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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8320a062b4c705f6f2f90053ca66ed686da534b5 | [
"if not root:\n return 0\nlDepth = self.minDepth(root.left)\nrDepth = self.minDepth(root.right)\nif 0 in [lDepth, rDepth]:\n return lDepth + rDepth + 1\nreturn min(lDepth, rDepth) + 1",
"if not root:\n return 0\nq = Queue()\nq.put(root)\nlevel = 1\nwhile not q.empty():\n level += 1\n n = q.qsize()\... | <|body_start_0|>
if not root:
return 0
lDepth = self.minDepth(root.left)
rDepth = self.minDepth(root.right)
if 0 in [lDepth, rDepth]:
return lDepth + rDepth + 1
return min(lDepth, rDepth) + 1
<|end_body_0|>
<|body_start_1|>
if not root:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minDepth(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def minDepthIter(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
return 0
lDepth ... | stack_v2_sparse_classes_36k_train_011600 | 1,284 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "minDepth",
"signature": "def minDepth(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "minDepthIter",
"signature": "def minDepthIter(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDepth(self, root): :type root: TreeNode :rtype: int
- def minDepthIter(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 minDepth(self, root): :type root: TreeNode :rtype: int
- def minDepthIter(self, root): :type root: TreeNode :rtype: int
<|skeleton|>
class Solution:
def minDepth(self, ... | 2d06ab566c8adc6d864c5565310b56008d2d4b31 | <|skeleton|>
class Solution:
def minDepth(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def minDepthIter(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minDepth(self, root):
""":type root: TreeNode :rtype: int"""
if not root:
return 0
lDepth = self.minDepth(root.left)
rDepth = self.minDepth(root.right)
if 0 in [lDepth, rDepth]:
return lDepth + rDepth + 1
return min(lDepth, ... | the_stack_v2_python_sparse | leetcode/Done/111_MinDepthBinaryTree.py | humachine/AlgoLearning | train | 1 | |
b5ed78144468806fc1cfd6fb5bd1b63a7afad66e | [
"ban = [0 for i in range(len(senate))]\ndn = 0\ncrt = ''\nflag = True\nwhile flag:\n i = 0\n flag = False\n while i < len(senate):\n if not ban[i]:\n if dn == 0:\n crt = senate[i]\n dn += 1\n elif crt == senate[i]:\n dn += 1\n ... | <|body_start_0|>
ban = [0 for i in range(len(senate))]
dn = 0
crt = ''
flag = True
while flag:
i = 0
flag = False
while i < len(senate):
if not ban[i]:
if dn == 0:
crt = senate[i]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def predictPartyVictory(self, senate):
""":type senate: str :rtype: str"""
<|body_0|>
def predictPartyVictory2(self, senate):
""":type senate: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ban = [0 for i in range(len(sena... | stack_v2_sparse_classes_36k_train_011601 | 4,412 | no_license | [
{
"docstring": ":type senate: str :rtype: str",
"name": "predictPartyVictory",
"signature": "def predictPartyVictory(self, senate)"
},
{
"docstring": ":type senate: str :rtype: str",
"name": "predictPartyVictory2",
"signature": "def predictPartyVictory2(self, senate)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004799 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def predictPartyVictory(self, senate): :type senate: str :rtype: str
- def predictPartyVictory2(self, senate): :type senate: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def predictPartyVictory(self, senate): :type senate: str :rtype: str
- def predictPartyVictory2(self, senate): :type senate: str :rtype: str
<|skeleton|>
class Solution:
de... | 416fed6e441612e1ad82467d07ee1b5570386a94 | <|skeleton|>
class Solution:
def predictPartyVictory(self, senate):
""":type senate: str :rtype: str"""
<|body_0|>
def predictPartyVictory2(self, senate):
""":type senate: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def predictPartyVictory(self, senate):
""":type senate: str :rtype: str"""
ban = [0 for i in range(len(senate))]
dn = 0
crt = ''
flag = True
while flag:
i = 0
flag = False
while i < len(senate):
if no... | the_stack_v2_python_sparse | src/python/greedy_algorithm/dota2_senate.py | liadbiz/Leetcode-Solutions | train | 1 | |
14b9e6a14811ffbb36dc88818c38256c5156f1ab | [
"calificacion = Calificacion.obtener_calificacion(id_cancion, id_usuario)\nif calificacion is not None:\n error = {'error': 'calificacion_registrada', 'mensaje': 'Ya existe una calificacion registrada'}\n return error",
"calificacion = Calificacion.obtener_calificacion(id_cancion, id_usuario)\nif calificaci... | <|body_start_0|>
calificacion = Calificacion.obtener_calificacion(id_cancion, id_usuario)
if calificacion is not None:
error = {'error': 'calificacion_registrada', 'mensaje': 'Ya existe una calificacion registrada'}
return error
<|end_body_0|>
<|body_start_1|>
calificaci... | ValidacionCalificacion | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValidacionCalificacion:
def validar_existe_calificacion(id_usuario, id_cancion):
"""Valida si existe una Calificacion con el id_usuario y el id_cancion :param id_usuario: El id del usuario que realizo la calificacion :param id_cancion: El id de la cancion calificada :return: Un diccionar... | stack_v2_sparse_classes_36k_train_011602 | 3,691 | no_license | [
{
"docstring": "Valida si existe una Calificacion con el id_usuario y el id_cancion :param id_usuario: El id del usuario que realizo la calificacion :param id_cancion: El id de la cancion calificada :return: Un diccionario indicando que existe una calificacion registrada o None si no",
"name": "validar_exis... | 5 | stack_v2_sparse_classes_30k_train_016513 | Implement the Python class `ValidacionCalificacion` described below.
Class description:
Implement the ValidacionCalificacion class.
Method signatures and docstrings:
- def validar_existe_calificacion(id_usuario, id_cancion): Valida si existe una Calificacion con el id_usuario y el id_cancion :param id_usuario: El id ... | Implement the Python class `ValidacionCalificacion` described below.
Class description:
Implement the ValidacionCalificacion class.
Method signatures and docstrings:
- def validar_existe_calificacion(id_usuario, id_cancion): Valida si existe una Calificacion con el id_usuario y el id_cancion :param id_usuario: El id ... | 49bbaaf0bd4d1bec2d81eb35882e5f073b1c149f | <|skeleton|>
class ValidacionCalificacion:
def validar_existe_calificacion(id_usuario, id_cancion):
"""Valida si existe una Calificacion con el id_usuario y el id_cancion :param id_usuario: El id del usuario que realizo la calificacion :param id_cancion: El id de la cancion calificada :return: Un diccionar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ValidacionCalificacion:
def validar_existe_calificacion(id_usuario, id_cancion):
"""Valida si existe una Calificacion con el id_usuario y el id_cancion :param id_usuario: El id del usuario que realizo la calificacion :param id_cancion: El id de la cancion calificada :return: Un diccionario indicando q... | the_stack_v2_python_sparse | app/util/validaciones/modelos/ValidacionCalificacion.py | codeChinoUV/EspotifeiAPI | train | 0 | |
e6376670bb76ef8b963e4188aa5075c3afb4535d | [
"found_categories = []\nprevious_categories = filter(lambda x: page in x.pages, Category.query.all())\npattern = re.compile('\\\\[\\\\[Category:[a-zA-Z0-9 _]+\\\\]\\\\]')\nmatches = re.findall(pattern, content)\nfor match in matches:\n category_name = match[11:-2].strip()\n is_new, category = CategoryAPI.get_... | <|body_start_0|>
found_categories = []
previous_categories = filter(lambda x: page in x.pages, Category.query.all())
pattern = re.compile('\\[\\[Category:[a-zA-Z0-9 _]+\\]\\]')
matches = re.findall(pattern, content)
for match in matches:
category_name = match[11:-2].s... | CategoryAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoryAPI:
def update_categories_from_content(content, page):
"""Parse the string content for categories. The found categories in the content are processed as followed: * Existing categories are updated to save the path. * Unknown categories are created and save the path. Categories wh... | stack_v2_sparse_classes_36k_train_011603 | 3,400 | permissive | [
{
"docstring": "Parse the string content for categories. The found categories in the content are processed as followed: * Existing categories are updated to save the path. * Unknown categories are created and save the path. Categories which had this page, but are not found in the content are updated if the cate... | 2 | stack_v2_sparse_classes_30k_train_001750 | Implement the Python class `CategoryAPI` described below.
Class description:
Implement the CategoryAPI class.
Method signatures and docstrings:
- def update_categories_from_content(content, page): Parse the string content for categories. The found categories in the content are processed as followed: * Existing catego... | Implement the Python class `CategoryAPI` described below.
Class description:
Implement the CategoryAPI class.
Method signatures and docstrings:
- def update_categories_from_content(content, page): Parse the string content for categories. The found categories in the content are processed as followed: * Existing catego... | 1faec7e123c3fae7e8dbe1a354ad27b68f2a8cef | <|skeleton|>
class CategoryAPI:
def update_categories_from_content(content, page):
"""Parse the string content for categories. The found categories in the content are processed as followed: * Existing categories are updated to save the path. * Unknown categories are created and save the path. Categories wh... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CategoryAPI:
def update_categories_from_content(content, page):
"""Parse the string content for categories. The found categories in the content are processed as followed: * Existing categories are updated to save the path. * Unknown categories are created and save the path. Categories which had this p... | the_stack_v2_python_sparse | app/utils/category.py | viaict/viaduct | train | 11 | |
adb19d7e105462a4841a136cb62fd3fe105c6b5f | [
"super().__init__(*args, **kwargs)\nsource_projects = DataRequestProject.objects.filter(approved=True).exclude(returned_data_description='')\nself.fields['requested_sources'].choices = [(p.id, p.name) for p in source_projects]\nself.fields['requested_sources'].widget = forms.CheckboxSelectMultiple()\nself.fields['r... | <|body_start_0|>
super().__init__(*args, **kwargs)
source_projects = DataRequestProject.objects.filter(approved=True).exclude(returned_data_description='')
self.fields['requested_sources'].choices = [(p.id, p.name) for p in source_projects]
self.fields['requested_sources'].widget = forms... | The base for all DataRequestProject forms | DataRequestProjectForm | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataRequestProjectForm:
"""The base for all DataRequestProject forms"""
def __init__(self, *args, **kwargs):
"""Add custom handling for requested_sources and override some widgets."""
<|body_0|>
def clean(self):
"""Logic to for conditional required elements in ou... | stack_v2_sparse_classes_36k_train_011604 | 16,550 | permissive | [
{
"docstring": "Add custom handling for requested_sources and override some widgets.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Logic to for conditional required elements in our form.",
"name": "clean",
"signature": "def clean(self)"
}
] | 2 | null | Implement the Python class `DataRequestProjectForm` described below.
Class description:
The base for all DataRequestProject forms
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Add custom handling for requested_sources and override some widgets.
- def clean(self): Logic to for conditional re... | Implement the Python class `DataRequestProjectForm` described below.
Class description:
The base for all DataRequestProject forms
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Add custom handling for requested_sources and override some widgets.
- def clean(self): Logic to for conditional re... | a2e3bf3b95d7fcfb2cdffe3a42f86cb6e09674e4 | <|skeleton|>
class DataRequestProjectForm:
"""The base for all DataRequestProject forms"""
def __init__(self, *args, **kwargs):
"""Add custom handling for requested_sources and override some widgets."""
<|body_0|>
def clean(self):
"""Logic to for conditional required elements in ou... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataRequestProjectForm:
"""The base for all DataRequestProject forms"""
def __init__(self, *args, **kwargs):
"""Add custom handling for requested_sources and override some widgets."""
super().__init__(*args, **kwargs)
source_projects = DataRequestProject.objects.filter(approved=Tr... | the_stack_v2_python_sparse | private_sharing/forms.py | madprime/open-humans | train | 2 |
9b567739910ae0a144a739f736d3bd34d00f2581 | [
"self.name = name\nself.base_energy = base_energy\nself.goals = goals",
"if time % 9 == 0:\n return self.base_energy - self.energy_expended * self.goals - self.energy_expended * 0.1\nelse:\n return self.base_energy - self.energy_expended * self.goals"
] | <|body_start_0|>
self.name = name
self.base_energy = base_energy
self.goals = goals
<|end_body_0|>
<|body_start_1|>
if time % 9 == 0:
return self.base_energy - self.energy_expended * self.goals - self.energy_expended * 0.1
else:
return self.base_energy - ... | Chaser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Chaser:
def __init__(self, name, base_energy, goals):
"""Chasers have a name, score goals, and begin with base_energy."""
<|body_0|>
def energy(self, time):
"""Returns the amount of energy left after playing for time minutes. For every goal they score, they use energ... | stack_v2_sparse_classes_36k_train_011605 | 9,033 | permissive | [
{
"docstring": "Chasers have a name, score goals, and begin with base_energy.",
"name": "__init__",
"signature": "def __init__(self, name, base_energy, goals)"
},
{
"docstring": "Returns the amount of energy left after playing for time minutes. For every goal they score, they use energy_expended... | 2 | stack_v2_sparse_classes_30k_train_018766 | Implement the Python class `Chaser` described below.
Class description:
Implement the Chaser class.
Method signatures and docstrings:
- def __init__(self, name, base_energy, goals): Chasers have a name, score goals, and begin with base_energy.
- def energy(self, time): Returns the amount of energy left after playing ... | Implement the Python class `Chaser` described below.
Class description:
Implement the Chaser class.
Method signatures and docstrings:
- def __init__(self, name, base_energy, goals): Chasers have a name, score goals, and begin with base_energy.
- def energy(self, time): Returns the amount of energy left after playing ... | 6d0da2c1468b9a571c742a62ab0b8cf625688591 | <|skeleton|>
class Chaser:
def __init__(self, name, base_energy, goals):
"""Chasers have a name, score goals, and begin with base_energy."""
<|body_0|>
def energy(self, time):
"""Returns the amount of energy left after playing for time minutes. For every goal they score, they use energ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Chaser:
def __init__(self, name, base_energy, goals):
"""Chasers have a name, score goals, and begin with base_energy."""
self.name = name
self.base_energy = base_energy
self.goals = goals
def energy(self, time):
"""Returns the amount of energy left after playing f... | the_stack_v2_python_sparse | lab/lab10/lab10.py | tingjunwong/cs88-python-data-analysis | train | 0 | |
b774ced2c55b630d7de8d6fd1cfb5b376cb36c53 | [
"ans = []\nnums1 = set(nums1)\nnums2 = set(nums2)\nif len(nums1) >= len(nums2):\n nums1 = list(nums1)\n for i in range(len(nums1)):\n if nums1[i] in nums2:\n ans.append(nums1[i])\nelse:\n nums2 = list(nums2)\n for i in range(len(nums2)):\n if nums2[i] in nums1:\n ans.... | <|body_start_0|>
ans = []
nums1 = set(nums1)
nums2 = set(nums2)
if len(nums1) >= len(nums2):
nums1 = list(nums1)
for i in range(len(nums1)):
if nums1[i] in nums2:
ans.append(nums1[i])
else:
nums2 = list(nums2... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def intersection(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: List[int]"""
<|body_0|>
def intersection(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: List[int]"""
<|body_1|>
def in... | stack_v2_sparse_classes_36k_train_011606 | 2,170 | no_license | [
{
"docstring": ":type nums1: List[int] :type nums2: List[int] :rtype: List[int]",
"name": "intersection",
"signature": "def intersection(self, nums1, nums2)"
},
{
"docstring": ":type nums1: List[int] :type nums2: List[int] :rtype: List[int]",
"name": "intersection",
"signature": "def int... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def intersection(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: List[int]
- def intersection(self, nums1, nums2): :type nums1: List[int] :type nums2: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def intersection(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: List[int]
- def intersection(self, nums1, nums2): :type nums1: List[int] :type nums2: ... | b925bb22d1daa4a56c5a238a5758a926905559b4 | <|skeleton|>
class Solution:
def intersection(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: List[int]"""
<|body_0|>
def intersection(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: List[int]"""
<|body_1|>
def in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def intersection(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: List[int]"""
ans = []
nums1 = set(nums1)
nums2 = set(nums2)
if len(nums1) >= len(nums2):
nums1 = list(nums1)
for i in range(len(nums1)):
... | the_stack_v2_python_sparse | Arrays/349. Intersection of Two Arrays.py | beninghton/notGivenUpToG | train | 0 | |
5a3dc80453bda0bfa9f74dbb92afebbec31fb5f7 | [
"if lang is not None and (not isinstance(lang, AlgorandLanguages)):\n raise TypeError('Language is not an enumerative of AlgorandLanguages')\nsuper().__init__(lang.value if lang is not None else lang, Bip39WordsListFinder, Bip39WordsListGetter)",
"mnemonic_obj = AlgorandMnemonic.FromString(mnemonic) if isinsta... | <|body_start_0|>
if lang is not None and (not isinstance(lang, AlgorandLanguages)):
raise TypeError('Language is not an enumerative of AlgorandLanguages')
super().__init__(lang.value if lang is not None else lang, Bip39WordsListFinder, Bip39WordsListGetter)
<|end_body_0|>
<|body_start_1|>
... | Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes. | AlgorandMnemonicDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlgorandMnemonicDecoder:
"""Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes."""
def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None:
"""Construct class. Language is set to English by default because Algorand mnemonic only sup... | stack_v2_sparse_classes_36k_train_011607 | 5,112 | permissive | [
{
"docstring": "Construct class. Language is set to English by default because Algorand mnemonic only support one language, so it's useless (and slower) to automatically detect the language. Args: lang (AlgorandLanguages, optional): Language, None for automatic detection Raises: TypeError: If the language is no... | 3 | stack_v2_sparse_classes_30k_train_015955 | Implement the Python class `AlgorandMnemonicDecoder` described below.
Class description:
Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes.
Method signatures and docstrings:
- def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None: Construct class. Language is set... | Implement the Python class `AlgorandMnemonicDecoder` described below.
Class description:
Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes.
Method signatures and docstrings:
- def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None: Construct class. Language is set... | d15c75ddd74e4838c396a0d036ef6faf11b06a4b | <|skeleton|>
class AlgorandMnemonicDecoder:
"""Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes."""
def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None:
"""Construct class. Language is set to English by default because Algorand mnemonic only sup... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlgorandMnemonicDecoder:
"""Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes."""
def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None:
"""Construct class. Language is set to English by default because Algorand mnemonic only support one lang... | the_stack_v2_python_sparse | bip_utils/algorand/mnemonic/algorand_mnemonic_decoder.py | ebellocchia/bip_utils | train | 244 |
5a96c15aba770dd768ce1a2343c71c1dc6d79302 | [
"self.enable_worm_on_external_target = enable_worm_on_external_target\nself.policy_type = policy_type\nself.retention_secs = retention_secs\nself.version = version",
"if dictionary is None:\n return None\nenable_worm_on_external_target = dictionary.get('enableWormOnExternalTarget')\npolicy_type = dictionary.ge... | <|body_start_0|>
self.enable_worm_on_external_target = enable_worm_on_external_target
self.policy_type = policy_type
self.retention_secs = retention_secs
self.version = version
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
enable_worm_on_... | Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited from policy at successful backup run completion..... | WormRetentionProto | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WormRetentionProto:
"""Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited fro... | stack_v2_sparse_classes_36k_train_011608 | 3,033 | permissive | [
{
"docstring": "Constructor for the WormRetentionProto class",
"name": "__init__",
"signature": "def __init__(self, enable_worm_on_external_target=None, policy_type=None, retention_secs=None, version=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (... | 2 | stack_v2_sparse_classes_30k_val_000359 | Implement the Python class `WormRetentionProto` described below.
Class description:
Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. ba... | Implement the Python class `WormRetentionProto` described below.
Class description:
Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. ba... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class WormRetentionProto:
"""Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited fro... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WormRetentionProto:
"""Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited from policy at s... | the_stack_v2_python_sparse | cohesity_management_sdk/models/worm_retention_proto.py | cohesity/management-sdk-python | train | 24 |
8266cc0b5f9919a245912d493cf2904d2b34d6a9 | [
"self.ld = ld\nself.parmsh = {}\nself.parmsh['file'] = True\nself.filename = filename\nself.cdf = []\nfor d in self.ld:\n try:\n self.save = d['save']\n except:\n self.save = True\n bound = d['bound']\n values = d['values']\n Nv = len(values)\n cdf = np.array([])\n for k in bound:... | <|body_start_0|>
self.ld = ld
self.parmsh = {}
self.parmsh['file'] = True
self.filename = filename
self.cdf = []
for d in self.ld:
try:
self.save = d['save']
except:
self.save = True
bound = d['bound']
... | CDF | [
"MIT",
"GPL-3.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CDF:
def __init__(self, ld, filename='cdf'):
"""cdf = CDF(ld) ld is a list of dictionnary d0 = ld[0] d0['bound'] : bornes en abscisses de la cdf 0 d0['values'] : valeurs d0['xlabel'] : d0['ylabel'] : d0['legend'] : legend d0['title] : title d0['filename] : filename d0['linewidth'] : line... | stack_v2_sparse_classes_36k_train_011609 | 3,799 | permissive | [
{
"docstring": "cdf = CDF(ld) ld is a list of dictionnary d0 = ld[0] d0['bound'] : bornes en abscisses de la cdf 0 d0['values'] : valeurs d0['xlabel'] : d0['ylabel'] : d0['legend'] : legend d0['title] : title d0['filename] : filename d0['linewidth'] : linewidth",
"name": "__init__",
"signature": "def __... | 2 | stack_v2_sparse_classes_30k_train_005625 | Implement the Python class `CDF` described below.
Class description:
Implement the CDF class.
Method signatures and docstrings:
- def __init__(self, ld, filename='cdf'): cdf = CDF(ld) ld is a list of dictionnary d0 = ld[0] d0['bound'] : bornes en abscisses de la cdf 0 d0['values'] : valeurs d0['xlabel'] : d0['ylabel'... | Implement the Python class `CDF` described below.
Class description:
Implement the CDF class.
Method signatures and docstrings:
- def __init__(self, ld, filename='cdf'): cdf = CDF(ld) ld is a list of dictionnary d0 = ld[0] d0['bound'] : bornes en abscisses de la cdf 0 d0['values'] : valeurs d0['xlabel'] : d0['ylabel'... | 84bdc924ae8055313b9ec833f0509000b0a30f45 | <|skeleton|>
class CDF:
def __init__(self, ld, filename='cdf'):
"""cdf = CDF(ld) ld is a list of dictionnary d0 = ld[0] d0['bound'] : bornes en abscisses de la cdf 0 d0['values'] : valeurs d0['xlabel'] : d0['ylabel'] : d0['legend'] : legend d0['title] : title d0['filename] : filename d0['linewidth'] : line... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CDF:
def __init__(self, ld, filename='cdf'):
"""cdf = CDF(ld) ld is a list of dictionnary d0 = ld[0] d0['bound'] : bornes en abscisses de la cdf 0 d0['values'] : valeurs d0['xlabel'] : d0['ylabel'] : d0['legend'] : legend d0['title] : title d0['filename] : filename d0['linewidth'] : linewidth"""
... | the_stack_v2_python_sparse | pylayers/location/geometric/util/cdf2.py | tom2rd/pylayers | train | 0 | |
3d317fbd1b08326fd870ca9d738ca97b660df64d | [
"self.text = ''\nself.keywords = None\nself.seg = Segmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags, delimiters=delimiters)\nself.sentences = None\nself.words_no_filter = None\nself.words_no_stop_words = None\nself.words_all_filters = None",
"self.text = text\nself.word_index = {}\... | <|body_start_0|>
self.text = ''
self.keywords = None
self.seg = Segmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags, delimiters=delimiters)
self.sentences = None
self.words_no_filter = None
self.words_no_stop_words = None
self.words_a... | TextRankKeyword | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextRankKeyword:
def __init__(self, stop_words_file='./stopwords.txt', allow_speech_tags=util.allow_speech_tags, delimiters=util.sentence_delimiters):
"""Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: w... | stack_v2_sparse_classes_36k_train_011610 | 8,424 | no_license | [
{
"docstring": "Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: words_no_filter -- 对sentences中每个句子分词而得到的两级列表。 words_no_stop_words -- 去掉words_no_filter中的停止词而得到的两级列表。 words_all_filters -- 保留words_no_stop_words中指定词性的单词而得到的两级列表。",
... | 4 | stack_v2_sparse_classes_30k_train_010320 | Implement the Python class `TextRankKeyword` described below.
