):
"""Return the state attributes."""
connections = self.data.connections[0]
if len(self.data.connections) > 1:
connections["next"] = self.data.connections[1]["departure"]
if len(self.data.connections) > 2:
connections["next_on"] = ... | [
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DeutscheBahnSensor.update | (self) | Get the latest delay from bahn.de and updates the state. | Get the latest delay from bahn.de and updates the state. | def update(self):
"""Get the latest delay from bahn.de and updates the state."""
self.data.update()
self._state = self.data.connections[0].get("departure", "Unknown")
if self.data.connections[0].get("delay", 0) != 0:
self._state += f" + {self.data.connections[0]['delay']}" | [
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SchieneData.__init__ | (self, start, goal, offset, only_direct) | Initialize the sensor. | Initialize the sensor. | def __init__(self, start, goal, offset, only_direct):
"""Initialize the sensor."""
self.start = start
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SchieneData.update | (self) | Update the connection data. | Update the connection data. | def update(self):
"""Update the connection data."""
self.connections = self.schiene.connections(
self.start,
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dt_util.as_local(dt_util.utcnow() + self.offset),
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async_get_device_config | (hass, config_entry) | Initiate the connection and services. | Initiate the connection and services. | async def async_get_device_config(hass, config_entry):
"""Initiate the connection and services."""
# Make a copy of addresses due to edge case where the list of devices could change during status update
# Cannot be done concurrently due to issues with the underlying protocol.
for address in list(devices... | [
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close_insteon_connection | (*args) | Close the Insteon connection. | Close the Insteon connection. | async def close_insteon_connection(*args):
"""Close the Insteon connection."""
await async_close() | [
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async_setup | (hass, config) | Set up the Insteon platform. | Set up the Insteon platform. | async def async_setup(hass, config):
"""Set up the Insteon platform."""
if DOMAIN not in config:
return True
conf = config[DOMAIN]
data, options = convert_yaml_to_config_flow(conf)
if options:
hass.data[DOMAIN] = {}
hass.data[DOMAIN][OPTIONS] = options
# Create a config ... | [
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async_setup_entry | (hass, entry) | Set up an Insteon entry. | Set up an Insteon entry. | async def async_setup_entry(hass, entry):
"""Set up an Insteon entry."""
if not devices.modem:
try:
await async_connect(**entry.data)
except ConnectionError as exception:
_LOGGER.error("Could not connect to Insteon modem")
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async_check_ha_config_file | (hass: HomeAssistant) | Load and check if Home Assistant configuration file is valid.
This method is a coroutine.
| Load and check if Home Assistant configuration file is valid. | async def async_check_ha_config_file(hass: HomeAssistant) -> HomeAssistantConfig:
"""Load and check if Home Assistant configuration file is valid.
This method is a coroutine.
"""
result = HomeAssistantConfig()
def _pack_error(
package: str, component: str, config: ConfigType, message: str
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empty_value | (value: Any) | Test if the user has the default config value from adding "zone:". | Test if the user has the default config value from adding "zone:". | def empty_value(value: Any) -> Any:
"""Test if the user has the default config value from adding "zone:"."""
if isinstance(value, dict) and len(value) == 0:
return []
raise vol.Invalid("Not a default value") | [
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async_active_zone | (
hass: HomeAssistant, latitude: float, longitude: float, radius: int = 0
) | Find the active zone for given latitude, longitude.
This method must be run in the event loop.
| Find the active zone for given latitude, longitude. | def async_active_zone(
hass: HomeAssistant, latitude: float, longitude: float, radius: int = 0
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"""Find the active zone for given latitude, longitude.
This method must be run in the event loop.
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# Sort entity IDs so that we are deterministic if equal distance to 2 zones
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in_zone | (zone: State, latitude: float, longitude: float, radius: float = 0) | Test if given latitude, longitude is in given zone.
Async friendly.
| Test if given latitude, longitude is in given zone. | def in_zone(zone: State, latitude: float, longitude: float, radius: float = 0) -> bool:
"""Test if given latitude, longitude is in given zone.
Async friendly.
