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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5b1ad6646a7d573ef1a49ef139c5882ad6079bbc | [
"super().__init__()\nsiiFiles = directory.glob('**/*.sii' if recursive is True else '*.sii')\nsiiFiles = sorted(siiFiles, key=lambda file: file.stat().st_size, reverse=True)\nwith Pool() as pool:\n subDefinitions = pool.map(DefinitionFile, siiFiles)\nfor subDefinition in subDefinitions:\n self.merge(subDefini... | <|body_start_0|>
super().__init__()
siiFiles = directory.glob('**/*.sii' if recursive is True else '*.sii')
siiFiles = sorted(siiFiles, key=lambda file: file.stat().st_size, reverse=True)
with Pool() as pool:
subDefinitions = pool.map(DefinitionFile, siiFiles)
for sub... | SCS definition data (*.sii) represented as a cross-referenced graph of dictionaries, lists and items | Definition | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Definition:
"""SCS definition data (*.sii) represented as a cross-referenced graph of dictionaries, lists and items"""
def __init__(self, directory: Path, recursive=False):
"""Read a SCS definition files (*.sii) from a directory and merge them into a single in-memory graph"""
... | stack_v2_sparse_classes_36k_train_034500 | 14,891 | permissive | [
{
"docstring": "Read a SCS definition files (*.sii) from a directory and merge them into a single in-memory graph",
"name": "__init__",
"signature": "def __init__(self, directory: Path, recursive=False)"
},
{
"docstring": "Recursively merge with another definition file and check for duplicate va... | 4 | stack_v2_sparse_classes_30k_train_006584 | Implement the Python class `Definition` described below.
Class description:
SCS definition data (*.sii) represented as a cross-referenced graph of dictionaries, lists and items
Method signatures and docstrings:
- def __init__(self, directory: Path, recursive=False): Read a SCS definition files (*.sii) from a director... | Implement the Python class `Definition` described below.
Class description:
SCS definition data (*.sii) represented as a cross-referenced graph of dictionaries, lists and items
Method signatures and docstrings:
- def __init__(self, directory: Path, recursive=False): Read a SCS definition files (*.sii) from a director... | 735f6a07eab7ccfd8632ea6ec93854b26e1902d0 | <|skeleton|>
class Definition:
"""SCS definition data (*.sii) represented as a cross-referenced graph of dictionaries, lists and items"""
def __init__(self, directory: Path, recursive=False):
"""Read a SCS definition files (*.sii) from a directory and merge them into a single in-memory graph"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Definition:
"""SCS definition data (*.sii) represented as a cross-referenced graph of dictionaries, lists and items"""
def __init__(self, directory: Path, recursive=False):
"""Read a SCS definition files (*.sii) from a directory and merge them into a single in-memory graph"""
super().__in... | the_stack_v2_python_sparse | autodrome/policeman/definition.py | OSSDC/autodrome | train | 2 |
95c52ef6fc41df3102fad5d0ffc46d2fdb8b506a | [
"super()._swap(i, j)\nself._data[i]._index = i\nself._data[j]._index = j",
"if j > 0 and self._data[j] < self._data[self._parent(j)]:\n self._upheap(j)\nelse:\n self._downheap(j)",
"token = self.Locator(key, value, len(self._data))\nself._data.append(token)\nself._upheap(len(self._data) - 1)\nreturn token... | <|body_start_0|>
super()._swap(i, j)
self._data[i]._index = i
self._data[j]._index = j
<|end_body_0|>
<|body_start_1|>
if j > 0 and self._data[j] < self._data[self._parent(j)]:
self._upheap(j)
else:
self._downheap(j)
<|end_body_1|>
<|body_start_2|>
... | a locator-based priority queue implemented with a binary heap | AdaptableHeapPriorityQueue | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdaptableHeapPriorityQueue:
"""a locator-based priority queue implemented with a binary heap"""
def _swap(self, i, j):
"""override swap to record new indices :param i: :param j: :return:"""
<|body_0|>
def _bubble(self, j):
"""manages the reinstatement of the heap... | stack_v2_sparse_classes_36k_train_034501 | 2,951 | no_license | [
{
"docstring": "override swap to record new indices :param i: :param j: :return:",
"name": "_swap",
"signature": "def _swap(self, i, j)"
},
{
"docstring": "manages the reinstatement of the heap-order property when a key has changed at an arbitrary position within the heap, either due to a key up... | 5 | stack_v2_sparse_classes_30k_train_005681 | Implement the Python class `AdaptableHeapPriorityQueue` described below.
Class description:
a locator-based priority queue implemented with a binary heap
Method signatures and docstrings:
- def _swap(self, i, j): override swap to record new indices :param i: :param j: :return:
- def _bubble(self, j): manages the rein... | Implement the Python class `AdaptableHeapPriorityQueue` described below.
Class description:
a locator-based priority queue implemented with a binary heap
Method signatures and docstrings:
- def _swap(self, i, j): override swap to record new indices :param i: :param j: :return:
- def _bubble(self, j): manages the rein... | f79b08021cebbfe0ff32abcc8e9dd56af32e4aad | <|skeleton|>
class AdaptableHeapPriorityQueue:
"""a locator-based priority queue implemented with a binary heap"""
def _swap(self, i, j):
"""override swap to record new indices :param i: :param j: :return:"""
<|body_0|>
def _bubble(self, j):
"""manages the reinstatement of the heap... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdaptableHeapPriorityQueue:
"""a locator-based priority queue implemented with a binary heap"""
def _swap(self, i, j):
"""override swap to record new indices :param i: :param j: :return:"""
super()._swap(i, j)
self._data[i]._index = i
self._data[j]._index = j
def _bub... | the_stack_v2_python_sparse | exercises/ch09_priority_queues/AdaptableHeapPriorityQueue.py | rarezhang/data_structures_and_algorithms_in_python | train | 0 |
cc7974301495a200f82301736e6ec4d6d05b4ca2 | [
"if current + towers[current] >= len(towers):\n return current + towers[current]\nsub = towers[current + 1:current + towers[current] + 1]\noptions = []\nfor i, v in enumerate(sub):\n if v == 0:\n options.append(v)\n else:\n options.append(v + i)\nif options:\n sub_next = options.index(max(... | <|body_start_0|>
if current + towers[current] >= len(towers):
return current + towers[current]
sub = towers[current + 1:current + towers[current] + 1]
options = []
for i, v in enumerate(sub):
if v == 0:
options.append(v)
else:
... | NativeSolution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NativeSolution:
def next_step(current, towers):
"""The following algorithm will try to identify the next best step :param current: :param towers: :return:"""
<|body_0|>
def is_hopable(towers):
"""Check if a given seq can be hap till user in position 0 will be able to... | stack_v2_sparse_classes_36k_train_034502 | 4,795 | permissive | [
{
"docstring": "The following algorithm will try to identify the next best step :param current: :param towers: :return:",
"name": "next_step",
"signature": "def next_step(current, towers)"
},
{
"docstring": "Check if a given seq can be hap till user in position 0 will be able to jump outside the... | 2 | stack_v2_sparse_classes_30k_train_014237 | Implement the Python class `NativeSolution` described below.
Class description:
Implement the NativeSolution class.
Method signatures and docstrings:
- def next_step(current, towers): The following algorithm will try to identify the next best step :param current: :param towers: :return:
- def is_hopable(towers): Chec... | Implement the Python class `NativeSolution` described below.
Class description:
Implement the NativeSolution class.
Method signatures and docstrings:
- def next_step(current, towers): The following algorithm will try to identify the next best step :param current: :param towers: :return:
- def is_hopable(towers): Chec... | fd30805aa94332a6c14c9d8631c7044673fb3e2c | <|skeleton|>
class NativeSolution:
def next_step(current, towers):
"""The following algorithm will try to identify the next best step :param current: :param towers: :return:"""
<|body_0|>
def is_hopable(towers):
"""Check if a given seq can be hap till user in position 0 will be able to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NativeSolution:
def next_step(current, towers):
"""The following algorithm will try to identify the next best step :param current: :param towers: :return:"""
if current + towers[current] >= len(towers):
return current + towers[current]
sub = towers[current + 1:current + tow... | the_stack_v2_python_sparse | algo/problems/tower_hopper_problem.py | avi3tal/knowledgebase | train | 0 | |
9838acaf9191e34cd5814edc3f9e10548000e3e1 | [
"import operator\nlookups = []\nfor frequency, name in FREQUENCY_CHOICES:\n time = datetime.now() - timedelta(seconds=frequency)\n lookups.append(models.Q(frequency=frequency, last_sent_time__lte=time))\nreturn self.filter(reduce(operator.or_, lookups))",
"feed_cache = {}\nfor subscription in self.due():\n ... | <|body_start_0|>
import operator
lookups = []
for frequency, name in FREQUENCY_CHOICES:
time = datetime.now() - timedelta(seconds=frequency)
lookups.append(models.Q(frequency=frequency, last_sent_time__lte=time))
return self.filter(reduce(operator.or_, lookups))
<... | SubscriptionManager | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubscriptionManager:
def due(self):
"""Returns a queryset of all ``Subcription`` objects that are due to be sent."""
<|body_0|>
def send_due(self, fail_silently=True):
"""Sends an alerts to each user who has a due ``Subscription`` if there are new items for their sub... | stack_v2_sparse_classes_36k_train_034503 | 6,918 | permissive | [
{
"docstring": "Returns a queryset of all ``Subcription`` objects that are due to be sent.",
"name": "due",
"signature": "def due(self)"
},
{
"docstring": "Sends an alerts to each user who has a due ``Subscription`` if there are new items for their subscription. *NOTE*: This can potentially take... | 2 | null | Implement the Python class `SubscriptionManager` described below.
Class description:
Implement the SubscriptionManager class.
Method signatures and docstrings:
- def due(self): Returns a queryset of all ``Subcription`` objects that are due to be sent.
- def send_due(self, fail_silently=True): Sends an alerts to each ... | Implement the Python class `SubscriptionManager` described below.
Class description:
Implement the SubscriptionManager class.
Method signatures and docstrings:
- def due(self): Returns a queryset of all ``Subcription`` objects that are due to be sent.
- def send_due(self, fail_silently=True): Sends an alerts to each ... | d07094f9d6b2528d282ed99af0063002480bc00b | <|skeleton|>
class SubscriptionManager:
def due(self):
"""Returns a queryset of all ``Subcription`` objects that are due to be sent."""
<|body_0|>
def send_due(self, fail_silently=True):
"""Sends an alerts to each user who has a due ``Subscription`` if there are new items for their sub... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubscriptionManager:
def due(self):
"""Returns a queryset of all ``Subcription`` objects that are due to be sent."""
import operator
lookups = []
for frequency, name in FREQUENCY_CHOICES:
time = datetime.now() - timedelta(seconds=frequency)
lookups.appen... | the_stack_v2_python_sparse | populous/alerts/models.py | caiges/populous | train | 2 | |
a1e36dad1270247857b22cc6d3fd457c26d66e85 | [
"if not root:\n return ''\nresult = [str(root.val)]\nqueue = deque([(root, result)])\nwhile queue:\n num_nodes = len(queue)\n for _ in range(len(queue)):\n currNode, currPath = queue.popleft()\n if currNode.left:\n currPath.append(str(currNode.left.val))\n queue.append((... | <|body_start_0|>
if not root:
return ''
result = [str(root.val)]
queue = deque([(root, result)])
while queue:
num_nodes = len(queue)
for _ in range(len(queue)):
currNode, currPath = queue.popleft()
if currNode.left:
... | 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_034504 | 2,046 | 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:... | cbc38001423cb4facea69bad0c5ba1b49feb4e57 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return ''
result = [str(root.val)]
queue = deque([(root, result)])
while queue:
num_nodes = len(queue)
for _ in r... | the_stack_v2_python_sparse | 297.SerializeAndDeserializeBinaryTree/serialize_deserialize.py | mayanbhadage/LeetCode | train | 0 | |
4c886ac1ce9e8d9461b3dfcf7a83fad8e8310861 | [
"users_l = []\nfor _p in positions_l:\n related_l = self.filter(position=_p)\n for _r in related_l:\n users_l.append(_r.user)\nreturn users_l",
"positions_l = []\nfor _u in users_l:\n related_l = self.filter(user=_u)\n for _r in related_l:\n positions_l.append(_r.position)\nreturn positi... | <|body_start_0|>
users_l = []
for _p in positions_l:
related_l = self.filter(position=_p)
for _r in related_l:
users_l.append(_r.user)
return users_l
<|end_body_0|>
<|body_start_1|>
positions_l = []
for _u in users_l:
related_l... | Custom manager for Comment extending over django's default manager. | CommentManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentManager:
"""Custom manager for Comment extending over django's default manager."""
def query_on_position(self, positions_l):
"""Queries for users related to positions in positions_l."""
<|body_0|>
def query_on_user(self, users_l):
"""Queries for positions ... | stack_v2_sparse_classes_36k_train_034505 | 8,407 | no_license | [
{
"docstring": "Queries for users related to positions in positions_l.",
"name": "query_on_position",
"signature": "def query_on_position(self, positions_l)"
},
{
"docstring": "Queries for positions related to users in users_l.",
"name": "query_on_user",
"signature": "def query_on_user(s... | 2 | stack_v2_sparse_classes_30k_train_009920 | Implement the Python class `CommentManager` described below.
Class description:
Custom manager for Comment extending over django's default manager.
Method signatures and docstrings:
- def query_on_position(self, positions_l): Queries for users related to positions in positions_l.
- def query_on_user(self, users_l): Q... | Implement the Python class `CommentManager` described below.
Class description:
Custom manager for Comment extending over django's default manager.
Method signatures and docstrings:
- def query_on_position(self, positions_l): Queries for users related to positions in positions_l.
- def query_on_user(self, users_l): Q... | a062df4c6cb3118996d38f1a29c3258f2a960d8e | <|skeleton|>
class CommentManager:
"""Custom manager for Comment extending over django's default manager."""
def query_on_position(self, positions_l):
"""Queries for users related to positions in positions_l."""
<|body_0|>
def query_on_user(self, users_l):
"""Queries for positions ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentManager:
"""Custom manager for Comment extending over django's default manager."""
def query_on_position(self, positions_l):
"""Queries for users related to positions in positions_l."""
users_l = []
for _p in positions_l:
related_l = self.filter(position=_p)
... | the_stack_v2_python_sparse | coup/positions/models.py | dsawali/Co-Up | train | 0 |
661f125bb46859dfd6afb06dec9ac6d2c6968f81 | [
"self.ser = serial.Serial(port, baud, timeout=timeout)\ntime.sleep(5)\nself.filename = filename\nwith open(filename, 'r') as f:\n self.lines = [line.rstrip('\\n') for line in f]\n for item in self.lines:\n if item in 'ABCDEFGHIJKLMNOPQRSTUVXYZ':\n self.lines.remove(item)\nself.step = 0\nself... | <|body_start_0|>
self.ser = serial.Serial(port, baud, timeout=timeout)
time.sleep(5)
self.filename = filename
with open(filename, 'r') as f:
self.lines = [line.rstrip('\n') for line in f]
for item in self.lines:
if item in 'ABCDEFGHIJKLMNOPQRSTUVXY... | . | Writer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Writer:
"""."""
def __init__(self, filename, port, timeout, baud=9600):
"""Constructor."""
<|body_0|>
def refresh(self):
"""Read in new data from the file and split it."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.ser = serial.Serial(por... | stack_v2_sparse_classes_36k_train_034506 | 1,258 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, filename, port, timeout, baud=9600)"
},
{
"docstring": "Read in new data from the file and split it.",
"name": "refresh",
"signature": "def refresh(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013962 | Implement the Python class `Writer` described below.
Class description:
.
Method signatures and docstrings:
- def __init__(self, filename, port, timeout, baud=9600): Constructor.
- def refresh(self): Read in new data from the file and split it. | Implement the Python class `Writer` described below.
Class description:
.
Method signatures and docstrings:
- def __init__(self, filename, port, timeout, baud=9600): Constructor.
- def refresh(self): Read in new data from the file and split it.
<|skeleton|>
class Writer:
"""."""
def __init__(self, filename,... | 480d96309746be125c28f3795d78c84931181c00 | <|skeleton|>
class Writer:
"""."""
def __init__(self, filename, port, timeout, baud=9600):
"""Constructor."""
<|body_0|>
def refresh(self):
"""Read in new data from the file and split it."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Writer:
"""."""
def __init__(self, filename, port, timeout, baud=9600):
"""Constructor."""
self.ser = serial.Serial(port, baud, timeout=timeout)
time.sleep(5)
self.filename = filename
with open(filename, 'r') as f:
self.lines = [line.rstrip('\n') for li... | the_stack_v2_python_sparse | New TRTL Prototype/boardless/serial_writer.py | mkpjnx/MEO_Sat_Tracking | train | 0 |
7524c704a01da2dafe7261aadd7ae98eba7a1c3a | [
"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. | PolicyAppServiceServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PolicyAppServiceServicer:
"""Missing associated documentation comment in .proto file."""
def users_include_access_roles(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def users_include_roles(self, request, context):
... | stack_v2_sparse_classes_36k_train_034507 | 11,397 | no_license | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "users_include_access_roles",
"signature": "def users_include_access_roles(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "users_include_roles",
... | 5 | stack_v2_sparse_classes_30k_test_000121 | Implement the Python class `PolicyAppServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def users_include_access_roles(self, request, context): Missing associated documentation comment in .proto file.
- def users_include_ro... | Implement the Python class `PolicyAppServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def users_include_access_roles(self, request, context): Missing associated documentation comment in .proto file.
- def users_include_ro... | 55d36c068e26e13ee5bae5c033e2e17784c63feb | <|skeleton|>
class PolicyAppServiceServicer:
"""Missing associated documentation comment in .proto file."""
def users_include_access_roles(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def users_include_roles(self, request, context):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PolicyAppServiceServicer:
"""Missing associated documentation comment in .proto file."""
def users_include_access_roles(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Meth... | the_stack_v2_python_sparse | src/resource/proto/_generated/identity/policy_app_service_pb2_grpc.py | arkanmgerges/cafm.identity | train | 0 |
e29ab9eb2e299c470e8d3d6bb3510d61c9bec0ea | [
"self.Whf = np.random.normal(size=(h + i, h))\nself.Whb = np.random.normal(size=(h + i, h))\nself.Wy = np.random.normal(size=(2 * h, o))\nself.bhb = np.zeros((1, h))\nself.bhf = np.zeros((1, h))\nself.by = np.zeros((1, o))",
"con = np.concatenate((h_prev, x_t), axis=1)\nh_next = np.tanh(np.matmul(con, self.Whf) +... | <|body_start_0|>
self.Whf = np.random.normal(size=(h + i, h))
self.Whb = np.random.normal(size=(h + i, h))
self.Wy = np.random.normal(size=(2 * h, o))
self.bhb = np.zeros((1, h))
self.bhf = np.zeros((1, h))
self.by = np.zeros((1, o))
<|end_body_0|>
<|body_start_1|>
... | represents a bidirectional cell of a RNN | BidirectionalCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BidirectionalCell:
"""represents a bidirectional cell of a RNN"""
def __init__(self, i, h, o):
"""Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Whf, Whb, Wy, bhf, bhb, by weights and bias... | stack_v2_sparse_classes_36k_train_034508 | 1,430 | no_license | [
{
"docstring": "Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Whf, Whb, Wy, bhf, bhb, by weights and biases @Whf and bhf are for the hidden states in the forward direction @Whb and bhb are for the hidden states in t... | 2 | null | Implement the Python class `BidirectionalCell` described below.
Class description:
represents a bidirectional cell of a RNN
Method signatures and docstrings:
- def __init__(self, i, h, o): Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instan... | Implement the Python class `BidirectionalCell` described below.
Class description:
represents a bidirectional cell of a RNN
Method signatures and docstrings:
- def __init__(self, i, h, o): Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instan... | e20b284d5f1841952104d7d9a0274cff80eb304d | <|skeleton|>
class BidirectionalCell:
"""represents a bidirectional cell of a RNN"""
def __init__(self, i, h, o):
"""Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Whf, Whb, Wy, bhf, bhb, by weights and bias... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BidirectionalCell:
"""represents a bidirectional cell of a RNN"""
def __init__(self, i, h, o):
"""Constructor @i: dimensionality of the data @h: dimensionality of the hidden state @o: dimensionality of the outputs public instance attributes Whf, Whb, Wy, bhf, bhb, by weights and biases @Whf and b... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/5-bi_forward.py | jgadelugo/holbertonschool-machine_learning | train | 1 |
652a1e2e8b6505c8509e78e302d308bdd1f23802 | [
"max_texture_size = pyglet.image.get_max_texture_size()\nwidth = min(width, max_texture_size)\nheight = min(height, max_texture_size)\nself.texture = pyglet.image.Texture.create(width, height)\nself.allocator = Allocator(width, height)",
"x, y = self.allocator.alloc(img.width + border * 2, img.height + border * 2... | <|body_start_0|>
max_texture_size = pyglet.image.get_max_texture_size()
width = min(width, max_texture_size)
height = min(height, max_texture_size)
self.texture = pyglet.image.Texture.create(width, height)
self.allocator = Allocator(width, height)
<|end_body_0|>
<|body_start_1|>... | Collection of images within a texture. | TextureAtlas | [
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextureAtlas:
"""Collection of images within a texture."""
def __init__(self, width: int=2048, height: int=2048) -> None:
"""Create a texture atlas of the given size. :Parameters: `width` : int Width of the underlying texture. `height` : int Height of the underlying texture."""
... | stack_v2_sparse_classes_36k_train_034509 | 10,284 | permissive | [
{
"docstring": "Create a texture atlas of the given size. :Parameters: `width` : int Width of the underlying texture. `height` : int Height of the underlying texture.",
"name": "__init__",
"signature": "def __init__(self, width: int=2048, height: int=2048) -> None"
},
{
"docstring": "Add an imag... | 2 | stack_v2_sparse_classes_30k_train_010755 | Implement the Python class `TextureAtlas` described below.
Class description:
Collection of images within a texture.
Method signatures and docstrings:
- def __init__(self, width: int=2048, height: int=2048) -> None: Create a texture atlas of the given size. :Parameters: `width` : int Width of the underlying texture. ... | Implement the Python class `TextureAtlas` described below.
Class description:
Collection of images within a texture.
Method signatures and docstrings:
- def __init__(self, width: int=2048, height: int=2048) -> None: Create a texture atlas of the given size. :Parameters: `width` : int Width of the underlying texture. ... | 094c638f0529fecab4e74556487b92453a78753c | <|skeleton|>
class TextureAtlas:
"""Collection of images within a texture."""
def __init__(self, width: int=2048, height: int=2048) -> None:
"""Create a texture atlas of the given size. :Parameters: `width` : int Width of the underlying texture. `height` : int Height of the underlying texture."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextureAtlas:
"""Collection of images within a texture."""
def __init__(self, width: int=2048, height: int=2048) -> None:
"""Create a texture atlas of the given size. :Parameters: `width` : int Width of the underlying texture. `height` : int Height of the underlying texture."""
max_textur... | the_stack_v2_python_sparse | pyglet/image/atlas.py | pyglet/pyglet | train | 1,687 |
2c5b120378b854c59e6aa7f15a525296597de0e4 | [
"content = '\\n\\n {% load static %}\\n Hi {{ taster_first_name }},\\n\\n {% if placed_order %}\\n Thank you so much for attending my Vinely Taste Party and ordering some wine! I hope you had a great time and you and your personality are getting along great. Don\\'t forget to rate your w... | <|body_start_0|>
content = '\n\n {% load static %}\n Hi {{ taster_first_name }},\n\n {% if placed_order %}\n Thank you so much for attending my Vinely Taste Party and ordering some wine! I hope you had a great time and you and your personality are getting along great. Don\'t forget t... | Migration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Migration:
def forwards(self, orm):
"""Write your forwards methods here."""
<|body_0|>
def backwards(self, orm):
"""Write your backwards methods here."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
content = '\n\n {% load static %}\n ... | stack_v2_sparse_classes_36k_train_034510 | 4,891 | no_license | [
{
"docstring": "Write your forwards methods here.",
"name": "forwards",
"signature": "def forwards(self, orm)"
},
{
"docstring": "Write your backwards methods here.",
"name": "backwards",
"signature": "def backwards(self, orm)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000555 | Implement the Python class `Migration` described below.
Class description:
Implement the Migration class.
Method signatures and docstrings:
- def forwards(self, orm): Write your forwards methods here.
- def backwards(self, orm): Write your backwards methods here. | Implement the Python class `Migration` described below.
Class description:
Implement the Migration class.
Method signatures and docstrings:
- def forwards(self, orm): Write your forwards methods here.
- def backwards(self, orm): Write your backwards methods here.
<|skeleton|>
class Migration:
def forwards(self,... | c5c7d8a0b1a297e07302870017d3fb03c5dbb009 | <|skeleton|>
class Migration:
def forwards(self, orm):
"""Write your forwards methods here."""
<|body_0|>
def backwards(self, orm):
"""Write your backwards methods here."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Migration:
def forwards(self, orm):
"""Write your forwards methods here."""
content = '\n\n {% load static %}\n Hi {{ taster_first_name }},\n\n {% if placed_order %}\n Thank you so much for attending my Vinely Taste Party and ordering some wine! I hope you had a gre... | the_stack_v2_python_sparse | cms/migrations/0008_add_thanks_message_section.py | RSV3/nuvine | train | 0 | |
9877e8e2fa65092ca98d590584bc5f0f7b15644d | [
"if sandbox_id in sandboxes:\n return (sandboxes[sandbox_id].to_dict(), 200)\nelse:\n return ('', 200)",
"if sandbox_id not in sandboxes:\n return (None, 404)\nelse:\n sandbox = sandboxes[sandbox_id]\n sandbox.stop()\n return ({}, 204)"
] | <|body_start_0|>
if sandbox_id in sandboxes:
return (sandboxes[sandbox_id].to_dict(), 200)
else:
return ('', 200)
<|end_body_0|>
<|body_start_1|>
if sandbox_id not in sandboxes:
return (None, 404)
else:
sandbox = sandboxes[sandbox_id]
... | The sandbox REST resource. | Sandbox | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sandbox:
"""The sandbox REST resource."""
def get(self, sandbox_id):
"""Get the current instance of the sandbox."""
<|body_0|>
def delete(self, sandbox_id):
"""Delete the current sandbox instance."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_034511 | 11,386 | permissive | [
{
"docstring": "Get the current instance of the sandbox.",
"name": "get",
"signature": "def get(self, sandbox_id)"
},
{
"docstring": "Delete the current sandbox instance.",
"name": "delete",
"signature": "def delete(self, sandbox_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004483 | Implement the Python class `Sandbox` described below.
Class description:
The sandbox REST resource.
Method signatures and docstrings:
- def get(self, sandbox_id): Get the current instance of the sandbox.
- def delete(self, sandbox_id): Delete the current sandbox instance. | Implement the Python class `Sandbox` described below.
Class description:
The sandbox REST resource.
Method signatures and docstrings:
- def get(self, sandbox_id): Get the current instance of the sandbox.
- def delete(self, sandbox_id): Delete the current sandbox instance.
<|skeleton|>
class Sandbox:
"""The sandb... | 33c4aa24ca8daf26f2c8f2d2fa38d7f4bf750cfa | <|skeleton|>
class Sandbox:
"""The sandbox REST resource."""
def get(self, sandbox_id):
"""Get the current instance of the sandbox."""
<|body_0|>
def delete(self, sandbox_id):
"""Delete the current sandbox instance."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sandbox:
"""The sandbox REST resource."""
def get(self, sandbox_id):
"""Get the current instance of the sandbox."""
if sandbox_id in sandboxes:
return (sandboxes[sandbox_id].to_dict(), 200)
else:
return ('', 200)
def delete(self, sandbox_id):
"... | the_stack_v2_python_sparse | tac/gui/launcher/api/resources/sandboxes.py | fetchai/agents-tac | train | 30 |
0138634a824ddfbf85c9d5dda1f3bd62afb2ca90 | [
"if not isinstance(data, np.ndarray) or len(data.shape) != 2:\n raise TypeError('data must be a 2D numpy.ndarray')\nd, n = data.shape\nif n < 2:\n raise ValueError('data must contain multiple data points')\nself.mean = np.mean(data, axis=1).reshape(d, 1)\ndev = data - self.mean\nself.cov = np.matmul(dev, dev.... | <|body_start_0|>
if not isinstance(data, np.ndarray) or len(data.shape) != 2:
raise TypeError('data must be a 2D numpy.ndarray')
d, n = data.shape
if n < 2:
raise ValueError('data must contain multiple data points')
self.mean = np.mean(data, axis=1).reshape(d, 1)
... | Class for MultiNormal that represents a multivariate Normal distribution | MultiNormal | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiNormal:
"""Class for MultiNormal that represents a multivariate Normal distribution"""
def __init__(self, data):
"""constructor @data: np.ndarray shape(d, n) with data set @n: number of data points @d: number of dimensions in each data point"""
<|body_0|>
def pdf(se... | stack_v2_sparse_classes_36k_train_034512 | 1,591 | no_license | [
{
"docstring": "constructor @data: np.ndarray shape(d, n) with data set @n: number of data points @d: number of dimensions in each data point",
"name": "__init__",
"signature": "def __init__(self, data)"
},
{
"docstring": "Calculates the PDF at a data point @x: is np.ndarray shape(d, 1) data poi... | 2 | stack_v2_sparse_classes_30k_train_009362 | Implement the Python class `MultiNormal` described below.
Class description:
Class for MultiNormal that represents a multivariate Normal distribution
Method signatures and docstrings:
- def __init__(self, data): constructor @data: np.ndarray shape(d, n) with data set @n: number of data points @d: number of dimensions... | Implement the Python class `MultiNormal` described below.
Class description:
Class for MultiNormal that represents a multivariate Normal distribution
Method signatures and docstrings:
- def __init__(self, data): constructor @data: np.ndarray shape(d, n) with data set @n: number of data points @d: number of dimensions... | e20b284d5f1841952104d7d9a0274cff80eb304d | <|skeleton|>
class MultiNormal:
"""Class for MultiNormal that represents a multivariate Normal distribution"""
def __init__(self, data):
"""constructor @data: np.ndarray shape(d, n) with data set @n: number of data points @d: number of dimensions in each data point"""
<|body_0|>
def pdf(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiNormal:
"""Class for MultiNormal that represents a multivariate Normal distribution"""
def __init__(self, data):
"""constructor @data: np.ndarray shape(d, n) with data set @n: number of data points @d: number of dimensions in each data point"""
if not isinstance(data, np.ndarray) or ... | the_stack_v2_python_sparse | math/0x06-multivariate_prob/multinormal.py | jgadelugo/holbertonschool-machine_learning | train | 1 |
7735d0f1280b12d0e40d1491c659c7ad4aae22b4 | [
"pattern = '^M?M?M?$'\npattern = '^M{0,3}$'\nfor t in self.case_values[0]:\n r = re.search(pattern, t)\n i = len(t)\n if i < 4:\n assert r is not None, 'ERROR: pattern %s should match %s' % (pattern, t)\n else:\n assert r is None, 'ERROR: pattern %s should NOT match %s' % (pattern, t)",
... | <|body_start_0|>
pattern = '^M?M?M?$'
pattern = '^M{0,3}$'
for t in self.case_values[0]:
r = re.search(pattern, t)
i = len(t)
if i < 4:
assert r is not None, 'ERROR: pattern %s should match %s' % (pattern, t)
else:
a... | RegexMatch | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegexMatch:
def testMatchM2N(self):
"""match 0 - 3 M only"""
<|body_0|>
def testMatchXorY(self):
"""match any of these cases: CM CD 0 - 3 C D + 0 - 3 C :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pattern = '^M?M?M?$'
pattern = '... | stack_v2_sparse_classes_36k_train_034513 | 1,205 | no_license | [
{
"docstring": "match 0 - 3 M only",
"name": "testMatchM2N",
"signature": "def testMatchM2N(self)"
},
{
"docstring": "match any of these cases: CM CD 0 - 3 C D + 0 - 3 C :return:",
"name": "testMatchXorY",
"signature": "def testMatchXorY(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008725 | Implement the Python class `RegexMatch` described below.
Class description:
Implement the RegexMatch class.
Method signatures and docstrings:
- def testMatchM2N(self): match 0 - 3 M only
- def testMatchXorY(self): match any of these cases: CM CD 0 - 3 C D + 0 - 3 C :return: | Implement the Python class `RegexMatch` described below.
Class description:
Implement the RegexMatch class.
