blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 467 8.64k | id stringlengths 40 40 | length_bytes int64 515 49.7k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 160 3.93k | prompted_full_text stringlengths 681 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.09k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 331 8.3k | source stringclasses 1
value | source_path stringlengths 5 177 | source_repo stringlengths 6 88 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d3076f357c7ba8fa3d1d33199cbb74411a6e5590 | [
"assert isinstance(response, scrapy.http.response.html.HtmlResponse)\nURLS = ['https://www.charterboats-uk.co.uk/wales?page=1', 'https://www.charterboats-uk.co.uk/wales?page=2', 'https://www.charterboats-uk.co.uk/wales?page=3', 'https://www.charterboats-uk.co.uk/wales?page=4', 'https://www.charterboats-uk.co.uk/sco... | <|body_start_0|>
assert isinstance(response, scrapy.http.response.html.HtmlResponse)
URLS = ['https://www.charterboats-uk.co.uk/wales?page=1', 'https://www.charterboats-uk.co.uk/wales?page=2', 'https://www.charterboats-uk.co.uk/wales?page=3', 'https://www.charterboats-uk.co.uk/wales?page=4', 'https://ww... | scrape all the text in on the boat details tab to write to ugc | CharterBoatUKBoatWalesScotlandTextSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CharterBoatUKBoatWalesScotlandTextSpider:
"""scrape all the text in on the boat details tab to write to ugc"""
def parse(self, response):
"""generate links to pages in a board"""
<|body_0|>
def crawl_boats(self, response):
"""each page with links to 10 boats deta... | stack_v2_sparse_classes_10k_train_000600 | 17,953 | no_license | [
{
"docstring": "generate links to pages in a board",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "each page with links to 10 boats details",
"name": "crawl_boats",
"signature": "def crawl_boats(self, response)"
},
{
"docstring": "crawl",
"name"... | 3 | null | Implement the Python class `CharterBoatUKBoatWalesScotlandTextSpider` described below.
Class description:
scrape all the text in on the boat details tab to write to ugc
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board
- def crawl_boats(self, response): each page with l... | Implement the Python class `CharterBoatUKBoatWalesScotlandTextSpider` described below.
Class description:
scrape all the text in on the boat details tab to write to ugc
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board
- def crawl_boats(self, response): each page with l... | 9123aa6baf538b662143b9098d963d55165e8409 | <|skeleton|>
class CharterBoatUKBoatWalesScotlandTextSpider:
"""scrape all the text in on the boat details tab to write to ugc"""
def parse(self, response):
"""generate links to pages in a board"""
<|body_0|>
def crawl_boats(self, response):
"""each page with links to 10 boats deta... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CharterBoatUKBoatWalesScotlandTextSpider:
"""scrape all the text in on the boat details tab to write to ugc"""
def parse(self, response):
"""generate links to pages in a board"""
assert isinstance(response, scrapy.http.response.html.HtmlResponse)
URLS = ['https://www.charterboats-... | the_stack_v2_python_sparse | imgscrape/spiders/charterboatuk.py | gmonkman/python | train | 0 |
eb329a3c3167ea5b7392eeeda6985c37d3a3b532 | [
"classes = {b'\\xff': Header, b'[': Package}\nif not packetPrefix in classes:\n return None\nreturn classes[packetPrefix]",
"if data is None:\n return\npacketPrefix = data[:1]\nCLASS = self.getClass(packetPrefix)\nif not CLASS:\n raise Exception('Packet %s is not found' % binascii.hexlify(packetPrefix).d... | <|body_start_0|>
classes = {b'\xff': Header, b'[': Package}
if not packetPrefix in classes:
return None
return classes[packetPrefix]
<|end_body_0|>
<|body_start_1|>
if data is None:
return
packetPrefix = data[:1]
CLASS = self.getClass(packetPrefix... | Packet factory | PacketFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PacketFactory:
"""Packet factory"""
def getClass(cls, packetPrefix):
"""Returns a tag class by number @param packetPrefix: one byte buffer"""
<|body_0|>
def getInstance(self, data=None):
"""Returns a tag instance by its number"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k_train_000601 | 15,657 | no_license | [
{
"docstring": "Returns a tag class by number @param packetPrefix: one byte buffer",
"name": "getClass",
"signature": "def getClass(cls, packetPrefix)"
},
{
"docstring": "Returns a tag instance by its number",
"name": "getInstance",
"signature": "def getInstance(self, data=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003367 | Implement the Python class `PacketFactory` described below.
Class description:
Packet factory
Method signatures and docstrings:
- def getClass(cls, packetPrefix): Returns a tag class by number @param packetPrefix: one byte buffer
- def getInstance(self, data=None): Returns a tag instance by its number | Implement the Python class `PacketFactory` described below.
Class description:
Packet factory
Method signatures and docstrings:
- def getClass(cls, packetPrefix): Returns a tag class by number @param packetPrefix: one byte buffer
- def getInstance(self, data=None): Returns a tag instance by its number
<|skeleton|>
c... | 4a4bc730252ece695b2773388812e2d59d4947ce | <|skeleton|>
class PacketFactory:
"""Packet factory"""
def getClass(cls, packetPrefix):
"""Returns a tag class by number @param packetPrefix: one byte buffer"""
<|body_0|>
def getInstance(self, data=None):
"""Returns a tag instance by its number"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PacketFactory:
"""Packet factory"""
def getClass(cls, packetPrefix):
"""Returns a tag class by number @param packetPrefix: one byte buffer"""
classes = {b'\xff': Header, b'[': Package}
if not packetPrefix in classes:
return None
return classes[packetPrefix]
... | the_stack_v2_python_sparse | lib/handlers/autolink/packets.py | maprox/pipe | train | 4 |
4e3c8e809c8e27e7a7e3a66fd252759063d857ba | [
"if not prices:\n return 0\nprofit = 0\nminPrice = prices[0]\nfor i in range(len(prices)):\n profit = max(profit, prices[i] - minPrice)\n minPrice = min(prices[i], minPrice)\nreturn profit",
"if not prices:\n return 0\nprofit = 0\nfor i in range(len(prices) - 1):\n profit += max(prices[i + 1] - pri... | <|body_start_0|>
if not prices:
return 0
profit = 0
minPrice = prices[0]
for i in range(len(prices)):
profit = max(profit, prices[i] - minPrice)
minPrice = min(prices[i], minPrice)
return profit
<|end_body_0|>
<|body_start_1|>
if not p... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfitMulti(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_1|>
def maxProfitTwice(self, prices):
""":type prices: List[int] :rtype... | stack_v2_sparse_classes_10k_train_000602 | 5,151 | no_license | [
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices)"
},
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfitMulti",
"signature": "def maxProfitMulti(self, prices)"
},
{
"docstring": ":type prices... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfitMulti(self, prices): :type prices: List[int] :rtype: int
- def maxProfitTwice(self, prices): :type... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfitMulti(self, prices): :type prices: List[int] :rtype: int
- def maxProfitTwice(self, prices): :type... | 0584b86642dff667f5bf6b7acfbbce86a41a55b6 | <|skeleton|>
class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfitMulti(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_1|>
def maxProfitTwice(self, prices):
""":type prices: List[int] :rtype... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
if not prices:
return 0
profit = 0
minPrice = prices[0]
for i in range(len(prices)):
profit = max(profit, prices[i] - minPrice)
minPrice = min(prices[i],... | the_stack_v2_python_sparse | python_solution/121_130/BuyAndSellStock.py | CescWang1991/LeetCode-Python | train | 1 | |
58084122f432a51e940bc912c977e4a59393de90 | [
"if root is None:\n return []\nqueue = [root]\ndata = []\n\ndef dfs():\n if queue == []:\n return\n node = queue.pop(0)\n data.append(node.val)\n if node.left:\n queue.append(node.left)\n dfs()\n else:\n data.append(None)\n if node.right:\n queue.append(node.r... | <|body_start_0|>
if root is None:
return []
queue = [root]
data = []
def dfs():
if queue == []:
return
node = queue.pop(0)
data.append(node.val)
if node.left:
queue.append(node.left)
... | Codec | [
"MIT"
] | 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_10k_train_000603 | 1,631 | permissive | [
{
"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:... | cdf785856941f7ea546aee56ebcda8801cbb04de | <|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_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if root is None:
return []
queue = [root]
data = []
def dfs():
if queue == []:
return
node = queue.pop(0)
... | the_stack_v2_python_sparse | ProgrammingQuestions/leetcode/297.py | strawsyz/straw | train | 2 | |
13bc22c36cac712e09d5ec992cd3225eb04fd438 | [
"super().setUp()\nself.request = self.request_context['request']\nself.request.path = '/api/v1/status/'\nself.request.META['QUERY_STRING'] = ''",
"middleware = RequestTimingMiddleware()\nmiddleware.process_request(self.request)\nself.assertTrue(hasattr(self.request, 'start_time'))",
"mock_get_tenant.return_valu... | <|body_start_0|>
super().setUp()
self.request = self.request_context['request']
self.request.path = '/api/v1/status/'
self.request.META['QUERY_STRING'] = ''
<|end_body_0|>
<|body_start_1|>
middleware = RequestTimingMiddleware()
middleware.process_request(self.request)
... | Tests against the koku tenant middleware. | RequestTimingMiddlewareTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RequestTimingMiddlewareTest:
"""Tests against the koku tenant middleware."""
def setUp(self):
"""Set up middleware tests."""
<|body_0|>
def test_process_request(self):
"""Test that the request gets a user."""
<|body_1|>
def test_process_response(self... | stack_v2_sparse_classes_10k_train_000604 | 27,733 | permissive | [
{
"docstring": "Set up middleware tests.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test that the request gets a user.",
"name": "test_process_request",
"signature": "def test_process_request(self)"
},
{
"docstring": "Test that the request gets a user.",... | 3 | stack_v2_sparse_classes_30k_train_005214 | Implement the Python class `RequestTimingMiddlewareTest` described below.
Class description:
Tests against the koku tenant middleware.
Method signatures and docstrings:
- def setUp(self): Set up middleware tests.
- def test_process_request(self): Test that the request gets a user.
- def test_process_response(self, mo... | Implement the Python class `RequestTimingMiddlewareTest` described below.
Class description:
Tests against the koku tenant middleware.
Method signatures and docstrings:
- def setUp(self): Set up middleware tests.
- def test_process_request(self): Test that the request gets a user.
- def test_process_response(self, mo... | 0416e5216eb1ec4b41c8dd4999adde218b1ab2e1 | <|skeleton|>
class RequestTimingMiddlewareTest:
"""Tests against the koku tenant middleware."""
def setUp(self):
"""Set up middleware tests."""
<|body_0|>
def test_process_request(self):
"""Test that the request gets a user."""
<|body_1|>
def test_process_response(self... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RequestTimingMiddlewareTest:
"""Tests against the koku tenant middleware."""
def setUp(self):
"""Set up middleware tests."""
super().setUp()
self.request = self.request_context['request']
self.request.path = '/api/v1/status/'
self.request.META['QUERY_STRING'] = ''
... | the_stack_v2_python_sparse | koku/koku/test_middleware.py | project-koku/koku | train | 225 |
6f446e2ef3f403c60ff5f7f25eb434eea7ca3343 | [
"if not root:\n return ''\nrt = []\nstk = [root]\nwhile stk:\n newstk = []\n while stk:\n p = stk.pop(0)\n if p == None:\n rt.append('#')\n else:\n rt.append(str(p.val))\n newstk.extend([p.left, p.right])\n stk = newstk\nreturn ':'.join(rt)",
"if n... | <|body_start_0|>
if not root:
return ''
rt = []
stk = [root]
while stk:
newstk = []
while stk:
p = stk.pop(0)
if p == None:
rt.append('#')
else:
rt.append(str(p.val... | Codec1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec1:
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_10k_train_000605 | 3,374 | 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 `Codec1` described below.
Class description:
Implement the Codec1 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 :rtyp... | Implement the Python class `Codec1` described below.
Class description:
Implement the Codec1 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 :rtyp... | 0e99f9a5226507706b3ee66fd04bae813755ef40 | <|skeleton|>
class Codec1:
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_10k | data/stack_v2_sparse_classes_30k | class Codec1:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return ''
rt = []
stk = [root]
while stk:
newstk = []
while stk:
p = stk.pop(0)
... | the_stack_v2_python_sparse | medium/tree/test_449_Serialize_and_Deserialize_BST.py | wuxu1019/leetcode_sophia | train | 1 | |
fd8c34f609363a555db9d7d6a84b88c6e4b6c466 | [
"@lru_cache(None)\ndef dfs(curSum: int, visited: int) -> bool:\n if curSum >= target:\n return True\n for select in range(1, upper + 1):\n if visited >> select & 1:\n continue\n if curSum + select >= target or not dfs(curSum + select, visited | 1 << select):\n return... | <|body_start_0|>
@lru_cache(None)
def dfs(curSum: int, visited: int) -> bool:
if curSum >= target:
return True
for select in range(1, upper + 1):
if visited >> select & 1:
continue
if curSum + select >= target or... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canIWin(self, upper: int, target: int) -> bool:
"""2^n*n 每次dfs不用重新计算cur，因为curSum由visted唯一确定，两种的时间复杂度都是 O(2^n*n)"""
<|body_0|>
def canIWin2(self, upper: int, target: int) -> bool:
"""2^n*n 会慢一些"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_000606 | 1,839 | no_license | [
{
"docstring": "2^n*n 每次dfs不用重新计算cur，因为curSum由visted唯一确定，两种的时间复杂度都是 O(2^n*n)",
"name": "canIWin",
"signature": "def canIWin(self, upper: int, target: int) -> bool"
},
{
"docstring": "2^n*n 会慢一些",
"name": "canIWin2",
"signature": "def canIWin2(self, upper: int, target: int) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_006490 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canIWin(self, upper: int, target: int) -> bool: 2^n*n 每次dfs不用重新计算cur，因为curSum由visted唯一确定，两种的时间复杂度都是 O(2^n*n)
- def canIWin2(self, upper: int, target: int) -> bool: 2^n*n 会慢一些 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canIWin(self, upper: int, target: int) -> bool: 2^n*n 每次dfs不用重新计算cur，因为curSum由visted唯一确定，两种的时间复杂度都是 O(2^n*n)
- def canIWin2(self, upper: int, target: int) -> bool: 2^n*n 会慢一些... | 7e79e26bb8f641868561b186e34c1127ed63c9e0 | <|skeleton|>
class Solution:
def canIWin(self, upper: int, target: int) -> bool:
"""2^n*n 每次dfs不用重新计算cur，因为curSum由visted唯一确定，两种的时间复杂度都是 O(2^n*n)"""
<|body_0|>
def canIWin2(self, upper: int, target: int) -> bool:
"""2^n*n 会慢一些"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def canIWin(self, upper: int, target: int) -> bool:
"""2^n*n 每次dfs不用重新计算cur，因为curSum由visted唯一确定，两种的时间复杂度都是 O(2^n*n)"""
@lru_cache(None)
def dfs(curSum: int, visited: int) -> bool:
if curSum >= target:
return True
for select in range(1, ... | the_stack_v2_python_sparse | 11_动态规划/dp分类/状压dp/visited上携带参数/464. 我能赢吗.py | 981377660LMT/algorithm-study | train | 225 | |
c1155c50c6fef91dd8c275c01c8503caee6206cb | [
"st = []\nres, start, n = (0, 0, len(s))\nfor i in range(n):\n if s[i] == '(':\n st.append(i)\n if s[i] == ')':\n if not st:\n start = i + 1\n else:\n st.pop()\n if not st:\n res = max(res, i - start + 1)\n else:\n ... | <|body_start_0|>
st = []
res, start, n = (0, 0, len(s))
for i in range(n):
if s[i] == '(':
st.append(i)
if s[i] == ')':
if not st:
start = i + 1
else:
st.pop()
if n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestValidParentheses(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestValidParenthesesDP(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
st = []
res, start, n = (0, 0, len(... | stack_v2_sparse_classes_10k_train_000607 | 2,705 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "longestValidParentheses",
"signature": "def longestValidParentheses(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "longestValidParenthesesDP",
"signature": "def longestValidParenthesesDP(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParentheses(self, s): :type s: str :rtype: int
- def longestValidParenthesesDP(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParentheses(self, s): :type s: str :rtype: int
- def longestValidParenthesesDP(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def longestVa... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def longestValidParentheses(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestValidParenthesesDP(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def longestValidParentheses(self, s):
""":type s: str :rtype: int"""
st = []
res, start, n = (0, 0, len(s))
for i in range(n):
if s[i] == '(':
st.append(i)
if s[i] == ')':
if not st:
start = i... | the_stack_v2_python_sparse | L/LongestValidParentheses.py | bssrdf/pyleet | train | 2 | |
4a0521e733d7580ef3eba6519f3e26a369b68637 | [
"super().__init__()\nself.pooling = pooling\nself.kernel_size = kernel_size\nself.enc_l5 = SphericalChebBN2(16, 32, 64, laps[5], self.kernel_size)\nself.enc_l4 = SphericalChebBNPool(64, 128, laps[4], self.pooling, self.kernel_size)\nself.enc_l3 = SphericalChebBNPool(128, 256, laps[3], self.pooling, self.kernel_size... | <|body_start_0|>
super().__init__()
self.pooling = pooling
self.kernel_size = kernel_size
self.enc_l5 = SphericalChebBN2(16, 32, 64, laps[5], self.kernel_size)
self.enc_l4 = SphericalChebBNPool(64, 128, laps[4], self.pooling, self.kernel_size)
self.enc_l3 = SphericalChebB... | Encoder for the Spherical UNet. | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""Encoder for the Spherical UNet."""
def __init__(self, pooling, laps, kernel_size):
"""Initialization. Args: pooling (:obj:`torch.nn.Module`): pooling layer. laps (list): List of laplacians. kernel_size (int): polynomial degree."""
<|body_0|>
def forward(self,... | stack_v2_sparse_classes_10k_train_000608 | 41,403 | no_license | [
{
"docstring": "Initialization. Args: pooling (:obj:`torch.nn.Module`): pooling layer. laps (list): List of laplacians. kernel_size (int): polynomial degree.",
"name": "__init__",
"signature": "def __init__(self, pooling, laps, kernel_size)"
},
{
"docstring": "Forward Pass. Args: x (:obj:`torch.... | 2 | null | Implement the Python class `Encoder` described below.
Class description:
Encoder for the Spherical UNet.
Method signatures and docstrings:
- def __init__(self, pooling, laps, kernel_size): Initialization. Args: pooling (:obj:`torch.nn.Module`): pooling layer. laps (list): List of laplacians. kernel_size (int): polyno... | Implement the Python class `Encoder` described below.
Class description:
Encoder for the Spherical UNet.
Method signatures and docstrings:
- def __init__(self, pooling, laps, kernel_size): Initialization. Args: pooling (:obj:`torch.nn.Module`): pooling layer. laps (list): List of laplacians. kernel_size (int): polyno... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class Encoder:
"""Encoder for the Spherical UNet."""
def __init__(self, pooling, laps, kernel_size):
"""Initialization. Args: pooling (:obj:`torch.nn.Module`): pooling layer. laps (list): List of laplacians. kernel_size (int): polynomial degree."""
<|body_0|>
def forward(self,... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Encoder:
"""Encoder for the Spherical UNet."""
def __init__(self, pooling, laps, kernel_size):
"""Initialization. Args: pooling (:obj:`torch.nn.Module`): pooling layer. laps (list): List of laplacians. kernel_size (int): polynomial degree."""
super().__init__()
self.pooling = pool... | the_stack_v2_python_sparse | generated/test_deepsphere_deepsphere_pytorch.py | jansel/pytorch-jit-paritybench | train | 35 |
5e689a13d9a6503791cb1d84dc0efc29d8ad5ab3 | [
"profit = 0\nfor i in range(1, len(prices)):\n if prices[i] > prices[i - 1]:\n profit += prices[i] - prices[i - 1]\nreturn profit",
"profit = day = 0\ntotal_days = len(prices) - 1\nwhile day < total_days:\n while day < total_days and prices[day] >= prices[day + 1]:\n day += 1\n valley = pri... | <|body_start_0|>
profit = 0
for i in range(1, len(prices)):
if prices[i] > prices[i - 1]:
profit += prices[i] - prices[i - 1]
return profit
<|end_body_0|>
<|body_start_1|>
profit = day = 0
total_days = len(prices) - 1
while day < total_days:
... | Stock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Stock:
def max_profit(self, prices: List[int]) -> int:
"""Approach: Greedy (One Pass) Time Complexity: O(N) Space Complexity: O(1) :param prices: :return:"""
<|body_0|>
def max_profit_(self, prices: List[int]) -> int:
"""Approach: Peak Valley Time Complexity: O(N) Sp... | stack_v2_sparse_classes_10k_train_000609 | 1,201 | no_license | [
{
"docstring": "Approach: Greedy (One Pass) Time Complexity: O(N) Space Complexity: O(1) :param prices: :return:",
"name": "max_profit",
"signature": "def max_profit(self, prices: List[int]) -> int"
},
{
"docstring": "Approach: Peak Valley Time Complexity: O(N) Space Complexity: O(1) :param pric... | 2 | null | Implement the Python class `Stock` described below.
Class description:
Implement the Stock class.
Method signatures and docstrings:
- def max_profit(self, prices: List[int]) -> int: Approach: Greedy (One Pass) Time Complexity: O(N) Space Complexity: O(1) :param prices: :return:
- def max_profit_(self, prices: List[in... | Implement the Python class `Stock` described below.
Class description:
Implement the Stock class.
Method signatures and docstrings:
- def max_profit(self, prices: List[int]) -> int: Approach: Greedy (One Pass) Time Complexity: O(N) Space Complexity: O(1) :param prices: :return:
- def max_profit_(self, prices: List[in... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class Stock:
def max_profit(self, prices: List[int]) -> int:
"""Approach: Greedy (One Pass) Time Complexity: O(N) Space Complexity: O(1) :param prices: :return:"""
<|body_0|>
def max_profit_(self, prices: List[int]) -> int:
"""Approach: Peak Valley Time Complexity: O(N) Sp... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Stock:
def max_profit(self, prices: List[int]) -> int:
"""Approach: Greedy (One Pass) Time Complexity: O(N) Space Complexity: O(1) :param prices: :return:"""
profit = 0
for i in range(1, len(prices)):
if prices[i] > prices[i - 1]:
profit += prices[i] - price... | the_stack_v2_python_sparse | expedia/best_time_to_buy_stock_ii.py | Shiv2157k/leet_code | train | 1 | |
e7d766f34572154b7a0fec27fef9cf801aa40c0f | [
"super(SpatialPath, self).__init__()\nself.conv_7x7 = ConvBnRelu(in_planes, inner_channel, 7, 2, 3, norm_layer=norm_layer, Conv2d=Conv2d)\nself.conv_3x3_1 = ConvBnRelu(inner_channel, inner_channel, 3, 2, 1, norm_layer=norm_layer, Conv2d=Conv2d)\nself.conv_3x3_2 = ConvBnRelu(inner_channel, inner_channel, 3, 2, 1, no... | <|body_start_0|>
super(SpatialPath, self).__init__()
self.conv_7x7 = ConvBnRelu(in_planes, inner_channel, 7, 2, 3, norm_layer=norm_layer, Conv2d=Conv2d)
self.conv_3x3_1 = ConvBnRelu(inner_channel, inner_channel, 3, 2, 1, norm_layer=norm_layer, Conv2d=Conv2d)
self.conv_3x3_2 = ConvBnRelu(... | SpatialPath module. | SpatialPath | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpatialPath:
"""SpatialPath module."""
def __init__(self, in_planes, out_planes, norm_layer='BN', Conv2d=nn.Conv2d, inner_channel=64, **kwargs):
"""Create SpatialPath. :param in_planes: input channels :param out_planes: output channels :param norm_layer: type of norm layer. :param Co... | stack_v2_sparse_classes_10k_train_000610 | 9,350 | permissive | [
{
"docstring": "Create SpatialPath. :param in_planes: input channels :param out_planes: output channels :param norm_layer: type of norm layer. :param Conv2d: type of conv layer. :param inner_channel: number of inner channels.",
"name": "__init__",
"signature": "def __init__(self, in_planes, out_planes, ... | 2 | null | Implement the Python class `SpatialPath` described below.
Class description:
SpatialPath module.
Method signatures and docstrings:
- def __init__(self, in_planes, out_planes, norm_layer='BN', Conv2d=nn.Conv2d, inner_channel=64, **kwargs): Create SpatialPath. :param in_planes: input channels :param out_planes: output ... | Implement the Python class `SpatialPath` described below.
Class description:
SpatialPath module.
Method signatures and docstrings:
- def __init__(self, in_planes, out_planes, norm_layer='BN', Conv2d=nn.Conv2d, inner_channel=64, **kwargs): Create SpatialPath. :param in_planes: input channels :param out_planes: output ... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class SpatialPath:
"""SpatialPath module."""
def __init__(self, in_planes, out_planes, norm_layer='BN', Conv2d=nn.Conv2d, inner_channel=64, **kwargs):
"""Create SpatialPath. :param in_planes: input channels :param out_planes: output channels :param norm_layer: type of norm layer. :param Co... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SpatialPath:
"""SpatialPath module."""
def __init__(self, in_planes, out_planes, norm_layer='BN', Conv2d=nn.Conv2d, inner_channel=64, **kwargs):
"""Create SpatialPath. :param in_planes: input channels :param out_planes: output channels :param norm_layer: type of norm layer. :param Conv2d: type of... | the_stack_v2_python_sparse | zeus/networks/pytorch/customs/bisenet.py | huawei-noah/xingtian | train | 308 |
711f71d8d6a8742fa7e90fe73dd1413ef9e953ee | [
"contact = Contacts.query.order_by(desc(Contacts.Created)).first_or_404()\ncontent = jsonify({'contacts': [{'id': contact.ContactsID, 'email': contact.Email, 'techRider': contact.TechRider, 'inputList': contact.InputList, 'backline': contact.Backline, 'createdAt': contact.Created, 'updatedAt': contact.Updated}]})\n... | <|body_start_0|>
contact = Contacts.query.order_by(desc(Contacts.Created)).first_or_404()
content = jsonify({'contacts': [{'id': contact.ContactsID, 'email': contact.Email, 'techRider': contact.TechRider, 'inputList': contact.InputList, 'backline': contact.Backline, 'createdAt': contact.Created, 'update... | ContactsView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContactsView:
def index(self):
"""Return the newest Contacts entry."""
<|body_0|>
def post(self):
"""Add a new Contacts entry."""
<|body_1|>
def patch(self):
"""Partially modify the newest contact."""
<|body_2|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_000611 | 2,412 | permissive | [
{
"docstring": "Return the newest Contacts entry.",
"name": "index",
"signature": "def index(self)"
},
{
"docstring": "Add a new Contacts entry.",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Partially modify the newest contact.",
"name": "patch",
"sig... | 3 | stack_v2_sparse_classes_30k_train_003828 | Implement the Python class `ContactsView` described below.
Class description:
Implement the ContactsView class.
Method signatures and docstrings:
- def index(self): Return the newest Contacts entry.
- def post(self): Add a new Contacts entry.
- def patch(self): Partially modify the newest contact. | Implement the Python class `ContactsView` described below.
Class description:
Implement the ContactsView class.
Method signatures and docstrings:
- def index(self): Return the newest Contacts entry.
- def post(self): Add a new Contacts entry.
- def patch(self): Partially modify the newest contact.
<|skeleton|>
class... | 62f8e8e904e379541193f0cbb91a8434b47f538f | <|skeleton|>
class ContactsView:
def index(self):
"""Return the newest Contacts entry."""
<|body_0|>
def post(self):
"""Add a new Contacts entry."""
<|body_1|>
def patch(self):
"""Partially modify the newest contact."""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ContactsView:
def index(self):
"""Return the newest Contacts entry."""
contact = Contacts.query.order_by(desc(Contacts.Created)).first_or_404()
content = jsonify({'contacts': [{'id': contact.ContactsID, 'email': contact.Email, 'techRider': contact.TechRider, 'inputList': contact.InputL... | the_stack_v2_python_sparse | apps/contacts/views.py | Torniojaws/vortech-backend | train | 0 | |
385d3cf9d5d1dd91ad4110eab40841a87aa3930c | [
"file_ = g.db.query(File).filter(File.id == id_).first()\ndata_dir = get_path('data')\ncache_timeout = 0 if g.debug else None\nif file_ is None:\n raise NotFound('No file exists with id={}'.format(id_))\nif file_.mime == 'application/zip':\n return send_from_directory(data_dir, file_.relpath(), mimetype=file_... | <|body_start_0|>
file_ = g.db.query(File).filter(File.id == id_).first()
data_dir = get_path('data')
cache_timeout = 0 if g.debug else None
if file_ is None:
raise NotFound('No file exists with id={}'.format(id_))
if file_.mime == 'application/zip':
return... | Manipulate files. | FileView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileView:
"""Manipulate files."""
def get(self, id_):
"""Get a file identified by ``id_``. :status 200: ok :status 401: authentication required :status 404: no file with that ID"""
<|body_0|>
def delete(self, id_):
"""Delete file identified by `id_`."""
<... | stack_v2_sparse_classes_10k_train_000612 | 2,365 | no_license | [
{
"docstring": "Get a file identified by ``id_``. :status 200: ok :status 401: authentication required :status 404: no file with that ID",
"name": "get",
"signature": "def get(self, id_)"
},
{
"docstring": "Delete file identified by `id_`.",
"name": "delete",
"signature": "def delete(sel... | 2 | stack_v2_sparse_classes_30k_train_000467 | Implement the Python class `FileView` described below.
Class description:
Manipulate files.
Method signatures and docstrings:
- def get(self, id_): Get a file identified by ``id_``. :status 200: ok :status 401: authentication required :status 404: no file with that ID
- def delete(self, id_): Delete file identified b... | Implement the Python class `FileView` described below.
Class description:
Manipulate files.
Method signatures and docstrings:
- def get(self, id_): Get a file identified by ``id_``. :status 200: ok :status 401: authentication required :status 404: no file with that ID
- def delete(self, id_): Delete file identified b... | 449630ebcacc1f7cb15c435152842b206cc1e8a3 | <|skeleton|>
class FileView:
"""Manipulate files."""
def get(self, id_):
"""Get a file identified by ``id_``. :status 200: ok :status 401: authentication required :status 404: no file with that ID"""
<|body_0|>
def delete(self, id_):
"""Delete file identified by `id_`."""
<... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FileView:
"""Manipulate files."""
def get(self, id_):
"""Get a file identified by ``id_``. :status 200: ok :status 401: authentication required :status 404: no file with that ID"""
file_ = g.db.query(File).filter(File.id == id_).first()
data_dir = get_path('data')
cache_ti... | the_stack_v2_python_sparse | lib/app/views/file.py | jaisanas/hgprofiler | train | 1 |
f6eabc8a08b00ca08ec6eae6491452c2401db6a2 | [
"self.id = id\nself.neighs = neighs\nif variance == 'false':\n self.data = data\nelse:\n n = old_div(np.sqrt(9 + 8 * (len(data) - 1)) - 3, 2)\n self.var = np.matrix(np.identity(n))\n index = n + 1\n for i in range(int(n)):\n for j in range(i + 1):\n self.var[i, j] = data[int(index)]... | <|body_start_0|>
self.id = id
self.neighs = neighs
if variance == 'false':
self.data = data
else:
n = old_div(np.sqrt(9 + 8 * (len(data) - 1)) - 3, 2)
self.var = np.matrix(np.identity(n))
index = n + 1
for i in range(int(n)):
... | Area Class for Regional Clustering. | AreaCl | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AreaCl:
"""Area Class for Regional Clustering."""
def __init__(self, id, neighs, data, variance='false'):
"""@type id: integer @param id: Id of the polygon/area @type neighs: list @param neighs: Neighborhood ids @type data: list. @param data: Data releated to the area. @type variance... | stack_v2_sparse_classes_10k_train_000613 | 2,124 | permissive | [
{
"docstring": "@type id: integer @param id: Id of the polygon/area @type neighs: list @param neighs: Neighborhood ids @type data: list. @param data: Data releated to the area. @type variance: boolean @keyword variance: Boolean indicating if the data have variance matrix",
"name": "__init__",
"signature... | 2 | stack_v2_sparse_classes_30k_train_000003 | Implement the Python class `AreaCl` described below.
