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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fecb3e22946d38845977e65758ca3ade8bc44058 | [
"super().__init__()\nself.lca_layer = lca_layer\nself.num_lca_dim = num_lca_dim\nself.num_simulations = num_simulations\nself.num_time_steps = num_time_steps",
"dev = ff_input.device\nactive = torch.ones(size=[int(self.num_simulations), 1], device=dev)\npre_activities = torch.zeros(size=[int(self.num_simulations)... | <|body_start_0|>
super().__init__()
self.lca_layer = lca_layer
self.num_lca_dim = num_lca_dim
self.num_simulations = num_simulations
self.num_time_steps = num_time_steps
<|end_body_0|>
<|body_start_1|>
dev = ff_input.device
active = torch.ones(size=[int(self.num_... | LCAModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LCAModel:
def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000):
"""A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time course of perceptual choice: the l... | stack_v2_sparse_classes_36k_train_010100 | 11,741 | permissive | [
{
"docstring": "A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time course of perceptual choice: the leaky, competing accumulator model. Psychol Rev. 2001 Jul;108(3):550-92. doi: 10.1037/0033-295x.108.3.550. PMID: 11488378. Args: lca_la... | 2 | stack_v2_sparse_classes_30k_train_004254 | Implement the Python class `LCAModel` described below.
Class description:
Implement the LCAModel class.
Method signatures and docstrings:
- def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000): A model that simulates a leaky competing accumulator model (Usher... | Implement the Python class `LCAModel` described below.
Class description:
Implement the LCAModel class.
Method signatures and docstrings:
- def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000): A model that simulates a leaky competing accumulator model (Usher... | 424971b04d55a2cddbae4c05a0aae2d7b3502c20 | <|skeleton|>
class LCAModel:
def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000):
"""A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time course of perceptual choice: the l... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LCAModel:
def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000):
"""A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time course of perceptual choice: the leaky, competin... | the_stack_v2_python_sparse | Scripts/Debug/lca/onnx_lca.py | PrincetonUniversity/PsyNeuLink | train | 79 | |
d5248ef4b524621538e7af886947d13b71a01b66 | [
"self.magnitude = magnitude\nself.c_i = c_i\nself.nsamples = nsamples\nself.stdev_spread = stdev_spread\nloss_c = model.output[0][c_i]\ngrad_symbolic = K.gradients(loss_c, model.input)[0]\nself.iterate = K.function([model.input], grad_symbolic)",
"total_gradients = np.zeros_like(x_value)\nstdev = self.stdev_sprea... | <|body_start_0|>
self.magnitude = magnitude
self.c_i = c_i
self.nsamples = nsamples
self.stdev_spread = stdev_spread
loss_c = model.output[0][c_i]
grad_symbolic = K.gradients(loss_c, model.input)[0]
self.iterate = K.function([model.input], grad_symbolic)
<|end_bod... | SmoothedMask | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmoothedMask:
def __init__(self, model, c_i, stdev_spread=0.15, nsamples=25, magnitude=True):
"""Define the smoothGrad Mask class to return the smooth grad mask model : the deep model used c_i : the index of the class concerned stdev_spread: Amount of noise to add to the input, as fracti... | stack_v2_sparse_classes_36k_train_010101 | 9,618 | no_license | [
{
"docstring": "Define the smoothGrad Mask class to return the smooth grad mask model : the deep model used c_i : the index of the class concerned stdev_spread: Amount of noise to add to the input, as fraction of the total spread (x_max - x_min). Defaults to 15%. Level of noise nsamples: Number of samples to av... | 2 | stack_v2_sparse_classes_30k_train_013919 | Implement the Python class `SmoothedMask` described below.
Class description:
Implement the SmoothedMask class.
Method signatures and docstrings:
- def __init__(self, model, c_i, stdev_spread=0.15, nsamples=25, magnitude=True): Define the smoothGrad Mask class to return the smooth grad mask model : the deep model use... | Implement the Python class `SmoothedMask` described below.
Class description:
Implement the SmoothedMask class.
Method signatures and docstrings:
- def __init__(self, model, c_i, stdev_spread=0.15, nsamples=25, magnitude=True): Define the smoothGrad Mask class to return the smooth grad mask model : the deep model use... | 60da35f58ffe9e24e99b6b20dd7a46b02815ad79 | <|skeleton|>
class SmoothedMask:
def __init__(self, model, c_i, stdev_spread=0.15, nsamples=25, magnitude=True):
"""Define the smoothGrad Mask class to return the smooth grad mask model : the deep model used c_i : the index of the class concerned stdev_spread: Amount of noise to add to the input, as fracti... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmoothedMask:
def __init__(self, model, c_i, stdev_spread=0.15, nsamples=25, magnitude=True):
"""Define the smoothGrad Mask class to return the smooth grad mask model : the deep model used c_i : the index of the class concerned stdev_spread: Amount of noise to add to the input, as fraction of the tota... | the_stack_v2_python_sparse | Classif_Paintings/saliencyMaps.py | ngonthier/Icono_Art_Analysis | train | 2 | |
a4951c685ffbc51514c66de5e3738a1605064731 | [
"if decay < 0.0 or decay > 1.0:\n raise ValueError('Decay must be between 0 and 1')\nself.decay = decay\nself.num_updates = 0 if use_num_updates else None\nself.shadow_params = [p.clone().detach() for p in parameters if p.requires_grad]",
"decay = self.decay\nif self.num_updates is not None:\n self.num_upda... | <|body_start_0|>
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.decay = decay
self.num_updates = 0 if use_num_updates else None
self.shadow_params = [p.clone().detach() for p in parameters if p.requires_grad]
<|end_body_0|>
<|body_start... | Maintains (exponential) moving average of a set of parameters. | ExponentialMovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExponentialMovingAverage:
"""Maintains (exponential) moving average of a set of parameters."""
def __init__(self, parameters, decay, use_num_updates=True):
"""Args: parameters: Iterable of `torch.nn.Parameter`; usually the result of `model.parameters()`. decay: The exponential decay.... | stack_v2_sparse_classes_36k_train_010102 | 7,328 | no_license | [
{
"docstring": "Args: parameters: Iterable of `torch.nn.Parameter`; usually the result of `model.parameters()`. decay: The exponential decay. use_num_updates: Whether to use number of updates when computing averages.",
"name": "__init__",
"signature": "def __init__(self, parameters, decay, use_num_updat... | 3 | stack_v2_sparse_classes_30k_val_000483 | Implement the Python class `ExponentialMovingAverage` described below.
Class description:
Maintains (exponential) moving average of a set of parameters.
Method signatures and docstrings:
- def __init__(self, parameters, decay, use_num_updates=True): Args: parameters: Iterable of `torch.nn.Parameter`; usually the resu... | Implement the Python class `ExponentialMovingAverage` described below.
Class description:
Maintains (exponential) moving average of a set of parameters.
Method signatures and docstrings:
- def __init__(self, parameters, decay, use_num_updates=True): Args: parameters: Iterable of `torch.nn.Parameter`; usually the resu... | aede6493126fdefa3961c8f3fc06bd84d2a9a8b8 | <|skeleton|>
class ExponentialMovingAverage:
"""Maintains (exponential) moving average of a set of parameters."""
def __init__(self, parameters, decay, use_num_updates=True):
"""Args: parameters: Iterable of `torch.nn.Parameter`; usually the result of `model.parameters()`. decay: The exponential decay.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExponentialMovingAverage:
"""Maintains (exponential) moving average of a set of parameters."""
def __init__(self, parameters, decay, use_num_updates=True):
"""Args: parameters: Iterable of `torch.nn.Parameter`; usually the result of `model.parameters()`. decay: The exponential decay. use_num_upda... | the_stack_v2_python_sparse | tasks/tumor_segmentation_task.py | Animatory/BrainTumorSegmentation | train | 0 |
f95e38ae5063b1b3aac06d3f32bb7fa3f487967d | [
"metrics_window = self.metrics_window[1:]\nmetrics_window.append(round_metric)\naverage_metric = tf.reduce_mean(metrics_window)\nlearning_rate = self.learning_rate\nbest = self.best\nwait = self.wait\ncooldown_counter = self.cooldown_counter\nif cooldown_counter > 0:\n cooldown_counter -= 1\n wait = 0\nif sel... | <|body_start_0|>
metrics_window = self.metrics_window[1:]
metrics_window.append(round_metric)
average_metric = tf.reduce_mean(metrics_window)
learning_rate = self.learning_rate
best = self.best
wait = self.wait
cooldown_counter = self.cooldown_counter
if c... | A callback for decaying a learning rate when a metric stops improving. Attributes: learning_rate: The current learning rate. monitor: The name of the metric governing the callback. decay_factor: Factor by which the learning rate will be reduced when a plateau occurs. minimize: A boolean, when `True` the metric will be ... | ReduceLROnPlateau | [
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReduceLROnPlateau:
"""A callback for decaying a learning rate when a metric stops improving. Attributes: learning_rate: The current learning rate. monitor: The name of the metric governing the callback. decay_factor: Factor by which the learning rate will be reduced when a plateau occurs. minimiz... | stack_v2_sparse_classes_36k_train_010103 | 6,991 | permissive | [
{
"docstring": "Updates the `ReduceLROnPlateau` callback based on the round metric.",
"name": "update",
"signature": "def update(self, round_metric)"
},
{
"docstring": "Determines if a round metric improves a given ReduceLROnPlateau`.",
"name": "improves_best",
"signature": "def improves... | 2 | null | Implement the Python class `ReduceLROnPlateau` described below.
Class description:
A callback for decaying a learning rate when a metric stops improving. Attributes: learning_rate: The current learning rate. monitor: The name of the metric governing the callback. decay_factor: Factor by which the learning rate will be... | Implement the Python class `ReduceLROnPlateau` described below.
Class description:
A callback for decaying a learning rate when a metric stops improving. Attributes: learning_rate: The current learning rate. monitor: The name of the metric governing the callback. decay_factor: Factor by which the learning rate will be... | 329e60fa56b87f691303638ceb9dfa1fc5083953 | <|skeleton|>
class ReduceLROnPlateau:
"""A callback for decaying a learning rate when a metric stops improving. Attributes: learning_rate: The current learning rate. monitor: The name of the metric governing the callback. decay_factor: Factor by which the learning rate will be reduced when a plateau occurs. minimiz... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReduceLROnPlateau:
"""A callback for decaying a learning rate when a metric stops improving. Attributes: learning_rate: The current learning rate. monitor: The name of the metric governing the callback. decay_factor: Factor by which the learning rate will be reduced when a plateau occurs. minimize: A boolean,... | the_stack_v2_python_sparse | adaptive_lr_decay/callbacks.py | google-research/federated | train | 595 |
b73e89959b4f49cc07ccee74978f068a3bb2c3ce | [
"if self.note:\n return self.note.note\nreturn None",
"if user:\n self.deleted_by = user\nself.deleted_at = deleted_at\nself.save()"
] | <|body_start_0|>
if self.note:
return self.note.note
return None
<|end_body_0|>
<|body_start_1|>
if user:
self.deleted_by = user
self.deleted_at = deleted_at
self.save()
<|end_body_1|>
| SubmissionReview Model Class | SubmissionReview | [
"BSD-2-Clause",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubmissionReview:
"""SubmissionReview Model Class"""
def get_note_text(self):
"""Custom Property returns associated note text"""
<|body_0|>
def set_deleted(self, deleted_at=timezone.now(), user=None):
"""Sets the deleted_at and deleted_by fields"""
<|body... | stack_v2_sparse_classes_36k_train_010104 | 2,651 | permissive | [
{
"docstring": "Custom Property returns associated note text",
"name": "get_note_text",
"signature": "def get_note_text(self)"
},
{
"docstring": "Sets the deleted_at and deleted_by fields",
"name": "set_deleted",
"signature": "def set_deleted(self, deleted_at=timezone.now(), user=None)"
... | 2 | null | Implement the Python class `SubmissionReview` described below.
Class description:
SubmissionReview Model Class
Method signatures and docstrings:
- def get_note_text(self): Custom Property returns associated note text
- def set_deleted(self, deleted_at=timezone.now(), user=None): Sets the deleted_at and deleted_by fie... | Implement the Python class `SubmissionReview` described below.
Class description:
SubmissionReview Model Class
Method signatures and docstrings:
- def get_note_text(self): Custom Property returns associated note text
- def set_deleted(self, deleted_at=timezone.now(), user=None): Sets the deleted_at and deleted_by fie... | e5bdec91cb47179172b515bbcb91701262ff3377 | <|skeleton|>
class SubmissionReview:
"""SubmissionReview Model Class"""
def get_note_text(self):
"""Custom Property returns associated note text"""
<|body_0|>
def set_deleted(self, deleted_at=timezone.now(), user=None):
"""Sets the deleted_at and deleted_by fields"""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubmissionReview:
"""SubmissionReview Model Class"""
def get_note_text(self):
"""Custom Property returns associated note text"""
if self.note:
return self.note.note
return None
def set_deleted(self, deleted_at=timezone.now(), user=None):
"""Sets the delete... | the_stack_v2_python_sparse | onadata/apps/logger/models/submission_review.py | onaio/onadata | train | 177 |
933bde235606baf6d6bcc568464e99ed3c46cf02 | [
"with self.assertRaises(TypeError):\n get_majorana_operator(1.0)\nwith self.assertRaises(TypeError):\n _fermion_operator_to_majorana_operator([1.0])\nwith self.assertRaises(TypeError):\n _fermion_term_to_majorana_operator(1.0)",
"fermion_op = -2j * (FermionOperator(((0, 0), (1, 0))) - FermionOperator(((0... | <|body_start_0|>
with self.assertRaises(TypeError):
get_majorana_operator(1.0)
with self.assertRaises(TypeError):
_fermion_operator_to_majorana_operator([1.0])
with self.assertRaises(TypeError):
_fermion_term_to_majorana_operator(1.0)
<|end_body_0|>
<|body_st... | Test class get Majorana Operator. | GetMajoranaOperatorTest | [
"LicenseRef-scancode-generic-cla",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetMajoranaOperatorTest:
"""Test class get Majorana Operator."""
def test_raises(self):
"""Test raises errors."""
<|body_0|>
def test_get_majorana_operator_fermion_operator(self):
"""Test conversion FermionOperator to MajoranaOperator."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_010105 | 8,235 | permissive | [
{
"docstring": "Test raises errors.",
"name": "test_raises",
"signature": "def test_raises(self)"
},
{
"docstring": "Test conversion FermionOperator to MajoranaOperator.",
"name": "test_get_majorana_operator_fermion_operator",
"signature": "def test_get_majorana_operator_fermion_operator... | 3 | stack_v2_sparse_classes_30k_train_012680 | Implement the Python class `GetMajoranaOperatorTest` described below.
Class description:
Test class get Majorana Operator.
Method signatures and docstrings:
- def test_raises(self): Test raises errors.
- def test_get_majorana_operator_fermion_operator(self): Test conversion FermionOperator to MajoranaOperator.
- def ... | Implement the Python class `GetMajoranaOperatorTest` described below.
Class description:
Test class get Majorana Operator.
Method signatures and docstrings:
- def test_raises(self): Test raises errors.
- def test_get_majorana_operator_fermion_operator(self): Test conversion FermionOperator to MajoranaOperator.
- def ... | 788481753c798a72c5cb3aa9f2aa9da3ce3190b0 | <|skeleton|>
class GetMajoranaOperatorTest:
"""Test class get Majorana Operator."""
def test_raises(self):
"""Test raises errors."""
<|body_0|>
def test_get_majorana_operator_fermion_operator(self):
"""Test conversion FermionOperator to MajoranaOperator."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetMajoranaOperatorTest:
"""Test class get Majorana Operator."""
def test_raises(self):
"""Test raises errors."""
with self.assertRaises(TypeError):
get_majorana_operator(1.0)
with self.assertRaises(TypeError):
_fermion_operator_to_majorana_operator([1.0])
... | the_stack_v2_python_sparse | src/openfermion/transforms/opconversions/conversions_test.py | quantumlib/OpenFermion | train | 1,481 |
748fb9809f78fc8eac9e2b45c3711fdce7bdbe3f | [
"Base.__init__(self, target, opts)\nself.host, self.port, self.scheme, self.path = self._parse_url(self.target)\nreturn",
"url = self.target\nif self.opts['attack_url']:\n url = self.opts['attack_url']\nif self.opts['login_url']:\n url = self.opts['login_url']\nwith timeout(self.opts['timeout']):\n self.... | <|body_start_0|>
Base.__init__(self, target, opts)
self.host, self.port, self.scheme, self.path = self._parse_url(self.target)
return
<|end_body_0|>
<|body_start_1|>
url = self.target
if self.opts['attack_url']:
url = self.opts['attack_url']
if self.opts['log... | Login Cracker module | Crack | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Crack:
"""Login Cracker module"""
def __init__(self, target, opts):
"""init"""
<|body_0|>
def crack_http_auth_web(self):
"""DESCR: Check HTTP auth type (basic, realm, etc.) and crack login. (int) TOOLS: python3"""
<|body_1|>
def crack_tomcat_web(self... | stack_v2_sparse_classes_36k_train_010106 | 3,279 | no_license | [
{
"docstring": "init",
"name": "__init__",
"signature": "def __init__(self, target, opts)"
},
{
"docstring": "DESCR: Check HTTP auth type (basic, realm, etc.) and crack login. (int) TOOLS: python3",
"name": "crack_http_auth_web",
"signature": "def crack_http_auth_web(self)"
},
{
... | 4 | stack_v2_sparse_classes_30k_test_000572 | Implement the Python class `Crack` described below.
Class description:
Login Cracker module
Method signatures and docstrings:
- def __init__(self, target, opts): init
- def crack_http_auth_web(self): DESCR: Check HTTP auth type (basic, realm, etc.) and crack login. (int) TOOLS: python3
- def crack_tomcat_web(self): D... | Implement the Python class `Crack` described below.
Class description:
Login Cracker module
Method signatures and docstrings:
- def __init__(self, target, opts): init
- def crack_http_auth_web(self): DESCR: Check HTTP auth type (basic, realm, etc.) and crack login. (int) TOOLS: python3
- def crack_tomcat_web(self): D... | ddc052c8d7d43a60fc00ea40d85111d5bd7a282e | <|skeleton|>
class Crack:
"""Login Cracker module"""
def __init__(self, target, opts):
"""init"""
<|body_0|>
def crack_http_auth_web(self):
"""DESCR: Check HTTP auth type (basic, realm, etc.) and crack login. (int) TOOLS: python3"""
<|body_1|>
def crack_tomcat_web(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Crack:
"""Login Cracker module"""
def __init__(self, target, opts):
"""init"""
Base.__init__(self, target, opts)
self.host, self.port, self.scheme, self.path = self._parse_url(self.target)
return
def crack_http_auth_web(self):
"""DESCR: Check HTTP auth type (b... | the_stack_v2_python_sparse | src/modules/web/crack.py | noptrix/nullscan | train | 52 |
560e7574f656daea9f6767e1ee6723757a833af8 | [
"if root:\n queue = [root]\nelse:\n return '[]'\nres = [root.val]\nwhile queue:\n node = queue.pop(0)\n if node.left:\n res.append(node.left.val)\n queue.append(node.left)\n else:\n res.append(None)\n if node.right:\n res.append(node.right.val)\n queue.append(nod... | <|body_start_0|>
if root:
queue = [root]
else:
return '[]'
res = [root.val]
while queue:
node = queue.pop(0)
if node.left:
res.append(node.left.val)
queue.append(node.left)
else:
r... | 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_010107 | 2,373 | 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_val_000114 | 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:... | c811c7fb2d203852eb35019f9e3c6b5f1a5a2133 | <|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"""
if root:
queue = [root]
else:
return '[]'
res = [root.val]
while queue:
node = queue.pop(0)
if node.left:
... | the_stack_v2_python_sparse | leetcode/297.二叉树的序列化与反序列化.py | guojiangwei/data-structure | train | 0 | |
e85e9f9f8890b0728d0f4ea677c3528ba5239040 | [
"super(ListOfListsAction, self).__init__(option_strings, dest, **kwargs)\nself.dtype = type\nif self.dtype is None:\n self.dtype = np.int32",
"decoded_list = []\nremoved1 = values.replace('[', '')\nremoved2 = removed1.replace(']', '')\nout_list = removed2.split(':')\nfor line in out_list:\n in_list = []\n ... | <|body_start_0|>
super(ListOfListsAction, self).__init__(option_strings, dest, **kwargs)
self.dtype = type
if self.dtype is None:
self.dtype = np.int32
<|end_body_0|>
<|body_start_1|>
decoded_list = []
removed1 = values.replace('[', '')
removed2 = removed1.re... | This class extends the argparse.Action class by instantiating an argparser that constructs a list-of-lists from an input (command-line option or argument) given as a string. | ListOfListsAction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListOfListsAction:
"""This class extends the argparse.Action class by instantiating an argparser that constructs a list-of-lists from an input (command-line option or argument) given as a string."""
def __init__(self, option_strings, dest, type, **kwargs):
"""Initialize a ListOfLists... | stack_v2_sparse_classes_36k_train_010108 | 28,110 | permissive | [
{
"docstring": "Initialize a ListOfListsAction object. If no type is specified, an integer is assumed by default as the type for the elements of the list-of-lists. Parameters ---------- option_strings : string String to parse dest : object Object to store the output (in this case the parsed list-of-lists). type... | 2 | null | Implement the Python class `ListOfListsAction` described below.
Class description:
This class extends the argparse.Action class by instantiating an argparser that constructs a list-of-lists from an input (command-line option or argument) given as a string.
Method signatures and docstrings:
- def __init__(self, option... | Implement the Python class `ListOfListsAction` described below.
Class description:
This class extends the argparse.Action class by instantiating an argparser that constructs a list-of-lists from an input (command-line option or argument) given as a string.
Method signatures and docstrings:
- def __init__(self, option... | f6a3da8818308c9edcd9fedbcf831dd5968efcdd | <|skeleton|>
class ListOfListsAction:
"""This class extends the argparse.Action class by instantiating an argparser that constructs a list-of-lists from an input (command-line option or argument) given as a string."""
def __init__(self, option_strings, dest, type, **kwargs):
"""Initialize a ListOfLists... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ListOfListsAction:
"""This class extends the argparse.Action class by instantiating an argparser that constructs a list-of-lists from an input (command-line option or argument) given as a string."""
def __init__(self, option_strings, dest, type, **kwargs):
"""Initialize a ListOfListsAction object... | the_stack_v2_python_sparse | common/parsing_utils.py | ECP-CANDLE/Benchmarks | train | 65 |
cece3091c1a56091f60fd4fcacc1f6469411addc | [
"try:\n dst_state = Status.objects.get(id=self.context.get('dst_state'))\nexcept Status.DoesNotExist:\n self.data.set_outputs('message', '对应的节点不存在')\n return False\nprocessors = self.data.get_one_of_inputs('processors')\nif not processors:\n self.data.set_outputs('message', '设置处理人为空')\n return False\... | <|body_start_0|>
try:
dst_state = Status.objects.get(id=self.context.get('dst_state'))
except Status.DoesNotExist:
self.data.set_outputs('message', '对应的节点不存在')
return False
processors = self.data.get_one_of_inputs('processors')
if not processors:
... | ModifyProcessorComponent | [
"MIT",
"LGPL-2.1-or-later",
"LGPL-3.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModifyProcessorComponent:
def _execute(self):
"""修改节点对应的处理人"""
<|body_0|>
def update_context(self):
"""手动操作的时候更新context"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
dst_state = Status.objects.get(id=self.context.get('dst_state'))... | stack_v2_sparse_classes_36k_train_010109 | 3,288 | permissive | [
{
"docstring": "修改节点对应的处理人",
"name": "_execute",
"signature": "def _execute(self)"
},
{
"docstring": "手动操作的时候更新context",
"name": "update_context",
"signature": "def update_context(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008654 | Implement the Python class `ModifyProcessorComponent` described below.
Class description:
Implement the ModifyProcessorComponent class.
Method signatures and docstrings:
- def _execute(self): 修改节点对应的处理人
- def update_context(self): 手动操作的时候更新context | Implement the Python class `ModifyProcessorComponent` described below.
Class description:
Implement the ModifyProcessorComponent class.
Method signatures and docstrings:
- def _execute(self): 修改节点对应的处理人
- def update_context(self): 手动操作的时候更新context
<|skeleton|>
class ModifyProcessorComponent:
def _execute(self):... | 2d708bd0d869d391456e0fb8d644af3b9f031acf | <|skeleton|>
class ModifyProcessorComponent:
def _execute(self):
"""修改节点对应的处理人"""
<|body_0|>
def update_context(self):
"""手动操作的时候更新context"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModifyProcessorComponent:
def _execute(self):
"""修改节点对应的处理人"""
try:
dst_state = Status.objects.get(id=self.context.get('dst_state'))
except Status.DoesNotExist:
self.data.set_outputs('message', '对应的节点不存在')
return False
processors = self.data.... | the_stack_v2_python_sparse | itsm/trigger/action/components/modify_processor.py | TencentBlueKing/bk-itsm | train | 100 | |
e34e584ad7d0eb51c4937d8790831c8680a02c9e | [
"post_reply = get_object_or_404(PostReply, pk=post_reply_id)\nserializer = PostReplySerializerUpdate(post_reply, data=request.data, context={'request': request}, partial=True)\nif serializer.is_valid():\n serializer.save()\n return Response(PostReplySerializer(serializer.instance).data)\nreturn Response(seria... | <|body_start_0|>
post_reply = get_object_or_404(PostReply, pk=post_reply_id)
serializer = PostReplySerializerUpdate(post_reply, data=request.data, context={'request': request}, partial=True)
if serializer.is_valid():
serializer.save()
return Response(PostReplySerializer(s... | PostReplyDetail | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PostReplyDetail:
def patch(request, post_reply_id):
"""Update post reply"""
<|body_0|>
def delete(request, post_reply_id):
"""Delete post reply"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
post_reply = get_object_or_404(PostReply, pk=post_reply_i... | stack_v2_sparse_classes_36k_train_010110 | 1,780 | permissive | [
{
"docstring": "Update post reply",
"name": "patch",
"signature": "def patch(request, post_reply_id)"
},
{
"docstring": "Delete post reply",
"name": "delete",
"signature": "def delete(request, post_reply_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015448 | Implement the Python class `PostReplyDetail` described below.
Class description:
Implement the PostReplyDetail class.
Method signatures and docstrings:
- def patch(request, post_reply_id): Update post reply
- def delete(request, post_reply_id): Delete post reply | Implement the Python class `PostReplyDetail` described below.
Class description:
Implement the PostReplyDetail class.
Method signatures and docstrings:
- def patch(request, post_reply_id): Update post reply
- def delete(request, post_reply_id): Delete post reply
<|skeleton|>
class PostReplyDetail:
def patch(req... | b93fa2fea8d45df9f19c3c58037e59dad4981921 | <|skeleton|>
class PostReplyDetail:
def patch(request, post_reply_id):
"""Update post reply"""
<|body_0|>
def delete(request, post_reply_id):
"""Delete post reply"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PostReplyDetail:
def patch(request, post_reply_id):
"""Update post reply"""
post_reply = get_object_or_404(PostReply, pk=post_reply_id)
serializer = PostReplySerializerUpdate(post_reply, data=request.data, context={'request': request}, partial=True)
if serializer.is_valid():
... | the_stack_v2_python_sparse | v1/replies/views/post_reply.py | lawiz22/PLOUC-Backend-master | train | 0 | |
5c580eb8776cf69b4e6395e0d76d1c4baad5a31f | [
"self.prototype = prototype\nself.config = ConfigParser()\nif fileName:\n if not os.path.isfile(fileName):\n path = Resource.getWritableResourcePath()\n fileName = os.path.join(path, fileName)\n self.config.read(fileName)\nself.fileName = fileName\nfor section, options in prototype.items():\n ... | <|body_start_0|>
self.prototype = prototype
self.config = ConfigParser()
if fileName:
if not os.path.isfile(fileName):
path = Resource.getWritableResourcePath()
fileName = os.path.join(path, fileName)
self.config.read(fileName)
self... | A configuration registry. | Config | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Config:
"""A configuration registry."""
def __init__(self, prototype, fileName=None):
"""@param prototype: The configuration protype mapping @param fileName: The file that holds this configuration registry"""
<|body_0|>
def get(self, section, option):
"""Read a c... | stack_v2_sparse_classes_36k_train_010111 | 5,757 | no_license | [
{
"docstring": "@param prototype: The configuration protype mapping @param fileName: The file that holds this configuration registry",
"name": "__init__",
"signature": "def __init__(self, prototype, fileName=None)"
},
{
"docstring": "Read a configuration key. @param section: Section name @param ... | 3 | null | Implement the Python class `Config` described below.
Class description:
A configuration registry.
Method signatures and docstrings:
- def __init__(self, prototype, fileName=None): @param prototype: The configuration protype mapping @param fileName: The file that holds this configuration registry
- def get(self, secti... | Implement the Python class `Config` described below.
Class description:
A configuration registry.
Method signatures and docstrings:
- def __init__(self, prototype, fileName=None): @param prototype: The configuration protype mapping @param fileName: The file that holds this configuration registry
- def get(self, secti... | 9d72169506172c40e7bbd69ceb4b649279c5b239 | <|skeleton|>
class Config:
"""A configuration registry."""
def __init__(self, prototype, fileName=None):
"""@param prototype: The configuration protype mapping @param fileName: The file that holds this configuration registry"""
<|body_0|>
def get(self, section, option):
"""Read a c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Config:
"""A configuration registry."""
def __init__(self, prototype, fileName=None):
"""@param prototype: The configuration protype mapping @param fileName: The file that holds this configuration registry"""
self.prototype = prototype
self.config = ConfigParser()
if fileN... | the_stack_v2_python_sparse | minifof/src/foflib/Config.py | jay3sh/altcanvas | train | 1 |
05cd67a57b00d1fa61a3dcfea266762edda91c78 | [
"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!')"
] | <|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... | Proto file describing the Feed service. Service to manage feeds. | FeedServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeedServiceServicer:
"""Proto file describing the Feed service. Service to manage feeds."""
def GetFeed(self, request, context):
"""Returns the requested feed in full detail."""
<|body_0|>
def MutateFeeds(self, request, context):
"""Creates, updates, or removes f... | stack_v2_sparse_classes_36k_train_010112 | 5,053 | permissive | [
{
"docstring": "Returns the requested feed in full detail.",
"name": "GetFeed",
"signature": "def GetFeed(self, request, context)"
},
{
"docstring": "Creates, updates, or removes feeds. Operation statuses are returned.",
"name": "MutateFeeds",
"signature": "def MutateFeeds(self, request,... | 2 | stack_v2_sparse_classes_30k_train_008236 | Implement the Python class `FeedServiceServicer` described below.
Class description:
Proto file describing the Feed service. Service to manage feeds.
Method signatures and docstrings:
- def GetFeed(self, request, context): Returns the requested feed in full detail.
- def MutateFeeds(self, request, context): Creates, ... | Implement the Python class `FeedServiceServicer` described below.
Class description:
Proto file describing the Feed service. Service to manage feeds.
Method signatures and docstrings:
- def GetFeed(self, request, context): Returns the requested feed in full detail.
- def MutateFeeds(self, request, context): Creates, ... | a5b6cede64f4d9912ae6ad26927a54e40448c9fe | <|skeleton|>
class FeedServiceServicer:
"""Proto file describing the Feed service. Service to manage feeds."""
def GetFeed(self, request, context):
"""Returns the requested feed in full detail."""
<|body_0|>
def MutateFeeds(self, request, context):
"""Creates, updates, or removes f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeedServiceServicer:
"""Proto file describing the Feed service. Service to manage feeds."""
def GetFeed(self, request, context):
"""Returns the requested feed in full detail."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
... | the_stack_v2_python_sparse | google/ads/google_ads/v5/proto/services/feed_service_pb2_grpc.py | fiboknacky/google-ads-python | train | 0 |
6ea84b27e1aacc2fef00b14632368c2a2a487eb4 | [
"self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else '__unused').set(thing_classes=['G1/G2', 'S', 'M', 'E'])\nself.cpu_device = torch.device('cpu')\nself.instance_mode = instance_mode\nself.parallel = parallel\nif parallel:\n num_gpu = torch.cuda.device_count()\n self.predi... | <|body_start_0|>
self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else '__unused').set(thing_classes=['G1/G2', 'S', 'M', 'E'])
self.cpu_device = torch.device('cpu')
self.instance_mode = instance_mode
self.parallel = parallel
if parallel:
... | VisualizationDemo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VisualizationDemo:
def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
"""Copied from Facebook Detectron2 Demo. Apache 2.0 Licence. Args: cfg (CfgNode): instance_mode (ColorMode): parallel (bool): whether to run the model in different processes from visualization. Use... | stack_v2_sparse_classes_36k_train_010113 | 13,593 | permissive | [
{
"docstring": "Copied from Facebook Detectron2 Demo. Apache 2.0 Licence. Args: cfg (CfgNode): instance_mode (ColorMode): parallel (bool): whether to run the model in different processes from visualization. Useful since the visualization logic can be slow.",
"name": "__init__",
"signature": "def __init_... | 2 | stack_v2_sparse_classes_30k_train_008199 | Implement the Python class `VisualizationDemo` described below.
