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test_send_preset_mode_device_timeout
( hass, discovery, device, mock_now, preset )
Test for sending preset mode command to the device with a device timeout.
Test for sending preset mode command to the device with a device timeout.
async def test_send_preset_mode_device_timeout( hass, discovery, device, mock_now, preset ): """Test for sending preset mode command to the device with a device timeout.""" device().push_state_update.side_effect = DeviceTimeoutError await async_setup_gree(hass) next_update = mock_now + timedelta(m...
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[ 435, 0 ]
[ 457, 59 ]
python
en
['en', 'en', 'en']
True
test_update_preset_mode
(hass, discovery, device, mock_now, preset)
Test for updating preset mode from the device.
Test for updating preset mode from the device.
async def test_update_preset_mode(hass, discovery, device, mock_now, preset): """Test for updating preset mode from the device.""" device().steady_heat = preset == PRESET_AWAY device().power_save = preset == PRESET_ECO device().sleep = preset == PRESET_SLEEP device().turbo = preset == PRESET_BOOST ...
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[ 463, 0 ]
[ 479, 59 ]
python
en
['en', 'en', 'en']
True
test_send_hvac_mode
(hass, discovery, device, mock_now, hvac_mode)
Test for sending hvac mode command to the device.
Test for sending hvac mode command to the device.
async def test_send_hvac_mode(hass, discovery, device, mock_now, hvac_mode): """Test for sending hvac mode command to the device.""" await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5) with patch("homeassistant.util.dt.utcnow", return_value=next_update): async_fire_time_c...
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[ 493, 0 ]
[ 511, 35 ]
python
en
['en', 'en', 'en']
True
test_send_hvac_mode_device_timeout
( hass, discovery, device, mock_now, hvac_mode )
Test for sending hvac mode command to the device with a device timeout.
Test for sending hvac mode command to the device with a device timeout.
async def test_send_hvac_mode_device_timeout( hass, discovery, device, mock_now, hvac_mode ): """Test for sending hvac mode command to the device with a device timeout.""" device().push_state_update.side_effect = DeviceTimeoutError await async_setup_gree(hass) next_update = mock_now + timedelta(mi...
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[ 518, 0 ]
[ 540, 35 ]
python
en
['en', 'en', 'en']
True
test_update_hvac_mode
(hass, discovery, device, mock_now, hvac_mode)
Test for updating hvac mode from the device.
Test for updating hvac mode from the device.
async def test_update_hvac_mode(hass, discovery, device, mock_now, hvac_mode): """Test for updating hvac mode from the device.""" device().power = hvac_mode != HVAC_MODE_OFF device().mode = HVAC_MODES_REVERSE.get(hvac_mode) await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5)...
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[ 554, 0 ]
[ 568, 35 ]
python
en
['en', 'en', 'en']
True
test_send_fan_mode
(hass, discovery, device, mock_now, fan_mode)
Test for sending fan mode command to the device.
Test for sending fan mode command to the device.
async def test_send_fan_mode(hass, discovery, device, mock_now, fan_mode): """Test for sending fan mode command to the device.""" await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5) with patch("homeassistant.util.dt.utcnow", return_value=next_update): async_fire_time_chan...
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[ 575, 0 ]
[ 593, 58 ]
python
en
['en', 'en', 'en']
True
test_send_invalid_fan_mode
(hass, discovery, device, mock_now)
Test for sending fan mode command to the device.
Test for sending fan mode command to the device.
async def test_send_invalid_fan_mode(hass, discovery, device, mock_now): """Test for sending fan mode command to the device.""" await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5) with patch("homeassistant.util.dt.utcnow", return_value=next_update): async_fire_time_change...
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[ 596, 0 ]
[ 615, 59 ]
python
en
['en', 'en', 'en']
True
test_send_fan_mode_device_timeout
( hass, discovery, device, mock_now, fan_mode )
Test for sending fan mode command to the device with a device timeout.
Test for sending fan mode command to the device with a device timeout.
async def test_send_fan_mode_device_timeout( hass, discovery, device, mock_now, fan_mode ): """Test for sending fan mode command to the device with a device timeout.""" device().push_state_update.side_effect = DeviceTimeoutError await async_setup_gree(hass) next_update = mock_now + timedelta(minut...
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[ 622, 0 ]
[ 644, 58 ]
python
en
['en', 'en', 'en']
True
test_update_fan_mode
(hass, discovery, device, mock_now, fan_mode)
Test for updating fan mode from the device.
Test for updating fan mode from the device.
async def test_update_fan_mode(hass, discovery, device, mock_now, fan_mode): """Test for updating fan mode from the device.""" device().fan_speed = FAN_MODES_REVERSE.get(fan_mode) await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5) with patch("homeassistant.util.dt.utcnow", ...
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[ 651, 0 ]
[ 664, 58 ]
python
en
['en', 'en', 'en']
True
test_send_swing_mode
(hass, discovery, device, mock_now, swing_mode)
Test for sending swing mode command to the device.
Test for sending swing mode command to the device.
async def test_send_swing_mode(hass, discovery, device, mock_now, swing_mode): """Test for sending swing mode command to the device.""" await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5) with patch("homeassistant.util.dt.utcnow", return_value=next_update): async_fire_tim...
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[ 670, 0 ]
[ 688, 62 ]
python
en
['en', 'en', 'en']
True
test_send_invalid_swing_mode
(hass, discovery, device, mock_now)
Test for sending swing mode command to the device.
