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 378 8.64k | id stringlengths 44 44 | length_bytes int64 505 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.88k | prompted_full_text stringlengths 565 12.5k | revision_id stringlengths 40 40 | skeleton stringlengths 162 5.05k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | snapshot_total_rows int64 75.8k 75.8k | solution stringlengths 242 8.3k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a4e43698cf434ac493e830391b9db58ed91d3944 | [
"self._face = freetype.Face(path)\nself._face.set_char_size(size * 64)\nGL.glPixelStorei(GL.GL_UNPACK_ALIGNMENT, 1)\nself._characters = {}\nfor char_num in range(255):\n char = chr(char_num)\n self._face.load_char(char)\n bitmap = self._face.glyph.bitmap\n texture = GL.glGenTextures(1)\n GL.glBindTex... | <|body_start_0|>
self._face = freetype.Face(path)
self._face.set_char_size(size * 64)
GL.glPixelStorei(GL.GL_UNPACK_ALIGNMENT, 1)
self._characters = {}
for char_num in range(255):
char = chr(char_num)
self._face.load_char(char)
bitmap = self._f... | Font | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Font:
def __init__(self, path='/System/Library/Fonts/Courier.dfont', size=16):
"""Args: path (str): path to the font file size (int): point size of the font"""
<|body_0|>
def render_3d(self, window_size, text, pos, scale=1.0, color=(1.0, 0.0, 0.0, 1.0)):
"""Render th... | stack_v2_sparse_classes_75kplus_train_067200 | 8,519 | no_license | [
{
"docstring": "Args: path (str): path to the font file size (int): point size of the font",
"name": "__init__",
"signature": "def __init__(self, path='/System/Library/Fonts/Courier.dfont', size=16)"
},
{
"docstring": "Render the provided text at a point in 3D space using this font Args: window_... | 2 | stack_v2_sparse_classes_30k_train_053580 | Implement the Python class `Font` described below.
Class description:
Implement the Font class.
Method signatures and docstrings:
- def __init__(self, path='/System/Library/Fonts/Courier.dfont', size=16): Args: path (str): path to the font file size (int): point size of the font
- def render_3d(self, window_size, tex... | Implement the Python class `Font` described below.
Class description:
Implement the Font class.
Method signatures and docstrings:
- def __init__(self, path='/System/Library/Fonts/Courier.dfont', size=16): Args: path (str): path to the font file size (int): point size of the font
- def render_3d(self, window_size, tex... | b40ef729353205a9751b1f975f8a487e24286bf9 | <|skeleton|>
class Font:
def __init__(self, path='/System/Library/Fonts/Courier.dfont', size=16):
"""Args: path (str): path to the font file size (int): point size of the font"""
<|body_0|>
def render_3d(self, window_size, text, pos, scale=1.0, color=(1.0, 0.0, 0.0, 1.0)):
"""Render th... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Font:
def __init__(self, path='/System/Library/Fonts/Courier.dfont', size=16):
"""Args: path (str): path to the font file size (int): point size of the font"""
self._face = freetype.Face(path)
self._face.set_char_size(size * 64)
GL.glPixelStorei(GL.GL_UNPACK_ALIGNMENT, 1)
... | the_stack_v2_python_sparse | python/game_core/drawing.py | tymonpitts/game_test | train | 0 | |
64aa211716d1ff8aa7a7f77a0930a1b04c1f87e2 | [
"if not root:\n return ''\nif root.left:\n if root.right:\n return '{}({})({})'.format(root.val, self.serialize(root.left), self.serialize(root.right))\n else:\n return '{}({})'.format(root.val, self.serialize(root.left))\nelif root.right:\n return '{}()({})'.format(root.val, self.serializ... | <|body_start_0|>
if not root:
return ''
if root.left:
if root.right:
return '{}({})({})'.format(root.val, self.serialize(root.left), self.serialize(root.right))
else:
return '{}({})'.format(root.val, self.serialize(root.left))
e... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_75kplus_train_067201 | 2,097 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_009336 | 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:... | 34a78e06d493e61b21d4442747e9102abf9b319b | <|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_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return ''
if root.left:
if root.right:
return '{}({})({})'.format(root.val, self.serialize(root.left), self.serialize(root.ri... | the_stack_v2_python_sparse | 449_Serialize_and_Deserialize_BST.py | sunnyyeti/Leetcode-solutions | train | 0 | |
233e464239c669c25b63d5d61222b39130ae3960 | [
"code = '\\na = 0\\nfor i in range(1, 10, 1):\\n a = i\\n '\nattr = self.prepare(code, self.attr_name)\nself.assertEqual(attr, 0)",
"code = '\\na = 2\\nfor i in range(1, 10, 1):\\n if i % a == 0:\\n a = 2\\n else:\\n a = 2\\n '\nattr = self.prepare(code, self.attr_name)\nself.... | <|body_start_0|>
code = '\na = 0\nfor i in range(1, 10, 1):\n a = i\n '
attr = self.prepare(code, self.attr_name)
self.assertEqual(attr, 0)
<|end_body_0|>
<|body_start_1|>
code = '\na = 2\nfor i in range(1, 10, 1):\n if i % a == 0:\n a = 2\n else:\n a = 2\n... | Test class for RepeatValues | TestRepeatValues | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRepeatValues:
"""Test class for RepeatValues"""
def test_noRepeatValues(self):
"""Tests the correct results for codes which do not have repeating values. Keyword arguments: self -- the TestRepeatValues instance"""
<|body_0|>
def test_repeatValues(self):
"""Te... | stack_v2_sparse_classes_75kplus_train_067202 | 20,005 | no_license | [
{
"docstring": "Tests the correct results for codes which do not have repeating values. Keyword arguments: self -- the TestRepeatValues instance",
"name": "test_noRepeatValues",
"signature": "def test_noRepeatValues(self)"
},
{
"docstring": "Tests the correct results for codes which have repeati... | 2 | stack_v2_sparse_classes_30k_train_040345 | Implement the Python class `TestRepeatValues` described below.
Class description:
Test class for RepeatValues
Method signatures and docstrings:
- def test_noRepeatValues(self): Tests the correct results for codes which do not have repeating values. Keyword arguments: self -- the TestRepeatValues instance
- def test_r... | Implement the Python class `TestRepeatValues` described below.
Class description:
Test class for RepeatValues
Method signatures and docstrings:
- def test_noRepeatValues(self): Tests the correct results for codes which do not have repeating values. Keyword arguments: self -- the TestRepeatValues instance
- def test_r... | 2c0b907f5d9e74265e87ab3e36753f764a965f21 | <|skeleton|>
class TestRepeatValues:
"""Test class for RepeatValues"""
def test_noRepeatValues(self):
"""Tests the correct results for codes which do not have repeating values. Keyword arguments: self -- the TestRepeatValues instance"""
<|body_0|>
def test_repeatValues(self):
"""Te... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestRepeatValues:
"""Test class for RepeatValues"""
def test_noRepeatValues(self):
"""Tests the correct results for codes which do not have repeating values. Keyword arguments: self -- the TestRepeatValues instance"""
code = '\na = 0\nfor i in range(1, 10, 1):\n a = i\n '
... | the_stack_v2_python_sparse | AlgoBooster/ab_ui/ab_main/ab_unittests/test_extraction.py | danielaboeing/algobooster | train | 0 |
254a77c058fb3ea4be7cd79c8d090676a1f141c3 | [
"nums.sort()\nfor i in range(len(nums)):\n if nums[i] == nums[i + 1]:\n return nums[i]\nreturn -1",
"s = set()\nfor num in nums:\n if num in s:\n return num\n else:\n s.add(num)\nreturn -1",
"slow = nums[0]\nfast = nums[nums[0]]\nwhile slow != fast:\n slow = nums[slow]\n fast... | <|body_start_0|>
nums.sort()
for i in range(len(nums)):
if nums[i] == nums[i + 1]:
return nums[i]
return -1
<|end_body_0|>
<|body_start_1|>
s = set()
for num in nums:
if num in s:
return num
else:
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def find_duplicate_number(self, nums: List[int]) -> int:
"""找出重复的数字 Args: arr: 数组 Returns: 重复数字"""
<|body_0|>
def find_duplicate_number2(self, nums: List[int]) -> int:
"""找出重复的数字 Args: arr: 数组 Returns: 重复数字"""
<|body_1|>
def find_duplicate_numb... | stack_v2_sparse_classes_75kplus_train_067203 | 2,766 | permissive | [
{
"docstring": "找出重复的数字 Args: arr: 数组 Returns: 重复数字",
"name": "find_duplicate_number",
"signature": "def find_duplicate_number(self, nums: List[int]) -> int"
},
{
"docstring": "找出重复的数字 Args: arr: 数组 Returns: 重复数字",
"name": "find_duplicate_number2",
"signature": "def find_duplicate_number... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find_duplicate_number(self, nums: List[int]) -> int: 找出重复的数字 Args: arr: 数组 Returns: 重复数字
- def find_duplicate_number2(self, nums: List[int]) -> int: 找出重复的数字 Args: arr: 数组 Ret... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find_duplicate_number(self, nums: List[int]) -> int: 找出重复的数字 Args: arr: 数组 Returns: 重复数字
- def find_duplicate_number2(self, nums: List[int]) -> int: 找出重复的数字 Args: arr: 数组 Ret... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def find_duplicate_number(self, nums: List[int]) -> int:
"""找出重复的数字 Args: arr: 数组 Returns: 重复数字"""
<|body_0|>
def find_duplicate_number2(self, nums: List[int]) -> int:
"""找出重复的数字 Args: arr: 数组 Returns: 重复数字"""
<|body_1|>
def find_duplicate_numb... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def find_duplicate_number(self, nums: List[int]) -> int:
"""找出重复的数字 Args: arr: 数组 Returns: 重复数字"""
nums.sort()
for i in range(len(nums)):
if nums[i] == nums[i + 1]:
return nums[i]
return -1
def find_duplicate_number2(self, nums: List[i... | the_stack_v2_python_sparse | src/leetcodepython/top100likedquestions/find_duplicate_number_287.py | zhangyu345293721/leetcode | train | 101 | |
4b329e510d6098851dd3c76c8716ab4018867840 | [
"n = len(nums)\nM = [0] * (n + 1)\nif n == 0:\n return 0\nM[0] = 0\nM[1] = nums[0]\nfor i in range(2, n + 1):\n M[i] = max(nums[i - 1] + M[i - 2], M[i - 1])\nreturn M[-1]",
"prev = 0\ncurr = 0\nfor i in nums:\n prev, curr = (curr, max(curr, prev + i))\nreturn curr",
"n = len(nums)\ndp_i_1 = 0\ndp_i_2 =... | <|body_start_0|>
n = len(nums)
M = [0] * (n + 1)
if n == 0:
return 0
M[0] = 0
M[1] = nums[0]
for i in range(2, n + 1):
M[i] = max(nums[i - 1] + M[i - 2], M[i - 1])
return M[-1]
<|end_body_0|>
<|body_start_1|>
prev = 0
curr ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_2|>
<|end_skele... | stack_v2_sparse_classes_75kplus_train_067204 | 1,312 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",... | 3 | stack_v2_sparse_classes_30k_train_050336 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob(self, nums): :type nums: List[int] :rtype: int
<|skelet... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_2|>
<|end_skele... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
n = len(nums)
M = [0] * (n + 1)
if n == 0:
return 0
M[0] = 0
M[1] = nums[0]
for i in range(2, n + 1):
M[i] = max(nums[i - 1] + M[i - 2], M[i - 1])
retu... | the_stack_v2_python_sparse | 0198_House_Robber.py | bingli8802/leetcode | train | 0 | |
2629fc1175b68277265ebdb5cee77bf2188cecb1 | [
"content_type = ContentType.objects.get_for_model(instance.__class__)\nobj_id = instance.id\nqueryset = super(LikeDislikeManager, self).filter(content_type=content_type, object_id=obj_id)\nreturn queryset",
"ip_address = get_client_ip(request)\ncontent_type = ContentType.objects.get_for_model(instance.__class__)\... | <|body_start_0|>
content_type = ContentType.objects.get_for_model(instance.__class__)
obj_id = instance.id
queryset = super(LikeDislikeManager, self).filter(content_type=content_type, object_id=obj_id)
return queryset
<|end_body_0|>
<|body_start_1|>
ip_address = get_client_ip(re... | mesle manager e Comments | LikeDislikeManager | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LikeDislikeManager:
"""mesle manager e Comments"""
def filter_by_model(self, instance):
"""bar assasse model filter mikonim"""
<|body_0|>
def create_for_instance_model(self, instance, request, likedislike, user=None):
"""bar assasse model create mkonim age user a... | stack_v2_sparse_classes_75kplus_train_067205 | 6,949 | permissive | [
{
"docstring": "bar assasse model filter mikonim",
"name": "filter_by_model",
"signature": "def filter_by_model(self, instance)"
},
{
"docstring": "bar assasse model create mkonim age user anonymous bud bar assasse IP_address taghirat emal mishe age authenticate bud ke nega mikone bebine ip sh h... | 2 | stack_v2_sparse_classes_30k_train_048908 | Implement the Python class `LikeDislikeManager` described below.
Class description:
mesle manager e Comments
Method signatures and docstrings:
- def filter_by_model(self, instance): bar assasse model filter mikonim
- def create_for_instance_model(self, instance, request, likedislike, user=None): bar assasse model cre... | Implement the Python class `LikeDislikeManager` described below.
Class description:
mesle manager e Comments
Method signatures and docstrings:
- def filter_by_model(self, instance): bar assasse model filter mikonim
- def create_for_instance_model(self, instance, request, likedislike, user=None): bar assasse model cre... | aef47922fdd6488550881ed9d42bf30a0d33a32a | <|skeleton|>
class LikeDislikeManager:
"""mesle manager e Comments"""
def filter_by_model(self, instance):
"""bar assasse model filter mikonim"""
<|body_0|>
def create_for_instance_model(self, instance, request, likedislike, user=None):
"""bar assasse model create mkonim age user a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LikeDislikeManager:
"""mesle manager e Comments"""
def filter_by_model(self, instance):
"""bar assasse model filter mikonim"""
content_type = ContentType.objects.get_for_model(instance.__class__)
obj_id = instance.id
queryset = super(LikeDislikeManager, self).filter(conten... | the_stack_v2_python_sparse | src/likes/models.py | m3h-D/Myinfoblog | train | 0 |
970b77954e3edb5d114b8701f2654963d2ef1263 | [
"\"\"\"\n You'll have to do a set of jumps, and choose for each one whether \n to do it using a rope or bricks. It's always optimal to use ropes \n in the largest jumps.\n\n \"\"\"\nA = heights\nheap = []\n'\\n Iterate on the buildings, maintaining the largest r jumps and the \\n ... | <|body_start_0|>
"""
You'll have to do a set of jumps, and choose for each one whether
to do it using a rope or bricks. It's always optimal to use ropes
in the largest jumps.
"""
A = heights
heap = []
'\n Iterate o... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def furthestBuilding(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type ladders: int :rtype: int"""
<|body_0|>
def furthestBuildingHeap(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type la... | stack_v2_sparse_classes_75kplus_train_067206 | 4,669 | no_license | [
{
"docstring": ":type heights: List[int] :type bricks: int :type ladders: int :rtype: int",
"name": "furthestBuilding",
"signature": "def furthestBuilding(self, heights, bricks, ladders)"
},
{
"docstring": ":type heights: List[int] :type bricks: int :type ladders: int :rtype: int",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_051727 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def furthestBuilding(self, heights, bricks, ladders): :type heights: List[int] :type bricks: int :type ladders: int :rtype: int
- def furthestBuildingHeap(self, heights, bricks, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def furthestBuilding(self, heights, bricks, ladders): :type heights: List[int] :type bricks: int :type ladders: int :rtype: int
- def furthestBuildingHeap(self, heights, bricks, ... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def furthestBuilding(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type ladders: int :rtype: int"""
<|body_0|>
def furthestBuildingHeap(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type la... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def furthestBuilding(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type ladders: int :rtype: int"""
"""
You'll have to do a set of jumps, and choose for each one whether
to do it using a rope or bricks. It's always op... | the_stack_v2_python_sparse | F/FurthestBuildingYouCanReach.py | bssrdf/pyleet | train | 2 | |
5c2f1e41b694cbfd877def321a88593fcda4fbca | [
"super(PeriodStrategy, self).__init__('periodStrategy')\nself.configDict = configDict\nassert int(configDict[CONF_STRATEGY_PERIOD]) >= 1\nself.perAmount = max(1, round(int(configDict[CONF_INIT_CASH]) / 100))\nself.period = int(configDict[CONF_STRATEGY_PERIOD])\nself.symbols = None\nself.counter = 0",
"self.counte... | <|body_start_0|>
super(PeriodStrategy, self).__init__('periodStrategy')
self.configDict = configDict
assert int(configDict[CONF_STRATEGY_PERIOD]) >= 1
self.perAmount = max(1, round(int(configDict[CONF_INIT_CASH]) / 100))
self.period = int(configDict[CONF_STRATEGY_PERIOD])
... | period strategy | PeriodStrategy | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PeriodStrategy:
"""period strategy"""
def __init__(self, configDict):
"""constructor"""
<|body_0|>
def increaseAndCheckCounter(self):
"""increase counter by one and check whether a period is end"""
<|body_1|>
def tickUpdate(self, tickDict):
"... | stack_v2_sparse_classes_75kplus_train_067207 | 1,684 | no_license | [
{
"docstring": "constructor",
"name": "__init__",
"signature": "def __init__(self, configDict)"
},
{
"docstring": "increase counter by one and check whether a period is end",
"name": "increaseAndCheckCounter",
"signature": "def increaseAndCheckCounter(self)"
},
{
"docstring": "co... | 3 | stack_v2_sparse_classes_30k_train_054502 | Implement the Python class `PeriodStrategy` described below.
Class description:
period strategy
Method signatures and docstrings:
- def __init__(self, configDict): constructor
- def increaseAndCheckCounter(self): increase counter by one and check whether a period is end
- def tickUpdate(self, tickDict): consume ticks | Implement the Python class `PeriodStrategy` described below.
Class description:
period strategy
Method signatures and docstrings:
- def __init__(self, configDict): constructor
- def increaseAndCheckCounter(self): increase counter by one and check whether a period is end
- def tickUpdate(self, tickDict): consume ticks... | d494b3041069d377d6a7a9c296a14334f2fa5acc | <|skeleton|>
class PeriodStrategy:
"""period strategy"""
def __init__(self, configDict):
"""constructor"""
<|body_0|>
def increaseAndCheckCounter(self):
"""increase counter by one and check whether a period is end"""
<|body_1|>
def tickUpdate(self, tickDict):
"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PeriodStrategy:
"""period strategy"""
def __init__(self, configDict):
"""constructor"""
super(PeriodStrategy, self).__init__('periodStrategy')
self.configDict = configDict
assert int(configDict[CONF_STRATEGY_PERIOD]) >= 1
self.perAmount = max(1, round(int(configDic... | the_stack_v2_python_sparse | python/panpanpandas_ultrafinance/ultrafinance-master/ultrafinance/backTest/tickSubscriber/strategies/periodStrategy.py | LiuFang816/SALSTM_py_data | train | 10 |
af93ab0728f64999c75d5999cef34b9f5eb27e9b | [
"try:\n print('收到获取教师信息的请求')\n self.sqlhandler = None\n body = json.loads(self.request.body)\n self.TeaUid = body['teaUid']\n if self.getTeaInfo():\n self.write({'success': True, 'data': self.TeaInfo})\n self.finish()\n else:\n raise RuntimeError\nexcept Exception as e:\n p... | <|body_start_0|>
try:
print('收到获取教师信息的请求')
self.sqlhandler = None
body = json.loads(self.request.body)
self.TeaUid = body['teaUid']
if self.getTeaInfo():
self.write({'success': True, 'data': self.TeaInfo})
self.finish()
... | TeaInfoRequestHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeaInfoRequestHandler:
def post(self):
"""从数据库获取教师信息返回给客户端"""
<|body_0|>
def getTeaInfo(self):
"""从数据库读取教师信息"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
print('收到获取教师信息的请求')
self.sqlhandler = None
body = ... | stack_v2_sparse_classes_75kplus_train_067208 | 1,819 | no_license | [
{
"docstring": "从数据库获取教师信息返回给客户端",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "从数据库读取教师信息",
"name": "getTeaInfo",
"signature": "def getTeaInfo(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_053025 | Implement the Python class `TeaInfoRequestHandler` described below.
Class description:
Implement the TeaInfoRequestHandler class.
Method signatures and docstrings:
- def post(self): 从数据库获取教师信息返回给客户端
- def getTeaInfo(self): 从数据库读取教师信息 | Implement the Python class `TeaInfoRequestHandler` described below.
Class description:
Implement the TeaInfoRequestHandler class.
Method signatures and docstrings:
- def post(self): 从数据库获取教师信息返回给客户端
- def getTeaInfo(self): 从数据库读取教师信息
<|skeleton|>
class TeaInfoRequestHandler:
def post(self):
"""从数据库获取教师信... | b28eb4163b02bd0a931653b94851592f2654b199 | <|skeleton|>
class TeaInfoRequestHandler:
def post(self):
"""从数据库获取教师信息返回给客户端"""
<|body_0|>
def getTeaInfo(self):
"""从数据库读取教师信息"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TeaInfoRequestHandler:
def post(self):
"""从数据库获取教师信息返回给客户端"""
try:
print('收到获取教师信息的请求')
self.sqlhandler = None
body = json.loads(self.request.body)
self.TeaUid = body['teaUid']
if self.getTeaInfo():
self.write({'succes... | the_stack_v2_python_sparse | server/teacher/TeaInfoRequestHandler.py | lyh-ADT/edu-app | train | 1 | |
1d555060c191b81e72dbeedb761a6de5c9dc12bf | [
"if not root:\n return ''\ns = []\nq = collections.deque()\nq.append(root)\nwhile q:\n node = q.popleft()\n if node:\n s.append(str(node.val))\n q.append(node.left)\n q.append(node.right)\n else:\n s.append('null')\nres = ','.join(s)\nreturn res",
"if not data:\n return ... | <|body_start_0|>
if not root:
return ''
s = []
q = collections.deque()
q.append(root)
while q:
node = q.popleft()
if node:
s.append(str(node.val))
q.append(node.left)
q.append(node.right)
... | 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_75kplus_train_067209 | 1,224 | 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:... | 95ca845e40c7c9f8ba589a45332791d5bbf49bbf | <|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_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return ''
s = []
q = collections.deque()
q.append(root)
while q:
node = q.popleft()
if node:
... | the_stack_v2_python_sparse | Tree/Leetcode 297. Serialize and Deserialize Binary Tree.py | sriharsha004/LeetCode | train | 0 | |
ec8c9badd21860ebf1ee710ba28f854561fe00b9 | [
"len_s = len(s)\nflags = []\nfor i in range(len_s // 2):\n if (len_s - i - 1) % (i + 1) != 0:\n continue\n target = s[0:i + 1]\n flag = True\n j = i + 1\n tmp = s[j:]\n while flag:\n if tmp[0:i + 1] != target:\n flag = False\n j += i + 1\n if j < len_s:\n ... | <|body_start_0|>
len_s = len(s)
flags = []
for i in range(len_s // 2):
if (len_s - i - 1) % (i + 1) != 0:
continue
target = s[0:i + 1]
flag = True
j = i + 1
tmp = s[j:]
while flag:
if tmp[0:i ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def repeatedSubstringPattern(self, s: str) -> bool:
"""Mine"""
<|body_0|>
def repeatedSubstringPattern(self, s):
"""枚举"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
len_s = len(s)
flags = []
for i in range(len_s // 2):
... | stack_v2_sparse_classes_75kplus_train_067210 | 1,113 | no_license | [
{
"docstring": "Mine",
"name": "repeatedSubstringPattern",
"signature": "def repeatedSubstringPattern(self, s: str) -> bool"
},
{
"docstring": "枚举",
"name": "repeatedSubstringPattern",
"signature": "def repeatedSubstringPattern(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def repeatedSubstringPattern(self, s: str) -> bool: Mine
- def repeatedSubstringPattern(self, s): 枚举 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def repeatedSubstringPattern(self, s: str) -> bool: Mine
- def repeatedSubstringPattern(self, s): 枚举
<|skeleton|>
class Solution:
def repeatedSubstringPattern(self, s: str)... | ae191a449619418e3eba23f18574c7841e7ba52a | <|skeleton|>
class Solution:
def repeatedSubstringPattern(self, s: str) -> bool:
"""Mine"""
<|body_0|>
def repeatedSubstringPattern(self, s):
"""枚举"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def repeatedSubstringPattern(self, s: str) -> bool:
"""Mine"""
len_s = len(s)
flags = []
for i in range(len_s // 2):
if (len_s - i - 1) % (i + 1) != 0:
continue
target = s[0:i + 1]
flag = True
j = i + 1
... | the_stack_v2_python_sparse | lc/459.py | zealfory/dive_python | train | 0 | |
c3b2dc381ad3ba2ddfdff0e05eb2ebcceb363bff | [
"self.protocol = protocol\nself.srp = srp\nself._atv_salt = None\nself._atv_pub_key = None",
"self.srp.initialize()\nawait self.protocol.start(skip_initial_messages=True)\nmsg = messages.crypto_pairing({TlvValue.Method: b'\\x00', TlvValue.SeqNo: b'\\x01'}, is_pairing=True)\nresp = await self.protocol.send_and_rec... | <|body_start_0|>
self.protocol = protocol
self.srp = srp
self._atv_salt = None
self._atv_pub_key = None
<|end_body_0|>
<|body_start_1|>
self.srp.initialize()
await self.protocol.start(skip_initial_messages=True)
msg = messages.crypto_pairing({TlvValue.Method: b'\... | Perform pairing and return new credentials. | MrpPairSetupProcedure | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MrpPairSetupProcedure:
"""Perform pairing and return new credentials."""
def __init__(self, protocol, srp):
"""Initialize a new MrpPairingHandler."""
<|body_0|>
async def start_pairing(self):
"""Start pairing procedure."""
<|body_1|>
async def finish... | stack_v2_sparse_classes_75kplus_train_067211 | 4,068 | permissive | [
{
"docstring": "Initialize a new MrpPairingHandler.",
"name": "__init__",
"signature": "def __init__(self, protocol, srp)"
},
{
"docstring": "Start pairing procedure.",
"name": "start_pairing",
"signature": "async def start_pairing(self)"
},
{
"docstring": "Finish pairing process... | 3 | null | Implement the Python class `MrpPairSetupProcedure` described below.
Class description:
Perform pairing and return new credentials.
Method signatures and docstrings:
- def __init__(self, protocol, srp): Initialize a new MrpPairingHandler.
- async def start_pairing(self): Start pairing procedure.
- async def finish_pai... | Implement the Python class `MrpPairSetupProcedure` described below.
Class description:
Perform pairing and return new credentials.
Method signatures and docstrings:
- def __init__(self, protocol, srp): Initialize a new MrpPairingHandler.
- async def start_pairing(self): Start pairing procedure.
- async def finish_pai... | 05ca46d2a8bbc8e725ad63794d14b2d1fb9913fa | <|skeleton|>
class MrpPairSetupProcedure:
"""Perform pairing and return new credentials."""
def __init__(self, protocol, srp):
"""Initialize a new MrpPairingHandler."""
<|body_0|>
async def start_pairing(self):
"""Start pairing procedure."""
<|body_1|>
async def finish... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MrpPairSetupProcedure:
"""Perform pairing and return new credentials."""
def __init__(self, protocol, srp):
"""Initialize a new MrpPairingHandler."""
self.protocol = protocol
self.srp = srp
self._atv_salt = None
self._atv_pub_key = None
async def start_pairing... | the_stack_v2_python_sparse | pyatv/protocols/mrp/auth.py | postlund/pyatv | train | 749 |
b2a45618b02c9babe1661231eefdf1d309e6fe6d | [
"assert isinstance(response, scrapy.http.response.html.HtmlResponse)\ncurboard = response.selector.xpath('//div[contains(@class, \"titleBar\")]/h1/text()').extract()\nlast_page = MAX_PAGE[curboard[0].lower()]\n'try:\\n last_page = int(response.selector.xpath(\\'//nav/a[@class=\"PageNavNext\"]/following::... | <|body_start_0|>
assert isinstance(response, scrapy.http.response.html.HtmlResponse)
curboard = response.selector.xpath('//div[contains(@class, "titleBar")]/h1/text()').extract()
last_page = MAX_PAGE[curboard[0].lower()]
'try:\n last_page = int(response.selector.xpath(\'//nav/... | scrape reports from angling addicts forum | worldseafishingAfloatSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class worldseafishingAfloatSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ..."""
<|body_0|>
def crawl_board_threads(sel... | stack_v2_sparse_classes_75kplus_train_067212 | 9,045 | no_license | [
{
"docstring": "generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ...",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "crawl",
"name": "crawl_board_threads",
"signature": "def crawl_board_t... | 3 | stack_v2_sparse_classes_30k_train_001682 | Implement the Python class `worldseafishingAfloatSpider` described below.
Class description:
scrape reports from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, .... | Implement the Python class `worldseafishingAfloatSpider` described below.
Class description:
scrape reports from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, .... | 9123aa6baf538b662143b9098d963d55165e8409 | <|skeleton|>
class worldseafishingAfloatSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ..."""
<|body_0|>
def crawl_board_threads(sel... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class worldseafishingAfloatSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: https://www.worldseafishing.com/forums/forums/south-east-catch-reports.39/, ..."""
assert isinstance(response, scrapy.http.response.html.Ht... | the_stack_v2_python_sparse | imgscrape/spiders/worldseafishing_reports.py | gmonkman/python | train | 0 |
240cdfff2b352b2e00ce100211ed85bbe16404c0 | [
"if connection_mode is None:\n connection_mode = pybullet.DIRECT\nself._client = pybullet.connect(pybullet.SHARED_MEMORY)\nif self._client < 0:\n self._client = pybullet.connect(connection_mode, options=options)\nself._shapes = {}",
"try:\n pybullet.disconnect(physicsClientId=self._client)\nexcept pybull... | <|body_start_0|>
if connection_mode is None:
connection_mode = pybullet.DIRECT
self._client = pybullet.connect(pybullet.SHARED_MEMORY)
if self._client < 0:
self._client = pybullet.connect(connection_mode, options=options)
self._shapes = {}
<|end_body_0|>
<|body_s... | A wrapper for pybullet to manage different clients. | MyBulletClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyBulletClient:
"""A wrapper for pybullet to manage different clients."""
def __init__(self, connection_mode=None, options=''):
"""Create a simulation and connect to it."""
<|body_0|>
def __del__(self):
"""Clean up connection if not already done."""
<|bod... | stack_v2_sparse_classes_75kplus_train_067213 | 28,029 | permissive | [
{
"docstring": "Create a simulation and connect to it.",
"name": "__init__",
"signature": "def __init__(self, connection_mode=None, options='')"
},
{
"docstring": "Clean up connection if not already done.",
"name": "__del__",
"signature": "def __del__(self)"
},
{
"docstring": "In... | 3 | stack_v2_sparse_classes_30k_train_000547 | Implement the Python class `MyBulletClient` described below.
Class description:
A wrapper for pybullet to manage different clients.
Method signatures and docstrings:
- def __init__(self, connection_mode=None, options=''): Create a simulation and connect to it.
- def __del__(self): Clean up connection if not already d... | Implement the Python class `MyBulletClient` described below.
Class description:
A wrapper for pybullet to manage different clients.
Method signatures and docstrings:
- def __init__(self, connection_mode=None, options=''): Create a simulation and connect to it.
- def __del__(self): Clean up connection if not already d... | cdd9bbdc2a3a832be24f20105b8c9fe28149cb63 | <|skeleton|>
class MyBulletClient:
"""A wrapper for pybullet to manage different clients."""
def __init__(self, connection_mode=None, options=''):
"""Create a simulation and connect to it."""