Class description:
Implement the VisualizationDemo class.
Method signatures and docstrings:
- def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): Copied from Facebook Detectron2 Demo. Apache 2.0 Licence. Args: cfg (CfgNode): instance_... | Implement the Python class `VisualizationDemo` described below.
Class description:
Implement the VisualizationDemo class.
Method signatures and docstrings:
- def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): Copied from Facebook Detectron2 Demo. Apache 2.0 Licence. Args: cfg (CfgNode): instance_... | 16f8128167c143dfd9cb6cf25046725a5cf1273a | <|skeleton|>
class VisualizationDemo:
def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
"""Copied from Facebook Detectron2 Demo. Apache 2.0 Licence. Args: cfg (CfgNode): instance_mode (ColorMode): parallel (bool): whether to run the model in different processes from visualization. Use... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VisualizationDemo:
def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
"""Copied from Facebook Detectron2 Demo. Apache 2.0 Licence. Args: cfg (CfgNode): instance_mode (ColorMode): parallel (bool): whether to run the model in different processes from visualization. Useful since the ... | the_stack_v2_python_sparse | bin/pcnaDeep/predictor.py | kuanyoow/PCNAdeep | train | 0 | |
39781d0d9c73f7a4cd9c61c994578beffed84f9c | [
"self.modern_dem_name = modern_dem_name\nself.modeled_dem_name = modeled_dem_name\nself.grid, self.z = self.read_topography(modern_dem_name)\nself.grid.set_watershed_boundary_condition_outlet_id(outlet_id, self.z, nodata_value=-9999)\nself.mgrid, self.mz = self.read_topography(modeled_dem_name)\nself.mgrid.set_wate... | <|body_start_0|>
self.modern_dem_name = modern_dem_name
self.modeled_dem_name = modeled_dem_name
self.grid, self.z = self.read_topography(modern_dem_name)
self.grid.set_watershed_boundary_condition_outlet_id(outlet_id, self.z, nodata_value=-9999)
self.mgrid, self.mz = self.read_t... | Calculator for topographic metrics used in sensitivity analysis and model evaluation. | GroupedDifferences | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupedDifferences:
"""Calculator for topographic metrics used in sensitivity analysis and model evaluation."""
def __init__(self, modeled_dem_name, modern_dem_name, outlet_id, category_file=None, category_values=None, weight_file=None, weight_values=None):
"""Initialize GroupedDiffe... | stack_v2_sparse_classes_36k_train_010114 | 5,390 | no_license | [
{
"docstring": "Initialize GroupedDifferences with names of postglacial and modern DEMs.",
"name": "__init__",
"signature": "def __init__(self, modeled_dem_name, modern_dem_name, outlet_id, category_file=None, category_values=None, weight_file=None, weight_values=None)"
},
{
"docstring": "Read a... | 5 | stack_v2_sparse_classes_30k_train_014027 | Implement the Python class `GroupedDifferences` described below.
Class description:
Calculator for topographic metrics used in sensitivity analysis and model evaluation.
Method signatures and docstrings:
- def __init__(self, modeled_dem_name, modern_dem_name, outlet_id, category_file=None, category_values=None, weigh... | Implement the Python class `GroupedDifferences` described below.
Class description:
Calculator for topographic metrics used in sensitivity analysis and model evaluation.
Method signatures and docstrings:
- def __init__(self, modeled_dem_name, modern_dem_name, outlet_id, category_file=None, category_values=None, weigh... | 3506ec741a7c8a170ea654d40c6119fefe1b93ba | <|skeleton|>
class GroupedDifferences:
"""Calculator for topographic metrics used in sensitivity analysis and model evaluation."""
def __init__(self, modeled_dem_name, modern_dem_name, outlet_id, category_file=None, category_values=None, weight_file=None, weight_values=None):
"""Initialize GroupedDiffe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GroupedDifferences:
"""Calculator for topographic metrics used in sensitivity analysis and model evaluation."""
def __init__(self, modeled_dem_name, modern_dem_name, outlet_id, category_file=None, category_values=None, weight_file=None, weight_values=None):
"""Initialize GroupedDifferences with n... | the_stack_v2_python_sparse | metric_and_objective_function_calculation/metric_calculator/grouped_differences.py | kbarnhart/inverting_topography_postglacial | train | 4 |
8047289b9f6d2fc1a28e952a519c7ea304faae95 | [
"procrowre = re.compile('\\\\s*([-/_a-zA-Z0-9%]+)\\\\s*=\\\\s*\"([^\"]+)\"')\nlstss = []\nfor line in rules_file_contents.split('\\n'):\n s = line.strip()\n m = procrowre.match(s)\n if not m:\n continue\n lstss.append((m.group(1), m.group(2)))\nreturn lstss",
"rulerowre = re.compile('^\\\\s*([0... | <|body_start_0|>
procrowre = re.compile('\\s*([-/_a-zA-Z0-9%]+)\\s*=\\s*"([^"]+)"')
lstss = []
for line in rules_file_contents.split('\n'):
s = line.strip()
m = procrowre.match(s)
if not m:
continue
lstss.append((m.group(1), m.group... | ExtRulesParser | [
"MIT",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtRulesParser:
def parseLstsFiles(self, rules_file_contents):
"""Parse the lsts files in the given string. The string is assumed to be in TVT extended rules format, that is <lsts#1> = "<lstsfilename1>" <lsts#2> = "<lstsfilename2>" ... Returns list of numbers and lsts filenames: [ (lsts#... | stack_v2_sparse_classes_36k_train_010115 | 3,149 | permissive | [
{
"docstring": "Parse the lsts files in the given string. The string is assumed to be in TVT extended rules format, that is <lsts#1> = \"<lstsfilename1>\" <lsts#2> = \"<lstsfilename2>\" ... Returns list of numbers and lsts filenames: [ (lsts#1, \"lstsfilename1\"), (lsts#2, \"lstsfilename2\"), ... ]",
"name"... | 2 | null | Implement the Python class `ExtRulesParser` described below.
Class description:
Implement the ExtRulesParser class.
Method signatures and docstrings:
- def parseLstsFiles(self, rules_file_contents): Parse the lsts files in the given string. The string is assumed to be in TVT extended rules format, that is <lsts#1> = ... | Implement the Python class `ExtRulesParser` described below.
Class description:
Implement the ExtRulesParser class.
Method signatures and docstrings:
- def parseLstsFiles(self, rules_file_contents): Parse the lsts files in the given string. The string is assumed to be in TVT extended rules format, that is <lsts#1> = ... | 9c3f119c8bf5cc565e6a3e8e9e6205037e326d89 | <|skeleton|>
class ExtRulesParser:
def parseLstsFiles(self, rules_file_contents):
"""Parse the lsts files in the given string. The string is assumed to be in TVT extended rules format, that is <lsts#1> = "<lstsfilename1>" <lsts#2> = "<lstsfilename2>" ... Returns list of numbers and lsts filenames: [ (lsts#... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExtRulesParser:
def parseLstsFiles(self, rules_file_contents):
"""Parse the lsts files in the given string. The string is assumed to be in TVT extended rules format, that is <lsts#1> = "<lstsfilename1>" <lsts#2> = "<lstsfilename2>" ... Returns list of numbers and lsts filenames: [ (lsts#1, "lstsfilena... | the_stack_v2_python_sparse | TemaLib/tema/rules/rules_parser.py | tema-tut/tema-tg | train | 1 | |
757de08854639016817e7589d3f65d5282b76581 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn TemporaryAccessPassAuthenticationMethod()",
"from .authentication_method import AuthenticationMethod\nfrom .authentication_method import AuthenticationMethod\nfields: Dict[str, Callable[[Any], None]] = {'createdDateTime': lambda n: set... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return TemporaryAccessPassAuthenticationMethod()
<|end_body_0|>
<|body_start_1|>
from .authentication_method import AuthenticationMethod
from .authentication_method import AuthenticationMethod
... | TemporaryAccessPassAuthenticationMethod | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TemporaryAccessPassAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TemporaryAccessPassAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read t... | stack_v2_sparse_classes_36k_train_010116 | 4,574 | 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: TemporaryAccessPassAuthenticationMethod",
"name": "create_from_discriminator_value",
"signature": "def creat... | 3 | stack_v2_sparse_classes_30k_train_020438 | Implement the Python class `TemporaryAccessPassAuthenticationMethod` described below.
Class description:
Implement the TemporaryAccessPassAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TemporaryAccessPassAuthenticationMethod... | Implement the Python class `TemporaryAccessPassAuthenticationMethod` described below.
Class description:
Implement the TemporaryAccessPassAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TemporaryAccessPassAuthenticationMethod... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class TemporaryAccessPassAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TemporaryAccessPassAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TemporaryAccessPassAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> TemporaryAccessPassAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminat... | the_stack_v2_python_sparse | msgraph/generated/models/temporary_access_pass_authentication_method.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
0e2229b616d9f49e9080b271842340ed4c456852 | [
"eps = 1e-09\nout = -np.sum(y * np.log(x + eps), axis=1).mean()\nreturn out",
"eps = 1e-09\ndx = -y / (len(y) * (x + eps))\nreturn dx"
] | <|body_start_0|>
eps = 1e-09
out = -np.sum(y * np.log(x + eps), axis=1).mean()
return out
<|end_body_0|>
<|body_start_1|>
eps = 1e-09
dx = -y / (len(y) * (x + eps))
return dx
<|end_body_1|>
| Cross entropy loss module. | CrossEntropyModule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CrossEntropyModule:
"""Cross entropy loss module."""
def forward(self, x, y):
"""Forward pass. Args: x: input to the module y: labels of the input Returns: out: cross entropy loss"""
<|body_0|>
def backward(self, x, y):
"""Backward pass. Args: x: input to the mod... | stack_v2_sparse_classes_36k_train_010117 | 5,181 | no_license | [
{
"docstring": "Forward pass. Args: x: input to the module y: labels of the input Returns: out: cross entropy loss",
"name": "forward",
"signature": "def forward(self, x, y)"
},
{
"docstring": "Backward pass. Args: x: input to the module y: labels of the input Returns: dx: gradient of the loss w... | 2 | stack_v2_sparse_classes_30k_train_019244 | Implement the Python class `CrossEntropyModule` described below.
Class description:
Cross entropy loss module.
Method signatures and docstrings:
- def forward(self, x, y): Forward pass. Args: x: input to the module y: labels of the input Returns: out: cross entropy loss
- def backward(self, x, y): Backward pass. Args... | Implement the Python class `CrossEntropyModule` described below.
Class description:
Cross entropy loss module.
Method signatures and docstrings:
- def forward(self, x, y): Forward pass. Args: x: input to the module y: labels of the input Returns: out: cross entropy loss
- def backward(self, x, y): Backward pass. Args... | b2cd0d67337b101f3e204e519625e1aaf3cea43b | <|skeleton|>
class CrossEntropyModule:
"""Cross entropy loss module."""
def forward(self, x, y):
"""Forward pass. Args: x: input to the module y: labels of the input Returns: out: cross entropy loss"""
<|body_0|>
def backward(self, x, y):
"""Backward pass. Args: x: input to the mod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CrossEntropyModule:
"""Cross entropy loss module."""
def forward(self, x, y):
"""Forward pass. Args: x: input to the module y: labels of the input Returns: out: cross entropy loss"""
eps = 1e-09
out = -np.sum(y * np.log(x + eps), axis=1).mean()
return out
def backward... | the_stack_v2_python_sparse | assignment_1/code/modules.py | Ivan-Yovchev/uvadlc_practicals_2019 | train | 0 |
90e03ffb126dfed8c9ede8b699f7557e3da83338 | [
"pygame.sprite.Sprite.__init__(self)\nif image:\n self.image = image\nelse:\n self.image = Puff.IMAGE.copy()\nself.rect = self.image.get_rect()\nif alpha:\n self.image.set_alpha(int(alpha))\nelse:\n self.image.set_alpha(255)\nself.rect.centerx = coord[0]\nself.rect.centery = coord[1]\nself.countdown = l... | <|body_start_0|>
pygame.sprite.Sprite.__init__(self)
if image:
self.image = image
else:
self.image = Puff.IMAGE.copy()
self.rect = self.image.get_rect()
if alpha:
self.image.set_alpha(int(alpha))
else:
self.image.set_alpha(2... | A class which acts as temporary image, it has a life, and Surface as well as a location The class can be given an alpha value, which can be made to change at a specified rate The object will automatically kill itself after its life runs out | Puff | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Puff:
"""A class which acts as temporary image, it has a life, and Surface as well as a location The class can be given an alpha value, which can be made to change at a specified rate The object will automatically kill itself after its life runs out"""
def __init__(self, coord, life=60, alph... | stack_v2_sparse_classes_36k_train_010118 | 2,118 | permissive | [
{
"docstring": "Creates a puff of smoke coord(list) --> (x,y) center point of the puff life(int) --> frames before dissapearing (PRESET: 60 frames)(-1 = infinite) alpha(int) --> Alpha value of the surface (PRESET: fully opaque) alpha_decrease --> Alpha decrease per frame (PRESET: No decrease) image(Surface) -->... | 2 | stack_v2_sparse_classes_30k_train_013864 | Implement the Python class `Puff` described below.
Class description:
A class which acts as temporary image, it has a life, and Surface as well as a location The class can be given an alpha value, which can be made to change at a specified rate The object will automatically kill itself after its life runs out
Method ... | Implement the Python class `Puff` described below.
Class description:
A class which acts as temporary image, it has a life, and Surface as well as a location The class can be given an alpha value, which can be made to change at a specified rate The object will automatically kill itself after its life runs out
Method ... | 35b7127f9af204798615d820ec664a00eb45e1d2 | <|skeleton|>
class Puff:
"""A class which acts as temporary image, it has a life, and Surface as well as a location The class can be given an alpha value, which can be made to change at a specified rate The object will automatically kill itself after its life runs out"""
def __init__(self, coord, life=60, alph... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Puff:
"""A class which acts as temporary image, it has a life, and Surface as well as a location The class can be given an alpha value, which can be made to change at a specified rate The object will automatically kill itself after its life runs out"""
def __init__(self, coord, life=60, alpha=None, alpha... | the_stack_v2_python_sparse | Puff.py | Saevon/Arkanoid | train | 0 |
9fd2f125908c23b13157f808aa7dc788f6e5be25 | [
"self.BranchLength = 0.0\nif branch_length:\n self.BranchLength = branch_length\nself.Name = name\nself.Printed = False\nself.NumChildren = 0\nself.NumChildrenPrinted = 0\nself.LastChild = None",
"delimiter = '-'\npdelimiter = '+'\nif self.Printed:\n delimiter = ' '\n pdelimiter = '|'\nlast_ct = 0\nif se... | <|body_start_0|>
self.BranchLength = 0.0
if branch_length:
self.BranchLength = branch_length
self.Name = name
self.Printed = False
self.NumChildren = 0
self.NumChildrenPrinted = 0
self.LastChild = None
<|end_body_0|>
<|body_start_1|>
delimiter... | Helper to display text phyolo tree | TextNode | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextNode:
"""Helper to display text phyolo tree"""
def __init__(self, branch_length, name):
"""Set up defaults"""
<|body_0|>
def display(self, max_dist, scale):
"""Display current node - should refactor this"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_010119 | 3,462 | permissive | [
{
"docstring": "Set up defaults",
"name": "__init__",
"signature": "def __init__(self, branch_length, name)"
},
{
"docstring": "Display current node - should refactor this",
"name": "display",
"signature": "def display(self, max_dist, scale)"
}
] | 2 | null | Implement the Python class `TextNode` described below.
Class description:
Helper to display text phyolo tree
Method signatures and docstrings:
- def __init__(self, branch_length, name): Set up defaults
- def display(self, max_dist, scale): Display current node - should refactor this | Implement the Python class `TextNode` described below.
Class description:
Helper to display text phyolo tree
Method signatures and docstrings:
- def __init__(self, branch_length, name): Set up defaults
- def display(self, max_dist, scale): Display current node - should refactor this
<|skeleton|>
class TextNode:
... | fe6f8c8dfed86d39c80f2804a753c05bb2e485b4 | <|skeleton|>
class TextNode:
"""Helper to display text phyolo tree"""
def __init__(self, branch_length, name):
"""Set up defaults"""
<|body_0|>
def display(self, max_dist, scale):
"""Display current node - should refactor this"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextNode:
"""Helper to display text phyolo tree"""
def __init__(self, branch_length, name):
"""Set up defaults"""
self.BranchLength = 0.0
if branch_length:
self.BranchLength = branch_length
self.Name = name
self.Printed = False
self.NumChildren ... | the_stack_v2_python_sparse | scripts/venv/lib/python2.7/site-packages/cogent/format/text_tree.py | sauloal/cnidaria | train | 3 |
4c60bb1677a5ac5a61022814e06d6496222ff4ed | [
"values, outdict = BaseWidget.process_form(self, instance=instance, field=field, form=form, empty_marker=empty_marker, emptyReturnsMarker=emptyReturnsMarker)\nfor index in range(len(values)):\n item = values[index]\n min_panic = self._get_spec_value(form, item['uid'], 'minpanic')\n max_panic = self._get_sp... | <|body_start_0|>
values, outdict = BaseWidget.process_form(self, instance=instance, field=field, form=form, empty_marker=empty_marker, emptyReturnsMarker=emptyReturnsMarker)
for index in range(len(values)):
item = values[index]
min_panic = self._get_spec_value(form, item['uid'], ... | AnalysisSpecificationWidget | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalysisSpecificationWidget:
def process_form(self, instance, field, form, empty_marker=None, emptyReturnsMarker=False):
"""Return a list of dictionaries fit for AnalysisSpecsResultsField consumption. If neither hidemin nor hidemax are specified, only services which have float()able entr... | stack_v2_sparse_classes_36k_train_010120 | 2,735 | no_license | [
{
"docstring": "Return a list of dictionaries fit for AnalysisSpecsResultsField consumption. If neither hidemin nor hidemax are specified, only services which have float()able entries in result,min and max field will be included. If hidemin and/or hidemax specified, results might contain empty min and/or max fi... | 2 | stack_v2_sparse_classes_30k_train_017640 | Implement the Python class `AnalysisSpecificationWidget` described below.
Class description:
Implement the AnalysisSpecificationWidget class.
Method signatures and docstrings:
- def process_form(self, instance, field, form, empty_marker=None, emptyReturnsMarker=False): Return a list of dictionaries fit for AnalysisSp... | Implement the Python class `AnalysisSpecificationWidget` described below.
Class description:
Implement the AnalysisSpecificationWidget class.
Method signatures and docstrings:
- def process_form(self, instance, field, form, empty_marker=None, emptyReturnsMarker=False): Return a list of dictionaries fit for AnalysisSp... | 683e87144bdca23c8b5b21161797773b5e694b90 | <|skeleton|>
class AnalysisSpecificationWidget:
def process_form(self, instance, field, form, empty_marker=None, emptyReturnsMarker=False):
"""Return a list of dictionaries fit for AnalysisSpecsResultsField consumption. If neither hidemin nor hidemax are specified, only services which have float()able entr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalysisSpecificationWidget:
def process_form(self, instance, field, form, empty_marker=None, emptyReturnsMarker=False):
"""Return a list of dictionaries fit for AnalysisSpecsResultsField consumption. If neither hidemin nor hidemax are specified, only services which have float()able entries in result,... | the_stack_v2_python_sparse | bhp/lims/browser/widgets/analysisspecificationwidget.py | tdiphale/bhp.lims-1 | train | 1 | |
561f4daf5df80351697eb628ceeca0f6f434710c | [
"\"\"\"\n solution1: 构造所有的可能子序列\n 复杂度： O(N2)\n \"\"\"\nif not S:\n return 0\nresult = set([''])\nfor x in S:\n new_str = set()\n for item in result:\n new_str.add(item + x)\n for item in new_str:\n if item not in result:\n result.add(item)\nreturn len(result... | <|body_start_0|>
"""
solution1: 构造所有的可能子序列
复杂度： O(N2)
"""
if not S:
return 0
result = set([''])
for x in S:
new_str = set()
for item in result:
new_str.add(item + x)
for item i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def distinctSubseqII0(self, S):
""":type S: str :rtype: int"""
<|body_0|>
def distinctSubseqII1(self, S):
"""solution2: end[c]标记所有以‘c’结尾的子序列 (跟顺序无关) 若‘c’在之前的序列里没出现过，则加 end[c] = sum[end] + 1; c为新增项 若‘c’在之前的序列里出现过，end[c] = sum[end] + 1，替换掉之前的项， 因为c与其他项的组合，包含之... | stack_v2_sparse_classes_36k_train_010121 | 1,864 | no_license | [
{
"docstring": ":type S: str :rtype: int",
"name": "distinctSubseqII0",
"signature": "def distinctSubseqII0(self, S)"
},
{
"docstring": "solution2: end[c]标记所有以‘c’结尾的子序列 (跟顺序无关) 若‘c’在之前的序列里没出现过，则加 end[c] = sum[end] + 1; c为新增项 若‘c’在之前的序列里出现过，end[c] = sum[end] + 1，替换掉之前的项， 因为c与其他项的组合，包含之前以c结尾的项 一个c... | 2 | stack_v2_sparse_classes_30k_train_000041 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def distinctSubseqII0(self, S): :type S: str :rtype: int
- def distinctSubseqII1(self, S): solution2: end[c]标记所有以‘c’结尾的子序列 (跟顺序无关) 若‘c’在之前的序列里没出现过，则加 end[c] = sum[end] + 1; c为新增项... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def distinctSubseqII0(self, S): :type S: str :rtype: int
- def distinctSubseqII1(self, S): solution2: end[c]标记所有以‘c’结尾的子序列 (跟顺序无关) 若‘c’在之前的序列里没出现过，则加 end[c] = sum[end] + 1; c为新增项... | 00abbb9909dc8a10f274a6e8605c665ba361fa3e | <|skeleton|>
class Solution:
def distinctSubseqII0(self, S):
""":type S: str :rtype: int"""
<|body_0|>
def distinctSubseqII1(self, S):
"""solution2: end[c]标记所有以‘c’结尾的子序列 (跟顺序无关) 若‘c’在之前的序列里没出现过，则加 end[c] = sum[end] + 1; c为新增项 若‘c’在之前的序列里出现过，end[c] = sum[end] + 1，替换掉之前的项， 因为c与其他项的组合，包含之... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def distinctSubseqII0(self, S):
""":type S: str :rtype: int"""
"""
solution1: 构造所有的可能子序列
复杂度： O(N2)
"""
if not S:
return 0
result = set([''])
for x in S:
new_str = set()
for it... | the_stack_v2_python_sparse | 01_string/06所有可能组合/940.DistinctSubsequencesII.py | harverywxu/algorithm_python | train | 0 | |
8caafb2e69682d5c6ff187dd10554eb4fc693cb5 | [
"if auth_header is None or type(auth_header) != str or (not auth_header.startswith('Basic ')):\n return None\nreturn auth_header.split(' ')[1]",
"if b64_auth_header is None or type(b64_auth_header) != str:\n return None\ntry:\n return base64.b64decode(b64_auth_header.encode('ascii')).decode('ascii')\nexc... | <|body_start_0|>
if auth_header is None or type(auth_header) != str or (not auth_header.startswith('Basic ')):
return None
return auth_header.split(' ')[1]
<|end_body_0|>
<|body_start_1|>
if b64_auth_header is None or type(b64_auth_header) != str:
return None
try... | BasicAuth class. | BasicAuth | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicAuth:
"""BasicAuth class."""
def extract_base64_authorization_header(self, auth_header: str) -> str:
"""Returns Base64 of the Authorization header"""
<|body_0|>
def decode_base64_authorization_header(self, b64_auth_header: str) -> str:
"""Returns decoded val... | stack_v2_sparse_classes_36k_train_010122 | 1,999 | no_license | [
{
"docstring": "Returns Base64 of the Authorization header",
"name": "extract_base64_authorization_header",
"signature": "def extract_base64_authorization_header(self, auth_header: str) -> str"
},
{
"docstring": "Returns decoded value of a Base64 string",
"name": "decode_base64_authorization... | 4 | stack_v2_sparse_classes_30k_train_012952 | Implement the Python class `BasicAuth` described below.
Class description:
BasicAuth class.
Method signatures and docstrings:
- def extract_base64_authorization_header(self, auth_header: str) -> str: Returns Base64 of the Authorization header
- def decode_base64_authorization_header(self, b64_auth_header: str) -> str... | Implement the Python class `BasicAuth` described below.
Class description:
BasicAuth class.
Method signatures and docstrings:
- def extract_base64_authorization_header(self, auth_header: str) -> str: Returns Base64 of the Authorization header
- def decode_base64_authorization_header(self, b64_auth_header: str) -> str... | 0aa1f87942a34efe436a8bebf95cc267cd846e04 | <|skeleton|>
class BasicAuth:
"""BasicAuth class."""
def extract_base64_authorization_header(self, auth_header: str) -> str:
"""Returns Base64 of the Authorization header"""
<|body_0|>
def decode_base64_authorization_header(self, b64_auth_header: str) -> str:
"""Returns decoded val... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BasicAuth:
"""BasicAuth class."""
def extract_base64_authorization_header(self, auth_header: str) -> str:
"""Returns Base64 of the Authorization header"""
if auth_header is None or type(auth_header) != str or (not auth_header.startswith('Basic ')):
return None
return a... | the_stack_v2_python_sparse | 0x06-Basic_authentication/api/v1/auth/basic_auth.py | maleksal/holbertonschool-web_back_end | train | 1 |
bc4a2db3a55671a2b192212ad1b0db2993ccb623 | [
"super(LightweightConvolution2D, self).__init__()\nassert n_feat % wshare == 0\nself.wshare = wshare\nself.use_kernel_mask = use_kernel_mask\nself.dropout_rate = dropout_rate\nself.kernel_size = kernel_size\nself.padding_size = int(kernel_size / 2)\nself.linear1 = nn.Linear(n_feat, n_feat * 2)\nself.linear2 = nn.Li... | <|body_start_0|>
super(LightweightConvolution2D, self).__init__()
assert n_feat % wshare == 0
self.wshare = wshare
self.use_kernel_mask = use_kernel_mask
self.dropout_rate = dropout_rate
self.kernel_size = kernel_size
self.padding_size = int(kernel_size / 2)
... | Lightweight 2-Dimensional Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features dropout_rate (float): dropout_rate kernel_size (int): kernel size (length) use_kernel_mask (boo... | LightweightConvolution2D | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LightweightConvolution2D:
"""Lightweight 2-Dimensional Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features dropout_rate (float): dropout_rate kernel_s... | stack_v2_sparse_classes_36k_train_010123 | 4,229 | permissive | [
{
"docstring": "Construct Lightweight 2-Dimensional Convolution layer.",
"name": "__init__",
"signature": "def __init__(self, wshare, n_feat, dropout_rate, kernel_size, use_kernel_mask=False, use_bias=False)"
},
{
"docstring": "Forward of 'Lightweight 2-Dimensional Convolution'. This function ta... | 2 | stack_v2_sparse_classes_30k_train_011709 | Implement the Python class `LightweightConvolution2D` described below.
Class description:
Lightweight 2-Dimensional Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features drop... | Implement the Python class `LightweightConvolution2D` described below.
Class description:
Lightweight 2-Dimensional Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features drop... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class LightweightConvolution2D:
"""Lightweight 2-Dimensional Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features dropout_rate (float): dropout_rate kernel_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LightweightConvolution2D:
"""Lightweight 2-Dimensional Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features dropout_rate (float): dropout_rate kernel_size (int): ke... | the_stack_v2_python_sparse | espnet/nets/pytorch_backend/transformer/lightconv2d.py | espnet/espnet | train | 7,242 |
9f3251ec709deeea020781473d4bb072437f9d16 | [
"self.extension = extension\nself.zipname = zipname\nself.temp_directory = Path('unzipped-{}'.format(zipname[:-4]))",
"self.unzip_files()\nself.process_files()\nself.zip_files()",
"self.temp_directory.mkdir()\nwith zipfile.ZipFile(self.zipname) as zp:\n zp.extractall(str(self.temp_directory))",
"with zipfi... | <|body_start_0|>
self.extension = extension
self.zipname = zipname
self.temp_directory = Path('unzipped-{}'.format(zipname[:-4]))
<|end_body_0|>
<|body_start_1|>
self.unzip_files()
self.process_files()
self.zip_files()
<|end_body_1|>
<|body_start_2|>
self.temp_d... | Represents process of zipping and unzipping | ZipProcessor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZipProcessor:
"""Represents process of zipping and unzipping"""
def __init__(self, zipname, extension):
"""Initializes variable"""
<|body_0|>
def process_zip(self):
"""Run process"""
<|body_1|>
def unzip_files(self):
"""Unzip files"""
... | stack_v2_sparse_classes_36k_train_010124 | 1,313 | no_license | [
{
"docstring": "Initializes variable",
"name": "__init__",
"signature": "def __init__(self, zipname, extension)"
},
{
"docstring": "Run process",
"name": "process_zip",
"signature": "def process_zip(self)"
},
{
"docstring": "Unzip files",
"name": "unzip_files",
"signature... | 4 | null | Implement the Python class `ZipProcessor` described below.
Class description:
Represents process of zipping and unzipping
Method signatures and docstrings:
- def __init__(self, zipname, extension): Initializes variable
- def process_zip(self): Run process
- def unzip_files(self): Unzip files
- def zip_files(self): Zi... | Implement the Python class `ZipProcessor` described below.
Class description:
Represents process of zipping and unzipping
Method signatures and docstrings:
- def __init__(self, zipname, extension): Initializes variable
- def process_zip(self): Run process
- def unzip_files(self): Unzip files
- def zip_files(self): Zi... | 1837d3234e5b4b5d46cd264bf4a0c4da75bfc3d2 | <|skeleton|>
class ZipProcessor:
"""Represents process of zipping and unzipping"""
def __init__(self, zipname, extension):
"""Initializes variable"""
<|body_0|>
def process_zip(self):
"""Run process"""
<|body_1|>
def unzip_files(self):
"""Unzip files"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ZipProcessor:
"""Represents process of zipping and unzipping"""
def __init__(self, zipname, extension):
"""Initializes variable"""
self.extension = extension
self.zipname = zipname
self.temp_directory = Path('unzipped-{}'.format(zipname[:-4]))
def process_zip(self):
... | the_stack_v2_python_sparse | lab07-cachingWebpage-scalingZipedImage/scale-zipped-images/scale_zip.py | 7ss8n/ProgrammingBasics2-Python | train | 0 |
d65bec5d7bf6d022f0e3faabb8f4b54a381bde70 | [
"self.value = value\nself.prev_node = prev_node\nself.next_node = next_node",
"if not new_value:\n return False\nelse:\n if not self.value:\n self.value = new_value\n else:\n current = self\n while current.next_node:\n current = current.next_node\n current.next_node... | <|body_start_0|>
self.value = value
self.prev_node = prev_node
self.next_node = next_node
<|end_body_0|>
<|body_start_1|>
if not new_value:
return False
else:
if not self.value:
self.value = new_value
else:
curr... | Node | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Node:
def __init__(self, value=None, prev_node=None, next_node=None):
"""Constructor for a new Node on the linked list"""
<|body_0|>
def add_value(self, new_value=None):
"""Adds a new value to the linked list"""
<|body_1|>
def delete(self):
"""De... | stack_v2_sparse_classes_36k_train_010125 | 4,372 | permissive | [
{
"docstring": "Constructor for a new Node on the linked list",
"name": "__init__",
"signature": "def __init__(self, value=None, prev_node=None, next_node=None)"
},
{
"docstring": "Adds a new value to the linked list",
"name": "add_value",
"signature": "def add_value(self, new_value=None... | 3 | null | Implement the Python class `Node` described below.
Class description:
Implement the Node class.
Method signatures and docstrings:
- def __init__(self, value=None, prev_node=None, next_node=None): Constructor for a new Node on the linked list
- def add_value(self, new_value=None): Adds a new value to the linked list
-... | Implement the Python class `Node` described below.
Class description:
Implement the Node class.
