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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c0208317c3cd2e932e1a36395c7ead42cc35ab08 | [
"if instance_id is None:\n instance_id = id(self)\nsuper().__init__('%s-%s' % (name, instance_id))\nself._proc = _BackgroundProcess(name, self)",
"super().start(rusage)\nwith _bg_metrics_lock:\n _background_processes_active_since_last_scrape.add(self._proc)",
"super().__exit__(type, value, traceback)\nwit... | <|body_start_0|>
if instance_id is None:
instance_id = id(self)
super().__init__('%s-%s' % (name, instance_id))
self._proc = _BackgroundProcess(name, self)
<|end_body_0|>
<|body_start_1|>
super().start(rusage)
with _bg_metrics_lock:
_background_processes_... | A logging context that tracks in flight metrics for background processes. | BackgroundProcessLoggingContext | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BackgroundProcessLoggingContext:
"""A logging context that tracks in flight metrics for background processes."""
def __init__(self, name: str, instance_id: Optional[Union[int, str]]=None):
"""Args: name: The name of the background process. Each distinct `name` gets a separate prometh... | stack_v2_sparse_classes_36k_train_032100 | 12,220 | permissive | [
{
"docstring": "Args: name: The name of the background process. Each distinct `name` gets a separate prometheus time series. instance_id: an identifer to add to `name` to distinguish this instance of the named background process in the logs. If this is `None`, one is made up based on id(self).",
"name": "__... | 3 | null | Implement the Python class `BackgroundProcessLoggingContext` described below.
Class description:
A logging context that tracks in flight metrics for background processes.
Method signatures and docstrings:
- def __init__(self, name: str, instance_id: Optional[Union[int, str]]=None): Args: name: The name of the backgro... | Implement the Python class `BackgroundProcessLoggingContext` described below.
Class description:
A logging context that tracks in flight metrics for background processes.
Method signatures and docstrings:
- def __init__(self, name: str, instance_id: Optional[Union[int, str]]=None): Args: name: The name of the backgro... | d35bed8369514fe727b4fe1afb68f48cc8b2655a | <|skeleton|>
class BackgroundProcessLoggingContext:
"""A logging context that tracks in flight metrics for background processes."""
def __init__(self, name: str, instance_id: Optional[Union[int, str]]=None):
"""Args: name: The name of the background process. Each distinct `name` gets a separate prometh... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BackgroundProcessLoggingContext:
"""A logging context that tracks in flight metrics for background processes."""
def __init__(self, name: str, instance_id: Optional[Union[int, str]]=None):
"""Args: name: The name of the background process. Each distinct `name` gets a separate prometheus time seri... | the_stack_v2_python_sparse | synapse/metrics/background_process_metrics.py | matrix-org/synapse | train | 12,215 |
0eaff126a683866b8a16b515778bdc1fe2b8e949 | [
"np.random.seed(seed)\nself.scale = scale\nif var is None:\n self.var = 10 ** (np.random.randn(ndim) * 1.5)\nelse:\n self.var = var\nif mu is None:\n self.mu = scipy.stats.norm(loc=0, scale=self.scale).rvs(ndim)\nelse:\n self.mu = mu",
"if np.all(x < 500) and np.all(x > -500):\n return scipy.stats.... | <|body_start_0|>
np.random.seed(seed)
self.scale = scale
if var is None:
self.var = 10 ** (np.random.randn(ndim) * 1.5)
else:
self.var = var
if mu is None:
self.mu = scipy.stats.norm(loc=0, scale=self.scale).rvs(ndim)
else:
... | N-Dimensional Gaussian distribution with mu ~ Normal(0, 10) var ~ LogNormal(0, 1.5) Prior on mean is U(-500, 500) | MultidimensionalGaussianPosterior | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultidimensionalGaussianPosterior:
"""N-Dimensional Gaussian distribution with mu ~ Normal(0, 10) var ~ LogNormal(0, 1.5) Prior on mean is U(-500, 500)"""
def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None):
"""_summary_ Parameters ---------- ndim : int, optional _desc... | stack_v2_sparse_classes_36k_train_032101 | 14,651 | permissive | [
{
"docstring": "_summary_ Parameters ---------- ndim : int, optional _description_, by default 2 seed : int, optional _description_, by default 12345 scale : int, optional _description_, by default 10",
"name": "__init__",
"signature": "def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None)"... | 2 | stack_v2_sparse_classes_30k_train_009495 | Implement the Python class `MultidimensionalGaussianPosterior` described below.
Class description:
N-Dimensional Gaussian distribution with mu ~ Normal(0, 10) var ~ LogNormal(0, 1.5) Prior on mean is U(-500, 500)
Method signatures and docstrings:
- def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None): _... | Implement the Python class `MultidimensionalGaussianPosterior` described below.
Class description:
N-Dimensional Gaussian distribution with mu ~ Normal(0, 10) var ~ LogNormal(0, 1.5) Prior on mean is U(-500, 500)
Method signatures and docstrings:
- def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None): _... | 5df233ea90aba16611d29c6a4b7717eb08ae7e09 | <|skeleton|>
class MultidimensionalGaussianPosterior:
"""N-Dimensional Gaussian distribution with mu ~ Normal(0, 10) var ~ LogNormal(0, 1.5) Prior on mean is U(-500, 500)"""
def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None):
"""_summary_ Parameters ---------- ndim : int, optional _desc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultidimensionalGaussianPosterior:
"""N-Dimensional Gaussian distribution with mu ~ Normal(0, 10) var ~ LogNormal(0, 1.5) Prior on mean is U(-500, 500)"""
def __init__(self, ndim=2, seed=12345, scale=3, mu=None, var=None):
"""_summary_ Parameters ---------- ndim : int, optional _description_, by ... | the_stack_v2_python_sparse | manim_ml/diffusion/mcmc.py | helblazer811/ManimML | train | 1,339 |
5cb10a7344a72eb9ab176a1bc5e4665e78299f14 | [
"dao = self.repo.get(gsid)\ndao.enabled = True\nself.repo.persist_dao(dao)",
"dao = self.repo.get(gsid)\ndao.enabled = False\nself.repo.persist_dao(dao)",
"assert kind == 'totp', 'HOTP support is deprecated.'\nif self.finder.has_active_otp(kind, gsid) and (not force):\n raise self.OneTimePasswordActive()\nse... | <|body_start_0|>
dao = self.repo.get(gsid)
dao.enabled = True
self.repo.persist_dao(dao)
<|end_body_0|>
<|body_start_1|>
dao = self.repo.get(gsid)
dao.enabled = False
self.repo.persist_dao(dao)
<|end_body_1|>
<|body_start_2|>
assert kind == 'totp', 'HOTP support... | Exposes an API to generate and verify One-Time Passwords (OTPs). | OneTimePasswordService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OneTimePasswordService:
"""Exposes an API to generate and verify One-Time Passwords (OTPs)."""
def enable(self, gsid):
"""Disables the TOTP for the **Subject** identified by `gsid`."""
<|body_0|>
def disable(self, gsid):
"""Disables the TOTP for the **Subject** i... | stack_v2_sparse_classes_36k_train_032102 | 1,556 | no_license | [
{
"docstring": "Disables the TOTP for the **Subject** identified by `gsid`.",
"name": "enable",
"signature": "def enable(self, gsid)"
},
{
"docstring": "Disables the TOTP for the **Subject** identified by `gsid`.",
"name": "disable",
"signature": "def disable(self, gsid)"
},
{
"d... | 3 | stack_v2_sparse_classes_30k_train_014153 | Implement the Python class `OneTimePasswordService` described below.
Class description:
Exposes an API to generate and verify One-Time Passwords (OTPs).
Method signatures and docstrings:
- def enable(self, gsid): Disables the TOTP for the **Subject** identified by `gsid`.
- def disable(self, gsid): Disables the TOTP ... | Implement the Python class `OneTimePasswordService` described below.
Class description:
Exposes an API to generate and verify One-Time Passwords (OTPs).
Method signatures and docstrings:
- def enable(self, gsid): Disables the TOTP for the **Subject** identified by `gsid`.
- def disable(self, gsid): Disables the TOTP ... | 6c97ae544444e90753e375ecc68f25534d97764a | <|skeleton|>
class OneTimePasswordService:
"""Exposes an API to generate and verify One-Time Passwords (OTPs)."""
def enable(self, gsid):
"""Disables the TOTP for the **Subject** identified by `gsid`."""
<|body_0|>
def disable(self, gsid):
"""Disables the TOTP for the **Subject** i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OneTimePasswordService:
"""Exposes an API to generate and verify One-Time Passwords (OTPs)."""
def enable(self, gsid):
"""Disables the TOTP for the **Subject** identified by `gsid`."""
dao = self.repo.get(gsid)
dao.enabled = True
self.repo.persist_dao(dao)
def disable... | the_stack_v2_python_sparse | safi/app/services/onetimepassword/impl.py | wizardsofindustry/quantum-safi | train | 0 |
ffeffc1a88b7c63ca777c6c62936f3cd1e974fc0 | [
"self._num_classes = num_classes\nself._mask_target_size = mask_target_size\nself._num_convs = num_convs\nself._num_filters = num_filters\nif use_separable_conv:\n self._conv2d_op = functools.partial(tf.keras.layers.SeparableConv2D, depth_multiplier=1, bias_initializer=tf.zeros_initializer())\nelse:\n self._c... | <|body_start_0|>
self._num_classes = num_classes
self._mask_target_size = mask_target_size
self._num_convs = num_convs
self._num_filters = num_filters
if use_separable_conv:
self._conv2d_op = functools.partial(tf.keras.layers.SeparableConv2D, depth_multiplier=1, bias_... | Mask R-CNN head. | MaskrcnnHead | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaskrcnnHead:
"""Mask R-CNN head."""
def __init__(self, num_classes, mask_target_size, num_convs=4, num_filters=256, use_separable_conv=False, activation='relu', use_batch_norm=True, norm_activation=nn_ops.norm_activation_builder(activation='relu')):
"""Initialize params to build Fas... | stack_v2_sparse_classes_36k_train_032103 | 45,524 | permissive | [
{
"docstring": "Initialize params to build Fast R-CNN head. Args: num_classes: a integer for the number of classes. mask_target_size: a integer that is the resolution of masks. num_convs: `int` number that represents the number of the intermediate conv layers before the prediction. num_filters: `int` number tha... | 2 | stack_v2_sparse_classes_30k_train_007277 | Implement the Python class `MaskrcnnHead` described below.
Class description:
Mask R-CNN head.
Method signatures and docstrings:
- def __init__(self, num_classes, mask_target_size, num_convs=4, num_filters=256, use_separable_conv=False, activation='relu', use_batch_norm=True, norm_activation=nn_ops.norm_activation_bu... | Implement the Python class `MaskrcnnHead` described below.
Class description:
Mask R-CNN head.
Method signatures and docstrings:
- def __init__(self, num_classes, mask_target_size, num_convs=4, num_filters=256, use_separable_conv=False, activation='relu', use_batch_norm=True, norm_activation=nn_ops.norm_activation_bu... | 965cc3eef54a3a173a2347fe0059a35f1e5f2496 | <|skeleton|>
class MaskrcnnHead:
"""Mask R-CNN head."""
def __init__(self, num_classes, mask_target_size, num_convs=4, num_filters=256, use_separable_conv=False, activation='relu', use_batch_norm=True, norm_activation=nn_ops.norm_activation_builder(activation='relu')):
"""Initialize params to build Fas... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MaskrcnnHead:
"""Mask R-CNN head."""
def __init__(self, num_classes, mask_target_size, num_convs=4, num_filters=256, use_separable_conv=False, activation='relu', use_batch_norm=True, norm_activation=nn_ops.norm_activation_builder(activation='relu')):
"""Initialize params to build Fast R-CNN head.... | the_stack_v2_python_sparse | official/vision/detection/modeling/architecture/heads.py | ayushmankumar7/models | train | 4 |
c5a33049dbadecbe7038f6ad4e9d102e76117904 | [
"request = kwargs['request']\nif request_from_master(request):\n config.master_contacted()\ntry:\n current_assignments = config['current_assignments'].itervalues\nexcept AttributeError:\n current_assignments = config['current_assignments'].values\ntasks = []\nfor assignment in current_assignments():\n t... | <|body_start_0|>
request = kwargs['request']
if request_from_master(request):
config.master_contacted()
try:
current_assignments = config['current_assignments'].itervalues
except AttributeError:
current_assignments = config['current_assignments'].value... | Tasks | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tasks:
def get(self, **kwargs):
"""Returns all tasks which are currently being processed locally by the agent. .. http:get:: /api/v1/tasks/ HTTP/1.1 **Request** .. sourcecode:: http GET /api/v1/tasks/ HTTP/1.1 **Response** .. sourcecode:: http HTTP/1.1 200 OK Content-Type: application/js... | stack_v2_sparse_classes_36k_train_032104 | 5,070 | permissive | [
{
"docstring": "Returns all tasks which are currently being processed locally by the agent. .. http:get:: /api/v1/tasks/ HTTP/1.1 **Request** .. sourcecode:: http GET /api/v1/tasks/ HTTP/1.1 **Response** .. sourcecode:: http HTTP/1.1 200 OK Content-Type: application/json [{ \"id\": \"732c1ef0-9488-4914-adef-c29... | 2 | stack_v2_sparse_classes_30k_train_002951 | Implement the Python class `Tasks` described below.
Class description:
Implement the Tasks class.
Method signatures and docstrings:
- def get(self, **kwargs): Returns all tasks which are currently being processed locally by the agent. .. http:get:: /api/v1/tasks/ HTTP/1.1 **Request** .. sourcecode:: http GET /api/v1/... | Implement the Python class `Tasks` described below.
Class description:
Implement the Tasks class.
Method signatures and docstrings:
- def get(self, **kwargs): Returns all tasks which are currently being processed locally by the agent. .. http:get:: /api/v1/tasks/ HTTP/1.1 **Request** .. sourcecode:: http GET /api/v1/... | aa87504ab8679db0ed8da4818729b8e5fc1c0ecb | <|skeleton|>
class Tasks:
def get(self, **kwargs):
"""Returns all tasks which are currently being processed locally by the agent. .. http:get:: /api/v1/tasks/ HTTP/1.1 **Request** .. sourcecode:: http GET /api/v1/tasks/ HTTP/1.1 **Response** .. sourcecode:: http HTTP/1.1 200 OK Content-Type: application/js... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tasks:
def get(self, **kwargs):
"""Returns all tasks which are currently being processed locally by the agent. .. http:get:: /api/v1/tasks/ HTTP/1.1 **Request** .. sourcecode:: http GET /api/v1/tasks/ HTTP/1.1 **Response** .. sourcecode:: http HTTP/1.1 200 OK Content-Type: application/json [{ "id": "7... | the_stack_v2_python_sparse | pyfarm/agent/http/api/tasks.py | pyfarm/pyfarm-agent | train | 1 | |
ed41bfc5515008d62eee2b4e11ec55f39c8710c4 | [
"query = request.GET.get('q')\nsort = request.GET.get('sort', 'name')\nasearch = Asignacion.objects.filter(server=kwargs['id']).first()\nform = AsignacionForm(instance=asearch)\nlist_asignacion = Asignacion.objects.filter(server=kwargs['id'])\nlist_server = Server.objects.filter(id=kwargs['id'])\nlist_client = Asig... | <|body_start_0|>
query = request.GET.get('q')
sort = request.GET.get('sort', 'name')
asearch = Asignacion.objects.filter(server=kwargs['id']).first()
form = AsignacionForm(instance=asearch)
list_asignacion = Asignacion.objects.filter(server=kwargs['id'])
list_server = Ser... | Clase para ver los detalles del servidor | ServerDetailView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServerDetailView:
"""Clase para ver los detalles del servidor"""
def get(self, request, *args, **kwargs):
"""Método get"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Método post"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
query ... | stack_v2_sparse_classes_36k_train_032105 | 22,221 | no_license | [
{
"docstring": "Método get",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "Método post",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016538 | Implement the Python class `ServerDetailView` described below.
Class description:
Clase para ver los detalles del servidor
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Método get
- def post(self, request, *args, **kwargs): Método post | Implement the Python class `ServerDetailView` described below.
Class description:
Clase para ver los detalles del servidor
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Método get
- def post(self, request, *args, **kwargs): Método post
<|skeleton|>
class ServerDetailView:
"""Clase ... | e28e2d968372609ad396c42fb572a00c2410a117 | <|skeleton|>
class ServerDetailView:
"""Clase para ver los detalles del servidor"""
def get(self, request, *args, **kwargs):
"""Método get"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Método post"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ServerDetailView:
"""Clase para ver los detalles del servidor"""
def get(self, request, *args, **kwargs):
"""Método get"""
query = request.GET.get('q')
sort = request.GET.get('sort', 'name')
asearch = Asignacion.objects.filter(server=kwargs['id']).first()
form = As... | the_stack_v2_python_sparse | list/views.py | damaos/server_list2 | train | 0 |
69b3a9aa9741fa2569400bf19628514633465aab | [
"super().__init__()\nself.cost_classes = cost_classes\nself.cost_segments = cost_segments\nself.cost_diou = cost_diou\nassert cost_classes != 0 or cost_segments != 0 or cost_diou != 0, 'all costs cant be 0'",
"bs, num_queries = outputs['classes'].shape[:2]\nout_classes = outputs['classes'].flatten(0, 1).softmax(-... | <|body_start_0|>
super().__init__()
self.cost_classes = cost_classes
self.cost_segments = cost_segments
self.cost_diou = cost_diou
assert cost_classes != 0 or cost_segments != 0 or cost_diou != 0, 'all costs cant be 0'
<|end_body_0|>
<|body_start_1|>
bs, num_queries = ou... | This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (... | HungarianMatcher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HungarianMatcher:
"""This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best pr... | stack_v2_sparse_classes_36k_train_032106 | 4,544 | no_license | [
{
"docstring": "Creates the matcher Params: cost_classes: This is the relative weight of the classification error in the matching cost cost_segments: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost cost_diou: This is the relative weight of the giou loss of the bo... | 2 | stack_v2_sparse_classes_30k_train_018849 | Implement the Python class `HungarianMatcher` described below.
Class description:
This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case,... | Implement the Python class `HungarianMatcher` described below.
Class description:
This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case,... | 11337eab8ff3f5c79b8e7aa5ea7bdfc704785b38 | <|skeleton|>
class HungarianMatcher:
"""This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HungarianMatcher:
"""This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, wh... | the_stack_v2_python_sparse | src/models/detr/matcher.py | BianbBb/EventDetection | train | 0 |
c49036d4a5743be390154fa3ed40305ca6d8947d | [
"event = {'current_location': (CourseURL('http://a/b/'), '2013-11-10 06:43:41'), 'time': '2013-12-10 06:44:00', 'page': ''}\nexpected_event = {'current_location': (CourseURL('http://a/b/'), '2013-11-10 06:43:41'), 'time': '2013-12-10 06:44:00', 'page': '', 'inherited': ''}\nno_url(event)\nself.assertEqual(expected_... | <|body_start_0|>
event = {'current_location': (CourseURL('http://a/b/'), '2013-11-10 06:43:41'), 'time': '2013-12-10 06:44:00', 'page': ''}
expected_event = {'current_location': (CourseURL('http://a/b/'), '2013-11-10 06:43:41'), 'time': '2013-12-10 06:44:00', 'page': '', 'inherited': ''}
no_url(... | Tester for the inheritloc functions | InheritLocTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InheritLocTest:
"""Tester for the inheritloc functions"""
def test_no_url_no_inheritance(self):
"""Test the no_url function with an event that should not inherit a url."""
<|body_0|>
def test_no_url_yes_inheritance(self):
"""Test the no_url function with an event... | stack_v2_sparse_classes_36k_train_032107 | 3,026 | permissive | [
{
"docstring": "Test the no_url function with an event that should not inherit a url.",
"name": "test_no_url_no_inheritance",
"signature": "def test_no_url_no_inheritance(self)"
},
{
"docstring": "Test the no_url function with an event that should inherit a url.",
"name": "test_no_url_yes_in... | 3 | stack_v2_sparse_classes_30k_train_019501 | Implement the Python class `InheritLocTest` described below.
Class description:
Tester for the inheritloc functions
Method signatures and docstrings:
- def test_no_url_no_inheritance(self): Test the no_url function with an event that should not inherit a url.
- def test_no_url_yes_inheritance(self): Test the no_url f... | Implement the Python class `InheritLocTest` described below.
Class description:
Tester for the inheritloc functions
Method signatures and docstrings:
- def test_no_url_no_inheritance(self): Test the no_url function with an event that should not inherit a url.
- def test_no_url_yes_inheritance(self): Test the no_url f... | ad5ae27476c1dc02b1a06d2d90d4c8b7ada97c02 | <|skeleton|>
class InheritLocTest:
"""Tester for the inheritloc functions"""
def test_no_url_no_inheritance(self):
"""Test the no_url function with an event that should not inherit a url."""
<|body_0|>
def test_no_url_yes_inheritance(self):
"""Test the no_url function with an event... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InheritLocTest:
"""Tester for the inheritloc functions"""
def test_no_url_no_inheritance(self):
"""Test the no_url function with an event that should not inherit a url."""
event = {'current_location': (CourseURL('http://a/b/'), '2013-11-10 06:43:41'), 'time': '2013-12-10 06:44:00', 'page'... | the_stack_v2_python_sparse | edx_pipe/qpipe/inheritloc_test.py | johnding1996/MOOC-Learner-Curated | train | 0 |
5368d96683e21ee297c4b244d41eca1bf0a81336 | [
"url = conf.get(_CFG_URL)\nport = conf.get(_CFG_PORT)\ncert_path = conf.get(_CFG_CERT_PATH)\nlog.debug(\"Using config '%s' = '%s'\", _CFG_URL, url)\nlog.debug(\"Using config '%s' = '%s'\", _CFG_PORT, port)\nlog.debug(\"Using config '%s' = '%s'\", _CFG_CERT_PATH, cert_path)\noptions = load_certificates(url, cert_pat... | <|body_start_0|>
url = conf.get(_CFG_URL)
port = conf.get(_CFG_PORT)
cert_path = conf.get(_CFG_CERT_PATH)
log.debug("Using config '%s' = '%s'", _CFG_URL, url)
log.debug("Using config '%s' = '%s'", _CFG_PORT, port)
log.debug("Using config '%s' = '%s'", _CFG_CERT_PATH, cert... | Provides an API to communicate with CyberArk. | CyberArkClient | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CyberArkClient:
"""Provides an API to communicate with CyberArk."""
def from_dict(conf):
"""Returns a new CyberArkClient object. :param conf: Contains the CyberArk configuration data :type conf: A dict-like object providing the 'get' method."""
<|body_0|>
def __init__(se... | stack_v2_sparse_classes_36k_train_032108 | 16,014 | no_license | [
{
"docstring": "Returns a new CyberArkClient object. :param conf: Contains the CyberArk configuration data :type conf: A dict-like object providing the 'get' method.",
"name": "from_dict",
"signature": "def from_dict(conf)"
},
{
"docstring": "Initializes a CyberArkClient instance. :param str url... | 3 | null | Implement the Python class `CyberArkClient` described below.
Class description:
Provides an API to communicate with CyberArk.
Method signatures and docstrings:
- def from_dict(conf): Returns a new CyberArkClient object. :param conf: Contains the CyberArk configuration data :type conf: A dict-like object providing the... | Implement the Python class `CyberArkClient` described below.
Class description:
Provides an API to communicate with CyberArk.
Method signatures and docstrings:
- def from_dict(conf): Returns a new CyberArkClient object. :param conf: Contains the CyberArk configuration data :type conf: A dict-like object providing the... | 1ea508c3d2b51742bc3b448c445cd0a3dba9e798 | <|skeleton|>
class CyberArkClient:
"""Provides an API to communicate with CyberArk."""
def from_dict(conf):
"""Returns a new CyberArkClient object. :param conf: Contains the CyberArk configuration data :type conf: A dict-like object providing the 'get' method."""
<|body_0|>
def __init__(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CyberArkClient:
"""Provides an API to communicate with CyberArk."""
def from_dict(conf):
"""Returns a new CyberArkClient object. :param conf: Contains the CyberArk configuration data :type conf: A dict-like object providing the 'get' method."""
url = conf.get(_CFG_URL)
port = conf... | the_stack_v2_python_sparse | Products/ZenCollector/cyberark.py | zenoss/zenoss-prodbin | train | 27 |
4426b5e70ae0d8dc5d3c3366c1d3be8d8770d09e | [
"logging.Formatter.__init__(self, fmt, datefmt)\nself.technicolor = technicolor\nself._isatty = sys.stderr.isatty()",
"if record.levelno == logging.INFO:\n msg = logging.Formatter.format(self, record)\n return msg\nif self.technicolor and self._isatty:\n colour = self.LEVEL_COLOURS[record.levelno]\n b... | <|body_start_0|>
logging.Formatter.__init__(self, fmt, datefmt)
self.technicolor = technicolor
self._isatty = sys.stderr.isatty()
<|end_body_0|>
<|body_start_1|>
if record.levelno == logging.INFO:
msg = logging.Formatter.format(self, record)
return msg
if... | Intelligent and pretty log formatting. Colourise output to a TTY and prepend logging level name to levels other than INFO. | TechnicolorFormatter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TechnicolorFormatter:
"""Intelligent and pretty log formatting. Colourise output to a TTY and prepend logging level name to levels other than INFO."""
def __init__(self, fmt=None, datefmt=None, technicolor=True):
"""Create new Formatter. Args: fmt (str): A `logging.Formatter` format ... | stack_v2_sparse_classes_36k_train_032109 | 9,687 | permissive | [
{
"docstring": "Create new Formatter. Args: fmt (str): A `logging.Formatter` format string. datefmt (str): `strftime` format string. technicolor (bool): Colourise TTY output?",
"name": "__init__",
"signature": "def __init__(self, fmt=None, datefmt=None, technicolor=True)"
},
{
"docstring": "Form... | 3 | stack_v2_sparse_classes_30k_train_014601 | Implement the Python class `TechnicolorFormatter` described below.
Class description:
Intelligent and pretty log formatting. Colourise output to a TTY and prepend logging level name to levels other than INFO.
Method signatures and docstrings:
- def __init__(self, fmt=None, datefmt=None, technicolor=True): Create new ... | Implement the Python class `TechnicolorFormatter` described below.
Class description:
Intelligent and pretty log formatting. Colourise output to a TTY and prepend logging level name to levels other than INFO.
Method signatures and docstrings:
- def __init__(self, fmt=None, datefmt=None, technicolor=True): Create new ... | 665d39a2bd82543d5196555f0801ef8fd4a3ee48 | <|skeleton|>
class TechnicolorFormatter:
"""Intelligent and pretty log formatting. Colourise output to a TTY and prepend logging level name to levels other than INFO."""
def __init__(self, fmt=None, datefmt=None, technicolor=True):
"""Create new Formatter. Args: fmt (str): A `logging.Formatter` format ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TechnicolorFormatter:
"""Intelligent and pretty log formatting. Colourise output to a TTY and prepend logging level name to levels other than INFO."""
def __init__(self, fmt=None, datefmt=None, technicolor=True):
"""Create new Formatter. Args: fmt (str): A `logging.Formatter` format string. datef... | the_stack_v2_python_sparse | all-gists/b16f018119ef3fe951af/snippet.py | gistable/gistable | train | 76 |
b4bb2d1df7ae79a7abf22b6b0af7b6f4050ac5f6 | [
"try:\n return_data = ''\n return Response(json.dumps(return_data))\nexcept Exception as e:\n return_data = {'status': '404', 'result': str(e)}\n return Response(json.dumps(return_data))",
"try:\n return_data = ''\n return Response(json.dumps(return_data))\nexcept Exception as e:\n return_dat... | <|body_start_0|>
try:
return_data = ''
return Response(json.dumps(return_data))
except Exception as e:
return_data = {'status': '404', 'result': str(e)}
return Response(json.dumps(return_data))
<|end_body_0|>
<|body_start_1|>
try:
retu... | RunManagerSchedule | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RunManagerSchedule:
def post(self, request, nnid):
"""Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update ... | stack_v2_sparse_classes_36k_train_032110 | 3,545 | permissive | [
{
"docstring": "Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update model automatically --- # Class Name : RunManagerSchedule # De... | 4 | null | Implement the Python class `RunManagerSchedule` described below.
Class description:
Implement the RunManagerSchedule class.
Method signatures and docstrings:
- def post(self, request, nnid): Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run... | Implement the Python class `RunManagerSchedule` described below.
Class description:
Implement the RunManagerSchedule class.
Method signatures and docstrings:
- def post(self, request, nnid): Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run... | 6ad2fbc7384e4dbe7e3e63bdb44c8ce0387f4b7f | <|skeleton|>
class RunManagerSchedule:
def post(self, request, nnid):
"""Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RunManagerSchedule:
def post(self, request, nnid):
"""Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update model automati... | the_stack_v2_python_sparse | api/views/runmanager_schedule.py | yurimkoo/tensormsa | train | 1 | |
4e81a90244c89798331ba754fe5de81a329c88ba | [
"res = super(event_registration, self).confirm_registration(cr, uid, ids, context=context)\nmoodle_pool = self.pool.get('event.moodle.config.wiz')\nmoodle_config_wiz_id = moodle_pool.find(cr, uid, context=context)\nfor register in self.browse(cr, uid, ids, context=context):\n if register.event_id.state == 'confi... | <|body_start_0|>
res = super(event_registration, self).confirm_registration(cr, uid, ids, context=context)
moodle_pool = self.pool.get('event.moodle.config.wiz')
moodle_config_wiz_id = moodle_pool.find(cr, uid, context=context)
for register in self.browse(cr, uid, ids, context=context):
... | event_registration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class event_registration:
def confirm_registration(self, cr, uid, ids, context=None):
"""create a user and match to a course if the event is already confirmed"""
<|body_0|>
def onchange_moodle_name(self, cr, uid, ids, moodle_username, context=None):
"""This onchange receiv... | stack_v2_sparse_classes_36k_train_032111 | 13,266 | no_license | [
{
"docstring": "create a user and match to a course if the event is already confirmed",
"name": "confirm_registration",
"signature": "def confirm_registration(self, cr, uid, ids, context=None)"
},
{
"docstring": "This onchange receive as parameter a username moddle and will fill the moodle_uid a... | 2 | null | Implement the Python class `event_registration` described below.
Class description:
Implement the event_registration class.
Method signatures and docstrings:
- def confirm_registration(self, cr, uid, ids, context=None): create a user and match to a course if the event is already confirmed
- def onchange_moodle_name(s... | Implement the Python class `event_registration` described below.
Class description:
Implement the event_registration class.
Method signatures and docstrings:
- def confirm_registration(self, cr, uid, ids, context=None): create a user and match to a course if the event is already confirmed
- def onchange_moodle_name(s... | e6b06ea17fa44e35e3c99a83c6f3ec433c33c894 | <|skeleton|>
class event_registration:
def confirm_registration(self, cr, uid, ids, context=None):
"""create a user and match to a course if the event is already confirmed"""
<|body_0|>
def onchange_moodle_name(self, cr, uid, ids, moodle_username, context=None):
"""This onchange receiv... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class event_registration:
def confirm_registration(self, cr, uid, ids, context=None):
"""create a user and match to a course if the event is already confirmed"""
res = super(event_registration, self).confirm_registration(cr, uid, ids, context=context)
moodle_pool = self.pool.get('event.moodl... | the_stack_v2_python_sparse | event_moodle/event_moodle.py | rvalyi/openerp-addons | train | 2 | |
f16c84514defbbc1319eead11515ff60d318300d | [
"if interp not in {'linear', 'nearest'}:\n raise ValueError('interp must be one of {linear, nearest}')\nself.translation = list(translation)\nself.reference = reference\nself.interp = interp",
"if X.pixeltype != 'float':\n raise ValueError('image.pixeltype must be float ... use TypeCast transform or clone t... | <|body_start_0|>
if interp not in {'linear', 'nearest'}:
raise ValueError('interp must be one of {linear, nearest}')
self.translation = list(translation)
self.reference = reference
self.interp = interp
<|end_body_0|>
<|body_start_1|>
if X.pixeltype != 'float':
... | Translate an image in physical space. This function calls highly optimized ITK/C++ code. | TranslateImage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TranslateImage:
"""Translate an image in physical space. This function calls highly optimized ITK/C++ code."""
def __init__(self, translation, reference=None, interp='linear'):
"""Initialize a TranslateImage transform Arguments --------- translation : list, tuple, or numpy.ndarray ab... | stack_v2_sparse_classes_36k_train_032112 | 24,297 | permissive | [
{
"docstring": "Initialize a TranslateImage transform Arguments --------- translation : list, tuple, or numpy.ndarray absolute pixel transformation in each axis reference : ANTsImage (optional) image which provides the reference physical space in which to perform the transform interp : string type of interpolat... | 2 | null | Implement the Python class `TranslateImage` described below.