<|body_0|>
def __del__(self):
"""Clean up connection if not already done."""
<|bod... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyBulletClient:
"""A wrapper for pybullet to manage different clients."""
def __init__(self, connection_mode=None, options=''):
"""Create a simulation and connect to it."""
if connection_mode is None:
connection_mode = pybullet.DIRECT
self._client = pybullet.connect(py... | the_stack_v2_python_sparse | rlscope/profiler/clib_wrap.py | UofT-EcoSystem/rlscope | train | 42 |
8c7e68cb4c9ff1e92b2d275f71f80f8ee84446da | [
"super(StackedLSTMCell, self).__init__()\nself.hidden_dim = hidden_dim\nself.num_layers = num_layers\nself.lstm_cells = nn.ModuleList([nn.LSTMCell(hidden_dim, hidden_dim) for _ in range(num_layers)])\nself.fc_layers = nn.ModuleList([nn.Sequential(nn.Linear(hidden_dim, hidden_dim), nn.ELU()) for _ in range(num_layer... | <|body_start_0|>
super(StackedLSTMCell, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.lstm_cells = nn.ModuleList([nn.LSTMCell(hidden_dim, hidden_dim) for _ in range(num_layers)])
self.fc_layers = nn.ModuleList([nn.Sequential(nn.Linear(hidden_dim,... | A looping stack of LSTM cells, with FC()=>ELU() linking between them, as well as both residual connections that additively hop over each RNN block. Between each layer, we have a fully-connected [hidden_dim=>hidden_dim] layer that transforms the hidden state into the input for the next layer. | StackedLSTMCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StackedLSTMCell:
"""A looping stack of LSTM cells, with FC()=>ELU() linking between them, as well as both residual connections that additively hop over each RNN block. Between each layer, we have a fully-connected [hidden_dim=>hidden_dim] layer that transforms the hidden state into the input for ... | stack_v2_sparse_classes_75kplus_train_067214 | 10,130 | no_license | [
{
"docstring": "Construct a stacked LSTM cell.",
"name": "__init__",
"signature": "def __init__(self, hidden_dim, num_layers)"
},
{
"docstring": "Run over one iteration. Args: * x: the bottom-most input. * h0s: ... * c0s: ... Returns: * outs: an output FloatTensor Variables of shape (batch_size,... | 2 | stack_v2_sparse_classes_30k_train_024854 | Implement the Python class `StackedLSTMCell` described below.
Class description:
A looping stack of LSTM cells, with FC()=>ELU() linking between them, as well as both residual connections that additively hop over each RNN block. Between each layer, we have a fully-connected [hidden_dim=>hidden_dim] layer that transfor... | Implement the Python class `StackedLSTMCell` described below.
Class description:
A looping stack of LSTM cells, with FC()=>ELU() linking between them, as well as both residual connections that additively hop over each RNN block. Between each layer, we have a fully-connected [hidden_dim=>hidden_dim] layer that transfor... | 7ad943d9cc7a6872a14bba5239a99755f70db4cd | <|skeleton|>
class StackedLSTMCell:
"""A looping stack of LSTM cells, with FC()=>ELU() linking between them, as well as both residual connections that additively hop over each RNN block. Between each layer, we have a fully-connected [hidden_dim=>hidden_dim] layer that transforms the hidden state into the input for ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StackedLSTMCell:
"""A looping stack of LSTM cells, with FC()=>ELU() linking between them, as well as both residual connections that additively hop over each RNN block. Between each layer, we have a fully-connected [hidden_dim=>hidden_dim] layer that transforms the hidden state into the input for the next laye... | the_stack_v2_python_sparse | modules/rnn_decoder.py | paultsw/wavenet-speech | train | 0 |
390d4bd508572d70e9645bc0b2b5017ece2b9f97 | [
"self.scales = scales\nself.ratios = ratios\nself.feature_strides = feature_strides",
"pad_shape = calc_batch_padded_shape(img_metas)\nfeature_shapes = [(pad_shape[0] // stride, pad_shape[1] // stride) for stride in self.feature_strides]\nanchors = [self._generate_level_anchors(level, feature_shape) for level, fe... | <|body_start_0|>
self.scales = scales
self.ratios = ratios
self.feature_strides = feature_strides
<|end_body_0|>
<|body_start_1|>
pad_shape = calc_batch_padded_shape(img_metas)
feature_shapes = [(pad_shape[0] // stride, pad_shape[1] // stride) for stride in self.feature_strides]... | AnchorGenerator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnchorGenerator:
def __init__(self, scales=(32, 64, 128, 256, 512), ratios=(0.5, 1, 2), feature_strides=(4, 8, 16, 32, 64)):
"""Anchor Generator Attributes --- scales: 1D array of anchor sizes in pixels. ratios: 1D array of anchor ratios of width/height. feature_strides: Stride of the fe... | stack_v2_sparse_classes_75kplus_train_067215 | 4,368 | permissive | [
{
"docstring": "Anchor Generator Attributes --- scales: 1D array of anchor sizes in pixels. ratios: 1D array of anchor ratios of width/height. feature_strides: Stride of the feature map relative to the image in pixels.",
"name": "__init__",
"signature": "def __init__(self, scales=(32, 64, 128, 256, 512)... | 4 | null | Implement the Python class `AnchorGenerator` described below.
Class description:
Implement the AnchorGenerator class.
Method signatures and docstrings:
- def __init__(self, scales=(32, 64, 128, 256, 512), ratios=(0.5, 1, 2), feature_strides=(4, 8, 16, 32, 64)): Anchor Generator Attributes --- scales: 1D array of anch... | Implement the Python class `AnchorGenerator` described below.
Class description:
Implement the AnchorGenerator class.
Method signatures and docstrings:
- def __init__(self, scales=(32, 64, 128, 256, 512), ratios=(0.5, 1, 2), feature_strides=(4, 8, 16, 32, 64)): Anchor Generator Attributes --- scales: 1D array of anch... | f756b811ab31c9dab2a8f8afe68f46465422f64b | <|skeleton|>
class AnchorGenerator:
def __init__(self, scales=(32, 64, 128, 256, 512), ratios=(0.5, 1, 2), feature_strides=(4, 8, 16, 32, 64)):
"""Anchor Generator Attributes --- scales: 1D array of anchor sizes in pixels. ratios: 1D array of anchor ratios of width/height. feature_strides: Stride of the fe... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AnchorGenerator:
def __init__(self, scales=(32, 64, 128, 256, 512), ratios=(0.5, 1, 2), feature_strides=(4, 8, 16, 32, 64)):
"""Anchor Generator Attributes --- scales: 1D array of anchor sizes in pixels. ratios: 1D array of anchor ratios of width/height. feature_strides: Stride of the feature map rela... | the_stack_v2_python_sparse | detection/core/anchor/anchor_generator.py | HirataYurina/cascade-rcnn-tf2.2 | train | 1 | |
44719f8ca7fbed3e3b076ea4b8065924a27f2049 | [
"self.child = child\nif not child:\n self.child = self\nself.logger = logger\nif not self.logger:\n logargs = utils.logargs(child.__class__, __file__)\n self.logger = utils.setup_logger(logargs.name, logargs.file, extra=logargs.extra)\n self.log('No logger passed into constructor. Creating new logger.')... | <|body_start_0|>
self.child = child
if not child:
self.child = self
self.logger = logger
if not self.logger:
logargs = utils.logargs(child.__class__, __file__)
self.logger = utils.setup_logger(logargs.name, logargs.file, extra=logargs.extra)
... | Creates a logger for the subclasses of this class. Handles printing of log messages to log file. | Loggable | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Loggable:
"""Creates a logger for the subclasses of this class. Handles printing of log messages to log file."""
def __init__(self, child=None, logger=None):
"""Sets the class logger if passed in. If no logger then uses the secondary option of creating a logger using the logargs argu... | stack_v2_sparse_classes_75kplus_train_067216 | 2,076 | permissive | [
{
"docstring": "Sets the class logger if passed in. If no logger then uses the secondary option of creating a logger using the logargs argument. If no child is passed in, meaning this class is not being subclassed but rather used as a standalone object, then it will set its own child as itself.",
"name": "_... | 2 | null | Implement the Python class `Loggable` described below.
Class description:
Creates a logger for the subclasses of this class. Handles printing of log messages to log file.
Method signatures and docstrings:
- def __init__(self, child=None, logger=None): Sets the class logger if passed in. If no logger then uses the sec... | Implement the Python class `Loggable` described below.
Class description:
Creates a logger for the subclasses of this class. Handles printing of log messages to log file.
Method signatures and docstrings:
- def __init__(self, child=None, logger=None): Sets the class logger if passed in. If no logger then uses the sec... | 78f3637b4c03c11d7f6ef15b20a1acf699d4be24 | <|skeleton|>
class Loggable:
"""Creates a logger for the subclasses of this class. Handles printing of log messages to log file."""
def __init__(self, child=None, logger=None):
"""Sets the class logger if passed in. If no logger then uses the secondary option of creating a logger using the logargs argu... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Loggable:
"""Creates a logger for the subclasses of this class. Handles printing of log messages to log file."""
def __init__(self, child=None, logger=None):
"""Sets the class logger if passed in. If no logger then uses the secondary option of creating a logger using the logargs argument. If no c... | the_stack_v2_python_sparse | source/logger.py | whitegreyblack/PyCurses | train | 0 |
680e48bfa094f2d671311329f9b0a6633f1dcfb9 | [
"type_option: Optional[NotificationSettingTypes] = kwargs.get('type')\nactor_mapping = {recipient.actor_id: recipient for recipient in item_list}\nnotifications_settings = NotificationSetting.objects._filter(type=type_option, target_ids=actor_mapping.keys())\nresults: MutableMapping[Union['Team', 'User'], MutableMa... | <|body_start_0|>
type_option: Optional[NotificationSettingTypes] = kwargs.get('type')
actor_mapping = {recipient.actor_id: recipient for recipient in item_list}
notifications_settings = NotificationSetting.objects._filter(type=type_option, target_ids=actor_mapping.keys())
results: Mutabl... | This Serializer fetches and serializes NotificationSettings for a list of targets (users or teams.) Pass filters like `project=project` and `type=NotificationSettingTypes.DEPLOY` to kwargs. | NotificationSettingsSerializer | [
"Apache-2.0",
"BUSL-1.1"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NotificationSettingsSerializer:
"""This Serializer fetches and serializes NotificationSettings for a list of targets (users or teams.) Pass filters like `project=project` and `type=NotificationSettingTypes.DEPLOY` to kwargs."""
def get_attrs(self, item_list: Iterable[Union['Team', 'User']], ... | stack_v2_sparse_classes_75kplus_train_067217 | 4,722 | permissive | [
{
"docstring": "This takes a list of recipients (which are either Users or Teams, because both can have Notification Settings). The function returns a mapping of targets to flat lists of object to be passed to the `serialize` function. :param item_list: Either a Set of User or Team objects whose notification se... | 2 | null | Implement the Python class `NotificationSettingsSerializer` described below.
Class description:
This Serializer fetches and serializes NotificationSettings for a list of targets (users or teams.) Pass filters like `project=project` and `type=NotificationSettingTypes.DEPLOY` to kwargs.
Method signatures and docstrings... | Implement the Python class `NotificationSettingsSerializer` described below.
Class description:
This Serializer fetches and serializes NotificationSettings for a list of targets (users or teams.) Pass filters like `project=project` and `type=NotificationSettingTypes.DEPLOY` to kwargs.
Method signatures and docstrings... | d9dd4f382f96b5c4576b64cbf015db651556c18b | <|skeleton|>
class NotificationSettingsSerializer:
"""This Serializer fetches and serializes NotificationSettings for a list of targets (users or teams.) Pass filters like `project=project` and `type=NotificationSettingTypes.DEPLOY` to kwargs."""
def get_attrs(self, item_list: Iterable[Union['Team', 'User']], ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NotificationSettingsSerializer:
"""This Serializer fetches and serializes NotificationSettings for a list of targets (users or teams.) Pass filters like `project=project` and `type=NotificationSettingTypes.DEPLOY` to kwargs."""
def get_attrs(self, item_list: Iterable[Union['Team', 'User']], user: User, *... | the_stack_v2_python_sparse | src/sentry/api/serializers/models/notification_setting.py | nagyist/sentry | train | 0 |
a858ed5244385c365bf716a1cab15a68c7b681f0 | [
"self.__main_line = main_line\nself.__position = position\nself.__where = where",
"if self.__where == 0:\n follower.move_to(self.__main_line.get_start() + self.__position)\nelif self.__where == 1:\n follower.move_to(self.__main_line.get_end() + self.__position)\nelse:\n raise AssertionError('whwre should... | <|body_start_0|>
self.__main_line = main_line
self.__position = position
self.__where = where
<|end_body_0|>
<|body_start_1|>
if self.__where == 0:
follower.move_to(self.__main_line.get_start() + self.__position)
elif self.__where == 1:
follower.move_to(s... | Glue two objects (a point at the edge of a line) for updater Put an follower object next to the end point of a line. + follower(right(where = 1)) / / / line / / follower(left(where = 0)) + This uses move_to() instead of next_to(). | Glue_line_point_edge_rotate_updater | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Glue_line_point_edge_rotate_updater:
"""Glue two objects (a point at the edge of a line) for updater Put an follower object next to the end point of a line. + follower(right(where = 1)) / / / line / / follower(left(where = 0)) + This uses move_to() instead of next_to()."""
def __init__(self,... | stack_v2_sparse_classes_75kplus_train_067218 | 28,855 | permissive | [
{
"docstring": "param[in] main_line the main object but a line, follower object will follow this main line object via updater param[in] position next_to position param[in] where 0 ... start, 1 ... end",
"name": "__init__",
"signature": "def __init__(self, main_line, position=LEFT, where=0)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_017911 | Implement the Python class `Glue_line_point_edge_rotate_updater` described below.
Class description:
Glue two objects (a point at the edge of a line) for updater Put an follower object next to the end point of a line. + follower(right(where = 1)) / / / line / / follower(left(where = 0)) + This uses move_to() instead o... | Implement the Python class `Glue_line_point_edge_rotate_updater` described below.
Class description:
Glue two objects (a point at the edge of a line) for updater Put an follower object next to the end point of a line. + follower(right(where = 1)) / / / line / / follower(left(where = 0)) + This uses move_to() instead o... | ff8b30ff6b6a8ea746e6739ac170784a08491e7a | <|skeleton|>
class Glue_line_point_edge_rotate_updater:
"""Glue two objects (a point at the edge of a line) for updater Put an follower object next to the end point of a line. + follower(right(where = 1)) / / / line / / follower(left(where = 0)) + This uses move_to() instead of next_to()."""
def __init__(self,... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Glue_line_point_edge_rotate_updater:
"""Glue two objects (a point at the edge of a line) for updater Put an follower object next to the end point of a line. + follower(right(where = 1)) / / / line / / follower(left(where = 0)) + This uses move_to() instead of next_to()."""
def __init__(self, main_line, p... | the_stack_v2_python_sparse | 202008_corner_cube_mirror/03_corresponding_angles.py | yamauchih/3b1b_manim_examples | train | 1 |
52ac05de1d1f508b94dee705a4401edafc44538a | [
"def parse(st):\n stack = []\n for x in st:\n if x == '#' and stack:\n stack.pop()\n elif x.isalpha():\n stack.append(x)\n return stack\nreturn parse(S) == parse(T)",
"i, j, del1, del2 = (len(S) - 1, len(T) - 1, 0, 0)\nwhile i >= 0 or j >= 0:\n while i >= 0:\n ... | <|body_start_0|>
def parse(st):
stack = []
for x in st:
if x == '#' and stack:
stack.pop()
elif x.isalpha():
stack.append(x)
return stack
return parse(S) == parse(T)
<|end_body_0|>
<|body_start_1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def backspaceCompare1(self, S: str, T: str) -> bool:
"""常规解法，利用栈，时间空间复杂度都是O(S+T)即O(n)"""
<|body_0|>
def backspaceCompare2(self, S: str, T: str) -> bool:
"""Follow up: 要求空间复杂度O(1)，双指针逆序遍历"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def ... | stack_v2_sparse_classes_75kplus_train_067219 | 1,998 | no_license | [
{
"docstring": "常规解法，利用栈，时间空间复杂度都是O(S+T)即O(n)",
"name": "backspaceCompare1",
"signature": "def backspaceCompare1(self, S: str, T: str) -> bool"
},
{
"docstring": "Follow up: 要求空间复杂度O(1)，双指针逆序遍历",
"name": "backspaceCompare2",
"signature": "def backspaceCompare2(self, S: str, T: str) -> bo... | 2 | stack_v2_sparse_classes_30k_train_043677 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def backspaceCompare1(self, S: str, T: str) -> bool: 常规解法，利用栈，时间空间复杂度都是O(S+T)即O(n)
- def backspaceCompare2(self, S: str, T: str) -> bool: Follow up: 要求空间复杂度O(1)，双指针逆序遍历 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def backspaceCompare1(self, S: str, T: str) -> bool: 常规解法，利用栈，时间空间复杂度都是O(S+T)即O(n)
- def backspaceCompare2(self, S: str, T: str) -> bool: Follow up: 要求空间复杂度O(1)，双指针逆序遍历
<|skelet... | 2bbb1640589aab34f2bc42489283033cc11fb885 | <|skeleton|>
class Solution:
def backspaceCompare1(self, S: str, T: str) -> bool:
"""常规解法，利用栈，时间空间复杂度都是O(S+T)即O(n)"""
<|body_0|>
def backspaceCompare2(self, S: str, T: str) -> bool:
"""Follow up: 要求空间复杂度O(1)，双指针逆序遍历"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def backspaceCompare1(self, S: str, T: str) -> bool:
"""常规解法，利用栈，时间空间复杂度都是O(S+T)即O(n)"""
def parse(st):
stack = []
for x in st:
if x == '#' and stack:
stack.pop()
elif x.isalpha():
stack.a... | the_stack_v2_python_sparse | 844_backspace-string-compare.py | helloocc/algorithm | train | 1 | |
2de5036a7a089c7c99425d83c4510d2e8881f2cb | [
"try:\n queryset = self.get_all_objects(User)\n print(queryset)\n serializer = serializers.UserSerializer(queryset, many=True)\n return Utils.dispatch_success(request, serializer.data)\nexcept Exception as e:\n return self.internal_server_error(request, e)",
"try:\n serializer = serializers.Crea... | <|body_start_0|>
try:
queryset = self.get_all_objects(User)
print(queryset)
serializer = serializers.UserSerializer(queryset, many=True)
return Utils.dispatch_success(request, serializer.data)
except Exception as e:
return self.internal_server_... | Lists all users | UserList | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserList:
"""Lists all users"""
def get(self, request, *args, **kwargs):
"""Get the List of users :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Create New User :param request: { "username" : "98765... | stack_v2_sparse_classes_75kplus_train_067220 | 4,727 | permissive | [
{
"docstring": "Get the List of users :param request: :param args: :param kwargs: :return:",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "Create New User :param request: { \"username\" : \"9876543210\", \"first_name\":\"Team 1\", \"email\" : \"team1@gm... | 2 | stack_v2_sparse_classes_30k_train_009429 | Implement the Python class `UserList` described below.
Class description:
Lists all users
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Get the List of users :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): Create New User :param request: ... | Implement the Python class `UserList` described below.
Class description:
Lists all users
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Get the List of users :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): Create New User :param request: ... | 1e31affddf60d2de72445a85dd2055bdeba6f670 | <|skeleton|>
class UserList:
"""Lists all users"""
def get(self, request, *args, **kwargs):
"""Get the List of users :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Create New User :param request: { "username" : "98765... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UserList:
"""Lists all users"""
def get(self, request, *args, **kwargs):
"""Get the List of users :param request: :param args: :param kwargs: :return:"""
try:
queryset = self.get_all_objects(User)
print(queryset)
serializer = serializers.UserSerializer(... | the_stack_v2_python_sparse | the_mechanic_backend/v0/accounts/views.py | muthukumar4999/the-mechanic-backend | train | 0 |
c086397c2c0a6dedb1fd39d2e2e2493ce9d92e1f | [
"if self.expires:\n return datetime.datetime.utcnow() > self.expires\nreturn False",
"if self.refresh_token_expires:\n return datetime.datetime.utcnow() > self.refresh_token_expires\nreturn False",
"if not self.expires:\n return None\nnow = datetime.datetime.utcnow()\nttl = self.expires - now\nttl_in_s... | <|body_start_0|>
if self.expires:
return datetime.datetime.utcnow() > self.expires
return False
<|end_body_0|>
<|body_start_1|>
if self.refresh_token_expires:
return datetime.datetime.utcnow() > self.refresh_token_expires
return False
<|end_body_1|>
<|body_start... | An API access token for a user. These fall into two categories: - Long-lived developer tokens, which are generated for an account for third-party integrations. These do not expire. - Temporary access tokens, which are currently only issued from JWTs generated by third party `AuthClient`\\\\ s. These do expire. | Token | [
"BSD-2-Clause",
"BSD-3-Clause",
"BSD-2-Clause-Views"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Token:
"""An API access token for a user. These fall into two categories: - Long-lived developer tokens, which are generated for an account for third-party integrations. These do not expire. - Temporary access tokens, which are currently only issued from JWTs generated by third party `AuthClient`... | stack_v2_sparse_classes_75kplus_train_067221 | 2,917 | permissive | [
{
"docstring": "Return True if this access token has expired, False otherwise.",
"name": "expired",
"signature": "def expired(self)"
},
{
"docstring": "Return True if this refresh token has expired, False otherwise.",
"name": "refresh_token_expired",
"signature": "def refresh_token_expir... | 3 | null | Implement the Python class `Token` described below.
Class description:
An API access token for a user. These fall into two categories: - Long-lived developer tokens, which are generated for an account for third-party integrations. These do not expire. - Temporary access tokens, which are currently only issued from JWT... | Implement the Python class `Token` described below.
Class description:
An API access token for a user. These fall into two categories: - Long-lived developer tokens, which are generated for an account for third-party integrations. These do not expire. - Temporary access tokens, which are currently only issued from JWT... | 232446d776fdb906d2fb253cf0a409c6813a08d6 | <|skeleton|>
class Token:
"""An API access token for a user. These fall into two categories: - Long-lived developer tokens, which are generated for an account for third-party integrations. These do not expire. - Temporary access tokens, which are currently only issued from JWTs generated by third party `AuthClient`... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Token:
"""An API access token for a user. These fall into two categories: - Long-lived developer tokens, which are generated for an account for third-party integrations. These do not expire. - Temporary access tokens, which are currently only issued from JWTs generated by third party `AuthClient`\\\\ s. These... | the_stack_v2_python_sparse | h/models/token.py | hypothesis/h | train | 2,558 |
c3e0e36d60c86b73441ae080f8dbe72e548d7451 | [
"self.fh = filehandle\nself.counter = 0\nself.chunk = chunksize\nself.extraspaces = extraspaces",
"self.fh.write(' ' * self.extraspaces)\nself.fh.write(f2s(value))\nself.counter += 1\nif self.counter == self.chunk:\n self.fh.write('\\n')\n self.counter = 0",
"if self.counter != 0:\n self.fh.write('\\n'... | <|body_start_0|>
self.fh = filehandle
self.counter = 0
self.chunk = chunksize
self.extraspaces = extraspaces
<|end_body_0|>
<|body_start_1|>
self.fh.write(' ' * self.extraspaces)
self.fh.write(f2s(value))
self.counter += 1
if self.counter == self.chunk:
... | This outputs values in lines, inserting newlines when needed. | ChunkOutput | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChunkOutput:
"""This outputs values in lines, inserting newlines when needed."""
def __init__(self, filehandle, chunksize=5, extraspaces=0):
"""filehandle output to write to chunksize number of values on a line extraspaces number of extra spaces between outputs"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_067222 | 2,205 | permissive | [
{
"docstring": "filehandle output to write to chunksize number of values on a line extraspaces number of extra spaces between outputs",
"name": "__init__",
"signature": "def __init__(self, filehandle, chunksize=5, extraspaces=0)"
},
{
"docstring": "\" Write a value to the output, adding a newlin... | 3 | stack_v2_sparse_classes_30k_train_049171 | Implement the Python class `ChunkOutput` described below.
Class description:
This outputs values in lines, inserting newlines when needed.
Method signatures and docstrings:
- def __init__(self, filehandle, chunksize=5, extraspaces=0): filehandle output to write to chunksize number of values on a line extraspaces numb... | Implement the Python class `ChunkOutput` described below.
Class description:
This outputs values in lines, inserting newlines when needed.
Method signatures and docstrings:
- def __init__(self, filehandle, chunksize=5, extraspaces=0): filehandle output to write to chunksize number of values on a line extraspaces numb... | af351c4aa4a40aa5bb5bdaa8083575344c0827e2 | <|skeleton|>
class ChunkOutput:
"""This outputs values in lines, inserting newlines when needed."""
def __init__(self, filehandle, chunksize=5, extraspaces=0):
"""filehandle output to write to chunksize number of values on a line extraspaces number of extra spaces between outputs"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ChunkOutput:
"""This outputs values in lines, inserting newlines when needed."""
def __init__(self, filehandle, chunksize=5, extraspaces=0):
"""filehandle output to write to chunksize number of values on a line extraspaces number of extra spaces between outputs"""
self.fh = filehandle
... | the_stack_v2_python_sparse | pleque/io/_fileutils.py | kripnerl/pleque | train | 15 |
994230ab0479ed58e3b84eb43673850649f3c682 | [
"data = self.data\ncode = data['code']\nreturn f'{PLATFORM_URL}reset-password/{code}'",
"payload = super().transform()\ndata = self.data\nexpires = data.get('expiration_date')\nif expires.endswith('Z'):\n expires = expires[:-1]\nexpires = self._format_datetime(expires)\npayload[0]['data']['CODE'] = data.get('c... | <|body_start_0|>
data = self.data
code = data['code']
return f'{PLATFORM_URL}reset-password/{code}'
<|end_body_0|>
<|body_start_1|>
payload = super().transform()
data = self.data
expires = data.get('expiration_date')
if expires.endswith('Z'):
expires ... | Send an email to the user the details of a password reset request. | PasswordReset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordReset:
"""Send an email to the user the details of a password reset request."""
def _action_url(self) -> str:
"""Return the action URL for the object."""
<|body_0|>
def transform(self) -> t.List[dict]:
"""Transform data."""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_75kplus_train_067223 | 4,062 | no_license | [
{
"docstring": "Return the action URL for the object.",
"name": "_action_url",
"signature": "def _action_url(self) -> str"
},
{
"docstring": "Transform data.",
"name": "transform",
"signature": "def transform(self) -> t.List[dict]"
}
] | 2 | stack_v2_sparse_classes_30k_train_037122 | Implement the Python class `PasswordReset` described below.
Class description:
Send an email to the user the details of a password reset request.
Method signatures and docstrings:
- def _action_url(self) -> str: Return the action URL for the object.
- def transform(self) -> t.List[dict]: Transform data. | Implement the Python class `PasswordReset` described below.
Class description:
Send an email to the user the details of a password reset request.
Method signatures and docstrings:
- def _action_url(self) -> str: Return the action URL for the object.
- def transform(self) -> t.List[dict]: Transform data.
<|skeleton|>... | cca179f55ebc3c420426eff59b23d7c8963ca9a3 | <|skeleton|>
class PasswordReset:
"""Send an email to the user the details of a password reset request."""
def _action_url(self) -> str:
"""Return the action URL for the object."""
<|body_0|>
def transform(self) -> t.List[dict]:
"""Transform data."""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PasswordReset:
"""Send an email to the user the details of a password reset request."""
def _action_url(self) -> str:
"""Return the action URL for the object."""
data = self.data
code = data['code']
return f'{PLATFORM_URL}reset-password/{code}'
def transform(self) -> ... | the_stack_v2_python_sparse | src/briefy/choreographer/actions/mail/user.py | BriefyHQ/briefy.choreographer | train | 0 |
ed0af6b1fc923c0c58411385cac89997e2a9a99c | [
"self.log = logging.getLogger('lapis.gyazo')\nself.headers = {'User-Agent': useragent}\nself.regex = re.compile('^(.*?\\\\.)?gyazo\\\\.com$')",
"try:\n if not self.regex.match(urlsplit(submission.url).netloc):\n return None\n data = {'author': 'a gyazo.com user', 'source': submission.url, 'importer_d... | <|body_start_0|>
self.log = logging.getLogger('lapis.gyazo')
self.headers = {'User-Agent': useragent}
self.regex = re.compile('^(.*?\\.)?gyazo\\.com$')
<|end_body_0|>
<|body_start_1|>
try:
if not self.regex.match(urlsplit(submission.url).netloc):
return None
... | A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots. | GyazoPlugin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GyazoPlugin:
"""A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots."""
def __init__(self, useragent: str, **options):
"""Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the con... | stack_v2_sparse_classes_75kplus_train_067224 | 2,843 | permissive | [
{
"docstring": "Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the configuration. Ignored.",
"name": "__init__",
"signature": "def __init__(self, useragent: str, **options)"
},
{
"docstring": "Import a submission from ... | 2 | stack_v2_sparse_classes_30k_test_001648 | Implement the Python class `GyazoPlugin` described below.
Class description:
A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots.
Method signatures and docstrings:
- def __init__(self, useragent: str, **options): Initialize the gyazo import API. :param useragent: The useragent to use for... | Implement the Python class `GyazoPlugin` described below.
Class description:
A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots.
Method signatures and docstrings:
- def __init__(self, useragent: str, **options): Initialize the gyazo import API. :param useragent: The useragent to use for... | 29503bb70b7b9e47a5cea1ea03098543b1a01efb | <|skeleton|>
class GyazoPlugin:
"""A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots."""
def __init__(self, useragent: str, **options):
"""Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the con... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GyazoPlugin:
"""A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots."""
def __init__(self, useragent: str, **options):
"""Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the configuration. I... | the_stack_v2_python_sparse | plugins/gyazo.py | spiral6/VelvetBot | train | 0 |
da9bb410435d894fa4d5dfe710a387976b1c1818 | [
"self.profile = profile\nsuper(ProfileForm, self).__init__(*args, **kwargs)\nself.fields['first_name'].initial = profile.user.first_name\nself.fields['last_name'].initial = profile.user.last_name\nself.fields['email'].initial = profile.user.email\nself.fields['email'].gender = profile.gender",
"users = User.objec... | <|body_start_0|>
self.profile = profile
super(ProfileForm, self).__init__(*args, **kwargs)
self.fields['first_name'].initial = profile.user.first_name
self.fields['last_name'].initial = profile.user.last_name
self.fields['email'].initial = profile.user.email
self.fields['... | The ProfileForm provides a form used to update the user's informations | ProfileForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProfileForm:
"""The ProfileForm provides a form used to update the user's informations"""
def __init__(self, profile, *args, **kwargs):
"""Initializes a new instance of the ChangePasswordForm class."""
<|body_0|>
def clean_email(self):
"""Validate that the suppli... | stack_v2_sparse_classes_75kplus_train_067225 | 5,957 | no_license | [
{
"docstring": "Initializes a new instance of the ChangePasswordForm class.",
"name": "__init__",
"signature": "def __init__(self, profile, *args, **kwargs)"
},
{
"docstring": "Validate that the supplied email address is unique for the site.",
"name": "clean_email",
"signature": "def cle... | 3 | stack_v2_sparse_classes_30k_train_040088 | Implement the Python class `ProfileForm` described below.
Class description:
The ProfileForm provides a form used to update the user's informations
Method signatures and docstrings:
- def __init__(self, profile, *args, **kwargs): Initializes a new instance of the ChangePasswordForm class.
- def clean_email(self): Val... | Implement the Python class `ProfileForm` described below.