Class description:
Implement the TextRankKeyword class.
Method signatures and docstrings:
- def __init__(self, stop_words_file='./stopwords.txt', allow_speech_tags=util.allow_speech_tags, delimiters=util.sentence_delimiters): Keyword arguments: stop_words_... | Implement the Python class `TextRankKeyword` described below.
Class description:
Implement the TextRankKeyword class.
Method signatures and docstrings:
- def __init__(self, stop_words_file='./stopwords.txt', allow_speech_tags=util.allow_speech_tags, delimiters=util.sentence_delimiters): Keyword arguments: stop_words_... | 7f7c4e56a8a66618feeb245b5394c0fc7d4f529a | <|skeleton|>
class TextRankKeyword:
def __init__(self, stop_words_file='./stopwords.txt', allow_speech_tags=util.allow_speech_tags, delimiters=util.sentence_delimiters):
"""Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: w... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextRankKeyword:
def __init__(self, stop_words_file='./stopwords.txt', allow_speech_tags=util.allow_speech_tags, delimiters=util.sentence_delimiters):
"""Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: words_no_filter... | the_stack_v2_python_sparse | KwordExtr/TextRank/TextRankKeyword.py | primingrx/NLP | train | 0 | |
50f8d7442322b239010632cd6c34d6a7a11f4d31 | [
"mocker.patch.object(client, 'get_detections', return_value=detections)\ncmd_res = get_detections_cmd(client, first_timestamp='')\nif detections:\n assert len(cmd_res.outputs) == len(detections.get('results'))\nelse:\n assert 'No detections found' in cmd_res.readable_output",
"mocker.patch.object(client, 'g... | <|body_start_0|>
mocker.patch.object(client, 'get_detections', return_value=detections)
cmd_res = get_detections_cmd(client, first_timestamp='')
if detections:
assert len(cmd_res.outputs) == len(detections.get('results'))
else:
assert 'No detections found' in cmd_... | TestCommands | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCommands:
def test_get_detections_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audits: Dict[str, Any]):
"""Test `vectra-get-events` method detections part."""
<|body_0|>
def test_get_audits_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audi... | stack_v2_sparse_classes_36k_train_011611 | 12,115 | permissive | [
{
"docstring": "Test `vectra-get-events` method detections part.",
"name": "test_get_detections_cmd",
"signature": "def test_get_detections_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audits: Dict[str, Any])"
},
{
"docstring": "Test `vectra-get-events` method audits part.",
... | 5 | stack_v2_sparse_classes_30k_train_008503 | Implement the Python class `TestCommands` described below.
Class description:
Implement the TestCommands class.
Method signatures and docstrings:
- def test_get_detections_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audits: Dict[str, Any]): Test `vectra-get-events` method detections part.
- def test_... | Implement the Python class `TestCommands` described below.
Class description:
Implement the TestCommands class.
Method signatures and docstrings:
- def test_get_detections_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audits: Dict[str, Any]): Test `vectra-get-events` method detections part.
- def test_... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class TestCommands:
def test_get_detections_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audits: Dict[str, Any]):
"""Test `vectra-get-events` method detections part."""
<|body_0|>
def test_get_audits_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestCommands:
def test_get_detections_cmd(self, mocker: MockerFixture, detections: Dict[str, Any], audits: Dict[str, Any]):
"""Test `vectra-get-events` method detections part."""
mocker.patch.object(client, 'get_detections', return_value=detections)
cmd_res = get_detections_cmd(client,... | the_stack_v2_python_sparse | Packs/Vectra_AI/Integrations/VectraAIEventCollector/VectraAIEventCollector_test.py | demisto/content | train | 1,023 | |
c459a5bfb6427bb215a67ce6c74b2e6f2cab813f | [
"self.size = len(words)\nself.word_pos = collections.defaultdict(list)\nfor i, word in enumerate(words):\n self.word_pos[word].append(i)",
"pos_list1 = self.word_pos[word1]\npos_list2 = self.word_pos[word2]\nshortest = self.size\nfor i in pos_list1:\n for j in pos_list2:\n if shortest > abs(i - j):\n... | <|body_start_0|>
self.size = len(words)
self.word_pos = collections.defaultdict(list)
for i, word in enumerate(words):
self.word_pos[word].append(i)
<|end_body_0|>
<|body_start_1|>
pos_list1 = self.word_pos[word1]
pos_list2 = self.word_pos[word2]
shortest = s... | WordDistance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordDistance:
def __init__(self, words):
"""Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and return the shortest distance between these two words in the list. Your method will be called repeatedly many time... | stack_v2_sparse_classes_36k_train_011612 | 1,603 | no_license | [
{
"docstring": "Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and return the shortest distance between these two words in the list. Your method will be called repeatedly many times with different parameters. Example: Assume that wo... | 2 | stack_v2_sparse_classes_30k_train_007509 | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and return the s... | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and return the s... | 08c6d27498e35f636045fed05a6f94b760ab69ca | <|skeleton|>
class WordDistance:
def __init__(self, words):
"""Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and return the shortest distance between these two words in the list. Your method will be called repeatedly many time... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordDistance:
def __init__(self, words):
"""Design a class which receives a list of words in the constructor, and implements a method that takes two words word1 and word2 and return the shortest distance between these two words in the list. Your method will be called repeatedly many times with differe... | the_stack_v2_python_sparse | solutions/hashtable/244.Shortest.Word.Distance.II.py | ljia2/leetcode.py | train | 0 | |
3ef678a1342741e610ad44441e2cd5f629f57173 | [
"self.__dataset_path = dataset_path\nself.__trainX = None\nself.__trainY = None\nself.__testX = None\nself.__testY = None\nself.__valX = None\nself.__valY = None\nself.__lb = LabelBinarizer()\nself.__data = []\nself.__labels = []",
"self.__loading_Images()\nself.__scale_Pixels()\nself.__divide_data()\nreturn (sel... | <|body_start_0|>
self.__dataset_path = dataset_path
self.__trainX = None
self.__trainY = None
self.__testX = None
self.__testY = None
self.__valX = None
self.__valY = None
self.__lb = LabelBinarizer()
self.__data = []
self.__labels = []
<|e... | LoadData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LoadData:
def __init__(self, dataset_path):
"""Create the HandleData class object. :param dataset_path: string :return: None"""
<|body_0|>
def handle_dataset(self):
"""this function manage the HandleData class :param: None :return self.__trainX: array :return self.__... | stack_v2_sparse_classes_36k_train_011613 | 5,531 | no_license | [
{
"docstring": "Create the HandleData class object. :param dataset_path: string :return: None",
"name": "__init__",
"signature": "def __init__(self, dataset_path)"
},
{
"docstring": "this function manage the HandleData class :param: None :return self.__trainX: array :return self.__trainY: array ... | 5 | stack_v2_sparse_classes_30k_train_013865 | Implement the Python class `LoadData` described below.
Class description:
Implement the LoadData class.
Method signatures and docstrings:
- def __init__(self, dataset_path): Create the HandleData class object. :param dataset_path: string :return: None
- def handle_dataset(self): this function manage the HandleData cl... | Implement the Python class `LoadData` described below.
Class description:
Implement the LoadData class.
Method signatures and docstrings:
- def __init__(self, dataset_path): Create the HandleData class object. :param dataset_path: string :return: None
- def handle_dataset(self): this function manage the HandleData cl... | 9b7f035dca04e9ac4d20d4d9fa9e687ce583603b | <|skeleton|>
class LoadData:
def __init__(self, dataset_path):
"""Create the HandleData class object. :param dataset_path: string :return: None"""
<|body_0|>
def handle_dataset(self):
"""this function manage the HandleData class :param: None :return self.__trainX: array :return self.__... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LoadData:
def __init__(self, dataset_path):
"""Create the HandleData class object. :param dataset_path: string :return: None"""
self.__dataset_path = dataset_path
self.__trainX = None
self.__trainY = None
self.__testX = None
self.__testY = None
self.__va... | the_stack_v2_python_sparse | python files/load_data.py | maayan121/project_python_letters-DL | train | 0 | |
2c0a83fe0542a074b7d2100d503da6c6bba6a5e1 | [
"self.T_myClass = 0\nself.T_otherClass = 0\nself.F_myClass = 0\nself.F_otherClass = 0",
"if my_class and is_correct:\n self.T_myClass += 1\nelif is_correct:\n self.T_otherClass += 1\nelif my_class:\n self.F_myClass += 1\nelse:\n self.F_otherClass += 1"
] | <|body_start_0|>
self.T_myClass = 0
self.T_otherClass = 0
self.F_myClass = 0
self.F_otherClass = 0
<|end_body_0|>
<|body_start_1|>
if my_class and is_correct:
self.T_myClass += 1
elif is_correct:
self.T_otherClass += 1
elif my_class:
... | ClassAccuracy | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassAccuracy:
def __init__(self):
"""Initialize the accuracy calculation for a single class"""
<|body_0|>
def updateAccuracy(self, my_class, is_correct):
"""Increment the appropriate cell of the confusion matrix"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_011614 | 840 | no_license | [
{
"docstring": "Initialize the accuracy calculation for a single class",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Increment the appropriate cell of the confusion matrix",
"name": "updateAccuracy",
"signature": "def updateAccuracy(self, my_class, is_correct... | 2 | stack_v2_sparse_classes_30k_train_015106 | Implement the Python class `ClassAccuracy` described below.
Class description:
Implement the ClassAccuracy class.
Method signatures and docstrings:
- def __init__(self): Initialize the accuracy calculation for a single class
- def updateAccuracy(self, my_class, is_correct): Increment the appropriate cell of the confu... | Implement the Python class `ClassAccuracy` described below.
Class description:
Implement the ClassAccuracy class.
Method signatures and docstrings:
- def __init__(self): Initialize the accuracy calculation for a single class
- def updateAccuracy(self, my_class, is_correct): Increment the appropriate cell of the confu... | bec4ebeee6bf800addc5a6d1fb434ac92855aff2 | <|skeleton|>
class ClassAccuracy:
def __init__(self):
"""Initialize the accuracy calculation for a single class"""
<|body_0|>
def updateAccuracy(self, my_class, is_correct):
"""Increment the appropriate cell of the confusion matrix"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassAccuracy:
def __init__(self):
"""Initialize the accuracy calculation for a single class"""
self.T_myClass = 0
self.T_otherClass = 0
self.F_myClass = 0
self.F_otherClass = 0
def updateAccuracy(self, my_class, is_correct):
"""Increment the appropriate ce... | the_stack_v2_python_sparse | XCS/XCS_ClassAccuracy.py | VasilyShcherbinin/LCS-Suite | train | 3 | |
14f58c051bd45199cc2f55a79f4e9885f8a9c5e8 | [
"gate_id = gate.key.integer_id()\nearliest_response = datetime.datetime.max\napprovers = approval_defs.get_approvers(gate.gate_type)\nactivities = Activity.get_activities(gate.feature_id, gate_id=gate_id, comments_only=True)\nfor a in activities:\n if gate.requested_on < a.created < earliest_response:\n i... | <|body_start_0|>
gate_id = gate.key.integer_id()
earliest_response = datetime.datetime.max
approvers = approval_defs.get_approvers(gate.gate_type)
activities = Activity.get_activities(gate.feature_id, gate_id=gate_id, comments_only=True)
for a in activities:
if gate.r... | BackfillRespondedOn | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BackfillRespondedOn:
def update_responded_on(self, gate):
"""Update gate.responded_on and return True if an update was needed."""
<|body_0|>
def get_template_data(self, **kwargs):
"""Backfill responded_on dates for existing gates."""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k_train_011615 | 7,549 | permissive | [
{
"docstring": "Update gate.responded_on and return True if an update was needed.",
"name": "update_responded_on",
"signature": "def update_responded_on(self, gate)"
},
{
"docstring": "Backfill responded_on dates for existing gates.",
"name": "get_template_data",
"signature": "def get_te... | 2 | stack_v2_sparse_classes_30k_train_007570 | Implement the Python class `BackfillRespondedOn` described below.
Class description:
Implement the BackfillRespondedOn class.
Method signatures and docstrings:
- def update_responded_on(self, gate): Update gate.responded_on and return True if an update was needed.
- def get_template_data(self, **kwargs): Backfill res... | Implement the Python class `BackfillRespondedOn` described below.
Class description:
Implement the BackfillRespondedOn class.
Method signatures and docstrings:
- def update_responded_on(self, gate): Update gate.responded_on and return True if an update was needed.
- def get_template_data(self, **kwargs): Backfill res... | 17f9886d064da5bda84006d5866077727646fff2 | <|skeleton|>
class BackfillRespondedOn:
def update_responded_on(self, gate):
"""Update gate.responded_on and return True if an update was needed."""
<|body_0|>
def get_template_data(self, **kwargs):
"""Backfill responded_on dates for existing gates."""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BackfillRespondedOn:
def update_responded_on(self, gate):
"""Update gate.responded_on and return True if an update was needed."""
gate_id = gate.key.integer_id()
earliest_response = datetime.datetime.max
approvers = approval_defs.get_approvers(gate.gate_type)
activities... | the_stack_v2_python_sparse | internals/maintenance_scripts.py | GoogleChrome/chromium-dashboard | train | 574 | |
6e278751b2349560d1adc8299bde7d08ab4bc9ef | [
"syntax_options: SyntaxOptions = assert_not_none(self.config.syntax)\nspacy_model = spacy.load(syntax_options.spacy_model)\nutterances = batch[self.config.columns.text_input]\nrecords: List[TaggingResponse] = []\nfor utterance in utterances:\n tag: Dict[Tag, bool] = {smart_tag: False for family in [SmartTagFamil... | <|body_start_0|>
syntax_options: SyntaxOptions = assert_not_none(self.config.syntax)
spacy_model = spacy.load(syntax_options.spacy_model)
utterances = batch[self.config.columns.text_input]
records: List[TaggingResponse] = []
for utterance in utterances:
tag: Dict[Tag,... | Calculate smart tags related to syntax. | SyntaxTaggingModule | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SyntaxTaggingModule:
"""Calculate smart tags related to syntax."""
def compute(self, batch: Dataset) -> List[TaggingResponse]:
"""Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the smart tags. Returns: tags: Newly calculated tags."""
... | stack_v2_sparse_classes_36k_train_011616 | 3,923 | permissive | [
{
"docstring": "Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the smart tags. Returns: tags: Newly calculated tags.",
"name": "compute",
"signature": "def compute(self, batch: Dataset) -> List[TaggingResponse]"
},
{
"docstring": "Save tags in a Dataset... | 2 | stack_v2_sparse_classes_30k_train_006733 | Implement the Python class `SyntaxTaggingModule` described below.
Class description:
Calculate smart tags related to syntax.
Method signatures and docstrings:
- def compute(self, batch: Dataset) -> List[TaggingResponse]: Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the sma... | Implement the Python class `SyntaxTaggingModule` described below.
Class description:
Calculate smart tags related to syntax.
Method signatures and docstrings:
- def compute(self, batch: Dataset) -> List[TaggingResponse]: Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the sma... | 34081048a4de3900ca29ac0d37bce7026113382c | <|skeleton|>
class SyntaxTaggingModule:
"""Calculate smart tags related to syntax."""
def compute(self, batch: Dataset) -> List[TaggingResponse]:
"""Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the smart tags. Returns: tags: Newly calculated tags."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SyntaxTaggingModule:
"""Calculate smart tags related to syntax."""
def compute(self, batch: Dataset) -> List[TaggingResponse]:
"""Get smart tags for provided indices. Args: batch: the batch on which we want to calculate the smart tags. Returns: tags: Newly calculated tags."""
syntax_optio... | the_stack_v2_python_sparse | azimuth/modules/dataset_analysis/syntax_tagging.py | ServiceNow/azimuth | train | 172 |
213e81d94c8249b283a002b2bdf368023db0a6d9 | [
"self.v = x\nself.c = None\nreturn None",
"if not a or not isinstance(a, list):\n return ListNode(None)\no = ListNode(None)\np = o\nn = len(a)\nfor i in range(n):\n p.c = ListNode(a[i])\n p = p.c\nreturn o.c",
"if not o or not isinstance(o, ListNode):\n return list()\na = list()\np = o\nwhile p:\n ... | <|body_start_0|>
self.v = x
self.c = None
return None
<|end_body_0|>
<|body_start_1|>
if not a or not isinstance(a, list):
return ListNode(None)
o = ListNode(None)
p = o
n = len(a)
for i in range(n):
p.c = ListNode(a[i])
... | Manage ListNode objects for linked lists. | ListNode | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListNode:
"""Manage ListNode objects for linked lists."""
def __init__(self, x=None):
"""Constructor for ListNode objects. :param (int or None) x: integer value for node :param ListNode c: pointer to child ListNode :return: None :rtype: None"""
<|body_0|>
def convert(sel... | stack_v2_sparse_classes_36k_train_011617 | 1,738 | permissive | [
{
"docstring": "Constructor for ListNode objects. :param (int or None) x: integer value for node :param ListNode c: pointer to child ListNode :return: None :rtype: None",
"name": "__init__",
"signature": "def __init__(self, x=None)"
},
{
"docstring": "Transforms array to linked list representati... | 3 | stack_v2_sparse_classes_30k_train_015511 | Implement the Python class `ListNode` described below.
Class description:
Manage ListNode objects for linked lists.
Method signatures and docstrings:
- def __init__(self, x=None): Constructor for ListNode objects. :param (int or None) x: integer value for node :param ListNode c: pointer to child ListNode :return: Non... | Implement the Python class `ListNode` described below.
Class description:
Manage ListNode objects for linked lists.
Method signatures and docstrings:
- def __init__(self, x=None): Constructor for ListNode objects. :param (int or None) x: integer value for node :param ListNode c: pointer to child ListNode :return: Non... | 69f90877c5466927e8b081c4268cbcda074813ec | <|skeleton|>
class ListNode:
"""Manage ListNode objects for linked lists."""
def __init__(self, x=None):
"""Constructor for ListNode objects. :param (int or None) x: integer value for node :param ListNode c: pointer to child ListNode :return: None :rtype: None"""
<|body_0|>
def convert(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ListNode:
"""Manage ListNode objects for linked lists."""
def __init__(self, x=None):
"""Constructor for ListNode objects. :param (int or None) x: integer value for node :param ListNode c: pointer to child ListNode :return: None :rtype: None"""
self.v = x
self.c = None
ret... | the_stack_v2_python_sparse | 0002_add_two_numbers/python_util.py | arthurdysart/LeetCode | train | 0 |
f3730fe39d468d47f60f130d5f5964e532b61f1f | [
"shareList = Share.objects.filter(delete_flag=0).order_by('team', 'shareperson')\nserializer = Shareserializers(shareList, many=True)\nreturn Response({'status': True, 'message': '成功', 'data': serializer.data})",
"if request.data['sharetitle'] == '':\n return Response({'status': True, 'message': '不新建分享', 'data... | <|body_start_0|>
shareList = Share.objects.filter(delete_flag=0).order_by('team', 'shareperson')
serializer = Shareserializers(shareList, many=True)
return Response({'status': True, 'message': '成功', 'data': serializer.data})
<|end_body_0|>
<|body_start_1|>
if request.data['sharetitle'] ... | 分享 | Shares | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Shares:
"""分享"""
def get(self, request):
"""获取分享列表"""
<|body_0|>
def post(self, request):
"""新建分享列表"""
<|body_1|>
def put(self, request):
"""修改分享列表"""
<|body_2|>
def delete(self, request):
"""删除分享"""
<|body_3|>
<... | stack_v2_sparse_classes_36k_train_011618 | 2,709 | no_license | [
{
"docstring": "获取分享列表",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "新建分享列表",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "修改分享列表",
"name": "put",
"signature": "def put(self, request)"
},
{
"docstring": "删除分... | 4 | stack_v2_sparse_classes_30k_train_016934 | Implement the Python class `Shares` described below.
Class description:
分享
Method signatures and docstrings:
- def get(self, request): 获取分享列表
- def post(self, request): 新建分享列表
- def put(self, request): 修改分享列表
- def delete(self, request): 删除分享 | Implement the Python class `Shares` described below.
Class description:
分享
Method signatures and docstrings:
- def get(self, request): 获取分享列表
- def post(self, request): 新建分享列表
- def put(self, request): 修改分享列表
- def delete(self, request): 删除分享
<|skeleton|>
class Shares:
"""分享"""
def get(self, request):
... | 9ccebcc6820af3f950c28fc2a4dee4f41a3157f1 | <|skeleton|>
class Shares:
"""分享"""
def get(self, request):
"""获取分享列表"""
<|body_0|>
def post(self, request):
"""新建分享列表"""
<|body_1|>
def put(self, request):
"""修改分享列表"""
<|body_2|>
def delete(self, request):
"""删除分享"""
<|body_3|>
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Shares:
"""分享"""
def get(self, request):
"""获取分享列表"""
shareList = Share.objects.filter(delete_flag=0).order_by('team', 'shareperson')
serializer = Shareserializers(shareList, many=True)
return Response({'status': True, 'message': '成功', 'data': serializer.data})
def po... | the_stack_v2_python_sparse | moon/share/views.py | xiaominwanglast/python | train | 0 |
807120c87a2fe923d9dd8c299b624d979c85f8dd | [
"py_typecheck.check_type(symbols, dict)\nfor k, v in symbols.items():\n py_typecheck.check_type(k, str)\n py_typecheck.check_type(v, LambdaExecutorValue)\nif parent is not None:\n py_typecheck.check_type(parent, LambdaExecutorScope)\nself._parent = parent\nself._symbols = {k: v for k, v in symbols.items()}... | <|body_start_0|>
py_typecheck.check_type(symbols, dict)
for k, v in symbols.items():
py_typecheck.check_type(k, str)
py_typecheck.check_type(v, LambdaExecutorValue)
if parent is not None:
py_typecheck.check_type(parent, LambdaExecutorScope)
self._paren... | Represents a naming scope for computations in the lambda executor. | LambdaExecutorScope | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LambdaExecutorScope:
"""Represents a naming scope for computations in the lambda executor."""
def __init__(self, symbols, parent=None):
"""Constructs a new scope. Args: symbols: A dict of symbols available in this scope, with keys being strings, and values being instances of `LambdaE... | stack_v2_sparse_classes_36k_train_011619 | 18,827 | permissive | [
{
"docstring": "Constructs a new scope. Args: symbols: A dict of symbols available in this scope, with keys being strings, and values being instances of `LambdaExecutorValue`. parent: The parent scope, or `None` if this is the root.",
"name": "__init__",
"signature": "def __init__(self, symbols, parent=... | 2 | stack_v2_sparse_classes_30k_val_000607 | Implement the Python class `LambdaExecutorScope` described below.
Class description:
Represents a naming scope for computations in the lambda executor.
Method signatures and docstrings:
- def __init__(self, symbols, parent=None): Constructs a new scope. Args: symbols: A dict of symbols available in this scope, with k... | Implement the Python class `LambdaExecutorScope` described below.
Class description:
Represents a naming scope for computations in the lambda executor.