Method signatures and docstrings:
- def testMatchM2N(self): match 0 - 3 M only
- def testMatchXorY(self): match any of these cases: CM CD 0 - 3 C D + 0 - 3 C :return:
<|skeleton|>
class RegexMatch:
def te... | eb171b45bbf2f29cd1307aefd8e4609b683773d8 | <|skeleton|>
class RegexMatch:
def testMatchM2N(self):
"""match 0 - 3 M only"""
<|body_0|>
def testMatchXorY(self):
"""match any of these cases: CM CD 0 - 3 C D + 0 - 3 C :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegexMatch:
def testMatchM2N(self):
"""match 0 - 3 M only"""
pattern = '^M?M?M?$'
pattern = '^M{0,3}$'
for t in self.case_values[0]:
r = re.search(pattern, t)
i = len(t)
if i < 4:
assert r is not None, 'ERROR: pattern %s shoul... | the_stack_v2_python_sparse | library-demos/regex/regex_m2n.py | lostsquirrel/python_test | train | 0 | |
5f9e3e72014e12bc210bdc5615fe9426b3b8ee88 | [
"def dfs(node, depth):\n if not node:\n return\n if depth == d - 1:\n left = node.left if node.left else None\n node.left = TreeNode(v)\n node.left.left = left\n right = node.right if node.right else None\n node.right = TreeNode(v)\n node.right.right = right\n ... | <|body_start_0|>
def dfs(node, depth):
if not node:
return
if depth == d - 1:
left = node.left if node.left else None
node.left = TreeNode(v)
node.left.left = left
right = node.right if node.right else None
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode:
"""06/20/2020 17:37"""
<|body_0|>
def addOneRow(self, root: Optional[TreeNode], val: int, depth: int) -> Optional[TreeNode]:
"""10/21/2022 20:39"""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_36k_train_034514 | 3,360 | no_license | [
{
"docstring": "06/20/2020 17:37",
"name": "addOneRow",
"signature": "def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode"
},
{
"docstring": "10/21/2022 20:39",
"name": "addOneRow",
"signature": "def addOneRow(self, root: Optional[TreeNode], val: int, depth: int) -> Optional[... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode: 06/20/2020 17:37
- def addOneRow(self, root: Optional[TreeNode], val: int, depth: int) -> Optional[TreeNode]: 10/... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode: 06/20/2020 17:37
- def addOneRow(self, root: Optional[TreeNode], val: int, depth: int) -> Optional[TreeNode]: 10/... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode:
"""06/20/2020 17:37"""
<|body_0|>
def addOneRow(self, root: Optional[TreeNode], val: int, depth: int) -> Optional[TreeNode]:
"""10/21/2022 20:39"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode:
"""06/20/2020 17:37"""
def dfs(node, depth):
if not node:
return
if depth == d - 1:
left = node.left if node.left else None
node.left = TreeNode(v)... | the_stack_v2_python_sparse | leetcode/solved/623_Add_One_Row_to_Tree/solution.py | sungminoh/algorithms | train | 0 | |
5b6c6a4254b98d27640723845896c5353c6cf183 | [
"store_view_obj = self.pool.get('magento.store.store_view')\nstore_view = store_view_obj.browse(cursor, user, context.get('active_id'))\nstore_view_obj.write(cursor, user, [store_view.id], {'last_order_export_time': time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}, context=context)\nsales = store_view_obj.export_orde... | <|body_start_0|>
store_view_obj = self.pool.get('magento.store.store_view')
store_view = store_view_obj.browse(cursor, user, context.get('active_id'))
store_view_obj.write(cursor, user, [store_view.id], {'last_order_export_time': time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}, context=context)
... | Export Orders | ExportOrders | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExportOrders:
"""Export Orders"""
def export_orders(self, cursor, user, ids, context):
"""Export Orders Status to magento for the current store view :param cursor: Database cursor :param user: ID of current user :param ids: List of ids of records for this model :param context: Applic... | stack_v2_sparse_classes_36k_train_034515 | 2,337 | no_license | [
{
"docstring": "Export Orders Status to magento for the current store view :param cursor: Database cursor :param user: ID of current user :param ids: List of ids of records for this model :param context: Application context :return: Sale order view",
"name": "export_orders",
"signature": "def export_ord... | 2 | stack_v2_sparse_classes_30k_train_008986 | Implement the Python class `ExportOrders` described below.
Class description:
Export Orders
Method signatures and docstrings:
- def export_orders(self, cursor, user, ids, context): Export Orders Status to magento for the current store view :param cursor: Database cursor :param user: ID of current user :param ids: Lis... | Implement the Python class `ExportOrders` described below.
Class description:
Export Orders
Method signatures and docstrings:
- def export_orders(self, cursor, user, ids, context): Export Orders Status to magento for the current store view :param cursor: Database cursor :param user: ID of current user :param ids: Lis... | f661c776973868c0414007791ae6a0b069b1038f | <|skeleton|>
class ExportOrders:
"""Export Orders"""
def export_orders(self, cursor, user, ids, context):
"""Export Orders Status to magento for the current store view :param cursor: Database cursor :param user: ID of current user :param ids: List of ids of records for this model :param context: Applic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExportOrders:
"""Export Orders"""
def export_orders(self, cursor, user, ids, context):
"""Export Orders Status to magento for the current store view :param cursor: Database cursor :param user: ID of current user :param ids: List of ids of records for this model :param context: Application context... | the_stack_v2_python_sparse | wizard/export_orders.py | openlabs/magento_integration | train | 23 |
5af610523def04185c9f244c5fa023d1230c4532 | [
"self.joint_limits = dict()\nself.state_lock = Lock()\nrospy.init_node('JointLimitsRecorder', anonymous=True)\nrospy.Subscriber('/joint_states', JointState, self.jointStateCallback)",
"with self.state_lock:\n for joint_id, joint_name in enumerate(data.name):\n if joint_name in self.joint_limits:\n ... | <|body_start_0|>
self.joint_limits = dict()
self.state_lock = Lock()
rospy.init_node('JointLimitsRecorder', anonymous=True)
rospy.Subscriber('/joint_states', JointState, self.jointStateCallback)
<|end_body_0|>
<|body_start_1|>
with self.state_lock:
for joint_id, join... | Record the mechanical joint limits while the joints are manually moved | JointLimits | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JointLimits:
"""Record the mechanical joint limits while the joints are manually moved"""
def __init__(self):
"""TODO: to be defined1."""
<|body_0|>
def jointStateCallback(self, data):
"""Receive the joint state updates and store the limits :data: JointState mess... | stack_v2_sparse_classes_36k_train_034516 | 1,540 | permissive | [
{
"docstring": "TODO: to be defined1.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Receive the joint state updates and store the limits :data: JointState message",
"name": "jointStateCallback",
"signature": "def jointStateCallback(self, data)"
},
{
"... | 3 | stack_v2_sparse_classes_30k_train_019611 | Implement the Python class `JointLimits` described below.
Class description:
Record the mechanical joint limits while the joints are manually moved
Method signatures and docstrings:
- def __init__(self): TODO: to be defined1.
- def jointStateCallback(self, data): Receive the joint state updates and store the limits :... | Implement the Python class `JointLimits` described below.
Class description:
Record the mechanical joint limits while the joints are manually moved
Method signatures and docstrings:
- def __init__(self): TODO: to be defined1.
- def jointStateCallback(self, data): Receive the joint state updates and store the limits :... | 7858fd3da9b31b4a17cc265722a4647567cda8cd | <|skeleton|>
class JointLimits:
"""Record the mechanical joint limits while the joints are manually moved"""
def __init__(self):
"""TODO: to be defined1."""
<|body_0|>
def jointStateCallback(self, data):
"""Receive the joint state updates and store the limits :data: JointState mess... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JointLimits:
"""Record the mechanical joint limits while the joints are manually moved"""
def __init__(self):
"""TODO: to be defined1."""
self.joint_limits = dict()
self.state_lock = Lock()
rospy.init_node('JointLimitsRecorder', anonymous=True)
rospy.Subscriber('/j... | the_stack_v2_python_sparse | talos/full-body-calibration/joint_limits.py | agimus/agimus-demos | train | 1 |
2d43ee9133a47b53caef7d151d3fb3622d3d8ba1 | [
"self.test_data_true = dictionary_class_helper_true()\nself.test_data_true.set_action(4)\nself.test_data_false = dictionary_class_helper_false()\nself.sock_conn = socketconnection.SocketConnection(self.test_data_true.car_id)\nself.valid_dict = self.test_data_true.get_socket_dictionary()\nself.invalid_dict = self.te... | <|body_start_0|>
self.test_data_true = dictionary_class_helper_true()
self.test_data_true.set_action(4)
self.test_data_false = dictionary_class_helper_false()
self.sock_conn = socketconnection.SocketConnection(self.test_data_true.car_id)
self.valid_dict = self.test_data_true.get_... | As in :class:`TestSocketResponseAction1`, but for action 4 (return a vehicle). | TestSocketResponseAction4 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestSocketResponseAction4:
"""As in :class:`TestSocketResponseAction1`, but for action 4 (return a vehicle)."""
def setUp(self):
"""It is necessary to instantiate the data classes and extract the relevant dictionaries. This testing suite also requires a socket connection."""
... | stack_v2_sparse_classes_36k_train_034517 | 23,291 | no_license | [
{
"docstring": "It is necessary to instantiate the data classes and extract the relevant dictionaries. This testing suite also requires a socket connection.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Tests an incorrect car_id",
"name": "test_a4_invalid_car_id",
... | 6 | null | Implement the Python class `TestSocketResponseAction4` described below.
Class description:
As in :class:`TestSocketResponseAction1`, but for action 4 (return a vehicle).
Method signatures and docstrings:
- def setUp(self): It is necessary to instantiate the data classes and extract the relevant dictionaries. This tes... | Implement the Python class `TestSocketResponseAction4` described below.
Class description:
As in :class:`TestSocketResponseAction1`, but for action 4 (return a vehicle).
Method signatures and docstrings:
- def setUp(self): It is necessary to instantiate the data classes and extract the relevant dictionaries. This tes... | 8f68cc2a6ca568e803a6bfea49a452a5b0c1a2cf | <|skeleton|>
class TestSocketResponseAction4:
"""As in :class:`TestSocketResponseAction1`, but for action 4 (return a vehicle)."""
def setUp(self):
"""It is necessary to instantiate the data classes and extract the relevant dictionaries. This testing suite also requires a socket connection."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestSocketResponseAction4:
"""As in :class:`TestSocketResponseAction1`, but for action 4 (return a vehicle)."""
def setUp(self):
"""It is necessary to instantiate the data classes and extract the relevant dictionaries. This testing suite also requires a socket connection."""
self.test_dat... | the_stack_v2_python_sparse | AgentPi/agenttesting.py | JiewenGuan/Iot-Carshare | train | 0 |
94634b0729a07fe5baedd13edede14b272dc39d5 | [
"if view.action in ['partial_update']:\n profile_name = request.user.StaffProfileID.Name\n return profile_name in ['Vente', 'Gestion', 'Support']\nreturn True",
"if view.action in ['partial_update']:\n if request.user.StaffProfileID.Name in ['Vente', 'Support']:\n return request.user in obj.get_st... | <|body_start_0|>
if view.action in ['partial_update']:
profile_name = request.user.StaffProfileID.Name
return profile_name in ['Vente', 'Gestion', 'Support']
return True
<|end_body_0|>
<|body_start_1|>
if view.action in ['partial_update']:
if request.user.Sta... | Permission checking if the staff contact or part of the management team. | IsStaffContactOrManagement | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IsStaffContactOrManagement:
"""Permission checking if the staff contact or part of the management team."""
def has_permission(self, request, view):
"""Check the permission."""
<|body_0|>
def has_object_permission(self, request, view, obj):
"""Check the object per... | stack_v2_sparse_classes_36k_train_034518 | 3,875 | no_license | [
{
"docstring": "Check the permission.",
"name": "has_permission",
"signature": "def has_permission(self, request, view)"
},
{
"docstring": "Check the object permission.",
"name": "has_object_permission",
"signature": "def has_object_permission(self, request, view, obj)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020535 | Implement the Python class `IsStaffContactOrManagement` described below.
Class description:
Permission checking if the staff contact or part of the management team.
Method signatures and docstrings:
- def has_permission(self, request, view): Check the permission.
- def has_object_permission(self, request, view, obj):... | Implement the Python class `IsStaffContactOrManagement` described below.
Class description:
Permission checking if the staff contact or part of the management team.
Method signatures and docstrings:
- def has_permission(self, request, view): Check the permission.
- def has_object_permission(self, request, view, obj):... | b76df0d62fc56e3c668827b18f0cce61124f0d53 | <|skeleton|>
class IsStaffContactOrManagement:
"""Permission checking if the staff contact or part of the management team."""
def has_permission(self, request, view):
"""Check the permission."""
<|body_0|>
def has_object_permission(self, request, view, obj):
"""Check the object per... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IsStaffContactOrManagement:
"""Permission checking if the staff contact or part of the management team."""
def has_permission(self, request, view):
"""Check the permission."""
if view.action in ['partial_update']:
profile_name = request.user.StaffProfileID.Name
ret... | the_stack_v2_python_sparse | settings/permissions.py | FortranVBA/DAP12 | train | 0 |
4a2686406b220a6c21244889000fa0b7a858aa81 | [
"tests = ['test.1', 'test.2']\nexpected = 'test.1:test.2'\nself.assertEqual(test_apps.get_gtest_filter(tests), expected)",
"tests = ['test.1', 'test.2']\nexpected = '-test.1:test.2'\nself.assertEqual(test_apps.get_gtest_filter(tests, invert=True), expected)"
] | <|body_start_0|>
tests = ['test.1', 'test.2']
expected = 'test.1:test.2'
self.assertEqual(test_apps.get_gtest_filter(tests), expected)
<|end_body_0|>
<|body_start_1|>
tests = ['test.1', 'test.2']
expected = '-test.1:test.2'
self.assertEqual(test_apps.get_gtest_filter(tes... | Tests for test_runner.get_gtest_filter. | GetGTestFilterTest | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetGTestFilterTest:
"""Tests for test_runner.get_gtest_filter."""
def test_correct(self):
"""Ensures correctness of filter."""
<|body_0|>
def test_correct_inverted(self):
"""Ensures correctness of inverted filter."""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_034519 | 1,492 | permissive | [
{
"docstring": "Ensures correctness of filter.",
"name": "test_correct",
"signature": "def test_correct(self)"
},
{
"docstring": "Ensures correctness of inverted filter.",
"name": "test_correct_inverted",
"signature": "def test_correct_inverted(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006763 | Implement the Python class `GetGTestFilterTest` described below.
Class description:
Tests for test_runner.get_gtest_filter.
Method signatures and docstrings:
- def test_correct(self): Ensures correctness of filter.
- def test_correct_inverted(self): Ensures correctness of inverted filter. | Implement the Python class `GetGTestFilterTest` described below.
Class description:
Tests for test_runner.get_gtest_filter.
Method signatures and docstrings:
- def test_correct(self): Ensures correctness of filter.
- def test_correct_inverted(self): Ensures correctness of inverted filter.
<|skeleton|>
class GetGTest... | 64bee65c921db7e78e25d08f1e98da2668b57be5 | <|skeleton|>
class GetGTestFilterTest:
"""Tests for test_runner.get_gtest_filter."""
def test_correct(self):
"""Ensures correctness of filter."""
<|body_0|>
def test_correct_inverted(self):
"""Ensures correctness of inverted filter."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetGTestFilterTest:
"""Tests for test_runner.get_gtest_filter."""
def test_correct(self):
"""Ensures correctness of filter."""
tests = ['test.1', 'test.2']
expected = 'test.1:test.2'
self.assertEqual(test_apps.get_gtest_filter(tests), expected)
def test_correct_invert... | the_stack_v2_python_sparse | ios/build/bots/scripts/test_apps_test.py | otcshare/chromium-src | train | 18 |
c6013f131a6b3e35cb594c75e7cd391dcc7d983f | [
"try:\n user = self.get_user(request, username)\nexcept PermissionDenied:\n return redirect(reverse('login') + '?next=' + request.path)\nlinks = Link.objects.select_related('category').filter(user=user).order_by('category__title', 'weight')\npalette = set((link.color for link in links))\ncategorized_links = d... | <|body_start_0|>
try:
user = self.get_user(request, username)
except PermissionDenied:
return redirect(reverse('login') + '?next=' + request.path)
links = Link.objects.select_related('category').filter(user=user).order_by('category__title', 'weight')
palette = set... | LinkView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkView:
def get(self, request, username=None):
"""Get list of links."""
<|body_0|>
def post(self, request, username=None):
"""Form submit."""
<|body_1|>
def delete(self, request, pk, username=None):
"""Delete link."""
<|body_2|>
<|end_... | stack_v2_sparse_classes_36k_train_034520 | 30,576 | permissive | [
{
"docstring": "Get list of links.",
"name": "get",
"signature": "def get(self, request, username=None)"
},
{
"docstring": "Form submit.",
"name": "post",
"signature": "def post(self, request, username=None)"
},
{
"docstring": "Delete link.",
"name": "delete",
"signature"... | 3 | stack_v2_sparse_classes_30k_train_006712 | Implement the Python class `LinkView` described below.
Class description:
Implement the LinkView class.
Method signatures and docstrings:
- def get(self, request, username=None): Get list of links.
- def post(self, request, username=None): Form submit.
- def delete(self, request, pk, username=None): Delete link. | Implement the Python class `LinkView` described below.
Class description:
Implement the LinkView class.
Method signatures and docstrings:
- def get(self, request, username=None): Get list of links.
- def post(self, request, username=None): Form submit.
- def delete(self, request, pk, username=None): Delete link.
<|s... | 51a2ae2b29ae5c91a3cf7171f89edf225cc8a6f0 | <|skeleton|>
class LinkView:
def get(self, request, username=None):
"""Get list of links."""
<|body_0|>
def post(self, request, username=None):
"""Form submit."""
<|body_1|>
def delete(self, request, pk, username=None):
"""Delete link."""
<|body_2|>
<|end_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkView:
def get(self, request, username=None):
"""Get list of links."""
try:
user = self.get_user(request, username)
except PermissionDenied:
return redirect(reverse('login') + '?next=' + request.path)
links = Link.objects.select_related('category').fi... | the_stack_v2_python_sparse | tool/views/views.py | mikekeda/tools | train | 0 | |
aa8e640e78b3314d2df0cdb2c0228830056cab9d | [
"if isinstance(x, valid_type):\n pass\nelse:\n raise TypeError(f'Expected type of {x} is an instance of {valid_type}, but got ``{type(x)}``.')",
"if x in valid_value:\n pass\nelse:\n raise ValueError(f'Expected `x` is one of {valid_value}, but got ``{x}``.')",
"out = []\nfor each in args:\n if is... | <|body_start_0|>
if isinstance(x, valid_type):
pass
else:
raise TypeError(f'Expected type of {x} is an instance of {valid_type}, but got ``{type(x)}``.')
<|end_body_0|>
<|body_start_1|>
if x in valid_value:
pass
else:
raise ValueError(f'Ex... | A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values. | Validation | [
"Apache-2.0",
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Validation:
"""A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values."""
def validate_type(x, valid_type):
"""Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verifi... | stack_v2_sparse_classes_36k_train_034521 | 3,049 | permissive | [
{
"docstring": "Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verified valid_type: type or tuple of type A tuple, as in ``validate_type(x, (A, B, ...))``, may be given as the target to check against. This is equivalent to ``validate_type(x, A) or validate_t... | 3 | stack_v2_sparse_classes_30k_train_005854 | Implement the Python class `Validation` described below.
Class description:
A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values.
Method signatures and docstrings:
- def validate_type(x, valid_type): Check whether an object is an instance of `valid_t... | Implement the Python class `Validation` described below.
Class description:
A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values.
Method signatures and docstrings:
- def validate_type(x, valid_type): Check whether an object is an instance of `valid_t... | 238cbc41865ddf629bb6ae92c2e1445be27f98b8 | <|skeleton|>
class Validation:
"""A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values."""
def validate_type(x, valid_type):
"""Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verifi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Validation:
"""A class for Parameters Validation Check whether the parameters are valid, including parameter types and parameter values."""
def validate_type(x, valid_type):
"""Check whether an object is an instance of `valid_type`. Parameters ---------- x: object object to be verified valid_type... | the_stack_v2_python_sparse | gcastle/castle/algorithms/gradient/corl/torch/utils/validation.py | huawei-noah/trustworthyAI | train | 832 |
2b80a2e2268e322b50264ae32cd1bb5371cb04f8 | [
"m, n = (len(mat), len(mat[0]))\nMAX = m * n\nret = [[MAX for _ in range(n)] for _ in range(m)]\ndir = [(1, 0), (0, 1), (-1, 0), (0, -1)]\ndq = collections.deque()\nfor i in range(m):\n for j in range(n):\n if mat[i][j] == 0:\n dq.append((i, j, 0))\nseen = set()\nwhile dq:\n x, y, d = dq.pop... | <|body_start_0|>
m, n = (len(mat), len(mat[0]))
MAX = m * n
ret = [[MAX for _ in range(n)] for _ in range(m)]
dir = [(1, 0), (0, 1), (-1, 0), (0, -1)]
dq = collections.deque()
for i in range(m):
for j in range(n):
if mat[i][j] == 0:
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def updateMatrix2(self, mat: List[List[int]]) -> List[List[int]]:
"""Updated at 2022/4/15 Runtime: 869 ms, faster than 54.07% Memory Usage: 19.8 MB, less than 6.29% m == mat.length n == mat[i].length 1 <= m, n <= 10^4 1 <= m * n <= 10^4 mat[i][j] is either 0 or 1. There is at l... | stack_v2_sparse_classes_36k_train_034522 | 4,849 | permissive | [
{
"docstring": "Updated at 2022/4/15 Runtime: 869 ms, faster than 54.07% Memory Usage: 19.8 MB, less than 6.29% m == mat.length n == mat[i].length 1 <= m, n <= 10^4 1 <= m * n <= 10^4 mat[i][j] is either 0 or 1. There is at least one 0 in mat. :param mat: :return:",
"name": "updateMatrix2",
"signature":... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def updateMatrix2(self, mat: List[List[int]]) -> List[List[int]]: Updated at 2022/4/15 Runtime: 869 ms, faster than 54.07% Memory Usage: 19.8 MB, less than 6.29% m == mat.length ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def updateMatrix2(self, mat: List[List[int]]) -> List[List[int]]: Updated at 2022/4/15 Runtime: 869 ms, faster than 54.07% Memory Usage: 19.8 MB, less than 6.29% m == mat.length ... | 4dd1e54d8d08f7e6590bc76abd08ecaacaf775e5 | <|skeleton|>
class Solution:
def updateMatrix2(self, mat: List[List[int]]) -> List[List[int]]:
"""Updated at 2022/4/15 Runtime: 869 ms, faster than 54.07% Memory Usage: 19.8 MB, less than 6.29% m == mat.length n == mat[i].length 1 <= m, n <= 10^4 1 <= m * n <= 10^4 mat[i][j] is either 0 or 1. There is at l... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def updateMatrix2(self, mat: List[List[int]]) -> List[List[int]]:
"""Updated at 2022/4/15 Runtime: 869 ms, faster than 54.07% Memory Usage: 19.8 MB, less than 6.29% m == mat.length n == mat[i].length 1 <= m, n <= 10^4 1 <= m * n <= 10^4 mat[i][j] is either 0 or 1. There is at least one 0 in ... | the_stack_v2_python_sparse | src/542-01Matrix.py | Jiezhi/myleetcode | train | 1 | |
56b568f68e2cc7147d2dd90f481e11b8ffd74ddb | [
"dest_list = []\ndest_file = open('csv_files/Destinations.csv', 'r')\nreader = csv.DictReader(dest_file)\nfor row in reader:\n dest_id = row['dest_id']\n dest_country = row['country']\n dest_city = row['city']\n dest_airport = row['airport']\n dest_flight_time = row['flight_time']\n dest_distance ... | <|body_start_0|>
dest_list = []
dest_file = open('csv_files/Destinations.csv', 'r')
reader = csv.DictReader(dest_file)
for row in reader:
dest_id = row['dest_id']
dest_country = row['country']
dest_city = row['city']
dest_airport = row['air... | DestinationIO | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DestinationIO:
def load_all_destinations(self):
"""Reads into the database. Returns a list of all destinations as instances"""
<|body_0|>
def store_new_destination(self, new_destination):
"""Stores new destination to the existing file"""
<|body_1|>
def s... | stack_v2_sparse_classes_36k_train_034523 | 2,033 | no_license | [
{
"docstring": "Reads into the database. Returns a list of all destinations as instances",
"name": "load_all_destinations",
"signature": "def load_all_destinations(self)"
},
{
"docstring": "Stores new destination to the existing file",
"name": "store_new_destination",
"signature": "def s... | 4 | stack_v2_sparse_classes_30k_train_015004 | Implement the Python class `DestinationIO` described below.
Class description:
Implement the DestinationIO class.
Method signatures and docstrings:
- def load_all_destinations(self): Reads into the database. Returns a list of all destinations as instances
- def store_new_destination(self, new_destination): Stores new... | Implement the Python class `DestinationIO` described below.
Class description:
Implement the DestinationIO class.
Method signatures and docstrings:
- def load_all_destinations(self): Reads into the database. Returns a list of all destinations as instances
- def store_new_destination(self, new_destination): Stores new... | 5dbce2a3d1cdc8a0614252fb77685211b395c2df | <|skeleton|>
class DestinationIO:
def load_all_destinations(self):
"""Reads into the database. Returns a list of all destinations as instances"""
<|body_0|>
def store_new_destination(self, new_destination):
"""Stores new destination to the existing file"""
<|body_1|>
def s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DestinationIO:
def load_all_destinations(self):
"""Reads into the database. Returns a list of all destinations as instances"""
dest_list = []
dest_file = open('csv_files/Destinations.csv', 'r')
reader = csv.DictReader(dest_file)
for row in reader:
dest_id = ... | the_stack_v2_python_sparse | DataLayer/DestinationIO.py | svana00/VLN1-NaN-Air | train | 0 | |
49340d701dbab51a459efe295fb9b8e7ab9b697e | [
"self.desiredTypes = desiredTypes\nself.result = []\nsuper(_Finder, self).__init__()",
"todo = [(0, node)]\ncurrentStack = []\nchildrenTypes = TRAVERSAL_TYPES + self.desiredTypes\nwhile todo:\n index, node = todo[0]\n del todo[0]\n del currentStack[index:]\n is_desired = isinstance(node, self.desiredT... | <|body_start_0|>
self.desiredTypes = desiredTypes
self.result = []
super(_Finder, self).__init__()
<|end_body_0|>
<|body_start_1|>
todo = [(0, node)]
currentStack = []
childrenTypes = TRAVERSAL_TYPES + self.desiredTypes
while todo:
index, node = todo[... | Traverse a scenegraph looking for bindable nodes This is a simple implementation of a scenegraph-search which looks for all nodes which are instances of any of a given set of classes/types. Attributes: result -- the resulting set of node-paths desiredTypes -- the node-types being searched for See the find function for ... | _Finder | [
"LicenseRef-scancode-warranty-disclaimer",
"GPL-1.0-or-later",
"LicenseRef-scancode-other-copyleft",
"LGPL-2.1-or-later",
"GPL-3.0-only",
"LGPL-2.0-or-later",
"GPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _Finder:
"""Traverse a scenegraph looking for bindable nodes This is a simple implementation of a scenegraph-search which looks for all nodes which are instances of any of a given set of classes/types. Attributes: result -- the resulting set of node-paths desiredTypes -- the node-types being sear... | stack_v2_sparse_classes_36k_train_034524 | 14,994 | permissive | [
{
"docstring": "Initialize the _Finder object desiredTypes -- sequence of types to be searched for",
"name": "__init__",
"signature": "def __init__(self, desiredTypes=())"
},
{
"docstring": "Visit an individual node, search for self.desiredTypes This is a heavily trimmed version of the superclas... | 2 | stack_v2_sparse_classes_30k_train_004641 | Implement the Python class `_Finder` described below.
Class description:
Traverse a scenegraph looking for bindable nodes This is a simple implementation of a scenegraph-search which looks for all nodes which are instances of any of a given set of classes/types. Attributes: result -- the resulting set of node-paths de... | Implement the Python class `_Finder` described below.
Class description:
Traverse a scenegraph looking for bindable nodes This is a simple implementation of a scenegraph-search which looks for all nodes which are instances of any of a given set of classes/types. Attributes: result -- the resulting set of node-paths de... | 7f600ad153270feff12aa7aa86d7ed0a49ebc71c | <|skeleton|>
class _Finder:
"""Traverse a scenegraph looking for bindable nodes This is a simple implementation of a scenegraph-search which looks for all nodes which are instances of any of a given set of classes/types. Attributes: result -- the resulting set of node-paths desiredTypes -- the node-types being sear... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _Finder:
"""Traverse a scenegraph looking for bindable nodes This is a simple implementation of a scenegraph-search which looks for all nodes which are instances of any of a given set of classes/types. Attributes: result -- the resulting set of node-paths desiredTypes -- the node-types being searched for See ... | the_stack_v2_python_sparse | pythonAnimations/pyOpenGLChess/engineDirectory/oglc-env/lib/python2.7/site-packages/OpenGLContext/visitor.py | alexus37/AugmentedRealityChess | train | 1 |
f39b6483965651b08ae6daa9797c6b53d66b3d8a | [
"if logger.isEnabledFor(logging.DEBUG):\n logger.debug('Entry with args=(ctx=%s, kwargs=%s, parameters=%s) called by=%s', ctx, kwargs, parameters, '::L'.join((str(i) for i in inspect.getouterframes(inspect.currentframe(), 2)[1][1:3])))\nctx.tapes = kwargs['tapes']\nctx.device = kwargs['device']\nctx.execute_fn =... | <|body_start_0|>
if logger.isEnabledFor(logging.DEBUG):
logger.debug('Entry with args=(ctx=%s, kwargs=%s, parameters=%s) called by=%s', ctx, kwargs, parameters, '::L'.join((str(i) for i in inspect.getouterframes(inspect.currentframe(), 2)[1][1:3])))
ctx.tapes = kwargs['tapes']
ctx.de... | The signature of this ``torch.autograd.Function`` is designed to work around Torch restrictions. In particular, ``torch.autograd.Function``: - Cannot accept keyword arguments. As a result, we pass a dictionary as the first argument ``kwargs``. This dictionary **must** contain: * ``"tapes"``: the quantum tapes to batch ... | ExecuteTapes | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExecuteTapes:
"""The signature of this ``torch.autograd.Function`` is designed to work around Torch restrictions. In particular, ``torch.autograd.Function``: - Cannot accept keyword arguments. As a result, we pass a dictionary as the first argument ``kwargs``. This dictionary **must** contain: * ... | stack_v2_sparse_classes_36k_train_034525 | 15,349 | permissive | [
{
"docstring": "Implements the forward pass batch tape evaluation.",
"name": "forward",
"signature": "def forward(ctx, kwargs, *parameters)"
},
{
"docstring": "Returns the vector-Jacobian product with given parameter values p and output gradient dy",
"name": "backward",
"signature": "def... | 2 | null | Implement the Python class `ExecuteTapes` described below.
Class description:
The signature of this ``torch.autograd.Function`` is designed to work around Torch restrictions. In particular, ``torch.autograd.Function``: - Cannot accept keyword arguments. As a result, we pass a dictionary as the first argument ``kwargs`... | Implement the Python class `ExecuteTapes` described below.