Class description:
The ProfileForm provides a form used to update the user's informations
Method signatures and docstrings:
- def __init__(self, profile, *args, **kwargs): Initializes a new instance of the ChangePasswordForm class.
- def clean_email(self): Val... | b0702a8f7f60de6db9de7f712108e68d66f07f61 | <|skeleton|>
class ProfileForm:
"""The ProfileForm provides a form used to update the user's informations"""
def __init__(self, profile, *args, **kwargs):
"""Initializes a new instance of the ChangePasswordForm class."""
<|body_0|>
def clean_email(self):
"""Validate that the suppli... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProfileForm:
"""The ProfileForm provides a form used to update the user's informations"""
def __init__(self, profile, *args, **kwargs):
"""Initializes a new instance of the ChangePasswordForm class."""
self.profile = profile
super(ProfileForm, self).__init__(*args, **kwargs)
... | the_stack_v2_python_sparse | getdeal/apps/profiles/forms.py | PankeshGupta/getdeal | train | 0 |
57306ecdfaf3aa610f81dae6b4c488a6adf14178 | [
"rslt = super(StockMove, self)._generate_valuation_lines_data(partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id)\nif self.picking_id.currency_rate1 > 0:\n purchase_currency = self.purchase_line_id.currency_id\n purchase_price_unit = self.purchase_line_id.price_unit\n if self.p... | <|body_start_0|>
rslt = super(StockMove, self)._generate_valuation_lines_data(partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id)
if self.picking_id.currency_rate1 > 0:
purchase_currency = self.purchase_line_id.currency_id
purchase_price_unit = self.p... | StockMove | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StockMove:
def _generate_valuation_lines_data(self, partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id):
"""Overridden from stock_account to support amount_currency on valuation lines generated from po"""
<|body_0|>
def _prepare_account_move_line... | stack_v2_sparse_classes_75kplus_train_067226 | 5,257 | no_license | [
{
"docstring": "Overridden from stock_account to support amount_currency on valuation lines generated from po",
"name": "_generate_valuation_lines_data",
"signature": "def _generate_valuation_lines_data(self, partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id)"
},
{
... | 2 | stack_v2_sparse_classes_30k_test_002965 | Implement the Python class `StockMove` described below.
Class description:
Implement the StockMove class.
Method signatures and docstrings:
- def _generate_valuation_lines_data(self, partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id): Overridden from stock_account to support amount_curre... | Implement the Python class `StockMove` described below.
Class description:
Implement the StockMove class.
Method signatures and docstrings:
- def _generate_valuation_lines_data(self, partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id): Overridden from stock_account to support amount_curre... | 5ed668bda8177586695f5dc2e68a48806eccf976 | <|skeleton|>
class StockMove:
def _generate_valuation_lines_data(self, partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id):
"""Overridden from stock_account to support amount_currency on valuation lines generated from po"""
<|body_0|>
def _prepare_account_move_line... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StockMove:
def _generate_valuation_lines_data(self, partner_id, qty, debit_value, credit_value, debit_account_id, credit_account_id):
"""Overridden from stock_account to support amount_currency on valuation lines generated from po"""
rslt = super(StockMove, self)._generate_valuation_lines_data... | the_stack_v2_python_sparse | purchase_exchange_currency_rate/models/stock.py | akradore/ACC_12 | train | 0 | |
82a601333d429a6f960fede0b1563805636fb4d6 | [
"self.bin_loc = bin_loc\nself.max_cube = max_cube\nself.available_cube = max_cube\nself.materials = dict()",
"if material.id not in self.materials:\n self.materials[material.id] = 0\nchange = cube\nunfulfilled = 0\nif material.bin_loc != self.bin_loc:\n return cube\nif self.available_cube < 0:\n return c... | <|body_start_0|>
self.bin_loc = bin_loc
self.max_cube = max_cube
self.available_cube = max_cube
self.materials = dict()
<|end_body_0|>
<|body_start_1|>
if material.id not in self.materials:
self.materials[material.id] = 0
change = cube
unfulfilled = 0... | Storage | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Storage:
def __init__(self, bin_loc, max_cube):
"""Represents a storage space in a Distribution Center :param bin_loc: BinLocation :param max_cube: int"""
<|body_0|>
def update_inventory(self, material, cube):
"""Attempt to place an order in the storage space :param ... | stack_v2_sparse_classes_75kplus_train_067227 | 5,030 | permissive | [
{
"docstring": "Represents a storage space in a Distribution Center :param bin_loc: BinLocation :param max_cube: int",
"name": "__init__",
"signature": "def __init__(self, bin_loc, max_cube)"
},
{
"docstring": "Attempt to place an order in the storage space :param material: Material :param cube:... | 3 | stack_v2_sparse_classes_30k_train_049866 | Implement the Python class `Storage` described below.
Class description:
Implement the Storage class.
Method signatures and docstrings:
- def __init__(self, bin_loc, max_cube): Represents a storage space in a Distribution Center :param bin_loc: BinLocation :param max_cube: int
- def update_inventory(self, material, c... | Implement the Python class `Storage` described below.
Class description:
Implement the Storage class.
Method signatures and docstrings:
- def __init__(self, bin_loc, max_cube): Represents a storage space in a Distribution Center :param bin_loc: BinLocation :param max_cube: int
- def update_inventory(self, material, c... | b9ed1bd924089f06854a9bd2b4a96171e4962e38 | <|skeleton|>
class Storage:
def __init__(self, bin_loc, max_cube):
"""Represents a storage space in a Distribution Center :param bin_loc: BinLocation :param max_cube: int"""
<|body_0|>
def update_inventory(self, material, cube):
"""Attempt to place an order in the storage space :param ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Storage:
def __init__(self, bin_loc, max_cube):
"""Represents a storage space in a Distribution Center :param bin_loc: BinLocation :param max_cube: int"""
self.bin_loc = bin_loc
self.max_cube = max_cube
self.available_cube = max_cube
self.materials = dict()
def upd... | the_stack_v2_python_sparse | src/opm_manager/models/sku.py | mattrwyrick/OPM-Manager | train | 0 | |
f644a5f3aaba16b7e0ec73805f46bc1cf886ed81 | [
"while n not in [0, 1, 4]:\n cur, n = (n, 0)\n for x in str(cur):\n x = int(x)\n n += x * x\nif n == 1:\n return True\nelse:\n return False",
"seen = set()\nwhile n not in seen:\n seen.add(n)\n n = sum(((ord(x) - ord('0')) ** 2 for x in str(n)))\nreturn n == 1"
] | <|body_start_0|>
while n not in [0, 1, 4]:
cur, n = (n, 0)
for x in str(cur):
x = int(x)
n += x * x
if n == 1:
return True
else:
return False
<|end_body_0|>
<|body_start_1|>
seen = set()
while n not ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isHappy1(self, n: int) -> bool:
"""最终如果是0,1,4，会陷入循环"""
<|body_0|>
def isHappy2(self, n: int) -> bool:
"""记录已经出现过的数字，如果再次出现则形成死循环。"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
while n not in [0, 1, 4]:
cur, n = (n, 0)
... | stack_v2_sparse_classes_75kplus_train_067228 | 1,030 | no_license | [
{
"docstring": "最终如果是0,1,4，会陷入循环",
"name": "isHappy1",
"signature": "def isHappy1(self, n: int) -> bool"
},
{
"docstring": "记录已经出现过的数字，如果再次出现则形成死循环。",
"name": "isHappy2",
"signature": "def isHappy2(self, n: int) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_033723 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy1(self, n: int) -> bool: 最终如果是0,1,4，会陷入循环
- def isHappy2(self, n: int) -> bool: 记录已经出现过的数字，如果再次出现则形成死循环。 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy1(self, n: int) -> bool: 最终如果是0,1,4，会陷入循环
- def isHappy2(self, n: int) -> bool: 记录已经出现过的数字，如果再次出现则形成死循环。
<|skeleton|>
class Solution:
def isHappy1(self, n: int) ... | 2bbb1640589aab34f2bc42489283033cc11fb885 | <|skeleton|>
class Solution:
def isHappy1(self, n: int) -> bool:
"""最终如果是0,1,4，会陷入循环"""
<|body_0|>
def isHappy2(self, n: int) -> bool:
"""记录已经出现过的数字，如果再次出现则形成死循环。"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def isHappy1(self, n: int) -> bool:
"""最终如果是0,1,4，会陷入循环"""
while n not in [0, 1, 4]:
cur, n = (n, 0)
for x in str(cur):
x = int(x)
n += x * x
if n == 1:
return True
else:
return False
... | the_stack_v2_python_sparse | 202_happy-number.py | helloocc/algorithm | train | 1 | |
812d66beafd7a774f5ea2094bc3e1e5125821d49 | [
"self.engine = engine\nself._allocated_qubit_ids = set()\nself._deallocated_qubit_ids = set()\nself._uncompute_eng = None",
"compute_eng = self.engine.next_engine\nif not isinstance(compute_eng, ComputeEngine):\n raise NoComputeSectionError(\"Invalid call to CustomUncompute: No corresponding 'with Compute' sta... | <|body_start_0|>
self.engine = engine
self._allocated_qubit_ids = set()
self._deallocated_qubit_ids = set()
self._uncompute_eng = None
<|end_body_0|>
<|body_start_1|>
compute_eng = self.engine.next_engine
if not isinstance(compute_eng, ComputeEngine):
raise N... | Start a custom uncompute-section. Example: .. code-block:: python with Compute(eng): do_something(qubits) action(qubits) with CustomUncompute(eng): do_something_inverse(qubits) Raises: QubitManagementError: If qubits are allocated within Compute or within CustomUncompute context but are not deallocated. | CustomUncompute | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomUncompute:
"""Start a custom uncompute-section. Example: .. code-block:: python with Compute(eng): do_something(qubits) action(qubits) with CustomUncompute(eng): do_something_inverse(qubits) Raises: QubitManagementError: If qubits are allocated within Compute or within CustomUncompute conte... | stack_v2_sparse_classes_75kplus_train_067229 | 16,074 | permissive | [
{
"docstring": "Initialize a CustomUncompute context. Args: engine (BasicEngine): Engine which is the first to receive all commands (normally: MainEngine).",
"name": "__init__",
"signature": "def __init__(self, engine)"
},
{
"docstring": "Context manager enter function.",
"name": "__enter__"... | 3 | stack_v2_sparse_classes_30k_train_023029 | Implement the Python class `CustomUncompute` described below.
Class description:
Start a custom uncompute-section. Example: .. code-block:: python with Compute(eng): do_something(qubits) action(qubits) with CustomUncompute(eng): do_something_inverse(qubits) Raises: QubitManagementError: If qubits are allocated within ... | Implement the Python class `CustomUncompute` described below.
Class description:
Start a custom uncompute-section. Example: .. code-block:: python with Compute(eng): do_something(qubits) action(qubits) with CustomUncompute(eng): do_something_inverse(qubits) Raises: QubitManagementError: If qubits are allocated within ... | 67c660ca18725d23ab0b261a45e34873b6a58d03 | <|skeleton|>
class CustomUncompute:
"""Start a custom uncompute-section. Example: .. code-block:: python with Compute(eng): do_something(qubits) action(qubits) with CustomUncompute(eng): do_something_inverse(qubits) Raises: QubitManagementError: If qubits are allocated within Compute or within CustomUncompute conte... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CustomUncompute:
"""Start a custom uncompute-section. Example: .. code-block:: python with Compute(eng): do_something(qubits) action(qubits) with CustomUncompute(eng): do_something_inverse(qubits) Raises: QubitManagementError: If qubits are allocated within Compute or within CustomUncompute context but are no... | the_stack_v2_python_sparse | projectq/meta/_compute.py | ProjectQ-Framework/ProjectQ | train | 886 |
14e7633d024e895a24315e735a1d7792d235b9c6 | [
"super(AdditiveUpsampleLayer, self).__init__(name=name)\nself.new_size = new_size\nself.n_splits = int(n_splits)",
"check_divisible_channels(input_tensor, self.n_splits)\nresizing_layer = ResizingLayer(self.new_size)\nsplit = tf.split(resizing_layer(input_tensor), self.n_splits, axis=-1)\nsplit_tensor = tf.stack(... | <|body_start_0|>
super(AdditiveUpsampleLayer, self).__init__(name=name)
self.new_size = new_size
self.n_splits = int(n_splits)
<|end_body_0|>
<|body_start_1|>
check_divisible_channels(input_tensor, self.n_splits)
resizing_layer = ResizingLayer(self.new_size)
split = tf.s... | Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4`` | AdditiveUpsampleLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdditiveUpsampleLayer:
"""Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4``"""
def __init__(self, new_... | stack_v2_sparse_classes_75kplus_train_067230 | 4,253 | permissive | [
{
"docstring": ":param new_size: integer or a list of integers set the output 2D/3D spatial shape. If the parameter is an integer ``d``, it'll be expanded to ``(d, d)`` and ``(d, d, d)`` for 2D and 3D inputs respectively. :param n_splits: integer, the output tensor will have ``C / n_splits`` channels, where ``C... | 2 | stack_v2_sparse_classes_30k_train_020027 | Implement the Python class `AdditiveUpsampleLayer` described below.
Class description:
Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_split... | Implement the Python class `AdditiveUpsampleLayer` described below.
Class description:
Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_split... | 84dd0f85c9a1ab8a72f4c55fcf073379acf5ae1b | <|skeleton|>
class AdditiveUpsampleLayer:
"""Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4``"""
def __init__(self, new_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AdditiveUpsampleLayer:
"""Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4``"""
def __init__(self, new_size, n_split... | the_stack_v2_python_sparse | niftynet/layer/additive_upsample.py | 12SigmaTechnologies/NiftyNet-1 | train | 2 |
bd771c814da2ec9ba4337f24a133ee01a51fccb8 | [
"params = Parameters.instance().place_params\ntransmission = params['place_transmission']\nplace_idx = place.place_type.value - 1\ntry:\n num_groups = params['mean_group_size'][place_idx]\nexcept IndexError:\n num_groups = 1\nplace_inf = 0 if hasattr(infector.microcell, 'closure_start_time') and infector.is_p... | <|body_start_0|>
params = Parameters.instance().place_params
transmission = params['place_transmission']
place_idx = place.place_type.value - 1
try:
num_groups = params['mean_group_size'][place_idx]
except IndexError:
num_groups = 1
place_inf = 0 i... | Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places. | PlaceInfection | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlaceInfection:
"""Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places."""
def place_inf(place, infector, time: float):
"""Calculate the infectiousness of a place. Does not include interventions such as isolation, or... | stack_v2_sparse_classes_75kplus_train_067231 | 6,023 | permissive | [
{
"docstring": "Calculate the infectiousness of a place. Does not include interventions such as isolation, or whether individual is a carehome resident. Does not yet differentiate between places as we have not decided which places to implement, and what transmission to give them. Parameters ---------- place : P... | 3 | stack_v2_sparse_classes_30k_train_039351 | Implement the Python class `PlaceInfection` described below.
Class description:
Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places.
Method signatures and docstrings:
- def place_inf(place, infector, time: float): Calculate the infectiousness of a pl... | Implement the Python class `PlaceInfection` described below.
Class description:
Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places.
Method signatures and docstrings:
- def place_inf(place, infector, time: float): Calculate the infectiousness of a pl... | c11de122c6bfdf9103162e4045758808da5df322 | <|skeleton|>
class PlaceInfection:
"""Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places."""
def place_inf(place, infector, time: float):
"""Calculate the infectiousness of a place. Does not include interventions such as isolation, or... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PlaceInfection:
"""Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places."""
def place_inf(place, infector, time: float):
"""Calculate the infectiousness of a place. Does not include interventions such as isolation, or whether indi... | the_stack_v2_python_sparse | pyEpiabm/pyEpiabm/property/place_foi.py | SABS-R3-Epidemiology/epiabm | train | 21 |
091a48314e9c600d648acbdf5d959d5bf0170cda | [
"expected = 62006\nmodel: cifar.Net = cifar.load_model()\nactual = sum((p.numel() for p in model.parameters() if p.requires_grad))\nassert actual == expected",
"model: cifar.Net = cifar.load_model()\nexpected = 10\nweights: NDArrays = model.get_weights()\nassert len(weights) == expected",
"weights_expected: NDA... | <|body_start_0|>
expected = 62006
model: cifar.Net = cifar.load_model()
actual = sum((p.numel() for p in model.parameters() if p.requires_grad))
assert actual == expected
<|end_body_0|>
<|body_start_1|>
model: cifar.Net = cifar.load_model()
expected = 10
weights:... | Tests for cifar module. | CifarTestCase | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CifarTestCase:
"""Tests for cifar module."""
def test_load_model(self) -> None:
"""Test the number of (trainable) model parameters."""
<|body_0|>
def test_get_weights(self) -> None:
"""Test get_weights."""
<|body_1|>
def test_set_weights(self) -> Non... | stack_v2_sparse_classes_75kplus_train_067232 | 2,193 | permissive | [
{
"docstring": "Test the number of (trainable) model parameters.",
"name": "test_load_model",
"signature": "def test_load_model(self) -> None"
},
{
"docstring": "Test get_weights.",
"name": "test_get_weights",
"signature": "def test_get_weights(self) -> None"
},
{
"docstring": "T... | 3 | stack_v2_sparse_classes_30k_train_010909 | Implement the Python class `CifarTestCase` described below.
Class description:
Tests for cifar module.
Method signatures and docstrings:
- def test_load_model(self) -> None: Test the number of (trainable) model parameters.
- def test_get_weights(self) -> None: Test get_weights.
- def test_set_weights(self) -> None: T... | Implement the Python class `CifarTestCase` described below.
Class description:
Tests for cifar module.
Method signatures and docstrings:
- def test_load_model(self) -> None: Test the number of (trainable) model parameters.
- def test_get_weights(self) -> None: Test get_weights.
- def test_set_weights(self) -> None: T... | 55be690535e5f3feb33c888c3e4a586b7bdbf489 | <|skeleton|>
class CifarTestCase:
"""Tests for cifar module."""
def test_load_model(self) -> None:
"""Test the number of (trainable) model parameters."""
<|body_0|>
def test_get_weights(self) -> None:
"""Test get_weights."""
<|body_1|>
def test_set_weights(self) -> Non... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CifarTestCase:
"""Tests for cifar module."""
def test_load_model(self) -> None:
"""Test the number of (trainable) model parameters."""
expected = 62006
model: cifar.Net = cifar.load_model()
actual = sum((p.numel() for p in model.parameters() if p.requires_grad))
as... | the_stack_v2_python_sparse | src/py/flwr_example/pytorch_cifar/cifar_test.py | adap/flower | train | 2,999 |
5e2d2f34748dcad069ca5e46fc81c59aeb27861f | [
"self.num_points = num_points\n'Setting all walks to start from(0,0)'\nself.x_values = [0]\nself.y_values = [0]",
"while len(self.x_values) < self.num_points:\n\n def get_step(self):\n self.direction = choice([1, -1])\n self.distance = choice([0, 1, 2, 3])\n self.step = self.direction * se... | <|body_start_0|>
self.num_points = num_points
'Setting all walks to start from(0,0)'
self.x_values = [0]
self.y_values = [0]
<|end_body_0|>
<|body_start_1|>
while len(self.x_values) < self.num_points:
def get_step(self):
self.direction = choice([1, -... | A class to generate random walks. | RandomWalk | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomWalk:
"""A class to generate random walks."""
def __init__(self, num_points=5000):
"""Initialize attributes"""
<|body_0|>
def fill_walk(self):
"""Calculate all the points in the walk."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.nu... | stack_v2_sparse_classes_75kplus_train_067233 | 894 | no_license | [
{
"docstring": "Initialize attributes",
"name": "__init__",
"signature": "def __init__(self, num_points=5000)"
},
{
"docstring": "Calculate all the points in the walk.",
"name": "fill_walk",
"signature": "def fill_walk(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_001255 | Implement the Python class `RandomWalk` described below.
Class description:
A class to generate random walks.
Method signatures and docstrings:
- def __init__(self, num_points=5000): Initialize attributes
- def fill_walk(self): Calculate all the points in the walk. | Implement the Python class `RandomWalk` described below.
Class description:
A class to generate random walks.
Method signatures and docstrings:
- def __init__(self, num_points=5000): Initialize attributes
- def fill_walk(self): Calculate all the points in the walk.
<|skeleton|>
class RandomWalk:
"""A class to ge... | 6be7694c49f8756faaf6eb009918726863cc01e5 | <|skeleton|>
class RandomWalk:
"""A class to generate random walks."""
def __init__(self, num_points=5000):
"""Initialize attributes"""
<|body_0|>
def fill_walk(self):
"""Calculate all the points in the walk."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RandomWalk:
"""A class to generate random walks."""
def __init__(self, num_points=5000):
"""Initialize attributes"""
self.num_points = num_points
'Setting all walks to start from(0,0)'
self.x_values = [0]
self.y_values = [0]
def fill_walk(self):
"""Cal... | the_stack_v2_python_sparse | data_visualization/mole_steps.py | osayi/python_intro | train | 1 |
bffd5dd0e85b9ac62352514b792cb874190ce438 | [
"try:\n import onnxruntime\n import skl2onnx\n return True\nexcept ImportError:\n return False",
"if input_types is None or not isinstance(input_types[0], tuple):\n raise RuntimeError('input_types argument should contain at least one tuple, e.g. [((1, 14), np.float32)]')\nif isinstance(model, RFOnn... | <|body_start_0|>
try:
import onnxruntime
import skl2onnx
return True
except ImportError:
return False
<|end_body_0|>
<|body_start_1|>
if input_types is None or not isinstance(input_types[0], tuple):
raise RuntimeError('input_types argu... | RFOnnxCompiler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RFOnnxCompiler:
def can_compile():
"""Verify whether the required package has been installed."""
<|body_0|>
def compile(model, path: str, input_types=None):
"""Compile the trained model for faster inference. Parameters ---------- model The native model that is expect... | stack_v2_sparse_classes_75kplus_train_067234 | 4,283 | permissive | [
{
"docstring": "Verify whether the required package has been installed.",
"name": "can_compile",
"signature": "def can_compile()"
},
{
"docstring": "Compile the trained model for faster inference. Parameters ---------- model The native model that is expected to be compiled. path : str The path f... | 4 | stack_v2_sparse_classes_30k_train_049993 | Implement the Python class `RFOnnxCompiler` described below.
Class description:
Implement the RFOnnxCompiler class.
Method signatures and docstrings:
- def can_compile(): Verify whether the required package has been installed.
- def compile(model, path: str, input_types=None): Compile the trained model for faster inf... | Implement the Python class `RFOnnxCompiler` described below.
Class description:
Implement the RFOnnxCompiler class.
Method signatures and docstrings:
- def can_compile(): Verify whether the required package has been installed.
- def compile(model, path: str, input_types=None): Compile the trained model for faster inf... | 6af92e149491f6e5062495d87306b3625d12d992 | <|skeleton|>
class RFOnnxCompiler:
def can_compile():
"""Verify whether the required package has been installed."""
<|body_0|>
def compile(model, path: str, input_types=None):
"""Compile the trained model for faster inference. Parameters ---------- model The native model that is expect... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RFOnnxCompiler:
def can_compile():
"""Verify whether the required package has been installed."""
try:
import onnxruntime
import skl2onnx
return True
except ImportError:
return False
def compile(model, path: str, input_types=None):
... | the_stack_v2_python_sparse | tabular/src/autogluon/tabular/models/rf/compilers/onnx.py | stjordanis/autogluon | train | 0 | |
b0057acdc0e2755c710d7497fef4414b7a2a71dd | [
"self.svc_name = svc_name\nself.set_name = set_name\nself.min = int(min)\nself.max = int(max)\nself.isContinuous = True\nself.vals = None",
"self.isContinuous = False\nself.vals = [int(val) for val in vals]\nself.min = min(self.vals)\nself.max = max(self.vals)"
] | <|body_start_0|>
self.svc_name = svc_name
self.set_name = set_name
self.min = int(min)
self.max = int(max)
self.isContinuous = True
self.vals = None
<|end_body_0|>
<|body_start_1|>
self.isContinuous = False
self.vals = [int(val) for val in vals]
s... | a knob setting with max and min settings. It's the smallest unit for RAPID-C | Knob | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Knob:
"""a knob setting with max and min settings. It's the smallest unit for RAPID-C"""
def __init__(self, svc_name, set_name, min, max):
"""Initialization :param svc_name: name of service :param set_name: name of setting :param min: lower bound :param max: upper bound"""
<|... | stack_v2_sparse_classes_75kplus_train_067235 | 5,554 | no_license | [
{
"docstring": "Initialization :param svc_name: name of service :param set_name: name of setting :param min: lower bound :param max: upper bound",
"name": "__init__",
"signature": "def __init__(self, svc_name, set_name, min, max)"
},
{
"docstring": "Trasform to a knob with vals (discrete)",
... | 2 | stack_v2_sparse_classes_30k_train_002596 | Implement the Python class `Knob` described below.
Class description:
a knob setting with max and min settings. It's the smallest unit for RAPID-C
Method signatures and docstrings:
- def __init__(self, svc_name, set_name, min, max): Initialization :param svc_name: name of service :param set_name: name of setting :par... | Implement the Python class `Knob` described below.
Class description:
a knob setting with max and min settings. It's the smallest unit for RAPID-C
Method signatures and docstrings:
- def __init__(self, svc_name, set_name, min, max): Initialization :param svc_name: name of service :param set_name: name of setting :par... | 63b50cc32c6f647ea34b5512f48688149f949a3c | <|skeleton|>
class Knob:
"""a knob setting with max and min settings. It's the smallest unit for RAPID-C"""
def __init__(self, svc_name, set_name, min, max):
"""Initialization :param svc_name: name of service :param set_name: name of setting :param min: lower bound :param max: upper bound"""
<|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Knob:
"""a knob setting with max and min settings. It's the smallest unit for RAPID-C"""
def __init__(self, svc_name, set_name, min, max):
"""Initialization :param svc_name: name of service :param set_name: name of setting :param min: lower bound :param max: upper bound"""
self.svc_name =... | the_stack_v2_python_sparse | modelConstr/Rapids/Rapids_Classes/KDG.py | niuye8911/rapidlib-linux | train | 0 |
d6bc85077b6c72d706ec16aa9aba38d9f4efa475 | [
"dir = [(-1, 0), (1, 0), (0, -1), (0, 1)]\nMAX_NUM_ELEMENTS = 10000\nnrows = len(matrix)\nncols = len(matrix[0])\ndist = [[0 for c in range(ncols)] for r in range(nrows)]\nqueue = []\nfor i in range(0, nrows):\n for j in range(0, ncols):\n if matrix[i][j] == 0:\n queue.append((i, j))\n e... | <|body_start_0|>
dir = [(-1, 0), (1, 0), (0, -1), (0, 1)]
MAX_NUM_ELEMENTS = 10000
nrows = len(matrix)
ncols = len(matrix[0])
dist = [[0 for c in range(ncols)] for r in range(nrows)]
queue = []
for i in range(0, nrows):
for j in range(0, ncols):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def updateMatrix(self, matrix):
""":type matrix: List[List[int]] :rtype: List[List[int]] matrix is stored row-wise"""
<|body_0|>
def updateMatrix2(self, matrix):
"""BFS :param matrix: :return:"""
<|body_1|>
def updateMatrix3(self, matrix):
... | stack_v2_sparse_classes_75kplus_train_067236 | 4,225 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :rtype: List[List[int]] matrix is stored row-wise",
"name": "updateMatrix",
"signature": "def updateMatrix(self, matrix)"
},
{
"docstring": "BFS :param matrix: :return:",
"name": "updateMatrix2",
"signature": "def updateMatrix2(self, matrix)"... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def updateMatrix(self, matrix): :type matrix: List[List[int]] :rtype: List[List[int]] matrix is stored row-wise
- def updateMatrix2(self, matrix): BFS :param matrix: :return:
- d... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def updateMatrix(self, matrix): :type matrix: List[List[int]] :rtype: List[List[int]] matrix is stored row-wise
- def updateMatrix2(self, matrix): BFS :param matrix: :return:
- d... | a5b02044ef39154b6a8d32eb57682f447e1632ba | <|skeleton|>
class Solution:
def updateMatrix(self, matrix):
""":type matrix: List[List[int]] :rtype: List[List[int]] matrix is stored row-wise"""
<|body_0|>
def updateMatrix2(self, matrix):
"""BFS :param matrix: :return:"""
<|body_1|>
def updateMatrix3(self, matrix):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def updateMatrix(self, matrix):
""":type matrix: List[List[int]] :rtype: List[List[int]] matrix is stored row-wise"""
dir = [(-1, 0), (1, 0), (0, -1), (0, 1)]
MAX_NUM_ELEMENTS = 10000
nrows = len(matrix)
ncols = len(matrix[0])
dist = [[0 for c in range... | the_stack_v2_python_sparse | algo/dp/01_matrix.py | xys234/coding-problems | train | 0 | |
4cdcd2254e219ca22f778cf162069424188aa01a | [
"super().__init__()\nself.in_dim = in_dim\nself.out_dim = out_dim\nself.hidden_dims = hidden_dims\nself.n_properties = n_properties\nself.min_var = min_var\nself.non_linearity = non_linearity\nself.restrict_var = restrict_var\nself.network = nn.ModuleList()\nfor i in range(self.n_properties):\n self.network.appe... | <|body_start_0|>
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.hidden_dims = hidden_dims
self.n_properties = n_properties
self.min_var = min_var
self.non_linearity = non_linearity
self.restrict_var = restrict_var
self.network ... | MultiProbabilisticVanillaNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiProbabilisticVanillaNN:
def __init__(self, in_dim, out_dim, hidden_dims, n_properties, non_linearity=F.tanh, min_var=0.01, restrict_var=False):
""":param input_size: An integer describing the dimensionality of the input, in this case r_size, (the dimensionality of the embedding r) :... | stack_v2_sparse_classes_75kplus_train_067237 | 16,175 | no_license | [
{
"docstring": ":param input_size: An integer describing the dimensionality of the input, in this case r_size, (the dimensionality of the embedding r) :param output_size: An integer describing the dimensionality of the output, in this case output_size = x_size :param decoder_n_hidden: An integer describing the ... | 2 | stack_v2_sparse_classes_30k_train_020431 | Implement the Python class `MultiProbabilisticVanillaNN` described below.
Class description:
Implement the MultiProbabilisticVanillaNN class.
Method signatures and docstrings:
- def __init__(self, in_dim, out_dim, hidden_dims, n_properties, non_linearity=F.tanh, min_var=0.01, restrict_var=False): :param input_size: A... | Implement the Python class `MultiProbabilisticVanillaNN` described below.
Class description:
Implement the MultiProbabilisticVanillaNN class.
