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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
870953d0d99265c39ec3458163d86e85e3e2948f | [
"self.buffer_size = buffer_size\nself.num_imgs = 0\nself.images = []",
"return_images = []\nfor image in images:\n image = torch.unsqueeze(image.data, 0)\n if self.num_imgs < self.buffer_size:\n self.images.append(image)\n return_images.append(image)\n self.num_imgs += 1\n else:\n ... | <|body_start_0|>
self.buffer_size = buffer_size
self.num_imgs = 0
self.images = []
<|end_body_0|>
<|body_start_1|>
return_images = []
for image in images:
image = torch.unsqueeze(image.data, 0)
if self.num_imgs < self.buffer_size:
self.ima... | Random choose previous generated images or ones produced by the latest generators. :param buffer_size: the size of image buffer :type buffer_size: int | ShuffleBuffer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShuffleBuffer:
"""Random choose previous generated images or ones produced by the latest generators. :param buffer_size: the size of image buffer :type buffer_size: int"""
def __init__(self, buffer_size):
"""Initialize the ImagePool class. :param buffer_size: the size of image buffer... | stack_v2_sparse_classes_36k_train_025100 | 10,238 | permissive | [
{
"docstring": "Initialize the ImagePool class. :param buffer_size: the size of image buffer :type buffer_size: int",
"name": "__init__",
"signature": "def __init__(self, buffer_size)"
},
{
"docstring": "Return an image from the pool. :param images: the latest generated images from the generator... | 2 | null | Implement the Python class `ShuffleBuffer` described below.
Class description:
Random choose previous generated images or ones produced by the latest generators. :param buffer_size: the size of image buffer :type buffer_size: int
Method signatures and docstrings:
- def __init__(self, buffer_size): Initialize the Imag... | Implement the Python class `ShuffleBuffer` described below.
Class description:
Random choose previous generated images or ones produced by the latest generators. :param buffer_size: the size of image buffer :type buffer_size: int
Method signatures and docstrings:
- def __init__(self, buffer_size): Initialize the Imag... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class ShuffleBuffer:
"""Random choose previous generated images or ones produced by the latest generators. :param buffer_size: the size of image buffer :type buffer_size: int"""
def __init__(self, buffer_size):
"""Initialize the ImagePool class. :param buffer_size: the size of image buffer... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShuffleBuffer:
"""Random choose previous generated images or ones produced by the latest generators. :param buffer_size: the size of image buffer :type buffer_size: int"""
def __init__(self, buffer_size):
"""Initialize the ImagePool class. :param buffer_size: the size of image buffer :type buffer... | the_stack_v2_python_sparse | zeus/networks/pytorch/cyclesrbodys/trans_model.py | huawei-noah/xingtian | train | 308 |
24279632f980c7ee174612d490f01a5ec4b5317f | [
"self.cap = capacity\nself.cnt = collections.defaultdict(int)\nself.cache = {}\nself.visited = collections.deque()",
"if key in self.cache:\n self.visited.append(key)\n self.cnt[key] += 1\n return self.cache[key]\nreturn -1",
"if key not in self.cache and len(self.cache) >= self.cap:\n while self.vi... | <|body_start_0|>
self.cap = capacity
self.cnt = collections.defaultdict(int)
self.cache = {}
self.visited = collections.deque()
<|end_body_0|>
<|body_start_1|>
if key in self.cache:
self.visited.append(key)
self.cnt[key] += 1
return self.cache... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k_train_025101 | 2,031 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | stack_v2_sparse_classes_30k_train_012587 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
<|sk... | 28d47c9488d47921769f40383ea9ffe2c56f3597 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.cap = capacity
self.cnt = collections.defaultdict(int)
self.cache = {}
self.visited = collections.deque()
def get(self, key):
""":type key: int :rtype: int"""
if key in self.cach... | the_stack_v2_python_sparse | 146. LRU Cache.py | liangliannie/LeetCode | train | 0 | |
ee7b9a5d70f529cf10799604b4860d09d3292ac7 | [
"fake_cfg = mock.MagicMock()\nfake_cfg.ssh_public_key_path = self.SSH_PUBLIC_KEY_PATH\nfake_cfg.machine_type = self.MACHINE_TYPE\nfake_cfg.network = self.NETWORK\nfake_cfg.zone = self.ZONE\nfake_cfg.resolution = '{x}x{y}x32x{dpi}'.format(x=self.X_RES, y=self.Y_RES, dpi=self.DPI)\nfake_cfg.metadata_variable = self.M... | <|body_start_0|>
fake_cfg = mock.MagicMock()
fake_cfg.ssh_public_key_path = self.SSH_PUBLIC_KEY_PATH
fake_cfg.machine_type = self.MACHINE_TYPE
fake_cfg.network = self.NETWORK
fake_cfg.zone = self.ZONE
fake_cfg.resolution = '{x}x{y}x32x{dpi}'.format(x=self.X_RES, y=self.Y_... | Test CheepsComputeClient. | CheepsComputeClientTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CheepsComputeClientTest:
"""Test CheepsComputeClient."""
def _GetFakeConfig(self):
"""Create a fake configuration object. Returns: A fake configuration mock object."""
<|body_0|>
def setUp(self):
"""Set up the test."""
<|body_1|>
def testCreateInstan... | stack_v2_sparse_classes_36k_train_025102 | 6,492 | permissive | [
{
"docstring": "Create a fake configuration object. Returns: A fake configuration mock object.",
"name": "_GetFakeConfig",
"signature": "def _GetFakeConfig(self)"
},
{
"docstring": "Set up the test.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test CreateI... | 4 | stack_v2_sparse_classes_30k_train_016688 | Implement the Python class `CheepsComputeClientTest` described below.
Class description:
Test CheepsComputeClient.
Method signatures and docstrings:
- def _GetFakeConfig(self): Create a fake configuration object. Returns: A fake configuration mock object.
- def setUp(self): Set up the test.
- def testCreateInstance(s... | Implement the Python class `CheepsComputeClientTest` described below.
Class description:
Test CheepsComputeClient.
Method signatures and docstrings:
- def _GetFakeConfig(self): Create a fake configuration object. Returns: A fake configuration mock object.
- def setUp(self): Set up the test.
- def testCreateInstance(s... | 78a61ca023cbf1a0cecfef8b97df2b274ac3a988 | <|skeleton|>
class CheepsComputeClientTest:
"""Test CheepsComputeClient."""
def _GetFakeConfig(self):
"""Create a fake configuration object. Returns: A fake configuration mock object."""
<|body_0|>
def setUp(self):
"""Set up the test."""
<|body_1|>
def testCreateInstan... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CheepsComputeClientTest:
"""Test CheepsComputeClient."""
def _GetFakeConfig(self):
"""Create a fake configuration object. Returns: A fake configuration mock object."""
fake_cfg = mock.MagicMock()
fake_cfg.ssh_public_key_path = self.SSH_PUBLIC_KEY_PATH
fake_cfg.machine_type... | the_stack_v2_python_sparse | tools/acloud/internal/lib/cheeps_compute_client_test.py | ZYHGOD-1/Aosp11 | train | 0 |
1cd16d219c678d753c96c367884211a3fed00a86 | [
"self.cluster_name = cluster_name\nself.environment = environment\nself.job_name = job_name\nself.job_uid = job_uid",
"if dictionary is None:\n return None\ncluster_name = dictionary.get('clusterName')\nenvironment = dictionary.get('environment')\njob_name = dictionary.get('jobName')\njob_uid = cohesity_manage... | <|body_start_0|>
self.cluster_name = cluster_name
self.environment = environment
self.job_name = job_name
self.job_uid = job_uid
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
cluster_name = dictionary.get('clusterName')
environmen... | Implementation of the 'RemoteProtectionJobInformation' model. Specifies details about the original Protection Job and its Snapshots, that were archived to a remote Vault. Attributes: cluster_name (string): Specifies the name of the original Cluster that archived the data to the Vault. environment (EnvironmentRemoteProt... | RemoteProtectionJobInformation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoteProtectionJobInformation:
"""Implementation of the 'RemoteProtectionJobInformation' model. Specifies details about the original Protection Job and its Snapshots, that were archived to a remote Vault. Attributes: cluster_name (string): Specifies the name of the original Cluster that archived... | stack_v2_sparse_classes_36k_train_025103 | 6,552 | permissive | [
{
"docstring": "Constructor for the RemoteProtectionJobInformation class",
"name": "__init__",
"signature": "def __init__(self, cluster_name=None, environment=None, job_name=None, job_uid=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary):... | 2 | null | Implement the Python class `RemoteProtectionJobInformation` described below.
Class description:
Implementation of the 'RemoteProtectionJobInformation' model. Specifies details about the original Protection Job and its Snapshots, that were archived to a remote Vault. Attributes: cluster_name (string): Specifies the nam... | Implement the Python class `RemoteProtectionJobInformation` described below.
Class description:
Implementation of the 'RemoteProtectionJobInformation' model. Specifies details about the original Protection Job and its Snapshots, that were archived to a remote Vault. Attributes: cluster_name (string): Specifies the nam... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RemoteProtectionJobInformation:
"""Implementation of the 'RemoteProtectionJobInformation' model. Specifies details about the original Protection Job and its Snapshots, that were archived to a remote Vault. Attributes: cluster_name (string): Specifies the name of the original Cluster that archived... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RemoteProtectionJobInformation:
"""Implementation of the 'RemoteProtectionJobInformation' model. Specifies details about the original Protection Job and its Snapshots, that were archived to a remote Vault. Attributes: cluster_name (string): Specifies the name of the original Cluster that archived the data to ... | the_stack_v2_python_sparse | cohesity_management_sdk/models/remote_protection_job_information.py | cohesity/management-sdk-python | train | 24 |
c84d997a2ce771d5bbc37273d9d6216bed642715 | [
"if p == None and q == None:\n return True\nelif p != None and q != None:\n p_stack = [p]\n q_stack = [q]\n while p_stack:\n p = p_stack.pop(0)\n q = q_stack.pop(0)\n left_flag = right_flag = True\n if p.val != q.val:\n return False\n if p.left != None and q... | <|body_start_0|>
if p == None and q == None:
return True
elif p != None and q != None:
p_stack = [p]
q_stack = [q]
while p_stack:
p = p_stack.pop(0)
q = q_stack.pop(0)
left_flag = right_flag = True
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSameTree1(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
<|body_0|>
def isSameTree2(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if p == None an... | stack_v2_sparse_classes_36k_train_025104 | 1,753 | no_license | [
{
"docstring": ":type p: TreeNode :type q: TreeNode :rtype: bool",
"name": "isSameTree1",
"signature": "def isSameTree1(self, p, q)"
},
{
"docstring": ":type p: TreeNode :type q: TreeNode :rtype: bool",
"name": "isSameTree2",
"signature": "def isSameTree2(self, p, q)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014344 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSameTree1(self, p, q): :type p: TreeNode :type q: TreeNode :rtype: bool
- def isSameTree2(self, p, q): :type p: TreeNode :type q: TreeNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSameTree1(self, p, q): :type p: TreeNode :type q: TreeNode :rtype: bool
- def isSameTree2(self, p, q): :type p: TreeNode :type q: TreeNode :rtype: bool
<|skeleton|>
class ... | e2fecd266bfced6208694b19a2d81182b13dacd6 | <|skeleton|>
class Solution:
def isSameTree1(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
<|body_0|>
def isSameTree2(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isSameTree1(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
if p == None and q == None:
return True
elif p != None and q != None:
p_stack = [p]
q_stack = [q]
while p_stack:
p = p_stack.pop... | the_stack_v2_python_sparse | isSameTree.py | HuipengXu/leetcode | train | 0 | |
3bf12d14d627ae6dff8b4657b87546705e414b0b | [
"try:\n return self._dao.execute('SELECT interest_id, interest_name FROM Student_Interest INNER JOIN Interest ON Student_Interest.interest_id=Interest.id where k_number = %s AND Student_Interest.scheme_id = %s', (k_number, scheme_id))\nexcept Exception as e:\n self._log.exception('Could not get interests')\n ... | <|body_start_0|>
try:
return self._dao.execute('SELECT interest_id, interest_name FROM Student_Interest INNER JOIN Interest ON Student_Interest.interest_id=Interest.id where k_number = %s AND Student_Interest.scheme_id = %s', (k_number, scheme_id))
except Exception as e:
self._lo... | StudentInterestModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StudentInterestModel:
def get_interests(self, scheme_id, k_number):
"""Given the k_number will return all the student's interests"""
<|body_0|>
def insert_interest(self, scheme_id, k_number, interest_id):
"""Will entirely populate an entry for Student_Interest table"... | stack_v2_sparse_classes_36k_train_025105 | 2,655 | no_license | [
{
"docstring": "Given the k_number will return all the student's interests",
"name": "get_interests",
"signature": "def get_interests(self, scheme_id, k_number)"
},
{
"docstring": "Will entirely populate an entry for Student_Interest table",
"name": "insert_interest",
"signature": "def i... | 4 | stack_v2_sparse_classes_30k_train_005423 | Implement the Python class `StudentInterestModel` described below.
Class description:
Implement the StudentInterestModel class.
Method signatures and docstrings:
- def get_interests(self, scheme_id, k_number): Given the k_number will return all the student's interests
- def insert_interest(self, scheme_id, k_number, ... | Implement the Python class `StudentInterestModel` described below.
Class description:
Implement the StudentInterestModel class.
Method signatures and docstrings:
- def get_interests(self, scheme_id, k_number): Given the k_number will return all the student's interests
- def insert_interest(self, scheme_id, k_number, ... | 649a3c1cdcc90443f9561dfa1262ae3b0e970729 | <|skeleton|>
class StudentInterestModel:
def get_interests(self, scheme_id, k_number):
"""Given the k_number will return all the student's interests"""
<|body_0|>
def insert_interest(self, scheme_id, k_number, interest_id):
"""Will entirely populate an entry for Student_Interest table"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StudentInterestModel:
def get_interests(self, scheme_id, k_number):
"""Given the k_number will return all the student's interests"""
try:
return self._dao.execute('SELECT interest_id, interest_name FROM Student_Interest INNER JOIN Interest ON Student_Interest.interest_id=Interest.i... | the_stack_v2_python_sparse | flaskr/models/student_interestmdl.py | nickpezzotti1/BuddySchemeWebApp | train | 2 | |
9419298407bf2c2ee1aafb71a1378315f2810685 | [
"self.module = module\nself.exclude = exclude\nself.include = include",
"if self.exclude:\n self.actions = [a for a in self.actions if a.__name__ not in self.exclude]\nif self.include:\n self.actions = [a for a in self.actions if a.__name__ in self.include]\nif self.exclude:\n self.cleanup = [c for c in ... | <|body_start_0|>
self.module = module
self.exclude = exclude
self.include = include
<|end_body_0|>
<|body_start_1|>
if self.exclude:
self.actions = [a for a in self.actions if a.__name__ not in self.exclude]
if self.include:
self.actions = [a for a in sel... | Model | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
def __init__(self, module, exclude, include):
"""initializations common to all derived classes"""
<|body_0|>
def revise_actions(self):
"""Revise lists of actions and cleanups accounting for -e --exclude -a --add Alter the copies, results might differ from self... | stack_v2_sparse_classes_36k_train_025106 | 2,307 | permissive | [
{
"docstring": "initializations common to all derived classes",
"name": "__init__",
"signature": "def __init__(self, module, exclude, include)"
},
{
"docstring": "Revise lists of actions and cleanups accounting for -e --exclude -a --add Alter the copies, results might differ from self.module.act... | 3 | stack_v2_sparse_classes_30k_train_002457 | Implement the Python class `Model` described below.
Class description:
Implement the Model class.
Method signatures and docstrings:
- def __init__(self, module, exclude, include): initializations common to all derived classes
- def revise_actions(self): Revise lists of actions and cleanups accounting for -e --exclude... | Implement the Python class `Model` described below.
Class description:
Implement the Model class.
Method signatures and docstrings:
- def __init__(self, module, exclude, include): initializations common to all derived classes
- def revise_actions(self): Revise lists of actions and cleanups accounting for -e --exclude... | 457ea284ea20703885f8e57fa5c1891051be9b03 | <|skeleton|>
class Model:
def __init__(self, module, exclude, include):
"""initializations common to all derived classes"""
<|body_0|>
def revise_actions(self):
"""Revise lists of actions and cleanups accounting for -e --exclude -a --add Alter the copies, results might differ from self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Model:
def __init__(self, module, exclude, include):
"""initializations common to all derived classes"""
self.module = module
self.exclude = exclude
self.include = include
def revise_actions(self):
"""Revise lists of actions and cleanups accounting for -e --exclude... | the_stack_v2_python_sparse | pymodel/model.py | jon-jacky/PyModel | train | 75 | |
9a3879df9d987b03d5e78edeaf561b327a4d9493 | [
"self.attribute_vec = attribute_vec\nself.dest_guid = dest_guid\nself.dest_prop_count = dest_prop_count\nself.excluded_prop_count = excluded_prop_count\nself.mismatch_prop_count = mismatch_prop_count\nself.object_flags = object_flags\nself.source_guid = source_guid\nself.source_prop_count = source_prop_count\nself.... | <|body_start_0|>
self.attribute_vec = attribute_vec
self.dest_guid = dest_guid
self.dest_prop_count = dest_prop_count
self.excluded_prop_count = excluded_prop_count
self.mismatch_prop_count = mismatch_prop_count
self.object_flags = object_flags
self.source_guid = ... | Implementation of the 'CompareADObjectsResult_ADObject' model. TODO: type description here. Attributes: attribute_vec (list of CompareADObjectsResult_ADAttribute): Array of AD attributes of AD object. This will contain distinct attributes from source and destination objects. dest_guid (string): Object guid from dest_se... | CompareADObjectsResult_ADObject | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompareADObjectsResult_ADObject:
"""Implementation of the 'CompareADObjectsResult_ADObject' model. TODO: type description here. Attributes: attribute_vec (list of CompareADObjectsResult_ADAttribute): Array of AD attributes of AD object. This will contain distinct attributes from source and destin... | stack_v2_sparse_classes_36k_train_025107 | 5,278 | permissive | [
{
"docstring": "Constructor for the CompareADObjectsResult_ADObject class",
"name": "__init__",
"signature": "def __init__(self, attribute_vec=None, dest_guid=None, dest_prop_count=None, excluded_prop_count=None, mismatch_prop_count=None, object_flags=None, source_guid=None, source_prop_count=None, stat... | 2 | null | Implement the Python class `CompareADObjectsResult_ADObject` described below.
Class description:
Implementation of the 'CompareADObjectsResult_ADObject' model. TODO: type description here. Attributes: attribute_vec (list of CompareADObjectsResult_ADAttribute): Array of AD attributes of AD object. This will contain dis... | Implement the Python class `CompareADObjectsResult_ADObject` described below.
Class description:
Implementation of the 'CompareADObjectsResult_ADObject' model. TODO: type description here. Attributes: attribute_vec (list of CompareADObjectsResult_ADAttribute): Array of AD attributes of AD object. This will contain dis... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class CompareADObjectsResult_ADObject:
"""Implementation of the 'CompareADObjectsResult_ADObject' model. TODO: type description here. Attributes: attribute_vec (list of CompareADObjectsResult_ADAttribute): Array of AD attributes of AD object. This will contain distinct attributes from source and destin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompareADObjectsResult_ADObject:
"""Implementation of the 'CompareADObjectsResult_ADObject' model. TODO: type description here. Attributes: attribute_vec (list of CompareADObjectsResult_ADAttribute): Array of AD attributes of AD object. This will contain distinct attributes from source and destination objects... | the_stack_v2_python_sparse | cohesity_management_sdk/models/compare_ad_objects_result_ad_object.py | cohesity/management-sdk-python | train | 24 |
b690d468aec3ffaadaff1b24f8bc28941501d648 | [
"self.biosqllog = LogIt().default(logname='BioSQL', logfile=None)\nself.biosql_utils = FullUtilities()\nself.biosql_proc = self.biosql_utils.system_cmd\nself.scripts = pkg_resources.resource_filename(sql_scripts.__name__, '')\nself.ncbi_taxon_script = pkg_resources.resource_filename(sql_scripts.__name__, 'load_ncbi... | <|body_start_0|>
self.biosqllog = LogIt().default(logname='BioSQL', logfile=None)
self.biosql_utils = FullUtilities()
self.biosql_proc = self.biosql_utils.system_cmd
self.scripts = pkg_resources.resource_filename(sql_scripts.__name__, '')
self.ncbi_taxon_script = pkg_resources.re... | BaseBioSQL | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseBioSQL:
def __init__(self, database_name, template_name='', project=None, project_path=None, proj_mana=ProjectManagement, **kwargs):
"""This is the base BioSQL class. It provides a general framework for managing the BioSQL workflow. Higher level classes provide more specific function... | stack_v2_sparse_classes_36k_train_025108 | 13,035 | no_license | [
{
"docstring": "This is the base BioSQL class. It provides a general framework for managing the BioSQL workflow. Higher level classes provide more specific functionality related to the various BioSQL supported database types. Taxonomy data can be found at: NCBI: ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy ITIS: htt... | 3 | stack_v2_sparse_classes_30k_train_017447 | Implement the Python class `BaseBioSQL` described below.
Class description:
Implement the BaseBioSQL class.
Method signatures and docstrings:
- def __init__(self, database_name, template_name='', project=None, project_path=None, proj_mana=ProjectManagement, **kwargs): This is the base BioSQL class. It provides a gene... | Implement the Python class `BaseBioSQL` described below.
Class description:
Implement the BaseBioSQL class.
Method signatures and docstrings:
- def __init__(self, database_name, template_name='', project=None, project_path=None, proj_mana=ProjectManagement, **kwargs): This is the base BioSQL class. It provides a gene... | e207046ec36387751fe2bba8b6782fdc2a580107 | <|skeleton|>
class BaseBioSQL:
def __init__(self, database_name, template_name='', project=None, project_path=None, proj_mana=ProjectManagement, **kwargs):
"""This is the base BioSQL class. It provides a general framework for managing the BioSQL workflow. Higher level classes provide more specific function... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseBioSQL:
def __init__(self, database_name, template_name='', project=None, project_path=None, proj_mana=ProjectManagement, **kwargs):
"""This is the base BioSQL class. It provides a general framework for managing the BioSQL workflow. Higher level classes provide more specific functionality related ... | the_stack_v2_python_sparse | OrthoEvol/Manager/biosql/biosql.py | datasnakes/OrthoEvolution | train | 19 | |
dbfe82fa28ce2a9e38bab882de22687fb3800cb2 | [
"self.state = np.array([[detection[0]], [detection[1]], [detection[2]], [detection[3]], [1], [1]])\nself.p_matrix = np.eye(6, dtype=int)\nself.h_matrix = np.array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0]])\nself.r_matrix = np.eye(4)\nself.predicted_state = type(None)\nself.pre... | <|body_start_0|>
self.state = np.array([[detection[0]], [detection[1]], [detection[2]], [detection[3]], [1], [1]])
self.p_matrix = np.eye(6, dtype=int)
self.h_matrix = np.array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0]])
self.r_matrix = np.eye(4)
... | Class KalmanFilter | KalmanFilter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KalmanFilter:
"""Class KalmanFilter"""
def __init__(self, detection):
"""This function initializes KalmanFilter object. Args: detection(list: 1x4): Detection of an object, which is used to initialize the state vector."""
<|body_0|>
def predict(self):
"""Function ... | stack_v2_sparse_classes_36k_train_025109 | 3,453 | no_license | [
{
"docstring": "This function initializes KalmanFilter object. Args: detection(list: 1x4): Detection of an object, which is used to initialize the state vector.",
"name": "__init__",
"signature": "def __init__(self, detection)"
},
{
"docstring": "Function for prediction. This function predicts t... | 3 | stack_v2_sparse_classes_30k_val_000747 | Implement the Python class `KalmanFilter` described below.
Class description:
Class KalmanFilter
Method signatures and docstrings:
- def __init__(self, detection): This function initializes KalmanFilter object. Args: detection(list: 1x4): Detection of an object, which is used to initialize the state vector.
- def pre... | Implement the Python class `KalmanFilter` described below.
Class description:
Class KalmanFilter
Method signatures and docstrings:
- def __init__(self, detection): This function initializes KalmanFilter object. Args: detection(list: 1x4): Detection of an object, which is used to initialize the state vector.
- def pre... | 7e2095142e604c157bc19fdca9cbe8c294376710 | <|skeleton|>
class KalmanFilter:
"""Class KalmanFilter"""
def __init__(self, detection):
"""This function initializes KalmanFilter object. Args: detection(list: 1x4): Detection of an object, which is used to initialize the state vector."""
<|body_0|>
def predict(self):
"""Function ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KalmanFilter:
"""Class KalmanFilter"""
def __init__(self, detection):
"""This function initializes KalmanFilter object. Args: detection(list: 1x4): Detection of an object, which is used to initialize the state vector."""
self.state = np.array([[detection[0]], [detection[1]], [detection[2]... | the_stack_v2_python_sparse | src/stabilisierung/kalman.py | Heatdh/advanced_machine_detection_gui | train | 0 |
c56ac94690fbec2ea84a0b954e0c54627a0dcb52 | [
"self.centre = centre\nif hasattr(centre, 'e') and centre.e is not None:\n self.e = centre.e\nelse:\n self.e = 0.0\nself.grad = grad\nself.hess = hess\nn_atoms = grad.shape[0]\nassert hess.shape == (n_atoms, n_atoms)",
"dx = (coords - self.centre).flatten()\nnew_e = self.e + np.dot(self.grad, dx)\nnew_e += ... | <|body_start_0|>
self.centre = centre
if hasattr(centre, 'e') and centre.e is not None:
self.e = centre.e
else:
self.e = 0.0
self.grad = grad
self.hess = hess
n_atoms = grad.shape[0]
assert hess.shape == (n_atoms, n_atoms)
<|end_body_0|>
<... | The truncated taylor surface from current grad and hessian | TruncatedTaylor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TruncatedTaylor:
"""The truncated taylor surface from current grad and hessian"""
def __init__(self, centre: Union[OptCoordinates, np.ndarray], grad: np.ndarray, hess: np.ndarray):
"""Second-order Taylor expansion around a point Args: centre (OptCoordinates|np.ndarray): The coordinat... | stack_v2_sparse_classes_36k_train_025110 | 26,310 | permissive | [
{
"docstring": "Second-order Taylor expansion around a point Args: centre (OptCoordinates|np.ndarray): The coordinate point grad (np.ndarray): Gradient at that point hess (np.ndarray): Hessian at that point",
"name": "__init__",
"signature": "def __init__(self, centre: Union[OptCoordinates, np.ndarray],... | 3 | stack_v2_sparse_classes_30k_train_016221 | Implement the Python class `TruncatedTaylor` described below.
Class description:
The truncated taylor surface from current grad and hessian
Method signatures and docstrings:
- def __init__(self, centre: Union[OptCoordinates, np.ndarray], grad: np.ndarray, hess: np.ndarray): Second-order Taylor expansion around a poin... | Implement the Python class `TruncatedTaylor` described below.
Class description:
The truncated taylor surface from current grad and hessian
Method signatures and docstrings:
- def __init__(self, centre: Union[OptCoordinates, np.ndarray], grad: np.ndarray, hess: np.ndarray): Second-order Taylor expansion around a poin... | 4d6667592f083dfcf38de6b75c4222c0a0e7b60b | <|skeleton|>
class TruncatedTaylor:
"""The truncated taylor surface from current grad and hessian"""
def __init__(self, centre: Union[OptCoordinates, np.ndarray], grad: np.ndarray, hess: np.ndarray):
"""Second-order Taylor expansion around a point Args: centre (OptCoordinates|np.ndarray): The coordinat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TruncatedTaylor:
"""The truncated taylor surface from current grad and hessian"""
def __init__(self, centre: Union[OptCoordinates, np.ndarray], grad: np.ndarray, hess: np.ndarray):
"""Second-order Taylor expansion around a point Args: centre (OptCoordinates|np.ndarray): The coordinate point grad ... | the_stack_v2_python_sparse | autode/bracket/dhs.py | duartegroup/autodE | train | 132 |
e533e5c8dd556553e5b616c34e61b9c5d26a19e3 | [
"self.model = model\nself.forward_relu_outputs: List[torch.Tensor] = []\nself.model.eval()\nself.unguided_gradient = unguided_gradient\nself.post_process_output = post_process_output\nif not unguided_gradient:\n self.update_relus()",
"def relu_backward_hook_function(module, grad_in, grad_out):\n \"\"\"\n ... | <|body_start_0|>
self.model = model
self.forward_relu_outputs: List[torch.Tensor] = []
self.model.eval()
self.unguided_gradient = unguided_gradient
self.post_process_output = post_process_output
if not unguided_gradient:
self.update_relus()
<|end_body_0|>
<|b... | Produces gradients generated with guided back propagation from the given image .. warning: * We assume the model is built with `Relu` activation function * the model will be instrumented, use `trw.train.CleanAddedHooks` to remove the hooks once guided back-propagation is finished | GuidedBackprop | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GuidedBackprop:
"""Produces gradients generated with guided back propagation from the given image .. warning: * We assume the model is built with `Relu` activation function * the model will be instrumented, use `trw.train.CleanAddedHooks` to remove the hooks once guided back-propagation is finish... | stack_v2_sparse_classes_36k_train_025111 | 6,548 | permissive | [
{
"docstring": "Args: model: the model unguided_gradient: if `False`, calculate the guided gradient. If `True`, calculate the gradient only post_process_output: a function to post-process the output of a model so that it is suitable for gradient attribution",
"name": "__init__",
"signature": "def __init... | 5 | null | Implement the Python class `GuidedBackprop` described below.
Class description:
Produces gradients generated with guided back propagation from the given image .. warning: * We assume the model is built with `Relu` activation function * the model will be instrumented, use `trw.train.CleanAddedHooks` to remove the hooks... | Implement the Python class `GuidedBackprop` described below.
Class description:
Produces gradients generated with guided back propagation from the given image .. warning: * We assume the model is built with `Relu` activation function * the model will be instrumented, use `trw.train.CleanAddedHooks` to remove the hooks... | 11c59dea0072d940b036166be22b392bb9e3b066 | <|skeleton|>
class GuidedBackprop:
"""Produces gradients generated with guided back propagation from the given image .. warning: * We assume the model is built with `Relu` activation function * the model will be instrumented, use `trw.train.CleanAddedHooks` to remove the hooks once guided back-propagation is finish... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GuidedBackprop:
"""Produces gradients generated with guided back propagation from the given image .. warning: * We assume the model is built with `Relu` activation function * the model will be instrumented, use `trw.train.CleanAddedHooks` to remove the hooks once guided back-propagation is finished"""
de... | the_stack_v2_python_sparse | src/trw/train/guided_back_propagation.py | civodlu/trw | train | 12 |
2fc34223ff7e19ba468a622f26857279da92dbc6 | [
"super().__init__()\nself.D = D\nself.W = W\nself.in_channels_dir = in_channels_dir\nfor i in range(D):\n if i == 0:\n layer = nn.Sequential(nn.Linear(in_channels_dir, W), nn.ReLU())\n else:\n layer = nn.Sequential(nn.Linear(W, W), nn.ReLU())\n setattr(self, f'dir_encoding_{i + 1}', layer)\ns... | <|body_start_0|>
super().__init__()
self.D = D
self.W = W
self.in_channels_dir = in_channels_dir
for i in range(D):
if i == 0:
layer = nn.Sequential(nn.Linear(in_channels_dir, W), nn.ReLU())
else:
layer = nn.Sequential(nn.Li... | BgNeRF | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BgNeRF:
def __init__(self, D, W, in_channels_dir=27, with_semantics=False, n_classes=6):
"""D: number of layers W: number of hidden units in each layer in_channels_dir: number of input channels for direction (3+3*4*2=27 by default) skips: add skip connection in the Dth layer"""
<... | stack_v2_sparse_classes_36k_train_025112 | 8,749 | permissive | [
{
"docstring": "D: number of layers W: number of hidden units in each layer in_channels_dir: number of input channels for direction (3+3*4*2=27 by default) skips: add skip connection in the Dth layer",
"name": "__init__",
"signature": "def __init__(self, D, W, in_channels_dir=27, with_semantics=False, n... | 2 | null | Implement the Python class `BgNeRF` described below.
