blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2f579a128aabe9b70332277d9e93074cefff1569 | [
"if self.machine in ['x86_64', 'AMD64', 'i686']:\n if self.architecture == '32bit':\n return 'i386'\n return 'amd64'\nelif self.machine == 'x86':\n return 'i386'\nreturn self.machine",
"result = self.pep425tag\nif result:\n return result\nraise ValueError('PEP 425 Signature not set - this is li... | <|body_start_0|>
if self.machine in ['x86_64', 'AMD64', 'i686']:
if self.architecture == '32bit':
return 'i386'
return 'amd64'
elif self.machine == 'x86':
return 'i386'
return self.machine
<|end_body_0|>
<|body_start_1|>
result = self.... | A protobuf to represent the current system. | Uname | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Uname:
"""A protobuf to represent the current system."""
def arch(self):
"""Return a more standard representation of the architecture."""
<|body_0|>
def signature(self):
"""Returns a unique string that encapsulates the architecture."""
<|body_1|>
def... | stack_v2_sparse_classes_36k_train_002600 | 21,106 | permissive | [
{
"docstring": "Return a more standard representation of the architecture.",
"name": "arch",
"signature": "def arch(self)"
},
{
"docstring": "Returns a unique string that encapsulates the architecture.",
"name": "signature",
"signature": "def signature(self)"
},
{
"docstring": "F... | 3 | null | Implement the Python class `Uname` described below.
Class description:
A protobuf to represent the current system.
Method signatures and docstrings:
- def arch(self): Return a more standard representation of the architecture.
- def signature(self): Returns a unique string that encapsulates the architecture.
- def Fro... | Implement the Python class `Uname` described below.
Class description:
A protobuf to represent the current system.
Method signatures and docstrings:
- def arch(self): Return a more standard representation of the architecture.
- def signature(self): Returns a unique string that encapsulates the architecture.
- def Fro... | 44c0eb8c938302098ef7efae8cfd6b90bcfbb2d6 | <|skeleton|>
class Uname:
"""A protobuf to represent the current system."""
def arch(self):
"""Return a more standard representation of the architecture."""
<|body_0|>
def signature(self):
"""Returns a unique string that encapsulates the architecture."""
<|body_1|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Uname:
"""A protobuf to represent the current system."""
def arch(self):
"""Return a more standard representation of the architecture."""
if self.machine in ['x86_64', 'AMD64', 'i686']:
if self.architecture == '32bit':
return 'i386'
return 'amd64'
... | the_stack_v2_python_sparse | grr/core/grr_response_core/lib/rdfvalues/client.py | google/grr | train | 4,683 |
00208201d290bf3ba95bb8f0797e723620c166e0 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn AttackSimulationSimulationUserCoverage()",
"from .attack_simulation_user import AttackSimulationUser\nfrom .attack_simulation_user import AttackSimulationUser\nfields: Dict[str, Callable[[Any], None]] = {'attackSimulationUser': lambda ... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return AttackSimulationSimulationUserCoverage()
<|end_body_0|>
<|body_start_1|>
from .attack_simulation_user import AttackSimulationUser
from .attack_simulation_user import AttackSimulationUser... | AttackSimulationSimulationUserCoverage | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttackSimulationSimulationUserCoverage:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AttackSimulationSimulationUserCoverage:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the... | stack_v2_sparse_classes_36k_train_002601 | 4,198 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: AttackSimulationSimulationUserCoverage",
"name": "create_from_discriminator_value",
"signature": "def create... | 3 | stack_v2_sparse_classes_30k_train_020824 | Implement the Python class `AttackSimulationSimulationUserCoverage` described below.
Class description:
Implement the AttackSimulationSimulationUserCoverage class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AttackSimulationSimulationUserCoverage: C... | Implement the Python class `AttackSimulationSimulationUserCoverage` described below.
Class description:
Implement the AttackSimulationSimulationUserCoverage class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AttackSimulationSimulationUserCoverage: C... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class AttackSimulationSimulationUserCoverage:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AttackSimulationSimulationUserCoverage:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttackSimulationSimulationUserCoverage:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AttackSimulationSimulationUserCoverage:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator... | the_stack_v2_python_sparse | msgraph/generated/models/attack_simulation_simulation_user_coverage.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
e5077a26d041cc9bbed47296fc66fa74cea5ead3 | [
"input_size = common.tuplify2d(input_size)\nsuper().__init__(input_tensor_spec=TensorSpec((input_channels,) + input_size), name=name)\nassert isinstance(conv_layer_params, tuple)\nassert len(conv_layer_params) > 0\nif kernel_initializer is None:\n kernel_initializer = functools.partial(variance_scaling_init, mod... | <|body_start_0|>
input_size = common.tuplify2d(input_size)
super().__init__(input_tensor_spec=TensorSpec((input_channels,) + input_size), name=name)
assert isinstance(conv_layer_params, tuple)
assert len(conv_layer_params) > 0
if kernel_initializer is None:
kernel_ini... | ParamConvNet | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParamConvNet:
def __init__(self, input_channels, input_size, conv_layer_params, same_padding=False, activation=torch.relu_, use_bias=False, use_ln=False, n_groups=None, kernel_initializer=None, flatten_output=False, name='ParamConvNet'):
"""A fully 2D conv network that does not maintain ... | stack_v2_sparse_classes_36k_train_002602 | 14,523 | permissive | [
{
"docstring": "A fully 2D conv network that does not maintain its own network parameters, but accepts them from users. If the given parameter tensor has an extra batch dimension (first dimension), it performs parallel operations. Args: input_channels (int): number of channels in the input image input_size (int... | 4 | stack_v2_sparse_classes_30k_train_018502 | Implement the Python class `ParamConvNet` described below.
Class description:
Implement the ParamConvNet class.
Method signatures and docstrings:
- def __init__(self, input_channels, input_size, conv_layer_params, same_padding=False, activation=torch.relu_, use_bias=False, use_ln=False, n_groups=None, kernel_initiali... | Implement the Python class `ParamConvNet` described below.
Class description:
Implement the ParamConvNet class.
Method signatures and docstrings:
- def __init__(self, input_channels, input_size, conv_layer_params, same_padding=False, activation=torch.relu_, use_bias=False, use_ln=False, n_groups=None, kernel_initiali... | b00ff2fa5e660de31020338ba340263183fbeaa4 | <|skeleton|>
class ParamConvNet:
def __init__(self, input_channels, input_size, conv_layer_params, same_padding=False, activation=torch.relu_, use_bias=False, use_ln=False, n_groups=None, kernel_initializer=None, flatten_output=False, name='ParamConvNet'):
"""A fully 2D conv network that does not maintain ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ParamConvNet:
def __init__(self, input_channels, input_size, conv_layer_params, same_padding=False, activation=torch.relu_, use_bias=False, use_ln=False, n_groups=None, kernel_initializer=None, flatten_output=False, name='ParamConvNet'):
"""A fully 2D conv network that does not maintain its own networ... | the_stack_v2_python_sparse | alf/networks/param_networks.py | HorizonRobotics/alf | train | 288 | |
db61ba8bb68ca237bade264cca61074760db525a | [
"integral_strategy_settings = member_models.IntegralStrategySettings.select().dj_where(webapp_id=self.corp.webapp_id).first()\nif not integral_strategy_settings:\n return MallConfigFactory.get(self.corp).create_default_integral_strategy()\nelse:\n return IntegralStrategy(integral_strategy_settings)",
"mall_... | <|body_start_0|>
integral_strategy_settings = member_models.IntegralStrategySettings.select().dj_where(webapp_id=self.corp.webapp_id).first()
if not integral_strategy_settings:
return MallConfigFactory.get(self.corp).create_default_integral_strategy()
else:
return Integra... | MallConfigRepository | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MallConfigRepository:
def get_integral_strategy(self):
"""获得积分策略"""
<|body_0|>
def get_webapp_config(self):
"""获得WebappConfig对象"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
integral_strategy_settings = member_models.IntegralStrategySettings.selec... | stack_v2_sparse_classes_36k_train_002603 | 1,596 | no_license | [
{
"docstring": "获得积分策略",
"name": "get_integral_strategy",
"signature": "def get_integral_strategy(self)"
},
{
"docstring": "获得WebappConfig对象",
"name": "get_webapp_config",
"signature": "def get_webapp_config(self)"
}
] | 2 | null | Implement the Python class `MallConfigRepository` described below.
Class description:
Implement the MallConfigRepository class.
Method signatures and docstrings:
- def get_integral_strategy(self): 获得积分策略
- def get_webapp_config(self): 获得WebappConfig对象 | Implement the Python class `MallConfigRepository` described below.
Class description:
Implement the MallConfigRepository class.
Method signatures and docstrings:
- def get_integral_strategy(self): 获得积分策略
- def get_webapp_config(self): 获得WebappConfig对象
<|skeleton|>
class MallConfigRepository:
def get_integral_st... | 39860a451678ab50ad93ce8ebe2ef8490af65d62 | <|skeleton|>
class MallConfigRepository:
def get_integral_strategy(self):
"""获得积分策略"""
<|body_0|>
def get_webapp_config(self):
"""获得WebappConfig对象"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MallConfigRepository:
def get_integral_strategy(self):
"""获得积分策略"""
integral_strategy_settings = member_models.IntegralStrategySettings.select().dj_where(webapp_id=self.corp.webapp_id).first()
if not integral_strategy_settings:
return MallConfigFactory.get(self.corp).create... | the_stack_v2_python_sparse | business/mall/config/mall_config_repository.py | chengdg/gaia | train | 0 | |
70f0d18e3441844b004032ffdd09af7e1781e974 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')\ncommGarden = get_Col('commGardens', repo)\nfoodEstabl = get_Col('foodEstabl', repo)\nmaster = count_city(commGarden, 'area')\ncount_gardens = master[0]\ncount_ci... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')
commGarden = get_Col('commGardens', repo)
foodEstabl = get_Col('foodEstabl', repo)
master = count... | same_city | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class same_city:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything hap... | stack_v2_sparse_classes_36k_train_002604 | 3,674 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | null | Implement the Python class `same_city` described below.
Class description:
Implement the same_city class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=N... | Implement the Python class `same_city` described below.
Class description:
Implement the same_city class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=N... | 9cb0ad789b6ff265222cbd3ea3561ff553b4cdff | <|skeleton|>
class same_city:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything hap... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class same_city:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')
... | the_stack_v2_python_sparse | ckarjadi_johnnyg7/same_city.py | yinghang/course-2016-fal-proj | train | 1 | |
457b1fe40503ebce32700436ac7fccb691749bc1 | [
"with SftpClient(**config) as writer:\n for configured_stream in configured_catalog.streams:\n if configured_stream.destination_sync_mode == DestinationSyncMode.overwrite:\n writer.delete(configured_stream.stream.name)\n for message in input_messages:\n if message.type == Type.STATE:\... | <|body_start_0|>
with SftpClient(**config) as writer:
for configured_stream in configured_catalog.streams:
if configured_stream.destination_sync_mode == DestinationSyncMode.overwrite:
writer.delete(configured_stream.stream.name)
for message in input_me... | DestinationSftpJson | [
"MIT",
"Apache-2.0",
"BSD-3-Clause",
"Elastic-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DestinationSftpJson:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method re... | stack_v2_sparse_classes_36k_train_002605 | 4,144 | permissive | [
{
"docstring": "Reads the input stream of messages, config, and catalog to write data to the destination. This method returns an iterable (typically a generator of AirbyteMessages via yield) containing state messages received in the input message stream. Outputting a state message means that every AirbyteRecord... | 2 | null | Implement the Python class `DestinationSftpJson` described below.
Class description:
Implement the DestinationSftpJson class.
Method signatures and docstrings:
- def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessag... | Implement the Python class `DestinationSftpJson` described below.
Class description:
Implement the DestinationSftpJson class.
Method signatures and docstrings:
- def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessag... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class DestinationSftpJson:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DestinationSftpJson:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method returns an itera... | the_stack_v2_python_sparse | dts/airbyte/airbyte-integrations/connectors/destination-sftp-json/destination_sftp_json/destination.py | alldatacenter/alldata | train | 774 | |
3b58def95598cd286c1400c798349ae3b4ea1114 | [
"super().__init__(*args, **kwargs)\n'Build layer.'\nself.theta_0 = self.add_weight(name='theta_0', shape=(1,), initializer=Constant(0.0), trainable=True, regularizer=self.regularizer)\ntheta_lc_shape = (1, self.L, self.C)\ntheta_lc_init = np.random.randn(*theta_lc_shape) / np.sqrt(self.L)\nself.theta_lc = self.add_... | <|body_start_0|>
super().__init__(*args, **kwargs)
'Build layer.'
self.theta_0 = self.add_weight(name='theta_0', shape=(1,), initializer=Constant(0.0), trainable=True, regularizer=self.regularizer)
theta_lc_shape = (1, self.L, self.C)
theta_lc_init = np.random.randn(*theta_lc_sha... | Represents an additive G-P map. | AdditiveGPMapLayer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdditiveGPMapLayer:
"""Represents an additive G-P map."""
def __init__(self, *args, **kwargs):
"""Construct layer instance."""
<|body_0|>
def call(self, x_lc):
"""Process layer input and return output."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_002606 | 14,529 | permissive | [
{
"docstring": "Construct layer instance.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Process layer input and return output.",
"name": "call",
"signature": "def call(self, x_lc)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000595 | Implement the Python class `AdditiveGPMapLayer` described below.
Class description:
Represents an additive G-P map.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Construct layer instance.
- def call(self, x_lc): Process layer input and return output. | Implement the Python class `AdditiveGPMapLayer` described below.
Class description:
Represents an additive G-P map.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Construct layer instance.
- def call(self, x_lc): Process layer input and return output.
<|skeleton|>
class AdditiveGPMapLayer:
... | f83f6e94d3d6ceeb7f19401d369da1908cfac31d | <|skeleton|>
class AdditiveGPMapLayer:
"""Represents an additive G-P map."""
def __init__(self, *args, **kwargs):
"""Construct layer instance."""
<|body_0|>
def call(self, x_lc):
"""Process layer input and return output."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdditiveGPMapLayer:
"""Represents an additive G-P map."""
def __init__(self, *args, **kwargs):
"""Construct layer instance."""
super().__init__(*args, **kwargs)
'Build layer.'
self.theta_0 = self.add_weight(name='theta_0', shape=(1,), initializer=Constant(0.0), trainable=T... | the_stack_v2_python_sparse | mavenn/src/layers/gpmap.py | jbkinney/mavenn | train | 21 |
0bda9f6572a872c51895eb44ebea3c386176da9a | [
"self.analytical_params = analytical_params\nself.geographical_params = geogr_params\nself.deformational_params = deform_params\nself.anal_param_values = self.get_analytical_param_values()\narray_range, array_size, formula = self.anal_param_values\na_min, a_max, b_min, b_max = array_range\na_range, b_range = (a_max... | <|body_start_0|>
self.analytical_params = analytical_params
self.geographical_params = geogr_params
self.deformational_params = deform_params
self.anal_param_values = self.get_analytical_param_values()
array_range, array_size, formula = self.anal_param_values
a_min, a_max... | AnalyticGeosurface | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalyticGeosurface:
def __init__(self, analytical_params, geogr_params, deform_params):
""":param analytical_params: :param geogr_params: :param deform_params:"""
<|body_0|>
def geosurface_center(self):
""":return:"""
<|body_1|>
def geosurface_XYZ(self):... | stack_v2_sparse_classes_36k_train_002607 | 18,232 | no_license | [
{
"docstring": ":param analytical_params: :param geogr_params: :param deform_params:",
"name": "__init__",
"signature": "def __init__(self, analytical_params, geogr_params, deform_params)"
},
{
"docstring": ":return:",
"name": "geosurface_center",
"signature": "def geosurface_center(self... | 6 | stack_v2_sparse_classes_30k_train_000275 | Implement the Python class `AnalyticGeosurface` described below.
Class description:
Implement the AnalyticGeosurface class.
Method signatures and docstrings:
- def __init__(self, analytical_params, geogr_params, deform_params): :param analytical_params: :param geogr_params: :param deform_params:
- def geosurface_cent... | Implement the Python class `AnalyticGeosurface` described below.
Class description:
Implement the AnalyticGeosurface class.
Method signatures and docstrings:
- def __init__(self, analytical_params, geogr_params, deform_params): :param analytical_params: :param geogr_params: :param deform_params:
- def geosurface_cent... | b07ab23400b4ff4151555c2e81392a7adf99fc33 | <|skeleton|>
class AnalyticGeosurface:
def __init__(self, analytical_params, geogr_params, deform_params):
""":param analytical_params: :param geogr_params: :param deform_params:"""
<|body_0|>
def geosurface_center(self):
""":return:"""
<|body_1|>
def geosurface_XYZ(self):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalyticGeosurface:
def __init__(self, analytical_params, geogr_params, deform_params):
""":param analytical_params: :param geogr_params: :param deform_params:"""
self.analytical_params = analytical_params
self.geographical_params = geogr_params
self.deformational_params = defo... | the_stack_v2_python_sparse | pygsf/spatial/vectorial/meshes.py | mauroalberti/qgSurf | train | 5 | |
cb5ac25b2b79196487cab106cc37255980f58030 | [
"super().__init__(config)\nself.constraints = config.get('constraints')\nif not isinstance(self.constraints, dict):\n raise RuntimeError('constraints should be a dict')\nself.qcs_config: QCSClientConfiguration = QCSClientConfiguration.load(profile_name=config.get('qcs_profile_name'), secrets_file_path=config.get... | <|body_start_0|>
super().__init__(config)
self.constraints = config.get('constraints')
if not isinstance(self.constraints, dict):
raise RuntimeError('constraints should be a dict')
self.qcs_config: QCSClientConfiguration = QCSClientConfiguration.load(profile_name=config.get('... | Billing costs for the Rigetti systems | RigettiBillingInfo | [
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RigettiBillingInfo:
"""Billing costs for the Rigetti systems"""
def __init__(self, config):
"""Read in our toml files and build a client we can use"""
<|body_0|>
def acquire(self):
"""Method to be called from Task Manager. redefines acquire from Source.py. Acquir... | stack_v2_sparse_classes_36k_train_002608 | 3,608 | permissive | [
{
"docstring": "Read in our toml files and build a client we can use",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "Method to be called from Task Manager. redefines acquire from Source.py. Acquire Regetti costs and return as pandas frame Returns: dict: A dicti... | 2 | null | Implement the Python class `RigettiBillingInfo` described below.
Class description:
Billing costs for the Rigetti systems
Method signatures and docstrings:
- def __init__(self, config): Read in our toml files and build a client we can use
- def acquire(self): Method to be called from Task Manager. redefines acquire f... | Implement the Python class `RigettiBillingInfo` described below.
Class description:
Billing costs for the Rigetti systems
Method signatures and docstrings:
- def __init__(self, config): Read in our toml files and build a client we can use
- def acquire(self): Method to be called from Task Manager. redefines acquire f... | 842fdc91a31879084906d71a7d0c317e5035a925 | <|skeleton|>
class RigettiBillingInfo:
"""Billing costs for the Rigetti systems"""
def __init__(self, config):
"""Read in our toml files and build a client we can use"""
<|body_0|>
def acquire(self):
"""Method to be called from Task Manager. redefines acquire from Source.py. Acquir... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RigettiBillingInfo:
"""Billing costs for the Rigetti systems"""
def __init__(self, config):
"""Read in our toml files and build a client we can use"""
super().__init__(config)
self.constraints = config.get('constraints')
if not isinstance(self.constraints, dict):
... | the_stack_v2_python_sparse | src/decisionengine_modules/Rigetti/sources/Rigetti_BillingInfo.py | HEPCloud/decisionengine_modules | train | 2 |
14935c8951193de3b8718182b8456168e31f57b4 | [
"self.smaller, self.bigger = ([], [])\nheapify(self.smaller)\nheapify(self.bigger)",
"heappush(self.bigger, num)\nheappush(self.smaller, -heappop(self.bigger))\nwhile len(self.smaller) > len(self.bigger):\n heappush(self.bigger, -heappop(self.smaller))",
"if len(self.smaller) == len(self.bigger):\n return... | <|body_start_0|>
self.smaller, self.bigger = ([], [])
heapify(self.smaller)
heapify(self.bigger)
<|end_body_0|>
<|body_start_1|>
heappush(self.bigger, num)
heappush(self.smaller, -heappop(self.bigger))
while len(self.smaller) > len(self.bigger):
heappush(self... | MedianFinder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MedianFinder:
def __init__(self):
"""initialize your data structure here."""
<|body_0|>
def addNum(self, num):
""":type num: int :rtype: None"""
<|body_1|>
def findMedian(self):
""":rtype: float"""
<|body_2|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_002609 | 1,169 | no_license | [
{
"docstring": "initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": ":type num: int :rtype: None",
"name": "addNum",
"signature": "def addNum(self, num)"
},
{
"docstring": ":rtype: float",
"name": "findMedian",
"s... | 3 | stack_v2_sparse_classes_30k_train_000224 | Implement the Python class `MedianFinder` described below.
Class description:
Implement the MedianFinder class.
Method signatures and docstrings:
- def __init__(self): initialize your data structure here.
- def addNum(self, num): :type num: int :rtype: None
- def findMedian(self): :rtype: float | Implement the Python class `MedianFinder` described below.
Class description:
Implement the MedianFinder class.
Method signatures and docstrings:
- def __init__(self): initialize your data structure here.
- def addNum(self, num): :type num: int :rtype: None
- def findMedian(self): :rtype: float
<|skeleton|>
class Me... | 76d767ec001649b2df07aac211ac4b43b415ebdd | <|skeleton|>
class MedianFinder:
def __init__(self):
"""initialize your data structure here."""
<|body_0|>
def addNum(self, num):
""":type num: int :rtype: None"""
<|body_1|>
def findMedian(self):
""":rtype: float"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MedianFinder:
def __init__(self):
"""initialize your data structure here."""
self.smaller, self.bigger = ([], [])
heapify(self.smaller)
heapify(self.bigger)
def addNum(self, num):
""":type num: int :rtype: None"""
heappush(self.bigger, num)
heappush... | the_stack_v2_python_sparse | leetcode295 Find Median from Data Stream.py | whglamrock/leetcode_series | train | 2 | |
624b25c664ee2cddfab8f45f13247f6f4b39ea9f | [
"if index_uuid in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'index_uuid'\")\nif accept_data_loss is None:\n raise ValueError(\"Empty value passed for parameter 'accept_data_loss'\")\n__path = f'/_dangling/{_quote(index_uuid)}'\n__query: t.Dict[str, t.Any] = {}\nif accept_data_loss is... | <|body_start_0|>
if index_uuid in SKIP_IN_PATH:
raise ValueError("Empty value passed for parameter 'index_uuid'")
if accept_data_loss is None:
raise ValueError("Empty value passed for parameter 'accept_data_loss'")
__path = f'/_dangling/{_quote(index_uuid)}'
__que... | DanglingIndicesClient | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DanglingIndicesClient:
def delete_dangling_index(self, *, index_uuid: str, accept_data_loss: bool, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, master_timeout: t.Optional[t.Union['t.Literal[... | stack_v2_sparse_classes_36k_train_002610 | 6,469 | permissive | [
{
"docstring": "Deletes the specified dangling index `<https://www.elastic.co/guide/en/elasticsearch/reference/master/modules-gateway-dangling-indices.html>`_ :param index_uuid: The UUID of the dangling index :param accept_data_loss: Must be set to true in order to delete the dangling index :param master_timeou... | 3 | stack_v2_sparse_classes_30k_train_012939 | Implement the Python class `DanglingIndicesClient` described below.
Class description:
Implement the DanglingIndicesClient class.
Method signatures and docstrings:
- def delete_dangling_index(self, *, index_uuid: str, accept_data_loss: bool, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.U... | Implement the Python class `DanglingIndicesClient` described below.
Class description:
Implement the DanglingIndicesClient class.
Method signatures and docstrings:
- def delete_dangling_index(self, *, index_uuid: str, accept_data_loss: bool, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.U... | 915bbd784831ccb84e1559af0f829736652d2e78 | <|skeleton|>
class DanglingIndicesClient:
def delete_dangling_index(self, *, index_uuid: str, accept_data_loss: bool, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, master_timeout: t.Optional[t.Union['t.Literal[... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DanglingIndicesClient:
def delete_dangling_index(self, *, index_uuid: str, accept_data_loss: bool, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, master_timeout: t.Optional[t.Union['t.Literal[-1]', 't.Liter... | the_stack_v2_python_sparse | elasticsearch/_sync/client/dangling_indices.py | elastic/elasticsearch-py | train | 3,845 | |
add483eab35ac4ad2938837fe89e066f11ac77ff | [
"super().__init__(coordinator=coordinator)\nself._sensor_key = sensor_key\nself._service_key = service_key\nself.entity_id = f'{SENSOR_DOMAIN}.{service_key}_{sensor_key}'\nself._attr_device_class = sensor.get(ATTR_DEVICE_CLASS)\nself._attr_entity_registry_enabled_default = sensor.get(ATTR_ENABLED_BY_DEFAULT, True)\... | <|body_start_0|>
super().__init__(coordinator=coordinator)
self._sensor_key = sensor_key
self._service_key = service_key
self.entity_id = f'{SENSOR_DOMAIN}.{service_key}_{sensor_key}'
self._attr_device_class = sensor.get(ATTR_DEVICE_CLASS)
self._attr_entity_registry_enabl... | Defines an Ambee sensor. | AmbeeSensorEntity | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AmbeeSensorEntity:
"""Defines an Ambee sensor."""
def __init__(self, *, coordinator: DataUpdateCoordinator, entry_id: str, sensor_key: str, sensor: AmbeeSensor, service_key: str, service: str) -> None:
"""Initialize Ambee sensor."""
<|body_0|>
def state(self) -> StateTyp... | stack_v2_sparse_classes_36k_train_002611 | 3,042 | permissive | [
{
"docstring": "Initialize Ambee sensor.",
"name": "__init__",
"signature": "def __init__(self, *, coordinator: DataUpdateCoordinator, entry_id: str, sensor_key: str, sensor: AmbeeSensor, service_key: str, service: str) -> None"
},
{
"docstring": "Return the state of the sensor.",
"name": "s... | 2 | null | Implement the Python class `AmbeeSensorEntity` described below.
Class description:
Defines an Ambee sensor.
Method signatures and docstrings:
- def __init__(self, *, coordinator: DataUpdateCoordinator, entry_id: str, sensor_key: str, sensor: AmbeeSensor, service_key: str, service: str) -> None: Initialize Ambee senso... | Implement the Python class `AmbeeSensorEntity` described below.
Class description:
Defines an Ambee sensor.
Method signatures and docstrings:
- def __init__(self, *, coordinator: DataUpdateCoordinator, entry_id: str, sensor_key: str, sensor: AmbeeSensor, service_key: str, service: str) -> None: Initialize Ambee senso... | 2fee32fce03bc49e86cf2e7b741a15621a97cce5 | <|skeleton|>
class AmbeeSensorEntity:
"""Defines an Ambee sensor."""
def __init__(self, *, coordinator: DataUpdateCoordinator, entry_id: str, sensor_key: str, sensor: AmbeeSensor, service_key: str, service: str) -> None:
"""Initialize Ambee sensor."""
<|body_0|>
def state(self) -> StateTyp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AmbeeSensorEntity:
"""Defines an Ambee sensor."""
def __init__(self, *, coordinator: DataUpdateCoordinator, entry_id: str, sensor_key: str, sensor: AmbeeSensor, service_key: str, service: str) -> None:
"""Initialize Ambee sensor."""
super().__init__(coordinator=coordinator)
self._... | the_stack_v2_python_sparse | homeassistant/components/ambee/sensor.py | BenWoodford/home-assistant | train | 11 |
b92fa70a8837a069fb303ff5c1e6e3011aa91780 | [
"red = white = blue = 0\nfor num in nums:\n if num == 0:\n red += 1\n elif num == 1:\n white += 1\n else:\n blue += 1\nfor i in range(len(nums)):\n if red:\n red -= 1\n nums[i] = 0\n elif white:\n white -= 1\n nums[i] = 1\n elif blue:\n blue ... | <|body_start_0|>
red = white = blue = 0
for num in nums:
if num == 0:
red += 1
elif num == 1:
white += 1
else:
blue += 1
for i in range(len(nums)):
if red:
red -= 1
num... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _sortColors(self, nums):
""":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead."""
<|body_0|>
def sortColors(self, nums):
""":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead."""... | stack_v2_sparse_classes_36k_train_002612 | 2,792 | permissive | [
{
"docstring": ":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead.",
"name": "_sortColors",
"signature": "def _sortColors(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead.",
"name": "... | 2 | stack_v2_sparse_classes_30k_train_016313 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _sortColors(self, nums): :type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead.
- def sortColors(self, nums): :type nums: List[int] :rtype: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _sortColors(self, nums): :type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead.
- def sortColors(self, nums): :type nums: List[int] :rtype: ... | 0dd67edca4e0b0323cb5a7239f02ea46383cd15a | <|skeleton|>
class Solution:
def _sortColors(self, nums):
""":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead."""
<|body_0|>
def sortColors(self, nums):
""":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead."""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def _sortColors(self, nums):
""":type nums: List[int] :rtype: None Do not return anything, modify nums in-place instead."""
red = white = blue = 0
for num in nums:
if num == 0:
red += 1
elif num == 1:
white += 1
... | the_stack_v2_python_sparse | 75.sort-colors.py | windard/leeeeee | train | 0 | |
0dbe5d508dfc4613ef8dd5352c6f2801ce135631 | [
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ndatabase = 'scratch/toy.db'\ntalon.get_counters(database)\nedge_dict = init_refs.make_edge_dict(cursor)\nlocation_dict = init_refs.make_location_dict(build, cursor)\nrun_info = talon.init_run_info(database, build)\ntranscript_dict = init_refs.make_transcript_dic... | <|body_start_0|>
conn, cursor = get_db_cursor()
build = 'toy_build'
database = 'scratch/toy.db'
talon.get_counters(database)
edge_dict = init_refs.make_edge_dict(cursor)
location_dict = init_refs.make_location_dict(build, cursor)
run_info = talon.init_run_info(dat... | TestIdentifyRemaining | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIdentifyRemaining:
def test_intergenic(self):
"""Example where the transcript is an NIC match to an existing one by virtue of a new splice donor."""
<|body_0|>
def test_antisense(self):
"""Example where the transcript is antisense but contains no known splice ver... | stack_v2_sparse_classes_36k_train_002613 | 6,699 | permissive | [
{
"docstring": "Example where the transcript is an NIC match to an existing one by virtue of a new splice donor.",
"name": "test_intergenic",
"signature": "def test_intergenic(self)"
},
{
"docstring": "Example where the transcript is antisense but contains no known splice vertices",
"name": ... | 3 | stack_v2_sparse_classes_30k_train_000715 | Implement the Python class `TestIdentifyRemaining` described below.
Class description:
Implement the TestIdentifyRemaining class.
Method signatures and docstrings:
- def test_intergenic(self): Example where the transcript is an NIC match to an existing one by virtue of a new splice donor.
- def test_antisense(self): ... | Implement the Python class `TestIdentifyRemaining` described below.
Class description:
Implement the TestIdentifyRemaining class.
Method signatures and docstrings:
- def test_intergenic(self): Example where the transcript is an NIC match to an existing one by virtue of a new splice donor.
- def test_antisense(self): ... | 8014faed5f982e5e106ec05239e47d65878e76c3 | <|skeleton|>
class TestIdentifyRemaining:
def test_intergenic(self):
"""Example where the transcript is an NIC match to an existing one by virtue of a new splice donor."""