Method signatures and docstrings:
- def __init__(self, in_dim, out_dim, hidden_dims, n_properties, non_linearity=F.tanh, min_var=0.01, restrict_var=False): :param input_size: A... | de60f831ee082ab2ae232c498cf2755da7c14c27 | <|skeleton|>
class MultiProbabilisticVanillaNN:
def __init__(self, in_dim, out_dim, hidden_dims, n_properties, non_linearity=F.tanh, min_var=0.01, restrict_var=False):
""":param input_size: An integer describing the dimensionality of the input, in this case r_size, (the dimensionality of the embedding r) :... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiProbabilisticVanillaNN:
def __init__(self, in_dim, out_dim, hidden_dims, n_properties, non_linearity=F.tanh, min_var=0.01, restrict_var=False):
""":param input_size: An integer describing the dimensionality of the input, in this case r_size, (the dimensionality of the embedding r) :param output_s... | the_stack_v2_python_sparse | models/networks/np_networks.py | PenelopeJones/neural_processes | train | 4 | |
c093250a430b218f136da1c3103d244cbcd8caa7 | [
"CommonProcessWrapper.__init__(self, command, arguments, environs, workingDir, state, timeout, stdout, stderr, displayName, **kwargs)\nself.stdout = '/dev/null'\nself.stderr = '/dev/null'\ntry:\n if stdout is not None:\n self.stdout = stdout\nexcept Exception:\n log.info('Unable to create file to captu... | <|body_start_0|>
CommonProcessWrapper.__init__(self, command, arguments, environs, workingDir, state, timeout, stdout, stderr, displayName, **kwargs)
self.stdout = '/dev/null'
self.stderr = '/dev/null'
try:
if stdout is not None:
self.stdout = stdout
e... | Unix process wrapper for process execution and management. The unix process wrapper provides the ability to start and stop an external process, setting the process environment, working directory and state i.e. a foreground process in which case a call to the L{start()} method will not return until the process has exite... | ProcessWrapper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProcessWrapper:
"""Unix process wrapper for process execution and management. The unix process wrapper provides the ability to start and stop an external process, setting the process environment, working directory and state i.e. a foreground process in which case a call to the L{start()} method w... | stack_v2_sparse_classes_75kplus_train_067238 | 7,828 | no_license | [
{
"docstring": "Create an instance of the process wrapper. @param command: The full path to the command to execute @param arguments: A list of arguments to the command @param environs: A dictionary of environment variables (key, value) for the process context execution @param workingDir: The working directory f... | 6 | stack_v2_sparse_classes_30k_val_002046 | Implement the Python class `ProcessWrapper` described below.
Class description:
Unix process wrapper for process execution and management. The unix process wrapper provides the ability to start and stop an external process, setting the process environment, working directory and state i.e. a foreground process in which... | Implement the Python class `ProcessWrapper` described below.
Class description:
Unix process wrapper for process execution and management. The unix process wrapper provides the ability to start and stop an external process, setting the process environment, working directory and state i.e. a foreground process in which... | 3f93cbedbb806b6c53de89358025f93c740ebdc3 | <|skeleton|>
class ProcessWrapper:
"""Unix process wrapper for process execution and management. The unix process wrapper provides the ability to start and stop an external process, setting the process environment, working directory and state i.e. a foreground process in which case a call to the L{start()} method w... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProcessWrapper:
"""Unix process wrapper for process execution and management. The unix process wrapper provides the ability to start and stop an external process, setting the process environment, working directory and state i.e. a foreground process in which case a call to the L{start()} method will not retur... | the_stack_v2_python_sparse | pysys/process/plat-unix/helper.py | moraygrieve/pysys | train | 0 |
3e121aa4796508a06b6501569e5f670b06a82d86 | [
"super().__init__()\nn_blocks = len(channels) - 1\nif type(kernel_size) is not list:\n kernel_size = [kernel_size] * n_blocks\nif stride is None:\n stride = kernel_size\nelif type(stride) is not list:\n stride = [stride] * n_blocks\nif type(nonlinear) is not list:\n nonlinear = [nonlinear] * n_blocks\ns... | <|body_start_0|>
super().__init__()
n_blocks = len(channels) - 1
if type(kernel_size) is not list:
kernel_size = [kernel_size] * n_blocks
if stride is None:
stride = kernel_size
elif type(stride) is not list:
stride = [stride] * n_blocks
... | Decoder1d | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder1d:
def __init__(self, channels, kernel_size, stride=None, dilated=False, separable=False, nonlinear='relu'):
"""Args: channels <list<int>> kernel_size <int> or <list<int>> stride <int> or <list<int>> dilated <bool> nonlinear <str> or <list<str>>"""
<|body_0|>
def for... | stack_v2_sparse_classes_75kplus_train_067239 | 19,840 | no_license | [
{
"docstring": "Args: channels <list<int>> kernel_size <int> or <list<int>> stride <int> or <list<int>> dilated <bool> nonlinear <str> or <list<str>>",
"name": "__init__",
"signature": "def __init__(self, channels, kernel_size, stride=None, dilated=False, separable=False, nonlinear='relu')"
},
{
... | 2 | null | Implement the Python class `Decoder1d` described below.
Class description:
Implement the Decoder1d class.
Method signatures and docstrings:
- def __init__(self, channels, kernel_size, stride=None, dilated=False, separable=False, nonlinear='relu'): Args: channels <list<int>> kernel_size <int> or <list<int>> stride <in... | Implement the Python class `Decoder1d` described below.
Class description:
Implement the Decoder1d class.
Method signatures and docstrings:
- def __init__(self, channels, kernel_size, stride=None, dilated=False, separable=False, nonlinear='relu'): Args: channels <list<int>> kernel_size <int> or <list<int>> stride <in... | 4f7f77406cf580785ebf932d78069e7d6e35b765 | <|skeleton|>
class Decoder1d:
def __init__(self, channels, kernel_size, stride=None, dilated=False, separable=False, nonlinear='relu'):
"""Args: channels <list<int>> kernel_size <int> or <list<int>> stride <int> or <list<int>> dilated <bool> nonlinear <str> or <list<str>>"""
<|body_0|>
def for... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Decoder1d:
def __init__(self, channels, kernel_size, stride=None, dilated=False, separable=False, nonlinear='relu'):
"""Args: channels <list<int>> kernel_size <int> or <list<int>> stride <int> or <list<int>> dilated <bool> nonlinear <str> or <list<str>>"""
super().__init__()
n_blocks =... | the_stack_v2_python_sparse | src/models/unet.py | shelly-tang/DNN-based_source_separation | train | 0 | |
621d3628827128962a0eb69f570c3053bf48520a | [
"count = [0] * (len(citations) + 1)\nfor i in range(len(citations)):\n if citations[i] >= len(citations):\n count[len(citations)] += 1\n else:\n count[citations[i]] += 1\nfor j in range(len(citations), -1, -1):\n if count[j] >= j:\n return j\n count[j - 1] += count[j]",
"level = 0... | <|body_start_0|>
count = [0] * (len(citations) + 1)
for i in range(len(citations)):
if citations[i] >= len(citations):
count[len(citations)] += 1
else:
count[citations[i]] += 1
for j in range(len(citations), -1, -1):
if count[j]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hIndex(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_0|>
def hIndex_sort(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
count = [0] * (len(citations) ... | stack_v2_sparse_classes_75kplus_train_067240 | 1,937 | no_license | [
{
"docstring": ":type citations: List[int] :rtype: int",
"name": "hIndex",
"signature": "def hIndex(self, citations)"
},
{
"docstring": ":type citations: List[int] :rtype: int",
"name": "hIndex_sort",
"signature": "def hIndex_sort(self, citations)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000639 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hIndex(self, citations): :type citations: List[int] :rtype: int
- def hIndex_sort(self, citations): :type citations: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hIndex(self, citations): :type citations: List[int] :rtype: int
- def hIndex_sort(self, citations): :type citations: List[int] :rtype: int
<|skeleton|>
class Solution:
... | 8595b04cf5a024c2cd8a97f750d890a818568401 | <|skeleton|>
class Solution:
def hIndex(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_0|>
def hIndex_sort(self, citations):
""":type citations: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def hIndex(self, citations):
""":type citations: List[int] :rtype: int"""
count = [0] * (len(citations) + 1)
for i in range(len(citations)):
if citations[i] >= len(citations):
count[len(citations)] += 1
else:
count[citat... | the_stack_v2_python_sparse | python/274.h-index.py | tainenko/Leetcode2019 | train | 5 | |
475b3f7924f7eb75612dd97a0062976ab35630fd | [
"self.buf = buf\nself.ptr = ptr\nself.endian = endian\nself._cache = {}",
"pkst = self._cache.get(fmt)\nif pkst is None:\n if self.endian is None or fmt[0] in _ENDIAN_CODES:\n pkst = Struct(fmt)\n else:\n endian_fmt = self.endian + fmt\n pkst = Struct(endian_fmt)\n self._cache[en... | <|body_start_0|>
self.buf = buf
self.ptr = ptr
self.endian = endian
self._cache = {}
<|end_body_0|>
<|body_start_1|>
pkst = self._cache.get(fmt)
if pkst is None:
if self.endian is None or fmt[0] in _ENDIAN_CODES:
pkst = Struct(fmt)
... | Class to unpack values from buffer object The buffer object is usually a string. Caches compiled :mod:`struct` format strings so that repeated unpacking with the same format string should be faster than using ``struct.unpack`` directly. Examples -------- >>> a = b'1234567890' >>> upk = Unpacker(a) >>> upk.unpack('2s') ... | Unpacker | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Unpacker:
"""Class to unpack values from buffer object The buffer object is usually a string. Caches compiled :mod:`struct` format strings so that repeated unpacking with the same format string should be faster than using ``struct.unpack`` directly. Examples -------- >>> a = b'1234567890' >>> upk... | stack_v2_sparse_classes_75kplus_train_067241 | 3,580 | permissive | [
{
"docstring": "Initialize unpacker Parameters ---------- buf : buffer object implementing buffer protocol (e.g. str) ptr : int, optional offset at which to begin reads from `buf` endian : None or str, optional endian code to prepend to format, as for ``unpack`` endian codes. None (the default) corresponds to t... | 3 | stack_v2_sparse_classes_30k_test_000046 | Implement the Python class `Unpacker` described below.
Class description:
Class to unpack values from buffer object The buffer object is usually a string. Caches compiled :mod:`struct` format strings so that repeated unpacking with the same format string should be faster than using ``struct.unpack`` directly. Examples... | Implement the Python class `Unpacker` described below.
Class description:
Class to unpack values from buffer object The buffer object is usually a string. Caches compiled :mod:`struct` format strings so that repeated unpacking with the same format string should be faster than using ``struct.unpack`` directly. Examples... | 3c3acc55de8ba741e673063378e6cbaf10b64c7a | <|skeleton|>
class Unpacker:
"""Class to unpack values from buffer object The buffer object is usually a string. Caches compiled :mod:`struct` format strings so that repeated unpacking with the same format string should be faster than using ``struct.unpack`` directly. Examples -------- >>> a = b'1234567890' >>> upk... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Unpacker:
"""Class to unpack values from buffer object The buffer object is usually a string. Caches compiled :mod:`struct` format strings so that repeated unpacking with the same format string should be faster than using ``struct.unpack`` directly. Examples -------- >>> a = b'1234567890' >>> upk = Unpacker(a... | the_stack_v2_python_sparse | env/lib/python3.6/site-packages/nibabel/nicom/structreader.py | Raniac/NEURO-LEARN | train | 9 |
73f8f091feb132f58118ef33bc4821b5a81fa3d7 | [
"super(LabelSmoothingLoss, self).__init__()\nself.use_label_smoothing = False\nif smoothing > 0.0:\n logging.info('Use label smoothing')\n self.smoothing = smoothing\n self.confidence = 1.0 - smoothing\n self.use_label_smoothing = True\n self.n_target_vocab = n_target_vocab\nself.normalize_length = n... | <|body_start_0|>
super(LabelSmoothingLoss, self).__init__()
self.use_label_smoothing = False
if smoothing > 0.0:
logging.info('Use label smoothing')
self.smoothing = smoothing
self.confidence = 1.0 - smoothing
self.use_label_smoothing = True
... | Label Smoothing Loss. Args: smoothing (float): smoothing rate (0.0 means the conventional CE). n_target_vocab (int): number of classes. normalize_length (bool): normalize loss by sequence length if True. | LabelSmoothingLoss | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabelSmoothingLoss:
"""Label Smoothing Loss. Args: smoothing (float): smoothing rate (0.0 means the conventional CE). n_target_vocab (int): number of classes. normalize_length (bool): normalize loss by sequence length if True."""
def __init__(self, smoothing, n_target_vocab, normalize_length... | stack_v2_sparse_classes_75kplus_train_067242 | 2,455 | permissive | [
{
"docstring": "Initialize Loss.",
"name": "__init__",
"signature": "def __init__(self, smoothing, n_target_vocab, normalize_length=False, ignore_id=-1)"
},
{
"docstring": "Forward Loss. Args: ys_block (chainer.Variable): Predicted labels. ys_pad (chainer.Variable): Target (true) labels. Returns... | 2 | null | Implement the Python class `LabelSmoothingLoss` described below.
Class description:
Label Smoothing Loss. Args: smoothing (float): smoothing rate (0.0 means the conventional CE). n_target_vocab (int): number of classes. normalize_length (bool): normalize loss by sequence length if True.
Method signatures and docstrin... | Implement the Python class `LabelSmoothingLoss` described below.
Class description:
Label Smoothing Loss. Args: smoothing (float): smoothing rate (0.0 means the conventional CE). n_target_vocab (int): number of classes. normalize_length (bool): normalize loss by sequence length if True.
Method signatures and docstrin... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class LabelSmoothingLoss:
"""Label Smoothing Loss. Args: smoothing (float): smoothing rate (0.0 means the conventional CE). n_target_vocab (int): number of classes. normalize_length (bool): normalize loss by sequence length if True."""
def __init__(self, smoothing, n_target_vocab, normalize_length... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LabelSmoothingLoss:
"""Label Smoothing Loss. Args: smoothing (float): smoothing rate (0.0 means the conventional CE). n_target_vocab (int): number of classes. normalize_length (bool): normalize loss by sequence length if True."""
def __init__(self, smoothing, n_target_vocab, normalize_length=False, ignor... | the_stack_v2_python_sparse | espnet/nets/chainer_backend/transformer/label_smoothing_loss.py | espnet/espnet | train | 7,242 |
dcaf7f5b15aa6b7a8631fab79d2ff601b3d56c9a | [
"auth_token = helpers.get_auth_token_for_testing()\nbatch = {'user_id': 'erik', 'auth_token': auth_token, 'annotations': [dict(account='r123', key='owner', value='erik'), dict(account='r124', key='owner', value='erik')]}\nresponse = self.client.post('/add', data=json.dumps(batch), content_type='application/json')\n... | <|body_start_0|>
auth_token = helpers.get_auth_token_for_testing()
batch = {'user_id': 'erik', 'auth_token': auth_token, 'annotations': [dict(account='r123', key='owner', value='erik'), dict(account='r124', key='owner', value='erik')]}
response = self.client.post('/add', data=json.dumps(batch), ... | Unit tests for the "/add" API endpoint. | AddTestCase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AddTestCase:
"""Unit tests for the "/add" API endpoint."""
def test_add_in_body(self):
"""Test the "/add" endpoint with a batch in the body of the request."""
<|body_0|>
def test_add_query_params(self):
"""Test the "/add" endpoint with a batch in a query paramete... | stack_v2_sparse_classes_75kplus_train_067243 | 25,230 | no_license | [
{
"docstring": "Test the \"/add\" endpoint with a batch in the body of the request.",
"name": "test_add_in_body",
"signature": "def test_add_in_body(self)"
},
{
"docstring": "Test the \"/add\" endpoint with a batch in a query parameter.",
"name": "test_add_query_params",
"signature": "de... | 3 | stack_v2_sparse_classes_30k_train_021989 | Implement the Python class `AddTestCase` described below.
Class description:
Unit tests for the "/add" API endpoint.
Method signatures and docstrings:
- def test_add_in_body(self): Test the "/add" endpoint with a batch in the body of the request.
- def test_add_query_params(self): Test the "/add" endpoint with a batc... | Implement the Python class `AddTestCase` described below.
Class description:
Unit tests for the "/add" API endpoint.
Method signatures and docstrings:
- def test_add_in_body(self): Test the "/add" endpoint with a batch in the body of the request.
- def test_add_query_params(self): Test the "/add" endpoint with a batc... | a7d49d463ea97900333885dd29cb2e70c1a0fdb9 | <|skeleton|>
class AddTestCase:
"""Unit tests for the "/add" API endpoint."""
def test_add_in_body(self):
"""Test the "/add" endpoint with a batch in the body of the request."""
<|body_0|>
def test_add_query_params(self):
"""Test the "/add" endpoint with a batch in a query paramete... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AddTestCase:
"""Unit tests for the "/add" API endpoint."""
def test_add_in_body(self):
"""Test the "/add" endpoint with a batch in the body of the request."""
auth_token = helpers.get_auth_token_for_testing()
batch = {'user_id': 'erik', 'auth_token': auth_token, 'annotations': [di... | the_stack_v2_python_sparse | annotationDatabase/api/tests.py | erikwestra/ripple-annotation-database | train | 0 |
817ddd7e812334c72daa8dc5d08d01555bfd629e | [
"request_data = request.GET\nusername = request.META.get('HTTP_USERNAME')\nif not username:\n username = request.user.username\nsearch_value = request_data.get('search_value', '')\nper_page = int(request_data.get('per_page', 10)) if request_data.get('per_page', 10) else 10\npage = int(request_data.get('page', 1)... | <|body_start_0|>
request_data = request.GET
username = request.META.get('HTTP_USERNAME')
if not username:
username = request.user.username
search_value = request_data.get('search_value', '')
per_page = int(request_data.get('per_page', 10)) if request_data.get('per_pag... | WorkflowRunScriptView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkflowRunScriptView:
def get(self, request, *args, **kwargs):
"""获取工作流执行脚本列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""新增脚本 :param request: :param args: :param kwargs: :return:"""
<|body_... | stack_v2_sparse_classes_75kplus_train_067244 | 35,200 | permissive | [
{
"docstring": "获取工作流执行脚本列表 :param request: :param args: :param kwargs: :return:",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "新增脚本 :param request: :param args: :param kwargs: :return:",
"name": "post",
"signature": "def post(self, request, *a... | 2 | stack_v2_sparse_classes_30k_train_049508 | Implement the Python class `WorkflowRunScriptView` described below.
Class description:
Implement the WorkflowRunScriptView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 获取工作流执行脚本列表 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 新增... | Implement the Python class `WorkflowRunScriptView` described below.
Class description:
Implement the WorkflowRunScriptView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 获取工作流执行脚本列表 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 新增... | 14d29d6669a6538fe176792d001710616719d050 | <|skeleton|>
class WorkflowRunScriptView:
def get(self, request, *args, **kwargs):
"""获取工作流执行脚本列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""新增脚本 :param request: :param args: :param kwargs: :return:"""
<|body_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WorkflowRunScriptView:
def get(self, request, *args, **kwargs):
"""获取工作流执行脚本列表 :param request: :param args: :param kwargs: :return:"""
request_data = request.GET
username = request.META.get('HTTP_USERNAME')
if not username:
username = request.user.username
s... | the_stack_v2_python_sparse | apps/workflow/views.py | Rgcsh/loonflow_custom | train | 2 | |
1b8f3861019759034db870907acf8af222ca9750 | [
"super().__init__()\nself.n_heads = n_heads\nself.head_dim = head_dim\nself.hid_dim = head_dim * n_heads\nscale = torch.tensor([self.hid_dim], dtype=torch.float32).rsqrt()\nself.register_buffer('scale', scale)\nif kv_emb_dim is None:\n kv_emb_dim = q_emb_dim\nself.fc_q = nn.Linear(q_emb_dim, self.hid_dim)\nself.... | <|body_start_0|>
super().__init__()
self.n_heads = n_heads
self.head_dim = head_dim
self.hid_dim = head_dim * n_heads
scale = torch.tensor([self.hid_dim], dtype=torch.float32).rsqrt()
self.register_buffer('scale', scale)
if kv_emb_dim is None:
kv_emb_d... | MultiHeadAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadAttention:
def __init__(self, q_emb_dim, head_dim, n_heads, kv_emb_dim=None, dropout=0.0):
""":param emb_dim: input embeddings dim :param head_dim: Single head hidden-dim :param n_heads: number of MHSA heads :param dropout: 0 by default (no dropout applied)"""
<|body_0|>... | stack_v2_sparse_classes_75kplus_train_067245 | 3,592 | no_license | [
{
"docstring": ":param emb_dim: input embeddings dim :param head_dim: Single head hidden-dim :param n_heads: number of MHSA heads :param dropout: 0 by default (no dropout applied)",
"name": "__init__",
"signature": "def __init__(self, q_emb_dim, head_dim, n_heads, kv_emb_dim=None, dropout=0.0)"
},
{... | 3 | null | Implement the Python class `MultiHeadAttention` described below.
Class description:
Implement the MultiHeadAttention class.
Method signatures and docstrings:
- def __init__(self, q_emb_dim, head_dim, n_heads, kv_emb_dim=None, dropout=0.0): :param emb_dim: input embeddings dim :param head_dim: Single head hidden-dim :... | Implement the Python class `MultiHeadAttention` described below.
Class description:
Implement the MultiHeadAttention class.
Method signatures and docstrings:
- def __init__(self, q_emb_dim, head_dim, n_heads, kv_emb_dim=None, dropout=0.0): :param emb_dim: input embeddings dim :param head_dim: Single head hidden-dim :... | 6a27856f3f5d71373e6d42657233f7af0447a795 | <|skeleton|>
class MultiHeadAttention:
def __init__(self, q_emb_dim, head_dim, n_heads, kv_emb_dim=None, dropout=0.0):
""":param emb_dim: input embeddings dim :param head_dim: Single head hidden-dim :param n_heads: number of MHSA heads :param dropout: 0 by default (no dropout applied)"""
<|body_0|>... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiHeadAttention:
def __init__(self, q_emb_dim, head_dim, n_heads, kv_emb_dim=None, dropout=0.0):
""":param emb_dim: input embeddings dim :param head_dim: Single head hidden-dim :param n_heads: number of MHSA heads :param dropout: 0 by default (no dropout applied)"""
super().__init__()
... | the_stack_v2_python_sparse | models/attention/attention.py | cscribano/AYCE_2021 | train | 8 | |
fa9e3f76e640b785da2708ce663b481298f995c1 | [
"self.response.headers['Content-Type'] = 'text/html'\nsubject = self.request.get('subject')\ncontent = self.request.get('content')\nvalues = {'subject': subject, 'content': content}\npath = os.path.join(os.path.dirname(__file__), '../templates/create_blog_entry.html')\nself.response.out.write(template.render(path, ... | <|body_start_0|>
self.response.headers['Content-Type'] = 'text/html'
subject = self.request.get('subject')
content = self.request.get('content')
values = {'subject': subject, 'content': content}
path = os.path.join(os.path.dirname(__file__), '../templates/create_blog_entry.html')... | CreateBlogEntry | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateBlogEntry:
def get(self):
"""Handles request for the create page"""
<|body_0|>
def post(self):
"""Handles blog entry creation"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.response.headers['Content-Type'] = 'text/html'
subject =... | stack_v2_sparse_classes_75kplus_train_067246 | 7,437 | permissive | [
{
"docstring": "Handles request for the create page",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Handles blog entry creation",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_047131 | Implement the Python class `CreateBlogEntry` described below.
Class description:
Implement the CreateBlogEntry class.
Method signatures and docstrings:
- def get(self): Handles request for the create page
- def post(self): Handles blog entry creation | Implement the Python class `CreateBlogEntry` described below.
Class description:
Implement the CreateBlogEntry class.
Method signatures and docstrings:
- def get(self): Handles request for the create page
- def post(self): Handles blog entry creation
<|skeleton|>
class CreateBlogEntry:
def get(self):
""... | 87cf5dd5d0e06ee745d3aba058d96fa46f2aeb6b | <|skeleton|>
class CreateBlogEntry:
def get(self):
"""Handles request for the create page"""
<|body_0|>
def post(self):
"""Handles blog entry creation"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CreateBlogEntry:
def get(self):
"""Handles request for the create page"""
self.response.headers['Content-Type'] = 'text/html'
subject = self.request.get('subject')
content = self.request.get('content')
values = {'subject': subject, 'content': content}
path = os.... | the_stack_v2_python_sparse | src/unit6/blog/blog_main.py | cdoremus/udacity-python_web_development-cs253 | train | 0 | |
c4478c74c8829ce931fcac75b79b93dbf132a965 | [
"annotations = task.annotations\nif 'request' in self.context and hasattr(self.context['request'], 'user'):\n user = self.context['request'].user\n if user.is_annotator:\n annotations = annotations.filter(completed_by=user)\nreturn AnnotationSerializer(annotations, many=True, read_only=True, default=Tr... | <|body_start_0|>
annotations = task.annotations
if 'request' in self.context and hasattr(self.context['request'], 'user'):
user = self.context['request'].user
if user.is_annotator:
annotations = annotations.filter(completed_by=user)
return AnnotationSerial... | TaskWithAnnotationsAndPredictionsAndDraftsSerializer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskWithAnnotationsAndPredictionsAndDraftsSerializer:
def get_annotations(self, task):
"""Return annotations only for the current user"""
<|body_0|>
def get_drafts(self, task):
"""Return drafts only for the current user"""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_75kplus_train_067247 | 18,964 | permissive | [
{
"docstring": "Return annotations only for the current user",
"name": "get_annotations",
"signature": "def get_annotations(self, task)"
},
{
"docstring": "Return drafts only for the current user",
"name": "get_drafts",
"signature": "def get_drafts(self, task)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010487 | Implement the Python class `TaskWithAnnotationsAndPredictionsAndDraftsSerializer` described below.
Class description:
Implement the TaskWithAnnotationsAndPredictionsAndDraftsSerializer class.
Method signatures and docstrings:
- def get_annotations(self, task): Return annotations only for the current user
- def get_dr... | Implement the Python class `TaskWithAnnotationsAndPredictionsAndDraftsSerializer` described below.
Class description:
Implement the TaskWithAnnotationsAndPredictionsAndDraftsSerializer class.
Method signatures and docstrings:
- def get_annotations(self, task): Return annotations only for the current user
- def get_dr... | 7c9e5777b7c0fe510b8585ae4c42b74a46929f73 | <|skeleton|>
class TaskWithAnnotationsAndPredictionsAndDraftsSerializer:
def get_annotations(self, task):
"""Return annotations only for the current user"""
<|body_0|>
def get_drafts(self, task):
"""Return drafts only for the current user"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TaskWithAnnotationsAndPredictionsAndDraftsSerializer:
def get_annotations(self, task):
"""Return annotations only for the current user"""
annotations = task.annotations
if 'request' in self.context and hasattr(self.context['request'], 'user'):
user = self.context['request']... | the_stack_v2_python_sparse | label_studio/tasks/serializers.py | mihirpurwar/label-studio | train | 1 | |
936dee37f4be4a748b26ed035e1d6f84f6ccb958 | [
"self.word = word\nw2i, i2w = early_model.index\nsim_early = np.dot(early_model.C, early_model.W[w2i[word]])\nsim_later = np.dot(later_model.C, later_model.W[w2i[word]])\nz_early = scipy.special.logsumexp(sim_early)\nz_later = scipy.special.logsumexp(sim_later)\nself.simdiff = sim_later - sim_early\nself.zdiff = z_... | <|body_start_0|>
self.word = word
w2i, i2w = early_model.index
sim_early = np.dot(early_model.C, early_model.W[w2i[word]])
sim_later = np.dot(later_model.C, later_model.W[w2i[word]])
z_early = scipy.special.logsumexp(sim_early)
z_later = scipy.special.logsumexp(sim_later)... | Scorer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Scorer:
def __init__(self, early_model, later_model, word):
"""constructor for scoring with respect to the given models and the word Args: early_model (:obj: model): embeddings from early documents. later_model (:obj: model): embeddings from later documents. word (:obj: str): the word"""... | stack_v2_sparse_classes_75kplus_train_067248 | 2,376 | permissive | [
{
"docstring": "constructor for scoring with respect to the given models and the word Args: early_model (:obj: model): embeddings from early documents. later_model (:obj: model): embeddings from later documents. word (:obj: str): the word",
"name": "__init__",
"signature": "def __init__(self, early_mode... | 2 | null | Implement the Python class `Scorer` described below.
Class description:
Implement the Scorer class.
Method signatures and docstrings:
- def __init__(self, early_model, later_model, word): constructor for scoring with respect to the given models and the word Args: early_model (:obj: model): embeddings from early docum... | Implement the Python class `Scorer` described below.
Class description:
Implement the Scorer class.
Method signatures and docstrings:
- def __init__(self, early_model, later_model, word): constructor for scoring with respect to the given models and the word Args: early_model (:obj: model): embeddings from early docum... | 824079b388d0eebc92b2197805b27ed320353f8f | <|skeleton|>
class Scorer:
def __init__(self, early_model, later_model, word):
"""constructor for scoring with respect to the given models and the word Args: early_model (:obj: model): embeddings from early documents. later_model (:obj: model): embeddings from later documents. word (:obj: str): the word"""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Scorer:
def __init__(self, early_model, later_model, word):
"""constructor for scoring with respect to the given models and the word Args: early_model (:obj: model): embeddings from early documents. later_model (:obj: model): embeddings from later documents. word (:obj: str): the word"""
self.... | the_stack_v2_python_sparse | modules/semshift/docscores.py | petershan1119/semantic-progressiveness | train | 0 | |
5105f143b28c00ad4d50a1182ccd43c91f0d3efe | [
"orm_question = self.get_nested_orm_question(data['id'])\nif orm_question is None:\n raise serializers.ValidationError('This question does not belong to this quiz.')\norm_options_by_id = {option.id: option for option in orm_question.options.all()}\nnew_options_by_id = {option['id']: option for option in data['op... | <|body_start_0|>
orm_question = self.get_nested_orm_question(data['id'])
if orm_question is None:
raise serializers.ValidationError('This question does not belong to this quiz.')
orm_options_by_id = {option.id: option for option in orm_question.options.all()}
new_options_by_i... | This serializer is supposed to be nested into EditableQuizSerializer in a ListSerializer field (i.e. with "many=True"). | EditableQuestionSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EditableQuestionSerializer:
"""This serializer is supposed to be nested into EditableQuizSerializer in a ListSerializer field (i.e. with "many=True")."""
def validate(self, data):
"""Object-level validation method. `data` is a dictionary of submitted field values. Because of validati... | stack_v2_sparse_classes_75kplus_train_067249 | 10,431 | no_license | [
{
"docstring": "Object-level validation method. `data` is a dictionary of submitted field values. Because of validation order, can assume that required fields are present.",
"name": "validate",
"signature": "def validate(self, data)"
},
{
"docstring": "Get Question ORM object assuming that the s... | 4 | null | Implement the Python class `EditableQuestionSerializer` described below.
Class description:
This serializer is supposed to be nested into EditableQuizSerializer in a ListSerializer field (i.e. with "many=True").
Method signatures and docstrings:
- def validate(self, data): Object-level validation method. `data` is a ... | Implement the Python class `EditableQuestionSerializer` described below.
Class description:
This serializer is supposed to be nested into EditableQuizSerializer in a ListSerializer field (i.e. with "many=True").
Method signatures and docstrings:
- def validate(self, data): Object-level validation method. `data` is a ... | e2fc1625f9082ebb722b4b7a459537f5d363944a | <|skeleton|>
class EditableQuestionSerializer:
"""This serializer is supposed to be nested into EditableQuizSerializer in a ListSerializer field (i.e. with "many=True")."""
def validate(self, data):
"""Object-level validation method. `data` is a dictionary of submitted field values. Because of validati... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EditableQuestionSerializer:
"""This serializer is supposed to be nested into EditableQuizSerializer in a ListSerializer field (i.e. with "many=True")."""
def validate(self, data):
"""Object-level validation method. `data` is a dictionary of submitted field values. Because of validation order, can... | the_stack_v2_python_sparse | drf/quizzz/quizzes/serializers.py | pyvlad/quizzz-spa | train | 0 |
1285ce461d8f440a98ade3c28397c317c5a16b3d | [
"try:\n data = {'first_name': user_data['localizedFirstName'], 'last_name': user_data['localizedLastName'], 'email': user_data['elements'][0]['handle~']['emailAddress'], 'photo_url': None}\n return data\nexcept KeyError:\n raise PermissionException",
"data = {'grant_type': 'authorization_code', 'code': a... | <|body_start_0|>
try:
data = {'first_name': user_data['localizedFirstName'], 'last_name': user_data['localizedLastName'], 'email': user_data['elements'][0]['handle~']['emailAddress'], 'photo_url': None}
return data
except KeyError:
raise PermissionException
<|end_body... | Linkedin backend. | LinkedinBackend | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkedinBackend:
"""Linkedin backend."""
def _normalize_user_data(user_data: dict) -> dict:
"""Normalize user data. :param user_data: User data from Linkedin :return: normalized user data."""
