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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2e0a284678a344763d16d6ed8100887908ec4bdc | [
"self.data = []\nif data is not None:\n if type(data) != list:\n raise TypeError('data must be a list')\n if len(data) < 2:\n raise ValueError('data must contain multiple values')\n self.lambtha = sum(data) / len(data)\nelif lambtha >= 1:\n self.lambtha = float(lambtha)\nelse:\n raise V... | <|body_start_0|>
self.data = []
if data is not None:
if type(data) != list:
raise TypeError('data must be a list')
if len(data) < 2:
raise ValueError('data must contain multiple values')
self.lambtha = sum(data) / len(data)
elif... | This class is to represent a poisson distribution | Poisson | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Poisson:
"""This class is to represent a poisson distribution"""
def __init__(self, data=None, lambtha=1.0):
"""All begins here"""
<|body_0|>
def pmf(self, k):
"""Method to calculate the pmf"""
<|body_1|>
def cdf(self, k):
"""This method calc... | stack_v2_sparse_classes_36k_train_009300 | 1,386 | permissive | [
{
"docstring": "All begins here",
"name": "__init__",
"signature": "def __init__(self, data=None, lambtha=1.0)"
},
{
"docstring": "Method to calculate the pmf",
"name": "pmf",
"signature": "def pmf(self, k)"
},
{
"docstring": "This method calculates the CDF",
"name": "cdf",
... | 3 | null | Implement the Python class `Poisson` described below.
Class description:
This class is to represent a poisson distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): All begins here
- def pmf(self, k): Method to calculate the pmf
- def cdf(self, k): This method calculates the CDF | Implement the Python class `Poisson` described below.
Class description:
This class is to represent a poisson distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): All begins here
- def pmf(self, k): Method to calculate the pmf
- def cdf(self, k): This method calculates the CDF
... | 58c367f3014919f95157426121093b9fe14d4035 | <|skeleton|>
class Poisson:
"""This class is to represent a poisson distribution"""
def __init__(self, data=None, lambtha=1.0):
"""All begins here"""
<|body_0|>
def pmf(self, k):
"""Method to calculate the pmf"""
<|body_1|>
def cdf(self, k):
"""This method calc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Poisson:
"""This class is to represent a poisson distribution"""
def __init__(self, data=None, lambtha=1.0):
"""All begins here"""
self.data = []
if data is not None:
if type(data) != list:
raise TypeError('data must be a list')
if len(data)... | the_stack_v2_python_sparse | math/0x03-probability/poisson.py | linkem97/holbertonschool-machine_learning | train | 0 |
e3e95ab004a4190267d621f5ef244f71697dd8e8 | [
"try:\n user_type_data = UserType.query.filter(UserType.id == user_type_id).first()\n if not user_type_data:\n raise UserTypeObjectNotFound('Users type with this id does not exit')\n result = user_type_schema.dump(user_type_data)\n logger.info('Response for get with id request for user type {}'.f... | <|body_start_0|>
try:
user_type_data = UserType.query.filter(UserType.id == user_type_id).first()
if not user_type_data:
raise UserTypeObjectNotFound('Users type with this id does not exit')
result = user_type_schema.dump(user_type_data)
logger.inf... | UserTypeResourceId | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserTypeResourceId:
def get(self, user_type_id):
"""This is GET API Call this api passing a user type id parameters: id:int user_type: string responses: 404: description: User type with this id does not exist 200: description: Users type with this id return successfully schema: UserTypeS... | stack_v2_sparse_classes_36k_train_009301 | 25,221 | no_license | [
{
"docstring": "This is GET API Call this api passing a user type id parameters: id:int user_type: string responses: 404: description: User type with this id does not exist 200: description: Users type with this id return successfully schema: UserTypeSchema",
"name": "get",
"signature": "def get(self, u... | 2 | stack_v2_sparse_classes_30k_train_014864 | Implement the Python class `UserTypeResourceId` described below.
Class description:
Implement the UserTypeResourceId class.
Method signatures and docstrings:
- def get(self, user_type_id): This is GET API Call this api passing a user type id parameters: id:int user_type: string responses: 404: description: User type ... | Implement the Python class `UserTypeResourceId` described below.
Class description:
Implement the UserTypeResourceId class.
Method signatures and docstrings:
- def get(self, user_type_id): This is GET API Call this api passing a user type id parameters: id:int user_type: string responses: 404: description: User type ... | b6bc7dc48d27e843a5d0d3657952464ad4707471 | <|skeleton|>
class UserTypeResourceId:
def get(self, user_type_id):
"""This is GET API Call this api passing a user type id parameters: id:int user_type: string responses: 404: description: User type with this id does not exist 200: description: Users type with this id return successfully schema: UserTypeS... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserTypeResourceId:
def get(self, user_type_id):
"""This is GET API Call this api passing a user type id parameters: id:int user_type: string responses: 404: description: User type with this id does not exist 200: description: Users type with this id return successfully schema: UserTypeSchema"""
... | the_stack_v2_python_sparse | app/views/user.py | pyarati/bank-system | train | 0 | |
530e55664d24765897171f933fc13463793da4b7 | [
"self._backend = subprocess.Popen(['python', 'backend.py'], stdout=subprocess.PIPE)\nsubprocess.Popen(['./frontend/node_modules/.bin/electron', './frontend'])\nself._noised = noised\nself._left_eye_gaze = np.array([0, -1, 0])\nself._right_eye_gaze = np.array([0, -1, 0])\nself._plane = np.array([0.0, 1.0, 0.0, -100.... | <|body_start_0|>
self._backend = subprocess.Popen(['python', 'backend.py'], stdout=subprocess.PIPE)
subprocess.Popen(['./frontend/node_modules/.bin/electron', './frontend'])
self._noised = noised
self._left_eye_gaze = np.array([0, -1, 0])
self._right_eye_gaze = np.array([0, -1, 0... | A watcher representation of visual debug predictor :ivar _backend: (subprocess.Popen), backend for debug predictor :ivar _noised: (bool), whether noising of gaze vectors is needed :ivar _left_eye_gaze: (np.ndarray, shape [3]), left eye gaze vector :ivar _right_eye_gaze: (np.ndarray, shape [3]), right eye gaze vector :i... | VisualDebugPredictor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VisualDebugPredictor:
"""A watcher representation of visual debug predictor :ivar _backend: (subprocess.Popen), backend for debug predictor :ivar _noised: (bool), whether noising of gaze vectors is needed :ivar _left_eye_gaze: (np.ndarray, shape [3]), left eye gaze vector :ivar _right_eye_gaze: (... | stack_v2_sparse_classes_36k_train_009302 | 6,279 | permissive | [
{
"docstring": "Construct object Constructor runs both backend and frontend subprocess Backend can't be run in a thread as gevent(zerorpc) doesn't like threads :param noised: whether to noise the predicted gaze vectors and fake landmarks",
"name": "__init__",
"signature": "def __init__(self, noised: boo... | 3 | stack_v2_sparse_classes_30k_train_015683 | Implement the Python class `VisualDebugPredictor` described below.
Class description:
A watcher representation of visual debug predictor :ivar _backend: (subprocess.Popen), backend for debug predictor :ivar _noised: (bool), whether noising of gaze vectors is needed :ivar _left_eye_gaze: (np.ndarray, shape [3]), left e... | Implement the Python class `VisualDebugPredictor` described below.
Class description:
A watcher representation of visual debug predictor :ivar _backend: (subprocess.Popen), backend for debug predictor :ivar _noised: (bool), whether noising of gaze vectors is needed :ivar _left_eye_gaze: (np.ndarray, shape [3]), left e... | 2b4a15b95b4e1f2e9e8c7359416747fd4d26d4a9 | <|skeleton|>
class VisualDebugPredictor:
"""A watcher representation of visual debug predictor :ivar _backend: (subprocess.Popen), backend for debug predictor :ivar _noised: (bool), whether noising of gaze vectors is needed :ivar _left_eye_gaze: (np.ndarray, shape [3]), left eye gaze vector :ivar _right_eye_gaze: (... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VisualDebugPredictor:
"""A watcher representation of visual debug predictor :ivar _backend: (subprocess.Popen), backend for debug predictor :ivar _noised: (bool), whether noising of gaze vectors is needed :ivar _left_eye_gaze: (np.ndarray, shape [3]), left eye gaze vector :ivar _right_eye_gaze: (np.ndarray, s... | the_stack_v2_python_sparse | watcher/predictor_module/visual_debug_predictor.py | framaz/eye_control | train | 3 |
5de6bd2a4c10705121b1334a795e857450d708bb | [
"self.type = self.__class__.__name__\nself.name = name\nself.hardwareComm = hardwareComm\nself.modelLock = modelLock",
"self.modelLock.Acquire()\npReturn1 = pGetFunction(False)\nself.modelLock.Release()\nreturn pReturn1",
"self.modelLock.Acquire()\npReturn1, pReturn2 = pGetFunction(False)\nself.modelLock.Releas... | <|body_start_0|>
self.type = self.__class__.__name__
self.name = name
self.hardwareComm = hardwareComm
self.modelLock = modelLock
<|end_body_0|>
<|body_start_1|>
self.modelLock.Acquire()
pReturn1 = pGetFunction(False)
self.modelLock.Release()
return pRetu... | ComponentModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ComponentModel:
def __init__(self, name, hardwareComm, modelLock):
"""ComponentModel base class constructor"""
<|body_0|>
def protectedReturn1(self, pGetFunction):
"""Returns the value of the variable as protected by the model lock"""
<|body_1|>
def prot... | stack_v2_sparse_classes_36k_train_009303 | 1,492 | no_license | [
{
"docstring": "ComponentModel base class constructor",
"name": "__init__",
"signature": "def __init__(self, name, hardwareComm, modelLock)"
},
{
"docstring": "Returns the value of the variable as protected by the model lock",
"name": "protectedReturn1",
"signature": "def protectedReturn... | 5 | null | Implement the Python class `ComponentModel` described below.
Class description:
Implement the ComponentModel class.
Method signatures and docstrings:
- def __init__(self, name, hardwareComm, modelLock): ComponentModel base class constructor
- def protectedReturn1(self, pGetFunction): Returns the value of the variable... | Implement the Python class `ComponentModel` described below.
Class description:
Implement the ComponentModel class.
Method signatures and docstrings:
- def __init__(self, name, hardwareComm, modelLock): ComponentModel base class constructor
- def protectedReturn1(self, pGetFunction): Returns the value of the variable... | c6954ca0fff935ce1eb8154744f6307743765dc5 | <|skeleton|>
class ComponentModel:
def __init__(self, name, hardwareComm, modelLock):
"""ComponentModel base class constructor"""
<|body_0|>
def protectedReturn1(self, pGetFunction):
"""Returns the value of the variable as protected by the model lock"""
<|body_1|>
def prot... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ComponentModel:
def __init__(self, name, hardwareComm, modelLock):
"""ComponentModel base class constructor"""
self.type = self.__class__.__name__
self.name = name
self.hardwareComm = hardwareComm
self.modelLock = modelLock
def protectedReturn1(self, pGetFunction):... | the_stack_v2_python_sparse | server/core/ComponentModel.py | henryeherman/elixys | train | 1 | |
19840523d2f4b992ef7ad6aacf8518ed7d68d3b6 | [
"if not nums or len(nums) < 1:\n return -1\nnum_set = set()\nfor ele in nums:\n if ele in num_set:\n num_set.remove(ele)\n else:\n num_set.add(ele)\nreturn num_set.pop()",
"if not nums or len(nums) < 1:\n return -1\nres = nums[0]\nfor i in range(1, len(nums)):\n res ^= nums[i]\nreturn... | <|body_start_0|>
if not nums or len(nums) < 1:
return -1
num_set = set()
for ele in nums:
if ele in num_set:
num_set.remove(ele)
else:
num_set.add(ele)
return num_set.pop()
<|end_body_0|>
<|body_start_1|>
if not... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def single_number(self, nums: List[int]) -> int:
"""求只出现一次的数字 Args: nums: 数组 Returns: 数字"""
<|body_0|>
def single_number2(self, nums: List[int]) -> int:
"""求只出现一次的数字 Args: nums: 数组 Returns: 数字"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_009304 | 1,924 | permissive | [
{
"docstring": "求只出现一次的数字 Args: nums: 数组 Returns: 数字",
"name": "single_number",
"signature": "def single_number(self, nums: List[int]) -> int"
},
{
"docstring": "求只出现一次的数字 Args: nums: 数组 Returns: 数字",
"name": "single_number2",
"signature": "def single_number2(self, nums: List[int]) -> in... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def single_number(self, nums: List[int]) -> int: 求只出现一次的数字 Args: nums: 数组 Returns: 数字
- def single_number2(self, nums: List[int]) -> int: 求只出现一次的数字 Args: nums: 数组 Returns: 数字 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def single_number(self, nums: List[int]) -> int: 求只出现一次的数字 Args: nums: 数组 Returns: 数字
- def single_number2(self, nums: List[int]) -> int: 求只出现一次的数字 Args: nums: 数组 Returns: 数字
<|... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def single_number(self, nums: List[int]) -> int:
"""求只出现一次的数字 Args: nums: 数组 Returns: 数字"""
<|body_0|>
def single_number2(self, nums: List[int]) -> int:
"""求只出现一次的数字 Args: nums: 数组 Returns: 数字"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def single_number(self, nums: List[int]) -> int:
"""求只出现一次的数字 Args: nums: 数组 Returns: 数字"""
if not nums or len(nums) < 1:
return -1
num_set = set()
for ele in nums:
if ele in num_set:
num_set.remove(ele)
else:
... | the_stack_v2_python_sparse | src/leetcodepython/array/single_number_136.py | zhangyu345293721/leetcode | train | 101 | |
508e92b584dbf04c6df9b34c38bc0776801c8f68 | [
"text = ''\nshortened = False\nif self.abstract:\n text = self.abstract\nelif self.description:\n for block in json.loads(self.description)['data']:\n if block.get('type') == 'text':\n data = block['data']\n if len(data['text']) > settings.ABSTRACT_LENGTH:\n trimmed... | <|body_start_0|>
text = ''
shortened = False
if self.abstract:
text = self.abstract
elif self.description:
for block in json.loads(self.description)['data']:
if block.get('type') == 'text':
data = block['data']
... | Adds the abstract_html method. Assumes the object has an abstract and description field, where the description field is sir-trevor json. Also assumes that the object has a primary_url method (for the read-more link). | AbstractHTMLMixin | [
"CC0-1.0",
"MIT",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbstractHTMLMixin:
"""Adds the abstract_html method. Assumes the object has an abstract and description field, where the description field is sir-trevor json. Also assumes that the object has a primary_url method (for the read-more link)."""
def abstract_plaintext(self, include_shortened=Fal... | stack_v2_sparse_classes_36k_train_009305 | 24,965 | permissive | [
{
"docstring": "If an explicit abstract is present, return it. Otherwise, return the first paragraph of the description",
"name": "abstract_plaintext",
"signature": "def abstract_plaintext(self, include_shortened=False)"
},
{
"docstring": "Take the plaintext and run it through a sir trevor templ... | 2 | stack_v2_sparse_classes_30k_train_017521 | Implement the Python class `AbstractHTMLMixin` described below.
Class description:
Adds the abstract_html method. Assumes the object has an abstract and description field, where the description field is sir-trevor json. Also assumes that the object has a primary_url method (for the read-more link).
Method signatures ... | Implement the Python class `AbstractHTMLMixin` described below.
Class description:
Adds the abstract_html method. Assumes the object has an abstract and description field, where the description field is sir-trevor json. Also assumes that the object has a primary_url method (for the read-more link).
Method signatures ... | 840c451eff415ebc57203bfeca55409131e9ab05 | <|skeleton|>
class AbstractHTMLMixin:
"""Adds the abstract_html method. Assumes the object has an abstract and description field, where the description field is sir-trevor json. Also assumes that the object has a primary_url method (for the read-more link)."""
def abstract_plaintext(self, include_shortened=Fal... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AbstractHTMLMixin:
"""Adds the abstract_html method. Assumes the object has an abstract and description field, where the description field is sir-trevor json. Also assumes that the object has a primary_url method (for the read-more link)."""
def abstract_plaintext(self, include_shortened=False):
... | the_stack_v2_python_sparse | peacecorps/peacecorps/models.py | forumone/peacecorps-site | train | 1 |
b484fffff2d6e59801407c6b9567f02f3ef51272 | [
"print('==== Test Edit Distance Recursive ====')\nstr1 = 'sunday'\nstr2 = 'saturday'\nprint('Given strs: {} and {}'.format(str1, str2))\nresult = edit_distance_rec(str1, str2, len(str1), len(str2))\nprint('Need to perform {} operations', result)\nself.assertEqual(result, 3)\nstr1 = ''\nstr2 = 'test'\nprint('Given s... | <|body_start_0|>
print('==== Test Edit Distance Recursive ====')
str1 = 'sunday'
str2 = 'saturday'
print('Given strs: {} and {}'.format(str1, str2))
result = edit_distance_rec(str1, str2, len(str1), len(str2))
print('Need to perform {} operations', result)
self.as... | Test cases for Edit Distance | TestEditDistance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEditDistance:
"""Test cases for Edit Distance"""
def test_edit_distance_rec(self):
"""Test Edit Distance Recursive"""
<|body_0|>
def test_edit_distance_dp(self):
"""Test Edit Distance DP"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
print(... | stack_v2_sparse_classes_36k_train_009306 | 5,036 | no_license | [
{
"docstring": "Test Edit Distance Recursive",
"name": "test_edit_distance_rec",
"signature": "def test_edit_distance_rec(self)"
},
{
"docstring": "Test Edit Distance DP",
"name": "test_edit_distance_dp",
"signature": "def test_edit_distance_dp(self)"
}
] | 2 | null | Implement the Python class `TestEditDistance` described below.
Class description:
Test cases for Edit Distance
Method signatures and docstrings:
- def test_edit_distance_rec(self): Test Edit Distance Recursive
- def test_edit_distance_dp(self): Test Edit Distance DP | Implement the Python class `TestEditDistance` described below.
Class description:
Test cases for Edit Distance
Method signatures and docstrings:
- def test_edit_distance_rec(self): Test Edit Distance Recursive
- def test_edit_distance_dp(self): Test Edit Distance DP
<|skeleton|>
class TestEditDistance:
"""Test c... | 74007a5ef4f8b0a7a1416dcc65eeeab3504792b4 | <|skeleton|>
class TestEditDistance:
"""Test cases for Edit Distance"""
def test_edit_distance_rec(self):
"""Test Edit Distance Recursive"""
<|body_0|>
def test_edit_distance_dp(self):
"""Test Edit Distance DP"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestEditDistance:
"""Test cases for Edit Distance"""
def test_edit_distance_rec(self):
"""Test Edit Distance Recursive"""
print('==== Test Edit Distance Recursive ====')
str1 = 'sunday'
str2 = 'saturday'
print('Given strs: {} and {}'.format(str1, str2))
res... | the_stack_v2_python_sparse | python/dynamic_programming/edit_distance/edit_distance.py | ktp-forked-repos/algorithms-8 | train | 0 |
182f83363a5447abdefea693b2c65c64e712ca04 | [
"if action is not None:\n raise APIError(422, 'Action must be None')\nif session_id is None:\n filter_data = dict(self.request.arguments)\n self.success(get_all_session_dicts(self.session, filter_data))\nelse:\n try:\n self.success(Session.get_by_id(self.session, session_id))\n except exceptio... | <|body_start_0|>
if action is not None:
raise APIError(422, 'Action must be None')
if session_id is None:
filter_data = dict(self.request.arguments)
self.success(get_all_session_dicts(self.session, filter_data))
else:
try:
self.succ... | Handles OWTF sessions. | OWTFSessionHandler | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OWTFSessionHandler:
"""Handles OWTF sessions."""
def get(self, session_id=None, action=None):
"""Get all registered sessions. **Example request**: .. sourcecode:: http GET /api/v1/sessions/ HTTP/1.1 Accept: application/json, text/javascript, */*; q=0.01 X-Requested-With: XMLHttpReque... | stack_v2_sparse_classes_36k_train_009307 | 5,183 | permissive | [
{
"docstring": "Get all registered sessions. **Example request**: .. sourcecode:: http GET /api/v1/sessions/ HTTP/1.1 Accept: application/json, text/javascript, */*; q=0.01 X-Requested-With: XMLHttpRequest **Example response**: .. sourcecode:: http HTTP/1.1 200 OK Content-Type: application/json { \"status\": \"... | 4 | null | Implement the Python class `OWTFSessionHandler` described below.
Class description:
Handles OWTF sessions.
Method signatures and docstrings:
- def get(self, session_id=None, action=None): Get all registered sessions. **Example request**: .. sourcecode:: http GET /api/v1/sessions/ HTTP/1.1 Accept: application/json, te... | Implement the Python class `OWTFSessionHandler` described below.
Class description:
Handles OWTF sessions.
Method signatures and docstrings:
- def get(self, session_id=None, action=None): Get all registered sessions. **Example request**: .. sourcecode:: http GET /api/v1/sessions/ HTTP/1.1 Accept: application/json, te... | 240825989a3850241b6b5dba6bcae1042a5dc384 | <|skeleton|>
class OWTFSessionHandler:
"""Handles OWTF sessions."""
def get(self, session_id=None, action=None):
"""Get all registered sessions. **Example request**: .. sourcecode:: http GET /api/v1/sessions/ HTTP/1.1 Accept: application/json, text/javascript, */*; q=0.01 X-Requested-With: XMLHttpReque... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OWTFSessionHandler:
"""Handles OWTF sessions."""
def get(self, session_id=None, action=None):
"""Get all registered sessions. **Example request**: .. sourcecode:: http GET /api/v1/sessions/ HTTP/1.1 Accept: application/json, text/javascript, */*; q=0.01 X-Requested-With: XMLHttpRequest **Example ... | the_stack_v2_python_sparse | owtf/api/handlers/session.py | owtf/owtf | train | 1,683 |
7da8491278a244e043ee142a75b55de0b6cfeac1 | [
"self.cone = Cone(3, 5)\nself.cube = Cube(3)\nself.cylinder = Cylinder(3, 7)\nself.sphere = Sphere(3)",
"attributes = [self.cone, self.cube, self.cylinder, self.sphere]\nvolume = 0\nfor a in attributes:\n volume += a.get_volume()\nreturn round(volume, 2)"
] | <|body_start_0|>
self.cone = Cone(3, 5)
self.cube = Cube(3)
self.cylinder = Cylinder(3, 7)
self.sphere = Sphere(3)
<|end_body_0|>
<|body_start_1|>
attributes = [self.cone, self.cube, self.cylinder, self.sphere]
volume = 0
for a in attributes:
volume +... | Class which generates a facade for computing the weirdly shaped object. | Facade | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Facade:
"""Class which generates a facade for computing the weirdly shaped object."""
def __init__(self):
"""Constructor of the Facade class."""
<|body_0|>
def get_volume(self):
"""Method which computes the volume of the weird shape. :return: The volume of the sh... | stack_v2_sparse_classes_36k_train_009308 | 904 | no_license | [
{
"docstring": "Constructor of the Facade class.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Method which computes the volume of the weird shape. :return: The volume of the shape.",
"name": "get_volume",
"signature": "def get_volume(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005804 | Implement the Python class `Facade` described below.
Class description:
Class which generates a facade for computing the weirdly shaped object.
Method signatures and docstrings:
- def __init__(self): Constructor of the Facade class.
- def get_volume(self): Method which computes the volume of the weird shape. :return:... | Implement the Python class `Facade` described below.
Class description:
Class which generates a facade for computing the weirdly shaped object.
Method signatures and docstrings:
- def __init__(self): Constructor of the Facade class.
- def get_volume(self): Method which computes the volume of the weird shape. :return:... | 7b3c92c151266cd3ccdd63e7dc0a37f7a60476fa | <|skeleton|>
class Facade:
"""Class which generates a facade for computing the weirdly shaped object."""
def __init__(self):
"""Constructor of the Facade class."""
<|body_0|>
def get_volume(self):
"""Method which computes the volume of the weird shape. :return: The volume of the sh... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Facade:
"""Class which generates a facade for computing the weirdly shaped object."""
def __init__(self):
"""Constructor of the Facade class."""
self.cone = Cone(3, 5)
self.cube = Cube(3)
self.cylinder = Cylinder(3, 7)
self.sphere = Sphere(3)
def get_volume(se... | the_stack_v2_python_sparse | Laboratory 9/problem2/facade.py | BabyCakes13/Python-Treasure | train | 0 |
75b91843c83e94bb16740c0dc597e2bfd21f3864 | [
"goods_id = request.POST.get('goods_id')\ngoods_lable = request.POST.get('goods_lable')\nu_login_name = request.session.get('u_login_name')\nuser = User.objects.get(u_login_name=u_login_name)\ngoods = Goods.objects.get(g_id=goods_id)\ngl = GoodsLable.objects.filter(gl_lable=goods_lable, gl_goods=goods)\nif len(gl) ... | <|body_start_0|>
goods_id = request.POST.get('goods_id')
goods_lable = request.POST.get('goods_lable')
u_login_name = request.session.get('u_login_name')
user = User.objects.get(u_login_name=u_login_name)
goods = Goods.objects.get(g_id=goods_id)
gl = GoodsLable.objects.fi... | 产品标签添加，删除 | GoodsLableHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GoodsLableHandler:
"""产品标签添加，删除"""
def post(self, request):
"""添加"""
<|body_0|>
def get(self, request):
"""删除"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
goods_id = request.POST.get('goods_id')
goods_lable = request.POST.get('goods_l... | stack_v2_sparse_classes_36k_train_009309 | 21,132 | no_license | [
{
"docstring": "添加",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "删除",
"name": "get",
"signature": "def get(self, request)"
}
] | 2 | null | Implement the Python class `GoodsLableHandler` described below.
Class description:
产品标签添加，删除
Method signatures and docstrings:
- def post(self, request): 添加
- def get(self, request): 删除 | Implement the Python class `GoodsLableHandler` described below.
Class description:
产品标签添加，删除
Method signatures and docstrings:
- def post(self, request): 添加
- def get(self, request): 删除
<|skeleton|>
class GoodsLableHandler:
"""产品标签添加，删除"""
def post(self, request):
"""添加"""
<|body_0|>
de... | b6185fe5fb138a5a124e0efc9c266a249cce5459 | <|skeleton|>
class GoodsLableHandler:
"""产品标签添加，删除"""
def post(self, request):
"""添加"""
<|body_0|>
def get(self, request):
"""删除"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GoodsLableHandler:
"""产品标签添加，删除"""
def post(self, request):
"""添加"""
goods_id = request.POST.get('goods_id')
goods_lable = request.POST.get('goods_lable')
u_login_name = request.session.get('u_login_name')
user = User.objects.get(u_login_name=u_login_name)
... | the_stack_v2_python_sparse | goods/views.py | bingfengxindong/goods_info | train | 0 |
0a2b8318c408ade9c01639129d4bdf4b9e82fe1f | [
"super(Decoder, self).__init__()\nself.N = N\nself.dm = dm\nself.embedding = tf.keras.layers.Embedding(target_vocab, dm)\nself.positional_encoding = positional_encoding(max_seq_len, dm)\nself.blocks = [DecoderBlock(dm, h, hidden, drop_rate) for i in range(N)]\nself.dropout = tf.keras.layers.Dropout(drop_rate)",
"... | <|body_start_0|>
super(Decoder, self).__init__()
self.N = N
self.dm = dm
self.embedding = tf.keras.layers.Embedding(target_vocab, dm)
self.positional_encoding = positional_encoding(max_seq_len, dm)
self.blocks = [DecoderBlock(dm, h, hidden, drop_rate) for i in range(N)]
... | create the decoder for a transformer | Decoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""create the decoder for a transformer"""
def __init__(self, N, dm, h, hidden, target_vocab, max_seq_len, drop_rate=0.1):
"""N - the number of blocks in the encoder dm - the dimensionality of the model h - the number of heads hidden - the number of hidden units in the fully... | stack_v2_sparse_classes_36k_train_009310 | 3,024 | no_license | [
{
"docstring": "N - the number of blocks in the encoder dm - the dimensionality of the model h - the number of heads hidden - the number of hidden units in the fully connected layer target_vocab - the size of the target vocabulary max_seq_len - the maximum sequence length possible drop_rate - the dropout rate p... | 2 | stack_v2_sparse_classes_30k_train_015494 | Implement the Python class `Decoder` described below.
Class description:
create the decoder for a transformer
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, target_vocab, max_seq_len, drop_rate=0.1): N - the number of blocks in the encoder dm - the dimensionality of the model h - the number ... | Implement the Python class `Decoder` described below.
Class description:
create the decoder for a transformer
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, target_vocab, max_seq_len, drop_rate=0.1): N - the number of blocks in the encoder dm - the dimensionality of the model h - the number ... | e10b4e9b6f3fa00639e6e9e5b35f0cdb43a339a3 | <|skeleton|>
class Decoder:
"""create the decoder for a transformer"""
def __init__(self, N, dm, h, hidden, target_vocab, max_seq_len, drop_rate=0.1):
"""N - the number of blocks in the encoder dm - the dimensionality of the model h - the number of heads hidden - the number of hidden units in the fully... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder:
"""create the decoder for a transformer"""
def __init__(self, N, dm, h, hidden, target_vocab, max_seq_len, drop_rate=0.1):
"""N - the number of blocks in the encoder dm - the dimensionality of the model h - the number of heads hidden - the number of hidden units in the fully connected la... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/10-transformer_decoder.py | HeimerR/holbertonschool-machine_learning | train | 0 |
4c11945770efad48978903132ba77619cc967fe3 | [
"self.kode_type_field = kode_type_field\nself.kode_tekst_field = kode_tekst_field\nself.navn_field = navn_field\nself.gate_adresse_field = gate_adresse_field\nself.gate_postboks_field = gate_postboks_field\nself.gate_postnr_field = gate_postnr_field\nself.gate_poststed_field = gate_poststed_field\nself.post_adresse... | <|body_start_0|>
self.kode_type_field = kode_type_field
self.kode_tekst_field = kode_tekst_field
self.navn_field = navn_field
self.gate_adresse_field = gate_adresse_field
self.gate_postboks_field = gate_postboks_field
self.gate_postnr_field = gate_postnr_field
sel... | Implementation of the 'NavnAdresse' model. TODO: type model description here. Attributes: kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type description here. gate_adresse_field (string): TODO: type description here. gate_postbo... | NavnAdresse | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NavnAdresse:
"""Implementation of the 'NavnAdresse' model. TODO: type model description here. Attributes: kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type description here. gate_adresse_field (string): T... | stack_v2_sparse_classes_36k_train_009311 | 4,971 | permissive | [
{
"docstring": "Constructor for the NavnAdresse class",
"name": "__init__",
"signature": "def __init__(self, kode_type_field=None, kode_tekst_field=None, navn_field=None, gate_adresse_field=None, gate_postboks_field=None, gate_postnr_field=None, gate_poststed_field=None, post_adresse_field=None, post_po... | 2 | null | Implement the Python class `NavnAdresse` described below.
Class description:
Implementation of the 'NavnAdresse' model. TODO: type model description here. Attributes: kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type descripti... | Implement the Python class `NavnAdresse` described below.
Class description:
Implementation of the 'NavnAdresse' model. TODO: type model description here. Attributes: kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type descripti... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class NavnAdresse:
"""Implementation of the 'NavnAdresse' model. TODO: type model description here. Attributes: kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type description here. gate_adresse_field (string): T... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NavnAdresse:
"""Implementation of the 'NavnAdresse' model. TODO: type model description here. Attributes: kode_type_field (string): TODO: type description here. kode_tekst_field (string): TODO: type description here. navn_field (string): TODO: type description here. gate_adresse_field (string): TODO: type des... | the_stack_v2_python_sparse | idfy_rest_client/models/navn_adresse.py | dealflowteam/Idfy | train | 0 |
cecd1d3885345eaaa5338945449e39ab49cdf41e | [
"for entry in cls:\n if entry.value.name == category_name:\n return entry\nraise KeyError(category_name)",
"for entry in cls:\n if entry.value.code_prefix == prefix:\n return entry\nraise KeyError(prefix)"
] | <|body_start_0|>
for entry in cls:
if entry.value.name == category_name:
return entry
raise KeyError(category_name)
<|end_body_0|>
<|body_start_1|>
for entry in cls:
if entry.value.code_prefix == prefix:
return entry
raise KeyError... | All enuemrated error categories. | ErrorCategories | [
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ErrorCategories:
"""All enuemrated error categories."""
def by_category_name(cls, category_name: str) -> 'ErrorCategories':
"""Get a subsystem by its category name."""