Class description:
Area Class for Regional Clustering.
Method signatures and docstrings:
- def __init__(self, id, neighs, data, variance='false'): @type id: integer @param id: Id of the polygon/area @type neighs: list @param neighs: Neighborhood ids @type data: lis... | Implement the Python class `AreaCl` described below.
Class description:
Area Class for Regional Clustering.
Method signatures and docstrings:
- def __init__(self, id, neighs, data, variance='false'): @type id: integer @param id: Id of the polygon/area @type neighs: list @param neighs: Neighborhood ids @type data: lis... | 5c2600b048836e54495dc5997a250af72f72f6e7 | <|skeleton|>
class AreaCl:
"""Area Class for Regional Clustering."""
def __init__(self, id, neighs, data, variance='false'):
"""@type id: integer @param id: Id of the polygon/area @type neighs: list @param neighs: Neighborhood ids @type data: list. @param data: Data releated to the area. @type variance... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AreaCl:
"""Area Class for Regional Clustering."""
def __init__(self, id, neighs, data, variance='false'):
"""@type id: integer @param id: Id of the polygon/area @type neighs: list @param neighs: Neighborhood ids @type data: list. @param data: Data releated to the area. @type variance: boolean @ke... | the_stack_v2_python_sparse | clusterpy/core/toolboxes/cluster/componentsAlg/areacl.py | CentroGeo/clusterpy | train | 4 |
64be7b63fe73434be9d0f771faf85ec51e86d8c7 | [
"assert_is_instance(l_plus, np.ndarray, 'L plus has to be a np array')\nassert_is_instance(l_minus, np.ndarray, 'L minus has to be a np array')\nassert_condition(l_plus.shape == l_minus.shape, TypeError, 'It is not an splitting')\nassert_is_instance(level, IMultigridLevel, 'Not the right level')\nself.order = kwarg... | <|body_start_0|>
assert_is_instance(l_plus, np.ndarray, 'L plus has to be a np array')
assert_is_instance(l_minus, np.ndarray, 'L minus has to be a np array')
assert_condition(l_plus.shape == l_minus.shape, TypeError, 'It is not an splitting')
assert_is_instance(level, IMultigridLevel, '... | A general Smoothing class which arises from splitting the main stencil This class of smoothers is easy to derive and really broad, it is statically linked to a certain level. | SplitSmoother | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SplitSmoother:
"""A general Smoothing class which arises from splitting the main stencil This class of smoothers is easy to derive and really broad, it is statically linked to a certain level."""
def __init__(self, l_plus, l_minus, level, *args, **kwargs):
"""init method of the split... | stack_v2_sparse_classes_10k_train_000614 | 9,259 | no_license | [
{
"docstring": "init method of the split smoother l_plus and l_minus have to be centralized",
"name": "__init__",
"signature": "def __init__(self, l_plus, l_minus, level, *args, **kwargs)"
},
{
"docstring": "Does the relaxation step several times on the lvl the hardship in this case is to use th... | 2 | stack_v2_sparse_classes_30k_train_003675 | Implement the Python class `SplitSmoother` described below.
Class description:
A general Smoothing class which arises from splitting the main stencil This class of smoothers is easy to derive and really broad, it is statically linked to a certain level.
Method signatures and docstrings:
- def __init__(self, l_plus, l... | Implement the Python class `SplitSmoother` described below.
Class description:
A general Smoothing class which arises from splitting the main stencil This class of smoothers is easy to derive and really broad, it is statically linked to a certain level.
Method signatures and docstrings:
- def __init__(self, l_plus, l... | 90aed34cf43d633e44f56444f6c5d4fa39619663 | <|skeleton|>
class SplitSmoother:
"""A general Smoothing class which arises from splitting the main stencil This class of smoothers is easy to derive and really broad, it is statically linked to a certain level."""
def __init__(self, l_plus, l_minus, level, *args, **kwargs):
"""init method of the split... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SplitSmoother:
"""A general Smoothing class which arises from splitting the main stencil This class of smoothers is easy to derive and really broad, it is statically linked to a certain level."""
def __init__(self, l_plus, l_minus, level, *args, **kwargs):
"""init method of the split smoother l_p... | the_stack_v2_python_sparse | pypint/plugins/multigrid/multigrid_smoother.py | Parallel-in-Time/PyPinT | train | 0 |
5f50cac7bab77100f94eb1a636a9bf86fef3c89c | [
"if obj.organization_address is None:\n return None\nserializer = OrganizationAddressSerializer(obj.organization_address, read_only=True)\nreturn serializer.data",
"request = self.context.get('request')\nif not request.user.has_perm('VIEW_FUEL_SUPPLIERS') and request.user.organization.id != obj.id:\n return... | <|body_start_0|>
if obj.organization_address is None:
return None
serializer = OrganizationAddressSerializer(obj.organization_address, read_only=True)
return serializer.data
<|end_body_0|>
<|body_start_1|>
request = self.context.get('request')
if not request.user.has... | Serializer for the Fuel Supplier Loads most of the fields and the balance for the Fuel Supplier | OrganizationSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrganizationSerializer:
"""Serializer for the Fuel Supplier Loads most of the fields and the balance for the Fuel Supplier"""
def get_organization_address(self, obj):
"""Shows the organization address"""
<|body_0|>
def get_organization_balance(self, obj):
"""Only... | stack_v2_sparse_classes_10k_train_000615 | 8,700 | permissive | [
{
"docstring": "Shows the organization address",
"name": "get_organization_address",
"signature": "def get_organization_address(self, obj)"
},
{
"docstring": "Only show the credit balance if the logged in user has permission to view fuel suppliers",
"name": "get_organization_balance",
"s... | 2 | stack_v2_sparse_classes_30k_train_000338 | Implement the Python class `OrganizationSerializer` described below.
Class description:
Serializer for the Fuel Supplier Loads most of the fields and the balance for the Fuel Supplier
Method signatures and docstrings:
- def get_organization_address(self, obj): Shows the organization address
- def get_organization_bal... | Implement the Python class `OrganizationSerializer` described below.
Class description:
Serializer for the Fuel Supplier Loads most of the fields and the balance for the Fuel Supplier
Method signatures and docstrings:
- def get_organization_address(self, obj): Shows the organization address
- def get_organization_bal... | 80ae1ef5938ef5e580128ed0c622071b307fc7e1 | <|skeleton|>
class OrganizationSerializer:
"""Serializer for the Fuel Supplier Loads most of the fields and the balance for the Fuel Supplier"""
def get_organization_address(self, obj):
"""Shows the organization address"""
<|body_0|>
def get_organization_balance(self, obj):
"""Only... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OrganizationSerializer:
"""Serializer for the Fuel Supplier Loads most of the fields and the balance for the Fuel Supplier"""
def get_organization_address(self, obj):
"""Shows the organization address"""
if obj.organization_address is None:
return None
serializer = Org... | the_stack_v2_python_sparse | backend/api/serializers/Organization.py | kuanfandevops/tfrs | train | 0 |
c1a7ec53d5d78103be18b6683a8c104e4bd2dcb1 | [
"try:\n import StringIO\n class_StringIO = StringIO.StringIO\nexcept Exception:\n import io\n class_StringIO = io.StringIO\nsio = class_StringIO('<foo> <bar> </foo>\\n')\ntry:\n ET.parse(sio)\nexcept Exception:\n self.ET_exc_class = sys.exc_info()[0]\nelse:\n self.ET_exc_class = Exception",
"... | <|body_start_0|>
try:
import StringIO
class_StringIO = StringIO.StringIO
except Exception:
import io
class_StringIO = io.StringIO
sio = class_StringIO('<foo> <bar> </foo>\n')
try:
ET.parse(sio)
except Exception:
... | Construct an XmlDocReader, call its readDoc(filename) method, then access the resulting DOM structure using XmlNode methods. If more than one document must be parsed, it is slightly faster to repeatedly call the readDoc method rather than constructing a new XmlDocReader each time. | XmlDocReader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XmlDocReader:
"""Construct an XmlDocReader, call its readDoc(filename) method, then access the resulting DOM structure using XmlNode methods. If more than one document must be parsed, it is slightly faster to repeatedly call the readDoc method rather than constructing a new XmlDocReader each time... | stack_v2_sparse_classes_10k_train_000616 | 8,265 | no_license | [
{
"docstring": "The constructor determines the error class used by ElementTree.parse().",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Open the XML file and read its contents into a tree of XmlNode objects. XML errors raise an XmlError exception.",
"name": "readDo... | 2 | stack_v2_sparse_classes_30k_train_006986 | Implement the Python class `XmlDocReader` described below.
Class description:
Construct an XmlDocReader, call its readDoc(filename) method, then access the resulting DOM structure using XmlNode methods. If more than one document must be parsed, it is slightly faster to repeatedly call the readDoc method rather than co... | Implement the Python class `XmlDocReader` described below.
Class description:
Construct an XmlDocReader, call its readDoc(filename) method, then access the resulting DOM structure using XmlNode methods. If more than one document must be parsed, it is slightly faster to repeatedly call the readDoc method rather than co... | 2d07558737127077f97e9347d84e6ca46885b0bc | <|skeleton|>
class XmlDocReader:
"""Construct an XmlDocReader, call its readDoc(filename) method, then access the resulting DOM structure using XmlNode methods. If more than one document must be parsed, it is slightly faster to repeatedly call the readDoc method rather than constructing a new XmlDocReader each time... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class XmlDocReader:
"""Construct an XmlDocReader, call its readDoc(filename) method, then access the resulting DOM structure using XmlNode methods. If more than one document must be parsed, it is slightly faster to repeatedly call the readDoc method rather than constructing a new XmlDocReader each time."""
def... | the_stack_v2_python_sparse | vvt/libvvtest/xmlwrapper.py | rrdrake/vvtools | train | 5 |
06c0fcfbc7a04ef95670ddba3889f66c1b6c2c84 | [
"self.readservice = readservice\nif u_context:\n self.user_context = u_context\n self.username = u_context.user\n if u_context.context == u_context.ChoicesOfView.COMMON:\n self.use_user = None\n else:\n self.use_user = u_context.user",
"from bl.person import Person\nsource = self.readser... | <|body_start_0|>
self.readservice = readservice
if u_context:
self.user_context = u_context
self.username = u_context.user
if u_context.context == u_context.ChoicesOfView.COMMON:
self.use_user = None
else:
self.use_user = u_... | Public methods for accessing active database. Returns a PersonResult object | DbReader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DbReader:
"""Public methods for accessing active database. Returns a PersonResult object"""
def __init__(self, readservice, u_context=None):
"""Create a reader object with db driver and user context. - readservice Neo4jReadService or Neo4jWriteDriver"""
<|body_0|>
def ge... | stack_v2_sparse_classes_10k_train_000617 | 3,432 | no_license | [
{
"docstring": "Create a reader object with db driver and user context. - readservice Neo4jReadService or Neo4jWriteDriver",
"name": "__init__",
"signature": "def __init__(self, readservice, u_context=None)"
},
{
"docstring": "Read the source, repository and events etc referencing this source. R... | 2 | stack_v2_sparse_classes_30k_train_006100 | Implement the Python class `DbReader` described below.
Class description:
Public methods for accessing active database. Returns a PersonResult object
Method signatures and docstrings:
- def __init__(self, readservice, u_context=None): Create a reader object with db driver and user context. - readservice Neo4jReadServ... | Implement the Python class `DbReader` described below.
Class description:
Public methods for accessing active database. Returns a PersonResult object
Method signatures and docstrings:
- def __init__(self, readservice, u_context=None): Create a reader object with db driver and user context. - readservice Neo4jReadServ... | 0f8d6ba035e3cca8dc756531b7cc51029a549a4f | <|skeleton|>
class DbReader:
"""Public methods for accessing active database. Returns a PersonResult object"""
def __init__(self, readservice, u_context=None):
"""Create a reader object with db driver and user context. - readservice Neo4jReadService or Neo4jWriteDriver"""
<|body_0|>
def ge... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DbReader:
"""Public methods for accessing active database. Returns a PersonResult object"""
def __init__(self, readservice, u_context=None):
"""Create a reader object with db driver and user context. - readservice Neo4jReadService or Neo4jWriteDriver"""
self.readservice = readservice
... | the_stack_v2_python_sparse | pe/db_reader.py | kkujansuu/stk | train | 0 |
01900c1d14a04ee43553c8602a07e0c6ecfabded | [
"request = self.context['request']\ndata.setdefault('user', request.user)\ndata.setdefault('device_user_token', None)\nif not request.user.is_authenticated():\n raise serializers.ValidationError('user is not logged in.')\ntry:\n self.instance = DeviceUser.objects.get(**data)\nexcept DeviceUser.DoesNotExist:\n... | <|body_start_0|>
request = self.context['request']
data.setdefault('user', request.user)
data.setdefault('device_user_token', None)
if not request.user.is_authenticated():
raise serializers.ValidationError('user is not logged in.')
try:
self.instance = Dev... | Serializer for log users out. | LogoutSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogoutSerializer:
"""Serializer for log users out."""
def validate(self, data):
"""Validate that the requesting user owns the given device."""
<|body_0|>
def update(self):
"""Mark the given device as inactive."""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_10k_train_000618 | 4,186 | permissive | [
{
"docstring": "Validate that the requesting user owns the given device.",
"name": "validate",
"signature": "def validate(self, data)"
},
{
"docstring": "Mark the given device as inactive.",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003767 | Implement the Python class `LogoutSerializer` described below.
Class description:
Serializer for log users out.
Method signatures and docstrings:
- def validate(self, data): Validate that the requesting user owns the given device.
- def update(self): Mark the given device as inactive. | Implement the Python class `LogoutSerializer` described below.
Class description:
Serializer for log users out.
Method signatures and docstrings:
- def validate(self, data): Validate that the requesting user owns the given device.
- def update(self): Mark the given device as inactive.
<|skeleton|>
class LogoutSerial... | 7349ce18f56658d67daedf5e1abb352b5c15a029 | <|skeleton|>
class LogoutSerializer:
"""Serializer for log users out."""
def validate(self, data):
"""Validate that the requesting user owns the given device."""
<|body_0|>
def update(self):
"""Mark the given device as inactive."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LogoutSerializer:
"""Serializer for log users out."""
def validate(self, data):
"""Validate that the requesting user owns the given device."""
request = self.context['request']
data.setdefault('user', request.user)
data.setdefault('device_user_token', None)
if not ... | the_stack_v2_python_sparse | src/tandlr/authentication/serializers.py | shrmoud/schoolapp | train | 0 |
41a99e6f5963771541a3d24bf3b54f019328a3e0 | [
"with document.file.open('rb') as file:\n text = extract_text(BytesIO(file.read()))\nreturn text",
"with document.file.open('rb') as file:\n result = mammoth.extract_raw_text(file)\nreturn result.value"
] | <|body_start_0|>
with document.file.open('rb') as file:
text = extract_text(BytesIO(file.read()))
return text
<|end_body_0|>
<|body_start_1|>
with document.file.open('rb') as file:
result = mammoth.extract_raw_text(file)
return result.value
<|end_body_1|>
| Extract files text | TextExtractor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextExtractor:
"""Extract files text"""
def from_pdf(document):
"""Extract pdf text"""
<|body_0|>
def from_docx(document):
"""Extract docx text"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
with document.file.open('rb') as file:
te... | stack_v2_sparse_classes_10k_train_000619 | 883 | permissive | [
{
"docstring": "Extract pdf text",
"name": "from_pdf",
"signature": "def from_pdf(document)"
},
{
"docstring": "Extract docx text",
"name": "from_docx",
"signature": "def from_docx(document)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000408 | Implement the Python class `TextExtractor` described below.
Class description:
Extract files text
Method signatures and docstrings:
- def from_pdf(document): Extract pdf text
- def from_docx(document): Extract docx text | Implement the Python class `TextExtractor` described below.
Class description:
Extract files text
Method signatures and docstrings:
- def from_pdf(document): Extract pdf text
- def from_docx(document): Extract docx text
<|skeleton|>
class TextExtractor:
"""Extract files text"""
def from_pdf(document):
... | 22e4afa728a851bb4c2479fbb6f5944a75984b9b | <|skeleton|>
class TextExtractor:
"""Extract files text"""
def from_pdf(document):
"""Extract pdf text"""
<|body_0|>
def from_docx(document):
"""Extract docx text"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TextExtractor:
"""Extract files text"""
def from_pdf(document):
"""Extract pdf text"""
with document.file.open('rb') as file:
text = extract_text(BytesIO(file.read()))
return text
def from_docx(document):
"""Extract docx text"""
with document.file.... | the_stack_v2_python_sparse | src/backend/partaj/core/services/file_handler.py | MTES-MCT/partaj | train | 4 |
9a9d3e5f1cb707ccd191b81b14244b49cfa75748 | [
"from sims4communitylib.utils.sims.common_occult_utils import CommonOccultUtils\nif CommonOccultUtils.is_alien(sim_info):\n return CommonOccultType.ALIEN\nelif CommonOccultUtils.is_ghost(sim_info):\n return CommonOccultType.GHOST\nelif CommonOccultUtils.is_mermaid(sim_info):\n return CommonOccultType.MERMA... | <|body_start_0|>
from sims4communitylib.utils.sims.common_occult_utils import CommonOccultUtils
if CommonOccultUtils.is_alien(sim_info):
return CommonOccultType.ALIEN
elif CommonOccultUtils.is_ghost(sim_info):
return CommonOccultType.GHOST
elif CommonOccultUtils.i... | Custom Occult Types enum containing all occults. DLC not required. | CommonOccultType | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonOccultType:
"""Custom Occult Types enum containing all occults. DLC not required."""
def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType':
"""determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_in... | stack_v2_sparse_classes_10k_train_000620 | 3,786 | permissive | [
{
"docstring": "determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_info: SimInfo :return: The CommonOccultType that represents what a Sim is. :rtype: CommonOccultType",
"name": "determine_occult_type",
"signature": "def determine_occul... | 2 | stack_v2_sparse_classes_30k_train_005844 | Implement the Python class `CommonOccultType` described below.
Class description:
Custom Occult Types enum containing all occults. DLC not required.
Method signatures and docstrings:
- def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType': determine_occult_type(sim_info) Determine the type of Occult a Si... | Implement the Python class `CommonOccultType` described below.
Class description:
Custom Occult Types enum containing all occults. DLC not required.
Method signatures and docstrings:
- def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType': determine_occult_type(sim_info) Determine the type of Occult a Si... | b59ea7e5f4bd01d3b3bd7603843d525a9c179867 | <|skeleton|>
class CommonOccultType:
"""Custom Occult Types enum containing all occults. DLC not required."""
def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType':
"""determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_in... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CommonOccultType:
"""Custom Occult Types enum containing all occults. DLC not required."""
def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType':
"""determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_info: SimInfo :... | the_stack_v2_python_sparse | src/sims4communitylib/enums/common_occult_type.py | velocist/TS4CheatsInfo | train | 1 |
611cff14b17f6cdc5846187ccf778a29da273728 | [
"if not head:\n return None\nif head.next is None:\n return head\nvals = []\ncur = head\nwhile cur:\n vals.append(cur.val)\n cur = cur.next\nvals = sorted(vals)\npHead = ListNode(None)\ncur = ListNode(vals[0])\npHead.next = cur\nfor v in vals[1:]:\n cur.next = ListNode(v)\n cur = cur.next\nreturn ... | <|body_start_0|>
if not head:
return None
if head.next is None:
return head
vals = []
cur = head
while cur:
vals.append(cur.val)
cur = cur.next
vals = sorted(vals)
pHead = ListNode(None)
cur = ListNode(vals[0... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortList2(self, head: ListNode) -> ListNode:
"""把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)"""
<|body_0|>
def sortList(self, head: ListNode) -> ListNode:
"""归并排序解法, 代码比较丑, 但是思路没问题: 先用快慢指针找到链表的中点, 断开, 然后分别排序, 再归并"""
<|... | stack_v2_sparse_classes_10k_train_000621 | 2,838 | no_license | [
{
"docstring": "把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)",
"name": "sortList2",
"signature": "def sortList2(self, head: ListNode) -> ListNode"
},
{
"docstring": "归并排序解法, 代码比较丑, 但是思路没问题: 先用快慢指针找到链表的中点, 断开, 然后分别排序, 再归并",
"name": "sortList",
"signature": "def ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortList2(self, head: ListNode) -> ListNode: 把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)
- def sortList(self, head: ListNode) -> ListNode: 归并排序解法,... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortList2(self, head: ListNode) -> ListNode: 把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)
- def sortList(self, head: ListNode) -> ListNode: 归并排序解法,... | 99a3abf1774933af73a8405f9b59e5e64906bca4 | <|skeleton|>
class Solution:
def sortList2(self, head: ListNode) -> ListNode:
"""把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)"""
<|body_0|>
def sortList(self, head: ListNode) -> ListNode:
"""归并排序解法, 代码比较丑, 但是思路没问题: 先用快慢指针找到链表的中点, 断开, 然后分别排序, 再归并"""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def sortList2(self, head: ListNode) -> ListNode:
"""把值都取出来, 然后排序, 再重新创建链表 时间复杂度是 O(n)+O(nlog(n))+O(n), 符合要求, 但是 空间复杂度是 O(n)"""
if not head:
return None
if head.next is None:
return head
vals = []
cur = head
while cur:
... | the_stack_v2_python_sparse | 2018年力扣高频算法面试题汇总/排序链表.py | iamkissg/leetcode | train | 0 | |
5b35ea4a5af61cc3193bbce9aee11e51dc5aa233 | [
"subscription.is_active = self.instance.expiration_time > timezone.now()\nsubscription.save()\nself.instance.subscription = subscription\nself.instance.save()",
"self.instance = self.instance or models.AppleReceipt()\nself.instance.receipt_data = data['receipt_data']\ntry:\n self.instance.update_info()\nexcept... | <|body_start_0|>
subscription.is_active = self.instance.expiration_time > timezone.now()
subscription.save()
self.instance.subscription = subscription
self.instance.save()
<|end_body_0|>
<|body_start_1|>
self.instance = self.instance or models.AppleReceipt()
self.instanc... | Serializer for an Apple receipt. | AppleReceiptSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppleReceiptSerializer:
"""Serializer for an Apple receipt."""
def save(self, subscription):
"""Save the :class:`AppleReceipt` instance associated with the serializer. Args: subscription: The subscription to associate the Apple receipt being saved with."""
<|body_0|>
def... | stack_v2_sparse_classes_10k_train_000622 | 8,796 | permissive | [
{
"docstring": "Save the :class:`AppleReceipt` instance associated with the serializer. Args: subscription: The subscription to associate the Apple receipt being saved with.",
"name": "save",
"signature": "def save(self, subscription)"
},
{
"docstring": "Validate that the provided receipt data c... | 2 | stack_v2_sparse_classes_30k_train_005920 | Implement the Python class `AppleReceiptSerializer` described below.
Class description:
Serializer for an Apple receipt.
Method signatures and docstrings:
- def save(self, subscription): Save the :class:`AppleReceipt` instance associated with the serializer. Args: subscription: The subscription to associate the Apple... | Implement the Python class `AppleReceiptSerializer` described below.
Class description:
Serializer for an Apple receipt.
Method signatures and docstrings:
- def save(self, subscription): Save the :class:`AppleReceipt` instance associated with the serializer. Args: subscription: The subscription to associate the Apple... | e4b72484c42e88a6c0087c9b1d5fef240e66cbb0 | <|skeleton|>
class AppleReceiptSerializer:
"""Serializer for an Apple receipt."""
def save(self, subscription):
"""Save the :class:`AppleReceipt` instance associated with the serializer. Args: subscription: The subscription to associate the Apple receipt being saved with."""
<|body_0|>
def... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AppleReceiptSerializer:
"""Serializer for an Apple receipt."""
def save(self, subscription):
"""Save the :class:`AppleReceipt` instance associated with the serializer. Args: subscription: The subscription to associate the Apple receipt being saved with."""
subscription.is_active = self.in... | the_stack_v2_python_sparse | km_api/know_me/serializers/subscription_serializers.py | knowmetools/km-api | train | 4 |
f46183fd5d77db4ad002a875a73fed28c2f8005c | [
"self.posterior = posterior\nmu = np.random.multivariate_normal(start, sigma, size=n)\nif student:\n self.components = [StudentsTComponent(1.0 / n, m, sigma, nu) for m in mu]\nelse:\n self.components = [GaussianComponent(1.0 / n, m, sigma) for m in mu]\nself.pool = pool\nself.quiet = quiet",
"self.kill_coun... | <|body_start_0|>
self.posterior = posterior
mu = np.random.multivariate_normal(start, sigma, size=n)
if student:
self.components = [StudentsTComponent(1.0 / n, m, sigma, nu) for m in mu]
else:
self.components = [GaussianComponent(1.0 / n, m, sigma) for m in mu]
... | A Population Monte Carlo (PMC) sampler, which combines expectation-maximization and importance sampling This code follows the notation and methodolgy in http://arxiv.org/pdf/0903.0837v1.pdf | PopulationMonteCarlo | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PopulationMonteCarlo:
"""A Population Monte Carlo (PMC) sampler, which combines expectation-maximization and importance sampling This code follows the notation and methodolgy in http://arxiv.org/pdf/0903.0837v1.pdf"""
def __init__(self, posterior, n, start, sigma, pool=None, quiet=False, stu... | stack_v2_sparse_classes_10k_train_000623 | 7,579 | permissive | [
{
"docstring": "posterior: the posterior function n: number of components to use in the mixture start: estimated mean of the distribution sigma: estimated covariance matrix pool (optional): an MPI or multiprocessing worker pool",
"name": "__init__",
"signature": "def __init__(self, posterior, n, start, ... | 4 | null | Implement the Python class `PopulationMonteCarlo` described below.
Class description:
A Population Monte Carlo (PMC) sampler, which combines expectation-maximization and importance sampling This code follows the notation and methodolgy in http://arxiv.org/pdf/0903.0837v1.pdf
Method signatures and docstrings:
- def __... | Implement the Python class `PopulationMonteCarlo` described below.
Class description:
A Population Monte Carlo (PMC) sampler, which combines expectation-maximization and importance sampling This code follows the notation and methodolgy in http://arxiv.org/pdf/0903.0837v1.pdf
Method signatures and docstrings:
- def __... | ce195564631b148bef0214a27a57470640c69a08 | <|skeleton|>
class PopulationMonteCarlo:
"""A Population Monte Carlo (PMC) sampler, which combines expectation-maximization and importance sampling This code follows the notation and methodolgy in http://arxiv.org/pdf/0903.0837v1.pdf"""
def __init__(self, posterior, n, start, sigma, pool=None, quiet=False, stu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PopulationMonteCarlo:
"""A Population Monte Carlo (PMC) sampler, which combines expectation-maximization and importance sampling This code follows the notation and methodolgy in http://arxiv.org/pdf/0903.0837v1.pdf"""
def __init__(self, posterior, n, start, sigma, pool=None, quiet=False, student=False, n... | the_stack_v2_python_sparse | cosmosis/samplers/pmc/pmc.py | ktanidis/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra | train | 1 |
ca0a2a6984b8214c0ce214c666b4c8423107166d | [
"self.meshing_enabled = meshing_enabled\nself.ipv_6_bridge_enabled = ipv_6_bridge_enabled\nself.location_analytics_enabled = location_analytics_enabled\nself.led_lights_on = led_lights_on",
"if dictionary is None:\n return None\nmeshing_enabled = dictionary.get('meshingEnabled')\nipv_6_bridge_enabled = diction... | <|body_start_0|>
self.meshing_enabled = meshing_enabled
self.ipv_6_bridge_enabled = ipv_6_bridge_enabled
self.location_analytics_enabled = location_analytics_enabled
self.led_lights_on = led_lights_on
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None... | Implementation of the 'updateNetworkWirelessSettings' model. TODO: type model description here. Attributes: meshing_enabled (bool): Toggle for enabling or disabling meshing in a network ipv_6_bridge_enabled (bool): Toggle for enabling or disabling IPv6 bridging in a network (Note: if enabled, SSIDs must also be configu... | UpdateNetworkWirelessSettingsModel | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateNetworkWirelessSettingsModel:
"""Implementation of the 'updateNetworkWirelessSettings' model. TODO: type model description here. Attributes: meshing_enabled (bool): Toggle for enabling or disabling meshing in a network ipv_6_bridge_enabled (bool): Toggle for enabling or disabling IPv6 bridg... | stack_v2_sparse_classes_10k_train_000624 | 2,821 | permissive | [
{
"docstring": "Constructor for the UpdateNetworkWirelessSettingsModel class",
"name": "__init__",
"signature": "def __init__(self, meshing_enabled=None, ipv_6_bridge_enabled=None, location_analytics_enabled=None, led_lights_on=None)"
},
{
"docstring": "Creates an instance of this model from a d... | 2 | stack_v2_sparse_classes_30k_train_004721 | Implement the Python class `UpdateNetworkWirelessSettingsModel` described below.
Class description:
Implementation of the 'updateNetworkWirelessSettings' model. TODO: type model description here. Attributes: meshing_enabled (bool): Toggle for enabling or disabling meshing in a network ipv_6_bridge_enabled (bool): Togg... | Implement the Python class `UpdateNetworkWirelessSettingsModel` described below.
Class description:
Implementation of the 'updateNetworkWirelessSettings' model. TODO: type model description here. Attributes: meshing_enabled (bool): Toggle for enabling or disabling meshing in a network ipv_6_bridge_enabled (bool): Togg... | 9894089eb013318243ae48869cc5130eb37f80c0 | <|skeleton|>
class UpdateNetworkWirelessSettingsModel:
"""Implementation of the 'updateNetworkWirelessSettings' model. TODO: type model description here. Attributes: meshing_enabled (bool): Toggle for enabling or disabling meshing in a network ipv_6_bridge_enabled (bool): Toggle for enabling or disabling IPv6 bridg... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UpdateNetworkWirelessSettingsModel:
"""Implementation of the 'updateNetworkWirelessSettings' model. TODO: type model description here. Attributes: meshing_enabled (bool): Toggle for enabling or disabling meshing in a network ipv_6_bridge_enabled (bool): Toggle for enabling or disabling IPv6 bridging in a netw... | the_stack_v2_python_sparse | meraki_sdk/models/update_network_wireless_settings_model.py | RaulCatalano/meraki-python-sdk | train | 1 |
221d3c789e5e6580f94d2daabddf663f1d249883 | [
"Component.__init__(self, name, ReduceTensor, config)\nself.key_inputs = self.stream_keys['inputs']\nself.key_outputs = self.stream_keys['outputs']\nself.num_inputs_dims = self.config['num_inputs_dims']\nself.input_size = self.globals['input_size']\nself.dim = self.config['reduction_dim']\nself.keepdim = self.confi... | <|body_start_0|>
Component.__init__(self, name, ReduceTensor, config)
self.key_inputs = self.stream_keys['inputs']
self.key_outputs = self.stream_keys['outputs']
self.num_inputs_dims = self.config['num_inputs_dims']
self.input_size = self.globals['input_size']
self.dim = ... | Class responsible for reducing tensor using indicated reduction method along a given dimension. | ReduceTensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReduceTensor:
"""Class responsible for reducing tensor using indicated reduction method along a given dimension."""
def __init__(self, name, config):
"""Initializes object. :param name: Name of the component loaded from the configuration file. :type name: str :param config: Dictionar... | stack_v2_sparse_classes_10k_train_000625 | 4,905 | permissive | [
{
"docstring": "Initializes object. :param name: Name of the component loaded from the configuration file. :type name: str :param config: Dictionary of parameters (read from the configuration ``.yaml`` file). :type config: :py:class:`ptp.configuration.ConfigInterface`",
"name": "__init__",
"signature": ... | 4 | stack_v2_sparse_classes_30k_train_007278 | Implement the Python class `ReduceTensor` described below.