<|body_0|>
def test_antisense(self):
"""Example where the transcript is antisense but contains no known splice ver... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestIdentifyRemaining:
def test_intergenic(self):
"""Example where the transcript is an NIC match to an existing one by virtue of a new splice donor."""
conn, cursor = get_db_cursor()
build = 'toy_build'
database = 'scratch/toy.db'
talon.get_counters(database)
e... | the_stack_v2_python_sparse | testing_suite/test_process_remaining_mult_cases.py | kopardev/TALON | train | 0 | |
47b0cdc517c1632cbee89a7b25476eb381d3c579 | [
"super(mesh_to_mesh_1d_periodic, self).__init__(fine_level, coarse_level, params)\nfine_grid = np.array([i * fine_level.prob.dx for i in range(fine_level.prob.nvars)])\ncoarse_grid = np.array([i * coarse_level.prob.dx for i in range(coarse_level.prob.nvars)])\nif self.init_c == self.init_f:\n self.Rspace = np.ey... | <|body_start_0|>
super(mesh_to_mesh_1d_periodic, self).__init__(fine_level, coarse_level, params)
fine_grid = np.array([i * fine_level.prob.dx for i in range(fine_level.prob.nvars)])
coarse_grid = np.array([i * coarse_level.prob.dx for i in range(coarse_level.prob.nvars)])
if self.init_c... | Custon transfer class, implements Transfer.py This implementation can restrict and prolong between 1d meshes via matrix-vector products Attributes: fine: reference to the fine level coarse: reference to the coarse level init_f: number of variables on the fine level (whatever init represents there) init_c: number of var... | mesh_to_mesh_1d_periodic | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class mesh_to_mesh_1d_periodic:
"""Custon transfer class, implements Transfer.py This implementation can restrict and prolong between 1d meshes via matrix-vector products Attributes: fine: reference to the fine level coarse: reference to the coarse level init_f: number of variables on the fine level (w... | stack_v2_sparse_classes_36k_train_002614 | 3,644 | permissive | [
{
"docstring": "Initialization routine Args: fine_level: fine level connected with the transfer operations (passed to parent) coarse_level: coarse level connected with the transfer operations (passed to parent) params: parameters for the transfer operators",
"name": "__init__",
"signature": "def __init_... | 3 | null | Implement the Python class `mesh_to_mesh_1d_periodic` described below.
Class description:
Custon transfer class, implements Transfer.py This implementation can restrict and prolong between 1d meshes via matrix-vector products Attributes: fine: reference to the fine level coarse: reference to the coarse level init_f: n... | Implement the Python class `mesh_to_mesh_1d_periodic` described below.
Class description:
Custon transfer class, implements Transfer.py This implementation can restrict and prolong between 1d meshes via matrix-vector products Attributes: fine: reference to the fine level coarse: reference to the coarse level init_f: n... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class mesh_to_mesh_1d_periodic:
"""Custon transfer class, implements Transfer.py This implementation can restrict and prolong between 1d meshes via matrix-vector products Attributes: fine: reference to the fine level coarse: reference to the coarse level init_f: number of variables on the fine level (w... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class mesh_to_mesh_1d_periodic:
"""Custon transfer class, implements Transfer.py This implementation can restrict and prolong between 1d meshes via matrix-vector products Attributes: fine: reference to the fine level coarse: reference to the coarse level init_f: number of variables on the fine level (whatever init ... | the_stack_v2_python_sparse | pySDC/playgrounds/deprecated/advection_1d_implicit/TransferClass.py | Parallel-in-Time/pySDC | train | 30 |
f6ef462086bcc8079410619ca20a62f022e5683c | [
"print('load Pre_process')\ndata = pd.concat([pd.read_csv(config.train_data_path, sep='\\t'), pd.read_csv(config.dev_data_path, sep='\\t'), pd.read_csv(config.test_data_path, sep='\\t')])\nprint('读取数据集完成')\ndata['text'] = data['title'] + data['desc']\ndata['text'] = data['text'].apply(query_cut)\ndata['text'] = dat... | <|body_start_0|>
print('load Pre_process')
data = pd.concat([pd.read_csv(config.train_data_path, sep='\t'), pd.read_csv(config.dev_data_path, sep='\t'), pd.read_csv(config.test_data_path, sep='\t')])
print('读取数据集完成')
data['text'] = data['title'] + data['desc']
data['text'] = data... | Build_Corpus | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Build_Corpus:
def load_data(self):
"""函数说明：该函数用于加载数据集 return: -data: 表示所有数据拼接的原始数据 -data_text: 表示数据集中的特征数据集 -datatext: 表示经过分词之后的特征数据集 -stopWords: 表示读取的停用词"""
<|body_0|>
def write_csv(self, datatext, stopWords):
"""将三个数据文件的 text 合并起来到一个文件，并且去除停用词 :param datatext: 三个数据... | stack_v2_sparse_classes_36k_train_002615 | 4,601 | no_license | [
{
"docstring": "函数说明：该函数用于加载数据集 return: -data: 表示所有数据拼接的原始数据 -data_text: 表示数据集中的特征数据集 -datatext: 表示经过分词之后的特征数据集 -stopWords: 表示读取的停用词",
"name": "load_data",
"signature": "def load_data(self)"
},
{
"docstring": "将三个数据文件的 text 合并起来到一个文件，并且去除停用词 :param datatext: 三个数据文件合并后的list，没有去除停用词 :param stopWor... | 3 | stack_v2_sparse_classes_30k_train_009546 | Implement the Python class `Build_Corpus` described below.
Class description:
Implement the Build_Corpus class.
Method signatures and docstrings:
- def load_data(self): 函数说明：该函数用于加载数据集 return: -data: 表示所有数据拼接的原始数据 -data_text: 表示数据集中的特征数据集 -datatext: 表示经过分词之后的特征数据集 -stopWords: 表示读取的停用词
- def write_csv(self, datatext, ... | Implement the Python class `Build_Corpus` described below.
Class description:
Implement the Build_Corpus class.
Method signatures and docstrings:
- def load_data(self): 函数说明：该函数用于加载数据集 return: -data: 表示所有数据拼接的原始数据 -data_text: 表示数据集中的特征数据集 -datatext: 表示经过分词之后的特征数据集 -stopWords: 表示读取的停用词
- def write_csv(self, datatext, ... | d9d3848f2075350141d7736c7d53126daa0e1751 | <|skeleton|>
class Build_Corpus:
def load_data(self):
"""函数说明：该函数用于加载数据集 return: -data: 表示所有数据拼接的原始数据 -data_text: 表示数据集中的特征数据集 -datatext: 表示经过分词之后的特征数据集 -stopWords: 表示读取的停用词"""
<|body_0|>
def write_csv(self, datatext, stopWords):
"""将三个数据文件的 text 合并起来到一个文件，并且去除停用词 :param datatext: 三个数据... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Build_Corpus:
def load_data(self):
"""函数说明：该函数用于加载数据集 return: -data: 表示所有数据拼接的原始数据 -data_text: 表示数据集中的特征数据集 -datatext: 表示经过分词之后的特征数据集 -stopWords: 表示读取的停用词"""
print('load Pre_process')
data = pd.concat([pd.read_csv(config.train_data_path, sep='\t'), pd.read_csv(config.dev_data_path, sep... | the_stack_v2_python_sparse | src/data_process/build_corpus.py | WAng91An/Book_Classification | train | 0 | |
4a0521e733d7580ef3eba6519f3e26a369b68637 | [
"super().__init__()\nself.pooling = pooling\nself.spherical_cheb_bn = SphericalChebBN(in_channels, out_channels, lap, kernel_size)",
"x = self.pooling(x)\nx = self.spherical_cheb_bn(x)\nreturn x"
] | <|body_start_0|>
super().__init__()
self.pooling = pooling
self.spherical_cheb_bn = SphericalChebBN(in_channels, out_channels, lap, kernel_size)
<|end_body_0|>
<|body_start_1|>
x = self.pooling(x)
x = self.spherical_cheb_bn(x)
return x
<|end_body_1|>
| Building Block with a pooling/unpooling, a calling the SphericalChebBN block. | SphericalChebBNPool | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SphericalChebBNPool:
"""Building Block with a pooling/unpooling, a calling the SphericalChebBN block."""
def __init__(self, in_channels, out_channels, lap, pooling, kernel_size):
"""Initialization. Args: in_channels (int): initial number of channels. out_channels (int): output number... | stack_v2_sparse_classes_36k_train_002616 | 41,403 | no_license | [
{
"docstring": "Initialization. Args: in_channels (int): initial number of channels. out_channels (int): output number of channels. lap (:obj:`torch.sparse.FloatTensor`): laplacian. pooling (:obj:`torch.nn.Module`): pooling/unpooling module. kernel_size (int, optional): polynomial degree. Defaults to 3.",
"... | 2 | stack_v2_sparse_classes_30k_train_015576 | Implement the Python class `SphericalChebBNPool` described below.
Class description:
Building Block with a pooling/unpooling, a calling the SphericalChebBN block.
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, lap, pooling, kernel_size): Initialization. Args: in_channels (int): init... | Implement the Python class `SphericalChebBNPool` described below.
Class description:
Building Block with a pooling/unpooling, a calling the SphericalChebBN block.
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, lap, pooling, kernel_size): Initialization. Args: in_channels (int): init... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class SphericalChebBNPool:
"""Building Block with a pooling/unpooling, a calling the SphericalChebBN block."""
def __init__(self, in_channels, out_channels, lap, pooling, kernel_size):
"""Initialization. Args: in_channels (int): initial number of channels. out_channels (int): output number... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SphericalChebBNPool:
"""Building Block with a pooling/unpooling, a calling the SphericalChebBN block."""
def __init__(self, in_channels, out_channels, lap, pooling, kernel_size):
"""Initialization. Args: in_channels (int): initial number of channels. out_channels (int): output number of channels.... | the_stack_v2_python_sparse | generated/test_deepsphere_deepsphere_pytorch.py | jansel/pytorch-jit-paritybench | train | 35 |
c5a20ac67445f00292775ffcf0888d6a074169e3 | [
"world = vehicle.get_world()\nblueprint = world.get_blueprint_library().find('sensor.lidar.ray_cast')\nblueprint.set_attribute('upper_fov', str(config_yaml['upper_fov']))\nblueprint.set_attribute('lower_fov', str(config_yaml['lower_fov']))\nblueprint.set_attribute('channels', str(config_yaml['channels']))\nblueprin... | <|body_start_0|>
world = vehicle.get_world()
blueprint = world.get_blueprint_library().find('sensor.lidar.ray_cast')
blueprint.set_attribute('upper_fov', str(config_yaml['upper_fov']))
blueprint.set_attribute('lower_fov', str(config_yaml['lower_fov']))
blueprint.set_attribute('ch... | Lidar sensor manager. Parameters -vehicle : carla.Vehicle The carla.Vehicle. We need this class to spawn sensors. -config_yaml : dict Configuration for lidar sensor. Attributes -o3d_pointcloud : o3d.PointCloud Recieved point cloud saved in o3d.PointCloud format. -sensor : CARLA actor The current sensor actors that will... | LidarSensor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LidarSensor:
"""Lidar sensor manager. Parameters -vehicle : carla.Vehicle The carla.Vehicle. We need this class to spawn sensors. -config_yaml : dict Configuration for lidar sensor. Attributes -o3d_pointcloud : o3d.PointCloud Recieved point cloud saved in o3d.PointCloud format. -sensor : CARLA ac... | stack_v2_sparse_classes_36k_train_002617 | 14,680 | permissive | [
{
"docstring": "Construct class. Args: vehicle (carla.Vehicle): The attached vehicle. config_yaml (dict): Configuration for lidar.",
"name": "__init__",
"signature": "def __init__(self, vehicle, config_yaml)"
},
{
"docstring": "CAMERA method",
"name": "_on_data_event",
"signature": "def ... | 2 | stack_v2_sparse_classes_30k_train_005941 | Implement the Python class `LidarSensor` described below.
Class description:
Lidar sensor manager. Parameters -vehicle : carla.Vehicle The carla.Vehicle. We need this class to spawn sensors. -config_yaml : dict Configuration for lidar sensor. Attributes -o3d_pointcloud : o3d.PointCloud Recieved point cloud saved in o3... | Implement the Python class `LidarSensor` described below.
Class description:
Lidar sensor manager. Parameters -vehicle : carla.Vehicle The carla.Vehicle. We need this class to spawn sensors. -config_yaml : dict Configuration for lidar sensor. Attributes -o3d_pointcloud : o3d.PointCloud Recieved point cloud saved in o3... | 1ad4b368d4287dae8b282bac1665816a496d57c6 | <|skeleton|>
class LidarSensor:
"""Lidar sensor manager. Parameters -vehicle : carla.Vehicle The carla.Vehicle. We need this class to spawn sensors. -config_yaml : dict Configuration for lidar sensor. Attributes -o3d_pointcloud : o3d.PointCloud Recieved point cloud saved in o3d.PointCloud format. -sensor : CARLA ac... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LidarSensor:
"""Lidar sensor manager. Parameters -vehicle : carla.Vehicle The carla.Vehicle. We need this class to spawn sensors. -config_yaml : dict Configuration for lidar sensor. Attributes -o3d_pointcloud : o3d.PointCloud Recieved point cloud saved in o3d.PointCloud format. -sensor : CARLA actor The curre... | the_stack_v2_python_sparse | opencda/core/sensing/perception/perception_manager.py | xiaxin2000/OpenCDA-Documents | train | 0 |
eafcd083db6bbd489e71a8f21a8fd33d464df199 | [
"data = request.get_json()\npath = data.get('config_file_path')\nif not path:\n path = '.'\ncode, image = client.build(data.get('repository_URL'), data.get('image_tag'), path)\nif code == '0':\n return ({'image': image.id}, 200)\nelse:\n return ({'code': code}, 400)",
"data = request.get_json()\nclient.d... | <|body_start_0|>
data = request.get_json()
path = data.get('config_file_path')
if not path:
path = '.'
code, image = client.build(data.get('repository_URL'), data.get('image_tag'), path)
if code == '0':
return ({'image': image.id}, 200)
else:
... | deployment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class deployment:
def post(self):
"""docker build"""
<|body_0|>
def delete(self):
"""docker rmi"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
data = request.get_json()
path = data.get('config_file_path')
if not path:
path = '... | stack_v2_sparse_classes_36k_train_002618 | 1,181 | no_license | [
{
"docstring": "docker build",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "docker rmi",
"name": "delete",
"signature": "def delete(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013033 | Implement the Python class `deployment` described below.
Class description:
Implement the deployment class.
Method signatures and docstrings:
- def post(self): docker build
- def delete(self): docker rmi | Implement the Python class `deployment` described below.
Class description:
Implement the deployment class.
Method signatures and docstrings:
- def post(self): docker build
- def delete(self): docker rmi
<|skeleton|>
class deployment:
def post(self):
"""docker build"""
<|body_0|>
def delete... | 2cd55cbfb023e79b92dc31a96b9de6d2db138767 | <|skeleton|>
class deployment:
def post(self):
"""docker build"""
<|body_0|>
def delete(self):
"""docker rmi"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class deployment:
def post(self):
"""docker build"""
data = request.get_json()
path = data.get('config_file_path')
if not path:
path = '.'
code, image = client.build(data.get('repository_URL'), data.get('image_tag'), path)
if code == '0':
retur... | the_stack_v2_python_sparse | src/image/namespaces/views.py | LCTheo/DeployT_PoC | train | 0 | |
3e7feda020db856d99ba9e26a11f5c088d2f4896 | [
"def ser(root, array):\n if not root:\n array.append('None')\n else:\n array.append(root.val)\n ser(root.left, array)\n ser(root.right, array)\n return array\nreturn ser(root, [])",
"def des(data_):\n if data_[0] == 'None':\n data_.pop(0)\n return None\n ro... | <|body_start_0|>
def ser(root, array):
if not root:
array.append('None')
else:
array.append(root.val)
ser(root.left, array)
ser(root.right, array)
return array
return ser(root, [])
<|end_body_0|>
<|body_... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_002619 | 1,312 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 1bcf3206cd3acc428ec690cb883c612aaf708aac | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def ser(root, array):
if not root:
array.append('None')
else:
array.append(root.val)
ser(root.left, array)
... | the_stack_v2_python_sparse | top100/297.py | KevinChen1994/leetcode-algorithm | train | 2 | |
5f477eaf8233cc0cd2d72366b75ff88c2b2b54f7 | [
"db = self.request.app['db']\nif not await db.otus.count_documents({'_id': otu_id, 'isolates.id': isolate_id}, limit=1):\n raise NotFound\nprojection = list(virtool.otus.db.SEQUENCE_PROJECTION)\nprojection.remove('otu_id')\nprojection.remove('isolate_id')\nreturn json_response([base_processor(d) async for d in d... | <|body_start_0|>
db = self.request.app['db']
if not await db.otus.count_documents({'_id': otu_id, 'isolates.id': isolate_id}, limit=1):
raise NotFound
projection = list(virtool.otus.db.SEQUENCE_PROJECTION)
projection.remove('otu_id')
projection.remove('isolate_id')
... | SequencesView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SequencesView:
async def get(self, otu_id: str, isolate_id: str, /) -> Union[r200[List[OTUIsolate]], r401, r403, r404]:
"""List sequences. Lists the sequences for an isolate."""
<|body_0|>
async def post(self, otu_id: str, isolate_id: str, /, data: CreateSequenceRequest) -> ... | stack_v2_sparse_classes_36k_train_002620 | 16,946 | permissive | [
{
"docstring": "List sequences. Lists the sequences for an isolate.",
"name": "get",
"signature": "async def get(self, otu_id: str, isolate_id: str, /) -> Union[r200[List[OTUIsolate]], r401, r403, r404]"
},
{
"docstring": "Create a sequence. Creates a new sequence for an isolate identified by `o... | 2 | null | Implement the Python class `SequencesView` described below.
Class description:
Implement the SequencesView class.
Method signatures and docstrings:
- async def get(self, otu_id: str, isolate_id: str, /) -> Union[r200[List[OTUIsolate]], r401, r403, r404]: List sequences. Lists the sequences for an isolate.
- async def... | Implement the Python class `SequencesView` described below.
Class description:
Implement the SequencesView class.
Method signatures and docstrings:
- async def get(self, otu_id: str, isolate_id: str, /) -> Union[r200[List[OTUIsolate]], r401, r403, r404]: List sequences. Lists the sequences for an isolate.
- async def... | 1d17d2ba570cf5487e7514bec29250a5b368bb0a | <|skeleton|>
class SequencesView:
async def get(self, otu_id: str, isolate_id: str, /) -> Union[r200[List[OTUIsolate]], r401, r403, r404]:
"""List sequences. Lists the sequences for an isolate."""
<|body_0|>
async def post(self, otu_id: str, isolate_id: str, /, data: CreateSequenceRequest) -> ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SequencesView:
async def get(self, otu_id: str, isolate_id: str, /) -> Union[r200[List[OTUIsolate]], r401, r403, r404]:
"""List sequences. Lists the sequences for an isolate."""
db = self.request.app['db']
if not await db.otus.count_documents({'_id': otu_id, 'isolates.id': isolate_id},... | the_stack_v2_python_sparse | virtool/otus/api.py | virtool/virtool | train | 45 | |
c420be450f11e407bbde261381ba750e6af00724 | [
"if api.dashboard_config.IS_ACTIVATE_AUTH:\n user_id = get_jwt_identity()\n role: ProjectUserRoleModel = db.session.query(ProjectUserRoleModel).filter(ProjectUserRoleModel.project_id == project_id, ProjectUserRoleModel.user_id == user_id).one_or_none()\n if role is None:\n raise ProjectUserRoleExcep... | <|body_start_0|>
if api.dashboard_config.IS_ACTIVATE_AUTH:
user_id = get_jwt_identity()
role: ProjectUserRoleModel = db.session.query(ProjectUserRoleModel).filter(ProjectUserRoleModel.project_id == project_id, ProjectUserRoleModel.user_id == user_id).one_or_none()
if role is ... | ApiKubernetes | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiKubernetes:
def get(self, project_id: int):
"""get_kubernetes"""
<|body_0|>
def post(self, project_id: int):
"""add_kubernetes"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if api.dashboard_config.IS_ACTIVATE_AUTH:
user_id = get_jwt... | stack_v2_sparse_classes_36k_train_002621 | 10,859 | permissive | [
{
"docstring": "get_kubernetes",
"name": "get",
"signature": "def get(self, project_id: int)"
},
{
"docstring": "add_kubernetes",
"name": "post",
"signature": "def post(self, project_id: int)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000280 | Implement the Python class `ApiKubernetes` described below.
Class description:
Implement the ApiKubernetes class.
Method signatures and docstrings:
- def get(self, project_id: int): get_kubernetes
- def post(self, project_id: int): add_kubernetes | Implement the Python class `ApiKubernetes` described below.
Class description:
Implement the ApiKubernetes class.
Method signatures and docstrings:
- def get(self, project_id: int): get_kubernetes
- def post(self, project_id: int): add_kubernetes
<|skeleton|>
class ApiKubernetes:
def get(self, project_id: int):... | c246a7c4c18c8aa6332482661cd55fc67cacf869 | <|skeleton|>
class ApiKubernetes:
def get(self, project_id: int):
"""get_kubernetes"""
<|body_0|>
def post(self, project_id: int):
"""add_kubernetes"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApiKubernetes:
def get(self, project_id: int):
"""get_kubernetes"""
if api.dashboard_config.IS_ACTIVATE_AUTH:
user_id = get_jwt_identity()
role: ProjectUserRoleModel = db.session.query(ProjectUserRoleModel).filter(ProjectUserRoleModel.project_id == project_id, ProjectUs... | the_stack_v2_python_sparse | rekcurd_dashboard/apis/api_kubernetes.py | frankiegu/dashboard | train | 0 | |
6873a63a854d455c62a31192f0699a1e89bec8a2 | [
"if not nums:\n return []\nmaxVal = max(nums[:k])\nresult = [maxVal]\nfor i in range(k, len(nums)):\n if nums[i - k] < maxVal:\n maxVal = max(maxVal, nums[i])\n else:\n maxVal = max(nums[i - k + 1:i + 1])\n result.append(maxVal)\nreturn result",
"if not nums:\n return []\nresult = []\... | <|body_start_0|>
if not nums:
return []
maxVal = max(nums[:k])
result = [maxVal]
for i in range(k, len(nums)):
if nums[i - k] < maxVal:
maxVal = max(maxVal, nums[i])
else:
maxVal = max(nums[i - k + 1:i + 1])
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSlidingWindow2(self, nums, k):
"""200ms maxVal是前一个窗口的最大值。 当把第i个元素加入到当前窗口时， if maxVal不是前一个窗口的第一个值： 当前窗口的最大值是max(maxVal, nums[i]) else: 当前窗口的最大值需要从整个窗口中查找 :type nums: List[int] :type k: int :rtype: List[int]"""
<|body_0|>
def maxSlidingWindow(self, nums, k):
... | stack_v2_sparse_classes_36k_train_002622 | 2,769 | permissive | [
{
"docstring": "200ms maxVal是前一个窗口的最大值。 当把第i个元素加入到当前窗口时， if maxVal不是前一个窗口的第一个值： 当前窗口的最大值是max(maxVal, nums[i]) else: 当前窗口的最大值需要从整个窗口中查找 :type nums: List[int] :type k: int :rtype: List[int]",
"name": "maxSlidingWindow2",
"signature": "def maxSlidingWindow2(self, nums, k)"
},
{
"docstring": "220ms ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow2(self, nums, k): 200ms maxVal是前一个窗口的最大值。 当把第i个元素加入到当前窗口时， if maxVal不是前一个窗口的第一个值： 当前窗口的最大值是max(maxVal, nums[i]) else: 当前窗口的最大值需要从整个窗口中查找 :type nums: List[int]... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow2(self, nums, k): 200ms maxVal是前一个窗口的最大值。 当把第i个元素加入到当前窗口时， if maxVal不是前一个窗口的第一个值： 当前窗口的最大值是max(maxVal, nums[i]) else: 当前窗口的最大值需要从整个窗口中查找 :type nums: List[int]... | 2830c7e2ada8dfd3dcdda7c06846116d4f944a27 | <|skeleton|>
class Solution:
def maxSlidingWindow2(self, nums, k):
"""200ms maxVal是前一个窗口的最大值。 当把第i个元素加入到当前窗口时， if maxVal不是前一个窗口的第一个值： 当前窗口的最大值是max(maxVal, nums[i]) else: 当前窗口的最大值需要从整个窗口中查找 :type nums: List[int] :type k: int :rtype: List[int]"""
<|body_0|>
def maxSlidingWindow(self, nums, k):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSlidingWindow2(self, nums, k):
"""200ms maxVal是前一个窗口的最大值。 当把第i个元素加入到当前窗口时， if maxVal不是前一个窗口的第一个值： 当前窗口的最大值是max(maxVal, nums[i]) else: 当前窗口的最大值需要从整个窗口中查找 :type nums: List[int] :type k: int :rtype: List[int]"""
if not nums:
return []
maxVal = max(nums[:k])
... | the_stack_v2_python_sparse | leetcode/hard/Sliding_Window_Maximum.py | shhuan/algorithms | train | 0 | |
194cdbbee0b4a77826161e6fcc423a12d5b82d40 | [
"x = 9\nn = 2\nresult = ReturnsNthRoot(x, n)\nroot = 3\nself.assertEqual(result, root)",
"x = 9\nn = 3\nresult = ReturnsNthRoot(x, n)\nroot = None\nself.assertEqual(result, root)"
] | <|body_start_0|>
x = 9
n = 2
result = ReturnsNthRoot(x, n)
root = 3
self.assertEqual(result, root)
<|end_body_0|>
<|body_start_1|>
x = 9
n = 3
result = ReturnsNthRoot(x, n)
root = None
self.assertEqual(result, root)
<|end_body_1|>
| TestRootPower | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRootPower:
def test_ReturnsNthRoot_Existent(self):
"""Test ReturnsNthRoot if it exists as an Integer"""
<|body_0|>
def test_ReturnsNthRoot_NonExistent(self):
"""Test ReturnsNthRoot if it exists as an Integer"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_002623 | 1,474 | no_license | [
{
"docstring": "Test ReturnsNthRoot if it exists as an Integer",
"name": "test_ReturnsNthRoot_Existent",
"signature": "def test_ReturnsNthRoot_Existent(self)"
},
{
"docstring": "Test ReturnsNthRoot if it exists as an Integer",
"name": "test_ReturnsNthRoot_NonExistent",
"signature": "def ... | 2 | null | Implement the Python class `TestRootPower` described below.
Class description:
Implement the TestRootPower class.
Method signatures and docstrings:
- def test_ReturnsNthRoot_Existent(self): Test ReturnsNthRoot if it exists as an Integer
- def test_ReturnsNthRoot_NonExistent(self): Test ReturnsNthRoot if it exists as ... | Implement the Python class `TestRootPower` described below.
Class description:
Implement the TestRootPower class.
Method signatures and docstrings:
- def test_ReturnsNthRoot_Existent(self): Test ReturnsNthRoot if it exists as an Integer
- def test_ReturnsNthRoot_NonExistent(self): Test ReturnsNthRoot if it exists as ... | cd85abea9d91ed8b17334f744ab150e790044da6 | <|skeleton|>
class TestRootPower:
def test_ReturnsNthRoot_Existent(self):
"""Test ReturnsNthRoot if it exists as an Integer"""
<|body_0|>
def test_ReturnsNthRoot_NonExistent(self):
"""Test ReturnsNthRoot if it exists as an Integer"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestRootPower:
def test_ReturnsNthRoot_Existent(self):
"""Test ReturnsNthRoot if it exists as an Integer"""
x = 9
n = 2
result = ReturnsNthRoot(x, n)
root = 3
self.assertEqual(result, root)
def test_ReturnsNthRoot_NonExistent(self):
"""Test ReturnsN... | the_stack_v2_python_sparse | Chapters/Chapter3/FingerExercises/RootPower/testRootPower.py | ppysjp93/Introduction-to-Computation-and-Programming-Using-Python- | train | 0 | |
ae4c923a26bf1f8cd1bd8039078c957125ca73ae | [
"self.old_x = 0\nself.old_y = 0\nself.vector = vector\nself.damage = damage\nself.duration = 150\nself.position_debt = (0.0, 0.0)\nif damage > 1:\n self.bullet = HotBulletSprite(center)\nelse:\n self.bullet = BulletSprite(center)",
"if self in GAME_DATA.elements.bullets:\n GAME_DATA.elements.bullets.remo... | <|body_start_0|>
self.old_x = 0
self.old_y = 0
self.vector = vector
self.damage = damage
self.duration = 150
self.position_debt = (0.0, 0.0)
if damage > 1:
self.bullet = HotBulletSprite(center)
else:
self.bullet = BulletSprite(cente... | Controls Bullet | Bullet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bullet:
"""Controls Bullet"""
def __init__(self, vector, center, damage=1):
""":param vector: path for bullet :param center: starting point for bullet :param damage: damage to be dealt on impact :param speed: movement speed of bullet :param duration: time after which bullet will be d... | stack_v2_sparse_classes_36k_train_002624 | 21,421 | no_license | [
{
"docstring": ":param vector: path for bullet :param center: starting point for bullet :param damage: damage to be dealt on impact :param speed: movement speed of bullet :param duration: time after which bullet will be destroyed",
"name": "__init__",
"signature": "def __init__(self, vector, center, dam... | 5 | stack_v2_sparse_classes_30k_train_015230 | Implement the Python class `Bullet` described below.
Class description:
Controls Bullet
Method signatures and docstrings:
- def __init__(self, vector, center, damage=1): :param vector: path for bullet :param center: starting point for bullet :param damage: damage to be dealt on impact :param speed: movement speed of ... | Implement the Python class `Bullet` described below.
Class description:
Controls Bullet
Method signatures and docstrings:
- def __init__(self, vector, center, damage=1): :param vector: path for bullet :param center: starting point for bullet :param damage: damage to be dealt on impact :param speed: movement speed of ... | 51a2f2ecc09a05672a2c3deb00ab8c273d3b756b | <|skeleton|>
class Bullet:
"""Controls Bullet"""
def __init__(self, vector, center, damage=1):
""":param vector: path for bullet :param center: starting point for bullet :param damage: damage to be dealt on impact :param speed: movement speed of bullet :param duration: time after which bullet will be d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bullet:
"""Controls Bullet"""
def __init__(self, vector, center, damage=1):
""":param vector: path for bullet :param center: starting point for bullet :param damage: damage to be dealt on impact :param speed: movement speed of bullet :param duration: time after which bullet will be destroyed"""
... | the_stack_v2_python_sparse | game_core/game_data.py | asmodeii/tanki | train | 0 |
706b7ad62c3bb54c939b0f103732f6893723af52 | [
"self._view = qw.QGraphicsView()\nself._view.setScene(qw.QGraphicsScene())\nself._project = project",
"rpt_dir, _, _ = make_report_file_names(self._project['proj_full_path'])\nimage_dir = rpt_dir.joinpath('images')\nimages = [i for i in image_dir.iterdir() if i.suffix == '.ppm']\nreturn images",
"images = self.... | <|body_start_0|>
self._view = qw.QGraphicsView()
self._view.setScene(qw.QGraphicsScene())
self._project = project
<|end_body_0|>
<|body_start_1|>
rpt_dir, _, _ = make_report_file_names(self._project['proj_full_path'])
image_dir = rpt_dir.joinpath('images')
images = [i fo... | an off-screen renderer for images | OffScreenRender | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OffScreenRender:
"""an off-screen renderer for images"""
def __init__(self, project):
"""set up the object Args: project (CGTProject): the project data"""
<|body_0|>
def list_pixmaps(self):
"""get a list of all the pixmaps"""
<|body_1|>
def render_re... | stack_v2_sparse_classes_36k_train_002625 | 3,848 | permissive | [
{
"docstring": "set up the object Args: project (CGTProject): the project data",
"name": "__init__",
"signature": "def __init__(self, project)"
},
{
"docstring": "get a list of all the pixmaps",
"name": "list_pixmaps",
"signature": "def list_pixmaps(self)"
},
{
"docstring": "rend... | 5 | stack_v2_sparse_classes_30k_train_009500 | Implement the Python class `OffScreenRender` described below.
Class description:
an off-screen renderer for images
Method signatures and docstrings:
- def __init__(self, project): set up the object Args: project (CGTProject): the project data
- def list_pixmaps(self): get a list of all the pixmaps
- def render_region... | Implement the Python class `OffScreenRender` described below.