Class description:
Implement the BgNeRF class.
Method signatures and docstrings:
- def __init__(self, D, W, in_channels_dir=27, with_semantics=False, n_classes=6): D: number of layers W: number of hidden units in each layer in_channels_dir: number of input channels... | Implement the Python class `BgNeRF` described below.
Class description:
Implement the BgNeRF class.
Method signatures and docstrings:
- def __init__(self, D, W, in_channels_dir=27, with_semantics=False, n_classes=6): D: number of layers W: number of hidden units in each layer in_channels_dir: number of input channels... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class BgNeRF:
def __init__(self, D, W, in_channels_dir=27, with_semantics=False, n_classes=6):
"""D: number of layers W: number of hidden units in each layer in_channels_dir: number of input channels for direction (3+3*4*2=27 by default) skips: add skip connection in the Dth layer"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BgNeRF:
def __init__(self, D, W, in_channels_dir=27, with_semantics=False, n_classes=6):
"""D: number of layers W: number of hidden units in each layer in_channels_dir: number of input channels for direction (3+3*4*2=27 by default) skips: add skip connection in the Dth layer"""
super().__init_... | the_stack_v2_python_sparse | nerflets/models/nerf.py | Jimmy-INL/google-research | train | 1 | |
8f5217c239308f19f1e873422c05455e42c4c0f4 | [
"self.num_map = defaultdict(list)\nfor i in range(len(nums)):\n self.num_map[nums[i]].append(i)",
"arr = self.num_map[target]\nif len(self.num_map) == 1:\n return arr[0]\nelse:\n index = randint(0, len(arr) - 1)\n return arr[index]"
] | <|body_start_0|>
self.num_map = defaultdict(list)
for i in range(len(nums)):
self.num_map[nums[i]].append(i)
<|end_body_0|>
<|body_start_1|>
arr = self.num_map[target]
if len(self.num_map) == 1:
return arr[0]
else:
index = randint(0, len(arr) ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def pick(self, target):
""":type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.num_map = defaultdict(list)
for i in range(len(nums)):
... | stack_v2_sparse_classes_36k_train_025113 | 1,594 | no_license | [
{
"docstring": ":type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": ":type target: int :rtype: int",
"name": "pick",
"signature": "def pick(self, target)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def pick(self, target): :type target: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def pick(self, target): :type target: int :rtype: int
<|skeleton|>
class Solution:
def __init__(self, nums):
""":t... | 1211eac167f33084f536007468ea10c1a0ceab08 | <|skeleton|>
class Solution:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def pick(self, target):
""":type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, nums):
""":type nums: List[int]"""
self.num_map = defaultdict(list)
for i in range(len(nums)):
self.num_map[nums[i]].append(i)
def pick(self, target):
""":type target: int :rtype: int"""
arr = self.num_map[target]
if... | the_stack_v2_python_sparse | 398_RandomPickIndex.py | renukadeshmukh/Leetcode_Solutions | train | 3 | |
e7604ed3ca0b5e41f97e5f7aa3cb550eecb484dc | [
"if isinstance(key, int):\n return Option(key)\nif key not in Option._member_map_:\n return extend_enum(Option, key, default)\nreturn Option[key]",
"if not (isinstance(value, int) and 0 <= value <= 255):\n raise ValueError('%r is not a valid %s' % (value, cls.__name__))\nreturn extend_enum(cls, 'Unassign... | <|body_start_0|>
if isinstance(key, int):
return Option(key)
if key not in Option._member_map_:
return extend_enum(Option, key, default)
return Option[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) and 0 <= value <= 255):
raise Va... | [Option] Mobility Options | Option | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Option:
"""[Option] Mobility Options"""
def get(key: 'int | str', default: 'int'=-1) -> 'Option':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_0|>
def _missing_(cls, value: 'int') -... | stack_v2_sparse_classes_36k_train_025114 | 7,031 | permissive | [
{
"docstring": "Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:",
"name": "get",
"signature": "def get(key: 'int | str', default: 'int'=-1) -> 'Option'"
},
{
"docstring": "Lookup function used when value is not found. Args... | 2 | null | Implement the Python class `Option` described below.
Class description:
[Option] Mobility Options
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'Option': Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:
- de... | Implement the Python class `Option` described below.
Class description:
[Option] Mobility Options
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'Option': Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:
- de... | a6fe49ec58f09e105bec5a00fb66d9b3f22730d9 | <|skeleton|>
class Option:
"""[Option] Mobility Options"""
def get(key: 'int | str', default: 'int'=-1) -> 'Option':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_0|>
def _missing_(cls, value: 'int') -... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Option:
"""[Option] Mobility Options"""
def get(key: 'int | str', default: 'int'=-1) -> 'Option':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
if isinstance(key, int):
return Option(key)
... | the_stack_v2_python_sparse | pcapkit/const/mh/option.py | JarryShaw/PyPCAPKit | train | 204 |
ac92d1a0eb628e22f9472b7215529fa745a83ebd | [
"if config.memoryProfile:\n config.memoryProfile.sample()\nif config.hotshotProfile:\n import hotshot\n config.hotshotProfile = hotshot.Profile(config.hotshotProfile)\nserver.Site.startFactory(self)",
"server.Site.stopFactory(self)\nif config.hotshotProfile:\n config.hotshotProfile.close()"
] | <|body_start_0|>
if config.memoryProfile:
config.memoryProfile.sample()
if config.hotshotProfile:
import hotshot
config.hotshotProfile = hotshot.Profile(config.hotshotProfile)
server.Site.startFactory(self)
<|end_body_0|>
<|body_start_1|>
server.Site.... | Moin site | MoinSite | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MoinSite:
"""Moin site"""
def startFactory(self):
"""Setup before starting"""
<|body_0|>
def stopFactory(self):
"""Cleaup before stoping"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if config.memoryProfile:
config.memoryProfile.sa... | stack_v2_sparse_classes_36k_train_025115 | 8,777 | no_license | [
{
"docstring": "Setup before starting",
"name": "startFactory",
"signature": "def startFactory(self)"
},
{
"docstring": "Cleaup before stoping",
"name": "stopFactory",
"signature": "def stopFactory(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001992 | Implement the Python class `MoinSite` described below.
Class description:
Moin site
Method signatures and docstrings:
- def startFactory(self): Setup before starting
- def stopFactory(self): Cleaup before stoping | Implement the Python class `MoinSite` described below.
Class description:
Moin site
Method signatures and docstrings:
- def startFactory(self): Setup before starting
- def stopFactory(self): Cleaup before stoping
<|skeleton|>
class MoinSite:
"""Moin site"""
def startFactory(self):
"""Setup before st... | a17b987c5adaa13befb0fd74ac400c8edbe62ef5 | <|skeleton|>
class MoinSite:
"""Moin site"""
def startFactory(self):
"""Setup before starting"""
<|body_0|>
def stopFactory(self):
"""Cleaup before stoping"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MoinSite:
"""Moin site"""
def startFactory(self):
"""Setup before starting"""
if config.memoryProfile:
config.memoryProfile.sample()
if config.hotshotProfile:
import hotshot
config.hotshotProfile = hotshot.Profile(config.hotshotProfile)
... | the_stack_v2_python_sparse | moin/lib/python2.4/site-packages/MoinMoin/server/twistedmoin.py | imosts/flume | train | 0 |
435f48322403ca8e571f3bccfe8cc3a0a1677b7e | [
"super().__init__()\nself.frequency = frequency\nself.quality_factor = quality_factor\nself.sampling_freq = sampling_freq",
"b_notch, a_notch = convert_to_tensor(iirnotch(self.frequency, self.quality_factor, self.sampling_freq), dtype=torch.float)\ny_notched = filtfilt(convert_to_tensor(signal), a_notch, b_notch)... | <|body_start_0|>
super().__init__()
self.frequency = frequency
self.quality_factor = quality_factor
self.sampling_freq = sampling_freq
<|end_body_0|>
<|body_start_1|>
b_notch, a_notch = convert_to_tensor(iirnotch(self.frequency, self.quality_factor, self.sampling_freq), dtype=to... | Remove a frequency from a signal | SignalRemoveFrequency | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignalRemoveFrequency:
"""Remove a frequency from a signal"""
def __init__(self, frequency: float | None=None, quality_factor: float | None=None, sampling_freq: float | None=None) -> None:
"""Args: frequency: frequency to be removed from the signal quality_factor: quality factor for ... | stack_v2_sparse_classes_36k_train_025116 | 16,322 | permissive | [
{
"docstring": "Args: frequency: frequency to be removed from the signal quality_factor: quality factor for notch filter see : https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirnotch.html sampling_freq: sampling frequency of the input signal",
"name": "__init__",
"signature": "def __i... | 2 | stack_v2_sparse_classes_30k_train_018552 | Implement the Python class `SignalRemoveFrequency` described below.
Class description:
Remove a frequency from a signal
Method signatures and docstrings:
- def __init__(self, frequency: float | None=None, quality_factor: float | None=None, sampling_freq: float | None=None) -> None: Args: frequency: frequency to be re... | Implement the Python class `SignalRemoveFrequency` described below.
Class description:
Remove a frequency from a signal
Method signatures and docstrings:
- def __init__(self, frequency: float | None=None, quality_factor: float | None=None, sampling_freq: float | None=None) -> None: Args: frequency: frequency to be re... | e48c3e2c741fa3fc705c4425d17ac4a5afac6c47 | <|skeleton|>
class SignalRemoveFrequency:
"""Remove a frequency from a signal"""
def __init__(self, frequency: float | None=None, quality_factor: float | None=None, sampling_freq: float | None=None) -> None:
"""Args: frequency: frequency to be removed from the signal quality_factor: quality factor for ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SignalRemoveFrequency:
"""Remove a frequency from a signal"""
def __init__(self, frequency: float | None=None, quality_factor: float | None=None, sampling_freq: float | None=None) -> None:
"""Args: frequency: frequency to be removed from the signal quality_factor: quality factor for notch filter ... | the_stack_v2_python_sparse | monai/transforms/signal/array.py | Project-MONAI/MONAI | train | 4,805 |
af11b83519e5d40ab468f52d17412432523ec196 | [
"self.bam_handler = PEPPER_VARIANT.BAM_handler(bam_file_path)\nself.fasta_handler = PEPPER_VARIANT.FASTA_handler(fasta_file_path)\nself.chromosome_name = chromosome_name",
"if not options.use_hp_info:\n alignment_summarizer = AlignmentSummarizer(self.bam_handler, self.fasta_handler, self.chromosome_name, start... | <|body_start_0|>
self.bam_handler = PEPPER_VARIANT.BAM_handler(bam_file_path)
self.fasta_handler = PEPPER_VARIANT.FASTA_handler(fasta_file_path)
self.chromosome_name = chromosome_name
<|end_body_0|>
<|body_start_1|>
if not options.use_hp_info:
alignment_summarizer = Alignmen... | Process manager that runs sequence of processes to generate images and their labels. | ImageGenerator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageGenerator:
"""Process manager that runs sequence of processes to generate images and their labels."""
def __init__(self, chromosome_name, bam_file_path, fasta_file_path):
"""Initialize a manager object :param chromosome_name: Name of the chromosome :param bam_file_path: Path to ... | stack_v2_sparse_classes_36k_train_025117 | 16,089 | permissive | [
{
"docstring": "Initialize a manager object :param chromosome_name: Name of the chromosome :param bam_file_path: Path to the BAM file :param fasta_file_path: Path to the reference FASTA file",
"name": "__init__",
"signature": "def __init__(self, chromosome_name, bam_file_path, fasta_file_path)"
},
{... | 2 | null | Implement the Python class `ImageGenerator` described below.
Class description:
Process manager that runs sequence of processes to generate images and their labels.
Method signatures and docstrings:
- def __init__(self, chromosome_name, bam_file_path, fasta_file_path): Initialize a manager object :param chromosome_na... | Implement the Python class `ImageGenerator` described below.
Class description:
Process manager that runs sequence of processes to generate images and their labels.
Method signatures and docstrings:
- def __init__(self, chromosome_name, bam_file_path, fasta_file_path): Initialize a manager object :param chromosome_na... | 30c8907501b254bb72d8f64dfb8cf54a1b7a60eb | <|skeleton|>
class ImageGenerator:
"""Process manager that runs sequence of processes to generate images and their labels."""
def __init__(self, chromosome_name, bam_file_path, fasta_file_path):
"""Initialize a manager object :param chromosome_name: Name of the chromosome :param bam_file_path: Path to ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageGenerator:
"""Process manager that runs sequence of processes to generate images and their labels."""
def __init__(self, chromosome_name, bam_file_path, fasta_file_path):
"""Initialize a manager object :param chromosome_name: Name of the chromosome :param bam_file_path: Path to the BAM file ... | the_stack_v2_python_sparse | pepper_variant/modules/python/ImageGenerationUI.py | kishwarshafin/pepper | train | 219 |
60e091d1b582be3cd32147f3374be9d3ce03cb25 | [
"self.foldername = foldername\nself.s3_config_json_filename = os.path.join(AWS_CRED_DIR, 'aws_s3_credentials.json')\nself.s3_util = S3Util(aws_cred_config_json_filename=self.s3_config_json_filename)\nself.msg_printer = wasabi.Printer()\nself.interact()",
"choices = []\npath = pathlib.Path(self.foldername)\nfor di... | <|body_start_0|>
self.foldername = foldername
self.s3_config_json_filename = os.path.join(AWS_CRED_DIR, 'aws_s3_credentials.json')
self.s3_util = S3Util(aws_cred_config_json_filename=self.s3_config_json_filename)
self.msg_printer = wasabi.Printer()
self.interact()
<|end_body_0|>
... | S3OutputMove | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class S3OutputMove:
def __init__(self, foldername: str):
"""Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket"""
<|body_0|>
def get_folder_choice(self):
"""Goes through the folder ... | stack_v2_sparse_classes_36k_train_025118 | 3,125 | permissive | [
{
"docstring": "Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket",
"name": "__init__",
"signature": "def __init__(self, foldername: str)"
},
{
"docstring": "Goes through the folder and gets the choice o... | 4 | stack_v2_sparse_classes_30k_train_009491 | Implement the Python class `S3OutputMove` described below.
Class description:
Implement the S3OutputMove class.
Method signatures and docstrings:
- def __init__(self, foldername: str): Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to ... | Implement the Python class `S3OutputMove` described below.
Class description:
Implement the S3OutputMove class.
Method signatures and docstrings:
- def __init__(self, foldername: str): Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to ... | 1c061b99a35a9d8b565d9762aaaf5db979b50112 | <|skeleton|>
class S3OutputMove:
def __init__(self, foldername: str):
"""Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket"""
<|body_0|>
def get_folder_choice(self):
"""Goes through the folder ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class S3OutputMove:
def __init__(self, foldername: str):
"""Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket"""
self.foldername = foldername
self.s3_config_json_filename = os.path.join(AWS_CRED_DIR,... | the_stack_v2_python_sparse | sciwing/cli/s3_mv_cli.py | abhinavkashyap/sciwing | train | 58 | |
a8e50982c1776966b621ddde4ab8d7844365cceb | [
"if self.__dict__.get('verbose', False):\n print('generating anagrams')\nself.anagrams = build_anagrams(jobs)\nfor job in jobs:\n try:\n remaining_letters = alphabetize(char_filter(self.word, job, 1))\n if remaining_letters in self.anagrams:\n for second_job in self.anagrams[remaining... | <|body_start_0|>
if self.__dict__.get('verbose', False):
print('generating anagrams')
self.anagrams = build_anagrams(jobs)
for job in jobs:
try:
remaining_letters = alphabetize(char_filter(self.word, job, 1))
if remaining_letters in self.an... | MySolver is an example solver which is a child of the solver class. | MySolver | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MySolver:
"""MySolver is an example solver which is a child of the solver class."""
def try_list(self, list_name, jobs):
"""Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized."""
<|body_0|>
def get_candidates(self):
... | stack_v2_sparse_classes_36k_train_025119 | 3,609 | permissive | [
{
"docstring": "Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized.",
"name": "try_list",
"signature": "def try_list(self, list_name, jobs)"
},
{
"docstring": "prints the current list of candidates",
"name": "get_candidates",
"signat... | 2 | stack_v2_sparse_classes_30k_train_004224 | Implement the Python class `MySolver` described below.
Class description:
MySolver is an example solver which is a child of the solver class.
Method signatures and docstrings:
- def try_list(self, list_name, jobs): Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetiz... | Implement the Python class `MySolver` described below.
Class description:
MySolver is an example solver which is a child of the solver class.
Method signatures and docstrings:
- def try_list(self, list_name, jobs): Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetiz... | c84c1b51d83c7e780430175e41588632441aa180 | <|skeleton|>
class MySolver:
"""MySolver is an example solver which is a child of the solver class."""
def try_list(self, list_name, jobs):
"""Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized."""
<|body_0|>
def get_candidates(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MySolver:
"""MySolver is an example solver which is a child of the solver class."""
def try_list(self, list_name, jobs):
"""Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized."""
if self.__dict__.get('verbose', False):
pri... | the_stack_v2_python_sparse | solutions/20150524/merchant_raider.py | johnobrien/pyshortz | train | 0 |
6e27f7b8c62d9a9632620af1d831dbe069c94492 | [
"super(Encoder, self).__init__()\nself.dm = dm\nself.N = N\nself.embedding = tf.keras.layers.Embedding(input_vocab, dm)\nself.positional_encoding = positional_encoding(max_seq_len, self.dm)\nself.blocks = [EncoderBlock(dm, h, hidden, drop_rate) for _ in range(N)]\nself.dropout = tf.keras.layers.Dropout(drop_rate)",... | <|body_start_0|>
super(Encoder, self).__init__()
self.dm = dm
self.N = N
self.embedding = tf.keras.layers.Embedding(input_vocab, dm)
self.positional_encoding = positional_encoding(max_seq_len, self.dm)
self.blocks = [EncoderBlock(dm, h, hidden, drop_rate) for _ in range(N... | Encoder represents a Transformers encoder layer | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""Encoder represents a Transformers encoder layer"""
def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1):
"""Encoder represents a Transformers encoder layer"""
<|body_0|>
def call(self, x, training, mask):
"""This calls the enc... | stack_v2_sparse_classes_36k_train_025120 | 1,422 | no_license | [
{
"docstring": "Encoder represents a Transformers encoder layer",
"name": "__init__",
"signature": "def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1)"
},
{
"docstring": "This calls the encoder algo and returns encoder output",
"name": "call",
"signature": "def... | 2 | stack_v2_sparse_classes_30k_val_001140 | Implement the Python class `Encoder` described below.
Class description:
Encoder represents a Transformers encoder layer
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1): Encoder represents a Transformers encoder layer
- def call(self, x, training, mask... | Implement the Python class `Encoder` described below.
Class description:
Encoder represents a Transformers encoder layer
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1): Encoder represents a Transformers encoder layer
- def call(self, x, training, mask... | 05eabebe5e5c050b1c4a7e1454b947638d883176 | <|skeleton|>
class Encoder:
"""Encoder represents a Transformers encoder layer"""
def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1):
"""Encoder represents a Transformers encoder layer"""
<|body_0|>
def call(self, x, training, mask):
"""This calls the enc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Encoder:
"""Encoder represents a Transformers encoder layer"""
def __init__(self, N, dm, h, hidden, input_vocab, max_seq_len, drop_rate=0.1):
"""Encoder represents a Transformers encoder layer"""
super(Encoder, self).__init__()
self.dm = dm
self.N = N
self.embeddin... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/9-transformer_encoder.py | chriswill88/holbertonschool-machine_learning | train | 0 |
37b7b25d91417ed99dc8b6b5fa6636c08d6fc391 | [
"super(PIFuhd_Surface_Head, self).__init__()\nself.name = 'PIFuhd_Surface_Head'\nif last_op == 'sigmoid':\n self.last_op = nn.Sigmoid()\nelse:\n raise NotImplementedError('only sigmoid function could be used in terms of sigmoid')\nself.filters = nn.ModuleList()\nself.norms = nn.ModuleList()\nself.merge_layer ... | <|body_start_0|>
super(PIFuhd_Surface_Head, self).__init__()
self.name = 'PIFuhd_Surface_Head'
if last_op == 'sigmoid':
self.last_op = nn.Sigmoid()
else:
raise NotImplementedError('only sigmoid function could be used in terms of sigmoid')
self.filters = nn... | MLP: aims at learn iso-surface function Implicit function where 0->outside, 1-> inside therefore, we define 0.5 is iso-surface | PIFuhd_Surface_Head | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PIFuhd_Surface_Head:
"""MLP: aims at learn iso-surface function Implicit function where 0->outside, 1-> inside therefore, we define 0.5 is iso-surface"""
def __init__(self, filter_channels, merge_layer=0, res_layers=[], norm='group', last_op=None):
"""Parameters: filter_channels: Lis... | stack_v2_sparse_classes_36k_train_025121 | 3,326 | permissive | [
{
"docstring": "Parameters: filter_channels: List mlp layers default [257, 1024, 512, 256, 128, 1] merge_layer: it means which layer you want to employ in fine PIFu model res_layers: whether you wana employ residual block ? Default [2,3,4] norm: use group normalization or not last_op: what kind of operator you ... | 2 | stack_v2_sparse_classes_30k_train_008983 | Implement the Python class `PIFuhd_Surface_Head` described below.
Class description:
MLP: aims at learn iso-surface function Implicit function where 0->outside, 1-> inside therefore, we define 0.5 is iso-surface
Method signatures and docstrings:
- def __init__(self, filter_channels, merge_layer=0, res_layers=[], norm... | Implement the Python class `PIFuhd_Surface_Head` described below.
Class description:
MLP: aims at learn iso-surface function Implicit function where 0->outside, 1-> inside therefore, we define 0.5 is iso-surface
Method signatures and docstrings:
- def __init__(self, filter_channels, merge_layer=0, res_layers=[], norm... | 3a66b647bcf5591e818af62735e64a93c4aaef85 | <|skeleton|>
class PIFuhd_Surface_Head:
"""MLP: aims at learn iso-surface function Implicit function where 0->outside, 1-> inside therefore, we define 0.5 is iso-surface"""
def __init__(self, filter_channels, merge_layer=0, res_layers=[], norm='group', last_op=None):
"""Parameters: filter_channels: Lis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PIFuhd_Surface_Head:
"""MLP: aims at learn iso-surface function Implicit function where 0->outside, 1-> inside therefore, we define 0.5 is iso-surface"""
def __init__(self, filter_channels, merge_layer=0, res_layers=[], norm='group', last_op=None):
"""Parameters: filter_channels: List mlp layers ... | the_stack_v2_python_sparse | engineer/models/heads/PIFuhd_Surface_head.py | yukangcao/Open-PIFuhd | train | 0 |
60db057abca1525edf81897dfc1b3748b2e430a8 | [
"List = []\nfor i in range(len(points) - 2):\n for j in range(i + 1, len(points) - 1):\n for k in range(j + 1, len(points)):\n S = self.TriangleArea(points[i], points[j], points[k])\n List.append(S)\nreturn max(List)",
"x1, x2, x3 = (A[0], B[0], C[0])\ny1, y2, y3 = (A[1], B[1], C[1... | <|body_start_0|>
List = []
for i in range(len(points) - 2):
for j in range(i + 1, len(points) - 1):
for k in range(j + 1, len(points)):
S = self.TriangleArea(points[i], points[j], points[k])
List.append(S)
return max(List)
<|end... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def largestTriangleArea(self, points):
""":type points: List[List[int]] :rtype: float"""
<|body_0|>
def TriangleArea(self, A, B, C):
"""给定三个坐标，求三角形面积"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
List = []
for i in range(len(poin... | stack_v2_sparse_classes_36k_train_025122 | 765 | no_license | [
{
"docstring": ":type points: List[List[int]] :rtype: float",
"name": "largestTriangleArea",
"signature": "def largestTriangleArea(self, points)"
},
{
"docstring": "给定三个坐标，求三角形面积",
"name": "TriangleArea",
"signature": "def TriangleArea(self, A, B, C)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019633 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestTriangleArea(self, points): :type points: List[List[int]] :rtype: float
- def TriangleArea(self, A, B, C): 给定三个坐标，求三角形面积 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestTriangleArea(self, points): :type points: List[List[int]] :rtype: float
- def TriangleArea(self, A, B, C): 给定三个坐标，求三角形面积
<|skeleton|>
class Solution:
def largest... | 2df5d3b361bc7d25cd3d2afd5ac1c64fbc303920 | <|skeleton|>
class Solution:
def largestTriangleArea(self, points):
""":type points: List[List[int]] :rtype: float"""
<|body_0|>
def TriangleArea(self, A, B, C):
"""给定三个坐标，求三角形面积"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def largestTriangleArea(self, points):
""":type points: List[List[int]] :rtype: float"""
List = []
for i in range(len(points) - 2):
for j in range(i + 1, len(points) - 1):
for k in range(j + 1, len(points)):
S = self.TriangleAre... | the_stack_v2_python_sparse | leetcode_812.py | SongJialiJiali/test | train | 0 | |
bd7724708e167478f1b6ccd9ab4aef77d1a9e9d0 | [
"writeFastaFile(sequences, 'sequences.fasta.tmp')\ncommand = 'mafft --auto'\nif anysymbol:\n command += ' --anysymbol'\ncommand += ' sequences.fasta.tmp > sequences.aligned.fasta.tmp'\nos.system(command)\nalignment = AlignIO.read('sequences.aligned.fasta.tmp', 'fasta')\nos.remove('sequences.fasta.tmp')\nif outpu... | <|body_start_0|>
writeFastaFile(sequences, 'sequences.fasta.tmp')
command = 'mafft --auto'
if anysymbol:
command += ' --anysymbol'
command += ' sequences.fasta.tmp > sequences.aligned.fasta.tmp'
os.system(command)
alignment = AlignIO.read('sequences.aligned.fa... | Class to hold methods to work with mafft executable. Methods ------- multipleSequenceAlignment() Execute a multiple sequence alignment of the input sequences | mafft | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class mafft:
"""Class to hold methods to work with mafft executable. Methods ------- multipleSequenceAlignment() Execute a multiple sequence alignment of the input sequences"""
def multipleSequenceAlignment(sequences, output=None, anysymbol=False):
"""Use the mafft executable to perform a ... | stack_v2_sparse_classes_36k_train_025123 | 5,064 | no_license | [
{
"docstring": "Use the mafft executable to perform a multiple sequence alignment. Parameters ---------- sequences : dict Dictionary containing as values the strings representing the sequences of the proteins to align and their identifiers as keys. output : str File name to write the fasta formatted alignment o... | 3 | stack_v2_sparse_classes_30k_train_005470 | Implement the Python class `mafft` described below.
Class description:
Class to hold methods to work with mafft executable. Methods ------- multipleSequenceAlignment() Execute a multiple sequence alignment of the input sequences
Method signatures and docstrings:
- def multipleSequenceAlignment(sequences, output=None,... | Implement the Python class `mafft` described below.
Class description:
Class to hold methods to work with mafft executable. Methods ------- multipleSequenceAlignment() Execute a multiple sequence alignment of the input sequences
Method signatures and docstrings:
- def multipleSequenceAlignment(sequences, output=None,... | 13dedb6ed7114286d922253819dc433478a6fbcb | <|skeleton|>
class mafft:
"""Class to hold methods to work with mafft executable. Methods ------- multipleSequenceAlignment() Execute a multiple sequence alignment of the input sequences"""
def multipleSequenceAlignment(sequences, output=None, anysymbol=False):
"""Use the mafft executable to perform a ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class mafft:
"""Class to hold methods to work with mafft executable. Methods ------- multipleSequenceAlignment() Execute a multiple sequence alignment of the input sequences"""
def multipleSequenceAlignment(sequences, output=None, anysymbol=False):
"""Use the mafft executable to perform a multiple sequ... | the_stack_v2_python_sparse | pycbbl/alignment/mafft_functions.py | CompBiochBiophLab/pycbbl | train | 1 |
590822e0dac13775cd3fb6cee93cb6794b4be3a6 | [
"test_array = [array_amqp_type]\nfor _ in range(repeat):\n for val in self.type_map[array_amqp_type]:\n test_array.append(val)\nreturn test_array",
"if test_type == 'binary':\n test_values = []\n bytes_test_values = super().get_test_values(test_type)\n for bytes_test_value in bytes_test_values:... | <|body_start_0|>
test_array = [array_amqp_type]
for _ in range(repeat):
for val in self.type_map[array_amqp_type]:
test_array.append(val)
return test_array
<|end_body_0|>
<|body_start_1|>
if test_type == 'binary':
test_values = []
byte... | Class which contains all the described AMQP primitive types and the test values to be used in testing. | AmqpPrimitiveTypes | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AmqpPrimitiveTypes:
"""Class which contains all the described AMQP primitive types and the test values to be used in testing."""
def create_array(self, array_amqp_type, repeat):
"""Create a single test array for a given AMQP type from the test values for that type. It can be optional... | stack_v2_sparse_classes_36k_train_025124 | 18,156 | permissive | [
{
"docstring": "Create a single test array for a given AMQP type from the test values for that type. It can be optionally repeated for greater number of elements.",
"name": "create_array",
"signature": "def create_array(self, array_amqp_type, repeat)"
},
{
"docstring": "Overload the parent metho... | 2 | stack_v2_sparse_classes_30k_train_000716 | Implement the Python class `AmqpPrimitiveTypes` described below.
Class description:
Class which contains all the described AMQP primitive types and the test values to be used in testing.
Method signatures and docstrings:
- def create_array(self, array_amqp_type, repeat): Create a single test array for a given AMQP ty... | Implement the Python class `AmqpPrimitiveTypes` described below.
Class description:
Class which contains all the described AMQP primitive types and the test values to be used in testing.
Method signatures and docstrings:
- def create_array(self, array_amqp_type, repeat): Create a single test array for a given AMQP ty... | 2ae24aa2ac6998b9598b02e5b181f187f7b51212 | <|skeleton|>
class AmqpPrimitiveTypes:
"""Class which contains all the described AMQP primitive types and the test values to be used in testing."""
def create_array(self, array_amqp_type, repeat):
"""Create a single test array for a given AMQP type from the test values for that type. It can be optional... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AmqpPrimitiveTypes:
"""Class which contains all the described AMQP primitive types and the test values to be used in testing."""
def create_array(self, array_amqp_type, repeat):
"""Create a single test array for a given AMQP type from the test values for that type. It can be optionally repeated f... | the_stack_v2_python_sparse | src/python/qpid_interop_test/amqp_types_test.py | apache/qpid-interop-test | train | 7 |
304f726f08b371ded8567a002c4eabed8624e2a1 | [
"super().__init__()\nself.factor_dependencies = factor_dependencies\nfor factor_type, model in factor_models.items():\n self.__setattr__(factor_type + '_factor_model', model)\nself.factor_models = factor_models\nself.factor_label_indices = dict()\nfor factor_type, dependencies in factor_dependencies.items():\n ... | <|body_start_0|>
super().__init__()
self.factor_dependencies = factor_dependencies
for factor_type, model in factor_models.items():
self.__setattr__(factor_type + '_factor_model', model)
self.factor_models = factor_models
self.factor_label_indices = dict()
for... | FactorGraph interface to work with cpp implementations. It manages the models for the factors and the inference of their scores. | FactorGraphCpp | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FactorGraphCpp:
"""FactorGraph interface to work with cpp implementations. It manages the models for the factors and the inference of their scores."""
def __init__(self, factor_dependencies: Dict[str, List[Tuple[int]]], factor_models: Dict[str, nn.Module]):
"""See nsr.graph.factor_gr... | stack_v2_sparse_classes_36k_train_025125 | 2,129 | permissive | [
{
"docstring": "See nsr.graph.factor_graph.FactorGraph.",
"name": "__init__",
"signature": "def __init__(self, factor_dependencies: Dict[str, List[Tuple[int]]], factor_models: Dict[str, nn.Module])"
},
{
"docstring": "Evaluate all the factor Tensors. Args: input_states: states for each label nod... | 2 | stack_v2_sparse_classes_30k_train_016510 | Implement the Python class `FactorGraphCpp` described below.