Method signatures and docstrings:
- def __init__(self, value=None, prev_node=None, next_node=None): Constructor for a new Node on the linked list
- def add_value(self, new_value=None): Adds a new value to the linked list
-... | 27ffb6b32d6d18d279c51cfa45bf305a409be5c2 | <|skeleton|>
class Node:
def __init__(self, value=None, prev_node=None, next_node=None):
"""Constructor for a new Node on the linked list"""
<|body_0|>
def add_value(self, new_value=None):
"""Adds a new value to the linked list"""
<|body_1|>
def delete(self):
"""De... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Node:
def __init__(self, value=None, prev_node=None, next_node=None):
"""Constructor for a new Node on the linked list"""
self.value = value
self.prev_node = prev_node
self.next_node = next_node
def add_value(self, new_value=None):
"""Adds a new value to the linked... | the_stack_v2_python_sparse | src/daily-coding-problem/medium/sort-linked-list/sort_linked_list.py | nwthomas/code-challenges | train | 2 | |
4b828af27dba1b0203f10ad0c536c68364991282 | [
"os.environ['SDL_VIDEO_CENTERED'] = '1'\npg.init()\npg.display.set_caption('Drag the Red Square')\nself.screen = pg.display.set_mode(SCREEN_SIZE)\nself.screen_rect = self.screen.get_rect()\nself.clock = pg.time.Clock()\nself.fps = 60.0\nself.done = False\nself.keys = pg.key.get_pressed()\nself.player = Character(0,... | <|body_start_0|>
os.environ['SDL_VIDEO_CENTERED'] = '1'
pg.init()
pg.display.set_caption('Drag the Red Square')
self.screen = pg.display.set_mode(SCREEN_SIZE)
self.screen_rect = self.screen.get_rect()
self.clock = pg.time.Clock()
self.fps = 60.0
self.done ... | A control class to manage our event and game loops. | Control | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Control:
"""A control class to manage our event and game loops."""
def __init__(self):
"""Here we have set up the pygame session within the init. Sometimes it is more convenient to do this elsewhere."""
<|body_0|>
def event_loop(self):
"""This is the event loop f... | stack_v2_sparse_classes_36k_train_010126 | 3,624 | no_license | [
{
"docstring": "Here we have set up the pygame session within the init. Sometimes it is more convenient to do this elsewhere.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This is the event loop for the whole program. Regardless of the complexity of a program, there ... | 3 | null | Implement the Python class `Control` described below.
Class description:
A control class to manage our event and game loops.
Method signatures and docstrings:
- def __init__(self): Here we have set up the pygame session within the init. Sometimes it is more convenient to do this elsewhere.
- def event_loop(self): Thi... | Implement the Python class `Control` described below.
Class description:
A control class to manage our event and game loops.
Method signatures and docstrings:
- def __init__(self): Here we have set up the pygame session within the init. Sometimes it is more convenient to do this elsewhere.
- def event_loop(self): Thi... | 7fc4e0d98d06b4e28b09844babb2452e229a603c | <|skeleton|>
class Control:
"""A control class to manage our event and game loops."""
def __init__(self):
"""Here we have set up the pygame session within the init. Sometimes it is more convenient to do this elsewhere."""
<|body_0|>
def event_loop(self):
"""This is the event loop f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Control:
"""A control class to manage our event and game loops."""
def __init__(self):
"""Here we have set up the pygame session within the init. Sometimes it is more convenient to do this elsewhere."""
os.environ['SDL_VIDEO_CENTERED'] = '1'
pg.init()
pg.display.set_captio... | the_stack_v2_python_sparse | meks-pygame-samples/drag_text.py | pk00749/Example_Python | train | 1 |
e81ce1a1a3ddec77988f145c8be6e1e2010c7c62 | [
"action = rule['action']\nuser_id = rule.get('user_id', '*')\nroom_id = rule.get('room_id', '*')\nalias = rule.get('alias', '*')\nif action in ('allow', 'deny'):\n self.action = action\nelse:\n raise ConfigError(\"%s rules can only have action of 'allow' or 'deny'\" % (option_name,))\nself._alias_matches_all ... | <|body_start_0|>
action = rule['action']
user_id = rule.get('user_id', '*')
room_id = rule.get('room_id', '*')
alias = rule.get('alias', '*')
if action in ('allow', 'deny'):
self.action = action
else:
raise ConfigError("%s rules can only have actio... | Helper class to test whether a room directory action is allowed, like creating an alias or publishing a room. | _RoomDirectoryRule | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _RoomDirectoryRule:
"""Helper class to test whether a room directory action is allowed, like creating an alias or publishing a room."""
def __init__(self, option_name: str, rule: JsonDict):
"""Args: option_name: Name of the config option this rule belongs to rule: The rule as specifi... | stack_v2_sparse_classes_36k_train_010127 | 5,480 | permissive | [
{
"docstring": "Args: option_name: Name of the config option this rule belongs to rule: The rule as specified in the config",
"name": "__init__",
"signature": "def __init__(self, option_name: str, rule: JsonDict)"
},
{
"docstring": "Tests if this rule matches the given user_id, room_id and alias... | 2 | null | Implement the Python class `_RoomDirectoryRule` described below.
Class description:
Helper class to test whether a room directory action is allowed, like creating an alias or publishing a room.
Method signatures and docstrings:
- def __init__(self, option_name: str, rule: JsonDict): Args: option_name: Name of the con... | Implement the Python class `_RoomDirectoryRule` described below.
Class description:
Helper class to test whether a room directory action is allowed, like creating an alias or publishing a room.
Method signatures and docstrings:
- def __init__(self, option_name: str, rule: JsonDict): Args: option_name: Name of the con... | d35bed8369514fe727b4fe1afb68f48cc8b2655a | <|skeleton|>
class _RoomDirectoryRule:
"""Helper class to test whether a room directory action is allowed, like creating an alias or publishing a room."""
def __init__(self, option_name: str, rule: JsonDict):
"""Args: option_name: Name of the config option this rule belongs to rule: The rule as specifi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _RoomDirectoryRule:
"""Helper class to test whether a room directory action is allowed, like creating an alias or publishing a room."""
def __init__(self, option_name: str, rule: JsonDict):
"""Args: option_name: Name of the config option this rule belongs to rule: The rule as specified in the con... | the_stack_v2_python_sparse | synapse/config/room_directory.py | matrix-org/synapse | train | 12,215 |
4ae90d6909c1f41eb1eb5177b62e646a975fd720 | [
"value = {'args': args, 'on_schedule': on_time}\nkey = JobModel.convert_name_into_job(name)\nid = self.client.api.create_changeset(key, value, targets)\nreturn id",
"status = self.client.api.get_current_device_status(device_id)\nmstatus = [self.prepare_model(s) for s in status]\nfor s in mstatus:\n if s.name =... | <|body_start_0|>
value = {'args': args, 'on_schedule': on_time}
key = JobModel.convert_name_into_job(name)
id = self.client.api.create_changeset(key, value, targets)
return id
<|end_body_0|>
<|body_start_1|>
status = self.client.api.get_current_device_status(device_id)
m... | JobCollection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JobCollection:
def schedule(self, name, args, targets, on_time=None):
"""Schedule a new Job Args: name (str): Name of the job. args (dict): Arguments of the job as key, value. targets (list): Targets of the changeset (devices). Returns: A :py:class:`JobModel` object. Raises: :py:class:`a... | stack_v2_sparse_classes_36k_train_010128 | 2,691 | no_license | [
{
"docstring": "Schedule a new Job Args: name (str): Name of the job. args (dict): Arguments of the job as key, value. targets (list): Targets of the changeset (devices). Returns: A :py:class:`JobModel` object. Raises: :py:class:`adm.errors.APIError` If the server returns an error.",
"name": "schedule",
... | 3 | null | Implement the Python class `JobCollection` described below.
Class description:
Implement the JobCollection class.
Method signatures and docstrings:
- def schedule(self, name, args, targets, on_time=None): Schedule a new Job Args: name (str): Name of the job. args (dict): Arguments of the job as key, value. targets (l... | Implement the Python class `JobCollection` described below.
Class description:
Implement the JobCollection class.
Method signatures and docstrings:
- def schedule(self, name, args, targets, on_time=None): Schedule a new Job Args: name (str): Name of the job. args (dict): Arguments of the job as key, value. targets (l... | d27b0d6ee47b9c4f320f518705074f1032fedf8a | <|skeleton|>
class JobCollection:
def schedule(self, name, args, targets, on_time=None):
"""Schedule a new Job Args: name (str): Name of the job. args (dict): Arguments of the job as key, value. targets (list): Targets of the changeset (devices). Returns: A :py:class:`JobModel` object. Raises: :py:class:`a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JobCollection:
def schedule(self, name, args, targets, on_time=None):
"""Schedule a new Job Args: name (str): Name of the job. args (dict): Arguments of the job as key, value. targets (list): Targets of the changeset (devices). Returns: A :py:class:`JobModel` object. Raises: :py:class:`adm.errors.APIE... | the_stack_v2_python_sparse | zdevicemanager/client/models/job.py | zerynth/core-zerynth-toolchain | train | 0 | |
94664bc0d86ea23078f31e65d3b6953f12d89f95 | [
"super(CustomSchedule, self).__init__()\nself.d_model = d_model\nself.d_model = tf.cast(self.d_model, tf.float32)\nself.warmup_steps = warmup_steps",
"arg1 = tf.math.rsqrt(step)\narg2 = step * self.warmup_steps ** (-1.5)\nreturn tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)"
] | <|body_start_0|>
super(CustomSchedule, self).__init__()
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
<|end_body_0|>
<|body_start_1|>
arg1 = tf.math.rsqrt(step)
arg2 = step * self.warmup_steps ** (-1.5)
r... | for decaying the lrate | CustomSchedule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomSchedule:
"""for decaying the lrate"""
def __init__(self, d_model, warmup_steps=4000):
"""initializer"""
<|body_0|>
def __call__(self, step):
"""call method"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(CustomSchedule, self).__init... | stack_v2_sparse_classes_36k_train_010129 | 3,937 | no_license | [
{
"docstring": "initializer",
"name": "__init__",
"signature": "def __init__(self, d_model, warmup_steps=4000)"
},
{
"docstring": "call method",
"name": "__call__",
"signature": "def __call__(self, step)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003204 | Implement the Python class `CustomSchedule` described below.
Class description:
for decaying the lrate
Method signatures and docstrings:
- def __init__(self, d_model, warmup_steps=4000): initializer
- def __call__(self, step): call method | Implement the Python class `CustomSchedule` described below.
Class description:
for decaying the lrate
Method signatures and docstrings:
- def __init__(self, d_model, warmup_steps=4000): initializer
- def __call__(self, step): call method
<|skeleton|>
class CustomSchedule:
"""for decaying the lrate"""
def _... | d86b0e0cae2dd07c761f84a493abc895007873ee | <|skeleton|>
class CustomSchedule:
"""for decaying the lrate"""
def __init__(self, d_model, warmup_steps=4000):
"""initializer"""
<|body_0|>
def __call__(self, step):
"""call method"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomSchedule:
"""for decaying the lrate"""
def __init__(self, d_model, warmup_steps=4000):
"""initializer"""
super(CustomSchedule, self).__init__()
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
def _... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/5-train.py | mag389/holbertonschool-machine_learning | train | 2 |
dcad37a8101e1054ceb0404e5dcec42041a1f2a3 | [
"BaseController.__init__(self, veh_id, car_following_params, delay=delay, fail_safe=fail_safe, noise=noise)\nself.v_desired = v0\nself.acc = acc\nself.b = b\nself.b_l = b_l\nself.s0 = s0\nself.tau = tau",
"v = env.k.vehicle.get_speed(self.veh_id)\nh = env.k.vehicle.get_headway(self.veh_id)\nv_l = env.k.vehicle.ge... | <|body_start_0|>
BaseController.__init__(self, veh_id, car_following_params, delay=delay, fail_safe=fail_safe, noise=noise)
self.v_desired = v0
self.acc = acc
self.b = b
self.b_l = b_l
self.s0 = s0
self.tau = tau
<|end_body_0|>
<|body_start_1|>
v = env.k.... | Gipps' Model controller. For more information on this controller, see: Traffic Flow Dynamics written by M.Treiber and A.Kesting By courtesy of Springer publisher, http://www.springer.com http://www.traffic-flow-dynamics.org/res/SampleChapter11.pdf Usage ----- See BaseController for usage example. Attributes ---------- ... | GippsController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GippsController:
"""Gipps' Model controller. For more information on this controller, see: Traffic Flow Dynamics written by M.Treiber and A.Kesting By courtesy of Springer publisher, http://www.springer.com http://www.traffic-flow-dynamics.org/res/SampleChapter11.pdf Usage ----- See BaseControlle... | stack_v2_sparse_classes_36k_train_010130 | 17,548 | permissive | [
{
"docstring": "Instantiate a Gipps' controller.",
"name": "__init__",
"signature": "def __init__(self, veh_id, car_following_params=None, v0=30, acc=1.5, b=-1, b_l=-1, s0=2, tau=1, delay=0, noise=0, fail_safe=None)"
},
{
"docstring": "See parent class.",
"name": "get_accel",
"signature"... | 2 | stack_v2_sparse_classes_30k_train_008078 | Implement the Python class `GippsController` described below.
Class description:
Gipps' Model controller. For more information on this controller, see: Traffic Flow Dynamics written by M.Treiber and A.Kesting By courtesy of Springer publisher, http://www.springer.com http://www.traffic-flow-dynamics.org/res/SampleChap... | Implement the Python class `GippsController` described below.
Class description:
Gipps' Model controller. For more information on this controller, see: Traffic Flow Dynamics written by M.Treiber and A.Kesting By courtesy of Springer publisher, http://www.springer.com http://www.traffic-flow-dynamics.org/res/SampleChap... | badac3da17f04d8d8ae5691ee8ba2af9d56fac35 | <|skeleton|>
class GippsController:
"""Gipps' Model controller. For more information on this controller, see: Traffic Flow Dynamics written by M.Treiber and A.Kesting By courtesy of Springer publisher, http://www.springer.com http://www.traffic-flow-dynamics.org/res/SampleChapter11.pdf Usage ----- See BaseControlle... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GippsController:
"""Gipps' Model controller. For more information on this controller, see: Traffic Flow Dynamics written by M.Treiber and A.Kesting By courtesy of Springer publisher, http://www.springer.com http://www.traffic-flow-dynamics.org/res/SampleChapter11.pdf Usage ----- See BaseController for usage e... | the_stack_v2_python_sparse | flow/controllers/car_following_models.py | parthjaggi/flow | train | 6 |
00c378a2f62a81f689737e0ab72de5577b10c6b2 | [
"super(CLI, self).__init__(protocol_attr, rse_settings, logger=logger)\nif not logger:\n logger = logging.getLogger('%s.null' % __name__)\nself.logger = logger",
"dest = os.path.abspath(dest)\nif ':' not in dest:\n dest = 'file://' + dest\ncmd = 'gfal-copy -vf -p -t %s -T %s %s %s' % (transfer_timeout, tran... | <|body_start_0|>
super(CLI, self).__init__(protocol_attr, rse_settings, logger=logger)
if not logger:
logger = logging.getLogger('%s.null' % __name__)
self.logger = logger
<|end_body_0|>
<|body_start_1|>
dest = os.path.abspath(dest)
if ':' not in dest:
de... | Implementing access to RSEs using the srm protocol through CLI with 'gfal' commands. | CLI | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CLI:
"""Implementing access to RSEs using the srm protocol through CLI with 'gfal' commands."""
def __init__(self, protocol_attr, rse_settings, logger=logging.log):
"""Initializes the object with information about the referred RSE. :param props: Properties derived from the RSE Reposi... | stack_v2_sparse_classes_36k_train_010131 | 29,035 | permissive | [
{
"docstring": "Initializes the object with information about the referred RSE. :param props: Properties derived from the RSE Repository",
"name": "__init__",
"signature": "def __init__(self, protocol_attr, rse_settings, logger=logging.log)"
},
{
"docstring": "Provides access to files stored ins... | 3 | null | Implement the Python class `CLI` described below.
Class description:
Implementing access to RSEs using the srm protocol through CLI with 'gfal' commands.
Method signatures and docstrings:
- def __init__(self, protocol_attr, rse_settings, logger=logging.log): Initializes the object with information about the referred ... | Implement the Python class `CLI` described below.
Class description:
Implementing access to RSEs using the srm protocol through CLI with 'gfal' commands.
Method signatures and docstrings:
- def __init__(self, protocol_attr, rse_settings, logger=logging.log): Initializes the object with information about the referred ... | 7f0d229ac0b3bc7dec12c6e158bea2b82d414a3b | <|skeleton|>
class CLI:
"""Implementing access to RSEs using the srm protocol through CLI with 'gfal' commands."""
def __init__(self, protocol_attr, rse_settings, logger=logging.log):
"""Initializes the object with information about the referred RSE. :param props: Properties derived from the RSE Reposi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CLI:
"""Implementing access to RSEs using the srm protocol through CLI with 'gfal' commands."""
def __init__(self, protocol_attr, rse_settings, logger=logging.log):
"""Initializes the object with information about the referred RSE. :param props: Properties derived from the RSE Repository"""
... | the_stack_v2_python_sparse | lib/rucio/rse/protocols/gfal.py | rucio/rucio | train | 232 |
23367faf94eea5a02c092d577c9e7764d4ed1799 | [
"super().__init__(**kwargs)\nself.dirty_path = os.path.expanduser(path)\nreturn",
"params = {}\nif self._mimetype:\n params['mime'] = self._mimetype\nif self._name:\n params['name'] = self._name\nreturn 'file://{path}{params}'.format(path=self.quote(self.dirty_path), params='?{}'.format(self.urlencode(param... | <|body_start_0|>
super().__init__(**kwargs)
self.dirty_path = os.path.expanduser(path)
return
<|end_body_0|>
<|body_start_1|>
params = {}
if self._mimetype:
params['mime'] = self._mimetype
if self._name:
params['name'] = self._name
return ... | A wrapper for File based attachment sources | AttachFile | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttachFile:
"""A wrapper for File based attachment sources"""
def __init__(self, path, **kwargs):
"""Initialize Local File Attachment Object"""
<|body_0|>
def url(self, privacy=False, *args, **kwargs):
"""Returns the URL built dynamically based on specified argum... | stack_v2_sparse_classes_36k_train_010132 | 4,970 | permissive | [
{
"docstring": "Initialize Local File Attachment Object",
"name": "__init__",
"signature": "def __init__(self, path, **kwargs)"
},
{
"docstring": "Returns the URL built dynamically based on specified arguments.",
"name": "url",
"signature": "def url(self, privacy=False, *args, **kwargs)"... | 4 | null | Implement the Python class `AttachFile` described below.
Class description:
A wrapper for File based attachment sources
Method signatures and docstrings:
- def __init__(self, path, **kwargs): Initialize Local File Attachment Object
- def url(self, privacy=False, *args, **kwargs): Returns the URL built dynamically bas... | Implement the Python class `AttachFile` described below.
Class description:
A wrapper for File based attachment sources
Method signatures and docstrings:
- def __init__(self, path, **kwargs): Initialize Local File Attachment Object
- def url(self, privacy=False, *args, **kwargs): Returns the URL built dynamically bas... | be3baed7e3d33bae973f1714df4ebbf65aa33f85 | <|skeleton|>
class AttachFile:
"""A wrapper for File based attachment sources"""
def __init__(self, path, **kwargs):
"""Initialize Local File Attachment Object"""
<|body_0|>
def url(self, privacy=False, *args, **kwargs):
"""Returns the URL built dynamically based on specified argum... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttachFile:
"""A wrapper for File based attachment sources"""
def __init__(self, path, **kwargs):
"""Initialize Local File Attachment Object"""
super().__init__(**kwargs)
self.dirty_path = os.path.expanduser(path)
return
def url(self, privacy=False, *args, **kwargs):
... | the_stack_v2_python_sparse | apprise/attachment/AttachFile.py | caronc/apprise | train | 8,426 |
75c38782e22c39493cdf913402a5fa2e072334b7 | [
"self.X = X\nself.K = K\nself.centrX = []\nself.C = []\nself.U = []\nself.Z = []\nself.centrX = self._centralized()\nself.C = self._cov()\nself.U = self._U()\nself.Z = self._Z()",
"print('样本矩阵X:\\n', self.X)\ncentrX = []\nmean = np.array([np.mean(attr) for attr in self.X.T])\nprint('样本集的特征均值:\\n', mean)\ncentrX =... | <|body_start_0|>
self.X = X
self.K = K
self.centrX = []
self.C = []
self.U = []
self.Z = []
self.centrX = self._centralized()
self.C = self._cov()
self.U = self._U()
self.Z = self._Z()
<|end_body_0|>
<|body_start_1|>
print('样本矩阵X:\... | 用PCA求样本矩阵X的K阶降维矩阵Z Note:请保证输入的样本矩阵X shape=(m, n)，m行样例，n个特征 | CPCA | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CPCA:
"""用PCA求样本矩阵X的K阶降维矩阵Z Note:请保证输入的样本矩阵X shape=(m, n)，m行样例，n个特征"""
def __init__(self, X, K):
""":param X,训练样本矩阵X :param K,X的降维矩阵的阶数，即X要特征降维成k阶"""
<|body_0|>
def _centralized(self):
"""矩阵X的中心化"""
<|body_1|>
def _cov(self):
"""求样本矩阵X的协方差矩阵C... | stack_v2_sparse_classes_36k_train_010133 | 3,330 | no_license | [
{
"docstring": ":param X,训练样本矩阵X :param K,X的降维矩阵的阶数，即X要特征降维成k阶",
"name": "__init__",
"signature": "def __init__(self, X, K)"
},
{
"docstring": "矩阵X的中心化",
"name": "_centralized",
"signature": "def _centralized(self)"
},
{
"docstring": "求样本矩阵X的协方差矩阵C",
"name": "_cov",
"sign... | 5 | null | Implement the Python class `CPCA` described below.
Class description:
用PCA求样本矩阵X的K阶降维矩阵Z Note:请保证输入的样本矩阵X shape=(m, n)，m行样例，n个特征
Method signatures and docstrings:
- def __init__(self, X, K): :param X,训练样本矩阵X :param K,X的降维矩阵的阶数，即X要特征降维成k阶
- def _centralized(self): 矩阵X的中心化
- def _cov(self): 求样本矩阵X的协方差矩阵C
- def _U(self)... | Implement the Python class `CPCA` described below.
Class description:
用PCA求样本矩阵X的K阶降维矩阵Z Note:请保证输入的样本矩阵X shape=(m, n)，m行样例，n个特征
Method signatures and docstrings:
- def __init__(self, X, K): :param X,训练样本矩阵X :param K,X的降维矩阵的阶数，即X要特征降维成k阶
- def _centralized(self): 矩阵X的中心化
- def _cov(self): 求样本矩阵X的协方差矩阵C
- def _U(self)... | f2a1b2f8b6b292815d92a294d49954616d3624d5 | <|skeleton|>
class CPCA:
"""用PCA求样本矩阵X的K阶降维矩阵Z Note:请保证输入的样本矩阵X shape=(m, n)，m行样例，n个特征"""
def __init__(self, X, K):
""":param X,训练样本矩阵X :param K,X的降维矩阵的阶数，即X要特征降维成k阶"""
<|body_0|>
def _centralized(self):
"""矩阵X的中心化"""
<|body_1|>
def _cov(self):
"""求样本矩阵X的协方差矩阵C... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CPCA:
"""用PCA求样本矩阵X的K阶降维矩阵Z Note:请保证输入的样本矩阵X shape=(m, n)，m行样例，n个特征"""
def __init__(self, X, K):
""":param X,训练样本矩阵X :param K,X的降维矩阵的阶数，即X要特征降维成k阶"""
self.X = X
self.K = K
self.centrX = []
self.C = []
self.U = []
self.Z = []
self.centrX = se... | the_stack_v2_python_sparse | 47+洪杰+上海/第三周/PCA_numpy_detail.py | Yang-chen205/badou-Turing | train | 1 |
7b63555531bf5c131e83c747dedb5da57e05de29 | [
"super().__init__()\nself.layers = nn.ModuleList(map(lambda x: AttentionLayer(**attention_layer), range(n_layers)))\npositional_encoding = position_encoding_init(max_len, features)\nself.register_buffer('positional_encoding', positional_encoding)\nself.p2x = nn.Linear(features, features * 2)",
"gamma, beta = self... | <|body_start_0|>
super().__init__()
self.layers = nn.ModuleList(map(lambda x: AttentionLayer(**attention_layer), range(n_layers)))
positional_encoding = position_encoding_init(max_len, features)
self.register_buffer('positional_encoding', positional_encoding)
self.p2x = nn.Linear... | Residual attention stacks based on the Attention Is All You Need paper | ResidualAttentionEncoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResidualAttentionEncoder:
"""Residual attention stacks based on the Attention Is All You Need paper"""
def __init__(self, features, attention_layer, max_len=200, n_layers=3):
"""features - the number of features per parameter c_features - the number of side conditioning features per ... | stack_v2_sparse_classes_36k_train_010134 | 2,486 | permissive | [
{
"docstring": "features - the number of features per parameter c_features - the number of side conditioning features per batch item attention_layer - a dictionary containing instantiation parameters for the AttentionLayer module max_len - the maximum needed size of the positional encodings n_layers - number of... | 2 | stack_v2_sparse_classes_30k_train_001440 | Implement the Python class `ResidualAttentionEncoder` described below.
Class description:
Residual attention stacks based on the Attention Is All You Need paper
Method signatures and docstrings:
- def __init__(self, features, attention_layer, max_len=200, n_layers=3): features - the number of features per parameter c... | Implement the Python class `ResidualAttentionEncoder` described below.
Class description:
Residual attention stacks based on the Attention Is All You Need paper
Method signatures and docstrings:
- def __init__(self, features, attention_layer, max_len=200, n_layers=3): features - the number of features per parameter c... | 327844cea18a6dfe35e0dc8f5de0832343487366 | <|skeleton|>
class ResidualAttentionEncoder:
"""Residual attention stacks based on the Attention Is All You Need paper"""
def __init__(self, features, attention_layer, max_len=200, n_layers=3):
"""features - the number of features per parameter c_features - the number of side conditioning features per ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResidualAttentionEncoder:
"""Residual attention stacks based on the Attention Is All You Need paper"""
def __init__(self, features, attention_layer, max_len=200, n_layers=3):
"""features - the number of features per parameter c_features - the number of side conditioning features per batch item at... | the_stack_v2_python_sparse | neuralDX7/models/attention/attention_encoder.py | jGambit/NeuralDX7 | train | 0 |
199689dc56fd6fd4410d7620620842f97fb43cf0 | [
"self.num_feat_per_dim = num_feat_per_dim\nself.scale = to.sqrt(to.tensor(2.0 / num_feat_per_dim))\nself.freq = to.randn(num_feat_per_dim, inp_dim)\nif not isinstance(bandwidth, to.Tensor):\n bandwidth = to.from_numpy(np.asanyarray(bandwidth))\nself.freq *= to.sqrt(to.tensor(2.0) / atleast_2D(bandwidth))\nself.s... | <|body_start_0|>
self.num_feat_per_dim = num_feat_per_dim
self.scale = to.sqrt(to.tensor(2.0 / num_feat_per_dim))
self.freq = to.randn(num_feat_per_dim, inp_dim)
if not isinstance(bandwidth, to.Tensor):
bandwidth = to.from_numpy(np.asanyarray(bandwidth))
self.freq *= ... | Random Fourier features .. seealso:: [1] A. Rahimi and B. Recht "Random Features for Large-Scale Kernel Machines", NIPS, 2007 | RandFourierFeat | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandFourierFeat:
"""Random Fourier features .. seealso:: [1] A. Rahimi and B. Recht "Random Features for Large-Scale Kernel Machines", NIPS, 2007"""
def __init__(self, inp_dim: int, num_feat_per_dim: int, bandwidth: [float, np.ndarray, to.Tensor]):
"""Gaussian kernel: $k(x,y) = \\exp... | stack_v2_sparse_classes_36k_train_010135 | 13,729 | permissive | [
{
"docstring": "Gaussian kernel: $k(x,y) = \\\\exp(-\\\\sigma**2 / (2*d) * ||x-y||^2)$ Sample from $\\\\mathcal{N}(0,1)$ and scale the result by $\\\\sigma / \\\\sqrt{2*d}$ :param inp_dim: flat dimension of the inputs i.e. the observations, called $d$ in [1] :param num_feat_per_dim: number of random Fourier fea... | 2 | null | Implement the Python class `RandFourierFeat` described below.
Class description:
Random Fourier features .. seealso:: [1] A. Rahimi and B. Recht "Random Features for Large-Scale Kernel Machines", NIPS, 2007
Method signatures and docstrings:
- def __init__(self, inp_dim: int, num_feat_per_dim: int, bandwidth: [float, ... | Implement the Python class `RandFourierFeat` described below.
Class description:
Random Fourier features .. seealso:: [1] A. Rahimi and B. Recht "Random Features for Large-Scale Kernel Machines", NIPS, 2007
Method signatures and docstrings:
- def __init__(self, inp_dim: int, num_feat_per_dim: int, bandwidth: [float, ... | a6c982862e2ab39a9f65d1c09aa59d9a8b7ac6c5 | <|skeleton|>
class RandFourierFeat:
"""Random Fourier features .. seealso:: [1] A. Rahimi and B. Recht "Random Features for Large-Scale Kernel Machines", NIPS, 2007"""
def __init__(self, inp_dim: int, num_feat_per_dim: int, bandwidth: [float, np.ndarray, to.Tensor]):
"""Gaussian kernel: $k(x,y) = \\exp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandFourierFeat:
"""Random Fourier features .. seealso:: [1] A. Rahimi and B. Recht "Random Features for Large-Scale Kernel Machines", NIPS, 2007"""
def __init__(self, inp_dim: int, num_feat_per_dim: int, bandwidth: [float, np.ndarray, to.Tensor]):
"""Gaussian kernel: $k(x,y) = \\exp(-\\sigma**2 ... | the_stack_v2_python_sparse | Pyrado/pyrado/policies/features.py | jacarvalho/SimuRLacra | train | 0 |
5a7342122b64427f7e2a282639173fd793391f3f | [
"super(UserExtended, self).__init__(parent=parent)\nself.applicationInfoWidget = QtWidgets.QLabel()\nself._userId = userId\nself._applications = applications\nself.setLayout(QtWidgets.QVBoxLayout())\nself.user = User(name, userId, group=None, applications=applications)\nself.layout().addWidget(self.user)\nself.layo... | <|body_start_0|>
super(UserExtended, self).__init__(parent=parent)
self.applicationInfoWidget = QtWidgets.QLabel()
self._userId = userId
self._applications = applications
self.setLayout(QtWidgets.QVBoxLayout())
self.user = User(name, userId, group=None, applications=appli... | Extended user information. | UserExtended | [
"Apache-2.0",
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserExtended:
"""Extended user information."""
def __init__(self, name, userId, applications, group=None, parent=None):
"""Initialise widget with initial component *value* and *parent*."""
<|body_0|>
def updateInformation(self, name, userId, applications):
"""Upd... | stack_v2_sparse_classes_36k_train_010136 | 7,036 | permissive | [
{
"docstring": "Initialise widget with initial component *value* and *parent*.",
"name": "__init__",
"signature": "def __init__(self, name, userId, applications, group=None, parent=None)"
},
{
"docstring": "Update widget with *name*, *userId* and *applications*.",
"name": "updateInformation"... | 2 | stack_v2_sparse_classes_30k_train_004528 | Implement the Python class `UserExtended` described below.
Class description:
Extended user information.
Method signatures and docstrings:
- def __init__(self, name, userId, applications, group=None, parent=None): Initialise widget with initial component *value* and *parent*.
- def updateInformation(self, name, userI... | Implement the Python class `UserExtended` described below.
Class description:
Extended user information.
Method signatures and docstrings:
- def __init__(self, name, userId, applications, group=None, parent=None): Initialise widget with initial component *value* and *parent*.
- def updateInformation(self, name, userI... | f55f52787484fcf931c4653e7e241791f052c04f | <|skeleton|>
class UserExtended:
"""Extended user information."""
def __init__(self, name, userId, applications, group=None, parent=None):
"""Initialise widget with initial component *value* and *parent*."""
<|body_0|>
def updateInformation(self, name, userId, applications):
"""Upd... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserExtended:
"""Extended user information."""
def __init__(self, name, userId, applications, group=None, parent=None):
"""Initialise widget with initial component *value* and *parent*."""
super(UserExtended, self).__init__(parent=parent)
self.applicationInfoWidget = QtWidgets.QLa... | the_stack_v2_python_sparse | source/ftrack_connect/ui/widget/user.py | IngenuityEngine/ftrack-connect | train | 0 |
b62173335183be65b5f41cc1779a5b84b3e2cbfb | [
"super(IQN, self).__init__()\nobs_shape, action_shape = (squeeze(obs_shape), squeeze(action_shape))\nif head_hidden_size is None:\n head_hidden_size = encoder_hidden_size_list[-1]\nif isinstance(obs_shape, int) or len(obs_shape) == 1:\n self.encoder = FCEncoder(obs_shape, encoder_hidden_size_list, activation=... | <|body_start_0|>
super(IQN, self).__init__()
obs_shape, action_shape = (squeeze(obs_shape), squeeze(action_shape))
if head_hidden_size is None:
head_hidden_size = encoder_hidden_size_list[-1]
if isinstance(obs_shape, int) or len(obs_shape) == 1:
self.encoder = FCE... | IQN | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IQN:
def __init__(self, obs_shape: Union[int, SequenceType], action_shape: Union[int, SequenceType], encoder_hidden_size_list: SequenceType=[128, 128, 64], head_hidden_size: Optional[int]=None, head_layer_num: int=1, num_quantiles: int=32, quantile_embedding_size: int=128, activation: Optional[n... | stack_v2_sparse_classes_36k_train_010137 | 30,380 | permissive | [
{
"docstring": "Overview: Init the IQN Model according to input arguments. Arguments: - obs_shape (:obj:`Union[int, SequenceType]`): Observation space shape. - action_shape (:obj:`Union[int, SequenceType]`): Action space shape. - encoder_hidden_size_list (:obj:`SequenceType`): Collection of ``hidden_size`` to p... | 2 | stack_v2_sparse_classes_30k_train_006548 | Implement the Python class `IQN` described below.