<|body_0|>
def by_code_prefix(cls, prefix: str) -> 'ErrorCategories':
"""Get an error category ... | stack_v2_sparse_classes_36k_train_009312 | 1,687 | permissive | [
{
"docstring": "Get a subsystem by its category name.",
"name": "by_category_name",
"signature": "def by_category_name(cls, category_name: str) -> 'ErrorCategories'"
},
{
"docstring": "Get an error category by its code prefix.",
"name": "by_code_prefix",
"signature": "def by_code_prefix(... | 2 | null | Implement the Python class `ErrorCategories` described below.
Class description:
All enuemrated error categories.
Method signatures and docstrings:
- def by_category_name(cls, category_name: str) -> 'ErrorCategories': Get a subsystem by its category name.
- def by_code_prefix(cls, prefix: str) -> 'ErrorCategories': G... | Implement the Python class `ErrorCategories` described below.
Class description:
All enuemrated error categories.
Method signatures and docstrings:
- def by_category_name(cls, category_name: str) -> 'ErrorCategories': Get a subsystem by its category name.
- def by_code_prefix(cls, prefix: str) -> 'ErrorCategories': G... | 026b523c8c9e5d45910c490efb89194d72595be9 | <|skeleton|>
class ErrorCategories:
"""All enuemrated error categories."""
def by_category_name(cls, category_name: str) -> 'ErrorCategories':
"""Get a subsystem by its category name."""
<|body_0|>
def by_code_prefix(cls, prefix: str) -> 'ErrorCategories':
"""Get an error category ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ErrorCategories:
"""All enuemrated error categories."""
def by_category_name(cls, category_name: str) -> 'ErrorCategories':
"""Get a subsystem by its category name."""
for entry in cls:
if entry.value.name == category_name:
return entry
raise KeyError(c... | the_stack_v2_python_sparse | shared-data/python/opentrons_shared_data/errors/categories.py | Opentrons/opentrons | train | 326 |
2d858c32871d5b958dc12f0ec79e92063d6e2c30 | [
"if root == None:\n return 0\nmax_sub_depth = 0\nfor node in root.children:\n cur_depth = self.maxDepthWithRecursion(node)\n max_sub_depth = max(cur_depth, max_sub_depth)\nreturn max_sub_depth + 1",
"if root == None:\n return 0\nmax_depth = 0\nqueue = []\nqueue.append([root, 1])\nwhile len(queue) > 0:... | <|body_start_0|>
if root == None:
return 0
max_sub_depth = 0
for node in root.children:
cur_depth = self.maxDepthWithRecursion(node)
max_sub_depth = max(cur_depth, max_sub_depth)
return max_sub_depth + 1
<|end_body_0|>
<|body_start_1|>
if root... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxDepthWithRecursion(self, root):
""":type root: Node :rtype: int"""
<|body_0|>
def maxDepthWithQueue(self, root):
""":type root: Node :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root == None:
return 0
... | stack_v2_sparse_classes_36k_train_009313 | 1,003 | no_license | [
{
"docstring": ":type root: Node :rtype: int",
"name": "maxDepthWithRecursion",
"signature": "def maxDepthWithRecursion(self, root)"
},
{
"docstring": ":type root: Node :rtype: int",
"name": "maxDepthWithQueue",
"signature": "def maxDepthWithQueue(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003423 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepthWithRecursion(self, root): :type root: Node :rtype: int
- def maxDepthWithQueue(self, root): :type root: Node :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepthWithRecursion(self, root): :type root: Node :rtype: int
- def maxDepthWithQueue(self, root): :type root: Node :rtype: int
<|skeleton|>
class Solution:
def maxDe... | 176cc1db3291843fb068f06d0180766dd8c3122c | <|skeleton|>
class Solution:
def maxDepthWithRecursion(self, root):
""":type root: Node :rtype: int"""
<|body_0|>
def maxDepthWithQueue(self, root):
""":type root: Node :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxDepthWithRecursion(self, root):
""":type root: Node :rtype: int"""
if root == None:
return 0
max_sub_depth = 0
for node in root.children:
cur_depth = self.maxDepthWithRecursion(node)
max_sub_depth = max(cur_depth, max_sub_dep... | the_stack_v2_python_sparse | 2020/tree/maximum_depth_of_n_ary_tree_559.py | yehongyu/acode | train | 0 | |
a61fb6ce4a881c67a866331b46431b13d2597472 | [
"hashdict = {}\nfor i in range(len(nums)):\n if nums[i] not in hashdict:\n hashdict[nums[i]] = 1\n else:\n hashdict[nums[i]] += 1\n if hashdict[nums[i]] > len(nums) // 2:\n return nums[i]",
"votes = 0\nfor num in nums:\n if votes == 0:\n x = num\n votes += 1 if num == x ... | <|body_start_0|>
hashdict = {}
for i in range(len(nums)):
if nums[i] not in hashdict:
hashdict[nums[i]] = 1
else:
hashdict[nums[i]] += 1
if hashdict[nums[i]] > len(nums) // 2:
return nums[i]
<|end_body_0|>
<|body_start_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def majorityElement(self, nums: List[int]) -> int:
"""寻找数组中出现次数超过数组长度的一半（哈希表法） :param nums: :return: 复杂度分析：时间复杂度O（N）"""
<|body_0|>
def majorityElementPlus(self, nums: List[int]) -> int:
"""正负抵消法（摩尔投票法） :param nums: :return: 复杂度分析：时间复杂度O（N），空间复杂度O（1）"""
... | stack_v2_sparse_classes_36k_train_009314 | 2,387 | no_license | [
{
"docstring": "寻找数组中出现次数超过数组长度的一半（哈希表法） :param nums: :return: 复杂度分析：时间复杂度O（N）",
"name": "majorityElement",
"signature": "def majorityElement(self, nums: List[int]) -> int"
},
{
"docstring": "正负抵消法（摩尔投票法） :param nums: :return: 复杂度分析：时间复杂度O（N），空间复杂度O（1）",
"name": "majorityElementPlus",
"s... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def majorityElement(self, nums: List[int]) -> int: 寻找数组中出现次数超过数组长度的一半（哈希表法） :param nums: :return: 复杂度分析：时间复杂度O（N）
- def majorityElementPlus(self, nums: List[int]) -> int: 正负抵消法（摩... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def majorityElement(self, nums: List[int]) -> int: 寻找数组中出现次数超过数组长度的一半（哈希表法） :param nums: :return: 复杂度分析：时间复杂度O（N）
- def majorityElementPlus(self, nums: List[int]) -> int: 正负抵消法（摩... | 32941ee052d0985a9569441d314378700ff4d225 | <|skeleton|>
class Solution:
def majorityElement(self, nums: List[int]) -> int:
"""寻找数组中出现次数超过数组长度的一半（哈希表法） :param nums: :return: 复杂度分析：时间复杂度O（N）"""
<|body_0|>
def majorityElementPlus(self, nums: List[int]) -> int:
"""正负抵消法（摩尔投票法） :param nums: :return: 复杂度分析：时间复杂度O（N），空间复杂度O（1）"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def majorityElement(self, nums: List[int]) -> int:
"""寻找数组中出现次数超过数组长度的一半（哈希表法） :param nums: :return: 复杂度分析：时间复杂度O（N）"""
hashdict = {}
for i in range(len(nums)):
if nums[i] not in hashdict:
hashdict[nums[i]] = 1
else:
has... | the_stack_v2_python_sparse | cecilia-python/剑指offer/chapter-6/MajorityElement.py | Cecilia520/algorithmic-learning-leetcode | train | 7 | |
5252d1b38d382d88a008fe795b8ab33ad96a1bf7 | [
"self.err_int = np.zeros(2)\nself.err_d1 = np.zeros(2)\nself.diff_d1 = np.zeros(2)\nself.ctrl_look = control_lookahead\nself.last_closest_idx = 0\nself.t_d1 = 0",
"self.err_int = np.zeros(2)\nself.err_d1 = np.zeros(2)\nself.diff_d1 = np.zeros(2)\nself.last_closest_idx = 0\nself.t_d1 = 0",
"dist_squared = [(stat... | <|body_start_0|>
self.err_int = np.zeros(2)
self.err_d1 = np.zeros(2)
self.diff_d1 = np.zeros(2)
self.ctrl_look = control_lookahead
self.last_closest_idx = 0
self.t_d1 = 0
<|end_body_0|>
<|body_start_1|>
self.err_int = np.zeros(2)
self.err_d1 = np.zeros(2... | NNControl | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NNControl:
def __init__(self, control_lookahead=50):
"""Initialization for a neural network controller. Inputs: control_lookahead: Number of points ahead of the previous closest point on the path that the controller looks to find the next closest point. Used to handle paths that cross ba... | stack_v2_sparse_classes_36k_train_009315 | 9,955 | no_license | [
{
"docstring": "Initialization for a neural network controller. Inputs: control_lookahead: Number of points ahead of the previous closest point on the path that the controller looks to find the next closest point. Used to handle paths that cross back on themselves.",
"name": "__init__",
"signature": "de... | 3 | stack_v2_sparse_classes_30k_train_012737 | Implement the Python class `NNControl` described below.
Class description:
Implement the NNControl class.
Method signatures and docstrings:
- def __init__(self, control_lookahead=50): Initialization for a neural network controller. Inputs: control_lookahead: Number of points ahead of the previous closest point on the... | Implement the Python class `NNControl` described below.
Class description:
Implement the NNControl class.
Method signatures and docstrings:
- def __init__(self, control_lookahead=50): Initialization for a neural network controller. Inputs: control_lookahead: Number of points ahead of the previous closest point on the... | cf864712c4cdb40b252bae3b01a5bd86318d32d2 | <|skeleton|>
class NNControl:
def __init__(self, control_lookahead=50):
"""Initialization for a neural network controller. Inputs: control_lookahead: Number of points ahead of the previous closest point on the path that the controller looks to find the next closest point. Used to handle paths that cross ba... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NNControl:
def __init__(self, control_lookahead=50):
"""Initialization for a neural network controller. Inputs: control_lookahead: Number of points ahead of the previous closest point on the path that the controller looks to find the next closest point. Used to handle paths that cross back on themselv... | the_stack_v2_python_sparse | src/ego_sim/nn_control.py | zabrock/tractor_trailer_learning_control | train | 1 | |
af6cea748f93ed25c5b702539b6858e064e93f8d | [
"Parametre.__init__(self, 'miens', 'mine')\nself.tronquer = True\nself.aide_courte = 'affiche vos familiers'\nself.aide_longue = 'Cette commande affiche la liste de vos familiers et donne un aperçu du lieu où ils se trouvent, ainsi que de leur condition (faim et soif).'",
"familiers = importeur.familier.familiers... | <|body_start_0|>
Parametre.__init__(self, 'miens', 'mine')
self.tronquer = True
self.aide_courte = 'affiche vos familiers'
self.aide_longue = 'Cette commande affiche la liste de vos familiers et donne un aperçu du lieu où ils se trouvent, ainsi que de leur condition (faim et soif).'
<|en... | Commande 'familier miens'. | PrmMiens | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmMiens:
"""Commande 'familier miens'."""
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_009316 | 2,981 | 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 | null | Implement the Python class `PrmMiens` described below.
Class description:
Commande 'familier miens'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre | Implement the Python class `PrmMiens` described below.
Class description:
Commande 'familier miens'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre
<|skeleton|>
class PrmMiens:
"""Commande 'familier m... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmMiens:
"""Commande 'familier miens'."""
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 PrmMiens:
"""Commande 'familier miens'."""
def __init__(self):
"""Constructeur du paramètre"""
Parametre.__init__(self, 'miens', 'mine')
self.tronquer = True
self.aide_courte = 'affiche vos familiers'
self.aide_longue = 'Cette commande affiche la liste de vos famil... | the_stack_v2_python_sparse | src/secondaires/familier/commandes/familier/miens.py | vincent-lg/tsunami | train | 5 |
d20646d560e273c3e86319dd707e4e97cd063a68 | [
"l = len(matrix)\ndp_row = [[0] * l for _ in range(l)]\ndp_col = [[0] * l for _ in range(l)]\nfor i in range(l):\n for j in range(l):\n if matrix[i][j] == 0:\n dp_row[i][j] = dp_row[i][j - 1] + 1\n dp_col[i][j] = dp_col[i - 1][j] + 1\nres = []\nfor i in range(l - 1, -1, -1):\n for... | <|body_start_0|>
l = len(matrix)
dp_row = [[0] * l for _ in range(l)]
dp_col = [[0] * l for _ in range(l)]
for i in range(l):
for j in range(l):
if matrix[i][j] == 0:
dp_row[i][j] = dp_row[i][j - 1] + 1
dp_col[i][j] = dp... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findSquare(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
<|body_0|>
def findSquare_2(self, matrix):
"""实现找到最大的边为全0的方阵，看做卷积运算，但是会超时 :type matrix: List[List[int]] :rtype: List[int]"""
<|body_1|>
def findSquare_3(self, m... | stack_v2_sparse_classes_36k_train_009317 | 4,012 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :rtype: List[int]",
"name": "findSquare",
"signature": "def findSquare(self, matrix)"
},
{
"docstring": "实现找到最大的边为全0的方阵，看做卷积运算，但是会超时 :type matrix: List[List[int]] :rtype: List[int]",
"name": "findSquare_2",
"signature": "def findSquare_2(self... | 3 | stack_v2_sparse_classes_30k_train_013715 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findSquare(self, matrix): :type matrix: List[List[int]] :rtype: List[int]
- def findSquare_2(self, matrix): 实现找到最大的边为全0的方阵，看做卷积运算，但是会超时 :type matrix: List[List[int]] :rtype: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findSquare(self, matrix): :type matrix: List[List[int]] :rtype: List[int]
- def findSquare_2(self, matrix): 实现找到最大的边为全0的方阵，看做卷积运算，但是会超时 :type matrix: List[List[int]] :rtype: ... | 64bc823e2a7325f36d09fd282b13da56962d8218 | <|skeleton|>
class Solution:
def findSquare(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
<|body_0|>
def findSquare_2(self, matrix):
"""实现找到最大的边为全0的方阵，看做卷积运算，但是会超时 :type matrix: List[List[int]] :rtype: List[int]"""
<|body_1|>
def findSquare_3(self, m... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findSquare(self, matrix):
""":type matrix: List[List[int]] :rtype: List[int]"""
l = len(matrix)
dp_row = [[0] * l for _ in range(l)]
dp_col = [[0] * l for _ in range(l)]
for i in range(l):
for j in range(l):
if matrix[i][j] == 0... | the_stack_v2_python_sparse | LCCI/0928M最大黑方阵.py | Kittyuzu1207/Leecode | train | 0 | |
6b9562f55aa4a283e035d02af087d05e4496be3c | [
"idx = {element: i for i, element in enumerate(inorder)}\n\ndef build(preorder_left, preorder_right, inorder_left, inorder_right: int) -> TreeNode:\n \"\"\"递归构造树。\"\"\"\n if preorder_left > preorder_right:\n return None\n root = TreeNode(preorder[preorder_left])\n inorder_root = idx[root.val]\n ... | <|body_start_0|>
idx = {element: i for i, element in enumerate(inorder)}
def build(preorder_left, preorder_right, inorder_left, inorder_right: int) -> TreeNode:
"""递归构造树。"""
if preorder_left > preorder_right:
return None
root = TreeNode(preorder[preor... | OfficialSolution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OfficialSolution:
def build_tree(self, preorder: List[int], inorder: List[int]) -> TreeNode:
"""递归。"""
<|body_0|>
def build_tree_2(self, preorder: List[int], inorder: List[int]) -> TreeNode:
"""递归。"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
idx... | stack_v2_sparse_classes_36k_train_009318 | 4,140 | no_license | [
{
"docstring": "递归。",
"name": "build_tree",
"signature": "def build_tree(self, preorder: List[int], inorder: List[int]) -> TreeNode"
},
{
"docstring": "递归。",
"name": "build_tree_2",
"signature": "def build_tree_2(self, preorder: List[int], inorder: List[int]) -> TreeNode"
}
] | 2 | stack_v2_sparse_classes_30k_test_000746 | Implement the Python class `OfficialSolution` described below.
Class description:
Implement the OfficialSolution class.
Method signatures and docstrings:
- def build_tree(self, preorder: List[int], inorder: List[int]) -> TreeNode: 递归。
- def build_tree_2(self, preorder: List[int], inorder: List[int]) -> TreeNode: 递归。 | Implement the Python class `OfficialSolution` described below.
Class description:
Implement the OfficialSolution class.
Method signatures and docstrings:
- def build_tree(self, preorder: List[int], inorder: List[int]) -> TreeNode: 递归。
- def build_tree_2(self, preorder: List[int], inorder: List[int]) -> TreeNode: 递归。
... | 6932d69353b94ec824dd0ddc86a92453f6673232 | <|skeleton|>
class OfficialSolution:
def build_tree(self, preorder: List[int], inorder: List[int]) -> TreeNode:
"""递归。"""
<|body_0|>
def build_tree_2(self, preorder: List[int], inorder: List[int]) -> TreeNode:
"""递归。"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OfficialSolution:
def build_tree(self, preorder: List[int], inorder: List[int]) -> TreeNode:
"""递归。"""
idx = {element: i for i, element in enumerate(inorder)}
def build(preorder_left, preorder_right, inorder_left, inorder_right: int) -> TreeNode:
"""递归构造树。"""
i... | the_stack_v2_python_sparse | 0105_construct-binary-tree-from-preorder-and-inorder-traversal.py | Nigirimeshi/leetcode | train | 0 | |
8cd43400e78b46ffa3a79e55e717553345d60e3e | [
"super(ListGroupConfigTest, cls).setUpClass()\ngc_name = rand_name('t_sg')\ncls.gc_name = gc_name\ncls.gc_max_entities = 10\ncls.gc_metadata = {'gc_meta_key_1': 'gc_meta_value_1', 'gc_meta_key_2': 'gc_meta_value_2'}\ncreate_resp = cls.autoscale_behaviors.create_scaling_group_given(gc_name=cls.gc_name, gc_max_entiti... | <|body_start_0|>
super(ListGroupConfigTest, cls).setUpClass()
gc_name = rand_name('t_sg')
cls.gc_name = gc_name
cls.gc_max_entities = 10
cls.gc_metadata = {'gc_meta_key_1': 'gc_meta_value_1', 'gc_meta_key_2': 'gc_meta_value_2'}
create_resp = cls.autoscale_behaviors.create... | Verify list group config. | ListGroupConfigTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListGroupConfigTest:
"""Verify list group config."""
def setUpClass(cls):
"""Create a scaling group with given data."""
<|body_0|>
def test_list_group_config_response(self):
"""Verify the list group config for response code 200, headers and data"""
<|body... | stack_v2_sparse_classes_36k_train_009319 | 2,842 | permissive | [
{
"docstring": "Create a scaling group with given data.",
"name": "setUpClass",
"signature": "def setUpClass(cls)"
},
{
"docstring": "Verify the list group config for response code 200, headers and data",
"name": "test_list_group_config_response",
"signature": "def test_list_group_config... | 2 | null | Implement the Python class `ListGroupConfigTest` described below.
Class description:
Verify list group config.
Method signatures and docstrings:
- def setUpClass(cls): Create a scaling group with given data.
- def test_list_group_config_response(self): Verify the list group config for response code 200, headers and d... | Implement the Python class `ListGroupConfigTest` described below.
Class description:
Verify list group config.
Method signatures and docstrings:
- def setUpClass(cls): Create a scaling group with given data.
- def test_list_group_config_response(self): Verify the list group config for response code 200, headers and d... | 7199cdd67255fe116dbcbedea660c13453671134 | <|skeleton|>
class ListGroupConfigTest:
"""Verify list group config."""
def setUpClass(cls):
"""Create a scaling group with given data."""
<|body_0|>
def test_list_group_config_response(self):
"""Verify the list group config for response code 200, headers and data"""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ListGroupConfigTest:
"""Verify list group config."""
def setUpClass(cls):
"""Create a scaling group with given data."""
super(ListGroupConfigTest, cls).setUpClass()
gc_name = rand_name('t_sg')
cls.gc_name = gc_name
cls.gc_max_entities = 10
cls.gc_metadata =... | the_stack_v2_python_sparse | autoscale_cloudroast/test_repo/autoscale/functional/scaling_group/test_list_group_config.py | rackerlabs/otter | train | 20 |
bb92a5de04c7aed4e2d94ac334c4478d328a9290 | [
"def dfs(pos: int, isLimit: bool, pre1: int, pre2: int):\n if pos == n:\n if not isLimit:\n yield path\n return\n lower = ords[pos] if isLimit else 97\n for cur in range(lower, 97 + k):\n if cur == pre1 or cur == pre2:\n continue\n path.append(cur)\n ... | <|body_start_0|>
def dfs(pos: int, isLimit: bool, pre1: int, pre2: int):
if pos == n:
if not isLimit:
yield path
return
lower = ords[pos] if isLimit else 97
for cur in range(lower, 97 + k):
if cur == pre1 or ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def smallestBeautifulString1(self, s: str, k: int) -> str:
"""生成器dfs返回路径."""
<|body_0|>
def smallestBeautifulString2(self, s: str, k: int) -> str:
"""!返回bool的dfs返回路径."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def dfs(pos: int, isLimi... | stack_v2_sparse_classes_36k_train_009320 | 1,921 | no_license | [
{
"docstring": "生成器dfs返回路径.",
"name": "smallestBeautifulString1",
"signature": "def smallestBeautifulString1(self, s: str, k: int) -> str"
},
{
"docstring": "!返回bool的dfs返回路径.",
"name": "smallestBeautifulString2",
"signature": "def smallestBeautifulString2(self, s: str, k: int) -> str"
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def smallestBeautifulString1(self, s: str, k: int) -> str: 生成器dfs返回路径.
- def smallestBeautifulString2(self, s: str, k: int) -> str: !返回bool的dfs返回路径. | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def smallestBeautifulString1(self, s: str, k: int) -> str: 生成器dfs返回路径.
- def smallestBeautifulString2(self, s: str, k: int) -> str: !返回bool的dfs返回路径.
<|skeleton|>
class Solution:... | 7e79e26bb8f641868561b186e34c1127ed63c9e0 | <|skeleton|>
class Solution:
def smallestBeautifulString1(self, s: str, k: int) -> str:
"""生成器dfs返回路径."""
<|body_0|>
def smallestBeautifulString2(self, s: str, k: int) -> str:
"""!返回bool的dfs返回路径."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def smallestBeautifulString1(self, s: str, k: int) -> str:
"""生成器dfs返回路径."""
def dfs(pos: int, isLimit: bool, pre1: int, pre2: int):
if pos == n:
if not isLimit:
yield path
return
lower = ords[pos] if isLimit... | the_stack_v2_python_sparse | 7_graph/dfs/yield与返回bool的dfs.py | 981377660LMT/algorithm-study | train | 225 | |
f93d2d62001fe3b076cf0568b99d910a5cfd5f1f | [
"super().__init__(apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight)\nself.alpha = alpha\nself.beta = beta\nself.eps = 1e-08",
"num_classes = yhat.shape[1]\ntarget_one_hot = tensor_one_hot(target, n_classes=num_classes)\nyhat_soft = F.softmax(yhat, dim=1)\nassert target_one_hot.shape == yhat.... | <|body_start_0|>
super().__init__(apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight)
self.alpha = alpha
self.beta = beta
self.eps = 1e-08
<|end_body_0|>
<|body_start_1|>
num_classes = yhat.shape[1]
target_one_hot = tensor_one_hot(target, n_classes=nu... | TverskyLoss | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TverskyLoss:
def __init__(self, alpha: float=0.7, beta: float=0.3, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, class_weights: torch.Tensor=None, **kwargs) -> None:
"""Tversky loss. https://arxiv.org/abs/1706.05721 P... | stack_v2_sparse_classes_36k_train_009321 | 3,889 | permissive | [
{
"docstring": "Tversky loss. https://arxiv.org/abs/1706.05721 Parameters ---------- alpha : float, default=0.7 False positive dice coefficient. beta : float, default=0.3 False negative tanimoto coefficient. apply_sd : bool, default=False If True, Spectral decoupling regularization will be applied to the loss m... | 2 | null | Implement the Python class `TverskyLoss` described below.
Class description:
Implement the TverskyLoss class.
Method signatures and docstrings:
- def __init__(self, alpha: float=0.7, beta: float=0.3, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, c... | Implement the Python class `TverskyLoss` described below.
Class description:
Implement the TverskyLoss class.
Method signatures and docstrings:
- def __init__(self, alpha: float=0.7, beta: float=0.3, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, c... | 7f79405012eb934b419bbdba8de23f35e840ca85 | <|skeleton|>
class TverskyLoss:
def __init__(self, alpha: float=0.7, beta: float=0.3, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, class_weights: torch.Tensor=None, **kwargs) -> None:
"""Tversky loss. https://arxiv.org/abs/1706.05721 P... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TverskyLoss:
def __init__(self, alpha: float=0.7, beta: float=0.3, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, class_weights: torch.Tensor=None, **kwargs) -> None:
"""Tversky loss. https://arxiv.org/abs/1706.05721 Parameters ----... | the_stack_v2_python_sparse | cellseg_models_pytorch/losses/criterions/tversky.py | okunator/cellseg_models.pytorch | train | 43 | |
047e4b34e9fae618860b4f3cd3a0abca271a8852 | [
"if sort:\n self.ls = sorted(ls)\nelse:\n self.ls = ls",
"_verification(max_length)\ntotal = []\nwhile len(line) > 0:\n word = line[-max_length:]\n while not binary_search(self.ls, word):\n if len(word) == 1:\n break\n else:\n word = word[1:]\n total.append(word)... | <|body_start_0|>
if sort:
self.ls = sorted(ls)
else:
self.ls = ls
<|end_body_0|>
<|body_start_1|>
_verification(max_length)
total = []
while len(line) > 0:
word = line[-max_length:]
while not binary_search(self.ls, word):
... | RMMA(Reverse Maximum Matching Algorithms)逆向最大匹配算法 >>> r = RMMA(ls=['我们', '野生', '动物园', '在野'], sort=True) >>> print(r.cut('我们在野生动物园玩', 3)) >>> # ['我们', '在', '野生', '动物园', '玩'] | RMMA | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RMMA:
"""RMMA(Reverse Maximum Matching Algorithms)逆向最大匹配算法 >>> r = RMMA(ls=['我们', '野生', '动物园', '在野'], sort=True) >>> print(r.cut('我们在野生动物园玩', 3)) >>> # ['我们', '在', '野生', '动物园', '玩']"""
def __init__(self, ls, sort=False):
"""逆向最大匹配算法、匹配的词典 :param ls: 词典 :param sort: 是否要排序"""
<... | stack_v2_sparse_classes_36k_train_009322 | 4,714 | permissive | [
{
"docstring": "逆向最大匹配算法、匹配的词典 :param ls: 词典 :param sort: 是否要排序",
"name": "__init__",
"signature": "def __init__(self, ls, sort=False)"
},
{
"docstring": "输入一行字符串，最大按照max_length拆分",
"name": "cut",
"signature": "def cut(self, line, max_length)"
}
] | 2 | null | Implement the Python class `RMMA` described below.
Class description:
RMMA(Reverse Maximum Matching Algorithms)逆向最大匹配算法 >>> r = RMMA(ls=['我们', '野生', '动物园', '在野'], sort=True) >>> print(r.cut('我们在野生动物园玩', 3)) >>> # ['我们', '在', '野生', '动物园', '玩']
Method signatures and docstrings:
- def __init__(self, ls, sort=False): 逆向最... | Implement the Python class `RMMA` described below.
Class description:
RMMA(Reverse Maximum Matching Algorithms)逆向最大匹配算法 >>> r = RMMA(ls=['我们', '野生', '动物园', '在野'], sort=True) >>> print(r.cut('我们在野生动物园玩', 3)) >>> # ['我们', '在', '野生', '动物园', '玩']
Method signatures and docstrings:
- def __init__(self, ls, sort=False): 逆向最... | 5a584cbf12d644b6c4fb13167d8841a383afbbac | <|skeleton|>
class RMMA:
"""RMMA(Reverse Maximum Matching Algorithms)逆向最大匹配算法 >>> r = RMMA(ls=['我们', '野生', '动物园', '在野'], sort=True) >>> print(r.cut('我们在野生动物园玩', 3)) >>> # ['我们', '在', '野生', '动物园', '玩']"""
def __init__(self, ls, sort=False):
"""逆向最大匹配算法、匹配的词典 :param ls: 词典 :param sort: 是否要排序"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RMMA:
"""RMMA(Reverse Maximum Matching Algorithms)逆向最大匹配算法 >>> r = RMMA(ls=['我们', '野生', '动物园', '在野'], sort=True) >>> print(r.cut('我们在野生动物园玩', 3)) >>> # ['我们', '在', '野生', '动物园', '玩']"""
def __init__(self, ls, sort=False):
"""逆向最大匹配算法、匹配的词典 :param ls: 词典 :param sort: 是否要排序"""
if sort:
... | the_stack_v2_python_sparse | jtyoui/algorithm/MatchingAlgorithm.py | liangxioa/Jtyoui | train | 1 |
f9b176409c00bb8ca3ffbd7bacc04d90a5ffcee8 | [
"super(Powerup, self).__init__()\nself.image = powerup_img\nself.rect = self.image.get_rect()\nself.rect.center = pos\nself.is_targeted = False\nself.boost = 2",
"enemy.max_health *= self.boost\nenemy.health = enemy.max_health\nenemy.speed *= self.boost * 0.7\nenemy.width = int(enemy.width * 1.5)\nenemy.height = ... | <|body_start_0|>
super(Powerup, self).__init__()
self.image = powerup_img
self.rect = self.image.get_rect()
self.rect.center = pos
self.is_targeted = False
self.boost = 2
<|end_body_0|>
<|body_start_1|>
enemy.max_health *= self.boost
enemy.health = enemy.... | Powerup | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Powerup:
def __init__(self, pos):
""":param pos: position."""
<|body_0|>
def power_up(self, enemy):
"""Increases attributes :return: none"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(Powerup, self).__init__()
self.image = powerup_im... | stack_v2_sparse_classes_36k_train_009323 | 1,094 | no_license | [
{
"docstring": ":param pos: position.",
"name": "__init__",
"signature": "def __init__(self, pos)"
},
{
"docstring": "Increases attributes :return: none",
"name": "power_up",
"signature": "def power_up(self, enemy)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017940 | Implement the Python class `Powerup` described below.
Class description:
Implement the Powerup class.
Method signatures and docstrings:
- def __init__(self, pos): :param pos: position.
- def power_up(self, enemy): Increases attributes :return: none | Implement the Python class `Powerup` described below.
Class description:
Implement the Powerup class.
Method signatures and docstrings:
- def __init__(self, pos): :param pos: position.
- def power_up(self, enemy): Increases attributes :return: none
<|skeleton|>
class Powerup:
def __init__(self, pos):
""... | 4f31b24565ac817ae95c5ca4ccb247a9ae18044e | <|skeleton|>
class Powerup:
def __init__(self, pos):
""":param pos: position."""