Test for sending swing mode command to the device.
async def test_send_invalid_swing_mode(hass, discovery, device, mock_now): """Test for sending swing mode command to the device.""" await async_setup_gree(hass) next_update = mock_now + timedelta(minutes=5) with patch("homeassistant.util.dt.utcnow", return_value=next_update): async_fire_time_ch...
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[ 691, 0 ]
[ 710, 61 ]
python
en
['en', 'en', 'en']
True
test_send_swing_mode_device_timeout
( hass, discovery, device, mock_now, swing_mode )
Test for sending swing mode command to the device with a device timeout.
Test for sending swing mode command to the device with a device timeout.
async def test_send_swing_mode_device_timeout( hass, discovery, device, mock_now, swing_mode ): """Test for sending swing mode command to the device with a device timeout.""" device().push_state_update.side_effect = DeviceTimeoutError await async_setup_gree(hass) next_update = mock_now + timedelta...
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[ 716, 0 ]
[ 738, 62 ]
python
en
['en', 'en', 'en']
True
test_update_swing_mode
(hass, discovery, device, mock_now, swing_mode)
Test for updating swing mode from the device.
Test for updating swing mode from the device.
async def test_update_swing_mode(hass, discovery, device, mock_now, swing_mode): """Test for updating swing mode from the device.""" device().horizontal_swing = ( HorizontalSwing.FullSwing if swing_mode in (SWING_BOTH, SWING_HORIZONTAL) else HorizontalSwing.Default ) device().ver...
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[ 744, 0 ]
[ 766, 62 ]
python
en
['en', 'en', 'en']
True
test_name
(hass, discovery, device)
Test for name property.
Test for name property.
async def test_name(hass, discovery, device): """Test for name property.""" await async_setup_gree(hass) state = hass.states.get(ENTITY_ID) assert state.attributes[ATTR_FRIENDLY_NAME] == "fake-device-1"
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[ 769, 0 ]
[ 773, 66 ]
python
en
['en', 'en', 'en']
True
test_supported_features_with_turnon
(hass, discovery, device)
Test for supported_features property.
Test for supported_features property.
async def test_supported_features_with_turnon(hass, discovery, device): """Test for supported_features property.""" await async_setup_gree(hass) state = hass.states.get(ENTITY_ID) assert state.attributes[ATTR_SUPPORTED_FEATURES] == SUPPORTED_FEATURES
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[ 776, 0 ]
[ 780, 74 ]
python
en
['en', 'en', 'en']
True
build_tf_to_pytorch_map
(model, config)
A map of modules from TF to PyTorch. This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
A map of modules from TF to PyTorch. This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
def build_tf_to_pytorch_map(model, config): """ A map of modules from TF to PyTorch. This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible. """ tf_to_pt_map = {} if hasattr(model, 'transformer'): # We are loading in a TransfoXLLMHeadModel...
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[ 54, 0 ]
[ 124, 23 ]
python
en
['en', 'en', 'en']
True
load_tf_weights_in_transfo_xl
(model, config, tf_path)
Load tf checkpoints in a pytorch model
Load tf checkpoints in a pytorch model
def load_tf_weights_in_transfo_xl(model, config, tf_path): """ Load tf checkpoints in a pytorch model """ try: import numpy as np import tensorflow as tf except ImportError: print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " ...
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[ 126, 0 ]
[ 179, 16 ]
python
en
['en', 'en', 'en']
True
TransfoXLConfig.__init__
(self, vocab_size_or_config_json_file=267735, cutoffs=[20000, 40000, 200000], d_model=1024, d_embed=1024, n_head=16, d_head=64, d_inner=4096, div_val=4, pre_lnorm=Fals...
Constructs TransfoXLConfig. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `TransfoXLModel` or a configuration json file. cutoffs: cutoffs for the adaptive softmax d_model: Dimensionality of the model's hidden states. d_embed: Dimensiona...
Constructs TransfoXLConfig.
def __init__(self, vocab_size_or_config_json_file=267735, cutoffs=[20000, 40000, 200000], d_model=1024, d_embed=1024, n_head=16, d_head=64, d_inner=4096, div_val=4, pr...
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[ 185, 4 ]
[ 287, 83 ]
python
en
['en', 'en', 'it']
False
TransfoXLConfig.from_dict
(cls, json_object)
Constructs a `TransfoXLConfig` from a Python dictionary of parameters.
Constructs a `TransfoXLConfig` from a Python dictionary of parameters.
def from_dict(cls, json_object): """Constructs a `TransfoXLConfig` from a Python dictionary of parameters.""" config = TransfoXLConfig(vocab_size_or_config_json_file=-1) for key, value in json_object.items(): config.__dict__[key] = value return config
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[ 290, 4 ]
[ 295, 21 ]
python
en
['en', 'en', 'en']
True
TransfoXLConfig.from_json_file
(cls, json_file)
Constructs a `TransfoXLConfig` from a json file of parameters.
Constructs a `TransfoXLConfig` from a json file of parameters.
def from_json_file(cls, json_file): """Constructs a `TransfoXLConfig` from a json file of parameters.""" with open(json_file, "r", encoding='utf-8') as reader: text = reader.read() return cls.from_dict(json.loads(text))
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[ 298, 4 ]
[ 302, 46 ]
python
en
['en', 'en', 'en']
True
TransfoXLConfig.to_dict
(self)
Serializes this instance to a Python dictionary.