Class description:
The signature of this ``torch.autograd.Function`` is designed to work around Torch restrictions. In particular, ``torch.autograd.Function``: - Cannot accept keyword arguments. As a result, we pass a dictionary as the first argument ``kwargs`... | 0843183ff15a013c2622af5e61fea431d18076d3 | <|skeleton|>
class ExecuteTapes:
"""The signature of this ``torch.autograd.Function`` is designed to work around Torch restrictions. In particular, ``torch.autograd.Function``: - Cannot accept keyword arguments. As a result, we pass a dictionary as the first argument ``kwargs``. This dictionary **must** contain: * ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExecuteTapes:
"""The signature of this ``torch.autograd.Function`` is designed to work around Torch restrictions. In particular, ``torch.autograd.Function``: - Cannot accept keyword arguments. As a result, we pass a dictionary as the first argument ``kwargs``. This dictionary **must** contain: * ``"tapes"``: ... | the_stack_v2_python_sparse | pennylane/interfaces/torch.py | PennyLaneAI/pennylane | train | 1,431 |
f14b844b5bcf5cd333c3325c16c84b5fca2a9b41 | [
"assert type(screen_name) == str\nfriends = tweepy_getter.get_friends_by_screen_name(screen_name, num_friends)\nuser_friends_setter.store_friends_by_screen_name(screen_name, friends)\nreturn friends",
"assert type(id) == int\nfriends = tweepy_getter.get_friends_by_id(id, num_friends)\nuser_friends_setter.store_fr... | <|body_start_0|>
assert type(screen_name) == str
friends = tweepy_getter.get_friends_by_screen_name(screen_name, num_friends)
user_friends_setter.store_friends_by_screen_name(screen_name, friends)
return friends
<|end_body_0|>
<|body_start_1|>
assert type(id) == int
frie... | Download Twitter Friends for use in future algorithms. | TwitterFriendsDownloader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwitterFriendsDownloader:
"""Download Twitter Friends for use in future algorithms."""
def gen_friends_by_screen_name(self, screen_name: str, tweepy_getter, user_friends_setter, num_friends=None) -> List[str]:
"""Retrieves a list of screen_names of friends for the user with the given... | stack_v2_sparse_classes_36k_train_034526 | 7,540 | no_license | [
{
"docstring": "Retrieves a list of screen_names of friends for the user with the given screen name @param screen_name the screen name of the user to query on @param tweepy_getter the getter to access twitter with @param user_friends_setter the dao to store the output @param num_friends Optional - if specified,... | 3 | stack_v2_sparse_classes_30k_train_008662 | Implement the Python class `TwitterFriendsDownloader` described below.
Class description:
Download Twitter Friends for use in future algorithms.
Method signatures and docstrings:
- def gen_friends_by_screen_name(self, screen_name: str, tweepy_getter, user_friends_setter, num_friends=None) -> List[str]: Retrieves a li... | Implement the Python class `TwitterFriendsDownloader` described below.
Class description:
Download Twitter Friends for use in future algorithms.
Method signatures and docstrings:
- def gen_friends_by_screen_name(self, screen_name: str, tweepy_getter, user_friends_setter, num_friends=None) -> List[str]: Retrieves a li... | 33a3fa38ad4dcdd54ff583da15dcd67c99ad9701 | <|skeleton|>
class TwitterFriendsDownloader:
"""Download Twitter Friends for use in future algorithms."""
def gen_friends_by_screen_name(self, screen_name: str, tweepy_getter, user_friends_setter, num_friends=None) -> List[str]:
"""Retrieves a list of screen_names of friends for the user with the given... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TwitterFriendsDownloader:
"""Download Twitter Friends for use in future algorithms."""
def gen_friends_by_screen_name(self, screen_name: str, tweepy_getter, user_friends_setter, num_friends=None) -> List[str]:
"""Retrieves a list of screen_names of friends for the user with the given screen name ... | the_stack_v2_python_sparse | src/process/download/twitter_downloader.py | ReinaKousaka/core | train | 0 |
9106775c35ea030ce6287facdd3a416f412dc686 | [
"super(GlobalAveragePool3D, self).__init__(**kwargs)\nself._keepdims = keepdims\nself._causal = causal\nself._frame_count = None",
"config = {'keepdims': self._keepdims, 'causal': self._causal}\nbase_config = super(GlobalAveragePool3D, self).get_config()\nreturn dict(list(base_config.items()) + list(config.items(... | <|body_start_0|>
super(GlobalAveragePool3D, self).__init__(**kwargs)
self._keepdims = keepdims
self._causal = causal
self._frame_count = None
<|end_body_0|>
<|body_start_1|>
config = {'keepdims': self._keepdims, 'causal': self._causal}
base_config = super(GlobalAveragePo... | Creates a global average pooling layer with causal mode. Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across frames in the time dimension, allowing the use of a stream buffer. Sums any valid input state with the current input to allow state to accumulate over several iterations. | GlobalAveragePool3D | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GlobalAveragePool3D:
"""Creates a global average pooling layer with causal mode. Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across frames in the time dimension, allowing the use of a stream buffer. Sums any valid input state with the current input to allow state to acc... | stack_v2_sparse_classes_36k_train_034527 | 33,772 | permissive | [
{
"docstring": "Initializes a global average pool layer. Args: keepdims: A `bool`. If True, keep the averaged dimensions. causal: A `bool` of whether to run in causal mode with a cumulative sum across frames. **kwargs: Additional keyword arguments to be passed to this layer. Returns: An output `tf.Tensor`.",
... | 4 | null | Implement the Python class `GlobalAveragePool3D` described below.
Class description:
Creates a global average pooling layer with causal mode. Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across frames in the time dimension, allowing the use of a stream buffer. Sums any valid input state with ... | Implement the Python class `GlobalAveragePool3D` described below.
Class description:
Creates a global average pooling layer with causal mode. Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across frames in the time dimension, allowing the use of a stream buffer. Sums any valid input state with ... | 192ae544169c1230c21141c033800aa1bd94e9b6 | <|skeleton|>
class GlobalAveragePool3D:
"""Creates a global average pooling layer with causal mode. Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across frames in the time dimension, allowing the use of a stream buffer. Sums any valid input state with the current input to allow state to acc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GlobalAveragePool3D:
"""Creates a global average pooling layer with causal mode. Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across frames in the time dimension, allowing the use of a stream buffer. Sums any valid input state with the current input to allow state to accumulate over ... | the_stack_v2_python_sparse | official/vision/beta/modeling/layers/nn_layers.py | DemonDamon/mask-detection-based-on-tf2odapi | train | 2 |
a4e1b3eec7650e8686693ace63f9653203d0a427 | [
"self.calibrator = calibrators.Calibrator(calibrators.WaveDivider())\nself.signal_start_calibrating = self.calibrator.signal_start_calibrating\nself.signal_stop_calibrating = self.calibrator.signal_stop_calibrating\nself.accumulator = collectors.DataAccumulator(samples=accum_samples)\nself.generator = generator",
... | <|body_start_0|>
self.calibrator = calibrators.Calibrator(calibrators.WaveDivider())
self.signal_start_calibrating = self.calibrator.signal_start_calibrating
self.signal_stop_calibrating = self.calibrator.signal_stop_calibrating
self.accumulator = collectors.DataAccumulator(samples=accum... | Process the EEG transforming it into waves. TODO | WaveProcessor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WaveProcessor:
"""Process the EEG transforming it into waves. TODO"""
def __init__(self, generator, accum_samples=1):
"""Constructor."""
<|body_0|>
def generate(self, timestamp, new_data):
"""Make a TF analysis of the data and delegates to a processor."""
... | stack_v2_sparse_classes_36k_train_034528 | 1,270 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, generator, accum_samples=1)"
},
{
"docstring": "Make a TF analysis of the data and delegates to a processor.",
"name": "generate",
"signature": "def generate(self, timestamp, new_data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007903 | Implement the Python class `WaveProcessor` described below.
Class description:
Process the EEG transforming it into waves. TODO
Method signatures and docstrings:
- def __init__(self, generator, accum_samples=1): Constructor.
- def generate(self, timestamp, new_data): Make a TF analysis of the data and delegates to a ... | Implement the Python class `WaveProcessor` described below.
Class description:
Process the EEG transforming it into waves. TODO
Method signatures and docstrings:
- def __init__(self, generator, accum_samples=1): Constructor.
- def generate(self, timestamp, new_data): Make a TF analysis of the data and delegates to a ... | 38cbb8d55cec730a03899692a37273f0817875eb | <|skeleton|>
class WaveProcessor:
"""Process the EEG transforming it into waves. TODO"""
def __init__(self, generator, accum_samples=1):
"""Constructor."""
<|body_0|>
def generate(self, timestamp, new_data):
"""Make a TF analysis of the data and delegates to a processor."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WaveProcessor:
"""Process the EEG transforming it into waves. TODO"""
def __init__(self, generator, accum_samples=1):
"""Constructor."""
self.calibrator = calibrators.Calibrator(calibrators.WaveDivider())
self.signal_start_calibrating = self.calibrator.signal_start_calibrating
... | the_stack_v2_python_sparse | backend/engine/processors/waves.py | pdpino/muse-player | train | 0 |
4cb57cab5178e80faf3156cd7422bc0a309559fe | [
"self.K = K\nself.X, self.T = ([], [])\nself.flagKLinReg = flagKLinReg",
"self.X, self.T = (np.array(X), np.array(T))\nself.N, self.D = self.X.shape\nself.kdtree = scipy.spatial.KDTree(self.X)",
"if K == None:\n K = self.K\nif flagKLinReg == None:\n flagKLinReg = self.flagKLinReg\nnn = self.kdtree.query(x... | <|body_start_0|>
self.K = K
self.X, self.T = ([], [])
self.flagKLinReg = flagKLinReg
<|end_body_0|>
<|body_start_1|>
self.X, self.T = (np.array(X), np.array(T))
self.N, self.D = self.X.shape
self.kdtree = scipy.spatial.KDTree(self.X)
<|end_body_1|>
<|body_start_2|>
... | Class for fast K-Nearest-Neighbor-Regression using KD-trees | KNNRegressifier | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KNNRegressifier:
"""Class for fast K-Nearest-Neighbor-Regression using KD-trees"""
def __init__(self, K, flagKLinReg=0):
"""Constructor of class KNNRegressifier :param K: number of nearest neighbors that are used to compute prediction :flagKLinReg: if >0 then the do a linear (least s... | stack_v2_sparse_classes_36k_train_034529 | 21,971 | no_license | [
{
"docstring": "Constructor of class KNNRegressifier :param K: number of nearest neighbors that are used to compute prediction :flagKLinReg: if >0 then the do a linear (least squares) regression on the the K nearest neighbors and their target values otherwise just take the mean of the K nearest neighbors target... | 3 | stack_v2_sparse_classes_30k_val_000827 | Implement the Python class `KNNRegressifier` described below.
Class description:
Class for fast K-Nearest-Neighbor-Regression using KD-trees
Method signatures and docstrings:
- def __init__(self, K, flagKLinReg=0): Constructor of class KNNRegressifier :param K: number of nearest neighbors that are used to compute pre... | Implement the Python class `KNNRegressifier` described below.
Class description:
Class for fast K-Nearest-Neighbor-Regression using KD-trees
Method signatures and docstrings:
- def __init__(self, K, flagKLinReg=0): Constructor of class KNNRegressifier :param K: number of nearest neighbors that are used to compute pre... | de2ba4e2afdad7e2e1ba0c145edbd341f8555802 | <|skeleton|>
class KNNRegressifier:
"""Class for fast K-Nearest-Neighbor-Regression using KD-trees"""
def __init__(self, K, flagKLinReg=0):
"""Constructor of class KNNRegressifier :param K: number of nearest neighbors that are used to compute prediction :flagKLinReg: if >0 then the do a linear (least s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KNNRegressifier:
"""Class for fast K-Nearest-Neighbor-Regression using KD-trees"""
def __init__(self, K, flagKLinReg=0):
"""Constructor of class KNNRegressifier :param K: number of nearest neighbors that are used to compute prediction :flagKLinReg: if >0 then the do a linear (least squares) regre... | the_stack_v2_python_sparse | versuch2/src/V2A2_Regression.py | xsjad0/ias-neuronale-netze | train | 1 |
c20b6ca58ce0a13a1ba70c53ee95c7a26c43fe8c | [
"pre = None\ncur = head\nwhile cur:\n tmp = cur.next\n cur.next = pre\n pre = cur\n cur = tmp\nreturn pre",
"p = head\nh = p\ntt = None\nhead = None\nwhile p is not None:\n for _ in range(k - 1):\n if p.next is None:\n if tt is not None:\n tt.next = h\n i... | <|body_start_0|>
pre = None
cur = head
while cur:
tmp = cur.next
cur.next = pre
pre = cur
cur = tmp
return pre
<|end_body_0|>
<|body_start_1|>
p = head
h = p
tt = None
head = None
while p is not None... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def reverseKGroup(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pre = None
... | stack_v2_sparse_classes_36k_train_034530 | 1,323 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "reverseList",
"signature": "def reverseList(self, head)"
},
{
"docstring": ":type head: ListNode :type k: int :rtype: ListNode",
"name": "reverseKGroup",
"signature": "def reverseKGroup(self, head, k)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000528 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def reverseKGroup(self, head, k): :type head: ListNode :type k: int :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def reverseKGroup(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
<|skeleton|>
class Solu... | d8ed762d1005975f0de4f07760c9671195621c88 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def reverseKGroup(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
pre = None
cur = head
while cur:
tmp = cur.next
cur.next = pre
pre = cur
cur = tmp
return pre
def reverseKGroup(self, head, k):
... | the_stack_v2_python_sparse | reverse-nodes-in-k-group/solution.py | uxlsl/leetcode_practice | train | 0 | |
c8f1c794c6bd84716bdd8ff7a6712fc363e54a7a | [
"super(Network, self).__init__()\nself.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=2, padding=2)\nself.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=2, padding=2)\nself.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=2, padding=2)\nself... | <|body_start_0|>
super(Network, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, ... | Network | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Network:
def __init__(self):
"""Define the NN layers and the initialization for the layers with parameters."""
<|body_0|>
def forward(self, x, pos, training):
"""The forward pass of the network. :param x: Input to the NN :return: return the output of the network"""
... | stack_v2_sparse_classes_36k_train_034531 | 8,776 | no_license | [
{
"docstring": "Define the NN layers and the initialization for the layers with parameters.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "The forward pass of the network. :param x: Input to the NN :return: return the output of the network",
"name": "forward",
... | 2 | stack_v2_sparse_classes_30k_train_000608 | Implement the Python class `Network` described below.
Class description:
Implement the Network class.
Method signatures and docstrings:
- def __init__(self): Define the NN layers and the initialization for the layers with parameters.
- def forward(self, x, pos, training): The forward pass of the network. :param x: In... | Implement the Python class `Network` described below.
Class description:
Implement the Network class.
Method signatures and docstrings:
- def __init__(self): Define the NN layers and the initialization for the layers with parameters.
- def forward(self, x, pos, training): The forward pass of the network. :param x: In... | e55e1e5a5e31dc2d9ad3f59b53fe8d2008c6dedf | <|skeleton|>
class Network:
def __init__(self):
"""Define the NN layers and the initialization for the layers with parameters."""
<|body_0|>
def forward(self, x, pos, training):
"""The forward pass of the network. :param x: Input to the NN :return: return the output of the network"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Network:
def __init__(self):
"""Define the NN layers and the initialization for the layers with parameters."""
super(Network, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_c... | the_stack_v2_python_sparse | custom_layer/drop_layer.py | adastimes/code | train | 0 | |
71830b7ddf96f880bc051c51fb916c4ba0df2edb | [
"t1 = (zconst - rays.origins[Ellipsis, -1]) / rays.directions[Ellipsis, -1]\nxy = rays.origins[Ellipsis, :2] + (t1[Ellipsis, None] * rays.directions)[Ellipsis, :2]\nreturn xy",
"st = self.ray_plane_intersection(self.config.st_plane, rays)\nuv = self.ray_plane_intersection(self.config.uv_plane, rays)\nlf_samples =... | <|body_start_0|>
t1 = (zconst - rays.origins[Ellipsis, -1]) / rays.directions[Ellipsis, -1]
xy = rays.origins[Ellipsis, :2] + (t1[Ellipsis, None] * rays.directions)[Ellipsis, :2]
return xy
<|end_body_0|>
<|body_start_1|>
st = self.ray_plane_intersection(self.config.st_plane, rays)
... | A class encapsulation the LightSlab utilities. | LightSlab | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LightSlab:
"""A class encapsulation the LightSlab utilities."""
def ray_plane_intersection(self, zconst, rays):
"""Compute intersection of the ray with a plane of the form z=const. Args: zconst: Fixed z-value for the plane. rays: data_type.Rays. Returns: xy: The free-coordinates of i... | stack_v2_sparse_classes_36k_train_034532 | 3,936 | permissive | [
{
"docstring": "Compute intersection of the ray with a plane of the form z=const. Args: zconst: Fixed z-value for the plane. rays: data_type.Rays. Returns: xy: The free-coordinates of intersection.",
"name": "ray_plane_intersection",
"signature": "def ray_plane_intersection(self, zconst, rays)"
},
{... | 2 | null | Implement the Python class `LightSlab` described below.
Class description:
A class encapsulation the LightSlab utilities.
Method signatures and docstrings:
- def ray_plane_intersection(self, zconst, rays): Compute intersection of the ray with a plane of the form z=const. Args: zconst: Fixed z-value for the plane. ray... | Implement the Python class `LightSlab` described below.
Class description:
A class encapsulation the LightSlab utilities.
Method signatures and docstrings:
- def ray_plane_intersection(self, zconst, rays): Compute intersection of the ray with a plane of the form z=const. Args: zconst: Fixed z-value for the plane. ray... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class LightSlab:
"""A class encapsulation the LightSlab utilities."""
def ray_plane_intersection(self, zconst, rays):
"""Compute intersection of the ray with a plane of the form z=const. Args: zconst: Fixed z-value for the plane. rays: data_type.Rays. Returns: xy: The free-coordinates of i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LightSlab:
"""A class encapsulation the LightSlab utilities."""
def ray_plane_intersection(self, zconst, rays):
"""Compute intersection of the ray with a plane of the form z=const. Args: zconst: Fixed z-value for the plane. rays: data_type.Rays. Returns: xy: The free-coordinates of intersection."... | the_stack_v2_python_sparse | gen_patch_neural_rendering/src/utils/lf_utils.py | Jimmy-INL/google-research | train | 1 |
a2d6af3d9ec871089d0fe30569f047ec486de9ac | [
"try:\n template_xsl_rendering_object = template_xsl_rendering_api.get_by_id(pk)\n template_xsl_rendering_serializer = TemplateXslRenderingSerializer(template_xsl_rendering_object)\n return Response(template_xsl_rendering_serializer.data)\nexcept exceptions.DoesNotExist:\n content = {'message': 'XSL ren... | <|body_start_0|>
try:
template_xsl_rendering_object = template_xsl_rendering_api.get_by_id(pk)
template_xsl_rendering_serializer = TemplateXslRenderingSerializer(template_xsl_rendering_object)
return Response(template_xsl_rendering_serializer.data)
except exceptions.D... | TemplateXslRendering details view | TemplateXslRenderingDetail | [
"NIST-Software"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TemplateXslRenderingDetail:
"""TemplateXslRendering details view"""
def get(self, request, pk):
"""Get `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Returns: TemplateXSLRendering"""
<|body_0|>
def patch(self, request, pk):
"""Edit... | stack_v2_sparse_classes_36k_train_034533 | 14,418 | permissive | [
{
"docstring": "Get `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Returns: TemplateXSLRendering",
"name": "get",
"signature": "def get(self, request, pk)"
},
{
"docstring": "Edit `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Retur... | 3 | null | Implement the Python class `TemplateXslRenderingDetail` described below.
Class description:
TemplateXslRendering details view
Method signatures and docstrings:
- def get(self, request, pk): Get `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Returns: TemplateXSLRendering
- def patch(sel... | Implement the Python class `TemplateXslRenderingDetail` described below.
Class description:
TemplateXslRendering details view
Method signatures and docstrings:
- def get(self, request, pk): Get `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Returns: TemplateXSLRendering
- def patch(sel... | f032036d95076f92b164389fdbec7415567e7b0f | <|skeleton|>
class TemplateXslRenderingDetail:
"""TemplateXslRendering details view"""
def get(self, request, pk):
"""Get `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Returns: TemplateXSLRendering"""
<|body_0|>
def patch(self, request, pk):
"""Edit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TemplateXslRenderingDetail:
"""TemplateXslRendering details view"""
def get(self, request, pk):
"""Get `TemplateXSLRendering` object from db Args: request: HTTP request pk: ObjectId Returns: TemplateXSLRendering"""
try:
template_xsl_rendering_object = template_xsl_rendering_ap... | the_stack_v2_python_sparse | core_main_app/rest/template_xsl_rendering/views.py | usnistgov/core_main_app | train | 3 |
2c194804dbc9b47fe347a9fc64f23402b0fa51d9 | [
"if context is None:\n context = {}\nprod = self.pool.get('mrp.production').browse(cr, uid, context.get('active_id', False), context=context)\nsn = prod.serialno\nif not sn:\n sn = self.pool.get('ir.sequence').get(cr, uid, 'lot.sn.seq', context=None)\nreturn sn",
"production_id = context.get('active_id', Fa... | <|body_start_0|>
if context is None:
context = {}
prod = self.pool.get('mrp.production').browse(cr, uid, context.get('active_id', False), context=context)
sn = prod.serialno
if not sn:
sn = self.pool.get('ir.sequence').get(cr, uid, 'lot.sn.seq', context=None)
... | mrp_product_produce | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class mrp_product_produce:
def _get_lot_sn(self, cr, uid, context=None):
"""To obtain MO SN @return: prod_lot"""
<|body_0|>
def do_produce(self, cr, uid, ids, context=None):
"""To create and add prod_lot in moves @param ids: The wizard id @return: Execute production with c... | stack_v2_sparse_classes_36k_train_034534 | 1,519 | no_license | [
{
"docstring": "To obtain MO SN @return: prod_lot",
"name": "_get_lot_sn",
"signature": "def _get_lot_sn(self, cr, uid, context=None)"
},
{
"docstring": "To create and add prod_lot in moves @param ids: The wizard id @return: Execute production with current MO's lot_id",
"name": "do_produce",... | 2 | stack_v2_sparse_classes_30k_train_014823 | Implement the Python class `mrp_product_produce` described below.
Class description:
Implement the mrp_product_produce class.
Method signatures and docstrings:
- def _get_lot_sn(self, cr, uid, context=None): To obtain MO SN @return: prod_lot
- def do_produce(self, cr, uid, ids, context=None): To create and add prod_l... | Implement the Python class `mrp_product_produce` described below.
Class description:
Implement the mrp_product_produce class.
Method signatures and docstrings:
- def _get_lot_sn(self, cr, uid, context=None): To obtain MO SN @return: prod_lot
- def do_produce(self, cr, uid, ids, context=None): To create and add prod_l... | c5a5678379649ccdf57a9d55b09b30436428b430 | <|skeleton|>
class mrp_product_produce:
def _get_lot_sn(self, cr, uid, context=None):
"""To obtain MO SN @return: prod_lot"""
<|body_0|>
def do_produce(self, cr, uid, ids, context=None):
"""To create and add prod_lot in moves @param ids: The wizard id @return: Execute production with c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class mrp_product_produce:
def _get_lot_sn(self, cr, uid, context=None):
"""To obtain MO SN @return: prod_lot"""
if context is None:
context = {}
prod = self.pool.get('mrp.production').browse(cr, uid, context.get('active_id', False), context=context)
sn = prod.serialno
... | the_stack_v2_python_sparse | bpkdomino/mrp_barcode/wizard/product_produce.py | adahra/addons | train | 1 | |
32ba18fbe88f3c914c3293bbfd141cb48a61416e | [
"self.username = sUsername\nself.database = pDatabase\npAllUsers = pDatabase.GetAllUsers(sUsername)\nself.emailAddresses = []\nself.phoneNumbers = []\nfor pUser in pAllUsers:\n if pUser['email'] != '':\n self.emailAddresses.append(pUser['email'])\n if pUser['phone'] != '':\n self.phoneNumbers.ap... | <|body_start_0|>
self.username = sUsername
self.database = pDatabase
pAllUsers = pDatabase.GetAllUsers(sUsername)
self.emailAddresses = []
self.phoneNumbers = []
for pUser in pAllUsers:
if pUser['email'] != '':
self.emailAddresses.append(pUser[... | Messaging | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Messaging:
def __init__(self, sUsername, pDatabase):
"""Constructor"""
<|body_0|>
def broadcastMessage(self, sMessage):
"""Broadcasts a message"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.username = sUsername
self.database = pDataba... | stack_v2_sparse_classes_36k_train_034535 | 922 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, sUsername, pDatabase)"
},
{
"docstring": "Broadcasts a message",
"name": "broadcastMessage",
"signature": "def broadcastMessage(self, sMessage)"
}
] | 2 | null | Implement the Python class `Messaging` described below.
Class description:
Implement the Messaging class.
Method signatures and docstrings:
- def __init__(self, sUsername, pDatabase): Constructor
- def broadcastMessage(self, sMessage): Broadcasts a message | Implement the Python class `Messaging` described below.
Class description:
Implement the Messaging class.
Method signatures and docstrings:
- def __init__(self, sUsername, pDatabase): Constructor
- def broadcastMessage(self, sMessage): Broadcasts a message
<|skeleton|>
class Messaging:
def __init__(self, sUsern... | c6954ca0fff935ce1eb8154744f6307743765dc5 | <|skeleton|>
class Messaging:
def __init__(self, sUsername, pDatabase):
"""Constructor"""
<|body_0|>
def broadcastMessage(self, sMessage):
"""Broadcasts a message"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Messaging:
def __init__(self, sUsername, pDatabase):
"""Constructor"""
self.username = sUsername
self.database = pDatabase
pAllUsers = pDatabase.GetAllUsers(sUsername)
self.emailAddresses = []
self.phoneNumbers = []
for pUser in pAllUsers:
if... | the_stack_v2_python_sparse | server/core/Messaging.py | henryeherman/elixys | train | 1 | |
2501aeb7c1d544958d038dd101de797b0c52793a | [
"super(Radar, self).__init__(vehicle_id)\nself.name = name\nself.sens_range = float(sens_range)\nself.sens_angle = float(sens_angle) * np.pi / 180.0\nself.pub_readings = rospy.Publisher(self.name + '_readings', RadarReadings, queue_size=10)",
"if not np.any(self.vehicle_states[0] == self.vehicle_id) or not self.v... | <|body_start_0|>
super(Radar, self).__init__(vehicle_id)
self.name = name
self.sens_range = float(sens_range)
self.sens_angle = float(sens_angle) * np.pi / 180.0
self.pub_readings = rospy.Publisher(self.name + '_readings', RadarReadings, queue_size=10)
<|end_body_0|>
<|body_star... | Radar sensor class. | Radar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Radar:
"""Radar sensor class."""
def __init__(self, vehicle_id, name, sens_range, sens_angle):
"""Initialize Radar sensor class. @param vehicle_id: I{(int)} ID of the vehicle this sensor belongs to. @param name: I{(str)} Name of the sensor under which it will publish its readings. @p... | stack_v2_sparse_classes_36k_train_034536 | 5,525 | no_license | [
{
"docstring": "Initialize Radar sensor class. @param vehicle_id: I{(int)} ID of the vehicle this sensor belongs to. @param name: I{(str)} Name of the sensor under which it will publish its readings. @param sens_range: I{(float)} Range of the randar sensor. @param sens_angle: I{(float)} Opening angle of the rad... | 2 | stack_v2_sparse_classes_30k_train_005107 | Implement the Python class `Radar` described below.
Class description:
Radar sensor class.
Method signatures and docstrings:
- def __init__(self, vehicle_id, name, sens_range, sens_angle): Initialize Radar sensor class. @param vehicle_id: I{(int)} ID of the vehicle this sensor belongs to. @param name: I{(str)} Name o... | Implement the Python class `Radar` described below.
Class description:
Radar sensor class.
Method signatures and docstrings:
- def __init__(self, vehicle_id, name, sens_range, sens_angle): Initialize Radar sensor class. @param vehicle_id: I{(int)} ID of the vehicle this sensor belongs to. @param name: I{(str)} Name o... | a759b0336b80b5647cc858d99d1fa40a0a9d826d | <|skeleton|>
class Radar:
"""Radar sensor class."""
def __init__(self, vehicle_id, name, sens_range, sens_angle):
"""Initialize Radar sensor class. @param vehicle_id: I{(int)} ID of the vehicle this sensor belongs to. @param name: I{(str)} Name of the sensor under which it will publish its readings. @p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Radar:
"""Radar sensor class."""
def __init__(self, vehicle_id, name, sens_range, sens_angle):
"""Initialize Radar sensor class. @param vehicle_id: I{(int)} ID of the vehicle this sensor belongs to. @param name: I{(str)} Name of the sensor under which it will publish its readings. @param sens_ran... | the_stack_v2_python_sparse | sml_world/scripts/sml_modules/sensor_models.py | marinarantanen/sml_world | train | 1 |
41ad63019fbeb3fdcf3db6c8b09fcde13324cb3b | [
"def result():\n shuffle('test')\nself.assertRaises(TypeError, result)",
"list_one = [1]\nshuffle(list_one)\nself.assertEqual(list_one, [1])\nlist_empty = []\nshuffle(list_empty)\nself.assertEqual(list_empty, [])",
"small_list = [8, 30, 2, 6]\ntracker = create_tracker_dict(small_list)\nshuffle(small_list)\ns... | <|body_start_0|>
def result():
shuffle('test')
self.assertRaises(TypeError, result)
<|end_body_0|>
<|body_start_1|>
list_one = [1]
shuffle(list_one)
self.assertEqual(list_one, [1])
list_empty = []
shuffle(list_empty)
self.assertEqual(list_empt... | TestShuffle | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestShuffle:
def test_raises_typeerror_for_non_list_argument(self):
"""Raises a new TypeError if the argument for shuffle is not type list"""
<|body_0|>
def test_returns_input_if_length_less_than_2(self):
"""Returns the inputted integer list if the length is less tha... | stack_v2_sparse_classes_36k_train_034537 | 2,026 | permissive | [
{
"docstring": "Raises a new TypeError if the argument for shuffle is not type list",
"name": "test_raises_typeerror_for_non_list_argument",
"signature": "def test_raises_typeerror_for_non_list_argument(self)"
},
{
"docstring": "Returns the inputted integer list if the length is less than 2",
... | 4 | null | Implement the Python class `TestShuffle` described below.
Class description:
Implement the TestShuffle class.
Method signatures and docstrings:
- def test_raises_typeerror_for_non_list_argument(self): Raises a new TypeError if the argument for shuffle is not type list
- def test_returns_input_if_length_less_than_2(se... | Implement the Python class `TestShuffle` described below.
Class description:
Implement the TestShuffle class.
Method signatures and docstrings:
- def test_raises_typeerror_for_non_list_argument(self): Raises a new TypeError if the argument for shuffle is not type list
- def test_returns_input_if_length_less_than_2(se... | 27ffb6b32d6d18d279c51cfa45bf305a409be5c2 | <|skeleton|>
class TestShuffle:
def test_raises_typeerror_for_non_list_argument(self):
"""Raises a new TypeError if the argument for shuffle is not type list"""
<|body_0|>
def test_returns_input_if_length_less_than_2(self):
"""Returns the inputted integer list if the length is less tha... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestShuffle:
def test_raises_typeerror_for_non_list_argument(self):
"""Raises a new TypeError if the argument for shuffle is not type list"""
def result():
shuffle('test')
self.assertRaises(TypeError, result)
def test_returns_input_if_length_less_than_2(self):
... | the_stack_v2_python_sparse | src/interview-cake/fisher-yates-shuffle/test_fisher_yates_shuffle.py | nwthomas/code-challenges | train | 2 | |
63eed6235fcbd320eb4281074d85cb279c3c3c1f | [
"def gotResults(results):\n for name, url, id in results:\n yield (u'\\x02%s\\x02: <%s>;' % (name, url))\n\ndef outputResults(results):\n source.reply(u' '.join(results))\nreturn imdb.searchByTitle(title, exact=False).addCallback(gotResults).addCallback(outputResults)",
"def gotInfo(info):\n sourc... | <|body_start_0|>
def gotResults(results):
for name, url, id in results:
yield (u'\x02%s\x02: <%s>;' % (name, url))
def outputResults(results):
source.reply(u' '.join(results))
return imdb.searchByTitle(title, exact=False).addCallback(gotResults).addCallba... | IMDB | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IMDB:
def cmd_search(self, source, title):
"""Search IMDB for artifacts whose titles match <title>."""
<|body_0|>
def cmd_plot(self, source, id):
"""Retrieve the plot information for an IMDB title with <id>."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_034538 | 1,477 | permissive | [
{
"docstring": "Search IMDB for artifacts whose titles match <title>.",
"name": "cmd_search",
"signature": "def cmd_search(self, source, title)"
},
{
"docstring": "Retrieve the plot information for an IMDB title with <id>.",
"name": "cmd_plot",
"signature": "def cmd_plot(self, source, id... | 2 | stack_v2_sparse_classes_30k_train_013986 | Implement the Python class `IMDB` described below.
Class description:
Implement the IMDB class.
Method signatures and docstrings:
- def cmd_search(self, source, title): Search IMDB for artifacts whose titles match <title>.
- def cmd_plot(self, source, id): Retrieve the plot information for an IMDB title with <id>. | Implement the Python class `IMDB` described below.
Class description:
Implement the IMDB class.
Method signatures and docstrings:
- def cmd_search(self, source, title): Search IMDB for artifacts whose titles match <title>.
- def cmd_plot(self, source, id): Retrieve the plot information for an IMDB title with <id>.
