body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
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def generator_fn(words_file, tags_file):
'Enumerator to enumerate through words_file and associated tags_file one line at a time\n\n :param words_file: file path of the words file (one sentence per line)\n :param tags_file: file path of tags file (tags corresponding to words file)\n :return enumerator that... | -4,083,911,911,165,067,300 | Enumerator to enumerate through words_file and associated tags_file one line at a time
:param words_file: file path of the words file (one sentence per line)
:param tags_file: file path of tags file (tags corresponding to words file)
:return enumerator that enumerates over the format (words, len(words)), tags one line... | src/model/lstm_crf/main.py | generator_fn | vikasbahirwani/SequenceTagging | python | def generator_fn(words_file, tags_file):
'Enumerator to enumerate through words_file and associated tags_file one line at a time\n\n :param words_file: file path of the words file (one sentence per line)\n :param tags_file: file path of tags file (tags corresponding to words file)\n :return enumerator that... |
def input_fn(words_file, tags_file, params=None, shuffle_and_repeat=False):
"Creates tensorflow dataset using the generator_fn\n\n :param words_file: file path of the words file (one sentence per line)\n :param tags_file: file path of tags file (tags corresponding to words file)\n :param params: if not Non... | -139,622,785,079,150,910 | Creates tensorflow dataset using the generator_fn
:param words_file: file path of the words file (one sentence per line)
:param tags_file: file path of tags file (tags corresponding to words file)
:param params: if not None then model hyperparameters expected - 'buffer' (as in buffer size) and 'epochs'
:param shuffle_... | src/model/lstm_crf/main.py | input_fn | vikasbahirwani/SequenceTagging | python | def input_fn(words_file, tags_file, params=None, shuffle_and_repeat=False):
"Creates tensorflow dataset using the generator_fn\n\n :param words_file: file path of the words file (one sentence per line)\n :param tags_file: file path of tags file (tags corresponding to words file)\n :param params: if not Non... |
def model_fn(features, labels, mode, params):
'\n\n :param features: words from sentence and number of words per sentence\n :param labels: One tag per word\n :param mode: tf.estimator.ModeKeys.TRAIN or tf.estimator.ModeKeys.PREDICT or tf.estimator.ModeKeys.EVAL\n :param params: dictionary of hyper pa... | 8,180,957,206,196,648,000 | :param features: words from sentence and number of words per sentence
:param labels: One tag per word
:param mode: tf.estimator.ModeKeys.TRAIN or tf.estimator.ModeKeys.PREDICT or tf.estimator.ModeKeys.EVAL
:param params: dictionary of hyper parameters for the model
:return: | src/model/lstm_crf/main.py | model_fn | vikasbahirwani/SequenceTagging | python | def model_fn(features, labels, mode, params):
'\n\n :param features: words from sentence and number of words per sentence\n :param labels: One tag per word\n :param mode: tf.estimator.ModeKeys.TRAIN or tf.estimator.ModeKeys.PREDICT or tf.estimator.ModeKeys.EVAL\n :param params: dictionary of hyper pa... |
def __virtual__():
'\n Load this state if the reg module exists\n '
if ('reg.read_value' not in __utils__):
return (False, 'reg state module failed to load: missing module function: reg.read_value')
if ('reg.set_value' not in __utils__):
return (False, 'reg state module failed to load:... | 8,883,516,520,131,150,000 | Load this state if the reg module exists | salt/states/reg.py | __virtual__ | Feeeenng/salt | python | def __virtual__():
'\n \n '
if ('reg.read_value' not in __utils__):
return (False, 'reg state module failed to load: missing module function: reg.read_value')
if ('reg.set_value' not in __utils__):
return (False, 'reg state module failed to load: missing module function: reg.set_value'... |
def _parse_key(key):
'\n split the hive from the key\n '
splt = key.split('\\')
hive = splt.pop(0)
key = '\\'.join(splt)
return (hive, key) | -1,644,809,154,807,784,400 | split the hive from the key | salt/states/reg.py | _parse_key | Feeeenng/salt | python | def _parse_key(key):
'\n \n '
splt = key.split('\\')
hive = splt.pop(0)
key = '\\'.join(splt)
return (hive, key) |
def present(name, vname=None, vdata=None, vtype='REG_SZ', use_32bit_registry=False):
"\n Ensure a registry key or value is present.\n\n :param str name: A string value representing the full path of the key to\n include the HIVE, Key, and all Subkeys. For example:\n\n ``HKEY_LOCAL_MACHINE\\SOFTWARE\\Salt... | -3,619,361,012,449,428,500 | Ensure a registry key or value is present.
:param str name: A string value representing the full path of the key to
include the HIVE, Key, and all Subkeys. For example:
``HKEY_LOCAL_MACHINE\SOFTWARE\Salt``
Valid hive values include:
- HKEY_CURRENT_USER or HKCU
- HKEY_LOCAL_MACHINE or HKLM
- HKEY_USERS or HKU
:param... | salt/states/reg.py | present | Feeeenng/salt | python | def present(name, vname=None, vdata=None, vtype='REG_SZ', use_32bit_registry=False):
"\n Ensure a registry key or value is present.\n\n :param str name: A string value representing the full path of the key to\n include the HIVE, Key, and all Subkeys. For example:\n\n ``HKEY_LOCAL_MACHINE\\SOFTWARE\\Salt... |
def absent(name, vname=None, use_32bit_registry=False):
"\n Ensure a registry value is removed. To remove a key use key_absent.\n\n :param str name: A string value representing the full path of the key to\n include the HIVE, Key, and all Subkeys. For example:\n\n ``HKEY_LOCAL_MACHINE\\SOFTWARE\\Salt``\n... | 8,684,500,273,568,656,000 | Ensure a registry value is removed. To remove a key use key_absent.
:param str name: A string value representing the full path of the key to
include the HIVE, Key, and all Subkeys. For example:
``HKEY_LOCAL_MACHINE\SOFTWARE\Salt``
Valid hive values include:
- HKEY_CURRENT_USER or HKCU
- HKEY_LOCAL_MACHINE or HKLM
-... | salt/states/reg.py | absent | Feeeenng/salt | python | def absent(name, vname=None, use_32bit_registry=False):
"\n Ensure a registry value is removed. To remove a key use key_absent.\n\n :param str name: A string value representing the full path of the key to\n include the HIVE, Key, and all Subkeys. For example:\n\n ``HKEY_LOCAL_MACHINE\\SOFTWARE\\Salt``\n... |
def key_absent(name, use_32bit_registry=False):
"\n .. versionadded:: 2015.5.4\n\n Ensure a registry key is removed. This will remove a key and all value\n entries it contains. It will fail if the key contains subkeys.\n\n :param str name: A string representing the full path to the key to be\n remove... | 7,864,107,645,527,735,000 | .. versionadded:: 2015.5.4
Ensure a registry key is removed. This will remove a key and all value
entries it contains. It will fail if the key contains subkeys.
:param str name: A string representing the full path to the key to be
removed to include the hive and the keypath. The hive can be any of the
following:
- H... | salt/states/reg.py | key_absent | Feeeenng/salt | python | def key_absent(name, use_32bit_registry=False):
"\n .. versionadded:: 2015.5.4\n\n Ensure a registry key is removed. This will remove a key and all value\n entries it contains. It will fail if the key contains subkeys.\n\n :param str name: A string representing the full path to the key to be\n remove... |
def test_authenticate_username_superuser(self):
'Test to authenticate as superuser.'
self.user.is_superuser = True
self.user.validated_by_email = False
self.user.validated_by_manager = False
self.user.save()
backend = DakaraModelBackend()
self.assertEqual(backend.authenticate(MagicMock(), us... | 8,255,383,177,069,123,000 | Test to authenticate as superuser. | dakara_server/users/tests/test_backends.py | test_authenticate_username_superuser | DakaraProject/dakara-server | python | def test_authenticate_username_superuser(self):
self.user.is_superuser = True
self.user.validated_by_email = False
self.user.validated_by_manager = False
self.user.save()
backend = DakaraModelBackend()
self.assertEqual(backend.authenticate(MagicMock(), username='TestUser', password='pass'),... |
def test_authenticate_username_not_active(self):
'Test to authenticate an inactive user.'
self.user.is_active = False
self.user.save()
backend = DakaraModelBackend()
self.assertIsNone(backend.authenticate(MagicMock(), username='TestUser', password='pass')) | -5,009,936,732,395,484,000 | Test to authenticate an inactive user. | dakara_server/users/tests/test_backends.py | test_authenticate_username_not_active | DakaraProject/dakara-server | python | def test_authenticate_username_not_active(self):
self.user.is_active = False
self.user.save()
backend = DakaraModelBackend()
self.assertIsNone(backend.authenticate(MagicMock(), username='TestUser', password='pass')) |
def test_authenticate_username_not_validated_by_email(self):
'Test to authenticate when not validated by email.'