"""
if zone.state == STATE_UNAVAILABLE:
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zone_dist = distance(
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longitude,
zone.attrib... | [
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async_setup | (hass: HomeAssistant, config: Dict) | Set up configured zones as well as Home Assistant zone if necessary. | Set up configured zones as well as Home Assistant zone if necessary. | async def async_setup(hass: HomeAssistant, config: Dict) -> bool:
"""Set up configured zones as well as Home Assistant zone if necessary."""
component = entity_component.EntityComponent(_LOGGER, DOMAIN, hass)
id_manager = collection.IDManager()
yaml_collection = collection.IDLessCollection(
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_home_conf | (hass: HomeAssistant) | Return the home zone config. | Return the home zone config. | def _home_conf(hass: HomeAssistant) -> Dict:
"""Return the home zone config."""
return {
CONF_NAME: hass.config.location_name,
CONF_LATITUDE: hass.config.latitude,
CONF_LONGITUDE: hass.config.longitude,
CONF_RADIUS: DEFAULT_RADIUS,
CONF_ICON: ICON_HOME,
CONF_PASSI... | [
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async_setup_entry | (
hass: HomeAssistant, config_entry: config_entries.ConfigEntry
) | Set up zone as config entry. | Set up zone as config entry. | async def async_setup_entry(
hass: HomeAssistant, config_entry: config_entries.ConfigEntry
) -> bool:
"""Set up zone as config entry."""
storage_collection = cast(ZoneStorageCollection, hass.data[DOMAIN])
data = dict(config_entry.data)
data.setdefault(CONF_PASSIVE, DEFAULT_PASSIVE)
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async_unload_entry | (
hass: HomeAssistant, config_entry: config_entries.ConfigEntry
) | Will be called once we remove it. | Will be called once we remove it. | async def async_unload_entry(
hass: HomeAssistant, config_entry: config_entries.ConfigEntry
) -> bool:
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return True | [
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ZoneStorageCollection._process_create_data | (self, data: Dict) | Validate the config is valid. | Validate the config is valid. | async def _process_create_data(self, data: Dict) -> Dict:
"""Validate the config is valid."""
return cast(Dict, self.CREATE_SCHEMA(data)) | [
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ZoneStorageCollection._get_suggested_id | (self, info: Dict) | Suggest an ID based on the config. | Suggest an ID based on the config. | def _get_suggested_id(self, info: Dict) -> str:
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ZoneStorageCollection._update_data | (self, data: dict, update_data: Dict) | Return a new updated data object. | Return a new updated data object. | async def _update_data(self, data: dict, update_data: Dict) -> Dict:
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Zone.__init__ | (self, config: Dict, editable: bool) | Initialize the zone. | Initialize the zone. | def __init__(self, config: Dict, editable: bool):
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Zone.state | (self) | Return the state property really does nothing for a zone. | Return the state property really does nothing for a zone. | def state(self) -> str:
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Zone.name | (self) | Return name. | Return name. | def name(self) -> str:
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Zone.unique_id | (self) | Return unique ID. | Return unique ID. | def unique_id(self) -> Optional[str]:
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Zone.icon | (self) | Return the icon if any. | Return the icon if any. | def icon(self) -> Optional[str]:
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Zone.state_attributes | (self) | Return the state attributes of the zone. | Return the state attributes of the zone. | def state_attributes(self) -> Optional[Dict]:
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Zone.should_poll | (self) | Zone does not poll. | Zone does not poll. | def should_poll(self) -> bool:
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Zone.async_update_config | (self, config: Dict) | Handle when the config is updated. | Handle when the config is updated. | async def async_update_config(self, config: Dict) -> None:
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Zone._generate_attrs | (self) | Generate new attrs based on config. | Generate new attrs based on config. | def _generate_attrs(self) -> None:
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Compressor.__init__ | (self, model, config_list, optimizer=None) |
Record necessary info in class members
Parameters
----------
model : pytorch model
the model user wants to compress
config_list : list
the configurations that users specify for compression
optimizer: pytorch optimizer
optimizer used t... |
Record necessary info in class members | def __init__(self, model, config_list, optimizer=None):
"""
Record necessary info in class members
Parameters
----------
model : pytorch model
the model user wants to compress
config_list : list
the configurations that users specify for compressio... | [
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Compressor.validate_config | (self, model, config_list) |
subclass can optionally implement this method to check if config_list if valid
|
subclass can optionally implement this method to check if config_list if valid
| def validate_config(self, model, config_list):
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Compressor.reset | (self, checkpoint=None) |
reset model state dict and model wrapper
|
reset model state dict and model wrapper
| def reset(self, checkpoint=None):
"""
reset model state dict and model wrapper
"""
self._unwrap_model()
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self.modules_to_compress = None
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Compressor._detect_modules_to_compress | (self) |
detect all modules should be compressed, and save the result in `self.modules_to_compress`.