<|body_0|>
def power_up(self, enemy):
"""Increases attributes :return: none"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Powerup:
def __init__(self, pos):
""":param pos: position."""
super(Powerup, self).__init__()
self.image = powerup_img
self.rect = self.image.get_rect()
self.rect.center = pos
self.is_targeted = False
self.boost = 2
def power_up(self, enemy):
... | the_stack_v2_python_sparse | enemies/powerboost.py | marikb/Tower-Defense | train | 0 | |
d6028db352bbaef9708738b26faeb7faec8a9523 | [
"argument_group.add_argument('--analysis', metavar='PLUGIN_LIST', dest='analysis_plugins', default='', action='store', type=str, help='A comma separated list of analysis plugin names to be loaded or \"--analysis list\" to see a list of available plugins.')\narguments = sys.argv[1:]\nargument_index = 0\nif '--analys... | <|body_start_0|>
argument_group.add_argument('--analysis', metavar='PLUGIN_LIST', dest='analysis_plugins', default='', action='store', type=str, help='A comma separated list of analysis plugin names to be loaded or "--analysis list" to see a list of available plugins.')
arguments = sys.argv[1:]
... | Analysis plugins CLI arguments helper. | AnalysisPluginsArgumentsHelper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalysisPluginsArgumentsHelper:
"""Analysis plugins CLI arguments helper."""
def AddArguments(cls, argument_group):
"""Adds command line arguments to an argument group. This function takes an argument parser or an argument group object and adds to it all the command line arguments th... | stack_v2_sparse_classes_36k_train_009324 | 2,915 | permissive | [
{
"docstring": "Adds command line arguments to an argument group. This function takes an argument parser or an argument group object and adds to it all the command line arguments this helper supports. Args: argument_group (argparse._ArgumentGroup|argparse.ArgumentParser): argparse group.",
"name": "AddArgum... | 2 | null | Implement the Python class `AnalysisPluginsArgumentsHelper` described below.
Class description:
Analysis plugins CLI arguments helper.
Method signatures and docstrings:
- def AddArguments(cls, argument_group): Adds command line arguments to an argument group. This function takes an argument parser or an argument grou... | Implement the Python class `AnalysisPluginsArgumentsHelper` described below.
Class description:
Analysis plugins CLI arguments helper.
Method signatures and docstrings:
- def AddArguments(cls, argument_group): Adds command line arguments to an argument group. This function takes an argument parser or an argument grou... | d6022f8cfebfddf2d08ab2d300a41b61f3349933 | <|skeleton|>
class AnalysisPluginsArgumentsHelper:
"""Analysis plugins CLI arguments helper."""
def AddArguments(cls, argument_group):
"""Adds command line arguments to an argument group. This function takes an argument parser or an argument group object and adds to it all the command line arguments th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalysisPluginsArgumentsHelper:
"""Analysis plugins CLI arguments helper."""
def AddArguments(cls, argument_group):
"""Adds command line arguments to an argument group. This function takes an argument parser or an argument group object and adds to it all the command line arguments this helper sup... | the_stack_v2_python_sparse | plaso/cli/helpers/analysis_plugins.py | log2timeline/plaso | train | 1,506 |
b39acc61a426fa92e15cd14488f9e111d19ac575 | [
"self.i = i\nself.neighbours = neighbours\nself.weights = weights\nself.dimension = dimension\nself.draw = draw_function",
"xyz = []\nfor i in range(self.dimension):\n xyz.append(index % length)\n index = index // length\nreturn xyz",
"for i in range(len(xyz)):\n if xyz[i] - nxyz[i] == length - 1:\n ... | <|body_start_0|>
self.i = i
self.neighbours = neighbours
self.weights = weights
self.dimension = dimension
self.draw = draw_function
<|end_body_0|>
<|body_start_1|>
xyz = []
for i in range(self.dimension):
xyz.append(index % length)
index ... | Site in a nD lattice | Site | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Site:
"""Site in a nD lattice"""
def __init__(self, i, neighbours, weights, dimension=3, draw_function=None):
"""Create neighbours and links"""
<|body_0|>
def convert_to_xyz(self, index, length):
"""Convert index to [x, y, z] coordinates :index: Int :length: Int ... | stack_v2_sparse_classes_36k_train_009325 | 3,698 | no_license | [
{
"docstring": "Create neighbours and links",
"name": "__init__",
"signature": "def __init__(self, i, neighbours, weights, dimension=3, draw_function=None)"
},
{
"docstring": "Convert index to [x, y, z] coordinates :index: Int :length: Int - system size :returns: 1xd array of ints",
"name": ... | 5 | stack_v2_sparse_classes_30k_train_016320 | Implement the Python class `Site` described below.
Class description:
Site in a nD lattice
Method signatures and docstrings:
- def __init__(self, i, neighbours, weights, dimension=3, draw_function=None): Create neighbours and links
- def convert_to_xyz(self, index, length): Convert index to [x, y, z] coordinates :ind... | Implement the Python class `Site` described below.
Class description:
Site in a nD lattice
Method signatures and docstrings:
- def __init__(self, i, neighbours, weights, dimension=3, draw_function=None): Create neighbours and links
- def convert_to_xyz(self, index, length): Convert index to [x, y, z] coordinates :ind... | 56f41e405226e69512067ad2e55409ff644b25d9 | <|skeleton|>
class Site:
"""Site in a nD lattice"""
def __init__(self, i, neighbours, weights, dimension=3, draw_function=None):
"""Create neighbours and links"""
<|body_0|>
def convert_to_xyz(self, index, length):
"""Convert index to [x, y, z] coordinates :index: Int :length: Int ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Site:
"""Site in a nD lattice"""
def __init__(self, i, neighbours, weights, dimension=3, draw_function=None):
"""Create neighbours and links"""
self.i = i
self.neighbours = neighbours
self.weights = weights
self.dimension = dimension
self.draw = draw_functi... | the_stack_v2_python_sparse | cpp/pyplot/helpers/site.py | srydell/thesis | train | 0 |
200f93291966cf9ba8d037ad0ca377c1088525ab | [
"self.card_game = []\nfor i in range(2, 15):\n for j in range(0, 4):\n self.card_game.append((i, j))\n j += 1\n i += 1",
"name_dict = {11: 'Jack', 12: 'Lady', 13: 'King', 14: 'Ace'}\nname = name_dict.get(card[0], card[0])\ncolor_dict = {0: 'Spades', 1: 'Clover', 2: 'Diamond', 3: 'Heart'}\ncolo... | <|body_start_0|>
self.card_game = []
for i in range(2, 15):
for j in range(0, 4):
self.card_game.append((i, j))
j += 1
i += 1
<|end_body_0|>
<|body_start_1|>
name_dict = {11: 'Jack', 12: 'Lady', 13: 'King', 14: 'Ace'}
name = name_d... | Definition of the Cardgame class with the following methods: constructor, card_name, shuffle, choose_card, choose_card_randomly | Cardgame | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cardgame:
"""Definition of the Cardgame class with the following methods: constructor, card_name, shuffle, choose_card, choose_card_randomly"""
def __init__(self):
"""Creates a list of 52 tuples, each tuples represent a card of the game (height & color)"""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_009326 | 4,269 | no_license | [
{
"docstring": "Creates a list of 52 tuples, each tuples represent a card of the game (height & color)",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Receive a tuple descriptor (for instance (14, 3)) & displays the card name: its heigth and color ('Ace of Spades' here... | 5 | null | Implement the Python class `Cardgame` described below.
Class description:
Definition of the Cardgame class with the following methods: constructor, card_name, shuffle, choose_card, choose_card_randomly
Method signatures and docstrings:
- def __init__(self): Creates a list of 52 tuples, each tuples represent a card of... | Implement the Python class `Cardgame` described below.
Class description:
Definition of the Cardgame class with the following methods: constructor, card_name, shuffle, choose_card, choose_card_randomly
Method signatures and docstrings:
- def __init__(self): Creates a list of 52 tuples, each tuples represent a card of... | e858542dd20a7454db462854ba736c4dfca2b267 | <|skeleton|>
class Cardgame:
"""Definition of the Cardgame class with the following methods: constructor, card_name, shuffle, choose_card, choose_card_randomly"""
def __init__(self):
"""Creates a list of 52 tuples, each tuples represent a card of the game (height & color)"""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cardgame:
"""Definition of the Cardgame class with the following methods: constructor, card_name, shuffle, choose_card, choose_card_randomly"""
def __init__(self):
"""Creates a list of 52 tuples, each tuples represent a card of the game (height & color)"""
self.card_game = []
for ... | the_stack_v2_python_sparse | 12.07.card_game.py | obrunet/Apprendre-a-programmer-Python3 | train | 0 |
761d059bc51ee29c9b235411e3002479972c7202 | [
"adm = ProjectAdministration()\nproj = adm.get_project_by_id(project_id)\nreturn proj",
"adm = ProjectAdministration()\nproj = adm.get_project_by_id(project_id)\nif proj is not None:\n adm.delete_project(proj)\n return ('gelöscht', 200)\nelse:\n return ('', 500)"
] | <|body_start_0|>
adm = ProjectAdministration()
proj = adm.get_project_by_id(project_id)
return proj
<|end_body_0|>
<|body_start_1|>
adm = ProjectAdministration()
proj = adm.get_project_by_id(project_id)
if proj is not None:
adm.delete_project(proj)
... | ProjectOperations | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProjectOperations:
def get(self, project_id):
"""Auslesen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird."""
<|body_0|>
def delete(self, project_id):
"""Löschen eines bestimmten Project-Objektes, welches durch die project_id ... | stack_v2_sparse_classes_36k_train_009327 | 44,493 | no_license | [
{
"docstring": "Auslesen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird.",
"name": "get",
"signature": "def get(self, project_id)"
},
{
"docstring": "Löschen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird.",
... | 2 | stack_v2_sparse_classes_30k_train_005264 | Implement the Python class `ProjectOperations` described below.
Class description:
Implement the ProjectOperations class.
Method signatures and docstrings:
- def get(self, project_id): Auslesen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird.
- def delete(self, project_id): Lö... | Implement the Python class `ProjectOperations` described below.
Class description:
Implement the ProjectOperations class.
Method signatures and docstrings:
- def get(self, project_id): Auslesen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird.
- def delete(self, project_id): Lö... | 4b2826225525ae855e15e1174f5cf90466097021 | <|skeleton|>
class ProjectOperations:
def get(self, project_id):
"""Auslesen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird."""
<|body_0|>
def delete(self, project_id):
"""Löschen eines bestimmten Project-Objektes, welches durch die project_id ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProjectOperations:
def get(self, project_id):
"""Auslesen eines bestimmten Project-Objektes, welches durch die project_id in dem URI bestimmt wird."""
adm = ProjectAdministration()
proj = adm.get_project_by_id(project_id)
return proj
def delete(self, project_id):
"... | the_stack_v2_python_sparse | src/main.py | KieserChristian/SW_Praktikum_Gruppe1 | train | 0 | |
22abae91d16e40cd4140cba7d96af09ef99d678e | [
"self.session = session\nself.auto_commit = auto_commit\nself.readonly = readonly",
"if self.readonly:\n stub_out_flush_operation(self.session)\nreturn self.session",
"try:\n if traceback is None and self.auto_commit:\n self.session.commit()\nfinally:\n if not self.readonly:\n self.sessio... | <|body_start_0|>
self.session = session
self.auto_commit = auto_commit
self.readonly = readonly
<|end_body_0|>
<|body_start_1|>
if self.readonly:
stub_out_flush_operation(self.session)
return self.session
<|end_body_1|>
<|body_start_2|>
try:
if t... | A scoped session is automatically released. | ScopedSession | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScopedSession:
"""A scoped session is automatically released."""
def __init__(self, session, auto_commit=False, readonly=False):
"""Constructor. Args: session (object): Database session to use scope. auto_commit (bool): Set to true, of commit should automatically happen upon close. r... | stack_v2_sparse_classes_36k_train_009328 | 4,807 | permissive | [
{
"docstring": "Constructor. Args: session (object): Database session to use scope. auto_commit (bool): Set to true, of commit should automatically happen upon close. readonly (bool): whether or not the session is read only.",
"name": "__init__",
"signature": "def __init__(self, session, auto_commit=Fal... | 3 | stack_v2_sparse_classes_30k_train_016878 | Implement the Python class `ScopedSession` described below.
Class description:
A scoped session is automatically released.
Method signatures and docstrings:
- def __init__(self, session, auto_commit=False, readonly=False): Constructor. Args: session (object): Database session to use scope. auto_commit (bool): Set to ... | Implement the Python class `ScopedSession` described below.
Class description:
A scoped session is automatically released.
Method signatures and docstrings:
- def __init__(self, session, auto_commit=False, readonly=False): Constructor. Args: session (object): Database session to use scope. auto_commit (bool): Set to ... | d4421afa50a17ed47cbebe942044ebab3720e0f5 | <|skeleton|>
class ScopedSession:
"""A scoped session is automatically released."""
def __init__(self, session, auto_commit=False, readonly=False):
"""Constructor. Args: session (object): Database session to use scope. auto_commit (bool): Set to true, of commit should automatically happen upon close. r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScopedSession:
"""A scoped session is automatically released."""
def __init__(self, session, auto_commit=False, readonly=False):
"""Constructor. Args: session (object): Database session to use scope. auto_commit (bool): Set to true, of commit should automatically happen upon close. readonly (bool... | the_stack_v2_python_sparse | google/cloud/forseti/services/db.py | kevensen/forseti-security | train | 1 |
ae2097b26203423e585052b988fdade692125acd | [
"usePrivateKey = True\nfor prop in self.required_props1:\n if prop not in list(self.properties.keys()):\n usePrivateKey = False\n break\nusePassword = True\nfor prop in self.required_props2:\n if prop not in list(self.properties.keys()):\n usePassword = False\n break\nif not usePri... | <|body_start_0|>
usePrivateKey = True
for prop in self.required_props1:
if prop not in list(self.properties.keys()):
usePrivateKey = False
break
usePassword = True
for prop in self.required_props2:
if prop not in list(self.propertie... | Class for sending and deleting files and directories via SSH. | SecureSender | [
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-public-domain-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SecureSender:
"""Class for sending and deleting files and directories via SSH."""
def connect(self):
"""Initiate an ssh connection with properties passed to constructor. :returns: Instance of the paramiko SSHClient class."""
<|body_0|>
def send(self):
"""Send any... | stack_v2_sparse_classes_36k_train_009329 | 3,806 | permissive | [
{
"docstring": "Initiate an ssh connection with properties passed to constructor. :returns: Instance of the paramiko SSHClient class.",
"name": "connect",
"signature": "def connect(self)"
},
{
"docstring": "Send any files or folders that have been passed to constructor. :returns: Number of files... | 3 | stack_v2_sparse_classes_30k_train_003870 | Implement the Python class `SecureSender` described below.
Class description:
Class for sending and deleting files and directories via SSH.
Method signatures and docstrings:
- def connect(self): Initiate an ssh connection with properties passed to constructor. :returns: Instance of the paramiko SSHClient class.
- def... | Implement the Python class `SecureSender` described below.
Class description:
Class for sending and deleting files and directories via SSH.
Method signatures and docstrings:
- def connect(self): Initiate an ssh connection with properties passed to constructor. :returns: Instance of the paramiko SSHClient class.
- def... | a55f488bbe19c45c6375c7102160dbc0a353d661 | <|skeleton|>
class SecureSender:
"""Class for sending and deleting files and directories via SSH."""
def connect(self):
"""Initiate an ssh connection with properties passed to constructor. :returns: Instance of the paramiko SSHClient class."""
<|body_0|>
def send(self):
"""Send any... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SecureSender:
"""Class for sending and deleting files and directories via SSH."""
def connect(self):
"""Initiate an ssh connection with properties passed to constructor. :returns: Instance of the paramiko SSHClient class."""
usePrivateKey = True
for prop in self.required_props1:
... | the_stack_v2_python_sparse | shakemap/transfer/securesender.py | kallstadt-usgs/shakemap | train | 0 |
2b7f590ab015531e8afe0c03e8b51a1a512ba500 | [
"self.shared_key = shared_key\nself.session_key = session_key\nself.crypto = MessageCrypto(session_key)\nself.auth = MessageAuthenticator(shared_key)",
"signed = self.auth.sign(plaintext)\nencrypted = self.crypto.encrypt(signed)\nreturn self.auth.sign(encrypted)",
"if self.auth.verify(ciphertext):\n decrypte... | <|body_start_0|>
self.shared_key = shared_key
self.session_key = session_key
self.crypto = MessageCrypto(session_key)
self.auth = MessageAuthenticator(shared_key)
<|end_body_0|>
<|body_start_1|>
signed = self.auth.sign(plaintext)
encrypted = self.crypto.encrypt(signed)
... | MessageCryptoSystem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MessageCryptoSystem:
def __init__(self, session_key, shared_key):
"""" Initialize the Message cryptosystem with a 128 byte session key (16 Chars) and a shared key"""
<|body_0|>
def wrap_message(self, plaintext):
"""Prepares signed, encrypted, and signed message"""
... | stack_v2_sparse_classes_36k_train_009330 | 1,027 | no_license | [
{
"docstring": "\" Initialize the Message cryptosystem with a 128 byte session key (16 Chars) and a shared key",
"name": "__init__",
"signature": "def __init__(self, session_key, shared_key)"
},
{
"docstring": "Prepares signed, encrypted, and signed message",
"name": "wrap_message",
"sig... | 3 | stack_v2_sparse_classes_30k_train_020042 | Implement the Python class `MessageCryptoSystem` described below.
Class description:
Implement the MessageCryptoSystem class.
Method signatures and docstrings:
- def __init__(self, session_key, shared_key): " Initialize the Message cryptosystem with a 128 byte session key (16 Chars) and a shared key
- def wrap_messag... | Implement the Python class `MessageCryptoSystem` described below.
Class description:
Implement the MessageCryptoSystem class.
Method signatures and docstrings:
- def __init__(self, session_key, shared_key): " Initialize the Message cryptosystem with a 128 byte session key (16 Chars) and a shared key
- def wrap_messag... | ca31e06e94f4325045f7066f78a4af57b00acab1 | <|skeleton|>
class MessageCryptoSystem:
def __init__(self, session_key, shared_key):
"""" Initialize the Message cryptosystem with a 128 byte session key (16 Chars) and a shared key"""
<|body_0|>
def wrap_message(self, plaintext):
"""Prepares signed, encrypted, and signed message"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MessageCryptoSystem:
def __init__(self, session_key, shared_key):
"""" Initialize the Message cryptosystem with a 128 byte session key (16 Chars) and a shared key"""
self.shared_key = shared_key
self.session_key = session_key
self.crypto = MessageCrypto(session_key)
sel... | the_stack_v2_python_sparse | assignment3/MessageCryptoSystem.py | tsiemens/eece-412-group | train | 0 | |
761d059bc51ee29c9b235411e3002479972c7202 | [
"adm = ProjectAdministration()\nstud = adm.get_student_by_id(student_id)\nreturn stud",
"adm = ProjectAdministration()\nstud = adm.get_student_by_id(student_id)\nif stud is not None:\n adm.delete_student(stud)\n return ('gelöscht', 200)\nelse:\n return ('There was no student object with this id', 500)"
] | <|body_start_0|>
adm = ProjectAdministration()
stud = adm.get_student_by_id(student_id)
return stud
<|end_body_0|>
<|body_start_1|>
adm = ProjectAdministration()
stud = adm.get_student_by_id(student_id)
if stud is not None:
adm.delete_student(stud)
... | StudentOperations | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StudentOperations:
def get(self, student_id):
"""Auslesen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird."""
<|body_0|>
def delete(self, student_id):
"""Löschen eines bestimmten Student-Objekts, welches durch die student_id in... | stack_v2_sparse_classes_36k_train_009331 | 44,493 | no_license | [
{
"docstring": "Auslesen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird.",
"name": "get",
"signature": "def get(self, student_id)"
},
{
"docstring": "Löschen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird.",
"n... | 2 | stack_v2_sparse_classes_30k_train_008220 | Implement the Python class `StudentOperations` described below.
Class description:
Implement the StudentOperations class.
Method signatures and docstrings:
- def get(self, student_id): Auslesen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird.
- def delete(self, student_id): Lös... | Implement the Python class `StudentOperations` described below.
Class description:
Implement the StudentOperations class.
Method signatures and docstrings:
- def get(self, student_id): Auslesen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird.
- def delete(self, student_id): Lös... | 4b2826225525ae855e15e1174f5cf90466097021 | <|skeleton|>
class StudentOperations:
def get(self, student_id):
"""Auslesen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird."""
<|body_0|>
def delete(self, student_id):
"""Löschen eines bestimmten Student-Objekts, welches durch die student_id in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StudentOperations:
def get(self, student_id):
"""Auslesen eines bestimmten Student-Objekts, welches durch die student_id in dem URI bestimmt wird."""
adm = ProjectAdministration()
stud = adm.get_student_by_id(student_id)
return stud
def delete(self, student_id):
""... | the_stack_v2_python_sparse | src/main.py | KieserChristian/SW_Praktikum_Gruppe1 | train | 0 | |
488451854a3c0df8eaf4c34fbf79defc064719fc | [
"self.scorer = UniEvaluator(model_name_or_path='MingZhong/unieval-fact' if model_name_or_path == '' else model_name_or_path, max_length=max_length, device=device, cache_dir=cache_dir)\nself.task = 'fact'\nself.dim = 'consistency'",
"n_data = len(data)\neval_scores = [{} for _ in range(n_data)]\nsrc_list, output_l... | <|body_start_0|>
self.scorer = UniEvaluator(model_name_or_path='MingZhong/unieval-fact' if model_name_or_path == '' else model_name_or_path, max_length=max_length, device=device, cache_dir=cache_dir)
self.task = 'fact'
self.dim = 'consistency'
<|end_body_0|>
<|body_start_1|>
n_data = le... | FactEvaluator | [
"BSD-3-Clause",
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FactEvaluator:
def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None):
"""Set up evaluator for factual consistency detection"""
<|body_0|>
def evaluate(self, data, category):
"""Get the factual consistency score (only 1 dimension for... | stack_v2_sparse_classes_36k_train_009332 | 14,573 | permissive | [
{
"docstring": "Set up evaluator for factual consistency detection",
"name": "__init__",
"signature": "def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None)"
},
{
"docstring": "Get the factual consistency score (only 1 dimension for this task) category: The cat... | 2 | stack_v2_sparse_classes_30k_train_001530 | Implement the Python class `FactEvaluator` described below.
Class description:
Implement the FactEvaluator class.
Method signatures and docstrings:
- def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): Set up evaluator for factual consistency detection
- def evaluate(self, data, ... | Implement the Python class `FactEvaluator` described below.
Class description:
Implement the FactEvaluator class.
Method signatures and docstrings:
- def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): Set up evaluator for factual consistency detection
- def evaluate(self, data, ... | c7b60f75470f067d1342705708810a660eabd684 | <|skeleton|>
class FactEvaluator:
def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None):
"""Set up evaluator for factual consistency detection"""
<|body_0|>
def evaluate(self, data, category):
"""Get the factual consistency score (only 1 dimension for... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FactEvaluator:
def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None):
"""Set up evaluator for factual consistency detection"""
self.scorer = UniEvaluator(model_name_or_path='MingZhong/unieval-fact' if model_name_or_path == '' else model_name_or_path, max_leng... | the_stack_v2_python_sparse | applications/Chat/evaluate/unieval/evaluator.py | hpcaitech/ColossalAI | train | 32,044 | |
a1d398944dcb5b864a09fa55eaa411cafc18b9dc | [
"self.root = root\nself.proc = Popen(cmd, stdout=PIPE, stderr=STDOUT)\nself.root.createfilehandler(self.proc.stdout, tk.READABLE, self.read_output)\nself._var = tk.StringVar()\ntk.Label(root, textvariable=self._var).pack()\ntk.Button(root, text='Stop subprocess', command=self.stop).pack()",
"data = os.read(pipe.f... | <|body_start_0|>
self.root = root
self.proc = Popen(cmd, stdout=PIPE, stderr=STDOUT)
self.root.createfilehandler(self.proc.stdout, tk.READABLE, self.read_output)
self._var = tk.StringVar()
tk.Label(root, textvariable=self._var).pack()
tk.Button(root, text='Stop subprocess... | ShowProcessOutputDemo | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShowProcessOutputDemo:
def __init__(self, root):
"""Start subprocess, make GUI widgets."""
<|body_0|>
def read_output(self, pipe, mask):
"""Read subprocess' output, pass it to the GUI."""
<|body_1|>
def stop(self, stopping=[]):
"""Stop subprocess... | stack_v2_sparse_classes_36k_train_009333 | 2,778 | permissive | [
{
"docstring": "Start subprocess, make GUI widgets.",
"name": "__init__",
"signature": "def __init__(self, root)"
},
{
"docstring": "Read subprocess' output, pass it to the GUI.",
"name": "read_output",
"signature": "def read_output(self, pipe, mask)"
},
{
"docstring": "Stop subp... | 3 | null | Implement the Python class `ShowProcessOutputDemo` described below.
Class description:
Implement the ShowProcessOutputDemo class.
Method signatures and docstrings:
- def __init__(self, root): Start subprocess, make GUI widgets.
- def read_output(self, pipe, mask): Read subprocess' output, pass it to the GUI.
- def st... | Implement the Python class `ShowProcessOutputDemo` described below.
Class description:
Implement the ShowProcessOutputDemo class.
Method signatures and docstrings:
- def __init__(self, root): Start subprocess, make GUI widgets.
- def read_output(self, pipe, mask): Read subprocess' output, pass it to the GUI.
- def st... | 665d39a2bd82543d5196555f0801ef8fd4a3ee48 | <|skeleton|>
class ShowProcessOutputDemo:
def __init__(self, root):
"""Start subprocess, make GUI widgets."""
<|body_0|>
def read_output(self, pipe, mask):
"""Read subprocess' output, pass it to the GUI."""
<|body_1|>
def stop(self, stopping=[]):
"""Stop subprocess... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShowProcessOutputDemo:
def __init__(self, root):
"""Start subprocess, make GUI widgets."""
self.root = root
self.proc = Popen(cmd, stdout=PIPE, stderr=STDOUT)
self.root.createfilehandler(self.proc.stdout, tk.READABLE, self.read_output)
self._var = tk.StringVar()
... | the_stack_v2_python_sparse | all-gists/9294978/snippet.py | gistable/gistable | train | 76 | |
0affaf83be401729d5f1d3b236cee14e140eb565 | [
"project_dir = Path(__file__).resolve().parents[2]\nself._trainset_path = str(project_dir) + '/data/raw/train_set.csv'\nself._testset_path = str(project_dir) + '/data/raw/test_set.csv'\nself._trainset = None\nself._testset = None",
"if self._trainset is None:\n self._trainset = self.read_dataset(self._trainset... | <|body_start_0|>
project_dir = Path(__file__).resolve().parents[2]
self._trainset_path = str(project_dir) + '/data/raw/train_set.csv'
self._testset_path = str(project_dir) + '/data/raw/test_set.csv'
self._trainset = None
self._testset = None
<|end_body_0|>
<|body_start_1|>
... | Utility class to easily load the datasets for training, development and testing. | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Utility class to easily load the datasets for training, development and testing."""
def __init__(self, in_notebook=False):
"""Defines the basic properties of the dataset reader. Args: language: The language of the dataset. dataset_name: The name of the dataset (all files ... | stack_v2_sparse_classes_36k_train_009334 | 2,265 | no_license | [
{
"docstring": "Defines the basic properties of the dataset reader. Args: language: The language of the dataset. dataset_name: The name of the dataset (all files should have it).",
"name": "__init__",
"signature": "def __init__(self, in_notebook=False)"
},
{
"docstring": "list. Getter method for... | 4 | stack_v2_sparse_classes_30k_train_002151 | Implement the Python class `Dataset` described below.
Class description:
Utility class to easily load the datasets for training, development and testing.
Method signatures and docstrings:
- def __init__(self, in_notebook=False): Defines the basic properties of the dataset reader. Args: language: The language of the d... | Implement the Python class `Dataset` described below.
Class description:
Utility class to easily load the datasets for training, development and testing.
Method signatures and docstrings:
- def __init__(self, in_notebook=False): Defines the basic properties of the dataset reader. Args: language: The language of the d... | 437d952d3c27d44319b5834d6a467e08d9ccad4c | <|skeleton|>
class Dataset:
"""Utility class to easily load the datasets for training, development and testing."""
def __init__(self, in_notebook=False):
"""Defines the basic properties of the dataset reader. Args: language: The language of the dataset. dataset_name: The name of the dataset (all files ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dataset:
"""Utility class to easily load the datasets for training, development and testing."""
def __init__(self, in_notebook=False):
"""Defines the basic properties of the dataset reader. Args: language: The language of the dataset. dataset_name: The name of the dataset (all files should have i... | the_stack_v2_python_sparse | src/data/dataset.py | willferreira/Brexit-Corpus-Stance-Classification-Project | train | 0 |
8e554176a3e8736c450e0e4eab0d85346423d7b3 | [
"logger.info('Overriding class: Corpus -> SentenceCorpus.')\nsuper(SentenceCorpus, self).__init__(min_frequency=min_frequency)\nif not tokens:\n sentences = loader.load_txt(from_file).splitlines()\n pipe = self._create_tokenizer(corpus_type)\n self.tokens = [pipe(sentence) for sentence in sentences]\nelse:... | <|body_start_0|>
logger.info('Overriding class: Corpus -> SentenceCorpus.')
super(SentenceCorpus, self).__init__(min_frequency=min_frequency)
if not tokens:
sentences = loader.load_txt(from_file).splitlines()
pipe = self._create_tokenizer(corpus_type)
self.tok... | A SentenceCorpus class is used to defined the first step of the workflow. It serves to load the raw sentences, pre-process them and create their tokens and vocabulary. | SentenceCorpus | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SentenceCorpus:
"""A SentenceCorpus class is used to defined the first step of the workflow. It serves to load the raw sentences, pre-process them and create their tokens and vocabulary."""
def __init__(self, tokens: Optional[List[str]]=None, from_file: Optional[str]=None, corpus_type: Optio... | stack_v2_sparse_classes_36k_train_009335 | 3,757 | permissive | [
{
"docstring": "Initialization method. Args: tokens: A list of tokens. from_file: An input file to load the sentences. corpus_type: The desired type to tokenize the sentences. Should be `char` or `word`. min_frequency: Minimum frequency of individual tokens. max_pad_length: Maximum length to pad the tokens. sos... | 4 | stack_v2_sparse_classes_30k_train_021443 | Implement the Python class `SentenceCorpus` described below.
Class description:
A SentenceCorpus class is used to defined the first step of the workflow. It serves to load the raw sentences, pre-process them and create their tokens and vocabulary.
Method signatures and docstrings:
- def __init__(self, tokens: Optiona... | Implement the Python class `SentenceCorpus` described below.
Class description:
A SentenceCorpus class is used to defined the first step of the workflow. It serves to load the raw sentences, pre-process them and create their tokens and vocabulary.
Method signatures and docstrings:
- def __init__(self, tokens: Optiona... | 4b7e7c1b1a304a5b37b21a972c50668e60b7bd7f | <|skeleton|>
class SentenceCorpus:
"""A SentenceCorpus class is used to defined the first step of the workflow. It serves to load the raw sentences, pre-process them and create their tokens and vocabulary."""
def __init__(self, tokens: Optional[List[str]]=None, from_file: Optional[str]=None, corpus_type: Optio... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SentenceCorpus:
"""A SentenceCorpus class is used to defined the first step of the workflow. It serves to load the raw sentences, pre-process them and create their tokens and vocabulary."""
def __init__(self, tokens: Optional[List[str]]=None, from_file: Optional[str]=None, corpus_type: Optional[str]='cha... | the_stack_v2_python_sparse | nalp/corpus/sentence.py | gugarosa/nalp | train | 25 |
b4aa4e115fe9d3e316c1a8679e0f0cb22ba82cf0 | [
"if self.listener.listener_kind == ListenerKind.TEMPERATURE:\n if not self.coordinator.data.user_preferences:\n return None\n if self.coordinator.data.user_preferences.celsius_enabled:\n return UnitOfTemperature.CELSIUS\n return UnitOfTemperature.FAHRENHEIT\nreturn None",
"if not self.liste... | <|body_start_0|>
if self.listener.listener_kind == ListenerKind.TEMPERATURE:
if not self.coordinator.data.user_preferences:
return None
if self.coordinator.data.user_preferences.celsius_enabled:
return UnitOfTemperature.CELSIUS
return UnitOfTem... | Define a Notion sensor. | NotionSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NotionSensor:
"""Define a Notion sensor."""
def native_unit_of_measurement(self) -> str | None:
"""Return the unit of measurement of the sensor."""