Class description:
Implement the IQN class.
Method signatures and docstrings:
- def __init__(self, obs_shape: Union[int, SequenceType], action_shape: Union[int, SequenceType], encoder_hidden_size_list: SequenceType=[128, 128, 64], head_hidden_size: Optional[int]=None,... | Implement the Python class `IQN` described below.
Class description:
Implement the IQN class.
Method signatures and docstrings:
- def __init__(self, obs_shape: Union[int, SequenceType], action_shape: Union[int, SequenceType], encoder_hidden_size_list: SequenceType=[128, 128, 64], head_hidden_size: Optional[int]=None,... | eb483fa6e46602d58c8e7d2ca1e566adca28e703 | <|skeleton|>
class IQN:
def __init__(self, obs_shape: Union[int, SequenceType], action_shape: Union[int, SequenceType], encoder_hidden_size_list: SequenceType=[128, 128, 64], head_hidden_size: Optional[int]=None, head_layer_num: int=1, num_quantiles: int=32, quantile_embedding_size: int=128, activation: Optional[n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IQN:
def __init__(self, obs_shape: Union[int, SequenceType], action_shape: Union[int, SequenceType], encoder_hidden_size_list: SequenceType=[128, 128, 64], head_hidden_size: Optional[int]=None, head_layer_num: int=1, num_quantiles: int=32, quantile_embedding_size: int=128, activation: Optional[nn.Module]=nn.R... | the_stack_v2_python_sparse | ding/model/template/q_learning.py | shengxuesun/DI-engine | train | 1 | |
0d134d5aeb37a1cdb23591dab954b13759e1bb7b | [
"if isinstance(data, str):\n try:\n data = yaml.safe_load(data)\n except Exception:\n pass\nreturn data",
"dtype = TYPES.UNKNOWN\nif pd.isnull(typed_data):\n dtype = TYPES.NULL\nelif isinstance(typed_data, bool):\n dtype = TYPES.BOOLEAN\nelif isinstance(typed_data, FLOAT_TYPES):\n dty... | <|body_start_0|>
if isinstance(data, str):
try:
data = yaml.safe_load(data)
except Exception:
pass
return data
<|end_body_0|>
<|body_start_1|>
dtype = TYPES.UNKNOWN
if pd.isnull(typed_data):
dtype = TYPES.NULL
e... | A class to coerce types on data. To see available types: .. code-block:: python >>> from whylogs.core.types.typeddataconverter import TYPES >>> print("\\n".join(sorted(TYPES.keys()))) | TypedDataConverter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TypedDataConverter:
"""A class to coerce types on data. To see available types: .. code-block:: python >>> from whylogs.core.types.typeddataconverter import TYPES >>> print("\\n".join(sorted(TYPES.keys())))"""
def convert(data):
"""Convert `data` to a typed value If a `data` is a str... | stack_v2_sparse_classes_36k_train_010138 | 2,137 | permissive | [
{
"docstring": "Convert `data` to a typed value If a `data` is a string, parse `data` with yaml. Else, return `data` unchanged Note: this method is very slow, since it relies on the complex and python-based implementation of yaml.",
"name": "convert",
"signature": "def convert(data)"
},
{
"docst... | 2 | stack_v2_sparse_classes_30k_val_000004 | Implement the Python class `TypedDataConverter` described below.
Class description:
A class to coerce types on data. To see available types: .. code-block:: python >>> from whylogs.core.types.typeddataconverter import TYPES >>> print("\\n".join(sorted(TYPES.keys())))
Method signatures and docstrings:
- def convert(da... | Implement the Python class `TypedDataConverter` described below.
Class description:
A class to coerce types on data. To see available types: .. code-block:: python >>> from whylogs.core.types.typeddataconverter import TYPES >>> print("\\n".join(sorted(TYPES.keys())))
Method signatures and docstrings:
- def convert(da... | f71cc98a250c68365ee19b15f7b1eba72677209a | <|skeleton|>
class TypedDataConverter:
"""A class to coerce types on data. To see available types: .. code-block:: python >>> from whylogs.core.types.typeddataconverter import TYPES >>> print("\\n".join(sorted(TYPES.keys())))"""
def convert(data):
"""Convert `data` to a typed value If a `data` is a str... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TypedDataConverter:
"""A class to coerce types on data. To see available types: .. code-block:: python >>> from whylogs.core.types.typeddataconverter import TYPES >>> print("\\n".join(sorted(TYPES.keys())))"""
def convert(data):
"""Convert `data` to a typed value If a `data` is a string, parse `d... | the_stack_v2_python_sparse | src/whylogs/core/types/typeddataconverter.py | valer-whylabs/whylogs | train | 0 |
05d5e58eecd206a5be107e6939df042a2ec5d78b | [
"super(ItemModelMLP, self).__init__()\nself._input_embedding_dimension = input_embedding_dimension\nself.item_input_embedding = tf.keras.layers.Embedding(vocab_size, input_embedding_dimension, name='item_embedding', embeddings_initializer=tf.keras.initializers.RandomUniform(minval=-0.1, maxval=0.1))\nself.item_mode... | <|body_start_0|>
super(ItemModelMLP, self).__init__()
self._input_embedding_dimension = input_embedding_dimension
self.item_input_embedding = tf.keras.layers.Embedding(vocab_size, input_embedding_dimension, name='item_embedding', embeddings_initializer=tf.keras.initializers.RandomUniform(minval=... | An MLP model that can be used as an item tower. | ItemModelMLP | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ItemModelMLP:
"""An MLP model that can be used as an item tower."""
def __init__(self, output_dimension, vocab_size, input_embedding_dimension, num_layers, dropout=0.0):
"""Initializes the parameteric attention model. Args: output_dimension: The output dimension of the user represent... | stack_v2_sparse_classes_36k_train_010139 | 2,480 | permissive | [
{
"docstring": "Initializes the parameteric attention model. Args: output_dimension: The output dimension of the user representation. vocab_size: The vocabulary size for input tokens/items. input_embedding_dimension: The embedding dimension for input tokens/items. num_layers: Number of layers in the MLP. dropou... | 2 | stack_v2_sparse_classes_30k_train_002819 | Implement the Python class `ItemModelMLP` described below.
Class description:
An MLP model that can be used as an item tower.
Method signatures and docstrings:
- def __init__(self, output_dimension, vocab_size, input_embedding_dimension, num_layers, dropout=0.0): Initializes the parameteric attention model. Args: out... | Implement the Python class `ItemModelMLP` described below.
Class description:
An MLP model that can be used as an item tower.
Method signatures and docstrings:
- def __init__(self, output_dimension, vocab_size, input_embedding_dimension, num_layers, dropout=0.0): Initializes the parameteric attention model. Args: out... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class ItemModelMLP:
"""An MLP model that can be used as an item tower."""
def __init__(self, output_dimension, vocab_size, input_embedding_dimension, num_layers, dropout=0.0):
"""Initializes the parameteric attention model. Args: output_dimension: The output dimension of the user represent... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ItemModelMLP:
"""An MLP model that can be used as an item tower."""
def __init__(self, output_dimension, vocab_size, input_embedding_dimension, num_layers, dropout=0.0):
"""Initializes the parameteric attention model. Args: output_dimension: The output dimension of the user representation. vocab_... | the_stack_v2_python_sparse | multiple_user_representations/models/mlp_item_model.py | Jimmy-INL/google-research | train | 1 |
ce4fb20f945c73eeacb94de47603e434754a23fd | [
"if filters is None:\n filters = {}\nresponse = self.client.get(self.list_url, filters, format='json')\nself.assertEqual(response.status_code, 200)\nreturn response.data",
"response = self.do_list()\nself.assertEqual(len(response), 0)\nresponse = self.do_list({'item': 10})\nresponse = self.do_list({'item': 100... | <|body_start_0|>
if filters is None:
filters = {}
response = self.client.get(self.list_url, filters, format='json')
self.assertEqual(response.status_code, 200)
return response.data
<|end_body_0|>
<|body_start_1|>
response = self.do_list()
self.assertEqual(len... | Tests for the StockItem TestReport templates. | TestReportTests | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestReportTests:
"""Tests for the StockItem TestReport templates."""
def do_list(self, filters=None):
"""Helper function to request list of labels with provided filters"""
<|body_0|>
def test_list(self):
"""Test the API list endpoint"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k_train_010140 | 1,475 | permissive | [
{
"docstring": "Helper function to request list of labels with provided filters",
"name": "do_list",
"signature": "def do_list(self, filters=None)"
},
{
"docstring": "Test the API list endpoint",
"name": "test_list",
"signature": "def test_list(self)"
}
] | 2 | null | Implement the Python class `TestReportTests` described below.
Class description:
Tests for the StockItem TestReport templates.
Method signatures and docstrings:
- def do_list(self, filters=None): Helper function to request list of labels with provided filters
- def test_list(self): Test the API list endpoint | Implement the Python class `TestReportTests` described below.
Class description:
Tests for the StockItem TestReport templates.
Method signatures and docstrings:
- def do_list(self, filters=None): Helper function to request list of labels with provided filters
- def test_list(self): Test the API list endpoint
<|skele... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class TestReportTests:
"""Tests for the StockItem TestReport templates."""
def do_list(self, filters=None):
"""Helper function to request list of labels with provided filters"""
<|body_0|>
def test_list(self):
"""Test the API list endpoint"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestReportTests:
"""Tests for the StockItem TestReport templates."""
def do_list(self, filters=None):
"""Helper function to request list of labels with provided filters"""
if filters is None:
filters = {}
response = self.client.get(self.list_url, filters, format='json'... | the_stack_v2_python_sparse | InvenTree/label/test_api.py | inventree/InvenTree | train | 3,077 |
4a0dc7be60ff96c3a52cde3d05cd70679b6f8b20 | [
"self.domain = domain\nself.ip = ip\nself.ucenter = ucenter\nself.user = user\nself.password = password\nself.server = Server(self.ip, get_info=ALL)\nself.conn = Connection(self.server, user=self.domain + '\\\\' + self.user, password=self.password, auto_bind=True, authentication=NTLM)",
"att_list = ['displayName'... | <|body_start_0|>
self.domain = domain
self.ip = ip
self.ucenter = ucenter
self.user = user
self.password = password
self.server = Server(self.ip, get_info=ALL)
self.conn = Connection(self.server, user=self.domain + '\\' + self.user, password=self.password, auto_bi... | AD域操作 | Adoper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Adoper:
"""AD域操作"""
def __init__(self, domain, ip, ucenter, user, password):
""":param domain:域名,格式为xxx.xxx.xxx :param ip:服务器地址,格式为域名xxx.xxx.xxx :param user:管理账户 :param password:密码 :param basedn:"""
<|body_0|>
def searchuser(self, orgunit):
""":param orgunit: 组织单... | stack_v2_sparse_classes_36k_train_010141 | 4,132 | no_license | [
{
"docstring": ":param domain:域名,格式为xxx.xxx.xxx :param ip:服务器地址,格式为域名xxx.xxx.xxx :param user:管理账户 :param password:密码 :param basedn:",
"name": "__init__",
"signature": "def __init__(self, domain, ip, ucenter, user, password)"
},
{
"docstring": ":param orgunit: 组织单元名,格式为aaa.bbb 即bbb组织下的aaa组织，不包含域地... | 4 | stack_v2_sparse_classes_30k_train_010626 | Implement the Python class `Adoper` described below.
Class description:
AD域操作
Method signatures and docstrings:
- def __init__(self, domain, ip, ucenter, user, password): :param domain:域名,格式为xxx.xxx.xxx :param ip:服务器地址,格式为域名xxx.xxx.xxx :param user:管理账户 :param password:密码 :param basedn:
- def searchuser(self, orgunit)... | Implement the Python class `Adoper` described below.
Class description:
AD域操作
Method signatures and docstrings:
- def __init__(self, domain, ip, ucenter, user, password): :param domain:域名,格式为xxx.xxx.xxx :param ip:服务器地址,格式为域名xxx.xxx.xxx :param user:管理账户 :param password:密码 :param basedn:
- def searchuser(self, orgunit)... | 2a733b34f337d4497051660f473a4cfb977fc15b | <|skeleton|>
class Adoper:
"""AD域操作"""
def __init__(self, domain, ip, ucenter, user, password):
""":param domain:域名,格式为xxx.xxx.xxx :param ip:服务器地址,格式为域名xxx.xxx.xxx :param user:管理账户 :param password:密码 :param basedn:"""
<|body_0|>
def searchuser(self, orgunit):
""":param orgunit: 组织单... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Adoper:
"""AD域操作"""
def __init__(self, domain, ip, ucenter, user, password):
""":param domain:域名,格式为xxx.xxx.xxx :param ip:服务器地址,格式为域名xxx.xxx.xxx :param user:管理账户 :param password:密码 :param basedn:"""
self.domain = domain
self.ip = ip
self.ucenter = ucenter
self.user... | the_stack_v2_python_sparse | VMwareAutoApi/kdldapapi/ldaptool/adoper.py | vkhaibao/PyProject | train | 1 |
7bc7a81f1d601bbf60f793307cc49475c38fc110 | [
"ai = float('INF')\nfor j in range(len(nums) - 1):\n ai = min(ai, nums[j])\n for k in range(j + 1, len(nums)):\n if nums[k] > ai and nums[k] < nums[j]:\n return True\nreturn False",
"if len(nums) < 3:\n return False\nintervals = []\ns = 0\nfor i in range(1, len(nums)):\n if nums[i] <... | <|body_start_0|>
ai = float('INF')
for j in range(len(nums) - 1):
ai = min(ai, nums[j])
for k in range(j + 1, len(nums)):
if nums[k] > ai and nums[k] < nums[j]:
return True
return False
<|end_body_0|>
<|body_start_1|>
if len(nu... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def find132pattern_brute_force_optimize(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def find132pattern_record_intervals(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
def find132pattern_nlogn(self, nums):
... | stack_v2_sparse_classes_36k_train_010142 | 2,996 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "find132pattern_brute_force_optimize",
"signature": "def find132pattern_brute_force_optimize(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "find132pattern_record_intervals",
"signature": "def find... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find132pattern_brute_force_optimize(self, nums): :type nums: List[int] :rtype: bool
- def find132pattern_record_intervals(self, nums): :type nums: List[int] :rtype: bool
- de... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find132pattern_brute_force_optimize(self, nums): :type nums: List[int] :rtype: bool
- def find132pattern_record_intervals(self, nums): :type nums: List[int] :rtype: bool
- de... | 0e99f9a5226507706b3ee66fd04bae813755ef40 | <|skeleton|>
class Solution:
def find132pattern_brute_force_optimize(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def find132pattern_record_intervals(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
def find132pattern_nlogn(self, nums):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def find132pattern_brute_force_optimize(self, nums):
""":type nums: List[int] :rtype: bool"""
ai = float('INF')
for j in range(len(nums) - 1):
ai = min(ai, nums[j])
for k in range(j + 1, len(nums)):
if nums[k] > ai and nums[k] < nums[j]... | the_stack_v2_python_sparse | medium/heapstack/test_456_132_Pattern.py | wuxu1019/leetcode_sophia | train | 1 | |
ada6b8f20ce70a8e13fcdc0109a4dc0d66a4bf96 | [
"super(Player, self).__init__()\nself.width = 75\nself.height = 15\nself.image = pygame.Surface([self.width, self.height])\nself.image.fill(white)\nself.rect = self.image.get_rect()\nself.screenheight = pygame.display.get_surface().get_height()\nself.screenwidth = pygame.display.get_surface().get_width()\nself.rect... | <|body_start_0|>
super(Player, self).__init__()
self.width = 75
self.height = 15
self.image = pygame.Surface([self.width, self.height])
self.image.fill(white)
self.rect = self.image.get_rect()
self.screenheight = pygame.display.get_surface().get_height()
s... | This class represents the bar at the bottom that the player controls. | Player | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Player:
"""This class represents the bar at the bottom that the player controls."""
def __init__(self):
"""Constructor for Player."""
<|body_0|>
def update(self, vel):
"""Update the player position."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_010143 | 15,458 | no_license | [
{
"docstring": "Constructor for Player.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Update the player position.",
"name": "update",
"signature": "def update(self, vel)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019821 | Implement the Python class `Player` described below.
Class description:
This class represents the bar at the bottom that the player controls.
Method signatures and docstrings:
- def __init__(self): Constructor for Player.
- def update(self, vel): Update the player position. | Implement the Python class `Player` described below.
Class description:
This class represents the bar at the bottom that the player controls.
Method signatures and docstrings:
- def __init__(self): Constructor for Player.
- def update(self, vel): Update the player position.
<|skeleton|>
class Player:
"""This cla... | 4419e1237986416379618981c7136dabed995a58 | <|skeleton|>
class Player:
"""This class represents the bar at the bottom that the player controls."""
def __init__(self):
"""Constructor for Player."""
<|body_0|>
def update(self, vel):
"""Update the player position."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Player:
"""This class represents the bar at the bottom that the player controls."""
def __init__(self):
"""Constructor for Player."""
super(Player, self).__init__()
self.width = 75
self.height = 15
self.image = pygame.Surface([self.width, self.height])
self... | the_stack_v2_python_sparse | project-3/breakout.py | kajal-puri/game-ai-bonn-ss18 | train | 0 |
4812224be3f79bdeb9b29422ef2e3aded8352afc | [
"self.clear_table()\nself.unhinge_db()\nself.logger.info('Vacuum analyzing tables now that db is unhinged')\nself.execute('VACUUM ANALYZE;')\nself.logger.info('Creating mrt_w_roas')\nsql = 'CREATE UNLOGGED TABLE IF NOT EXISTS\\n mrt_w_roas AS (\\n SELECT DISTINCT ON (m.prefix, m.as_path, m... | <|body_start_0|>
self.clear_table()
self.unhinge_db()
self.logger.info('Vacuum analyzing tables now that db is unhinged')
self.execute('VACUUM ANALYZE;')
self.logger.info('Creating mrt_w_roas')
sql = 'CREATE UNLOGGED TABLE IF NOT EXISTS\n mrt_w_roas AS (\n ... | Announcements table class | MRT_W_Roas_Table | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MRT_W_Roas_Table:
"""Announcements table class"""
def _create_tables(self):
"""Creates tables if they do not exist"""
<|body_0|>
def clear_table(self):
"""Clears the tables. Should be called at the start of every run"""
<|body_1|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_36k_train_010144 | 2,215 | permissive | [
{
"docstring": "Creates tables if they do not exist",
"name": "_create_tables",
"signature": "def _create_tables(self)"
},
{
"docstring": "Clears the tables. Should be called at the start of every run",
"name": "clear_table",
"signature": "def clear_table(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000448 | Implement the Python class `MRT_W_Roas_Table` described below.
Class description:
Announcements table class
Method signatures and docstrings:
- def _create_tables(self): Creates tables if they do not exist
- def clear_table(self): Clears the tables. Should be called at the start of every run | Implement the Python class `MRT_W_Roas_Table` described below.
Class description:
Announcements table class
Method signatures and docstrings:
- def _create_tables(self): Creates tables if they do not exist
- def clear_table(self): Clears the tables. Should be called at the start of every run
<|skeleton|>
class MRT_W... | 91c92584b31bd128d818c7fee86c738367c0712e | <|skeleton|>
class MRT_W_Roas_Table:
"""Announcements table class"""
def _create_tables(self):
"""Creates tables if they do not exist"""
<|body_0|>
def clear_table(self):
"""Clears the tables. Should be called at the start of every run"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MRT_W_Roas_Table:
"""Announcements table class"""
def _create_tables(self):
"""Creates tables if they do not exist"""
self.clear_table()
self.unhinge_db()
self.logger.info('Vacuum analyzing tables now that db is unhinged')
self.execute('VACUUM ANALYZE;')
se... | the_stack_v2_python_sparse | lib_bgp_data/forecast/tables.py | jfuruness/lib_bgp_data | train | 16 |
5e474669e014a54dde3bd14b3005e5c8f704ac02 | [
"super(DeviceDriverTest, self).setUp()\nself.build_client = mock.MagicMock()\nself.Patch(android_build_client, 'AndroidBuildClient', return_value=self.build_client)\nself.storage_client = mock.MagicMock()\nself.Patch(gstorage_client, 'StorageClient', return_value=self.storage_client)\nself.compute_client = mock.Mag... | <|body_start_0|>
super(DeviceDriverTest, self).setUp()
self.build_client = mock.MagicMock()
self.Patch(android_build_client, 'AndroidBuildClient', return_value=self.build_client)
self.storage_client = mock.MagicMock()
self.Patch(gstorage_client, 'StorageClient', return_value=self... | Test device_driver. | DeviceDriverTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeviceDriverTest:
"""Test device_driver."""
def setUp(self):
"""Set up the test."""
<|body_0|>
def testCreateGCETypeAVD(self):
"""Test CreateGCETypeAVD."""
<|body_1|>
def testCreateGCETypeAVDInternalIP(self):
"""Test CreateGCETypeAVD with int... | stack_v2_sparse_classes_36k_train_010145 | 6,901 | permissive | [
{
"docstring": "Set up the test.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test CreateGCETypeAVD.",
"name": "testCreateGCETypeAVD",
"signature": "def testCreateGCETypeAVD(self)"
},
{
"docstring": "Test CreateGCETypeAVD with internal IP.",
"name": "t... | 4 | null | Implement the Python class `DeviceDriverTest` described below.
Class description:
Test device_driver.
Method signatures and docstrings:
- def setUp(self): Set up the test.
- def testCreateGCETypeAVD(self): Test CreateGCETypeAVD.
- def testCreateGCETypeAVDInternalIP(self): Test CreateGCETypeAVD with internal IP.
- def... | Implement the Python class `DeviceDriverTest` described below.
Class description:
Test device_driver.
Method signatures and docstrings:
- def setUp(self): Set up the test.
- def testCreateGCETypeAVD(self): Test CreateGCETypeAVD.
- def testCreateGCETypeAVDInternalIP(self): Test CreateGCETypeAVD with internal IP.
- def... | 78a61ca023cbf1a0cecfef8b97df2b274ac3a988 | <|skeleton|>
class DeviceDriverTest:
"""Test device_driver."""
def setUp(self):
"""Set up the test."""
<|body_0|>
def testCreateGCETypeAVD(self):
"""Test CreateGCETypeAVD."""
<|body_1|>
def testCreateGCETypeAVDInternalIP(self):
"""Test CreateGCETypeAVD with int... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeviceDriverTest:
"""Test device_driver."""
def setUp(self):
"""Set up the test."""
super(DeviceDriverTest, self).setUp()
self.build_client = mock.MagicMock()
self.Patch(android_build_client, 'AndroidBuildClient', return_value=self.build_client)
self.storage_client... | the_stack_v2_python_sparse | tools/acloud/public/device_driver_test.py | ZYHGOD-1/Aosp11 | train | 0 |
18374e3a0394f568e6ee8f47cc759e82eb8b5893 | [
"self.input = input.clone()\noutput = input.clamp(min=0)\nreturn output",
"assert self.input is not None\nassert grad_output.size() == self.input.size()\ngrad_input = grad_output.clone()\ngrad_input[self.input < 0] = 0\nreturn grad_input"
] | <|body_start_0|>
self.input = input.clone()
output = input.clamp(min=0)
return output
<|end_body_0|>
<|body_start_1|>
assert self.input is not None
assert grad_output.size() == self.input.size()
grad_input = grad_output.clone()
grad_input[self.input < 0] = 0
... | Class representing the rectified linear unit activation function. | ReLU | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReLU:
"""Class representing the rectified linear unit activation function."""
def forward(self, input):
"""Apllies the relu function to the input. Args: input -- tensor of size (N, *) Return: output -- tensor of same size as input"""
<|body_0|>
def backward(self, grad_ou... | stack_v2_sparse_classes_36k_train_010146 | 2,186 | permissive | [
{
"docstring": "Apllies the relu function to the input. Args: input -- tensor of size (N, *) Return: output -- tensor of same size as input",
"name": "forward",
"signature": "def forward(self, input)"
},
{
"docstring": "Given the gradient w.r.t. to the output of the activation, computes the grad... | 2 | stack_v2_sparse_classes_30k_val_001025 | Implement the Python class `ReLU` described below.
Class description:
Class representing the rectified linear unit activation function.
Method signatures and docstrings:
- def forward(self, input): Apllies the relu function to the input. Args: input -- tensor of size (N, *) Return: output -- tensor of same size as in... | Implement the Python class `ReLU` described below.
Class description:
Class representing the rectified linear unit activation function.
Method signatures and docstrings:
- def forward(self, input): Apllies the relu function to the input. Args: input -- tensor of size (N, *) Return: output -- tensor of same size as in... | 056b1be878b77c5a7dd5cff8d29ecb390be8b5de | <|skeleton|>
class ReLU:
"""Class representing the rectified linear unit activation function."""
def forward(self, input):
"""Apllies the relu function to the input. Args: input -- tensor of size (N, *) Return: output -- tensor of same size as input"""
<|body_0|>
def backward(self, grad_ou... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReLU:
"""Class representing the rectified linear unit activation function."""
def forward(self, input):
"""Apllies the relu function to the input. Args: input -- tensor of size (N, *) Return: output -- tensor of same size as input"""
self.input = input.clone()
output = input.clamp... | the_stack_v2_python_sparse | Proj2/modules/Activations.py | jouvemax/DeepLearning | train | 0 |
0a404c1421be4c6d39a1a7333cc6bd83b07ac0be | [
"n = self.xdim\nx = x.detach()\nx.requires_grad_(True)\nnx = self.step(t, x, u)\njac_x = torch.stack([torch.autograd.grad(nx[:, :, i].sum(), x, retain_graph=True)[0] for i in range(n)], dim=2)\nreturn jac_x",
"n = self.xdim\nu = u.detach()\nu.requires_grad_(True)\nnx = self.step(t, x, u)\njac_u = torch.stack([tor... | <|body_start_0|>
n = self.xdim
x = x.detach()
x.requires_grad_(True)
nx = self.step(t, x, u)
jac_x = torch.stack([torch.autograd.grad(nx[:, :, i].sum(), x, retain_graph=True)[0] for i in range(n)], dim=2)
return jac_x
<|end_body_0|>
<|body_start_1|>
n = self.xdim... | Mixin for computing jacobians of dynamics. Defaults to using autograd. | DynJacMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DynJacMixin:
"""Mixin for computing jacobians of dynamics. Defaults to using autograd."""
def jac_step_x(self, t, x, u=None):
"""Returns the Jacobian of step at time t Args: t (torch.tensor): (B, T,) shaped time indices x (torch.tensor): (B, T, n) shaped system states u (torch.tensor... | stack_v2_sparse_classes_36k_train_010147 | 11,101 | permissive | [
{
"docstring": "Returns the Jacobian of step at time t Args: t (torch.tensor): (B, T,) shaped time indices x (torch.tensor): (B, T, n) shaped system states u (torch.tensor): (B, T, m) shaped control inputs Returns jac_x_t (torch.tensor): (B, T, n, n) shaped jacobian of the next state",
"name": "jac_step_x",... | 3 | stack_v2_sparse_classes_30k_train_019198 | Implement the Python class `DynJacMixin` described below.
Class description:
Mixin for computing jacobians of dynamics. Defaults to using autograd.
Method signatures and docstrings:
- def jac_step_x(self, t, x, u=None): Returns the Jacobian of step at time t Args: t (torch.tensor): (B, T,) shaped time indices x (torc... | Implement the Python class `DynJacMixin` described below.
Class description:
Mixin for computing jacobians of dynamics. Defaults to using autograd.
Method signatures and docstrings:
- def jac_step_x(self, t, x, u=None): Returns the Jacobian of step at time t Args: t (torch.tensor): (B, T,) shaped time indices x (torc... | 6154587fe3cdb92e8b7f70eedb1262caa1553cc8 | <|skeleton|>
class DynJacMixin:
"""Mixin for computing jacobians of dynamics. Defaults to using autograd."""
def jac_step_x(self, t, x, u=None):
"""Returns the Jacobian of step at time t Args: t (torch.tensor): (B, T,) shaped time indices x (torch.tensor): (B, T, n) shaped system states u (torch.tensor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DynJacMixin:
"""Mixin for computing jacobians of dynamics. Defaults to using autograd."""
def jac_step_x(self, t, x, u=None):
"""Returns the Jacobian of step at time t Args: t (torch.tensor): (B, T,) shaped time indices x (torch.tensor): (B, T, n) shaped system states u (torch.tensor): (B, T, m) ... | the_stack_v2_python_sparse | ceem/dynamics.py | sisl/CEEM | train | 6 |
e82d1080472569ba433d54900246adfcc73093c3 | [
"self.ensure_one()\nif self.country_id.code != 'CL':\n return super()._format_document_number(document_number)\nif not document_number:\n return False\nreturn document_number.zfill(6)",
"self.ensure_one()\nif self.country_id.code == 'CL' and self.code in ['39', '41', '110', '111', '112', '34']:\n return ... | <|body_start_0|>
self.ensure_one()
if self.country_id.code != 'CL':
return super()._format_document_number(document_number)
if not document_number:
return False
return document_number.zfill(6)
<|end_body_0|>
<|body_start_1|>
self.ensure_one()
if s... | L10nLatamDocumentType | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class L10nLatamDocumentType:
def _format_document_number(self, document_number):
"""Make validation of Import Dispatch Number * making validations on the document_number. If it is wrong it should raise an exception * format the document_number against a pattern and return it"""
<|body_... | stack_v2_sparse_classes_36k_train_010148 | 1,398 | permissive | [
{
"docstring": "Make validation of Import Dispatch Number * making validations on the document_number. If it is wrong it should raise an exception * format the document_number against a pattern and return it",
"name": "_format_document_number",
"signature": "def _format_document_number(self, document_nu... | 2 | stack_v2_sparse_classes_30k_train_005509 | Implement the Python class `L10nLatamDocumentType` described below.
Class description:
Implement the L10nLatamDocumentType class.
Method signatures and docstrings:
- def _format_document_number(self, document_number): Make validation of Import Dispatch Number * making validations on the document_number. If it is wron... | Implement the Python class `L10nLatamDocumentType` described below.
Class description:
Implement the L10nLatamDocumentType class.
Method signatures and docstrings:
- def _format_document_number(self, document_number): Make validation of Import Dispatch Number * making validations on the document_number. If it is wron... | 310497a9872db7844b521e6dab5f7a9f61d365a4 | <|skeleton|>
class L10nLatamDocumentType:
def _format_document_number(self, document_number):
"""Make validation of Import Dispatch Number * making validations on the document_number. If it is wrong it should raise an exception * format the document_number against a pattern and return it"""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class L10nLatamDocumentType:
def _format_document_number(self, document_number):
"""Make validation of Import Dispatch Number * making validations on the document_number. If it is wrong it should raise an exception * format the document_number against a pattern and return it"""
self.ensure_one()
... | the_stack_v2_python_sparse | addons/l10n_cl/models/l10n_latam_document_type.py | SHIVJITH/Odoo_Machine_Test | train | 0 | |
d441b85e15392c2057bfe64dde8c30c8d627129b | [
"subcategory = self.cleaned_data['subcategory']\nif subcategory.parent_category is None:\n raise forms.ValidationError(INVALID_SUBCATEGORY, code='invalid')\nreturn subcategory",
"images = self.data.get('images', [])\ncurrent_images = Image.objects.filter(offer_id=offer.id)\nif current_images:\n excluded_ima... | <|body_start_0|>
subcategory = self.cleaned_data['subcategory']
if subcategory.parent_category is None:
raise forms.ValidationError(INVALID_SUBCATEGORY, code='invalid')
return subcategory
<|end_body_0|>
<|body_start_1|>
images = self.data.get('images', [])
current_im... | Form to create an offer from admin | AdminCreateOfferForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdminCreateOfferForm:
"""Form to create an offer from admin"""
def clean_subcategory(self):
"""Validate subcategory is actually a subcategory, not a parent category"""
<|body_0|>
def clean_images(self, offer):
"""Clean images"""
<|body_1|>
def clean_... | stack_v2_sparse_classes_36k_train_010149 | 8,911 | no_license | [
{
"docstring": "Validate subcategory is actually a subcategory, not a parent category",
"name": "clean_subcategory",
"signature": "def clean_subcategory(self)"
},
{
"docstring": "Clean images",
"name": "clean_images",
"signature": "def clean_images(self, offer)"
},
{
"docstring":... | 4 | stack_v2_sparse_classes_30k_train_001423 | Implement the Python class `AdminCreateOfferForm` described below.
Class description:
Form to create an offer from admin
Method signatures and docstrings:
- def clean_subcategory(self): Validate subcategory is actually a subcategory, not a parent category
- def clean_images(self, offer): Clean images
- def clean_mate... | Implement the Python class `AdminCreateOfferForm` described below.