Class description:
Translate an image in physical space. This function calls highly optimized ITK/C++ code.
Method signatures and docstrings:
- def __init__(self, translation, reference=None, interp='linear'): Initialize a TranslateImage transform Arguments... | Implement the Python class `TranslateImage` described below.
Class description:
Translate an image in physical space. This function calls highly optimized ITK/C++ code.
Method signatures and docstrings:
- def __init__(self, translation, reference=None, interp='linear'): Initialize a TranslateImage transform Arguments... | 41f2dd3fcf72654f284dac1a9448033e963f0afb | <|skeleton|>
class TranslateImage:
"""Translate an image in physical space. This function calls highly optimized ITK/C++ code."""
def __init__(self, translation, reference=None, interp='linear'):
"""Initialize a TranslateImage transform Arguments --------- translation : list, tuple, or numpy.ndarray ab... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TranslateImage:
"""Translate an image in physical space. This function calls highly optimized ITK/C++ code."""
def __init__(self, translation, reference=None, interp='linear'):
"""Initialize a TranslateImage transform Arguments --------- translation : list, tuple, or numpy.ndarray absolute pixel ... | the_stack_v2_python_sparse | ants/contrib/sampling/transforms.py | ANTsX/ANTsPy | train | 483 |
6a7f18cc07ca7e0724fc6d87bed510cdfc5778ad | [
"self.click_(self.loc_paihangbang)\nresult_1 = self.is_element_Exist(self.loc_dy_Day_1)\nresult_2 = self.is_element_Exist(self.loc_dy_Day_2)\nresult_3 = self.is_element_Exist(self.loc_dy_Day_3)\nif result_1 and result_2 and result_3:\n return True\nelse:\n return False",
"self.click_(self.loc_Week)\nself.cl... | <|body_start_0|>
self.click_(self.loc_paihangbang)
result_1 = self.is_element_Exist(self.loc_dy_Day_1)
result_2 = self.is_element_Exist(self.loc_dy_Day_2)
result_3 = self.is_element_Exist(self.loc_dy_Day_3)
if result_1 and result_2 and result_3:
return True
el... | 学习-学习排行榜 | Ranking_List | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ranking_List:
"""学习-学习排行榜"""
def day(self):
"""学习排行榜-Day"""
<|body_0|>
def week(self):
"""学习排行榜-Week"""
<|body_1|>
def month(self):
"""学习排行榜-Month"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
self.click_(self.loc_paihangb... | stack_v2_sparse_classes_36k_train_032113 | 3,460 | no_license | [
{
"docstring": "学习排行榜-Day",
"name": "day",
"signature": "def day(self)"
},
{
"docstring": "学习排行榜-Week",
"name": "week",
"signature": "def week(self)"
},
{
"docstring": "学习排行榜-Month",
"name": "month",
"signature": "def month(self)"
}
] | 3 | stack_v2_sparse_classes_30k_val_000384 | Implement the Python class `Ranking_List` described below.
Class description:
学习-学习排行榜
Method signatures and docstrings:
- def day(self): 学习排行榜-Day
- def week(self): 学习排行榜-Week
- def month(self): 学习排行榜-Month | Implement the Python class `Ranking_List` described below.
Class description:
学习-学习排行榜
Method signatures and docstrings:
- def day(self): 学习排行榜-Day
- def week(self): 学习排行榜-Week
- def month(self): 学习排行榜-Month
<|skeleton|>
class Ranking_List:
"""学习-学习排行榜"""
def day(self):
"""学习排行榜-Day"""
<|bod... | 9d8ad54fc982d3b2f8244e439705bcfee12ebd0c | <|skeleton|>
class Ranking_List:
"""学习-学习排行榜"""
def day(self):
"""学习排行榜-Day"""
<|body_0|>
def week(self):
"""学习排行榜-Week"""
<|body_1|>
def month(self):
"""学习排行榜-Month"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Ranking_List:
"""学习-学习排行榜"""
def day(self):
"""学习排行榜-Day"""
self.click_(self.loc_paihangbang)
result_1 = self.is_element_Exist(self.loc_dy_Day_1)
result_2 = self.is_element_Exist(self.loc_dy_Day_2)
result_3 = self.is_element_Exist(self.loc_dy_Day_3)
if resu... | the_stack_v2_python_sparse | wyt/page/ranking_list.py | mengmengxidi/wyt-APP-Automation-code | train | 0 |
5f22af9a5bdcbd5cdec51e744c1f7117ba3c64b3 | [
"slower = head\nfaster = head\nwhile faster and faster.next:\n slower = slower.next\n faster = faster.next.next\n if faster == slower:\n return True\nreturn False",
"listSet = set()\nwhile head:\n if head == None:\n return False\n elif head in listSet:\n return True\n else:\... | <|body_start_0|>
slower = head
faster = head
while faster and faster.next:
slower = slower.next
faster = faster.next.next
if faster == slower:
return True
return False
<|end_body_0|>
<|body_start_1|>
listSet = set()
whi... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasCycle(self, head):
""":type head: ListNode :rtype: bool"""
<|body_0|>
def hasCycle1(self, head):
""":type head: ListNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
slower = head
faster = head
while ... | stack_v2_sparse_classes_36k_train_032114 | 1,012 | no_license | [
{
"docstring": ":type head: ListNode :rtype: bool",
"name": "hasCycle",
"signature": "def hasCycle(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: bool",
"name": "hasCycle1",
"signature": "def hasCycle1(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001655 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle(self, head): :type head: ListNode :rtype: bool
- def hasCycle1(self, head): :type head: ListNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle(self, head): :type head: ListNode :rtype: bool
- def hasCycle1(self, head): :type head: ListNode :rtype: bool
<|skeleton|>
class Solution:
def hasCycle(self, h... | 639f4686308522d59cd8b818247d70ce57dc5c10 | <|skeleton|>
class Solution:
def hasCycle(self, head):
""":type head: ListNode :rtype: bool"""
<|body_0|>
def hasCycle1(self, head):
""":type head: ListNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasCycle(self, head):
""":type head: ListNode :rtype: bool"""
slower = head
faster = head
while faster and faster.next:
slower = slower.next
faster = faster.next.next
if faster == slower:
return True
retu... | the_stack_v2_python_sparse | src/141. Linked List Cycle.py | YoungXueya/LeetcodeSolution | train | 0 | |
d295aaa08b9473ce8f33ee75624d17147a66fbac | [
"for addr, comment in comments.items():\n db_comment = session.query(DbComment).filter_by(kb=db_kb, addr=addr).scalar()\n if db_comment is not None:\n if comment == db_comment.comment:\n continue\n db_comment.comment = comment\n else:\n db_comment = DbComment(kb=db_kb, addr=... | <|body_start_0|>
for addr, comment in comments.items():
db_comment = session.query(DbComment).filter_by(kb=db_kb, addr=addr).scalar()
if db_comment is not None:
if comment == db_comment.comment:
continue
db_comment.comment = comment
... | Serialize/unserialize comments to/from a database session. | CommentsSerializer | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentsSerializer:
"""Serialize/unserialize comments to/from a database session."""
def dump(session, db_kb, comments):
""":param session: :param DbKnowledgeBase db_kb: :param Comments comments: :return: None"""
<|body_0|>
def load(session, db_kb, kb):
""":param... | stack_v2_sparse_classes_36k_train_032115 | 1,531 | permissive | [
{
"docstring": ":param session: :param DbKnowledgeBase db_kb: :param Comments comments: :return: None",
"name": "dump",
"signature": "def dump(session, db_kb, comments)"
},
{
"docstring": ":param session: :param DbKnowledgeBase db_kb: :param KnowledgeBase kb: :return:",
"name": "load",
"... | 2 | stack_v2_sparse_classes_30k_train_005019 | Implement the Python class `CommentsSerializer` described below.
Class description:
Serialize/unserialize comments to/from a database session.
Method signatures and docstrings:
- def dump(session, db_kb, comments): :param session: :param DbKnowledgeBase db_kb: :param Comments comments: :return: None
- def load(sessio... | Implement the Python class `CommentsSerializer` described below.
Class description:
Serialize/unserialize comments to/from a database session.
Method signatures and docstrings:
- def dump(session, db_kb, comments): :param session: :param DbKnowledgeBase db_kb: :param Comments comments: :return: None
- def load(sessio... | 37e8ca1c3308ec601ad1d7c6bc8081ff38a7cffd | <|skeleton|>
class CommentsSerializer:
"""Serialize/unserialize comments to/from a database session."""
def dump(session, db_kb, comments):
""":param session: :param DbKnowledgeBase db_kb: :param Comments comments: :return: None"""
<|body_0|>
def load(session, db_kb, kb):
""":param... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentsSerializer:
"""Serialize/unserialize comments to/from a database session."""
def dump(session, db_kb, comments):
""":param session: :param DbKnowledgeBase db_kb: :param Comments comments: :return: None"""
for addr, comment in comments.items():
db_comment = session.quer... | the_stack_v2_python_sparse | angr/angrdb/serializers/comments.py | angr/angr | train | 7,184 |
07e661ead5350de08a61d284cd7fdab2a7b7d42b | [
"self.file_path = file_path\nself.count_episodes = None\nself.count_states = None\nself.data = None",
"with open(file=self.file_path, mode='a', buffering=1) as file:\n msg_annotated = '{0}\\t{1}\\t{2}\\n'.format(count_episodes, count_states, msg)\n file.write(msg_annotated)",
"with open(self.file_path) as... | <|body_start_0|>
self.file_path = file_path
self.count_episodes = None
self.count_states = None
self.data = None
<|end_body_0|>
<|body_start_1|>
with open(file=self.file_path, mode='a', buffering=1) as file:
msg_annotated = '{0}\t{1}\t{2}\n'.format(count_episodes, co... | Base-class for logging data to a text-file during training. It is possible to use TensorFlow / TensorBoard for this, but it is quite awkward to implement, as it was intended for logging variables and other aspects of the TensorFlow graph. We want to log the reward and Q-values which are not in that graph. | Log | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Log:
"""Base-class for logging data to a text-file during training. It is possible to use TensorFlow / TensorBoard for this, but it is quite awkward to implement, as it was intended for logging variables and other aspects of the TensorFlow graph. We want to log the reward and Q-values which are n... | stack_v2_sparse_classes_36k_train_032116 | 4,975 | permissive | [
{
"docstring": "Set the path for the log-file. Nothing is saved or loaded yet.",
"name": "__init__",
"signature": "def __init__(self, file_path)"
},
{
"docstring": "Write a line to the log-file. This is only called by sub-classes. :param count_episodes: Counter for the number of episodes process... | 3 | stack_v2_sparse_classes_30k_train_007235 | Implement the Python class `Log` described below.
Class description:
Base-class for logging data to a text-file during training. It is possible to use TensorFlow / TensorBoard for this, but it is quite awkward to implement, as it was intended for logging variables and other aspects of the TensorFlow graph. We want to ... | Implement the Python class `Log` described below.
Class description:
Base-class for logging data to a text-file during training. It is possible to use TensorFlow / TensorBoard for this, but it is quite awkward to implement, as it was intended for logging variables and other aspects of the TensorFlow graph. We want to ... | cc4181ed1a951ec9e82e86358a53f69b63d5328a | <|skeleton|>
class Log:
"""Base-class for logging data to a text-file during training. It is possible to use TensorFlow / TensorBoard for this, but it is quite awkward to implement, as it was intended for logging variables and other aspects of the TensorFlow graph. We want to log the reward and Q-values which are n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Log:
"""Base-class for logging data to a text-file during training. It is possible to use TensorFlow / TensorBoard for this, but it is quite awkward to implement, as it was intended for logging variables and other aspects of the TensorFlow graph. We want to log the reward and Q-values which are not in that gr... | the_stack_v2_python_sparse | log.py | sandip824/Reinforcement-Learning-for-Self-Driving-Cars | train | 1 |
1b0bd28ce5705593a2dc05215f003a4c3640adf1 | [
"def backtrack(res, path, index, s):\n if len(s) == index:\n res.append(path[:])\n return\n for i in range(index, len(s)):\n substr = s[index:i + 1]\n if substr == substr[::-1]:\n path.append(substr)\n backtrack(res, path, i + 1, s)\n path.pop()\nan... | <|body_start_0|>
def backtrack(res, path, index, s):
if len(s) == index:
res.append(path[:])
return
for i in range(index, len(s)):
substr = s[index:i + 1]
if substr == substr[::-1]:
path.append(substr)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def partition(self, s):
""":type s: str :rtype: List[List[str]]"""
<|body_0|>
def partition_wrong_answer(self, s):
""":type s: str :rtype: List[List[str]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def backtrack(res, path, index, s):
... | stack_v2_sparse_classes_36k_train_032117 | 2,206 | no_license | [
{
"docstring": ":type s: str :rtype: List[List[str]]",
"name": "partition",
"signature": "def partition(self, s)"
},
{
"docstring": ":type s: str :rtype: List[List[str]]",
"name": "partition_wrong_answer",
"signature": "def partition_wrong_answer(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_val_001175 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def partition(self, s): :type s: str :rtype: List[List[str]]
- def partition_wrong_answer(self, s): :type s: str :rtype: List[List[str]] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def partition(self, s): :type s: str :rtype: List[List[str]]
- def partition_wrong_answer(self, s): :type s: str :rtype: List[List[str]]
<|skeleton|>
class Solution:
def pa... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def partition(self, s):
""":type s: str :rtype: List[List[str]]"""
<|body_0|>
def partition_wrong_answer(self, s):
""":type s: str :rtype: List[List[str]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def partition(self, s):
""":type s: str :rtype: List[List[str]]"""
def backtrack(res, path, index, s):
if len(s) == index:
res.append(path[:])
return
for i in range(index, len(s)):
substr = s[index:i + 1]
... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00131.Palindrome Partitioning.py | roger6blog/LeetCode | train | 0 | |
5170b1cd5e706e01684a23760d574769852e3fae | [
"def buildList(root, lst):\n if not root:\n lst.append('N')\n else:\n lst.append(str(root.val))\n buildList(root.left, lst)\n buildList(root.right, lst)\nlst = []\nbuildList(root, lst)\nreturn ','.join(lst)",
"def buildTree(it):\n val = next(it)\n if val == 'N':\n ro... | <|body_start_0|>
def buildList(root, lst):
if not root:
lst.append('N')
else:
lst.append(str(root.val))
buildList(root.left, lst)
buildList(root.right, lst)
lst = []
buildList(root, lst)
return ','.jo... | 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_032118 | 1,719 | 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 | stack_v2_sparse_classes_30k_train_016462 | 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:... | 672816c504e56a1d2dfea72f96312f27cd9a3133 | <|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 buildList(root, lst):
if not root:
lst.append('N')
else:
lst.append(str(root.val))
buildList(root.left, lst)
... | the_stack_v2_python_sparse | 297.Serialize_and_Deserialize_Binary_Tree.py | mikehung/leetcode | train | 3 | |
74bce0dbec2c1a92c40a962f3a099097be735c96 | [
"super().__init__()\nself.input_size = input_size\nself.d_model = d_model\nif input_size != d_model:\n self.proj = nn.Linear(input_size, d_model)\nlayer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)\nself.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)])\nself.num_la... | <|body_start_0|>
super().__init__()
self.input_size = input_size
self.d_model = d_model
if input_size != d_model:
self.proj = nn.Linear(input_size, d_model)
layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)
self.layers = nn.ModuleList([... | TransformerDecoder is a stack of N decoder layers | TransformerDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerDecoder:
"""TransformerDecoder is a stack of N decoder layers"""
def __init__(self, input_size: int, d_model: int, nhead: int, num_layers: int, dim_feedforward: int=2048, dropout: float=0.1) -> None:
"""Initialize the TransformerDecoder. Parameters --------- input_size : i... | stack_v2_sparse_classes_36k_train_032119 | 20,460 | permissive | [
{
"docstring": "Initialize the TransformerDecoder. Parameters --------- input_size : int The embedding dimension of the model. If different from d_model, a linear projection layer is added. d_model : int The number of expected features in encoder/decoder inputs. nhead : int, optional The number of heads in the ... | 3 | stack_v2_sparse_classes_30k_train_010709 | Implement the Python class `TransformerDecoder` described below.
Class description:
TransformerDecoder is a stack of N decoder layers
Method signatures and docstrings:
- def __init__(self, input_size: int, d_model: int, nhead: int, num_layers: int, dim_feedforward: int=2048, dropout: float=0.1) -> None: Initialize th... | Implement the Python class `TransformerDecoder` described below.
Class description:
TransformerDecoder is a stack of N decoder layers
Method signatures and docstrings:
- def __init__(self, input_size: int, d_model: int, nhead: int, num_layers: int, dim_feedforward: int=2048, dropout: float=0.1) -> None: Initialize th... | 0dc2f5b2b286694defe8abf450fe5be9ae12c097 | <|skeleton|>
class TransformerDecoder:
"""TransformerDecoder is a stack of N decoder layers"""
def __init__(self, input_size: int, d_model: int, nhead: int, num_layers: int, dim_feedforward: int=2048, dropout: float=0.1) -> None:
"""Initialize the TransformerDecoder. Parameters --------- input_size : i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransformerDecoder:
"""TransformerDecoder is a stack of N decoder layers"""
def __init__(self, input_size: int, d_model: int, nhead: int, num_layers: int, dim_feedforward: int=2048, dropout: float=0.1) -> None:
"""Initialize the TransformerDecoder. Parameters --------- input_size : int The embedd... | the_stack_v2_python_sparse | flambe/nn/transformer.py | cle-ros/flambe | train | 1 |
3c1b5c68516c2da141143af08e62c31bc43d3d43 | [
"self.trade_date = None\nself.trade_no = None\nself.settlement_date = None\nself.security_descr = None\nself.buyer = None\nself.seller = None\nself.isin = None\nself.issuer = None\nself.maturity_date = None\nself.currency = None\nself.nominal = None\nself.yield_to_maturity = None\nself.clean_price = None\nself.clea... | <|body_start_0|>
self.trade_date = None
self.trade_no = None
self.settlement_date = None
self.security_descr = None
self.buyer = None
self.seller = None
self.isin = None
self.issuer = None
self.maturity_date = None
self.currency = None
... | A class representing the details of a trade for display on a broker note bulk. | Trade | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trade:
"""A class representing the details of a trade for display on a broker note bulk."""
def __init__(self):
"""Constructor."""
<|body_0|>
def to_xml_element(self):
"""Convert this Trade instance to its representation as an XML Element."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_032120 | 26,096 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Convert this Trade instance to its representation as an XML Element.",
"name": "to_xml_element",
"signature": "def to_xml_element(self)"
},
{
"docstring": "Convert the XML Elem... | 3 | stack_v2_sparse_classes_30k_train_004901 | Implement the Python class `Trade` described below.
Class description:
A class representing the details of a trade for display on a broker note bulk.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def to_xml_element(self): Convert this Trade instance to its representation as an XML Element.
- ... | Implement the Python class `Trade` described below.
Class description:
A class representing the details of a trade for display on a broker note bulk.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def to_xml_element(self): Convert this Trade instance to its representation as an XML Element.
- ... | 5e7cc7de3495145501ca53deb9efee2233ab7e1c | <|skeleton|>
class Trade:
"""A class representing the details of a trade for display on a broker note bulk."""
def __init__(self):
"""Constructor."""
<|body_0|>
def to_xml_element(self):
"""Convert this Trade instance to its representation as an XML Element."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Trade:
"""A class representing the details of a trade for display on a broker note bulk."""
def __init__(self):
"""Constructor."""
self.trade_date = None
self.trade_no = None
self.settlement_date = None
self.security_descr = None
self.buyer = None
s... | the_stack_v2_python_sparse | Extensions/ABSA Documentation/FPythonCode/BrokerNoteBulkGeneral.py | webclinic017/fa-absa-py3 | train | 0 |
bf16bd030d10ceb7836ecce43222d882a34b1410 | [
"if not super(self.__class__, self).validate(mention, mention_index):\n return False\nif not mention['form'] in dictionaries.pronouns.relative:\n self.debug('MENTION FILTERED Not a relative pronoun: %s ', mention['form'])\n return False\nreturn True",
"candidate_tag = candidate.get('tag', False)\nif cand... | <|body_start_0|>
if not super(self.__class__, self).validate(mention, mention_index):
return False
if not mention['form'] in dictionaries.pronouns.relative:
self.debug('MENTION FILTERED Not a relative pronoun: %s ', mention['form'])
return False
return True
<|... | A relative pronoun is referent to the NP that modified. | RelativePronoun | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelativePronoun:
"""A relative pronoun is referent to the NP that modified."""
def validate(self, mention, mention_index):
"""Entity must be relative pronoun."""
<|body_0|>
def are_coreferent(self, entity, mention, candidate):
"""Candidate is the NP that the rela... | stack_v2_sparse_classes_36k_train_032121 | 10,579 | permissive | [
{
"docstring": "Entity must be relative pronoun.",
"name": "validate",
"signature": "def validate(self, mention, mention_index)"
},
{
"docstring": "Candidate is the NP that the relative pronoun modified.",
"name": "are_coreferent",
"signature": "def are_coreferent(self, entity, mention, ... | 2 | stack_v2_sparse_classes_30k_train_018511 | Implement the Python class `RelativePronoun` described below.
Class description:
A relative pronoun is referent to the NP that modified.
Method signatures and docstrings:
- def validate(self, mention, mention_index): Entity must be relative pronoun.
- def are_coreferent(self, entity, mention, candidate): Candidate is... | Implement the Python class `RelativePronoun` described below.
Class description:
A relative pronoun is referent to the NP that modified.
Method signatures and docstrings:
- def validate(self, mention, mention_index): Entity must be relative pronoun.
- def are_coreferent(self, entity, mention, candidate): Candidate is... | 5fe41dda0da83a37a01220f8f0552a336f2294ef | <|skeleton|>
class RelativePronoun:
"""A relative pronoun is referent to the NP that modified."""
def validate(self, mention, mention_index):
"""Entity must be relative pronoun."""
<|body_0|>
def are_coreferent(self, entity, mention, candidate):
"""Candidate is the NP that the rela... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelativePronoun:
"""A relative pronoun is referent to the NP that modified."""
def validate(self, mention, mention_index):
"""Entity must be relative pronoun."""
if not super(self.__class__, self).validate(mention, mention_index):
return False
if not mention['form'] in... | the_stack_v2_python_sparse | core/corefgraph/multisieve/sieves/preciseConstruct.py | malv007/coreference-base | train | 0 |
81e3e13225ba8589d7204242681c688ce44e7de2 | [
"test_numlist = [[1, 2], [-5, 6]]\nfor pos in test_numlist:\n qpos = QtCore.QPoint(pos[0], pos[1])\n npos = QtUtil.QPoint2numpy(qpos)\n assert npos[0] == float(pos[0])\n assert npos[1] == float(pos[1])",
"ctrlmod = QtCore.Qt.KeyboardModifiers(QtCore.Qt.ControlModifier)\naltmod = QtCore.Qt.KeyboardModi... | <|body_start_0|>
test_numlist = [[1, 2], [-5, 6]]
for pos in test_numlist:
qpos = QtCore.QPoint(pos[0], pos[1])
npos = QtUtil.QPoint2numpy(qpos)
assert npos[0] == float(pos[0])
assert npos[1] == float(pos[1])
<|end_body_0|>
<|body_start_1|>
ctrlmo... | test: QtUtil | TestQtUtil | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestQtUtil:
"""test: QtUtil"""
def test_QPoint2numpy(self):
"""test mapToSphere function"""
<|body_0|>
def test_key_modifier(self):
"""test key modifiers"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
test_numlist = [[1, 2], [-5, 6]]
fo... | stack_v2_sparse_classes_36k_train_032122 | 2,019 | no_license | [
{
"docstring": "test mapToSphere function",
"name": "test_QPoint2numpy",
"signature": "def test_QPoint2numpy(self)"
},
{
"docstring": "test key modifiers",
"name": "test_key_modifier",
"signature": "def test_key_modifier(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017692 | Implement the Python class `TestQtUtil` described below.
Class description:
test: QtUtil
Method signatures and docstrings:
- def test_QPoint2numpy(self): test mapToSphere function
- def test_key_modifier(self): test key modifiers | Implement the Python class `TestQtUtil` described below.
Class description:
test: QtUtil
Method signatures and docstrings:
- def test_QPoint2numpy(self): test mapToSphere function
- def test_key_modifier(self): test key modifiers
<|skeleton|>
class TestQtUtil:
"""test: QtUtil"""
def test_QPoint2numpy(self):... | f163b6b9e15100d223ddf4e180727a2b63fbae2d | <|skeleton|>
class TestQtUtil:
"""test: QtUtil"""
def test_QPoint2numpy(self):
"""test mapToSphere function"""
<|body_0|>
def test_key_modifier(self):
"""test key modifiers"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestQtUtil:
"""test: QtUtil"""
def test_QPoint2numpy(self):
"""test mapToSphere function"""
test_numlist = [[1, 2], [-5, 6]]
for pos in test_numlist:
qpos = QtCore.QPoint(pos[0], pos[1])
npos = QtUtil.QPoint2numpy(qpos)
assert npos[0] == float(p... | the_stack_v2_python_sparse | examiner00/test_QtUtil.py | yamauchih/ifgi-path-tracer | train | 0 |
b05c89fdba66c10dbccbcac279ac924066219267 | [
"player = g.db.query(MatchPlayer).filter(MatchPlayer.match_id == match_id, MatchPlayer.player_id == player_id).first()\nif not player:\n abort(http_client.NOT_FOUND)\nret = player.as_dict()\nret['team_url'] = None\nif player.team_id:\n ret['team_url'] = url_for('matches.team', match_id=match_id, team_id=playe... | <|body_start_0|>
player = g.db.query(MatchPlayer).filter(MatchPlayer.match_id == match_id, MatchPlayer.player_id == player_id).first()
if not player:
abort(http_client.NOT_FOUND)
ret = player.as_dict()
ret['team_url'] = None
if player.team_id:
ret['team_ur... | A specific player in a specific match | MatchPlayerAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MatchPlayerAPI:
"""A specific player in a specific match"""
def get(self, match_id, player_id):
"""Get a specific player from a battle"""
<|body_0|>
def delete(self, match_id, player_id):
"""A player has left an ongoing battle"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k_train_032123 | 24,829 | permissive | [
{
"docstring": "Get a specific player from a battle",
"name": "get",
"signature": "def get(self, match_id, player_id)"
},
{
"docstring": "A player has left an ongoing battle",
"name": "delete",
"signature": "def delete(self, match_id, player_id)"
}
] | 2 | null | Implement the Python class `MatchPlayerAPI` described below.
Class description:
A specific player in a specific match
Method signatures and docstrings:
- def get(self, match_id, player_id): Get a specific player from a battle
- def delete(self, match_id, player_id): A player has left an ongoing battle | Implement the Python class `MatchPlayerAPI` described below.
Class description:
A specific player in a specific match
Method signatures and docstrings:
- def get(self, match_id, player_id): Get a specific player from a battle
- def delete(self, match_id, player_id): A player has left an ongoing battle
<|skeleton|>
c... | 9825cb22b26b577b715f2ce95453363bf90ecc7e | <|skeleton|>
class MatchPlayerAPI:
"""A specific player in a specific match"""
def get(self, match_id, player_id):
"""Get a specific player from a battle"""
<|body_0|>
def delete(self, match_id, player_id):
"""A player has left an ongoing battle"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MatchPlayerAPI:
"""A specific player in a specific match"""
def get(self, match_id, player_id):
"""Get a specific player from a battle"""
player = g.db.query(MatchPlayer).filter(MatchPlayer.match_id == match_id, MatchPlayer.player_id == player_id).first()
if not player:
... | the_stack_v2_python_sparse | driftbase/api/matches.py | dgnorth/drift-base | train | 1 |
898b7cd6651312a4be77fae5e2c8c3914fed7b5a | [
"if len(strs) == 0:\n return ''\npreStr = strs[0]\nfor i in range(1, len(strs)):\n nowStr = strs[i]\n if preStr == '':\n break\n preStr = self.longestCommonPrefixTwo(preStr, nowStr)\nreturn preStr",
"i = 0\nwhile i < len(str1) and i < len(str2):\n if str1[i] == str2[i]:\n i += 1\n ... | <|body_start_0|>
if len(strs) == 0:
return ''
preStr = strs[0]
for i in range(1, len(strs)):
nowStr = strs[i]
if preStr == '':
break
preStr = self.longestCommonPrefixTwo(preStr, nowStr)
return preStr
<|end_body_0|>
<|body_s... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestCommonPrefix(self, strs):
""":type strs: List[str] :rtype: str"""
<|body_0|>
def longestCommonPrefixTwo(self, str1, str2):
""":type str1: str1 :type str2: str2 :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(s... | stack_v2_sparse_classes_36k_train_032124 | 754 | no_license | [
{
"docstring": ":type strs: List[str] :rtype: str",
"name": "longestCommonPrefix",
"signature": "def longestCommonPrefix(self, strs)"
},
{
"docstring": ":type str1: str1 :type str2: str2 :rtype: str",
"name": "longestCommonPrefixTwo",
"signature": "def longestCommonPrefixTwo(self, str1, ... | 2 | stack_v2_sparse_classes_30k_train_001349 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestCommonPrefix(self, strs): :type strs: List[str] :rtype: str
- def longestCommonPrefixTwo(self, str1, str2): :type str1: str1 :type str2: str2 :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestCommonPrefix(self, strs): :type strs: List[str] :rtype: str
- def longestCommonPrefixTwo(self, str1, str2): :type str1: str1 :type str2: str2 :rtype: str
<|skeleton|>... | ba264d6d218afefc0af385b036840ae451b4d714 | <|skeleton|>
class Solution:
def longestCommonPrefix(self, strs):
""":type strs: List[str] :rtype: str"""
<|body_0|>
def longestCommonPrefixTwo(self, str1, str2):
""":type str1: str1 :type str2: str2 :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestCommonPrefix(self, strs):
""":type strs: List[str] :rtype: str"""
if len(strs) == 0:
return ''
preStr = strs[0]
for i in range(1, len(strs)):
nowStr = strs[i]
if preStr == '':
break
preStr = se... | the_stack_v2_python_sparse | python-code/15-LongestCommonPrefix.py | Yingminzhou/leetcode | train | 0 | |
5c7a4736cbbbd8f71bc1462f2b3e84dc787063ac | [
"guess_str = (str(merkle_root) + str(previous_hash) + str(nonce)).encode('utf8')\nguess_hash = FuncUtil.hashfunc_sha256(guess_str)\ndifficulty = 1\nwhile int('f' * difficulty, 16) < sum_stake:\n difficulty += 1\nguess_weight = int(guess_hash[:difficulty], 16) / int('f' * difficulty, 16)\nreturn guess_weight < st... | <|body_start_0|>
guess_str = (str(merkle_root) + str(previous_hash) + str(nonce)).encode('utf8')
guess_hash = FuncUtil.hashfunc_sha256(guess_str)
difficulty = 1
while int('f' * difficulty, 16) < sum_stake:
difficulty += 1
guess_weight = int(guess_hash[:difficulty], 16... | Proof-of-Stake consenses mechanism | POS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class POS:
"""Proof-of-Stake consenses mechanism"""
def valid_proof(merkle_root, previous_hash, nonce, stake_weight=1, sum_stake=1):
"""Check if a guessing hash value satisfies the mining difficulty conditions. @ previous_hash: The hash of parent block @ nonce: the stake deposit value @ me... | stack_v2_sparse_classes_36k_train_032125 | 5,661 | no_license | [
{
"docstring": "Check if a guessing hash value satisfies the mining difficulty conditions. @ previous_hash: The hash of parent block @ nonce: the stake deposit value @ merkle_root: merkle tree root of transactions in block",
"name": "valid_proof",
"signature": "def valid_proof(merkle_root, previous_hash... | 3 | null | Implement the Python class `POS` described below.
Class description:
Proof-of-Stake consenses mechanism
Method signatures and docstrings:
- def valid_proof(merkle_root, previous_hash, nonce, stake_weight=1, sum_stake=1): Check if a guessing hash value satisfies the mining difficulty conditions. @ previous_hash: The h... | Implement the Python class `POS` described below.