The model will be instrumented and user should never edit it after calling this method.
|
detect all modules should be compressed, and save the result in `self.modules_to_compress`.
The model will be instrumented and user should never edit it after calling this method.
| def _detect_modules_to_compress(self):
"""
detect all modules should be compressed, and save the result in `self.modules_to_compress`.
The model will be instrumented and user should never edit it after calling this method.
"""
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Compressor._wrap_model | (self) |
wrap all modules that needed to be compressed
|
wrap all modules that needed to be compressed | def _wrap_model(self):
"""
wrap all modules that needed to be compressed
"""
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Compressor._unwrap_model | (self) |
unwrap all modules that needed to be compressed
|
unwrap all modules that needed to be compressed | def _unwrap_model(self):
"""
unwrap all modules that needed to be compressed
"""
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Compressor.compress | (self) |
Compress the model with algorithm implemented by subclass.
The model will be instrumented and user should never edit it after calling this method.
`self.modules_to_compress` records all the to-be-compressed layers
Returns
-------
torch.nn.Module
model with ... |
Compress the model with algorithm implemented by subclass. | def compress(self):
"""
Compress the model with algorithm implemented by subclass.
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-------
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Compressor.set_wrappers_attribute | (self, name, value) |
To register attributes used in wrapped module's forward method.
If the type of the value is Torch.tensor, then this value is registered as a buffer in wrapper,
which will be saved by model.state_dict. Otherwise, this value is just a regular variable in wrapper.
Parameters
-----... |
To register attributes used in wrapped module's forward method.
If the type of the value is Torch.tensor, then this value is registered as a buffer in wrapper,
which will be saved by model.state_dict. Otherwise, this value is just a regular variable in wrapper. | def set_wrappers_attribute(self, name, value):
"""
To register attributes used in wrapped module's forward method.
If the type of the value is Torch.tensor, then this value is registered as a buffer in wrapper,
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Compressor.get_modules_to_compress | (self) |
To obtain all the to-be-compressed modules.
Returns
-------
list
a list of the layers, each of which is a tuple (`layer`, `config`),
`layer` is `LayerInfo`, `config` is a `dict`
|
To obtain all the to-be-compressed modules. | def get_modules_to_compress(self):
"""
To obtain all the to-be-compressed modules.
Returns
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list
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Compressor.get_modules_wrapper | (self) |
To obtain all the wrapped modules.
Returns
-------
list
a list of the wrapped modules
|
To obtain all the wrapped modules. | def get_modules_wrapper(self):
"""
To obtain all the wrapped modules.
Returns
-------
list
a list of the wrapped modules
"""
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Compressor.select_config | (self, layer) |
Find the configuration for `layer` by parsing `self.config_list`
Parameters
----------
layer : LayerInfo
one layer
Returns
-------
config or None
the retrieved configuration for this layer, if None, this layer should
not be c... |
Find the configuration for `layer` by parsing `self.config_list` | def select_config(self, layer):
"""
Find the configuration for `layer` by parsing `self.config_list`
Parameters
----------
layer : LayerInfo
one layer
Returns
-------
config or None
the retrieved configuration for this layer, if N... | [
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Compressor.update_epoch | (self, epoch) |
If user want to update model every epoch, user can override this method.
This method should be called at the beginning of each epoch
Parameters
----------
epoch : num
the current epoch number
|
If user want to update model every epoch, user can override this method.
This method should be called at the beginning of each epoch | def update_epoch(self, epoch):
"""
If user want to update model every epoch, user can override this method.
This method should be called at the beginning of each epoch
Parameters
----------
epoch : num
the current epoch number
"""
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Compressor._wrap_modules | (self, layer, config) |
This method is implemented in the subclasses, i.e., `Pruner` and `Quantizer`
Parameters
----------
layer : LayerInfo
the layer to instrument the compression operation
config : dict
the configuration for compressing this layer
|
This method is implemented in the subclasses, i.e., `Pruner` and `Quantizer` | def _wrap_modules(self, layer, config):
"""
This method is implemented in the subclasses, i.e., `Pruner` and `Quantizer`
Parameters
----------
layer : LayerInfo
the layer to instrument the compression operation
config : dict
the configuration for ... | [
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PrunerModuleWrapper.__init__ | (self, module, module_name, module_type, config, pruner) |
Wrap an module to enable data parallel, forward method customization and buffer registeration.