self.user.validated_by_email = False
self.user.validated_by_manager = True
self.user.save()
backend = DakaraModelBackend()
with self.assertRaisesRegex(ValidationError, 'This user emai... | 7,254,216,078,541,471,000 | Test to authenticate when not validated by email. | dakara_server/users/tests/test_backends.py | test_authenticate_username_not_validated_by_email | DakaraProject/dakara-server | python | def test_authenticate_username_not_validated_by_email(self):
self.user.validated_by_email = False
self.user.validated_by_manager = True
self.user.save()
backend = DakaraModelBackend()
with self.assertRaisesRegex(ValidationError, 'This user email has not been validated'):
backend.authent... |
@config_email_disabled
def test_authenticate_username_not_validated_by_email_no_email(self):
'Test to authenticate when not validated by email and emails disabled.'
self.user.validated_by_email = False
self.user.validated_by_manager = True
self.user.save()
backend = DakaraModelBackend()
self.ass... | -7,744,552,453,736,005,000 | Test to authenticate when not validated by email and emails disabled. | dakara_server/users/tests/test_backends.py | test_authenticate_username_not_validated_by_email_no_email | DakaraProject/dakara-server | python | @config_email_disabled
def test_authenticate_username_not_validated_by_email_no_email(self):
self.user.validated_by_email = False
self.user.validated_by_manager = True
self.user.save()
backend = DakaraModelBackend()
self.assertEqual(backend.authenticate(MagicMock(), username='TestUser', passwor... |
def test_authenticate_username_not_validated_by_manager(self):
'Test to authenticate when not validated by manager.'
self.user.validated_by_email = True
self.user.validated_by_manager = False
self.user.save()
backend = DakaraModelBackend()
with self.assertRaisesRegex(ValidationError, 'This user ... | 8,615,451,956,186,013,000 | Test to authenticate when not validated by manager. | dakara_server/users/tests/test_backends.py | test_authenticate_username_not_validated_by_manager | DakaraProject/dakara-server | python | def test_authenticate_username_not_validated_by_manager(self):
self.user.validated_by_email = True
self.user.validated_by_manager = False
self.user.save()
backend = DakaraModelBackend()
with self.assertRaisesRegex(ValidationError, 'This user account has not been validated by a manager'):
... |
def test_authenticate_username_ok(self):
'Test to authenticate.'
self.user.validated_by_email = True
self.user.validated_by_manager = True
self.user.save()
backend = DakaraModelBackend()
self.assertEqual(backend.authenticate(MagicMock(), username='TestUser', password='pass'), self.user) | 6,897,010,371,461,581,000 | Test to authenticate. | dakara_server/users/tests/test_backends.py | test_authenticate_username_ok | DakaraProject/dakara-server | python | def test_authenticate_username_ok(self):
self.user.validated_by_email = True
self.user.validated_by_manager = True
self.user.save()
backend = DakaraModelBackend()
self.assertEqual(backend.authenticate(MagicMock(), username='TestUser', password='pass'), self.user) |
def new_admissions_chart(alt, projection_admits: pd.DataFrame, parameters: Parameters) -> Chart:
'docstring'
plot_projection_days = (parameters.n_days - 10)
max_y_axis = parameters.max_y_axis
as_date = parameters.as_date
y_scale = alt.Scale()
if (max_y_axis is not None):
y_scale.domain =... | 8,149,229,652,189,678,000 | docstring | src/penn_chime/charts.py | new_admissions_chart | degerli/chime-1 | python | def new_admissions_chart(alt, projection_admits: pd.DataFrame, parameters: Parameters) -> Chart:
plot_projection_days = (parameters.n_days - 10)
max_y_axis = parameters.max_y_axis
as_date = parameters.as_date
y_scale = alt.Scale()
if (max_y_axis is not None):
y_scale.domain = (0, max_y_... |
def admitted_patients_chart(alt, census: pd.DataFrame, parameters: Parameters) -> Chart:
'docstring'
plot_projection_days = (parameters.n_days - 10)
max_y_axis = parameters.max_y_axis
as_date = parameters.as_date
if as_date:
census = add_date_column(census)
x_kwargs = {'shorthand': '... | 5,839,135,649,114,293,000 | docstring | src/penn_chime/charts.py | admitted_patients_chart | degerli/chime-1 | python | def admitted_patients_chart(alt, census: pd.DataFrame, parameters: Parameters) -> Chart:
plot_projection_days = (parameters.n_days - 10)
max_y_axis = parameters.max_y_axis
as_date = parameters.as_date
if as_date:
census = add_date_column(census)
x_kwargs = {'shorthand': 'date:T', 't... |
def chart_descriptions(chart: Chart, labels, suffix: str=''):
'\n\n :param chart: Chart: The alt chart to be used in finding max points\n :param suffix: str: The assumption is that the charts have similar column names.\n The census chart adds " Census" to the column names.\n ... | -3,031,882,789,968,356,000 | :param chart: Chart: The alt chart to be used in finding max points
:param suffix: str: The assumption is that the charts have similar column names.
The census chart adds " Census" to the column names.
Make sure to include a space or underscore as appropriate
:return: str: Returns a multi-... | src/penn_chime/charts.py | chart_descriptions | degerli/chime-1 | python | def chart_descriptions(chart: Chart, labels, suffix: str=):
'\n\n :param chart: Chart: The alt chart to be used in finding max points\n :param suffix: str: The assumption is that the charts have similar column names.\n The census chart adds " Census" to the column names.\n ... |
@classmethod
def setup(cls: Type[Dataclass], arguments: Optional[str]='', dest: Optional[str]=None, default: Optional[Dataclass]=None, conflict_resolution_mode: ConflictResolution=ConflictResolution.AUTO, add_option_string_dash_variants: DashVariant=DashVariant.AUTO, parse_known_args: bool=False, attempt_to_reorder: bo... | 5,784,539,640,100,981,000 | Basic setup for a test.
Keyword Arguments:
arguments {Optional[str]} -- The arguments to pass to the parser (default: {""})
dest {Optional[str]} -- the attribute where the argument should be stored. (default: {None})
Returns:
{cls}} -- the class's type. | test/testutils.py | setup | idoby/SimpleParsing | python | @classmethod
def setup(cls: Type[Dataclass], arguments: Optional[str]=, dest: Optional[str]=None, default: Optional[Dataclass]=None, conflict_resolution_mode: ConflictResolution=ConflictResolution.AUTO, add_option_string_dash_variants: DashVariant=DashVariant.AUTO, parse_known_args: bool=False, attempt_to_reorder: bool... |
@classmethod
def required_components(cls) -> List[Type]:
'Components that should be included in the pipeline before this component.'
return [Featurizer] | -3,653,919,952,852,145,000 | Components that should be included in the pipeline before this component. | rasa/nlu/classifiers/diet_classifier.py | required_components | Adarshsng/rasa | python | @classmethod
def required_components(cls) -> List[Type]:
return [Featurizer] |
@staticmethod
def get_default_config() -> Dict[(Text, Any)]:
"The component's default config (see parent class for full docstring)."
return {HIDDEN_LAYERS_SIZES: {TEXT: [], LABEL: []}, SHARE_HIDDEN_LAYERS: False, TRANSFORMER_SIZE: DEFAULT_TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS: 2, NUM_HEADS: 4, KEY_RELATIVE_A... | -3,262,418,372,835,260,400 | The component's default config (see parent class for full docstring). | rasa/nlu/classifiers/diet_classifier.py | get_default_config | Adarshsng/rasa | python | @staticmethod
def get_default_config() -> Dict[(Text, Any)]:
return {HIDDEN_LAYERS_SIZES: {TEXT: [], LABEL: []}, SHARE_HIDDEN_LAYERS: False, TRANSFORMER_SIZE: DEFAULT_TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS: 2, NUM_HEADS: 4, KEY_RELATIVE_ATTENTION: False, VALUE_RELATIVE_ATTENTION: False, MAX_RELATIVE_POSITION... |
def __init__(self, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, index_label_id_mapping: Optional[Dict[(int, Text)]]=None, entity_tag_specs: Optional[List[EntityTagSpec]]=None, model: Optional[RasaModel]=None, sparse_feature_sizes: Optional[Dict[(Text, ... | -106,568,300,991,616,510 | Declare instance variables with default values. | rasa/nlu/classifiers/diet_classifier.py | __init__ | Adarshsng/rasa | python | def __init__(self, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, index_label_id_mapping: Optional[Dict[(int, Text)]]=None, entity_tag_specs: Optional[List[EntityTagSpec]]=None, model: Optional[RasaModel]=None, sparse_feature_sizes: Optional[Dict[(Text, ... |
@classmethod
def create(cls, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext) -> DIETClassifier:
'Creates a new untrained component (see parent class for full docstring).'
return cls(config, model_storage, resource, execution_context) | 3,003,322,216,744,029,000 | Creates a new untrained component (see parent class for full docstring). | rasa/nlu/classifiers/diet_classifier.py | create | Adarshsng/rasa | python | @classmethod
def create(cls, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext) -> DIETClassifier:
return cls(config, model_storage, resource, execution_context) |
@property
def label_key(self) -> Optional[Text]:
'Return key if intent classification is activated.'