Serializes this instance to a Python dictionary.
def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output
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[ 307, 4 ]
[ 310, 21 ]
python
en
['en', 'en', 'en']
True
TransfoXLConfig.to_json_string
(self)
Serializes this instance to a JSON string.
Serializes this instance to a JSON string.
def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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[ 312, 4 ]
[ 314, 74 ]
python
en
['en', 'en', 'en']
True
TransfoXLPreTrainedModel.init_weights
(self, m)
Initialize the weights.
Initialize the weights.
def init_weights(self, m): """ Initialize the weights. """ classname = m.__class__.__name__ if classname.find('Linear') != -1: if hasattr(m, 'weight') and m.weight is not None: self.init_weight(m.weight) if hasattr(m, 'bias') and m.bias is not None...
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[ 839, 4 ]
[ 878, 40 ]
python
en
['en', 'en', 'en']
True
TransfoXLPreTrainedModel.from_pretrained
(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs)
Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: pretrained_model_name_or_path: either: - a str with the name of a pre-trained model to load selected in the...
Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed.
def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs): """ Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if n...
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[ 884, 4 ]
[ 979, 20 ]
python
en
['en', 'error', 'th']
False
TransfoXLModel.forward
(self, input_ids, mems=None)
Params: input_ids :: [bsz, len] mems :: optional mems from previous forwar passes (or init_mems) list (num layers) of mem states at the entry of each layer shape :: [self.config.mem_len, bsz, self.config.d_model] Note that ...
Params: input_ids :: [bsz, len] mems :: optional mems from previous forwar passes (or init_mems) list (num layers) of mem states at the entry of each layer shape :: [self.config.mem_len, bsz, self.config.d_model] Note that ...
def forward(self, input_ids, mems=None): """ Params: input_ids :: [bsz, len] mems :: optional mems from previous forwar passes (or init_mems) list (num layers) of mem states at the entry of each layer shape :: [self.config.mem_len, bsz,...
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[ 1232, 4 ]
[ 1256, 38 ]
python
en
['en', 'lt', 'pt']
False
TransfoXLLMHeadModel.tie_weights
(self)
Run this to be sure output and input (adaptive) softmax weights are tied
Run this to be sure output and input (adaptive) softmax weights are tied
def tie_weights(self): """ Run this to be sure output and input (adaptive) softmax weights are tied """ # sampled softmax if self.sample_softmax > 0: if self.config.tie_weight: self.out_layer.weight = self.transformer.word_emb.weight # adaptive softmax (includ...
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[ 1324, 4 ]
[ 1340, 87 ]
python
en
['en', 'en', 'en']
True
TransfoXLLMHeadModel.forward
(self, input_ids, target=None, mems=None)
Params: input_ids :: [bsz, len] target :: [bsz, len] Returns: tuple(softmax_output, new_mems) where: new_mems: list (num layers) of hidden states at the entry of each layer shape :: [mem_len, bsz, self.config.d_mode...
Params: input_ids :: [bsz, len] target :: [bsz, len] Returns: tuple(softmax_output, new_mems) where: new_mems: list (num layers) of hidden states at the entry of each layer shape :: [mem_len, bsz, self.config.d_mode...
def forward(self, input_ids, target=None, mems=None): """ Params: input_ids :: [bsz, len] target :: [bsz, len] Returns: tuple(softmax_output, new_mems) where: new_mems: list (num layers) of hidden states at the entry of each layer ...
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[ 1348, 4 ]
[ 1380, 41 ]
python
en
['en', 'lt', 'pt']
False
async_setup
(hass: HomeAssistant, config: ConfigType)
Set up the BSB-Lan component.
Set up the BSB-Lan component.
async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool: """Set up the BSB-Lan component.""" return True
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[ 18, 0 ]
[ 20, 15 ]
python
en
['en', 'en', 'en']
True
async_setup_entry
(hass: HomeAssistant, entry: ConfigEntry)
Set up BSB-Lan from a config entry.
Set up BSB-Lan from a config entry.
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Set up BSB-Lan from a config entry.""" session = async_get_clientsession(hass) bsblan = BSBLan( entry.data[CONF_HOST], passkey=entry.data[CONF_PASSKEY], port=entry.data[CONF_PORT], session=sessi...
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[ 23, 0 ]
[ 46, 15 ]
python
en
['en', 'en', 'en']
True
async_unload_entry
(hass: HomeAssistant, entry: ConfigEntry)
Unload BSBLan config entry.
Unload BSBLan config entry.
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Unload BSBLan config entry.""" await hass.config_entries.async_forward_entry_unload(entry, CLIMATE_DOMAIN) # Cleanup del hass.data[DOMAIN][entry.entry_id] if not hass.data[DOMAIN]: del hass.data[DOMAIN] ...
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[ 49, 0 ]
[ 59, 15 ]
python
da
['da', 'es', 'en']
False
async_setup
(hass: HomeAssistant, config: Dict)
Set up the IPP component.
Set up the IPP component.
async def async_setup(hass: HomeAssistant, config: Dict) -> bool: """Set up the IPP component.""" hass.data.setdefault(DOMAIN, {}) return True
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[ 41, 0 ]
[ 44, 15 ]
python
en
['en', 'en', 'en']
True
async_setup_entry
(hass: HomeAssistant, entry: ConfigEntry)
Set up IPP from a config entry.