Method signatures and docstrings:
- def __init__(self, symbols, parent=None): Constructs a new scope. Args: symbols: A dict of symbols available in this scope, with k... | 91be7e99906112c008f04ef8534eb6ea87a7053d | <|skeleton|>
class LambdaExecutorScope:
"""Represents a naming scope for computations in the lambda executor."""
def __init__(self, symbols, parent=None):
"""Constructs a new scope. Args: symbols: A dict of symbols available in this scope, with keys being strings, and values being instances of `LambdaE... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LambdaExecutorScope:
"""Represents a naming scope for computations in the lambda executor."""
def __init__(self, symbols, parent=None):
"""Constructs a new scope. Args: symbols: A dict of symbols available in this scope, with keys being strings, and values being instances of `LambdaExecutorValue`... | the_stack_v2_python_sparse | tensorflow_federated/python/core/impl/lambda_executor.py | igabriel85/federated | train | 2 |
450016e1acacda541140e9f73bbff8a8dcd5145a | [
"params = dict(workspace_name='harfangletest_corp')\nform = WorkspaceCreateForm(params)\nself.assertTrue(form.is_valid())\nself.assertEquals(form.cleaned_data['workspace_name'], 'harfangletest_corp')",
"params = dict(workspace_name='全角テスト株式会社')\nform = WorkspaceCreateForm(params)\nself.assertTrue(form.is_valid())... | <|body_start_0|>
params = dict(workspace_name='harfangletest_corp')
form = WorkspaceCreateForm(params)
self.assertTrue(form.is_valid())
self.assertEquals(form.cleaned_data['workspace_name'], 'harfangletest_corp')
<|end_body_0|>
<|body_start_1|>
params = dict(workspace_name='全角テス... | WorkspaceCreateFormTests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkspaceCreateFormTests:
def test_workspace_create_forms_normal_1(self):
"""顧客情報登録フォーム 正常系 1"""
<|body_0|>
def test_workspace_create_forms_normal_2(self):
"""顧客情報登録フォーム 正常系 ２"""
<|body_1|>
def test_workspace_create_forms_normal_3(self):
"""顧客情報登... | stack_v2_sparse_classes_36k_train_011620 | 4,355 | permissive | [
{
"docstring": "顧客情報登録フォーム 正常系 1",
"name": "test_workspace_create_forms_normal_1",
"signature": "def test_workspace_create_forms_normal_1(self)"
},
{
"docstring": "顧客情報登録フォーム 正常系 ２",
"name": "test_workspace_create_forms_normal_2",
"signature": "def test_workspace_create_forms_normal_2(se... | 5 | stack_v2_sparse_classes_30k_train_015158 | Implement the Python class `WorkspaceCreateFormTests` described below.
Class description:
Implement the WorkspaceCreateFormTests class.
Method signatures and docstrings:
- def test_workspace_create_forms_normal_1(self): 顧客情報登録フォーム 正常系 1
- def test_workspace_create_forms_normal_2(self): 顧客情報登録フォーム 正常系 ２
- def test_wor... | Implement the Python class `WorkspaceCreateFormTests` described below.
Class description:
Implement the WorkspaceCreateFormTests class.
Method signatures and docstrings:
- def test_workspace_create_forms_normal_1(self): 顧客情報登録フォーム 正常系 1
- def test_workspace_create_forms_normal_2(self): 顧客情報登録フォーム 正常系 ２
- def test_wor... | 049058a37b9ee45b58be5f4393a0b3191362043c | <|skeleton|>
class WorkspaceCreateFormTests:
def test_workspace_create_forms_normal_1(self):
"""顧客情報登録フォーム 正常系 1"""
<|body_0|>
def test_workspace_create_forms_normal_2(self):
"""顧客情報登録フォーム 正常系 ２"""
<|body_1|>
def test_workspace_create_forms_normal_3(self):
"""顧客情報登... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WorkspaceCreateFormTests:
def test_workspace_create_forms_normal_1(self):
"""顧客情報登録フォーム 正常系 1"""
params = dict(workspace_name='harfangletest_corp')
form = WorkspaceCreateForm(params)
self.assertTrue(form.is_valid())
self.assertEquals(form.cleaned_data['workspace_name'],... | the_stack_v2_python_sparse | register/tests_forms.py | yashiki-takajin/sfa-next | train | 0 | |
cd81149eb09663da0f05505c52c051ed7981f3aa | [
"super().__init__(parent=parent)\nself.plotWidget: Optional['PlotWidget'] = None\nself.data: Optional[DataDictBase] = None\nlayout: QtWidgets.QVBoxLayout = QtWidgets.QVBoxLayout(self)\nlayout.setContentsMargins(0, 0, 0, 0)\nself.setLayout(layout)",
"if widget is self.plotWidget:\n return\nif self.plotWidget is... | <|body_start_0|>
super().__init__(parent=parent)
self.plotWidget: Optional['PlotWidget'] = None
self.data: Optional[DataDictBase] = None
layout: QtWidgets.QVBoxLayout = QtWidgets.QVBoxLayout(self)
layout.setContentsMargins(0, 0, 0, 0)
self.setLayout(layout)
<|end_body_0|>... | This is the base widget for Plots, derived from `QWidget`. This widget does not implement any plotting. It merely is a wrapping widget that contains the actual plot widget in it. This actual plot widget can be set dynamically. Use :class:`PlotWidget` as base for implementing widgets that can be added to this container. | PlotWidgetContainer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlotWidgetContainer:
"""This is the base widget for Plots, derived from `QWidget`. This widget does not implement any plotting. It merely is a wrapping widget that contains the actual plot widget in it. This actual plot widget can be set dynamically. Use :class:`PlotWidget` as base for implementi... | stack_v2_sparse_classes_36k_train_011621 | 23,346 | permissive | [
{
"docstring": "Constructor for :class:`PlotWidgetContainer`.",
"name": "__init__",
"signature": "def __init__(self, parent: Optional[QtWidgets.QWidget]=None)"
},
{
"docstring": "Set the plot widget. Makes sure that the added widget receives new data. :param widget: plot widget",
"name": "se... | 3 | stack_v2_sparse_classes_30k_train_003170 | Implement the Python class `PlotWidgetContainer` described below.
Class description:
This is the base widget for Plots, derived from `QWidget`. This widget does not implement any plotting. It merely is a wrapping widget that contains the actual plot widget in it. This actual plot widget can be set dynamically. Use :cl... | Implement the Python class `PlotWidgetContainer` described below.
Class description:
This is the base widget for Plots, derived from `QWidget`. This widget does not implement any plotting. It merely is a wrapping widget that contains the actual plot widget in it. This actual plot widget can be set dynamically. Use :cl... | 0ccdeb76d44fcc57e5b986c8b75cb0696fbff03b | <|skeleton|>
class PlotWidgetContainer:
"""This is the base widget for Plots, derived from `QWidget`. This widget does not implement any plotting. It merely is a wrapping widget that contains the actual plot widget in it. This actual plot widget can be set dynamically. Use :class:`PlotWidget` as base for implementi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlotWidgetContainer:
"""This is the base widget for Plots, derived from `QWidget`. This widget does not implement any plotting. It merely is a wrapping widget that contains the actual plot widget in it. This actual plot widget can be set dynamically. Use :class:`PlotWidget` as base for implementing widgets th... | the_stack_v2_python_sparse | plottr/plot/base.py | labist/plottr | train | 0 |
7aaee75a4f1b6a275c1cef8d344f4f92051525bb | [
"super(PermissionForm, self).__init__(*args, **kwargs)\nself.current_user = current_user\nself.user_id.choices = [(-1, _('--- Select User ---'))] + [(user.id, user.email) for user in User.query.order_by('email').filter_by(is_deleted=False)]\nself.collection_id.choices = [(-1, _('--- Select Collection ---'))]\nif cu... | <|body_start_0|>
super(PermissionForm, self).__init__(*args, **kwargs)
self.current_user = current_user
self.user_id.choices = [(-1, _('--- Select User ---'))] + [(user.id, user.email) for user in User.query.order_by('email').filter_by(is_deleted=False)]
self.collection_id.choices = [(-1... | Permission form. | PermissionForm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PermissionForm:
"""Permission form."""
def __init__(self, current_user, *args, **kwargs):
"""Create instance."""
<|body_0|>
def validate_user_id(self, field):
"""Validate user ID is selected and exists in 'users' table."""
<|body_1|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_36k_train_011622 | 10,553 | permissive | [
{
"docstring": "Create instance.",
"name": "__init__",
"signature": "def __init__(self, current_user, *args, **kwargs)"
},
{
"docstring": "Validate user ID is selected and exists in 'users' table.",
"name": "validate_user_id",
"signature": "def validate_user_id(self, field)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000237 | Implement the Python class `PermissionForm` described below.
Class description:
Permission form.
Method signatures and docstrings:
- def __init__(self, current_user, *args, **kwargs): Create instance.
- def validate_user_id(self, field): Validate user ID is selected and exists in 'users' table. | Implement the Python class `PermissionForm` described below.
Class description:
Permission form.
Method signatures and docstrings:
- def __init__(self, current_user, *args, **kwargs): Create instance.
- def validate_user_id(self, field): Validate user ID is selected and exists in 'users' table.
<|skeleton|>
class Pe... | d2b66717d87ee2452edf0f6c04f6fdf4533091ba | <|skeleton|>
class PermissionForm:
"""Permission form."""
def __init__(self, current_user, *args, **kwargs):
"""Create instance."""
<|body_0|>
def validate_user_id(self, field):
"""Validate user ID is selected and exists in 'users' table."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PermissionForm:
"""Permission form."""
def __init__(self, current_user, *args, **kwargs):
"""Create instance."""
super(PermissionForm, self).__init__(*args, **kwargs)
self.current_user = current_user
self.user_id.choices = [(-1, _('--- Select User ---'))] + [(user.id, user... | the_stack_v2_python_sparse | xl_auth/permission/forms.py | libris/xl_auth | train | 8 |
2e15561c381df2c7cea833256eea7e0e80a66a3a | [
"if isinstance(text, unicode):\n return text\nreturn unicode(text, encoding, errors=errors)",
"if isinstance(text, unicode):\n return text.encode('utf8')\nreturn unicode(text, encoding, errors=errors).encode('utf8')"
] | <|body_start_0|>
if isinstance(text, unicode):
return text
return unicode(text, encoding, errors=errors)
<|end_body_0|>
<|body_start_1|>
if isinstance(text, unicode):
return text.encode('utf8')
return unicode(text, encoding, errors=errors).encode('utf8')
<|end_bo... | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
def str2uni(text, encoding='utf8', errors='strict'):
"""Convert `text` to unicode. Parameters ---------- text : str Input text. errors : str, optional Error handling behaviour, used as parameter for `unicode` function (python2 only). encoding : str, optional Encoding of `text` f... | stack_v2_sparse_classes_36k_train_011623 | 9,000 | no_license | [
{
"docstring": "Convert `text` to unicode. Parameters ---------- text : str Input text. errors : str, optional Error handling behaviour, used as parameter for `unicode` function (python2 only). encoding : str, optional Encoding of `text` for `unicode` function (python2 only). Returns ------- str Unicode version... | 2 | stack_v2_sparse_classes_30k_train_001233 | Implement the Python class `Encoder` described below.
Class description:
Implement the Encoder class.
Method signatures and docstrings:
- def str2uni(text, encoding='utf8', errors='strict'): Convert `text` to unicode. Parameters ---------- text : str Input text. errors : str, optional Error handling behaviour, used a... | Implement the Python class `Encoder` described below.
Class description:
Implement the Encoder class.
Method signatures and docstrings:
- def str2uni(text, encoding='utf8', errors='strict'): Convert `text` to unicode. Parameters ---------- text : str Input text. errors : str, optional Error handling behaviour, used a... | b798e7b1286e043c5685aa8375022ee50f91024a | <|skeleton|>
class Encoder:
def str2uni(text, encoding='utf8', errors='strict'):
"""Convert `text` to unicode. Parameters ---------- text : str Input text. errors : str, optional Error handling behaviour, used as parameter for `unicode` function (python2 only). encoding : str, optional Encoding of `text` f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Encoder:
def str2uni(text, encoding='utf8', errors='strict'):
"""Convert `text` to unicode. Parameters ---------- text : str Input text. errors : str, optional Error handling behaviour, used as parameter for `unicode` function (python2 only). encoding : str, optional Encoding of `text` for `unicode` f... | the_stack_v2_python_sparse | utils/other_utils.py | duytinvo/sequence_labeller | train | 3 | |
8d8515bf8a5aceea7b95a52ee632ca9eac95fa12 | [
"content = [w for w in menu_list]\nheight = len(menu_list)\nwidth = 0\nfor entry in menu_list:\n if len(entry.original_widget.text) > width:\n width = len(entry.original_widget.text)\nself._listbox = urwid.AttrWrap(urwid.ListBox(content), attr[0])\noverlay = urwid.Overlay(self._listbox, body, 'center', wi... | <|body_start_0|>
content = [w for w in menu_list]
height = len(menu_list)
width = 0
for entry in menu_list:
if len(entry.original_widget.text) > width:
width = len(entry.original_widget.text)
self._listbox = urwid.AttrWrap(urwid.ListBox(content), attr[... | Creates a popup menu on top of another BoxWidget. Attributes: selected -- Contains the item the user has selected by pressing <RETURN>, or None if nothing has been selected. | Popup | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Popup:
"""Creates a popup menu on top of another BoxWidget. Attributes: selected -- Contains the item the user has selected by pressing <RETURN>, or None if nothing has been selected."""
def __init__(self, menu_list, attr, pos, body):
"""menu_list -- a list of strings with the menu e... | stack_v2_sparse_classes_36k_train_011624 | 45,709 | no_license | [
{
"docstring": "menu_list -- a list of strings with the menu entries attr -- a tuple (background, active_item) of attributes pos -- a tuple (x, y), position of the menu widget body -- widget displayed beneath the message widget",
"name": "__init__",
"signature": "def __init__(self, menu_list, attr, pos,... | 2 | null | Implement the Python class `Popup` described below.
Class description:
Creates a popup menu on top of another BoxWidget. Attributes: selected -- Contains the item the user has selected by pressing <RETURN>, or None if nothing has been selected.
Method signatures and docstrings:
- def __init__(self, menu_list, attr, p... | Implement the Python class `Popup` described below.
Class description:
Creates a popup menu on top of another BoxWidget. Attributes: selected -- Contains the item the user has selected by pressing <RETURN>, or None if nothing has been selected.
Method signatures and docstrings:
- def __init__(self, menu_list, attr, p... | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | <|skeleton|>
class Popup:
"""Creates a popup menu on top of another BoxWidget. Attributes: selected -- Contains the item the user has selected by pressing <RETURN>, or None if nothing has been selected."""
def __init__(self, menu_list, attr, pos, body):
"""menu_list -- a list of strings with the menu e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Popup:
"""Creates a popup menu on top of another BoxWidget. Attributes: selected -- Contains the item the user has selected by pressing <RETURN>, or None if nothing has been selected."""
def __init__(self, menu_list, attr, pos, body):
"""menu_list -- a list of strings with the menu entries attr -... | the_stack_v2_python_sparse | repoData/socketubs-pyhn/allPythonContent.py | aCoffeeYin/pyreco | train | 0 |
e0adfb50c1bd69ef89412b53925f436a7192be76 | [
"if intervals == None or len(intervals) == 0:\n return [newInterval]\nres = []\ni = 0\nwhile i < len(intervals):\n if intervals[i].end < newInterval.start:\n res.append(intervals[i])\n i += 1\n continue\n if intervals[i].start > newInterval.end:\n res.append(newInterval)\n ... | <|body_start_0|>
if intervals == None or len(intervals) == 0:
return [newInterval]
res = []
i = 0
while i < len(intervals):
if intervals[i].end < newInterval.start:
res.append(intervals[i])
i += 1
continue
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def insert_before(self, intervals, newInterval):
""":type intervals: List[Interval] :type newInterval: Interval :rtype: List[Interval]"""
<|body_0|>
def insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :... | stack_v2_sparse_classes_36k_train_011625 | 2,021 | no_license | [
{
"docstring": ":type intervals: List[Interval] :type newInterval: Interval :rtype: List[Interval]",
"name": "insert_before",
"signature": "def insert_before(self, intervals, newInterval)"
},
{
"docstring": ":type intervals: List[List[int]] :type newInterval: List[int] :rtype: List[List[int]]",
... | 2 | stack_v2_sparse_classes_30k_train_021509 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def insert_before(self, intervals, newInterval): :type intervals: List[Interval] :type newInterval: Interval :rtype: List[Interval]
- def insert(self, intervals, newInterval): :t... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def insert_before(self, intervals, newInterval): :type intervals: List[Interval] :type newInterval: Interval :rtype: List[Interval]
- def insert(self, intervals, newInterval): :t... | 238995bd23c8a6c40c6035890e94baa2473d4bbc | <|skeleton|>
class Solution:
def insert_before(self, intervals, newInterval):
""":type intervals: List[Interval] :type newInterval: Interval :rtype: List[Interval]"""
<|body_0|>
def insert(self, intervals, newInterval):
""":type intervals: List[List[int]] :type newInterval: List[int] :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def insert_before(self, intervals, newInterval):
""":type intervals: List[Interval] :type newInterval: Interval :rtype: List[Interval]"""
if intervals == None or len(intervals) == 0:
return [newInterval]
res = []
i = 0
while i < len(intervals):
... | the_stack_v2_python_sparse | problems/InsertInterval.py | wan-catherine/Leetcode | train | 5 | |
38db0a0fb7dc7f33f73137602a12c097b8cc68fe | [
"super().__init__()\ndeconv_kwargs = {'kernel_size': 3, 'padding': 1}\ndeconv_kwargs.update(kwargs)\nself.stride = stride\nself.outp = 1 if stride == 2 else 0\nself.use_upsampling = use_upsampling\nif in_c != out_c or stride > 1:\n assert self.use_upsampling == True, 'Input needs to be adjusted in (h,w) and/or n... | <|body_start_0|>
super().__init__()
deconv_kwargs = {'kernel_size': 3, 'padding': 1}
deconv_kwargs.update(kwargs)
self.stride = stride
self.outp = 1 if stride == 2 else 0
self.use_upsampling = use_upsampling
if in_c != out_c or stride > 1:
assert self.... | A module that implements a single flow of residual operation for ResNet. Each conv layer uses kernel of size 3x3, stride=stride, and padding=1. First the input's (h,w) are shrinked by `stride`, then the num of channels is increased to out_c via subsequent conv operations. input ---> (bn-relu-convTranspose2d) -> z1 --->... | ResidualDeconv | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResidualDeconv:
"""A module that implements a single flow of residual operation for ResNet. Each conv layer uses kernel of size 3x3, stride=stride, and padding=1. First the input's (h,w) are shrinked by `stride`, then the num of channels is increased to out_c via subsequent conv operations. input... | stack_v2_sparse_classes_36k_train_011626 | 6,408 | no_license | [
{
"docstring": "To double the input's (h,w), ie. stride=2, use kernel_size=3, padding=1, stride=2, output_padding=1 When the output needs bo have the same (h,w) as the input, ie. stride=1, use output_padding = 0",
"name": "__init__",
"signature": "def __init__(self, in_c, out_c, *, stride=2, use_upsampl... | 2 | null | Implement the Python class `ResidualDeconv` described below.
Class description:
A module that implements a single flow of residual operation for ResNet. Each conv layer uses kernel of size 3x3, stride=stride, and padding=1. First the input's (h,w) are shrinked by `stride`, then the num of channels is increased to out_... | Implement the Python class `ResidualDeconv` described below.
Class description:
A module that implements a single flow of residual operation for ResNet. Each conv layer uses kernel of size 3x3, stride=stride, and padding=1. First the input's (h,w) are shrinked by `stride`, then the num of channels is increased to out_... | 6f6e8ee74bc9f597def9559f74a6841c37c0a214 | <|skeleton|>
class ResidualDeconv:
"""A module that implements a single flow of residual operation for ResNet. Each conv layer uses kernel of size 3x3, stride=stride, and padding=1. First the input's (h,w) are shrinked by `stride`, then the num of channels is increased to out_c via subsequent conv operations. input... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResidualDeconv:
"""A module that implements a single flow of residual operation for ResNet. Each conv layer uses kernel of size 3x3, stride=stride, and padding=1. First the input's (h,w) are shrinked by `stride`, then the num of channels is increased to out_c via subsequent conv operations. input ---> (bn-rel... | the_stack_v2_python_sparse | src/models/resnet_deconv.py | cocoaaa/Tenenbaum2000 | train | 0 |
1401cc908f52a3afd8d9cbcac01e8902dfb3b2b9 | [
"len_a = self.get_length(headA)\nlen_b = self.get_length(headB)\nlonger_list = len_a if len_a >= len_b else len_b\nk = len_a - len_b if len_a >= len_b else len_b - len_a\nif len_a >= len_b:\n for _ in range(k):\n headA = headA.next\nelse:\n for _ in range(k):\n headB = headB.next\nwhile headA !=... | <|body_start_0|>
len_a = self.get_length(headA)
len_b = self.get_length(headB)
longer_list = len_a if len_a >= len_b else len_b
k = len_a - len_b if len_a >= len_b else len_b - len_a
if len_a >= len_b:
for _ in range(k):
headA = headA.next
else... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_0|>
def get_length(self, head):
"""Return the length of the linked list"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
len_a = self.ge... | stack_v2_sparse_classes_36k_train_011627 | 1,702 | permissive | [
{
"docstring": ":type head1, head1: ListNode :rtype: ListNode",
"name": "getIntersectionNode",
"signature": "def getIntersectionNode(self, headA, headB)"
},
{
"docstring": "Return the length of the linked list",
"name": "get_length",
"signature": "def get_length(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009354 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: ListNode
- def get_length(self, head): Return the length of the linked list | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: ListNode
- def get_length(self, head): Return the length of the linked list
<|skeleton|>
class ... | 547c200b627c774535bc22880b16d5390183aeba | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_0|>
def get_length(self, head):
"""Return the length of the linked list"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
len_a = self.get_length(headA)
len_b = self.get_length(headB)
longer_list = len_a if len_a >= len_b else len_b
k = len_a - len_b if len_a >= len_b else len_b - len... | the_stack_v2_python_sparse | easy/160_intersection_of_two_linked_list.py | Sukhrobjon/leetcode | train | 0 | |
cbcc0ea464e8a19eab943526758560c312febd77 | [
"parser = reqparse.RequestParser()\nparser.add_argument('user', type=str, location='args')\nargs = parser.parse_args()\nuser = args['user']\nif user is None:\n return errors.all_errors('CLIENT_MISSING_PARAMETER', 'user (str) parameter is required')\ntry:\n check_existing_key = ApiKeys.query.filter_by(user=use... | <|body_start_0|>
parser = reqparse.RequestParser()
parser.add_argument('user', type=str, location='args')
args = parser.parse_args()
user = args['user']
if user is None:
return errors.all_errors('CLIENT_MISSING_PARAMETER', 'user (str) parameter is required')
t... | ApiKey | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"MIT",
"BSD-3-Clause",
"GPL-1.0-or-later",
"AGPL-3.0-only",
"AGPL-3.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiKey:
def get(self):
"""Retrieve API key of the user --- tags: - User Management parameters: - in: body name: body schema: required: - user properties: user: type: string description: user of the SOCA user responses: 200: description: Return the token associated to the user 203: descri... | stack_v2_sparse_classes_36k_train_011628 | 6,408 | permissive | [
{
"docstring": "Retrieve API key of the user --- tags: - User Management parameters: - in: body name: body schema: required: - user properties: user: type: string description: user of the SOCA user responses: 200: description: Return the token associated to the user 203: description: No token detected 400: desc... | 2 | stack_v2_sparse_classes_30k_train_010122 | Implement the Python class `ApiKey` described below.
Class description:
Implement the ApiKey class.
Method signatures and docstrings:
- def get(self): Retrieve API key of the user --- tags: - User Management parameters: - in: body name: body schema: required: - user properties: user: type: string description: user of... | Implement the Python class `ApiKey` described below.
Class description:
Implement the ApiKey class.
