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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ad03cda38b6757ced0ac89e2c3df586205499aa3 | [
"x = [int(c) for c in a]\ny = [int(c) for c in b]\nif len(x) > len(y):\n x, y = (y, x)\ncarry = 0\nfor i in range(len(x)):\n y[~i] = x[~i] + y[~i] + carry\n if y[~i] >= 2:\n y[~i] -= 2\n carry = 1\n else:\n carry = 0\ni = len(x)\nwhile i < len(y) and carry:\n y[~i] = y[~i] + carr... | <|body_start_0|>
x = [int(c) for c in a]
y = [int(c) for c in b]
if len(x) > len(y):
x, y = (y, x)
carry = 0
for i in range(len(x)):
y[~i] = x[~i] + y[~i] + carry
if y[~i] >= 2:
y[~i] -= 2
carry = 1
e... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addBinary(self, a, b):
""":type a: str :type b: str :rtype: str"""
<|body_0|>
def addBinary1(self, a, b):
""":type a: str :type b: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
x = [int(c) for c in a]
y = [int... | stack_v2_sparse_classes_36k_train_000100 | 2,097 | no_license | [
{
"docstring": ":type a: str :type b: str :rtype: str",
"name": "addBinary",
"signature": "def addBinary(self, a, b)"
},
{
"docstring": ":type a: str :type b: str :rtype: str",
"name": "addBinary1",
"signature": "def addBinary1(self, a, b)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addBinary(self, a, b): :type a: str :type b: str :rtype: str
- def addBinary1(self, a, b): :type a: str :type b: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addBinary(self, a, b): :type a: str :type b: str :rtype: str
- def addBinary1(self, a, b): :type a: str :type b: str :rtype: str
<|skeleton|>
class Solution:
def addBin... | 4a1747b6497305f3821612d9c358a6795b1690da | <|skeleton|>
class Solution:
def addBinary(self, a, b):
""":type a: str :type b: str :rtype: str"""
<|body_0|>
def addBinary1(self, a, b):
""":type a: str :type b: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def addBinary(self, a, b):
""":type a: str :type b: str :rtype: str"""
x = [int(c) for c in a]
y = [int(c) for c in b]
if len(x) > len(y):
x, y = (y, x)
carry = 0
for i in range(len(x)):
y[~i] = x[~i] + y[~i] + carry
... | the_stack_v2_python_sparse | Math/q067_add_binary.py | sevenhe716/LeetCode | train | 0 | |
d5b36cf5a8606b93c54d857930836efb5157838d | [
"self.dic = {}\nself.preorder = preorder\nfor i in range(len(inorder)):\n self.dic[inorder[i]] = i\nreturn self._build_tree(0, 0, len(inorder) - 1)",
"if in_left > in_right:\n return\nroot = TreeNode(self.preorder[pre_left])\nroot_index = self.dic[root.val]\nleft_len = root_index - in_left\nroot.left = self... | <|body_start_0|>
self.dic = {}
self.preorder = preorder
for i in range(len(inorder)):
self.dic[inorder[i]] = i
return self._build_tree(0, 0, len(inorder) - 1)
<|end_body_0|>
<|body_start_1|>
if in_left > in_right:
return
root = TreeNode(self.preor... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def buildTree(self, preorder, inorder):
"""time: O(N) space: O(N) Args: preorder: list[int] inorder: list[int] Return: TreeNode"""
<|body_0|>
def _build_tree(self, pre_left, in_left, in_right):
"""Args: pre_left: int in_left: int in_right: int Return: TreeN... | stack_v2_sparse_classes_36k_train_000101 | 2,596 | no_license | [
{
"docstring": "time: O(N) space: O(N) Args: preorder: list[int] inorder: list[int] Return: TreeNode",
"name": "buildTree",
"signature": "def buildTree(self, preorder, inorder)"
},
{
"docstring": "Args: pre_left: int in_left: int in_right: int Return: TreeNode",
"name": "_build_tree",
"s... | 2 | stack_v2_sparse_classes_30k_train_020880 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def buildTree(self, preorder, inorder): time: O(N) space: O(N) Args: preorder: list[int] inorder: list[int] Return: TreeNode
- def _build_tree(self, pre_left, in_left, in_right):... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def buildTree(self, preorder, inorder): time: O(N) space: O(N) Args: preorder: list[int] inorder: list[int] Return: TreeNode
- def _build_tree(self, pre_left, in_left, in_right):... | 101bce2fac8b188a4eb2f5e017293d21ad0ecb21 | <|skeleton|>
class Solution:
def buildTree(self, preorder, inorder):
"""time: O(N) space: O(N) Args: preorder: list[int] inorder: list[int] Return: TreeNode"""
<|body_0|>
def _build_tree(self, pre_left, in_left, in_right):
"""Args: pre_left: int in_left: int in_right: int Return: TreeN... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def buildTree(self, preorder, inorder):
"""time: O(N) space: O(N) Args: preorder: list[int] inorder: list[int] Return: TreeNode"""
self.dic = {}
self.preorder = preorder
for i in range(len(inorder)):
self.dic[inorder[i]] = i
return self._build_tree... | the_stack_v2_python_sparse | code/面试题07. 重建二叉树.py | AiZhanghan/Leetcode | train | 0 | |
b2caca50b861acd136c5211fc6d478e9b6671a05 | [
"self.xmax = max(self.xmax, x)\nif node.left:\n xleft = x + 1 if node.left.val == node.val + 1 else 1\n self.backtrack(xleft, node.left)\nif node.right:\n xright = x + 1 if node.right.val == node.val + 1 else 1\n self.backtrack(xright, node.right)",
"self.xmax = 0\nif root:\n self.backtrack(1, root... | <|body_start_0|>
self.xmax = max(self.xmax, x)
if node.left:
xleft = x + 1 if node.left.val == node.val + 1 else 1
self.backtrack(xleft, node.left)
if node.right:
xright = x + 1 if node.right.val == node.val + 1 else 1
self.backtrack(xright, node.r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def backtrack(self, x, node):
"""x: length of consecutive path to this node."""
<|body_0|>
def longestConsecutive(self, root: TreeNode) -> int:
"""DFS"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.xmax = max(self.xmax, x)
if... | stack_v2_sparse_classes_36k_train_000102 | 1,006 | no_license | [
{
"docstring": "x: length of consecutive path to this node.",
"name": "backtrack",
"signature": "def backtrack(self, x, node)"
},
{
"docstring": "DFS",
"name": "longestConsecutive",
"signature": "def longestConsecutive(self, root: TreeNode) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_015035 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def backtrack(self, x, node): x: length of consecutive path to this node.
- def longestConsecutive(self, root: TreeNode) -> int: DFS | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def backtrack(self, x, node): x: length of consecutive path to this node.
- def longestConsecutive(self, root: TreeNode) -> int: DFS
<|skeleton|>
class Solution:
def backtr... | 6043134736452a6f4704b62857d0aed2e9571164 | <|skeleton|>
class Solution:
def backtrack(self, x, node):
"""x: length of consecutive path to this node."""
<|body_0|>
def longestConsecutive(self, root: TreeNode) -> int:
"""DFS"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def backtrack(self, x, node):
"""x: length of consecutive path to this node."""
self.xmax = max(self.xmax, x)
if node.left:
xleft = x + 1 if node.left.val == node.val + 1 else 1
self.backtrack(xleft, node.left)
if node.right:
xright... | the_stack_v2_python_sparse | src/0200-0299/0298.longest.consecutive.path.bt.py | gyang274/leetcode | train | 1 | |
187b9ad59a5c4ec00b649a64717e7fd7d6701a83 | [
"data = {'label': [], 'group_by_data': [{'seriesNum': 0, 'seriesName': '校内培训', 'data': []}, {'seriesNum': 1, 'seriesName': '校外培训', 'data': []}]}\nrandom_key = list(group_records['campus_records'].keys())[0]\nlabels = [EnumData.AGE_LABEL, EnumData.EDUCATION_BACKGROUD_LABEL, EnumData.TITLE_LABEL]\nfor label in labels... | <|body_start_0|>
data = {'label': [], 'group_by_data': [{'seriesNum': 0, 'seriesName': '校内培训', 'data': []}, {'seriesNum': 1, 'seriesName': '校外培训', 'data': []}]}
random_key = list(group_records['campus_records'].keys())[0]
labels = [EnumData.AGE_LABEL, EnumData.EDUCATION_BACKGROUD_LABEL, EnumData... | format data for canvas | CanvasDataFormater | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CanvasDataFormater:
"""format data for canvas"""
def format_records_statistics_data(group_records):
"""format records statistics data Parameters: ---------- group_records: dict { 'campus_records': dict 'off_campus_records': dict }"""
<|body_0|>
def format_teachers_statis... | stack_v2_sparse_classes_36k_train_000103 | 7,426 | no_license | [
{
"docstring": "format records statistics data Parameters: ---------- group_records: dict { 'campus_records': dict 'off_campus_records': dict }",
"name": "format_records_statistics_data",
"signature": "def format_records_statistics_data(group_records)"
},
{
"docstring": "format statistics data P... | 4 | null | Implement the Python class `CanvasDataFormater` described below.
Class description:
format data for canvas
Method signatures and docstrings:
- def format_records_statistics_data(group_records): format records statistics data Parameters: ---------- group_records: dict { 'campus_records': dict 'off_campus_records': dic... | Implement the Python class `CanvasDataFormater` described below.
Class description:
format data for canvas
Method signatures and docstrings:
- def format_records_statistics_data(group_records): format records statistics data Parameters: ---------- group_records: dict { 'campus_records': dict 'off_campus_records': dic... | 48cccddbe8347167cb6120a1cd7d61f9fc57cc7c | <|skeleton|>
class CanvasDataFormater:
"""format data for canvas"""
def format_records_statistics_data(group_records):
"""format records statistics data Parameters: ---------- group_records: dict { 'campus_records': dict 'off_campus_records': dict }"""
<|body_0|>
def format_teachers_statis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CanvasDataFormater:
"""format data for canvas"""
def format_records_statistics_data(group_records):
"""format records statistics data Parameters: ---------- group_records: dict { 'campus_records': dict 'off_campus_records': dict }"""
data = {'label': [], 'group_by_data': [{'seriesNum': 0,... | the_stack_v2_python_sparse | data_warehouse/services/canvas_data_formater_service.py | DLUT-SIE/TMSFTT-BE | train | 1 |
7d260fc3f3b9de7d635f8b1acfd65fbd72ca8f14 | [
"super().__init__(d_model, q, v, h, attention_size, **kwargs)\nself._chunk_size = chunk_size\nself._future_mask = nn.Parameter(torch.triu(torch.ones((self._chunk_size, self._chunk_size)), diagonal=1).bool(), requires_grad=False)\nif self._attention_size is not None:\n self._attention_mask = nn.Parameter(generate... | <|body_start_0|>
super().__init__(d_model, q, v, h, attention_size, **kwargs)
self._chunk_size = chunk_size
self._future_mask = nn.Parameter(torch.triu(torch.ones((self._chunk_size, self._chunk_size)), diagonal=1).bool(), requires_grad=False)
if self._attention_size is not None:
... | Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant size. Parameters ---------- d_model: Dimension of ... | MultiHeadAttentionChunk | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadAttentionChunk:
"""Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant... | stack_v2_sparse_classes_36k_train_000104 | 13,552 | permissive | [
{
"docstring": "Initialize the Multi Head Block.",
"name": "__init__",
"signature": "def __init__(self, d_model: int, q: int, v: int, h: int, attention_size: int=None, chunk_size: Optional[int]=168, **kwargs)"
},
{
"docstring": "Propagate forward the input through the MHB. We compute for each he... | 2 | null | Implement the Python class `MultiHeadAttentionChunk` described below.
Class description:
Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and v... | Implement the Python class `MultiHeadAttentionChunk` described below.
Class description:
Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and v... | 0b801d2d2e828ac480d1097cb3bdd82b1e25c15b | <|skeleton|>
class MultiHeadAttentionChunk:
"""Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadAttentionChunk:
"""Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant size. Parame... | the_stack_v2_python_sparse | code/deep/adarnn/tst/multiHeadAttention.py | jindongwang/transferlearning | train | 12,773 |
01d4256b4fad17f594b71d8aa983f6c744797115 | [
"print('Loading resources...')\nself.create_chitchat_bot()\nself.intent_recognizer = unpickle_file(Path(*paths['INTENT_RECOGNIZER']))\nself.tfidf_vectorizer = unpickle_file(Path(*paths['TFIDF_VECTORIZER']))\nself.ANSWER_TEMPLATE = 'I think its about {}\\nThis thread might help you: https://stackoverflow.com/questio... | <|body_start_0|>
print('Loading resources...')
self.create_chitchat_bot()
self.intent_recognizer = unpickle_file(Path(*paths['INTENT_RECOGNIZER']))
self.tfidf_vectorizer = unpickle_file(Path(*paths['TFIDF_VECTORIZER']))
self.ANSWER_TEMPLATE = 'I think its about {}\nThis thread mi... | Class for the dialogue manager | DialogueManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DialogueManager:
"""Class for the dialogue manager"""
def __init__(self, paths):
"""Constructor for the DialogueManager - Loads the intent recognizer (is this about programming, or just chit-chatting?) - Loads the tf-idf vectorizer (the vectorizer trained on the dialogue and StackOve... | stack_v2_sparse_classes_36k_train_000105 | 6,341 | no_license | [
{
"docstring": "Constructor for the DialogueManager - Loads the intent recognizer (is this about programming, or just chit-chatting?) - Loads the tf-idf vectorizer (the vectorizer trained on the dialogue and StackOverflow thread questions) Parameters ---------- paths : dict Where the keys are names, and the val... | 3 | stack_v2_sparse_classes_30k_train_014169 | Implement the Python class `DialogueManager` described below.
Class description:
Class for the dialogue manager
Method signatures and docstrings:
- def __init__(self, paths): Constructor for the DialogueManager - Loads the intent recognizer (is this about programming, or just chit-chatting?) - Loads the tf-idf vector... | Implement the Python class `DialogueManager` described below.
Class description:
Class for the dialogue manager
Method signatures and docstrings:
- def __init__(self, paths): Constructor for the DialogueManager - Loads the intent recognizer (is this about programming, or just chit-chatting?) - Loads the tf-idf vector... | 06ae79e02a1c528c50efa26c96efe0852e4bb795 | <|skeleton|>
class DialogueManager:
"""Class for the dialogue manager"""
def __init__(self, paths):
"""Constructor for the DialogueManager - Loads the intent recognizer (is this about programming, or just chit-chatting?) - Loads the tf-idf vectorizer (the vectorizer trained on the dialogue and StackOve... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DialogueManager:
"""Class for the dialogue manager"""
def __init__(self, paths):
"""Constructor for the DialogueManager - Loads the intent recognizer (is this about programming, or just chit-chatting?) - Loads the tf-idf vectorizer (the vectorizer trained on the dialogue and StackOverflow thread ... | the_stack_v2_python_sparse | course_6_natural_language_processing/week5_dialog_systems/dialogue_manager.py | loeiten/coursera_advanced_machine_learning | train | 8 |
a8302935955d2a76d2c1ebbd9a08730968fcecac | [
"self.interpol = interpol\nself.gatherer = gatherer\nself.pointgen = pointgen\nself.shell = shell",
"self.initial_points = initial_points\nself.shell.newline()\nself.shell.dashes()\nself.shell.say('-------------------------- Gathering Initial Data Set ' + '------------------------')\nself.shell.dashes()\nself.she... | <|body_start_0|>
self.interpol = interpol
self.gatherer = gatherer
self.pointgen = pointgen
self.shell = shell
<|end_body_0|>
<|body_start_1|>
self.initial_points = initial_points
self.shell.newline()
self.shell.dashes()
self.shell.say('------------------... | Training | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Training:
def __init__(self, interpol, gatherer, pointgen, shell):
"""Training must be provided with and interpolator, a 'gatherer', which may be a benchmarking program or otherwise, a point generator, and a shell."""
<|body_0|>
def initialize(self, initial_points):
... | stack_v2_sparse_classes_36k_train_000106 | 5,207 | no_license | [
{
"docstring": "Training must be provided with and interpolator, a 'gatherer', which may be a benchmarking program or otherwise, a point generator, and a shell.",
"name": "__init__",
"signature": "def __init__(self, interpol, gatherer, pointgen, shell)"
},
{
"docstring": "Gather an initial set o... | 4 | stack_v2_sparse_classes_30k_train_000271 | Implement the Python class `Training` described below.
Class description:
Implement the Training class.
Method signatures and docstrings:
- def __init__(self, interpol, gatherer, pointgen, shell): Training must be provided with and interpolator, a 'gatherer', which may be a benchmarking program or otherwise, a point ... | Implement the Python class `Training` described below.
Class description:
Implement the Training class.
Method signatures and docstrings:
- def __init__(self, interpol, gatherer, pointgen, shell): Training must be provided with and interpolator, a 'gatherer', which may be a benchmarking program or otherwise, a point ... | e5fa2f1262d7a72ab770d919cc3b9a849a577267 | <|skeleton|>
class Training:
def __init__(self, interpol, gatherer, pointgen, shell):
"""Training must be provided with and interpolator, a 'gatherer', which may be a benchmarking program or otherwise, a point generator, and a shell."""
<|body_0|>
def initialize(self, initial_points):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Training:
def __init__(self, interpol, gatherer, pointgen, shell):
"""Training must be provided with and interpolator, a 'gatherer', which may be a benchmarking program or otherwise, a point generator, and a shell."""
self.interpol = interpol
self.gatherer = gatherer
self.point... | the_stack_v2_python_sparse | Benchmarks/Tile_Benchmarks/calibration/interpolator/lib/training.py | UFCCMT/behavioural_emulation | train | 0 | |
040721f192918114ed0ae438c061f107e3931fab | [
"self.mouse = tcod.Mouse()\nself.tile_width = tile_width\nself.tile_height = tile_height\nself.mouse_x = None\nself.mouse_y = None\nself.mouse_moved = False\nself.lclick = False\nself.rclick = False\nself.key = tcod.Key()\nself.quit = False\nself.bus = bus",
"tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS | tcod.E... | <|body_start_0|>
self.mouse = tcod.Mouse()
self.tile_width = tile_width
self.tile_height = tile_height
self.mouse_x = None
self.mouse_y = None
self.mouse_moved = False
self.lclick = False
self.rclick = False
self.key = tcod.Key()
self.quit ... | Inputs | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Inputs:
def __init__(self, bus, tile_width, tile_height):
"""Building the input reader simply requires to give the event bus so we can write inside."""
<|body_0|>
def poll(self):
"""Check key and mouse input."""
<|body_1|>
def poll_keys(self):
""... | stack_v2_sparse_classes_36k_train_000107 | 4,073 | no_license | [
{
"docstring": "Building the input reader simply requires to give the event bus so we can write inside.",
"name": "__init__",
"signature": "def __init__(self, bus, tile_width, tile_height)"
},
{
"docstring": "Check key and mouse input.",
"name": "poll",
"signature": "def poll(self)"
},... | 4 | stack_v2_sparse_classes_30k_train_009781 | Implement the Python class `Inputs` described below.
Class description:
Implement the Inputs class.
Method signatures and docstrings:
- def __init__(self, bus, tile_width, tile_height): Building the input reader simply requires to give the event bus so we can write inside.
- def poll(self): Check key and mouse input.... | Implement the Python class `Inputs` described below.
Class description:
Implement the Inputs class.
Method signatures and docstrings:
- def __init__(self, bus, tile_width, tile_height): Building the input reader simply requires to give the event bus so we can write inside.
- def poll(self): Check key and mouse input.... | 049141c31fc165bb5cf4b2d224b90cbe9655997c | <|skeleton|>
class Inputs:
def __init__(self, bus, tile_width, tile_height):
"""Building the input reader simply requires to give the event bus so we can write inside."""
<|body_0|>
def poll(self):
"""Check key and mouse input."""
<|body_1|>
def poll_keys(self):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Inputs:
def __init__(self, bus, tile_width, tile_height):
"""Building the input reader simply requires to give the event bus so we can write inside."""
self.mouse = tcod.Mouse()
self.tile_width = tile_width
self.tile_height = tile_height
self.mouse_x = None
self... | the_stack_v2_python_sparse | groggy/inputs/input.py | Raveline/groggy | train | 0 | |
0d72bce6b37703c275e5706c4ec71a85eeb0efeb | [
"self.api = None\nself.user_login = None\nself.user_pass = None\nself.user_token = None\nself._login()",
"home = os.path.abspath(os.environ.get('HOME', ''))\nconfig_file_path = os.path.join(home, config_file_name)\nreturn config_file_path",
"config = self._github_config(self.CONFIG)\nparser = configparser.RawCo... | <|body_start_0|>
self.api = None
self.user_login = None
self.user_pass = None
self.user_token = None
self._login()
<|end_body_0|>
<|body_start_1|>
home = os.path.abspath(os.environ.get('HOME', ''))
config_file_path = os.path.join(home, config_file_name)
r... | Provides integration with the GitHub API. Attributes: * api: An instance of github3 to interact with the GitHub API. * CONFIG: A string representing the config file name. * CONFIG_SECTION: A string representing the main config file section. * CONFIG_USER_LOGIN: A string representing the user login config. * CONFIG_USER... | GitHub | [
"MIT",
"LicenseRef-scancode-free-unknown",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GitHub:
"""Provides integration with the GitHub API. Attributes: * api: An instance of github3 to interact with the GitHub API. * CONFIG: A string representing the config file name. * CONFIG_SECTION: A string representing the main config file section. * CONFIG_USER_LOGIN: A string representing th... | stack_v2_sparse_classes_36k_train_000108 | 6,388 | permissive | [
{
"docstring": "Inits GitHub. Args: * None. Returns: None.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Attempts to find the github config file. Adapted from https://github.com/sigmavirus24/github-cli. Args: * config_file_name: A String that represents the config fi... | 4 | stack_v2_sparse_classes_30k_train_012484 | Implement the Python class `GitHub` described below.
Class description:
Provides integration with the GitHub API. Attributes: * api: An instance of github3 to interact with the GitHub API. * CONFIG: A string representing the config file name. * CONFIG_SECTION: A string representing the main config file section. * CONF... | Implement the Python class `GitHub` described below.
Class description:
Provides integration with the GitHub API. Attributes: * api: An instance of github3 to interact with the GitHub API. * CONFIG: A string representing the config file name. * CONFIG_SECTION: A string representing the main config file section. * CONF... | dd27b767cdc0c667655ab8e32e020ed4248bd112 | <|skeleton|>
class GitHub:
"""Provides integration with the GitHub API. Attributes: * api: An instance of github3 to interact with the GitHub API. * CONFIG: A string representing the config file name. * CONFIG_SECTION: A string representing the main config file section. * CONFIG_USER_LOGIN: A string representing th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GitHub:
"""Provides integration with the GitHub API. Attributes: * api: An instance of github3 to interact with the GitHub API. * CONFIG: A string representing the config file name. * CONFIG_SECTION: A string representing the main config file section. * CONFIG_USER_LOGIN: A string representing the user login ... | the_stack_v2_python_sparse | something-learned/Cloud-Computing/AWS/01-awesome-aws/awesome/lib/github.py | agarrharr/code-rush-101 | train | 4 |
7cbdc61620c4b00c9b6d4fec96623574ffd6ffb3 | [
"super(NotifyMailgun, self).__init__(**kwargs)\nself.apikey = validate_regex(apikey)\nif not self.apikey:\n msg = 'An invalid Mailgun API Key ({}) was specified.'.format(apikey)\n self.logger.warning(msg)\n raise TypeError(msg)\nif not self.user:\n msg = 'No Mailgun username was specified.'\n self.lo... | <|body_start_0|>
super(NotifyMailgun, self).__init__(**kwargs)
self.apikey = validate_regex(apikey)
if not self.apikey:
msg = 'An invalid Mailgun API Key ({}) was specified.'.format(apikey)
self.logger.warning(msg)
raise TypeError(msg)
if not self.user... | A wrapper for Mailgun Notifications | NotifyMailgun | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NotifyMailgun:
"""A wrapper for Mailgun Notifications"""
def __init__(self, apikey, targets, from_name=None, region_name=None, **kwargs):
"""Initialize Mailgun Object"""
<|body_0|>
def send(self, body, title='', notify_type=NotifyType.INFO, **kwargs):
"""Perform ... | stack_v2_sparse_classes_36k_train_000109 | 12,916 | permissive | [
{
"docstring": "Initialize Mailgun Object",
"name": "__init__",
"signature": "def __init__(self, apikey, targets, from_name=None, region_name=None, **kwargs)"
},
{
"docstring": "Perform Mailgun Notification",
"name": "send",
"signature": "def send(self, body, title='', notify_type=Notify... | 4 | stack_v2_sparse_classes_30k_train_001628 | Implement the Python class `NotifyMailgun` described below.
Class description:
A wrapper for Mailgun Notifications
Method signatures and docstrings:
- def __init__(self, apikey, targets, from_name=None, region_name=None, **kwargs): Initialize Mailgun Object
- def send(self, body, title='', notify_type=NotifyType.INFO... | Implement the Python class `NotifyMailgun` described below.
Class description:
A wrapper for Mailgun Notifications
Method signatures and docstrings:
- def __init__(self, apikey, targets, from_name=None, region_name=None, **kwargs): Initialize Mailgun Object
- def send(self, body, title='', notify_type=NotifyType.INFO... | 784e073eea64d2ee37cc52e7a2391bce35b05720 | <|skeleton|>
class NotifyMailgun:
"""A wrapper for Mailgun Notifications"""
def __init__(self, apikey, targets, from_name=None, region_name=None, **kwargs):
"""Initialize Mailgun Object"""
<|body_0|>
def send(self, body, title='', notify_type=NotifyType.INFO, **kwargs):
"""Perform ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NotifyMailgun:
"""A wrapper for Mailgun Notifications"""
def __init__(self, apikey, targets, from_name=None, region_name=None, **kwargs):
"""Initialize Mailgun Object"""
super(NotifyMailgun, self).__init__(**kwargs)
self.apikey = validate_regex(apikey)
if not self.apikey:
... | the_stack_v2_python_sparse | apprise/plugins/NotifyMailgun.py | raman325/apprise | train | 1 |
dfc5681b10b8d6eb3b321d6b91ff592ee23f1880 | [
"if amount < 1:\n return 0\nreturn self.coin_change(coins, amount, [0] * (amount + 1))",
"if remainder < 0:\n return -1\n'\\n NOTE: BASE CASE\\n The minimum coins needed to make change for 0 is always 0\\n coins no matter what coins we have.\\n '\nif remainder == 0:\n return 0... | <|body_start_0|>
if amount < 1:
return 0
return self.coin_change(coins, amount, [0] * (amount + 1))
<|end_body_0|>
<|body_start_1|>
if remainder < 0:
return -1
'\n NOTE: BASE CASE\n The minimum coins needed to make change for 0 is always 0\n ... | TopDownSolution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TopDownSolution:
def leastCoins(self, coins, amount):
"""Interface ---- :type coins: list of int :type amount: int :rtype: int Approach ---- 1. We use a recursive approach 2. At each stack frame we consider what choices do we have to make and how does this get us to our goal 3. What are ... | stack_v2_sparse_classes_36k_train_000110 | 5,135 | no_license | [
{
"docstring": "Interface ---- :type coins: list of int :type amount: int :rtype: int Approach ---- 1. We use a recursive approach 2. At each stack frame we consider what choices do we have to make and how does this get us to our goal 3. What are the base cases? - When the remainder is less then 0 - When the re... | 2 | stack_v2_sparse_classes_30k_train_015036 | Implement the Python class `TopDownSolution` described below.
Class description:
Implement the TopDownSolution class.
Method signatures and docstrings:
- def leastCoins(self, coins, amount): Interface ---- :type coins: list of int :type amount: int :rtype: int Approach ---- 1. We use a recursive approach 2. At each s... | Implement the Python class `TopDownSolution` described below.
Class description:
Implement the TopDownSolution class.
Method signatures and docstrings:
- def leastCoins(self, coins, amount): Interface ---- :type coins: list of int :type amount: int :rtype: int Approach ---- 1. We use a recursive approach 2. At each s... | c0d49423885832b616ae3c7cd58e8f24c17cfd4d | <|skeleton|>
class TopDownSolution:
def leastCoins(self, coins, amount):
"""Interface ---- :type coins: list of int :type amount: int :rtype: int Approach ---- 1. We use a recursive approach 2. At each stack frame we consider what choices do we have to make and how does this get us to our goal 3. What are ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TopDownSolution:
def leastCoins(self, coins, amount):
"""Interface ---- :type coins: list of int :type amount: int :rtype: int Approach ---- 1. We use a recursive approach 2. At each stack frame we consider what choices do we have to make and how does this get us to our goal 3. What are the base cases... | the_stack_v2_python_sparse | dynamicProgramming/min_coins_make_change.py | miaviles/Data-Structures-Algorithms-Python | train | 0 | |
45fe5814285e36df32f860f0546176d80f9c4a0e | [
"self._on_not_found: Callable[[str, str], None] | None = None\nrel_paths = {(key_map or {}).get(key, key): file for key, file in type(self).__dict__.items() if not key.startswith('_')}\nabs_paths = {key: f'{root}/{file}' for key, file in rel_paths.items()}\nself.__dict__ = abs_paths\nsuper().__init__(abs_paths)",
... | <|body_start_0|>
self._on_not_found: Callable[[str, str], None] | None = None
rel_paths = {(key_map or {}).get(key, key): file for key, file in type(self).__dict__.items() if not key.startswith('_')}
abs_paths = {key: f'{root}/{file}' for key, file in rel_paths.items()}
self.__dict__ = a... | Files instance inherits from dict so that .values(), items(), etc. are supported but also allows accessing attributes by dot notation. E.g. FILES.wbm_summary instead of FILES["wbm_summary"]. This enables tab completion in IDEs and auto-updating attribute names across the code base when changing the key of a file. | Files | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Files:
"""Files instance inherits from dict so that .values(), items(), etc. are supported but also allows accessing attributes by dot notation. E.g. FILES.wbm_summary instead of FILES["wbm_summary"]. This enables tab completion in IDEs and auto-updating attribute names across the code base when ... | stack_v2_sparse_classes_36k_train_000111 | 10,259 | permissive | [
{
"docstring": "Create a Files instance. Args: root (str, optional): Root directory used to absolufy every file path. Defaults to '~/.cache/matbench-discovery/[latest_figshare_release]' where latest_figshare_release is e.g. 1.0.0. Can also be set through env var MATBENCH_DISCOVERY_CACHE_DIR. key_map (dict[str, ... | 2 | null | Implement the Python class `Files` described below.
Class description:
Files instance inherits from dict so that .values(), items(), etc. are supported but also allows accessing attributes by dot notation. E.g. FILES.wbm_summary instead of FILES["wbm_summary"]. This enables tab completion in IDEs and auto-updating att... | Implement the Python class `Files` described below.
Class description:
Files instance inherits from dict so that .values(), items(), etc. are supported but also allows accessing attributes by dot notation. E.g. FILES.wbm_summary instead of FILES["wbm_summary"]. This enables tab completion in IDEs and auto-updating att... | 5df80efbc23541d38f9f300256cc30d92c61cbce | <|skeleton|>
class Files:
"""Files instance inherits from dict so that .values(), items(), etc. are supported but also allows accessing attributes by dot notation. E.g. FILES.wbm_summary instead of FILES["wbm_summary"]. This enables tab completion in IDEs and auto-updating attribute names across the code base when ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Files:
"""Files instance inherits from dict so that .values(), items(), etc. are supported but also allows accessing attributes by dot notation. E.g. FILES.wbm_summary instead of FILES["wbm_summary"]. This enables tab completion in IDEs and auto-updating attribute names across the code base when changing the ... | the_stack_v2_python_sparse | matbench_discovery/data.py | janosh/matbench-discovery | train | 30 |
4ac01e9db0266170690e0c450adeb2258ce5ce60 | [
"super(DiscriminatorNet, self).__init__()\nself.n_features = 784\nself.n_out = 1\nself.__model_fn()\nself.optimizer = optim.Adam(self.parameters(), lr=0.0002)",
"self.hidden0 = nn.Sequential(nn.Linear(self.n_features, 1024), nn.LeakyReLU(0.2), nn.Dropout(0.3))\nself.hidden1 = nn.Sequential(nn.Linear(1024, 512), n... | <|body_start_0|>
super(DiscriminatorNet, self).__init__()
self.n_features = 784
self.n_out = 1
self.__model_fn()
self.optimizer = optim.Adam(self.parameters(), lr=0.0002)
<|end_body_0|>
<|body_start_1|>
self.hidden0 = nn.Sequential(nn.Linear(self.n_features, 1024), nn.Le... | Class DiscriminatorNet. | DiscriminatorNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DiscriminatorNet:
"""Class DiscriminatorNet."""
def __init__(self):
"""Constructor."""