Class description:
Class responsible for reducing tensor using indicated reduction method along a given dimension.
Method signatures and docstrings:
- def __init__(self, name, config): Initializes object. :param name: Name of the component loaded from the con... | Implement the Python class `ReduceTensor` described below.
Class description:
Class responsible for reducing tensor using indicated reduction method along a given dimension.
Method signatures and docstrings:
- def __init__(self, name, config): Initializes object. :param name: Name of the component loaded from the con... | 9cb17271666061cb19fe24197ecd5e4c8d32c5da | <|skeleton|>
class ReduceTensor:
"""Class responsible for reducing tensor using indicated reduction method along a given dimension."""
def __init__(self, name, config):
"""Initializes object. :param name: Name of the component loaded from the configuration file. :type name: str :param config: Dictionar... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ReduceTensor:
"""Class responsible for reducing tensor using indicated reduction method along a given dimension."""
def __init__(self, name, config):
"""Initializes object. :param name: Name of the component loaded from the configuration file. :type name: str :param config: Dictionary of paramete... | the_stack_v2_python_sparse | ptp/components/transforms/reduce_tensor.py | ConnectionMaster/pytorchpipe | train | 1 |
9b73c17c9b10bcc2f777a8ab1c07abc053f02a42 | [
"identifier = identifiers.primary_identifier\nfor required_perm in permission_s:\n required_permission = CaseSensitivePermission(wildcard_string=required_perm)\n domain = next(iter(required_permission.domain))\n assigned_permission_s = self.get_authzd_permissions(identifier, domain)\n is_permitted = Fal... | <|body_start_0|>
identifier = identifiers.primary_identifier
for required_perm in permission_s:
required_permission = CaseSensitivePermission(wildcard_string=required_perm)
domain = next(iter(required_permission.domain))
assigned_permission_s = self.get_authzd_permiss... | Customized version of hte default AccountStoreRealm. This is required to get case-sensitive permission behavior, which is not supported by default. | AnchoreNativeRealm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnchoreNativeRealm:
"""Customized version of hte default AccountStoreRealm. This is required to get case-sensitive permission behavior, which is not supported by default."""
def is_permitted(self, identifiers, permission_s):
"""If the authorization info cannot be obtained from the ac... | stack_v2_sparse_classes_10k_train_000626 | 7,371 | permissive | [
{
"docstring": "If the authorization info cannot be obtained from the accountstore, permission check tuple yields False. :type identifiers: subject_abcs.IdentifierCollection :param permission_s: a collection of one or more permissions, represented as string-based permissions or Permission objects and NEVER comi... | 2 | stack_v2_sparse_classes_30k_train_003436 | Implement the Python class `AnchoreNativeRealm` described below.
Class description:
Customized version of hte default AccountStoreRealm. This is required to get case-sensitive permission behavior, which is not supported by default.
Method signatures and docstrings:
- def is_permitted(self, identifiers, permission_s):... | Implement the Python class `AnchoreNativeRealm` described below.
Class description:
Customized version of hte default AccountStoreRealm. This is required to get case-sensitive permission behavior, which is not supported by default.
Method signatures and docstrings:
- def is_permitted(self, identifiers, permission_s):... | 0f3c2a381febe1ab82918014fc421a54dedcdaeb | <|skeleton|>
class AnchoreNativeRealm:
"""Customized version of hte default AccountStoreRealm. This is required to get case-sensitive permission behavior, which is not supported by default."""
def is_permitted(self, identifiers, permission_s):
"""If the authorization info cannot be obtained from the ac... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AnchoreNativeRealm:
"""Customized version of hte default AccountStoreRealm. This is required to get case-sensitive permission behavior, which is not supported by default."""
def is_permitted(self, identifiers, permission_s):
"""If the authorization info cannot be obtained from the accountstore, p... | the_stack_v2_python_sparse | anchore_engine/subsys/auth/realms.py | rkhozinov/anchore-engine | train | 1 |
67c0ed03c8f71a462b43ce90fd9c89502261f5d4 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\ntry:\n mapping_value = parse_node.get_child_node('@odata.type').get_str_value()\nexcept AttributeError:\n mapping_value = None\nif mapping_value and mapping_value.casefold() == '#microsoft.graph.targetedManagedAppConfiguration'.casefold()... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
try:
mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
except AttributeError:
mapping_value = None
if mapping_value and mapping_value.casefold() ==... | Configuration used to deliver a set of custom settings as-is to apps for users to whom the configuration is scoped | ManagedAppConfiguration | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ManagedAppConfiguration:
"""Configuration used to deliver a set of custom settings as-is to apps for users to whom the configuration is scoped"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ManagedAppConfiguration:
"""Creates a new instance of the app... | stack_v2_sparse_classes_10k_train_000627 | 3,291 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: ManagedAppConfiguration",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrimin... | 3 | null | Implement the Python class `ManagedAppConfiguration` described below.
Class description:
Configuration used to deliver a set of custom settings as-is to apps for users to whom the configuration is scoped
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> M... | Implement the Python class `ManagedAppConfiguration` described below.
Class description:
Configuration used to deliver a set of custom settings as-is to apps for users to whom the configuration is scoped
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> M... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class ManagedAppConfiguration:
"""Configuration used to deliver a set of custom settings as-is to apps for users to whom the configuration is scoped"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ManagedAppConfiguration:
"""Creates a new instance of the app... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ManagedAppConfiguration:
"""Configuration used to deliver a set of custom settings as-is to apps for users to whom the configuration is scoped"""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ManagedAppConfiguration:
"""Creates a new instance of the appropriate clas... | the_stack_v2_python_sparse | msgraph/generated/models/managed_app_configuration.py | microsoftgraph/msgraph-sdk-python | train | 135 |
e445912300bf78c0a1ba487a9cb69dbbcf6e74b6 | [
"if not nums:\n return -1\nl, r = (0, len(nums) - 1)\nwhile l <= r:\n m = (l + r) // 2\n if nums[m] == target:\n return m\n if nums[l] <= nums[m]:\n if nums[l] <= target < nums[m]:\n r = m - 1\n else:\n l = m + 1\n elif nums[m] < target <= nums[r]:\n ... | <|body_start_0|>
if not nums:
return -1
l, r = (0, len(nums) - 1)
while l <= r:
m = (l + r) // 2
if nums[m] == target:
return m
if nums[l] <= nums[m]:
if nums[l] <= target < nums[m]:
r = m - 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def search(self, nums: List[int], target: int) -> int:
"""33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.20% 的用户 内存消耗： 14.9 MB , 在所有 Python3 提交中击败了 91.10% 的用户"""
<|body_0|>
def search1(self, nums: List[int], target: int) -> int:
"""33 执行用时： 44 ms , 在所有 Python3 提交中击... | stack_v2_sparse_classes_10k_train_000628 | 4,457 | no_license | [
{
"docstring": "33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.20% 的用户 内存消耗： 14.9 MB , 在所有 Python3 提交中击败了 91.10% 的用户",
"name": "search",
"signature": "def search(self, nums: List[int], target: int) -> int"
},
{
"docstring": "33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.48% 的用户 内存消耗： 15.2 MB , 在所有 Python3 提交中击... | 3 | stack_v2_sparse_classes_30k_train_002189 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> int: 33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.20% 的用户 内存消耗： 14.9 MB , 在所有 Python3 提交中击败了 91.10% 的用户
- def search1(self, nums: List... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> int: 33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.20% 的用户 内存消耗： 14.9 MB , 在所有 Python3 提交中击败了 91.10% 的用户
- def search1(self, nums: List... | d613ed8a5a2c15ace7d513965b372d128845d66a | <|skeleton|>
class Solution:
def search(self, nums: List[int], target: int) -> int:
"""33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.20% 的用户 内存消耗： 14.9 MB , 在所有 Python3 提交中击败了 91.10% 的用户"""
<|body_0|>
def search1(self, nums: List[int], target: int) -> int:
"""33 执行用时： 44 ms , 在所有 Python3 提交中击... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def search(self, nums: List[int], target: int) -> int:
"""33 执行用时： 44 ms , 在所有 Python3 提交中击败了 36.20% 的用户 内存消耗： 14.9 MB , 在所有 Python3 提交中击败了 91.10% 的用户"""
if not nums:
return -1
l, r = (0, len(nums) - 1)
while l <= r:
m = (l + r) // 2
... | the_stack_v2_python_sparse | 搜索旋转排序数组1&2.py | nomboy/leetcode | train | 0 | |
23552b6bd9a0076dff9fb1bde82366f6f0bf6595 | [
"while get_current_window_id() == 10101:\n xbmc.sleep(100)\nsuper(progress_dialog, self).__init__()",
"if kodi_version_major() < 19:\n lines = message.split('\\n', 2)\n line1, line2, line3 = lines + [None] * (3 - len(lines))\n return super(progress_dialog, self).create(heading, line1=line1, line2=line... | <|body_start_0|>
while get_current_window_id() == 10101:
xbmc.sleep(100)
super(progress_dialog, self).__init__()
<|end_body_0|>
<|body_start_1|>
if kodi_version_major() < 19:
lines = message.split('\n', 2)
line1, line2, line3 = lines + [None] * (3 - len(lines... | Show Kodi's Progress dialog | progress_dialog | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class progress_dialog:
"""Show Kodi's Progress dialog"""
def __init__(self):
"""Initialize a new progress dialog"""
<|body_0|>
def create(self, heading, message=''):
"""Create and show a progress dialog"""
<|body_1|>
def update(self, percent, message=''):
... | stack_v2_sparse_classes_10k_train_000629 | 14,153 | permissive | [
{
"docstring": "Initialize a new progress dialog",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Create and show a progress dialog",
"name": "create",
"signature": "def create(self, heading, message='')"
},
{
"docstring": "Update the progress dialog",
... | 3 | null | Implement the Python class `progress_dialog` described below.
Class description:
Show Kodi's Progress dialog
Method signatures and docstrings:
- def __init__(self): Initialize a new progress dialog
- def create(self, heading, message=''): Create and show a progress dialog
- def update(self, percent, message=''): Upda... | Implement the Python class `progress_dialog` described below.
Class description:
Show Kodi's Progress dialog
Method signatures and docstrings:
- def __init__(self): Initialize a new progress dialog
- def create(self, heading, message=''): Create and show a progress dialog
- def update(self, percent, message=''): Upda... | 9b7cab3656c2497c812ab101a56ed661dd8cf4a7 | <|skeleton|>
class progress_dialog:
"""Show Kodi's Progress dialog"""
def __init__(self):
"""Initialize a new progress dialog"""
<|body_0|>
def create(self, heading, message=''):
"""Create and show a progress dialog"""
<|body_1|>
def update(self, percent, message=''):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class progress_dialog:
"""Show Kodi's Progress dialog"""
def __init__(self):
"""Initialize a new progress dialog"""
while get_current_window_id() == 10101:
xbmc.sleep(100)
super(progress_dialog, self).__init__()
def create(self, heading, message=''):
"""Create a... | the_stack_v2_python_sparse | repo/script.module.inputstreamhelper/lib/inputstreamhelper/kodiutils.py | irmu/arda | train | 7 |
7d2bf70a1736b50409a315ef6f4f601f3d63e250 | [
"super(Exponential, self).__init__(n_dims=n_dims, active_dims=active_dims, name=name)\nlogger.debug('Initializing %s kernel.' % self.name)\nself.variance = np.float64(variance)\nself.lengthscale = np.float64(lengthscale)\nself.parameter_list = ['variance', 'lengthscale']\nself.constraint_map = {'variance': '+ve', '... | <|body_start_0|>
super(Exponential, self).__init__(n_dims=n_dims, active_dims=active_dims, name=name)
logger.debug('Initializing %s kernel.' % self.name)
self.variance = np.float64(variance)
self.lengthscale = np.float64(lengthscale)
self.parameter_list = ['variance', 'lengthscal... | Exponential | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Exponential:
def __init__(self, n_dims, variance=1.0, lengthscale=1.0, active_dims=None, name=None):
"""squared exponential kernel Inputs: n_dims : number of dimensions variance : kernel variance lengthscale : kernel lengthscale active_dims : all dims active by default, subset can be spe... | stack_v2_sparse_classes_10k_train_000630 | 9,047 | no_license | [
{
"docstring": "squared exponential kernel Inputs: n_dims : number of dimensions variance : kernel variance lengthscale : kernel lengthscale active_dims : all dims active by default, subset can be specified",
"name": "__init__",
"signature": "def __init__(self, n_dims, variance=1.0, lengthscale=1.0, act... | 2 | stack_v2_sparse_classes_30k_train_007217 | Implement the Python class `Exponential` described below.
Class description:
Implement the Exponential class.
Method signatures and docstrings:
- def __init__(self, n_dims, variance=1.0, lengthscale=1.0, active_dims=None, name=None): squared exponential kernel Inputs: n_dims : number of dimensions variance : kernel v... | Implement the Python class `Exponential` described below.
Class description:
Implement the Exponential class.
Method signatures and docstrings:
- def __init__(self, n_dims, variance=1.0, lengthscale=1.0, active_dims=None, name=None): squared exponential kernel Inputs: n_dims : number of dimensions variance : kernel v... | 1bed882b8a94ee58fd0bde6920ee85f81ffb77bb | <|skeleton|>
class Exponential:
def __init__(self, n_dims, variance=1.0, lengthscale=1.0, active_dims=None, name=None):
"""squared exponential kernel Inputs: n_dims : number of dimensions variance : kernel variance lengthscale : kernel lengthscale active_dims : all dims active by default, subset can be spe... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Exponential:
def __init__(self, n_dims, variance=1.0, lengthscale=1.0, active_dims=None, name=None):
"""squared exponential kernel Inputs: n_dims : number of dimensions variance : kernel variance lengthscale : kernel lengthscale active_dims : all dims active by default, subset can be specified"""
... | the_stack_v2_python_sparse | gp_grief/kern/stationary.py | scwolof/gp_grief | train | 2 | |
71451c77233a845fd30a13fa73dc2c918dce6e58 | [
"super().__init__(repo_path)\nself.oss_fuzz_project_name = oss_fuzz_project_name\nself.fuzzer_stats_url = _get_oss_fuzz_fuzzer_stats_dir_url(self.oss_fuzz_project_name)\nif self.fuzzer_stats_url is None:\n raise CoverageError('Could not get latest coverage.')",
"if not self.fuzzer_stats_url:\n return None\n... | <|body_start_0|>
super().__init__(repo_path)
self.oss_fuzz_project_name = oss_fuzz_project_name
self.fuzzer_stats_url = _get_oss_fuzz_fuzzer_stats_dir_url(self.oss_fuzz_project_name)
if self.fuzzer_stats_url is None:
raise CoverageError('Could not get latest coverage.')
<|end... | Gets coverage data for a project from OSS-Fuzz. | OSSFuzzCoverage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OSSFuzzCoverage:
"""Gets coverage data for a project from OSS-Fuzz."""
def __init__(self, repo_path, oss_fuzz_project_name):
"""Constructor for OSSFuzzCoverage."""
<|body_0|>
def get_target_coverage(self, target):
"""Get the coverage report for a specific fuzz ta... | stack_v2_sparse_classes_10k_train_000631 | 7,002 | permissive | [
{
"docstring": "Constructor for OSSFuzzCoverage.",
"name": "__init__",
"signature": "def __init__(self, repo_path, oss_fuzz_project_name)"
},
{
"docstring": "Get the coverage report for a specific fuzz target. Args: target: The name of the fuzz target whose coverage is requested. Returns: The ta... | 2 | stack_v2_sparse_classes_30k_train_003451 | Implement the Python class `OSSFuzzCoverage` described below.
Class description:
Gets coverage data for a project from OSS-Fuzz.
Method signatures and docstrings:
- def __init__(self, repo_path, oss_fuzz_project_name): Constructor for OSSFuzzCoverage.
- def get_target_coverage(self, target): Get the coverage report f... | Implement the Python class `OSSFuzzCoverage` described below.
Class description:
Gets coverage data for a project from OSS-Fuzz.
Method signatures and docstrings:
- def __init__(self, repo_path, oss_fuzz_project_name): Constructor for OSSFuzzCoverage.
- def get_target_coverage(self, target): Get the coverage report f... | f0275421f84b8f80ee767fb9230134ac97cb687b | <|skeleton|>
class OSSFuzzCoverage:
"""Gets coverage data for a project from OSS-Fuzz."""
def __init__(self, repo_path, oss_fuzz_project_name):
"""Constructor for OSSFuzzCoverage."""
<|body_0|>
def get_target_coverage(self, target):
"""Get the coverage report for a specific fuzz ta... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OSSFuzzCoverage:
"""Gets coverage data for a project from OSS-Fuzz."""
def __init__(self, repo_path, oss_fuzz_project_name):
"""Constructor for OSSFuzzCoverage."""
super().__init__(repo_path)
self.oss_fuzz_project_name = oss_fuzz_project_name
self.fuzzer_stats_url = _get_o... | the_stack_v2_python_sparse | infra/cifuzz/get_coverage.py | google/oss-fuzz | train | 9,438 |
d50b9ec43ce411c27531f7a4e837e90aacff257b | [
"memo = dict()\n\ndef dfs(nums, index, total, target):\n if index == len(nums):\n return 1 if total == target else 0\n if (index, total) in memo.keys():\n return memo[index, total]\n else:\n memo[index, total] = dfs(nums, index + 1, total + nums[index], target) + dfs(nums, index + 1, t... | <|body_start_0|>
memo = dict()
def dfs(nums, index, total, target):
if index == len(nums):
return 1 if total == target else 0
if (index, total) in memo.keys():
return memo[index, total]
else:
memo[index, total] = dfs(nu... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findTargetSumWays1(self, nums: List[int], S: int) -> int:
"""简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底... | stack_v2_sparse_classes_10k_train_000632 | 3,042 | no_license | [
{
"docstring": "简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\\\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\\\ /\\\\ /\\\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底往上的返回值，即所有结果）",
"name": "findTargetSumWays1",
"signature": "def fin... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findTargetSumWays1(self, nums: List[int], S: int) -> int: 简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findTargetSumWays1(self, nums: List[int], S: int) -> int: 简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -... | 2bbb1640589aab34f2bc42489283033cc11fb885 | <|skeleton|>
class Solution:
def findTargetSumWays1(self, nums: List[int], S: int) -> int:
"""简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def findTargetSumWays1(self, nums: List[int], S: int) -> int:
"""简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底往上的返回值，即所有结果）"... | the_stack_v2_python_sparse | 494_target-sum.py | helloocc/algorithm | train | 1 | |
1c3ca6b81fe71d475d65a203b8b1de5b1ec5459b | [
"data = request.get_json()\nseat = 1\nif data:\n seat = data.get('seat')\ncurrent_user = get_jwt_identity()\ntry:\n flight = get_flight(flight_id)\n if not flight:\n return generate_response('Selected flight not available', 400)\n if seat == 1 and flight.booked_economy < flight.airplane.economy_s... | <|body_start_0|>
data = request.get_json()
seat = 1
if data:
seat = data.get('seat')
current_user = get_jwt_identity()
try:
flight = get_flight(flight_id)
if not flight:
return generate_response('Selected flight not available', ... | Class to manipulate the airport details | BookingManipulation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BookingManipulation:
"""Class to manipulate the airport details"""
def post(self, flight_id):
"""POST method to add a new flight booking"""
<|body_0|>
def get(self, flight_id):
"""Return a list of all reservations in a given day"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_10k_train_000633 | 2,955 | permissive | [
{
"docstring": "POST method to add a new flight booking",
"name": "post",
"signature": "def post(self, flight_id)"
},
{
"docstring": "Return a list of all reservations in a given day",
"name": "get",
"signature": "def get(self, flight_id)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000322 | Implement the Python class `BookingManipulation` described below.
Class description:
Class to manipulate the airport details
Method signatures and docstrings:
- def post(self, flight_id): POST method to add a new flight booking
- def get(self, flight_id): Return a list of all reservations in a given day | Implement the Python class `BookingManipulation` described below.
Class description:
Class to manipulate the airport details
Method signatures and docstrings:
- def post(self, flight_id): POST method to add a new flight booking
- def get(self, flight_id): Return a list of all reservations in a given day
<|skeleton|>... | 77b157098d618582737979382197e5302d347017 | <|skeleton|>
class BookingManipulation:
"""Class to manipulate the airport details"""
def post(self, flight_id):
"""POST method to add a new flight booking"""
<|body_0|>
def get(self, flight_id):
"""Return a list of all reservations in a given day"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BookingManipulation:
"""Class to manipulate the airport details"""
def post(self, flight_id):
"""POST method to add a new flight booking"""
data = request.get_json()
seat = 1
if data:
seat = data.get('seat')
current_user = get_jwt_identity()
try... | the_stack_v2_python_sparse | app/bookings/views.py | muthash/flight-booking-flask | train | 6 |
f2b0fbc98fc2bd8fe90294854641aa4a3e97e921 | [
"self.event = None\nself.concrete_categories = None\nself.abstract_categories = None\nself.key = key\nif not regex_objects:\n if model:\n regex_objs = RegexCategory.objects.select_related().filter(model_type=model)\n else:\n regex_objs = RegexCategory.objects.all()\nelse:\n regex_objs = regex... | <|body_start_0|>
self.event = None
self.concrete_categories = None
self.abstract_categories = None
self.key = key
if not regex_objects:
if model:
regex_objs = RegexCategory.objects.select_related().filter(model_type=model)
else:
... | Use a regular expression to map the external category text to internal categories | RegexRule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegexRule:
"""Use a regular expression to map the external category text to internal categories"""
def __init__(self, key, model, regex_objects=None):
"""# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules... | stack_v2_sparse_classes_10k_train_000634 | 10,087 | no_license | [
{
"docstring": "# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules = [] # from items in table with source NULL for row in model: key(row) -> category each model is of the form: regular_expression We basically build 2 different types... | 2 | stack_v2_sparse_classes_30k_train_005717 | Implement the Python class `RegexRule` described below.
Class description:
Use a regular expression to map the external category text to internal categories
Method signatures and docstrings:
- def __init__(self, key, model, regex_objects=None): # note: regexes are compiled here- they are not strings self.source_rules... | Implement the Python class `RegexRule` described below.
Class description:
Use a regular expression to map the external category text to internal categories
Method signatures and docstrings:
- def __init__(self, key, model, regex_objects=None): # note: regexes are compiled here- they are not strings self.source_rules... | c4992d80f984f3360eb2018c5a1b13ce962a55b4 | <|skeleton|>
class RegexRule:
"""Use a regular expression to map the external category text to internal categories"""
def __init__(self, key, model, regex_objects=None):
"""# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RegexRule:
"""Use a regular expression to map the external category text to internal categories"""
def __init__(self, key, model, regex_objects=None):
"""# note: regexes are compiled here- they are not strings self.source_rules = defaultdict(list) # built from a table self.null_rules = [] # from ... | the_stack_v2_python_sparse | abextra/pundit/classification_rules.py | danbretl/mid-tier | train | 0 |
1156dae05e641403082e891e000e447d1090a9b7 | [
"self.key2node = {}\nself.freq2list = defaultdict(OrderedDict)\nself.capacity = capacity\nself.minf = 0",
"if key not in self.key2node:\n return -1\nkey, value, freq = self.key2node[key]\nself.freq2list[freq].pop(key)\nself.key2node.pop(key)\nif not self.freq2list[freq] and freq == self.minf:\n self.minf +=... | <|body_start_0|>
self.key2node = {}
self.freq2list = defaultdict(OrderedDict)
self.capacity = capacity
self.minf = 0
<|end_body_0|>
<|body_start_1|>
if key not in self.key2node:
return -1
key, value, freq = self.key2node[key]
self.freq2list[freq].pop(... | LFUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k_train_000635 | 2,950 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: None",
"name": "pu... | 3 | null | Implement the Python class `LFUCache` described below.
Class description:
Implement the LFUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None | Implement the Python class `LFUCache` described below.
Class description:
Implement the LFUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None
<|sk... | 547cdc7365716660a9d9590c1cc97d95bc38d315 | <|skeleton|>
class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.key2node = {}
self.freq2list = defaultdict(OrderedDict)
self.capacity = capacity
self.minf = 0
def get(self, key):
""":type key: int :rtype: int"""
if key not in self.key2node:
... | the_stack_v2_python_sparse | 460.lfu-cache.py | zhch-sun/leetcode_szc | train | 0 | |
ab055e3c903c0a1c51ea4e8407d5df8c4b964dc7 | [
"try:\n http_method = self._resolve_method(request)\n http_headers = self._convert_list_tuples_to_dict(headers_list=request.headers)\n parsed_url = parse_url(request.url)\n if parsed_url.scheme is None or parsed_url.scheme != 'https':\n raise ApiClientException('Requests against non-HTTPS endpoin... | <|body_start_0|>
try:
http_method = self._resolve_method(request)
http_headers = self._convert_list_tuples_to_dict(headers_list=request.headers)
parsed_url = parse_url(request.url)
if parsed_url.scheme is None or parsed_url.scheme != 'https':
raise... | Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library. | DefaultApiClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultApiClient:
"""Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library."""
def invoke(self, request):
"""Dispatches a request to an API endpoint described in the request. Resolves the method from input request obj... | stack_v2_sparse_classes_10k_train_000636 | 6,077 | permissive | [
{
"docstring": "Dispatches a request to an API endpoint described in the request. Resolves the method from input request object, converts the list of header tuples to the required format (dict) for the `requests` lib call and invokes the method with corresponding parameters on `requests` library. The response f... | 4 | stack_v2_sparse_classes_30k_train_002203 | Implement the Python class `DefaultApiClient` described below.
Class description:
Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library.
Method signatures and docstrings:
- def invoke(self, request): Dispatches a request to an API endpoint described i... | Implement the Python class `DefaultApiClient` described below.
Class description:
Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library.
Method signatures and docstrings:
- def invoke(self, request): Dispatches a request to an API endpoint described i... | 7e13ca69b240985584dff6ec633a27598a154ca1 | <|skeleton|>
class DefaultApiClient:
"""Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library."""
def invoke(self, request):
"""Dispatches a request to an API endpoint described in the request. Resolves the method from input request obj... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DefaultApiClient:
"""Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library."""
def invoke(self, request):
"""Dispatches a request to an API endpoint described in the request. Resolves the method from input request object, converts... | the_stack_v2_python_sparse | ask-sdk-core/ask_sdk_core/api_client.py | alexa/alexa-skills-kit-sdk-for-python | train | 560 |
0122accf959081acb64d200093feee0f2bd3b16b | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\ntry:\n mapping_value = parse_node.get_child_node('@odata.type').get_str_value()\nexcept AttributeError:\n mapping_value = None\nif mapping_value and mapping_value.casefold() == '#microsoft.graph.printer'.casefold():\n from .printer imp... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
try:
mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
except AttributeError:
mapping_value = None
if mapping_value and mapping_value.casefold() ==... | PrinterBase | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrinterBase:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PrinterBase:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Pr... | stack_v2_sparse_classes_10k_train_000637 | 5,587 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: PrinterBase",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(p... | 3 | null | Implement the Python class `PrinterBase` described below.
Class description:
Implement the PrinterBase class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PrinterBase: Creates a new instance of the appropriate class based on discriminator value Args:... | Implement the Python class `PrinterBase` described below.
Class description:
Implement the PrinterBase class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PrinterBase: Creates a new instance of the appropriate class based on discriminator value Args:... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class PrinterBase:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PrinterBase:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Pr... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PrinterBase:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PrinterBase:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: PrinterBase"""
... | the_stack_v2_python_sparse | msgraph/generated/models/printer_base.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
a1fff8fc17bb72491be6d189cfc5ff8610bec0b7 | [
"self.val = None\nself.prev = None\nself.next = None",
"if index == 0:\n if self.val != None:\n return self.val\n else:\n return -1\nif self.next:\n return self.next.get(index - 1)\nelse:\n return -1",
"new = MyLinkedList()\nnew.val = self.val\nnew.prev = self\nnew.next = self.next\nif... | <|body_start_0|>
self.val = None
self.prev = None
self.next = None
<|end_body_0|>
<|body_start_1|>
if index == 0:
if self.val != None:
return self.val
else:
return -1
if self.next:
return self.next.get(index - 1... | MyLinkedList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyLinkedList:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def get(self, index: int) -> int:
"""Get the value of the index-th node in the linked list. If the index is invalid, return -1."""
<|body_1|>
def addAtHead(self, val:... | stack_v2_sparse_classes_10k_train_000638 | 4,061 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Get the value of the index-th node in the linked list. If the index is invalid, return -1.",
"name": "get",
"signature": "def get(self, index: int) -> int"
},... | 6 | null | Implement the Python class `MyLinkedList` described below.
Class description:
Implement the MyLinkedList class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def get(self, index: int) -> int: Get the value of the index-th node in the linked list. If the index is invali... | Implement the Python class `MyLinkedList` described below.
Class description:
Implement the MyLinkedList class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def get(self, index: int) -> int: Get the value of the index-th node in the linked list. If the index is invali... | 4bf1a7814b5c76e11242e7933e09c59ede3284a3 | <|skeleton|>
class MyLinkedList:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def get(self, index: int) -> int:
"""Get the value of the index-th node in the linked list. If the index is invalid, return -1."""
<|body_1|>
def addAtHead(self, val:... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MyLinkedList:
def __init__(self):
"""Initialize your data structure here."""
self.val = None
self.prev = None
self.next = None
def get(self, index: int) -> int:
"""Get the value of the index-th node in the linked list. If the index is invalid, return -1."""
... | the_stack_v2_python_sparse | Explore/Linked List/Singly Linked List/0707_Design_Linked_List.py | actcheng/leetcode-solutions | train | 2 | |
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_10k_train_000639 | 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_001432 | 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_10k | 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 | |
e0b089fd82815c1cf94829106bbaa11934353e5b | [
"super().__init__(unique_id, zha_device, cluster_handlers, **kwargs)\nself._presets = [PRESET_NONE, self.PRESET_HOLIDAY, PRESET_SCHEDULE, self.PRESET_FROST]\nself._supported_flags |= ClimateEntityFeature.PRESET_MODE",
"if record.attr_name == 'operation_preset':\n if record.value == 0:\n self._preset = P... | <|body_start_0|>
super().__init__(unique_id, zha_device, cluster_handlers, **kwargs)
self._presets = [PRESET_NONE, self.PRESET_HOLIDAY, PRESET_SCHEDULE, self.PRESET_FROST]
self._supported_flags |= ClimateEntityFeature.PRESET_MODE
<|end_body_0|>
<|body_start_1|>
if record.attr_name == 'o... | ZONNSMART Thermostat implementation. Notice that this device uses two holiday presets (2: HolidayMode, 3: HolidayModeTemp), but only one of them can be set. | ZONNSMARTThermostat | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZONNSMARTThermostat:
"""ZONNSMART Thermostat implementation. Notice that this device uses two holiday presets (2: HolidayMode, 3: HolidayModeTemp), but only one of them can be set."""
def __init__(self, unique_id, zha_device, cluster_handlers, **kwargs):
"""Initialize ZHA Thermostat ... | stack_v2_sparse_classes_10k_train_000640 | 29,216 | permissive | [
{
"docstring": "Initialize ZHA Thermostat instance.",
"name": "__init__",
"signature": "def __init__(self, unique_id, zha_device, cluster_handlers, **kwargs)"
},
{
"docstring": "Handle attribute update from device.",
"name": "async_attribute_updated",
"signature": "async def async_attrib... | 3 | null | Implement the Python class `ZONNSMARTThermostat` described below.
Class description:
ZONNSMART Thermostat implementation. Notice that this device uses two holiday presets (2: HolidayMode, 3: HolidayModeTemp), but only one of them can be set.
Method signatures and docstrings:
- def __init__(self, unique_id, zha_device... | Implement the Python class `ZONNSMARTThermostat` described below.
Class description:
ZONNSMART Thermostat implementation. Notice that this device uses two holiday presets (2: HolidayMode, 3: HolidayModeTemp), but only one of them can be set.