Method signatures and docstrings:
- def get(self): Retrieve API key of the user --- tags: - User Management parameters: - in: body name: body schema: required: - user properties: user: type: string description: user of... | e1617b14bb1b4a29a5bc6e02fa76c1312a333389 | <|skeleton|>
class ApiKey:
def get(self):
"""Retrieve API key of the user --- tags: - User Management parameters: - in: body name: body schema: required: - user properties: user: type: string description: user of the SOCA user responses: 200: description: Return the token associated to the user 203: descri... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApiKey:
def get(self):
"""Retrieve API key of the user --- tags: - User Management parameters: - in: body name: body schema: required: - user properties: user: type: string description: user of the SOCA user responses: 200: description: Return the token associated to the user 203: description: No toke... | the_stack_v2_python_sparse | source/soca/cluster_web_ui/api/v1/user/api_key.py | awslabs/scale-out-computing-on-aws | train | 110 | |
0ce35f4a37ef0f9b0bc791242bb3dc0e16daf286 | [
"self._compute = compute_client.apitools_client\nself._messages = compute_client.messages\nself._http = compute_client.apitools_client.http\nself._batch_url = compute_client.batch_url",
"errors = []\nrequests = []\nzone_names = set()\nfor resource_ref in resource_refs:\n if resource_ref.zone not in zone_names:... | <|body_start_0|>
self._compute = compute_client.apitools_client
self._messages = compute_client.messages
self._http = compute_client.apitools_client.http
self._batch_url = compute_client.batch_url
<|end_body_0|>
<|body_start_1|>
errors = []
requests = []
zone_nam... | A (small) collection of utils for working with zones. | ZoneResourceFetcher | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZoneResourceFetcher:
"""A (small) collection of utils for working with zones."""
def __init__(self, compute_client):
"""Instantiate ZoneResourceFetcher and embed all required data into it. ZoneResourceFetcher is a class depending on "base_classes" class layout (properties side-derive... | stack_v2_sparse_classes_36k_train_011629 | 4,350 | permissive | [
{
"docstring": "Instantiate ZoneResourceFetcher and embed all required data into it. ZoneResourceFetcher is a class depending on \"base_classes\" class layout (properties side-derived from one of base_class class). This function can be used to avoid unfeasible inheritance and use composition instead when refact... | 3 | null | Implement the Python class `ZoneResourceFetcher` described below.
Class description:
A (small) collection of utils for working with zones.
Method signatures and docstrings:
- def __init__(self, compute_client): Instantiate ZoneResourceFetcher and embed all required data into it. ZoneResourceFetcher is a class dependi... | Implement the Python class `ZoneResourceFetcher` described below.
Class description:
A (small) collection of utils for working with zones.
Method signatures and docstrings:
- def __init__(self, compute_client): Instantiate ZoneResourceFetcher and embed all required data into it. ZoneResourceFetcher is a class dependi... | c98b58aeb0994e011df960163541e9379ae7ea06 | <|skeleton|>
class ZoneResourceFetcher:
"""A (small) collection of utils for working with zones."""
def __init__(self, compute_client):
"""Instantiate ZoneResourceFetcher and embed all required data into it. ZoneResourceFetcher is a class depending on "base_classes" class layout (properties side-derive... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ZoneResourceFetcher:
"""A (small) collection of utils for working with zones."""
def __init__(self, compute_client):
"""Instantiate ZoneResourceFetcher and embed all required data into it. ZoneResourceFetcher is a class depending on "base_classes" class layout (properties side-derived from one of... | the_stack_v2_python_sparse | google-cloud-sdk/.install/.backup/lib/googlecloudsdk/api_lib/compute/zone_utils.py | KaranToor/MA450 | train | 1 |
ea572610a435687ba7aeb220e785bbdceb38619f | [
"self.width = width\nself.height = height\nself.food = deque([(item[0], item[1]) for item in food])\nself.snake = deque([(0, 0)])\nself.offsets = {'R': (0, 1), 'L': (0, -1), 'U': (-1, 0), 'D': (1, 0)}",
"row_offset, col_offset = self.offsets[direction]\nrow, col = self.snake[0]\nnew_row, new_col = (row + row_offs... | <|body_start_0|>
self.width = width
self.height = height
self.food = deque([(item[0], item[1]) for item in food])
self.snake = deque([(0, 0)])
self.offsets = {'R': (0, 1), 'L': (0, -1), 'U': (-1, 0), 'D': (1, 0)}
<|end_body_0|>
<|body_start_1|>
row_offset, col_offset = s... | SnakeGame | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], t... | stack_v2_sparse_classes_36k_train_011630 | 1,713 | permissive | [
{
"docstring": "Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], the second is at [1,0].",
"name": "__init__",
"signature": "def __init__(self, widt... | 2 | null | Implement the Python class `SnakeGame` described below.
Class description:
Implement the SnakeGame class.
Method signatures and docstrings:
- def __init__(self, width: int, height: int, food: List[List[int]]): Initialize your data structure here. @param width - screen width @param height - screen height @param food -... | Implement the Python class `SnakeGame` described below.
Class description:
Implement the SnakeGame class.
Method signatures and docstrings:
- def __init__(self, width: int, height: int, food: List[List[int]]): Initialize your data structure here. @param width - screen width @param height - screen height @param food -... | bf03743a3676ca9a8c107f92cf3858b6887d0308 | <|skeleton|>
class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], the second is a... | the_stack_v2_python_sparse | python/353_design_snake_game.py | liaison/LeetCode | train | 17 | |
a59b3e9bdad38206fb724febb069a86cf5cc96ae | [
"i, j, n = (0, len(A) - 1, len(A))\nwhile i + 1 < n and A[i] < A[i + 1]:\n i += 1\nwhile j > 0 and A[j - 1] > A[j]:\n j -= 1\nreturn 0 < i == j < n - 1",
"if len(A) < 3:\n return False\nif A[0] >= A[1]:\n return False\nclimb = True\nfor a, b in zip(A, A[1:]):\n if climb:\n if a < b:\n ... | <|body_start_0|>
i, j, n = (0, len(A) - 1, len(A))
while i + 1 < n and A[i] < A[i + 1]:
i += 1
while j > 0 and A[j - 1] > A[j]:
j -= 1
return 0 < i == j < n - 1
<|end_body_0|>
<|body_start_1|>
if len(A) < 3:
return False
if A[0] >= A[1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def validMountainArray(self, A):
""":type A: List[int] :rtype: bool"""
<|body_0|>
def validMountainArray(self, A):
""":type A: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
i, j, n = (0, len(A) - 1, len(A))
... | stack_v2_sparse_classes_36k_train_011631 | 839 | no_license | [
{
"docstring": ":type A: List[int] :rtype: bool",
"name": "validMountainArray",
"signature": "def validMountainArray(self, A)"
},
{
"docstring": ":type A: List[int] :rtype: bool",
"name": "validMountainArray",
"signature": "def validMountainArray(self, A)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016721 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validMountainArray(self, A): :type A: List[int] :rtype: bool
- def validMountainArray(self, A): :type A: List[int] :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validMountainArray(self, A): :type A: List[int] :rtype: bool
- def validMountainArray(self, A): :type A: List[int] :rtype: bool
<|skeleton|>
class Solution:
def validMo... | c92a5ddcc56e3f69be1e6fb25e9c8ed277e57ee0 | <|skeleton|>
class Solution:
def validMountainArray(self, A):
""":type A: List[int] :rtype: bool"""
<|body_0|>
def validMountainArray(self, A):
""":type A: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def validMountainArray(self, A):
""":type A: List[int] :rtype: bool"""
i, j, n = (0, len(A) - 1, len(A))
while i + 1 < n and A[i] < A[i + 1]:
i += 1
while j > 0 and A[j - 1] > A[j]:
j -= 1
return 0 < i == j < n - 1
def validMountai... | the_stack_v2_python_sparse | code/941#Valid Mountain Array.py | EachenKuang/LeetCode | train | 28 | |
01d958fe033b0bec75d9314add6be9c67e5c3dcd | [
"self.obs_type = obs_type\nself.stale_age = stale_age\nself.old_timestamp = None\nself.old_value = None",
"if self.obs_type not in record:\n raise weewx.CannotCalculate(self.obs_type)\nderivative = None\nif record[self.obs_type] is not None:\n if self.old_timestamp:\n if record['dateTime'] < self.old... | <|body_start_0|>
self.obs_type = obs_type
self.stale_age = stale_age
self.old_timestamp = None
self.old_value = None
<|end_body_0|>
<|body_start_1|>
if self.obs_type not in record:
raise weewx.CannotCalculate(self.obs_type)
derivative = None
if record... | Calculate time derivative for a specific observation type. | TimeDerivative | [
"GPL-1.0-or-later",
"GPL-3.0-only",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimeDerivative:
"""Calculate time derivative for a specific observation type."""
def __init__(self, obs_type, stale_age):
"""Initialize. obs_type: the observation type for which the derivative will be calculated. stale_age: Derivatives are calculated as a difference over time. This i... | stack_v2_sparse_classes_36k_train_011632 | 2,064 | permissive | [
{
"docstring": "Initialize. obs_type: the observation type for which the derivative will be calculated. stale_age: Derivatives are calculated as a difference over time. This is how old the old value can be and still be considered useful.",
"name": "__init__",
"signature": "def __init__(self, obs_type, s... | 2 | null | Implement the Python class `TimeDerivative` described below.
Class description:
Calculate time derivative for a specific observation type.
Method signatures and docstrings:
- def __init__(self, obs_type, stale_age): Initialize. obs_type: the observation type for which the derivative will be calculated. stale_age: Der... | Implement the Python class `TimeDerivative` described below.
Class description:
Calculate time derivative for a specific observation type.
Method signatures and docstrings:
- def __init__(self, obs_type, stale_age): Initialize. obs_type: the observation type for which the derivative will be calculated. stale_age: Der... | 7085654f455d39b06acc688738fde27e1f78ad1e | <|skeleton|>
class TimeDerivative:
"""Calculate time derivative for a specific observation type."""
def __init__(self, obs_type, stale_age):
"""Initialize. obs_type: the observation type for which the derivative will be calculated. stale_age: Derivatives are calculated as a difference over time. This i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TimeDerivative:
"""Calculate time derivative for a specific observation type."""
def __init__(self, obs_type, stale_age):
"""Initialize. obs_type: the observation type for which the derivative will be calculated. stale_age: Derivatives are calculated as a difference over time. This is how old the... | the_stack_v2_python_sparse | dist/weewx-4.3.0b2/bin/weeutil/timediff.py | tomdotorg/docker-weewx | train | 21 |
918bff455975ffdd513038de15195eaf39667f01 | [
"self._list = []\nself._value = {}\nself._length = capacity",
"ans = self._value.get(key)\nif ans is not None:\n self.set(key, ans)\n return ans\nreturn -1",
"if not self._value.get(key):\n if len(self._list) == self._length:\n del self._value[self._list[0]]\n self._list[0:1] = []\nelse:\... | <|body_start_0|>
self._list = []
self._value = {}
self._length = capacity
<|end_body_0|>
<|body_start_1|>
ans = self._value.get(key)
if ans is not None:
self.set(key, ans)
return ans
return -1
<|end_body_1|>
<|body_start_2|>
if not self._... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_011633 | 869 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: nothing",
"name": "set",
"sig... | 3 | stack_v2_sparse_classes_30k_train_012065 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing
<|skeleton|>
cla... | bd6b18f134336513bbc3112be6e33c79374a7cb1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self._list = []
self._value = {}
self._length = capacity
def get(self, key):
""":rtype: int"""
ans = self._value.get(key)
if ans is not None:
self.set(key, ans)
... | the_stack_v2_python_sparse | python/146. LRU Cache.py | AlisonZXQ/leetcode | train | 1 | |
90df319fe3828a41e093c3cf1750d0cb985b4698 | [
"if target_vector is None:\n target_vector = np.zeros(cluster_subspace.num_orbits)\nif target_weights is None:\n target_weights = np.ones(cluster_subspace.num_orbits - 1)\nif interaction_tensors is None:\n interaction_tensors = (0.0,) + tuple((sum((m * tensor for i, (m, tensor) in enumerate(zip(orbit.bit_c... | <|body_start_0|>
if target_vector is None:
target_vector = np.zeros(cluster_subspace.num_orbits)
if target_weights is None:
target_weights = np.ones(cluster_subspace.num_orbits - 1)
if interaction_tensors is None:
interaction_tensors = (0.0,) + tuple((sum((m *... | Compute distances from a fixed cluster interaction vector. The distance used to measure distance is, .. math:: d = -wL + ||W^T(f - f_T)||_1 where f is the cluster interaction vector, f_T is the target vector, and L is the diameter for which all correlation values of clusters of smaller diameter are exactly equal to the... | ClusterInteractionDistanceProcessor | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClusterInteractionDistanceProcessor:
"""Compute distances from a fixed cluster interaction vector. The distance used to measure distance is, .. math:: d = -wL + ||W^T(f - f_T)||_1 where f is the cluster interaction vector, f_T is the target vector, and L is the diameter for which all correlation ... | stack_v2_sparse_classes_36k_train_011634 | 17,001 | permissive | [
{
"docstring": "Initialize a CorrelationDistanceProcessor. Args: cluster_subspace (ClusterSubspace): a cluster subspace supercell_matrix (ndarray): an array representing the supercell matrix with respect to the Cluster Expansion prim structure. interaction_tensors (sequence of ndarray): optional Sequence of nda... | 3 | null | Implement the Python class `ClusterInteractionDistanceProcessor` described below.
Class description:
Compute distances from a fixed cluster interaction vector. The distance used to measure distance is, .. math:: d = -wL + ||W^T(f - f_T)||_1 where f is the cluster interaction vector, f_T is the target vector, and L is ... | Implement the Python class `ClusterInteractionDistanceProcessor` described below.
Class description:
Compute distances from a fixed cluster interaction vector. The distance used to measure distance is, .. math:: d = -wL + ||W^T(f - f_T)||_1 where f is the cluster interaction vector, f_T is the target vector, and L is ... | 457518b9a27729fd4d5c1c23231bd37f8fee364b | <|skeleton|>
class ClusterInteractionDistanceProcessor:
"""Compute distances from a fixed cluster interaction vector. The distance used to measure distance is, .. math:: d = -wL + ||W^T(f - f_T)||_1 where f is the cluster interaction vector, f_T is the target vector, and L is the diameter for which all correlation ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClusterInteractionDistanceProcessor:
"""Compute distances from a fixed cluster interaction vector. The distance used to measure distance is, .. math:: d = -wL + ||W^T(f - f_T)||_1 where f is the cluster interaction vector, f_T is the target vector, and L is the diameter for which all correlation values of clu... | the_stack_v2_python_sparse | smol/moca/processor/distance.py | CederGroupHub/smol | train | 40 |
421b264ed83f666dc9998d21f67a5b38a797eba7 | [
"def target1(x, pattern: BaseGeometry, pos: BaseGeometry):\n params = _TargetTransformParams(x[0], x[1], x[2], x[3])\n pos_trans = _target_affine_transform(pos, params)\n pos_overlap = pattern.intersection(pos_trans).area\n false_pos_overlap = pos_trans.difference(pattern).area\n return false_pos_ove... | <|body_start_0|>
def target1(x, pattern: BaseGeometry, pos: BaseGeometry):
params = _TargetTransformParams(x[0], x[1], x[2], x[3])
pos_trans = _target_affine_transform(pos, params)
pos_overlap = pattern.intersection(pos_trans).area
false_pos_overlap = pos_trans.di... | Aligner | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Aligner:
def align(self, pattern: BaseGeometry, pos: BaseGeometry) -> TransformMatrix:
"""this method try to apply an affine transformation on pos, so the overlap between pattern and pos is maximized :param pattern: keep-it-still background :param pos: a shape we try to align it with pat... | stack_v2_sparse_classes_36k_train_011635 | 6,594 | no_license | [
{
"docstring": "this method try to apply an affine transformation on pos, so the overlap between pattern and pos is maximized :param pattern: keep-it-still background :param pos: a shape we try to align it with pattern :return: transformation parameters",
"name": "align",
"signature": "def align(self, p... | 2 | stack_v2_sparse_classes_30k_train_019686 | Implement the Python class `Aligner` described below.
Class description:
Implement the Aligner class.
Method signatures and docstrings:
- def align(self, pattern: BaseGeometry, pos: BaseGeometry) -> TransformMatrix: this method try to apply an affine transformation on pos, so the overlap between pattern and pos is ma... | Implement the Python class `Aligner` described below.
Class description:
Implement the Aligner class.
Method signatures and docstrings:
- def align(self, pattern: BaseGeometry, pos: BaseGeometry) -> TransformMatrix: this method try to apply an affine transformation on pos, so the overlap between pattern and pos is ma... | 86446637d87077388b318ce9be7317139b5e5428 | <|skeleton|>
class Aligner:
def align(self, pattern: BaseGeometry, pos: BaseGeometry) -> TransformMatrix:
"""this method try to apply an affine transformation on pos, so the overlap between pattern and pos is maximized :param pattern: keep-it-still background :param pos: a shape we try to align it with pat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Aligner:
def align(self, pattern: BaseGeometry, pos: BaseGeometry) -> TransformMatrix:
"""this method try to apply an affine transformation on pos, so the overlap between pattern and pos is maximized :param pattern: keep-it-still background :param pos: a shape we try to align it with pattern :return: ... | the_stack_v2_python_sparse | backend/prototype/shape_match.py | snowdmonkey/solar | train | 1 | |
dcc0d7caad437457765389524b9c16a6abd2bb2f | [
"super().__init__()\nimport sklearn\nimport sklearn.discriminant_analysis\nself.model = sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
"specs = super(LinearDiscriminantAnalysisClassifier, cls).getInputSpecification()\nspecs.description = \"The \\\\xmlNode{LinearDiscriminantAnalysisClassifier} is a cla... | <|body_start_0|>
super().__init__()
import sklearn
import sklearn.discriminant_analysis
self.model = sklearn.discriminant_analysis.LinearDiscriminantAnalysis
<|end_body_0|>
<|body_start_1|>
specs = super(LinearDiscriminantAnalysisClassifier, cls).getInputSpecification()
... | KNeighborsClassifier Classifier implementing the k-nearest neighbors vote. | LinearDiscriminantAnalysisClassifier | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-2-Clause",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearDiscriminantAnalysisClassifier:
"""KNeighborsClassifier Classifier implementing the k-nearest neighbors vote."""
def __init__(self):
"""Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None"""
<|body_0|>
def getInputSpec... | stack_v2_sparse_classes_36k_train_011636 | 7,036 | permissive | [
{
"docstring": "Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Method to get a reference to a class that specifies the input data for class cls. @ In, cls, the class for... | 3 | null | Implement the Python class `LinearDiscriminantAnalysisClassifier` described below.
Class description:
KNeighborsClassifier Classifier implementing the k-nearest neighbors vote.
Method signatures and docstrings:
- def __init__(self): Constructor that will appropriately initialize a supervised learning object @ In, Non... | Implement the Python class `LinearDiscriminantAnalysisClassifier` described below.
Class description:
KNeighborsClassifier Classifier implementing the k-nearest neighbors vote.
Method signatures and docstrings:
- def __init__(self): Constructor that will appropriately initialize a supervised learning object @ In, Non... | 2b16e7aa3325fe84cab2477947a951414c635381 | <|skeleton|>
class LinearDiscriminantAnalysisClassifier:
"""KNeighborsClassifier Classifier implementing the k-nearest neighbors vote."""
def __init__(self):
"""Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None"""
<|body_0|>
def getInputSpec... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinearDiscriminantAnalysisClassifier:
"""KNeighborsClassifier Classifier implementing the k-nearest neighbors vote."""
def __init__(self):
"""Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None"""
super().__init__()
import sklearn
... | the_stack_v2_python_sparse | ravenframework/SupervisedLearning/ScikitLearn/DiscriminantAnalysis/LinearDiscriminantAnalysis.py | idaholab/raven | train | 201 |
03cf94c6ce8ba1a25d1d14423071f393a949d212 | [
"if root:\n if root.left and root.right:\n return self.isSameTree(root.left, root.right)\n elif not root.left and (not root.right):\n return True\n else:\n return False\nelse:\n return True",
"if not (p and q or (not p and (not q))):\n return False\nif p:\n if p.val != q.val... | <|body_start_0|>
if root:
if root.left and root.right:
return self.isSameTree(root.left, root.right)
elif not root.left and (not root.right):
return True
else:
return False
else:
return True
<|end_body_0|>
<... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSymmetric(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isSameTree(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root:
if root.le... | stack_v2_sparse_classes_36k_train_011637 | 885 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isSymmetric",
"signature": "def isSymmetric(self, root)"
},
{
"docstring": ":type p: TreeNode :type q: TreeNode :rtype: bool",
"name": "isSameTree",
"signature": "def isSameTree(self, p, q)"
}
] | 2 | stack_v2_sparse_classes_30k_train_021103 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric(self, root): :type root: TreeNode :rtype: bool
- def isSameTree(self, p, q): :type p: TreeNode :type q: TreeNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric(self, root): :type root: TreeNode :rtype: bool
- def isSameTree(self, p, q): :type p: TreeNode :type q: TreeNode :rtype: bool
<|skeleton|>
class Solution:
d... | 2711bc08f15266bec4ca135e8e3e629df46713eb | <|skeleton|>
class Solution:
def isSymmetric(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isSameTree(self, p, q):
""":type p: TreeNode :type q: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isSymmetric(self, root):
""":type root: TreeNode :rtype: bool"""
if root:
if root.left and root.right:
return self.isSameTree(root.left, root.right)
elif not root.left and (not root.right):
return True
else:
... | the_stack_v2_python_sparse | 0.算法/101_Symmetric_Tree.py | unlimitediw/CheckCode | train | 0 | |
8f137a8946329e233cbd259009419d3f32b7a449 | [
"user = request.user\nif not way_id:\n data = [way.get_way_with_routes() for way in user.ways.all()]\n return JsonResponse(data, status=200, safe=False)\nway = Way.get_by_id(obj_id=way_id)\nif not way:\n return RESPONSE_400_OBJECT_NOT_FOUND\nif not way.user == user:\n return RESPONSE_403_ACCESS_DENIED\n... | <|body_start_0|>
user = request.user
if not way_id:
data = [way.get_way_with_routes() for way in user.ways.all()]
return JsonResponse(data, status=200, safe=False)
way = Way.get_by_id(obj_id=way_id)
if not way:
return RESPONSE_400_OBJECT_NOT_FOUND
... | Class-based view for way model. | WayView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WayView:
"""Class-based view for way model."""
def get(self, request, way_id=None):
"""Method for HTTP GET request :param request: client HttpRequest. Is required :type request: HttpRequest :param way_id: id of Way model :type way_id: int :return JsonResponse with way data and list w... | stack_v2_sparse_classes_36k_train_011638 | 7,225 | no_license | [
{
"docstring": "Method for HTTP GET request :param request: client HttpRequest. Is required :type request: HttpRequest :param way_id: id of Way model :type way_id: int :return JsonResponse with way data and list with routes and status 200 if parameters are good and way_id is specified, JsonResponse with all way... | 3 | null | Implement the Python class `WayView` described below.
Class description:
Class-based view for way model.
Method signatures and docstrings:
- def get(self, request, way_id=None): Method for HTTP GET request :param request: client HttpRequest. Is required :type request: HttpRequest :param way_id: id of Way model :type ... | Implement the Python class `WayView` described below.
Class description:
Class-based view for way model.