<|body_0|>
def __model_fn(self):
"""Specifies the network."""
<|body_1|>
def forward(self, X):
"""Performs a forward-pass on the data. :param X: network in... | stack_v2_sparse_classes_36k_train_000112 | 11,950 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Specifies the network.",
"name": "__model_fn",
"signature": "def __model_fn(self)"
},
{
"docstring": "Performs a forward-pass on the data. :param X: network input",
"name":... | 3 | stack_v2_sparse_classes_30k_train_004924 | Implement the Python class `DiscriminatorNet` described below.
Class description:
Class DiscriminatorNet.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def __model_fn(self): Specifies the network.
- def forward(self, X): Performs a forward-pass on the data. :param X: network input | Implement the Python class `DiscriminatorNet` described below.
Class description:
Class DiscriminatorNet.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def __model_fn(self): Specifies the network.
- def forward(self, X): Performs a forward-pass on the data. :param X: network input
<|skeleton... | 98b71b76f664d5f6493bd7f90036531d8f6644a7 | <|skeleton|>
class DiscriminatorNet:
"""Class DiscriminatorNet."""
def __init__(self):
"""Constructor."""
<|body_0|>
def __model_fn(self):
"""Specifies the network."""
<|body_1|>
def forward(self, X):
"""Performs a forward-pass on the data. :param X: network in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DiscriminatorNet:
"""Class DiscriminatorNet."""
def __init__(self):
"""Constructor."""
super(DiscriminatorNet, self).__init__()
self.n_features = 784
self.n_out = 1
self.__model_fn()
self.optimizer = optim.Adam(self.parameters(), lr=0.0002)
def __model... | the_stack_v2_python_sparse | 06_python/misc/gan.py | pfisterer/Applied_ML_Fundamentals | train | 0 |
0e160113b3b1ac49d900309bd380978b70a76ef0 | [
"self.log = LogHandler(logger=logger)\nser = serial.Serial(port=port, baudrate=9600, timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS)\nself.sio = io.TextIOWrapper(io.BufferedRWPair(ser, ser))\ninit_pressure = self.get_pressure()\nself.log.info(f'Successfully reading {in... | <|body_start_0|>
self.log = LogHandler(logger=logger)
ser = serial.Serial(port=port, baudrate=9600, timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS)
self.sio = io.TextIOWrapper(io.BufferedRWPair(ser, ser))
init_pressure = self.get_pressure()
... | AGC_100 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AGC_100:
def __init__(self, port, logger=None):
"""Instantiates serial connection to pressure gauge"""
<|body_0|>
def get_pressure(self):
"""Returns pressure in mBar"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.log = LogHandler(logger=logger... | stack_v2_sparse_classes_36k_train_000113 | 939 | permissive | [
{
"docstring": "Instantiates serial connection to pressure gauge",
"name": "__init__",
"signature": "def __init__(self, port, logger=None)"
},
{
"docstring": "Returns pressure in mBar",
"name": "get_pressure",
"signature": "def get_pressure(self)"
}
] | 2 | null | Implement the Python class `AGC_100` described below.
Class description:
Implement the AGC_100 class.
Method signatures and docstrings:
- def __init__(self, port, logger=None): Instantiates serial connection to pressure gauge
- def get_pressure(self): Returns pressure in mBar | Implement the Python class `AGC_100` described below.
Class description:
Implement the AGC_100 class.
Method signatures and docstrings:
- def __init__(self, port, logger=None): Instantiates serial connection to pressure gauge
- def get_pressure(self): Returns pressure in mBar
<|skeleton|>
class AGC_100:
def __i... | c8794a342d30119a6be93b2dd30ea61b5c946d8a | <|skeleton|>
class AGC_100:
def __init__(self, port, logger=None):
"""Instantiates serial connection to pressure gauge"""
<|body_0|>
def get_pressure(self):
"""Returns pressure in mBar"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AGC_100:
def __init__(self, port, logger=None):
"""Instantiates serial connection to pressure gauge"""
self.log = LogHandler(logger=logger)
ser = serial.Serial(port=port, baudrate=9600, timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS)
... | the_stack_v2_python_sparse | pylabnet/hardware/pressure_gauge/agc_100.py | lukingroup/pylabnet | train | 15 | |
98140917a74b692d3f3d75c92dcf992fcff49e5d | [
"self.event_domain = event_domain\nself.params = params\nself.max_sent_length = params.get_int('max_sent_length')\nself.statistics = defaultdict(int)",
"self.statistics.clear()\nexamples = []\n':type: list[nlplingo.tasks.event_sentence.EventSentenceExample]'\nfor doc in docs:\n for sent in doc.sentences:\n ... | <|body_start_0|>
self.event_domain = event_domain
self.params = params
self.max_sent_length = params.get_int('max_sent_length')
self.statistics = defaultdict(int)
<|end_body_0|>
<|body_start_1|>
self.statistics.clear()
examples = []
':type: list[nlplingo.tasks.ev... | EventSentenceGenerator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventSentenceGenerator:
def __init__(self, event_domain, params):
""":type event_domain: nlplingo.event.event_domain.EventDomain :type params: nlplingo.common.parameters.Parameters"""
<|body_0|>
def generate(self, docs):
""":type docs: list[nlplingo.text.text_theory.... | stack_v2_sparse_classes_36k_train_000114 | 6,318 | permissive | [
{
"docstring": ":type event_domain: nlplingo.event.event_domain.EventDomain :type params: nlplingo.common.parameters.Parameters",
"name": "__init__",
"signature": "def __init__(self, event_domain, params)"
},
{
"docstring": ":type docs: list[nlplingo.text.text_theory.Document]",
"name": "gen... | 6 | null | Implement the Python class `EventSentenceGenerator` described below.
Class description:
Implement the EventSentenceGenerator class.
Method signatures and docstrings:
- def __init__(self, event_domain, params): :type event_domain: nlplingo.event.event_domain.EventDomain :type params: nlplingo.common.parameters.Paramet... | Implement the Python class `EventSentenceGenerator` described below.
Class description:
Implement the EventSentenceGenerator class.
Method signatures and docstrings:
- def __init__(self, event_domain, params): :type event_domain: nlplingo.event.event_domain.EventDomain :type params: nlplingo.common.parameters.Paramet... | 32ff17b1320937faa3d3ebe727032f4b3e7a353d | <|skeleton|>
class EventSentenceGenerator:
def __init__(self, event_domain, params):
""":type event_domain: nlplingo.event.event_domain.EventDomain :type params: nlplingo.common.parameters.Parameters"""
<|body_0|>
def generate(self, docs):
""":type docs: list[nlplingo.text.text_theory.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventSentenceGenerator:
def __init__(self, event_domain, params):
""":type event_domain: nlplingo.event.event_domain.EventDomain :type params: nlplingo.common.parameters.Parameters"""
self.event_domain = event_domain
self.params = params
self.max_sent_length = params.get_int('m... | the_stack_v2_python_sparse | nlplingo/sandbox/misc/event_sentence.py | BBN-E/nlplingo | train | 3 | |
e6cda8c5842637fb7563c576797a75381c898f84 | [
"if isinstance(obj, SecuritySavings):\n return SecuritySavingsSerializer(obj, context=self.context).to_representation(obj)\nelif isinstance(obj, SecurityShares):\n return SecuritySharesSerializer(obj, context=self.context).to_representation(obj)\nelif isinstance(obj, SecurityArticle):\n return SecurityArti... | <|body_start_0|>
if isinstance(obj, SecuritySavings):
return SecuritySavingsSerializer(obj, context=self.context).to_representation(obj)
elif isinstance(obj, SecurityShares):
return SecuritySharesSerializer(obj, context=self.context).to_representation(obj)
elif isinstance... | SecuritySerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SecuritySerializer:
def to_representation(self, obj):
"""The Serializer is selected depending on the type of security"""
<|body_0|>
def to_internal_value(self, data):
"""The Serializer is selected depending on the type of security"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k_train_000115 | 4,478 | no_license | [
{
"docstring": "The Serializer is selected depending on the type of security",
"name": "to_representation",
"signature": "def to_representation(self, obj)"
},
{
"docstring": "The Serializer is selected depending on the type of security",
"name": "to_internal_value",
"signature": "def to_... | 2 | stack_v2_sparse_classes_30k_val_000488 | Implement the Python class `SecuritySerializer` described below.
Class description:
Implement the SecuritySerializer class.
Method signatures and docstrings:
- def to_representation(self, obj): The Serializer is selected depending on the type of security
- def to_internal_value(self, data): The Serializer is selected... | Implement the Python class `SecuritySerializer` described below.
Class description:
Implement the SecuritySerializer class.
Method signatures and docstrings:
- def to_representation(self, obj): The Serializer is selected depending on the type of security
- def to_internal_value(self, data): The Serializer is selected... | c5ac11e40a628c93c3865363e97b4f255a104ca8 | <|skeleton|>
class SecuritySerializer:
def to_representation(self, obj):
"""The Serializer is selected depending on the type of security"""
<|body_0|>
def to_internal_value(self, data):
"""The Serializer is selected depending on the type of security"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SecuritySerializer:
def to_representation(self, obj):
"""The Serializer is selected depending on the type of security"""
if isinstance(obj, SecuritySavings):
return SecuritySavingsSerializer(obj, context=self.context).to_representation(obj)
elif isinstance(obj, SecurityShar... | the_stack_v2_python_sparse | loans/serializers.py | lubegamark/gosacco | train | 2 | |
ba5a4e19bc1af156ac0e7caf39cf21953f8089f7 | [
"super().__init__()\nself._encoder = Encoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension)\nself._decoder = Decoder(layer_spec_target_res, layer_spec_input_res, kernel_size, filters_cap, initial_filters, channels)",
"encoding = self._encoder(inputs, tr... | <|body_start_0|>
super().__init__()
self._encoder = Encoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension)
self._decoder = Decoder(layer_spec_target_res, layer_spec_input_res, kernel_size, filters_cap, initial_filters, channels)
<|end_... | Primitive Model for all convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3, ) encoding, reconstruction = autoencoder(tf.zeros((1... | Autoencoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Autoencoder:
"""Primitive Model for all convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3, ) encoding, r... | stack_v2_sparse_classes_36k_train_000116 | 6,511 | permissive | [
{
"docstring": "Instantiate the :py:class:`BaseAutoEncoder`. Args: layer_spec_input_res (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of the input tensors. layer_spec_target_res: (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of tensor desired as output of :func:`_get_layer_spec`. kernel_size (int): Kern... | 2 | stack_v2_sparse_classes_30k_train_016512 | Implement the Python class `Autoencoder` described below.
Class description:
Primitive Model for all convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_d... | Implement the Python class `Autoencoder` described below.
Class description:
Primitive Model for all convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_d... | 92ac86fb0c962854e0d80c44165e0e7ff126b3c1 | <|skeleton|>
class Autoencoder:
"""Primitive Model for all convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3, ) encoding, r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Autoencoder:
"""Primitive Model for all convolutional autoencoders. Examples: * Direct Usage: .. testcode:: autoencoder = Autoencoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=32, filters_cap=128, encoding_dimension=100, channels=3, ) encoding, reconstruction... | the_stack_v2_python_sparse | src/ashpy/models/convolutional/autoencoders.py | zurutech/ashpy | train | 89 |
e27d87eaf11730efd6ab4859a71433ef6fd3ebc7 | [
"Xth = len(nums1) + len(nums2)\nif Xth / 2 == Xth // 2:\n return (self.findXthSortedArrarys(nums1, nums2, Xth // 2) + self.findXthSortedArrarys(nums1, nums2, Xth // 2 + 1)) / 2\nelse:\n return self.findXthSortedArrarys(nums1, nums2, Xth // 2 + 1)",
"if Xth > len(nums1) + len(nums2):\n return -1\nto_drop_... | <|body_start_0|>
Xth = len(nums1) + len(nums2)
if Xth / 2 == Xth // 2:
return (self.findXthSortedArrarys(nums1, nums2, Xth // 2) + self.findXthSortedArrarys(nums1, nums2, Xth // 2 + 1)) / 2
else:
return self.findXthSortedArrarys(nums1, nums2, Xth // 2 + 1)
<|end_body_0|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|body_0|>
def findXthSortedArrarys(self, nums1, nums2, Xth=None):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|... | stack_v2_sparse_classes_36k_train_000117 | 1,819 | no_license | [
{
"docstring": ":type nums1: List[int] :type nums2: List[int] :rtype: float",
"name": "findMedianSortedArrays",
"signature": "def findMedianSortedArrays(self, nums1, nums2)"
},
{
"docstring": ":type nums1: List[int] :type nums2: List[int] :rtype: float",
"name": "findXthSortedArrarys",
"... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: float
- def findXthSortedArrarys(self, nums1, nums2, Xth=None): :type nums1:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: float
- def findXthSortedArrarys(self, nums1, nums2, Xth=None): :type nums1:... | d6ddbef76dd8630234f669d272d1f8065c6be128 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|body_0|>
def findXthSortedArrarys(self, nums1, nums2, Xth=None):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
Xth = len(nums1) + len(nums2)
if Xth / 2 == Xth // 2:
return (self.findXthSortedArrarys(nums1, nums2, Xth // 2) + self.findXthSortedArrarys(nums1, num... | the_stack_v2_python_sparse | 4_findMedianSortedArrays.py | Mang0o/leetcode | train | 0 | |
a4c77e6f218bde221ccf02bb1acd23a757f7a586 | [
"super().__init__(coordinator)\ndescription = SENSOR_TYPES[kind]\nself._attrs: dict[str, Any] = {}\nself._attr_device_class = description.get(ATTR_DEVICE_CLASS)\nself._attr_device_info = device_info\nself._attr_entity_registry_enabled_default = description[ATTR_ENABLED]\nself._attr_icon = description[ATTR_ICON]\nse... | <|body_start_0|>
super().__init__(coordinator)
description = SENSOR_TYPES[kind]
self._attrs: dict[str, Any] = {}
self._attr_device_class = description.get(ATTR_DEVICE_CLASS)
self._attr_device_info = device_info
self._attr_entity_registry_enabled_default = description[ATTR... | Define an Brother Printer sensor. | BrotherPrinterSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BrotherPrinterSensor:
"""Define an Brother Printer sensor."""
def __init__(self, coordinator: BrotherDataUpdateCoordinator, kind: str, device_info: DeviceInfo) -> None:
"""Initialize."""
<|body_0|>
def state(self) -> Any:
"""Return the state."""
<|body_1|... | stack_v2_sparse_classes_36k_train_000118 | 3,217 | permissive | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, coordinator: BrotherDataUpdateCoordinator, kind: str, device_info: DeviceInfo) -> None"
},
{
"docstring": "Return the state.",
"name": "state",
"signature": "def state(self) -> Any"
},
{
"docstring... | 3 | null | Implement the Python class `BrotherPrinterSensor` described below.
Class description:
Define an Brother Printer sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: BrotherDataUpdateCoordinator, kind: str, device_info: DeviceInfo) -> None: Initialize.
- def state(self) -> Any: Return the state.... | Implement the Python class `BrotherPrinterSensor` described below.
Class description:
Define an Brother Printer sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: BrotherDataUpdateCoordinator, kind: str, device_info: DeviceInfo) -> None: Initialize.
- def state(self) -> Any: Return the state.... | 2fee32fce03bc49e86cf2e7b741a15621a97cce5 | <|skeleton|>
class BrotherPrinterSensor:
"""Define an Brother Printer sensor."""
def __init__(self, coordinator: BrotherDataUpdateCoordinator, kind: str, device_info: DeviceInfo) -> None:
"""Initialize."""
<|body_0|>
def state(self) -> Any:
"""Return the state."""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BrotherPrinterSensor:
"""Define an Brother Printer sensor."""
def __init__(self, coordinator: BrotherDataUpdateCoordinator, kind: str, device_info: DeviceInfo) -> None:
"""Initialize."""
super().__init__(coordinator)
description = SENSOR_TYPES[kind]
self._attrs: dict[str, ... | the_stack_v2_python_sparse | homeassistant/components/brother/sensor.py | BenWoodford/home-assistant | train | 11 |
5d1aadb7c69aab49f4a3ba6a4c20d9919aaa38fb | [
"iqCustomComboCtrl.__init__(self, *args, **kwargs)\nself.choice = list()\nself.filter_env = None\nself.choice_idx = -1\nself.data = None",
"if data is None:\n self.data = list()\nelse:\n self.data = data\n choice = [element['description'] for element in self.data]\n self.setChoice(choice)\nreturn self... | <|body_start_0|>
iqCustomComboCtrl.__init__(self, *args, **kwargs)
self.choice = list()
self.filter_env = None
self.choice_idx = -1
self.data = None
<|end_body_0|>
<|body_start_1|>
if data is None:
self.data = list()
else:
self.data = data... | The control class is an extended selection from the specified list. | iqCustomChoice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class iqCustomChoice:
"""The control class is an extended selection from the specified list."""
def __init__(self, *args, **kwargs):
"""Constructor."""
<|body_0|>
def setData(self, data=None):
"""Set data."""
<|body_1|>
def setChoice(self, choice=None):
... | stack_v2_sparse_classes_36k_train_000119 | 19,825 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Set data.",
"name": "setData",
"signature": "def setData(self, data=None)"
},
{
"docstring": "Set choices.",
"name": "setChoice",
"signature": "def set... | 4 | null | Implement the Python class `iqCustomChoice` described below.
Class description:
The control class is an extended selection from the specified list.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Constructor.
- def setData(self, data=None): Set data.
- def setChoice(self, choice=None): Set ch... | Implement the Python class `iqCustomChoice` described below.
Class description:
The control class is an extended selection from the specified list.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Constructor.
- def setData(self, data=None): Set data.
- def setChoice(self, choice=None): Set ch... | 7550e242746cb2fb1219474463f8db21f8e3e114 | <|skeleton|>
class iqCustomChoice:
"""The control class is an extended selection from the specified list."""
def __init__(self, *args, **kwargs):
"""Constructor."""
<|body_0|>
def setData(self, data=None):
"""Set data."""
<|body_1|>
def setChoice(self, choice=None):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class iqCustomChoice:
"""The control class is an extended selection from the specified list."""
def __init__(self, *args, **kwargs):
"""Constructor."""
iqCustomComboCtrl.__init__(self, *args, **kwargs)
self.choice = list()
self.filter_env = None
self.choice_idx = -1
... | the_stack_v2_python_sparse | iq/components/wx_filterchoicectrl/filter_builder_ctrl.py | XHermitOne/iq_framework | train | 1 |
d15d3c0d50820b63dd4b4dee44cd473f17f6cdce | [
"super(ProjectQueueManager, self).__init__()\nself.zk_client = zk_client\nself.project_id = project_id\nproject_dsn_node = '/appscale/projects/{}/postgres_dsn'.format(project_id)\nglobal_dsn_node = '/appscale/tasks/postgres_dsn'\nif self.zk_client.exists(project_dsn_node):\n pg_dsn = self.zk_client.get(project_d... | <|body_start_0|>
super(ProjectQueueManager, self).__init__()
self.zk_client = zk_client
self.project_id = project_id
project_dsn_node = '/appscale/projects/{}/postgres_dsn'.format(project_id)
global_dsn_node = '/appscale/tasks/postgres_dsn'
if self.zk_client.exists(projec... | Keeps track of queue configuration details for a single project. | ProjectQueueManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProjectQueueManager:
"""Keeps track of queue configuration details for a single project."""
def __init__(self, zk_client, project_id):
"""Creates a new ProjectQueueManager. Args: zk_client: A KazooClient. project_id: A string specifying a project ID."""
<|body_0|>
def up... | stack_v2_sparse_classes_36k_train_000120 | 7,391 | permissive | [
{
"docstring": "Creates a new ProjectQueueManager. Args: zk_client: A KazooClient. project_id: A string specifying a project ID.",
"name": "__init__",
"signature": "def __init__(self, zk_client, project_id)"
},
{
"docstring": "Caches new configuration details and cleans up old state. Args: queue... | 6 | stack_v2_sparse_classes_30k_train_015179 | Implement the Python class `ProjectQueueManager` described below.
Class description:
Keeps track of queue configuration details for a single project.
Method signatures and docstrings:
- def __init__(self, zk_client, project_id): Creates a new ProjectQueueManager. Args: zk_client: A KazooClient. project_id: A string s... | Implement the Python class `ProjectQueueManager` described below.
Class description:
Keeps track of queue configuration details for a single project.
Method signatures and docstrings:
- def __init__(self, zk_client, project_id): Creates a new ProjectQueueManager. Args: zk_client: A KazooClient. project_id: A string s... | be17e5f658d7b42b5aa7eeb7a5ddd4962f3ea82f | <|skeleton|>
class ProjectQueueManager:
"""Keeps track of queue configuration details for a single project."""
def __init__(self, zk_client, project_id):
"""Creates a new ProjectQueueManager. Args: zk_client: A KazooClient. project_id: A string specifying a project ID."""
<|body_0|>
def up... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProjectQueueManager:
"""Keeps track of queue configuration details for a single project."""
def __init__(self, zk_client, project_id):
"""Creates a new ProjectQueueManager. Args: zk_client: A KazooClient. project_id: A string specifying a project ID."""
super(ProjectQueueManager, self).__... | the_stack_v2_python_sparse | AppTaskQueue/appscale/taskqueue/queue_manager.py | obino/appscale | train | 1 |
71c46eab5e499496a2392bc6e1d452ee44cbb960 | [
"toolshed_name = 'toolshed.g2.bx.psu.edu/repos/jjohnson/igvtools/igvtools_tile/1.0'\nshort_name = 'igvtools_tile'\nself.assertEqual(galaxy_workflow.parse_tool_name(toolshed_name), short_name)",
"name = 'igvtools_tile'\nshort_name = 'igvtools_tile'\nself.assertEqual(galaxy_workflow.parse_tool_name(name), short_nam... | <|body_start_0|>
toolshed_name = 'toolshed.g2.bx.psu.edu/repos/jjohnson/igvtools/igvtools_tile/1.0'
short_name = 'igvtools_tile'
self.assertEqual(galaxy_workflow.parse_tool_name(toolshed_name), short_name)
<|end_body_0|>
<|body_start_1|>
name = 'igvtools_tile'
short_name = 'igvt... | Test all functions in the galaxy_workflow module | GalaxyWorkflowTest | [
"LicenseRef-scancode-warranty-disclaimer",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GalaxyWorkflowTest:
"""Test all functions in the galaxy_workflow module"""
def test_parse_tool_name_full(self):
"""Test with a toolshed name"""
<|body_0|>
def test_parse_tool_name_short(self):
"""Test with a non-toolshed name"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_000121 | 738 | permissive | [
{
"docstring": "Test with a toolshed name",
"name": "test_parse_tool_name_full",
"signature": "def test_parse_tool_name_full(self)"
},
{
"docstring": "Test with a non-toolshed name",
"name": "test_parse_tool_name_short",
"signature": "def test_parse_tool_name_short(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017718 | Implement the Python class `GalaxyWorkflowTest` described below.
Class description:
Test all functions in the galaxy_workflow module
Method signatures and docstrings:
- def test_parse_tool_name_full(self): Test with a toolshed name
- def test_parse_tool_name_short(self): Test with a non-toolshed name | Implement the Python class `GalaxyWorkflowTest` described below.
Class description:
Test all functions in the galaxy_workflow module
Method signatures and docstrings:
- def test_parse_tool_name_full(self): Test with a toolshed name
- def test_parse_tool_name_short(self): Test with a non-toolshed name
<|skeleton|>
cl... | fca97c904be407c6619608e13437f25a9fc9e979 | <|skeleton|>
class GalaxyWorkflowTest:
"""Test all functions in the galaxy_workflow module"""
def test_parse_tool_name_full(self):
"""Test with a toolshed name"""
<|body_0|>
def test_parse_tool_name_short(self):
"""Test with a non-toolshed name"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GalaxyWorkflowTest:
"""Test all functions in the galaxy_workflow module"""
def test_parse_tool_name_full(self):
"""Test with a toolshed name"""
toolshed_name = 'toolshed.g2.bx.psu.edu/repos/jjohnson/igvtools/igvtools_tile/1.0'
short_name = 'igvtools_tile'
self.assertEqual(... | the_stack_v2_python_sparse | refinery/galaxy_connector/tests.py | ShuhratBek/refinery-platform | train | 1 |
a2f72edc82606e21cf58be2fa1e349e4938d1a49 | [
"self.start_number = start_number\nself.rename_pairs = []\nself.zfillsize = None\nself.process_rename()",
"files = os.listdir('.')\nsorted(files)\ntotal_files = len(files)\nself.zfillsize = len(str(total_files))\nfileseq = self.start_number\nfor i, filename in enumerate(files):\n new_filename = str(fileseq).zf... | <|body_start_0|>
self.start_number = start_number
self.rename_pairs = []
self.zfillsize = None
self.process_rename()
<|end_body_0|>
<|body_start_1|>
files = os.listdir('.')
sorted(files)
total_files = len(files)
self.zfillsize = len(str(total_files))
... | This class takes a start number as parameter, loops thru all files on folder, prefixes numbers sequencially to all files, asks rename confirmation, if confirmed, renames all files on folder. | Renamer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Renamer:
"""This class takes a start number as parameter, loops thru all files on folder, prefixes numbers sequencially to all files, asks rename confirmation, if confirmed, renames all files on folder."""
def __init__(self, start_number):
"""start_number determines the first prefix ... | stack_v2_sparse_classes_36k_train_000122 | 2,741 | no_license | [
{
"docstring": "start_number determines the first prefix number for renames The numbering goes along the alphanumeric ordering given by sorted(files)",
"name": "__init__",
"signature": "def __init__(self, start_number)"
},
{
"docstring": "Prepare renames",
"name": "prep_rename",
"signatu... | 5 | stack_v2_sparse_classes_30k_train_016903 | Implement the Python class `Renamer` described below.
Class description:
This class takes a start number as parameter, loops thru all files on folder, prefixes numbers sequencially to all files, asks rename confirmation, if confirmed, renames all files on folder.
Method signatures and docstrings:
- def __init__(self,... | Implement the Python class `Renamer` described below.
Class description:
This class takes a start number as parameter, loops thru all files on folder, prefixes numbers sequencially to all files, asks rename confirmation, if confirmed, renames all files on folder.
Method signatures and docstrings:
- def __init__(self,... | b4c5642c8d5843846d529630f8d93a7103676539 | <|skeleton|>
class Renamer:
"""This class takes a start number as parameter, loops thru all files on folder, prefixes numbers sequencially to all files, asks rename confirmation, if confirmed, renames all files on folder."""
def __init__(self, start_number):
"""start_number determines the first prefix ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Renamer:
"""This class takes a start number as parameter, loops thru all files on folder, prefixes numbers sequencially to all files, asks rename confirmation, if confirmed, renames all files on folder."""
def __init__(self, start_number):
"""start_number determines the first prefix number for re... | the_stack_v2_python_sparse | renameNumberPrefix.py | alclass/bin | train | 0 |
c8c975b5de40c3c6b78dbebe52dea33b098d6e43 | [
"def maxLengthBeforeI(num):\n lstBeforeI = [dp[j] for j in range(num) if nums[j] < nums[i]]\n return max(lstBeforeI) if lstBeforeI else 0\nn = len(nums)\ndp = [0] * n\ndp[0] = 1\nfor i in range(1, n):\n dp[i] = 1 + maxLengthBeforeI(i)\nreturn max(dp)",
"if not nums:\n return 0\ndp = []\nfor i in range... | <|body_start_0|>
def maxLengthBeforeI(num):
lstBeforeI = [dp[j] for j in range(num) if nums[j] < nums[i]]
return max(lstBeforeI) if lstBeforeI else 0
n = len(nums)
dp = [0] * n
dp[0] = 1
for i in range(1, n):
dp[i] = 1 + maxLengthBeforeI(i)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
... | stack_v2_sparse_classes_36k_train_000123 | 2,098 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "lengthOfLIS",
"signature": "def lengthOfLIS(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "lengthOfLIS",
"signature": "def lengthOfLIS(self, nums)"
},
{
"docstring": ":type nums: List[int] ... | 4 | stack_v2_sparse_classes_30k_val_000790 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS(self, nums): :type nums: List[in... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS(self, nums): :type nums: List[int] :rtype: int
- def lengthOfLIS(self, nums): :type nums: List[in... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def lengthOfLIS(self, nums):
""":type nums: List[int] :rtype: int"""
def maxLengthBeforeI(num):
lstBeforeI = [dp[j] for j in range(num) if nums[j] < nums[i]]
return max(lstBeforeI) if lstBeforeI else 0
n = len(nums)
dp = [0] * n
dp[0] =... | the_stack_v2_python_sparse | 0300_Longest_Increasing_Subsequence.py | bingli8802/leetcode | train | 0 | |
0d20c6f7ff0f58faade08b1e5b77340fd3878fec | [
"self.am_coeffs = None\nself.alt_coeffs = None\nself.reference_transmission = 1.0\nself.poly_am = None\nself.poly_alt = None\nself.configure_options(options)",
"if not isinstance(options, dict):\n raise ValueError(f'Options must be a {dict}. Received {options}.')\nam_coeffs = get_float_list(options.get('amcoe... | <|body_start_0|>
self.am_coeffs = None
self.alt_coeffs = None
self.reference_transmission = 1.0
self.poly_am = None
self.poly_alt = None
self.configure_options(options)
<|end_body_0|>
<|body_start_1|>
if not isinstance(options, dict):
raise ValueError... | AtranModel | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AtranModel:
def __init__(self, options):
"""Initialize the ATRAN model for SOFIA. The ATRAN model is used to derive the relative transmission correction factor for a given altitude above the Earth's surface, observing a source at a given elevation. This can be used to determine the atmos... | stack_v2_sparse_classes_36k_train_000124 | 6,143 | permissive | [
{
"docstring": "Initialize the ATRAN model for SOFIA. The ATRAN model is used to derive the relative transmission correction factor for a given altitude above the Earth's surface, observing a source at a given elevation. This can be used to determine the atmospheric opacity. Please see :func:`AtranModel.get_rel... | 4 | stack_v2_sparse_classes_30k_train_010373 | Implement the Python class `AtranModel` described below.
Class description:
Implement the AtranModel class.
Method signatures and docstrings:
- def __init__(self, options): Initialize the ATRAN model for SOFIA. The ATRAN model is used to derive the relative transmission correction factor for a given altitude above th... | Implement the Python class `AtranModel` described below.
Class description:
Implement the AtranModel class.
Method signatures and docstrings:
- def __init__(self, options): Initialize the ATRAN model for SOFIA. The ATRAN model is used to derive the relative transmission correction factor for a given altitude above th... | 493700340cd34d5f319af6f3a562a82135bb30dd | <|skeleton|>
class AtranModel:
def __init__(self, options):
"""Initialize the ATRAN model for SOFIA. The ATRAN model is used to derive the relative transmission correction factor for a given altitude above the Earth's surface, observing a source at a given elevation. This can be used to determine the atmos... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AtranModel:
def __init__(self, options):
"""Initialize the ATRAN model for SOFIA. The ATRAN model is used to derive the relative transmission correction factor for a given altitude above the Earth's surface, observing a source at a given elevation. This can be used to determine the atmospheric opacity... | the_stack_v2_python_sparse | sofia_redux/scan/custom/sofia/integration/models/atran.py | SOFIA-USRA/sofia_redux | train | 12 | |
0f6e53c6a63858aefee4676e267f685aa1a01006 | [
"self.id = id\nself.backup_run_type = backup_run_type\nself.copy_partial = copy_partial\nself.datalock_config = datalock_config\nself.days_to_keep = days_to_keep\nself.multiplier = multiplier\nself.periodicity = periodicity\nself.source_cluster_id = source_cluster_id\nself.target = target",
"if dictionary is None... | <|body_start_0|>
self.id = id
self.backup_run_type = backup_run_type
self.copy_partial = copy_partial
self.datalock_config = datalock_config
self.days_to_keep = days_to_keep
self.multiplier = multiplier
self.periodicity = periodicity
self.source_cluster_id... | Implementation of the 'SnapshotArchivalCopyPolicy' model. Specifies settings for copying Snapshots External Targets (such as AWS or Tape). This also specifies the retention policy that should be applied to Snapshots after they have been copied to the specified target. Attributes: id (string): Specified the Id for a sna... | SnapshotArchivalCopyPolicy | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SnapshotArchivalCopyPolicy:
"""Implementation of the 'SnapshotArchivalCopyPolicy' model. Specifies settings for copying Snapshots External Targets (such as AWS or Tape). This also specifies the retention policy that should be applied to Snapshots after they have been copied to the specified targe... | stack_v2_sparse_classes_36k_train_000125 | 7,301 | permissive | [
{
"docstring": "Constructor for the SnapshotArchivalCopyPolicy class",
"name": "__init__",
"signature": "def __init__(self, id=None, backup_run_type=None, copy_partial=None, datalock_config=None, days_to_keep=None, multiplier=None, periodicity=None, source_cluster_id=None, target=None)"
},
{
"do... | 2 | null | Implement the Python class `SnapshotArchivalCopyPolicy` described below.