Class description:
Proof-of-Stake consenses mechanism
Method signatures and docstrings:
- def valid_proof(merkle_root, previous_hash, nonce, stake_weight=1, sum_stake=1): Check if a guessing hash value satisfies the mining difficulty conditions. @ previous_hash: The h... | 03ff57e6fe0114ffd2dd953e79a73a893a6bc0ad | <|skeleton|>
class POS:
"""Proof-of-Stake consenses mechanism"""
def valid_proof(merkle_root, previous_hash, nonce, stake_weight=1, sum_stake=1):
"""Check if a guessing hash value satisfies the mining difficulty conditions. @ previous_hash: The hash of parent block @ nonce: the stake deposit value @ me... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class POS:
"""Proof-of-Stake consenses mechanism"""
def valid_proof(merkle_root, previous_hash, nonce, stake_weight=1, sum_stake=1):
"""Check if a guessing hash value satisfies the mining difficulty conditions. @ previous_hash: The hash of parent block @ nonce: the stake deposit value @ merkle_root: me... | the_stack_v2_python_sparse | Security/py_dev/ENF_chain/consensus/consensus.py | samuelxu999/Research | train | 1 |
e809ff6e95859b7e5e82607d1bb31e0676eeabc6 | [
"queryset = self.queryset\npage = self.paginate_queryset(queryset)\nif page is not None:\n serializer = self.get_serializer(page, many=True)\n return self.get_paginated_response(serializer.data)\nserializer = self.get_serializer(queryset, many=True)\nreturn Response(serializer.data)",
"merchant = Message.ob... | <|body_start_0|>
queryset = self.queryset
page = self.paginate_queryset(queryset)
if page is not None:
serializer = self.get_serializer(page, many=True)
return self.get_paginated_response(serializer.data)
serializer = self.get_serializer(queryset, many=True)
... | MessageViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MessageViewSet:
def list(self, request, *args, **kwargs):
"""消息列表"""
<|body_0|>
def partial_update(self, request, *args, **kwargs):
"""更新消息状态"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
queryset = self.queryset
page = self.paginate_query... | stack_v2_sparse_classes_36k_train_032126 | 1,718 | no_license | [
{
"docstring": "消息列表",
"name": "list",
"signature": "def list(self, request, *args, **kwargs)"
},
{
"docstring": "更新消息状态",
"name": "partial_update",
"signature": "def partial_update(self, request, *args, **kwargs)"
}
] | 2 | null | Implement the Python class `MessageViewSet` described below.
Class description:
Implement the MessageViewSet class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): 消息列表
- def partial_update(self, request, *args, **kwargs): 更新消息状态 | Implement the Python class `MessageViewSet` described below.
Class description:
Implement the MessageViewSet class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): 消息列表
- def partial_update(self, request, *args, **kwargs): 更新消息状态
<|skeleton|>
class MessageViewSet:
def list(self, re... | 0d32f98f42591b43e0b4da5e978b627da517f758 | <|skeleton|>
class MessageViewSet:
def list(self, request, *args, **kwargs):
"""消息列表"""
<|body_0|>
def partial_update(self, request, *args, **kwargs):
"""更新消息状态"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MessageViewSet:
def list(self, request, *args, **kwargs):
"""消息列表"""
queryset = self.queryset
page = self.paginate_queryset(queryset)
if page is not None:
serializer = self.get_serializer(page, many=True)
return self.get_paginated_response(serializer.dat... | the_stack_v2_python_sparse | payserver/padmin/views/message.py | yiyuhao/FukuanUnion | train | 0 | |
ae8e8d2a59a255d7b3aa52264a2a0f95e87f3d57 | [
"self._registered = {}\nself._discovered = {}\nself._lock = threading.Lock()\nself._browser = None",
"with self._lock:\n if device.serial in self._discovered:\n callback(self._discovered[device.serial])\n else:\n self._registered[device.serial] = callback",
"if info.type == TYPE_DYSON_360_EY... | <|body_start_0|>
self._registered = {}
self._discovered = {}
self._lock = threading.Lock()
self._browser = None
<|end_body_0|>
<|body_start_1|>
with self._lock:
if device.serial in self._discovered:
callback(self._discovered[device.serial])
... | Dyson device discovery. | DysonDiscovery | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DysonDiscovery:
"""Dyson device discovery."""
def __init__(self):
"""Initialize the instance."""
<|body_0|>
def register_device(self, device: DysonDevice, callback: Callable[[str], None]) -> None:
"""Register a device."""
<|body_1|>
def device_discov... | stack_v2_sparse_classes_36k_train_032127 | 2,876 | permissive | [
{
"docstring": "Initialize the instance.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Register a device.",
"name": "register_device",
"signature": "def register_device(self, device: DysonDevice, callback: Callable[[str], None]) -> None"
},
{
"docstri... | 5 | stack_v2_sparse_classes_30k_train_001773 | Implement the Python class `DysonDiscovery` described below.
Class description:
Dyson device discovery.
Method signatures and docstrings:
- def __init__(self): Initialize the instance.
- def register_device(self, device: DysonDevice, callback: Callable[[str], None]) -> None: Register a device.
- def device_discovered... | Implement the Python class `DysonDiscovery` described below.
Class description:
Dyson device discovery.
Method signatures and docstrings:
- def __init__(self): Initialize the instance.
- def register_device(self, device: DysonDevice, callback: Callable[[str], None]) -> None: Register a device.
- def device_discovered... | 8548d9999ddd54f13d6a307e013abcb8c897a74e | <|skeleton|>
class DysonDiscovery:
"""Dyson device discovery."""
def __init__(self):
"""Initialize the instance."""
<|body_0|>
def register_device(self, device: DysonDevice, callback: Callable[[str], None]) -> None:
"""Register a device."""
<|body_1|>
def device_discov... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DysonDiscovery:
"""Dyson device discovery."""
def __init__(self):
"""Initialize the instance."""
self._registered = {}
self._discovered = {}
self._lock = threading.Lock()
self._browser = None
def register_device(self, device: DysonDevice, callback: Callable[[s... | the_stack_v2_python_sparse | custom_components/dyson_local/vendor/libdyson/discovery.py | bacco007/HomeAssistantConfig | train | 98 |
d97a50c66eb719c680ab68d943d9fa12f37a1c09 | [
"field_names = list(super().get_field_names(declared_fields, info))\nexclude = getattr(self.Meta, 'exclude', None)\nif exclude is None or 'id' not in exclude:\n if 'id' not in field_names:\n field_names.insert(0, 'id')\nif hasattr(self.Meta.model, 'ObjectMeta') and hasattr(self.Meta.model.ObjectMeta, 'det... | <|body_start_0|>
field_names = list(super().get_field_names(declared_fields, info))
exclude = getattr(self.Meta, 'exclude', None)
if exclude is None or 'id' not in exclude:
if 'id' not in field_names:
field_names.insert(0, 'id')
if hasattr(self.Meta.model, 'Ob... | Serializer forcing id as field and adding 'url_field_name' (default: 'detail_url') if objects model has 'ObjectMeta'-class with 'detail_view_name' to view taking 'pk'. | DBObjectSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DBObjectSerializer:
"""Serializer forcing id as field and adding 'url_field_name' (default: 'detail_url') if objects model has 'ObjectMeta'-class with 'detail_view_name' to view taking 'pk'."""
def get_field_names(self, declared_fields, info):
"""Adds fields 'id' and self.url_field_n... | stack_v2_sparse_classes_36k_train_032128 | 11,760 | no_license | [
{
"docstring": "Adds fields 'id' and self.url_field_name if not explicitly said otherwise in exclude of metaclass.",
"name": "get_field_names",
"signature": "def get_field_names(self, declared_fields, info)"
},
{
"docstring": "Create a field representing the object's own URL. Uses objects",
... | 2 | stack_v2_sparse_classes_30k_train_012533 | Implement the Python class `DBObjectSerializer` described below.
Class description:
Serializer forcing id as field and adding 'url_field_name' (default: 'detail_url') if objects model has 'ObjectMeta'-class with 'detail_view_name' to view taking 'pk'.
Method signatures and docstrings:
- def get_field_names(self, decl... | Implement the Python class `DBObjectSerializer` described below.
Class description:
Serializer forcing id as field and adding 'url_field_name' (default: 'detail_url') if objects model has 'ObjectMeta'-class with 'detail_view_name' to view taking 'pk'.
Method signatures and docstrings:
- def get_field_names(self, decl... | e5af3fff2ec3e2b54ae0c2583f7994714c83dd39 | <|skeleton|>
class DBObjectSerializer:
"""Serializer forcing id as field and adding 'url_field_name' (default: 'detail_url') if objects model has 'ObjectMeta'-class with 'detail_view_name' to view taking 'pk'."""
def get_field_names(self, declared_fields, info):
"""Adds fields 'id' and self.url_field_n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DBObjectSerializer:
"""Serializer forcing id as field and adding 'url_field_name' (default: 'detail_url') if objects model has 'ObjectMeta'-class with 'detail_view_name' to view taking 'pk'."""
def get_field_names(self, declared_fields, info):
"""Adds fields 'id' and self.url_field_name if not ex... | the_stack_v2_python_sparse | utils/serializers.py | Fysiksektionen/Hemsida-Backend | train | 0 |
73a08a944039b4fc8c0b18c7f70d89221dee9841 | [
"_id = request.args.get('id', None)\nif not _id:\n return ({'msg': 'params error !'}, 400)\ntry:\n result = mongo_algo.db.algo_info.find_one({'_id': bson.ObjectId(_id)}, {'hyper_params': 1})\n if not result:\n return ({'msg': 'id is not exist !'}, 200)\nexcept Exception as e:\n logging.error(e, e... | <|body_start_0|>
_id = request.args.get('id', None)
if not _id:
return ({'msg': 'params error !'}, 400)
try:
result = mongo_algo.db.algo_info.find_one({'_id': bson.ObjectId(_id)}, {'hyper_params': 1})
if not result:
return ({'msg': 'id is not e... | HyperParamsViews | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HyperParamsViews:
def get(self):
"""get one hyper params through id :return:"""
<|body_0|>
def post(self):
"""add an hyper params record :return:"""
<|body_1|>
def put(self):
"""update hyper params record :return:"""
<|body_2|>
<|end_ske... | stack_v2_sparse_classes_36k_train_032129 | 20,183 | no_license | [
{
"docstring": "get one hyper params through id :return:",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "add an hyper params record :return:",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "update hyper params record :return:",
"name": "put"... | 3 | stack_v2_sparse_classes_30k_train_020715 | Implement the Python class `HyperParamsViews` described below.
Class description:
Implement the HyperParamsViews class.
Method signatures and docstrings:
- def get(self): get one hyper params through id :return:
- def post(self): add an hyper params record :return:
- def put(self): update hyper params record :return: | Implement the Python class `HyperParamsViews` described below.
Class description:
Implement the HyperParamsViews class.
Method signatures and docstrings:
- def get(self): get one hyper params through id :return:
- def post(self): add an hyper params record :return:
- def put(self): update hyper params record :return:... | 054324b50e807d6f4e98f4a1b67afac9a0653b06 | <|skeleton|>
class HyperParamsViews:
def get(self):
"""get one hyper params through id :return:"""
<|body_0|>
def post(self):
"""add an hyper params record :return:"""
<|body_1|>
def put(self):
"""update hyper params record :return:"""
<|body_2|>
<|end_ske... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HyperParamsViews:
def get(self):
"""get one hyper params through id :return:"""
_id = request.args.get('id', None)
if not _id:
return ({'msg': 'params error !'}, 400)
try:
result = mongo_algo.db.algo_info.find_one({'_id': bson.ObjectId(_id)}, {'hyper_par... | the_stack_v2_python_sparse | services/AlgoVersion/views.py | condilin/DMS | train | 0 | |
df0278dea15e46cdbb666674db84128cb87dad3d | [
"super().configure_data(cfg, data_cfg)\nif self.training:\n if cfg.data.get('unlabeled', False) and cfg.data.unlabeled.get('otx_dataset', False):\n if len(cfg.data.unlabeled.get('pipeline', [])) == 0:\n cfg.data.unlabeled.pipeline = cfg.data.train.pipeline.copy()\n self.configure_unlabel... | <|body_start_0|>
super().configure_data(cfg, data_cfg)
if self.training:
if cfg.data.get('unlabeled', False) and cfg.data.unlabeled.get('otx_dataset', False):
if len(cfg.data.unlabeled.get('pipeline', [])) == 0:
cfg.data.unlabeled.pipeline = cfg.data.train... | Patch config to support semi supervised learning. | SemiSLConfigurerMixin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SemiSLConfigurerMixin:
"""Patch config to support semi supervised learning."""
def configure_data(self, cfg, data_cfg):
"""Patch cfg.data."""
<|body_0|>
def configure_unlabeled_dataloader(cfg: Config):
"""Patch for unlabled dataloader."""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k_train_032130 | 2,319 | permissive | [
{
"docstring": "Patch cfg.data.",
"name": "configure_data",
"signature": "def configure_data(self, cfg, data_cfg)"
},
{
"docstring": "Patch for unlabled dataloader.",
"name": "configure_unlabeled_dataloader",
"signature": "def configure_unlabeled_dataloader(cfg: Config)"
}
] | 2 | null | Implement the Python class `SemiSLConfigurerMixin` described below.
Class description:
Patch config to support semi supervised learning.
Method signatures and docstrings:
- def configure_data(self, cfg, data_cfg): Patch cfg.data.
- def configure_unlabeled_dataloader(cfg: Config): Patch for unlabled dataloader. | Implement the Python class `SemiSLConfigurerMixin` described below.
Class description:
Patch config to support semi supervised learning.
Method signatures and docstrings:
- def configure_data(self, cfg, data_cfg): Patch cfg.data.
- def configure_unlabeled_dataloader(cfg: Config): Patch for unlabled dataloader.
<|ske... | 80454808b38727e358e8b880043eeac0f18152fb | <|skeleton|>
class SemiSLConfigurerMixin:
"""Patch config to support semi supervised learning."""
def configure_data(self, cfg, data_cfg):
"""Patch cfg.data."""
<|body_0|>
def configure_unlabeled_dataloader(cfg: Config):
"""Patch for unlabled dataloader."""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SemiSLConfigurerMixin:
"""Patch config to support semi supervised learning."""
def configure_data(self, cfg, data_cfg):
"""Patch cfg.data."""
super().configure_data(cfg, data_cfg)
if self.training:
if cfg.data.get('unlabeled', False) and cfg.data.unlabeled.get('otx_dat... | the_stack_v2_python_sparse | src/otx/algorithms/common/adapters/mmcv/semisl_mixin.py | openvinotoolkit/training_extensions | train | 397 |
962b2476b73530dac7b44dad1d2e5214fcddec46 | [
"raise_error = kwargs.get(RAISE_ERROR, True)\ntry:\n cls.check_valid_status(appointment, cls.status_enum.ON_MODERATION)\nexcept WrongStatusError as err:\n valid_err = cls.reject_error(title=err.title)\n if raise_error:\n raise valid_err\n else:\n logging.warning(valid_err, extra={APPOINTME... | <|body_start_0|>
raise_error = kwargs.get(RAISE_ERROR, True)
try:
cls.check_valid_status(appointment, cls.status_enum.ON_MODERATION)
except WrongStatusError as err:
valid_err = cls.reject_error(title=err.title)
if raise_error:
raise valid_err
... | AppointmentModerationValidator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppointmentModerationValidator:
def validate_before_reject(cls, appointment: Appointment, **kwargs) -> None:
""":raises: apps.appointments.exceptions.AppointmentRejectError"""
<|body_0|>
def validate_before_approve(cls, appointment: Appointment) -> None:
""":raises: ... | stack_v2_sparse_classes_36k_train_032131 | 12,944 | no_license | [
{
"docstring": ":raises: apps.appointments.exceptions.AppointmentRejectError",
"name": "validate_before_reject",
"signature": "def validate_before_reject(cls, appointment: Appointment, **kwargs) -> None"
},
{
"docstring": ":raises: apps.appointments.exceptions.AppointmentApproveError",
"name... | 3 | stack_v2_sparse_classes_30k_train_020771 | Implement the Python class `AppointmentModerationValidator` described below.
Class description:
Implement the AppointmentModerationValidator class.
Method signatures and docstrings:
- def validate_before_reject(cls, appointment: Appointment, **kwargs) -> None: :raises: apps.appointments.exceptions.AppointmentRejectEr... | Implement the Python class `AppointmentModerationValidator` described below.
Class description:
Implement the AppointmentModerationValidator class.
Method signatures and docstrings:
- def validate_before_reject(cls, appointment: Appointment, **kwargs) -> None: :raises: apps.appointments.exceptions.AppointmentRejectEr... | 447a4c46e578f9aa1ae015edd39752d3b9b5cb28 | <|skeleton|>
class AppointmentModerationValidator:
def validate_before_reject(cls, appointment: Appointment, **kwargs) -> None:
""":raises: apps.appointments.exceptions.AppointmentRejectError"""
<|body_0|>
def validate_before_approve(cls, appointment: Appointment) -> None:
""":raises: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AppointmentModerationValidator:
def validate_before_reject(cls, appointment: Appointment, **kwargs) -> None:
""":raises: apps.appointments.exceptions.AppointmentRejectError"""
raise_error = kwargs.get(RAISE_ERROR, True)
try:
cls.check_valid_status(appointment, cls.status_en... | the_stack_v2_python_sparse | apps/appointments/validators.py | kirmalyshev/django_example | train | 0 | |
b7968d334791692082fdd99c2624240106846c7e | [
"sums = [0]\nfor x in nums:\n sums.append(sums[-1] + x)\ncount = 0\nfor j in range(len(sums)):\n for i in range(j):\n if sums[j] - sums[i] == k:\n count += 1\nreturn count",
"count, sums = (0, 0)\ndic = {0: 1}\nfor x in nums:\n sums += x\n count += dic.get(sums - k, 0)\n dic[sums]... | <|body_start_0|>
sums = [0]
for x in nums:
sums.append(sums[-1] + x)
count = 0
for j in range(len(sums)):
for i in range(j):
if sums[j] - sums[i] == k:
count += 1
return count
<|end_body_0|>
<|body_start_1|>
cou... | 求连续子数组的和，直接用暴力破解，穷举出所有子数组，算出和等于k即可。 如何求子数组的和sum(i,j)？ 暴力法需要重复多次计算，而使用前缀和记录则是O(1) 即：sum(i,j) = sum(0,j) - sum(0,i-1) index: 0 1 2 4 nums: [ 1, 2, 1, 3 ] sum: 0, 1, 3, 4, 7 补位0，num==k的场景 | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""求连续子数组的和，直接用暴力破解，穷举出所有子数组，算出和等于k即可。 如何求子数组的和sum(i,j)？ 暴力法需要重复多次计算，而使用前缀和记录则是O(1) 即：sum(i,j) = sum(0,j) - sum(0,i-1) index: 0 1 2 4 nums: [ 1, 2, 1, 3 ] sum: 0, 1, 3, 4, 7 补位0，num==k的场景"""
def subarraySum1(self, nums: List[int], k: int) -> int:
"""前缀和：会超时，仅提供思路"""
... | stack_v2_sparse_classes_36k_train_032132 | 1,897 | no_license | [
{
"docstring": "前缀和：会超时，仅提供思路",
"name": "subarraySum1",
"signature": "def subarraySum1(self, nums: List[int], k: int) -> int"
},
{
"docstring": "前缀和+hash表优化: 问题转化为: 求i<j，满足sum(i) = sum(j) - k 对于每个j，记录之前所有的presum，然后查找有多少个presum==sum(j)-k",
"name": "subarraySum2",
"signature": "def subarra... | 2 | stack_v2_sparse_classes_30k_train_000374 | Implement the Python class `Solution` described below.
Class description:
求连续子数组的和，直接用暴力破解，穷举出所有子数组，算出和等于k即可。 如何求子数组的和sum(i,j)？ 暴力法需要重复多次计算，而使用前缀和记录则是O(1) 即：sum(i,j) = sum(0,j) - sum(0,i-1) index: 0 1 2 4 nums: [ 1, 2, 1, 3 ] sum: 0, 1, 3, 4, 7 补位0，num==k的场景
Method signatures and docstrings:
- def subarraySum1(self, ... | Implement the Python class `Solution` described below.
Class description:
求连续子数组的和，直接用暴力破解，穷举出所有子数组，算出和等于k即可。 如何求子数组的和sum(i,j)？ 暴力法需要重复多次计算，而使用前缀和记录则是O(1) 即：sum(i,j) = sum(0,j) - sum(0,i-1) index: 0 1 2 4 nums: [ 1, 2, 1, 3 ] sum: 0, 1, 3, 4, 7 补位0，num==k的场景
Method signatures and docstrings:
- def subarraySum1(self, ... | 2bbb1640589aab34f2bc42489283033cc11fb885 | <|skeleton|>
class Solution:
"""求连续子数组的和，直接用暴力破解，穷举出所有子数组，算出和等于k即可。 如何求子数组的和sum(i,j)？ 暴力法需要重复多次计算，而使用前缀和记录则是O(1) 即：sum(i,j) = sum(0,j) - sum(0,i-1) index: 0 1 2 4 nums: [ 1, 2, 1, 3 ] sum: 0, 1, 3, 4, 7 补位0，num==k的场景"""
def subarraySum1(self, nums: List[int], k: int) -> int:
"""前缀和：会超时，仅提供思路"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""求连续子数组的和，直接用暴力破解，穷举出所有子数组，算出和等于k即可。 如何求子数组的和sum(i,j)？ 暴力法需要重复多次计算，而使用前缀和记录则是O(1) 即：sum(i,j) = sum(0,j) - sum(0,i-1) index: 0 1 2 4 nums: [ 1, 2, 1, 3 ] sum: 0, 1, 3, 4, 7 补位0，num==k的场景"""
def subarraySum1(self, nums: List[int], k: int) -> int:
"""前缀和：会超时，仅提供思路"""
sums = [0]
... | the_stack_v2_python_sparse | 560_subarray-sum-equals-k.py | helloocc/algorithm | train | 1 |
f5fb50d0ef37b2b9bf4de53a09c355740486895a | [
"inputs = np.random.rand(2, input_size, input_size, 3)\ntf.keras.backend.set_image_data_format('channels_last')\nbackbone = basnet_model.BASNetEncoder()\ndecoder = basnet_model.BASNetDecoder()\nrefinement = refunet.RefUnet()\nmodel = basnet_model.BASNetModel(backbone=backbone, decoder=decoder, refinement=refinement... | <|body_start_0|>
inputs = np.random.rand(2, input_size, input_size, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = basnet_model.BASNetEncoder()
decoder = basnet_model.BASNetDecoder()
refinement = refunet.RefUnet()
model = basnet_model.BASNetModel(ba... | BASNetNetworkTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BASNetNetworkTest:
def test_basnet_network_creation(self, input_size):
"""Test for creation of a segmentation network."""
<|body_0|>
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k_train_032133 | 2,356 | permissive | [
{
"docstring": "Test for creation of a segmentation network.",
"name": "test_basnet_network_creation",
"signature": "def test_basnet_network_creation(self, input_size)"
},
{
"docstring": "Validate the network can be serialized and deserialized.",
"name": "test_serialize_deserialize",
"si... | 2 | null | Implement the Python class `BASNetNetworkTest` described below.
Class description:
Implement the BASNetNetworkTest class.
Method signatures and docstrings:
- def test_basnet_network_creation(self, input_size): Test for creation of a segmentation network.
- def test_serialize_deserialize(self): Validate the network ca... | Implement the Python class `BASNetNetworkTest` described below.
Class description:
Implement the BASNetNetworkTest class.
Method signatures and docstrings:
- def test_basnet_network_creation(self, input_size): Test for creation of a segmentation network.
- def test_serialize_deserialize(self): Validate the network ca... | d3507b550a3ade40cade60a79eb5b8978b56c7ae | <|skeleton|>
class BASNetNetworkTest:
def test_basnet_network_creation(self, input_size):
"""Test for creation of a segmentation network."""
<|body_0|>
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BASNetNetworkTest:
def test_basnet_network_creation(self, input_size):
"""Test for creation of a segmentation network."""
inputs = np.random.rand(2, input_size, input_size, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = basnet_model.BASNetEncoder()
... | the_stack_v2_python_sparse | official/projects/basnet/modeling/basnet_model_test.py | jianzhnie/models | train | 2 | |
04947dc78c4c62c5ad3066f80a4a7181443c8d43 | [
"if len(s) % 2 != 0:\n return False\nif len(s) == 0:\n return True\nss1 = ['(', ')', '{', '}', '[', ']']\nfor i in range(0, 6, 2):\n if s.count(ss1[i]) != s.count(ss1[i + 1]):\n return False\nwhile '()' in s or '[]' in s or '{}' in s:\n s = s.replace('()', '')\n s = s.replace('[]', '')\n s ... | <|body_start_0|>
if len(s) % 2 != 0:
return False
if len(s) == 0:
return True
ss1 = ['(', ')', '{', '}', '[', ']']
for i in range(0, 6, 2):
if s.count(ss1[i]) != s.count(ss1[i + 1]):
return False
while '()' in s or '[]' in s or ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isValid1(self, s):
""":type s: str :rtype: bool"""
<|body_0|>
def isValid(self, s):
""":type s: str :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(s) % 2 != 0:
return False
if len(s) == 0:
... | stack_v2_sparse_classes_36k_train_032134 | 1,310 | no_license | [
{
"docstring": ":type s: str :rtype: bool",
"name": "isValid1",
"signature": "def isValid1(self, s)"
},
{
"docstring": ":type s: str :rtype: bool",
"name": "isValid",
"signature": "def isValid(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isValid1(self, s): :type s: str :rtype: bool
- def isValid(self, s): :type s: str :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isValid1(self, s): :type s: str :rtype: bool
- def isValid(self, s): :type s: str :rtype: bool
<|skeleton|>
class Solution:
def isValid1(self, s):
""":type s: s... | f1d780b7e8b91b4df704651514018143c6931f9d | <|skeleton|>
class Solution:
def isValid1(self, s):
""":type s: str :rtype: bool"""
<|body_0|>
def isValid(self, s):
""":type s: str :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isValid1(self, s):
""":type s: str :rtype: bool"""
if len(s) % 2 != 0:
return False
if len(s) == 0:
return True
ss1 = ['(', ')', '{', '}', '[', ']']
for i in range(0, 6, 2):
if s.count(ss1[i]) != s.count(ss1[i + 1]):
... | the_stack_v2_python_sparse | ProgramForLeetCode/LeetCode/20_isvalid.py | DQDH/Algorithm_Code | train | 0 | |
116910a56c9988d6690a9e57ded9f98de6908be2 | [
"i, n, res = (0, len(nums), [])\nwhile i < n:\n if nums[i] != i + 1 and nums[i] != nums[nums[i] - 1]:\n temp = nums[i]\n nums[i] = nums[nums[i] - 1]\n nums[temp - 1] = temp\n else:\n i += 1\nfor i, num in enumerate(nums):\n if i + 1 != num:\n res.append(i + 1)\nreturn res... | <|body_start_0|>
i, n, res = (0, len(nums), [])
while i < n:
if nums[i] != i + 1 and nums[i] != nums[nums[i] - 1]:
temp = nums[i]
nums[i] = nums[nums[i] - 1]
nums[temp - 1] = temp
else:
i += 1
for i, num in e... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDisappearedNumbers(self, nums: List[int]) -> List[int]:
"""Swap numbers to be at their correct indices. Time: O(n) Space: O(1) excluding output list"""
<|body_0|>
def findDisappearedNumbers2(self, nums: List[int]) -> List[int]:
"""Sort and add numbe... | stack_v2_sparse_classes_36k_train_032135 | 1,581 | no_license | [
{
"docstring": "Swap numbers to be at their correct indices. Time: O(n) Space: O(1) excluding output list",
"name": "findDisappearedNumbers",
"signature": "def findDisappearedNumbers(self, nums: List[int]) -> List[int]"
},
{
"docstring": "Sort and add numbers that aren't in nums. Time: O(nlogn) ... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers(self, nums: List[int]) -> List[int]: Swap numbers to be at their correct indices. Time: O(n) Space: O(1) excluding output list
- def findDisappearedNum... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDisappearedNumbers(self, nums: List[int]) -> List[int]: Swap numbers to be at their correct indices. Time: O(n) Space: O(1) excluding output list
- def findDisappearedNum... | c14d8829c95f61ff6691816e8c0de76b9319f389 | <|skeleton|>
class Solution:
def findDisappearedNumbers(self, nums: List[int]) -> List[int]:
"""Swap numbers to be at their correct indices. Time: O(n) Space: O(1) excluding output list"""
<|body_0|>
def findDisappearedNumbers2(self, nums: List[int]) -> List[int]:
"""Sort and add numbe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findDisappearedNumbers(self, nums: List[int]) -> List[int]:
"""Swap numbers to be at their correct indices. Time: O(n) Space: O(1) excluding output list"""
i, n, res = (0, len(nums), [])
while i < n:
if nums[i] != i + 1 and nums[i] != nums[nums[i] - 1]:
... | the_stack_v2_python_sparse | easy/find-all-numbers-disappeared-in-an-array/solution.py | hsuanhauliu/leetcode-solutions | train | 0 | |
226826158e2f6b8d9717a4ca44c6bc8690282af4 | [
"try:\n self.request_control = request.RequestController(endopoint=accounting_endpoint)\nexcept Exception as e:\n raise exceptions.ConfigurationException('Accounting server configuration failed %s. ' % e.message)",
"path = '/set_accounting'\nparameters = {'admin_token': admin_token, 'accounting': accounting... | <|body_start_0|>
try:
self.request_control = request.RequestController(endopoint=accounting_endpoint)
except Exception as e:
raise exceptions.ConfigurationException('Accounting server configuration failed %s. ' % e.message)
<|end_body_0|>
<|body_start_1|>
path = '/set_ac... | Notification controller for batch systems. | BatchNotificationController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BatchNotificationController:
"""Notification controller for batch systems."""
def __init__(self, accounting_endpoint):
"""Initialize the controller It set attributes and creates a Request controller by using the endpoint related to the accounting server. :param accounting_endpoint: :... | stack_v2_sparse_classes_36k_train_032136 | 29,683 | permissive | [
{
"docstring": "Initialize the controller It set attributes and creates a Request controller by using the endpoint related to the accounting server. :param accounting_endpoint: :return:",
"name": "__init__",
"signature": "def __init__(self, accounting_endpoint)"
},
{
"docstring": "Execute a PUT ... | 2 | stack_v2_sparse_classes_30k_train_021390 | Implement the Python class `BatchNotificationController` described below.
Class description:
Notification controller for batch systems.
Method signatures and docstrings:
- def __init__(self, accounting_endpoint): Initialize the controller It set attributes and creates a Request controller by using the endpoint relate... | Implement the Python class `BatchNotificationController` described below.
Class description:
Notification controller for batch systems.
Method signatures and docstrings:
- def __init__(self, accounting_endpoint): Initialize the controller It set attributes and creates a Request controller by using the endpoint relate... | 346f5bdd7a1ff6c705c30172661a93540d9f0985 | <|skeleton|>
class BatchNotificationController:
"""Notification controller for batch systems."""
def __init__(self, accounting_endpoint):
"""Initialize the controller It set attributes and creates a Request controller by using the endpoint related to the accounting server. :param accounting_endpoint: :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BatchNotificationController:
"""Notification controller for batch systems."""
def __init__(self, accounting_endpoint):
"""Initialize the controller It set attributes and creates a Request controller by using the endpoint related to the accounting server. :param accounting_endpoint: :return:"""
... | the_stack_v2_python_sparse | bdocker/modules/batch.py | indigo-dc/bdocker | train | 4 |
2973a96cc233a04d740c4c88e720cc72fcb2afe8 | [
"query.add_extra({'_': 'akeys(\"%s\")' % attr}, None, None, None, None, None)\nresult = query.get_compiler(self.db).execute_sql(SINGLE)\nreturn result[0] if result else []",
"query.add_extra({'_': '%s -> %%s' % attr}, [key], None, None, None, None)\nresult = query.get_compiler(self.db).execute_sql(SINGLE)\nif res... | <|body_start_0|>
query.add_extra({'_': 'akeys("%s")' % attr}, None, None, None, None, None)
result = query.get_compiler(self.db).execute_sql(SINGLE)
return result[0] if result else []
<|end_body_0|>
<|body_start_1|>
query.add_extra({'_': '%s -> %%s' % attr}, [key], None, None, None, Non... | HStoreQuerysetMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HStoreQuerysetMixin:
def hkeys(self, query, attr):
"""Enumerates the keys in the specified hstore."""
<|body_0|>
def hpeek(self, query, attr, key):
"""Peeks at a value of the specified key."""