<|body_0|>
def native_value(self) -> str | None:
"""Return the value reported by the sensor."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_009336 | 3,068 | permissive | [
{
"docstring": "Return the unit of measurement of the sensor.",
"name": "native_unit_of_measurement",
"signature": "def native_unit_of_measurement(self) -> str | None"
},
{
"docstring": "Return the value reported by the sensor.",
"name": "native_value",
"signature": "def native_value(sel... | 2 | null | Implement the Python class `NotionSensor` described below.
Class description:
Define a Notion sensor.
Method signatures and docstrings:
- def native_unit_of_measurement(self) -> str | None: Return the unit of measurement of the sensor.
- def native_value(self) -> str | None: Return the value reported by the sensor. | Implement the Python class `NotionSensor` described below.
Class description:
Define a Notion sensor.
Method signatures and docstrings:
- def native_unit_of_measurement(self) -> str | None: Return the unit of measurement of the sensor.
- def native_value(self) -> str | None: Return the value reported by the sensor.
... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class NotionSensor:
"""Define a Notion sensor."""
def native_unit_of_measurement(self) -> str | None:
"""Return the unit of measurement of the sensor."""
<|body_0|>
def native_value(self) -> str | None:
"""Return the value reported by the sensor."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NotionSensor:
"""Define a Notion sensor."""
def native_unit_of_measurement(self) -> str | None:
"""Return the unit of measurement of the sensor."""
if self.listener.listener_kind == ListenerKind.TEMPERATURE:
if not self.coordinator.data.user_preferences:
return... | the_stack_v2_python_sparse | homeassistant/components/notion/sensor.py | home-assistant/core | train | 35,501 |
c38fd6ce2c3d7cb7414613c5b16789f4fe2d8031 | [
"if len(nums) < 2:\n return len(nums)\n_nums, cnt, result = (sorted(nums), 1, 0)\nfor i in xrange(1, len(_nums)):\n if _nums[i] - _nums[i - 1] == 1:\n cnt += 1\n else:\n result = max(cnt, result)\n cnt = 1\nreturn max(result, cnt)",
"nums = set(nums)\nif len(nums) < 2:\n return le... | <|body_start_0|>
if len(nums) < 2:
return len(nums)
_nums, cnt, result = (sorted(nums), 1, 0)
for i in xrange(1, len(_nums)):
if _nums[i] - _nums[i - 1] == 1:
cnt += 1
else:
result = max(cnt, result)
cnt = 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestConsecutive1(self, nums):
""":type nums: List[int] :rtype: int 明显超时(NlogN)"""
<|body_0|>
def longestConsecutive2(self, nums):
""":type nums: List[int] :rtype: int unionfind(N)"""
<|body_1|>
def longestConsecutive(self, nums):
... | stack_v2_sparse_classes_36k_train_009337 | 2,676 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int 明显超时(NlogN)",
"name": "longestConsecutive1",
"signature": "def longestConsecutive1(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int unionfind(N)",
"name": "longestConsecutive2",
"signature": "def longestConsecutive2(self,... | 3 | stack_v2_sparse_classes_30k_train_017973 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestConsecutive1(self, nums): :type nums: List[int] :rtype: int 明显超时(NlogN)
- def longestConsecutive2(self, nums): :type nums: List[int] :rtype: int unionfind(N)
- def lon... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestConsecutive1(self, nums): :type nums: List[int] :rtype: int 明显超时(NlogN)
- def longestConsecutive2(self, nums): :type nums: List[int] :rtype: int unionfind(N)
- def lon... | 9687f8e743a8b6396fff192f22b5256d1025f86b | <|skeleton|>
class Solution:
def longestConsecutive1(self, nums):
""":type nums: List[int] :rtype: int 明显超时(NlogN)"""
<|body_0|>
def longestConsecutive2(self, nums):
""":type nums: List[int] :rtype: int unionfind(N)"""
<|body_1|>
def longestConsecutive(self, nums):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestConsecutive1(self, nums):
""":type nums: List[int] :rtype: int 明显超时(NlogN)"""
if len(nums) < 2:
return len(nums)
_nums, cnt, result = (sorted(nums), 1, 0)
for i in xrange(1, len(_nums)):
if _nums[i] - _nums[i - 1] == 1:
... | the_stack_v2_python_sparse | 2017/array/Longest_Consecutive_Sequence.py | buhuipao/LeetCode | train | 5 | |
ed411b57209fc233dd0fa1f6c7e03911fba9efef | [
"if request.args.get('pass_id'):\n data = db.session.query(Staff).get(request.args.get('pass_id'))\n return marshal(data, staff_struct) if data else 'No such staff!'\nelif not request.args:\n data = db.session.query(Staff).all()\n return marshal(data, staff_struct)\nelse:\n return 'Unknown query'",
... | <|body_start_0|>
if request.args.get('pass_id'):
data = db.session.query(Staff).get(request.args.get('pass_id'))
return marshal(data, staff_struct) if data else 'No such staff!'
elif not request.args:
data = db.session.query(Staff).all()
return marshal(dat... | StaffRes | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StaffRes:
def get(self):
"""Get particular staff by pass_id if '?pass_id=' specified or get all staff if not. :return:"""
<|body_0|>
def post(self):
"""Add new staff to DB. :return:"""
<|body_1|>
def delete(self):
"""Delete staff by pass_id :retu... | stack_v2_sparse_classes_36k_train_009338 | 2,573 | no_license | [
{
"docstring": "Get particular staff by pass_id if '?pass_id=' specified or get all staff if not. :return:",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Add new staff to DB. :return:",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Delete staf... | 4 | stack_v2_sparse_classes_30k_train_019637 | Implement the Python class `StaffRes` described below.
Class description:
Implement the StaffRes class.
Method signatures and docstrings:
- def get(self): Get particular staff by pass_id if '?pass_id=' specified or get all staff if not. :return:
- def post(self): Add new staff to DB. :return:
- def delete(self): Dele... | Implement the Python class `StaffRes` described below.
Class description:
Implement the StaffRes class.
Method signatures and docstrings:
- def get(self): Get particular staff by pass_id if '?pass_id=' specified or get all staff if not. :return:
- def post(self): Add new staff to DB. :return:
- def delete(self): Dele... | d3759f773f9abc0e917e75c174c28feb7d4a0692 | <|skeleton|>
class StaffRes:
def get(self):
"""Get particular staff by pass_id if '?pass_id=' specified or get all staff if not. :return:"""
<|body_0|>
def post(self):
"""Add new staff to DB. :return:"""
<|body_1|>
def delete(self):
"""Delete staff by pass_id :retu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StaffRes:
def get(self):
"""Get particular staff by pass_id if '?pass_id=' specified or get all staff if not. :return:"""
if request.args.get('pass_id'):
data = db.session.query(Staff).get(request.args.get('pass_id'))
return marshal(data, staff_struct) if data else 'No ... | the_stack_v2_python_sparse | rest_alchemy/staff/routes.py | serhiihoriaiev/common | train | 0 | |
1dc26503f6554d6c0d719cfdff42f34119dcb0a6 | [
"VapiInterface.__init__(self, config, _CsrStub)\nself._VAPI_OPERATION_IDS = {}\nself._VAPI_OPERATION_IDS.update({'create_task': 'create$task'})\nself._VAPI_OPERATION_IDS.update({'get_task': 'get$task'})",
"task_id = self._invoke('create$task', {'cluster': cluster, 'provider': provider})\ntask_svc = Tasks(self._co... | <|body_start_0|>
VapiInterface.__init__(self, config, _CsrStub)
self._VAPI_OPERATION_IDS = {}
self._VAPI_OPERATION_IDS.update({'create_task': 'create$task'})
self._VAPI_OPERATION_IDS.update({'get_task': 'get$task'})
<|end_body_0|>
<|body_start_1|>
task_id = self._invoke('create$... | The ``Csr`` interface provides methods to create a certificate signing request(CSR). This class was added in vSphere API 7.0.0. | Csr | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Csr:
"""The ``Csr`` interface provides methods to create a certificate signing request(CSR). This class was added in vSphere API 7.0.0."""
def __init__(self, config):
""":type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for cre... | stack_v2_sparse_classes_36k_train_009339 | 11,504 | permissive | [
{
"docstring": ":type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stub.",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "Generate a certificate signing request (CSR) for the client certifi... | 3 | null | Implement the Python class `Csr` described below.
Class description:
The ``Csr`` interface provides methods to create a certificate signing request(CSR). This class was added in vSphere API 7.0.0.
Method signatures and docstrings:
- def __init__(self, config): :type config: :class:`vmware.vapi.bindings.stub.StubConfi... | Implement the Python class `Csr` described below.
Class description:
The ``Csr`` interface provides methods to create a certificate signing request(CSR). This class was added in vSphere API 7.0.0.
Method signatures and docstrings:
- def __init__(self, config): :type config: :class:`vmware.vapi.bindings.stub.StubConfi... | c07e1be98615201139b26c28db3aa584c4254b66 | <|skeleton|>
class Csr:
"""The ``Csr`` interface provides methods to create a certificate signing request(CSR). This class was added in vSphere API 7.0.0."""
def __init__(self, config):
""":type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for cre... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Csr:
"""The ``Csr`` interface provides methods to create a certificate signing request(CSR). This class was added in vSphere API 7.0.0."""
def __init__(self, config):
""":type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stu... | the_stack_v2_python_sparse | com/vmware/vcenter/trusted_infrastructure/trust_authority_clusters/kms/providers/client_certificate_client.py | adammillerio/vsphere-automation-sdk-python | train | 0 |
5234a2e733c2b76f6f965f4182d2c06044eb665e | [
"super(InvertedResidualSE, self).__init__()\nself.identity = stride == 1 and inp == oup\nself.ir_block = Sequential(ops.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), ops.BatchNorm2d(hidden_dim, momentum=momentum), ops.Hswish() if use_hs else ops.Relu(inplace=True), ops.Conv2d(hidden_dim, hidden_dim, kernel_size, st... | <|body_start_0|>
super(InvertedResidualSE, self).__init__()
self.identity = stride == 1 and inp == oup
self.ir_block = Sequential(ops.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), ops.BatchNorm2d(hidden_dim, momentum=momentum), ops.Hswish() if use_hs else ops.Relu(inplace=True), ops.Conv2d(hidde... | This is the class of InvertedResidual with SELayer for MobileNetV3. | InvertedResidualSE | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InvertedResidualSE:
"""This is the class of InvertedResidual with SELayer for MobileNetV3."""
def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1):
"""Init InvertedResidualSE."""
<|body_0|>
def __call__(self, x):
... | stack_v2_sparse_classes_36k_train_009340 | 9,288 | permissive | [
{
"docstring": "Init InvertedResidualSE.",
"name": "__init__",
"signature": "def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1)"
},
{
"docstring": "Forward compute of InvertedResidualSE.",
"name": "__call__",
"signature": "def __call__... | 2 | null | Implement the Python class `InvertedResidualSE` described below.
Class description:
This is the class of InvertedResidual with SELayer for MobileNetV3.
Method signatures and docstrings:
- def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1): Init InvertedResidualSE.
... | Implement the Python class `InvertedResidualSE` described below.
Class description:
This is the class of InvertedResidual with SELayer for MobileNetV3.
Method signatures and docstrings:
- def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1): Init InvertedResidualSE.
... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class InvertedResidualSE:
"""This is the class of InvertedResidual with SELayer for MobileNetV3."""
def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1):
"""Init InvertedResidualSE."""
<|body_0|>
def __call__(self, x):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InvertedResidualSE:
"""This is the class of InvertedResidual with SELayer for MobileNetV3."""
def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1):
"""Init InvertedResidualSE."""
super(InvertedResidualSE, self).__init__()
self.ide... | the_stack_v2_python_sparse | zeus/networks/mobilenetv3.py | huawei-noah/xingtian | train | 308 |
e125a49c19f46b57ecb8d88813ebb87df024a35a | [
"if request.version != 'v1':\n return Response(status=status.HTTP_505_HTTP_VERSION_NOT_SUPPORTED)\nserializer = AccountNotRequiredSerializer(data={}, context={'request': request})\nserializer.is_valid(raise_exception=True)\nreturn Response(data=serializer.data, status=status.HTTP_200_OK)",
"if request.version ... | <|body_start_0|>
if request.version != 'v1':
return Response(status=status.HTTP_505_HTTP_VERSION_NOT_SUPPORTED)
serializer = AccountNotRequiredSerializer(data={}, context={'request': request})
serializer.is_valid(raise_exception=True)
return Response(data=serializer.data, sta... | Account view. | AccountView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountView:
"""Account view."""
def get(self, request, *args, **kwargs):
"""GET request."""
<|body_0|>
def patch(self, request, *args, **kwargs):
"""PATCH request."""
<|body_1|>
def delete(self, request, *args, **kwargs):
"""DELETE request."... | stack_v2_sparse_classes_36k_train_009341 | 6,798 | no_license | [
{
"docstring": "GET request.",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "PATCH request.",
"name": "patch",
"signature": "def patch(self, request, *args, **kwargs)"
},
{
"docstring": "DELETE request.",
"name": "delete",
"signa... | 3 | stack_v2_sparse_classes_30k_val_001180 | Implement the Python class `AccountView` described below.
Class description:
Account view.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): GET request.
- def patch(self, request, *args, **kwargs): PATCH request.
- def delete(self, request, *args, **kwargs): DELETE request. | Implement the Python class `AccountView` described below.
Class description:
Account view.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): GET request.
- def patch(self, request, *args, **kwargs): PATCH request.
- def delete(self, request, *args, **kwargs): DELETE request.
<|skeleton|>
c... | cd8767b5eeaef3a09d77c936781b4126fd8591de | <|skeleton|>
class AccountView:
"""Account view."""
def get(self, request, *args, **kwargs):
"""GET request."""
<|body_0|>
def patch(self, request, *args, **kwargs):
"""PATCH request."""
<|body_1|>
def delete(self, request, *args, **kwargs):
"""DELETE request."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccountView:
"""Account view."""
def get(self, request, *args, **kwargs):
"""GET request."""
if request.version != 'v1':
return Response(status=status.HTTP_505_HTTP_VERSION_NOT_SUPPORTED)
serializer = AccountNotRequiredSerializer(data={}, context={'request': request})
... | the_stack_v2_python_sparse | api/auths/views.py | ignite7/backproject | train | 0 |
a26659093be29bf01d959ebb83c840f44e33725a | [
"super().__init__()\nself.conditional = conditional\nif self.conditional:\n layer_sizes[0] += num_labels\nself.MLP = nn.Sequential()\nfor i, (in_size, out_size) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n self.MLP.add_module(name='L{:d}'.format(i), module=nn.Linear(in_size, out_size))\n self.ML... | <|body_start_0|>
super().__init__()
self.conditional = conditional
if self.conditional:
layer_sizes[0] += num_labels
self.MLP = nn.Sequential()
for i, (in_size, out_size) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
self.MLP.add_module(name='L{:d}... | Encoder class for CVAE | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""Encoder class for CVAE"""
def __init__(self, layer_sizes, latent_size, conditional, num_labels):
"""Initialization"""
<|body_0|>
def forward(self, x, c=None):
"""Forward process"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super()... | stack_v2_sparse_classes_36k_train_009342 | 4,298 | no_license | [
{
"docstring": "Initialization",
"name": "__init__",
"signature": "def __init__(self, layer_sizes, latent_size, conditional, num_labels)"
},
{
"docstring": "Forward process",
"name": "forward",
"signature": "def forward(self, x, c=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011442 | Implement the Python class `Encoder` described below.
Class description:
Encoder class for CVAE
Method signatures and docstrings:
- def __init__(self, layer_sizes, latent_size, conditional, num_labels): Initialization
- def forward(self, x, c=None): Forward process | Implement the Python class `Encoder` described below.
Class description:
Encoder class for CVAE
Method signatures and docstrings:
- def __init__(self, layer_sizes, latent_size, conditional, num_labels): Initialization
- def forward(self, x, c=None): Forward process
<|skeleton|>
class Encoder:
"""Encoder class fo... | 21c0bf459388bd616a64afc1a34441123b1f41fe | <|skeleton|>
class Encoder:
"""Encoder class for CVAE"""
def __init__(self, layer_sizes, latent_size, conditional, num_labels):
"""Initialization"""
<|body_0|>
def forward(self, x, c=None):
"""Forward process"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Encoder:
"""Encoder class for CVAE"""
def __init__(self, layer_sizes, latent_size, conditional, num_labels):
"""Initialization"""
super().__init__()
self.conditional = conditional
if self.conditional:
layer_sizes[0] += num_labels
self.MLP = nn.Sequentia... | the_stack_v2_python_sparse | Reconstruction/models/CVAE.py | CHOcho-quan/CS385ML | train | 1 |
c4234a0419a44ee60309840f3ab593a506829a8c | [
"self.k = k\nself.heap = []\nfor n in nums:\n if self.k > 0:\n heappush(self.heap, n)\n self.k -= 1\n else:\n heappushpop(self.heap, n)",
"if self.k > 0:\n heappush(self.heap, val)\n self.k -= 1\nelse:\n heappushpop(self.heap, val)\nreturn self.heap[0]"
] | <|body_start_0|>
self.k = k
self.heap = []
for n in nums:
if self.k > 0:
heappush(self.heap, n)
self.k -= 1
else:
heappushpop(self.heap, n)
<|end_body_0|>
<|body_start_1|>
if self.k > 0:
heappush(self.he... | KthLargest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.k = k
self.heap = []
for n in nums:
... | stack_v2_sparse_classes_36k_train_009343 | 3,175 | no_license | [
{
"docstring": ":type k: int :type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, k, nums)"
},
{
"docstring": ":type val: int :rtype: int",
"name": "add",
"signature": "def add(self, val)"
}
] | 2 | null | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int | Implement the Python class `KthLargest` described below.
Class description:
Implement the KthLargest class.
Method signatures and docstrings:
- def __init__(self, k, nums): :type k: int :type nums: List[int]
- def add(self, val): :type val: int :rtype: int
<|skeleton|>
class KthLargest:
def __init__(self, k, nu... | b1764cd62e1c8cb062869992d9eaa8b2d2fdf9c2 | <|skeleton|>
class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
<|body_0|>
def add(self, val):
""":type val: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KthLargest:
def __init__(self, k, nums):
""":type k: int :type nums: List[int]"""
self.k = k
self.heap = []
for n in nums:
if self.k > 0:
heappush(self.heap, n)
self.k -= 1
else:
heappushpop(self.heap, n)
... | the_stack_v2_python_sparse | leetcode/heap/easy/703. Kth Largest Element in a Stream.py | Hk4Fun/algorithm_offer | train | 1 | |
0dc767eede6d702c292036ba8f68b6b17fd85c1a | [
"uid = request._request.uid\ns = LabelCreateSerializer(data=request.data)\ns.is_valid()\nif s.errors:\n return self.error(errorcode.MSG_INVALID_DATA, errorcode.INVALID_DATA)\ntry:\n instance = s.create(s.validated_data)\nexcept:\n return self.error(errorcode.MSG_DB_ERROR, errorcode.DB_ERROR)\ns = LabelCrea... | <|body_start_0|>
uid = request._request.uid
s = LabelCreateSerializer(data=request.data)
s.is_valid()
if s.errors:
return self.error(errorcode.MSG_INVALID_DATA, errorcode.INVALID_DATA)
try:
instance = s.create(s.validated_data)
except:
... | LabelView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabelView:
def post(self, request):
"""新建标签"""
<|body_0|>
def get(self, request):
"""获取所有顶级标签"""
<|body_1|>
def delete(self, request):
"""删除标签，同时删除它与其他标签、文章、问答等的关系"""
<|body_2|>
def put(self, request):
"""修改标签"""
<|bo... | stack_v2_sparse_classes_36k_train_009344 | 9,306 | no_license | [
{
"docstring": "新建标签",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "获取所有顶级标签",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "删除标签，同时删除它与其他标签、文章、问答等的关系",
"name": "delete",
"signature": "def delete(self, request)"
},
... | 4 | stack_v2_sparse_classes_30k_train_008477 | Implement the Python class `LabelView` described below.
Class description:
Implement the LabelView class.
Method signatures and docstrings:
- def post(self, request): 新建标签
- def get(self, request): 获取所有顶级标签
- def delete(self, request): 删除标签，同时删除它与其他标签、文章、问答等的关系
- def put(self, request): 修改标签 | Implement the Python class `LabelView` described below.
Class description:
Implement the LabelView class.
Method signatures and docstrings:
- def post(self, request): 新建标签
- def get(self, request): 获取所有顶级标签
- def delete(self, request): 删除标签，同时删除它与其他标签、文章、问答等的关系
- def put(self, request): 修改标签
<|skeleton|>
class Label... | 6a68fb207f43e5ed65299cc08535b35d5e934ead | <|skeleton|>
class LabelView:
def post(self, request):
"""新建标签"""
<|body_0|>
def get(self, request):
"""获取所有顶级标签"""
<|body_1|>
def delete(self, request):
"""删除标签，同时删除它与其他标签、文章、问答等的关系"""
<|body_2|>
def put(self, request):
"""修改标签"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LabelView:
def post(self, request):
"""新建标签"""
uid = request._request.uid
s = LabelCreateSerializer(data=request.data)
s.is_valid()
if s.errors:
return self.error(errorcode.MSG_INVALID_DATA, errorcode.INVALID_DATA)
try:
instance = s.creat... | the_stack_v2_python_sparse | apps/labels/views.py | Slowhalfframe/fanyijiang-API | train | 0 | |
d932241abdb58e727d4f45ecceb80d2e968bbe02 | [
"try:\n wish_word = Wish_Word.objects.get(pk=pk)\n serializer = WishWordsSerializer(wish_word, context={'request': request})\n return Response(serializer.data)\nexcept Exception as ex:\n return HttpResponseServerError(ex)",
"word_values = self.request.query_params.get('word_values')\nif word_values:\n... | <|body_start_0|>
try:
wish_word = Wish_Word.objects.get(pk=pk)
serializer = WishWordsSerializer(wish_word, context={'request': request})
return Response(serializer.data)
except Exception as ex:
return HttpResponseServerError(ex)
<|end_body_0|>
<|body_star... | Wish_Words | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Wish_Words:
def retrieve(self, request, pk=None):
"""Handle GET requests for single word Returns: Response -- JSON serialized word instance"""
<|body_0|>
def list(self, request):
"""Handle GET requests to words resource Returns: Response -- JSON serialized list of wo... | stack_v2_sparse_classes_36k_train_009345 | 2,724 | no_license | [
{
"docstring": "Handle GET requests for single word Returns: Response -- JSON serialized word instance",
"name": "retrieve",
"signature": "def retrieve(self, request, pk=None)"
},
{
"docstring": "Handle GET requests to words resource Returns: Response -- JSON serialized list of words",
"name... | 3 | stack_v2_sparse_classes_30k_train_014675 | Implement the Python class `Wish_Words` described below.
Class description:
Implement the Wish_Words class.
Method signatures and docstrings:
- def retrieve(self, request, pk=None): Handle GET requests for single word Returns: Response -- JSON serialized word instance
- def list(self, request): Handle GET requests to... | Implement the Python class `Wish_Words` described below.
Class description:
Implement the Wish_Words class.
Method signatures and docstrings:
- def retrieve(self, request, pk=None): Handle GET requests for single word Returns: Response -- JSON serialized word instance
- def list(self, request): Handle GET requests to... | 582048dafa7e354fffdc0478ec68088e8bbf42b1 | <|skeleton|>
class Wish_Words:
def retrieve(self, request, pk=None):
"""Handle GET requests for single word Returns: Response -- JSON serialized word instance"""
<|body_0|>
def list(self, request):
"""Handle GET requests to words resource Returns: Response -- JSON serialized list of wo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Wish_Words:
def retrieve(self, request, pk=None):
"""Handle GET requests for single word Returns: Response -- JSON serialized word instance"""
try:
wish_word = Wish_Word.objects.get(pk=pk)
serializer = WishWordsSerializer(wish_word, context={'request': request})
... | the_stack_v2_python_sparse | genieioapp/views/wish_words.py | cherkesky/GenieIO | train | 1 | |
279f0192a9e44244a6febbfa5127f5e402a48c00 | [
"self.folder_id = folder_id\nself.public_folder_item_id_list = public_folder_item_id_list\nself.restore_entire_folder = restore_entire_folder",
"if dictionary is None:\n return None\nfolder_id = dictionary.get('folderId')\npublic_folder_item_id_list = dictionary.get('publicFolderItemIdList')\nrestore_entire_fo... | <|body_start_0|>
self.folder_id = folder_id
self.public_folder_item_id_list = public_folder_item_id_list
self.restore_entire_folder = restore_entire_folder
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
folder_id = dictionary.get('folderId')
... | Implementation of the 'PublicFolder' model. Specifies the O365 PublicFolder details. Attributes: folder_id (string): Specifies the unique ID of the folder. public_folder_item_id_list (list of string): Specifies the PublicFolder items within the folder to be restored incase the user wishes not to restore the entire fold... | PublicFolder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PublicFolder:
"""Implementation of the 'PublicFolder' model. Specifies the O365 PublicFolder details. Attributes: folder_id (string): Specifies the unique ID of the folder. public_folder_item_id_list (list of string): Specifies the PublicFolder items within the folder to be restored incase the us... | stack_v2_sparse_classes_36k_train_009346 | 2,185 | permissive | [
{
"docstring": "Constructor for the PublicFolder class",
"name": "__init__",
"signature": "def __init__(self, folder_id=None, public_folder_item_id_list=None, restore_entire_folder=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dict... | 2 | stack_v2_sparse_classes_30k_train_003895 | Implement the Python class `PublicFolder` described below.
Class description:
Implementation of the 'PublicFolder' model. Specifies the O365 PublicFolder details. Attributes: folder_id (string): Specifies the unique ID of the folder. public_folder_item_id_list (list of string): Specifies the PublicFolder items within ... | Implement the Python class `PublicFolder` described below.
Class description:
Implementation of the 'PublicFolder' model. Specifies the O365 PublicFolder details. Attributes: folder_id (string): Specifies the unique ID of the folder. public_folder_item_id_list (list of string): Specifies the PublicFolder items within ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class PublicFolder:
"""Implementation of the 'PublicFolder' model. Specifies the O365 PublicFolder details. Attributes: folder_id (string): Specifies the unique ID of the folder. public_folder_item_id_list (list of string): Specifies the PublicFolder items within the folder to be restored incase the us... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PublicFolder:
"""Implementation of the 'PublicFolder' model. Specifies the O365 PublicFolder details. Attributes: folder_id (string): Specifies the unique ID of the folder. public_folder_item_id_list (list of string): Specifies the PublicFolder items within the folder to be restored incase the user wishes not... | the_stack_v2_python_sparse | cohesity_management_sdk/models/public_folder.py | cohesity/management-sdk-python | train | 24 |
c7fec254c6fa3a109f6b2b603bca9e8848ce56b5 | [
"super().__init__()\nself._img_shape = img_shape\nself.model = torch.nn.Sequential(torch.nn.Linear(latent_dim, 512), torch.nn.LeakyReLU(0.2, inplace=True), torch.nn.Linear(512, 512), torch.nn.BatchNorm1d(512), torch.nn.LeakyReLU(0.2, inplace=True), torch.nn.Linear(512, int(reduce(mul, img_shape, 1))), torch.nn.Tanh... | <|body_start_0|>
super().__init__()
self._img_shape = img_shape
self.model = torch.nn.Sequential(torch.nn.Linear(latent_dim, 512), torch.nn.LeakyReLU(0.2, inplace=True), torch.nn.Linear(512, 512), torch.nn.BatchNorm1d(512), torch.nn.LeakyReLU(0.2, inplace=True), torch.nn.Linear(512, int(reduce(m... | Decodes an already encoded image signal | Decoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""Decodes an already encoded image signal"""
def __init__(self, latent_dim, img_shape):
"""Parameters ---------- latent_dim : int the size of the latent dimension img_shape : tuple the shape of the input image"""
<|body_0|>
def forward(self, z):
"""Feed... | stack_v2_sparse_classes_36k_train_009347 | 5,204 | permissive | [
{
"docstring": "Parameters ---------- latent_dim : int the size of the latent dimension img_shape : tuple the shape of the input image",
"name": "__init__",
"signature": "def __init__(self, latent_dim, img_shape)"
},
{
"docstring": "Feeds an encoded signal through the network for decoding Parame... | 2 | stack_v2_sparse_classes_30k_train_015564 | Implement the Python class `Decoder` described below.
Class description:
Decodes an already encoded image signal
Method signatures and docstrings:
- def __init__(self, latent_dim, img_shape): Parameters ---------- latent_dim : int the size of the latent dimension img_shape : tuple the shape of the input image
- def f... | Implement the Python class `Decoder` described below.
Class description:
Decodes an already encoded image signal
Method signatures and docstrings:
- def __init__(self, latent_dim, img_shape): Parameters ---------- latent_dim : int the size of the latent dimension img_shape : tuple the shape of the input image
- def f... | 1078f5030b8aac2bf022daf5fa14d66f74c3c893 | <|skeleton|>
class Decoder:
"""Decodes an already encoded image signal"""
def __init__(self, latent_dim, img_shape):
"""Parameters ---------- latent_dim : int the size of the latent dimension img_shape : tuple the shape of the input image"""
<|body_0|>
def forward(self, z):
"""Feed... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder:
"""Decodes an already encoded image signal"""
def __init__(self, latent_dim, img_shape):
"""Parameters ---------- latent_dim : int the size of the latent dimension img_shape : tuple the shape of the input image"""
super().__init__()
self._img_shape = img_shape
sel... | the_stack_v2_python_sparse | dlutils/models/gans/adversarial_autoencoder/models.py | justusschock/dl-utils | train | 15 |
6d7a2eb11cf2f14d3092f0d9a0d0d4afd66740c3 | [
"super().__init__()\npadding = int((kSize - 1) / 2)\nself.conv = nn.Conv2d(nIn, nOut, (kSize, kSize), stride=stride, padding=(padding, padding), bias=False)\nself.bn = nn.BatchNorm2d(nOut, eps=0.001)",
"output = self.conv(input)\noutput = self.bn(output)\nreturn output"
] | <|body_start_0|>
super().__init__()
padding = int((kSize - 1) / 2)
self.conv = nn.Conv2d(nIn, nOut, (kSize, kSize), stride=stride, padding=(padding, padding), bias=False)
self.bn = nn.BatchNorm2d(nOut, eps=0.001)
<|end_body_0|>
<|body_start_1|>
output = self.conv(input)
... | This class groups the convolution and batch normalization | CB | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CB:
"""This class groups the convolution and batch normalization"""
def __init__(self, nIn, nOut, kSize, stride=1):
""":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: optinal stide for down-sampling"""
<|bod... | stack_v2_sparse_classes_36k_train_009348 | 15,567 | permissive | [
{
"docstring": ":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: optinal stide for down-sampling",
"name": "__init__",
"signature": "def __init__(self, nIn, nOut, kSize, stride=1)"
},
{
"docstring": ":param input: input feature ... | 2 | null | Implement the Python class `CB` described below.
Class description:
This class groups the convolution and batch normalization
Method signatures and docstrings:
- def __init__(self, nIn, nOut, kSize, stride=1): :param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param... | Implement the Python class `CB` described below.