<|body_0|>
def _auth(self, auth_code: str) -> None:
"""Get user access ... | stack_v2_sparse_classes_75kplus_train_067250 | 3,876 | no_license | [
{
"docstring": "Normalize user data. :param user_data: User data from Linkedin :return: normalized user data.",
"name": "_normalize_user_data",
"signature": "def _normalize_user_data(user_data: dict) -> dict"
},
{
"docstring": "Get user access token by authorization code. :param auth_code: Autho... | 5 | stack_v2_sparse_classes_30k_train_026398 | Implement the Python class `LinkedinBackend` described below.
Class description:
Linkedin backend.
Method signatures and docstrings:
- def _normalize_user_data(user_data: dict) -> dict: Normalize user data. :param user_data: User data from Linkedin :return: normalized user data.
- def _auth(self, auth_code: str) -> N... | Implement the Python class `LinkedinBackend` described below.
Class description:
Linkedin backend.
Method signatures and docstrings:
- def _normalize_user_data(user_data: dict) -> dict: Normalize user data. :param user_data: User data from Linkedin :return: normalized user data.
- def _auth(self, auth_code: str) -> N... | 713b9d84ac70d964d46f189ab1f9c7b944b9684b | <|skeleton|>
class LinkedinBackend:
"""Linkedin backend."""
def _normalize_user_data(user_data: dict) -> dict:
"""Normalize user data. :param user_data: User data from Linkedin :return: normalized user data."""
<|body_0|>
def _auth(self, auth_code: str) -> None:
"""Get user access ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LinkedinBackend:
"""Linkedin backend."""
def _normalize_user_data(user_data: dict) -> dict:
"""Normalize user data. :param user_data: User data from Linkedin :return: normalized user data."""
try:
data = {'first_name': user_data['localizedFirstName'], 'last_name': user_data['l... | the_stack_v2_python_sparse | jobadvisor/authentication/backends/linkedin.py | ewgen19892/jobadvisor | train | 0 |
87e4bcf8dc95312fe7c8dee6a9f6ba459c6a1ca8 | [
"super().__init__(hyper_parameters)\nself.atrous_rates = hyper_parameters['graph'].get('atrous_rates', [2, 1, 2])\nself.crf_lr_multiplier = hyper_parameters.get('train', {}).get('crf_lr_multiplier', 1 if self.embed_type in ['WARD', 'RANDOM'] else 3200)",
"conv_pools = []\nfor i in range(len(self.filters_size)):\n... | <|body_start_0|>
super().__init__(hyper_parameters)
self.atrous_rates = hyper_parameters['graph'].get('atrous_rates', [2, 1, 2])
self.crf_lr_multiplier = hyper_parameters.get('train', {}).get('crf_lr_multiplier', 1 if self.embed_type in ['WARD', 'RANDOM'] else 3200)
<|end_body_0|>
<|body_start_... | DGCNNGraph | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DGCNNGraph:
def __init__(self, hyper_parameters):
"""Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains "sharing", "embed", "graph", "train", "save" and "data". Returns: None"""
<|body_0|>
def build_model(self, inputs, o... | stack_v2_sparse_classes_75kplus_train_067251 | 3,746 | permissive | [
{
"docstring": "Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains \"sharing\", \"embed\", \"graph\", \"train\", \"save\" and \"data\". Returns: None",
"name": "__init__",
"signature": "def __init__(self, hyper_parameters)"
},
{
"docstring":... | 2 | stack_v2_sparse_classes_30k_test_000949 | Implement the Python class `DGCNNGraph` described below.
Class description:
Implement the DGCNNGraph class.
Method signatures and docstrings:
- def __init__(self, hyper_parameters): Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains "sharing", "embed", "graph", "... | Implement the Python class `DGCNNGraph` described below.
Class description:
Implement the DGCNNGraph class.
Method signatures and docstrings:
- def __init__(self, hyper_parameters): Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains "sharing", "embed", "graph", "... | 5237381459db5909f392737e33618a16c1e0452a | <|skeleton|>
class DGCNNGraph:
def __init__(self, hyper_parameters):
"""Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains "sharing", "embed", "graph", "train", "save" and "data". Returns: None"""
<|body_0|>
def build_model(self, inputs, o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DGCNNGraph:
def __init__(self, hyper_parameters):
"""Init of hyper_parameters and build_embed. Args: hyper_parameters: hyper_parameters of all, which contains "sharing", "embed", "graph", "train", "save" and "data". Returns: None"""
super().__init__(hyper_parameters)
self.atrous_rates ... | the_stack_v2_python_sparse | macadam/sl/s04_dgcnn.py | payiz-asj/Macadam | train | 1 | |
4834fd049d46da9bfd1841d0444678ed30fe8512 | [
"self.output_dir = kwargs.get('output_dir', '.')\nif not os.path.isdir(self.output_dir):\n os.mkidr(self.output_dir)\nself.view_solution_pvd = True\nself.all_variables = ['velocity', 'p', 'T', 'density', 'viscosity', 'sp_upper', 'sp_lower']\nHAS_PLATE_EDGE = True\nif HAS_PLATE_EDGE:\n self.all_variables.appen... | <|body_start_0|>
self.output_dir = kwargs.get('output_dir', '.')
if not os.path.isdir(self.output_dir):
os.mkidr(self.output_dir)
self.view_solution_pvd = True
self.all_variables = ['velocity', 'p', 'T', 'density', 'viscosity', 'sp_upper', 'sp_lower']
HAS_PLATE_EDGE =... | PARAVIEW_PLOT | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PARAVIEW_PLOT:
def __init__(self, filein, **kwargs):
"""initiate Args: filein(str): path of vtu file plots(list): lists of plot"""
<|body_0|>
def goto_time(self, _time):
"""go to a different snapshot"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
s... | stack_v2_sparse_classes_75kplus_train_067252 | 9,251 | no_license | [
{
"docstring": "initiate Args: filein(str): path of vtu file plots(list): lists of plot",
"name": "__init__",
"signature": "def __init__(self, filein, **kwargs)"
},
{
"docstring": "go to a different snapshot",
"name": "goto_time",
"signature": "def goto_time(self, _time)"
}
] | 2 | null | Implement the Python class `PARAVIEW_PLOT` described below.
Class description:
Implement the PARAVIEW_PLOT class.
Method signatures and docstrings:
- def __init__(self, filein, **kwargs): initiate Args: filein(str): path of vtu file plots(list): lists of plot
- def goto_time(self, _time): go to a different snapshot | Implement the Python class `PARAVIEW_PLOT` described below.
Class description:
Implement the PARAVIEW_PLOT class.
Method signatures and docstrings:
- def __init__(self, filein, **kwargs): initiate Args: filein(str): path of vtu file plots(list): lists of plot
- def goto_time(self, _time): go to a different snapshot
... | d919cadce2b57811351c0615d94da5c6ebfff800 | <|skeleton|>
class PARAVIEW_PLOT:
def __init__(self, filein, **kwargs):
"""initiate Args: filein(str): path of vtu file plots(list): lists of plot"""
<|body_0|>
def goto_time(self, _time):
"""go to a different snapshot"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PARAVIEW_PLOT:
def __init__(self, filein, **kwargs):
"""initiate Args: filein(str): path of vtu file plots(list): lists of plot"""
self.output_dir = kwargs.get('output_dir', '.')
if not os.path.isdir(self.output_dir):
os.mkidr(self.output_dir)
self.view_solution_pvd... | the_stack_v2_python_sparse | paraview_scripts/base.py | lhy11009/aspectLib | train | 0 | |
9791e5658365f86124c8501423c9844651c25687 | [
"noval_lis = response.xpath('//ul[@class=\"all-img-list cf\"]/li')\nfor noval_li in noval_lis:\n novalItem = QidianNovalItem()\n novalItem['coverImage'] = 'https:' + noval_li.xpath('.//div[@class=\"book-img-box\"]//img/@src').extract_first('')\n novalItem['coverImage'] = 'https:' + noval_li.css('div.book-i... | <|body_start_0|>
noval_lis = response.xpath('//ul[@class="all-img-list cf"]/li')
for noval_li in noval_lis:
novalItem = QidianNovalItem()
novalItem['coverImage'] = 'https:' + noval_li.xpath('.//div[@class="book-img-box"]//img/@src').extract_first('')
novalItem['coverI... | QdSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QdSpider:
def parse(self, response):
"""获取当前页书籍的列表，遍历 :param response: :return:"""
<|body_0|>
def parse_noval_detail(self, response):
"""获取书籍详情信息，提取章节信息 :param response: :return:"""
<|body_1|>
def parse_dynamic_chpater(self, response):
"""解析动态加载的... | stack_v2_sparse_classes_75kplus_train_067253 | 6,824 | no_license | [
{
"docstring": "获取当前页书籍的列表，遍历 :param response: :return:",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "获取书籍详情信息，提取章节信息 :param response: :return:",
"name": "parse_noval_detail",
"signature": "def parse_noval_detail(self, response)"
},
{
"docstring":... | 4 | stack_v2_sparse_classes_30k_train_039728 | Implement the Python class `QdSpider` described below.
Class description:
Implement the QdSpider class.
Method signatures and docstrings:
- def parse(self, response): 获取当前页书籍的列表，遍历 :param response: :return:
- def parse_noval_detail(self, response): 获取书籍详情信息，提取章节信息 :param response: :return:
- def parse_dynamic_chpater... | Implement the Python class `QdSpider` described below.
Class description:
Implement the QdSpider class.
Method signatures and docstrings:
- def parse(self, response): 获取当前页书籍的列表，遍历 :param response: :return:
- def parse_noval_detail(self, response): 获取书籍详情信息，提取章节信息 :param response: :return:
- def parse_dynamic_chpater... | 15e1322dcc36de4f1d1e467525761746cadb58fa | <|skeleton|>
class QdSpider:
def parse(self, response):
"""获取当前页书籍的列表，遍历 :param response: :return:"""
<|body_0|>
def parse_noval_detail(self, response):
"""获取书籍详情信息，提取章节信息 :param response: :return:"""
<|body_1|>
def parse_dynamic_chpater(self, response):
"""解析动态加载的... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class QdSpider:
def parse(self, response):
"""获取当前页书籍的列表，遍历 :param response: :return:"""
noval_lis = response.xpath('//ul[@class="all-img-list cf"]/li')
for noval_li in noval_lis:
novalItem = QidianNovalItem()
novalItem['coverImage'] = 'https:' + noval_li.xpath('.//di... | the_stack_v2_python_sparse | Code/爬虫/scrapy框架/qidian3/qidian/qidian/spiders/qd.py | wangxuyongkang/chengxuyuanhh | train | 1 | |
eea24e4cdef7ddc1605764259cfdbf57b61415e2 | [
"self.text = text\nself.rect = pygame.Rect(pos, size)\nself.action = action\nself.params = params if params is not None else []\nself.enabled = enabled\nself._font = pygame.font.SysFont('comicsansms', 18)",
"color = (255, 255, 255)\ntext_color = (0, 0, 0) if self.enabled else (200, 200, 200)\nif self.rect.collide... | <|body_start_0|>
self.text = text
self.rect = pygame.Rect(pos, size)
self.action = action
self.params = params if params is not None else []
self.enabled = enabled
self._font = pygame.font.SysFont('comicsansms', 18)
<|end_body_0|>
<|body_start_1|>
color = (255, 2... | Class to assist in creation of buttons. | Button | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Button:
"""Class to assist in creation of buttons."""
def __init__(self, text, pos, size, action, params=None, enabled=True):
"""Initialize a single Button. :param text: Text to write on button. :parm pos: Position to place topleft corner of button (x, y). :parm size: Size of button ... | stack_v2_sparse_classes_75kplus_train_067254 | 6,310 | no_license | [
{
"docstring": "Initialize a single Button. :param text: Text to write on button. :parm pos: Position to place topleft corner of button (x, y). :parm size: Size of button (width, height). :parm action: Function to call when clicked. :parm params: Parameters to call action with when called. :parm enabled: Parame... | 3 | stack_v2_sparse_classes_30k_train_032632 | Implement the Python class `Button` described below.
Class description:
Class to assist in creation of buttons.
Method signatures and docstrings:
- def __init__(self, text, pos, size, action, params=None, enabled=True): Initialize a single Button. :param text: Text to write on button. :parm pos: Position to place top... | Implement the Python class `Button` described below.
Class description:
Class to assist in creation of buttons.
Method signatures and docstrings:
- def __init__(self, text, pos, size, action, params=None, enabled=True): Initialize a single Button. :param text: Text to write on button. :parm pos: Position to place top... | 4faeace5327e4e702ba57e8fdc59e313c29d1a42 | <|skeleton|>
class Button:
"""Class to assist in creation of buttons."""
def __init__(self, text, pos, size, action, params=None, enabled=True):
"""Initialize a single Button. :param text: Text to write on button. :parm pos: Position to place topleft corner of button (x, y). :parm size: Size of button ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Button:
"""Class to assist in creation of buttons."""
def __init__(self, text, pos, size, action, params=None, enabled=True):
"""Initialize a single Button. :param text: Text to write on button. :parm pos: Position to place topleft corner of button (x, y). :parm size: Size of button (width, heigh... | the_stack_v2_python_sparse | src/gui/utils.py | antatranta/Waids-and-Wyverns | train | 0 |
7e7e44d69fba30084174a0f87524e7f731b27a51 | [
"self.policy_manager = PolicyManager(self.project, self.kb)\nif policies is not None:\n for policy in policies:\n self.policy_manager.register_policy(policy, policy.name)",
"if functions is None:\n functions = self.policy_manager.fast_cfg.functions.values()\nfor function in functions:\n self.polic... | <|body_start_0|>
self.policy_manager = PolicyManager(self.project, self.kb)
if policies is not None:
for policy in policies:
self.policy_manager.register_policy(policy, policy.name)
<|end_body_0|>
<|body_start_1|>
if functions is None:
functions = self.po... | An angr analysis that performs policy checks with a given set of policies against the binary program. | StaticPolice | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StaticPolice:
"""An angr analysis that performs policy checks with a given set of policies against the binary program."""
def __init__(self, policies=None):
"""Constructor. :param iterable policies: A collection of policies to be registered with the policy manager."""
<|body_... | stack_v2_sparse_classes_75kplus_train_067255 | 1,151 | permissive | [
{
"docstring": "Constructor. :param iterable policies: A collection of policies to be registered with the policy manager.",
"name": "__init__",
"signature": "def __init__(self, policies=None)"
},
{
"docstring": "Enforce the policy. :param iterable functions: A collection of functions to enforce ... | 2 | stack_v2_sparse_classes_30k_train_023125 | Implement the Python class `StaticPolice` described below.
Class description:
An angr analysis that performs policy checks with a given set of policies against the binary program.
Method signatures and docstrings:
- def __init__(self, policies=None): Constructor. :param iterable policies: A collection of policies to ... | Implement the Python class `StaticPolice` described below.
Class description:
An angr analysis that performs policy checks with a given set of policies against the binary program.
Method signatures and docstrings:
- def __init__(self, policies=None): Constructor. :param iterable policies: A collection of policies to ... | 964dc80c758e25c698c2cbcc454ef5954c5fa0a0 | <|skeleton|>
class StaticPolice:
"""An angr analysis that performs policy checks with a given set of policies against the binary program."""
def __init__(self, policies=None):
"""Constructor. :param iterable policies: A collection of policies to be registered with the policy manager."""
<|body_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StaticPolice:
"""An angr analysis that performs policy checks with a given set of policies against the binary program."""
def __init__(self, policies=None):
"""Constructor. :param iterable policies: A collection of policies to be registered with the policy manager."""
self.policy_manager ... | the_stack_v2_python_sparse | exercises/slide_104/static-police/staticpolice/staticpolice.py | Ruide/angr-dev | train | 0 |
8cde3ad6fe45973fbc75ad87986a27d0b8f8a12e | [
"self._app = _app\nself.viewer = viewer\nself.player = None\nself.tournament = tournament",
"from app.controllers.edit_player import EditPlayerController\nself._app.change_controller(EditPlayerController(self.player, self.tournament))\nself.viewer.warning = ''\nreturn False",
"from app.models.player import Play... | <|body_start_0|>
self._app = _app
self.viewer = viewer
self.player = None
self.tournament = tournament
<|end_body_0|>
<|body_start_1|>
from app.controllers.edit_player import EditPlayerController
self._app.change_controller(EditPlayerController(self.player, self.tourname... | Project go_to_edit_player command class. | GotoEditPlayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GotoEditPlayer:
"""Project go_to_edit_player command class."""
def __init__(self, _app, viewer, tournament):
"""Init go_to_edit_player command class."""
<|body_0|>
def exe_command(self):
"""Execute command and return False to not exit application."""
<|bo... | stack_v2_sparse_classes_75kplus_train_067256 | 9,466 | no_license | [
{
"docstring": "Init go_to_edit_player command class.",
"name": "__init__",
"signature": "def __init__(self, _app, viewer, tournament)"
},
{
"docstring": "Execute command and return False to not exit application.",
"name": "exe_command",
"signature": "def exe_command(self)"
},
{
... | 3 | null | Implement the Python class `GotoEditPlayer` described below.
Class description:
Project go_to_edit_player command class.
Method signatures and docstrings:
- def __init__(self, _app, viewer, tournament): Init go_to_edit_player command class.
- def exe_command(self): Execute command and return False to not exit applica... | Implement the Python class `GotoEditPlayer` described below.
Class description:
Project go_to_edit_player command class.
Method signatures and docstrings:
- def __init__(self, _app, viewer, tournament): Init go_to_edit_player command class.
- def exe_command(self): Execute command and return False to not exit applica... | be6089cd71c762f23725b61e8d2745cfabe4f0c0 | <|skeleton|>
class GotoEditPlayer:
"""Project go_to_edit_player command class."""
def __init__(self, _app, viewer, tournament):
"""Init go_to_edit_player command class."""
<|body_0|>
def exe_command(self):
"""Execute command and return False to not exit application."""
<|bo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GotoEditPlayer:
"""Project go_to_edit_player command class."""
def __init__(self, _app, viewer, tournament):
"""Init go_to_edit_player command class."""
self._app = _app
self.viewer = viewer
self.player = None
self.tournament = tournament
def exe_command(self)... | the_stack_v2_python_sparse | app/commands/navigation.py | FortranVBA/DAP4 | train | 0 |
ba61ba7954b2a7866bb9c7f494df973efe361271 | [
"super().__init__(*args, **kwargs)\nif 'direct_course' not in self.fields:\n return\nif self.instance.pk:\n course_query = self.instance.get_course().get_snapshots(include_self=True).filter(extended_object__publisher_is_draft=True).distinct()\nelse:\n course_query = models.Course.objects.filter(extended_ob... | <|body_start_0|>
super().__init__(*args, **kwargs)
if 'direct_course' not in self.fields:
return
if self.instance.pk:
course_query = self.instance.get_course().get_snapshots(include_self=True).filter(extended_object__publisher_is_draft=True).distinct()
else:
... | Admin form used for frontend editing. | CourseRunAdminForm | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CourseRunAdminForm:
"""Admin form used for frontend editing."""
def __init__(self, *args, **kwargs):
"""If the form is instanciated to update an existing course run: > show the direct course select box only if the course has one or more snapshots and limit choices to either the maste... | stack_v2_sparse_classes_75kplus_train_067257 | 11,939 | permissive | [
{
"docstring": "If the form is instanciated to update an existing course run: > show the direct course select box only if the course has one or more snapshots and limit choices to either the master course or one of its snapshots If the form is instanciated to create a new course run and the \"Add\" form is open... | 2 | stack_v2_sparse_classes_30k_train_032102 | Implement the Python class `CourseRunAdminForm` described below.
Class description:
Admin form used for frontend editing.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): If the form is instanciated to update an existing course run: > show the direct course select box only if the course has on... | Implement the Python class `CourseRunAdminForm` described below.
Class description:
Admin form used for frontend editing.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): If the form is instanciated to update an existing course run: > show the direct course select box only if the course has on... | f2d46fc46b271eb3b4d565039a29c15ba15f027c | <|skeleton|>
class CourseRunAdminForm:
"""Admin form used for frontend editing."""
def __init__(self, *args, **kwargs):
"""If the form is instanciated to update an existing course run: > show the direct course select box only if the course has one or more snapshots and limit choices to either the maste... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CourseRunAdminForm:
"""Admin form used for frontend editing."""
def __init__(self, *args, **kwargs):
"""If the form is instanciated to update an existing course run: > show the direct course select box only if the course has one or more snapshots and limit choices to either the master course or o... | the_stack_v2_python_sparse | src/richie/apps/courses/admin.py | openfun/richie | train | 238 |
6584c32016b1dbe77f5bd43c1fdda232d6a80c72 | [
"current_app.logger.info('Method called')\ntry:\n response = g.requests.get('{}/{}/state/{}'.format(SESSION_API_URL, token_id, subsection, headers={'X-Trace-ID': g.trace_id}))\nexcept Exception as ex:\n current_app.logger.warning('Failed to get session state. TraceID : {} - Exception - {}'.format(g.trace_id, ... | <|body_start_0|>
current_app.logger.info('Method called')
try:
response = g.requests.get('{}/{}/state/{}'.format(SESSION_API_URL, token_id, subsection, headers={'X-Trace-ID': g.trace_id}))
except Exception as ex:
current_app.logger.warning('Failed to get session state. Tr... | SessionAPIService | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SessionAPIService:
def get_session_state(token_id, subsection):
"""Returns either a JSON object containing the session contents, or returns None if the session isn't found. :param token_id: Identifier for the session. :param subsection: The subsection of session state that should be retr... | stack_v2_sparse_classes_75kplus_train_067258 | 5,336 | permissive | [
{
"docstring": "Returns either a JSON object containing the session contents, or returns None if the session isn't found. :param token_id: Identifier for the session. :param subsection: The subsection of session state that should be retrieved. :return: Returns dictionary representation of the response or None."... | 5 | stack_v2_sparse_classes_30k_train_025891 | Implement the Python class `SessionAPIService` described below.
Class description:
Implement the SessionAPIService class.
Method signatures and docstrings:
- def get_session_state(token_id, subsection): Returns either a JSON object containing the session contents, or returns None if the session isn't found. :param to... | Implement the Python class `SessionAPIService` described below.
Class description:
Implement the SessionAPIService class.
Method signatures and docstrings:
- def get_session_state(token_id, subsection): Returns either a JSON object containing the session contents, or returns None if the session isn't found. :param to... | d92446a9972ebbcd9a43a7a7444a528aa2f30bf7 | <|skeleton|>
class SessionAPIService:
def get_session_state(token_id, subsection):
"""Returns either a JSON object containing the session contents, or returns None if the session isn't found. :param token_id: Identifier for the session. :param subsection: The subsection of session state that should be retr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SessionAPIService:
def get_session_state(token_id, subsection):
"""Returns either a JSON object containing the session contents, or returns None if the session isn't found. :param token_id: Identifier for the session. :param subsection: The subsection of session state that should be retrieved. :return... | the_stack_v2_python_sparse | maintain_frontend/dependencies/session_api/session_service.py | uk-gov-mirror/LandRegistry.maintain-frontend | train | 0 | |
1090157872bb59bd816ef4117e8c76b82eb82e70 | [
"tag_name = obj.tag_name\nlogger.info('delete tag {tag}'.format(tag=ensure_utf8(tag_name)))\ngs = Gallery.objects.filter(tags__contains=tag_name + ',') | Gallery.objects.filter(tags__contains=',' + tag_name)\nfor gallery in gs:\n logger.info('delete tag for gallery : {g_id}'.format(g_id=gallery.gallery_id))\n ... | <|body_start_0|>
tag_name = obj.tag_name
logger.info('delete tag {tag}'.format(tag=ensure_utf8(tag_name)))
gs = Gallery.objects.filter(tags__contains=tag_name + ',') | Gallery.objects.filter(tags__contains=',' + tag_name)
for gallery in gs:
logger.info('delete tag for gallery... | TagAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TagAdmin:
def delete_model(self, request, obj):
"""TODO 清理相关tag的缓存 删除tag，重量级操作 1、找到所有包含这个tag的图集，修改tag字段 2、删除redis中的tag索引 3、删除该tag，刷新缓存 :param request: :param obj: :return:"""
<|body_0|>
def save_model(self, request, obj, form, change):
"""新增或更新tag，重量级操作 1、数据库中查找tag_n... | stack_v2_sparse_classes_75kplus_train_067259 | 3,458 | no_license | [
{
"docstring": "TODO 清理相关tag的缓存 删除tag，重量级操作 1、找到所有包含这个tag的图集，修改tag字段 2、删除redis中的tag索引 3、删除该tag，刷新缓存 :param request: :param obj: :return:",
"name": "delete_model",
"signature": "def delete_model(self, request, obj)"
},
{
"docstring": "新增或更新tag，重量级操作 1、数据库中查找tag_name不存在，存在则直接返回 2、数据库中插入tag 3、按照gal... | 2 | null | Implement the Python class `TagAdmin` described below.
Class description:
Implement the TagAdmin class.
Method signatures and docstrings:
- def delete_model(self, request, obj): TODO 清理相关tag的缓存 删除tag，重量级操作 1、找到所有包含这个tag的图集，修改tag字段 2、删除redis中的tag索引 3、删除该tag，刷新缓存 :param request: :param obj: :return:
- def save_model(se... | Implement the Python class `TagAdmin` described below.
Class description:
Implement the TagAdmin class.
Method signatures and docstrings:
- def delete_model(self, request, obj): TODO 清理相关tag的缓存 删除tag，重量级操作 1、找到所有包含这个tag的图集，修改tag字段 2、删除redis中的tag索引 3、删除该tag，刷新缓存 :param request: :param obj: :return:
- def save_model(se... | b65b053adb7f574eef76612913de962e75b93b26 | <|skeleton|>
class TagAdmin:
def delete_model(self, request, obj):
"""TODO 清理相关tag的缓存 删除tag，重量级操作 1、找到所有包含这个tag的图集，修改tag字段 2、删除redis中的tag索引 3、删除该tag，刷新缓存 :param request: :param obj: :return:"""
<|body_0|>
def save_model(self, request, obj, form, change):
"""新增或更新tag，重量级操作 1、数据库中查找tag_n... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TagAdmin:
def delete_model(self, request, obj):
"""TODO 清理相关tag的缓存 删除tag，重量级操作 1、找到所有包含这个tag的图集，修改tag字段 2、删除redis中的tag索引 3、删除该tag，刷新缓存 :param request: :param obj: :return:"""
tag_name = obj.tag_name
logger.info('delete tag {tag}'.format(tag=ensure_utf8(tag_name)))
gs = Gallery.... | the_stack_v2_python_sparse | mysite/beauty/admin.py | gaoconghui/image_site | train | 8 | |
ed407e708913b85db248b3493889e67babb38946 | [
"self.lsa_components = lsa_components\nself.tfidf_parameters = tfidf_parameters\nself._tfv = TfidfVectorizer(**self.tfidf_parameters)\nself._svd = TruncatedSVD(self.lsa_components)",
"X.fillna('NA', inplace=True)\ntfidf_output_csr = self._tfv.fit_transform(X, y)\nself._svd.fit(tfidf_output_csr)\nprint('LSA explai... | <|body_start_0|>
self.lsa_components = lsa_components
self.tfidf_parameters = tfidf_parameters
self._tfv = TfidfVectorizer(**self.tfidf_parameters)
self._svd = TruncatedSVD(self.lsa_components)
<|end_body_0|>
<|body_start_1|>
X.fillna('NA', inplace=True)
tfidf_output_csr... | This class defines a Scikit-Learn transformer that implements a Latent Semantic Analysis. | LSAVectorizer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSAVectorizer:
"""This class defines a Scikit-Learn transformer that implements a Latent Semantic Analysis."""
def __init__(self, lsa_components, tfidf_parameters={'analyzer': 'word', 'ngram_range': (1, 1), 'min_df': 10}):
"""This is the class' constructor. Parameters ---------- lsa_... | stack_v2_sparse_classes_75kplus_train_067260 | 14,227 | no_license | [
{
"docstring": "This is the class' constructor. Parameters ---------- lsa_components : positive integer Number of components we want to keep. tfidf_parameters : dict (default = {\"analyzer\": \"word\", \"ngram_range\": (1, 1), \"min_df\": 10}) Dict containing parameters of TfidfVectorizer used in this class. Ea... | 3 | stack_v2_sparse_classes_30k_train_040824 | Implement the Python class `LSAVectorizer` described below.
Class description:
This class defines a Scikit-Learn transformer that implements a Latent Semantic Analysis.
Method signatures and docstrings:
- def __init__(self, lsa_components, tfidf_parameters={'analyzer': 'word', 'ngram_range': (1, 1), 'min_df': 10}): T... | Implement the Python class `LSAVectorizer` described below.
Class description:
This class defines a Scikit-Learn transformer that implements a Latent Semantic Analysis.
Method signatures and docstrings:
- def __init__(self, lsa_components, tfidf_parameters={'analyzer': 'word', 'ngram_range': (1, 1), 'min_df': 10}): T... | ba9a7a15a3ae8b65cb09044489ee1d907a702909 | <|skeleton|>
class LSAVectorizer:
"""This class defines a Scikit-Learn transformer that implements a Latent Semantic Analysis."""
def __init__(self, lsa_components, tfidf_parameters={'analyzer': 'word', 'ngram_range': (1, 1), 'min_df': 10}):
"""This is the class' constructor. Parameters ---------- lsa_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LSAVectorizer:
"""This class defines a Scikit-Learn transformer that implements a Latent Semantic Analysis."""
def __init__(self, lsa_components, tfidf_parameters={'analyzer': 'word', 'ngram_range': (1, 1), 'min_df': 10}):
"""This is the class' constructor. Parameters ---------- lsa_components : ... | the_stack_v2_python_sparse | python_code/M5_Forecasting_Accuracy/m5_forecasting_accuracy/preprocessing/text_encoders.py | ThomasSELECK/src | train | 0 |
b6038537aaa2227adcf6fca88dc1df24e58d8157 | [
"history_values = history_table.get_attribute(units)\nhistory_values_without_zeros_idx = where(history_values > 0)[0]\nnbuildings = history_values_without_zeros_idx.size\nids = arange(nbuildings)\ngrid_ids = history_table.get_attribute_by_index('grid_id', history_values_without_zeros_idx)\nstorage = StorageFactory(... | <|body_start_0|>
history_values = history_table.get_attribute(units)
history_values_without_zeros_idx = where(history_values > 0)[0]
nbuildings = history_values_without_zeros_idx.size
ids = arange(nbuildings)
grid_ids = history_table.get_attribute_by_index('grid_id', history_valu... | BuildingCreator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildingCreator:
def create_buildings_from_history(self, history_table, type, type_code, units, building_categories):
"""Returns a BuildingDataset created from the information this development_event_history table. 'type' is the type of the buildings ('residential' or 'commercial') 'type_... | stack_v2_sparse_classes_75kplus_train_067261 | 5,570 | no_license | [
{
"docstring": "Returns a BuildingDataset created from the information this development_event_history table. 'type' is the type of the buildings ('residential' or 'commercial') 'type_code' is an interger code for the corresponding building type. 'units' is a string like 'residential_units' or 'commercial_sqft'.... | 3 | null | Implement the Python class `BuildingCreator` described below.
Class description:
Implement the BuildingCreator class.
Method signatures and docstrings:
- def create_buildings_from_history(self, history_table, type, type_code, units, building_categories): Returns a BuildingDataset created from the information this dev... | Implement the Python class `BuildingCreator` described below.
Class description:
Implement the BuildingCreator class.