Parameters
----------
module : pytorch module
the module user wants to compress
config : dict
the configurations that users specify for compression
mod... |
Wrap an module to enable data parallel, forward method customization and buffer registeration. | def __init__(self, module, module_name, module_type, config, pruner):
"""
Wrap an module to enable data parallel, forward method customization and buffer registeration.
Parameters
----------
module : pytorch module
the module user wants to compress
config : d... | [
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Pruner.calc_mask | (self, wrapper, **kwargs) |
Pruners should overload this method to provide mask for weight tensors.
The mask must have the same shape and type comparing to the weight.
It will be applied with `mul()` operation on the weight.
This method is effectively hooked to `forward()` method of the model.
Parameters
... |
Pruners should overload this method to provide mask for weight tensors.
The mask must have the same shape and type comparing to the weight.
It will be applied with `mul()` operation on the weight.
This method is effectively hooked to `forward()` method of the model. | def calc_mask(self, wrapper, **kwargs):
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Pruner._wrap_modules | (self, layer, config) |
Create a wrapper module to replace the original one.
Parameters
----------
layer : LayerInfo
the layer to instrument the mask
config : dict
the configuration for generating the mask
|
Create a wrapper module to replace the original one. | def _wrap_modules(self, layer, config):
"""
Create a wrapper module to replace the original one.
Parameters
----------
layer : LayerInfo
the layer to instrument the mask
config : dict
the configuration for generating the mask
"""
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Pruner.export_model | (self, model_path, mask_path=None, onnx_path=None, input_shape=None, device=None) |
Export pruned model weights, masks and onnx model(optional)
Parameters
----------
model_path : str
path to save pruned model state_dict
mask_path : str
(optional) path to save mask dict
onnx_path : str
(optional) path to save onnx mod... |
Export pruned model weights, masks and onnx model(optional) | def export_model(self, model_path, mask_path=None, onnx_path=None, input_shape=None, device=None):
"""
Export pruned model weights, masks and onnx model(optional)
Parameters
----------
model_path : str
path to save pruned model state_dict
mask_path : str
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Pruner.load_model_state_dict | (self, model_state) |
Load the state dict saved from unwrapped model.
Parameters
----------
model_state : dict
state dict saved from unwrapped model
|
Load the state dict saved from unwrapped model. | def load_model_state_dict(self, model_state):
"""
Load the state dict saved from unwrapped model.
Parameters
----------
model_state : dict
state dict saved from unwrapped model
"""
if self.is_wrapped:
self._unwrap_model()
self.... | [
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Pruner.get_pruned_weights | (self, dim=0) |
Log the simulated prune sparsity.
Parameters
----------
dim : int
the pruned dim.
|
Log the simulated prune sparsity. | def get_pruned_weights(self, dim=0):
"""
Log the simulated prune sparsity.
Parameters
----------
dim : int
the pruned dim.
"""
for _, wrapper in enumerate(self.get_modules_wrapper()):
weight_mask = wrapper.weight_mask
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QuantizerModuleWrapper.__init__ | (self, module, module_name, module_type, config, quantizer) |
Wrap an module to enable data parallel, forward method customization and buffer registeration.
Parameters
----------
module : pytorch module
the module user wants to compress
config : dict
the configurations that users specify for compression
mod... |
Wrap an module to enable data parallel, forward method customization and buffer registeration. | def __init__(self, module, module_name, module_type, config, quantizer):
"""
Wrap an module to enable data parallel, forward method customization and buffer registeration.
Parameters
----------
module : pytorch module
the module user wants to compress
config ... | [
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Quantizer.quantize_weight | (self, wrapper, **kwargs) |
quantize should overload this method to quantize weight.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
wrapper : QuantizerModuleWrapper
the wrapper for origin module
|
quantize should overload this method to quantize weight.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
wrapper : QuantizerModuleWrapper
the wrapper for origin module
| def quantize_weight(self, wrapper, **kwargs):
"""
quantize should overload this method to quantize weight.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
wrapper : QuantizerModuleWrapper
the wrapper for origin module
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Quantizer.quantize_output | (self, output, wrapper, **kwargs) |
quantize should overload this method to quantize output.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
output : Tensor
output that needs to be quantized
wrapper : QuantizerModuleWrapper
the wrapper for or... |
quantize should overload this method to quantize output.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
output : Tensor
output that needs to be quantized
wrapper : QuantizerModuleWrapper
the wrapper for or... | def quantize_output(self, output, wrapper, **kwargs):
"""
quantize should overload this method to quantize output.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
output : Tensor
output that needs to be quantized
... | [
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Quantizer.quantize_input | (self, *inputs, wrapper, **kwargs) |
quantize should overload this method to quantize input.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
inputs : Tensor
inputs that needs to be quantized
wrapper : QuantizerModuleWrapper
the wrapper for ori... |
quantize should overload this method to quantize input.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
inputs : Tensor
inputs that needs to be quantized
wrapper : QuantizerModuleWrapper
the wrapper for ori... | def quantize_input(self, *inputs, wrapper, **kwargs):
"""
quantize should overload this method to quantize input.
