blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4fece7da81705a66820b035552fa8b9fa9ddcc6c | [
"super(LinearRegression, self).__init__()\nself.num_steps = 1\nself.define_placeholder(shape_x=[None, para.CONTINUOUS_WINDOW], shape_y=[None, para.NUM_CLASSES])\nself.define_parameters_totrack()",
"with tf.name_scope('output'):\n W = self.weight_variable([para.CONTINUOUS_WINDOW, para.NUM_CLASSES])\n b = sel... | <|body_start_0|>
super(LinearRegression, self).__init__()
self.num_steps = 1
self.define_placeholder(shape_x=[None, para.CONTINUOUS_WINDOW], shape_y=[None, para.NUM_CLASSES])
self.define_parameters_totrack()
<|end_body_0|>
<|body_start_1|>
with tf.name_scope('output'):
... | use linear regression for trend prediction. | LinearRegression | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearRegression:
"""use linear regression for trend prediction."""
def __init__(self):
"""init."""
<|body_0|>
def inference(self, input):
"""use linear regression to output the result."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(Linea... | stack_v2_sparse_classes_36k_train_010000 | 1,122 | no_license | [
{
"docstring": "init.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "use linear regression to output the result.",
"name": "inference",
"signature": "def inference(self, input)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019310 | Implement the Python class `LinearRegression` described below.
Class description:
use linear regression for trend prediction.
Method signatures and docstrings:
- def __init__(self): init.
- def inference(self, input): use linear regression to output the result. | Implement the Python class `LinearRegression` described below.
Class description:
use linear regression for trend prediction.
Method signatures and docstrings:
- def __init__(self): init.
- def inference(self, input): use linear regression to output the result.
<|skeleton|>
class LinearRegression:
"""use linear ... | bd792ea4aa052248b65002595c6613a43c63275e | <|skeleton|>
class LinearRegression:
"""use linear regression for trend prediction."""
def __init__(self):
"""init."""
<|body_0|>
def inference(self, input):
"""use linear regression to output the result."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinearRegression:
"""use linear regression for trend prediction."""
def __init__(self):
"""init."""
super(LinearRegression, self).__init__()
self.num_steps = 1
self.define_placeholder(shape_x=[None, para.CONTINUOUS_WINDOW], shape_y=[None, para.NUM_CLASSES])
self.de... | the_stack_v2_python_sparse | model/linearRegression.py | weilai0980/TrendNet | train | 6 |
a5fc04de933383c7358c86eedacf8724925494b3 | [
"r = re.compile('\\\\s+')\nsearch_target = r.sub('%20', search_target)\nenc_search_target = urllib.parse.quote_plus(search_target)\nurl = 'http://www.google.com/complete/search?hl=ja&q={}&output=toolbar'.format(enc_search_target)\nresponse = urllib.request.urlopen(url)\ntext = response.readlines()\ntext = text[0].d... | <|body_start_0|>
r = re.compile('\\s+')
search_target = r.sub('%20', search_target)
enc_search_target = urllib.parse.quote_plus(search_target)
url = 'http://www.google.com/complete/search?hl=ja&q={}&output=toolbar'.format(enc_search_target)
response = urllib.request.urlopen(url)
... | GetGoogleSuggest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetGoogleSuggest:
def __init__(self, search_target, get_max_count=6):
"""XMLを読み込んでitemタグのリストを作る"""
<|body_0|>
def get_data(self):
"""欲しいデータがディクショナリ形式で入ったリストを返す"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
r = re.compile('\\s+')
search_tar... | stack_v2_sparse_classes_36k_train_010001 | 1,828 | no_license | [
{
"docstring": "XMLを読み込んでitemタグのリストを作る",
"name": "__init__",
"signature": "def __init__(self, search_target, get_max_count=6)"
},
{
"docstring": "欲しいデータがディクショナリ形式で入ったリストを返す",
"name": "get_data",
"signature": "def get_data(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000921 | Implement the Python class `GetGoogleSuggest` described below.
Class description:
Implement the GetGoogleSuggest class.
Method signatures and docstrings:
- def __init__(self, search_target, get_max_count=6): XMLを読み込んでitemタグのリストを作る
- def get_data(self): 欲しいデータがディクショナリ形式で入ったリストを返す | Implement the Python class `GetGoogleSuggest` described below.
Class description:
Implement the GetGoogleSuggest class.
Method signatures and docstrings:
- def __init__(self, search_target, get_max_count=6): XMLを読み込んでitemタグのリストを作る
- def get_data(self): 欲しいデータがディクショナリ形式で入ったリストを返す
<|skeleton|>
class GetGoogleSuggest:
... | d70a0c21858e5d37a3cf3fca81b69ea7f73af661 | <|skeleton|>
class GetGoogleSuggest:
def __init__(self, search_target, get_max_count=6):
"""XMLを読み込んでitemタグのリストを作る"""
<|body_0|>
def get_data(self):
"""欲しいデータがディクショナリ形式で入ったリストを返す"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetGoogleSuggest:
def __init__(self, search_target, get_max_count=6):
"""XMLを読み込んでitemタグのリストを作る"""
r = re.compile('\\s+')
search_target = r.sub('%20', search_target)
enc_search_target = urllib.parse.quote_plus(search_target)
url = 'http://www.google.com/complete/search?... | the_stack_v2_python_sparse | application/module/misc/get_googlesuggest.py | fujimisakari/otherbu | train | 0 | |
dfa701858419849bdcd76451123b7b224635bba0 | [
"if crn is None:\n raise ValueError('crn must be provided')\nif zone_id is None:\n raise ValueError('zone_id must be provided')\nauthenticator = get_authenticator_from_environment(service_name)\nservice = cls(crn, zone_id, authenticator)\nservice.configure_service(service_name)\nreturn service",
"if crn is ... | <|body_start_0|>
if crn is None:
raise ValueError('crn must be provided')
if zone_id is None:
raise ValueError('zone_id must be provided')
authenticator = get_authenticator_from_environment(service_name)
service = cls(crn, zone_id, authenticator)
service.c... | The Security Events API V1 service. | SecurityEventsApiV1 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SecurityEventsApiV1:
"""The Security Events API V1 service."""
def new_instance(cls, crn: str, zone_id: str, service_name: str=DEFAULT_SERVICE_NAME) -> 'SecurityEventsApiV1':
"""Return a new client for the Security Events API service using the specified parameters and external config... | stack_v2_sparse_classes_36k_train_010002 | 39,687 | permissive | [
{
"docstring": "Return a new client for the Security Events API service using the specified parameters and external configuration. :param str crn: Full url-encoded cloud resource name (CRN) of resource instance. :param str zone_id: zone identifier.",
"name": "new_instance",
"signature": "def new_instanc... | 3 | stack_v2_sparse_classes_30k_train_009809 | Implement the Python class `SecurityEventsApiV1` described below.
Class description:
The Security Events API V1 service.
Method signatures and docstrings:
- def new_instance(cls, crn: str, zone_id: str, service_name: str=DEFAULT_SERVICE_NAME) -> 'SecurityEventsApiV1': Return a new client for the Security Events API s... | Implement the Python class `SecurityEventsApiV1` described below.
Class description:
The Security Events API V1 service.
Method signatures and docstrings:
- def new_instance(cls, crn: str, zone_id: str, service_name: str=DEFAULT_SERVICE_NAME) -> 'SecurityEventsApiV1': Return a new client for the Security Events API s... | 7eed5185f1e93a57e43d0d7a1e83ee8c708179e0 | <|skeleton|>
class SecurityEventsApiV1:
"""The Security Events API V1 service."""
def new_instance(cls, crn: str, zone_id: str, service_name: str=DEFAULT_SERVICE_NAME) -> 'SecurityEventsApiV1':
"""Return a new client for the Security Events API service using the specified parameters and external config... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SecurityEventsApiV1:
"""The Security Events API V1 service."""
def new_instance(cls, crn: str, zone_id: str, service_name: str=DEFAULT_SERVICE_NAME) -> 'SecurityEventsApiV1':
"""Return a new client for the Security Events API service using the specified parameters and external configuration. :par... | the_stack_v2_python_sparse | ibm_cloud_networking_services/security_events_api_v1.py | mauriceDevsM/networking-python-sdk | train | 0 |
338a23412a8a3b71a47114e91227e4b6ec30f28e | [
"graph = [[1, 3], [0, 2], [1, 3], [0, 2]]\nself.assertEqual(is_bipartite(graph), True)\noutput = '\\n The graph looks like this:\\n 0----1\\n | |\\n | |\\n 3----2\\n We can divide the vertices into two groups: {0, 2} and {1, 3}.\\n '\nprint(f'Ex... | <|body_start_0|>
graph = [[1, 3], [0, 2], [1, 3], [0, 2]]
self.assertEqual(is_bipartite(graph), True)
output = '\n The graph looks like this:\n 0----1\n | |\n | |\n 3----2\n We can divide the vertices into two groups: {0, 2} and {1,... | Unit test for is_bipartite. | TestIsBipartite | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIsBipartite:
"""Unit test for is_bipartite."""
def test_1(self):
"""Test for graph: 0----1 | | | | 3----2"""
<|body_0|>
def test_2(self):
"""Test for graph: 0----1 | \\ | | \\ | 3----2"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
graph = ... | stack_v2_sparse_classes_36k_train_010003 | 2,565 | no_license | [
{
"docstring": "Test for graph: 0----1 | | | | 3----2",
"name": "test_1",
"signature": "def test_1(self)"
},
{
"docstring": "Test for graph: 0----1 | \\\\ | | \\\\ | 3----2",
"name": "test_2",
"signature": "def test_2(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011083 | Implement the Python class `TestIsBipartite` described below.
Class description:
Unit test for is_bipartite.
Method signatures and docstrings:
- def test_1(self): Test for graph: 0----1 | | | | 3----2
- def test_2(self): Test for graph: 0----1 | \\ | | \\ | 3----2 | Implement the Python class `TestIsBipartite` described below.
Class description:
Unit test for is_bipartite.
Method signatures and docstrings:
- def test_1(self): Test for graph: 0----1 | | | | 3----2
- def test_2(self): Test for graph: 0----1 | \\ | | \\ | 3----2
<|skeleton|>
class TestIsBipartite:
"""Unit test... | 8105e1b20bf450a03a9bb910f344fc140e5ba703 | <|skeleton|>
class TestIsBipartite:
"""Unit test for is_bipartite."""
def test_1(self):
"""Test for graph: 0----1 | | | | 3----2"""
<|body_0|>
def test_2(self):
"""Test for graph: 0----1 | \\ | | \\ | 3----2"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestIsBipartite:
"""Unit test for is_bipartite."""
def test_1(self):
"""Test for graph: 0----1 | | | | 3----2"""
graph = [[1, 3], [0, 2], [1, 3], [0, 2]]
self.assertEqual(is_bipartite(graph), True)
output = '\n The graph looks like this:\n 0----1\n ... | the_stack_v2_python_sparse | module_1/python/is_bipartite.py | vprusso/6-Weeks-to-Interview-Ready | train | 6 |
03a92d81b28c99b16053ed2b71e9553298fb6e52 | [
"super().__init__(data, batch_size=batch_size, epochs=epochs, input_size=input_size, blacklist=blacklist)\nself.rnd_pool_ = []\nself.rnd = RandomState(seed=random_seed)",
"if not self.rnd_pool_:\n self.rnd_pool_ = self.rnd.randint(0, self.input_size - 1, self.batch_size * 10).tolist()\nreturn self.rnd_pool_.po... | <|body_start_0|>
super().__init__(data, batch_size=batch_size, epochs=epochs, input_size=input_size, blacklist=blacklist)
self.rnd_pool_ = []
self.rnd = RandomState(seed=random_seed)
<|end_body_0|>
<|body_start_1|>
if not self.rnd_pool_:
self.rnd_pool_ = self.rnd.randint(0, ... | Random sampling produces samples uniformly distributed from the input data. Individual samples are not guaranteed to be unique in the output, they can be repeated by the process of random sampling. The simplest usage of `RandomSampler` is no different than `OrderedSampler`: ```python sampler = RandomSampler(dataset) ba... | RandomSampler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomSampler:
"""Random sampling produces samples uniformly distributed from the input data. Individual samples are not guaranteed to be unique in the output, they can be repeated by the process of random sampling. The simplest usage of `RandomSampler` is no different than `OrderedSampler`: ```p... | stack_v2_sparse_classes_36k_train_010004 | 2,026 | permissive | [
{
"docstring": "Parameters ---------- data TODO batch_size : int TODO epochs : int TODO input_size : int TODO blacklist : set TODO random_seed : int TODO",
"name": "__init__",
"signature": "def __init__(self, data, batch_size: int=128, epochs: int=None, input_size: int=None, blacklist: set=None, random_... | 2 | stack_v2_sparse_classes_30k_train_002254 | Implement the Python class `RandomSampler` described below.
Class description:
Random sampling produces samples uniformly distributed from the input data. Individual samples are not guaranteed to be unique in the output, they can be repeated by the process of random sampling. The simplest usage of `RandomSampler` is n... | Implement the Python class `RandomSampler` described below.
Class description:
Random sampling produces samples uniformly distributed from the input data. Individual samples are not guaranteed to be unique in the output, they can be repeated by the process of random sampling. The simplest usage of `RandomSampler` is n... | 4d37af1713b7f166ead3459a7004748f954d336e | <|skeleton|>
class RandomSampler:
"""Random sampling produces samples uniformly distributed from the input data. Individual samples are not guaranteed to be unique in the output, they can be repeated by the process of random sampling. The simplest usage of `RandomSampler` is no different than `OrderedSampler`: ```p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomSampler:
"""Random sampling produces samples uniformly distributed from the input data. Individual samples are not guaranteed to be unique in the output, they can be repeated by the process of random sampling. The simplest usage of `RandomSampler` is no different than `OrderedSampler`: ```python sampler... | the_stack_v2_python_sparse | bananas/sampling/random.py | owahltinez/bananas | train | 0 |
2373044a9e7cddcff1bd79b34500cc44ae84909b | [
"def isPalindrome(i, j) -> bool:\n return s[i:j + 1] == s[i:j + 1][::-1]\ni = 0\ncount = 0\nslen = len(s)\nwhile i < slen:\n j = slen - 1\n while j >= i:\n if isPalindrome(i, j):\n count += 1\n j -= 1\n i += 1\nreturn count",
"count = 0\nslen = len(s)\ni = 0\n\ndef isPalin(i, ... | <|body_start_0|>
def isPalindrome(i, j) -> bool:
return s[i:j + 1] == s[i:j + 1][::-1]
i = 0
count = 0
slen = len(s)
while i < slen:
j = slen - 1
while j >= i:
if isPalindrome(i, j):
count += 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countSubString(self, s) -> int:
""":type s: String :return: int"""
<|body_0|>
def countString2(self, s):
"""while (i < len(s) -1) : within while loop : call inner method isPalin(i, j) where i = 0, j = len(s) -1 within isPalin - check if the subArray (i.... | stack_v2_sparse_classes_36k_train_010005 | 1,558 | no_license | [
{
"docstring": ":type s: String :return: int",
"name": "countSubString",
"signature": "def countSubString(self, s) -> int"
},
{
"docstring": "while (i < len(s) -1) : within while loop : call inner method isPalin(i, j) where i = 0, j = len(s) -1 within isPalin - check if the subArray (i.e s[i:j] ... | 2 | stack_v2_sparse_classes_30k_train_007727 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countSubString(self, s) -> int: :type s: String :return: int
- def countString2(self, s): while (i < len(s) -1) : within while loop : call inner method isPalin(i, j) where i ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countSubString(self, s) -> int: :type s: String :return: int
- def countString2(self, s): while (i < len(s) -1) : within while loop : call inner method isPalin(i, j) where i ... | e3e076206b34ff6edf00596a03bc2b5911051cd8 | <|skeleton|>
class Solution:
def countSubString(self, s) -> int:
""":type s: String :return: int"""
<|body_0|>
def countString2(self, s):
"""while (i < len(s) -1) : within while loop : call inner method isPalin(i, j) where i = 0, j = len(s) -1 within isPalin - check if the subArray (i.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def countSubString(self, s) -> int:
""":type s: String :return: int"""
def isPalindrome(i, j) -> bool:
return s[i:j + 1] == s[i:j + 1][::-1]
i = 0
count = 0
slen = len(s)
while i < slen:
j = slen - 1
while j >= i:
... | the_stack_v2_python_sparse | Code/DataStructures/python/leetcode_ds/Py_PalindromicSubstrings_1.py | karanalang/technology | train | 0 | |
6f417cb8e15460a639c2c39bac57ede471f263c8 | [
"node = root\nqueue = [node]\nwhile queue:\n node = queue.pop(0)\n if node.right:\n queue.append(node.right)\n if node.left:\n queue.append(node.left)\nreturn node.val",
"queue = [root]\nfor node in queue:\n queue += filter(None, (node.right, node.left))\nreturn node.val"
] | <|body_start_0|>
node = root
queue = [node]
while queue:
node = queue.pop(0)
if node.right:
queue.append(node.right)
if node.left:
queue.append(node.left)
return node.val
<|end_body_0|>
<|body_start_1|>
queue = ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findBottomLeftValue(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def find_bottom_left_value(self, root):
"""pythonic :param root: TreeNode :return: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
node = root
... | stack_v2_sparse_classes_36k_train_010006 | 1,315 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "findBottomLeftValue",
"signature": "def findBottomLeftValue(self, root)"
},
{
"docstring": "pythonic :param root: TreeNode :return: int",
"name": "find_bottom_left_value",
"signature": "def find_bottom_left_value(self, root)"
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findBottomLeftValue(self, root): :type root: TreeNode :rtype: int
- def find_bottom_left_value(self, root): pythonic :param root: TreeNode :return: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findBottomLeftValue(self, root): :type root: TreeNode :rtype: int
- def find_bottom_left_value(self, root): pythonic :param root: TreeNode :return: int
<|skeleton|>
class So... | 215d513b3564a7a76db3d2b29e4acc341a68e8ee | <|skeleton|>
class Solution:
def findBottomLeftValue(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def find_bottom_left_value(self, root):
"""pythonic :param root: TreeNode :return: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findBottomLeftValue(self, root):
""":type root: TreeNode :rtype: int"""
node = root
queue = [node]
while queue:
node = queue.pop(0)
if node.right:
queue.append(node.right)
if node.left:
queue.appe... | the_stack_v2_python_sparse | python/tree/find-bottom-left-tree-value.py | euxuoh/leetcode | train | 0 | |
0c52e3ae22cfc15f691c9f03c5b58222eed69558 | [
"current1 = l1\ncurrent2 = l2\nprev1 = None\nprev2 = None\nhead_of_merged = None\nend_of_merged = None\nwhile current1 is not None and current2 is not None:\n if current1.val <= current2.val:\n prev1 = current1\n current1 = current1.next\n prev1.next = None\n head_of_merged, end_of_me... | <|body_start_0|>
current1 = l1
current2 = l2
prev1 = None
prev2 = None
head_of_merged = None
end_of_merged = None
while current1 is not None and current2 is not None:
if current1.val <= current2.val:
prev1 = current1
cur... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""(Solution, ListNode, ListNode) -> ListNode Given two linked lists whose elements are in sorted order, return a new merged linked list containing elements of both lists in sorted order"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_010007 | 2,464 | no_license | [
{
"docstring": "(Solution, ListNode, ListNode) -> ListNode Given two linked lists whose elements are in sorted order, return a new merged linked list containing elements of both lists in sorted order",
"name": "mergeTwoLists",
"signature": "def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: (Solution, ListNode, ListNode) -> ListNode Given two linked lists whose elements are in sorted order, return a ne... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: (Solution, ListNode, ListNode) -> ListNode Given two linked lists whose elements are in sorted order, return a ne... | 6812253b90bdd5a35c6bfba8eac54da9be26d56c | <|skeleton|>
class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""(Solution, ListNode, ListNode) -> ListNode Given two linked lists whose elements are in sorted order, return a new merged linked list containing elements of both lists in sorted order"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""(Solution, ListNode, ListNode) -> ListNode Given two linked lists whose elements are in sorted order, return a new merged linked list containing elements of both lists in sorted order"""
current1 = l1
current... | the_stack_v2_python_sparse | python3/mergeSortedLists.py | yichuanma95/leetcode-solns | train | 2 | |
0a3a6d5b043bf63b1612215436d68c6bf92dca3a | [
"pygame.sprite.Sprite.__init__(self)\nself.image = pygame.Surface((0, 0))\nself.rect = self.image.get_rect()",
"too_long: bool = True\nfont: pygame.Font\nsize: int = 38\nif pygame.font:\n while too_long and size >= 10:\n size -= 2\n font = pygame.font.SysFont('Bauhaus 93', size)\n too_long... | <|body_start_0|>
pygame.sprite.Sprite.__init__(self)
self.image = pygame.Surface((0, 0))
self.rect = self.image.get_rect()
<|end_body_0|>
<|body_start_1|>
too_long: bool = True
font: pygame.Font
size: int = 38
if pygame.font:
while too_long and size >... | A message centered in the screen. | Message | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Message:
"""A message centered in the screen."""
def __init__(self) -> None:
"""Initialize from parameters."""
<|body_0|>
def set_message(self, message: str, screen: pygame.Surface) -> bool:
"""Change to new message."""
<|body_1|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_36k_train_010008 | 15,940 | no_license | [
{
"docstring": "Initialize from parameters.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Change to new message.",
"name": "set_message",
"signature": "def set_message(self, message: str, screen: pygame.Surface) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_007268 | Implement the Python class `Message` described below.
Class description:
A message centered in the screen.
Method signatures and docstrings:
- def __init__(self) -> None: Initialize from parameters.
- def set_message(self, message: str, screen: pygame.Surface) -> bool: Change to new message. | Implement the Python class `Message` described below.
Class description:
A message centered in the screen.
Method signatures and docstrings:
- def __init__(self) -> None: Initialize from parameters.
- def set_message(self, message: str, screen: pygame.Surface) -> bool: Change to new message.
<|skeleton|>
class Messa... | 0fe17edf6ffcb35265032c6449d866b9434fda00 | <|skeleton|>
class Message:
"""A message centered in the screen."""
def __init__(self) -> None:
"""Initialize from parameters."""
<|body_0|>
def set_message(self, message: str, screen: pygame.Surface) -> bool:
"""Change to new message."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Message:
"""A message centered in the screen."""
def __init__(self) -> None:
"""Initialize from parameters."""
pygame.sprite.Sprite.__init__(self)
self.image = pygame.Surface((0, 0))
self.rect = self.image.get_rect()
def set_message(self, message: str, screen: pygame.... | the_stack_v2_python_sparse | Chapter11TextbookCode/Listing 11-4.py | ProfessorBurke/PythonObjectsGames | train | 3 |
c1fb96d281ff340126642b38e421cb45381803dd | [
"config = current_app.cea_config\ndashboards = cea.plots.read_dashboards(config, current_app.plot_cache)\nreturn dashboard_to_dict(dashboards[dashboard_index])",
"config = current_app.cea_config\ncea.plots.delete_dashboard(config, dashboard_index)\nreturn {'message': 'deleted dashboard'}",
"form = api.payload\n... | <|body_start_0|>
config = current_app.cea_config
dashboards = cea.plots.read_dashboards(config, current_app.plot_cache)
return dashboard_to_dict(dashboards[dashboard_index])
<|end_body_0|>
<|body_start_1|>
config = current_app.cea_config
cea.plots.delete_dashboard(config, dashbo... | Dashboard | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dashboard:
def get(self, dashboard_index):
"""Get Dashboard"""
<|body_0|>
def delete(self, dashboard_index):
"""Delete Dashboard"""
<|body_1|>
def patch(self, dashboard_index):
"""Update Dashboard properties"""
<|body_2|>
<|end_skeleton|... | stack_v2_sparse_classes_36k_train_010009 | 9,106 | permissive | [
{
"docstring": "Get Dashboard",
"name": "get",
"signature": "def get(self, dashboard_index)"
},
{
"docstring": "Delete Dashboard",
"name": "delete",
"signature": "def delete(self, dashboard_index)"
},
{
"docstring": "Update Dashboard properties",
"name": "patch",
"signatu... | 3 | stack_v2_sparse_classes_30k_train_008658 | Implement the Python class `Dashboard` described below.
Class description:
Implement the Dashboard class.
Method signatures and docstrings:
- def get(self, dashboard_index): Get Dashboard
- def delete(self, dashboard_index): Delete Dashboard
- def patch(self, dashboard_index): Update Dashboard properties | Implement the Python class `Dashboard` described below.
Class description:
Implement the Dashboard class.
Method signatures and docstrings:
- def get(self, dashboard_index): Get Dashboard
- def delete(self, dashboard_index): Delete Dashboard
- def patch(self, dashboard_index): Update Dashboard properties
<|skeleton|... | b84bcefdfdfc2bc0e009b5284b74391a957995ac | <|skeleton|>
class Dashboard:
def get(self, dashboard_index):
"""Get Dashboard"""
<|body_0|>
def delete(self, dashboard_index):
"""Delete Dashboard"""
<|body_1|>
def patch(self, dashboard_index):
"""Update Dashboard properties"""
<|body_2|>
<|end_skeleton|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dashboard:
def get(self, dashboard_index):
"""Get Dashboard"""
config = current_app.cea_config
dashboards = cea.plots.read_dashboards(config, current_app.plot_cache)
return dashboard_to_dict(dashboards[dashboard_index])
def delete(self, dashboard_index):
"""Delete ... | the_stack_v2_python_sparse | cea/interfaces/dashboard/api/dashboard.py | architecture-building-systems/CityEnergyAnalyst | train | 166 | |
c7e8906553914cb951cb023b1af42c20752c8be1 | [
"assert_pycocotools_installed('PyCOCOWrapper')\nCOCO.__init__(self, annotation_file=None)\nself._eval_type = 'box'\nif gt_dataset:\n self.dataset = gt_dataset\n self.createIndex()",
"res = COCO()\nres.dataset['images'] = copy.deepcopy(self.dataset['images'])\nres.dataset['categories'] = copy.deepcopy(self.d... | <|body_start_0|>
assert_pycocotools_installed('PyCOCOWrapper')
COCO.__init__(self, annotation_file=None)
self._eval_type = 'box'
if gt_dataset:
self.dataset = gt_dataset
self.createIndex()
<|end_body_0|>
<|body_start_1|>
res = COCO()
res.dataset['... | COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using the external annotation dictionary. | PyCOCOWrapper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PyCOCOWrapper:
"""COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using th... | stack_v2_sparse_classes_36k_train_010010 | 8,149 | permissive | [
{
"docstring": "Instantiates a COCO-style API object. Args: eval_type: either 'box' or 'mask'. annotation_file: a JSON file that stores annotations of the eval dataset. This is required if `gt_dataset` is not provided. gt_dataset: the groundtruth eval dataset in COCO API format.",
"name": "__init__",
"s... | 2 | stack_v2_sparse_classes_30k_test_000313 | Implement the Python class `PyCOCOWrapper` described below.
Class description:
COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support ... | Implement the Python class `PyCOCOWrapper` described below.
Class description:
COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support ... | e83f229f1b7b847cd712d5cd4810097d3e06d14e | <|skeleton|>
class PyCOCOWrapper:
"""COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PyCOCOWrapper:
"""COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using the external an... | the_stack_v2_python_sparse | keras_cv/metrics/coco/pycoco_wrapper.py | keras-team/keras-cv | train | 818 |
f30f7723054f61236e0a227abffbe041122e92e5 | [
"main.clear_collections()\nresult = main.import_data('./data/', 'products', 'customers', 'rentals')\nself.assertEqual(result[0][1], 1000)\nself.assertEqual(result[0][2], 0)\nself.assertEqual(result[1][0], 'products')\nself.assertEqual(result[1][3], 1000)\nself.assertEqual(result[2][2], 0)\nself.assertGreater(result... | <|body_start_0|>
main.clear_collections()
result = main.import_data('./data/', 'products', 'customers', 'rentals')
self.assertEqual(result[0][1], 1000)
self.assertEqual(result[0][2], 0)
self.assertEqual(result[1][0], 'products')
self.assertEqual(result[1][3], 1000)
... | Class for testing HP Norton database | ModuleTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModuleTests:
"""Class for testing HP Norton database"""
def test_import_data(self):
"""Test CSV import and correct database insertion functionality"""
<|body_0|>
def test_failed_import_data(self):
"""Test CSV import failure"""
<|body_1|>
def test_sho... | stack_v2_sparse_classes_36k_train_010011 | 2,539 | no_license | [
{
"docstring": "Test CSV import and correct database insertion functionality",
"name": "test_import_data",
"signature": "def test_import_data(self)"
},
{
"docstring": "Test CSV import failure",
"name": "test_failed_import_data",
"signature": "def test_failed_import_data(self)"
},
{
... | 4 | null | Implement the Python class `ModuleTests` described below.
Class description:
Class for testing HP Norton database
Method signatures and docstrings:
- def test_import_data(self): Test CSV import and correct database insertion functionality
- def test_failed_import_data(self): Test CSV import failure
- def test_show_av... | Implement the Python class `ModuleTests` described below.
Class description:
Class for testing HP Norton database
Method signatures and docstrings:
- def test_import_data(self): Test CSV import and correct database insertion functionality
- def test_failed_import_data(self): Test CSV import failure
- def test_show_av... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class ModuleTests:
"""Class for testing HP Norton database"""
def test_import_data(self):
"""Test CSV import and correct database insertion functionality"""
<|body_0|>
def test_failed_import_data(self):
"""Test CSV import failure"""
<|body_1|>
def test_sho... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModuleTests:
"""Class for testing HP Norton database"""
def test_import_data(self):
"""Test CSV import and correct database insertion functionality"""
main.clear_collections()
result = main.import_data('./data/', 'products', 'customers', 'rentals')
self.assertEqual(result[... | the_stack_v2_python_sparse | students/stellie/lesson07/assignment/test_linear.py | JavaRod/SP_Python220B_2019 | train | 1 |
defce6771f3c747bb26af5b3e0de29b0f1874a5e | [
"cross = tensor([[[0, 1, 0], [1, 1, 1], [0, 1, 0]]])\nbound = tensor([[[0, 0, 0], [0, 1, 0], [0, 0, 0]]])\nkernel = stack([bound, cross, bound], 1) * (1 / 7)\nreturn kernel[None]",
"if pred.dim() != 5:\n raise ValueError(f'Only 3D images supported. Got {pred.dim()}.')\nreturn super().forward(pred, target)"
] | <|body_start_0|>
cross = tensor([[[0, 1, 0], [1, 1, 1], [0, 1, 0]]])
bound = tensor([[[0, 0, 0], [0, 1, 0], [0, 0, 0]]])
kernel = stack([bound, cross, bound], 1) * (1 / 7)
return kernel[None]
<|end_body_0|>
<|body_start_1|>
if pred.dim() != 5:
raise ValueError(f'Only... | Binary 3D Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .. math:: hd(X,Y) = \\max_{x \\in X} \\min_{y ... | HausdorffERLoss3D | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HausdorffERLoss3D:
"""Binary 3D Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .... | stack_v2_sparse_classes_36k_train_010012 | 9,826 | permissive | [
{
"docstring": "Get kernel for image morphology convolution.",
"name": "get_kernel",
"signature": "def get_kernel(self) -> Tensor"
},
{
"docstring": "Compute 3D Hausdorff loss. Args: pred: predicted tensor with a shape of :math:`(B, C, D, H, W)`. Each channel is as binary as: 1 -> fg, 0 -> bg. t... | 2 | stack_v2_sparse_classes_30k_train_016439 | Implement the Python class `HausdorffERLoss3D` described below.