Class description:
an off-screen renderer for images
Method signatures and docstrings:
- def __init__(self, project): set up the object Args: project (CGTProject): the project data
- def list_pixmaps(self): get a list of all the pixmaps
- def render_region... | d92f82d400cbc41f73d2f476ca6227e767ab3d4b | <|skeleton|>
class OffScreenRender:
"""an off-screen renderer for images"""
def __init__(self, project):
"""set up the object Args: project (CGTProject): the project data"""
<|body_0|>
def list_pixmaps(self):
"""get a list of all the pixmaps"""
<|body_1|>
def render_re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OffScreenRender:
"""an off-screen renderer for images"""
def __init__(self, project):
"""set up the object Args: project (CGTProject): the project data"""
self._view = qw.QGraphicsView()
self._view.setScene(qw.QGraphicsScene())
self._project = project
def list_pixmaps... | the_stack_v2_python_sparse | cgt/io/offscreenrender.py | jonathanHuwP/CrystalGrowthTracker | train | 0 |
bbcdc1844d97f559edeb426e83156f6881993669 | [
"self.GET_PARSER.add_argument('id', required=True, type=int, location='args')\nargs = self.GET_PARSER.parse_args()\nid_ = args['id']\npassenger = common.query_single_by_id(models.Passenger, id_)\nif passenger is None:\n return ({'error': 'not found'}, 404)\nreturn marshal(passenger, PASSENGER_STRUCTURE)",
"sel... | <|body_start_0|>
self.GET_PARSER.add_argument('id', required=True, type=int, location='args')
args = self.GET_PARSER.parse_args()
id_ = args['id']
passenger = common.query_single_by_id(models.Passenger, id_)
if passenger is None:
return ({'error': 'not found'}, 404)
... | PassengerResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PassengerResource:
def get(self):
"""获取拼车的人的信息 API请求地址: /interaction/api/v2/passenger 方法: GET 参数: 必选参数: id 乘客的id"""
<|body_0|>
def post(self):
"""加入某个拼车 API请求地址: /interaction/api/v2/passenger 方法: POST 参数: 参数位置为form 必选参数: carpool_id 已经存在的某个拼车id uid 用户id token 用户token ... | stack_v2_sparse_classes_36k_train_002626 | 7,122 | no_license | [
{
"docstring": "获取拼车的人的信息 API请求地址: /interaction/api/v2/passenger 方法: GET 参数: 必选参数: id 乘客的id",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "加入某个拼车 API请求地址: /interaction/api/v2/passenger 方法: POST 参数: 参数位置为form 必选参数: carpool_id 已经存在的某个拼车id uid 用户id token 用户token contact 用户自己的联系信息,... | 4 | stack_v2_sparse_classes_30k_train_009191 | Implement the Python class `PassengerResource` described below.
Class description:
Implement the PassengerResource class.
Method signatures and docstrings:
- def get(self): 获取拼车的人的信息 API请求地址: /interaction/api/v2/passenger 方法: GET 参数: 必选参数: id 乘客的id
- def post(self): 加入某个拼车 API请求地址: /interaction/api/v2/passenger 方法: P... | Implement the Python class `PassengerResource` described below.
Class description:
Implement the PassengerResource class.
Method signatures and docstrings:
- def get(self): 获取拼车的人的信息 API请求地址: /interaction/api/v2/passenger 方法: GET 参数: 必选参数: id 乘客的id
- def post(self): 加入某个拼车 API请求地址: /interaction/api/v2/passenger 方法: P... | 076f2a6ed334f8a96b741d0c5c9d268f3716c8b3 | <|skeleton|>
class PassengerResource:
def get(self):
"""获取拼车的人的信息 API请求地址: /interaction/api/v2/passenger 方法: GET 参数: 必选参数: id 乘客的id"""
<|body_0|>
def post(self):
"""加入某个拼车 API请求地址: /interaction/api/v2/passenger 方法: POST 参数: 参数位置为form 必选参数: carpool_id 已经存在的某个拼车id uid 用户id token 用户token ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PassengerResource:
def get(self):
"""获取拼车的人的信息 API请求地址: /interaction/api/v2/passenger 方法: GET 参数: 必选参数: id 乘客的id"""
self.GET_PARSER.add_argument('id', required=True, type=int, location='args')
args = self.GET_PARSER.parse_args()
id_ = args['id']
passenger = common.query... | the_stack_v2_python_sparse | app/mod_interaction/resources/PassengerResource.py | xiaofud/syllabus_backend | train | 0 | |
93d0667bffb15dcd26766440ea7a204f309186ab | [
"m, n = (len(s), len(p))\ndp = [[0] * (n + 1) for _ in range(m + 1)]\ns = ' ' + s\np = ' ' + p\ndp[0][0] = 1\nfor i in range(0, m + 1):\n for j in range(1, n + 1):\n if i > 0 and (s[i] == p[j] or p[j] == '.'):\n dp[i][j] = dp[i - 1][j - 1] | dp[i][j]\n if p[j] == '*':\n dp[i][... | <|body_start_0|>
m, n = (len(s), len(p))
dp = [[0] * (n + 1) for _ in range(m + 1)]
s = ' ' + s
p = ' ' + p
dp[0][0] = 1
for i in range(0, m + 1):
for j in range(1, n + 1):
if i > 0 and (s[i] == p[j] or p[j] == '.'):
dp[i][j... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
<|body_0|>
def isMatch2(self, s, p):
"""直接使用re :param s: :param p: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
m, n = (len(s), len(p))
dp = [[0] * (... | stack_v2_sparse_classes_36k_train_002627 | 2,903 | no_license | [
{
"docstring": ":type s: str :type p: str :rtype: bool",
"name": "isMatch",
"signature": "def isMatch(self, s, p)"
},
{
"docstring": "直接使用re :param s: :param p: :return:",
"name": "isMatch2",
"signature": "def isMatch2(self, s, p)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007236 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isMatch(self, s, p): :type s: str :type p: str :rtype: bool
- def isMatch2(self, s, p): 直接使用re :param s: :param p: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isMatch(self, s, p): :type s: str :type p: str :rtype: bool
- def isMatch2(self, s, p): 直接使用re :param s: :param p: :return:
<|skeleton|>
class Solution:
def isMatch(sel... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
<|body_0|>
def isMatch2(self, s, p):
"""直接使用re :param s: :param p: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
m, n = (len(s), len(p))
dp = [[0] * (n + 1) for _ in range(m + 1)]
s = ' ' + s
p = ' ' + p
dp[0][0] = 1
for i in range(0, m + 1):
for j in range(1, n + 1):
... | the_stack_v2_python_sparse | 10_ 正则表达式匹配.py | lovehhf/LeetCode | train | 0 | |
edd9edce56896948402695c41423ff48c23c1503 | [
"edges = defaultdict(set)\nindegrees = [0] * numCourses\nfor second, first in prerequisites:\n indegrees[second] += 1\n edges[first].add(second)\nqueue = []\nfor i in range(numCourses):\n if indegrees[i] == 0:\n queue.append(i)\nvisited = 0\nwhile queue:\n course = queue.pop(0)\n visited += 1\... | <|body_start_0|>
edges = defaultdict(set)
indegrees = [0] * numCourses
for second, first in prerequisites:
indegrees[second] += 1
edges[first].add(second)
queue = []
for i in range(numCourses):
if indegrees[i] == 0:
queue.append... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:
"""BFS"""
<|body_0|>
def canFinishDFS(self, numCourses: int, prerequisites: List[List[int]]) -> bool:
"""DFS"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
edge... | stack_v2_sparse_classes_36k_train_002628 | 3,200 | no_license | [
{
"docstring": "BFS",
"name": "canFinish",
"signature": "def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool"
},
{
"docstring": "DFS",
"name": "canFinishDFS",
"signature": "def canFinishDFS(self, numCourses: int, prerequisites: List[List[int]]) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_000014 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool: BFS
- def canFinishDFS(self, numCourses: int, prerequisites: List[List[int]]) -> bool: DFS | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool: BFS
- def canFinishDFS(self, numCourses: int, prerequisites: List[List[int]]) -> bool: DFS
<|skelet... | 52756b30e9d51794591aca030bc918e707f473f1 | <|skeleton|>
class Solution:
def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:
"""BFS"""
<|body_0|>
def canFinishDFS(self, numCourses: int, prerequisites: List[List[int]]) -> bool:
"""DFS"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:
"""BFS"""
edges = defaultdict(set)
indegrees = [0] * numCourses
for second, first in prerequisites:
indegrees[second] += 1
edges[first].add(second)
queue = []... | the_stack_v2_python_sparse | 207.课程表/solution.py | QtTao/daily_leetcode | train | 0 | |
90a8560e68210f05645872a3614e8aeea3b75853 | [
"try:\n parser = DocumentParser.post_parser()\n request_data = parser.parse_args()\n size_in_limit = DocumentHandler.max_document_size(request_data['documents'][0])\n if not size_in_limit:\n return ('File size limit exceeds the max size', 413)\n result = DocumentHandler.upload_document(request... | <|body_start_0|>
try:
parser = DocumentParser.post_parser()
request_data = parser.parse_args()
size_in_limit = DocumentHandler.max_document_size(request_data['documents'][0])
if not size_in_limit:
return ('File size limit exceeds the max size', 413... | Documents | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Documents:
def post(self):
"""The POST request will upload the document on the S3 bucket, If the upload is successful then we will save the Details in the database @return: Saved Document details in Json Format"""
<|body_0|>
def get(self):
"""This GET request will fe... | stack_v2_sparse_classes_36k_train_002629 | 7,627 | no_license | [
{
"docstring": "The POST request will upload the document on the S3 bucket, If the upload is successful then we will save the Details in the database @return: Saved Document details in Json Format",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "This GET request will fetch the ... | 3 | null | Implement the Python class `Documents` described below.
Class description:
Implement the Documents class.
Method signatures and docstrings:
- def post(self): The POST request will upload the document on the S3 bucket, If the upload is successful then we will save the Details in the database @return: Saved Document de... | Implement the Python class `Documents` described below.
Class description:
Implement the Documents class.
Method signatures and docstrings:
- def post(self): The POST request will upload the document on the S3 bucket, If the upload is successful then we will save the Details in the database @return: Saved Document de... | 17b93889c6945db15ed8b57147def2ae89a07de5 | <|skeleton|>
class Documents:
def post(self):
"""The POST request will upload the document on the S3 bucket, If the upload is successful then we will save the Details in the database @return: Saved Document details in Json Format"""
<|body_0|>
def get(self):
"""This GET request will fe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Documents:
def post(self):
"""The POST request will upload the document on the S3 bucket, If the upload is successful then we will save the Details in the database @return: Saved Document details in Json Format"""
try:
parser = DocumentParser.post_parser()
request_data ... | the_stack_v2_python_sparse | FiledInfluencer/Api/routers.py | jssellars/aniket_filed | train | 0 | |
fa4e99b78515dc081319c1bdd6bb716e9d53dbe8 | [
"token_verification.verify_tokens()\nquery = \"SELECT * FROM products WHERE product_id = '{}'\".format(product_id)\nfetched_product = db.select_from_db(query)\nif not fetched_product:\n return make_response(jsonify({'message': 'Product with id {} is not existing'.format(product_id)}), 404)\nreturn make_response(... | <|body_start_0|>
token_verification.verify_tokens()
query = "SELECT * FROM products WHERE product_id = '{}'".format(product_id)
fetched_product = db.select_from_db(query)
if not fetched_product:
return make_response(jsonify({'message': 'Product with id {} is not existing'.for... | Simple class to fetch specific product | FetchSpecificProduct | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FetchSpecificProduct:
"""Simple class to fetch specific product"""
def get(self, product_id):
"""GET /products/<int:product_id> fetches specific product"""
<|body_0|>
def put(self, product_id):
"""PUT /product/"""
<|body_1|>
def delete(self, product_... | stack_v2_sparse_classes_36k_train_002630 | 5,144 | no_license | [
{
"docstring": "GET /products/<int:product_id> fetches specific product",
"name": "get",
"signature": "def get(self, product_id)"
},
{
"docstring": "PUT /product/",
"name": "put",
"signature": "def put(self, product_id)"
},
{
"docstring": "Delete products by id",
"name": "del... | 3 | stack_v2_sparse_classes_30k_train_000953 | Implement the Python class `FetchSpecificProduct` described below.
Class description:
Simple class to fetch specific product
Method signatures and docstrings:
- def get(self, product_id): GET /products/<int:product_id> fetches specific product
- def put(self, product_id): PUT /product/
- def delete(self, product_id):... | Implement the Python class `FetchSpecificProduct` described below.
Class description:
Simple class to fetch specific product
Method signatures and docstrings:
- def get(self, product_id): GET /products/<int:product_id> fetches specific product
- def put(self, product_id): PUT /product/
- def delete(self, product_id):... | 944173cf41648ea218fd8440c3741939d9cd2754 | <|skeleton|>
class FetchSpecificProduct:
"""Simple class to fetch specific product"""
def get(self, product_id):
"""GET /products/<int:product_id> fetches specific product"""
<|body_0|>
def put(self, product_id):
"""PUT /product/"""
<|body_1|>
def delete(self, product_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FetchSpecificProduct:
"""Simple class to fetch specific product"""
def get(self, product_id):
"""GET /products/<int:product_id> fetches specific product"""
token_verification.verify_tokens()
query = "SELECT * FROM products WHERE product_id = '{}'".format(product_id)
fetche... | the_stack_v2_python_sparse | app/api/v2/views/products.py | TooColline/Store-Manager-Api-V2 | train | 1 |
db6989622dcdc274ab2ca809178b0a4f327c1ecb | [
"nums = {}\nfor task in tasks:\n nums[task] = nums.get(task, 0) + 1\nsorted_nums = sorted(nums.items(), key=lambda x: x[1], reverse=True)\nmax_count = sorted_nums[0][1]\nres = (max_count - 1) * (n + 1)\nfor tmp in sorted_nums:\n if tmp[1] == max_count:\n res += 1\nres = res if res >= len(tasks) else le... | <|body_start_0|>
nums = {}
for task in tasks:
nums[task] = nums.get(task, 0) + 1
sorted_nums = sorted(nums.items(), key=lambda x: x[1], reverse=True)
max_count = sorted_nums[0][1]
res = (max_count - 1) * (n + 1)
for tmp in sorted_nums:
if tmp[1] ==... | 桶思想： 自己实现了Counter功能 | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""桶思想： 自己实现了Counter功能"""
def leastInterval(self, tasks, n):
""":type tasks: List[str] :type n: int :rtype: int"""
<|body_0|>
def leastInterval(self, tasks, n):
""":type tasks: List[str] :type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_002631 | 1,556 | no_license | [
{
"docstring": ":type tasks: List[str] :type n: int :rtype: int",
"name": "leastInterval",
"signature": "def leastInterval(self, tasks, n)"
},
{
"docstring": ":type tasks: List[str] :type n: int :rtype: int",
"name": "leastInterval",
"signature": "def leastInterval(self, tasks, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
桶思想： 自己实现了Counter功能
Method signatures and docstrings:
- def leastInterval(self, tasks, n): :type tasks: List[str] :type n: int :rtype: int
- def leastInterval(self, tasks, n): :type tasks: List[str] :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
桶思想： 自己实现了Counter功能
Method signatures and docstrings:
- def leastInterval(self, tasks, n): :type tasks: List[str] :type n: int :rtype: int
- def leastInterval(self, tasks, n): :type tasks: List[str] :type n: int :rtype: int
<|skeleton|>
class ... | c162817f717b78997197649c084c27af48c3fd6f | <|skeleton|>
class Solution:
"""桶思想： 自己实现了Counter功能"""
def leastInterval(self, tasks, n):
""":type tasks: List[str] :type n: int :rtype: int"""
<|body_0|>
def leastInterval(self, tasks, n):
""":type tasks: List[str] :type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""桶思想： 自己实现了Counter功能"""
def leastInterval(self, tasks, n):
""":type tasks: List[str] :type n: int :rtype: int"""
nums = {}
for task in tasks:
nums[task] = nums.get(task, 0) + 1
sorted_nums = sorted(nums.items(), key=lambda x: x[1], reverse=True)
... | the_stack_v2_python_sparse | Week_06/621.任务调度器.py | dream201188/algorithm017 | train | 1 |
6449243f07327101a2eaa009506ec67d49b74395 | [
"min_date = timezone.now() + timedelta(minutes=10)\nif data < min_date:\n raise serializers.ValidationError('Departure time must be at least pass the next 20 minutes window.')\nreturn data",
"if self.context['request'].user != data['offered_by']:\n raise serializers.ValidationError('Rides offered on behalf ... | <|body_start_0|>
min_date = timezone.now() + timedelta(minutes=10)
if data < min_date:
raise serializers.ValidationError('Departure time must be at least pass the next 20 minutes window.')
return data
<|end_body_0|>
<|body_start_1|>
if self.context['request'].user != data['o... | Ride model serializer. | CreateRideSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateRideSerializer:
"""Ride model serializer."""
def validate_departure_date(self, data):
"""Verify date is not in the past."""
<|body_0|>
def validate(self, data):
"""Validate. Verify that the person who offers the ride is member and also the same user making ... | stack_v2_sparse_classes_36k_train_002632 | 5,840 | permissive | [
{
"docstring": "Verify date is not in the past.",
"name": "validate_departure_date",
"signature": "def validate_departure_date(self, data)"
},
{
"docstring": "Validate. Verify that the person who offers the ride is member and also the same user making the request.",
"name": "validate",
"... | 3 | stack_v2_sparse_classes_30k_train_006284 | Implement the Python class `CreateRideSerializer` described below.
Class description:
Ride model serializer.
Method signatures and docstrings:
- def validate_departure_date(self, data): Verify date is not in the past.
- def validate(self, data): Validate. Verify that the person who offers the ride is member and also ... | Implement the Python class `CreateRideSerializer` described below.
Class description:
Ride model serializer.
Method signatures and docstrings:
- def validate_departure_date(self, data): Verify date is not in the past.
- def validate(self, data): Validate. Verify that the person who offers the ride is member and also ... | 642576deaf569663d5dbc0d5820cfbc49c17fd2e | <|skeleton|>
class CreateRideSerializer:
"""Ride model serializer."""
def validate_departure_date(self, data):
"""Verify date is not in the past."""
<|body_0|>
def validate(self, data):
"""Validate. Verify that the person who offers the ride is member and also the same user making ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateRideSerializer:
"""Ride model serializer."""
def validate_departure_date(self, data):
"""Verify date is not in the past."""
min_date = timezone.now() + timedelta(minutes=10)
if data < min_date:
raise serializers.ValidationError('Departure time must be at least pa... | the_stack_v2_python_sparse | cride/rides/serializers/rides.py | stalinch98/Advanced_Django | train | 2 |
45468e7a7f60f0a25a453b7be12ab91a254defda | [
"self.image = pygame.Surface((size[0], size[1]))\nif type(background) == str:\n background = pygame.image.load(background).convert()\n background = pygame.transform.scale(background, size)\n self.image.blit(background, (0, 0))\nelif type(background) == tuple:\n self.image.fill(background)\nself.pos = (0... | <|body_start_0|>
self.image = pygame.Surface((size[0], size[1]))
if type(background) == str:
background = pygame.image.load(background).convert()
background = pygame.transform.scale(background, size)
self.image.blit(background, (0, 0))
elif type(background) ==... | BaseScreen | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseScreen:
def __init__(self, size, background=None):
"""This is a base class for the other screens offered in the pygametools module. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Inputs: size - an (x,y) tuple defining the width and height of the screen to be... | stack_v2_sparse_classes_36k_train_002633 | 35,070 | no_license | [
{
"docstring": "This is a base class for the other screens offered in the pygametools module. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Inputs: size - an (x,y) tuple defining the width and height of the screen to be made (in pixels). background - The background can either be a rgb... | 2 | stack_v2_sparse_classes_30k_test_000574 | Implement the Python class `BaseScreen` described below.
Class description:
Implement the BaseScreen class.
Method signatures and docstrings:
- def __init__(self, size, background=None): This is a base class for the other screens offered in the pygametools module. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... | Implement the Python class `BaseScreen` described below.
Class description:
Implement the BaseScreen class.
Method signatures and docstrings:
- def __init__(self, size, background=None): This is a base class for the other screens offered in the pygametools module. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... | 3eae1428fdd30fddc66669d40b8bb0a715d5595a | <|skeleton|>
class BaseScreen:
def __init__(self, size, background=None):
"""This is a base class for the other screens offered in the pygametools module. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Inputs: size - an (x,y) tuple defining the width and height of the screen to be... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseScreen:
def __init__(self, size, background=None):
"""This is a base class for the other screens offered in the pygametools module. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Inputs: size - an (x,y) tuple defining the width and height of the screen to be made (in pixe... | the_stack_v2_python_sparse | Games/Torric's Quest/pygametools.py | jbm950/personal_projects | train | 0 | |
4d318608495c9bdf6111d54cc9713e621d18a33f | [
"self.dateValueDict = dateValueDict\nself.length = len(dateValueDict.keys())\nself.lowMargin = lowMargin\nself.upMargin = upMargin\nself.leftMargin = leftMargin\nself.rightMargin = rightMargin\nself.dateFormat = dateFormat\nself.rect = []\nheight = float(1 - self.lowMargin - self.upMargin - (self.length - 1) * betw... | <|body_start_0|>
self.dateValueDict = dateValueDict
self.length = len(dateValueDict.keys())
self.lowMargin = lowMargin
self.upMargin = upMargin
self.leftMargin = leftMargin
self.rightMargin = rightMargin
self.dateFormat = dateFormat
self.rect = []
... | plot dict with date value | PlotDateValueDict | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlotDateValueDict:
"""plot dict with date value"""
def __init__(self, dateValueDict, dateFormat='%Y%m%d', lowMargin=0.05, upMargin=0.05, rightMargin=0.05, leftMargin=0.05, betweenMargin=0.05):
"""constructor"""
<|body_0|>
def plot(self):
"""plot dataValue"""
... | stack_v2_sparse_classes_36k_train_002634 | 2,303 | no_license | [
{
"docstring": "constructor",
"name": "__init__",
"signature": "def __init__(self, dateValueDict, dateFormat='%Y%m%d', lowMargin=0.05, upMargin=0.05, rightMargin=0.05, leftMargin=0.05, betweenMargin=0.05)"
},
{
"docstring": "plot dataValue",
"name": "plot",
"signature": "def plot(self)"
... | 2 | null | Implement the Python class `PlotDateValueDict` described below.
Class description:
plot dict with date value
Method signatures and docstrings:
- def __init__(self, dateValueDict, dateFormat='%Y%m%d', lowMargin=0.05, upMargin=0.05, rightMargin=0.05, leftMargin=0.05, betweenMargin=0.05): constructor
- def plot(self): p... | Implement the Python class `PlotDateValueDict` described below.
Class description:
plot dict with date value
Method signatures and docstrings:
- def __init__(self, dateValueDict, dateFormat='%Y%m%d', lowMargin=0.05, upMargin=0.05, rightMargin=0.05, leftMargin=0.05, betweenMargin=0.05): constructor
- def plot(self): p... | d494b3041069d377d6a7a9c296a14334f2fa5acc | <|skeleton|>
class PlotDateValueDict:
"""plot dict with date value"""
def __init__(self, dateValueDict, dateFormat='%Y%m%d', lowMargin=0.05, upMargin=0.05, rightMargin=0.05, leftMargin=0.05, betweenMargin=0.05):
"""constructor"""
<|body_0|>
def plot(self):
"""plot dataValue"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlotDateValueDict:
"""plot dict with date value"""
def __init__(self, dateValueDict, dateFormat='%Y%m%d', lowMargin=0.05, upMargin=0.05, rightMargin=0.05, leftMargin=0.05, betweenMargin=0.05):
"""constructor"""
self.dateValueDict = dateValueDict
self.length = len(dateValueDict.key... | the_stack_v2_python_sparse | python/panpanpandas_ultrafinance/ultrafinance-master/ultrafinance/lib/plotDateValueDict.py | LiuFang816/SALSTM_py_data | train | 10 |
3b9c8e90d27032b1555763a46768ec74570b2d23 | [
"Maze.__init__(self, length, width, initialP)\nself.fire = fire\nself.fireSpots = []\nself.probabilityDistribution = {}\nfor state in self.maze.keys():\n self.probabilityDistribution[state] = 0\nself.utility = {}",
"coordinates = []\nfor x in range(self.length // 4 + 1, 3 * self.length // 4):\n for y in ran... | <|body_start_0|>
Maze.__init__(self, length, width, initialP)
self.fire = fire
self.fireSpots = []
self.probabilityDistribution = {}
for state in self.maze.keys():
self.probabilityDistribution[state] = 0
self.utility = {}
<|end_body_0|>
<|body_start_1|>
... | FireMaze | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FireMaze:
def __init__(self, length, width, initialP, fire):
"""initialize the number of the fire spots"""
<|body_0|>
def setFire(self):
"""randomly distribute the fire spots"""
<|body_1|>
def fireProbability(self, state):
"""Input: - a state coo... | stack_v2_sparse_classes_36k_train_002635 | 22,307 | no_license | [
{
"docstring": "initialize the number of the fire spots",
"name": "__init__",
"signature": "def __init__(self, length, width, initialP, fire)"
},
{
"docstring": "randomly distribute the fire spots",
"name": "setFire",
"signature": "def setFire(self)"
},
{
"docstring": "Input: - a... | 6 | stack_v2_sparse_classes_30k_train_019706 | Implement the Python class `FireMaze` described below.
Class description:
Implement the FireMaze class.
Method signatures and docstrings:
- def __init__(self, length, width, initialP, fire): initialize the number of the fire spots
- def setFire(self): randomly distribute the fire spots
- def fireProbability(self, sta... | Implement the Python class `FireMaze` described below.
Class description:
Implement the FireMaze class.
Method signatures and docstrings:
- def __init__(self, length, width, initialP, fire): initialize the number of the fire spots
- def setFire(self): randomly distribute the fire spots
- def fireProbability(self, sta... | ea0ca361e3c47c3b38d6e5bcbe69762ab18a1262 | <|skeleton|>
class FireMaze:
def __init__(self, length, width, initialP, fire):
"""initialize the number of the fire spots"""
<|body_0|>
def setFire(self):
"""randomly distribute the fire spots"""
<|body_1|>
def fireProbability(self, state):
"""Input: - a state coo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FireMaze:
def __init__(self, length, width, initialP, fire):
"""initialize the number of the fire spots"""
Maze.__init__(self, length, width, initialP)
self.fire = fire
self.fireSpots = []
self.probabilityDistribution = {}
for state in self.maze.keys():
... | the_stack_v2_python_sparse | Introduction to Artificial Intelligence/Assignment/MazeRunner/maze.py | hello-roderickwang/CourseWork | train | 3 | |
edcb7ee2c2b5737fd3bc387079b573c5293a9f48 | [
"if data is None:\n if n <= 0:\n raise ValueError('n must be a positive value')\n if p >= 1 or p <= 0:\n raise ValueError('p must be greater than 0 and less than 1')\n self.n = n\n self.p = p\nelse:\n if type(data) is not list:\n raise TypeError('data must be a list')\n if len... | <|body_start_0|>
if data is None:
if n <= 0:
raise ValueError('n must be a positive value')
if p >= 1 or p <= 0:
raise ValueError('p must be greater than 0 and less than 1')
self.n = n
self.p = p
else:
if type(da... | binomial dist class | Binomial | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Binomial:
"""binomial dist class"""
def __init__(self, data=None, n=1, p=0.5):
"""initialize constructor for binomial class"""
<|body_0|>
def pmf(self, k):
"""calculates PMF for a given number of successes Args: k (int):s the number of “successes” Returns: (float... | stack_v2_sparse_classes_36k_train_002636 | 1,977 | no_license | [
{
"docstring": "initialize constructor for binomial class",
"name": "__init__",
"signature": "def __init__(self, data=None, n=1, p=0.5)"
},
{
"docstring": "calculates PMF for a given number of successes Args: k (int):s the number of “successes” Returns: (float) the PMF value for k",
"name": ... | 3 | null | Implement the Python class `Binomial` described below.
Class description:
binomial dist class
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): initialize constructor for binomial class
- def pmf(self, k): calculates PMF for a given number of successes Args: k (int):s the number of “succe... | Implement the Python class `Binomial` described below.
Class description:
binomial dist class
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): initialize constructor for binomial class
- def pmf(self, k): calculates PMF for a given number of successes Args: k (int):s the number of “succe... | 2eb7965900fd018f4092d2fb1e2055d35ba4899e | <|skeleton|>
class Binomial:
"""binomial dist class"""
def __init__(self, data=None, n=1, p=0.5):
"""initialize constructor for binomial class"""
<|body_0|>
def pmf(self, k):
"""calculates PMF for a given number of successes Args: k (int):s the number of “successes” Returns: (float... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Binomial:
"""binomial dist class"""
def __init__(self, data=None, n=1, p=0.5):
"""initialize constructor for binomial class"""
if data is None:
if n <= 0:
raise ValueError('n must be a positive value')
if p >= 1 or p <= 0:
raise Valu... | the_stack_v2_python_sparse | math/0x03-probability/binomial.py | s0m35h1t/holbertonschool-machine_learning | train | 0 |
6f3979926a7fcc962601e2867f7d6c01bc51fe5f | [
"i = 0\nfor user, msg in self._queue:\n if user == nick:\n return i\n i += 1\nreturn -1",
"outfile = self.registryValue('dumpFile')\nwith open(outfile, 'w') as h:\n i = 1\n for nick, msg in self._queue:\n if msg is None:\n msg = '[no message]'\n h.write('% 2d\\t%s\\t%s\... | <|body_start_0|>
i = 0
for user, msg in self._queue:
if user == nick:
return i
i += 1
return -1
<|end_body_0|>
<|body_start_1|>
outfile = self.registryValue('dumpFile')
with open(outfile, 'w') as h:
i = 1
for nick, ... | A simple queue manager for meetings. You can add yourself to the queue by using the queue command, giving an optional notice that the bot can display when it's your turn. If you call the queue command again, you can change the saved notice. Doing so won't make you lose your queue position. In case you changed your mind... | Queue | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Queue:
"""A simple queue manager for meetings. You can add yourself to the queue by using the queue command, giving an optional notice that the bot can display when it's your turn. If you call the queue command again, you can change the saved notice. Doing so won't make you lose your queue positi... | stack_v2_sparse_classes_36k_train_002637 | 5,983 | permissive | [
{
"docstring": "Check if a given user is in the queue",
"name": "_find_in_queue",
"signature": "def _find_in_queue(self, nick)"
},
{
"docstring": "Dump the queue to a file",
"name": "_dump_queue",
"signature": "def _dump_queue(self)"
},
{
"docstring": "[<notice>] Queue up for say... | 6 | stack_v2_sparse_classes_30k_train_005424 | Implement the Python class `Queue` described below.
Class description:
A simple queue manager for meetings. You can add yourself to the queue by using the queue command, giving an optional notice that the bot can display when it's your turn. If you call the queue command again, you can change the saved notice. Doing s... | Implement the Python class `Queue` described below.
Class description:
A simple queue manager for meetings. You can add yourself to the queue by using the queue command, giving an optional notice that the bot can display when it's your turn. If you call the queue command again, you can change the saved notice. Doing s... | 656f42f8d6b3fe4544a5270e0dab816fd3603118 | <|skeleton|>
class Queue:
"""A simple queue manager for meetings. You can add yourself to the queue by using the queue command, giving an optional notice that the bot can display when it's your turn. If you call the queue command again, you can change the saved notice. Doing so won't make you lose your queue positi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Queue:
"""A simple queue manager for meetings. You can add yourself to the queue by using the queue command, giving an optional notice that the bot can display when it's your turn. If you call the queue command again, you can change the saved notice. Doing so won't make you lose your queue position. In case y... | the_stack_v2_python_sparse | plugins/Queue/plugin.py | kblin/supybot-gsoc | train | 2 |
d0de5f960c50d72d8860ae9ba0b7270b3a707df1 | [
"super(NFM, self).__init__()\nself.cate_fea_size = len(cate_fea_uniques)\nself.num_fea_size = num_fea_size\nself.emb_size = emb_size\nself.embed_layers = nn.ModuleList([nn.Embedding(voc_size, self.emb_size) for voc_size in cate_fea_uniques])\nself.all_dims = [self.emb_size + self.num_fea_size] + hidden_dims\nfor i ... | <|body_start_0|>
super(NFM, self).__init__()
self.cate_fea_size = len(cate_fea_uniques)
self.num_fea_size = num_fea_size
self.emb_size = emb_size
self.embed_layers = nn.ModuleList([nn.Embedding(voc_size, self.emb_size) for voc_size in cate_fea_uniques])
self.all_dims = [s... | NFM | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NFM:
def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_classes=1):
""":param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: embed_dim"""
<|body_0|>
def forward(self, X_sparse, X_dense=None):... | stack_v2_sparse_classes_36k_train_002638 | 4,551 | permissive | [
{
"docstring": ":param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: embed_dim",
"name": "__init__",
"signature": "def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_classes=1)"
},
{
"docstring": "X_sparse: spar... | 2 | null | Implement the Python class `NFM` described below.