This method is effectively hooked to :meth:`forward` of the model.
Parameters
----------
inputs : Tensor
inputs that needs to be quantized
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Quantizer._wrap_modules | (self, layer, config) |
Create a wrapper forward function to replace the original one.
Parameters
----------
layer : LayerInfo
the layer to instrument the mask
config : dict
the configuration for quantization
|
Create a wrapper forward function to replace the original one.
Parameters
----------
layer : LayerInfo
the layer to instrument the mask
config : dict
the configuration for quantization
| def _wrap_modules(self, layer, config):
"""
Create a wrapper forward function to replace the original one.
Parameters
----------
layer : LayerInfo
the layer to instrument the mask
config : dict
the configuration for quantization
"""
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Quantizer.export_model_save | (self, model, model_path, calibration_config=None, calibration_path=None, onnx_path=None,
input_shape=None, device=None) |
This method helps save pytorch model, calibration config, onnx model in quantizer.
Parameters
----------
model : pytorch model
pytorch model to be saved
model_path : str
path to save pytorch
calibration_config: dict
(optional) config ... |
This method helps save pytorch model, calibration config, onnx model in quantizer. | def export_model_save(self, model, model_path, calibration_config=None, calibration_path=None, onnx_path=None,
input_shape=None, device=None):
"""
This method helps save pytorch model, calibration config, onnx model in quantizer.
Parameters
----------
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Quantizer.export_model | (self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None) |
Export quantized model weights and calibration parameters
Parameters
----------
model_path : str
path to save quantized model weight
calibration_path : str
(optional) path to save quantize parameters after calibration
onnx_path : str
... |
Export quantized model weights and calibration parameters | def export_model(self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None):
"""
Export quantized model weights and calibration parameters
Parameters
----------
model_path : str
path to save quantized model weight
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QuantGrad._quantize | (cls, x, scale, zero_point) |
Reference function for quantizing x -- non-clamped.
Parameters
----------
x : Tensor
tensor to be quantized
scale : Tensor
scale for quantizing x
zero_point : Tensor
zero_point for quantizing x
Returns
-------
t... |
Reference function for quantizing x -- non-clamped.
Parameters
----------
x : Tensor
tensor to be quantized
scale : Tensor
scale for quantizing x
zero_point : Tensor
zero_point for quantizing x
Returns
-------
t... | def _quantize(cls, x, scale, zero_point):
"""
Reference function for quantizing x -- non-clamped.
Parameters
----------
x : Tensor
tensor to be quantized
scale : Tensor
scale for quantizing x
zero_point : Tensor
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QuantGrad.get_bits_length | (cls, config, quant_type) |
Get bit for quantize config
Parameters
----------
config : Dict
the configuration for quantization
quant_type : str
quant type
Returns
-------
int
n-bits for quantization configuration
|
Get bit for quantize config
Parameters
----------
config : Dict
the configuration for quantization
quant_type : str
quant type
Returns
-------
int
n-bits for quantization configuration
| def get_bits_length(cls, config, quant_type):
"""
Get bit for quantize config
Parameters
----------
config : Dict
the configuration for quantization
quant_type : str
quant type
Returns
-------
int
n-bits for quan... | [
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QuantGrad.quant_backward | (tensor, grad_output, quant_type, scale, zero_point, qmin, qmax) |
This method should be overrided by subclass to provide customized backward function,
default implementation is Straight-Through Estimator
Parameters
----------
tensor : Tensor
input of quantization operation
grad_output : Tensor
gradient of the ou... |
This method should be overrided by subclass to provide customized backward function,
default implementation is Straight-Through Estimator
Parameters
----------
tensor : Tensor
input of quantization operation
grad_output : Tensor
gradient of the ou... | def quant_backward(tensor, grad_output, quant_type, scale, zero_point, qmin, qmax):
"""
This method should be overrided by subclass to provide customized backward function,
default implementation is Straight-Through Estimator
Parameters
----------
tensor : Tensor
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test_platform_manually_configured | (hass) | Test that nothing happens when platform is manually configured. | Test that nothing happens when platform is manually configured. | async def test_platform_manually_configured(hass):
"""Test that nothing happens when platform is manually configured."""