Class description:
Binary 3D Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the on... | Implement the Python class `HausdorffERLoss3D` described below.
Class description:
Binary 3D Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the on... | 1e0f8baa7318c05b17ea6dbb48605691bca8972f | <|skeleton|>
class HausdorffERLoss3D:
"""Binary 3D Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HausdorffERLoss3D:
"""Binary 3D Hausdorff loss based on morphological erosion. Hausdorff Distance loss measures the maximum distance of a predicted segmentation boundary to the nearest ground-truth edge pixel. For two segmentation point sets X and Y , the one-sided HD from X to Y is defined as: .. math:: hd(X... | the_stack_v2_python_sparse | kornia/losses/hausdorff.py | kornia/kornia | train | 7,351 |
5733999d4b86225b12dfd80042180415e535018e | [
"ans = bal = 0\nfor symbol in S:\n bal += 1 if symbol == '(' else -1\n if bal == -1:\n ans += 1\n bal += 1\nreturn ans + bal",
"while '()' in S:\n S = S.replace('()', '')\nreturn len(S)"
] | <|body_start_0|>
ans = bal = 0
for symbol in S:
bal += 1 if symbol == '(' else -1
if bal == -1:
ans += 1
bal += 1
return ans + bal
<|end_body_0|>
<|body_start_1|>
while '()' in S:
S = S.replace('()', '')
return ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minAddToMakeValid(self, S):
"""解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。"""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_010013 | 1,364 | no_license | [
{
"docstring": "解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。",
"name": "minAddToMakeValid",
"signature": "def minAddToMakeValid(self, S)... | 2 | stack_v2_sparse_classes_30k_train_015766 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAddToMakeValid(self, S): 解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAddToMakeValid(self, S): 解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，... | 18c06a96bb14688e4a1d5fb6baf235a6b53bd3ae | <|skeleton|>
class Solution:
def minAddToMakeValid(self, S):
"""解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。"""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minAddToMakeValid(self, S):
"""解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。"""
ans = bal = 0
for symbol ... | the_stack_v2_python_sparse | medium/others/minimum-add-to-make-parentheses-valid.py | congyingTech/Basic-Algorithm | train | 10 | |
26ff039ab9f070e6116b9ddffd9e3e28df03564e | [
"super().__init__()\nself.input_channel_size = input_channels\nself.output_channel_size = output_channels\nself.num_nodes = num_nodes\nself.parallel_strategy = parallel_strategy\nself.instance = 0\nself.is_distconv = False\nif parallel_strategy:\n if list(parallel_strategy.values()[0]) > 0:\n self.is_dist... | <|body_start_0|>
super().__init__()
self.input_channel_size = input_channels
self.output_channel_size = output_channels
self.num_nodes = num_nodes
self.parallel_strategy = parallel_strategy
self.instance = 0
self.is_distconv = False
if parallel_strategy:
... | GCN Conv later. See: https://arxiv.org/abs/1609.02907 | GCNConv | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GCNConv:
"""GCN Conv later. See: https://arxiv.org/abs/1609.02907"""
def __init__(self, input_channels, output_channels, num_nodes, bias=True, activation=lbann.Relu, name=None, parallel_strategy={}):
"""Initialize GCN layer Args: input_channels (int): The size of the input node featu... | stack_v2_sparse_classes_36k_train_010014 | 5,080 | permissive | [
{
"docstring": "Initialize GCN layer Args: input_channels (int): The size of the input node features output_channels (int): The output size of the node features num_nodes (int): Number of vertices in the graph bias (bool): Whether to apply biases after weights transform activation (type): Activation leyer for t... | 2 | null | Implement the Python class `GCNConv` described below.
Class description:
GCN Conv later. See: https://arxiv.org/abs/1609.02907
Method signatures and docstrings:
- def __init__(self, input_channels, output_channels, num_nodes, bias=True, activation=lbann.Relu, name=None, parallel_strategy={}): Initialize GCN layer Arg... | Implement the Python class `GCNConv` described below.
Class description:
GCN Conv later. See: https://arxiv.org/abs/1609.02907
Method signatures and docstrings:
- def __init__(self, input_channels, output_channels, num_nodes, bias=True, activation=lbann.Relu, name=None, parallel_strategy={}): Initialize GCN layer Arg... | e8cf85eed2acbd3383892bf7cb2d88b44c194f4f | <|skeleton|>
class GCNConv:
"""GCN Conv later. See: https://arxiv.org/abs/1609.02907"""
def __init__(self, input_channels, output_channels, num_nodes, bias=True, activation=lbann.Relu, name=None, parallel_strategy={}):
"""Initialize GCN layer Args: input_channels (int): The size of the input node featu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GCNConv:
"""GCN Conv later. See: https://arxiv.org/abs/1609.02907"""
def __init__(self, input_channels, output_channels, num_nodes, bias=True, activation=lbann.Relu, name=None, parallel_strategy={}):
"""Initialize GCN layer Args: input_channels (int): The size of the input node features output_ch... | the_stack_v2_python_sparse | python/lbann/modules/graph/sparse/GCNConv.py | LLNL/lbann | train | 225 |
4d8412de4fc831bfbb7f9eaba085fe5a1788f24c | [
"if not root:\n return ''\nstack = [root]\nres = ''\nwhile stack:\n childs = []\n for node in stack:\n if not node:\n res += ','\n childs.append(None)\n childs.append(None)\n else:\n res += str(node.val) + ','\n childs.append(node.left)\n... | <|body_start_0|>
if not root:
return ''
stack = [root]
res = ''
while stack:
childs = []
for node in stack:
if not node:
res += ','
childs.append(None)
childs.append(None)
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_010015 | 2,077 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 14dcf9029486283b5e4685d95ebfe9979ade03c3 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return ''
stack = [root]
res = ''
while stack:
childs = []
for node in stack:
if not node:
... | the_stack_v2_python_sparse | 449-SerializeandDeserializeBST.py | dq-code/leetcode | train | 0 | |
ca7d1013e72b241f8bb21e2ccb9cd40fdf9528bf | [
"try:\n results = self.api.photosets.getList(user_id=self.account.user.nsid, per_page=self.items_per_page, page=self.page_number)\nexcept FlickrError as e:\n raise FetchError('Error when fetching photosets (page %s): %s' % (self.page_number, e))\nif self.page_number == 1 and 'photosets' in results and ('pages... | <|body_start_0|>
try:
results = self.api.photosets.getList(user_id=self.account.user.nsid, per_page=self.items_per_page, page=self.page_number)
except FlickrError as e:
raise FetchError('Error when fetching photosets (page %s): %s' % (self.page_number, e))
if self.page_nu... | PhotosetsFetcher | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PhotosetsFetcher:
def _call_api(self):
"""Fetch one page of results. https://www.flickr.com/services/api/flickr.photosets.getList.htm"""
<|body_0|>
def _fetch_extra(self):
"""Before saving we need to get the list of photos in each photoset."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_010016 | 20,064 | permissive | [
{
"docstring": "Fetch one page of results. https://www.flickr.com/services/api/flickr.photosets.getList.htm",
"name": "_call_api",
"signature": "def _call_api(self)"
},
{
"docstring": "Before saving we need to get the list of photos in each photoset.",
"name": "_fetch_extra",
"signature"... | 4 | null | Implement the Python class `PhotosetsFetcher` described below.
Class description:
Implement the PhotosetsFetcher class.
Method signatures and docstrings:
- def _call_api(self): Fetch one page of results. https://www.flickr.com/services/api/flickr.photosets.getList.htm
- def _fetch_extra(self): Before saving we need t... | Implement the Python class `PhotosetsFetcher` described below.
Class description:
Implement the PhotosetsFetcher class.
Method signatures and docstrings:
- def _call_api(self): Fetch one page of results. https://www.flickr.com/services/api/flickr.photosets.getList.htm
- def _fetch_extra(self): Before saving we need t... | 57ee6f6657b41705af71ef67924d8ef06c60ae4f | <|skeleton|>
class PhotosetsFetcher:
def _call_api(self):
"""Fetch one page of results. https://www.flickr.com/services/api/flickr.photosets.getList.htm"""
<|body_0|>
def _fetch_extra(self):
"""Before saving we need to get the list of photos in each photoset."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PhotosetsFetcher:
def _call_api(self):
"""Fetch one page of results. https://www.flickr.com/services/api/flickr.photosets.getList.htm"""
try:
results = self.api.photosets.getList(user_id=self.account.user.nsid, per_page=self.items_per_page, page=self.page_number)
except Fli... | the_stack_v2_python_sparse | ditto/flickr/fetch/fetchers.py | philgyford/django-ditto | train | 59 | |
102348c4c95182462ffd019aceb4e040eaee7bbf | [
"if not issubclass(encodable, Encodable):\n msg = 'EncodableRegistry only accepts Encodable subclasses for registration. Got: {encodable}'\n error = ValueError(msg, encodable, reg_args)\n raise self._log_exception(error, msg)\nreturn True",
"try:\n name = encodable.type_field()\nexcept NotImplementedE... | <|body_start_0|>
if not issubclass(encodable, Encodable):
msg = 'EncodableRegistry only accepts Encodable subclasses for registration. Got: {encodable}'
error = ValueError(msg, encodable, reg_args)
raise self._log_exception(error, msg)
return True
<|end_body_0|>
<|bo... | Registry for all the encodable types. | EncodableRegistry | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncodableRegistry:
"""Registry for all the encodable types."""
def _init_register(self, encodable: Type[Encodable], reg_args: Iterable[str]) -> bool:
"""This is called before anything happens in `register()`. Raise an error if a non-Encodable class tries to register."""
<|bod... | stack_v2_sparse_classes_36k_train_010017 | 12,550 | no_license | [
{
"docstring": "This is called before anything happens in `register()`. Raise an error if a non-Encodable class tries to register.",
"name": "_init_register",
"signature": "def _init_register(self, encodable: Type[Encodable], reg_args: Iterable[str]) -> bool"
},
{
"docstring": "We want to regist... | 5 | null | Implement the Python class `EncodableRegistry` described below.
Class description:
Registry for all the encodable types.
Method signatures and docstrings:
- def _init_register(self, encodable: Type[Encodable], reg_args: Iterable[str]) -> bool: This is called before anything happens in `register()`. Raise an error if ... | Implement the Python class `EncodableRegistry` described below.
Class description:
Registry for all the encodable types.
Method signatures and docstrings:
- def _init_register(self, encodable: Type[Encodable], reg_args: Iterable[str]) -> bool: This is called before anything happens in `register()`. Raise an error if ... | 8c9fc1170ceac335985686571568eebf08b0db7a | <|skeleton|>
class EncodableRegistry:
"""Registry for all the encodable types."""
def _init_register(self, encodable: Type[Encodable], reg_args: Iterable[str]) -> bool:
"""This is called before anything happens in `register()`. Raise an error if a non-Encodable class tries to register."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncodableRegistry:
"""Registry for all the encodable types."""
def _init_register(self, encodable: Type[Encodable], reg_args: Iterable[str]) -> bool:
"""This is called before anything happens in `register()`. Raise an error if a non-Encodable class tries to register."""
if not issubclass(... | the_stack_v2_python_sparse | data/codec/registry.py | cole-brown/veredi-code | train | 1 |
646d8b468b6646117d08b8bc81eae2e7936ff9e3 | [
"result = {}\noutput = []\nfor i in nums:\n result[i] = result.get(i, 0) + 1\nfor k, v in result.items():\n if v == 1:\n output.append(k)\nreturn output",
"res1, res2 = (0, 0)\nfor num in nums:\n res1 ^= num\nfor num in nums[::-1]:\n res2 ^= num\nreturn [res1, res2]"
] | <|body_start_0|>
result = {}
output = []
for i in nums:
result[i] = result.get(i, 0) + 1
for k, v in result.items():
if v == 1:
output.append(k)
return output
<|end_body_0|>
<|body_start_1|>
res1, res2 = (0, 0)
for num in n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumber_1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def singleNumber_2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = {}
output = []
... | stack_v2_sparse_classes_36k_train_010018 | 672 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber_1",
"signature": "def singleNumber_1(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber_2",
"signature": "def singleNumber_2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003661 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber_1(self, nums): :type nums: List[int] :rtype: int
- def singleNumber_2(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 singleNumber_1(self, nums): :type nums: List[int] :rtype: int
- def singleNumber_2(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def singl... | 8a62b397a5fa107c70efc8ea65d0edfb74f8baa7 | <|skeleton|>
class Solution:
def singleNumber_1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def singleNumber_2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def singleNumber_1(self, nums):
""":type nums: List[int] :rtype: int"""
result = {}
output = []
for i in nums:
result[i] = result.get(i, 0) + 1
for k, v in result.items():
if v == 1:
output.append(k)
return outpu... | the_stack_v2_python_sparse | LeetCode-Solution/Algorithms/Single-Number-III.py | LFYG/leetcode-acm-euler-other | train | 0 | |
048e1ff72a18e453b02fb3b9308a630fecbbb5a9 | [
"if not form.is_valid():\n return\nurl = form.cleaned_data.get('url', None)\nresponse = requests.get(url, stream=True)\ncontent_length = response.headers.get('Content-Length', '0')\ntry:\n content_length = int(content_length)\nexcept ValueError:\n content_length = 0\nMAX_IMG_LENGTH = 10 * 1024 * 1024\nif c... | <|body_start_0|>
if not form.is_valid():
return
url = form.cleaned_data.get('url', None)
response = requests.get(url, stream=True)
content_length = response.headers.get('Content-Length', '0')
try:
content_length = int(content_length)
except ValueEr... | View for downloading an image from a provided URL | CompanyImageDownloadFromURL | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompanyImageDownloadFromURL:
"""View for downloading an image from a provided URL"""
def validate(self, company, form):
"""Validate that the image data are correct"""
<|body_0|>
def save(self, company, form, **kwargs):
"""Save the downloaded image to the company"... | stack_v2_sparse_classes_36k_train_010019 | 6,294 | permissive | [
{
"docstring": "Validate that the image data are correct",
"name": "validate",
"signature": "def validate(self, company, form)"
},
{
"docstring": "Save the downloaded image to the company",
"name": "save",
"signature": "def save(self, company, form, **kwargs)"
}
] | 2 | null | Implement the Python class `CompanyImageDownloadFromURL` described below.
Class description:
View for downloading an image from a provided URL
Method signatures and docstrings:
- def validate(self, company, form): Validate that the image data are correct
- def save(self, company, form, **kwargs): Save the downloaded ... | Implement the Python class `CompanyImageDownloadFromURL` described below.
Class description:
View for downloading an image from a provided URL
Method signatures and docstrings:
- def validate(self, company, form): Validate that the image data are correct
- def save(self, company, form, **kwargs): Save the downloaded ... | 2a0ea66f6591756eeb62da28d24daec3ad4209e8 | <|skeleton|>
class CompanyImageDownloadFromURL:
"""View for downloading an image from a provided URL"""
def validate(self, company, form):
"""Validate that the image data are correct"""
<|body_0|>
def save(self, company, form, **kwargs):
"""Save the downloaded image to the company"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompanyImageDownloadFromURL:
"""View for downloading an image from a provided URL"""
def validate(self, company, form):
"""Validate that the image data are correct"""
if not form.is_valid():
return
url = form.cleaned_data.get('url', None)
response = requests.ge... | the_stack_v2_python_sparse | InvenTree/company/views.py | MedShift/InvenTree | train | 0 |
6dbf79057eb4f87f921d84257bebf8d3a75b3690 | [
"permissions = [AllowAny]\nif self.action in ['update', 'partial_update', 'delete', 'create']:\n permissions = [IsAdminUser, IsAuthenticated]\nreturn [permission() for permission in permissions]",
"serializer = MovieCreateSerializer(data=request.data)\nserializer.is_valid(raise_exception=True)\nmovie = seriali... | <|body_start_0|>
permissions = [AllowAny]
if self.action in ['update', 'partial_update', 'delete', 'create']:
permissions = [IsAdminUser, IsAuthenticated]
return [permission() for permission in permissions]
<|end_body_0|>
<|body_start_1|>
serializer = MovieCreateSerializer(d... | A Movies view Set | MoviesViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MoviesViewSet:
"""A Movies view Set"""
def get_permissions(self):
"""Asign permisions based on actions"""
<|body_0|>
def create(self, request):
"""Create a movie"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
permissions = [AllowAny]
if... | stack_v2_sparse_classes_36k_train_010020 | 2,069 | permissive | [
{
"docstring": "Asign permisions based on actions",
"name": "get_permissions",
"signature": "def get_permissions(self)"
},
{
"docstring": "Create a movie",
"name": "create",
"signature": "def create(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012315 | Implement the Python class `MoviesViewSet` described below.
Class description:
A Movies view Set
Method signatures and docstrings:
- def get_permissions(self): Asign permisions based on actions
- def create(self, request): Create a movie | Implement the Python class `MoviesViewSet` described below.
Class description:
A Movies view Set
Method signatures and docstrings:
- def get_permissions(self): Asign permisions based on actions
- def create(self, request): Create a movie
<|skeleton|>
class MoviesViewSet:
"""A Movies view Set"""
def get_perm... | d83a9f57223842378e413936d4ccdba0463f1f0a | <|skeleton|>
class MoviesViewSet:
"""A Movies view Set"""
def get_permissions(self):
"""Asign permisions based on actions"""
<|body_0|>
def create(self, request):
"""Create a movie"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MoviesViewSet:
"""A Movies view Set"""
def get_permissions(self):
"""Asign permisions based on actions"""
permissions = [AllowAny]
if self.action in ['update', 'partial_update', 'delete', 'create']:
permissions = [IsAdminUser, IsAuthenticated]
return [permissio... | the_stack_v2_python_sparse | cinema/movies/views/movies.py | kevinGarcia15/cinemaAPI | train | 0 |
f128e0c6b367f85e005a274cda3023d9873401ba | [
"self.__base_output_dir = base_output_dir\nself.__satellite_tle = satellite_tle\nself.__ground_station = ground_station\nself.__tz = tz\nself.__report_timezone = tz.tzname(datetime.now())\nself.__start_day = start_day\nself.__end_day = end_day\nself.__out = sys.stdout",
"end = start = datetime.now()\nstart = star... | <|body_start_0|>
self.__base_output_dir = base_output_dir
self.__satellite_tle = satellite_tle
self.__ground_station = ground_station
self.__tz = tz
self.__report_timezone = tz.tzname(datetime.now())
self.__start_day = start_day
self.__end_day = end_day
se... | Class to create a list of inviews report for a given satellite and ground station for a specific time period | InviewListReportGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InviewListReportGenerator:
"""Class to create a list of inviews report for a given satellite and ground station for a specific time period"""
def __init__(self, base_output_dir, satellite_tle, ground_station, tz, start_day=0, end_day=0):
"""Constructor"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_010021 | 2,403 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, base_output_dir, satellite_tle, ground_station, tz, start_day=0, end_day=0)"
},
{
"docstring": "Method to generate the inview report",
"name": "generate_report",
"signature": "def generate_report(self)"
... | 2 | stack_v2_sparse_classes_30k_train_003375 | Implement the Python class `InviewListReportGenerator` described below.
Class description:
Class to create a list of inviews report for a given satellite and ground station for a specific time period
Method signatures and docstrings:
- def __init__(self, base_output_dir, satellite_tle, ground_station, tz, start_day=0... | Implement the Python class `InviewListReportGenerator` described below.
Class description:
Class to create a list of inviews report for a given satellite and ground station for a specific time period
Method signatures and docstrings:
- def __init__(self, base_output_dir, satellite_tle, ground_station, tz, start_day=0... | 0eed643eeaa9bcc35020b0b38399c25b616421c2 | <|skeleton|>
class InviewListReportGenerator:
"""Class to create a list of inviews report for a given satellite and ground station for a specific time period"""
def __init__(self, base_output_dir, satellite_tle, ground_station, tz, start_day=0, end_day=0):
"""Constructor"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InviewListReportGenerator:
"""Class to create a list of inviews report for a given satellite and ground station for a specific time period"""
def __init__(self, base_output_dir, satellite_tle, ground_station, tz, start_day=0, end_day=0):
"""Constructor"""
self.__base_output_dir = base_out... | the_stack_v2_python_sparse | scripts/inview_list_report_generator.py | nasa-itc/OrbitInviewPowerPrediction | train | 0 |
2ae12c270e15950e2a9cf86d55e1865b6a2f354f | [
"QMimeData.__init__(self)\nself._local_instance = data\nif data is not None:\n try:\n pdata = dumps(data)\n except:\n return\n self.setData(self.MIME_TYPE, dumps(data.__class__) + pdata)",
"if isinstance(md, cls):\n return md\nif not md.hasFormat(cls.MIME_TYPE):\n return None\nnmd = c... | <|body_start_0|>
QMimeData.__init__(self)
self._local_instance = data
if data is not None:
try:
pdata = dumps(data)
except:
return
self.setData(self.MIME_TYPE, dumps(data.__class__) + pdata)
<|end_body_0|>
<|body_start_1|>
... | The PyMimeData wraps a Python instance as MIME data. | PyMimeData | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PyMimeData:
"""The PyMimeData wraps a Python instance as MIME data."""
def __init__(self, data=None):
"""Initialise the instance."""
<|body_0|>
def coerce(cls, md):
"""Coerce a QMimeData instance to a PyMimeData instance if possible."""
<|body_1|>
de... | stack_v2_sparse_classes_36k_train_010022 | 9,642 | permissive | [
{
"docstring": "Initialise the instance.",
"name": "__init__",
"signature": "def __init__(self, data=None)"
},
{
"docstring": "Coerce a QMimeData instance to a PyMimeData instance if possible.",
"name": "coerce",
"signature": "def coerce(cls, md)"
},
{
"docstring": "Return the in... | 4 | stack_v2_sparse_classes_30k_train_001262 | Implement the Python class `PyMimeData` described below.
Class description:
The PyMimeData wraps a Python instance as MIME data.
Method signatures and docstrings:
- def __init__(self, data=None): Initialise the instance.
- def coerce(cls, md): Coerce a QMimeData instance to a PyMimeData instance if possible.
- def in... | Implement the Python class `PyMimeData` described below.
Class description:
The PyMimeData wraps a Python instance as MIME data.
Method signatures and docstrings:
- def __init__(self, data=None): Initialise the instance.
- def coerce(cls, md): Coerce a QMimeData instance to a PyMimeData instance if possible.
- def in... | 4d42121e4af850ba1bf9a4140c11fe10ba218cdd | <|skeleton|>
class PyMimeData:
"""The PyMimeData wraps a Python instance as MIME data."""
def __init__(self, data=None):
"""Initialise the instance."""
<|body_0|>
def coerce(cls, md):
"""Coerce a QMimeData instance to a PyMimeData instance if possible."""
<|body_1|>
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PyMimeData:
"""The PyMimeData wraps a Python instance as MIME data."""
def __init__(self, data=None):
"""Initialise the instance."""
QMimeData.__init__(self)
self._local_instance = data
if data is not None:
try:
pdata = dumps(data)
e... | the_stack_v2_python_sparse | yy.py | shyamal388/PythonBlocks | train | 0 |
fccee119f790eb0e2de214de35bb39aefe689935 | [
"if not root:\n return 0\nreturn 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))",
"max_depth = 0\nstack = deque([(root, 0)])\nwhile stack:\n node, depth = stack.pop()\n max_depth = max(max_depth, depth)\n if node:\n stack.append((node.left, depth + 1))\n stack.append((node.... | <|body_start_0|>
if not root:
return 0
return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))
<|end_body_0|>
<|body_start_1|>
max_depth = 0
stack = deque([(root, 0)])
while stack:
node, depth = stack.pop()
max_depth = max(max_dept... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxDepth(self, root: Optional[TreeNode]) -> int:
"""Recursive DFS Time complexity: O(n) Space complexity: O(n)"""
<|body_0|>
def maxDepthIterativeDFS(self, root: Optional[TreeNode]) -> int:
"""Iterative DFS Time complexity: O(n) Space complexity: O(n)""... | stack_v2_sparse_classes_36k_train_010023 | 1,684 | permissive | [
{
"docstring": "Recursive DFS Time complexity: O(n) Space complexity: O(n)",
"name": "maxDepth",
"signature": "def maxDepth(self, root: Optional[TreeNode]) -> int"
},
{
"docstring": "Iterative DFS Time complexity: O(n) Space complexity: O(n)",
"name": "maxDepthIterativeDFS",
"signature":... | 3 | stack_v2_sparse_classes_30k_train_001973 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepth(self, root: Optional[TreeNode]) -> int: Recursive DFS Time complexity: O(n) Space complexity: O(n)
- def maxDepthIterativeDFS(self, root: Optional[TreeNode]) -> int:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepth(self, root: Optional[TreeNode]) -> int: Recursive DFS Time complexity: O(n) Space complexity: O(n)
- def maxDepthIterativeDFS(self, root: Optional[TreeNode]) -> int:... | 32b0878f63e5edd19a1fbe13bfa4c518a4261e23 | <|skeleton|>
class Solution:
def maxDepth(self, root: Optional[TreeNode]) -> int:
"""Recursive DFS Time complexity: O(n) Space complexity: O(n)"""
<|body_0|>
def maxDepthIterativeDFS(self, root: Optional[TreeNode]) -> int:
"""Iterative DFS Time complexity: O(n) Space complexity: O(n)""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxDepth(self, root: Optional[TreeNode]) -> int:
"""Recursive DFS Time complexity: O(n) Space complexity: O(n)"""
if not root:
return 0
return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))
def maxDepthIterativeDFS(self, root: Optional[TreeN... | the_stack_v2_python_sparse | leetcode/Trees/104. Maximum Depth of Binary Tree.py | danielfsousa/algorithms-solutions | train | 2 | |
d0a128757b94b9c3ee4a33433551fb53148ebf3c | [
"data = NDLabel(**{'annotations': json_data})\nres = data.to_common()\nreturn res",
"for example in NDLabel.from_common(labels):\n res = example.dict(by_alias=True)\n for k, v in list(res.items()):\n if k in IGNORE_IF_NONE and v is None:\n del res[k]\n yield res"
] | <|body_start_0|>
data = NDLabel(**{'annotations': json_data})
res = data.to_common()
return res
<|end_body_0|>
<|body_start_1|>
for example in NDLabel.from_common(labels):
res = example.dict(by_alias=True)
for k, v in list(res.items()):
if k in IG... | NDJsonConverter | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NDJsonConverter:
def deserialize(json_data: Iterable[Dict[str, Any]]) -> LabelGenerator:
"""Converts ndjson data (prediction import format) into the common labelbox format. Args: json_data: An iterable representing the ndjson data Returns: LabelGenerator containing the ndjson data."""
... | stack_v2_sparse_classes_36k_train_010024 | 1,728 | permissive | [
{
"docstring": "Converts ndjson data (prediction import format) into the common labelbox format. Args: json_data: An iterable representing the ndjson data Returns: LabelGenerator containing the ndjson data.",
"name": "deserialize",
"signature": "def deserialize(json_data: Iterable[Dict[str, Any]]) -> La... | 2 | stack_v2_sparse_classes_30k_train_000154 | Implement the Python class `NDJsonConverter` described below.
Class description:
Implement the NDJsonConverter class.
Method signatures and docstrings:
- def deserialize(json_data: Iterable[Dict[str, Any]]) -> LabelGenerator: Converts ndjson data (prediction import format) into the common labelbox format. Args: json_... | Implement the Python class `NDJsonConverter` described below.
Class description:
Implement the NDJsonConverter class.
Method signatures and docstrings:
- def deserialize(json_data: Iterable[Dict[str, Any]]) -> LabelGenerator: Converts ndjson data (prediction import format) into the common labelbox format. Args: json_... | 29f0d3fa19cc62721f5c67022259ded320ae01ed | <|skeleton|>
class NDJsonConverter:
def deserialize(json_data: Iterable[Dict[str, Any]]) -> LabelGenerator:
"""Converts ndjson data (prediction import format) into the common labelbox format. Args: json_data: An iterable representing the ndjson data Returns: LabelGenerator containing the ndjson data."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NDJsonConverter:
def deserialize(json_data: Iterable[Dict[str, Any]]) -> LabelGenerator:
"""Converts ndjson data (prediction import format) into the common labelbox format. Args: json_data: An iterable representing the ndjson data Returns: LabelGenerator containing the ndjson data."""
data = N... | the_stack_v2_python_sparse | labelbox/data/serialization/ndjson/converter.py | Labelbox/labelbox-python | train | 81 | |
b7965422ed2e90ebb81b5d5af35798dac106e409 | [
"import collections\ndicts_row = collections.defaultdict(set)\nfor point in points:\n dicts_row[point[0]].add(point[1])\nl = len(points)\nmin_val = float('inf')\nfor i in range(l - 1):\n for j in range(i + 1, l):\n if points[i][0] != points[j][0] and points[i][1] != points[j][1]:\n if points... | <|body_start_0|>
import collections
dicts_row = collections.defaultdict(set)
for point in points:
dicts_row[point[0]].add(point[1])
l = len(points)
min_val = float('inf')
for i in range(l - 1):
for j in range(i + 1, l):
if points[i]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minAreaRect(self, points):
""":type points: List[List[int]] :rtype: int 2028 ms"""
<|body_0|>
def minAreaRect_1(self, points):
"""112ms :param points: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
import collections
... | stack_v2_sparse_classes_36k_train_010025 | 2,528 | no_license | [
{
"docstring": ":type points: List[List[int]] :rtype: int 2028 ms",
"name": "minAreaRect",
"signature": "def minAreaRect(self, points)"
},
{
"docstring": "112ms :param points: :return:",
"name": "minAreaRect_1",
"signature": "def minAreaRect_1(self, points)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020243 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAreaRect(self, points): :type points: List[List[int]] :rtype: int 2028 ms
- def minAreaRect_1(self, points): 112ms :param points: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAreaRect(self, points): :type points: List[List[int]] :rtype: int 2028 ms
- def minAreaRect_1(self, points): 112ms :param points: :return:
<|skeleton|>
class Solution:
... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def minAreaRect(self, points):
""":type points: List[List[int]] :rtype: int 2028 ms"""
<|body_0|>
def minAreaRect_1(self, points):
"""112ms :param points: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minAreaRect(self, points):
""":type points: List[List[int]] :rtype: int 2028 ms"""
import collections
dicts_row = collections.defaultdict(set)
for point in points:
dicts_row[point[0]].add(point[1])
l = len(points)
min_val = float('inf')... | the_stack_v2_python_sparse | MinimumAreaRectangle_MID_939.py | 953250587/leetcode-python | train | 2 | |
0aca9c6495fbeea9f85a0da8b67ca4af4231ff7a | [
"ver = state.pop('version')\nassert ver == cls.VERSION\nreturn cls(**state)",
"state = attr.asdict(self)\nstate['version'] = self.VERSION\nreturn state"
] | <|body_start_0|>
ver = state.pop('version')
assert ver == cls.VERSION
return cls(**state)
<|end_body_0|>
<|body_start_1|>
state = attr.asdict(self)
state['version'] = self.VERSION
return state
<|end_body_1|>
| Base class supplying basic serialization methods and used to mark things that are intended to be serialzable. By serializable, we mean 'able to be rendered to dict of base types' such that this state can be easily pickled. | Serializable | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Serializable:
"""Base class supplying basic serialization methods and used to mark things that are intended to be serialzable. By serializable, we mean 'able to be rendered to dict of base types' such that this state can be easily pickled."""
def deserialize(cls, state):
"""Deseriali... | stack_v2_sparse_classes_36k_train_010026 | 1,206 | permissive | [
{
"docstring": "Deserialize the object from the dict of basic types. :param state dict: dict (serialized) representation of the object :return Serializable: the deserialized object",
"name": "deserialize",
"signature": "def deserialize(cls, state)"
},
{
"docstring": "Serialize object to dict of ... | 2 | stack_v2_sparse_classes_30k_test_000570 | Implement the Python class `Serializable` described below.