Class description:
Implementation of the 'SnapshotArchivalCopyPolicy' model. Specifies settings for copying Snapshots External Targets (such as AWS or Tape). This also specifies the retention policy that should be applied to Snapshots after they ... | Implement the Python class `SnapshotArchivalCopyPolicy` described below.
Class description:
Implementation of the 'SnapshotArchivalCopyPolicy' model. Specifies settings for copying Snapshots External Targets (such as AWS or Tape). This also specifies the retention policy that should be applied to Snapshots after they ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SnapshotArchivalCopyPolicy:
"""Implementation of the 'SnapshotArchivalCopyPolicy' model. Specifies settings for copying Snapshots External Targets (such as AWS or Tape). This also specifies the retention policy that should be applied to Snapshots after they have been copied to the specified targe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SnapshotArchivalCopyPolicy:
"""Implementation of the 'SnapshotArchivalCopyPolicy' model. Specifies settings for copying Snapshots External Targets (such as AWS or Tape). This also specifies the retention policy that should be applied to Snapshots after they have been copied to the specified target. Attributes... | the_stack_v2_python_sparse | cohesity_management_sdk/models/snapshot_archival_copy_policy.py | cohesity/management-sdk-python | train | 24 |
0efe5d502ffa1cb9a3ed2551e6d24b833f646ec0 | [
"dp = [float('inf')] * (T + 1)\ndp[0] = 0\npre = [float('inf')] * (100 + 1)\nfor l, r in clips:\n for j in range(l, r + 1):\n pre[j] = min(pre[j], l)\nfor i in range(1, T + 1):\n if pre[i] != float('inf'):\n dp[i] = min(dp[i], dp[pre[i]] + 1)\nreturn dp[-1] if dp[-1] != float('inf') else -1",
... | <|body_start_0|>
dp = [float('inf')] * (T + 1)
dp[0] = 0
pre = [float('inf')] * (100 + 1)
for l, r in clips:
for j in range(l, r + 1):
pre[j] = min(pre[j], l)
for i in range(1, T + 1):
if pre[i] != float('inf'):
dp[i] = min(... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def videoStitching1(self, clips: List[List[int]], T: int) -> int:
"""思路：动态规划法 1. 获取每个位置对应的左边可以到达的最小位置，动态规划时从最小的位置转移 @param clips: @param T: @return:"""
<|body_0|>
def videoStitching2(self, clips: List[List[int]], T: int) -> int:
"""思路：贪心算法 @param clips: @pa... | stack_v2_sparse_classes_36k_train_000126 | 3,323 | no_license | [
{
"docstring": "思路：动态规划法 1. 获取每个位置对应的左边可以到达的最小位置，动态规划时从最小的位置转移 @param clips: @param T: @return:",
"name": "videoStitching1",
"signature": "def videoStitching1(self, clips: List[List[int]], T: int) -> int"
},
{
"docstring": "思路：贪心算法 @param clips: @param T: @return:",
"name": "videoStitching2"... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def videoStitching1(self, clips: List[List[int]], T: int) -> int: 思路：动态规划法 1. 获取每个位置对应的左边可以到达的最小位置，动态规划时从最小的位置转移 @param clips: @param T: @return:
- def videoStitching2(self, clip... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def videoStitching1(self, clips: List[List[int]], T: int) -> int: 思路：动态规划法 1. 获取每个位置对应的左边可以到达的最小位置，动态规划时从最小的位置转移 @param clips: @param T: @return:
- def videoStitching2(self, clip... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def videoStitching1(self, clips: List[List[int]], T: int) -> int:
"""思路：动态规划法 1. 获取每个位置对应的左边可以到达的最小位置，动态规划时从最小的位置转移 @param clips: @param T: @return:"""
<|body_0|>
def videoStitching2(self, clips: List[List[int]], T: int) -> int:
"""思路：贪心算法 @param clips: @pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def videoStitching1(self, clips: List[List[int]], T: int) -> int:
"""思路：动态规划法 1. 获取每个位置对应的左边可以到达的最小位置，动态规划时从最小的位置转移 @param clips: @param T: @return:"""
dp = [float('inf')] * (T + 1)
dp[0] = 0
pre = [float('inf')] * (100 + 1)
for l, r in clips:
for ... | the_stack_v2_python_sparse | LeetCode/动态规划法(dp)/1024. 视频拼接.py | yiming1012/MyLeetCode | train | 2 | |
0f57db67533c3c2f0c4880f67ab5c1d5b05f233f | [
"assert isinstance(values, np.ndarray)\nassert len(values.shape) == 3\nself.values = values\nself.idx_map = idx_map",
"if self.idx_map is not None:\n series_idx = np.array([self.idx_map[i] for i in series_idx])\nbatch_size = series_idx.shape[0] * time_idx.shape[0]\nseq_len = time_idx.shape[1]\nif seq_last:\n ... | <|body_start_0|>
assert isinstance(values, np.ndarray)
assert len(values.shape) == 3
self.values = values
self.idx_map = idx_map
<|end_body_0|>
<|body_start_1|>
if self.idx_map is not None:
series_idx = np.array([self.idx_map[i] for i in series_idx])
batch_si... | TimeSeries | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimeSeries:
def __init__(self, values, idx_map=None):
"""Args: values: shape(N, dim, seq) idx_map: dict"""
<|body_0|>
def read_batch(self, series_idx, time_idx, seq_last):
"""Args: series_idx: shape(I) time_idx: shape(J, seq) seq_last(bool) Returns: shape(batch, dim,... | stack_v2_sparse_classes_36k_train_000127 | 17,496 | permissive | [
{
"docstring": "Args: values: shape(N, dim, seq) idx_map: dict",
"name": "__init__",
"signature": "def __init__(self, values, idx_map=None)"
},
{
"docstring": "Args: series_idx: shape(I) time_idx: shape(J, seq) seq_last(bool) Returns: shape(batch, dim, seq)",
"name": "read_batch",
"signa... | 2 | stack_v2_sparse_classes_30k_train_013814 | Implement the Python class `TimeSeries` described below.
Class description:
Implement the TimeSeries class.
Method signatures and docstrings:
- def __init__(self, values, idx_map=None): Args: values: shape(N, dim, seq) idx_map: dict
- def read_batch(self, series_idx, time_idx, seq_last): Args: series_idx: shape(I) ti... | Implement the Python class `TimeSeries` described below.
Class description:
Implement the TimeSeries class.
Method signatures and docstrings:
- def __init__(self, values, idx_map=None): Args: values: shape(N, dim, seq) idx_map: dict
- def read_batch(self, series_idx, time_idx, seq_last): Args: series_idx: shape(I) ti... | 04f8dfc8a508d433c8536c0e57f3c3d9df12c69c | <|skeleton|>
class TimeSeries:
def __init__(self, values, idx_map=None):
"""Args: values: shape(N, dim, seq) idx_map: dict"""
<|body_0|>
def read_batch(self, series_idx, time_idx, seq_last):
"""Args: series_idx: shape(I) time_idx: shape(J, seq) seq_last(bool) Returns: shape(batch, dim,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TimeSeries:
def __init__(self, values, idx_map=None):
"""Args: values: shape(N, dim, seq) idx_map: dict"""
assert isinstance(values, np.ndarray)
assert len(values.shape) == 3
self.values = values
self.idx_map = idx_map
def read_batch(self, series_idx, time_idx, seq... | the_stack_v2_python_sparse | deepseries/old_dataset.py | Relevation-143/Deep-Time-Series-Prediction | train | 0 | |
80b9bd6dd5fa29cffac45bae9693c4f343b66bfa | [
"request.user = None\nauth_header = authentication.get_authorization_header(request).split()\nauth_header_prefix = self.authentication_header_prefix.lower()\nif not auth_header:\n return None\nif len(auth_header) == 1:\n return None\nelif len(auth_header) > 2:\n return None\nprefix = auth_header[0].decode(... | <|body_start_0|>
request.user = None
auth_header = authentication.get_authorization_header(request).split()
auth_header_prefix = self.authentication_header_prefix.lower()
if not auth_header:
return None
if len(auth_header) == 1:
return None
elif le... | JWTAuthentication | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JWTAuthentication:
def authenticate(self, request):
"""The `authenticate` method is called on every request, regardless of whether the endpoint requires authentication. `authenticate` has two possible return values: 1) `None` - We return `None` if we do not wish to authenticate. Usually ... | stack_v2_sparse_classes_36k_train_000128 | 3,554 | permissive | [
{
"docstring": "The `authenticate` method is called on every request, regardless of whether the endpoint requires authentication. `authenticate` has two possible return values: 1) `None` - We return `None` if we do not wish to authenticate. Usually this means we know authentication will fail. An example of this... | 2 | null | Implement the Python class `JWTAuthentication` described below.
Class description:
Implement the JWTAuthentication class.
Method signatures and docstrings:
- def authenticate(self, request): The `authenticate` method is called on every request, regardless of whether the endpoint requires authentication. `authenticate... | Implement the Python class `JWTAuthentication` described below.
Class description:
Implement the JWTAuthentication class.
Method signatures and docstrings:
- def authenticate(self, request): The `authenticate` method is called on every request, regardless of whether the endpoint requires authentication. `authenticate... | 121636bbae446fb93f56c14a83ba819faf327d1f | <|skeleton|>
class JWTAuthentication:
def authenticate(self, request):
"""The `authenticate` method is called on every request, regardless of whether the endpoint requires authentication. `authenticate` has two possible return values: 1) `None` - We return `None` if we do not wish to authenticate. Usually ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JWTAuthentication:
def authenticate(self, request):
"""The `authenticate` method is called on every request, regardless of whether the endpoint requires authentication. `authenticate` has two possible return values: 1) `None` - We return `None` if we do not wish to authenticate. Usually this means we ... | the_stack_v2_python_sparse | python/django/django-realworld/django-realworld-example-app/conduit/apps/authentication/backends.py | DataDog/trace-examples | train | 106 | |
56193e4a7cfda0884088689b116a56ef6c698665 | [
"if params:\n raise ValueError(f'Observation parameters not supported; passed {params}')\npieces = [('player', 2, (2,))]\nif iig_obs_type.private_info == pyspiel.PrivateInfoType.SINGLE_PLAYER:\n pieces.append(('private_card', 3, (3,)))\nif iig_obs_type.public_info:\n if iig_obs_type.perfect_recall:\n ... | <|body_start_0|>
if params:
raise ValueError(f'Observation parameters not supported; passed {params}')
pieces = [('player', 2, (2,))]
if iig_obs_type.private_info == pyspiel.PrivateInfoType.SINGLE_PLAYER:
pieces.append(('private_card', 3, (3,)))
if iig_obs_type.pu... | Observer, conforming to the PyObserver interface (see observation.py). | KuhnPokerObserver | [
"LicenseRef-scancode-generic-cla",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KuhnPokerObserver:
"""Observer, conforming to the PyObserver interface (see observation.py)."""
def __init__(self, iig_obs_type, params):
"""Initializes an empty observation tensor."""
<|body_0|>
def set_from(self, state, player):
"""Updates `tensor` and `dict` t... | stack_v2_sparse_classes_36k_train_000129 | 7,683 | permissive | [
{
"docstring": "Initializes an empty observation tensor.",
"name": "__init__",
"signature": "def __init__(self, iig_obs_type, params)"
},
{
"docstring": "Updates `tensor` and `dict` to reflect `state` from PoV of `player`.",
"name": "set_from",
"signature": "def set_from(self, state, pla... | 3 | stack_v2_sparse_classes_30k_train_021499 | Implement the Python class `KuhnPokerObserver` described below.
Class description:
Observer, conforming to the PyObserver interface (see observation.py).
Method signatures and docstrings:
- def __init__(self, iig_obs_type, params): Initializes an empty observation tensor.
- def set_from(self, state, player): Updates ... | Implement the Python class `KuhnPokerObserver` described below.
Class description:
Observer, conforming to the PyObserver interface (see observation.py).
Method signatures and docstrings:
- def __init__(self, iig_obs_type, params): Initializes an empty observation tensor.
- def set_from(self, state, player): Updates ... | 6f3551fd990053cf2287b380fb9ad0b2a2607c18 | <|skeleton|>
class KuhnPokerObserver:
"""Observer, conforming to the PyObserver interface (see observation.py)."""
def __init__(self, iig_obs_type, params):
"""Initializes an empty observation tensor."""
<|body_0|>
def set_from(self, state, player):
"""Updates `tensor` and `dict` t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KuhnPokerObserver:
"""Observer, conforming to the PyObserver interface (see observation.py)."""
def __init__(self, iig_obs_type, params):
"""Initializes an empty observation tensor."""
if params:
raise ValueError(f'Observation parameters not supported; passed {params}')
... | the_stack_v2_python_sparse | open_spiel/python/games/kuhn_poker.py | sarahperrin/open_spiel | train | 3 |
7980cac317f328fd6387eab10ef8309b30a155e7 | [
"self.pool = heapq.nlargest(k, nums)\nheapq.heapify(self.pool)\nself.k = k",
"if len(self.pool) < self.k:\n heapq.heappush(self.pool, val)\nelse:\n heapq.heappushpop(self.pool, val)\nreturn self.pool[0]"
] | <|body_start_0|>
self.pool = heapq.nlargest(k, nums)
heapq.heapify(self.pool)
self.k = k
<|end_body_0|>
<|body_start_1|>
if len(self.pool) < self.k:
heapq.heappush(self.pool, val)
else:
heapq.heappushpop(self.pool, val)
return self.pool[0]
<|end_b... | KthLargest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.pool = heapq.nlargest(k, nums)
heapq.heapify(... | stack_v2_sparse_classes_36k_train_000130 | 822 | no_license | [
{
"docstring": ":type k: int :type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, k, nums)"
},
{
"docstring": ":type val: int :rtype: int",
"name": "add",
"signature": "def add(self, val)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013807 | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int
<|skeleton|>
class KthLargest:
def __init__(self, k, nu... | bf900574ebec530f4af4494f81c84b31d36c9933 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
self.pool = heapq.nlargest(k, nums)
heapq.heapify(self.pool)
self.k = k
def add(self, val):
""":type val: int :rtype: int"""
if len(self.pool) < self.k:
heapq.heap... | the_stack_v2_python_sparse | easy/kth_largest_element_in_a_stream.py | luozhiping/leetcode | train | 5 | |
09edf147777f1ff5956cb528ee35ae9c7e033b25 | [
"super(TaskTarget, self).__init__()\nself.service_name = service_name\nself.job_name = job_name\nself.region = region\nself.hostname = hostname\nself.task_num = task_num\nself._fields = ('service_name', 'job_name', 'region', 'hostname', 'task_num')",
"collection.task.service_name = self.service_name\ncollection.t... | <|body_start_0|>
super(TaskTarget, self).__init__()
self.service_name = service_name
self.job_name = job_name
self.region = region
self.hostname = hostname
self.task_num = task_num
self._fields = ('service_name', 'job_name', 'region', 'hostname', 'task_num')
<|end... | Monitoring interface class for monitoring active jobs or processes. | TaskTarget | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskTarget:
"""Monitoring interface class for monitoring active jobs or processes."""
def __init__(self, service_name, job_name, region, hostname, task_num=0):
"""Create a Target object exporting info about a specific task. Args: service_name (str): service of which this task is a pa... | stack_v2_sparse_classes_36k_train_000131 | 4,448 | permissive | [
{
"docstring": "Create a Target object exporting info about a specific task. Args: service_name (str): service of which this task is a part. job_name (str): specific name of this task. region (str): general region in which this task is running. hostname (str): specific machine on which this task is running. tas... | 2 | null | Implement the Python class `TaskTarget` described below.
Class description:
Monitoring interface class for monitoring active jobs or processes.
Method signatures and docstrings:
- def __init__(self, service_name, job_name, region, hostname, task_num=0): Create a Target object exporting info about a specific task. Arg... | Implement the Python class `TaskTarget` described below.
Class description:
Monitoring interface class for monitoring active jobs or processes.
Method signatures and docstrings:
- def __init__(self, service_name, job_name, region, hostname, task_num=0): Create a Target object exporting info about a specific task. Arg... | 53102de187a48ac2cfc241fef54dcbc29c453a8e | <|skeleton|>
class TaskTarget:
"""Monitoring interface class for monitoring active jobs or processes."""
def __init__(self, service_name, job_name, region, hostname, task_num=0):
"""Create a Target object exporting info about a specific task. Args: service_name (str): service of which this task is a pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaskTarget:
"""Monitoring interface class for monitoring active jobs or processes."""
def __init__(self, service_name, job_name, region, hostname, task_num=0):
"""Create a Target object exporting info about a specific task. Args: service_name (str): service of which this task is a part. job_name ... | the_stack_v2_python_sparse | third_party/gae_ts_mon/gae_ts_mon/common/targets.py | catapult-project/catapult | train | 2,032 |
c7f88a6aea530ddce980a1920194f6cf1ace4630 | [
"N = len(nums)\nsums = list(accumulate(nums))\nans = 0\nf, t = (0, 0)\nfor i in range(N - 2):\n while f <= i or (f < N - 1 and sums[i] > sums[f] - sums[i]):\n f += 1\n while t < f or (t < N - 1 and sums[t] - sums[i] <= sums[-1] - sums[t]):\n t += 1\n ans = (ans + t - f) % (10 ** 9 + 7)\nretur... | <|body_start_0|>
N = len(nums)
sums = list(accumulate(nums))
ans = 0
f, t = (0, 0)
for i in range(N - 2):
while f <= i or (f < N - 1 and sums[i] > sums[f] - sums[i]):
f += 1
while t < f or (t < N - 1 and sums[t] - sums[i] <= sums[-1] - sums... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def waysToSplit(self, nums: List[int]) -> int:
"""1번 방법을 two pointer 문제로 풀어서 계산량을 줄임. i를 키우면 l이 커지고, m이 작아짐. j를 키우면 m이 커지고, r이 작아짐. i를 키우면서 j의 범위 f, t을 구함 i를 키우면 f를 키워야만 하고, t도 더 커질 수 있다. O(N) / O(N)"""
<|body_0|>
def waysToSplit1(self, nums: List[int]) -> int:
... | stack_v2_sparse_classes_36k_train_000132 | 1,569 | no_license | [
{
"docstring": "1번 방법을 two pointer 문제로 풀어서 계산량을 줄임. i를 키우면 l이 커지고, m이 작아짐. j를 키우면 m이 커지고, r이 작아짐. i를 키우면서 j의 범위 f, t을 구함 i를 키우면 f를 키워야만 하고, t도 더 커질 수 있다. O(N) / O(N)",
"name": "waysToSplit",
"signature": "def waysToSplit(self, nums: List[int]) -> int"
},
{
"docstring": "Prefix sum으로 2개의 포인트를 결정하... | 2 | stack_v2_sparse_classes_30k_train_003742 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def waysToSplit(self, nums: List[int]) -> int: 1번 방법을 two pointer 문제로 풀어서 계산량을 줄임. i를 키우면 l이 커지고, m이 작아짐. j를 키우면 m이 커지고, r이 작아짐. i를 키우면서 j의 범위 f, t을 구함 i를 키우면 f를 키워야만 하고, t도 더 커질... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def waysToSplit(self, nums: List[int]) -> int: 1번 방법을 two pointer 문제로 풀어서 계산량을 줄임. i를 키우면 l이 커지고, m이 작아짐. j를 키우면 m이 커지고, r이 작아짐. i를 키우면서 j의 범위 f, t을 구함 i를 키우면 f를 키워야만 하고, t도 더 커질... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def waysToSplit(self, nums: List[int]) -> int:
"""1번 방법을 two pointer 문제로 풀어서 계산량을 줄임. i를 키우면 l이 커지고, m이 작아짐. j를 키우면 m이 커지고, r이 작아짐. i를 키우면서 j의 범위 f, t을 구함 i를 키우면 f를 키워야만 하고, t도 더 커질 수 있다. O(N) / O(N)"""
<|body_0|>
def waysToSplit1(self, nums: List[int]) -> int:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def waysToSplit(self, nums: List[int]) -> int:
"""1번 방법을 two pointer 문제로 풀어서 계산량을 줄임. i를 키우면 l이 커지고, m이 작아짐. j를 키우면 m이 커지고, r이 작아짐. i를 키우면서 j의 범위 f, t을 구함 i를 키우면 f를 키워야만 하고, t도 더 커질 수 있다. O(N) / O(N)"""
N = len(nums)
sums = list(accumulate(nums))
ans = 0
f, t ... | the_stack_v2_python_sparse | Leetcode/1712.py | hanwgyu/algorithm_problem_solving | train | 5 | |
77c7b36eb83a0a6c36584a62e3bc93afafbe6257 | [
"super(SelfAttention, self).__init__()\nself.W = tf.keras.layers.Dense(units)\nself.U = tf.keras.layers.Dense(units)\nself.V = tf.keras.layers.Dense(1)",
"s_prev_with_time_axis = tf.expand_dims(s_prev, 1)\nW = self.W(s_prev_with_time_axis)\nU = self.U(hidden_states)\nscore = self.V(tf.nn.tanh(W + U))\nattention_w... | <|body_start_0|>
super(SelfAttention, self).__init__()
self.W = tf.keras.layers.Dense(units)
self.U = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
<|end_body_0|>
<|body_start_1|>
s_prev_with_time_axis = tf.expand_dims(s_prev, 1)
W = self.W(s_prev_with_t... | class that instantiates a self-attention layer | SelfAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SelfAttention:
"""class that instantiates a self-attention layer"""
def __init__(self, units):
"""constructor"""
<|body_0|>
def call(self, s_prev, hidden_states):
"""function that builds the self-attention layer"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_000133 | 929 | no_license | [
{
"docstring": "constructor",
"name": "__init__",
"signature": "def __init__(self, units)"
},
{
"docstring": "function that builds the self-attention layer",
"name": "call",
"signature": "def call(self, s_prev, hidden_states)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007834 | Implement the Python class `SelfAttention` described below.
Class description:
class that instantiates a self-attention layer
Method signatures and docstrings:
- def __init__(self, units): constructor
- def call(self, s_prev, hidden_states): function that builds the self-attention layer | Implement the Python class `SelfAttention` described below.
Class description:
class that instantiates a self-attention layer
Method signatures and docstrings:
- def __init__(self, units): constructor
- def call(self, s_prev, hidden_states): function that builds the self-attention layer
<|skeleton|>
class SelfAttent... | 7d3b348aec3b20da25b162b71f150c87c7c28d71 | <|skeleton|>
class SelfAttention:
"""class that instantiates a self-attention layer"""
def __init__(self, units):
"""constructor"""
<|body_0|>
def call(self, s_prev, hidden_states):
"""function that builds the self-attention layer"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SelfAttention:
"""class that instantiates a self-attention layer"""
def __init__(self, units):
"""constructor"""
super(SelfAttention, self).__init__()
self.W = tf.keras.layers.Dense(units)
self.U = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/1-self_attention.py | dacastanogo/holbertonschool-machine_learning | train | 0 |
0a7f7793d7bc9384f2dd8db7f6888ef3d4779a72 | [
"super(CheckpointEventHandler, self).__init__()\nself.watches = set()\nself.handler = handler\nself.verbose = verbose\nself.experiment = experiment",
"root, ext = os.path.splitext(event.src_path)\nbasename = os.path.basename(root)\nif ext == '.incomplete' and basename == 'checkpoint.pt':\n self.watches.add(eve... | <|body_start_0|>
super(CheckpointEventHandler, self).__init__()
self.watches = set()
self.handler = handler
self.verbose = verbose
self.experiment = experiment
<|end_body_0|>
<|body_start_1|>
root, ext = os.path.splitext(event.src_path)
basename = os.path.basenam... | A filesystem event handler for new checkpoints | CheckpointEventHandler | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CheckpointEventHandler:
"""A filesystem event handler for new checkpoints"""
def __init__(self, handler, experiment, verbose=0):
"""Initialize the CheckpointEventHandler"""
<|body_0|>
def on_created(self, event):
"""Watcher for a new file"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_000134 | 6,605 | permissive | [
{
"docstring": "Initialize the CheckpointEventHandler",
"name": "__init__",
"signature": "def __init__(self, handler, experiment, verbose=0)"
},
{
"docstring": "Watcher for a new file",
"name": "on_created",
"signature": "def on_created(self, event)"
},
{
"docstring": "Handle whe... | 3 | stack_v2_sparse_classes_30k_test_000642 | Implement the Python class `CheckpointEventHandler` described below.
Class description:
A filesystem event handler for new checkpoints
Method signatures and docstrings:
- def __init__(self, handler, experiment, verbose=0): Initialize the CheckpointEventHandler
- def on_created(self, event): Watcher for a new file
- d... | Implement the Python class `CheckpointEventHandler` described below.
Class description:
A filesystem event handler for new checkpoints
Method signatures and docstrings:
- def __init__(self, handler, experiment, verbose=0): Initialize the CheckpointEventHandler
- def on_created(self, event): Watcher for a new file
- d... | bbe1cdaecf1d7d104d27b1035a591ebbd3b5141e | <|skeleton|>
class CheckpointEventHandler:
"""A filesystem event handler for new checkpoints"""
def __init__(self, handler, experiment, verbose=0):
"""Initialize the CheckpointEventHandler"""
<|body_0|>
def on_created(self, event):
"""Watcher for a new file"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CheckpointEventHandler:
"""A filesystem event handler for new checkpoints"""
def __init__(self, handler, experiment, verbose=0):
"""Initialize the CheckpointEventHandler"""
super(CheckpointEventHandler, self).__init__()
self.watches = set()
self.handler = handler
s... | the_stack_v2_python_sparse | actions/lambada_acc.py | SimengSun/revisit-nplm | train | 13 |
2d818f2e97dac66db26807791e04dbcf161508cd | [
"erase_parser = argparse.ArgumentParser(description=cls.HELP, add_help=False)\nerase_options = erase_parser.add_argument_group('erase options')\nerase_options.add_argument('-c', '--chip', dest='erase_mode', action='store_const', const=FlashEraser.Mode.CHIP, help='Perform a chip erase.')\nerase_options.add_argument(... | <|body_start_0|>
erase_parser = argparse.ArgumentParser(description=cls.HELP, add_help=False)
erase_options = erase_parser.add_argument_group('erase options')
erase_options.add_argument('-c', '--chip', dest='erase_mode', action='store_const', const=FlashEraser.Mode.CHIP, help='Perform a chip era... | @brief `pyocd erase` subcommand. | EraseSubcommand | [
"CC-BY-4.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EraseSubcommand:
"""@brief `pyocd erase` subcommand."""
def get_args(cls) -> List[argparse.ArgumentParser]:
"""@brief Add this subcommand to the subparsers object."""
<|body_0|>
def invoke(self) -> int:
"""@brief Handle 'erase' subcommand."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_000135 | 4,957 | permissive | [
{
"docstring": "@brief Add this subcommand to the subparsers object.",
"name": "get_args",
"signature": "def get_args(cls) -> List[argparse.ArgumentParser]"
},
{
"docstring": "@brief Handle 'erase' subcommand.",
"name": "invoke",
"signature": "def invoke(self) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_test_000471 | Implement the Python class `EraseSubcommand` described below.
Class description:
@brief `pyocd erase` subcommand.
Method signatures and docstrings:
- def get_args(cls) -> List[argparse.ArgumentParser]: @brief Add this subcommand to the subparsers object.
- def invoke(self) -> int: @brief Handle 'erase' subcommand. | Implement the Python class `EraseSubcommand` described below.
Class description:
@brief `pyocd erase` subcommand.
Method signatures and docstrings:
- def get_args(cls) -> List[argparse.ArgumentParser]: @brief Add this subcommand to the subparsers object.
- def invoke(self) -> int: @brief Handle 'erase' subcommand.
<... | 9253740baf46ebf4eacbce6bf3369150c5fb8ee0 | <|skeleton|>
class EraseSubcommand:
"""@brief `pyocd erase` subcommand."""
def get_args(cls) -> List[argparse.ArgumentParser]:
"""@brief Add this subcommand to the subparsers object."""
<|body_0|>
def invoke(self) -> int:
"""@brief Handle 'erase' subcommand."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EraseSubcommand:
"""@brief `pyocd erase` subcommand."""
def get_args(cls) -> List[argparse.ArgumentParser]:
"""@brief Add this subcommand to the subparsers object."""
erase_parser = argparse.ArgumentParser(description=cls.HELP, add_help=False)
erase_options = erase_parser.add_argu... | the_stack_v2_python_sparse | pyocd/subcommands/erase_cmd.py | pyocd/pyOCD | train | 507 |
b1dcd0b9dcf074b5fde24a6e436e1acef0235e98 | [
"trashs_json = []\nemail = request.user.username\ntrash_repos = syncwerk_api.get_trash_repos_by_owner(email)\nfor r in trash_repos:\n trash = {'repo_id': r.repo_id, 'owner_email': email, 'owner_name': email2nickname(email), 'owner_contact_email': email2contact_email(email), 'repo_name': r.repo_name, 'org_id': r.... | <|body_start_0|>
trashs_json = []
email = request.user.username
trash_repos = syncwerk_api.get_trash_repos_by_owner(email)
for r in trash_repos:
trash = {'repo_id': r.repo_id, 'owner_email': email, 'owner_name': email2nickname(email), 'owner_contact_email': email2contact_emai... | DeletedRepos | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeletedRepos:
def get(self, request):
"""get the deleted-repos of owner"""
<|body_0|>
def post(self, request):
"""restore deleted-repo return: return True if success, otherwise api_error"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
trashs_json = ... | stack_v2_sparse_classes_36k_train_000136 | 2,806 | permissive | [
{
"docstring": "get the deleted-repos of owner",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "restore deleted-repo return: return True if success, otherwise api_error",
"name": "post",
"signature": "def post(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018342 | Implement the Python class `DeletedRepos` described below.
Class description:
Implement the DeletedRepos class.
Method signatures and docstrings:
- def get(self, request): get the deleted-repos of owner
- def post(self, request): restore deleted-repo return: return True if success, otherwise api_error | Implement the Python class `DeletedRepos` described below.
Class description:
Implement the DeletedRepos class.