Class description:
FactorGraph interface to work with cpp implementations. It manages the models for the factors and the inference of their scores.
Method signatures and docstrings:
- def __init__(self, factor_dependencies: Dict[str, List[Tuple[int]]], fact... | Implement the Python class `FactorGraphCpp` described below.
Class description:
FactorGraph interface to work with cpp implementations. It manages the models for the factors and the inference of their scores.
Method signatures and docstrings:
- def __init__(self, factor_dependencies: Dict[str, List[Tuple[int]]], fact... | 8b4a7a40cc34bff608f19d3f7eb64bda76669c5b | <|skeleton|>
class FactorGraphCpp:
"""FactorGraph interface to work with cpp implementations. It manages the models for the factors and the inference of their scores."""
def __init__(self, factor_dependencies: Dict[str, List[Tuple[int]]], factor_models: Dict[str, nn.Module]):
"""See nsr.graph.factor_gr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FactorGraphCpp:
"""FactorGraph interface to work with cpp implementations. It manages the models for the factors and the inference of their scores."""
def __init__(self, factor_dependencies: Dict[str, List[Tuple[int]]], factor_models: Dict[str, nn.Module]):
"""See nsr.graph.factor_graph.FactorGra... | the_stack_v2_python_sparse | nsr/graph_cpp/factor_graph_cpp.py | GaoSida/Neural-SampleRank | train | 3 |
92a1915fa2859bf57ef59813821caedd96bfdbaa | [
"if not nums:\n return False\ncount = {}\nfor i in nums:\n count[i] = count.get(i, 0) + 1\n if count.get(i, 0) > 1:\n return True\nreturn False",
"if not nums:\n return False\nnums.sort()\ni = 1\nwhile i < len(nums):\n if nums[i] == nums[i - 1]:\n return True\n i += 1\nreturn False... | <|body_start_0|>
if not nums:
return False
count = {}
for i in nums:
count[i] = count.get(i, 0) + 1
if count.get(i, 0) > 1:
return True
return False
<|end_body_0|>
<|body_start_1|>
if not nums:
return False
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsDuplicate(self, nums: List[int]) -> bool:
"""统计"""
<|body_0|>
def containsDuplicate1(self, nums: List[int]) -> bool:
"""指针"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not nums:
return False
count = {}... | stack_v2_sparse_classes_36k_train_025126 | 1,003 | no_license | [
{
"docstring": "统计",
"name": "containsDuplicate",
"signature": "def containsDuplicate(self, nums: List[int]) -> bool"
},
{
"docstring": "指针",
"name": "containsDuplicate1",
"signature": "def containsDuplicate1(self, nums: List[int]) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_017162 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsDuplicate(self, nums: List[int]) -> bool: 统计
- def containsDuplicate1(self, nums: List[int]) -> bool: 指针 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsDuplicate(self, nums: List[int]) -> bool: 统计
- def containsDuplicate1(self, nums: List[int]) -> bool: 指针
<|skeleton|>
class Solution:
def containsDuplicate(self... | 069bb0b751ef7f469036b9897436eb5d138ffa24 | <|skeleton|>
class Solution:
def containsDuplicate(self, nums: List[int]) -> bool:
"""统计"""
<|body_0|>
def containsDuplicate1(self, nums: List[int]) -> bool:
"""指针"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def containsDuplicate(self, nums: List[int]) -> bool:
"""统计"""
if not nums:
return False
count = {}
for i in nums:
count[i] = count.get(i, 0) + 1
if count.get(i, 0) > 1:
return True
return False
def cont... | the_stack_v2_python_sparse | 算法/Week_03/217. 存在重复元素.py | RichieSong/algorithm | train | 0 | |
16735da1a755908f562d3d59cf5ed009f837f213 | [
"dqrqsj = datetime.datetime.now().strftime('%Y%m%d%H:%M:%S')\ndqrq = dqrqsj[:8]\ndqsj = dqrqsj[8:]\nqtrqsj = (datetime.datetime.strptime(dqrqsj, '%Y%m%d%H:%M:%S') - datetime.timedelta(minutes=int(min))).strftime('%Y%m%d%H:%M:%S')\nqtrq = qtrqsj[:8]\nqtsj = qtrqsj[8:]\nxym_str_lst = sbxymlb.split(',')\njym_str_lst =... | <|body_start_0|>
dqrqsj = datetime.datetime.now().strftime('%Y%m%d%H:%M:%S')
dqrq = dqrqsj[:8]
dqsj = dqrqsj[8:]
qtrqsj = (datetime.datetime.strptime(dqrqsj, '%Y%m%d%H:%M:%S') - datetime.timedelta(minutes=int(min))).strftime('%Y%m%d%H:%M:%S')
qtrq = qtrqsj[:8]
qtsj = qtrq... | Business | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Business:
def get_failtrans(self, jymlb, sbxymlb, min):
"""获取交易失败笔数"""
<|body_0|>
def get_firstrans(self, jymlb):
"""获取业务第一笔交易"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
dqrqsj = datetime.datetime.now().strftime('%Y%m%d%H:%M:%S')
dqrq =... | stack_v2_sparse_classes_36k_train_025127 | 16,132 | no_license | [
{
"docstring": "获取交易失败笔数",
"name": "get_failtrans",
"signature": "def get_failtrans(self, jymlb, sbxymlb, min)"
},
{
"docstring": "获取业务第一笔交易",
"name": "get_firstrans",
"signature": "def get_firstrans(self, jymlb)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013729 | Implement the Python class `Business` described below.
Class description:
Implement the Business class.
Method signatures and docstrings:
- def get_failtrans(self, jymlb, sbxymlb, min): 获取交易失败笔数
- def get_firstrans(self, jymlb): 获取业务第一笔交易 | Implement the Python class `Business` described below.
Class description:
Implement the Business class.
Method signatures and docstrings:
- def get_failtrans(self, jymlb, sbxymlb, min): 获取交易失败笔数
- def get_firstrans(self, jymlb): 获取业务第一笔交易
<|skeleton|>
class Business:
def get_failtrans(self, jymlb, sbxymlb, min)... | 68ddf3df6d2cd731e6634b09d27aff4c22debd8e | <|skeleton|>
class Business:
def get_failtrans(self, jymlb, sbxymlb, min):
"""获取交易失败笔数"""
<|body_0|>
def get_firstrans(self, jymlb):
"""获取业务第一笔交易"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Business:
def get_failtrans(self, jymlb, sbxymlb, min):
"""获取交易失败笔数"""
dqrqsj = datetime.datetime.now().strftime('%Y%m%d%H:%M:%S')
dqrq = dqrqsj[:8]
dqsj = dqrqsj[8:]
qtrqsj = (datetime.datetime.strptime(dqrqsj, '%Y%m%d%H:%M:%S') - datetime.timedelta(minutes=int(min))).... | the_stack_v2_python_sparse | zh_manage/apps/_init/oa/yw_jkgl/yw_jkgl_001/yw_jkgl_001.py | yizhong120110/CPOS | train | 0 | |
5506856d790b0315ee9a2092553d08dae7830412 | [
"self._generator = generator\nself._pageSize = pageSize\nif otherOptions is None:\n otherOptions = []\nself._otherOptions = otherOptions\nself._preMessage = preMessage\nself._postMessage = postMessage\nself._emptyMessage = emptyMessage\nself._displayedItems = []\nself._displayedItemStrings = []\nself._exhaustedI... | <|body_start_0|>
self._generator = generator
self._pageSize = pageSize
if otherOptions is None:
otherOptions = []
self._otherOptions = otherOptions
self._preMessage = preMessage
self._postMessage = postMessage
self._emptyMessage = emptyMessage
... | A menu that displays a few options at a time with a "See more" option Gets options using a given generator | TerminalGeneratorMenu | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TerminalGeneratorMenu:
"""A menu that displays a few options at a time with a "See more" option Gets options using a given generator"""
def __init__(self, generator, pageSize=5, otherOptions=None, preMessage=None, postMessage=None, emptyMessage='Nothing to display'):
"""Arguments: ge... | stack_v2_sparse_classes_36k_train_025128 | 7,322 | no_license | [
{
"docstring": "Arguments: generator -- the generator to get the options from pageSize -- the number of items to show per page otherOptions -- any options to show below the options from the generator preMessage -- the message to show above the menu postMessage -- the message to show below the menu emptyMessage ... | 5 | stack_v2_sparse_classes_30k_train_015821 | Implement the Python class `TerminalGeneratorMenu` described below.
Class description:
A menu that displays a few options at a time with a "See more" option Gets options using a given generator
Method signatures and docstrings:
- def __init__(self, generator, pageSize=5, otherOptions=None, preMessage=None, postMessag... | Implement the Python class `TerminalGeneratorMenu` described below.
Class description:
A menu that displays a few options at a time with a "See more" option Gets options using a given generator
Method signatures and docstrings:
- def __init__(self, generator, pageSize=5, otherOptions=None, preMessage=None, postMessag... | 46b7e084234227f925a24ea2eb41ed5d9ac14b7a | <|skeleton|>
class TerminalGeneratorMenu:
"""A menu that displays a few options at a time with a "See more" option Gets options using a given generator"""
def __init__(self, generator, pageSize=5, otherOptions=None, preMessage=None, postMessage=None, emptyMessage='Nothing to display'):
"""Arguments: ge... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TerminalGeneratorMenu:
"""A menu that displays a few options at a time with a "See more" option Gets options using a given generator"""
def __init__(self, generator, pageSize=5, otherOptions=None, preMessage=None, postMessage=None, emptyMessage='Nothing to display'):
"""Arguments: generator -- th... | the_stack_v2_python_sparse | Source/TerminalMenu.py | csahmad/291-Mini-Project-1 | train | 0 |
1f1d3e5c277becf8ed0e388acad0bb7c7dd4912a | [
"new_trans = transaction()\nnew_trans.isBuyTransaction = True\nnew_trans.amount = amount\nnew_trans.desired_value = value\nreturn new_trans",
"new_trans = transaction()\nnew_trans.isBuyTransaction = False\nnew_trans.amount = amount\nnew_trans.desired_value = value\nreturn new_trans",
"if self.isBuyTransaction:\... | <|body_start_0|>
new_trans = transaction()
new_trans.isBuyTransaction = True
new_trans.amount = amount
new_trans.desired_value = value
return new_trans
<|end_body_0|>
<|body_start_1|>
new_trans = transaction()
new_trans.isBuyTransaction = False
new_trans.... | Transaction factory class. Produces a transaction based on buying or selling. | transaction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class transaction:
"""Transaction factory class. Produces a transaction based on buying or selling."""
def buyTransactionFactory(amount, value):
"""Create a long transaction object."""
<|body_0|>
def sellTransactionFactory(amount, value):
"""Create a short transaction ... | stack_v2_sparse_classes_36k_train_025129 | 1,093 | permissive | [
{
"docstring": "Create a long transaction object.",
"name": "buyTransactionFactory",
"signature": "def buyTransactionFactory(amount, value)"
},
{
"docstring": "Create a short transaction object.",
"name": "sellTransactionFactory",
"signature": "def sellTransactionFactory(amount, value)"
... | 3 | stack_v2_sparse_classes_30k_train_002218 | Implement the Python class `transaction` described below.
Class description:
Transaction factory class. Produces a transaction based on buying or selling.
Method signatures and docstrings:
- def buyTransactionFactory(amount, value): Create a long transaction object.
- def sellTransactionFactory(amount, value): Create... | Implement the Python class `transaction` described below.
Class description:
Transaction factory class. Produces a transaction based on buying or selling.
Method signatures and docstrings:
- def buyTransactionFactory(amount, value): Create a long transaction object.
- def sellTransactionFactory(amount, value): Create... | e4cfc62f2daa45cb939bb544491cdb1c1a7294ef | <|skeleton|>
class transaction:
"""Transaction factory class. Produces a transaction based on buying or selling."""
def buyTransactionFactory(amount, value):
"""Create a long transaction object."""
<|body_0|>
def sellTransactionFactory(amount, value):
"""Create a short transaction ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class transaction:
"""Transaction factory class. Produces a transaction based on buying or selling."""
def buyTransactionFactory(amount, value):
"""Create a long transaction object."""
new_trans = transaction()
new_trans.isBuyTransaction = True
new_trans.amount = amount
... | the_stack_v2_python_sparse | app/common/common_classes.py | DataScienceHobbyGroup/nacho-b | train | 0 |
1fc23e16fa9033fb5d66df522f7ba9b1829e8bc0 | [
"a = [[None] * n for x in range(n)]\ni = 0\nj = 0\np = 0\na, j, x = self.set_initial_row(a, i, j, n)\nfor q in self.gen_part_len(n):\n for t in range(0, q):\n i, j = self.eval_next_loc(p, i, j)\n a[i][j] = x\n x += 1\n p += 1\nreturn a",
"for x in range(1, n):\n a[i][j] = x\n j +=... | <|body_start_0|>
a = [[None] * n for x in range(n)]
i = 0
j = 0
p = 0
a, j, x = self.set_initial_row(a, i, j, n)
for q in self.gen_part_len(n):
for t in range(0, q):
i, j = self.eval_next_loc(p, i, j)
a[i][j] = x
... | Solution | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def make_spiral_matrix(self, n):
"""Creates spiral matrix from given characteristic factor "n". :param int n: characteristic factor for spiral matrix :return: completed 2D spiral matrix :rtype: list[list[int]]"""
<|body_0|>
def set_initial_row(self, a, i, j, n):
... | stack_v2_sparse_classes_36k_train_025130 | 3,472 | permissive | [
{
"docstring": "Creates spiral matrix from given characteristic factor \"n\". :param int n: characteristic factor for spiral matrix :return: completed 2D spiral matrix :rtype: list[list[int]]",
"name": "make_spiral_matrix",
"signature": "def make_spiral_matrix(self, n)"
},
{
"docstring": "Comple... | 4 | stack_v2_sparse_classes_30k_train_014290 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def make_spiral_matrix(self, n): Creates spiral matrix from given characteristic factor "n". :param int n: characteristic factor for spiral matrix :return: completed 2D spiral ma... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def make_spiral_matrix(self, n): Creates spiral matrix from given characteristic factor "n". :param int n: characteristic factor for spiral matrix :return: completed 2D spiral ma... | 69f90877c5466927e8b081c4268cbcda074813ec | <|skeleton|>
class Solution:
def make_spiral_matrix(self, n):
"""Creates spiral matrix from given characteristic factor "n". :param int n: characteristic factor for spiral matrix :return: completed 2D spiral matrix :rtype: list[list[int]]"""
<|body_0|>
def set_initial_row(self, a, i, j, n):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def make_spiral_matrix(self, n):
"""Creates spiral matrix from given characteristic factor "n". :param int n: characteristic factor for spiral matrix :return: completed 2D spiral matrix :rtype: list[list[int]]"""
a = [[None] * n for x in range(n)]
i = 0
j = 0
... | the_stack_v2_python_sparse | 0059_spiral_matrix_2/python_source.py | arthurdysart/LeetCode | train | 0 | |
78684c89a06df79f0c2207c4a41c7b4ebdf4a036 | [
"y = 0\nfor x in range(len(nums)):\n if nums[x]:\n nums[x], nums[y] = (nums[y], nums[x])\n y += 1",
"l = len(nums)\nfor i in range(l):\n if nums[i] == 0:\n j = i + 1\n while j < l and nums[j] == 0:\n j += 1\n if j < l:\n nums[i], nums[j] = (nums[j], n... | <|body_start_0|>
y = 0
for x in range(len(nums)):
if nums[x]:
nums[x], nums[y] = (nums[y], nums[x])
y += 1
<|end_body_0|>
<|body_start_1|>
l = len(nums)
for i in range(l):
if nums[i] == 0:
j = i + 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def moveZeroes(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_0|>
def moveZeroes(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_1... | stack_v2_sparse_classes_36k_train_025131 | 1,129 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.",
"name": "moveZeroes",
"signature": "def moveZeroes(self, nums)"
},
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "moveZeroes",
"signature": "def mo... | 2 | stack_v2_sparse_classes_30k_train_003847 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def moveZeroes(self, nums: List[int]) -> None: Do not retur... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def moveZeroes(self, nums: List[int]) -> None: Do not retur... | 378cb9b53e7710c5cf546f5d75e572060e2a211a | <|skeleton|>
class Solution:
def moveZeroes(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_0|>
def moveZeroes(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def moveZeroes(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
y = 0
for x in range(len(nums)):
if nums[x]:
nums[x], nums[y] = (nums[y], nums[x])
y += 1
def moveZeroes... | the_stack_v2_python_sparse | 283.MoveZeroes.py | RupertMa/LeetCode-Playground | train | 0 | |
1d33c339a471d73df0253d0cda5ebe5e059f8fc7 | [
"length = len(nums)\nif length > 0:\n maxSoFar = nums[0]\n maxEndingHere = nums[0]\n for i in range(1, length):\n maxEndingHere = max(maxEndingHere + nums[i], nums[i])\n maxSoFar = max(maxSoFar, maxEndingHere)\n return maxSoFar\nelse:\n return 0",
"count = len(nums)\nif count == 0:\n ... | <|body_start_0|>
length = len(nums)
if length > 0:
maxSoFar = nums[0]
maxEndingHere = nums[0]
for i in range(1, length):
maxEndingHere = max(maxEndingHere + nums[i], nums[i])
maxSoFar = max(maxSoFar, maxEndingHere)
return ma... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSubArray(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def maxSubArray_self(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
length = len(nums)
if length > 0... | stack_v2_sparse_classes_36k_train_025132 | 896 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "maxSubArray",
"signature": "def maxSubArray(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "maxSubArray_self",
"signature": "def maxSubArray_self(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000611 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSubArray(self, nums): :type nums: List[int] :rtype: int
- def maxSubArray_self(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSubArray(self, nums): :type nums: List[int] :rtype: int
- def maxSubArray_self(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def maxSub... | ea492ec864b50547214ecbbb2cdeeac21e70229b | <|skeleton|>
class Solution:
def maxSubArray(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def maxSubArray_self(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSubArray(self, nums):
""":type nums: List[int] :rtype: int"""
length = len(nums)
if length > 0:
maxSoFar = nums[0]
maxEndingHere = nums[0]
for i in range(1, length):
maxEndingHere = max(maxEndingHere + nums[i], nums[i... | the_stack_v2_python_sparse | 53_maximum_subarray/sol.py | lianke123321/leetcode_sol | train | 0 | |
2230b1ab69065d8df6f1ca845c6264e9acd12962 | [
"dimod.Composite.__init__(self, sampler)\ntry:\n target_nodelist, target_edgelist, target_adjacency = sampler.structure\nexcept:\n raise\nself.chain_strength = self._validate_chain_strength(chain_strength)\nif isinstance(embedding, str):\n embedding = get_embedding_from_tag(embedding, target_nodelist, targ... | <|body_start_0|>
dimod.Composite.__init__(self, sampler)
try:
target_nodelist, target_edgelist, target_adjacency = sampler.structure
except:
raise
self.chain_strength = self._validate_chain_strength(chain_strength)
if isinstance(embedding, str):
... | VirtualGraph | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VirtualGraph:
def __init__(self, sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600):
"""Apply the VirtualGraph composite layer to the given solver. Args: sampler (:class:`dwave_micro_client_dimod.DWaveSampler`): A dimod_ sampler. ... | stack_v2_sparse_classes_36k_train_025133 | 13,081 | permissive | [
{
"docstring": "Apply the VirtualGraph composite layer to the given solver. Args: sampler (:class:`dwave_micro_client_dimod.DWaveSampler`): A dimod_ sampler. Normally :class:`dwave_micro_client_dimod.DWaveSampler`, or a derived composite sampler. Other samplers in general will not work or will not make sense wi... | 3 | stack_v2_sparse_classes_30k_train_020639 | Implement the Python class `VirtualGraph` described below.
Class description:
Implement the VirtualGraph class.
Method signatures and docstrings:
- def __init__(self, sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600): Apply the VirtualGraph composite layer to... | Implement the Python class `VirtualGraph` described below.
Class description:
Implement the VirtualGraph class.
Method signatures and docstrings:
- def __init__(self, sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600): Apply the VirtualGraph composite layer to... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class VirtualGraph:
def __init__(self, sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600):
"""Apply the VirtualGraph composite layer to the given solver. Args: sampler (:class:`dwave_micro_client_dimod.DWaveSampler`): A dimod_ sampler. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VirtualGraph:
def __init__(self, sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600):
"""Apply the VirtualGraph composite layer to the given solver. Args: sampler (:class:`dwave_micro_client_dimod.DWaveSampler`): A dimod_ sampler. Normally :clas... | the_stack_v2_python_sparse | artifacts/minimal_bugfixes/dwave-system/dwave-system#16/before/virtual_graph.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 | |
9cece4bc21262697605da0124afabe8c1263e14d | [
"for lcl, rmt in cls._to_ref:\n cls._decl_class_registry[lcl]._reference_table(cls._decl_class_registry[rmt].__table__)\ncls._to_ref.clear()",
"cols = [(Column(), refcol) for refcol in ref_table.primary_key]\nfor col, refcol in cols:\n setattr(cls, '%s_%s' % (ref_table.name, refcol.name), col)\ncls.__table_... | <|body_start_0|>
for lcl, rmt in cls._to_ref:
cls._decl_class_registry[lcl]._reference_table(cls._decl_class_registry[rmt].__table__)
cls._to_ref.clear()
<|end_body_0|>
<|body_start_1|>
cols = [(Column(), refcol) for refcol in ref_table.primary_key]
for col, refcol in cols:
... | A mixin which creates foreign key references to related classes. | References | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class References:
"""A mixin which creates foreign key references to related classes."""
def __declare_first__(cls):
"""declarative hook called within the 'before_configure' mapper event."""
<|body_0|>
def _reference_table(cls, ref_table):
"""Create a foreign key refer... | stack_v2_sparse_classes_36k_train_025134 | 19,400 | permissive | [
{
"docstring": "declarative hook called within the 'before_configure' mapper event.",
"name": "__declare_first__",
"signature": "def __declare_first__(cls)"
},
{
"docstring": "Create a foreign key reference from the local class to the given remote table. Adds column references to the declarative... | 2 | null | Implement the Python class `References` described below.
Class description:
A mixin which creates foreign key references to related classes.
Method signatures and docstrings:
- def __declare_first__(cls): declarative hook called within the 'before_configure' mapper event.
- def _reference_table(cls, ref_table): Creat... | Implement the Python class `References` described below.
Class description:
A mixin which creates foreign key references to related classes.
Method signatures and docstrings:
- def __declare_first__(cls): declarative hook called within the 'before_configure' mapper event.
- def _reference_table(cls, ref_table): Creat... | cdc18ee0c8bab67f049228d28d03578babfafb3d | <|skeleton|>
class References:
"""A mixin which creates foreign key references to related classes."""
def __declare_first__(cls):
"""declarative hook called within the 'before_configure' mapper event."""
<|body_0|>
def _reference_table(cls, ref_table):
"""Create a foreign key refer... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class References:
"""A mixin which creates foreign key references to related classes."""
def __declare_first__(cls):
"""declarative hook called within the 'before_configure' mapper event."""
for lcl, rmt in cls._to_ref:
cls._decl_class_registry[lcl]._reference_table(cls._decl_class_... | the_stack_v2_python_sparse | systemcheck/models/meta/schema.py | team-fasel/systemcheck | train | 2 |
bd0f1abfcf830758fb58ba5e12d93d44f79d7085 | [
"super(LayerNorm, self).__init__()\nself.a_2 = nn.Parameter(torch.ones(features))\nself.b_2 = nn.Parameter(torch.zeros(features))\nself.eps = eps",
"mean = x.mean(-1, keepdim=True)\nstd = x.std(-1, keepdim=True)\nreturn self.a_2 * (x - mean) / (std + self.eps) + self.b_2"
] | <|body_start_0|>
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
<|end_body_0|>
<|body_start_1|>
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return sel... | Layer normalization module. | LayerNorm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LayerNorm:
"""Layer normalization module."""
def __init__(self, features, eps=1e-06):
""":param features: shape of normalized features :param eps: epsilon used for standard deviation"""
<|body_0|>
def forward(self, x):
"""Forward pass through the layer normalizat... | stack_v2_sparse_classes_36k_train_025135 | 21,238 | no_license | [
{
"docstring": ":param features: shape of normalized features :param eps: epsilon used for standard deviation",
"name": "__init__",
"signature": "def __init__(self, features, eps=1e-06)"
},
{
"docstring": "Forward pass through the layer normalization. :param x: input of shape [batch_size, slate_... | 2 | stack_v2_sparse_classes_30k_train_011993 | Implement the Python class `LayerNorm` described below.
Class description:
Layer normalization module.
Method signatures and docstrings:
- def __init__(self, features, eps=1e-06): :param features: shape of normalized features :param eps: epsilon used for standard deviation
- def forward(self, x): Forward pass through... | Implement the Python class `LayerNorm` described below.
Class description:
Layer normalization module.
Method signatures and docstrings:
- def __init__(self, features, eps=1e-06): :param features: shape of normalized features :param eps: epsilon used for standard deviation
- def forward(self, x): Forward pass through... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class LayerNorm:
"""Layer normalization module."""
def __init__(self, features, eps=1e-06):
""":param features: shape of normalized features :param eps: epsilon used for standard deviation"""
<|body_0|>
def forward(self, x):
"""Forward pass through the layer normalizat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LayerNorm:
"""Layer normalization module."""
def __init__(self, features, eps=1e-06):
""":param features: shape of normalized features :param eps: epsilon used for standard deviation"""
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_... | the_stack_v2_python_sparse | generated/test_allegro_allRank.py | jansel/pytorch-jit-paritybench | train | 35 |
1eb3edc4948f2d7a8a2f7bf6908311519bb30962 | [
"super().__init__()\nself.image_uri_key = image_uri_key\nself.image_good_counter = Metrics.counter(self.__class__, 'image_good')\nself.image_bad_counter = Metrics.counter(self.__class__, 'image_bad')",
"d = {}\ntry:\n image_uri = element.pop(self.image_uri_key)\n image = load(image_uri)\n element['image_... | <|body_start_0|>
super().__init__()
self.image_uri_key = image_uri_key
self.image_good_counter = Metrics.counter(self.__class__, 'image_good')
self.image_bad_counter = Metrics.counter(self.__class__, 'image_bad')
<|end_body_0|>
<|body_start_1|>
d = {}
try:
im... | Adds image to PCollection. | ExtractImagesDoFn | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtractImagesDoFn:
"""Adds image to PCollection."""
def __init__(self, image_uri_key: str):
"""Constructor."""
<|body_0|>
def process(self, element: Dict[str, Any], *args: Tuple[Any, ...], **kwargs: Dict) -> Generator[Dict[str, Any], None, None]:
"""Loads image a... | stack_v2_sparse_classes_36k_train_025136 | 3,408 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, image_uri_key: str)"
},
{
"docstring": "Loads image and creates image features. This DoFn extracts an image being stored on local disk or GCS and yields a base64 encoded image, the image height, image width, and ... | 2 | stack_v2_sparse_classes_30k_train_011381 | Implement the Python class `ExtractImagesDoFn` described below.
Class description:
Adds image to PCollection.
Method signatures and docstrings:
- def __init__(self, image_uri_key: str): Constructor.
- def process(self, element: Dict[str, Any], *args: Tuple[Any, ...], **kwargs: Dict) -> Generator[Dict[str, Any], None,... | Implement the Python class `ExtractImagesDoFn` described below.
Class description:
Adds image to PCollection.
Method signatures and docstrings:
- def __init__(self, image_uri_key: str): Constructor.
- def process(self, element: Dict[str, Any], *args: Tuple[Any, ...], **kwargs: Dict) -> Generator[Dict[str, Any], None,... | 80f421ef53778ad2dc793d344d079ab4a814687a | <|skeleton|>
class ExtractImagesDoFn:
"""Adds image to PCollection."""
def __init__(self, image_uri_key: str):
"""Constructor."""
<|body_0|>
def process(self, element: Dict[str, Any], *args: Tuple[Any, ...], **kwargs: Dict) -> Generator[Dict[str, Any], None, None]:
"""Loads image a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExtractImagesDoFn:
"""Adds image to PCollection."""
def __init__(self, image_uri_key: str):
"""Constructor."""
super().__init__()
self.image_uri_key = image_uri_key
self.image_good_counter = Metrics.counter(self.__class__, 'image_good')
self.image_bad_counter = Met... | the_stack_v2_python_sparse | tfrecorder/beam_image.py | jared-burns/tensorflow-recorder | train | 0 |
311b4c55f3f2c3d0fbc1a9d7913350227fabc42a | [
"prog = sf.TDMProgram(2)\neng = sf.Engine('gaussian')\nwith prog.context([1, 2], [3, 4]) as (p, q):\n ops.Sgate(p[0]) | q[0]\n ops.MeasureHomodyne(p[1]) | q[0]\nresults = eng.run(prog)\nassert results.samples.shape[0] == 1",
"prog = sf.TDMProgram(2)\neng = sf.Engine('gaussian')\nwith prog.context([1, 2], [3... | <|body_start_0|>
prog = sf.TDMProgram(2)
eng = sf.Engine('gaussian')
with prog.context([1, 2], [3, 4]) as (p, q):
ops.Sgate(p[0]) | q[0]
ops.MeasureHomodyne(p[1]) | q[0]
results = eng.run(prog)
assert results.samples.shape[0] == 1
<|end_body_0|>
<|body_st... | Test the Engine class and its interaction with TDMProgram instances. | TestEngineTDMProgramInteraction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEngineTDMProgramInteraction:
"""Test the Engine class and its interaction with TDMProgram instances."""
def test_shots_default(self):
"""Test that default shots (1) is used"""
<|body_0|>
def test_shots_run_options(self):
"""Test that run_options takes precede... | stack_v2_sparse_classes_36k_train_025137 | 29,620 | permissive | [
{
"docstring": "Test that default shots (1) is used",
"name": "test_shots_default",
"signature": "def test_shots_default(self)"
},
{
"docstring": "Test that run_options takes precedence over default",
"name": "test_shots_run_options",
"signature": "def test_shots_run_options(self)"
},
... | 3 | stack_v2_sparse_classes_30k_train_018361 | Implement the Python class `TestEngineTDMProgramInteraction` described below.
Class description:
Test the Engine class and its interaction with TDMProgram instances.
Method signatures and docstrings:
- def test_shots_default(self): Test that default shots (1) is used
- def test_shots_run_options(self): Test that run_... | Implement the Python class `TestEngineTDMProgramInteraction` described below.
Class description:
Test the Engine class and its interaction with TDMProgram instances.