Method signatures and docstrings:
- def __init__(self, unique_id, zha_device... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ZONNSMARTThermostat:
"""ZONNSMART Thermostat implementation. Notice that this device uses two holiday presets (2: HolidayMode, 3: HolidayModeTemp), but only one of them can be set."""
def __init__(self, unique_id, zha_device, cluster_handlers, **kwargs):
"""Initialize ZHA Thermostat ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ZONNSMARTThermostat:
"""ZONNSMART Thermostat implementation. Notice that this device uses two holiday presets (2: HolidayMode, 3: HolidayModeTemp), but only one of them can be set."""
def __init__(self, unique_id, zha_device, cluster_handlers, **kwargs):
"""Initialize ZHA Thermostat instance."""
... | the_stack_v2_python_sparse | homeassistant/components/zha/climate.py | home-assistant/core | train | 35,501 |
08c20671c2a17fca57a62e61251f33f66fd8d397 | [
"super().__init__()\nself.v2_proj = nn.Linear(hidden_size, hidden_size)\nself.proj = nn.Linear(hidden_size * 4, hidden_size * 2)\nself.dropout = nn.Dropout(p=dropout_rate)",
"proj_v2 = self.v2_proj(v2)\nsimilarity_matrix = v1.bmm(proj_v2.transpose(2, 1).contiguous())\nv1_v2_attn = F.softmax(similarity_matrix.mask... | <|body_start_0|>
super().__init__()
self.v2_proj = nn.Linear(hidden_size, hidden_size)
self.proj = nn.Linear(hidden_size * 4, hidden_size * 2)
self.dropout = nn.Dropout(p=dropout_rate)
<|end_body_0|>
<|body_start_1|>
proj_v2 = self.v2_proj(v2)
similarity_matrix = v1.bmm(... | Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.shape torch.Size([4, 5, 10]) >>> v2 = torc... | MatchModule | [
"MIT",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.1-or-later",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MatchModule:
"""Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.sha... | stack_v2_sparse_classes_10k_train_000641 | 3,191 | permissive | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, hidden_size, dropout_rate=0)"
},
{
"docstring": "Computing attention vectors and projection vectors.",
"name": "forward",
"signature": "def forward(self, v1, v2, v2_mask)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007026 | Implement the Python class `MatchModule` described below.
Class description:
Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>... | Implement the Python class `MatchModule` described below.
Class description:
Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>... | 4198ebce942f4afe7ddca6a96ab6f4464ade4518 | <|skeleton|>
class MatchModule:
"""Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.sha... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MatchModule:
"""Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.shape torch.Size... | the_stack_v2_python_sparse | poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/attention.py | microsoft/ContextualSP | train | 332 |
9287250760624f3e344034fb105186a21f6ae4f2 | [
"response = utility.ExecutorResponse()\nerr_msg = 'Testing handling of OSError'\nwith patch('subprocess.run') as mock_run:\n mock_run.side_effect = MagicMock(side_effect=OSError(err_msg))\n response.execute_command([])\nself.assertEqual(response._stdout, '')\nself.assertEqual(response._stderr, err_msg)",
"t... | <|body_start_0|>
response = utility.ExecutorResponse()
err_msg = 'Testing handling of OSError'
with patch('subprocess.run') as mock_run:
mock_run.side_effect = MagicMock(side_effect=OSError(err_msg))
response.execute_command([])
self.assertEqual(response._stdout, ... | UtilityTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UtilityTest:
def test_execute_command_oserror(self):
"""Testing stdout and stderr is correctly captured upon OSError."""
<|body_0|>
def test_execute_command_stdout(self):
"""Testing stdout output is correctly captured."""
<|body_1|>
def test_execute_comm... | stack_v2_sparse_classes_10k_train_000642 | 2,714 | permissive | [
{
"docstring": "Testing stdout and stderr is correctly captured upon OSError.",
"name": "test_execute_command_oserror",
"signature": "def test_execute_command_oserror(self)"
},
{
"docstring": "Testing stdout output is correctly captured.",
"name": "test_execute_command_stdout",
"signatur... | 5 | null | Implement the Python class `UtilityTest` described below.
Class description:
Implement the UtilityTest class.
Method signatures and docstrings:
- def test_execute_command_oserror(self): Testing stdout and stderr is correctly captured upon OSError.
- def test_execute_command_stdout(self): Testing stdout output is corr... | Implement the Python class `UtilityTest` described below.
Class description:
Implement the UtilityTest class.
Method signatures and docstrings:
- def test_execute_command_oserror(self): Testing stdout and stderr is correctly captured upon OSError.
- def test_execute_command_stdout(self): Testing stdout output is corr... | 3fb199658f68e7debf4906d9ce32a9a307e39243 | <|skeleton|>
class UtilityTest:
def test_execute_command_oserror(self):
"""Testing stdout and stderr is correctly captured upon OSError."""
<|body_0|>
def test_execute_command_stdout(self):
"""Testing stdout output is correctly captured."""
<|body_1|>
def test_execute_comm... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UtilityTest:
def test_execute_command_oserror(self):
"""Testing stdout and stderr is correctly captured upon OSError."""
response = utility.ExecutorResponse()
err_msg = 'Testing handling of OSError'
with patch('subprocess.run') as mock_run:
mock_run.side_effect = Ma... | the_stack_v2_python_sparse | sdk/python/kfp/cli/diagnose_me/utility_test.py | kubeflow/pipelines | train | 3,434 | |
ec53f19c80eb1e296e12949fc45f5cd3af31f792 | [
"super().__init__()\nself.tade1 = TADELayer(in_channels=in_channels, aux_channels=aux_channels, kernel_size=kernel_size, bias=bias, upsample_factor=1, upsample_mode=upsample_mode)\nself.gated_conv1 = torch.nn.Conv1d(in_channels, in_channels * 2, kernel_size, 1, bias=bias, padding=(kernel_size - 1) // 2)\nself.tade2... | <|body_start_0|>
super().__init__()
self.tade1 = TADELayer(in_channels=in_channels, aux_channels=aux_channels, kernel_size=kernel_size, bias=bias, upsample_factor=1, upsample_mode=upsample_mode)
self.gated_conv1 = torch.nn.Conv1d(in_channels, in_channels * 2, kernel_size, 1, bias=bias, padding=(... | TADEResBlock module. | TADEResBlock | [
"MIT",
"LicenseRef-scancode-proprietary-license",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TADEResBlock:
"""TADEResBlock module."""
def __init__(self, in_channels=64, aux_channels=80, kernel_size=9, dilation=2, bias=True, upsample_factor=2, upsample_mode='nearest', gated_function='softmax'):
"""Initialize TADEResBlock module."""
<|body_0|>
def forward(self, x,... | stack_v2_sparse_classes_10k_train_000643 | 4,805 | permissive | [
{
"docstring": "Initialize TADEResBlock module.",
"name": "__init__",
"signature": "def __init__(self, in_channels=64, aux_channels=80, kernel_size=9, dilation=2, bias=True, upsample_factor=2, upsample_mode='nearest', gated_function='softmax')"
},
{
"docstring": "Calculate forward propagation. A... | 2 | stack_v2_sparse_classes_30k_train_000062 | Implement the Python class `TADEResBlock` described below.
Class description:
TADEResBlock module.
Method signatures and docstrings:
- def __init__(self, in_channels=64, aux_channels=80, kernel_size=9, dilation=2, bias=True, upsample_factor=2, upsample_mode='nearest', gated_function='softmax'): Initialize TADEResBloc... | Implement the Python class `TADEResBlock` described below.
Class description:
TADEResBlock module.
Method signatures and docstrings:
- def __init__(self, in_channels=64, aux_channels=80, kernel_size=9, dilation=2, bias=True, upsample_factor=2, upsample_mode='nearest', gated_function='softmax'): Initialize TADEResBloc... | c68b4590ab20eaf55e0b96b82325a90177fffd5c | <|skeleton|>
class TADEResBlock:
"""TADEResBlock module."""
def __init__(self, in_channels=64, aux_channels=80, kernel_size=9, dilation=2, bias=True, upsample_factor=2, upsample_mode='nearest', gated_function='softmax'):
"""Initialize TADEResBlock module."""
<|body_0|>
def forward(self, x,... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TADEResBlock:
"""TADEResBlock module."""
def __init__(self, in_channels=64, aux_channels=80, kernel_size=9, dilation=2, bias=True, upsample_factor=2, upsample_mode='nearest', gated_function='softmax'):
"""Initialize TADEResBlock module."""
super().__init__()
self.tade1 = TADELayer... | the_stack_v2_python_sparse | parallel_wavegan/layers/tade_res_block.py | kan-bayashi/ParallelWaveGAN | train | 1,405 |
dc12b70e917bb37106704c6059efc1f325368d92 | [
"self.items = sorted(items) if items else []\nself.sorted = False\nself.sorting_parameters = sorting_parameters",
"if not newitem in self.items:\n self.items.append(newitem)\n self.sorted = False",
"if not self.sorted:\n self.items.sort(**self.sorting_parameters)\n self.sorted = True"
] | <|body_start_0|>
self.items = sorted(items) if items else []
self.sorted = False
self.sorting_parameters = sorting_parameters
<|end_body_0|>
<|body_start_1|>
if not newitem in self.items:
self.items.append(newitem)
self.sorted = False
<|end_body_1|>
<|body_start... | Loose Python equivalent of std::set. makes sure list is sorted when it is accessed (searched, iterated over, etc.), but not until then. Basically, sort lazily. Items are also unique. | OrderedSet | [
"BSD-3-Clause",
"BSD-2-Clause-Views",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrderedSet:
"""Loose Python equivalent of std::set. makes sure list is sorted when it is accessed (searched, iterated over, etc.), but not until then. Basically, sort lazily. Items are also unique."""
def __init__(self, items=None, **sorting_parameters):
"""Initialize the set with a ... | stack_v2_sparse_classes_10k_train_000644 | 1,239 | permissive | [
{
"docstring": "Initialize the set with a list of items, or not at all. They will be sorted automatically. items = list of initial items sorting_parameters = keyword arguments which will be passed to sort",
"name": "__init__",
"signature": "def __init__(self, items=None, **sorting_parameters)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_001361 | Implement the Python class `OrderedSet` described below.
Class description:
Loose Python equivalent of std::set. makes sure list is sorted when it is accessed (searched, iterated over, etc.), but not until then. Basically, sort lazily. Items are also unique.
Method signatures and docstrings:
- def __init__(self, item... | Implement the Python class `OrderedSet` described below.
Class description:
Loose Python equivalent of std::set. makes sure list is sorted when it is accessed (searched, iterated over, etc.), but not until then. Basically, sort lazily. Items are also unique.
Method signatures and docstrings:
- def __init__(self, item... | 9a85b997ddf0a3d7c50ab109cb3b91e71743c7a3 | <|skeleton|>
class OrderedSet:
"""Loose Python equivalent of std::set. makes sure list is sorted when it is accessed (searched, iterated over, etc.), but not until then. Basically, sort lazily. Items are also unique."""
def __init__(self, items=None, **sorting_parameters):
"""Initialize the set with a ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OrderedSet:
"""Loose Python equivalent of std::set. makes sure list is sorted when it is accessed (searched, iterated over, etc.), but not until then. Basically, sort lazily. Items are also unique."""
def __init__(self, items=None, **sorting_parameters):
"""Initialize the set with a list of items... | the_stack_v2_python_sparse | mobile/pcap2har/orderedset.py | jpvincent/WPT-server | train | 17 |
0e7a0fb6d3bae406de8ccf2e614514c9c2fff34b | [
"cur_obj = self\nfor key in list_of_keys:\n cur_obj = cur_obj.get(key)\n if not cur_obj:\n break\nreturn cur_obj",
"inv_map = {}\nfor k, v in self.items():\n if sys.version_info < (3, 0):\n acceptable_v_instance = isinstance(v, (str, int, float, long))\n else:\n acceptable_v_insta... | <|body_start_0|>
cur_obj = self
for key in list_of_keys:
cur_obj = cur_obj.get(key)
if not cur_obj:
break
return cur_obj
<|end_body_0|>
<|body_start_1|>
inv_map = {}
for k, v in self.items():
if sys.version_info < (3, 0):
... | This class expands on the dictionary class by adding the gettree class method. | adv_dict | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class adv_dict:
"""This class expands on the dictionary class by adding the gettree class method."""
def get_tree(self, list_of_keys):
"""gettree will extract the value from a nested tree INPUT list_of_keys: a list of keys ie. ['key1', 'key2'] USAGE >>> # Access the value for key2 within t... | stack_v2_sparse_classes_10k_train_000645 | 22,796 | permissive | [
{
"docstring": "gettree will extract the value from a nested tree INPUT list_of_keys: a list of keys ie. ['key1', 'key2'] USAGE >>> # Access the value for key2 within the nested dictionary >>> adv_dict({'key1': {'key2': 'value'}}).gettree(['key1', 'key2']) 'value'",
"name": "get_tree",
"signature": "def... | 2 | stack_v2_sparse_classes_30k_train_006726 | Implement the Python class `adv_dict` described below.
Class description:
This class expands on the dictionary class by adding the gettree class method.
Method signatures and docstrings:
- def get_tree(self, list_of_keys): gettree will extract the value from a nested tree INPUT list_of_keys: a list of keys ie. ['key1... | Implement the Python class `adv_dict` described below.
Class description:
This class expands on the dictionary class by adding the gettree class method.
Method signatures and docstrings:
- def get_tree(self, list_of_keys): gettree will extract the value from a nested tree INPUT list_of_keys: a list of keys ie. ['key1... | b4cc546485bddbbe26de6a80b629350314db6422 | <|skeleton|>
class adv_dict:
"""This class expands on the dictionary class by adding the gettree class method."""
def get_tree(self, list_of_keys):
"""gettree will extract the value from a nested tree INPUT list_of_keys: a list of keys ie. ['key1', 'key2'] USAGE >>> # Access the value for key2 within t... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class adv_dict:
"""This class expands on the dictionary class by adding the gettree class method."""
def get_tree(self, list_of_keys):
"""gettree will extract the value from a nested tree INPUT list_of_keys: a list of keys ie. ['key1', 'key2'] USAGE >>> # Access the value for key2 within the nested dic... | the_stack_v2_python_sparse | genemethods/cgecore/utility.py | OLC-LOC-Bioinformatics/genemethods | train | 1 |
54c4f3520d5d633aec41806b924b17ff1faf61a8 | [
"QtWidgets.QDialog.__init__(self)\nself.df = pandaTable\nself.layout = QtWidgets.QGridLayout(self)\nself.columnSelect = QtWidgets.QComboBox()\nself.columnSelect.addItems(self.df.columns.values)\nself.layout.addWidget(self.columnSelect, 0, 1)\nself.layout.addWidget(QtWidgets.QLabel('Column:'), 0, 0)\nself.separatorL... | <|body_start_0|>
QtWidgets.QDialog.__init__(self)
self.df = pandaTable
self.layout = QtWidgets.QGridLayout(self)
self.columnSelect = QtWidgets.QComboBox()
self.columnSelect.addItems(self.df.columns.values)
self.layout.addWidget(self.columnSelect, 0, 1)
self.layout... | A dialog box to get the information required by the newRowsOnSeparator function. | NewRowsOnSeparatorDialogBox | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NewRowsOnSeparatorDialogBox:
"""A dialog box to get the information required by the newRowsOnSeparator function."""
def __init__(self, pandaTable, parent):
"""Initializes the UI and sets the two dropdowns to display column names of the active Panda."""
<|body_0|>
def get... | stack_v2_sparse_classes_10k_train_000646 | 29,548 | no_license | [
{
"docstring": "Initializes the UI and sets the two dropdowns to display column names of the active Panda.",
"name": "__init__",
"signature": "def __init__(self, pandaTable, parent)"
},
{
"docstring": "Returns the user's input",
"name": "getResults",
"signature": "def getResults(self, pa... | 2 | stack_v2_sparse_classes_30k_train_001999 | Implement the Python class `NewRowsOnSeparatorDialogBox` described below.
Class description:
A dialog box to get the information required by the newRowsOnSeparator function.
Method signatures and docstrings:
- def __init__(self, pandaTable, parent): Initializes the UI and sets the two dropdowns to display column name... | Implement the Python class `NewRowsOnSeparatorDialogBox` described below.
Class description:
A dialog box to get the information required by the newRowsOnSeparator function.
Method signatures and docstrings:
- def __init__(self, pandaTable, parent): Initializes the UI and sets the two dropdowns to display column name... | 1a3c5ad967472faf66236a311cc07a5128f5f911 | <|skeleton|>
class NewRowsOnSeparatorDialogBox:
"""A dialog box to get the information required by the newRowsOnSeparator function."""
def __init__(self, pandaTable, parent):
"""Initializes the UI and sets the two dropdowns to display column names of the active Panda."""
<|body_0|>
def get... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NewRowsOnSeparatorDialogBox:
"""A dialog box to get the information required by the newRowsOnSeparator function."""
def __init__(self, pandaTable, parent):
"""Initializes the UI and sets the two dropdowns to display column names of the active Panda."""
QtWidgets.QDialog.__init__(self)
... | the_stack_v2_python_sparse | datatool/gui/Model.py | scottawalton/datatool | train | 0 |
4982f4aff17a94f65f753fb1ec1b6a2656bd97ea | [
"message = (tag, ':', text_string)\nif global_step is not None:\n message = ('Global step', global_step, ',') + message\nprint(*message)\nreturn super().add_text(tag, text_string, global_step, walltime)",
"message = (tag, ':', scalar_value)\nif global_step:\n message = ('Global step', global_step, ',') + me... | <|body_start_0|>
message = (tag, ':', text_string)
if global_step is not None:
message = ('Global step', global_step, ',') + message
print(*message)
return super().add_text(tag, text_string, global_step, walltime)
<|end_body_0|>
<|body_start_1|>
message = (tag, ':', ... | SummaryWriter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SummaryWriter:
def add_text(self, tag, text_string, global_step=None, walltime=None):
"""Prints to console before running tensorboardX.SummaryWriter.add_text()"""
<|body_0|>
def add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
"""Prints to consol... | stack_v2_sparse_classes_10k_train_000647 | 5,926 | no_license | [
{
"docstring": "Prints to console before running tensorboardX.SummaryWriter.add_text()",
"name": "add_text",
"signature": "def add_text(self, tag, text_string, global_step=None, walltime=None)"
},
{
"docstring": "Prints to console before running tensorboardX.SummaryWriter.add_scalar()",
"nam... | 2 | stack_v2_sparse_classes_30k_train_004527 | Implement the Python class `SummaryWriter` described below.
Class description:
Implement the SummaryWriter class.
Method signatures and docstrings:
- def add_text(self, tag, text_string, global_step=None, walltime=None): Prints to console before running tensorboardX.SummaryWriter.add_text()
- def add_scalar(self, tag... | Implement the Python class `SummaryWriter` described below.
Class description:
Implement the SummaryWriter class.
Method signatures and docstrings:
- def add_text(self, tag, text_string, global_step=None, walltime=None): Prints to console before running tensorboardX.SummaryWriter.add_text()
- def add_scalar(self, tag... | e0f6183e6b669c078793b326839665f69b3f324e | <|skeleton|>
class SummaryWriter:
def add_text(self, tag, text_string, global_step=None, walltime=None):
"""Prints to console before running tensorboardX.SummaryWriter.add_text()"""
<|body_0|>
def add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
"""Prints to consol... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SummaryWriter:
def add_text(self, tag, text_string, global_step=None, walltime=None):
"""Prints to console before running tensorboardX.SummaryWriter.add_text()"""
message = (tag, ':', text_string)
if global_step is not None:
message = ('Global step', global_step, ',') + mes... | the_stack_v2_python_sparse | experiments/base.py | ctrl-q/pass-the-torch | train | 0 | |
bb170026178c714934d782b7989b93ed3159126d | [
"min_height = float('inf')\nupdated = False\n\ndef traverse(n, height=0):\n nonlocal min_height, updated\n if n == None:\n if height < min_height:\n if updated:\n return False\n else:\n if min_height < float('inf'):\n updated = True... | <|body_start_0|>
min_height = float('inf')
updated = False
def traverse(n, height=0):
nonlocal min_height, updated
if n == None:
if height < min_height:
if updated:
return False
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isCompleteTree(self, root: TreeNode) -> bool:
"""May 30, 2020 18:23"""
<|body_0|>
def isCompleteTree(self, root: Optional[TreeNode]) -> bool:
"""Apr 23, 2023 17:45"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
min_height = float('inf... | stack_v2_sparse_classes_10k_train_000648 | 3,289 | no_license | [
{
"docstring": "May 30, 2020 18:23",
"name": "isCompleteTree",
"signature": "def isCompleteTree(self, root: TreeNode) -> bool"
},
{
"docstring": "Apr 23, 2023 17:45",
"name": "isCompleteTree",
"signature": "def isCompleteTree(self, root: Optional[TreeNode]) -> bool"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root: TreeNode) -> bool: May 30, 2020 18:23
- def isCompleteTree(self, root: Optional[TreeNode]) -> bool: Apr 23, 2023 17:45 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root: TreeNode) -> bool: May 30, 2020 18:23
- def isCompleteTree(self, root: Optional[TreeNode]) -> bool: Apr 23, 2023 17:45
<|skeleton|>
class Solution... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def isCompleteTree(self, root: TreeNode) -> bool:
"""May 30, 2020 18:23"""
<|body_0|>
def isCompleteTree(self, root: Optional[TreeNode]) -> bool:
"""Apr 23, 2023 17:45"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isCompleteTree(self, root: TreeNode) -> bool:
"""May 30, 2020 18:23"""
min_height = float('inf')
updated = False
def traverse(n, height=0):
nonlocal min_height, updated
if n == None:
if height < min_height:
... | the_stack_v2_python_sparse | leetcode/solved/998_Check_Completeness_of_a_Binary_Tree/solution.py | sungminoh/algorithms | train | 0 | |
8ec6597d5796ab7a2256d297d46718de9915f5ba | [
"self._linode = li\nself._node_id = node_id\nself._attr_extra_state_attributes = {}\nself._attr_name = None",
"data = None\nself._linode.update()\nif self._linode.data is not None:\n for node in self._linode.data:\n if node.id == self._node_id:\n data = node\nif data is not None:\n self._a... | <|body_start_0|>
self._linode = li
self._node_id = node_id
self._attr_extra_state_attributes = {}
self._attr_name = None
<|end_body_0|>
<|body_start_1|>
data = None
self._linode.update()
if self._linode.data is not None:
for node in self._linode.data:... | Representation of a Linode droplet sensor. | LinodeBinarySensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinodeBinarySensor:
"""Representation of a Linode droplet sensor."""
def __init__(self, li, node_id):
"""Initialize a new Linode sensor."""
<|body_0|>
def update(self) -> None:
"""Update state of sensor."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_000649 | 2,638 | permissive | [
{
"docstring": "Initialize a new Linode sensor.",
"name": "__init__",
"signature": "def __init__(self, li, node_id)"
},
{
"docstring": "Update state of sensor.",
"name": "update",
"signature": "def update(self) -> None"
}
] | 2 | null | Implement the Python class `LinodeBinarySensor` described below.
Class description:
Representation of a Linode droplet sensor.
Method signatures and docstrings:
- def __init__(self, li, node_id): Initialize a new Linode sensor.
- def update(self) -> None: Update state of sensor. | Implement the Python class `LinodeBinarySensor` described below.
Class description:
Representation of a Linode droplet sensor.
Method signatures and docstrings:
- def __init__(self, li, node_id): Initialize a new Linode sensor.
- def update(self) -> None: Update state of sensor.
<|skeleton|>
class LinodeBinarySensor... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class LinodeBinarySensor:
"""Representation of a Linode droplet sensor."""
def __init__(self, li, node_id):
"""Initialize a new Linode sensor."""
<|body_0|>
def update(self) -> None:
"""Update state of sensor."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LinodeBinarySensor:
"""Representation of a Linode droplet sensor."""
def __init__(self, li, node_id):
"""Initialize a new Linode sensor."""
self._linode = li
self._node_id = node_id
self._attr_extra_state_attributes = {}
self._attr_name = None
def update(self)... | the_stack_v2_python_sparse | homeassistant/components/linode/binary_sensor.py | home-assistant/core | train | 35,501 |
c68e0d1d68fe9ae72cc4539b93bb6b6d35ac49e1 | [
"self.cluster_partition_id = cluster_partition_id\nself.job_id = job_id\nself.job_name = job_name\nself.job_uid = job_uid\nself.object_name = object_name\nself.os_type = os_type\nself.registered_source = registered_source\nself.snapshotted_source = snapshotted_source\nself.versions = versions\nself.view_box_id = vi... | <|body_start_0|>
self.cluster_partition_id = cluster_partition_id
self.job_id = job_id
self.job_name = job_name
self.job_uid = job_uid
self.object_name = object_name
self.os_type = os_type
self.registered_source = registered_source
self.snapshotted_source ... | Implementation of the 'ObjectSnapshotInfo' model. Specifies information about an object that has been backed up. Attributes: cluster_partition_id (long|int): Specifies the Cohesity Cluster partition id where this object is stored. job_id (long|int): Specifies the id for the Protection Job that is currently associated w... | ObjectSnapshotInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjectSnapshotInfo:
"""Implementation of the 'ObjectSnapshotInfo' model. Specifies information about an object that has been backed up. Attributes: cluster_partition_id (long|int): Specifies the Cohesity Cluster partition id where this object is stored. job_id (long|int): Specifies the id for the... | stack_v2_sparse_classes_10k_train_000650 | 6,352 | permissive | [
{
"docstring": "Constructor for the ObjectSnapshotInfo class",
"name": "__init__",
"signature": "def __init__(self, cluster_partition_id=None, job_id=None, job_name=None, job_uid=None, object_name=None, os_type=None, registered_source=None, snapshotted_source=None, versions=None, view_box_id=None, view_... | 2 | null | Implement the Python class `ObjectSnapshotInfo` described below.
Class description:
Implementation of the 'ObjectSnapshotInfo' model. Specifies information about an object that has been backed up. Attributes: cluster_partition_id (long|int): Specifies the Cohesity Cluster partition id where this object is stored. job_... | Implement the Python class `ObjectSnapshotInfo` described below.
Class description:
Implementation of the 'ObjectSnapshotInfo' model. Specifies information about an object that has been backed up. Attributes: cluster_partition_id (long|int): Specifies the Cohesity Cluster partition id where this object is stored. job_... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class ObjectSnapshotInfo:
"""Implementation of the 'ObjectSnapshotInfo' model. Specifies information about an object that has been backed up. Attributes: cluster_partition_id (long|int): Specifies the Cohesity Cluster partition id where this object is stored. job_id (long|int): Specifies the id for the... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ObjectSnapshotInfo:
"""Implementation of the 'ObjectSnapshotInfo' model. Specifies information about an object that has been backed up. Attributes: cluster_partition_id (long|int): Specifies the Cohesity Cluster partition id where this object is stored. job_id (long|int): Specifies the id for the Protection J... | the_stack_v2_python_sparse | cohesity_management_sdk/models/object_snapshot_info.py | cohesity/management-sdk-python | train | 24 |
e21a20afbea56534d5163025dc7f9af39c5520cd | [
"super().__init__()\naction_latent_features = 128\nif action_converter.is_singular_discrete:\n self.action_encoder = nn.Embedding(action_converter.shape[0], action_latent_features)\nelse:\n self.action_encoder = nn.Linear(action_converter.shape[0], action_latent_features)\nself.hidden = nn.Sequential(nn.Linea... | <|body_start_0|>
super().__init__()
action_latent_features = 128
if action_converter.is_singular_discrete:
self.action_encoder = nn.Embedding(action_converter.shape[0], action_latent_features)
else:
self.action_encoder = nn.Linear(action_converter.shape[0], action... | ForwardModel | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ForwardModel:
def __init__(self, action_converter: ActionSpace, state_latent_features: int):
""":param action_converter: :param state_latent_features:"""
<|body_0|>
def forward(self, state_latent: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
""":param state_l... | stack_v2_sparse_classes_10k_train_000651 | 8,668 | permissive | [
{
"docstring": ":param action_converter: :param state_latent_features:",
"name": "__init__",
"signature": "def __init__(self, action_converter: ActionSpace, state_latent_features: int)"
},
{
"docstring": ":param state_latent: :param action: :return:",
"name": "forward",
"signature": "def... | 2 | stack_v2_sparse_classes_30k_train_004056 | Implement the Python class `ForwardModel` described below.
Class description:
Implement the ForwardModel class.
Method signatures and docstrings:
- def __init__(self, action_converter: ActionSpace, state_latent_features: int): :param action_converter: :param state_latent_features:
- def forward(self, state_latent: to... | Implement the Python class `ForwardModel` described below.
Class description:
Implement the ForwardModel class.
Method signatures and docstrings:
- def __init__(self, action_converter: ActionSpace, state_latent_features: int): :param action_converter: :param state_latent_features:
- def forward(self, state_latent: to... | 21e3564696062b67151b013fd5e47df46cf44aa5 | <|skeleton|>
class ForwardModel:
def __init__(self, action_converter: ActionSpace, state_latent_features: int):
""":param action_converter: :param state_latent_features:"""
<|body_0|>
def forward(self, state_latent: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
""":param state_l... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ForwardModel:
def __init__(self, action_converter: ActionSpace, state_latent_features: int):
""":param action_converter: :param state_latent_features:"""
super().__init__()
action_latent_features = 128
if action_converter.is_singular_discrete:
self.action_encoder = ... | the_stack_v2_python_sparse | neodroidagent/utilities/exploration/intrinsic_signals/torch_isp/curiosity/icm.py | sintefneodroid/agent | train | 9 | |
28b2d67b6584d79dcc907f6282c07027b59eac75 | [
"with tf.Graph().as_default():\n lp_dict = layer_collection.LayerParametersDict()\n x = tf.constant(0)\n y0 = tf.constant(0)\n y1 = tf.constant(0)\n z0 = tf.constant(0)\n z1 = tf.constant(0)\n keys = [x, (y0, y1), [z0, z1]]\n for key in keys:\n lp_dict[key] = key\n for key in keys:... | <|body_start_0|>
with tf.Graph().as_default():
lp_dict = layer_collection.LayerParametersDict()
x = tf.constant(0)
y0 = tf.constant(0)
y1 = tf.constant(0)
z0 = tf.constant(0)
z1 = tf.constant(0)
keys = [x, (y0, y1), [z0, z1]]
... | LayerParametersDictTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LayerParametersDictTest:
def testSetItem(self):
"""Ensure insertion, contains, retrieval works for supported key types."""
<|body_0|>
def testSetItemOverlap(self):
"""Ensure insertion fails if key overlaps with existing key."""
<|body_1|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_10k_train_000652 | 22,448 | permissive | [
{
"docstring": "Ensure insertion, contains, retrieval works for supported key types.",
"name": "testSetItem",
"signature": "def testSetItem(self)"
},
{
"docstring": "Ensure insertion fails if key overlaps with existing key.",
"name": "testSetItemOverlap",
"signature": "def testSetItemOve... | 2 | stack_v2_sparse_classes_30k_train_006355 | Implement the Python class `LayerParametersDictTest` described below.
Class description:
Implement the LayerParametersDictTest class.
Method signatures and docstrings:
- def testSetItem(self): Ensure insertion, contains, retrieval works for supported key types.
- def testSetItemOverlap(self): Ensure insertion fails i... | Implement the Python class `LayerParametersDictTest` described below.
Class description:
Implement the LayerParametersDictTest class.
Method signatures and docstrings:
- def testSetItem(self): Ensure insertion, contains, retrieval works for supported key types.
- def testSetItemOverlap(self): Ensure insertion fails i... | ddad6375bbdebfae809bccfd3a5c3db073128764 | <|skeleton|>
class LayerParametersDictTest:
def testSetItem(self):
"""Ensure insertion, contains, retrieval works for supported key types."""