Method signatures and docstrings:
- def get(self, request, way_id=None): Method for HTTP GET request :param request: client HttpRequest. Is required :type request: HttpRequest :param way_id: id of Way model :type ... | c5f533bd049f6939037b14045e2aa2550aaac36a | <|skeleton|>
class WayView:
"""Class-based view for way model."""
def get(self, request, way_id=None):
"""Method for HTTP GET request :param request: client HttpRequest. Is required :type request: HttpRequest :param way_id: id of Way model :type way_id: int :return JsonResponse with way data and list w... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WayView:
"""Class-based view for way model."""
def get(self, request, way_id=None):
"""Method for HTTP GET request :param request: client HttpRequest. Is required :type request: HttpRequest :param way_id: id of Way model :type way_id: int :return JsonResponse with way data and list with routes an... | the_stack_v2_python_sparse | way_to_home/way/views.py | Lv-365python/wayToHome | train | 1 |
817f8c4a36a79b254fc6e92bfa08df0426a5bd93 | [
"self.depth = depth\nself.qubits = qubits\nself.quantum_program = QuantumProgram()\nself.quantum_register = self.quantum_program.create_quantum_register('qr', qubits)\nself.classical_register = self.quantum_program.create_classical_register('cr', qubits)\nif seed is not None:\n random.seed(a=seed)",
"circuit_n... | <|body_start_0|>
self.depth = depth
self.qubits = qubits
self.quantum_program = QuantumProgram()
self.quantum_register = self.quantum_program.create_quantum_register('qr', qubits)
self.classical_register = self.quantum_program.create_classical_register('cr', qubits)
if se... | Generate circuits with random operations for profiling. | RandomQasmGenerator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomQasmGenerator:
"""Generate circuits with random operations for profiling."""
def __init__(self, seed=None, qubits=16, depth=40):
"""Args: seed: Random number seed. If none, don't seed the generator. depth: Number of operations in the circuit. qubits: Number of qubits in the cir... | stack_v2_sparse_classes_36k_train_011639 | 4,544 | permissive | [
{
"docstring": "Args: seed: Random number seed. If none, don't seed the generator. depth: Number of operations in the circuit. qubits: Number of qubits in the circuit.",
"name": "__init__",
"signature": "def __init__(self, seed=None, qubits=16, depth=40)"
},
{
"docstring": "Creates a circuit Gen... | 2 | stack_v2_sparse_classes_30k_train_020452 | Implement the Python class `RandomQasmGenerator` described below.
Class description:
Generate circuits with random operations for profiling.
Method signatures and docstrings:
- def __init__(self, seed=None, qubits=16, depth=40): Args: seed: Random number seed. If none, don't seed the generator. depth: Number of opera... | Implement the Python class `RandomQasmGenerator` described below.
Class description:
Generate circuits with random operations for profiling.
Method signatures and docstrings:
- def __init__(self, seed=None, qubits=16, depth=40): Args: seed: Random number seed. If none, don't seed the generator. depth: Number of opera... | e0f8d9fd61322623fe59a11b4d03cbd3f54d9015 | <|skeleton|>
class RandomQasmGenerator:
"""Generate circuits with random operations for profiling."""
def __init__(self, seed=None, qubits=16, depth=40):
"""Args: seed: Random number seed. If none, don't seed the generator. depth: Number of operations in the circuit. qubits: Number of qubits in the cir... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomQasmGenerator:
"""Generate circuits with random operations for profiling."""
def __init__(self, seed=None, qubits=16, depth=40):
"""Args: seed: Random number seed. If none, don't seed the generator. depth: Number of operations in the circuit. qubits: Number of qubits in the circuit."""
... | the_stack_v2_python_sparse | tools/random_qasm_generator.py | sulavtimilsina/qiskit-terra | train | 1 |
3cf63d4630aa0a0d5d7db7d48d5654514830631a | [
"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... | EventListener: Receives Event protos, e.g., from debugged TensorFlow runtime(s). | EventListenerServicer | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventListenerServicer:
"""EventListener: Receives Event protos, e.g., from debugged TensorFlow runtime(s)."""
def SendEvents(self, request_iterator, context):
"""Client(s) can use this RPC method to send the EventListener Event protos. The Event protos can hold information such as: 1... | stack_v2_sparse_classes_36k_train_011640 | 5,031 | permissive | [
{
"docstring": "Client(s) can use this RPC method to send the EventListener Event protos. The Event protos can hold information such as: 1) intermediate tensors from a debugged graph being executed, which can be sent from DebugIdentity ops configured with grpc URLs. 2) GraphDefs of partition graphs, which can b... | 3 | null | Implement the Python class `EventListenerServicer` described below.
Class description:
EventListener: Receives Event protos, e.g., from debugged TensorFlow runtime(s).
Method signatures and docstrings:
- def SendEvents(self, request_iterator, context): Client(s) can use this RPC method to send the EventListener Event... | Implement the Python class `EventListenerServicer` described below.
Class description:
EventListener: Receives Event protos, e.g., from debugged TensorFlow runtime(s).
Method signatures and docstrings:
- def SendEvents(self, request_iterator, context): Client(s) can use this RPC method to send the EventListener Event... | a7f3934a67900720af3d3b15389551483bee50b8 | <|skeleton|>
class EventListenerServicer:
"""EventListener: Receives Event protos, e.g., from debugged TensorFlow runtime(s)."""
def SendEvents(self, request_iterator, context):
"""Client(s) can use this RPC method to send the EventListener Event protos. The Event protos can hold information such as: 1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventListenerServicer:
"""EventListener: Receives Event protos, e.g., from debugged TensorFlow runtime(s)."""
def SendEvents(self, request_iterator, context):
"""Client(s) can use this RPC method to send the EventListener Event protos. The Event protos can hold information such as: 1) intermediat... | the_stack_v2_python_sparse | tensorflow/python/debug/lib/debug_service_pb2_grpc.py | tensorflow/tensorflow | train | 208,740 |
230f5f17b1dc1a7d637581d25d54f89adaa38d6f | [
"super(CNN, self).__init__()\nself.cnn = nn.SequentialCell([nn.Conv1d(n_in, n_hid, kernel_size=5, pad_mode='valid', has_bias=True, weight_init=init.Normal(math.sqrt(2.0 / (5 * n_hid))), bias_init=init.Constant(0.1)), nn.ReLU(), MyBatchNorm1d(n_hid, dim=1), nn.Dropout(p=do_prob), nn.MaxPool1d(kernel_size=2, stride=2... | <|body_start_0|>
super(CNN, self).__init__()
self.cnn = nn.SequentialCell([nn.Conv1d(n_in, n_hid, kernel_size=5, pad_mode='valid', has_bias=True, weight_init=init.Normal(math.sqrt(2.0 / (5 * n_hid))), bias_init=init.Constant(0.1)), nn.ReLU(), MyBatchNorm1d(n_hid, dim=1), nn.Dropout(p=do_prob), nn.MaxPoo... | CNN | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CNN:
def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0.0):
"""Parameters ---------- n_in : int input dimension. n_hid : int dimension of hidden layers. n_out : int output dimension. do_prob : float, optional rate of dropout. The default is 0.."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_011641 | 9,199 | permissive | [
{
"docstring": "Parameters ---------- n_in : int input dimension. n_hid : int dimension of hidden layers. n_out : int output dimension. do_prob : float, optional rate of dropout. The default is 0..",
"name": "__init__",
"signature": "def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0... | 2 | stack_v2_sparse_classes_30k_train_009214 | Implement the Python class `CNN` described below.
Class description:
Implement the CNN class.
Method signatures and docstrings:
- def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0.0): Parameters ---------- n_in : int input dimension. n_hid : int dimension of hidden layers. n_out : int output dime... | Implement the Python class `CNN` described below.
Class description:
Implement the CNN class.
Method signatures and docstrings:
- def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0.0): Parameters ---------- n_in : int input dimension. n_hid : int dimension of hidden layers. n_out : int output dime... | eab643f51336dbf7d711f02d27e6516e5affee59 | <|skeleton|>
class CNN:
def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0.0):
"""Parameters ---------- n_in : int input dimension. n_hid : int dimension of hidden layers. n_out : int output dimension. do_prob : float, optional rate of dropout. The default is 0.."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CNN:
def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0.0):
"""Parameters ---------- n_in : int input dimension. n_hid : int dimension of hidden layers. n_out : int output dimension. do_prob : float, optional rate of dropout. The default is 0.."""
super(CNN, self).__init__(... | the_stack_v2_python_sparse | research/gnn/nri-mpm/models/base.py | mindspore-ai/models | train | 301 | |
c2dad5c69981fc815e3c0878c4b852f0c3da5a04 | [
"f = self.dtype_f(self.init)\nself.AMat.mult(u, f)\nfa = self.init.getVecArray(f)\nxa = self.init.getVecArray(u)\nfor i in range(self.xs, self.xe):\n for j in range(self.ys, self.ye):\n fa[i, j, 0] += -xa[i, j, 0] * xa[i, j, 1] ** 2 + self.A * (1 - xa[i, j, 0])\n fa[i, j, 1] += xa[i, j, 0] * xa[i, ... | <|body_start_0|>
f = self.dtype_f(self.init)
self.AMat.mult(u, f)
fa = self.init.getVecArray(f)
xa = self.init.getVecArray(u)
for i in range(self.xs, self.xe):
for j in range(self.ys, self.ye):
fa[i, j, 0] += -xa[i, j, 0] * xa[i, j, 1] ** 2 + self.A * ... | Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc | petsc_grayscott_fullyimplicit | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class petsc_grayscott_fullyimplicit:
"""Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc"""
def eval_f(self, u, t):
"""Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: t... | stack_v2_sparse_classes_36k_train_011642 | 20,605 | permissive | [
{
"docstring": "Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: the RHS",
"name": "eval_f",
"signature": "def eval_f(self, u, t)"
},
{
"docstring": "Nonlinear solver for (I-factor*F)(u) = rhs Args: rhs (dtype_f): right-hand side for the lin... | 2 | null | Implement the Python class `petsc_grayscott_fullyimplicit` described below.
Class description:
Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc
Method signatures and docstrings:
- def eval_f(self, u, t): Routine to evaluate the RHS Args: u (dtype_u): c... | Implement the Python class `petsc_grayscott_fullyimplicit` described below.
Class description:
Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc
Method signatures and docstrings:
- def eval_f(self, u, t): Routine to evaluate the RHS Args: u (dtype_u): c... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class petsc_grayscott_fullyimplicit:
"""Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc"""
def eval_f(self, u, t):
"""Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class petsc_grayscott_fullyimplicit:
"""Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc"""
def eval_f(self, u, t):
"""Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: the RHS"""
... | the_stack_v2_python_sparse | pySDC/implementations/problem_classes/GrayScott_2D_PETSc_periodic.py | Parallel-in-Time/pySDC | train | 30 |
a155f34bbe13a1ec52071b4086b404e330d3827a | [
"stack = []\nfor token in tokens:\n if token not in ('+', '-', '*', '/'):\n stack.append(token)\n else:\n x, y = (int(stack.pop()), int(stack.pop()))\n if token == '+':\n stack.append(x + y)\n elif token == '-':\n stack.append(y - x)\n elif token == '*'... | <|body_start_0|>
stack = []
for token in tokens:
if token not in ('+', '-', '*', '/'):
stack.append(token)
else:
x, y = (int(stack.pop()), int(stack.pop()))
if token == '+':
stack.append(x + y)
el... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def evalRPN(self, tokens: List[str]) -> int:
"""栈"""
<|body_0|>
def evalRPNArray(self, tokens: List[str]) -> int:
"""数组模拟栈"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
stack = []
for token in tokens:
if token not in ... | stack_v2_sparse_classes_36k_train_011643 | 1,540 | no_license | [
{
"docstring": "栈",
"name": "evalRPN",
"signature": "def evalRPN(self, tokens: List[str]) -> int"
},
{
"docstring": "数组模拟栈",
"name": "evalRPNArray",
"signature": "def evalRPNArray(self, tokens: List[str]) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_007936 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def evalRPN(self, tokens: List[str]) -> int: 栈
- def evalRPNArray(self, tokens: List[str]) -> int: 数组模拟栈 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def evalRPN(self, tokens: List[str]) -> int: 栈
- def evalRPNArray(self, tokens: List[str]) -> int: 数组模拟栈
<|skeleton|>
class Solution:
def evalRPN(self, tokens: List[str]) -... | 52756b30e9d51794591aca030bc918e707f473f1 | <|skeleton|>
class Solution:
def evalRPN(self, tokens: List[str]) -> int:
"""栈"""
<|body_0|>
def evalRPNArray(self, tokens: List[str]) -> int:
"""数组模拟栈"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def evalRPN(self, tokens: List[str]) -> int:
"""栈"""
stack = []
for token in tokens:
if token not in ('+', '-', '*', '/'):
stack.append(token)
else:
x, y = (int(stack.pop()), int(stack.pop()))
if token ==... | the_stack_v2_python_sparse | 150.逆波兰表达式求值/solution.py | QtTao/daily_leetcode | train | 0 | |
5c0ebdbadc44c0e63f02830f0d70a756f1273091 | [
"name = homework.get_slug()\nrepos_root = Config.REPOS_ROOT + '/' + name\ntry:\n os.mkdir(repos_root)\nexcept FileExistsError:\n pass\nrepositories = Repository.clone(Repository.search(name), repos_root, homework)\nfor repo in repositories:\n Repository.parse(repo)",
"repos = []\nresponse_len = Config.GI... | <|body_start_0|>
name = homework.get_slug()
repos_root = Config.REPOS_ROOT + '/' + name
try:
os.mkdir(repos_root)
except FileExistsError:
pass
repositories = Repository.clone(Repository.search(name), repos_root, homework)
for repo in repositories:
... | Repository | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Repository:
def clone_n_parse(homework):
"""Clone all repositories for given homework and parse all solution files for each repository"""
<|body_0|>
def search(name):
"""Searches for gitea repositories with given name. Returns a list of dictionaries representing repo... | stack_v2_sparse_classes_36k_train_011644 | 6,212 | no_license | [
{
"docstring": "Clone all repositories for given homework and parse all solution files for each repository",
"name": "clone_n_parse",
"signature": "def clone_n_parse(homework)"
},
{
"docstring": "Searches for gitea repositories with given name. Returns a list of dictionaries representing reposit... | 6 | stack_v2_sparse_classes_30k_train_014815 | Implement the Python class `Repository` described below.
Class description:
Implement the Repository class.
Method signatures and docstrings:
- def clone_n_parse(homework): Clone all repositories for given homework and parse all solution files for each repository
- def search(name): Searches for gitea repositories wi... | Implement the Python class `Repository` described below.
Class description:
Implement the Repository class.
Method signatures and docstrings:
- def clone_n_parse(homework): Clone all repositories for given homework and parse all solution files for each repository
- def search(name): Searches for gitea repositories wi... | 86f343f8fc734207799fdc30b2266e982be24213 | <|skeleton|>
class Repository:
def clone_n_parse(homework):
"""Clone all repositories for given homework and parse all solution files for each repository"""
<|body_0|>
def search(name):
"""Searches for gitea repositories with given name. Returns a list of dictionaries representing repo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Repository:
def clone_n_parse(homework):
"""Clone all repositories for given homework and parse all solution files for each repository"""
name = homework.get_slug()
repos_root = Config.REPOS_ROOT + '/' + name
try:
os.mkdir(repos_root)
except FileExistsError:... | the_stack_v2_python_sparse | app/repository.py | KSET/OKOSL_homeworks | train | 1 | |
3516fd875d8d17c1990134a41508fcdc715dc79c | [
"self.id = identifier\nself.name = name\nself.wfos = wfos if wfos is not None else []\nself.geometry = Point([lon, lat])",
"if self.name is None:\n return f'(({self.id}))'\nreturn self.name",
"if key == 'lat':\n return self.geometry.y\nif key == 'lon':\n return self.geometry.x\nreturn getattr(self, key... | <|body_start_0|>
self.id = identifier
self.name = name
self.wfos = wfos if wfos is not None else []
self.geometry = Point([lon, lat])
<|end_body_0|>
<|body_start_1|>
if self.name is None:
return f'(({self.id}))'
return self.name
<|end_body_1|>
<|body_start_2... | National Weather Service Location Idenitifiers (NWSLI) | NWSLI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NWSLI:
"""National Weather Service Location Idenitifiers (NWSLI)"""
def __init__(self, identifier, name=None, wfos=None, lon=0, lat=0):
"""Constructor Args: identifier(str): The string identifier for this NWSLI name(str, optional): The free-form text name of this location wfo(list, o... | stack_v2_sparse_classes_36k_train_011645 | 1,359 | permissive | [
{
"docstring": "Constructor Args: identifier(str): The string identifier for this NWSLI name(str, optional): The free-form text name of this location wfo(list, optional): The wfo(s) associated with this NWSLI lon(float, optional): The longitude in decimal degrees lat(float, optional): The latitude in decimal de... | 3 | stack_v2_sparse_classes_30k_train_010137 | Implement the Python class `NWSLI` described below.
Class description:
National Weather Service Location Idenitifiers (NWSLI)
Method signatures and docstrings:
- def __init__(self, identifier, name=None, wfos=None, lon=0, lat=0): Constructor Args: identifier(str): The string identifier for this NWSLI name(str, option... | Implement the Python class `NWSLI` described below.
Class description:
National Weather Service Location Idenitifiers (NWSLI)
Method signatures and docstrings:
- def __init__(self, identifier, name=None, wfos=None, lon=0, lat=0): Constructor Args: identifier(str): The string identifier for this NWSLI name(str, option... | 460f44394be05e1b655111595a3d7de3f7e47757 | <|skeleton|>
class NWSLI:
"""National Weather Service Location Idenitifiers (NWSLI)"""
def __init__(self, identifier, name=None, wfos=None, lon=0, lat=0):
"""Constructor Args: identifier(str): The string identifier for this NWSLI name(str, optional): The free-form text name of this location wfo(list, o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NWSLI:
"""National Weather Service Location Idenitifiers (NWSLI)"""
def __init__(self, identifier, name=None, wfos=None, lon=0, lat=0):
"""Constructor Args: identifier(str): The string identifier for this NWSLI name(str, optional): The free-form text name of this location wfo(list, optional): The... | the_stack_v2_python_sparse | src/pyiem/nws/nwsli.py | akrherz/pyIEM | train | 38 |
a198b6670e57eaaaf383b6271f5d6cc0750bd141 | [
"row, col = (len(board), len(board[0]))\nflags = [[False] * col] * row\nprint(flags)\nfor i in range(row):\n for j in range(col):\n count_1 = 0\n print('i,j:', i, j)\n print('第', i, j, '个 flags', flags[i][j])\n flags[i][j] = False\n for [x, y] in [[-1, -1], [-1, 0], [-1, 1], [0... | <|body_start_0|>
row, col = (len(board), len(board[0]))
flags = [[False] * col] * row
print(flags)
for i in range(row):
for j in range(col):
count_1 = 0
print('i,j:', i, j)
print('第', i, j, '个 flags', flags[i][j])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def gameOfLife(self, board) -> None:
"""Do not return anything, modify board in-place instead."""
<|body_0|>
def gameOfLife_2(self, board) -> None:
"""Do not return anything, modify board in-place instead."""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_011646 | 4,020 | no_license | [
{
"docstring": "Do not return anything, modify board in-place instead.",
"name": "gameOfLife",
"signature": "def gameOfLife(self, board) -> None"
},
{
"docstring": "Do not return anything, modify board in-place instead.",
"name": "gameOfLife_2",
"signature": "def gameOfLife_2(self, board... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def gameOfLife(self, board) -> None: Do not return anything, modify board in-place instead.
- def gameOfLife_2(self, board) -> None: Do not return anything, modify board in-place... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def gameOfLife(self, board) -> None: Do not return anything, modify board in-place instead.
- def gameOfLife_2(self, board) -> None: Do not return anything, modify board in-place... | e29b8b553adaaa2ab55851fa79b21df84eb17452 | <|skeleton|>
class Solution:
def gameOfLife(self, board) -> None:
"""Do not return anything, modify board in-place instead."""
<|body_0|>
def gameOfLife_2(self, board) -> None:
"""Do not return anything, modify board in-place instead."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def gameOfLife(self, board) -> None:
"""Do not return anything, modify board in-place instead."""
row, col = (len(board), len(board[0]))
flags = [[False] * col] * row
print(flags)
for i in range(row):
for j in range(col):
count_1 = ... | the_stack_v2_python_sparse | 289_生命游戏.py | Yujunw/leetcode_python | train | 0 | |
421560b36a98fd94be0190d8421d18f4e66d66ab | [
"if not root:\n return\nleft = self.flatten(root.left)\nright = self.flatten(root.right)\nroot.right = left\nnode, next_ = (root, root.right)\nwhile next_:\n node.left, node, next_ = (None, next_, next_.right)\nnode.right = right\nreturn root",
"nodes = list()\n\ndef dfs(root):\n if not root:\n re... | <|body_start_0|>
if not root:
return
left = self.flatten(root.left)
right = self.flatten(root.right)
root.right = left
node, next_ = (root, root.right)
while next_:
node.left, node, next_ = (None, next_, next_.right)
node.right = right
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def flatten(self, root: TreeNode) -> None:
"""Do not return anything, modify root in-place instead."""
<|body_0|>
def flatten(self, root: TreeNode) -> None:
"""Do not return anything, modify root in-place instead."""
<|body_1|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_36k_train_011647 | 1,279 | no_license | [
{
"docstring": "Do not return anything, modify root in-place instead.",
"name": "flatten",
"signature": "def flatten(self, root: TreeNode) -> None"
},
{
"docstring": "Do not return anything, modify root in-place instead.",
"name": "flatten",
"signature": "def flatten(self, root: TreeNode... | 2 | stack_v2_sparse_classes_30k_train_018283 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, root: TreeNode) -> None: Do not return anything, modify root in-place instead.
- def flatten(self, root: TreeNode) -> None: Do not return anything, modify root ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, root: TreeNode) -> None: Do not return anything, modify root in-place instead.
- def flatten(self, root: TreeNode) -> None: Do not return anything, modify root ... | 5d29bcf7ea1a9e489a92bc36d2158456de25829e | <|skeleton|>
class Solution:
def flatten(self, root: TreeNode) -> None:
"""Do not return anything, modify root in-place instead."""
<|body_0|>
def flatten(self, root: TreeNode) -> None:
"""Do not return anything, modify root in-place instead."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def flatten(self, root: TreeNode) -> None:
"""Do not return anything, modify root in-place instead."""
if not root:
return
left = self.flatten(root.left)
right = self.flatten(root.right)
root.right = left
node, next_ = (root, root.right)
... | the_stack_v2_python_sparse | 114.二叉树展开为链表.py | oceanbei333/leetcode | train | 0 | |
7b2b1c18377a9cb3b9cf0ce37624c37013f4c92c | [
"items = self.node(parent)\nif items and kwargs:\n for item in items:\n for key, value in kwargs.iteritems():\n setattr(item, key, value)\nreturn items",
"try:\n return self.node.scope[name]\nexcept KeyError:\n msg = \"'%s' object has no attribute '%s'\"\n raise AttributeError(msg % ... | <|body_start_0|>
items = self.node(parent)
if items and kwargs:
for item in items:
for key, value in kwargs.iteritems():
setattr(item, key, value)
return items
<|end_body_0|>
<|body_start_1|>
try:
return self.node.scope[name]
... | A class representing a template instantiation. Instances of this class are created by instances of Template. They should not be created directly by user code. | TemplateInstance | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TemplateInstance:
"""A class representing a template instantiation. Instances of this class are created by instances of Template. They should not be created directly by user code."""
def __call__(self, parent=None, **kwargs):
"""Instantiate the list of items for the template. Paramet... | stack_v2_sparse_classes_36k_train_011648 | 8,361 | permissive | [
{
"docstring": "Instantiate the list of items for the template. Parameters ---------- parent : Object, optional The parent object for the generated objects. **kwargs Additional keyword arguments to apply to the returned items. Returns ------- result : list The list of objects generated by the template.",
"n... | 2 | stack_v2_sparse_classes_30k_train_017580 | Implement the Python class `TemplateInstance` described below.
Class description:
A class representing a template instantiation. Instances of this class are created by instances of Template. They should not be created directly by user code.
Method signatures and docstrings:
- def __call__(self, parent=None, **kwargs)... | Implement the Python class `TemplateInstance` described below.
Class description:
A class representing a template instantiation. Instances of this class are created by instances of Template. They should not be created directly by user code.