<|body_1|>
def hslice(self, query, attr, keys):
"""Sl... | stack_v2_sparse_classes_36k_train_032137 | 22,243 | no_license | [
{
"docstring": "Enumerates the keys in the specified hstore.",
"name": "hkeys",
"signature": "def hkeys(self, query, attr)"
},
{
"docstring": "Peeks at a value of the specified key.",
"name": "hpeek",
"signature": "def hpeek(self, query, attr, key)"
},
{
"docstring": "Slices the ... | 5 | null | Implement the Python class `HStoreQuerysetMixin` described below.
Class description:
Implement the HStoreQuerysetMixin class.
Method signatures and docstrings:
- def hkeys(self, query, attr): Enumerates the keys in the specified hstore.
- def hpeek(self, query, attr, key): Peeks at a value of the specified key.
- def... | Implement the Python class `HStoreQuerysetMixin` described below.
Class description:
Implement the HStoreQuerysetMixin class.
Method signatures and docstrings:
- def hkeys(self, query, attr): Enumerates the keys in the specified hstore.
- def hpeek(self, query, attr, key): Peeks at a value of the specified key.
- def... | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | <|skeleton|>
class HStoreQuerysetMixin:
def hkeys(self, query, attr):
"""Enumerates the keys in the specified hstore."""
<|body_0|>
def hpeek(self, query, attr, key):
"""Peeks at a value of the specified key."""
<|body_1|>
def hslice(self, query, attr, keys):
"""Sl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HStoreQuerysetMixin:
def hkeys(self, query, attr):
"""Enumerates the keys in the specified hstore."""
query.add_extra({'_': 'akeys("%s")' % attr}, None, None, None, None, None)
result = query.get_compiler(self.db).execute_sql(SINGLE)
return result[0] if result else []
def ... | the_stack_v2_python_sparse | repoData/djangonauts-djorm-ext-hstore/allPythonContent.py | aCoffeeYin/pyreco | train | 0 | |
27445d915c623dd4455d27eccec82fd15c242a74 | [
"self._query = query\nself._doc_fields = doc_fields\nself._usr_fields = usr_fields\nself._hparams = hparams\nself._mode = mode\nself.text_ftr_size = len(hparams.filter_window_sizes) * hparams.num_filters\nself.embedding = model_utils.init_word_embedding(self._hparams, self._mode)\nwith tf.variable_scope('cnn', dtyp... | <|body_start_0|>
self._query = query
self._doc_fields = doc_fields
self._usr_fields = usr_fields
self._hparams = hparams
self._mode = mode
self.text_ftr_size = len(hparams.filter_window_sizes) * hparams.num_filters
self.embedding = model_utils.init_word_embedding(... | CnnModel | [
"BSD-2-Clause",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CnnModel:
def __init__(self, query, doc_fields, usr_fields, hparams, mode):
"""Applies CNN to convert text to a fix length embedding :param query: Tensor(dtype=tf.int) Shape=[batch_size, query_length] :param doc_fields: list(Tensor(dtype=int))/Tensor A list of document fields. Each has s... | stack_v2_sparse_classes_36k_train_032138 | 7,785 | permissive | [
{
"docstring": "Applies CNN to convert text to a fix length embedding :param query: Tensor(dtype=tf.int) Shape=[batch_size, query_length] :param doc_fields: list(Tensor(dtype=int))/Tensor A list of document fields. Each has shape= [batch_size, max_group_size, doc_field_length]. For online scoring, these fields ... | 4 | stack_v2_sparse_classes_30k_train_007429 | Implement the Python class `CnnModel` described below.
Class description:
Implement the CnnModel class.
Method signatures and docstrings:
- def __init__(self, query, doc_fields, usr_fields, hparams, mode): Applies CNN to convert text to a fix length embedding :param query: Tensor(dtype=tf.int) Shape=[batch_size, quer... | Implement the Python class `CnnModel` described below.
Class description:
Implement the CnnModel class.
Method signatures and docstrings:
- def __init__(self, query, doc_fields, usr_fields, hparams, mode): Applies CNN to convert text to a fix length embedding :param query: Tensor(dtype=tf.int) Shape=[batch_size, quer... | 38e7b74879debd8ae5f2685367c81cc3a8aa003b | <|skeleton|>
class CnnModel:
def __init__(self, query, doc_fields, usr_fields, hparams, mode):
"""Applies CNN to convert text to a fix length embedding :param query: Tensor(dtype=tf.int) Shape=[batch_size, query_length] :param doc_fields: list(Tensor(dtype=int))/Tensor A list of document fields. Each has s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CnnModel:
def __init__(self, query, doc_fields, usr_fields, hparams, mode):
"""Applies CNN to convert text to a fix length embedding :param query: Tensor(dtype=tf.int) Shape=[batch_size, query_length] :param doc_fields: list(Tensor(dtype=int))/Tensor A list of document fields. Each has shape= [batch_s... | the_stack_v2_python_sparse | src/detext/model/cnn_model.py | naimmalek/detext | train | 1 | |
80e6d23d72bfca7d4581cdae011a0567d315b373 | [
"primary = self.getPrimaryField()\nif primary:\n return primary.getRaw(self)\nelse:\n return ''",
"histories = list(self.getHistories(1))\nif not histories:\n return None\nuser = histories[0][3].split(' ')[-1].strip()\nreturn user",
"mTool = getToolByName(self, 'portal_membership')\nhistories = list(se... | <|body_start_0|>
primary = self.getPrimaryField()
if primary:
return primary.getRaw(self)
else:
return ''
<|end_body_0|>
<|body_start_1|>
histories = list(self.getHistories(1))
if not histories:
return None
user = histories[0][3].split... | History aware mixin class Shows a unified diff history of the content This mixin is using some low level functions of the ZODB to get the last transaction states (versions) of the current object. Older histories will disapear after packing the database so DO NOT rely on the history functionality. It's more a gimmick an... | HistoryAwareMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HistoryAwareMixin:
"""History aware mixin class Shows a unified diff history of the content This mixin is using some low level functions of the ZODB to get the last transaction states (versions) of the current object. Older histories will disapear after packing the database so DO NOT rely on the ... | stack_v2_sparse_classes_36k_train_032139 | 3,584 | no_license | [
{
"docstring": "get source for HistoryAwareMixin Must return a (raw) string",
"name": "getHistorySource",
"signature": "def getHistorySource(self)"
},
{
"docstring": "Returns the user name of the last editor. Returns None if no last editor is known.",
"name": "getLastEditor",
"signature"... | 3 | null | Implement the Python class `HistoryAwareMixin` described below.
Class description:
History aware mixin class Shows a unified diff history of the content This mixin is using some low level functions of the ZODB to get the last transaction states (versions) of the current object. Older histories will disapear after pack... | Implement the Python class `HistoryAwareMixin` described below.
Class description:
History aware mixin class Shows a unified diff history of the content This mixin is using some low level functions of the ZODB to get the last transaction states (versions) of the current object. Older histories will disapear after pack... | 9c59626073daa97162c2b1d33a39a043f386cd8e | <|skeleton|>
class HistoryAwareMixin:
"""History aware mixin class Shows a unified diff history of the content This mixin is using some low level functions of the ZODB to get the last transaction states (versions) of the current object. Older histories will disapear after packing the database so DO NOT rely on the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HistoryAwareMixin:
"""History aware mixin class Shows a unified diff history of the content This mixin is using some low level functions of the ZODB to get the last transaction states (versions) of the current object. Older histories will disapear after packing the database so DO NOT rely on the history funct... | the_stack_v2_python_sparse | Products/ATContentTypes/lib/historyaware.py | plone/Products.ATContentTypes | train | 1 |
05fd99b059a217e2030ac146acccfae954a3078b | [
"src_type, src_path = self.identify_type(src)\ndest_type, dest_path = self.identify_type(dest)\nformat_table = {'s3': self.s3_format, 'local': self.local_format}\ndir_op = parameters['dir_op']\nsrc_path = format_table[src_type](src_path, dir_op)[0]\ndest_path, use_src_name = format_table[dest_type](dest_path, dir_o... | <|body_start_0|>
src_type, src_path = self.identify_type(src)
dest_type, dest_path = self.identify_type(dest)
format_table = {'s3': self.s3_format, 'local': self.local_format}
dir_op = parameters['dir_op']
src_path = format_table[src_type](src_path, dir_op)[0]
dest_path, ... | FileFormat | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileFormat:
def format(self, src, dest, parameters):
"""This function formats the source and destination path to the proper form for a file generator. Note that a file is designated as an s3 file if it begins with s3:// :param src: The path of the source :type src: string :param dest: Th... | stack_v2_sparse_classes_36k_train_032140 | 6,027 | permissive | [
{
"docstring": "This function formats the source and destination path to the proper form for a file generator. Note that a file is designated as an s3 file if it begins with s3:// :param src: The path of the source :type src: string :param dest: The path of the dest :type dest: string :param parameters: A dicti... | 4 | null | Implement the Python class `FileFormat` described below.
Class description:
Implement the FileFormat class.
Method signatures and docstrings:
- def format(self, src, dest, parameters): This function formats the source and destination path to the proper form for a file generator. Note that a file is designated as an s... | Implement the Python class `FileFormat` described below.
Class description:
Implement the FileFormat class.
Method signatures and docstrings:
- def format(self, src, dest, parameters): This function formats the source and destination path to the proper form for a file generator. Note that a file is designated as an s... | 147d16dfdb72dc9cf362b676a57e46a49375afbd | <|skeleton|>
class FileFormat:
def format(self, src, dest, parameters):
"""This function formats the source and destination path to the proper form for a file generator. Note that a file is designated as an s3 file if it begins with s3:// :param src: The path of the source :type src: string :param dest: Th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileFormat:
def format(self, src, dest, parameters):
"""This function formats the source and destination path to the proper form for a file generator. Note that a file is designated as an s3 file if it begins with s3:// :param src: The path of the source :type src: string :param dest: The path of the ... | the_stack_v2_python_sparse | awscli/customizations/s3/fileformat.py | aws/aws-cli | train | 13,038 | |
528c8b5d28b583c4a0e0d04572fdcf3a2e36fd38 | [
"args = {}\nargs.update(cls.args_map_export())\nargs.update({'json_flat': False})\nreturn args",
"super(Json, self).start(**kwargs)\nflat = self.get_arg_value('json_flat')\nself._first_row = True\nself.open_fd()\nbegin = '' if flat else '['\nself._fd.write(begin)",
"super(Json, self).stop(**kwargs)\nflat = self... | <|body_start_0|>
args = {}
args.update(cls.args_map_export())
args.update({'json_flat': False})
return args
<|end_body_0|>
<|body_start_1|>
super(Json, self).start(**kwargs)
flat = self.get_arg_value('json_flat')
self._first_row = True
self.open_fd()
... | Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for callback generic arguments to for... | Json | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Json:
"""Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for c... | stack_v2_sparse_classes_36k_train_032141 | 4,497 | permissive | [
{
"docstring": "Get the custom argument names and their defaults for this callbacks object. Examples: Export the output to STDOUT. If ``export_file`` is not supplied, the default is to print the output to STDOUT. >>> assets = apiobj.get(export=\"json\") Export the output to a file in the default path :attr:`axo... | 6 | stack_v2_sparse_classes_30k_train_003949 | Implement the Python class `Json` described below.
Class description:
Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # o... | Implement the Python class `Json` described below.
Class description:
Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # o... | be49566e590834df1b46494c8588651fa029b8c5 | <|skeleton|>
class Json:
"""Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Json:
"""Callbacks for formatting asset data and exporting it in JSON format. Examples: Create a ``client`` using :obj:`axonius_api_client.connect.Connect` and assume ``apiobj`` is either ``client.devices`` or ``client.users`` >>> apiobj = client.devices # or client.users * :meth:`args_map` for callback gener... | the_stack_v2_python_sparse | axonius_api_client/api/asset_callbacks/base_json.py | Axonius/axonius_api_client | train | 17 |
0c5dc20af8517fcbf26c935fce8d1bc1a5ac746d | [
"assert number_rows > 0\nassert number_columns > 0\nsuper().__init__(self.PROBLEM_NAME)\nself.number_rows = number_rows\nself.number_columns = number_columns",
"print('Solving {} problem ...'.format(self.PROBLEM_NAME))\nm = self.number_rows\nn = self.number_columns\npath_matrix = [[-1 for i in range(n)] for j in ... | <|body_start_0|>
assert number_rows > 0
assert number_columns > 0
super().__init__(self.PROBLEM_NAME)
self.number_rows = number_rows
self.number_columns = number_columns
<|end_body_0|>
<|body_start_1|>
print('Solving {} problem ...'.format(self.PROBLEM_NAME))
m =... | Unique Paths | UniquePaths | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UniquePaths:
"""Unique Paths"""
def __init__(self, number_rows, number_columns):
"""Unique Paths Args: number_rows: Number of rows in the matrix number_columns: Number of columns in the matrix Returns: None Raises: None"""
<|body_0|>
def solve(self):
"""Solve the... | stack_v2_sparse_classes_36k_train_032142 | 1,885 | no_license | [
{
"docstring": "Unique Paths Args: number_rows: Number of rows in the matrix number_columns: Number of columns in the matrix Returns: None Raises: None",
"name": "__init__",
"signature": "def __init__(self, number_rows, number_columns)"
},
{
"docstring": "Solve the problem Note: O(mn) solution w... | 2 | null | Implement the Python class `UniquePaths` described below.
Class description:
Unique Paths
Method signatures and docstrings:
- def __init__(self, number_rows, number_columns): Unique Paths Args: number_rows: Number of rows in the matrix number_columns: Number of columns in the matrix Returns: None Raises: None
- def s... | Implement the Python class `UniquePaths` described below.
Class description:
Unique Paths
Method signatures and docstrings:
- def __init__(self, number_rows, number_columns): Unique Paths Args: number_rows: Number of rows in the matrix number_columns: Number of columns in the matrix Returns: None Raises: None
- def s... | 11f4d25cb211740514c119a60962d075a0817abd | <|skeleton|>
class UniquePaths:
"""Unique Paths"""
def __init__(self, number_rows, number_columns):
"""Unique Paths Args: number_rows: Number of rows in the matrix number_columns: Number of columns in the matrix Returns: None Raises: None"""
<|body_0|>
def solve(self):
"""Solve the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UniquePaths:
"""Unique Paths"""
def __init__(self, number_rows, number_columns):
"""Unique Paths Args: number_rows: Number of rows in the matrix number_columns: Number of columns in the matrix Returns: None Raises: None"""
assert number_rows > 0
assert number_columns > 0
s... | the_stack_v2_python_sparse | python/problems/dynamic_programming/unique_paths.py | santhosh-kumar/AlgorithmsAndDataStructures | train | 2 |
a7af196c40923c3f22ed2facd7df2162b85ffe15 | [
"super(FeatureFusionBlock_custom, self).__init__()\nself.deconv = deconv\nself.align_corners = align_corners\nself.groups = 1\nself.expand = expand\nout_features = features\nif self.expand == True:\n out_features = features // 2\nself.out_conv = nn.Conv2D(features, out_features, kernel_size=1, stride=1, padding=... | <|body_start_0|>
super(FeatureFusionBlock_custom, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups = 1
self.expand = expand
out_features = features
if self.expand == True:
out_features = features // 2
self.ou... | Feature fusion block. | FeatureFusionBlock_custom | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureFusionBlock_custom:
"""Feature fusion block."""
def __init__(self, features, activation=nn.ReLU(), deconv=False, bn=False, expand=False, align_corners=True):
"""Init. Args: features (int): number of features"""
<|body_0|>
def forward(self, *xs):
"""Forward... | stack_v2_sparse_classes_36k_train_032143 | 7,931 | permissive | [
{
"docstring": "Init. Args: features (int): number of features",
"name": "__init__",
"signature": "def __init__(self, features, activation=nn.ReLU(), deconv=False, bn=False, expand=False, align_corners=True)"
},
{
"docstring": "Forward pass. Returns: tensor: output",
"name": "forward",
"... | 2 | null | Implement the Python class `FeatureFusionBlock_custom` described below.
Class description:
Feature fusion block.
Method signatures and docstrings:
- def __init__(self, features, activation=nn.ReLU(), deconv=False, bn=False, expand=False, align_corners=True): Init. Args: features (int): number of features
- def forwar... | Implement the Python class `FeatureFusionBlock_custom` described below.
Class description:
Feature fusion block.
Method signatures and docstrings:
- def __init__(self, features, activation=nn.ReLU(), deconv=False, bn=False, expand=False, align_corners=True): Init. Args: features (int): number of features
- def forwar... | b402610a6f0b382a978e82473b541ea1fc6cf09a | <|skeleton|>
class FeatureFusionBlock_custom:
"""Feature fusion block."""
def __init__(self, features, activation=nn.ReLU(), deconv=False, bn=False, expand=False, align_corners=True):
"""Init. Args: features (int): number of features"""
<|body_0|>
def forward(self, *xs):
"""Forward... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureFusionBlock_custom:
"""Feature fusion block."""
def __init__(self, features, activation=nn.ReLU(), deconv=False, bn=False, expand=False, align_corners=True):
"""Init. Args: features (int): number of features"""
super(FeatureFusionBlock_custom, self).__init__()
self.deconv =... | the_stack_v2_python_sparse | modules/image/semantic_segmentation/lseg/models/scratch.py | PaddlePaddle/PaddleHub | train | 12,914 |
8f4a9be4611d012c6f8a05fe6183fda335c322e5 | [
"self.nums = nums\nself.dicts = {}\n\ndef dfs(start, end):\n if start > end:\n return 0\n if (start, end) in self.dicts.keys():\n return self.dicts[start, end]\n if start == end:\n self.dicts[start, end] = self.nums[start]\n return self.nums[start]\n else:\n a = dfs(st... | <|body_start_0|>
self.nums = nums
self.dicts = {}
def dfs(start, end):
if start > end:
return 0
if (start, end) in self.dicts.keys():
return self.dicts[start, end]
if start == end:
self.dicts[start, end] = self.... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def PredictTheWinner(self, nums):
""":type nums: List[int] :rtype: bool 62ms"""
<|body_0|>
def PredictTheWinner_1(self, nums):
""":type nums: List[int] :rtype: bool 32ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.nums = nums
... | stack_v2_sparse_classes_36k_train_032144 | 2,954 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool 62ms",
"name": "PredictTheWinner",
"signature": "def PredictTheWinner(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool 32ms",
"name": "PredictTheWinner_1",
"signature": "def PredictTheWinner_1(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013648 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def PredictTheWinner(self, nums): :type nums: List[int] :rtype: bool 62ms
- def PredictTheWinner_1(self, nums): :type nums: List[int] :rtype: bool 32ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def PredictTheWinner(self, nums): :type nums: List[int] :rtype: bool 62ms
- def PredictTheWinner_1(self, nums): :type nums: List[int] :rtype: bool 32ms
<|skeleton|>
class Soluti... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def PredictTheWinner(self, nums):
""":type nums: List[int] :rtype: bool 62ms"""
<|body_0|>
def PredictTheWinner_1(self, nums):
""":type nums: List[int] :rtype: bool 32ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def PredictTheWinner(self, nums):
""":type nums: List[int] :rtype: bool 62ms"""
self.nums = nums
self.dicts = {}
def dfs(start, end):
if start > end:
return 0
if (start, end) in self.dicts.keys():
return self.di... | the_stack_v2_python_sparse | PredictTheWinner_MID_486.py | 953250587/leetcode-python | train | 2 | |
a9a323d15c906da2be29e958fcc561740b986a22 | [
"self.db = database\nself.cache = cache\nif auto_define:\n self.define_tables(migrate=migrate, fake_migrate=fake_migrate)\n self._get_settings()",
"self.db.define_table('settings', Field('kkey'), Field('name'), Field('value', 'text'), Field('value_type'), Field('description', 'text'), Field('created_on', 'd... | <|body_start_0|>
self.db = database
self.cache = cache
if auto_define:
self.define_tables(migrate=migrate, fake_migrate=fake_migrate)
self._get_settings()
<|end_body_0|>
<|body_start_1|>
self.db.define_table('settings', Field('kkey'), Field('name'), Field('value'... | This class implements a configurable set of options for use in anything that needs settings that are to be stored in the database. | Configure | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Configure:
"""This class implements a configurable set of options for use in anything that needs settings that are to be stored in the database."""
def __init__(self, database, auto_define=True, migrate=True, fake_migrate=False, cache=None):
"""Initialize configure class. Keyword aru... | stack_v2_sparse_classes_36k_train_032145 | 6,588 | no_license | [
{
"docstring": "Initialize configure class. Keyword arugments: database -- web2py DAL instance auto_define -- auto define database tables (default: True) migrate -- migrate the database tables (default: True) cache -- cache object to use for pulling database settings, this is a tuple object consisting of cache ... | 6 | stack_v2_sparse_classes_30k_train_014231 | Implement the Python class `Configure` described below.
Class description:
This class implements a configurable set of options for use in anything that needs settings that are to be stored in the database.
Method signatures and docstrings:
- def __init__(self, database, auto_define=True, migrate=True, fake_migrate=Fa... | Implement the Python class `Configure` described below.
Class description:
This class implements a configurable set of options for use in anything that needs settings that are to be stored in the database.
Method signatures and docstrings:
- def __init__(self, database, auto_define=True, migrate=True, fake_migrate=Fa... | 44e9250a28ae3284bef242ad042624338e405fbb | <|skeleton|>
class Configure:
"""This class implements a configurable set of options for use in anything that needs settings that are to be stored in the database."""
def __init__(self, database, auto_define=True, migrate=True, fake_migrate=False, cache=None):
"""Initialize configure class. Keyword aru... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Configure:
"""This class implements a configurable set of options for use in anything that needs settings that are to be stored in the database."""
def __init__(self, database, auto_define=True, migrate=True, fake_migrate=False, cache=None):
"""Initialize configure class. Keyword arugments: datab... | the_stack_v2_python_sparse | modules/web2py_utils/configure.py | Baffour/iapt-spring | train | 1 |
c63d21c730744bed4915141c8afc7ac15d35c718 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | A service that handles tenant management, including CRUD and enumeration. | TenantServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TenantServiceServicer:
"""A service that handles tenant management, including CRUD and enumeration."""
def CreateTenant(self, request, context):
"""Creates a new tenant entity."""
<|body_0|>
def GetTenant(self, request, context):
"""Retrieves specified tenant."""... | stack_v2_sparse_classes_36k_train_032146 | 6,155 | permissive | [
{
"docstring": "Creates a new tenant entity.",
"name": "CreateTenant",
"signature": "def CreateTenant(self, request, context)"
},
{
"docstring": "Retrieves specified tenant.",
"name": "GetTenant",
"signature": "def GetTenant(self, request, context)"
},
{
"docstring": "Updates spe... | 5 | null | Implement the Python class `TenantServiceServicer` described below.
Class description:
A service that handles tenant management, including CRUD and enumeration.
Method signatures and docstrings:
- def CreateTenant(self, request, context): Creates a new tenant entity.
- def GetTenant(self, request, context): Retrieves... | Implement the Python class `TenantServiceServicer` described below.
Class description:
A service that handles tenant management, including CRUD and enumeration.
Method signatures and docstrings:
- def CreateTenant(self, request, context): Creates a new tenant entity.
- def GetTenant(self, request, context): Retrieves... | d897d56bce03d1fda98b79afb08264e51d46c421 | <|skeleton|>
class TenantServiceServicer:
"""A service that handles tenant management, including CRUD and enumeration."""
def CreateTenant(self, request, context):
"""Creates a new tenant entity."""
<|body_0|>
def GetTenant(self, request, context):
"""Retrieves specified tenant."""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TenantServiceServicer:
"""A service that handles tenant management, including CRUD and enumeration."""
def CreateTenant(self, request, context):
"""Creates a new tenant entity."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
... | the_stack_v2_python_sparse | talent/google/cloud/talent_v4beta1/proto/tenant_service_pb2_grpc.py | tswast/google-cloud-python | train | 1 |
36d7f35ff2d557ab4a3036d467a7da54ccacc7f1 | [
"x = len(matrix)\ny = 0\nif x != 0:\n y = len(matrix[0])\nfor i in range(x):\n for j in range(y):\n left = 0\n top = 0\n leftTop = 0\n if j - 1 >= 0:\n left = matrix[i][j - 1]\n if i - 1 >= 0:\n top = matrix[i - 1][j]\n if i - 1 >= 0 and j - 1 >=... | <|body_start_0|>
x = len(matrix)
y = 0
if x != 0:
y = len(matrix[0])
for i in range(x):
for j in range(y):
left = 0
top = 0
leftTop = 0
if j - 1 >= 0:
left = matrix[i][j - 1]
... | NumMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
""":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_032147 | 1,387 | no_license | [
{
"docstring": ":type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": ":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int",
"name": "sumRegion",
"signature": "def sumRegion(self, row1, col1, row2, col2)"
... | 2 | null | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype:... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype:... | b890a5ea050cfe3886b5275cc26c1593b35b58db | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
""":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
x = len(matrix)
y = 0
if x != 0:
y = len(matrix[0])
for i in range(x):
for j in range(y):
left = 0
top = 0
leftTop = 0
... | the_stack_v2_python_sparse | 面试练习/304.py | QkqBeer/PythonSubject | train | 4 | |
9861268e721429a9d8da4a0385df2b1c08e517ef | [
"super().__init__(parent)\nself.figure_waveform = Figure()\nself.figure_spectrum = Figure()\nself.canvas_waveform = FigureCanvas(self.figure_waveform)\nself.canvas_spectrum = FigureCanvas(self.figure_spectrum)\nself.layout = QGridLayout(self)\nself.layout.addWidget(self.canvas_waveform, 0, 0)\nself.layout.addWidget... | <|body_start_0|>
super().__init__(parent)
self.figure_waveform = Figure()
self.figure_spectrum = Figure()
self.canvas_waveform = FigureCanvas(self.figure_waveform)
self.canvas_spectrum = FigureCanvas(self.figure_spectrum)
self.layout = QGridLayout(self)
self.layou... | A QWidget that displays audio waveform and spectrum using Matplotlib. | AudioWidget | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AudioWidget:
"""A QWidget that displays audio waveform and spectrum using Matplotlib."""
def __init__(self, audio_path, parent=None):
"""Constructor for the AudioWidget class. Args: audio_path (str): Path to the audio file. parent (QWidget): Optional parent widget."""
<|body_... | stack_v2_sparse_classes_36k_train_032148 | 3,129 | permissive | [
{
"docstring": "Constructor for the AudioWidget class. Args: audio_path (str): Path to the audio file. parent (QWidget): Optional parent widget.",
"name": "__init__",
"signature": "def __init__(self, audio_path, parent=None)"
},
{
"docstring": "Plot the audio waveform and spectrum.",
"name":... | 3 | stack_v2_sparse_classes_30k_train_011400 | Implement the Python class `AudioWidget` described below.
Class description:
A QWidget that displays audio waveform and spectrum using Matplotlib.
Method signatures and docstrings:
- def __init__(self, audio_path, parent=None): Constructor for the AudioWidget class. Args: audio_path (str): Path to the audio file. par... | Implement the Python class `AudioWidget` described below.
Class description:
A QWidget that displays audio waveform and spectrum using Matplotlib.
Method signatures and docstrings:
- def __init__(self, audio_path, parent=None): Constructor for the AudioWidget class. Args: audio_path (str): Path to the audio file. par... | 730f7dff2239ef716841390311b5b9250149acaf | <|skeleton|>
class AudioWidget:
"""A QWidget that displays audio waveform and spectrum using Matplotlib."""
def __init__(self, audio_path, parent=None):
"""Constructor for the AudioWidget class. Args: audio_path (str): Path to the audio file. parent (QWidget): Optional parent widget."""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AudioWidget:
"""A QWidget that displays audio waveform and spectrum using Matplotlib."""
def __init__(self, audio_path, parent=None):
"""Constructor for the AudioWidget class. Args: audio_path (str): Path to the audio file. parent (QWidget): Optional parent widget."""
super().__init__(par... | the_stack_v2_python_sparse | annolid/gui/widgets/audio.py | healthonrails/annolid | train | 25 |
3b8c14b1c911048b737599c27dc773218fc4b61a | [
"super(DecoderRNN, self).__init__()\nself.embed = nn.Embedding(vocab_size, embed_size)\nself.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)\nself.linear = nn.Linear(hidden_size, vocab_size)",
"captions = captions[:, :-1]\nembeddings = self.embed(captions)\ninputs = torch.cat((features.unsqu... | <|body_start_0|>
super(DecoderRNN, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
<|end_body_0|>
<|body_start_1|>
captions = captions[:... | DecoderRNN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecoderRNN:
def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
"""Set the hyper-parameters and build the layers."""
<|body_0|>
def forward(self, features, captions):
"""Decode image feature vectors and generates captions."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_032149 | 4,303 | permissive | [
{
"docstring": "Set the hyper-parameters and build the layers.",
"name": "__init__",
"signature": "def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1)"
},
{
"docstring": "Decode image feature vectors and generates captions.",
"name": "forward",
"signature": "def forward... | 4 | stack_v2_sparse_classes_30k_train_010957 | Implement the Python class `DecoderRNN` described below.
Class description:
Implement the DecoderRNN class.
Method signatures and docstrings:
- def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1): Set the hyper-parameters and build the layers.
- def forward(self, features, captions): Decode image fe... | Implement the Python class `DecoderRNN` described below.
Class description:
Implement the DecoderRNN class.
Method signatures and docstrings:
- def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1): Set the hyper-parameters and build the layers.
- def forward(self, features, captions): Decode image fe... | 2b558076dd7467acc2bcaf4c7480d48b129688a3 | <|skeleton|>
class DecoderRNN:
def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
"""Set the hyper-parameters and build the layers."""
<|body_0|>
def forward(self, features, captions):
"""Decode image feature vectors and generates captions."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DecoderRNN:
def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
"""Set the hyper-parameters and build the layers."""
super(DecoderRNN, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, ... | the_stack_v2_python_sparse | NIC/image_captioning/model.py | jomycs/Book-KnowledgeGraph-Recommendation | train | 1 | |
62ba06d61266d78b7ab6fc03e77c8d0a7fe8feed | [
"if n == 1:\n return k\ntwo_posts_back = k\none_post_back = k * k\nfor i in range(3, n + 1):\n curr = (k - 1) * (one_post_back + two_posts_back)\n two_posts_back = one_post_back\n one_post_back = curr\nreturn one_post_back",
"if n == 1:\n return k\nif n == 2:\n return k * k\ndp = [0] * (n + 1)\n... | <|body_start_0|>
if n == 1:
return k
two_posts_back = k
one_post_back = k * k
for i in range(3, n + 1):
curr = (k - 1) * (one_post_back + two_posts_back)
two_posts_back = one_post_back
one_post_back = curr
return one_post_back
<|end... | Fence | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Fence:
def number_of_ways_to_paint(self, n: int, k: int) -> int:
"""Approach: DP - Bottom Up (Constant Space) Time Complexity: O(N) Space Complexity: O(1) :param n: :param k: :return:"""
<|body_0|>
def number_of_ways_to_paint_(self, n: int, k: int) -> int:
"""Approac... | stack_v2_sparse_classes_36k_train_032150 | 1,833 | no_license | [
{
"docstring": "Approach: DP - Bottom Up (Constant Space) Time Complexity: O(N) Space Complexity: O(1) :param n: :param k: :return:",
"name": "number_of_ways_to_paint",
"signature": "def number_of_ways_to_paint(self, n: int, k: int) -> int"
},
{
"docstring": "Approach: DP - Bottom Up Time Comple... | 3 | null | Implement the Python class `Fence` described below.
Class description:
Implement the Fence class.
Method signatures and docstrings:
- def number_of_ways_to_paint(self, n: int, k: int) -> int: Approach: DP - Bottom Up (Constant Space) Time Complexity: O(N) Space Complexity: O(1) :param n: :param k: :return:
- def numb... | Implement the Python class `Fence` described below.
Class description:
Implement the Fence class.