<... | 11c80c7024548ce7c41800b077d3d0a738a04875 | <|skeleton|>
class IMDB:
def cmd_search(self, source, title):
"""Search IMDB for artifacts whose titles match <title>."""
<|body_0|>
def cmd_plot(self, source, id):
"""Retrieve the plot information for an IMDB title with <id>."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IMDB:
def cmd_search(self, source, title):
"""Search IMDB for artifacts whose titles match <title>."""
def gotResults(results):
for name, url, id in results:
yield (u'\x02%s\x02: <%s>;' % (name, url))
def outputResults(results):
source.reply(u' ... | the_stack_v2_python_sparse | eridanusstd/plugindefs/imdb.py | mithrandi/eridanus | train | 0 | |
7adb6c04ff21b3aed12c470993a6d0eb2bef5dfd | [
"res = {}\nres[1] = 1\nres[2] = 2\nfor i in range(3, n + 1):\n res[i] = res[i - 1] + res[i - 2]\nreturn res[n]",
"a = b = 1\nfor _ in range(n):\n a, b = (b, a + b)\nreturn a"
] | <|body_start_0|>
res = {}
res[1] = 1
res[2] = 2
for i in range(3, n + 1):
res[i] = res[i - 1] + res[i - 2]
return res[n]
<|end_body_0|>
<|body_start_1|>
a = b = 1
for _ in range(n):
a, b = (b, a + b)
return a
<|end_body_1|>
| Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def climbStairs(self, n):
""":param n: int :return: int"""
<|body_0|>
def climbStairs2(self, n):
""":param n: int :return: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = {}
res[1] = 1
res[2] = 2
for i in ... | stack_v2_sparse_classes_36k_train_034539 | 913 | no_license | [
{
"docstring": ":param n: int :return: int",
"name": "climbStairs",
"signature": "def climbStairs(self, n)"
},
{
"docstring": ":param n: int :return: int",
"name": "climbStairs2",
"signature": "def climbStairs2(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000071 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def climbStairs(self, n): :param n: int :return: int
- def climbStairs2(self, n): :param n: int :return: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def climbStairs(self, n): :param n: int :return: int
- def climbStairs2(self, n): :param n: int :return: int
<|skeleton|>
class Solution:
def climbStairs(self, n):
... | 16b6fc4247c91a919d38bf18835f10fc29fccca7 | <|skeleton|>
class Solution:
def climbStairs(self, n):
""":param n: int :return: int"""
<|body_0|>
def climbStairs2(self, n):
""":param n: int :return: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def climbStairs(self, n):
""":param n: int :return: int"""
res = {}
res[1] = 1
res[2] = 2
for i in range(3, n + 1):
res[i] = res[i - 1] + res[i - 2]
return res[n]
def climbStairs2(self, n):
""":param n: int :return: int"""
... | the_stack_v2_python_sparse | problem_79.py | SeanLau/leetcode | train | 0 | |
41ec649af4c99f83e1c92288a0d671c9bf7cdfb3 | [
"PISM.IP_SSATaucTaoTikhonovProblemLCLListener.__init__(self)\nself.owner = owner\nself.listener = listener",
"data = Bunch(tikhonov_penalty=eta, JDesign=objVal, JState=penaltyVal, zeta=d, zeta_step=diff_d, grad_JDesign=grad_d, u=u, residual=diff_u, grad_JState=grad_u, constraints=constraints)\ntry:\n self.list... | <|body_start_0|>
PISM.IP_SSATaucTaoTikhonovProblemLCLListener.__init__(self)
self.owner = owner
self.listener = listener
<|end_body_0|>
<|body_start_1|>
data = Bunch(tikhonov_penalty=eta, JDesign=objVal, JState=penaltyVal, zeta=d, zeta_step=diff_d, grad_JDesign=grad_d, u=u, residual=dif... | Adaptor converting calls to a C++ :cpp:class:`IP_SSATaucTaoTikhonovProblemListener` on to a standard python-based listener. Used internally by :class:`InvSSATaucSolver_Tikhonov`. I.e. don't make one of these for yourself. | TaucLCLIterationListenerAdaptor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaucLCLIterationListenerAdaptor:
"""Adaptor converting calls to a C++ :cpp:class:`IP_SSATaucTaoTikhonovProblemListener` on to a standard python-based listener. Used internally by :class:`InvSSATaucSolver_Tikhonov`. I.e. don't make one of these for yourself."""
def __init__(self, owner, liste... | stack_v2_sparse_classes_36k_train_034540 | 10,589 | no_license | [
{
"docstring": ":param owner: The :class:`InvSSATaucSolver_Tikhonov` that constructed us :param listener: The python-based listener.",
"name": "__init__",
"signature": "def __init__(self, owner, listener)"
},
{
"docstring": "Called during IP_SSATaucTaoTikhonovProblemLCL iterations. Gathers toget... | 2 | stack_v2_sparse_classes_30k_train_006237 | Implement the Python class `TaucLCLIterationListenerAdaptor` described below.
Class description:
Adaptor converting calls to a C++ :cpp:class:`IP_SSATaucTaoTikhonovProblemListener` on to a standard python-based listener. Used internally by :class:`InvSSATaucSolver_Tikhonov`. I.e. don't make one of these for yourself.
... | Implement the Python class `TaucLCLIterationListenerAdaptor` described below.
Class description:
Adaptor converting calls to a C++ :cpp:class:`IP_SSATaucTaoTikhonovProblemListener` on to a standard python-based listener. Used internally by :class:`InvSSATaucSolver_Tikhonov`. I.e. don't make one of these for yourself.
... | 88664f50a2f7075b6e96a06a5976986aac0302ed | <|skeleton|>
class TaucLCLIterationListenerAdaptor:
"""Adaptor converting calls to a C++ :cpp:class:`IP_SSATaucTaoTikhonovProblemListener` on to a standard python-based listener. Used internally by :class:`InvSSATaucSolver_Tikhonov`. I.e. don't make one of these for yourself."""
def __init__(self, owner, liste... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaucLCLIterationListenerAdaptor:
"""Adaptor converting calls to a C++ :cpp:class:`IP_SSATaucTaoTikhonovProblemListener` on to a standard python-based listener. Used internally by :class:`InvSSATaucSolver_Tikhonov`. I.e. don't make one of these for yourself."""
def __init__(self, owner, listener):
... | the_stack_v2_python_sparse | site-packages/PISM/invert/ssa_tao.py | flapo099/test | train | 0 |
473b7264a210362b0ce31e3450bbd7aabd145c1c | [
"t = {}\nfor n in nums:\n if n in t:\n t[n] += 1\n else:\n t[n] = 1\n if t[n] == 2:\n del t[n]\nres = []\nfor key, val in t.items():\n res.append(key)\nreturn res",
"d = set()\nfor n in nums:\n if n in d:\n d.remove(n)\n else:\n d.add(n)\nreturn list(d)"
] | <|body_start_0|>
t = {}
for n in nums:
if n in t:
t[n] += 1
else:
t[n] = 1
if t[n] == 2:
del t[n]
res = []
for key, val in t.items():
res.append(key)
return res
<|end_body_0|>
<|body_... | https://leetcode.com/problems/single-number-iii/description/ | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/single-number-iii/description/"""
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def singleNumber2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|... | stack_v2_sparse_classes_36k_train_034541 | 843 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "singleNumber",
"signature": "def singleNumber(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "singleNumber2",
"signature": "def singleNumber2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005976 | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/single-number-iii/description/
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: List[int]
- def singleNumber2(self, nums): :type nums: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/single-number-iii/description/
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: List[int]
- def singleNumber2(self, nums): :type nums: List[int] :rtype: List[int]
<|s... | 54d3d9530b25272d4a2e5dc33e7035c44f506dc5 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/single-number-iii/description/"""
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def singleNumber2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""https://leetcode.com/problems/single-number-iii/description/"""
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
t = {}
for n in nums:
if n in t:
t[n] += 1
else:
t[n] = 1
if... | the_stack_v2_python_sparse | old/Session002/Arrays/SingleNumberIII.py | MaxIakovliev/algorithms | train | 0 |
3bc9dbc5765edcd53722c2f7fe155f4c3644209d | [
"lookup = {}\nrows = len(grid)\ncols = len(grid[0])\nones, overlaps = (0, 0)\nfor i in range(rows):\n for j in range(cols):\n if grid[i][j] == 1:\n ones += 1\n lookup[i, j] = 1\n if (i - 1, j) in lookup:\n overlaps += 1\n if (i + 1, j) in lookup:\... | <|body_start_0|>
lookup = {}
rows = len(grid)
cols = len(grid[0])
ones, overlaps = (0, 0)
for i in range(rows):
for j in range(cols):
if grid[i][j] == 1:
ones += 1
lookup[i, j] = 1
if (i - 1, ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def islandPerimeter(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_0|>
def islandPerimeter2(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_1|>
def islandPerimeter3(self, grid):
""":type grid: List[L... | stack_v2_sparse_classes_36k_train_034542 | 2,869 | no_license | [
{
"docstring": ":type grid: List[List[int]] :rtype: int",
"name": "islandPerimeter",
"signature": "def islandPerimeter(self, grid)"
},
{
"docstring": ":type grid: List[List[int]] :rtype: int",
"name": "islandPerimeter2",
"signature": "def islandPerimeter2(self, grid)"
},
{
"docst... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def islandPerimeter(self, grid): :type grid: List[List[int]] :rtype: int
- def islandPerimeter2(self, grid): :type grid: List[List[int]] :rtype: int
- def islandPerimeter3(self, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def islandPerimeter(self, grid): :type grid: List[List[int]] :rtype: int
- def islandPerimeter2(self, grid): :type grid: List[List[int]] :rtype: int
- def islandPerimeter3(self, ... | 602dfdf41b72e84f7d8e2f5e1a9ac6e0f8a71967 | <|skeleton|>
class Solution:
def islandPerimeter(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_0|>
def islandPerimeter2(self, grid):
""":type grid: List[List[int]] :rtype: int"""
<|body_1|>
def islandPerimeter3(self, grid):
""":type grid: List[L... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def islandPerimeter(self, grid):
""":type grid: List[List[int]] :rtype: int"""
lookup = {}
rows = len(grid)
cols = len(grid[0])
ones, overlaps = (0, 0)
for i in range(rows):
for j in range(cols):
if grid[i][j] == 1:
... | the_stack_v2_python_sparse | 463_island_perimeter.py | YaohuiZeng/Leetcode | train | 0 | |
0471e63d594822bbde055d2572c539bfecce6b1a | [
"for c in inspect.getmro(class_obj):\n if c.__name__ == parent_class_name:\n return True\nreturn False",
"if 'type' not in config:\n raise ConfigurationError(\"Section {} does not contain the key 'type' defining the component type\".format(name))\nc_type = config['type']\nif c_type.find('ptp.') != -1... | <|body_start_0|>
for c in inspect.getmro(class_obj):
if c.__name__ == parent_class_name:
return True
return False
<|end_body_0|>
<|body_start_1|>
if 'type' not in config:
raise ConfigurationError("Section {} does not contain the key 'type' defining the co... | Class instantiating the components using the passed config. | ComponentFactory | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ComponentFactory:
"""Class instantiating the components using the passed config."""
def check_inheritance(class_obj, parent_class_name):
"""Checks whether given class inherits (even indirectly) from parent class."""
<|body_0|>
def build(name, config):
"""Method c... | stack_v2_sparse_classes_36k_train_034543 | 2,795 | permissive | [
{
"docstring": "Checks whether given class inherits (even indirectly) from parent class.",
"name": "check_inheritance",
"signature": "def check_inheritance(class_obj, parent_class_name)"
},
{
"docstring": "Method creates a single component on the basis of configuration section. Raises Configurat... | 2 | stack_v2_sparse_classes_30k_train_015344 | Implement the Python class `ComponentFactory` described below.
Class description:
Class instantiating the components using the passed config.
Method signatures and docstrings:
- def check_inheritance(class_obj, parent_class_name): Checks whether given class inherits (even indirectly) from parent class.
- def build(na... | Implement the Python class `ComponentFactory` described below.
Class description:
Class instantiating the components using the passed config.
Method signatures and docstrings:
- def check_inheritance(class_obj, parent_class_name): Checks whether given class inherits (even indirectly) from parent class.
- def build(na... | 9cb17271666061cb19fe24197ecd5e4c8d32c5da | <|skeleton|>
class ComponentFactory:
"""Class instantiating the components using the passed config."""
def check_inheritance(class_obj, parent_class_name):
"""Checks whether given class inherits (even indirectly) from parent class."""
<|body_0|>
def build(name, config):
"""Method c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ComponentFactory:
"""Class instantiating the components using the passed config."""
def check_inheritance(class_obj, parent_class_name):
"""Checks whether given class inherits (even indirectly) from parent class."""
for c in inspect.getmro(class_obj):
if c.__name__ == parent_c... | the_stack_v2_python_sparse | ptp/application/component_factory.py | ConnectionMaster/pytorchpipe | train | 1 |
aa1ac397ff453ac5122e4ba163472945f89a516c | [
"self.archival_target = archival_target\nself.cloud_replication_target = cloud_replication_target\nself.replication_target = replication_target\nself.mtype = mtype",
"if dictionary is None:\n return None\narchival_target = cohesity_management_sdk.models.archival_external_target.ArchivalExternalTarget.from_dict... | <|body_start_0|>
self.archival_target = archival_target
self.cloud_replication_target = cloud_replication_target
self.replication_target = replication_target
self.mtype = mtype
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
archival_target... | Implementation of the 'SnapshotTargetSettings' model. Specifies settings about a target where a copied Snapshot is stored. A target can be a Remote Cluster or an Archival External Target such as AWS or Tape. Attributes: archival_target (ArchivalExternalTarget): Specifies the Archival External Target for storing a copie... | SnapshotTargetSettings | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SnapshotTargetSettings:
"""Implementation of the 'SnapshotTargetSettings' model. Specifies settings about a target where a copied Snapshot is stored. A target can be a Remote Cluster or an Archival External Target such as AWS or Tape. Attributes: archival_target (ArchivalExternalTarget): Specifie... | stack_v2_sparse_classes_36k_train_034544 | 4,209 | permissive | [
{
"docstring": "Constructor for the SnapshotTargetSettings class",
"name": "__init__",
"signature": "def __init__(self, archival_target=None, cloud_replication_target=None, replication_target=None, mtype=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictiona... | 2 | stack_v2_sparse_classes_30k_train_020021 | Implement the Python class `SnapshotTargetSettings` described below.
Class description:
Implementation of the 'SnapshotTargetSettings' model. Specifies settings about a target where a copied Snapshot is stored. A target can be a Remote Cluster or an Archival External Target such as AWS or Tape. Attributes: archival_ta... | Implement the Python class `SnapshotTargetSettings` described below.
Class description:
Implementation of the 'SnapshotTargetSettings' model. Specifies settings about a target where a copied Snapshot is stored. A target can be a Remote Cluster or an Archival External Target such as AWS or Tape. Attributes: archival_ta... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SnapshotTargetSettings:
"""Implementation of the 'SnapshotTargetSettings' model. Specifies settings about a target where a copied Snapshot is stored. A target can be a Remote Cluster or an Archival External Target such as AWS or Tape. Attributes: archival_target (ArchivalExternalTarget): Specifie... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SnapshotTargetSettings:
"""Implementation of the 'SnapshotTargetSettings' model. Specifies settings about a target where a copied Snapshot is stored. A target can be a Remote Cluster or an Archival External Target such as AWS or Tape. Attributes: archival_target (ArchivalExternalTarget): Specifies the Archiva... | the_stack_v2_python_sparse | cohesity_management_sdk/models/snapshot_target_settings.py | cohesity/management-sdk-python | train | 24 |
c498d341c49f21f50670398038b28d76fbda3e5e | [
"super(test_resize_volume_instances, cls).setUpClass()\ncls.dbaas = cls.client.reddwarfclient\nNAME = 'qe-resize_instances'\nFLAVOR = 1\nVOLUME = test_resize_volume_instances.ResizeUpSizes.origLevel\ninstance = test_resize_volume_instances.dbaas.instances.create(name=NAME, flavor_id=FLAVOR, volume={'size': VOLUME},... | <|body_start_0|>
super(test_resize_volume_instances, cls).setUpClass()
cls.dbaas = cls.client.reddwarfclient
NAME = 'qe-resize_instances'
FLAVOR = 1
VOLUME = test_resize_volume_instances.ResizeUpSizes.origLevel
instance = test_resize_volume_instances.dbaas.instances.creat... | test_resize_volume_instances | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test_resize_volume_instances:
def setUpClass(cls):
"""Creating an instance for smoke testing"""
<|body_0|>
def tearDownClass(cls):
"""Tearing down: Deleting the instance if in active state"""
<|body_1|>
def test_resize_volume_instance(self):
"""R... | stack_v2_sparse_classes_36k_train_034545 | 30,991 | permissive | [
{
"docstring": "Creating an instance for smoke testing",
"name": "setUpClass",
"signature": "def setUpClass(cls)"
},
{
"docstring": "Tearing down: Deleting the instance if in active state",
"name": "tearDownClass",
"signature": "def tearDownClass(cls)"
},
{
"docstring": "Resize t... | 3 | null | Implement the Python class `test_resize_volume_instances` described below.
Class description:
Implement the test_resize_volume_instances class.
Method signatures and docstrings:
- def setUpClass(cls): Creating an instance for smoke testing
- def tearDownClass(cls): Tearing down: Deleting the instance if in active sta... | Implement the Python class `test_resize_volume_instances` described below.
Class description:
Implement the test_resize_volume_instances class.
Method signatures and docstrings:
- def setUpClass(cls): Creating an instance for smoke testing
- def tearDownClass(cls): Tearing down: Deleting the instance if in active sta... | 30f0e64672676c3f90b4a582fe90fac6621475b3 | <|skeleton|>
class test_resize_volume_instances:
def setUpClass(cls):
"""Creating an instance for smoke testing"""
<|body_0|>
def tearDownClass(cls):
"""Tearing down: Deleting the instance if in active state"""
<|body_1|>
def test_resize_volume_instance(self):
"""R... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class test_resize_volume_instances:
def setUpClass(cls):
"""Creating an instance for smoke testing"""
super(test_resize_volume_instances, cls).setUpClass()
cls.dbaas = cls.client.reddwarfclient
NAME = 'qe-resize_instances'
FLAVOR = 1
VOLUME = test_resize_volume_instan... | the_stack_v2_python_sparse | cloudroast/database/pos_regr/instances.py | RULCSoft/cloudroast | train | 1 | |
ec55e7e8567fa0615f54ad5159259e02c2c55149 | [
"print('Querying database')\nif sort not in ['created_at', 'cook_time', 'num_of_servings']:\n sort = 'created_at'\nif order not in ['asc', 'desc']:\n order = 'desc'\npaginated_recipes = Recipe.get_all_published(q, page, per_page, sort, order)\nreturn (recipe_pagination_schema.dump(paginated_recipes).data, HTT... | <|body_start_0|>
print('Querying database')
if sort not in ['created_at', 'cook_time', 'num_of_servings']:
sort = 'created_at'
if order not in ['asc', 'desc']:
order = 'desc'
paginated_recipes = Recipe.get_all_published(q, page, per_page, sort, order)
retu... | RecipeListResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RecipeListResource:
def get(self, q, page, per_page, sort, order):
"""Passes three arguments in the get_all_published method and gets the pagination object back. returns the paginated recipes as serialized and back to front end client. The q parameter passed the search string into the AP... | stack_v2_sparse_classes_36k_train_034546 | 8,070 | no_license | [
{
"docstring": "Passes three arguments in the get_all_published method and gets the pagination object back. returns the paginated recipes as serialized and back to front end client. The q parameter passed the search string into the API",
"name": "get",
"signature": "def get(self, q, page, per_page, sort... | 2 | stack_v2_sparse_classes_30k_train_003393 | Implement the Python class `RecipeListResource` described below.
Class description:
Implement the RecipeListResource class.
Method signatures and docstrings:
- def get(self, q, page, per_page, sort, order): Passes three arguments in the get_all_published method and gets the pagination object back. returns the paginat... | Implement the Python class `RecipeListResource` described below.
Class description:
Implement the RecipeListResource class.
Method signatures and docstrings:
- def get(self, q, page, per_page, sort, order): Passes three arguments in the get_all_published method and gets the pagination object back. returns the paginat... | 875b8bc3cc5315efe8ccee8ce9b312056802c49d | <|skeleton|>
class RecipeListResource:
def get(self, q, page, per_page, sort, order):
"""Passes three arguments in the get_all_published method and gets the pagination object back. returns the paginated recipes as serialized and back to front end client. The q parameter passed the search string into the AP... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RecipeListResource:
def get(self, q, page, per_page, sort, order):
"""Passes three arguments in the get_all_published method and gets the pagination object back. returns the paginated recipes as serialized and back to front end client. The q parameter passed the search string into the API"""
p... | the_stack_v2_python_sparse | resources/recipe.py | ShayanRiyaz/TheDailyCook | train | 1 | |
4c5e0cb42c5a1c375ca0d6015d511deb485d3905 | [
"GeneticAlgorithm.__init__(self, max_iter, max_time, max_f_evals, verbose, verbose_interspace, plot_pareto_front, plot_pareto_solutions, plot_dpi, pop_size)\nself._survival_strategy = MemeticSurvivalStrategy(pop_size, crowding_quantile)\nGradientBasedAlgorithm.__init__(self, max_iter, max_time, max_f_evals, verbose... | <|body_start_0|>
GeneticAlgorithm.__init__(self, max_iter, max_time, max_f_evals, verbose, verbose_interspace, plot_pareto_front, plot_pareto_solutions, plot_dpi, pop_size)
self._survival_strategy = MemeticSurvivalStrategy(pop_size, crowding_quantile)
GradientBasedAlgorithm.__init__(self, max_it... | Abstract class for memetic algorithms The main functions are: - Initialize a memetic algorithm instance; - Add a specified value to the current one(s) of a stopping condition (see StoppingCondition.py); - Update the current value of a stopping condition. | MemeticAlgorithm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MemeticAlgorithm:
"""Abstract class for memetic algorithms The main functions are: - Initialize a memetic algorithm instance; - Add a specified value to the current one(s) of a stopping condition (see StoppingCondition.py); - Update the current value of a stopping condition."""
def __init__(... | stack_v2_sparse_classes_36k_train_034547 | 8,306 | permissive | [
{
"docstring": "Initialize a memetic algorithm instance :param max_iter: maximum number of iterations :param max_time: maximum number of elapsed minutes on a problem :param max_f_evals: maximum number of function evaluations :param verbose: if set to True, then the VerboseSystem instance is used during the algo... | 3 | stack_v2_sparse_classes_30k_train_010847 | Implement the Python class `MemeticAlgorithm` described below.
Class description:
Abstract class for memetic algorithms The main functions are: - Initialize a memetic algorithm instance; - Add a specified value to the current one(s) of a stopping condition (see StoppingCondition.py); - Update the current value of a st... | Implement the Python class `MemeticAlgorithm` described below.
Class description:
Abstract class for memetic algorithms The main functions are: - Initialize a memetic algorithm instance; - Add a specified value to the current one(s) of a stopping condition (see StoppingCondition.py); - Update the current value of a st... | 22610b3dbc308fe89309ac99204992feac048908 | <|skeleton|>
class MemeticAlgorithm:
"""Abstract class for memetic algorithms The main functions are: - Initialize a memetic algorithm instance; - Add a specified value to the current one(s) of a stopping condition (see StoppingCondition.py); - Update the current value of a stopping condition."""
def __init__(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MemeticAlgorithm:
"""Abstract class for memetic algorithms The main functions are: - Initialize a memetic algorithm instance; - Add a specified value to the current one(s) of a stopping condition (see StoppingCondition.py); - Update the current value of a stopping condition."""
def __init__(self, max_ite... | the_stack_v2_python_sparse | algorithms/memetic/memetic_algorithm.py | pierlumanzu/nsma | train | 5 |
d2d27c2d852ca82fa3c013df2f1cf08049f297db | [
"value = proposal['value']\nif value is None:\n return value\nif self.min and self.min > value:\n value = max(value, self.min)\nif self.max and self.max < value:\n value = min(value, self.max)\nreturn value",
"min = proposal['value']\nif min is None:\n return min\nif self.max and min > self.max:\n ... | <|body_start_0|>
value = proposal['value']
if value is None:
return value
if self.min and self.min > value:
value = max(value, self.min)
if self.max and self.max < value:
value = min(value, self.max)
return value
<|end_body_0|>
<|body_start_1|... | Display a widget for picking dates. Parameters ---------- value: datetime.date The current value of the widget. disabled: bool Whether to disable user changes. Examples -------- >>> import datetime >>> import ipywidgets as widgets >>> date_pick = widgets.DatePicker() >>> date_pick.value = datetime.date(2019, 7, 9) | DatePicker | [
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatePicker:
"""Display a widget for picking dates. Parameters ---------- value: datetime.date The current value of the widget. disabled: bool Whether to disable user changes. Examples -------- >>> import datetime >>> import ipywidgets as widgets >>> date_pick = widgets.DatePicker() >>> date_pick.... | stack_v2_sparse_classes_36k_train_034548 | 2,597 | permissive | [
{
"docstring": "Cap and floor value",
"name": "_validate_value",
"signature": "def _validate_value(self, proposal)"
},
{
"docstring": "Enforce min <= value <= max",
"name": "_validate_min",
"signature": "def _validate_min(self, proposal)"
},
{
"docstring": "Enforce min <= value <... | 3 | null | Implement the Python class `DatePicker` described below.
Class description:
Display a widget for picking dates. Parameters ---------- value: datetime.date The current value of the widget. disabled: bool Whether to disable user changes. Examples -------- >>> import datetime >>> import ipywidgets as widgets >>> date_pic... | Implement the Python class `DatePicker` described below.
Class description:
Display a widget for picking dates. Parameters ---------- value: datetime.date The current value of the widget. disabled: bool Whether to disable user changes. Examples -------- >>> import datetime >>> import ipywidgets as widgets >>> date_pic... | f5042e35b945aded77b23470ead62d7eacefde92 | <|skeleton|>
class DatePicker:
"""Display a widget for picking dates. Parameters ---------- value: datetime.date The current value of the widget. disabled: bool Whether to disable user changes. Examples -------- >>> import datetime >>> import ipywidgets as widgets >>> date_pick = widgets.DatePicker() >>> date_pick.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatePicker:
"""Display a widget for picking dates. Parameters ---------- value: datetime.date The current value of the widget. disabled: bool Whether to disable user changes. Examples -------- >>> import datetime >>> import ipywidgets as widgets >>> date_pick = widgets.DatePicker() >>> date_pick.value = datet... | the_stack_v2_python_sparse | contrib/python/ipywidgets/py3/ipywidgets/widgets/widget_date.py | catboost/catboost | train | 8,012 |
bc4ff3761dd848b32d7f57f9ebb29f35d9faa02f | [
"self.folderA = folderA\nself.folderB = folderB\nself.fileMask = fileMask\nself.processedFilesList = []",
"try:\n print('- copying \"%s\" -> \"%s\"...' % (fileNameSrc, fileNameDst))\n shutil.copyfile(fileNameSrc, fileNameDst)\n shutil.copystat(fileNameSrc, fileNameDst)\nexcept Exception as error:\n pr... | <|body_start_0|>
self.folderA = folderA
self.folderB = folderB
self.fileMask = fileMask
self.processedFilesList = []
<|end_body_0|>
<|body_start_1|>
try:
print('- copying "%s" -> "%s"...' % (fileNameSrc, fileNameDst))
shutil.copyfile(fileNameSrc, fileName... | Синхронизация папок | FolderSync | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FolderSync:
"""Синхронизация папок"""
def __init__(self, folderA, folderB, fileMask):
"""Инициализация"""
<|body_0|>
def _CopyFile(self, fileNameSrc, fileNameDst):
"""Копируем файл и подавляем исключения"""
<|body_1|>
def _SyncOneWay(self, folder1, f... | stack_v2_sparse_classes_36k_train_034549 | 2,568 | no_license | [
{
"docstring": "Инициализация",
"name": "__init__",
"signature": "def __init__(self, folderA, folderB, fileMask)"
},
{
"docstring": "Копируем файл и подавляем исключения",
"name": "_CopyFile",
"signature": "def _CopyFile(self, fileNameSrc, fileNameDst)"
},
{
"docstring": "Односто... | 4 | stack_v2_sparse_classes_30k_train_005401 | Implement the Python class `FolderSync` described below.
Class description:
Синхронизация папок
Method signatures and docstrings:
- def __init__(self, folderA, folderB, fileMask): Инициализация
- def _CopyFile(self, fileNameSrc, fileNameDst): Копируем файл и подавляем исключения
- def _SyncOneWay(self, folder1, folde... | Implement the Python class `FolderSync` described below.
Class description:
Синхронизация папок
Method signatures and docstrings:
- def __init__(self, folderA, folderB, fileMask): Инициализация
- def _CopyFile(self, fileNameSrc, fileNameDst): Копируем файл и подавляем исключения
- def _SyncOneWay(self, folder1, folde... | d2771bf04aa187dda6d468883a5a167237589369 | <|skeleton|>
class FolderSync:
"""Синхронизация папок"""
def __init__(self, folderA, folderB, fileMask):
"""Инициализация"""
<|body_0|>
def _CopyFile(self, fileNameSrc, fileNameDst):
"""Копируем файл и подавляем исключения"""
<|body_1|>
def _SyncOneWay(self, folder1, f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FolderSync:
"""Синхронизация папок"""
def __init__(self, folderA, folderB, fileMask):
"""Инициализация"""
self.folderA = folderA
self.folderB = folderB
self.fileMask = fileMask
self.processedFilesList = []
def _CopyFile(self, fileNameSrc, fileNameDst):
... | the_stack_v2_python_sparse | doorsadmin/tools/sync.py | cash2one/doorscenter | train | 0 |
c598542fa0ca5311f3c2025aaab7e697b148e067 | [
"self = real_self.magic\ngf = getFormatter\nargs = {'presentation_of': real_self.directTranslate(Message(u'text_presentation_of', 'recensio', default='presentation of:')), 'in': real_self.directTranslate(Message(u'text_in', 'recensio', default='in:')), 'page': real_self.directTranslate(Message(u'text_pages', 'recen... | <|body_start_0|>
self = real_self.magic
gf = getFormatter
args = {'presentation_of': real_self.directTranslate(Message(u'text_presentation_of', 'recensio', default='presentation of:')), 'in': real_self.directTranslate(Message(u'text_in', 'recensio', default='in:')), 'page': real_self.directTrans... | PresentationCollectionNoMagic | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PresentationCollectionNoMagic:
def get_citation_string(real_self):
"""NOTE: There is no differentiation between title and subtitle of the collection book. Either return the custom citation or the generated one >>> from mock import Mock >>> at_mock = Mock() >>> at_mock.customCitation = ''... | stack_v2_sparse_classes_36k_train_034550 | 16,138 | no_license | [
{
"docstring": "NOTE: There is no differentiation between title and subtitle of the collection book. Either return the custom citation or the generated one >>> from mock import Mock >>> at_mock = Mock() >>> at_mock.customCitation = '' >>> at_mock.get = lambda x: None >>> at_mock.authors = [{'firstname': x[0], '... | 2 | stack_v2_sparse_classes_30k_train_016409 | Implement the Python class `PresentationCollectionNoMagic` described below.
Class description:
Implement the PresentationCollectionNoMagic class.
Method signatures and docstrings:
- def get_citation_string(real_self): NOTE: There is no differentiation between title and subtitle of the collection book. Either return t... | Implement the Python class `PresentationCollectionNoMagic` described below.
Class description:
Implement the PresentationCollectionNoMagic class.