return (LABEL_KEY if self.component_config[INTENT_CLASSIFICATION] else None) | 1,831,871,081,288,990,200 | Return key if intent classification is activated. | rasa/nlu/classifiers/diet_classifier.py | label_key | Adarshsng/rasa | python | @property
def label_key(self) -> Optional[Text]:
return (LABEL_KEY if self.component_config[INTENT_CLASSIFICATION] else None) |
@property
def label_sub_key(self) -> Optional[Text]:
'Return sub key if intent classification is activated.'
return (LABEL_SUB_KEY if self.component_config[INTENT_CLASSIFICATION] else None) | 7,158,035,865,042,439,000 | Return sub key if intent classification is activated. | rasa/nlu/classifiers/diet_classifier.py | label_sub_key | Adarshsng/rasa | python | @property
def label_sub_key(self) -> Optional[Text]:
return (LABEL_SUB_KEY if self.component_config[INTENT_CLASSIFICATION] else None) |
@staticmethod
def _label_id_index_mapping(training_data: TrainingData, attribute: Text) -> Dict[(Text, int)]:
'Create label_id dictionary.'
distinct_label_ids = ({example.get(attribute) for example in training_data.intent_examples} - {None})
return {label_id: idx for (idx, label_id) in enumerate(sorted(dist... | 4,189,876,990,410,381,300 | Create label_id dictionary. | rasa/nlu/classifiers/diet_classifier.py | _label_id_index_mapping | Adarshsng/rasa | python | @staticmethod
def _label_id_index_mapping(training_data: TrainingData, attribute: Text) -> Dict[(Text, int)]:
distinct_label_ids = ({example.get(attribute) for example in training_data.intent_examples} - {None})
return {label_id: idx for (idx, label_id) in enumerate(sorted(distinct_label_ids))} |
def _create_entity_tag_specs(self, training_data: TrainingData) -> List[EntityTagSpec]:
'Create entity tag specifications with their respective tag id mappings.'
_tag_specs = []
for tag_name in POSSIBLE_TAGS:
if self.component_config[BILOU_FLAG]:
tag_id_index_mapping = bilou_utils.build_... | -2,915,100,119,132,239,000 | Create entity tag specifications with their respective tag id mappings. | rasa/nlu/classifiers/diet_classifier.py | _create_entity_tag_specs | Adarshsng/rasa | python | def _create_entity_tag_specs(self, training_data: TrainingData) -> List[EntityTagSpec]:
_tag_specs = []
for tag_name in POSSIBLE_TAGS:
if self.component_config[BILOU_FLAG]:
tag_id_index_mapping = bilou_utils.build_tag_id_dict(training_data, tag_name)
else:
tag_id_ind... |
@staticmethod
def _tag_id_index_mapping_for(tag_name: Text, training_data: TrainingData) -> Optional[Dict[(Text, int)]]:
'Create mapping from tag name to id.'
if (tag_name == ENTITY_ATTRIBUTE_ROLE):
distinct_tags = training_data.entity_roles
elif (tag_name == ENTITY_ATTRIBUTE_GROUP):
distinc... | -7,342,428,663,700,129,000 | Create mapping from tag name to id. | rasa/nlu/classifiers/diet_classifier.py | _tag_id_index_mapping_for | Adarshsng/rasa | python | @staticmethod
def _tag_id_index_mapping_for(tag_name: Text, training_data: TrainingData) -> Optional[Dict[(Text, int)]]:
if (tag_name == ENTITY_ATTRIBUTE_ROLE):
distinct_tags = training_data.entity_roles
elif (tag_name == ENTITY_ATTRIBUTE_GROUP):
distinct_tags = training_data.entity_groups
... |
def _check_labels_features_exist(self, labels_example: List[Message], attribute: Text) -> bool:
'Checks if all labels have features set.'
return all((label_example.features_present(attribute, self.component_config[FEATURIZERS]) for label_example in labels_example)) | 7,485,533,832,181,726,000 | Checks if all labels have features set. | rasa/nlu/classifiers/diet_classifier.py | _check_labels_features_exist | Adarshsng/rasa | python | def _check_labels_features_exist(self, labels_example: List[Message], attribute: Text) -> bool:
return all((label_example.features_present(attribute, self.component_config[FEATURIZERS]) for label_example in labels_example)) |
def _check_input_dimension_consistency(self, model_data: RasaModelData) -> None:
'Checks if features have same dimensionality if hidden layers are shared.'
if self.component_config.get(SHARE_HIDDEN_LAYERS):
num_text_sentence_features = model_data.number_of_units(TEXT, SENTENCE)
num_label_sentenc... | -2,929,522,057,481,884,700 | Checks if features have same dimensionality if hidden layers are shared. | rasa/nlu/classifiers/diet_classifier.py | _check_input_dimension_consistency | Adarshsng/rasa | python | def _check_input_dimension_consistency(self, model_data: RasaModelData) -> None:
if self.component_config.get(SHARE_HIDDEN_LAYERS):
num_text_sentence_features = model_data.number_of_units(TEXT, SENTENCE)
num_label_sentence_features = model_data.number_of_units(LABEL, SENTENCE)
num_text_... |
def _extract_labels_precomputed_features(self, label_examples: List[Message], attribute: Text=INTENT) -> Tuple[(List[FeatureArray], List[FeatureArray])]:
'Collects precomputed encodings.'
features = defaultdict(list)
for e in label_examples:
label_features = self._extract_features(e, attribute)
... | -1,011,031,399,489,427,300 | Collects precomputed encodings. | rasa/nlu/classifiers/diet_classifier.py | _extract_labels_precomputed_features | Adarshsng/rasa | python | def _extract_labels_precomputed_features(self, label_examples: List[Message], attribute: Text=INTENT) -> Tuple[(List[FeatureArray], List[FeatureArray])]:
features = defaultdict(list)
for e in label_examples:
label_features = self._extract_features(e, attribute)
for (feature_key, feature_val... |
@staticmethod
def _compute_default_label_features(labels_example: List[Message]) -> List[FeatureArray]:
'Computes one-hot representation for the labels.'
logger.debug('No label features found. Computing default label features.')
eye_matrix = np.eye(len(labels_example), dtype=np.float32)
return [FeatureA... | 8,457,130,274,428,411,000 | Computes one-hot representation for the labels. | rasa/nlu/classifiers/diet_classifier.py | _compute_default_label_features | Adarshsng/rasa | python | @staticmethod
def _compute_default_label_features(labels_example: List[Message]) -> List[FeatureArray]:
logger.debug('No label features found. Computing default label features.')
eye_matrix = np.eye(len(labels_example), dtype=np.float32)
return [FeatureArray(np.array([np.expand_dims(a, 0) for a in eye_... |
def _create_label_data(self, training_data: TrainingData, label_id_dict: Dict[(Text, int)], attribute: Text) -> RasaModelData:
'Create matrix with label_ids encoded in rows as bag of words.\n\n Find a training example for each label and get the encoded features\n from the corresponding Message object.... | -1,096,988,557,676,506,600 | Create matrix with label_ids encoded in rows as bag of words.
Find a training example for each label and get the encoded features
from the corresponding Message object.
If the features are already computed, fetch them from the message object
else compute a one hot encoding for the label as the feature vector. | rasa/nlu/classifiers/diet_classifier.py | _create_label_data | Adarshsng/rasa | python | def _create_label_data(self, training_data: TrainingData, label_id_dict: Dict[(Text, int)], attribute: Text) -> RasaModelData:
'Create matrix with label_ids encoded in rows as bag of words.\n\n Find a training example for each label and get the encoded features\n from the corresponding Message object.... |
def _create_model_data(self, training_data: List[Message], label_id_dict: Optional[Dict[(Text, int)]]=None, label_attribute: Optional[Text]=None, training: bool=True) -> RasaModelData:
'Prepare data for training and create a RasaModelData object.'
from rasa.utils.tensorflow import model_data_utils
attribute... | 1,932,037,351,382,631,700 | Prepare data for training and create a RasaModelData object. | rasa/nlu/classifiers/diet_classifier.py | _create_model_data | Adarshsng/rasa | python | def _create_model_data(self, training_data: List[Message], label_id_dict: Optional[Dict[(Text, int)]]=None, label_attribute: Optional[Text]=None, training: bool=True) -> RasaModelData:
from rasa.utils.tensorflow import model_data_utils
attributes_to_consider = [TEXT]
if (training and self.component_con... |
def preprocess_train_data(self, training_data: TrainingData) -> RasaModelData:
'Prepares data for training.\n\n Performs sanity checks on training data, extracts encodings for labels.\n '
if self.component_config[BILOU_FLAG]:
bilou_utils.apply_bilou_schema(training_data)
label_id_index... | 557,687,017,003,223,200 | Prepares data for training.