Method signatures and docstrings:
- def __call__(self, parent=None, **kwargs)... | 1544e7fb371b8f941cfa2fde682795e479380284 | <|skeleton|>
class TemplateInstance:
"""A class representing a template instantiation. Instances of this class are created by instances of Template. They should not be created directly by user code."""
def __call__(self, parent=None, **kwargs):
"""Instantiate the list of items for the template. Paramet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TemplateInstance:
"""A class representing a template instantiation. Instances of this class are created by instances of Template. They should not be created directly by user code."""
def __call__(self, parent=None, **kwargs):
"""Instantiate the list of items for the template. Parameters ---------... | the_stack_v2_python_sparse | enaml/core/template.py | MatthieuDartiailh/enaml | train | 26 |
c6a2e843cdec45985dca42d733c47ba5d87c8bb7 | [
"user_id = request.user.pk\nqueryset = User.objects.filter(id=user_id)\nserializer = UserSerializer(queryset, many=True)\nreturn Response(serializer.data[0])",
"user_id = request.user.pk\ntry:\n update_user = User.objects.get(id=user_id)\n update_user.name = request.data['name']\n update_user.nickname = ... | <|body_start_0|>
user_id = request.user.pk
queryset = User.objects.filter(id=user_id)
serializer = UserSerializer(queryset, many=True)
return Response(serializer.data[0])
<|end_body_0|>
<|body_start_1|>
user_id = request.user.pk
try:
update_user = User.object... | MyPageView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyPageView:
def get(self, request):
"""나의 정보 api --- 결과: 나의정보(이메일,이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 반환"""
<|body_0|>
def patch(self, request):
"""회원수정 api --- 결과: 나의정보(이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 수정 후 저장"""
<|body_1|>
def delete(self, request):... | stack_v2_sparse_classes_36k_train_011649 | 4,479 | no_license | [
{
"docstring": "나의 정보 api --- 결과: 나의정보(이메일,이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 반환",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "회원수정 api --- 결과: 나의정보(이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 수정 후 저장",
"name": "patch",
"signature": "def patch(self, request)"
},
... | 3 | stack_v2_sparse_classes_30k_val_000972 | Implement the Python class `MyPageView` described below.
Class description:
Implement the MyPageView class.
Method signatures and docstrings:
- def get(self, request): 나의 정보 api --- 결과: 나의정보(이메일,이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 반환
- def patch(self, request): 회원수정 api --- 결과: 나의정보(이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비... | Implement the Python class `MyPageView` described below.
Class description:
Implement the MyPageView class.
Method signatures and docstrings:
- def get(self, request): 나의 정보 api --- 결과: 나의정보(이메일,이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 반환
- def patch(self, request): 회원수정 api --- 결과: 나의정보(이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비... | 54cbf4ee4e4a38e1e30722472715e89eb9fc84a2 | <|skeleton|>
class MyPageView:
def get(self, request):
"""나의 정보 api --- 결과: 나의정보(이메일,이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 반환"""
<|body_0|>
def patch(self, request):
"""회원수정 api --- 결과: 나의정보(이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 수정 후 저장"""
<|body_1|>
def delete(self, request):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyPageView:
def get(self, request):
"""나의 정보 api --- 결과: 나의정보(이메일,이름,닉네임,연락처), 관심정보(주량,최애술,최애안주,최애콤비) 반환"""
user_id = request.user.pk
queryset = User.objects.filter(id=user_id)
serializer = UserSerializer(queryset, many=True)
return Response(serializer.data[0])
def... | the_stack_v2_python_sparse | mypage/views.py | liketoy/honsuri-backend | train | 0 | |
e5101eb2823b74724e422188dda13b0345cf4723 | [
"threading.Thread.__init__(self)\nself.daemon = True\nself.metaQueue = metaQueue\nself.commandsQueue = commandsQueue\nself.text = text",
"self.commandsQueue.join()\nif self.text:\n common.ThreadSafePrint(self.text)\nself.metaQueue.task_done()"
] | <|body_start_0|>
threading.Thread.__init__(self)
self.daemon = True
self.metaQueue = metaQueue
self.commandsQueue = commandsQueue
self.text = text
<|end_body_0|>
<|body_start_1|>
self.commandsQueue.join()
if self.text:
common.ThreadSafePrint(self.text... | Ожидаем завершения обработки очереди команд и выводим заданный текст | SocialBotLauncherWaitingThread | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SocialBotLauncherWaitingThread:
"""Ожидаем завершения обработки очереди команд и выводим заданный текст"""
def __init__(self, metaQueue, commandsQueue, text=None):
"""Инициализация"""
<|body_0|>
def run(self):
"""Главный метод"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k_train_011650 | 7,553 | no_license | [
{
"docstring": "Инициализация",
"name": "__init__",
"signature": "def __init__(self, metaQueue, commandsQueue, text=None)"
},
{
"docstring": "Главный метод",
"name": "run",
"signature": "def run(self)"
}
] | 2 | null | Implement the Python class `SocialBotLauncherWaitingThread` described below.
Class description:
Ожидаем завершения обработки очереди команд и выводим заданный текст
Method signatures and docstrings:
- def __init__(self, metaQueue, commandsQueue, text=None): Инициализация
- def run(self): Главный метод | Implement the Python class `SocialBotLauncherWaitingThread` described below.
Class description:
Ожидаем завершения обработки очереди команд и выводим заданный текст
Method signatures and docstrings:
- def __init__(self, metaQueue, commandsQueue, text=None): Инициализация
- def run(self): Главный метод
<|skeleton|>
c... | d2771bf04aa187dda6d468883a5a167237589369 | <|skeleton|>
class SocialBotLauncherWaitingThread:
"""Ожидаем завершения обработки очереди команд и выводим заданный текст"""
def __init__(self, metaQueue, commandsQueue, text=None):
"""Инициализация"""
<|body_0|>
def run(self):
"""Главный метод"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SocialBotLauncherWaitingThread:
"""Ожидаем завершения обработки очереди команд и выводим заданный текст"""
def __init__(self, metaQueue, commandsQueue, text=None):
"""Инициализация"""
threading.Thread.__init__(self)
self.daemon = True
self.metaQueue = metaQueue
sel... | the_stack_v2_python_sparse | stumbleupon/botlaunch.py | cash2one/doorscenter | train | 0 |
2e15561c381df2c7cea833256eea7e0e80a66a3a | [
"print('Writing tokens into %s file: ' % filename)\nwith open(filename, 'w') as f:\n for word, idx in tok2idx.iteritems():\n if idx != len(tok2idx) - 1:\n f.write('{} {}\\n'.format(word, idx))\n else:\n f.write('{} {}'.format(word, idx))\nprint('\\t- Done: {} tokens.'.format(l... | <|body_start_0|>
print('Writing tokens into %s file: ' % filename)
with open(filename, 'w') as f:
for word, idx in tok2idx.iteritems():
if idx != len(tok2idx) - 1:
f.write('{} {}\n'.format(word, idx))
else:
f.write('{} {... | RWfile | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RWfile:
def write_vocab(tok2idx, filename):
"""Writes a vocab to a file Writes one word per line. Args: vocab: iterable that yields word filename: path to vocab file Returns: write a word per line"""
<|body_0|>
def load_vocab(filename):
"""Loads vocab from a file Arg... | stack_v2_sparse_classes_36k_train_011651 | 9,000 | no_license | [
{
"docstring": "Writes a vocab to a file Writes one word per line. Args: vocab: iterable that yields word filename: path to vocab file Returns: write a word per line",
"name": "write_vocab",
"signature": "def write_vocab(tok2idx, filename)"
},
{
"docstring": "Loads vocab from a file Args: filena... | 2 | stack_v2_sparse_classes_30k_train_006931 | Implement the Python class `RWfile` described below.
Class description:
Implement the RWfile class.
Method signatures and docstrings:
- def write_vocab(tok2idx, filename): Writes a vocab to a file Writes one word per line. Args: vocab: iterable that yields word filename: path to vocab file Returns: write a word per l... | Implement the Python class `RWfile` described below.
Class description:
Implement the RWfile class.
Method signatures and docstrings:
- def write_vocab(tok2idx, filename): Writes a vocab to a file Writes one word per line. Args: vocab: iterable that yields word filename: path to vocab file Returns: write a word per l... | b798e7b1286e043c5685aa8375022ee50f91024a | <|skeleton|>
class RWfile:
def write_vocab(tok2idx, filename):
"""Writes a vocab to a file Writes one word per line. Args: vocab: iterable that yields word filename: path to vocab file Returns: write a word per line"""
<|body_0|>
def load_vocab(filename):
"""Loads vocab from a file Arg... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RWfile:
def write_vocab(tok2idx, filename):
"""Writes a vocab to a file Writes one word per line. Args: vocab: iterable that yields word filename: path to vocab file Returns: write a word per line"""
print('Writing tokens into %s file: ' % filename)
with open(filename, 'w') as f:
... | the_stack_v2_python_sparse | utils/other_utils.py | duytinvo/sequence_labeller | train | 3 | |
78ab31b9110b2db8827f9d727aa675481686baaf | [
"label = SettingSidebarLabel(text=name, uid=uid, menu=self)\nif len(self.buttons_layout.children) == 0:\n label.selected = True\nif self.buttons_layout is not None:\n self.buttons_layout.add_widget(label)",
"for button in self.buttons_layout.children:\n if button.uid != self.selected_uid:\n button... | <|body_start_0|>
label = SettingSidebarLabel(text=name, uid=uid, menu=self)
if len(self.buttons_layout.children) == 0:
label.selected = True
if self.buttons_layout is not None:
self.buttons_layout.add_widget(label)
<|end_body_0|>
<|body_start_1|>
for button in se... | The menu used by :class:`InterfaceWithSidebar`. It provides a sidebar with an entry for each settings panel, which the user may click to select. | MenuSidebar | [
"LGPL-2.1-only",
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MenuSidebar:
"""The menu used by :class:`InterfaceWithSidebar`. It provides a sidebar with an entry for each settings panel, which the user may click to select."""
def add_item(self, name, uid):
"""This method is used to add new panels to the menu. :Parameters: `name`: The name (a st... | stack_v2_sparse_classes_36k_train_011652 | 45,276 | permissive | [
{
"docstring": "This method is used to add new panels to the menu. :Parameters: `name`: The name (a string) of the panel. It should be used to represent the panel in the menu. `uid`: The name (an int) of the panel. It should be used internally to represent the panel and used to set self.selected_uid when the pa... | 2 | null | Implement the Python class `MenuSidebar` described below.
Class description:
The menu used by :class:`InterfaceWithSidebar`. It provides a sidebar with an entry for each settings panel, which the user may click to select.
Method signatures and docstrings:
- def add_item(self, name, uid): This method is used to add ne... | Implement the Python class `MenuSidebar` described below.
Class description:
The menu used by :class:`InterfaceWithSidebar`. It provides a sidebar with an entry for each settings panel, which the user may click to select.
Method signatures and docstrings:
- def add_item(self, name, uid): This method is used to add ne... | ca1b918c656f23e401707388f25f4a63d9b8ae7d | <|skeleton|>
class MenuSidebar:
"""The menu used by :class:`InterfaceWithSidebar`. It provides a sidebar with an entry for each settings panel, which the user may click to select."""
def add_item(self, name, uid):
"""This method is used to add new panels to the menu. :Parameters: `name`: The name (a st... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MenuSidebar:
"""The menu used by :class:`InterfaceWithSidebar`. It provides a sidebar with an entry for each settings panel, which the user may click to select."""
def add_item(self, name, uid):
"""This method is used to add new panels to the menu. :Parameters: `name`: The name (a string) of the ... | the_stack_v2_python_sparse | kivy/uix/settings.py | kivy/kivy | train | 16,076 |
8c0d72ae7cc97b27b3868462300ef474434533dd | [
"discussion = Discussion.query.get(discussion_id)\nif discussion is None:\n return abort(HTTPStatus.NOT_FOUND, message='Discussion is not found')\ndiscussion.view_count += 1\ndb.session.commit()\nreturn discussion",
"discussion = Discussion.query.get(discussion_id)\nif discussion is None:\n return abort(HTT... | <|body_start_0|>
discussion = Discussion.query.get(discussion_id)
if discussion is None:
return abort(HTTPStatus.NOT_FOUND, message='Discussion is not found')
discussion.view_count += 1
db.session.commit()
return discussion
<|end_body_0|>
<|body_start_1|>
dis... | DiscussionItem | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DiscussionItem:
def get(self, discussion_id):
"""Get discussion info * This action **increases the views count**"""
<|body_0|>
def patch(self, discussion_id):
"""Edit discussion info * User can edit **their discussion** (not frozen) * User with permission to **"edit ... | stack_v2_sparse_classes_36k_train_011653 | 3,433 | permissive | [
{
"docstring": "Get discussion info * This action **increases the views count**",
"name": "get",
"signature": "def get(self, discussion_id)"
},
{
"docstring": "Edit discussion info * User can edit **their discussion** (not frozen) * User with permission to **\"edit discussions\"** can edit the d... | 3 | null | Implement the Python class `DiscussionItem` described below.
Class description:
Implement the DiscussionItem class.
Method signatures and docstrings:
- def get(self, discussion_id): Get discussion info * This action **increases the views count**
- def patch(self, discussion_id): Edit discussion info * User can edit *... | Implement the Python class `DiscussionItem` described below.
Class description:
Implement the DiscussionItem class.
Method signatures and docstrings:
- def get(self, discussion_id): Get discussion info * This action **increases the views count**
- def patch(self, discussion_id): Edit discussion info * User can edit *... | dce87ffe395ae4bd08b47f28e07594e1889da819 | <|skeleton|>
class DiscussionItem:
def get(self, discussion_id):
"""Get discussion info * This action **increases the views count**"""
<|body_0|>
def patch(self, discussion_id):
"""Edit discussion info * User can edit **their discussion** (not frozen) * User with permission to **"edit ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DiscussionItem:
def get(self, discussion_id):
"""Get discussion info * This action **increases the views count**"""
discussion = Discussion.query.get(discussion_id)
if discussion is None:
return abort(HTTPStatus.NOT_FOUND, message='Discussion is not found')
discussi... | the_stack_v2_python_sparse | src/backend/app/api/public/discussions/discussion/discussion.py | aimanow/sft | train | 0 | |
5715f7d24162506d83ec7477e829ef9175953b85 | [
"objFile = open(FileName, 'r')\nfor line in objFile:\n lstData = line.split(',')\n dicRow = {'Task': lstData[0].strip(), 'Priority': lstData[1].strip()}\n lstTable.append(dicRow)\nobjFile.close()\nreturn lstTable",
"print('\\n Menu of Options\\n 1) Show current data\\n 2) Add a new item.\\n 3... | <|body_start_0|>
objFile = open(FileName, 'r')
for line in objFile:
lstData = line.split(',')
dicRow = {'Task': lstData[0].strip(), 'Priority': lstData[1].strip()}
lstTable.append(dicRow)
objFile.close()
return lstTable
<|end_body_0|>
<|body_start_1|>... | Desc: A section of functions with in a class Functions: ReadFileData() menu() RawData() AddData() RemoveData() SaveData() | PresentationFunctions | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PresentationFunctions:
"""Desc: A section of functions with in a class Functions: ReadFileData() menu() RawData() AddData() RemoveData() SaveData()"""
def ReadFileData(FileName):
"""Desc: This Function reads from a text file and writes it to a dictionary then appends it in a list obj... | stack_v2_sparse_classes_36k_train_011654 | 8,289 | no_license | [
{
"docstring": "Desc: This Function reads from a text file and writes it to a dictionary then appends it in a list objFile: Text file variable for the text file to be opened open: Opens the file at the directory listed in FileName FileName: Variable where input of directory needs to be line: Line in the text fi... | 6 | stack_v2_sparse_classes_30k_train_002338 | Implement the Python class `PresentationFunctions` described below.
Class description:
Desc: A section of functions with in a class Functions: ReadFileData() menu() RawData() AddData() RemoveData() SaveData()
Method signatures and docstrings:
- def ReadFileData(FileName): Desc: This Function reads from a text file an... | Implement the Python class `PresentationFunctions` described below.
Class description:
Desc: A section of functions with in a class Functions: ReadFileData() menu() RawData() AddData() RemoveData() SaveData()
Method signatures and docstrings:
- def ReadFileData(FileName): Desc: This Function reads from a text file an... | 209b826ea0d8c2fcdd82c3d8493b322ba00f2543 | <|skeleton|>
class PresentationFunctions:
"""Desc: A section of functions with in a class Functions: ReadFileData() menu() RawData() AddData() RemoveData() SaveData()"""
def ReadFileData(FileName):
"""Desc: This Function reads from a text file and writes it to a dictionary then appends it in a list obj... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PresentationFunctions:
"""Desc: A section of functions with in a class Functions: ReadFileData() menu() RawData() AddData() RemoveData() SaveData()"""
def ReadFileData(FileName):
"""Desc: This Function reads from a text file and writes it to a dictionary then appends it in a list objFile: Text fi... | the_stack_v2_python_sparse | Module06/Assignment06/Assignment06.py | gjkim44/Test | train | 0 |
a0a5e946f0cd499d8594073f283852e5b5f6a194 | [
"super().__init__()\nallowed = list(IMG_TRANSFORMS.keys())\nif not all([tr in allowed for tr in img_transforms]):\n raise ValueError(f'Wrong img transformation. Got: {img_transforms}. Allowed: {allowed}.')\nallowed = list(NORM_TRANSFORMS.keys()) + [None]\nif normalization not in allowed:\n raise ValueError(f'... | <|body_start_0|>
super().__init__()
allowed = list(IMG_TRANSFORMS.keys())
if not all([tr in allowed for tr in img_transforms]):
raise ValueError(f'Wrong img transformation. Got: {img_transforms}. Allowed: {allowed}.')
allowed = list(NORM_TRANSFORMS.keys()) + [None]
if... | TrainDatasetBase | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrainDatasetBase:
def __init__(self, img_transforms: List[str], inst_transforms: List[str], normalization: str=None, return_inst: bool=False, return_binary: bool=True, return_type: bool=True, return_sem: bool=False, return_weight: bool=False, **kwargs) -> None:
"""Training dataset basecl... | stack_v2_sparse_classes_36k_train_011655 | 6,392 | permissive | [
{
"docstring": "Training dataset baseclass. Parameters ---------- img_transforms : List[str] A list containing all the transformations that are applied to the input images and corresponding masks. Allowed ones: \"blur\", \"non_spatial\", \"non_rigid\", \"rigid\", \"hue_sat\", \"random_crop\", \"center_crop\", \... | 2 | stack_v2_sparse_classes_30k_train_007785 | Implement the Python class `TrainDatasetBase` described below.
Class description:
Implement the TrainDatasetBase class.
Method signatures and docstrings:
- def __init__(self, img_transforms: List[str], inst_transforms: List[str], normalization: str=None, return_inst: bool=False, return_binary: bool=True, return_type:... | Implement the Python class `TrainDatasetBase` described below.
Class description:
Implement the TrainDatasetBase class.
Method signatures and docstrings:
- def __init__(self, img_transforms: List[str], inst_transforms: List[str], normalization: str=None, return_inst: bool=False, return_binary: bool=True, return_type:... | 7f79405012eb934b419bbdba8de23f35e840ca85 | <|skeleton|>
class TrainDatasetBase:
def __init__(self, img_transforms: List[str], inst_transforms: List[str], normalization: str=None, return_inst: bool=False, return_binary: bool=True, return_type: bool=True, return_sem: bool=False, return_weight: bool=False, **kwargs) -> None:
"""Training dataset basecl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrainDatasetBase:
def __init__(self, img_transforms: List[str], inst_transforms: List[str], normalization: str=None, return_inst: bool=False, return_binary: bool=True, return_type: bool=True, return_sem: bool=False, return_weight: bool=False, **kwargs) -> None:
"""Training dataset baseclass. Parameter... | the_stack_v2_python_sparse | cellseg_models_pytorch/datasets/_base_dataset.py | okunator/cellseg_models.pytorch | train | 43 | |
2698e7f76fb281f94e39a4d9fb828ca79de8c800 | [
"self.head = None\nself.tail = None\nself.cnt = 0",
"if index >= self.cnt or index < 0:\n return -1\ncurr = self.head\nfor i in range(index):\n curr = curr.next\nreturn curr.val",
"node = ListNode(val)\nif self.head is None:\n self.head = node\n self.tail = node\nelse:\n self.head.prev = node\n ... | <|body_start_0|>
self.head = None
self.tail = None
self.cnt = 0
<|end_body_0|>
<|body_start_1|>
if index >= self.cnt or index < 0:
return -1
curr = self.head
for i in range(index):
curr = curr.next
return curr.val
<|end_body_1|>
<|body_st... | MyLinkedList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyLinkedList:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def get(self, index):
"""Get the value of the index-th node in the linked list. If the index is invalid, return -1. :type index: int :rtype: int"""
<|body_1|>
def add... | stack_v2_sparse_classes_36k_train_011656 | 5,409 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Get the value of the index-th node in the linked list. If the index is invalid, return -1. :type index: int :rtype: int",
"name": "get",
"signature": "def get(s... | 6 | null | Implement the Python class `MyLinkedList` described below.
Class description:
Implement the MyLinkedList class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def get(self, index): Get the value of the index-th node in the linked list. If the index is invalid, return -1... | Implement the Python class `MyLinkedList` described below.
Class description:
Implement the MyLinkedList class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def get(self, index): Get the value of the index-th node in the linked list. If the index is invalid, return -1... | 4d73e4c1f2017828ff2d36058819988146356abe | <|skeleton|>
class MyLinkedList:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def get(self, index):
"""Get the value of the index-th node in the linked list. If the index is invalid, return -1. :type index: int :rtype: int"""
<|body_1|>
def add... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyLinkedList:
def __init__(self):
"""Initialize your data structure here."""
self.head = None
self.tail = None
self.cnt = 0
def get(self, index):
"""Get the value of the index-th node in the linked list. If the index is invalid, return -1. :type index: int :rtype: ... | the_stack_v2_python_sparse | python/leetcode/system_design/707_design_linked_list.py | zchen0211/topcoder | train | 0 | |
5d3d9db9b20a368869d6dff5d8433bc27781b601 | [
"self.url = url\nself.location = location\nself.geo = None\nself.produce = None",
"if self.url.startswith('file:'):\n with open(self.url[6:]) as jsonfile:\n json_data = json.load(jsonfile)\nelse:\n response = magtag.network.fetch(self.url)\n if response.status_code == 200:\n json_data = res... | <|body_start_0|>
self.url = url
self.location = location
self.geo = None
self.produce = None
<|end_body_0|>
<|body_start_1|>
if self.url.startswith('file:'):
with open(self.url[6:]) as jsonfile:
json_data = json.load(jsonfile)
else:
... | Class to generate seasonal produce lists from server-based JSON data. | Produce | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Produce:
"""Class to generate seasonal produce lists from server-based JSON data."""
def __init__(self, url, location):
"""Constructor"""
<|body_0|>
def fetch(self, magtag=None):
"""Retrieves current seasonal produce data from server, does some deserializing and ... | stack_v2_sparse_classes_36k_train_011657 | 8,879 | permissive | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, url, location)"
},
{
"docstring": "Retrieves current seasonal produce data from server, does some deserializing and processing for later filtering. This is currently tied to a MagTag object -- would prefer to func... | 4 | stack_v2_sparse_classes_30k_train_004392 | Implement the Python class `Produce` described below.
Class description:
Class to generate seasonal produce lists from server-based JSON data.
Method signatures and docstrings:
- def __init__(self, url, location): Constructor
- def fetch(self, magtag=None): Retrieves current seasonal produce data from server, does so... | Implement the Python class `Produce` described below.
Class description:
Class to generate seasonal produce lists from server-based JSON data.