Method signatures and docstrings:
- def get(self, request): get the deleted-repos of owner
- def post(self, request): restore deleted-repo return: return True if success, otherwise api_error
<|skeleton|>
c... | 13b3ed26a04248211ef91ca70dccc617be27a3c3 | <|skeleton|>
class DeletedRepos:
def get(self, request):
"""get the deleted-repos of owner"""
<|body_0|>
def post(self, request):
"""restore deleted-repo return: return True if success, otherwise api_error"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeletedRepos:
def get(self, request):
"""get the deleted-repos of owner"""
trashs_json = []
email = request.user.username
trash_repos = syncwerk_api.get_trash_repos_by_owner(email)
for r in trash_repos:
trash = {'repo_id': r.repo_id, 'owner_email': email, 'o... | the_stack_v2_python_sparse | fhs/usr/share/python/syncwerk/restapi/restapi/api2/endpoints/deleted_repos.py | syncwerk/syncwerk-server-restapi | train | 0 | |
f4d621441d5f26ee0b92113d34a61302aa84f900 | [
"start = ListNode(-1)\nstart.next = head\np = start\nwhile p.next:\n if p.next.val == val:\n p.next = p.next.next\n else:\n p = p.next\nreturn start.next",
"if head is None:\n return None\nhead.next = self._removeElements(head.next, val)\nreturn head.next if head.val == val else head"
] | <|body_start_0|>
start = ListNode(-1)
start.next = head
p = start
while p.next:
if p.next.val == val:
p.next = p.next.next
else:
p = p.next
return start.next
<|end_body_0|>
<|body_start_1|>
if head is None:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_0|>
def _removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_36k_train_000137 | 1,540 | no_license | [
{
"docstring": ":type head: ListNode :type val: int :rtype: ListNode",
"name": "removeElements",
"signature": "def removeElements(self, head, val)"
},
{
"docstring": ":type head: ListNode :type val: int :rtype: ListNode",
"name": "_removeElements",
"signature": "def _removeElements(self,... | 2 | stack_v2_sparse_classes_30k_train_020621 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeElements(self, head, val): :type head: ListNode :type val: int :rtype: ListNode
- def _removeElements(self, head, val): :type head: ListNode :type val: int :rtype: List... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeElements(self, head, val): :type head: ListNode :type val: int :rtype: ListNode
- def _removeElements(self, head, val): :type head: ListNode :type val: int :rtype: List... | 1d1ffe25d8b49832acc1791261c959ce436a6362 | <|skeleton|>
class Solution:
def removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_0|>
def _removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
start = ListNode(-1)
start.next = head
p = start
while p.next:
if p.next.val == val:
p.next = p.next.next
else:
... | the_stack_v2_python_sparse | 03-单链表/3-虚拟头结点/01-203.py | qiaozhi827/leetcode-1 | train | 0 | |
f01ae55610534fc174aeed663f397cd84fdeffeb | [
"user_uuid = get_jwt_identity()\ntry:\n page = int(request.args.get('page'))\nexcept (ValueError, TypeError):\n page = 1\nreturn MovieService.get_popular_movies(page, user_uuid)",
"user_uuid = get_jwt_identity()\ndata = request.get_json()\nreturn MovieService.add_additional_movie(user_uuid, data)"
] | <|body_start_0|>
user_uuid = get_jwt_identity()
try:
page = int(request.args.get('page'))
except (ValueError, TypeError):
page = 1
return MovieService.get_popular_movies(page, user_uuid)
<|end_body_0|>
<|body_start_1|>
user_uuid = get_jwt_identity()
... | MovieResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovieResource:
def get(self):
"""Get list of the most popular Movies"""
<|body_0|>
def post(self):
"""Add additional Movie for validation"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
user_uuid = get_jwt_identity()
try:
page = ... | stack_v2_sparse_classes_36k_train_000138 | 8,217 | no_license | [
{
"docstring": "Get list of the most popular Movies",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Add additional Movie for validation",
"name": "post",
"signature": "def post(self)"
}
] | 2 | null | Implement the Python class `MovieResource` described below.
Class description:
Implement the MovieResource class.
Method signatures and docstrings:
- def get(self): Get list of the most popular Movies
- def post(self): Add additional Movie for validation | Implement the Python class `MovieResource` described below.
Class description:
Implement the MovieResource class.
Method signatures and docstrings:
- def get(self): Get list of the most popular Movies
- def post(self): Add additional Movie for validation
<|skeleton|>
class MovieResource:
def get(self):
... | 2e7b4e07f149ede884cfe37130d9842ff9bb7be2 | <|skeleton|>
class MovieResource:
def get(self):
"""Get list of the most popular Movies"""
<|body_0|>
def post(self):
"""Add additional Movie for validation"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovieResource:
def get(self):
"""Get list of the most popular Movies"""
user_uuid = get_jwt_identity()
try:
page = int(request.args.get('page'))
except (ValueError, TypeError):
page = 1
return MovieService.get_popular_movies(page, user_uuid)
... | the_stack_v2_python_sparse | src/resources/movie_resource.py | RomainCtl/RecoFinement-api | train | 0 | |
258dd787b9119b8928eafee95259b747e61dcef3 | [
"super().__init__()\nself.hidden_size = hidden_size\nself.embedding = nn.Embedding(output_size, hidden_size)\nself.gru = nn.GRU(hidden_size, hidden_size, num_layers=numlayers, batch_first=True)\nself.out = nn.Linear(hidden_size, output_size)\nself.softmax = nn.LogSoftmax(dim=2)",
"emb = self.embedding(input)\nrel... | <|body_start_0|>
super().__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(output_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size, num_layers=numlayers, batch_first=True)
self.out = nn.Linear(hidden_size, output_size)
self.softmax = nn.... | Generates a sequence of tokens in response to context. | DecoderRNN | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecoderRNN:
"""Generates a sequence of tokens in response to context."""
def __init__(self, output_size, hidden_size, numlayers):
"""Initialize decoder. :param input_size: size of embedding :param hidden_size: size of GRU hidden layers :param numlayers: number of GRU layers"""
... | stack_v2_sparse_classes_36k_train_000139 | 10,301 | permissive | [
{
"docstring": "Initialize decoder. :param input_size: size of embedding :param hidden_size: size of GRU hidden layers :param numlayers: number of GRU layers",
"name": "__init__",
"signature": "def __init__(self, output_size, hidden_size, numlayers)"
},
{
"docstring": "Return encoded state. :par... | 2 | null | Implement the Python class `DecoderRNN` described below.
Class description:
Generates a sequence of tokens in response to context.
Method signatures and docstrings:
- def __init__(self, output_size, hidden_size, numlayers): Initialize decoder. :param input_size: size of embedding :param hidden_size: size of GRU hidde... | Implement the Python class `DecoderRNN` described below.
Class description:
Generates a sequence of tokens in response to context.
Method signatures and docstrings:
- def __init__(self, output_size, hidden_size, numlayers): Initialize decoder. :param input_size: size of embedding :param hidden_size: size of GRU hidde... | ccf60824b28f0ce8ceda44a7ce52a0d117669115 | <|skeleton|>
class DecoderRNN:
"""Generates a sequence of tokens in response to context."""
def __init__(self, output_size, hidden_size, numlayers):
"""Initialize decoder. :param input_size: size of embedding :param hidden_size: size of GRU hidden layers :param numlayers: number of GRU layers"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DecoderRNN:
"""Generates a sequence of tokens in response to context."""
def __init__(self, output_size, hidden_size, numlayers):
"""Initialize decoder. :param input_size: size of embedding :param hidden_size: size of GRU hidden layers :param numlayers: number of GRU layers"""
super().__i... | the_stack_v2_python_sparse | ParlAI/parlai/agents/example_seq2seq/example_seq2seq.py | ethanjperez/convince | train | 27 |
4ae1bfd0f4ffa051b92d3188993bb0d667edc03a | [
"info = OrderedDict({})\ntry:\n if obj.student:\n info['student'] = obj.student.pen_name\nexcept Pupil.DoesNotExist as e:\n info['student'] = str(e)\ntry:\n if obj.exam:\n info['exam'] = obj.exam.name\nexcept Pupil.DoesNotExist as e:\n info['exam'] = str(e)\ntry:\n info_problems = Order... | <|body_start_0|>
info = OrderedDict({})
try:
if obj.student:
info['student'] = obj.student.pen_name
except Pupil.DoesNotExist as e:
info['student'] = str(e)
try:
if obj.exam:
info['exam'] = obj.exam.name
except P... | Serialize the Exam Answer Key with links and info | ExamAnswersSerializers | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExamAnswersSerializers:
"""Serialize the Exam Answer Key with links and info"""
def get_info_data(self, obj, *args, **kwargs):
"""Get Information data :param obj: :param args: :param kwargs: :return:"""
<|body_0|>
def get_links_url(self, obj, *args, **kwargs):
""... | stack_v2_sparse_classes_36k_train_000140 | 7,433 | no_license | [
{
"docstring": "Get Information data :param obj: :param args: :param kwargs: :return:",
"name": "get_info_data",
"signature": "def get_info_data(self, obj, *args, **kwargs)"
},
{
"docstring": "Get links url :param obj: :param args: :param kwargs: :return:",
"name": "get_links_url",
"sign... | 2 | stack_v2_sparse_classes_30k_train_019209 | Implement the Python class `ExamAnswersSerializers` described below.
Class description:
Serialize the Exam Answer Key with links and info
Method signatures and docstrings:
- def get_info_data(self, obj, *args, **kwargs): Get Information data :param obj: :param args: :param kwargs: :return:
- def get_links_url(self, o... | Implement the Python class `ExamAnswersSerializers` described below.
Class description:
Serialize the Exam Answer Key with links and info
Method signatures and docstrings:
- def get_info_data(self, obj, *args, **kwargs): Get Information data :param obj: :param args: :param kwargs: :return:
- def get_links_url(self, o... | acd31a2f43d7ea83fc9bb34627f5dca94763eade | <|skeleton|>
class ExamAnswersSerializers:
"""Serialize the Exam Answer Key with links and info"""
def get_info_data(self, obj, *args, **kwargs):
"""Get Information data :param obj: :param args: :param kwargs: :return:"""
<|body_0|>
def get_links_url(self, obj, *args, **kwargs):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExamAnswersSerializers:
"""Serialize the Exam Answer Key with links and info"""
def get_info_data(self, obj, *args, **kwargs):
"""Get Information data :param obj: :param args: :param kwargs: :return:"""
info = OrderedDict({})
try:
if obj.student:
info['... | the_stack_v2_python_sparse | classroom/serializers.py | JoenyBui/mywaterbuffalo | train | 0 |
01e3427781ed8033e95d9b54233100af3ac383da | [
"print('Output Class Started')\nmultiprocessing.Process.__init__(self, *args, **kw)\nself.queue = q\nself.workers = N\nself.sorting = sorting\nself.output = []",
"while self.workers:\n p = self.queue.get()\n if p is None:\n self.workers -= 1\n else:\n self.output.append(p)\nprint(''.join((c... | <|body_start_0|>
print('Output Class Started')
multiprocessing.Process.__init__(self, *args, **kw)
self.queue = q
self.workers = N
self.sorting = sorting
self.output = []
<|end_body_0|>
<|body_start_1|>
while self.workers:
p = self.queue.get()
... | OutThread | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OutThread:
def __init__(self, N, q, sorting=True, *args, **kw):
"""Initialize process and save queue reference"""
<|body_0|>
def run(self):
"""Extracts items from the output queue and print untill all are done"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_000141 | 1,724 | no_license | [
{
"docstring": "Initialize process and save queue reference",
"name": "__init__",
"signature": "def __init__(self, N, q, sorting=True, *args, **kw)"
},
{
"docstring": "Extracts items from the output queue and print untill all are done",
"name": "run",
"signature": "def run(self)"
}
] | 2 | null | Implement the Python class `OutThread` described below.
Class description:
Implement the OutThread class.
Method signatures and docstrings:
- def __init__(self, N, q, sorting=True, *args, **kw): Initialize process and save queue reference
- def run(self): Extracts items from the output queue and print untill all are ... | Implement the Python class `OutThread` described below.
Class description:
Implement the OutThread class.
Method signatures and docstrings:
- def __init__(self, N, q, sorting=True, *args, **kw): Initialize process and save queue reference
- def run(self): Extracts items from the output queue and print untill all are ... | 7306581d542d6d045a9b2e6377ade0fc5ab8bc0e | <|skeleton|>
class OutThread:
def __init__(self, N, q, sorting=True, *args, **kw):
"""Initialize process and save queue reference"""
<|body_0|>
def run(self):
"""Extracts items from the output queue and print untill all are done"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OutThread:
def __init__(self, N, q, sorting=True, *args, **kw):
"""Initialize process and save queue reference"""
print('Output Class Started')
multiprocessing.Process.__init__(self, *args, **kw)
self.queue = q
self.workers = N
self.sorting = sorting
sel... | the_stack_v2_python_sparse | PythonHomeWork/Py4/Py4_Lesson12/src/output.py | rduvalwa5/OReillyPy | train | 0 | |
0d7013bde870150a09a59ac82cc2a5dd5a3efb8d | [
"TokenAuthenticator(request.headers.get('Authorization')).authenticate()\nbanned_list = Session().query(User.userID, User.username).join(BannedList, User.userID == BannedList.bannedUserID).filter(BannedList.userID == g.userID)\nreturn ([{'userID': userID, 'username': username} for userID, username in banned_list], ... | <|body_start_0|>
TokenAuthenticator(request.headers.get('Authorization')).authenticate()
banned_list = Session().query(User.userID, User.username).join(BannedList, User.userID == BannedList.bannedUserID).filter(BannedList.userID == g.userID)
return ([{'userID': userID, 'username': username} for ... | BannedLists | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BannedLists:
def get(self):
"""View your Banned List."""
<|body_0|>
def post(self):
"""Add a FilmFinder to your Banned List."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
TokenAuthenticator(request.headers.get('Authorization')).authenticate()
... | stack_v2_sparse_classes_36k_train_000142 | 3,081 | no_license | [
{
"docstring": "View your Banned List.",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Add a FilmFinder to your Banned List.",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018499 | Implement the Python class `BannedLists` described below.
Class description:
Implement the BannedLists class.
Method signatures and docstrings:
- def get(self): View your Banned List.
- def post(self): Add a FilmFinder to your Banned List. | Implement the Python class `BannedLists` described below.
Class description:
Implement the BannedLists class.
Method signatures and docstrings:
- def get(self): View your Banned List.
- def post(self): Add a FilmFinder to your Banned List.
<|skeleton|>
class BannedLists:
def get(self):
"""View your Bann... | db8862ea20ee441aed84099d44dd5695f0d950ee | <|skeleton|>
class BannedLists:
def get(self):
"""View your Banned List."""
<|body_0|>
def post(self):
"""Add a FilmFinder to your Banned List."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BannedLists:
def get(self):
"""View your Banned List."""
TokenAuthenticator(request.headers.get('Authorization')).authenticate()
banned_list = Session().query(User.userID, User.username).join(BannedList, User.userID == BannedList.bannedUserID).filter(BannedList.userID == g.userID)
... | the_stack_v2_python_sparse | server/apis/banned_list.py | NishantChokkarapu/capstone-project-comp9900-h16a-tahelka | train | 0 | |
fa7434d1e859e4d94c2dade90d9bf66a990629cf | [
"if not root:\n return True\nnodes = [root]\nwhile nodes:\n node = nodes.pop()\n if abs(self.maxDepth(node.left) - self.maxDepth(node.right)) > 1:\n return False\n if node.left:\n nodes.append(node.left)\n if node.right:\n nodes.append(node.right)\nreturn True",
"if not root:\n... | <|body_start_0|>
if not root:
return True
nodes = [root]
while nodes:
node = nodes.pop()
if abs(self.maxDepth(node.left) - self.maxDepth(node.right)) > 1:
return False
if node.left:
nodes.append(node.left)
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isBalanced(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def maxDepth(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
return True
nodes... | stack_v2_sparse_classes_36k_train_000143 | 2,310 | permissive | [
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isBalanced",
"signature": "def isBalanced(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "maxDepth",
"signature": "def maxDepth(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000881 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isBalanced(self, root): :type root: TreeNode :rtype: bool
- def maxDepth(self, root): :type root: TreeNode :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isBalanced(self, root): :type root: TreeNode :rtype: bool
- def maxDepth(self, root): :type root: TreeNode :rtype: int
<|skeleton|>
class Solution:
def isBalanced(self,... | 57080da5fbe5d62cbc0b8a34e362a8b0978d5b59 | <|skeleton|>
class Solution:
def isBalanced(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def maxDepth(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isBalanced(self, root):
""":type root: TreeNode :rtype: bool"""
if not root:
return True
nodes = [root]
while nodes:
node = nodes.pop()
if abs(self.maxDepth(node.left) - self.maxDepth(node.right)) > 1:
return Fal... | the_stack_v2_python_sparse | python/tree/0110_balanced_binary_tree.py | linshaoyong/leetcode | train | 6 | |
8aefaba9b382d84fe30da2e717b8ae922bd23c4f | [
"self.department = department\nself.dependents = dependents\nself.hired_at = APIHelper.HttpDateTime(hired_at) if hired_at else None\nself.joining_day = joining_day\nself.salary = salary\nself.working_days = working_days\nself.boss = boss\nself.additional_properties = additional_properties\nsuper(Employee, self).__i... | <|body_start_0|>
self.department = department
self.dependents = dependents
self.hired_at = APIHelper.HttpDateTime(hired_at) if hired_at else None
self.joining_day = joining_day
self.salary = salary
self.working_days = working_days
self.boss = boss
self.add... | Implementation of the 'Employee' model. TODO: type model description here. NOTE: This class inherits from 'Person'. Attributes: department (string): TODO: type description here. dependents (list of Person): TODO: type description here. hired_at (datetime): TODO: type description here. joining_day (Days): TODO: type des... | Employee | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Employee:
"""Implementation of the 'Employee' model. TODO: type model description here. NOTE: This class inherits from 'Person'. Attributes: department (string): TODO: type description here. dependents (list of Person): TODO: type description here. hired_at (datetime): TODO: type description here... | stack_v2_sparse_classes_36k_train_000144 | 13,843 | permissive | [
{
"docstring": "Constructor for the Employee class",
"name": "__init__",
"signature": "def __init__(self, address=None, age=None, birthday=None, birthtime=None, department=None, dependents=None, hired_at=None, joining_day='Monday', name=None, salary=None, uid=None, working_days=None, boss=None, person_t... | 2 | stack_v2_sparse_classes_30k_test_000183 | Implement the Python class `Employee` described below.
Class description:
Implementation of the 'Employee' model. TODO: type model description here. NOTE: This class inherits from 'Person'. Attributes: department (string): TODO: type description here. dependents (list of Person): TODO: type description here. hired_at ... | Implement the Python class `Employee` described below.
Class description:
Implementation of the 'Employee' model. TODO: type model description here. NOTE: This class inherits from 'Person'. Attributes: department (string): TODO: type description here. dependents (list of Person): TODO: type description here. hired_at ... | 49acc3d416a1dde7ea43b178d070484baf1b7f2b | <|skeleton|>
class Employee:
"""Implementation of the 'Employee' model. TODO: type model description here. NOTE: This class inherits from 'Person'. Attributes: department (string): TODO: type description here. dependents (list of Person): TODO: type description here. hired_at (datetime): TODO: type description here... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Employee:
"""Implementation of the 'Employee' model. TODO: type model description here. NOTE: This class inherits from 'Person'. Attributes: department (string): TODO: type description here. dependents (list of Person): TODO: type description here. hired_at (datetime): TODO: type description here. joining_day... | the_stack_v2_python_sparse | PYTHON_GENERIC_LIB/tester/models/person.py | MaryamAdnan3/Tester1 | train | 0 |
b7ece0e7c282c97bfd4cc03f518aee043389ebe7 | [
"self.apikey = apikey\nself.headers = {'User-Agent': 'Prowlpy/%s' % str(__version__), 'Content-type': 'application/x-www-form-urlencoded'}\nself.add = self.post",
"h = Https(API_DOMAIN)\ndata = {'apikey': self.apikey, 'application': application, 'event': event, 'description': description, 'priority': priority}\ni... | <|body_start_0|>
self.apikey = apikey
self.headers = {'User-Agent': 'Prowlpy/%s' % str(__version__), 'Content-type': 'application/x-www-form-urlencoded'}
self.add = self.post
<|end_body_0|>
<|body_start_1|>
h = Https(API_DOMAIN)
data = {'apikey': self.apikey, 'application': appl... | Prowl | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Prowl:
def __init__(self, apikey, providerkey=None):
"""Initialize a Prowl instance."""
<|body_0|>
def post(self, application=None, event=None, description=None, priority=0, providerkey=None):
"""Post a notification.. You must provide either event or description or b... | stack_v2_sparse_classes_36k_train_000145 | 2,977 | no_license | [
{
"docstring": "Initialize a Prowl instance.",
"name": "__init__",
"signature": "def __init__(self, apikey, providerkey=None)"
},
{
"docstring": "Post a notification.. You must provide either event or description or both. The parameters are : - application ; The name of your application or the a... | 3 | stack_v2_sparse_classes_30k_test_000449 | Implement the Python class `Prowl` described below.
Class description:
Implement the Prowl class.
Method signatures and docstrings:
- def __init__(self, apikey, providerkey=None): Initialize a Prowl instance.
- def post(self, application=None, event=None, description=None, priority=0, providerkey=None): Post a notifi... | Implement the Python class `Prowl` described below.
Class description:
Implement the Prowl class.
Method signatures and docstrings:
- def __init__(self, apikey, providerkey=None): Initialize a Prowl instance.
- def post(self, application=None, event=None, description=None, priority=0, providerkey=None): Post a notifi... | 6a1e71a1c001e6577c45cca06fa1b57be968d0ae | <|skeleton|>
class Prowl:
def __init__(self, apikey, providerkey=None):
"""Initialize a Prowl instance."""
<|body_0|>
def post(self, application=None, event=None, description=None, priority=0, providerkey=None):
"""Post a notification.. You must provide either event or description or b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Prowl:
def __init__(self, apikey, providerkey=None):
"""Initialize a Prowl instance."""
self.apikey = apikey
self.headers = {'User-Agent': 'Prowlpy/%s' % str(__version__), 'Content-type': 'application/x-www-form-urlencoded'}
self.add = self.post
def post(self, application=... | the_stack_v2_python_sparse | prowlpy.py | minrivertea/laowailai | train | 1 | |
887cf0464b44e4287a33716e02adb5ba5c625a08 | [
"BaseDiscretizer.__init__(self, feature_names=feature_names, fill_na=fill_na, return_numeric=return_numeric, return_array=return_array, decimal=decimal)\nif bins <= 1:\n raise Exception('bins必须大于1！')\nself.bins = bins",
"if len(X.shape) == 1:\n vmax = X.max()\n vmin = X.min()\n if vmin == vmax:\n ... | <|body_start_0|>
BaseDiscretizer.__init__(self, feature_names=feature_names, fill_na=fill_na, return_numeric=return_numeric, return_array=return_array, decimal=decimal)
if bins <= 1:
raise Exception('bins必须大于1！')
self.bins = bins
<|end_body_0|>
<|body_start_1|>
if len(X.shap... | SimpleBinsDiscretizer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleBinsDiscretizer:
def __init__(self, feature_names=None, bins=10, fill_na=-1, return_numeric=True, return_array=False, decimal=2):
"""根据等分点离散化。 feature_names: 需要离散化的变量列表，非负整数或者字符串。 bins: 等分区间数。"""
<|body_0|>
def fit(self, X, y=None):
"""对feature_names中的变量获取各自离散化... | stack_v2_sparse_classes_36k_train_000146 | 13,441 | no_license | [
{
"docstring": "根据等分点离散化。 feature_names: 需要离散化的变量列表，非负整数或者字符串。 bins: 等分区间数。",
"name": "__init__",
"signature": "def __init__(self, feature_names=None, bins=10, fill_na=-1, return_numeric=True, return_array=False, decimal=2)"
},
{
"docstring": "对feature_names中的变量获取各自离散化分割点。 X: 一维或二维数组，或DataFrame，... | 2 | stack_v2_sparse_classes_30k_train_011470 | Implement the Python class `SimpleBinsDiscretizer` described below.
Class description:
Implement the SimpleBinsDiscretizer class.
Method signatures and docstrings:
- def __init__(self, feature_names=None, bins=10, fill_na=-1, return_numeric=True, return_array=False, decimal=2): 根据等分点离散化。 feature_names: 需要离散化的变量列表，非负整... | Implement the Python class `SimpleBinsDiscretizer` described below.
Class description:
Implement the SimpleBinsDiscretizer class.
Method signatures and docstrings:
- def __init__(self, feature_names=None, bins=10, fill_na=-1, return_numeric=True, return_array=False, decimal=2): 根据等分点离散化。 feature_names: 需要离散化的变量列表，非负整... | 2e5304fe3152509b60003ac41a60c0ed7f5cf6c7 | <|skeleton|>
class SimpleBinsDiscretizer:
def __init__(self, feature_names=None, bins=10, fill_na=-1, return_numeric=True, return_array=False, decimal=2):
"""根据等分点离散化。 feature_names: 需要离散化的变量列表，非负整数或者字符串。 bins: 等分区间数。"""
<|body_0|>
def fit(self, X, y=None):
"""对feature_names中的变量获取各自离散化... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimpleBinsDiscretizer:
def __init__(self, feature_names=None, bins=10, fill_na=-1, return_numeric=True, return_array=False, decimal=2):
"""根据等分点离散化。 feature_names: 需要离散化的变量列表，非负整数或者字符串。 bins: 等分区间数。"""
BaseDiscretizer.__init__(self, feature_names=feature_names, fill_na=fill_na, return_numeric=... | the_stack_v2_python_sparse | installment_consume_model/discretize.py | tesla2349/Kaiyuan-Financial-Consumption-Risk-Control-Model | train | 0 | |
2e21c4a74dc7da26ce34caee047415bb0563d1b1 | [
"if model_kwargs is None:\n model_kwargs = {}\nif stl_kwargs is None:\n stl_kwargs = {}\nself.in_column = in_column\nself.period = period\nif isinstance(model, str):\n if model == 'arima':\n self.model = ARIMA\n model_kwargs = {'order': (1, 1, 0)}\n elif model == 'holt':\n self.mode... | <|body_start_0|>
if model_kwargs is None:
model_kwargs = {}
if stl_kwargs is None:
stl_kwargs = {}
self.in_column = in_column
self.period = period
if isinstance(model, str):
if model == 'arima':
self.model = ARIMA
... | _OneSegmentSTLTransform | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _OneSegmentSTLTransform:
def __init__(self, in_column: str, period: int, model: Union[str, TimeSeriesModel]='arima', robust: bool=False, model_kwargs: Optional[Dict[str, Any]]=None, stl_kwargs: Optional[Dict[str, Any]]=None):
"""Init _OneSegmentSTLTransform. Parameters ---------- in_colu... | stack_v2_sparse_classes_36k_train_000147 | 5,887 | permissive | [
{
"docstring": "Init _OneSegmentSTLTransform. Parameters ---------- in_column: name of processed column period: size of seasonality model: model to predict trend, default options are: 1. \"arima\": `ARIMA(data, 1, 1, 0)` (default) 2. \"holt\": `ETSModel(data, trend='add')` Custom model should be a subclass of s... | 4 | null | Implement the Python class `_OneSegmentSTLTransform` described below.
Class description:
Implement the _OneSegmentSTLTransform class.
Method signatures and docstrings:
- def __init__(self, in_column: str, period: int, model: Union[str, TimeSeriesModel]='arima', robust: bool=False, model_kwargs: Optional[Dict[str, Any... | Implement the Python class `_OneSegmentSTLTransform` described below.
Class description:
Implement the _OneSegmentSTLTransform class.
Method signatures and docstrings:
- def __init__(self, in_column: str, period: int, model: Union[str, TimeSeriesModel]='arima', robust: bool=False, model_kwargs: Optional[Dict[str, Any... | b2453671b00affe2af23c4b10f556f6fb5d7d602 | <|skeleton|>
class _OneSegmentSTLTransform:
def __init__(self, in_column: str, period: int, model: Union[str, TimeSeriesModel]='arima', robust: bool=False, model_kwargs: Optional[Dict[str, Any]]=None, stl_kwargs: Optional[Dict[str, Any]]=None):
"""Init _OneSegmentSTLTransform. Parameters ---------- in_colu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _OneSegmentSTLTransform:
def __init__(self, in_column: str, period: int, model: Union[str, TimeSeriesModel]='arima', robust: bool=False, model_kwargs: Optional[Dict[str, Any]]=None, stl_kwargs: Optional[Dict[str, Any]]=None):
"""Init _OneSegmentSTLTransform. Parameters ---------- in_column: name of pr... | the_stack_v2_python_sparse | etna/transforms/stl.py | jingmouren/etna-ts | train | 0 | |
ff7dd86d50971ddd6dc7a4305177f33ff11dfe35 | [
"self._app_process = None\nself._lib_directories = None\nself.lib_directory = None\nself.lib_major_version = 'lib_{}'.format(sys.version_info.major)\nself.lib_minor_version = '{}.{}'.format(self.lib_major_version, sys.version_info.minor)\nself.lib_micro_version = '{}.{}'.format(self.lib_minor_version, sys.version_i... | <|body_start_0|>
self._app_process = None
self._lib_directories = None
self.lib_directory = None
self.lib_major_version = 'lib_{}'.format(sys.version_info.major)
self.lib_minor_version = '{}.{}'.format(self.lib_major_version, sys.version_info.minor)
self.lib_micro_version... | Set App Lib Directory | AppLib | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppLib:
"""Set App Lib Directory"""
def __init__(self):
"""Initialize App properties."""
<|body_0|>
def find_lib_directory(self):
"""Find the optimal lib directory."""
<|body_1|>
def lib_directories(self):
"""Get all "lib" directories."""
... | stack_v2_sparse_classes_36k_train_000148 | 4,138 | permissive | [
{
"docstring": "Initialize App properties.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Find the optimal lib directory.",
"name": "find_lib_directory",
"signature": "def find_lib_directory(self)"
},
{
"docstring": "Get all \"lib\" directories.",
... | 5 | stack_v2_sparse_classes_30k_train_003827 | Implement the Python class `AppLib` described below.
Class description:
Set App Lib Directory
Method signatures and docstrings:
- def __init__(self): Initialize App properties.
- def find_lib_directory(self): Find the optimal lib directory.
- def lib_directories(self): Get all "lib" directories.
- def run_app(self): ... | Implement the Python class `AppLib` described below.
Class description:
Set App Lib Directory
Method signatures and docstrings:
- def __init__(self): Initialize App properties.
- def find_lib_directory(self): Find the optimal lib directory.
- def lib_directories(self): Get all "lib" directories.
- def run_app(self): ... | 0f2e6a2d1c71f104b1522fd68ec01b9f9f3b92f9 | <|skeleton|>
class AppLib:
"""Set App Lib Directory"""
def __init__(self):
"""Initialize App properties."""
<|body_0|>
def find_lib_directory(self):
"""Find the optimal lib directory."""
<|body_1|>
def lib_directories(self):
"""Get all "lib" directories."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AppLib:
"""Set App Lib Directory"""
def __init__(self):
"""Initialize App properties."""
self._app_process = None
self._lib_directories = None
self.lib_directory = None
self.lib_major_version = 'lib_{}'.format(sys.version_info.major)
self.lib_minor_version ... | the_stack_v2_python_sparse | apps/TCPB_-_Disposable_Email_Address_Identifier/__main__.py | ThreatConnect-Inc/threatconnect-playbooks | train | 76 |
ae1291bbee8b039669bad1292cf31c250d983a2e | [
"data = cls.createDataForReceipt(order, menu)\ntemplateFile = open('utils/receiptTemplate.html', 'r').read()\ntemplate = Template(templateFile.decode('utf8'))\nreturn template.render(data=data)",
"data = cls.createDataForReceipt(order, menu)\noptions = {'page-size': 'A4', 'dpi': 400}\nhtmlReceipt = cls.generateHt... | <|body_start_0|>
data = cls.createDataForReceipt(order, menu)
templateFile = open('utils/receiptTemplate.html', 'r').read()
template = Template(templateFile.decode('utf8'))
return template.render(data=data)
<|end_body_0|>
<|body_start_1|>
data = cls.createDataForReceipt(order, m... | Description: This class is used to generate pdf receipt from a given order object | ReceiptGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReceiptGenerator:
"""Description: This class is used to generate pdf receipt from a given order object"""
def generateHtmlReceipt(cls, order, menu):
"""DESCRIPTION: This class used Jinja2 templating engine to generate an HTML receipt for the given order ARGUMENTS: order: order to gen... | stack_v2_sparse_classes_36k_train_000149 | 3,828 | no_license | [
{
"docstring": "DESCRIPTION: This class used Jinja2 templating engine to generate an HTML receipt for the given order ARGUMENTS: order: order to generate the receipt menu: list of all the menus, this is used to get menu descriptions RETURNS: rendered html receipt file for the order",
"name": "generateHtmlRe... | 3 | stack_v2_sparse_classes_30k_train_004474 | Implement the Python class `ReceiptGenerator` described below.
Class description:
Description: This class is used to generate pdf receipt from a given order object
Method signatures and docstrings:
- def generateHtmlReceipt(cls, order, menu): DESCRIPTION: This class used Jinja2 templating engine to generate an HTML r... | Implement the Python class `ReceiptGenerator` described below.