Method signatures and docstrings:
- def test_shots_default(self): Test that default shots (1) is used
- def test_shots_run_options(self): Test that run_... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class TestEngineTDMProgramInteraction:
"""Test the Engine class and its interaction with TDMProgram instances."""
def test_shots_default(self):
"""Test that default shots (1) is used"""
<|body_0|>
def test_shots_run_options(self):
"""Test that run_options takes precede... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestEngineTDMProgramInteraction:
"""Test the Engine class and its interaction with TDMProgram instances."""
def test_shots_default(self):
"""Test that default shots (1) is used"""
prog = sf.TDMProgram(2)
eng = sf.Engine('gaussian')
with prog.context([1, 2], [3, 4]) as (p, ... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits/strawberryfields/strawberryfields#611/before/test_tdmprogram.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
592fa985d3be9e61737e78892a28d3124a1c4974 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn WindowsHelloForBusinessAuthenticationMethod()",
"from .authentication_method import AuthenticationMethod\nfrom .authentication_method_key_strength import AuthenticationMethodKeyStrength\nfrom .device import Device\nfrom .authentication... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return WindowsHelloForBusinessAuthenticationMethod()
<|end_body_0|>
<|body_start_1|>
from .authentication_method import AuthenticationMethod
from .authentication_method_key_strength import Auth... | WindowsHelloForBusinessAuthenticationMethod | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WindowsHelloForBusinessAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsHelloForBusinessAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use t... | stack_v2_sparse_classes_36k_train_025138 | 3,929 | 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: WindowsHelloForBusinessAuthenticationMethod",
"name": "create_from_discriminator_value",
"signature": "def c... | 3 | null | Implement the Python class `WindowsHelloForBusinessAuthenticationMethod` described below.
Class description:
Implement the WindowsHelloForBusinessAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsHelloForBusinessAuthenti... | Implement the Python class `WindowsHelloForBusinessAuthenticationMethod` described below.
Class description:
Implement the WindowsHelloForBusinessAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsHelloForBusinessAuthenti... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class WindowsHelloForBusinessAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsHelloForBusinessAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WindowsHelloForBusinessAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsHelloForBusinessAuthenticationMethod:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the dis... | the_stack_v2_python_sparse | msgraph/generated/models/windows_hello_for_business_authentication_method.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
6a49f704aa0351a21a848b0fc40c7b451f35bdbd | [
"validated_data['bundle_name'] = validated_data.pop('name')\nvalidated_data['bundle_note'] = validated_data.pop('note')\nreturn Bundle.UploadNew(**validated_data)",
"project_name = validated_data.pop('project_name')\nbundle_name = instance.name\ndata = {'dst_bundle_name': validated_data.pop('new_name', None), 'no... | <|body_start_0|>
validated_data['bundle_name'] = validated_data.pop('name')
validated_data['bundle_note'] = validated_data.pop('note')
return Bundle.UploadNew(**validated_data)
<|end_body_0|>
<|body_start_1|>
project_name = validated_data.pop('project_name')
bundle_name = instan... | Serialize or deserialize Bundle objects. | BundleSerializer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BundleSerializer:
"""Serialize or deserialize Bundle objects."""
def create(self, validated_data):
"""Override parent's method."""
<|body_0|>
def update(self, instance, validated_data):
"""Override parent's method."""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_025139 | 6,625 | permissive | [
{
"docstring": "Override parent's method.",
"name": "create",
"signature": "def create(self, validated_data)"
},
{
"docstring": "Override parent's method.",
"name": "update",
"signature": "def update(self, instance, validated_data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008274 | Implement the Python class `BundleSerializer` described below.
Class description:
Serialize or deserialize Bundle objects.
Method signatures and docstrings:
- def create(self, validated_data): Override parent's method.
- def update(self, instance, validated_data): Override parent's method. | Implement the Python class `BundleSerializer` described below.
Class description:
Serialize or deserialize Bundle objects.
Method signatures and docstrings:
- def create(self, validated_data): Override parent's method.
- def update(self, instance, validated_data): Override parent's method.
<|skeleton|>
class BundleS... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class BundleSerializer:
"""Serialize or deserialize Bundle objects."""
def create(self, validated_data):
"""Override parent's method."""
<|body_0|>
def update(self, instance, validated_data):
"""Override parent's method."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BundleSerializer:
"""Serialize or deserialize Bundle objects."""
def create(self, validated_data):
"""Override parent's method."""
validated_data['bundle_name'] = validated_data.pop('name')
validated_data['bundle_note'] = validated_data.pop('note')
return Bundle.UploadNew(... | the_stack_v2_python_sparse | py/dome/backend/serializers.py | bridder/factory | train | 0 |
361814879fbd1019509550d74cd6f07ac4827d05 | [
"citations.sort()\nN = len(citations)\nlow, high = (0, N - 1)\nwhile low <= high:\n mid = (low + high) // 2\n if N - mid > citations[mid]:\n low = mid + 1\n else:\n high = mid - 1\nreturn N - low",
"citations.sort()\nprint(citations)\nn = len(citations)\nfor i in range(n):\n length = n -... | <|body_start_0|>
citations.sort()
N = len(citations)
low, high = (0, N - 1)
while low <= high:
mid = (low + high) // 2
if N - mid > citations[mid]:
low = mid + 1
else:
high = mid - 1
return N - low
<|end_body_0|>... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hIndex(self, citations):
"""二分查找 :type citations: List[int] :rtype: int"""
<|body_0|>
def hIndex2(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
citations.sort()
N = ... | stack_v2_sparse_classes_36k_train_025140 | 1,636 | no_license | [
{
"docstring": "二分查找 :type citations: List[int] :rtype: int",
"name": "hIndex",
"signature": "def hIndex(self, citations)"
},
{
"docstring": ":type citations: List[int] :rtype: int",
"name": "hIndex2",
"signature": "def hIndex2(self, citations)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hIndex(self, citations): 二分查找 :type citations: List[int] :rtype: int
- def hIndex2(self, citations): :type citations: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hIndex(self, citations): 二分查找 :type citations: List[int] :rtype: int
- def hIndex2(self, citations): :type citations: List[int] :rtype: int
<|skeleton|>
class Solution:
... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def hIndex(self, citations):
"""二分查找 :type citations: List[int] :rtype: int"""
<|body_0|>
def hIndex2(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hIndex(self, citations):
"""二分查找 :type citations: List[int] :rtype: int"""
citations.sort()
N = len(citations)
low, high = (0, N - 1)
while low <= high:
mid = (low + high) // 2
if N - mid > citations[mid]:
low = mid ... | the_stack_v2_python_sparse | 274_H指数.py | lovehhf/LeetCode | train | 0 | |
f43517cbdb4cb79b1f6241414780018e198a6e3f | [
"self.students = []\nself.grades = {}\nself.is_sorted = True",
"if student in self.students:\n raise ValueError('Duplicate student')\nself.students.append(student)\nself.grades[student.get_id_num()] = []\nself.is_sorted = False",
"try:\n self.grades[student.get_id_num()].append(grade)\nexcept KeyError:\n ... | <|body_start_0|>
self.students = []
self.grades = {}
self.is_sorted = True
<|end_body_0|>
<|body_start_1|>
if student in self.students:
raise ValueError('Duplicate student')
self.students.append(student)
self.grades[student.get_id_num()] = []
self.is_... | A mapping from students to a list of grades | Grades | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Grades:
"""A mapping from students to a list of grades"""
def __init__(self):
"""Create empty grade book"""
<|body_0|>
def add_student(self, student):
"""Assume: student is if type Student add student to the grade book"""
<|body_1|>
def add_grade(sel... | stack_v2_sparse_classes_36k_train_025141 | 2,494 | no_license | [
{
"docstring": "Create empty grade book",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Assume: student is if type Student add student to the grade book",
"name": "add_student",
"signature": "def add_student(self, student)"
},
{
"docstring": "Assume gra... | 5 | stack_v2_sparse_classes_30k_train_018038 | Implement the Python class `Grades` described below.
Class description:
A mapping from students to a list of grades
Method signatures and docstrings:
- def __init__(self): Create empty grade book
- def add_student(self, student): Assume: student is if type Student add student to the grade book
- def add_grade(self, s... | Implement the Python class `Grades` described below.
Class description:
A mapping from students to a list of grades
Method signatures and docstrings:
- def __init__(self): Create empty grade book
- def add_student(self, student): Assume: student is if type Student add student to the grade book
- def add_grade(self, s... | cae706564efdc41e435b309493484ea9348c908d | <|skeleton|>
class Grades:
"""A mapping from students to a list of grades"""
def __init__(self):
"""Create empty grade book"""
<|body_0|>
def add_student(self, student):
"""Assume: student is if type Student add student to the grade book"""
<|body_1|>
def add_grade(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Grades:
"""A mapping from students to a list of grades"""
def __init__(self):
"""Create empty grade book"""
self.students = []
self.grades = {}
self.is_sorted = True
def add_student(self, student):
"""Assume: student is if type Student add student to the grade... | the_stack_v2_python_sparse | Week_05/OOP_Part_02/class_grade_book.py | xeusteerapat/MIT-6.00.1x-EDX | train | 0 |
cb6f3da2f7cb83f8ab52ee54c9479923a60a8bb7 | [
"dic = {*wordDict}\nn = len(s)\ndp = [0] * (n + 1)\ndp[0] = 1\nfor i in range(1, n + 1):\n for j in wordDict:\n if s[i - len(j):i] in dic and dp[i - len(j)] == 1:\n dp[i] = 1\n break\nreturn True if dp[-1] == 1 else False",
"@lru_cache()\ndef dfs(l):\n if l == len(s):\n s... | <|body_start_0|>
dic = {*wordDict}
n = len(s)
dp = [0] * (n + 1)
dp[0] = 1
for i in range(1, n + 1):
for j in wordDict:
if s[i - len(j):i] in dic and dp[i - len(j)] == 1:
dp[i] = 1
break
return True if dp... | 题意：判断给定的s能否由wordDict中的word连起来，word可以使用多次 | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""题意：判断给定的s能否由wordDict中的word连起来，word可以使用多次"""
def wordBreak1(self, s: str, wordDict: List[str]) -> bool:
"""思路：动态规划法 1. 判断s中每个位置前面是否有"""
<|body_0|>
def wordBreak2(self, s: str, wordDict: List[str]) -> bool:
"""思路：dfs 1. 用字符串s去wordDict中匹配，匹配到就从s剩下的字符串中匹... | stack_v2_sparse_classes_36k_train_025142 | 2,350 | no_license | [
{
"docstring": "思路：动态规划法 1. 判断s中每个位置前面是否有",
"name": "wordBreak1",
"signature": "def wordBreak1(self, s: str, wordDict: List[str]) -> bool"
},
{
"docstring": "思路：dfs 1. 用字符串s去wordDict中匹配，匹配到就从s剩下的字符串中匹配wordDict，直到正好匹配完s 2.",
"name": "wordBreak2",
"signature": "def wordBreak2(self, s: str,... | 2 | stack_v2_sparse_classes_30k_train_013703 | Implement the Python class `Solution` described below.
Class description:
题意：判断给定的s能否由wordDict中的word连起来，word可以使用多次
Method signatures and docstrings:
- def wordBreak1(self, s: str, wordDict: List[str]) -> bool: 思路：动态规划法 1. 判断s中每个位置前面是否有
- def wordBreak2(self, s: str, wordDict: List[str]) -> bool: 思路：dfs 1. 用字符串s去wordD... | Implement the Python class `Solution` described below.
Class description:
题意：判断给定的s能否由wordDict中的word连起来，word可以使用多次
Method signatures and docstrings:
- def wordBreak1(self, s: str, wordDict: List[str]) -> bool: 思路：动态规划法 1. 判断s中每个位置前面是否有
- def wordBreak2(self, s: str, wordDict: List[str]) -> bool: 思路：dfs 1. 用字符串s去wordD... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
"""题意：判断给定的s能否由wordDict中的word连起来，word可以使用多次"""
def wordBreak1(self, s: str, wordDict: List[str]) -> bool:
"""思路：动态规划法 1. 判断s中每个位置前面是否有"""
<|body_0|>
def wordBreak2(self, s: str, wordDict: List[str]) -> bool:
"""思路：dfs 1. 用字符串s去wordDict中匹配，匹配到就从s剩下的字符串中匹... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""题意：判断给定的s能否由wordDict中的word连起来，word可以使用多次"""
def wordBreak1(self, s: str, wordDict: List[str]) -> bool:
"""思路：动态规划法 1. 判断s中每个位置前面是否有"""
dic = {*wordDict}
n = len(s)
dp = [0] * (n + 1)
dp[0] = 1
for i in range(1, n + 1):
for j in word... | the_stack_v2_python_sparse | LeetCode/动态规划法(dp)/139. 单词拆分.py | yiming1012/MyLeetCode | train | 2 |
3f0852cff7c49d86edf3535a79447785075f4ccb | [
"if not root:\n return -1\nret = 1 + max((self.find_leaves_helper(child, results) for child in (root.left, root.right)))\nif ret >= len(results):\n results.append([])\nresults[ret].append(root.val)\nreturn ret",
"ret = []\nself.find_leaves_helper(root, ret)\nreturn ret"
] | <|body_start_0|>
if not root:
return -1
ret = 1 + max((self.find_leaves_helper(child, results) for child in (root.left, root.right)))
if ret >= len(results):
results.append([])
results[ret].append(root.val)
return ret
<|end_body_0|>
<|body_start_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def find_leaves_helper(self, root, results):
"""push root and all descendants to results return the distance from root to farthest leaf"""
<|body_0|>
def find_leaves(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_s... | stack_v2_sparse_classes_36k_train_025143 | 1,688 | no_license | [
{
"docstring": "push root and all descendants to results return the distance from root to farthest leaf",
"name": "find_leaves_helper",
"signature": "def find_leaves_helper(self, root, results)"
},
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "find_leaves",
"sig... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find_leaves_helper(self, root, results): push root and all descendants to results return the distance from root to farthest leaf
- def find_leaves(self, root): :type root: Tr... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find_leaves_helper(self, root, results): push root and all descendants to results return the distance from root to farthest leaf
- def find_leaves(self, root): :type root: Tr... | e3637e293c5e4e8b4e5cc2e24dcd638ef796c560 | <|skeleton|>
class Solution:
def find_leaves_helper(self, root, results):
"""push root and all descendants to results return the distance from root to farthest leaf"""
<|body_0|>
def find_leaves(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def find_leaves_helper(self, root, results):
"""push root and all descendants to results return the distance from root to farthest leaf"""
if not root:
return -1
ret = 1 + max((self.find_leaves_helper(child, results) for child in (root.left, root.right)))
... | the_stack_v2_python_sparse | tree/findLeavesOfBinaryTree.py | ay701/Coding_Challenges | train | 1 | |
69891e9fdce4619a4a9b093225cf7c8fd9c063a5 | [
"self.producers = producers\nself.consumer = consumer\nself.is_unary = is_unary",
"values = tuple([f(row) for f in self.producers])\nif self.is_unary:\n return self.consumer(values)\nelse:\n return self.consumer(*values)"
] | <|body_start_0|>
self.producers = producers
self.consumer = consumer
self.is_unary = is_unary
<|end_body_0|>
<|body_start_1|>
values = tuple([f(row) for f in self.producers])
if self.is_unary:
return self.consumer(values)
else:
return self.consume... | A ternary stream function extracts values using multiple producers and passes them to a single consumer. The consumer may either be a unary or a ternary function. An unary function will receive a tuple of extracted values as the argument. | TernaryStreamFunction | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TernaryStreamFunction:
"""A ternary stream function extracts values using multiple producers and passes them to a single consumer. The consumer may either be a unary or a ternary function. An unary function will receive a tuple of extracted values as the argument."""
def __init__(self, produ... | stack_v2_sparse_classes_36k_train_025144 | 44,356 | permissive | [
{
"docstring": "Initialize the list of producers and the consumer for values that are extracted (by the producers) from data stream rows. Parameters ---------- producers: list of openclean.data.stream.base.StreamFunction List of stream functions that are used to extract values from data stream rows. consumer: c... | 2 | stack_v2_sparse_classes_30k_train_004188 | Implement the Python class `TernaryStreamFunction` described below.
Class description:
A ternary stream function extracts values using multiple producers and passes them to a single consumer. The consumer may either be a unary or a ternary function. An unary function will receive a tuple of extracted values as the arg... | Implement the Python class `TernaryStreamFunction` described below.
Class description:
A ternary stream function extracts values using multiple producers and passes them to a single consumer. The consumer may either be a unary or a ternary function. An unary function will receive a tuple of extracted values as the arg... | e3d0e04f90468c49f29ca53edc2feb12465c24d5 | <|skeleton|>
class TernaryStreamFunction:
"""A ternary stream function extracts values using multiple producers and passes them to a single consumer. The consumer may either be a unary or a ternary function. An unary function will receive a tuple of extracted values as the argument."""
def __init__(self, produ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TernaryStreamFunction:
"""A ternary stream function extracts values using multiple producers and passes them to a single consumer. The consumer may either be a unary or a ternary function. An unary function will receive a tuple of extracted values as the argument."""
def __init__(self, producers: StreamF... | the_stack_v2_python_sparse | openclean/function/eval/base.py | Denisfench/openclean-core | train | 0 |
532e4a663a1d4e7801ae2baa8487f45bc07173f9 | [
"self._params = Parameters()\nfor path, param in network.get_variables().items():\n self._params.add(path + '_mean_sqr_gradient', numpy.zeros_like(param.get_value()))\n self._params.add(path + '_mean_sqr_delta', numpy.zeros_like(param.get_value()))\nif 'gradient_decay_rate' not in optimization_options:\n r... | <|body_start_0|>
self._params = Parameters()
for path, param in network.get_variables().items():
self._params.add(path + '_mean_sqr_gradient', numpy.zeros_like(param.get_value()))
self._params.add(path + '_mean_sqr_delta', numpy.zeros_like(param.get_value()))
if 'gradient... | ADADELTA Optimization Method ADADELTA optimization method has been derived from AdaGrad. AdaGrad accumulates the sum of squared gradients over all time, which is used to scale the learning rate smaller and smaller. ADADELTA uses an exponentially decaying average of the squared gradients. This implementation scales the ... | AdadeltaOptimizer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdadeltaOptimizer:
"""ADADELTA Optimization Method ADADELTA optimization method has been derived from AdaGrad. AdaGrad accumulates the sum of squared gradients over all time, which is used to scale the learning rate smaller and smaller. ADADELTA uses an exponentially decaying average of the squar... | stack_v2_sparse_classes_36k_train_025145 | 3,644 | permissive | [
{
"docstring": "Creates an Adadelta optimizer. :type optimization_options: dict :param optimization_options: a dictionary of optimization options :type network: Network :param network: the neural network object",
"name": "__init__",
"signature": "def __init__(self, optimization_options, network, *args, ... | 2 | stack_v2_sparse_classes_30k_train_001435 | Implement the Python class `AdadeltaOptimizer` described below.
Class description:
ADADELTA Optimization Method ADADELTA optimization method has been derived from AdaGrad. AdaGrad accumulates the sum of squared gradients over all time, which is used to scale the learning rate smaller and smaller. ADADELTA uses an expo... | Implement the Python class `AdadeltaOptimizer` described below.
Class description:
ADADELTA Optimization Method ADADELTA optimization method has been derived from AdaGrad. AdaGrad accumulates the sum of squared gradients over all time, which is used to scale the learning rate smaller and smaller. ADADELTA uses an expo... | 9904faec19ad5718470f21927229aad2656e5686 | <|skeleton|>
class AdadeltaOptimizer:
"""ADADELTA Optimization Method ADADELTA optimization method has been derived from AdaGrad. AdaGrad accumulates the sum of squared gradients over all time, which is used to scale the learning rate smaller and smaller. ADADELTA uses an exponentially decaying average of the squar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdadeltaOptimizer:
"""ADADELTA Optimization Method ADADELTA optimization method has been derived from AdaGrad. AdaGrad accumulates the sum of squared gradients over all time, which is used to scale the learning rate smaller and smaller. ADADELTA uses an exponentially decaying average of the squared gradients.... | the_stack_v2_python_sparse | theanolm/training/adadeltaoptimizer.py | senarvi/theanolm | train | 95 |
bf8c6a7dc4b51b2c4bf86d0078c45a3de872197f | [
"found = 1\nstep = 2\nwhile step <= number:\n found *= step\n step += 1\nreturn found",
"trimmed = re.compile('[^a-zA-Z0-9 ]').sub('', sentence)\nchunks = trimmed.split(' ')\nlongest = 0\nindex = -1\nfor i, x in enumerate(chunks):\n if len(x) > longest:\n longest = len(x)\n index = i\nretur... | <|body_start_0|>
found = 1
step = 2
while step <= number:
found *= step
step += 1
return found
<|end_body_0|>
<|body_start_1|>
trimmed = re.compile('[^a-zA-Z0-9 ]').sub('', sentence)
chunks = trimmed.split(' ')
longest = 0
index = ... | Challenges | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Challenges:
def first_factorial(number: int) -> int:
"""Iterative approach :param number: an input, first factorial of a number :return: factorial"""
<|body_0|>
def longest_word(sentence: str) -> str:
"""Detect longest word in a sentence :param sentence: :return:"""
... | stack_v2_sparse_classes_36k_train_025146 | 1,505 | permissive | [
{
"docstring": "Iterative approach :param number: an input, first factorial of a number :return: factorial",
"name": "first_factorial",
"signature": "def first_factorial(number: int) -> int"
},
{
"docstring": "Detect longest word in a sentence :param sentence: :return:",
"name": "longest_wor... | 3 | stack_v2_sparse_classes_30k_train_005035 | Implement the Python class `Challenges` described below.
Class description:
Implement the Challenges class.
Method signatures and docstrings:
- def first_factorial(number: int) -> int: Iterative approach :param number: an input, first factorial of a number :return: factorial
- def longest_word(sentence: str) -> str: ... | Implement the Python class `Challenges` described below.
Class description:
Implement the Challenges class.
Method signatures and docstrings:
- def first_factorial(number: int) -> int: Iterative approach :param number: an input, first factorial of a number :return: factorial
- def longest_word(sentence: str) -> str: ... | ce7cf332483e01d05bcad98921d736c33a33a66c | <|skeleton|>
class Challenges:
def first_factorial(number: int) -> int:
"""Iterative approach :param number: an input, first factorial of a number :return: factorial"""
<|body_0|>
def longest_word(sentence: str) -> str:
"""Detect longest word in a sentence :param sentence: :return:"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Challenges:
def first_factorial(number: int) -> int:
"""Iterative approach :param number: an input, first factorial of a number :return: factorial"""
found = 1
step = 2
while step <= number:
found *= step
step += 1
return found
def longest_w... | the_stack_v2_python_sparse | py_algorithms/challenges/challenges.py | ktp-forked-repos/py-algorithms | train | 0 | |
fa6bde2bb36b271c4a327f81a9459bf91fe8cf38 | [
"super().__init__()\nself.vote_factor = vote_factor\nself.in_dim = seed_feature_dim\nself.out_dim = self.in_dim\nself.conv1 = torch.nn.Conv1d(self.in_dim, self.in_dim, 1)\nself.conv2 = torch.nn.Conv1d(self.in_dim, self.in_dim, 1)\nself.conv3 = torch.nn.Conv1d(self.in_dim, (3 + self.out_dim) * self.vote_factor, 1)\n... | <|body_start_0|>
super().__init__()
self.vote_factor = vote_factor
self.in_dim = seed_feature_dim
self.out_dim = self.in_dim
self.conv1 = torch.nn.Conv1d(self.in_dim, self.in_dim, 1)
self.conv2 = torch.nn.Conv1d(self.in_dim, self.in_dim, 1)
self.conv3 = torch.nn.C... | VotingModule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VotingModule:
def __init__(self, vote_factor, seed_feature_dim):
"""Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point seed_feature_dim: int number of channels of seed point features vote_feature_dim: int number of channels of... | stack_v2_sparse_classes_36k_train_025147 | 40,928 | no_license | [
{
"docstring": "Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point seed_feature_dim: int number of channels of seed point features vote_feature_dim: int number of channels of vote features",
"name": "__init__",
"signature": "def __init__(self... | 2 | stack_v2_sparse_classes_30k_test_000724 | Implement the Python class `VotingModule` described below.
Class description:
Implement the VotingModule class.
Method signatures and docstrings:
- def __init__(self, vote_factor, seed_feature_dim): Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point seed_f... | Implement the Python class `VotingModule` described below.
Class description:
Implement the VotingModule class.
Method signatures and docstrings:
- def __init__(self, vote_factor, seed_feature_dim): Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point seed_f... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class VotingModule:
def __init__(self, vote_factor, seed_feature_dim):
"""Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point seed_feature_dim: int number of channels of seed point features vote_feature_dim: int number of channels of... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VotingModule:
def __init__(self, vote_factor, seed_feature_dim):
"""Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point seed_feature_dim: int number of channels of seed point features vote_feature_dim: int number of channels of vote features... | the_stack_v2_python_sparse | generated/test_Na_Z_sess.py | jansel/pytorch-jit-paritybench | train | 35 | |
da196a044126e5086f8feff65e92ad55d98f9f55 | [
"result = []\nif not root:\n return result\nqueue = [root]\nwhile queue:\n levelNum = len(queue)\n temp = []\n for i in range(levelNum):\n cur = queue[0]\n temp.append(cur.val)\n if cur.left:\n queue.append(cur.left)\n if cur.right:\n queue.append(cur.ri... | <|body_start_0|>
result = []
if not root:
return result
queue = [root]
while queue:
levelNum = len(queue)
temp = []
for i in range(levelNum):
cur = queue[0]
temp.append(cur.val)
if cur.left:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rightSideView(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def rightSideView_self(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = []
if not... | stack_v2_sparse_classes_36k_train_025148 | 1,417 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[int]",
"name": "rightSideView",
"signature": "def rightSideView(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: List[int]",
"name": "rightSideView_self",
"signature": "def rightSideView_self(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rightSideView(self, root): :type root: TreeNode :rtype: List[int]
- def rightSideView_self(self, root): :type root: TreeNode :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rightSideView(self, root): :type root: TreeNode :rtype: List[int]
- def rightSideView_self(self, root): :type root: TreeNode :rtype: List[int]
<|skeleton|>
class Solution:
... | ea492ec864b50547214ecbbb2cdeeac21e70229b | <|skeleton|>
class Solution:
def rightSideView(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def rightSideView_self(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rightSideView(self, root):
""":type root: TreeNode :rtype: List[int]"""
result = []
if not root:
return result
queue = [root]
while queue:
levelNum = len(queue)
temp = []
for i in range(levelNum):
... | the_stack_v2_python_sparse | 199_binary_tree_right_side_view/sol.py | lianke123321/leetcode_sol | train | 0 | |
787a1b61ae0bc890ca0772ea5aed183e4cbe89aa | [
"self.folder = folder\nself.is_latin_required = is_latin_required\nif root:\n self.folder = os.path.join(root, self.folder)\nassert os.path.exists(os.path.join(self.folder, 'ImagesPart1'))\nassert os.path.exists(os.path.join(self.folder, 'ImagesPart2'))\nassert os.path.exists(os.path.join(self.folder, 'train_gt_... | <|body_start_0|>
self.folder = folder
self.is_latin_required = is_latin_required
if root:
self.folder = os.path.join(root, self.folder)
assert os.path.exists(os.path.join(self.folder, 'ImagesPart1'))
assert os.path.exists(os.path.join(self.folder, 'ImagesPart2'))
... | Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation. | ICDAR2019MLTDatasetConverter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ICDAR2019MLTDatasetConverter:
"""Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation."""
def __init__(self, folder, is_latin_required, root=''):
"""Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder with extracted zip archives containing images and annotat... | stack_v2_sparse_classes_36k_train_025149 | 25,441 | permissive | [
{
"docstring": "Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder with extracted zip archives containing images and annotation. :param is_latin_required: if it is True than images that do not contain latin text will be filtered out.",
"name": "__init__",
"signature": "def __init__(s... | 4 | null | Implement the Python class `ICDAR2019MLTDatasetConverter` described below.
Class description:
Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation.
Method signatures and docstrings:
- def __init__(self, folder, is_latin_required, root=''): Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder ... | Implement the Python class `ICDAR2019MLTDatasetConverter` described below.
Class description:
Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation.
Method signatures and docstrings:
- def __init__(self, folder, is_latin_required, root=''): Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder ... | c553a56088f0055baba838b68c9299e19683227e | <|skeleton|>
class ICDAR2019MLTDatasetConverter:
"""Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation."""
def __init__(self, folder, is_latin_required, root=''):
"""Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder with extracted zip archives containing images and annotat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ICDAR2019MLTDatasetConverter:
"""Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation."""
def __init__(self, folder, is_latin_required, root=''):
"""Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder with extracted zip archives containing images and annotation. :param i... | the_stack_v2_python_sparse | pytorch_toolkit/text_spotting/text_spotting/datasets/datasets.py | DmitriySidnev/openvino_training_extensions | train | 0 |
df4ea4d2c92528ca2a3c8af7c4ac627e5d563437 | [
"if n == 0:\n return 1\nif x == 0:\n if n > 0:\n return 0\n else:\n return inf\nres = 1\nflag = 1\nif n < 0:\n flag = -1\n n = abs(n)\nfor i in range(n):\n res *= x\nreturn res if flag == 1 else 1 / res",
"if n == 0:\n return 1\nif x == 0:\n if n > 0:\n return 0\n e... | <|body_start_0|>
if n == 0:
return 1
if x == 0:
if n > 0:
return 0
else:
return inf
res = 1
flag = 1
if n < 0:
flag = -1
n = abs(n)
for i in range(n):
res *= x
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def myPow1(self, x: float, n: int) -> float:
"""思路：迭代 时间复杂度：O(N)"""
<|body_0|>
def myPow2(self, x: float, n: int) -> float:
"""执行用时 :28 ms, 在所有 Python3 提交中击败了94.37%的用户 内存消耗 :13.7 MB, 在所有 Python3 提交中击败了5.05%的用户 思路：快速幂 1、首先，任何数的0次幂都是 2、负数的n次幂，为这个数绝对值的n次幂的倒数 3... | stack_v2_sparse_classes_36k_train_025150 | 2,333 | no_license | [
{
"docstring": "思路：迭代 时间复杂度：O(N)",
"name": "myPow1",
"signature": "def myPow1(self, x: float, n: int) -> float"
},
{
"docstring": "执行用时 :28 ms, 在所有 Python3 提交中击败了94.37%的用户 内存消耗 :13.7 MB, 在所有 Python3 提交中击败了5.05%的用户 思路：快速幂 1、首先，任何数的0次幂都是 2、负数的n次幂，为这个数绝对值的n次幂的倒数 3、将n转化为2进制，如10对应的二进制为：1010，即n=1*(2**... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def myPow1(self, x: float, n: int) -> float: 思路：迭代 时间复杂度：O(N)
- def myPow2(self, x: float, n: int) -> float: 执行用时 :28 ms, 在所有 Python3 提交中击败了94.37%的用户 内存消耗 :13.7 MB, 在所有 Python3 提... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def myPow1(self, x: float, n: int) -> float: 思路：迭代 时间复杂度：O(N)
- def myPow2(self, x: float, n: int) -> float: 执行用时 :28 ms, 在所有 Python3 提交中击败了94.37%的用户 内存消耗 :13.7 MB, 在所有 Python3 提... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def myPow1(self, x: float, n: int) -> float:
"""思路：迭代 时间复杂度：O(N)"""
<|body_0|>
def myPow2(self, x: float, n: int) -> float:
"""执行用时 :28 ms, 在所有 Python3 提交中击败了94.37%的用户 内存消耗 :13.7 MB, 在所有 Python3 提交中击败了5.05%的用户 思路：快速幂 1、首先，任何数的0次幂都是 2、负数的n次幂，为这个数绝对值的n次幂的倒数 3... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def myPow1(self, x: float, n: int) -> float:
"""思路：迭代 时间复杂度：O(N)"""
if n == 0:
return 1
if x == 0:
if n > 0:
return 0
else:
return inf
res = 1
flag = 1
if n < 0:
flag = -1
... | the_stack_v2_python_sparse | LeetCode/快速幂/50. Pow(x, n).py | yiming1012/MyLeetCode | train | 2 | |
a67028db1ac6093b14f47bcdae8bfaf5e41bcc51 | [
"sets = collections()\nboss = Char(104, 8, 1)\nreturn part1(sets, boss)",
"sets = collections()\nboss = Char(104, 8, 1)\nreturn part2(sets, boss)"
] | <|body_start_0|>
sets = collections()
boss = Char(104, 8, 1)
return part1(sets, boss)
<|end_body_0|>
<|body_start_1|>
sets = collections()
boss = Char(104, 8, 1)
return part2(sets, boss)
<|end_body_1|>
| AoC 2015 Day 21 | Day21 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Day21:
"""AoC 2015 Day 21"""
def part1(_filename: str) -> int:
"""Given a filename, solve 2015 day 21 part 1"""
<|body_0|>
def part2(_filename: str) -> int:
"""Given a filename, solve 2015 day 21 part 2"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_025151 | 3,590 | no_license | [
{
"docstring": "Given a filename, solve 2015 day 21 part 1",
"name": "part1",
"signature": "def part1(_filename: str) -> int"
},
{
"docstring": "Given a filename, solve 2015 day 21 part 2",
"name": "part2",
"signature": "def part2(_filename: str) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_015538 | Implement the Python class `Day21` described below.