Class description:
Form to create an offer from admin
Method signatures and docstrings:
- def clean_subcategory(self): Validate subcategory is actually a subcategory, not a parent category
- def clean_images(self, offer): Clean images
- def clean_mate... | 4dc6362ef624eb6591aad9d5c7de95eee40a01c9 | <|skeleton|>
class AdminCreateOfferForm:
"""Form to create an offer from admin"""
def clean_subcategory(self):
"""Validate subcategory is actually a subcategory, not a parent category"""
<|body_0|>
def clean_images(self, offer):
"""Clean images"""
<|body_1|>
def clean_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdminCreateOfferForm:
"""Form to create an offer from admin"""
def clean_subcategory(self):
"""Validate subcategory is actually a subcategory, not a parent category"""
subcategory = self.cleaned_data['subcategory']
if subcategory.parent_category is None:
raise forms.Va... | the_stack_v2_python_sparse | app/offers/forms.py | arielMilan1899/orbita-api | train | 0 |
e58e6fce59535aaaeab0fd30bac2bc5fad985af1 | [
"self.w = w\nself.n = len(w)\nself.s = sum(self.w)\nfor i in range(1, self.n):\n w[i] += w[i - 1]",
"seed = random.randint(1, self.s)\nl, r = (0, self.n - 1)\nwhile l < r:\n mid = (l + r) // 2\n if seed <= self.w[mid]:\n r = mid\n else:\n l = mid + 1\nreturn l"
] | <|body_start_0|>
self.w = w
self.n = len(w)
self.s = sum(self.w)
for i in range(1, self.n):
w[i] += w[i - 1]
<|end_body_0|>
<|body_start_1|>
seed = random.randint(1, self.s)
l, r = (0, self.n - 1)
while l < r:
mid = (l + r) // 2
... | Given an array w of positive integers, where w[i] describes the weight of index i, write a function pickIndex which randomly picks an index in proportion to its weight. | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Given an array w of positive integers, where w[i] describes the weight of index i, write a function pickIndex which randomly picks an index in proportion to its weight."""
def __init__(self, w):
""":type w: List[int]"""
<|body_0|>
def pickIndex(self):
... | stack_v2_sparse_classes_36k_train_010150 | 752 | permissive | [
{
"docstring": ":type w: List[int]",
"name": "__init__",
"signature": "def __init__(self, w)"
},
{
"docstring": ":rtype: int",
"name": "pickIndex",
"signature": "def pickIndex(self)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Given an array w of positive integers, where w[i] describes the weight of index i, write a function pickIndex which randomly picks an index in proportion to its weight.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[in... | Implement the Python class `Solution` described below.
Class description:
Given an array w of positive integers, where w[i] describes the weight of index i, write a function pickIndex which randomly picks an index in proportion to its weight.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[in... | e3accd22d8cf25546f33883aac634a9bfe108b34 | <|skeleton|>
class Solution:
"""Given an array w of positive integers, where w[i] describes the weight of index i, write a function pickIndex which randomly picks an index in proportion to its weight."""
def __init__(self, w):
""":type w: List[int]"""
<|body_0|>
def pickIndex(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""Given an array w of positive integers, where w[i] describes the weight of index i, write a function pickIndex which randomly picks an index in proportion to its weight."""
def __init__(self, w):
""":type w: List[int]"""
self.w = w
self.n = len(w)
self.s = sum(... | the_stack_v2_python_sparse | LeetCode/random_pick_with_rate.py | milkrong/Basic-Python-DS-Algs | train | 0 |
b7c9d0a622882f42d91684702fba4df6979b3b9e | [
"security_group = self.os_conn.create_sec_group_for_ssh()\nself.instance_keypair = self.os_conn.create_key(key_name='instancekey')\nself.os_conn.nova.security_group_rules.create(security_group.id, ip_protocol='tcp', from_port=1, to_port=65535, cidr='0.0.0.0/0')\nnet, subnet = self.create_internal_network_with_subne... | <|body_start_0|>
security_group = self.os_conn.create_sec_group_for_ssh()
self.instance_keypair = self.os_conn.create_key(key_name='instancekey')
self.os_conn.nova.security_group_rules.create(security_group.id, ip_protocol='tcp', from_port=1, to_port=65535, cidr='0.0.0.0/0')
net, subnet ... | Check association and disassociation floating ip | TestFloatingIP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFloatingIP:
"""Check association and disassociation floating ip"""
def prepare_openstack(self):
"""Prepare OpenStack for scenarios run Steps: 1. Create network net01, subnet net01__subnet with CIDR 10.1.1.0/24 2. Create new security group sec_group1 3. Add Ingress rule for TCP pr... | stack_v2_sparse_classes_36k_train_010151 | 5,007 | no_license | [
{
"docstring": "Prepare OpenStack for scenarios run Steps: 1. Create network net01, subnet net01__subnet with CIDR 10.1.1.0/24 2. Create new security group sec_group1 3. Add Ingress rule for TCP protocol to sec_group1 4. Boot vm1 net01 with sec_group1",
"name": "prepare_openstack",
"signature": "def pre... | 2 | stack_v2_sparse_classes_30k_train_010560 | Implement the Python class `TestFloatingIP` described below.
Class description:
Check association and disassociation floating ip
Method signatures and docstrings:
- def prepare_openstack(self): Prepare OpenStack for scenarios run Steps: 1. Create network net01, subnet net01__subnet with CIDR 10.1.1.0/24 2. Create new... | Implement the Python class `TestFloatingIP` described below.
Class description:
Check association and disassociation floating ip
Method signatures and docstrings:
- def prepare_openstack(self): Prepare OpenStack for scenarios run Steps: 1. Create network net01, subnet net01__subnet with CIDR 10.1.1.0/24 2. Create new... | 8aced2855b78b5f123195d188c80e27b43888a2e | <|skeleton|>
class TestFloatingIP:
"""Check association and disassociation floating ip"""
def prepare_openstack(self):
"""Prepare OpenStack for scenarios run Steps: 1. Create network net01, subnet net01__subnet with CIDR 10.1.1.0/24 2. Create new security group sec_group1 3. Add Ingress rule for TCP pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestFloatingIP:
"""Check association and disassociation floating ip"""
def prepare_openstack(self):
"""Prepare OpenStack for scenarios run Steps: 1. Create network net01, subnet net01__subnet with CIDR 10.1.1.0/24 2. Create new security group sec_group1 3. Add Ingress rule for TCP protocol to sec... | the_stack_v2_python_sparse | mos_tests/neutron/python_tests/test_floating_ip.py | Mirantis/mos-integration-tests | train | 16 |
e020960945365660d61f3fdfca91ec5631ee0331 | [
"if type(sources) == int:\n sources = list(sources)\nself.marked = [False for _ in range(G.V)]\nfor s in sources:\n if not self.marked[s]:\n self.dfs(G, s)",
"self.marked[v] = True\nfor w in G.adj[v]:\n self.dfs(G, w)"
] | <|body_start_0|>
if type(sources) == int:
sources = list(sources)
self.marked = [False for _ in range(G.V)]
for s in sources:
if not self.marked[s]:
self.dfs(G, s)
<|end_body_0|>
<|body_start_1|>
self.marked[v] = True
for w in G.adj[v]:
... | DirectedDFS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DirectedDFS:
def __init__(self, G, sources):
"""Args: G: Digraph sources: int or list[int]"""
<|body_0|>
def dfs(self, G, v):
"""Args: G: Digraph v: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if type(sources) == int:
sources = l... | stack_v2_sparse_classes_36k_train_010152 | 575 | no_license | [
{
"docstring": "Args: G: Digraph sources: int or list[int]",
"name": "__init__",
"signature": "def __init__(self, G, sources)"
},
{
"docstring": "Args: G: Digraph v: int",
"name": "dfs",
"signature": "def dfs(self, G, v)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011763 | Implement the Python class `DirectedDFS` described below.
Class description:
Implement the DirectedDFS class.
Method signatures and docstrings:
- def __init__(self, G, sources): Args: G: Digraph sources: int or list[int]
- def dfs(self, G, v): Args: G: Digraph v: int | Implement the Python class `DirectedDFS` described below.
Class description:
Implement the DirectedDFS class.
Method signatures and docstrings:
- def __init__(self, G, sources): Args: G: Digraph sources: int or list[int]
- def dfs(self, G, v): Args: G: Digraph v: int
<|skeleton|>
class DirectedDFS:
def __init__... | c4e5cce32826ae8ead955277b85f6b3377286d51 | <|skeleton|>
class DirectedDFS:
def __init__(self, G, sources):
"""Args: G: Digraph sources: int or list[int]"""
<|body_0|>
def dfs(self, G, v):
"""Args: G: Digraph v: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DirectedDFS:
def __init__(self, G, sources):
"""Args: G: Digraph sources: int or list[int]"""
if type(sources) == int:
sources = list(sources)
self.marked = [False for _ in range(G.V)]
for s in sources:
if not self.marked[s]:
self.dfs(G, ... | the_stack_v2_python_sparse | code/chapter4/DirectedDFS.py | AiZhanghan/Algorithms-Fourth-Edition | train | 0 | |
c3f831e21b3afc2ba0364626e10f86eb08ea51ba | [
"super(TFC_RNN, self).__init__()\nself.skip_connection = skip_connection\nself.tfc = TFC(in_channels, num_layers_tfc, gr, kt, kf, activation)\nself.bn = nn.BatchNorm2d(gr)\nhidden_units_rnn = max(f // bn_factor_rnn, min_bn_units_rnn)\nself.rnn = nn.GRU(f, hidden_units_rnn, num_layers_rnn, bias=bias_rnn, batch_first... | <|body_start_0|>
super(TFC_RNN, self).__init__()
self.skip_connection = skip_connection
self.tfc = TFC(in_channels, num_layers_tfc, gr, kt, kf, activation)
self.bn = nn.BatchNorm2d(gr)
hidden_units_rnn = max(f // bn_factor_rnn, min_bn_units_rnn)
self.rnn = nn.GRU(f, hidde... | [B, in_channels, T, F] => [B, gr, T, F] | TFC_RNN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TFC_RNN:
"""[B, in_channels, T, F] => [B, gr, T, F]"""
def __init__(self, in_channels, num_layers_tfc, gr, kt, kf, f, bn_factor_rnn, num_layers_rnn, bidirectional=True, min_bn_units_rnn=16, bias_rnn=True, bn_factor_tdf=16, bias_tdf=True, skip_connection=True, activation=nn.ReLU):
"""... | stack_v2_sparse_classes_36k_train_010153 | 6,895 | permissive | [
{
"docstring": "in_channels: number of input channels num_layers_tfc: number of densely connected conv layers gr: growth rate kt: kernel size of the temporal axis. kf: kernel size of the freq. axis f: num of frequency bins bn_factor_rnn: bottleneck factor of rnn num_layers_rnn: number of layers of rnn bidirecti... | 2 | stack_v2_sparse_classes_30k_val_000938 | Implement the Python class `TFC_RNN` described below.
Class description:
[B, in_channels, T, F] => [B, gr, T, F]
Method signatures and docstrings:
- def __init__(self, in_channels, num_layers_tfc, gr, kt, kf, f, bn_factor_rnn, num_layers_rnn, bidirectional=True, min_bn_units_rnn=16, bias_rnn=True, bn_factor_tdf=16, b... | Implement the Python class `TFC_RNN` described below.
Class description:
[B, in_channels, T, F] => [B, gr, T, F]
Method signatures and docstrings:
- def __init__(self, in_channels, num_layers_tfc, gr, kt, kf, f, bn_factor_rnn, num_layers_rnn, bidirectional=True, min_bn_units_rnn=16, bias_rnn=True, bn_factor_tdf=16, b... | 366b92b601f324a5937fb902d3cd123a0980c9b8 | <|skeleton|>
class TFC_RNN:
"""[B, in_channels, T, F] => [B, gr, T, F]"""
def __init__(self, in_channels, num_layers_tfc, gr, kt, kf, f, bn_factor_rnn, num_layers_rnn, bidirectional=True, min_bn_units_rnn=16, bias_rnn=True, bn_factor_tdf=16, bias_tdf=True, skip_connection=True, activation=nn.ReLU):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TFC_RNN:
"""[B, in_channels, T, F] => [B, gr, T, F]"""
def __init__(self, in_channels, num_layers_tfc, gr, kt, kf, f, bn_factor_rnn, num_layers_rnn, bidirectional=True, min_bn_units_rnn=16, bias_rnn=True, bn_factor_tdf=16, bias_tdf=True, skip_connection=True, activation=nn.ReLU):
"""in_channels: ... | the_stack_v2_python_sparse | src/lasaft/source_separation/sub_modules/building_blocks.py | loretoparisi/Conditioned-Source-Separation-LaSAFT | train | 3 |
24f201f8fefa926be75ce2c69d19785742966365 | [
"classname = self.__class__.__name__\nobj_dir = settings.CLASSNAME_TO_DIR[classname]\nobj_path = os.path.join(obj_dir, self.id)\npickle.dump(self, open(obj_path, 'wb'))",
"classname = cls.__name__\nobj_dir = settings.CLASSNAME_TO_DIR[classname]\nobj_list = []\nfor obj_name in os.listdir(obj_dir):\n obj_path = ... | <|body_start_0|>
classname = self.__class__.__name__
obj_dir = settings.CLASSNAME_TO_DIR[classname]
obj_path = os.path.join(obj_dir, self.id)
pickle.dump(self, open(obj_path, 'wb'))
<|end_body_0|>
<|body_start_1|>
classname = cls.__name__
obj_dir = settings.CLASSNAME_TO_... | 基础功能类 | Base | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Base:
"""基础功能类"""
def save(self):
"""存储对象功能"""
<|body_0|>
def get_all_obj(cls):
"""获取所有对象功能"""
<|body_1|>
def enroll(self, logger):
"""注册功能"""
<|body_2|>
def login(cls, logger):
"""登陆功能"""
<|body_3|>
<|end_skelet... | stack_v2_sparse_classes_36k_train_010154 | 2,893 | no_license | [
{
"docstring": "存储对象功能",
"name": "save",
"signature": "def save(self)"
},
{
"docstring": "获取所有对象功能",
"name": "get_all_obj",
"signature": "def get_all_obj(cls)"
},
{
"docstring": "注册功能",
"name": "enroll",
"signature": "def enroll(self, logger)"
},
{
"docstring": "登... | 4 | stack_v2_sparse_classes_30k_train_006199 | Implement the Python class `Base` described below.
Class description:
基础功能类
Method signatures and docstrings:
- def save(self): 存储对象功能
- def get_all_obj(cls): 获取所有对象功能
- def enroll(self, logger): 注册功能
- def login(cls, logger): 登陆功能 | Implement the Python class `Base` described below.
Class description:
基础功能类
Method signatures and docstrings:
- def save(self): 存储对象功能
- def get_all_obj(cls): 获取所有对象功能
- def enroll(self, logger): 注册功能
- def login(cls, logger): 登陆功能
<|skeleton|>
class Base:
"""基础功能类"""
def save(self):
"""存储对象功能"""
... | 4d497a6261de17cc2fc058cea50e127e885e5095 | <|skeleton|>
class Base:
"""基础功能类"""
def save(self):
"""存储对象功能"""
<|body_0|>
def get_all_obj(cls):
"""获取所有对象功能"""
<|body_1|>
def enroll(self, logger):
"""注册功能"""
<|body_2|>
def login(cls, logger):
"""登陆功能"""
<|body_3|>
<|end_skelet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Base:
"""基础功能类"""
def save(self):
"""存储对象功能"""
classname = self.__class__.__name__
obj_dir = settings.CLASSNAME_TO_DIR[classname]
obj_path = os.path.join(obj_dir, self.id)
pickle.dump(self, open(obj_path, 'wb'))
def get_all_obj(cls):
"""获取所有对象功能"""
... | the_stack_v2_python_sparse | day09/LikeFabric/core/base.py | phully/PythonHomeWork | train | 0 |
e7c9fee0370d61548a563c60e0c201d38f4431c7 | [
"super().__init__()\nself.decoder = Decoder(config)\nself.encoder = Encoder(config)\nhop_length = frame_length = int(config.sample_rate * config.frame_resolution)\nself.harmonic_oscillator = HarmonicOscillator(sr=config.sample_rate, frame_length=hop_length)\nself.filtered_noise = FilteredNoise(frame_length=hop_leng... | <|body_start_0|>
super().__init__()
self.decoder = Decoder(config)
self.encoder = Encoder(config)
hop_length = frame_length = int(config.sample_rate * config.frame_resolution)
self.harmonic_oscillator = HarmonicOscillator(sr=config.sample_rate, frame_length=hop_length)
se... | AutoEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutoEncoder:
def __init__(self, config):
"""encoder_config use_z=False, sample_rate=16000, z_units=16, n_fft=2048, hop_length=64, n_mels=128, n_mfcc=30, gru_units=512 decoder_config mlp_units=512, mlp_layers=3, use_z=False, z_units=16, n_harmonics=101, n_freq=65, gru_units=512, component... | stack_v2_sparse_classes_36k_train_010155 | 32,670 | no_license | [
{
"docstring": "encoder_config use_z=False, sample_rate=16000, z_units=16, n_fft=2048, hop_length=64, n_mels=128, n_mfcc=30, gru_units=512 decoder_config mlp_units=512, mlp_layers=3, use_z=False, z_units=16, n_harmonics=101, n_freq=65, gru_units=512, components_config sample_rate hop_length",
"name": "__ini... | 4 | null | Implement the Python class `AutoEncoder` described below.
Class description:
Implement the AutoEncoder class.
Method signatures and docstrings:
- def __init__(self, config): encoder_config use_z=False, sample_rate=16000, z_units=16, n_fft=2048, hop_length=64, n_mels=128, n_mfcc=30, gru_units=512 decoder_config mlp_un... | Implement the Python class `AutoEncoder` described below.
Class description:
Implement the AutoEncoder class.
Method signatures and docstrings:
- def __init__(self, config): encoder_config use_z=False, sample_rate=16000, z_units=16, n_fft=2048, hop_length=64, n_mels=128, n_mfcc=30, gru_units=512 decoder_config mlp_un... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class AutoEncoder:
def __init__(self, config):
"""encoder_config use_z=False, sample_rate=16000, z_units=16, n_fft=2048, hop_length=64, n_mels=128, n_mfcc=30, gru_units=512 decoder_config mlp_units=512, mlp_layers=3, use_z=False, z_units=16, n_harmonics=101, n_freq=65, gru_units=512, component... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AutoEncoder:
def __init__(self, config):
"""encoder_config use_z=False, sample_rate=16000, z_units=16, n_fft=2048, hop_length=64, n_mels=128, n_mfcc=30, gru_units=512 decoder_config mlp_units=512, mlp_layers=3, use_z=False, z_units=16, n_harmonics=101, n_freq=65, gru_units=512, components_config sampl... | the_stack_v2_python_sparse | generated/test_sweetcocoa_ddsp_pytorch.py | jansel/pytorch-jit-paritybench | train | 35 | |
6a9013415fcd27f786424a343215399b2549464c | [
"self.need_header_flag = False\nself.header = None\nself.data = input_data(img, no_header=True)\nself.num_bins = num_bins\nself.periodic = periodic\nif lags is None:\n self.lags = [1, 2, 4, 8, 16, 32, 64]\nelse:\n self.lags = lags\nself.tsallis_arrays = np.empty((len(self.lags), self.data.shape[0], self.data.... | <|body_start_0|>
self.need_header_flag = False
self.header = None
self.data = input_data(img, no_header=True)
self.num_bins = num_bins
self.periodic = periodic
if lags is None:
self.lags = [1, 2, 4, 8, 16, 32, 64]
else:
self.lags = lags
... | The Tsallis Distribution (see Tofflemire et al., 2011) Parameters ---------- img : %(dtypes)s 2D image. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. periodic : bool, optional Sets whether the boundaries are periodic. | Tsallis | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tsallis:
"""The Tsallis Distribution (see Tofflemire et al., 2011) Parameters ---------- img : %(dtypes)s 2D image. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. periodic : bool, optional Sets whether the boundaries are period... | stack_v2_sparse_classes_36k_train_010156 | 8,719 | permissive | [
{
"docstring": "Parameters ---------- periodic : bool, optional Use for simulations with periodic boundaries.",
"name": "__init__",
"signature": "def __init__(self, img, lags=None, num_bins=500, periodic=False)"
},
{
"docstring": "Calculate the Tsallis distribution at each lag. We standardize ea... | 4 | stack_v2_sparse_classes_30k_train_005033 | Implement the Python class `Tsallis` described below.
Class description:
The Tsallis Distribution (see Tofflemire et al., 2011) Parameters ---------- img : %(dtypes)s 2D image. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. periodic : bool, optional... | Implement the Python class `Tsallis` described below.
Class description:
The Tsallis Distribution (see Tofflemire et al., 2011) Parameters ---------- img : %(dtypes)s 2D image. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. periodic : bool, optional... | 6bdcae10dd27e8e81413e422fd9dd5ade3868a33 | <|skeleton|>
class Tsallis:
"""The Tsallis Distribution (see Tofflemire et al., 2011) Parameters ---------- img : %(dtypes)s 2D image. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. periodic : bool, optional Sets whether the boundaries are period... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tsallis:
"""The Tsallis Distribution (see Tofflemire et al., 2011) Parameters ---------- img : %(dtypes)s 2D image. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. periodic : bool, optional Sets whether the boundaries are periodic."""
d... | the_stack_v2_python_sparse | TurbuStat/turbustat/statistics/tsallis/tsallis.py | jrobbfed/carma | train | 0 |
ed01d783da097b71de112097f1bbaa9a5a37a6fe | [
"if not s:\n return ''\nif len(s) == 1:\n return s\n\ndef isPalindrome(l, r):\n while l < r:\n if s[l] != s[r]:\n return False\n l += 1\n r -= 1\n return True\nindicies = []\nmax_lenght = 0\nfor i in range(0, len(s) - 1):\n for j in range(i + 1, len(s)):\n if is... | <|body_start_0|>
if not s:
return ''
if len(s) == 1:
return s
def isPalindrome(l, r):
while l < r:
if s[l] != s[r]:
return False
l += 1
r -= 1
return True
indicies = []
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_2|>
<|en... | stack_v2_sparse_classes_36k_train_010157 | 4,948 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome2",
"signature": "def longestPalindrome2(self, s)"
},
{
"docstring": ":type s: str :rtype:... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: str
- def longestPalindrome2(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s): :type s: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: str
- def longestPalindrome2(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s): :type s: str :rtype: str
<... | b925bb22d1daa4a56c5a238a5758a926905559b4 | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_2|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
if not s:
return ''
if len(s) == 1:
return s
def isPalindrome(l, r):
while l < r:
if s[l] != s[r]:
return False
l +=... | the_stack_v2_python_sparse | String/5. Longest Palindromic Substring.py | beninghton/notGivenUpToG | train | 0 | |
75da30ac44e3af5b3c94b184ef2a07e1600a96f4 | [
"dct = {}\nfor i in A:\n if i not in dct.keys():\n dct[i] = 1\n else:\n dct[i] += 1\nfor k, v in dct.items():\n if v == 1:\n return k",
"for i in A:\n if A.count(i) == 1:\n return i",
"lst1 = []\nlst2 = []\nfor i in A:\n if i not in lst1:\n if i not in lst2:\n ... | <|body_start_0|>
dct = {}
for i in A:
if i not in dct.keys():
dct[i] = 1
else:
dct[i] += 1
for k, v in dct.items():
if v == 1:
return k
<|end_body_0|>
<|body_start_1|>
for i in A:
if A.count(... | @param: A: An integer array @return: An integer | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""@param: A: An integer array @return: An integer"""
def singleNumberII_1(self, A):
"""字典,lintcode居然超时了, 这台垃圾电脑秒出结果"""
<|body_0|>
def singleNumberII_2(self, A):
"""逗比答案, 还是超时"""
<|body_1|>
def singleNumberII_3(self, A):
"""不用怀疑还是超时... | stack_v2_sparse_classes_36k_train_010158 | 2,222 | no_license | [
{
"docstring": "字典,lintcode居然超时了, 这台垃圾电脑秒出结果",
"name": "singleNumberII_1",
"signature": "def singleNumberII_1(self, A)"
},
{
"docstring": "逗比答案, 还是超时",
"name": "singleNumberII_2",
"signature": "def singleNumberII_2(self, A)"
},
{
"docstring": "不用怀疑还是超时...",
"name": "singleNum... | 4 | stack_v2_sparse_classes_30k_train_005039 | Implement the Python class `Solution` described below.
Class description:
@param: A: An integer array @return: An integer
Method signatures and docstrings:
- def singleNumberII_1(self, A): 字典,lintcode居然超时了, 这台垃圾电脑秒出结果
- def singleNumberII_2(self, A): 逗比答案, 还是超时
- def singleNumberII_3(self, A): 不用怀疑还是超时...
- def singl... | Implement the Python class `Solution` described below.
Class description:
@param: A: An integer array @return: An integer
Method signatures and docstrings:
- def singleNumberII_1(self, A): 字典,lintcode居然超时了, 这台垃圾电脑秒出结果
- def singleNumberII_2(self, A): 逗比答案, 还是超时
- def singleNumberII_3(self, A): 不用怀疑还是超时...
- def singl... | 87592a39d67d8e734e693327d6b063be334b37e2 | <|skeleton|>
class Solution:
"""@param: A: An integer array @return: An integer"""
def singleNumberII_1(self, A):
"""字典,lintcode居然超时了, 这台垃圾电脑秒出结果"""
<|body_0|>
def singleNumberII_2(self, A):
"""逗比答案, 还是超时"""
<|body_1|>
def singleNumberII_3(self, A):
"""不用怀疑还是超时... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""@param: A: An integer array @return: An integer"""
def singleNumberII_1(self, A):
"""字典,lintcode居然超时了, 这台垃圾电脑秒出结果"""
dct = {}
for i in A:
if i not in dct.keys():
dct[i] = 1
else:
dct[i] += 1
for k, v in d... | the_stack_v2_python_sparse | LintCode/LintCode-83：落单的数 II.py | hoshizorahikari/PythonExercise | train | 0 |
bbe0e77c4d10909107a65a10439caff196654794 | [
"cart_obj = self.request.user.cart_set.order_by('-created_time')[0]\ncart_items = CartItem.objects.select_related('menu', 'menu__restaurant').prefetch_related('menu__tastes').filter(cart__id=cart_obj.id)\nif not cart_items.exists():\n return JsonResponse(data={'message': '주문표는 현재 비어있습니다.'})\nfirst_menu = cart_it... | <|body_start_0|>
cart_obj = self.request.user.cart_set.order_by('-created_time')[0]
cart_items = CartItem.objects.select_related('menu', 'menu__restaurant').prefetch_related('menu__tastes').filter(cart__id=cart_obj.id)
if not cart_items.exists():
return JsonResponse(data={'message': ... | CartListCreateAPIView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CartListCreateAPIView:
def get(self, request, *args, **kwargs):
"""주문표 내 메뉴의 리스트를 보여준다."""
<|body_0|>
def post(self, request, *args, **kwargs):
"""주문표을 생성한다."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cart_obj = self.request.user.cart_set.order... | stack_v2_sparse_classes_36k_train_010159 | 10,133 | no_license | [
{
"docstring": "주문표 내 메뉴의 리스트를 보여준다.",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "주문표을 생성한다.",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003432 | Implement the Python class `CartListCreateAPIView` described below.
Class description:
Implement the CartListCreateAPIView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 주문표 내 메뉴의 리스트를 보여준다.
- def post(self, request, *args, **kwargs): 주문표을 생성한다. | Implement the Python class `CartListCreateAPIView` described below.
Class description:
Implement the CartListCreateAPIView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 주문표 내 메뉴의 리스트를 보여준다.
- def post(self, request, *args, **kwargs): 주문표을 생성한다.
<|skeleton|>
class CartListCreateA... | 659d9757d1f369a6713aa5a66bab2aa5d6381b8e | <|skeleton|>
class CartListCreateAPIView:
def get(self, request, *args, **kwargs):
"""주문표 내 메뉴의 리스트를 보여준다."""
<|body_0|>
def post(self, request, *args, **kwargs):
"""주문표을 생성한다."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CartListCreateAPIView:
def get(self, request, *args, **kwargs):
"""주문표 내 메뉴의 리스트를 보여준다."""
cart_obj = self.request.user.cart_set.order_by('-created_time')[0]
cart_items = CartItem.objects.select_related('menu', 'menu__restaurant').prefetch_related('menu__tastes').filter(cart__id=cart_o... | the_stack_v2_python_sparse | project/src/cart/api/views.py | ssr03/MiniDelivery | train | 0 | |
e2f5c0369cbd798ab3e41e4207d461f0a64f3f79 | [
"dummy = ListNode(0)\ncurr = dummy.next = head\nh = l = ListNode(0)\nwhile curr and curr.next:\n if curr.val != curr.next.val:\n l.next = curr\n curr = curr.next\n l = l.next\n else:\n tmp = curr.val\n while curr and curr.val == tmp:\n curr = curr.next\nl.next = c... | <|body_start_0|>
dummy = ListNode(0)
curr = dummy.next = head
h = l = ListNode(0)
while curr and curr.next:
if curr.val != curr.next.val:
l.next = curr
curr = curr.next
l = l.next
else:
tmp = curr.val... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def deleteDuplicates1(self, head: ListNode) -> ListNode:
"""额外链表 :param head: :return:"""
<|body_0|>
def deleteDuplicates2(self, head: ListNode) -> ListNode:
"""in-place :param head: :return:"""
<|body_1|>
def deleteDuplicates3(self, head: List... | stack_v2_sparse_classes_36k_train_010160 | 2,518 | no_license | [
{
"docstring": "额外链表 :param head: :return:",
"name": "deleteDuplicates1",
"signature": "def deleteDuplicates1(self, head: ListNode) -> ListNode"
},
{
"docstring": "in-place :param head: :return:",
"name": "deleteDuplicates2",
"signature": "def deleteDuplicates2(self, head: ListNode) -> L... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteDuplicates1(self, head: ListNode) -> ListNode: 额外链表 :param head: :return:
- def deleteDuplicates2(self, head: ListNode) -> ListNode: in-place :param head: :return:
- de... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteDuplicates1(self, head: ListNode) -> ListNode: 额外链表 :param head: :return:
- def deleteDuplicates2(self, head: ListNode) -> ListNode: in-place :param head: :return:
- de... | 25f2795b6e7f9f68833f2fddc6cc4f4d977121a6 | <|skeleton|>
class Solution:
def deleteDuplicates1(self, head: ListNode) -> ListNode:
"""额外链表 :param head: :return:"""
<|body_0|>
def deleteDuplicates2(self, head: ListNode) -> ListNode:
"""in-place :param head: :return:"""
<|body_1|>
def deleteDuplicates3(self, head: List... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def deleteDuplicates1(self, head: ListNode) -> ListNode:
"""额外链表 :param head: :return:"""
dummy = ListNode(0)
curr = dummy.next = head
h = l = ListNode(0)
while curr and curr.next:
if curr.val != curr.next.val:
l.next = curr
... | the_stack_v2_python_sparse | 82.py | Darkxiete/leetcode_python | train | 0 | |
37e7b6c744bbb139f8eb8174d412166fdc09bd0e | [
"req = request.query_params.dict()\nif len(req) != 0:\n raise ArgumentError()\ndata = VERSION.objects.all()\nlength = data.count()\nif length > 0:\n return Response(data.values('version_number', 'update_date', 'announcement', 'download_address')[length - 1])\nreturn Response({'version_number': '', 'update_dat... | <|body_start_0|>
req = request.query_params.dict()
if len(req) != 0:
raise ArgumentError()
data = VERSION.objects.all()
length = data.count()
if length > 0:
return Response(data.values('version_number', 'update_date', 'announcement', 'download_address')[le... | Version | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Version:
def get(request):
"""前端调用得到最新版本信息 访问方法 http://127.0.0.1:8000/version/ 无参数 返回结果：版本号，更新日期，更新公告，下载地址"""
<|body_0|>
def post(request):
"""pm调用在数据库中插入最新版本信息 访问方法 http://127.0.0.1:8000/version/ 参数:版本号，更新日期，更新公告，下载地址 返回结果：插入状态"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k_train_010161 | 1,785 | no_license | [
{
"docstring": "前端调用得到最新版本信息 访问方法 http://127.0.0.1:8000/version/ 无参数 返回结果：版本号，更新日期，更新公告，下载地址",
"name": "get",
"signature": "def get(request)"
},
{
"docstring": "pm调用在数据库中插入最新版本信息 访问方法 http://127.0.0.1:8000/version/ 参数:版本号，更新日期，更新公告，下载地址 返回结果：插入状态",
"name": "post",
"signature": "def post(... | 2 | stack_v2_sparse_classes_30k_train_017946 | Implement the Python class `Version` described below.
Class description:
Implement the Version class.
Method signatures and docstrings:
- def get(request): 前端调用得到最新版本信息 访问方法 http://127.0.0.1:8000/version/ 无参数 返回结果：版本号，更新日期，更新公告，下载地址
- def post(request): pm调用在数据库中插入最新版本信息 访问方法 http://127.0.0.1:8000/version/ 参数:版本号，更新日... | Implement the Python class `Version` described below.