Class description:
Description: This class is used to generate pdf receipt from a given order object
Method signatures and docstrings:
- def generateHtmlReceipt(cls, order, menu): DESCRIPTION: This class used Jinja2 templating engine to generate an HTML r... | e5139e131282d527753c142b0da8041819922424 | <|skeleton|>
class ReceiptGenerator:
"""Description: This class is used to generate pdf receipt from a given order object"""
def generateHtmlReceipt(cls, order, menu):
"""DESCRIPTION: This class used Jinja2 templating engine to generate an HTML receipt for the given order ARGUMENTS: order: order to gen... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReceiptGenerator:
"""Description: This class is used to generate pdf receipt from a given order object"""
def generateHtmlReceipt(cls, order, menu):
"""DESCRIPTION: This class used Jinja2 templating engine to generate an HTML receipt for the given order ARGUMENTS: order: order to generate the rec... | the_stack_v2_python_sparse | utils/ReceiptGenerator.py | BeeShall/POS-Python | train | 0 |
6c518816ee557ceb7ca1b9b9d8394e9f60ffe162 | [
"self.has_archival_copy = has_archival_copy\nself.has_local_copy = has_local_copy\nself.has_remote_copy = has_remote_copy\nself.modified_time_usecs = modified_time_usecs\nself.replica_info_list = replica_info_list\nself.size_bytes = size_bytes\nself.snapshot = snapshot",
"if dictionary is None:\n return None\n... | <|body_start_0|>
self.has_archival_copy = has_archival_copy
self.has_local_copy = has_local_copy
self.has_remote_copy = has_remote_copy
self.modified_time_usecs = modified_time_usecs
self.replica_info_list = replica_info_list
self.size_bytes = size_bytes
self.snap... | Implementation of the 'FileSnapshotInformation' model. Specifies the information about the snapshot that contains the file or folder. In addition, information about the file or folder is provided. Attributes: has_archival_copy (bool): If true, this snapshot is located on an archival target (such as a tape or AWS). has_... | FileSnapshotInformation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileSnapshotInformation:
"""Implementation of the 'FileSnapshotInformation' model. Specifies the information about the snapshot that contains the file or folder. In addition, information about the file or folder is provided. Attributes: has_archival_copy (bool): If true, this snapshot is located ... | stack_v2_sparse_classes_36k_train_000150 | 4,043 | permissive | [
{
"docstring": "Constructor for the FileSnapshotInformation class",
"name": "__init__",
"signature": "def __init__(self, has_archival_copy=None, has_local_copy=None, has_remote_copy=None, modified_time_usecs=None, replica_info_list=None, size_bytes=None, snapshot=None)"
},
{
"docstring": "Create... | 2 | stack_v2_sparse_classes_30k_train_012126 | Implement the Python class `FileSnapshotInformation` described below.
Class description:
Implementation of the 'FileSnapshotInformation' model. Specifies the information about the snapshot that contains the file or folder. In addition, information about the file or folder is provided. Attributes: has_archival_copy (bo... | Implement the Python class `FileSnapshotInformation` described below.
Class description:
Implementation of the 'FileSnapshotInformation' model. Specifies the information about the snapshot that contains the file or folder. In addition, information about the file or folder is provided. Attributes: has_archival_copy (bo... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class FileSnapshotInformation:
"""Implementation of the 'FileSnapshotInformation' model. Specifies the information about the snapshot that contains the file or folder. In addition, information about the file or folder is provided. Attributes: has_archival_copy (bool): If true, this snapshot is located ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileSnapshotInformation:
"""Implementation of the 'FileSnapshotInformation' model. Specifies the information about the snapshot that contains the file or folder. In addition, information about the file or folder is provided. Attributes: has_archival_copy (bool): If true, this snapshot is located on an archiva... | the_stack_v2_python_sparse | cohesity_management_sdk/models/file_snapshot_information.py | cohesity/management-sdk-python | train | 24 |
c1cd41f0c9f2b559e3a5b65ee403196ed4936e17 | [
"\"\"\"\n Treat each node as root, calculate their depths, return the minimum roots.\n This method will get TLE\n \"\"\"\ngraph = [[] for _ in range(n)]\nfor a, b in edges:\n graph[a].append(b)\n graph[b].append(a)\n\ndef get_height(root, visited):\n visited.add(root)\n heig... | <|body_start_0|>
"""
Treat each node as root, calculate their depths, return the minimum roots.
This method will get TLE
"""
graph = [[] for _ in range(n)]
for a, b in edges:
graph[a].append(b)
graph[b].append(a)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]:
"""Brute Force, Time: O(V^2), Space: O(V)"""
<|body_0|>
def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]:
"""BFS, Time: O(V), Space: O(V)"""
<|body_1... | stack_v2_sparse_classes_36k_train_000151 | 3,079 | no_license | [
{
"docstring": "Brute Force, Time: O(V^2), Space: O(V)",
"name": "findMinHeightTrees",
"signature": "def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]"
},
{
"docstring": "BFS, Time: O(V), Space: O(V)",
"name": "findMinHeightTrees",
"signature": "def findMinHeightT... | 2 | stack_v2_sparse_classes_30k_test_000398 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]: Brute Force, Time: O(V^2), Space: O(V)
- def findMinHeightTrees(self, n: int, edges: List[List[int]]) -... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]: Brute Force, Time: O(V^2), Space: O(V)
- def findMinHeightTrees(self, n: int, edges: List[List[int]]) -... | 72136e3487d239f5b37e2d6393e034262a6bf599 | <|skeleton|>
class Solution:
def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]:
"""Brute Force, Time: O(V^2), Space: O(V)"""
<|body_0|>
def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]:
"""BFS, Time: O(V), Space: O(V)"""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]:
"""Brute Force, Time: O(V^2), Space: O(V)"""
"""
Treat each node as root, calculate their depths, return the minimum roots.
This method will get TLE
"""
... | the_stack_v2_python_sparse | python/310-Minimum Height Trees.py | cwza/leetcode | train | 0 | |
bd1a9b9d54a1c15e3cf7769fd8823aafc6754247 | [
"quizTakerId = kwargs['pk']\nquizTaker = QuizTakers.objects.filter(id=quizTakerId).first()\nresponse = StudentResponse.objects.filter(quiztaker=quizTaker)\nserializer = ResponseSerializer(response, many=True)\nreturn Response(serializer.data)",
"quizTakerId = kwargs['pk']\nquizTaker = QuizTakers.objects.filter(id... | <|body_start_0|>
quizTakerId = kwargs['pk']
quizTaker = QuizTakers.objects.filter(id=quizTakerId).first()
response = StudentResponse.objects.filter(quiztaker=quizTaker)
serializer = ResponseSerializer(response, many=True)
return Response(serializer.data)
<|end_body_0|>
<|body_st... | ListCreateResponse | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListCreateResponse:
def get(self, request, *args, **kwargs):
"""return quiz taker answers"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""add quiz taker answers"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
quizTakerId = kwargs['pk']
... | stack_v2_sparse_classes_36k_train_000152 | 1,434 | permissive | [
{
"docstring": "return quiz taker answers",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "add quiz taker answers",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010401 | Implement the Python class `ListCreateResponse` described below.
Class description:
Implement the ListCreateResponse class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): return quiz taker answers
- def post(self, request, *args, **kwargs): add quiz taker answers | Implement the Python class `ListCreateResponse` described below.
Class description:
Implement the ListCreateResponse class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): return quiz taker answers
- def post(self, request, *args, **kwargs): add quiz taker answers
<|skeleton|>
class List... | bebeff8d055ea769773cd1c749f42408aa83f5b9 | <|skeleton|>
class ListCreateResponse:
def get(self, request, *args, **kwargs):
"""return quiz taker answers"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""add quiz taker answers"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ListCreateResponse:
def get(self, request, *args, **kwargs):
"""return quiz taker answers"""
quizTakerId = kwargs['pk']
quizTaker = QuizTakers.objects.filter(id=quizTakerId).first()
response = StudentResponse.objects.filter(quiztaker=quizTaker)
serializer = ResponseSeri... | the_stack_v2_python_sparse | backend/quiz/api/views/response.py | mahmoud-batman/quizz-app | train | 0 | |
80c065a92f132f34bbd1343dd0f91840b37e7bdf | [
"if not root:\n return '[]'\nque = [root]\nres = []\nwhile que:\n node = que.pop(0)\n if node:\n res.append(str(node.val))\n que.append(node.left)\n que.append(node.right)\n else:\n res.append('null')\nreturn '[' + ','.join(res) + ']'",
"if data == '[]':\n return\nvals, ... | <|body_start_0|>
if not root:
return '[]'
que = [root]
res = []
while que:
node = que.pop(0)
if node:
res.append(str(node.val))
que.append(node.left)
que.append(node.right)
else:
... | 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_000153 | 2,077 | 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:... | 8343f4258d20661f70f0462c358ef8b118a03de4 | <|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 '[]'
que = [root]
res = []
while que:
node = que.pop(0)
if node:
res.append(str(node.val))... | the_stack_v2_python_sparse | python/offer_37_Codec.py | Aiooon/MyLeetcode | train | 0 | |
d6e46b24598cb54012bcd51c7b2ef901e88e0d30 | [
"_, _, nodes = node_to_edge(self.edges, directed=False)\nnodes_indices = dict(((n, i) for i, n in enumerate(nodes)))\nnnodes = len(nodes)\nweights = [x[-1] for x in self.edges]\nmax_x, min_x = (max(weights), min(weights))\ninf = 2 * max(abs(max_x), abs(min_x))\nfactor = 10 ** precision\nlogging.debug('TSP rescale: ... | <|body_start_0|>
_, _, nodes = node_to_edge(self.edges, directed=False)
nodes_indices = dict(((n, i) for i, n in enumerate(nodes)))
nnodes = len(nodes)
weights = [x[-1] for x in self.edges]
max_x, min_x = (max(weights), min(weights))
inf = 2 * max(abs(max_x), abs(min_x))
... | TSPDataModel | [
"BSD-3-Clause",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TSPDataModel:
def distance_matrix(self, precision=0) -> tuple:
"""Compute the distance matrix Returns: np.array: Numpy square matrix with integer entries as distance"""
<|body_0|>
def solve(self, time_limit=5, concorde=False, precision=0) -> list:
"""Solve the TSP in... | stack_v2_sparse_classes_36k_train_000154 | 12,559 | permissive | [
{
"docstring": "Compute the distance matrix Returns: np.array: Numpy square matrix with integer entries as distance",
"name": "distance_matrix",
"signature": "def distance_matrix(self, precision=0) -> tuple"
},
{
"docstring": "Solve the TSP instance. Args: time_limit (int, optional): Time limit ... | 2 | stack_v2_sparse_classes_30k_train_012855 | Implement the Python class `TSPDataModel` described below.
Class description:
Implement the TSPDataModel class.
Method signatures and docstrings:
- def distance_matrix(self, precision=0) -> tuple: Compute the distance matrix Returns: np.array: Numpy square matrix with integer entries as distance
- def solve(self, tim... | Implement the Python class `TSPDataModel` described below.
Class description:
Implement the TSPDataModel class.
Method signatures and docstrings:
- def distance_matrix(self, precision=0) -> tuple: Compute the distance matrix Returns: np.array: Numpy square matrix with integer entries as distance
- def solve(self, tim... | 695bd2eee98b14118b54fc37e38cd0222ce6a5e9 | <|skeleton|>
class TSPDataModel:
def distance_matrix(self, precision=0) -> tuple:
"""Compute the distance matrix Returns: np.array: Numpy square matrix with integer entries as distance"""
<|body_0|>
def solve(self, time_limit=5, concorde=False, precision=0) -> list:
"""Solve the TSP in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TSPDataModel:
def distance_matrix(self, precision=0) -> tuple:
"""Compute the distance matrix Returns: np.array: Numpy square matrix with integer entries as distance"""
_, _, nodes = node_to_edge(self.edges, directed=False)
nodes_indices = dict(((n, i) for i, n in enumerate(nodes)))
... | the_stack_v2_python_sparse | jcvi/algorithms/tsp.py | tanghaibao/jcvi | train | 641 | |
e378688782bc4d1f01ce5456f1b8ee24cb8c919f | [
"if not isinstance(actionspace, Dict):\n raise ValueError('actionspace must be Dict but found ' + str(actionspace))\nif len(agentComponentList) == 0:\n raise ValueError('There must be at least 1 agent in the list')\nfor agent in agentComponentList:\n if not isinstance(agent, QAgentComponent):\n rais... | <|body_start_0|>
if not isinstance(actionspace, Dict):
raise ValueError('actionspace must be Dict but found ' + str(actionspace))
if len(agentComponentList) == 0:
raise ValueError('There must be at least 1 agent in the list')
for agent in agentComponentList:
i... | A QCoordinator is an agent that can coordinate a set of sub-agents in such a way that the sum of their actions is optimal. See #step for more details. The QCoordinator should work with environments that have a dictionary action space of the form: A={ "entityId1": Discrete(n1) "entitityId2": Discrete(n2) "entityId3": Di... | QCoordinator | [
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QCoordinator:
"""A QCoordinator is an agent that can coordinate a set of sub-agents in such a way that the sum of their actions is optimal. See #step for more details. The QCoordinator should work with environments that have a dictionary action space of the form: A={ "entityId1": Discrete(n1) "en... | stack_v2_sparse_classes_36k_train_000155 | 4,795 | no_license | [
{
"docstring": "@param AgentComponentList: a list of QAgentComponent. Size must be >=1. The agent environments should be equal to our environment, or to a Packed version of it. We can't check this because environments do not implement equals at this moment. @param environment the openAI Gym Env. Must have actio... | 2 | stack_v2_sparse_classes_30k_train_021055 | Implement the Python class `QCoordinator` described below.
Class description:
A QCoordinator is an agent that can coordinate a set of sub-agents in such a way that the sum of their actions is optimal. See #step for more details. The QCoordinator should work with environments that have a dictionary action space of the ... | Implement the Python class `QCoordinator` described below.
Class description:
A QCoordinator is an agent that can coordinate a set of sub-agents in such a way that the sum of their actions is optimal. See #step for more details. The QCoordinator should work with environments that have a dictionary action space of the ... | e7d052c6a01e0470bfd011d9dc5ba95247466494 | <|skeleton|>
class QCoordinator:
"""A QCoordinator is an agent that can coordinate a set of sub-agents in such a way that the sum of their actions is optimal. See #step for more details. The QCoordinator should work with environments that have a dictionary action space of the form: A={ "entityId1": Discrete(n1) "en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QCoordinator:
"""A QCoordinator is an agent that can coordinate a set of sub-agents in such a way that the sum of their actions is optimal. See #step for more details. The QCoordinator should work with environments that have a dictionary action space of the form: A={ "entityId1": Discrete(n1) "entitityId2": D... | the_stack_v2_python_sparse | aiagents/multi/QCoordinator.py | INFLUENCEorg/aiagents | train | 0 |
85817c87532492f212b765e0a5b13ad9f9c578f4 | [
"super(ExperimentalLearner, self).__init__(**kwargs)\nif not isinstance(self.embedding_fn, tf.Module):\n raise ValueError('The `embedding_fn` provided to `ExperimentalLearner`s must be an instance of `tf.Module`.')\nself._built = False",
"del onehot_labels\ndel predictions\nreturn tf.reduce_sum(input_tensor=se... | <|body_start_0|>
super(ExperimentalLearner, self).__init__(**kwargs)
if not isinstance(self.embedding_fn, tf.Module):
raise ValueError('The `embedding_fn` provided to `ExperimentalLearner`s must be an instance of `tf.Module`.')
self._built = False
<|end_body_0|>
<|body_start_1|>
... | An experimental learner. | ExperimentalLearner | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExperimentalLearner:
"""An experimental learner."""
def __init__(self, **kwargs):
"""Constructs an `ExperimentalLearner`. Args: **kwargs: Keyword arguments common to all `Learner`s. Raises: ValueError: If the `embedding_fn` provided is not an instance of `tf.Module`."""
<|bod... | stack_v2_sparse_classes_36k_train_000156 | 3,466 | permissive | [
{
"docstring": "Constructs an `ExperimentalLearner`. Args: **kwargs: Keyword arguments common to all `Learner`s. Raises: ValueError: If the `embedding_fn` provided is not an instance of `tf.Module`.",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "Computes a r... | 5 | stack_v2_sparse_classes_30k_train_014953 | Implement the Python class `ExperimentalLearner` described below.
Class description:
An experimental learner.
Method signatures and docstrings:
- def __init__(self, **kwargs): Constructs an `ExperimentalLearner`. Args: **kwargs: Keyword arguments common to all `Learner`s. Raises: ValueError: If the `embedding_fn` pro... | Implement the Python class `ExperimentalLearner` described below.
Class description:
An experimental learner.
Method signatures and docstrings:
- def __init__(self, **kwargs): Constructs an `ExperimentalLearner`. Args: **kwargs: Keyword arguments common to all `Learner`s. Raises: ValueError: If the `embedding_fn` pro... | 13ca9ed2533056909f232168c759c096ae291740 | <|skeleton|>
class ExperimentalLearner:
"""An experimental learner."""
def __init__(self, **kwargs):
"""Constructs an `ExperimentalLearner`. Args: **kwargs: Keyword arguments common to all `Learner`s. Raises: ValueError: If the `embedding_fn` provided is not an instance of `tf.Module`."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExperimentalLearner:
"""An experimental learner."""
def __init__(self, **kwargs):
"""Constructs an `ExperimentalLearner`. Args: **kwargs: Keyword arguments common to all `Learner`s. Raises: ValueError: If the `embedding_fn` provided is not an instance of `tf.Module`."""
super(Experimental... | the_stack_v2_python_sparse | meta_dataset/learners/experimental/base.py | google-research/meta-dataset | train | 753 |
c4459562e654698cfad03bc7b8c8659413657eb2 | [
"super(AdelaideFastNAS, self).__init__()\nself.desc = copy.deepcopy(net_desc)\nself.backbone_load_path = self.desc['backbone_load_path']\nif 'data_format' in self.desc:\n self.data_format = self.desc.get('data_format')\nelse:\n self.data_format = self.desc.get('data_format', 'channels_first')\n self.desc['... | <|body_start_0|>
super(AdelaideFastNAS, self).__init__()
self.desc = copy.deepcopy(net_desc)
self.backbone_load_path = self.desc['backbone_load_path']
if 'data_format' in self.desc:
self.data_format = self.desc.get('data_format')
else:
self.data_format = s... | Search space of AdelaideFastNAS. | AdelaideFastNAS | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdelaideFastNAS:
"""Search space of AdelaideFastNAS."""
def __init__(self, net_desc):
"""Construct the AdelaideFastNAS class. :param net_desc: config of the searched structure"""
<|body_0|>
def __call__(self, input_var, training):
"""Do an inference on AdelaideFa... | stack_v2_sparse_classes_36k_train_000157 | 2,352 | permissive | [
{
"docstring": "Construct the AdelaideFastNAS class. :param net_desc: config of the searched structure",
"name": "__init__",
"signature": "def __init__(self, net_desc)"
},
{
"docstring": "Do an inference on AdelaideFastNAS model. :param input_var: input tensor :return: output tensor",
"name"... | 2 | null | Implement the Python class `AdelaideFastNAS` described below.
Class description:
Search space of AdelaideFastNAS.
Method signatures and docstrings:
- def __init__(self, net_desc): Construct the AdelaideFastNAS class. :param net_desc: config of the searched structure
- def __call__(self, input_var, training): Do an in... | Implement the Python class `AdelaideFastNAS` described below.
Class description:
Search space of AdelaideFastNAS.
Method signatures and docstrings:
- def __init__(self, net_desc): Construct the AdelaideFastNAS class. :param net_desc: config of the searched structure
- def __call__(self, input_var, training): Do an in... | df51ed9c1d6dbde1deef63f2a037a369f8554406 | <|skeleton|>
class AdelaideFastNAS:
"""Search space of AdelaideFastNAS."""
def __init__(self, net_desc):
"""Construct the AdelaideFastNAS class. :param net_desc: config of the searched structure"""
<|body_0|>
def __call__(self, input_var, training):
"""Do an inference on AdelaideFa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdelaideFastNAS:
"""Search space of AdelaideFastNAS."""
def __init__(self, net_desc):
"""Construct the AdelaideFastNAS class. :param net_desc: config of the searched structure"""
super(AdelaideFastNAS, self).__init__()
self.desc = copy.deepcopy(net_desc)
self.backbone_load... | the_stack_v2_python_sparse | built-in/TensorFlow/Research/cv/image_classification/Cars_for_TensorFlow/automl/vega/search_space/networks/tensorflow/customs/adelaide.py | Huawei-Ascend/modelzoo | train | 1 |
ca126b5979e94a5a80047e9d2c3c03b1005b568e | [
"def generate(A):\n if len(A) == 2 * n:\n if valid(A):\n ret.append(''.join(A))\n print(''.join(A))\n else:\n A.append('(')\n generate(A)\n A.pop()\n A.append(')')\n generate(A)\n A.pop()\n\ndef valid(A):\n bal = 0\n for i in A:\n ... | <|body_start_0|>
def generate(A):
if len(A) == 2 * n:
if valid(A):
ret.append(''.join(A))
print(''.join(A))
else:
A.append('(')
generate(A)
A.pop()
A.append(')')
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def generateParenthesis(self, n):
""":type n: int :rtype: List[str]"""
<|body_0|>
def generateParenthesis(self, n):
""":type n: int :rtype: List[str]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def generate(A):
if len(A) ==... | stack_v2_sparse_classes_36k_train_000158 | 1,568 | no_license | [
{
"docstring": ":type n: int :rtype: List[str]",
"name": "generateParenthesis",
"signature": "def generateParenthesis(self, n)"
},
{
"docstring": ":type n: int :rtype: List[str]",
"name": "generateParenthesis",
"signature": "def generateParenthesis(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014444 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def generateParenthesis(self, n): :type n: int :rtype: List[str]
- def generateParenthesis(self, n): :type n: int :rtype: List[str] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def generateParenthesis(self, n): :type n: int :rtype: List[str]
- def generateParenthesis(self, n): :type n: int :rtype: List[str]
<|skeleton|>
class Solution:
def generat... | d3e8669f932fc2e22711e8b7590d3365d020e189 | <|skeleton|>
class Solution:
def generateParenthesis(self, n):
""":type n: int :rtype: List[str]"""
<|body_0|>
def generateParenthesis(self, n):
""":type n: int :rtype: List[str]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def generateParenthesis(self, n):
""":type n: int :rtype: List[str]"""
def generate(A):
if len(A) == 2 * n:
if valid(A):
ret.append(''.join(A))
print(''.join(A))
else:
A.append('(')
... | the_stack_v2_python_sparse | leetcode/22.py | liuweilin17/algorithm | train | 3 | |
1aedacd65b28e892af82b6d63e0cc404b2b67712 | [
"context = kwargs.get('context', {})\ncontext['tiers'] = cls.related_serializers['tiers'](context.get('tier_objects', []), context=context, many=True).data\ncontext['levels'] = cls.related_serializers['levels'](context.get('level_objects', []), context=context, many=True).data\nprogram = Program.rf_aware_objects.se... | <|body_start_0|>
context = kwargs.get('context', {})
context['tiers'] = cls.related_serializers['tiers'](context.get('tier_objects', []), context=context, many=True).data
context['levels'] = cls.related_serializers['levels'](context.get('level_objects', []), context=context, many=True).data
... | Program Serializer component to serialize just program data for excel rendered IPTT output | IPTTExcelMixin | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IPTTExcelMixin:
"""Program Serializer component to serialize just program data for excel rendered IPTT output"""
def load_for_pk(cls, program_pk, **kwargs):
"""Main entry point - loads a program with prefetched context for rendering Excel export of IPTT"""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_000159 | 10,971 | permissive | [
{
"docstring": "Main entry point - loads a program with prefetched context for rendering Excel export of IPTT",
"name": "load_for_pk",
"signature": "def load_for_pk(cls, program_pk, **kwargs)"
},
{
"docstring": "Returns serialized data _in order_ based on context key",
"name": "get_levels",
... | 2 | null | Implement the Python class `IPTTExcelMixin` described below.
Class description:
Program Serializer component to serialize just program data for excel rendered IPTT output
Method signatures and docstrings:
- def load_for_pk(cls, program_pk, **kwargs): Main entry point - loads a program with prefetched context for rend... | Implement the Python class `IPTTExcelMixin` described below.
Class description:
Program Serializer component to serialize just program data for excel rendered IPTT output
Method signatures and docstrings:
- def load_for_pk(cls, program_pk, **kwargs): Main entry point - loads a program with prefetched context for rend... | 7ca89ab1e5f55cbe4577d16d7281c6cf0936fc3d | <|skeleton|>
class IPTTExcelMixin:
"""Program Serializer component to serialize just program data for excel rendered IPTT output"""
def load_for_pk(cls, program_pk, **kwargs):
"""Main entry point - loads a program with prefetched context for rendering Excel export of IPTT"""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IPTTExcelMixin:
"""Program Serializer component to serialize just program data for excel rendered IPTT output"""
def load_for_pk(cls, program_pk, **kwargs):
"""Main entry point - loads a program with prefetched context for rendering Excel export of IPTT"""
context = kwargs.get('context', ... | the_stack_v2_python_sparse | workflow/serializers_new/iptt_program_serializers.py | mercycorps/toladata | train | 0 |
d7cf0ec28adb38a224fc46d9222248a5a06c4f91 | [
"response = self.client.get(reverse('home'))\nself.assertEqual(response.status_code, 200)\nself.assertContains(response, 'About Me')\nself.assertContains(response, 'March Madness')\nself.assertContains(response, 'graduation rates')",
"response = self.client.get(reverse('aboutme'))\nself.assertEqual(response.statu... | <|body_start_0|>
response = self.client.get(reverse('home'))
self.assertEqual(response.status_code, 200)
self.assertContains(response, 'About Me')
self.assertContains(response, 'March Madness')
self.assertContains(response, 'graduation rates')
<|end_body_0|>
<|body_start_1|>
... | AllPagesOpen | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllPagesOpen:
def test_index_loads(self):
"""Make sure main index page loads and displays content"""
<|body_0|>
def test_about_me_loads(self):
"""Make sure about me page loads and displays content"""
<|body_1|>
def test_basketball_project_loads(self):
... | stack_v2_sparse_classes_36k_train_000160 | 4,601 | no_license | [
{
"docstring": "Make sure main index page loads and displays content",
"name": "test_index_loads",
"signature": "def test_index_loads(self)"
},
{
"docstring": "Make sure about me page loads and displays content",
"name": "test_about_me_loads",
"signature": "def test_about_me_loads(self)"... | 4 | stack_v2_sparse_classes_30k_train_017055 | Implement the Python class `AllPagesOpen` described below.
Class description:
Implement the AllPagesOpen class.
Method signatures and docstrings:
- def test_index_loads(self): Make sure main index page loads and displays content
- def test_about_me_loads(self): Make sure about me page loads and displays content
- def... | Implement the Python class `AllPagesOpen` described below.
Class description:
Implement the AllPagesOpen class.
Method signatures and docstrings:
- def test_index_loads(self): Make sure main index page loads and displays content
- def test_about_me_loads(self): Make sure about me page loads and displays content
- def... | 2a8e2dc4e9b3cb92d4d437b37e61940a9486b81f | <|skeleton|>
class AllPagesOpen:
def test_index_loads(self):
"""Make sure main index page loads and displays content"""
<|body_0|>
def test_about_me_loads(self):
"""Make sure about me page loads and displays content"""
<|body_1|>
def test_basketball_project_loads(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllPagesOpen:
def test_index_loads(self):
"""Make sure main index page loads and displays content"""
response = self.client.get(reverse('home'))
self.assertEqual(response.status_code, 200)
self.assertContains(response, 'About Me')
self.assertContains(response, 'March Ma... | the_stack_v2_python_sparse | mysite/tests.py | smeds1/mysite | train | 1 | |
16ed45389b39fe2b182b29a726e1da79007c5736 | [
"super().__init__(rho=rho, mus_prior=mus_prior, stds_prior=stds_prior, number_of_classes=number_of_classes, dim_input=dim_input, dim_channels=dim_channels, hidden_activation=hidden_activation, last_activation=last_activation)\nif rho == 'determinist':\n self.determinist = True\nelif type(rho) in [int, float]:\n ... | <|body_start_0|>
super().__init__(rho=rho, mus_prior=mus_prior, stds_prior=stds_prior, number_of_classes=number_of_classes, dim_input=dim_input, dim_channels=dim_channels, hidden_activation=hidden_activation, last_activation=last_activation)
if rho == 'determinist':
self.determinist = True
... | Same as GaussianClassifier but without batch norm layers | GaussianClassifierNoBatchNorm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianClassifierNoBatchNorm:
"""Same as GaussianClassifier but without batch norm layers"""
def __init__(self, rho=-5, mus_prior=(0, 0), stds_prior=None, number_of_classes=10, dim_input=28, dim_channels=1, hidden_activation=F.relu, last_activation=F.softmax):
"""Args: rho (float): ... | stack_v2_sparse_classes_36k_train_000161 | 14,393 | no_license | [
{
"docstring": "Args: rho (float): parameter to get the std. std = log(1+exp(rho)) mus_prior (tuple): the means of the prior (weight and bias). Often will be (0,0) stds_prior (tuple): the stds of the prior (weight and bias) number_of_classes (int): number of different classes in the problem dim_input (int): dim... | 2 | stack_v2_sparse_classes_30k_train_009570 | Implement the Python class `GaussianClassifierNoBatchNorm` described below.
Class description:
Same as GaussianClassifier but without batch norm layers
Method signatures and docstrings:
- def __init__(self, rho=-5, mus_prior=(0, 0), stds_prior=None, number_of_classes=10, dim_input=28, dim_channels=1, hidden_activatio... | Implement the Python class `GaussianClassifierNoBatchNorm` described below.
Class description:
Same as GaussianClassifier but without batch norm layers
Method signatures and docstrings:
- def __init__(self, rho=-5, mus_prior=(0, 0), stds_prior=None, number_of_classes=10, dim_input=28, dim_channels=1, hidden_activatio... | e0daf7d01a90ff6ce4f969b1f01075f7e2cc7c58 | <|skeleton|>
class GaussianClassifierNoBatchNorm:
"""Same as GaussianClassifier but without batch norm layers"""
def __init__(self, rho=-5, mus_prior=(0, 0), stds_prior=None, number_of_classes=10, dim_input=28, dim_channels=1, hidden_activation=F.relu, last_activation=F.softmax):
"""Args: rho (float): ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianClassifierNoBatchNorm:
"""Same as GaussianClassifier but without batch norm layers"""
def __init__(self, rho=-5, mus_prior=(0, 0), stds_prior=None, number_of_classes=10, dim_input=28, dim_channels=1, hidden_activation=F.relu, last_activation=F.softmax):
"""Args: rho (float): parameter to ... | the_stack_v2_python_sparse | src/models/bayesian_models/gaussian_classifiers.py | TheodoreAouad/Deterministic_vs_Bayesian | train | 0 |
08635877872385efbd79cba81e702e25886cf1cc | [
"if resampler == None:\n self._resampler = ResamplerLayer(interpolation='LINEAR', boundary='REPLICATE')\n self._interpolation = 'LINEAR'\nelse:\n self._resampler = resampler\n self._interpolation = self._resampler.interpolation\nself._field_transform = field_transform\nsuper(ResampledFieldGridWarperLaye... | <|body_start_0|>
if resampler == None:
self._resampler = ResamplerLayer(interpolation='LINEAR', boundary='REPLICATE')
self._interpolation = 'LINEAR'
else:
self._resampler = resampler
self._interpolation = self._resampler.interpolation
self._field_t... | The resampled field grid warper defines a grid based on sampling coordinate values from a spatially varying displacement field (passed as a tensor input) along an affine grid pattern in the field. This enables grids representing small patches of a larger transform, as well as the composition of multiple transforms befo... | ResampledFieldGridWarperLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResampledFieldGridWarperLayer:
"""The resampled field grid warper defines a grid based on sampling coordinate values from a spatially varying displacement field (passed as a tensor input) along an affine grid pattern in the field. This enables grids representing small patches of a larger transfor... | stack_v2_sparse_classes_36k_train_000162 | 11,338 | permissive | [
{
"docstring": "Constructs an ResampledFieldingGridWarperLayer. Args: source_shape: Iterable of integers determining the size of the source signal domain. output_shape: Iterable of integers determining the size of the destination resampled signal domain. coeff_shape: Shape of displacement field. interpolation: ... | 3 | null | Implement the Python class `ResampledFieldGridWarperLayer` described below.
Class description:
The resampled field grid warper defines a grid based on sampling coordinate values from a spatially varying displacement field (passed as a tensor input) along an affine grid pattern in the field. This enables grids represen... | Implement the Python class `ResampledFieldGridWarperLayer` described below.