Class description:
AoC 2015 Day 21
Method signatures and docstrings:
- def part1(_filename: str) -> int: Given a filename, solve 2015 day 21 part 1
- def part2(_filename: str) -> int: Given a filename, solve 2015 day 21 part 2 | Implement the Python class `Day21` described below.
Class description:
AoC 2015 Day 21
Method signatures and docstrings:
- def part1(_filename: str) -> int: Given a filename, solve 2015 day 21 part 1
- def part2(_filename: str) -> int: Given a filename, solve 2015 day 21 part 2
<|skeleton|>
class Day21:
"""AoC 2... | e89db235837d2d05848210a18c9c2a4456085570 | <|skeleton|>
class Day21:
"""AoC 2015 Day 21"""
def part1(_filename: str) -> int:
"""Given a filename, solve 2015 day 21 part 1"""
<|body_0|>
def part2(_filename: str) -> int:
"""Given a filename, solve 2015 day 21 part 2"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Day21:
"""AoC 2015 Day 21"""
def part1(_filename: str) -> int:
"""Given a filename, solve 2015 day 21 part 1"""
sets = collections()
boss = Char(104, 8, 1)
return part1(sets, boss)
def part2(_filename: str) -> int:
"""Given a filename, solve 2015 day 21 part 2... | the_stack_v2_python_sparse | 2015/python2015/aoc/day21.py | mreishus/aoc | train | 16 |
1b7575a64366b7da437ac0ffd9fddd6860b639ac | [
"self.cutoff = cutoff\nself.angle_cutoff = angle_cutoff\nself.box_width = box_width\nself.voxel_width = voxel_width",
"if 'complex' in kwargs:\n datapoint = kwargs.get('complex')\n raise DeprecationWarning('Complex is being phased out as a parameter, please pass \"datapoint\" instead.')\ntry:\n fragments... | <|body_start_0|>
self.cutoff = cutoff
self.angle_cutoff = angle_cutoff
self.box_width = box_width
self.voxel_width = voxel_width
<|end_body_0|>
<|body_start_1|>
if 'complex' in kwargs:
datapoint = kwargs.get('complex')
raise DeprecationWarning('Complex is... | Localize cation-Pi interactions between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute cation-Pi between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which it originated to create a local cation-pi ar... | CationPiVoxelizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CationPiVoxelizer:
"""Localize cation-Pi interactions between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute cation-Pi between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which... | stack_v2_sparse_classes_36k_train_025152 | 27,676 | permissive | [
{
"docstring": "Parameters ---------- cutoff: float, optional (default 6.5) The distance in angstroms within which atoms must be to be considered for a cation-pi interaction between them. angle_cutoff: float, optional (default 30.0) Angle cutoff. Max allowed deviation from the ideal (0deg) angle between ring no... | 2 | null | Implement the Python class `CationPiVoxelizer` described below.
Class description:
Localize cation-Pi interactions between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute cation-Pi between atoms in the macromolecular complex. For each atom, localize... | Implement the Python class `CationPiVoxelizer` described below.
Class description:
Localize cation-Pi interactions between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute cation-Pi between atoms in the macromolecular complex. For each atom, localize... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class CationPiVoxelizer:
"""Localize cation-Pi interactions between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute cation-Pi between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CationPiVoxelizer:
"""Localize cation-Pi interactions between atoms in macromolecular complexes. Given a macromolecular complex made up of multiple constitutent molecules, compute cation-Pi between atoms in the macromolecular complex. For each atom, localize this salt bridge in the voxel in which it originate... | the_stack_v2_python_sparse | deepchem/feat/complex_featurizers/grid_featurizers.py | deepchem/deepchem | train | 4,876 |
a949cbe2c39bd9425420f981d4178fd687913534 | [
"rst = []\ncounter = dict([(n, int(0)) for n in range(nums[0] - 1, nums[-1] + 2)])\nfor n in nums:\n counter[n] += 1\nfor i in range(nums[0], nums[-1] + 1):\n rst.extend([i] * (counter[i] - counter[i - 1]))\nreturn rst",
"rst = []\ncounter = dict([(n, int(0)) for n in range(nums[0] - 1, nums[-1] + 2)])\nfor... | <|body_start_0|>
rst = []
counter = dict([(n, int(0)) for n in range(nums[0] - 1, nums[-1] + 2)])
for n in nums:
counter[n] += 1
for i in range(nums[0], nums[-1] + 1):
rst.extend([i] * (counter[i] - counter[i - 1]))
return rst
<|end_body_0|>
<|body_start_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findStarsPoints(self, nums: list[int]) -> list[int]:
""">>> s = Solution() >>> s.findStarsPoints([1,2,4,5]) [1, 4] >>> s.findStarsPoints([1,1,1,2,2,3]) [1, 1, 1] >>> s.findStarsPoints([1,2,3]) [1] >>> s.findStarsPoints([1,2,3,4,4,5]) [1, 4] >>> s.findStarsPoints([1,2,3,3,4,... | stack_v2_sparse_classes_36k_train_025153 | 3,767 | no_license | [
{
"docstring": ">>> s = Solution() >>> s.findStarsPoints([1,2,4,5]) [1, 4] >>> s.findStarsPoints([1,1,1,2,2,3]) [1, 1, 1] >>> s.findStarsPoints([1,2,3]) [1] >>> s.findStarsPoints([1,2,3,4,4,5]) [1, 4] >>> s.findStarsPoints([1,2,3,3,4,5]) [1, 3] >>> s.findStarsPoints([1,2,3,3,4,4,5,5]) [1, 3]",
"name": "find... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findStarsPoints(self, nums: list[int]) -> list[int]: >>> s = Solution() >>> s.findStarsPoints([1,2,4,5]) [1, 4] >>> s.findStarsPoints([1,1,1,2,2,3]) [1, 1, 1] >>> s.findStars... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findStarsPoints(self, nums: list[int]) -> list[int]: >>> s = Solution() >>> s.findStarsPoints([1,2,4,5]) [1, 4] >>> s.findStarsPoints([1,1,1,2,2,3]) [1, 1, 1] >>> s.findStars... | d2e8b2dca40fc955045eb62e576c776bad8ee5f1 | <|skeleton|>
class Solution:
def findStarsPoints(self, nums: list[int]) -> list[int]:
""">>> s = Solution() >>> s.findStarsPoints([1,2,4,5]) [1, 4] >>> s.findStarsPoints([1,1,1,2,2,3]) [1, 1, 1] >>> s.findStarsPoints([1,2,3]) [1] >>> s.findStarsPoints([1,2,3,4,4,5]) [1, 4] >>> s.findStarsPoints([1,2,3,3,4,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findStarsPoints(self, nums: list[int]) -> list[int]:
""">>> s = Solution() >>> s.findStarsPoints([1,2,4,5]) [1, 4] >>> s.findStarsPoints([1,1,1,2,2,3]) [1, 1, 1] >>> s.findStarsPoints([1,2,3]) [1] >>> s.findStarsPoints([1,2,3,4,4,5]) [1, 4] >>> s.findStarsPoints([1,2,3,3,4,5]) [1, 3] >>>... | the_stack_v2_python_sparse | split-array-into-consecutive-subsequences/solution.py | childe/leetcode | train | 2 | |
3d9e78c57add055f3642a9768fd751b0c6955765 | [
"if context is None:\n context = {}\njournal_pool = self.pool.get('account.journal')\ninvoice_pool = self.pool.get('account.invoice')\nif context.get('invoice_id', False):\n currency_id = invoice_pool.browse(cr, uid, context['invoice_id'], context=context).currency_id.id\n journal_id = journal_pool.search(... | <|body_start_0|>
if context is None:
context = {}
journal_pool = self.pool.get('account.journal')
invoice_pool = self.pool.get('account.invoice')
if context.get('invoice_id', False):
currency_id = invoice_pool.browse(cr, uid, context['invoice_id'], context=context... | account_voucher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class account_voucher:
def _get_journal(self, cr, uid, context=None):
"""Function to initialise the variable journal_id"""
<|body_0|>
def onchange_partner_id(self, cr, uid, ids, partner_id, journal_id, price, currency_id, ttype, date, context=None):
"""Inherited - add amou... | stack_v2_sparse_classes_36k_train_025154 | 8,345 | no_license | [
{
"docstring": "Function to initialise the variable journal_id",
"name": "_get_journal",
"signature": "def _get_journal(self, cr, uid, context=None)"
},
{
"docstring": "Inherited - add amount_in_word in return value dictionary cr: cursor uid: user id ids: ids of account voucher partner_id: partn... | 3 | null | Implement the Python class `account_voucher` described below.
Class description:
Implement the account_voucher class.
Method signatures and docstrings:
- def _get_journal(self, cr, uid, context=None): Function to initialise the variable journal_id
- def onchange_partner_id(self, cr, uid, ids, partner_id, journal_id, ... | Implement the Python class `account_voucher` described below.
Class description:
Implement the account_voucher class.
Method signatures and docstrings:
- def _get_journal(self, cr, uid, context=None): Function to initialise the variable journal_id
- def onchange_partner_id(self, cr, uid, ids, partner_id, journal_id, ... | b5cf28bdbb347df4c39ffe3ca32355bd2206077b | <|skeleton|>
class account_voucher:
def _get_journal(self, cr, uid, context=None):
"""Function to initialise the variable journal_id"""
<|body_0|>
def onchange_partner_id(self, cr, uid, ids, partner_id, journal_id, price, currency_id, ttype, date, context=None):
"""Inherited - add amou... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class account_voucher:
def _get_journal(self, cr, uid, context=None):
"""Function to initialise the variable journal_id"""
if context is None:
context = {}
journal_pool = self.pool.get('account.journal')
invoice_pool = self.pool.get('account.invoice')
if context.g... | the_stack_v2_python_sparse | account_check_writing/account_voucher.py | aryaadiputra/addons60_ptgbu_2013 | train | 0 | |
544d1e6504d9719f684ba5aaa653dd09aa20ee96 | [
"if self.ssl:\n url = 'https://'\nelse:\n url = 'http://'\nurl += '{}:{}{}'.format(self.target, self.port, path)\nkwargs.setdefault('timeout', HTTP_TIMEOUT)\nkwargs.setdefault('verify', False)\nkwargs.setdefault('allow_redirects', False)\ntry:\n return getattr(session, method.lower())(url, **kwargs)\nexcep... | <|body_start_0|>
if self.ssl:
url = 'https://'
else:
url = 'http://'
url += '{}:{}{}'.format(self.target, self.port, path)
kwargs.setdefault('timeout', HTTP_TIMEOUT)
kwargs.setdefault('verify', False)
kwargs.setdefault('allow_redirects', False)
... | HTTP Client provides methods to handle communication with HTTP server | HTTPClient | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTTPClient:
"""HTTP Client provides methods to handle communication with HTTP server"""
def http_request(self, method: str, path: str, session: requests=requests, **kwargs) -> requests.Response:
"""Requests HTTP resource :param str method: method that should be issued e.g. GET, POST ... | stack_v2_sparse_classes_36k_train_025155 | 2,798 | permissive | [
{
"docstring": "Requests HTTP resource :param str method: method that should be issued e.g. GET, POST :param str path: path to the resource that should be requested :param requests session: session manager that should be used :param kwargs: kwargs passed to request method :return Response: Response object",
... | 3 | stack_v2_sparse_classes_30k_train_020283 | Implement the Python class `HTTPClient` described below.
Class description:
HTTP Client provides methods to handle communication with HTTP server
Method signatures and docstrings:
- def http_request(self, method: str, path: str, session: requests=requests, **kwargs) -> requests.Response: Requests HTTP resource :param... | Implement the Python class `HTTPClient` described below.
Class description:
HTTP Client provides methods to handle communication with HTTP server
Method signatures and docstrings:
- def http_request(self, method: str, path: str, session: requests=requests, **kwargs) -> requests.Response: Requests HTTP resource :param... | 56ae6325c08bcedd22c57b9fe11b58f1b38314ca | <|skeleton|>
class HTTPClient:
"""HTTP Client provides methods to handle communication with HTTP server"""
def http_request(self, method: str, path: str, session: requests=requests, **kwargs) -> requests.Response:
"""Requests HTTP resource :param str method: method that should be issued e.g. GET, POST ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HTTPClient:
"""HTTP Client provides methods to handle communication with HTTP server"""
def http_request(self, method: str, path: str, session: requests=requests, **kwargs) -> requests.Response:
"""Requests HTTP resource :param str method: method that should be issued e.g. GET, POST :param str pa... | the_stack_v2_python_sparse | maza/core/http/http_client.py | ArturSpirin/maza | train | 2 |
ee2e9245e55be32ed9ba104cfed089cf46abbe76 | [
"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... | Converter is a service for converting between other ecosystems and Pulumi. This is currently unstable and experimental. | ConverterServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConverterServicer:
"""Converter is a service for converting between other ecosystems and Pulumi. This is currently unstable and experimental."""
def ConvertState(self, request, context):
"""ConvertState converts state from the target ecosystem into a form that can be imported into Pu... | stack_v2_sparse_classes_36k_train_025156 | 4,535 | permissive | [
{
"docstring": "ConvertState converts state from the target ecosystem into a form that can be imported into Pulumi.",
"name": "ConvertState",
"signature": "def ConvertState(self, request, context)"
},
{
"docstring": "ConvertProgram converts a program from the target ecosystem into a form that ca... | 2 | stack_v2_sparse_classes_30k_train_017910 | Implement the Python class `ConverterServicer` described below.
Class description:
Converter is a service for converting between other ecosystems and Pulumi. This is currently unstable and experimental.
Method signatures and docstrings:
- def ConvertState(self, request, context): ConvertState converts state from the ... | Implement the Python class `ConverterServicer` described below.
Class description:
Converter is a service for converting between other ecosystems and Pulumi. This is currently unstable and experimental.
Method signatures and docstrings:
- def ConvertState(self, request, context): ConvertState converts state from the ... | 46e2753d02d46a1c077930eeccdfe6738f46c0d2 | <|skeleton|>
class ConverterServicer:
"""Converter is a service for converting between other ecosystems and Pulumi. This is currently unstable and experimental."""
def ConvertState(self, request, context):
"""ConvertState converts state from the target ecosystem into a form that can be imported into Pu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConverterServicer:
"""Converter is a service for converting between other ecosystems and Pulumi. This is currently unstable and experimental."""
def ConvertState(self, request, context):
"""ConvertState converts state from the target ecosystem into a form that can be imported into Pulumi."""
... | the_stack_v2_python_sparse | sdk/python/lib/pulumi/runtime/proto/converter_pb2_grpc.py | pulumi/pulumi | train | 17,553 |
c9a2c399126ae3cebc7ed720ce6a98d1d184a261 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | BoxSecretServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BoxSecretServiceServicer:
"""Missing associated documentation comment in .proto file."""
def getBoxSecret(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def createBoxSecret(self, request, context):
"""Missing as... | stack_v2_sparse_classes_36k_train_025157 | 7,947 | permissive | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "getBoxSecret",
"signature": "def getBoxSecret(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "createBoxSecret",
"signature": "def createBoxS... | 4 | stack_v2_sparse_classes_30k_train_002453 | Implement the Python class `BoxSecretServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def getBoxSecret(self, request, context): Missing associated documentation comment in .proto file.
- def createBoxSecret(self, request,... | Implement the Python class `BoxSecretServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def getBoxSecret(self, request, context): Missing associated documentation comment in .proto file.
- def createBoxSecret(self, request,... | c69e14b409add099d151434b9add711e41f41b20 | <|skeleton|>
class BoxSecretServiceServicer:
"""Missing associated documentation comment in .proto file."""
def getBoxSecret(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def createBoxSecret(self, request, context):
"""Missing as... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BoxSecretServiceServicer:
"""Missing associated documentation comment in .proto file."""
def getBoxSecret(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not impleme... | the_stack_v2_python_sparse | python-sdk/src/airavata_mft_sdk/box/BoxSecretService_pb2_grpc.py | apache/airavata-mft | train | 23 |
f0a1f12694e99ba46af996e444ef32d7ebce0b22 | [
"if model._meta.app_label == 'researcherquery':\n return 'safedb'\nreturn None",
"if model._meta.app_label == 'researcherquery':\n return 'safedb'\nreturn None",
"if obj1._meta.app_label == 'researcherquery' and obj2._meta.app_label == 'researcherquery':\n return True\nreturn None",
"if app_label == ... | <|body_start_0|>
if model._meta.app_label == 'researcherquery':
return 'safedb'
return None
<|end_body_0|>
<|body_start_1|>
if model._meta.app_label == 'researcherquery':
return 'safedb'
return None
<|end_body_1|>
<|body_start_2|>
if obj1._meta.app_label... | A router to control all database operations on models in the researcherquery application. | ResearcherqueryRouter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResearcherqueryRouter:
"""A router to control all database operations on models in the researcherquery application."""
def db_for_read(self, model, **hints):
"""Attempts to read researcherquery models go to safedb."""
<|body_0|>
def db_for_write(self, model, **hints):
... | stack_v2_sparse_classes_36k_train_025158 | 1,295 | no_license | [
{
"docstring": "Attempts to read researcherquery models go to safedb.",
"name": "db_for_read",
"signature": "def db_for_read(self, model, **hints)"
},
{
"docstring": "Attempts to write researcherquery models go to safedb.",
"name": "db_for_write",
"signature": "def db_for_write(self, mod... | 4 | stack_v2_sparse_classes_30k_train_003232 | Implement the Python class `ResearcherqueryRouter` described below.
Class description:
A router to control all database operations on models in the researcherquery application.
Method signatures and docstrings:
- def db_for_read(self, model, **hints): Attempts to read researcherquery models go to safedb.
- def db_for... | Implement the Python class `ResearcherqueryRouter` described below.
Class description:
A router to control all database operations on models in the researcherquery application.
Method signatures and docstrings:
- def db_for_read(self, model, **hints): Attempts to read researcherquery models go to safedb.
- def db_for... | 685c2b9d40fb24ca1735352846a39fdf5d3728eb | <|skeleton|>
class ResearcherqueryRouter:
"""A router to control all database operations on models in the researcherquery application."""
def db_for_read(self, model, **hints):
"""Attempts to read researcherquery models go to safedb."""
<|body_0|>
def db_for_write(self, model, **hints):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResearcherqueryRouter:
"""A router to control all database operations on models in the researcherquery application."""
def db_for_read(self, model, **hints):
"""Attempts to read researcherquery models go to safedb."""
if model._meta.app_label == 'researcherquery':
return 'safe... | the_stack_v2_python_sparse | researcherquery/router.py | guekling/ifs4205team1 | train | 0 |
79817d45b4be6f79b525a78cb4ea720a165d7453 | [
"stock_company = get_object_or_404(UserCompany, user=request.user, pk=id)\nurl = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={stock_company.companystock.symbol}&outputsize=compact&apikey={settings.STOCK_API_KEY}'\nres = requests.get(url)\ndata = res.json()\ndata = data['Time Serie... | <|body_start_0|>
stock_company = get_object_or_404(UserCompany, user=request.user, pk=id)
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={stock_company.companystock.symbol}&outputsize=compact&apikey={settings.STOCK_API_KEY}'
res = requests.get(url)
d... | CompanyStockView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompanyStockView:
def get(self, request, id):
"""Retorna los datos del mercado de la compañía"""
<|body_0|>
def post(self, request, id):
"""Suscribirse a una acción"""
<|body_1|>
def delete(self, request, id):
"""Elimina la suscripción a una acci... | stack_v2_sparse_classes_36k_train_025159 | 3,455 | no_license | [
{
"docstring": "Retorna los datos del mercado de la compañía",
"name": "get",
"signature": "def get(self, request, id)"
},
{
"docstring": "Suscribirse a una acción",
"name": "post",
"signature": "def post(self, request, id)"
},
{
"docstring": "Elimina la suscripción a una acción"... | 3 | stack_v2_sparse_classes_30k_train_014606 | Implement the Python class `CompanyStockView` described below.
Class description:
Implement the CompanyStockView class.
Method signatures and docstrings:
- def get(self, request, id): Retorna los datos del mercado de la compañía
- def post(self, request, id): Suscribirse a una acción
- def delete(self, request, id): ... | Implement the Python class `CompanyStockView` described below.
Class description:
Implement the CompanyStockView class.
Method signatures and docstrings:
- def get(self, request, id): Retorna los datos del mercado de la compañía
- def post(self, request, id): Suscribirse a una acción
- def delete(self, request, id): ... | db0c61cd66da56f9c904cffeae807b2605a96cc7 | <|skeleton|>
class CompanyStockView:
def get(self, request, id):
"""Retorna los datos del mercado de la compañía"""
<|body_0|>
def post(self, request, id):
"""Suscribirse a una acción"""
<|body_1|>
def delete(self, request, id):
"""Elimina la suscripción a una acci... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompanyStockView:
def get(self, request, id):
"""Retorna los datos del mercado de la compañía"""
stock_company = get_object_or_404(UserCompany, user=request.user, pk=id)
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={stock_company.companystock.sym... | the_stack_v2_python_sparse | api/views/companystock.py | klasinky/FinaccessBackend | train | 2 | |
15a16734f497adee8dab1202558e8ae8814ab2e7 | [
"index = 0\ncount = 0\nwhile index <= n:\n count += str(index).count('1')\n index += 1\nreturn count",
"if n < 0:\n return 0\nlevel = 1\ncount = 0\nwhile True:\n step, redundancy = divmod(n, level * 10)\n count += level * step\n r, l = divmod(redundancy, level)\n if r > 1:\n count += l... | <|body_start_0|>
index = 0
count = 0
while index <= n:
count += str(index).count('1')
index += 1
return count
<|end_body_0|>
<|body_start_1|>
if n < 0:
return 0
level = 1
count = 0
while True:
step, redundan... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _countDigitOne(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def countDigitOne(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
index = 0
count = 0
while index <= n:
... | stack_v2_sparse_classes_36k_train_025160 | 1,818 | permissive | [
{
"docstring": ":type n: int :rtype: int",
"name": "_countDigitOne",
"signature": "def _countDigitOne(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "countDigitOne",
"signature": "def countDigitOne(self, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _countDigitOne(self, n): :type n: int :rtype: int
- def countDigitOne(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _countDigitOne(self, n): :type n: int :rtype: int
- def countDigitOne(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def _countDigitOne(self, n):
... | 0dd67edca4e0b0323cb5a7239f02ea46383cd15a | <|skeleton|>
class Solution:
def _countDigitOne(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def countDigitOne(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def _countDigitOne(self, n):
""":type n: int :rtype: int"""
index = 0
count = 0
while index <= n:
count += str(index).count('1')
index += 1
return count
def countDigitOne(self, n):
""":type n: int :rtype: int"""
if ... | the_stack_v2_python_sparse | 233.number-of-digit-one.py | windard/leeeeee | train | 0 | |
38eb2f43acf9033951650652027b33bd1f59b1df | [
"self._categorical_features = set(schema_util.get_categorical_numeric_features(schema) if schema else [])\nself._weight_feature = weight_feature\nself._num_top_values = num_top_values\nself._num_rank_histogram_buckets = num_rank_histogram_buckets",
"feature_values_with_weights = pcoll | 'TopK_ConvertInputToFeatur... | <|body_start_0|>
self._categorical_features = set(schema_util.get_categorical_numeric_features(schema) if schema else [])
self._weight_feature = weight_feature
self._num_top_values = num_top_values
self._num_rank_histogram_buckets = num_rank_histogram_buckets
<|end_body_0|>
<|body_start... | A ptransform that computes the top-k most frequent feature values for string features. | _ComputeTopKStats | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ComputeTopKStats:
"""A ptransform that computes the top-k most frequent feature values for string features."""
def __init__(self, schema, weight_feature, num_top_values, num_rank_histogram_buckets):
"""Initializes _ComputeTopKStats. Args: schema: An schema for the dataset. None if n... | stack_v2_sparse_classes_36k_train_025161 | 14,116 | permissive | [
{
"docstring": "Initializes _ComputeTopKStats. Args: schema: An schema for the dataset. None if no schema is available. weight_feature: Feature name whose numeric value represents the weight of an example. None if there is no weight feature. num_top_values: The number of most frequent feature values to keep for... | 2 | stack_v2_sparse_classes_30k_train_006321 | Implement the Python class `_ComputeTopKStats` described below.
Class description:
A ptransform that computes the top-k most frequent feature values for string features.
Method signatures and docstrings:
- def __init__(self, schema, weight_feature, num_top_values, num_rank_histogram_buckets): Initializes _ComputeTopK... | Implement the Python class `_ComputeTopKStats` described below.
Class description:
A ptransform that computes the top-k most frequent feature values for string features.
Method signatures and docstrings:
- def __init__(self, schema, weight_feature, num_top_values, num_rank_histogram_buckets): Initializes _ComputeTopK... | 9c29b0dc92e73d794db90d0676ab6b43ed73815d | <|skeleton|>
class _ComputeTopKStats:
"""A ptransform that computes the top-k most frequent feature values for string features."""
def __init__(self, schema, weight_feature, num_top_values, num_rank_histogram_buckets):
"""Initializes _ComputeTopKStats. Args: schema: An schema for the dataset. None if n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _ComputeTopKStats:
"""A ptransform that computes the top-k most frequent feature values for string features."""
def __init__(self, schema, weight_feature, num_top_values, num_rank_histogram_buckets):
"""Initializes _ComputeTopKStats. Args: schema: An schema for the dataset. None if no schema is a... | the_stack_v2_python_sparse | tensorflow_data_validation/statistics/generators/top_k_stats_generator.py | paulgc/data-validation | train | 1 |
27de429078601ca9d7f472a83261c7eeb4e16318 | [
"request = await stream.recv_message()\nassert request is not None\nuser: str | None = None\nuser_data: UserData | None = None\nentity_tag: int | None = None\nasync with self.catch_errors('GetUser') as result, self.login_as(stream.metadata, request.user) as identity:\n user = identity.name\n metadata = await ... | <|body_start_0|>
request = await stream.recv_message()
assert request is not None
user: str | None = None
user_data: UserData | None = None
entity_tag: int | None = None
async with self.catch_errors('GetUser') as result, self.login_as(stream.metadata, request.user) as ide... | The GRPC handlers, executed when an admin request is received. Each handler should receive a request, take action, and send the response. See Also: :class:`grpclib.server.Server` | UserHandlers | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserHandlers:
"""The GRPC handlers, executed when an admin request is received. Each handler should receive a request, take action, and send the response. See Also: :class:`grpclib.server.Server`"""
async def GetUser(self, stream: _GetUserStream) -> None:
"""See ``pymap-admin get-use... | stack_v2_sparse_classes_36k_train_025162 | 4,142 | permissive | [
{
"docstring": "See ``pymap-admin get-user --help`` for more options. Args: stream (:class:`~grpclib.server.Stream`): The stream for the request and response.",
"name": "GetUser",
"signature": "async def GetUser(self, stream: _GetUserStream) -> None"
},
{
"docstring": "See ``pymap-admin set-user... | 3 | null | Implement the Python class `UserHandlers` described below.
Class description:
The GRPC handlers, executed when an admin request is received. Each handler should receive a request, take action, and send the response. See Also: :class:`grpclib.server.Server`
Method signatures and docstrings:
- async def GetUser(self, s... | Implement the Python class `UserHandlers` described below.
Class description:
The GRPC handlers, executed when an admin request is received. Each handler should receive a request, take action, and send the response. See Also: :class:`grpclib.server.Server`
Method signatures and docstrings:
- async def GetUser(self, s... | 0b7cc3f0a1a000b96cb9873af7a2bbdf5cb7dc34 | <|skeleton|>
class UserHandlers:
"""The GRPC handlers, executed when an admin request is received. Each handler should receive a request, take action, and send the response. See Also: :class:`grpclib.server.Server`"""
async def GetUser(self, stream: _GetUserStream) -> None:
"""See ``pymap-admin get-use... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserHandlers:
"""The GRPC handlers, executed when an admin request is received. Each handler should receive a request, take action, and send the response. See Also: :class:`grpclib.server.Server`"""
async def GetUser(self, stream: _GetUserStream) -> None:
"""See ``pymap-admin get-user --help`` fo... | the_stack_v2_python_sparse | pymap/admin/handlers/user.py | icgood/pymap | train | 23 |
45a2e00e9038699c6335a2be5be2b1c7cf2686e7 | [
"self.clean_number()\nself.clean_letters()\nsuper().save(*args, **kwargs)",
"if not self.number:\n if not self.letters:\n raise ValidationError('You must enter either a phonenumber or a letter representation of the phonenumber!')\n self.number = dectutil.letters_to_number(self.letters)\ntry:\n int... | <|body_start_0|>
self.clean_number()
self.clean_letters()
super().save(*args, **kwargs)
<|end_body_0|>
<|body_start_1|>
if not self.number:
if not self.letters:
raise ValidationError('You must enter either a phonenumber or a letter representation of the phone... | This model contains DECT registrations for users and services | DectRegistration | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DectRegistration:
"""This model contains DECT registrations for users and services"""
def save(self, *args, **kwargs):
"""This is just here so we get the validation in the admin as well."""
<|body_0|>
def clean_number(self):
"""We call this from the views form_va... | stack_v2_sparse_classes_36k_train_025163 | 5,767 | permissive | [
{
"docstring": "This is just here so we get the validation in the admin as well.",
"name": "save",
"signature": "def save(self, *args, **kwargs)"
},
{
"docstring": "We call this from the views form_valid() so we have a Camp object available for the validation. This code really belongs in model.c... | 3 | null | Implement the Python class `DectRegistration` described below.
Class description:
This model contains DECT registrations for users and services
Method signatures and docstrings:
- def save(self, *args, **kwargs): This is just here so we get the validation in the admin as well.
- def clean_number(self): We call this f... | Implement the Python class `DectRegistration` described below.
Class description:
This model contains DECT registrations for users and services
Method signatures and docstrings:
- def save(self, *args, **kwargs): This is just here so we get the validation in the admin as well.