<|body_0|>
def testSetItemOverlap(self):
"""Ensure insertion fails if key overlaps with existing key."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LayerParametersDictTest:
def testSetItem(self):
"""Ensure insertion, contains, retrieval works for supported key types."""
with tf.Graph().as_default():
lp_dict = layer_collection.LayerParametersDict()
x = tf.constant(0)
y0 = tf.constant(0)
y1 = ... | the_stack_v2_python_sparse | kfac/python/kernel_tests/layer_collection_test.py | tensorflow/kfac | train | 193 | |
02210bcca96804748a836b6cc4f8043162ef4535 | [
"try:\n db_creds = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../config/db_creds.json')\n user_creds = json.load(open(db_creds, 'r'))['elastic'][access]\n hosts = user_creds['hosts']\n http_auth = (user_creds['user'], user_creds['pass'])\nexcept:\n env_vars = ['ELASTIC_HOST', 'ELASTIC_... | <|body_start_0|>
try:
db_creds = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../config/db_creds.json')
user_creds = json.load(open(db_creds, 'r'))['elastic'][access]
hosts = user_creds['hosts']
http_auth = (user_creds['user'], user_creds['pass'])
... | Class representing a connection to the Elastic Cloud cluster (ElasticSearch) | ElasticConnection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ElasticConnection:
"""Class representing a connection to the Elastic Cloud cluster (ElasticSearch)"""
def __init__(self, local=local, access='read_only'):
"""Args: local (bool): True to use local config file, False for environment variables. Default: False access (str): Level of acce... | stack_v2_sparse_classes_10k_train_000653 | 6,259 | no_license | [
{
"docstring": "Args: local (bool): True to use local config file, False for environment variables. Default: False access (str): Level of access. e.g. \"admin\", \"read_only\", or \"annotator\" db (str): Desired database. e.g. \"test\" or \"production\" Returns: pymongo Client.",
"name": "__init__",
"si... | 2 | stack_v2_sparse_classes_30k_train_005297 | Implement the Python class `ElasticConnection` described below.
Class description:
Class representing a connection to the Elastic Cloud cluster (ElasticSearch)
Method signatures and docstrings:
- def __init__(self, local=local, access='read_only'): Args: local (bool): True to use local config file, False for environm... | Implement the Python class `ElasticConnection` described below.
Class description:
Class representing a connection to the Elastic Cloud cluster (ElasticSearch)
Method signatures and docstrings:
- def __init__(self, local=local, access='read_only'): Args: local (bool): True to use local config file, False for environm... | b10cd933cbfaa969e00f68ad7a895be447472163 | <|skeleton|>
class ElasticConnection:
"""Class representing a connection to the Elastic Cloud cluster (ElasticSearch)"""
def __init__(self, local=local, access='read_only'):
"""Args: local (bool): True to use local config file, False for environment variables. Default: False access (str): Level of acce... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ElasticConnection:
"""Class representing a connection to the Elastic Cloud cluster (ElasticSearch)"""
def __init__(self, local=local, access='read_only'):
"""Args: local (bool): True to use local config file, False for environment variables. Default: False access (str): Level of access. e.g. "adm... | the_stack_v2_python_sparse | matstract/models/database.py | abhinavwidak/matstract | train | 0 |
197829184cb24021170fa3b05836ea0ef845c0dc | [
"self.type = type\nself.name = name\nself.width = width\nself.cls = cls",
"assert elem.tag in ['input', 'output', 'clock'], elem.tag\nport = Port(type=PortType.from_string(elem.tag), name=elem.attrib['name'], width=int(elem.get('num_pins', '1')), cls=elem.get('port_class', None))\nreturn port",
"if range_spec i... | <|body_start_0|>
self.type = type
self.name = name
self.width = width
self.cls = cls
<|end_body_0|>
<|body_start_1|>
assert elem.tag in ['input', 'output', 'clock'], elem.tag
port = Port(type=PortType.from_string(elem.tag), name=elem.attrib['name'], width=int(elem.get('n... | A port of pb_type | Port | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Port:
"""A port of pb_type"""
def __init__(self, type, name, width=1, cls=None):
"""Basic constructor"""
<|body_0|>
def from_etree(elem):
"""Create the object from its ElementTree representation"""
<|body_1|>
def yield_pins(self, range_spec=None):
... | stack_v2_sparse_classes_10k_train_000654 | 11,880 | permissive | [
{
"docstring": "Basic constructor",
"name": "__init__",
"signature": "def __init__(self, type, name, width=1, cls=None)"
},
{
"docstring": "Create the object from its ElementTree representation",
"name": "from_etree",
"signature": "def from_etree(elem)"
},
{
"docstring": "Yields ... | 3 | stack_v2_sparse_classes_30k_train_006160 | Implement the Python class `Port` described below.
Class description:
A port of pb_type
Method signatures and docstrings:
- def __init__(self, type, name, width=1, cls=None): Basic constructor
- def from_etree(elem): Create the object from its ElementTree representation
- def yield_pins(self, range_spec=None): Yields... | Implement the Python class `Port` described below.
Class description:
A port of pb_type
Method signatures and docstrings:
- def __init__(self, type, name, width=1, cls=None): Basic constructor
- def from_etree(elem): Create the object from its ElementTree representation
- def yield_pins(self, range_spec=None): Yields... | 835a40534f9efd70770d74f56f25fef6cfc6ebc6 | <|skeleton|>
class Port:
"""A port of pb_type"""
def __init__(self, type, name, width=1, cls=None):
"""Basic constructor"""
<|body_0|>
def from_etree(elem):
"""Create the object from its ElementTree representation"""
<|body_1|>
def yield_pins(self, range_spec=None):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Port:
"""A port of pb_type"""
def __init__(self, type, name, width=1, cls=None):
"""Basic constructor"""
self.type = type
self.name = name
self.width = width
self.cls = cls
def from_etree(elem):
"""Create the object from its ElementTree representation"... | the_stack_v2_python_sparse | f4pga/utils/quicklogic/repacker/pb_type.py | f4pga/f4pga | train | 19 |
b9cd4e1ab9cb30bf63b8487027b548f0388e16b0 | [
"super().__init__()\nself.norm = nn.LayerNorm(query_dim)\nself.att = SelfAttention(name=name, how=how, query_dim=query_dim, cross_attention_dim=cross_attention_dim, head_dim=head_dim, num_heads=num_heads, dropout=dropout, bias=bias, slice_size=slice_size, **kwargs)",
"residual = x\nx = self.norm(x)\nx = self.att(... | <|body_start_0|>
super().__init__()
self.norm = nn.LayerNorm(query_dim)
self.att = SelfAttention(name=name, how=how, query_dim=query_dim, cross_attention_dim=cross_attention_dim, head_dim=head_dim, num_heads=num_heads, dropout=dropout, bias=bias, slice_size=slice_size, **kwargs)
<|end_body_0|>
... | SelfAttentionBlock | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SelfAttentionBlock:
def __init__(self, how: str, query_dim: int, name: str='exact', cross_attention_dim: int=None, num_heads: int=8, head_dim: int=64, dropout: float=0.0, bias: bool=False, slice_size: int=4, **kwargs) -> None:
"""Singular self-attention block. These can be stacked to for... | stack_v2_sparse_classes_10k_train_000655 | 10,093 | permissive | [
{
"docstring": "Singular self-attention block. These can be stacked to form a tranformer block. NOTE: Can be used as a cross-attention block if `cross_attention_dim` is given. Input Shape: (B, H'*W', query_dim). Output Shape: (B, H'*W', query_dim). Parameters ---------- name : str Name of the attention method. ... | 2 | stack_v2_sparse_classes_30k_train_002195 | Implement the Python class `SelfAttentionBlock` described below.
Class description:
Implement the SelfAttentionBlock class.
Method signatures and docstrings:
- def __init__(self, how: str, query_dim: int, name: str='exact', cross_attention_dim: int=None, num_heads: int=8, head_dim: int=64, dropout: float=0.0, bias: b... | Implement the Python class `SelfAttentionBlock` described below.
Class description:
Implement the SelfAttentionBlock class.
Method signatures and docstrings:
- def __init__(self, how: str, query_dim: int, name: str='exact', cross_attention_dim: int=None, num_heads: int=8, head_dim: int=64, dropout: float=0.0, bias: b... | 7f79405012eb934b419bbdba8de23f35e840ca85 | <|skeleton|>
class SelfAttentionBlock:
def __init__(self, how: str, query_dim: int, name: str='exact', cross_attention_dim: int=None, num_heads: int=8, head_dim: int=64, dropout: float=0.0, bias: bool=False, slice_size: int=4, **kwargs) -> None:
"""Singular self-attention block. These can be stacked to for... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SelfAttentionBlock:
def __init__(self, how: str, query_dim: int, name: str='exact', cross_attention_dim: int=None, num_heads: int=8, head_dim: int=64, dropout: float=0.0, bias: bool=False, slice_size: int=4, **kwargs) -> None:
"""Singular self-attention block. These can be stacked to form a tranformer... | the_stack_v2_python_sparse | cellseg_models_pytorch/modules/self_attention_modules.py | okunator/cellseg_models.pytorch | train | 43 | |
7fbc240e809f275c94cd1eae474ee11c6d760030 | [
"@skip(False)\ndef func(data):\n \"\"\"dummy the func method\"\"\"\n return data\nself.assert_(func('test') == 'test')",
"@skip(True)\ndef func(data):\n \"\"\"dummy the func method\"\"\"\n return data\nself.assert_(func('test') == None)",
"@skip(True, 'stub')\ndef func(data):\n \"\"\"dummy the fu... | <|body_start_0|>
@skip(False)
def func(data):
"""dummy the func method"""
return data
self.assert_(func('test') == 'test')
<|end_body_0|>
<|body_start_1|>
@skip(True)
def func(data):
"""dummy the func method"""
return data
... | Acceptance tests for unittestadditions.py | TestSkipDecorator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestSkipDecorator:
"""Acceptance tests for unittestadditions.py"""
def test_skip_false(self):
"""A skip(False) decorated function is executed properly"""
<|body_0|>
def test_skip_true(self):
"""A skip(True) decorated function is executed properly"""
<|bod... | stack_v2_sparse_classes_10k_train_000656 | 1,723 | no_license | [
{
"docstring": "A skip(False) decorated function is executed properly",
"name": "test_skip_false",
"signature": "def test_skip_false(self)"
},
{
"docstring": "A skip(True) decorated function is executed properly",
"name": "test_skip_true",
"signature": "def test_skip_true(self)"
},
{... | 3 | stack_v2_sparse_classes_30k_test_000024 | Implement the Python class `TestSkipDecorator` described below.
Class description:
Acceptance tests for unittestadditions.py
Method signatures and docstrings:
- def test_skip_false(self): A skip(False) decorated function is executed properly
- def test_skip_true(self): A skip(True) decorated function is executed prop... | Implement the Python class `TestSkipDecorator` described below.
Class description:
Acceptance tests for unittestadditions.py
Method signatures and docstrings:
- def test_skip_false(self): A skip(False) decorated function is executed properly
- def test_skip_true(self): A skip(True) decorated function is executed prop... | f458a4ce83f74d603362fe6b71eaa647ccc62fee | <|skeleton|>
class TestSkipDecorator:
"""Acceptance tests for unittestadditions.py"""
def test_skip_false(self):
"""A skip(False) decorated function is executed properly"""
<|body_0|>
def test_skip_true(self):
"""A skip(True) decorated function is executed properly"""
<|bod... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestSkipDecorator:
"""Acceptance tests for unittestadditions.py"""
def test_skip_false(self):
"""A skip(False) decorated function is executed properly"""
@skip(False)
def func(data):
"""dummy the func method"""
return data
self.assert_(func('test') ... | the_stack_v2_python_sparse | buildframework/helium/sf/python/pythoncore/lib/pythoncoretests/test_unittestadditions.py | anagovitsyn/oss.FCL.sftools.dev.build | train | 0 |
46e1b6040e5c41d9cd5760ec9902fce4a5acda80 | [
"self.client = aff4.FACTORY.Open(self.client_id, token=self.token)\nself.system = str(self.client.Get(self.client.Schema.SYSTEM))\nself.os_version = str(self.client.Get(self.client.Schema.OS_VERSION))\nself.os_major_version = self.os_version.split('.')[0]\nif self.use_tsk:\n self.path_type = rdfvalue.PathSpec.Pa... | <|body_start_0|>
self.client = aff4.FACTORY.Open(self.client_id, token=self.token)
self.system = str(self.client.Get(self.client.Schema.SYSTEM))
self.os_version = str(self.client.Get(self.client.Schema.OS_VERSION))
self.os_major_version = self.os_version.split('.')[0]
if self.use... | Do the initial work for a system investigation. This encapsulates the different platform specific modules. | WinSystemActivityInvestigation | [
"Apache-2.0",
"DOC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WinSystemActivityInvestigation:
"""Do the initial work for a system investigation. This encapsulates the different platform specific modules."""
def Start(self):
"""Start."""
<|body_0|>
def FinishFlow(self, responses):
"""Complete anything we need to do for each ... | stack_v2_sparse_classes_10k_train_000657 | 10,734 | permissive | [
{
"docstring": "Start.",
"name": "Start",
"signature": "def Start(self)"
},
{
"docstring": "Complete anything we need to do for each flow finishing.",
"name": "FinishFlow",
"signature": "def FinishFlow(self, responses)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000274 | Implement the Python class `WinSystemActivityInvestigation` described below.
Class description:
Do the initial work for a system investigation. This encapsulates the different platform specific modules.
Method signatures and docstrings:
- def Start(self): Start.
- def FinishFlow(self, responses): Complete anything we... | Implement the Python class `WinSystemActivityInvestigation` described below.
Class description:
Do the initial work for a system investigation. This encapsulates the different platform specific modules.
Method signatures and docstrings:
- def Start(self): Start.
- def FinishFlow(self, responses): Complete anything we... | ba1648b97a76f844ffb8e1891cc9e2680f9b1c6e | <|skeleton|>
class WinSystemActivityInvestigation:
"""Do the initial work for a system investigation. This encapsulates the different platform specific modules."""
def Start(self):
"""Start."""
<|body_0|>
def FinishFlow(self, responses):
"""Complete anything we need to do for each ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WinSystemActivityInvestigation:
"""Do the initial work for a system investigation. This encapsulates the different platform specific modules."""
def Start(self):
"""Start."""
self.client = aff4.FACTORY.Open(self.client_id, token=self.token)
self.system = str(self.client.Get(self.c... | the_stack_v2_python_sparse | lib/flows/general/automation.py | defaultnamehere/grr | train | 3 |
1a75e952d9d3e270051b2c7f7aae701f30d74a93 | [
"self.base_filters = filters or []\nself.polling_interval = polling_interval\nself.continue_interval = continue_interval or polling_interval\nself.should_continue = continue_func\nstart_timestamp = _GetTailStartingTimestamp(filters, num_prev_entries)\nlog.debug('start timestamp: {}'.format(start_timestamp))\nself.l... | <|body_start_0|>
self.base_filters = filters or []
self.polling_interval = polling_interval
self.continue_interval = continue_interval or polling_interval
self.should_continue = continue_func
start_timestamp = _GetTailStartingTimestamp(filters, num_prev_entries)
log.debug... | A class which fetches job logs. | LogFetcher | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogFetcher:
"""A class which fetches job logs."""
def __init__(self, filters=None, polling_interval=10, continue_func=lambda x: True, continue_interval=None, num_prev_entries=None):
"""Initializes the LogFetcher. Args: filters: list of string filters used in the API call. polling_int... | stack_v2_sparse_classes_10k_train_000658 | 9,319 | permissive | [
{
"docstring": "Initializes the LogFetcher. Args: filters: list of string filters used in the API call. polling_interval: amount of time to sleep between each poll. continue_func: One-arg function that takes in the number of empty polls and outputs a boolean to decide if we should keep polling or not. If not gi... | 3 | stack_v2_sparse_classes_30k_train_003018 | Implement the Python class `LogFetcher` described below.
Class description:
A class which fetches job logs.
Method signatures and docstrings:
- def __init__(self, filters=None, polling_interval=10, continue_func=lambda x: True, continue_interval=None, num_prev_entries=None): Initializes the LogFetcher. Args: filters:... | Implement the Python class `LogFetcher` described below.
Class description:
A class which fetches job logs.
Method signatures and docstrings:
- def __init__(self, filters=None, polling_interval=10, continue_func=lambda x: True, continue_interval=None, num_prev_entries=None): Initializes the LogFetcher. Args: filters:... | 85bb264e273568b5a0408f733b403c56373e2508 | <|skeleton|>
class LogFetcher:
"""A class which fetches job logs."""
def __init__(self, filters=None, polling_interval=10, continue_func=lambda x: True, continue_interval=None, num_prev_entries=None):
"""Initializes the LogFetcher. Args: filters: list of string filters used in the API call. polling_int... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LogFetcher:
"""A class which fetches job logs."""
def __init__(self, filters=None, polling_interval=10, continue_func=lambda x: True, continue_interval=None, num_prev_entries=None):
"""Initializes the LogFetcher. Args: filters: list of string filters used in the API call. polling_interval: amount... | the_stack_v2_python_sparse | google-cloud-sdk/lib/googlecloudsdk/command_lib/logs/stream.py | bopopescu/socialliteapp | train | 0 |
3792202cbca60b4a19deac319b8a16b7cba5d625 | [
"group1_antall, group1_image = detectFace(group1)\ngroup1_expectedNumber = 7\nself.assertEqual(group1_antall, group1_expectedNumber)",
"couple_antall, couple_image = detectFace(couple)\ncouple_expectedNumber = 2\nself.assertEqual(couple_antall, couple_expectedNumber)"
] | <|body_start_0|>
group1_antall, group1_image = detectFace(group1)
group1_expectedNumber = 7
self.assertEqual(group1_antall, group1_expectedNumber)
<|end_body_0|>
<|body_start_1|>
couple_antall, couple_image = detectFace(couple)
couple_expectedNumber = 2
self.assertEqual(... | Tests for the anonymise module | test_Anon | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test_Anon:
"""Tests for the anonymise module"""
def test_faceDetect_Group1(self):
"""Test for face detection of image (group1.jpg)"""
<|body_0|>
def test_faceDetect_Couple(self):
"""Test for face detection of image (couple.jpg)"""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_10k_train_000659 | 817 | no_license | [
{
"docstring": "Test for face detection of image (group1.jpg)",
"name": "test_faceDetect_Group1",
"signature": "def test_faceDetect_Group1(self)"
},
{
"docstring": "Test for face detection of image (couple.jpg)",
"name": "test_faceDetect_Couple",
"signature": "def test_faceDetect_Couple(... | 2 | stack_v2_sparse_classes_30k_train_000170 | Implement the Python class `test_Anon` described below.
Class description:
Tests for the anonymise module
Method signatures and docstrings:
- def test_faceDetect_Group1(self): Test for face detection of image (group1.jpg)
- def test_faceDetect_Couple(self): Test for face detection of image (couple.jpg) | Implement the Python class `test_Anon` described below.
Class description:
Tests for the anonymise module
Method signatures and docstrings:
- def test_faceDetect_Group1(self): Test for face detection of image (group1.jpg)
- def test_faceDetect_Couple(self): Test for face detection of image (couple.jpg)
<|skeleton|>
... | dd40d095231ed397cf69e3598f21483a3bcf11a6 | <|skeleton|>
class test_Anon:
"""Tests for the anonymise module"""
def test_faceDetect_Group1(self):
"""Test for face detection of image (group1.jpg)"""
<|body_0|>
def test_faceDetect_Couple(self):
"""Test for face detection of image (couple.jpg)"""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class test_Anon:
"""Tests for the anonymise module"""
def test_faceDetect_Group1(self):
"""Test for face detection of image (group1.jpg)"""
group1_antall, group1_image = detectFace(group1)
group1_expectedNumber = 7
self.assertEqual(group1_antall, group1_expectedNumber)
def ... | the_stack_v2_python_sparse | src/test_FaceDetect.py | jegerud/Sciprog2020project | train | 0 |
143758b7a4c3091ebe280a532eb9e012acd08437 | [
"super().__init__()\nself.extends: Sequence[Union[str, Dict[str, Any]]] = ''\nself.css: Optional[Union[Dict[str, Any], Combine]] = None",
"new_obj = self.__class__(self)\nnew_obj.extends = extends\nnew_obj.css = css\nreturn new_obj"
] | <|body_start_0|>
super().__init__()
self.extends: Sequence[Union[str, Dict[str, Any]]] = ''
self.css: Optional[Union[Dict[str, Any], Combine]] = None
<|end_body_0|>
<|body_start_1|>
new_obj = self.__class__(self)
new_obj.extends = extends
new_obj.css = css
return... | ``Var`` that, when called, allows to extend a previously defined css dict. Attributes ---------- extends : Sequence[Union[str, Dict[str, Any]]] The list of css we extend. An extend can be the name of a previously defined "extend" (key starting with `%`) in the same css dict (same level or above), or directly a dict. Se... | Extend | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Extend:
"""``Var`` that, when called, allows to extend a previously defined css dict. Attributes ---------- extends : Sequence[Union[str, Dict[str, Any]]] The list of css we extend. An extend can be the name of a previously defined "extend" (key starting with `%`) in the same css dict (same level... | stack_v2_sparse_classes_10k_train_000660 | 44,402 | permissive | [
{
"docstring": "Init the var and its attributes. For the parameters, see ``str``.",
"name": "__init__",
"signature": "def __init__(self, _str_: str) -> None"
},
{
"docstring": "Allow to have a css key defined multiple times. Parameters ---------- extends : Tuple[Union[str, Dict[str, Any]]] The l... | 2 | stack_v2_sparse_classes_30k_test_000145 | Implement the Python class `Extend` described below.
Class description:
``Var`` that, when called, allows to extend a previously defined css dict. Attributes ---------- extends : Sequence[Union[str, Dict[str, Any]]] The list of css we extend. An extend can be the name of a previously defined "extend" (key starting wit... | Implement the Python class `Extend` described below.
Class description:
``Var`` that, when called, allows to extend a previously defined css dict. Attributes ---------- extends : Sequence[Union[str, Dict[str, Any]]] The list of css we extend. An extend can be the name of a previously defined "extend" (key starting wit... | adeff652784f0d814835fd16a8cacab09f426922 | <|skeleton|>
class Extend:
"""``Var`` that, when called, allows to extend a previously defined css dict. Attributes ---------- extends : Sequence[Union[str, Dict[str, Any]]] The list of css we extend. An extend can be the name of a previously defined "extend" (key starting with `%`) in the same css dict (same level... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Extend:
"""``Var`` that, when called, allows to extend a previously defined css dict. Attributes ---------- extends : Sequence[Union[str, Dict[str, Any]]] The list of css we extend. An extend can be the name of a previously defined "extend" (key starting with `%`) in the same css dict (same level or above), o... | the_stack_v2_python_sparse | src/mixt/contrib/css/vars.py | twidi/mixt | train | 37 |
dc53df9448c0be28b7793616e9afa47ca9da9db9 | [
"self.chassis_serial = chassis_serial\nself.connected_to = connected_to\nself.hostname = hostname\nself.id = id\nself.ip = ip\nself.ipmi_ip = ipmi_ip\nself.ips = ips\nself.node_serial = node_serial\nself.node_ui_slot = node_ui_slot\nself.num_slots_in_chassis = num_slots_in_chassis\nself.product_model = product_mode... | <|body_start_0|>
self.chassis_serial = chassis_serial
self.connected_to = connected_to
self.hostname = hostname
self.id = id
self.ip = ip
self.ipmi_ip = ipmi_ip
self.ips = ips
self.node_serial = node_serial
self.node_ui_slot = node_ui_slot
... | Implementation of the 'FreeNodeInformation' model. Specifies the Metadata of a free Node on the network. Attributes: chassis_serial (string): Specifies the serial number of the Chassis the Node is installed in. connected_to (bool): Specifies whether or not this is the Node that is sending the response. hostname (string... | FreeNodeInformation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FreeNodeInformation:
"""Implementation of the 'FreeNodeInformation' model. Specifies the Metadata of a free Node on the network. Attributes: chassis_serial (string): Specifies the serial number of the Chassis the Node is installed in. connected_to (bool): Specifies whether or not this is the Node... | stack_v2_sparse_classes_10k_train_000661 | 4,722 | permissive | [
{
"docstring": "Constructor for the FreeNodeInformation class",
"name": "__init__",
"signature": "def __init__(self, chassis_serial=None, connected_to=None, hostname=None, id=None, ip=None, ipmi_ip=None, ips=None, node_serial=None, node_ui_slot=None, num_slots_in_chassis=None, product_model=None, slot_n... | 2 | null | Implement the Python class `FreeNodeInformation` described below.
Class description:
Implementation of the 'FreeNodeInformation' model. Specifies the Metadata of a free Node on the network. Attributes: chassis_serial (string): Specifies the serial number of the Chassis the Node is installed in. connected_to (bool): Sp... | Implement the Python class `FreeNodeInformation` described below.
Class description:
Implementation of the 'FreeNodeInformation' model. Specifies the Metadata of a free Node on the network. Attributes: chassis_serial (string): Specifies the serial number of the Chassis the Node is installed in. connected_to (bool): Sp... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class FreeNodeInformation:
"""Implementation of the 'FreeNodeInformation' model. Specifies the Metadata of a free Node on the network. Attributes: chassis_serial (string): Specifies the serial number of the Chassis the Node is installed in. connected_to (bool): Specifies whether or not this is the Node... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FreeNodeInformation:
"""Implementation of the 'FreeNodeInformation' model. Specifies the Metadata of a free Node on the network. Attributes: chassis_serial (string): Specifies the serial number of the Chassis the Node is installed in. connected_to (bool): Specifies whether or not this is the Node that is send... | the_stack_v2_python_sparse | cohesity_management_sdk/models/free_node_information.py | cohesity/management-sdk-python | train | 24 |
9788f13284e58b468d12fd133c8028ffc2ca2dce | [
"tempdir = tempfile.mkdtemp()\nmodel_path = os.path.join(tempdir, cls.MODEL_FILENAME)\nstripped_state_dict = consume_prefix_in_state_dict_if_present_not_in_place(state_dict, 'module.')\ntorch.save(stripped_state_dict, model_path)\ncheckpoint = cls.from_directory(tempdir)\nif preprocessor:\n checkpoint.set_prepro... | <|body_start_0|>
tempdir = tempfile.mkdtemp()
model_path = os.path.join(tempdir, cls.MODEL_FILENAME)
stripped_state_dict = consume_prefix_in_state_dict_if_present_not_in_place(state_dict, 'module.')
torch.save(stripped_state_dict, model_path)
checkpoint = cls.from_directory(tempd... | A :class:`~ray.train.Checkpoint` with Torch-specific functionality. | TorchCheckpoint | [
"MIT",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TorchCheckpoint:
"""A :class:`~ray.train.Checkpoint` with Torch-specific functionality."""
def from_state_dict(cls, state_dict: Dict[str, Any], *, preprocessor: Optional['Preprocessor']=None) -> 'TorchCheckpoint':
"""Create a :class:`~ray.train.Checkpoint` that stores a model state d... | stack_v2_sparse_classes_10k_train_000662 | 15,178 | permissive | [
{
"docstring": "Create a :class:`~ray.train.Checkpoint` that stores a model state dictionary. .. tip:: This is the recommended method for creating :class:`TorchCheckpoints<TorchCheckpoint>`. Args: state_dict: The model state dictionary to store in the checkpoint. preprocessor: A fitted preprocessor to be applie... | 3 | stack_v2_sparse_classes_30k_train_003668 | Implement the Python class `TorchCheckpoint` described below.
Class description:
A :class:`~ray.train.Checkpoint` with Torch-specific functionality.
Method signatures and docstrings:
- def from_state_dict(cls, state_dict: Dict[str, Any], *, preprocessor: Optional['Preprocessor']=None) -> 'TorchCheckpoint': Create a :... | Implement the Python class `TorchCheckpoint` described below.
Class description:
A :class:`~ray.train.Checkpoint` with Torch-specific functionality.
Method signatures and docstrings:
- def from_state_dict(cls, state_dict: Dict[str, Any], *, preprocessor: Optional['Preprocessor']=None) -> 'TorchCheckpoint': Create a :... | edba68c3e7cf255d1d6479329f305adb7fa4c3ed | <|skeleton|>
class TorchCheckpoint:
"""A :class:`~ray.train.Checkpoint` with Torch-specific functionality."""
def from_state_dict(cls, state_dict: Dict[str, Any], *, preprocessor: Optional['Preprocessor']=None) -> 'TorchCheckpoint':
"""Create a :class:`~ray.train.Checkpoint` that stores a model state d... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TorchCheckpoint:
"""A :class:`~ray.train.Checkpoint` with Torch-specific functionality."""
def from_state_dict(cls, state_dict: Dict[str, Any], *, preprocessor: Optional['Preprocessor']=None) -> 'TorchCheckpoint':
"""Create a :class:`~ray.train.Checkpoint` that stores a model state dictionary. ..... | the_stack_v2_python_sparse | python/ray/train/torch/torch_checkpoint.py | ray-project/ray | train | 29,482 |
0eadfbfa1e83c474a949ac72b99f3851e324c0f4 | [
"targets = (app_engine_http_target, http_target)\nif sum([1 if x is not None else 0 for x in targets]) > 1:\n raise CreatingHttpAndAppEngineQueueError('Attempting to send multiple queue target types simultaneously: {} , {}'.format(six.text_type(app_engine_http_target), six.text_type(http_target)))\ntargets = (pu... | <|body_start_0|>
targets = (app_engine_http_target, http_target)
if sum([1 if x is not None else 0 for x in targets]) > 1:
raise CreatingHttpAndAppEngineQueueError('Attempting to send multiple queue target types simultaneously: {} , {}'.format(six.text_type(app_engine_http_target), six.text_... | Client for queues service in the Cloud Tasks API. | AlphaQueues | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlphaQueues:
"""Client for queues service in the Cloud Tasks API."""
def Create(self, parent_ref, queue_ref, retry_config=None, rate_limits=None, pull_target=None, app_engine_http_target=None, http_target=None):
"""Prepares and sends a Create request for creating a queue."""
... | stack_v2_sparse_classes_10k_train_000663 | 19,528 | permissive | [
{
"docstring": "Prepares and sends a Create request for creating a queue.",
"name": "Create",
"signature": "def Create(self, parent_ref, queue_ref, retry_config=None, rate_limits=None, pull_target=None, app_engine_http_target=None, http_target=None)"
},
{
"docstring": "Prepares and sends a Patch... | 2 | null | Implement the Python class `AlphaQueues` described below.
Class description:
Client for queues service in the Cloud Tasks API.
Method signatures and docstrings:
- def Create(self, parent_ref, queue_ref, retry_config=None, rate_limits=None, pull_target=None, app_engine_http_target=None, http_target=None): Prepares and... | Implement the Python class `AlphaQueues` described below.
Class description:
Client for queues service in the Cloud Tasks API.
Method signatures and docstrings:
- def Create(self, parent_ref, queue_ref, retry_config=None, rate_limits=None, pull_target=None, app_engine_http_target=None, http_target=None): Prepares and... | 392abf004b16203030e6efd2f0af24db7c8d669e | <|skeleton|>
class AlphaQueues:
"""Client for queues service in the Cloud Tasks API."""
def Create(self, parent_ref, queue_ref, retry_config=None, rate_limits=None, pull_target=None, app_engine_http_target=None, http_target=None):
"""Prepares and sends a Create request for creating a queue."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AlphaQueues:
"""Client for queues service in the Cloud Tasks API."""
def Create(self, parent_ref, queue_ref, retry_config=None, rate_limits=None, pull_target=None, app_engine_http_target=None, http_target=None):
"""Prepares and sends a Create request for creating a queue."""
targets = (ap... | the_stack_v2_python_sparse | lib/googlecloudsdk/api_lib/tasks/queues.py | google-cloud-sdk-unofficial/google-cloud-sdk | train | 9 |
7659c8a25fb676ee8e232bb0543b536a311029b3 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | ModelRepositoryServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelRepositoryServiceServicer:
"""Missing associated documentation comment in .proto file."""
def RepositoryIndex(self, request, context):
"""Get the index of model repository contents."""