Class description:
The resampled field grid warper defines a grid based on sampling coordinate values from a spatially varying displacement field (passed as a tensor input) along an affine grid pattern in the field. This enables grids represen... | 84dd0f85c9a1ab8a72f4c55fcf073379acf5ae1b | <|skeleton|>
class ResampledFieldGridWarperLayer:
"""The resampled field grid warper defines a grid based on sampling coordinate values from a spatially varying displacement field (passed as a tensor input) along an affine grid pattern in the field. This enables grids representing small patches of a larger transfor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResampledFieldGridWarperLayer:
"""The resampled field grid warper defines a grid based on sampling coordinate values from a spatially varying displacement field (passed as a tensor input) along an affine grid pattern in the field. This enables grids representing small patches of a larger transform, as well as... | the_stack_v2_python_sparse | niftynet/layer/spatial_transformer.py | 12SigmaTechnologies/NiftyNet-1 | train | 2 |
85807726b59473110c5a2dc4510cd979470af406 | [
"super(Basic, self).__init__(input_file=input_file, params=params, BaselevelHandlerClass=BaselevelHandlerClass)\nK_sp = self.get_parameter_from_exponent('K_sp', raise_error=False)\nK_ss = self.get_parameter_from_exponent('K_ss', raise_error=False)\nlinear_diffusivity = self._length_factor ** 2.0 * self.get_paramete... | <|body_start_0|>
super(Basic, self).__init__(input_file=input_file, params=params, BaselevelHandlerClass=BaselevelHandlerClass)
K_sp = self.get_parameter_from_exponent('K_sp', raise_error=False)
K_ss = self.get_parameter_from_exponent('K_ss', raise_error=False)
linear_diffusivity = self.... | A Basic computes erosion using linear diffusion, basic stream power, and Q~A. | Basic | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Basic:
"""A Basic computes erosion using linear diffusion, basic stream power, and Q~A."""
def __init__(self, input_file=None, params=None, BaselevelHandlerClass=None):
"""Initialize the Basic model."""
<|body_0|>
def run_one_step(self, dt):
"""Advance model for ... | stack_v2_sparse_classes_36k_train_000163 | 4,084 | permissive | [
{
"docstring": "Initialize the Basic model.",
"name": "__init__",
"signature": "def __init__(self, input_file=None, params=None, BaselevelHandlerClass=None)"
},
{
"docstring": "Advance model for one time-step of duration dt.",
"name": "run_one_step",
"signature": "def run_one_step(self, ... | 2 | stack_v2_sparse_classes_30k_train_006412 | Implement the Python class `Basic` described below.
Class description:
A Basic computes erosion using linear diffusion, basic stream power, and Q~A.
Method signatures and docstrings:
- def __init__(self, input_file=None, params=None, BaselevelHandlerClass=None): Initialize the Basic model.
- def run_one_step(self, dt... | Implement the Python class `Basic` described below.
Class description:
A Basic computes erosion using linear diffusion, basic stream power, and Q~A.
Method signatures and docstrings:
- def __init__(self, input_file=None, params=None, BaselevelHandlerClass=None): Initialize the Basic model.
- def run_one_step(self, dt... | 1b756477b8a8ab6a8f1275b1b30ec84855c840ea | <|skeleton|>
class Basic:
"""A Basic computes erosion using linear diffusion, basic stream power, and Q~A."""
def __init__(self, input_file=None, params=None, BaselevelHandlerClass=None):
"""Initialize the Basic model."""
<|body_0|>
def run_one_step(self, dt):
"""Advance model for ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Basic:
"""A Basic computes erosion using linear diffusion, basic stream power, and Q~A."""
def __init__(self, input_file=None, params=None, BaselevelHandlerClass=None):
"""Initialize the Basic model."""
super(Basic, self).__init__(input_file=input_file, params=params, BaselevelHandlerClas... | the_stack_v2_python_sparse | terrainbento/derived_models/model_000_basic/model_000_basic.py | mcflugen/terrainbento | train | 0 |
9bc1ff1330fa9eeaef7a82fca4b19c0c1341762b | [
"def recursive(root, ans):\n if not root:\n return\n recursive(root.left, ans)\n if root.val:\n ans.append(root.val)\n recursive(root.right, ans)\nans = []\nrecursive(root, ans)\nreturn ans",
"stack = []\nans = []\ncurr_node = root\nwhile curr_node or stack:\n while curr_node:\n ... | <|body_start_0|>
def recursive(root, ans):
if not root:
return
recursive(root.left, ans)
if root.val:
ans.append(root.val)
recursive(root.right, ans)
ans = []
recursive(root, ans)
return ans
<|end_body_0|>
<... | Solution | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def inorderTraversal(self, root: TreeNode) -> List[int]:
"""Recursive solution"""
<|body_0|>
def inorderTraversal(self, root: TreeNode) -> List[int]:
"""Iterative solution"""
<|body_1|>
def inorderTraversal(self, root: TreeNode) -> List[int]:
... | stack_v2_sparse_classes_36k_train_000164 | 2,156 | permissive | [
{
"docstring": "Recursive solution",
"name": "inorderTraversal",
"signature": "def inorderTraversal(self, root: TreeNode) -> List[int]"
},
{
"docstring": "Iterative solution",
"name": "inorderTraversal",
"signature": "def inorderTraversal(self, root: TreeNode) -> List[int]"
},
{
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def inorderTraversal(self, root: TreeNode) -> List[int]: Recursive solution
- def inorderTraversal(self, root: TreeNode) -> List[int]: Iterative solution
- def inorderTraversal(s... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def inorderTraversal(self, root: TreeNode) -> List[int]: Recursive solution
- def inorderTraversal(self, root: TreeNode) -> List[int]: Iterative solution
- def inorderTraversal(s... | 226cecde136531341ce23cdf88529345be1912fc | <|skeleton|>
class Solution:
def inorderTraversal(self, root: TreeNode) -> List[int]:
"""Recursive solution"""
<|body_0|>
def inorderTraversal(self, root: TreeNode) -> List[int]:
"""Iterative solution"""
<|body_1|>
def inorderTraversal(self, root: TreeNode) -> List[int]:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def inorderTraversal(self, root: TreeNode) -> List[int]:
"""Recursive solution"""
def recursive(root, ans):
if not root:
return
recursive(root.left, ans)
if root.val:
ans.append(root.val)
recursive(root.r... | the_stack_v2_python_sparse | Leetcode/Intermediate/Tree and graph/94_Binary_Tree_Inorder_Traversal.py | ZR-Huang/AlgorithmsPractices | train | 1 | |
f79227895895befb9d6477caac884e53171e7c28 | [
"super().__init__(parent)\nself.parent = parent\nself.order = self.parent.getParent().holder.getOrder()\nself.items = self.order.getItems()\nself.initUi()",
"style = 'QLabel {\\n font-family: Asap;\\n font-weight: bold;\\n font-size: 25pt;\\n ... | <|body_start_0|>
super().__init__(parent)
self.parent = parent
self.order = self.parent.getParent().holder.getOrder()
self.items = self.order.getItems()
self.initUi()
<|end_body_0|>
<|body_start_1|>
style = 'QLabel {\n font-family: Asap;\n ... | Widget to select items to pop from one order to another one. | PopOrderWidget | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PopOrderWidget:
"""Widget to select items to pop from one order to another one."""
def __init__(self, parent):
"""Init."""
<|body_0|>
def initUi(self):
"""UI setup."""
<|body_1|>
def paintEvent(self, event):
"""Set window background color."""... | stack_v2_sparse_classes_36k_train_000165 | 27,111 | no_license | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, parent)"
},
{
"docstring": "UI setup.",
"name": "initUi",
"signature": "def initUi(self)"
},
{
"docstring": "Set window background color.",
"name": "paintEvent",
"signature": "def paintEvent(self... | 3 | stack_v2_sparse_classes_30k_train_021404 | Implement the Python class `PopOrderWidget` described below.
Class description:
Widget to select items to pop from one order to another one.
Method signatures and docstrings:
- def __init__(self, parent): Init.
- def initUi(self): UI setup.
- def paintEvent(self, event): Set window background color. | Implement the Python class `PopOrderWidget` described below.
Class description:
Widget to select items to pop from one order to another one.
Method signatures and docstrings:
- def __init__(self, parent): Init.
- def initUi(self): UI setup.
- def paintEvent(self, event): Set window background color.
<|skeleton|>
cla... | a5d18593e689123cac34af552628ed2818ca5d59 | <|skeleton|>
class PopOrderWidget:
"""Widget to select items to pop from one order to another one."""
def __init__(self, parent):
"""Init."""
<|body_0|>
def initUi(self):
"""UI setup."""
<|body_1|>
def paintEvent(self, event):
"""Set window background color."""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PopOrderWidget:
"""Widget to select items to pop from one order to another one."""
def __init__(self, parent):
"""Init."""
super().__init__(parent)
self.parent = parent
self.order = self.parent.getParent().holder.getOrder()
self.items = self.order.getItems()
... | the_stack_v2_python_sparse | Dialogs.py | edgary777/lonchepos | train | 0 |
08747748a4c5268ae195f91b7576f0a88fa76a08 | [
"current = self.head\nwhile current is not None:\n if current.value[0] == key:\n return current.value[1]\n current = current.next\nreturn None",
"if self.is_empty():\n print('Список пуст')\nelse:\n current = self.head\n ind = 0\n while current is not None:\n if current.value[0] == ... | <|body_start_0|>
current = self.head
while current is not None:
if current.value[0] == key:
return current.value[1]
current = current.next
return None
<|end_body_0|>
<|body_start_1|>
if self.is_empty():
print('Список пуст')
els... | This is the linked list class for the hash table | LinkedListHash | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkedListHash:
"""This is the linked list class for the hash table"""
def this_value(self, key):
"""Method for checking the existence of a node with a given value in the list"""
<|body_0|>
def delete_value(self, key):
"""Method for removing a node / nodes by a g... | stack_v2_sparse_classes_36k_train_000166 | 2,309 | no_license | [
{
"docstring": "Method for checking the existence of a node with a given value in the list",
"name": "this_value",
"signature": "def this_value(self, key)"
},
{
"docstring": "Method for removing a node / nodes by a given key from the list",
"name": "delete_value",
"signature": "def delet... | 2 | stack_v2_sparse_classes_30k_train_020773 | Implement the Python class `LinkedListHash` described below.
Class description:
This is the linked list class for the hash table
Method signatures and docstrings:
- def this_value(self, key): Method for checking the existence of a node with a given value in the list
- def delete_value(self, key): Method for removing ... | Implement the Python class `LinkedListHash` described below.
Class description:
This is the linked list class for the hash table
Method signatures and docstrings:
- def this_value(self, key): Method for checking the existence of a node with a given value in the list
- def delete_value(self, key): Method for removing ... | 44d27242789d670efa64dd72f9a112a80df8373c | <|skeleton|>
class LinkedListHash:
"""This is the linked list class for the hash table"""
def this_value(self, key):
"""Method for checking the existence of a node with a given value in the list"""
<|body_0|>
def delete_value(self, key):
"""Method for removing a node / nodes by a g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkedListHash:
"""This is the linked list class for the hash table"""
def this_value(self, key):
"""Method for checking the existence of a node with a given value in the list"""
current = self.head
while current is not None:
if current.value[0] == key:
... | the_stack_v2_python_sparse | structures/hash_table.py | SvetlanaSumets11/python-education | train | 0 |
22998bf2ee66a5879dfc44b581a6708a3eec786b | [
"user = get_a_UserRoles(UserRoleId)\nif not user:\n api.abort(404)\nelse:\n return user\ndata = request.json\nreturn get_a_UserRoles(data=data)",
"user = complete_UserRoles(UserRoleId)\nif not user:\n api.abort(404)\nelse:\n return user\ndata = request.json\nreturn complete_UserRoles(data=data)",
"u... | <|body_start_0|>
user = get_a_UserRoles(UserRoleId)
if not user:
api.abort(404)
else:
return user
data = request.json
return get_a_UserRoles(data=data)
<|end_body_0|>
<|body_start_1|>
user = complete_UserRoles(UserRoleId)
if not user:
... | UserRoles | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserRoles:
def get(self, UserRoleId):
"""get a UserRoles given its identifier"""
<|body_0|>
def put(self, UserRoleId):
"""UserRoles updated"""
<|body_1|>
def delete(self, UserRoleId):
"""UserRoles deleted"""
<|body_2|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_000167 | 2,719 | no_license | [
{
"docstring": "get a UserRoles given its identifier",
"name": "get",
"signature": "def get(self, UserRoleId)"
},
{
"docstring": "UserRoles updated",
"name": "put",
"signature": "def put(self, UserRoleId)"
},
{
"docstring": "UserRoles deleted",
"name": "delete",
"signatur... | 3 | null | Implement the Python class `UserRoles` described below.
Class description:
Implement the UserRoles class.
Method signatures and docstrings:
- def get(self, UserRoleId): get a UserRoles given its identifier
- def put(self, UserRoleId): UserRoles updated
- def delete(self, UserRoleId): UserRoles deleted | Implement the Python class `UserRoles` described below.
Class description:
Implement the UserRoles class.
Method signatures and docstrings:
- def get(self, UserRoleId): get a UserRoles given its identifier
- def put(self, UserRoleId): UserRoles updated
- def delete(self, UserRoleId): UserRoles deleted
<|skeleton|>
c... | 4fa4042304ee01cf23ecc81f9c27977fd12c31b9 | <|skeleton|>
class UserRoles:
def get(self, UserRoleId):
"""get a UserRoles given its identifier"""
<|body_0|>
def put(self, UserRoleId):
"""UserRoles updated"""
<|body_1|>
def delete(self, UserRoleId):
"""UserRoles deleted"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserRoles:
def get(self, UserRoleId):
"""get a UserRoles given its identifier"""
user = get_a_UserRoles(UserRoleId)
if not user:
api.abort(404)
else:
return user
data = request.json
return get_a_UserRoles(data=data)
def put(self, Use... | the_stack_v2_python_sparse | main/controller/UserRoles_controller.py | Gauravkumar45/Flask-RESTPlus-API | train | 0 | |
6ba23a850b67a8fb27034340ccc0aee22c6e62b1 | [
"_type, qname, qclass, qtype, _id, ip = query\nself.has_result = False\nqname_lower = qname.lower()\n'List of servers to round-robin'\nservers = ['192.168.1.201', '192.168.1.202']\nserver = random.choice(servers)\nself.results = []\nif (qtype == 'A' or qtype == 'ANY') and qname_lower == 'test.domain.org':\n self... | <|body_start_0|>
_type, qname, qclass, qtype, _id, ip = query
self.has_result = False
qname_lower = qname.lower()
'List of servers to round-robin'
servers = ['192.168.1.201', '192.168.1.202']
server = random.choice(servers)
self.results = []
if (qtype == '... | Handle PowerDNS pipe-backend domain name lookups. | DNSLookup | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DNSLookup:
"""Handle PowerDNS pipe-backend domain name lookups."""
def __init__(self, query):
"""parse DNS query and produce lookup result. query: a sequence containing the DNS query as per PowerDNS manual appendix A: http://downloads.powerdns.com/documentation/html/backends-detail.h... | stack_v2_sparse_classes_36k_train_000168 | 4,118 | permissive | [
{
"docstring": "parse DNS query and produce lookup result. query: a sequence containing the DNS query as per PowerDNS manual appendix A: http://downloads.powerdns.com/documentation/html/backends-detail.html#PIPEBACKEND-PROTOCOL",
"name": "__init__",
"signature": "def __init__(self, query)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_009523 | Implement the Python class `DNSLookup` described below.
Class description:
Handle PowerDNS pipe-backend domain name lookups.
Method signatures and docstrings:
- def __init__(self, query): parse DNS query and produce lookup result. query: a sequence containing the DNS query as per PowerDNS manual appendix A: http://do... | Implement the Python class `DNSLookup` described below.
Class description:
Handle PowerDNS pipe-backend domain name lookups.
Method signatures and docstrings:
- def __init__(self, query): parse DNS query and produce lookup result. query: a sequence containing the DNS query as per PowerDNS manual appendix A: http://do... | 665d39a2bd82543d5196555f0801ef8fd4a3ee48 | <|skeleton|>
class DNSLookup:
"""Handle PowerDNS pipe-backend domain name lookups."""
def __init__(self, query):
"""parse DNS query and produce lookup result. query: a sequence containing the DNS query as per PowerDNS manual appendix A: http://downloads.powerdns.com/documentation/html/backends-detail.h... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DNSLookup:
"""Handle PowerDNS pipe-backend domain name lookups."""
def __init__(self, query):
"""parse DNS query and produce lookup result. query: a sequence containing the DNS query as per PowerDNS manual appendix A: http://downloads.powerdns.com/documentation/html/backends-detail.html#PIPEBACKE... | the_stack_v2_python_sparse | dockerized-gists/10069682/snippet.py | gistable/gistable | train | 76 |
a9e79ced7f79f55849f13742310819e73a64dfb1 | [
"self.cluster_info = cluster_info\nself.keyspace_info = keyspace_info\nself.name = name\nself.mtype = mtype\nself.uuid = uuid",
"if dictionary is None:\n return None\ncluster_info = cohesity_management_sdk.models.cassandra_cluster.CassandraCluster.from_dictionary(dictionary.get('clusterInfo')) if dictionary.ge... | <|body_start_0|>
self.cluster_info = cluster_info
self.keyspace_info = keyspace_info
self.name = name
self.mtype = mtype
self.uuid = uuid
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
cluster_info = cohesity_management_sdk.models.... | Implementation of the 'CassandraProtectionSource' model. Specifies an Object representing Cassandra. Attributes: cluster_info (CassandraCluster): Information of a Cassandra cluster, only valid for an entity of type kCluster. keyspace_info (CassandraKeyspace): Information of a cassandra keyspapce, only valid for an enti... | CassandraProtectionSource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CassandraProtectionSource:
"""Implementation of the 'CassandraProtectionSource' model. Specifies an Object representing Cassandra. Attributes: cluster_info (CassandraCluster): Information of a Cassandra cluster, only valid for an entity of type kCluster. keyspace_info (CassandraKeyspace): Informa... | stack_v2_sparse_classes_36k_train_000169 | 3,340 | permissive | [
{
"docstring": "Constructor for the CassandraProtectionSource class",
"name": "__init__",
"signature": "def __init__(self, cluster_info=None, keyspace_info=None, name=None, mtype=None, uuid=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary... | 2 | null | Implement the Python class `CassandraProtectionSource` described below.
Class description:
Implementation of the 'CassandraProtectionSource' model. Specifies an Object representing Cassandra. Attributes: cluster_info (CassandraCluster): Information of a Cassandra cluster, only valid for an entity of type kCluster. key... | Implement the Python class `CassandraProtectionSource` described below.
Class description:
Implementation of the 'CassandraProtectionSource' model. Specifies an Object representing Cassandra. Attributes: cluster_info (CassandraCluster): Information of a Cassandra cluster, only valid for an entity of type kCluster. key... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class CassandraProtectionSource:
"""Implementation of the 'CassandraProtectionSource' model. Specifies an Object representing Cassandra. Attributes: cluster_info (CassandraCluster): Information of a Cassandra cluster, only valid for an entity of type kCluster. keyspace_info (CassandraKeyspace): Informa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CassandraProtectionSource:
"""Implementation of the 'CassandraProtectionSource' model. Specifies an Object representing Cassandra. Attributes: cluster_info (CassandraCluster): Information of a Cassandra cluster, only valid for an entity of type kCluster. keyspace_info (CassandraKeyspace): Information of a cas... | the_stack_v2_python_sparse | cohesity_management_sdk/models/cassandra_protection_source.py | cohesity/management-sdk-python | train | 24 |
66b0d04f9ff8ff6a25a73968d0081278f7593e60 | [
"context = super(EntitiesView, self).get_context_data(**kwargs)\ncontext['entities'] = get_entities_list(self.request.session.get('token', False), self.kwargs.get('aiid')).get('entities')\ncontext['allow_regex'] = get_experiments_list(self.request.session.get('token', False), self.kwargs.get('aiid', False), 'regex-... | <|body_start_0|>
context = super(EntitiesView, self).get_context_data(**kwargs)
context['entities'] = get_entities_list(self.request.session.get('token', False), self.kwargs.get('aiid')).get('entities')
context['allow_regex'] = get_experiments_list(self.request.session.get('token', False), self.... | Manage AI Entities | EntitiesView | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntitiesView:
"""Manage AI Entities"""
def get_context_data(self, **kwargs):
"""Update context with Entities list"""
<|body_0|>
def form_valid(self, form):
"""Try to save Entity, can still be invalid"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_000170 | 39,842 | permissive | [
{
"docstring": "Update context with Entities list",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
},
{
"docstring": "Try to save Entity, can still be invalid",
"name": "form_valid",
"signature": "def form_valid(self, form)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014699 | Implement the Python class `EntitiesView` described below.
Class description:
Manage AI Entities
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Update context with Entities list
- def form_valid(self, form): Try to save Entity, can still be invalid | Implement the Python class `EntitiesView` described below.
Class description:
Manage AI Entities
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Update context with Entities list
- def form_valid(self, form): Try to save Entity, can still be invalid
<|skeleton|>
class EntitiesView:
"""M... | d632d00f9a22a7a826bba4896a7102b2ac8690ff | <|skeleton|>
class EntitiesView:
"""Manage AI Entities"""
def get_context_data(self, **kwargs):
"""Update context with Entities list"""
<|body_0|>
def form_valid(self, form):
"""Try to save Entity, can still be invalid"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntitiesView:
"""Manage AI Entities"""
def get_context_data(self, **kwargs):
"""Update context with Entities list"""
context = super(EntitiesView, self).get_context_data(**kwargs)
context['entities'] = get_entities_list(self.request.session.get('token', False), self.kwargs.get('ai... | the_stack_v2_python_sparse | src/studio/views.py | hutomadotAI/web-console | train | 6 |
cfaeaf3d6153e8ebf5eda75940a73f497a4e8fcf | [
"self.pv_name = pv_name\nself.storage_class = storage_class\nself.volume = volume\nself.volume_path = volume_path",
"if dictionary is None:\n return None\npv_name = dictionary.get('pvName')\nstorage_class = dictionary.get('storageClass')\nvolume = cohesity_management_sdk.models.pod_info_pod_spec_volume_info.Po... | <|body_start_0|>
self.pv_name = pv_name
self.storage_class = storage_class
self.volume = volume
self.volume_path = volume_path
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
pv_name = dictionary.get('pvName')
storage_class = dictio... | Implementation of the 'PodMetadata_VolumeInfo' model. TODO: type description here. Attributes: pv_name (string): The underlying PV name if this volume is a PVC. This will be used to identify name of the directory containing PVC data at the path /var/lib/kubelet/pods. storage_class (string): Name of the storage class. T... | PodMetadata_VolumeInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PodMetadata_VolumeInfo:
"""Implementation of the 'PodMetadata_VolumeInfo' model. TODO: type description here. Attributes: pv_name (string): The underlying PV name if this volume is a PVC. This will be used to identify name of the directory containing PVC data at the path /var/lib/kubelet/pods. st... | stack_v2_sparse_classes_36k_train_000171 | 2,473 | permissive | [
{
"docstring": "Constructor for the PodMetadata_VolumeInfo class",
"name": "__init__",
"signature": "def __init__(self, pv_name=None, storage_class=None, volume=None, volume_path=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictio... | 2 | stack_v2_sparse_classes_30k_train_016587 | Implement the Python class `PodMetadata_VolumeInfo` described below.
Class description:
Implementation of the 'PodMetadata_VolumeInfo' model. TODO: type description here. Attributes: pv_name (string): The underlying PV name if this volume is a PVC. This will be used to identify name of the directory containing PVC dat... | Implement the Python class `PodMetadata_VolumeInfo` described below.
Class description:
Implementation of the 'PodMetadata_VolumeInfo' model. TODO: type description here. Attributes: pv_name (string): The underlying PV name if this volume is a PVC. This will be used to identify name of the directory containing PVC dat... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class PodMetadata_VolumeInfo:
"""Implementation of the 'PodMetadata_VolumeInfo' model. TODO: type description here. Attributes: pv_name (string): The underlying PV name if this volume is a PVC. This will be used to identify name of the directory containing PVC data at the path /var/lib/kubelet/pods. st... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PodMetadata_VolumeInfo:
"""Implementation of the 'PodMetadata_VolumeInfo' model. TODO: type description here. Attributes: pv_name (string): The underlying PV name if this volume is a PVC. This will be used to identify name of the directory containing PVC data at the path /var/lib/kubelet/pods. storage_class (... | the_stack_v2_python_sparse | cohesity_management_sdk/models/pod_metadata_volume_info.py | cohesity/management-sdk-python | train | 24 |
3dbe257b5126749aad80941db64961baf9fcfc29 | [
"user = instance\nuser_id = user.id\nnew_groups = validated_data.pop('groups', None)\nif new_groups is not None:\n UserInGroups = User.groups.through\n group_qs = UserInGroups.objects.filter(user=user)\n group_qs.exclude(group_id__in=(gr.id for gr in new_groups)).delete()\n for gr in new_groups:\n ... | <|body_start_0|>
user = instance
user_id = user.id
new_groups = validated_data.pop('groups', None)
if new_groups is not None:
UserInGroups = User.groups.through
group_qs = UserInGroups.objects.filter(user=user)
group_qs.exclude(group_id__in=(gr.id for ... | Serializer for put and post requests | UserSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserSerializer:
"""Serializer for put and post requests"""
def update(self, instance, validated_data):
"""update the user-attributes, including profile information"""
<|body_0|>
def create(self, validated_data):
"""Create a new user and its profile"""
<|b... | stack_v2_sparse_classes_36k_train_000172 | 7,759 | no_license | [
{
"docstring": "update the user-attributes, including profile information",
"name": "update",
"signature": "def update(self, instance, validated_data)"
},
{
"docstring": "Create a new user and its profile",
"name": "create",
"signature": "def create(self, validated_data)"
}
] | 2 | null | Implement the Python class `UserSerializer` described below.
Class description:
Serializer for put and post requests
Method signatures and docstrings:
- def update(self, instance, validated_data): update the user-attributes, including profile information
- def create(self, validated_data): Create a new user and its p... | Implement the Python class `UserSerializer` described below.
Class description:
Serializer for put and post requests
Method signatures and docstrings:
- def update(self, instance, validated_data): update the user-attributes, including profile information
- def create(self, validated_data): Create a new user and its p... | a5ba34f085f0d5af5ea3ded24706ea54ab39e7cb | <|skeleton|>
class UserSerializer:
"""Serializer for put and post requests"""
def update(self, instance, validated_data):
"""update the user-attributes, including profile information"""
<|body_0|>
def create(self, validated_data):
"""Create a new user and its profile"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserSerializer:
"""Serializer for put and post requests"""
def update(self, instance, validated_data):
"""update the user-attributes, including profile information"""
user = instance
user_id = user.id
new_groups = validated_data.pop('groups', None)
if new_groups is... | the_stack_v2_python_sparse | repair/apps/login/serializers/users.py | MaxBo/REPAiR-Web | train | 9 |
309c85fdf9231ca1d2f9311f344808ff18f9d27f | [
"hconfig: Dict[str, Any] = dict()\nconfigs = hyperrun.generate(hconfig)\nself.assertEqual(len(configs), 1)\nself.assertFalse(configs[0])",
"hconfig = {'foo': 'bar'}\nconfigs = hyperrun.generate(hconfig)\nself.assertEqual(len(configs), 1)\nself.assertEqual(configs[0], hconfig)",
"hconfig = {'foo': ['bar', 'car']... | <|body_start_0|>
hconfig: Dict[str, Any] = dict()
configs = hyperrun.generate(hconfig)
self.assertEqual(len(configs), 1)
self.assertFalse(configs[0])
<|end_body_0|>
<|body_start_1|>
hconfig = {'foo': 'bar'}
configs = hyperrun.generate(hconfig)
self.assertEqual(le... | Test cases for the hyper configuration generation function. | TestHyperRunGeneration | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestHyperRunGeneration:
"""Test cases for the hyper configuration generation function."""
def test_empty_dict_gives_empty_dict(self):
"""The configuration generator gives an empty dictionary on an empty dictionary."""
<|body_0|>
def test_constant_dict_gives_single_dict(s... | stack_v2_sparse_classes_36k_train_000173 | 3,278 | permissive | [
{
"docstring": "The configuration generator gives an empty dictionary on an empty dictionary.",
"name": "test_empty_dict_gives_empty_dict",
"signature": "def test_empty_dict_gives_empty_dict(self)"
},
{
"docstring": "A single dictionary with constant values give a single dictionary.",
"name"... | 6 | stack_v2_sparse_classes_30k_train_007368 | Implement the Python class `TestHyperRunGeneration` described below.
Class description:
Test cases for the hyper configuration generation function.
Method signatures and docstrings:
- def test_empty_dict_gives_empty_dict(self): The configuration generator gives an empty dictionary on an empty dictionary.
- def test_c... | Implement the Python class `TestHyperRunGeneration` described below.
Class description:
Test cases for the hyper configuration generation function.
Method signatures and docstrings:
- def test_empty_dict_gives_empty_dict(self): The configuration generator gives an empty dictionary on an empty dictionary.
- def test_c... | 0f0f654e488a1839455786ccc4ad023c0aa0c2e8 | <|skeleton|>
class TestHyperRunGeneration:
"""Test cases for the hyper configuration generation function."""
def test_empty_dict_gives_empty_dict(self):
"""The configuration generator gives an empty dictionary on an empty dictionary."""
<|body_0|>
def test_constant_dict_gives_single_dict(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestHyperRunGeneration:
"""Test cases for the hyper configuration generation function."""
def test_empty_dict_gives_empty_dict(self):
"""The configuration generator gives an empty dictionary on an empty dictionary."""
hconfig: Dict[str, Any] = dict()
configs = hyperrun.generate(hc... | the_stack_v2_python_sparse | utils/test_hyperrun.py | nuric/pix2rule | train | 10 |
d791c9966a30ffb8256dbcc5013f7b90ff57dbc2 | [
"stack = []\np = root\nLastVisited = None\ns = 0\nwhile p != None and p.val != '#' or stack:\n while p != None:\n stack.append(p)\n s += p.val\n p = p.left\n p = stack[-1]\n if p.left == None and p.right == None and (s == sum):\n return True\n if p.right == None or LastVisite... | <|body_start_0|>
stack = []
p = root
LastVisited = None
s = 0
while p != None and p.val != '#' or stack:
while p != None:
stack.append(p)
s += p.val
p = p.left
p = stack[-1]
if p.left == None and ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
<|body_0|>
def hasPathSum_self(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
st... | stack_v2_sparse_classes_36k_train_000174 | 1,467 | no_license | [
{
"docstring": ":type root: TreeNode :type sum: int :rtype: bool",
"name": "hasPathSum",
"signature": "def hasPathSum(self, root, sum)"
},
{
"docstring": ":type root: TreeNode :type sum: int :rtype: bool",
"name": "hasPathSum_self",
"signature": "def hasPathSum_self(self, root, sum)"
}... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasPathSum(self, root, sum): :type root: TreeNode :type sum: int :rtype: bool
- def hasPathSum_self(self, root, sum): :type root: TreeNode :type sum: int :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasPathSum(self, root, sum): :type root: TreeNode :type sum: int :rtype: bool
- def hasPathSum_self(self, root, sum): :type root: TreeNode :type sum: int :rtype: bool
<|skel... | ea492ec864b50547214ecbbb2cdeeac21e70229b | <|skeleton|>
class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
<|body_0|>
def hasPathSum_self(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :type sum: int :rtype: bool"""
stack = []
p = root
LastVisited = None
s = 0
while p != None and p.val != '#' or stack:
while p != None:
stack.append(p)
... | the_stack_v2_python_sparse | 112_path_sum/sol.py | lianke123321/leetcode_sol | train | 0 | |
4b3e2fda65bef530db734d9cb1d0d156dcb11eab | [
"if isinstance(key, int):\n return QoSAttribute(key)\nif key not in QoSAttribute._member_map_:\n return extend_enum(QoSAttribute, key, default)\nreturn QoSAttribute[key]",
"if not (isinstance(value, int) and 0 <= value <= 255):\n raise ValueError('%r is not a valid %s' % (value, cls.__name__))\nif 12 <= ... | <|body_start_0|>
if isinstance(key, int):
return QoSAttribute(key)
if key not in QoSAttribute._member_map_:
return extend_enum(QoSAttribute, key, default)
return QoSAttribute[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) and 0 <= value <= 25... | [QoSAttribute] Quality-of-Service Attribute Registry | QoSAttribute | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QoSAttribute:
"""[QoSAttribute] Quality-of-Service Attribute Registry"""
def get(key: 'int | str', default: 'int'=-1) -> 'QoSAttribute':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_000175 | 2,638 | permissive | [
{
"docstring": "Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:",
"name": "get",
"signature": "def get(key: 'int | str', default: 'int'=-1) -> 'QoSAttribute'"
},
{
"docstring": "Lookup function used when value is not found... | 2 | null | Implement the Python class `QoSAttribute` described below.