- def clean_number(self): We call this f... | 767deb7f58429e9162e0c2ef79be9f0f38f37ce1 | <|skeleton|>
class DectRegistration:
"""This model contains DECT registrations for users and services"""
def save(self, *args, **kwargs):
"""This is just here so we get the validation in the admin as well."""
<|body_0|>
def clean_number(self):
"""We call this from the views form_va... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DectRegistration:
"""This model contains DECT registrations for users and services"""
def save(self, *args, **kwargs):
"""This is just here so we get the validation in the admin as well."""
self.clean_number()
self.clean_letters()
super().save(*args, **kwargs)
def cle... | the_stack_v2_python_sparse | src/phonebook/models.py | bornhack/bornhack-website | train | 9 |
c433331b0118f1588dfda0428440ea85685c5baf | [
"super().__init__()\nself.encoder = Encoder(**encoder_config)\nself.decoder = Decoder(**decoder_config)",
"out = self.encoder(x)\nout = self.decoder(out)\nreturn out"
] | <|body_start_0|>
super().__init__()
self.encoder = Encoder(**encoder_config)
self.decoder = Decoder(**decoder_config)
<|end_body_0|>
<|body_start_1|>
out = self.encoder(x)
out = self.decoder(out)
return out
<|end_body_1|>
| A PyTorch implementation of the translation model proposed by Guillaume Genthial and Romain Sauvestre for the Im2Latex-100k dataset. | Im2Latex | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Im2Latex:
"""A PyTorch implementation of the translation model proposed by Guillaume Genthial and Romain Sauvestre for the Im2Latex-100k dataset."""
def __init__(self, encoder_config, decoder_config):
"""Initializes the stages of the Im2Latex model. Inputs: encoder_config : dict A di... | stack_v2_sparse_classes_36k_train_025164 | 2,334 | no_license | [
{
"docstring": "Initializes the stages of the Im2Latex model. Inputs: encoder_config : dict A dictionary of keyword arguments used to configure the Encoder. decoder_config : dict A dictionary of keyword arguments used to configure the Decoder. Outputs: None, but initializes the layers.",
"name": "__init__",... | 2 | stack_v2_sparse_classes_30k_train_019220 | Implement the Python class `Im2Latex` described below.
Class description:
A PyTorch implementation of the translation model proposed by Guillaume Genthial and Romain Sauvestre for the Im2Latex-100k dataset.
Method signatures and docstrings:
- def __init__(self, encoder_config, decoder_config): Initializes the stages ... | Implement the Python class `Im2Latex` described below.
Class description:
A PyTorch implementation of the translation model proposed by Guillaume Genthial and Romain Sauvestre for the Im2Latex-100k dataset.
Method signatures and docstrings:
- def __init__(self, encoder_config, decoder_config): Initializes the stages ... | 9820aca2d37d2b320a3e3c1283f6c2e0e16bbb75 | <|skeleton|>
class Im2Latex:
"""A PyTorch implementation of the translation model proposed by Guillaume Genthial and Romain Sauvestre for the Im2Latex-100k dataset."""
def __init__(self, encoder_config, decoder_config):
"""Initializes the stages of the Im2Latex model. Inputs: encoder_config : dict A di... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Im2Latex:
"""A PyTorch implementation of the translation model proposed by Guillaume Genthial and Romain Sauvestre for the Im2Latex-100k dataset."""
def __init__(self, encoder_config, decoder_config):
"""Initializes the stages of the Im2Latex model. Inputs: encoder_config : dict A dictionary of k... | the_stack_v2_python_sparse | src/models/im2latex/model.py | davidlin001/equation2latex | train | 2 |
5321ec5ec76882045f25df52ff1a4e1a25142d2b | [
"m = len(matrix)\nif m == 0:\n self.F = None\n return\nn = len(matrix[0])\nself.F = [[0 for _ in xrange(n + 1)] for _ in xrange(m + 1)]\nfor i in xrange(1, m + 1):\n for j in xrange(1, n + 1):\n self.F[i][j] = self.F[i - 1][j] + self.F[i][j - 1] - self.F[i - 1][j - 1] + matrix[i - 1][j - 1]",
"if ... | <|body_start_0|>
m = len(matrix)
if m == 0:
self.F = None
return
n = len(matrix[0])
self.F = [[0 for _ in xrange(n + 1)] for _ in xrange(m + 1)]
for i in xrange(1, m + 1):
for j in xrange(1, n + 1):
self.F[i][j] = self.F[i - 1][... | NumMatrix | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. dp F[i][j] = F[i-1][j]+F[i][j-1]-F[i-1][j-1]+mat[i][j] :type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
"""sum of elements matrix[(row1,col1)..(row2... | stack_v2_sparse_classes_36k_train_025165 | 1,587 | permissive | [
{
"docstring": "initialize your data structure here. dp F[i][j] = F[i-1][j]+F[i][j-1]-F[i-1][j-1]+mat[i][j] :type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": "sum of elements matrix[(row1,col1)..(row2,col2)], inclusive.",
"name": ... | 2 | null | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here. dp F[i][j] = F[i-1][j]+F[i][j-1]-F[i-1][j-1]+mat[i][j] :type matrix: List[List[int]]
- def sumRegion(self, row1... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here. dp F[i][j] = F[i-1][j]+F[i][j-1]-F[i-1][j-1]+mat[i][j] :type matrix: List[List[int]]
- def sumRegion(self, row1... | cbbd4a67ab342ada2421e13f82d660b1d47d4d20 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. dp F[i][j] = F[i-1][j]+F[i][j-1]-F[i-1][j-1]+mat[i][j] :type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
"""sum of elements matrix[(row1,col1)..(row2... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. dp F[i][j] = F[i-1][j]+F[i][j-1]-F[i-1][j-1]+mat[i][j] :type matrix: List[List[int]]"""
m = len(matrix)
if m == 0:
self.F = None
return
n = len(matrix[0])
self.F = [[0... | the_stack_v2_python_sparse | 304 Range Sum Query 2D - Immutable.py | Aminaba123/LeetCode | train | 1 | |
b193fabdd3da5642332a5e14485acaf24d7f40d4 | [
"super(MeanAggregator, self).__init__()\nself.features = features\nself.cuda = cuda\nself.gcn = gcn",
"_set = set\nif num_sample:\n _sample = random.sample\n samp_neighs = [_set(_sample(to_neigh, num_sample)) if len(to_neigh) >= num_sample else to_neigh for to_neigh in to_neighs]\nelse:\n samp_neighs = t... | <|body_start_0|>
super(MeanAggregator, self).__init__()
self.features = features
self.cuda = cuda
self.gcn = gcn
<|end_body_0|>
<|body_start_1|>
_set = set
if num_sample:
_sample = random.sample
samp_neighs = [_set(_sample(to_neigh, num_sample)) i... | Aggregates a node's embeddings using mean of neighbors' embeddings | MeanAggregator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MeanAggregator:
"""Aggregates a node's embeddings using mean of neighbors' embeddings"""
def __init__(self, features, cuda=False, gcn=False):
"""Initializes the aggregator for a specific graph. Parameters ---------- features : [type] function mapping LongTensor of node ids to FloatTe... | stack_v2_sparse_classes_36k_train_025166 | 14,546 | no_license | [
{
"docstring": "Initializes the aggregator for a specific graph. Parameters ---------- features : [type] function mapping LongTensor of node ids to FloatTensor of feature values. cuda : bool, optional whether to use GPU, by default False gcn : bool, optional whether to perform concatenation GraphSAGE-style, or ... | 2 | stack_v2_sparse_classes_30k_train_003943 | Implement the Python class `MeanAggregator` described below.
Class description:
Aggregates a node's embeddings using mean of neighbors' embeddings
Method signatures and docstrings:
- def __init__(self, features, cuda=False, gcn=False): Initializes the aggregator for a specific graph. Parameters ---------- features : ... | Implement the Python class `MeanAggregator` described below.
Class description:
Aggregates a node's embeddings using mean of neighbors' embeddings
Method signatures and docstrings:
- def __init__(self, features, cuda=False, gcn=False): Initializes the aggregator for a specific graph. Parameters ---------- features : ... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class MeanAggregator:
"""Aggregates a node's embeddings using mean of neighbors' embeddings"""
def __init__(self, features, cuda=False, gcn=False):
"""Initializes the aggregator for a specific graph. Parameters ---------- features : [type] function mapping LongTensor of node ids to FloatTe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MeanAggregator:
"""Aggregates a node's embeddings using mean of neighbors' embeddings"""
def __init__(self, features, cuda=False, gcn=False):
"""Initializes the aggregator for a specific graph. Parameters ---------- features : [type] function mapping LongTensor of node ids to FloatTensor of featu... | the_stack_v2_python_sparse | generated/test_dreamhomes_PyTorch_GNNs.py | jansel/pytorch-jit-paritybench | train | 35 |
cc878044d30b3563836322d6f429d72294c9dd17 | [
"from facebook import GraphAPI, GraphAPIError\nself.GraphAPI = GraphAPI\nself.GraphAPIError = GraphAPIError\nrequest = current.request\nsettings = current.deployment_settings\nscope = 'email,user_about_me,user_location,user_photos,user_relationships,user_birthday,user_website,create_event,user_events,publish_stream... | <|body_start_0|>
from facebook import GraphAPI, GraphAPIError
self.GraphAPI = GraphAPI
self.GraphAPIError = GraphAPIError
request = current.request
settings = current.deployment_settings
scope = 'email,user_about_me,user_location,user_photos,user_relationships,user_birthd... | OAuth implementation for FaceBook | FaceBookAccount | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FaceBookAccount:
"""OAuth implementation for FaceBook"""
def __init__(self, channel):
"""Constructor @param channel: Facebook channel (Row) with API credentials: {app_id=clientID, app_secret=clientSecret}"""
<|body_0|>
def login_url(self, next='/'):
"""Overriding... | stack_v2_sparse_classes_36k_train_025167 | 31,965 | permissive | [
{
"docstring": "Constructor @param channel: Facebook channel (Row) with API credentials: {app_id=clientID, app_secret=clientSecret}",
"name": "__init__",
"signature": "def __init__(self, channel)"
},
{
"docstring": "Overriding to produce a different redirect_uri",
"name": "login_url",
"s... | 3 | stack_v2_sparse_classes_30k_train_019720 | Implement the Python class `FaceBookAccount` described below.
Class description:
OAuth implementation for FaceBook
Method signatures and docstrings:
- def __init__(self, channel): Constructor @param channel: Facebook channel (Row) with API credentials: {app_id=clientID, app_secret=clientSecret}
- def login_url(self, ... | Implement the Python class `FaceBookAccount` described below.
Class description:
OAuth implementation for FaceBook
Method signatures and docstrings:
- def __init__(self, channel): Constructor @param channel: Facebook channel (Row) with API credentials: {app_id=clientID, app_secret=clientSecret}
- def login_url(self, ... | 7ec4b959d009daf26d5ca6ce91dd9c3c0bd978d6 | <|skeleton|>
class FaceBookAccount:
"""OAuth implementation for FaceBook"""
def __init__(self, channel):
"""Constructor @param channel: Facebook channel (Row) with API credentials: {app_id=clientID, app_secret=clientSecret}"""
<|body_0|>
def login_url(self, next='/'):
"""Overriding... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FaceBookAccount:
"""OAuth implementation for FaceBook"""
def __init__(self, channel):
"""Constructor @param channel: Facebook channel (Row) with API credentials: {app_id=clientID, app_secret=clientSecret}"""
from facebook import GraphAPI, GraphAPIError
self.GraphAPI = GraphAPI
... | the_stack_v2_python_sparse | modules/core/aaa/oauth.py | nursix/drkcm | train | 3 |
4d542d06223e08a24c96a31d4d834483f1bf64da | [
"queue = [root]\nans = ''\nwhile queue:\n cur = queue.pop(0)\n if cur:\n ans += str(cur.val) + ','\n queue.append(cur.left)\n queue.append(cur.right)\n else:\n ans += '#,'\nreturn ans[:-1]",
"if data == '#' or not data:\n return None\nnodes = data.split(',')\nroot = TreeNod... | <|body_start_0|>
queue = [root]
ans = ''
while queue:
cur = queue.pop(0)
if cur:
ans += str(cur.val) + ','
queue.append(cur.left)
queue.append(cur.right)
else:
ans += '#,'
return ans[:-1]
... | 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_025168 | 2,652 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 3a5649357e0f21cbbc5e238351300cd706d533b3 | <|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"""
queue = [root]
ans = ''
while queue:
cur = queue.pop(0)
if cur:
ans += str(cur.val) + ','
queue.append(cur.left)
... | the_stack_v2_python_sparse | leetcode-py/leetcode297.py | cicihou/LearningProject | train | 0 | |
9fc0ab610b5b525f0bfa5669faa026122c4b4e0c | [
"assert FinancialAid.objects.count() == 0\nassert User.objects.count() == 0\nassert Profile.objects.count() == 0\nFinancialAidFactory.create()\nassert FinancialAid.objects.count() == 1\nassert User.objects.count() == 1\nassert Profile.objects.count() == 1",
"with mute_signals(post_save):\n user = UserFactory.c... | <|body_start_0|>
assert FinancialAid.objects.count() == 0
assert User.objects.count() == 0
assert Profile.objects.count() == 0
FinancialAidFactory.create()
assert FinancialAid.objects.count() == 1
assert User.objects.count() == 1
assert Profile.objects.count() == ... | Tests for financialaid factories | FinancialAidModelsTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FinancialAidModelsTests:
"""Tests for financialaid factories"""
def test_financial_aid_factory_create(self):
"""Tests that FinancialAidFactory.create() will create a profile for the user field if a user is not specified"""
<|body_0|>
def test_financial_aid_factory_create... | stack_v2_sparse_classes_36k_train_025169 | 1,554 | permissive | [
{
"docstring": "Tests that FinancialAidFactory.create() will create a profile for the user field if a user is not specified",
"name": "test_financial_aid_factory_create",
"signature": "def test_financial_aid_factory_create(self)"
},
{
"docstring": "Tests that FinancialAidFactory.create() will st... | 2 | null | Implement the Python class `FinancialAidModelsTests` described below.
Class description:
Tests for financialaid factories
Method signatures and docstrings:
- def test_financial_aid_factory_create(self): Tests that FinancialAidFactory.create() will create a profile for the user field if a user is not specified
- def t... | Implement the Python class `FinancialAidModelsTests` described below.
Class description:
Tests for financialaid factories
Method signatures and docstrings:
- def test_financial_aid_factory_create(self): Tests that FinancialAidFactory.create() will create a profile for the user field if a user is not specified
- def t... | d6564caca0b7bbfd31e67a751564107fd17d6eb0 | <|skeleton|>
class FinancialAidModelsTests:
"""Tests for financialaid factories"""
def test_financial_aid_factory_create(self):
"""Tests that FinancialAidFactory.create() will create a profile for the user field if a user is not specified"""
<|body_0|>
def test_financial_aid_factory_create... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FinancialAidModelsTests:
"""Tests for financialaid factories"""
def test_financial_aid_factory_create(self):
"""Tests that FinancialAidFactory.create() will create a profile for the user field if a user is not specified"""
assert FinancialAid.objects.count() == 0
assert User.objec... | the_stack_v2_python_sparse | financialaid/factories_test.py | mitodl/micromasters | train | 35 |
d295f56e7d03aad68ac25921d46879b8b8e5e39d | [
"super(AttentionalDecoder, self).__init__(output_size, hidden_size, embedding_dim, max_length, enc_dim, device, dropout_p, pad_token)\nself._gru = nn.GRU(input_size=self._embedding_dim + self._enc_dim, hidden_size=self._hidden_size)\nself._attention = AdditiveAttention(key_dim=self._enc_dim, value_dim=self._enc_dim... | <|body_start_0|>
super(AttentionalDecoder, self).__init__(output_size, hidden_size, embedding_dim, max_length, enc_dim, device, dropout_p, pad_token)
self._gru = nn.GRU(input_size=self._embedding_dim + self._enc_dim, hidden_size=self._hidden_size)
self._attention = AdditiveAttention(key_dim=self... | AttentionalDecoder | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttentionalDecoder:
def __init__(self, hidden_size, output_size, embedding_dim, max_length, enc_dim, device, dropout_p=0.1, pad_token=0):
""":param embedding_dim: dimension of :param hidden_size: :param output_size: :param max_length: :param device: :param dropout_p:"""
<|body_0|... | stack_v2_sparse_classes_36k_train_025170 | 2,837 | permissive | [
{
"docstring": ":param embedding_dim: dimension of :param hidden_size: :param output_size: :param max_length: :param device: :param dropout_p:",
"name": "__init__",
"signature": "def __init__(self, hidden_size, output_size, embedding_dim, max_length, enc_dim, device, dropout_p=0.1, pad_token=0)"
},
... | 2 | stack_v2_sparse_classes_30k_train_004275 | Implement the Python class `AttentionalDecoder` described below.
Class description:
Implement the AttentionalDecoder class.
Method signatures and docstrings:
- def __init__(self, hidden_size, output_size, embedding_dim, max_length, enc_dim, device, dropout_p=0.1, pad_token=0): :param embedding_dim: dimension of :para... | Implement the Python class `AttentionalDecoder` described below.
Class description:
Implement the AttentionalDecoder class.
Method signatures and docstrings:
- def __init__(self, hidden_size, output_size, embedding_dim, max_length, enc_dim, device, dropout_p=0.1, pad_token=0): :param embedding_dim: dimension of :para... | 689b9924d3c88a433f8f350b89c13a878ac7d7c3 | <|skeleton|>
class AttentionalDecoder:
def __init__(self, hidden_size, output_size, embedding_dim, max_length, enc_dim, device, dropout_p=0.1, pad_token=0):
""":param embedding_dim: dimension of :param hidden_size: :param output_size: :param max_length: :param device: :param dropout_p:"""
<|body_0|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttentionalDecoder:
def __init__(self, hidden_size, output_size, embedding_dim, max_length, enc_dim, device, dropout_p=0.1, pad_token=0):
""":param embedding_dim: dimension of :param hidden_size: :param output_size: :param max_length: :param device: :param dropout_p:"""
super(AttentionalDecode... | the_stack_v2_python_sparse | nntoolbox/sequence/models/decoder.py | nhatsmrt/nn-toolbox | train | 19 | |
12029a082f09dc317def3d8adaae053dfd10f99f | [
"if not userid:\n raise ValueError('Users must have an email address')\nuser = self.model(userid=userid, date_of_birth=date_of_birth)\nuser.set_password(password)\nuser.save(using=self._db)\nreturn user",
"user = self.create_user(userid, password=password, date_of_birth=date_of_birth)\nuser.is_admin = True\nus... | <|body_start_0|>
if not userid:
raise ValueError('Users must have an email address')
user = self.model(userid=userid, date_of_birth=date_of_birth)
user.set_password(password)
user.save(using=self._db)
return user
<|end_body_0|>
<|body_start_1|>
user = self.cr... | MyUserManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyUserManager:
def create_user(self, userid, date_of_birth, password=None):
"""Creates and saves a User with the given email, date of birth and password."""
<|body_0|>
def create_superuser(self, userid, date_of_birth, password):
"""Creates and saves a superuser with ... | stack_v2_sparse_classes_36k_train_025171 | 15,631 | no_license | [
{
"docstring": "Creates and saves a User with the given email, date of birth and password.",
"name": "create_user",
"signature": "def create_user(self, userid, date_of_birth, password=None)"
},
{
"docstring": "Creates and saves a superuser with the given email, date of birth and password.",
... | 2 | stack_v2_sparse_classes_30k_train_003485 | Implement the Python class `MyUserManager` described below.
Class description:
Implement the MyUserManager class.
Method signatures and docstrings:
- def create_user(self, userid, date_of_birth, password=None): Creates and saves a User with the given email, date of birth and password.
- def create_superuser(self, use... | Implement the Python class `MyUserManager` described below.
Class description:
Implement the MyUserManager class.
Method signatures and docstrings:
- def create_user(self, userid, date_of_birth, password=None): Creates and saves a User with the given email, date of birth and password.
- def create_superuser(self, use... | 54f2b945e5214c7f7e85984cafd5b22c999a3640 | <|skeleton|>
class MyUserManager:
def create_user(self, userid, date_of_birth, password=None):
"""Creates and saves a User with the given email, date of birth and password."""
<|body_0|>
def create_superuser(self, userid, date_of_birth, password):
"""Creates and saves a superuser with ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyUserManager:
def create_user(self, userid, date_of_birth, password=None):
"""Creates and saves a User with the given email, date of birth and password."""
if not userid:
raise ValueError('Users must have an email address')
user = self.model(userid=userid, date_of_birth=da... | the_stack_v2_python_sparse | testuser/newapp/models.py | vaibhavs95/NSIT-Hostel-MS | train | 0 | |
09ceeff88db61da4ecf6a84878bedc5302bdf39a | [
"if request.version == 'v6':\n return self._post_v6(request)\nelif request.version == 'v7':\n return self._post_v6(request)\nraise Http404()",
"configuration = rest_util.parse_dict(request, 'configuration')\nvalidation = Scan.objects.validate_scan_v6(configuration=configuration)\nresp_dict = {'is_valid': va... | <|body_start_0|>
if request.version == 'v6':
return self._post_v6(request)
elif request.version == 'v7':
return self._post_v6(request)
raise Http404()
<|end_body_0|>
<|body_start_1|>
configuration = rest_util.parse_dict(request, 'configuration')
validatio... | This view is the endpoint for validating a new Scan process before attempting to actually create it | ScansValidationView | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScansValidationView:
"""This view is the endpoint for validating a new Scan process before attempting to actually create it"""
def post(self, request):
"""Validates a new Scan process and returns any warnings discovered :param request: the HTTP POST request :type request: :class:`res... | stack_v2_sparse_classes_36k_train_025172 | 30,689 | permissive | [
{
"docstring": "Validates a new Scan process and returns any warnings discovered :param request: the HTTP POST request :type request: :class:`rest_framework.request.Request` :rtype: :class:`rest_framework.response.Response` :returns: the HTTP response to send back to the user",
"name": "post",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_002858 | Implement the Python class `ScansValidationView` described below.
Class description:
This view is the endpoint for validating a new Scan process before attempting to actually create it
Method signatures and docstrings:
- def post(self, request): Validates a new Scan process and returns any warnings discovered :param ... | Implement the Python class `ScansValidationView` described below.
Class description:
This view is the endpoint for validating a new Scan process before attempting to actually create it
Method signatures and docstrings:
- def post(self, request): Validates a new Scan process and returns any warnings discovered :param ... | 28618aee07ceed9e4a6eb7b8d0e6f05b31d8fd6b | <|skeleton|>
class ScansValidationView:
"""This view is the endpoint for validating a new Scan process before attempting to actually create it"""
def post(self, request):
"""Validates a new Scan process and returns any warnings discovered :param request: the HTTP POST request :type request: :class:`res... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScansValidationView:
"""This view is the endpoint for validating a new Scan process before attempting to actually create it"""
def post(self, request):
"""Validates a new Scan process and returns any warnings discovered :param request: the HTTP POST request :type request: :class:`rest_framework.r... | the_stack_v2_python_sparse | scale/ingest/views.py | kfconsultant/scale | train | 0 |
d91a8c1a3cad5d695140c97462da3ab7b49fa1cb | [
"self.graph = graph\nself.weight = weight\nself.threshold = threshold\nself.distance = dict(nx.all_pairs_dijkstra_path_length(self.graph, weight=self.weight))",
"def length(nodes):\n return np.sum([self.graph.edges[edge].get(self.weight, 1.0) for edge in zip(nodes[:-1], nodes[1:])])\ncoverage = np.mean([np.exp... | <|body_start_0|>
self.graph = graph
self.weight = weight
self.threshold = threshold
self.distance = dict(nx.all_pairs_dijkstra_path_length(self.graph, weight=self.weight))
<|end_body_0|>
<|body_start_1|>
def length(nodes):
return np.sum([self.graph.edges[edge].get(se... | Coverage weighted by length score (CLS). Python doctest: >>> cls = CLS(nx.grid_graph([3, 4])) >>> reference = [(0, 0), (1, 0), (1, 1), (2, 1), (2, 2), (3, 2)] >>> assert np.isclose(cls(reference, reference), 1.0) >>> prediction = [(0, 0), (0, 1), (1, 1), (2, 1), (3, 1), (3, 2)] >>> assert np.isclose(cls(reference, pred... | CLS | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CLS:
"""Coverage weighted by length score (CLS). Python doctest: >>> cls = CLS(nx.grid_graph([3, 4])) >>> reference = [(0, 0), (1, 0), (1, 1), (2, 1), (2, 2), (3, 2)] >>> assert np.isclose(cls(reference, reference), 1.0) >>> prediction = [(0, 0), (0, 1), (1, 1), (2, 1), (3, 1), (3, 2)] >>> assert... | stack_v2_sparse_classes_36k_train_025173 | 2,653 | permissive | [
{
"docstring": "Initializes a CLS object. Args: graph: networkx graph for the environment. weight: networkx edge weight key (str). threshold: distance threshold $d_{th}$ (float).",
"name": "__init__",
"signature": "def __init__(self, graph, weight='weight', threshold=3.0)"
},
{
"docstring": "Com... | 2 | null | Implement the Python class `CLS` described below.
Class description:
Coverage weighted by length score (CLS). Python doctest: >>> cls = CLS(nx.grid_graph([3, 4])) >>> reference = [(0, 0), (1, 0), (1, 1), (2, 1), (2, 2), (3, 2)] >>> assert np.isclose(cls(reference, reference), 1.0) >>> prediction = [(0, 0), (0, 1), (1,... | Implement the Python class `CLS` described below.
Class description:
Coverage weighted by length score (CLS). Python doctest: >>> cls = CLS(nx.grid_graph([3, 4])) >>> reference = [(0, 0), (1, 0), (1, 1), (2, 1), (2, 2), (3, 2)] >>> assert np.isclose(cls(reference, reference), 1.0) >>> prediction = [(0, 0), (0, 1), (1,... | dea327aa9e7ef7f7bca5a6c225dbdca1077a06e9 | <|skeleton|>
class CLS:
"""Coverage weighted by length score (CLS). Python doctest: >>> cls = CLS(nx.grid_graph([3, 4])) >>> reference = [(0, 0), (1, 0), (1, 1), (2, 1), (2, 2), (3, 2)] >>> assert np.isclose(cls(reference, reference), 1.0) >>> prediction = [(0, 0), (0, 1), (1, 1), (2, 1), (3, 1), (3, 2)] >>> assert... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CLS:
"""Coverage weighted by length score (CLS). Python doctest: >>> cls = CLS(nx.grid_graph([3, 4])) >>> reference = [(0, 0), (1, 0), (1, 1), (2, 1), (2, 2), (3, 2)] >>> assert np.isclose(cls(reference, reference), 1.0) >>> prediction = [(0, 0), (0, 1), (1, 1), (2, 1), (3, 1), (3, 2)] >>> assert np.isclose(c... | the_stack_v2_python_sparse | r4r/cls.py | Tarkiyah/googleResearch | train | 11 |
d96679ad499aae31ac5a6441972f7f057e12f357 | [
"super().__init__(args)\nself.batch_matmul_shapes = [[16, 512, 64, 512]]\nself.dispatches_collection_list = []",
"for bmm_shape in bmm_shapes:\n operation = BatchMatmulOperation(bmm_shape, TensorDescription(data_type[0], LayoutType.RowMajor), TensorDescription(data_type[1], LayoutType.RowMajor), TensorDescript... | <|body_start_0|>
super().__init__(args)
self.batch_matmul_shapes = [[16, 512, 64, 512]]
self.dispatches_collection_list = []
<|end_body_0|>
<|body_start_1|>
for bmm_shape in bmm_shapes:
operation = BatchMatmulOperation(bmm_shape, TensorDescription(data_type[0], LayoutType.Ro... | Batch matmul dispatch generator class. | CudaBatchMatmulGenerator | [
"Apache-2.0",
"LLVM-exception",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CudaBatchMatmulGenerator:
"""Batch matmul dispatch generator class."""
def __init__(self, args):
"""Initializes the batch matmul dispatch generator."""
<|body_0|>
def _append_matmul_dispatch_collection(self, bmm_shapes, data_type, configuration_list):
"""Update t... | stack_v2_sparse_classes_36k_train_025174 | 11,380 | permissive | [
{
"docstring": "Initializes the batch matmul dispatch generator.",
"name": "__init__",
"signature": "def __init__(self, args)"
},
{
"docstring": "Update the batch matmul dispatch collection with the given configuration list.",
"name": "_append_matmul_dispatch_collection",
"signature": "d... | 5 | null | Implement the Python class `CudaBatchMatmulGenerator` described below.
Class description:
Batch matmul dispatch generator class.
Method signatures and docstrings:
- def __init__(self, args): Initializes the batch matmul dispatch generator.
- def _append_matmul_dispatch_collection(self, bmm_shapes, data_type, configur... | Implement the Python class `CudaBatchMatmulGenerator` described below.
Class description:
Batch matmul dispatch generator class.
Method signatures and docstrings:
- def __init__(self, args): Initializes the batch matmul dispatch generator.
- def _append_matmul_dispatch_collection(self, bmm_shapes, data_type, configur... | 13ef677e556d0a1d154e45b052fe016256057f65 | <|skeleton|>
class CudaBatchMatmulGenerator:
"""Batch matmul dispatch generator class."""
def __init__(self, args):
"""Initializes the batch matmul dispatch generator."""
<|body_0|>
def _append_matmul_dispatch_collection(self, bmm_shapes, data_type, configuration_list):
"""Update t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CudaBatchMatmulGenerator:
"""Batch matmul dispatch generator class."""
def __init__(self, args):
"""Initializes the batch matmul dispatch generator."""
super().__init__(args)
self.batch_matmul_shapes = [[16, 512, 64, 512]]
self.dispatches_collection_list = []
def _app... | the_stack_v2_python_sparse | experimental/dispatch_profiler/batch_matmul.py | openxla/iree | train | 387 |
17de4550a8bb03ab737dcdf55647de45cf68d523 | [
"assert isinstance(item, User)\nitem.password = bcrypt.encrypt(item.password)\nsuper().insert(item)",
"errors = {}\nif not item.get('firstname'):\n errors['firstname'] = 'First name is required'\nif not item.get('lastname'):\n errors['lastname'] = 'Last name is required'\nif not item.get('username'):\n e... | <|body_start_0|>
assert isinstance(item, User)
item.password = bcrypt.encrypt(item.password)
super().insert(item)
<|end_body_0|>
<|body_start_1|>
errors = {}
if not item.get('firstname'):
errors['firstname'] = 'First name is required'
if not item.get('lastnam... | UserCollection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserCollection:
def insert(self, item):
"""Encrypt password before adding user into the collection"""
<|body_0|>
def is_valid(self, item):
"""Check if the response has valid user details"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
assert isinsta... | stack_v2_sparse_classes_36k_train_025175 | 4,439 | no_license | [
{
"docstring": "Encrypt password before adding user into the collection",
"name": "insert",
"signature": "def insert(self, item)"
},
{
"docstring": "Check if the response has valid user details",
"name": "is_valid",
"signature": "def is_valid(self, item)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006882 | Implement the Python class `UserCollection` described below.
Class description:
Implement the UserCollection class.
Method signatures and docstrings:
- def insert(self, item): Encrypt password before adding user into the collection
- def is_valid(self, item): Check if the response has valid user details | Implement the Python class `UserCollection` described below.
Class description:
Implement the UserCollection class.