Class description:
This class groups the convolution and batch normalization
Method signatures and docstrings:
- def __init__(self, nIn, nOut, kSize, stride=1): :param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param... | f2993d3ce73a2f7ddba05da3891defb08547d504 | <|skeleton|>
class CB:
"""This class groups the convolution and batch normalization"""
def __init__(self, nIn, nOut, kSize, stride=1):
""":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: optinal stide for down-sampling"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CB:
"""This class groups the convolution and batch normalization"""
def __init__(self, nIn, nOut, kSize, stride=1):
""":param nIn: number of input channels :param nOut: number of output channels :param kSize: kernel size :param stride: optinal stide for down-sampling"""
super().__init__()... | the_stack_v2_python_sparse | pytorch/pytorchcv/models/others/oth_espnet.py | osmr/imgclsmob | train | 3,017 |
03dbe21f557017fe3259d0a4dbc62745d4e68736 | [
"if not root:\n return str([])\nqueue = deque()\nresult = []\nqueue.append(root)\nwhile queue:\n r = queue.popleft()\n if not r:\n result.append(None)\n continue\n result.append(r.val)\n queue.append(r.left)\n queue.append(r.right)\nreturn str(result)",
"data = deque(eval(data))\ni... | <|body_start_0|>
if not root:
return str([])
queue = deque()
result = []
queue.append(root)
while queue:
r = queue.popleft()
if not r:
result.append(None)
continue
result.append(r.val)
que... | 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_009349 | 1,773 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_015957 | 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:... | 8e338ee7a5c9f124e897491d6a1f4bcd1d1a6270 | <|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 str([])
queue = deque()
result = []
queue.append(root)
while queue:
r = queue.popleft()
if not r:
... | the_stack_v2_python_sparse | src/297.二叉树的序列化与反序列化.py | hysapphire/leetcode-python | train | 0 | |
63ac9559230b26bd798ba9dc965582b176cb69ef | [
"nums = sorted(nums)\nL = len(nums)\nr = float('inf')\nif L < 3:\n return sum(nums)\nfor i in range(L - 2):\n j = i + 1\n k = L - 1\n while j < k:\n Sum = nums[i] + nums[j] + nums[k]\n if abs(r - target) > abs(Sum - target):\n r = Sum\n if Sum == target:\n retu... | <|body_start_0|>
nums = sorted(nums)
L = len(nums)
r = float('inf')
if L < 3:
return sum(nums)
for i in range(L - 2):
j = i + 1
k = L - 1
while j < k:
Sum = nums[i] + nums[j] + nums[k]
if abs(r - targ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def threeSumClosest(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int Runtime: 132 ms, faster than 34.04% of Python online submissions for 3Sum Closest. Memory Usage: 11.9 MB, less than 9.68% of Python online submissions for 3Sum Closest."""
<|... | stack_v2_sparse_classes_36k_train_009350 | 3,378 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: int Runtime: 132 ms, faster than 34.04% of Python online submissions for 3Sum Closest. Memory Usage: 11.9 MB, less than 9.68% of Python online submissions for 3Sum Closest.",
"name": "threeSumClosest",
"signature": "def threeSumClosest(self... | 3 | stack_v2_sparse_classes_30k_train_011580 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSumClosest(self, nums, target): :type nums: List[int] :type target: int :rtype: int Runtime: 132 ms, faster than 34.04% of Python online submissions for 3Sum Closest. Me... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSumClosest(self, nums, target): :type nums: List[int] :type target: int :rtype: int Runtime: 132 ms, faster than 34.04% of Python online submissions for 3Sum Closest. Me... | bad06f681d8d3f2b841cb3c8a969198b8643f864 | <|skeleton|>
class Solution:
def threeSumClosest(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int Runtime: 132 ms, faster than 34.04% of Python online submissions for 3Sum Closest. Memory Usage: 11.9 MB, less than 9.68% of Python online submissions for 3Sum Closest."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def threeSumClosest(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int Runtime: 132 ms, faster than 34.04% of Python online submissions for 3Sum Closest. Memory Usage: 11.9 MB, less than 9.68% of Python online submissions for 3Sum Closest."""
nums = sorted(nu... | the_stack_v2_python_sparse | 16_3sum_closest.py | subicWang/leetcode_aotang | train | 0 | |
441a13a3359174644eab0deca73e2743880ee24e | [
"self._caffe = kwargs.pop('caffe')\nkwargs.setdefault('label_suffix', '')\nsuper(CompanyForm, self).__init__(*args, **kwargs)\nself.fields['name'].label = 'Nazwa'",
"name = self.cleaned_data['name']\nquery = Company.objects.filter(name=name, caffe=self._caffe)\nif query.exists():\n raise ValidationError(_('Naz... | <|body_start_0|>
self._caffe = kwargs.pop('caffe')
kwargs.setdefault('label_suffix', '')
super(CompanyForm, self).__init__(*args, **kwargs)
self.fields['name'].label = 'Nazwa'
<|end_body_0|>
<|body_start_1|>
name = self.cleaned_data['name']
query = Company.objects.filter... | Responsible for creating a Company. | CompanyForm | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompanyForm:
"""Responsible for creating a Company."""
def __init__(self, *args, **kwargs):
"""Initialize all Company's fields."""
<|body_0|>
def clean_name(self):
"""Check name field."""
<|body_1|>
def save(self, commit=True):
"""Override of... | stack_v2_sparse_classes_36k_train_009351 | 4,623 | permissive | [
{
"docstring": "Initialize all Company's fields.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Check name field.",
"name": "clean_name",
"signature": "def clean_name(self)"
},
{
"docstring": "Override of save method, to add Caffe rela... | 3 | stack_v2_sparse_classes_30k_train_010051 | Implement the Python class `CompanyForm` described below.
Class description:
Responsible for creating a Company.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize all Company's fields.
- def clean_name(self): Check name field.
- def save(self, commit=True): Override of save method, t... | Implement the Python class `CompanyForm` described below.
Class description:
Responsible for creating a Company.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize all Company's fields.
- def clean_name(self): Check name field.
- def save(self, commit=True): Override of save method, t... | cdb7f5edb29255c7e874eaa6231621063210a8b0 | <|skeleton|>
class CompanyForm:
"""Responsible for creating a Company."""
def __init__(self, *args, **kwargs):
"""Initialize all Company's fields."""
<|body_0|>
def clean_name(self):
"""Check name field."""
<|body_1|>
def save(self, commit=True):
"""Override of... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompanyForm:
"""Responsible for creating a Company."""
def __init__(self, *args, **kwargs):
"""Initialize all Company's fields."""
self._caffe = kwargs.pop('caffe')
kwargs.setdefault('label_suffix', '')
super(CompanyForm, self).__init__(*args, **kwargs)
self.fields... | the_stack_v2_python_sparse | caffe/cash/forms.py | VirrageS/io-kawiarnie | train | 3 |
4806eadc7770d7b6c0a4479286b27987541156dc | [
"left, right, width, res = (0, len(height) - 1, len(height) - 1, 0)\nfor w in range(width, 0, -1):\n if height[left] < height[right]:\n res, left = (max(res, height[left] * w), left + 1)\n else:\n res, right = (max(res, height[right] * w), right - 1)\nreturn res",
"left = 0\nright = len(height... | <|body_start_0|>
left, right, width, res = (0, len(height) - 1, len(height) - 1, 0)
for w in range(width, 0, -1):
if height[left] < height[right]:
res, left = (max(res, height[left] * w), left + 1)
else:
res, right = (max(res, height[right] * w), r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxArea(self, height):
""":type height: List[int] :rtype: int the largest width is from start to end later heights can compete only with larger heights, so move smaller between left and right heights beats 97.39%"""
<|body_0|>
def maxArea1(self, height):
... | stack_v2_sparse_classes_36k_train_009352 | 2,053 | no_license | [
{
"docstring": ":type height: List[int] :rtype: int the largest width is from start to end later heights can compete only with larger heights, so move smaller between left and right heights beats 97.39%",
"name": "maxArea",
"signature": "def maxArea(self, height)"
},
{
"docstring": ":param heigh... | 3 | stack_v2_sparse_classes_30k_train_004438 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxArea(self, height): :type height: List[int] :rtype: int the largest width is from start to end later heights can compete only with larger heights, so move smaller between ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxArea(self, height): :type height: List[int] :rtype: int the largest width is from start to end later heights can compete only with larger heights, so move smaller between ... | 7e0e917c15d3e35f49da3a00ef395bd5ff180d79 | <|skeleton|>
class Solution:
def maxArea(self, height):
""":type height: List[int] :rtype: int the largest width is from start to end later heights can compete only with larger heights, so move smaller between left and right heights beats 97.39%"""
<|body_0|>
def maxArea1(self, height):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxArea(self, height):
""":type height: List[int] :rtype: int the largest width is from start to end later heights can compete only with larger heights, so move smaller between left and right heights beats 97.39%"""
left, right, width, res = (0, len(height) - 1, len(height) - 1, ... | the_stack_v2_python_sparse | LeetCode/011_container_with_most_water.py | yao23/Machine_Learning_Playground | train | 12 | |
d3d0422bbd5eb2937afc6c090eab49d4c8170f69 | [
"item = super().transform_record(pid, record, links_factory=links_factory, **kwargs)\nfilter_circulation(item)\nreturn item",
"hit = super().transform_search_hit(pid, record_hit, links_factory=links_factory, **kwargs)\nfilter_circulation(hit)\nreturn hit"
] | <|body_start_0|>
item = super().transform_record(pid, record, links_factory=links_factory, **kwargs)
filter_circulation(item)
return item
<|end_body_0|>
<|body_start_1|>
hit = super().transform_search_hit(pid, record_hit, links_factory=links_factory, **kwargs)
filter_circulation... | Serialize and filter item circulation status. | ItemJSONSerializer | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ItemJSONSerializer:
"""Serialize and filter item circulation status."""
def transform_record(self, pid, record, links_factory=None, **kwargs):
"""Transform record into an intermediate representation."""
<|body_0|>
def transform_search_hit(self, pid, record_hit, links_fac... | stack_v2_sparse_classes_36k_train_009353 | 2,583 | permissive | [
{
"docstring": "Transform record into an intermediate representation.",
"name": "transform_record",
"signature": "def transform_record(self, pid, record, links_factory=None, **kwargs)"
},
{
"docstring": "Transform search result hit into an intermediate representation.",
"name": "transform_se... | 2 | null | Implement the Python class `ItemJSONSerializer` described below.
Class description:
Serialize and filter item circulation status.
Method signatures and docstrings:
- def transform_record(self, pid, record, links_factory=None, **kwargs): Transform record into an intermediate representation.
- def transform_search_hit(... | Implement the Python class `ItemJSONSerializer` described below.
Class description:
Serialize and filter item circulation status.
Method signatures and docstrings:
- def transform_record(self, pid, record, links_factory=None, **kwargs): Transform record into an intermediate representation.
- def transform_search_hit(... | 1c36526e85510100c5f64059518d1b716d87ac10 | <|skeleton|>
class ItemJSONSerializer:
"""Serialize and filter item circulation status."""
def transform_record(self, pid, record, links_factory=None, **kwargs):
"""Transform record into an intermediate representation."""
<|body_0|>
def transform_search_hit(self, pid, record_hit, links_fac... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ItemJSONSerializer:
"""Serialize and filter item circulation status."""
def transform_record(self, pid, record, links_factory=None, **kwargs):
"""Transform record into an intermediate representation."""
item = super().transform_record(pid, record, links_factory=links_factory, **kwargs)
... | the_stack_v2_python_sparse | invenio_app_ils/items/serializers/item.py | inveniosoftware/invenio-app-ils | train | 64 |
0fc3a2ed33206875f71dde5dac974fd2acdbe63d | [
"@lru_cache(None)\ndef dfs(n):\n if n == 1:\n return 0\n ans = 0\n if n & 1:\n ans += 1 + min(dfs(n + 1), dfs(n - 1))\n else:\n ans += 1 + dfs(n // 2)\n return ans\nreturn dfs(n)",
"def dfs(n):\n if n in memo:\n return memo[n]\n ans = 0\n if n & 1:\n ans ... | <|body_start_0|>
@lru_cache(None)
def dfs(n):
if n == 1:
return 0
ans = 0
if n & 1:
ans += 1 + min(dfs(n + 1), dfs(n - 1))
else:
ans += 1 + dfs(n // 2)
return ans
return dfs(n)
<|end_body_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def integerReplacement1(self, n: int) -> int:
"""思路：记忆化递归-标准库 @param n: @return:"""
<|body_0|>
def integerReplacement2(self, n: int) -> int:
"""思路：记忆化递归-备忘录 @param n: @return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
@lru_cache(None... | stack_v2_sparse_classes_36k_train_009354 | 1,555 | no_license | [
{
"docstring": "思路：记忆化递归-标准库 @param n: @return:",
"name": "integerReplacement1",
"signature": "def integerReplacement1(self, n: int) -> int"
},
{
"docstring": "思路：记忆化递归-备忘录 @param n: @return:",
"name": "integerReplacement2",
"signature": "def integerReplacement2(self, n: int) -> int"
}... | 2 | stack_v2_sparse_classes_30k_train_005206 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def integerReplacement1(self, n: int) -> int: 思路：记忆化递归-标准库 @param n: @return:
- def integerReplacement2(self, n: int) -> int: 思路：记忆化递归-备忘录 @param n: @return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def integerReplacement1(self, n: int) -> int: 思路：记忆化递归-标准库 @param n: @return:
- def integerReplacement2(self, n: int) -> int: 思路：记忆化递归-备忘录 @param n: @return:
<|skeleton|>
class ... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def integerReplacement1(self, n: int) -> int:
"""思路：记忆化递归-标准库 @param n: @return:"""
<|body_0|>
def integerReplacement2(self, n: int) -> int:
"""思路：记忆化递归-备忘录 @param n: @return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def integerReplacement1(self, n: int) -> int:
"""思路：记忆化递归-标准库 @param n: @return:"""
@lru_cache(None)
def dfs(n):
if n == 1:
return 0
ans = 0
if n & 1:
ans += 1 + min(dfs(n + 1), dfs(n - 1))
else:
... | the_stack_v2_python_sparse | LeetCode/记忆化/397. 整数替换.py | yiming1012/MyLeetCode | train | 2 | |
5d755d25a57408a713ea354bad709ec6e61f0c12 | [
"super(Discriminator, self).__init__()\nself.conv_dim = conv_dim\nself.conv1 = conv(3, conv_dim, 4, batch_norm=False)\nself.conv2 = conv(conv_dim, conv_dim * 2, 4)\nself.conv3 = conv(conv_dim * 2, conv_dim * 4, 4)\nself.fc = nn.Linear(conv_dim * 4 * 4 * 4, 1)",
"x = F.leaky_relu(self.conv1(x), 0.2)\nx = F.leaky_r... | <|body_start_0|>
super(Discriminator, self).__init__()
self.conv_dim = conv_dim
self.conv1 = conv(3, conv_dim, 4, batch_norm=False)
self.conv2 = conv(conv_dim, conv_dim * 2, 4)
self.conv3 = conv(conv_dim * 2, conv_dim * 4, 4)
self.fc = nn.Linear(conv_dim * 4 * 4 * 4, 1)
<... | Discriminator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Discriminator:
def __init__(self, conv_dim=32):
"""Initialize the Discriminator Module :param conv_dim: The depth of the first convolutional layer"""
<|body_0|>
def forward(self, x):
"""Forward propagation of the neural network :param x: The input to the neural netwo... | stack_v2_sparse_classes_36k_train_009355 | 12,896 | permissive | [
{
"docstring": "Initialize the Discriminator Module :param conv_dim: The depth of the first convolutional layer",
"name": "__init__",
"signature": "def __init__(self, conv_dim=32)"
},
{
"docstring": "Forward propagation of the neural network :param x: The input to the neural network :return: Dis... | 2 | stack_v2_sparse_classes_30k_train_021075 | Implement the Python class `Discriminator` described below.
Class description:
Implement the Discriminator class.
Method signatures and docstrings:
- def __init__(self, conv_dim=32): Initialize the Discriminator Module :param conv_dim: The depth of the first convolutional layer
- def forward(self, x): Forward propaga... | Implement the Python class `Discriminator` described below.
Class description:
Implement the Discriminator class.
Method signatures and docstrings:
- def __init__(self, conv_dim=32): Initialize the Discriminator Module :param conv_dim: The depth of the first convolutional layer
- def forward(self, x): Forward propaga... | b9b54564f94aadfc3c71ff513da0f05ef85d22a8 | <|skeleton|>
class Discriminator:
def __init__(self, conv_dim=32):
"""Initialize the Discriminator Module :param conv_dim: The depth of the first convolutional layer"""
<|body_0|>
def forward(self, x):
"""Forward propagation of the neural network :param x: The input to the neural netwo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Discriminator:
def __init__(self, conv_dim=32):
"""Initialize the Discriminator Module :param conv_dim: The depth of the first convolutional layer"""
super(Discriminator, self).__init__()
self.conv_dim = conv_dim
self.conv1 = conv(3, conv_dim, 4, batch_norm=False)
self.... | the_stack_v2_python_sparse | dl/pytorch/gan/face_gan.py | xta0/Python-Playground | train | 0 | |
44d160bd335180af752386c8ffa6662bacf81c5c | [
"self._dbg = debug\nself._log = get_logger(self.__class__.__name__, self._dbg)\nself._log.debug('midi_file=%s, channel=%s', midi_file, channel)\nself._log.debug('dst=%s', dst)\nself._log.debug('note_origin=%s', note_origin)\nself._log.debug('no_note_offset_flag=%s', no_note_offset_flag)\nself._log.debug('wav_mode=%... | <|body_start_0|>
self._dbg = debug
self._log = get_logger(self.__class__.__name__, self._dbg)
self._log.debug('midi_file=%s, channel=%s', midi_file, channel)
self._log.debug('dst=%s', dst)
self._log.debug('note_origin=%s', note_origin)
self._log.debug('no_note_offset_flag... | MidiApp | MidiApp | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MidiApp:
"""MidiApp"""
def __init__(self, midi_file, dst=(), channel=[], note_origin=-1, no_note_offset_flag=False, wav_mode=0, debug=False) -> None:
"""Constructor Parameters ---------- midi_file: str dst: str channel: list of int note_origin: int no_note_offset_flag: bool wav_mode:... | stack_v2_sparse_classes_36k_train_009356 | 25,197 | no_license | [
{
"docstring": "Constructor Parameters ---------- midi_file: str dst: str channel: list of int note_origin: int no_note_offset_flag: bool wav_mode: int",
"name": "__init__",
"signature": "def __init__(self, midi_file, dst=(), channel=[], note_origin=-1, no_note_offset_flag=False, wav_mode=0, debug=False... | 2 | stack_v2_sparse_classes_30k_train_015522 | Implement the Python class `MidiApp` described below.
Class description:
MidiApp
Method signatures and docstrings:
- def __init__(self, midi_file, dst=(), channel=[], note_origin=-1, no_note_offset_flag=False, wav_mode=0, debug=False) -> None: Constructor Parameters ---------- midi_file: str dst: str channel: list of... | Implement the Python class `MidiApp` described below.
Class description:
MidiApp
Method signatures and docstrings:
- def __init__(self, midi_file, dst=(), channel=[], note_origin=-1, no_note_offset_flag=False, wav_mode=0, debug=False) -> None: Constructor Parameters ---------- midi_file: str dst: str channel: list of... | b8264118d19c7f6c6be9b11f18c890c598eb1295 | <|skeleton|>
class MidiApp:
"""MidiApp"""
def __init__(self, midi_file, dst=(), channel=[], note_origin=-1, no_note_offset_flag=False, wav_mode=0, debug=False) -> None:
"""Constructor Parameters ---------- midi_file: str dst: str channel: list of int note_origin: int no_note_offset_flag: bool wav_mode:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MidiApp:
"""MidiApp"""
def __init__(self, midi_file, dst=(), channel=[], note_origin=-1, no_note_offset_flag=False, wav_mode=0, debug=False) -> None:
"""Constructor Parameters ---------- midi_file: str dst: str channel: list of int note_origin: int no_note_offset_flag: bool wav_mode: int"""
... | the_stack_v2_python_sparse | musicbox/__main__.py | ytani01/MusicBox | train | 1 |
9cfa265a1dbfe5f394575eb74dc3fca408a743a5 | [
"sigma_rules = []\nall_sigma_rules = SigmaRule.query.all()\nfor rule in all_sigma_rules:\n sigma_rules.append(ts_sigma_lib.enrich_sigma_rule_object(rule=rule, parse_yaml=False))\nmeta = {'rules_count': len(sigma_rules)}\nreturn jsonify({'objects': sigma_rules, 'meta': meta})",
"rule_yaml = request.json.get('ru... | <|body_start_0|>
sigma_rules = []
all_sigma_rules = SigmaRule.query.all()
for rule in all_sigma_rules:
sigma_rules.append(ts_sigma_lib.enrich_sigma_rule_object(rule=rule, parse_yaml=False))
meta = {'rules_count': len(sigma_rules)}
return jsonify({'objects': sigma_rule... | Resource to get list of all SigmaRules. | SigmaRuleListResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SigmaRuleListResource:
"""Resource to get list of all SigmaRules."""
def get(self):
"""Fetches Sigma rules from the database. Fetches all Sigma rules stored in the database on the system and returns a list of JSON representations of the rules. Returns: List of sigma rules represented... | stack_v2_sparse_classes_36k_train_009357 | 12,205 | permissive | [
{
"docstring": "Fetches Sigma rules from the database. Fetches all Sigma rules stored in the database on the system and returns a list of JSON representations of the rules. Returns: List of sigma rules represented in JSON e.g. {\"objects\": [sigma_rules], \"meta\": {\"rules_count\": 42}.",
"name": "get",
... | 2 | stack_v2_sparse_classes_30k_train_018385 | Implement the Python class `SigmaRuleListResource` described below.
Class description:
Resource to get list of all SigmaRules.
Method signatures and docstrings:
- def get(self): Fetches Sigma rules from the database. Fetches all Sigma rules stored in the database on the system and returns a list of JSON representatio... | Implement the Python class `SigmaRuleListResource` described below.
Class description:
Resource to get list of all SigmaRules.
Method signatures and docstrings:
- def get(self): Fetches Sigma rules from the database. Fetches all Sigma rules stored in the database on the system and returns a list of JSON representatio... | 24f471b58ca4a87cb053961b5f05c07a544ca7b8 | <|skeleton|>
class SigmaRuleListResource:
"""Resource to get list of all SigmaRules."""
def get(self):
"""Fetches Sigma rules from the database. Fetches all Sigma rules stored in the database on the system and returns a list of JSON representations of the rules. Returns: List of sigma rules represented... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SigmaRuleListResource:
"""Resource to get list of all SigmaRules."""
def get(self):
"""Fetches Sigma rules from the database. Fetches all Sigma rules stored in the database on the system and returns a list of JSON representations of the rules. Returns: List of sigma rules represented in JSON e.g.... | the_stack_v2_python_sparse | timesketch/api/v1/resources/sigma.py | google/timesketch | train | 2,263 |
2393814dd49e482eca8fe96263f8bd409df4b7c4 | [
"visited = {}\nwhile head is not None:\n if head in visited:\n return True\n visited[head] = 1\n head = head.next\nreturn False",
"faster = slow = head\nwhile faster != None and faster.next != None:\n faster = faster.next.next\n slow = slow.next\n if faster == slow:\n return True\n... | <|body_start_0|>
visited = {}
while head is not None:
if head in visited:
return True
visited[head] = 1
head = head.next
return False
<|end_body_0|>
<|body_start_1|>
faster = slow = head
while faster != None and faster.next != ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def naive_hasCycle(self, head):
""":type head: ListNode :rtype: bool O(n) space complexity"""
<|body_0|>
def hasCycle(self, head):
""":type head: ListNode :rtype: bool proof: https://stackoverflow.com/questions/3952805/proof-of-detecting-the-start-of-cycle-... | stack_v2_sparse_classes_36k_train_009358 | 837 | no_license | [
{
"docstring": ":type head: ListNode :rtype: bool O(n) space complexity",
"name": "naive_hasCycle",
"signature": "def naive_hasCycle(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: bool proof: https://stackoverflow.com/questions/3952805/proof-of-detecting-the-start-of-cycle-in-linke... | 2 | stack_v2_sparse_classes_30k_train_003772 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def naive_hasCycle(self, head): :type head: ListNode :rtype: bool O(n) space complexity
- def hasCycle(self, head): :type head: ListNode :rtype: bool proof: https://stackoverflow... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def naive_hasCycle(self, head): :type head: ListNode :rtype: bool O(n) space complexity
- def hasCycle(self, head): :type head: ListNode :rtype: bool proof: https://stackoverflow... | 9746205998338fb4d7fd51300a21149c4181fc8f | <|skeleton|>
class Solution:
def naive_hasCycle(self, head):
""":type head: ListNode :rtype: bool O(n) space complexity"""
<|body_0|>
def hasCycle(self, head):
""":type head: ListNode :rtype: bool proof: https://stackoverflow.com/questions/3952805/proof-of-detecting-the-start-of-cycle-... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def naive_hasCycle(self, head):
""":type head: ListNode :rtype: bool O(n) space complexity"""
visited = {}
while head is not None:
if head in visited:
return True
visited[head] = 1
head = head.next
return False
... | the_stack_v2_python_sparse | leetcode/linkedList/4_linked_list_cycle.py | RuizhenMai/academic-blog | train | 0 | |
ac27729641320ff682f79f4bc86bd665046dbdbc | [
"self.archival_target = archival_target\nself.attempt_number = attempt_number\nself.cloud_deploy_target = cloud_deploy_target\nself.job_run_id = job_run_id\nself.job_uid = job_uid\nself.parent_source = parent_source\nself.restore_time_usecs = restore_time_usecs\nself.snapshot_relative_dir_path = snapshot_relative_d... | <|body_start_0|>
self.archival_target = archival_target
self.attempt_number = attempt_number
self.cloud_deploy_target = cloud_deploy_target
self.job_run_id = job_run_id
self.job_uid = job_uid
self.parent_source = parent_source
self.restore_time_usecs = restore_tim... | Implementation of the 'RestoreInfo' model. Specifies the info regarding a full SQL snapshot. Attributes: archival_target (ArchivalExternalTarget): Specifies the info related to the archival target. attempt_number (int): Specifies the attempt number. cloud_deploy_target (CloudDeployTargetDetails): Specifies the info rel... | RestoreInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestoreInfo:
"""Implementation of the 'RestoreInfo' model. Specifies the info regarding a full SQL snapshot. Attributes: archival_target (ArchivalExternalTarget): Specifies the info related to the archival target. attempt_number (int): Specifies the attempt number. cloud_deploy_target (CloudDeplo... | stack_v2_sparse_classes_36k_train_009359 | 5,776 | permissive | [
{
"docstring": "Constructor for the RestoreInfo class",
"name": "__init__",
"signature": "def __init__(self, archival_target=None, attempt_number=None, cloud_deploy_target=None, job_run_id=None, job_uid=None, parent_source=None, restore_time_usecs=None, snapshot_relative_dir_path=None, source=None, star... | 2 | stack_v2_sparse_classes_30k_train_015258 | Implement the Python class `RestoreInfo` described below.
Class description:
Implementation of the 'RestoreInfo' model. Specifies the info regarding a full SQL snapshot. Attributes: archival_target (ArchivalExternalTarget): Specifies the info related to the archival target. attempt_number (int): Specifies the attempt ... | Implement the Python class `RestoreInfo` described below.
Class description:
Implementation of the 'RestoreInfo' model. Specifies the info regarding a full SQL snapshot. Attributes: archival_target (ArchivalExternalTarget): Specifies the info related to the archival target. attempt_number (int): Specifies the attempt ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RestoreInfo:
"""Implementation of the 'RestoreInfo' model. Specifies the info regarding a full SQL snapshot. Attributes: archival_target (ArchivalExternalTarget): Specifies the info related to the archival target. attempt_number (int): Specifies the attempt number. cloud_deploy_target (CloudDeplo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RestoreInfo:
"""Implementation of the 'RestoreInfo' model. Specifies the info regarding a full SQL snapshot. Attributes: archival_target (ArchivalExternalTarget): Specifies the info related to the archival target. attempt_number (int): Specifies the attempt number. cloud_deploy_target (CloudDeployTargetDetail... | the_stack_v2_python_sparse | cohesity_management_sdk/models/restore_info.py | cohesity/management-sdk-python | train | 24 |
1c9c233b175fe713ecfa3d98ad640f9291151b73 | [
"self.generator = random_number_generator\nself.length = length\nself.num_generated_numbers = None",
"if self.num_generated_numbers is not None:\n raise RuntimeError\nself.num_generated_numbers = 0\nreturn self",
"if self.num_generated_numbers is None:\n raise RuntimeError('Cannot call \"next\" before Ran... | <|body_start_0|>
self.generator = random_number_generator
self.length = length
self.num_generated_numbers = None
<|end_body_0|>
<|body_start_1|>
if self.num_generated_numbers is not None:
raise RuntimeError
self.num_generated_numbers = 0
return self
<|end_bod... | RandIter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandIter:
def __init__(self, random_number_generator, length):
"""Arguments --------- random_number_generator : A random number generator with a ``rand`` method that takes no arguments and returns a random number. length : int The number of random numbers to generate"""
<|body_0|... | stack_v2_sparse_classes_36k_train_009360 | 3,027 | no_license | [
{
"docstring": "Arguments --------- random_number_generator : A random number generator with a ``rand`` method that takes no arguments and returns a random number. length : int The number of random numbers to generate",
"name": "__init__",
"signature": "def __init__(self, random_number_generator, length... | 3 | stack_v2_sparse_classes_30k_train_018137 | Implement the Python class `RandIter` described below.
Class description:
Implement the RandIter class.
Method signatures and docstrings:
- def __init__(self, random_number_generator, length): Arguments --------- random_number_generator : A random number generator with a ``rand`` method that takes no arguments and re... | Implement the Python class `RandIter` described below.
Class description:
Implement the RandIter class.
Method signatures and docstrings:
- def __init__(self, random_number_generator, length): Arguments --------- random_number_generator : A random number generator with a ``rand`` method that takes no arguments and re... | 527f908422b559e6afc1ec025c04336d7a13828d | <|skeleton|>
class RandIter:
def __init__(self, random_number_generator, length):
"""Arguments --------- random_number_generator : A random number generator with a ``rand`` method that takes no arguments and returns a random number. length : int The number of random numbers to generate"""
<|body_0|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandIter:
def __init__(self, random_number_generator, length):
"""Arguments --------- random_number_generator : A random number generator with a ``rand`` method that takes no arguments and returns a random number. length : int The number of random numbers to generate"""
self.generator = random... | the_stack_v2_python_sparse | src/nicolai_munsterhjelm_ex/ex05/myrand.py | Nicomunster/INF200-2019-Exercises | train | 0 | |
143877f20d97a019cee8058b5a95313fc362d974 | [
"tests = set()\nfunctions = filter(lambda f: not f.properties.get('presence_tested', False), Database().get_functions())\nfor func in functions:\n test = self.generate_test(func)\n tests.add(test)\nreturn tests",
"logging.info('function presence generating test for %s', function.name)\ntest = Test(f'functio... | <|body_start_0|>
tests = set()
functions = filter(lambda f: not f.properties.get('presence_tested', False), Database().get_functions())
for func in functions:
test = self.generate_test(func)
tests.add(test)
return tests
<|end_body_0|>
<|body_start_1|>
log... | FunctionPresenceGenerator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FunctionPresenceGenerator:
def generate(self) -> Set[Test]:
"""Generates all presence test objects for all functions in the database."""
<|body_0|>
def generate_test(self, function: Function) -> Test:
"""Generates a Test object containing a valid main block and a fun... | stack_v2_sparse_classes_36k_train_009361 | 2,662 | permissive | [
{
"docstring": "Generates all presence test objects for all functions in the database.",
"name": "generate",
"signature": "def generate(self) -> Set[Test]"
},
{
"docstring": "Generates a Test object containing a valid main block and a function call without check or print statements.",
"name"... | 2 | stack_v2_sparse_classes_30k_train_012245 | Implement the Python class `FunctionPresenceGenerator` described below.
Class description:
Implement the FunctionPresenceGenerator class.
Method signatures and docstrings:
- def generate(self) -> Set[Test]: Generates all presence test objects for all functions in the database.
- def generate_test(self, function: Func... | Implement the Python class `FunctionPresenceGenerator` described below.
Class description:
Implement the FunctionPresenceGenerator class.