Class description:
[QoSAttribute] Quality-of-Service Attribute Registry
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'QoSAttribute': Backport support for original codes. Args: key: Key to get enum item. default: Default ... | Implement the Python class `QoSAttribute` described below.
Class description:
[QoSAttribute] Quality-of-Service Attribute Registry
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'QoSAttribute': Backport support for original codes. Args: key: Key to get enum item. default: Default ... | a6fe49ec58f09e105bec5a00fb66d9b3f22730d9 | <|skeleton|>
class QoSAttribute:
"""[QoSAttribute] Quality-of-Service Attribute Registry"""
def get(key: 'int | str', default: 'int'=-1) -> 'QoSAttribute':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QoSAttribute:
"""[QoSAttribute] Quality-of-Service Attribute Registry"""
def get(key: 'int | str', default: 'int'=-1) -> 'QoSAttribute':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
if isinstance(key, int):... | the_stack_v2_python_sparse | pcapkit/const/mh/qos_attribute.py | JarryShaw/PyPCAPKit | train | 204 |
d5b1a3a90afbce4c4d262058ae6d71fc57075a07 | [
"self.pos = f_positive\nself.neg = f_negative\nself.max_lines = 100000\nself.lemmatizer = WordNetLemmatizer()\nself.lexicon = self._create_lexicon()",
"lexicon = []\nwith open(self.pos, 'r') as f_handle:\n contents = f_handle.readlines()\n for word in contents[:self.max_lines]:\n all_words = word_tok... | <|body_start_0|>
self.pos = f_positive
self.neg = f_negative
self.max_lines = 100000
self.lemmatizer = WordNetLemmatizer()
self.lexicon = self._create_lexicon()
<|end_body_0|>
<|body_start_1|>
lexicon = []
with open(self.pos, 'r') as f_handle:
content... | Process Sentiment Data. | Data | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Data:
"""Process Sentiment Data."""
def __init__(self, f_positive, f_negative):
"""Method to instantiate the class. Args: f_positive: File with positive sentiments f_negative: File with negative sentiments Returns: None"""
<|body_0|>
def _create_lexicon(self):
""... | stack_v2_sparse_classes_36k_train_000176 | 8,117 | no_license | [
{
"docstring": "Method to instantiate the class. Args: f_positive: File with positive sentiments f_negative: File with negative sentiments Returns: None",
"name": "__init__",
"signature": "def __init__(self, f_positive, f_negative)"
},
{
"docstring": "Create the lexicon from files. Args: None Re... | 4 | stack_v2_sparse_classes_30k_train_005964 | Implement the Python class `Data` described below.
Class description:
Process Sentiment Data.
Method signatures and docstrings:
- def __init__(self, f_positive, f_negative): Method to instantiate the class. Args: f_positive: File with positive sentiments f_negative: File with negative sentiments Returns: None
- def _... | Implement the Python class `Data` described below.
Class description:
Process Sentiment Data.
Method signatures and docstrings:
- def __init__(self, f_positive, f_negative): Method to instantiate the class. Args: f_positive: File with positive sentiments f_negative: File with negative sentiments Returns: None
- def _... | 36a7996b140cccb9003cba8367364645e2d65d85 | <|skeleton|>
class Data:
"""Process Sentiment Data."""
def __init__(self, f_positive, f_negative):
"""Method to instantiate the class. Args: f_positive: File with positive sentiments f_negative: File with negative sentiments Returns: None"""
<|body_0|>
def _create_lexicon(self):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Data:
"""Process Sentiment Data."""
def __init__(self, f_positive, f_negative):
"""Method to instantiate the class. Args: f_positive: File with positive sentiments f_negative: File with negative sentiments Returns: None"""
self.pos = f_positive
self.neg = f_negative
self.m... | the_stack_v2_python_sparse | general/sentdex/tf-nltk-multilayer-perceptron.py | palisadoes/AI | train | 1 |
d331132c1033ceb3ee702a119d156300fdec8b30 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Proto file describing the Campaign Label service. Service to manage labels on campaigns. | CampaignLabelServiceServicer | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CampaignLabelServiceServicer:
"""Proto file describing the Campaign Label service. Service to manage labels on campaigns."""
def GetCampaignLabel(self, request, context):
"""Returns the requested campaign-label relationship in full detail."""
<|body_0|>
def MutateCampaig... | stack_v2_sparse_classes_36k_train_000177 | 3,479 | permissive | [
{
"docstring": "Returns the requested campaign-label relationship in full detail.",
"name": "GetCampaignLabel",
"signature": "def GetCampaignLabel(self, request, context)"
},
{
"docstring": "Creates and removes campaign-label relationships. Operation statuses are returned.",
"name": "MutateC... | 2 | null | Implement the Python class `CampaignLabelServiceServicer` described below.
Class description:
Proto file describing the Campaign Label service. Service to manage labels on campaigns.
Method signatures and docstrings:
- def GetCampaignLabel(self, request, context): Returns the requested campaign-label relationship in ... | Implement the Python class `CampaignLabelServiceServicer` described below.
Class description:
Proto file describing the Campaign Label service. Service to manage labels on campaigns.
Method signatures and docstrings:
- def GetCampaignLabel(self, request, context): Returns the requested campaign-label relationship in ... | 0fc8a7dbf31d9e8e2a4364df93bec5f6b7edd50a | <|skeleton|>
class CampaignLabelServiceServicer:
"""Proto file describing the Campaign Label service. Service to manage labels on campaigns."""
def GetCampaignLabel(self, request, context):
"""Returns the requested campaign-label relationship in full detail."""
<|body_0|>
def MutateCampaig... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CampaignLabelServiceServicer:
"""Proto file describing the Campaign Label service. Service to manage labels on campaigns."""
def GetCampaignLabel(self, request, context):
"""Returns the requested campaign-label relationship in full detail."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED... | the_stack_v2_python_sparse | google/ads/google_ads/v2/proto/services/campaign_label_service_pb2_grpc.py | juanmacugat/google-ads-python | train | 1 |
d8bb70588fd341da77e3f0f82a6dbd984f6da13a | [
"title = request.GET.get('title', '')\ninterface_case_name = request.GET.get('interface_case_name', '')\nobj = RelevanceCaseSet.objects.filter(parent__interface_case_set_name=title)\nif interface_case_name:\n obj = RelevanceCaseSet.objects.filter(Q(parent__interface_case_set_name=title) & Q(interface_case_name__... | <|body_start_0|>
title = request.GET.get('title', '')
interface_case_name = request.GET.get('interface_case_name', '')
obj = RelevanceCaseSet.objects.filter(parent__interface_case_set_name=title)
if interface_case_name:
obj = RelevanceCaseSet.objects.filter(Q(parent__interfac... | 接口分类 | RelevanceCaseSetList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelevanceCaseSetList:
"""接口分类"""
def get(self, request, *args, **kwargs):
"""获取用例集关联用例列表"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""添加用例集关联用例"""
<|body_1|>
def delete(self, request, pk, *args, **kwargs):
"""删除用例集关联用例"""
... | stack_v2_sparse_classes_36k_train_000178 | 5,044 | no_license | [
{
"docstring": "获取用例集关联用例列表",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "添加用例集关联用例",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
},
{
"docstring": "删除用例集关联用例",
"name": "delete",
"signature": "def de... | 3 | stack_v2_sparse_classes_30k_train_008904 | Implement the Python class `RelevanceCaseSetList` described below.
Class description:
接口分类
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 获取用例集关联用例列表
- def post(self, request, *args, **kwargs): 添加用例集关联用例
- def delete(self, request, pk, *args, **kwargs): 删除用例集关联用例 | Implement the Python class `RelevanceCaseSetList` described below.
Class description:
接口分类
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 获取用例集关联用例列表
- def post(self, request, *args, **kwargs): 添加用例集关联用例
- def delete(self, request, pk, *args, **kwargs): 删除用例集关联用例
<|skeleton|>
class Rele... | e5247d56eb3af770dca1eeb18571281355e58c08 | <|skeleton|>
class RelevanceCaseSetList:
"""接口分类"""
def get(self, request, *args, **kwargs):
"""获取用例集关联用例列表"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""添加用例集关联用例"""
<|body_1|>
def delete(self, request, pk, *args, **kwargs):
"""删除用例集关联用例"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelevanceCaseSetList:
"""接口分类"""
def get(self, request, *args, **kwargs):
"""获取用例集关联用例列表"""
title = request.GET.get('title', '')
interface_case_name = request.GET.get('interface_case_name', '')
obj = RelevanceCaseSet.objects.filter(parent__interface_case_set_name=title)
... | the_stack_v2_python_sparse | easy/api/interFace/interfaceCaseSetMange.py | zhuzhanhao1/Easytest | train | 1 |
ac92d98280c94f3bc8d3d4dc39547210bc6be7bc | [
"tags = result.tags.split()\nif ('blogger' in tags or ('undecided' in tags and 'SHORT_BIO_50' in tags)) and (any([t in tags for t in self.OR_TAGS]) if self.OR_TAGS else True):\n return True\nreturn False",
"log.info('Started %s.pipeline(profile_id=%s, route=%s)' % (type(self).__name__, profile_id, route))\ntry... | <|body_start_0|>
tags = result.tags.split()
if ('blogger' in tags or ('undecided' in tags and 'SHORT_BIO_50' in tags)) and (any([t in tags for t in self.OR_TAGS]) if self.OR_TAGS else True):
return True
return False
<|end_body_0|>
<|body_start_1|>
log.info('Started %s.pipeli... | This processor is used as a filter to proceed only if 2 conditions are satisfied: * have either 'blogger' or both 'SHORT_LEN_50' and 'undecided' tags AND * there is one or more of OR_TAGS tags | OnlyBloggersProcessor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OnlyBloggersProcessor:
"""This processor is used as a filter to proceed only if 2 conditions are satisfied: * have either 'blogger' or both 'SHORT_LEN_50' and 'undecided' tags AND * there is one or more of OR_TAGS tags"""
def proceed(self, result):
"""This function determines conditi... | stack_v2_sparse_classes_36k_train_000179 | 30,721 | no_license | [
{
"docstring": "This function determines condition when it will proceed to the next Processor, Classifier or Upgrader in chain. gets Profile as result",
"name": "proceed",
"signature": "def proceed(self, result)"
},
{
"docstring": "This function is called when performing Processor as a part of p... | 2 | null | Implement the Python class `OnlyBloggersProcessor` described below.
Class description:
This processor is used as a filter to proceed only if 2 conditions are satisfied: * have either 'blogger' or both 'SHORT_LEN_50' and 'undecided' tags AND * there is one or more of OR_TAGS tags
Method signatures and docstrings:
- de... | Implement the Python class `OnlyBloggersProcessor` described below.
Class description:
This processor is used as a filter to proceed only if 2 conditions are satisfied: * have either 'blogger' or both 'SHORT_LEN_50' and 'undecided' tags AND * there is one or more of OR_TAGS tags
Method signatures and docstrings:
- de... | 2f15c4ddd8bbb112c407d222ae48746b626c674f | <|skeleton|>
class OnlyBloggersProcessor:
"""This processor is used as a filter to proceed only if 2 conditions are satisfied: * have either 'blogger' or both 'SHORT_LEN_50' and 'undecided' tags AND * there is one or more of OR_TAGS tags"""
def proceed(self, result):
"""This function determines conditi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OnlyBloggersProcessor:
"""This processor is used as a filter to proceed only if 2 conditions are satisfied: * have either 'blogger' or both 'SHORT_LEN_50' and 'undecided' tags AND * there is one or more of OR_TAGS tags"""
def proceed(self, result):
"""This function determines condition when it wi... | the_stack_v2_python_sparse | Projects/miami_metro/social_discovery/processors.py | TopWebGhost/Angular-Influencer | train | 1 |
36ebfbd99598734f82e688a20403ec0c57c577b6 | [
"self.generic_visit(node)\nis_multiple = len(node.targets) > 1\nis_compound = any(map(is_sequence_node, node.targets))\nis_simple = not is_compound\nif is_simple and is_multiple:\n return self.visit_simple_assign(node)\nelif is_compound and (is_multiple or is_sequence_node(node.value)):\n return self.visit_co... | <|body_start_0|>
self.generic_visit(node)
is_multiple = len(node.targets) > 1
is_compound = any(map(is_sequence_node, node.targets))
is_simple = not is_compound
if is_simple and is_multiple:
return self.visit_simple_assign(node)
elif is_compound and (is_multip... | Eliminate statements with multiple targets. Converts any assignment or deletion statement with multiple targets to a sequences of statements which each have a single target. We are careful to preserve Python's evaluation order: https://docs.python.org/3/reference/expressions.html#evaluation-order This normalization is ... | EliminateMultipleTargets | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EliminateMultipleTargets:
"""Eliminate statements with multiple targets. Converts any assignment or deletion statement with multiple targets to a sequences of statements which each have a single target. We are careful to preserve Python's evaluation order: https://docs.python.org/3/reference/expr... | stack_v2_sparse_classes_36k_train_000180 | 15,969 | permissive | [
{
"docstring": "Replace multiple assignment with single assignments.",
"name": "visit_Assign",
"signature": "def visit_Assign(self, node)"
},
{
"docstring": "Visit assignment node whose targets are all simple.",
"name": "visit_simple_assign",
"signature": "def visit_simple_assign(self, n... | 4 | stack_v2_sparse_classes_30k_train_005779 | Implement the Python class `EliminateMultipleTargets` described below.
Class description:
Eliminate statements with multiple targets. Converts any assignment or deletion statement with multiple targets to a sequences of statements which each have a single target. We are careful to preserve Python's evaluation order: h... | Implement the Python class `EliminateMultipleTargets` described below.
Class description:
Eliminate statements with multiple targets. Converts any assignment or deletion statement with multiple targets to a sequences of statements which each have a single target. We are careful to preserve Python's evaluation order: h... | a6097d36c8863925c774f04155e2af6cc8cb3859 | <|skeleton|>
class EliminateMultipleTargets:
"""Eliminate statements with multiple targets. Converts any assignment or deletion statement with multiple targets to a sequences of statements which each have a single target. We are careful to preserve Python's evaluation order: https://docs.python.org/3/reference/expr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EliminateMultipleTargets:
"""Eliminate statements with multiple targets. Converts any assignment or deletion statement with multiple targets to a sequences of statements which each have a single target. We are careful to preserve Python's evaluation order: https://docs.python.org/3/reference/expressions.html#... | the_stack_v2_python_sparse | flowgraph/trace/ast_transform.py | epatters/pyflowgraph | train | 2 |
e065a55bed130229f0fa1da93016e9e42e6bc8d3 | [
"self.host = host\nself.port = port\nself.secret = secret",
"if dictionary is None:\n return None\nhost = dictionary.get('host')\nsecret = dictionary.get('secret')\nport = dictionary.get('port')\nreturn cls(host, secret, port)"
] | <|body_start_0|>
self.host = host
self.port = port
self.secret = secret
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
host = dictionary.get('host')
secret = dictionary.get('secret')
port = dictionary.get('port')
return cls... | Implementation of the 'RadiusAccountingServer' model. TODO: type model description here. Attributes: host (string): IP address to which the APs will send RADIUS accounting messages port (int): Port on the RADIUS server that is listening for accounting messages secret (string): Shared key used to authenticate messages b... | RadiusAccountingServerModel | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RadiusAccountingServerModel:
"""Implementation of the 'RadiusAccountingServer' model. TODO: type model description here. Attributes: host (string): IP address to which the APs will send RADIUS accounting messages port (int): Port on the RADIUS server that is listening for accounting messages secr... | stack_v2_sparse_classes_36k_train_000181 | 1,986 | permissive | [
{
"docstring": "Constructor for the RadiusAccountingServerModel class",
"name": "__init__",
"signature": "def __init__(self, host=None, secret=None, port=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of th... | 2 | null | Implement the Python class `RadiusAccountingServerModel` described below.
Class description:
Implementation of the 'RadiusAccountingServer' model. TODO: type model description here. Attributes: host (string): IP address to which the APs will send RADIUS accounting messages port (int): Port on the RADIUS server that is... | Implement the Python class `RadiusAccountingServerModel` described below.
Class description:
Implementation of the 'RadiusAccountingServer' model. TODO: type model description here. Attributes: host (string): IP address to which the APs will send RADIUS accounting messages port (int): Port on the RADIUS server that is... | 9894089eb013318243ae48869cc5130eb37f80c0 | <|skeleton|>
class RadiusAccountingServerModel:
"""Implementation of the 'RadiusAccountingServer' model. TODO: type model description here. Attributes: host (string): IP address to which the APs will send RADIUS accounting messages port (int): Port on the RADIUS server that is listening for accounting messages secr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RadiusAccountingServerModel:
"""Implementation of the 'RadiusAccountingServer' model. TODO: type model description here. Attributes: host (string): IP address to which the APs will send RADIUS accounting messages port (int): Port on the RADIUS server that is listening for accounting messages secret (string): ... | the_stack_v2_python_sparse | meraki_sdk/models/radius_accounting_server_model.py | RaulCatalano/meraki-python-sdk | train | 1 |
276fd56c742ca1ba7de4d4fd2a785853918cacac | [
"s = DynamicArrayStack()\nself.assertEqual(0, len(s))\nself.assertEqual(0, len(s._array))",
"s = DynamicArrayStack()\nself.assertEqual(0, len(s))\ns.push(1)\nself.assertEqual(1, len(s))\nself.assertEqual(1, len(s._array))\nself.assertEqual(1, s._array[0])\ns.push(StackEmptyException)\nself.assertEqual(2, len(s))\... | <|body_start_0|>
s = DynamicArrayStack()
self.assertEqual(0, len(s))
self.assertEqual(0, len(s._array))
<|end_body_0|>
<|body_start_1|>
s = DynamicArrayStack()
self.assertEqual(0, len(s))
s.push(1)
self.assertEqual(1, len(s))
self.assertEqual(1, len(s._ar... | TestDynamicArrayStack | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDynamicArrayStack:
def test_instantiation(self):
"""Test basic object creation."""
<|body_0|>
def test_push(self):
"""Test stack push operation."""
<|body_1|>
def test_pop(self):
"""Test stack pop operation."""
<|body_2|>
def tes... | stack_v2_sparse_classes_36k_train_000182 | 6,008 | no_license | [
{
"docstring": "Test basic object creation.",
"name": "test_instantiation",
"signature": "def test_instantiation(self)"
},
{
"docstring": "Test stack push operation.",
"name": "test_push",
"signature": "def test_push(self)"
},
{
"docstring": "Test stack pop operation.",
"name... | 4 | stack_v2_sparse_classes_30k_train_015565 | Implement the Python class `TestDynamicArrayStack` described below.
Class description:
Implement the TestDynamicArrayStack class.
Method signatures and docstrings:
- def test_instantiation(self): Test basic object creation.
- def test_push(self): Test stack push operation.
- def test_pop(self): Test stack pop operati... | Implement the Python class `TestDynamicArrayStack` described below.
Class description:
Implement the TestDynamicArrayStack class.
Method signatures and docstrings:
- def test_instantiation(self): Test basic object creation.
- def test_push(self): Test stack push operation.
- def test_pop(self): Test stack pop operati... | 66e553842998e22ee8ec4f9ebe901f76089128de | <|skeleton|>
class TestDynamicArrayStack:
def test_instantiation(self):
"""Test basic object creation."""
<|body_0|>
def test_push(self):
"""Test stack push operation."""
<|body_1|>
def test_pop(self):
"""Test stack pop operation."""
<|body_2|>
def tes... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestDynamicArrayStack:
def test_instantiation(self):
"""Test basic object creation."""
s = DynamicArrayStack()
self.assertEqual(0, len(s))
self.assertEqual(0, len(s._array))
def test_push(self):
"""Test stack push operation."""
s = DynamicArrayStack()
... | the_stack_v2_python_sparse | python/dsa/stacks_test.py | nehararora/practise-code | train | 0 | |
4615cf2c86a7030a971ff4d1032e513b92d588c1 | [
"super(SCPCheckOKResponse, self).__init__()\nself._operation = operation\nself._command = command",
"result = self.scp_response_header.result\nif result != SCPResult.RC_OK:\n raise SpinnmanUnexpectedResponseCodeException(self._operation, self._command, result.name)"
] | <|body_start_0|>
super(SCPCheckOKResponse, self).__init__()
self._operation = operation
self._command = command
<|end_body_0|>
<|body_start_1|>
result = self.scp_response_header.result
if result != SCPResult.RC_OK:
raise SpinnmanUnexpectedResponseCodeException(self._... | An SCP response to a request which returns nothing other than OK | SCPCheckOKResponse | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SCPCheckOKResponse:
"""An SCP response to a request which returns nothing other than OK"""
def __init__(self, operation, command):
""":param operation: The operation being performed :type operation: str :param command: The command that was sent :type command: str"""
<|body_0|... | stack_v2_sparse_classes_36k_train_000183 | 1,103 | permissive | [
{
"docstring": ":param operation: The operation being performed :type operation: str :param command: The command that was sent :type command: str",
"name": "__init__",
"signature": "def __init__(self, operation, command)"
},
{
"docstring": "See :py:meth:`spinnman.messages.scp.abstract_scp_respon... | 2 | stack_v2_sparse_classes_30k_train_019913 | Implement the Python class `SCPCheckOKResponse` described below.
Class description:
An SCP response to a request which returns nothing other than OK
Method signatures and docstrings:
- def __init__(self, operation, command): :param operation: The operation being performed :type operation: str :param command: The comm... | Implement the Python class `SCPCheckOKResponse` described below.
Class description:
An SCP response to a request which returns nothing other than OK
Method signatures and docstrings:
- def __init__(self, operation, command): :param operation: The operation being performed :type operation: str :param command: The comm... | 04fa1eaf78778edea3ba3afa4c527d20c491718e | <|skeleton|>
class SCPCheckOKResponse:
"""An SCP response to a request which returns nothing other than OK"""
def __init__(self, operation, command):
""":param operation: The operation being performed :type operation: str :param command: The command that was sent :type command: str"""
<|body_0|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SCPCheckOKResponse:
"""An SCP response to a request which returns nothing other than OK"""
def __init__(self, operation, command):
""":param operation: The operation being performed :type operation: str :param command: The command that was sent :type command: str"""
super(SCPCheckOKRespon... | the_stack_v2_python_sparse | src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/spinnman/messages/scp/impl/scp_check_ok_response.py | Roboy/LSM_SpiNNaker_MyoArm | train | 2 |
487367437e6ac8a14e1c6ee02d26d21d87de8fb1 | [
"if author.lower() == 'beals':\n freqs = {'A': 0.074, 'R': 0.042, 'C': 0.033, 'G': 0.074, 'H': 0.029, 'N': 0.044, 'D': 0.059, 'E': 0.058, 'Q': 0.037, 'I': 0.038, 'L': 0.076, 'K': 0.072, 'M': 0.018, 'F': 0.04, 'P': 0.05, 'S': 0.081, 'T': 0.062, 'W': 0.013, 'Y': 0.033, 'V': 0.068}\nelif author.lower() == 'dayhoff'... | <|body_start_0|>
if author.lower() == 'beals':
freqs = {'A': 0.074, 'R': 0.042, 'C': 0.033, 'G': 0.074, 'H': 0.029, 'N': 0.044, 'D': 0.059, 'E': 0.058, 'Q': 0.037, 'I': 0.038, 'L': 0.076, 'K': 0.072, 'M': 0.018, 'F': 0.04, 'P': 0.05, 'S': 0.081, 'T': 0.062, 'W': 0.013, 'Y': 0.033, 'V': 0.068}
... | AminoAcidStats | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AminoAcidStats:
def composition(cls, author='beals'):
"""Amino acid frequencies observed in the sequence data-bases. The index corresponds to different publications: M. Beals, L. Gross, S. Harrell www.tiem.utk.edu/~harrell/webmodules/aminoacid.htm Dayhoff (1978) Jones, D.T. Taylor, W.R. ... | stack_v2_sparse_classes_36k_train_000184 | 9,435 | permissive | [
{
"docstring": "Amino acid frequencies observed in the sequence data-bases. The index corresponds to different publications: M. Beals, L. Gross, S. Harrell www.tiem.utk.edu/~harrell/webmodules/aminoacid.htm Dayhoff (1978) Jones, D.T. Taylor, W.R. & Thornton, J.M.(1991) CABIOS 8:275-282",
"name": "compositio... | 2 | null | Implement the Python class `AminoAcidStats` described below.
Class description:
Implement the AminoAcidStats class.
Method signatures and docstrings:
- def composition(cls, author='beals'): Amino acid frequencies observed in the sequence data-bases. The index corresponds to different publications: M. Beals, L. Gross,... | Implement the Python class `AminoAcidStats` described below.
Class description:
Implement the AminoAcidStats class.
Method signatures and docstrings:
- def composition(cls, author='beals'): Amino acid frequencies observed in the sequence data-bases. The index corresponds to different publications: M. Beals, L. Gross,... | a27152ef5437eb87ee31c317091356c4787f82a4 | <|skeleton|>
class AminoAcidStats:
def composition(cls, author='beals'):
"""Amino acid frequencies observed in the sequence data-bases. The index corresponds to different publications: M. Beals, L. Gross, S. Harrell www.tiem.utk.edu/~harrell/webmodules/aminoacid.htm Dayhoff (1978) Jones, D.T. Taylor, W.R. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AminoAcidStats:
def composition(cls, author='beals'):
"""Amino acid frequencies observed in the sequence data-bases. The index corresponds to different publications: M. Beals, L. Gross, S. Harrell www.tiem.utk.edu/~harrell/webmodules/aminoacid.htm Dayhoff (1978) Jones, D.T. Taylor, W.R. & Thornton, J.... | the_stack_v2_python_sparse | mainPackage/clustalo.py | dtklinh/Protein-Rigid-Domains-Estimation | train | 0 | |
4daa02e84d42b8e83e5775b4360c825f68aa1b12 | [
"res = [[]]\nfor i in nums:\n temp = []\n for j in res:\n temp.append(j + [i])\n res += temp\nreturn res",
"nums.sort()\nres = []\nn = len(nums)\n\ndef dfs(cur, path):\n res.append(path)\n for i in range(cur, n):\n if i > cur and nums[i] == nums[i - 1]:\n continue\n ... | <|body_start_0|>
res = [[]]
for i in nums:
temp = []
for j in res:
temp.append(j + [i])
res += temp
return res
<|end_body_0|>
<|body_start_1|>
nums.sort()
res = []
n = len(nums)
def dfs(cur, path):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def subsetsWithDup78(self, nums: List[int]) -> List[List[int]]:
"""78. 子集 数组元素互不相同"""
<|body_0|>
def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:
"""90. 子集 II 存在重复元素 看成 n叉树，去重只去同一层的相同元素，同一树分支的元素不去重。 对于数组[2,2,3]比如第一层取2；3,2的下一层取2，3；3没有下一层"""
... | stack_v2_sparse_classes_36k_train_000185 | 1,688 | no_license | [
{
"docstring": "78. 子集 数组元素互不相同",
"name": "subsetsWithDup78",
"signature": "def subsetsWithDup78(self, nums: List[int]) -> List[List[int]]"
},
{
"docstring": "90. 子集 II 存在重复元素 看成 n叉树，去重只去同一层的相同元素，同一树分支的元素不去重。 对于数组[2,2,3]比如第一层取2；3,2的下一层取2，3；3没有下一层",
"name": "subsetsWithDup",
"signature": ... | 2 | stack_v2_sparse_classes_30k_train_012830 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subsetsWithDup78(self, nums: List[int]) -> List[List[int]]: 78. 子集 数组元素互不相同
- def subsetsWithDup(self, nums: List[int]) -> List[List[int]]: 90. 子集 II 存在重复元素 看成 n叉树，去重只去同一层的相同... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def subsetsWithDup78(self, nums: List[int]) -> List[List[int]]: 78. 子集 数组元素互不相同
- def subsetsWithDup(self, nums: List[int]) -> List[List[int]]: 90. 子集 II 存在重复元素 看成 n叉树，去重只去同一层的相同... | 3fd69b85f52af861ff7e2c74d8aacc515b192615 | <|skeleton|>
class Solution:
def subsetsWithDup78(self, nums: List[int]) -> List[List[int]]:
"""78. 子集 数组元素互不相同"""
<|body_0|>
def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:
"""90. 子集 II 存在重复元素 看成 n叉树，去重只去同一层的相同元素，同一树分支的元素不去重。 对于数组[2,2,3]比如第一层取2；3,2的下一层取2，3；3没有下一层"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def subsetsWithDup78(self, nums: List[int]) -> List[List[int]]:
"""78. 子集 数组元素互不相同"""
res = [[]]
for i in nums:
temp = []
for j in res:
temp.append(j + [i])
res += temp
return res
def subsetsWithDup(self, nums: ... | the_stack_v2_python_sparse | Array/Array_Subset_78_90.py | helloprogram6/leetcode_Cookbook_python | train | 0 | |
e9ccdde08dba6093cb43b287248cb76bbc806744 | [
"self.head = head\nself.count = 0\nrepre = self.head\nwhile repre.next:\n repre = repre.next\n self.count += 1",
"selected = random.randint(0, self.count)\nrepre = self.head\nwhile selected > 0:\n repre = repre.next\n selected -= 1\nreturn repre.val"
] | <|body_start_0|>
self.head = head
self.count = 0
repre = self.head
while repre.next:
repre = repre.next
self.count += 1
<|end_body_0|>
<|body_start_1|>
selected = random.randint(0, self.count)
repre = self.head
while selected > 0:
... | RandomLinkedList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomLinkedList:
def __init__(self, head):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode"""
<|body_0|>
def getRandom(self):
"""Returns a random node's value. :rtype: int... | stack_v2_sparse_classes_36k_train_000186 | 4,813 | no_license | [
{
"docstring": "@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode",
"name": "__init__",
"signature": "def __init__(self, head)"
},
{
"docstring": "Returns a random node's value. :rtype: int",
"name": "g... | 2 | null | Implement the Python class `RandomLinkedList` described below.
Class description:
Implement the RandomLinkedList class.
Method signatures and docstrings:
- def __init__(self, head): @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListN... | Implement the Python class `RandomLinkedList` described below.
Class description:
Implement the RandomLinkedList class.
Method signatures and docstrings:
- def __init__(self, head): @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListN... | 2711bc08f15266bec4ca135e8e3e629df46713eb | <|skeleton|>
class RandomLinkedList:
def __init__(self, head):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode"""
<|body_0|>
def getRandom(self):
"""Returns a random node's value. :rtype: int... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomLinkedList:
def __init__(self, head):
"""@param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. :type head: ListNode"""
self.head = head
self.count = 0
repre = self.head
while repre.next:
... | the_stack_v2_python_sparse | 0.算法/20180814.py | unlimitediw/CheckCode | train | 0 | |
0ad294331f0ec042368d38f798268ff134f91dc7 | [
"try:\n return cfg.evelocations.Get(locationID).locationName\nexcept KeyError:\n log.LogException()\n return '[no location: %d]' % locationID",
"try:\n return cfg.evelocations.Get(locationID).GetRawName(languageID)\nexcept KeyError:\n log.LogException()\n return '[no location: %d]' % locationID"... | <|body_start_0|>
try:
return cfg.evelocations.Get(locationID).locationName
except KeyError:
log.LogException()
return '[no location: %d]' % locationID
<|end_body_0|>
<|body_start_1|>
try:
return cfg.evelocations.Get(locationID).GetRawName(language... | The location property handler class that defines the methods to retrieve location-specific property data. | LocationPropertyHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LocationPropertyHandler:
"""The location property handler class that defines the methods to retrieve location-specific property data."""
def _GetName(self, locationID, languageID, *args, **kwargs):
"""Retrieve name of the location"""
<|body_0|>
def _GetRawName(self, loca... | stack_v2_sparse_classes_36k_train_000187 | 3,191 | no_license | [
{
"docstring": "Retrieve name of the location",
"name": "_GetName",
"signature": "def _GetName(self, locationID, languageID, *args, **kwargs)"
},
{
"docstring": "Returns the localized name without respect to bilingual functionlity settings. Note that this does NOT work for celestials or stations... | 3 | null | Implement the Python class `LocationPropertyHandler` described below.
Class description:
The location property handler class that defines the methods to retrieve location-specific property data.
Method signatures and docstrings:
- def _GetName(self, locationID, languageID, *args, **kwargs): Retrieve name of the locat... | Implement the Python class `LocationPropertyHandler` described below.
Class description:
The location property handler class that defines the methods to retrieve location-specific property data.