Method signatures and docstrings:
- def get_citation_string(real_self): NOTE: There is no differentiation between title and subtitle of the collection book. Either return t... | acf6ca3c962bfcf50600739087973de3ba7ad124 | <|skeleton|>
class PresentationCollectionNoMagic:
def get_citation_string(real_self):
"""NOTE: There is no differentiation between title and subtitle of the collection book. Either return the custom citation or the generated one >>> from mock import Mock >>> at_mock = Mock() >>> at_mock.customCitation = ''... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PresentationCollectionNoMagic:
def get_citation_string(real_self):
"""NOTE: There is no differentiation between title and subtitle of the collection book. Either return the custom citation or the generated one >>> from mock import Mock >>> at_mock = Mock() >>> at_mock.customCitation = '' >>> at_mock.g... | the_stack_v2_python_sparse | recensio/contenttypes/content/presentationcollection.py | syslabcom/recensio.contenttypes | train | 0 | |
b1533134e20fed855f4e2c8ac4a02ad0397ba0d2 | [
"ImageObj.__init__(self, name, interpolation=interpolation, max_pts=max_pts, parent=parent, transform=transform, verbose=verbose, **kw)\nif isinstance(data, np.ndarray):\n self.set_data(data, sf, f_pha, f_amp, idpac, n_window, clim, cmap, vmin, under, vmax, over, **pac_kw)",
"is_tensorpac_installed(raise_error... | <|body_start_0|>
ImageObj.__init__(self, name, interpolation=interpolation, max_pts=max_pts, parent=parent, transform=transform, verbose=verbose, **kw)
if isinstance(data, np.ndarray):
self.set_data(data, sf, f_pha, f_amp, idpac, n_window, clim, cmap, vmin, under, vmax, over, **pac_kw)
<|end... | Create a Phase-Amplitude Coupling (PAC) object. The PAC is computed using the tensorpac package. The Pacmap can be used to visualize : * PAC, across time for a fixed phase and several amplitude frequencies * PAC, across time for a fixed amplitude and several phase frequencies * PAC is computed across time for several a... | PacmapObj | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PacmapObj:
"""Create a Phase-Amplitude Coupling (PAC) object. The PAC is computed using the tensorpac package. The Pacmap can be used to visualize : * PAC, across time for a fixed phase and several amplitude frequencies * PAC, across time for a fixed amplitude and several phase frequencies * PAC ... | stack_v2_sparse_classes_36k_train_034551 | 5,525 | permissive | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, name, data=None, sf=1.0, f_pha=[(2, 4), (5, 7), (8, 13)], f_amp=[(40, 60), (60, 100)], idpac=(4, 0, 0), n_window=None, cmap='viridis', clim=None, vmin=None, under='gray', vmax=None, over='red', interpolation='nearest', max_pts=... | 2 | stack_v2_sparse_classes_30k_test_000727 | Implement the Python class `PacmapObj` described below.
Class description:
Create a Phase-Amplitude Coupling (PAC) object. The PAC is computed using the tensorpac package. The Pacmap can be used to visualize : * PAC, across time for a fixed phase and several amplitude frequencies * PAC, across time for a fixed amplitu... | Implement the Python class `PacmapObj` described below.
Class description:
Create a Phase-Amplitude Coupling (PAC) object. The PAC is computed using the tensorpac package. The Pacmap can be used to visualize : * PAC, across time for a fixed phase and several amplitude frequencies * PAC, across time for a fixed amplitu... | be096aa8a7058c329e7120d0bdb45d3c9eb8be42 | <|skeleton|>
class PacmapObj:
"""Create a Phase-Amplitude Coupling (PAC) object. The PAC is computed using the tensorpac package. The Pacmap can be used to visualize : * PAC, across time for a fixed phase and several amplitude frequencies * PAC, across time for a fixed amplitude and several phase frequencies * PAC ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PacmapObj:
"""Create a Phase-Amplitude Coupling (PAC) object. The PAC is computed using the tensorpac package. The Pacmap can be used to visualize : * PAC, across time for a fixed phase and several amplitude frequencies * PAC, across time for a fixed amplitude and several phase frequencies * PAC is computed a... | the_stack_v2_python_sparse | visbrain/objects/pacmap_obj.py | lassemadsen/visbrain | train | 0 |
1aa1a0058a6e7d11a04fac660de3b22d5d2813f1 | [
"super().__init__(params)\nself.lr = lr\nself.momentum = momentum\nself.v_weights = [np.zeros_like(t.weights) for t in self.params]\nself.v_bias = [np.zeros_like(t.bias) for t in self.params]",
"for i, object in enumerate(self.params):\n self.v_weights[i] = self.momentum * self.v_weights[i]\n self.v_bias[i]... | <|body_start_0|>
super().__init__(params)
self.lr = lr
self.momentum = momentum
self.v_weights = [np.zeros_like(t.weights) for t in self.params]
self.v_bias = [np.zeros_like(t.bias) for t in self.params]
<|end_body_0|>
<|body_start_1|>
for i, object in enumerate(self.par... | GD | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GD:
def __init__(self, params, lr=0.001, momentum=0.0):
"""The Gradient Descent optimizer (GD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent"""
<|body_0|>
def step(self):
"""Step p... | stack_v2_sparse_classes_36k_train_034552 | 3,421 | no_license | [
{
"docstring": "The Gradient Descent optimizer (GD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent",
"name": "__init__",
"signature": "def __init__(self, params, lr=0.001, momentum=0.0)"
},
{
"docstring": "Step... | 2 | stack_v2_sparse_classes_30k_train_006117 | Implement the Python class `GD` described below.
Class description:
Implement the GD class.
Method signatures and docstrings:
- def __init__(self, params, lr=0.001, momentum=0.0): The Gradient Descent optimizer (GD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the ve... | Implement the Python class `GD` described below.
Class description:
Implement the GD class.
Method signatures and docstrings:
- def __init__(self, params, lr=0.001, momentum=0.0): The Gradient Descent optimizer (GD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the ve... | 07ff58ae68264e3a9b820d10e84d82f8a3ca99b5 | <|skeleton|>
class GD:
def __init__(self, params, lr=0.001, momentum=0.0):
"""The Gradient Descent optimizer (GD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent"""
<|body_0|>
def step(self):
"""Step p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GD:
def __init__(self, params, lr=0.001, momentum=0.0):
"""The Gradient Descent optimizer (GD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent"""
super().__init__(params)
self.lr = lr
self.mome... | the_stack_v2_python_sparse | wavegrad/optimizers.py | vlnraf/WaveGrad | train | 1 | |
546e546c24584a066a8ba1b280b923c8897bf616 | [
"if kwargs and object_id:\n raise Exception('Specify an object id or keyword arguments, but not both')\nif kwargs:\n object_id = self._get_object(**kwargs)['Id']\nurl_template = '{root}/lightning/r/{object_name}/{object_id}/view'\nurl = url_template.format(root=self.cumulusci.org.lightning_base_url, object_na... | <|body_start_0|>
if kwargs and object_id:
raise Exception('Specify an object id or keyword arguments, but not both')
if kwargs:
object_id = self._get_object(**kwargs)['Id']
url_template = '{root}/lightning/r/{object_name}/{object_id}/view'
url = url_template.forma... | A page object representing the standard Detail page. When going to this page via the standard `Go to page` keyword, you can specify either an object id, or a set of keyword arguments which will be used to look up the object id. When using keyword arguments, they need to represent a unique user. Example | ${contact_id} ... | DetailPage | [
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DetailPage:
"""A page object representing the standard Detail page. When going to this page via the standard `Go to page` keyword, you can specify either an object id, or a set of keyword arguments which will be used to look up the object id. When using keyword arguments, they need to represent a... | stack_v2_sparse_classes_36k_train_034553 | 16,279 | permissive | [
{
"docstring": "Go to the detail page for the given record. You may pass in an object id, or you may pass in keyword arguments which can be used to look up the object. Example | Go to page Detail Contact firstName=John lastName=Smith",
"name": "_go_to_page",
"signature": "def _go_to_page(self, object_id... | 2 | stack_v2_sparse_classes_30k_train_017958 | Implement the Python class `DetailPage` described below.
Class description:
A page object representing the standard Detail page. When going to this page via the standard `Go to page` keyword, you can specify either an object id, or a set of keyword arguments which will be used to look up the object id. When using keyw... | Implement the Python class `DetailPage` described below.
Class description:
A page object representing the standard Detail page. When going to this page via the standard `Go to page` keyword, you can specify either an object id, or a set of keyword arguments which will be used to look up the object id. When using keyw... | 9ccf3c9566f78c6e9102ac214db30470cef660c1 | <|skeleton|>
class DetailPage:
"""A page object representing the standard Detail page. When going to this page via the standard `Go to page` keyword, you can specify either an object id, or a set of keyword arguments which will be used to look up the object id. When using keyword arguments, they need to represent a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DetailPage:
"""A page object representing the standard Detail page. When going to this page via the standard `Go to page` keyword, you can specify either an object id, or a set of keyword arguments which will be used to look up the object id. When using keyword arguments, they need to represent a unique user.... | the_stack_v2_python_sparse | cumulusci/robotframework/pageobjects/BasePageObjects.py | SFDO-Tooling/CumulusCI | train | 226 |
8a16fbe82e5c72ada13cb843d973957b35fc6506 | [
"super().__init__()\nmodel_file_name_full_path = os.path.join(resource_manager.getResourceRootDir(), model_file_name)\nif not os.path.exists(model_file_name_full_path):\n raise Exception(f'Missing model file: {model_file_name_full_path}')\nself.model = resource_manager.loadRankLibModel(model_file_name)\nself.fea... | <|body_start_0|>
super().__init__()
model_file_name_full_path = os.path.join(resource_manager.getResourceRootDir(), model_file_name)
if not os.path.exists(model_file_name_full_path):
raise Exception(f'Missing model file: {model_file_name_full_path}')
self.model = resource_man... | An interface to classic (non-neural) rankers, which are implemented a the Java layer. Model and configuration files are relative to the collection directory (resource root directory). | ClassicRanker | [
"BSD-2-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassicRanker:
"""An interface to classic (non-neural) rankers, which are implemented a the Java layer. Model and configuration files are relative to the collection directory (resource root directory)."""
def __init__(self, resource_manager, feat_extr_file_name, model_file_name):
"""... | stack_v2_sparse_classes_36k_train_034554 | 3,723 | permissive | [
{
"docstring": "Reranker constructor. :param resource_manager: a resource manager object :param feat_extr_file_name: feature extractor JSON configuration file. :param model_file_name: a (previously trained/created) model file name",
"name": "__init__",
"signature": "def __init__(self, resource_manager, ... | 2 | stack_v2_sparse_classes_30k_train_010418 | Implement the Python class `ClassicRanker` described below.
Class description:
An interface to classic (non-neural) rankers, which are implemented a the Java layer. Model and configuration files are relative to the collection directory (resource root directory).
Method signatures and docstrings:
- def __init__(self, ... | Implement the Python class `ClassicRanker` described below.
Class description:
An interface to classic (non-neural) rankers, which are implemented a the Java layer. Model and configuration files are relative to the collection directory (resource root directory).
Method signatures and docstrings:
- def __init__(self, ... | 0bd3e06735ff705731fb6cee62d3486276beccdf | <|skeleton|>
class ClassicRanker:
"""An interface to classic (non-neural) rankers, which are implemented a the Java layer. Model and configuration files are relative to the collection directory (resource root directory)."""
def __init__(self, resource_manager, feat_extr_file_name, model_file_name):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassicRanker:
"""An interface to classic (non-neural) rankers, which are implemented a the Java layer. Model and configuration files are relative to the collection directory (resource root directory)."""
def __init__(self, resource_manager, feat_extr_file_name, model_file_name):
"""Reranker cons... | the_stack_v2_python_sparse | flexneuart/ranker/classic.py | oaqa/FlexNeuART | train | 156 |
3fbad9abe1ddc3fdf17c50ca1ca108b45440e426 | [
"Q = self.coll.Qmat if Q is None else Q\nQI = np.zeros_like(Q) if QI is None else QI\nQE = np.zeros_like(Q) if QE is None else QE\nL = self.level\nme = []\nfor m in range(1, self.coll.num_nodes + 1):\n me.append(L.dt * ((Q - QI)[m, 1] * L.f[1].impl + (Q - QE)[m, 1] * L.f[1].expl))\n for j in range(2, self.col... | <|body_start_0|>
Q = self.coll.Qmat if Q is None else Q
QI = np.zeros_like(Q) if QI is None else QI
QE = np.zeros_like(Q) if QE is None else QE
L = self.level
me = []
for m in range(1, self.coll.num_nodes + 1):
me.append(L.dt * ((Q - QI)[m, 1] * L.f[1].impl + ... | Duplicate of `imex_1st_order` sweeper which is slightly more efficient at the cost of code readability. | imex_1st_order_efficient | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class imex_1st_order_efficient:
"""Duplicate of `imex_1st_order` sweeper which is slightly more efficient at the cost of code readability."""
def integrate(self, Q=None, QI=None, QE=None):
"""Integrates the right-hand side (here impl + expl) Args: Q (numpy.ndarray): Full quadrature rule QI... | stack_v2_sparse_classes_36k_train_034555 | 7,777 | permissive | [
{
"docstring": "Integrates the right-hand side (here impl + expl) Args: Q (numpy.ndarray): Full quadrature rule QI (numpy.ndarray): Implicit preconditioner QE (numpy.ndarray): Explicit preconditioner Returns: list of dtype_u: containing the integral as values",
"name": "integrate",
"signature": "def int... | 2 | stack_v2_sparse_classes_30k_train_019783 | Implement the Python class `imex_1st_order_efficient` described below.
Class description:
Duplicate of `imex_1st_order` sweeper which is slightly more efficient at the cost of code readability.
Method signatures and docstrings:
- def integrate(self, Q=None, QI=None, QE=None): Integrates the right-hand side (here impl... | Implement the Python class `imex_1st_order_efficient` described below.
Class description:
Duplicate of `imex_1st_order` sweeper which is slightly more efficient at the cost of code readability.
Method signatures and docstrings:
- def integrate(self, Q=None, QI=None, QE=None): Integrates the right-hand side (here impl... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class imex_1st_order_efficient:
"""Duplicate of `imex_1st_order` sweeper which is slightly more efficient at the cost of code readability."""
def integrate(self, Q=None, QI=None, QE=None):
"""Integrates the right-hand side (here impl + expl) Args: Q (numpy.ndarray): Full quadrature rule QI... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class imex_1st_order_efficient:
"""Duplicate of `imex_1st_order` sweeper which is slightly more efficient at the cost of code readability."""
def integrate(self, Q=None, QI=None, QE=None):
"""Integrates the right-hand side (here impl + expl) Args: Q (numpy.ndarray): Full quadrature rule QI (numpy.ndarr... | the_stack_v2_python_sparse | pySDC/projects/Resilience/sweepers.py | Parallel-in-Time/pySDC | train | 30 |
30cc6899eeebca39d6974a9cf1f251b627d138d4 | [
"import main\nmain.moteur_1_b.set(position)\nmoteur_bassin_1(position)",
"import main\nmain.moteur_2_b.set(position)\nmoteur_bassin_2(position)"
] | <|body_start_0|>
import main
main.moteur_1_b.set(position)
moteur_bassin_1(position)
<|end_body_0|>
<|body_start_1|>
import main
main.moteur_2_b.set(position)
moteur_bassin_2(position)
<|end_body_1|>
| pelvis | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class pelvis:
def rocker(position: int):
"""position en % du mouvement total"""
<|body_0|>
def rotation(position: int):
"""position en % du mouvement total"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
import main
main.moteur_1_b.set(position)
... | stack_v2_sparse_classes_36k_train_034556 | 18,170 | no_license | [
{
"docstring": "position en % du mouvement total",
"name": "rocker",
"signature": "def rocker(position: int)"
},
{
"docstring": "position en % du mouvement total",
"name": "rotation",
"signature": "def rotation(position: int)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008645 | Implement the Python class `pelvis` described below.
Class description:
Implement the pelvis class.
Method signatures and docstrings:
- def rocker(position: int): position en % du mouvement total
- def rotation(position: int): position en % du mouvement total | Implement the Python class `pelvis` described below.
Class description:
Implement the pelvis class.
Method signatures and docstrings:
- def rocker(position: int): position en % du mouvement total
- def rotation(position: int): position en % du mouvement total
<|skeleton|>
class pelvis:
def rocker(position: int)... | 68872f2845464b151ab0ddc809cef1d758e4a703 | <|skeleton|>
class pelvis:
def rocker(position: int):
"""position en % du mouvement total"""
<|body_0|>
def rotation(position: int):
"""position en % du mouvement total"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class pelvis:
def rocker(position: int):
"""position en % du mouvement total"""
import main
main.moteur_1_b.set(position)
moteur_bassin_1(position)
def rotation(position: int):
"""position en % du mouvement total"""
import main
main.moteur_2_b.set(positio... | the_stack_v2_python_sparse | body.py | ppgg88/InMoov_app | train | 1 | |
f218a16813a9351c677fdbc7a6ebe9d0242e2284 | [
"TextAnswerFormRecord._init_map(self)\nFilesAnswerFormRecord._init_map(self)\nsuper(AnswerTextAndFilesMixin, self)._init_map()",
"TextAnswerFormRecord._init_metadata(self)\nFilesAnswerFormRecord._init_metadata(self)\nsuper(AnswerTextAndFilesMixin, self)._init_metadata()"
] | <|body_start_0|>
TextAnswerFormRecord._init_map(self)
FilesAnswerFormRecord._init_map(self)
super(AnswerTextAndFilesMixin, self)._init_map()
<|end_body_0|>
<|body_start_1|>
TextAnswerFormRecord._init_metadata(self)
FilesAnswerFormRecord._init_metadata(self)
super(AnswerT... | Mixin class to make the two classes compatible with super() for _init_map and _init_metadata | AnswerTextAndFilesMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnswerTextAndFilesMixin:
"""Mixin class to make the two classes compatible with super() for _init_map and _init_metadata"""
def _init_map(self):
"""stub"""
<|body_0|>
def _init_metadata(self):
"""stub"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_034557 | 22,562 | permissive | [
{
"docstring": "stub",
"name": "_init_map",
"signature": "def _init_map(self)"
},
{
"docstring": "stub",
"name": "_init_metadata",
"signature": "def _init_metadata(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013259 | Implement the Python class `AnswerTextAndFilesMixin` described below.
Class description:
Mixin class to make the two classes compatible with super() for _init_map and _init_metadata
Method signatures and docstrings:
- def _init_map(self): stub
- def _init_metadata(self): stub | Implement the Python class `AnswerTextAndFilesMixin` described below.
Class description:
Mixin class to make the two classes compatible with super() for _init_map and _init_metadata
Method signatures and docstrings:
- def _init_map(self): stub
- def _init_metadata(self): stub
<|skeleton|>
class AnswerTextAndFilesMix... | 445f968a175d61c8d92c0f617a3c17dc1dc7c584 | <|skeleton|>
class AnswerTextAndFilesMixin:
"""Mixin class to make the two classes compatible with super() for _init_map and _init_metadata"""
def _init_map(self):
"""stub"""
<|body_0|>
def _init_metadata(self):
"""stub"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnswerTextAndFilesMixin:
"""Mixin class to make the two classes compatible with super() for _init_map and _init_metadata"""
def _init_map(self):
"""stub"""
TextAnswerFormRecord._init_map(self)
FilesAnswerFormRecord._init_map(self)
super(AnswerTextAndFilesMixin, self)._init... | the_stack_v2_python_sparse | dlkit/records/assessment/basic/simple_records.py | mitsei/dlkit | train | 2 |
48b4e75b53c36217b319de28103a3ade9799f768 | [
"import os\nfrom MDSplus import Uint32\ndebug = os.getenv('DEBUG_DEVICES')\ntry:\n host = str(self.node.record.data())\nexcept:\n host = 'local'\nif Data.execute('mdsconnect($)', host) == 0:\n raise Exception('Error connecting to host: ' + host)\nboard = int(self.board.record)\nfor i in range(4):\n do_n... | <|body_start_0|>
import os
from MDSplus import Uint32
debug = os.getenv('DEBUG_DEVICES')
try:
host = str(self.node.record.data())
except:
host = 'local'
if Data.execute('mdsconnect($)', host) == 0:
raise Exception('Error connecting to h... | Adlink CP7452 DIO | CP7452 | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CP7452:
"""Adlink CP7452 DIO"""
def init(self):
"""Initialize digital outputs of CP7452 cpci board. Connects to the host and for each of the DIGITAL_OUTS nodes which are turned on, write the value to the digital output."""
<|body_0|>
def store(self):
"""Stores th... | stack_v2_sparse_classes_36k_train_034558 | 5,266 | permissive | [
{
"docstring": "Initialize digital outputs of CP7452 cpci board. Connects to the host and for each of the DIGITAL_OUTS nodes which are turned on, write the value to the digital output.",
"name": "init",
"signature": "def init(self)"
},
{
"docstring": "Stores the digital input values into the tre... | 2 | null | Implement the Python class `CP7452` described below.
Class description:
Adlink CP7452 DIO
Method signatures and docstrings:
- def init(self): Initialize digital outputs of CP7452 cpci board. Connects to the host and for each of the DIGITAL_OUTS nodes which are turned on, write the value to the digital output.
- def s... | Implement the Python class `CP7452` described below.
Class description:
Adlink CP7452 DIO
Method signatures and docstrings:
- def init(self): Initialize digital outputs of CP7452 cpci board. Connects to the host and for each of the DIGITAL_OUTS nodes which are turned on, write the value to the digital output.
- def s... | 9cb20ed47249576d100a5e395028605a220e6146 | <|skeleton|>
class CP7452:
"""Adlink CP7452 DIO"""
def init(self):
"""Initialize digital outputs of CP7452 cpci board. Connects to the host and for each of the DIGITAL_OUTS nodes which are turned on, write the value to the digital output."""
<|body_0|>
def store(self):
"""Stores th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CP7452:
"""Adlink CP7452 DIO"""
def init(self):
"""Initialize digital outputs of CP7452 cpci board. Connects to the host and for each of the DIGITAL_OUTS nodes which are turned on, write the value to the digital output."""
import os
from MDSplus import Uint32
debug = os.ge... | the_stack_v2_python_sparse | pydevices/MitDevices/cp7452.py | MDSplus/mdsplus | train | 61 |
5c144fc89b07f85459d6b92764fb9c0112265826 | [
"self.name = name\nself.dictionary = Dictionary()\nself.res_random = RandomResponder('Random', self.dictionary)\nself.res_what = RepeatResponder('Repeat', self.dictionary)\nself.res_pattern = PatternResponder('Pattern', self.dictionary)",
"x = random.randint(0, 100)\nif x <= 60:\n self.responder = self.res_pat... | <|body_start_0|>
self.name = name
self.dictionary = Dictionary()
self.res_random = RandomResponder('Random', self.dictionary)
self.res_what = RepeatResponder('Repeat', self.dictionary)
self.res_pattern = PatternResponder('Pattern', self.dictionary)
<|end_body_0|>
<|body_start_1|... | ピティナの本体クラス | Ptna | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ptna:
"""ピティナの本体クラス"""
def __init__(self, name):
"""Ptnaオブジェクトの名前をnameに格納 応答オブジェクトをランダムに生成してresponderに格納 @param name Ptnaオブジェクトの名前"""
<|body_0|>
def dialogue(self, input):
"""応答オブジェクトのresponse()を呼び出して 応答文字列を取得する @param input ユーザーによって入力された文字列 戻り値 応答文字列"""
... | stack_v2_sparse_classes_36k_train_034559 | 1,559 | no_license | [
{
"docstring": "Ptnaオブジェクトの名前をnameに格納 応答オブジェクトをランダムに生成してresponderに格納 @param name Ptnaオブジェクトの名前",
"name": "__init__",
"signature": "def __init__(self, name)"
},
{
"docstring": "応答オブジェクトのresponse()を呼び出して 応答文字列を取得する @param input ユーザーによって入力された文字列 戻り値 応答文字列",
"name": "dialogue",
"signature": ... | 2 | stack_v2_sparse_classes_30k_train_000044 | Implement the Python class `Ptna` described below.
Class description:
ピティナの本体クラス
Method signatures and docstrings:
- def __init__(self, name): Ptnaオブジェクトの名前をnameに格納 応答オブジェクトをランダムに生成してresponderに格納 @param name Ptnaオブジェクトの名前
- def dialogue(self, input): 応答オブジェクトのresponse()を呼び出して 応答文字列を取得する @param input ユーザーによって入力された文字列 ... | Implement the Python class `Ptna` described below.
Class description:
ピティナの本体クラス
Method signatures and docstrings:
- def __init__(self, name): Ptnaオブジェクトの名前をnameに格納 応答オブジェクトをランダムに生成してresponderに格納 @param name Ptnaオブジェクトの名前
- def dialogue(self, input): 応答オブジェクトのresponse()を呼び出して 応答文字列を取得する @param input ユーザーによって入力された文字列 ... | 26126c02cfa0dc4c0db726f2f2cabb162511a5b5 | <|skeleton|>
class Ptna:
"""ピティナの本体クラス"""
def __init__(self, name):
"""Ptnaオブジェクトの名前をnameに格納 応答オブジェクトをランダムに生成してresponderに格納 @param name Ptnaオブジェクトの名前"""
<|body_0|>
def dialogue(self, input):
"""応答オブジェクトのresponse()を呼び出して 応答文字列を取得する @param input ユーザーによって入力された文字列 戻り値 応答文字列"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Ptna:
"""ピティナの本体クラス"""
def __init__(self, name):
"""Ptnaオブジェクトの名前をnameに格納 応答オブジェクトをランダムに生成してresponderに格納 @param name Ptnaオブジェクトの名前"""
self.name = name
self.dictionary = Dictionary()
self.res_random = RandomResponder('Random', self.dictionary)
self.res_what = Repeat... | the_stack_v2_python_sparse | normal/PythonAI/chap05/sec03/Ptna/ptna.py | munezou/PycharmProject | train | 2 |
f0bdc18bbdc65c9e968d24215b8e50bef2352cc7 | [
"super(Conv2dSubsampling2, self).__init__()\nself.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU(), torch.nn.Conv2d(odim, odim, 3, 1), torch.nn.ReLU())\nself.out = torch.nn.Sequential(torch.nn.Linear(odim * ((idim - 1) // 2 - 2), odim), pos_enc if pos_enc is not None else PositionalEncodin... | <|body_start_0|>
super(Conv2dSubsampling2, self).__init__()
self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU(), torch.nn.Conv2d(odim, odim, 3, 1), torch.nn.ReLU())
self.out = torch.nn.Sequential(torch.nn.Linear(odim * ((idim - 1) // 2 - 2), odim), pos_enc if pos_enc ... | Convolutional 2D subsampling (to 1/2 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer. | Conv2dSubsampling2 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Conv2dSubsampling2:
"""Convolutional 2D subsampling (to 1/2 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer."""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
... | stack_v2_sparse_classes_36k_train_034560 | 14,351 | permissive | [
{
"docstring": "Construct an Conv2dSubsampling2 object.",
"name": "__init__",
"signature": "def __init__(self, idim, odim, dropout_rate, pos_enc=None)"
},
{
"docstring": "Subsample x. Args: x (torch.Tensor): Input tensor (#batch, time, idim). x_mask (torch.Tensor): Input mask (#batch, 1, time). ... | 3 | null | Implement the Python class `Conv2dSubsampling2` described below.
Class description:
Convolutional 2D subsampling (to 1/2 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer.
Method signatures and docstr... | Implement the Python class `Conv2dSubsampling2` described below.
Class description:
Convolutional 2D subsampling (to 1/2 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer.
Method signatures and docstr... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class Conv2dSubsampling2:
"""Convolutional 2D subsampling (to 1/2 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer."""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Conv2dSubsampling2:
"""Convolutional 2D subsampling (to 1/2 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (torch.nn.Module): Custom position encoding layer."""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
"""Co... | the_stack_v2_python_sparse | espnet/nets/pytorch_backend/transformer/subsampling.py | espnet/espnet | train | 7,242 |
92ec0c8402c7a245a8bc3b6c25bf4a1918ca35a6 | [
"self.key = key\nself.value = value\nself.prev = prev\nself.next = next",
"if self.prev:\n self.prev.next = self.next\nif self.next:\n self.next.prev = self.prev"
] | <|body_start_0|>
self.key = key
self.value = value
self.prev = prev
self.next = next
<|end_body_0|>
<|body_start_1|>
if self.prev:
self.prev.next = self.next
if self.next:
self.next.prev = self.prev
<|end_body_1|>
| ListNode | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListNode:
def __init__(self, key, value, prev=None, next=None):
"""A node in a doubly-linked list-based LRU cache. :param key : Key by which to access nodes. :param value : Value accessed by key. :param prev [ListNode] : Previous ListNode in list, defaults to None :param next [ListNode] ... | stack_v2_sparse_classes_36k_train_034561 | 6,249 | permissive | [
{
"docstring": "A node in a doubly-linked list-based LRU cache. :param key : Key by which to access nodes. :param value : Value accessed by key. :param prev [ListNode] : Previous ListNode in list, defaults to None :param next [ListNode] : Next ListNode in list, defaults to None",
"name": "__init__",
"si... | 2 | null | Implement the Python class `ListNode` described below.
Class description:
Implement the ListNode class.
Method signatures and docstrings:
- def __init__(self, key, value, prev=None, next=None): A node in a doubly-linked list-based LRU cache. :param key : Key by which to access nodes. :param value : Value accessed by ... | Implement the Python class `ListNode` described below.
Class description:
Implement the ListNode class.
Method signatures and docstrings:
- def __init__(self, key, value, prev=None, next=None): A node in a doubly-linked list-based LRU cache. :param key : Key by which to access nodes. :param value : Value accessed by ... | b0b3d3c6dc3fa397c8c7a492098a02cf75e0ff82 | <|skeleton|>
class ListNode:
def __init__(self, key, value, prev=None, next=None):
"""A node in a doubly-linked list-based LRU cache. :param key : Key by which to access nodes. :param value : Value accessed by key. :param prev [ListNode] : Previous ListNode in list, defaults to None :param next [ListNode] ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ListNode:
def __init__(self, key, value, prev=None, next=None):
"""A node in a doubly-linked list-based LRU cache. :param key : Key by which to access nodes. :param value : Value accessed by key. :param prev [ListNode] : Previous ListNode in list, defaults to None :param next [ListNode] : Next ListNod... | the_stack_v2_python_sparse | cs/lambda_cs/03_data_structures/lru_cache/lru_cache.py | tobias-fyi/vela | train | 0 | |
43f76bd818fece5e38f43c1023ec4a76a3322321 | [
"suggestions = super(Partner, self).get_static_mention_suggestions()\ntry:\n employee_group = self.env.ref('base.group_user')\n hr_suggestions = [{'id': user.partner_id.id, 'name': user.name, 'email': user.email} for user in employee_group.users]\n suggestions.append(hr_suggestions)\n return suggestions... | <|body_start_0|>
suggestions = super(Partner, self).get_static_mention_suggestions()
try:
employee_group = self.env.ref('base.group_user')
hr_suggestions = [{'id': user.partner_id.id, 'name': user.name, 'email': user.email} for user in employee_group.users]
suggestion... | Partner | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Partner:
def get_static_mention_suggestions(self):
"""Extend the mail's static mention suggestions by adding the employees."""
<|body_0|>
def name_get(self):
"""Override to allow an employee to see its private address in his profile. This avoids to relax access rules... | stack_v2_sparse_classes_36k_train_034562 | 1,457 | no_license | [
{
"docstring": "Extend the mail's static mention suggestions by adding the employees.",
"name": "get_static_mention_suggestions",
"signature": "def get_static_mention_suggestions(self)"
},
{
"docstring": "Override to allow an employee to see its private address in his profile. This avoids to rel... | 2 | null | Implement the Python class `Partner` described below.
Class description:
Implement the Partner class.
Method signatures and docstrings:
- def get_static_mention_suggestions(self): Extend the mail's static mention suggestions by adding the employees.
- def name_get(self): Override to allow an employee to see its priva... | Implement the Python class `Partner` described below.
Class description:
Implement the Partner class.
Method signatures and docstrings:
- def get_static_mention_suggestions(self): Extend the mail's static mention suggestions by adding the employees.
- def name_get(self): Override to allow an employee to see its priva... | 148dd95d991a348ebbaff9396759a7dd1fe6e101 | <|skeleton|>
class Partner:
def get_static_mention_suggestions(self):
"""Extend the mail's static mention suggestions by adding the employees."""