Performs sanity checks on training data, extracts encodings for labels. | rasa/nlu/classifiers/diet_classifier.py | preprocess_train_data | Adarshsng/rasa | python | def preprocess_train_data(self, training_data: TrainingData) -> RasaModelData:
'Prepares data for training.\n\n Performs sanity checks on training data, extracts encodings for labels.\n '
if self.component_config[BILOU_FLAG]:
bilou_utils.apply_bilou_schema(training_data)
label_id_index... |
def train(self, training_data: TrainingData) -> Resource:
'Train the embedding intent classifier on a data set.'
model_data = self.preprocess_train_data(training_data)
if model_data.is_empty():
logger.debug(f"Cannot train '{self.__class__.__name__}'. No data was provided. Skipping training of the cl... | -3,389,418,153,500,108,300 | Train the embedding intent classifier on a data set. | rasa/nlu/classifiers/diet_classifier.py | train | Adarshsng/rasa | python | def train(self, training_data: TrainingData) -> Resource:
model_data = self.preprocess_train_data(training_data)
if model_data.is_empty():
logger.debug(f"Cannot train '{self.__class__.__name__}'. No data was provided. Skipping training of the classifier.")
return self._resource
if ((not... |
def _predict_label(self, predict_out: Optional[Dict[(Text, tf.Tensor)]]) -> Tuple[(Dict[(Text, Any)], List[Dict[(Text, Any)]])]:
'Predicts the intent of the provided message.'
label: Dict[(Text, Any)] = {'name': None, 'confidence': 0.0}
label_ranking = []
if (predict_out is None):
return (label,... | 5,663,114,333,651,413,000 | Predicts the intent of the provided message. | rasa/nlu/classifiers/diet_classifier.py | _predict_label | Adarshsng/rasa | python | def _predict_label(self, predict_out: Optional[Dict[(Text, tf.Tensor)]]) -> Tuple[(Dict[(Text, Any)], List[Dict[(Text, Any)]])]:
label: Dict[(Text, Any)] = {'name': None, 'confidence': 0.0}
label_ranking = []
if (predict_out is None):
return (label, label_ranking)
message_sim = predict_out[... |
def process(self, messages: List[Message]) -> List[Message]:
'Augments the message with intents, entities, and diagnostic data.'
for message in messages:
out = self._predict(message)
if self.component_config[INTENT_CLASSIFICATION]:
(label, label_ranking) = self._predict_label(out)
... | 109,263,573,167,095,840 | Augments the message with intents, entities, and diagnostic data. | rasa/nlu/classifiers/diet_classifier.py | process | Adarshsng/rasa | python | def process(self, messages: List[Message]) -> List[Message]:
for message in messages:
out = self._predict(message)
if self.component_config[INTENT_CLASSIFICATION]:
(label, label_ranking) = self._predict_label(out)
message.set(INTENT, label, add_to_output=True)
... |
def persist(self) -> None:
'Persist this model into the passed directory.'
if (self.model is None):
return None
with self._model_storage.write_to(self._resource) as model_path:
file_name = self.__class__.__name__
tf_model_file = (model_path / f'{file_name}.tf_model')
rasa.sha... | 211,940,360,593,332,900 | Persist this model into the passed directory. | rasa/nlu/classifiers/diet_classifier.py | persist | Adarshsng/rasa | python | def persist(self) -> None:
if (self.model is None):
return None
with self._model_storage.write_to(self._resource) as model_path:
file_name = self.__class__.__name__
tf_model_file = (model_path / f'{file_name}.tf_model')
rasa.shared.utils.io.create_directory_for_file(tf_model... |
@classmethod
def load(cls, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, **kwargs: Any) -> DIETClassifier:
'Loads a policy from the storage (see parent class for full docstring).'
try:
with model_storage.read_from(resource) as model_path... | 2,106,258,776,940,941,800 | Loads a policy from the storage (see parent class for full docstring). | rasa/nlu/classifiers/diet_classifier.py | load | Adarshsng/rasa | python | @classmethod
def load(cls, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, **kwargs: Any) -> DIETClassifier:
try:
with model_storage.read_from(resource) as model_path:
return cls._load(model_path, config, model_storage, resour... |
@classmethod
def _load(cls, model_path: Path, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext) -> 'DIETClassifier':
'Loads the trained model from the provided directory.'
(index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_f... | 1,848,851,608,624,442,400 | Loads the trained model from the provided directory. | rasa/nlu/classifiers/diet_classifier.py | _load | Adarshsng/rasa | python | @classmethod
def _load(cls, model_path: Path, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext) -> 'DIETClassifier':
(index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes) = cls._load_from_files(model_path)
c... |
@staticmethod
def _ordered_tag_specs(entity_tag_specs: Optional[List[EntityTagSpec]]) -> List[EntityTagSpec]:
'Ensure that order of entity tag specs matches CRF layer order.'
if (entity_tag_specs is None):
return []
crf_order = [ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP]
... | 7,247,348,547,029,321,000 | Ensure that order of entity tag specs matches CRF layer order. | rasa/nlu/classifiers/diet_classifier.py | _ordered_tag_specs | Adarshsng/rasa | python | @staticmethod
def _ordered_tag_specs(entity_tag_specs: Optional[List[EntityTagSpec]]) -> List[EntityTagSpec]:
if (entity_tag_specs is None):
return []
crf_order = [ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP]
ordered_tag_spec = []
for tag_name in crf_order:
... |
def batch_loss(self, batch_in: Union[(Tuple[tf.Tensor], Tuple[np.ndarray])]) -> tf.Tensor:
'Calculates the loss for the given batch.\n\n Args:\n batch_in: The batch.\n\n Returns:\n The loss of the given batch.\n '
tf_batch_data = self.batch_to_model_data_format(batch_i... | 1,193,514,375,686,330,000 | Calculates the loss for the given batch.
Args:
batch_in: The batch.
Returns:
The loss of the given batch. | rasa/nlu/classifiers/diet_classifier.py | batch_loss | Adarshsng/rasa | python | def batch_loss(self, batch_in: Union[(Tuple[tf.Tensor], Tuple[np.ndarray])]) -> tf.Tensor:
'Calculates the loss for the given batch.\n\n Args:\n batch_in: The batch.\n\n Returns:\n The loss of the given batch.\n '
tf_batch_data = self.batch_to_model_data_format(batch_i... |
def prepare_for_predict(self) -> None:
'Prepares the model for prediction.'
if self.config[INTENT_CLASSIFICATION]:
(_, self.all_labels_embed) = self._create_all_labels() | 1,332,386,574,597,657,600 | Prepares the model for prediction. | rasa/nlu/classifiers/diet_classifier.py | prepare_for_predict | Adarshsng/rasa | python | def prepare_for_predict(self) -> None:
if self.config[INTENT_CLASSIFICATION]:
(_, self.all_labels_embed) = self._create_all_labels() |
def batch_predict(self, batch_in: Union[(Tuple[tf.Tensor], Tuple[np.ndarray])]) -> Dict[(Text, tf.Tensor)]:
'Predicts the output of the given batch.\n\n Args:\n batch_in: The batch.\n\n Returns:\n The output to predict.\n '
tf_batch_data = self.batch_to_model_data_form... | 1,617,009,859,647,779,300 | Predicts the output of the given batch.
Args:
batch_in: The batch.
Returns:
The output to predict. | rasa/nlu/classifiers/diet_classifier.py | batch_predict | Adarshsng/rasa | python | def batch_predict(self, batch_in: Union[(Tuple[tf.Tensor], Tuple[np.ndarray])]) -> Dict[(Text, tf.Tensor)]:
'Predicts the output of the given batch.\n\n Args:\n batch_in: The batch.\n\n Returns:\n The output to predict.\n '
tf_batch_data = self.batch_to_model_data_form... |
def _create_dictionary(self, document):
'Creates mapping key = word, value = row index'
words = map(self.normalize_word, document.words)
unique_words = frozenset((self.stem_word(w) for w in words if (w not in self._stop_words)))
return dict(((w, i) for (i, w) in enumerate(unique_words))) | 2,916,173,340,952,540,000 | Creates mapping key = word, value = row index | util_common/nlp/Sumy/summarizers/lsa.py | _create_dictionary | Sohone-Guo/Pointer-Generator | python | def _create_dictionary(self, document):
words = map(self.normalize_word, document.words)
unique_words = frozenset((self.stem_word(w) for w in words if (w not in self._stop_words)))
return dict(((w, i) for (i, w) in enumerate(unique_words))) |
def _create_matrix(self, document, dictionary):
'\n Creates matrix of shape |unique words|×|sentences| where cells\n contains number of occurences of words (rows) in senteces (cols).\n '
sentences = document.sentences
words_count = len(dictionary)
sentences_count = len(sentences)
... | -3,011,865,140,669,539,300 | Creates matrix of shape |unique words|×|sentences| where cells
contains number of occurences of words (rows) in senteces (cols). | util_common/nlp/Sumy/summarizers/lsa.py | _create_matrix | Sohone-Guo/Pointer-Generator | python | def _create_matrix(self, document, dictionary):
'\n Creates matrix of shape |unique words|×|sentences| where cells\n contains number of occurences of words (rows) in senteces (cols).\n '
sentences = document.sentences
words_count = len(dictionary)
sentences_count = len(sentences)
... |
def _compute_term_frequency(self, matrix, smooth=0.4):
'\n Computes TF metrics for each sentence (column) in the given matrix.\n You can read more about smoothing parameter at URL below:\n http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html\n '
assert (0.... | 1,475,393,266,677,919,200 | Computes TF metrics for each sentence (column) in the given matrix.