Method signatures and docstrings:
- def number_of_ways_to_paint(self, n: int, k: int) -> int: Approach: DP - Bottom Up (Constant Space) Time Complexity: O(N) Space Complexity: O(1) :param n: :param k: :return:
- def numb... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class Fence:
def number_of_ways_to_paint(self, n: int, k: int) -> int:
"""Approach: DP - Bottom Up (Constant Space) Time Complexity: O(N) Space Complexity: O(1) :param n: :param k: :return:"""
<|body_0|>
def number_of_ways_to_paint_(self, n: int, k: int) -> int:
"""Approac... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Fence:
def number_of_ways_to_paint(self, n: int, k: int) -> int:
"""Approach: DP - Bottom Up (Constant Space) Time Complexity: O(N) Space Complexity: O(1) :param n: :param k: :return:"""
if n == 1:
return k
two_posts_back = k
one_post_back = k * k
for i in r... | the_stack_v2_python_sparse | expedia/paint_fence.py | Shiv2157k/leet_code | train | 1 | |
3ef17ef6d07db3ad0d73923b1c598b17382b9274 | [
"if version:\n if version == 4:\n return Command.executeIp(logger, IpConstant.IPV4, IpOption.RULE, IpAction.SHOW)\n elif version == 6:\n return Command.executeIp(logger, IpConstant.IPV6, IpOption.RULE, IpAction.SHOW)\nrc = Command.executeIp(logger, IpOption.RULE, IpAction.SHOW)\nreturn rc",
"i... | <|body_start_0|>
if version:
if version == 4:
return Command.executeIp(logger, IpConstant.IPV4, IpOption.RULE, IpAction.SHOW)
elif version == 6:
return Command.executeIp(logger, IpConstant.IPV6, IpOption.RULE, IpAction.SHOW)
rc = Command.executeIp(... | IpRule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IpRule:
def showRules(logger, version=None):
"""This function list all rules Args: logger Return: tuple (rc, stdout, stderr) Raise: None"""
<|body_0|>
def addRule(logger, source, table, version=None):
"""This function inserts a new rule Args: logger source - select t... | stack_v2_sparse_classes_36k_train_032151 | 23,984 | no_license | [
{
"docstring": "This function list all rules Args: logger Return: tuple (rc, stdout, stderr) Raise: None",
"name": "showRules",
"signature": "def showRules(logger, version=None)"
},
{
"docstring": "This function inserts a new rule Args: logger source - select the source prefix to match. table - ... | 4 | null | Implement the Python class `IpRule` described below.
Class description:
Implement the IpRule class.
Method signatures and docstrings:
- def showRules(logger, version=None): This function list all rules Args: logger Return: tuple (rc, stdout, stderr) Raise: None
- def addRule(logger, source, table, version=None): This... | Implement the Python class `IpRule` described below.
Class description:
Implement the IpRule class.
Method signatures and docstrings:
- def showRules(logger, version=None): This function list all rules Args: logger Return: tuple (rc, stdout, stderr) Raise: None
- def addRule(logger, source, table, version=None): This... | 81bcc74fe7c0ca036ec483f634d7be0bab19a6d0 | <|skeleton|>
class IpRule:
def showRules(logger, version=None):
"""This function list all rules Args: logger Return: tuple (rc, stdout, stderr) Raise: None"""
<|body_0|>
def addRule(logger, source, table, version=None):
"""This function inserts a new rule Args: logger source - select t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IpRule:
def showRules(logger, version=None):
"""This function list all rules Args: logger Return: tuple (rc, stdout, stderr) Raise: None"""
if version:
if version == 4:
return Command.executeIp(logger, IpConstant.IPV4, IpOption.RULE, IpAction.SHOW)
elif ... | the_stack_v2_python_sparse | oscar/a/sys/net/lnx/route.py | afeset/miner2-tools | train | 0 | |
31c53490c06bfa0e3c5ccca769db2e318c4527ff | [
"super(SimpleNet, self).__init__()\nself.conv_layers = None\nself.fc_layers = None\nself.loss_criterion = None\nself.conv_layers = nn.Sequential(nn.Conv2d(1, 10, kernel_size=5, stride=1), nn.ReLU(), nn.MaxPool2d(3), nn.Conv2d(10, 20, kernel_size=5, stride=1), nn.ReLU(), nn.MaxPool2d(3))\nconv_out = int(20 * 5 * 5)\... | <|body_start_0|>
super(SimpleNet, self).__init__()
self.conv_layers = None
self.fc_layers = None
self.loss_criterion = None
self.conv_layers = nn.Sequential(nn.Conv2d(1, 10, kernel_size=5, stride=1), nn.ReLU(), nn.MaxPool2d(3), nn.Conv2d(10, 20, kernel_size=5, stride=1), nn.ReLU(... | SimpleNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleNet:
def __init__(self):
"""Constructor for SimpleNet class to define the layers and loss function. Note: Use 'mean' reduction in the loss_criterion. Read Pytorch's documention to understand what this means."""
<|body_0|>
def forward(self, x: torch.Tensor) -> torch.Ten... | stack_v2_sparse_classes_36k_train_032152 | 2,210 | no_license | [
{
"docstring": "Constructor for SimpleNet class to define the layers and loss function. Note: Use 'mean' reduction in the loss_criterion. Read Pytorch's documention to understand what this means.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Perform the forward pass ... | 2 | stack_v2_sparse_classes_30k_train_001832 | Implement the Python class `SimpleNet` described below.
Class description:
Implement the SimpleNet class.
Method signatures and docstrings:
- def __init__(self): Constructor for SimpleNet class to define the layers and loss function. Note: Use 'mean' reduction in the loss_criterion. Read Pytorch's documention to unde... | Implement the Python class `SimpleNet` described below.
Class description:
Implement the SimpleNet class.
Method signatures and docstrings:
- def __init__(self): Constructor for SimpleNet class to define the layers and loss function. Note: Use 'mean' reduction in the loss_criterion. Read Pytorch's documention to unde... | fd47764547131cb6382124b27fe7d428cbf4c64a | <|skeleton|>
class SimpleNet:
def __init__(self):
"""Constructor for SimpleNet class to define the layers and loss function. Note: Use 'mean' reduction in the loss_criterion. Read Pytorch's documention to understand what this means."""
<|body_0|>
def forward(self, x: torch.Tensor) -> torch.Ten... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleNet:
def __init__(self):
"""Constructor for SimpleNet class to define the layers and loss function. Note: Use 'mean' reduction in the loss_criterion. Read Pytorch's documention to understand what this means."""
super(SimpleNet, self).__init__()
self.conv_layers = None
sel... | the_stack_v2_python_sparse | proj5/proj5_code/simple_net.py | pranavshenoykp/computerVision | train | 2 | |
4924ccdfd2b632022e21bf12fc5b2288190ba973 | [
"stable = False\nwhile not stable:\n board, stable = self._crush(board)\n if not stable:\n board = self._drop(board)\nreturn board",
"m, n = (len(board), len(board[0]))\ncrush = set()\nstable = True\nfor i in range(m):\n for j in range(1, n - 1):\n if board[i][j] == 0:\n continue... | <|body_start_0|>
stable = False
while not stable:
board, stable = self._crush(board)
if not stable:
board = self._drop(board)
return board
<|end_body_0|>
<|body_start_1|>
m, n = (len(board), len(board[0]))
crush = set()
stable = Tr... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def candyCrush(self, board: List[List[int]]) -> List[List[int]]:
"""Array."""
<|body_0|>
def _crush(self, board):
"""Running time: O(k) where k is the number of items in board."""
<|body_1|>
def _drop(self, board):
"""Running time: O(k)... | stack_v2_sparse_classes_36k_train_032153 | 1,691 | permissive | [
{
"docstring": "Array.",
"name": "candyCrush",
"signature": "def candyCrush(self, board: List[List[int]]) -> List[List[int]]"
},
{
"docstring": "Running time: O(k) where k is the number of items in board.",
"name": "_crush",
"signature": "def _crush(self, board)"
},
{
"docstring"... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def candyCrush(self, board: List[List[int]]) -> List[List[int]]: Array.
- def _crush(self, board): Running time: O(k) where k is the number of items in board.
- def _drop(self, b... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def candyCrush(self, board: List[List[int]]) -> List[List[int]]: Array.
- def _crush(self, board): Running time: O(k) where k is the number of items in board.
- def _drop(self, b... | 4a508a982b125a3a90ea893ae70863df7c99cc70 | <|skeleton|>
class Solution:
def candyCrush(self, board: List[List[int]]) -> List[List[int]]:
"""Array."""
<|body_0|>
def _crush(self, board):
"""Running time: O(k) where k is the number of items in board."""
<|body_1|>
def _drop(self, board):
"""Running time: O(k)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def candyCrush(self, board: List[List[int]]) -> List[List[int]]:
"""Array."""
stable = False
while not stable:
board, stable = self._crush(board)
if not stable:
board = self._drop(board)
return board
def _crush(self, board)... | the_stack_v2_python_sparse | solutions/723_candy_crush.py | YiqunPeng/leetcode_pro | train | 0 | |
1f253857a7da706aa6ef79334ea3cf5457783677 | [
"searchMin = self.config.searchMin\nsearchMax = self.config.searchMax\nsearchStep = self.config.searchStep\nsearchNum = 1 + int(math.ceil((searchMax - searchMin) / searchStep))\nsearchVelocity = np.linspace(searchMin, searchMax, num=searchNum, endpoint=True)\nbeta = searchVelocity / const.c.to('km/s').value\ndopple... | <|body_start_0|>
searchMin = self.config.searchMin
searchMax = self.config.searchMax
searchStep = self.config.searchStep
searchNum = 1 + int(math.ceil((searchMax - searchMin) / searchStep))
searchVelocity = np.linspace(searchMin, searchMax, num=searchNum, endpoint=True)
b... | Estimate the radial velocity. | EstimateRadialVelocityTask | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EstimateRadialVelocityTask:
"""Estimate the radial velocity."""
def run(self, spectrum: PfsFiberArray, modelSpectrum: PfsSimpleSpectrum) -> Struct:
"""Get the radial velocity of ``spectrum`` in comparison with ``modelSpectrum``. Parameters ---------- spectrum : `pfs.datamodel.pfsFibe... | stack_v2_sparse_classes_36k_train_032154 | 13,859 | no_license | [
{
"docstring": "Get the radial velocity of ``spectrum`` in comparison with ``modelSpectrum``. Parameters ---------- spectrum : `pfs.datamodel.pfsFiberArray.PfsFiberArray` Observed spectrum. It must be whitened (Continuum is 1.0 everywhere.) modelSpectrum : `pfs.datamodel.pfsSimpleSpectrum.PfsSimpleSpectrum` Mod... | 2 | stack_v2_sparse_classes_30k_train_002571 | Implement the Python class `EstimateRadialVelocityTask` described below.
Class description:
Estimate the radial velocity.
Method signatures and docstrings:
- def run(self, spectrum: PfsFiberArray, modelSpectrum: PfsSimpleSpectrum) -> Struct: Get the radial velocity of ``spectrum`` in comparison with ``modelSpectrum``... | Implement the Python class `EstimateRadialVelocityTask` described below.
Class description:
Estimate the radial velocity.
Method signatures and docstrings:
- def run(self, spectrum: PfsFiberArray, modelSpectrum: PfsSimpleSpectrum) -> Struct: Get the radial velocity of ``spectrum`` in comparison with ``modelSpectrum``... | 85602eea2485ac24e0831046dc74f1b2d1a3d89f | <|skeleton|>
class EstimateRadialVelocityTask:
"""Estimate the radial velocity."""
def run(self, spectrum: PfsFiberArray, modelSpectrum: PfsSimpleSpectrum) -> Struct:
"""Get the radial velocity of ``spectrum`` in comparison with ``modelSpectrum``. Parameters ---------- spectrum : `pfs.datamodel.pfsFibe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EstimateRadialVelocityTask:
"""Estimate the radial velocity."""
def run(self, spectrum: PfsFiberArray, modelSpectrum: PfsSimpleSpectrum) -> Struct:
"""Get the radial velocity of ``spectrum`` in comparison with ``modelSpectrum``. Parameters ---------- spectrum : `pfs.datamodel.pfsFiberArray.PfsFib... | the_stack_v2_python_sparse | python/pfs/drp/stella/estimateRadialVelocity.py | Subaru-PFS/drp_stella | train | 3 |
0d7435c9c3f78fea8212d02288beb662458c31ff | [
"roles = get_list_or_404(Role)\nif request.GET.get('pagination'):\n pagination = request.GET.get('pagination')\n if pagination == 'true':\n paginator = AdministratorPagination()\n results = paginator.paginate_queryset(roles, request)\n serializer = RoleSerializer(results, many=True)\n ... | <|body_start_0|>
roles = get_list_or_404(Role)
if request.GET.get('pagination'):
pagination = request.GET.get('pagination')
if pagination == 'true':
paginator = AdministratorPagination()
results = paginator.paginate_queryset(roles, request)
... | RoleList | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RoleList:
def get(self, request, format=None):
"""List all roles --- serializer: administrator.serializers.RoleSerializer parameters: - name: pagination required: false type: string paramType: query"""
<|body_0|>
def post(self, request, format=None):
"""Create new Ro... | stack_v2_sparse_classes_36k_train_032155 | 30,608 | permissive | [
{
"docstring": "List all roles --- serializer: administrator.serializers.RoleSerializer parameters: - name: pagination required: false type: string paramType: query",
"name": "get",
"signature": "def get(self, request, format=None)"
},
{
"docstring": "Create new Role --- serializer: administrato... | 2 | stack_v2_sparse_classes_30k_train_012502 | Implement the Python class `RoleList` described below.
Class description:
Implement the RoleList class.
Method signatures and docstrings:
- def get(self, request, format=None): List all roles --- serializer: administrator.serializers.RoleSerializer parameters: - name: pagination required: false type: string paramType... | Implement the Python class `RoleList` described below.
Class description:
Implement the RoleList class.
Method signatures and docstrings:
- def get(self, request, format=None): List all roles --- serializer: administrator.serializers.RoleSerializer parameters: - name: pagination required: false type: string paramType... | 73728463badb3bfd4413aa0f7aeb44a9606fdfea | <|skeleton|>
class RoleList:
def get(self, request, format=None):
"""List all roles --- serializer: administrator.serializers.RoleSerializer parameters: - name: pagination required: false type: string paramType: query"""
<|body_0|>
def post(self, request, format=None):
"""Create new Ro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RoleList:
def get(self, request, format=None):
"""List all roles --- serializer: administrator.serializers.RoleSerializer parameters: - name: pagination required: false type: string paramType: query"""
roles = get_list_or_404(Role)
if request.GET.get('pagination'):
paginati... | the_stack_v2_python_sparse | administrator/views.py | belatrix/BackendAllStars | train | 5 | |
e6edd16db1a07b6eb96fe15c7c2147bbbac28d17 | [
"super().__init__()\nself.low = low\nself.high = high\nself.seed = seed",
"if self.seed:\n np.random.seed(self.seed)\nreturn np.random.randint(self.low, self.high)"
] | <|body_start_0|>
super().__init__()
self.low = low
self.high = high
self.seed = seed
<|end_body_0|>
<|body_start_1|>
if self.seed:
np.random.seed(self.seed)
return np.random.randint(self.low, self.high)
<|end_body_1|>
| Derived class of HexHeuristic to quantify a state by generating random integers. | RandomHeuristic | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomHeuristic:
"""Derived class of HexHeuristic to quantify a state by generating random integers."""
def __init__(self, low, high, seed=None):
"""Initialize the Random heuristic with a low and high boundary for the random integer generation. For experimentation purposes a random s... | stack_v2_sparse_classes_36k_train_032156 | 13,925 | permissive | [
{
"docstring": "Initialize the Random heuristic with a low and high boundary for the random integer generation. For experimentation purposes a random seed can be provided. :param low: int Lower boundary for the random heuristic value :param high: int Upper boundary for the random heuristic value :param seed: in... | 2 | stack_v2_sparse_classes_30k_train_018883 | Implement the Python class `RandomHeuristic` described below.
Class description:
Derived class of HexHeuristic to quantify a state by generating random integers.
Method signatures and docstrings:
- def __init__(self, low, high, seed=None): Initialize the Random heuristic with a low and high boundary for the random in... | Implement the Python class `RandomHeuristic` described below.
Class description:
Derived class of HexHeuristic to quantify a state by generating random integers.
Method signatures and docstrings:
- def __init__(self, low, high, seed=None): Initialize the Random heuristic with a low and high boundary for the random in... | 78478c6a8a0f0e0e740159236d6cbb30a9396f5a | <|skeleton|>
class RandomHeuristic:
"""Derived class of HexHeuristic to quantify a state by generating random integers."""
def __init__(self, low, high, seed=None):
"""Initialize the Random heuristic with a low and high boundary for the random integer generation. For experimentation purposes a random s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomHeuristic:
"""Derived class of HexHeuristic to quantify a state by generating random integers."""
def __init__(self, low, high, seed=None):
"""Initialize the Random heuristic with a low and high boundary for the random integer generation. For experimentation purposes a random seed can be pr... | the_stack_v2_python_sparse | Games/hex/legacy/hex_heuristics.py | frankbryce/muzero | train | 1 |
16e917bb6ce255885127b76de3339f49cb5919b5 | [
"n = len(height)\nif n < 3:\n return 0\nl_max = height[0]\nr_max = height[n - 1]\nleft, right = (1, n - 2)\nres = 0\nwhile left <= right:\n l_max = max(l_max, height[left])\n r_max = max(r_max, height[right])\n if l_max < r_max:\n res += l_max - height[left]\n left += 1\n else:\n ... | <|body_start_0|>
n = len(height)
if n < 3:
return 0
l_max = height[0]
r_max = height[n - 1]
left, right = (1, n - 2)
res = 0
while left <= right:
l_max = max(l_max, height[left])
r_max = max(r_max, height[right])
if ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def trap(self, height):
""":type height: List[int] :rtype: int 备忘录的方法用了两个数组， 但是发现其实每一次只需要用到第i次的值 因此可以边计算最大值边遍历， 考虑到左右两边，可以采用双指针"""
<|body_0|>
def trap0(self, height):
""":type height: List[int] :rtype: int 因为每个位置我只需要知道它之前和之后的最大值， 所以可以用两个数组提前算好， 再遍历取对应的元素比较计... | stack_v2_sparse_classes_36k_train_032157 | 2,713 | no_license | [
{
"docstring": ":type height: List[int] :rtype: int 备忘录的方法用了两个数组， 但是发现其实每一次只需要用到第i次的值 因此可以边计算最大值边遍历， 考虑到左右两边，可以采用双指针",
"name": "trap",
"signature": "def trap(self, height)"
},
{
"docstring": ":type height: List[int] :rtype: int 因为每个位置我只需要知道它之前和之后的最大值， 所以可以用两个数组提前算好， 再遍历取对应的元素比较计算就好",
"name":... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def trap(self, height): :type height: List[int] :rtype: int 备忘录的方法用了两个数组， 但是发现其实每一次只需要用到第i次的值 因此可以边计算最大值边遍历， 考虑到左右两边，可以采用双指针
- def trap0(self, height): :type height: List[int] :r... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def trap(self, height): :type height: List[int] :rtype: int 备忘录的方法用了两个数组， 但是发现其实每一次只需要用到第i次的值 因此可以边计算最大值边遍历， 考虑到左右两边，可以采用双指针
- def trap0(self, height): :type height: List[int] :r... | 6e18c5d257840489cc3fb1079ae3804c743982a4 | <|skeleton|>
class Solution:
def trap(self, height):
""":type height: List[int] :rtype: int 备忘录的方法用了两个数组， 但是发现其实每一次只需要用到第i次的值 因此可以边计算最大值边遍历， 考虑到左右两边，可以采用双指针"""
<|body_0|>
def trap0(self, height):
""":type height: List[int] :rtype: int 因为每个位置我只需要知道它之前和之后的最大值， 所以可以用两个数组提前算好， 再遍历取对应的元素比较计... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def trap(self, height):
""":type height: List[int] :rtype: int 备忘录的方法用了两个数组， 但是发现其实每一次只需要用到第i次的值 因此可以边计算最大值边遍历， 考虑到左右两边，可以采用双指针"""
n = len(height)
if n < 3:
return 0
l_max = height[0]
r_max = height[n - 1]
left, right = (1, n - 2)
r... | the_stack_v2_python_sparse | out/production/leetcode/42.接雨水.py | yangyuxiang1996/leetcode | train | 0 | |
1867420c10cf52535175179e1a037af8095fb690 | [
"doc, _ = self._to_xml_doc(frame_data, sasentry_attrs)\nif self.encoding is None:\n self.encoding = 'UTF-8'\nwith open(filename, 'wb') as file_ref:\n doc.write(file_ref, encoding=self.encoding, pretty_print=True, xml_declaration=True)",
"valid_class = all([issubclass(data.__class__, Data1D) for data in fram... | <|body_start_0|>
doc, _ = self._to_xml_doc(frame_data, sasentry_attrs)
if self.encoding is None:
self.encoding = 'UTF-8'
with open(filename, 'wb') as file_ref:
doc.write(file_ref, encoding=self.encoding, pretty_print=True, xml_declaration=True)
<|end_body_0|>
<|body_star... | CansasWriter | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CansasWriter:
def write(self, filename, frame_data, sasentry_attrs=None):
"""Write the content of a Data1D as a CanSAS XML file :param filename: name of the file to write :param datainfo: Data1D object"""
<|body_0|>
def _to_xml_doc(self, frame_data, sasentry_attrs=None):
... | stack_v2_sparse_classes_36k_train_032158 | 4,460 | permissive | [
{
"docstring": "Write the content of a Data1D as a CanSAS XML file :param filename: name of the file to write :param datainfo: Data1D object",
"name": "write",
"signature": "def write(self, filename, frame_data, sasentry_attrs=None)"
},
{
"docstring": "Create an XML document to contain the conte... | 3 | stack_v2_sparse_classes_30k_test_001052 | Implement the Python class `CansasWriter` described below.
Class description:
Implement the CansasWriter class.
Method signatures and docstrings:
- def write(self, filename, frame_data, sasentry_attrs=None): Write the content of a Data1D as a CanSAS XML file :param filename: name of the file to write :param datainfo:... | Implement the Python class `CansasWriter` described below.
Class description:
Implement the CansasWriter class.
Method signatures and docstrings:
- def write(self, filename, frame_data, sasentry_attrs=None): Write the content of a Data1D as a CanSAS XML file :param filename: name of the file to write :param datainfo:... | a5e91705b5a0d4b6c6bf6f4374418fd71c58dc8f | <|skeleton|>
class CansasWriter:
def write(self, filename, frame_data, sasentry_attrs=None):
"""Write the content of a Data1D as a CanSAS XML file :param filename: name of the file to write :param datainfo: Data1D object"""
<|body_0|>
def _to_xml_doc(self, frame_data, sasentry_attrs=None):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CansasWriter:
def write(self, filename, frame_data, sasentry_attrs=None):
"""Write the content of a Data1D as a CanSAS XML file :param filename: name of the file to write :param datainfo: Data1D object"""
doc, _ = self._to_xml_doc(frame_data, sasentry_attrs)
if self.encoding is None:
... | the_stack_v2_python_sparse | src/sas/sascalc/file_converter/cansas_writer.py | m2cci-NMZ/sasview | train | 0 | |
f7d11a9f574ad5fb94f3a694e357c64cc75b4edc | [
"res = ''\nlength = len(s)\ni = 0\nwhile i < length:\n start = i\n while i < length and s[i] != ' ':\n i += 1\n for p in range(start, i)[::-1]:\n res += s[p]\n while i < length and s[i] == ' ':\n i += 1\n res += ' '\nreturn res",
"strs = s.split(' ')\nres = []\nfor sub in s... | <|body_start_0|>
res = ''
length = len(s)
i = 0
while i < length:
start = i
while i < length and s[i] != ' ':
i += 1
for p in range(start, i)[::-1]:
res += s[p]
while i < length and s[i] == ' ':
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverse_words(self, s: str) -> str:
"""单词反转 Args: arr: 字符串 Returns: 反转后字符串"""
<|body_0|>
def reverse_words2(self, s: str) -> str:
"""单词反转 Args: arr: 字符串 Returns: 反转后字符串"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = ''
l... | stack_v2_sparse_classes_36k_train_032159 | 2,076 | permissive | [
{
"docstring": "单词反转 Args: arr: 字符串 Returns: 反转后字符串",
"name": "reverse_words",
"signature": "def reverse_words(self, s: str) -> str"
},
{
"docstring": "单词反转 Args: arr: 字符串 Returns: 反转后字符串",
"name": "reverse_words2",
"signature": "def reverse_words2(self, s: str) -> str"
}
] | 2 | stack_v2_sparse_classes_30k_train_001224 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverse_words(self, s: str) -> str: 单词反转 Args: arr: 字符串 Returns: 反转后字符串
- def reverse_words2(self, s: str) -> str: 单词反转 Args: arr: 字符串 Returns: 反转后字符串 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverse_words(self, s: str) -> str: 单词反转 Args: arr: 字符串 Returns: 反转后字符串
- def reverse_words2(self, s: str) -> str: 单词反转 Args: arr: 字符串 Returns: 反转后字符串
<|skeleton|>
class Sol... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def reverse_words(self, s: str) -> str:
"""单词反转 Args: arr: 字符串 Returns: 反转后字符串"""
<|body_0|>
def reverse_words2(self, s: str) -> str:
"""单词反转 Args: arr: 字符串 Returns: 反转后字符串"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverse_words(self, s: str) -> str:
"""单词反转 Args: arr: 字符串 Returns: 反转后字符串"""
res = ''
length = len(s)
i = 0
while i < length:
start = i
while i < length and s[i] != ' ':
i += 1
for p in range(start, i)[:... | the_stack_v2_python_sparse | src/leetcodepython/string/reverse_words_string_557.py | zhangyu345293721/leetcode | train | 101 | |
127b536d18395e0b543d55cd05265cf700cf6b83 | [
"ENFORCER.enforce_call(action='identity:check_grant', build_target=functools.partial(_build_enforcement_target_attr, domain_id=domain_id, group_id=group_id, role_id=role_id))\nPROVIDERS.assignment_api.get_grant(domain_id=domain_id, group_id=group_id, role_id=role_id, inherited_to_projects=True)\nreturn (None, http_... | <|body_start_0|>
ENFORCER.enforce_call(action='identity:check_grant', build_target=functools.partial(_build_enforcement_target_attr, domain_id=domain_id, group_id=group_id, role_id=role_id))
PROVIDERS.assignment_api.get_grant(domain_id=domain_id, group_id=group_id, role_id=role_id, inherited_to_projects... | OSInheritDomainGroupRolesResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OSInheritDomainGroupRolesResource:
def get(self, domain_id, group_id, role_id):
"""Check for an inherited grant for a group on a domain. GET/HEAD /OS-INHERIT/domains/{domain_id}/groups/{group_id} /roles/{role_id}/inherited_to_projects"""
<|body_0|>
def put(self, domain_id, g... | stack_v2_sparse_classes_36k_train_032160 | 19,022 | permissive | [
{
"docstring": "Check for an inherited grant for a group on a domain. GET/HEAD /OS-INHERIT/domains/{domain_id}/groups/{group_id} /roles/{role_id}/inherited_to_projects",
"name": "get",
"signature": "def get(self, domain_id, group_id, role_id)"
},
{
"docstring": "Create an inherited grant for a g... | 3 | stack_v2_sparse_classes_30k_train_009843 | Implement the Python class `OSInheritDomainGroupRolesResource` described below.
Class description:
Implement the OSInheritDomainGroupRolesResource class.
Method signatures and docstrings:
- def get(self, domain_id, group_id, role_id): Check for an inherited grant for a group on a domain. GET/HEAD /OS-INHERIT/domains/... | Implement the Python class `OSInheritDomainGroupRolesResource` described below.
Class description:
Implement the OSInheritDomainGroupRolesResource class.
Method signatures and docstrings:
- def get(self, domain_id, group_id, role_id): Check for an inherited grant for a group on a domain. GET/HEAD /OS-INHERIT/domains/... | 03a0a8146a78682ede9eca12a5a7fdacde2035c8 | <|skeleton|>
class OSInheritDomainGroupRolesResource:
def get(self, domain_id, group_id, role_id):
"""Check for an inherited grant for a group on a domain. GET/HEAD /OS-INHERIT/domains/{domain_id}/groups/{group_id} /roles/{role_id}/inherited_to_projects"""
<|body_0|>
def put(self, domain_id, g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OSInheritDomainGroupRolesResource:
def get(self, domain_id, group_id, role_id):
"""Check for an inherited grant for a group on a domain. GET/HEAD /OS-INHERIT/domains/{domain_id}/groups/{group_id} /roles/{role_id}/inherited_to_projects"""
ENFORCER.enforce_call(action='identity:check_grant', bui... | the_stack_v2_python_sparse | keystone/api/os_inherit.py | sapcc/keystone | train | 0 | |
deebaff45b12c9ca011e660f33feb8bfd5b7bb35 | [
"develop.initialize_options(self)\nself.no_npm = None\nself.with_doc_deps = None\nself.use_npm_cache = None",
"if self.no_deps:\n develop.install_for_development(self)\n return\nself._run_pip(['install', '-e', '.'])\nself._run_pip(['install', '-r', 'dev-requirements.txt'])\nif self.with_doc_deps:\n self.... | <|body_start_0|>
develop.initialize_options(self)
self.no_npm = None
self.with_doc_deps = None
self.use_npm_cache = None
<|end_body_0|>
<|body_start_1|>
if self.no_deps:
develop.install_for_development(self)
return
self._run_pip(['install', '-e', ... | Installs Review Board in developer mode. This will install all standard and development dependencies (using Python wheels and node.js packages from npm) and add the source tree to the Python module search path. That includes updating the versions of pip and setuptools on the system. To speed up subsequent runs, callers... | DevelopCommand | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DevelopCommand:
"""Installs Review Board in developer mode. This will install all standard and development dependencies (using Python wheels and node.js packages from npm) and add the source tree to the Python module search path. That includes updating the versions of pip and setuptools on the sy... | stack_v2_sparse_classes_36k_train_032161 | 14,185 | permissive | [
{
"docstring": "Initialize options for the command.",
"name": "initialize_options",
"signature": "def initialize_options(self)"
},
{
"docstring": "Install the package for development. This takes care of the work of installing all dependencies.",
"name": "install_for_development",
"signat... | 3 | stack_v2_sparse_classes_30k_train_021045 | Implement the Python class `DevelopCommand` described below.
Class description:
Installs Review Board in developer mode. This will install all standard and development dependencies (using Python wheels and node.js packages from npm) and add the source tree to the Python module search path. That includes updating the v... | Implement the Python class `DevelopCommand` described below.
Class description:
Installs Review Board in developer mode. This will install all standard and development dependencies (using Python wheels and node.js packages from npm) and add the source tree to the Python module search path. That includes updating the v... | c3a991f1e9d7682239a1ab0e8661cee6da01d537 | <|skeleton|>
class DevelopCommand:
"""Installs Review Board in developer mode. This will install all standard and development dependencies (using Python wheels and node.js packages from npm) and add the source tree to the Python module search path. That includes updating the versions of pip and setuptools on the sy... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DevelopCommand:
"""Installs Review Board in developer mode. This will install all standard and development dependencies (using Python wheels and node.js packages from npm) and add the source tree to the Python module search path. That includes updating the versions of pip and setuptools on the system. To spee... | the_stack_v2_python_sparse | setup.py | reviewboard/reviewboard | train | 1,141 |
c3f9f694d3276ac32d547368f54c637501f7a53a | [
"repo = repo.strip()\npath = os.path.join(os.getcwd(), path.strip())\n_ = {'ssh': '', 'name': 'name', 'email': 'name@gmail.com'}\n_.update(user)\nuser = _\nif not repo.startswith('git@'):\n raise Exception(f'Invalid checkout url: {repo}\\n\\nPlease use the valid ssh url with the following format:\\n `git@github.... | <|body_start_0|>
repo = repo.strip()
path = os.path.join(os.getcwd(), path.strip())
_ = {'ssh': '', 'name': 'name', 'email': 'name@gmail.com'}
_.update(user)
user = _
if not repo.startswith('git@'):
raise Exception(f'Invalid checkout url: {repo}\n\nPlease use ... | GitUtil | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GitUtil:
def __init__(self, repo: str, user: dict, path: str=''):
"""initial method To use this class, first make sure you have set ssh private key and give the path in the following wapper. Save the wrapper as a bash script and give required permissions using "sudo chmod a+x /path/to/ss... | stack_v2_sparse_classes_36k_train_032162 | 4,122 | permissive | [
{
"docstring": "initial method To use this class, first make sure you have set ssh private key and give the path in the following wapper. Save the wrapper as a bash script and give required permissions using \"sudo chmod a+x /path/to/ssh_key\": #!/bin/bash ssh -i path/to/ssh/key -oIdentitiesOnly=yes -oStrictHos... | 2 | null | Implement the Python class `GitUtil` described below.
Class description:
Implement the GitUtil class.
Method signatures and docstrings:
- def __init__(self, repo: str, user: dict, path: str=''): initial method To use this class, first make sure you have set ssh private key and give the path in the following wapper. S... | Implement the Python class `GitUtil` described below.