Set up IPP from a config entry.
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Set up IPP from a config entry.""" # Create IPP instance for this entry coordinator = IPPDataUpdateCoordinator( hass, host=entry.data[CONF_HOST], port=entry.data[CONF_PORT], base_path=entry.data...
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[ 47, 0 ]
[ 71, 15 ]
python
en
['en', 'en', 'en']
True
async_unload_entry
(hass: HomeAssistant, entry: ConfigEntry)
Unload a config entry.
Unload a config entry.
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Unload a config entry.""" unload_ok = all( await asyncio.gather( *[ hass.config_entries.async_forward_entry_unload(entry, component) for component in PLATFORMS ] ...
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[ 74, 0 ]
[ 88, 20 ]
python
en
['en', 'es', 'en']
True
IPPDataUpdateCoordinator.__init__
( self, hass: HomeAssistant, *, host: str, port: int, base_path: str, tls: bool, verify_ssl: bool, )
Initialize global IPP data updater.
Initialize global IPP data updater.
def __init__( self, hass: HomeAssistant, *, host: str, port: int, base_path: str, tls: bool, verify_ssl: bool, ): """Initialize global IPP data updater.""" self.ipp = IPP( host=host, port=port, base_p...
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[ 94, 4 ]
[ 119, 9 ]
python
en
['en', 'en', 'it']
True
IPPDataUpdateCoordinator._async_update_data
(self)
Fetch data from IPP.
Fetch data from IPP.
async def _async_update_data(self) -> IPPPrinter: """Fetch data from IPP.""" try: return await self.ipp.printer() except IPPError as error: raise UpdateFailed(f"Invalid response from API: {error}") from error
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[ 121, 4 ]
[ 126, 80 ]
python
en
['en', 'en', 'en']
True
IPPEntity.__init__
( self, *, entry_id: str, device_id: str, coordinator: IPPDataUpdateCoordinator, name: str, icon: str, enabled_default: bool = True, )
Initialize the IPP entity.
Initialize the IPP entity.
def __init__( self, *, entry_id: str, device_id: str, coordinator: IPPDataUpdateCoordinator, name: str, icon: str, enabled_default: bool = True, ) -> None: """Initialize the IPP entity.""" super().__init__(coordinator) self._dev...
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[ 132, 4 ]
[ 148, 25 ]
python
en
['en', 'en', 'en']
True
IPPEntity.name
(self)
Return the name of the entity.
Return the name of the entity.
def name(self) -> str: """Return the name of the entity.""" return self._name
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[ 151, 4 ]
[ 153, 25 ]
python
en
['en', 'en', 'en']
True
IPPEntity.icon
(self)
Return the mdi icon of the entity.
Return the mdi icon of the entity.
def icon(self) -> str: """Return the mdi icon of the entity.""" return self._icon
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[ 156, 4 ]
[ 158, 25 ]
python
en
['en', 'en', 'en']
True
IPPEntity.entity_registry_enabled_default
(self)
Return if the entity should be enabled when first added to the entity registry.
Return if the entity should be enabled when first added to the entity registry.
def entity_registry_enabled_default(self) -> bool: """Return if the entity should be enabled when first added to the entity registry.""" return self._enabled_default
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[ 161, 4 ]
[ 163, 36 ]
python
en
['en', 'en', 'en']
True
IPPEntity.device_info
(self)
Return device information about this IPP device.
Return device information about this IPP device.
def device_info(self) -> Dict[str, Any]: """Return device information about this IPP device.""" if self._device_id is None: return None return { ATTR_IDENTIFIERS: {(DOMAIN, self._device_id)}, ATTR_NAME: self.coordinator.data.info.name, ATTR_MANUFA...
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[ 166, 4 ]
[ 177, 9 ]
python
en
['en', 'en', 'en']
True
conv2d
(x_input, w_matrix)
conv2d returns a 2d convolution layer with full stride.
conv2d returns a 2d convolution layer with full stride.
def conv2d(x_input, w_matrix): """conv2d returns a 2d convolution layer with full stride.""" return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
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[ 124, 0 ]
[ 126, 80 ]
python
en
['en', 'en', 'en']
True
max_pool
(x_input, pool_size)
max_pool downsamples a feature map by 2X.
max_pool downsamples a feature map by 2X.
def max_pool(x_input, pool_size): """max_pool downsamples a feature map by 2X.""" return tf.nn.max_pool(x_input, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME')
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[ 129, 0 ]
[ 132, 79 ]
python
en
['en', 'en', 'en']
True
weight_variable
(shape)
weight_variable generates a weight variable of a given shape.
weight_variable generates a weight variable of a given shape.
def weight_variable(shape): """weight_variable generates a weight variable of a given shape.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial)
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[ 135, 0 ]
[ 138, 31 ]
python
en
['en', 'en', 'en']
True
bias_variable
(shape)
bias_variable generates a bias variable of a given shape.
bias_variable generates a bias variable of a given shape.
def bias_variable(shape): """bias_variable generates a bias variable of a given shape.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial)
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[ 141, 0 ]
[ 144, 31 ]
python
en
['en', 'en', 'en']
True
download_mnist_retry
(data_dir, max_num_retries=20)
Try to download mnist dataset and avoid errors
Try to download mnist dataset and avoid errors
def download_mnist_retry(data_dir, max_num_retries=20): """Try to download mnist dataset and avoid errors""" for _ in range(max_num_retries): try: return input_data.read_data_sets(data_dir, one_hot=True) except tf.errors.AlreadyExistsError: time.sleep(1) raise Excepti...