Method signatures and docstrings:
- def __init__(self, url, location): Constructor
- def fetch(self, magtag=None): Retrieves current seasonal produce data from server, does so... | 5eaa7a15a437c533b89f359a25983e24bb6b5438 | <|skeleton|>
class Produce:
"""Class to generate seasonal produce lists from server-based JSON data."""
def __init__(self, url, location):
"""Constructor"""
<|body_0|>
def fetch(self, magtag=None):
"""Retrieves current seasonal produce data from server, does some deserializing and ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Produce:
"""Class to generate seasonal produce lists from server-based JSON data."""
def __init__(self, url, location):
"""Constructor"""
self.url = url
self.location = location
self.geo = None
self.produce = None
def fetch(self, magtag=None):
"""Retri... | the_stack_v2_python_sparse | MagTag_Seasonal_Produce/produce.py | adafruit/Adafruit_Learning_System_Guides | train | 937 |
ea0a5546d075ac8df98d2a3cf61382bf514b33df | [
"try:\n prepend = COLOR_CODES[color] if color else ''\n append = COLOR_CODES['reset'] if color else ''\nexcept KeyError:\n prepend = ''\n append = ''\nself.txt = prepend + txt + append\nself.newline = newline",
"self.start = time()\nif not self.newline:\n print(self.txt + '... ', end='')\n sys.s... | <|body_start_0|>
try:
prepend = COLOR_CODES[color] if color else ''
append = COLOR_CODES['reset'] if color else ''
except KeyError:
prepend = ''
append = ''
self.txt = prepend + txt + append
self.newline = newline
<|end_body_0|>
<|body_sta... | Example of usage: with Timer("TimedFunction", newline=True, color='blue'): ... # do something | Timer | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Timer:
"""Example of usage: with Timer("TimedFunction", newline=True, color='blue'): ... # do something"""
def __init__(self, txt: str, newline: bool=False, color: str=None):
"""Parameters ---------- txt: str Name of this timer. newline: bool Wheter you want prints to end with newlin... | stack_v2_sparse_classes_36k_train_011658 | 1,784 | permissive | [
{
"docstring": "Parameters ---------- txt: str Name of this timer. newline: bool Wheter you want prints to end with newlines. color: str One of 'black', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan', or 'white'.",
"name": "__init__",
"signature": "def __init__(self, txt: str, newline: bool=False, ... | 3 | stack_v2_sparse_classes_30k_train_007291 | Implement the Python class `Timer` described below.
Class description:
Example of usage: with Timer("TimedFunction", newline=True, color='blue'): ... # do something
Method signatures and docstrings:
- def __init__(self, txt: str, newline: bool=False, color: str=None): Parameters ---------- txt: str Name of this timer... | Implement the Python class `Timer` described below.
Class description:
Example of usage: with Timer("TimedFunction", newline=True, color='blue'): ... # do something
Method signatures and docstrings:
- def __init__(self, txt: str, newline: bool=False, color: str=None): Parameters ---------- txt: str Name of this timer... | 229456e0d4a9b73c0fd1069b062ca02c49dece00 | <|skeleton|>
class Timer:
"""Example of usage: with Timer("TimedFunction", newline=True, color='blue'): ... # do something"""
def __init__(self, txt: str, newline: bool=False, color: str=None):
"""Parameters ---------- txt: str Name of this timer. newline: bool Wheter you want prints to end with newlin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Timer:
"""Example of usage: with Timer("TimedFunction", newline=True, color='blue'): ... # do something"""
def __init__(self, txt: str, newline: bool=False, color: str=None):
"""Parameters ---------- txt: str Name of this timer. newline: bool Wheter you want prints to end with newlines. color: st... | the_stack_v2_python_sparse | dwi_ml/experiment_utils/timer.py | scil-vital/dwi_ml | train | 8 |
8fb7a4d6d30caf511926517ede2956aeb46c728c | [
"if not self.caller.location or not self.caller.location.allow_combat:\n self.msg(\"Can't fight here!\")\n raise InterruptCommand()",
"self.args = args = self.args.strip()\nself.lhs, self.rhs = ('', '')\nif not args:\n return\nif ' on ' in args:\n lhs, rhs = args.split(' on ', 1)\nelse:\n lhs, *rhs... | <|body_start_0|>
if not self.caller.location or not self.caller.location.allow_combat:
self.msg("Can't fight here!")
raise InterruptCommand()
<|end_body_0|>
<|body_start_1|>
self.args = args = self.args.strip()
self.lhs, self.rhs = ('', '')
if not args:
... | Parent class for all twitch-combat commnads. | _BaseTwitchCombatCommand | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"CC-BY-4.0",
"CC-BY-3.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _BaseTwitchCombatCommand:
"""Parent class for all twitch-combat commnads."""
def at_pre_command(self):
"""Called before parsing."""
<|body_0|>
def parse(self):
"""Handle parsing of most supported combat syntaxes (except stunts). <action> [<target>|<item>] or <act... | stack_v2_sparse_classes_36k_train_011659 | 18,175 | permissive | [
{
"docstring": "Called before parsing.",
"name": "at_pre_command",
"signature": "def at_pre_command(self)"
},
{
"docstring": "Handle parsing of most supported combat syntaxes (except stunts). <action> [<target>|<item>] or <action> <item> [on] <target> Use 'on' to differentiate if names/items hav... | 3 | stack_v2_sparse_classes_30k_train_000368 | Implement the Python class `_BaseTwitchCombatCommand` described below.
Class description:
Parent class for all twitch-combat commnads.
Method signatures and docstrings:
- def at_pre_command(self): Called before parsing.
- def parse(self): Handle parsing of most supported combat syntaxes (except stunts). <action> [<ta... | Implement the Python class `_BaseTwitchCombatCommand` described below.
Class description:
Parent class for all twitch-combat commnads.
Method signatures and docstrings:
- def at_pre_command(self): Called before parsing.
- def parse(self): Handle parsing of most supported combat syntaxes (except stunts). <action> [<ta... | b3ca58b5c1325a3bf57051dfe23560a08d2947b7 | <|skeleton|>
class _BaseTwitchCombatCommand:
"""Parent class for all twitch-combat commnads."""
def at_pre_command(self):
"""Called before parsing."""
<|body_0|>
def parse(self):
"""Handle parsing of most supported combat syntaxes (except stunts). <action> [<target>|<item>] or <act... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _BaseTwitchCombatCommand:
"""Parent class for all twitch-combat commnads."""
def at_pre_command(self):
"""Called before parsing."""
if not self.caller.location or not self.caller.location.allow_combat:
self.msg("Can't fight here!")
raise InterruptCommand()
def... | the_stack_v2_python_sparse | evennia/contrib/tutorials/evadventure/combat_twitch.py | evennia/evennia | train | 1,781 |
40c4fba5035b15fb6efa1167d4c6f28dea89cc35 | [
"parser.add_argument('sink_name', help='The name for the sink.')\nparser.add_argument('destination', help='The destination must be a fully qualified BigQuery resource name. The destination can be for the same Google Cloud project or for a different Google Cloud project in the same organization.')\nparser.add_argume... | <|body_start_0|>
parser.add_argument('sink_name', help='The name for the sink.')
parser.add_argument('destination', help='The destination must be a fully qualified BigQuery resource name. The destination can be for the same Google Cloud project or for a different Google Cloud project in the same organiz... | Creates a sink. | Create | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Create:
"""Creates a sink."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this com... | stack_v2_sparse_classes_36k_train_011660 | 3,928 | permissive | [
{
"docstring": "Register flags for this command.",
"name": "Args",
"signature": "def Args(parser)"
},
{
"docstring": "This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: The created... | 2 | null | Implement the Python class `Create` described below.
Class description:
Creates a sink.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were... | Implement the Python class `Create` described below.
Class description:
Creates a sink.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were... | 85bb264e273568b5a0408f733b403c56373e2508 | <|skeleton|>
class Create:
"""Creates a sink."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this com... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Create:
"""Creates a sink."""
def Args(parser):
"""Register flags for this command."""
parser.add_argument('sink_name', help='The name for the sink.')
parser.add_argument('destination', help='The destination must be a fully qualified BigQuery resource name. The destination can be ... | the_stack_v2_python_sparse | google-cloud-sdk/lib/surface/trace/sinks/create.py | bopopescu/socialliteapp | train | 0 |
343ce02f0094686a96dabe662cd1effc7ecf7c73 | [
"row = len(M)\ncolumn = len(M[0])\nret = [[0] * column for x in range(row)]\nfor i in range(row):\n for j in range(column):\n ret[i][j] = self.get_num_of_num_filter(M, i, j, row, column)\nreturn ret",
"ret = 0\nret_num = 0\nfor i in range(x - 1, x + 2):\n for j in range(y - 1, y + 2):\n if 0 <... | <|body_start_0|>
row = len(M)
column = len(M[0])
ret = [[0] * column for x in range(row)]
for i in range(row):
for j in range(column):
ret[i][j] = self.get_num_of_num_filter(M, i, j, row, column)
return ret
<|end_body_0|>
<|body_start_1|>
ret ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def imageSmoother(self, M):
""":type M: List[List[int]] :rtype: List[List[int]]"""
<|body_0|>
def get_num_of_num_filter(self, image, x, y, row, column):
""":type image:list[list[int]] :type x: int :type y: int :type row: int :type column: int :rtype : int""... | stack_v2_sparse_classes_36k_train_011661 | 2,520 | no_license | [
{
"docstring": ":type M: List[List[int]] :rtype: List[List[int]]",
"name": "imageSmoother",
"signature": "def imageSmoother(self, M)"
},
{
"docstring": ":type image:list[list[int]] :type x: int :type y: int :type row: int :type column: int :rtype : int",
"name": "get_num_of_num_filter",
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def imageSmoother(self, M): :type M: List[List[int]] :rtype: List[List[int]]
- def get_num_of_num_filter(self, image, x, y, row, column): :type image:list[list[int]] :type x: int... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def imageSmoother(self, M): :type M: List[List[int]] :rtype: List[List[int]]
- def get_num_of_num_filter(self, image, x, y, row, column): :type image:list[list[int]] :type x: int... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def imageSmoother(self, M):
""":type M: List[List[int]] :rtype: List[List[int]]"""
<|body_0|>
def get_num_of_num_filter(self, image, x, y, row, column):
""":type image:list[list[int]] :type x: int :type y: int :type row: int :type column: int :rtype : int""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def imageSmoother(self, M):
""":type M: List[List[int]] :rtype: List[List[int]]"""
row = len(M)
column = len(M[0])
ret = [[0] * column for x in range(row)]
for i in range(row):
for j in range(column):
ret[i][j] = self.get_num_of_num... | the_stack_v2_python_sparse | python/leetcode/661_Image_Smoother.py | bobcaoge/my-code | train | 0 | |
62da1c75cfc3b08a5c306e4bee070e1e3de30cf2 | [
"self.snake = collections.deque([(0, 0)])\nself.snake_set = {(0, 0): 1}\nself.width = width\nself.height = height\nself.food = food\nself.food_index = 0\nself.movement = {'U': [-1, 0], 'L': [0, -1], 'R': [0, 1], 'D': [1, 0]}",
"newHead = (self.snake[0][0] + self.movement[direction][0], self.snake[0][1] + self.mov... | <|body_start_0|>
self.snake = collections.deque([(0, 0)])
self.snake_set = {(0, 0): 1}
self.width = width
self.height = height
self.food = food
self.food_index = 0
self.movement = {'U': [-1, 0], 'L': [0, -1], 'R': [0, 1], 'D': [1, 0]}
<|end_body_0|>
<|body_start_... | SnakeGame | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], t... | stack_v2_sparse_classes_36k_train_011662 | 15,245 | no_license | [
{
"docstring": "Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], the second is at [1,0].",
"name": "__init__",
"signature": "def __init__(self, widt... | 2 | stack_v2_sparse_classes_30k_train_002236 | Implement the Python class `SnakeGame` described below.
Class description:
Implement the SnakeGame class.
Method signatures and docstrings:
- def __init__(self, width: int, height: int, food: List[List[int]]): Initialize your data structure here. @param width - screen width @param height - screen height @param food -... | Implement the Python class `SnakeGame` described below.
Class description:
Implement the SnakeGame class.
Method signatures and docstrings:
- def __init__(self, width: int, height: int, food: List[List[int]]): Initialize your data structure here. @param width - screen width @param height - screen height @param food -... | 035ef08434fa1ca781a6fb2f9eed3538b7d20c02 | <|skeleton|>
class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], the second is a... | the_stack_v2_python_sparse | leetcode_python/Design/design-snake-game.py | yennanliu/CS_basics | train | 64 | |
381789bae6d2d87363a5e0c651b765565a2019c1 | [
"self.folder_base = folder\nsuper().__init__(folder, image_extension='JPG')\nif not self.explicit_extrinsics_paths:\n self.explicit_extrinsics_paths = self.__generate_extrinsics_from_reconstruction()",
"reconstruction_path = os.path.join(self.folder_base, 'reconstruction', 'data.mat')\nextrinsics_path_template... | <|body_start_0|>
self.folder_base = folder
super().__init__(folder, image_extension='JPG')
if not self.explicit_extrinsics_paths:
self.explicit_extrinsics_paths = self.__generate_extrinsics_from_reconstruction()
<|end_body_0|>
<|body_start_1|>
reconstruction_path = os.path.j... | Simple loader class that reads from a folder on disk. Folder layout structure: - RGB Images: images/ - Extrinsics data (optional): extrinsics/ - numpy array with the same name as images If explicit intrinsics are not provided, the exif data will be used. | LundDatasetLoader | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LundDatasetLoader:
"""Simple loader class that reads from a folder on disk. Folder layout structure: - RGB Images: images/ - Extrinsics data (optional): extrinsics/ - numpy array with the same name as images If explicit intrinsics are not provided, the exif data will be used."""
def __init__... | stack_v2_sparse_classes_36k_train_011663 | 2,397 | permissive | [
{
"docstring": "Initialize object to load image data from a specified folder on disk Args: folder: the base folder for a given scene.",
"name": "__init__",
"signature": "def __init__(self, folder: str) -> None"
},
{
"docstring": "Extract extrinsics from mat file and stores them as numpy arrays. ... | 2 | stack_v2_sparse_classes_30k_train_017014 | Implement the Python class `LundDatasetLoader` described below.
Class description:
Simple loader class that reads from a folder on disk. Folder layout structure: - RGB Images: images/ - Extrinsics data (optional): extrinsics/ - numpy array with the same name as images If explicit intrinsics are not provided, the exif ... | Implement the Python class `LundDatasetLoader` described below.
Class description:
Simple loader class that reads from a folder on disk. Folder layout structure: - RGB Images: images/ - Extrinsics data (optional): extrinsics/ - numpy array with the same name as images If explicit intrinsics are not provided, the exif ... | 245fb4d90bf6d63d45af8f77a4debfe46ea52ff0 | <|skeleton|>
class LundDatasetLoader:
"""Simple loader class that reads from a folder on disk. Folder layout structure: - RGB Images: images/ - Extrinsics data (optional): extrinsics/ - numpy array with the same name as images If explicit intrinsics are not provided, the exif data will be used."""
def __init__... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LundDatasetLoader:
"""Simple loader class that reads from a folder on disk. Folder layout structure: - RGB Images: images/ - Extrinsics data (optional): extrinsics/ - numpy array with the same name as images If explicit intrinsics are not provided, the exif data will be used."""
def __init__(self, folder... | the_stack_v2_python_sparse | gtsfm/loader/lund_dataset_loader.py | asa/gtsfm | train | 0 |
36f4eddd7c3ffbb7af96c4c5fde322ae25efe9f7 | [
"super().__init__(add_help=False, **kwargs)\nif config_options:\n self.add_argument('-c', '--show-config', action='store_true', default=False, dest='show_config', help='Show the configuration parameters.')\n self.add_argument('-a', '--attributes-level', default=0, type=int, dest='attributes_level', help='Set ... | <|body_start_0|>
super().__init__(add_help=False, **kwargs)
if config_options:
self.add_argument('-c', '--show-config', action='store_true', default=False, dest='show_config', help='Show the configuration parameters.')
self.add_argument('-a', '--attributes-level', default=0, type... | The base class for the option parser. | ArgumentParserBase | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ArgumentParserBase:
"""The base class for the option parser."""
def __init__(self, config_options=False, sort_options=False, **kwargs):
"""Initialise the class. :param config_options: True/False show configuration options :param sort_options: True/False show argument sorting options"... | stack_v2_sparse_classes_36k_train_011664 | 39,454 | permissive | [
{
"docstring": "Initialise the class. :param config_options: True/False show configuration options :param sort_options: True/False show argument sorting options",
"name": "__init__",
"signature": "def __init__(self, config_options=False, sort_options=False, **kwargs)"
},
{
"docstring": "Parse th... | 3 | stack_v2_sparse_classes_30k_train_016702 | Implement the Python class `ArgumentParserBase` described below.
Class description:
The base class for the option parser.
Method signatures and docstrings:
- def __init__(self, config_options=False, sort_options=False, **kwargs): Initialise the class. :param config_options: True/False show configuration options :para... | Implement the Python class `ArgumentParserBase` described below.
Class description:
The base class for the option parser.
Method signatures and docstrings:
- def __init__(self, config_options=False, sort_options=False, **kwargs): Initialise the class. :param config_options: True/False show configuration options :para... | 88bf7f7c5ac44defc046ebf0719cde748092cfff | <|skeleton|>
class ArgumentParserBase:
"""The base class for the option parser."""
def __init__(self, config_options=False, sort_options=False, **kwargs):
"""Initialise the class. :param config_options: True/False show configuration options :param sort_options: True/False show argument sorting options"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ArgumentParserBase:
"""The base class for the option parser."""
def __init__(self, config_options=False, sort_options=False, **kwargs):
"""Initialise the class. :param config_options: True/False show configuration options :param sort_options: True/False show argument sorting options"""
su... | the_stack_v2_python_sparse | src/dials/util/options.py | dials/dials | train | 71 |
e08c8f6be26f584a8d3174e4862ed7a88d85b385 | [
"try:\n logger.info('服务器地址为空的rsever注册测试')\n self.login()\n self.register_rserver('', readconfig.name, readconfig.pwd, readconfig.machineName)\n sleep(3)\n self.assertEqual(self.error_hint(), u'服务器地址不能为空！')\n WebDriverWait(self.driver, 5, 0.5).until(ES.alert_is_present())\n sleep(2)\n self.ac... | <|body_start_0|>
try:
logger.info('服务器地址为空的rsever注册测试')
self.login()
self.register_rserver('', readconfig.name, readconfig.pwd, readconfig.machineName)
sleep(3)
self.assertEqual(self.error_hint(), u'服务器地址不能为空！')
WebDriverWait(self.driver, 5... | 快速配置，rserver注册相关测试 | ConfigureRegisterTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigureRegisterTest:
"""快速配置，rserver注册相关测试"""
def test_register1(self):
"""服务器地址为空的rsever注册测试"""
<|body_0|>
def test_register2(self):
"""服务账号为空的rsever注册测试"""
<|body_1|>
def test_register3(self):
"""服务昵称为空的rsever注册测试"""
<|body_2|>
... | stack_v2_sparse_classes_36k_train_011665 | 4,945 | no_license | [
{
"docstring": "服务器地址为空的rsever注册测试",
"name": "test_register1",
"signature": "def test_register1(self)"
},
{
"docstring": "服务账号为空的rsever注册测试",
"name": "test_register2",
"signature": "def test_register2(self)"
},
{
"docstring": "服务昵称为空的rsever注册测试",
"name": "test_register3",
... | 6 | stack_v2_sparse_classes_30k_train_000147 | Implement the Python class `ConfigureRegisterTest` described below.
Class description:
快速配置，rserver注册相关测试
Method signatures and docstrings:
- def test_register1(self): 服务器地址为空的rsever注册测试
- def test_register2(self): 服务账号为空的rsever注册测试
- def test_register3(self): 服务昵称为空的rsever注册测试
- def test_register4(self): 非白名单的用户注册
-... | Implement the Python class `ConfigureRegisterTest` described below.
Class description:
快速配置，rserver注册相关测试
Method signatures and docstrings:
- def test_register1(self): 服务器地址为空的rsever注册测试
- def test_register2(self): 服务账号为空的rsever注册测试
- def test_register3(self): 服务昵称为空的rsever注册测试
- def test_register4(self): 非白名单的用户注册
-... | fd552eeb47fd4838c2c5caef4deea7480ab75ce9 | <|skeleton|>
class ConfigureRegisterTest:
"""快速配置，rserver注册相关测试"""
def test_register1(self):
"""服务器地址为空的rsever注册测试"""
<|body_0|>
def test_register2(self):
"""服务账号为空的rsever注册测试"""
<|body_1|>
def test_register3(self):
"""服务昵称为空的rsever注册测试"""
<|body_2|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConfigureRegisterTest:
"""快速配置，rserver注册相关测试"""
def test_register1(self):
"""服务器地址为空的rsever注册测试"""
try:
logger.info('服务器地址为空的rsever注册测试')
self.login()
self.register_rserver('', readconfig.name, readconfig.pwd, readconfig.machineName)
sleep(3... | the_stack_v2_python_sparse | test_case/A003_configure_register_test.py | luhuifnag/AVA_UIauto_test | train | 0 |
bfdd49cd90a026198a67008d7f3c9a5d10e9bb98 | [
"import itertools\nfor p in itertools.permutations(pieces):\n l = list(chain.from_iterable(p))\n if l == arr:\n return True\nreturn False",
"mp = {x[0]: x for x in pieces}\nret = []\nfor num in arr:\n ret += mp.get(num, [])\nreturn ret == arr"
] | <|body_start_0|>
import itertools
for p in itertools.permutations(pieces):
l = list(chain.from_iterable(p))
if l == arr:
return True
return False
<|end_body_0|>
<|body_start_1|>
mp = {x[0]: x for x in pieces}
ret = []
for num in ar... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canFormArray_TLE(self, arr, pieces):
""":type arr: List[int] :type pieces: List[List[int]] :rtype: bool thought: get all permutation of pieces, choose length == len(arr) in all subset and compare with arr. general solution but TLE."""
<|body_0|>
def canFormArra... | stack_v2_sparse_classes_36k_train_011666 | 2,422 | no_license | [
{
"docstring": ":type arr: List[int] :type pieces: List[List[int]] :rtype: bool thought: get all permutation of pieces, choose length == len(arr) in all subset and compare with arr. general solution but TLE.",
"name": "canFormArray_TLE",
"signature": "def canFormArray_TLE(self, arr, pieces)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_010427 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFormArray_TLE(self, arr, pieces): :type arr: List[int] :type pieces: List[List[int]] :rtype: bool thought: get all permutation of pieces, choose length == len(arr) in all ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFormArray_TLE(self, arr, pieces): :type arr: List[int] :type pieces: List[List[int]] :rtype: bool thought: get all permutation of pieces, choose length == len(arr) in all ... | 02726da394971ef02616a038dadc126c6ff260de | <|skeleton|>
class Solution:
def canFormArray_TLE(self, arr, pieces):
""":type arr: List[int] :type pieces: List[List[int]] :rtype: bool thought: get all permutation of pieces, choose length == len(arr) in all subset and compare with arr. general solution but TLE."""
<|body_0|>
def canFormArra... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canFormArray_TLE(self, arr, pieces):
""":type arr: List[int] :type pieces: List[List[int]] :rtype: bool thought: get all permutation of pieces, choose length == len(arr) in all subset and compare with arr. general solution but TLE."""
import itertools
for p in itertools.p... | the_stack_v2_python_sparse | N1640_CheckArrayFormationThroughConcatenation.py | zerghua/leetcode-python | train | 2 | |
0569be77a6d67aa51f731f17f6fcd0ae387e6eb9 | [
"self.server = server\nself.NN = NN\nself.op = optimizer\nself.criterion = criterion\nnum_servers = server.num_servers\nnum_customers = len(server.customer_rewards)\nself.state_to_basis = {}\nfor free_servers in range(num_servers + 1):\n for customer in range(num_customers):\n basis = torch.zeros((num_ser... | <|body_start_0|>
self.server = server
self.NN = NN
self.op = optimizer
self.criterion = criterion
num_servers = server.num_servers
num_customers = len(server.customer_rewards)
self.state_to_basis = {}
for free_servers in range(num_servers + 1):
... | Agent object that uses differential semi-gradient SARSA to find optimal policy. | Agent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Agent:
"""Agent object that uses differential semi-gradient SARSA to find optimal policy."""
def __init__(self, server, NN, optimizer, criterion):
"""Initializes the agent. @type server: ServerQueue Server queue environment. @type NN: NeuralNet Neural network for computing the state-... | stack_v2_sparse_classes_36k_train_011667 | 8,549 | permissive | [
{
"docstring": "Initializes the agent. @type server: ServerQueue Server queue environment. @type NN: NeuralNet Neural network for computing the state-action values @type optimizer: optimizer Optimizer from the torch.optim module. @type scheduler: lr_scheduler Learning rate scheduler from the torch.optim module.... | 5 | stack_v2_sparse_classes_30k_test_000165 | Implement the Python class `Agent` described below.