<|body_0|>
def RepositoryModelLoad(self, request, context):
"""Load or re... | stack_v2_sparse_classes_10k_train_000664 | 6,338 | permissive | [
{
"docstring": "Get the index of model repository contents.",
"name": "RepositoryIndex",
"signature": "def RepositoryIndex(self, request, context)"
},
{
"docstring": "Load or reload a model from a repository.",
"name": "RepositoryModelLoad",
"signature": "def RepositoryModelLoad(self, re... | 3 | stack_v2_sparse_classes_30k_train_001322 | Implement the Python class `ModelRepositoryServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def RepositoryIndex(self, request, context): Get the index of model repository contents.
- def RepositoryModelLoad(self, request,... | Implement the Python class `ModelRepositoryServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def RepositoryIndex(self, request, context): Get the index of model repository contents.
- def RepositoryModelLoad(self, request,... | 07a36f2cacf4eea6ed3ac28436792f31adb04d01 | <|skeleton|>
class ModelRepositoryServiceServicer:
"""Missing associated documentation comment in .proto file."""
def RepositoryIndex(self, request, context):
"""Get the index of model repository contents."""
<|body_0|>
def RepositoryModelLoad(self, request, context):
"""Load or re... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ModelRepositoryServiceServicer:
"""Missing associated documentation comment in .proto file."""
def RepositoryIndex(self, request, context):
"""Get the index of model repository contents."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented... | the_stack_v2_python_sparse | mlserver/grpc/model_repository_pb2_grpc.py | LiangTsao/MLServer | train | 0 |
47a2344dde34deb1af4a49840057086e5567530e | [
"super().__init__()\nlogger.debug('Create PaddleVectorConnectionHandler to process the vector request')\nself.vector_engine = vector_engine\nself.executor = self.vector_engine.executor\nself.task = self.vector_engine.executor.task\nself.model = self.vector_engine.executor.model\nself.config = self.vector_engine.exe... | <|body_start_0|>
super().__init__()
logger.debug('Create PaddleVectorConnectionHandler to process the vector request')
self.vector_engine = vector_engine
self.executor = self.vector_engine.executor
self.task = self.vector_engine.executor.task
self.model = self.vector_engi... | PaddleVectorConnectionHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PaddleVectorConnectionHandler:
def __init__(self, vector_engine):
"""The PaddleSpeech Vector Server Connection Handler This connection process every server request Args: vector_engine (VectorEngine): The Vector engine"""
<|body_0|>
def run(self, audio_data, task='spk'):
... | stack_v2_sparse_classes_10k_train_000665 | 7,306 | permissive | [
{
"docstring": "The PaddleSpeech Vector Server Connection Handler This connection process every server request Args: vector_engine (VectorEngine): The Vector engine",
"name": "__init__",
"signature": "def __init__(self, vector_engine)"
},
{
"docstring": "The connection process the http request a... | 4 | null | Implement the Python class `PaddleVectorConnectionHandler` described below.
Class description:
Implement the PaddleVectorConnectionHandler class.
Method signatures and docstrings:
- def __init__(self, vector_engine): The PaddleSpeech Vector Server Connection Handler This connection process every server request Args: ... | Implement the Python class `PaddleVectorConnectionHandler` described below.
Class description:
Implement the PaddleVectorConnectionHandler class.
Method signatures and docstrings:
- def __init__(self, vector_engine): The PaddleSpeech Vector Server Connection Handler This connection process every server request Args: ... | 17854a04d43c231eff66bfed9d6aa55e94a29e79 | <|skeleton|>
class PaddleVectorConnectionHandler:
def __init__(self, vector_engine):
"""The PaddleSpeech Vector Server Connection Handler This connection process every server request Args: vector_engine (VectorEngine): The Vector engine"""
<|body_0|>
def run(self, audio_data, task='spk'):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PaddleVectorConnectionHandler:
def __init__(self, vector_engine):
"""The PaddleSpeech Vector Server Connection Handler This connection process every server request Args: vector_engine (VectorEngine): The Vector engine"""
super().__init__()
logger.debug('Create PaddleVectorConnectionHan... | the_stack_v2_python_sparse | paddlespeech/server/engine/vector/python/vector_engine.py | anniyanvr/DeepSpeech-1 | train | 0 | |
62698644a9c83f32aa05e8b3c146af2b974449cb | [
"v_axis_item = Render2DNeuronIdentityLinesMixin._setup_custom_neuron_ticks(plot_widget, n_cells)\nv_axis_item = Render2DNeuronIdentityLinesMixin._add_lines(plot_widget)\nreturn v_axis_item",
"neuron_id_ticks = [[(float(i), '') for i in np.arange(n_cells + 1)]]\nv_axis_item = plot_widget.axes['left']['item']\nv_ax... | <|body_start_0|>
v_axis_item = Render2DNeuronIdentityLinesMixin._setup_custom_neuron_ticks(plot_widget, n_cells)
v_axis_item = Render2DNeuronIdentityLinesMixin._add_lines(plot_widget)
return v_axis_item
<|end_body_0|>
<|body_start_1|>
neuron_id_ticks = [[(float(i), '') for i in np.arang... | renders the horizontal lines separating the neurons on the 2D raster plots Review 2022-08-30 - Confirmed working as implemented! (Actually, the correct spacing/grid layout of the lines wasn't validated and doesn't look perfect, but the whole thing works. TODO: This is not really a mixin, need to figure out how I want t... | Render2DNeuronIdentityLinesMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Render2DNeuronIdentityLinesMixin:
"""renders the horizontal lines separating the neurons on the 2D raster plots Review 2022-08-30 - Confirmed working as implemented! (Actually, the correct spacing/grid layout of the lines wasn't validated and doesn't look perfect, but the whole thing works. TODO:... | stack_v2_sparse_classes_10k_train_000666 | 3,487 | permissive | [
{
"docstring": "Completely sets up the custom 2D neuron identity axis vertical/y-axis) by adding one minor tick per neuron and displaying the horizontal grid.",
"name": "setup_custom_neuron_identity_axis",
"signature": "def setup_custom_neuron_identity_axis(plot_widget, n_cells)"
},
{
"docstring... | 3 | stack_v2_sparse_classes_30k_train_003208 | Implement the Python class `Render2DNeuronIdentityLinesMixin` described below.
Class description:
renders the horizontal lines separating the neurons on the 2D raster plots Review 2022-08-30 - Confirmed working as implemented! (Actually, the correct spacing/grid layout of the lines wasn't validated and doesn't look pe... | Implement the Python class `Render2DNeuronIdentityLinesMixin` described below.
Class description:
renders the horizontal lines separating the neurons on the 2D raster plots Review 2022-08-30 - Confirmed working as implemented! (Actually, the correct spacing/grid layout of the lines wasn't validated and doesn't look pe... | 212399d826284b394fce8894ff1a93133aef783f | <|skeleton|>
class Render2DNeuronIdentityLinesMixin:
"""renders the horizontal lines separating the neurons on the 2D raster plots Review 2022-08-30 - Confirmed working as implemented! (Actually, the correct spacing/grid layout of the lines wasn't validated and doesn't look perfect, but the whole thing works. TODO:... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Render2DNeuronIdentityLinesMixin:
"""renders the horizontal lines separating the neurons on the 2D raster plots Review 2022-08-30 - Confirmed working as implemented! (Actually, the correct spacing/grid layout of the lines wasn't validated and doesn't look perfect, but the whole thing works. TODO: This is not ... | the_stack_v2_python_sparse | src/pyphoplacecellanalysis/GUI/PyQtPlot/Widgets/Mixins/Render2DNeuronIdentityLinesMixin.py | CommanderPho/pyPhoPlaceCellAnalysis | train | 1 |
592daa4458b950c2da134bed83460a70b2106749 | [
"user = request.user\nif not order_id:\n return redirect(reverse('user:order'))\ntry:\n order = OrderInfo.objects.get(order_id=order_id, user=user)\nexcept OrderInfo.DoesNotExist:\n return redirect(reverse('user:order'))\norder.order_status_name = OrderInfo.ORDER_STATUS[order.order_status]\norder_skus = Or... | <|body_start_0|>
user = request.user
if not order_id:
return redirect(reverse('user:order'))
try:
order = OrderInfo.objects.get(order_id=order_id, user=user)
except OrderInfo.DoesNotExist:
return redirect(reverse('user:order'))
order.order_stat... | 订单评论 | OrderCommentView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrderCommentView:
"""订单评论"""
def get(self, request, order_id):
"""提供评论页面"""
<|body_0|>
def post(self, request, order_id):
"""处理评论内容"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
user = request.user
if not order_id:
return r... | stack_v2_sparse_classes_10k_train_000667 | 18,227 | no_license | [
{
"docstring": "提供评论页面",
"name": "get",
"signature": "def get(self, request, order_id)"
},
{
"docstring": "处理评论内容",
"name": "post",
"signature": "def post(self, request, order_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003223 | Implement the Python class `OrderCommentView` described below.
Class description:
订单评论
Method signatures and docstrings:
- def get(self, request, order_id): 提供评论页面
- def post(self, request, order_id): 处理评论内容 | Implement the Python class `OrderCommentView` described below.
Class description:
订单评论
Method signatures and docstrings:
- def get(self, request, order_id): 提供评论页面
- def post(self, request, order_id): 处理评论内容
<|skeleton|>
class OrderCommentView:
"""订单评论"""
def get(self, request, order_id):
"""提供评论页面"... | 96ed9be42ba487c065b5ea1783555b053f051586 | <|skeleton|>
class OrderCommentView:
"""订单评论"""
def get(self, request, order_id):
"""提供评论页面"""
<|body_0|>
def post(self, request, order_id):
"""处理评论内容"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OrderCommentView:
"""订单评论"""
def get(self, request, order_id):
"""提供评论页面"""
user = request.user
if not order_id:
return redirect(reverse('user:order'))
try:
order = OrderInfo.objects.get(order_id=order_id, user=user)
except OrderInfo.DoesNot... | the_stack_v2_python_sparse | Web开发/02_web_django/celery_worker_env/dailyfresh/apps/order/views.py | YuanXianguo/Python-IT-Heima | train | 0 |
b237319759db97fe256952d750a2f154decc69fc | [
"if isinstance(udf, Column) or not hasattr(udf, 'func') or udf.evalType != PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:\n raise ValueError('Invalid udf: the udf argument must be a pandas_udf of type GROUPED_MAP.')\nwarnings.warn(\"It is preferred to use 'applyInPandas' over this API. This API will be deprecated in... | <|body_start_0|>
if isinstance(udf, Column) or not hasattr(udf, 'func') or udf.evalType != PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF:
raise ValueError('Invalid udf: the udf argument must be a pandas_udf of type GROUPED_MAP.')
warnings.warn("It is preferred to use 'applyInPandas' over this AP... | Min-in for pandas grouped operations. Currently, only :class:`GroupedData` can use this class. | PandasGroupedOpsMixin | [
"BSD-3-Clause",
"CC0-1.0",
"CDDL-1.1",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference",
"EPL-2.0",
"CDDL-1.0",
"MIT",
"LGPL-2.0-or-later",
"Python-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-free-unknown",... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PandasGroupedOpsMixin:
"""Min-in for pandas grouped operations. Currently, only :class:`GroupedData` can use this class."""
def apply(self, udf: 'GroupedMapPandasUserDefinedFunction') -> DataFrame:
"""It is an alias of :meth:`pyspark.sql.GroupedData.applyInPandas`; however, it takes ... | stack_v2_sparse_classes_10k_train_000668 | 21,716 | permissive | [
{
"docstring": "It is an alias of :meth:`pyspark.sql.GroupedData.applyInPandas`; however, it takes a :meth:`pyspark.sql.functions.pandas_udf` whereas :meth:`pyspark.sql.GroupedData.applyInPandas` takes a Python native function. .. versionadded:: 2.3.0 .. versionchanged:: 3.4.0 Support Spark Connect. Parameters ... | 4 | null | Implement the Python class `PandasGroupedOpsMixin` described below.
Class description:
Min-in for pandas grouped operations. Currently, only :class:`GroupedData` can use this class.
Method signatures and docstrings:
- def apply(self, udf: 'GroupedMapPandasUserDefinedFunction') -> DataFrame: It is an alias of :meth:`p... | Implement the Python class `PandasGroupedOpsMixin` described below.
Class description:
Min-in for pandas grouped operations. Currently, only :class:`GroupedData` can use this class.
Method signatures and docstrings:
- def apply(self, udf: 'GroupedMapPandasUserDefinedFunction') -> DataFrame: It is an alias of :meth:`p... | 60d8fc49bec5dae1b8cf39a0670cb640b430f520 | <|skeleton|>
class PandasGroupedOpsMixin:
"""Min-in for pandas grouped operations. Currently, only :class:`GroupedData` can use this class."""
def apply(self, udf: 'GroupedMapPandasUserDefinedFunction') -> DataFrame:
"""It is an alias of :meth:`pyspark.sql.GroupedData.applyInPandas`; however, it takes ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PandasGroupedOpsMixin:
"""Min-in for pandas grouped operations. Currently, only :class:`GroupedData` can use this class."""
def apply(self, udf: 'GroupedMapPandasUserDefinedFunction') -> DataFrame:
"""It is an alias of :meth:`pyspark.sql.GroupedData.applyInPandas`; however, it takes a :meth:`pysp... | the_stack_v2_python_sparse | python/pyspark/sql/pandas/group_ops.py | apache/spark | train | 39,983 |
690d8df99963f2c2fb15488e5f0c9ed73244cbd1 | [
"prev_control_input = self.system.control_input\ncloud_node = self.system.cloud_node\nselected_nodes = None\nsolution = None\nif prev_control_input is None:\n solution, selected_nodes = MOGAOperator._decode_part_1(self, individual)\nelse:\n solution = OptSolution.create_empty(self.system)\n selected_nodes ... | <|body_start_0|>
prev_control_input = self.system.control_input
cloud_node = self.system.cloud_node
selected_nodes = None
solution = None
if prev_control_input is None:
solution, selected_nodes = MOGAOperator._decode_part_1(self, individual)
else:
... | No Migration GA Operator | NoMigrationGAOperator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NoMigrationGAOperator:
"""No Migration GA Operator"""
def _decode_part_1(self, individual):
"""Decode Part I. It selects candidate nodes to host applications Args: individual (GAIndividual): individual Returns: (OptSolution, dict): solution, list of selected nodes per application"""
... | stack_v2_sparse_classes_10k_train_000669 | 5,124 | no_license | [
{
"docstring": "Decode Part I. It selects candidate nodes to host applications Args: individual (GAIndividual): individual Returns: (OptSolution, dict): solution, list of selected nodes per application",
"name": "_decode_part_1",
"signature": "def _decode_part_1(self, individual)"
},
{
"docstrin... | 3 | stack_v2_sparse_classes_30k_train_003633 | Implement the Python class `NoMigrationGAOperator` described below.
Class description:
No Migration GA Operator
Method signatures and docstrings:
- def _decode_part_1(self, individual): Decode Part I. It selects candidate nodes to host applications Args: individual (GAIndividual): individual Returns: (OptSolution, di... | Implement the Python class `NoMigrationGAOperator` described below.
Class description:
No Migration GA Operator
Method signatures and docstrings:
- def _decode_part_1(self, individual): Decode Part I. It selects candidate nodes to host applications Args: individual (GAIndividual): individual Returns: (OptSolution, di... | ce7045918f60c92ce1ed5ca4389b969bf28e6b82 | <|skeleton|>
class NoMigrationGAOperator:
"""No Migration GA Operator"""
def _decode_part_1(self, individual):
"""Decode Part I. It selects candidate nodes to host applications Args: individual (GAIndividual): individual Returns: (OptSolution, dict): solution, list of selected nodes per application"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NoMigrationGAOperator:
"""No Migration GA Operator"""
def _decode_part_1(self, individual):
"""Decode Part I. It selects candidate nodes to host applications Args: individual (GAIndividual): individual Returns: (OptSolution, dict): solution, list of selected nodes per application"""
prev_... | the_stack_v2_python_sparse | sp/system_controller/optimizer/no_migration.py | adysonmaia/phd-sp-dynamic | train | 0 |
d97f2d45da395ceb5ae6d8b2c7e8b6070515ed3d | [
"if not nums or len(nums) < 4:\n return []\nnums.sort()\nreturn [list(t) for t in self.kSum(nums, target, 4)]",
"res = set()\nif k == 2:\n lo, hi = (0, len(nums) - 1)\n while lo < hi:\n if nums[lo] + nums[hi] == target:\n res.add((nums[lo], nums[hi]))\n lo += 1\n elif ... | <|body_start_0|>
if not nums or len(nums) < 4:
return []
nums.sort()
return [list(t) for t in self.kSum(nums, target, 4)]
<|end_body_0|>
<|body_start_1|>
res = set()
if k == 2:
lo, hi = (0, len(nums) - 1)
while lo < hi:
if nums... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def fourSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[List[int]]"""
<|body_0|>
def kSum(self, nums, target, k):
""":type nums = List[int] :type target: int :type k: int :rtype: List[int]"""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_10k_train_000670 | 1,102 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: List[List[int]]",
"name": "fourSum",
"signature": "def fourSum(self, nums, target)"
},
{
"docstring": ":type nums = List[int] :type target: int :type k: int :rtype: List[int]",
"name": "kSum",
"signature": "def kSum(self, nu... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def fourSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[List[int]]
- def kSum(self, nums, target, k): :type nums = List[int] :type target: int :type... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def fourSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[List[int]]
- def kSum(self, nums, target, k): :type nums = List[int] :type target: int :type... | 24988428cada3b1f8a6c0cf0140e288511cd9a6d | <|skeleton|>
class Solution:
def fourSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[List[int]]"""
<|body_0|>
def kSum(self, nums, target, k):
""":type nums = List[int] :type target: int :type k: int :rtype: List[int]"""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def fourSum(self, nums, target):
""":type nums: List[int] :type target: int :rtype: List[List[int]]"""
if not nums or len(nums) < 4:
return []
nums.sort()
return [list(t) for t in self.kSum(nums, target, 4)]
def kSum(self, nums, target, k):
""... | the_stack_v2_python_sparse | LC_n_Misc/LC_18_4Sum.py | abhikrish06/PythonPractice | train | 0 | |
a071ae6dc0156217830ddfd3789d3b86e824d90e | [
"engine = MainEngine()\nqubits = engine.allocate_qureg(3)\nH | qubits[0]\nengine.flush()\nprint(engine.backend.cheat())\nX | qubits[2]\nengine.flush()\nprint(engine.backend.cheat())\nCNOT | (qubits[0], qubits[1])\nengine.flush()\nprint(engine.backend.cheat())",
"drawer = CircuitDrawer()\nengine = MainEngine(drawe... | <|body_start_0|>
engine = MainEngine()
qubits = engine.allocate_qureg(3)
H | qubits[0]
engine.flush()
print(engine.backend.cheat())
X | qubits[2]
engine.flush()
print(engine.backend.cheat())
CNOT | (qubits[0], qubits[1])
engine.flush()
... | This class contains demonstrations of ProjectQ's debugging and diagnostic features. | DebuggingFeatures | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DebuggingFeatures:
"""This class contains demonstrations of ProjectQ's debugging and diagnostic features."""
def test_step_by_step_circuit_inspection(self):
"""This function demonstrates how to use ProjectQ to print the state vector of every step (moment) in a circuit. It also shows ... | stack_v2_sparse_classes_10k_train_000671 | 3,023 | permissive | [
{
"docstring": "This function demonstrates how to use ProjectQ to print the state vector of every step (moment) in a circuit. It also shows how to get the state vector at each step, and how to print it in ket notation.",
"name": "test_step_by_step_circuit_inspection",
"signature": "def test_step_by_step... | 3 | stack_v2_sparse_classes_30k_train_003539 | Implement the Python class `DebuggingFeatures` described below.
Class description:
This class contains demonstrations of ProjectQ's debugging and diagnostic features.
Method signatures and docstrings:
- def test_step_by_step_circuit_inspection(self): This function demonstrates how to use ProjectQ to print the state v... | Implement the Python class `DebuggingFeatures` described below.
Class description:
This class contains demonstrations of ProjectQ's debugging and diagnostic features.
Method signatures and docstrings:
- def test_step_by_step_circuit_inspection(self): This function demonstrates how to use ProjectQ to print the state v... | 941488f8f8a81a4b7d7fe28414ce14fa478a692a | <|skeleton|>
class DebuggingFeatures:
"""This class contains demonstrations of ProjectQ's debugging and diagnostic features."""
def test_step_by_step_circuit_inspection(self):
"""This function demonstrates how to use ProjectQ to print the state vector of every step (moment) in a circuit. It also shows ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DebuggingFeatures:
"""This class contains demonstrations of ProjectQ's debugging and diagnostic features."""
def test_step_by_step_circuit_inspection(self):
"""This function demonstrates how to use ProjectQ to print the state vector of every step (moment) in a circuit. It also shows how to get th... | the_stack_v2_python_sparse | ProjectQ/ProjectQDebugging/debugging_features.py | taibah/qsfe | train | 0 |
f29c67392d53702387c32d9e921e15f1a9d29a95 | [
"self._attr_available = False\nself._gitlab_data = gitlab_data\nself._attr_name = name",
"if self.native_value == 'success':\n return ICON_HAPPY\nif self.native_value == 'failed':\n return ICON_SAD\nreturn ICON_OTHER",
"self._gitlab_data.update()\nself._attr_native_value = self._gitlab_data.status\nself._... | <|body_start_0|>
self._attr_available = False
self._gitlab_data = gitlab_data
self._attr_name = name
<|end_body_0|>
<|body_start_1|>
if self.native_value == 'success':
return ICON_HAPPY
if self.native_value == 'failed':
return ICON_SAD
return ICON... | Representation of a GitLab sensor. | GitLabSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GitLabSensor:
"""Representation of a GitLab sensor."""
def __init__(self, gitlab_data: GitLabData, name: str) -> None:
"""Initialize the GitLab sensor."""
<|body_0|>
def icon(self) -> str:
"""Return the icon to use in the frontend."""
<|body_1|>
def ... | stack_v2_sparse_classes_10k_train_000672 | 5,282 | permissive | [
{
"docstring": "Initialize the GitLab sensor.",
"name": "__init__",
"signature": "def __init__(self, gitlab_data: GitLabData, name: str) -> None"
},
{
"docstring": "Return the icon to use in the frontend.",
"name": "icon",
"signature": "def icon(self) -> str"
},
{
"docstring": "C... | 3 | null | Implement the Python class `GitLabSensor` described below.
Class description:
Representation of a GitLab sensor.
Method signatures and docstrings:
- def __init__(self, gitlab_data: GitLabData, name: str) -> None: Initialize the GitLab sensor.
- def icon(self) -> str: Return the icon to use in the frontend.
- def upda... | Implement the Python class `GitLabSensor` described below.
Class description:
Representation of a GitLab sensor.
Method signatures and docstrings:
- def __init__(self, gitlab_data: GitLabData, name: str) -> None: Initialize the GitLab sensor.
- def icon(self) -> str: Return the icon to use in the frontend.
- def upda... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class GitLabSensor:
"""Representation of a GitLab sensor."""
def __init__(self, gitlab_data: GitLabData, name: str) -> None:
"""Initialize the GitLab sensor."""
<|body_0|>
def icon(self) -> str:
"""Return the icon to use in the frontend."""
<|body_1|>
def ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GitLabSensor:
"""Representation of a GitLab sensor."""
def __init__(self, gitlab_data: GitLabData, name: str) -> None:
"""Initialize the GitLab sensor."""
self._attr_available = False
self._gitlab_data = gitlab_data
self._attr_name = name
def icon(self) -> str:
... | the_stack_v2_python_sparse | homeassistant/components/gitlab_ci/sensor.py | home-assistant/core | train | 35,501 |
65069c192bdcfc8bf792f8d1e63112e0837c7ea7 | [
"unique_digit_square_sums = set()\nwhile n not in unique_digit_square_sums:\n unique_digit_square_sums.add(n)\n n = self.sum_of_digit_squares(n)\n if n == 1:\n return True\nreturn False",
"digit_square_sum = 0\nwhile n > 0:\n digit_square_sum += (n % 10) ** 2\n n //= 10\nreturn digit_square_... | <|body_start_0|>
unique_digit_square_sums = set()
while n not in unique_digit_square_sums:
unique_digit_square_sums.add(n)
n = self.sum_of_digit_squares(n)
if n == 1:
return True
return False
<|end_body_0|>
<|body_start_1|>
digit_squar... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isHappy(self, n: int) -> bool:
"""(Solution, int) -> bool Returns True iff n is a "happy" number, which is a number that results in a 1 after a repetitive process of replacing the original number by the sum of digit squares. >>> soln = Solution() >>> soln.isHappy(19) True""... | stack_v2_sparse_classes_10k_train_000673 | 2,132 | no_license | [
{
"docstring": "(Solution, int) -> bool Returns True iff n is a \"happy\" number, which is a number that results in a 1 after a repetitive process of replacing the original number by the sum of digit squares. >>> soln = Solution() >>> soln.isHappy(19) True",
"name": "isHappy",
"signature": "def isHappy(... | 2 | stack_v2_sparse_classes_30k_train_006423 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy(self, n: int) -> bool: (Solution, int) -> bool Returns True iff n is a "happy" number, which is a number that results in a 1 after a repetitive process of replacing t... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy(self, n: int) -> bool: (Solution, int) -> bool Returns True iff n is a "happy" number, which is a number that results in a 1 after a repetitive process of replacing t... | 6812253b90bdd5a35c6bfba8eac54da9be26d56c | <|skeleton|>
class Solution:
def isHappy(self, n: int) -> bool:
"""(Solution, int) -> bool Returns True iff n is a "happy" number, which is a number that results in a 1 after a repetitive process of replacing the original number by the sum of digit squares. >>> soln = Solution() >>> soln.isHappy(19) True""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isHappy(self, n: int) -> bool:
"""(Solution, int) -> bool Returns True iff n is a "happy" number, which is a number that results in a 1 after a repetitive process of replacing the original number by the sum of digit squares. >>> soln = Solution() >>> soln.isHappy(19) True"""
uniq... | the_stack_v2_python_sparse | python3/happyNum.py | yichuanma95/leetcode-solns | train | 2 | |
dabb4b405eb88bdbb1304adde116f707797e5bc8 | [
"super(TextSubNet, self).__init__()\nself.rnn = nn.LSTM(in_size, hidden_size, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional, batch_first=True)\nself.dropout = nn.Dropout(dropout)\nself.linear_1 = nn.Linear(hidden_size, out_size)",
"_, final_states = self.rnn(x)\nh = self.dropout(final_states... | <|body_start_0|>
super(TextSubNet, self).__init__()
self.rnn = nn.LSTM(in_size, hidden_size, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional, batch_first=True)
self.dropout = nn.Dropout(dropout)
self.linear_1 = nn.Linear(hidden_size, out_size)
<|end_body_0|>
<|body_s... | The LSTM-based subnetwork that is used in LMF for text | TextSubNet | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextSubNet:
"""The LSTM-based subnetwork that is used in LMF for text"""
def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False):
"""Args: in_size: input dimension hidden_size: hidden layer dimension num_layers: specify the number of layers ... | stack_v2_sparse_classes_10k_train_000674 | 7,292 | permissive | [
{
"docstring": "Args: in_size: input dimension hidden_size: hidden layer dimension num_layers: specify the number of layers of LSTMs. dropout: dropout probability bidirectional: specify usage of bidirectional LSTM Output: (return value in forward) a tensor of shape (batch_size, out_size)",
"name": "__init__... | 2 | stack_v2_sparse_classes_30k_train_003199 | Implement the Python class `TextSubNet` described below.
Class description:
The LSTM-based subnetwork that is used in LMF for text
Method signatures and docstrings:
- def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False): Args: in_size: input dimension hidden_size: hidden ... | Implement the Python class `TextSubNet` described below.
Class description:
The LSTM-based subnetwork that is used in LMF for text
Method signatures and docstrings:
- def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False): Args: in_size: input dimension hidden_size: hidden ... | 92acc188d3a0f634de58463b6676e70df83ef808 | <|skeleton|>
class TextSubNet:
"""The LSTM-based subnetwork that is used in LMF for text"""
def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False):
"""Args: in_size: input dimension hidden_size: hidden layer dimension num_layers: specify the number of layers ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TextSubNet:
"""The LSTM-based subnetwork that is used in LMF for text"""
def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False):
"""Args: in_size: input dimension hidden_size: hidden layer dimension num_layers: specify the number of layers of LSTMs. dro... | the_stack_v2_python_sparse | PyTorch/contrib/others/Low-rank-Multimodal-Fusion_ID2983_for_Pytorch/model.py | Ascend/ModelZoo-PyTorch | train | 23 |
64b9ccc9a378d91f9c4f2ce10be7cb95d950dfb6 | [
"self.debug = debug\nself.n = n\nself.a = a\nself.b = b\nself.x = np.zeros(n + 1, dtype=np.int8)\nsuper().__init__()\nif self.debug:\n print(sys._getframe().f_code.co_name, ':', self.n, self.a, self.b, self.debug)",
"count = self.n\nfor i in range(1, self.n + 1):\n nxt = self.a * i + self.b\n if self.x[i... | <|body_start_0|>
self.debug = debug
self.n = n
self.a = a
self.b = b
self.x = np.zeros(n + 1, dtype=np.int8)
super().__init__()
if self.debug:
print(sys._getframe().f_code.co_name, ':', self.n, self.a, self.b, self.debug)
<|end_body_0|>
<|body_start_1... | A class used to get the count of elements url : https://codeup.kr/problem.php?id=2128&rid=0 n, a, b이 주어진다. (1<=n<=10100) (1<=a,b<=103) 집합 A의 임의의 원소 x를 선택했을 때 ax+b가 집합 A에 존재하지 않도록 A의 원소를 빼버리자! 하지만 a, b는 배려심이 많기 때문에 A의 크기를 최대로 하려고 한다. ax+b = n : hate a and b in set 10 2 2 : 2x + 2 <= 10 x=1 => 4 remove 1 2 3 5 6 7 8 9 10... | CountAntiSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CountAntiSet:
"""A class used to get the count of elements url : https://codeup.kr/problem.php?id=2128&rid=0 n, a, b이 주어진다. (1<=n<=10100) (1<=a,b<=103) 집합 A의 임의의 원소 x를 선택했을 때 ax+b가 집합 A에 존재하지 않도록 A의 원소를 빼버리자! 하지만 a, b는 배려심이 많기 때문에 A의 크기를 최대로 하려고 한다. ax+b = n : hate a and b in set 10 2 2 : 2x + 2 ... | stack_v2_sparse_classes_10k_train_000675 | 3,185 | no_license | [
{
"docstring": "get the count of elements to meet the rule. (2) :param n: max number :param a: ax+b :param b: ax+b :param debug: debug mode :return:",
"name": "__init__",
"signature": "def __init__(self, n, a, b, debug=0)"
},
{
"docstring": "get the count of elements to meet the rule. (2)",
... | 3 | stack_v2_sparse_classes_30k_train_007271 | Implement the Python class `CountAntiSet` described below.
Class description:
A class used to get the count of elements url : https://codeup.kr/problem.php?id=2128&rid=0 n, a, b이 주어진다. (1<=n<=10100) (1<=a,b<=103) 집합 A의 임의의 원소 x를 선택했을 때 ax+b가 집합 A에 존재하지 않도록 A의 원소를 빼버리자! 하지만 a, b는 배려심이 많기 때문에 A의 크기를 최대로 하려고 한다. ax+b = n... | Implement the Python class `CountAntiSet` described below.