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[ 146, 0 ]
[ 153, 48 ]
python
en
['en', 'en', 'en']
True
main
(params)
Main function, build mnist network, run and send result to NNI.
Main function, build mnist network, run and send result to NNI.
def main(params): ''' Main function, build mnist network, run and send result to NNI. ''' # Import data mnist = download_mnist_retry(params['data_dir']) print('Mnist download data done.') logger.debug('Mnist download data done.') # Create the model # Build the graph for the deep net...
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[ 155, 0 ]
[ 208, 47 ]
python
en
['en', 'error', 'th']
False
get_params
()
Get parameters from command line
Get parameters from command line
def get_params(): ''' Get parameters from command line ''' parser = argparse.ArgumentParser() parser.add_argument("--data_dir", type=str, default='/tmp/tensorflow/mnist/input_data', help="data directory") parser.add_argument("--dropout_rate", type=float, default=0.5, help="dropout rate") parser.add_...
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[ 210, 0 ]
[ 225, 15 ]
python
en
['en', 'en', 'en']
True
MnistNetwork.build_network
(self)
Building network for mnist
Building network for mnist
def build_network(self): ''' Building network for mnist ''' # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is only one here, since images are # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. with tf.n...
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[ 47, 4 ]
[ 121, 56 ]
python
en
['en', 'error', 'th']
False
_patch_media_setup
()
Patch media_player.async_setup_entry.
Patch media_player.async_setup_entry.
def _patch_media_setup(): """Patch media_player.async_setup_entry.""" async def _async_return(): return True return patch( "homeassistant.components.songpal.media_player.async_setup_entry", side_effect=_async_return, )
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[ 15, 0 ]
[ 24, 5 ]
python
en
['en', 'cs', 'en']
False
test_setup_empty
(hass)
Test setup without any configuration.
Test setup without any configuration.
async def test_setup_empty(hass): """Test setup without any configuration.""" with _patch_media_setup() as setup: assert await async_setup_component(hass, songpal.DOMAIN, {}) is True await hass.async_block_till_done() setup.assert_not_called()
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[ 27, 0 ]
[ 32, 29 ]
python
en
['en', 'zu', 'en']
True
test_setup
(hass)
Test setup the platform.
Test setup the platform.
async def test_setup(hass): """Test setup the platform.""" mocked_device = _create_mocked_device() with _patch_config_flow_device(mocked_device), _patch_media_setup() as setup: assert ( await async_setup_component( hass, songpal.DOMAIN, {songpal.DOMAIN: [CONF_DATA]} ...
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[ 35, 0 ]
[ 48, 30 ]
python
en
['en', 'da', 'en']
True
test_unload
(hass)
Test unload entity.
Test unload entity.
async def test_unload(hass): """Test unload entity.""" entry = MockConfigEntry(domain=songpal.DOMAIN, data=CONF_DATA) entry.add_to_hass(hass) mocked_device = _create_mocked_device() with _patch_config_flow_device(mocked_device), _patch_media_player_device( mocked_device ): asser...
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[ 51, 0 ]
[ 65, 64 ]
python
de
['de', 'fr', 'it']
False
TFT5LayerNorm.__init__
(self, epsilon=1e-6, **kwargs)
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
def __init__(self, epsilon=1e-6, **kwargs): """ Construct a layernorm module in the T5 style No bias and no subtraction of mean. """ super().__init__(**kwargs) self.variance_epsilon = epsilon
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[ 74, 4 ]
[ 79, 39 ]
python
en
['en', 'error', 'th']
False
TFT5LayerNorm.build
(self, input_shape)
Build shared word embedding layer
Build shared word embedding layer
def build(self, input_shape): """Build shared word embedding layer """ self.weight = self.add_weight("weight", shape=(input_shape[-1],), initializer="ones") super().build(input_shape)
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[ 81, 4 ]
[ 84, 34 ]
python
en
['en', 'en', 'en']
True
TFT5Attention._relative_position_bucket
(relative_position, bidirectional=True, num_buckets=32, max_distance=128)
Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_positi...
Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate rel...
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[ 187, 4 ]
[ 229, 31 ]
python
en
['en', 'error', 'th']
False
TFT5Attention.compute_bias
(self, query_length, key_length)
Compute binned relative position bias
Compute binned relative position bias
def compute_bias(self, query_length, key_length): """ Compute binned relative position bias """ context_position = tf.range(query_length)[:, None] memory_position = tf.range(key_length)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) ...
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[ 231, 4 ]
[ 247, 21 ]
python
en
['en', 'en', 'nl']
True
TFT5Attention.call
( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, training=False, output_attentions=False, )
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
def call( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, training=False, output_attentions=False, ): """ ...
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[ 249, 4 ]
[ 371, 22 ]
python
en
['en', 'error', 'th']
False
async_setup_entity_state
( config, async_add_entities, config_entry, discovery_data )
Set up a State MQTT Vacuum.
Set up a State MQTT Vacuum.
async def async_setup_entity_state( config, async_add_entities, config_entry, discovery_data ): """Set up a State MQTT Vacuum.""" async_add_entities([MqttStateVacuum(config, config_entry, discovery_data)])
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[ 156, 0 ]
[ 160, 79 ]
python
en
['en', 'co', 'en']
True
MqttStateVacuum.__init__
(self, config, config_entry, discovery_info)
Initialize the vacuum.