Method signatures and docstrings:
- def insert(self, item): Encrypt password before adding user into the collection
- def is_valid(self, item): Check if the response has valid user details
<|skeleton|>... | 22228f996b8e1d7d571bc6ade184087c396da655 | <|skeleton|>
class UserCollection:
def insert(self, item):
"""Encrypt password before adding user into the collection"""
<|body_0|>
def is_valid(self, item):
"""Check if the response has valid user details"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserCollection:
def insert(self, item):
"""Encrypt password before adding user into the collection"""
assert isinstance(item, User)
item.password = bcrypt.encrypt(item.password)
super().insert(item)
def is_valid(self, item):
"""Check if the response has valid user ... | the_stack_v2_python_sparse | API/v1/data_store/collections.py | gitaumoses4/maintenance-tracker | train | 2 | |
a240bec196f7bd6d82fd9d657c39e2d362513690 | [
"try:\n logger.info('上传片头,片尾等图片测试')\n self.login()\n texts = self.upload()\n self.assertEqual(texts, ['上传文件成功!', '上传文件成功!', '上传文件成功!', '上传文件成功!', '上传文件成功!'])\nexcept Exception as msg:\n logger.error(u'异常原因：%s' % msg)\n self.driver.get_screenshot_as_file(os.path.join(readconfig.screen_path, 'test_u... | <|body_start_0|>
try:
logger.info('上传片头,片尾等图片测试')
self.login()
texts = self.upload()
self.assertEqual(texts, ['上传文件成功!', '上传文件成功!', '上传文件成功!', '上传文件成功!', '上传文件成功!'])
except Exception as msg:
logger.error(u'异常原因：%s' % msg)
self.drive... | 片头片尾相关功能测试 | HeadTailTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeadTailTest:
"""片头片尾相关功能测试"""
def test_upload(self):
"""上传片头,片尾等图片测试"""
<|body_0|>
def test_set_title_trailer_time(self):
"""片头片尾机显示视频信息测试"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
logger.info('上传片头,片尾等图片测试')
... | stack_v2_sparse_classes_36k_train_025176 | 1,940 | no_license | [
{
"docstring": "上传片头,片尾等图片测试",
"name": "test_upload",
"signature": "def test_upload(self)"
},
{
"docstring": "片头片尾机显示视频信息测试",
"name": "test_set_title_trailer_time",
"signature": "def test_set_title_trailer_time(self)"
}
] | 2 | null | Implement the Python class `HeadTailTest` described below.
Class description:
片头片尾相关功能测试
Method signatures and docstrings:
- def test_upload(self): 上传片头,片尾等图片测试
- def test_set_title_trailer_time(self): 片头片尾机显示视频信息测试 | Implement the Python class `HeadTailTest` described below.
Class description:
片头片尾相关功能测试
Method signatures and docstrings:
- def test_upload(self): 上传片头,片尾等图片测试
- def test_set_title_trailer_time(self): 片头片尾机显示视频信息测试
<|skeleton|>
class HeadTailTest:
"""片头片尾相关功能测试"""
def test_upload(self):
"""上传片头,片尾等... | fd552eeb47fd4838c2c5caef4deea7480ab75ce9 | <|skeleton|>
class HeadTailTest:
"""片头片尾相关功能测试"""
def test_upload(self):
"""上传片头,片尾等图片测试"""
<|body_0|>
def test_set_title_trailer_time(self):
"""片头片尾机显示视频信息测试"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HeadTailTest:
"""片头片尾相关功能测试"""
def test_upload(self):
"""上传片头,片尾等图片测试"""
try:
logger.info('上传片头,片尾等图片测试')
self.login()
texts = self.upload()
self.assertEqual(texts, ['上传文件成功!', '上传文件成功!', '上传文件成功!', '上传文件成功!', '上传文件成功!'])
except Exce... | the_stack_v2_python_sparse | test_case/B004_head_tail_test.py | luhuifnag/AVA_UIauto_test | train | 0 |
c1f4411390ee078534c99a7c6f52ff9ac7ea58b4 | [
"self.capacity = capacity\nself.head = {}\nself.tail = {}\nself.head['next'] = self.tail\nself.tail['prev'] = self.head\nself.cache = {}",
"if key not in self.cache:\n return -1\nnode = self.cache.get(key)\nnode['prev']['next'] = node['next']\nnode['next']['prev'] = node['prev']\nnode['next'] = self.tail\nnode... | <|body_start_0|>
self.capacity = capacity
self.head = {}
self.tail = {}
self.head['next'] = self.tail
self.tail['prev'] = self.head
self.cache = {}
<|end_body_0|>
<|body_start_1|>
if key not in self.cache:
return -1
node = self.cache.get(key)
... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity: int):
"""init empty head and empty tail dict point the head and tail to each other init empty cache dict"""
<|body_0|>
def get(self, key: int) -> int:
"""if key is not in the cache, return -1 re-position the key's prev's next an... | stack_v2_sparse_classes_36k_train_025177 | 3,112 | no_license | [
{
"docstring": "init empty head and empty tail dict point the head and tail to each other init empty cache dict",
"name": "__init__",
"signature": "def __init__(self, capacity: int)"
},
{
"docstring": "if key is not in the cache, return -1 re-position the key's prev's next and the next's prev po... | 3 | stack_v2_sparse_classes_30k_train_021307 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity: int): init empty head and empty tail dict point the head and tail to each other init empty cache dict
- def get(self, key: int) -> int: if key is not... | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity: int): init empty head and empty tail dict point the head and tail to each other init empty cache dict
- def get(self, key: int) -> int: if key is not... | 8499dcee8c1f3e310a39153324216d25c3ca36cf | <|skeleton|>
class LRUCache:
def __init__(self, capacity: int):
"""init empty head and empty tail dict point the head and tail to each other init empty cache dict"""
<|body_0|>
def get(self, key: int) -> int:
"""if key is not in the cache, return -1 re-position the key's prev's next an... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity: int):
"""init empty head and empty tail dict point the head and tail to each other init empty cache dict"""
self.capacity = capacity
self.head = {}
self.tail = {}
self.head['next'] = self.tail
self.tail['prev'] = self.head
... | the_stack_v2_python_sparse | leetCode/py3/lru_cache.py | spark721/ds_al | train | 0 | |
db51d54f75ae6bff35a70ae7afa7d0c3cb11d26a | [
"args = parser.parse_args()\nname = args.get('name')\npgnum = args.get('pgnum')\nif not pgnum:\n pgnum = 1\noptions = {'name': name, 'pgnum': pgnum}\nrole, pg = role_list_c(options)\nresponse_data = {'code': 1200, 'ok': True, 'data': role, 'msg': '获取角色信息成功', 'pg': pg}\nreturn response_data",
"args = parser.par... | <|body_start_0|>
args = parser.parse_args()
name = args.get('name')
pgnum = args.get('pgnum')
if not pgnum:
pgnum = 1
options = {'name': name, 'pgnum': pgnum}
role, pg = role_list_c(options)
response_data = {'code': 1200, 'ok': True, 'data': role, 'msg... | RoleManage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RoleManage:
def get(self):
"""获取角色信息 --- tags: - role summary: Add a new pet to the store parameters: - in: query name: name type: string description: 角色名 - in: query name: pgnum type: string description: 页码 responses: 200: description: 获取角色信息 schema: id: Role properties: name: type: str... | stack_v2_sparse_classes_36k_train_025178 | 5,564 | no_license | [
{
"docstring": "获取角色信息 --- tags: - role summary: Add a new pet to the store parameters: - in: query name: name type: string description: 角色名 - in: query name: pgnum type: string description: 页码 responses: 200: description: 获取角色信息 schema: id: Role properties: name: type: string description: The name of the user ... | 4 | stack_v2_sparse_classes_30k_train_007807 | Implement the Python class `RoleManage` described below.
Class description:
Implement the RoleManage class.
Method signatures and docstrings:
- def get(self): 获取角色信息 --- tags: - role summary: Add a new pet to the store parameters: - in: query name: name type: string description: 角色名 - in: query name: pgnum type: stri... | Implement the Python class `RoleManage` described below.
Class description:
Implement the RoleManage class.
Method signatures and docstrings:
- def get(self): 获取角色信息 --- tags: - role summary: Add a new pet to the store parameters: - in: query name: name type: string description: 角色名 - in: query name: pgnum type: stri... | 73246bbd492fd991e0329b9a011b5380b11a1618 | <|skeleton|>
class RoleManage:
def get(self):
"""获取角色信息 --- tags: - role summary: Add a new pet to the store parameters: - in: query name: name type: string description: 角色名 - in: query name: pgnum type: string description: 页码 responses: 200: description: 获取角色信息 schema: id: Role properties: name: type: str... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RoleManage:
def get(self):
"""获取角色信息 --- tags: - role summary: Add a new pet to the store parameters: - in: query name: name type: string description: 角色名 - in: query name: pgnum type: string description: 页码 responses: 200: description: 获取角色信息 schema: id: Role properties: name: type: string descriptio... | the_stack_v2_python_sparse | app/main/base/apis/role.py | zhouliang0v0/naguan-kpy | train | 0 | |
18ff1b945320dfcdb39010ff71532dbee8b13d88 | [
"descriptor = self._select(self._descriptors)\nnoun = self._select(self._nouns)\nnumbers = ''.join((self._select(chars) for _ in range(length)))\nreturn delim.join([descriptor, noun, numbers])",
"if len(select_from) <= 0:\n return ''\nreturn choice(select_from)"
] | <|body_start_0|>
descriptor = self._select(self._descriptors)
noun = self._select(self._nouns)
numbers = ''.join((self._select(chars) for _ in range(length)))
return delim.join([descriptor, noun, numbers])
<|end_body_0|>
<|body_start_1|>
if len(select_from) <= 0:
ret... | RobotNamer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RobotNamer:
def generate(self, delim='-', length=4, chars='0123456789'):
"""Generate a robot name. Inspiration from Haikunator, but much more poorly implemented ;) Parameters ========== delim: Delimiter length: TokenLength chars: TokenChars"""
<|body_0|>
def _select(self, se... | stack_v2_sparse_classes_36k_train_025179 | 4,323 | permissive | [
{
"docstring": "Generate a robot name. Inspiration from Haikunator, but much more poorly implemented ;) Parameters ========== delim: Delimiter length: TokenLength chars: TokenChars",
"name": "generate",
"signature": "def generate(self, delim='-', length=4, chars='0123456789')"
},
{
"docstring": ... | 2 | null | Implement the Python class `RobotNamer` described below.
Class description:
Implement the RobotNamer class.
Method signatures and docstrings:
- def generate(self, delim='-', length=4, chars='0123456789'): Generate a robot name. Inspiration from Haikunator, but much more poorly implemented ;) Parameters ========== del... | Implement the Python class `RobotNamer` described below.
Class description:
Implement the RobotNamer class.
Method signatures and docstrings:
- def generate(self, delim='-', length=4, chars='0123456789'): Generate a robot name. Inspiration from Haikunator, but much more poorly implemented ;) Parameters ========== del... | 31fd3fb1233f39ea2252a7a44160ff8a2140f7bd | <|skeleton|>
class RobotNamer:
def generate(self, delim='-', length=4, chars='0123456789'):
"""Generate a robot name. Inspiration from Haikunator, but much more poorly implemented ;) Parameters ========== delim: Delimiter length: TokenLength chars: TokenChars"""
<|body_0|>
def _select(self, se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RobotNamer:
def generate(self, delim='-', length=4, chars='0123456789'):
"""Generate a robot name. Inspiration from Haikunator, but much more poorly implemented ;) Parameters ========== delim: Delimiter length: TokenLength chars: TokenChars"""
descriptor = self._select(self._descriptors)
... | the_stack_v2_python_sparse | Python/Telegram_Bot/markovmeme/namer.py | HarshCasper/Rotten-Scripts | train | 1,474 | |
a3f1dbccdcb751e47254742818a93ef6ca6fc047 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('yjunchoi_yzhang71', 'yjunchoi_yzhang71')\nPL = repo['yjunchoi_yzhang71.pollingLocation'].find()\nPE = repo['yjunchoi_yzhang71.presidentElectionByPrecinct'].find()\nmapPEList = []\nvoterTotal = 0\nfor row... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('yjunchoi_yzhang71', 'yjunchoi_yzhang71')
PL = repo['yjunchoi_yzhang71.pollingLocation'].find()
PE = repo['yjunchoi_yzhang71.presidentElectionByPre... | countPollingLocationByWard | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class countPollingLocationByWard:
def execute(trial=False):
"""Merging data sets"""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything happening in this script. Each run of the scr... | stack_v2_sparse_classes_36k_train_025180 | 5,202 | no_license | [
{
"docstring": "Merging data sets",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.",
"name": "pr... | 2 | null | Implement the Python class `countPollingLocationByWard` described below.
Class description:
Implement the countPollingLocationByWard class.
Method signatures and docstrings:
- def execute(trial=False): Merging data sets
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenan... | Implement the Python class `countPollingLocationByWard` described below.
Class description:
Implement the countPollingLocationByWard class.
Method signatures and docstrings:
- def execute(trial=False): Merging data sets
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenan... | 97e72731ffadbeae57d7a332decd58706e7c08de | <|skeleton|>
class countPollingLocationByWard:
def execute(trial=False):
"""Merging data sets"""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything happening in this script. Each run of the scr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class countPollingLocationByWard:
def execute(trial=False):
"""Merging data sets"""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('yjunchoi_yzhang71', 'yjunchoi_yzhang71')
PL = repo['yjunchoi_yzhang71.polli... | the_stack_v2_python_sparse | yjunchoi_yzhang71/countPollingLocationByWard.py | ROODAY/course-2017-fal-proj | train | 3 | |
02d264799b8f25eda1bd7c799735a4538b695e68 | [
"timestamp = self._GetRowValue(query_hash, row, value_name)\nif timestamp is None:\n return None\nreturn dfdatetime_cocoa_time.CocoaTime(timestamp=timestamp)",
"query_hash = hash(query)\nzhtmlstring = self._GetRowValue(query_hash, row, 'zhtmlstring')\ntext_extractor = _ZHTMLStringTextExtractor()\ntext = text_e... | <|body_start_0|>
timestamp = self._GetRowValue(query_hash, row, value_name)
if timestamp is None:
return None
return dfdatetime_cocoa_time.CocoaTime(timestamp=timestamp)
<|end_body_0|>
<|body_start_1|>
query_hash = hash(query)
zhtmlstring = self._GetRowValue(query_ha... | SQLite parser plugin for MacOS notes database files. The MacOS Notes database file is typically stored in: test_data/NotesV7.storedata | MacOSNotesPlugin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MacOSNotesPlugin:
"""SQLite parser plugin for MacOS notes database files. The MacOS Notes database file is typically stored in: test_data/NotesV7.storedata"""
def _GetDateTimeRowValue(self, query_hash, row, value_name):
"""Retrieves a date and time value from the row. Args: query_has... | stack_v2_sparse_classes_36k_train_025181 | 7,734 | permissive | [
{
"docstring": "Retrieves a date and time value from the row. Args: query_hash (int): hash of the query, that uniquely identifies the query that produced the row. row (sqlite3.Row): row. value_name (str): name of the value. Returns: dfdatetime.CocoaTime: date and time value or None if not available.",
"name... | 2 | stack_v2_sparse_classes_30k_train_017329 | Implement the Python class `MacOSNotesPlugin` described below.
Class description:
SQLite parser plugin for MacOS notes database files. The MacOS Notes database file is typically stored in: test_data/NotesV7.storedata
Method signatures and docstrings:
- def _GetDateTimeRowValue(self, query_hash, row, value_name): Retr... | Implement the Python class `MacOSNotesPlugin` described below.
Class description:
SQLite parser plugin for MacOS notes database files. The MacOS Notes database file is typically stored in: test_data/NotesV7.storedata
Method signatures and docstrings:
- def _GetDateTimeRowValue(self, query_hash, row, value_name): Retr... | d6022f8cfebfddf2d08ab2d300a41b61f3349933 | <|skeleton|>
class MacOSNotesPlugin:
"""SQLite parser plugin for MacOS notes database files. The MacOS Notes database file is typically stored in: test_data/NotesV7.storedata"""
def _GetDateTimeRowValue(self, query_hash, row, value_name):
"""Retrieves a date and time value from the row. Args: query_has... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MacOSNotesPlugin:
"""SQLite parser plugin for MacOS notes database files. The MacOS Notes database file is typically stored in: test_data/NotesV7.storedata"""
def _GetDateTimeRowValue(self, query_hash, row, value_name):
"""Retrieves a date and time value from the row. Args: query_hash (int): hash... | the_stack_v2_python_sparse | plaso/parsers/sqlite_plugins/macos_notes.py | log2timeline/plaso | train | 1,506 |
c55960c80f23835a9bafb2dce4d22c3ae89bd214 | [
"request_payload = {}\nc = commons.parse_coordinates(coordinates).transform_to('icrs')\nra_dec_str = str(c.ra.hour) + ' ' + str(c.dec.degree)\nrequest_payload['RA'] = ra_dec_str\nrequest_payload['Equinox'] = 'J2000'\nrequest_payload['ImageSize'] = coord.Angle(image_size).arcmin\nrequest_payload['ImageType'] = 'FITS... | <|body_start_0|>
request_payload = {}
c = commons.parse_coordinates(coordinates).transform_to('icrs')
ra_dec_str = str(c.ra.hour) + ' ' + str(c.dec.degree)
request_payload['RA'] = ra_dec_str
request_payload['Equinox'] = 'J2000'
request_payload['ImageSize'] = coord.Angle(i... | FirstClass | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FirstClass:
def _args_to_payload(self, coordinates, *, image_size=1 * u.arcmin, maximsize=None):
"""Fetches image cutouts from FIRST survey. Parameters ---------- coordinates : str or `astropy.coordinates` object The target around which to search. It may be specified as a string in which... | stack_v2_sparse_classes_36k_train_025182 | 3,737 | permissive | [
{
"docstring": "Fetches image cutouts from FIRST survey. Parameters ---------- coordinates : str or `astropy.coordinates` object The target around which to search. It may be specified as a string in which case it is resolved using online services or as the appropriate `astropy.coordinates` object. ICRS coordina... | 3 | stack_v2_sparse_classes_30k_train_019297 | Implement the Python class `FirstClass` described below.
Class description:
Implement the FirstClass class.
Method signatures and docstrings:
- def _args_to_payload(self, coordinates, *, image_size=1 * u.arcmin, maximsize=None): Fetches image cutouts from FIRST survey. Parameters ---------- coordinates : str or `astr... | Implement the Python class `FirstClass` described below.
Class description:
Implement the FirstClass class.
Method signatures and docstrings:
- def _args_to_payload(self, coordinates, *, image_size=1 * u.arcmin, maximsize=None): Fetches image cutouts from FIRST survey. Parameters ---------- coordinates : str or `astr... | 51316d7417d7daf01a8b29d1df99037b9227c2bc | <|skeleton|>
class FirstClass:
def _args_to_payload(self, coordinates, *, image_size=1 * u.arcmin, maximsize=None):
"""Fetches image cutouts from FIRST survey. Parameters ---------- coordinates : str or `astropy.coordinates` object The target around which to search. It may be specified as a string in which... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FirstClass:
def _args_to_payload(self, coordinates, *, image_size=1 * u.arcmin, maximsize=None):
"""Fetches image cutouts from FIRST survey. Parameters ---------- coordinates : str or `astropy.coordinates` object The target around which to search. It may be specified as a string in which case it is re... | the_stack_v2_python_sparse | astroquery/image_cutouts/first/core.py | astropy/astroquery | train | 636 | |
f73e4bcf78273940cbba1f31c19d6d28be77824b | [
"for fld in ['TargetNameFields', 'TargetInfoFields']:\n yield (fld, self[fld])\nreturn",
"for _, item in self.enumerate():\n yield item\nreturn",
"for item in self.iterate():\n yield item\nreturn"
] | <|body_start_0|>
for fld in ['TargetNameFields', 'TargetInfoFields']:
yield (fld, self[fld])
return
<|end_body_0|>
<|body_start_1|>
for _, item in self.enumerate():
yield item
return
<|end_body_1|>
<|body_start_2|>
for item in self.iterate():
... | CHALLENGE_MESSAGE | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CHALLENGE_MESSAGE:
def enumerate(self):
"""Yield the name and field that compose the message type payload."""
<|body_0|>
def iterate(self):
"""Yield each field that composes the message type payload."""
<|body_1|>
def Fields(self):
"""Yield all o... | stack_v2_sparse_classes_36k_train_025183 | 31,838 | permissive | [
{
"docstring": "Yield the name and field that compose the message type payload.",
"name": "enumerate",
"signature": "def enumerate(self)"
},
{
"docstring": "Yield each field that composes the message type payload.",
"name": "iterate",
"signature": "def iterate(self)"
},
{
"docstr... | 3 | null | Implement the Python class `CHALLENGE_MESSAGE` described below.
Class description:
Implement the CHALLENGE_MESSAGE class.
Method signatures and docstrings:
- def enumerate(self): Yield the name and field that compose the message type payload.
- def iterate(self): Yield each field that composes the message type payloa... | Implement the Python class `CHALLENGE_MESSAGE` described below.
Class description:
Implement the CHALLENGE_MESSAGE class.
Method signatures and docstrings:
- def enumerate(self): Yield the name and field that compose the message type payload.
- def iterate(self): Yield each field that composes the message type payloa... | e02b014dc764ed822288210248c9438a843af8a9 | <|skeleton|>
class CHALLENGE_MESSAGE:
def enumerate(self):
"""Yield the name and field that compose the message type payload."""
<|body_0|>
def iterate(self):
"""Yield each field that composes the message type payload."""
<|body_1|>
def Fields(self):
"""Yield all o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CHALLENGE_MESSAGE:
def enumerate(self):
"""Yield the name and field that compose the message type payload."""
for fld in ['TargetNameFields', 'TargetInfoFields']:
yield (fld, self[fld])
return
def iterate(self):
"""Yield each field that composes the message typ... | the_stack_v2_python_sparse | template/protocol/nlmp.py | arizvisa/syringe | train | 36 | |
9f961a4a2ab99e5eec3193af6039e5b57ad5bfce | [
"_input = DataFrame({'A': [1, 2, 3]})\n_expected = DataFrame({'A': [1, 2, 3]})\n_groupings = [{'operator': 'difference', 'columns': ['A'], 'value': 0}]\n_vc = VariableCleaner(_input)\n_vc.clean(_groupings)\nassert_frame_equal(_expected, _vc.frame)",
"_input = DataFrame({'A': [1, 2, 3]})\n_expected = DataFrame({'A... | <|body_start_0|>
_input = DataFrame({'A': [1, 2, 3]})
_expected = DataFrame({'A': [1, 2, 3]})
_groupings = [{'operator': 'difference', 'columns': ['A'], 'value': 0}]
_vc = VariableCleaner(_input)
_vc.clean(_groupings)
assert_frame_equal(_expected, _vc.frame)
<|end_body_0|... | Tests for the ``preprocess._aggregate_columns._difference`` module. Assert final data frames match expectations. | PreprocessConstantDifferenceTests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreprocessConstantDifferenceTests:
"""Tests for the ``preprocess._aggregate_columns._difference`` module. Assert final data frames match expectations."""
def test_clean_difference_ints_0():
"""Test subtracting 0 from a column."""
<|body_0|>
def test_clean_difference_ints... | stack_v2_sparse_classes_36k_train_025184 | 5,083 | permissive | [
{
"docstring": "Test subtracting 0 from a column.",
"name": "test_clean_difference_ints_0",
"signature": "def test_clean_difference_ints_0()"
},
{
"docstring": "Test subtracting 1 from a column.",
"name": "test_clean_difference_ints_1",
"signature": "def test_clean_difference_ints_1()"
... | 4 | stack_v2_sparse_classes_30k_train_011841 | Implement the Python class `PreprocessConstantDifferenceTests` described below.
Class description:
Tests for the ``preprocess._aggregate_columns._difference`` module. Assert final data frames match expectations.
Method signatures and docstrings:
- def test_clean_difference_ints_0(): Test subtracting 0 from a column.
... | Implement the Python class `PreprocessConstantDifferenceTests` described below.
Class description:
Tests for the ``preprocess._aggregate_columns._difference`` module. Assert final data frames match expectations.
Method signatures and docstrings:
- def test_clean_difference_ints_0(): Test subtracting 0 from a column.
... | 2e89bc55a61ce2a4ce77646bb427f5b3040f672c | <|skeleton|>
class PreprocessConstantDifferenceTests:
"""Tests for the ``preprocess._aggregate_columns._difference`` module. Assert final data frames match expectations."""
def test_clean_difference_ints_0():
"""Test subtracting 0 from a column."""
<|body_0|>
def test_clean_difference_ints... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PreprocessConstantDifferenceTests:
"""Tests for the ``preprocess._aggregate_columns._difference`` module. Assert final data frames match expectations."""
def test_clean_difference_ints_0():
"""Test subtracting 0 from a column."""
_input = DataFrame({'A': [1, 2, 3]})
_expected = Da... | the_stack_v2_python_sparse | numom2b_preprocessing/unittests/cleaning_tests/test_difference.py | hayesall/nuMoM2b_preprocessing | train | 2 |
65be1fcf50a650c9e5286a0c1028136ca431d64b | [
"if not head or not head.next:\n return head\nlength, cur = (1, head)\nwhile cur.next:\n cur = cur.next\n length += 1\ncur.next = head\ncur, tail = (head, cur)\nfor _ in xrange(length - k % length):\n tail = cur\n cur = cur.next\ntail.next = None\nreturn cur",
"if not head or not head.next:\n re... | <|body_start_0|>
if not head or not head.next:
return head
length, cur = (1, head)
while cur.next:
cur = cur.next
length += 1
cur.next = head
cur, tail = (head, cur)
for _ in xrange(length - k % length):
tail = cur
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rotateRight(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_0|>
def rotate_without_count(self, head, k):
"""不需要计算链表的长度, 但是当K很大时, 会遍历多次, 浪费时间 :type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k_train_025185 | 1,740 | no_license | [
{
"docstring": ":type head: ListNode :type k: int :rtype: ListNode",
"name": "rotateRight",
"signature": "def rotateRight(self, head, k)"
},
{
"docstring": "不需要计算链表的长度, 但是当K很大时, 会遍历多次, 浪费时间 :type head: ListNode :type k: int :rtype: ListNode",
"name": "rotate_without_count",
"signature": ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotateRight(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
- def rotate_without_count(self, head, k): 不需要计算链表的长度, 但是当K很大时, 会遍历多次, 浪费时间 :type head: ListNod... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotateRight(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
- def rotate_without_count(self, head, k): 不需要计算链表的长度, 但是当K很大时, 会遍历多次, 浪费时间 :type head: ListNod... | 215d513b3564a7a76db3d2b29e4acc341a68e8ee | <|skeleton|>
class Solution:
def rotateRight(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_0|>
def rotate_without_count(self, head, k):
"""不需要计算链表的长度, 但是当K很大时, 会遍历多次, 浪费时间 :type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rotateRight(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
if not head or not head.next:
return head
length, cur = (1, head)
while cur.next:
cur = cur.next
length += 1
cur.next = head
cu... | the_stack_v2_python_sparse | python/two-pointer/rotate-list.py | euxuoh/leetcode | train | 0 | |
47c71ca91a7dd0bd27a51e4b155dab3c1279b638 | [
"video = VideosPerm.objects.filter(videos_id=self, username=healthcare, perm_value__in=[2, 3])\nif video.count() == 0:\n return False\nelse:\n return True",
"if self.patient_id == patient:\n return True\nelse:\n return False"
] | <|body_start_0|>
video = VideosPerm.objects.filter(videos_id=self, username=healthcare, perm_value__in=[2, 3])
if video.count() == 0:
return False
else:
return True
<|end_body_0|>
<|body_start_1|>
if self.patient_id == patient:
return True
els... | Videos | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Videos:
def has_permission(self, healthcare):
"""Checks if a user has permissions to view the video."""
<|body_0|>
def is_patient(self, patient):
"""Checks if the record belongs to the patient."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
video =... | stack_v2_sparse_classes_36k_train_025186 | 12,031 | no_license | [
{
"docstring": "Checks if a user has permissions to view the video.",
"name": "has_permission",
"signature": "def has_permission(self, healthcare)"
},
{
"docstring": "Checks if the record belongs to the patient.",
"name": "is_patient",
"signature": "def is_patient(self, patient)"
}
] | 2 | null | Implement the Python class `Videos` described below.
Class description:
Implement the Videos class.
Method signatures and docstrings:
- def has_permission(self, healthcare): Checks if a user has permissions to view the video.
- def is_patient(self, patient): Checks if the record belongs to the patient. | Implement the Python class `Videos` described below.
Class description:
Implement the Videos class.
Method signatures and docstrings:
- def has_permission(self, healthcare): Checks if a user has permissions to view the video.
- def is_patient(self, patient): Checks if the record belongs to the patient.
<|skeleton|>
... | 685c2b9d40fb24ca1735352846a39fdf5d3728eb | <|skeleton|>
class Videos:
def has_permission(self, healthcare):
"""Checks if a user has permissions to view the video."""
<|body_0|>
def is_patient(self, patient):
"""Checks if the record belongs to the patient."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Videos:
def has_permission(self, healthcare):
"""Checks if a user has permissions to view the video."""
video = VideosPerm.objects.filter(videos_id=self, username=healthcare, perm_value__in=[2, 3])
if video.count() == 0:
return False
else:
return True
... | the_stack_v2_python_sparse | patientrecords/models.py | guekling/ifs4205team1 | train | 0 | |
518ded4a160bfb0dbd8c78461458c62ad7c9bd04 | [
"if component is None:\n return\nif isinstance(component, INotifiable):\n component.notify(correlation_id, args)",
"if components is None:\n return\nargs = args if not args is None else Parameters()\nfor component in components:\n Notifier.notify_one(correlation_id, component, args)"
] | <|body_start_0|>
if component is None:
return
if isinstance(component, INotifiable):
component.notify(correlation_id, args)
<|end_body_0|>
<|body_start_1|>
if components is None:
return
args = args if not args is None else Parameters()
for com... | Helper class that notifies components. | Notifier | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Notifier:
"""Helper class that notifies components."""
def notify_one(correlation_id: Optional[str], component: Any, args: Parameters):
"""Notifies specific component. To be notiied components must implement :class:`INotifiable <pip_services3_commons.run.INotifiable.INotifiable>` int... | stack_v2_sparse_classes_36k_train_025187 | 2,000 | permissive | [
{
"docstring": "Notifies specific component. To be notiied components must implement :class:`INotifiable <pip_services3_commons.run.INotifiable.INotifiable>` interface. If they don't the call to this method has no effect. :param correlation_id: (optional) transaction id to trace execution through call chain. :p... | 2 | null | Implement the Python class `Notifier` described below.
Class description:
Helper class that notifies components.
Method signatures and docstrings:
- def notify_one(correlation_id: Optional[str], component: Any, args: Parameters): Notifies specific component. To be notiied components must implement :class:`INotifiable... | Implement the Python class `Notifier` described below.
Class description:
Helper class that notifies components.