Class description:
A class used to get the count of elements url : https://codeup.kr/problem.php?id=2128&rid=0 n, a, b이 주어진다. (1<=n<=10100) (1<=a,b<=103) 집합 A의 임의의 원소 x를 선택했을 때 ax+b가 집합 A에 존재하지 않도록 A의 원소를 빼버리자! 하지만 a, b는 배려심이 많기 때문에 A의 크기를 최대로 하려고 한다. ax+b = n... | 2fb6246be3bf48eb8ad626217300a1a9328f541a | <|skeleton|>
class CountAntiSet:
"""A class used to get the count of elements url : https://codeup.kr/problem.php?id=2128&rid=0 n, a, b이 주어진다. (1<=n<=10100) (1<=a,b<=103) 집합 A의 임의의 원소 x를 선택했을 때 ax+b가 집합 A에 존재하지 않도록 A의 원소를 빼버리자! 하지만 a, b는 배려심이 많기 때문에 A의 크기를 최대로 하려고 한다. ax+b = n : hate a and b in set 10 2 2 : 2x + 2 ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CountAntiSet:
"""A class used to get the count of elements url : https://codeup.kr/problem.php?id=2128&rid=0 n, a, b이 주어진다. (1<=n<=10100) (1<=a,b<=103) 집합 A의 임의의 원소 x를 선택했을 때 ax+b가 집합 A에 존재하지 않도록 A의 원소를 빼버리자! 하지만 a, b는 배려심이 많기 때문에 A의 크기를 최대로 하려고 한다. ax+b = n : hate a and b in set 10 2 2 : 2x + 2 <= 10 x=1 => ... | the_stack_v2_python_sparse | 2022/1.py | cheoljoo/problemSolving | train | 1 |
13482df4285582a2ac66ff8b947e92a06c22d56b | [
"try:\n self.administrator\nexcept:\n return False\nreturn True",
"try:\n self.coordinator\nexcept:\n return False\nreturn True"
] | <|body_start_0|>
try:
self.administrator
except:
return False
return True
<|end_body_0|>
<|body_start_1|>
try:
self.coordinator
except:
return False
return True
<|end_body_1|>
| Proxy for the main User class. | User | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class User:
"""Proxy for the main User class."""
def is_administrator(self):
"""Returns True if the user is an administrator."""
<|body_0|>
def is_coordinator(self):
"""Returns True if the user is a coordinator."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_10k_train_000676 | 758 | no_license | [
{
"docstring": "Returns True if the user is an administrator.",
"name": "is_administrator",
"signature": "def is_administrator(self)"
},
{
"docstring": "Returns True if the user is a coordinator.",
"name": "is_coordinator",
"signature": "def is_coordinator(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006298 | Implement the Python class `User` described below.
Class description:
Proxy for the main User class.
Method signatures and docstrings:
- def is_administrator(self): Returns True if the user is an administrator.
- def is_coordinator(self): Returns True if the user is a coordinator. | Implement the Python class `User` described below.
Class description:
Proxy for the main User class.
Method signatures and docstrings:
- def is_administrator(self): Returns True if the user is an administrator.
- def is_coordinator(self): Returns True if the user is a coordinator.
<|skeleton|>
class User:
"""Pro... | b9992dc1ea27fe5e3a87cb10e691d277689008a5 | <|skeleton|>
class User:
"""Proxy for the main User class."""
def is_administrator(self):
"""Returns True if the user is an administrator."""
<|body_0|>
def is_coordinator(self):
"""Returns True if the user is a coordinator."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class User:
"""Proxy for the main User class."""
def is_administrator(self):
"""Returns True if the user is an administrator."""
try:
self.administrator
except:
return False
return True
def is_coordinator(self):
"""Returns True if the user is... | the_stack_v2_python_sparse | foji_project/foji/models/user.py | SoporteFoji/catastro | train | 0 |
43521f506538523eb06306c5cfbdd46471880438 | [
"QStandardItem.__init__(self, principal.displayName)\nself._initIcons()\nself.principal = principal\nif principal.type == USER_PRINCIPAL_TYPE:\n self.setIcon(self._userIcon)\nelse:\n self.setIcon(self._groupIcon)\nself.setEditable(False)\nself.setToolTip(self.principal.type.displayName)",
"if self._groupIco... | <|body_start_0|>
QStandardItem.__init__(self, principal.displayName)
self._initIcons()
self.principal = principal
if principal.type == USER_PRINCIPAL_TYPE:
self.setIcon(self._userIcon)
else:
self.setIcon(self._groupIcon)
self.setEditable(False)
... | Principal-specific item. | PrincipalItem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrincipalItem:
"""Principal-specific item."""
def __init__(self, principal):
"""Constructor. @param principal: The associated principal. @type principal: L{<Principal>datafinder.core.principal.Principal}"""
<|body_0|>
def _initIcons(self):
"""Initializes the icon... | stack_v2_sparse_classes_10k_train_000677 | 4,825 | no_license | [
{
"docstring": "Constructor. @param principal: The associated principal. @type principal: L{<Principal>datafinder.core.principal.Principal}",
"name": "__init__",
"signature": "def __init__(self, principal)"
},
{
"docstring": "Initializes the icons.",
"name": "_initIcons",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_001151 | Implement the Python class `PrincipalItem` described below.
Class description:
Principal-specific item.
Method signatures and docstrings:
- def __init__(self, principal): Constructor. @param principal: The associated principal. @type principal: L{<Principal>datafinder.core.principal.Principal}
- def _initIcons(self):... | Implement the Python class `PrincipalItem` described below.
Class description:
Principal-specific item.
Method signatures and docstrings:
- def __init__(self, principal): Constructor. @param principal: The associated principal. @type principal: L{<Principal>datafinder.core.principal.Principal}
- def _initIcons(self):... | 958fda4f3064f9f6b2034da396a20ac9d9abd52f | <|skeleton|>
class PrincipalItem:
"""Principal-specific item."""
def __init__(self, principal):
"""Constructor. @param principal: The associated principal. @type principal: L{<Principal>datafinder.core.principal.Principal}"""
<|body_0|>
def _initIcons(self):
"""Initializes the icon... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PrincipalItem:
"""Principal-specific item."""
def __init__(self, principal):
"""Constructor. @param principal: The associated principal. @type principal: L{<Principal>datafinder.core.principal.Principal}"""
QStandardItem.__init__(self, principal.displayName)
self._initIcons()
... | the_stack_v2_python_sparse | src/datafinder/gui/user/dialogs/privilege_dialog/items.py | DLR-SC/DataFinder | train | 9 |
940afa7569cbec382be52baa393d226516ca4fb9 | [
"assert size > 0\nself.size = size\nself.graph = [[] for _ in range(size)]\nself.cost_edge = [[] for _ in range(size)]",
"assert 0 <= x < self.size\nassert 0 <= y < self.size\nself.graph[x].append(y)\nself.graph[y].append(x)\nself.cost_edge[x].append(cost)\nself.cost_edge[y].append(cost)",
"s2 = 1\nwhile 1 << s... | <|body_start_0|>
assert size > 0
self.size = size
self.graph = [[] for _ in range(size)]
self.cost_edge = [[] for _ in range(size)]
<|end_body_0|>
<|body_start_1|>
assert 0 <= x < self.size
assert 0 <= y < self.size
self.graph[x].append(y)
self.graph[y].a... | Lowest Common Ancestor | LCA | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LCA:
"""Lowest Common Ancestor"""
def __init__(self, size):
"""保存領域の初期化。 辺に重みがついている時の頂点間の重み計算にはcostを使えます。 size: 頂点の数"""
<|body_0|>
def add_edge(self, x: int, y: int, cost: int=1) -> None:
"""木の辺を追加します。"""
<|body_1|>
def init(self) -> None:
""... | stack_v2_sparse_classes_10k_train_000678 | 25,752 | no_license | [
{
"docstring": "保存領域の初期化。 辺に重みがついている時の頂点間の重み計算にはcostを使えます。 size: 頂点の数",
"name": "__init__",
"signature": "def __init__(self, size)"
},
{
"docstring": "木の辺を追加します。",
"name": "add_edge",
"signature": "def add_edge(self, x: int, y: int, cost: int=1) -> None"
},
{
"docstring": "全ての辺を追... | 6 | stack_v2_sparse_classes_30k_train_000498 | Implement the Python class `LCA` described below.
Class description:
Lowest Common Ancestor
Method signatures and docstrings:
- def __init__(self, size): 保存領域の初期化。 辺に重みがついている時の頂点間の重み計算にはcostを使えます。 size: 頂点の数
- def add_edge(self, x: int, y: int, cost: int=1) -> None: 木の辺を追加します。
- def init(self) -> None: 全ての辺を追加した後に、LC... | Implement the Python class `LCA` described below.
Class description:
Lowest Common Ancestor
Method signatures and docstrings:
- def __init__(self, size): 保存領域の初期化。 辺に重みがついている時の頂点間の重み計算にはcostを使えます。 size: 頂点の数
- def add_edge(self, x: int, y: int, cost: int=1) -> None: 木の辺を追加します。
- def init(self) -> None: 全ての辺を追加した後に、LC... | f214ef92f13bc5d6b290746d5a94e2faad20d8b0 | <|skeleton|>
class LCA:
"""Lowest Common Ancestor"""
def __init__(self, size):
"""保存領域の初期化。 辺に重みがついている時の頂点間の重み計算にはcostを使えます。 size: 頂点の数"""
<|body_0|>
def add_edge(self, x: int, y: int, cost: int=1) -> None:
"""木の辺を追加します。"""
<|body_1|>
def init(self) -> None:
""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LCA:
"""Lowest Common Ancestor"""
def __init__(self, size):
"""保存領域の初期化。 辺に重みがついている時の頂点間の重み計算にはcostを使えます。 size: 頂点の数"""
assert size > 0
self.size = size
self.graph = [[] for _ in range(size)]
self.cost_edge = [[] for _ in range(size)]
def add_edge(self, x: int... | the_stack_v2_python_sparse | lib/lib.py | silphire/atcoder | train | 0 |
b8c7c97b09145b1b580d8f8a55d1d7a4bdc0d47e | [
"text = ''\nwith open(file_path, 'r') as file:\n for line in file.readlines():\n text += line\nreturn text",
"read = sys.stdin.readlines()\ntext = ''\nfor line in read:\n text += line\nreturn text"
] | <|body_start_0|>
text = ''
with open(file_path, 'r') as file:
for line in file.readlines():
text += line
return text
<|end_body_0|>
<|body_start_1|>
read = sys.stdin.readlines()
text = ''
for line in read:
text += line
retu... | Used in handling reading input. | Reader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Reader:
"""Used in handling reading input."""
def read_file(file_path):
"""Reads a file and outputs it in a single string. :param str file_path: File pathname. :return str: File content in a string"""
<|body_0|>
def read_input():
"""Reads a direct input. An input... | stack_v2_sparse_classes_10k_train_000679 | 855 | permissive | [
{
"docstring": "Reads a file and outputs it in a single string. :param str file_path: File pathname. :return str: File content in a string",
"name": "read_file",
"signature": "def read_file(file_path)"
},
{
"docstring": "Reads a direct input. An input ends with CTRL+D. Enables inputting from a f... | 2 | stack_v2_sparse_classes_30k_train_003031 | Implement the Python class `Reader` described below.
Class description:
Used in handling reading input.
Method signatures and docstrings:
- def read_file(file_path): Reads a file and outputs it in a single string. :param str file_path: File pathname. :return str: File content in a string
- def read_input(): Reads a d... | Implement the Python class `Reader` described below.
Class description:
Used in handling reading input.
Method signatures and docstrings:
- def read_file(file_path): Reads a file and outputs it in a single string. :param str file_path: File pathname. :return str: File content in a string
- def read_input(): Reads a d... | 1f7bea099dac93696d5d2ebb8d76926efe5ceda4 | <|skeleton|>
class Reader:
"""Used in handling reading input."""
def read_file(file_path):
"""Reads a file and outputs it in a single string. :param str file_path: File pathname. :return str: File content in a string"""
<|body_0|>
def read_input():
"""Reads a direct input. An input... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Reader:
"""Used in handling reading input."""
def read_file(file_path):
"""Reads a file and outputs it in a single string. :param str file_path: File pathname. :return str: File content in a string"""
text = ''
with open(file_path, 'r') as file:
for line in file.readli... | the_stack_v2_python_sparse | form/readers.py | ffhan/lingua | train | 0 |
21d742e41aa04052e1d11888dab2f2ae0d5976ea | [
"device_obj = Device(context=context, type=self.type, vendor=self.vendor, model=self.model, hostname=host)\nif hasattr(self, 'std_board_info'):\n device_obj.std_board_info = self.std_board_info\nif hasattr(self, 'vendor_board_info'):\n device_obj.vendor_board_info = self.vendor_board_info\ndevice_obj.create(c... | <|body_start_0|>
device_obj = Device(context=context, type=self.type, vendor=self.vendor, model=self.model, hostname=host)
if hasattr(self, 'std_board_info'):
device_obj.std_board_info = self.std_board_info
if hasattr(self, 'vendor_board_info'):
device_obj.vendor_board_in... | DriverDevice | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DriverDevice:
def create(self, context, host):
"""Create a driver-side Device Object into DB. This object will be stored in many db tables: device, deployable, attach_handle, controlpath_id etc. by calling related Object."""
<|body_0|>
def destroy(self, context, host):
... | stack_v2_sparse_classes_10k_train_000680 | 7,119 | permissive | [
{
"docstring": "Create a driver-side Device Object into DB. This object will be stored in many db tables: device, deployable, attach_handle, controlpath_id etc. by calling related Object.",
"name": "create",
"signature": "def create(self, context, host)"
},
{
"docstring": "Delete a driver-side D... | 5 | stack_v2_sparse_classes_30k_train_000922 | Implement the Python class `DriverDevice` described below.
Class description:
Implement the DriverDevice class.
Method signatures and docstrings:
- def create(self, context, host): Create a driver-side Device Object into DB. This object will be stored in many db tables: device, deployable, attach_handle, controlpath_... | Implement the Python class `DriverDevice` described below.
Class description:
Implement the DriverDevice class.
Method signatures and docstrings:
- def create(self, context, host): Create a driver-side Device Object into DB. This object will be stored in many db tables: device, deployable, attach_handle, controlpath_... | ab8b8514242895b8adc2ec3dfbbb63a49f02c89e | <|skeleton|>
class DriverDevice:
def create(self, context, host):
"""Create a driver-side Device Object into DB. This object will be stored in many db tables: device, deployable, attach_handle, controlpath_id etc. by calling related Object."""
<|body_0|>
def destroy(self, context, host):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DriverDevice:
def create(self, context, host):
"""Create a driver-side Device Object into DB. This object will be stored in many db tables: device, deployable, attach_handle, controlpath_id etc. by calling related Object."""
device_obj = Device(context=context, type=self.type, vendor=self.vend... | the_stack_v2_python_sparse | cyborg/objects/driver_objects/driver_device.py | openstack/cyborg | train | 41 | |
d2fb199ca9b7a3023a6745bb13613567a408c2f6 | [
"self.thresholds = np.array([276, 277], dtype=np.float32)\nself.rain_name = 'probability_of_falling_rain_level_above_surface'\nself.snow_name = 'probability_of_falling_snow_level_below_surface'\nrain_prob = np.array([[[0.5, 0.1, 1.0], [0.0, 0.2, 0.5], [0.1, 0.1, 0.3]], [[0.5, 0.1, 1.0], [0.0, 0.2, 0.5], [0.1, 0.1, ... | <|body_start_0|>
self.thresholds = np.array([276, 277], dtype=np.float32)
self.rain_name = 'probability_of_falling_rain_level_above_surface'
self.snow_name = 'probability_of_falling_snow_level_below_surface'
rain_prob = np.array([[[0.5, 0.1, 1.0], [0.0, 0.2, 0.5], [0.1, 0.1, 0.3]], [[0.5... | Tests the calculate sleet probability function. | Test_calculate_sleet_probability | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_calculate_sleet_probability:
"""Tests the calculate sleet probability function."""
def setUp(self):
"""Create cubes to input into the function."""
<|body_0|>
def test_basic_calculation(self):
"""Test the basic sleet calculation works."""
<|body_1|>
... | stack_v2_sparse_classes_10k_train_000681 | 5,635 | permissive | [
{
"docstring": "Create cubes to input into the function.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test the basic sleet calculation works.",
"name": "test_basic_calculation",
"signature": "def test_basic_calculation(self)"
},
{
"docstring": "Test the ba... | 5 | stack_v2_sparse_classes_30k_val_000210 | Implement the Python class `Test_calculate_sleet_probability` described below.
Class description:
Tests the calculate sleet probability function.
Method signatures and docstrings:
- def setUp(self): Create cubes to input into the function.
- def test_basic_calculation(self): Test the basic sleet calculation works.
- ... | Implement the Python class `Test_calculate_sleet_probability` described below.
Class description:
Tests the calculate sleet probability function.
Method signatures and docstrings:
- def setUp(self): Create cubes to input into the function.
- def test_basic_calculation(self): Test the basic sleet calculation works.
- ... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_calculate_sleet_probability:
"""Tests the calculate sleet probability function."""
def setUp(self):
"""Create cubes to input into the function."""
<|body_0|>
def test_basic_calculation(self):
"""Test the basic sleet calculation works."""
<|body_1|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Test_calculate_sleet_probability:
"""Tests the calculate sleet probability function."""
def setUp(self):
"""Create cubes to input into the function."""
self.thresholds = np.array([276, 277], dtype=np.float32)
self.rain_name = 'probability_of_falling_rain_level_above_surface'
... | the_stack_v2_python_sparse | improver_tests/precipitation_type/calculate_sleet_prob/test_calculate_sleet_probability.py | metoppv/improver | train | 101 |
be790ce3e8df5304abc4c98cac52a2ff1068da94 | [
"super(PG_LOSS, self).__init__()\nself.simulations = simulations\nself.trajectory_length = trajectory_length\nself.max_ent = max_ent",
"flat_states = torch.flatten(state_tensor, start_dim=0, end_dim=1)\nflat_actions = torch.flatten(action_tensor, start_dim=0, end_dim=1)\nflat_cumsum = torch.flatten(cumulative_rol... | <|body_start_0|>
super(PG_LOSS, self).__init__()
self.simulations = simulations
self.trajectory_length = trajectory_length
self.max_ent = max_ent
<|end_body_0|>
<|body_start_1|>
flat_states = torch.flatten(state_tensor, start_dim=0, end_dim=1)
flat_actions = torch.flatte... | POLICY GRADIENTS LOSS FUNCTION | PG_LOSS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PG_LOSS:
"""POLICY GRADIENTS LOSS FUNCTION"""
def __init__(self, trajectory_length, simulations, max_ent=0, TRPO=0, PPO=0, beta=None):
"""INITIALIZATIONS"""
<|body_0|>
def forward(self, policy, state_tensor, action_tensor, reward_tensor, cumulative_rollout):
"""C... | stack_v2_sparse_classes_10k_train_000682 | 1,741 | no_license | [
{
"docstring": "INITIALIZATIONS",
"name": "__init__",
"signature": "def __init__(self, trajectory_length, simulations, max_ent=0, TRPO=0, PPO=0, beta=None)"
},
{
"docstring": "CONVERT FORMAT",
"name": "forward",
"signature": "def forward(self, policy, state_tensor, action_tensor, reward_... | 2 | stack_v2_sparse_classes_30k_train_004608 | Implement the Python class `PG_LOSS` described below.
Class description:
POLICY GRADIENTS LOSS FUNCTION
Method signatures and docstrings:
- def __init__(self, trajectory_length, simulations, max_ent=0, TRPO=0, PPO=0, beta=None): INITIALIZATIONS
- def forward(self, policy, state_tensor, action_tensor, reward_tensor, c... | Implement the Python class `PG_LOSS` described below.
Class description:
POLICY GRADIENTS LOSS FUNCTION
Method signatures and docstrings:
- def __init__(self, trajectory_length, simulations, max_ent=0, TRPO=0, PPO=0, beta=None): INITIALIZATIONS
- def forward(self, policy, state_tensor, action_tensor, reward_tensor, c... | 5790d3b3214dcf60657a92149162511d12cdfea7 | <|skeleton|>
class PG_LOSS:
"""POLICY GRADIENTS LOSS FUNCTION"""
def __init__(self, trajectory_length, simulations, max_ent=0, TRPO=0, PPO=0, beta=None):
"""INITIALIZATIONS"""
<|body_0|>
def forward(self, policy, state_tensor, action_tensor, reward_tensor, cumulative_rollout):
"""C... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PG_LOSS:
"""POLICY GRADIENTS LOSS FUNCTION"""
def __init__(self, trajectory_length, simulations, max_ent=0, TRPO=0, PPO=0, beta=None):
"""INITIALIZATIONS"""
super(PG_LOSS, self).__init__()
self.simulations = simulations
self.trajectory_length = trajectory_length
se... | the_stack_v2_python_sparse | Vanilla Policy Gradients/objective_function.py | WilderLavington/Probabalistic_RL | train | 1 |
9321b297db24abbafe12bf8e629a626aae4868fb | [
"if not nums:\n return [[]]\nres = []\nself.helper(res, [], nums)\nreturn res",
"total.append(part)\nif len(nums) == 1:\n total.append(part + [nums[0]])\n return\nfor i, e in enumerate(nums):\n self.helper(total, part + [e], nums[i + 1:])"
] | <|body_start_0|>
if not nums:
return [[]]
res = []
self.helper(res, [], nums)
return res
<|end_body_0|>
<|body_start_1|>
total.append(part)
if len(nums) == 1:
total.append(part + [nums[0]])
return
for i, e in enumerate(nums):
... | Solution description | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Solution description"""
def func(self, nums):
"""Solution function description"""
<|body_0|>
def helper(self, total, part, nums):
"""Solution function description"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not nums:
... | stack_v2_sparse_classes_10k_train_000683 | 795 | permissive | [
{
"docstring": "Solution function description",
"name": "func",
"signature": "def func(self, nums)"
},
{
"docstring": "Solution function description",
"name": "helper",
"signature": "def helper(self, total, part, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004705 | Implement the Python class `Solution` described below.
Class description:
Solution description
Method signatures and docstrings:
- def func(self, nums): Solution function description
- def helper(self, total, part, nums): Solution function description | Implement the Python class `Solution` described below.
Class description:
Solution description
Method signatures and docstrings:
- def func(self, nums): Solution function description
- def helper(self, total, part, nums): Solution function description
<|skeleton|>
class Solution:
"""Solution description"""
... | 869ee24c50c08403b170e8f7868699185e9dfdd1 | <|skeleton|>
class Solution:
"""Solution description"""
def func(self, nums):
"""Solution function description"""
<|body_0|>
def helper(self, total, part, nums):
"""Solution function description"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
"""Solution description"""
def func(self, nums):
"""Solution function description"""
if not nums:
return [[]]
res = []
self.helper(res, [], nums)
return res
def helper(self, total, part, nums):
"""Solution function description"""
... | the_stack_v2_python_sparse | 78.Subsets/2.py | cerebrumaize/leetcode | train | 0 |
8ee7b1947e3933d394e8244375cea3e346504307 | [
"print('***DEBUG PARAMS PARAMS: |%s|' % param)\nparam = param[0]\nif param.get('getFields'):\n return [{'name': {'type': 'string'}}]\nsearch_args = []\np_name = param.get('p_name')\nif p_name:\n search_args.extend([('name', 'ilike', p_name)])\nids = self.search(cr, uid, search_args)\nresult = []\nfor partner ... | <|body_start_0|>
print('***DEBUG PARAMS PARAMS: |%s|' % param)
param = param[0]
if param.get('getFields'):
return [{'name': {'type': 'string'}}]
search_args = []
p_name = param.get('p_name')
if p_name:
search_args.extend([('name', 'ilike', p_name)]... | res_partner | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class res_partner:
def report_custom_data_params(self, cr, uid, *param):
"""Custom data method for 'params' report. 'param' is a tuple where the first element is a dict with any report parameters (keyed by the parameter name) and other environmental info. In this example the report has a singl... | stack_v2_sparse_classes_10k_train_000684 | 2,925 | no_license | [
{
"docstring": "Custom data method for 'params' report. 'param' is a tuple where the first element is a dict with any report parameters (keyed by the parameter name) and other environmental info. In this example the report has a single defined parameter 'p_name' which is a string. The code below uses 'ilike' to... | 2 | stack_v2_sparse_classes_30k_test_000160 | Implement the Python class `res_partner` described below.
Class description:
Implement the res_partner class.
Method signatures and docstrings:
- def report_custom_data_params(self, cr, uid, *param): Custom data method for 'params' report. 'param' is a tuple where the first element is a dict with any report parameter... | Implement the Python class `res_partner` described below.
Class description:
Implement the res_partner class.
Method signatures and docstrings:
- def report_custom_data_params(self, cr, uid, *param): Custom data method for 'params' report. 'param' is a tuple where the first element is a dict with any report parameter... | 7f36c019ed5405bfdff809c43ae74fb369cd1fd6 | <|skeleton|>
class res_partner:
def report_custom_data_params(self, cr, uid, *param):
"""Custom data method for 'params' report. 'param' is a tuple where the first element is a dict with any report parameters (keyed by the parameter name) and other environmental info. In this example the report has a singl... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class res_partner:
def report_custom_data_params(self, cr, uid, *param):
"""Custom data method for 'params' report. 'param' is a tuple where the first element is a dict with any report parameters (keyed by the parameter name) and other environmental info. In this example the report has a single defined para... | the_stack_v2_python_sparse | dme_pentaho_example/models/res_partner.py | turbodavid/dme | train | 0 | |
0f645ee91782f286c68d418dd060752e7354a76d | [
"self._host = host\nself._port = port\nself._dsmr_version = dsmr_version\nself._telegram = {}",
"if obis_ref.EQUIPMENT_IDENTIFIER in self._telegram:\n dsmr_object = self._telegram[obis_ref.EQUIPMENT_IDENTIFIER]\n return getattr(dsmr_object, 'value', None)",
"if obis_ref.EQUIPMENT_IDENTIFIER_GAS in self._t... | <|body_start_0|>
self._host = host
self._port = port
self._dsmr_version = dsmr_version
self._telegram = {}
<|end_body_0|>
<|body_start_1|>
if obis_ref.EQUIPMENT_IDENTIFIER in self._telegram:
dsmr_object = self._telegram[obis_ref.EQUIPMENT_IDENTIFIER]
retu... | Test the connection to DSMR and receive telegram to read serial ids. | DSMRConnection | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DSMRConnection:
"""Test the connection to DSMR and receive telegram to read serial ids."""
def __init__(self, host, port, dsmr_version):
"""Initialize."""
<|body_0|>
def equipment_identifier(self):
"""Equipment identifier."""
<|body_1|>
def equipment... | stack_v2_sparse_classes_10k_train_000685 | 6,055 | permissive | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, host, port, dsmr_version)"
},
{
"docstring": "Equipment identifier.",
"name": "equipment_identifier",
"signature": "def equipment_identifier(self)"
},
{
"docstring": "Equipment identifier gas.",
... | 4 | stack_v2_sparse_classes_30k_train_003344 | Implement the Python class `DSMRConnection` described below.
Class description:
Test the connection to DSMR and receive telegram to read serial ids.
Method signatures and docstrings:
- def __init__(self, host, port, dsmr_version): Initialize.
- def equipment_identifier(self): Equipment identifier.
- def equipment_ide... | Implement the Python class `DSMRConnection` described below.
Class description:
Test the connection to DSMR and receive telegram to read serial ids.
Method signatures and docstrings:
- def __init__(self, host, port, dsmr_version): Initialize.
- def equipment_identifier(self): Equipment identifier.
- def equipment_ide... | ed4ab403deaed9e8c95e0db728477fcb012bf4fa | <|skeleton|>
class DSMRConnection:
"""Test the connection to DSMR and receive telegram to read serial ids."""
def __init__(self, host, port, dsmr_version):
"""Initialize."""
<|body_0|>
def equipment_identifier(self):
"""Equipment identifier."""
<|body_1|>
def equipment... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DSMRConnection:
"""Test the connection to DSMR and receive telegram to read serial ids."""
def __init__(self, host, port, dsmr_version):
"""Initialize."""
self._host = host
self._port = port
self._dsmr_version = dsmr_version
self._telegram = {}
def equipment_i... | the_stack_v2_python_sparse | homeassistant/components/dsmr/config_flow.py | tchellomello/home-assistant | train | 8 |
60171a16cd96548f1768ae642e4e5494d52933c3 | [
"ret = 0\nif len(A) < 3:\n return ret\nstart, end = (0, 1)\nwhile end < len(A):\n if A[end] <= A[start]:\n start = end\n end += 1\n else:\n peak = end + 1\n while peak < len(A) and A[peak] > A[peak - 1]:\n peak += 1\n peak -= 1\n end = peak + 1\n ... | <|body_start_0|>
ret = 0
if len(A) < 3:
return ret
start, end = (0, 1)
while end < len(A):
if A[end] <= A[start]:
start = end
end += 1
else:
peak = end + 1
while peak < len(A) and A[peak] ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestMountain(self, A):
""":type A: List[int] :rtype: int"""
<|body_0|>
def longestMountain2(self, A):
""":type A: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ret = 0
if len(A) < 3:
retur... | stack_v2_sparse_classes_10k_train_000686 | 2,431 | no_license | [
{
"docstring": ":type A: List[int] :rtype: int",
"name": "longestMountain",
"signature": "def longestMountain(self, A)"
},
{
"docstring": ":type A: List[int] :rtype: int",
"name": "longestMountain2",
"signature": "def longestMountain2(self, A)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestMountain(self, A): :type A: List[int] :rtype: int
- def longestMountain2(self, A): :type A: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestMountain(self, A): :type A: List[int] :rtype: int
- def longestMountain2(self, A): :type A: List[int] :rtype: int
<|skeleton|>
class Solution:
def longestMountai... | 9190d3d178f1733aa226973757ee7e045b7bab00 | <|skeleton|>
class Solution:
def longestMountain(self, A):
""":type A: List[int] :rtype: int"""
<|body_0|>
def longestMountain2(self, A):
""":type A: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def longestMountain(self, A):
""":type A: List[int] :rtype: int"""
ret = 0
if len(A) < 3:
return ret
start, end = (0, 1)
while end < len(A):
if A[end] <= A[start]:
start = end
end += 1
else:
... | the_stack_v2_python_sparse | LongestMountainInArray.py | ellinx/LC-python | train | 1 | |
ed94486254899116b94770c0259e0fb6dc50c06d | [
"data_list = []\nresults = self.query.all()\nformatter = date.getLocaleFormatter(self.request, 'date', 'long')\nfor result in results:\n data = {}\n data['qid'] = 'm_' + str(result.motion_id)\n data['subject'] = u'M ' + str(result.motion_number) + u' ' + result.short_name\n data['title'] = result.short_... | <|body_start_0|>
data_list = []
results = self.query.all()
formatter = date.getLocaleFormatter(self.request, 'date', 'long')
for result in results:
data = {}
data['qid'] = 'm_' + str(result.motion_id)
data['subject'] = u'M ' + str(result.motion_number)... | MotionInStateViewlet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MotionInStateViewlet:
def _setData(self):
"""return the data of the query"""
<|body_0|>
def update(self):
"""refresh the query"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
data_list = []
results = self.query.all()
formatter = date... | stack_v2_sparse_classes_10k_train_000687 | 27,657 | no_license | [
{
"docstring": "return the data of the query",
"name": "_setData",
"signature": "def _setData(self)"
},
{
"docstring": "refresh the query",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003982 | Implement the Python class `MotionInStateViewlet` described below.
Class description:
Implement the MotionInStateViewlet class.
Method signatures and docstrings:
- def _setData(self): return the data of the query
- def update(self): refresh the query | Implement the Python class `MotionInStateViewlet` described below.
Class description:
Implement the MotionInStateViewlet class.