Method signatures and docstrings:
- def generate(self) -> Set[Test]: Generates all presence test objects for all functions in the database.
- def generate_test(self, function: Func... | 4e24759aded6536bbb3cdcc311e5eaf72d52c4e3 | <|skeleton|>
class FunctionPresenceGenerator:
def generate(self) -> Set[Test]:
"""Generates all presence test objects for all functions in the database."""
<|body_0|>
def generate_test(self, function: Function) -> Test:
"""Generates a Test object containing a valid main block and a fun... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FunctionPresenceGenerator:
def generate(self) -> Set[Test]:
"""Generates all presence test objects for all functions in the database."""
tests = set()
functions = filter(lambda f: not f.properties.get('presence_tested', False), Database().get_functions())
for func in functions:... | the_stack_v2_python_sparse | lemonspotter/generators/functionpresence.py | martinruefenacht/lemonspotter | train | 0 | |
d50b9ec43ce411c27531f7a4e837e90aacff257b | [
"memo = dict()\n\ndef dfs(nums, index, total, target):\n if index == len(nums):\n return 1 if total == target else 0\n if (index, total) in memo.keys():\n return memo[index, total]\n else:\n memo[index, total] = dfs(nums, index + 1, total + nums[index], target) + dfs(nums, index + 1, t... | <|body_start_0|>
memo = dict()
def dfs(nums, index, total, target):
if index == len(nums):
return 1 if total == target else 0
if (index, total) in memo.keys():
return memo[index, total]
else:
memo[index, total] = dfs(nu... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findTargetSumWays1(self, nums: List[int], S: int) -> int:
"""简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底... | stack_v2_sparse_classes_36k_train_009362 | 3,042 | no_license | [
{
"docstring": "简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\\\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\\\ /\\\\ /\\\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底往上的返回值，即所有结果）",
"name": "findTargetSumWays1",
"signature": "def fin... | 2 | stack_v2_sparse_classes_30k_train_016947 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findTargetSumWays1(self, nums: List[int], S: int) -> int: 简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findTargetSumWays1(self, nums: List[int], S: int) -> int: 简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -... | 2bbb1640589aab34f2bc42489283033cc11fb885 | <|skeleton|>
class Solution:
def findTargetSumWays1(self, nums: List[int], S: int) -> int:
"""简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findTargetSumWays1(self, nums: List[int], S: int) -> int:
"""简单的dfs会超时，需要进行优化。 以[1,1,1] 为例: 0 / -1 1 / \\ / -2 0 0 2 # 当所在层数相同且sum相同时，则后面dfs的结果也是相同的，不用重复计算 /\\ /\\ /\\ / -3 -1 -1 1 -1 1 1 3 将当前层数及sum值存到dict，后面计算时遇到相同key直接取vaule即可（这里需要理解，dfs是自底向上返回值，所以从dict中取到的value，是当前key从底往上的返回值，即所有结果）"... | the_stack_v2_python_sparse | 494_target-sum.py | helloocc/algorithm | train | 1 | |
3d36bdb873ad1f95ad1d742f11b18fa1027e862f | [
"chair = Part.objects.get(pk=10000)\nself.assertEqual(chair.stock_entries(include_variants=False).count(), 0)\nself.assertEqual(chair.stock_entries().count(), 12)\ngreen = Part.objects.get(pk=10003)\nself.assertEqual(green.stock_entries(include_variants=False).count(), 0)\nself.assertEqual(green.stock_entries().cou... | <|body_start_0|>
chair = Part.objects.get(pk=10000)
self.assertEqual(chair.stock_entries(include_variants=False).count(), 0)
self.assertEqual(chair.stock_entries().count(), 12)
green = Part.objects.get(pk=10003)
self.assertEqual(green.stock_entries(include_variants=False).count()... | Tests for calculation stock counts against templates / variants. | VariantTest | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VariantTest:
"""Tests for calculation stock counts against templates / variants."""
def test_variant_stock(self):
"""Test variant functions."""
<|body_0|>
def test_serial_numbers(self):
"""Test serial number functionality for variant / template parts."""
... | stack_v2_sparse_classes_36k_train_009363 | 40,181 | permissive | [
{
"docstring": "Test variant functions.",
"name": "test_variant_stock",
"signature": "def test_variant_stock(self)"
},
{
"docstring": "Test serial number functionality for variant / template parts.",
"name": "test_serial_numbers",
"signature": "def test_serial_numbers(self)"
}
] | 2 | null | Implement the Python class `VariantTest` described below.
Class description:
Tests for calculation stock counts against templates / variants.
Method signatures and docstrings:
- def test_variant_stock(self): Test variant functions.
- def test_serial_numbers(self): Test serial number functionality for variant / templa... | Implement the Python class `VariantTest` described below.
Class description:
Tests for calculation stock counts against templates / variants.
Method signatures and docstrings:
- def test_variant_stock(self): Test variant functions.
- def test_serial_numbers(self): Test serial number functionality for variant / templa... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class VariantTest:
"""Tests for calculation stock counts against templates / variants."""
def test_variant_stock(self):
"""Test variant functions."""
<|body_0|>
def test_serial_numbers(self):
"""Test serial number functionality for variant / template parts."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VariantTest:
"""Tests for calculation stock counts against templates / variants."""
def test_variant_stock(self):
"""Test variant functions."""
chair = Part.objects.get(pk=10000)
self.assertEqual(chair.stock_entries(include_variants=False).count(), 0)
self.assertEqual(chai... | the_stack_v2_python_sparse | InvenTree/stock/tests.py | inventree/InvenTree | train | 3,077 |
435f48322403ca8e571f3bccfe8cc3a0a1677b7e | [
"super().__init__()\ncheck_boundaries(boundaries)\nself.boundaries = boundaries\nself.frequencies = frequencies",
"self.randomize(None)\nself.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])\nself.freqs = self.R.uniform(low=self.frequencies[0], high=self.frequencies[1])\nlength = signal... | <|body_start_0|>
super().__init__()
check_boundaries(boundaries)
self.boundaries = boundaries
self.frequencies = frequencies
<|end_body_0|>
<|body_start_1|>
self.randomize(None)
self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])
self... | Add a random sinusoidal signal to the input signal | SignalRandAddSine | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignalRandAddSine:
"""Add a random sinusoidal signal to the input signal"""
def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lowe... | stack_v2_sparse_classes_36k_train_009364 | 16,322 | permissive | [
{
"docstring": "Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lower and upper values need to be positive ,default : ``[0.1, 0.3]`` frequencies: list defining lower and upper frequencies for sinusoidal signal generation ,default : ``[0.001, 0.02]``",
"name": "__init... | 2 | stack_v2_sparse_classes_30k_train_017063 | Implement the Python class `SignalRandAddSine` described below.
Class description:
Add a random sinusoidal signal to the input signal
Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None: Args: boundaries: list defining lowe... | Implement the Python class `SignalRandAddSine` described below.
Class description:
Add a random sinusoidal signal to the input signal
Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None: Args: boundaries: list defining lowe... | e48c3e2c741fa3fc705c4425d17ac4a5afac6c47 | <|skeleton|>
class SignalRandAddSine:
"""Add a random sinusoidal signal to the input signal"""
def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lowe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SignalRandAddSine:
"""Add a random sinusoidal signal to the input signal"""
def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lower and upper v... | the_stack_v2_python_sparse | monai/transforms/signal/array.py | Project-MONAI/MONAI | train | 4,805 |
234bf63c9005d3e1e95d4239536d7a91edf01da3 | [
"if not root:\n print('serialize: ', '')\n return ''\nqueue = collections.deque([root])\nstringArr = []\nstring = ''\nlayer = 1\nnum_in_layer = 2 ** (layer - 1)\nwhile queue:\n level = []\n for _ in range(len(queue)):\n node = queue.popleft()\n if node:\n queue.append(node.left)... | <|body_start_0|>
if not root:
print('serialize: ', '')
return ''
queue = collections.deque([root])
stringArr = []
string = ''
layer = 1
num_in_layer = 2 ** (layer - 1)
while queue:
level = []
for _ in range(len(queue... | 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_009365 | 6,668 | 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:... | ca95110b77152258573b6f1d43e39a316cdcb459 | <|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:
print('serialize: ', '')
return ''
queue = collections.deque([root])
stringArr = []
string = ''
layer = 1
num... | the_stack_v2_python_sparse | algo/tree/_0297_SerializeAndDeserializeBinaryTree.py | ianlai/Note-Python | train | 0 | |
e67e4e412d280ff36b1a73f4eb01962d5d0f7c81 | [
"super(SelectBox, self).__init__(parent)\nself._leftBrushColor = QColor()\nself._rightBrushColor = QColor()\nself._leftPenColor = QColor()\nself._rightPenColor = QColor()\nself._alpha = 255\nself._dirBrush = QBrush()\nself._leftBrush = QBrush()\nself._rightBrush = QBrush()\nself._dirPen = QPen()\nself._leftPen = QP... | <|body_start_0|>
super(SelectBox, self).__init__(parent)
self._leftBrushColor = QColor()
self._rightBrushColor = QColor()
self._leftPenColor = QColor()
self._rightPenColor = QColor()
self._alpha = 255
self._dirBrush = QBrush()
self._leftBrush = QBrush()
... | Subclass of `QRubberBand`_ TOWRITE | SelectBox | [
"Zlib",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SelectBox:
"""Subclass of `QRubberBand`_ TOWRITE"""
def __init__(self, s, parent=None):
"""Default class constructor. :param `s`: TOWRITE :type `s`: QRubberBand.Shape :param `parent`: Pointer to a parent widget instance. :type `parent`: `QWidget`_"""
<|body_0|>
def setDi... | stack_v2_sparse_classes_36k_train_009366 | 4,928 | permissive | [
{
"docstring": "Default class constructor. :param `s`: TOWRITE :type `s`: QRubberBand.Shape :param `parent`: Pointer to a parent widget instance. :type `parent`: `QWidget`_",
"name": "__init__",
"signature": "def __init__(self, s, parent=None)"
},
{
"docstring": "TOWRITE :param `dir`: TOWRITE :t... | 5 | stack_v2_sparse_classes_30k_train_020041 | Implement the Python class `SelectBox` described below.
Class description:
Subclass of `QRubberBand`_ TOWRITE
Method signatures and docstrings:
- def __init__(self, s, parent=None): Default class constructor. :param `s`: TOWRITE :type `s`: QRubberBand.Shape :param `parent`: Pointer to a parent widget instance. :type ... | Implement the Python class `SelectBox` described below.
Class description:
Subclass of `QRubberBand`_ TOWRITE
Method signatures and docstrings:
- def __init__(self, s, parent=None): Default class constructor. :param `s`: TOWRITE :type `s`: QRubberBand.Shape :param `parent`: Pointer to a parent widget instance. :type ... | 9c5c2baea3bf3897470495e2a50eb70ee1363637 | <|skeleton|>
class SelectBox:
"""Subclass of `QRubberBand`_ TOWRITE"""
def __init__(self, s, parent=None):
"""Default class constructor. :param `s`: TOWRITE :type `s`: QRubberBand.Shape :param `parent`: Pointer to a parent widget instance. :type `parent`: `QWidget`_"""
<|body_0|>
def setDi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SelectBox:
"""Subclass of `QRubberBand`_ TOWRITE"""
def __init__(self, s, parent=None):
"""Default class constructor. :param `s`: TOWRITE :type `s`: QRubberBand.Shape :param `parent`: Pointer to a parent widget instance. :type `parent`: `QWidget`_"""
super(SelectBox, self).__init__(parent... | the_stack_v2_python_sparse | experimental/python/gui/selectbox.py | Fran89/Embroidermodder | train | 1 |
8ca995af99324163d5c136f25b82c3a7314852ca | [
"conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels * upscale_factor ** 2, kernel_size=1, padding=0)\nself.initialize_conv(conv, in_channels, out_channels, upscale_factor)\nlayers = [conv, activation(), normalization(num_features=out_channels * upscale_factor ** 2), nn.PixelShuffle(upscale_factor)]... | <|body_start_0|>
conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels * upscale_factor ** 2, kernel_size=1, padding=0)
self.initialize_conv(conv, in_channels, out_channels, upscale_factor)
layers = [conv, activation(), normalization(num_features=out_channels * upscale_factor ** 2)... | Upsample the image using normal convolution follow by pixel shuffling References: https://arxiv.org/pdf/1609.05158.pdf https://arxiv.org/pdf/1806.02658.pdf (additional blurring at the end) | PixelShuffleConvolutionLayer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PixelShuffleConvolutionLayer:
"""Upsample the image using normal convolution follow by pixel shuffling References: https://arxiv.org/pdf/1609.05158.pdf https://arxiv.org/pdf/1806.02658.pdf (additional blurring at the end)"""
def __init__(self, in_channels: int, out_channels: int, upscale_fac... | stack_v2_sparse_classes_36k_train_009367 | 3,091 | permissive | [
{
"docstring": ":param in_channels: input channels :param out_channels: output channels :param upscale_factor: factor to increase spatial resolution by :param activation: activation function :param normalization: normalization function :param: whether to blur at the end to remove checkerboard artifact",
"na... | 2 | null | Implement the Python class `PixelShuffleConvolutionLayer` described below.
Class description:
Upsample the image using normal convolution follow by pixel shuffling References: https://arxiv.org/pdf/1609.05158.pdf https://arxiv.org/pdf/1806.02658.pdf (additional blurring at the end)
Method signatures and docstrings:
-... | Implement the Python class `PixelShuffleConvolutionLayer` described below.
Class description:
Upsample the image using normal convolution follow by pixel shuffling References: https://arxiv.org/pdf/1609.05158.pdf https://arxiv.org/pdf/1806.02658.pdf (additional blurring at the end)
Method signatures and docstrings:
-... | 689b9924d3c88a433f8f350b89c13a878ac7d7c3 | <|skeleton|>
class PixelShuffleConvolutionLayer:
"""Upsample the image using normal convolution follow by pixel shuffling References: https://arxiv.org/pdf/1609.05158.pdf https://arxiv.org/pdf/1806.02658.pdf (additional blurring at the end)"""
def __init__(self, in_channels: int, out_channels: int, upscale_fac... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PixelShuffleConvolutionLayer:
"""Upsample the image using normal convolution follow by pixel shuffling References: https://arxiv.org/pdf/1609.05158.pdf https://arxiv.org/pdf/1806.02658.pdf (additional blurring at the end)"""
def __init__(self, in_channels: int, out_channels: int, upscale_factor: int, act... | the_stack_v2_python_sparse | nntoolbox/vision/components/upsample.py | nhatsmrt/nn-toolbox | train | 19 |
fb8f07ce47cd5e35911bc23911a20393a1d01004 | [
"def helper(root):\n res = [0, 0]\n if not root:\n return res\n left, right = (helper(root.left), helper(root.right))\n res[0] = max(left) + max(right)\n res[1] = root.val + left[0] + right[0]\n return res\nreturn max(helper(root))",
"def helper(root, mem):\n if not root:\n retu... | <|body_start_0|>
def helper(root):
res = [0, 0]
if not root:
return res
left, right = (helper(root.left), helper(root.right))
res[0] = max(left) + max(right)
res[1] = root.val + left[0] + right[0]
return res
return m... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def rob2(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
def rob3(self, root):
""":type root: TreeNode :rtype: int"""
<|body_2|>
<|end_skelet... | stack_v2_sparse_classes_36k_train_009368 | 3,633 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "rob",
"signature": "def rob(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "rob2",
"signature": "def rob2(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "rob3",... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, root): :type root: TreeNode :rtype: int
- def rob2(self, root): :type root: TreeNode :rtype: int
- def rob3(self, root): :type root: TreeNode :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, root): :type root: TreeNode :rtype: int
- def rob2(self, root): :type root: TreeNode :rtype: int
- def rob3(self, root): :type root: TreeNode :rtype: int
<|skeleto... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def rob(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def rob2(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
def rob3(self, root):
""":type root: TreeNode :rtype: int"""
<|body_2|>
<|end_skelet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rob(self, root):
""":type root: TreeNode :rtype: int"""
def helper(root):
res = [0, 0]
if not root:
return res
left, right = (helper(root.left), helper(root.right))
res[0] = max(left) + max(right)
res[1] ... | the_stack_v2_python_sparse | code337HouseRobberIII.py | cybelewang/leetcode-python | train | 0 | |
32375c144ac9769a1fdd8a7233902032f042ba3e | [
"def compare(x, y):\n \"\"\" 比较函数，从大到小排序 \"\"\"\n if y + x > x + y:\n return 1\n return -1\nnums = sorted(map(str, nums), key=cmp_to_key(compare))\nif nums[0] == '0':\n return '0'\nreturn ''.join(nums)",
"def func(x):\n if not x:\n return 0\n n = int(math.log10(x)) + 1\n return ... | <|body_start_0|>
def compare(x, y):
""" 比较函数，从大到小排序 """
if y + x > x + y:
return 1
return -1
nums = sorted(map(str, nums), key=cmp_to_key(compare))
if nums[0] == '0':
return '0'
return ''.join(nums)
<|end_body_0|>
<|body_st... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def largestNumber(self, nums: List[int]) -> str:
"""比较"""
<|body_0|>
def largestNumberMath(self, nums: List[int]) -> str:
"""数学"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def compare(x, y):
""" 比较函数，从大到小排序 """
... | stack_v2_sparse_classes_36k_train_009369 | 1,302 | no_license | [
{
"docstring": "比较",
"name": "largestNumber",
"signature": "def largestNumber(self, nums: List[int]) -> str"
},
{
"docstring": "数学",
"name": "largestNumberMath",
"signature": "def largestNumberMath(self, nums: List[int]) -> str"
}
] | 2 | stack_v2_sparse_classes_30k_train_008733 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestNumber(self, nums: List[int]) -> str: 比较
- def largestNumberMath(self, nums: List[int]) -> str: 数学 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestNumber(self, nums: List[int]) -> str: 比较
- def largestNumberMath(self, nums: List[int]) -> str: 数学
<|skeleton|>
class Solution:
def largestNumber(self, nums: Lis... | 52756b30e9d51794591aca030bc918e707f473f1 | <|skeleton|>
class Solution:
def largestNumber(self, nums: List[int]) -> str:
"""比较"""
<|body_0|>
def largestNumberMath(self, nums: List[int]) -> str:
"""数学"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def largestNumber(self, nums: List[int]) -> str:
"""比较"""
def compare(x, y):
""" 比较函数，从大到小排序 """
if y + x > x + y:
return 1
return -1
nums = sorted(map(str, nums), key=cmp_to_key(compare))
if nums[0] == '0':
... | the_stack_v2_python_sparse | 179.最大数/solution.py | QtTao/daily_leetcode | train | 0 | |
69806b954a1de8eb08071a7774aacb5b8fe74dd8 | [
"dval = {}\nmodel = type(self)\nmapper = inspect(model)\nfor col in mapper.attrs:\n col_key = col.key\n dval[col_key] = str(getattr(self, col_key))\nreturn dval",
"model_dict = self.to_dict()\njson_str = json.dumps(model_dict, indent=indent)\nreturn json_str"
] | <|body_start_0|>
dval = {}
model = type(self)
mapper = inspect(model)
for col in mapper.attrs:
col_key = col.key
dval[col_key] = str(getattr(self, col_key))
return dval
<|end_body_0|>
<|body_start_1|>
model_dict = self.to_dict()
json_str =... | Mixin style class that adds serialization to data model objects. | SerializableModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SerializableModel:
"""Mixin style class that adds serialization to data model objects."""
def to_dict(self):
"""Iterates the formal data attributes of a model and outputs a dictionary with the data based on the model."""
<|body_0|>
def to_json(self, indent=4):
""... | stack_v2_sparse_classes_36k_train_009370 | 6,583 | no_license | [
{
"docstring": "Iterates the formal data attributes of a model and outputs a dictionary with the data based on the model.",
"name": "to_dict",
"signature": "def to_dict(self)"
},
{
"docstring": "Iterates the formal data attributes of a model and creates a dictionary with the data based on the mo... | 2 | stack_v2_sparse_classes_30k_train_015669 | Implement the Python class `SerializableModel` described below.
Class description:
Mixin style class that adds serialization to data model objects.
Method signatures and docstrings:
- def to_dict(self): Iterates the formal data attributes of a model and outputs a dictionary with the data based on the model.
- def to_... | Implement the Python class `SerializableModel` described below.
Class description:
Mixin style class that adds serialization to data model objects.
Method signatures and docstrings:
- def to_dict(self): Iterates the formal data attributes of a model and outputs a dictionary with the data based on the model.
- def to_... | 530ea184f29add6f42bee1465343f6ddb51a1e51 | <|skeleton|>
class SerializableModel:
"""Mixin style class that adds serialization to data model objects."""
def to_dict(self):
"""Iterates the formal data attributes of a model and outputs a dictionary with the data based on the model."""
<|body_0|>
def to_json(self, indent=4):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SerializableModel:
"""Mixin style class that adds serialization to data model objects."""
def to_dict(self):
"""Iterates the formal data attributes of a model and outputs a dictionary with the data based on the model."""
dval = {}
model = type(self)
mapper = inspect(model)... | the_stack_v2_python_sparse | packages/akit/datum/orm.py | TrendingTechnology/automationkit | train | 0 |
5df7507e614c1a2ef8a1bd1716481c4ef702b4e3 | [
"if not root:\n return ''\nq = [root]\ncoded = [root.val]\nwhile q:\n n = len(q)\n for _ in range(n):\n node = q.pop(0)\n if not node:\n continue\n q.append(node.left)\n q.append(node.right)\n coded += [node.val if node else 'N' for node in q]\nreturn coded",
"if... | <|body_start_0|>
if not root:
return ''
q = [root]
coded = [root.val]
while q:
n = len(q)
for _ in range(n):
node = q.pop(0)
if not node:
continue
q.append(node.left)
q... | 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_009371 | 2,617 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_017630 | 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:... | 0127190b27862ec7e7f4f2fcce5ce958d480cdac | <|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 ''
q = [root]
coded = [root.val]
while q:
n = len(q)
for _ in range(n):
node = q.pop(0)
... | the_stack_v2_python_sparse | 449.serialize-and-deserialize-bst.py | Iverance/leetcode | train | 0 | |
6a0a0783428edc8dca7a1a5f1b7346a6c8d1cfe9 | [
"self.sums = []\nfor weight in w:\n if not self.sums:\n self.sums.append(weight)\n else:\n self.sums.append(weight + self.sums[-1])",
"import bisect\npick = random.uniform(0, self.sums[-1])\nreturn bisect.bisect_left(self.sums, pick)"
] | <|body_start_0|>
self.sums = []
for weight in w:
if not self.sums:
self.sums.append(weight)
else:
self.sums.append(weight + self.sums[-1])
<|end_body_0|>
<|body_start_1|>
import bisect
pick = random.uniform(0, self.sums[-1])
... | Solution_1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution_1:
def __init__(self, w):
""":type w: List[int] 176ms"""
<|body_0|>
def pickIndex(self):
""":rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.sums = []
for weight in w:
if not self.sums:
se... | stack_v2_sparse_classes_36k_train_009372 | 1,901 | no_license | [
{
"docstring": ":type w: List[int] 176ms",
"name": "__init__",
"signature": "def __init__(self, w)"
},
{
"docstring": ":rtype: int",
"name": "pickIndex",
"signature": "def pickIndex(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016824 | Implement the Python class `Solution_1` described below.
Class description:
Implement the Solution_1 class.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[int] 176ms
- def pickIndex(self): :rtype: int | Implement the Python class `Solution_1` described below.
Class description:
Implement the Solution_1 class.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[int] 176ms
- def pickIndex(self): :rtype: int
<|skeleton|>
class Solution_1:
def __init__(self, w):
""":type w: List[int] 1... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution_1:
def __init__(self, w):
""":type w: List[int] 176ms"""
<|body_0|>
def pickIndex(self):
""":rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution_1:
def __init__(self, w):
""":type w: List[int] 176ms"""
self.sums = []
for weight in w:
if not self.sums:
self.sums.append(weight)
else:
self.sums.append(weight + self.sums[-1])
def pickIndex(self):
""":rtyp... | the_stack_v2_python_sparse | RandomPickWithWeight_MID_880.py | 953250587/leetcode-python | train | 2 | |
d0c34a3185e2333a46fa0f7369266aa4e10b1685 | [
"super(ConditionalAutoencoder, self).__init__()\nn_enc_blks = len(enc_channels) - 1\nn_dec_blks = len(dec_channels) - 1\nassert n_enc_blks > 0\nassert n_dec_blks > 0\nself.n_enc_blks = n_enc_blks\nself.n_dec_blks = n_dec_blks\nself.bottom_width = 4\nself.nonlinearity = nn.ReLU()\nresblk_cls = ConditionalResidualBlo... | <|body_start_0|>
super(ConditionalAutoencoder, self).__init__()
n_enc_blks = len(enc_channels) - 1
n_dec_blks = len(dec_channels) - 1
assert n_enc_blks > 0
assert n_dec_blks > 0
self.n_enc_blks = n_enc_blks
self.n_dec_blks = n_dec_blks
self.bottom_width = ... | ConditionalAutoencoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConditionalAutoencoder:
def __init__(self, enc_channels, dec_channels, num_classes, dim_z=128, im_channels=3):
"""enc_channels [64, 128, 256, 256] c1 c2 c3 dec_channels [256, 128, 64, 64] c1 c2 c3 num_classes if not None, use conditional batchnorm"""
<|body_0|>
def forward(s... | stack_v2_sparse_classes_36k_train_009373 | 18,748 | no_license | [
{
"docstring": "enc_channels [64, 128, 256, 256] c1 c2 c3 dec_channels [256, 128, 64, 64] c1 c2 c3 num_classes if not None, use conditional batchnorm",
"name": "__init__",
"signature": "def __init__(self, enc_channels, dec_channels, num_classes, dim_z=128, im_channels=3)"
},
{
"docstring": "x ba... | 2 | stack_v2_sparse_classes_30k_test_001183 | Implement the Python class `ConditionalAutoencoder` described below.
Class description:
Implement the ConditionalAutoencoder class.
Method signatures and docstrings:
- def __init__(self, enc_channels, dec_channels, num_classes, dim_z=128, im_channels=3): enc_channels [64, 128, 256, 256] c1 c2 c3 dec_channels [256, 12... | Implement the Python class `ConditionalAutoencoder` described below.
Class description:
Implement the ConditionalAutoencoder class.
Method signatures and docstrings:
- def __init__(self, enc_channels, dec_channels, num_classes, dim_z=128, im_channels=3): enc_channels [64, 128, 256, 256] c1 c2 c3 dec_channels [256, 12... | 0a6653a66f1fb2590df9d6697e4cd69d32a2baaa | <|skeleton|>
class ConditionalAutoencoder:
def __init__(self, enc_channels, dec_channels, num_classes, dim_z=128, im_channels=3):
"""enc_channels [64, 128, 256, 256] c1 c2 c3 dec_channels [256, 128, 64, 64] c1 c2 c3 num_classes if not None, use conditional batchnorm"""
<|body_0|>
def forward(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConditionalAutoencoder:
def __init__(self, enc_channels, dec_channels, num_classes, dim_z=128, im_channels=3):
"""enc_channels [64, 128, 256, 256] c1 c2 c3 dec_channels [256, 128, 64, 64] c1 c2 c3 num_classes if not None, use conditional batchnorm"""
super(ConditionalAutoencoder, self).__init_... | the_stack_v2_python_sparse | pe/models_cgan.py | tt6746690/misc_impl | train | 0 | |
767d9c3833c0818432748ddbfc8a00274ae2ac76 | [
"super().__init__(**kwargs)\nself._sublayers = []\nfor num_units in units[:-1]:\n self._sublayers.append(tf.keras.layers.Dense(num_units, activation=activation, use_bias=use_bias))\nself._sublayers.append(tf.keras.layers.Dense(units[-1], activation=final_activation, use_bias=use_bias))",
"for layer in self._su... | <|body_start_0|>
super().__init__(**kwargs)
self._sublayers = []
for num_units in units[:-1]:
self._sublayers.append(tf.keras.layers.Dense(num_units, activation=activation, use_bias=use_bias))
self._sublayers.append(tf.keras.layers.Dense(units[-1], activation=final_activation... | Sequential multi-layer perceptron (MLP) block. | MLP | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MLP:
"""Sequential multi-layer perceptron (MLP) block."""
def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) -> None:
"""Initializes the MLP layer. Args: units: Sequenti... | stack_v2_sparse_classes_36k_train_009374 | 1,936 | permissive | [
{
"docstring": "Initializes the MLP layer. Args: units: Sequential list of layer sizes. use_bias: Whether to include a bias term. activation: Type of activation to use on all except the last layer. final_activation: Type of activation to use on last layer. **kwargs: Extra args passed to the Keras Layer base cla... | 2 | stack_v2_sparse_classes_30k_train_003744 | Implement the Python class `MLP` described below.
Class description:
Sequential multi-layer perceptron (MLP) block.
Method signatures and docstrings:
- def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) ... | Implement the Python class `MLP` described below.
Class description:
Sequential multi-layer perceptron (MLP) block.
Method signatures and docstrings:
- def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) ... | f4f42c1a183a262539e21f5ab8d25f0dc3e5621d | <|skeleton|>
class MLP:
"""Sequential multi-layer perceptron (MLP) block."""
def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) -> None:
"""Initializes the MLP layer. Args: units: Sequenti... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MLP:
"""Sequential multi-layer perceptron (MLP) block."""
def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) -> None:
"""Initializes the MLP layer. Args: units: Sequential list of la... | the_stack_v2_python_sparse | tensorflow_recommenders/layers/blocks.py | tensorflow/recommenders | train | 1,666 |
e4ac9fff359d5afdd054b10d08f4f85a321d86cc | [
"if not usernames and (not addresses):\n return 0\nselection = models.Q()\nif usernames:\n selection |= models.Q(username__in=set(usernames))\nif addresses:\n selection |= models.Q(source_address__in=set(addresses))\nreturn self.get_queryset().filter(selection, lockout=True).update(lockout=False)",
"sele... | <|body_start_0|>
if not usernames and (not addresses):
return 0
selection = models.Q()
if usernames:
selection |= models.Q(username__in=set(usernames))
if addresses:
selection |= models.Q(source_address__in=set(addresses))
return self.get_query... | Manager to handle Logins. | LoginAttemptManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LoginAttemptManager:
"""Manager to handle Logins."""
def unlock(self, usernames=[], addresses=[]):
"""To Unlock given usernames and IP addresses. Returns the number of attempts that have been unlocked."""
<|body_0|>
def unlock_queryset(self, queryset):
"""To unlo... | stack_v2_sparse_classes_36k_train_009375 | 10,201 | no_license | [
{
"docstring": "To Unlock given usernames and IP addresses. Returns the number of attempts that have been unlocked.",
"name": "unlock",
"signature": "def unlock(self, usernames=[], addresses=[])"
},
{
"docstring": "To unlock all usernames and IP addresses found in ``queryset``. Returns the numbe... | 2 | stack_v2_sparse_classes_30k_train_015637 | Implement the Python class `LoginAttemptManager` described below.
Class description:
Manager to handle Logins.
Method signatures and docstrings:
- def unlock(self, usernames=[], addresses=[]): To Unlock given usernames and IP addresses. Returns the number of attempts that have been unlocked.
- def unlock_queryset(sel... | Implement the Python class `LoginAttemptManager` described below.
Class description:
Manager to handle Logins.
Method signatures and docstrings:
- def unlock(self, usernames=[], addresses=[]): To Unlock given usernames and IP addresses. Returns the number of attempts that have been unlocked.
- def unlock_queryset(sel... | cb392be0402543acf074425fcaf8edf054269012 | <|skeleton|>
class LoginAttemptManager:
"""Manager to handle Logins."""
def unlock(self, usernames=[], addresses=[]):
"""To Unlock given usernames and IP addresses. Returns the number of attempts that have been unlocked."""