Class description:
Implement the NFM class.
Method signatures and docstrings:
- def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_classes=1): :param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :par... | Implement the Python class `NFM` described below.
Class description:
Implement the NFM class.
Method signatures and docstrings:
- def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_classes=1): :param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :par... | 92acc188d3a0f634de58463b6676e70df83ef808 | <|skeleton|>
class NFM:
def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_classes=1):
""":param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: embed_dim"""
<|body_0|>
def forward(self, X_sparse, X_dense=None):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NFM:
def __init__(self, cate_fea_uniques, num_fea_size=0, emb_size=8, hidden_dims=[256, 128], dropout=[0.2, 0.2], num_classes=1):
""":param cate_fea_uniques: :param num_fea_size: 数字特征 也就是连续特征 :param emb_size: embed_dim"""
super(NFM, self).__init__()
self.cate_fea_size = len(cate_fea_un... | the_stack_v2_python_sparse | PyTorch/dev/others/Widedeep_ID2866_for_PyTorch/NFM/model.py | Ascend/ModelZoo-PyTorch | train | 23 | |
63f69511a39807cdf3e749c84e04b5daffbee5a6 | [
"@lru_cache(None)\ndef _rec(i, sign):\n if i == 0:\n return [nums[i]]\n max_arr = []\n for j in range(i - 1, -1, -1):\n if j + 2 <= len(max_arr):\n break\n arr = _rec(j, -sign)\n if sign * (nums[i] - arr[-1]) > 0:\n arr = arr + [nums[i]]\n else:\n ... | <|body_start_0|>
@lru_cache(None)
def _rec(i, sign):
if i == 0:
return [nums[i]]
max_arr = []
for j in range(i - 1, -1, -1):
if j + 2 <= len(max_arr):
break
arr = _rec(j, -sign)
if sig... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def wiggleMaxLength(self, nums: List[int]) -> int:
"""04/12/2020 19:20"""
<|body_0|>
def wiggleMaxLength(self, nums: List[int]) -> int:
"""04/12/2020 20:18"""
<|body_1|>
def wiggleMaxLength(self, nums: List[int]) -> int:
"""07/24/2022 1... | stack_v2_sparse_classes_36k_train_002639 | 3,929 | no_license | [
{
"docstring": "04/12/2020 19:20",
"name": "wiggleMaxLength",
"signature": "def wiggleMaxLength(self, nums: List[int]) -> int"
},
{
"docstring": "04/12/2020 20:18",
"name": "wiggleMaxLength",
"signature": "def wiggleMaxLength(self, nums: List[int]) -> int"
},
{
"docstring": "07/2... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wiggleMaxLength(self, nums: List[int]) -> int: 04/12/2020 19:20
- def wiggleMaxLength(self, nums: List[int]) -> int: 04/12/2020 20:18
- def wiggleMaxLength(self, nums: List[i... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wiggleMaxLength(self, nums: List[int]) -> int: 04/12/2020 19:20
- def wiggleMaxLength(self, nums: List[int]) -> int: 04/12/2020 20:18
- def wiggleMaxLength(self, nums: List[i... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def wiggleMaxLength(self, nums: List[int]) -> int:
"""04/12/2020 19:20"""
<|body_0|>
def wiggleMaxLength(self, nums: List[int]) -> int:
"""04/12/2020 20:18"""
<|body_1|>
def wiggleMaxLength(self, nums: List[int]) -> int:
"""07/24/2022 1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def wiggleMaxLength(self, nums: List[int]) -> int:
"""04/12/2020 19:20"""
@lru_cache(None)
def _rec(i, sign):
if i == 0:
return [nums[i]]
max_arr = []
for j in range(i - 1, -1, -1):
if j + 2 <= len(max_arr):
... | the_stack_v2_python_sparse | leetcode/solved/376_Wiggle_Subsequence/solution.py | sungminoh/algorithms | train | 0 | |
663fe62f82d8b7b2433a3e65f432f1b76483a40a | [
"if len(args_translators) < 2:\n return None\nret = []\nbase_args_t = args_translators[0]\nfor arg_name, arg_val in base_args_t.actual_args.items():\n for args_t in args_translators[1:]:\n val = args_t.actual_args.get(arg_name)\n if val is None:\n raise ValueError('Unable to find the ... | <|body_start_0|>
if len(args_translators) < 2:
return None
ret = []
base_args_t = args_translators[0]
for arg_name, arg_val in base_args_t.actual_args.items():
for args_t in args_translators[1:]:
val = args_t.actual_args.get(arg_name)
... | Define operations related to ArgsTranslation instances. | ArgsTranslationHelper | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause",
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ArgsTranslationHelper:
"""Define operations related to ArgsTranslation instances."""
def find_formal_args_in_modules(args_translators):
"""Find formal args among multiple args translators. Args: args_translators(list[ArgsTranslation]): a list of args translator to be checked. Returns... | stack_v2_sparse_classes_36k_train_002640 | 9,892 | permissive | [
{
"docstring": "Find formal args among multiple args translators. Args: args_translators(list[ArgsTranslation]): a list of args translator to be checked. Returns: list, name of args to be formal.",
"name": "find_formal_args_in_modules",
"signature": "def find_formal_args_in_modules(args_translators)"
... | 2 | stack_v2_sparse_classes_30k_train_012326 | Implement the Python class `ArgsTranslationHelper` described below.
Class description:
Define operations related to ArgsTranslation instances.
Method signatures and docstrings:
- def find_formal_args_in_modules(args_translators): Find formal args among multiple args translators. Args: args_translators(list[ArgsTransl... | Implement the Python class `ArgsTranslationHelper` described below.
Class description:
Define operations related to ArgsTranslation instances.
Method signatures and docstrings:
- def find_formal_args_in_modules(args_translators): Find formal args among multiple args translators. Args: args_translators(list[ArgsTransl... | db5769eb80cbd13a2a9af7682c11f5667d8bf141 | <|skeleton|>
class ArgsTranslationHelper:
"""Define operations related to ArgsTranslation instances."""
def find_formal_args_in_modules(args_translators):
"""Find formal args among multiple args translators. Args: args_translators(list[ArgsTranslation]): a list of args translator to be checked. Returns... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ArgsTranslationHelper:
"""Define operations related to ArgsTranslation instances."""
def find_formal_args_in_modules(args_translators):
"""Find formal args among multiple args translators. Args: args_translators(list[ArgsTranslation]): a list of args translator to be checked. Returns: list, name ... | the_stack_v2_python_sparse | mindinsight/mindconverter/graph_based_converter/generator/args_translator.py | fapbatista/mindinsight | train | 0 |
1140f76c2b095e5c0a953fabea8160566e617335 | [
"if queryset is None:\n queryset = self.get_queryset()\npk = self.kwargs.get('pk', None)\nslug = self.kwargs.get('slug', None)\ntry:\n if pk is not None:\n return queryset.get(pk=self.kwargs['pk'])\n elif slug is not None:\n slug_field = self.get_slug_field()\n return queryset.get(**{s... | <|body_start_0|>
if queryset is None:
queryset = self.get_queryset()
pk = self.kwargs.get('pk', None)
slug = self.kwargs.get('slug', None)
try:
if pk is not None:
return queryset.get(pk=self.kwargs['pk'])
elif slug is not None:
... | SingleObjectMixin | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SingleObjectMixin:
def get_object(self, queryset=None):
"""Returns the object the view is displaying. By default this requires `self.queryset` and a `pk` or `slug` argument in the URLconf, but subclasses can override this to return any object."""
<|body_0|>
def get_queryset(... | stack_v2_sparse_classes_36k_train_002641 | 2,256 | permissive | [
{
"docstring": "Returns the object the view is displaying. By default this requires `self.queryset` and a `pk` or `slug` argument in the URLconf, but subclasses can override this to return any object.",
"name": "get_object",
"signature": "def get_object(self, queryset=None)"
},
{
"docstring": "G... | 2 | stack_v2_sparse_classes_30k_train_006665 | Implement the Python class `SingleObjectMixin` described below.
Class description:
Implement the SingleObjectMixin class.
Method signatures and docstrings:
- def get_object(self, queryset=None): Returns the object the view is displaying. By default this requires `self.queryset` and a `pk` or `slug` argument in the UR... | Implement the Python class `SingleObjectMixin` described below.
Class description:
Implement the SingleObjectMixin class.
Method signatures and docstrings:
- def get_object(self, queryset=None): Returns the object the view is displaying. By default this requires `self.queryset` and a `pk` or `slug` argument in the UR... | 406734280ca6b55f66b73b3b4ec5e97ba58f045d | <|skeleton|>
class SingleObjectMixin:
def get_object(self, queryset=None):
"""Returns the object the view is displaying. By default this requires `self.queryset` and a `pk` or `slug` argument in the URLconf, but subclasses can override this to return any object."""
<|body_0|>
def get_queryset(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SingleObjectMixin:
def get_object(self, queryset=None):
"""Returns the object the view is displaying. By default this requires `self.queryset` and a `pk` or `slug` argument in the URLconf, but subclasses can override this to return any object."""
if queryset is None:
queryset = sel... | the_stack_v2_python_sparse | dockit/views/detail.py | cuker/django-dockit | train | 0 | |
2e98cccd2886e0945089a80f0c302bc3e6a7fbaa | [
"self.mins_to_checkpoint = 0.05\nself.timer_checkpoint = -1\nself.debug = debug\nitem_structure = {'start_time': float, 'end_time': float, 'frequency': int, 'targets': list}\nself.context_tracker = managers['tracking'].init_tracker('context', item_structure)\nself.current_context = self.context_tracker.new_item([0,... | <|body_start_0|>
self.mins_to_checkpoint = 0.05
self.timer_checkpoint = -1
self.debug = debug
item_structure = {'start_time': float, 'end_time': float, 'frequency': int, 'targets': list}
self.context_tracker = managers['tracking'].init_tracker('context', item_structure)
s... | A manager for Aria's context tracking system. Only one ContextManager should be active at a time. | ContextManager | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContextManager:
"""A manager for Aria's context tracking system. Only one ContextManager should be active at a time."""
def __init__(self, managers, debug=False):
"""Constructs a ContextManager object. Parameters: managers : [Manager] - A list of references to all manager objects. de... | stack_v2_sparse_classes_36k_train_002642 | 23,341 | permissive | [
{
"docstring": "Constructs a ContextManager object. Parameters: managers : [Manager] - A list of references to all manager objects. debug : boolean - Optional setting to enable verbose feedback.",
"name": "__init__",
"signature": "def __init__(self, managers, debug=False)"
},
{
"docstring": "Upd... | 5 | stack_v2_sparse_classes_30k_train_013587 | Implement the Python class `ContextManager` described below.
Class description:
A manager for Aria's context tracking system. Only one ContextManager should be active at a time.
Method signatures and docstrings:
- def __init__(self, managers, debug=False): Constructs a ContextManager object. Parameters: managers : [M... | Implement the Python class `ContextManager` described below.
Class description:
A manager for Aria's context tracking system. Only one ContextManager should be active at a time.
Method signatures and docstrings:
- def __init__(self, managers, debug=False): Constructs a ContextManager object. Parameters: managers : [M... | aff4c3a69f24026974bc6f944bb769a30f31e405 | <|skeleton|>
class ContextManager:
"""A manager for Aria's context tracking system. Only one ContextManager should be active at a time."""
def __init__(self, managers, debug=False):
"""Constructs a ContextManager object. Parameters: managers : [Manager] - A list of references to all manager objects. de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ContextManager:
"""A manager for Aria's context tracking system. Only one ContextManager should be active at a time."""
def __init__(self, managers, debug=False):
"""Constructs a ContextManager object. Parameters: managers : [Manager] - A list of references to all manager objects. debug : boolean... | the_stack_v2_python_sparse | Managers.py | SKaplanOfficial/Aria | train | 2 |
8ca541803db3cc0f2be5551f1758cbbea8bb5c20 | [
"self.movie = movie\nself.creator = creator\nself.__dict__.update(kwargs)",
"stream = {'href': url_for('streamdetailview', stream_id=self.id, _external=True), 'name': self.name, 'published': self.public, 'description': self.description, 'author': {'username': self.creator.username, 'href': url_for('userdetailview... | <|body_start_0|>
self.movie = movie
self.creator = creator
self.__dict__.update(kwargs)
<|end_body_0|>
<|body_start_1|>
stream = {'href': url_for('streamdetailview', stream_id=self.id, _external=True), 'name': self.name, 'published': self.public, 'description': self.description, 'author... | A collection of related, timecoded entries that accompanies a movie. Entries in a stream will usually have some common theme, like annoucing new actors that enter the screen, or providing references for topics mentioned in a movie. | Stream | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Stream:
"""A collection of related, timecoded entries that accompanies a movie. Entries in a stream will usually have some common theme, like annoucing new actors that enter the screen, or providing references for topics mentioned in a movie."""
def __init__(self, movie=None, creator=None, *... | stack_v2_sparse_classes_36k_train_002643 | 15,064 | permissive | [
{
"docstring": "Create new stream. :param movie: The movie this stream should be associated to. :param creator: The user that created the stream. :param kwargs: Set object properties from constructor.",
"name": "__init__",
"signature": "def __init__(self, movie=None, creator=None, **kwargs)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_002861 | Implement the Python class `Stream` described below.
Class description:
A collection of related, timecoded entries that accompanies a movie. Entries in a stream will usually have some common theme, like annoucing new actors that enter the screen, or providing references for topics mentioned in a movie.
Method signatu... | Implement the Python class `Stream` described below.
Class description:
A collection of related, timecoded entries that accompanies a movie. Entries in a stream will usually have some common theme, like annoucing new actors that enter the screen, or providing references for topics mentioned in a movie.
Method signatu... | 7a44abd88283ac5d145137a87f3d87f42e449032 | <|skeleton|>
class Stream:
"""A collection of related, timecoded entries that accompanies a movie. Entries in a stream will usually have some common theme, like annoucing new actors that enter the screen, or providing references for topics mentioned in a movie."""
def __init__(self, movie=None, creator=None, *... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Stream:
"""A collection of related, timecoded entries that accompanies a movie. Entries in a stream will usually have some common theme, like annoucing new actors that enter the screen, or providing references for topics mentioned in a movie."""
def __init__(self, movie=None, creator=None, **kwargs):
... | the_stack_v2_python_sparse | marvin/models.py | streamr/marvin | train | 0 |
9b3641d6d88aeee24e7ca0539d59f916e5a8c513 | [
"major, count = (0, 0)\nfor n in nums:\n if count == 0:\n count += 1\n major = n\n elif major == n:\n count += 1\n else:\n count -= 1\nreturn major",
"d = {}\nlength = len(nums)\nfor n in nums:\n d[n] = d.get(n, 0) + 1\n if d[n] > length // 2:\n return n",
"coun... | <|body_start_0|>
major, count = (0, 0)
for n in nums:
if count == 0:
count += 1
major = n
elif major == n:
count += 1
else:
count -= 1
return major
<|end_body_0|>
<|body_start_1|>
d = {}
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def majorityElement(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def majorityElement1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def majorityElement_II(self, nums):
""":type nums: List[int] :rtype... | stack_v2_sparse_classes_36k_train_002644 | 1,805 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "majorityElement",
"signature": "def majorityElement(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "majorityElement1",
"signature": "def majorityElement1(self, nums)"
},
{
"docstring": ":typ... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def majorityElement(self, nums): :type nums: List[int] :rtype: int
- def majorityElement1(self, nums): :type nums: List[int] :rtype: int
- def majorityElement_II(self, nums): :ty... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def majorityElement(self, nums): :type nums: List[int] :rtype: int
- def majorityElement1(self, nums): :type nums: List[int] :rtype: int
- def majorityElement_II(self, nums): :ty... | f234bd7b62cb7bc2150faa764bf05a9095e19192 | <|skeleton|>
class Solution:
def majorityElement(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def majorityElement1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def majorityElement_II(self, nums):
""":type nums: List[int] :rtype... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def majorityElement(self, nums):
""":type nums: List[int] :rtype: int"""
major, count = (0, 0)
for n in nums:
if count == 0:
count += 1
major = n
elif major == n:
count += 1
else:
... | the_stack_v2_python_sparse | alg/majority_element.py | nyannko/leetcode-python | train | 0 | |
ff8a5b4bdc35b434c17814a02ed456594c7c3a01 | [
"if not root or not root.left:\n return root\nl_root = self.upsideDownBinaryTree(root.left)\nr_most = l_root\nwhile r_most.right:\n r_most = r_most.right\nroot = l_root\nr_most.left = root.right\nr_most.right = TreeNode(root.val)\nreturn root",
"if not root or not root.left:\n return root\nparent = None\... | <|body_start_0|>
if not root or not root.left:
return root
l_root = self.upsideDownBinaryTree(root.left)
r_most = l_root
while r_most.right:
r_most = r_most.right
root = l_root
r_most.left = root.right
r_most.right = TreeNode(root.val)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def upsideDownBinaryTree(self, root):
""":type root: TreeNode :rtype: TreeNode beats 24.21%"""
<|body_0|>
def upsideDownBinaryTree1(self, root):
""":param root: :return: beats 55.79%"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not r... | stack_v2_sparse_classes_36k_train_002645 | 1,159 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: TreeNode beats 24.21%",
"name": "upsideDownBinaryTree",
"signature": "def upsideDownBinaryTree(self, root)"
},
{
"docstring": ":param root: :return: beats 55.79%",
"name": "upsideDownBinaryTree1",
"signature": "def upsideDownBinaryTree1(self, ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def upsideDownBinaryTree(self, root): :type root: TreeNode :rtype: TreeNode beats 24.21%
- def upsideDownBinaryTree1(self, root): :param root: :return: beats 55.79% | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def upsideDownBinaryTree(self, root): :type root: TreeNode :rtype: TreeNode beats 24.21%
- def upsideDownBinaryTree1(self, root): :param root: :return: beats 55.79%
<|skeleton|>... | 7e0e917c15d3e35f49da3a00ef395bd5ff180d79 | <|skeleton|>
class Solution:
def upsideDownBinaryTree(self, root):
""":type root: TreeNode :rtype: TreeNode beats 24.21%"""
<|body_0|>
def upsideDownBinaryTree1(self, root):
""":param root: :return: beats 55.79%"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def upsideDownBinaryTree(self, root):
""":type root: TreeNode :rtype: TreeNode beats 24.21%"""
if not root or not root.left:
return root
l_root = self.upsideDownBinaryTree(root.left)
r_most = l_root
while r_most.right:
r_most = r_most.r... | the_stack_v2_python_sparse | LeetCode/156_binary_tree_upside_down.py | yao23/Machine_Learning_Playground | train | 12 | |
fc63e69a02c228ba2121691b51d8be44b8102992 | [
"EventHandler.__init__(self)\nself._win = win\nself._button = Rectangle(120, 100, (100, 630))\nself._button.setFillColor('lavender')\nself._text = Text('Done with Turn!', (100, 630), 16)\nself._button.addHandler(self)\nself._block = Rectangle(210, 30, (340, 645))\nself._block.setDepth(1)\nself._block.setFillColor('... | <|body_start_0|>
EventHandler.__init__(self)
self._win = win
self._button = Rectangle(120, 100, (100, 630))
self._button.setFillColor('lavender')
self._text = Text('Done with Turn!', (100, 630), 16)
self._button.addHandler(self)
self._block = Rectangle(210, 30, (3... | A class that is used to create a done button that will switch players' turns | Done | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Done:
"""A class that is used to create a done button that will switch players' turns"""
def __init__(self, win):
"""Creates the parts of the button and the rectangular block"""
<|body_0|>
def addTo(self, win):
"""Adds the button and block to the window"""
... | stack_v2_sparse_classes_36k_train_002646 | 23,085 | no_license | [
{
"docstring": "Creates the parts of the button and the rectangular block",
"name": "__init__",
"signature": "def __init__(self, win)"
},
{
"docstring": "Adds the button and block to the window",
"name": "addTo",
"signature": "def addTo(self, win)"
},
{
"docstring": "Changes the ... | 3 | null | Implement the Python class `Done` described below.
Class description:
A class that is used to create a done button that will switch players' turns
Method signatures and docstrings:
- def __init__(self, win): Creates the parts of the button and the rectangular block
- def addTo(self, win): Adds the button and block to... | Implement the Python class `Done` described below.
Class description:
A class that is used to create a done button that will switch players' turns
Method signatures and docstrings:
- def __init__(self, win): Creates the parts of the button and the rectangular block
- def addTo(self, win): Adds the button and block to... | e5d96a65fc84481b85072cfb55dea9a0666634b5 | <|skeleton|>
class Done:
"""A class that is used to create a done button that will switch players' turns"""
def __init__(self, win):
"""Creates the parts of the button and the rectangular block"""
<|body_0|>
def addTo(self, win):
"""Adds the button and block to the window"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Done:
"""A class that is used to create a done button that will switch players' turns"""
def __init__(self, win):
"""Creates the parts of the button and the rectangular block"""
EventHandler.__init__(self)
self._win = win
self._button = Rectangle(120, 100, (100, 630))
... | the_stack_v2_python_sparse | Games-2017/21/Game.py | paulmagnus/CSPy | train | 0 |
eff864014e5328433d897d836c0bbd3de8948fca | [
"self._top = Toplevel()\nself._top.focus_set()\nself._top.grab_set()\nself._reason = reason_window\nself._a = a\nself._b = b\nself._make_widgets(n)",
"self._all_input = []\nfor i in range(n):\n tmp_frame = Frame(self._top)\n Label(tmp_frame, text='x{}:'.format(i), font=('arial', '16', 'bold')).pack(side=LEF... | <|body_start_0|>
self._top = Toplevel()
self._top.focus_set()
self._top.grab_set()
self._reason = reason_window
self._a = a
self._b = b
self._make_widgets(n)
<|end_body_0|>
<|body_start_1|>
self._all_input = []
for i in range(n):
tmp_f... | Діалогове вікно, в якому необхідно ввести n координат вектора. | Dialog | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dialog:
"""Діалогове вікно, в якому необхідно ввести n координат вектора."""
def __init__(self, n: int, a: float, b: float, reason_window):
"""Ініціалізація :param n: к-ть компонент вектора :param a: ліва межа відрізку :param b: права межа відрізку :param reason_window: вікно, яке сп... | stack_v2_sparse_classes_36k_train_002647 | 5,760 | no_license | [
{
"docstring": "Ініціалізація :param n: к-ть компонент вектора :param a: ліва межа відрізку :param b: права межа відрізку :param reason_window: вікно, яке спричинило даний екземпляр класу",
"name": "__init__",
"signature": "def __init__(self, n: int, a: float, b: float, reason_window)"
},
{
"doc... | 3 | stack_v2_sparse_classes_30k_train_021060 | Implement the Python class `Dialog` described below.
Class description:
Діалогове вікно, в якому необхідно ввести n координат вектора.
Method signatures and docstrings:
- def __init__(self, n: int, a: float, b: float, reason_window): Ініціалізація :param n: к-ть компонент вектора :param a: ліва межа відрізку :param b... | Implement the Python class `Dialog` described below.
Class description:
Діалогове вікно, в якому необхідно ввести n координат вектора.
Method signatures and docstrings:
- def __init__(self, n: int, a: float, b: float, reason_window): Ініціалізація :param n: к-ть компонент вектора :param a: ліва межа відрізку :param b... | e44bf2b535b34bc31fb323c20901a77b0b3072f2 | <|skeleton|>
class Dialog:
"""Діалогове вікно, в якому необхідно ввести n координат вектора."""
def __init__(self, n: int, a: float, b: float, reason_window):
"""Ініціалізація :param n: к-ть компонент вектора :param a: ліва межа відрізку :param b: права межа відрізку :param reason_window: вікно, яке сп... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dialog:
"""Діалогове вікно, в якому необхідно ввести n координат вектора."""
def __init__(self, n: int, a: float, b: float, reason_window):
"""Ініціалізація :param n: к-ть компонент вектора :param a: ліва межа відрізку :param b: права межа відрізку :param reason_window: вікно, яке спричинило дани... | the_stack_v2_python_sparse | dz_others/subject24_gui/t24_22/t24_7.py | davendiy/ads_course2 | train | 0 |
1dba2fc0eedf3346a22d55a8da465066eecdfa0f | [
"if n <= 1:\n return 1\nugly = []\nfactor = {}\nfor _ in primes:\n factor[_] = 0\nugly.append(1)\nfor i in xrange(1, n):\n ugly.append(sys.maxint)\n for p in factor:\n ugly[i] = min(ugly[i], ugly[factor[p]] * p)\n for p in factor:\n if ugly[i] == ugly[factor[p]] * p:\n factor... | <|body_start_0|>
if n <= 1:
return 1
ugly = []
factor = {}
for _ in primes:
factor[_] = 0
ugly.append(1)
for i in xrange(1, n):
ugly.append(sys.maxint)
for p in factor:
ugly[i] = min(ugly[i], ugly[factor[p]] ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nthSuperUglyNumber(self, n, primes):
""":type n: int :type primes: List[int] :rtype: int"""
<|body_0|>
def nthSuperUglyNumber_heapq_TLE(self, n, primes):
""":type n: int :type primes: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_36k_train_002648 | 2,165 | no_license | [
{
"docstring": ":type n: int :type primes: List[int] :rtype: int",
"name": "nthSuperUglyNumber",
"signature": "def nthSuperUglyNumber(self, n, primes)"
},
{
"docstring": ":type n: int :type primes: List[int] :rtype: int",
"name": "nthSuperUglyNumber_heapq_TLE",
"signature": "def nthSuper... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nthSuperUglyNumber(self, n, primes): :type n: int :type primes: List[int] :rtype: int
- def nthSuperUglyNumber_heapq_TLE(self, n, primes): :type n: int :type primes: List[int... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nthSuperUglyNumber(self, n, primes): :type n: int :type primes: List[int] :rtype: int
- def nthSuperUglyNumber_heapq_TLE(self, n, primes): :type n: int :type primes: List[int... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def nthSuperUglyNumber(self, n, primes):
""":type n: int :type primes: List[int] :rtype: int"""
<|body_0|>
def nthSuperUglyNumber_heapq_TLE(self, n, primes):
""":type n: int :type primes: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def nthSuperUglyNumber(self, n, primes):
""":type n: int :type primes: List[int] :rtype: int"""
if n <= 1:
return 1
ugly = []
factor = {}
for _ in primes:
factor[_] = 0
ugly.append(1)
for i in xrange(1, n):
u... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00313.Super Ugly Number.py | roger6blog/LeetCode | train | 0 | |
19d886d1ddfa8484f38c9a367e8b349cc80fd200 | [
"parts = shlex.split(args)\nsearch = self._search_terms(parts, user_default_filter=False)\nheaders = ['id', 'hostname', 'ip', 'max_vms', 'running_vms']\nvalues = []\nfor slave in self._talus_client.slave_iter(**search):\n values.append([slave.id, slave.hostname, slave.ip, slave.max_vms, slave.running_vms])\nprin... | <|body_start_0|>
parts = shlex.split(args)
search = self._search_terms(parts, user_default_filter=False)
headers = ['id', 'hostname', 'ip', 'max_vms', 'running_vms']
values = []
for slave in self._talus_client.slave_iter(**search):
values.append([slave.id, slave.hostn... | The Talus slave command processor | SlaveCmd | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SlaveCmd:
"""The Talus slave command processor"""
def do_list(self, args):
"""List existing slaves connected to Talus. slave list"""
<|body_0|>
def do_info(self, args):
"""List information about a slave talus slave info ID_OR_HOSTNAME_OR_IP"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_002649 | 2,389 | permissive | [
{
"docstring": "List existing slaves connected to Talus. slave list",
"name": "do_list",
"signature": "def do_list(self, args)"
},
{
"docstring": "List information about a slave talus slave info ID_OR_HOSTNAME_OR_IP",
"name": "do_info",
"signature": "def do_info(self, args)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015984 | Implement the Python class `SlaveCmd` described below.
Class description:
The Talus slave command processor
Method signatures and docstrings:
- def do_list(self, args): List existing slaves connected to Talus. slave list
- def do_info(self, args): List information about a slave talus slave info ID_OR_HOSTNAME_OR_IP | Implement the Python class `SlaveCmd` described below.
Class description:
The Talus slave command processor
Method signatures and docstrings:
- def do_list(self, args): List existing slaves connected to Talus. slave list
- def do_info(self, args): List information about a slave talus slave info ID_OR_HOSTNAME_OR_IP
... | 0168e92e7b4a872270f3c246960d0664f1e43eb2 | <|skeleton|>
class SlaveCmd:
"""The Talus slave command processor"""
def do_list(self, args):
"""List existing slaves connected to Talus. slave list"""
<|body_0|>
def do_info(self, args):
"""List information about a slave talus slave info ID_OR_HOSTNAME_OR_IP"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SlaveCmd:
"""The Talus slave command processor"""
def do_list(self, args):
"""List existing slaves connected to Talus. slave list"""
parts = shlex.split(args)
search = self._search_terms(parts, user_default_filter=False)
headers = ['id', 'hostname', 'ip', 'max_vms', 'runni... | the_stack_v2_python_sparse | talus_client/cmds/slaves.py | d0c-s4vage/talus_client | train | 4 |
c96c3895a12e4cc80fc65eb4603d2df319cb9b3f | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('jgrishey', 'jgrishey')\nurl = 'http://datamechanics.io/data/hospitalsgeo.json'\ndata = urllib.request.urlopen(url).read().decode('utf-8')\nresponse = json.loads(data)\nhospitals = []\nID = 0\nfor hospita... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jgrishey', 'jgrishey')
url = 'http://datamechanics.io/data/hospitalsgeo.json'
data = urllib.request.urlopen(url).read().decode('utf-8')
re... | getHospitals | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class getHospitals:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything ... | stack_v2_sparse_classes_36k_train_002650 | 3,600 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_train_014319 | Implement the Python class `getHospitals` described below.
Class description:
Implement the getHospitals class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, end... | Implement the Python class `getHospitals` described below.
Class description:
Implement the getHospitals class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, end... | 0df485d0469c5451ebdcd684bed2a0960ba3ab84 | <|skeleton|>
class getHospitals:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class getHospitals:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jgrishey', 'jgrishey')
url = '... | the_stack_v2_python_sparse | jgrishey/getHospitals.py | lingyigu/course-2017-spr-proj | train | 0 | |
9a33814ae5deb069859d26d54dc75e9e56c3dacf | [
"params = request.body\njsonParams = json.loads(params)\nuser = User_Info.objects.get(email__exact=jsonParams.get('email'))\nrecord = EmailVerifyRecord.objects.filter(code__exact=jsonParams.get('r_code'))\nif not record.exists():\n return JsonResponse({'status': False, 'err': '重置失败'}, status=404)\nrecord.code_st... | <|body_start_0|>
params = request.body
jsonParams = json.loads(params)
user = User_Info.objects.get(email__exact=jsonParams.get('email'))
record = EmailVerifyRecord.objects.filter(code__exact=jsonParams.get('r_code'))
if not record.exists():
return JsonResponse({'stat... | ResetView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResetView:
def post(self, request):
"""忘记密码后重置密码 :param request: :return:"""
<|body_0|>
def get(self, request, r_code):
"""检查重置密码验证码正确性 :param request: :param r_code: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
params = request.body
... | stack_v2_sparse_classes_36k_train_002651 | 2,218 | no_license | [
{
"docstring": "忘记密码后重置密码 :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "检查重置密码验证码正确性 :param request: :param r_code: :return:",
"name": "get",
"signature": "def get(self, request, r_code)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016019 | Implement the Python class `ResetView` described below.
Class description:
Implement the ResetView class.