Method signatures and docstrings:
- def create_buildings_from_history(self, history_table, type, type_code, units, building_categories): Returns a BuildingDataset created from the information this dev... | c392d15b35aa1d47bbc185ed76314f8e6dd9f92f | <|skeleton|>
class BuildingCreator:
def create_buildings_from_history(self, history_table, type, type_code, units, building_categories):
"""Returns a BuildingDataset created from the information this development_event_history table. 'type' is the type of the buildings ('residential' or 'commercial') 'type_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BuildingCreator:
def create_buildings_from_history(self, history_table, type, type_code, units, building_categories):
"""Returns a BuildingDataset created from the information this development_event_history table. 'type' is the type of the buildings ('residential' or 'commercial') 'type_code' is an in... | the_stack_v2_python_sparse | urbansim/datasets/building_dataset.py | psrc/urbansim | train | 4 | |
b1bd9176ed8c7004b2fe625b5ced7b081418164c | [
"self.sigmoid_layers = []\nself.rbm_layers = []\nself.params = []\nself.n_layers = len(hidden_layers_sizes)\nassert self.n_layers > 0\nif not theano_rng:\n theano_rng = MRG_RandomStreams(numpy_rng.randint(2 ** 30))\nself.x = T.matrix('x')\nself.y = T.ivector('y')\nfor i in range(self.n_layers):\n if i == 0:\n... | <|body_start_0|>
self.sigmoid_layers = []
self.rbm_layers = []
self.params = []
self.n_layers = len(hidden_layers_sizes)
assert self.n_layers > 0
if not theano_rng:
theano_rng = MRG_RandomStreams(numpy_rng.randint(2 ** 30))
self.x = T.matrix('x')
... | Deep Belief Network | DBN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DBN:
"""Deep Belief Network"""
def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10):
"""This class is made to support a variable number of layers. numpy_rng is the random state number. numpy_rng is the generator to draw initial weights.... | stack_v2_sparse_classes_75kplus_train_067262 | 9,191 | no_license | [
{
"docstring": "This class is made to support a variable number of layers. numpy_rng is the random state number. numpy_rng is the generator to draw initial weights. theano_rng is for tensor's theano rng. n_ins is an integer of the input dimension. hidden_layer_sizes is the intermediate layers size. n_outs is ou... | 3 | stack_v2_sparse_classes_30k_val_001104 | Implement the Python class `DBN` described below.
Class description:
Deep Belief Network
Method signatures and docstrings:
- def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10): This class is made to support a variable number of layers. numpy_rng is the random state nu... | Implement the Python class `DBN` described below.
Class description:
Deep Belief Network
Method signatures and docstrings:
- def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10): This class is made to support a variable number of layers. numpy_rng is the random state nu... | 6849cc891bbb9ac69cb20dfb13fe6bb5bd77d8c5 | <|skeleton|>
class DBN:
"""Deep Belief Network"""
def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10):
"""This class is made to support a variable number of layers. numpy_rng is the random state number. numpy_rng is the generator to draw initial weights.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DBN:
"""Deep Belief Network"""
def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10):
"""This class is made to support a variable number of layers. numpy_rng is the random state number. numpy_rng is the generator to draw initial weights. theano_rng i... | the_stack_v2_python_sparse | algebra/linear/deepbeliefnetwork.py | HussainAther/mathematics | train | 2 |
f04e4966acd9085b3a334aa40a4e031c10e82e54 | [
"self.__args__ = args\nself.__kargs__ = kargs\nself.__topic__ = topic\nself.__conn__ = connection\nself.__balanced__ = balanced",
"cons = None\ntopic = self.__conn__.create_topic(self.__topic__)\nif self.__balanced__ is True:\n cons = topic.get_balanced_consumer(*self.__args__, **self.__kargs__)\nelse:\n co... | <|body_start_0|>
self.__args__ = args
self.__kargs__ = kargs
self.__topic__ = topic
self.__conn__ = connection
self.__balanced__ = balanced
<|end_body_0|>
<|body_start_1|>
cons = None
topic = self.__conn__.create_topic(self.__topic__)
if self.__balanced__... | A class capable of create a consumer connection. | ConsumerFactory | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConsumerFactory:
"""A class capable of create a consumer connection."""
def __init__(self, connection, topic, balanced, args, kargs):
"""Create a ConsumerFactory. A object capable of create a consumer connection Parameters ---------- connection: ConnectionBuilder A object that holds ... | stack_v2_sparse_classes_75kplus_train_067263 | 2,257 | permissive | [
{
"docstring": "Create a ConsumerFactory. A object capable of create a consumer connection Parameters ---------- connection: ConnectionBuilder A object that holds the connection information topic: str Name of topic that messages will be read from balanced: bool True for a balanced consumer False for a simple co... | 3 | stack_v2_sparse_classes_30k_train_010245 | Implement the Python class `ConsumerFactory` described below.
Class description:
A class capable of create a consumer connection.
Method signatures and docstrings:
- def __init__(self, connection, topic, balanced, args, kargs): Create a ConsumerFactory. A object capable of create a consumer connection Parameters ----... | Implement the Python class `ConsumerFactory` described below.
Class description:
A class capable of create a consumer connection.
Method signatures and docstrings:
- def __init__(self, connection, topic, balanced, args, kargs): Create a ConsumerFactory. A object capable of create a consumer connection Parameters ----... | f2c958df88c5698148aae4c5314dd39e31e995c3 | <|skeleton|>
class ConsumerFactory:
"""A class capable of create a consumer connection."""
def __init__(self, connection, topic, balanced, args, kargs):
"""Create a ConsumerFactory. A object capable of create a consumer connection Parameters ---------- connection: ConnectionBuilder A object that holds ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConsumerFactory:
"""A class capable of create a consumer connection."""
def __init__(self, connection, topic, balanced, args, kargs):
"""Create a ConsumerFactory. A object capable of create a consumer connection Parameters ---------- connection: ConnectionBuilder A object that holds the connectio... | the_stack_v2_python_sparse | kafka_client_decorators/kafka/consumer_factory.py | cdsedson/kafka-decorator | train | 1 |
4cbc13db75557fa69d6deda7d0ec57075e7e4bdd | [
"self._time_intervals = time_intervals\nif index is not None:\n self._index = index\n self.shape = (index.sum(),)\nelse:\n self._index = slice(None)\n self.shape = (len(time_intervals),)\nself.name = epoch_name",
"if time_slice:\n raise NotImplementedError('todo')\nelse:\n start_times = self._ti... | <|body_start_0|>
self._time_intervals = time_intervals
if index is not None:
self._index = index
self.shape = (index.sum(),)
else:
self._index = slice(None)
self.shape = (len(time_intervals),)
self.name = epoch_name
<|end_body_0|>
<|body_s... | EpochProxy | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EpochProxy:
def __init__(self, time_intervals, epoch_name=None, index=None):
""":param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long periods :param epoch_name: (str) Name of the epoch object :param index: (np.array, slice) Slice obje... | stack_v2_sparse_classes_75kplus_train_067264 | 36,998 | permissive | [
{
"docstring": ":param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long periods :param epoch_name: (str) Name of the epoch object :param index: (np.array, slice) Slice object or array of bool values masking time_intervals to be used. In case of an array it has... | 2 | null | Implement the Python class `EpochProxy` described below.
Class description:
Implement the EpochProxy class.
Method signatures and docstrings:
- def __init__(self, time_intervals, epoch_name=None, index=None): :param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long p... | Implement the Python class `EpochProxy` described below.
Class description:
Implement the EpochProxy class.
Method signatures and docstrings:
- def __init__(self, time_intervals, epoch_name=None, index=None): :param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long p... | 354c8d9d5fbc4daad3547773d2f281f8c163d208 | <|skeleton|>
class EpochProxy:
def __init__(self, time_intervals, epoch_name=None, index=None):
""":param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long periods :param epoch_name: (str) Name of the epoch object :param index: (np.array, slice) Slice obje... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EpochProxy:
def __init__(self, time_intervals, epoch_name=None, index=None):
""":param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long periods :param epoch_name: (str) Name of the epoch object :param index: (np.array, slice) Slice object or array of... | the_stack_v2_python_sparse | neo/io/nwbio.py | NeuralEnsemble/python-neo | train | 265 | |
a861ed283f44f9704de3a8b7a1ba861f0fc0dc5f | [
"self.log = log or self.log\nself.ssl_config = ssl_config or self.ssl_config_cls()\nif not qnam:\n if not parent:\n qnam = QNetworkAccessManager()\n else:\n qnam = QNetworkAccessManager(parent)\n self._qnam = qnam\nqnam.finished.connect(self._finished)\nself.transfers = {}",
"transfer = sel... | <|body_start_0|>
self.log = log or self.log
self.ssl_config = ssl_config or self.ssl_config_cls()
if not qnam:
if not parent:
qnam = QNetworkAccessManager()
else:
qnam = QNetworkAccessManager(parent)
self._qnam = qnam
qn... | Make HTTP requests according to a specified policy. | Client | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Client:
"""Make HTTP requests according to a specified policy."""
def __init__(self, ssl_config=None, log=None, qnam=None, parent=None):
""":arg ssl_config: Optional. SSLConfig instance to use. :arg log: Optional. Logger to use. Defaults to logger .qt.client.Client. :arg qnam: Option... | stack_v2_sparse_classes_75kplus_train_067265 | 9,933 | no_license | [
{
"docstring": ":arg ssl_config: Optional. SSLConfig instance to use. :arg log: Optional. Logger to use. Defaults to logger .qt.client.Client. :arg qnam: Optional. A QNetworkAccessManager to be used by this client. If None is passed, the QNAM will be created automatically. :arg parent: Optional. A QObject to pa... | 3 | stack_v2_sparse_classes_30k_train_030973 | Implement the Python class `Client` described below.
Class description:
Make HTTP requests according to a specified policy.
Method signatures and docstrings:
- def __init__(self, ssl_config=None, log=None, qnam=None, parent=None): :arg ssl_config: Optional. SSLConfig instance to use. :arg log: Optional. Logger to use... | Implement the Python class `Client` described below.
Class description:
Make HTTP requests according to a specified policy.
Method signatures and docstrings:
- def __init__(self, ssl_config=None, log=None, qnam=None, parent=None): :arg ssl_config: Optional. SSLConfig instance to use. :arg log: Optional. Logger to use... | 71db923df2862ff246755f1d819b0eecc76960ef | <|skeleton|>
class Client:
"""Make HTTP requests according to a specified policy."""
def __init__(self, ssl_config=None, log=None, qnam=None, parent=None):
""":arg ssl_config: Optional. SSLConfig instance to use. :arg log: Optional. Logger to use. Defaults to logger .qt.client.Client. :arg qnam: Option... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Client:
"""Make HTTP requests according to a specified policy."""
def __init__(self, ssl_config=None, log=None, qnam=None, parent=None):
""":arg ssl_config: Optional. SSLConfig instance to use. :arg log: Optional. Logger to use. Defaults to logger .qt.client.Client. :arg qnam: Optional. A QNetwor... | the_stack_v2_python_sparse | because/interfaces/qt/client.py | jrbeckwith/because | train | 3 |
15ae9750fd1fe44be7d58f78d711e9958bbdacb7 | [
"serializer = self.get_serializer(data=request.data)\nif serializer.is_valid():\n user = serializer.create(serializer.data)\n login_response_serializer = serializers.LoginResponseV2Serializer(user)\n return Response(login_response_serializer.data)\nreturn Response(serializer.errors, status=status.HTTP_400_... | <|body_start_0|>
serializer = self.get_serializer(data=request.data)
if serializer.is_valid():
user = serializer.create(serializer.data)
login_response_serializer = serializers.LoginResponseV2Serializer(user)
return Response(login_response_serializer.data)
ret... | AuthViewSet | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthViewSet:
def login(self, request, *args, **kwargs):
"""Logs the user in. --- type: email: required: true type: string password: required: true type: string device_os: required: true type: string device_user_token: required: false type: string parameters: - name: email description: em... | stack_v2_sparse_classes_75kplus_train_067266 | 3,412 | permissive | [
{
"docstring": "Logs the user in. --- type: email: required: true type: string password: required: true type: string device_os: required: true type: string device_user_token: required: false type: string parameters: - name: email description: email user. required: true type: string paramType: form - name: passw... | 3 | stack_v2_sparse_classes_30k_train_026787 | Implement the Python class `AuthViewSet` described below.
Class description:
Implement the AuthViewSet class.
Method signatures and docstrings:
- def login(self, request, *args, **kwargs): Logs the user in. --- type: email: required: true type: string password: required: true type: string device_os: required: true ty... | Implement the Python class `AuthViewSet` described below.
Class description:
Implement the AuthViewSet class.
Method signatures and docstrings:
- def login(self, request, *args, **kwargs): Logs the user in. --- type: email: required: true type: string password: required: true type: string device_os: required: true ty... | 7349ce18f56658d67daedf5e1abb352b5c15a029 | <|skeleton|>
class AuthViewSet:
def login(self, request, *args, **kwargs):
"""Logs the user in. --- type: email: required: true type: string password: required: true type: string device_os: required: true type: string device_user_token: required: false type: string parameters: - name: email description: em... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AuthViewSet:
def login(self, request, *args, **kwargs):
"""Logs the user in. --- type: email: required: true type: string password: required: true type: string device_os: required: true type: string device_user_token: required: false type: string parameters: - name: email description: email user. requ... | the_stack_v2_python_sparse | src/tandlr/authentication/api.py | shrmoud/schoolapp | train | 0 | |
aafcdbced26dc2792040871de952ec7290b5ffdf | [
"if n < 2:\n raise ValueError('There must be at least 2 atoms.')\nnx = 3 * n\nnf = n * (n - 1) // 2\nsuper().__init__(self._fdist, nf=nf, nx=nx, maxderiv=None, zlevel=None)\nself.n = n\nreturn",
"x = nitrogen.dfun.X2adf(X, deriv, var)\nn = self.n\ny = []\nfor i in range(n):\n for j in range(i + 1, n):\n ... | <|body_start_0|>
if n < 2:
raise ValueError('There must be at least 2 atoms.')
nx = 3 * n
nf = n * (n - 1) // 2
super().__init__(self._fdist, nf=nf, nx=nx, maxderiv=None, zlevel=None)
self.n = n
return
<|end_body_0|>
<|body_start_1|>
x = nitrogen.dfun... | Internuclear coordinate function for linear distance Attributes ---------- n : integer The number of atoms. | InternuclearR | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InternuclearR:
"""Internuclear coordinate function for linear distance Attributes ---------- n : integer The number of atoms."""
def __init__(self, n):
"""Parameters ---------- n : integer The number of atoms"""
<|body_0|>
def _fdist(self, X, deriv=0, out=None, var=None)... | stack_v2_sparse_classes_75kplus_train_067267 | 37,911 | permissive | [
{
"docstring": "Parameters ---------- n : integer The number of atoms",
"name": "__init__",
"signature": "def __init__(self, n)"
},
{
"docstring": "evaluation function",
"name": "_fdist",
"signature": "def _fdist(self, X, deriv=0, out=None, var=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_022764 | Implement the Python class `InternuclearR` described below.
Class description:
Internuclear coordinate function for linear distance Attributes ---------- n : integer The number of atoms.
Method signatures and docstrings:
- def __init__(self, n): Parameters ---------- n : integer The number of atoms
- def _fdist(self,... | Implement the Python class `InternuclearR` described below.
Class description:
Internuclear coordinate function for linear distance Attributes ---------- n : integer The number of atoms.
Method signatures and docstrings:
- def __init__(self, n): Parameters ---------- n : integer The number of atoms
- def _fdist(self,... | c6341a58331deef3728cc43c627c556139deb673 | <|skeleton|>
class InternuclearR:
"""Internuclear coordinate function for linear distance Attributes ---------- n : integer The number of atoms."""
def __init__(self, n):
"""Parameters ---------- n : integer The number of atoms"""
<|body_0|>
def _fdist(self, X, deriv=0, out=None, var=None)... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class InternuclearR:
"""Internuclear coordinate function for linear distance Attributes ---------- n : integer The number of atoms."""
def __init__(self, n):
"""Parameters ---------- n : integer The number of atoms"""
if n < 2:
raise ValueError('There must be at least 2 atoms.')
... | the_stack_v2_python_sparse | nitrogen/pes/fit.py | bchangala/nitrogen | train | 11 |
23de18d020f15fe24dcd19bb09b7180e18bab111 | [
"input_file = self.get_data(self.test_dir, 'jw88600073001_gs-id_7_image-uncal.fits')\nGuiderPipeline.call(input_file, output_file='jw88600073001_gs-id_7_image-cal.fits')\noutputs = [('jw88600073001_gs-id_7_image-cal.fits', 'jw88600073001_gs-id_7_image-cal_ref.fits', ['primary', 'sci', 'dq'])]\nself.compare_outputs(... | <|body_start_0|>
input_file = self.get_data(self.test_dir, 'jw88600073001_gs-id_7_image-uncal.fits')
GuiderPipeline.call(input_file, output_file='jw88600073001_gs-id_7_image-cal.fits')
outputs = [('jw88600073001_gs-id_7_image-cal.fits', 'jw88600073001_gs-id_7_image-cal_ref.fits', ['primary', 'sc... | TestGuiderPipeline | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestGuiderPipeline:
def test_guider_pipeline1(self):
"""Regression test of calwebb_guider pipeline performed on ID-image data."""
<|body_0|>
def test_guider_pipeline2(self):
"""Regression test of calwebb_guider pipeline performed on ACQ-1 data."""
<|body_1|>
... | stack_v2_sparse_classes_75kplus_train_067268 | 2,333 | permissive | [
{
"docstring": "Regression test of calwebb_guider pipeline performed on ID-image data.",
"name": "test_guider_pipeline1",
"signature": "def test_guider_pipeline1(self)"
},
{
"docstring": "Regression test of calwebb_guider pipeline performed on ACQ-1 data.",
"name": "test_guider_pipeline2",
... | 3 | stack_v2_sparse_classes_30k_train_012027 | Implement the Python class `TestGuiderPipeline` described below.
Class description:
Implement the TestGuiderPipeline class.
Method signatures and docstrings:
- def test_guider_pipeline1(self): Regression test of calwebb_guider pipeline performed on ID-image data.
- def test_guider_pipeline2(self): Regression test of ... | Implement the Python class `TestGuiderPipeline` described below.
Class description:
Implement the TestGuiderPipeline class.
Method signatures and docstrings:
- def test_guider_pipeline1(self): Regression test of calwebb_guider pipeline performed on ID-image data.
- def test_guider_pipeline2(self): Regression test of ... | e3a5b2d8bb50d92ccca46cd3bbd6585d5238000a | <|skeleton|>
class TestGuiderPipeline:
def test_guider_pipeline1(self):
"""Regression test of calwebb_guider pipeline performed on ID-image data."""
<|body_0|>
def test_guider_pipeline2(self):
"""Regression test of calwebb_guider pipeline performed on ACQ-1 data."""
<|body_1|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestGuiderPipeline:
def test_guider_pipeline1(self):
"""Regression test of calwebb_guider pipeline performed on ID-image data."""
input_file = self.get_data(self.test_dir, 'jw88600073001_gs-id_7_image-uncal.fits')
GuiderPipeline.call(input_file, output_file='jw88600073001_gs-id_7_image... | the_stack_v2_python_sparse | jwst/tests_nightly/general/fgs/test_guider_pipeline.py | mperrin/jwst | train | 0 | |
2002a2cb97c69129b4ac16445eee3d9be3f38a2e | [
"attrs = attrs or []\nself.attrs = list(attrs)\nif ORTH in self.attrs:\n self.attrs.pop(ORTH)\nif SPACY in self.attrs:\n self.attrs.pop(SPACY)\nself.attrs.insert(0, ORTH)\nself.tokens = []\nself.spaces = []\nself.strings = set()",
"array = doc.to_array(self.attrs)\nif len(array.shape) == 1:\n array = arr... | <|body_start_0|>
attrs = attrs or []
self.attrs = list(attrs)
if ORTH in self.attrs:
self.attrs.pop(ORTH)
if SPACY in self.attrs:
self.attrs.pop(SPACY)
self.attrs.insert(0, ORTH)
self.tokens = []
self.spaces = []
self.strings = set(... | Serialize analyses from a collection of doc objects. | Binder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Binder:
"""Serialize analyses from a collection of doc objects."""
def __init__(self, attrs=None):
"""Create a Binder object, to hold serialized annotations. attrs (list): List of attributes to serialize. 'orth' and 'spacy' are always serialized, so they're not required. Defaults to ... | stack_v2_sparse_classes_75kplus_train_067269 | 4,226 | permissive | [
{
"docstring": "Create a Binder object, to hold serialized annotations. attrs (list): List of attributes to serialize. 'orth' and 'spacy' are always serialized, so they're not required. Defaults to None.",
"name": "__init__",
"signature": "def __init__(self, attrs=None)"
},
{
"docstring": "Add a... | 6 | stack_v2_sparse_classes_30k_train_016457 | Implement the Python class `Binder` described below.
Class description:
Serialize analyses from a collection of doc objects.
Method signatures and docstrings:
- def __init__(self, attrs=None): Create a Binder object, to hold serialized annotations. attrs (list): List of attributes to serialize. 'orth' and 'spacy' are... | Implement the Python class `Binder` described below.
Class description:
Serialize analyses from a collection of doc objects.
Method signatures and docstrings:
- def __init__(self, attrs=None): Create a Binder object, to hold serialized annotations. attrs (list): List of attributes to serialize. 'orth' and 'spacy' are... | a062c118f12b93172e31e8ca115ce3f871b64461 | <|skeleton|>
class Binder:
"""Serialize analyses from a collection of doc objects."""
def __init__(self, attrs=None):
"""Create a Binder object, to hold serialized annotations. attrs (list): List of attributes to serialize. 'orth' and 'spacy' are always serialized, so they're not required. Defaults to ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Binder:
"""Serialize analyses from a collection of doc objects."""
def __init__(self, attrs=None):
"""Create a Binder object, to hold serialized annotations. attrs (list): List of attributes to serialize. 'orth' and 'spacy' are always serialized, so they're not required. Defaults to None."""
... | the_stack_v2_python_sparse | python/spaCy/2018/12/_serialize.py | rosoareslv/SED99 | train | 1 |
ba957e50b86531f9cb94124952a63c75386fb644 | [
"self.start = datetime.now()\nself.duration = timedelta(seconds=30)\nself.end = self.start + self.duration\nself.assertEqual(time_it(self.start, self.end), self.duration.seconds)\nself.assertRaises(BadTimeObjError, time_it, 'first', 'second')",
"numlist = []\nfor num in range(0, 1000):\n numlist.append(gennum(... | <|body_start_0|>
self.start = datetime.now()
self.duration = timedelta(seconds=30)
self.end = self.start + self.duration
self.assertEqual(time_it(self.start, self.end), self.duration.seconds)
self.assertRaises(BadTimeObjError, time_it, 'first', 'second')
<|end_body_0|>
<|body_st... | MathQuiz | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MathQuiz:
def test_time_it(self):
"""Test the time_it function"""
<|body_0|>
def test_randint(self):
"""Tests the random number generator part of math_quiz.py"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.start = datetime.now()
self.d... | stack_v2_sparse_classes_75kplus_train_067270 | 1,825 | no_license | [
{
"docstring": "Test the time_it function",
"name": "test_time_it",
"signature": "def test_time_it(self)"
},
{
"docstring": "Tests the random number generator part of math_quiz.py",
"name": "test_randint",
"signature": "def test_randint(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_026767 | Implement the Python class `MathQuiz` described below.
Class description:
Implement the MathQuiz class.
Method signatures and docstrings:
- def test_time_it(self): Test the time_it function
- def test_randint(self): Tests the random number generator part of math_quiz.py | Implement the Python class `MathQuiz` described below.
Class description:
Implement the MathQuiz class.
Method signatures and docstrings:
- def test_time_it(self): Test the time_it function
- def test_randint(self): Tests the random number generator part of math_quiz.py
<|skeleton|>
class MathQuiz:
def test_tim... | b32f83aa1b705a5ad384b73c618f04f7d2622753 | <|skeleton|>
class MathQuiz:
def test_time_it(self):
"""Test the time_it function"""
<|body_0|>
def test_randint(self):
"""Tests the random number generator part of math_quiz.py"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MathQuiz:
def test_time_it(self):
"""Test the time_it function"""
self.start = datetime.now()
self.duration = timedelta(seconds=30)
self.end = self.start + self.duration
self.assertEqual(time_it(self.start, self.end), self.duration.seconds)
self.assertRaises(Bad... | the_stack_v2_python_sparse | ostPython3/test_math_quiz.py | deepbsd/OST_Python | train | 1 | |
5f1383e0f413352dc8eb3ff69217fcd55831d78d | [
"self.database_uri = None\nself.config = dict()\nself.max_pool = 100\nself.min_pool = 0\nself.max_idle_time = None\nself.connection_timeout = 10000\nself.heartbeat_frequency = 10000\nself.server_timeout = 100",
"self.database_uri = app.config.get('MONGODB_DATABASE_URI')\nself.config['maxPoolSize'] = app.config.ge... | <|body_start_0|>
self.database_uri = None
self.config = dict()
self.max_pool = 100
self.min_pool = 0
self.max_idle_time = None
self.connection_timeout = 10000
self.heartbeat_frequency = 10000
self.server_timeout = 100
<|end_body_0|>
<|body_start_1|>
... | MongoAlchemy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MongoAlchemy:
def __init__(self):
"""Initialize variables for the MongoDB database connection"""
<|body_0|>
def init_app(self, app: Flask=None) -> None:
"""This function will get config values to set configurations for connection :param app: Instance of the Flask App... | stack_v2_sparse_classes_75kplus_train_067271 | 2,517 | permissive | [
{
"docstring": "Initialize variables for the MongoDB database connection",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This function will get config values to set configurations for connection :param app: Instance of the Flask App :return: None",
"name": "init_ap... | 4 | stack_v2_sparse_classes_30k_train_018550 | Implement the Python class `MongoAlchemy` described below.
Class description:
Implement the MongoAlchemy class.
Method signatures and docstrings:
- def __init__(self): Initialize variables for the MongoDB database connection
- def init_app(self, app: Flask=None) -> None: This function will get config values to set co... | Implement the Python class `MongoAlchemy` described below.
Class description:
Implement the MongoAlchemy class.
Method signatures and docstrings:
- def __init__(self): Initialize variables for the MongoDB database connection
- def init_app(self, app: Flask=None) -> None: This function will get config values to set co... | a008845ca403c4bb07666ed9f9293fffe360bdd8 | <|skeleton|>
class MongoAlchemy:
def __init__(self):
"""Initialize variables for the MongoDB database connection"""
<|body_0|>
def init_app(self, app: Flask=None) -> None:
"""This function will get config values to set configurations for connection :param app: Instance of the Flask App... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MongoAlchemy:
def __init__(self):
"""Initialize variables for the MongoDB database connection"""
self.database_uri = None
self.config = dict()
self.max_pool = 100
self.min_pool = 0
self.max_idle_time = None
self.connection_timeout = 10000
self.he... | the_stack_v2_python_sparse | app/db.py | amanjaiswalofficial/infinity-reads-blog | train | 0 | |
e06fb8c1064aa1dcfcf8cef4a7d54ac17f5c084e | [
"self.capacity = capacity\nself.size = 0\nself.cache = dict()\nself.cachelist = DoubleList()",
"if key not in self.cache:\n return -1\nnode = self.cache[key]\nself.cachelist.delete(node)\nself.cachelist.append(node)\nreturn node.val",
"if key in self.cache:\n node = self.cache[key]\n node.val = value\n... | <|body_start_0|>
self.capacity = capacity
self.size = 0
self.cache = dict()
self.cachelist = DoubleList()
<|end_body_0|>
<|body_start_1|>
if key not in self.cache:
return -1
node = self.cache[key]
self.cachelist.delete(node)
self.cachelist.app... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_75kplus_train_067272 | 2,925 | 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 | stack_v2_sparse_classes_30k_train_006829 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None
<|sk... | 837957ea22aa07ce28a6c23ea0419bd2011e1f88 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.size = 0
self.cache = dict()
self.cachelist = DoubleList()
def get(self, key):
""":type key: int :rtype: int"""
if key not in self.cache:
ret... | the_stack_v2_python_sparse | Tencent/midum/LRU缓存.py | 2226171237/Algorithmpractice | train | 0 | |
6c521f53ee011b2eed03b83d1b7031f36ec31102 | [
"APIAdminCommon.verifySecurityOfAdminAPICall(appObj, request, tenant)\ncontent_raw = request.get_json()\ncontent = marshal(content_raw, getUserPersonLinkModel(appObj))\nrequiredInPayload(content, ['UserID', 'personGUID'])\nif userID != content['UserID']:\n raise BadRequest('UserID in payload not the same as in U... | <|body_start_0|>
APIAdminCommon.verifySecurityOfAdminAPICall(appObj, request, tenant)
content_raw = request.get_json()
content = marshal(content_raw, getUserPersonLinkModel(appObj))
requiredInPayload(content, ['UserID', 'personGUID'])
if userID != content['UserID']:
r... | userpersonlinkInfo | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class userpersonlinkInfo:
def post(self, tenant, userID, personGUID):
"""Create userpersonlink"""
<|body_0|>
def delete(self, tenant, userID, personGUID):
"""Delete userpersonlink"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
APIAdminCommon.verifySecuri... | stack_v2_sparse_classes_75kplus_train_067273 | 35,114 | permissive | [
{
"docstring": "Create userpersonlink",
"name": "post",
"signature": "def post(self, tenant, userID, personGUID)"
},
{
"docstring": "Delete userpersonlink",
"name": "delete",
"signature": "def delete(self, tenant, userID, personGUID)"
}
] | 2 | stack_v2_sparse_classes_30k_train_025226 | Implement the Python class `userpersonlinkInfo` described below.
Class description:
Implement the userpersonlinkInfo class.
Method signatures and docstrings:
- def post(self, tenant, userID, personGUID): Create userpersonlink
- def delete(self, tenant, userID, personGUID): Delete userpersonlink | Implement the Python class `userpersonlinkInfo` described below.
Class description:
Implement the userpersonlinkInfo class.
Method signatures and docstrings:
- def post(self, tenant, userID, personGUID): Create userpersonlink
- def delete(self, tenant, userID, personGUID): Delete userpersonlink
<|skeleton|>
class us... | d3908c46614fb1b638553282cd72ba3634277495 | <|skeleton|>
class userpersonlinkInfo:
def post(self, tenant, userID, personGUID):
"""Create userpersonlink"""
<|body_0|>
def delete(self, tenant, userID, personGUID):
"""Delete userpersonlink"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class userpersonlinkInfo:
def post(self, tenant, userID, personGUID):
"""Create userpersonlink"""
APIAdminCommon.verifySecurityOfAdminAPICall(appObj, request, tenant)
content_raw = request.get_json()
content = marshal(content_raw, getUserPersonLinkModel(appObj))
requiredInPay... | the_stack_v2_python_sparse | services/src/APIadmin_Legacy.py | rmetcalf9/saas_user_management_system | train | 1 | |
ea0e3eafbbd443920d6add021877bd4d85b9d0bf | [
"self.exist = False\nif address is not None:\n self.exist = True\n if len(address) == 7:\n if address[0] == 1:\n self.universe, self.address_start, balance = ((address[4], address[5], cut_little_ledstrip), (address[1], address[2], 0))[idx_led < cut_little_ledstrip]\n self.address_... | <|body_start_0|>
self.exist = False
if address is not None:
self.exist = True
if len(address) == 7:
if address[0] == 1:
self.universe, self.address_start, balance = ((address[4], address[5], cut_little_ledstrip), (address[1], address[2], 0))[id... | Represent a led of the cube | Led | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Led:
"""Represent a led of the cube"""
def __init__(self, address, idx_led):
"""Constructor of led :param address: tuple of led address :param idx_led: led index"""
<|body_0|>
def show(self, brightness):
"""Illuminate the led with brightness :param brightness: in... | stack_v2_sparse_classes_75kplus_train_067274 | 3,537 | no_license | [
{
"docstring": "Constructor of led :param address: tuple of led address :param idx_led: led index",
"name": "__init__",
"signature": "def __init__(self, address, idx_led)"
},
{
"docstring": "Illuminate the led with brightness :param brightness: int between 0 and 15 include :raise exception",
... | 2 | stack_v2_sparse_classes_30k_train_009173 | Implement the Python class `Led` described below.