You can read more about smoothing parameter at URL below:
http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html | util_common/nlp/Sumy/summarizers/lsa.py | _compute_term_frequency | Sohone-Guo/Pointer-Generator | python | def _compute_term_frequency(self, matrix, smooth=0.4):
'\n Computes TF metrics for each sentence (column) in the given matrix.\n You can read more about smoothing parameter at URL below:\n http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html\n '
assert (0.... |
@skipIfReproducer
def test_read_memory(self):
'Test Python SBProcess.ReadMemory() API.'
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation('main.cpp', self.line)
self.asser... | -3,021,106,652,205,118,500 | Test Python SBProcess.ReadMemory() API. | lldb/test/API/python_api/process/TestProcessAPI.py | test_read_memory | AaronBallman/llvm | python | @skipIfReproducer
def test_read_memory(self):
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation('main.cpp', self.line)
self.assertTrue(breakpoint, VALID_BREAKPOINT)
p... |
@skipIfReproducer
def test_write_memory(self):
'Test Python SBProcess.WriteMemory() API.'
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation('main.cpp', self.line)
self.ass... | -1,242,185,819,707,027,200 | Test Python SBProcess.WriteMemory() API. | lldb/test/API/python_api/process/TestProcessAPI.py | test_write_memory | AaronBallman/llvm | python | @skipIfReproducer
def test_write_memory(self):
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation('main.cpp', self.line)
self.assertTrue(breakpoint, VALID_BREAKPOINT)
... |
@skipIfReproducer
def test_access_my_int(self):
"Test access 'my_int' using Python SBProcess.GetByteOrder() and other APIs."
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation(... | 2,923,101,617,713,456,000 | Test access 'my_int' using Python SBProcess.GetByteOrder() and other APIs. | lldb/test/API/python_api/process/TestProcessAPI.py | test_access_my_int | AaronBallman/llvm | python | @skipIfReproducer
def test_access_my_int(self):
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation('main.cpp', self.line)
self.assertTrue(breakpoint, VALID_BREAKPOINT)
... |
def test_remote_launch(self):
'Test SBProcess.RemoteLaunch() API with a process not in eStateConnected, and it should fail.'
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
process = target.LaunchSimple(None, None, self.... | 323,723,085,478,817,100 | Test SBProcess.RemoteLaunch() API with a process not in eStateConnected, and it should fail. | lldb/test/API/python_api/process/TestProcessAPI.py | test_remote_launch | AaronBallman/llvm | python | def test_remote_launch(self):
self.build()
exe = self.getBuildArtifact('a.out')
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
process = target.LaunchSimple(None, None, self.get_process_working_directory())
if self.TraceOn():
print('process state:', state_... |
def test_get_num_supported_hardware_watchpoints(self):
'Test SBProcess.GetNumSupportedHardwareWatchpoints() API with a process.'
self.build()
exe = self.getBuildArtifact('a.out')
self.runCmd(('file ' + exe), CURRENT_EXECUTABLE_SET)
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALI... | -8,014,264,649,563,344,000 | Test SBProcess.GetNumSupportedHardwareWatchpoints() API with a process. | lldb/test/API/python_api/process/TestProcessAPI.py | test_get_num_supported_hardware_watchpoints | AaronBallman/llvm | python | def test_get_num_supported_hardware_watchpoints(self):
self.build()
exe = self.getBuildArtifact('a.out')
self.runCmd(('file ' + exe), CURRENT_EXECUTABLE_SET)
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
breakpoint = target.BreakpointCreateByLocation('main.cpp', ... |
@no_debug_info_test
def test_get_process_info(self):
'Test SBProcess::GetProcessInfo() API with a locally launched process.'
self.build()
exe = self.getBuildArtifact('a.out')
self.runCmd(('file ' + exe), CURRENT_EXECUTABLE_SET)
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TA... | 7,278,161,170,177,846,000 | Test SBProcess::GetProcessInfo() API with a locally launched process. | lldb/test/API/python_api/process/TestProcessAPI.py | test_get_process_info | AaronBallman/llvm | python | @no_debug_info_test
def test_get_process_info(self):
self.build()
exe = self.getBuildArtifact('a.out')
self.runCmd(('file ' + exe), CURRENT_EXECUTABLE_SET)
target = self.dbg.CreateTarget(exe)
self.assertTrue(target, VALID_TARGET)
launch_info = target.GetLaunchInfo()
launch_info.SetWorki... |
def test_allocate_deallocate_memory(self):
'Test Python SBProcess.AllocateMemory() and SBProcess.DeallocateMemory() APIs.'
self.build()
(target, process, main_thread, main_breakpoint) = lldbutil.run_to_source_breakpoint(self, '// Set break point at this line', lldb.SBFileSpec('main.cpp'))
error = lldb.S... | -1,831,053,923,018,552,000 | Test Python SBProcess.AllocateMemory() and SBProcess.DeallocateMemory() APIs. | lldb/test/API/python_api/process/TestProcessAPI.py | test_allocate_deallocate_memory | AaronBallman/llvm | python | def test_allocate_deallocate_memory(self):
self.build()
(target, process, main_thread, main_breakpoint) = lldbutil.run_to_source_breakpoint(self, '// Set break point at this line', lldb.SBFileSpec('main.cpp'))
error = lldb.SBError()
addr = process.AllocateMemory(16384, lldb.ePermissionsReadable, er... |
def plot_gll(x, y, z):
' Plots values on 2D unstructured GLL mesh\n '
r = ((max(x) - min(x)) / (max(y) - min(y)))
rx = (r / np.sqrt((1 + (r ** 2))))
ry = (1 / np.sqrt((1 + (r ** 2))))
f = plt.figure(figsize=((10 * rx), (10 * ry)))
p = plt.tricontourf(x, y, z, 125)
plt.axis('image')
re... | -6,270,445,130,626,942,000 | Plots values on 2D unstructured GLL mesh | seisflows/tools/graphics.py | plot_gll | fanwu8/SeisFlowsQ | python | def plot_gll(x, y, z):
' \n '
r = ((max(x) - min(x)) / (max(y) - min(y)))
rx = (r / np.sqrt((1 + (r ** 2))))
ry = (1 / np.sqrt((1 + (r ** 2))))
f = plt.figure(figsize=((10 * rx), (10 * ry)))
p = plt.tricontourf(x, y, z, 125)
plt.axis('image')
return (f, p) |
def plot_vector(t, v, xlabel='', ylabel='', title=''):
' Plots a vector or time series.\n\n Parameters\n ----------\n v: ndarray, ndims = 1/2\n Vector or time series to plot\n xlabel: str\n x axis label\n ylabel: str\n y axis label\n title: str\n plot title\n\n Raise... | 8,343,828,763,393,565,000 | Plots a vector or time series.
Parameters
----------
v: ndarray, ndims = 1/2
Vector or time series to plot
xlabel: str
x axis label
ylabel: str
y axis label
title: str
plot title
Raises
------
ValueError
If dimensions of v are greater than 2 | seisflows/tools/graphics.py | plot_vector | fanwu8/SeisFlowsQ | python | def plot_vector(t, v, xlabel=, ylabel=, title=):
' Plots a vector or time series.\n\n Parameters\n ----------\n v: ndarray, ndims = 1/2\n Vector or time series to plot\n xlabel: str\n x axis label\n ylabel: str\n y axis label\n title: str\n plot title\n\n Raises\n ... |
def plot_section(stream, ax=None, cmap='seismic', clip=100, title='', x_interval=1.0, y_interval=1.0):
' Plots a seismic section from an obspy stream.\n\n Parameters\n ----------\n stream: Obspy stream object\n Obspy stream object created from a SU data file\n ax: Matplotlib Axes object\n ... | -203,811,772,725,341,600 | Plots a seismic section from an obspy stream.
Parameters
----------
stream: Obspy stream object
Obspy stream object created from a SU data file
ax: Matplotlib Axes object
Optional axis object
cmap: str
Matplotlib colormap option.
clip: float
Percentage value (0-100) for amplitude clipping
title: str
... | seisflows/tools/graphics.py | plot_section | fanwu8/SeisFlowsQ | python | def plot_section(stream, ax=None, cmap='seismic', clip=100, title=, x_interval=1.0, y_interval=1.0):
' Plots a seismic section from an obspy stream.\n\n Parameters\n ----------\n stream: Obspy stream object\n Obspy stream object created from a SU data file\n ax: Matplotlib Axes object\n O... |
def _convert_to_array(stream):
' Extracts trace data from an obspy stream and returns a 2D array.\n\n Parameters\n ----------\n stream: Obspy stream object\n Stream storing trace data\n\n Returns\n -------\n output: ndarray, ndim=2\n Returns an (nt*nr) array. nt and nr are the number... | 5,514,239,679,669,107,000 | Extracts trace data from an obspy stream and returns a 2D array.