Method signatures and docstrings:
- def post(self, request): 忘记密码后重置密码 :param request: :return:
- def get(self, request, r_code): 检查重置密码验证码正确性 :param request: :param r_code: :return: | Implement the Python class `ResetView` described below.
Class description:
Implement the ResetView class.
Method signatures and docstrings:
- def post(self, request): 忘记密码后重置密码 :param request: :return:
- def get(self, request, r_code): 检查重置密码验证码正确性 :param request: :param r_code: :return:
<|skeleton|>
class ResetView... | 526dea540048fc92260bce611c520c50af744e0b | <|skeleton|>
class ResetView:
def post(self, request):
"""忘记密码后重置密码 :param request: :return:"""
<|body_0|>
def get(self, request, r_code):
"""检查重置密码验证码正确性 :param request: :param r_code: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResetView:
def post(self, request):
"""忘记密码后重置密码 :param request: :return:"""
params = request.body
jsonParams = json.loads(params)
user = User_Info.objects.get(email__exact=jsonParams.get('email'))
record = EmailVerifyRecord.objects.filter(code__exact=jsonParams.get('r_... | the_stack_v2_python_sparse | apps/account/views/userInfo/reset_password.py | DICKQI/ALGYunXS | train | 0 | |
a9c81744cc487fd9de4159c139d6aa6e01b28b52 | [
"self._orders = orders\nself._traded = []\nself._max_heap = MaxHeapPriorityQueue()\nself._min_heap = HeapPriorityQueue()\nself._construct_heaps()",
"for movement, price, quantity in self._orders:\n if movement == 'buy':\n self._max_heap.add(price, quantity)\n elif movement == 'sell':\n self._m... | <|body_start_0|>
self._orders = orders
self._traded = []
self._max_heap = MaxHeapPriorityQueue()
self._min_heap = HeapPriorityQueue()
self._construct_heaps()
<|end_body_0|>
<|body_start_1|>
for movement, price, quantity in self._orders:
if movement == 'buy':
... | OnlineStockTrade | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OnlineStockTrade:
def __init__(self, orders):
"""Construct two heaps from orders. NOTE: the quantiry of all orders are the same for now."""
<|body_0|>
def _construct_heaps(self):
"""construct the max-oriented heap for buying order and the min-oriented heap for sellin... | stack_v2_sparse_classes_36k_train_002652 | 2,295 | no_license | [
{
"docstring": "Construct two heaps from orders. NOTE: the quantiry of all orders are the same for now.",
"name": "__init__",
"signature": "def __init__(self, orders)"
},
{
"docstring": "construct the max-oriented heap for buying order and the min-oriented heap for selling order.",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_014653 | Implement the Python class `OnlineStockTrade` described below.
Class description:
Implement the OnlineStockTrade class.
Method signatures and docstrings:
- def __init__(self, orders): Construct two heaps from orders. NOTE: the quantiry of all orders are the same for now.
- def _construct_heaps(self): construct the ma... | Implement the Python class `OnlineStockTrade` described below.
Class description:
Implement the OnlineStockTrade class.
Method signatures and docstrings:
- def __init__(self, orders): Construct two heaps from orders. NOTE: the quantiry of all orders are the same for now.
- def _construct_heaps(self): construct the ma... | 70b23ead7a89e46a84d9d914e7c8fa678edd1f90 | <|skeleton|>
class OnlineStockTrade:
def __init__(self, orders):
"""Construct two heaps from orders. NOTE: the quantiry of all orders are the same for now."""
<|body_0|>
def _construct_heaps(self):
"""construct the max-oriented heap for buying order and the min-oriented heap for sellin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OnlineStockTrade:
def __init__(self, orders):
"""Construct two heaps from orders. NOTE: the quantiry of all orders are the same for now."""
self._orders = orders
self._traded = []
self._max_heap = MaxHeapPriorityQueue()
self._min_heap = HeapPriorityQueue()
self.... | the_stack_v2_python_sparse | priority_queue_ch09/projects/online_stock_trade_p9_55.py | wanyikang/dsap | train | 1 | |
b515fc2c779e886541ef78cb5230f62679e8e254 | [
"super().__init__()\nself.local_coordinate_system: LocalCoordinateSystem = local_coordinate_system\nself.length: float = float(length)\nself.aperture_radius = float(aperture_radius)\nself.magnetic_field = float(magnetic_field)\nself.magnetic_field_vector: P3 = self.local_coordinate_system.YI * self.magnetic_field",... | <|body_start_0|>
super().__init__()
self.local_coordinate_system: LocalCoordinateSystem = local_coordinate_system
self.length: float = float(length)
self.aperture_radius = float(aperture_radius)
self.magnetic_field = float(magnetic_field)
self.magnetic_field_vector: P3 = ... | 局部圆柱区域产生均匀磁场的磁铁，可以看作一个直线二极磁铁 local_coordinate_system 局部坐标系 局部坐标系的原点即二极磁铁的起点 局部坐标系的 z 方向即二极磁铁延申方向 局部坐标系的 y 方向即磁场方向 length 磁铁长度 aperture_radius 磁铁孔径，磁铁外部磁场为零 magnetic_field 磁场大小，标量。磁场方向由 local_coordinate_system 确定 | LocalUniformMagnet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LocalUniformMagnet:
"""局部圆柱区域产生均匀磁场的磁铁，可以看作一个直线二极磁铁 local_coordinate_system 局部坐标系 局部坐标系的原点即二极磁铁的起点 局部坐标系的 z 方向即二极磁铁延申方向 局部坐标系的 y 方向即磁场方向 length 磁铁长度 aperture_radius 磁铁孔径，磁铁外部磁场为零 magnetic_field 磁场大小，标量。磁场方向由 local_coordinate_system 确定"""
def __init__(self, local_coordinate_system: LocalCoord... | stack_v2_sparse_classes_36k_train_002653 | 28,633 | permissive | [
{
"docstring": "构造器 输入磁场 magnetic_field",
"name": "__init__",
"signature": "def __init__(self, local_coordinate_system: LocalCoordinateSystem, length: float, aperture_radius: float, magnetic_field: float) -> None"
},
{
"docstring": "point 全局坐标系的点",
"name": "magnetic_field_at",
"signature... | 4 | null | Implement the Python class `LocalUniformMagnet` described below.
Class description:
局部圆柱区域产生均匀磁场的磁铁，可以看作一个直线二极磁铁 local_coordinate_system 局部坐标系 局部坐标系的原点即二极磁铁的起点 局部坐标系的 z 方向即二极磁铁延申方向 局部坐标系的 y 方向即磁场方向 length 磁铁长度 aperture_radius 磁铁孔径，磁铁外部磁场为零 magnetic_field 磁场大小，标量。磁场方向由 local_coordinate_system 确定
Method signatures and ... | Implement the Python class `LocalUniformMagnet` described below.
Class description:
局部圆柱区域产生均匀磁场的磁铁，可以看作一个直线二极磁铁 local_coordinate_system 局部坐标系 局部坐标系的原点即二极磁铁的起点 局部坐标系的 z 方向即二极磁铁延申方向 局部坐标系的 y 方向即磁场方向 length 磁铁长度 aperture_radius 磁铁孔径，磁铁外部磁场为零 magnetic_field 磁场大小，标量。磁场方向由 local_coordinate_system 确定
Method signatures and ... | b02c64220ea533a4fc9cad0b882d1be6edadf1c0 | <|skeleton|>
class LocalUniformMagnet:
"""局部圆柱区域产生均匀磁场的磁铁，可以看作一个直线二极磁铁 local_coordinate_system 局部坐标系 局部坐标系的原点即二极磁铁的起点 局部坐标系的 z 方向即二极磁铁延申方向 局部坐标系的 y 方向即磁场方向 length 磁铁长度 aperture_radius 磁铁孔径，磁铁外部磁场为零 magnetic_field 磁场大小，标量。磁场方向由 local_coordinate_system 确定"""
def __init__(self, local_coordinate_system: LocalCoord... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LocalUniformMagnet:
"""局部圆柱区域产生均匀磁场的磁铁，可以看作一个直线二极磁铁 local_coordinate_system 局部坐标系 局部坐标系的原点即二极磁铁的起点 局部坐标系的 z 方向即二极磁铁延申方向 局部坐标系的 y 方向即磁场方向 length 磁铁长度 aperture_radius 磁铁孔径，磁铁外部磁场为零 magnetic_field 磁场大小，标量。磁场方向由 local_coordinate_system 确定"""
def __init__(self, local_coordinate_system: LocalCoordinateSystem, ... | the_stack_v2_python_sparse | final_code/packages/magnets.py | madokast/cctpy | train | 1 |
c9814933116a5858e2482310c4b30a68b4f749d1 | [
"self.logger = Loggers.get_logger()\nself.connection_successful = False\nnumber_of_retries = 0\nif from_email is None:\n from_email = settings.SFU_CSSS_GMAIL_USERNAME\nif password is None:\n password = settings.SFU_CSSS_GMAIL_PASSWORD\nself.from_email = from_email\nself.max_number_of_retries = max_number_of_r... | <|body_start_0|>
self.logger = Loggers.get_logger()
self.connection_successful = False
number_of_retries = 0
if from_email is None:
from_email = settings.SFU_CSSS_GMAIL_USERNAME
if password is None:
password = settings.SFU_CSSS_GMAIL_PASSWORD
self.... | Gmail | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Gmail:
def __init__(self, from_email=None, password=None, smtp='smtp.gmail.com', port=587, max_number_of_retries=5):
"""initialized the gmail object for a gmail account Keyword Argument from_email -- the email address to log into password -- the password for the email to log into smtp --... | stack_v2_sparse_classes_36k_train_002654 | 5,999 | no_license | [
{
"docstring": "initialized the gmail object for a gmail account Keyword Argument from_email -- the email address to log into password -- the password for the email to log into smtp -- the server that hosts the smptlib server for gmail port -- the port for the smptlib server for gmail max_number_of_retries -- t... | 3 | null | Implement the Python class `Gmail` described below.
Class description:
Implement the Gmail class.
Method signatures and docstrings:
- def __init__(self, from_email=None, password=None, smtp='smtp.gmail.com', port=587, max_number_of_retries=5): initialized the gmail object for a gmail account Keyword Argument from_ema... | Implement the Python class `Gmail` described below.
Class description:
Implement the Gmail class.
Method signatures and docstrings:
- def __init__(self, from_email=None, password=None, smtp='smtp.gmail.com', port=587, max_number_of_retries=5): initialized the gmail object for a gmail account Keyword Argument from_ema... | 5152787e8db3b1c4a73362e8f06a117f5fadc817 | <|skeleton|>
class Gmail:
def __init__(self, from_email=None, password=None, smtp='smtp.gmail.com', port=587, max_number_of_retries=5):
"""initialized the gmail object for a gmail account Keyword Argument from_email -- the email address to log into password -- the password for the email to log into smtp --... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Gmail:
def __init__(self, from_email=None, password=None, smtp='smtp.gmail.com', port=587, max_number_of_retries=5):
"""initialized the gmail object for a gmail account Keyword Argument from_email -- the email address to log into password -- the password for the email to log into smtp -- the server th... | the_stack_v2_python_sparse | csss-site/src/csss/Gmail.py | CSSS/csss-site | train | 9 | |
5614132ffaceb5ea3e84b0434d7dc4e71450f20b | [
"super().__init__(adguard, entry)\nself.entity_description = description\nself._attr_unique_id = '_'.join([DOMAIN, adguard.host, str(adguard.port), 'sensor', description.key])",
"value = await self.entity_description.value_fn(self.adguard)\nself._attr_native_value = value\nif isinstance(value, float):\n self._... | <|body_start_0|>
super().__init__(adguard, entry)
self.entity_description = description
self._attr_unique_id = '_'.join([DOMAIN, adguard.host, str(adguard.port), 'sensor', description.key])
<|end_body_0|>
<|body_start_1|>
value = await self.entity_description.value_fn(self.adguard)
... | Defines a AdGuard Home sensor. | AdGuardHomeSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdGuardHomeSensor:
"""Defines a AdGuard Home sensor."""
def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None:
"""Initialize AdGuard Home sensor."""
<|body_0|>
async def _adguard_update(self) -> None:
"""U... | stack_v2_sparse_classes_36k_train_002655 | 4,982 | permissive | [
{
"docstring": "Initialize AdGuard Home sensor.",
"name": "__init__",
"signature": "def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None"
},
{
"docstring": "Update AdGuard Home entity.",
"name": "_adguard_update",
"signature": "a... | 2 | stack_v2_sparse_classes_30k_train_007873 | Implement the Python class `AdGuardHomeSensor` described below.
Class description:
Defines a AdGuard Home sensor.
Method signatures and docstrings:
- def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None: Initialize AdGuard Home sensor.
- async def _adguard_up... | Implement the Python class `AdGuardHomeSensor` described below.
Class description:
Defines a AdGuard Home sensor.
Method signatures and docstrings:
- def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None: Initialize AdGuard Home sensor.
- async def _adguard_up... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class AdGuardHomeSensor:
"""Defines a AdGuard Home sensor."""
def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None:
"""Initialize AdGuard Home sensor."""
<|body_0|>
async def _adguard_update(self) -> None:
"""U... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdGuardHomeSensor:
"""Defines a AdGuard Home sensor."""
def __init__(self, adguard: AdGuardHome, entry: ConfigEntry, description: AdGuardHomeEntityDescription) -> None:
"""Initialize AdGuard Home sensor."""
super().__init__(adguard, entry)
self.entity_description = description
... | the_stack_v2_python_sparse | homeassistant/components/adguard/sensor.py | home-assistant/core | train | 35,501 |
cf8dcdce5fa201e390cada3b4aeefb034f093d58 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn Room()",
"from .booking_type import BookingType\nfrom .place import Place\nfrom .booking_type import BookingType\nfrom .place import Place\nfields: Dict[str, Callable[[Any], None]] = {'audioDeviceName': lambda n: setattr(self, 'audio_d... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return Room()
<|end_body_0|>
<|body_start_1|>
from .booking_type import BookingType
from .place import Place
from .booking_type import BookingType
from .place import Place
... | Room | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Room:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Room:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Room"""
... | stack_v2_sparse_classes_36k_train_002656 | 5,259 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Room",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(parse_no... | 3 | stack_v2_sparse_classes_30k_train_011261 | Implement the Python class `Room` described below.
Class description:
Implement the Room class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Room: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pars... | Implement the Python class `Room` described below.
Class description:
Implement the Room class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Room: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pars... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class Room:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Room:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Room"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Room:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Room:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Room"""
if not parse_n... | the_stack_v2_python_sparse | msgraph/generated/models/room.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
1b7575a64366b7da437ac0ffd9fddd6860b639ac | [
"self.cutoff = cutoff\nself.box_width = box_width\nself.voxel_width = voxel_width\nself.reduce_to_contacts = reduce_to_contacts",
"if 'complex' in kwargs:\n datapoint = kwargs.get('complex')\n raise DeprecationWarning('Complex is being phased out as a parameter, please pass \"datapoint\" instead.')\ntry:\n ... | <|body_start_0|>
self.cutoff = cutoff
self.box_width = box_width
self.voxel_width = voxel_width
self.reduce_to_contacts = reduce_to_contacts
<|end_body_0|>
<|body_start_1|>
if 'complex' in kwargs:
datapoint = kwargs.get('complex')
raise DeprecationWarning... | Localize partial charges of atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute the partial (Gasteiger charge) on each molecule. For each atom, localize this partial charge in the voxel in which it originated to create a local charge array. Sum contribut... | ChargeVoxelizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChargeVoxelizer:
"""Localize partial charges of atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute the partial (Gasteiger charge) on each molecule. For each atom, localize this partial charge in the voxel in which it originated to... | stack_v2_sparse_classes_36k_train_002657 | 27,676 | permissive | [
{
"docstring": "Parameters ---------- cutoff: float (default 4.5) Distance cutoff in angstroms for molecules in complex. box_width: float, optional (default 16.0) Size of a box in which voxel features are calculated. Box is centered on a ligand centroid. voxel_width: float, optional (default 1.0) Size of a 3D v... | 2 | null | Implement the Python class `ChargeVoxelizer` described below.
Class description:
Localize partial charges of atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute the partial (Gasteiger charge) on each molecule. For each atom, localize this partial charge... | Implement the Python class `ChargeVoxelizer` described below.
Class description:
Localize partial charges of atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute the partial (Gasteiger charge) on each molecule. For each atom, localize this partial charge... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class ChargeVoxelizer:
"""Localize partial charges of atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute the partial (Gasteiger charge) on each molecule. For each atom, localize this partial charge in the voxel in which it originated to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChargeVoxelizer:
"""Localize partial charges of atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute the partial (Gasteiger charge) on each molecule. For each atom, localize this partial charge in the voxel in which it originated to create a loc... | the_stack_v2_python_sparse | deepchem/feat/complex_featurizers/grid_featurizers.py | deepchem/deepchem | train | 4,876 |
ef8c6d0f9594ac8273f5ab09744c1f3096aab49e | [
"if not fpath:\n fpath = self.generate_fpath(rootdir, product, utc)\nsuper().__init__(fpath)",
"valid_products = ('PRECIPRATE',)\npdb.set_trace()\nif prod not in valid_products:\n raise Exception('Product name not valid.')\nfname = '{0}.{1}{2}{3}.{4}{5}{6}'.format(prod, utc.year, utc.month, utc.day, utc.hou... | <|body_start_0|>
if not fpath:
fpath = self.generate_fpath(rootdir, product, utc)
super().__init__(fpath)
<|end_body_0|>
<|body_start_1|>
valid_products = ('PRECIPRATE',)
pdb.set_trace()
if prod not in valid_products:
raise Exception('Product name not val... | MRMS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MRMS:
def __init__(self, fpath=False, rootdir=False, product=False, utc=False):
"""A helper function to automatically generate file name to load needs to be written. Arguments: fpath (str, optional): file path to data. If False, other information must be presented to search for data (bel... | stack_v2_sparse_classes_36k_train_002658 | 18,802 | no_license | [
{
"docstring": "A helper function to automatically generate file name to load needs to be written. Arguments: fpath (str, optional): file path to data. If False, other information must be presented to search for data (below). rootdir (str, optional): if fpath is False, this is required to search for data based ... | 2 | stack_v2_sparse_classes_30k_train_018159 | Implement the Python class `MRMS` described below.
Class description:
Implement the MRMS class.
Method signatures and docstrings:
- def __init__(self, fpath=False, rootdir=False, product=False, utc=False): A helper function to automatically generate file name to load needs to be written. Arguments: fpath (str, option... | Implement the Python class `MRMS` described below.
Class description:
Implement the MRMS class.
Method signatures and docstrings:
- def __init__(self, fpath=False, rootdir=False, product=False, utc=False): A helper function to automatically generate file name to load needs to be written. Arguments: fpath (str, option... | 08f0472a59a910aaeb1c52baedffc2cec45fb54d | <|skeleton|>
class MRMS:
def __init__(self, fpath=False, rootdir=False, product=False, utc=False):
"""A helper function to automatically generate file name to load needs to be written. Arguments: fpath (str, optional): file path to data. If False, other information must be presented to search for data (bel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MRMS:
def __init__(self, fpath=False, rootdir=False, product=False, utc=False):
"""A helper function to automatically generate file name to load needs to be written. Arguments: fpath (str, optional): file path to data. If False, other information must be presented to search for data (below). rootdir (... | the_stack_v2_python_sparse | postWRF/postWRF/obs.py | johnrobertlawson/WEM | train | 33 | |
f163413944d747da568e3daa022813b03070f873 | [
"if not kwargs.get('obj_ids'):\n obj_model = facade.get_vlan_by_search(self.search)\n vlans = obj_model['query_set']\n only_main_property = False\nelse:\n obj_ids = kwargs['obj_ids'].split(';')\n vlans = facade.get_vlan_by_ids(obj_ids)\n obj_model = None\n only_main_property = True\nserializer_... | <|body_start_0|>
if not kwargs.get('obj_ids'):
obj_model = facade.get_vlan_by_search(self.search)
vlans = obj_model['query_set']
only_main_property = False
else:
obj_ids = kwargs['obj_ids'].split(';')
vlans = facade.get_vlan_by_ids(obj_ids)
... | VlanDBView | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VlanDBView:
def get(self, request, *args, **kwargs):
"""Returns a list of vlans with details by ids ou dict."""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Creates list of vlans."""
<|body_1|>
def put(self, request, *args, **kwargs):
... | stack_v2_sparse_classes_36k_train_002659 | 6,313 | permissive | [
{
"docstring": "Returns a list of vlans with details by ids ou dict.",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "Creates list of vlans.",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
},
{
"docstring": "Upda... | 4 | stack_v2_sparse_classes_30k_train_011739 | Implement the Python class `VlanDBView` described below.
Class description:
Implement the VlanDBView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Returns a list of vlans with details by ids ou dict.
- def post(self, request, *args, **kwargs): Creates list of vlans.
- def put(sel... | Implement the Python class `VlanDBView` described below.
Class description:
Implement the VlanDBView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Returns a list of vlans with details by ids ou dict.
- def post(self, request, *args, **kwargs): Creates list of vlans.
- def put(sel... | eb27e1d977a1c4bb1fee8fb51b8d8050c64696d9 | <|skeleton|>
class VlanDBView:
def get(self, request, *args, **kwargs):
"""Returns a list of vlans with details by ids ou dict."""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Creates list of vlans."""
<|body_1|>
def put(self, request, *args, **kwargs):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VlanDBView:
def get(self, request, *args, **kwargs):
"""Returns a list of vlans with details by ids ou dict."""
if not kwargs.get('obj_ids'):
obj_model = facade.get_vlan_by_search(self.search)
vlans = obj_model['query_set']
only_main_property = False
... | the_stack_v2_python_sparse | networkapi/api_vlan/views/v3.py | globocom/GloboNetworkAPI | train | 86 | |
fa786575b9c7c156ee9e2d03d4d477d0db8db939 | [
"book = get_object_or_404(models.Edition, id=book_id)\ndata = {'file_link_form': forms.FileLinkForm(), 'book': book}\nreturn TemplateResponse(request, 'book/file_links/file_link_page.html', data)",
"book = get_object_or_404(models.Book.objects.select_subclasses(), id=book_id)\nlink = get_object_or_404(models.File... | <|body_start_0|>
book = get_object_or_404(models.Edition, id=book_id)
data = {'file_link_form': forms.FileLinkForm(), 'book': book}
return TemplateResponse(request, 'book/file_links/file_link_page.html', data)
<|end_body_0|>
<|body_start_1|>
book = get_object_or_404(models.Book.objects.... | a book! this is the stuff | AddFileLink | [
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AddFileLink:
"""a book! this is the stuff"""
def get(self, request, book_id):
"""Create link form"""
<|body_0|>
def post(self, request, book_id, link_id=None):
"""Add a link to a copy of the book you can read"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_002660 | 3,716 | no_license | [
{
"docstring": "Create link form",
"name": "get",
"signature": "def get(self, request, book_id)"
},
{
"docstring": "Add a link to a copy of the book you can read",
"name": "post",
"signature": "def post(self, request, book_id, link_id=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014255 | Implement the Python class `AddFileLink` described below.
Class description:
a book! this is the stuff
Method signatures and docstrings:
- def get(self, request, book_id): Create link form
- def post(self, request, book_id, link_id=None): Add a link to a copy of the book you can read | Implement the Python class `AddFileLink` described below.
Class description:
a book! this is the stuff
Method signatures and docstrings:
- def get(self, request, book_id): Create link form
- def post(self, request, book_id, link_id=None): Add a link to a copy of the book you can read
<|skeleton|>
class AddFileLink:
... | 0f8da5b738047f3c34d60d93f59bdedd8f797224 | <|skeleton|>
class AddFileLink:
"""a book! this is the stuff"""
def get(self, request, book_id):
"""Create link form"""
<|body_0|>
def post(self, request, book_id, link_id=None):
"""Add a link to a copy of the book you can read"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AddFileLink:
"""a book! this is the stuff"""
def get(self, request, book_id):
"""Create link form"""
book = get_object_or_404(models.Edition, id=book_id)
data = {'file_link_form': forms.FileLinkForm(), 'book': book}
return TemplateResponse(request, 'book/file_links/file_li... | the_stack_v2_python_sparse | bookwyrm/views/books/links.py | bookwyrm-social/bookwyrm | train | 1,398 |
9be1c49ec9fa16f1d1f430d11f07db66827e4410 | [
"self._observer = observer\nself._path_handler = path_handler\nself._watch: Optional[ObservedWatch] = initial_watch",
"if event.event_type == EVENT_TYPE_OPENED:\n LOG.debug('Ignoring file system OPENED event.')\n return\nif self._watch and (not self._path_handler.path.exists()):\n if self._path_handler.s... | <|body_start_0|>
self._observer = observer
self._path_handler = path_handler
self._watch: Optional[ObservedWatch] = initial_watch
<|end_body_0|>
<|body_start_1|>
if event.event_type == EVENT_TYPE_OPENED:
LOG.debug('Ignoring file system OPENED event.')
return
... | This class is used to alter the behavior of watchdog folder watches. https://github.com/gorakhargosh/watchdog/issues/415 By default, if a folder is renamed, the handler will still get triggered for the new folder Ex: 1. Create FolderA 2. Watch FolderA 3. Rename FolderA to FolderB 4. Add file to FolderB 5. Handler will ... | StaticFolderWrapper | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StaticFolderWrapper:
"""This class is used to alter the behavior of watchdog folder watches. https://github.com/gorakhargosh/watchdog/issues/415 By default, if a folder is renamed, the handler will still get triggered for the new folder Ex: 1. Create FolderA 2. Watch FolderA 3. Rename FolderA to ... | stack_v2_sparse_classes_36k_train_002661 | 6,436 | permissive | [
{
"docstring": "[summary] Parameters ---------- observer : HandlerObserver HandlerObserver initial_watch : ObservedWatch Initial watch for the folder to be watched that gets returned by HandlerObserver path_handler : PathHandler PathHandler of the folder to be watched.",
"name": "__init__",
"signature":... | 3 | null | Implement the Python class `StaticFolderWrapper` described below.
Class description:
This class is used to alter the behavior of watchdog folder watches. https://github.com/gorakhargosh/watchdog/issues/415 By default, if a folder is renamed, the handler will still get triggered for the new folder Ex: 1. Create FolderA... | Implement the Python class `StaticFolderWrapper` described below.
Class description:
This class is used to alter the behavior of watchdog folder watches. https://github.com/gorakhargosh/watchdog/issues/415 By default, if a folder is renamed, the handler will still get triggered for the new folder Ex: 1. Create FolderA... | b297ff015f2b69d7c74059c2d42ece1c29ea73ee | <|skeleton|>
class StaticFolderWrapper:
"""This class is used to alter the behavior of watchdog folder watches. https://github.com/gorakhargosh/watchdog/issues/415 By default, if a folder is renamed, the handler will still get triggered for the new folder Ex: 1. Create FolderA 2. Watch FolderA 3. Rename FolderA to ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StaticFolderWrapper:
"""This class is used to alter the behavior of watchdog folder watches. https://github.com/gorakhargosh/watchdog/issues/415 By default, if a folder is renamed, the handler will still get triggered for the new folder Ex: 1. Create FolderA 2. Watch FolderA 3. Rename FolderA to FolderB 4. Ad... | the_stack_v2_python_sparse | samcli/lib/utils/path_observer.py | aws/aws-sam-cli | train | 1,402 |
2ce1ac1110cb38ece8fb709bb976e905e64b62ec | [
"params = book_review_getter.parse_args()\npg_size, pg_num = (int(params['pg-size']), int(params['pg-num']))\nstart = (pg_num - 1) * pg_size\nend = start + pg_size\nnew_records = LiveReview.find_by_asin(asin)\nold_records = OldReview.find_by_asin(asin)\nrecords = old_records + new_records\nif records == [] or start... | <|body_start_0|>
params = book_review_getter.parse_args()
pg_size, pg_num = (int(params['pg-size']), int(params['pg-num']))
start = (pg_num - 1) * pg_size
end = start + pg_size
new_records = LiveReview.find_by_asin(asin)
old_records = OldReview.find_by_asin(asin)
... | BookReview | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BookReview:
def get(self, asin):
"""Returns new and old Reviews for the book given asin (B000FA64PK). New and old reviews are differentiated by their "old_review" field's value. Page Number starts from 1"""
<|body_0|>
def post(self, asin):
"""JWT required in Headers ... | stack_v2_sparse_classes_36k_train_002662 | 5,907 | no_license | [
{
"docstring": "Returns new and old Reviews for the book given asin (B000FA64PK). New and old reviews are differentiated by their \"old_review\" field's value. Page Number starts from 1",
"name": "get",
"signature": "def get(self, asin)"
},
{
"docstring": "JWT required in Headers {Authorization:... | 3 | stack_v2_sparse_classes_30k_train_020843 | Implement the Python class `BookReview` described below.
Class description:
Implement the BookReview class.
Method signatures and docstrings:
- def get(self, asin): Returns new and old Reviews for the book given asin (B000FA64PK). New and old reviews are differentiated by their "old_review" field's value. Page Number... | Implement the Python class `BookReview` described below.
Class description:
Implement the BookReview class.
Method signatures and docstrings:
- def get(self, asin): Returns new and old Reviews for the book given asin (B000FA64PK). New and old reviews are differentiated by their "old_review" field's value. Page Number... | ef4336ad7a9e8b30281cc3cb828d08ebba9022e5 | <|skeleton|>
class BookReview:
def get(self, asin):
"""Returns new and old Reviews for the book given asin (B000FA64PK). New and old reviews are differentiated by their "old_review" field's value. Page Number starts from 1"""
<|body_0|>
def post(self, asin):
"""JWT required in Headers ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BookReview:
def get(self, asin):
"""Returns new and old Reviews for the book given asin (B000FA64PK). New and old reviews are differentiated by their "old_review" field's value. Page Number starts from 1"""
params = book_review_getter.parse_args()
pg_size, pg_num = (int(params['pg-size... | the_stack_v2_python_sparse | app/namespaces/book_review.py | FavebookSUTD/favebook_backend | train | 0 | |
50b3595b58b65b73ed9cc313d9088143e904aa8e | [
"self.model_class = model_class\nself.graph_db = graph_db\nself.results: Optional[pd.DataFrame] = []\nif not self.graph_db.splits:\n raise ValueError('Database contains no splits')\nif self.graph_db.splits != list(range(len(self.graph_db.splits))):\n raise ValueError(f'Graph database splits are not a contiguo... | <|body_start_0|>
self.model_class = model_class
self.graph_db = graph_db
self.results: Optional[pd.DataFrame] = []
if not self.graph_db.splits:
raise ValueError('Database contains no splits')
if self.graph_db.splits != list(range(len(self.graph_db.splits))):
... | A k cross-validation jobs. This runs the requested train/val/test schedule using every split in the graph database. | KFoldCrossValidation | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KFoldCrossValidation:
"""A k cross-validation jobs. This runs the requested train/val/test schedule using every split in the graph database."""
def __init__(self, model_class, graph_db: graph_tuple_database.Database):
"""Constructor. Args: model_class: A model constructor. Raises: Va... | stack_v2_sparse_classes_36k_train_002663 | 21,195 | permissive | [
{
"docstring": "Constructor. Args: model_class: A model constructor. Raises: ValueError: If the database contains invalid splits.",
"name": "__init__",
"signature": "def __init__(self, model_class, graph_db: graph_tuple_database.Database)"
},
{
"docstring": "Run the train/val/test loop.",
"n... | 2 | null | Implement the Python class `KFoldCrossValidation` described below.
Class description:
A k cross-validation jobs. This runs the requested train/val/test schedule using every split in the graph database.
Method signatures and docstrings:
- def __init__(self, model_class, graph_db: graph_tuple_database.Database): Constr... | Implement the Python class `KFoldCrossValidation` described below.
Class description:
A k cross-validation jobs. This runs the requested train/val/test schedule using every split in the graph database.