Initialize the vacuum.
def __init__(self, config, config_entry, discovery_info): """Initialize the vacuum.""" self._state = None self._state_attrs = {} self._fan_speed_list = [] self._sub_state = None self._unique_id = config.get(CONF_UNIQUE_ID) # Load config self._setup_from_c...
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[ 172, 4 ]
[ 188, 72 ]
python
en
['en', 'la', 'en']
True
MqttStateVacuum.discovery_update
(self, discovery_payload)
Handle updated discovery message.
Handle updated discovery message.
async def discovery_update(self, discovery_payload): """Handle updated discovery message.""" config = PLATFORM_SCHEMA_STATE(discovery_payload) self._setup_from_config(config) await self.attributes_discovery_update(config) await self.availability_discovery_update(config) a...
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[ 214, 4 ]
[ 222, 35 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.async_added_to_hass
(self)
Subscribe MQTT events.
Subscribe MQTT events.
async def async_added_to_hass(self): """Subscribe MQTT events.""" await super().async_added_to_hass() await self._subscribe_topics()
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[ 224, 4 ]
[ 227, 38 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.async_will_remove_from_hass
(self)
Unsubscribe when removed.
Unsubscribe when removed.
async def async_will_remove_from_hass(self): """Unsubscribe when removed.""" self._sub_state = await subscription.async_unsubscribe_topics( self.hass, self._sub_state ) await MqttAttributes.async_will_remove_from_hass(self) await MqttAvailability.async_will_remove_fro...
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[ 229, 4 ]
[ 236, 67 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum._subscribe_topics
(self)
(Re)Subscribe to topics.
(Re)Subscribe to topics.
async def _subscribe_topics(self): """(Re)Subscribe to topics.""" topics = {} @callback @log_messages(self.hass, self.entity_id) def state_message_received(msg): """Handle state MQTT message.""" payload = json.loads(msg.payload) if STATE in pa...
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[ 238, 4 ]
[ 261, 9 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.name
(self)
Return the name of the vacuum.
Return the name of the vacuum.
def name(self): """Return the name of the vacuum.""" return self._name
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[ 264, 4 ]
[ 266, 25 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.state
(self)
Return state of vacuum.
Return state of vacuum.
def state(self): """Return state of vacuum.""" return self._state
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[ 269, 4 ]
[ 271, 26 ]
python
en
['en', 'la', 'en']
True
MqttStateVacuum.unique_id
(self)
Return a unique ID.
Return a unique ID.
def unique_id(self): """Return a unique ID.""" return self._unique_id
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[ 274, 4 ]
[ 276, 30 ]
python
ca
['fr', 'ca', 'en']
False
MqttStateVacuum.fan_speed
(self)
Return fan speed of the vacuum.
Return fan speed of the vacuum.
def fan_speed(self): """Return fan speed of the vacuum.""" return self._state_attrs.get(FAN_SPEED, 0)
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[ 279, 4 ]
[ 281, 50 ]
python
en
['en', 'fy', 'en']
True
MqttStateVacuum.fan_speed_list
(self)
Return fan speed list of the vacuum.
Return fan speed list of the vacuum.
def fan_speed_list(self): """Return fan speed list of the vacuum.""" return self._fan_speed_list
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[ 284, 4 ]
[ 286, 35 ]
python
en
['en', 'fy', 'en']
True
MqttStateVacuum.battery_level
(self)
Return battery level of the vacuum.
Return battery level of the vacuum.
def battery_level(self): """Return battery level of the vacuum.""" return max(0, min(100, self._state_attrs.get(BATTERY, 0)))
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[ 289, 4 ]
[ 291, 66 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.supported_features
(self)
Flag supported features.
Flag supported features.
def supported_features(self): """Flag supported features.""" return self._supported_features
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[ 294, 4 ]
[ 296, 39 ]
python
en
['da', 'en', 'en']
True
MqttStateVacuum.async_start
(self)
Start the vacuum.
Start the vacuum.
async def async_start(self): """Start the vacuum.""" if self.supported_features & SUPPORT_START == 0: return None mqtt.async_publish( self.hass, self._command_topic, self._config[CONF_PAYLOAD_START], self._config[CONF_QOS], ...
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[ 298, 4 ]
[ 308, 9 ]
python
en
['en', 'la', 'en']
True
MqttStateVacuum.async_pause
(self)
Pause the vacuum.
Pause the vacuum.
async def async_pause(self): """Pause the vacuum.""" if self.supported_features & SUPPORT_PAUSE == 0: return None mqtt.async_publish( self.hass, self._command_topic, self._config[CONF_PAYLOAD_PAUSE], self._config[CONF_QOS], ...
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[ 310, 4 ]
[ 320, 9 ]
python
en
['en', 'la', 'en']
True
MqttStateVacuum.async_stop
(self, **kwargs)
Stop the vacuum.
Stop the vacuum.
async def async_stop(self, **kwargs): """Stop the vacuum.""" if self.supported_features & SUPPORT_STOP == 0: return None mqtt.async_publish( self.hass, self._command_topic, self._config[CONF_PAYLOAD_STOP], self._config[CONF_QOS], ...