Class description:
Implement the Version class.
Method signatures and docstrings:
- def get(request): 前端调用得到最新版本信息 访问方法 http://127.0.0.1:8000/version/ 无参数 返回结果：版本号，更新日期，更新公告，下载地址
- def post(request): pm调用在数据库中插入最新版本信息 访问方法 http://127.0.0.1:8000/version/ 参数:版本号，更新日... | 7dfa07283d4130b931a92c80bf4f499f97a33b62 | <|skeleton|>
class Version:
def get(request):
"""前端调用得到最新版本信息 访问方法 http://127.0.0.1:8000/version/ 无参数 返回结果：版本号，更新日期，更新公告，下载地址"""
<|body_0|>
def post(request):
"""pm调用在数据库中插入最新版本信息 访问方法 http://127.0.0.1:8000/version/ 参数:版本号，更新日期，更新公告，下载地址 返回结果：插入状态"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Version:
def get(request):
"""前端调用得到最新版本信息 访问方法 http://127.0.0.1:8000/version/ 无参数 返回结果：版本号，更新日期，更新公告，下载地址"""
req = request.query_params.dict()
if len(req) != 0:
raise ArgumentError()
data = VERSION.objects.all()
length = data.count()
if length > 0:
... | the_stack_v2_python_sparse | version_information/views.py | SE2020-TopUnderstanding/BUAA-Campus-Tools-Backend | train | 7 | |
b8c07cbce56f5499ce9970ac0d9450bc4b56240d | [
"try:\n return project_context.database['project']\nexcept KeyError as e:\n raise ValueError('Cannot get project') from e",
"from renku import __version__\ndatabase = project_context.database\ntry:\n if database['project']:\n database.remove_root_object('project')\nexcept KeyError:\n pass\nproj... | <|body_start_0|>
try:
return project_context.database['project']
except KeyError as e:
raise ValueError('Cannot get project') from e
<|end_body_0|>
<|body_start_1|>
from renku import __version__
database = project_context.database
try:
if data... | Gateway for project database operations. | ProjectGateway | [
"Apache-2.0",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProjectGateway:
"""Gateway for project database operations."""
def get_project(self) -> Project:
"""Get project metadata."""
<|body_0|>
def update_project(self, project: Project):
"""Update project metadata."""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_010162 | 1,711 | permissive | [
{
"docstring": "Get project metadata.",
"name": "get_project",
"signature": "def get_project(self) -> Project"
},
{
"docstring": "Update project metadata.",
"name": "update_project",
"signature": "def update_project(self, project: Project)"
}
] | 2 | null | Implement the Python class `ProjectGateway` described below.
Class description:
Gateway for project database operations.
Method signatures and docstrings:
- def get_project(self) -> Project: Get project metadata.
- def update_project(self, project: Project): Update project metadata. | Implement the Python class `ProjectGateway` described below.
Class description:
Gateway for project database operations.
Method signatures and docstrings:
- def get_project(self) -> Project: Get project metadata.
- def update_project(self, project: Project): Update project metadata.
<|skeleton|>
class ProjectGateway... | e0ff587f507d049eeeb873e8488ba8bb10ac1a15 | <|skeleton|>
class ProjectGateway:
"""Gateway for project database operations."""
def get_project(self) -> Project:
"""Get project metadata."""
<|body_0|>
def update_project(self, project: Project):
"""Update project metadata."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProjectGateway:
"""Gateway for project database operations."""
def get_project(self) -> Project:
"""Get project metadata."""
try:
return project_context.database['project']
except KeyError as e:
raise ValueError('Cannot get project') from e
def update_... | the_stack_v2_python_sparse | renku/infrastructure/gateway/project_gateway.py | SwissDataScienceCenter/renku-python | train | 30 |
b3a8b0f807275f77fd678f130c0c9d4cb12bbfc0 | [
"super().__init__(inter_class=False)\nself.num_frames = num_frames\nself.num_ctx_frames = num_ctx_frames",
"def _remove_ctx_frames(frame):\n s, h, w, c = frame.shape\n seq_len = s // self.num_ctx_frames\n frame = frame.reshape(seq_len, self.num_ctx_frames, h, w, c)\n return frame[:, -1]\nframes = [o.f... | <|body_start_0|>
super().__init__(inter_class=False)
self.num_frames = num_frames
self.num_ctx_frames = num_ctx_frames
<|end_body_0|>
<|body_start_1|>
def _remove_ctx_frames(frame):
s, h, w, c = frame.shape
seq_len = s // self.num_ctx_frames
frame = f... | Frame reconstruction visualizer. | ReconstructionVisualizer | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReconstructionVisualizer:
"""Frame reconstruction visualizer."""
def __init__(self, num_frames, num_ctx_frames):
"""Constructor. Args: num_frames: The number of reconstructed frames in a sequence to display. num_ctx_frames: The number of context frames stacked together for each indiv... | stack_v2_sparse_classes_36k_train_010163 | 2,727 | permissive | [
{
"docstring": "Constructor. Args: num_frames: The number of reconstructed frames in a sequence to display. num_ctx_frames: The number of context frames stacked together for each individual video frame.",
"name": "__init__",
"signature": "def __init__(self, num_frames, num_ctx_frames)"
},
{
"doc... | 2 | null | Implement the Python class `ReconstructionVisualizer` described below.
Class description:
Frame reconstruction visualizer.
Method signatures and docstrings:
- def __init__(self, num_frames, num_ctx_frames): Constructor. Args: num_frames: The number of reconstructed frames in a sequence to display. num_ctx_frames: The... | Implement the Python class `ReconstructionVisualizer` described below.
Class description:
Frame reconstruction visualizer.
Method signatures and docstrings:
- def __init__(self, num_frames, num_ctx_frames): Constructor. Args: num_frames: The number of reconstructed frames in a sequence to display. num_ctx_frames: The... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class ReconstructionVisualizer:
"""Frame reconstruction visualizer."""
def __init__(self, num_frames, num_ctx_frames):
"""Constructor. Args: num_frames: The number of reconstructed frames in a sequence to display. num_ctx_frames: The number of context frames stacked together for each indiv... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReconstructionVisualizer:
"""Frame reconstruction visualizer."""
def __init__(self, num_frames, num_ctx_frames):
"""Constructor. Args: num_frames: The number of reconstructed frames in a sequence to display. num_ctx_frames: The number of context frames stacked together for each individual video f... | the_stack_v2_python_sparse | xirl/xirl/evaluators/reconstruction_visualizer.py | Jimmy-INL/google-research | train | 1 |
2d2e5ce95f9d0a33a80f091c0b58cfe18653b78b | [
"self.p = {}\nfor word in words:\n for i in xrange(len(word) + 1):\n if word[:i] in self.p:\n self.p[word[:i]].add(word)\n else:\n self.p[word[:i]] = set([word])\nself.s = {}\nfor word in words:\n for i in xrange(len(word) + 1):\n if word[i:] in self.s:\n ... | <|body_start_0|>
self.p = {}
for word in words:
for i in xrange(len(word) + 1):
if word[:i] in self.p:
self.p[word[:i]].add(word)
else:
self.p[word[:i]] = set([word])
self.s = {}
for word in words:
... | WordFilter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordFilter:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def f(self, prefix, suffix):
""":type prefix: str :type suffix: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.p = {}
for word in words:
... | stack_v2_sparse_classes_36k_train_010164 | 1,920 | permissive | [
{
"docstring": ":type words: List[str]",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": ":type prefix: str :type suffix: str :rtype: int",
"name": "f",
"signature": "def f(self, prefix, suffix)"
}
] | 2 | null | Implement the Python class `WordFilter` described below.
Class description:
Implement the WordFilter class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def f(self, prefix, suffix): :type prefix: str :type suffix: str :rtype: int | Implement the Python class `WordFilter` described below.
Class description:
Implement the WordFilter class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def f(self, prefix, suffix): :type prefix: str :type suffix: str :rtype: int
<|skeleton|>
class WordFilter:
def __in... | 6facec2a54d1d9f133f420c9bce1d1043f57ebc6 | <|skeleton|>
class WordFilter:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def f(self, prefix, suffix):
""":type prefix: str :type suffix: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordFilter:
def __init__(self, words):
""":type words: List[str]"""
self.p = {}
for word in words:
for i in xrange(len(word) + 1):
if word[:i] in self.p:
self.p[word[:i]].add(word)
else:
self.p[word[:i]... | the_stack_v2_python_sparse | Prefix and Suffix Search.py | sugia/leetcode | train | 1 | |
2dc46641f941ec4ad473e343b8d6e2e7978a0f74 | [
"super().__init__()\nself._alpha = torch.tensor(alpha)\nself._noise_val = self._alpha / n_classes\nself._n_classes = n_classes\nself.gamma = gamma\nself.class_weight_tensor = torch.tensor(weights).view(-1).cuda() if weights else None",
"if teacher.ndim == 1:\n teacher = torch.eye(self._n_classes)[teacher]\ntea... | <|body_start_0|>
super().__init__()
self._alpha = torch.tensor(alpha)
self._noise_val = self._alpha / n_classes
self._n_classes = n_classes
self.gamma = gamma
self.class_weight_tensor = torch.tensor(weights).view(-1).cuda() if weights else None
<|end_body_0|>
<|body_star... | ClassificationFocalLossWithLabelSmoothing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassificationFocalLossWithLabelSmoothing:
def __init__(self, n_classes: int, alpha=[0.2, 0.2, 0.2, 0.2, 0.2], gamma=2, weights: List[float]=None):
""":param alpha: parameter of Label Smoothing. :param n_classes: :param gamma: 簡単なサンプルの重み. 大きいほど簡単なサンプルを重視しない. :param weights: weights by cl... | stack_v2_sparse_classes_36k_train_010165 | 9,953 | no_license | [
{
"docstring": ":param alpha: parameter of Label Smoothing. :param n_classes: :param gamma: 簡単なサンプルの重み. 大きいほど簡単なサンプルを重視しない. :param weights: weights by classes, :param logits:",
"name": "__init__",
"signature": "def __init__(self, n_classes: int, alpha=[0.2, 0.2, 0.2, 0.2, 0.2], gamma=2, weights: List[fl... | 2 | stack_v2_sparse_classes_30k_train_014893 | Implement the Python class `ClassificationFocalLossWithLabelSmoothing` described below.
Class description:
Implement the ClassificationFocalLossWithLabelSmoothing class.
Method signatures and docstrings:
- def __init__(self, n_classes: int, alpha=[0.2, 0.2, 0.2, 0.2, 0.2], gamma=2, weights: List[float]=None): :param ... | Implement the Python class `ClassificationFocalLossWithLabelSmoothing` described below.
Class description:
Implement the ClassificationFocalLossWithLabelSmoothing class.
Method signatures and docstrings:
- def __init__(self, n_classes: int, alpha=[0.2, 0.2, 0.2, 0.2, 0.2], gamma=2, weights: List[float]=None): :param ... | e64deb19918c7a7462d1c2b79cb9bc41c57318d3 | <|skeleton|>
class ClassificationFocalLossWithLabelSmoothing:
def __init__(self, n_classes: int, alpha=[0.2, 0.2, 0.2, 0.2, 0.2], gamma=2, weights: List[float]=None):
""":param alpha: parameter of Label Smoothing. :param n_classes: :param gamma: 簡単なサンプルの重み. 大きいほど簡単なサンプルを重視しない. :param weights: weights by cl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassificationFocalLossWithLabelSmoothing:
def __init__(self, n_classes: int, alpha=[0.2, 0.2, 0.2, 0.2, 0.2], gamma=2, weights: List[float]=None):
""":param alpha: parameter of Label Smoothing. :param n_classes: :param gamma: 簡単なサンプルの重み. 大きいほど簡単なサンプルを重視しない. :param weights: weights by classes, :param ... | the_stack_v2_python_sparse | losses/loss.py | monofo/kaggle_cassava_leaf_disease_classification | train | 0 | |
7990368a530c83377c1c11cb43a1fa37ff7ad768 | [
"self.debug = False\nself.name = 'featurizer'\nself.epsilon = 1e-08\nself.max_num_inputs = max_num_inputs\nif max_num_features is None:\n self.max_num_bundles = 3 * self.max_num_inputs\n self.max_num_features = self.max_num_inputs + self.max_num_bundles\nelse:\n self.max_num_features = max_num_features\n ... | <|body_start_0|>
self.debug = False
self.name = 'featurizer'
self.epsilon = 1e-08
self.max_num_inputs = max_num_inputs
if max_num_features is None:
self.max_num_bundles = 3 * self.max_num_inputs
self.max_num_features = self.max_num_inputs + self.max_num_bu... | Convert inputs to bundles and learn new bundles. Inputs are transformed into bundles, sets of inputs that tend to co-occur. | Featurizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Featurizer:
"""Convert inputs to bundles and learn new bundles. Inputs are transformed into bundles, sets of inputs that tend to co-occur."""
def __init__(self, max_num_inputs, max_num_features=None):
"""Configure the featurizer. Parameters --------- max_num_inputs : int See Featuriz... | stack_v2_sparse_classes_36k_train_010166 | 8,848 | permissive | [
{
"docstring": "Configure the featurizer. Parameters --------- max_num_inputs : int See Featurizer.max_num_inputs. max_num_features : int See Featurizer.max_num_features.",
"name": "__init__",
"signature": "def __init__(self, max_num_inputs, max_num_features=None)"
},
{
"docstring": "Learn bundl... | 5 | stack_v2_sparse_classes_30k_train_007919 | Implement the Python class `Featurizer` described below.
Class description:
Convert inputs to bundles and learn new bundles. Inputs are transformed into bundles, sets of inputs that tend to co-occur.
Method signatures and docstrings:
- def __init__(self, max_num_inputs, max_num_features=None): Configure the featurize... | Implement the Python class `Featurizer` described below.
Class description:
Convert inputs to bundles and learn new bundles. Inputs are transformed into bundles, sets of inputs that tend to co-occur.
Method signatures and docstrings:
- def __init__(self, max_num_inputs, max_num_features=None): Configure the featurize... | 85ee5f530717518b1b43ba9a310e4f0d70b290a4 | <|skeleton|>
class Featurizer:
"""Convert inputs to bundles and learn new bundles. Inputs are transformed into bundles, sets of inputs that tend to co-occur."""
def __init__(self, max_num_inputs, max_num_features=None):
"""Configure the featurizer. Parameters --------- max_num_inputs : int See Featuriz... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Featurizer:
"""Convert inputs to bundles and learn new bundles. Inputs are transformed into bundles, sets of inputs that tend to co-occur."""
def __init__(self, max_num_inputs, max_num_features=None):
"""Configure the featurizer. Parameters --------- max_num_inputs : int See Featurizer.max_num_in... | the_stack_v2_python_sparse | becca/featurizer.py | microgold/Becca35 | train | 1 |
e5f8766a3122e2169e4a176077362189c06bd107 | [
"self.data = []\nself.size = size\nself.front = 0\nself.total = 0",
"self.total += val\nif len(self.data) == self.size:\n self.total -= self.data[self.front]\n self.data[self.front] = val\n self.front = (self.front + 1) % self.size\nelse:\n self.data.append(val)\nreturn self.total / len(self.data)"
] | <|body_start_0|>
self.data = []
self.size = size
self.front = 0
self.total = 0
<|end_body_0|>
<|body_start_1|>
self.total += val
if len(self.data) == self.size:
self.total -= self.data[self.front]
self.data[self.front] = val
self.front... | MovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.data = []
self.size = si... | stack_v2_sparse_classes_36k_train_010167 | 1,375 | no_license | [
{
"docstring": "Initialize your data structure here. :type size: int",
"name": "__init__",
"signature": "def __init__(self, size)"
},
{
"docstring": ":type val: int :rtype: float",
"name": "next",
"signature": "def next(self, val)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020898 | Implement the Python class `MovingAverage` described below.
Class description:
Implement the MovingAverage class.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float | Implement the Python class `MovingAverage` described below.
Class description:
Implement the MovingAverage class.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float
<|skeleton|>
class MovingAverage:
... | 9d0ff0f8705451947a6605ab5ef92bb3e27a7147 | <|skeleton|>
class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
self.data = []
self.size = size
self.front = 0
self.total = 0
def next(self, val):
""":type val: int :rtype: float"""
self.total += val
if le... | the_stack_v2_python_sparse | premium/amazon/design/moving_average_from_data_stream.py | rayt579/leetcode | train | 0 | |
e79c70e3491210e29a50ac3cb8fef7c3023b80db | [
"sortable_fields = self.get_sortable_fields(request)\nordering = request.GET.get('o')\ntry:\n column = abs(int(ordering))\n current_sfield = sortable_fields[column]\nexcept (IndexError, TypeError, ValueError):\n return self.ordering\ncurrent_sfield.update(ordering)\nsort_by = current_sfield.sort_by()\nsfie... | <|body_start_0|>
sortable_fields = self.get_sortable_fields(request)
ordering = request.GET.get('o')
try:
column = abs(int(ordering))
current_sfield = sortable_fields[column]
except (IndexError, TypeError, ValueError):
return self.ordering
curr... | Inline that provides sortable functionallity Supports field override for related object attribute sort e.g. sortable_fields = {'node': 'node__name'} should replace sort by node id with sort by node name. Example usage: class SliverInline(PermissionTabularInline, SortableTabularInline): sortable_fields = {'node_link':'n... | SortableTabularInline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SortableTabularInline:
"""Inline that provides sortable functionallity Supports field override for related object attribute sort e.g. sortable_fields = {'node': 'node__name'} should replace sort by node id with sort by node name. Example usage: class SliverInline(PermissionTabularInline, Sortable... | stack_v2_sparse_classes_36k_train_010168 | 7,765 | no_license | [
{
"docstring": "Define dynamic ordering based on request parameters",
"name": "get_ordering",
"signature": "def get_ordering(self, request)"
},
{
"docstring": "Hack for get the inline fields SHOWED at admin page NOTE: excludes primary key and hidden foreign key Returns SortableFields list",
... | 2 | stack_v2_sparse_classes_30k_train_017976 | Implement the Python class `SortableTabularInline` described below.
Class description:
Inline that provides sortable functionallity Supports field override for related object attribute sort e.g. sortable_fields = {'node': 'node__name'} should replace sort by node id with sort by node name. Example usage: class SliverI... | Implement the Python class `SortableTabularInline` described below.
Class description:
Inline that provides sortable functionallity Supports field override for related object attribute sort e.g. sortable_fields = {'node': 'node__name'} should replace sort by node id with sort by node name. Example usage: class SliverI... | dd798dc9bd3321b17007ff131e7b1288a2cd3c36 | <|skeleton|>
class SortableTabularInline:
"""Inline that provides sortable functionallity Supports field override for related object attribute sort e.g. sortable_fields = {'node': 'node__name'} should replace sort by node id with sort by node name. Example usage: class SliverInline(PermissionTabularInline, Sortable... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SortableTabularInline:
"""Inline that provides sortable functionallity Supports field override for related object attribute sort e.g. sortable_fields = {'node': 'node__name'} should replace sort by node id with sort by node name. Example usage: class SliverInline(PermissionTabularInline, SortableTabularInline... | the_stack_v2_python_sparse | controller/admin/options.py | m00dy/vct-controller | train | 2 |
44a0a60fe395f4a1d0f6ce198eb4dfa8ca45ba7a | [
"if not file_name:\n file_name = config.getStringValue('default_statistic_file')\ntoWrite = statistic\nif append:\n toWrite = StatisticWriter.getAppendedStatistic(statistic=statistic, file_name=file_name)\nCsvWriter.writeListStatic(file_name, list(toWrite.getData().items()), lambda item: [item[0], CsvWriter.s... | <|body_start_0|>
if not file_name:
file_name = config.getStringValue('default_statistic_file')
toWrite = statistic
if append:
toWrite = StatisticWriter.getAppendedStatistic(statistic=statistic, file_name=file_name)
CsvWriter.writeListStatic(file_name, list(toWrite... | Class implementing a static method to write a statistic. Call writeStatistic. | StatisticWriter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StatisticWriter:
"""Class implementing a static method to write a statistic. Call writeStatistic."""
def write(statistic: Statistic=Statistic.getDefaultStatistic(), file_name: str='', config: Config=Config.getDefaultConfig(), append: bool=True) -> None:
"""Write the given statistic (... | stack_v2_sparse_classes_36k_train_010169 | 4,209 | permissive | [
{
"docstring": "Write the given statistic (or the default statistic, when none is given). :param statistic: the statistic to write. Will default to the static default statistic :param file_name: the file to write to. When none is given, this will be read from the config :param config: used to read the file name... | 2 | null | Implement the Python class `StatisticWriter` described below.
Class description:
Class implementing a static method to write a statistic. Call writeStatistic.
Method signatures and docstrings:
- def write(statistic: Statistic=Statistic.getDefaultStatistic(), file_name: str='', config: Config=Config.getDefaultConfig()... | Implement the Python class `StatisticWriter` described below.
Class description:
Class implementing a static method to write a statistic. Call writeStatistic.
Method signatures and docstrings:
- def write(statistic: Statistic=Statistic.getDefaultStatistic(), file_name: str='', config: Config=Config.getDefaultConfig()... | 27eba8b6038946ce162e9f7bbc0bd23045029d51 | <|skeleton|>
class StatisticWriter:
"""Class implementing a static method to write a statistic. Call writeStatistic."""
def write(statistic: Statistic=Statistic.getDefaultStatistic(), file_name: str='', config: Config=Config.getDefaultConfig(), append: bool=True) -> None:
"""Write the given statistic (... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StatisticWriter:
"""Class implementing a static method to write a statistic. Call writeStatistic."""
def write(statistic: Statistic=Statistic.getDefaultStatistic(), file_name: str='', config: Config=Config.getDefaultConfig(), append: bool=True) -> None:
"""Write the given statistic (or the defaul... | the_stack_v2_python_sparse | src/core/python/core/io/statistic.py | kaat0/OpenLinTim | train | 0 |
b24b1b037c75d47e73a927b2b4d624f695055678 | [
"super(OrderMonitor, self).init_ui()\nself.setToolTip('双击单元格撤单')\nself.itemDoubleClicked.connect(self.cancel_order)",
"order = cell.get_data()\nreq = order.create_cancel_request()\nself.main_engine.cancel_order(req, order.gateway_name)"
] | <|body_start_0|>
super(OrderMonitor, self).init_ui()
self.setToolTip('双击单元格撤单')
self.itemDoubleClicked.connect(self.cancel_order)
<|end_body_0|>
<|body_start_1|>
order = cell.get_data()
req = order.create_cancel_request()
self.main_engine.cancel_order(req, order.gateway_... | Monitor for order data. | OrderMonitor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrderMonitor:
"""Monitor for order data."""
def init_ui(self):
"""Connect signal."""
<|body_0|>
def cancel_order(self, cell: BaseCell) -> None:
"""Cancel order if cell double clicked."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(OrderMo... | stack_v2_sparse_classes_36k_train_010170 | 41,585 | permissive | [
{
"docstring": "Connect signal.",
"name": "init_ui",
"signature": "def init_ui(self)"
},
{
"docstring": "Cancel order if cell double clicked.",
"name": "cancel_order",
"signature": "def cancel_order(self, cell: BaseCell) -> None"
}
] | 2 | null | Implement the Python class `OrderMonitor` described below.
Class description:
Monitor for order data.
Method signatures and docstrings:
- def init_ui(self): Connect signal.
- def cancel_order(self, cell: BaseCell) -> None: Cancel order if cell double clicked. | Implement the Python class `OrderMonitor` described below.
Class description:
Monitor for order data.
Method signatures and docstrings:
- def init_ui(self): Connect signal.
- def cancel_order(self, cell: BaseCell) -> None: Cancel order if cell double clicked.
<|skeleton|>
class OrderMonitor:
"""Monitor for order... | 7f4fd3cd202712b083ed7dc2f346ba4bb1bda6d7 | <|skeleton|>
class OrderMonitor:
"""Monitor for order data."""
def init_ui(self):
"""Connect signal."""
<|body_0|>
def cancel_order(self, cell: BaseCell) -> None:
"""Cancel order if cell double clicked."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OrderMonitor:
"""Monitor for order data."""
def init_ui(self):
"""Connect signal."""
super(OrderMonitor, self).init_ui()
self.setToolTip('双击单元格撤单')
self.itemDoubleClicked.connect(self.cancel_order)
def cancel_order(self, cell: BaseCell) -> None:
"""Cancel orde... | the_stack_v2_python_sparse | vnpy/trader/ui/widget.py | msincenselee/vnpy | train | 359 |
61d4495028e6fdadadbd108d67e4777505efd28f | [
"FAKE_DATA = False\nrule = None\nif FAKE_DATA:\n rule = {'answer': [{'content': '欢迎光临', 'type': 'text'}, {'content': '1', 'type': 'text'}, {'content': '2', 'type': 'news', 'newses': [{'id': 1, 'title': '标题一'}, {'id': 2, 'title': '标题二'}]}], 'active_type': 2, 'active_days': {'Mon': True, 'Tue': False, 'Wed': True,... | <|body_start_0|>
FAKE_DATA = False
rule = None
if FAKE_DATA:
rule = {'answer': [{'content': '欢迎光临', 'type': 'text'}, {'content': '1', 'type': 'text'}, {'content': '2', 'type': 'news', 'newses': [{'id': 1, 'title': '标题一'}, {'id': 2, 'title': '标题二'}]}], 'active_type': 2, 'active_days':... | UnmatchRules | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnmatchRules:
def get(request):
"""未匹配自动回复的消息 active_type等于2表示分时段开启，等于1表示始终开启，等于0表示禁用"""
<|body_0|>
def api_put(request):
"""创建未匹配自动回复消息"""
<|body_1|>
def api_post(request):
"""更新关键词自动回复消息"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_010171 | 5,825 | no_license | [
{
"docstring": "未匹配自动回复的消息 active_type等于2表示分时段开启，等于1表示始终开启，等于0表示禁用",
"name": "get",
"signature": "def get(request)"
},
{
"docstring": "创建未匹配自动回复消息",
"name": "api_put",
"signature": "def api_put(request)"
},
{
"docstring": "更新关键词自动回复消息",
"name": "api_post",
"signature": "d... | 3 | null | Implement the Python class `UnmatchRules` described below.
Class description:
Implement the UnmatchRules class.
Method signatures and docstrings:
- def get(request): 未匹配自动回复的消息 active_type等于2表示分时段开启，等于1表示始终开启，等于0表示禁用
- def api_put(request): 创建未匹配自动回复消息
- def api_post(request): 更新关键词自动回复消息 | Implement the Python class `UnmatchRules` described below.
Class description:
Implement the UnmatchRules class.
Method signatures and docstrings:
- def get(request): 未匹配自动回复的消息 active_type等于2表示分时段开启，等于1表示始终开启，等于0表示禁用
- def api_put(request): 创建未匹配自动回复消息
- def api_post(request): 更新关键词自动回复消息
<|skeleton|>
class UnmatchR... | 8b2f7befe92841bcc35e0e60cac5958ef3f3af54 | <|skeleton|>
class UnmatchRules:
def get(request):
"""未匹配自动回复的消息 active_type等于2表示分时段开启，等于1表示始终开启，等于0表示禁用"""
<|body_0|>
def api_put(request):
"""创建未匹配自动回复消息"""
<|body_1|>
def api_post(request):
"""更新关键词自动回复消息"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnmatchRules:
def get(request):
"""未匹配自动回复的消息 active_type等于2表示分时段开启，等于1表示始终开启，等于0表示禁用"""
FAKE_DATA = False
rule = None
if FAKE_DATA:
rule = {'answer': [{'content': '欢迎光临', 'type': 'text'}, {'content': '1', 'type': 'text'}, {'content': '2', 'type': 'news', 'newses': ... | the_stack_v2_python_sparse | weapp/weixin2/message/unmatch_rules.py | chengdg/weizoom | train | 1 | |
0c8215b6e6d8d5bc52a543074e536e5e78292187 | [
"user = get_user_model()\nemail = self.cleaned_data['inputemail'].lower()\ntry:\n user.objects.get(email=email)\n raise ValidationError('Email already exist')\nexcept ObjectDoesNotExist:\n return email",
"user = get_user_model()\nusername = self.cleaned_data['inputUsername'].lower()\ntry:\n user.objec... | <|body_start_0|>
user = get_user_model()
email = self.cleaned_data['inputemail'].lower()
try:
user.objects.get(email=email)
raise ValidationError('Email already exist')
except ObjectDoesNotExist:
return email
<|end_body_0|>
<|body_start_1|>
us... | class dor custom user creation form Args: forms Form: super class Form Raises: ValidationError: "Email already exist" ValidationError: "Username already exist" ValidationError: "Password doesn't match" Returns: form: form | CustomUserCreationForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomUserCreationForm:
"""class dor custom user creation form Args: forms Form: super class Form Raises: ValidationError: "Email already exist" ValidationError: "Username already exist" ValidationError: "Password doesn't match" Returns: form: form"""
def clean_email(self):
"""This m... | stack_v2_sparse_classes_36k_train_010172 | 3,995 | no_license | [
{
"docstring": "This method clean email in lower case and test if it exist Raises: ValidationError: \"Email already exist\" Returns: str: email of form in lower case",
"name": "clean_email",
"signature": "def clean_email(self)"
},
{
"docstring": "This method clean pseudo in lower case and test i... | 4 | stack_v2_sparse_classes_30k_train_012512 | Implement the Python class `CustomUserCreationForm` described below.
Class description:
class dor custom user creation form Args: forms Form: super class Form Raises: ValidationError: "Email already exist" ValidationError: "Username already exist" ValidationError: "Password doesn't match" Returns: form: form
Method s... | Implement the Python class `CustomUserCreationForm` described below.
Class description:
class dor custom user creation form Args: forms Form: super class Form Raises: ValidationError: "Email already exist" ValidationError: "Username already exist" ValidationError: "Password doesn't match" Returns: form: form
Method s... | 2f183c3a87a3e34e3edba207b65d86066d06b603 | <|skeleton|>
class CustomUserCreationForm:
"""class dor custom user creation form Args: forms Form: super class Form Raises: ValidationError: "Email already exist" ValidationError: "Username already exist" ValidationError: "Password doesn't match" Returns: form: form"""
def clean_email(self):
"""This m... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomUserCreationForm:
"""class dor custom user creation form Args: forms Form: super class Form Raises: ValidationError: "Email already exist" ValidationError: "Username already exist" ValidationError: "Password doesn't match" Returns: form: form"""
def clean_email(self):
"""This method clean e... | the_stack_v2_python_sparse | free_chess_gui/auth/forms.py | Anthony10700/P13 | train | 0 |
976ef4a4e02f900221e023f10dda42e190459cf7 | [
"BaseNet.__init__(self, name=name)\nself.global_net = INetAffine(decay=decay, affine_w_initializer=affine_w_initializer, affine_b_initializer=affine_b_initializer, acti_func=acti_func, name='inet-global')\nself.local_net = INetDense(decay=decay, disp_w_initializer=disp_w_initializer, disp_b_initializer=disp_b_initi... | <|body_start_0|>
BaseNet.__init__(self, name=name)
self.global_net = INetAffine(decay=decay, affine_w_initializer=affine_w_initializer, affine_b_initializer=affine_b_initializer, acti_func=acti_func, name='inet-global')
self.local_net = INetDense(decay=decay, disp_w_initializer=disp_w_initialize... | ### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration, Medi... | INetHybridPreWarp | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class INetHybridPreWarp:
"""### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Net... | stack_v2_sparse_classes_36k_train_010173 | 7,784 | permissive | [
{
"docstring": ":param decay: float, regularisation decay :param affine_w_initializer: weight initialisation for affine registration network :param affine_b_initializer: bias initialisation for affine registration network :param disp_w_initializer: weight initialisation for dense registration network :param dis... | 2 | stack_v2_sparse_classes_30k_train_000598 | Implement the Python class `INetHybridPreWarp` described below.
Class description:
### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Wea... | Implement the Python class `INetHybridPreWarp` described below.
Class description:
### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Wea... | 67db048685705e36622bc2851b4c7794e56065ad | <|skeleton|>
class INetHybridPreWarp:
"""### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Net... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class INetHybridPreWarp:
"""### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Networks for Mul... | the_stack_v2_python_sparse | niftynet/network/interventional_hybrid_net.py | BRAINSia/NiftyNet | train | 0 |
2fc34223ff7e19ba468a622f26857279da92dbc6 | [
"super().__init__()\nself.n = n\nself.D = D\nself.W = W\nself.in_channels_xyz = in_channels_xyz\nself.in_channels_dir = in_channels_dir\nself.skips = skips\nif topk > 0:\n StackedFcLayers = StackedFcSlow\nelse:\n StackedFcLayers = StackedFcDense\nfor i in range(D):\n if i == 0:\n layer = StackedFcLa... | <|body_start_0|>
super().__init__()
self.n = n
self.D = D
self.W = W
self.in_channels_xyz = in_channels_xyz
self.in_channels_dir = in_channels_dir
self.skips = skips
if topk > 0:
StackedFcLayers = StackedFcSlow
else:
Stacked... | Nerflets. | Nerflets | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Nerflets:
"""Nerflets."""
def __init__(self, n, D, W, in_channels_xyz=63, in_channels_dir=27, skips=[4], with_semantics=False, n_classes=6, topk=0):
"""n: number of nerflets D: number of layers for density (sigma) encoder W: number of hidden units in each layer in_channels_xyz: numbe... | stack_v2_sparse_classes_36k_train_010174 | 8,749 | permissive | [
{
"docstring": "n: number of nerflets D: number of layers for density (sigma) encoder W: number of hidden units in each layer in_channels_xyz: number of input channels for xyz (3+3*10*2=63 by default) in_channels_dir: number of input channels for direction (3+3*4*2=27 by default) skips: add skip connection in t... | 2 | null | Implement the Python class `Nerflets` described below.