Method signatures and docstrings:
- def _GetName(self, locationID, languageID, *args, **kwargs): Retrieve name of the locat... | 50de3488a2140343c364efc2615cf6e67f152be0 | <|skeleton|>
class LocationPropertyHandler:
"""The location property handler class that defines the methods to retrieve location-specific property data."""
def _GetName(self, locationID, languageID, *args, **kwargs):
"""Retrieve name of the location"""
<|body_0|>
def _GetRawName(self, loca... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LocationPropertyHandler:
"""The location property handler class that defines the methods to retrieve location-specific property data."""
def _GetName(self, locationID, languageID, *args, **kwargs):
"""Retrieve name of the location"""
try:
return cfg.evelocations.Get(locationID... | the_stack_v2_python_sparse | localization/propertyHandlers/locationPropertyHandler.py | nanxijw/Clara-Pretty-One-Dick | train | 0 |
5f5b10ad4cb85707afd21c08795d542ccc0ec1a9 | [
"dtypes = data.dtypes\ndtypes = dtypes.reset_index()\ndtypes.columns = ['feature_name', 'types']\nobj_list = list(dtypes.loc[dtypes['types'] == 'object', 'feature_name'])\nobj_data = data.loc[:, obj_list]\nreturn obj_data",
"from sklearn.preprocessing import LabelEncoder\ndata = data.loc[:, var_list]\ncols = data... | <|body_start_0|>
dtypes = data.dtypes
dtypes = dtypes.reset_index()
dtypes.columns = ['feature_name', 'types']
obj_list = list(dtypes.loc[dtypes['types'] == 'object', 'feature_name'])
obj_data = data.loc[:, obj_list]
return obj_data
<|end_body_0|>
<|body_start_1|>
... | Label_Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Label_Encoder:
def slice_obj_data(self, data):
"""提取数据类型为object的数据，可能需要填充缺失值，可能是类别型特征需要进行label或one-hot Args: data: Returns: 数据类型为object的数据子集"""
<|body_0|>
def label_encoder_fit(self, data, var_list):
"""提认为模拟label_encoder的fit过程，得到fit的字典，要求data为类别型的数据 Args: data:原数据框 ... | stack_v2_sparse_classes_36k_train_000188 | 4,010 | no_license | [
{
"docstring": "提取数据类型为object的数据，可能需要填充缺失值，可能是类别型特征需要进行label或one-hot Args: data: Returns: 数据类型为object的数据子集",
"name": "slice_obj_data",
"signature": "def slice_obj_data(self, data)"
},
{
"docstring": "提认为模拟label_encoder的fit过程，得到fit的字典，要求data为类别型的数据 Args: data:原数据框 var_list:数据类型为类别型的字段名列表 Returns:... | 3 | stack_v2_sparse_classes_30k_train_018776 | Implement the Python class `Label_Encoder` described below.
Class description:
Implement the Label_Encoder class.
Method signatures and docstrings:
- def slice_obj_data(self, data): 提取数据类型为object的数据，可能需要填充缺失值，可能是类别型特征需要进行label或one-hot Args: data: Returns: 数据类型为object的数据子集
- def label_encoder_fit(self, data, var_list)... | Implement the Python class `Label_Encoder` described below.
Class description:
Implement the Label_Encoder class.
Method signatures and docstrings:
- def slice_obj_data(self, data): 提取数据类型为object的数据，可能需要填充缺失值，可能是类别型特征需要进行label或one-hot Args: data: Returns: 数据类型为object的数据子集
- def label_encoder_fit(self, data, var_list)... | cc25cb60f1c1c89b4591bbdaec8db1eeba818377 | <|skeleton|>
class Label_Encoder:
def slice_obj_data(self, data):
"""提取数据类型为object的数据，可能需要填充缺失值，可能是类别型特征需要进行label或one-hot Args: data: Returns: 数据类型为object的数据子集"""
<|body_0|>
def label_encoder_fit(self, data, var_list):
"""提认为模拟label_encoder的fit过程，得到fit的字典，要求data为类别型的数据 Args: data:原数据框 ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Label_Encoder:
def slice_obj_data(self, data):
"""提取数据类型为object的数据，可能需要填充缺失值，可能是类别型特征需要进行label或one-hot Args: data: Returns: 数据类型为object的数据子集"""
dtypes = data.dtypes
dtypes = dtypes.reset_index()
dtypes.columns = ['feature_name', 'types']
obj_list = list(dtypes.loc[dtype... | the_stack_v2_python_sparse | bus_drop/loans_drop/feature_engineering/label_encoder.py | xeon-ye/dgg-pro | train | 0 | |
35cd4b3cf420be17e04d516e265e77d9d72c28e5 | [
"self.switch_profiles = switch_profiles\nself.switches = switches\nself.stacks = stacks\nself.igmp_snooping_enabled = igmp_snooping_enabled\nself.flood_unknown_multicast_traffic_enabled = flood_unknown_multicast_traffic_enabled",
"if dictionary is None:\n return None\nigmp_snooping_enabled = dictionary.get('ig... | <|body_start_0|>
self.switch_profiles = switch_profiles
self.switches = switches
self.stacks = stacks
self.igmp_snooping_enabled = igmp_snooping_enabled
self.flood_unknown_multicast_traffic_enabled = flood_unknown_multicast_traffic_enabled
<|end_body_0|>
<|body_start_1|>
... | Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of switch stack ids for non-template network... | Override1Model | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Override1Model:
"""Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of... | stack_v2_sparse_classes_36k_train_000189 | 3,029 | permissive | [
{
"docstring": "Constructor for the Override1Model class",
"name": "__init__",
"signature": "def __init__(self, igmp_snooping_enabled=None, flood_unknown_multicast_traffic_enabled=None, switch_profiles=None, switches=None, stacks=None)"
},
{
"docstring": "Creates an instance of this model from a... | 2 | null | Implement the Python class `Override1Model` described below.
Class description:
Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template n... | Implement the Python class `Override1Model` described below.
Class description:
Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template n... | 9894089eb013318243ae48869cc5130eb37f80c0 | <|skeleton|>
class Override1Model:
"""Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Override1Model:
"""Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of switch stack... | the_stack_v2_python_sparse | meraki_sdk/models/override_1_model.py | RaulCatalano/meraki-python-sdk | train | 1 |
093d8a9bba46d5917950997bdca51a8bfa1432b5 | [
"account = v['user.account']\npassword = v['user.password']\napp = Demo()\nres = app.login(account, password).json()\nassert res['code'] == 10000\nassert res['msg'] == 'login success'",
"email = v['user.account']\npassword = '123456'\napp = Demo()\nres = app.login(email, password).json()\nassert res['code'] == 20... | <|body_start_0|>
account = v['user.account']
password = v['user.password']
app = Demo()
res = app.login(account, password).json()
assert res['code'] == 10000
assert res['msg'] == 'login success'
<|end_body_0|>
<|body_start_1|>
email = v['user.account']
pa... | TestLogin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestLogin:
def test_login_success(self):
"""测试登录成功场景"""
<|body_0|>
def test_login_with_error_password(self):
"""测试使用错误的密码登录"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
account = v['user.account']
password = v['user.password']
app... | stack_v2_sparse_classes_36k_train_000190 | 722 | permissive | [
{
"docstring": "测试登录成功场景",
"name": "test_login_success",
"signature": "def test_login_success(self)"
},
{
"docstring": "测试使用错误的密码登录",
"name": "test_login_with_error_password",
"signature": "def test_login_with_error_password(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000926 | Implement the Python class `TestLogin` described below.
Class description:
Implement the TestLogin class.
Method signatures and docstrings:
- def test_login_success(self): 测试登录成功场景
- def test_login_with_error_password(self): 测试使用错误的密码登录 | Implement the Python class `TestLogin` described below.
Class description:
Implement the TestLogin class.
Method signatures and docstrings:
- def test_login_success(self): 测试登录成功场景
- def test_login_with_error_password(self): 测试使用错误的密码登录
<|skeleton|>
class TestLogin:
def test_login_success(self):
"""测试登录... | 36be435d4aab7a730a267a985fc4ea6493cab232 | <|skeleton|>
class TestLogin:
def test_login_success(self):
"""测试登录成功场景"""
<|body_0|>
def test_login_with_error_password(self):
"""测试使用错误的密码登录"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestLogin:
def test_login_success(self):
"""测试登录成功场景"""
account = v['user.account']
password = v['user.password']
app = Demo()
res = app.login(account, password).json()
assert res['code'] == 10000
assert res['msg'] == 'login success'
def test_login_... | the_stack_v2_python_sparse | src/walnuts/templates/test_suites/test_login.py | hzs618/walnuts | train | 0 | |
d5f3f5672bbc26e921e40e75080ad7584c1d2bba | [
"super(GlobalBodyHeadDiscriminator, self).__init__()\ncond_nc = cfg.cond_nc\nbg_cond_nc = cfg.bg_cond_nc\nndf = cfg.ndf\nn_layers = cfg.n_layers\nmax_nf_mult = cfg.max_nf_mult\nnorm_type = cfg.norm_type\nuse_sigmoid = cfg.use_sigmoid\nself.global_model = PatchDiscriminator(cond_nc, ndf=ndf, n_layers=n_layers, max_n... | <|body_start_0|>
super(GlobalBodyHeadDiscriminator, self).__init__()
cond_nc = cfg.cond_nc
bg_cond_nc = cfg.bg_cond_nc
ndf = cfg.ndf
n_layers = cfg.n_layers
max_nf_mult = cfg.max_nf_mult
norm_type = cfg.norm_type
use_sigmoid = cfg.use_sigmoid
self.... | GlobalBodyHeadDiscriminator | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license",
"Apache-2.0",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GlobalBodyHeadDiscriminator:
def __init__(self, cfg, use_aug_bg=False):
"""Args: cfg (dict or EasyDict): the configurations, and it contains the followings, --cond_nc (int): the input dimension; --ndf (int): the number of filters at the first layer, default is 64; --n_layers (int): the n... | stack_v2_sparse_classes_36k_train_000191 | 11,501 | permissive | [
{
"docstring": "Args: cfg (dict or EasyDict): the configurations, and it contains the followings, --cond_nc (int): the input dimension; --ndf (int): the number of filters at the first layer, default is 64; --n_layers (int): the number of downsampling operations, such as the convolution with stride 2, default is... | 2 | stack_v2_sparse_classes_30k_train_008115 | Implement the Python class `GlobalBodyHeadDiscriminator` described below.
Class description:
Implement the GlobalBodyHeadDiscriminator class.
Method signatures and docstrings:
- def __init__(self, cfg, use_aug_bg=False): Args: cfg (dict or EasyDict): the configurations, and it contains the followings, --cond_nc (int)... | Implement the Python class `GlobalBodyHeadDiscriminator` described below.
Class description:
Implement the GlobalBodyHeadDiscriminator class.
Method signatures and docstrings:
- def __init__(self, cfg, use_aug_bg=False): Args: cfg (dict or EasyDict): the configurations, and it contains the followings, --cond_nc (int)... | fcf9a18ffd66bf3fdd3eea4153a3bc4785131848 | <|skeleton|>
class GlobalBodyHeadDiscriminator:
def __init__(self, cfg, use_aug_bg=False):
"""Args: cfg (dict or EasyDict): the configurations, and it contains the followings, --cond_nc (int): the input dimension; --ndf (int): the number of filters at the first layer, default is 64; --n_layers (int): the n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GlobalBodyHeadDiscriminator:
def __init__(self, cfg, use_aug_bg=False):
"""Args: cfg (dict or EasyDict): the configurations, and it contains the followings, --cond_nc (int): the input dimension; --ndf (int): the number of filters at the first layer, default is 64; --n_layers (int): the number of downs... | the_stack_v2_python_sparse | iPERCore/models/networks/discriminators/multi_scale_dis.py | iPERDance/iPERCore | train | 2,520 | |
48853dbce7fb7d3b3746786f2fa925a6ea952785 | [
"q_points = np.array(data[:, :2])\nim_points = np.array(data[:, 2:])\nH, mask = cv2.findHomography(srcPoints=q_points, dstPoints=im_points, method=0)\nreturn H",
"list_ones = np.ones((data.shape[0], 1))\nsrc_p = np.append(data[:, :2], list_ones, axis=1)\ndst_p = np.append(data[:, 2:], list_ones, axis=1)\nsrc_p_tr... | <|body_start_0|>
q_points = np.array(data[:, :2])
im_points = np.array(data[:, 2:])
H, mask = cv2.findHomography(srcPoints=q_points, dstPoints=im_points, method=0)
return H
<|end_body_0|>
<|body_start_1|>
list_ones = np.ones((data.shape[0], 1))
src_p = np.append(data[:, ... | Class for Homography estimation. It implements the interface needed by the ransac() function. | HomographyModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HomographyModel:
"""Class for Homography estimation. It implements the interface needed by the ransac() function."""
def fit(data):
"""Find and return perspective transformation H between the source (src) and the destination (dst) planes. The transformation minimizes the error of thi... | stack_v2_sparse_classes_36k_train_000192 | 16,214 | no_license | [
{
"docstring": "Find and return perspective transformation H between the source (src) and the destination (dst) planes. The transformation minimizes the error of this constraint H * src_points = dst_points for all given points, using homogeneous coordinates i.e., it fits the homography to ALL given input data. ... | 2 | null | Implement the Python class `HomographyModel` described below.
Class description:
Class for Homography estimation. It implements the interface needed by the ransac() function.
Method signatures and docstrings:
- def fit(data): Find and return perspective transformation H between the source (src) and the destination (d... | Implement the Python class `HomographyModel` described below.
Class description:
Class for Homography estimation. It implements the interface needed by the ransac() function.
Method signatures and docstrings:
- def fit(data): Find and return perspective transformation H between the source (src) and the destination (d... | 2f9c33c4e1a26b3e9e699210ac974047936f49e1 | <|skeleton|>
class HomographyModel:
"""Class for Homography estimation. It implements the interface needed by the ransac() function."""
def fit(data):
"""Find and return perspective transformation H between the source (src) and the destination (dst) planes. The transformation minimizes the error of thi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HomographyModel:
"""Class for Homography estimation. It implements the interface needed by the ransac() function."""
def fit(data):
"""Find and return perspective transformation H between the source (src) and the destination (dst) planes. The transformation minimizes the error of this constraint ... | the_stack_v2_python_sparse | model/robust_matching.py | winkash/image-classification | train | 0 |
2ea96482745dcc4cfd6c3417777055c6044370f7 | [
"self.host = host\nself.port = port\nself.verbose = verbose\nself.meth = meth\nself.opts = opts\nself.flags = flags\nself.connect()",
"context = zmq.Context()\npusher = context.socket(zmq.PUSH)\nfor opt in self.opts:\n pusher.setsockopt(opt, 1)\nprint('{0}://{1}:{2}'.format(self.meth, self.host, self.port))\np... | <|body_start_0|>
self.host = host
self.port = port
self.verbose = verbose
self.meth = meth
self.opts = opts
self.flags = flags
self.connect()
<|end_body_0|>
<|body_start_1|>
context = zmq.Context()
pusher = context.socket(zmq.PUSH)
for opt... | ZMQPush | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZMQPush:
def __init__(self, host, port, opts=[], flags=0, meth='tcp', verbose=False):
"""create a Default ZMQ Pull socket"""
<|body_0|>
def connect(self):
"""open ZMQ pull socket return receiver object"""
<|body_1|>
def send(self, msg):
"""receiv... | stack_v2_sparse_classes_36k_train_000193 | 12,974 | no_license | [
{
"docstring": "create a Default ZMQ Pull socket",
"name": "__init__",
"signature": "def __init__(self, host, port, opts=[], flags=0, meth='tcp', verbose=False)"
},
{
"docstring": "open ZMQ pull socket return receiver object",
"name": "connect",
"signature": "def connect(self)"
},
{
... | 3 | null | Implement the Python class `ZMQPush` described below.
Class description:
Implement the ZMQPush class.
Method signatures and docstrings:
- def __init__(self, host, port, opts=[], flags=0, meth='tcp', verbose=False): create a Default ZMQ Pull socket
- def connect(self): open ZMQ pull socket return receiver object
- def... | Implement the Python class `ZMQPush` described below.
Class description:
Implement the ZMQPush class.
Method signatures and docstrings:
- def __init__(self, host, port, opts=[], flags=0, meth='tcp', verbose=False): create a Default ZMQ Pull socket
- def connect(self): open ZMQ pull socket return receiver object
- def... | 55041e6947b888242ff01cb18bd5f1ee4c4c8f28 | <|skeleton|>
class ZMQPush:
def __init__(self, host, port, opts=[], flags=0, meth='tcp', verbose=False):
"""create a Default ZMQ Pull socket"""
<|body_0|>
def connect(self):
"""open ZMQ pull socket return receiver object"""
<|body_1|>
def send(self, msg):
"""receiv... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ZMQPush:
def __init__(self, host, port, opts=[], flags=0, meth='tcp', verbose=False):
"""create a Default ZMQ Pull socket"""
self.host = host
self.port = port
self.verbose = verbose
self.meth = meth
self.opts = opts
self.flags = flags
self.connec... | the_stack_v2_python_sparse | NPC/gui/ZmqSockets.py | coquellen/NanoPeakCell | train | 6 | |
3085c74c4ec045d8afd05324f7931e3155871a40 | [
"parser.add_argument('-show', dest='show', type=bool, default=True, help='show all the entries in category')\nparser.add_argument('-add', '--a', dest='add', type=str, help=\"Add a new category if it's possible\")\nparser.add_argument('-delete', '--d', dest='del', type=str, help=\"Delete a category if it's possible\... | <|body_start_0|>
parser.add_argument('-show', dest='show', type=bool, default=True, help='show all the entries in category')
parser.add_argument('-add', '--a', dest='add', type=str, help="Add a new category if it's possible")
parser.add_argument('-delete', '--d', dest='del', type=str, help="Dele... | this class manages the parameters you can pass to python manage.py | Command | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Command:
"""this class manages the parameters you can pass to python manage.py"""
def add_arguments(self, parser):
"""manages the args to pass to category"""
<|body_0|>
def _show(self):
"""shows the cat from the list"""
<|body_1|>
def _add(self, new_... | stack_v2_sparse_classes_36k_train_000194 | 3,635 | no_license | [
{
"docstring": "manages the args to pass to category",
"name": "add_arguments",
"signature": "def add_arguments(self, parser)"
},
{
"docstring": "shows the cat from the list",
"name": "_show",
"signature": "def _show(self)"
},
{
"docstring": "add a category to the list if it exis... | 5 | stack_v2_sparse_classes_30k_train_005458 | Implement the Python class `Command` described below.
Class description:
this class manages the parameters you can pass to python manage.py
Method signatures and docstrings:
- def add_arguments(self, parser): manages the args to pass to category
- def _show(self): shows the cat from the list
- def _add(self, new_cat)... | Implement the Python class `Command` described below.
Class description:
this class manages the parameters you can pass to python manage.py
Method signatures and docstrings:
- def add_arguments(self, parser): manages the args to pass to category
- def _show(self): shows the cat from the list
- def _add(self, new_cat)... | 378244474186a2fe25f91377f3628a1479329f99 | <|skeleton|>
class Command:
"""this class manages the parameters you can pass to python manage.py"""
def add_arguments(self, parser):
"""manages the args to pass to category"""
<|body_0|>
def _show(self):
"""shows the cat from the list"""
<|body_1|>
def _add(self, new_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Command:
"""this class manages the parameters you can pass to python manage.py"""
def add_arguments(self, parser):
"""manages the args to pass to category"""
parser.add_argument('-show', dest='show', type=bool, default=True, help='show all the entries in category')
parser.add_argu... | the_stack_v2_python_sparse | products/management/commands/category.py | blingstand/projet8 | train | 0 |
c6155b355152d4d1086fb83a5604c3b17cf973b4 | [
"pressure = self.getPressure(target)\nif self.parent.currPowerPoints > 0:\n self.parent.currPowerPoints -= pressure\nreturn []",
"if isinstance(self.parent.hitDelegate, HitSelfDelegate):\n return 1\nelse:\n return target.getAbility().powerPointsPressure()"
] | <|body_start_0|>
pressure = self.getPressure(target)
if self.parent.currPowerPoints > 0:
self.parent.currPowerPoints -= pressure
return []
<|end_body_0|>
<|body_start_1|>
if isinstance(self.parent.hitDelegate, HitSelfDelegate):
return 1
else:
... | Represents the Remove PP Step in the Attack Process | RemovePPStep | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemovePPStep:
"""Represents the Remove PP Step in the Attack Process"""
def perform(self, user, target, environment):
"""Perform this step"""
<|body_0|>
def getPressure(self, target):
"""Return the Pressure exerted when using the attack"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k_train_000195 | 769 | no_license | [
{
"docstring": "Perform this step",
"name": "perform",
"signature": "def perform(self, user, target, environment)"
},
{
"docstring": "Return the Pressure exerted when using the attack",
"name": "getPressure",
"signature": "def getPressure(self, target)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000123 | Implement the Python class `RemovePPStep` described below.
Class description:
Represents the Remove PP Step in the Attack Process
Method signatures and docstrings:
- def perform(self, user, target, environment): Perform this step
- def getPressure(self, target): Return the Pressure exerted when using the attack | Implement the Python class `RemovePPStep` described below.
Class description:
Represents the Remove PP Step in the Attack Process
Method signatures and docstrings:
- def perform(self, user, target, environment): Perform this step
- def getPressure(self, target): Return the Pressure exerted when using the attack
<|sk... | 3931eee5fd04e18bb1738a0b27a4c6979dc4db01 | <|skeleton|>
class RemovePPStep:
"""Represents the Remove PP Step in the Attack Process"""
def perform(self, user, target, environment):
"""Perform this step"""
<|body_0|>
def getPressure(self, target):
"""Return the Pressure exerted when using the attack"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RemovePPStep:
"""Represents the Remove PP Step in the Attack Process"""
def perform(self, user, target, environment):
"""Perform this step"""
pressure = self.getPressure(target)
if self.parent.currPowerPoints > 0:
self.parent.currPowerPoints -= pressure
return ... | the_stack_v2_python_sparse | src/Battle/Attack/Steps/remove_pp_step.py | sgtnourry/Pokemon-Project | train | 0 |
de8058ed1ee31e9988382a212a6fb1e2a35e6d0e | [
"if directory is None:\n directory = compat.get_saver_or_default().directory\n if not directory.endswith('/'):\n directory += '/'\n directory += 'best'\nsuper(KeepBestCheckpointSaver, self).__init__(checkpoint=tf.train.Checkpoint(**dict([(x.name.split(':')[0], x) for x in model.weights])), directory... | <|body_start_0|>
if directory is None:
directory = compat.get_saver_or_default().directory
if not directory.endswith('/'):
directory += '/'
directory += 'best'
super(KeepBestCheckpointSaver, self).__init__(checkpoint=tf.train.Checkpoint(**dict([(x.name... | Custom Checkpoint manager for saving and restoring variables. | KeepBestCheckpointSaver | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KeepBestCheckpointSaver:
"""Custom Checkpoint manager for saving and restoring variables."""
def __init__(self, model, directory, metric, max_to_keep=8, checkpoint_name='ckpt'):
"""Initializes a custom checkpoint manager. Args: model: A keras model. directory: The path to a directory... | stack_v2_sparse_classes_36k_train_000196 | 17,097 | permissive | [
{
"docstring": "Initializes a custom checkpoint manager. Args: model: A keras model. directory: The path to a directory in which to write checkpoints. metric: A metric object. max_to_keep: The maximum checkpoint numbers to keep. checkpoint_name: The name of each checkpoint.",
"name": "__init__",
"signat... | 2 | null | Implement the Python class `KeepBestCheckpointSaver` described below.
Class description:
Custom Checkpoint manager for saving and restoring variables.
Method signatures and docstrings:
- def __init__(self, model, directory, metric, max_to_keep=8, checkpoint_name='ckpt'): Initializes a custom checkpoint manager. Args:... | Implement the Python class `KeepBestCheckpointSaver` described below.
Class description:
Custom Checkpoint manager for saving and restoring variables.
Method signatures and docstrings:
- def __init__(self, model, directory, metric, max_to_keep=8, checkpoint_name='ckpt'): Initializes a custom checkpoint manager. Args:... | 06613a99305f02312a0e64ee3c3c50e7b00dcf0e | <|skeleton|>
class KeepBestCheckpointSaver:
"""Custom Checkpoint manager for saving and restoring variables."""
def __init__(self, model, directory, metric, max_to_keep=8, checkpoint_name='ckpt'):
"""Initializes a custom checkpoint manager. Args: model: A keras model. directory: The path to a directory... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KeepBestCheckpointSaver:
"""Custom Checkpoint manager for saving and restoring variables."""
def __init__(self, model, directory, metric, max_to_keep=8, checkpoint_name='ckpt'):
"""Initializes a custom checkpoint manager. Args: model: A keras model. directory: The path to a directory in which to ... | the_stack_v2_python_sparse | neurst/neurst/utils/checkpoints.py | ohlionel/Prune-Tune | train | 12 |
baf8338eddf4621f2f8e0ab2efc93c014e15355f | [
"self._name = name\nself._version = version\nself._release = release\nself._override = override",
"full_version = None\nif self.version:\n full_version = self.version\n if self.release:\n full_version = '{}-{}'.format(self.version, self.release)\nreturn full_version",
"if self.full_version:\n re... | <|body_start_0|>
self._name = name
self._version = version
self._release = release
self._override = override
<|end_body_0|>
<|body_start_1|>
full_version = None
if self.version:
full_version = self.version
if self.release:
full_ver... | A package's authoritative version data. This class contains version information for a named package, and provides helper methods for formatting version/release data as well as version-enriched package name, for all supported OS families. | PackageVersion | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PackageVersion:
"""A package's authoritative version data. This class contains version information for a named package, and provides helper methods for formatting version/release data as well as version-enriched package name, for all supported OS families."""
def __init__(self, name: str, ve... | stack_v2_sparse_classes_36k_train_000197 | 14,999 | permissive | [
{
"docstring": "Initializes a package version. Arguments: name: the name of the package version: the version of the package release: the release of the package",
"name": "__init__",
"signature": "def __init__(self, name: str, version: Optional[str]=None, release: Optional[str]=None, override: Optional[s... | 3 | stack_v2_sparse_classes_30k_train_017203 | Implement the Python class `PackageVersion` described below.
Class description:
A package's authoritative version data. This class contains version information for a named package, and provides helper methods for formatting version/release data as well as version-enriched package name, for all supported OS families.
... | Implement the Python class `PackageVersion` described below.
Class description:
A package's authoritative version data. This class contains version information for a named package, and provides helper methods for formatting version/release data as well as version-enriched package name, for all supported OS families.
... | 6854d582f58592675afb3759585ce614b3db08f3 | <|skeleton|>
class PackageVersion:
"""A package's authoritative version data. This class contains version information for a named package, and provides helper methods for formatting version/release data as well as version-enriched package name, for all supported OS families."""
def __init__(self, name: str, ve... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PackageVersion:
"""A package's authoritative version data. This class contains version information for a named package, and provides helper methods for formatting version/release data as well as version-enriched package name, for all supported OS families."""
def __init__(self, name: str, version: Option... | the_stack_v2_python_sparse | buildchain/buildchain/versions.py | scality/metalk8s | train | 321 |
f2c26e624e2c36cc619ada0a7b1565e4dc29b59a | [
"if not gui:\n gui = self.kernel.gui\nself.active_eventloop = gui",
"if not gui:\n gui = self.kernel.gui\nreturn super().enable_matplotlib(gui)",
"if not gui:\n gui = self.kernel.gui\nreturn super().enable_pylab(gui, import_all, welcome_message)"
] | <|body_start_0|>
if not gui:
gui = self.kernel.gui
self.active_eventloop = gui
<|end_body_0|>
<|body_start_1|>
if not gui:
gui = self.kernel.gui
return super().enable_matplotlib(gui)
<|end_body_1|>
<|body_start_2|>
if not gui:
gui = self.kern... | An in-process interactive shell. | InProcessInteractiveShell | [
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InProcessInteractiveShell:
"""An in-process interactive shell."""
def enable_gui(self, gui=None):
"""Enable GUI integration for the kernel."""
<|body_0|>
def enable_matplotlib(self, gui=None):
"""Enable matplotlib integration for the kernel."""
<|body_1|>... | stack_v2_sparse_classes_36k_train_000198 | 7,067 | permissive | [
{
"docstring": "Enable GUI integration for the kernel.",
"name": "enable_gui",
"signature": "def enable_gui(self, gui=None)"
},
{
"docstring": "Enable matplotlib integration for the kernel.",
"name": "enable_matplotlib",
"signature": "def enable_matplotlib(self, gui=None)"
},
{
"... | 3 | null | Implement the Python class `InProcessInteractiveShell` described below.
Class description:
An in-process interactive shell.
Method signatures and docstrings:
- def enable_gui(self, gui=None): Enable GUI integration for the kernel.
- def enable_matplotlib(self, gui=None): Enable matplotlib integration for the kernel.
... | Implement the Python class `InProcessInteractiveShell` described below.
Class description:
An in-process interactive shell.
Method signatures and docstrings:
- def enable_gui(self, gui=None): Enable GUI integration for the kernel.
- def enable_matplotlib(self, gui=None): Enable matplotlib integration for the kernel.
... | f5042e35b945aded77b23470ead62d7eacefde92 | <|skeleton|>
class InProcessInteractiveShell:
"""An in-process interactive shell."""
def enable_gui(self, gui=None):
"""Enable GUI integration for the kernel."""
<|body_0|>
def enable_matplotlib(self, gui=None):
"""Enable matplotlib integration for the kernel."""
<|body_1|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InProcessInteractiveShell:
"""An in-process interactive shell."""
def enable_gui(self, gui=None):
"""Enable GUI integration for the kernel."""
if not gui:
gui = self.kernel.gui
self.active_eventloop = gui
def enable_matplotlib(self, gui=None):
"""Enable ma... | the_stack_v2_python_sparse | contrib/python/ipykernel/py3/ipykernel/inprocess/ipkernel.py | catboost/catboost | train | 8,012 |
e306a5abb4a27fb915ec65c51672cef7591b576a | [
"for tv in self._testData:\n s2v = _S2V.new(t2b(tv[1]), tv[3])\n for s in tv[0]:\n s2v.update(t2b(s))\n result = s2v.derive()\n self.assertEqual(result, t2b(tv[2]))",
"key = bchr(0) * 8 + bchr(255) * 8\nfor module in (AES, DES3):\n s2v = _S2V.new(key, module)\n max_comps = module.block_si... | <|body_start_0|>
for tv in self._testData:
s2v = _S2V.new(t2b(tv[1]), tv[3])
for s in tv[0]:
s2v.update(t2b(s))
result = s2v.derive()
self.assertEqual(result, t2b(tv[2]))
<|end_body_0|>
<|body_start_1|>
key = bchr(0) * 8 + bchr(255) * 8
... | S2V_Tests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class S2V_Tests:
def test1(self):
"""Verify correctness of test vector"""
<|body_0|>
def test2(self):
"""Verify that no more than 127(AES) and 63(TDES) components are accepted."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
for tv in self._testData:
... | stack_v2_sparse_classes_36k_train_000199 | 34,497 | permissive | [
{
"docstring": "Verify correctness of test vector",
"name": "test1",
"signature": "def test1(self)"
},
{
"docstring": "Verify that no more than 127(AES) and 63(TDES) components are accepted.",
"name": "test2",
"signature": "def test2(self)"
}
] | 2 | null | Implement the Python class `S2V_Tests` described below.
Class description:
Implement the S2V_Tests class.
Method signatures and docstrings:
- def test1(self): Verify correctness of test vector
- def test2(self): Verify that no more than 127(AES) and 63(TDES) components are accepted. | Implement the Python class `S2V_Tests` described below.
Class description:
Implement the S2V_Tests class.
Method signatures and docstrings:
- def test1(self): Verify correctness of test vector
- def test2(self): Verify that no more than 127(AES) and 63(TDES) components are accepted.
<|skeleton|>
class S2V_Tests:
... | fa82044a2dc2f0f1f7454f5394e6d68fa923c289 | <|skeleton|>
class S2V_Tests:
def test1(self):
"""Verify correctness of test vector"""
<|body_0|>
def test2(self):
"""Verify that no more than 127(AES) and 63(TDES) components are accepted."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class S2V_Tests:
def test1(self):
"""Verify correctness of test vector"""
for tv in self._testData:
s2v = _S2V.new(t2b(tv[1]), tv[3])
for s in tv[0]:
s2v.update(t2b(s))
result = s2v.derive()
self.assertEqual(result, t2b(tv[2]))
def... | the_stack_v2_python_sparse | venv/lib/python3.6/site-packages/Crypto/SelfTest/Protocol/test_KDF.py | masora1030/eigoyurusan | train | 11 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.