<|body_0|>
def name_get(self):
"""Override to allow an employee to see its private address in his profile. This avoids to relax access rules... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Partner:
def get_static_mention_suggestions(self):
"""Extend the mail's static mention suggestions by adding the employees."""
suggestions = super(Partner, self).get_static_mention_suggestions()
try:
employee_group = self.env.ref('base.group_user')
hr_suggestion... | the_stack_v2_python_sparse | addons/hr/models/res_partner.py | marionumza/saas | train | 0 | |
6c39e0d144ae45bb76ee7f90bccef400bc802762 | [
"count = 0\nsorted_array = sorted(arr)\ns1 = s2 = 0\nfor num1, num2 in zip(arr, sorted_array):\n s1 += num1\n s2 += num2\n if s1 == s2:\n count += 1\nreturn count",
"count = max_value = 0\nfor i in range(len(arr)):\n max_value = max(max_value, arr[i])\n if max_value == i:\n count += 1... | <|body_start_0|>
count = 0
sorted_array = sorted(arr)
s1 = s2 = 0
for num1, num2 in zip(arr, sorted_array):
s1 += num1
s2 += num2
if s1 == s2:
count += 1
return count
<|end_body_0|>
<|body_start_1|>
count = max_value = ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxChunksToSorted(self, arr: List[int]) -> int:
"""sorting and compare sum"""
<|body_0|>
def maxChunksToSorted(self, arr: List[int]) -> int:
"""use index"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
count = 0
sorted_array = ... | stack_v2_sparse_classes_36k_train_034563 | 905 | no_license | [
{
"docstring": "sorting and compare sum",
"name": "maxChunksToSorted",
"signature": "def maxChunksToSorted(self, arr: List[int]) -> int"
},
{
"docstring": "use index",
"name": "maxChunksToSorted",
"signature": "def maxChunksToSorted(self, arr: List[int]) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_008142 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxChunksToSorted(self, arr: List[int]) -> int: sorting and compare sum
- def maxChunksToSorted(self, arr: List[int]) -> int: use index | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxChunksToSorted(self, arr: List[int]) -> int: sorting and compare sum
- def maxChunksToSorted(self, arr: List[int]) -> int: use index
<|skeleton|>
class Solution:
def... | fce451090ecaf5471aab5a9413ac0675639ace5d | <|skeleton|>
class Solution:
def maxChunksToSorted(self, arr: List[int]) -> int:
"""sorting and compare sum"""
<|body_0|>
def maxChunksToSorted(self, arr: List[int]) -> int:
"""use index"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxChunksToSorted(self, arr: List[int]) -> int:
"""sorting and compare sum"""
count = 0
sorted_array = sorted(arr)
s1 = s2 = 0
for num1, num2 in zip(arr, sorted_array):
s1 += num1
s2 += num2
if s1 == s2:
... | the_stack_v2_python_sparse | array_stack_queue/769MaxChunksToMakeSorted.py | kidexp/91leetcode | train | 0 | |
106082d1896bc5e9d7fd85b24424562c75f46632 | [
"self.box = box\nself._method = method\nsuper().__init__(hass, _LOGGER, name=name, update_interval=_SCAN_INTERVAL)",
"try:\n return await self._method(self.box)\nexcept SFRBoxError as err:\n raise UpdateFailed() from err"
] | <|body_start_0|>
self.box = box
self._method = method
super().__init__(hass, _LOGGER, name=name, update_interval=_SCAN_INTERVAL)
<|end_body_0|>
<|body_start_1|>
try:
return await self._method(self.box)
except SFRBoxError as err:
raise UpdateFailed() from ... | Coordinator to manage data updates. | SFRDataUpdateCoordinator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SFRDataUpdateCoordinator:
"""Coordinator to manage data updates."""
def __init__(self, hass: HomeAssistant, box: SFRBox, name: str, method: Callable[[SFRBox], Coroutine[Any, Any, _T]]) -> None:
"""Initialize coordinator."""
<|body_0|>
async def _async_update_data(self) -... | stack_v2_sparse_classes_36k_train_034564 | 1,140 | permissive | [
{
"docstring": "Initialize coordinator.",
"name": "__init__",
"signature": "def __init__(self, hass: HomeAssistant, box: SFRBox, name: str, method: Callable[[SFRBox], Coroutine[Any, Any, _T]]) -> None"
},
{
"docstring": "Update data.",
"name": "_async_update_data",
"signature": "async de... | 2 | stack_v2_sparse_classes_30k_train_001067 | Implement the Python class `SFRDataUpdateCoordinator` described below.
Class description:
Coordinator to manage data updates.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, box: SFRBox, name: str, method: Callable[[SFRBox], Coroutine[Any, Any, _T]]) -> None: Initialize coordinator.
- asyn... | Implement the Python class `SFRDataUpdateCoordinator` described below.
Class description:
Coordinator to manage data updates.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, box: SFRBox, name: str, method: Callable[[SFRBox], Coroutine[Any, Any, _T]]) -> None: Initialize coordinator.
- asyn... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class SFRDataUpdateCoordinator:
"""Coordinator to manage data updates."""
def __init__(self, hass: HomeAssistant, box: SFRBox, name: str, method: Callable[[SFRBox], Coroutine[Any, Any, _T]]) -> None:
"""Initialize coordinator."""
<|body_0|>
async def _async_update_data(self) -... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SFRDataUpdateCoordinator:
"""Coordinator to manage data updates."""
def __init__(self, hass: HomeAssistant, box: SFRBox, name: str, method: Callable[[SFRBox], Coroutine[Any, Any, _T]]) -> None:
"""Initialize coordinator."""
self.box = box
self._method = method
super().__in... | the_stack_v2_python_sparse | homeassistant/components/sfr_box/coordinator.py | home-assistant/core | train | 35,501 |
415ba941b0c4e3d0b613fff35a888e305867c049 | [
"try:\n user_pk = kwargs['user_pk']\nexcept KeyError:\n return JsonResponse({'Error': 'Not Found'}, status=status.HTTP_404_NOT_FOUND)\nuser = get_object_or_404(User, pk=user_pk, is_active=True)\ndefault_params = DefaultParams.default_value(key=user.group.pk)\ndefault_combustibles_params = default_params.combu... | <|body_start_0|>
try:
user_pk = kwargs['user_pk']
except KeyError:
return JsonResponse({'Error': 'Not Found'}, status=status.HTTP_404_NOT_FOUND)
user = get_object_or_404(User, pk=user_pk, is_active=True)
default_params = DefaultParams.default_value(key=user.group.... | ParamsView requires authenticated advisor get :model:`autodiag_copro.Params` | DefaultParamsView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultParamsView:
"""ParamsView requires authenticated advisor get :model:`autodiag_copro.Params`"""
def get(self, request, *args, **kwargs):
"""Get :model:`autodiag_copro.DefaultParams` by [pk]"""
<|body_0|>
def patch(self, request, *args, **kwargs):
"""Update ... | stack_v2_sparse_classes_36k_train_034565 | 2,999 | no_license | [
{
"docstring": "Get :model:`autodiag_copro.DefaultParams` by [pk]",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "Update :model:`autodiag_copro.DefaultParams` by [pk] Must have default_params.change permission",
"name": "patch",
"signature": "de... | 2 | null | Implement the Python class `DefaultParamsView` described below.
Class description:
ParamsView requires authenticated advisor get :model:`autodiag_copro.Params`
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Get :model:`autodiag_copro.DefaultParams` by [pk]
- def patch(self, request, *arg... | Implement the Python class `DefaultParamsView` described below.
Class description:
ParamsView requires authenticated advisor get :model:`autodiag_copro.Params`
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Get :model:`autodiag_copro.DefaultParams` by [pk]
- def patch(self, request, *arg... | 95d21cd6036a99c5f399b700a5426e9e2e17e878 | <|skeleton|>
class DefaultParamsView:
"""ParamsView requires authenticated advisor get :model:`autodiag_copro.Params`"""
def get(self, request, *args, **kwargs):
"""Get :model:`autodiag_copro.DefaultParams` by [pk]"""
<|body_0|>
def patch(self, request, *args, **kwargs):
"""Update ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DefaultParamsView:
"""ParamsView requires authenticated advisor get :model:`autodiag_copro.Params`"""
def get(self, request, *args, **kwargs):
"""Get :model:`autodiag_copro.DefaultParams` by [pk]"""
try:
user_pk = kwargs['user_pk']
except KeyError:
return J... | the_stack_v2_python_sparse | autodiag_copro/views/default_params.py | alexandrenorman/mixeur | train | 0 |
a6663d9c1154db81bb9c325d4d87fbf214957add | [
"super(MLP, self).__init__()\nself.neurons = [input_dim] + hidden_dims + [output_class]\nself.layers = []\ndropout_each_layer = dropout\nif not isinstance(dropout_each_layer, (tuple, list)):\n dropout_each_layer = [dropout] * len(hidden_dims)\nfor idx, (in_dim, out_dim, dropout_this_layer) in enumerate(zip(self.... | <|body_start_0|>
super(MLP, self).__init__()
self.neurons = [input_dim] + hidden_dims + [output_class]
self.layers = []
dropout_each_layer = dropout
if not isinstance(dropout_each_layer, (tuple, list)):
dropout_each_layer = [dropout] * len(hidden_dims)
for idx... | >>> General class for multilayer perceptron >>> Suitable for MNIST | MLP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MLP:
""">>> General class for multilayer perceptron >>> Suitable for MNIST"""
def __init__(self, input_dim=784, hidden_dims=[], output_class=10, dropout=None):
""">>> input_dim, hidden_dims, output_class: the dim of input neurons, hidden neurons and output neurons >>> dropout: the dr... | stack_v2_sparse_classes_36k_train_034566 | 1,712 | no_license | [
{
"docstring": ">>> input_dim, hidden_dims, output_class: the dim of input neurons, hidden neurons and output neurons >>> dropout: the dropout rate i.e. the probability to deactivate the neuron, None means no dropout",
"name": "__init__",
"signature": "def __init__(self, input_dim=784, hidden_dims=[], o... | 2 | stack_v2_sparse_classes_30k_train_006525 | Implement the Python class `MLP` described below.
Class description:
>>> General class for multilayer perceptron >>> Suitable for MNIST
Method signatures and docstrings:
- def __init__(self, input_dim=784, hidden_dims=[], output_class=10, dropout=None): >>> input_dim, hidden_dims, output_class: the dim of input neuro... | Implement the Python class `MLP` described below.
Class description:
>>> General class for multilayer perceptron >>> Suitable for MNIST
Method signatures and docstrings:
- def __init__(self, input_dim=784, hidden_dims=[], output_class=10, dropout=None): >>> input_dim, hidden_dims, output_class: the dim of input neuro... | 14d9bc5b25699dd275466c82b6d3748fd90e6e4e | <|skeleton|>
class MLP:
""">>> General class for multilayer perceptron >>> Suitable for MNIST"""
def __init__(self, input_dim=784, hidden_dims=[], output_class=10, dropout=None):
""">>> input_dim, hidden_dims, output_class: the dim of input neurons, hidden neurons and output neurons >>> dropout: the dr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MLP:
""">>> General class for multilayer perceptron >>> Suitable for MNIST"""
def __init__(self, input_dim=784, hidden_dims=[], output_class=10, dropout=None):
""">>> input_dim, hidden_dims, output_class: the dim of input neurons, hidden neurons and output neurons >>> dropout: the dropout rate i.... | the_stack_v2_python_sparse | pytorch/cv/mnist/models/mlp.py | liuchen11/dl_benchmark | train | 0 |
94daa731248d091e5571f9b46d0d2eefe5c9b118 | [
"super().__init__()\nself.channels = in_channels\nself.n_filters = n_filters\npadding = int(kernel_size / 2)\nself.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, padding=padding)\nself.conv2 = nn.Conv2d(in_channels=n_filters, out_channels=n_filters, kernel_size=kernel_si... | <|body_start_0|>
super().__init__()
self.channels = in_channels
self.n_filters = n_filters
padding = int(kernel_size / 2)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=n_filters, kernel_size=kernel_size, padding=padding)
self.conv2 = nn.Conv2d(in_channels=n... | A residual block that up-samples the input image by a factor of 2. | ResidualBlock | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResidualBlock:
"""A residual block that up-samples the input image by a factor of 2."""
def __init__(self, in_channels, n_filters=64, kernel_size=3, dtype=torch.float):
"""Instantiate the residual block, composed by a 2x up-sampling and two convolutional layers. Args: in_channels (in... | stack_v2_sparse_classes_36k_train_034567 | 6,461 | permissive | [
{
"docstring": "Instantiate the residual block, composed by a 2x up-sampling and two convolutional layers. Args: in_channels (int): Number of input channels. n_filters (int): Number of filters, and thus output channels. kernel_size (int): Size of the convolutional kernels. dtype (torch.dtype): Type to be used i... | 2 | stack_v2_sparse_classes_30k_train_003010 | Implement the Python class `ResidualBlock` described below.
Class description:
A residual block that up-samples the input image by a factor of 2.
Method signatures and docstrings:
- def __init__(self, in_channels, n_filters=64, kernel_size=3, dtype=torch.float): Instantiate the residual block, composed by a 2x up-sam... | Implement the Python class `ResidualBlock` described below.
Class description:
A residual block that up-samples the input image by a factor of 2.
Method signatures and docstrings:
- def __init__(self, in_channels, n_filters=64, kernel_size=3, dtype=torch.float): Instantiate the residual block, composed by a 2x up-sam... | 702d3ff3aec40eba20e17c5a1612b5b0b1e2f831 | <|skeleton|>
class ResidualBlock:
"""A residual block that up-samples the input image by a factor of 2."""
def __init__(self, in_channels, n_filters=64, kernel_size=3, dtype=torch.float):
"""Instantiate the residual block, composed by a 2x up-sampling and two convolutional layers. Args: in_channels (in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResidualBlock:
"""A residual block that up-samples the input image by a factor of 2."""
def __init__(self, in_channels, n_filters=64, kernel_size=3, dtype=torch.float):
"""Instantiate the residual block, composed by a 2x up-sampling and two convolutional layers. Args: in_channels (int): Number of... | the_stack_v2_python_sparse | networks/decoder_net.py | CampusAI/Hamiltonian-Generative-Networks | train | 35 |
545fed3f1a27a00c8da8f7b56d5a0ab5ff200dce | [
"self.urlroot = urlroot\nself.headers = headers or {}\nself.cookies = cookies or {}",
"url = self.urlroot + path\nif self.headers:\n if 'headers' in kwargs:\n headers = self.headers.copy()\n headers.update(kwargs['headers'])\n else:\n headers = self.headers\n kwargs['headers'] = head... | <|body_start_0|>
self.urlroot = urlroot
self.headers = headers or {}
self.cookies = cookies or {}
<|end_body_0|>
<|body_start_1|>
url = self.urlroot + path
if self.headers:
if 'headers' in kwargs:
headers = self.headers.copy()
headers.... | Proxy | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Proxy:
def __init__(self, urlroot, headers=None, cookies=None):
""":param urlroot: 平台接口根路径(比如 http://mapi.m.jxtbkt.com) :param headers: 自定义头部字典 :param cookies: 自定义cookie字典"""
<|body_0|>
def post(self, path, data=None, json=None, **kwargs):
"""Sends a POST request. :p... | stack_v2_sparse_classes_36k_train_034568 | 4,472 | no_license | [
{
"docstring": ":param urlroot: 平台接口根路径(比如 http://mapi.m.jxtbkt.com) :param headers: 自定义头部字典 :param cookies: 自定义cookie字典",
"name": "__init__",
"signature": "def __init__(self, urlroot, headers=None, cookies=None)"
},
{
"docstring": "Sends a POST request. :param path: 接口路径(比如 \"/account/profile\"... | 3 | stack_v2_sparse_classes_30k_train_003172 | Implement the Python class `Proxy` described below.
Class description:
Implement the Proxy class.
Method signatures and docstrings:
- def __init__(self, urlroot, headers=None, cookies=None): :param urlroot: 平台接口根路径(比如 http://mapi.m.jxtbkt.com) :param headers: 自定义头部字典 :param cookies: 自定义cookie字典
- def post(self, path,... | Implement the Python class `Proxy` described below.
Class description:
Implement the Proxy class.
Method signatures and docstrings:
- def __init__(self, urlroot, headers=None, cookies=None): :param urlroot: 平台接口根路径(比如 http://mapi.m.jxtbkt.com) :param headers: 自定义头部字典 :param cookies: 自定义cookie字典
- def post(self, path,... | 1f08cbfccc1ae2123d92670c0afed9b59ae645b8 | <|skeleton|>
class Proxy:
def __init__(self, urlroot, headers=None, cookies=None):
""":param urlroot: 平台接口根路径(比如 http://mapi.m.jxtbkt.com) :param headers: 自定义头部字典 :param cookies: 自定义cookie字典"""
<|body_0|>
def post(self, path, data=None, json=None, **kwargs):
"""Sends a POST request. :p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Proxy:
def __init__(self, urlroot, headers=None, cookies=None):
""":param urlroot: 平台接口根路径(比如 http://mapi.m.jxtbkt.com) :param headers: 自定义头部字典 :param cookies: 自定义cookie字典"""
self.urlroot = urlroot
self.headers = headers or {}
self.cookies = cookies or {}
def post(self, pa... | the_stack_v2_python_sparse | tbkt/libs/utils/tbktapi.py | GUAN-YE/hd_api_djs | train | 1 | |
7c38b8a071d09314ccc063999da239fc84e737f2 | [
"self.session: object = session\nself.tcex: object = tcex\nself.args: Namespace = tcex.args\nself.allow_redirects: bool = True\nself.data: Optional[Union[dict, str]] = None\nself.headers: dict = {}\nself.max_mb: int = 500\nself.mt: callable = MimeTypes()\nself.output_prefix: str = self.tcex.ij.output_prefix\nself.p... | <|body_start_0|>
self.session: object = session
self.tcex: object = tcex
self.args: Namespace = tcex.args
self.allow_redirects: bool = True
self.data: Optional[Union[dict, str]] = None
self.headers: dict = {}
self.max_mb: int = 500
self.mt: callable = Mime... | App Feature Advanced Request Module Args: session (object): An instance of Requests Session object. tcex (object): An instance of Tcex object. timeout (Optional[int] = 600): The timeout value for the request. | AdvancedRequest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdvancedRequest:
"""App Feature Advanced Request Module Args: session (object): An instance of Requests Session object. tcex (object): An instance of Tcex object. timeout (Optional[int] = 600): The timeout value for the request."""
def __init__(self, session: object, tcex: object, timeout: O... | stack_v2_sparse_classes_36k_train_034569 | 6,443 | permissive | [
{
"docstring": "Initialize class properties.",
"name": "__init__",
"signature": "def __init__(self, session: object, tcex: object, timeout: Optional[int]=600)"
},
{
"docstring": "Configure Body Args: tc_adv_req_body (Union[bytes, str]): The request body.",
"name": "configure_body",
"sign... | 5 | stack_v2_sparse_classes_30k_val_000838 | Implement the Python class `AdvancedRequest` described below.
Class description:
App Feature Advanced Request Module Args: session (object): An instance of Requests Session object. tcex (object): An instance of Tcex object. timeout (Optional[int] = 600): The timeout value for the request.
Method signatures and docstr... | Implement the Python class `AdvancedRequest` described below.
Class description:
App Feature Advanced Request Module Args: session (object): An instance of Requests Session object. tcex (object): An instance of Tcex object. timeout (Optional[int] = 600): The timeout value for the request.
Method signatures and docstr... | 7cf04fec048fadc71ff851970045b8a587269ccf | <|skeleton|>
class AdvancedRequest:
"""App Feature Advanced Request Module Args: session (object): An instance of Requests Session object. tcex (object): An instance of Tcex object. timeout (Optional[int] = 600): The timeout value for the request."""
def __init__(self, session: object, tcex: object, timeout: O... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdvancedRequest:
"""App Feature Advanced Request Module Args: session (object): An instance of Requests Session object. tcex (object): An instance of Tcex object. timeout (Optional[int] = 600): The timeout value for the request."""
def __init__(self, session: object, tcex: object, timeout: Optional[int]=... | the_stack_v2_python_sparse | tcex/app_feature/advanced_request.py | TpyoKnig/tcex | train | 0 |
f14bed489841b35f5cb0103699c0d3c0a3594674 | [
"def dfs(root1, root2):\n if root1 is None and root2 is None:\n return True\n if root1 is None or root2 is None:\n return False\n if root1.val != root2.val:\n return False\n return dfs(root1.left, root2.left) and dfs(root1.right, root2.right) or (dfs(root1.left, root2.right) and dfs... | <|body_start_0|>
def dfs(root1, root2):
if root1 is None and root2 is None:
return True
if root1 is None or root2 is None:
return False
if root1.val != root2.val:
return False
return dfs(root1.left, root2.left) and d... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def flipEquiv(self, root1, root2):
""":type root1: TreeNode :type root2: TreeNode :rtype: bool"""
<|body_0|>
def flipEquiv2(self, root1, root2):
""":type root1: TreeNode :type root2: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_36k_train_034570 | 2,038 | no_license | [
{
"docstring": ":type root1: TreeNode :type root2: TreeNode :rtype: bool",
"name": "flipEquiv",
"signature": "def flipEquiv(self, root1, root2)"
},
{
"docstring": ":type root1: TreeNode :type root2: TreeNode :rtype: bool",
"name": "flipEquiv2",
"signature": "def flipEquiv2(self, root1, r... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flipEquiv(self, root1, root2): :type root1: TreeNode :type root2: TreeNode :rtype: bool
- def flipEquiv2(self, root1, root2): :type root1: TreeNode :type root2: TreeNode :rty... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flipEquiv(self, root1, root2): :type root1: TreeNode :type root2: TreeNode :rtype: bool
- def flipEquiv2(self, root1, root2): :type root1: TreeNode :type root2: TreeNode :rty... | 813235789ce422a3bab198317aafc46fbc61625e | <|skeleton|>
class Solution:
def flipEquiv(self, root1, root2):
""":type root1: TreeNode :type root2: TreeNode :rtype: bool"""
<|body_0|>
def flipEquiv2(self, root1, root2):
""":type root1: TreeNode :type root2: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def flipEquiv(self, root1, root2):
""":type root1: TreeNode :type root2: TreeNode :rtype: bool"""
def dfs(root1, root2):
if root1 is None and root2 is None:
return True
if root1 is None or root2 is None:
return False
... | the_stack_v2_python_sparse | 7.BINARY TREE and BST/951_flip equivalent binary tree_MED/solution.py | kimmyoo/python_leetcode | train | 1 | |
6f0940ad0339e69f7d7e7ef410baa3f2df474e82 | [
"user = get_a_user(assetid)\nif not user:\n api.abort(404)\nelse:\n return user\ndata = request.json\nreturn get_a_user(data=data)",
"user = complete_users(assetid)\nif not user:\n api.abort(404)\nelse:\n return user\ndata = request.json\nreturn complete_users(data=data)",
"user = delete_user(asseti... | <|body_start_0|>
user = get_a_user(assetid)
if not user:
api.abort(404)
else:
return user
data = request.json
return get_a_user(data=data)
<|end_body_0|>
<|body_start_1|>
user = complete_users(assetid)
if not user:
api.abort(40... | Assets | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Assets:
def get(self, assetid):
"""get a Assets given its identifier"""
<|body_0|>
def put(self, assetid):
"""Assets Updated"""
<|body_1|>
def delete(self, assetid):
"""Assets Deleted"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_034571 | 2,510 | no_license | [
{
"docstring": "get a Assets given its identifier",
"name": "get",
"signature": "def get(self, assetid)"
},
{
"docstring": "Assets Updated",
"name": "put",
"signature": "def put(self, assetid)"
},
{
"docstring": "Assets Deleted",
"name": "delete",
"signature": "def delete... | 3 | stack_v2_sparse_classes_30k_train_008558 | Implement the Python class `Assets` described below.
Class description:
Implement the Assets class.
Method signatures and docstrings:
- def get(self, assetid): get a Assets given its identifier
- def put(self, assetid): Assets Updated
- def delete(self, assetid): Assets Deleted | Implement the Python class `Assets` described below.
Class description:
Implement the Assets class.
Method signatures and docstrings:
- def get(self, assetid): get a Assets given its identifier
- def put(self, assetid): Assets Updated
- def delete(self, assetid): Assets Deleted
<|skeleton|>
class Assets:
def ge... | 4fa4042304ee01cf23ecc81f9c27977fd12c31b9 | <|skeleton|>
class Assets:
def get(self, assetid):
"""get a Assets given its identifier"""
<|body_0|>
def put(self, assetid):
"""Assets Updated"""
<|body_1|>
def delete(self, assetid):
"""Assets Deleted"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Assets:
def get(self, assetid):
"""get a Assets given its identifier"""
user = get_a_user(assetid)
if not user:
api.abort(404)
else:
return user
data = request.json
return get_a_user(data=data)
def put(self, assetid):
"""Asse... | the_stack_v2_python_sparse | main/controller/assets_controller.py | Gauravkumar45/Flask-RESTPlus-API | train | 0 | |
706ef83f8504f257c5a7d287bf342f597038bd88 | [
"cpacs_path = mif.get_toolinput_file_path('SMUse')\ntixi = cpsf.open_tixi(cpacs_path)\nself.Model = smu.load_surrogate(tixi)\ncpsf.close_tixi(tixi, cpacs_path)\ndf = self.Model.df\ndf.set_index('Name', inplace=True)\nfor name in df.index:\n if df.loc[name, 'type'] == 'obj':\n self.add_output(name)\n el... | <|body_start_0|>
cpacs_path = mif.get_toolinput_file_path('SMUse')
tixi = cpsf.open_tixi(cpacs_path)
self.Model = smu.load_surrogate(tixi)
cpsf.close_tixi(tixi, cpacs_path)
df = self.Model.df
df.set_index('Name', inplace=True)
for name in df.index:
if ... | Uses a surrogate model to make a prediction | SmComp | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmComp:
"""Uses a surrogate model to make a prediction"""
def setup(self):
"""Setup inputs and outputs"""
<|body_0|>
def compute(self, inputs, outputs):
"""Make a prediction"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cpacs_path = mif.get_to... | stack_v2_sparse_classes_36k_train_034572 | 21,151 | permissive | [
{
"docstring": "Setup inputs and outputs",
"name": "setup",
"signature": "def setup(self)"
},
{
"docstring": "Make a prediction",
"name": "compute",
"signature": "def compute(self, inputs, outputs)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000446 | Implement the Python class `SmComp` described below.
Class description:
Uses a surrogate model to make a prediction
Method signatures and docstrings:
- def setup(self): Setup inputs and outputs
- def compute(self, inputs, outputs): Make a prediction | Implement the Python class `SmComp` described below.
Class description:
Uses a surrogate model to make a prediction
Method signatures and docstrings:
- def setup(self): Setup inputs and outputs
- def compute(self, inputs, outputs): Make a prediction
<|skeleton|>
class SmComp:
"""Uses a surrogate model to make a ... | 3cc211507caab176a76213e442238abfa43afa42 | <|skeleton|>
class SmComp:
"""Uses a surrogate model to make a prediction"""
def setup(self):
"""Setup inputs and outputs"""
<|body_0|>
def compute(self, inputs, outputs):
"""Make a prediction"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmComp:
"""Uses a surrogate model to make a prediction"""
def setup(self):
"""Setup inputs and outputs"""
cpacs_path = mif.get_toolinput_file_path('SMUse')
tixi = cpsf.open_tixi(cpacs_path)
self.Model = smu.load_surrogate(tixi)
cpsf.close_tixi(tixi, cpacs_path)
... | the_stack_v2_python_sparse | ceasiompy/Optimisation/optimisation.py | schneo/CEASIOMpy | train | 0 |
b468362ab150b41326c9f328203652541a2d3b9d | [
"while left >= 0 and right < len(s) and (s[left] == s[right]):\n left -= 1\n right += 1\nreturn right - left - 1",
"if not s or len(s) < 1:\n return ''\nleft = right = 0\nfor index in range(len(s)):\n odd_len = self.expand_center(s, index, index)\n even_len = self.expand_center(s, index, index + 1)... | <|body_start_0|>
while left >= 0 and right < len(s) and (s[left] == s[right]):
left -= 1
right += 1
return right - left - 1
<|end_body_0|>
<|body_start_1|>
if not s or len(s) < 1:
return ''
left = right = 0
for index in range(len(s)):
... | String | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class String:
def expand_center(self, s: str, left: int, right: int) -> int:
""":param s: :param l: :param r: :return:"""
<|body_0|>
def longest_palindromic_substring(self, s: str) -> str:
"""Approach: Expand Center Time Complexity: O(N^2) Space Complexity: O(1) :param s: ... | stack_v2_sparse_classes_36k_train_034573 | 1,441 | no_license | [
{
"docstring": ":param s: :param l: :param r: :return:",
"name": "expand_center",
"signature": "def expand_center(self, s: str, left: int, right: int) -> int"
},
{
"docstring": "Approach: Expand Center Time Complexity: O(N^2) Space Complexity: O(1) :param s: :return:",
"name": "longest_palin... | 2 | stack_v2_sparse_classes_30k_train_018585 | Implement the Python class `String` described below.
Class description:
Implement the String class.
Method signatures and docstrings:
- def expand_center(self, s: str, left: int, right: int) -> int: :param s: :param l: :param r: :return:
- def longest_palindromic_substring(self, s: str) -> str: Approach: Expand Cente... | Implement the Python class `String` described below.
Class description:
Implement the String class.
Method signatures and docstrings:
- def expand_center(self, s: str, left: int, right: int) -> int: :param s: :param l: :param r: :return:
- def longest_palindromic_substring(self, s: str) -> str: Approach: Expand Cente... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class String:
def expand_center(self, s: str, left: int, right: int) -> int:
""":param s: :param l: :param r: :return:"""
<|body_0|>
def longest_palindromic_substring(self, s: str) -> str:
"""Approach: Expand Center Time Complexity: O(N^2) Space Complexity: O(1) :param s: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class String:
def expand_center(self, s: str, left: int, right: int) -> int:
""":param s: :param l: :param r: :return:"""
while left >= 0 and right < len(s) and (s[left] == s[right]):
left -= 1
right += 1
return right - left - 1
def longest_palindromic_substring(... | the_stack_v2_python_sparse | revisited__2021/math_and_string/longest_palindromic_substring.py | Shiv2157k/leet_code | train | 1 | |
49424f28451f9046cae9f98a1b8c11a6feeef880 | [
"test = 'aabc'\nd = Palindrom(test)\nself.assertEqual(d.cnt['c'], 1)\nself.assertEqual(d.pcnt['c'], 0)\nself.assertEqual(Palindrom(test).calculate(), 'abba')\ntest = 'aabcd'\nself.assertEqual(Palindrom(test).calculate(), 'abcba')\ntest = 'aabbcccdd'\nself.assertEqual(Palindrom(test).calculate(), 'abcdcdcba')\ntest ... | <|body_start_0|>
test = 'aabc'
d = Palindrom(test)
self.assertEqual(d.cnt['c'], 1)
self.assertEqual(d.pcnt['c'], 0)
self.assertEqual(Palindrom(test).calculate(), 'abba')
test = 'aabcd'
self.assertEqual(Palindrom(test).calculate(), 'abcba')
test = 'aabbcccd... | unitTests | [
"Unlicense",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class unitTests:
def test_single_test(self):
"""Palindrom class testing"""
<|body_0|>
def time_limit_test(self, nmax):
"""Timelimit testing"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
test = 'aabc'
d = Palindrom(test)
self.assertEqual(... | stack_v2_sparse_classes_36k_train_034574 | 3,524 | permissive | [
{
"docstring": "Palindrom class testing",
"name": "test_single_test",
"signature": "def test_single_test(self)"
},
{
"docstring": "Timelimit testing",
"name": "time_limit_test",
"signature": "def time_limit_test(self, nmax)"
}
] | 2 | stack_v2_sparse_classes_30k_train_021159 | Implement the Python class `unitTests` described below.
Class description:
Implement the unitTests class.
Method signatures and docstrings:
- def test_single_test(self): Palindrom class testing
- def time_limit_test(self, nmax): Timelimit testing | Implement the Python class `unitTests` described below.
Class description:
Implement the unitTests class.
Method signatures and docstrings:
- def test_single_test(self): Palindrom class testing
- def time_limit_test(self, nmax): Timelimit testing
<|skeleton|>
class unitTests:
def test_single_test(self):
... | ae02ea872ca91ef98630cc172a844b82cc56f621 | <|skeleton|>
class unitTests:
def test_single_test(self):
"""Palindrom class testing"""
<|body_0|>
def time_limit_test(self, nmax):
"""Timelimit testing"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class unitTests:
def test_single_test(self):
"""Palindrom class testing"""
test = 'aabc'
d = Palindrom(test)
self.assertEqual(d.cnt['c'], 1)
self.assertEqual(d.pcnt['c'], 0)
self.assertEqual(Palindrom(test).calculate(), 'abba')
test = 'aabcd'
self.asse... | the_stack_v2_python_sparse | codeforces/600C_palindrom.py | snsokolov/contests | train | 1 | |
458a9c1bcfbad838e32131bc43236e48ecb6fd87 | [
"super(ProcessResourceType, self).setUp()\nfor nodetype in NODE_TYPES:\n nodetype.objects.all().delete()",
"ggc.return_value = {'client': self.EmptyClientObject(), 'region': 'Siberia'}\nprocess_resource_type(Image)\nself.assertEqual(PolyResource.objects.count(), 0)",
"NODES = [(Image, 'a'), (Image, 'ab'), (I... | <|body_start_0|>
super(ProcessResourceType, self).setUp()
for nodetype in NODE_TYPES:
nodetype.objects.all().delete()
<|end_body_0|>
<|body_start_1|>
ggc.return_value = {'client': self.EmptyClientObject(), 'region': 'Siberia'}
process_resource_type(Image)
self.assert... | Test utilities.process_resource_type. | ProcessResourceType | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProcessResourceType:
"""Test utilities.process_resource_type."""
def setUp(self):
"""Run before every test."""