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[ 322, 4 ]
[ 332, 9 ]
python
en
['en', 'la', 'en']
True
MqttStateVacuum.async_set_fan_speed
(self, fan_speed, **kwargs)
Set fan speed.
Set fan speed.
async def async_set_fan_speed(self, fan_speed, **kwargs): """Set fan speed.""" if (self.supported_features & SUPPORT_FAN_SPEED == 0) or ( fan_speed not in self._fan_speed_list ): return None mqtt.async_publish( self.hass, self._set_fan_spee...
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[ 334, 4 ]
[ 346, 9 ]
python
fy
['sv', 'fy', 'ur']
False
MqttStateVacuum.async_return_to_base
(self, **kwargs)
Tell the vacuum to return to its dock.
Tell the vacuum to return to its dock.
async def async_return_to_base(self, **kwargs): """Tell the vacuum to return to its dock.""" if self.supported_features & SUPPORT_RETURN_HOME == 0: return None mqtt.async_publish( self.hass, self._command_topic, self._config[CONF_PAYLOAD_RETURN_TO_...
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[ 348, 4 ]
[ 358, 9 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.async_clean_spot
(self, **kwargs)
Perform a spot clean-up.
Perform a spot clean-up.
async def async_clean_spot(self, **kwargs): """Perform a spot clean-up.""" if self.supported_features & SUPPORT_CLEAN_SPOT == 0: return None mqtt.async_publish( self.hass, self._command_topic, self._config[CONF_PAYLOAD_CLEAN_SPOT], self...
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[ 360, 4 ]
[ 370, 9 ]
python
en
['pt', 'en', 'en']
True
MqttStateVacuum.async_locate
(self, **kwargs)
Locate the vacuum (usually by playing a song).
Locate the vacuum (usually by playing a song).
async def async_locate(self, **kwargs): """Locate the vacuum (usually by playing a song).""" if self.supported_features & SUPPORT_LOCATE == 0: return None mqtt.async_publish( self.hass, self._command_topic, self._config[CONF_PAYLOAD_LOCATE], ...
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[ 372, 4 ]
[ 382, 9 ]
python
en
['en', 'en', 'en']
True
MqttStateVacuum.async_send_command
(self, command, params=None, **kwargs)
Send a command to a vacuum cleaner.
Send a command to a vacuum cleaner.
async def async_send_command(self, command, params=None, **kwargs): """Send a command to a vacuum cleaner.""" if self.supported_features & SUPPORT_SEND_COMMAND == 0: return None if params: message = {"command": command} message.update(params) messa...
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[ 384, 4 ]
[ 400, 9 ]
python
en
['en', 'en', 'en']
True
test_config_entry_not_ready
( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker )
Test configuration entry not ready on library error.
Test configuration entry not ready on library error.
async def test_config_entry_not_ready( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker ) -> None: """Test configuration entry not ready on library error.""" aioclient_mock.post("http://127.0.0.1:10000/retrieve", exc=aiohttp.ClientError) entry = await init_integration(hass, aioclient_mock) a...
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[ 12, 0 ]
[ 18, 49 ]
python
en
['en', 'en', 'en']
True
test_config_entry_empty_reply
( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker )
Test configuration entry not ready when library returns False.
Test configuration entry not ready when library returns False.
async def test_config_entry_empty_reply( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker ) -> None: """Test configuration entry not ready when library returns False.""" with patch("pyatag.AtagOne.update", return_value=False): entry = await init_integration(hass, aioclient_mock) asse...
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[ 21, 0 ]
[ 27, 53 ]
python
en
['en', 'en', 'en']
True
test_unload_config_entry
( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker )
Test the ATAG configuration entry unloading.
Test the ATAG configuration entry unloading.
async def test_unload_config_entry( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker ) -> None: """Test the ATAG configuration entry unloading.""" entry = await init_integration(hass, aioclient_mock) assert hass.data[DOMAIN] await hass.config_entries.async_unload(entry.entry_id) await ha...
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[ 30, 0 ]
[ 38, 36 ]
python
en
['en', 'en', 'en']
True
setup_platform
(hass, config, add_entities, discovery_info=None)
Set up switch platform for ADS.
Set up switch platform for ADS.
def setup_platform(hass, config, add_entities, discovery_info=None): """Set up switch platform for ADS.""" ads_hub = hass.data.get(DATA_ADS) name = config[CONF_NAME] ads_var = config[CONF_ADS_VAR] add_entities([AdsSwitch(ads_hub, name, ads_var)])
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[ 19, 0 ]
[ 26, 53 ]
python
en
['en', 'da', 'en']
True
AdsSwitch.async_added_to_hass
(self)
Register device notification.
Register device notification.
async def async_added_to_hass(self): """Register device notification.""" await self.async_initialize_device(self._ads_var, self._ads_hub.PLCTYPE_BOOL)
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[ 32, 4 ]
[ 34, 85 ]
python
en
['da', 'en', 'en']
True
AdsSwitch.is_on
(self)
Return True if the entity is on.
Return True if the entity is on.
def is_on(self): """Return True if the entity is on.""" return self._state_dict[STATE_KEY_STATE]
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[ 37, 4 ]
[ 39, 48 ]
python
en
['en', 'en', 'en']
True
AdsSwitch.turn_on
(self, **kwargs)
Turn the switch on.