Class description:
Base class supplying basic serialization methods and used to mark things that are intended to be serialzable. By serializable, we mean 'able to be rendered to dict of base types' such that this state can be easily pickled.
Method signatures... | Implement the Python class `Serializable` described below.
Class description:
Base class supplying basic serialization methods and used to mark things that are intended to be serialzable. By serializable, we mean 'able to be rendered to dict of base types' such that this state can be easily pickled.
Method signatures... | e2864d88eb971e327d7e886e75d00140673006ef | <|skeleton|>
class Serializable:
"""Base class supplying basic serialization methods and used to mark things that are intended to be serialzable. By serializable, we mean 'able to be rendered to dict of base types' such that this state can be easily pickled."""
def deserialize(cls, state):
"""Deseriali... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Serializable:
"""Base class supplying basic serialization methods and used to mark things that are intended to be serialzable. By serializable, we mean 'able to be rendered to dict of base types' such that this state can be easily pickled."""
def deserialize(cls, state):
"""Deserialize the object... | the_stack_v2_python_sparse | bc_gym_planning_env/utilities/serialize.py | braincorp/bc-gym-planning-env | train | 2 |
fc4a4393d99239b8a52cb01d62501e6de538cbcc | [
"super().__init__(client, info, mac)\nself.entity_description = ButtonEntityDescription(key='identify', name='Identify', icon='mdi:help', entity_category=EntityCategory.CONFIG)\nself._attr_unique_id = f'{info.serial_number}_{self.entity_description.key}'",
"try:\n await self.client.identify()\nexcept ElgatoErr... | <|body_start_0|>
super().__init__(client, info, mac)
self.entity_description = ButtonEntityDescription(key='identify', name='Identify', icon='mdi:help', entity_category=EntityCategory.CONFIG)
self._attr_unique_id = f'{info.serial_number}_{self.entity_description.key}'
<|end_body_0|>
<|body_star... | Defines an Elgato identify button. | ElgatoIdentifyButton | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ElgatoIdentifyButton:
"""Defines an Elgato identify button."""
def __init__(self, client: Elgato, info: Info, mac: str | None) -> None:
"""Initialize the button entity."""
<|body_0|>
async def async_press(self) -> None:
"""Identify the light, will make it blink."... | stack_v2_sparse_classes_36k_train_010027 | 1,925 | permissive | [
{
"docstring": "Initialize the button entity.",
"name": "__init__",
"signature": "def __init__(self, client: Elgato, info: Info, mac: str | None) -> None"
},
{
"docstring": "Identify the light, will make it blink.",
"name": "async_press",
"signature": "async def async_press(self) -> None... | 2 | stack_v2_sparse_classes_30k_train_008583 | Implement the Python class `ElgatoIdentifyButton` described below.
Class description:
Defines an Elgato identify button.
Method signatures and docstrings:
- def __init__(self, client: Elgato, info: Info, mac: str | None) -> None: Initialize the button entity.
- async def async_press(self) -> None: Identify the light,... | Implement the Python class `ElgatoIdentifyButton` described below.
Class description:
Defines an Elgato identify button.
Method signatures and docstrings:
- def __init__(self, client: Elgato, info: Info, mac: str | None) -> None: Initialize the button entity.
- async def async_press(self) -> None: Identify the light,... | dcf68d768e4f628d038f1fdd6e40bad713fbc222 | <|skeleton|>
class ElgatoIdentifyButton:
"""Defines an Elgato identify button."""
def __init__(self, client: Elgato, info: Info, mac: str | None) -> None:
"""Initialize the button entity."""
<|body_0|>
async def async_press(self) -> None:
"""Identify the light, will make it blink."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ElgatoIdentifyButton:
"""Defines an Elgato identify button."""
def __init__(self, client: Elgato, info: Info, mac: str | None) -> None:
"""Initialize the button entity."""
super().__init__(client, info, mac)
self.entity_description = ButtonEntityDescription(key='identify', name='I... | the_stack_v2_python_sparse | homeassistant/components/elgato/button.py | Adminiuga/home-assistant | train | 5 |
14388c43e0808f12454f282c0f52bea3563b8e96 | [
"log.info('Setup Section verifyProcessorDetails')\nhost_ip = classparam['host_ip']\nboot_order_obj = classparam['boot_order_obj']\nself.host_serial_handle = classparam['host_serial_handle']\nself.host_serial_handle.connect_to_host_serial()\nlog.info('Create boot device from CIMC config and boot from it')\nif boot_o... | <|body_start_0|>
log.info('Setup Section verifyProcessorDetails')
host_ip = classparam['host_ip']
boot_order_obj = classparam['boot_order_obj']
self.host_serial_handle = classparam['host_serial_handle']
self.host_serial_handle.connect_to_host_serial()
log.info('Create boo... | Configure boot device to bios, pxe, hdd, cdrom, floppy drive options in non persistent mode when boot device set from CIMC config and booted from it | CimcConfigIPMICmdNonPersistentBootDevice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CimcConfigIPMICmdNonPersistentBootDevice:
"""Configure boot device to bios, pxe, hdd, cdrom, floppy drive options in non persistent mode when boot device set from CIMC config and booted from it"""
def setup(self, cimc_util_obj):
"""Test Case Setup"""
<|body_0|>
def test(... | stack_v2_sparse_classes_36k_train_010028 | 19,363 | no_license | [
{
"docstring": "Test Case Setup",
"name": "setup",
"signature": "def setup(self, cimc_util_obj)"
},
{
"docstring": "ipmi command to set boot to bios, pxe, hdd, cdrom, floppy drive options in non-persistent mode",
"name": "test",
"signature": "def test(self, cimc_util_obj, config, paramet... | 3 | stack_v2_sparse_classes_30k_train_012209 | Implement the Python class `CimcConfigIPMICmdNonPersistentBootDevice` described below.
Class description:
Configure boot device to bios, pxe, hdd, cdrom, floppy drive options in non persistent mode when boot device set from CIMC config and booted from it
Method signatures and docstrings:
- def setup(self, cimc_util_o... | Implement the Python class `CimcConfigIPMICmdNonPersistentBootDevice` described below.
Class description:
Configure boot device to bios, pxe, hdd, cdrom, floppy drive options in non persistent mode when boot device set from CIMC config and booted from it
Method signatures and docstrings:
- def setup(self, cimc_util_o... | c255e045a4950a0d8868a10012d5ce6e5c6a9c23 | <|skeleton|>
class CimcConfigIPMICmdNonPersistentBootDevice:
"""Configure boot device to bios, pxe, hdd, cdrom, floppy drive options in non persistent mode when boot device set from CIMC config and booted from it"""
def setup(self, cimc_util_obj):
"""Test Case Setup"""
<|body_0|>
def test(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CimcConfigIPMICmdNonPersistentBootDevice:
"""Configure boot device to bios, pxe, hdd, cdrom, floppy drive options in non persistent mode when boot device set from CIMC config and booted from it"""
def setup(self, cimc_util_obj):
"""Test Case Setup"""
log.info('Setup Section verifyProcesso... | the_stack_v2_python_sparse | ipmi_cmnd_bootorder.py | jrchanda/MyRepo | train | 0 |
d33f5928e4414fbed5d4a09ae32baa2c6f413c19 | [
"super(Decoder, self).__init__()\nself.input_size = input_size\nself.hidden_size = hidden_size\nself.num_layers = num_layers\nself.proba_output = proba_output\nif rnn_type == 'LSTM':\n self.model = nn.LSTM(input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers, batch_first=batch_fir... | <|body_start_0|>
super(Decoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.proba_output = proba_output
if rnn_type == 'LSTM':
self.model = nn.LSTM(input_size=self.input_size, hidden_size=self.... | Decoder Network | Decoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""Decoder Network"""
def __init__(self, input_size, hidden_size, num_layers, dropout=0, batch_first=True, rnn_type='LSTM', proba_output=False):
"""Create Encoder Args: input_size (int): number of features per time step hidden_size (int): number of hidden nodes per time step... | stack_v2_sparse_classes_36k_train_010029 | 14,969 | permissive | [
{
"docstring": "Create Encoder Args: input_size (int): number of features per time step hidden_size (int): number of hidden nodes per time step num_layers (int): number of layers dropout (float, optional): percentage of nodes that should switched out at any term. Defaults to 0. batch_first (bool, optional): if ... | 2 | stack_v2_sparse_classes_30k_train_016472 | Implement the Python class `Decoder` described below.
Class description:
Decoder Network
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, num_layers, dropout=0, batch_first=True, rnn_type='LSTM', proba_output=False): Create Encoder Args: input_size (int): number of features per time ste... | Implement the Python class `Decoder` described below.
Class description:
Decoder Network
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, num_layers, dropout=0, batch_first=True, rnn_type='LSTM', proba_output=False): Create Encoder Args: input_size (int): number of features per time ste... | 5b4a61b5dd0bc259ffe68223877949ef4ebfa5e3 | <|skeleton|>
class Decoder:
"""Decoder Network"""
def __init__(self, input_size, hidden_size, num_layers, dropout=0, batch_first=True, rnn_type='LSTM', proba_output=False):
"""Create Encoder Args: input_size (int): number of features per time step hidden_size (int): number of hidden nodes per time step... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder:
"""Decoder Network"""
def __init__(self, input_size, hidden_size, num_layers, dropout=0, batch_first=True, rnn_type='LSTM', proba_output=False):
"""Create Encoder Args: input_size (int): number of features per time step hidden_size (int): number of hidden nodes per time step num_layers (... | the_stack_v2_python_sparse | src/models/anomalia/layers.py | maurony/ts-vrae | train | 1 |
979c5a410b0afe485bff6be64e685171bfedb812 | [
"self.connection = None\nself.differential_drive = DifferentialDrive()\nself.camera = None\nself.buzzer = None\nself.led = LED()\nself.left_bump_sensor = BumpSensor('left')\nself.right_bump_sensor = BumpSensor('right')\nself.left_proximity_sensor = ProximitySensor('left')\nself.front_proximity_sensor = ProximitySen... | <|body_start_0|>
self.connection = None
self.differential_drive = DifferentialDrive()
self.camera = None
self.buzzer = None
self.led = LED()
self.left_bump_sensor = BumpSensor('left')
self.right_bump_sensor = BumpSensor('right')
self.left_proximity_sensor ... | RoseBot | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RoseBot:
def __init__(self):
"""Initializes a RoseBot that has: -- self.connection: for Python-to-robot communication -- self.differential_drive: for making the robot move -- self.camera: for doing things with the Pixy camera -- self.buzzer: for making noises -- self.led: for turning the... | stack_v2_sparse_classes_36k_train_010030 | 14,163 | no_license | [
{
"docstring": "Initializes a RoseBot that has: -- self.connection: for Python-to-robot communication -- self.differential_drive: for making the robot move -- self.camera: for doing things with the Pixy camera -- self.buzzer: for making noises -- self.led: for turning the LED on and off -- Sensor objects for se... | 4 | stack_v2_sparse_classes_30k_train_009532 | Implement the Python class `RoseBot` described below.
Class description:
Implement the RoseBot class.
Method signatures and docstrings:
- def __init__(self): Initializes a RoseBot that has: -- self.connection: for Python-to-robot communication -- self.differential_drive: for making the robot move -- self.camera: for ... | Implement the Python class `RoseBot` described below.
Class description:
Implement the RoseBot class.
Method signatures and docstrings:
- def __init__(self): Initializes a RoseBot that has: -- self.connection: for Python-to-robot communication -- self.differential_drive: for making the robot move -- self.camera: for ... | 7f5906ce0cde57a5537a3068513575da32b33df5 | <|skeleton|>
class RoseBot:
def __init__(self):
"""Initializes a RoseBot that has: -- self.connection: for Python-to-robot communication -- self.differential_drive: for making the robot move -- self.camera: for doing things with the Pixy camera -- self.buzzer: for making noises -- self.led: for turning the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RoseBot:
def __init__(self):
"""Initializes a RoseBot that has: -- self.connection: for Python-to-robot communication -- self.differential_drive: for making the robot move -- self.camera: for doing things with the Pixy camera -- self.buzzer: for making noises -- self.led: for turning the LED on and of... | the_stack_v2_python_sparse | Session20_RobotPixyCamera/src/rosebot.py | rhinomikey/Python-Projects | train | 0 | |
46eaff679d7537647f4bc1f3722d59179c6db870 | [
"try:\n content_type = decide_content_type(self.request.headers.getall(hdrs.ACCEPT), SUPPORTED_CONTENT_TYPES)\nexcept NoAgreeableContentTypeError as e:\n raise web.HTTPNotAcceptable() from e\nid = self.request.match_info['id']\nlogging.debug(f'Getting catalog with id {id}')\ncatalog = await get_catalog_by_id(... | <|body_start_0|>
try:
content_type = decide_content_type(self.request.headers.getall(hdrs.ACCEPT), SUPPORTED_CONTENT_TYPES)
except NoAgreeableContentTypeError as e:
raise web.HTTPNotAcceptable() from e
id = self.request.match_info['id']
logging.debug(f'Getting cat... | Class representing catalog resource. | Catalog | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Catalog:
"""Class representing catalog resource."""
async def get(self) -> web.Response:
"""Get catalog by id."""
<|body_0|>
async def delete(self) -> web.Response:
"""Delete catalog given by id."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
t... | stack_v2_sparse_classes_36k_train_010031 | 3,608 | permissive | [
{
"docstring": "Get catalog by id.",
"name": "get",
"signature": "async def get(self) -> web.Response"
},
{
"docstring": "Delete catalog given by id.",
"name": "delete",
"signature": "async def delete(self) -> web.Response"
}
] | 2 | stack_v2_sparse_classes_30k_train_001614 | Implement the Python class `Catalog` described below.
Class description:
Class representing catalog resource.
Method signatures and docstrings:
- async def get(self) -> web.Response: Get catalog by id.
- async def delete(self) -> web.Response: Delete catalog given by id. | Implement the Python class `Catalog` described below.
Class description:
Class representing catalog resource.
Method signatures and docstrings:
- async def get(self) -> web.Response: Get catalog by id.
- async def delete(self) -> web.Response: Delete catalog given by id.
<|skeleton|>
class Catalog:
"""Class repr... | 86d1525d9bd58644384e1760711968adb948956e | <|skeleton|>
class Catalog:
"""Class representing catalog resource."""
async def get(self) -> web.Response:
"""Get catalog by id."""
<|body_0|>
async def delete(self) -> web.Response:
"""Delete catalog given by id."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Catalog:
"""Class representing catalog resource."""
async def get(self) -> web.Response:
"""Get catalog by id."""
try:
content_type = decide_content_type(self.request.headers.getall(hdrs.ACCEPT), SUPPORTED_CONTENT_TYPES)
except NoAgreeableContentTypeError as e:
... | the_stack_v2_python_sparse | dataservice_publisher/resources/catalogs.py | Informasjonsforvaltning/dataservice-publisher | train | 1 |
e995551f989d9afd3ce9a01a19a5ec812d41e6aa | [
"self.__logger = State().getLogger('DetectionCore_Component_Logger')\nself.__logger.info('Starting __init__()', 'HorizontalLineRemoveDetector:__init__')\nself.__indexOfProcessMat = indexOfProcessMat\nself.__anchorPoint = anchorPoint\nself.__kernelWidth = kernelWidth\nself.__kernelHeight = kernelHeight\nself.__morph... | <|body_start_0|>
self.__logger = State().getLogger('DetectionCore_Component_Logger')
self.__logger.info('Starting __init__()', 'HorizontalLineRemoveDetector:__init__')
self.__indexOfProcessMat = indexOfProcessMat
self.__anchorPoint = anchorPoint
self.__kernelWidth = kernelWidth
... | HorizontalLineRemoveDetector | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HorizontalLineRemoveDetector:
def __init__(self, indexOfProcessMat=0, anchorPoint=(-1, -1), kernelWidth=1, kernelHeight=3, morphOfKernel=cv2.MORPH_RECT, showImagesInWindow=True):
"""To-Do: Bitte Kommentar bzw. Dokumentaion erstellen!"""
<|body_0|>
def detect(self, mats):
... | stack_v2_sparse_classes_36k_train_010032 | 3,792 | no_license | [
{
"docstring": "To-Do: Bitte Kommentar bzw. Dokumentaion erstellen!",
"name": "__init__",
"signature": "def __init__(self, indexOfProcessMat=0, anchorPoint=(-1, -1), kernelWidth=1, kernelHeight=3, morphOfKernel=cv2.MORPH_RECT, showImagesInWindow=True)"
},
{
"docstring": "To-Do: Bitte Kommentar b... | 2 | stack_v2_sparse_classes_30k_train_004233 | Implement the Python class `HorizontalLineRemoveDetector` described below.
Class description:
Implement the HorizontalLineRemoveDetector class.
Method signatures and docstrings:
- def __init__(self, indexOfProcessMat=0, anchorPoint=(-1, -1), kernelWidth=1, kernelHeight=3, morphOfKernel=cv2.MORPH_RECT, showImagesInWin... | Implement the Python class `HorizontalLineRemoveDetector` described below.
Class description:
Implement the HorizontalLineRemoveDetector class.
Method signatures and docstrings:
- def __init__(self, indexOfProcessMat=0, anchorPoint=(-1, -1), kernelWidth=1, kernelHeight=3, morphOfKernel=cv2.MORPH_RECT, showImagesInWin... | 3daaa72b193ebfb55894b47b6a752cdc2192bb6b | <|skeleton|>
class HorizontalLineRemoveDetector:
def __init__(self, indexOfProcessMat=0, anchorPoint=(-1, -1), kernelWidth=1, kernelHeight=3, morphOfKernel=cv2.MORPH_RECT, showImagesInWindow=True):
"""To-Do: Bitte Kommentar bzw. Dokumentaion erstellen!"""
<|body_0|>
def detect(self, mats):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HorizontalLineRemoveDetector:
def __init__(self, indexOfProcessMat=0, anchorPoint=(-1, -1), kernelWidth=1, kernelHeight=3, morphOfKernel=cv2.MORPH_RECT, showImagesInWindow=True):
"""To-Do: Bitte Kommentar bzw. Dokumentaion erstellen!"""
self.__logger = State().getLogger('DetectionCore_Componen... | the_stack_v2_python_sparse | SheetMusicScanner/DetectionCore_Component/Detector/HorizontalLineRemoveDetector.py | jadeskon/score-scan | train | 0 | |
35ada8c093f51a36f07c8045251cfb339c84d5be | [
"if n < 1:\n return False\nwhile n != 1:\n if n % 5 == 0:\n n = n / 5\n elif n % 3 == 0:\n n = n / 3\n elif n % 2 == 0:\n n = n / 2\n else:\n return False\nreturn True",
"dp2, dp3, dp5 = (1, 1, 1)\ndp = [0] * (n + 1)\ndp[1] = 1\nfor i in range(2, n + 1):\n dp[i] = min... | <|body_start_0|>
if n < 1:
return False
while n != 1:
if n % 5 == 0:
n = n / 5
elif n % 3 == 0:
n = n / 3
elif n % 2 == 0:
n = n / 2
else:
return False
return True
<|end_bo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isUgly(self, n: int) -> bool:
"""263 判断 n 是否为丑数"""
<|body_0|>
def nthUglyNumber(self, n: int) -> int:
"""264 返回第 n 个丑数 用三个指针dp2,dp3,dp5"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if n < 1:
return False
while n ... | stack_v2_sparse_classes_36k_train_010033 | 1,417 | no_license | [
{
"docstring": "263 判断 n 是否为丑数",
"name": "isUgly",
"signature": "def isUgly(self, n: int) -> bool"
},
{
"docstring": "264 返回第 n 个丑数 用三个指针dp2,dp3,dp5",
"name": "nthUglyNumber",
"signature": "def nthUglyNumber(self, n: int) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_008312 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isUgly(self, n: int) -> bool: 263 判断 n 是否为丑数
- def nthUglyNumber(self, n: int) -> int: 264 返回第 n 个丑数 用三个指针dp2,dp3,dp5 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isUgly(self, n: int) -> bool: 263 判断 n 是否为丑数
- def nthUglyNumber(self, n: int) -> int: 264 返回第 n 个丑数 用三个指针dp2,dp3,dp5
<|skeleton|>
class Solution:
def isUgly(self, n: i... | 3fd69b85f52af861ff7e2c74d8aacc515b192615 | <|skeleton|>
class Solution:
def isUgly(self, n: int) -> bool:
"""263 判断 n 是否为丑数"""
<|body_0|>
def nthUglyNumber(self, n: int) -> int:
"""264 返回第 n 个丑数 用三个指针dp2,dp3,dp5"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isUgly(self, n: int) -> bool:
"""263 判断 n 是否为丑数"""
if n < 1:
return False
while n != 1:
if n % 5 == 0:
n = n / 5
elif n % 3 == 0:
n = n / 3
elif n % 2 == 0:
n = n / 2
... | the_stack_v2_python_sparse | Other/Ugly_263_264.py | helloprogram6/leetcode_Cookbook_python | 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_36k_train_010034 | 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 | null | 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_36k | data/stack_v2_sparse_classes_30k | 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 |
4dbe354bcbb961bd1170a72e3f0b30afc0d11572 | [
"ver_inst, rec_type, rec_size = unpack('<HHL', self.stream.read(8))\ninstance, version = divmod(ver_inst, 2 ** 4)\nreturn (rec_type, rec_size, (instance, version))",
"if rec_type == PptRecordCurrentUser.TYPE:\n return (PptRecordCurrentUser, True)\nelif rec_type == PptRecordExOleObjAtom.TYPE:\n return (PptRe... | <|body_start_0|>
ver_inst, rec_type, rec_size = unpack('<HHL', self.stream.read(8))
instance, version = divmod(ver_inst, 2 ** 4)
return (rec_type, rec_size, (instance, version))
<|end_body_0|>
<|body_start_1|>
if rec_type == PptRecordCurrentUser.TYPE:
return (PptRecordCurren... | a stream of records in a ppt file | PptStream | [
"MIT",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PptStream:
"""a stream of records in a ppt file"""
def read_record_head(self):
"""read first few bytes of record to determine size and type returns (type, size, other) where other is (instance, version)"""
<|body_0|>
def record_class_for_type(cls, rec_type):
"""d... | stack_v2_sparse_classes_36k_train_010035 | 29,559 | permissive | [
{
"docstring": "read first few bytes of record to determine size and type returns (type, size, other) where other is (instance, version)",
"name": "read_record_head",
"signature": "def read_record_head(self)"
},
{
"docstring": "determine a class for given record type returns (clz, force_read)",
... | 2 | stack_v2_sparse_classes_30k_train_005812 | Implement the Python class `PptStream` described below.
Class description:
a stream of records in a ppt file
Method signatures and docstrings:
- def read_record_head(self): read first few bytes of record to determine size and type returns (type, size, other) where other is (instance, version)
- def record_class_for_t... | Implement the Python class `PptStream` described below.
Class description:
a stream of records in a ppt file
Method signatures and docstrings:
- def read_record_head(self): read first few bytes of record to determine size and type returns (type, size, other) where other is (instance, version)
- def record_class_for_t... | fb4546ec1be5f46d53856161e46ea53d7b7e532a | <|skeleton|>
class PptStream:
"""a stream of records in a ppt file"""
def read_record_head(self):
"""read first few bytes of record to determine size and type returns (type, size, other) where other is (instance, version)"""
<|body_0|>
def record_class_for_type(cls, rec_type):
"""d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PptStream:
"""a stream of records in a ppt file"""
def read_record_head(self):
"""read first few bytes of record to determine size and type returns (type, size, other) where other is (instance, version)"""
ver_inst, rec_type, rec_size = unpack('<HHL', self.stream.read(8))
instance... | the_stack_v2_python_sparse | oletools/ppt_record_parser.py | decalage2/oletools | train | 2,601 |
893b376d2c61918c5442881d42e813a65d871326 | [
"logger_admin = self.setup_logger(logging.INFO, 'scoreboard_admin.log')\n'\\n Use the same configuration file as the python controller. The conf file contains a user\\n name and password that is critical to retrieve hint entitlements. This user and password\\n allows us to retrieve a second ses... | <|body_start_0|>
logger_admin = self.setup_logger(logging.INFO, 'scoreboard_admin.log')
'\n Use the same configuration file as the python controller. The conf file contains a user\n name and password that is critical to retrieve hint entitlements. This user and password\n allows us ... | getanswerCommand | [
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class getanswerCommand:
def stream(self, records):
"""Configure the logger. In this custom search command we only need to write to scoreboard_admin.log."""
<|body_0|>
def setup_logger(self, level, filename):
"""Setup a logger for the custom search command."""
<|bod... | stack_v2_sparse_classes_36k_train_010036 | 3,066 | permissive | [
{
"docstring": "Configure the logger. In this custom search command we only need to write to scoreboard_admin.log.",
"name": "stream",
"signature": "def stream(self, records)"
},
{
"docstring": "Setup a logger for the custom search command.",
"name": "setup_logger",
"signature": "def set... | 2 | null | Implement the Python class `getanswerCommand` described below.
Class description:
Implement the getanswerCommand class.
Method signatures and docstrings:
- def stream(self, records): Configure the logger. In this custom search command we only need to write to scoreboard_admin.log.
- def setup_logger(self, level, file... | Implement the Python class `getanswerCommand` described below.
Class description:
Implement the getanswerCommand class.
Method signatures and docstrings:
- def stream(self, records): Configure the logger. In this custom search command we only need to write to scoreboard_admin.log.
- def setup_logger(self, level, file... | bef2d5cb254b0d6d4699f4f445e6fc914a35ceed | <|skeleton|>
class getanswerCommand:
def stream(self, records):
"""Configure the logger. In this custom search command we only need to write to scoreboard_admin.log."""
<|body_0|>
def setup_logger(self, level, filename):
"""Setup a logger for the custom search command."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class getanswerCommand:
def stream(self, records):
"""Configure the logger. In this custom search command we only need to write to scoreboard_admin.log."""
logger_admin = self.setup_logger(logging.INFO, 'scoreboard_admin.log')
'\n Use the same configuration file as the python controll... | the_stack_v2_python_sparse | bin/validateevents.py | splunk/SA-ctf_scoreboard | train | 113 | |
ae15c9a0dc1ab1c8c72b2b91c65eab3e0bb32eac | [
"params = ParamsParser(request.JSON)\npassword = params.str('password', desc='密码', min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)\nusername = params.str('username', desc='用户名', max_length=MAX_USERNAME_LENGTH)\naccounts = Account.objects.filter_cache(username=username)\nif len(accounts) == 0 or not ... | <|body_start_0|>
params = ParamsParser(request.JSON)
password = params.str('password', desc='密码', min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
username = params.str('username', desc='用户名', max_length=MAX_USERNAME_LENGTH)
accounts = Account.objects.filter_cache(username... | AccountLoginView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountLoginView:
def post(self, request):
"""登录 :param request: :return:"""
<|body_0|>
def get(self, request):
"""检查是否登录 :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
params = ParamsParser(request.JSON)
password =... | stack_v2_sparse_classes_36k_train_010037 | 2,013 | no_license | [
{
"docstring": "登录 :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "检查是否登录 :param request: :return:",
"name": "get",
"signature": "def get(self, request)"
}
] | 2 | null | Implement the Python class `AccountLoginView` described below.
Class description:
Implement the AccountLoginView class.
Method signatures and docstrings:
- def post(self, request): 登录 :param request: :return:
- def get(self, request): 检查是否登录 :param request: :return: | Implement the Python class `AccountLoginView` described below.
Class description:
Implement the AccountLoginView class.
Method signatures and docstrings:
- def post(self, request): 登录 :param request: :return:
- def get(self, request): 检查是否登录 :param request: :return:
<|skeleton|>
class AccountLoginView:
def post... | 7467cd66e1fc91f0b3a264f8fc9b93f22f09fe7b | <|skeleton|>
class AccountLoginView:
def post(self, request):
"""登录 :param request: :return:"""
<|body_0|>
def get(self, request):
"""检查是否登录 :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccountLoginView:
def post(self, request):
"""登录 :param request: :return:"""
params = ParamsParser(request.JSON)
password = params.str('password', desc='密码', min_length=MIN_PASSWORD_LENGTH, max_length=MAX_PASSWORD_LENGTH)
username = params.str('username', desc='用户名', max_length... | the_stack_v2_python_sparse | FireHydrant/server/account/views/login.py | shoogoome/FireHydrant | train | 4 | |
2cf542f1d5004fd1181585648b974f5f25dd57ac | [
"Callbacks.current = self\ntry:\n yield\nfinally:\n if Callbacks.current is self:\n Callbacks.current = None",
"with self.set_current():\n ret = None\n try:\n for i in self:\n try:\n ret = i(*args, **kwargs) or ret\n except AbortedError:\n ... | <|body_start_0|>
Callbacks.current = self
try:
yield
finally:
if Callbacks.current is self:
Callbacks.current = None
<|end_body_0|>
<|body_start_1|>
with self.set_current():
ret = None
try:
for i in self:
... | Failsafe callbacks executor. | Callbacks | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Callbacks:
"""Failsafe callbacks executor."""
def set_current(self):
"""Set current object during context."""
<|body_0|>
def execute(self, *args, **kwargs):
"""Execute callbacks."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Callbacks.current ... | stack_v2_sparse_classes_36k_train_010038 | 3,157 | no_license | [
{
"docstring": "Set current object during context.",
"name": "set_current",
"signature": "def set_current(self)"
},
{
"docstring": "Execute callbacks.",
"name": "execute",
"signature": "def execute(self, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019308 | Implement the Python class `Callbacks` described below.
Class description:
Failsafe callbacks executor.
Method signatures and docstrings:
- def set_current(self): Set current object during context.
- def execute(self, *args, **kwargs): Execute callbacks. | Implement the Python class `Callbacks` described below.
Class description:
Failsafe callbacks executor.
Method signatures and docstrings:
- def set_current(self): Set current object during context.
- def execute(self, *args, **kwargs): Execute callbacks.
<|skeleton|>
class Callbacks:
"""Failsafe callbacks execut... | e346c61db83397da1a8d80ed3a0e33aa7f677533 | <|skeleton|>
class Callbacks:
"""Failsafe callbacks executor."""
def set_current(self):
"""Set current object during context."""