<|body_0|>
def test_empty_rg_empty_cloud(self, ggc):
"""Nothing in the resource graph, nothing in the cloud. Nothing should be done."""
<|body_1... | stack_v2_sparse_classes_36k_train_034575 | 22,971 | permissive | [
{
"docstring": "Run before every test.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Nothing in the resource graph, nothing in the cloud. Nothing should be done.",
"name": "test_empty_rg_empty_cloud",
"signature": "def test_empty_rg_empty_cloud(self, ggc)"
},
{... | 4 | stack_v2_sparse_classes_30k_train_017598 | Implement the Python class `ProcessResourceType` described below.
Class description:
Test utilities.process_resource_type.
Method signatures and docstrings:
- def setUp(self): Run before every test.
- def test_empty_rg_empty_cloud(self, ggc): Nothing in the resource graph, nothing in the cloud. Nothing should be done... | Implement the Python class `ProcessResourceType` described below.
Class description:
Test utilities.process_resource_type.
Method signatures and docstrings:
- def setUp(self): Run before every test.
- def test_empty_rg_empty_cloud(self, ggc): Nothing in the resource graph, nothing in the cloud. Nothing should be done... | 73d334a9f0df7c044c06989977a9a22dd2ff9b7a | <|skeleton|>
class ProcessResourceType:
"""Test utilities.process_resource_type."""
def setUp(self):
"""Run before every test."""
<|body_0|>
def test_empty_rg_empty_cloud(self, ggc):
"""Nothing in the resource graph, nothing in the cloud. Nothing should be done."""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProcessResourceType:
"""Test utilities.process_resource_type."""
def setUp(self):
"""Run before every test."""
super(ProcessResourceType, self).setUp()
for nodetype in NODE_TYPES:
nodetype.objects.all().delete()
def test_empty_rg_empty_cloud(self, ggc):
""... | the_stack_v2_python_sparse | goldstone/core/tests.py | bhuvan-rk/goldstone-server | train | 0 |
c094085a00556364cd1fd0c5880fedba70cbff66 | [
"dp_row, dp_col, dp_len = (len(s1), len(s2), len(s3))\nif dp_row + dp_col != dp_len:\n return False\ndp = [True for _ in xrange(dp_col + 1)]\nfor j in xrange(1, dp_col + 1):\n dp[j] = dp[j - 1] and s2[j - 1] == s3[j - 1]\nfor i in xrange(1, dp_row + 1):\n dp[0] = dp[0] and s1[i - 1] == s3[i - 1]\n for j... | <|body_start_0|>
dp_row, dp_col, dp_len = (len(s1), len(s2), len(s3))
if dp_row + dp_col != dp_len:
return False
dp = [True for _ in xrange(dp_col + 1)]
for j in xrange(1, dp_col + 1):
dp[j] = dp[j - 1] and s2[j - 1] == s3[j - 1]
for i in xrange(1, dp_row ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isInterleave(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool DP, O(n) space beats 80.27%"""
<|body_0|>
def isInterleave1(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool DFS beats 97.99%"""
... | stack_v2_sparse_classes_36k_train_034576 | 2,418 | no_license | [
{
"docstring": ":type s1: str :type s2: str :type s3: str :rtype: bool DP, O(n) space beats 80.27%",
"name": "isInterleave",
"signature": "def isInterleave(self, s1, s2, s3)"
},
{
"docstring": ":type s1: str :type s2: str :type s3: str :rtype: bool DFS beats 97.99%",
"name": "isInterleave1",... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isInterleave(self, s1, s2, s3): :type s1: str :type s2: str :type s3: str :rtype: bool DP, O(n) space beats 80.27%
- def isInterleave1(self, s1, s2, s3): :type s1: str :type ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isInterleave(self, s1, s2, s3): :type s1: str :type s2: str :type s3: str :rtype: bool DP, O(n) space beats 80.27%
- def isInterleave1(self, s1, s2, s3): :type s1: str :type ... | 7e0e917c15d3e35f49da3a00ef395bd5ff180d79 | <|skeleton|>
class Solution:
def isInterleave(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool DP, O(n) space beats 80.27%"""
<|body_0|>
def isInterleave1(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool DFS beats 97.99%"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isInterleave(self, s1, s2, s3):
""":type s1: str :type s2: str :type s3: str :rtype: bool DP, O(n) space beats 80.27%"""
dp_row, dp_col, dp_len = (len(s1), len(s2), len(s3))
if dp_row + dp_col != dp_len:
return False
dp = [True for _ in xrange(dp_col +... | the_stack_v2_python_sparse | LeetCode/097_interleaving_string.py | yao23/Machine_Learning_Playground | train | 12 | |
1819e7b35e58e1b194098dc0cf6a56c9b7ef8165 | [
"super(CnnMaxpoolLayer, self).__init__()\nif not isinstance(filter_size, (tuple, list)):\n filter_size = [filter_size]\nif not isinstance(output_num, (tuple, list)):\n output_num = [output_num] * len(filter_size)\nassert len(filter_size) == len(output_num), 'Filter size len is not equal output_num len'\nprint... | <|body_start_0|>
super(CnnMaxpoolLayer, self).__init__()
if not isinstance(filter_size, (tuple, list)):
filter_size = [filter_size]
if not isinstance(output_num, (tuple, list)):
output_num = [output_num] * len(filter_size)
assert len(filter_size) == len(output_num... | CnnMaxpoolLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CnnMaxpoolLayer:
def __init__(self, input_num, output_num, filter_size, stride=1, padding=0, activation='relu', initial_method=None, **kwargs):
"""cnn+maxpooling的结构做encoder :params input_num int 输入的维度 :params output_num int|list 每个卷积核输出的维度 :params filter_size list 卷积核的大小 :params init_met... | stack_v2_sparse_classes_36k_train_034577 | 2,594 | permissive | [
{
"docstring": "cnn+maxpooling的结构做encoder :params input_num int 输入的维度 :params output_num int|list 每个卷积核输出的维度 :params filter_size list 卷积核的大小 :params init_method str 网络参数初始化的方法，默认为xavier_uniform",
"name": "__init__",
"signature": "def __init__(self, input_num, output_num, filter_size, stride=1, padding=0... | 2 | stack_v2_sparse_classes_30k_train_013883 | Implement the Python class `CnnMaxpoolLayer` described below.
Class description:
Implement the CnnMaxpoolLayer class.
Method signatures and docstrings:
- def __init__(self, input_num, output_num, filter_size, stride=1, padding=0, activation='relu', initial_method=None, **kwargs): cnn+maxpooling的结构做encoder :params inp... | Implement the Python class `CnnMaxpoolLayer` described below.
Class description:
Implement the CnnMaxpoolLayer class.
Method signatures and docstrings:
- def __init__(self, input_num, output_num, filter_size, stride=1, padding=0, activation='relu', initial_method=None, **kwargs): cnn+maxpooling的结构做encoder :params inp... | 5eda8e7c60116735f595f4b21b24547708b36cf5 | <|skeleton|>
class CnnMaxpoolLayer:
def __init__(self, input_num, output_num, filter_size, stride=1, padding=0, activation='relu', initial_method=None, **kwargs):
"""cnn+maxpooling的结构做encoder :params input_num int 输入的维度 :params output_num int|list 每个卷积核输出的维度 :params filter_size list 卷积核的大小 :params init_met... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CnnMaxpoolLayer:
def __init__(self, input_num, output_num, filter_size, stride=1, padding=0, activation='relu', initial_method=None, **kwargs):
"""cnn+maxpooling的结构做encoder :params input_num int 输入的维度 :params output_num int|list 每个卷积核输出的维度 :params filter_size list 卷积核的大小 :params init_method str 网络参数初始... | the_stack_v2_python_sparse | UNF/modules/encoder/cnn_maxpool.py | Dreamliking/UNF | train | 1 | |
735bae795469da88214a623f5221ad6616d7a6e0 | [
"try:\n AppUser.objects.get(username__iexact=self.cleaned_data['username'])\nexcept AppUser.DoesNotExist:\n return self.cleaned_data['username']\nraise forms.ValidationError(_('The username already exists. Please try another one.'))",
"form_obj = self.cleaned_data\nif 'password1' in form_obj and 'password2'... | <|body_start_0|>
try:
AppUser.objects.get(username__iexact=self.cleaned_data['username'])
except AppUser.DoesNotExist:
return self.cleaned_data['username']
raise forms.ValidationError(_('The username already exists. Please try another one.'))
<|end_body_0|>
<|body_start_... | Registration form. | RegistrationForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegistrationForm:
"""Registration form."""
def clean_username(self):
"""Method to clean username."""
<|body_0|>
def clean(self):
"""Method to compare passwords."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
AppUser.objects.get... | stack_v2_sparse_classes_36k_train_034578 | 9,900 | no_license | [
{
"docstring": "Method to clean username.",
"name": "clean_username",
"signature": "def clean_username(self)"
},
{
"docstring": "Method to compare passwords.",
"name": "clean",
"signature": "def clean(self)"
}
] | 2 | null | Implement the Python class `RegistrationForm` described below.
Class description:
Registration form.
Method signatures and docstrings:
- def clean_username(self): Method to clean username.
- def clean(self): Method to compare passwords. | Implement the Python class `RegistrationForm` described below.
Class description:
Registration form.
Method signatures and docstrings:
- def clean_username(self): Method to clean username.
- def clean(self): Method to compare passwords.
<|skeleton|>
class RegistrationForm:
"""Registration form."""
def clean... | cb392be0402543acf074425fcaf8edf054269012 | <|skeleton|>
class RegistrationForm:
"""Registration form."""
def clean_username(self):
"""Method to clean username."""
<|body_0|>
def clean(self):
"""Method to compare passwords."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegistrationForm:
"""Registration form."""
def clean_username(self):
"""Method to clean username."""
try:
AppUser.objects.get(username__iexact=self.cleaned_data['username'])
except AppUser.DoesNotExist:
return self.cleaned_data['username']
raise for... | the_stack_v2_python_sparse | cpovc_auth/forms.py | uonafya/cpims-2.3beta | train | 4 |
76c6f16aa84d897cb725dfded456fc9a9f473b0c | [
"mid = 0\nwhile low < high:\n mid = low + int((high - low) / 2)\n if nums[mid] < target:\n low = mid + 1\n else:\n high = mid\nreturn low",
"res = []\nfor i in range(N):\n if not res or treasures[i] > res[-1]:\n res.append(treasures[i])\n else:\n idx = self.binary_search... | <|body_start_0|>
mid = 0
while low < high:
mid = low + int((high - low) / 2)
if nums[mid] < target:
low = mid + 1
else:
high = mid
return low
<|end_body_0|>
<|body_start_1|>
res = []
for i in range(N):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def binary_search(self, nums, low, high, target):
"""根据指定的数据寻找相应的位置 @param: nums @param: low @param: high @param: target"""
<|body_0|>
def LIS(self, N, treasures):
"""最长上升子序列 @param: N @param: treasures"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_034579 | 2,565 | no_license | [
{
"docstring": "根据指定的数据寻找相应的位置 @param: nums @param: low @param: high @param: target",
"name": "binary_search",
"signature": "def binary_search(self, nums, low, high, target)"
},
{
"docstring": "最长上升子序列 @param: N @param: treasures",
"name": "LIS",
"signature": "def LIS(self, N, treasures)... | 2 | stack_v2_sparse_classes_30k_train_014275 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binary_search(self, nums, low, high, target): 根据指定的数据寻找相应的位置 @param: nums @param: low @param: high @param: target
- def LIS(self, N, treasures): 最长上升子序列 @param: N @param: tre... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binary_search(self, nums, low, high, target): 根据指定的数据寻找相应的位置 @param: nums @param: low @param: high @param: target
- def LIS(self, N, treasures): 最长上升子序列 @param: N @param: tre... | 32941ee052d0985a9569441d314378700ff4d225 | <|skeleton|>
class Solution:
def binary_search(self, nums, low, high, target):
"""根据指定的数据寻找相应的位置 @param: nums @param: low @param: high @param: target"""
<|body_0|>
def LIS(self, N, treasures):
"""最长上升子序列 @param: N @param: treasures"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def binary_search(self, nums, low, high, target):
"""根据指定的数据寻找相应的位置 @param: nums @param: low @param: high @param: target"""
mid = 0
while low < high:
mid = low + int((high - low) / 2)
if nums[mid] < target:
low = mid + 1
els... | the_stack_v2_python_sparse | cecilia-python/company-title/xiaohongshu/ResellingLoot.py | Cecilia520/algorithmic-learning-leetcode | train | 7 | |
c206ec838b481aad4fc7e94ee84a55271c9af01c | [
"if dividend == 0:\n return 0\nif divisor == 1:\n return dividend\nif divisor == -1:\n if dividend > INT_MIN:\n return -dividend\n else:\n return INT_MAX\na = dividend\nb = divisor\nsign = 1\nif a > 0 and b < 0 or (a < 0 and b > 0):\n sign = -1\na = abs(a)\nb = abs(b)\nres = self.div(a,... | <|body_start_0|>
if dividend == 0:
return 0
if divisor == 1:
return dividend
if divisor == -1:
if dividend > INT_MIN:
return -dividend
else:
return INT_MAX
a = dividend
b = divisor
sign = 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def divide(self, dividend, divisor):
"""Args: dividend: int divisor: int Return: int"""
<|body_0|>
def div(self, a, b):
"""Args: a: int b: int Return: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if dividend == 0:
return... | stack_v2_sparse_classes_36k_train_034580 | 1,113 | no_license | [
{
"docstring": "Args: dividend: int divisor: int Return: int",
"name": "divide",
"signature": "def divide(self, dividend, divisor)"
},
{
"docstring": "Args: a: int b: int Return: int",
"name": "div",
"signature": "def div(self, a, b)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def divide(self, dividend, divisor): Args: dividend: int divisor: int Return: int
- def div(self, a, b): Args: a: int b: int Return: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def divide(self, dividend, divisor): Args: dividend: int divisor: int Return: int
- def div(self, a, b): Args: a: int b: int Return: int
<|skeleton|>
class Solution:
def di... | 101bce2fac8b188a4eb2f5e017293d21ad0ecb21 | <|skeleton|>
class Solution:
def divide(self, dividend, divisor):
"""Args: dividend: int divisor: int Return: int"""
<|body_0|>
def div(self, a, b):
"""Args: a: int b: int Return: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def divide(self, dividend, divisor):
"""Args: dividend: int divisor: int Return: int"""
if dividend == 0:
return 0
if divisor == 1:
return dividend
if divisor == -1:
if dividend > INT_MIN:
return -dividend
... | the_stack_v2_python_sparse | code/29. 两数相除.py | AiZhanghan/Leetcode | train | 0 | |
680703241b85a329c551e73b3e5e6eafb45133dc | [
"pipeline_cfg = cfg.test_dataloader.dataset.pipeline\nif 'meta_keys' in pipeline_cfg[-1]:\n pipeline_cfg[-1]['meta_keys'] = tuple((meta_key for meta_key in pipeline_cfg[-1]['meta_keys'] if meta_key != 'img_id'))\nload_img_idx = self._get_transform_idx(pipeline_cfg, 'LoadImageFromFile')\nif load_img_idx == -1:\n ... | <|body_start_0|>
pipeline_cfg = cfg.test_dataloader.dataset.pipeline
if 'meta_keys' in pipeline_cfg[-1]:
pipeline_cfg[-1]['meta_keys'] = tuple((meta_key for meta_key in pipeline_cfg[-1]['meta_keys'] if meta_key != 'img_id'))
load_img_idx = self._get_transform_idx(pipeline_cfg, 'LoadI... | RefImageCaptionInferencer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RefImageCaptionInferencer:
def _init_pipeline(self, cfg: ConfigType) -> Compose:
"""Initialize the test pipeline."""
<|body_0|>
def _get_chunk_data(self, inputs: Iterable, chunk_size: int):
"""Get batch data from inputs. Args: inputs (Iterable): An iterable dataset. ... | stack_v2_sparse_classes_36k_train_034581 | 11,521 | permissive | [
{
"docstring": "Initialize the test pipeline.",
"name": "_init_pipeline",
"signature": "def _init_pipeline(self, cfg: ConfigType) -> Compose"
},
{
"docstring": "Get batch data from inputs. Args: inputs (Iterable): An iterable dataset. chunk_size (int): Equivalent to batch size. Yields: list: bat... | 3 | stack_v2_sparse_classes_30k_train_004150 | Implement the Python class `RefImageCaptionInferencer` described below.
Class description:
Implement the RefImageCaptionInferencer class.
Method signatures and docstrings:
- def _init_pipeline(self, cfg: ConfigType) -> Compose: Initialize the test pipeline.
- def _get_chunk_data(self, inputs: Iterable, chunk_size: in... | Implement the Python class `RefImageCaptionInferencer` described below.
Class description:
Implement the RefImageCaptionInferencer class.
Method signatures and docstrings:
- def _init_pipeline(self, cfg: ConfigType) -> Compose: Initialize the test pipeline.
- def _get_chunk_data(self, inputs: Iterable, chunk_size: in... | f78af7785ada87f1ced75a2313746e4ba3149760 | <|skeleton|>
class RefImageCaptionInferencer:
def _init_pipeline(self, cfg: ConfigType) -> Compose:
"""Initialize the test pipeline."""
<|body_0|>
def _get_chunk_data(self, inputs: Iterable, chunk_size: int):
"""Get batch data from inputs. Args: inputs (Iterable): An iterable dataset. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RefImageCaptionInferencer:
def _init_pipeline(self, cfg: ConfigType) -> Compose:
"""Initialize the test pipeline."""
pipeline_cfg = cfg.test_dataloader.dataset.pipeline
if 'meta_keys' in pipeline_cfg[-1]:
pipeline_cfg[-1]['meta_keys'] = tuple((meta_key for meta_key in pipel... | the_stack_v2_python_sparse | projects/XDecoder/xdecoder/inference/image_caption.py | wencheng256/mmdetection | train | 0 | |
0800c4305b83ed8a597e5c257d6492c5ad238238 | [
"if not space.extruded:\n raise ValueError('The Theta Limiter can only be used on an extruded mesh')\nsub_elements = space.ufl_element().sub_elements()\nif sub_elements[0].family() not in ['Discontinuous Lagrange', 'DQ'] or sub_elements[1].family() != 'Lagrange' or space.ufl_element().degree() != (1, 2):\n ra... | <|body_start_0|>
if not space.extruded:
raise ValueError('The Theta Limiter can only be used on an extruded mesh')
sub_elements = space.ufl_element().sub_elements()
if sub_elements[0].family() not in ['Discontinuous Lagrange', 'DQ'] or sub_elements[1].family() != 'Lagrange' or space.... | A vertex-based limiter for the degree 1 temperature space. A vertex based limiter for fields in the DG1xCG2 space, i.e. temperature variables in the next-to-lowest order set of spaces. This acts like the vertex-based limiter implemented in Firedrake, but in addition corrects the central nodes to prevent new maxima or m... | ThetaLimiter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ThetaLimiter:
"""A vertex-based limiter for the degree 1 temperature space. A vertex based limiter for fields in the DG1xCG2 space, i.e. temperature variables in the next-to-lowest order set of spaces. This acts like the vertex-based limiter implemented in Firedrake, but in addition corrects the ... | stack_v2_sparse_classes_36k_train_034582 | 7,242 | permissive | [
{
"docstring": "Args: space (:class:`FunctionSpace`): the space in which the transported variables lies. It should be a form of the DG1xCG2 space. Raises: ValueError: If the mesh is not extruded. ValueError: If the space is not appropriate for the limiter.",
"name": "__init__",
"signature": "def __init_... | 2 | stack_v2_sparse_classes_30k_test_000582 | Implement the Python class `ThetaLimiter` described below.
Class description:
A vertex-based limiter for the degree 1 temperature space. A vertex based limiter for fields in the DG1xCG2 space, i.e. temperature variables in the next-to-lowest order set of spaces. This acts like the vertex-based limiter implemented in F... | Implement the Python class `ThetaLimiter` described below.
Class description:
A vertex-based limiter for the degree 1 temperature space. A vertex based limiter for fields in the DG1xCG2 space, i.e. temperature variables in the next-to-lowest order set of spaces. This acts like the vertex-based limiter implemented in F... | ab93672a84d4a71019abad4249529403e4b0c8d7 | <|skeleton|>
class ThetaLimiter:
"""A vertex-based limiter for the degree 1 temperature space. A vertex based limiter for fields in the DG1xCG2 space, i.e. temperature variables in the next-to-lowest order set of spaces. This acts like the vertex-based limiter implemented in Firedrake, but in addition corrects the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ThetaLimiter:
"""A vertex-based limiter for the degree 1 temperature space. A vertex based limiter for fields in the DG1xCG2 space, i.e. temperature variables in the next-to-lowest order set of spaces. This acts like the vertex-based limiter implemented in Firedrake, but in addition corrects the central nodes... | the_stack_v2_python_sparse | gusto/limiters.py | firedrakeproject/gusto | train | 10 |
ab7fb73c88324b13417d63221146e357fb04be9f | [
"if not prices:\n return 0\n\ndef foo(i):\n if i == 0:\n return [-prices[0], 0, 0]\n sub = foo(i - 1)\n return [max(sub[0], sub[2] - prices[i]), sub[0] + prices[i], max(sub[1], sub[2])]\nreturn max(foo(len(prices) - 1))",
"if not prices:\n return 0\nmoney = [-prices[0], 0, 0]\nfor p in price... | <|body_start_0|>
if not prices:
return 0
def foo(i):
if i == 0:
return [-prices[0], 0, 0]
sub = foo(i - 1)
return [max(sub[0], sub[2] - prices[i]), sub[0] + prices[i], max(sub[1], sub[2])]
return max(foo(len(prices) - 1))
<|end_bod... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""12/27/2019 02:51"""
<|body_0|>
def maxProfit(self, prices: List[int]) -> int:
"""12/27/2019 02:53"""
<|body_1|>
def maxProfit(self, prices: List[int]) -> int:
"""Oct 20, 2021 22:24"""
... | stack_v2_sparse_classes_36k_train_034583 | 3,236 | no_license | [
{
"docstring": "12/27/2019 02:51",
"name": "maxProfit",
"signature": "def maxProfit(self, prices: List[int]) -> int"
},
{
"docstring": "12/27/2019 02:53",
"name": "maxProfit",
"signature": "def maxProfit(self, prices: List[int]) -> int"
},
{
"docstring": "Oct 20, 2021 22:24",
... | 5 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices: List[int]) -> int: 12/27/2019 02:51
- def maxProfit(self, prices: List[int]) -> int: 12/27/2019 02:53
- def maxProfit(self, prices: List[int]) -> int:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices: List[int]) -> int: 12/27/2019 02:51
- def maxProfit(self, prices: List[int]) -> int: 12/27/2019 02:53
- def maxProfit(self, prices: List[int]) -> int:... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""12/27/2019 02:51"""
<|body_0|>
def maxProfit(self, prices: List[int]) -> int:
"""12/27/2019 02:53"""
<|body_1|>
def maxProfit(self, prices: List[int]) -> int:
"""Oct 20, 2021 22:24"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""12/27/2019 02:51"""
if not prices:
return 0
def foo(i):
if i == 0:
return [-prices[0], 0, 0]
sub = foo(i - 1)
return [max(sub[0], sub[2] - prices[i]), sub[0] + p... | the_stack_v2_python_sparse | leetcode/solved/309_Best_Time_to_Buy_and_Sell_Stock_with_Cooldown/solution.py | sungminoh/algorithms | train | 0 | |
ae8884b8e062cbaa08adf683663309ab8bc90c97 | [
"p = psutil.Process(pid=number)\npname = p.name()\nctime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(p.create_time()))\npcpu = p.cpu_percent()\nmem = p.memory_info().rss / unit['m']\npmen = p.memory_percent()\nwdata = p.io_counters().write_bytes / unit['k']\nreturn [number, pname, ctime, pcpu, pmen, mem, wd... | <|body_start_0|>
p = psutil.Process(pid=number)
pname = p.name()
ctime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(p.create_time()))
pcpu = p.cpu_percent()
mem = p.memory_info().rss / unit['m']
pmen = p.memory_percent()
wdata = p.io_counters().write_bytes ... | MyServer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyServer:
def getMessage(number):
""":param number: 进程号"""
<|body_0|>
def handle(self):
""":return:要想实现并发效果必须重写父类中的handler方法，在此方法中实现服务端的逻辑代码（不用再写连接准备，包括bind()、listen()、accept()方法）"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
p = psutil.Process(pi... | stack_v2_sparse_classes_36k_train_034584 | 3,784 | no_license | [
{
"docstring": ":param number: 进程号",
"name": "getMessage",
"signature": "def getMessage(number)"
},
{
"docstring": ":return:要想实现并发效果必须重写父类中的handler方法，在此方法中实现服务端的逻辑代码（不用再写连接准备，包括bind()、listen()、accept()方法）",
"name": "handle",
"signature": "def handle(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006104 | Implement the Python class `MyServer` described below.
Class description:
Implement the MyServer class.
Method signatures and docstrings:
- def getMessage(number): :param number: 进程号
- def handle(self): :return:要想实现并发效果必须重写父类中的handler方法，在此方法中实现服务端的逻辑代码（不用再写连接准备，包括bind()、listen()、accept()方法） | Implement the Python class `MyServer` described below.
Class description:
Implement the MyServer class.
Method signatures and docstrings:
- def getMessage(number): :param number: 进程号
- def handle(self): :return:要想实现并发效果必须重写父类中的handler方法，在此方法中实现服务端的逻辑代码（不用再写连接准备，包括bind()、listen()、accept()方法）
<|skeleton|>
class MyServ... | 8f6c6f806f3f8a8fe4ab8937d5c5a00986522c40 | <|skeleton|>
class MyServer:
def getMessage(number):
""":param number: 进程号"""
<|body_0|>
def handle(self):
""":return:要想实现并发效果必须重写父类中的handler方法，在此方法中实现服务端的逻辑代码（不用再写连接准备，包括bind()、listen()、accept()方法）"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyServer:
def getMessage(number):
""":param number: 进程号"""
p = psutil.Process(pid=number)
pname = p.name()
ctime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(p.create_time()))
pcpu = p.cpu_percent()
mem = p.memory_info().rss / unit['m']
pmen = p.m... | the_stack_v2_python_sparse | QiCrawlEgine/Handler/miniMonitor.py | tiankangbo/dashboard | train | 0 | |
b60abee824efde5d51e958cc28248f23a2410e01 | [
"username = self.cleaned_data.get('username')\nuser_obj = models.UserInfo.objects.filter(username=username)\nif not user_obj:\n return username\nelse:\n raise ValidationError('该用户已存在')",
"password = self.cleaned_data.get('password')\nrepwd = self.cleaned_data.get('re_pwd')\nif password == repwd:\n return... | <|body_start_0|>
username = self.cleaned_data.get('username')
user_obj = models.UserInfo.objects.filter(username=username)
if not user_obj:
return username
else:
raise ValidationError('该用户已存在')
<|end_body_0|>
<|body_start_1|>
password = self.cleaned_data.... | 注册form表单校验 | RegisterForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegisterForm:
"""注册form表单校验"""
def clean_username(self):
"""校验用户是否存在 :return:"""
<|body_0|>
def clean(self):
"""校验两次输入的密码是否一致 :return:"""
<|body_1|>
def clean_email(self):
"""校验注册邮箱是否已经注册 :return:"""
<|body_2|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_034585 | 2,622 | no_license | [
{
"docstring": "校验用户是否存在 :return:",
"name": "clean_username",
"signature": "def clean_username(self)"
},
{
"docstring": "校验两次输入的密码是否一致 :return:",
"name": "clean",
"signature": "def clean(self)"
},
{
"docstring": "校验注册邮箱是否已经注册 :return:",
"name": "clean_email",
"signature":... | 3 | stack_v2_sparse_classes_30k_train_000162 | Implement the Python class `RegisterForm` described below.
Class description:
注册form表单校验
Method signatures and docstrings:
- def clean_username(self): 校验用户是否存在 :return:
- def clean(self): 校验两次输入的密码是否一致 :return:
- def clean_email(self): 校验注册邮箱是否已经注册 :return: | Implement the Python class `RegisterForm` described below.
Class description:
注册form表单校验
Method signatures and docstrings:
- def clean_username(self): 校验用户是否存在 :return:
- def clean(self): 校验两次输入的密码是否一致 :return:
- def clean_email(self): 校验注册邮箱是否已经注册 :return:
<|skeleton|>
class RegisterForm:
"""注册form表单校验"""
... | 7768b74164ad6e3b9337b1f5aed043ec209fcddb | <|skeleton|>
class RegisterForm:
"""注册form表单校验"""
def clean_username(self):
"""校验用户是否存在 :return:"""
<|body_0|>
def clean(self):
"""校验两次输入的密码是否一致 :return:"""
<|body_1|>
def clean_email(self):
"""校验注册邮箱是否已经注册 :return:"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegisterForm:
"""注册form表单校验"""
def clean_username(self):
"""校验用户是否存在 :return:"""
username = self.cleaned_data.get('username')
user_obj = models.UserInfo.objects.filter(username=username)
if not user_obj:
return username
else:
raise Validatio... | the_stack_v2_python_sparse | files/ce2dcf55-e457-4610-a495-c725f3aa7ace/图书馆系统/cnblog/Blog/blog_forms.py | cs4224485/Flaskd- | train | 0 |
3064504dc9bc70e64d4a51d972c78837678d0c61 | [
"features = dict()\nneighbor_config = configs.GraphNeighborConfig()\nsample_features = utils.strip_neighbor_features(features, neighbor_config)\ndummy_tensor = tf.constant(1.0)\nsample_features, dummy_tensor = self.evaluate([sample_features, dummy_tensor])\nself.assertEmpty(sample_features)",
"features = {'F0': t... | <|body_start_0|>
features = dict()
neighbor_config = configs.GraphNeighborConfig()
sample_features = utils.strip_neighbor_features(features, neighbor_config)
dummy_tensor = tf.constant(1.0)
sample_features, dummy_tensor = self.evaluate([sample_features, dummy_tensor])
sel... | Tests removal of neighbor features from a feature dictionary. | StripNeighborFeaturesTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StripNeighborFeaturesTest:
"""Tests removal of neighbor features from a feature dictionary."""
def testEmptyFeatures(self):
"""Tests strip_neighbor_features with empty input."""
<|body_0|>
def testNoNeighborFeatures(self):
"""Tests strip_neighbor_features when th... | stack_v2_sparse_classes_36k_train_034586 | 36,436 | permissive | [
{
"docstring": "Tests strip_neighbor_features with empty input.",
"name": "testEmptyFeatures",
"signature": "def testEmptyFeatures(self)"
},
{
"docstring": "Tests strip_neighbor_features when there are no neighbor features.",
"name": "testNoNeighborFeatures",
"signature": "def testNoNeig... | 4 | stack_v2_sparse_classes_30k_train_021449 | Implement the Python class `StripNeighborFeaturesTest` described below.
Class description:
Tests removal of neighbor features from a feature dictionary.
Method signatures and docstrings:
- def testEmptyFeatures(self): Tests strip_neighbor_features with empty input.
- def testNoNeighborFeatures(self): Tests strip_neig... | Implement the Python class `StripNeighborFeaturesTest` described below.
Class description:
Tests removal of neighbor features from a feature dictionary.
Method signatures and docstrings:
- def testEmptyFeatures(self): Tests strip_neighbor_features with empty input.
- def testNoNeighborFeatures(self): Tests strip_neig... | 995064233479e806a3187ede8a395319520db75e | <|skeleton|>
class StripNeighborFeaturesTest:
"""Tests removal of neighbor features from a feature dictionary."""
def testEmptyFeatures(self):
"""Tests strip_neighbor_features with empty input."""