<|body_0|>
def unlock_queryset(self, queryset):
"""To unlo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LoginAttemptManager:
"""Manager to handle Logins."""
def unlock(self, usernames=[], addresses=[]):
"""To Unlock given usernames and IP addresses. Returns the number of attempts that have been unlocked."""
if not usernames and (not addresses):
return 0
selection = model... | the_stack_v2_python_sparse | cpovc_access/models.py | uonafya/cpims-2.3beta | train | 4 |
892cbc07a1524f47caaf9eddeb1e1485bb79c915 | [
"data = form.cleaned_data\nself.success_url = reverse('tokens', kwargs={'level': int(data['level']), 'semester': int(data['semester']), 'course': int(data['course'].id)})\nreturn super().form_valid(form)",
"context = super().get_context_data(**kwargs)\ncontext['title_text'] = 'Choose Course To View The Tokens'\nc... | <|body_start_0|>
data = form.cleaned_data
self.success_url = reverse('tokens', kwargs={'level': int(data['level']), 'semester': int(data['semester']), 'course': int(data['course'].id)})
return super().form_valid(form)
<|end_body_0|>
<|body_start_1|>
context = super().get_context_data(**... | View for choosing course tokens that will be displayed. Check that the user's account is still active. Redirects to tokens view on form valid. | ShowTokensView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShowTokensView:
"""View for choosing course tokens that will be displayed. Check that the user's account is still active. Redirects to tokens view on form valid."""
def form_valid(self, form):
"""Compute the success URL and call super.form_valid()"""
<|body_0|>
def get_c... | stack_v2_sparse_classes_36k_train_009376 | 29,759 | no_license | [
{
"docstring": "Compute the success URL and call super.form_valid()",
"name": "form_valid",
"signature": "def form_valid(self, form)"
},
{
"docstring": "Return the data used in the templates rendering.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
}
... | 2 | stack_v2_sparse_classes_30k_val_000439 | Implement the Python class `ShowTokensView` described below.
Class description:
View for choosing course tokens that will be displayed. Check that the user's account is still active. Redirects to tokens view on form valid.
Method signatures and docstrings:
- def form_valid(self, form): Compute the success URL and cal... | Implement the Python class `ShowTokensView` described below.
Class description:
View for choosing course tokens that will be displayed. Check that the user's account is still active. Redirects to tokens view on form valid.
Method signatures and docstrings:
- def form_valid(self, form): Compute the success URL and cal... | 06bc577d01d3dbf6c425e03dcb903977a38e377c | <|skeleton|>
class ShowTokensView:
"""View for choosing course tokens that will be displayed. Check that the user's account is still active. Redirects to tokens view on form valid."""
def form_valid(self, form):
"""Compute the success URL and call super.form_valid()"""
<|body_0|>
def get_c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShowTokensView:
"""View for choosing course tokens that will be displayed. Check that the user's account is still active. Redirects to tokens view on form valid."""
def form_valid(self, form):
"""Compute the success URL and call super.form_valid()"""
data = form.cleaned_data
self.... | the_stack_v2_python_sparse | cbt/views.py | Festusali/CBTest | train | 6 |
c296f2751865c7f5f948a68ae90e26f5e00985b4 | [
"self.max_read_iops = max_read_iops\nself.max_write_iops = max_write_iops\nself.read_iops_samples = read_iops_samples\nself.write_iops_samples = write_iops_samples",
"if dictionary is None:\n return None\nmax_read_iops = dictionary.get('maxReadIops')\nmax_write_iops = dictionary.get('maxWriteIops')\nread_iops_... | <|body_start_0|>
self.max_read_iops = max_read_iops
self.max_write_iops = max_write_iops
self.read_iops_samples = read_iops_samples
self.write_iops_samples = write_iops_samples
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
max_read_iops =... | Implementation of the 'IopsTile' model. IOPs information for dashboard. Attributes: max_read_iops (long|int): Maximum Read IOs per second in last 24 hours. max_write_iops (long|int): Maximum number of Write IOs per second in last 24 hours. read_iops_samples (list of Sample): Read IOs per second samples taken for the pa... | IopsTile | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IopsTile:
"""Implementation of the 'IopsTile' model. IOPs information for dashboard. Attributes: max_read_iops (long|int): Maximum Read IOs per second in last 24 hours. max_write_iops (long|int): Maximum number of Write IOs per second in last 24 hours. read_iops_samples (list of Sample): Read IOs... | stack_v2_sparse_classes_36k_train_009377 | 3,022 | permissive | [
{
"docstring": "Constructor for the IopsTile class",
"name": "__init__",
"signature": "def __init__(self, max_read_iops=None, max_write_iops=None, read_iops_samples=None, write_iops_samples=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary... | 2 | stack_v2_sparse_classes_30k_train_006975 | Implement the Python class `IopsTile` described below.
Class description:
Implementation of the 'IopsTile' model. IOPs information for dashboard. Attributes: max_read_iops (long|int): Maximum Read IOs per second in last 24 hours. max_write_iops (long|int): Maximum number of Write IOs per second in last 24 hours. read_... | Implement the Python class `IopsTile` described below.
Class description:
Implementation of the 'IopsTile' model. IOPs information for dashboard. Attributes: max_read_iops (long|int): Maximum Read IOs per second in last 24 hours. max_write_iops (long|int): Maximum number of Write IOs per second in last 24 hours. read_... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class IopsTile:
"""Implementation of the 'IopsTile' model. IOPs information for dashboard. Attributes: max_read_iops (long|int): Maximum Read IOs per second in last 24 hours. max_write_iops (long|int): Maximum number of Write IOs per second in last 24 hours. read_iops_samples (list of Sample): Read IOs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IopsTile:
"""Implementation of the 'IopsTile' model. IOPs information for dashboard. Attributes: max_read_iops (long|int): Maximum Read IOs per second in last 24 hours. max_write_iops (long|int): Maximum number of Write IOs per second in last 24 hours. read_iops_samples (list of Sample): Read IOs per second s... | the_stack_v2_python_sparse | cohesity_management_sdk/models/iops_tile.py | cohesity/management-sdk-python | train | 24 |
8b3aaf3bd64ee5974784f01d2050272f652f9b50 | [
"archive = models.Entry.objects.filter(is_published=True).order_by('-pub_date')\nif not archive:\n return {'list': archive, 'display_year': None, 'display_month': None}\nif display_year is None and display_month is None:\n display_year, display_month = (archive[0].pub_date.year, archive[0].pub_date.month)\nel... | <|body_start_0|>
archive = models.Entry.objects.filter(is_published=True).order_by('-pub_date')
if not archive:
return {'list': archive, 'display_year': None, 'display_month': None}
if display_year is None and display_month is None:
display_year, display_month = (archive[... | BlogMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlogMixin:
def get_archive(request, display_year=None, display_month=None):
"""Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param disp... | stack_v2_sparse_classes_36k_train_009378 | 7,485 | no_license | [
{
"docstring": "Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param display_year : The year (integer or None) which should be opened by default when this archi... | 3 | stack_v2_sparse_classes_30k_train_013449 | Implement the Python class `BlogMixin` described below.
Class description:
Implement the BlogMixin class.
Method signatures and docstrings:
- def get_archive(request, display_year=None, display_month=None): Generate a query set which list which provides a calendarised archive :param request : The WSGI request which t... | Implement the Python class `BlogMixin` described below.
Class description:
Implement the BlogMixin class.
Method signatures and docstrings:
- def get_archive(request, display_year=None, display_month=None): Generate a query set which list which provides a calendarised archive :param request : The WSGI request which t... | 3379c5d5f2105a2cefc63ca6a5bf2bc3b995a8a3 | <|skeleton|>
class BlogMixin:
def get_archive(request, display_year=None, display_month=None):
"""Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param disp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BlogMixin:
def get_archive(request, display_year=None, display_month=None):
"""Generate a query set which list which provides a calendarised archive :param request : The WSGI request which triggers this archive display, used to identify if draft documents should be displayed. :param display_year : The... | the_stack_v2_python_sparse | blog/views.py | TonyFlury/SuffolkCycleDjango | train | 0 | |
df1ef78c6479f5da68addb41e3f82c2f3815efa1 | [
"if not v:\n raise InvalidOrderData(order_id='order_id is required')\nif type(v) != int:\n raise InvalidOrderData(order_id='order_id must be integer')\nif v < 0 or v > 9223372036854775807:\n raise InvalidOrderData(order_id='order_id out of allowed range')\nreturn v",
"if not v:\n raise InvalidOrderDat... | <|body_start_0|>
if not v:
raise InvalidOrderData(order_id='order_id is required')
if type(v) != int:
raise InvalidOrderData(order_id='order_id must be integer')
if v < 0 or v > 9223372036854775807:
raise InvalidOrderData(order_id='order_id out of allowed rang... | Структура данных, описывающая заказ | OrderDataModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrderDataModel:
"""Структура данных, описывающая заказ"""
def validate_order_id(cls, v: int) -> int:
"""Валидирует order_id заказа"""
<|body_0|>
def validate_region(cls, v):
"""Валидирует регион заказа"""
<|body_1|>
def validate_weight(cls, v):
... | stack_v2_sparse_classes_36k_train_009379 | 8,762 | no_license | [
{
"docstring": "Валидирует order_id заказа",
"name": "validate_order_id",
"signature": "def validate_order_id(cls, v: int) -> int"
},
{
"docstring": "Валидирует регион заказа",
"name": "validate_region",
"signature": "def validate_region(cls, v)"
},
{
"docstring": "Валидирует вес... | 5 | stack_v2_sparse_classes_30k_train_016108 | Implement the Python class `OrderDataModel` described below.
Class description:
Структура данных, описывающая заказ
Method signatures and docstrings:
- def validate_order_id(cls, v: int) -> int: Валидирует order_id заказа
- def validate_region(cls, v): Валидирует регион заказа
- def validate_weight(cls, v): Валидируе... | Implement the Python class `OrderDataModel` described below.
Class description:
Структура данных, описывающая заказ
Method signatures and docstrings:
- def validate_order_id(cls, v: int) -> int: Валидирует order_id заказа
- def validate_region(cls, v): Валидирует регион заказа
- def validate_weight(cls, v): Валидируе... | f1a908e5d6b30b826c38d24c52a721764f056fde | <|skeleton|>
class OrderDataModel:
"""Структура данных, описывающая заказ"""
def validate_order_id(cls, v: int) -> int:
"""Валидирует order_id заказа"""
<|body_0|>
def validate_region(cls, v):
"""Валидирует регион заказа"""
<|body_1|>
def validate_weight(cls, v):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OrderDataModel:
"""Структура данных, описывающая заказ"""
def validate_order_id(cls, v: int) -> int:
"""Валидирует order_id заказа"""
if not v:
raise InvalidOrderData(order_id='order_id is required')
if type(v) != int:
raise InvalidOrderData(order_id='order... | the_stack_v2_python_sparse | candyapi/orders/validators.py | IntAlgambra/candyapi | train | 0 |
de6e762640a323ce1881869d5afa307dc0c3eeb5 | [
"self.keymap = {}\nself.freqmap = defaultdict(OrderedDict)\nself.cap = capacity\nself.minfreq = 1",
"if key not in self.keymap:\n return -1\nval, freq = self.keymap[key]\ndel self.freqmap[freq][key]\nself.keymap[key] = (val, freq + 1)\nself.freqmap[freq + 1][key] = 0\nif not self.freqmap[self.minfreq]:\n se... | <|body_start_0|>
self.keymap = {}
self.freqmap = defaultdict(OrderedDict)
self.cap = capacity
self.minfreq = 1
<|end_body_0|>
<|body_start_1|>
if key not in self.keymap:
return -1
val, freq = self.keymap[key]
del self.freqmap[freq][key]
self.k... | LFUCache2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LFUCache2:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, val):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_sk... | stack_v2_sparse_classes_36k_train_009380 | 4,619 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | null | Implement the Python class `LFUCache2` described below.
Class description:
Implement the LFUCache2 class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, val): :type key: int :type value: int :rtype: void | Implement the Python class `LFUCache2` described below.
Class description:
Implement the LFUCache2 class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, val): :type key: int :type value: int :rtype: void
<|sk... | b1764cd62e1c8cb062869992d9eaa8b2d2fdf9c2 | <|skeleton|>
class LFUCache2:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, val):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_sk... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LFUCache2:
def __init__(self, capacity):
""":type capacity: int"""
self.keymap = {}
self.freqmap = defaultdict(OrderedDict)
self.cap = capacity
self.minfreq = 1
def get(self, key):
""":type key: int :rtype: int"""
if key not in self.keymap:
... | the_stack_v2_python_sparse | leetcode/design/hard/460. LFU Cache.py | Hk4Fun/algorithm_offer | train | 1 | |
bfb353d20936944ba22ac4c21d6befdc24ba3eff | [
"self.sequence = rnaSequence\nself.pairedBases = {}\nself.computationMatrix = [[]]",
"self.computationMatrix = [[0 for i in range(len(self.sequence) + 1)] for j in range(len(self.sequence))]\ni = 2\nwhile i <= len(self.sequence):\n k = i\n j = 0\n while j <= len(self.sequence) - 2 and k <= len(self.seque... | <|body_start_0|>
self.sequence = rnaSequence
self.pairedBases = {}
self.computationMatrix = [[]]
<|end_body_0|>
<|body_start_1|>
self.computationMatrix = [[0 for i in range(len(self.sequence) + 1)] for j in range(len(self.sequence))]
i = 2
while i <= len(self.sequence):
... | The algorithm of Nussinov is a RNA secondary structure folding algorithm. It was developed by Ruth Nussinov et al. and was published in 1978: Nussinov, Ruth, et al. "Algorithms for loop matchings." SIAM Journal on Applied mathematics 35.1 (1978): 68-82. http://rci.rutgers.edu/~piecze/GriggsNussinovKleitmanPieczenik.pdf | Nussinov | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Nussinov:
"""The algorithm of Nussinov is a RNA secondary structure folding algorithm. It was developed by Ruth Nussinov et al. and was published in 1978: Nussinov, Ruth, et al. "Algorithms for loop matchings." SIAM Journal on Applied mathematics 35.1 (1978): 68-82. http://rci.rutgers.edu/~piecze... | stack_v2_sparse_classes_36k_train_009381 | 4,360 | no_license | [
{
"docstring": "rnaSequence: The RNA sequence for which the folding should be computed.",
"name": "__init__",
"signature": "def __init__(self, rnaSequence)"
},
{
"docstring": "This function computes the matrix which the Nussinov-algorithm is based on.",
"name": "computeMatrix",
"signatur... | 6 | null | Implement the Python class `Nussinov` described below.
Class description:
The algorithm of Nussinov is a RNA secondary structure folding algorithm. It was developed by Ruth Nussinov et al. and was published in 1978: Nussinov, Ruth, et al. "Algorithms for loop matchings." SIAM Journal on Applied mathematics 35.1 (1978)... | Implement the Python class `Nussinov` described below.
Class description:
The algorithm of Nussinov is a RNA secondary structure folding algorithm. It was developed by Ruth Nussinov et al. and was published in 1978: Nussinov, Ruth, et al. "Algorithms for loop matchings." SIAM Journal on Applied mathematics 35.1 (1978)... | 20d8df6172906337f81583dabb841d66b8f31857 | <|skeleton|>
class Nussinov:
"""The algorithm of Nussinov is a RNA secondary structure folding algorithm. It was developed by Ruth Nussinov et al. and was published in 1978: Nussinov, Ruth, et al. "Algorithms for loop matchings." SIAM Journal on Applied mathematics 35.1 (1978): 68-82. http://rci.rutgers.edu/~piecze... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Nussinov:
"""The algorithm of Nussinov is a RNA secondary structure folding algorithm. It was developed by Ruth Nussinov et al. and was published in 1978: Nussinov, Ruth, et al. "Algorithms for loop matchings." SIAM Journal on Applied mathematics 35.1 (1978): 68-82. http://rci.rutgers.edu/~piecze/GriggsNussin... | the_stack_v2_python_sparse | new_algs/Sequence+algorithms/Needleman-Wunsch+algorithm/nussinov.py | coolsnake/JupyterNotebook | train | 0 |
00bc4d8f7226100738131c6b2696c0838e227e72 | [
"self.__hazard_func = hazard_func\nself.__likelihood_func = likelihood_func\nself.__eps = eps\nself.__R_prev = np.ones(1)\nself.__run_length = 0",
"predprobs = self.__likelihood_func.pdf(x)\nself.__run_length = len(self.__R_prev)\nH = self.__hazard_func(self.__run_length)\nR = np.zeros(self.__run_length + 1)\nR[1... | <|body_start_0|>
self.__hazard_func = hazard_func
self.__likelihood_func = likelihood_func
self.__eps = eps
self.__R_prev = np.ones(1)
self.__run_length = 0
<|end_body_0|>
<|body_start_1|>
predprobs = self.__likelihood_func.pdf(x)
self.__run_length = len(self.__R... | BOCPD (Prospective) | Prospective | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Prospective:
"""BOCPD (Prospective)"""
def __init__(self, hazard_func, likelihood_func, eps=0.0001):
"""Args: hazard_func: hazard function likelihood_func: likelihood function"""
<|body_0|>
def update(self, x):
"""calculate the score of the input datum Args: x: i... | stack_v2_sparse_classes_36k_train_009382 | 5,419 | permissive | [
{
"docstring": "Args: hazard_func: hazard function likelihood_func: likelihood function",
"name": "__init__",
"signature": "def __init__(self, hazard_func, likelihood_func, eps=0.0001)"
},
{
"docstring": "calculate the score of the input datum Args: x: input datum Returns: float: score of the in... | 2 | stack_v2_sparse_classes_30k_test_000569 | Implement the Python class `Prospective` described below.
Class description:
BOCPD (Prospective)
Method signatures and docstrings:
- def __init__(self, hazard_func, likelihood_func, eps=0.0001): Args: hazard_func: hazard function likelihood_func: likelihood function
- def update(self, x): calculate the score of the i... | Implement the Python class `Prospective` described below.
Class description:
BOCPD (Prospective)
Method signatures and docstrings:
- def __init__(self, hazard_func, likelihood_func, eps=0.0001): Args: hazard_func: hazard function likelihood_func: likelihood function
- def update(self, x): calculate the score of the i... | 7faf99f36ac012799602f32b359dcda089bcd119 | <|skeleton|>
class Prospective:
"""BOCPD (Prospective)"""
def __init__(self, hazard_func, likelihood_func, eps=0.0001):
"""Args: hazard_func: hazard function likelihood_func: likelihood function"""
<|body_0|>
def update(self, x):
"""calculate the score of the input datum Args: x: i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Prospective:
"""BOCPD (Prospective)"""
def __init__(self, hazard_func, likelihood_func, eps=0.0001):
"""Args: hazard_func: hazard function likelihood_func: likelihood function"""
self.__hazard_func = hazard_func
self.__likelihood_func = likelihood_func
self.__eps = eps
... | the_stack_v2_python_sparse | bocpd/bocpd.py | IbarakikenYukishi/two-stage-MDL | train | 4 |
d73db0d469c9ef6549e05e9f8e33365764b552e0 | [
"mode = 'r'\nif byte:\n mode += 'b'\ntry:\n with open(src, mode) as file:\n content = file.read()\n file.close()\n return content\nexcept FileNotFoundError:\n return None",
"with open(src, 'w') as file:\n file.write(content)\n file.close()"
] | <|body_start_0|>
mode = 'r'
if byte:
mode += 'b'
try:
with open(src, mode) as file:
content = file.read()
file.close()
return content
except FileNotFoundError:
return None
<|end_body_0|>
<|body_start_1|>... | Class to handle file opening and saving in operating system. | FileRepository | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileRepository:
"""Class to handle file opening and saving in operating system."""
def open_file(self, src, byte=False):
"""open_file handles opening files for importing memos as markdown files and importing images to database. Handles interaction with OS. Args: src: location for fil... | stack_v2_sparse_classes_36k_train_009383 | 1,312 | no_license | [
{
"docstring": "open_file handles opening files for importing memos as markdown files and importing images to database. Handles interaction with OS. Args: src: location for file to be opened byte: optional, used to read images as bytes to be coverted to base64-string. If True, function will add byte indicator i... | 2 | stack_v2_sparse_classes_30k_train_011607 | Implement the Python class `FileRepository` described below.
Class description:
Class to handle file opening and saving in operating system.
Method signatures and docstrings:
- def open_file(self, src, byte=False): open_file handles opening files for importing memos as markdown files and importing images to database.... | Implement the Python class `FileRepository` described below.
Class description:
Class to handle file opening and saving in operating system.
Method signatures and docstrings:
- def open_file(self, src, byte=False): open_file handles opening files for importing memos as markdown files and importing images to database.... | 816990c4432d4e9db0818f6747a9ee482bb9f192 | <|skeleton|>
class FileRepository:
"""Class to handle file opening and saving in operating system."""
def open_file(self, src, byte=False):
"""open_file handles opening files for importing memos as markdown files and importing images to database. Handles interaction with OS. Args: src: location for fil... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileRepository:
"""Class to handle file opening and saving in operating system."""
def open_file(self, src, byte=False):
"""open_file handles opening files for importing memos as markdown files and importing images to database. Handles interaction with OS. Args: src: location for file to be opene... | the_stack_v2_python_sparse | src/repositories/file_repository.py | FinThunderstorm/ohte | train | 0 |
1a3f2350e08643506ab97436b3bac296f297cd0e | [
"body = eval(response_self.request.body)\nuser_id = str(body['userId'])\ndata = str(body['data'])\nif judgeIfPermiss(user_id=user_id, mode=1, page='systemUserTeam') == False:\n return {'status': 0, 'errorInfo': '用户没有权限设置'}\nelse:\n return self.insertInMysql(data)",
"try:\n data = eval(data)\nexcept:\n ... | <|body_start_0|>
body = eval(response_self.request.body)
user_id = str(body['userId'])
data = str(body['data'])
if judgeIfPermiss(user_id=user_id, mode=1, page='systemUserTeam') == False:
return {'status': 0, 'errorInfo': '用户没有权限设置'}
else:
return self.inse... | 添加一个用户，前端发送来的信息为： "userId": "admin", "data": { "name": "用户组三", "description": "巴拉巴拉", } 本函数接收该信息，判断userId用户是否拥有该权限并根据结果将其添加入库，返回： { "status": 1, #1表示成功，0表示失败 "errorInfo": "用户没有权限设置", #status为0时，前端展示errorinfo } | AddOneUserTeam | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AddOneUserTeam:
"""添加一个用户，前端发送来的信息为： "userId": "admin", "data": { "name": "用户组三", "description": "巴拉巴拉", } 本函数接收该信息，判断userId用户是否拥有该权限并根据结果将其添加入库，返回： { "status": 1, #1表示成功，0表示失败 "errorInfo": "用户没有权限设置", #status为0时，前端展示errorinfo }"""
def entry(self, response_self):
"""response为tornado下... | stack_v2_sparse_classes_36k_train_009384 | 2,571 | no_license | [
{
"docstring": "response为tornado下get函数接收到前端数据后的self",
"name": "entry",
"signature": "def entry(self, response_self)"
},
{
"docstring": "对前端发来的data进行校验",
"name": "judgePara",
"signature": "def judgePara(self, data)"
},
{
"docstring": "将data中用户组信息入库",
"name": "insertInMysql",
... | 3 | stack_v2_sparse_classes_30k_train_020998 | Implement the Python class `AddOneUserTeam` described below.
Class description:
添加一个用户，前端发送来的信息为： "userId": "admin", "data": { "name": "用户组三", "description": "巴拉巴拉", } 本函数接收该信息，判断userId用户是否拥有该权限并根据结果将其添加入库，返回： { "status": 1, #1表示成功，0表示失败 "errorInfo": "用户没有权限设置", #status为0时，前端展示errorinfo }
Method signatures and docstr... | Implement the Python class `AddOneUserTeam` described below.
Class description:
添加一个用户，前端发送来的信息为： "userId": "admin", "data": { "name": "用户组三", "description": "巴拉巴拉", } 本函数接收该信息，判断userId用户是否拥有该权限并根据结果将其添加入库，返回： { "status": 1, #1表示成功，0表示失败 "errorInfo": "用户没有权限设置", #status为0时，前端展示errorinfo }
Method signatures and docstr... | a31364869894c72349e3587944ecb4fda018e020 | <|skeleton|>
class AddOneUserTeam:
"""添加一个用户，前端发送来的信息为： "userId": "admin", "data": { "name": "用户组三", "description": "巴拉巴拉", } 本函数接收该信息，判断userId用户是否拥有该权限并根据结果将其添加入库，返回： { "status": 1, #1表示成功，0表示失败 "errorInfo": "用户没有权限设置", #status为0时，前端展示errorinfo }"""
def entry(self, response_self):
"""response为tornado下... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AddOneUserTeam:
"""添加一个用户，前端发送来的信息为： "userId": "admin", "data": { "name": "用户组三", "description": "巴拉巴拉", } 本函数接收该信息，判断userId用户是否拥有该权限并根据结果将其添加入库，返回： { "status": 1, #1表示成功，0表示失败 "errorInfo": "用户没有权限设置", #status为0时，前端展示errorinfo }"""
def entry(self, response_self):
"""response为tornado下get函数接收到前端数据后... | the_stack_v2_python_sparse | tornado/system/add_one_user_team.py | fxrc/care-system | train | 1 |
ed0e5486a930eac5e46f41a6091d2fb40fd24738 | [
"for key, value in son.items():\n if isinstance(value, api.CachedValue):\n son[key] = value.payload\n son['meta'] = value.metadata\n elif isinstance(value, dict):\n son[key] = self.transform_incoming(value, collection)\nreturn son",
"metadata = None\nif isinstance(son, dict) and all((k ... | <|body_start_0|>
for key, value in son.items():
if isinstance(value, api.CachedValue):
son[key] = value.payload
son['meta'] = value.metadata
elif isinstance(value, dict):
son[key] = self.transform_incoming(value, collection)
return ... | Base transformation class to store and read dogpile cached data from MongoDB. This is needed as dogpile internally stores data as a custom class i.e. dogpile.cache.api.CachedValue Note: Custom manipulator needs to always override ``transform_incoming`` and ``transform_outgoing`` methods. MongoDB manipulator logic speci... | BaseTransform | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseTransform:
"""Base transformation class to store and read dogpile cached data from MongoDB. This is needed as dogpile internally stores data as a custom class i.e. dogpile.cache.api.CachedValue Note: Custom manipulator needs to always override ``transform_incoming`` and ``transform_outgoing``... | stack_v2_sparse_classes_36k_train_009385 | 23,527 | permissive | [
{
"docstring": "Used while saving data to MongoDB.",
"name": "transform_incoming",
"signature": "def transform_incoming(self, son, collection)"
},
{
"docstring": "Used while reading data from MongoDB.",
"name": "transform_outgoing",
"signature": "def transform_outgoing(self, son, collect... | 2 | stack_v2_sparse_classes_30k_train_002476 | Implement the Python class `BaseTransform` described below.
Class description:
Base transformation class to store and read dogpile cached data from MongoDB. This is needed as dogpile internally stores data as a custom class i.e. dogpile.cache.api.CachedValue Note: Custom manipulator needs to always override ``transfor... | Implement the Python class `BaseTransform` described below.
Class description:
Base transformation class to store and read dogpile cached data from MongoDB. This is needed as dogpile internally stores data as a custom class i.e. dogpile.cache.api.CachedValue Note: Custom manipulator needs to always override ``transfor... | 0dfdfcfbc239d55d0669cd32e92b93487939ef84 | <|skeleton|>
class BaseTransform:
"""Base transformation class to store and read dogpile cached data from MongoDB. This is needed as dogpile internally stores data as a custom class i.e. dogpile.cache.api.CachedValue Note: Custom manipulator needs to always override ``transform_incoming`` and ``transform_outgoing``... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseTransform:
"""Base transformation class to store and read dogpile cached data from MongoDB. This is needed as dogpile internally stores data as a custom class i.e. dogpile.cache.api.CachedValue Note: Custom manipulator needs to always override ``transform_incoming`` and ``transform_outgoing`` methods. Mon... | the_stack_v2_python_sparse | keystone/common/cache/backends/mongo.py | ging/keystone | train | 4 |
345280c6c4b3d63a3e64f006a2960838ac3c0c5c | [
"self.templdirs = [os.path.dirname(__file__)]\nif dirs:\n self.templdirs.extend(dirs)\nself._charset = charset\ndu = False\nif self._charset.lower() != 'utf-8':\n du = True\nself.tlookup = TemplateLookup(directories=self.templdirs, disable_unicode=du, input_encoding=self._charset, output_encoding=self._charse... | <|body_start_0|>
self.templdirs = [os.path.dirname(__file__)]
if dirs:
self.templdirs.extend(dirs)
self._charset = charset
du = False
if self._charset.lower() != 'utf-8':
du = True
self.tlookup = TemplateLookup(directories=self.templdirs, disable_u... | A TemplateEngine class for Mako template. | MakoTemplateEngine | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MakoTemplateEngine:
"""A TemplateEngine class for Mako template."""
def __init__(self, extension=DEFAULT_EXTENSION, dirs=[], charset='utf-8'):
"""Initialization method"""
<|body_0|>
def get_template(self, path='', string='', tid=''):
"""A method to obtain templat... | stack_v2_sparse_classes_36k_train_009386 | 8,943 | permissive | [
{
"docstring": "Initialization method",
"name": "__init__",
"signature": "def __init__(self, extension=DEFAULT_EXTENSION, dirs=[], charset='utf-8')"
},
{
"docstring": "A method to obtain template object, by using given path or string. When argment path is given, method produce template string vi... | 3 | stack_v2_sparse_classes_30k_train_012372 | Implement the Python class `MakoTemplateEngine` described below.
Class description:
A TemplateEngine class for Mako template.
Method signatures and docstrings:
- def __init__(self, extension=DEFAULT_EXTENSION, dirs=[], charset='utf-8'): Initialization method
- def get_template(self, path='', string='', tid=''): A met... | Implement the Python class `MakoTemplateEngine` described below.
Class description:
A TemplateEngine class for Mako template.
Method signatures and docstrings:
- def __init__(self, extension=DEFAULT_EXTENSION, dirs=[], charset='utf-8'): Initialization method
- def get_template(self, path='', string='', tid=''): A met... | e1209f7d44d1c59ff9d373b7d89d414f31a9c28b | <|skeleton|>
class MakoTemplateEngine:
"""A TemplateEngine class for Mako template."""
def __init__(self, extension=DEFAULT_EXTENSION, dirs=[], charset='utf-8'):
"""Initialization method"""
<|body_0|>
def get_template(self, path='', string='', tid=''):
"""A method to obtain templat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MakoTemplateEngine:
"""A TemplateEngine class for Mako template."""
def __init__(self, extension=DEFAULT_EXTENSION, dirs=[], charset='utf-8'):
"""Initialization method"""
self.templdirs = [os.path.dirname(__file__)]
if dirs:
self.templdirs.extend(dirs)
self._ch... | the_stack_v2_python_sparse | aha/widget/handler.py | Letractively/aha-gae | train | 0 |
5f9dd74edaf1bd7e7a8605ccd8aa461770078303 | [
"ArgsUtils.addIfMissing('yLabel', 'Frequency', kwargs)\nsuper(BarPlot, self).__init__(**kwargs)\nself.color = kwargs.get('color', 'b')\nself.strokeColor = kwargs.get('strokeColor', 'none')\nself.data = kwargs.get('data', [])\nself.isLog = kwargs.get('isLog', False)",
"if not self.xLimits or not len(self.xLimits) ... | <|body_start_0|>
ArgsUtils.addIfMissing('yLabel', 'Frequency', kwargs)
super(BarPlot, self).__init__(**kwargs)
self.color = kwargs.get('color', 'b')
self.strokeColor = kwargs.get('strokeColor', 'none')
self.data = kwargs.get('data', [])
self.isLog = kwargs.get('isLog', Fa... | A class for... | BarPlot | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BarPlot:
"""A class for..."""
def __init__(self, **kwargs):
"""Creates a new instance of BarPlot."""
<|body_0|>
def shaveDataToXLimits(self):
"""shaveData doc..."""
<|body_1|>
def _plot(self):
"""_plot doc..."""