<|body_0|>
def execute(self, *args, **kwargs):
"""Execute callbacks."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Callbacks:
"""Failsafe callbacks executor."""
def set_current(self):
"""Set current object during context."""
Callbacks.current = self
try:
yield
finally:
if Callbacks.current is self:
Callbacks.current = None
def execute(self, ... | the_stack_v2_python_sparse | lib/callback.py | tws0002/Nuke-2 | train | 1 |
4b81d6fe1efac29ceeb7066b77453049ceb4647b | [
"if not root:\n return []\nlevel = [(root, 0)]\nres = {}\nmincol, maxcol = (1 << 31, -1 << 31)\nwhile level:\n for node, col in level:\n mincol = min(mincol, col)\n maxcol = max(maxcol, col)\n res[col] = res.get(col, []) + [node.val]\n tmp = []\n for node, col in level:\n if ... | <|body_start_0|>
if not root:
return []
level = [(root, 0)]
res = {}
mincol, maxcol = (1 << 31, -1 << 31)
while level:
for node, col in level:
mincol = min(mincol, col)
maxcol = max(maxcol, col)
res[col] = re... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def levelOrdervectical(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_0|>
def levelOrder(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
... | stack_v2_sparse_classes_36k_train_010039 | 1,540 | permissive | [
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "levelOrdervectical",
"signature": "def levelOrdervectical(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: List[List[int]]",
"name": "levelOrder",
"signature": "def levelOrder(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def levelOrdervectical(self, root): :type root: TreeNode :rtype: List[List[int]]
- def levelOrder(self, root): :type root: TreeNode :rtype: List[List[int]] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def levelOrdervectical(self, root): :type root: TreeNode :rtype: List[List[int]]
- def levelOrder(self, root): :type root: TreeNode :rtype: List[List[int]]
<|skeleton|>
class So... | 86f1cb98de801f58c39d9a48ce9de12df7303d20 | <|skeleton|>
class Solution:
def levelOrdervectical(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_0|>
def levelOrder(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def levelOrdervectical(self, root):
""":type root: TreeNode :rtype: List[List[int]]"""
if not root:
return []
level = [(root, 0)]
res = {}
mincol, maxcol = (1 << 31, -1 << 31)
while level:
for node, col in level:
... | the_stack_v2_python_sparse | 102-Binary-Tree-Level-Order-Traversal/solution.py | Tanych/CodeTracking | train | 0 | |
1bb69a91efb77ee151f70f2ba35860b4a4cbaaea | [
"os_walk_input_iter = (('a1', ['b1', 'b2'], ['c1', 'd1']), ('a2', ['b3', 'b4'], ['c2', 'd2']), ('a3', ['b5', 'b6'], ['c3', 'd3']))\nos_walk_expected_output = ('a1/b1', 'a1/b2', 'a2/b3', 'a2/b4', 'a3/b5', 'a3/b6')\nos_walk_actual_output = tuple(da.lwc.search._adapt_os_walk_to_dirpath(os_walk_input_iter))\nassert os_... | <|body_start_0|>
os_walk_input_iter = (('a1', ['b1', 'b2'], ['c1', 'd1']), ('a2', ['b3', 'b4'], ['c2', 'd2']), ('a3', ['b5', 'b6'], ['c3', 'd3']))
os_walk_expected_output = ('a1/b1', 'a1/b2', 'a2/b3', 'a2/b4', 'a3/b5', 'a3/b6')
os_walk_actual_output = tuple(da.lwc.search._adapt_os_walk_to_dirpat... | Tet cases for the _adapt_os_walk_to_dirpath function. | Specify_AdaptOsWalkToDirpath | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Specify_AdaptOsWalkToDirpath:
"""Tet cases for the _adapt_os_walk_to_dirpath function."""
def it_serialises_a_simple_tree(self):
"""Test _adapt_os_walk_to_dirpath handles a simple use case as expected. The _adapt_os_walk_to_dirpath should take output in the form provided by os.walk a... | stack_v2_sparse_classes_36k_train_010040 | 29,518 | permissive | [
{
"docstring": "Test _adapt_os_walk_to_dirpath handles a simple use case as expected. The _adapt_os_walk_to_dirpath should take output in the form provided by os.walk and should adapt it to produce a sequence of \"flat\" directory-paths.",
"name": "it_serialises_a_simple_tree",
"signature": "def it_seri... | 2 | null | Implement the Python class `Specify_AdaptOsWalkToDirpath` described below.
Class description:
Tet cases for the _adapt_os_walk_to_dirpath function.
Method signatures and docstrings:
- def it_serialises_a_simple_tree(self): Test _adapt_os_walk_to_dirpath handles a simple use case as expected. The _adapt_os_walk_to_dir... | Implement the Python class `Specify_AdaptOsWalkToDirpath` described below.
Class description:
Tet cases for the _adapt_os_walk_to_dirpath function.
Method signatures and docstrings:
- def it_serialises_a_simple_tree(self): Test _adapt_os_walk_to_dirpath handles a simple use case as expected. The _adapt_os_walk_to_dir... | 04a13be2792323e3f9fdb83fd236a8e9cfe6aa2d | <|skeleton|>
class Specify_AdaptOsWalkToDirpath:
"""Tet cases for the _adapt_os_walk_to_dirpath function."""
def it_serialises_a_simple_tree(self):
"""Test _adapt_os_walk_to_dirpath handles a simple use case as expected. The _adapt_os_walk_to_dirpath should take output in the form provided by os.walk a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Specify_AdaptOsWalkToDirpath:
"""Tet cases for the _adapt_os_walk_to_dirpath function."""
def it_serialises_a_simple_tree(self):
"""Test _adapt_os_walk_to_dirpath handles a simple use case as expected. The _adapt_os_walk_to_dirpath should take output in the form provided by os.walk and should ada... | the_stack_v2_python_sparse | a3_src/h70_internal/da/lwc/spec/spec_search.py | wtpayne/hiai | train | 5 |
89535d84d776914b034e79d2f2b27389175f8a03 | [
"self.modules = configuration['sed_modules']\nself.parameters = [self._param_dict_combine(configuration['sed_modules_params'][module]) for module in self.modules]\nself.shape = tuple((len(parameter) for parameter in self.parameters))\nself.size = int(np.product(self.shape))",
"dictionary = dict(dictionary)\nfor k... | <|body_start_0|>
self.modules = configuration['sed_modules']
self.parameters = [self._param_dict_combine(configuration['sed_modules_params'][module]) for module in self.modules]
self.shape = tuple((len(parameter) for parameter in self.parameters))
self.size = int(np.product(self.shape))
... | Class to generate a parameters handler for a systematic grid using the parameters given in the pcigale.ini file. | ParametersHandlerGrid | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParametersHandlerGrid:
"""Class to generate a parameters handler for a systematic grid using the parameters given in the pcigale.ini file."""
def __init__(self, configuration):
"""Instantiate the class. Parameters ---------- configuration: dictionary Contains the modules in the order... | stack_v2_sparse_classes_36k_train_010041 | 7,942 | no_license | [
{
"docstring": "Instantiate the class. Parameters ---------- configuration: dictionary Contains the modules in the order they are called",
"name": "__init__",
"signature": "def __init__(self, configuration)"
},
{
"docstring": "Given a dictionary associating to each key an array, returns all the ... | 4 | stack_v2_sparse_classes_30k_train_013404 | Implement the Python class `ParametersHandlerGrid` described below.
Class description:
Class to generate a parameters handler for a systematic grid using the parameters given in the pcigale.ini file.
Method signatures and docstrings:
- def __init__(self, configuration): Instantiate the class. Parameters ---------- co... | Implement the Python class `ParametersHandlerGrid` described below.
Class description:
Class to generate a parameters handler for a systematic grid using the parameters given in the pcigale.ini file.
Method signatures and docstrings:
- def __init__(self, configuration): Instantiate the class. Parameters ---------- co... | 9ef9b99425537350b8706fddfe90ed47301107a5 | <|skeleton|>
class ParametersHandlerGrid:
"""Class to generate a parameters handler for a systematic grid using the parameters given in the pcigale.ini file."""
def __init__(self, configuration):
"""Instantiate the class. Parameters ---------- configuration: dictionary Contains the modules in the order... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ParametersHandlerGrid:
"""Class to generate a parameters handler for a systematic grid using the parameters given in the pcigale.ini file."""
def __init__(self, configuration):
"""Instantiate the class. Parameters ---------- configuration: dictionary Contains the modules in the order they are cal... | the_stack_v2_python_sparse | pcigale/handlers/parameters_handler.py | JohannesBuchner/cigale | train | 5 |
3cfcee4c5b7fdbea4bc8a26213279457f5ce63e9 | [
"self.num_positions = num_positions\nself.num_trials = num_trials\nself.position_value = 1000 / self.num_positions",
"uniform_list = np.random.uniform(0, 1, self.num_positions)\nresult = np.zeros(self.num_positions)\nfor i in range(self.num_positions):\n if uniform_list[i] <= 0.49:\n result[i] = self.po... | <|body_start_0|>
self.num_positions = num_positions
self.num_trials = num_trials
self.position_value = 1000 / self.num_positions
<|end_body_0|>
<|body_start_1|>
uniform_list = np.random.uniform(0, 1, self.num_positions)
result = np.zeros(self.num_positions)
for i in rang... | The investment class inits number of shares to buy(num_positions) and number of times to repeated the test(num_trials). It also contains two methods: get_cumu_ret and get_daily_ret. | investment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class investment:
"""The investment class inits number of shares to buy(num_positions) and number of times to repeated the test(num_trials). It also contains two methods: get_cumu_ret and get_daily_ret."""
def __init__(self, num_positions, num_trials):
"""Constructor"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_010042 | 2,603 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, num_positions, num_trials)"
},
{
"docstring": "The method returns cumulative return, which is the outcome of simulation of one day's investment for different choice of positions.",
"name": "get_cumu_ret",
... | 3 | stack_v2_sparse_classes_30k_train_007645 | Implement the Python class `investment` described below.
Class description:
The investment class inits number of shares to buy(num_positions) and number of times to repeated the test(num_trials). It also contains two methods: get_cumu_ret and get_daily_ret.
Method signatures and docstrings:
- def __init__(self, num_p... | Implement the Python class `investment` described below.
Class description:
The investment class inits number of shares to buy(num_positions) and number of times to repeated the test(num_trials). It also contains two methods: get_cumu_ret and get_daily_ret.
Method signatures and docstrings:
- def __init__(self, num_p... | 5b904060e8bced7f91547ad7f7819773a7450a1e | <|skeleton|>
class investment:
"""The investment class inits number of shares to buy(num_positions) and number of times to repeated the test(num_trials). It also contains two methods: get_cumu_ret and get_daily_ret."""
def __init__(self, num_positions, num_trials):
"""Constructor"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class investment:
"""The investment class inits number of shares to buy(num_positions) and number of times to repeated the test(num_trials). It also contains two methods: get_cumu_ret and get_daily_ret."""
def __init__(self, num_positions, num_trials):
"""Constructor"""
self.num_positions = num... | the_stack_v2_python_sparse | zg475/investment.py | ds-ga-1007/assignment8 | train | 1 |
6eeb2d6be78771a9465d6d2e7b9be50ccaa4674f | [
"super().__init__(num_locations, coverages_per_location)\nself.dtypes = OrderedDict([('from_agg_id', 'i'), ('level_id', 'i'), ('to_agg_id', 'i')])\nself.data_length = num_locations * coverages_per_location * 2\nself.file_name = os.path.join(directory, 'fm_programme.bin')",
"levels = [1, 10]\nlevels = range(1, len... | <|body_start_0|>
super().__init__(num_locations, coverages_per_location)
self.dtypes = OrderedDict([('from_agg_id', 'i'), ('level_id', 'i'), ('to_agg_id', 'i')])
self.data_length = num_locations * coverages_per_location * 2
self.file_name = os.path.join(directory, 'fm_programme.bin')
<|e... | Generate data for Financial Model Programme dummy model Oasis file. This file shows the level hierarchy. Attributes: generate_data: Generate Financial Model Programme dummy model Oasis file data. | FMProgrammeFile | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FMProgrammeFile:
"""Generate data for Financial Model Programme dummy model Oasis file. This file shows the level hierarchy. Attributes: generate_data: Generate Financial Model Programme dummy model Oasis file data."""
def __init__(self, num_locations, coverages_per_location, directory):
... | stack_v2_sparse_classes_36k_train_010043 | 39,722 | permissive | [
{
"docstring": "Initialise Financial Model Programme file class. Args: num_locations (int): number of locations. coverages_per_location (int): number of coverage types per location. directory (str): dummy model file destination.",
"name": "__init__",
"signature": "def __init__(self, num_locations, cover... | 2 | null | Implement the Python class `FMProgrammeFile` described below.
Class description:
Generate data for Financial Model Programme dummy model Oasis file. This file shows the level hierarchy. Attributes: generate_data: Generate Financial Model Programme dummy model Oasis file data.
Method signatures and docstrings:
- def _... | Implement the Python class `FMProgrammeFile` described below.
Class description:
Generate data for Financial Model Programme dummy model Oasis file. This file shows the level hierarchy. Attributes: generate_data: Generate Financial Model Programme dummy model Oasis file data.
Method signatures and docstrings:
- def _... | 23e704c335629ccd010969b1090446cfa3f384d5 | <|skeleton|>
class FMProgrammeFile:
"""Generate data for Financial Model Programme dummy model Oasis file. This file shows the level hierarchy. Attributes: generate_data: Generate Financial Model Programme dummy model Oasis file data."""
def __init__(self, num_locations, coverages_per_location, directory):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FMProgrammeFile:
"""Generate data for Financial Model Programme dummy model Oasis file. This file shows the level hierarchy. Attributes: generate_data: Generate Financial Model Programme dummy model Oasis file data."""
def __init__(self, num_locations, coverages_per_location, directory):
"""Initi... | the_stack_v2_python_sparse | oasislmf/computation/data/dummy_model/generate.py | OasisLMF/OasisLMF | train | 122 |
60b6733d4e19a6b371aa03ae83f031e09701cc8a | [
"if bib_number <= 0:\n raise ValueError('bib_number must be strictely positive')\nself.bib_number = bib_number\nself.name = name\nif last_state:\n self.current_stage: WatchedProperty = WatchedProperty(last_state.current_stage.get_value())\n self.rank: WatchedProperty = WatchedProperty(last_state.rank.get_v... | <|body_start_0|>
if bib_number <= 0:
raise ValueError('bib_number must be strictely positive')
self.bib_number = bib_number
self.name = name
if last_state:
self.current_stage: WatchedProperty = WatchedProperty(last_state.current_stage.get_value())
self... | Represents the state of a team in a race file A state is a line in the race file | TeamState | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeamState:
"""Represents the state of a team in a race file A state is a line in the race file"""
def __init__(self, bib_number: int, name: str, last_state: Optional['TeamState']=None):
"""Creates a new team state A team state represents a line in a race file. The last_state paramete... | stack_v2_sparse_classes_36k_train_010044 | 5,116 | no_license | [
{
"docstring": "Creates a new team state A team state represents a line in a race file. The last_state parameter is used to keep old values. :param bib_number: bib number of the team :param name: name of the team :param last_state: previous state from the last reading, could be None :raises ValueError: if bib_n... | 3 | stack_v2_sparse_classes_30k_train_011124 | Implement the Python class `TeamState` described below.
Class description:
Represents the state of a team in a race file A state is a line in the race file
Method signatures and docstrings:
- def __init__(self, bib_number: int, name: str, last_state: Optional['TeamState']=None): Creates a new team state A team state ... | Implement the Python class `TeamState` described below.
Class description:
Represents the state of a team in a race file A state is a line in the race file
Method signatures and docstrings:
- def __init__(self, bib_number: int, name: str, last_state: Optional['TeamState']=None): Creates a new team state A team state ... | 07cff53642f34c0897c81c506ac1c93437de9bca | <|skeleton|>
class TeamState:
"""Represents the state of a team in a race file A state is a line in the race file"""
def __init__(self, bib_number: int, name: str, last_state: Optional['TeamState']=None):
"""Creates a new team state A team state represents a line in a race file. The last_state paramete... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TeamState:
"""Represents the state of a team in a race file A state is a line in the race file"""
def __init__(self, bib_number: int, name: str, last_state: Optional['TeamState']=None):
"""Creates a new team state A team state represents a line in a race file. The last_state parameter is used to ... | the_stack_v2_python_sparse | uctl2_back/team_state.py | mdesmarais/UCTL2_Broadcaster | train | 0 |
6cd96feb94dd5bcf9262e63c7a84f197ea6f8015 | [
"super().__init__()\nassert hidden_size % 2 == 0\nself.hidden_size = hidden_size\nself._forward_lstm = nn.LSTMCell(input_size, hidden_size // 2)\nself._backward_lstm = nn.LSTMCell(input_size, hidden_size // 2)\nself._dropout_in = nn.Dropout(dropout)\nself._dropout_h = nn.Dropout(dropout)",
"L, B, _ = x.data.shape... | <|body_start_0|>
super().__init__()
assert hidden_size % 2 == 0
self.hidden_size = hidden_size
self._forward_lstm = nn.LSTMCell(input_size, hidden_size // 2)
self._backward_lstm = nn.LSTMCell(input_size, hidden_size // 2)
self._dropout_in = nn.Dropout(dropout)
sel... | BidirectionalLSTM | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BidirectionalLSTM:
def __init__(self, input_size: int, hidden_size: int, dropout: float=0.0):
"""Parameters ---------- n_elem: int The number of words including <unknown> label. embedding_size: int The dimention of each embedding hidden_size: int The number of features in LSTM dropout: f... | stack_v2_sparse_classes_36k_train_010045 | 2,691 | permissive | [
{
"docstring": "Parameters ---------- n_elem: int The number of words including <unknown> label. embedding_size: int The dimention of each embedding hidden_size: int The number of features in LSTM dropout: float The probability of dropout",
"name": "__init__",
"signature": "def __init__(self, input_size... | 2 | stack_v2_sparse_classes_30k_train_013459 | Implement the Python class `BidirectionalLSTM` described below.
Class description:
Implement the BidirectionalLSTM class.
Method signatures and docstrings:
- def __init__(self, input_size: int, hidden_size: int, dropout: float=0.0): Parameters ---------- n_elem: int The number of words including <unknown> label. embe... | Implement the Python class `BidirectionalLSTM` described below.
Class description:
Implement the BidirectionalLSTM class.
Method signatures and docstrings:
- def __init__(self, input_size: int, hidden_size: int, dropout: float=0.0): Parameters ---------- n_elem: int The number of words including <unknown> label. embe... | 573e94c567064705fa65267dd83946bf183197de | <|skeleton|>
class BidirectionalLSTM:
def __init__(self, input_size: int, hidden_size: int, dropout: float=0.0):
"""Parameters ---------- n_elem: int The number of words including <unknown> label. embedding_size: int The dimention of each embedding hidden_size: int The number of features in LSTM dropout: f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BidirectionalLSTM:
def __init__(self, input_size: int, hidden_size: int, dropout: float=0.0):
"""Parameters ---------- n_elem: int The number of words including <unknown> label. embedding_size: int The dimention of each embedding hidden_size: int The number of features in LSTM dropout: float The proba... | the_stack_v2_python_sparse | mlprogram/nn/bidirectional_lstm.py | brando90/mlprogram | train | 0 | |
3ef97edba459a85ab0d974d3391e0ee7e938875a | [
"adb_monkey2 = 'adb shell pm list packages | find \"%s\" ' % package\nstatus = os.popen(adb_monkey2).read()\nif status:\n return True\nelse:\n return False",
"adb = 'adb install %s' % apk_path\nprint(adb)\nos.system(adb)",
"time.sleep(5)\nadb = 'adb uninstall %s' % package\nos.system(adb)"
] | <|body_start_0|>
adb_monkey2 = 'adb shell pm list packages | find "%s" ' % package
status = os.popen(adb_monkey2).read()
if status:
return True
else:
return False
<|end_body_0|>
<|body_start_1|>
adb = 'adb install %s' % apk_path
print(adb)
... | InstallUninstall | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InstallUninstall:
def apk_install_status(self, package):
"""功能查找是否已经安装此安装包 package：包名 apk_path：包存放路径"""
<|body_0|>
def apk_install(self, apk_path):
"""功能安装APP package：包名 apk_path：包存放路径"""
<|body_1|>
def apk_uninstall(self, package):
"""功能： 卸载APP ... | stack_v2_sparse_classes_36k_train_010046 | 951 | permissive | [
{
"docstring": "功能查找是否已经安装此安装包 package：包名 apk_path：包存放路径",
"name": "apk_install_status",
"signature": "def apk_install_status(self, package)"
},
{
"docstring": "功能安装APP package：包名 apk_path：包存放路径",
"name": "apk_install",
"signature": "def apk_install(self, apk_path)"
},
{
"docstri... | 3 | stack_v2_sparse_classes_30k_train_011991 | Implement the Python class `InstallUninstall` described below.
Class description:
Implement the InstallUninstall class.
Method signatures and docstrings:
- def apk_install_status(self, package): 功能查找是否已经安装此安装包 package：包名 apk_path：包存放路径
- def apk_install(self, apk_path): 功能安装APP package：包名 apk_path：包存放路径
- def apk_uni... | Implement the Python class `InstallUninstall` described below.
Class description:
Implement the InstallUninstall class.
Method signatures and docstrings:
- def apk_install_status(self, package): 功能查找是否已经安装此安装包 package：包名 apk_path：包存放路径
- def apk_install(self, apk_path): 功能安装APP package：包名 apk_path：包存放路径
- def apk_uni... | a95d891a8b1204a9e38071b480aae484dd21e70a | <|skeleton|>
class InstallUninstall:
def apk_install_status(self, package):
"""功能查找是否已经安装此安装包 package：包名 apk_path：包存放路径"""
<|body_0|>
def apk_install(self, apk_path):
"""功能安装APP package：包名 apk_path：包存放路径"""
<|body_1|>
def apk_uninstall(self, package):
"""功能： 卸载APP ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InstallUninstall:
def apk_install_status(self, package):
"""功能查找是否已经安装此安装包 package：包名 apk_path：包存放路径"""
adb_monkey2 = 'adb shell pm list packages | find "%s" ' % package
status = os.popen(adb_monkey2).read()
if status:
return True
else:
return ... | the_stack_v2_python_sparse | common/app_common/shell_install_adb.py | lineOneTwo/Test_Api_App | train | 0 | |
50137f3a6036d37ad5ac4d14a5f25d2215d4384a | [
"precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\nfor line in self:\n if not float_is_zero(qty, precision_digits=precision):\n vals = line._prepare_invoice_line(qty=qty)\n vals.update({'invoice_id': invoice_id, 'purchase_line_id': line.id, 'price_unit': self.price... | <|body_start_0|>
precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')
for line in self:
if not float_is_zero(qty, precision_digits=precision):
vals = line._prepare_invoice_line(qty=qty)
vals.update({'invoice_id': invoice_id, 'purc... | PurchaseOrderLine | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PurchaseOrderLine:
def invoice_line_create(self, invoice_id, qty):
"""Create an invoice line. The quantity to invoice can be positive (invoice) or negative (refund). :param invoice_id: integer :param qty: float quantity to invoice"""
<|body_0|>
def _prepare_invoice_line(self... | stack_v2_sparse_classes_36k_train_010047 | 9,530 | no_license | [
{
"docstring": "Create an invoice line. The quantity to invoice can be positive (invoice) or negative (refund). :param invoice_id: integer :param qty: float quantity to invoice",
"name": "invoice_line_create",
"signature": "def invoice_line_create(self, invoice_id, qty)"
},
{
"docstring": "Prepa... | 4 | stack_v2_sparse_classes_30k_train_020857 | Implement the Python class `PurchaseOrderLine` described below.
Class description:
Implement the PurchaseOrderLine class.
Method signatures and docstrings:
- def invoice_line_create(self, invoice_id, qty): Create an invoice line. The quantity to invoice can be positive (invoice) or negative (refund). :param invoice_i... | Implement the Python class `PurchaseOrderLine` described below.
Class description:
Implement the PurchaseOrderLine class.
Method signatures and docstrings:
- def invoice_line_create(self, invoice_id, qty): Create an invoice line. The quantity to invoice can be positive (invoice) or negative (refund). :param invoice_i... | eea92d44bee76053619be00aa601b1efc4249589 | <|skeleton|>
class PurchaseOrderLine:
def invoice_line_create(self, invoice_id, qty):
"""Create an invoice line. The quantity to invoice can be positive (invoice) or negative (refund). :param invoice_id: integer :param qty: float quantity to invoice"""
<|body_0|>
def _prepare_invoice_line(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PurchaseOrderLine:
def invoice_line_create(self, invoice_id, qty):
"""Create an invoice line. The quantity to invoice can be positive (invoice) or negative (refund). :param invoice_id: integer :param qty: float quantity to invoice"""
precision = self.env['decimal.precision'].precision_get('Pro... | the_stack_v2_python_sparse | linkloving_purchase_invoice/models/purchase_order.py | iverson2937/linklovingaddons | train | 1 | |
14d1908f657668fba848745530c877edceb280ed | [
"self.records = records\nself.cheapMetric = cheapDistanceMetric\nif not callable(self.cheapMetric):\n raise ValueError('Cheap distance metric must be callable function.')\nself.method = ermethod\nself.t1 = t1\nself.t2 = t2\nif scoreType == ScoreTypes.DISTANCE:\n self.scoreIsBetter = lambda score, best: score ... | <|body_start_0|>
self.records = records
self.cheapMetric = cheapDistanceMetric
if not callable(self.cheapMetric):
raise ValueError('Cheap distance metric must be callable function.')
self.method = ermethod
self.t1 = t1
self.t2 = t2
if scoreType == Scor... | Class to do blocking using canopies. | CanopiesBlocker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CanopiesBlocker:
"""Class to do blocking using canopies."""
def __init__(self, records, cheapDistanceMetric, ermethod, t1, t2, scoreType, randomize=True):
"""Constructor :param records: all the records to cluster :param cheapDistanceMetric: A cheap distance metric used to form the ca... | stack_v2_sparse_classes_36k_train_010048 | 6,582 | no_license | [
{
"docstring": "Constructor :param records: all the records to cluster :param cheapDistanceMetric: A cheap distance metric used to form the canopies. :param ermethod: An ER method to do the full clustering within each canopy. :param t1: First threshold for canopies :param t2: Second threshold for canopies :para... | 6 | stack_v2_sparse_classes_30k_train_018894 | Implement the Python class `CanopiesBlocker` described below.
Class description:
Class to do blocking using canopies.
Method signatures and docstrings:
- def __init__(self, records, cheapDistanceMetric, ermethod, t1, t2, scoreType, randomize=True): Constructor :param records: all the records to cluster :param cheapDi... | Implement the Python class `CanopiesBlocker` described below.
Class description:
Class to do blocking using canopies.
Method signatures and docstrings:
- def __init__(self, records, cheapDistanceMetric, ermethod, t1, t2, scoreType, randomize=True): Constructor :param records: all the records to cluster :param cheapDi... | 8399c88ab0fdc7736dddcf5239eea655d613c61d | <|skeleton|>
class CanopiesBlocker:
"""Class to do blocking using canopies."""
def __init__(self, records, cheapDistanceMetric, ermethod, t1, t2, scoreType, randomize=True):
"""Constructor :param records: all the records to cluster :param cheapDistanceMetric: A cheap distance metric used to form the ca... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CanopiesBlocker:
"""Class to do blocking using canopies."""
def __init__(self, records, cheapDistanceMetric, ermethod, t1, t2, scoreType, randomize=True):
"""Constructor :param records: all the records to cluster :param cheapDistanceMetric: A cheap distance metric used to form the canopies. :para... | the_stack_v2_python_sparse | canopies.py | timdestan/quiz-bowl-entity-resolution | train | 1 |
f6400a1157a563dc0a1162d9bed14478f096afaa | [
"super(ReluNet, self).__init__()\nself.input_dim = input_dim\nself.output_dim = output_dim\nself.hidden_dim = hidden_dim\nself.num_layers = num_layers\nself.num_epochs = num_epochs\nself.threshold = threshold\nself.learning_rate = learning_rate\nself.layers = nn.ModuleList()\nself.layers.append(nn.Linear(input_dim,... | <|body_start_0|>
super(ReluNet, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.num_epochs = num_epochs
self.threshold = threshold
self.learning_rate = learning_rate
... | Fully connected neural network with relu activation | ReluNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReluNet:
"""Fully connected neural network with relu activation"""
def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1):
"""Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the ... | stack_v2_sparse_classes_36k_train_010049 | 4,882 | no_license | [
{
"docstring": "Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the input feature output_dim: int -- dimension of the output feature hidden_dim: int -- number of hidden units at each layer num_layers: int -- number of hidden layers num_epochs: int -- number of epochs to trai... | 5 | stack_v2_sparse_classes_30k_train_017125 | Implement the Python class `ReluNet` described below.
Class description:
Fully connected neural network with relu activation
Method signatures and docstrings:
- def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1): Initilize a Neural Network with ... | Implement the Python class `ReluNet` described below.
Class description:
Fully connected neural network with relu activation
Method signatures and docstrings:
- def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1): Initilize a Neural Network with ... | d7e651024b07587b46497183d90934561a4839e2 | <|skeleton|>
class ReluNet:
"""Fully connected neural network with relu activation"""
def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1):
"""Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReluNet:
"""Fully connected neural network with relu activation"""
def __init__(self, input_dim, output_dim, hidden_dim=100, num_layers=2, num_epochs=100, learning_rate=0.001, threshold=0.1):
"""Initilize a Neural Network with Relu activation Args: input_dim: int -- dimension of the input feature... | the_stack_v2_python_sparse | model/relunet.py | SSF-climate/SSF | train | 7 |
4f9fe08196bd278eaa4eab75ea0ee1170a17978a | [
"super().__init__()\nlayers = OrderedDict([('inp_layers', nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(in_channels=inp_channel_dim, out_channels=hidden_channel_dim, kernel_size=7, bias=False), nn.BatchNorm2d(hidden_channel_dim), nn.ReLU(True)))])\nfor i in range(2):\n cur_inp_dim = 2 ** i * hidden_channel_dim\... | <|body_start_0|>
super().__init__()
layers = OrderedDict([('inp_layers', nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(in_channels=inp_channel_dim, out_channels=hidden_channel_dim, kernel_size=7, bias=False), nn.BatchNorm2d(hidden_channel_dim), nn.ReLU(True)))])
for i in range(2):
c... | Generator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
def __init__(self, inp_channel_dim: int, out_channel_dim: int, hidden_channel_dim: int=64, n_blocks: int=6):
"""Generator with downsampling, residual blocks and upsampling. Args: inp_channel_dim: number of channels of the input image out_channel_dim: number of channels of the ... | stack_v2_sparse_classes_36k_train_010050 | 6,411 | permissive | [
{
"docstring": "Generator with downsampling, residual blocks and upsampling. Args: inp_channel_dim: number of channels of the input image out_channel_dim: number of channels of the output image hidden_channel_dim: number of channels after first layer. Number of channels in res_blocks will be 4*hidden_channels_d... | 2 | stack_v2_sparse_classes_30k_train_004879 | Implement the Python class `Generator` described below.
Class description:
Implement the Generator class.