<|body_0|>
def testNoNeighborFeatures(self):
"""Tests strip_neighbor_features when th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StripNeighborFeaturesTest:
"""Tests removal of neighbor features from a feature dictionary."""
def testEmptyFeatures(self):
"""Tests strip_neighbor_features with empty input."""
features = dict()
neighbor_config = configs.GraphNeighborConfig()
sample_features = utils.strip... | the_stack_v2_python_sparse | neural_structured_learning/lib/utils_test.py | RubensZimbres/neural-structured-learning | train | 1 |
143c7dfca68b77df1dd05ea162b0bdc93aa26a5a | [
"if not nums:\n return 0\nif len(nums) == 1:\n return nums[0]\ndp0 = self.rob_helper(nums[0:-1])\ndp1 = self.rob_helper(nums[1:])\ndp2 = self.rob_helper(nums[1:-1])\nreturn max(dp0, dp1, dp2)",
"if not nums:\n return 0\nif len(nums) == 1:\n return nums[0]\ndp = [0] * len(nums)\ndp[0] = nums[0]\ndp[1] ... | <|body_start_0|>
if not nums:
return 0
if len(nums) == 1:
return nums[0]
dp0 = self.rob_helper(nums[0:-1])
dp1 = self.rob_helper(nums[1:])
dp2 = self.rob_helper(nums[1:-1])
return max(dp0, dp1, dp2)
<|end_body_0|>
<|body_start_1|>
if not n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob_helper(self, nums):
"""maximum sum without the circle formation :param nums: a list of positive integers :return: the maximum sum of a sub-sequence that does not contain 2 conse... | stack_v2_sparse_classes_36k_train_034587 | 2,455 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": "maximum sum without the circle formation :param nums: a list of positive integers :return: the maximum sum of a sub-sequence that does not contain 2 consecutive elements",
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob_helper(self, nums): maximum sum without the circle formation :param nums: a list of positive integers :return: th... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob_helper(self, nums): maximum sum without the circle formation :param nums: a list of positive integers :return: th... | a5b02044ef39154b6a8d32eb57682f447e1632ba | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob_helper(self, nums):
"""maximum sum without the circle formation :param nums: a list of positive integers :return: the maximum sum of a sub-sequence that does not contain 2 conse... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
if not nums:
return 0
if len(nums) == 1:
return nums[0]
dp0 = self.rob_helper(nums[0:-1])
dp1 = self.rob_helper(nums[1:])
dp2 = self.rob_helper(nums[1:-1])
ret... | the_stack_v2_python_sparse | algo/dp/house_robber_II.py | xys234/coding-problems | train | 0 | |
5e9bc7871ecf7efbd709d7839bb9b5a7246137c9 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn WindowsPhone81CustomConfiguration()",
"from .device_configuration import DeviceConfiguration\nfrom .oma_setting import OmaSetting\nfrom .device_configuration import DeviceConfiguration\nfrom .oma_setting import OmaSetting\nfields: Dict... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return WindowsPhone81CustomConfiguration()
<|end_body_0|>
<|body_start_1|>
from .device_configuration import DeviceConfiguration
from .oma_setting import OmaSetting
from .device_configu... | This topic provides descriptions of the declared methods, properties and relationships exposed by the windowsPhone81CustomConfiguration resource. | WindowsPhone81CustomConfiguration | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WindowsPhone81CustomConfiguration:
"""This topic provides descriptions of the declared methods, properties and relationships exposed by the windowsPhone81CustomConfiguration resource."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsPhone81CustomConfigur... | stack_v2_sparse_classes_36k_train_034588 | 2,607 | 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: WindowsPhone81CustomConfiguration",
"name": "create_from_discriminator_value",
"signature": "def create_from... | 3 | null | Implement the Python class `WindowsPhone81CustomConfiguration` described below.
Class description:
This topic provides descriptions of the declared methods, properties and relationships exposed by the windowsPhone81CustomConfiguration resource.
Method signatures and docstrings:
- def create_from_discriminator_value(p... | Implement the Python class `WindowsPhone81CustomConfiguration` described below.
Class description:
This topic provides descriptions of the declared methods, properties and relationships exposed by the windowsPhone81CustomConfiguration resource.
Method signatures and docstrings:
- def create_from_discriminator_value(p... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class WindowsPhone81CustomConfiguration:
"""This topic provides descriptions of the declared methods, properties and relationships exposed by the windowsPhone81CustomConfiguration resource."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsPhone81CustomConfigur... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WindowsPhone81CustomConfiguration:
"""This topic provides descriptions of the declared methods, properties and relationships exposed by the windowsPhone81CustomConfiguration resource."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WindowsPhone81CustomConfiguration:
... | the_stack_v2_python_sparse | msgraph/generated/models/windows_phone81_custom_configuration.py | microsoftgraph/msgraph-sdk-python | train | 135 |
b115a1e596447ad5429d1c24cb51e57a030417d0 | [
"super().__init__()\nself._google_config = google_config\nself._attr_entity_category = EntityCategory.DIAGNOSTIC\nself._attr_unique_id = f'{project_id}_sync'\nself._attr_device_info = DeviceInfo(identifiers={(DOMAIN, project_id)}, name='Google Assistant')",
"assert self._context\nagent_user_id = self._google_conf... | <|body_start_0|>
super().__init__()
self._google_config = google_config
self._attr_entity_category = EntityCategory.DIAGNOSTIC
self._attr_unique_id = f'{project_id}_sync'
self._attr_device_info = DeviceInfo(identifiers={(DOMAIN, project_id)}, name='Google Assistant')
<|end_body_0... | Representation of a synchronization button. | SyncButton | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SyncButton:
"""Representation of a synchronization button."""
def __init__(self, project_id: str, google_config: GoogleConfig) -> None:
"""Initialize button."""
<|body_0|>
async def async_press(self) -> None:
"""Press the button."""
<|body_1|>
<|end_skel... | stack_v2_sparse_classes_36k_train_034589 | 2,165 | permissive | [
{
"docstring": "Initialize button.",
"name": "__init__",
"signature": "def __init__(self, project_id: str, google_config: GoogleConfig) -> None"
},
{
"docstring": "Press the button.",
"name": "async_press",
"signature": "async def async_press(self) -> None"
}
] | 2 | null | Implement the Python class `SyncButton` described below.
Class description:
Representation of a synchronization button.
Method signatures and docstrings:
- def __init__(self, project_id: str, google_config: GoogleConfig) -> None: Initialize button.
- async def async_press(self) -> None: Press the button. | Implement the Python class `SyncButton` described below.
Class description:
Representation of a synchronization button.
Method signatures and docstrings:
- def __init__(self, project_id: str, google_config: GoogleConfig) -> None: Initialize button.
- async def async_press(self) -> None: Press the button.
<|skeleton|... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class SyncButton:
"""Representation of a synchronization button."""
def __init__(self, project_id: str, google_config: GoogleConfig) -> None:
"""Initialize button."""
<|body_0|>
async def async_press(self) -> None:
"""Press the button."""
<|body_1|>
<|end_skel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SyncButton:
"""Representation of a synchronization button."""
def __init__(self, project_id: str, google_config: GoogleConfig) -> None:
"""Initialize button."""
super().__init__()
self._google_config = google_config
self._attr_entity_category = EntityCategory.DIAGNOSTIC
... | the_stack_v2_python_sparse | homeassistant/components/google_assistant/button.py | home-assistant/core | train | 35,501 |
6fb7b458440d3ba8dc05dfdda7a028499e11a1f8 | [
"xdata = []\nydata = []\nfor i in range(0, len(ls_dataset)):\n xdata.append(ls_dataset[i][0])\n ydata.append(ls_dataset[i][1])\nmatplotlib.rcParams.update({'font.size': 12})\nfig, ax = plt.subplots()\nax.xaxis.set_major_locator(MaxNLocator(integer=True))\nax.plot(xdata, ydata, '.')\nplt.xlabel(x_label, fontsi... | <|body_start_0|>
xdata = []
ydata = []
for i in range(0, len(ls_dataset)):
xdata.append(ls_dataset[i][0])
ydata.append(ls_dataset[i][1])
matplotlib.rcParams.update({'font.size': 12})
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(MaxNLocator(i... | PlotUtil | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlotUtil:
def PlotData(ls_dataset, x_label, y_label, font_size=14, is_show=True, is_savefig=False, datafile=''):
"""Function: plot data on fig @arguments: (in) ls_dataset: return line list object"""
<|body_0|>
def Plotfit(xdata, ydata, x, y, x_label, y_label, plt_title='', f... | stack_v2_sparse_classes_36k_train_034590 | 9,796 | no_license | [
{
"docstring": "Function: plot data on fig @arguments: (in) ls_dataset: return line list object",
"name": "PlotData",
"signature": "def PlotData(ls_dataset, x_label, y_label, font_size=14, is_show=True, is_savefig=False, datafile='')"
},
{
"docstring": "Function: plot data and fit curve on fig @... | 3 | stack_v2_sparse_classes_30k_train_018233 | Implement the Python class `PlotUtil` described below.
Class description:
Implement the PlotUtil class.
Method signatures and docstrings:
- def PlotData(ls_dataset, x_label, y_label, font_size=14, is_show=True, is_savefig=False, datafile=''): Function: plot data on fig @arguments: (in) ls_dataset: return line list ob... | Implement the Python class `PlotUtil` described below.
Class description:
Implement the PlotUtil class.
Method signatures and docstrings:
- def PlotData(ls_dataset, x_label, y_label, font_size=14, is_show=True, is_savefig=False, datafile=''): Function: plot data on fig @arguments: (in) ls_dataset: return line list ob... | 03ff57e6fe0114ffd2dd953e79a73a893a6bc0ad | <|skeleton|>
class PlotUtil:
def PlotData(ls_dataset, x_label, y_label, font_size=14, is_show=True, is_savefig=False, datafile=''):
"""Function: plot data on fig @arguments: (in) ls_dataset: return line list object"""
<|body_0|>
def Plotfit(xdata, ydata, x, y, x_label, y_label, plt_title='', f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlotUtil:
def PlotData(ls_dataset, x_label, y_label, font_size=14, is_show=True, is_savefig=False, datafile=''):
"""Function: plot data on fig @arguments: (in) ls_dataset: return line list object"""
xdata = []
ydata = []
for i in range(0, len(ls_dataset)):
xdata.app... | the_stack_v2_python_sparse | GIS_DataAnalysis/proj_py/src/utilities.py | samuelxu999/Research | train | 1 | |
c6ef643551dfb8508cda8c878e72f447e75cb54d | [
"value = self.get_capability(COMMAND_TIMEOUTS)\nif value is None:\n return None\nif isinstance(value, dict):\n return {k: timedelta(milliseconds=v) for k, v in value.items()}\nreturn timedelta(milliseconds=int(value))",
"if isinstance(value, dict):\n self.set_capability(COMMAND_TIMEOUTS, {k: int(v.total_... | <|body_start_0|>
value = self.get_capability(COMMAND_TIMEOUTS)
if value is None:
return None
if isinstance(value, dict):
return {k: timedelta(milliseconds=v) for k, v in value.items()}
return timedelta(milliseconds=int(value))
<|end_body_0|>
<|body_start_1|>
... | CommandTimeoutsOption | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommandTimeoutsOption:
def command_timeouts(self) -> Optional[Union[Dict[str, timedelta], timedelta]]:
"""Custom timeout(s) for WDA backend commands execution."""
<|body_0|>
def command_timeouts(self, value: Union[Dict[str, timedelta], timedelta, int]) -> None:
"""Cu... | stack_v2_sparse_classes_36k_train_034591 | 2,627 | permissive | [
{
"docstring": "Custom timeout(s) for WDA backend commands execution.",
"name": "command_timeouts",
"signature": "def command_timeouts(self) -> Optional[Union[Dict[str, timedelta], timedelta]]"
},
{
"docstring": "Custom timeout for all WDA backend commands execution. This might be useful if WDA ... | 2 | null | Implement the Python class `CommandTimeoutsOption` described below.
Class description:
Implement the CommandTimeoutsOption class.
Method signatures and docstrings:
- def command_timeouts(self) -> Optional[Union[Dict[str, timedelta], timedelta]]: Custom timeout(s) for WDA backend commands execution.
- def command_time... | Implement the Python class `CommandTimeoutsOption` described below.
Class description:
Implement the CommandTimeoutsOption class.
Method signatures and docstrings:
- def command_timeouts(self) -> Optional[Union[Dict[str, timedelta], timedelta]]: Custom timeout(s) for WDA backend commands execution.
- def command_time... | 2e49569ed45751df4c6953466f9769336698c033 | <|skeleton|>
class CommandTimeoutsOption:
def command_timeouts(self) -> Optional[Union[Dict[str, timedelta], timedelta]]:
"""Custom timeout(s) for WDA backend commands execution."""
<|body_0|>
def command_timeouts(self, value: Union[Dict[str, timedelta], timedelta, int]) -> None:
"""Cu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommandTimeoutsOption:
def command_timeouts(self) -> Optional[Union[Dict[str, timedelta], timedelta]]:
"""Custom timeout(s) for WDA backend commands execution."""
value = self.get_capability(COMMAND_TIMEOUTS)
if value is None:
return None
if isinstance(value, dict):... | the_stack_v2_python_sparse | appium/options/ios/xcuitest/other/command_timeouts_option.py | appium/python-client | train | 1,588 | |
2ddecd9114f9d28791355050c36172b4f42fbdf5 | [
"self.text = text_data.get('DetectedText')\nself.kind = text_data.get('Type')\nself.id = text_data.get('Id')\nself.parent_id = text_data.get('ParentId')\nself.confidence = text_data.get('Confidence')\nself.geometry = text_data.get('Geometry')",
"rendering = {}\nif self.text is not None:\n rendering['text'] = s... | <|body_start_0|>
self.text = text_data.get('DetectedText')
self.kind = text_data.get('Type')
self.id = text_data.get('Id')
self.parent_id = text_data.get('ParentId')
self.confidence = text_data.get('Confidence')
self.geometry = text_data.get('Geometry')
<|end_body_0|>
<|... | Encapsulates an Amazon Rekognition text element. | RekognitionText | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RekognitionText:
"""Encapsulates an Amazon Rekognition text element."""
def __init__(self, text_data):
"""Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions."""
<|body_0|>
def to_dict(self):
"""Renders... | stack_v2_sparse_classes_36k_train_034592 | 11,689 | permissive | [
{
"docstring": "Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions.",
"name": "__init__",
"signature": "def __init__(self, text_data)"
},
{
"docstring": "Renders some of the text data to a dict. :return: A dict that contains the text ... | 2 | null | Implement the Python class `RekognitionText` described below.
Class description:
Encapsulates an Amazon Rekognition text element.
Method signatures and docstrings:
- def __init__(self, text_data): Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions.
- def t... | Implement the Python class `RekognitionText` described below.
Class description:
Encapsulates an Amazon Rekognition text element.
Method signatures and docstrings:
- def __init__(self, text_data): Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions.
- def t... | dec41fb589043ac9d8667aac36fb88a53c3abe50 | <|skeleton|>
class RekognitionText:
"""Encapsulates an Amazon Rekognition text element."""
def __init__(self, text_data):
"""Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions."""
<|body_0|>
def to_dict(self):
"""Renders... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RekognitionText:
"""Encapsulates an Amazon Rekognition text element."""
def __init__(self, text_data):
"""Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions."""
self.text = text_data.get('DetectedText')
self.kind = text... | the_stack_v2_python_sparse | python/example_code/rekognition/rekognition_objects.py | awsdocs/aws-doc-sdk-examples | train | 8,240 |
d1df998163385d3e3106ffdc620f2d659c647e17 | [
"device = self.device\nif hasattr(device.api, 'check_sensors'):\n data = await device.async_request(device.api.check_sensors)\n return self.normalize(data, self.coordinator.data)\nawait device.async_request(device.api.update)\nreturn {}",
"if data['temperature'] == -7:\n if previous_data is None or previ... | <|body_start_0|>
device = self.device
if hasattr(device.api, 'check_sensors'):
data = await device.async_request(device.api.check_sensors)
return self.normalize(data, self.coordinator.data)
await device.async_request(device.api.update)
return {}
<|end_body_0|>
<|... | Manages updates for Broadlink remotes. | BroadlinkRMUpdateManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BroadlinkRMUpdateManager:
"""Manages updates for Broadlink remotes."""
async def async_fetch_data(self):
"""Fetch data from the device."""
<|body_0|>
def normalize(data, previous_data):
"""Fix firmware issue. See https://github.com/home-assistant/core/issues/4210... | stack_v2_sparse_classes_36k_train_034593 | 6,174 | permissive | [
{
"docstring": "Fetch data from the device.",
"name": "async_fetch_data",
"signature": "async def async_fetch_data(self)"
},
{
"docstring": "Fix firmware issue. See https://github.com/home-assistant/core/issues/42100.",
"name": "normalize",
"signature": "def normalize(data, previous_data... | 2 | stack_v2_sparse_classes_30k_train_006101 | Implement the Python class `BroadlinkRMUpdateManager` described below.
Class description:
Manages updates for Broadlink remotes.
Method signatures and docstrings:
- async def async_fetch_data(self): Fetch data from the device.
- def normalize(data, previous_data): Fix firmware issue. See https://github.com/home-assis... | Implement the Python class `BroadlinkRMUpdateManager` described below.
Class description:
Manages updates for Broadlink remotes.
Method signatures and docstrings:
- async def async_fetch_data(self): Fetch data from the device.
- def normalize(data, previous_data): Fix firmware issue. See https://github.com/home-assis... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class BroadlinkRMUpdateManager:
"""Manages updates for Broadlink remotes."""
async def async_fetch_data(self):
"""Fetch data from the device."""
<|body_0|>
def normalize(data, previous_data):
"""Fix firmware issue. See https://github.com/home-assistant/core/issues/4210... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BroadlinkRMUpdateManager:
"""Manages updates for Broadlink remotes."""
async def async_fetch_data(self):
"""Fetch data from the device."""
device = self.device
if hasattr(device.api, 'check_sensors'):
data = await device.async_request(device.api.check_sensors)
... | the_stack_v2_python_sparse | homeassistant/components/broadlink/updater.py | home-assistant/core | train | 35,501 |
41818c7c5d3901d55346b6f1941abf3c6f4a7b80 | [
"self.data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)\nself.data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)\ntokenizer_pt, tokenizer_en = self.tokenize_dataset(self.data_train)\nself.tokenizer_pt = tokenizer_pt\nself.tokenizer_en... | <|body_start_0|>
self.data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
self.data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)
tokenizer_pt, tokenizer_en = self.tokenize_dataset(self.data_train)
self.token... | load en-pt lengues dataset and tokenize sentece | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""load en-pt lengues dataset and tokenize sentece"""
def __init__(self):
"""costructor"""
<|body_0|>
def tokenize_dataset(self, data):
"""tokenize for return 2 tensor"""
<|body_1|>
def encode(self, pt, en):
"""that encodes a transla... | stack_v2_sparse_classes_36k_train_034594 | 1,933 | no_license | [
{
"docstring": "costructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "tokenize for return 2 tensor",
"name": "tokenize_dataset",
"signature": "def tokenize_dataset(self, data)"
},
{
"docstring": "that encodes a translation into tokens",
"name":... | 3 | stack_v2_sparse_classes_30k_train_004949 | Implement the Python class `Dataset` described below.
Class description:
load en-pt lengues dataset and tokenize sentece
Method signatures and docstrings:
- def __init__(self): costructor
- def tokenize_dataset(self, data): tokenize for return 2 tensor
- def encode(self, pt, en): that encodes a translation into token... | Implement the Python class `Dataset` described below.
Class description:
load en-pt lengues dataset and tokenize sentece
Method signatures and docstrings:
- def __init__(self): costructor
- def tokenize_dataset(self, data): tokenize for return 2 tensor
- def encode(self, pt, en): that encodes a translation into token... | bda9efa60075afa834433ff1b5179db80f2487ae | <|skeleton|>
class Dataset:
"""load en-pt lengues dataset and tokenize sentece"""
def __init__(self):
"""costructor"""
<|body_0|>
def tokenize_dataset(self, data):
"""tokenize for return 2 tensor"""
<|body_1|>
def encode(self, pt, en):
"""that encodes a transla... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dataset:
"""load en-pt lengues dataset and tokenize sentece"""
def __init__(self):
"""costructor"""
self.data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
self.data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_super... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/1-dataset.py | vandeldiegoc/holbertonschool-machine_learning | train | 0 |
a64070b7ffb6e15f16acce48aca79358f52a620b | [
"super(MultiResolutionSTFTLoss, self).__init__()\nassert len(fft_sizes) == len(hop_sizes) == len(win_lengths)\nself.stft_losses = torch.nn.ModuleList()\nfor fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):\n self.stft_losses += [STFTLoss(fs, ss, wl, window)]\nself.factor_sc = factor_sc\nself.factor_mag = fa... | <|body_start_0|>
super(MultiResolutionSTFTLoss, self).__init__()
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
self.stft_losses = torch.nn.ModuleList()
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
self.stft_losses += [STFTLoss(fs, ss, wl, window)]
... | Multi resolution STFT loss module. | MultiResolutionSTFTLoss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiResolutionSTFTLoss:
"""Multi resolution STFT loss module."""
def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window='hann_window', factor_sc=0.1, factor_mag=0.1):
"""Initialize Multi resolution STFT loss module. Args: fft_s... | stack_v2_sparse_classes_36k_train_034595 | 24,374 | no_license | [
{
"docstring": "Initialize Multi resolution STFT loss module. Args: fft_sizes (list): List of FFT sizes. hop_sizes (list): List of hop sizes. win_lengths (list): List of window lengths. window (str): Window function type. factor (float): a balancing factor across different losses.",
"name": "__init__",
... | 2 | null | Implement the Python class `MultiResolutionSTFTLoss` described below.
Class description:
Multi resolution STFT loss module.
Method signatures and docstrings:
- def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window='hann_window', factor_sc=0.1, factor_mag=0.1): ... | Implement the Python class `MultiResolutionSTFTLoss` described below.
Class description:
Multi resolution STFT loss module.
Method signatures and docstrings:
- def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window='hann_window', factor_sc=0.1, factor_mag=0.1): ... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class MultiResolutionSTFTLoss:
"""Multi resolution STFT loss module."""
def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window='hann_window', factor_sc=0.1, factor_mag=0.1):
"""Initialize Multi resolution STFT loss module. Args: fft_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiResolutionSTFTLoss:
"""Multi resolution STFT loss module."""
def __init__(self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window='hann_window', factor_sc=0.1, factor_mag=0.1):
"""Initialize Multi resolution STFT loss module. Args: fft_sizes (list): ... | the_stack_v2_python_sparse | generated/test_facebookresearch_denoiser.py | jansel/pytorch-jit-paritybench | train | 35 |
534ed75b9977e53a30a6442493d578f1a44c4c3f | [
"self.domain = domain\nself.domain_id = domain_id\nself.id = id\nself.name = name",
"if dictionary is None:\n return None\ndomain = cohesity_management_sdk.models.domain.Domain.from_dictionary(dictionary.get('domain')) if dictionary.get('domain') else None\ndomain_id = dictionary.get('domainId')\nid = dictiona... | <|body_start_0|>
self.domain = domain
self.domain_id = domain_id
self.id = id
self.name = name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
domain = cohesity_management_sdk.models.domain.Domain.from_dictionary(dictionary.get('domain')) i... | Implementation of the 'Project' model. TODO: type description here. Attributes: domain (Domain): Domain to which the project is scoped. domain_id (string): The ID of the domain to which the project is scoped. This field is used in the reponse of Keystone API. id (string): The ID of the project. name (string): The name ... | Project | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Project:
"""Implementation of the 'Project' model. TODO: type description here. Attributes: domain (Domain): Domain to which the project is scoped. domain_id (string): The ID of the domain to which the project is scoped. This field is used in the reponse of Keystone API. id (string): The ID of th... | stack_v2_sparse_classes_36k_train_034596 | 2,065 | permissive | [
{
"docstring": "Constructor for the Project class",
"name": "__init__",
"signature": "def __init__(self, domain=None, domain_id=None, id=None, name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the obje... | 2 | null | Implement the Python class `Project` described below.
Class description:
Implementation of the 'Project' model. TODO: type description here. Attributes: domain (Domain): Domain to which the project is scoped. domain_id (string): The ID of the domain to which the project is scoped. This field is used in the reponse of ... | Implement the Python class `Project` described below.
Class description:
Implementation of the 'Project' model. TODO: type description here. Attributes: domain (Domain): Domain to which the project is scoped. domain_id (string): The ID of the domain to which the project is scoped. This field is used in the reponse of ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class Project:
"""Implementation of the 'Project' model. TODO: type description here. Attributes: domain (Domain): Domain to which the project is scoped. domain_id (string): The ID of the domain to which the project is scoped. This field is used in the reponse of Keystone API. id (string): The ID of th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Project:
"""Implementation of the 'Project' model. TODO: type description here. Attributes: domain (Domain): Domain to which the project is scoped. domain_id (string): The ID of the domain to which the project is scoped. This field is used in the reponse of Keystone API. id (string): The ID of the project. na... | the_stack_v2_python_sparse | cohesity_management_sdk/models/project.py | cohesity/management-sdk-python | train | 24 |
7996a42298386061344a45afe4b427e9a64074c1 | [
"try:\n installed_list_file = open(from_location + '/installed.lst')\nexcept IOError:\n pass\nelse:\n for line in installed_list_file:\n l = line.rstrip().split(' ')\n if l:\n self.installed_packages_list[l[0]] = this_package = self.package_info(from_location, l[0], l[1], l[2], url... | <|body_start_0|>
try:
installed_list_file = open(from_location + '/installed.lst')
except IOError:
pass
else:
for line in installed_list_file:
l = line.rstrip().split(' ')
if l:
self.installed_packages_list[l... | dataset_resolver | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dataset_resolver:
def read_installed_packages_list(self, from_location):
"""Reads a given configuration file, and updates the internal installed packages list. :param from_location: a path (string) to a directory containing an installed.lst file"""
<|body_0|>
def resolve_dat... | stack_v2_sparse_classes_36k_train_034597 | 3,438 | permissive | [
{
"docstring": "Reads a given configuration file, and updates the internal installed packages list. :param from_location: a path (string) to a directory containing an installed.lst file",
"name": "read_installed_packages_list",
"signature": "def read_installed_packages_list(self, from_location)"
},
... | 3 | null | Implement the Python class `dataset_resolver` described below.
Class description:
Implement the dataset_resolver class.
Method signatures and docstrings:
- def read_installed_packages_list(self, from_location): Reads a given configuration file, and updates the internal installed packages list. :param from_location: a... | Implement the Python class `dataset_resolver` described below.
Class description:
Implement the dataset_resolver class.
Method signatures and docstrings:
- def read_installed_packages_list(self, from_location): Reads a given configuration file, and updates the internal installed packages list. :param from_location: a... | 96edb376ced1b828962c749240059903686da549 | <|skeleton|>
class dataset_resolver:
def read_installed_packages_list(self, from_location):
"""Reads a given configuration file, and updates the internal installed packages list. :param from_location: a path (string) to a directory containing an installed.lst file"""
<|body_0|>
def resolve_dat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class dataset_resolver:
def read_installed_packages_list(self, from_location):
"""Reads a given configuration file, and updates the internal installed packages list. :param from_location: a path (string) to a directory containing an installed.lst file"""
try:
installed_list_file = open(f... | the_stack_v2_python_sparse | pylearn2/dataset_get/dataset_resolver.py | Coderx7/pylearn2 | train | 1 | |
f8c76d2c6a5a6595b6842ab4913feb18e056bb7a | [
"g = Grammar()\ng.train_string('Hello, world!')\nself.assertEqual('0 --(0)--> H e l l o , _ w o r l d ! \\n', g.print_grammar())",
"g = Grammar()\ng.train_string('abcabdabcabd')\nself.assertEqual('0 --(0)--> 1 1 \\n1 --(2)--> 2 c 2 d abcabd\\n2 --(2)--> a b ... | <|body_start_0|>
g = Grammar()
g.train_string('Hello, world!')
self.assertEqual('0 --(0)--> H e l l o , _ w o r l d ! \n', g.print_grammar())
<|end_body_0|>
<|body_start_1|>
g = Grammar()
g.train_string('abcabdabcabd')
self.assertEqual('0 --(0)--> 1 1 \n1 --(2)--> 2 c 2 ... | TestSequitur | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestSequitur:
def test_sequitur(self):
"""docstring for test_sequitur"""
<|body_0|>
def test_sequitur_base(self):
"""docstring for test_sequitur_base"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
g = Grammar()
g.train_string('Hello, world!... | stack_v2_sparse_classes_36k_train_034598 | 696 | permissive | [
{
"docstring": "docstring for test_sequitur",
"name": "test_sequitur",
"signature": "def test_sequitur(self)"
},
{
"docstring": "docstring for test_sequitur_base",
"name": "test_sequitur_base",
"signature": "def test_sequitur_base(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017610 | Implement the Python class `TestSequitur` described below.
Class description:
Implement the TestSequitur class.
Method signatures and docstrings:
- def test_sequitur(self): docstring for test_sequitur
- def test_sequitur_base(self): docstring for test_sequitur_base | Implement the Python class `TestSequitur` described below.
Class description:
Implement the TestSequitur class.
Method signatures and docstrings:
- def test_sequitur(self): docstring for test_sequitur
- def test_sequitur_base(self): docstring for test_sequitur_base
<|skeleton|>
class TestSequitur:
def test_sequ... | 7192f0bf26378d8aacb21c0220cc705cb577c6dc | <|skeleton|>
class TestSequitur:
def test_sequitur(self):
"""docstring for test_sequitur"""
<|body_0|>
def test_sequitur_base(self):
"""docstring for test_sequitur_base"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestSequitur:
def test_sequitur(self):
"""docstring for test_sequitur"""
g = Grammar()
g.train_string('Hello, world!')
self.assertEqual('0 --(0)--> H e l l o , _ w o r l d ! \n', g.print_grammar())
def test_sequitur_base(self):
"""docstring for test_sequitur_base""... | the_stack_v2_python_sparse | make_demo_discover_rt/pysequitur/sequiturpython/sequitur_test.py | sjtuytc/AAAI21-RoutineAugmentedPolicyLearning | train | 15 | |
a6db8d95776f07f9fa129ab238739577057429a4 | [
"self.assertEqual(super_algos.find_min(''), -1)\nself.assertEqual(super_algos.sum_all([]), -1)\nself.assertEqual(super_algos.find_min([1, 'a', 5, 6]), -1)\nself.assertEqual(super_algos.find_min([1, 1.3, 5, 6]), -1)\nself.assertEqual(super_algos.find_min([1, 2, 3, 4]), min([1, 2, 3, 4]))",
"self.assertEqual(super_... | <|body_start_0|>
self.assertEqual(super_algos.find_min(''), -1)
self.assertEqual(super_algos.sum_all([]), -1)
self.assertEqual(super_algos.find_min([1, 'a', 5, 6]), -1)
self.assertEqual(super_algos.find_min([1, 1.3, 5, 6]), -1)
self.assertEqual(super_algos.find_min([1, 2, 3, 4]),... | MyTestCases | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyTestCases:
def test_find_min(self):
"""Test Function: * Tests if all instructions are followed for find_min(list)"""
<|body_0|>
def test_sum_all(self):
"""Test Function: * Tests if all instructions are followed for sum_all(list)"""
<|body_1|>
def test_... | stack_v2_sparse_classes_36k_train_034599 | 1,720 | no_license | [
{
"docstring": "Test Function: * Tests if all instructions are followed for find_min(list)",
"name": "test_find_min",
"signature": "def test_find_min(self)"
},
{
"docstring": "Test Function: * Tests if all instructions are followed for sum_all(list)",
"name": "test_sum_all",
"signature":... | 3 | stack_v2_sparse_classes_30k_train_009317 | Implement the Python class `MyTestCases` described below.
Class description:
Implement the MyTestCases class.
Method signatures and docstrings:
- def test_find_min(self): Test Function: * Tests if all instructions are followed for find_min(list)
- def test_sum_all(self): Test Function: * Tests if all instructions are... | Implement the Python class `MyTestCases` described below.
Class description:
Implement the MyTestCases class.
Method signatures and docstrings:
- def test_find_min(self): Test Function: * Tests if all instructions are followed for find_min(list)
- def test_sum_all(self): Test Function: * Tests if all instructions are... | c27509693894b54c077bc40a4d4dfbfa311e029b | <|skeleton|>
class MyTestCases:
def test_find_min(self):
"""Test Function: * Tests if all instructions are followed for find_min(list)"""
<|body_0|>
def test_sum_all(self):
"""Test Function: * Tests if all instructions are followed for sum_all(list)"""
<|body_1|>
def test_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyTestCases:
def test_find_min(self):
"""Test Function: * Tests if all instructions are followed for find_min(list)"""
self.assertEqual(super_algos.find_min(''), -1)
self.assertEqual(super_algos.sum_all([]), -1)
self.assertEqual(super_algos.find_min([1, 'a', 5, 6]), -1)
... | the_stack_v2_python_sparse | python_projects/Recurrsion/submission_004-problem/test_algos.py | Mbuso21/WeThinkCode-Projects | train | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.