Parameters
----------
stream: Obspy stream object
Stream storing trace data
Returns
-------
output: ndarray, ndim=2
Returns an (nt*nr) array. nt and nr are the number of sample points
and number of traces respectively. Assumes trace lengths ... | seisflows/tools/graphics.py | _convert_to_array | fanwu8/SeisFlowsQ | python | def _convert_to_array(stream):
' Extracts trace data from an obspy stream and returns a 2D array.\n\n Parameters\n ----------\n stream: Obspy stream object\n Stream storing trace data\n\n Returns\n -------\n output: ndarray, ndim=2\n Returns an (nt*nr) array. nt and nr are the number... |
def _cscale(v, clip=100):
' Return limits for colormap.\n '
perc = (clip / 100.0)
return (((- perc) * abs(v).max()), (perc * abs(v).max())) | 1,486,020,659,555,331,300 | Return limits for colormap. | seisflows/tools/graphics.py | _cscale | fanwu8/SeisFlowsQ | python | def _cscale(v, clip=100):
' \n '
perc = (clip / 100.0)
return (((- perc) * abs(v).max()), (perc * abs(v).max())) |
def _get_time(stream):
' Get fixed time vector for stream object.\n '
dt = stream[0].stats.delta
nt = len(stream[0].data)
return np.arange(0, (nt * dt), dt) | 3,518,869,647,590,698,500 | Get fixed time vector for stream object. | seisflows/tools/graphics.py | _get_time | fanwu8/SeisFlowsQ | python | def _get_time(stream):
' \n '
dt = stream[0].stats.delta
nt = len(stream[0].data)
return np.arange(0, (nt * dt), dt) |
def _get_offsets(stream):
' Return offsets.\n '
nr = len(stream)
offsets = np.zeros(nr)
scalco = stream[0].stats.su.trace_header.scalar_to_be_applied_to_all_coordinates
if (scalco == 0):
scalco = 0.001
else:
scalco = (0.001 / scalco)
for (i, tr) in enumerate(stream):
... | 8,875,202,697,701,741,000 | Return offsets. | seisflows/tools/graphics.py | _get_offsets | fanwu8/SeisFlowsQ | python | def _get_offsets(stream):
' \n '
nr = len(stream)
offsets = np.zeros(nr)
scalco = stream[0].stats.su.trace_header.scalar_to_be_applied_to_all_coordinates
if (scalco == 0):
scalco = 0.001
else:
scalco = (0.001 / scalco)
for (i, tr) in enumerate(stream):
offsets[i] =... |
def get_regular_ticks(v, interval):
' Returns regular tick intervals.\n '
f = interp1d(v, list(range(len(v))))
begin = (int((v[0] / interval)) * interval)
end = v[(- 1)]
tick_labels = np.arange(begin, end, interval)
ticks = f(tick_labels)
return (ticks, tick_labels) | -4,995,927,191,784,999,000 | Returns regular tick intervals. | seisflows/tools/graphics.py | get_regular_ticks | fanwu8/SeisFlowsQ | python | def get_regular_ticks(v, interval):
' \n '
f = interp1d(v, list(range(len(v))))
begin = (int((v[0] / interval)) * interval)
end = v[(- 1)]
tick_labels = np.arange(begin, end, interval)
ticks = f(tick_labels)
return (ticks, tick_labels) |
def generate_kernel_pod_yaml(keywords):
'Return the kubernetes pod spec as a yaml string.\n\n - load jinja2 template from this file directory.\n - substitute template variables with keywords items.\n '
j_env = Environment(loader=FileSystemLoader(os.path.dirname(__file__)), trim_blocks=True, lstrip_bloc... | 5,852,460,280,651,974,000 | Return the kubernetes pod spec as a yaml string.
- load jinja2 template from this file directory.
- substitute template variables with keywords items. | tools/kernelspecs/kernels/R_kubernetes/scripts/launch_kubernetes.py | generate_kernel_pod_yaml | spotinst/wave-operator | python | def generate_kernel_pod_yaml(keywords):
'Return the kubernetes pod spec as a yaml string.\n\n - load jinja2 template from this file directory.\n - substitute template variables with keywords items.\n '
j_env = Environment(loader=FileSystemLoader(os.path.dirname(__file__)), trim_blocks=True, lstrip_bloc... |
def error_run(arguments: list[str], message: bytes) -> None:
'Run command that should fail and check error message.'
with Popen((['map-machine'] + arguments), stderr=PIPE) as pipe:
(_, error) = pipe.communicate()
assert (pipe.returncode != 0)
assert (error == message) | 8,372,083,281,896,209,000 | Run command that should fail and check error message. | tests/test_command_line.py | error_run | LaoshuBaby/map-machine | python | def error_run(arguments: list[str], message: bytes) -> None:
with Popen((['map-machine'] + arguments), stderr=PIPE) as pipe:
(_, error) = pipe.communicate()
assert (pipe.returncode != 0)
assert (error == message) |
def run(arguments: list[str], message: bytes) -> None:
'Run command that should fail and check error message.'
with Popen((['map-machine'] + arguments), stderr=PIPE) as pipe:
(_, error) = pipe.communicate()
assert (pipe.returncode == 0)
assert (error == message) | 1,844,572,730,295,534,300 | Run command that should fail and check error message. | tests/test_command_line.py | run | LaoshuBaby/map-machine | python | def run(arguments: list[str], message: bytes) -> None:
with Popen((['map-machine'] + arguments), stderr=PIPE) as pipe:
(_, error) = pipe.communicate()
assert (pipe.returncode == 0)
assert (error == message) |
def test_wrong_render_arguments() -> None:
'Test `render` command with wrong arguments.'
error_run(['render', '-z', '17'], b'CRITICAL Specify either --input, or --boundary-box, or --coordinates and --size.\n') | 5,530,455,509,936,444,000 | Test `render` command with wrong arguments. | tests/test_command_line.py | test_wrong_render_arguments | LaoshuBaby/map-machine | python | def test_wrong_render_arguments() -> None:
error_run(['render', '-z', '17'], b'CRITICAL Specify either --input, or --boundary-box, or --coordinates and --size.\n') |
def test_render() -> None:
'Test `render` command.'
run((COMMAND_LINES['render'] + ['--cache', 'tests/data']), (LOG + b'INFO Writing output SVG to out/map.svg...\n'))
with Path('out/map.svg').open(encoding='utf-8') as output_file:
root: Element = ElementTree.parse(output_file).getroot()
assert (... | -8,102,067,962,734,609,000 | Test `render` command. | tests/test_command_line.py | test_render | LaoshuBaby/map-machine | python | def test_render() -> None:
run((COMMAND_LINES['render'] + ['--cache', 'tests/data']), (LOG + b'INFO Writing output SVG to out/map.svg...\n'))
with Path('out/map.svg').open(encoding='utf-8') as output_file:
root: Element = ElementTree.parse(output_file).getroot()
assert (len(root) == 8)
asse... |
def test_render_with_tooltips() -> None:
'Test `render` command.'
run((COMMAND_LINES['render_with_tooltips'] + ['--cache', 'tests/data']), (LOG + b'INFO Writing output SVG to out/map.svg...\n'))
with Path('out/map.svg').open(encoding='utf-8') as output_file:
root: Element = ElementTree.parse(output_... | -3,097,467,188,967,107,000 | Test `render` command. | tests/test_command_line.py | test_render_with_tooltips | LaoshuBaby/map-machine | python | def test_render_with_tooltips() -> None:
run((COMMAND_LINES['render_with_tooltips'] + ['--cache', 'tests/data']), (LOG + b'INFO Writing output SVG to out/map.svg...\n'))
with Path('out/map.svg').open(encoding='utf-8') as output_file:
root: Element = ElementTree.parse(output_file).getroot()
asse... |
def test_icons() -> None:
'Test `icons` command.'
run(COMMAND_LINES['icons'], b'INFO Icons are written to out/icons_by_name and out/icons_by_id.\nINFO Icon grid is written to out/icon_grid.svg.\nINFO Icon grid is written to doc/grid.svg.\n')
assert (Path('out') / 'icon_grid.svg').is_file()
assert (Path(... | -1,171,357,103,594,110,700 | Test `icons` command. | tests/test_command_line.py | test_icons | LaoshuBaby/map-machine | python | def test_icons() -> None:
run(COMMAND_LINES['icons'], b'INFO Icons are written to out/icons_by_name and out/icons_by_id.\nINFO Icon grid is written to out/icon_grid.svg.\nINFO Icon grid is written to doc/grid.svg.\n')
assert (Path('out') / 'icon_grid.svg').is_file()
assert (Path('out') / 'icons_by_name... |
def test_mapcss() -> None:
'Test `mapcss` command.'
run(COMMAND_LINES['mapcss'], b'INFO MapCSS 0.2 scheme is written to out/map_machine_mapcss.\n')
assert (Path('out') / 'map_machine_mapcss').is_dir()
assert ((Path('out') / 'map_machine_mapcss') / 'icons').is_dir()
assert (((Path('out') / 'map_machi... | -34,753,347,926,657,268 | Test `mapcss` command. | tests/test_command_line.py | test_mapcss | LaoshuBaby/map-machine | python | def test_mapcss() -> None:
run(COMMAND_LINES['mapcss'], b'INFO MapCSS 0.2 scheme is written to out/map_machine_mapcss.\n')
assert (Path('out') / 'map_machine_mapcss').is_dir()
assert ((Path('out') / 'map_machine_mapcss') / 'icons').is_dir()
assert (((Path('out') / 'map_machine_mapcss') / 'icons') /... |
def test_element() -> None:
'Test `element` command.'