Method signatures and docstrings:
- def __init__(self, model_class, graph_db: graph_tuple_database.Database): Constr... | cd99d2c5362acd0b24ee224492bb3e8c4d4736fb | <|skeleton|>
class KFoldCrossValidation:
"""A k cross-validation jobs. This runs the requested train/val/test schedule using every split in the graph database."""
def __init__(self, model_class, graph_db: graph_tuple_database.Database):
"""Constructor. Args: model_class: A model constructor. Raises: Va... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KFoldCrossValidation:
"""A k cross-validation jobs. This runs the requested train/val/test schedule using every split in the graph database."""
def __init__(self, model_class, graph_db: graph_tuple_database.Database):
"""Constructor. Args: model_class: A model constructor. Raises: ValueError: If ... | the_stack_v2_python_sparse | deeplearning/ml4pl/models/run.py | Zacharias030/ProGraML | train | 0 |
e986bbb719341bbf139eec56a34e63e96a1881d7 | [
"if value is None:\n return ''\nordered_uuids = [(k, v) for k, v in value.items()]\nordered_uuids.sort(key=lambda x: x[1]['order'])\nreturn '\\r\\n'.join([i[0] for i in ordered_uuids])",
"if not len(value) or not isinstance(value, dict):\n return self.field.missing_value\nreturn value"
] | <|body_start_0|>
if value is None:
return ''
ordered_uuids = [(k, v) for k, v in value.items()]
ordered_uuids.sort(key=lambda x: x[1]['order'])
return '\r\n'.join([i[0] for i in ordered_uuids])
<|end_body_0|>
<|body_start_1|>
if not len(value) or not isinstance(value... | A data converter using the field's ``fromUnicode()`` method. | UUIDSFieldDataConverter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UUIDSFieldDataConverter:
"""A data converter using the field's ``fromUnicode()`` method."""
def toWidgetValue(self, value):
"""Convert the internal stored value into something that a z3c.form widget understands. :param value: [required] The internally stored value :type value: Dict :... | stack_v2_sparse_classes_36k_train_002664 | 6,453 | no_license | [
{
"docstring": "Convert the internal stored value into something that a z3c.form widget understands. :param value: [required] The internally stored value :type value: Dict :returns: A string with UUIDs separated by",
"name": "toWidgetValue",
"signature": "def toWidgetValue(self, value)"
},
{
"do... | 2 | stack_v2_sparse_classes_30k_train_012163 | Implement the Python class `UUIDSFieldDataConverter` described below.
Class description:
A data converter using the field's ``fromUnicode()`` method.
Method signatures and docstrings:
- def toWidgetValue(self, value): Convert the internal stored value into something that a z3c.form widget understands. :param value: [... | Implement the Python class `UUIDSFieldDataConverter` described below.
Class description:
A data converter using the field's ``fromUnicode()`` method.
Method signatures and docstrings:
- def toWidgetValue(self, value): Convert the internal stored value into something that a z3c.form widget understands. :param value: [... | c3a27fa8a48eb527d52179b8c007df5bc28220ee | <|skeleton|>
class UUIDSFieldDataConverter:
"""A data converter using the field's ``fromUnicode()`` method."""
def toWidgetValue(self, value):
"""Convert the internal stored value into something that a z3c.form widget understands. :param value: [required] The internally stored value :type value: Dict :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UUIDSFieldDataConverter:
"""A data converter using the field's ``fromUnicode()`` method."""
def toWidgetValue(self, value):
"""Convert the internal stored value into something that a z3c.form widget understands. :param value: [required] The internally stored value :type value: Dict :returns: A st... | the_stack_v2_python_sparse | src/collective/cover/tiles/carousel.py | collective/collective.cover | train | 23 |
3f2182fed3b446fed3e3b342c1f22ddb93e5d681 | [
"self._dbName = os.environ['MOPS_DBINSTANCE']\nself._instance = mopsInstance = Instance(self._dbName)\nself._conn = self._instance.get_dbh()\nself._cursor = self._conn.cursor()\nsql = 'select f.field_id, f.epoch_mjd, f.ra_deg, f.dec_deg, ' + 'f.survey_mode, f.time_start, f.time_stop, f.filter_id, ' + 'f.limiting_ma... | <|body_start_0|>
self._dbName = os.environ['MOPS_DBINSTANCE']
self._instance = mopsInstance = Instance(self._dbName)
self._conn = self._instance.get_dbh()
self._cursor = self._conn.cursor()
sql = 'select f.field_id, f.epoch_mjd, f.ra_deg, f.dec_deg, ' + 'f.survey_mode, f.time_sta... | FieldTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FieldTest:
def setUp(self):
"""Just create a Field instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it."""
<|body_0|>
def testInsert(self):
"""Test the insertion of a dummy Field instance in the DB."... | stack_v2_sparse_classes_36k_train_002665 | 17,613 | no_license | [
{
"docstring": "Just create a Field instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test the insertion of a dummy Field instance in the DB.",
"name": ... | 3 | stack_v2_sparse_classes_30k_train_003731 | Implement the Python class `FieldTest` described below.
Class description:
Implement the FieldTest class.
Method signatures and docstrings:
- def setUp(self): Just create a Field instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it.
- def testInsert(s... | Implement the Python class `FieldTest` described below.
Class description:
Implement the FieldTest class.
Method signatures and docstrings:
- def setUp(self): Just create a Field instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it.
- def testInsert(s... | 06858b7e935243da7a3f55b3e5097d6440e0c1c2 | <|skeleton|>
class FieldTest:
def setUp(self):
"""Just create a Field instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it."""
<|body_0|>
def testInsert(self):
"""Test the insertion of a dummy Field instance in the DB."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FieldTest:
def setUp(self):
"""Just create a Field instance from data in the DB. We will use that instance in our tests. We choose a field with tracklets associated to it."""
self._dbName = os.environ['MOPS_DBINSTANCE']
self._instance = mopsInstance = Instance(self._dbName)
sel... | the_stack_v2_python_sparse | python/MOPS/test.py | ldenneau/mopsng | train | 0 | |
efb06163268d634718ad95ead6295f3d41378371 | [
"if filters is None:\n self.filters = []\nelse:\n self.filters = filters",
"flags = self.check_flags(flags, **kwargs)\nfor f in self.filters:\n flags = f(flags, **kwargs)\nreturn flags",
"if flags is None:\n length = 0\n if predictions:\n length = len(predictions)\n if observations:\n ... | <|body_start_0|>
if filters is None:
self.filters = []
else:
self.filters = filters
<|end_body_0|>
<|body_start_1|>
flags = self.check_flags(flags, **kwargs)
for f in self.filters:
flags = f(flags, **kwargs)
return flags
<|end_body_1|>
<|body... | A class to run multiple filters in succession. | FilterRunner | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FilterRunner:
"""A class to run multiple filters in succession."""
def __init__(self, filters=None):
"""Initialise with a list of filters. :param filters: The list of filters"""
<|body_0|>
def __call__(self, flags, **kwargs):
"""Call the filters one by one. :para... | stack_v2_sparse_classes_36k_train_002666 | 17,726 | permissive | [
{
"docstring": "Initialise with a list of filters. :param filters: The list of filters",
"name": "__init__",
"signature": "def __init__(self, filters=None)"
},
{
"docstring": "Call the filters one by one. :param flags: The input flags :returns: The filtered flags",
"name": "__call__",
"s... | 3 | null | Implement the Python class `FilterRunner` described below.
Class description:
A class to run multiple filters in succession.
Method signatures and docstrings:
- def __init__(self, filters=None): Initialise with a list of filters. :param filters: The list of filters
- def __call__(self, flags, **kwargs): Call the filt... | Implement the Python class `FilterRunner` described below.
Class description:
A class to run multiple filters in succession.
Method signatures and docstrings:
- def __init__(self, filters=None): Initialise with a list of filters. :param filters: The list of filters
- def __call__(self, flags, **kwargs): Call the filt... | 88bf7f7c5ac44defc046ebf0719cde748092cfff | <|skeleton|>
class FilterRunner:
"""A class to run multiple filters in succession."""
def __init__(self, filters=None):
"""Initialise with a list of filters. :param filters: The list of filters"""
<|body_0|>
def __call__(self, flags, **kwargs):
"""Call the filters one by one. :para... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FilterRunner:
"""A class to run multiple filters in succession."""
def __init__(self, filters=None):
"""Initialise with a list of filters. :param filters: The list of filters"""
if filters is None:
self.filters = []
else:
self.filters = filters
def __c... | the_stack_v2_python_sparse | src/dials/algorithms/spot_finding/factory.py | dials/dials | train | 71 |
8e254edc3fe9fe441d9316ae640f7cdc342ba064 | [
"if isinstance(tags, str):\n return tags.split(',')\nreturn list(tags)",
"obj = super().dict(**kwargs)\nif 'tags' in obj:\n obj['tags'] = ','.join(obj['tags'])\nreturn obj"
] | <|body_start_0|>
if isinstance(tags, str):
return tags.split(',')
return list(tags)
<|end_body_0|>
<|body_start_1|>
obj = super().dict(**kwargs)
if 'tags' in obj:
obj['tags'] = ','.join(obj['tags'])
return obj
<|end_body_1|>
| Define a base class for elements and relationships. Attributes: id (str): tags (set of str): properties (dict): perspectives (set of Perspective): | ModelItemIO | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelItemIO:
"""Define a base class for elements and relationships. Attributes: id (str): tags (set of str): properties (dict): perspectives (set of Perspective):"""
def split_tags(cls, tags: Union[str, Iterable[str]]) -> List[str]:
"""Convert comma-separated tag list into list if ne... | stack_v2_sparse_classes_36k_train_002667 | 3,127 | permissive | [
{
"docstring": "Convert comma-separated tag list into list if needed.",
"name": "split_tags",
"signature": "def split_tags(cls, tags: Union[str, Iterable[str]]) -> List[str]"
},
{
"docstring": "Map this IO into a dictionary suitable for serialisation.",
"name": "dict",
"signature": "def ... | 2 | stack_v2_sparse_classes_30k_train_011193 | Implement the Python class `ModelItemIO` described below.
Class description:
Define a base class for elements and relationships. Attributes: id (str): tags (set of str): properties (dict): perspectives (set of Perspective):
Method signatures and docstrings:
- def split_tags(cls, tags: Union[str, Iterable[str]]) -> Li... | Implement the Python class `ModelItemIO` described below.
Class description:
Define a base class for elements and relationships. Attributes: id (str): tags (set of str): properties (dict): perspectives (set of Perspective):
Method signatures and docstrings:
- def split_tags(cls, tags: Union[str, Iterable[str]]) -> Li... | 31f1dcadb3ff113d8a77ce132657237ea01c307b | <|skeleton|>
class ModelItemIO:
"""Define a base class for elements and relationships. Attributes: id (str): tags (set of str): properties (dict): perspectives (set of Perspective):"""
def split_tags(cls, tags: Union[str, Iterable[str]]) -> List[str]:
"""Convert comma-separated tag list into list if ne... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelItemIO:
"""Define a base class for elements and relationships. Attributes: id (str): tags (set of str): properties (dict): perspectives (set of Perspective):"""
def split_tags(cls, tags: Union[str, Iterable[str]]) -> List[str]:
"""Convert comma-separated tag list into list if needed."""
... | the_stack_v2_python_sparse | src/structurizr/model/model_item.py | Midnighter/structurizr-python | train | 61 |
2cd96929bc880be241eaffb5995dc6f3db974730 | [
"self.capture_tail_logs = capture_tail_logs\nself.continue_after_error = continue_after_error\nself.data_file_destination = data_file_destination\nself.db_restore_overwrite_policy = db_restore_overwrite_policy\nself.enable_checksum = enable_checksum\nself.instance_name = instance_name\nself.is_auto_sync_enabled = i... | <|body_start_0|>
self.capture_tail_logs = capture_tail_logs
self.continue_after_error = continue_after_error
self.data_file_destination = data_file_destination
self.db_restore_overwrite_policy = db_restore_overwrite_policy
self.enable_checksum = enable_checksum
self.insta... | Implementation of the 'RestoreSqlAppObjectParams' model. TODO: type description here. Attributes: capture_tail_logs (bool): Set to true if tail logs are to be captured before the restore operation. This is only applicable if we are restoring the SQL database to its original source, and the database is not being renamed... | RestoreSqlAppObjectParams | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestoreSqlAppObjectParams:
"""Implementation of the 'RestoreSqlAppObjectParams' model. TODO: type description here. Attributes: capture_tail_logs (bool): Set to true if tail logs are to be captured before the restore operation. This is only applicable if we are restoring the SQL database to its o... | stack_v2_sparse_classes_36k_train_002668 | 10,408 | permissive | [
{
"docstring": "Constructor for the RestoreSqlAppObjectParams class",
"name": "__init__",
"signature": "def __init__(self, capture_tail_logs=None, continue_after_error=None, data_file_destination=None, db_restore_overwrite_policy=None, enable_checksum=None, instance_name=None, is_auto_sync_enabled=None,... | 2 | null | Implement the Python class `RestoreSqlAppObjectParams` described below.
Class description:
Implementation of the 'RestoreSqlAppObjectParams' model. TODO: type description here. Attributes: capture_tail_logs (bool): Set to true if tail logs are to be captured before the restore operation. This is only applicable if we ... | Implement the Python class `RestoreSqlAppObjectParams` described below.
Class description:
Implementation of the 'RestoreSqlAppObjectParams' model. TODO: type description here. Attributes: capture_tail_logs (bool): Set to true if tail logs are to be captured before the restore operation. This is only applicable if we ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RestoreSqlAppObjectParams:
"""Implementation of the 'RestoreSqlAppObjectParams' model. TODO: type description here. Attributes: capture_tail_logs (bool): Set to true if tail logs are to be captured before the restore operation. This is only applicable if we are restoring the SQL database to its o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RestoreSqlAppObjectParams:
"""Implementation of the 'RestoreSqlAppObjectParams' model. TODO: type description here. Attributes: capture_tail_logs (bool): Set to true if tail logs are to be captured before the restore operation. This is only applicable if we are restoring the SQL database to its original sourc... | the_stack_v2_python_sparse | cohesity_management_sdk/models/restore_sql_app_object_params.py | cohesity/management-sdk-python | train | 24 |
81869ed310e4298eb98d90991cda817223667f52 | [
"super(Csv, self).start(**kwargs)\nself._temp_file = tempfile.NamedTemporaryFile(mode='w+', encoding='utf-8')\nself.echo(msg=f'Writing JSON to temporary file {self._temp_file.name!r}')",
"super(Csv, self).stop(**kwargs)\nself._start(**kwargs)\nself.echo(msg='Re-reading temporary file and converting to CSV')\nself... | <|body_start_0|>
super(Csv, self).start(**kwargs)
self._temp_file = tempfile.NamedTemporaryFile(mode='w+', encoding='utf-8')
self.echo(msg=f'Writing JSON to temporary file {self._temp_file.name!r}')
<|end_body_0|>
<|body_start_1|>
super(Csv, self).stop(**kwargs)
self._start(**kw... | Pass. | JsonToCsv | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JsonToCsv:
"""Pass."""
def start(self, **kwargs):
"""Create temp file for writing to."""
<|body_0|>
def stop(self, **kwargs):
"""Create CSV file, process each row in temp file, then close temp and csv."""
<|body_1|>
def process_row(self, row):
... | stack_v2_sparse_classes_36k_train_002669 | 1,648 | permissive | [
{
"docstring": "Create temp file for writing to.",
"name": "start",
"signature": "def start(self, **kwargs)"
},
{
"docstring": "Create CSV file, process each row in temp file, then close temp and csv.",
"name": "stop",
"signature": "def stop(self, **kwargs)"
},
{
"docstring": "Wr... | 3 | null | Implement the Python class `JsonToCsv` described below.
Class description:
Pass.
Method signatures and docstrings:
- def start(self, **kwargs): Create temp file for writing to.
- def stop(self, **kwargs): Create CSV file, process each row in temp file, then close temp and csv.
- def process_row(self, row): Write row ... | Implement the Python class `JsonToCsv` described below.
Class description:
Pass.
Method signatures and docstrings:
- def start(self, **kwargs): Create temp file for writing to.
- def stop(self, **kwargs): Create CSV file, process each row in temp file, then close temp and csv.
- def process_row(self, row): Write row ... | 09fd564d62f0ddf7aa44db14a509eaafaf0c930f | <|skeleton|>
class JsonToCsv:
"""Pass."""
def start(self, **kwargs):
"""Create temp file for writing to."""
<|body_0|>
def stop(self, **kwargs):
"""Create CSV file, process each row in temp file, then close temp and csv."""
<|body_1|>
def process_row(self, row):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JsonToCsv:
"""Pass."""
def start(self, **kwargs):
"""Create temp file for writing to."""
super(Csv, self).start(**kwargs)
self._temp_file = tempfile.NamedTemporaryFile(mode='w+', encoding='utf-8')
self.echo(msg=f'Writing JSON to temporary file {self._temp_file.name!r}')
... | the_stack_v2_python_sparse | axonius_api_client/api/asset_callbacks/base_json_to_csv.py | geransmith/axonius_api_client | train | 0 |
39dfb3338d71ee5da837cfd2f2aacf3e63c43563 | [
"k_bits = k_max.bit_length()\ndub = [[0] * n for _ in range(k_bits)]\nfor j in range(n):\n dub[0][j] = f(j)\nfor i in range(1, k_bits):\n for j in range(n):\n dub[i][j] = dub[i - 1][dub[i - 1][j]]\nself.doubling_table = dub",
"now = x\nfor i in range(k.bit_length()):\n if k >> i & 1:\n now ... | <|body_start_0|>
k_bits = k_max.bit_length()
dub = [[0] * n for _ in range(k_bits)]
for j in range(n):
dub[0][j] = f(j)
for i in range(1, k_bits):
for j in range(n):
dub[i][j] = dub[i - 1][dub[i - 1][j]]
self.doubling_table = dub
<|end_body... | Doubling | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Doubling:
def __init__(self, n, k_max, f) -> None:
"""要素数nのダブリングテーブルを作成します。"""
<|body_0|>
def get(self, x, k):
"""xをk回操作した値を取得します。"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
k_bits = k_max.bit_length()
dub = [[0] * n for _ in range(k_bi... | stack_v2_sparse_classes_36k_train_002670 | 1,506 | no_license | [
{
"docstring": "要素数nのダブリングテーブルを作成します。",
"name": "__init__",
"signature": "def __init__(self, n, k_max, f) -> None"
},
{
"docstring": "xをk回操作した値を取得します。",
"name": "get",
"signature": "def get(self, x, k)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009649 | Implement the Python class `Doubling` described below.
Class description:
Implement the Doubling class.
Method signatures and docstrings:
- def __init__(self, n, k_max, f) -> None: 要素数nのダブリングテーブルを作成します。
- def get(self, x, k): xをk回操作した値を取得します。 | Implement the Python class `Doubling` described below.
Class description:
Implement the Doubling class.
Method signatures and docstrings:
- def __init__(self, n, k_max, f) -> None: 要素数nのダブリングテーブルを作成します。
- def get(self, x, k): xをk回操作した値を取得します。
<|skeleton|>
class Doubling:
def __init__(self, n, k_max, f) -> None:... | 1259be8d4214209b7c7d3783f33aa6de4ea04a01 | <|skeleton|>
class Doubling:
def __init__(self, n, k_max, f) -> None:
"""要素数nのダブリングテーブルを作成します。"""
<|body_0|>
def get(self, x, k):
"""xをk回操作した値を取得します。"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Doubling:
def __init__(self, n, k_max, f) -> None:
"""要素数nのダブリングテーブルを作成します。"""
k_bits = k_max.bit_length()
dub = [[0] * n for _ in range(k_bits)]
for j in range(n):
dub[0][j] = f(j)
for i in range(1, k_bits):
for j in range(n):
du... | the_stack_v2_python_sparse | solve_python/058.py | Nishin-0141/kyopro_educational_90_python | train | 0 | |
6054d4546b89cf5f69a90fe5a87df736fea407b3 | [
"name = base_name + str(len(self.containers[base_name]))\ncontainer = KubernetesContainer(container_spec, name)\nself.containers[base_name].append(container)\ncontainer.Create()",
"service = KubernetesContainerService(container_spec, name)\nself.services[name] = service\nservice.Create()"
] | <|body_start_0|>
name = base_name + str(len(self.containers[base_name]))
container = KubernetesContainer(container_spec, name)
self.containers[base_name].append(container)
container.Create()
<|end_body_0|>
<|body_start_1|>
service = KubernetesContainerService(container_spec, nam... | A Kubernetes flavor of Container Cluster. | KubernetesCluster | [
"Apache-2.0",
"GPL-2.0-only",
"LicenseRef-scancode-public-domain",
"Classpath-exception-2.0",
"AGPL-3.0-only",
"MIT",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KubernetesCluster:
"""A Kubernetes flavor of Container Cluster."""
def DeployContainer(self, base_name, container_spec):
"""Deploys Containers according to the ContainerSpec."""
<|body_0|>
def DeployContainerService(self, name, container_spec):
"""Deploys a Conta... | stack_v2_sparse_classes_36k_train_002671 | 22,185 | permissive | [
{
"docstring": "Deploys Containers according to the ContainerSpec.",
"name": "DeployContainer",
"signature": "def DeployContainer(self, base_name, container_spec)"
},
{
"docstring": "Deploys a ContainerSerivice according to the ContainerSpec.",
"name": "DeployContainerService",
"signatur... | 2 | null | Implement the Python class `KubernetesCluster` described below.
Class description:
A Kubernetes flavor of Container Cluster.
Method signatures and docstrings:
- def DeployContainer(self, base_name, container_spec): Deploys Containers according to the ContainerSpec.
- def DeployContainerService(self, name, container_s... | Implement the Python class `KubernetesCluster` described below.
Class description:
A Kubernetes flavor of Container Cluster.
Method signatures and docstrings:
- def DeployContainer(self, base_name, container_spec): Deploys Containers according to the ContainerSpec.
- def DeployContainerService(self, name, container_s... | 86ff6a8c3aaf2407f6692d207208b7c60bca0d2f | <|skeleton|>
class KubernetesCluster:
"""A Kubernetes flavor of Container Cluster."""
def DeployContainer(self, base_name, container_spec):
"""Deploys Containers according to the ContainerSpec."""
<|body_0|>
def DeployContainerService(self, name, container_spec):
"""Deploys a Conta... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KubernetesCluster:
"""A Kubernetes flavor of Container Cluster."""
def DeployContainer(self, base_name, container_spec):
"""Deploys Containers according to the ContainerSpec."""
name = base_name + str(len(self.containers[base_name]))
container = KubernetesContainer(container_spec,... | the_stack_v2_python_sparse | perfkitbenchmarker/container_service.py | sylvanasbeta/PerfKitBenchmarker | train | 1 |
3d118cc93d51dbb468cf22f532c3136620d16d7e | [
"counter = 0\nlength = len(nums)\narray = {}\nfor i in range(len(nums) - 1, -1, -1):\n for index in range(length - i):\n sum = array.get(index, 0)\n array[index] = nums[i] + sum\n if array[index] == k:\n counter += 1\nreturn counter",
"count = sum = 0\nmap = {}\nmap[0] = 1\nfor ... | <|body_start_0|>
counter = 0
length = len(nums)
array = {}
for i in range(len(nums) - 1, -1, -1):
for index in range(length - i):
sum = array.get(index, 0)
array[index] = nums[i] + sum
if array[index] == k:
c... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def subarraySum(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int"""
<|body_0|>
def subarraySum2(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
counter = 0
... | stack_v2_sparse_classes_36k_train_002672 | 1,119 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: int",
"name": "subarraySum",
"signature": "def subarraySum(self, nums, k)"
},
{
"docstring": ":type nums: List[int] :type k: int :rtype: int",
"name": "subarraySum2",
"signature": "def subarraySum2(self, nums, k)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017851 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subarraySum(self, nums, k): :type nums: List[int] :type k: int :rtype: int
- def subarraySum2(self, nums, k): :type nums: List[int] :type k: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subarraySum(self, nums, k): :type nums: List[int] :type k: int :rtype: int
- def subarraySum2(self, nums, k): :type nums: List[int] :type k: int :rtype: int
<|skeleton|>
cla... | a8b59573dc201438ebd5a5ab64e9ac61255a4abd | <|skeleton|>
class Solution:
def subarraySum(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int"""
<|body_0|>
def subarraySum2(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def subarraySum(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int"""
counter = 0
length = len(nums)
array = {}
for i in range(len(nums) - 1, -1, -1):
for index in range(length - i):
sum = array.get(index, 0)
... | the_stack_v2_python_sparse | summer/2018_07_13/subarray-sum-equals-k.py | shaheming/leecode | train | 0 | |
06bdb34f3ffc77a71db13eed330ae21a129c0a1f | [
"self.body_spec = body_spec\nself.nn_spec = nn_spec\nself.body_encoder = BodyEncoder(body_spec)\nself.brain_encoder = NeuralNetworkEncoder(nn_spec)",
"yaml.add_representer(unicode, unicode_representer)\nbot_yaml = {}\nid = bot_pb.id\nbody = bot_pb.body\nbrain = bot_pb.brain\nbot_yaml['id'] = id\nbot_yaml['body'] ... | <|body_start_0|>
self.body_spec = body_spec
self.nn_spec = nn_spec
self.body_encoder = BodyEncoder(body_spec)
self.brain_encoder = NeuralNetworkEncoder(nn_spec)
<|end_body_0|>
<|body_start_1|>
yaml.add_representer(unicode, unicode_representer)
bot_yaml = {}
id = ... | Sample converter creates a YAML stream from a Robot protobuf message and a body / neural net spec. | RobotToYaml | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RobotToYaml:
"""Sample converter creates a YAML stream from a Robot protobuf message and a body / neural net spec."""
def __init__(self, body_spec, nn_spec):
"""::param body_spec: :type body_spec: BodyImplementation :param nn_spec: :type nn_spec: NeuralNetImplementation"""
<|... | stack_v2_sparse_classes_36k_train_002673 | 3,202 | permissive | [
{
"docstring": "::param body_spec: :type body_spec: BodyImplementation :param nn_spec: :type nn_spec: NeuralNetImplementation",
"name": "__init__",
"signature": "def __init__(self, body_spec, nn_spec)"
},
{
"docstring": "Converts a protobuf robot object into YAML file which it then returns :para... | 2 | stack_v2_sparse_classes_30k_test_000950 | Implement the Python class `RobotToYaml` described below.
Class description:
Sample converter creates a YAML stream from a Robot protobuf message and a body / neural net spec.
Method signatures and docstrings:
- def __init__(self, body_spec, nn_spec): ::param body_spec: :type body_spec: BodyImplementation :param nn_s... | Implement the Python class `RobotToYaml` described below.
Class description:
Sample converter creates a YAML stream from a Robot protobuf message and a body / neural net spec.
Method signatures and docstrings:
- def __init__(self, body_spec, nn_spec): ::param body_spec: :type body_spec: BodyImplementation :param nn_s... | 70e65320a28fe04e121145b2cdde289d3052728a | <|skeleton|>
class RobotToYaml:
"""Sample converter creates a YAML stream from a Robot protobuf message and a body / neural net spec."""
def __init__(self, body_spec, nn_spec):
"""::param body_spec: :type body_spec: BodyImplementation :param nn_spec: :type nn_spec: NeuralNetImplementation"""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RobotToYaml:
"""Sample converter creates a YAML stream from a Robot protobuf message and a body / neural net spec."""
def __init__(self, body_spec, nn_spec):
"""::param body_spec: :type body_spec: BodyImplementation :param nn_spec: :type nn_spec: NeuralNetImplementation"""
self.body_spec ... | the_stack_v2_python_sparse | revolve/convert/yaml.py | ElteHupkes/revolve | train | 0 |
220d9922ce7854b19ab7d07d532f37bb7d621402 | [
"if n == 0:\n return ['']\noutput = []\nfor c in range(n):\n for left in self.generates(c):\n for right in self.generates(n - 1 - c):\n output.append('({}){}'.format(left, right))\nreturn output",
"def back_track(s='', left=0, right=0):\n if len(s) == n * 2:\n output.append(s)\n ... | <|body_start_0|>
if n == 0:
return ['']
output = []
for c in range(n):
for left in self.generates(c):
for right in self.generates(n - 1 - c):
output.append('({}){}'.format(left, right))
return output
<|end_body_0|>
<|body_start... | Parentheses | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Parentheses:
def generates(self, n: int) -> List[str]:
"""Approach: Closure Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/root(n)) :param n: :return:"""
<|body_0|>
def generate(self, n: int) -> List[str]:
"""Approach: Back tracking Time Complexity: O(4^n/ro... | stack_v2_sparse_classes_36k_train_002674 | 1,294 | no_license | [
{
"docstring": "Approach: Closure Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/root(n)) :param n: :return:",
"name": "generates",
"signature": "def generates(self, n: int) -> List[str]"
},
{
"docstring": "Approach: Back tracking Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/... | 2 | null | Implement the Python class `Parentheses` described below.
Class description:
Implement the Parentheses class.
Method signatures and docstrings:
- def generates(self, n: int) -> List[str]: Approach: Closure Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/root(n)) :param n: :return:
- def generate(self, n: int)... | Implement the Python class `Parentheses` described below.
Class description:
Implement the Parentheses class.
Method signatures and docstrings:
- def generates(self, n: int) -> List[str]: Approach: Closure Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/root(n)) :param n: :return:
- def generate(self, n: int)... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class Parentheses:
def generates(self, n: int) -> List[str]:
"""Approach: Closure Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/root(n)) :param n: :return:"""
<|body_0|>
def generate(self, n: int) -> List[str]:
"""Approach: Back tracking Time Complexity: O(4^n/ro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Parentheses:
def generates(self, n: int) -> List[str]:
"""Approach: Closure Time Complexity: O(4^n/root(n)) Space Complexity: O(4^n/root(n)) :param n: :return:"""
if n == 0:
return ['']
output = []
for c in range(n):
for left in self.generates(c):
... | the_stack_v2_python_sparse | revisited/back_tracking/generate_parentheses.py | Shiv2157k/leet_code | train | 1 | |
b19697dd2141337e43e8637db3d1b3194c5727c4 | [
"inps = [model.input, K.learning_phase()]\nouts = [layer.output for layer in model.layers]\nself.forward_pass = K.function(inps, outs)\nself.model = model",
"x_value = np.expand_dims(input_image, axis=0)\nvisual_bpr = None\nlayer_outs = self.forward_pass([x_value, 0])\nfor i in range(len(self.model.layers) - 1, -... | <|body_start_0|>
inps = [model.input, K.learning_phase()]
outs = [layer.output for layer in model.layers]
self.forward_pass = K.function(inps, outs)
self.model = model
<|end_body_0|>
<|body_start_1|>
x_value = np.expand_dims(input_image, axis=0)
visual_bpr = None
... | A SaliencyMask class that computes saliency masks with VisualBackprop (https://arxiv.org/abs/1611.05418). | VisualBackprop | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VisualBackprop:
"""A SaliencyMask class that computes saliency masks with VisualBackprop (https://arxiv.org/abs/1611.05418)."""
def __init__(self, model, output_index=0):
"""Constructs a VisualProp SaliencyMask."""
<|body_0|>
def get_mask(self, input_image):
"""R... | stack_v2_sparse_classes_36k_train_002675 | 2,437 | permissive | [
{
"docstring": "Constructs a VisualProp SaliencyMask.",
"name": "__init__",
"signature": "def __init__(self, model, output_index=0)"
},
{
"docstring": "Returns a VisualBackprop mask.",
"name": "get_mask",
"signature": "def get_mask(self, input_image)"
},
{
"docstring": "The decon... | 3 | stack_v2_sparse_classes_30k_train_009507 | Implement the Python class `VisualBackprop` described below.
Class description:
A SaliencyMask class that computes saliency masks with VisualBackprop (https://arxiv.org/abs/1611.05418).
Method signatures and docstrings:
- def __init__(self, model, output_index=0): Constructs a VisualProp SaliencyMask.
- def get_mask(... | Implement the Python class `VisualBackprop` described below.
Class description:
A SaliencyMask class that computes saliency masks with VisualBackprop (https://arxiv.org/abs/1611.05418).
Method signatures and docstrings:
- def __init__(self, model, output_index=0): Constructs a VisualProp SaliencyMask.
- def get_mask(... | 692785e881dc12a1500a9e651b07c7ff6fef6f9d | <|skeleton|>
class VisualBackprop:
"""A SaliencyMask class that computes saliency masks with VisualBackprop (https://arxiv.org/abs/1611.05418)."""
def __init__(self, model, output_index=0):
"""Constructs a VisualProp SaliencyMask."""