Method signatures and docstrings:
- def __init__(self, inp_channel_dim: int, out_channel_dim: int, hidden_channel_dim: int=64, n_blocks: int=6): Generator with downsampling, residual blocks and upsampling. Args: ... | Implement the Python class `Generator` described below.
Class description:
Implement the Generator class.
Method signatures and docstrings:
- def __init__(self, inp_channel_dim: int, out_channel_dim: int, hidden_channel_dim: int=64, n_blocks: int=6): Generator with downsampling, residual blocks and upsampling. Args: ... | b6caf28b1d56f9abef80e8fd34eede39f45c8c7d | <|skeleton|>
class Generator:
def __init__(self, inp_channel_dim: int, out_channel_dim: int, hidden_channel_dim: int=64, n_blocks: int=6):
"""Generator with downsampling, residual blocks and upsampling. Args: inp_channel_dim: number of channels of the input image out_channel_dim: number of channels of the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Generator:
def __init__(self, inp_channel_dim: int, out_channel_dim: int, hidden_channel_dim: int=64, n_blocks: int=6):
"""Generator with downsampling, residual blocks and upsampling. Args: inp_channel_dim: number of channels of the input image out_channel_dim: number of channels of the output image h... | the_stack_v2_python_sparse | src/modules/generator.py | elephantmipt/cycle-gan-distillation | train | 1 | |
ffb50588b53355aa5e320c30d1631be9fec3b144 | [
"self.undo = nuke.Undo\nself.__disabled = self.undo.disabled()\nself.script = script\nif save_func:\n self.save_func = save_func\nelse:\n self.save_func = nuke.scriptSave",
"if self.__disabled:\n self.undo.enable()\nself.undo.begin()",
"self.save_func(self.script)\nself.undo.cancel()\nif self.__disable... | <|body_start_0|>
self.undo = nuke.Undo
self.__disabled = self.undo.disabled()
self.script = script
if save_func:
self.save_func = save_func
else:
self.save_func = nuke.scriptSave
<|end_body_0|>
<|body_start_1|>
if self.__disabled:
self... | Given a script to save to, will save all of the changes made in the with block to the script, then undoes those changes in the current script. For example: with WriteChanges('/Volumes/af/show/omg/script.nk'): for node in nuke.allNodes(): node.setYpos(100) | WriteChanges | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WriteChanges:
"""Given a script to save to, will save all of the changes made in the with block to the script, then undoes those changes in the current script. For example: with WriteChanges('/Volumes/af/show/omg/script.nk'): for node in nuke.allNodes(): node.setYpos(100)"""
def __init__(sel... | stack_v2_sparse_classes_36k_train_010051 | 23,998 | no_license | [
{
"docstring": "Initialize a WriteChanges context manager. Must provide a script to write to. If you provide a save_func, it will be called instead of the default `nuke.scriptSave`. The function must have the same interface as `nuke.scriptSave`. A possible alternative is `nuke.nodeCopy`.",
"name": "__init__... | 3 | stack_v2_sparse_classes_30k_train_003438 | Implement the Python class `WriteChanges` described below.
Class description:
Given a script to save to, will save all of the changes made in the with block to the script, then undoes those changes in the current script. For example: with WriteChanges('/Volumes/af/show/omg/script.nk'): for node in nuke.allNodes(): nod... | Implement the Python class `WriteChanges` described below.
Class description:
Given a script to save to, will save all of the changes made in the with block to the script, then undoes those changes in the current script. For example: with WriteChanges('/Volumes/af/show/omg/script.nk'): for node in nuke.allNodes(): nod... | ffd112312632731db53aa94c77a0bb6d63243474 | <|skeleton|>
class WriteChanges:
"""Given a script to save to, will save all of the changes made in the with block to the script, then undoes those changes in the current script. For example: with WriteChanges('/Volumes/af/show/omg/script.nk'): for node in nuke.allNodes(): node.setYpos(100)"""
def __init__(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WriteChanges:
"""Given a script to save to, will save all of the changes made in the with block to the script, then undoes those changes in the current script. For example: with WriteChanges('/Volumes/af/show/omg/script.nk'): for node in nuke.allNodes(): node.setYpos(100)"""
def __init__(self, script, sa... | the_stack_v2_python_sparse | apps/nuke/scripts/python/zync_nuke.py | tws0002/gs-code | train | 1 |
7ee6d4e52f129fa35d6ec7f568a63af4754ad2aa | [
"positive = (dividend < 0) is (divisor < 0)\ndividend, divisor = (abs(dividend), abs(divisor))\nres = 0\nc, sub = (1, divisor)\nwhile dividend >= divisor:\n '\\n for example, if we want to calc (17/2)\\n ret = 0\\n 17-2 ,ret+=1 left=15\\n 15-4 ,ret+=2 left=11\\n ... | <|body_start_0|>
positive = (dividend < 0) is (divisor < 0)
dividend, divisor = (abs(dividend), abs(divisor))
res = 0
c, sub = (1, divisor)
while dividend >= divisor:
'\n for example, if we want to calc (17/2)\n ret = 0\n 17-2 ,ret+=1 ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def divide(self, dividend, divisor):
""":param dividend: :param divisor: :return:"""
<|body_0|>
def divide2(self, dividend, divisor):
"""效率低 :type dividend: int :type divisor: int :rtype: int"""
<|body_1|>
def divide3(self, dividend, divisor):
... | stack_v2_sparse_classes_36k_train_010052 | 4,179 | no_license | [
{
"docstring": ":param dividend: :param divisor: :return:",
"name": "divide",
"signature": "def divide(self, dividend, divisor)"
},
{
"docstring": "效率低 :type dividend: int :type divisor: int :rtype: int",
"name": "divide2",
"signature": "def divide2(self, dividend, divisor)"
},
{
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def divide(self, dividend, divisor): :param dividend: :param divisor: :return:
- def divide2(self, dividend, divisor): 效率低 :type dividend: int :type divisor: int :rtype: int
- de... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def divide(self, dividend, divisor): :param dividend: :param divisor: :return:
- def divide2(self, dividend, divisor): 效率低 :type dividend: int :type divisor: int :rtype: int
- de... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def divide(self, dividend, divisor):
""":param dividend: :param divisor: :return:"""
<|body_0|>
def divide2(self, dividend, divisor):
"""效率低 :type dividend: int :type divisor: int :rtype: int"""
<|body_1|>
def divide3(self, dividend, divisor):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def divide(self, dividend, divisor):
""":param dividend: :param divisor: :return:"""
positive = (dividend < 0) is (divisor < 0)
dividend, divisor = (abs(dividend), abs(divisor))
res = 0
c, sub = (1, divisor)
while dividend >= divisor:
'\n ... | the_stack_v2_python_sparse | 29_两数相除.py | lovehhf/LeetCode | train | 0 | |
9bd7ec7c5615c33b3a7ee22155991f6dd92cb125 | [
"self.info = forq_haplotype\nself.length = len(self.info)\nself.founders = [haplotype_id for position, haplotype_id in self.info]\nself.founders_set = list(set(self.founders))\nself.positions = None\nself.position_map_offset = None\nself.temp_map = None\nself.map = list()",
"if self.length == 1:\n self.map.app... | <|body_start_0|>
self.info = forq_haplotype
self.length = len(self.info)
self.founders = [haplotype_id for position, haplotype_id in self.info]
self.founders_set = list(set(self.founders))
self.positions = None
self.position_map_offset = None
self.temp_map = None
... | Forqs Haplotype Object Example [(0, 91), (1235052, 105), (1251805, 105)] or [(0, 91)] | ForqsHaplotype | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ForqsHaplotype:
"""Forqs Haplotype Object Example [(0, 91), (1235052, 105), (1251805, 105)] or [(0, 91)]"""
def __init__(self, forq_haplotype):
"""Forqs Haplotype Instance Creation"""
<|body_0|>
def haplotype_map_single(self):
"""Create the haplotype guide for as... | stack_v2_sparse_classes_36k_train_010053 | 2,539 | no_license | [
{
"docstring": "Forqs Haplotype Instance Creation",
"name": "__init__",
"signature": "def __init__(self, forq_haplotype)"
},
{
"docstring": "Create the haplotype guide for assembling the recombined forqs haplotype outputs",
"name": "haplotype_map_single",
"signature": "def haplotype_map_... | 2 | stack_v2_sparse_classes_30k_test_000396 | Implement the Python class `ForqsHaplotype` described below.
Class description:
Forqs Haplotype Object Example [(0, 91), (1235052, 105), (1251805, 105)] or [(0, 91)]
Method signatures and docstrings:
- def __init__(self, forq_haplotype): Forqs Haplotype Instance Creation
- def haplotype_map_single(self): Create the h... | Implement the Python class `ForqsHaplotype` described below.
Class description:
Forqs Haplotype Object Example [(0, 91), (1235052, 105), (1251805, 105)] or [(0, 91)]
Method signatures and docstrings:
- def __init__(self, forq_haplotype): Forqs Haplotype Instance Creation
- def haplotype_map_single(self): Create the h... | 13ccb51ab30bbd8a45228986017b23038c04357d | <|skeleton|>
class ForqsHaplotype:
"""Forqs Haplotype Object Example [(0, 91), (1235052, 105), (1251805, 105)] or [(0, 91)]"""
def __init__(self, forq_haplotype):
"""Forqs Haplotype Instance Creation"""
<|body_0|>
def haplotype_map_single(self):
"""Create the haplotype guide for as... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ForqsHaplotype:
"""Forqs Haplotype Object Example [(0, 91), (1235052, 105), (1251805, 105)] or [(0, 91)]"""
def __init__(self, forq_haplotype):
"""Forqs Haplotype Instance Creation"""
self.info = forq_haplotype
self.length = len(self.info)
self.founders = [haplotype_id for... | the_stack_v2_python_sparse | simulations/haptools/forqshaplotype.py | transferome/Simulations | train | 0 |
5b162013a237cb549fdf1e577bf7fa9e9048aff4 | [
"fast = slow = head\nwhile fast and slow and fast.next:\n fast = fast.next.next\n slow = slow.next\n if fast is slow:\n return True\nreturn False",
"cache = set()\nwhile head:\n if head in cache:\n return True\n else:\n cache.add(head)\n head = head.next\nreturn False"
] | <|body_start_0|>
fast = slow = head
while fast and slow and fast.next:
fast = fast.next.next
slow = slow.next
if fast is slow:
return True
return False
<|end_body_0|>
<|body_start_1|>
cache = set()
while head:
if he... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasCycle1(self, head: ListNode):
"""判断链表是否有环： 解法: 快慢指针 :type head: ListNode :rtype: bool"""
<|body_0|>
def hasCycle2(self, head: ListNode):
"""判断链表是否有环 解法: 哈希表判重方式 时间复杂度： O(n) 空间复杂度: O(n) :type head: ListNode :rtype: bool"""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_36k_train_010054 | 1,249 | no_license | [
{
"docstring": "判断链表是否有环： 解法: 快慢指针 :type head: ListNode :rtype: bool",
"name": "hasCycle1",
"signature": "def hasCycle1(self, head: ListNode)"
},
{
"docstring": "判断链表是否有环 解法: 哈希表判重方式 时间复杂度： O(n) 空间复杂度: O(n) :type head: ListNode :rtype: bool",
"name": "hasCycle2",
"signature": "def hasCyc... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle1(self, head: ListNode): 判断链表是否有环： 解法: 快慢指针 :type head: ListNode :rtype: bool
- def hasCycle2(self, head: ListNode): 判断链表是否有环 解法: 哈希表判重方式 时间复杂度： O(n) 空间复杂度: O(n) :typ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle1(self, head: ListNode): 判断链表是否有环： 解法: 快慢指针 :type head: ListNode :rtype: bool
- def hasCycle2(self, head: ListNode): 判断链表是否有环 解法: 哈希表判重方式 时间复杂度： O(n) 空间复杂度: O(n) :typ... | c0dd577481b46129d950354d567d332a4d091137 | <|skeleton|>
class Solution:
def hasCycle1(self, head: ListNode):
"""判断链表是否有环： 解法: 快慢指针 :type head: ListNode :rtype: bool"""
<|body_0|>
def hasCycle2(self, head: ListNode):
"""判断链表是否有环 解法: 哈希表判重方式 时间复杂度： O(n) 空间复杂度: O(n) :type head: ListNode :rtype: bool"""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasCycle1(self, head: ListNode):
"""判断链表是否有环： 解法: 快慢指针 :type head: ListNode :rtype: bool"""
fast = slow = head
while fast and slow and fast.next:
fast = fast.next.next
slow = slow.next
if fast is slow:
return True
... | the_stack_v2_python_sparse | leetcode/141_环形链表_是否有环.py | tenqaz/crazy_arithmetic | train | 0 | |
27446b7a109f296100c0a523f8a2cd544faa817e | [
"data = super(CharacterControl, self).parse(args)\nvalue = data[self.name]\nif value is not None:\n if self.strip is not None:\n value = self.strip.sub('', value)\n data[self.name] = value\n if self.minlen is not None and len(value) < self.minlen:\n m = tr('Input for field %s is too short... | <|body_start_0|>
data = super(CharacterControl, self).parse(args)
value = data[self.name]
if value is not None:
if self.strip is not None:
value = self.strip.sub('', value)
data[self.name] = value
if self.minlen is not None and len(value) <... | Base class for text controls. | CharacterControl | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CharacterControl:
"""Base class for text controls."""
def parse(self, args):
"""Parse `args' to Python format."""
<|body_0|>
def unparse(self, object):
"""Parse `object' to string format."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
data = su... | stack_v2_sparse_classes_36k_train_010055 | 12,353 | permissive | [
{
"docstring": "Parse `args' to Python format.",
"name": "parse",
"signature": "def parse(self, args)"
},
{
"docstring": "Parse `object' to string format.",
"name": "unparse",
"signature": "def unparse(self, object)"
}
] | 2 | null | Implement the Python class `CharacterControl` described below.
Class description:
Base class for text controls.
Method signatures and docstrings:
- def parse(self, args): Parse `args' to Python format.
- def unparse(self, object): Parse `object' to string format. | Implement the Python class `CharacterControl` described below.
Class description:
Base class for text controls.
Method signatures and docstrings:
- def parse(self, args): Parse `args' to Python format.
- def unparse(self, object): Parse `object' to string format.
<|skeleton|>
class CharacterControl:
"""Base clas... | 3a533d3158860102866eaf603840691618f39f81 | <|skeleton|>
class CharacterControl:
"""Base class for text controls."""
def parse(self, args):
"""Parse `args' to Python format."""
<|body_0|>
def unparse(self, object):
"""Parse `object' to string format."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CharacterControl:
"""Base class for text controls."""
def parse(self, args):
"""Parse `args' to Python format."""
data = super(CharacterControl, self).parse(args)
value = data[self.name]
if value is not None:
if self.strip is not None:
value = s... | the_stack_v2_python_sparse | draco2/form/control.py | geertj/draco2 | train | 0 |
ad9d35ce66fb9909cee3e2fea5ab582161c4691e | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn RichLongRunningOperation()",
"from .long_running_operation import LongRunningOperation\nfrom .public_error import PublicError\nfrom .long_running_operation import LongRunningOperation\nfrom .public_error import PublicError\nfields: Dic... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return RichLongRunningOperation()
<|end_body_0|>
<|body_start_1|>
from .long_running_operation import LongRunningOperation
from .public_error import PublicError
from .long_running_opera... | RichLongRunningOperation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RichLongRunningOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> RichLongRunningOperation:
"""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 cre... | stack_v2_sparse_classes_36k_train_010056 | 3,003 | 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: RichLongRunningOperation",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrimi... | 3 | null | Implement the Python class `RichLongRunningOperation` described below.
Class description:
Implement the RichLongRunningOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> RichLongRunningOperation: Creates a new instance of the appropriate c... | Implement the Python class `RichLongRunningOperation` described below.
Class description:
Implement the RichLongRunningOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> RichLongRunningOperation: Creates a new instance of the appropriate c... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class RichLongRunningOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> RichLongRunningOperation:
"""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 cre... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RichLongRunningOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> RichLongRunningOperation:
"""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... | the_stack_v2_python_sparse | msgraph/generated/models/rich_long_running_operation.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
fe61f19f2b83fd465fb0a05ffb77f3aeec435276 | [
"dp = [float('inf')] * len(triangle[-1])\ndp[0] = triangle[0][0]\nfor i in range(1, len(triangle)):\n previous = [n for n in dp]\n for j in range(i + 1):\n if j == 0:\n dp[j] = previous[0] + triangle[i][j]\n elif j == i:\n dp[j] = previous[j - 1] + triangle[i][j]\n e... | <|body_start_0|>
dp = [float('inf')] * len(triangle[-1])
dp[0] = triangle[0][0]
for i in range(1, len(triangle)):
previous = [n for n in dp]
for j in range(i + 1):
if j == 0:
dp[j] = previous[0] + triangle[i][j]
elif j =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal_dp2(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
def minimumTotal_bottomup(self, triangle):
... | stack_v2_sparse_classes_36k_train_010057 | 2,435 | no_license | [
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal",
"signature": "def minimumTotal(self, triangle)"
},
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal_dp2",
"signature": "def minimumTotal_dp2(self, triangle)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_020122 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal_dp2(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTot... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal_dp2(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTot... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal_dp2(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
def minimumTotal_bottomup(self, triangle):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
dp = [float('inf')] * len(triangle[-1])
dp[0] = triangle[0][0]
for i in range(1, len(triangle)):
previous = [n for n in dp]
for j in range(i + 1):
... | the_stack_v2_python_sparse | src/lt_120.py | oxhead/CodingYourWay | train | 0 | |
b3f6c4d46220f118e742a1cbcdbb7d90d43eb5d7 | [
"self.source_table = None\nself.source_field = None\nself.target_table = None\nself.target_field = None",
"tokens = re.findall('[\\\\w]+', lines[0])\ndir = re.findall('<|>', lines[0])\nif dir[0] == '>':\n self.source_table = tokens[0]\n self.source_field = tokens[1]\n self.target_table = tokens[3]\n s... | <|body_start_0|>
self.source_table = None
self.source_field = None
self.target_table = None
self.target_field = None
<|end_body_0|>
<|body_start_1|>
tokens = re.findall('[\\w]+', lines[0])
dir = re.findall('<|>', lines[0])
if dir[0] == '>':
self.sourc... | Parses a foreign key from PlantUML file. | ForeignKey | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ForeignKey:
"""Parses a foreign key from PlantUML file."""
def __init__(self):
"""Constructor."""
<|body_0|>
def parse(self, lines):
"""Parse a foreign key relatinoship. :param lines: The remaining lines of the PUML file."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_010058 | 11,534 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Parse a foreign key relatinoship. :param lines: The remaining lines of the PUML file.",
"name": "parse",
"signature": "def parse(self, lines)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009902 | Implement the Python class `ForeignKey` described below.
Class description:
Parses a foreign key from PlantUML file.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def parse(self, lines): Parse a foreign key relatinoship. :param lines: The remaining lines of the PUML file. | Implement the Python class `ForeignKey` described below.
Class description:
Parses a foreign key from PlantUML file.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def parse(self, lines): Parse a foreign key relatinoship. :param lines: The remaining lines of the PUML file.
<|skeleton|>
class ... | 33bf532b397f21290d6f85631466d90964aab4ad | <|skeleton|>
class ForeignKey:
"""Parses a foreign key from PlantUML file."""
def __init__(self):
"""Constructor."""
<|body_0|>
def parse(self, lines):
"""Parse a foreign key relatinoship. :param lines: The remaining lines of the PUML file."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ForeignKey:
"""Parses a foreign key from PlantUML file."""
def __init__(self):
"""Constructor."""
self.source_table = None
self.source_field = None
self.target_table = None
self.target_field = None
def parse(self, lines):
"""Parse a foreign key relatin... | the_stack_v2_python_sparse | SQLite 21/tools/dbdia2sql.py | deadbok/eal_programming | train | 1 |
95d6498de0d4ca1828bfc7dfce95665474176ea1 | [
"publisher = Publisher.query.filter_by(id=id).first()\nif publisher is None:\n return ({'message': 'Publisher does not exist'}, 404)\nreturn publisher_schema.dump(publisher)",
"req = api.payload\npublisher = Publisher.query.filter_by(id=id).first()\nif publisher is None:\n return ({'message': 'Publisher doe... | <|body_start_0|>
publisher = Publisher.query.filter_by(id=id).first()
if publisher is None:
return ({'message': 'Publisher does not exist'}, 404)
return publisher_schema.dump(publisher)
<|end_body_0|>
<|body_start_1|>
req = api.payload
publisher = Publisher.query.fil... | SinglePublisher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SinglePublisher:
def get(self, id):
"""Get Publisher by id"""
<|body_0|>
def put(self, id):
"""Update a Publisher"""
<|body_1|>
def delete(self, id):
"""Delete a Publisher by id"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
pu... | stack_v2_sparse_classes_36k_train_010059 | 3,380 | no_license | [
{
"docstring": "Get Publisher by id",
"name": "get",
"signature": "def get(self, id)"
},
{
"docstring": "Update a Publisher",
"name": "put",
"signature": "def put(self, id)"
},
{
"docstring": "Delete a Publisher by id",
"name": "delete",
"signature": "def delete(self, id)... | 3 | stack_v2_sparse_classes_30k_train_009703 | Implement the Python class `SinglePublisher` described below.
Class description:
Implement the SinglePublisher class.
Method signatures and docstrings:
- def get(self, id): Get Publisher by id
- def put(self, id): Update a Publisher
- def delete(self, id): Delete a Publisher by id | Implement the Python class `SinglePublisher` described below.
Class description:
Implement the SinglePublisher class.
Method signatures and docstrings:
- def get(self, id): Get Publisher by id
- def put(self, id): Update a Publisher
- def delete(self, id): Delete a Publisher by id
<|skeleton|>
class SinglePublisher:... | ae78fff9888b0f68d9403d7f65cba086dabb3802 | <|skeleton|>
class SinglePublisher:
def get(self, id):
"""Get Publisher by id"""
<|body_0|>
def put(self, id):
"""Update a Publisher"""
<|body_1|>
def delete(self, id):
"""Delete a Publisher by id"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SinglePublisher:
def get(self, id):
"""Get Publisher by id"""
publisher = Publisher.query.filter_by(id=id).first()
if publisher is None:
return ({'message': 'Publisher does not exist'}, 404)
return publisher_schema.dump(publisher)
def put(self, id):
"""... | the_stack_v2_python_sparse | api/v1/publishers.py | mythril-io/flask-api | train | 0 | |
de2402386462a87de1821e7a28ac943a01cfe012 | [
"if value is not None and value.tzinfo is None:\n default_tzinfo = pytz.timezone(settings.TIME_ZONE)\n value = default_tzinfo.localize(value)\n value = value.astimezone(pytz.utc)\nreturn super(UTCDateTimeField, self).to_representation(value)",
"result = super(UTCDateTimeField, self).to_internal_value(val... | <|body_start_0|>
if value is not None and value.tzinfo is None:
default_tzinfo = pytz.timezone(settings.TIME_ZONE)
value = default_tzinfo.localize(value)
value = value.astimezone(pytz.utc)
return super(UTCDateTimeField, self).to_representation(value)
<|end_body_0|>
<... | Like DateTimeField, except it is always in UTC in the API | UTCDateTimeField | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UTCDateTimeField:
"""Like DateTimeField, except it is always in UTC in the API"""
def to_representation(self, value):
"""Convert outgoing datetimes into UTC strings Input currently saves everything in Pacific time, but without timezone info. So this takes the datetimes and converts t... | stack_v2_sparse_classes_36k_train_010060 | 3,305 | permissive | [
{
"docstring": "Convert outgoing datetimes into UTC strings Input currently saves everything in Pacific time, but without timezone info. So this takes the datetimes and converts them from Pacific time to UTC. If this situation ever changes, then we'd change settings.TIME_ZONE and this should continue to work.",... | 2 | stack_v2_sparse_classes_30k_train_020259 | Implement the Python class `UTCDateTimeField` described below.
Class description:
Like DateTimeField, except it is always in UTC in the API
Method signatures and docstrings:
- def to_representation(self, value): Convert outgoing datetimes into UTC strings Input currently saves everything in Pacific time, but without ... | Implement the Python class `UTCDateTimeField` described below.
Class description:
Like DateTimeField, except it is always in UTC in the API
Method signatures and docstrings:
- def to_representation(self, value): Convert outgoing datetimes into UTC strings Input currently saves everything in Pacific time, but without ... | 0fcb81e6a5edaf42c00c64faf001fc43b24e11c0 | <|skeleton|>
class UTCDateTimeField:
"""Like DateTimeField, except it is always in UTC in the API"""
def to_representation(self, value):
"""Convert outgoing datetimes into UTC strings Input currently saves everything in Pacific time, but without timezone info. So this takes the datetimes and converts t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UTCDateTimeField:
"""Like DateTimeField, except it is always in UTC in the API"""
def to_representation(self, value):
"""Convert outgoing datetimes into UTC strings Input currently saves everything in Pacific time, but without timezone info. So this takes the datetimes and converts them from Paci... | the_stack_v2_python_sparse | fjord/base/api_utils.py | mozilla/fjord | train | 18 |
860f137d1e13cc74c627e4ed4df8ede91e3abcec | [
"self.interface_name = interface_name\nself.ip_family = ip_family\nself.ips = ips\nself.node_ids = node_ids\nself.role = role\nself.subnet_gateway = subnet_gateway\nself.subnet_mask_bits = subnet_mask_bits",
"if dictionary is None:\n return None\ninterface_name = dictionary.get('interfaceName')\nip_family = di... | <|body_start_0|>
self.interface_name = interface_name
self.ip_family = ip_family
self.ips = ips
self.node_ids = node_ids
self.role = role
self.subnet_gateway = subnet_gateway
self.subnet_mask_bits = subnet_mask_bits
<|end_body_0|>
<|body_start_1|>
if dict... | Implementation of the 'IpConfig' model. Specifies the configuration of an IP. Attributes: interface_name (string): The interface name. Specifies which interface to assign IP to. ip_family (int): IpFamily of this config. ips (list of string): The interface ips. node_ids (list of long|int): Node ids. role (string): The i... | IpConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IpConfig:
"""Implementation of the 'IpConfig' model. Specifies the configuration of an IP. Attributes: interface_name (string): The interface name. Specifies which interface to assign IP to. ip_family (int): IpFamily of this config. ips (list of string): The interface ips. node_ids (list of long|... | stack_v2_sparse_classes_36k_train_010061 | 2,714 | permissive | [
{
"docstring": "Constructor for the IpConfig class",
"name": "__init__",
"signature": "def __init__(self, interface_name=None, ip_family=None, ips=None, node_ids=None, role=None, subnet_gateway=None, subnet_mask_bits=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary A... | 2 | stack_v2_sparse_classes_30k_train_015664 | Implement the Python class `IpConfig` described below.
Class description:
Implementation of the 'IpConfig' model. Specifies the configuration of an IP. Attributes: interface_name (string): The interface name. Specifies which interface to assign IP to. ip_family (int): IpFamily of this config. ips (list of string): The... | Implement the Python class `IpConfig` described below.
Class description:
Implementation of the 'IpConfig' model. Specifies the configuration of an IP. Attributes: interface_name (string): The interface name. Specifies which interface to assign IP to. ip_family (int): IpFamily of this config. ips (list of string): The... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class IpConfig:
"""Implementation of the 'IpConfig' model. Specifies the configuration of an IP. Attributes: interface_name (string): The interface name. Specifies which interface to assign IP to. ip_family (int): IpFamily of this config. ips (list of string): The interface ips. node_ids (list of long|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IpConfig:
"""Implementation of the 'IpConfig' model. Specifies the configuration of an IP. Attributes: interface_name (string): The interface name. Specifies which interface to assign IP to. ip_family (int): IpFamily of this config. ips (list of string): The interface ips. node_ids (list of long|int): Node id... | the_stack_v2_python_sparse | cohesity_management_sdk/models/ip_config.py | cohesity/management-sdk-python | train | 24 |
0a2449e76df8df27abfc4329685d69d939e1530c | [
"startTime = datetime.datetime.now()\nif trial:\n endTime = datetime.datetime.now()\n return {'start': startTime, 'end': endTime}\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate(TEAM_NAME, TEAM_NAME)\nurl = FUSION_TABLE_URL\ncsv_string = urllib.request.urlopen(url).read().decode('ut... | <|body_start_0|>
startTime = datetime.datetime.now()
if trial:
endTime = datetime.datetime.now()
return {'start': startTime, 'end': endTime}
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate(TEAM_NAME, TEAM_NAME)
url = FUSION_... | countyShapes | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class countyShapes:
def execute(trial=False):
"""Read from Google Fusion table (uploaded to datamechanics.io) to get geoJSON data about each county and insert into collection ex) { "_id" : "7322", "Name" : Barnstable, "Shape" : "<Polygon> ... ", "Geo_ID" : "25001", }"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_010062 | 4,703 | no_license | [
{
"docstring": "Read from Google Fusion table (uploaded to datamechanics.io) to get geoJSON data about each county and insert into collection ex) { \"_id\" : \"7322\", \"Name\" : Barnstable, \"Shape\" : \"<Polygon> ... \", \"Geo_ID\" : \"25001\", }",
"name": "execute",
"signature": "def execute(trial=Fa... | 2 | stack_v2_sparse_classes_30k_test_000882 | Implement the Python class `countyShapes` described below.
Class description:
Implement the countyShapes class.
Method signatures and docstrings:
- def execute(trial=False): Read from Google Fusion table (uploaded to datamechanics.io) to get geoJSON data about each county and insert into collection ex) { "_id" : "732... | Implement the Python class `countyShapes` described below.
Class description:
Implement the countyShapes class.
Method signatures and docstrings:
- def execute(trial=False): Read from Google Fusion table (uploaded to datamechanics.io) to get geoJSON data about each county and insert into collection ex) { "_id" : "732... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class countyShapes:
def execute(trial=False):
"""Read from Google Fusion table (uploaded to datamechanics.io) to get geoJSON data about each county and insert into collection ex) { "_id" : "7322", "Name" : Barnstable, "Shape" : "<Polygon> ... ", "Geo_ID" : "25001", }"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class countyShapes:
def execute(trial=False):
"""Read from Google Fusion table (uploaded to datamechanics.io) to get geoJSON data about each county and insert into collection ex) { "_id" : "7322", "Name" : Barnstable, "Shape" : "<Polygon> ... ", "Geo_ID" : "25001", }"""
startTime = datetime.datetime... | the_stack_v2_python_sparse | ldisalvo_skeesara_vidyaap/countyShapes.py | maximega/course-2019-spr-proj | train | 2 | |
10ee6eb07536dd1c49755073b616ab1520b23731 | [
"try:\n time.sleep(0.5)\n elements = dr.find_visible_elements(selectory[0])\n if len(elements) == 1:\n time.sleep(0.5)\n elements[0].click()\n else:\n element = elements[selectory[1]]\n element.click()\nexcept IndexError:\n elements = dr.find_elements(selectory[0])\n if... | <|body_start_0|>
try:
time.sleep(0.5)
elements = dr.find_visible_elements(selectory[0])
if len(elements) == 1:
time.sleep(0.5)
elements[0].click()
else:
element = elements[selectory[1]]
element.click(... | MyElements | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyElements:
def elements_click(self, dr, selectory):
"""探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(".ivu-btn.ivu-btn-primary.ivu-btn-large")#确定"""
<|body_0|>
def elements_text_update(self, dr, selectory, msg):
"""探针卸载获得的class都是第一个 #elements=dr.find_visible_... | stack_v2_sparse_classes_36k_train_010063 | 3,395 | no_license | [
{
"docstring": "探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(\".ivu-btn.ivu-btn-primary.ivu-btn-large\")#确定",
"name": "elements_click",
"signature": "def elements_click(self, dr, selectory)"
},
{
"docstring": "探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(\".ivu-btn.ivu-btn-primar... | 2 | stack_v2_sparse_classes_30k_test_001007 | Implement the Python class `MyElements` described below.