Class description:
Nerflets.
Method signatures and docstrings:
- def __init__(self, n, D, W, in_channels_xyz=63, in_channels_dir=27, skips=[4], with_semantics=False, n_classes=6, topk=0): n: number of nerflets D: number of layers for density (sigma) encoder W: nu... | Implement the Python class `Nerflets` described below.
Class description:
Nerflets.
Method signatures and docstrings:
- def __init__(self, n, D, W, in_channels_xyz=63, in_channels_dir=27, skips=[4], with_semantics=False, n_classes=6, topk=0): n: number of nerflets D: number of layers for density (sigma) encoder W: nu... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class Nerflets:
"""Nerflets."""
def __init__(self, n, D, W, in_channels_xyz=63, in_channels_dir=27, skips=[4], with_semantics=False, n_classes=6, topk=0):
"""n: number of nerflets D: number of layers for density (sigma) encoder W: number of hidden units in each layer in_channels_xyz: numbe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Nerflets:
"""Nerflets."""
def __init__(self, n, D, W, in_channels_xyz=63, in_channels_dir=27, skips=[4], with_semantics=False, n_classes=6, topk=0):
"""n: number of nerflets D: number of layers for density (sigma) encoder W: number of hidden units in each layer in_channels_xyz: number of input ch... | the_stack_v2_python_sparse | nerflets/models/nerf.py | Jimmy-INL/google-research | train | 1 |
2a2be11a0d0379ccdf33e5e0db3d05ff1e40b26f | [
"super(Sensor, self).__init__()\nself.link = link\nself.type = sensor_type if sensor_type is not None else sensor.type\nself.is_virtual = getattr(sensor, 'is_virtual', False)\nself.sensor = sensor\nself.part_id = part_id",
"attrs = super(Sensor, self).render_attributes()\nattrs.update({'link': self.link.name, 'pa... | <|body_start_0|>
super(Sensor, self).__init__()
self.link = link
self.type = sensor_type if sensor_type is not None else sensor.type
self.is_virtual = getattr(sensor, 'is_virtual', False)
self.sensor = sensor
self.part_id = part_id
<|end_body_0|>
<|body_start_1|>
... | Plugin sensor base class. This is used to communicate sensor configuration through the SDF plugin to the model controller in Gazebo. | Sensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sensor:
"""Plugin sensor base class. This is used to communicate sensor configuration through the SDF plugin to the model controller in Gazebo."""
def __init__(self, part_id, link, sensor, sensor_type=None):
""":param link: :type link: Link :param part_id: ID of the part this sensor ... | stack_v2_sparse_classes_36k_train_010175 | 4,432 | permissive | [
{
"docstring": ":param link: :type link: Link :param part_id: ID of the part this sensor belongs to, required to identify the corresponding input neuron(s). :type part_id: str :param sensor: SDF element for this sensor, or a `VirtualSensor` instance if applicable. :type sensor: SdfSensor|VirtualSensor :param se... | 3 | null | Implement the Python class `Sensor` described below.
Class description:
Plugin sensor base class. This is used to communicate sensor configuration through the SDF plugin to the model controller in Gazebo.
Method signatures and docstrings:
- def __init__(self, part_id, link, sensor, sensor_type=None): :param link: :ty... | Implement the Python class `Sensor` described below.
Class description:
Plugin sensor base class. This is used to communicate sensor configuration through the SDF plugin to the model controller in Gazebo.
Method signatures and docstrings:
- def __init__(self, part_id, link, sensor, sensor_type=None): :param link: :ty... | 70e65320a28fe04e121145b2cdde289d3052728a | <|skeleton|>
class Sensor:
"""Plugin sensor base class. This is used to communicate sensor configuration through the SDF plugin to the model controller in Gazebo."""
def __init__(self, part_id, link, sensor, sensor_type=None):
""":param link: :type link: Link :param part_id: ID of the part this sensor ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sensor:
"""Plugin sensor base class. This is used to communicate sensor configuration through the SDF plugin to the model controller in Gazebo."""
def __init__(self, part_id, link, sensor, sensor_type=None):
""":param link: :type link: Link :param part_id: ID of the part this sensor belongs to, r... | the_stack_v2_python_sparse | revolve/build/sdf/sensor.py | ElteHupkes/revolve | train | 0 |
4cdcd2254e219ca22f778cf162069424188aa01a | [
"super().__init__()\nself.in_dim = in_dim\nself.r_dim = r_dim\nself.attention_dims = attention_dims\nif probabilistic_dims is None:\n self.probabilistic_dims = [self.r_dim, self.r_dim]\nelse:\n self.probabilistic_dims = probabilistic_dims\nself.self_att = self_att\nself.self_attentive_network = SelfAttentiveV... | <|body_start_0|>
super().__init__()
self.in_dim = in_dim
self.r_dim = r_dim
self.attention_dims = attention_dims
if probabilistic_dims is None:
self.probabilistic_dims = [self.r_dim, self.r_dim]
else:
self.probabilistic_dims = probabilistic_dims
... | Attentive probabilistic encoder as implemented in the ANP paper, where it is described as the Latent Encoder. Includes option of self attention only. | AttentiveProbabilisticEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttentiveProbabilisticEncoder:
"""Attentive probabilistic encoder as implemented in the ANP paper, where it is described as the Latent Encoder. Includes option of self attention only."""
def __init__(self, in_dim, r_dim, attention_dims, probabilistic_dims=None, self_att=True, min_var=0.001):... | stack_v2_sparse_classes_36k_train_010176 | 16,175 | no_license | [
{
"docstring": ":param in_dim: An integer describing the dimensionality of the input to the encoder; in this case the sum of x_dim and y_dim :param r_dim: An integer describing the dimensionality of the embedding, r_i :param encoder_n_hidden: An integer describing the number of hidden layers in the neural netwo... | 2 | stack_v2_sparse_classes_30k_train_003402 | Implement the Python class `AttentiveProbabilisticEncoder` described below.
Class description:
Attentive probabilistic encoder as implemented in the ANP paper, where it is described as the Latent Encoder. Includes option of self attention only.
Method signatures and docstrings:
- def __init__(self, in_dim, r_dim, att... | Implement the Python class `AttentiveProbabilisticEncoder` described below.
Class description:
Attentive probabilistic encoder as implemented in the ANP paper, where it is described as the Latent Encoder. Includes option of self attention only.
Method signatures and docstrings:
- def __init__(self, in_dim, r_dim, att... | de60f831ee082ab2ae232c498cf2755da7c14c27 | <|skeleton|>
class AttentiveProbabilisticEncoder:
"""Attentive probabilistic encoder as implemented in the ANP paper, where it is described as the Latent Encoder. Includes option of self attention only."""
def __init__(self, in_dim, r_dim, attention_dims, probabilistic_dims=None, self_att=True, min_var=0.001):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttentiveProbabilisticEncoder:
"""Attentive probabilistic encoder as implemented in the ANP paper, where it is described as the Latent Encoder. Includes option of self attention only."""
def __init__(self, in_dim, r_dim, attention_dims, probabilistic_dims=None, self_att=True, min_var=0.001):
""":... | the_stack_v2_python_sparse | models/networks/np_networks.py | PenelopeJones/neural_processes | train | 4 |
6f5dd294ad55df4bafaf58136d7f79820b3dca5a | [
"self.graph = graph\nself.mst = None\nself.distance = dict(((node, float('inf')) for node in self.graph.iternodes()))\nself.parent = dict(((node, None) for node in self.graph.iternodes()))\nself._in_queue = dict(((node, True) for node in self.graph.iternodes()))\nself._pq = PriorityQueue()",
"if source is None:\n... | <|body_start_0|>
self.graph = graph
self.mst = None
self.distance = dict(((node, float('inf')) for node in self.graph.iternodes()))
self.parent = dict(((node, None) for node in self.graph.iternodes()))
self._in_queue = dict(((node, True) for node in self.graph.iternodes()))
... | Prim's algorithm for finding a minimum spanning tree. Attributes ---------- graph : input undirected weighted graph or multigraph mst : graph (MST) distance : dict with nodes parent : dict with nodes (MST) _in_queue : dict, private _pq : priority queue, private | PrimMSTWithEdges | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrimMSTWithEdges:
"""Prim's algorithm for finding a minimum spanning tree. Attributes ---------- graph : input undirected weighted graph or multigraph mst : graph (MST) distance : dict with nodes parent : dict with nodes (MST) _in_queue : dict, private _pq : priority queue, private"""
def __... | stack_v2_sparse_classes_36k_train_010177 | 14,685 | permissive | [
{
"docstring": "The algorithm initialization.",
"name": "__init__",
"signature": "def __init__(self, graph)"
},
{
"docstring": "Executable pseudocode.",
"name": "run",
"signature": "def run(self, source=None)"
},
{
"docstring": "The minimum spanning tree is built.",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_000984 | Implement the Python class `PrimMSTWithEdges` described below.
Class description:
Prim's algorithm for finding a minimum spanning tree. Attributes ---------- graph : input undirected weighted graph or multigraph mst : graph (MST) distance : dict with nodes parent : dict with nodes (MST) _in_queue : dict, private _pq :... | Implement the Python class `PrimMSTWithEdges` described below.
Class description:
Prim's algorithm for finding a minimum spanning tree. Attributes ---------- graph : input undirected weighted graph or multigraph mst : graph (MST) distance : dict with nodes parent : dict with nodes (MST) _in_queue : dict, private _pq :... | 0ff4ae303e8824e6bb8474d23b29a7b3e5ed8e60 | <|skeleton|>
class PrimMSTWithEdges:
"""Prim's algorithm for finding a minimum spanning tree. Attributes ---------- graph : input undirected weighted graph or multigraph mst : graph (MST) distance : dict with nodes parent : dict with nodes (MST) _in_queue : dict, private _pq : priority queue, private"""
def __... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PrimMSTWithEdges:
"""Prim's algorithm for finding a minimum spanning tree. Attributes ---------- graph : input undirected weighted graph or multigraph mst : graph (MST) distance : dict with nodes parent : dict with nodes (MST) _in_queue : dict, private _pq : priority queue, private"""
def __init__(self, ... | the_stack_v2_python_sparse | graphtheory/spanningtrees/prim.py | kgashok/graphs-dict | train | 0 |
1e6c10a940ae3b54e24efb2013c27b872b606985 | [
"logger.info(f'Ecommerce loader is loaded with catalog {catalog}')\nif not isinstance(catalog, list):\n catalog = [catalog]\nec_data_global: List[Any] = []\ndata_path = Path(expand_path(data_path))\nif not is_done(data_path):\n self._download_data(data_path)\nif data_path.is_dir():\n for fname in data_path... | <|body_start_0|>
logger.info(f'Ecommerce loader is loaded with catalog {catalog}')
if not isinstance(catalog, list):
catalog = [catalog]
ec_data_global: List[Any] = []
data_path = Path(expand_path(data_path))
if not is_done(data_path):
self._download_data(... | Class to download and load ecommerce data catalog | AmazonEcommerceReader | [
"Python-2.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AmazonEcommerceReader:
"""Class to download and load ecommerce data catalog"""
def read(self, data_path: str, catalog: list, **kwargs) -> Dict[str, List[Tuple[Any, Any]]]:
"""Load data from specific catalog Parameters: data_path: where the dataset is located catalog: names of the spe... | stack_v2_sparse_classes_36k_train_010178 | 3,627 | permissive | [
{
"docstring": "Load data from specific catalog Parameters: data_path: where the dataset is located catalog: names of the specific subcategories Returns: dataset: loaded dataset",
"name": "read",
"signature": "def read(self, data_path: str, catalog: list, **kwargs) -> Dict[str, List[Tuple[Any, Any]]]"
... | 3 | null | Implement the Python class `AmazonEcommerceReader` described below.
Class description:
Class to download and load ecommerce data catalog
Method signatures and docstrings:
- def read(self, data_path: str, catalog: list, **kwargs) -> Dict[str, List[Tuple[Any, Any]]]: Load data from specific catalog Parameters: data_pat... | Implement the Python class `AmazonEcommerceReader` described below.
Class description:
Class to download and load ecommerce data catalog
Method signatures and docstrings:
- def read(self, data_path: str, catalog: list, **kwargs) -> Dict[str, List[Tuple[Any, Any]]]: Load data from specific catalog Parameters: data_pat... | 65f69dfb898f5444cc2c98ae03ec7b3f44266df2 | <|skeleton|>
class AmazonEcommerceReader:
"""Class to download and load ecommerce data catalog"""
def read(self, data_path: str, catalog: list, **kwargs) -> Dict[str, List[Tuple[Any, Any]]]:
"""Load data from specific catalog Parameters: data_path: where the dataset is located catalog: names of the spe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AmazonEcommerceReader:
"""Class to download and load ecommerce data catalog"""
def read(self, data_path: str, catalog: list, **kwargs) -> Dict[str, List[Tuple[Any, Any]]]:
"""Load data from specific catalog Parameters: data_path: where the dataset is located catalog: names of the specific subcate... | the_stack_v2_python_sparse | deeppavlov/dataset_readers/amazon_ecommerce_reader.py | vintagexav/DeepPavlov | train | 2 |
3f2c58771047de95642c976bcd07dff68c672bab | [
"self.generator = dictGenerator\nself.site = pywikibot.Site('commons', 'commons')\nself.repo = pywikibot.Site().data_repository()\nself.create = create",
"for metadata in self.generator:\n grave_item = None\n if metadata.get('wikidata'):\n grave_item = pywikibot.ItemPage(self.repo, title=metadata.get... | <|body_start_0|>
self.generator = dictGenerator
self.site = pywikibot.Site('commons', 'commons')
self.repo = pywikibot.Site().data_repository()
self.create = create
<|end_body_0|>
<|body_start_1|>
for metadata in self.generator:
grave_item = None
if metad... | A bot to enrich and create paintings on Wikidata | GraveBot | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraveBot:
"""A bot to enrich and create paintings on Wikidata"""
def __init__(self, dictGenerator, create=False):
"""Arguments: * generator - A generator that yields Dict objects. The dict in this generator needs to contain 'idpid' and 'collectionqid' * create - Boolean to say if you... | stack_v2_sparse_classes_36k_train_010179 | 13,014 | no_license | [
{
"docstring": "Arguments: * generator - A generator that yields Dict objects. The dict in this generator needs to contain 'idpid' and 'collectionqid' * create - Boolean to say if you want to create new items or just update existing",
"name": "__init__",
"signature": "def __init__(self, dictGenerator, c... | 5 | null | Implement the Python class `GraveBot` described below.
Class description:
A bot to enrich and create paintings on Wikidata
Method signatures and docstrings:
- def __init__(self, dictGenerator, create=False): Arguments: * generator - A generator that yields Dict objects. The dict in this generator needs to contain 'id... | Implement the Python class `GraveBot` described below.
Class description:
A bot to enrich and create paintings on Wikidata
Method signatures and docstrings:
- def __init__(self, dictGenerator, create=False): Arguments: * generator - A generator that yields Dict objects. The dict in this generator needs to contain 'id... | 99a96e49cfe6b2d3151da7ad5469792d80171be3 | <|skeleton|>
class GraveBot:
"""A bot to enrich and create paintings on Wikidata"""
def __init__(self, dictGenerator, create=False):
"""Arguments: * generator - A generator that yields Dict objects. The dict in this generator needs to contain 'idpid' and 'collectionqid' * create - Boolean to say if you... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GraveBot:
"""A bot to enrich and create paintings on Wikidata"""
def __init__(self, dictGenerator, create=False):
"""Arguments: * generator - A generator that yields Dict objects. The dict in this generator needs to contain 'idpid' and 'collectionqid' * create - Boolean to say if you want to crea... | the_stack_v2_python_sparse | bot/wikidata/pere-lachaise_import.py | multichill/toollabs | train | 18 |
770b9820c2d24942f223a2a60425a3342850966b | [
"if resource_name is not None:\n if is_valid_ipv4(resource_name):\n resource_name = resource_name + '::inst0::INSTR'\n else:\n logger.error('Invalid IP address for BNC845: {}.'.format(resource_name))\nsuper(BNC845, self).__init__(resource_name, *args, **kwargs)",
"if resource_name is not None:... | <|body_start_0|>
if resource_name is not None:
if is_valid_ipv4(resource_name):
resource_name = resource_name + '::inst0::INSTR'
else:
logger.error('Invalid IP address for BNC845: {}.'.format(resource_name))
super(BNC845, self).__init__(resource_na... | SCPI instrument driver for Berkely Nucleonics BNC845-M RF Signal Generator. Properties: frequency: Set the RF generator frequency, in Hz. 0.01-20 GHz. power: Set the RF generator output power, in dBm. No effect, BNC845M output is always +16dBm. output: Toggle RF signal output on/off. pulse: Toggle RF pulsed mode on/off... | BNC845 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BNC845:
"""SCPI instrument driver for Berkely Nucleonics BNC845-M RF Signal Generator. Properties: frequency: Set the RF generator frequency, in Hz. 0.01-20 GHz. power: Set the RF generator output power, in dBm. No effect, BNC845M output is always +16dBm. output: Toggle RF signal output on/off. p... | stack_v2_sparse_classes_36k_train_010180 | 4,136 | permissive | [
{
"docstring": "Berkely Nucleonics BNC845-M RF Signal Generator Args: resource_name: The IP address of the source to conenct to, as string.",
"name": "__init__",
"signature": "def __init__(self, resource_name=None, *args, **kwargs)"
},
{
"docstring": "Connect to the RF source via a specified phy... | 2 | stack_v2_sparse_classes_30k_train_017225 | Implement the Python class `BNC845` described below.
Class description:
SCPI instrument driver for Berkely Nucleonics BNC845-M RF Signal Generator. Properties: frequency: Set the RF generator frequency, in Hz. 0.01-20 GHz. power: Set the RF generator output power, in dBm. No effect, BNC845M output is always +16dBm. ou... | Implement the Python class `BNC845` described below.
Class description:
SCPI instrument driver for Berkely Nucleonics BNC845-M RF Signal Generator. Properties: frequency: Set the RF generator frequency, in Hz. 0.01-20 GHz. power: Set the RF generator output power, in dBm. No effect, BNC845M output is always +16dBm. ou... | 7f68f19dcda4a8937a10bd52270c883f2cf63fcb | <|skeleton|>
class BNC845:
"""SCPI instrument driver for Berkely Nucleonics BNC845-M RF Signal Generator. Properties: frequency: Set the RF generator frequency, in Hz. 0.01-20 GHz. power: Set the RF generator output power, in dBm. No effect, BNC845M output is always +16dBm. output: Toggle RF signal output on/off. p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BNC845:
"""SCPI instrument driver for Berkely Nucleonics BNC845-M RF Signal Generator. Properties: frequency: Set the RF generator frequency, in Hz. 0.01-20 GHz. power: Set the RF generator output power, in dBm. No effect, BNC845M output is always +16dBm. output: Toggle RF signal output on/off. pulse: Toggle ... | the_stack_v2_python_sparse | src/auspex/instruments/bnc.py | BBN-Q/Auspex | train | 35 |
b73c9813a5eeeefc76d8a3e11dbda6456ee83c45 | [
"if not coins:\n return -1\nif amount == 0:\n return 0\nMAX = 2147483647\ndp = [0] + [MAX] * amount\nfor i in range(1, amount + 1):\n dp[i] = min([dp[i - c] if i - c >= 0 else MAX for c in coins]) + 1\nreturn dp[amount] if dp[amount] < MAX else -1",
"if not coins:\n return -1\nif amount == 0:\n ret... | <|body_start_0|>
if not coins:
return -1
if amount == 0:
return 0
MAX = 2147483647
dp = [0] + [MAX] * amount
for i in range(1, amount + 1):
dp[i] = min([dp[i - c] if i - c >= 0 else MAX for c in coins]) + 1
return dp[amount] if dp[amoun... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_0|>
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_010181 | 2,201 | no_license | [
{
"docstring": ":type coins: List[int] :type amount: int :rtype: int",
"name": "coinChange",
"signature": "def coinChange(self, coins, amount)"
},
{
"docstring": ":type coins: List[int] :type amount: int :rtype: int",
"name": "coinChange",
"signature": "def coinChange(self, coins, amount... | 2 | stack_v2_sparse_classes_30k_train_011723 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: ... | 88da6b274e49ce97d432e1f4d4de8efa55593836 | <|skeleton|>
class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_0|>
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
if not coins:
return -1
if amount == 0:
return 0
MAX = 2147483647
dp = [0] + [MAX] * amount
for i in range(1, amount + 1):
... | the_stack_v2_python_sparse | 4th/homework_9/id_69/322-CoinChange.py | StuQAlgorithm/AlgorithmHomework | train | 6 | |
aaca2af582c834401ab3bf841da77663d419e736 | [
"def build(left, right):\n if left > right:\n return None\n mid = nums.index(max(nums[left:right + 1]))\n root = TreeNode(nums[mid])\n root.left = build(left, mid - 1)\n root.right = build(mid + 1, right)\n return root\nreturn build(0, len(nums) - 1)",
"stack = []\nfor n in nums:\n nod... | <|body_start_0|>
def build(left, right):
if left > right:
return None
mid = nums.index(max(nums[left:right + 1]))
root = TreeNode(nums[mid])
root.left = build(left, mid - 1)
root.right = build(mid + 1, right)
return root
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_0|>
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def b... | stack_v2_sparse_classes_36k_train_010182 | 2,219 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: TreeNode",
"name": "constructMaximumBinaryTree",
"signature": "def constructMaximumBinaryTree(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: TreeNode",
"name": "constructMaximumBinaryTree",
"signature": "def constructMaximumBin... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def constructMaximumBinaryTree(self, nums): :type nums: List[int] :rtype: TreeNode
- def constructMaximumBinaryTree(self, nums): :type nums: List[int] :rtype: TreeNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def constructMaximumBinaryTree(self, nums): :type nums: List[int] :rtype: TreeNode
- def constructMaximumBinaryTree(self, nums): :type nums: List[int] :rtype: TreeNode
<|skeleto... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_0|>
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
def build(left, right):
if left > right:
return None
mid = nums.index(max(nums[left:right + 1]))
root = TreeNode(nums[mid])
root.le... | the_stack_v2_python_sparse | src/lt_654.py | oxhead/CodingYourWay | train | 0 | |
b7099be81f1b1317a457e9f947bf6c8ecc0e46c7 | [
"categories = Category.objects.eligible_for_demos()\ncategories = categories.order_by('?')[:10]\nfor n, cat in enumerate(categories):\n yield models.CategoryUsage(demonstration=self.object, category=cat, position=n)",
"usages = self.object.categoryusage_set.all()\npending = usages.exclude(chance__finished_at__... | <|body_start_0|>
categories = Category.objects.eligible_for_demos()
categories = categories.order_by('?')[:10]
for n, cat in enumerate(categories):
yield models.CategoryUsage(demonstration=self.object, category=cat, position=n)
<|end_body_0|>
<|body_start_1|>
usages = self.o... | Offers the time for user read the terms of usage. | DemonstrationStartView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DemonstrationStartView:
"""Offers the time for user read the terms of usage."""
def get_new_usages(self):
"""Chooses 10 random categories to be used for demonstration."""
<|body_0|>
def get_success_url(self):
"""Clears any previous attempt and start/restart the d... | stack_v2_sparse_classes_36k_train_010183 | 10,067 | no_license | [
{
"docstring": "Chooses 10 random categories to be used for demonstration.",
"name": "get_new_usages",
"signature": "def get_new_usages(self)"
},
{
"docstring": "Clears any previous attempt and start/restart the demonstration for the user.",
"name": "get_success_url",
"signature": "def g... | 2 | null | Implement the Python class `DemonstrationStartView` described below.
Class description:
Offers the time for user read the terms of usage.
Method signatures and docstrings:
- def get_new_usages(self): Chooses 10 random categories to be used for demonstration.
- def get_success_url(self): Clears any previous attempt an... | Implement the Python class `DemonstrationStartView` described below.
Class description:
Offers the time for user read the terms of usage.
Method signatures and docstrings:
- def get_new_usages(self): Chooses 10 random categories to be used for demonstration.
- def get_success_url(self): Clears any previous attempt an... | e2d24a82462a735fc722f0b228be04a4495185c1 | <|skeleton|>
class DemonstrationStartView:
"""Offers the time for user read the terms of usage."""
def get_new_usages(self):
"""Chooses 10 random categories to be used for demonstration."""
<|body_0|>
def get_success_url(self):
"""Clears any previous attempt and start/restart the d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DemonstrationStartView:
"""Offers the time for user read the terms of usage."""
def get_new_usages(self):
"""Chooses 10 random categories to be used for demonstration."""
categories = Category.objects.eligible_for_demos()
categories = categories.order_by('?')[:10]
for n, c... | the_stack_v2_python_sparse | demonstrations/views/main.py | fredericosachweh/amostra2 | train | 0 |
f3853432fb03d2156f017c26e3788b82d4517541 | [
"self.res = 0\nvisited = [[False for _ in range(n)] for _ in range(m)]\nself.dfs(m, n, k, 0, 0, visited)\nreturn self.res",
"if row < 0 or row >= m or col < 0 or (col >= n) or visited[row][col] or (not self.valid(row, col, k)):\n return\nvisited[row][col] = True\nself.res += 1\nself.dfs(m, n, k, row + 1, col, ... | <|body_start_0|>
self.res = 0
visited = [[False for _ in range(n)] for _ in range(m)]
self.dfs(m, n, k, 0, 0, visited)
return self.res
<|end_body_0|>
<|body_start_1|>
if row < 0 or row >= m or col < 0 or (col >= n) or visited[row][col] or (not self.valid(row, col, k)):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def movingCount(self, m, n, k):
"""Args: m: int n: int k: int Return: int"""
<|body_0|>
def dfs(self, m, n, k, row, col, visited):
"""Args: m: int n: int k: int row: int col: int visited: list[bool]"""
<|body_1|>
def valid(self, row, col, k):
... | stack_v2_sparse_classes_36k_train_010184 | 1,346 | no_license | [
{
"docstring": "Args: m: int n: int k: int Return: int",
"name": "movingCount",
"signature": "def movingCount(self, m, n, k)"
},
{
"docstring": "Args: m: int n: int k: int row: int col: int visited: list[bool]",
"name": "dfs",
"signature": "def dfs(self, m, n, k, row, col, visited)"
},... | 3 | stack_v2_sparse_classes_30k_train_015786 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def movingCount(self, m, n, k): Args: m: int n: int k: int Return: int
- def dfs(self, m, n, k, row, col, visited): Args: m: int n: int k: int row: int col: int visited: list[boo... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def movingCount(self, m, n, k): Args: m: int n: int k: int Return: int
- def dfs(self, m, n, k, row, col, visited): Args: m: int n: int k: int row: int col: int visited: list[boo... | 101bce2fac8b188a4eb2f5e017293d21ad0ecb21 | <|skeleton|>
class Solution:
def movingCount(self, m, n, k):
"""Args: m: int n: int k: int Return: int"""
<|body_0|>
def dfs(self, m, n, k, row, col, visited):
"""Args: m: int n: int k: int row: int col: int visited: list[bool]"""
<|body_1|>
def valid(self, row, col, k):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def movingCount(self, m, n, k):
"""Args: m: int n: int k: int Return: int"""
self.res = 0
visited = [[False for _ in range(n)] for _ in range(m)]
self.dfs(m, n, k, 0, 0, visited)
return self.res
def dfs(self, m, n, k, row, col, visited):
"""Args: ... | the_stack_v2_python_sparse | 剑指offer/剑指 Offer 13. 机器人的运动范围.py | AiZhanghan/Leetcode | train | 0 | |
bf591b69389a0101c355a618358a9bd142eabafc | [
"slow = head\ncurr = head\nwhile curr and curr.next:\n slow = slow.next\n curr = curr.next.next\nnew_head = self.reverse(slow)\nwhile new_head:\n if head.val != new_head.val:\n return False\n head = head.next\n new_head = new_head.next\nreturn True",
"new_head = None\nc = head\nwhile c:\n ... | <|body_start_0|>
slow = head
curr = head
while curr and curr.next:
slow = slow.next
curr = curr.next.next
new_head = self.reverse(slow)
while new_head:
if head.val != new_head.val:
return False
head = head.next
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPalindrome(self, head):
""":type head: ListNode :rtype: bool"""
<|body_0|>
def reverse(self, head):
"""Reverse second half of linked list"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
slow = head
curr = head
while c... | stack_v2_sparse_classes_36k_train_010185 | 1,580 | no_license | [
{
"docstring": ":type head: ListNode :rtype: bool",
"name": "isPalindrome",
"signature": "def isPalindrome(self, head)"
},
{
"docstring": "Reverse second half of linked list",
"name": "reverse",
"signature": "def reverse(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008366 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, head): :type head: ListNode :rtype: bool
- def reverse(self, head): Reverse second half of linked list | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, head): :type head: ListNode :rtype: bool
- def reverse(self, head): Reverse second half of linked list
<|skeleton|>
class Solution:
def isPalindrome(... | 1639a4b13c692d87c658a7e0a11212bf0e98d443 | <|skeleton|>
class Solution:
def isPalindrome(self, head):
""":type head: ListNode :rtype: bool"""
<|body_0|>
def reverse(self, head):
"""Reverse second half of linked list"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isPalindrome(self, head):
""":type head: ListNode :rtype: bool"""
slow = head
curr = head
while curr and curr.next:
slow = slow.next
curr = curr.next.next
new_head = self.reverse(slow)
while new_head:
if head.val... | the_stack_v2_python_sparse | easy/isPalindrome.py | Hashah1/Leetcode-Practice | train | 0 | |
7a70f4187813414faae3b899984e6b612d518e8a | [
"if self.state_model.op_state in [DevState.FAULT, DevState.UNKNOWN, DevState.DISABLE]:\n tango.Except.throw_exception(f'Abort() is not allowed in current state {self.state_model.op_state}', 'Failed to invoke Abort command on CspSubarrayLeafNode.', 'cspsubarrayleafnode.Abort()', tango.ErrSeverity.ERR)\nthis_serve... | <|body_start_0|>
if self.state_model.op_state in [DevState.FAULT, DevState.UNKNOWN, DevState.DISABLE]:
tango.Except.throw_exception(f'Abort() is not allowed in current state {self.state_model.op_state}', 'Failed to invoke Abort command on CspSubarrayLeafNode.', 'cspsubarrayleafnode.Abort()', tango.E... | A class for CSPSubarrayLeafNode's Abort() command. Command to abort the current operation being done on the CSP Subarray. | AbortCommand | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbortCommand:
"""A class for CSPSubarrayLeafNode's Abort() command. Command to abort the current operation being done on the CSP Subarray."""
def check_allowed(self):
"""Checks whether this command is allowed to be run in current device state :return: True if this command is allowed ... | stack_v2_sparse_classes_36k_train_010186 | 4,767 | permissive | [
{
"docstring": "Checks whether this command is allowed to be run in current device state :return: True if this command is allowed to be run in current device state :rtype: boolean :raises: DevFailed if this command is not allowed to be run in current device state",
"name": "check_allowed",
"signature": ... | 3 | stack_v2_sparse_classes_30k_train_007241 | Implement the Python class `AbortCommand` described below.
Class description:
A class for CSPSubarrayLeafNode's Abort() command. Command to abort the current operation being done on the CSP Subarray.
Method signatures and docstrings:
- def check_allowed(self): Checks whether this command is allowed to be run in curre... | Implement the Python class `AbortCommand` described below.
Class description:
A class for CSPSubarrayLeafNode's Abort() command. Command to abort the current operation being done on the CSP Subarray.