<|body_0|>
def get_mask(self, input_image):
"""R... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VisualBackprop:
"""A SaliencyMask class that computes saliency masks with VisualBackprop (https://arxiv.org/abs/1611.05418)."""
def __init__(self, model, output_index=0):
"""Constructs a VisualProp SaliencyMask."""
inps = [model.input, K.learning_phase()]
outs = [layer.output for ... | the_stack_v2_python_sparse | keras_explain/deep_viz_keras/visual_backprop.py | PrimozGodec/keras-explain | train | 18 |
8bb21978e524a29431c1ac7687cd13b09d51e8dc | [
"super(Transformer, self).__init__()\nencoder_layer = TransformerEncoderLayer(d_model, num_heads, units, dropout, activation)\nencoder_norm = torch.nn.LayerNorm(d_model)\nself.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\ndecoder_layer = TransformerDecoderLayer(d_model, num_heads, u... | <|body_start_0|>
super(Transformer, self).__init__()
encoder_layer = TransformerEncoderLayer(d_model, num_heads, units, dropout, activation)
encoder_norm = torch.nn.LayerNorm(d_model)
self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
decoder_layer... | Transformer Model | Transformer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Transformer:
"""Transformer Model"""
def __init__(self, d_model: int=512, num_heads: int=8, num_encoder_layers: int=6, num_decoder_layers: int=6, units: int=2048, dropout: float=0.1, activation: str='relu') -> NoReturn:
""":param d_model: 深度，词嵌入维度 :param num_heads: 注意力头数 :param num_e... | stack_v2_sparse_classes_36k_train_002676 | 4,188 | permissive | [
{
"docstring": ":param d_model: 深度，词嵌入维度 :param num_heads: 注意力头数 :param num_encoder_layers: encoder层数 :param num_decoder_layers: decoder层数 :param units: 单元数 :param dropout: 采样率 :param activation: 激活方法",
"name": "__init__",
"signature": "def __init__(self, d_model: int=512, num_heads: int=8, num_encoder_... | 3 | stack_v2_sparse_classes_30k_train_011323 | Implement the Python class `Transformer` described below.
Class description:
Transformer Model
Method signatures and docstrings:
- def __init__(self, d_model: int=512, num_heads: int=8, num_encoder_layers: int=6, num_decoder_layers: int=6, units: int=2048, dropout: float=0.1, activation: str='relu') -> NoReturn: :par... | Implement the Python class `Transformer` described below.
Class description:
Transformer Model
Method signatures and docstrings:
- def __init__(self, d_model: int=512, num_heads: int=8, num_encoder_layers: int=6, num_decoder_layers: int=6, units: int=2048, dropout: float=0.1, activation: str='relu') -> NoReturn: :par... | d47c1438cb5c45c2c2aebfb82fea92bef4c3d65c | <|skeleton|>
class Transformer:
"""Transformer Model"""
def __init__(self, d_model: int=512, num_heads: int=8, num_encoder_layers: int=6, num_decoder_layers: int=6, units: int=2048, dropout: float=0.1, activation: str='relu') -> NoReturn:
""":param d_model: 深度，词嵌入维度 :param num_heads: 注意力头数 :param num_e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Transformer:
"""Transformer Model"""
def __init__(self, d_model: int=512, num_heads: int=8, num_encoder_layers: int=6, num_decoder_layers: int=6, units: int=2048, dropout: float=0.1, activation: str='relu') -> NoReturn:
""":param d_model: 深度，词嵌入维度 :param num_heads: 注意力头数 :param num_encoder_layers... | the_stack_v2_python_sparse | dialogue/pytorch/transformer/model.py | Jiadwu2/nlp-dialogue | train | 0 |
f13b1fa990e6406d844d49d3acfb179377bb6dda | [
"super(ADBShellSSHConnection, self).__init__(*args, **kwargs)\nself.command_prefix += ' shell '\nself._unknown_command = None\nreturn",
"if self._unknown_command is None:\n self._unknown_command = re.compile(SPACES.join([NAMED.format(n=COMMAND_GROUP, p=ALPHA + ONE_OR_MORE) + '/sh:', EVERYTHING, 'not', 'found']... | <|body_start_0|>
super(ADBShellSSHConnection, self).__init__(*args, **kwargs)
self.command_prefix += ' shell '
self._unknown_command = None
return
<|end_body_0|>
<|body_start_1|>
if self._unknown_command is None:
self._unknown_command = re.compile(SPACES.join([NAMED.... | A class to talk to the shell, note the adb-server | ADBShellSSHConnection | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ADBShellSSHConnection:
"""A class to talk to the shell, note the adb-server"""
def __init__(self, *args, **kwargs):
""":param: (see the ADBSSHConnection)"""
<|body_0|>
def unknown_command(self):
"""A regular expression to match unknown command errors. Uses: '\\w+... | stack_v2_sparse_classes_36k_train_002677 | 9,298 | permissive | [
{
"docstring": ":param: (see the ADBSSHConnection)",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "A regular expression to match unknown command errors. Uses: '\\\\w+/sh: *.* *not *found' :rtype: SRE_Pattern :return: regex to match unknown_command erro... | 3 | null | Implement the Python class `ADBShellSSHConnection` described below.
Class description:
A class to talk to the shell, note the adb-server
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): :param: (see the ADBSSHConnection)
- def unknown_command(self): A regular expression to match unknown comman... | Implement the Python class `ADBShellSSHConnection` described below.
Class description:
A class to talk to the shell, note the adb-server
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): :param: (see the ADBSSHConnection)
- def unknown_command(self): A regular expression to match unknown comman... | b4d1c77e1d611fe2b30768b42bdc7493afb0ea95 | <|skeleton|>
class ADBShellSSHConnection:
"""A class to talk to the shell, note the adb-server"""
def __init__(self, *args, **kwargs):
""":param: (see the ADBSSHConnection)"""
<|body_0|>
def unknown_command(self):
"""A regular expression to match unknown command errors. Uses: '\\w+... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ADBShellSSHConnection:
"""A class to talk to the shell, note the adb-server"""
def __init__(self, *args, **kwargs):
""":param: (see the ADBSSHConnection)"""
super(ADBShellSSHConnection, self).__init__(*args, **kwargs)
self.command_prefix += ' shell '
self._unknown_command ... | the_stack_v2_python_sparse | apetools/connections/adbconnection.py | russell-n/oldape | train | 0 |
765e942119ad5c735ba49c67ca98f3ce40a55ace | [
"node_list = response.xpath(\"//tr[@class='even'] | //tr[@class='odd']\")\nfor node in node_list:\n item = TencentItem()\n item['position_name'] = node.xpath('./td[1]/a/text()').extract_first()\n item['position_link'] = 'https://hr.tencent.com/' + node.xpath('./td[1]/a/@href').extract_first()\n item['po... | <|body_start_0|>
node_list = response.xpath("//tr[@class='even'] | //tr[@class='odd']")
for node in node_list:
item = TencentItem()
item['position_name'] = node.xpath('./td[1]/a/text()').extract_first()
item['position_link'] = 'https://hr.tencent.com/' + node.xpath('.... | TencentSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TencentSpider:
def parse(self, response):
"""默认列表页的解析方法"""
<|body_0|>
def parse_detail(self, response):
"""解析详情页的响应内容"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
node_list = response.xpath("//tr[@class='even'] | //tr[@class='odd']")
for ... | stack_v2_sparse_classes_36k_train_002678 | 2,985 | no_license | [
{
"docstring": "默认列表页的解析方法",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "解析详情页的响应内容",
"name": "parse_detail",
"signature": "def parse_detail(self, response)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019682 | Implement the Python class `TencentSpider` described below.
Class description:
Implement the TencentSpider class.
Method signatures and docstrings:
- def parse(self, response): 默认列表页的解析方法
- def parse_detail(self, response): 解析详情页的响应内容 | Implement the Python class `TencentSpider` described below.
Class description:
Implement the TencentSpider class.
Method signatures and docstrings:
- def parse(self, response): 默认列表页的解析方法
- def parse_detail(self, response): 解析详情页的响应内容
<|skeleton|>
class TencentSpider:
def parse(self, response):
"""默认列表页... | a51e31acff41292e568ac22b0e213e6cb48218fa | <|skeleton|>
class TencentSpider:
def parse(self, response):
"""默认列表页的解析方法"""
<|body_0|>
def parse_detail(self, response):
"""解析详情页的响应内容"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TencentSpider:
def parse(self, response):
"""默认列表页的解析方法"""
node_list = response.xpath("//tr[@class='even'] | //tr[@class='odd']")
for node in node_list:
item = TencentItem()
item['position_name'] = node.xpath('./td[1]/a/text()').extract_first()
item[... | the_stack_v2_python_sparse | 爬虫项目/code10/2.Spider类多级页面数据采集/Tencent2/Tencent2/spiders/tencent2.py | byst4nder/his_spider | train | 1 | |
3b68a7c7b99f0457a500ab76a2ca35bb0552376c | [
"self.uy = uy\nself.kocha = kocha\nself.tuman = tuman\nself.viloyat = viloyat",
"manzil = f'{self.viloyat} viloyati, {self.tuman} tumani, '\nmanzil += f\"{self.kocha} ko'chasi, {self.uy}-uy\"\nreturn manzil"
] | <|body_start_0|>
self.uy = uy
self.kocha = kocha
self.tuman = tuman
self.viloyat = viloyat
<|end_body_0|>
<|body_start_1|>
manzil = f'{self.viloyat} viloyati, {self.tuman} tumani, '
manzil += f"{self.kocha} ko'chasi, {self.uy}-uy"
return manzil
<|end_body_1|>
| Manzil saqlash uchun klass | Manzil | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Manzil:
"""Manzil saqlash uchun klass"""
def __init__(self, uy, kocha, tuman, viloyat):
"""Manzil xususiyatlari"""
<|body_0|>
def get_manzil(self):
"""Manzilni ko'rish"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.uy = uy
self.koc... | stack_v2_sparse_classes_36k_train_002679 | 1,994 | no_license | [
{
"docstring": "Manzil xususiyatlari",
"name": "__init__",
"signature": "def __init__(self, uy, kocha, tuman, viloyat)"
},
{
"docstring": "Manzilni ko'rish",
"name": "get_manzil",
"signature": "def get_manzil(self)"
}
] | 2 | null | Implement the Python class `Manzil` described below.
Class description:
Manzil saqlash uchun klass
Method signatures and docstrings:
- def __init__(self, uy, kocha, tuman, viloyat): Manzil xususiyatlari
- def get_manzil(self): Manzilni ko'rish | Implement the Python class `Manzil` described below.
Class description:
Manzil saqlash uchun klass
Method signatures and docstrings:
- def __init__(self, uy, kocha, tuman, viloyat): Manzil xususiyatlari
- def get_manzil(self): Manzilni ko'rish
<|skeleton|>
class Manzil:
"""Manzil saqlash uchun klass"""
def ... | 1fd2409b6b532fe6dd8cc9d6cb19753a40bbe83c | <|skeleton|>
class Manzil:
"""Manzil saqlash uchun klass"""
def __init__(self, uy, kocha, tuman, viloyat):
"""Manzil xususiyatlari"""
<|body_0|>
def get_manzil(self):
"""Manzilni ko'rish"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Manzil:
"""Manzil saqlash uchun klass"""
def __init__(self, uy, kocha, tuman, viloyat):
"""Manzil xususiyatlari"""
self.uy = uy
self.kocha = kocha
self.tuman = tuman
self.viloyat = viloyat
def get_manzil(self):
"""Manzilni ko'rish"""
manzil = f... | the_stack_v2_python_sparse | 30-inheritance/darslar-30-manzil.py | eldorbaxtiyorov/python-darslar | train | 1 |
9c7e6e5d58e57bc5a0209e2dcb38a8a2cec6a092 | [
"super(RNNDecoder, self).__init__()\nself.units = units\nself.batch = batch\nself.embedding = tf.keras.layers.Embedding(vocab, embedding)\nself.gru = tf.keras.layers.GRU(units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')\nself.F = tf.keras.layers.Dense(vocab)",
"self_attentio... | <|body_start_0|>
super(RNNDecoder, self).__init__()
self.units = units
self.batch = batch
self.embedding = tf.keras.layers.Embedding(vocab, embedding)
self.gru = tf.keras.layers.GRU(units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
s... | RNNDecoder class decodes for machine translation | RNNDecoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNDecoder:
"""RNNDecoder class decodes for machine translation"""
def __init__(self, vocab, embedding, units, batch) -> None:
"""Initializer Arguments: vocab {int} -- Is representing the size of the input vocabulary embedding {int} -- Is representing the dimensionality of embedding ... | stack_v2_sparse_classes_36k_train_002680 | 2,173 | no_license | [
{
"docstring": "Initializer Arguments: vocab {int} -- Is representing the size of the input vocabulary embedding {int} -- Is representing the dimensionality of embedding units {int} -- Is the number of hidden units in the RNN cell batch {int} -- Is representing the batch size",
"name": "__init__",
"sign... | 2 | null | Implement the Python class `RNNDecoder` described below.
Class description:
RNNDecoder class decodes for machine translation
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch) -> None: Initializer Arguments: vocab {int} -- Is representing the size of the input vocabulary embedding ... | Implement the Python class `RNNDecoder` described below.
Class description:
RNNDecoder class decodes for machine translation
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch) -> None: Initializer Arguments: vocab {int} -- Is representing the size of the input vocabulary embedding ... | 2ddae38cc25d914488451b8c30e1234f1fa55ebe | <|skeleton|>
class RNNDecoder:
"""RNNDecoder class decodes for machine translation"""
def __init__(self, vocab, embedding, units, batch) -> None:
"""Initializer Arguments: vocab {int} -- Is representing the size of the input vocabulary embedding {int} -- Is representing the dimensionality of embedding ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RNNDecoder:
"""RNNDecoder class decodes for machine translation"""
def __init__(self, vocab, embedding, units, batch) -> None:
"""Initializer Arguments: vocab {int} -- Is representing the size of the input vocabulary embedding {int} -- Is representing the dimensionality of embedding units {int} -... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/2-rnn_decoder.py | KoeusIss/holbertonschool-machine_learning | train | 0 |
bc4bd704e0e28659c92d5af3cf8dc43c1e6965be | [
"super(DecomposedLinear, self).__init__()\ndevice = layer.weight.device\nweight = layer.weight.data\nout_dim, in_dim = weight.shape\nout_rank, in_rank = ranks\nself.in_layer = nn.Linear(in_features=in_dim, out_features=in_rank, bias=False).to(device)\nself.core_layer = nn.Linear(in_features=in_rank, out_features=ou... | <|body_start_0|>
super(DecomposedLinear, self).__init__()
device = layer.weight.device
weight = layer.weight.data
out_dim, in_dim = weight.shape
out_rank, in_rank = ranks
self.in_layer = nn.Linear(in_features=in_dim, out_features=in_rank, bias=False).to(device)
se... | Decomposed (or compressed) linear layer. | DecomposedLinear | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecomposedLinear:
"""Decomposed (or compressed) linear layer."""
def __init__(self, layer, ranks, init=True):
"""Class initializer."""
<|body_0|>
def forward(self, x):
"""Forward propagation."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super... | stack_v2_sparse_classes_36k_train_002681 | 5,386 | permissive | [
{
"docstring": "Class initializer.",
"name": "__init__",
"signature": "def __init__(self, layer, ranks, init=True)"
},
{
"docstring": "Forward propagation.",
"name": "forward",
"signature": "def forward(self, x)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015207 | Implement the Python class `DecomposedLinear` described below.
Class description:
Decomposed (or compressed) linear layer.
Method signatures and docstrings:
- def __init__(self, layer, ranks, init=True): Class initializer.
- def forward(self, x): Forward propagation. | Implement the Python class `DecomposedLinear` described below.
Class description:
Decomposed (or compressed) linear layer.
Method signatures and docstrings:
- def __init__(self, layer, ranks, init=True): Class initializer.
- def forward(self, x): Forward propagation.
<|skeleton|>
class DecomposedLinear:
"""Decom... | fe5d1eb5ab5453be70c4be473fd3da71afe4b06c | <|skeleton|>
class DecomposedLinear:
"""Decomposed (or compressed) linear layer."""
def __init__(self, layer, ranks, init=True):
"""Class initializer."""
<|body_0|>
def forward(self, x):
"""Forward propagation."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DecomposedLinear:
"""Decomposed (or compressed) linear layer."""
def __init__(self, layer, ranks, init=True):
"""Class initializer."""
super(DecomposedLinear, self).__init__()
device = layer.weight.device
weight = layer.weight.data
out_dim, in_dim = weight.shape
... | the_stack_v2_python_sparse | src/kegnet/utils/tucker.py | videoturingtest/KegNet | train | 0 |
85074eba64968786f4a0832b269de08ebfc4a5b3 | [
"super().__init__()\nself.transformer = nn.Transformer(hidden_dim * 2, nheads, num_encoder_layers, num_decoder_layers, dropout=0.05)\nself.query_pos = nn.Parameter(torch.rand(100, hidden_dim * 2))\nself.row_embed = nn.Parameter(torch.rand(50, hidden_dim))\nself.col_embed = nn.Parameter(torch.rand(50, hidden_dim))\n... | <|body_start_0|>
super().__init__()
self.transformer = nn.Transformer(hidden_dim * 2, nheads, num_encoder_layers, num_decoder_layers, dropout=0.05)
self.query_pos = nn.Parameter(torch.rand(100, hidden_dim * 2))
self.row_embed = nn.Parameter(torch.rand(50, hidden_dim))
self.col_em... | Decoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
def __init__(self, num_classes, hidden_dim: int=256, nheads: int=16, num_encoder_layers=6, num_decoder_layers=6):
"""num_classes: int, should be number of classes WITHOUT "no object" class"""
<|body_0|>
def forward(self, h_left: torch.Tensor, h_right: torch.Tensor):... | stack_v2_sparse_classes_36k_train_002682 | 9,933 | permissive | [
{
"docstring": "num_classes: int, should be number of classes WITHOUT \"no object\" class",
"name": "__init__",
"signature": "def __init__(self, num_classes, hidden_dim: int=256, nheads: int=16, num_encoder_layers=6, num_decoder_layers=6)"
},
{
"docstring": "h_left: N, C, H, W h_right: N, C, H, ... | 2 | stack_v2_sparse_classes_30k_train_012537 | Implement the Python class `Decoder` described below.
Class description:
Implement the Decoder class.
Method signatures and docstrings:
- def __init__(self, num_classes, hidden_dim: int=256, nheads: int=16, num_encoder_layers=6, num_decoder_layers=6): num_classes: int, should be number of classes WITHOUT "no object" ... | Implement the Python class `Decoder` described below.
Class description:
Implement the Decoder class.
Method signatures and docstrings:
- def __init__(self, num_classes, hidden_dim: int=256, nheads: int=16, num_encoder_layers=6, num_decoder_layers=6): num_classes: int, should be number of classes WITHOUT "no object" ... | 2f97eae4750ea9766bcad60c26dee9f9a14fafa4 | <|skeleton|>
class Decoder:
def __init__(self, num_classes, hidden_dim: int=256, nheads: int=16, num_encoder_layers=6, num_decoder_layers=6):
"""num_classes: int, should be number of classes WITHOUT "no object" class"""
<|body_0|>
def forward(self, h_left: torch.Tensor, h_right: torch.Tensor):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder:
def __init__(self, num_classes, hidden_dim: int=256, nheads: int=16, num_encoder_layers=6, num_decoder_layers=6):
"""num_classes: int, should be number of classes WITHOUT "no object" class"""
super().__init__()
self.transformer = nn.Transformer(hidden_dim * 2, nheads, num_enco... | the_stack_v2_python_sparse | volume/fishdetr3d_alt.py | Napam/INF399-Master-DeepLearning | train | 1 | |
cc2bb94cdc1a11747fb1863437e7bc3f1f00b61c | [
"self._server_config = CONFIG\nsnmp_config = CONFIG_SNMP\nself._hostname = hostname\nself._snmp_object = None\nvalidate = snmp_manager.Validate(hostname, snmp_config.snmp_auth())\nsnmp_params = validate.credentials()\nif _do_poll(snmp_params) is True:\n self._snmp_object = snmp_manager.Interact(snmp_params)\nels... | <|body_start_0|>
self._server_config = CONFIG
snmp_config = CONFIG_SNMP
self._hostname = hostname
self._snmp_object = None
validate = snmp_manager.Validate(hostname, snmp_config.snmp_auth())
snmp_params = validate.credentials()
if _do_poll(snmp_params) is True:
... | Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post: | Poll | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Poll:
"""Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:"""
def __init__(self, hostname):
"""Method initializing the class. Args: hostname: Hostname to poll Returns: None"""
<|body_0|>
def query(self):
"""Query ... | stack_v2_sparse_classes_36k_train_002683 | 2,651 | permissive | [
{
"docstring": "Method initializing the class. Args: hostname: Hostname to poll Returns: None",
"name": "__init__",
"signature": "def __init__(self, hostname)"
},
{
"docstring": "Query all remote hosts for data. Args: None Returns: None",
"name": "query",
"signature": "def query(self)"
... | 2 | null | Implement the Python class `Poll` described below.
Class description:
Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:
Method signatures and docstrings:
- def __init__(self, hostname): Method initializing the class. Args: hostname: Hostname to poll Returns: None
- de... | Implement the Python class `Poll` described below.
Class description:
Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:
Method signatures and docstrings:
- def __init__(self, hostname): Method initializing the class. Args: hostname: Hostname to poll Returns: None
- de... | ae82589fbbab77fef6d6be09c1fcca5846f595a8 | <|skeleton|>
class Poll:
"""Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:"""
def __init__(self, hostname):
"""Method initializing the class. Args: hostname: Hostname to poll Returns: None"""
<|body_0|>
def query(self):
"""Query ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Poll:
"""Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:"""
def __init__(self, hostname):
"""Method initializing the class. Args: hostname: Hostname to poll Returns: None"""
self._server_config = CONFIG
snmp_config = CONFIG_SNMP
... | the_stack_v2_python_sparse | switchmap/snmp/poller.py | PalisadoesFoundation/switchmap-ng | train | 8 |
6ddec57a7d6617d15cc6cea4d8d1852ca282272a | [
"len_a = self.len_list(headA)\nlen_b = self.len_list(headB)\nif len_a > len_b:\n gap = len_a - len_b\n while gap > 0:\n headA = headA.next\n gap -= 1\nelse:\n gap = len_b - len_a\n while gap > 0:\n headB = headB.next\n gap -= 1\nwhile headA and headB:\n if headA == headB:\... | <|body_start_0|>
len_a = self.len_list(headA)
len_b = self.len_list(headB)
if len_a > len_b:
gap = len_a - len_b
while gap > 0:
headA = headA.next
gap -= 1
else:
gap = len_b - len_a
while gap > 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type headA, headB: ListNode :rtype: ListNode"""
<|body_0|>
def len_list(self, head):
""":param head: ListNode :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
len_a = self.len_list(h... | stack_v2_sparse_classes_36k_train_002684 | 1,212 | no_license | [
{
"docstring": ":type headA, headB: ListNode :rtype: ListNode",
"name": "getIntersectionNode",
"signature": "def getIntersectionNode(self, headA, headB)"
},
{
"docstring": ":param head: ListNode :return:",
"name": "len_list",
"signature": "def len_list(self, head)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type headA, headB: ListNode :rtype: ListNode
- def len_list(self, head): :param head: ListNode :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type headA, headB: ListNode :rtype: ListNode
- def len_list(self, head): :param head: ListNode :return:
<|skeleton|>
class Solution... | a75310a96d2b165b15d5ee10ec409a17cdc880ba | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type headA, headB: ListNode :rtype: ListNode"""
<|body_0|>
def len_list(self, head):
""":param head: ListNode :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getIntersectionNode(self, headA, headB):
""":type headA, headB: ListNode :rtype: ListNode"""
len_a = self.len_list(headA)
len_b = self.len_list(headB)
if len_a > len_b:
gap = len_a - len_b
while gap > 0:
headA = headA.next
... | the_stack_v2_python_sparse | leetcode/linked_list/code/offer-52.py | skyxyz-lang/CS_Note | train | 0 | |
0e1c35cd8ab0cbcf2cacc0aae5f3a3d1ecdb0faa | [
"self.page = page\nself.per_page_count = per_page\nself.page_count = page_count\nself.page_url = page_url\npage_show_count = current_page_count\navg_page_count = page_show_count // 2\nself.start_page_show = page - avg_page_count\nself.end_page_show = page + avg_page_count\nif self.start_page_show <= 0:\n self.st... | <|body_start_0|>
self.page = page
self.per_page_count = per_page
self.page_count = page_count
self.page_url = page_url
page_show_count = current_page_count
avg_page_count = page_show_count // 2
self.start_page_show = page - avg_page_count
self.end_page_sho... | Page | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Page:
def __init__(self, page, page_count, page_url, per_page=10, current_page_count=10):
"""初始化分页的所有参数 :param page: :param page_count: :param page_url: :param per_page: :param current_page_count:"""
<|body_0|>
def get_page_show(self):
"""根据初始化的参数拼接page分页 :return:"""... | stack_v2_sparse_classes_36k_train_002685 | 4,235 | no_license | [
{
"docstring": "初始化分页的所有参数 :param page: :param page_count: :param page_url: :param per_page: :param current_page_count:",
"name": "__init__",
"signature": "def __init__(self, page, page_count, page_url, per_page=10, current_page_count=10)"
},
{
"docstring": "根据初始化的参数拼接page分页 :return:",
"name... | 2 | stack_v2_sparse_classes_30k_train_009697 | Implement the Python class `Page` described below.
Class description:
Implement the Page class.
Method signatures and docstrings:
- def __init__(self, page, page_count, page_url, per_page=10, current_page_count=10): 初始化分页的所有参数 :param page: :param page_count: :param page_url: :param per_page: :param current_page_count... | Implement the Python class `Page` described below.
Class description:
Implement the Page class.
Method signatures and docstrings:
- def __init__(self, page, page_count, page_url, per_page=10, current_page_count=10): 初始化分页的所有参数 :param page: :param page_count: :param page_url: :param per_page: :param current_page_count... | 5a1a6dd59cdd903563389fa7c73a283e8657d731 | <|skeleton|>
class Page:
def __init__(self, page, page_count, page_url, per_page=10, current_page_count=10):
"""初始化分页的所有参数 :param page: :param page_count: :param page_url: :param per_page: :param current_page_count:"""
<|body_0|>
def get_page_show(self):
"""根据初始化的参数拼接page分页 :return:"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Page:
def __init__(self, page, page_count, page_url, per_page=10, current_page_count=10):
"""初始化分页的所有参数 :param page: :param page_count: :param page_url: :param per_page: :param current_page_count:"""
self.page = page
self.per_page_count = per_page
self.page_count = page_count
... | the_stack_v2_python_sparse | python/Django/20190625/test01/utils/page.py | wjl626nice/1902 | train | 4 | |
e28b0b77a0b0103929b573c155153bdf76f45e91 | [
"super(GaussianNoise, self).__init__()\nself._mu = mu\nassert sigma >= 0, \"GaussianNoise's sigma should be positive.\"\nself._sigma = sigma",
"noise = torch.randn(shape, device=device)\nnoise = noise * self._sigma + self._mu\nreturn noise"
] | <|body_start_0|>
super(GaussianNoise, self).__init__()
self._mu = mu
assert sigma >= 0, "GaussianNoise's sigma should be positive."
self._sigma = sigma
<|end_body_0|>
<|body_start_1|>
noise = torch.randn(shape, device=device)
noise = noise * self._sigma + self._mu
... | Overview: Derived class for generating gaussian noise, which satisfies :math:`X \\sim N(\\mu, \\sigma^2)` Interface: __init__, __call__ | GaussianNoise | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianNoise:
"""Overview: Derived class for generating gaussian noise, which satisfies :math:`X \\sim N(\\mu, \\sigma^2)` Interface: __init__, __call__"""
def __init__(self, mu: float=0.0, sigma: float=1.0) -> None:
"""Overview: Initialize :math:`\\mu` and :math:`\\sigma` in Gaussi... | stack_v2_sparse_classes_36k_train_002686 | 7,610 | permissive | [
{
"docstring": "Overview: Initialize :math:`\\\\mu` and :math:`\\\\sigma` in Gaussian Distribution Arguments: - mu (:obj:`float`): :math:`\\\\mu` , mean value - sigma (:obj:`float`): :math:`\\\\sigma` , standard deviation, should be positive",
"name": "__init__",
"signature": "def __init__(self, mu: flo... | 2 | null | Implement the Python class `GaussianNoise` described below.
Class description:
Overview: Derived class for generating gaussian noise, which satisfies :math:`X \\sim N(\\mu, \\sigma^2)` Interface: __init__, __call__
Method signatures and docstrings:
- def __init__(self, mu: float=0.0, sigma: float=1.0) -> None: Overvi... | Implement the Python class `GaussianNoise` described below.
Class description:
Overview: Derived class for generating gaussian noise, which satisfies :math:`X \\sim N(\\mu, \\sigma^2)` Interface: __init__, __call__
Method signatures and docstrings:
- def __init__(self, mu: float=0.0, sigma: float=1.0) -> None: Overvi... | eb483fa6e46602d58c8e7d2ca1e566adca28e703 | <|skeleton|>
class GaussianNoise:
"""Overview: Derived class for generating gaussian noise, which satisfies :math:`X \\sim N(\\mu, \\sigma^2)` Interface: __init__, __call__"""
def __init__(self, mu: float=0.0, sigma: float=1.0) -> None:
"""Overview: Initialize :math:`\\mu` and :math:`\\sigma` in Gaussi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianNoise:
"""Overview: Derived class for generating gaussian noise, which satisfies :math:`X \\sim N(\\mu, \\sigma^2)` Interface: __init__, __call__"""
def __init__(self, mu: float=0.0, sigma: float=1.0) -> None:
"""Overview: Initialize :math:`\\mu` and :math:`\\sigma` in Gaussian Distributi... | the_stack_v2_python_sparse | ding/rl_utils/exploration.py | shengxuesun/DI-engine | train | 1 |
51c6a749ce8e9be0b4b63a8604f008556254ccbb | [
"super(DecodePredictions, self).__init__(**kwargs)\nself.anchor_box = AnchorBox()\nself.box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2], dtype=tf.float32)\nself.max_detections_per_class = max_detections_per_class\nself.max_detections = max_detections\nself.nms_iou_threshold = nms_iou_threshold\nself.confid... | <|body_start_0|>
super(DecodePredictions, self).__init__(**kwargs)
self.anchor_box = AnchorBox()
self.box_variance = tf.convert_to_tensor([0.1, 0.1, 0.2, 0.2], dtype=tf.float32)
self.max_detections_per_class = max_detections_per_class
self.max_detections = max_detections
... | 解码预测值网络层, 将RetinaNet的预测值使用非极大值抑制解码成人类可读形式. Attributes: anchor_box: RetinaNet.preprocessing.label_ops.AnchorBox, 锚框. box_variance: tf.Tensor, 框方差, 用来增大损失(小于1), 便于计算梯度. max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detections: int, default=100, 图片上出现的目前的最大数量. nms_iou_threshold: float, default=0.5, 使用非极大值抑制... | DecodePredictions | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecodePredictions:
"""解码预测值网络层, 将RetinaNet的预测值使用非极大值抑制解码成人类可读形式. Attributes: anchor_box: RetinaNet.preprocessing.label_ops.AnchorBox, 锚框. box_variance: tf.Tensor, 框方差, 用来增大损失(小于1), 便于计算梯度. max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detections: int, default=100, 图片上出现的目前的最大数量. nm... | stack_v2_sparse_classes_36k_train_002687 | 3,904 | permissive | [
{
"docstring": "初始化解码预测值网络层. Args: max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detections: int, default=100, 图片上出现的目前的最大数量. nms_iou_threshold: float, default=0.5, 使用非极大值抑制时的IoU阈值. confidence_threshold: float, default=00.5, 样本置信度阈值.",
"name": "__init__",
"signature": "def __init__(self, ... | 3 | stack_v2_sparse_classes_30k_train_009483 | Implement the Python class `DecodePredictions` described below.