run(COMMAND_LINES['element'], b'INFO Element is written to out/element.svg.\n')
assert (Path('out') / 'element.svg').is_file() | 3,954,329,336,101,989,000 | Test `element` command. | tests/test_command_line.py | test_element | LaoshuBaby/map-machine | python | def test_element() -> None:
run(COMMAND_LINES['element'], b'INFO Element is written to out/element.svg.\n')
assert (Path('out') / 'element.svg').is_file() |
def test_tile() -> None:
'Test `tile` command.'
run((COMMAND_LINES['tile'] + ['--cache', 'tests/data']), (LOG + b'INFO Tile is drawn to out/tiles/tile_18_160199_88904.svg.\nINFO SVG file is rasterized to out/tiles/tile_18_160199_88904.png.\n'))
assert ((Path('out') / 'tiles') / 'tile_18_160199_88904.svg').i... | -5,938,328,673,918,745,000 | Test `tile` command. | tests/test_command_line.py | test_tile | LaoshuBaby/map-machine | python | def test_tile() -> None:
run((COMMAND_LINES['tile'] + ['--cache', 'tests/data']), (LOG + b'INFO Tile is drawn to out/tiles/tile_18_160199_88904.svg.\nINFO SVG file is rasterized to out/tiles/tile_18_160199_88904.png.\n'))
assert ((Path('out') / 'tiles') / 'tile_18_160199_88904.svg').is_file()
assert ((... |
def run(path: str, output_file: str='', mongo=False) -> Union[(None, List[dict])]:
'Invoca o utilitário `isis2json` com os parâmetros adaptados para a\n leitura de arquivos MST de acordo com as definições padrões utilizadas\n pelo __main__ da ferramenta `isis2json`.\n\n O resultado de saída pode ser escrit... | 5,417,742,210,112,840,000 | Invoca o utilitário `isis2json` com os parâmetros adaptados para a
leitura de arquivos MST de acordo com as definições padrões utilizadas
pelo __main__ da ferramenta `isis2json`.
O resultado de saída pode ser escrito diretamente para um arquivo em disco
ou retornará uma lista contento as linhas passíveis de conversão ... | documentstore_migracao/utils/extract_isis.py | run | patymori/document-store-migracao | python | def run(path: str, output_file: str=, mongo=False) -> Union[(None, List[dict])]:
'Invoca o utilitário `isis2json` com os parâmetros adaptados para a\n leitura de arquivos MST de acordo com as definições padrões utilizadas\n pelo __main__ da ferramenta `isis2json`.\n\n O resultado de saída pode ser escrito ... |
def __init__(__self__, *, host_account_names: pulumi.Input[Sequence[pulumi.Input[str]]], host_group_id: pulumi.Input[str], instance_id: pulumi.Input[str], user_group_id: pulumi.Input[str]):
'\n The set of arguments for constructing a HostGroupAccountUserGroupAttachment resource.\n :param pulumi.Input[... | 8,620,752,537,416,545,000 | The set of arguments for constructing a HostGroupAccountUserGroupAttachment resource.
:param pulumi.Input[Sequence[pulumi.Input[str]]] host_account_names: A list names of the host account.
:param pulumi.Input[str] host_group_id: The ID of the host group.
:param pulumi.Input[str] instance_id: The ID of the Bastionhost i... | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | __init__ | pulumi/pulumi-alicloud | python | def __init__(__self__, *, host_account_names: pulumi.Input[Sequence[pulumi.Input[str]]], host_group_id: pulumi.Input[str], instance_id: pulumi.Input[str], user_group_id: pulumi.Input[str]):
'\n The set of arguments for constructing a HostGroupAccountUserGroupAttachment resource.\n :param pulumi.Input[... |
@property
@pulumi.getter(name='hostAccountNames')
def host_account_names(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
'\n A list names of the host account.\n '
return pulumi.get(self, 'host_account_names') | 8,201,247,303,138,980,000 | A list names of the host account. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | host_account_names | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='hostAccountNames')
def host_account_names(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
'\n \n '
return pulumi.get(self, 'host_account_names') |
@property
@pulumi.getter(name='hostGroupId')
def host_group_id(self) -> pulumi.Input[str]:
'\n The ID of the host group.\n '
return pulumi.get(self, 'host_group_id') | 3,970,179,039,349,197,000 | The ID of the host group. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | host_group_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='hostGroupId')
def host_group_id(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'host_group_id') |
@property
@pulumi.getter(name='instanceId')
def instance_id(self) -> pulumi.Input[str]:
'\n The ID of the Bastionhost instance where you want to authorize the user to manage the specified hosts and host accounts.\n '
return pulumi.get(self, 'instance_id') | -3,452,605,385,748,844,000 | The ID of the Bastionhost instance where you want to authorize the user to manage the specified hosts and host accounts. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | instance_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='instanceId')
def instance_id(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'instance_id') |
@property
@pulumi.getter(name='userGroupId')
def user_group_id(self) -> pulumi.Input[str]:
'\n The ID of the user group that you want to authorize to manage the specified hosts and host accounts.\n '
return pulumi.get(self, 'user_group_id') | -8,345,498,789,096,243,000 | The ID of the user group that you want to authorize to manage the specified hosts and host accounts. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | user_group_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='userGroupId')
def user_group_id(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'user_group_id') |
def __init__(__self__, *, host_account_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, host_group_id: Optional[pulumi.Input[str]]=None, instance_id: Optional[pulumi.Input[str]]=None, user_group_id: Optional[pulumi.Input[str]]=None):
'\n Input properties used for looking up and filtering Host... | 4,413,283,507,895,842,300 | Input properties used for looking up and filtering HostGroupAccountUserGroupAttachment resources.
:param pulumi.Input[Sequence[pulumi.Input[str]]] host_account_names: A list names of the host account.
:param pulumi.Input[str] host_group_id: The ID of the host group.
:param pulumi.Input[str] instance_id: The ID of the B... | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | __init__ | pulumi/pulumi-alicloud | python | def __init__(__self__, *, host_account_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, host_group_id: Optional[pulumi.Input[str]]=None, instance_id: Optional[pulumi.Input[str]]=None, user_group_id: Optional[pulumi.Input[str]]=None):
'\n Input properties used for looking up and filtering Host... |
@property
@pulumi.getter(name='hostAccountNames')
def host_account_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
'\n A list names of the host account.\n '
return pulumi.get(self, 'host_account_names') | -6,218,258,946,766,746,000 | A list names of the host account. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | host_account_names | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='hostAccountNames')
def host_account_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
'\n \n '
return pulumi.get(self, 'host_account_names') |
@property
@pulumi.getter(name='hostGroupId')
def host_group_id(self) -> Optional[pulumi.Input[str]]:
'\n The ID of the host group.\n '
return pulumi.get(self, 'host_group_id') | -1,706,495,266,037,631,500 | The ID of the host group. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | host_group_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='hostGroupId')
def host_group_id(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'host_group_id') |
@property
@pulumi.getter(name='instanceId')
def instance_id(self) -> Optional[pulumi.Input[str]]:
'\n The ID of the Bastionhost instance where you want to authorize the user to manage the specified hosts and host accounts.\n '
return pulumi.get(self, 'instance_id') | 1,680,588,397,272,546,600 | The ID of the Bastionhost instance where you want to authorize the user to manage the specified hosts and host accounts. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | instance_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='instanceId')
def instance_id(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'instance_id') |
@property
@pulumi.getter(name='userGroupId')
def user_group_id(self) -> Optional[pulumi.Input[str]]:
'\n The ID of the user group that you want to authorize to manage the specified hosts and host accounts.\n '
return pulumi.get(self, 'user_group_id') | -1,121,814,425,541,299,700 | The ID of the user group that you want to authorize to manage the specified hosts and host accounts. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | user_group_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='userGroupId')
def user_group_id(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'user_group_id') |
@overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, host_account_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, host_group_id: Optional[pulumi.Input[str]]=None, instance_id: Optional[pulumi.Input[str]]=None, user_group_id: Optional[pulumi.Input[str]]=None,... | 8,111,317,736,864,197,000 | Provides a Bastion Host Host Account Attachment resource to add list host accounts into one user group and one host group.
> **NOTE:** Available in v1.135.0+.
## Example Usage
Basic Usage
```python
import pulumi
import pulumi_alicloud as alicloud
default_host = alicloud.bastionhost.Host("defaultHost",
instance... | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | __init__ | pulumi/pulumi-alicloud | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, host_account_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, host_group_id: Optional[pulumi.Input[str]]=None, instance_id: Optional[pulumi.Input[str]]=None, user_group_id: Optional[pulumi.Input[str]]=None,... |
@overload
def __init__(__self__, resource_name: str, args: HostGroupAccountUserGroupAttachmentArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Provides a Bastion Host Host Account Attachment resource to add list host accounts into one user group and one host group.\n\n > **NOTE:** Available in ... | 8,345,629,500,227,280,000 | Provides a Bastion Host Host Account Attachment resource to add list host accounts into one user group and one host group.
> **NOTE:** Available in v1.135.0+.