Turn the switch on.
def turn_on(self, **kwargs): """Turn the switch on.""" self._ads_hub.write_by_name(self._ads_var, True, self._ads_hub.PLCTYPE_BOOL)
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[ 41, 4 ]
[ 43, 84 ]
python
en
['en', 'en', 'en']
True
AdsSwitch.turn_off
(self, **kwargs)
Turn the switch off.
Turn the switch off.
def turn_off(self, **kwargs): """Turn the switch off.""" self._ads_hub.write_by_name(self._ads_var, False, self._ads_hub.PLCTYPE_BOOL)
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[ 45, 4 ]
[ 47, 85 ]
python
en
['en', 'en', 'en']
True
load_vocab
(vocab_file)
Loads a vocabulary file into a dictionary.
Loads a vocabulary file into a dictionary.
def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() with open(vocab_file, "r", encoding="utf-8") as reader: tokens = reader.readlines() for index, token in enumerate(tokens): token = token.rstrip("\n") vocab[token] = inde...
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[ 96, 0 ]
[ 104, 16 ]
python
en
['en', 'en', 'en']
True
whitespace_tokenize
(text)
Runs basic whitespace cleaning and splitting on a piece of text.
Runs basic whitespace cleaning and splitting on a piece of text.
def whitespace_tokenize(text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() if not text: return [] tokens = text.split() return tokens
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[ 107, 0 ]
[ 113, 17 ]
python
en
['en', 'en', 'en']
True
BertTokenizer._convert_token_to_id
(self, token)
Converts a token (str) in an id using the vocab.
Converts a token (str) in an id using the vocab.
def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ return self.vocab.get(token, self.vocab.get(self.unk_token))
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[ 234, 4 ]
[ 236, 68 ]
python
en
['en', 'en', 'en']
True
BertTokenizer._convert_id_to_token
(self, index)
Converts an index (integer) in a token (str) using the vocab.
Converts an index (integer) in a token (str) using the vocab.
def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.ids_to_tokens.get(index, self.unk_token)
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[ 238, 4 ]
[ 240, 60 ]
python
en
['en', 'en', 'en']
True
BertTokenizer.convert_tokens_to_string
(self, tokens)
Converts a sequence of tokens (string) in a single string.
Converts a sequence of tokens (string) in a single string.
def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
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[ 242, 4 ]
[ 245, 25 ]
python
en
['en', 'en', 'en']
True
BertTokenizer.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 BERT sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` Args: ...
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT 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 BERT sequence has t...
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[ 247, 4 ]
[ 270, 58 ]
python
en
['en', 'error', 'th']
False
BertTokenizer.get_special_tokens_mask
( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False )
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` method. Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`,...
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` method.
def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens ...
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[ 272, 4 ]
[ 301, 51 ]
python
en
['en', 'error', 'th']
False
BertTokenizer.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. A BERT sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If :obj:`token_ids_1` is :obj:...
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format:
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. A BERT sequence pair mask has the following format: ...
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[ 303, 4 ]
[ 331, 80 ]
python
en
['en', 'error', 'th']
False
BasicTokenizer.tokenize
(self, text, never_split=None)
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer. Args: **never_split**: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see ...
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer.
def tokenize(self, text, never_split=None): """ Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer. Args: **never_split**: (`optional`) list of str Kept for backward compatibility purposes. N...
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[ 382, 4 ]
[ 417, 28 ]
python
en
['en', 'error', 'th']
False
BasicTokenizer._run_strip_accents
(self, text)
Strips accents from a piece of text.
Strips accents from a piece of text.
def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) ...
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[ 419, 4 ]
[ 428, 30 ]
python
en
['en', 'en', 'en']
True
BasicTokenizer._run_split_on_punc
(self, text, never_split=None)
Splits punctuation on a piece of text.
Splits punctuation on a piece of text.
def _run_split_on_punc(self, text, never_split=None): """Splits punctuation on a piece of text.""" if never_split is not None and text in never_split: return [text] chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): ...
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[ 430, 4 ]
[ 450, 43 ]
python
en
['en', 'en', 'en']
True
BasicTokenizer._tokenize_chinese_chars
(self, text)
Adds whitespace around any CJK character.
Adds whitespace around any CJK character.
def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") ...
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[ 452, 4 ]
[ 463, 30 ]
python
en
['en', 'en', 'en']
True
BasicTokenizer._is_chinese_char
(self, cp)
Checks whether CP is the codepoint of a CJK character.
Checks whether CP is the codepoint of a CJK character.
def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is ...
[ "def", "_is_chinese_char", "(", "self", ",", "cp", ")", ":", "# This defines a \"chinese character\" as anything in the CJK Unicode block:", "# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)", "#", "# Note that the CJK Unicode block is NOT all Japanese and Korean charac...
[ 465, 4 ]
[ 487, 20 ]
python
en
['en', 'en', 'en']
True
BasicTokenizer._clean_text
(self, text)
Performs invalid character removal and whitespace cleanup on text.
Performs invalid character removal and whitespace cleanup on text.
def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xFFFD or _is_control(char): continue if _is_whitespace(char): ...
[ "def", "_clean_text", "(", "self", ",", "text", ")", ":", "output", "=", "[", "]", "for", "char", "in", "text", ":", "cp", "=", "ord", "(", "char", ")", "if", "cp", "==", "0", "or", "cp", "==", "0xFFFD", "or", "_is_control", "(", "char", ")", "...
[ 489, 4 ]
[ 500, 30 ]
python
en
['en', 'en', 'en']
True