<|body_2|>
def _d... | stack_v2_sparse_classes_36k_train_009387 | 3,328 | no_license | [
{
"docstring": "Creates a new instance of BarPlot.",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "shaveData doc...",
"name": "shaveDataToXLimits",
"signature": "def shaveDataToXLimits(self)"
},
{
"docstring": "_plot doc...",
"name": "_plo... | 4 | null | Implement the Python class `BarPlot` described below.
Class description:
A class for...
Method signatures and docstrings:
- def __init__(self, **kwargs): Creates a new instance of BarPlot.
- def shaveDataToXLimits(self): shaveData doc...
- def _plot(self): _plot doc...
- def _dataItemToValue(cls, value): _dataItemToV... | Implement the Python class `BarPlot` described below.
Class description:
A class for...
Method signatures and docstrings:
- def __init__(self, **kwargs): Creates a new instance of BarPlot.
- def shaveDataToXLimits(self): shaveData doc...
- def _plot(self): _plot doc...
- def _dataItemToValue(cls, value): _dataItemToV... | bcd0d80077c68cf4bb515d643e51f62dd6c4caaa | <|skeleton|>
class BarPlot:
"""A class for..."""
def __init__(self, **kwargs):
"""Creates a new instance of BarPlot."""
<|body_0|>
def shaveDataToXLimits(self):
"""shaveData doc..."""
<|body_1|>
def _plot(self):
"""_plot doc..."""
<|body_2|>
def _d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BarPlot:
"""A class for..."""
def __init__(self, **kwargs):
"""Creates a new instance of BarPlot."""
ArgsUtils.addIfMissing('yLabel', 'Frequency', kwargs)
super(BarPlot, self).__init__(**kwargs)
self.color = kwargs.get('color', 'b')
self.strokeColor = kwargs.get('s... | the_stack_v2_python_sparse | src/cadence/analysis/shared/plotting/BarPlot.py | sernst/Cadence | train | 2 |
8e174c9f68a2282c3ff7ef48282a2251f300468b | [
"self.episodes = []\nself.buffer_size = buffer_size\nself.timesteps = 0\nself.rollout_length = rollout_length\nself.batch_size = batch_size\nself.learning_starts = learning_starts",
"self.timesteps += batch.count\nepisodes = batch.split_by_episode()\nfor i, e in enumerate(episodes):\n episodes[i] = self.prepro... | <|body_start_0|>
self.episodes = []
self.buffer_size = buffer_size
self.timesteps = 0
self.rollout_length = rollout_length
self.batch_size = batch_size
self.learning_starts = learning_starts
<|end_body_0|>
<|body_start_1|>
self.timesteps += batch.count
ep... | EpisodicBuffer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EpisodicBuffer:
def __init__(self, buffer_size: int=1000, rollout_length: int=50, batch_size: int=50, learning_starts: int=1000):
"""Data structure that stores episodes and samples chunks of size length from episodes Args: max_length: Maximum episodes it can store length: Episode chunkin... | stack_v2_sparse_classes_36k_train_009388 | 9,345 | no_license | [
{
"docstring": "Data structure that stores episodes and samples chunks of size length from episodes Args: max_length: Maximum episodes it can store length: Episode chunking lengh in sample()",
"name": "__init__",
"signature": "def __init__(self, buffer_size: int=1000, rollout_length: int=50, batch_size:... | 4 | null | Implement the Python class `EpisodicBuffer` described below.
Class description:
Implement the EpisodicBuffer class.
Method signatures and docstrings:
- def __init__(self, buffer_size: int=1000, rollout_length: int=50, batch_size: int=50, learning_starts: int=1000): Data structure that stores episodes and samples chun... | Implement the Python class `EpisodicBuffer` described below.
Class description:
Implement the EpisodicBuffer class.
Method signatures and docstrings:
- def __init__(self, buffer_size: int=1000, rollout_length: int=50, batch_size: int=50, learning_starts: int=1000): Data structure that stores episodes and samples chun... | b96284768a5bd7e5d7f407b28ca1a905a7575c93 | <|skeleton|>
class EpisodicBuffer:
def __init__(self, buffer_size: int=1000, rollout_length: int=50, batch_size: int=50, learning_starts: int=1000):
"""Data structure that stores episodes and samples chunks of size length from episodes Args: max_length: Maximum episodes it can store length: Episode chunkin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EpisodicBuffer:
def __init__(self, buffer_size: int=1000, rollout_length: int=50, batch_size: int=50, learning_starts: int=1000):
"""Data structure that stores episodes and samples chunks of size length from episodes Args: max_length: Maximum episodes it can store length: Episode chunking lengh in sam... | the_stack_v2_python_sparse | agents/dreamer/dreamer.py | zizai/notebooks | train | 3 | |
3bc73e5bad6d23843d9e5538e1a24e56462adc64 | [
"use_base_name = base_name\nif use_base_name is None:\n use_base_name = self.get_base_name(candidate_id, generation)\nuse_dir = experiment_dir\nif generation is not None:\n filer = GenerationFiler(experiment_dir, generation)\n use_dir = filer.get_generation_dir()\ndictionary_converter = CandidateDictionary... | <|body_start_0|>
use_base_name = base_name
if use_base_name is None:
use_base_name = self.get_base_name(candidate_id, generation)
use_dir = experiment_dir
if generation is not None:
filer = GenerationFiler(experiment_dir, generation)
use_dir = filer.ge... | A class which knows how to persist a candidate dict to/from file(s). A candidate contains a few major fields: * id - the string id of the candidate unique to at least the experiment * identity - a dictionary containing information about the birth circumstances of the candidate * interpretation - which contains a digest... | CandidatePersistence | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CandidatePersistence:
"""A class which knows how to persist a candidate dict to/from file(s). A candidate contains a few major fields: * id - the string id of the candidate unique to at least the experiment * identity - a dictionary containing information about the birth circumstances of the cand... | stack_v2_sparse_classes_36k_train_009389 | 2,857 | no_license | [
{
"docstring": "Constructor. :param experiment_dir: the directory where experiment results go :param candidate_id: the id of the candidate :param generation: the generation number for the candidate :param base_name: a full base name to use (minus extension) :param logger: The logger to use for messaging",
"... | 2 | null | Implement the Python class `CandidatePersistence` described below.
Class description:
A class which knows how to persist a candidate dict to/from file(s). A candidate contains a few major fields: * id - the string id of the candidate unique to at least the experiment * identity - a dictionary containing information ab... | Implement the Python class `CandidatePersistence` described below.
Class description:
A class which knows how to persist a candidate dict to/from file(s). A candidate contains a few major fields: * id - the string id of the candidate unique to at least the experiment * identity - a dictionary containing information ab... | 99c2f401d6c4b203ee439ed607985a918d0c3c7e | <|skeleton|>
class CandidatePersistence:
"""A class which knows how to persist a candidate dict to/from file(s). A candidate contains a few major fields: * id - the string id of the candidate unique to at least the experiment * identity - a dictionary containing information about the birth circumstances of the cand... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CandidatePersistence:
"""A class which knows how to persist a candidate dict to/from file(s). A candidate contains a few major fields: * id - the string id of the candidate unique to at least the experiment * identity - a dictionary containing information about the birth circumstances of the candidate * inter... | the_stack_v2_python_sparse | framework/persistence/candidate_persistence.py | Cognizant-CDB-AIA-BAI-AI-OI/LEAF-ENN-Training-V2 | train | 0 |
76dfa2d0dbb2711c5c98fefe831a2e84e4733d1e | [
"now_sum = cnt = max_num = 0\nfor i, v in enumerate(light):\n now_sum += 1\n max_num = max(v, max_num)\n if max_num == now_sum:\n cnt += 1\nreturn cnt",
"answer = 0\nmax_num = 0\nfor index_i, i in enumerate(light):\n if max_num < i:\n max_num = i\n if index_i + 1 == max_num:\n ... | <|body_start_0|>
now_sum = cnt = max_num = 0
for i, v in enumerate(light):
now_sum += 1
max_num = max(v, max_num)
if max_num == now_sum:
cnt += 1
return cnt
<|end_body_0|>
<|body_start_1|>
answer = 0
max_num = 0
for ind... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numTimesAllBlue(self, light):
""":type light: List[int] :rtype: int"""
<|body_0|>
def numTimesAllBlue(self, light):
""":type light: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
now_sum = cnt = max_num = 0
... | stack_v2_sparse_classes_36k_train_009390 | 875 | no_license | [
{
"docstring": ":type light: List[int] :rtype: int",
"name": "numTimesAllBlue",
"signature": "def numTimesAllBlue(self, light)"
},
{
"docstring": ":type light: List[int] :rtype: int",
"name": "numTimesAllBlue",
"signature": "def numTimesAllBlue(self, light)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTimesAllBlue(self, light): :type light: List[int] :rtype: int
- def numTimesAllBlue(self, light): :type light: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTimesAllBlue(self, light): :type light: List[int] :rtype: int
- def numTimesAllBlue(self, light): :type light: List[int] :rtype: int
<|skeleton|>
class Solution:
def... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def numTimesAllBlue(self, light):
""":type light: List[int] :rtype: int"""
<|body_0|>
def numTimesAllBlue(self, light):
""":type light: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def numTimesAllBlue(self, light):
""":type light: List[int] :rtype: int"""
now_sum = cnt = max_num = 0
for i, v in enumerate(light):
now_sum += 1
max_num = max(v, max_num)
if max_num == now_sum:
cnt += 1
return cnt
... | the_stack_v2_python_sparse | 1375_Bulb_Switcher_III.py | bingli8802/leetcode | train | 0 | |
ab5fcb81574a38223b9c2e5f54c6b8cc599c6491 | [
"session = self.login()\nitems = session.query(NavbarItems)\nresponse = [row2dict(item) for item in items]\nself.logout(session)\nreturn response",
"session = self.login()\nitems = session.query(JqlLinks)\nresponse = [row2dict(item) for item in items]\nself.logout(session)\nreturn response",
"session = self.log... | <|body_start_0|>
session = self.login()
items = session.query(NavbarItems)
response = [row2dict(item) for item in items]
self.logout(session)
return response
<|end_body_0|>
<|body_start_1|>
session = self.login()
items = session.query(JqlLinks)
response =... | Actions for navbar in the DB. | SQLNavBar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SQLNavBar:
"""Actions for navbar in the DB."""
def get_navbar_items(self):
"""Gets all navbar items from DB."""
<|body_0|>
def get_jql_links(self):
"""Gets all JQL links for the DB."""
<|body_1|>
def set_navbar_item(self, item):
"""Sets a nav... | stack_v2_sparse_classes_36k_train_009391 | 1,226 | no_license | [
{
"docstring": "Gets all navbar items from DB.",
"name": "get_navbar_items",
"signature": "def get_navbar_items(self)"
},
{
"docstring": "Gets all JQL links for the DB.",
"name": "get_jql_links",
"signature": "def get_jql_links(self)"
},
{
"docstring": "Sets a navbar item's data.... | 3 | stack_v2_sparse_classes_30k_val_000126 | Implement the Python class `SQLNavBar` described below.
Class description:
Actions for navbar in the DB.
Method signatures and docstrings:
- def get_navbar_items(self): Gets all navbar items from DB.
- def get_jql_links(self): Gets all JQL links for the DB.
- def set_navbar_item(self, item): Sets a navbar item's data... | Implement the Python class `SQLNavBar` described below.
Class description:
Actions for navbar in the DB.
Method signatures and docstrings:
- def get_navbar_items(self): Gets all navbar items from DB.
- def get_jql_links(self): Gets all JQL links for the DB.
- def set_navbar_item(self, item): Sets a navbar item's data... | 52ba4eecd727c200f8ad82652434d171655c5f0a | <|skeleton|>
class SQLNavBar:
"""Actions for navbar in the DB."""
def get_navbar_items(self):
"""Gets all navbar items from DB."""
<|body_0|>
def get_jql_links(self):
"""Gets all JQL links for the DB."""
<|body_1|>
def set_navbar_item(self, item):
"""Sets a nav... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SQLNavBar:
"""Actions for navbar in the DB."""
def get_navbar_items(self):
"""Gets all navbar items from DB."""
session = self.login()
items = session.query(NavbarItems)
response = [row2dict(item) for item in items]
self.logout(session)
return response
... | the_stack_v2_python_sparse | devcenter/sql/navbar.py | ljmerza/devCenter | train | 0 |
01757b69845e10b6b1e6169254f76a7e45bb57a3 | [
"super(ServerXMLRPCLog, self).parse_content(content)\nself.last = None\nmsg_info = {}\nfor l in reversed(self.lines):\n msg_info = self._parse_line(l)\n if 'client_ip' in msg_info:\n break\nself.last = msg_info",
"msg_info = dict()\nmsg_info['raw_message'] = line\nmatch = self._LINE_RE.search(line)\n... | <|body_start_0|>
super(ServerXMLRPCLog, self).parse_content(content)
self.last = None
msg_info = {}
for l in reversed(self.lines):
msg_info = self._parse_line(l)
if 'client_ip' in msg_info:
break
self.last = msg_info
<|end_body_0|>
<|body_... | Class for parsing the ``rhn_server_xmlrpc.log`` file. Sample log line:: 2016/04/11 05:52:01 -04:00 23630 10.4.4.17: xmlrpc/registration.welcome_message('lang: None',) Attributes: lines (list): All lines captured in this file. last (dict): Dict of the last log line. Examples: >>> log = shared[ServerXMLRPCLog] >>> log.fi... | ServerXMLRPCLog | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServerXMLRPCLog:
"""Class for parsing the ``rhn_server_xmlrpc.log`` file. Sample log line:: 2016/04/11 05:52:01 -04:00 23630 10.4.4.17: xmlrpc/registration.welcome_message('lang: None',) Attributes: lines (list): All lines captured in this file. last (dict): Dict of the last log line. Examples: >... | stack_v2_sparse_classes_36k_train_009392 | 7,383 | permissive | [
{
"docstring": "Parse the logs as its super class LogFileOutput. And get the last complete log. If the last line is not complete, then get from its previous line.",
"name": "parse_content",
"signature": "def parse_content(self, content)"
},
{
"docstring": "Parse a log line using the XMLRPC regul... | 2 | null | Implement the Python class `ServerXMLRPCLog` described below.
Class description:
Class for parsing the ``rhn_server_xmlrpc.log`` file. Sample log line:: 2016/04/11 05:52:01 -04:00 23630 10.4.4.17: xmlrpc/registration.welcome_message('lang: None',) Attributes: lines (list): All lines captured in this file. last (dict):... | Implement the Python class `ServerXMLRPCLog` described below.
Class description:
Class for parsing the ``rhn_server_xmlrpc.log`` file. Sample log line:: 2016/04/11 05:52:01 -04:00 23630 10.4.4.17: xmlrpc/registration.welcome_message('lang: None',) Attributes: lines (list): All lines captured in this file. last (dict):... | b0ea07fc3f4dd8801b505fe70e9b36e628152c4a | <|skeleton|>
class ServerXMLRPCLog:
"""Class for parsing the ``rhn_server_xmlrpc.log`` file. Sample log line:: 2016/04/11 05:52:01 -04:00 23630 10.4.4.17: xmlrpc/registration.welcome_message('lang: None',) Attributes: lines (list): All lines captured in this file. last (dict): Dict of the last log line. Examples: >... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ServerXMLRPCLog:
"""Class for parsing the ``rhn_server_xmlrpc.log`` file. Sample log line:: 2016/04/11 05:52:01 -04:00 23630 10.4.4.17: xmlrpc/registration.welcome_message('lang: None',) Attributes: lines (list): All lines captured in this file. last (dict): Dict of the last log line. Examples: >>> log = shar... | the_stack_v2_python_sparse | insights/parsers/rhn_logs.py | RedHatInsights/insights-core | train | 144 |
e85e146b6da17ff5b9efeb4c044d6b3c5e360557 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn UserExperienceAnalyticsBaseline()",
"from .entity import Entity\nfrom .user_experience_analytics_category import UserExperienceAnalyticsCategory\nfrom .entity import Entity\nfrom .user_experience_analytics_category import UserExperienc... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return UserExperienceAnalyticsBaseline()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .user_experience_analytics_category import UserExperienceAnalyticsCategory
from ... | The user experience analytics baseline entity contains baseline values against which to compare the user experience analytics scores. | UserExperienceAnalyticsBaseline | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserExperienceAnalyticsBaseline:
"""The user experience analytics baseline entity contains baseline values against which to compare the user experience analytics scores."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserExperienceAnalyticsBaseline:
"... | stack_v2_sparse_classes_36k_train_009393 | 6,064 | 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: UserExperienceAnalyticsBaseline",
"name": "create_from_discriminator_value",
"signature": "def create_from_d... | 3 | stack_v2_sparse_classes_30k_train_015180 | Implement the Python class `UserExperienceAnalyticsBaseline` described below.
Class description:
The user experience analytics baseline entity contains baseline values against which to compare the user experience analytics scores.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Opt... | Implement the Python class `UserExperienceAnalyticsBaseline` described below.
Class description:
The user experience analytics baseline entity contains baseline values against which to compare the user experience analytics scores.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Opt... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class UserExperienceAnalyticsBaseline:
"""The user experience analytics baseline entity contains baseline values against which to compare the user experience analytics scores."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserExperienceAnalyticsBaseline:
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserExperienceAnalyticsBaseline:
"""The user experience analytics baseline entity contains baseline values against which to compare the user experience analytics scores."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserExperienceAnalyticsBaseline:
"""Creates a n... | the_stack_v2_python_sparse | msgraph/generated/models/user_experience_analytics_baseline.py | microsoftgraph/msgraph-sdk-python | train | 135 |
582f5f7cc2cbdc26dc47ba28039f489fab195fb4 | [
"self.output_path = output_path\nself.max_concurrent_invocations_per_instance = max_concurrent_invocations_per_instance\nself.kms_key_id = kms_key_id\nself.notification_config = notification_config\nself.failure_path = failure_path",
"request_dict = {'OutputConfig': {'S3OutputPath': self.output_path, 'S3FailurePa... | <|body_start_0|>
self.output_path = output_path
self.max_concurrent_invocations_per_instance = max_concurrent_invocations_per_instance
self.kms_key_id = kms_key_id
self.notification_config = notification_config
self.failure_path = failure_path
<|end_body_0|>
<|body_start_1|>
... | Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference | AsyncInferenceConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AsyncInferenceConfig:
"""Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference"""
def __init__(self, output_path=N... | stack_v2_sparse_classes_36k_train_009394 | 4,694 | permissive | [
{
"docstring": "Initialize an AsyncInferenceConfig object for async inference configuration. Args: output_path (str): Optional. The Amazon S3 location that endpoints upload inference responses to. If no value is provided, Amazon SageMaker will use default Amazon S3 Async Inference output path. (Default: None) m... | 2 | stack_v2_sparse_classes_30k_train_007890 | Implement the Python class `AsyncInferenceConfig` described below.
Class description:
Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference
... | Implement the Python class `AsyncInferenceConfig` described below.
Class description:
Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference
... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class AsyncInferenceConfig:
"""Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference"""
def __init__(self, output_path=N... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AsyncInferenceConfig:
"""Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference"""
def __init__(self, output_path=None, max_conc... | the_stack_v2_python_sparse | src/sagemaker/async_inference/async_inference_config.py | aws/sagemaker-python-sdk | train | 2,050 |
e4be228ce98ccdb2030d79c2cae1b6d44268ee6f | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | A set of methods for managing ClickHouse Database resources. NOTE: these methods are available only if database management through SQL is disabled. | DatabaseServiceServicer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatabaseServiceServicer:
"""A set of methods for managing ClickHouse Database resources. NOTE: these methods are available only if database management through SQL is disabled."""
def Get(self, request, context):
"""Returns the specified ClickHouse Database resource. To get the list o... | stack_v2_sparse_classes_36k_train_009395 | 9,134 | permissive | [
{
"docstring": "Returns the specified ClickHouse Database resource. To get the list of available ClickHouse Database resources, make a [List] request.",
"name": "Get",
"signature": "def Get(self, request, context)"
},
{
"docstring": "Retrieves the list of ClickHouse Database resources in the spe... | 4 | null | Implement the Python class `DatabaseServiceServicer` described below.
Class description:
A set of methods for managing ClickHouse Database resources. NOTE: these methods are available only if database management through SQL is disabled.
Method signatures and docstrings:
- def Get(self, request, context): Returns the ... | Implement the Python class `DatabaseServiceServicer` described below.
Class description:
A set of methods for managing ClickHouse Database resources. NOTE: these methods are available only if database management through SQL is disabled.
Method signatures and docstrings:
- def Get(self, request, context): Returns the ... | b906a014dd893e2697864e1e48e814a8d9fbc48c | <|skeleton|>
class DatabaseServiceServicer:
"""A set of methods for managing ClickHouse Database resources. NOTE: these methods are available only if database management through SQL is disabled."""
def Get(self, request, context):
"""Returns the specified ClickHouse Database resource. To get the list o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatabaseServiceServicer:
"""A set of methods for managing ClickHouse Database resources. NOTE: these methods are available only if database management through SQL is disabled."""
def Get(self, request, context):
"""Returns the specified ClickHouse Database resource. To get the list of available C... | the_stack_v2_python_sparse | yandex/cloud/mdb/clickhouse/v1/database_service_pb2_grpc.py | yandex-cloud/python-sdk | train | 63 |
0fbb2829d370ad36e4ee4d5a5ff212c23e4a335a | [
"super(GaussianDist, self).__init__(configs=configs, hidden_activation=hidden_activation, use_output_layer=False)\nself.mu_activation = mu_activation\nself.log_std_min = log_std_min\nself.log_std_max = log_std_max\nin_size = configs.hidden_sizes[-1]\nself.fixed_logstd = configs.fixed_logstd\nif self.fixed_logstd:\n... | <|body_start_0|>
super(GaussianDist, self).__init__(configs=configs, hidden_activation=hidden_activation, use_output_layer=False)
self.mu_activation = mu_activation
self.log_std_min = log_std_min
self.log_std_max = log_std_max
in_size = configs.hidden_sizes[-1]
self.fixed... | Multilayer perceptron with Gaussian distribution output. Attributes: mu_activation (function): bounding function for mean log_std_min (float): lower bound of log std log_std_max (float): upper bound of log std mu_layer (nn.Linear): output layer for mean log_std_layer (nn.Linear): output layer for log std | GaussianDist | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianDist:
"""Multilayer perceptron with Gaussian distribution output. Attributes: mu_activation (function): bounding function for mean log_std_min (float): lower bound of log std log_std_max (float): upper bound of log std mu_layer (nn.Linear): output layer for mean log_std_layer (nn.Linear):... | stack_v2_sparse_classes_36k_train_009396 | 7,663 | permissive | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, configs: ConfigDict, hidden_activation: Callable=F.relu, mu_activation: Callable=torch.tanh, log_std_min: float=-20, log_std_max: float=2, init_fn: Callable=init_layer_uniform)"
},
{
"docstring": "Return gausian d... | 3 | stack_v2_sparse_classes_30k_train_012899 | Implement the Python class `GaussianDist` described below.
Class description:
Multilayer perceptron with Gaussian distribution output. Attributes: mu_activation (function): bounding function for mean log_std_min (float): lower bound of log std log_std_max (float): upper bound of log std mu_layer (nn.Linear): output la... | Implement the Python class `GaussianDist` described below.
Class description:
Multilayer perceptron with Gaussian distribution output. Attributes: mu_activation (function): bounding function for mean log_std_min (float): lower bound of log std log_std_max (float): upper bound of log std mu_layer (nn.Linear): output la... | fdfac4e7056ee5a9d5b48b7b9653ce844a03ca22 | <|skeleton|>
class GaussianDist:
"""Multilayer perceptron with Gaussian distribution output. Attributes: mu_activation (function): bounding function for mean log_std_min (float): lower bound of log std log_std_max (float): upper bound of log std mu_layer (nn.Linear): output layer for mean log_std_layer (nn.Linear):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianDist:
"""Multilayer perceptron with Gaussian distribution output. Attributes: mu_activation (function): bounding function for mean log_std_min (float): lower bound of log std log_std_max (float): upper bound of log std mu_layer (nn.Linear): output layer for mean log_std_layer (nn.Linear): output layer... | the_stack_v2_python_sparse | rl_algorithms/common/networks/heads.py | medipixel/rl_algorithms | train | 525 |
74740fad7b96eeb46118e5d97bf81abef5df8f6e | [
"super().__init__(coordinator, device, 'extra', 'Extra meter', f'extra_{dev_type}')\nself._type = dev_type\nself._attr_name = f'Extra {dev_type}'",
"if self.coordinator.data.extra_meter is None:\n return None\nreturn getattr(self.coordinator.data.extra_meter, f'_{self._type}', None)"
] | <|body_start_0|>
super().__init__(coordinator, device, 'extra', 'Extra meter', f'extra_{dev_type}')
self._type = dev_type
self._attr_name = f'Extra {dev_type}'
<|end_body_0|>
<|body_start_1|>
if self.coordinator.data.extra_meter is None:
return None
return getattr(se... | The Youless extra meter power value sensor (s0). | ExtraMeterPowerSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtraMeterPowerSensor:
"""The Youless extra meter power value sensor (s0)."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None:
"""Instantiate an extra meter power sensor."""
<|body_0|>
def get_sensor(self) -> Youless... | stack_v2_sparse_classes_36k_train_009397 | 11,812 | permissive | [
{
"docstring": "Instantiate an extra meter power sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None"
},
{
"docstring": "Get the sensor for providing the value.",
"name": "get_sensor",
"signatu... | 2 | null | Implement the Python class `ExtraMeterPowerSensor` described below.
Class description:
The Youless extra meter power value sensor (s0).
Method signatures and docstrings:
- def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None: Instantiate an extra meter power sensor.
-... | Implement the Python class `ExtraMeterPowerSensor` described below.
Class description:
The Youless extra meter power value sensor (s0).
Method signatures and docstrings:
- def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None: Instantiate an extra meter power sensor.
-... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ExtraMeterPowerSensor:
"""The Youless extra meter power value sensor (s0)."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None:
"""Instantiate an extra meter power sensor."""
<|body_0|>
def get_sensor(self) -> Youless... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExtraMeterPowerSensor:
"""The Youless extra meter power value sensor (s0)."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str) -> None:
"""Instantiate an extra meter power sensor."""
super().__init__(coordinator, device, 'extra', 'Extra meter'... | the_stack_v2_python_sparse | homeassistant/components/youless/sensor.py | home-assistant/core | train | 35,501 |
25ea46673f5cd6641610961618d119b9f708fb5a | [
"test_response = self.client.get('/posts/fixture-post')\nself.assertEqual(test_response.status_code, 200)\nself.assertTemplateUsed(test_response, 'post_detail.html')\nself.assertTemplateUsed(test_response, 'base.html')\nself.assertTemplateUsed(test_response, 'disqus_snippet.html')\nself.assertTemplateUsed(test_resp... | <|body_start_0|>
test_response = self.client.get('/posts/fixture-post')
self.assertEqual(test_response.status_code, 200)
self.assertTemplateUsed(test_response, 'post_detail.html')
self.assertTemplateUsed(test_response, 'base.html')
self.assertTemplateUsed(test_response, 'disqus_s... | These test the views associated with post objects. | PostViewTests | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PostViewTests:
"""These test the views associated with post objects."""
def test_post_details_view(self):
"""This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for thi... | stack_v2_sparse_classes_36k_train_009398 | 14,526 | permissive | [
{
"docstring": "This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.",
"name": "test_post_details_view",
"signature": "def test_post_details_view(self)"
},
{
"docstri... | 5 | stack_v2_sparse_classes_30k_train_007312 | Implement the Python class `PostViewTests` described below.
Class description:
These test the views associated with post objects.
Method signatures and docstrings:
- def test_post_details_view(self): This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser p... | Implement the Python class `PostViewTests` described below.
Class description:
These test the views associated with post objects.
Method signatures and docstrings:
- def test_post_details_view(self): This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser p... | d6f6c9c068bbf668c253e5943d9514947023e66d | <|skeleton|>
class PostViewTests:
"""These test the views associated with post objects."""
def test_post_details_view(self):
"""This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for thi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PostViewTests:
"""These test the views associated with post objects."""
def test_post_details_view(self):
"""This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view."""
... | the_stack_v2_python_sparse | communication/tests.py | BridgesLab/Lab-Website | train | 0 |
9b41c29c1f36dc3cba691d01f18252f53a53f8fc | [
"if not is_MPolynomialRing(domain):\n raise ValueError('domain should be a multivariate polynomial ring')\nif not is_PolynomialRing(codomain) and (not is_MPolynomialRing(codomain)):\n raise ValueError('codomain should be a polynomial ring')\nring = codomain\nintermediate_rings = []\nwhile is_PolynomialRing(ri... | <|body_start_0|>
if not is_MPolynomialRing(domain):
raise ValueError('domain should be a multivariate polynomial ring')
if not is_PolynomialRing(codomain) and (not is_MPolynomialRing(codomain)):
raise ValueError('codomain should be a polynomial ring')
ring = codomain
... | Inverses for :class:`FlatteningMorphism` EXAMPLES:: sage: R = QQ['c','x','y','z'] sage: S = QQ['c']['x','y','z'] sage: from sage.rings.polynomial.flatten import UnflatteningMorphism sage: f = UnflatteningMorphism(R, S) sage: g = f(R('x^2 + c*y^2 - z^2'));g x^2 + c*y^2 - z^2 sage: g.parent() Multivariate Polynomial Ring... | UnflatteningMorphism | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnflatteningMorphism:
"""Inverses for :class:`FlatteningMorphism` EXAMPLES:: sage: R = QQ['c','x','y','z'] sage: S = QQ['c']['x','y','z'] sage: from sage.rings.polynomial.flatten import UnflatteningMorphism sage: f = UnflatteningMorphism(R, S) sage: g = f(R('x^2 + c*y^2 - z^2'));g x^2 + c*y^2 - z... | stack_v2_sparse_classes_36k_train_009399 | 12,176 | no_license | [
{
"docstring": "The Python constructor EXAMPLES:: sage: R = QQ['x']['y']['s','t']['X'] sage: p = R.random_element() sage: from sage.rings.polynomial.flatten import FlatteningMorphism sage: f = FlatteningMorphism(R) sage: g = f.section() sage: g(f(p)) == p True :: sage: R = QQ['a','b','x','y'] sage: S = ZZ['a','... | 2 | null | Implement the Python class `UnflatteningMorphism` described below.
Class description:
Inverses for :class:`FlatteningMorphism` EXAMPLES:: sage: R = QQ['c','x','y','z'] sage: S = QQ['c']['x','y','z'] sage: from sage.rings.polynomial.flatten import UnflatteningMorphism sage: f = UnflatteningMorphism(R, S) sage: g = f(R(... | Implement the Python class `UnflatteningMorphism` described below.
Class description:
Inverses for :class:`FlatteningMorphism` EXAMPLES:: sage: R = QQ['c','x','y','z'] sage: S = QQ['c']['x','y','z'] sage: from sage.rings.polynomial.flatten import UnflatteningMorphism sage: f = UnflatteningMorphism(R, S) sage: g = f(R(... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class UnflatteningMorphism:
"""Inverses for :class:`FlatteningMorphism` EXAMPLES:: sage: R = QQ['c','x','y','z'] sage: S = QQ['c']['x','y','z'] sage: from sage.rings.polynomial.flatten import UnflatteningMorphism sage: f = UnflatteningMorphism(R, S) sage: g = f(R('x^2 + c*y^2 - z^2'));g x^2 + c*y^2 - z... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnflatteningMorphism:
"""Inverses for :class:`FlatteningMorphism` EXAMPLES:: sage: R = QQ['c','x','y','z'] sage: S = QQ['c']['x','y','z'] sage: from sage.rings.polynomial.flatten import UnflatteningMorphism sage: f = UnflatteningMorphism(R, S) sage: g = f(R('x^2 + c*y^2 - z^2'));g x^2 + c*y^2 - z^2 sage: g.pa... | the_stack_v2_python_sparse | sage/src/sage/rings/polynomial/flatten.py | bopopescu/geosci | train | 0 |
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