Class description:
Implement the MyElements class.
Method signatures and docstrings:
- def elements_click(self, dr, selectory): 探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(".ivu-btn.ivu-btn-primary.ivu-btn-large")#确定
- def elements_text_update(self, dr... | Implement the Python class `MyElements` described below.
Class description:
Implement the MyElements class.
Method signatures and docstrings:
- def elements_click(self, dr, selectory): 探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(".ivu-btn.ivu-btn-primary.ivu-btn-large")#确定
- def elements_text_update(self, dr... | a5cf2ab372d1e1e09cae1904c22dba2eab3c29de | <|skeleton|>
class MyElements:
def elements_click(self, dr, selectory):
"""探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(".ivu-btn.ivu-btn-primary.ivu-btn-large")#确定"""
<|body_0|>
def elements_text_update(self, dr, selectory, msg):
"""探针卸载获得的class都是第一个 #elements=dr.find_visible_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyElements:
def elements_click(self, dr, selectory):
"""探针卸载获得的class都是第一个 #elements=dr.find_visible_elements(".ivu-btn.ivu-btn-primary.ivu-btn-large")#确定"""
try:
time.sleep(0.5)
elements = dr.find_visible_elements(selectory[0])
if len(elements) == 1:
... | the_stack_v2_python_sparse | common/elements_base.py | ItTestKing/Base_test | train | 0 | |
10b976bbbe35096eb28886df89f563697ed23780 | [
"cls.testDir = os.path.join(originalPath, 'rmg', 'test_data', 'restartTest')\ncls.outputDir = os.path.join(cls.testDir, 'output_w_filters')\ncls.databaseDirectory = settings['database.directory']\nos.mkdir(cls.outputDir)\ninitialize_log(logging.INFO, os.path.join(cls.outputDir, 'RMG.log'))\ncls.rmg = RMG(input_file... | <|body_start_0|>
cls.testDir = os.path.join(originalPath, 'rmg', 'test_data', 'restartTest')
cls.outputDir = os.path.join(cls.testDir, 'output_w_filters')
cls.databaseDirectory = settings['database.directory']
os.mkdir(cls.outputDir)
initialize_log(logging.INFO, os.path.join(cls.... | TestRestartWithFilters | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRestartWithFilters:
def setUpClass(cls):
"""A function that is run ONCE before all unit tests in this class."""
<|body_0|>
def test_restart_with_filters(self):
"""Test that the RMG restart job with filters included completed without problems"""
<|body_1|>... | stack_v2_sparse_classes_36k_train_010064 | 15,751 | permissive | [
{
"docstring": "A function that is run ONCE before all unit tests in this class.",
"name": "setUpClass",
"signature": "def setUpClass(cls)"
},
{
"docstring": "Test that the RMG restart job with filters included completed without problems",
"name": "test_restart_with_filters",
"signature"... | 3 | null | Implement the Python class `TestRestartWithFilters` described below.
Class description:
Implement the TestRestartWithFilters class.
Method signatures and docstrings:
- def setUpClass(cls): A function that is run ONCE before all unit tests in this class.
- def test_restart_with_filters(self): Test that the RMG restart... | Implement the Python class `TestRestartWithFilters` described below.
Class description:
Implement the TestRestartWithFilters class.
Method signatures and docstrings:
- def setUpClass(cls): A function that is run ONCE before all unit tests in this class.
- def test_restart_with_filters(self): Test that the RMG restart... | 349a4af759cf8877197772cd7eaca1e51d46eff5 | <|skeleton|>
class TestRestartWithFilters:
def setUpClass(cls):
"""A function that is run ONCE before all unit tests in this class."""
<|body_0|>
def test_restart_with_filters(self):
"""Test that the RMG restart job with filters included completed without problems"""
<|body_1|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestRestartWithFilters:
def setUpClass(cls):
"""A function that is run ONCE before all unit tests in this class."""
cls.testDir = os.path.join(originalPath, 'rmg', 'test_data', 'restartTest')
cls.outputDir = os.path.join(cls.testDir, 'output_w_filters')
cls.databaseDirectory = ... | the_stack_v2_python_sparse | rmgpy/rmg/mainTest.py | CanePan-cc/CanePanWorkshop | train | 2 | |
f37caeab91c56167944a7f3ea680647680eedb1b | [
"kwargs = {}\nfor key, value in d.items():\n if key in ('version_check', 'convert_case', 'include_reflection_data'):\n kwargs[key] = value\n elif key == 'text_type':\n kwargs[key] = TextType.parse(value)\n else:\n raise ValueError('Unknown option: %s' % key)\nreturn cls(**kwargs)",
"... | <|body_start_0|>
kwargs = {}
for key, value in d.items():
if key in ('version_check', 'convert_case', 'include_reflection_data'):
kwargs[key] = value
elif key == 'text_type':
kwargs[key] = TextType.parse(value)
else:
rai... | Options | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Options:
def from_dict(cls, d):
"""Create an Options instance from the given dict. Each option is expressed as either a normal bool or a string; strings are parsed (e.g. "bytes" becomes TextType.bytes)"""
<|body_0|>
def combine(self, other):
"""Combine the options of... | stack_v2_sparse_classes_36k_train_010065 | 2,890 | permissive | [
{
"docstring": "Create an Options instance from the given dict. Each option is expressed as either a normal bool or a string; strings are parsed (e.g. \"bytes\" becomes TextType.bytes)",
"name": "from_dict",
"signature": "def from_dict(cls, d)"
},
{
"docstring": "Combine the options of ``self`` ... | 2 | stack_v2_sparse_classes_30k_train_014314 | Implement the Python class `Options` described below.
Class description:
Implement the Options class.
Method signatures and docstrings:
- def from_dict(cls, d): Create an Options instance from the given dict. Each option is expressed as either a normal bool or a string; strings are parsed (e.g. "bytes" becomes TextTy... | Implement the Python class `Options` described below.
Class description:
Implement the Options class.
Method signatures and docstrings:
- def from_dict(cls, d): Create an Options instance from the given dict. Each option is expressed as either a normal bool or a string; strings are parsed (e.g. "bytes" becomes TextTy... | 618af51656836d6a183e6e8e7b5781074c5fbe85 | <|skeleton|>
class Options:
def from_dict(cls, d):
"""Create an Options instance from the given dict. Each option is expressed as either a normal bool or a string; strings are parsed (e.g. "bytes" becomes TextType.bytes)"""
<|body_0|>
def combine(self, other):
"""Combine the options of... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Options:
def from_dict(cls, d):
"""Create an Options instance from the given dict. Each option is expressed as either a normal bool or a string; strings are parsed (e.g. "bytes" becomes TextType.bytes)"""
kwargs = {}
for key, value in d.items():
if key in ('version_check', ... | the_stack_v2_python_sparse | capnpy/annotate_extended.py | antocuni/capnpy | train | 46 | |
9b0c63d67aca437a5e71ffcde213b9bddcb5220e | [
"self.container_name = container_name\nself.divisor = divisor\nself.resource = resource",
"if dictionary is None:\n return None\ncontainer_name = dictionary.get('containerName')\ndivisor = dictionary.get('divisor')\nresource = dictionary.get('resource')\nreturn cls(container_name, divisor, resource)"
] | <|body_start_0|>
self.container_name = container_name
self.divisor = divisor
self.resource = resource
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
container_name = dictionary.get('containerName')
divisor = dictionary.get('divisor')
... | Implementation of the 'PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector' model. TODO: type description here. Attributes: container_name (string): TODO: Type description here. divisor (string): TODO: Type description here. resource (string): TODO: Type description here. | PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector:
"""Implementation of the 'PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector' model. TODO: type description here. Attributes: container_name (string): TODO: Type description here. divisor (string): TODO: T... | stack_v2_sparse_classes_36k_train_010066 | 1,944 | permissive | [
{
"docstring": "Constructor for the PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector class",
"name": "__init__",
"signature": "def __init__(self, container_name=None, divisor=None, resource=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: ... | 2 | null | Implement the Python class `PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector` described below.
Class description:
Implementation of the 'PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector' model. TODO: type description here. Attributes: container_name (string): TODO: Type desc... | Implement the Python class `PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector` described below.
Class description:
Implementation of the 'PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector' model. TODO: type description here. Attributes: container_name (string): TODO: Type desc... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector:
"""Implementation of the 'PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector' model. TODO: type description here. Attributes: container_name (string): TODO: Type description here. divisor (string): TODO: T... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector:
"""Implementation of the 'PodInfo_PodSpec_VolumeInfo_DownwardAPIVolumeFile_ResourceFieldSelector' model. TODO: type description here. Attributes: container_name (string): TODO: Type description here. divisor (string): TODO: Type descripti... | the_stack_v2_python_sparse | cohesity_management_sdk/models/pod_info_pod_spec_volume_info_downward_api_volume_file_resource_field_selector.py | cohesity/management-sdk-python | train | 24 |
ec767776e8e4ac655759cb1797c499dd083b7d76 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn ConditionalAccessGrantControls()",
"from .authentication_strength_policy import AuthenticationStrengthPolicy\nfrom .conditional_access_grant_control import ConditionalAccessGrantControl\nfrom .authentication_strength_policy import Auth... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return ConditionalAccessGrantControls()
<|end_body_0|>
<|body_start_1|>
from .authentication_strength_policy import AuthenticationStrengthPolicy
from .conditional_access_grant_control import Co... | ConditionalAccessGrantControls | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConditionalAccessGrantControls:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ConditionalAccessGrantControls:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator v... | stack_v2_sparse_classes_36k_train_010067 | 4,649 | 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: ConditionalAccessGrantControls",
"name": "create_from_discriminator_value",
"signature": "def create_from_di... | 3 | null | Implement the Python class `ConditionalAccessGrantControls` described below.
Class description:
Implement the ConditionalAccessGrantControls class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ConditionalAccessGrantControls: Creates a new instance of... | Implement the Python class `ConditionalAccessGrantControls` described below.
Class description:
Implement the ConditionalAccessGrantControls class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ConditionalAccessGrantControls: Creates a new instance of... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class ConditionalAccessGrantControls:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ConditionalAccessGrantControls:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator v... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConditionalAccessGrantControls:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> ConditionalAccessGrantControls:
"""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 creat... | the_stack_v2_python_sparse | msgraph/generated/models/conditional_access_grant_controls.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
a2d5ba4a37c7f1b6b066eaa2b252e070df19ea64 | [
"super().__init__()\nself.w = nn.Parameter(torch.randn(q_dim, s_dim) * 0.001)\nself.out = nn.Linear(s_dim, out_dim)",
"attn_score = torch.einsum('bqi,ij,bsj->bqs', q, self.w, s)\nattn_score.masked_fill_(mask == 0, -10000000000.0)\nattn_w = F.softmax(attn_score, dim=-1)\nattn_out = attn_w @ s\nattn_out = self.out(... | <|body_start_0|>
super().__init__()
self.w = nn.Parameter(torch.randn(q_dim, s_dim) * 0.001)
self.out = nn.Linear(s_dim, out_dim)
<|end_body_0|>
<|body_start_1|>
attn_score = torch.einsum('bqi,ij,bsj->bqs', q, self.w, s)
attn_score.masked_fill_(mask == 0, -10000000000.0)
... | Luong attention score = q*W*s [q_len, q_dim] * [q_dim, s_dim] * [s_dim, s_len] = [q_len, s_len] | MultiplicativeAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiplicativeAttention:
"""Luong attention score = q*W*s [q_len, q_dim] * [q_dim, s_dim] * [s_dim, s_len] = [q_len, s_len]"""
def __init__(self, q_dim, s_dim, h_dim, out_dim):
"""params: q_dim: query dim s_dim: source dim h_dim: attn hidden dim out_dim: final out dim"""
<|bo... | stack_v2_sparse_classes_36k_train_010068 | 4,057 | no_license | [
{
"docstring": "params: q_dim: query dim s_dim: source dim h_dim: attn hidden dim out_dim: final out dim",
"name": "__init__",
"signature": "def __init__(self, q_dim, s_dim, h_dim, out_dim)"
},
{
"docstring": "q: [B, q_len, q_dim] s: [B, s_len, s_dim] mask: [B, 1, s_len]",
"name": "forward",... | 2 | stack_v2_sparse_classes_30k_train_014611 | Implement the Python class `MultiplicativeAttention` described below.
Class description:
Luong attention score = q*W*s [q_len, q_dim] * [q_dim, s_dim] * [s_dim, s_len] = [q_len, s_len]
Method signatures and docstrings:
- def __init__(self, q_dim, s_dim, h_dim, out_dim): params: q_dim: query dim s_dim: source dim h_di... | Implement the Python class `MultiplicativeAttention` described below.
Class description:
Luong attention score = q*W*s [q_len, q_dim] * [q_dim, s_dim] * [s_dim, s_len] = [q_len, s_len]
Method signatures and docstrings:
- def __init__(self, q_dim, s_dim, h_dim, out_dim): params: q_dim: query dim s_dim: source dim h_di... | 54dcd23112d452b856e4f8000cf697d352cfec05 | <|skeleton|>
class MultiplicativeAttention:
"""Luong attention score = q*W*s [q_len, q_dim] * [q_dim, s_dim] * [s_dim, s_len] = [q_len, s_len]"""
def __init__(self, q_dim, s_dim, h_dim, out_dim):
"""params: q_dim: query dim s_dim: source dim h_dim: attn hidden dim out_dim: final out dim"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiplicativeAttention:
"""Luong attention score = q*W*s [q_len, q_dim] * [q_dim, s_dim] * [s_dim, s_len] = [q_len, s_len]"""
def __init__(self, q_dim, s_dim, h_dim, out_dim):
"""params: q_dim: query dim s_dim: source dim h_dim: attn hidden dim out_dim: final out dim"""
super().__init__(... | the_stack_v2_python_sparse | models/rnn/attention.py | khanrc/pt.seq2seq | train | 3 |
339ca0b9b4c4fc9ad8d09864da097e917006b846 | [
"with codecs.open(input_file, mode='r', encoding=enc) as in_f, codecs.open(output_file, mode='w', encoding='utf-8') as out_f:\n reader = csv.DictReader(in_f)\n writer = csv.writer(out_f)\n writer.writerow(remains)\n for row in reader:\n contents = []\n for col in remains:\n cont... | <|body_start_0|>
with codecs.open(input_file, mode='r', encoding=enc) as in_f, codecs.open(output_file, mode='w', encoding='utf-8') as out_f:
reader = csv.DictReader(in_f)
writer = csv.writer(out_f)
writer.writerow(remains)
for row in reader:
conte... | File | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class File:
def remove_csv_col(self, input_file, output_file, remains, enc='utf-8'):
"""Extract only the necessary columns from the CSV data and output a new CSV Args: input_file (str): Input csv file name output_file (str): Output csv file name remains (str[]): Columns which remain for new cs... | stack_v2_sparse_classes_36k_train_010069 | 2,975 | permissive | [
{
"docstring": "Extract only the necessary columns from the CSV data and output a new CSV Args: input_file (str): Input csv file name output_file (str): Output csv file name remains (str[]): Columns which remain for new csv enc=utf-8 (str): encording",
"name": "remove_csv_col",
"signature": "def remove_... | 3 | stack_v2_sparse_classes_30k_train_000866 | Implement the Python class `File` described below.
Class description:
Implement the File class.
Method signatures and docstrings:
- def remove_csv_col(self, input_file, output_file, remains, enc='utf-8'): Extract only the necessary columns from the CSV data and output a new CSV Args: input_file (str): Input csv file ... | Implement the Python class `File` described below.
Class description:
Implement the File class.
Method signatures and docstrings:
- def remove_csv_col(self, input_file, output_file, remains, enc='utf-8'): Extract only the necessary columns from the CSV data and output a new CSV Args: input_file (str): Input csv file ... | e523653a9f96f84810c06824133c3a146a053b75 | <|skeleton|>
class File:
def remove_csv_col(self, input_file, output_file, remains, enc='utf-8'):
"""Extract only the necessary columns from the CSV data and output a new CSV Args: input_file (str): Input csv file name output_file (str): Output csv file name remains (str[]): Columns which remain for new cs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class File:
def remove_csv_col(self, input_file, output_file, remains, enc='utf-8'):
"""Extract only the necessary columns from the CSV data and output a new CSV Args: input_file (str): Input csv file name output_file (str): Output csv file name remains (str[]): Columns which remain for new csv enc=utf-8 (s... | the_stack_v2_python_sparse | cliboa/adapter/file.py | BrainPad/cliboa | train | 27 | |
0ae6e7e9df1719abab515aefc60ecfbea54ea66e | [
"self.name = name\nself._address = address\nself._initialized = False\nself._led = SevenSegment.SevenSegment(address=self._address)\ntry:\n self._led.begin()\n self.clear()\n self._initialized = True\nexcept IOError:\n msg = 'Could not connect to %s LED at I2C address %s' % (self.name, hex(self._address... | <|body_start_0|>
self.name = name
self._address = address
self._initialized = False
self._led = SevenSegment.SevenSegment(address=self._address)
try:
self._led.begin()
self.clear()
self._initialized = True
except IOError:
ms... | Numeric display uses the Adafruit SevenSegment display with i2c backpack | Numeric_Display_Adapter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Numeric_Display_Adapter:
"""Numeric display uses the Adafruit SevenSegment display with i2c backpack"""
def __init__(self, name, address):
"""Initalize a seven segment display with i2c"""
<|body_0|>
def clear(self):
"""Clear the display"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_010070 | 1,232 | no_license | [
{
"docstring": "Initalize a seven segment display with i2c",
"name": "__init__",
"signature": "def __init__(self, name, address)"
},
{
"docstring": "Clear the display",
"name": "clear",
"signature": "def clear(self)"
},
{
"docstring": "Display value",
"name": "display",
"... | 4 | stack_v2_sparse_classes_30k_train_006308 | Implement the Python class `Numeric_Display_Adapter` described below.
Class description:
Numeric display uses the Adafruit SevenSegment display with i2c backpack
Method signatures and docstrings:
- def __init__(self, name, address): Initalize a seven segment display with i2c
- def clear(self): Clear the display
- def... | Implement the Python class `Numeric_Display_Adapter` described below.
Class description:
Numeric display uses the Adafruit SevenSegment display with i2c backpack
Method signatures and docstrings:
- def __init__(self, name, address): Initalize a seven segment display with i2c
- def clear(self): Clear the display
- def... | 35ef4d55155d7d60ab15113ff068276c29ace510 | <|skeleton|>
class Numeric_Display_Adapter:
"""Numeric display uses the Adafruit SevenSegment display with i2c backpack"""
def __init__(self, name, address):
"""Initalize a seven segment display with i2c"""
<|body_0|>
def clear(self):
"""Clear the display"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Numeric_Display_Adapter:
"""Numeric display uses the Adafruit SevenSegment display with i2c backpack"""
def __init__(self, name, address):
"""Initalize a seven segment display with i2c"""
self.name = name
self._address = address
self._initialized = False
self._led ... | the_stack_v2_python_sparse | liberty_bell/components/numeric_display_adapter.py | mattgrogan/liberty_bell | train | 0 |
00aebdf3dfd86c7ea7580ca6118a1db55fb135ab | [
"self.y = np.empty(0)\nself.ts_period = ts_period\nself.timestamp_interval = -1\nself.last_timestamp = -1\nself._fitted = False\nself.copy = copy\nif self.ts_period is None:\n raise ValueError(\"'ts_period' must be given.\")",
"if X.size != y.size:\n raise ValueError(\"'X' and 'y' size must match.\")\nif se... | <|body_start_0|>
self.y = np.empty(0)
self.ts_period = ts_period
self.timestamp_interval = -1
self.last_timestamp = -1
self._fitted = False
self.copy = copy
if self.ts_period is None:
raise ValueError("'ts_period' must be given.")
<|end_body_0|>
<|bod... | Seasonal Naive model for time-series forecasting. This model is similar to the Naive model, but instead of using only the very last observation from the fitted time-series, it is used the whole past period. Then, each prediction is equal to the value in the corresponding timestamp of the previous period. | TSNaiveSeasonal | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TSNaiveSeasonal:
"""Seasonal Naive model for time-series forecasting. This model is similar to the Naive model, but instead of using only the very last observation from the fitted time-series, it is used the whole past period. Then, each prediction is equal to the value in the corresponding times... | stack_v2_sparse_classes_36k_train_010071 | 12,299 | permissive | [
{
"docstring": "Init a Seasonal Naive Model.",
"name": "__init__",
"signature": "def __init__(self, ts_period: int, copy: bool=False)"
},
{
"docstring": "Fit a Seasonal Naive model.",
"name": "fit",
"signature": "def fit(self, X: np.ndarray, y: np.ndarray, **kwargs) -> 'TSNaiveSeasonal'"... | 3 | stack_v2_sparse_classes_30k_train_008277 | Implement the Python class `TSNaiveSeasonal` described below.
Class description:
Seasonal Naive model for time-series forecasting. This model is similar to the Naive model, but instead of using only the very last observation from the fitted time-series, it is used the whole past period. Then, each prediction is equal ... | Implement the Python class `TSNaiveSeasonal` described below.
Class description:
Seasonal Naive model for time-series forecasting. This model is similar to the Naive model, but instead of using only the very last observation from the fitted time-series, it is used the whole past period. Then, each prediction is equal ... | 61cc1f63fa055c7466151cfefa7baff8df1702b7 | <|skeleton|>
class TSNaiveSeasonal:
"""Seasonal Naive model for time-series forecasting. This model is similar to the Naive model, but instead of using only the very last observation from the fitted time-series, it is used the whole past period. Then, each prediction is equal to the value in the corresponding times... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TSNaiveSeasonal:
"""Seasonal Naive model for time-series forecasting. This model is similar to the Naive model, but instead of using only the very last observation from the fitted time-series, it is used the whole past period. Then, each prediction is equal to the value in the corresponding timestamp of the p... | the_stack_v2_python_sparse | tspymfe/_models.py | FelSiq/ts-pymfe | train | 9 |
42c6755132e715958b21025cf490d7e4fad79cfa | [
"self.__run1_file = run1_file\nself.__run2_file = run2_file\nself.__qrels = qrels\nself.__metric = metric",
"te_method1 = TrecEval()\nte_method1.evaluate(self.__qrels, self.__run1_file)\nte_method2 = TrecEval()\nte_method2.evaluate(self.__qrels, self.__run2_file)\ndata = []\nfor query_id in te_method1.get_query_i... | <|body_start_0|>
self.__run1_file = run1_file
self.__run2_file = run2_file
self.__qrels = qrels
self.__metric = metric
<|end_body_0|>
<|body_start_1|>
te_method1 = TrecEval()
te_method1.evaluate(self.__qrels, self.__run1_file)
te_method2 = TrecEval()
te_m... | QueryDiff | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QueryDiff:
def __init__(self, run1_file, run2_file, qrels, metric):
""":param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:"""
<|body_0|>
def dump_differences(self, out... | stack_v2_sparse_classes_36k_train_010072 | 1,678 | permissive | [
{
"docstring": ":param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:",
"name": "__init__",
"signature": "def __init__(self, run1_file, run2_file, qrels, metric)"
},
{
"docstring": "Outputs ... | 2 | stack_v2_sparse_classes_30k_train_001646 | Implement the Python class `QueryDiff` described below.
Class description:
Implement the QueryDiff class.
Method signatures and docstrings:
- def __init__(self, run1_file, run2_file, qrels, metric): :param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of q... | Implement the Python class `QueryDiff` described below.
Class description:
Implement the QueryDiff class.
Method signatures and docstrings:
- def __init__(self, run1_file, run2_file, qrels, metric): :param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of q... | 7027699009504c72be4a087cf9730cad3051979b | <|skeleton|>
class QueryDiff:
def __init__(self, run1_file, run2_file, qrels, metric):
""":param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:"""
<|body_0|>
def dump_differences(self, out... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QueryDiff:
def __init__(self, run1_file, run2_file, qrels, metric):
""":param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:"""
self.__run1_file = run1_file
self.__run2_file = run2... | the_stack_v2_python_sparse | nordlys/core/eval/query_diff.py | iai-group/nordlys | train | 35 | |
757f9f1c376d7191407d1bf2685bb5eb85e33432 | [
"max_area = 0\nn = len(coord)\nfor x1 in range(n - 1):\n for x2 in range(x1 + 1, n):\n y1, y2 = (coord[x1], coord[x2])\n curr_area = (x2 - x1) * min(y1, y2)\n max_area = max(max_area, curr_area)\nreturn max_area",
"n = len(coord)\ni, j = (0, n - 1)\nmax_area = 0\nwhile i < j:\n curr_are... | <|body_start_0|>
max_area = 0
n = len(coord)
for x1 in range(n - 1):
for x2 in range(x1 + 1, n):
y1, y2 = (coord[x1], coord[x2])
curr_area = (x2 - x1) * min(y1, y2)
max_area = max(max_area, curr_area)
return max_area
<|end_body_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def max_area_brute(self, coord):
"""Brute force algorithm. Time complexity: O(n ^ 2). Space complexity: O(1), n is len(coord)."""
<|body_0|>
def max_area_2p(self, coord):
"""Two pointers algorithm. Time complexity: O(n). Space complexity: O(1), n is len(coo... | stack_v2_sparse_classes_36k_train_010073 | 2,759 | no_license | [
{
"docstring": "Brute force algorithm. Time complexity: O(n ^ 2). Space complexity: O(1), n is len(coord).",
"name": "max_area_brute",
"signature": "def max_area_brute(self, coord)"
},
{
"docstring": "Two pointers algorithm. Time complexity: O(n). Space complexity: O(1), n is len(coord).",
"... | 3 | stack_v2_sparse_classes_30k_train_014052 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def max_area_brute(self, coord): Brute force algorithm. Time complexity: O(n ^ 2). Space complexity: O(1), n is len(coord).
- def max_area_2p(self, coord): Two pointers algorithm... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def max_area_brute(self, coord): Brute force algorithm. Time complexity: O(n ^ 2). Space complexity: O(1), n is len(coord).
- def max_area_2p(self, coord): Two pointers algorithm... | 71b722ddfe8da04572e527b055cf8723d5c87bbf | <|skeleton|>
class Solution:
def max_area_brute(self, coord):
"""Brute force algorithm. Time complexity: O(n ^ 2). Space complexity: O(1), n is len(coord)."""
<|body_0|>
def max_area_2p(self, coord):
"""Two pointers algorithm. Time complexity: O(n). Space complexity: O(1), n is len(coo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def max_area_brute(self, coord):
"""Brute force algorithm. Time complexity: O(n ^ 2). Space complexity: O(1), n is len(coord)."""
max_area = 0
n = len(coord)
for x1 in range(n - 1):
for x2 in range(x1 + 1, n):
y1, y2 = (coord[x1], coord[x2]... | the_stack_v2_python_sparse | Arrays/container_with_most_water.py | vladn90/Algorithms | train | 0 | |
b5860aa2d88c14a262d25d49adbfd3f20ac760d6 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\ntry:\n mapping_value = parse_node.get_child_node('@odata.type').get_str_value()\nexcept AttributeError:\n mapping_value = None\nif mapping_value and mapping_value.casefold() == '#microsoft.graph.accessReviewInstanceDecisionItemAccessPacka... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
try:
mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
except AttributeError:
mapping_value = None
if mapping_value and mapping_value.casefold() ==... | AccessReviewInstanceDecisionItemResource | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccessReviewInstanceDecisionItemResource:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewInstanceDecisionItemResource:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read... | stack_v2_sparse_classes_36k_train_010074 | 5,643 | 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: AccessReviewInstanceDecisionItemResource",
"name": "create_from_discriminator_value",
"signature": "def crea... | 3 | stack_v2_sparse_classes_30k_train_012826 | Implement the Python class `AccessReviewInstanceDecisionItemResource` described below.
Class description:
Implement the AccessReviewInstanceDecisionItemResource class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewInstanceDecisionItemResou... | Implement the Python class `AccessReviewInstanceDecisionItemResource` described below.
Class description:
Implement the AccessReviewInstanceDecisionItemResource class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewInstanceDecisionItemResou... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class AccessReviewInstanceDecisionItemResource:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewInstanceDecisionItemResource:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccessReviewInstanceDecisionItemResource:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewInstanceDecisionItemResource:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discrimin... | the_stack_v2_python_sparse | msgraph/generated/models/access_review_instance_decision_item_resource.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
c09cbd7e0df64503ede470a8f3622926489627b6 | [
"super(StoreProductImages, self).__init__(*args, **kwargs)\nself.endpoint = 'ecommerce/stores'\nself.store_id = None\nself.product_id = None\nself.image_id = None",
"self.store_id = store_id\nself.product_id = product_id\nif 'id' not in data:\n raise KeyError('The product image must have an id')\nif 'title' no... | <|body_start_0|>
super(StoreProductImages, self).__init__(*args, **kwargs)
self.endpoint = 'ecommerce/stores'
self.store_id = None
self.product_id = None
self.image_id = None
<|end_body_0|>
<|body_start_1|>
self.store_id = store_id
self.product_id = product_id
... | A Product Image represents a specific product image. | StoreProductImages | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StoreProductImages:
"""A Product Image represents a specific product image."""
def __init__(self, *args, **kwargs):
"""Initialize the endpoint"""
<|body_0|>
def create(self, store_id, product_id, data):
"""Add a new image to the product. :param store_id: The stor... | stack_v2_sparse_classes_36k_train_010075 | 4,947 | permissive | [
{
"docstring": "Initialize the endpoint",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Add a new image to the product. :param store_id: The store id. :type store_id: :py:class:`str` :param product_id: The id for the product of a store. :type product_i... | 6 | stack_v2_sparse_classes_30k_train_003927 | Implement the Python class `StoreProductImages` described below.
Class description:
A Product Image represents a specific product image.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize the endpoint
- def create(self, store_id, product_id, data): Add a new image to the product. :par... | Implement the Python class `StoreProductImages` described below.