Class description:
Implement the GitUtil class.
Method signatures and docstrings:
- def __init__(self, repo: str, user: dict, path: str=''): initial method To use this class, first make sure you have set ssh private key and give the path in the following wapper. S... | 5ebec640c634ef57569d7ef4e1dc0dd1b188dcb3 | <|skeleton|>
class GitUtil:
def __init__(self, repo: str, user: dict, path: str=''):
"""initial method To use this class, first make sure you have set ssh private key and give the path in the following wapper. Save the wrapper as a bash script and give required permissions using "sudo chmod a+x /path/to/ss... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GitUtil:
def __init__(self, repo: str, user: dict, path: str=''):
"""initial method To use this class, first make sure you have set ssh private key and give the path in the following wapper. Save the wrapper as a bash script and give required permissions using "sudo chmod a+x /path/to/ssh_key": #!/bin... | the_stack_v2_python_sparse | openiti/git/git_util.py | OpenITI/openiti | train | 10 | |
fd94796047c557b42d455180121d18b4c96ee72f | [
"from scoop.content.models import Attachment\nuuid = self.value\nlink = Attachment.objects.get_link_by_uuid(uuid)\nreturn {'link': link}",
"base = super(AttachmentInline, self).get_template_name()[0]\npath = 'content/{}'.format(base)\nreturn path"
] | <|body_start_0|>
from scoop.content.models import Attachment
uuid = self.value
link = Attachment.objects.get_link_by_uuid(uuid)
return {'link': link}
<|end_body_0|>
<|body_start_1|>
base = super(AttachmentInline, self).get_template_name()[0]
path = 'content/{}'.format(ba... | Inline d'insertion de pièces jointes Format : {{attachment uuid}} | AttachmentInline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttachmentInline:
"""Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""
def get_context(self):
"""Renvoyer le contexte de rendu de l'inline"""
<|body_0|>
def get_template_name(self):
"""Renvoyer le chemin du template"""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k_train_032163 | 6,816 | no_license | [
{
"docstring": "Renvoyer le contexte de rendu de l'inline",
"name": "get_context",
"signature": "def get_context(self)"
},
{
"docstring": "Renvoyer le chemin du template",
"name": "get_template_name",
"signature": "def get_template_name(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003124 | Implement the Python class `AttachmentInline` described below.
Class description:
Inline d'insertion de pièces jointes Format : {{attachment uuid}}
Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte de rendu de l'inline
- def get_template_name(self): Renvoyer le chemin du template | Implement the Python class `AttachmentInline` described below.
Class description:
Inline d'insertion de pièces jointes Format : {{attachment uuid}}
Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte de rendu de l'inline
- def get_template_name(self): Renvoyer le chemin du template
<|skel... | 8cef6f6e89c1990e2b25f83e54e0c3481d83b6d7 | <|skeleton|>
class AttachmentInline:
"""Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""
def get_context(self):
"""Renvoyer le contexte de rendu de l'inline"""
<|body_0|>
def get_template_name(self):
"""Renvoyer le chemin du template"""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttachmentInline:
"""Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""
def get_context(self):
"""Renvoyer le contexte de rendu de l'inline"""
from scoop.content.models import Attachment
uuid = self.value
link = Attachment.objects.get_link_by_uuid(uuid)
... | the_stack_v2_python_sparse | scoop/content/util/inlines.py | artscoop/scoop | train | 0 |
445e81ce4a6ada54c133f22d1ba751d188a29f17 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn IosDeviceFeaturesConfiguration()",
"from .apple_device_features_configuration_base import AppleDeviceFeaturesConfigurationBase\nfrom .ios_home_screen_item import IosHomeScreenItem\nfrom .ios_home_screen_page import IosHomeScreenPage\nf... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return IosDeviceFeaturesConfiguration()
<|end_body_0|>
<|body_start_1|>
from .apple_device_features_configuration_base import AppleDeviceFeaturesConfigurationBase
from .ios_home_screen_item imp... | iOS Device Features Configuration Profile. | IosDeviceFeaturesConfiguration | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IosDeviceFeaturesConfiguration:
"""iOS Device Features Configuration Profile."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IosDeviceFeaturesConfiguration:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_no... | stack_v2_sparse_classes_36k_train_032164 | 4,709 | 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: IosDeviceFeaturesConfiguration",
"name": "create_from_discriminator_value",
"signature": "def create_from_di... | 3 | null | Implement the Python class `IosDeviceFeaturesConfiguration` described below.
Class description:
iOS Device Features Configuration Profile.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IosDeviceFeaturesConfiguration: Creates a new instance of the appr... | Implement the Python class `IosDeviceFeaturesConfiguration` described below.
Class description:
iOS Device Features Configuration Profile.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IosDeviceFeaturesConfiguration: Creates a new instance of the appr... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class IosDeviceFeaturesConfiguration:
"""iOS Device Features Configuration Profile."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IosDeviceFeaturesConfiguration:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_no... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IosDeviceFeaturesConfiguration:
"""iOS Device Features Configuration Profile."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IosDeviceFeaturesConfiguration:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse... | the_stack_v2_python_sparse | msgraph/generated/models/ios_device_features_configuration.py | microsoftgraph/msgraph-sdk-python | train | 135 |
478a11221eb3e26ef4fb53042ae055876d5bc868 | [
"try:\n self.stream = Stream._member_map_[data.get('stream', 'PRINT').upper()]\nexcept KeyError:\n raise ValueError(f\"invalid stream specifier: {data['stream']}\")\nself.binary = binary\nself.payload = data.get('payload', '{command}')\nself.args = data.get('args', [])\nself.suid = data.get('suid', None)\nsel... | <|body_start_0|>
try:
self.stream = Stream._member_map_[data.get('stream', 'PRINT').upper()]
except KeyError:
raise ValueError(f"invalid stream specifier: {data['stream']}")
self.binary = binary
self.payload = data.get('payload', '{command}')
self.args = d... | Abstract method class built from the JSON database | Method | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Method:
"""Abstract method class built from the JSON database"""
def __init__(self, binary: 'Binary', cap: Capability, data: Dict[str, Any]):
"""Create a new method associated with the given binary."""
<|body_0|>
def sudo_args(self, binary_path: str, spec: str) -> bool:
... | stack_v2_sparse_classes_36k_train_032165 | 17,537 | permissive | [
{
"docstring": "Create a new method associated with the given binary.",
"name": "__init__",
"signature": "def __init__(self, binary: 'Binary', cap: Capability, data: Dict[str, Any])"
},
{
"docstring": "Check if this method is compatible with the given sudo command spec. It will evaluate whether ... | 3 | null | Implement the Python class `Method` described below.
Class description:
Abstract method class built from the JSON database
Method signatures and docstrings:
- def __init__(self, binary: 'Binary', cap: Capability, data: Dict[str, Any]): Create a new method associated with the given binary.
- def sudo_args(self, binary... | Implement the Python class `Method` described below.
Class description:
Abstract method class built from the JSON database
Method signatures and docstrings:
- def __init__(self, binary: 'Binary', cap: Capability, data: Dict[str, Any]): Create a new method associated with the given binary.
- def sudo_args(self, binary... | 37f04d4e16ff47c7fd70e95162f9fccd327cca7e | <|skeleton|>
class Method:
"""Abstract method class built from the JSON database"""
def __init__(self, binary: 'Binary', cap: Capability, data: Dict[str, Any]):
"""Create a new method associated with the given binary."""
<|body_0|>
def sudo_args(self, binary_path: str, spec: str) -> bool:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Method:
"""Abstract method class built from the JSON database"""
def __init__(self, binary: 'Binary', cap: Capability, data: Dict[str, Any]):
"""Create a new method associated with the given binary."""
try:
self.stream = Stream._member_map_[data.get('stream', 'PRINT').upper()]... | the_stack_v2_python_sparse | pwncat/gtfobins.py | calebstewart/pwncat | train | 2,177 |
13e51e95931398f9d7cfd0d2043d004ed6f4e878 | [
"self.V = V\nself.D = D\nself.W = np.random.randn(self.V, self.D)\nself.W /= 100\nself.x = None",
"if W is not None:\n self.W = W\nself.x = x\nreturn self.W[x]",
"if self.x is None:\n raise 'forward pass must occur before backward pass'\ngrad_W = np.zeros(self.W.shape)\nnp.add.at(grad_W, self.x, grad_out)... | <|body_start_0|>
self.V = V
self.D = D
self.W = np.random.randn(self.V, self.D)
self.W /= 100
self.x = None
<|end_body_0|>
<|body_start_1|>
if W is not None:
self.W = W
self.x = x
return self.W[x]
<|end_body_1|>
<|body_start_2|>
if se... | Word embedding layer represents words using vectors. Each word of the vocabulary be associated with a vector and these vectors will be learned jointly with the rest of the system. | WordEmbeddingLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordEmbeddingLayer:
"""Word embedding layer represents words using vectors. Each word of the vocabulary be associated with a vector and these vectors will be learned jointly with the rest of the system."""
def __init__(self, V, D):
"""Args: V (int): Number of words in the dictionary.... | stack_v2_sparse_classes_36k_train_032166 | 2,635 | no_license | [
{
"docstring": "Args: V (int): Number of words in the dictionary. D (int): Dimension of the desired word vector.",
"name": "__init__",
"signature": "def __init__(self, V, D)"
},
{
"docstring": "Forward pass for word embedding layer. This function operates on mini-batch of size N where each seque... | 4 | null | Implement the Python class `WordEmbeddingLayer` described below.
Class description:
Word embedding layer represents words using vectors. Each word of the vocabulary be associated with a vector and these vectors will be learned jointly with the rest of the system.
Method signatures and docstrings:
- def __init__(self,... | Implement the Python class `WordEmbeddingLayer` described below.
Class description:
Word embedding layer represents words using vectors. Each word of the vocabulary be associated with a vector and these vectors will be learned jointly with the rest of the system.
Method signatures and docstrings:
- def __init__(self,... | 7da789ef34d5e5bcf9033cfbe0ff5df607b2437a | <|skeleton|>
class WordEmbeddingLayer:
"""Word embedding layer represents words using vectors. Each word of the vocabulary be associated with a vector and these vectors will be learned jointly with the rest of the system."""
def __init__(self, V, D):
"""Args: V (int): Number of words in the dictionary.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordEmbeddingLayer:
"""Word embedding layer represents words using vectors. Each word of the vocabulary be associated with a vector and these vectors will be learned jointly with the rest of the system."""
def __init__(self, V, D):
"""Args: V (int): Number of words in the dictionary. D (int): Dim... | the_stack_v2_python_sparse | recurrent_neural_networks/rnn/word_embedding_layer.py | calvinfeng/machine-learning-notebook | train | 38 |
fd115993a258640a5f69874c5d7cd34822113baa | [
"super(APConnect, self).__init__()\nself.nodes = nodes\nreturn",
"self.logger.info(\"Connecting '{0}' to SSID: '{1}'\".format(parameters.nodes.parameters, parameters.ssids.parameters))\nself.nodes[parameters.nodes.parameters].connect(parameters.ssids.parameters)\nreturn"
] | <|body_start_0|>
super(APConnect, self).__init__()
self.nodes = nodes
return
<|end_body_0|>
<|body_start_1|>
self.logger.info("Connecting '{0}' to SSID: '{1}'".format(parameters.nodes.parameters, parameters.ssids.parameters))
self.nodes[parameters.nodes.parameters].connect(param... | A class to connect a device to an AP | APConnect | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class APConnect:
"""A class to connect a device to an AP"""
def __init__(self, nodes):
""":param: - `nodes`: dictionary of id:device pairs"""
<|body_0|>
def __call__(self, parameters):
""":param: - `parameters`: a named tuple with `nodes.parameters` and `ssids.paramete... | stack_v2_sparse_classes_36k_train_032167 | 970 | permissive | [
{
"docstring": ":param: - `nodes`: dictionary of id:device pairs",
"name": "__init__",
"signature": "def __init__(self, nodes)"
},
{
"docstring": ":param: - `parameters`: a named tuple with `nodes.parameters` and `ssids.parameters` attributes",
"name": "__call__",
"signature": "def __cal... | 2 | stack_v2_sparse_classes_30k_test_000136 | Implement the Python class `APConnect` described below.
Class description:
A class to connect a device to an AP
Method signatures and docstrings:
- def __init__(self, nodes): :param: - `nodes`: dictionary of id:device pairs
- def __call__(self, parameters): :param: - `parameters`: a named tuple with `nodes.parameters... | Implement the Python class `APConnect` described below.
Class description:
A class to connect a device to an AP
Method signatures and docstrings:
- def __init__(self, nodes): :param: - `nodes`: dictionary of id:device pairs
- def __call__(self, parameters): :param: - `parameters`: a named tuple with `nodes.parameters... | b4d1c77e1d611fe2b30768b42bdc7493afb0ea95 | <|skeleton|>
class APConnect:
"""A class to connect a device to an AP"""
def __init__(self, nodes):
""":param: - `nodes`: dictionary of id:device pairs"""
<|body_0|>
def __call__(self, parameters):
""":param: - `parameters`: a named tuple with `nodes.parameters` and `ssids.paramete... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class APConnect:
"""A class to connect a device to an AP"""
def __init__(self, nodes):
""":param: - `nodes`: dictionary of id:device pairs"""
super(APConnect, self).__init__()
self.nodes = nodes
return
def __call__(self, parameters):
""":param: - `parameters`: a nam... | the_stack_v2_python_sparse | apetools/affectors/apconnect.py | russell-n/oldape | train | 0 |
7219e6eda24c85b742be0a2092d97eceb8dde47a | [
"np.random.seed(12345)\nN, M = image.shape\ndata = np.zeros((int(N * M / B / B), B * B))\nfor i in range(N // B):\n for j in range(M // B):\n data[i * M // B + j] = image[i * B:(i + 1) * B, j * B:(j + 1) * B].reshape(-1)\ncluster = KMeans(n_clusters=K)\ncluster.fit(data)\ncodebook = cluster.cluster_center... | <|body_start_0|>
np.random.seed(12345)
N, M = image.shape
data = np.zeros((int(N * M / B / B), B * B))
for i in range(N // B):
for j in range(M // B):
data[i * M // B + j] = image[i * B:(i + 1) * B, j * B:(j + 1) * B].reshape(-1)
cluster = KMeans(n_clu... | Question2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Question2:
def trainVQ(self, image, B, K):
"""Generate a codebook for vector quantization. You can use the KMeans function from the sklearn package. **For grading purposes only:** Do NOT change the random seed, otherwise we are not able to grade your code! Please flatten any matrix in *r... | stack_v2_sparse_classes_36k_train_032168 | 15,694 | no_license | [
{
"docstring": "Generate a codebook for vector quantization. You can use the KMeans function from the sklearn package. **For grading purposes only:** Do NOT change the random seed, otherwise we are not able to grade your code! Please flatten any matrix in *row-major* order. If you prefer, you can use np.flatten... | 3 | null | Implement the Python class `Question2` described below.
Class description:
Implement the Question2 class.
Method signatures and docstrings:
- def trainVQ(self, image, B, K): Generate a codebook for vector quantization. You can use the KMeans function from the sklearn package. **For grading purposes only:** Do NOT cha... | Implement the Python class `Question2` described below.
Class description:
Implement the Question2 class.
Method signatures and docstrings:
- def trainVQ(self, image, B, K): Generate a codebook for vector quantization. You can use the KMeans function from the sklearn package. **For grading purposes only:** Do NOT cha... | adcb6b47164a909fe8b3cd3969c8bc3f3696893a | <|skeleton|>
class Question2:
def trainVQ(self, image, B, K):
"""Generate a codebook for vector quantization. You can use the KMeans function from the sklearn package. **For grading purposes only:** Do NOT change the random seed, otherwise we are not able to grade your code! Please flatten any matrix in *r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Question2:
def trainVQ(self, image, B, K):
"""Generate a codebook for vector quantization. You can use the KMeans function from the sklearn package. **For grading purposes only:** Do NOT change the random seed, otherwise we are not able to grade your code! Please flatten any matrix in *row-major* orde... | the_stack_v2_python_sparse | ECE365/ML/lab4/main.py | RickyL-2000/ZJUI-lib | train | 1 | |
bf99336bd2c29ad714006bdfb35a6ce2bc7dcc2e | [
"self.license_key = license_key\nself.signed_by_user = signed_by_user\nself.signed_time = signed_time\nself.signed_version = signed_version",
"if dictionary is None:\n return None\nlicense_key = dictionary.get('licenseKey')\nsigned_by_user = dictionary.get('signedByUser')\nsigned_time = dictionary.get('signedT... | <|body_start_0|>
self.license_key = license_key
self.signed_by_user = signed_by_user
self.signed_time = signed_time
self.signed_version = signed_version
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
license_key = dictionary.get('licenseKe... | Implementation of the 'EulaConfig' model. Specifies the End User License Agreement acceptance information. Attributes: license_key (string, required): Specifies the license key. signed_by_user (string): Specifies the login account name for the Cohesity user who accepted the End User License Agreement. signed_time (long... | EulaConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EulaConfig:
"""Implementation of the 'EulaConfig' model. Specifies the End User License Agreement acceptance information. Attributes: license_key (string, required): Specifies the license key. signed_by_user (string): Specifies the login account name for the Cohesity user who accepted the End Use... | stack_v2_sparse_classes_36k_train_032169 | 2,344 | permissive | [
{
"docstring": "Constructor for the EulaConfig class",
"name": "__init__",
"signature": "def __init__(self, license_key=None, signed_by_user=None, signed_time=None, signed_version=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dicti... | 2 | null | Implement the Python class `EulaConfig` described below.
Class description:
Implementation of the 'EulaConfig' model. Specifies the End User License Agreement acceptance information. Attributes: license_key (string, required): Specifies the license key. signed_by_user (string): Specifies the login account name for the... | Implement the Python class `EulaConfig` described below.
Class description:
Implementation of the 'EulaConfig' model. Specifies the End User License Agreement acceptance information. Attributes: license_key (string, required): Specifies the license key. signed_by_user (string): Specifies the login account name for the... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class EulaConfig:
"""Implementation of the 'EulaConfig' model. Specifies the End User License Agreement acceptance information. Attributes: license_key (string, required): Specifies the license key. signed_by_user (string): Specifies the login account name for the Cohesity user who accepted the End Use... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EulaConfig:
"""Implementation of the 'EulaConfig' model. Specifies the End User License Agreement acceptance information. Attributes: license_key (string, required): Specifies the license key. signed_by_user (string): Specifies the login account name for the Cohesity user who accepted the End User License Agr... | the_stack_v2_python_sparse | cohesity_management_sdk/models/eula_config.py | cohesity/management-sdk-python | train | 24 |
924db1e689a1e67ca2cd0b7a1e9b1ce183cdb833 | [
"create_l7policy_flow = linear_flow.Flow(constants.CREATE_L7POLICY_FLOW)\ncreate_l7policy_flow.add(lifecycle_tasks.L7PolicyToErrorOnRevertTask(requires=[constants.L7POLICY, constants.LISTENERS, constants.LOADBALANCER_ID]))\ncreate_l7policy_flow.add(database_tasks.MarkL7PolicyPendingCreateInDB(requires=constants.L7P... | <|body_start_0|>
create_l7policy_flow = linear_flow.Flow(constants.CREATE_L7POLICY_FLOW)
create_l7policy_flow.add(lifecycle_tasks.L7PolicyToErrorOnRevertTask(requires=[constants.L7POLICY, constants.LISTENERS, constants.LOADBALANCER_ID]))
create_l7policy_flow.add(database_tasks.MarkL7PolicyPendin... | L7PolicyFlows | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class L7PolicyFlows:
def get_create_l7policy_flow(self):
"""Create a flow to create an L7 policy :returns: The flow for creating an L7 policy"""
<|body_0|>
def get_delete_l7policy_flow(self):
"""Create a flow to delete an L7 policy :returns: The flow for deleting an L7 pol... | stack_v2_sparse_classes_36k_train_032170 | 4,109 | permissive | [
{
"docstring": "Create a flow to create an L7 policy :returns: The flow for creating an L7 policy",
"name": "get_create_l7policy_flow",
"signature": "def get_create_l7policy_flow(self)"
},
{
"docstring": "Create a flow to delete an L7 policy :returns: The flow for deleting an L7 policy",
"na... | 3 | null | Implement the Python class `L7PolicyFlows` described below.
Class description:
Implement the L7PolicyFlows class.
Method signatures and docstrings:
- def get_create_l7policy_flow(self): Create a flow to create an L7 policy :returns: The flow for creating an L7 policy
- def get_delete_l7policy_flow(self): Create a flo... | Implement the Python class `L7PolicyFlows` described below.
Class description:
Implement the L7PolicyFlows class.
Method signatures and docstrings:
- def get_create_l7policy_flow(self): Create a flow to create an L7 policy :returns: The flow for creating an L7 policy
- def get_delete_l7policy_flow(self): Create a flo... | 0426285a41464a5015494584f109eed35a0d44db | <|skeleton|>
class L7PolicyFlows:
def get_create_l7policy_flow(self):
"""Create a flow to create an L7 policy :returns: The flow for creating an L7 policy"""
<|body_0|>
def get_delete_l7policy_flow(self):
"""Create a flow to delete an L7 policy :returns: The flow for deleting an L7 pol... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class L7PolicyFlows:
def get_create_l7policy_flow(self):
"""Create a flow to create an L7 policy :returns: The flow for creating an L7 policy"""
create_l7policy_flow = linear_flow.Flow(constants.CREATE_L7POLICY_FLOW)
create_l7policy_flow.add(lifecycle_tasks.L7PolicyToErrorOnRevertTask(requir... | the_stack_v2_python_sparse | octavia/controller/worker/v2/flows/l7policy_flows.py | openstack/octavia | train | 147 | |
6418721c19ed2fbed5bacacf0555ad2ddfd5ce07 | [
"self.S = S\nscore = 0\ni = 0\nwhile i < len(S):\n subscore, i = self._get_score(i)\n score += subscore\nreturn score",
"if i_start >= len(self.S):\n return [0, len(self.S)]\nscore = 0\ni = i_start + 1\nwhile i < len(self.S) and self.S[i] != ')':\n sub_score, i = self._get_score(i)\n score += sub_s... | <|body_start_0|>
self.S = S
score = 0
i = 0
while i < len(S):
subscore, i = self._get_score(i)
score += subscore
return score
<|end_body_0|>
<|body_start_1|>
if i_start >= len(self.S):
return [0, len(self.S)]
score = 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def scoreOfParentheses(self, S: str) -> int:
"""Time complexity. Each symbol is read once and processed in O(1) manner, so total complexity is O(n) Space complexity: O(1) without stacking memory, O(max depth) with stacking memory."""
<|body_0|>
def _get_score(self,... | stack_v2_sparse_classes_36k_train_032171 | 2,047 | no_license | [
{
"docstring": "Time complexity. Each symbol is read once and processed in O(1) manner, so total complexity is O(n) Space complexity: O(1) without stacking memory, O(max depth) with stacking memory.",
"name": "scoreOfParentheses",
"signature": "def scoreOfParentheses(self, S: str) -> int"
},
{
"... | 2 | stack_v2_sparse_classes_30k_train_015999 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def scoreOfParentheses(self, S: str) -> int: Time complexity. Each symbol is read once and processed in O(1) manner, so total complexity is O(n) Space complexity: O(1) without st... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def scoreOfParentheses(self, S: str) -> int: Time complexity. Each symbol is read once and processed in O(1) manner, so total complexity is O(n) Space complexity: O(1) without st... | ee8237b66975fb5584a3d68b311e762c0462c8aa | <|skeleton|>
class Solution:
def scoreOfParentheses(self, S: str) -> int:
"""Time complexity. Each symbol is read once and processed in O(1) manner, so total complexity is O(n) Space complexity: O(1) without stacking memory, O(max depth) with stacking memory."""
<|body_0|>
def _get_score(self,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def scoreOfParentheses(self, S: str) -> int:
"""Time complexity. Each symbol is read once and processed in O(1) manner, so total complexity is O(n) Space complexity: O(1) without stacking memory, O(max depth) with stacking memory."""
self.S = S
score = 0
i = 0
... | the_stack_v2_python_sparse | LC856-Score-of-Parentheses.py | kate-melnykova/LeetCode-solutions | train | 2 | |
21bbcf03a738abc709bbc3e54ed0c46b913fbe44 | [
"super(ObservationPadEnv, self).__init__(env)\nself._padded_shape = padded_shape\nself._center = center\nold_space = self.observation_space\nself.observation_space = gym.spaces.Box(low=self.observation(old_space.low), high=self.observation(old_space.high), dtype=old_space.dtype)",
"total_pads = tuple((target - cu... | <|body_start_0|>
super(ObservationPadEnv, self).__init__(env)
self._padded_shape = padded_shape
self._center = center
old_space = self.observation_space
self.observation_space = gym.spaces.Box(low=self.observation(old_space.low), high=self.observation(old_space.high), dtype=old_s... | An environment that zero-pads the observation. Supports any Box observation space. | ObservationPadEnv | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObservationPadEnv:
"""An environment that zero-pads the observation. Supports any Box observation space."""
def __init__(self, env, padded_shape, center=True):
"""Create a padded environment. Args: env: the environment to wrap. padded_shape: the shape after padding. center: if True, ... | stack_v2_sparse_classes_36k_train_032172 | 2,666 | permissive | [
{
"docstring": "Create a padded environment. Args: env: the environment to wrap. padded_shape: the shape after padding. center: if True, attempt to center the original observation in the padded one. Otherwise, put the original image at the beginning of the padded image (e.g. the top-left corner).",
"name": ... | 2 | stack_v2_sparse_classes_30k_train_005665 | Implement the Python class `ObservationPadEnv` described below.
Class description:
An environment that zero-pads the observation. Supports any Box observation space.
Method signatures and docstrings:
- def __init__(self, env, padded_shape, center=True): Create a padded environment. Args: env: the environment to wrap.... | Implement the Python class `ObservationPadEnv` described below.
Class description:
An environment that zero-pads the observation. Supports any Box observation space.
Method signatures and docstrings:
- def __init__(self, env, padded_shape, center=True): Create a padded environment. Args: env: the environment to wrap.... | bba80d7049fc1586a42c05905bae75c271657761 | <|skeleton|>
class ObservationPadEnv:
"""An environment that zero-pads the observation. Supports any Box observation space."""
def __init__(self, env, padded_shape, center=True):
"""Create a padded environment. Args: env: the environment to wrap. padded_shape: the shape after padding. center: if True, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ObservationPadEnv:
"""An environment that zero-pads the observation. Supports any Box observation space."""
def __init__(self, env, padded_shape, center=True):
"""Create a padded environment. Args: env: the environment to wrap. padded_shape: the shape after padding. center: if True, attempt to ce... | the_stack_v2_python_sparse | anyrl-py/anyrl/envs/wrappers/padding.py | onursahin93/sonic_contest | train | 0 |
b4f2bf194cce2fd88682429504a88de29c1b1245 | [
"ctxt = context.get_admin_context()\nservices = db.service_get_all(ctxt)\nprint_format = '%-16s %-36s %-16s %-10s %-5s %-10s'\nprint(print_format % (_('Binary'), _('Host'), _('Zone'), _('Status'), _('State'), _('Updated At')))\nfor svc in services:\n alive = utils.service_is_up(svc)\n art = ':-)' if alive els... | <|body_start_0|>
ctxt = context.get_admin_context()
services = db.service_get_all(ctxt)
print_format = '%-16s %-36s %-16s %-10s %-5s %-10s'
print(print_format % (_('Binary'), _('Host'), _('Zone'), _('Status'), _('State'), _('Updated At')))
for svc in services:
alive =... | Methods for managing services. | ServiceCommands | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServiceCommands:
"""Methods for managing services."""
def list(self):
"""Show a list of all manila services."""
<|body_0|>
def cleanup(self):
"""Remove manila services reporting as 'down'."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ctxt = c... | stack_v2_sparse_classes_36k_train_032173 | 19,425 | permissive | [
{
"docstring": "Show a list of all manila services.",
"name": "list",
"signature": "def list(self)"
},
{
"docstring": "Remove manila services reporting as 'down'.",
"name": "cleanup",
"signature": "def cleanup(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015811 | Implement the Python class `ServiceCommands` described below.
Class description:
Methods for managing services.
Method signatures and docstrings:
- def list(self): Show a list of all manila services.
- def cleanup(self): Remove manila services reporting as 'down'. | Implement the Python class `ServiceCommands` described below.
Class description:
Methods for managing services.
Method signatures and docstrings:
- def list(self): Show a list of all manila services.
- def cleanup(self): Remove manila services reporting as 'down'.
<|skeleton|>
class ServiceCommands:
"""Methods f... | a93a844398a11a8a85f204782fb9456f7caccdbe | <|skeleton|>
class ServiceCommands:
"""Methods for managing services."""
def list(self):
"""Show a list of all manila services."""
<|body_0|>
def cleanup(self):
"""Remove manila services reporting as 'down'."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ServiceCommands:
"""Methods for managing services."""
def list(self):
"""Show a list of all manila services."""
ctxt = context.get_admin_context()
services = db.service_get_all(ctxt)
print_format = '%-16s %-36s %-16s %-10s %-5s %-10s'
print(print_format % (_('Binar... | the_stack_v2_python_sparse | manila/cmd/manage.py | openstack/manila | train | 178 |
2c6c8d7866a60ca28a31866c8b738eaf043934ca | [
"supported_hashes = ', '.join(cls._SUPPORTED_HASHES)\nargument_group.add_argument('--nsrlsvr-hash', '--nsrlsvr_hash', dest='nsrlsvr_hash', type=str, action='store', choices=cls._SUPPORTED_HASHES, default=cls._DEFAULT_HASH, metavar='HASH', help=f'Type of hash to use to query nsrlsvr instance, the default is: {cls._D... | <|body_start_0|>
supported_hashes = ', '.join(cls._SUPPORTED_HASHES)
argument_group.add_argument('--nsrlsvr-hash', '--nsrlsvr_hash', dest='nsrlsvr_hash', type=str, action='store', choices=cls._SUPPORTED_HASHES, default=cls._DEFAULT_HASH, metavar='HASH', help=f'Type of hash to use to query nsrlsvr instan... | Nsrlsvr analysis plugin CLI arguments helper. | NsrlsvrAnalysisArgumentsHelper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NsrlsvrAnalysisArgumentsHelper:
"""Nsrlsvr analysis plugin CLI arguments helper."""
def AddArguments(cls, argument_group):
"""Adds command line arguments the helper supports to an argument group. This function takes an argument parser or an argument group object and adds to it all th... | stack_v2_sparse_classes_36k_train_032174 | 3,840 | permissive | [
{
"docstring": "Adds command line arguments the helper supports to an argument group. This function takes an argument parser or an argument group object and adds to it all the command line arguments this helper supports. Args: argument_group (argparse._ArgumentGroup|argparse.ArgumentParser): group to append arg... | 2 | null | Implement the Python class `NsrlsvrAnalysisArgumentsHelper` described below.
Class description:
Nsrlsvr analysis plugin CLI arguments helper.
Method signatures and docstrings:
- def AddArguments(cls, argument_group): Adds command line arguments the helper supports to an argument group. This function takes an argument... | Implement the Python class `NsrlsvrAnalysisArgumentsHelper` described below.
Class description:
Nsrlsvr analysis plugin CLI arguments helper.