Class description:
Represent a led of the cube
Method signatures and docstrings:
- def __init__(self, address, idx_led): Constructor of led :param address: tuple of led address :param idx_led: led index
- def show(self, brightness): Illuminate the led with brightness ... | Implement the Python class `Led` described below.
Class description:
Represent a led of the cube
Method signatures and docstrings:
- def __init__(self, address, idx_led): Constructor of led :param address: tuple of led address :param idx_led: led index
- def show(self, brightness): Illuminate the led with brightness ... | de1408317d5071b7e0c6b2fea6f281660115d728 | <|skeleton|>
class Led:
"""Represent a led of the cube"""
def __init__(self, address, idx_led):
"""Constructor of led :param address: tuple of led address :param idx_led: led index"""
<|body_0|>
def show(self, brightness):
"""Illuminate the led with brightness :param brightness: in... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Led:
"""Represent a led of the cube"""
def __init__(self, address, idx_led):
"""Constructor of led :param address: tuple of led address :param idx_led: led index"""
self.exist = False
if address is not None:
self.exist = True
if len(address) == 7:
... | the_stack_v2_python_sparse | api/package/cube/led.py | HE-Arc/Extrusion---web-interface | train | 4 |
5cc2e67d948f786c3302c61c6071119f1bca556d | [
"rmgpy_path = os.path.normpath(os.path.join(get_path(), '..'))\nqm = QMCalculator(software='gaussian', method='pm3', fileStore=os.path.join(rmgpy_path, 'testing', 'qm', 'QMfiles'), scratchDirectory=os.path.join(rmgpy_path, 'testing', 'qm', 'QMscratch'))\nif not os.path.exists(qm.settings.fileStore):\n os.makedir... | <|body_start_0|>
rmgpy_path = os.path.normpath(os.path.join(get_path(), '..'))
qm = QMCalculator(software='gaussian', method='pm3', fileStore=os.path.join(rmgpy_path, 'testing', 'qm', 'QMfiles'), scratchDirectory=os.path.join(rmgpy_path, 'testing', 'qm', 'QMscratch'))
if not os.path.exists(qm.se... | Contains unit tests for the Geometry class. | TestGaussianMolPM3 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestGaussianMolPM3:
"""Contains unit tests for the Geometry class."""
def setUp(self):
"""A function run before each unit test in this class."""
<|body_0|>
def test_generate_thermo_data(self):
"""Test that generate_thermo_data() works correctly on gaussian PM3.""... | stack_v2_sparse_classes_75kplus_train_067275 | 7,452 | permissive | [
{
"docstring": "A function run before each unit test in this class.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test that generate_thermo_data() works correctly on gaussian PM3.",
"name": "test_generate_thermo_data",
"signature": "def test_generate_thermo_data(se... | 3 | null | Implement the Python class `TestGaussianMolPM3` described below.
Class description:
Contains unit tests for the Geometry class.
Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def test_generate_thermo_data(self): Test that generate_thermo_data() works correct... | Implement the Python class `TestGaussianMolPM3` described below.
Class description:
Contains unit tests for the Geometry class.
Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def test_generate_thermo_data(self): Test that generate_thermo_data() works correct... | 349a4af759cf8877197772cd7eaca1e51d46eff5 | <|skeleton|>
class TestGaussianMolPM3:
"""Contains unit tests for the Geometry class."""
def setUp(self):
"""A function run before each unit test in this class."""
<|body_0|>
def test_generate_thermo_data(self):
"""Test that generate_thermo_data() works correctly on gaussian PM3.""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestGaussianMolPM3:
"""Contains unit tests for the Geometry class."""
def setUp(self):
"""A function run before each unit test in this class."""
rmgpy_path = os.path.normpath(os.path.join(get_path(), '..'))
qm = QMCalculator(software='gaussian', method='pm3', fileStore=os.path.joi... | the_stack_v2_python_sparse | rmgpy/qm/gaussianTest.py | CanePan-cc/CanePanWorkshop | train | 2 |
473ea668bd8cd63beafea221b74d1212abdf1525 | [
"self._to = dest\nself._from = source\nself._pwd = pwds\nself.status = None",
"file_path = os.path.join(self._from, filename)\nfor pwd in self._pwd:\n unr = unrar.UnrarSpoon(file_path, self._to, pwd)\n status = unr.update_loop(callback=proc)\n self.status = status\n if status[unrar.STATUS_OK]:\n ... | <|body_start_0|>
self._to = dest
self._from = source
self._pwd = pwds
self.status = None
<|end_body_0|>
<|body_start_1|>
file_path = os.path.join(self._from, filename)
for pwd in self._pwd:
unr = unrar.UnrarSpoon(file_path, self._to, pwd)
status =... | extractor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class extractor:
def __init__(self, source, dest, pwds):
"""Creates and configures an extractor-object"""
<|body_0|>
def extract(self, filename, proc=None):
"""Starts extractor-utility and extracts packets."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_75kplus_train_067276 | 845 | no_license | [
{
"docstring": "Creates and configures an extractor-object",
"name": "__init__",
"signature": "def __init__(self, source, dest, pwds)"
},
{
"docstring": "Starts extractor-utility and extracts packets.",
"name": "extract",
"signature": "def extract(self, filename, proc=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008469 | Implement the Python class `extractor` described below.
Class description:
Implement the extractor class.
Method signatures and docstrings:
- def __init__(self, source, dest, pwds): Creates and configures an extractor-object
- def extract(self, filename, proc=None): Starts extractor-utility and extracts packets. | Implement the Python class `extractor` described below.
Class description:
Implement the extractor class.
Method signatures and docstrings:
- def __init__(self, source, dest, pwds): Creates and configures an extractor-object
- def extract(self, filename, proc=None): Starts extractor-utility and extracts packets.
<|s... | 353ab0f9a651233ad1cbc1c463dd052fa28dc35d | <|skeleton|>
class extractor:
def __init__(self, source, dest, pwds):
"""Creates and configures an extractor-object"""
<|body_0|>
def extract(self, filename, proc=None):
"""Starts extractor-utility and extracts packets."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class extractor:
def __init__(self, source, dest, pwds):
"""Creates and configures an extractor-object"""
self._to = dest
self._from = source
self._pwd = pwds
self.status = None
def extract(self, filename, proc=None):
"""Starts extractor-utility and extracts pack... | the_stack_v2_python_sparse | src/pfextractor.py | boon-code/pfrinc4 | train | 0 | |
e43fadc6fbd7ebdd13c520e8c7f46c0a1cacb0e6 | [
"self.opt = opt\nself.dataset = None\nself.dataset_loader = None",
"if self.opt.dataset in ['imagenet', 'folder', 'lfw']:\n self.dataset = dset.ImageFolder(root=self.opt.dataroot, transform=transforms.Compose([transforms.Scale(self.opt.imageSize), transforms.CenterCrop(self.opt.imageSize), transforms.ToTensor(... | <|body_start_0|>
self.opt = opt
self.dataset = None
self.dataset_loader = None
<|end_body_0|>
<|body_start_1|>
if self.opt.dataset in ['imagenet', 'folder', 'lfw']:
self.dataset = dset.ImageFolder(root=self.opt.dataroot, transform=transforms.Compose([transforms.Scale(self.op... | Load a dataset. | Dataset_load | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset_load:
"""Load a dataset."""
def __init__(self, opt):
"""Constructor."""
<|body_0|>
def initialize_dataset(self):
"""Initialize."""
<|body_1|>
def initialize_dataset_loader(self, batchSize=None):
"""Create the datset loader."""
... | stack_v2_sparse_classes_75kplus_train_067277 | 3,119 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, opt)"
},
{
"docstring": "Initialize.",
"name": "initialize_dataset",
"signature": "def initialize_dataset(self)"
},
{
"docstring": "Create the datset loader.",
"name": "initialize_dataset_load... | 5 | stack_v2_sparse_classes_30k_train_000792 | Implement the Python class `Dataset_load` described below.
Class description:
Load a dataset.
Method signatures and docstrings:
- def __init__(self, opt): Constructor.
- def initialize_dataset(self): Initialize.
- def initialize_dataset_loader(self, batchSize=None): Create the datset loader.
- def get_dataset(self): ... | Implement the Python class `Dataset_load` described below.
Class description:
Load a dataset.
Method signatures and docstrings:
- def __init__(self, opt): Constructor.
- def initialize_dataset(self): Initialize.
- def initialize_dataset_loader(self, batchSize=None): Create the datset loader.
- def get_dataset(self): ... | e1e4a8d9a2ab51c2108a4d167bc37fab101f0c2c | <|skeleton|>
class Dataset_load:
"""Load a dataset."""
def __init__(self, opt):
"""Constructor."""
<|body_0|>
def initialize_dataset(self):
"""Initialize."""
<|body_1|>
def initialize_dataset_loader(self, batchSize=None):
"""Create the datset loader."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Dataset_load:
"""Load a dataset."""
def __init__(self, opt):
"""Constructor."""
self.opt = opt
self.dataset = None
self.dataset_loader = None
def initialize_dataset(self):
"""Initialize."""
if self.opt.dataset in ['imagenet', 'folder', 'lfw']:
... | the_stack_v2_python_sparse | diffrend/torch/GAN/datasets.py | sainatarajan/pix2shape | train | 0 |
bcf1b9c13fa954c345b9ae9778b1cea8e402d049 | [
"super(KleinConstraint, self).__init__()\nself.norm = Norm(axis=-1)\nself.min_norm = min_norm\nself.maxnorm = 1 - 0.004\nself.shape = Shape()\nself.reshape = Reshape()",
"last_dim_val = self.shape(x)[-1]\nnorm = self.reshape(self.norm(x), (-1, 1))\nmaxnorm = self.maxnorm\ncond = norm > maxnorm\nx_reshape = self.r... | <|body_start_0|>
super(KleinConstraint, self).__init__()
self.norm = Norm(axis=-1)
self.min_norm = min_norm
self.maxnorm = 1 - 0.004
self.shape = Shape()
self.reshape = Reshape()
<|end_body_0|>
<|body_start_1|>
last_dim_val = self.shape(x)[-1]
norm = self... | klein constraint class | KleinConstraint | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KleinConstraint:
"""klein constraint class"""
def __init__(self, min_norm):
"""init fun"""
<|body_0|>
def construct(self, x):
"""class construction"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(KleinConstraint, self).__init__()
s... | stack_v2_sparse_classes_75kplus_train_067278 | 8,596 | permissive | [
{
"docstring": "init fun",
"name": "__init__",
"signature": "def __init__(self, min_norm)"
},
{
"docstring": "class construction",
"name": "construct",
"signature": "def construct(self, x)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000175 | Implement the Python class `KleinConstraint` described below.
Class description:
klein constraint class
Method signatures and docstrings:
- def __init__(self, min_norm): init fun
- def construct(self, x): class construction | Implement the Python class `KleinConstraint` described below.
Class description:
klein constraint class
Method signatures and docstrings:
- def __init__(self, min_norm): init fun
- def construct(self, x): class construction
<|skeleton|>
class KleinConstraint:
"""klein constraint class"""
def __init__(self, ... | eab643f51336dbf7d711f02d27e6516e5affee59 | <|skeleton|>
class KleinConstraint:
"""klein constraint class"""
def __init__(self, min_norm):
"""init fun"""
<|body_0|>
def construct(self, x):
"""class construction"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KleinConstraint:
"""klein constraint class"""
def __init__(self, min_norm):
"""init fun"""
super(KleinConstraint, self).__init__()
self.norm = Norm(axis=-1)
self.min_norm = min_norm
self.maxnorm = 1 - 0.004
self.shape = Shape()
self.reshape = Reshap... | the_stack_v2_python_sparse | research/nlp/hypertext/src/poincare.py | mindspore-ai/models | train | 301 |
e27f524b287f45bdacf68e26fc8e63fceee91e06 | [
"n = len(init_val)\nself.segfunc = segfunc\nself.ide_ele = ide_ele\nself.num = 1 << (n - 1).bit_length()\nself.tree = [ide_ele] * 2 * self.num\nfor i in range(n):\n self.tree[self.num + i] = init_val[i]\nfor i in range(self.num - 1, 0, -1):\n self.tree[i] = self.segfunc(self.tree[2 * i], self.tree[2 * i + 1])... | <|body_start_0|>
n = len(init_val)
self.segfunc = segfunc
self.ide_ele = ide_ele
self.num = 1 << (n - 1).bit_length()
self.tree = [ide_ele] * 2 * self.num
for i in range(n):
self.tree[self.num + i] = init_val[i]
for i in range(self.num - 1, 0, -1):
... | init(init_val, ide_ele): 配列init_valで初期化 O(N) update(k, x): k番目の値をxに更新 O(logN) query(l, r): 区間[l, r)をsegfuncしたものを返す O(logN) | SegTree | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SegTree:
"""init(init_val, ide_ele): 配列init_valで初期化 O(N) update(k, x): k番目の値をxに更新 O(logN) query(l, r): 区間[l, r)をsegfuncしたものを返す O(logN)"""
def __init__(self, init_val, segfunc, ide_ele):
"""init_val: 配列の初期値 segfunc: 区間にしたい操作 ide_ele: 単位元 n: 要素数 num: n以上の最小の2のべき乗 tree: セグメント木(1-index)"... | stack_v2_sparse_classes_75kplus_train_067279 | 2,945 | no_license | [
{
"docstring": "init_val: 配列の初期値 segfunc: 区間にしたい操作 ide_ele: 単位元 n: 要素数 num: n以上の最小の2のべき乗 tree: セグメント木(1-index)",
"name": "__init__",
"signature": "def __init__(self, init_val, segfunc, ide_ele)"
},
{
"docstring": "k番目の値をxに更新 k: index(0-index) x: update value",
"name": "update",
"signatur... | 3 | stack_v2_sparse_classes_30k_train_024057 | Implement the Python class `SegTree` described below.
Class description:
init(init_val, ide_ele): 配列init_valで初期化 O(N) update(k, x): k番目の値をxに更新 O(logN) query(l, r): 区間[l, r)をsegfuncしたものを返す O(logN)
Method signatures and docstrings:
- def __init__(self, init_val, segfunc, ide_ele): init_val: 配列の初期値 segfunc: 区間にしたい操作 ide... | Implement the Python class `SegTree` described below.
Class description:
init(init_val, ide_ele): 配列init_valで初期化 O(N) update(k, x): k番目の値をxに更新 O(logN) query(l, r): 区間[l, r)をsegfuncしたものを返す O(logN)
Method signatures and docstrings:
- def __init__(self, init_val, segfunc, ide_ele): init_val: 配列の初期値 segfunc: 区間にしたい操作 ide... | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | <|skeleton|>
class SegTree:
"""init(init_val, ide_ele): 配列init_valで初期化 O(N) update(k, x): k番目の値をxに更新 O(logN) query(l, r): 区間[l, r)をsegfuncしたものを返す O(logN)"""
def __init__(self, init_val, segfunc, ide_ele):
"""init_val: 配列の初期値 segfunc: 区間にしたい操作 ide_ele: 単位元 n: 要素数 num: n以上の最小の2のべき乗 tree: セグメント木(1-index)"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SegTree:
"""init(init_val, ide_ele): 配列init_valで初期化 O(N) update(k, x): k番目の値をxに更新 O(logN) query(l, r): 区間[l, r)をsegfuncしたものを返す O(logN)"""
def __init__(self, init_val, segfunc, ide_ele):
"""init_val: 配列の初期値 segfunc: 区間にしたい操作 ide_ele: 単位元 n: 要素数 num: n以上の最小の2のべき乗 tree: セグメント木(1-index)"""
n ... | the_stack_v2_python_sparse | Python_codes/p03061/s654525017.py | Aasthaengg/IBMdataset | train | 0 |
53c6fd4999622528c9b791d5a22851709f29d7c6 | [
"if not root:\n return True\nreturn self.isValidBSTUtil(root)[0]",
"if not root.left and (not root.right):\n return (True, root.val, root.val)\nif root.left:\n isLeft, leftMin, leftMax = self.isValidBSTUtil(root.left)\nelse:\n isLeft, leftMin, leftMax = (True, float('inf'), float('-inf'))\nif root.rig... | <|body_start_0|>
if not root:
return True
return self.isValidBSTUtil(root)[0]
<|end_body_0|>
<|body_start_1|>
if not root.left and (not root.right):
return (True, root.val, root.val)
if root.left:
isLeft, leftMin, leftMax = self.isValidBSTUtil(root.le... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isValidBST(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isValidBSTUtil(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
return True
... | stack_v2_sparse_classes_75kplus_train_067280 | 1,439 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isValidBST",
"signature": "def isValidBST(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isValidBSTUtil",
"signature": "def isValidBSTUtil(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isValidBST(self, root): :type root: TreeNode :rtype: bool
- def isValidBSTUtil(self, root): :type root: TreeNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isValidBST(self, root): :type root: TreeNode :rtype: bool
- def isValidBSTUtil(self, root): :type root: TreeNode :rtype: bool
<|skeleton|>
class Solution:
def isValidBS... | 962803824b4173d553cb76940750dc249927b972 | <|skeleton|>
class Solution:
def isValidBST(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isValidBSTUtil(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def isValidBST(self, root):
""":type root: TreeNode :rtype: bool"""
if not root:
return True
return self.isValidBSTUtil(root)[0]
def isValidBSTUtil(self, root):
""":type root: TreeNode :rtype: bool"""
if not root.left and (not root.right):
... | the_stack_v2_python_sparse | ValidateBST.py | divyanshk/algorithms-and-data-structures | train | 0 | |
4ba6cc6f9c1795b0f091aef3592a11398adc4085 | [
"connection = sqlite3.connect(Constants.DATABASE)\nconnection.row_factory = sqlite3.Row\ncursor = connection.cursor()\nquery = 'SELECT * FROM ' + table + ' WHERE ' + attribute + ' = ' + str(value)\ncursor.execute(query)\nreturn cursor.fetchone()",
"connection = sqlite3.connect(Constants.DATABASE)\nconnection.row_... | <|body_start_0|>
connection = sqlite3.connect(Constants.DATABASE)
connection.row_factory = sqlite3.Row
cursor = connection.cursor()
query = 'SELECT * FROM ' + table + ' WHERE ' + attribute + ' = ' + str(value)
cursor.execute(query)
return cursor.fetchone()
<|end_body_0|>
... | DatabaseHelper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatabaseHelper:
def dbSelectRowByID(table, attribute, value):
"""Generic method to fetch contents from the local database."""
<|body_0|>
def dbUpdate(table, key_name, key_id, attribute, value):
"""Generic mathod to update data in table with the key_name matches key_i... | stack_v2_sparse_classes_75kplus_train_067281 | 2,600 | no_license | [
{
"docstring": "Generic method to fetch contents from the local database.",
"name": "dbSelectRowByID",
"signature": "def dbSelectRowByID(table, attribute, value)"
},
{
"docstring": "Generic mathod to update data in table with the key_name matches key_id",
"name": "dbUpdate",
"signature":... | 4 | stack_v2_sparse_classes_30k_train_050002 | Implement the Python class `DatabaseHelper` described below.
Class description:
Implement the DatabaseHelper class.
Method signatures and docstrings:
- def dbSelectRowByID(table, attribute, value): Generic method to fetch contents from the local database.
- def dbUpdate(table, key_name, key_id, attribute, value): Gen... | Implement the Python class `DatabaseHelper` described below.
Class description:
Implement the DatabaseHelper class.
Method signatures and docstrings:
- def dbSelectRowByID(table, attribute, value): Generic method to fetch contents from the local database.
- def dbUpdate(table, key_name, key_id, attribute, value): Gen... | 8273b84ff8ee0fdb9e4779fe5b605a44636f27a2 | <|skeleton|>
class DatabaseHelper:
def dbSelectRowByID(table, attribute, value):
"""Generic method to fetch contents from the local database."""
<|body_0|>
def dbUpdate(table, key_name, key_id, attribute, value):
"""Generic mathod to update data in table with the key_name matches key_i... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DatabaseHelper:
def dbSelectRowByID(table, attribute, value):
"""Generic method to fetch contents from the local database."""
connection = sqlite3.connect(Constants.DATABASE)
connection.row_factory = sqlite3.Row
cursor = connection.cursor()
query = 'SELECT * FROM ' + ta... | the_stack_v2_python_sparse | branches/hunvilbranch/src/common/DatabaseHelper.py | Panda3D-public-projects-archive/sfsu-multiplayer-game-dev-2011 | train | 0 | |
2628fa0c9f34505426440711d9d743c2dc218b38 | [
"m = 300\nctx.save_for_backward(k)\nk = k.double()\nanswer = (m / 2 - 1) * torch.log(k) - torch.log(sc.ive(m / 2 - 1, k)).cuda() - k - m / 2 * np.log(2 * np.pi)\nanswer = answer.float()\nreturn answer",
"k, = ctx.saved_tensors\nm = 300\nk = k.double()\nx = -(scipy.special.ive(m / 2, k) / scipy.special.ive(m / 2 -... | <|body_start_0|>
m = 300
ctx.save_for_backward(k)
k = k.double()
answer = (m / 2 - 1) * torch.log(k) - torch.log(sc.ive(m / 2 - 1, k)).cuda() - k - m / 2 * np.log(2 * np.pi)
answer = answer.float()
return answer
<|end_body_0|>
<|body_start_1|>
k, = ctx.saved_tens... | The exponentially scaled modified Bessel function of the first kind | Logcmk | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Logcmk:
"""The exponentially scaled modified Bessel function of the first kind"""
def forward(ctx, k):
"""In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward... | stack_v2_sparse_classes_75kplus_train_067282 | 2,692 | permissive | [
{
"docstring": "In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the ctx.save_for_backward method.",
... | 2 | stack_v2_sparse_classes_30k_train_029156 | Implement the Python class `Logcmk` described below.
Class description:
The exponentially scaled modified Bessel function of the first kind
Method signatures and docstrings:
- def forward(ctx, k): In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context ... | Implement the Python class `Logcmk` described below.
Class description:
The exponentially scaled modified Bessel function of the first kind
Method signatures and docstrings:
- def forward(ctx, k): In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context ... | 99cba1030ed8c012a453bc7715830fc99fb980dc | <|skeleton|>
class Logcmk:
"""The exponentially scaled modified Bessel function of the first kind"""
def forward(ctx, k):
"""In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Logcmk:
"""The exponentially scaled modified Bessel function of the first kind"""
def forward(ctx, k):
"""In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation.... | the_stack_v2_python_sparse | models/loss/old_vonmises.py | jamesoneill12/LayerFusion | train | 2 |
8fed59678ddeabe8b7060bdccc4745817cd442ab | [
"self.expression_data = expression_data\nself.calculator = calculator\nself.rm_outliers = rm_outliers",
"data = None\nif isinstance(measurments, dict):\n data = measurments\n measurments = list(measurments.values())\nmeasurments = np.array(measurments)\nupper_quartile = np.percentile(measurments, 75)\nlower... | <|body_start_0|>
self.expression_data = expression_data
self.calculator = calculator
self.rm_outliers = rm_outliers
<|end_body_0|>
<|body_start_1|>
data = None
if isinstance(measurments, dict):
data = measurments
measurments = list(measurments.values())
... | Base class for navigation of similarity calculation between specified genes. | SimilarityCalculatorNavigator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimilarityCalculatorNavigator:
"""Base class for navigation of similarity calculation between specified genes."""
def __init__(self, expression_data: GeneExpression, calculator: SimilarityCalculator, rm_outliers: bool=True):
""":param expression_data: Data for all genes :param calcul... | stack_v2_sparse_classes_75kplus_train_067283 | 43,977 | no_license | [
{
"docstring": ":param expression_data: Data for all genes :param calculator: SimilarityCalculator used in all calculations :param rm_outliers: should outliers be removed before similarity statistics calculation",
"name": "__init__",
"signature": "def __init__(self, expression_data: GeneExpression, calc... | 2 | stack_v2_sparse_classes_30k_train_032550 | Implement the Python class `SimilarityCalculatorNavigator` described below.
Class description:
Base class for navigation of similarity calculation between specified genes.
Method signatures and docstrings:
- def __init__(self, expression_data: GeneExpression, calculator: SimilarityCalculator, rm_outliers: bool=True):... | Implement the Python class `SimilarityCalculatorNavigator` described below.
Class description:
Base class for navigation of similarity calculation between specified genes.
Method signatures and docstrings:
- def __init__(self, expression_data: GeneExpression, calculator: SimilarityCalculator, rm_outliers: bool=True):... | 6d11df5e8ca37e53e048d261ac287f859ba6e9b9 | <|skeleton|>
class SimilarityCalculatorNavigator:
"""Base class for navigation of similarity calculation between specified genes."""
def __init__(self, expression_data: GeneExpression, calculator: SimilarityCalculator, rm_outliers: bool=True):
""":param expression_data: Data for all genes :param calcul... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SimilarityCalculatorNavigator:
"""Base class for navigation of similarity calculation between specified genes."""
def __init__(self, expression_data: GeneExpression, calculator: SimilarityCalculator, rm_outliers: bool=True):
""":param expression_data: Data for all genes :param calculator: Similar... | the_stack_v2_python_sparse | correlation_enrichment/library_correlation_enrichment.py | biolab/baylor-dicty | train | 0 |
4b1bb7bb46dc83b5312d2116b49f0b1aacea8a9a | [
"dist_utils.validate_callbacks(input_callbacks=callbacks, optimizer=model.optimizer)\ndist_utils.validate_inputs(x, y)\nbatch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(model._distribution_strategy, x, batch_size, steps_per_epoch, ModeKeys.TRAIN, validation_split=validation_split)\nbatch_size = ... | <|body_start_0|>
dist_utils.validate_callbacks(input_callbacks=callbacks, optimizer=model.optimizer)
dist_utils.validate_inputs(x, y)
batch_size, steps_per_epoch = dist_utils.process_batch_and_step_size(model._distribution_strategy, x, batch_size, steps_per_epoch, ModeKeys.TRAIN, validation_spli... | Training loop for distribution strategy with single worker. | DistributionSingleWorkerTrainingLoop | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DistributionSingleWorkerTrainingLoop:
"""Training loop for distribution strategy with single worker."""
def fit(self, model, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, ... | stack_v2_sparse_classes_75kplus_train_067284 | 29,780 | permissive | [
{
"docstring": "Fit loop for Distribution Strategies.",
"name": "fit",
"signature": "def fit(self, model, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoc... | 3 | stack_v2_sparse_classes_30k_train_007087 | Implement the Python class `DistributionSingleWorkerTrainingLoop` described below.
Class description:
Training loop for distribution strategy with single worker.
Method signatures and docstrings:
- def fit(self, model, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validat... | Implement the Python class `DistributionSingleWorkerTrainingLoop` described below.
Class description:
Training loop for distribution strategy with single worker.
Method signatures and docstrings:
- def fit(self, model, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validat... | a7f3934a67900720af3d3b15389551483bee50b8 | <|skeleton|>
class DistributionSingleWorkerTrainingLoop:
"""Training loop for distribution strategy with single worker."""
def fit(self, model, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DistributionSingleWorkerTrainingLoop:
"""Training loop for distribution strategy with single worker."""
def fit(self, model, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch... | the_stack_v2_python_sparse | tensorflow/python/keras/engine/training_distributed_v1.py | tensorflow/tensorflow | train | 208,740 |
a34e7410e522eb674682abd5dd5eebc8cf6d3306 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn EdiscoveryEstimateOperation()",
"from .case_operation import CaseOperation\nfrom .ediscovery_search import EdiscoverySearch\nfrom .case_operation import CaseOperation\nfrom .ediscovery_search import EdiscoverySearch\nfields: Dict[str, ... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return EdiscoveryEstimateOperation()
<|end_body_0|>
<|body_start_1|>
from .case_operation import CaseOperation
from .ediscovery_search import EdiscoverySearch
from .case_operation impor... | EdiscoveryEstimateOperation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EdiscoveryEstimateOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryEstimateOperation:
"""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 a... | stack_v2_sparse_classes_75kplus_train_067285 | 3,923 | 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: EdiscoveryEstimateOperation",
"name": "create_from_discriminator_value",
"signature": "def create_from_discr... | 3 | stack_v2_sparse_classes_30k_train_026976 | Implement the Python class `EdiscoveryEstimateOperation` described below.
Class description:
Implement the EdiscoveryEstimateOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryEstimateOperation: Creates a new instance of the appr... | Implement the Python class `EdiscoveryEstimateOperation` described below.
Class description:
Implement the EdiscoveryEstimateOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryEstimateOperation: Creates a new instance of the appr... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class EdiscoveryEstimateOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryEstimateOperation:
"""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 a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EdiscoveryEstimateOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryEstimateOperation:
"""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 ... | the_stack_v2_python_sparse | msgraph/generated/models/security/ediscovery_estimate_operation.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
5c8681a6edfe120479a4d663235935e2e5882fa1 | [
"super().__init__()\nBlock = self.name_to_block(block_name)\nN = self.num_blocks_per_layer(Block, depth)\nself.in_channels = 3\nself.channels = 16\nself.conv1 = nn.Conv2d(self.in_channels, self.channels, kernel_size=3, padding=1, bias=False)\nself.in_channels = 16\nself.layer1 = self.make_layer(Block, self.channels... | <|body_start_0|>
super().__init__()
Block = self.name_to_block(block_name)
N = self.num_blocks_per_layer(Block, depth)
self.in_channels = 3
self.channels = 16
self.conv1 = nn.Conv2d(self.in_channels, self.channels, kernel_size=3, padding=1, bias=False)
self.in_cha... | pre-activated ResNet. | PreResNet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreResNet:
"""pre-activated ResNet."""
def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs):
"""CTOR for the model."""
<|body_0|>
def make_layer(self, Block, channels, num_blocks, stride=1, **kwargs):
"""create a layer of blocks. NOTE: s... | stack_v2_sparse_classes_75kplus_train_067286 | 6,340 | permissive | [
{
"docstring": "CTOR for the model.",
"name": "__init__",
"signature": "def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs)"
},
{
"docstring": "create a layer of blocks. NOTE: self.in_channels will be updated NOTE: channels is the unexpended number of channels",
"n... | 5 | stack_v2_sparse_classes_30k_train_031752 | Implement the Python class `PreResNet` described below.
Class description:
pre-activated ResNet.
Method signatures and docstrings:
- def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs): CTOR for the model.
- def make_layer(self, Block, channels, num_blocks, stride=1, **kwargs): create a lay... | Implement the Python class `PreResNet` described below.
Class description:
pre-activated ResNet.
Method signatures and docstrings:
- def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs): CTOR for the model.
- def make_layer(self, Block, channels, num_blocks, stride=1, **kwargs): create a lay... | f81c417d3754102c902bd153809130e12607bd7d | <|skeleton|>
class PreResNet:
"""pre-activated ResNet."""
def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs):
"""CTOR for the model."""