Method signatures and docstrings:
- def notify_one(correlation_id: Optional[str], component: Any, args: Parameters): Notifies specific component. To be notiied components must implement :class:`INotifiable... | 17f8a231fb75684032ec57b24025c9a3ca3dcdd6 | <|skeleton|>
class Notifier:
"""Helper class that notifies components."""
def notify_one(correlation_id: Optional[str], component: Any, args: Parameters):
"""Notifies specific component. To be notiied components must implement :class:`INotifiable <pip_services3_commons.run.INotifiable.INotifiable>` int... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Notifier:
"""Helper class that notifies components."""
def notify_one(correlation_id: Optional[str], component: Any, args: Parameters):
"""Notifies specific component. To be notiied components must implement :class:`INotifiable <pip_services3_commons.run.INotifiable.INotifiable>` interface. If th... | the_stack_v2_python_sparse | pip_services3_commons/run/Notifier.py | pip-services3-python/pip-services3-commons-python | train | 0 |
85e28405e5fc34617e37429fafc14ae82bc4ba14 | [
"if _has_content(self):\n chunk_size = cast(int, self._connection_data_block_size)\n for i in range(0, len(self.content), chunk_size):\n yield self.content[i:i + chunk_size]\nelse:\n for part in _stream_download_helper(decompress=True, response=self):\n yield part\nself.close()",
"for raw_b... | <|body_start_0|>
if _has_content(self):
chunk_size = cast(int, self._connection_data_block_size)
for i in range(0, len(self.content), chunk_size):
yield self.content[i:i + chunk_size]
else:
for part in _stream_download_helper(decompress=True, response=... | RestRequestsTransportResponse | [
"MIT",
"LicenseRef-scancode-generic-cla",
"LGPL-2.1-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestRequestsTransportResponse:
def iter_bytes(self):
"""Iterates over the response's bytes. Will decompress in the process :return: An iterator of bytes from the response :rtype: Iterator[str]"""
<|body_0|>
def iter_raw(self):
"""Iterates over the response's bytes. W... | stack_v2_sparse_classes_36k_train_025188 | 5,630 | permissive | [
{
"docstring": "Iterates over the response's bytes. Will decompress in the process :return: An iterator of bytes from the response :rtype: Iterator[str]",
"name": "iter_bytes",
"signature": "def iter_bytes(self)"
},
{
"docstring": "Iterates over the response's bytes. Will not decompress in the p... | 3 | stack_v2_sparse_classes_30k_train_004269 | Implement the Python class `RestRequestsTransportResponse` described below.
Class description:
Implement the RestRequestsTransportResponse class.
Method signatures and docstrings:
- def iter_bytes(self): Iterates over the response's bytes. Will decompress in the process :return: An iterator of bytes from the response... | Implement the Python class `RestRequestsTransportResponse` described below.
Class description:
Implement the RestRequestsTransportResponse class.
Method signatures and docstrings:
- def iter_bytes(self): Iterates over the response's bytes. Will decompress in the process :return: An iterator of bytes from the response... | b2bdfe659210998d6d479e73b133b6c51eb2c009 | <|skeleton|>
class RestRequestsTransportResponse:
def iter_bytes(self):
"""Iterates over the response's bytes. Will decompress in the process :return: An iterator of bytes from the response :rtype: Iterator[str]"""
<|body_0|>
def iter_raw(self):
"""Iterates over the response's bytes. W... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RestRequestsTransportResponse:
def iter_bytes(self):
"""Iterates over the response's bytes. Will decompress in the process :return: An iterator of bytes from the response :rtype: Iterator[str]"""
if _has_content(self):
chunk_size = cast(int, self._connection_data_block_size)
... | the_stack_v2_python_sparse | sdk/core/azure-core/azure/core/rest/_requests_basic.py | adriananeci/azure-sdk-for-python | train | 1 | |
74221624a22d31c8b2d1df4a5024a1190ddd40b6 | [
"ori_shapes = tuple((meta['ori_shape'] for meta in img_metas))\nscale_factors = tuple((meta['scale_factor'] for meta in img_metas))\nif isinstance(scale_factors[0], float):\n warnings.warn('Scale factor in img_metas should be a ndarray with shape (4,) arrange as (factor_w, factor_h, factor_w, factor_h), The scal... | <|body_start_0|>
ori_shapes = tuple((meta['ori_shape'] for meta in img_metas))
scale_factors = tuple((meta['scale_factor'] for meta in img_metas))
if isinstance(scale_factors[0], float):
warnings.warn('Scale factor in img_metas should be a ndarray with shape (4,) arrange as (factor_w... | MaskTestMixin | [
"Apache-2.0",
"BSD-2-Clause-Views",
"MIT",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaskTestMixin:
def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False):
"""Simple test for mask head without augmentation."""
<|body_0|>
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
"""Test for mask head with test time au... | stack_v2_sparse_classes_36k_train_025189 | 13,557 | permissive | [
{
"docstring": "Simple test for mask head without augmentation.",
"name": "simple_test_mask",
"signature": "def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False)"
},
{
"docstring": "Test for mask head with test time augmentation.",
"name": "aug_test_mask",
"sign... | 2 | null | Implement the Python class `MaskTestMixin` described below.
Class description:
Implement the MaskTestMixin class.
Method signatures and docstrings:
- def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False): Simple test for mask head without augmentation.
- def aug_test_mask(self, feats, img_me... | Implement the Python class `MaskTestMixin` described below.
Class description:
Implement the MaskTestMixin class.
Method signatures and docstrings:
- def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False): Simple test for mask head without augmentation.
- def aug_test_mask(self, feats, img_me... | 3652b18c7ce68122dae7a32670624727d50e0914 | <|skeleton|>
class MaskTestMixin:
def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False):
"""Simple test for mask head without augmentation."""
<|body_0|>
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
"""Test for mask head with test time au... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MaskTestMixin:
def simple_test_mask(self, x, img_metas, det_bboxes, det_labels, rescale=False):
"""Simple test for mask head without augmentation."""
ori_shapes = tuple((meta['ori_shape'] for meta in img_metas))
scale_factors = tuple((meta['scale_factor'] for meta in img_metas))
... | the_stack_v2_python_sparse | mmdet/models/roi_heads/test_mixins.py | shinya7y/UniverseNet | train | 407 | |
fa80ffa5f9e2f1eefcacd7f4fdd044de9574029e | [
"if name is None:\n name = self._default_cache_dir\nif dirpath is None:\n dirpath = self._default_cache_dir\ncachedir = self._default_cache_dir\nif not os.path.exists(cachedir):\n os.makedirs(cachedir)\nself._name = name\ncachefile = self._default_cache_dir + '/' + name\nself._dbm = _gdbm.open(cachefile, '... | <|body_start_0|>
if name is None:
name = self._default_cache_dir
if dirpath is None:
dirpath = self._default_cache_dir
cachedir = self._default_cache_dir
if not os.path.exists(cachedir):
os.makedirs(cachedir)
self._name = name
cachefile... | gun dbm cache class | GNUDBMCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GNUDBMCache:
"""gun dbm cache class"""
def __init__(self, name=_default_cache_name, dirpath=_default_cache_dir):
"""initialize file cache with cache file path :param name: str, cache name :param dirpath: str, cache directory path"""
<|body_0|>
def cachedir(self, dirpath=... | stack_v2_sparse_classes_36k_train_025190 | 4,045 | no_license | [
{
"docstring": "initialize file cache with cache file path :param name: str, cache name :param dirpath: str, cache directory path",
"name": "__init__",
"signature": "def __init__(self, name=_default_cache_name, dirpath=_default_cache_dir)"
},
{
"docstring": "get or set the cache file directory p... | 6 | stack_v2_sparse_classes_30k_val_000690 | Implement the Python class `GNUDBMCache` described below.
Class description:
gun dbm cache class
Method signatures and docstrings:
- def __init__(self, name=_default_cache_name, dirpath=_default_cache_dir): initialize file cache with cache file path :param name: str, cache name :param dirpath: str, cache directory pa... | Implement the Python class `GNUDBMCache` described below.
Class description:
gun dbm cache class
Method signatures and docstrings:
- def __init__(self, name=_default_cache_name, dirpath=_default_cache_dir): initialize file cache with cache file path :param name: str, cache name :param dirpath: str, cache directory pa... | 9f452b6c57ff211b38ca8ce971396e94c0b2194b | <|skeleton|>
class GNUDBMCache:
"""gun dbm cache class"""
def __init__(self, name=_default_cache_name, dirpath=_default_cache_dir):
"""initialize file cache with cache file path :param name: str, cache name :param dirpath: str, cache directory path"""
<|body_0|>
def cachedir(self, dirpath=... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GNUDBMCache:
"""gun dbm cache class"""
def __init__(self, name=_default_cache_name, dirpath=_default_cache_dir):
"""initialize file cache with cache file path :param name: str, cache name :param dirpath: str, cache directory path"""
if name is None:
name = self._default_cache_... | the_stack_v2_python_sparse | python/security/utl/cache/gnuc.py | wruibo/tools | train | 0 |
aac0d5477577bb48862068d44bd862f2a34d51bf | [
"if num <= 0:\n return False\nreturn num & 1431655765 != 0 and (not num & num - 1)",
"if num <= 0:\n return False\nreturn not num & num - 1 and (num - 1) % 3 == 0"
] | <|body_start_0|>
if num <= 0:
return False
return num & 1431655765 != 0 and (not num & num - 1)
<|end_body_0|>
<|body_start_1|>
if num <= 0:
return False
return not num & num - 1 and (num - 1) % 3 == 0
<|end_body_1|>
| Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPowerOfFour(self, num):
""":type num: int :rtype: bool"""
<|body_0|>
def isPowerOfFour(self, num):
""":type num: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if num <= 0:
return False
return nu... | stack_v2_sparse_classes_36k_train_025191 | 801 | no_license | [
{
"docstring": ":type num: int :rtype: bool",
"name": "isPowerOfFour",
"signature": "def isPowerOfFour(self, num)"
},
{
"docstring": ":type num: int :rtype: bool",
"name": "isPowerOfFour",
"signature": "def isPowerOfFour(self, num)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPowerOfFour(self, num): :type num: int :rtype: bool
- def isPowerOfFour(self, num): :type num: int :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPowerOfFour(self, num): :type num: int :rtype: bool
- def isPowerOfFour(self, num): :type num: int :rtype: bool
<|skeleton|>
class Solution:
def isPowerOfFour(self, n... | 6fec95b9b4d735727160905e754a698513bfb7d8 | <|skeleton|>
class Solution:
def isPowerOfFour(self, num):
""":type num: int :rtype: bool"""
<|body_0|>
def isPowerOfFour(self, num):
""":type num: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isPowerOfFour(self, num):
""":type num: int :rtype: bool"""
if num <= 0:
return False
return num & 1431655765 != 0 and (not num & num - 1)
def isPowerOfFour(self, num):
""":type num: int :rtype: bool"""
if num <= 0:
return Fals... | the_stack_v2_python_sparse | leetcode/bit-manipulation/power-of-four.py | jwyx3/practices | train | 2 | |
30e6805e0a422fa6ccf9d8ab40ef4acdc19569c5 | [
"super().__init__(**kwargs)\nself.strategies = [self.PARALLEL_DOWNLOADER_CLASS(min_part_size=self.DEFAULT_MIN_PART_SIZE, min_chunk_size=self.MIN_CHUNK_SIZE, max_chunk_size=max(self.MAX_CHUNK_SIZE, write_buffer_size or 0), align_factor=write_buffer_size, thread_pool=self._thread_pool, check_hash=check_hash, max_stre... | <|body_start_0|>
super().__init__(**kwargs)
self.strategies = [self.PARALLEL_DOWNLOADER_CLASS(min_part_size=self.DEFAULT_MIN_PART_SIZE, min_chunk_size=self.MIN_CHUNK_SIZE, max_chunk_size=max(self.MAX_CHUNK_SIZE, write_buffer_size or 0), align_factor=write_buffer_size, thread_pool=self._thread_pool, chec... | Handle complex actions around downloads to free raw_api from that responsibility. | DownloadManager | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DownloadManager:
"""Handle complex actions around downloads to free raw_api from that responsibility."""
def __init__(self, write_buffer_size: int | None=None, check_hash: bool=True, max_download_streams_per_file: int | None=None, **kwargs):
"""Initialize the DownloadManager using th... | stack_v2_sparse_classes_36k_train_025192 | 4,349 | permissive | [
{
"docstring": "Initialize the DownloadManager using the given services object.",
"name": "__init__",
"signature": "def __init__(self, write_buffer_size: int | None=None, check_hash: bool=True, max_download_streams_per_file: int | None=None, **kwargs)"
},
{
"docstring": ":param url: url from whi... | 2 | null | Implement the Python class `DownloadManager` described below.
Class description:
Handle complex actions around downloads to free raw_api from that responsibility.
Method signatures and docstrings:
- def __init__(self, write_buffer_size: int | None=None, check_hash: bool=True, max_download_streams_per_file: int | None... | Implement the Python class `DownloadManager` described below.
Class description:
Handle complex actions around downloads to free raw_api from that responsibility.
Method signatures and docstrings:
- def __init__(self, write_buffer_size: int | None=None, check_hash: bool=True, max_download_streams_per_file: int | None... | 072f96dfe90ff191cb74dd2b657564ed5649553c | <|skeleton|>
class DownloadManager:
"""Handle complex actions around downloads to free raw_api from that responsibility."""
def __init__(self, write_buffer_size: int | None=None, check_hash: bool=True, max_download_streams_per_file: int | None=None, **kwargs):
"""Initialize the DownloadManager using th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DownloadManager:
"""Handle complex actions around downloads to free raw_api from that responsibility."""
def __init__(self, write_buffer_size: int | None=None, check_hash: bool=True, max_download_streams_per_file: int | None=None, **kwargs):
"""Initialize the DownloadManager using the given servi... | the_stack_v2_python_sparse | b2sdk/transfer/inbound/download_manager.py | Backblaze/b2-sdk-python | train | 160 |
cb5ad5f9d984c2c1d78d4d445e7c7631cfbe5bfb | [
"if not isinstance(trial, AbstractArchiveTrial):\n raise TypeError('The trial must an instance of AbstractArchiveTrial.')\nself.__trial = trial\nself.__name = name\nself.__locked = False",
"if not (value := getattr(self, f'_{self.__class__.__name__}__placeholder', None)) is not None:\n raise ValueError(\"Tr... | <|body_start_0|>
if not isinstance(trial, AbstractArchiveTrial):
raise TypeError('The trial must an instance of AbstractArchiveTrial.')
self.__trial = trial
self.__name = name
self.__locked = False
<|end_body_0|>
<|body_start_1|>
if not (value := getattr(self, f'_{se... | ArchiveTrialDescriptor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ArchiveTrialDescriptor:
def __init__(self, name: str, trial: AbstractArchiveTrial):
"""Initializes the ArchiveTrialDescriptor object, by injecting an AbstractArchiveTrial instance as a trial. :param trial:AbstractArchiveTrial:"""
<|body_0|>
async def __get__(self, instance, ... | stack_v2_sparse_classes_36k_train_025193 | 2,160 | no_license | [
{
"docstring": "Initializes the ArchiveTrialDescriptor object, by injecting an AbstractArchiveTrial instance as a trial. :param trial:AbstractArchiveTrial:",
"name": "__init__",
"signature": "def __init__(self, name: str, trial: AbstractArchiveTrial)"
},
{
"docstring": "Given that a trial value ... | 3 | null | Implement the Python class `ArchiveTrialDescriptor` described below.
Class description:
Implement the ArchiveTrialDescriptor class.
Method signatures and docstrings:
- def __init__(self, name: str, trial: AbstractArchiveTrial): Initializes the ArchiveTrialDescriptor object, by injecting an AbstractArchiveTrial instan... | Implement the Python class `ArchiveTrialDescriptor` described below.
Class description:
Implement the ArchiveTrialDescriptor class.
Method signatures and docstrings:
- def __init__(self, name: str, trial: AbstractArchiveTrial): Initializes the ArchiveTrialDescriptor object, by injecting an AbstractArchiveTrial instan... | 6eeedc6b61247bed79ec4d65e1ef77e89a39352f | <|skeleton|>
class ArchiveTrialDescriptor:
def __init__(self, name: str, trial: AbstractArchiveTrial):
"""Initializes the ArchiveTrialDescriptor object, by injecting an AbstractArchiveTrial instance as a trial. :param trial:AbstractArchiveTrial:"""
<|body_0|>
async def __get__(self, instance, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ArchiveTrialDescriptor:
def __init__(self, name: str, trial: AbstractArchiveTrial):
"""Initializes the ArchiveTrialDescriptor object, by injecting an AbstractArchiveTrial instance as a trial. :param trial:AbstractArchiveTrial:"""
if not isinstance(trial, AbstractArchiveTrial):
rais... | the_stack_v2_python_sparse | application/utils/protocol/descriptor/archive.py | VictorJan/Bruteforcer | train | 0 | |
5bce473350076a4b36778b6a885ec49b6fd68e36 | [
"super(MPN, self).__init__()\nself.args = args\nself.atom_fdim = atom_fdim or get_atom_fdim(args)\nself.bond_fdim = bond_fdim or get_bond_fdim(args) + (not args.atom_messages) * self.atom_fdim\nself.graph_input = graph_input\nself.encoder = MPNEncoder(self.args, self.atom_fdim, self.bond_fdim)",
"if not self.grap... | <|body_start_0|>
super(MPN, self).__init__()
self.args = args
self.atom_fdim = atom_fdim or get_atom_fdim(args)
self.bond_fdim = bond_fdim or get_bond_fdim(args) + (not args.atom_messages) * self.atom_fdim
self.graph_input = graph_input
self.encoder = MPNEncoder(self.args... | A message passing neural network for encoding a molecule. | MPN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MPN:
"""A message passing neural network for encoding a molecule."""
def __init__(self, args: Namespace, atom_fdim: int=None, bond_fdim: int=None, graph_input: bool=False):
"""Initializes the MPN. :param args: Arguments. :param atom_fdim: Atom features dimension. :param bond_fdim: Bo... | stack_v2_sparse_classes_36k_train_025194 | 26,669 | no_license | [
{
"docstring": "Initializes the MPN. :param args: Arguments. :param atom_fdim: Atom features dimension. :param bond_fdim: Bond features dimension. :param graph_input: If true, expects BatchMolGraph as input. Otherwise expects a list of smiles strings as input.",
"name": "__init__",
"signature": "def __i... | 2 | stack_v2_sparse_classes_30k_train_003654 | Implement the Python class `MPN` described below.
Class description:
A message passing neural network for encoding a molecule.
Method signatures and docstrings:
- def __init__(self, args: Namespace, atom_fdim: int=None, bond_fdim: int=None, graph_input: bool=False): Initializes the MPN. :param args: Arguments. :param... | Implement the Python class `MPN` described below.
Class description:
A message passing neural network for encoding a molecule.
Method signatures and docstrings:
- def __init__(self, args: Namespace, atom_fdim: int=None, bond_fdim: int=None, graph_input: bool=False): Initializes the MPN. :param args: Arguments. :param... | 1851765edfd77f4a1ebd1702b32a11a6e8e8f01d | <|skeleton|>
class MPN:
"""A message passing neural network for encoding a molecule."""
def __init__(self, args: Namespace, atom_fdim: int=None, bond_fdim: int=None, graph_input: bool=False):
"""Initializes the MPN. :param args: Arguments. :param atom_fdim: Atom features dimension. :param bond_fdim: Bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MPN:
"""A message passing neural network for encoding a molecule."""
def __init__(self, args: Namespace, atom_fdim: int=None, bond_fdim: int=None, graph_input: bool=False):
"""Initializes the MPN. :param args: Arguments. :param atom_fdim: Atom features dimension. :param bond_fdim: Bond features d... | the_stack_v2_python_sparse | MIRACLE/models/mpn.py | aabbccgithub/MIRACLE | train | 0 |
893c89076649bf25c15c091b0fe36b0b673a3e31 | [
"self.mobmondir = self.tempdir\nself.staticdir = os.path.join(self.mobmondir, self.STATICDIR)\nosutils.SafeMakedirs(self.staticdir)",
"cfm = MockCheckFileManager()\nroot = mobmonitor.MobMonitorRoot(cfm, staticdir=self.staticdir)\nself.assertEqual(cfm.GetServiceList(), json.loads(root.GetServiceList()))",
"cfm =... | <|body_start_0|>
self.mobmondir = self.tempdir
self.staticdir = os.path.join(self.mobmondir, self.STATICDIR)
osutils.SafeMakedirs(self.staticdir)
<|end_body_0|>
<|body_start_1|>
cfm = MockCheckFileManager()
root = mobmonitor.MobMonitorRoot(cfm, staticdir=self.staticdir)
... | Unittests for the MobMonitorRoot. | MobMonitorRootTest | [
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0",
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MobMonitorRootTest:
"""Unittests for the MobMonitorRoot."""
def setUp(self):
"""Setup directories expected by the Mob* Monitor."""
<|body_0|>
def testGetServiceList(self):
"""Test the GetServiceList RPC."""
<|body_1|>
def testGetStatus(self):
... | stack_v2_sparse_classes_36k_train_025195 | 4,242 | permissive | [
{
"docstring": "Setup directories expected by the Mob* Monitor.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test the GetServiceList RPC.",
"name": "testGetServiceList",
"signature": "def testGetServiceList(self)"
},
{
"docstring": "Test the GetStatus RPC.... | 5 | stack_v2_sparse_classes_30k_train_009973 | Implement the Python class `MobMonitorRootTest` described below.
Class description:
Unittests for the MobMonitorRoot.
Method signatures and docstrings:
- def setUp(self): Setup directories expected by the Mob* Monitor.
- def testGetServiceList(self): Test the GetServiceList RPC.
- def testGetStatus(self): Test the Ge... | Implement the Python class `MobMonitorRootTest` described below.
Class description:
Unittests for the MobMonitorRoot.
Method signatures and docstrings:
- def setUp(self): Setup directories expected by the Mob* Monitor.
- def testGetServiceList(self): Test the GetServiceList RPC.
- def testGetStatus(self): Test the Ge... | 72a05af97787001756bae2511b7985e61498c965 | <|skeleton|>
class MobMonitorRootTest:
"""Unittests for the MobMonitorRoot."""
def setUp(self):
"""Setup directories expected by the Mob* Monitor."""
<|body_0|>
def testGetServiceList(self):
"""Test the GetServiceList RPC."""
<|body_1|>
def testGetStatus(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MobMonitorRootTest:
"""Unittests for the MobMonitorRoot."""
def setUp(self):
"""Setup directories expected by the Mob* Monitor."""
self.mobmondir = self.tempdir
self.staticdir = os.path.join(self.mobmondir, self.STATICDIR)
osutils.SafeMakedirs(self.staticdir)
def test... | the_stack_v2_python_sparse | third_party/chromite/mobmonitor/scripts/mobmonitor_unittest.py | metux/chromium-suckless | train | 5 |
c9fe72e7ad5f51722deccaa06d2e4a3e7e3a0c16 | [
"self.commit_id = commit_id\nself.repository = repository\nself.generated_by = generated_by\nself.project_id = project_id",
"metadata_properties_request = dict()\nif self.commit_id:\n metadata_properties_request['CommitId'] = self.commit_id\nif self.repository:\n metadata_properties_request['Repository'] = ... | <|body_start_0|>
self.commit_id = commit_id
self.repository = repository
self.generated_by = generated_by
self.project_id = project_id
<|end_body_0|>
<|body_start_1|>
metadata_properties_request = dict()
if self.commit_id:
metadata_properties_request['CommitI... | Accepts metadata properties parameters for conversion to request dict. | MetadataProperties | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MetadataProperties:
"""Accepts metadata properties parameters for conversion to request dict."""
def __init__(self, commit_id: Optional[Union[str, PipelineVariable]]=None, repository: Optional[Union[str, PipelineVariable]]=None, generated_by: Optional[Union[str, PipelineVariable]]=None, proj... | stack_v2_sparse_classes_36k_train_025196 | 2,293 | permissive | [
{
"docstring": "Initialize a ``MetadataProperties`` instance and turn parameters into dict. # TODO: flesh out docstrings Args: commit_id (str or PipelineVariable): repository (str or PipelineVariable): generated_by (str or PipelineVariable): project_id (str or PipelineVariable):",
"name": "__init__",
"s... | 2 | stack_v2_sparse_classes_30k_train_010953 | Implement the Python class `MetadataProperties` described below.
Class description:
Accepts metadata properties parameters for conversion to request dict.
Method signatures and docstrings:
- def __init__(self, commit_id: Optional[Union[str, PipelineVariable]]=None, repository: Optional[Union[str, PipelineVariable]]=N... | Implement the Python class `MetadataProperties` described below.
Class description:
Accepts metadata properties parameters for conversion to request dict.
Method signatures and docstrings:
- def __init__(self, commit_id: Optional[Union[str, PipelineVariable]]=None, repository: Optional[Union[str, PipelineVariable]]=N... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class MetadataProperties:
"""Accepts metadata properties parameters for conversion to request dict."""
def __init__(self, commit_id: Optional[Union[str, PipelineVariable]]=None, repository: Optional[Union[str, PipelineVariable]]=None, generated_by: Optional[Union[str, PipelineVariable]]=None, proj... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MetadataProperties:
"""Accepts metadata properties parameters for conversion to request dict."""
def __init__(self, commit_id: Optional[Union[str, PipelineVariable]]=None, repository: Optional[Union[str, PipelineVariable]]=None, generated_by: Optional[Union[str, PipelineVariable]]=None, project_id: Optio... | the_stack_v2_python_sparse | src/sagemaker/metadata_properties.py | aws/sagemaker-python-sdk | train | 2,050 |
2663073006aa9f021f66bed56b3b6f75c73c03b8 | [
"d = {}\n\ndef help(n):\n if n in d:\n return d[n]\n if n <= 2:\n d[n] = n\n return n\n result = 0\n for i in range(1, n - 1):\n result += help(i) * help(n - i - 1)\n result += 2 * help(n - 1)\n d[n] = result\n return result\nhelp(n)\nreturn d[n]",
"dp = [0] * (n +... | <|body_start_0|>
d = {}
def help(n):
if n in d:
return d[n]
if n <= 2:
d[n] = n
return n
result = 0
for i in range(1, n - 1):
result += help(i) * help(n - i - 1)
result += 2 * hel... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numTrees(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numTreesDP(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
d = {}
def help(n):
if n in d:
retur... | stack_v2_sparse_classes_36k_train_025197 | 823 | permissive | [
{
"docstring": ":type n: int :rtype: int",
"name": "numTrees",
"signature": "def numTrees(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "numTreesDP",
"signature": "def numTreesDP(self, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTrees(self, n): :type n: int :rtype: int
- def numTreesDP(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTrees(self, n): :type n: int :rtype: int
- def numTreesDP(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def numTrees(self, n):
""":type n: ... | eb7f2fb142b8a30d987c5ac8002a96ead0aa56f4 | <|skeleton|>
class Solution:
def numTrees(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numTreesDP(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def numTrees(self, n):
""":type n: int :rtype: int"""
d = {}
def help(n):
if n in d:
return d[n]
if n <= 2:
d[n] = n
return n
result = 0
for i in range(1, n - 1):
... | the_stack_v2_python_sparse | python/96.UniqueBinarySearchTrees.py | Wanger-SJTU/leetcode-solutions | train | 1 | |
4871a08c11a277745bb2baafa5412da60abd12a6 | [
"self.plist = []\nself.limit = syze\nself.sieve = syze * [True]\nfor i in range(4, syze, 2):\n self.sieve[i] = False\nfinished = False\nloop = iter(range(3, syze, 2))\nwhile not finished:\n nprime = next(loop)\n if self.sieve[nprime] is False:\n continue\n spt = nprime * nprime\n if spt > syze... | <|body_start_0|>
self.plist = []
self.limit = syze
self.sieve = syze * [True]
for i in range(4, syze, 2):
self.sieve[i] = False
finished = False
loop = iter(range(3, syze, 2))
while not finished:
nprime = next(loop)
if self.siev... | Prime number generator | primez | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class primez:
"""Prime number generator"""
def __init__(self, syze):
"""Initialize a sieve to handle a prime number table of the size specified."""
<|body_0|>
def getList(self):
"""Return a list of prime numbers."""
<|body_1|>
def amIprime(self, number):
... | stack_v2_sparse_classes_36k_train_025198 | 16,005 | no_license | [
{
"docstring": "Initialize a sieve to handle a prime number table of the size specified.",
"name": "__init__",
"signature": "def __init__(self, syze)"
},
{
"docstring": "Return a list of prime numbers.",
"name": "getList",
"signature": "def getList(self)"
},
{
"docstring": "Retur... | 3 | null | Implement the Python class `primez` described below.
Class description:
Prime number generator
Method signatures and docstrings:
- def __init__(self, syze): Initialize a sieve to handle a prime number table of the size specified.
- def getList(self): Return a list of prime numbers.
- def amIprime(self, number): Retur... | Implement the Python class `primez` described below.
Class description:
Prime number generator
Method signatures and docstrings:
- def __init__(self, syze): Initialize a sieve to handle a prime number table of the size specified.
- def getList(self): Return a list of prime numbers.
- def amIprime(self, number): Retur... | 2ebb0a525d2d1c0ee28e83fdc2638c2bec97ac99 | <|skeleton|>
class primez:
"""Prime number generator"""
def __init__(self, syze):
"""Initialize a sieve to handle a prime number table of the size specified."""
<|body_0|>
def getList(self):
"""Return a list of prime numbers."""
<|body_1|>
def amIprime(self, number):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class primez:
"""Prime number generator"""
def __init__(self, syze):
"""Initialize a sieve to handle a prime number table of the size specified."""
self.plist = []
self.limit = syze
self.sieve = syze * [True]
for i in range(4, syze, 2):
self.sieve[i] = False
... | the_stack_v2_python_sparse | src/pemjh/challenge211/main.py | mattjhussey/pemjh | train | 0 |
1bb0e22f8d35f7fd2dd38700928af1c91c3f7001 | [
"patient_request = json.loads(request.body.decode('utf-8'))\nPatientView.validate_patient_request(patient_request)\nnew_patient_info = PatientService.add_patient(patient_request)\nreturn JsonResponse(new_patient_info)",
"pagination_args = PaginationViewUtils.get_pagination_args(request)\ncurrent_path = request.bu... | <|body_start_0|>
patient_request = json.loads(request.body.decode('utf-8'))
PatientView.validate_patient_request(patient_request)
new_patient_info = PatientService.add_patient(patient_request)
return JsonResponse(new_patient_info)
<|end_body_0|>
<|body_start_1|>
pagination_args ... | All endpoints related to patient actions | PatientView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PatientView:
"""All endpoints related to patient actions"""
def post(request):
"""Action when calling the endpoint with POST :param request: request for patient adding :return: json response with new patient info"""
<|body_0|>
def get(request):
"""Action when cal... | stack_v2_sparse_classes_36k_train_025199 | 7,260 | no_license | [
{
"docstring": "Action when calling the endpoint with POST :param request: request for patient adding :return: json response with new patient info",
"name": "post",
"signature": "def post(request)"
},
{
"docstring": "Action when calling the endpoint with GET Return list of patients :param reques... | 3 | stack_v2_sparse_classes_30k_train_008266 | Implement the Python class `PatientView` described below.
Class description:
All endpoints related to patient actions
Method signatures and docstrings:
- def post(request): Action when calling the endpoint with POST :param request: request for patient adding :return: json response with new patient info
- def get(requ... | Implement the Python class `PatientView` described below.
Class description:
All endpoints related to patient actions
Method signatures and docstrings:
- def post(request): Action when calling the endpoint with POST :param request: request for patient adding :return: json response with new patient info
- def get(requ... | 941e8b2870f8724db3d5103dda5157fd597cfcc7 | <|skeleton|>
class PatientView:
"""All endpoints related to patient actions"""
def post(request):
"""Action when calling the endpoint with POST :param request: request for patient adding :return: json response with new patient info"""
<|body_0|>
def get(request):
"""Action when cal... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PatientView:
"""All endpoints related to patient actions"""
def post(request):
"""Action when calling the endpoint with POST :param request: request for patient adding :return: json response with new patient info"""
patient_request = json.loads(request.body.decode('utf-8'))
Patien... | the_stack_v2_python_sparse | backend/martin_helder/views/patient_view.py | JoaoAlvaroFerreira/FEUP-LGP | train | 1 |
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