Method signatures and docstrings:
- def check_allowed(self): Checks whether this command is allowed to be run in curre... | 7ee65a9c8dada9b28893144b372a398bd0646195 | <|skeleton|>
class AbortCommand:
"""A class for CSPSubarrayLeafNode's Abort() command. Command to abort the current operation being done on the CSP Subarray."""
def check_allowed(self):
"""Checks whether this command is allowed to be run in current device state :return: True if this command is allowed ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AbortCommand:
"""A class for CSPSubarrayLeafNode's Abort() command. Command to abort the current operation being done on the CSP Subarray."""
def check_allowed(self):
"""Checks whether this command is allowed to be run in current device state :return: True if this command is allowed to be run in ... | the_stack_v2_python_sparse | temp_src/ska_tmc_cspsubarrayleafnode_mid/abort_command.py | ska-telescope/tmc-prototype | train | 4 |
f54606f8d5746eecda25cec1c99b1ab0058a9ce7 | [
"layout = self.layout\nfor i in range(len(self.phobos_data)):\n name = self.phobos_data[i].name[2:].replace('_', ' ')\n self.phobos_data[i].draw(layout, name)\nif self.annotation_checks:\n box = layout.box()\n box.label(text='Include annotations:')\n for i in range(len(self.annotation_checks)):\n ... | <|body_start_0|>
layout = self.layout
for i in range(len(self.phobos_data)):
name = self.phobos_data[i].name[2:].replace('_', ' ')
self.phobos_data[i].draw(layout, name)
if self.annotation_checks:
box = layout.box()
box.label(text='Include annotati... | Temporary operator to add a generic object. | TempObjAddOperator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TempObjAddOperator:
"""Temporary operator to add a generic object."""
def draw(self, context):
"""Args: context: Returns:"""
<|body_0|>
def invoke(self, context, event):
"""Args: context: event: Returns:"""
<|body_1|>
def execute(self, context):
... | stack_v2_sparse_classes_36k_train_010187 | 17,163 | permissive | [
{
"docstring": "Args: context: Returns:",
"name": "draw",
"signature": "def draw(self, context)"
},
{
"docstring": "Args: context: event: Returns:",
"name": "invoke",
"signature": "def invoke(self, context, event)"
},
{
"docstring": "Args: context: Returns:",
"name": "execute... | 3 | null | Implement the Python class `TempObjAddOperator` described below.
Class description:
Temporary operator to add a generic object.
Method signatures and docstrings:
- def draw(self, context): Args: context: Returns:
- def invoke(self, context, event): Args: context: event: Returns:
- def execute(self, context): Args: co... | Implement the Python class `TempObjAddOperator` described below.
Class description:
Temporary operator to add a generic object.
Method signatures and docstrings:
- def draw(self, context): Args: context: Returns:
- def invoke(self, context, event): Args: context: event: Returns:
- def execute(self, context): Args: co... | 543d220c65bbee0e23e810d89307e23aa79eb0cd | <|skeleton|>
class TempObjAddOperator:
"""Temporary operator to add a generic object."""
def draw(self, context):
"""Args: context: Returns:"""
<|body_0|>
def invoke(self, context, event):
"""Args: context: event: Returns:"""
<|body_1|>
def execute(self, context):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TempObjAddOperator:
"""Temporary operator to add a generic object."""
def draw(self, context):
"""Args: context: Returns:"""
layout = self.layout
for i in range(len(self.phobos_data)):
name = self.phobos_data[i].name[2:].replace('_', ' ')
self.phobos_data[i... | the_stack_v2_python_sparse | phobos/blender/operators/generic.py | dfki-ric/phobos | train | 483 |
9a111069bb59de3a04178f934f5b3cf61d0887ef | [
"sim_id = CommonSimUtils.get_sim_id(sim_info)\nrabbit_hole_service = CommonRabbitHoleUtils.get_rabbit_hole_service()\nrabbit_hole_id = rabbit_hole_service.get_head_rabbit_hole_id(sim_id)\nif rabbit_hole_id is None or rabbit_hole_id < 0:\n return None\nreturn rabbit_hole_id",
"if sim_info is None:\n raise As... | <|body_start_0|>
sim_id = CommonSimUtils.get_sim_id(sim_info)
rabbit_hole_service = CommonRabbitHoleUtils.get_rabbit_hole_service()
rabbit_hole_id = rabbit_hole_service.get_head_rabbit_hole_id(sim_id)
if rabbit_hole_id is None or rabbit_hole_id < 0:
return None
return... | Utilities for manipulating Rabbit Holes for Sims. | CommonSimRabbitHoleUtils | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonSimRabbitHoleUtils:
"""Utilities for manipulating Rabbit Holes for Sims."""
def get_first_rabbit_hole_id_for_sim(cls, sim_info: SimInfo) -> Union[int, None]:
"""get_rabbit_hole_id(sim_info) Retrieve the id of the rabbit hole a Sim is in. :param sim_info: An instance of a Sim. :... | stack_v2_sparse_classes_36k_train_010188 | 5,289 | permissive | [
{
"docstring": "get_rabbit_hole_id(sim_info) Retrieve the id of the rabbit hole a Sim is in. :param sim_info: An instance of a Sim. :type sim_info: SimInfo :return: The id of the first rabbit hole the Sim is in or None if not found. :rtype: Union[int, None]",
"name": "get_first_rabbit_hole_id_for_sim",
... | 3 | null | Implement the Python class `CommonSimRabbitHoleUtils` described below.
Class description:
Utilities for manipulating Rabbit Holes for Sims.
Method signatures and docstrings:
- def get_first_rabbit_hole_id_for_sim(cls, sim_info: SimInfo) -> Union[int, None]: get_rabbit_hole_id(sim_info) Retrieve the id of the rabbit h... | Implement the Python class `CommonSimRabbitHoleUtils` described below.
Class description:
Utilities for manipulating Rabbit Holes for Sims.
Method signatures and docstrings:
- def get_first_rabbit_hole_id_for_sim(cls, sim_info: SimInfo) -> Union[int, None]: get_rabbit_hole_id(sim_info) Retrieve the id of the rabbit h... | 58e7beb30b9c818b294d35abd2436a0192cd3e82 | <|skeleton|>
class CommonSimRabbitHoleUtils:
"""Utilities for manipulating Rabbit Holes for Sims."""
def get_first_rabbit_hole_id_for_sim(cls, sim_info: SimInfo) -> Union[int, None]:
"""get_rabbit_hole_id(sim_info) Retrieve the id of the rabbit hole a Sim is in. :param sim_info: An instance of a Sim. :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommonSimRabbitHoleUtils:
"""Utilities for manipulating Rabbit Holes for Sims."""
def get_first_rabbit_hole_id_for_sim(cls, sim_info: SimInfo) -> Union[int, None]:
"""get_rabbit_hole_id(sim_info) Retrieve the id of the rabbit hole a Sim is in. :param sim_info: An instance of a Sim. :type sim_info... | the_stack_v2_python_sparse | Scripts/sims4communitylib/utils/sims/common_sim_rabbit_hole_utils.py | ColonolNutty/Sims4CommunityLibrary | train | 183 |
9242462fd3e4800f63bcc87a5500dda60145900e | [
"geo_api = GeoNamesAPI()\nextra_args = {}\nself.mode = mode\nif self.mode == 'country':\n extra_args.update({'feature_class': 'A', 'feature_code': 'PCLI'})\nresults = geo_api.search(self.q, max_rows=50, name_start=True, **extra_args)\nreturn JsonResponse({'results': [dict(id=geo_api.uri_from_id(item['geonameId']... | <|body_start_0|>
geo_api = GeoNamesAPI()
extra_args = {}
self.mode = mode
if self.mode == 'country':
extra_args.update({'feature_class': 'A', 'feature_code': 'PCLI'})
results = geo_api.search(self.q, max_rows=50, name_start=True, **extra_args)
return JsonRespo... | GeoNames ajax lookup for use as autocomplete. Optional mode parameter to restrict to countries only. | GeoNamesLookup | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeoNamesLookup:
"""GeoNames ajax lookup for use as autocomplete. Optional mode parameter to restrict to countries only."""
def get(self, request, mode=None, *args, **kwargs):
""""Return option list json response."""
<|body_0|>
def get_label(self, item):
"""displa... | stack_v2_sparse_classes_36k_train_010189 | 39,534 | permissive | [
{
"docstring": "\"Return option list json response.",
"name": "get",
"signature": "def get(self, request, mode=None, *args, **kwargs)"
},
{
"docstring": "display country for context, if available",
"name": "get_label",
"signature": "def get_label(self, item)"
}
] | 2 | null | Implement the Python class `GeoNamesLookup` described below.
Class description:
GeoNames ajax lookup for use as autocomplete. Optional mode parameter to restrict to countries only.
Method signatures and docstrings:
- def get(self, request, mode=None, *args, **kwargs): "Return option list json response.
- def get_labe... | Implement the Python class `GeoNamesLookup` described below.
Class description:
GeoNames ajax lookup for use as autocomplete. Optional mode parameter to restrict to countries only.
Method signatures and docstrings:
- def get(self, request, mode=None, *args, **kwargs): "Return option list json response.
- def get_labe... | 6103855f07c2c0123ab21b93b794ea5d5ca39aa2 | <|skeleton|>
class GeoNamesLookup:
"""GeoNames ajax lookup for use as autocomplete. Optional mode parameter to restrict to countries only."""
def get(self, request, mode=None, *args, **kwargs):
""""Return option list json response."""
<|body_0|>
def get_label(self, item):
"""displa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GeoNamesLookup:
"""GeoNames ajax lookup for use as autocomplete. Optional mode parameter to restrict to countries only."""
def get(self, request, mode=None, *args, **kwargs):
""""Return option list json response."""
geo_api = GeoNamesAPI()
extra_args = {}
self.mode = mode
... | the_stack_v2_python_sparse | mep/people/views.py | Princeton-CDH/mep-django | train | 6 |
008acc477c6de384db81166439581d21a3140acd | [
"def rserialize(root, string):\n \"\"\" a recursive helper function for the serialize() function.\"\"\"\n if root is None:\n string += 'None,'\n else:\n string += str(root.val) + ','\n string = rserialize(root.left, string)\n string = rserialize(root.right, string)\n return s... | <|body_start_0|>
def rserialize(root, string):
""" a recursive helper function for the serialize() function."""
if root is None:
string += 'None,'
else:
string += str(root.val) + ','
string = rserialize(root.left, string)
... | 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_010190 | 17,936 | 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_010181 | 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:... | 035ef08434fa1ca781a6fb2f9eed3538b7d20c02 | <|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 rserialize(root, string):
""" a recursive helper function for the serialize() function."""
if root is None:
string += 'None,'
else... | the_stack_v2_python_sparse | leetcode_python/Tree/serialize-and-deserialize-binary-tree.py | yennanliu/CS_basics | train | 64 | |
f5629e682fd4bc02a02a90ffed28960461a4f6c6 | [
"event = rdfvalue.AuditEvent(user=self.token.username, action='CLIENT_APPROVAL_BREAK_GLASS_REQUEST', client=self.client_id, description=self.args.reason)\nflow.Events.PublishEvent('Audit', event, token=self.token)\nreturn self.ApprovalUrnBuilder(self.client_id.Path(), self.token.username, self.args.reason)",
"cli... | <|body_start_0|>
event = rdfvalue.AuditEvent(user=self.token.username, action='CLIENT_APPROVAL_BREAK_GLASS_REQUEST', client=self.client_id, description=self.args.reason)
flow.Events.PublishEvent('Audit', event, token=self.token)
return self.ApprovalUrnBuilder(self.client_id.Path(), self.token.us... | Grant an approval in an emergency. | BreakGlassGrantClientApprovalFlow | [
"Apache-2.0",
"DOC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BreakGlassGrantClientApprovalFlow:
"""Grant an approval in an emergency."""
def BuildApprovalUrn(self):
"""Builds approval object urn."""
<|body_0|>
def BuildSubjectTitle(self):
"""Returns the string with subject's title."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_010191 | 28,119 | permissive | [
{
"docstring": "Builds approval object urn.",
"name": "BuildApprovalUrn",
"signature": "def BuildApprovalUrn(self)"
},
{
"docstring": "Returns the string with subject's title.",
"name": "BuildSubjectTitle",
"signature": "def BuildSubjectTitle(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010932 | Implement the Python class `BreakGlassGrantClientApprovalFlow` described below.
Class description:
Grant an approval in an emergency.
Method signatures and docstrings:
- def BuildApprovalUrn(self): Builds approval object urn.
- def BuildSubjectTitle(self): Returns the string with subject's title. | Implement the Python class `BreakGlassGrantClientApprovalFlow` described below.
Class description:
Grant an approval in an emergency.
Method signatures and docstrings:
- def BuildApprovalUrn(self): Builds approval object urn.
- def BuildSubjectTitle(self): Returns the string with subject's title.
<|skeleton|>
class ... | ba1648b97a76f844ffb8e1891cc9e2680f9b1c6e | <|skeleton|>
class BreakGlassGrantClientApprovalFlow:
"""Grant an approval in an emergency."""
def BuildApprovalUrn(self):
"""Builds approval object urn."""
<|body_0|>
def BuildSubjectTitle(self):
"""Returns the string with subject's title."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BreakGlassGrantClientApprovalFlow:
"""Grant an approval in an emergency."""
def BuildApprovalUrn(self):
"""Builds approval object urn."""
event = rdfvalue.AuditEvent(user=self.token.username, action='CLIENT_APPROVAL_BREAK_GLASS_REQUEST', client=self.client_id, description=self.args.reason... | the_stack_v2_python_sparse | lib/aff4_objects/security.py | defaultnamehere/grr | train | 3 |
ea8e48ee03ef06a913aa6b02068065f5808793cf | [
"spark.sparkContext.setLogLevel('INFO')\nlog4jLogger = spark.sparkContext._jvm.org.apache.log4j\nlogger = log4jLogger.LogManager.getLogger(__name__)\nindex_mapper_handle = globals()['XML2kvpMapper']\n\ndef es_mapper_pt_udf(pt):\n mapper = index_mapper_handle(field_mapper_config=field_mapper_config)\n for row ... | <|body_start_0|>
spark.sparkContext.setLogLevel('INFO')
log4jLogger = spark.sparkContext._jvm.org.apache.log4j
logger = log4jLogger.LogManager.getLogger(__name__)
index_mapper_handle = globals()['XML2kvpMapper']
def es_mapper_pt_udf(pt):
mapper = index_mapper_handle(... | Class to organize methods for indexing mapped/flattened metadata into ElasticSearch (ES) | ESIndex | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ESIndex:
"""Class to organize methods for indexing mapped/flattened metadata into ElasticSearch (ES)"""
def index_job_to_es_spark(spark, job, records_df, field_mapper_config):
"""Method to index records dataframe into ES Args: spark (pyspark.sql.session.SparkSession): spark instance ... | stack_v2_sparse_classes_36k_train_010192 | 11,830 | permissive | [
{
"docstring": "Method to index records dataframe into ES Args: spark (pyspark.sql.session.SparkSession): spark instance from static job methods job (core.models.Job): Job for records records_df (pyspark.sql.DataFrame): records as pyspark DataFrame field_mapper_config (dict): XML2kvp field mapper configurations... | 2 | stack_v2_sparse_classes_30k_train_004522 | Implement the Python class `ESIndex` described below.
Class description:
Class to organize methods for indexing mapped/flattened metadata into ElasticSearch (ES)
Method signatures and docstrings:
- def index_job_to_es_spark(spark, job, records_df, field_mapper_config): Method to index records dataframe into ES Args: ... | Implement the Python class `ESIndex` described below.
Class description:
Class to organize methods for indexing mapped/flattened metadata into ElasticSearch (ES)
Method signatures and docstrings:
- def index_job_to_es_spark(spark, job, records_df, field_mapper_config): Method to index records dataframe into ES Args: ... | eb100ea17193d65485aa6c4a7f05a41b4cab7515 | <|skeleton|>
class ESIndex:
"""Class to organize methods for indexing mapped/flattened metadata into ElasticSearch (ES)"""
def index_job_to_es_spark(spark, job, records_df, field_mapper_config):
"""Method to index records dataframe into ES Args: spark (pyspark.sql.session.SparkSession): spark instance ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ESIndex:
"""Class to organize methods for indexing mapped/flattened metadata into ElasticSearch (ES)"""
def index_job_to_es_spark(spark, job, records_df, field_mapper_config):
"""Method to index records dataframe into ES Args: spark (pyspark.sql.session.SparkSession): spark instance from static j... | the_stack_v2_python_sparse | core/spark/es.py | tulibraries/combine | train | 1 |
641545540bcfb7496d5a8ce06bae2ef61738eee0 | [
"tp = type(e)\ntb = e.__traceback__\ntraceback_str = 'Traceback (most recent call last):\\n' + ''.join(traceback.format_tb(tb))\ntry:\n attributes = e.get_attributes()\nexcept AttributeError:\n attributes = {}\nreturn (sy.serde.msgpack.serde._simplify(worker, tp.__name__), sy.serde.msgpack.serde._simplify(wor... | <|body_start_0|>
tp = type(e)
tb = e.__traceback__
traceback_str = 'Traceback (most recent call last):\n' + ''.join(traceback.format_tb(tb))
try:
attributes = e.get_attributes()
except AttributeError:
attributes = {}
return (sy.serde.msgpack.serde.... | Raised when calling get on a pointer to a tensor which does not allow get to be called on it. This can happen do to sensitivity being too high | GetNotPermittedError | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetNotPermittedError:
"""Raised when calling get on a pointer to a tensor which does not allow get to be called on it. This can happen do to sensitivity being too high"""
def simplify(worker: 'sy.workers.AbstractWorker', e):
"""Serialize information about an Exception which was raise... | stack_v2_sparse_classes_36k_train_010193 | 15,166 | permissive | [
{
"docstring": "Serialize information about an Exception which was raised to forward it",
"name": "simplify",
"signature": "def simplify(worker: 'sy.workers.AbstractWorker', e)"
},
{
"docstring": "Detail and re-raise an Exception forwarded by another worker",
"name": "detail",
"signature... | 2 | stack_v2_sparse_classes_30k_train_013977 | Implement the Python class `GetNotPermittedError` described below.
Class description:
Raised when calling get on a pointer to a tensor which does not allow get to be called on it. This can happen do to sensitivity being too high
Method signatures and docstrings:
- def simplify(worker: 'sy.workers.AbstractWorker', e):... | Implement the Python class `GetNotPermittedError` described below.
Class description:
Raised when calling get on a pointer to a tensor which does not allow get to be called on it. This can happen do to sensitivity being too high
Method signatures and docstrings:
- def simplify(worker: 'sy.workers.AbstractWorker', e):... | cc4765bed880ad38a02505834f63df39e0815328 | <|skeleton|>
class GetNotPermittedError:
"""Raised when calling get on a pointer to a tensor which does not allow get to be called on it. This can happen do to sensitivity being too high"""
def simplify(worker: 'sy.workers.AbstractWorker', e):
"""Serialize information about an Exception which was raise... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetNotPermittedError:
"""Raised when calling get on a pointer to a tensor which does not allow get to be called on it. This can happen do to sensitivity being too high"""
def simplify(worker: 'sy.workers.AbstractWorker', e):
"""Serialize information about an Exception which was raised to forward ... | the_stack_v2_python_sparse | syft/exceptions.py | tudorcebere/PySyft | train | 2 |
c120acd5af964ec3df331bad4fdbd6ba6a8889a2 | [
"super(BertAttention, self).__init__()\nself.self1 = BertSelfAttention(config)\nself.output = BertSelfOutput(config)\nself.pruned_heads = set()\nself.view = P.Reshape()\nself.eq = ops.Equal()",
"self_outputs = self.self1(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask)\natt... | <|body_start_0|>
super(BertAttention, self).__init__()
self.self1 = BertSelfAttention(config)
self.output = BertSelfOutput(config)
self.pruned_heads = set()
self.view = P.Reshape()
self.eq = ops.Equal()
<|end_body_0|>
<|body_start_1|>
self_outputs = self.self1(hi... | bert attention | BertAttention | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BertAttention:
"""bert attention"""
def __init__(self, config):
"""init fun"""
<|body_0|>
def construct(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None):
"""construct fun"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k_train_010194 | 16,172 | permissive | [
{
"docstring": "init fun",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "construct fun",
"name": "construct",
"signature": "def construct(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None)"... | 2 | null | Implement the Python class `BertAttention` described below.
Class description:
bert attention
Method signatures and docstrings:
- def __init__(self, config): init fun
- def construct(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None): construct fun | Implement the Python class `BertAttention` described below.
Class description:
bert attention
Method signatures and docstrings:
- def __init__(self, config): init fun
- def construct(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None): construct fun
<|sk... | eab643f51336dbf7d711f02d27e6516e5affee59 | <|skeleton|>
class BertAttention:
"""bert attention"""
def __init__(self, config):
"""init fun"""
<|body_0|>
def construct(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None):
"""construct fun"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BertAttention:
"""bert attention"""
def __init__(self, config):
"""init fun"""
super(BertAttention, self).__init__()
self.self1 = BertSelfAttention(config)
self.output = BertSelfOutput(config)
self.pruned_heads = set()
self.view = P.Reshape()
self.e... | the_stack_v2_python_sparse | research/nlp/luke/src/luke/robert.py | mindspore-ai/models | train | 301 |
311058ba9aa7fb7985af4c25b0aaeb5b8e92d78d | [
"interpol = pero.LogInterpol()\nself.assertEqual(interpol.normalize(10, 1, 100), 0.5)\nself.assertEqual(interpol.denormalize(0.5, 1, 100), 10)\nself.assertAlmostEqual(interpol.normalize(0.1, 1, 100), -0.5, 10)\nself.assertAlmostEqual(interpol.denormalize(-0.5, 1, 100), 0.1, 10)\nself.assertAlmostEqual(interpol.norm... | <|body_start_0|>
interpol = pero.LogInterpol()
self.assertEqual(interpol.normalize(10, 1, 100), 0.5)
self.assertEqual(interpol.denormalize(0.5, 1, 100), 10)
self.assertAlmostEqual(interpol.normalize(0.1, 1, 100), -0.5, 10)
self.assertAlmostEqual(interpol.denormalize(-0.5, 1, 100)... | Test case for linear interpolator. | TestCase | [
"LicenseRef-scancode-philippe-de-muyter",
"LicenseRef-scancode-commercial-license",
"AGPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCase:
"""Test case for linear interpolator."""
def test_log10(self):
"""Tests whether interpolator works for log10 range."""
<|body_0|>
def test_log2(self):
"""Tests whether interpolator works for log2 range."""
<|body_1|>
def test_arrays(self):
... | stack_v2_sparse_classes_36k_train_010195 | 2,040 | permissive | [
{
"docstring": "Tests whether interpolator works for log10 range.",
"name": "test_log10",
"signature": "def test_log10(self)"
},
{
"docstring": "Tests whether interpolator works for log2 range.",
"name": "test_log2",
"signature": "def test_log2(self)"
},
{
"docstring": "Tests whe... | 3 | null | Implement the Python class `TestCase` described below.
Class description:
Test case for linear interpolator.
Method signatures and docstrings:
- def test_log10(self): Tests whether interpolator works for log10 range.
- def test_log2(self): Tests whether interpolator works for log2 range.
- def test_arrays(self): Test... | Implement the Python class `TestCase` described below.
Class description:
Test case for linear interpolator.
Method signatures and docstrings:
- def test_log10(self): Tests whether interpolator works for log10 range.
- def test_log2(self): Tests whether interpolator works for log2 range.
- def test_arrays(self): Test... | d59b1bc056f3037b7b7ab635b6deb41120612965 | <|skeleton|>
class TestCase:
"""Test case for linear interpolator."""
def test_log10(self):
"""Tests whether interpolator works for log10 range."""
<|body_0|>
def test_log2(self):
"""Tests whether interpolator works for log2 range."""
<|body_1|>
def test_arrays(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestCase:
"""Test case for linear interpolator."""
def test_log10(self):
"""Tests whether interpolator works for log10 range."""
interpol = pero.LogInterpol()
self.assertEqual(interpol.normalize(10, 1, 100), 0.5)
self.assertEqual(interpol.denormalize(0.5, 1, 100), 10)
... | the_stack_v2_python_sparse | unittests/scales/test_log.py | xxao/pero | train | 31 |
d5ed7c2006d589b4d866b7c55e5b49cd11e0caf7 | [
"elements = set(arr)\nsorted_ranks = sorted(list(elements))\nres = []\nfor x in arr:\n res.append(sorted_ranks.index(x) + 1)\nreturn res",
"elements = list(set(arr))\nsorted_ranks = sorted(elements)\nd = {}\nfor i, x in enumerate(sorted_ranks):\n d[x] = i + 1\nres = []\nfor x in arr:\n res.append(d[x])\n... | <|body_start_0|>
elements = set(arr)
sorted_ranks = sorted(list(elements))
res = []
for x in arr:
res.append(sorted_ranks.index(x) + 1)
return res
<|end_body_0|>
<|body_start_1|>
elements = list(set(arr))
sorted_ranks = sorted(elements)
d = {}... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def arrayRankTransform0(self, arr: List[int]) -> List[int]:
"""Purpose: Replaces each element in integer array with its rank. Note: - Rank is an integer starting from 1; - The larger the element, the larger the rank; - If two elements are equal, their rank must be the same; - R... | stack_v2_sparse_classes_36k_train_010196 | 1,061 | no_license | [
{
"docstring": "Purpose: Replaces each element in integer array with its rank. Note: - Rank is an integer starting from 1; - The larger the element, the larger the rank; - If two elements are equal, their rank must be the same; - Rank should be as small as possible.",
"name": "arrayRankTransform0",
"sig... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def arrayRankTransform0(self, arr: List[int]) -> List[int]: Purpose: Replaces each element in integer array with its rank. Note: - Rank is an integer starting from 1; - The large... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def arrayRankTransform0(self, arr: List[int]) -> List[int]: Purpose: Replaces each element in integer array with its rank. Note: - Rank is an integer starting from 1; - The large... | 95a86cbbca28d0c0f6d72d28a2f1cb5a86327934 | <|skeleton|>
class Solution:
def arrayRankTransform0(self, arr: List[int]) -> List[int]:
"""Purpose: Replaces each element in integer array with its rank. Note: - Rank is an integer starting from 1; - The larger the element, the larger the rank; - If two elements are equal, their rank must be the same; - R... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def arrayRankTransform0(self, arr: List[int]) -> List[int]:
"""Purpose: Replaces each element in integer array with its rank. Note: - Rank is an integer starting from 1; - The larger the element, the larger the rank; - If two elements are equal, their rank must be the same; - Rank should be ... | the_stack_v2_python_sparse | rankTransformArr.py | tashakim/puzzles_python | train | 8 | |
1285e3ae0f167499f21506afefb5a0da279b101e | [
"main = MainPage()\nmain.goto_register().register()\nsleep(3)",
"main = MainPage()\nmain.goto_login().goto_register().register()\nsleep(3)"
] | <|body_start_0|>
main = MainPage()
main.goto_register().register()
sleep(3)
<|end_body_0|>
<|body_start_1|>
main = MainPage()
main.goto_login().goto_register().register()
sleep(3)
<|end_body_1|>
| TestRegister | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRegister:
def test_register(self):
"""从首页-注册页 :return:"""
<|body_0|>
def test_login_register(self):
"""从首页-登录页-注册页 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
main = MainPage()
main.goto_register().register()
sleep(3... | stack_v2_sparse_classes_36k_train_010197 | 673 | no_license | [
{
"docstring": "从首页-注册页 :return:",
"name": "test_register",
"signature": "def test_register(self)"
},
{
"docstring": "从首页-登录页-注册页 :return:",
"name": "test_login_register",
"signature": "def test_login_register(self)"
}
] | 2 | null | Implement the Python class `TestRegister` described below.
Class description:
Implement the TestRegister class.
Method signatures and docstrings:
- def test_register(self): 从首页-注册页 :return:
- def test_login_register(self): 从首页-登录页-注册页 :return: | Implement the Python class `TestRegister` described below.
Class description:
Implement the TestRegister class.
Method signatures and docstrings:
- def test_register(self): 从首页-注册页 :return:
- def test_login_register(self): 从首页-登录页-注册页 :return:
<|skeleton|>
class TestRegister:
def test_register(self):
""... | 6648dbfb640b065ff2c76cb6889a8f9e4f124b91 | <|skeleton|>
class TestRegister:
def test_register(self):
"""从首页-注册页 :return:"""
<|body_0|>
def test_login_register(self):
"""从首页-登录页-注册页 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestRegister:
def test_register(self):
"""从首页-注册页 :return:"""
main = MainPage()
main.goto_register().register()
sleep(3)
def test_login_register(self):
"""从首页-登录页-注册页 :return:"""
main = MainPage()
main.goto_login().goto_register().register()
... | the_stack_v2_python_sparse | test_ProjectPractice/test_selenium/testcase/test_register.py | Veraun/HogwartsSDET17-1 | train | 0 | |
420ff28a65c5b7c760df03e57724a4d6e949cfc2 | [
"super(FeatureExtractor, self).__init__(model_path, profiling_file=profiling_file, decode=False)\nassert output_feature in self._outputs_map, 'invalid output_name %s, not in model signature' % output_feature\nself._outputs = [output_feature]",
"output_list = super(FeatureExtractor, self).predict(images, self._out... | <|body_start_0|>
super(FeatureExtractor, self).__init__(model_path, profiling_file=profiling_file, decode=False)
assert output_feature in self._outputs_map, 'invalid output_name %s, not in model signature' % output_feature
self._outputs = [output_feature]
<|end_body_0|>
<|body_start_1|>
... | FeatureExtractor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureExtractor:
def __init__(self, model_path, output_feature, profiling_file=None):
"""Args: model_path: saved model path or frozenpb path output_feature: output node name"""
<|body_0|>
def predict(self, images, batch_size=1):
"""Args: images: a list of numpy uint... | stack_v2_sparse_classes_36k_train_010198 | 18,831 | permissive | [
{
"docstring": "Args: model_path: saved model path or frozenpb path output_feature: output node name",
"name": "__init__",
"signature": "def __init__(self, model_path, output_feature, profiling_file=None)"
},
{
"docstring": "Args: images: a list of numpy uint8 array batch_size: batch_size used t... | 2 | stack_v2_sparse_classes_30k_train_019406 | Implement the Python class `FeatureExtractor` described below.
Class description:
Implement the FeatureExtractor class.
Method signatures and docstrings:
- def __init__(self, model_path, output_feature, profiling_file=None): Args: model_path: saved model path or frozenpb path output_feature: output node name
- def pr... | Implement the Python class `FeatureExtractor` described below.
Class description:
Implement the FeatureExtractor class.
Method signatures and docstrings:
- def __init__(self, model_path, output_feature, profiling_file=None): Args: model_path: saved model path or frozenpb path output_feature: output node name
- def pr... | d6912e2da3061935bf82bd429bc7a16a7a06e3a3 | <|skeleton|>
class FeatureExtractor:
def __init__(self, model_path, output_feature, profiling_file=None):
"""Args: model_path: saved model path or frozenpb path output_feature: output node name"""
<|body_0|>
def predict(self, images, batch_size=1):
"""Args: images: a list of numpy uint... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureExtractor:
def __init__(self, model_path, output_feature, profiling_file=None):
"""Args: model_path: saved model path or frozenpb path output_feature: output node name"""
super(FeatureExtractor, self).__init__(model_path, profiling_file=profiling_file, decode=False)
assert outpu... | the_stack_v2_python_sparse | scripts/fashion_bert/image_feature_extract.py | alibaba/EasyTransfer | train | 900 | |
9634dfb3d421affdce4416511aa6888e9f65533d | [
"super(SearchRequestHandler, self).__init__(application, request, **kwargs)\nself.status = None\nself.timestamp = None\nself.issues = []\nself.total = 0",
"def _decode_request():\n \"\"\"Decodes request.\n\n \"\"\"\n if self.get_argument(_PARAM_STATUS) != '*':\n self.status = self.get_argu... | <|body_start_0|>
super(SearchRequestHandler, self).__init__(application, request, **kwargs)
self.status = None
self.timestamp = None
self.issues = []
self.total = 0
<|end_body_0|>
<|body_start_1|>
def _decode_request():
"""Decodes request.
... | Search issue request handler. | SearchRequestHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SearchRequestHandler:
"""Search issue request handler."""
def __init__(self, application, request, **kwargs):
"""Instance constructor."""
<|body_0|>
def get(self):
"""HTTP GET handler."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(Search... | stack_v2_sparse_classes_36k_train_010199 | 1,876 | no_license | [
{
"docstring": "Instance constructor.",
"name": "__init__",
"signature": "def __init__(self, application, request, **kwargs)"
},
{
"docstring": "HTTP GET handler.",
"name": "get",
"signature": "def get(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000483 | Implement the Python class `SearchRequestHandler` described below.
Class description:
Search issue request handler.
Method signatures and docstrings:
- def __init__(self, application, request, **kwargs): Instance constructor.
- def get(self): HTTP GET handler. | Implement the Python class `SearchRequestHandler` described below.
Class description:
Search issue request handler.
Method signatures and docstrings:
- def __init__(self, application, request, **kwargs): Instance constructor.
- def get(self): HTTP GET handler.
<|skeleton|>
class SearchRequestHandler:
"""Search i... | 3058848dcb263aa1214166252e528850e73cb006 | <|skeleton|>
class SearchRequestHandler:
"""Search issue request handler."""
def __init__(self, application, request, **kwargs):
"""Instance constructor."""
<|body_0|>
def get(self):
"""HTTP GET handler."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SearchRequestHandler:
"""Search issue request handler."""
def __init__(self, application, request, **kwargs):
"""Instance constructor."""
super(SearchRequestHandler, self).__init__(application, request, **kwargs)
self.status = None
self.timestamp = None
self.issues... | the_stack_v2_python_sparse | ws/errata/handlers/search.py | AtefBN/esdoc-errata | train | 0 |
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