assert (
await async_setup_component(
hass, CAMERA_DOMAIN, {CAMERA_DOMAIN: {"platform": AXIS_DOMAIN}}
)
is True
)
assert AXIS_DOMAIN not in hass.d... | [
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test_camera | (hass) | Test that Axis camera platform is loaded properly. | Test that Axis camera platform is loaded properly. | async def test_camera(hass):
"""Test that Axis camera platform is loaded properly."""
await setup_axis_integration(hass)
assert len(hass.states.async_entity_ids(CAMERA_DOMAIN)) == 1
entity_id = f"{CAMERA_DOMAIN}.{NAME}"
cam = hass.states.get(entity_id)
assert cam.state == STATE_IDLE
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test_camera_with_stream_profile | (hass) | Test that Axis camera entity is using the correct path with stream profike. | Test that Axis camera entity is using the correct path with stream profike. | async def test_camera_with_stream_profile(hass):
"""Test that Axis camera entity is using the correct path with stream profike."""
with patch.dict(ENTRY_OPTIONS, {CONF_STREAM_PROFILE: "profile_1"}):
await setup_axis_integration(hass)
assert len(hass.states.async_entity_ids(CAMERA_DOMAIN)) == 1
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test_camera_disabled | (hass) | Test that Axis camera platform is loaded properly but does not create camera entity. | Test that Axis camera platform is loaded properly but does not create camera entity. | async def test_camera_disabled(hass):
"""Test that Axis camera platform is loaded properly but does not create camera entity."""
with patch("axis.vapix.Params.image_format", new=None):
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infer_framework_from_model | (model, model_classes: Optional[Dict[str, type]] = None, revision: Optional[str] = None) |
Select framework (TensorFlow or PyTorch) to use from the :obj:`model` passed. Returns a tuple (framework, model).
If :obj:`model` is instantiated, this function will just infer the framework from the model class. Otherwise
:obj:`model` is actually a checkpoint name and this method will try to instantiate ... |
Select framework (TensorFlow or PyTorch) to use from the :obj:`model` passed. Returns a tuple (framework, model). | def infer_framework_from_model(model, model_classes: Optional[Dict[str, type]] = None, revision: Optional[str] = None):
"""
Select framework (TensorFlow or PyTorch) to use from the :obj:`model` passed. Returns a tuple (framework, model).
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get_framework | (model, revision: Optional[str] = None) |
Select framework (TensorFlow or PyTorch) to use.
Args:
model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
If both frameworks are installed, picks the one corresponding to the model passed (either a model class or
the model na... |
Select framework (TensorFlow or PyTorch) to use. | def get_framework(model, revision: Optional[str] = None):
"""
Select framework (TensorFlow or PyTorch) to use.
Args:
model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
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get_default_model | (targeted_task: Dict, framework: Optional[str], task_options: Optional[Any]) |
Select a default model to use for a given task. Defaults to pytorch if ambiguous.
Args:
targeted_task (:obj:`Dict` ):
Dictionary representing the given task, that should contain default models
framework (:obj:`str`, None)
"pt", "tf" or None, representing a specific frame... |
Select a default model to use for a given task. Defaults to pytorch if ambiguous. | def get_default_model(targeted_task: Dict, framework: Optional[str], task_options: Optional[Any]) -> str:
"""
Select a default model to use for a given task. Defaults to pytorch if ambiguous.
Args:
targeted_task (:obj:`Dict` ):
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PipelineDataFormat.save | (self, data: Union[dict, List[dict]]) |
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`.
Args:
data (:obj:`dict` or list of :obj:`dict`): The data to store.
|
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`. | def save(self, data: Union[dict, List[dict]]):
"""
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`.
Args:
data (:obj:`dict` or list of :obj:`dict`): The data to store.
"""
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PipelineDataFormat.save_binary | (self, data: Union[dict, List[dict]]) |
Save the provided data object as a pickle-formatted binary data on the disk.
Args:
data (:obj:`dict` or list of :obj:`dict`): The data to store.