Class description:
A Product Image represents a specific product image.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize the endpoint
- def create(self, store_id, product_id, data): Add a new image to the product. :par... | bf61cd602dc44cbff32fbf6f6dcdd33cf6f782e8 | <|skeleton|>
class StoreProductImages:
"""A Product Image represents a specific product image."""
def __init__(self, *args, **kwargs):
"""Initialize the endpoint"""
<|body_0|>
def create(self, store_id, product_id, data):
"""Add a new image to the product. :param store_id: The stor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StoreProductImages:
"""A Product Image represents a specific product image."""
def __init__(self, *args, **kwargs):
"""Initialize the endpoint"""
super(StoreProductImages, self).__init__(*args, **kwargs)
self.endpoint = 'ecommerce/stores'
self.store_id = None
self.... | the_stack_v2_python_sparse | mailchimp3/entities/storeproductimages.py | VingtCinq/python-mailchimp | train | 190 |
06bcbb512cd57c15b52f386ddd3e751622d36f62 | [
"self.row_i = row_i\nself.start_i = start_i\nself.end_i = end_i\nself.n = n\nself.gap = None",
"g = self.gap\nif g is not None:\n return abs(g[1] - g[0]) < abs(f - s)\nelse:\n return True",
"g = self.gap\nif g is None:\n self.gap = (s, f)\nelif abs(f - s) > abs(g[1] - g[0]):\n self.gap = (s, f)"
] | <|body_start_0|>
self.row_i = row_i
self.start_i = start_i
self.end_i = end_i
self.n = n
self.gap = None
<|end_body_0|>
<|body_start_1|>
g = self.gap
if g is not None:
return abs(g[1] - g[0]) < abs(f - s)
else:
return True
<|end_bo... | Private Object that will keep track of current row data. | __GapData | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class __GapData:
"""Private Object that will keep track of current row data."""
def __init__(self, row_i, start_i, end_i, n):
"""Parameters ---------- row_i Current row of GapData start_i Starting row end_i End row n"""
<|body_0|>
def compareGap(self, s, f):
"""Compare... | stack_v2_sparse_classes_36k_train_010076 | 4,098 | permissive | [
{
"docstring": "Parameters ---------- row_i Current row of GapData start_i Starting row end_i End row n",
"name": "__init__",
"signature": "def __init__(self, row_i, start_i, end_i, n)"
},
{
"docstring": "Compare start, s, and finish, f, values with that of the current gaps values. If smaller, r... | 3 | stack_v2_sparse_classes_30k_train_018225 | Implement the Python class `__GapData` described below.
Class description:
Private Object that will keep track of current row data.
Method signatures and docstrings:
- def __init__(self, row_i, start_i, end_i, n): Parameters ---------- row_i Current row of GapData start_i Starting row end_i End row n
- def compareGap... | Implement the Python class `__GapData` described below.
Class description:
Private Object that will keep track of current row data.
Method signatures and docstrings:
- def __init__(self, row_i, start_i, end_i, n): Parameters ---------- row_i Current row of GapData start_i Starting row end_i End row n
- def compareGap... | 08fe54fe37df89ffc7e6378125bb14ad5bead421 | <|skeleton|>
class __GapData:
"""Private Object that will keep track of current row data."""
def __init__(self, row_i, start_i, end_i, n):
"""Parameters ---------- row_i Current row of GapData start_i Starting row end_i End row n"""
<|body_0|>
def compareGap(self, s, f):
"""Compare... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class __GapData:
"""Private Object that will keep track of current row data."""
def __init__(self, row_i, start_i, end_i, n):
"""Parameters ---------- row_i Current row of GapData start_i Starting row end_i End row n"""
self.row_i = row_i
self.start_i = start_i
self.end_i = end_... | the_stack_v2_python_sparse | Algorithms/gap_detection.py | marioliu/AutonomousQuadblade | train | 0 |
347fc19ffb749ca1d5eab4c51dab41b8cd3c41ce | [
"self.last_i = 0\nself.last_j = 0\nself.last_sum = 0\nself.nums2 = nums\nself.firstcall = 1",
"if not firstcall:\n if self.last_i <= i <= self.last_j and self.last_i <= j <= self.last_j:\n res = self.last_sum - sum(self.nums2[self.last_i:i]) - sum(self.nums2[j + 1:self.last_j + 1])\n elif self.last_i... | <|body_start_0|>
self.last_i = 0
self.last_j = 0
self.last_sum = 0
self.nums2 = nums
self.firstcall = 1
<|end_body_0|>
<|body_start_1|>
if not firstcall:
if self.last_i <= i <= self.last_j and self.last_i <= j <= self.last_j:
res = self.last_s... | NumArray | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumArray:
def __init__(self, nums):
"""initialize your data structure here. :type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
"""sum of elements nums[i..j], inclusive. :type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_010077 | 1,794 | no_license | [
{
"docstring": "initialize your data structure here. :type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": "sum of elements nums[i..j], inclusive. :type i: int :type j: int :rtype: int",
"name": "sumRange",
"signature": "def sumRange(self, ... | 2 | stack_v2_sparse_classes_30k_train_016105 | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): initialize your data structure here. :type nums: List[int]
- def sumRange(self, i, j): sum of elements nums[i..j], inclusive. :type i: int :type j: int ... | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): initialize your data structure here. :type nums: List[int]
- def sumRange(self, i, j): sum of elements nums[i..j], inclusive. :type i: int :type j: int ... | 7a1c3aba65f338f6e11afd2864dabd2b26142b6c | <|skeleton|>
class NumArray:
def __init__(self, nums):
"""initialize your data structure here. :type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
"""sum of elements nums[i..j], inclusive. :type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumArray:
def __init__(self, nums):
"""initialize your data structure here. :type nums: List[int]"""
self.last_i = 0
self.last_j = 0
self.last_sum = 0
self.nums2 = nums
self.firstcall = 1
def sumRange(self, i, j):
"""sum of elements nums[i..j], incl... | the_stack_v2_python_sparse | exercise/leetcode/python_src/by2017_Sep/Leet303.py | SS4G/AlgorithmTraining | train | 2 | |
4da788f91748938da32e1d054e1d85fdf50cbf57 | [
"if len(predictions_and_ratings) == 0:\n return 0\ndiffs = map(lambda x: (x[0] - x[1]) * (x[0] - x[1]), predictions_and_ratings)\nreturn math.sqrt(sum(diffs) / float(len(predictions_and_ratings)))",
"if len(predictions_and_ratings) == 0:\n return 0\ndiffs = map(lambda x: abs(x[0] - x[1]), predictions_and_ra... | <|body_start_0|>
if len(predictions_and_ratings) == 0:
return 0
diffs = map(lambda x: (x[0] - x[1]) * (x[0] - x[1]), predictions_and_ratings)
return math.sqrt(sum(diffs) / float(len(predictions_and_ratings)))
<|end_body_0|>
<|body_start_1|>
if len(predictions_and_ratings) ==... | AccMetrics | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccMetrics:
def calculate_rmse(predictions_and_ratings):
"""predictions_and_ratings is list of pairs of predicts and real rating"""
<|body_0|>
def calculate_mae(predictions_and_ratings):
"""predictions_and_ratings is list of pairs of predicts and real rating"""
... | stack_v2_sparse_classes_36k_train_010078 | 963 | no_license | [
{
"docstring": "predictions_and_ratings is list of pairs of predicts and real rating",
"name": "calculate_rmse",
"signature": "def calculate_rmse(predictions_and_ratings)"
},
{
"docstring": "predictions_and_ratings is list of pairs of predicts and real rating",
"name": "calculate_mae",
"... | 2 | stack_v2_sparse_classes_30k_train_008648 | Implement the Python class `AccMetrics` described below.
Class description:
Implement the AccMetrics class.
Method signatures and docstrings:
- def calculate_rmse(predictions_and_ratings): predictions_and_ratings is list of pairs of predicts and real rating
- def calculate_mae(predictions_and_ratings): predictions_an... | Implement the Python class `AccMetrics` described below.
Class description:
Implement the AccMetrics class.
Method signatures and docstrings:
- def calculate_rmse(predictions_and_ratings): predictions_and_ratings is list of pairs of predicts and real rating
- def calculate_mae(predictions_and_ratings): predictions_an... | d27654192efc7063e5d691efae9626775cb91940 | <|skeleton|>
class AccMetrics:
def calculate_rmse(predictions_and_ratings):
"""predictions_and_ratings is list of pairs of predicts and real rating"""
<|body_0|>
def calculate_mae(predictions_and_ratings):
"""predictions_and_ratings is list of pairs of predicts and real rating"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccMetrics:
def calculate_rmse(predictions_and_ratings):
"""predictions_and_ratings is list of pairs of predicts and real rating"""
if len(predictions_and_ratings) == 0:
return 0
diffs = map(lambda x: (x[0] - x[1]) * (x[0] - x[1]), predictions_and_ratings)
return ma... | the_stack_v2_python_sparse | deployment/scripts/metrics/AccMetrics.py | gmiejski/movies-recommender-api | train | 1 | |
08e328e884ead0778f24f6f56efb5ac20dcbab56 | [
"super(PlotCellTypeStack, self).__init__(experiment, name='PlotCellTypeStack', label=label)\nself.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0)\nself.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end', default=self.experiment.config.getint('Experiment', '... | <|body_start_0|>
super(PlotCellTypeStack, self).__init__(experiment, name='PlotCellTypeStack', label=label)
self.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0)
self.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end', default=self.experi... | TODO: description Configuration is done in the [PlotCellTypeStack] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment) frequency The frequency (epochs) at which to execute (default: 1) priority The prio... | PlotCellTypeStack | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlotCellTypeStack:
"""TODO: description Configuration is done in the [PlotCellTypeStack] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment) frequency The frequency (epochs) at wh... | stack_v2_sparse_classes_36k_train_010079 | 3,421 | permissive | [
{
"docstring": "Initialize the PlotCellTypeStack Action",
"name": "__init__",
"signature": "def __init__(self, experiment, label=None)"
},
{
"docstring": "Execute the action",
"name": "update",
"signature": "def update(self)"
},
{
"docstring": "Since we're at the end of the run, ... | 3 | stack_v2_sparse_classes_30k_train_011453 | Implement the Python class `PlotCellTypeStack` described below.
Class description:
TODO: description Configuration is done in the [PlotCellTypeStack] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment)... | Implement the Python class `PlotCellTypeStack` described below.
Class description:
TODO: description Configuration is done in the [PlotCellTypeStack] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment)... | a114ac66e62a960e18127faf52cff9e48831e212 | <|skeleton|>
class PlotCellTypeStack:
"""TODO: description Configuration is done in the [PlotCellTypeStack] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment) frequency The frequency (epochs) at wh... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlotCellTypeStack:
"""TODO: description Configuration is done in the [PlotCellTypeStack] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment) frequency The frequency (epochs) at which to execut... | the_stack_v2_python_sparse | contrib/actions/PlotCellTypeStack.py | namlehai/seeds | train | 0 |
9cd339b5d17c52f4fa43920848b7d13b3c406331 | [
"comment = PublicComment(**data)\nanon = comment.is_anonymous\nuser = comment.user.user if comment.user else None\nneeds_moderation = user and (not user.has_perm('comments.can_post_directly'))\nfrom_swat = is_from_swat(user=comment.user, ip=comment.ip_address)\nif needs_moderation or (anon and (not from_swat)):\n ... | <|body_start_0|>
comment = PublicComment(**data)
anon = comment.is_anonymous
user = comment.user.user if comment.user else None
needs_moderation = user and (not user.has_perm('comments.can_post_directly'))
from_swat = is_from_swat(user=comment.user, ip=comment.ip_address)
... | CommentsManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentsManager:
def new(self, check_spam=True, pre_approved=False, **data):
"""Makes a new comment, dealing with spam checking and pre-approval as necessary. If spam, raise a CommentIsSpam error, with the unsaved comment in its .comment attribute. This comment is all ready to go; it jus... | stack_v2_sparse_classes_36k_train_010080 | 13,162 | no_license | [
{
"docstring": "Makes a new comment, dealing with spam checking and pre-approval as necessary. If spam, raise a CommentIsSpam error, with the unsaved comment in its .comment attribute. This comment is all ready to go; it just needs to be saved, should it be decided that it's not actually spam. Comments from Swa... | 2 | stack_v2_sparse_classes_30k_train_005094 | Implement the Python class `CommentsManager` described below.
Class description:
Implement the CommentsManager class.
Method signatures and docstrings:
- def new(self, check_spam=True, pre_approved=False, **data): Makes a new comment, dealing with spam checking and pre-approval as necessary. If spam, raise a CommentI... | Implement the Python class `CommentsManager` described below.
Class description:
Implement the CommentsManager class.
Method signatures and docstrings:
- def new(self, check_spam=True, pre_approved=False, **data): Makes a new comment, dealing with spam checking and pre-approval as necessary. If spam, raise a CommentI... | ac19bce192fe2ac29bca7bafcf973109c0d1b43e | <|skeleton|>
class CommentsManager:
def new(self, check_spam=True, pre_approved=False, **data):
"""Makes a new comment, dealing with spam checking and pre-approval as necessary. If spam, raise a CommentIsSpam error, with the unsaved comment in its .comment attribute. This comment is all ready to go; it jus... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentsManager:
def new(self, check_spam=True, pre_approved=False, **data):
"""Makes a new comment, dealing with spam checking and pre-approval as necessary. If spam, raise a CommentIsSpam error, with the unsaved comment in its .comment attribute. This comment is all ready to go; it just needs to be ... | the_stack_v2_python_sparse | gazjango/comments/models.py | iambikash007/gazjango | train | 0 | |
3aa288bcb6ff8ca58a13eb63fec9961fb2fe910e | [
"assert len(prog) == 0\nwith prog.context as q:\n ops.Dgate(0.5) | q[0]\nassert len(prog) == 1\nwith prog.context as q:\n ops.BSgate(0.5, 0.3) | (q[1], q[0])\nassert len(prog) == 2",
"identity = program.Command(None, prog.register[0])\nprog.circuit.append(identity)\nassert len(prog) == 1\nprog = prog.compil... | <|body_start_0|>
assert len(prog) == 0
with prog.context as q:
ops.Dgate(0.5) | q[0]
assert len(prog) == 1
with prog.context as q:
ops.BSgate(0.5, 0.3) | (q[1], q[0])
assert len(prog) == 2
<|end_body_0|>
<|body_start_1|>
identity = program.Command... | Tests the Program class. | TestProgram | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestProgram:
"""Tests the Program class."""
def test_with_block(self, prog):
"""Gate application using a with block."""
<|body_0|>
def test_identity_command(self, prog):
"""Tests that the None command acts as the identity"""
<|body_1|>
def test_paren... | stack_v2_sparse_classes_36k_train_010081 | 19,472 | permissive | [
{
"docstring": "Gate application using a with block.",
"name": "test_with_block",
"signature": "def test_with_block(self, prog)"
},
{
"docstring": "Tests that the None command acts as the identity",
"name": "test_identity_command",
"signature": "def test_identity_command(self, prog)"
}... | 4 | null | Implement the Python class `TestProgram` described below.
Class description:
Tests the Program class.
Method signatures and docstrings:
- def test_with_block(self, prog): Gate application using a with block.
- def test_identity_command(self, prog): Tests that the None command acts as the identity
- def test_parent_pr... | Implement the Python class `TestProgram` described below.
Class description:
Tests the Program class.
Method signatures and docstrings:
- def test_with_block(self, prog): Gate application using a with block.
- def test_identity_command(self, prog): Tests that the None command acts as the identity
- def test_parent_pr... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class TestProgram:
"""Tests the Program class."""
def test_with_block(self, prog):
"""Gate application using a with block."""
<|body_0|>
def test_identity_command(self, prog):
"""Tests that the None command acts as the identity"""
<|body_1|>
def test_paren... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestProgram:
"""Tests the Program class."""
def test_with_block(self, prog):
"""Gate application using a with block."""
assert len(prog) == 0
with prog.context as q:
ops.Dgate(0.5) | q[0]
assert len(prog) == 1
with prog.context as q:
ops.BSg... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits_backup/strawberryfields/strawberryfields#90/after/test_program.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
5609f308e6e34a9744fc49cf60a57c3314eeb2e9 | [
"supernet = '172.31.0.0/16'\ncidr_bits = 17\nnetwork_factory = DockerClusterNetworkFactory(supernet, cidr_bits)\nprint('*** test_create_network_then_try_creating_it_again ***')\nprint('This test will use the main network %s' % supernet)\nfirst_name = 'shouldbenew'\nsg = network_factory.cluster_network_candidates(fi... | <|body_start_0|>
supernet = '172.31.0.0/16'
cidr_bits = 17
network_factory = DockerClusterNetworkFactory(supernet, cidr_bits)
print('*** test_create_network_then_try_creating_it_again ***')
print('This test will use the main network %s' % supernet)
first_name = 'shouldben... | Some tests that affect the system. This should not destroy existing networks, but better to make sure that the system is 'clean' before executing these tests! | TestDockerClusterNetworkFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDockerClusterNetworkFactory:
"""Some tests that affect the system. This should not destroy existing networks, but better to make sure that the system is 'clean' before executing these tests!"""
def test_create_network_then_try_creating_it_again(self):
"""Tries to create a named n... | stack_v2_sparse_classes_36k_train_010082 | 15,846 | no_license | [
{
"docstring": "Tries to create a named network, then tries again (expect error message), then remove the network. If the test succeeds, the system returns to the original state.",
"name": "test_create_network_then_try_creating_it_again",
"signature": "def test_create_network_then_try_creating_it_again(... | 2 | stack_v2_sparse_classes_30k_train_013628 | Implement the Python class `TestDockerClusterNetworkFactory` described below.
Class description:
Some tests that affect the system. This should not destroy existing networks, but better to make sure that the system is 'clean' before executing these tests!
Method signatures and docstrings:
- def test_create_network_th... | Implement the Python class `TestDockerClusterNetworkFactory` described below.
Class description:
Some tests that affect the system. This should not destroy existing networks, but better to make sure that the system is 'clean' before executing these tests!
Method signatures and docstrings:
- def test_create_network_th... | a9058d49d166205326cfb5f63d58dd42ef70343d | <|skeleton|>
class TestDockerClusterNetworkFactory:
"""Some tests that affect the system. This should not destroy existing networks, but better to make sure that the system is 'clean' before executing these tests!"""
def test_create_network_then_try_creating_it_again(self):
"""Tries to create a named n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestDockerClusterNetworkFactory:
"""Some tests that affect the system. This should not destroy existing networks, but better to make sure that the system is 'clean' before executing these tests!"""
def test_create_network_then_try_creating_it_again(self):
"""Tries to create a named network, then ... | the_stack_v2_python_sparse | dcluster/infra/networking.py | luciorq/dcluster | train | 0 |
14fb1f79292a79bb569ac8b964e3c211c10be157 | [
"essential_keys = ['nvars', 'c', 'freq']\nfor key in essential_keys:\n if key not in problem_params:\n msg = 'need %s to instantiate problem, only got %s' % (key, str(problem_params.keys()))\n raise ParameterError(msg)\nif (problem_params['nvars'] + 1) % 2 != 0:\n raise ProblemError('setup requi... | <|body_start_0|>
essential_keys = ['nvars', 'c', 'freq']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (key, str(problem_params.keys()))
raise ParameterError(msg)
if (problem_params['nvar... | Example implementing the unforced 1D advection equation with periodic BC in [0,1], discretized using upwinding finite differences Attributes: A: FD discretization of the gradient operator using upwinding dx: distance between two spatial nodes | advection1d_dirichlet | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class advection1d_dirichlet:
"""Example implementing the unforced 1D advection equation with periodic BC in [0,1], discretized using upwinding finite differences Attributes: A: FD discretization of the gradient operator using upwinding dx: distance between two spatial nodes"""
def __init__(self, p... | stack_v2_sparse_classes_36k_train_010083 | 5,002 | permissive | [
{
"docstring": "Initialization routine Args: problem_params (dict): custom parameters for the example dtype_u: mesh data type (will be passed parent class) dtype_f: mesh data type (will be passed parent class)",
"name": "__init__",
"signature": "def __init__(self, problem_params, dtype_u=mesh, dtype_f=m... | 5 | stack_v2_sparse_classes_30k_train_015070 | Implement the Python class `advection1d_dirichlet` described below.
Class description:
Example implementing the unforced 1D advection equation with periodic BC in [0,1], discretized using upwinding finite differences Attributes: A: FD discretization of the gradient operator using upwinding dx: distance between two spa... | Implement the Python class `advection1d_dirichlet` described below.
Class description:
Example implementing the unforced 1D advection equation with periodic BC in [0,1], discretized using upwinding finite differences Attributes: A: FD discretization of the gradient operator using upwinding dx: distance between two spa... | de2cd523411276083355389d7e7993106cedf93d | <|skeleton|>
class advection1d_dirichlet:
"""Example implementing the unforced 1D advection equation with periodic BC in [0,1], discretized using upwinding finite differences Attributes: A: FD discretization of the gradient operator using upwinding dx: distance between two spatial nodes"""
def __init__(self, p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class advection1d_dirichlet:
"""Example implementing the unforced 1D advection equation with periodic BC in [0,1], discretized using upwinding finite differences Attributes: A: FD discretization of the gradient operator using upwinding dx: distance between two spatial nodes"""
def __init__(self, problem_params... | the_stack_v2_python_sparse | pySDC/implementations/problem_classes/AdvectionEquation_1D_FD_dirichlet.py | ruthschoebel/pySDC | train | 0 |
1864cb01bf4603872053936a47741ae277730906 | [
"body = dict(self._body.dirty)\njob = ExecutableJob.new(**dict(job))\nbody['add_jobs'] = [dict(job._body.dirty)]\nendpoint_override = self.service.get_endpoint_override()\nresponse = session.post('/run-job-flow', headers={}, endpoint_filter=self.service, endpoint_override=endpoint_override, json=dict(body))\nself._... | <|body_start_0|>
body = dict(self._body.dirty)
job = ExecutableJob.new(**dict(job))
body['add_jobs'] = [dict(job._body.dirty)]
endpoint_override = self.service.get_endpoint_override()
response = session.post('/run-job-flow', headers={}, endpoint_filter=self.service, endpoint_over... | HuaWei Cluster extends | ClusterInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClusterInfo:
"""HuaWei Cluster extends"""
def create_and_run(self, session, job):
"""Create a new cluster and run a job on the created cluster :param session: the openstack session :param dict job: Keyword arguments which will be used to create a :class:`~openstack.map_reduce.v1.clus... | stack_v2_sparse_classes_36k_train_010084 | 18,095 | permissive | [
{
"docstring": "Create a new cluster and run a job on the created cluster :param session: the openstack session :param dict job: Keyword arguments which will be used to create a :class:`~openstack.map_reduce.v1.cluster.ExecutableJob`, comprised of the properties on the ExecutableJob class. :return:",
"name"... | 3 | null | Implement the Python class `ClusterInfo` described below.
Class description:
HuaWei Cluster extends
Method signatures and docstrings:
- def create_and_run(self, session, job): Create a new cluster and run a job on the created cluster :param session: the openstack session :param dict job: Keyword arguments which will ... | Implement the Python class `ClusterInfo` described below.
Class description:
HuaWei Cluster extends
Method signatures and docstrings:
- def create_and_run(self, session, job): Create a new cluster and run a job on the created cluster :param session: the openstack session :param dict job: Keyword arguments which will ... | 60d75438d71ffb7998f5dc407ffa890cc98d3171 | <|skeleton|>
class ClusterInfo:
"""HuaWei Cluster extends"""
def create_and_run(self, session, job):
"""Create a new cluster and run a job on the created cluster :param session: the openstack session :param dict job: Keyword arguments which will be used to create a :class:`~openstack.map_reduce.v1.clus... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClusterInfo:
"""HuaWei Cluster extends"""
def create_and_run(self, session, job):
"""Create a new cluster and run a job on the created cluster :param session: the openstack session :param dict job: Keyword arguments which will be used to create a :class:`~openstack.map_reduce.v1.cluster.Executabl... | the_stack_v2_python_sparse | openstack/map_reduce/v1/cluster.py | huaweicloudsdk/sdk-python | train | 20 |
44b74fe503294d161105c92c218f160926cabe17 | [
"self.explanation_type = explanation_type\nself._internal_obj = internal_obj\nself.feature_names = feature_names\nself.feature_types = feature_types\nself.name = name\nself.selector = selector",
"if key is None:\n return self._internal_obj['overall']\nreturn self._internal_obj['specific'][key]",
"from ..visu... | <|body_start_0|>
self.explanation_type = explanation_type
self._internal_obj = internal_obj
self.feature_names = feature_names
self.feature_types = feature_types
self.name = name
self.selector = selector
<|end_body_0|>
<|body_start_1|>
if key is None:
... | Visualizes rules as HTML for both global and local explanations. | RulesExplanation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RulesExplanation:
"""Visualizes rules as HTML for both global and local explanations."""
def __init__(self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None):
"""Initializes class. Args: explanation_type: Type of explanation. internal_o... | stack_v2_sparse_classes_36k_train_010085 | 14,262 | permissive | [
{
"docstring": "Initializes class. Args: explanation_type: Type of explanation. internal_obj: A jsonable object that backs the explanation. feature_names: List of feature names. feature_types: List of feature types. name: User-defined name of explanation. selector: A dataframe whose indices correspond to explan... | 3 | stack_v2_sparse_classes_30k_train_004049 | Implement the Python class `RulesExplanation` described below.
Class description:
Visualizes rules as HTML for both global and local explanations.
Method signatures and docstrings:
- def __init__(self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None): Initializes class... | Implement the Python class `RulesExplanation` described below.
Class description:
Visualizes rules as HTML for both global and local explanations.
Method signatures and docstrings:
- def __init__(self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None): Initializes class... | e6f38ea195aecbbd9d28c7183a83c65ada16e1ae | <|skeleton|>
class RulesExplanation:
"""Visualizes rules as HTML for both global and local explanations."""
def __init__(self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None):
"""Initializes class. Args: explanation_type: Type of explanation. internal_o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RulesExplanation:
"""Visualizes rules as HTML for both global and local explanations."""
def __init__(self, explanation_type, internal_obj, feature_names=None, feature_types=None, name=None, selector=None):
"""Initializes class. Args: explanation_type: Type of explanation. internal_obj: A jsonabl... | the_stack_v2_python_sparse | python/interpret-core/interpret/glassbox/_skoperules.py | interpretml/interpret | train | 3,731 |
ea9bb0106ff2976431bf2ddba81caa154cd35d80 | [
"n = len(nums)\nseen = set()\nfor i in nums:\n seen.add(i)\nfor i in range(n + 1):\n if i not in seen:\n return i\nreturn -1",
"for i, n in enumerate(nums):\n if i != n:\n return i + 1\nreturn len(nums) + 1"
] | <|body_start_0|>
n = len(nums)
seen = set()
for i in nums:
seen.add(i)
for i in range(n + 1):
if i not in seen:
return i
return -1
<|end_body_0|>
<|body_start_1|>
for i, n in enumerate(nums):
if i != n:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def missingNumber(self, nums: List[int]) -> int:
"""My Solution: O(n) Time IDEA: Create a set of seen numbers. Then, iterate 0...n to see which number is missing. # Time Complexity: O(n) # Space Complexity: O(n)"""
<|body_0|>
def missingNumber(self, nums: List[int]... | stack_v2_sparse_classes_36k_train_010086 | 1,355 | no_license | [
{
"docstring": "My Solution: O(n) Time IDEA: Create a set of seen numbers. Then, iterate 0...n to see which number is missing. # Time Complexity: O(n) # Space Complexity: O(n)",
"name": "missingNumber",
"signature": "def missingNumber(self, nums: List[int]) -> int"
},
{
"docstring": "My Solution... | 2 | stack_v2_sparse_classes_30k_train_012653 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def missingNumber(self, nums: List[int]) -> int: My Solution: O(n) Time IDEA: Create a set of seen numbers. Then, iterate 0...n to see which number is missing. # Time Complexity:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def missingNumber(self, nums: List[int]) -> int: My Solution: O(n) Time IDEA: Create a set of seen numbers. Then, iterate 0...n to see which number is missing. # Time Complexity:... | 8b11ceb675089a12a4a44f9b044dac7c3e666819 | <|skeleton|>
class Solution:
def missingNumber(self, nums: List[int]) -> int:
"""My Solution: O(n) Time IDEA: Create a set of seen numbers. Then, iterate 0...n to see which number is missing. # Time Complexity: O(n) # Space Complexity: O(n)"""
<|body_0|>
def missingNumber(self, nums: List[int]... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def missingNumber(self, nums: List[int]) -> int:
"""My Solution: O(n) Time IDEA: Create a set of seen numbers. Then, iterate 0...n to see which number is missing. # Time Complexity: O(n) # Space Complexity: O(n)"""
n = len(nums)
seen = set()
for i in nums:
... | the_stack_v2_python_sparse | Python_Solutions/268_Missing_Number.py | lw75251/leetcode | train | 0 | |
be4c15a46d621582bd8651a5a00a806ea546c6e8 | [
"if self.voucher_id and self.voucher_id.pay_now == 'installments':\n if str(fields.datetime.now()) < self.date:\n raise UserError(_(\"you can't post this move yet untill move date come %s\" % self.date))\n else:\n self.line_ids.with_context(check_move_validity=False)._onchange_amount_currency()\... | <|body_start_0|>
if self.voucher_id and self.voucher_id.pay_now == 'installments':
if str(fields.datetime.now()) < self.date:
raise UserError(_("you can't post this move yet untill move date come %s" % self.date))
else:
self.line_ids.with_context(check_mov... | AccountMove | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountMove:
def post(self):
"""override post func to set required constraints to installment voucher :return:"""
<|body_0|>
def open_payment_view(self):
"""open register payment wizard :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if sel... | stack_v2_sparse_classes_36k_train_010087 | 40,481 | no_license | [
{
"docstring": "override post func to set required constraints to installment voucher :return:",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "open register payment wizard :return:",
"name": "open_payment_view",
"signature": "def open_payment_view(self)"
}
] | 2 | null | Implement the Python class `AccountMove` described below.
Class description:
Implement the AccountMove class.
Method signatures and docstrings:
- def post(self): override post func to set required constraints to installment voucher :return:
- def open_payment_view(self): open register payment wizard :return: | Implement the Python class `AccountMove` described below.
Class description:
Implement the AccountMove class.
Method signatures and docstrings:
- def post(self): override post func to set required constraints to installment voucher :return:
- def open_payment_view(self): open register payment wizard :return:
<|skele... | 0b997095c260d58b026440967fea3a202bef7efb | <|skeleton|>
class AccountMove:
def post(self):
"""override post func to set required constraints to installment voucher :return:"""
<|body_0|>
def open_payment_view(self):
"""open register payment wizard :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccountMove:
def post(self):
"""override post func to set required constraints to installment voucher :return:"""
if self.voucher_id and self.voucher_id.pay_now == 'installments':
if str(fields.datetime.now()) < self.date:
raise UserError(_("you can't post this move... | the_stack_v2_python_sparse | v_11/EBS-SVN/branches/ebs/account_voucher_custom/models/account_voucher.py | musabahmed/baba | train | 0 | |
9cdb059585a0c8cc794f4e7c24d6d7a451e7e21b | [
"population_size = param_value(graph, parameters, Parameter.POPULATION_SIZE)\nno_of_processes = param_value(graph, parameters, Parameter.NO_OF_PROCESSES)\nindividuals = generate_individuals(graph, parameters)\npopulations = []\nchunks = ChainChunkFactory._list_chunks(individuals, population_size, no_of_processes)\n... | <|body_start_0|>
population_size = param_value(graph, parameters, Parameter.POPULATION_SIZE)
no_of_processes = param_value(graph, parameters, Parameter.NO_OF_PROCESSES)
individuals = generate_individuals(graph, parameters)
populations = []
chunks = ChainChunkFactory._list_chunks(... | ChainChunkFactory | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChainChunkFactory:
def create(graph: Graph, parameters: Dict[str, Any]=None) -> Optional[List[Population]]:
"""Returns a list of no_of_processes populations that have approximately population_size / no_of_processes individuals."""