Method signatures and docstrings:
- def _setData(self): return the data of the query
- def update(self): refresh the query
<|skeleton|>
class MotionInStateViewlet:
def _setData(self):
... | 5cf0ba31dfbff8d2c1b4aa8ab6f69c7a0ae9870d | <|skeleton|>
class MotionInStateViewlet:
def _setData(self):
"""return the data of the query"""
<|body_0|>
def update(self):
"""refresh the query"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MotionInStateViewlet:
def _setData(self):
"""return the data of the query"""
data_list = []
results = self.query.all()
formatter = date.getLocaleFormatter(self.request, 'date', 'long')
for result in results:
data = {}
data['qid'] = 'm_' + str(res... | the_stack_v2_python_sparse | bungeni.main/branches/mr/bungeni/ui/viewlets/workspace.py | malangalanga/bungeni-portal | train | 0 | |
d467e4aad74e0b846830603633fb678cca505b25 | [
"super().__init__()\nself.conv = torch.nn.Conv1d(idim, odim, kernel_size, stride=stride, dilation=dilation, groups=groups, bias=bias)\nself.dropout = torch.nn.Dropout(p=dropout_rate)\nif relu:\n self.relu_func = torch.nn.ReLU()\nif batch_norm:\n self.bn = torch.nn.BatchNorm1d(odim)\nself.relu = relu\nself.bat... | <|body_start_0|>
super().__init__()
self.conv = torch.nn.Conv1d(idim, odim, kernel_size, stride=stride, dilation=dilation, groups=groups, bias=bias)
self.dropout = torch.nn.Dropout(p=dropout_rate)
if relu:
self.relu_func = torch.nn.ReLU()
if batch_norm:
se... | 1D convolution module for custom encoder. Args: idim: Input dimension. odim: Output dimension. kernel_size: Size of the convolving kernel. stride: Stride of the convolution. dilation: Spacing between the kernel points. groups: Number of blocked connections from input channels to output channels. bias: Whether to add a ... | Conv1d | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Conv1d:
"""1D convolution module for custom encoder. Args: idim: Input dimension. odim: Output dimension. kernel_size: Size of the convolving kernel. stride: Stride of the convolution. dilation: Spacing between the kernel points. groups: Number of blocked connections from input channels to output... | stack_v2_sparse_classes_10k_train_000688 | 7,246 | permissive | [
{
"docstring": "Construct a Conv1d module object.",
"name": "__init__",
"signature": "def __init__(self, idim: int, odim: int, kernel_size: Union[int, Tuple], stride: Union[int, Tuple]=1, dilation: Union[int, Tuple]=1, groups: Union[int, Tuple]=1, bias: bool=True, batch_norm: bool=False, relu: bool=True... | 4 | stack_v2_sparse_classes_30k_train_002277 | Implement the Python class `Conv1d` described below.
Class description:
1D convolution module for custom encoder. Args: idim: Input dimension. odim: Output dimension. kernel_size: Size of the convolving kernel. stride: Stride of the convolution. dilation: Spacing between the kernel points. groups: Number of blocked co... | Implement the Python class `Conv1d` described below.
Class description:
1D convolution module for custom encoder. Args: idim: Input dimension. odim: Output dimension. kernel_size: Size of the convolving kernel. stride: Stride of the convolution. dilation: Spacing between the kernel points. groups: Number of blocked co... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class Conv1d:
"""1D convolution module for custom encoder. Args: idim: Input dimension. odim: Output dimension. kernel_size: Size of the convolving kernel. stride: Stride of the convolution. dilation: Spacing between the kernel points. groups: Number of blocked connections from input channels to output... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Conv1d:
"""1D convolution module for custom encoder. Args: idim: Input dimension. odim: Output dimension. kernel_size: Size of the convolving kernel. stride: Stride of the convolution. dilation: Spacing between the kernel points. groups: Number of blocked connections from input channels to output channels. bi... | the_stack_v2_python_sparse | espnet/nets/pytorch_backend/transducer/conv1d_nets.py | espnet/espnet | train | 7,242 |
3529772f5f0d5a938dfb56da9dce974eb1930df3 | [
"globalsettings = sublime.load_settings('Preferences.sublime-settings')\nsettings = sublime.load_settings('AnacondaKite.sublime-settings')\nenabled = settings.get('integrate_with_kite', False)\nnot_ignored = 'Kite' not in globalsettings.get('ignored_packages')\nif enabled and not_ignored:\n try:\n from Ki... | <|body_start_0|>
globalsettings = sublime.load_settings('Preferences.sublime-settings')
settings = sublime.load_settings('AnacondaKite.sublime-settings')
enabled = settings.get('integrate_with_kite', False)
not_ignored = 'Kite' not in globalsettings.get('ignored_packages')
if ena... | Checks if Kite integration is turned on | Integration | [
"MIT",
"GPL-1.0-or-later",
"LGPL-2.1-or-later",
"GPL-3.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"GPL-3.0-or-later",
"LGPL-2.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Integration:
"""Checks if Kite integration is turned on"""
def enabled(cls):
"""Returns True if Kite integration is enabled"""
<|body_0|>
def enable(cls):
"""Enable Kite integration"""
<|body_1|>
def disable(cls):
"""Disable Kite integration"... | stack_v2_sparse_classes_10k_train_000689 | 1,521 | permissive | [
{
"docstring": "Returns True if Kite integration is enabled",
"name": "enabled",
"signature": "def enabled(cls)"
},
{
"docstring": "Enable Kite integration",
"name": "enable",
"signature": "def enable(cls)"
},
{
"docstring": "Disable Kite integration",
"name": "disable",
... | 3 | stack_v2_sparse_classes_30k_train_002325 | Implement the Python class `Integration` described below.
Class description:
Checks if Kite integration is turned on
Method signatures and docstrings:
- def enabled(cls): Returns True if Kite integration is enabled
- def enable(cls): Enable Kite integration
- def disable(cls): Disable Kite integration | Implement the Python class `Integration` described below.
Class description:
Checks if Kite integration is turned on
Method signatures and docstrings:
- def enabled(cls): Returns True if Kite integration is enabled
- def enable(cls): Enable Kite integration
- def disable(cls): Disable Kite integration
<|skeleton|>
c... | 9a3808d0d79504b488a407084b489b9d687a528a | <|skeleton|>
class Integration:
"""Checks if Kite integration is turned on"""
def enabled(cls):
"""Returns True if Kite integration is enabled"""
<|body_0|>
def enable(cls):
"""Enable Kite integration"""
<|body_1|>
def disable(cls):
"""Disable Kite integration"... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Integration:
"""Checks if Kite integration is turned on"""
def enabled(cls):
"""Returns True if Kite integration is enabled"""
globalsettings = sublime.load_settings('Preferences.sublime-settings')
settings = sublime.load_settings('AnacondaKite.sublime-settings')
enabled =... | the_stack_v2_python_sparse | sublime/Packages/Anaconda/anaconda_lib/kite.py | Kisura/dotfiles | train | 0 |
9997f09387522570e5cfb1369655a98206fcf4bb | [
"self.product_code = product_code\nself.description = description\nself.market_price = market_price\nself.rental_price = rental_price",
"output_dict = {}\noutput_dict['productCode'] = self.product_code\noutput_dict['description'] = self.description\noutput_dict['marketPrice'] = self.market_price\noutput_dict['ren... | <|body_start_0|>
self.product_code = product_code
self.description = description
self.market_price = market_price
self.rental_price = rental_price
<|end_body_0|>
<|body_start_1|>
output_dict = {}
output_dict['productCode'] = self.product_code
output_dict['descrip... | inventory base class | Inventory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Inventory:
"""inventory base class"""
def __init__(self, product_code, description, market_price, rental_price):
"""initializing"""
<|body_0|>
def return_as_dictionary(self):
"""returns a dictionary"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_000690 | 772 | no_license | [
{
"docstring": "initializing",
"name": "__init__",
"signature": "def __init__(self, product_code, description, market_price, rental_price)"
},
{
"docstring": "returns a dictionary",
"name": "return_as_dictionary",
"signature": "def return_as_dictionary(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000427 | Implement the Python class `Inventory` described below.
Class description:
inventory base class
Method signatures and docstrings:
- def __init__(self, product_code, description, market_price, rental_price): initializing
- def return_as_dictionary(self): returns a dictionary | Implement the Python class `Inventory` described below.
Class description:
inventory base class
Method signatures and docstrings:
- def __init__(self, product_code, description, market_price, rental_price): initializing
- def return_as_dictionary(self): returns a dictionary
<|skeleton|>
class Inventory:
"""inven... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class Inventory:
"""inventory base class"""
def __init__(self, product_code, description, market_price, rental_price):
"""initializing"""
<|body_0|>
def return_as_dictionary(self):
"""returns a dictionary"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Inventory:
"""inventory base class"""
def __init__(self, product_code, description, market_price, rental_price):
"""initializing"""
self.product_code = product_code
self.description = description
self.market_price = market_price
self.rental_price = rental_price
... | the_stack_v2_python_sparse | students/humberto_gonzalez/lesson01/inventory_management/inventory_class.py | JavaRod/SP_Python220B_2019 | train | 1 |
4b83163dd1d632ad4e4d61fef5d6cd547ed515f9 | [
"self.transform_types: Dict[str, List[str]] = {'str': ['interpolation', 'dtype', 'label_file', 'vocab_file'], 'int': ['x', 'y', 'height', 'width', 'offset_height', 'offset_width', 'target_height', 'target_width', 'dim', 'resize_side', 'label_shift'], 'float': ['scale', 'central_fraction'], 'list<float>': ['mean', '... | <|body_start_0|>
self.transform_types: Dict[str, List[str]] = {'str': ['interpolation', 'dtype', 'label_file', 'vocab_file'], 'int': ['x', 'y', 'height', 'width', 'offset_height', 'offset_width', 'target_height', 'target_width', 'dim', 'resize_side', 'label_shift'], 'float': ['scale', 'central_fraction'], 'list... | Configuration type parser class. | ConfigurationParser | [
"MIT",
"Intel",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigurationParser:
"""Configuration type parser class."""
def __init__(self) -> None:
"""Initialize configuration type parser."""
<|body_0|>
def parse(self, data: dict) -> dict:
"""Parse configuration."""
<|body_1|>
def parse_transforms(self, trans... | stack_v2_sparse_classes_10k_train_000691 | 9,825 | permissive | [
{
"docstring": "Initialize configuration type parser.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Parse configuration.",
"name": "parse",
"signature": "def parse(self, data: dict) -> dict"
},
{
"docstring": "Parse transforms list.",
"nam... | 6 | stack_v2_sparse_classes_30k_train_000881 | Implement the Python class `ConfigurationParser` described below.
Class description:
Configuration type parser class.
Method signatures and docstrings:
- def __init__(self) -> None: Initialize configuration type parser.
- def parse(self, data: dict) -> dict: Parse configuration.
- def parse_transforms(self, transform... | Implement the Python class `ConfigurationParser` described below.
Class description:
Configuration type parser class.
Method signatures and docstrings:
- def __init__(self) -> None: Initialize configuration type parser.
- def parse(self, data: dict) -> dict: Parse configuration.
- def parse_transforms(self, transform... | 3976edc4215398e69ce0213f87ec295f5dc96e0e | <|skeleton|>
class ConfigurationParser:
"""Configuration type parser class."""
def __init__(self) -> None:
"""Initialize configuration type parser."""
<|body_0|>
def parse(self, data: dict) -> dict:
"""Parse configuration."""
<|body_1|>
def parse_transforms(self, trans... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ConfigurationParser:
"""Configuration type parser class."""
def __init__(self) -> None:
"""Initialize configuration type parser."""
self.transform_types: Dict[str, List[str]] = {'str': ['interpolation', 'dtype', 'label_file', 'vocab_file'], 'int': ['x', 'y', 'height', 'width', 'offset_hei... | the_stack_v2_python_sparse | neural_compressor/ux/components/configuration_wizard/configuration_parser.py | Skp80/neural-compressor | train | 0 |
692b526d309f5dc65e96b4fa97641b156035f350 | [
"if criteria is None:\n criteria = {}\nself.trials = get_trials(base_dir, criteria=criteria)\nassert len(self.trials) > 0, 'Nothing loaded.'\nself.label = 'AverageReturn'",
"if criteria is None:\n criteria = {}\nreturn [trial for trial in self.trials if matches_dict(criteria, trial.variant)]"
] | <|body_start_0|>
if criteria is None:
criteria = {}
self.trials = get_trials(base_dir, criteria=criteria)
assert len(self.trials) > 0, 'Nothing loaded.'
self.label = 'AverageReturn'
<|end_body_0|>
<|body_start_1|>
if criteria is None:
criteria = {}
... | Represents an experiment, which consists of many Trials. | Experiment | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Experiment:
"""Represents an experiment, which consists of many Trials."""
def __init__(self, base_dir, criteria=None):
""":param base_dir: A path. Directory structure should be something like: ``` base_dir/ foo/ bar/ arbtrarily_deep/ trial_one/ variant.json progress.csv trial_two/ v... | stack_v2_sparse_classes_10k_train_000692 | 5,929 | permissive | [
{
"docstring": ":param base_dir: A path. Directory structure should be something like: ``` base_dir/ foo/ bar/ arbtrarily_deep/ trial_one/ variant.json progress.csv trial_two/ variant.json progress.csv trial_three/ variant.json progress.csv ... variant.json # <-- base_dir/foo/bar has its own Trial progress.csv ... | 2 | stack_v2_sparse_classes_30k_train_001520 | Implement the Python class `Experiment` described below.
Class description:
Represents an experiment, which consists of many Trials.
Method signatures and docstrings:
- def __init__(self, base_dir, criteria=None): :param base_dir: A path. Directory structure should be something like: ``` base_dir/ foo/ bar/ arbtraril... | Implement the Python class `Experiment` described below.
Class description:
Represents an experiment, which consists of many Trials.
Method signatures and docstrings:
- def __init__(self, base_dir, criteria=None): :param base_dir: A path. Directory structure should be something like: ``` base_dir/ foo/ bar/ arbtraril... | baba8ce634d32a48c7dfe4dc03b123e18e96e0a3 | <|skeleton|>
class Experiment:
"""Represents an experiment, which consists of many Trials."""
def __init__(self, base_dir, criteria=None):
""":param base_dir: A path. Directory structure should be something like: ``` base_dir/ foo/ bar/ arbtrarily_deep/ trial_one/ variant.json progress.csv trial_two/ v... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Experiment:
"""Represents an experiment, which consists of many Trials."""
def __init__(self, base_dir, criteria=None):
""":param base_dir: A path. Directory structure should be something like: ``` base_dir/ foo/ bar/ arbtrarily_deep/ trial_one/ variant.json progress.csv trial_two/ variant.json p... | the_stack_v2_python_sparse | rlkit/misc/data_processing.py | Asap7772/railrl_evalsawyer | train | 1 |
d870bc14e27f410951e7876aeb88f69f19ec363f | [
"self.require_collection()\nrequest = http.Request('POST', self.get_url(), self.wrap_object(text))\nreturn (request, parsers.parse_json)",
"self.require_item()\nrequest = http.Request('PUT', self.get_url(), self.wrap_object(text))\nreturn (request, parsers.parse_json)"
] | <|body_start_0|>
self.require_collection()
request = http.Request('POST', self.get_url(), self.wrap_object(text))
return (request, parsers.parse_json)
<|end_body_0|>
<|body_start_1|>
self.require_item()
request = http.Request('PUT', self.get_url(), self.wrap_object(text))
... | UserVoiceTextResource | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserVoiceTextResource:
def create(self, text):
"""Create a new resource. :var text: the text of the resource to be created. :vartype text: str"""
<|body_0|>
def update(self, text):
"""Update this resource. :var text: the new text of the resource. :vartype text: str""... | stack_v2_sparse_classes_10k_train_000693 | 2,920 | permissive | [
{
"docstring": "Create a new resource. :var text: the text of the resource to be created. :vartype text: str",
"name": "create",
"signature": "def create(self, text)"
},
{
"docstring": "Update this resource. :var text: the new text of the resource. :vartype text: str",
"name": "update",
... | 2 | null | Implement the Python class `UserVoiceTextResource` described below.
Class description:
Implement the UserVoiceTextResource class.
Method signatures and docstrings:
- def create(self, text): Create a new resource. :var text: the text of the resource to be created. :vartype text: str
- def update(self, text): Update th... | Implement the Python class `UserVoiceTextResource` described below.
Class description:
Implement the UserVoiceTextResource class.
Method signatures and docstrings:
- def create(self, text): Create a new resource. :var text: the text of the resource to be created. :vartype text: str
- def update(self, text): Update th... | 25caa745a104c8dc209584fa359294c65dbf88bb | <|skeleton|>
class UserVoiceTextResource:
def create(self, text):
"""Create a new resource. :var text: the text of the resource to be created. :vartype text: str"""
<|body_0|>
def update(self, text):
"""Update this resource. :var text: the new text of the resource. :vartype text: str""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserVoiceTextResource:
def create(self, text):
"""Create a new resource. :var text: the text of the resource to be created. :vartype text: str"""
self.require_collection()
request = http.Request('POST', self.get_url(), self.wrap_object(text))
return (request, parsers.parse_json... | the_stack_v2_python_sparse | libsaas/services/uservoice/resource.py | piplcom/libsaas | train | 1 | |
1abea7b3f0164561422c7c796110264f1979584a | [
"if not root:\n return TreeNode(val)\ncur_node = root\npre_node = root\nwhile cur_node:\n pre_node = cur_node\n if cur_node.val < val:\n cur_node = cur_node.right\n else:\n cur_node = cur_node.left\nnew_node = TreeNode(val)\nif pre_node.val > val:\n pre_node.left = new_node\nelse:\n ... | <|body_start_0|>
if not root:
return TreeNode(val)
cur_node = root
pre_node = root
while cur_node:
pre_node = cur_node
if cur_node.val < val:
cur_node = cur_node.right
else:
cur_node = cur_node.left
n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode:
"""遍历方式，找到合适的叶子节点，插入 :param root: :param val: :return:"""
<|body_0|>
def insertIntoBST1(self, root: TreeNode, val: int) -> TreeNode:
"""递归算法 :param root: :param val: :return:"""
<|body_1... | stack_v2_sparse_classes_10k_train_000694 | 1,691 | no_license | [
{
"docstring": "遍历方式，找到合适的叶子节点，插入 :param root: :param val: :return:",
"name": "insertIntoBST",
"signature": "def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode"
},
{
"docstring": "递归算法 :param root: :param val: :return:",
"name": "insertIntoBST1",
"signature": "def insertIntoBS... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode: 遍历方式，找到合适的叶子节点，插入 :param root: :param val: :return:
- def insertIntoBST1(self, root: TreeNode, val: int) -> TreeNod... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode: 遍历方式，找到合适的叶子节点，插入 :param root: :param val: :return:
- def insertIntoBST1(self, root: TreeNode, val: int) -> TreeNod... | 9acba92695c06406f12f997a720bfe1deb9464a8 | <|skeleton|>
class Solution:
def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode:
"""遍历方式，找到合适的叶子节点，插入 :param root: :param val: :return:"""
<|body_0|>
def insertIntoBST1(self, root: TreeNode, val: int) -> TreeNode:
"""递归算法 :param root: :param val: :return:"""
<|body_1... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode:
"""遍历方式，找到合适的叶子节点，插入 :param root: :param val: :return:"""
if not root:
return TreeNode(val)
cur_node = root
pre_node = root
while cur_node:
pre_node = cur_node
i... | the_stack_v2_python_sparse | datastructure/binary_search_tree/InsertIntoBST.py | yinhuax/leet_code | train | 0 | |
0c8dbca23cac908046cf4b261aa2d4d0d57964ba | [
"add_tuple, error_tuple = database.import_data('csv_files', 'products.csv', 'customers.csv', 'rentals.csv')\nself.assertEqual(add_tuple, (3, 3, 5))\nself.assertEqual(error_tuple, (0, 0, 0))\nadd_tuple, error_tuple = database.import_data('csv_files', 'products1.csv', 'customers1.csv', 'rentals1.csv')\nself.assertEqu... | <|body_start_0|>
add_tuple, error_tuple = database.import_data('csv_files', 'products.csv', 'customers.csv', 'rentals.csv')
self.assertEqual(add_tuple, (3, 3, 5))
self.assertEqual(error_tuple, (0, 0, 0))
add_tuple, error_tuple = database.import_data('csv_files', 'products1.csv', 'custome... | Define a class for testing database functions | DatabaseTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatabaseTests:
"""Define a class for testing database functions"""
def test_import_data(self):
"""Test importing data from csv files"""
<|body_0|>
def test_show_available_products(self):
"""Test showing available products"""
<|body_1|>
def test_show_... | stack_v2_sparse_classes_10k_train_000695 | 2,440 | no_license | [
{
"docstring": "Test importing data from csv files",
"name": "test_import_data",
"signature": "def test_import_data(self)"
},
{
"docstring": "Test showing available products",
"name": "test_show_available_products",
"signature": "def test_show_available_products(self)"
},
{
"docs... | 3 | null | Implement the Python class `DatabaseTests` described below.
Class description:
Define a class for testing database functions
Method signatures and docstrings:
- def test_import_data(self): Test importing data from csv files
- def test_show_available_products(self): Test showing available products
- def test_show_rent... | Implement the Python class `DatabaseTests` described below.
Class description:
Define a class for testing database functions
Method signatures and docstrings:
- def test_import_data(self): Test importing data from csv files
- def test_show_available_products(self): Test showing available products
- def test_show_rent... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class DatabaseTests:
"""Define a class for testing database functions"""
def test_import_data(self):
"""Test importing data from csv files"""
<|body_0|>
def test_show_available_products(self):
"""Test showing available products"""
<|body_1|>
def test_show_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DatabaseTests:
"""Define a class for testing database functions"""
def test_import_data(self):
"""Test importing data from csv files"""
add_tuple, error_tuple = database.import_data('csv_files', 'products.csv', 'customers.csv', 'rentals.csv')
self.assertEqual(add_tuple, (3, 3, 5))... | the_stack_v2_python_sparse | students/bcoates/lesson05/test_database.py | JavaRod/SP_Python220B_2019 | train | 1 |
1756ea43dc98dc035a6c13be858c9d6390111b92 | [
"from math import sqrt\nself.min_instances = min_instances\nself.drift_level = float(drift_level)\nself.i = None\nself.pi = None\nself.si = None\nself.pi_min = None\nself.si_min = None\nself.sqrt = sqrt\nself.reset()",
"self.i = 0\nself.pi = 1.0\nself.si = 0.0\nself.pi_min = float('inf')\nself.si_min = float('inf... | <|body_start_0|>
from math import sqrt
self.min_instances = min_instances
self.drift_level = float(drift_level)
self.i = None
self.pi = None
self.si = None
self.pi_min = None
self.si_min = None
self.sqrt = sqrt
self.reset()
<|end_body_0|>
... | Implements the DDM drift detection method. This drift detector is based on the paper on the DDM Paper (João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295). We keep the highest alarm level of drift detection (out of control), leaving out warning level. Att... | DDM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DDM:
"""Implements the DDM drift detection method. This drift detector is based on the paper on the DDM Paper (João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295). We keep the highest alarm level of drift detection (out of control),... | stack_v2_sparse_classes_10k_train_000696 | 3,161 | no_license | [
{
"docstring": "Initialize the DDM Drift Detector Initialize the DDM Drift detector. Default parameters are provided as well. Args: min_instances: INT. Minimum number of instances for Detector to return a result. drift_level: Alarm level for drift detector. 3.0 is from the DDM paper. 2.0 would be for drift warn... | 3 | stack_v2_sparse_classes_30k_train_001141 | Implement the Python class `DDM` described below.
Class description:
Implements the DDM drift detection method. This drift detector is based on the paper on the DDM Paper (João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295). We keep the highest alarm lev... | Implement the Python class `DDM` described below.
Class description:
Implements the DDM drift detection method. This drift detector is based on the paper on the DDM Paper (João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295). We keep the highest alarm lev... | 4938936dbf08b5331275d4413dbad51acbaf7da9 | <|skeleton|>
class DDM:
"""Implements the DDM drift detection method. This drift detector is based on the paper on the DDM Paper (João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295). We keep the highest alarm level of drift detection (out of control),... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DDM:
"""Implements the DDM drift detection method. This drift detector is based on the paper on the DDM Paper (João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295). We keep the highest alarm level of drift detection (out of control), leaving out ... | the_stack_v2_python_sparse | mlep_odin_main/mlep/mlep/drift_detector/LabeledDriftDetector/DDM.py | asuprem/ODIN | train | 7 |
c6bbbc932da09ce1eacb63de1c7b238510195e60 | [
"row = index / columns\ncolumn = index - row * columns\nreturn (row, column)",
"if len(matrix) == 0 or len(matrix[0]) == 0:\n return False\nrows = len(matrix)\ncolumns = len(matrix[0])\nleft, right = (0, rows * columns - 1)\nwhile left <= right:\n middle = (left + right) / 2\n row, column = self.one_d_to... | <|body_start_0|>
row = index / columns
column = index - row * columns
return (row, column)
<|end_body_0|>
<|body_start_1|>
if len(matrix) == 0 or len(matrix[0]) == 0:
return False
rows = len(matrix)
columns = len(matrix[0])
left, right = (0, rows * co... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def one_d_to_two_d_index(self, index, columns):
""">>> s = Solution() >>> s.one_d_to_two_d_index(0, 3, 4) (0, 0) >>> s.one_d_to_two_d_index(11, 3, 4) (2, 3) >>> s.one_d_to_two_d_index(5, 3, 4) (1, 1) >>> s.one_d_to_two_d_index(4, 3, 4) (1, 0) >>> s.one_d_to_two_d_index(8, 3, 4)... | stack_v2_sparse_classes_10k_train_000697 | 1,790 | no_license | [
{
"docstring": ">>> s = Solution() >>> s.one_d_to_two_d_index(0, 3, 4) (0, 0) >>> s.one_d_to_two_d_index(11, 3, 4) (2, 3) >>> s.one_d_to_two_d_index(5, 3, 4) (1, 1) >>> s.one_d_to_two_d_index(4, 3, 4) (1, 0) >>> s.one_d_to_two_d_index(8, 3, 4) (2, 0) >>> s.one_d_to_two_d_index(7, 3, 4) (1, 3)",
"name": "one... | 2 | stack_v2_sparse_classes_30k_train_006980 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def one_d_to_two_d_index(self, index, columns): >>> s = Solution() >>> s.one_d_to_two_d_index(0, 3, 4) (0, 0) >>> s.one_d_to_two_d_index(11, 3, 4) (2, 3) >>> s.one_d_to_two_d_ind... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def one_d_to_two_d_index(self, index, columns): >>> s = Solution() >>> s.one_d_to_two_d_index(0, 3, 4) (0, 0) >>> s.one_d_to_two_d_index(11, 3, 4) (2, 3) >>> s.one_d_to_two_d_ind... | 3b13a02f9c8273f9794a57b948d2655792707f37 | <|skeleton|>
class Solution:
def one_d_to_two_d_index(self, index, columns):
""">>> s = Solution() >>> s.one_d_to_two_d_index(0, 3, 4) (0, 0) >>> s.one_d_to_two_d_index(11, 3, 4) (2, 3) >>> s.one_d_to_two_d_index(5, 3, 4) (1, 1) >>> s.one_d_to_two_d_index(4, 3, 4) (1, 0) >>> s.one_d_to_two_d_index(8, 3, 4)... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def one_d_to_two_d_index(self, index, columns):
""">>> s = Solution() >>> s.one_d_to_two_d_index(0, 3, 4) (0, 0) >>> s.one_d_to_two_d_index(11, 3, 4) (2, 3) >>> s.one_d_to_two_d_index(5, 3, 4) (1, 1) >>> s.one_d_to_two_d_index(4, 3, 4) (1, 0) >>> s.one_d_to_two_d_index(8, 3, 4) (2, 0) >>> s.... | the_stack_v2_python_sparse | search_2d_matrix.py | gsy/leetcode | train | 1 | |
2d238817366df2702990d6d077524275b689a3a2 | [
"Inventory.__init__(self, product_code, description, market_price, rental_price)\nself.material = material\nself.size = size",
"output_dict = Inventory.return_as_dictionary(self)\noutput_dict['material'] = self.material\noutput_dict['size'] = self.size\nreturn output_dict"
] | <|body_start_0|>
Inventory.__init__(self, product_code, description, market_price, rental_price)
self.material = material
self.size = size
<|end_body_0|>
<|body_start_1|>
output_dict = Inventory.return_as_dictionary(self)
output_dict['material'] = self.material
output_di... | Class for creating furniture object, inherits from Inventory class Methods: return_as_dictionary: Convert furniture object to a dictionary with keys for each attribute name and values for attribute value | Furniture | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Furniture:
"""Class for creating furniture object, inherits from Inventory class Methods: return_as_dictionary: Convert furniture object to a dictionary with keys for each attribute name and values for attribute value"""
def __init__(self, product_code, description, market_price, rental_pric... | stack_v2_sparse_classes_10k_train_000698 | 1,759 | no_license | [
{
"docstring": "Create instance of furniture object Args: product_code (alphanumeric): Unique product code description (string): Description of product market_price (numeric): Product price rental_price (numeric): Product rental price material (string): Product material size (string): Product size",
"name":... | 2 | null | Implement the Python class `Furniture` described below.
Class description:
Class for creating furniture object, inherits from Inventory class Methods: return_as_dictionary: Convert furniture object to a dictionary with keys for each attribute name and values for attribute value
Method signatures and docstrings:
- def... | Implement the Python class `Furniture` described below.
Class description:
Class for creating furniture object, inherits from Inventory class Methods: return_as_dictionary: Convert furniture object to a dictionary with keys for each attribute name and values for attribute value
Method signatures and docstrings:
- def... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class Furniture:
"""Class for creating furniture object, inherits from Inventory class Methods: return_as_dictionary: Convert furniture object to a dictionary with keys for each attribute name and values for attribute value"""
def __init__(self, product_code, description, market_price, rental_pric... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Furniture:
"""Class for creating furniture object, inherits from Inventory class Methods: return_as_dictionary: Convert furniture object to a dictionary with keys for each attribute name and values for attribute value"""
def __init__(self, product_code, description, market_price, rental_price, material, ... | the_stack_v2_python_sparse | students/gregdevore/lesson01/assignment/inventory_management/furniture_class.py | JavaRod/SP_Python220B_2019 | train | 1 |
cedcf3ef85cc1853886cbbfd65ab0178b77f5ca6 | [
"super().__init__()\nself.eps = eps\nself.apply_softmax = apply_softmax\nself.class_weight_power = class_weight_power",
"if not torch.is_tensor(output) or not torch.is_tensor(target):\n raise TypeError('Output and target must be torch.Tensors (type(output): {}, type(target): {})'.format(type(output), type(targ... | <|body_start_0|>
super().__init__()
self.eps = eps
self.apply_softmax = apply_softmax
self.class_weight_power = class_weight_power
<|end_body_0|>
<|body_start_1|>
if not torch.is_tensor(output) or not torch.is_tensor(target):
raise TypeError('Output and target must b... | Implementation of Soft-Dice Loss. Reference: Milletari, F., Navab, N., & Ahmadi, S. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In International Conference on 3D Vision (3DV). | SoftDiceLoss | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoftDiceLoss:
"""Implementation of Soft-Dice Loss. Reference: Milletari, F., Navab, N., & Ahmadi, S. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In International Conference on 3D Vision (3DV)."""
def __init__(self, eps: float=1e-10, apply_sof... | stack_v2_sparse_classes_10k_train_000699 | 4,335 | permissive | [
{
"docstring": ":param eps: A small constant to smooth Sorensen-Dice Loss function. Additionally, it avoids division by zero. :param apply_softmax: If true, the input to the loss function will be first fed through a Softmax operation. If false, the input to the loss function will be used as is. :param class_wei... | 2 | null | Implement the Python class `SoftDiceLoss` described below.
Class description:
Implementation of Soft-Dice Loss. Reference: Milletari, F., Navab, N., & Ahmadi, S. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In International Conference on 3D Vision (3DV).
Method signatu... | Implement the Python class `SoftDiceLoss` described below.
Class description:
Implementation of Soft-Dice Loss. Reference: Milletari, F., Navab, N., & Ahmadi, S. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In International Conference on 3D Vision (3DV).
Method signatu... | 2877002d50d3a34d80f647c18cb561025d9066cc | <|skeleton|>
class SoftDiceLoss:
"""Implementation of Soft-Dice Loss. Reference: Milletari, F., Navab, N., & Ahmadi, S. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In International Conference on 3D Vision (3DV)."""
def __init__(self, eps: float=1e-10, apply_sof... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SoftDiceLoss:
"""Implementation of Soft-Dice Loss. Reference: Milletari, F., Navab, N., & Ahmadi, S. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In International Conference on 3D Vision (3DV)."""
def __init__(self, eps: float=1e-10, apply_softmax: bool=Tr... | the_stack_v2_python_sparse | InnerEye/ML/models/losses/soft_dice.py | microsoft/InnerEye-DeepLearning | train | 511 |
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