Class description:
解码预测值网络层, 将RetinaNet的预测值使用非极大值抑制解码成人类可读形式. Attributes: anchor_box: RetinaNet.preprocessing.label_ops.AnchorBox, 锚框. box_variance: tf.Tensor, 框方差, 用来增大损失(小于1), 便于计算梯度. max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detectio... | Implement the Python class `DecodePredictions` described below.
Class description:
解码预测值网络层, 将RetinaNet的预测值使用非极大值抑制解码成人类可读形式. Attributes: anchor_box: RetinaNet.preprocessing.label_ops.AnchorBox, 锚框. box_variance: tf.Tensor, 框方差, 用来增大损失(小于1), 便于计算梯度. max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detectio... | 357edda03cdc1f976764b6ed4fcad6e639646142 | <|skeleton|>
class DecodePredictions:
"""解码预测值网络层, 将RetinaNet的预测值使用非极大值抑制解码成人类可读形式. Attributes: anchor_box: RetinaNet.preprocessing.label_ops.AnchorBox, 锚框. box_variance: tf.Tensor, 框方差, 用来增大损失(小于1), 便于计算梯度. max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detections: int, default=100, 图片上出现的目前的最大数量. nm... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DecodePredictions:
"""解码预测值网络层, 将RetinaNet的预测值使用非极大值抑制解码成人类可读形式. Attributes: anchor_box: RetinaNet.preprocessing.label_ops.AnchorBox, 锚框. box_variance: tf.Tensor, 框方差, 用来增大损失(小于1), 便于计算梯度. max_detections_per_class: int, default=100, 每类目标出现的最大数量. max_detections: int, default=100, 图片上出现的目前的最大数量. nms_iou_thresho... | the_stack_v2_python_sparse | RetinaNet/model/decode_predictions.py | sun1638650145/RetinaNet | train | 2 |
8a6066a5bb7e4ea269a605e65c427388c0d8bdd4 | [
"output = []\nif len(matrix[0]) == 0:\n return []\nexpected_out_len = len(matrix) * len(matrix[0])\nfor i in matrix[0]:\n output.append(i)\ndel matrix[0]\nreversed = list(zip(*matrix))\nwhile reversed:\n for i in reversed[-1]:\n output.append(i)\n del reversed[-1]\n reversed = list(zip(*revers... | <|body_start_0|>
output = []
if len(matrix[0]) == 0:
return []
expected_out_len = len(matrix) * len(matrix[0])
for i in matrix[0]:
output.append(i)
del matrix[0]
reversed = list(zip(*matrix))
while reversed:
for i in reversed[-1... | SpiralMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpiralMatrix:
def spiralOrder(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
<|body_0|>
def spiralOrder1(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
output =... | stack_v2_sparse_classes_36k_train_002688 | 1,808 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :rtype: List[int]",
"name": "spiralOrder",
"signature": "def spiralOrder(self, matrix)"
},
{
"docstring": ":type matrix: List[List[int]] :rtype: List[int]",
"name": "spiralOrder1",
"signature": "def spiralOrder1(self, matrix)"
}
] | 2 | null | Implement the Python class `SpiralMatrix` described below.
Class description:
Implement the SpiralMatrix class.
Method signatures and docstrings:
- def spiralOrder(self, matrix): :type matrix: List[List[int]] :rtype: List[int]
- def spiralOrder1(self, matrix): :type matrix: List[List[int]] :rtype: List[int] | Implement the Python class `SpiralMatrix` described below.
Class description:
Implement the SpiralMatrix class.
Method signatures and docstrings:
- def spiralOrder(self, matrix): :type matrix: List[List[int]] :rtype: List[int]
- def spiralOrder1(self, matrix): :type matrix: List[List[int]] :rtype: List[int]
<|skelet... | 6163a781364e4a79f03543cc151cea77c6738369 | <|skeleton|>
class SpiralMatrix:
def spiralOrder(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
<|body_0|>
def spiralOrder1(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SpiralMatrix:
def spiralOrder(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
output = []
if len(matrix[0]) == 0:
return []
expected_out_len = len(matrix) * len(matrix[0])
for i in matrix[0]:
output.append(i)
del matr... | the_stack_v2_python_sparse | SpiralMatrix.py | MichaelArslangul/python_Sandbox | train | 0 | |
dc51a51fdb8487c872371d1707baa72d27f6027e | [
"tag = get_object_or_404(Tag, pk=pk)\nself.check_object_permissions(request, tag)\nserializer = TagRetrieveUpdateDestroySerializer(tag, many=False)\nreturn Response(data=serializer.data, status=status.HTTP_200_OK)",
"tag = get_object_or_404(Tag, pk=pk)\nself.check_object_permissions(request, tag)\nserializer = Ta... | <|body_start_0|>
tag = get_object_or_404(Tag, pk=pk)
self.check_object_permissions(request, tag)
serializer = TagRetrieveUpdateDestroySerializer(tag, many=False)
return Response(data=serializer.data, status=status.HTTP_200_OK)
<|end_body_0|>
<|body_start_1|>
tag = get_object_or_... | TagRetrieveUpdateDestroyAPIView | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TagRetrieveUpdateDestroyAPIView:
def get(self, request, pk=None):
"""Retrieve"""
<|body_0|>
def put(self, request, pk=None):
"""Update"""
<|body_1|>
def delete(self, request, pk=None):
"""Delete"""
<|body_2|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_002689 | 2,948 | permissive | [
{
"docstring": "Retrieve",
"name": "get",
"signature": "def get(self, request, pk=None)"
},
{
"docstring": "Update",
"name": "put",
"signature": "def put(self, request, pk=None)"
},
{
"docstring": "Delete",
"name": "delete",
"signature": "def delete(self, request, pk=None... | 3 | stack_v2_sparse_classes_30k_test_000794 | Implement the Python class `TagRetrieveUpdateDestroyAPIView` described below.
Class description:
Implement the TagRetrieveUpdateDestroyAPIView class.
Method signatures and docstrings:
- def get(self, request, pk=None): Retrieve
- def put(self, request, pk=None): Update
- def delete(self, request, pk=None): Delete | Implement the Python class `TagRetrieveUpdateDestroyAPIView` described below.
Class description:
Implement the TagRetrieveUpdateDestroyAPIView class.
Method signatures and docstrings:
- def get(self, request, pk=None): Retrieve
- def put(self, request, pk=None): Update
- def delete(self, request, pk=None): Delete
<|... | 289318b0333d830c089f4492716c38d409c365ed | <|skeleton|>
class TagRetrieveUpdateDestroyAPIView:
def get(self, request, pk=None):
"""Retrieve"""
<|body_0|>
def put(self, request, pk=None):
"""Update"""
<|body_1|>
def delete(self, request, pk=None):
"""Delete"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TagRetrieveUpdateDestroyAPIView:
def get(self, request, pk=None):
"""Retrieve"""
tag = get_object_or_404(Tag, pk=pk)
self.check_object_permissions(request, tag)
serializer = TagRetrieveUpdateDestroySerializer(tag, many=False)
return Response(data=serializer.data, status... | the_stack_v2_python_sparse | workery/tenant_api/views/tag.py | wahello/workery-django | train | 0 | |
67aa284bdcc63ba77e510d6cb03cdedaa9c3ddc8 | [
"latest = self._latest()\nif latest is None:\n return None\nreturn SparkCloud(SparkSettings().API_URI, latest.access_token)",
"latest = self._latest()\nif latest is None:\n return None\nreturn latest.access_token",
"latest = self._latest()\nif latest is None or latest.expires_soon():\n cloud = SparkClo... | <|body_start_0|>
latest = self._latest()
if latest is None:
return None
return SparkCloud(SparkSettings().API_URI, latest.access_token)
<|end_body_0|>
<|body_start_1|>
latest = self._latest()
if latest is None:
return None
return latest.access_tok... | Custom model manager for `CloudCredentials`. | CloudCredentialsManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CloudCredentialsManager:
"""Custom model manager for `CloudCredentials`."""
def cloud_service(self):
"""Get a cloud service instance initialized with the most current credentials."""
<|body_0|>
def _access_token(self):
"""Get the most recent valid `access_token`,... | stack_v2_sparse_classes_36k_train_002690 | 5,716 | no_license | [
{
"docstring": "Get a cloud service instance initialized with the most current credentials.",
"name": "cloud_service",
"signature": "def cloud_service(self)"
},
{
"docstring": "Get the most recent valid `access_token`, if one isn't found then None is returned.",
"name": "_access_token",
... | 6 | stack_v2_sparse_classes_30k_train_016687 | Implement the Python class `CloudCredentialsManager` described below.
Class description:
Custom model manager for `CloudCredentials`.
Method signatures and docstrings:
- def cloud_service(self): Get a cloud service instance initialized with the most current credentials.
- def _access_token(self): Get the most recent ... | Implement the Python class `CloudCredentialsManager` described below.
Class description:
Custom model manager for `CloudCredentials`.
Method signatures and docstrings:
- def cloud_service(self): Get a cloud service instance initialized with the most current credentials.
- def _access_token(self): Get the most recent ... | c7d792db975b72b9b058298f9309238da05351a9 | <|skeleton|>
class CloudCredentialsManager:
"""Custom model manager for `CloudCredentials`."""
def cloud_service(self):
"""Get a cloud service instance initialized with the most current credentials."""
<|body_0|>
def _access_token(self):
"""Get the most recent valid `access_token`,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CloudCredentialsManager:
"""Custom model manager for `CloudCredentials`."""
def cloud_service(self):
"""Get a cloud service instance initialized with the most current credentials."""
latest = self._latest()
if latest is None:
return None
return SparkCloud(Spark... | the_stack_v2_python_sparse | sparkdoor/apps/spark/models.py | dummerbd/sparkdoor | train | 0 |
a35d3b721d3bda8b25c3dec503597fc393dda2fa | [
"self.Whf = np.random.normal(size=(i + h, h))\nself.Whb = np.random.normal(size=(i + h, h))\nself.Wy = np.random.normal(size=(h + h, o))\nself.bhf = np.zeros((1, h))\nself.bhb = np.zeros((1, h))\nself.by = np.zeros((1, o))",
"concat = np.concatenate((h_prev, x_t), axis=1)\nh_next = np.tanh(concat @ self.Whf + sel... | <|body_start_0|>
self.Whf = np.random.normal(size=(i + h, h))
self.Whb = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(h + h, o))
self.bhf = np.zeros((1, h))
self.bhb = np.zeros((1, h))
self.by = np.zeros((1, o))
<|end_body_0|>
<|body_start_1|>
... | Represents a bidirectional cell of an RNN | BidirectionalCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BidirectionalCell:
"""Represents a bidirectional cell of an RNN"""
def __init__(self, i, h, o):
"""Class constructor"""
<|body_0|>
def forward(self, h_prev, x_t):
"""Calculates the hidden state in the forward direction for one time step. Returns: h_next"""
... | stack_v2_sparse_classes_36k_train_002691 | 1,460 | no_license | [
{
"docstring": "Class constructor",
"name": "__init__",
"signature": "def __init__(self, i, h, o)"
},
{
"docstring": "Calculates the hidden state in the forward direction for one time step. Returns: h_next",
"name": "forward",
"signature": "def forward(self, h_prev, x_t)"
},
{
"d... | 3 | null | Implement the Python class `BidirectionalCell` described below.
Class description:
Represents a bidirectional cell of an RNN
Method signatures and docstrings:
- def __init__(self, i, h, o): Class constructor
- def forward(self, h_prev, x_t): Calculates the hidden state in the forward direction for one time step. Retu... | Implement the Python class `BidirectionalCell` described below.
Class description:
Represents a bidirectional cell of an RNN
Method signatures and docstrings:
- def __init__(self, i, h, o): Class constructor
- def forward(self, h_prev, x_t): Calculates the hidden state in the forward direction for one time step. Retu... | 161e33b23d398d7d01ad0d7740b78dda3f27e787 | <|skeleton|>
class BidirectionalCell:
"""Represents a bidirectional cell of an RNN"""
def __init__(self, i, h, o):
"""Class constructor"""
<|body_0|>
def forward(self, h_prev, x_t):
"""Calculates the hidden state in the forward direction for one time step. Returns: h_next"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BidirectionalCell:
"""Represents a bidirectional cell of an RNN"""
def __init__(self, i, h, o):
"""Class constructor"""
self.Whf = np.random.normal(size=(i + h, h))
self.Whb = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(h + h, o))
self.bhf = ... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/6-bi_backward.py | felipeserna/holbertonschool-machine_learning | train | 0 |
eab857f30da47331032acbe062f2a8aa906a001f | [
"self._memory_raise = memory_raise\nself._kernel = kernel\nself._conv_pixels = conv_pixels\nself._nx, self._ny = np.shape(conv_pixels)\nif compute_pixels is None:\n compute_pixels = np.ones_like(conv_pixels)\n compute_pixels = np.array(compute_pixels, dtype=bool)\nassert np.shape(conv_pixels) == np.shape(comp... | <|body_start_0|>
self._memory_raise = memory_raise
self._kernel = kernel
self._conv_pixels = conv_pixels
self._nx, self._ny = np.shape(conv_pixels)
if compute_pixels is None:
compute_pixels = np.ones_like(conv_pixels)
compute_pixels = np.array(compute_pixe... | class to convolve explicit pixels only the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens | NumbaConvolution | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumbaConvolution:
"""class to convolve explicit pixels only the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens"""
def __init__(self, kernel, conv_pixels, compute_pixels=None, nopython=True, cache=True, parallel=False, memory_raise=True):
""":param kern... | stack_v2_sparse_classes_36k_train_002692 | 11,652 | permissive | [
{
"docstring": ":param kernel: convolution kernel in units of the image pixels provided, odd length per axis :param conv_pixels: bool array same size as data, pixels to be convolved and their light to be blurred :param compute_pixels: bool array of size of image, these pixels (if True) will get blurred light fr... | 4 | null | Implement the Python class `NumbaConvolution` described below.
Class description:
class to convolve explicit pixels only the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens
Method signatures and docstrings:
- def __init__(self, kernel, conv_pixels, compute_pixels=None, nopython=True, ca... | Implement the Python class `NumbaConvolution` described below.
Class description:
class to convolve explicit pixels only the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens
Method signatures and docstrings:
- def __init__(self, kernel, conv_pixels, compute_pixels=None, nopython=True, ca... | 73c9645f26f6983fe7961104075ebe8bf7a4b54c | <|skeleton|>
class NumbaConvolution:
"""class to convolve explicit pixels only the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens"""
def __init__(self, kernel, conv_pixels, compute_pixels=None, nopython=True, cache=True, parallel=False, memory_raise=True):
""":param kern... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumbaConvolution:
"""class to convolve explicit pixels only the convolution is inspired by pyautolens: https://github.com/Jammy2211/PyAutoLens"""
def __init__(self, kernel, conv_pixels, compute_pixels=None, nopython=True, cache=True, parallel=False, memory_raise=True):
""":param kernel: convoluti... | the_stack_v2_python_sparse | lenstronomy/ImSim/Numerics/numba_convolution.py | lenstronomy/lenstronomy | train | 41 |
aa9a8f904c78f4d42eb038f8faccabf856f48b0a | [
"with tf.gfile.GFile(model_path, 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\ngraph = tf.Graph()\nwith graph.as_default():\n tf.import_graph_def(graph_def, name='import')\nself.input_image = graph.get_tensor_by_name('import/image_tensor:0')\nself.output_ops = [graph.get_te... | <|body_start_0|>
with tf.gfile.GFile(model_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.Graph()
with graph.as_default():
tf.import_graph_def(graph_def, name='import')
self.input_image = graph.get_tensor_by_... | FaceDetector | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FaceDetector:
def __init__(self, model_path):
"""Arguments: model_path: a string, path to a pb file."""
<|body_0|>
def __call__(self, image, score_threshold=0.5):
"""Detect faces. Arguments: image: a numpy uint8 array with shape [height, width, 3], that represents a ... | stack_v2_sparse_classes_36k_train_002693 | 2,412 | permissive | [
{
"docstring": "Arguments: model_path: a string, path to a pb file.",
"name": "__init__",
"signature": "def __init__(self, model_path)"
},
{
"docstring": "Detect faces. Arguments: image: a numpy uint8 array with shape [height, width, 3], that represents a RGB image. score_threshold: a float numb... | 2 | stack_v2_sparse_classes_30k_train_003227 | Implement the Python class `FaceDetector` described below.
Class description:
Implement the FaceDetector class.
Method signatures and docstrings:
- def __init__(self, model_path): Arguments: model_path: a string, path to a pb file.
- def __call__(self, image, score_threshold=0.5): Detect faces. Arguments: image: a nu... | Implement the Python class `FaceDetector` described below.
Class description:
Implement the FaceDetector class.
Method signatures and docstrings:
- def __init__(self, model_path): Arguments: model_path: a string, path to a pb file.
- def __call__(self, image, score_threshold=0.5): Detect faces. Arguments: image: a nu... | 8f701ea68515927163d5904d58262d1b480a9a97 | <|skeleton|>
class FaceDetector:
def __init__(self, model_path):
"""Arguments: model_path: a string, path to a pb file."""
<|body_0|>
def __call__(self, image, score_threshold=0.5):
"""Detect faces. Arguments: image: a numpy uint8 array with shape [height, width, 3], that represents a ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FaceDetector:
def __init__(self, model_path):
"""Arguments: model_path: a string, path to a pb file."""
with tf.gfile.GFile(model_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.Graph()
with graph.as_default():
... | the_stack_v2_python_sparse | faceR/alignment/face_boxes.py | hritools/faceR | train | 0 | |
7b11f370668ec6220db220bb569eae5578e71243 | [
"self.sigma = sigma\nself.use_errors = use_errors\nself.use_widths = use_widths",
"sigma = config('prior_passer', 'sigma')\nuse_errors = config('prior_passer', 'use_errors')\nuse_widths = config('prior_passer', 'use_widths')\nreturn PriorPasser(sigma=sigma, use_errors=use_errors, use_widths=use_widths)"
] | <|body_start_0|>
self.sigma = sigma
self.use_errors = use_errors
self.use_widths = use_widths
<|end_body_0|>
<|body_start_1|>
sigma = config('prior_passer', 'sigma')
use_errors = config('prior_passer', 'use_errors')
use_widths = config('prior_passer', 'use_widths')
... | PriorPasser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PriorPasser:
def __init__(self, sigma, use_errors, use_widths):
"""Class to package the API for prior passing. This class contains the parameters that controls how priors are passed from the results of one non-linear search to the next. Using the Phase API, we can pass priors from the re... | stack_v2_sparse_classes_36k_train_002694 | 29,292 | permissive | [
{
"docstring": "Class to package the API for prior passing. This class contains the parameters that controls how priors are passed from the results of one non-linear search to the next. Using the Phase API, we can pass priors from the result of one search to another follows: model_component.parameter = search1_... | 2 | null | Implement the Python class `PriorPasser` described below.
Class description:
Implement the PriorPasser class.
Method signatures and docstrings:
- def __init__(self, sigma, use_errors, use_widths): Class to package the API for prior passing. This class contains the parameters that controls how priors are passed from t... | Implement the Python class `PriorPasser` described below.
Class description:
Implement the PriorPasser class.
Method signatures and docstrings:
- def __init__(self, sigma, use_errors, use_widths): Class to package the API for prior passing. This class contains the parameters that controls how priors are passed from t... | 324007a6bbda32baf94f09918e0aef04fda0c7d0 | <|skeleton|>
class PriorPasser:
def __init__(self, sigma, use_errors, use_widths):
"""Class to package the API for prior passing. This class contains the parameters that controls how priors are passed from the results of one non-linear search to the next. Using the Phase API, we can pass priors from the re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PriorPasser:
def __init__(self, sigma, use_errors, use_widths):
"""Class to package the API for prior passing. This class contains the parameters that controls how priors are passed from the results of one non-linear search to the next. Using the Phase API, we can pass priors from the result of one se... | the_stack_v2_python_sparse | autofit/non_linear/abstract_search.py | philastrophist/PyAutoFit | train | 0 | |
608bb69fed018b3543f702428b642d34a23c2328 | [
"if not os.path.isdir(path + 'birdvox_dcase_20k'):\n print('Creating birdvox_dcase_20k Directory')\n os.mkdir(path + 'birdvox_dcase_20k')\nbase = 'https://zenodo.org/record/1208080/files/'\nfilename = 'BirdVox-DCASE-20k.zip'\nif not os.path.exists(path + 'birdvox_dcase_20k/' + filename):\n url = base + fil... | <|body_start_0|>
if not os.path.isdir(path + 'birdvox_dcase_20k'):
print('Creating birdvox_dcase_20k Directory')
os.mkdir(path + 'birdvox_dcase_20k')
base = 'https://zenodo.org/record/1208080/files/'
filename = 'BirdVox-DCASE-20k.zip'
if not os.path.exists(path + ... | Binary bird detection classification Dataset is 16.5Go compressed. BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips Version 2.0, March 2018. Created By Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3). (1): Cornell Lab of O... | birdvox_dcase_20k | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class birdvox_dcase_20k:
"""Binary bird detection classification Dataset is 16.5Go compressed. BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips Version 2.0, March 2018. Created By Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and J... | stack_v2_sparse_classes_36k_train_002695 | 6,924 | permissive | [
{
"docstring": "Download the Birdvox dataset and store the result into the given path Parameters ---------- path: str the path where the downloaded files will be stored. If the directory does not exist, it is created.",
"name": "download",
"signature": "def download(path)"
},
{
"docstring": "Par... | 2 | stack_v2_sparse_classes_30k_train_015024 | Implement the Python class `birdvox_dcase_20k` described below.
Class description:
Binary bird detection classification Dataset is 16.5Go compressed. BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips Version 2.0, March 2018. Created By Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew F... | Implement the Python class `birdvox_dcase_20k` described below.
Class description:
Binary bird detection classification Dataset is 16.5Go compressed. BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips Version 2.0, March 2018. Created By Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew F... | d8778c2eb3254b478cef4f45d934bf921e695619 | <|skeleton|>
class birdvox_dcase_20k:
"""Binary bird detection classification Dataset is 16.5Go compressed. BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips Version 2.0, March 2018. Created By Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and J... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class birdvox_dcase_20k:
"""Binary bird detection classification Dataset is 16.5Go compressed. BirdVox-DCASE-20k: a dataset for bird audio detection in 10-second clips Version 2.0, March 2018. Created By Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bel... | the_stack_v2_python_sparse | symjax/data/birdvox_dcase_20k.py | SymJAX/SymJAX | train | 52 |
a6bd5408f79b2c52f1f1677df3da3f21e634fc84 | [
"if k == 1 or not head:\n return head\nfull_cycle = False\ncount = 1\ncur = head\ndummy = new_head = ListNode(0)\nwhile cur:\n next_head = cur.next\n if count % k != 0:\n full_cycle = False\n cur = cur.next\n else:\n full_cycle = True\n cur.next = None\n new_head.next ... | <|body_start_0|>
if k == 1 or not head:
return head
full_cycle = False
count = 1
cur = head
dummy = new_head = ListNode(0)
while cur:
next_head = cur.next
if count % k != 0:
full_cycle = False
cur = cur.n... | Solution_B | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution_B:
def reverseKGroup(self, head: ListNode, k: int) -> ListNode:
"""Non-recursive, using counter cycling Slower than recursive"""
<|body_0|>
def reverseNodes(self, head: ListNode) -> ListNode:
"""Helper for both Solution A and Solution B 参见Leetcode LC206, rev... | stack_v2_sparse_classes_36k_train_002696 | 4,335 | permissive | [
{
"docstring": "Non-recursive, using counter cycling Slower than recursive",
"name": "reverseKGroup",
"signature": "def reverseKGroup(self, head: ListNode, k: int) -> ListNode"
},
{
"docstring": "Helper for both Solution A and Solution B 参见Leetcode LC206, reverse the whole linked-list",
"nam... | 2 | null | Implement the Python class `Solution_B` described below.
Class description:
Implement the Solution_B class.
Method signatures and docstrings:
- def reverseKGroup(self, head: ListNode, k: int) -> ListNode: Non-recursive, using counter cycling Slower than recursive
- def reverseNodes(self, head: ListNode) -> ListNode: ... | Implement the Python class `Solution_B` described below.
Class description:
Implement the Solution_B class.
Method signatures and docstrings:
- def reverseKGroup(self, head: ListNode, k: int) -> ListNode: Non-recursive, using counter cycling Slower than recursive
- def reverseNodes(self, head: ListNode) -> ListNode: ... | 143422321cbc3715ca08f6c3af8f960a55887ced | <|skeleton|>
class Solution_B:
def reverseKGroup(self, head: ListNode, k: int) -> ListNode:
"""Non-recursive, using counter cycling Slower than recursive"""
<|body_0|>
def reverseNodes(self, head: ListNode) -> ListNode:
"""Helper for both Solution A and Solution B 参见Leetcode LC206, rev... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution_B:
def reverseKGroup(self, head: ListNode, k: int) -> ListNode:
"""Non-recursive, using counter cycling Slower than recursive"""
if k == 1 or not head:
return head
full_cycle = False
count = 1
cur = head
dummy = new_head = ListNode(0)
... | the_stack_v2_python_sparse | LeetCode/LC025_reverse_nodes_in_k_group.py | jxie0755/Learning_Python | train | 0 | |
0fbb6316cf1ef95d3d0799e23cca30eb4cdd2e5e | [
"s_counter = Counter(s)\nfor i, char in enumerate(s):\n if s_counter.get(char) == 1:\n return i\nreturn -1",
"checked = set()\nfor i, char in enumerate(s):\n if char not in checked and s.count(char, i) == 1:\n return i\n checked.add(char)\nreturn -1"
] | <|body_start_0|>
s_counter = Counter(s)
for i, char in enumerate(s):
if s_counter.get(char) == 1:
return i
return -1
<|end_body_0|>
<|body_start_1|>
checked = set()
for i, char in enumerate(s):
if char not in checked and s.count(char, i) =... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def firstUniqChar(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def firstUniqChar(self, s):
"""Time: N^2 Space: N :type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
s_counter = Counter(s)
for i, char in ... | stack_v2_sparse_classes_36k_train_002697 | 847 | permissive | [
{
"docstring": ":type s: str :rtype: int",
"name": "firstUniqChar",
"signature": "def firstUniqChar(self, s)"
},
{
"docstring": "Time: N^2 Space: N :type s: str :rtype: int",
"name": "firstUniqChar",
"signature": "def firstUniqChar(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstUniqChar(self, s): :type s: str :rtype: int
- def firstUniqChar(self, s): Time: N^2 Space: N :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstUniqChar(self, s): :type s: str :rtype: int
- def firstUniqChar(self, s): Time: N^2 Space: N :type s: str :rtype: int
<|skeleton|>
class Solution:
def firstUniqCha... | d2ffcccede5d1543aea48f18a39cdbd3d83e3ed8 | <|skeleton|>
class Solution:
def firstUniqChar(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def firstUniqChar(self, s):
"""Time: N^2 Space: N :type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def firstUniqChar(self, s):
""":type s: str :rtype: int"""
s_counter = Counter(s)
for i, char in enumerate(s):
if s_counter.get(char) == 1:
return i
return -1
def firstUniqChar(self, s):
"""Time: N^2 Space: N :type s: str :rtyp... | the_stack_v2_python_sparse | strings/first_unique_char.py | kandarpck/leetcode | train | 0 | |
5a55163857061eb0ad0b238870270e86ccdeb487 | [
"super().__init__(*args, **kwargs)\nself._all_traces = None\nself._syscall_information_content = None",
"if self._all_traces is not all_traces or self._syscall_information_content is None:\n logger.info('Computing document frequencies.')\n self._all_traces = all_traces\n counter = Counter(chain.from_iter... | <|body_start_0|>
super().__init__(*args, **kwargs)
self._all_traces = None
self._syscall_information_content = None
<|end_body_0|>
<|body_start_1|>
if self._all_traces is not all_traces or self._syscall_information_content is None:
logger.info('Computing document frequencies... | Scoring method using information content. The final similarity score for two straces is the sum of information content for each matching syscall found in both traces. Syscall information content is computed using the standard definition where P is the probability of finding that syscall in any given strace (from all_tr... | NormalizedInformationContent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NormalizedInformationContent:
"""Scoring method using information content. The final similarity score for two straces is the sum of information content for each matching syscall found in both traces. Syscall information content is computed using the standard definition where P is the probability ... | stack_v2_sparse_classes_36k_train_002698 | 34,744 | no_license | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Compute and cache normalized information content.",
"name": "_information_content",
"signature": "def _information_content(self, all_traces: Set[Strace]) -> Dict[Syscall, flo... | 3 | stack_v2_sparse_classes_30k_train_018802 | Implement the Python class `NormalizedInformationContent` described below.
Class description:
Scoring method using information content. The final similarity score for two straces is the sum of information content for each matching syscall found in both traces. Syscall information content is computed using the standard... | Implement the Python class `NormalizedInformationContent` described below.
Class description:
Scoring method using information content. The final similarity score for two straces is the sum of information content for each matching syscall found in both traces. Syscall information content is computed using the standard... | 96ca42d65a91b5505316831318863f1963ff7c23 | <|skeleton|>
class NormalizedInformationContent:
"""Scoring method using information content. The final similarity score for two straces is the sum of information content for each matching syscall found in both traces. Syscall information content is computed using the standard definition where P is the probability ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NormalizedInformationContent:
"""Scoring method using information content. The final similarity score for two straces is the sum of information content for each matching syscall found in both traces. Syscall information content is computed using the standard definition where P is the probability of finding th... | the_stack_v2_python_sparse | lib/strace/comparison/scoring.py | config-migration/dozer | train | 2 |
cb4730bb7558a3616d5ca131cef9cc014a13ca06 | [
"self.auth_url = auth_url\nself.domain_id = domain_id\nself.domain_name = domain_name\nself.project_id = project_id\nself.project_name = project_name\nself.project_domain_id = project_domain_id\nself.project_domain_name = project_domain_name",
"scope_headers = {}\nif self.project_id:\n scope_headers['X-Project... | <|body_start_0|>
self.auth_url = auth_url
self.domain_id = domain_id
self.domain_name = domain_name
self.project_id = project_id
self.project_name = project_name
self.project_domain_id = project_domain_id
self.project_domain_name = project_domain_name
<|end_body_0... | A plugin for authenticating with Tokenless Auth. This is for Tokenless Authentication. Scoped information like domain name and project ID will be passed in the headers and token validation request will be authenticated based on the provided HTTPS certificate along with the scope information. | TokenlessAuth | [
"Apache-2.0",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TokenlessAuth:
"""A plugin for authenticating with Tokenless Auth. This is for Tokenless Authentication. Scoped information like domain name and project ID will be passed in the headers and token validation request will be authenticated based on the provided HTTPS certificate along with the scope... | stack_v2_sparse_classes_36k_train_002699 | 4,706 | permissive | [
{
"docstring": "A init method for TokenlessAuth. :param string auth_url: Identity service endpoint for authentication. The URL must include a version or any request will result in a 404 NotFound error. :param string domain_id: Domain ID for domain scoping. :param string domain_name: Domain name for domain scopi... | 3 | stack_v2_sparse_classes_30k_train_018522 | Implement the Python class `TokenlessAuth` described below.
Class description:
A plugin for authenticating with Tokenless Auth. This is for Tokenless Authentication. Scoped information like domain name and project ID will be passed in the headers and token validation request will be authenticated based on the provided... | Implement the Python class `TokenlessAuth` described below.
Class description:
A plugin for authenticating with Tokenless Auth. This is for Tokenless Authentication. Scoped information like domain name and project ID will be passed in the headers and token validation request will be authenticated based on the provided... | e6f3999c6f2f846e3dda505343166ab8c8346c2a | <|skeleton|>
class TokenlessAuth:
"""A plugin for authenticating with Tokenless Auth. This is for Tokenless Authentication. Scoped information like domain name and project ID will be passed in the headers and token validation request will be authenticated based on the provided HTTPS certificate along with the scope... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TokenlessAuth:
"""A plugin for authenticating with Tokenless Auth. This is for Tokenless Authentication. Scoped information like domain name and project ID will be passed in the headers and token validation request will be authenticated based on the provided HTTPS certificate along with the scope information.... | the_stack_v2_python_sparse | keystoneauth1/identity/v3/tokenless_auth.py | openstack/keystoneauth | train | 51 |
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