Returns:
:obj:`str`: Path where the data has been saved.
|
Save the provided data object as a pickle-formatted binary data on the disk. | def save_binary(self, data: Union[dict, List[dict]]) -> str:
"""
Save the provided data object as a pickle-formatted binary data on the disk.
Args:
data (:obj:`dict` or list of :obj:`dict`): The data to store.
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PipelineDataFormat.from_str | (
format: str,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
) |
Creates an instance of the right subclass of :class:`~transformers.pipelines.PipelineDataFormat` depending on
:obj:`format`.
Args:
format: (:obj:`str`):
The format of the desired pipeline. Acceptable values are :obj:`"json"`, :obj:`"csv"` or :obj:`"pipe"`.
... |
Creates an instance of the right subclass of :class:`~transformers.pipelines.PipelineDataFormat` depending on
:obj:`format`. | def from_str(
format: str,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
) -> "PipelineDataFormat":
"""
Creates an instance of the right subclass of :class:`~transformers.pipelines.PipelineDataFormat` depending ... | [
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280,
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313,
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CsvPipelineDataFormat.save | (self, data: List[dict]) |
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`.
Args:
data (:obj:`List[dict]`): The data to store.
|
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`. | def save(self, data: List[dict]):
"""
Save the provided data object with the representation for the current
:class:`~transformers.pipelines.PipelineDataFormat`.
Args:
data (:obj:`List[dict]`): The data to store.
"""
with open(self.output_path, "w") as f:
... | [
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JsonPipelineDataFormat.save | (self, data: dict) |
Save the provided data object in a json file.
Args:
data (:obj:`dict`): The data to store.
|
Save the provided data object in a json file. | def save(self, data: dict):
"""
Save the provided data object in a json file.
Args:
data (:obj:`dict`): The data to store.
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with open(self.output_path, "w") as f:
json.dump(data, f) | [
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PipedPipelineDataFormat.save | (self, data: dict) |
Print the data.
Args:
data (:obj:`dict`): The data to store.
|
Print the data. | def save(self, data: dict):
"""
Print the data.
Args:
data (:obj:`dict`): The data to store.
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async_setup | (hass) | Set up the Automation config API. | Set up the Automation config API. | async def async_setup(hass):
"""Set up the Automation config API."""
async def hook(action, config_key):
"""post_write_hook for Config View that reloads automations."""
await hass.services.async_call(DOMAIN, SERVICE_RELOAD)
if action != ACTION_DELETE:
return
ent_re... | [
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async_describe_on_off_states | (
hass: HomeAssistantType, registry: GroupIntegrationRegistry
) | Describe group on off states. | Describe group on off states. | def async_describe_on_off_states(
hass: HomeAssistantType, registry: GroupIntegrationRegistry
) -> None:
"""Describe group on off states."""
registry.on_off_states(
{STATE_CLEANING, STATE_ON, STATE_RETURNING, STATE_ERROR}, STATE_OFF
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RoonServer.__init__ | (self, hass, config_entry) | Initialize the system. | Initialize the system. | def __init__(self, hass, config_entry):
"""Initialize the system."""
self.config_entry = config_entry
self.hass = hass
self.roonapi = None
self.all_player_ids = set()
self.all_playlists = []
self.offline_devices = set()
self._exit = False
self._roo... | [
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RoonServer.host | (self) | Return the host of this server. | Return the host of this server. | def host(self):
"""Return the host of this server."""
return self.config_entry.data[CONF_HOST] | [
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RoonServer.async_setup | (self, tries=0) | Set up a roon server based on host parameter. | Set up a roon server based on host parameter. | async def async_setup(self, tries=0):
"""Set up a roon server based on host parameter."""
host = self.host
hass = self.hass
token = self.config_entry.data[CONF_API_KEY]
_LOGGER.debug("async_setup: %s %s", token, host)
self.roonapi = RoonApi(ROON_APPINFO, token, host, bloc... | [
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RoonServer.async_reset | (self) | Reset this connection to default state.
Will cancel any scheduled setup retry and will unload
the config entry.
| Reset this connection to default state. | async def async_reset(self):
"""Reset this connection to default state.
Will cancel any scheduled setup retry and will unload
the config entry.