<|body_0|>
def make_layer(self, Block, channels, num_blocks, stride=1, **kwargs):
"""create a layer of blocks. NOTE: s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PreResNet:
"""pre-activated ResNet."""
def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs):
"""CTOR for the model."""
super().__init__()
Block = self.name_to_block(block_name)
N = self.num_blocks_per_layer(Block, depth)
self.in_channels =... | the_stack_v2_python_sparse | gumi/models/preresnet.py | kumasento/gconv-prune | train | 10 |
1d4e7c826e5fce9e495448509ddbb2c71ae57ec7 | [
"if embedding_dict is None:\n embedding_dict = get_elemental_embeddings()\nself.embedding_dict = embedding_dict",
"features = []\nfor atom in atoms:\n emb = 0\n for k, v in atom.items():\n emb += np.array(self.embedding_dict[k]) * v\n features.append(emb)\nreturn np.array(features).reshape((len... | <|body_start_0|>
if embedding_dict is None:
embedding_dict = get_elemental_embeddings()
self.embedding_dict = embedding_dict
<|end_body_0|>
<|body_start_1|>
features = []
for atom in atoms:
emb = 0
for k, v in atom.items():
emb += np.a... | Fixed Atom embedding map, used with CrystalGraphDisordered | _AtomEmbeddingMap | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _AtomEmbeddingMap:
"""Fixed Atom embedding map, used with CrystalGraphDisordered"""
def __init__(self, embedding_dict: dict | None=None):
"""Args: embedding_dict (dict): element to element vector dictionary"""
<|body_0|>
def convert(self, atoms: list) -> np.ndarray:
... | stack_v2_sparse_classes_75kplus_train_067287 | 5,715 | permissive | [
{
"docstring": "Args: embedding_dict (dict): element to element vector dictionary",
"name": "__init__",
"signature": "def __init__(self, embedding_dict: dict | None=None)"
},
{
"docstring": "Convert atom {symbol: fraction} list to numeric features",
"name": "convert",
"signature": "def c... | 2 | null | Implement the Python class `_AtomEmbeddingMap` described below.
Class description:
Fixed Atom embedding map, used with CrystalGraphDisordered
Method signatures and docstrings:
- def __init__(self, embedding_dict: dict | None=None): Args: embedding_dict (dict): element to element vector dictionary
- def convert(self, ... | Implement the Python class `_AtomEmbeddingMap` described below.
Class description:
Fixed Atom embedding map, used with CrystalGraphDisordered
Method signatures and docstrings:
- def __init__(self, embedding_dict: dict | None=None): Args: embedding_dict (dict): element to element vector dictionary
- def convert(self, ... | f3705760266f24b62c4810dd4abacfc25f4f64ae | <|skeleton|>
class _AtomEmbeddingMap:
"""Fixed Atom embedding map, used with CrystalGraphDisordered"""
def __init__(self, embedding_dict: dict | None=None):
"""Args: embedding_dict (dict): element to element vector dictionary"""
<|body_0|>
def convert(self, atoms: list) -> np.ndarray:
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _AtomEmbeddingMap:
"""Fixed Atom embedding map, used with CrystalGraphDisordered"""
def __init__(self, embedding_dict: dict | None=None):
"""Args: embedding_dict (dict): element to element vector dictionary"""
if embedding_dict is None:
embedding_dict = get_elemental_embedding... | the_stack_v2_python_sparse | megnet/data/crystal.py | materialsvirtuallab/megnet | train | 471 |
95be8e650123f600fa47ac72721f7c89e0f709f3 | [
"self.game_screen = Background()\nself.check_mouse = True\nself.check_die = False\nself.check_create = True\npets = create_pets(self.game_screen, CAT_IMAGES.CAT, FOX_IMAGES.FOX, STICH_IMAGES.STICH)\nself.newpet = choose_pet(pets)\nself.check_not_die = True\nself.check_moving = 'none'\nself.check_play = False\nself.... | <|body_start_0|>
self.game_screen = Background()
self.check_mouse = True
self.check_die = False
self.check_create = True
pets = create_pets(self.game_screen, CAT_IMAGES.CAT, FOX_IMAGES.FOX, STICH_IMAGES.STICH)
self.newpet = choose_pet(pets)
self.check_not_die = Tr... | Game | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Game:
def __init__(self):
"""Creating a screen where user can choose a pet"""
<|body_0|>
def run(self):
"""The main game loop"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.game_screen = Background()
self.check_mouse = True
sel... | stack_v2_sparse_classes_75kplus_train_067288 | 2,153 | no_license | [
{
"docstring": "Creating a screen where user can choose a pet",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "The main game loop",
"name": "run",
"signature": "def run(self)"
}
] | 2 | null | Implement the Python class `Game` described below.
Class description:
Implement the Game class.
Method signatures and docstrings:
- def __init__(self): Creating a screen where user can choose a pet
- def run(self): The main game loop | Implement the Python class `Game` described below.
Class description:
Implement the Game class.
Method signatures and docstrings:
- def __init__(self): Creating a screen where user can choose a pet
- def run(self): The main game loop
<|skeleton|>
class Game:
def __init__(self):
"""Creating a screen wher... | 106586ca5a2bd41a23d3f9de614263796882955f | <|skeleton|>
class Game:
def __init__(self):
"""Creating a screen where user can choose a pet"""
<|body_0|>
def run(self):
"""The main game loop"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Game:
def __init__(self):
"""Creating a screen where user can choose a pet"""
self.game_screen = Background()
self.check_mouse = True
self.check_die = False
self.check_create = True
pets = create_pets(self.game_screen, CAT_IMAGES.CAT, FOX_IMAGES.FOX, STICH_IMAGE... | the_stack_v2_python_sparse | Tamagochi/game.py | frewerins/all_projects | train | 0 | |
243f3ea2c07fa52e090941717b5923167bde9de1 | [
"try:\n html2pdf_data = kwargs.get('html2pdf_data')\n html2pdf_data_type = kwargs.get('html2pdf_data_type')\n html2pdf_stylesheet = self.get_select_param(kwargs.get('html2pdf_stylesheet'))\n log = logging.getLogger(__name__)\n log.info('html2pdf_data: %s', html2pdf_data)\n log.info('html2pdf_data_... | <|body_start_0|>
try:
html2pdf_data = kwargs.get('html2pdf_data')
html2pdf_data_type = kwargs.get('html2pdf_data_type')
html2pdf_stylesheet = self.get_select_param(kwargs.get('html2pdf_stylesheet'))
log = logging.getLogger(__name__)
log.info('html2pdf_... | Component that implements Resilient function 'utilities_html2pdf' This function takes html, either complete "<html> ... </html>" or a fragment "<table> ... </table>" and converts it to a pdf image. That image is converted to base64 and returned. | FunctionComponent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FunctionComponent:
"""Component that implements Resilient function 'utilities_html2pdf' This function takes html, either complete "<html> ... </html>" or a fragment "<table> ... </table>" and converts it to a pdf image. That image is converted to base64 and returned."""
def _fn_html2pdf_func... | stack_v2_sparse_classes_75kplus_train_067289 | 3,058 | permissive | [
{
"docstring": "Function: function accessible from Resilient to render html to binary pdf format",
"name": "_fn_html2pdf_function",
"signature": "def _fn_html2pdf_function(self, event, *args, **kwargs)"
},
{
"docstring": "convert html data to pdf :param input_data: url to read html or html alrea... | 2 | stack_v2_sparse_classes_30k_train_030016 | Implement the Python class `FunctionComponent` described below.
Class description:
Component that implements Resilient function 'utilities_html2pdf' This function takes html, either complete "<html> ... </html>" or a fragment "<table> ... </table>" and converts it to a pdf image. That image is converted to base64 and ... | Implement the Python class `FunctionComponent` described below.
Class description:
Component that implements Resilient function 'utilities_html2pdf' This function takes html, either complete "<html> ... </html>" or a fragment "<table> ... </table>" and converts it to a pdf image. That image is converted to base64 and ... | 6878c78b94eeca407998a41ce8db2cc00f2b6758 | <|skeleton|>
class FunctionComponent:
"""Component that implements Resilient function 'utilities_html2pdf' This function takes html, either complete "<html> ... </html>" or a fragment "<table> ... </table>" and converts it to a pdf image. That image is converted to base64 and returned."""
def _fn_html2pdf_func... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FunctionComponent:
"""Component that implements Resilient function 'utilities_html2pdf' This function takes html, either complete "<html> ... </html>" or a fragment "<table> ... </table>" and converts it to a pdf image. That image is converted to base64 and returned."""
def _fn_html2pdf_function(self, ev... | the_stack_v2_python_sparse | fn_html2pdf/fn_html2pdf/components/html2pdf.py | ibmresilient/resilient-community-apps | train | 81 |
7e2c2b446199a82141470cc9a5b4c74c3b0e2ae2 | [
"keys = {key: value for key, value in cls.__dict__.items() if not isinstance(value, classmethod) and (not isinstance(value, staticmethod)) and (not callable(value)) and (not key.startswith('__'))}\nrequired = [v for k, v in keys.items() if not k.endswith('_')]\noptional = [v for k, v in keys.items() if k.endswith('... | <|body_start_0|>
keys = {key: value for key, value in cls.__dict__.items() if not isinstance(value, classmethod) and (not isinstance(value, staticmethod)) and (not callable(value)) and (not key.startswith('__'))}
required = [v for k, v in keys.items() if not k.endswith('_')]
optional = [v for k,... | Class to validate dictionary configurations. | ConfigKeys | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigKeys:
"""Class to validate dictionary configurations."""
def get_keys(cls) -> Tuple[List[str], List[str]]:
"""Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class."""
... | stack_v2_sparse_classes_75kplus_train_067290 | 3,020 | permissive | [
{
"docstring": "Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class.",
"name": "get_keys",
"signature": "def get_keys(cls) -> Tuple[List[str], List[str]]"
},
{
"docstring": "Checks wheth... | 2 | stack_v2_sparse_classes_30k_train_003105 | Implement the Python class `ConfigKeys` described below.
Class description:
Class to validate dictionary configurations.
Method signatures and docstrings:
- def get_keys(cls) -> Tuple[List[str], List[str]]: Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are ... | Implement the Python class `ConfigKeys` described below.
Class description:
Class to validate dictionary configurations.
Method signatures and docstrings:
- def get_keys(cls) -> Tuple[List[str], List[str]]: Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are ... | f1499e9c3fee00fd1d66de14cab66c4472c0085d | <|skeleton|>
class ConfigKeys:
"""Class to validate dictionary configurations."""
def get_keys(cls) -> Tuple[List[str], List[str]]:
"""Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConfigKeys:
"""Class to validate dictionary configurations."""
def get_keys(cls) -> Tuple[List[str], List[str]]:
"""Gets all the required and optional config keys for this class. Returns: A tuple (required, optional) which are lists of the required/optional keys for this class."""
keys = ... | the_stack_v2_python_sparse | src/zenml/config/config_keys.py | stefannica/zenml | train | 0 |
f00796aca417e00edbf8d3541fb9bd657e037183 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn List_()",
"from .base_item import BaseItem\nfrom .column_definition import ColumnDefinition\nfrom .content_type import ContentType\nfrom .drive import Drive\nfrom .list_info import ListInfo\nfrom .list_item import ListItem\nfrom .rich_... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return List_()
<|end_body_0|>
<|body_start_1|>
from .base_item import BaseItem
from .column_definition import ColumnDefinition
from .content_type import ContentType
from .drive ... | List_ | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class List_:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> List_:
"""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: List_"""
... | stack_v2_sparse_classes_75kplus_train_067291 | 5,839 | 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: List_",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(parse_n... | 3 | stack_v2_sparse_classes_30k_train_037808 | Implement the Python class `List_` described below.
Class description:
Implement the List_ class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> List_: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The p... | Implement the Python class `List_` described below.
Class description:
Implement the List_ class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> List_: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The p... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class List_:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> List_:
"""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: List_"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class List_:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> List_:
"""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: List_"""
if not pars... | the_stack_v2_python_sparse | msgraph/generated/models/list_.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
1c9e8611fe8d8d15df32d591b35e4be511bdd2cd | [
"import collections\nassert isinstance(e, collections.Hashable), 'input is not hashable'\nself.vals.pop(e, 0)",
"import collections\nassert isinstance(e, collections.Hashable), 'input is not hashable'\nreturn e in self.vals.keys()"
] | <|body_start_0|>
import collections
assert isinstance(e, collections.Hashable), 'input is not hashable'
self.vals.pop(e, 0)
<|end_body_0|>
<|body_start_1|>
import collections
assert isinstance(e, collections.Hashable), 'input is not hashable'
return e in self.vals.keys()... | ASet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ASet:
def remove(self, e):
"""assumes e is hashable removes e from self"""
<|body_0|>
def is_in(self, e):
"""assumes e is hashable returns True if e has been inserted in self and not subsequently removed, and False otherwise."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_75kplus_train_067292 | 3,068 | no_license | [
{
"docstring": "assumes e is hashable removes e from self",
"name": "remove",
"signature": "def remove(self, e)"
},
{
"docstring": "assumes e is hashable returns True if e has been inserted in self and not subsequently removed, and False otherwise.",
"name": "is_in",
"signature": "def is... | 2 | stack_v2_sparse_classes_30k_train_054380 | Implement the Python class `ASet` described below.
Class description:
Implement the ASet class.
Method signatures and docstrings:
- def remove(self, e): assumes e is hashable removes e from self
- def is_in(self, e): assumes e is hashable returns True if e has been inserted in self and not subsequently removed, and F... | Implement the Python class `ASet` described below.
Class description:
Implement the ASet class.
Method signatures and docstrings:
- def remove(self, e): assumes e is hashable removes e from self
- def is_in(self, e): assumes e is hashable returns True if e has been inserted in self and not subsequently removed, and F... | b60eaed255598118319c1efe6da9a78cbcf58f25 | <|skeleton|>
class ASet:
def remove(self, e):
"""assumes e is hashable removes e from self"""
<|body_0|>
def is_in(self, e):
"""assumes e is hashable returns True if e has been inserted in self and not subsequently removed, and False otherwise."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ASet:
def remove(self, e):
"""assumes e is hashable removes e from self"""
import collections
assert isinstance(e, collections.Hashable), 'input is not hashable'
self.vals.pop(e, 0)
def is_in(self, e):
"""assumes e is hashable returns True if e has been inserted in... | the_stack_v2_python_sparse | Final/problem6.py | aanzolaavila/MITx-6.00.1x | train | 0 | |
35cd4b3cf420be17e04d516e265e77d9d72c28e5 | [
"self.switch_profiles = switch_profiles\nself.switches = switches\nself.stacks = stacks\nself.igmp_snooping_enabled = igmp_snooping_enabled\nself.flood_unknown_multicast_traffic_enabled = flood_unknown_multicast_traffic_enabled",
"if dictionary is None:\n return None\nigmp_snooping_enabled = dictionary.get('ig... | <|body_start_0|>
self.switch_profiles = switch_profiles
self.switches = switches
self.stacks = stacks
self.igmp_snooping_enabled = igmp_snooping_enabled
self.flood_unknown_multicast_traffic_enabled = flood_unknown_multicast_traffic_enabled
<|end_body_0|>
<|body_start_1|>
... | Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of switch stack ids for non-template network... | Override1Model | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Override1Model:
"""Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of... | stack_v2_sparse_classes_75kplus_train_067293 | 3,029 | permissive | [
{
"docstring": "Constructor for the Override1Model class",
"name": "__init__",
"signature": "def __init__(self, igmp_snooping_enabled=None, flood_unknown_multicast_traffic_enabled=None, switch_profiles=None, switches=None, stacks=None)"
},
{
"docstring": "Creates an instance of this model from a... | 2 | stack_v2_sparse_classes_30k_train_005191 | Implement the Python class `Override1Model` described below.
Class description:
Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template n... | Implement the Python class `Override1Model` described below.
Class description:
Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template n... | 9894089eb013318243ae48869cc5130eb37f80c0 | <|skeleton|>
class Override1Model:
"""Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Override1Model:
"""Implementation of the 'Override1' model. TODO: type model description here. Attributes: switch_profiles (list of string): List of switch profiles ids for template network switches (list of string): List of switch serials for non-template network stacks (list of string): List of switch stack... | the_stack_v2_python_sparse | meraki_sdk/models/override_1_model.py | RaulCatalano/meraki-python-sdk | train | 1 |
8a80e3f9249f3fae3b1e26d093550f08fc1aae47 | [
"max_idx = nums.index(max(nums))\nfor idx, x in enumerate(nums):\n if idx != max_idx:\n if nums[max_idx] < 2 * x:\n return -1\nreturn max_idx",
"m = max(nums)\nif all((m >= 2 * x for x in nums if x != m)):\n return nums.index(m)\nreturn -1"
] | <|body_start_0|>
max_idx = nums.index(max(nums))
for idx, x in enumerate(nums):
if idx != max_idx:
if nums[max_idx] < 2 * x:
return -1
return max_idx
<|end_body_0|>
<|body_start_1|>
m = max(nums)
if all((m >= 2 * x for x in nums if... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def dominant_index(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def dominantIndex(self, nums):
"""any(iterable, /) Return True if bool(x) is True for any x in the iterable. If the iterable is empty, return False. all(iterable, /) Return T... | stack_v2_sparse_classes_75kplus_train_067294 | 846 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "dominant_index",
"signature": "def dominant_index(self, nums)"
},
{
"docstring": "any(iterable, /) Return True if bool(x) is True for any x in the iterable. If the iterable is empty, return False. all(iterable, /) Return True if bool(x... | 2 | stack_v2_sparse_classes_30k_train_041620 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def dominant_index(self, nums): :type nums: List[int] :rtype: int
- def dominantIndex(self, nums): any(iterable, /) Return True if bool(x) is True for any x in the iterable. If t... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def dominant_index(self, nums): :type nums: List[int] :rtype: int
- def dominantIndex(self, nums): any(iterable, /) Return True if bool(x) is True for any x in the iterable. If t... | cc7740026c3774be21ab924b99ae7596ef20d0e4 | <|skeleton|>
class Solution:
def dominant_index(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def dominantIndex(self, nums):
"""any(iterable, /) Return True if bool(x) is True for any x in the iterable. If the iterable is empty, return False. all(iterable, /) Return T... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def dominant_index(self, nums):
""":type nums: List[int] :rtype: int"""
max_idx = nums.index(max(nums))
for idx, x in enumerate(nums):
if idx != max_idx:
if nums[max_idx] < 2 * x:
return -1
return max_idx
def domina... | the_stack_v2_python_sparse | data_structure/arrays_and_strings/747_dominant_index.py | yangtao0304/hands-on-programming-exercise | train | 0 | |
d5a55efd4830497d20281ac9a049b8c32ce863c2 | [
"data = {'ok': False, 'message': exception.message}\nresult = dumps(data) + '\\n'\nresp = make_response(result, exception.code)\nresp.headers['Content-Type'] = self.content_type\nreturn resp",
"method = getattr(self, request.method.lower(), None)\nif method is None and request.method == 'HEAD':\n method = geta... | <|body_start_0|>
data = {'ok': False, 'message': exception.message}
result = dumps(data) + '\n'
resp = make_response(result, exception.code)
resp.headers['Content-Type'] = self.content_type
return resp
<|end_body_0|>
<|body_start_1|>
method = getattr(self, request.method... | 自定义 View 类 json 序列化，异常处理，装饰器支持 | RestView | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestView:
"""自定义 View 类 json 序列化，异常处理，装饰器支持"""
def handler_error(self, exception):
"""处理异常"""
<|body_0|>
def dispatch_request(self, *args, **kwargs):
"""重写父类方法，支持数据自动序列化"""
<|body_1|>
def unpack(value):
"""解析视图方法返回值"""
<|body_2|>
<|e... | stack_v2_sparse_classes_75kplus_train_067295 | 3,102 | permissive | [
{
"docstring": "处理异常",
"name": "handler_error",
"signature": "def handler_error(self, exception)"
},
{
"docstring": "重写父类方法，支持数据自动序列化",
"name": "dispatch_request",
"signature": "def dispatch_request(self, *args, **kwargs)"
},
{
"docstring": "解析视图方法返回值",
"name": "unpack",
... | 3 | stack_v2_sparse_classes_30k_train_036454 | Implement the Python class `RestView` described below.
Class description:
自定义 View 类 json 序列化，异常处理，装饰器支持
Method signatures and docstrings:
- def handler_error(self, exception): 处理异常
- def dispatch_request(self, *args, **kwargs): 重写父类方法，支持数据自动序列化
- def unpack(value): 解析视图方法返回值 | Implement the Python class `RestView` described below.
Class description:
自定义 View 类 json 序列化，异常处理，装饰器支持
Method signatures and docstrings:
- def handler_error(self, exception): 处理异常
- def dispatch_request(self, *args, **kwargs): 重写父类方法，支持数据自动序列化
- def unpack(value): 解析视图方法返回值
<|skeleton|>
class RestView:
"""自定义 ... | 655bb48711537efb7856a50dcab55f2380ea127a | <|skeleton|>
class RestView:
"""自定义 View 类 json 序列化，异常处理，装饰器支持"""
def handler_error(self, exception):
"""处理异常"""
<|body_0|>
def dispatch_request(self, *args, **kwargs):
"""重写父类方法，支持数据自动序列化"""
<|body_1|>
def unpack(value):
"""解析视图方法返回值"""
<|body_2|>
<|e... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RestView:
"""自定义 View 类 json 序列化，异常处理，装饰器支持"""
def handler_error(self, exception):
"""处理异常"""
data = {'ok': False, 'message': exception.message}
result = dumps(data) + '\n'
resp = make_response(result, exception.code)
resp.headers['Content-Type'] = self.content_typ... | the_stack_v2_python_sparse | rmon/common/rest.py | lvsoso/rmon | train | 0 |
46c1bf38178e10091bce874178c79c8292b3cf46 | [
"event = {'event': 'pause'}\nredis.publish(config.PLAYER_CHANNEL, json.dumps(event))\nreturn http.Created(event)",
"event = {'event': 'resume'}\nredis.publish(config.PLAYER_CHANNEL, json.dumps(event))\nreturn http.OK(event)"
] | <|body_start_0|>
event = {'event': 'pause'}
redis.publish(config.PLAYER_CHANNEL, json.dumps(event))
return http.Created(event)
<|end_body_0|>
<|body_start_1|>
event = {'event': 'resume'}
redis.publish(config.PLAYER_CHANNEL, json.dumps(event))
return http.OK(event)
<|end_... | The pause resources allows the payer to paused and unpaused via POST for pause and DELTETE to unpause the player. | PauseView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PauseView:
"""The pause resources allows the payer to paused and unpaused via POST for pause and DELTETE to unpause the player."""
def post(self):
"""Pauses the player."""
<|body_0|>
def delete(self):
"""Unapuses the player."""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_75kplus_train_067296 | 12,943 | no_license | [
{
"docstring": "Pauses the player.",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Unapuses the player.",
"name": "delete",
"signature": "def delete(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017598 | Implement the Python class `PauseView` described below.
Class description:
The pause resources allows the payer to paused and unpaused via POST for pause and DELTETE to unpause the player.
Method signatures and docstrings:
- def post(self): Pauses the player.
- def delete(self): Unapuses the player. | Implement the Python class `PauseView` described below.
Class description:
The pause resources allows the payer to paused and unpaused via POST for pause and DELTETE to unpause the player.
Method signatures and docstrings:
- def post(self): Pauses the player.
- def delete(self): Unapuses the player.
<|skeleton|>
cla... | 817766c6d2e2660291b723274d345ce5eb40ab77 | <|skeleton|>
class PauseView:
"""The pause resources allows the payer to paused and unpaused via POST for pause and DELTETE to unpause the player."""
def post(self):
"""Pauses the player."""
<|body_0|>
def delete(self):
"""Unapuses the player."""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PauseView:
"""The pause resources allows the payer to paused and unpaused via POST for pause and DELTETE to unpause the player."""
def post(self):
"""Pauses the player."""
event = {'event': 'pause'}
redis.publish(config.PLAYER_CHANNEL, json.dumps(event))
return http.Create... | the_stack_v2_python_sparse | fm/views/player.py | thisissoon/FM-API | train | 3 |
1851520e5358f6e3556853cb985294009951490d | [
"crc = Crc(g32)\nstr_rep = 'poly = 0x104C11DB7\\nreverse = True\\ninitCrc = 0xFFFFFFFF\\nxorOut = 0x00000000\\ncrcValue = 0xFFFFFFFF'\nself.assertEqual(str(crc), str_rep)\nself.assertEqual(crc.digest(), b'\\xff\\xff\\xff\\xff')\nself.assertEqual(crc.hexdigest(), 'FFFFFFFF')\ncrc.update(self.msg)\nself.assertEqua... | <|body_start_0|>
crc = Crc(g32)
str_rep = 'poly = 0x104C11DB7\nreverse = True\ninitCrc = 0xFFFFFFFF\nxorOut = 0x00000000\ncrcValue = 0xFFFFFFFF'
self.assertEqual(str(crc), str_rep)
self.assertEqual(crc.digest(), b'\xff\xff\xff\xff')
self.assertEqual(crc.hexdigest(), 'FFFFFFFF'... | Verify the Crc class | CrcClassTest | [
"BSD-3-Clause",
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CrcClassTest:
"""Verify the Crc class"""
def test_simple_crc32_class(self):
"""Verify the CRC class when not using xorOut"""
<|body_0|>
def test_full_crc32_class(self):
"""Verify the CRC class when using xorOut"""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_75kplus_train_067297 | 18,433 | permissive | [
{
"docstring": "Verify the CRC class when not using xorOut",
"name": "test_simple_crc32_class",
"signature": "def test_simple_crc32_class(self)"
},
{
"docstring": "Verify the CRC class when using xorOut",
"name": "test_full_crc32_class",
"signature": "def test_full_crc32_class(self)"
}... | 2 | stack_v2_sparse_classes_30k_train_043082 | Implement the Python class `CrcClassTest` described below.
Class description:
Verify the Crc class
Method signatures and docstrings:
- def test_simple_crc32_class(self): Verify the CRC class when not using xorOut
- def test_full_crc32_class(self): Verify the CRC class when using xorOut | Implement the Python class `CrcClassTest` described below.
Class description:
Verify the Crc class
Method signatures and docstrings:
- def test_simple_crc32_class(self): Verify the CRC class when not using xorOut
- def test_full_crc32_class(self): Verify the CRC class when using xorOut
<|skeleton|>
class CrcClassTes... | 53102de187a48ac2cfc241fef54dcbc29c453a8e | <|skeleton|>
class CrcClassTest:
"""Verify the Crc class"""
def test_simple_crc32_class(self):
"""Verify the CRC class when not using xorOut"""
<|body_0|>
def test_full_crc32_class(self):
"""Verify the CRC class when using xorOut"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CrcClassTest:
"""Verify the Crc class"""
def test_simple_crc32_class(self):
"""Verify the CRC class when not using xorOut"""
crc = Crc(g32)
str_rep = 'poly = 0x104C11DB7\nreverse = True\ninitCrc = 0xFFFFFFFF\nxorOut = 0x00000000\ncrcValue = 0xFFFFFFFF'
self.assertEqual(... | the_stack_v2_python_sparse | third_party/gsutil/third_party/crcmod/python3/crcmod/test.py | catapult-project/catapult | train | 2,032 |
700f976c1465bfdf9256c7a2e2cc916226e22762 | [
"q = []\nfor x, y in points:\n heapq.heappush(q, (math.sqrt(x ** 2 + y ** 2), x, y))\nclosest = [[x, y] for _, x, y in heapq.nsmallest(k, q)]\nreturn closest",
"q = []\nfor x, y in points:\n if len(q) < k:\n heapq.heappush(q, (-math.sqrt(x ** 2 + y ** 2), x, y))\n else:\n distance = math.sq... | <|body_start_0|>
q = []
for x, y in points:
heapq.heappush(q, (math.sqrt(x ** 2 + y ** 2), x, y))
closest = [[x, y] for _, x, y in heapq.nsmallest(k, q)]
return closest
<|end_body_0|>
<|body_start_1|>
q = []
for x, y in points:
if len(q) < k:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:
"""O(N*log(N)) Solution"""
<|body_0|>
def kClosest2(self, points: List[List[int]], k: int) -> List[List[int]]:
"""O(N*log(K)) Solution"""
<|body_1|>
<|end_skeleton|>
<|body_st... | stack_v2_sparse_classes_75kplus_train_067298 | 916 | no_license | [
{
"docstring": "O(N*log(N)) Solution",
"name": "kClosest",
"signature": "def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]"
},
{
"docstring": "O(N*log(K)) Solution",
"name": "kClosest2",
"signature": "def kClosest2(self, points: List[List[int]], k: int) -> List[List[... | 2 | stack_v2_sparse_classes_30k_test_000354 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]: O(N*log(N)) Solution
- def kClosest2(self, points: List[List[int]], k: int) -> List[List[int]]: O(N*log(K)... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]: O(N*log(N)) Solution
- def kClosest2(self, points: List[List[int]], k: int) -> List[List[int]]: O(N*log(K)... | b72229c50e87d1ff32d3538d13779953451b9daf | <|skeleton|>
class Solution:
def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:
"""O(N*log(N)) Solution"""
<|body_0|>
def kClosest2(self, points: List[List[int]], k: int) -> List[List[int]]:
"""O(N*log(K)) Solution"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:
"""O(N*log(N)) Solution"""
q = []
for x, y in points:
heapq.heappush(q, (math.sqrt(x ** 2 + y ** 2), x, y))
closest = [[x, y] for _, x, y in heapq.nsmallest(k, q)]
return close... | the_stack_v2_python_sparse | leetcode/K Closest Points to Origin/main.py | dalleng/Interview-Practice | train | 2 | |
b5de5853d5b005b2134dc53a25d517a1763c575e | [
"values = list(map(str.strip, values))\nfor col in cls.boolean_columns:\n mapping = {'yes': True, 'no': False}\n try:\n values[col] = mapping[values[col].lower()]\n except KeyError:\n raise ValueError(\"Invalid value for boolean column '{}'\".format(values[col]))\nfor col in cls.int_columns:\... | <|body_start_0|>
values = list(map(str.strip, values))
for col in cls.boolean_columns:
mapping = {'yes': True, 'no': False}
try:
values[col] = mapping[values[col].lower()]
except KeyError:
raise ValueError("Invalid value for boolean col... | Class to represent a row in the CSV file that corresponds to a dataset | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Class to represent a row in the CSV file that corresponds to a dataset"""
def from_strings(cls, values):
"""Instantiate a Dataset object from a line of the CSV file, and convert Yes/No to True/False"""
<|body_0|>
def get_dict(self):
"""Return a dictio... | stack_v2_sparse_classes_75kplus_train_067299 | 3,919 | no_license | [
{
"docstring": "Instantiate a Dataset object from a line of the CSV file, and convert Yes/No to True/False",
"name": "from_strings",
"signature": "def from_strings(cls, values)"
},
{
"docstring": "Return a dictionary that contains metadata about the dataset for this row and its data files",
... | 2 | stack_v2_sparse_classes_30k_train_031203 | Implement the Python class `Dataset` described below.
Class description:
Class to represent a row in the CSV file that corresponds to a dataset
Method signatures and docstrings:
- def from_strings(cls, values): Instantiate a Dataset object from a line of the CSV file, and convert Yes/No to True/False
- def get_dict(s... | Implement the Python class `Dataset` described below.
Class description:
Class to represent a row in the CSV file that corresponds to a dataset
Method signatures and docstrings:
- def from_strings(cls, values): Instantiate a Dataset object from a line of the CSV file, and convert Yes/No to True/False
- def get_dict(s... | e376d650ecd7da2938d1b49215fdd945b8395d13 | <|skeleton|>
class Dataset:
"""Class to represent a row in the CSV file that corresponds to a dataset"""
def from_strings(cls, values):
"""Instantiate a Dataset object from a line of the CSV file, and convert Yes/No to True/False"""
<|body_0|>
def get_dict(self):
"""Return a dictio... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Dataset:
"""Class to represent a row in the CSV file that corresponds to a dataset"""
def from_strings(cls, values):
"""Instantiate a Dataset object from a line of the CSV file, and convert Yes/No to True/False"""
values = list(map(str.strip, values))
for col in cls.boolean_column... | the_stack_v2_python_sparse | esacci_esgf/input/merge_csv_json.py | cedadev/esacci-esgf | train | 1 |
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