Method signatures and docstrings:
- def AddArguments(cls, argument_group): Adds command line arguments the helper supports to an argument group. This function takes an argument... | d6022f8cfebfddf2d08ab2d300a41b61f3349933 | <|skeleton|>
class NsrlsvrAnalysisArgumentsHelper:
"""Nsrlsvr analysis plugin CLI arguments helper."""
def AddArguments(cls, argument_group):
"""Adds command line arguments the helper supports to an argument group. This function takes an argument parser or an argument group object and adds to it all th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NsrlsvrAnalysisArgumentsHelper:
"""Nsrlsvr analysis plugin CLI arguments helper."""
def AddArguments(cls, argument_group):
"""Adds command line arguments the helper supports to an argument group. This function takes an argument parser or an argument group object and adds to it all the command lin... | the_stack_v2_python_sparse | plaso/cli/helpers/nsrlsvr_analysis.py | log2timeline/plaso | train | 1,506 |
988321bb3f0f3978f5f741829432c2bfdde4be58 | [
"if token.base_form == '*':\n return WordRepr(token.surface, token.surface)\nelse:\n return WordRepr(token.surface, token.base_form)",
"base_form = token_detail.feature.base_form\nif base_form == '*':\n return WordRepr(token_detail.surface, token_detail.surface)\nelse:\n return WordRepr(token_detail.s... | <|body_start_0|>
if token.base_form == '*':
return WordRepr(token.surface, token.surface)
else:
return WordRepr(token.surface, token.base_form)
<|end_body_0|>
<|body_start_1|>
base_form = token_detail.feature.base_form
if base_form == '*':
return Word... | 単語の表現情報 Attributes: surface (str): 表層形 base_form (str): 原形 | WordRepr | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordRepr:
"""単語の表現情報 Attributes: surface (str): 表層形 base_form (str): 原形"""
def from_token(cls, token: Token):
"""TokenインスタンスからWordReprインスタンスを作成 Args: token (Token): Tokenインスタンス Returns: WordReprインスタンス"""
<|body_0|>
def from_token_detail(cls, token_detail: TokenDetail):
... | stack_v2_sparse_classes_36k_train_032175 | 12,296 | no_license | [
{
"docstring": "TokenインスタンスからWordReprインスタンスを作成 Args: token (Token): Tokenインスタンス Returns: WordReprインスタンス",
"name": "from_token",
"signature": "def from_token(cls, token: Token)"
},
{
"docstring": "TokenDetailインスタンスからWordReprインスタンスを作成 Args: token_detail (TokenDetail): TokenDetailインスタンス Returns: Wo... | 2 | stack_v2_sparse_classes_30k_train_010639 | Implement the Python class `WordRepr` described below.
Class description:
単語の表現情報 Attributes: surface (str): 表層形 base_form (str): 原形
Method signatures and docstrings:
- def from_token(cls, token: Token): TokenインスタンスからWordReprインスタンスを作成 Args: token (Token): Tokenインスタンス Returns: WordReprインスタンス
- def from_token_detail(cl... | Implement the Python class `WordRepr` described below.
Class description:
単語の表現情報 Attributes: surface (str): 表層形 base_form (str): 原形
Method signatures and docstrings:
- def from_token(cls, token: Token): TokenインスタンスからWordReprインスタンスを作成 Args: token (Token): Tokenインスタンス Returns: WordReprインスタンス
- def from_token_detail(cl... | a4c6334b779a94814b7798a0fbfe9a148bf18d3a | <|skeleton|>
class WordRepr:
"""単語の表現情報 Attributes: surface (str): 表層形 base_form (str): 原形"""
def from_token(cls, token: Token):
"""TokenインスタンスからWordReprインスタンスを作成 Args: token (Token): Tokenインスタンス Returns: WordReprインスタンス"""
<|body_0|>
def from_token_detail(cls, token_detail: TokenDetail):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordRepr:
"""単語の表現情報 Attributes: surface (str): 表層形 base_form (str): 原形"""
def from_token(cls, token: Token):
"""TokenインスタンスからWordReprインスタンスを作成 Args: token (Token): Tokenインスタンス Returns: WordReprインスタンス"""
if token.base_form == '*':
return WordRepr(token.surface, token.surface)
... | the_stack_v2_python_sparse | src/review_research/nlp/nlp_types.py | S38knt-ks/ReviewResearch | train | 0 |
5a835443cd21a6f169a9dd6c125659a8acc8016b | [
"if self.source_port is not None:\n return self.source_port.is_local\nreturn True",
"if self.dest_port is not None:\n return self.dest_port.is_local\nreturn True",
"super(FlowClassifier, self).validate()\nif self.source_port is None and self.dest_port is None:\n raise errors.ValidationError('One of sou... | <|body_start_0|>
if self.source_port is not None:
return self.source_port.is_local
return True
<|end_body_0|>
<|body_start_1|>
if self.dest_port is not None:
return self.dest_port.is_local
return True
<|end_body_1|>
<|body_start_2|>
super(FlowClassifier,... | FlowClassifier | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlowClassifier:
def is_classification_local(self):
"""Should the flow classifier classification flows be installed locally For classification on source lport, we match using reg6, which is available only after classification app sets it, so there is no use installing it on other hosts. F... | stack_v2_sparse_classes_36k_train_032176 | 5,427 | permissive | [
{
"docstring": "Should the flow classifier classification flows be installed locally For classification on source lport, we match using reg6, which is available only after classification app sets it, so there is no use installing it on other hosts. For classification on dest lport, we match using reg7. reg7 is ... | 3 | stack_v2_sparse_classes_30k_test_000816 | Implement the Python class `FlowClassifier` described below.
Class description:
Implement the FlowClassifier class.
Method signatures and docstrings:
- def is_classification_local(self): Should the flow classifier classification flows be installed locally For classification on source lport, we match using reg6, which... | Implement the Python class `FlowClassifier` described below.
Class description:
Implement the FlowClassifier class.
Method signatures and docstrings:
- def is_classification_local(self): Should the flow classifier classification flows be installed locally For classification on source lport, we match using reg6, which... | 0f154d4f794b02ac5b7fd61a3417d89e7b10912d | <|skeleton|>
class FlowClassifier:
def is_classification_local(self):
"""Should the flow classifier classification flows be installed locally For classification on source lport, we match using reg6, which is available only after classification app sets it, so there is no use installing it on other hosts. F... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FlowClassifier:
def is_classification_local(self):
"""Should the flow classifier classification flows be installed locally For classification on source lport, we match using reg6, which is available only after classification app sets it, so there is no use installing it on other hosts. For classificat... | the_stack_v2_python_sparse | dragonflow/db/models/sfc.py | qianyuqiao/dragonflow | train | 0 | |
567a387287bf9c62c3cd299a59c85a542af781f5 | [
"self.serializer_class = CommentSerializer\nimage_id = self.kwargs.get('image_id')\nif not image_id:\n return JsonResponse({'status': 'fail', 'code': 406, 'data': None, 'messages': ['Error: No image id provided!']})\nqueryset_list = Comment.objects.filter(image__pk=image_id)\nreturn queryset_list",
"self.seria... | <|body_start_0|>
self.serializer_class = CommentSerializer
image_id = self.kwargs.get('image_id')
if not image_id:
return JsonResponse({'status': 'fail', 'code': 406, 'data': None, 'messages': ['Error: No image id provided!']})
queryset_list = Comment.objects.filter(image__pk... | Класа која се користи за креирање коментара и за добављање листе свих коментара слике репрезентованих у JSON формату | CreateGetCommentsAPI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateGetCommentsAPI:
"""Класа која се користи за креирање коментара и за добављање листе свих коментара слике репрезентованих у JSON формату"""
def get_queryset(self, *args, **kwargs):
"""Mетода помоћу које се добављају сви подаци :param args: :param kwargs: image_id :return: QueryS... | stack_v2_sparse_classes_36k_train_032177 | 6,327 | no_license | [
{
"docstring": "Mетода помоћу које се добављају сви подаци :param args: :param kwargs: image_id :return: QuerySet",
"name": "get_queryset",
"signature": "def get_queryset(self, *args, **kwargs)"
},
{
"docstring": "Mетода помоћу које се врши креирање :param request: :param args: :param kwargs: im... | 2 | stack_v2_sparse_classes_30k_train_002529 | Implement the Python class `CreateGetCommentsAPI` described below.
Class description:
Класа која се користи за креирање коментара и за добављање листе свих коментара слике репрезентованих у JSON формату
Method signatures and docstrings:
- def get_queryset(self, *args, **kwargs): Mетода помоћу које се добављају сви по... | Implement the Python class `CreateGetCommentsAPI` described below.
Class description:
Класа која се користи за креирање коментара и за добављање листе свих коментара слике репрезентованих у JSON формату
Method signatures and docstrings:
- def get_queryset(self, *args, **kwargs): Mетода помоћу које се добављају сви по... | 9b49cdfdcfbbc911cec23ed30ded30f6c4042522 | <|skeleton|>
class CreateGetCommentsAPI:
"""Класа која се користи за креирање коментара и за добављање листе свих коментара слике репрезентованих у JSON формату"""
def get_queryset(self, *args, **kwargs):
"""Mетода помоћу које се добављају сви подаци :param args: :param kwargs: image_id :return: QueryS... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateGetCommentsAPI:
"""Класа која се користи за креирање коментара и за добављање листе свих коментара слике репрезентованих у JSON формату"""
def get_queryset(self, *args, **kwargs):
"""Mетода помоћу које се добављају сви подаци :param args: :param kwargs: image_id :return: QuerySet"""
... | the_stack_v2_python_sparse | src/comments/api/views.py | milosb793/django-gallery-api | train | 0 |
76636a1200484ae7d2ef3113785f4f033cdaf370 | [
"links = response.xpath('//a[@class=\"product-title-link\"]/@href').extract()\nfor link in links:\n yield response.follow('https://www.labirint.ru' + link, callback=self.parse_item)\nnext_page = response.xpath('//a[@class=\"pagination-next__text\"]/@href').extract_first()\nif next_page:\n yield response.follo... | <|body_start_0|>
links = response.xpath('//a[@class="product-title-link"]/@href').extract()
for link in links:
yield response.follow('https://www.labirint.ru' + link, callback=self.parse_item)
next_page = response.xpath('//a[@class="pagination-next__text"]/@href').extract_first()
... | Labirint.ru spider. | LabirintruSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabirintruSpider:
"""Labirint.ru spider."""
def parse(self, response):
"""Novelty page parser."""
<|body_0|>
def parse_item(self, response: HtmlResponse):
"""Book item parser."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
links = response.xpat... | stack_v2_sparse_classes_36k_train_032178 | 1,756 | no_license | [
{
"docstring": "Novelty page parser.",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "Book item parser.",
"name": "parse_item",
"signature": "def parse_item(self, response: HtmlResponse)"
}
] | 2 | stack_v2_sparse_classes_30k_test_001166 | Implement the Python class `LabirintruSpider` described below.
Class description:
Labirint.ru spider.
Method signatures and docstrings:
- def parse(self, response): Novelty page parser.
- def parse_item(self, response: HtmlResponse): Book item parser. | Implement the Python class `LabirintruSpider` described below.
Class description:
Labirint.ru spider.
Method signatures and docstrings:
- def parse(self, response): Novelty page parser.
- def parse_item(self, response: HtmlResponse): Book item parser.
<|skeleton|>
class LabirintruSpider:
"""Labirint.ru spider.""... | 69488f0e788578722bf4f1cf171508f1e5624145 | <|skeleton|>
class LabirintruSpider:
"""Labirint.ru spider."""
def parse(self, response):
"""Novelty page parser."""
<|body_0|>
def parse_item(self, response: HtmlResponse):
"""Book item parser."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LabirintruSpider:
"""Labirint.ru spider."""
def parse(self, response):
"""Novelty page parser."""
links = response.xpath('//a[@class="product-title-link"]/@href').extract()
for link in links:
yield response.follow('https://www.labirint.ru' + link, callback=self.parse_i... | the_stack_v2_python_sparse | lesson_6/bookparser/spiders/labirintru.py | IInvasion/collecting_and_processing_web_data | train | 0 |
83b027bbe9a384bab501f1ad937396ebf4515b3f | [
"logger.info('Checking Appfollow API connection...')\ntry:\n ext_id = config['ext_id']\n cid = config['cid']\n api_secret = config['api_secret']\n response = requests.get(f'https://api.appfollow.io/ratings?ext_id={ext_id}&cid={cid}', auth=HTTPBasicAuth(api_secret, api_secret))\n if response.status_co... | <|body_start_0|>
logger.info('Checking Appfollow API connection...')
try:
ext_id = config['ext_id']
cid = config['cid']
api_secret = config['api_secret']
response = requests.get(f'https://api.appfollow.io/ratings?ext_id={ext_id}&cid={cid}', auth=HTTPBasicA... | SourceAppfollow | [
"MIT",
"Apache-2.0",
"BSD-3-Clause",
"Elastic-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SourceAppfollow:
def check_connection(self, logger, config) -> Tuple[bool, any]:
"""A connection check to validate that the user-provided config can be used to connect to the underlying API :param config: the user-input config object conforming to the connector's spec.yaml :param logger:... | stack_v2_sparse_classes_36k_train_032179 | 3,806 | permissive | [
{
"docstring": "A connection check to validate that the user-provided config can be used to connect to the underlying API :param config: the user-input config object conforming to the connector's spec.yaml :param logger: logger object :return Tuple[bool, any]: (True, None) if the input config can be used to con... | 2 | stack_v2_sparse_classes_30k_train_007124 | Implement the Python class `SourceAppfollow` described below.
Class description:
Implement the SourceAppfollow class.
Method signatures and docstrings:
- def check_connection(self, logger, config) -> Tuple[bool, any]: A connection check to validate that the user-provided config can be used to connect to the underlyin... | Implement the Python class `SourceAppfollow` described below.
Class description:
Implement the SourceAppfollow class.
Method signatures and docstrings:
- def check_connection(self, logger, config) -> Tuple[bool, any]: A connection check to validate that the user-provided config can be used to connect to the underlyin... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class SourceAppfollow:
def check_connection(self, logger, config) -> Tuple[bool, any]:
"""A connection check to validate that the user-provided config can be used to connect to the underlying API :param config: the user-input config object conforming to the connector's spec.yaml :param logger:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SourceAppfollow:
def check_connection(self, logger, config) -> Tuple[bool, any]:
"""A connection check to validate that the user-provided config can be used to connect to the underlying API :param config: the user-input config object conforming to the connector's spec.yaml :param logger: logger object... | the_stack_v2_python_sparse | dts/airbyte/airbyte-integrations/connectors/source-appfollow/source_appfollow/source.py | alldatacenter/alldata | train | 774 | |
7aa0653bafc11b065bb47d21a55d0c4e8f3ba002 | [
"def find(parent, i):\n \"\"\"\n find the \"root\" node which is connected to i\n \"\"\"\n while parent[i] != i:\n i = parent[i]\n return parent[i]\nN = len(edges)\nparent = [0] * (N + 1)\nA, B = ([], [])\nfor i in range(N):\n u, v = edges[i]\n if parent[v] == 0:\n ... | <|body_start_0|>
def find(parent, i):
"""
find the "root" node which is connected to i
"""
while parent[i] != i:
i = parent[i]
return parent[i]
N = len(edges)
parent = [0] * (N + 1)
A, B = ([], []... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findRedundantDirectedConnection(self, edges):
""":type edges: List[List[int]] :rtype: List[int]"""
<|body_0|>
def findRedundantDirectedConnection2(self, edges):
""":type edges: List[List[int]] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_032180 | 7,752 | no_license | [
{
"docstring": ":type edges: List[List[int]] :rtype: List[int]",
"name": "findRedundantDirectedConnection",
"signature": "def findRedundantDirectedConnection(self, edges)"
},
{
"docstring": ":type edges: List[List[int]] :rtype: List[int]",
"name": "findRedundantDirectedConnection2",
"sig... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findRedundantDirectedConnection(self, edges): :type edges: List[List[int]] :rtype: List[int]
- def findRedundantDirectedConnection2(self, edges): :type edges: List[List[int]]... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findRedundantDirectedConnection(self, edges): :type edges: List[List[int]] :rtype: List[int]
- def findRedundantDirectedConnection2(self, edges): :type edges: List[List[int]]... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def findRedundantDirectedConnection(self, edges):
""":type edges: List[List[int]] :rtype: List[int]"""
<|body_0|>
def findRedundantDirectedConnection2(self, edges):
""":type edges: List[List[int]] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findRedundantDirectedConnection(self, edges):
""":type edges: List[List[int]] :rtype: List[int]"""
def find(parent, i):
"""
find the "root" node which is connected to i
"""
while parent[i] != i:
i = p... | the_stack_v2_python_sparse | code685RedundantConnectionII.py | cybelewang/leetcode-python | train | 0 | |
f187006e01fc57789e714d7778b7868779b7bbe4 | [
"try:\n comment = self.update_order_comment()\nexcept Exception:\n return HttpResponseBadRequest()\nelse:\n return JsonResponse({'comment': comment})",
"product_id = self.request.POST['product_id']\ncomment = self.request.POST['comment']\nproduct = get_object_or_404(BaseProduct, id=product_id)\nreturn mo... | <|body_start_0|>
try:
comment = self.update_order_comment()
except Exception:
return HttpResponseBadRequest()
else:
return JsonResponse({'comment': comment})
<|end_body_0|>
<|body_start_1|>
product_id = self.request.POST['product_id']
comment ... | View for setting re-order comments. | SetOrderComment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SetOrderComment:
"""View for setting re-order comments."""
def post(self, *args, **kwargs):
"""Update re-order comment."""
<|body_0|>
def update_order_comment(self):
"""Update re-order comment."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try... | stack_v2_sparse_classes_36k_train_032181 | 6,889 | no_license | [
{
"docstring": "Update re-order comment.",
"name": "post",
"signature": "def post(self, *args, **kwargs)"
},
{
"docstring": "Update re-order comment.",
"name": "update_order_comment",
"signature": "def update_order_comment(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018647 | Implement the Python class `SetOrderComment` described below.
Class description:
View for setting re-order comments.
Method signatures and docstrings:
- def post(self, *args, **kwargs): Update re-order comment.
- def update_order_comment(self): Update re-order comment. | Implement the Python class `SetOrderComment` described below.
Class description:
View for setting re-order comments.
Method signatures and docstrings:
- def post(self, *args, **kwargs): Update re-order comment.
- def update_order_comment(self): Update re-order comment.
<|skeleton|>
class SetOrderComment:
"""View... | ba51d4e304b1aeb296fa2fe16611c892fcdbd471 | <|skeleton|>
class SetOrderComment:
"""View for setting re-order comments."""
def post(self, *args, **kwargs):
"""Update re-order comment."""
<|body_0|>
def update_order_comment(self):
"""Update re-order comment."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SetOrderComment:
"""View for setting re-order comments."""
def post(self, *args, **kwargs):
"""Update re-order comment."""
try:
comment = self.update_order_comment()
except Exception:
return HttpResponseBadRequest()
else:
return JsonResp... | the_stack_v2_python_sparse | restock/views.py | stcstores/stcadmin | train | 0 |
b4cc6bcb27a43d153bc09ea98392be10defb41b1 | [
"cluster = self.get_object_or_404(objects.Cluster, cluster_id)\nself.check_net_provider(cluster)\nreturn self.serializer.serialize_for_cluster(cluster)",
"data = jsonutils.loads(web.data())\ncluster = self.get_object_or_404(objects.Cluster, cluster_id)\nself.check_net_provider(cluster)\nself.check_if_network_conf... | <|body_start_0|>
cluster = self.get_object_or_404(objects.Cluster, cluster_id)
self.check_net_provider(cluster)
return self.serializer.serialize_for_cluster(cluster)
<|end_body_0|>
<|body_start_1|>
data = jsonutils.loads(web.data())
cluster = self.get_object_or_404(objects.Clust... | Neutron Network configuration handler | NeutronNetworkConfigurationHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NeutronNetworkConfigurationHandler:
"""Neutron Network configuration handler"""
def GET(self, cluster_id):
""":returns: JSONized network configuration for cluster. :http: * 200 (OK) * 404 (cluster not found in db)"""
<|body_0|>
def PUT(self, cluster_id):
""":retu... | stack_v2_sparse_classes_36k_train_032182 | 8,763 | permissive | [
{
"docstring": ":returns: JSONized network configuration for cluster. :http: * 200 (OK) * 404 (cluster not found in db)",
"name": "GET",
"signature": "def GET(self, cluster_id)"
},
{
"docstring": ":returns: JSONized Task object. :http: * 200 (task successfully executed) * 202 (network checking t... | 2 | null | Implement the Python class `NeutronNetworkConfigurationHandler` described below.
Class description:
Neutron Network configuration handler
Method signatures and docstrings:
- def GET(self, cluster_id): :returns: JSONized network configuration for cluster. :http: * 200 (OK) * 404 (cluster not found in db)
- def PUT(sel... | Implement the Python class `NeutronNetworkConfigurationHandler` described below.
Class description:
Neutron Network configuration handler
Method signatures and docstrings:
- def GET(self, cluster_id): :returns: JSONized network configuration for cluster. :http: * 200 (OK) * 404 (cluster not found in db)
- def PUT(sel... | 976baf842242a5f97c95bdc3e20328fa0558bf69 | <|skeleton|>
class NeutronNetworkConfigurationHandler:
"""Neutron Network configuration handler"""
def GET(self, cluster_id):
""":returns: JSONized network configuration for cluster. :http: * 200 (OK) * 404 (cluster not found in db)"""
<|body_0|>
def PUT(self, cluster_id):
""":retu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NeutronNetworkConfigurationHandler:
"""Neutron Network configuration handler"""
def GET(self, cluster_id):
""":returns: JSONized network configuration for cluster. :http: * 200 (OK) * 404 (cluster not found in db)"""
cluster = self.get_object_or_404(objects.Cluster, cluster_id)
se... | the_stack_v2_python_sparse | nailgun/nailgun/api/v1/handlers/network_configuration.py | nebril/fuel-web | train | 1 |
3a710c6f39d176e199527f6694694b0639a7e78c | [
"super().__init__(model, step_type, predictor_kwargs=predictor_kwargs)\nself.model = model\nself.hidden_size = hidden_size\nself.num_samples = num_samples\nself.mc_dropout = mc_dropout\nself.weight_decay = weight_decay\nself.prior_scale = prior_scale\nself.data_length = data_length\nself.mc_dropout_layer = nn.Dropo... | <|body_start_0|>
super().__init__(model, step_type, predictor_kwargs=predictor_kwargs)
self.model = model
self.hidden_size = hidden_size
self.num_samples = num_samples
self.mc_dropout = mc_dropout
self.weight_decay = weight_decay
self.prior_scale = prior_scale
... | Recoding mechanism that bases its recoding on the predictive uncertainty of the decoder, where the uncertainty is estimate using MC Dropout [1]. [1] http://proceedings.mlr.press/v48/gal16.pdf | MCDropoutMechanism | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MCDropoutMechanism:
"""Recoding mechanism that bases its recoding on the predictive uncertainty of the decoder, where the uncertainty is estimate using MC Dropout [1]. [1] http://proceedings.mlr.press/v48/gal16.pdf"""
def __init__(self, model: AbstractRNN, hidden_size: int, num_samples: int,... | stack_v2_sparse_classes_36k_train_032183 | 5,682 | no_license | [
{
"docstring": "Initialize the mechanism. Parameters ---------- model: AbstractRNN Model the mechanism is being applied to. hidden_size: int Dimensionality of hidden activations. num_samples: int Number of samples used to estimate uncertainty. mc_dropout: float Dropout probability used to estimate uncertainty. ... | 4 | stack_v2_sparse_classes_30k_train_018693 | Implement the Python class `MCDropoutMechanism` described below.
Class description:
Recoding mechanism that bases its recoding on the predictive uncertainty of the decoder, where the uncertainty is estimate using MC Dropout [1]. [1] http://proceedings.mlr.press/v48/gal16.pdf
Method signatures and docstrings:
- def __... | Implement the Python class `MCDropoutMechanism` described below.
Class description:
Recoding mechanism that bases its recoding on the predictive uncertainty of the decoder, where the uncertainty is estimate using MC Dropout [1]. [1] http://proceedings.mlr.press/v48/gal16.pdf
Method signatures and docstrings:
- def __... | 6443bea8d325fa948e117b32a063e5383db2de14 | <|skeleton|>
class MCDropoutMechanism:
"""Recoding mechanism that bases its recoding on the predictive uncertainty of the decoder, where the uncertainty is estimate using MC Dropout [1]. [1] http://proceedings.mlr.press/v48/gal16.pdf"""
def __init__(self, model: AbstractRNN, hidden_size: int, num_samples: int,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MCDropoutMechanism:
"""Recoding mechanism that bases its recoding on the predictive uncertainty of the decoder, where the uncertainty is estimate using MC Dropout [1]. [1] http://proceedings.mlr.press/v48/gal16.pdf"""
def __init__(self, model: AbstractRNN, hidden_size: int, num_samples: int, mc_dropout: ... | the_stack_v2_python_sparse | src/recoding/mc_dropout.py | Kaleidophon/tenacious-toucan | train | 0 |
b8a2e0b8e6fcc9ed26aea820e37dec857fabdd56 | [
"keyword = data.get('keyword')\ndescription = data.get('description')\n_keyword = Keyword(keyword)\nfunction_name = _keyword.get_function_name_from_source()\ndata = {'name': function_name, 'description': description, 'keyword': keyword}\nmodel = KeywordModel(**data)\ndb.session.add(model)\ndb.session.commit()\nretu... | <|body_start_0|>
keyword = data.get('keyword')
description = data.get('description')
_keyword = Keyword(keyword)
function_name = _keyword.get_function_name_from_source()
data = {'name': function_name, 'description': description, 'keyword': keyword}
model = KeywordModel(**... | KeywordService | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KeywordService:
def create(self, data):
"""# 暂时没有前端页面 -- SQL暂时不更换 # 这里需要先提取函数名，然后关键字用函数名进行索引，存到数据库。 # 如果数据库中函数名已经存在怎么办，是否需要先查询，重复则失败？ :param data: :return:"""
<|body_0|>
def delete(self, data):
""":param data: :return:"""
<|body_1|>
def update(self, data... | stack_v2_sparse_classes_36k_train_032184 | 3,159 | permissive | [
{
"docstring": "# 暂时没有前端页面 -- SQL暂时不更换 # 这里需要先提取函数名，然后关键字用函数名进行索引，存到数据库。 # 如果数据库中函数名已经存在怎么办，是否需要先查询，重复则失败？ :param data: :return:",
"name": "create",
"signature": "def create(self, data)"
},
{
"docstring": ":param data: :return:",
"name": "delete",
"signature": "def delete(self, data)"
... | 5 | stack_v2_sparse_classes_30k_train_003011 | Implement the Python class `KeywordService` described below.
Class description:
Implement the KeywordService class.
Method signatures and docstrings:
- def create(self, data): # 暂时没有前端页面 -- SQL暂时不更换 # 这里需要先提取函数名，然后关键字用函数名进行索引，存到数据库。 # 如果数据库中函数名已经存在怎么办，是否需要先查询，重复则失败？ :param data: :return:
- def delete(self, data): :pa... | Implement the Python class `KeywordService` described below.
Class description:
Implement the KeywordService class.
Method signatures and docstrings:
- def create(self, data): # 暂时没有前端页面 -- SQL暂时不更换 # 这里需要先提取函数名，然后关键字用函数名进行索引，存到数据库。 # 如果数据库中函数名已经存在怎么办，是否需要先查询，重复则失败？ :param data: :return:
- def delete(self, data): :pa... | 54dc4000263ab9e8873f0d429a7fe48b11fb727a | <|skeleton|>
class KeywordService:
def create(self, data):
"""# 暂时没有前端页面 -- SQL暂时不更换 # 这里需要先提取函数名，然后关键字用函数名进行索引，存到数据库。 # 如果数据库中函数名已经存在怎么办，是否需要先查询，重复则失败？ :param data: :return:"""
<|body_0|>
def delete(self, data):
""":param data: :return:"""
<|body_1|>
def update(self, data... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KeywordService:
def create(self, data):
"""# 暂时没有前端页面 -- SQL暂时不更换 # 这里需要先提取函数名，然后关键字用函数名进行索引，存到数据库。 # 如果数据库中函数名已经存在怎么办，是否需要先查询，重复则失败？ :param data: :return:"""
keyword = data.get('keyword')
description = data.get('description')
_keyword = Keyword(keyword)
function_name =... | the_stack_v2_python_sparse | clover/keyword/service.py | taoyanli0808/clover | train | 18 | |
3dc14a6646dc4003095a61985af6e816f58b3fd6 | [
"try:\n word_value = Word_Counter.objects.get(pk=pk)\n serializer = WordCounterSerializer(word_value, context={'request': request})\n return Response(serializer.data)\nexcept Exception as ex:\n return HttpResponseServerError(ex)",
"word_values = self.request.query_params.get('word_values')\nif word_va... | <|body_start_0|>
try:
word_value = Word_Counter.objects.get(pk=pk)
serializer = WordCounterSerializer(word_value, context={'request': request})
return Response(serializer.data)
except Exception as ex:
return HttpResponseServerError(ex)
<|end_body_0|>
<|bo... | Words_Counter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Words_Counter:
def retrieve(self, request, pk=None):
"""Handle GET requests for single word Returns: Response -- JSON serialized word instance"""
<|body_0|>
def list(self, request):
"""Handle GET requests to words resource Returns: Response -- JSON serialized list of... | stack_v2_sparse_classes_36k_train_032185 | 2,351 | no_license | [
{
"docstring": "Handle GET requests for single word Returns: Response -- JSON serialized word instance",
"name": "retrieve",
"signature": "def retrieve(self, request, pk=None)"
},
{
"docstring": "Handle GET requests to words resource Returns: Response -- JSON serialized list of words",
"name... | 2 | stack_v2_sparse_classes_30k_train_009840 | Implement the Python class `Words_Counter` described below.
Class description:
Implement the Words_Counter class.
Method signatures and docstrings:
- def retrieve(self, request, pk=None): Handle GET requests for single word Returns: Response -- JSON serialized word instance
- def list(self, request): Handle GET reque... | Implement the Python class `Words_Counter` described below.
Class description:
Implement the Words_Counter class.
Method signatures and docstrings:
- def retrieve(self, request, pk=None): Handle GET requests for single word Returns: Response -- JSON serialized word instance
- def list(self, request): Handle GET reque... | 582048dafa7e354fffdc0478ec68088e8bbf42b1 | <|skeleton|>
class Words_Counter:
def retrieve(self, request, pk=None):
"""Handle GET requests for single word Returns: Response -- JSON serialized word instance"""
<|body_0|>
def list(self, request):
"""Handle GET requests to words resource Returns: Response -- JSON serialized list of... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Words_Counter:
def retrieve(self, request, pk=None):
"""Handle GET requests for single word Returns: Response -- JSON serialized word instance"""
try:
word_value = Word_Counter.objects.get(pk=pk)
serializer = WordCounterSerializer(word_value, context={'request': request... | the_stack_v2_python_sparse | genieioapp/views/words_counter.py | cherkesky/GenieIO | train | 1 | |
011c2c4de5f89e0f33ce9287fc819cfd630ace89 | [
"self._num_gaussians = len(sigma_list)\nself._sigmas_scaled = np.array(sigma_list) / pixel_scale\nif supersampling_convolution is True:\n self._sigmas_scaled *= supersampling_factor\nself._fraction_list = fraction_list / np.sum(fraction_list)\nassert len(self._sigmas_scaled) == len(self._fraction_list)\nself._tr... | <|body_start_0|>
self._num_gaussians = len(sigma_list)
self._sigmas_scaled = np.array(sigma_list) / pixel_scale
if supersampling_convolution is True:
self._sigmas_scaled *= supersampling_factor
self._fraction_list = fraction_list / np.sum(fraction_list)
assert len(sel... | class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel. | MultiGaussianConvolution | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiGaussianConvolution:
"""class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel."""
def __init__(self, sigma_list, fraction_list, pi... | stack_v2_sparse_classes_36k_train_032186 | 15,375 | permissive | [
{
"docstring": ":param sigma_list: list of std value of Gaussian kernel :param fraction_list: fraction of flux to be convoled with each Gaussian kernel :param pixel_scale: scale of pixel width (to convert sigmas into units of pixels) :param truncation: float. Truncate the filter at this many standard deviations... | 4 | null | Implement the Python class `MultiGaussianConvolution` described below.
Class description:
class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel.
Method signature... | Implement the Python class `MultiGaussianConvolution` described below.
Class description:
class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel.
Method signature... | 73c9645f26f6983fe7961104075ebe8bf7a4b54c | <|skeleton|>
class MultiGaussianConvolution:
"""class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel."""
def __init__(self, sigma_list, fraction_list, pi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiGaussianConvolution:
"""class to perform a convolution consisting of multiple 2d Gaussians This is aimed to lead to a speed-up without significant loss of accuracy do to the simplified convolution kernel relative to a pixelized kernel."""
def __init__(self, sigma_list, fraction_list, pixel_scale, su... | the_stack_v2_python_sparse | lenstronomy/ImSim/Numerics/convolution.py | lenstronomy/lenstronomy | train | 41 |
d45662f4dd4be5a127e11579b8d510877b610a82 | [
"self.ss = ss\nself.n_step = n_step\nself.mu = mu\nself.sigma = sigma\nself.step_time = step_time\nself.saw_time = saw_time / delta_t",
"step_vector = np.abs([round(gauss(self.mu, self.sigma), 1) for _ in range(self.n_step)])\nstep_vector[0] = self.ss\nu = np.zeros(shape=dim)\nj = 0\nramp_Step = self.saw_time\nco... | <|body_start_0|>
self.ss = ss
self.n_step = n_step
self.mu = mu
self.sigma = sigma
self.step_time = step_time
self.saw_time = saw_time / delta_t
<|end_body_0|>
<|body_start_1|>
step_vector = np.abs([round(gauss(self.mu, self.sigma), 1) for _ in range(self.n_step)... | SawGaussStep | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SawGaussStep:
def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None):
"""Settings for a random step sequence Args: step_time: Time to perform step change n_step (int): Number of steps"""
<|body_0|>
def out(self, t: any, dim=(None, None)) ... | stack_v2_sparse_classes_36k_train_032187 | 8,036 | no_license | [
{
"docstring": "Settings for a random step sequence Args: step_time: Time to perform step change n_step (int): Number of steps",
"name": "__init__",
"signature": "def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None)"
},
{
"docstring": "Generate a random seq... | 2 | stack_v2_sparse_classes_30k_train_008396 | Implement the Python class `SawGaussStep` described below.