## Example Usage
Basic Usage
```python
import pulumi
import pulumi_alicloud as alicloud
default_host = alicloud.bastionhost.Host("defaultHost",
instance... | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | __init__ | pulumi/pulumi-alicloud | python | @overload
def __init__(__self__, resource_name: str, args: HostGroupAccountUserGroupAttachmentArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Provides a Bastion Host Host Account Attachment resource to add list host accounts into one user group and one host group.\n\n > **NOTE:** Available in ... |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, host_account_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, host_group_id: Optional[pulumi.Input[str]]=None, instance_id: Optional[pulumi.Input[str]]=None, user_group_id: Optional[pulumi.Input... | -4,569,158,122,378,688,500 | Get an existing HostGroupAccountUserGroupAttachment resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.Resou... | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | get | pulumi/pulumi-alicloud | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, host_account_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]=None, host_group_id: Optional[pulumi.Input[str]]=None, instance_id: Optional[pulumi.Input[str]]=None, user_group_id: Optional[pulumi.Input... |
@property
@pulumi.getter(name='hostAccountNames')
def host_account_names(self) -> pulumi.Output[Sequence[str]]:
'\n A list names of the host account.\n '
return pulumi.get(self, 'host_account_names') | 8,301,108,355,006,178,000 | A list names of the host account. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | host_account_names | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='hostAccountNames')
def host_account_names(self) -> pulumi.Output[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'host_account_names') |
@property
@pulumi.getter(name='hostGroupId')
def host_group_id(self) -> pulumi.Output[str]:
'\n The ID of the host group.\n '
return pulumi.get(self, 'host_group_id') | -2,142,794,302,333,448,700 | The ID of the host group. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | host_group_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='hostGroupId')
def host_group_id(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'host_group_id') |
@property
@pulumi.getter(name='instanceId')
def instance_id(self) -> pulumi.Output[str]:
'\n The ID of the Bastionhost instance where you want to authorize the user to manage the specified hosts and host accounts.\n '
return pulumi.get(self, 'instance_id') | 6,237,722,831,590,990,000 | The ID of the Bastionhost instance where you want to authorize the user to manage the specified hosts and host accounts. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | instance_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='instanceId')
def instance_id(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'instance_id') |
@property
@pulumi.getter(name='userGroupId')
def user_group_id(self) -> pulumi.Output[str]:
'\n The ID of the user group that you want to authorize to manage the specified hosts and host accounts.\n '
return pulumi.get(self, 'user_group_id') | -7,346,775,687,300,818,000 | The ID of the user group that you want to authorize to manage the specified hosts and host accounts. | sdk/python/pulumi_alicloud/bastionhost/host_group_account_user_group_attachment.py | user_group_id | pulumi/pulumi-alicloud | python | @property
@pulumi.getter(name='userGroupId')
def user_group_id(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'user_group_id') |
def is_start_piece(piece):
'Check if the current word piece is the starting piece (BERT).'
return (not piece.startswith('##')) | 6,410,489,472,930,046,000 | Check if the current word piece is the starting piece (BERT). | libai/data/datasets/bert_dataset.py | is_start_piece | Oneflow-Inc/libai | python | def is_start_piece(piece):
return (not piece.startswith('##')) |
def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, np_rng):
'Truncate sequence pair to a maximum sequence length.'
(len_a, len_b) = (len(tokens_a), len(tokens_b))
while True:
total_length = (len_a + len_b)
if (total_length <= max_num_tokens):
break
if (len_a ... | -3,613,704,199,210,650,000 | Truncate sequence pair to a maximum sequence length. | libai/data/datasets/bert_dataset.py | truncate_seq_pair | Oneflow-Inc/libai | python | def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, np_rng):
(len_a, len_b) = (len(tokens_a), len(tokens_b))
while True:
total_length = (len_a + len_b)
if (total_length <= max_num_tokens):
break
if (len_a > len_b):
trunc_tokens = tokens_a
... |
def create_tokens_and_token_types(self, tokens_a, tokens_b):
'Merge segments A and B, add [CLS] and [SEP] and build token types.'
tokens = (([self.cls_id] + tokens_a) + [self.sep_id])
token_types = ([0] * (len(tokens_a) + 2))
if (len(tokens_b) > 0):
tokens = ((tokens + tokens_b) + [self.sep_id])... | -7,094,127,822,114,704,000 | Merge segments A and B, add [CLS] and [SEP] and build token types. | libai/data/datasets/bert_dataset.py | create_tokens_and_token_types | Oneflow-Inc/libai | python | def create_tokens_and_token_types(self, tokens_a, tokens_b):
tokens = (([self.cls_id] + tokens_a) + [self.sep_id])
token_types = ([0] * (len(tokens_a) + 2))
if (len(tokens_b) > 0):
tokens = ((tokens + tokens_b) + [self.sep_id])
token_types = (token_types + ([1] * (len(tokens_b) + 1)))
... |
def mask_token(self, idx, tokens, np_rng):
'\n Helper function to mask `idx` token from `tokens` according to\n section 3.3.1 of https://arxiv.org/pdf/1810.04805.pdf\n '
label = tokens[idx]
if (np_rng.random() < 0.8):
new_label = self.mask_id
elif (np_rng.random() < 0.5):
... | 5,268,266,264,196,588,000 | Helper function to mask `idx` token from `tokens` according to
section 3.3.1 of https://arxiv.org/pdf/1810.04805.pdf | libai/data/datasets/bert_dataset.py | mask_token | Oneflow-Inc/libai | python | def mask_token(self, idx, tokens, np_rng):
'\n Helper function to mask `idx` token from `tokens` according to\n section 3.3.1 of https://arxiv.org/pdf/1810.04805.pdf\n '
label = tokens[idx]
if (np_rng.random() < 0.8):
new_label = self.mask_id
elif (np_rng.random() < 0.5):
... |
def create_masked_lm_predictions(self, tokens, np_rng, max_ngrams=3, do_whole_word_mask=True, favor_longer_ngram=False, geometric_dist=False):
'Creates the predictions for the masked LM objective.\n Note: Tokens here are vocab ids and not text tokens.'
cand_indexes = []
token_boundary = ([0] * len(to... | -3,165,146,543,570,706,400 | Creates the predictions for the masked LM objective.
Note: Tokens here are vocab ids and not text tokens. | libai/data/datasets/bert_dataset.py | create_masked_lm_predictions | Oneflow-Inc/libai | python | def create_masked_lm_predictions(self, tokens, np_rng, max_ngrams=3, do_whole_word_mask=True, favor_longer_ngram=False, geometric_dist=False):
'Creates the predictions for the masked LM objective.\n Note: Tokens here are vocab ids and not text tokens.'
cand_indexes = []
token_boundary = ([0] * len(to... |
def pad_and_convert_to_tensor(self, tokens, token_types, masked_positions, masked_labels):
'Pad sequences and convert them to tensor.'
num_tokens = len(tokens)
num_pad = (self.max_seq_length - num_tokens)
assert (num_pad >= 0)
assert (len(token_types) == num_tokens)
assert (len(masked_positions)... | -7,624,780,470,859,637,000 | Pad sequences and convert them to tensor. | libai/data/datasets/bert_dataset.py | pad_and_convert_to_tensor | Oneflow-Inc/libai | python | def pad_and_convert_to_tensor(self, tokens, token_types, masked_positions, masked_labels):
num_tokens = len(tokens)
num_pad = (self.max_seq_length - num_tokens)
assert (num_pad >= 0)
assert (len(token_types) == num_tokens)
assert (len(masked_positions) == len(masked_labels))
filler = ([self... |
def escape(s):
'\n Do the standard xml escapes, and replace newlines and tabs.\n '
return saxutils.escape(s, {'\n': '<br />', '\t': ' '}) | 776,089,565,378,620,500 | Do the standard xml escapes, and replace newlines and tabs. | leo/plugins/leowapp.py | escape | Anu082000/leo-editor | python | def escape(s):
'\n \n '
return saxutils.escape(s, {'\n': '<br />', '\t': ' '}) |
def init():
'Return True if the plugin has loaded successfully.'
if (not websockets):
return False
g.plugin_signon(__name__)
return True | 1,146,193,736,146,613,200 | Return True if the plugin has loaded successfully. | leo/plugins/leowapp.py | init | Anu082000/leo-editor | python | def init():
if (not websockets):
return False
g.plugin_signon(__name__)
return True |
def __getattr__(self, attr):
'Handle an missing attribute.'
if (attr in ('frameFactory', 'set_minibuffer_label')):
raise AttributeError
return self.message(attr) | -3,310,694,605,964,671,000 | Handle an missing attribute. | leo/plugins/leowapp.py | __getattr__ | Anu082000/leo-editor | python | def __getattr__(self, attr):
if (attr in ('frameFactory', 'set_minibuffer_label')):
raise AttributeError
return self.message(attr) |
def message(self, func):
'\n Send a message to the framework.\n '
g.trace('=====', func, g.callers()) | 1,021,533,959,370,079,700 | Send a message to the framework. | leo/plugins/leowapp.py | message | Anu082000/leo-editor | python | def message(self, func):
'\n \n '
g.trace('=====', func, g.callers()) |
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