"""
self.stop_roon()
return True | [
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RoonServer.zones | (self) | Return list of zones. | Return list of zones. | def zones(self):
"""Return list of zones."""
return self.roonapi.zones | [
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] | python | en | ['en', 'nl', 'en'] | True |
RoonServer.add_player_id | (self, entity_id, roon_name) | Register a roon player. | Register a roon player. | def add_player_id(self, entity_id, roon_name):
"""Register a roon player."""
self._roon_name_by_id[entity_id] = roon_name | [
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] | python | en | ['en', 'en', 'en'] | True |
RoonServer.roon_name | (self, entity_id) | Get the name of the roon player from entity_id. | Get the name of the roon player from entity_id. | def roon_name(self, entity_id):
"""Get the name of the roon player from entity_id."""
return self._roon_name_by_id.get(entity_id) | [
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RoonServer.stop_roon | (self) | Stop background worker. | Stop background worker. | def stop_roon(self):
"""Stop background worker."""
self.roonapi.stop()
self._exit = True | [
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RoonServer.roonapi_state_callback | (self, event, changed_zones) | Callbacks from the roon api websockets. | Callbacks from the roon api websockets. | def roonapi_state_callback(self, event, changed_zones):
"""Callbacks from the roon api websockets."""
self.hass.add_job(self.async_update_changed_players(changed_zones)) | [
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RoonServer.async_do_loop | (self) | Background work loop. | Background work loop. | async def async_do_loop(self):
"""Background work loop."""
self._exit = False
while not self._exit:
await self.async_update_players()
# await self.async_update_playlists()
await asyncio.sleep(FULL_SYNC_INTERVAL) | [
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RoonServer.async_update_changed_players | (self, changed_zones_ids) | Update the players which were reported as changed by the Roon API. | Update the players which were reported as changed by the Roon API. | async def async_update_changed_players(self, changed_zones_ids):
"""Update the players which were reported as changed by the Roon API."""
for zone_id in changed_zones_ids:
if zone_id not in self.roonapi.zones:
# device was removed ?
continue
zone =... | [
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RoonServer.async_update_players | (self) | Periodic full scan of all devices. | Periodic full scan of all devices. | async def async_update_players(self):
"""Periodic full scan of all devices."""
zone_ids = self.roonapi.zones.keys()
await self.async_update_changed_players(zone_ids)
# check for any removed devices
all_devs = {}
for zone in self.roonapi.zones.values():
for dev... | [
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RoonServer.async_update_playlists | (self) | Store lists in memory with all playlists - could be used by a custom lovelace card. | Store lists in memory with all playlists - could be used by a custom lovelace card. | async def async_update_playlists(self):
"""Store lists in memory with all playlists - could be used by a custom lovelace card."""
all_playlists = []
roon_playlists = self.roonapi.playlists()
if roon_playlists and "items" in roon_playlists:
all_playlists += [item["title"] for ... | [
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RoonServer.async_create_player_data | (self, zone, output) | Create player object dict by combining zone with output. | Create player object dict by combining zone with output. | async def async_create_player_data(self, zone, output):
"""Create player object dict by combining zone with output."""
new_dict = zone.copy()
new_dict.update(output)
new_dict.pop("outputs")
new_dict["host"] = self.host
new_dict["is_synced"] = len(zone["outputs"]) > 1
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] | python | en | ['en', 'en', 'en'] | True |
_generate_mock_feed_entry | (
external_id,
title,
alert_level,
distance_to_home,
coordinates,
attribution=None,
activity=None,
hazards=None,
) | Construct a mock feed entry for testing purposes. | Construct a mock feed entry for testing purposes. | def _generate_mock_feed_entry(
external_id,
title,
alert_level,
distance_to_home,
coordinates,
attribution=None,
activity=None,
hazards=None,
):
"""Construct a mock feed entry for testing purposes."""
feed_entry = MagicMock()
feed_entry.external_id = external_id
feed_entr... | [
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async_setup | (hass, config) | Set up the ProgettiHWSW Automation component. | Set up the ProgettiHWSW Automation component. | async def async_setup(hass, config):
"""Set up the ProgettiHWSW Automation component."""
hass.data[DOMAIN] = {}
return True | [
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async_setup_entry | (hass: HomeAssistant, entry: ConfigEntry) | Set up ProgettiHWSW Automation from a config entry. | Set up ProgettiHWSW Automation from a config entry. | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):
"""Set up ProgettiHWSW Automation from a config entry."""
hass.data[DOMAIN][entry.entry_id] = ProgettiHWSWAPI(
f'{entry.data["host"]}:{entry.data["port"]}'
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# Check board validation again to load new values to API.
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