<|body_0|>
def _list_chunks(individuals: Lis... | stack_v2_sparse_classes_36k_train_010088 | 5,463 | permissive | [
{
"docstring": "Returns a list of no_of_processes populations that have approximately population_size / no_of_processes individuals.",
"name": "create",
"signature": "def create(graph: Graph, parameters: Dict[str, Any]=None) -> Optional[List[Population]]"
},
{
"docstring": "Splits a list into eq... | 2 | null | Implement the Python class `ChainChunkFactory` described below.
Class description:
Implement the ChainChunkFactory class.
Method signatures and docstrings:
- def create(graph: Graph, parameters: Dict[str, Any]=None) -> Optional[List[Population]]: Returns a list of no_of_processes populations that have approximately p... | Implement the Python class `ChainChunkFactory` described below.
Class description:
Implement the ChainChunkFactory class.
Method signatures and docstrings:
- def create(graph: Graph, parameters: Dict[str, Any]=None) -> Optional[List[Population]]: Returns a list of no_of_processes populations that have approximately p... | 69f0242aceb47fc383d0e56077f08b2b061273b5 | <|skeleton|>
class ChainChunkFactory:
def create(graph: Graph, parameters: Dict[str, Any]=None) -> Optional[List[Population]]:
"""Returns a list of no_of_processes populations that have approximately population_size / no_of_processes individuals."""
<|body_0|>
def _list_chunks(individuals: Lis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChainChunkFactory:
def create(graph: Graph, parameters: Dict[str, Any]=None) -> Optional[List[Population]]:
"""Returns a list of no_of_processes populations that have approximately population_size / no_of_processes individuals."""
population_size = param_value(graph, parameters, Parameter.POPU... | the_stack_v2_python_sparse | python/mage/graph_coloring_module/components/chain_chunk.py | gitbuda/mage | train | 0 | |
0cfb0517ec15ac37abab6339c68ab3702a128347 | [
"super(Attention, self).__init__()\nself.encoder_att = nn.Linear(encoder_dim, attention_dim)\nself.decoder_att = nn.Linear(decoder_dim, attention_dim)\nself.full_att = nn.Linear(attention_dim, 1)\nself.tanh = nn.Tanh()\nself.softmax = nn.Softmax(dim=1)",
"att1 = self.encoder_att(encoder_out)\natt2 = self.decoder_... | <|body_start_0|>
super(Attention, self).__init__()
self.encoder_att = nn.Linear(encoder_dim, attention_dim)
self.decoder_att = nn.Linear(decoder_dim, attention_dim)
self.full_att = nn.Linear(attention_dim, 1)
self.tanh = nn.Tanh()
self.softmax = nn.Softmax(dim=1)
<|end_bo... | Attention Network. | Attention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Attention:
"""Attention Network."""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
""":param encoder_dim: feature size of encoded images :param decoder_dim: size of decoder's RNN :param attention_dim: size of the attention network"""
<|body_0|>
def forward(... | stack_v2_sparse_classes_36k_train_010089 | 5,269 | no_license | [
{
"docstring": ":param encoder_dim: feature size of encoded images :param decoder_dim: size of decoder's RNN :param attention_dim: size of the attention network",
"name": "__init__",
"signature": "def __init__(self, encoder_dim, decoder_dim, attention_dim)"
},
{
"docstring": "Forward propagation... | 2 | stack_v2_sparse_classes_30k_train_003212 | Implement the Python class `Attention` described below.
Class description:
Attention Network.
Method signatures and docstrings:
- def __init__(self, encoder_dim, decoder_dim, attention_dim): :param encoder_dim: feature size of encoded images :param decoder_dim: size of decoder's RNN :param attention_dim: size of the ... | Implement the Python class `Attention` described below.
Class description:
Attention Network.
Method signatures and docstrings:
- def __init__(self, encoder_dim, decoder_dim, attention_dim): :param encoder_dim: feature size of encoded images :param decoder_dim: size of decoder's RNN :param attention_dim: size of the ... | 02d93ee55bde386455eb1b146810528ffa3739d0 | <|skeleton|>
class Attention:
"""Attention Network."""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
""":param encoder_dim: feature size of encoded images :param decoder_dim: size of decoder's RNN :param attention_dim: size of the attention network"""
<|body_0|>
def forward(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Attention:
"""Attention Network."""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
""":param encoder_dim: feature size of encoded images :param decoder_dim: size of decoder's RNN :param attention_dim: size of the attention network"""
super(Attention, self).__init__()
... | the_stack_v2_python_sparse | modules.py | euyniy/785-visual-story-telling | train | 0 |
b9e15a7a0d8ab884552ac29b91dc1c465decd9be | [
"super().__init__(creator, flock_size, buffer_size)\nself._delay_used = delay_used\nself.inputs = self._create_storage('inputs', (flock_size, buffer_size, *input_shape), force_cpu=False)\nself.targets = self._create_storage('targets', (flock_size, buffer_size, *target_shape))\nself.learning_coefficients = self._cre... | <|body_start_0|>
super().__init__(creator, flock_size, buffer_size)
self._delay_used = delay_used
self.inputs = self._create_storage('inputs', (flock_size, buffer_size, *input_shape), force_cpu=False)
self.targets = self._create_storage('targets', (flock_size, buffer_size, *target_shape)... | Defines a circular buffer for a flock of neural networks. It has two storages: for inputs (flock_size, buffer_size, input_size) and for outputs. Then there are learning_coefficients (flock_size, 1) determining how much each network should learn the particular IO. | NetworkFlockBuffer | [
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NetworkFlockBuffer:
"""Defines a circular buffer for a flock of neural networks. It has two storages: for inputs (flock_size, buffer_size, input_size) and for outputs. Then there are learning_coefficients (flock_size, 1) determining how much each network should learn the particular IO."""
de... | stack_v2_sparse_classes_36k_train_010090 | 3,879 | permissive | [
{
"docstring": "Initialize the buffer Args: flock_size (int): Number of networks in the flock buffer_size (int): Number of elements that can be stored in the buffer before rewriting occurs input_shape (Tuple): The shape of the inputs target_shape (Tuple): The shape of the target delay_used (bool): whether any o... | 4 | stack_v2_sparse_classes_30k_train_013636 | Implement the Python class `NetworkFlockBuffer` described below.
Class description:
Defines a circular buffer for a flock of neural networks. It has two storages: for inputs (flock_size, buffer_size, input_size) and for outputs. Then there are learning_coefficients (flock_size, 1) determining how much each network sho... | Implement the Python class `NetworkFlockBuffer` described below.
Class description:
Defines a circular buffer for a flock of neural networks. It has two storages: for inputs (flock_size, buffer_size, input_size) and for outputs. Then there are learning_coefficients (flock_size, 1) determining how much each network sho... | 81d72b82ec96948c26d292d709f18c9c77a17ba4 | <|skeleton|>
class NetworkFlockBuffer:
"""Defines a circular buffer for a flock of neural networks. It has two storages: for inputs (flock_size, buffer_size, input_size) and for outputs. Then there are learning_coefficients (flock_size, 1) determining how much each network should learn the particular IO."""
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NetworkFlockBuffer:
"""Defines a circular buffer for a flock of neural networks. It has two storages: for inputs (flock_size, buffer_size, input_size) and for outputs. Then there are learning_coefficients (flock_size, 1) determining how much each network should learn the particular IO."""
def __init__(se... | the_stack_v2_python_sparse | torchsim/core/models/neural_network/network_flock_buffer.py | andreofner/torchsim | train | 0 |
b84e71ba1220bdb44966376d31cc7b02176e1112 | [
"kwargs.setdefault('sheriffs', ['sheriff'])\nkwargs.setdefault('sendToInterestedUsers', True)\nkwargs.setdefault('status_header', 'Automatically closing tree for \"%(steps)s\" on \"%(builder)s\"')\nchromium_notifier.ChromiumNotifier.__init__(self, **kwargs)\nself.tree_status_url = tree_status_url\nself.check_revisi... | <|body_start_0|>
kwargs.setdefault('sheriffs', ['sheriff'])
kwargs.setdefault('sendToInterestedUsers', True)
kwargs.setdefault('status_header', 'Automatically closing tree for "%(steps)s" on "%(builder)s"')
chromium_notifier.ChromiumNotifier.__init__(self, **kwargs)
self.tree_sta... | This is a status notifier which closes the tree upon failures. See builder.interfaces.IStatusReceiver to have more information about the parameters type. | GateKeeper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GateKeeper:
"""This is a status notifier which closes the tree upon failures. See builder.interfaces.IStatusReceiver to have more information about the parameters type."""
def __init__(self, tree_status_url, tree_message=None, check_revisions=True, **kwargs):
"""Constructor with foll... | stack_v2_sparse_classes_36k_train_010091 | 8,792 | no_license | [
{
"docstring": "Constructor with following specific arguments (on top of base class'). @type tree_status_url: String. @param tree_status_url: Web end-point for tree status updates. @type tree_message: String. @param tree_message: Message posted to the tree status site when closed. @type check_revisions: Boolean... | 4 | null | Implement the Python class `GateKeeper` described below.
Class description:
This is a status notifier which closes the tree upon failures. See builder.interfaces.IStatusReceiver to have more information about the parameters type.
Method signatures and docstrings:
- def __init__(self, tree_status_url, tree_message=Non... | Implement the Python class `GateKeeper` described below.
Class description:
This is a status notifier which closes the tree upon failures. See builder.interfaces.IStatusReceiver to have more information about the parameters type.
Method signatures and docstrings:
- def __init__(self, tree_status_url, tree_message=Non... | 516718f9b7b95c4280257b2d319638d4728a90e1 | <|skeleton|>
class GateKeeper:
"""This is a status notifier which closes the tree upon failures. See builder.interfaces.IStatusReceiver to have more information about the parameters type."""
def __init__(self, tree_status_url, tree_message=None, check_revisions=True, **kwargs):
"""Constructor with foll... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GateKeeper:
"""This is a status notifier which closes the tree upon failures. See builder.interfaces.IStatusReceiver to have more information about the parameters type."""
def __init__(self, tree_status_url, tree_message=None, check_revisions=True, **kwargs):
"""Constructor with following specifi... | the_stack_v2_python_sparse | build/scripts/master/gatekeeper.py | mhcchang/chromium30 | train | 0 |
a2cace009e5ae6907d422bb2d22796e400f550c1 | [
"course = Course.objects.get(id=pk)\nqueryset = course.lecture_set\nserializer = self.lecture_serializer(queryset.all(), many=True)\nreturn Response(serializer.data)",
"course = Course.objects.get(id=pk)\nqueryset = course.lecture_set\nserializer = self.emotion_serializer(queryset.all(), many=True)\nreturn Respon... | <|body_start_0|>
course = Course.objects.get(id=pk)
queryset = course.lecture_set
serializer = self.lecture_serializer(queryset.all(), many=True)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
course = Course.objects.get(id=pk)
queryset = course.lecture... | Viewset for handling course queries | CourseViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CourseViewSet:
"""Viewset for handling course queries"""
def lectures(self, request, pk=None):
"""Returns all lectures for a particular course"""
<|body_0|>
def emotions(self, request, pk=None):
"""Returns all emotions for a particular course"""
<|body_1|... | stack_v2_sparse_classes_36k_train_010092 | 7,767 | no_license | [
{
"docstring": "Returns all lectures for a particular course",
"name": "lectures",
"signature": "def lectures(self, request, pk=None)"
},
{
"docstring": "Returns all emotions for a particular course",
"name": "emotions",
"signature": "def emotions(self, request, pk=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002637 | Implement the Python class `CourseViewSet` described below.
Class description:
Viewset for handling course queries
Method signatures and docstrings:
- def lectures(self, request, pk=None): Returns all lectures for a particular course
- def emotions(self, request, pk=None): Returns all emotions for a particular course | Implement the Python class `CourseViewSet` described below.
Class description:
Viewset for handling course queries
Method signatures and docstrings:
- def lectures(self, request, pk=None): Returns all lectures for a particular course
- def emotions(self, request, pk=None): Returns all emotions for a particular course... | 4254efd246b954538a463c03e56c126ed63beec2 | <|skeleton|>
class CourseViewSet:
"""Viewset for handling course queries"""
def lectures(self, request, pk=None):
"""Returns all lectures for a particular course"""
<|body_0|>
def emotions(self, request, pk=None):
"""Returns all emotions for a particular course"""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CourseViewSet:
"""Viewset for handling course queries"""
def lectures(self, request, pk=None):
"""Returns all lectures for a particular course"""
course = Course.objects.get(id=pk)
queryset = course.lecture_set
serializer = self.lecture_serializer(queryset.all(), many=True... | the_stack_v2_python_sparse | backend/fuskar/views.py | deven96/fuskar-backend | train | 5 |
7875dcbd86ab6ab959596c7ee82b35ff816bdfe4 | [
"db = conn_mysqldb()\ndb_cursor = db.cursor()\nif id is None:\n sql = '\\n SELECT menu_id, menu_name, stock, note\\n FROM menus;\\n '\n db_cursor.execute(sql)\nelse:\n sql = '\\n SELECT menu_id, menu_name, stock, note\\n ... | <|body_start_0|>
db = conn_mysqldb()
db_cursor = db.cursor()
if id is None:
sql = '\n SELECT menu_id, menu_name, stock, note\n FROM menus;\n '
db_cursor.execute(sql)
else:
sql = '\n ... | StockManagement | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StockManagement:
def getStock(id=None):
"""id(menu_id) 값을 받지 않으면 전체 행을 반환 id가 있을 경우 해당하는 값만 반환 반환 형태 [ { menu_id: "..", menu_name: "..", stock: 0, note: "..", }, { ... } ]"""
<|body_0|>
def setStock(data: list[dict[str, str]]):
"""인수 형태 [ { menu_id: "..", menu_name: ... | stack_v2_sparse_classes_36k_train_010093 | 3,325 | no_license | [
{
"docstring": "id(menu_id) 값을 받지 않으면 전체 행을 반환 id가 있을 경우 해당하는 값만 반환 반환 형태 [ { menu_id: \"..\", menu_name: \"..\", stock: 0, note: \"..\", }, { ... } ]",
"name": "getStock",
"signature": "def getStock(id=None)"
},
{
"docstring": "인수 형태 [ { menu_id: \"..\", menu_name: \"..\", stock: 0, note: \"..\... | 2 | stack_v2_sparse_classes_30k_train_001492 | Implement the Python class `StockManagement` described below.
Class description:
Implement the StockManagement class.
Method signatures and docstrings:
- def getStock(id=None): id(menu_id) 값을 받지 않으면 전체 행을 반환 id가 있을 경우 해당하는 값만 반환 반환 형태 [ { menu_id: "..", menu_name: "..", stock: 0, note: "..", }, { ... } ]
- def setSto... | Implement the Python class `StockManagement` described below.
Class description:
Implement the StockManagement class.
Method signatures and docstrings:
- def getStock(id=None): id(menu_id) 값을 받지 않으면 전체 행을 반환 id가 있을 경우 해당하는 값만 반환 반환 형태 [ { menu_id: "..", menu_name: "..", stock: 0, note: "..", }, { ... } ]
- def setSto... | 8d8824c89f7f1c60944f972ec8ef178b4b5ad227 | <|skeleton|>
class StockManagement:
def getStock(id=None):
"""id(menu_id) 값을 받지 않으면 전체 행을 반환 id가 있을 경우 해당하는 값만 반환 반환 형태 [ { menu_id: "..", menu_name: "..", stock: 0, note: "..", }, { ... } ]"""
<|body_0|>
def setStock(data: list[dict[str, str]]):
"""인수 형태 [ { menu_id: "..", menu_name: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StockManagement:
def getStock(id=None):
"""id(menu_id) 값을 받지 않으면 전체 행을 반환 id가 있을 경우 해당하는 값만 반환 반환 형태 [ { menu_id: "..", menu_name: "..", stock: 0, note: "..", }, { ... } ]"""
db = conn_mysqldb()
db_cursor = db.cursor()
if id is None:
sql = '\n SEL... | the_stack_v2_python_sparse | packages/stock_management/StockManagement.py | Tanney-102/MrDaeBak_back | train | 0 | |
2fee95e03bf8acb9aaf31a09fbfd873f737f3553 | [
"text = TextBlob(text)\nresult = text.sentiment\nreturn (result[0], result[1])",
"txt = pd.Series(textSeries)\ntxt = txt.map(self.__mapfun)\ntxt = txt.apply(pd.Series)\ntxt.columns = ['polar', 'subj']\nreturn txt"
] | <|body_start_0|>
text = TextBlob(text)
result = text.sentiment
return (result[0], result[1])
<|end_body_0|>
<|body_start_1|>
txt = pd.Series(textSeries)
txt = txt.map(self.__mapfun)
txt = txt.apply(pd.Series)
txt.columns = ['polar', 'subj']
return txt
<|e... | Textblob | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Textblob:
def __mapfun(self, text):
"""text: string"""
<|body_0|>
def fit(self, textSeries):
"""Input: list/tuple/pandas.Series [sentence, sentence, ...] Output: dataframe with column name "polar", "subj" Explanation of output: (1) The polarity score is a float withi... | stack_v2_sparse_classes_36k_train_010094 | 2,696 | no_license | [
{
"docstring": "text: string",
"name": "__mapfun",
"signature": "def __mapfun(self, text)"
},
{
"docstring": "Input: list/tuple/pandas.Series [sentence, sentence, ...] Output: dataframe with column name \"polar\", \"subj\" Explanation of output: (1) The polarity score is a float within the range... | 2 | stack_v2_sparse_classes_30k_train_011364 | Implement the Python class `Textblob` described below.
Class description:
Implement the Textblob class.
Method signatures and docstrings:
- def __mapfun(self, text): text: string
- def fit(self, textSeries): Input: list/tuple/pandas.Series [sentence, sentence, ...] Output: dataframe with column name "polar", "subj" E... | Implement the Python class `Textblob` described below.
Class description:
Implement the Textblob class.
Method signatures and docstrings:
- def __mapfun(self, text): text: string
- def fit(self, textSeries): Input: list/tuple/pandas.Series [sentence, sentence, ...] Output: dataframe with column name "polar", "subj" E... | 1a3d46cee9bc8cae79e076d6336de5de9fb7427b | <|skeleton|>
class Textblob:
def __mapfun(self, text):
"""text: string"""
<|body_0|>
def fit(self, textSeries):
"""Input: list/tuple/pandas.Series [sentence, sentence, ...] Output: dataframe with column name "polar", "subj" Explanation of output: (1) The polarity score is a float withi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Textblob:
def __mapfun(self, text):
"""text: string"""
text = TextBlob(text)
result = text.sentiment
return (result[0], result[1])
def fit(self, textSeries):
"""Input: list/tuple/pandas.Series [sentence, sentence, ...] Output: dataframe with column name "polar", "s... | the_stack_v2_python_sparse | scripts/_sentiment/auto_sentiment_app/brexit_sentiment.py | my2582/gep | train | 0 | |
d604215a961a6056e05a25bae247bd20231a252f | [
"try:\n args = [self.config.image_converter, '-version']\n logger.debug('Invoking %r ...', args)\n subprocess.run(args, capture_output=True, check=True)\n return True\nexcept OSError as exc:\n logger.warning(__(\"Unable to run the image conversion command %r. 'sphinx.ext.imgconverter' requires ImageM... | <|body_start_0|>
try:
args = [self.config.image_converter, '-version']
logger.debug('Invoking %r ...', args)
subprocess.run(args, capture_output=True, check=True)
return True
except OSError as exc:
logger.warning(__("Unable to run the image con... | ImagemagickConverter | [
"BSD-3-Clause",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImagemagickConverter:
def is_available(self) -> bool:
"""Confirms the converter is available or not."""
<|body_0|>
def convert(self, _from: str, _to: str) -> bool:
"""Converts the image to expected one."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_010095 | 3,620 | permissive | [
{
"docstring": "Confirms the converter is available or not.",
"name": "is_available",
"signature": "def is_available(self) -> bool"
},
{
"docstring": "Converts the image to expected one.",
"name": "convert",
"signature": "def convert(self, _from: str, _to: str) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_018578 | Implement the Python class `ImagemagickConverter` described below.
Class description:
Implement the ImagemagickConverter class.
Method signatures and docstrings:
- def is_available(self) -> bool: Confirms the converter is available or not.
- def convert(self, _from: str, _to: str) -> bool: Converts the image to expec... | Implement the Python class `ImagemagickConverter` described below.
Class description:
Implement the ImagemagickConverter class.
Method signatures and docstrings:
- def is_available(self) -> bool: Confirms the converter is available or not.
- def convert(self, _from: str, _to: str) -> bool: Converts the image to expec... | eab54533a56119c5badd5aac647c595a9adae720 | <|skeleton|>
class ImagemagickConverter:
def is_available(self) -> bool:
"""Confirms the converter is available or not."""
<|body_0|>
def convert(self, _from: str, _to: str) -> bool:
"""Converts the image to expected one."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImagemagickConverter:
def is_available(self) -> bool:
"""Confirms the converter is available or not."""
try:
args = [self.config.image_converter, '-version']
logger.debug('Invoking %r ...', args)
subprocess.run(args, capture_output=True, check=True)
... | the_stack_v2_python_sparse | sphinx/ext/imgconverter.py | sphinx-doc/sphinx | train | 6,138 | |
ba1045b4a133fee2f842f1993b3470169335c839 | [
"Parametre.__init__(self, 'hotboot', 'hotboot')\nself.aide_courte = 'permet de redémarrer les modules du MUD'\nself.aide_longue = \"Cette commande permet de redémarrer un ou plusieurs modules pendant l'exécution du MUD. Cela permet de corriger des bugs, intégrer des modifications, ajouter ou retirer des commandes s... | <|body_start_0|>
Parametre.__init__(self, 'hotboot', 'hotboot')
self.aide_courte = 'permet de redémarrer les modules du MUD'
self.aide_longue = "Cette commande permet de redémarrer un ou plusieurs modules pendant l'exécution du MUD. Cela permet de corriger des bugs, intégrer des modifications, a... | Commande 'module hotboot'. | PrmHotboot | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmHotboot:
"""Commande 'module hotboot'."""
def __init__(self):
"""Constructeur du paramètre"""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Interprétation du paramètre"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Parametre.... | stack_v2_sparse_classes_36k_train_010096 | 3,062 | permissive | [
{
"docstring": "Constructeur du paramètre",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Interprétation du paramètre",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001650 | Implement the Python class `PrmHotboot` described below.
Class description:
Commande 'module hotboot'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre | Implement the Python class `PrmHotboot` described below.
Class description:
Commande 'module hotboot'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre
<|skeleton|>
class PrmHotboot:
"""Commande 'module... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmHotboot:
"""Commande 'module hotboot'."""
def __init__(self):
"""Constructeur du paramètre"""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Interprétation du paramètre"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PrmHotboot:
"""Commande 'module hotboot'."""
def __init__(self):
"""Constructeur du paramètre"""
Parametre.__init__(self, 'hotboot', 'hotboot')
self.aide_courte = 'permet de redémarrer les modules du MUD'
self.aide_longue = "Cette commande permet de redémarrer un ou plusie... | the_stack_v2_python_sparse | src/primaires/joueur/commandes/module/hotboot.py | vincent-lg/tsunami | train | 5 |
311b4c55f3f2c3d0fbc1a9d7913350227fabc42a | [
"sq_r = 1.0\nc = 4\nshots = 10\nalpha = [0, np.pi / 4] * c\nphi = [np.pi / 2, 0] * c\ntheta = [0, 0] + [0, np.pi / 2] + [np.pi / 2, 0] + [np.pi / 2]\nwith pytest.raises(ValueError, match='Gate-parameter lists must be of equal length.'):\n singleloop(sq_r, alpha, phi, theta, shots)",
"prog = tdmprogram.TDMProgr... | <|body_start_0|>
sq_r = 1.0
c = 4
shots = 10
alpha = [0, np.pi / 4] * c
phi = [np.pi / 2, 0] * c
theta = [0, 0] + [0, np.pi / 2] + [np.pi / 2, 0] + [np.pi / 2]
with pytest.raises(ValueError, match='Gate-parameter lists must be of equal length.'):
singl... | Test that the correct error messages are raised when a TDMProgram is created | TestTDMErrorRaising | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTDMErrorRaising:
"""Test that the correct error messages are raised when a TDMProgram is created"""
def test_gates_equal_length(self):
"""Checks gate list parameters have same length"""
<|body_0|>
def test_passing_list_of_tdmprograms(self):
"""Test that error... | stack_v2_sparse_classes_36k_train_010097 | 29,620 | permissive | [
{
"docstring": "Checks gate list parameters have same length",
"name": "test_gates_equal_length",
"signature": "def test_gates_equal_length(self)"
},
{
"docstring": "Test that error is raised when passing a list containing TDM programs",
"name": "test_passing_list_of_tdmprograms",
"signa... | 2 | stack_v2_sparse_classes_30k_train_016893 | Implement the Python class `TestTDMErrorRaising` described below.
Class description:
Test that the correct error messages are raised when a TDMProgram is created
Method signatures and docstrings:
- def test_gates_equal_length(self): Checks gate list parameters have same length
- def test_passing_list_of_tdmprograms(s... | Implement the Python class `TestTDMErrorRaising` described below.
Class description:
Test that the correct error messages are raised when a TDMProgram is created
Method signatures and docstrings:
- def test_gates_equal_length(self): Checks gate list parameters have same length
- def test_passing_list_of_tdmprograms(s... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class TestTDMErrorRaising:
"""Test that the correct error messages are raised when a TDMProgram is created"""
def test_gates_equal_length(self):
"""Checks gate list parameters have same length"""
<|body_0|>
def test_passing_list_of_tdmprograms(self):
"""Test that error... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestTDMErrorRaising:
"""Test that the correct error messages are raised when a TDMProgram is created"""
def test_gates_equal_length(self):
"""Checks gate list parameters have same length"""
sq_r = 1.0
c = 4
shots = 10
alpha = [0, np.pi / 4] * c
phi = [np.pi... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits/strawberryfields/strawberryfields#611/before/test_tdmprogram.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
067303081f1241503e9e2c984898ab472cccb0f2 | [
"self.resolver = resolver\nself.lock_cache = lock_cache\nself.dependency = dependency",
"path = self.resolver.resolve()\ncheckout = None\nif self.dependency.git_tag:\n checkout = self.dependency.git_tag\nelif self.dependency.git_commit:\n checkout = self.dependency.git_commit\nelse:\n raise WurfError('No... | <|body_start_0|>
self.resolver = resolver
self.lock_cache = lock_cache
self.dependency = dependency
<|end_body_0|>
<|body_start_1|>
path = self.resolver.resolve()
checkout = None
if self.dependency.git_tag:
checkout = self.dependency.git_tag
elif self... | StoreLockVersionResolver | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StoreLockVersionResolver:
def __init__(self, resolver, lock_cache, dependency):
"""Construct an instance. :param resolver: A resolver which will do the actual job :param lock_cache: The lock cache to store the version information in. :param dependency: A Dependency instance."""
<... | stack_v2_sparse_classes_36k_train_010098 | 1,223 | permissive | [
{
"docstring": "Construct an instance. :param resolver: A resolver which will do the actual job :param lock_cache: The lock cache to store the version information in. :param dependency: A Dependency instance.",
"name": "__init__",
"signature": "def __init__(self, resolver, lock_cache, dependency)"
},
... | 2 | stack_v2_sparse_classes_30k_train_005690 | Implement the Python class `StoreLockVersionResolver` described below.
Class description:
Implement the StoreLockVersionResolver class.
Method signatures and docstrings:
- def __init__(self, resolver, lock_cache, dependency): Construct an instance. :param resolver: A resolver which will do the actual job :param lock_... | Implement the Python class `StoreLockVersionResolver` described below.
Class description:
Implement the StoreLockVersionResolver class.
Method signatures and docstrings:
- def __init__(self, resolver, lock_cache, dependency): Construct an instance. :param resolver: A resolver which will do the actual job :param lock_... | ba94d46ce58ac7e936fc45acaca1168ae0d7780b | <|skeleton|>
class StoreLockVersionResolver:
def __init__(self, resolver, lock_cache, dependency):
"""Construct an instance. :param resolver: A resolver which will do the actual job :param lock_cache: The lock cache to store the version information in. :param dependency: A Dependency instance."""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StoreLockVersionResolver:
def __init__(self, resolver, lock_cache, dependency):
"""Construct an instance. :param resolver: A resolver which will do the actual job :param lock_cache: The lock cache to store the version information in. :param dependency: A Dependency instance."""
self.resolver =... | the_stack_v2_python_sparse | src/wurf/store_lock_version_resolver.py | steinwurf/waf | train | 15 | |
5ce53657f5e5895538279d88432d3d4e4ad8f5e1 | [
"self.directory = directory\nself.function = function\nself.iterable = iterable",
"for i, item in enumerate(self.iterable, start=1):\n with open(os.path.join(self.directory, '%d.input' % i), 'wb') as outfile:\n pickle.dump((self.function, item), outfile, protocol=pickle.HIGHEST_PROTOCOL)"
] | <|body_start_0|>
self.directory = directory
self.function = function
self.iterable = iterable
<|end_body_0|>
<|body_start_1|>
for i, item in enumerate(self.iterable, start=1):
with open(os.path.join(self.directory, '%d.input' % i), 'wb') as outfile:
pickle.du... | A class to write the input files | InputWriter | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InputWriter:
"""A class to write the input files"""
def __init__(self, directory, function, iterable):
"""Save the function and iterable"""
<|body_0|>
def __call__(self):
"""Call this to write input files"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_010099 | 4,398 | permissive | [
{
"docstring": "Save the function and iterable",
"name": "__init__",
"signature": "def __init__(self, directory, function, iterable)"
},
{
"docstring": "Call this to write input files",
"name": "__call__",
"signature": "def __call__(self)"
}
] | 2 | null | Implement the Python class `InputWriter` described below.
Class description:
A class to write the input files
Method signatures and docstrings:
- def __init__(self, directory, function, iterable): Save the function and iterable
- def __call__(self): Call this to write input files | Implement the Python class `InputWriter` described below.
Class description:
A class to write the input files
Method signatures and docstrings:
- def __init__(self, directory, function, iterable): Save the function and iterable
- def __call__(self): Call this to write input files
<|skeleton|>
class InputWriter:
... | 88bf7f7c5ac44defc046ebf0719cde748092cfff | <|skeleton|>
class InputWriter:
"""A class to write the input files"""
def __init__(self, directory, function, iterable):
"""Save the function and iterable"""
<|body_0|>
def __call__(self):
"""Call this to write input files"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InputWriter:
"""A class to write the input files"""
def __init__(self, directory, function, iterable):
"""Save the function and iterable"""
self.directory = directory
self.function = function
self.iterable = iterable
def __call__(self):
"""Call this to write i... | the_stack_v2_python_sparse | src/dials/util/cluster_map.py | dials/dials | train | 71 |
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