Class description:
Implement the SawGaussStep class.
Method signatures and docstrings:
- def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None): Settings for a random step sequence Args: step_time: Time to perform step cha... | Implement the Python class `SawGaussStep` described below.
Class description:
Implement the SawGaussStep class.
Method signatures and docstrings:
- def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None): Settings for a random step sequence Args: step_time: Time to perform step cha... | cf548475295f25407ba968546c2fc85c26f9343c | <|skeleton|>
class SawGaussStep:
def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None):
"""Settings for a random step sequence Args: step_time: Time to perform step change n_step (int): Number of steps"""
<|body_0|>
def out(self, t: any, dim=(None, None)) ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SawGaussStep:
def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None):
"""Settings for a random step sequence Args: step_time: Time to perform step change n_step (int): Number of steps"""
self.ss = ss
self.n_step = n_step
self.mu = mu
... | the_stack_v2_python_sparse | SourceCode/simulation/signal.py | martin-bachorik/Master-Thesis-Project | train | 0 | |
99c18086dc0c85ef0eb1cc727aa1f7d3b8e629ff | [
"stack = [root]\nrAns = [str(root.val)]\nwhile stack:\n temp = stack.pop(0)\n if temp.left and temp.right:\n stack.append(temp.left)\n stack.append(temp.right)\n rAns.append(str(temp.left.val))\n rAns.append(str(temp.right.val))\n elif not temp.left and temp.right:\n stac... | <|body_start_0|>
stack = [root]
rAns = [str(root.val)]
while stack:
temp = stack.pop(0)
if temp.left and temp.right:
stack.append(temp.left)
stack.append(temp.right)
rAns.append(str(temp.left.val))
rAns.appen... | 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_032188 | 2,440 | 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 | stack_v2_sparse_classes_30k_train_007634 | 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:... | 7ee9e603b0ef612193a522a32bb150781c6bb416 | <|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"""
stack = [root]
rAns = [str(root.val)]
while stack:
temp = stack.pop(0)
if temp.left and temp.right:
stack.append(temp.left)
... | the_stack_v2_python_sparse | pySolution/Codec.py | Qanora/leetcode | train | 0 | |
71d85026ba072ce67091fcdd952249e861ee28fb | [
"self.state_list = state_list\nself.domain = domain\nself.start_state = start_state\nself.acceptor_states = acceptor_states\nself.state_transition_table = state_transition_table",
"current_state = self.start_state\nfor char in input_string:\n if not current_state in self.state_list:\n return 'REJECT'\n ... | <|body_start_0|>
self.state_list = state_list
self.domain = domain
self.start_state = start_state
self.acceptor_states = acceptor_states
self.state_transition_table = state_transition_table
<|end_body_0|>
<|body_start_1|>
current_state = self.start_state
for char... | Class containing functions for defining finite state automata and running finite state machines using textual input | Finite_State_Automata | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Finite_State_Automata:
"""Class containing functions for defining finite state automata and running finite state machines using textual input"""
def __init__(self, state_list, domain, start_state, acceptor_states, state_transition_table):
"""Initialize an FSA by its own definition Pa... | stack_v2_sparse_classes_36k_train_032189 | 5,491 | no_license | [
{
"docstring": "Initialize an FSA by its own definition Parameters --- state_list -> List of all possible states domain -> Domain of all symbols that the machine can accept start_state -> The starting state of the machine acceptor_states -> list of states that are defined as the final accepting states state_tra... | 3 | stack_v2_sparse_classes_30k_train_008187 | Implement the Python class `Finite_State_Automata` described below.
Class description:
Class containing functions for defining finite state automata and running finite state machines using textual input
Method signatures and docstrings:
- def __init__(self, state_list, domain, start_state, acceptor_states, state_tran... | Implement the Python class `Finite_State_Automata` described below.
Class description:
Class containing functions for defining finite state automata and running finite state machines using textual input
Method signatures and docstrings:
- def __init__(self, state_list, domain, start_state, acceptor_states, state_tran... | 1f7dd50123b5b69d8268bc071a4adc5b3b8a76e6 | <|skeleton|>
class Finite_State_Automata:
"""Class containing functions for defining finite state automata and running finite state machines using textual input"""
def __init__(self, state_list, domain, start_state, acceptor_states, state_transition_table):
"""Initialize an FSA by its own definition Pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Finite_State_Automata:
"""Class containing functions for defining finite state automata and running finite state machines using textual input"""
def __init__(self, state_list, domain, start_state, acceptor_states, state_transition_table):
"""Initialize an FSA by its own definition Parameters --- ... | the_stack_v2_python_sparse | state_machine.py | lzambella/csc470-proj1 | train | 0 |
5e7b52ecb441c4972fd4855ac4edcc1747d8f79f | [
"super(SegNet_1, self).__init__()\nself.layer_1 = SegnetLayer_Encoder(in_channels, 64, 2)\nself.layer_2 = SegnetLayer_Encoder(64, 128, 2)\nself.layer_3 = SegnetLayer_Encoder(128, 256, 3)\nself.layer_4 = SegnetLayer_Encoder(256, 512, 3)\nself.layer_5 = SegnetLayer_Encoder(512, 1024, 3)\nself.layer_6 = SegnetLayer_En... | <|body_start_0|>
super(SegNet_1, self).__init__()
self.layer_1 = SegnetLayer_Encoder(in_channels, 64, 2)
self.layer_2 = SegnetLayer_Encoder(64, 128, 2)
self.layer_3 = SegnetLayer_Encoder(128, 256, 3)
self.layer_4 = SegnetLayer_Encoder(256, 512, 3)
self.layer_5 = SegnetLay... | Derived Class to define a Segnet Architecture of NN Attributes ---------- in_channels : int The input size of the network. n_classes : int The output size of the network. References ---------- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, Alex Kendall, Roberto Ci... | SegNet_1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SegNet_1:
"""Derived Class to define a Segnet Architecture of NN Attributes ---------- in_channels : int The input size of the network. n_classes : int The output size of the network. References ---------- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badr... | stack_v2_sparse_classes_36k_train_032190 | 20,094 | no_license | [
{
"docstring": "Sequential Instanciation of the different Layers",
"name": "__init__",
"signature": "def __init__(self, in_channels=3, n_classes=21)"
},
{
"docstring": "Sequential Computation, see nn.Module.forward methods PyTorch",
"name": "forward",
"signature": "def forward(self, inpu... | 2 | stack_v2_sparse_classes_30k_train_019076 | Implement the Python class `SegNet_1` described below.
Class description:
Derived Class to define a Segnet Architecture of NN Attributes ---------- in_channels : int The input size of the network. n_classes : int The output size of the network. References ---------- SegNet: A Deep Convolutional Encoder-Decoder Archite... | Implement the Python class `SegNet_1` described below.
Class description:
Derived Class to define a Segnet Architecture of NN Attributes ---------- in_channels : int The input size of the network. n_classes : int The output size of the network. References ---------- SegNet: A Deep Convolutional Encoder-Decoder Archite... | 3b63f360e67013d5962082e57fb36ebfb37d8920 | <|skeleton|>
class SegNet_1:
"""Derived Class to define a Segnet Architecture of NN Attributes ---------- in_channels : int The input size of the network. n_classes : int The output size of the network. References ---------- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SegNet_1:
"""Derived Class to define a Segnet Architecture of NN Attributes ---------- in_channels : int The input size of the network. n_classes : int The output size of the network. References ---------- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, A... | the_stack_v2_python_sparse | segmentation/models/nn.py | Kivo0/vibotorch | train | 0 |
74740fad7b96eeb46118e5d97bf81abef5df8f6e | [
"super().__init__(coordinator, device, 'delivery', 'Energy delivery', f'delivery_{dev_type}')\nself._type = dev_type\nself._attr_name = f'Energy delivery {dev_type}'",
"if self.coordinator.data.delivery_meter is None:\n return None\nreturn getattr(self.coordinator.data.delivery_meter, f'_{self._type}', None)"
... | <|body_start_0|>
super().__init__(coordinator, device, 'delivery', 'Energy delivery', f'delivery_{dev_type}')
self._type = dev_type
self._attr_name = f'Energy delivery {dev_type}'
<|end_body_0|>
<|body_start_1|>
if self.coordinator.data.delivery_meter is None:
return None
... | The Youless delivery meter value sensor. | DeliveryMeterSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeliveryMeterSensor:
"""The Youless delivery meter value sensor."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None:
"""Instantiate a delivery meter sensor."""
<|body_0|>
def get_sensor(self) -> YoulessSensor | None:... | stack_v2_sparse_classes_36k_train_032191 | 11,812 | permissive | [
{
"docstring": "Instantiate a delivery meter sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None"
},
{
"docstring": "Get the sensor for providing the value.",
"name": "get_sensor",
"signature":... | 2 | null | Implement the Python class `DeliveryMeterSensor` described below.
Class description:
The Youless delivery meter value sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None: Instantiate a delivery meter sensor.
- def get_senso... | Implement the Python class `DeliveryMeterSensor` described below.
Class description:
The Youless delivery meter value sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None: Instantiate a delivery meter sensor.
- def get_senso... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class DeliveryMeterSensor:
"""The Youless delivery meter value sensor."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None:
"""Instantiate a delivery meter sensor."""
<|body_0|>
def get_sensor(self) -> YoulessSensor | None:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeliveryMeterSensor:
"""The Youless delivery meter value sensor."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None:
"""Instantiate a delivery meter sensor."""
super().__init__(coordinator, device, 'delivery', 'Energy delivery', f'del... | the_stack_v2_python_sparse | homeassistant/components/youless/sensor.py | home-assistant/core | train | 35,501 |
19c9bcf19d67e868f2b5c68808f5f8201bfd4dc7 | [
"try:\n return (int(key[0] // 16), int(key[1] // 16), int(key[2] // 16))\nexcept ValueError:\n return KeyError(\"Key %s isn't usable here!\" % repr(key))",
"minx, innerx = divmod(key[0], 16)\nminy, innery = divmod(key[1], 16)\nminz, innerz = divmod(key[2], 16)\nminx = int(minx)\nminy = int(miny)\nminz = int... | <|body_start_0|>
try:
return (int(key[0] // 16), int(key[1] // 16), int(key[2] // 16))
except ValueError:
return KeyError("Key %s isn't usable here!" % repr(key))
<|end_body_0|>
<|body_start_1|>
minx, innerx = divmod(key[0], 16)
miny, innery = divmod(key[1], 16)
... | Class for tracking blocks in the XZ-plane. | Block3DSpatialDict | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Block3DSpatialDict:
"""Class for tracking blocks in the XZ-plane."""
def key_for_bucket(self, key):
"""Partition keys into chunk-sized buckets."""
<|body_0|>
def keys_near(self, key, radius):
"""Get all bucket keys "near" this key. This method may return a genera... | stack_v2_sparse_classes_36k_train_032192 | 5,213 | permissive | [
{
"docstring": "Partition keys into chunk-sized buckets.",
"name": "key_for_bucket",
"signature": "def key_for_bucket(self, key)"
},
{
"docstring": "Get all bucket keys \"near\" this key. This method may return a generator.",
"name": "keys_near",
"signature": "def keys_near(self, key, ra... | 2 | stack_v2_sparse_classes_30k_train_012028 | Implement the Python class `Block3DSpatialDict` described below.
Class description:
Class for tracking blocks in the XZ-plane.
Method signatures and docstrings:
- def key_for_bucket(self, key): Partition keys into chunk-sized buckets.
- def keys_near(self, key, radius): Get all bucket keys "near" this key. This metho... | Implement the Python class `Block3DSpatialDict` described below.
Class description:
Class for tracking blocks in the XZ-plane.
Method signatures and docstrings:
- def key_for_bucket(self, key): Partition keys into chunk-sized buckets.
- def keys_near(self, key, radius): Get all bucket keys "near" this key. This metho... | 7be5d792871a8447499911fa1502c6a7c1437dc3 | <|skeleton|>
class Block3DSpatialDict:
"""Class for tracking blocks in the XZ-plane."""
def key_for_bucket(self, key):
"""Partition keys into chunk-sized buckets."""
<|body_0|>
def keys_near(self, key, radius):
"""Get all bucket keys "near" this key. This method may return a genera... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Block3DSpatialDict:
"""Class for tracking blocks in the XZ-plane."""
def key_for_bucket(self, key):
"""Partition keys into chunk-sized buckets."""
try:
return (int(key[0] // 16), int(key[1] // 16), int(key[2] // 16))
except ValueError:
return KeyError("Key ... | the_stack_v2_python_sparse | bravo/utilities/spatial.py | CyberFlameGO/bravo | train | 0 |
32d8973b1b805ce5e395966c008f30e5c9649e33 | [
"self.str_a = str_a\nself.str_b = str_b\nself.ratio = distance_ratio\nself.author_a = None\nself.author_b = None\nself.work_a = None\nself.work_b = None\nself.subwork_a = None\nself.subwork_b = None\nself.text_n_a = None\nself.text_n_b = None\nself.language_a = None\nself.language_b = None\nreturn",
"if 'author' ... | <|body_start_0|>
self.str_a = str_a
self.str_b = str_b
self.ratio = distance_ratio
self.author_a = None
self.author_b = None
self.work_a = None
self.work_b = None
self.subwork_a = None
self.subwork_b = None
self.text_n_a = None
self... | A class to increase ease of working with text reuse data. | Comparison | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Comparison:
"""A class to increase ease of working with text reuse data."""
def __init__(self, str_a, str_b, distance_ratio):
"""Initialize class with compared strings and ratio of comparison :param str_a: str :param str_b: str :param distance_ratio: float"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_032193 | 7,939 | permissive | [
{
"docstring": "Initialize class with compared strings and ratio of comparison :param str_a: str :param str_b: str :param distance_ratio: float",
"name": "__init__",
"signature": "def __init__(self, str_a, str_b, distance_ratio)"
},
{
"docstring": "Set the reference values related to the str_a c... | 3 | null | Implement the Python class `Comparison` described below.
Class description:
A class to increase ease of working with text reuse data.
Method signatures and docstrings:
- def __init__(self, str_a, str_b, distance_ratio): Initialize class with compared strings and ratio of comparison :param str_a: str :param str_b: str... | Implement the Python class `Comparison` described below.
Class description:
A class to increase ease of working with text reuse data.
Method signatures and docstrings:
- def __init__(self, str_a, str_b, distance_ratio): Initialize class with compared strings and ratio of comparison :param str_a: str :param str_b: str... | 085420eaed7055fbcb311714eebb67861fd1b241 | <|skeleton|>
class Comparison:
"""A class to increase ease of working with text reuse data."""
def __init__(self, str_a, str_b, distance_ratio):
"""Initialize class with compared strings and ratio of comparison :param str_a: str :param str_b: str :param distance_ratio: float"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Comparison:
"""A class to increase ease of working with text reuse data."""
def __init__(self, str_a, str_b, distance_ratio):
"""Initialize class with compared strings and ratio of comparison :param str_a: str :param str_b: str :param distance_ratio: float"""
self.str_a = str_a
se... | the_stack_v2_python_sparse | cltk/text_reuse/comparison.py | jerryfrancis-97/cltk | train | 1 |
60fe1647f69d4d734dee6722d20fb0df14b30295 | [
"super().__init__(**kwargs)\nself.rnn = rnn\nself.reducer = reducer\nself.bidirectional = bidirectional\nif bidirectional:\n self.rnn = tf.keras.layers.Bidirectional(self.rnn, merge_mode=None)",
"outputs = self.rnn(*args, **kwargs)\nif self.bidirectional:\n sequences = outputs[0:2]\n states = outputs[2:]... | <|body_start_0|>
super().__init__(**kwargs)
self.rnn = rnn
self.reducer = reducer
self.bidirectional = bidirectional
if bidirectional:
self.rnn = tf.keras.layers.Bidirectional(self.rnn, merge_mode=None)
<|end_body_0|>
<|body_start_1|>
outputs = self.rnn(*args... | Extend a RNN layer to possibly make it bidirectional and format its outputs. | _RNNWrapper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _RNNWrapper:
"""Extend a RNN layer to possibly make it bidirectional and format its outputs."""
def __init__(self, rnn, bidirectional=False, reducer=reducer_lib.ConcatReducer(), **kwargs):
"""Initializes the layer. Args: rnn: The RNN layer to extend, built with ``return_sequences`` a... | stack_v2_sparse_classes_36k_train_032194 | 9,747 | permissive | [
{
"docstring": "Initializes the layer. Args: rnn: The RNN layer to extend, built with ``return_sequences`` and ``return_state`` enabled. bidirectional: Make this layer bidirectional. reducer: A :class:`opennmt.layers.Reducer` instance to merge bidirectional states and outputs. **kwargs: Additional layer argumen... | 2 | null | Implement the Python class `_RNNWrapper` described below.
Class description:
Extend a RNN layer to possibly make it bidirectional and format its outputs.
Method signatures and docstrings:
- def __init__(self, rnn, bidirectional=False, reducer=reducer_lib.ConcatReducer(), **kwargs): Initializes the layer. Args: rnn: T... | Implement the Python class `_RNNWrapper` described below.
Class description:
Extend a RNN layer to possibly make it bidirectional and format its outputs.
Method signatures and docstrings:
- def __init__(self, rnn, bidirectional=False, reducer=reducer_lib.ConcatReducer(), **kwargs): Initializes the layer. Args: rnn: T... | 6f3b952ebb973dec31250a806bf0f56ff730d0b5 | <|skeleton|>
class _RNNWrapper:
"""Extend a RNN layer to possibly make it bidirectional and format its outputs."""
def __init__(self, rnn, bidirectional=False, reducer=reducer_lib.ConcatReducer(), **kwargs):
"""Initializes the layer. Args: rnn: The RNN layer to extend, built with ``return_sequences`` a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _RNNWrapper:
"""Extend a RNN layer to possibly make it bidirectional and format its outputs."""
def __init__(self, rnn, bidirectional=False, reducer=reducer_lib.ConcatReducer(), **kwargs):
"""Initializes the layer. Args: rnn: The RNN layer to extend, built with ``return_sequences`` and ``return_s... | the_stack_v2_python_sparse | opennmt/layers/rnn.py | OpenNMT/OpenNMT-tf | train | 1,487 |
61777ce76fa0aa407cfce3f0c6fc81db02d04523 | [
"super(WeightQuantizerGF, self).__init__()\nself.k = k\nfor i in range(1, k + 1):\n self.register_buffer(f'v{i}', torch.tensor([0.0] * size))",
"if self.training:\n vs, x_q = quantization.quantizer_gf(x, k=self.k)\n for i in range(self.k):\n getattr(self, f'v{i + 1}').copy_(vs[i])\nelse:\n vs =... | <|body_start_0|>
super(WeightQuantizerGF, self).__init__()
self.k = k
for i in range(1, k + 1):
self.register_buffer(f'v{i}', torch.tensor([0.0] * size))
<|end_body_0|>
<|body_start_1|>
if self.training:
vs, x_q = quantization.quantizer_gf(x, k=self.k)
... | Weight greedy foldable quantizer. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization. | WeightQuantizerGF | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeightQuantizerGF:
"""Weight greedy foldable quantizer. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization."""
def __init__(self, size: int, k: int) -> None:
"""Construct a greedy-foldable quantizer wit... | stack_v2_sparse_classes_36k_train_032195 | 4,037 | no_license | [
{
"docstring": "Construct a greedy-foldable quantizer with `k`-bits.",
"name": "__init__",
"signature": "def __init__(self, size: int, k: int) -> None"
},
{
"docstring": "Forward pass of greedy foldable quantizer with `k`-bits.",
"name": "forward",
"signature": "def forward(self, x: torc... | 2 | stack_v2_sparse_classes_30k_train_005115 | Implement the Python class `WeightQuantizerGF` described below.
Class description:
Weight greedy foldable quantizer. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization.
Method signatures and docstrings:
- def __init__(self, size: int, k... | Implement the Python class `WeightQuantizerGF` described below.
Class description:
Weight greedy foldable quantizer. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization.
Method signatures and docstrings:
- def __init__(self, size: int, k... | 39197b5f54cd84ff35022c851dd2dcb753ca6b89 | <|skeleton|>
class WeightQuantizerGF:
"""Weight greedy foldable quantizer. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization."""
def __init__(self, size: int, k: int) -> None:
"""Construct a greedy-foldable quantizer wit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WeightQuantizerGF:
"""Weight greedy foldable quantizer. In training mode, the optimal scalars are computed and cached. In eval mode, the cached scalars are used to compute the quantization."""
def __init__(self, size: int, k: int) -> None:
"""Construct a greedy-foldable quantizer with `k`-bits.""... | the_stack_v2_python_sparse | quant/binary/weight_quantization.py | mikechen66/ml-quant | train | 0 |
06c9deea268d960d8d0ee98a1bfd9a07619be465 | [
"self.n = int(n)\nself.p = float(p)\nif data is None:\n if self.n < 1:\n raise ValueError('n must be a positive value')\n elif self.p <= 0 or self.p >= 1:\n raise ValueError('p must be greater than 0 and less than 1')\nelif isinstance(data, list):\n if len(data) > 1:\n self.data = data... | <|body_start_0|>
self.n = int(n)
self.p = float(p)
if data is None:
if self.n < 1:
raise ValueError('n must be a positive value')
elif self.p <= 0 or self.p >= 1:
raise ValueError('p must be greater than 0 and less than 1')
elif isi... | Class Binomial | Binomial | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Binomial:
"""Class Binomial"""
def __init__(self, data=None, n=1, p=0.5):
"""Initializes the distribution. Args: data (list): distribution data n (int): Bernoulli number. p (float): success probability."""
<|body_0|>
def pmf(self, k):
"""Calculates the pmf at a g... | stack_v2_sparse_classes_36k_train_032196 | 2,389 | no_license | [
{
"docstring": "Initializes the distribution. Args: data (list): distribution data n (int): Bernoulli number. p (float): success probability.",
"name": "__init__",
"signature": "def __init__(self, data=None, n=1, p=0.5)"
},
{
"docstring": "Calculates the pmf at a given k Args: k (int): point to ... | 3 | stack_v2_sparse_classes_30k_train_011654 | Implement the Python class `Binomial` described below.
Class description:
Class Binomial
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): Initializes the distribution. Args: data (list): distribution data n (int): Bernoulli number. p (float): success probability.
- def pmf(self, k): Calc... | Implement the Python class `Binomial` described below.
Class description:
Class Binomial
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): Initializes the distribution. Args: data (list): distribution data n (int): Bernoulli number. p (float): success probability.
- def pmf(self, k): Calc... | 5aff923277cfe9f2b5324a773e4e5c3cac810a0c | <|skeleton|>
class Binomial:
"""Class Binomial"""
def __init__(self, data=None, n=1, p=0.5):
"""Initializes the distribution. Args: data (list): distribution data n (int): Bernoulli number. p (float): success probability."""
<|body_0|>
def pmf(self, k):
"""Calculates the pmf at a g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Binomial:
"""Class Binomial"""
def __init__(self, data=None, n=1, p=0.5):
"""Initializes the distribution. Args: data (list): distribution data n (int): Bernoulli number. p (float): success probability."""
self.n = int(n)
self.p = float(p)
if data is None:
if s... | the_stack_v2_python_sparse | math/0x03-probability/binomial.py | cmmolanos1/holbertonschool-machine_learning | train | 1 |
368f3d65b3dcbbfb9b9ff08b82a8748cb8826381 | [
"super().setUp()\nself.login(self.CURRICULUM_ADMIN_EMAIL, is_super_admin=True)\ncsrf_token = self.get_new_csrf_token()\nself.post_json('/adminhandler', {'action': 'reload_exploration', 'exploration_id': '3'}, csrf_token=csrf_token)\nself.logout()",
"library_groups = summary_services.get_library_groups([])\nexpect... | <|body_start_0|>
super().setUp()
self.login(self.CURRICULUM_ADMIN_EMAIL, is_super_admin=True)
csrf_token = self.get_new_csrf_token()
self.post_json('/adminhandler', {'action': 'reload_exploration', 'exploration_id': '3'}, csrf_token=csrf_token)
self.logout()
<|end_body_0|>
<|bod... | Test functions for getting summary dicts for library groups. | LibraryGroupsTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LibraryGroupsTest:
"""Test functions for getting summary dicts for library groups."""
def setUp(self) -> None:
"""Populate the database of explorations and their summaries. The sequence of events is: - (1) Admin logs in. - (2) Admin access admin page. - (3) Admin reloads exploration ... | stack_v2_sparse_classes_36k_train_032197 | 47,358 | permissive | [
{
"docstring": "Populate the database of explorations and their summaries. The sequence of events is: - (1) Admin logs in. - (2) Admin access admin page. - (3) Admin reloads exploration with id '3'. - (4) Admin logs out.",
"name": "setUp",
"signature": "def setUp(self) -> None"
},
{
"docstring":... | 2 | stack_v2_sparse_classes_30k_train_015085 | Implement the Python class `LibraryGroupsTest` described below.
Class description:
Test functions for getting summary dicts for library groups.
Method signatures and docstrings:
- def setUp(self) -> None: Populate the database of explorations and their summaries. The sequence of events is: - (1) Admin logs in. - (2) ... | Implement the Python class `LibraryGroupsTest` described below.
Class description:
Test functions for getting summary dicts for library groups.
Method signatures and docstrings:
- def setUp(self) -> None: Populate the database of explorations and their summaries. The sequence of events is: - (1) Admin logs in. - (2) ... | d16fdf23d790eafd63812bd7239532256e30a21d | <|skeleton|>
class LibraryGroupsTest:
"""Test functions for getting summary dicts for library groups."""
def setUp(self) -> None:
"""Populate the database of explorations and their summaries. The sequence of events is: - (1) Admin logs in. - (2) Admin access admin page. - (3) Admin reloads exploration ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LibraryGroupsTest:
"""Test functions for getting summary dicts for library groups."""
def setUp(self) -> None:
"""Populate the database of explorations and their summaries. The sequence of events is: - (1) Admin logs in. - (2) Admin access admin page. - (3) Admin reloads exploration with id '3'. ... | the_stack_v2_python_sparse | core/domain/summary_services_test.py | oppia/oppia | train | 6,172 |
c8c24ab8b6e196d8eb0c64920251021a94acfa9e | [
"event_columns_string = QueryHelper.get_columns_string(EventMapping, 'events')\nevent_type_columns_string = QueryHelper.get_columns_string(EventTypeMapping, 'event_types')\ndog_columns_string = QueryHelper.get_columns_string(DogMapping, 'dogs')\nstmt = text('SELECT {event_columns}, {event_type_columns}, {dog_column... | <|body_start_0|>
event_columns_string = QueryHelper.get_columns_string(EventMapping, 'events')
event_type_columns_string = QueryHelper.get_columns_string(EventTypeMapping, 'event_types')
dog_columns_string = QueryHelper.get_columns_string(DogMapping, 'dogs')
stmt = text('SELECT {event_co... | EventRepository | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventRepository:
def get_events_by_expenditure_id(cls, expenditure_id: int):
"""Gets all events by expenditure id, joined with dog and type"""
<|body_0|>
def get_event_by_id(cls, event_id: int):
"""Get event by id"""
<|body_1|>
def add_new_event(cls, eve... | stack_v2_sparse_classes_36k_train_032198 | 7,079 | no_license | [
{
"docstring": "Gets all events by expenditure id, joined with dog and type",
"name": "get_events_by_expenditure_id",
"signature": "def get_events_by_expenditure_id(cls, expenditure_id: int)"
},
{
"docstring": "Get event by id",
"name": "get_event_by_id",
"signature": "def get_event_by_i... | 4 | stack_v2_sparse_classes_30k_train_001236 | Implement the Python class `EventRepository` described below.
Class description:
Implement the EventRepository class.
Method signatures and docstrings:
- def get_events_by_expenditure_id(cls, expenditure_id: int): Gets all events by expenditure id, joined with dog and type
- def get_event_by_id(cls, event_id: int): G... | Implement the Python class `EventRepository` described below.
Class description:
Implement the EventRepository class.
Method signatures and docstrings:
- def get_events_by_expenditure_id(cls, expenditure_id: int): Gets all events by expenditure id, joined with dog and type
- def get_event_by_id(cls, event_id: int): G... | d5e383a3a703c973d038627f35d405e716cfd25c | <|skeleton|>
class EventRepository:
def get_events_by_expenditure_id(cls, expenditure_id: int):
"""Gets all events by expenditure id, joined with dog and type"""
<|body_0|>
def get_event_by_id(cls, event_id: int):
"""Get event by id"""
<|body_1|>
def add_new_event(cls, eve... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventRepository:
def get_events_by_expenditure_id(cls, expenditure_id: int):
"""Gets all events by expenditure id, joined with dog and type"""
event_columns_string = QueryHelper.get_columns_string(EventMapping, 'events')
event_type_columns_string = QueryHelper.get_columns_string(EventT... | the_stack_v2_python_sparse | app/events/repository.py | Innodogs/Innodogs | train | 0 | |
c6e1b2e3f9b1b14f4881ee9baa0e1999835e5ac2 | [
"cube = set_up_variable_cube(np.zeros((2, 2), dtype=np.float32), name='lwe_thickness_of_precipitation_amount', units='m', time=dt(2017, 1, 10, 4, 0), frt=dt(2017, 1, 10, 3, 0))\nself.cube = add_coordinate(cube, [dt(2017, 1, 10, 3, 0), dt(2017, 1, 10, 4, 0)], 'time', is_datetime=True)\ndata = np.array([[[1.0, 1.0], ... | <|body_start_0|>
cube = set_up_variable_cube(np.zeros((2, 2), dtype=np.float32), name='lwe_thickness_of_precipitation_amount', units='m', time=dt(2017, 1, 10, 4, 0), frt=dt(2017, 1, 10, 3, 0))
self.cube = add_coordinate(cube, [dt(2017, 1, 10, 3, 0), dt(2017, 1, 10, 4, 0)], 'time', is_datetime=True)
... | Tests for the process method in ChooseDefaultWeightsTriangular. | Test_process | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_process:
"""Tests for the process method in ChooseDefaultWeightsTriangular."""
def setUp(self):
"""Set up cubes used in unit tests"""
<|body_0|>
def test_same_units(self):
"""Test plugin produces the correct weights when the parameters for the triangle are i... | stack_v2_sparse_classes_36k_train_032199 | 13,166 | permissive | [
{
"docstring": "Set up cubes used in unit tests",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test plugin produces the correct weights when the parameters for the triangle are in the same units as the input cube's coordinate",
"name": "test_same_units",
"signature"... | 4 | stack_v2_sparse_classes_30k_train_003089 | Implement the Python class `Test_process` described below.
Class description:
Tests for the process method in ChooseDefaultWeightsTriangular.
Method signatures and docstrings:
- def setUp(self): Set up cubes used in unit tests
- def test_same_units(self): Test plugin produces the correct weights when the parameters f... | Implement the Python class `Test_process` described below.
Class description:
Tests for the process method in ChooseDefaultWeightsTriangular.
Method signatures and docstrings:
- def setUp(self): Set up cubes used in unit tests
- def test_same_units(self): Test plugin produces the correct weights when the parameters f... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_process:
"""Tests for the process method in ChooseDefaultWeightsTriangular."""
def setUp(self):
"""Set up cubes used in unit tests"""
<|body_0|>
def test_same_units(self):
"""Test plugin produces the correct weights when the parameters for the triangle are i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test_process:
"""Tests for the process method in ChooseDefaultWeightsTriangular."""
def setUp(self):
"""Set up cubes used in unit tests"""
cube = set_up_variable_cube(np.zeros((2, 2), dtype=np.float32), name='lwe_thickness_of_precipitation_amount', units='m', time=dt(2017, 1, 10, 4, 0), f... | the_stack_v2_python_sparse | improver_tests/blending/weights/test_ChooseDefaultWeightsTriangular.py | metoppv/improver | train | 101 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.