Class description:
Agent object that uses differential semi-gradient SARSA to find optimal policy.
Method signatures and docstrings:
- def __init__(self, server, NN, optimizer, criterion): Initializes the agent. @type server: ServerQueue Server queue environment. @t... | Implement the Python class `Agent` described below.
Class description:
Agent object that uses differential semi-gradient SARSA to find optimal policy.
Method signatures and docstrings:
- def __init__(self, server, NN, optimizer, criterion): Initializes the agent. @type server: ServerQueue Server queue environment. @t... | 127d3fe10fe5774be7f8db3b00f6737f3eed363d | <|skeleton|>
class Agent:
"""Agent object that uses differential semi-gradient SARSA to find optimal policy."""
def __init__(self, server, NN, optimizer, criterion):
"""Initializes the agent. @type server: ServerQueue Server queue environment. @type NN: NeuralNet Neural network for computing the state-... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Agent:
"""Agent object that uses differential semi-gradient SARSA to find optimal policy."""
def __init__(self, server, NN, optimizer, criterion):
"""Initializes the agent. @type server: ServerQueue Server queue environment. @type NN: NeuralNet Neural network for computing the state-action values... | the_stack_v2_python_sparse | Ch10/access_control/server_queue.py | lolcharles2/Reinforcement_learning_book_implementations | train | 0 |
13bfc82205060e869be1b0269cb60790f7616558 | [
"page = request.args.get('page', 1, type=int)\nper_page = request.args.get('per_page', 10, type=int)\norderBy = request.args.get('orderBy', '', type=str)\ndesc = request.args.get('desc', 'False', type=str)\ntopicTitle = request.args.get('topicTitle', '', type=str)\nreportId = request.args.get('reportId', '', type=i... | <|body_start_0|>
page = request.args.get('page', 1, type=int)
per_page = request.args.get('per_page', 10, type=int)
orderBy = request.args.get('orderBy', '', type=str)
desc = request.args.get('desc', 'False', type=str)
topicTitle = request.args.get('topicTitle', '', type=str)
... | SimilarityAlgorithmsController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimilarityAlgorithmsController:
def get(self):
"""Returns a page of tweets with scores"""
<|body_0|>
def post(self):
"""Calculates the similarity between tweets of a topic and a report using various algorithms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_011668 | 3,460 | no_license | [
{
"docstring": "Returns a page of tweets with scores",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Calculates the similarity between tweets of a topic and a report using various algorithms",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007129 | Implement the Python class `SimilarityAlgorithmsController` described below.
Class description:
Implement the SimilarityAlgorithmsController class.
Method signatures and docstrings:
- def get(self): Returns a page of tweets with scores
- def post(self): Calculates the similarity between tweets of a topic and a report... | Implement the Python class `SimilarityAlgorithmsController` described below.
Class description:
Implement the SimilarityAlgorithmsController class.
Method signatures and docstrings:
- def get(self): Returns a page of tweets with scores
- def post(self): Calculates the similarity between tweets of a topic and a report... | e8d5fd562724df1ad26b90d0c731e133b052df24 | <|skeleton|>
class SimilarityAlgorithmsController:
def get(self):
"""Returns a page of tweets with scores"""
<|body_0|>
def post(self):
"""Calculates the similarity between tweets of a topic and a report using various algorithms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimilarityAlgorithmsController:
def get(self):
"""Returns a page of tweets with scores"""
page = request.args.get('page', 1, type=int)
per_page = request.args.get('per_page', 10, type=int)
orderBy = request.args.get('orderBy', '', type=str)
desc = request.args.get('desc... | the_stack_v2_python_sparse | app/main/controllers/similarityAlgorithmsController.py | ProyectoFinal2020/TweetAnalyzer-Backend | train | 0 | |
46b1c23f2c736425f31da4a8156aba803b8a11eb | [
"super().__init__(fmt, datefmt, style)\nself.enable_color = enable_color\nself.name_colors = {}",
"rec = logging.getLogRecordFactory()(level=record.levelno, **record.__dict__)\ntry:\n rec.message = rec.getMessage()\nexcept Exception as e:\n rec.message = 'Bad message (%r): %r' % (e, rec.__dict__)\nrec.ascti... | <|body_start_0|>
super().__init__(fmt, datefmt, style)
self.enable_color = enable_color
self.name_colors = {}
<|end_body_0|>
<|body_start_1|>
rec = logging.getLogRecordFactory()(level=record.levelno, **record.__dict__)
try:
rec.message = rec.getMessage()
exce... | Log formatter for Python logging module. This formatter assigns a color to individual logger names. Note that it may overlap because the number of defined colors is only 6. The following additional variables are available n a format string to colorize the output. * ``time_color`` * ``time_color_end`` * ``name_color`` *... | LogFormatter | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogFormatter:
"""Log formatter for Python logging module. This formatter assigns a color to individual logger names. Note that it may overlap because the number of defined colors is only 6. The following additional variables are available n a format string to colorize the output. * ``time_color``... | stack_v2_sparse_classes_36k_train_011669 | 6,001 | permissive | [
{
"docstring": "Initialize the formatter with specified format strings. Parameters ---------- fmt : str Format string. datefmt : str Date format. style : str Style parameter. enable_color : bool Whether to enable color output.",
"name": "__init__",
"signature": "def __init__(self, fmt=None, datefmt=None... | 4 | null | Implement the Python class `LogFormatter` described below.
Class description:
Log formatter for Python logging module. This formatter assigns a color to individual logger names. Note that it may overlap because the number of defined colors is only 6. The following additional variables are available n a format string t... | Implement the Python class `LogFormatter` described below.
Class description:
Log formatter for Python logging module. This formatter assigns a color to individual logger names. Note that it may overlap because the number of defined colors is only 6. The following additional variables are available n a format string t... | ab8352716c973eef9c224ff80d0dd66b95c606a3 | <|skeleton|>
class LogFormatter:
"""Log formatter for Python logging module. This formatter assigns a color to individual logger names. Note that it may overlap because the number of defined colors is only 6. The following additional variables are available n a format string to colorize the output. * ``time_color``... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LogFormatter:
"""Log formatter for Python logging module. This formatter assigns a color to individual logger names. Note that it may overlap because the number of defined colors is only 6. The following additional variables are available n a format string to colorize the output. * ``time_color`` * ``time_col... | the_stack_v2_python_sparse | jaffle/logging.py | daniel-covelli/jaffle | train | 0 |
a2fa9eceb408a52e7cf5872cb1865cf8e7f95a64 | [
"if not node:\n return (True, 0, MAX_INT, MIN_INT)\nif node.left is None and node.right is None:\n self.maxTotal = max(self.maxTotal, 1)\n return (True, 1, node.val, node.val)\nisBSTLeft, totalLeft, minLeft, maxLeft = self.postorder(node.left)\nisBSTRight, totalRight, minRight, maxRight = self.postorder(no... | <|body_start_0|>
if not node:
return (True, 0, MAX_INT, MIN_INT)
if node.left is None and node.right is None:
self.maxTotal = max(self.maxTotal, 1)
return (True, 1, node.val, node.val)
isBSTLeft, totalLeft, minLeft, maxLeft = self.postorder(node.left)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def postorder(self, node: TreeNode) -> Tuple[bool, int, int, int]:
"""@return: the status of a tree with node as root 1. Whether this tree is BST 2. Total node count 3. The minimum node value 4. The maximum node value"""
<|body_0|>
def largestBSTSubtree(self, root:... | stack_v2_sparse_classes_36k_train_011670 | 2,207 | no_license | [
{
"docstring": "@return: the status of a tree with node as root 1. Whether this tree is BST 2. Total node count 3. The minimum node value 4. The maximum node value",
"name": "postorder",
"signature": "def postorder(self, node: TreeNode) -> Tuple[bool, int, int, int]"
},
{
"docstring": "Same ques... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def postorder(self, node: TreeNode) -> Tuple[bool, int, int, int]: @return: the status of a tree with node as root 1. Whether this tree is BST 2. Total node count 3. The minimum ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def postorder(self, node: TreeNode) -> Tuple[bool, int, int, int]: @return: the status of a tree with node as root 1. Whether this tree is BST 2. Total node count 3. The minimum ... | ad2f5bd0aec3d2c2c77b7c18627c1dd8fe8c0653 | <|skeleton|>
class Solution:
def postorder(self, node: TreeNode) -> Tuple[bool, int, int, int]:
"""@return: the status of a tree with node as root 1. Whether this tree is BST 2. Total node count 3. The minimum node value 4. The maximum node value"""
<|body_0|>
def largestBSTSubtree(self, root:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def postorder(self, node: TreeNode) -> Tuple[bool, int, int, int]:
"""@return: the status of a tree with node as root 1. Whether this tree is BST 2. Total node count 3. The minimum node value 4. The maximum node value"""
if not node:
return (True, 0, MAX_INT, MIN_INT)
... | the_stack_v2_python_sparse | 333 Largest BST Subtree.py | jz33/LeetCodeSolutions | train | 8 | |
a52197b59eda44c3b937a925affb83433f628d98 | [
"super().__init__()\nKp = diagonalize_gain(to_tensor(Kp))\nKd = diagonalize_gain(to_tensor(Kd))\nassert Kp.shape == torch.Size([6, 6])\nassert Kd.shape == torch.Size([6, 6])\nself.Kp = torch.nn.Parameter(Kp)\nself.Kd = torch.nn.Parameter(Kd)\nself.robot_model = robot_model",
"ee_pos_current, ee_quat_current = sel... | <|body_start_0|>
super().__init__()
Kp = diagonalize_gain(to_tensor(Kp))
Kd = diagonalize_gain(to_tensor(Kd))
assert Kp.shape == torch.Size([6, 6])
assert Kd.shape == torch.Size([6, 6])
self.Kp = torch.nn.Parameter(Kp)
self.Kd = torch.nn.Parameter(Kd)
self... | PD feedback control in operational space. Errors are computed in Cartesian space, then projected back into joint space to compute joint torques. nA is the action dimension and N is the number of degrees of freedom Module parameters: - Kp: P gain matrix of shape (nA, N) - Kd: D gain matrix of shape (nA, N) | OperationalSpacePD | [
"LicenseRef-scancode-warranty-disclaimer",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OperationalSpacePD:
"""PD feedback control in operational space. Errors are computed in Cartesian space, then projected back into joint space to compute joint torques. nA is the action dimension and N is the number of degrees of freedom Module parameters: - Kp: P gain matrix of shape (nA, N) - Kd... | stack_v2_sparse_classes_36k_train_011671 | 9,060 | permissive | [
{
"docstring": "Args: Kp: P gain matrix of shape (nA, N) or shape (N,) representing a N-by-N diagonal matrix (if nA=N) Kd: D gain matrix of shape (nA, N) or shape (N,) representing a N-by-N diagonal matrix (if nA=N) robot_model: A valid robot model module from torchcontrol.models",
"name": "__init__",
"... | 2 | stack_v2_sparse_classes_30k_train_009868 | Implement the Python class `OperationalSpacePD` described below.
Class description:
PD feedback control in operational space. Errors are computed in Cartesian space, then projected back into joint space to compute joint torques. nA is the action dimension and N is the number of degrees of freedom Module parameters: - ... | Implement the Python class `OperationalSpacePD` described below.
Class description:
PD feedback control in operational space. Errors are computed in Cartesian space, then projected back into joint space to compute joint torques. nA is the action dimension and N is the number of degrees of freedom Module parameters: - ... | 1b2ea8528d4fb9ad72cec9c766be4cbdbdf76f18 | <|skeleton|>
class OperationalSpacePD:
"""PD feedback control in operational space. Errors are computed in Cartesian space, then projected back into joint space to compute joint torques. nA is the action dimension and N is the number of degrees of freedom Module parameters: - Kp: P gain matrix of shape (nA, N) - Kd... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OperationalSpacePD:
"""PD feedback control in operational space. Errors are computed in Cartesian space, then projected back into joint space to compute joint torques. nA is the action dimension and N is the number of degrees of freedom Module parameters: - Kp: P gain matrix of shape (nA, N) - Kd: D gain matr... | the_stack_v2_python_sparse | polymetis/python/torchcontrol/modules/feedback.py | facebookresearch/polymetis | train | 44 |
9b09f6925ad2fda7f3f4ebbfd3f22dc9f9432627 | [
"super(TenorNetworkModule, self).__init__()\nself.setup_weights()\nself.init_parameters()",
"self.weight_matrix = torch.nn.Parameter(torch.Tensor(256, 256, 16))\nself.weight_matrix_block = torch.nn.Parameter(torch.Tensor(16, 2 * 256))\nself.bias = torch.nn.Parameter(torch.Tensor(16, 1))",
"torch.nn.init.xavier_... | <|body_start_0|>
super(TenorNetworkModule, self).__init__()
self.setup_weights()
self.init_parameters()
<|end_body_0|>
<|body_start_1|>
self.weight_matrix = torch.nn.Parameter(torch.Tensor(256, 256, 16))
self.weight_matrix_block = torch.nn.Parameter(torch.Tensor(16, 2 * 256))
... | SimGNN Tensor Network module to calculate similarity vector. | TenorNetworkModule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TenorNetworkModule:
"""SimGNN Tensor Network module to calculate similarity vector."""
def __init__(self):
""":param args: Arguments object."""
<|body_0|>
def setup_weights(self):
"""Defining weights."""
<|body_1|>
def init_parameters(self):
... | stack_v2_sparse_classes_36k_train_011672 | 4,926 | no_license | [
{
"docstring": ":param args: Arguments object.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Defining weights.",
"name": "setup_weights",
"signature": "def setup_weights(self)"
},
{
"docstring": "Initializing weights.",
"name": "init_parameters",
... | 4 | stack_v2_sparse_classes_30k_train_021353 | Implement the Python class `TenorNetworkModule` described below.
Class description:
SimGNN Tensor Network module to calculate similarity vector.
Method signatures and docstrings:
- def __init__(self): :param args: Arguments object.
- def setup_weights(self): Defining weights.
- def init_parameters(self): Initializing... | Implement the Python class `TenorNetworkModule` described below.
Class description:
SimGNN Tensor Network module to calculate similarity vector.
Method signatures and docstrings:
- def __init__(self): :param args: Arguments object.
- def setup_weights(self): Defining weights.
- def init_parameters(self): Initializing... | 96b3cb5b392f08924ac0fd6df5eea2b6f680c1c8 | <|skeleton|>
class TenorNetworkModule:
"""SimGNN Tensor Network module to calculate similarity vector."""
def __init__(self):
""":param args: Arguments object."""
<|body_0|>
def setup_weights(self):
"""Defining weights."""
<|body_1|>
def init_parameters(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TenorNetworkModule:
"""SimGNN Tensor Network module to calculate similarity vector."""
def __init__(self):
""":param args: Arguments object."""
super(TenorNetworkModule, self).__init__()
self.setup_weights()
self.init_parameters()
def setup_weights(self):
"""D... | the_stack_v2_python_sparse | models/graph_similarity.py | GPNU-Frank/ABN_FER | train | 1 |
c188c7d5519946c0894cc0465caf51a6f79290c6 | [
"def bivariate(data):\n return holoviews.Bivariate(data, *args, **kwargs)\ndefault_bokeh_opts = {'height': 350, 'width': 400, 'tools': ['hover'], 'shared_axes': False}\ndefault_mpl_opts = {}\nmpl_opts, bokeh_opts = self.update_default_opts(default_mpl_opts, mpl_opts, default_bokeh_opts, bokeh_opts)\nsuper(Bivari... | <|body_start_0|>
def bivariate(data):
return holoviews.Bivariate(data, *args, **kwargs)
default_bokeh_opts = {'height': 350, 'width': 400, 'tools': ['hover'], 'shared_axes': False}
default_mpl_opts = {}
mpl_opts, bokeh_opts = self.update_default_opts(default_mpl_opts, mpl_opt... | Create a ``holoviews.Bivariate`` plot that plots steaming data. The streaming process is handled using a :class:`Pipe`. | Bivariate | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bivariate:
"""Create a ``holoviews.Bivariate`` plot that plots steaming data. The streaming process is handled using a :class:`Pipe`."""
def __init__(self, data=None, bokeh_opts=None, mpl_opts=None, *args, **kwargs):
"""Initialize a :class:`Bivariate`. Args: data: Passed to ``holovie... | stack_v2_sparse_classes_36k_train_011673 | 22,669 | permissive | [
{
"docstring": "Initialize a :class:`Bivariate`. Args: data: Passed to ``holoviews.Bivariate``. bokeh_opts: Default options for the plot when rendered using the \"bokeh\" backend. mpl_opts: Default options for the plot when rendered using the \"matplotlib\" backend. *args: Passed to ``holoviews.Bivariate``. **k... | 2 | stack_v2_sparse_classes_30k_train_005343 | Implement the Python class `Bivariate` described below.
Class description:
Create a ``holoviews.Bivariate`` plot that plots steaming data. The streaming process is handled using a :class:`Pipe`.
Method signatures and docstrings:
- def __init__(self, data=None, bokeh_opts=None, mpl_opts=None, *args, **kwargs): Initial... | Implement the Python class `Bivariate` described below.
Class description:
Create a ``holoviews.Bivariate`` plot that plots steaming data. The streaming process is handled using a :class:`Pipe`.
Method signatures and docstrings:
- def __init__(self, data=None, bokeh_opts=None, mpl_opts=None, *args, **kwargs): Initial... | 5e69c50e5b220859d65406d803086406b50a8e78 | <|skeleton|>
class Bivariate:
"""Create a ``holoviews.Bivariate`` plot that plots steaming data. The streaming process is handled using a :class:`Pipe`."""
def __init__(self, data=None, bokeh_opts=None, mpl_opts=None, *args, **kwargs):
"""Initialize a :class:`Bivariate`. Args: data: Passed to ``holovie... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bivariate:
"""Create a ``holoviews.Bivariate`` plot that plots steaming data. The streaming process is handled using a :class:`Pipe`."""
def __init__(self, data=None, bokeh_opts=None, mpl_opts=None, *args, **kwargs):
"""Initialize a :class:`Bivariate`. Args: data: Passed to ``holoviews.Bivariate`... | the_stack_v2_python_sparse | fragile/dataviz/streaming.py | sergio-hcsoft/fragile-1 | train | 0 |
e3556fcfbfb2469184e9c829f6ef1cf18b030162 | [
"self.delay = delay\nself.ticks = ticks\nself.tick_count = 0\nself.timer = None\nself.done = False",
"if not self.timer:\n self.timer = now\n return True\nelif not self.done and now - self.timer > self.delay:\n self.tick_count += 1\n self.timer = now\n if self.ticks != -1 and self.tick_count >= sel... | <|body_start_0|>
self.delay = delay
self.ticks = ticks
self.tick_count = 0
self.timer = None
self.done = False
<|end_body_0|>
<|body_start_1|>
if not self.timer:
self.timer = now
return True
elif not self.done and now - self.timer > self.d... | A very simple timer for events that are not directly tied to animation. | Timer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Timer:
"""A very simple timer for events that are not directly tied to animation."""
def __init__(self, delay, ticks=-1):
"""The delay is given in milliseconds; ticks is the number of ticks the timer will make before flipping self.done to True. Pass a value of -1 to bypass this."""
... | stack_v2_sparse_classes_36k_train_011674 | 11,413 | no_license | [
{
"docstring": "The delay is given in milliseconds; ticks is the number of ticks the timer will make before flipping self.done to True. Pass a value of -1 to bypass this.",
"name": "__init__",
"signature": "def __init__(self, delay, ticks=-1)"
},
{
"docstring": "Returns true if a tick worth of t... | 2 | null | Implement the Python class `Timer` described below.
Class description:
A very simple timer for events that are not directly tied to animation.
Method signatures and docstrings:
- def __init__(self, delay, ticks=-1): The delay is given in milliseconds; ticks is the number of ticks the timer will make before flipping s... | Implement the Python class `Timer` described below.
Class description:
A very simple timer for events that are not directly tied to animation.
Method signatures and docstrings:
- def __init__(self, delay, ticks=-1): The delay is given in milliseconds; ticks is the number of ticks the timer will make before flipping s... | cee7e4b5dc28c57a6c912852827652b5f51005ae | <|skeleton|>
class Timer:
"""A very simple timer for events that are not directly tied to animation."""
def __init__(self, delay, ticks=-1):
"""The delay is given in milliseconds; ticks is the number of ticks the timer will make before flipping self.done to True. Pass a value of -1 to bypass this."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Timer:
"""A very simple timer for events that are not directly tied to animation."""
def __init__(self, delay, ticks=-1):
"""The delay is given in milliseconds; ticks is the number of ticks the timer will make before flipping self.done to True. Pass a value of -1 to bypass this."""
self.d... | the_stack_v2_python_sparse | IE_games_3/cabbages-and-kings-master/data/tools.py | IndexErrorCoders/PygamesCompilation | train | 2 |
c01ddf1193a1d44a0a89c05efc524b313f967a8b | [
"if len(nums) < 2:\n return []\ni, j = (0, len(nums) - 1)\nresult = []\nunique_num = []\nwhile i < j:\n sum = nums[i] + nums[j]\n if sum > target:\n j -= 1\n elif sum < target:\n i += 1\n elif nums[i] in unique_num:\n i += 1\n else:\n result.append([nums[i], nums[j]])\n... | <|body_start_0|>
if len(nums) < 2:
return []
i, j = (0, len(nums) - 1)
result = []
unique_num = []
while i < j:
sum = nums[i] + nums[j]
if sum > target:
j -= 1
elif sum < target:
i += 1
el... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def twoSum(self, nums, target):
""":param nums: :param target: :return: >>> s = Solution() >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], 0) [[-1, 1], [0, 0]] >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], -2) [[-2, 0]]"""
<|body_0|>
def threeSum(self, nums, target):
"""... | stack_v2_sparse_classes_36k_train_011675 | 3,912 | no_license | [
{
"docstring": ":param nums: :param target: :return: >>> s = Solution() >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], 0) [[-1, 1], [0, 0]] >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], -2) [[-2, 0]]",
"name": "twoSum",
"signature": "def twoSum(self, nums, target)"
},
{
"docstring": ":param nums: :param targ... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def twoSum(self, nums, target): :param nums: :param target: :return: >>> s = Solution() >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], 0) [[-1, 1], [0, 0]] >>> s.twoSum([-2, -2, -2, -1,... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def twoSum(self, nums, target): :param nums: :param target: :return: >>> s = Solution() >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], 0) [[-1, 1], [0, 0]] >>> s.twoSum([-2, -2, -2, -1,... | 3b13a02f9c8273f9794a57b948d2655792707f37 | <|skeleton|>
class Solution:
def twoSum(self, nums, target):
""":param nums: :param target: :return: >>> s = Solution() >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], 0) [[-1, 1], [0, 0]] >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], -2) [[-2, 0]]"""
<|body_0|>
def threeSum(self, nums, target):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def twoSum(self, nums, target):
""":param nums: :param target: :return: >>> s = Solution() >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], 0) [[-1, 1], [0, 0]] >>> s.twoSum([-2, -2, -2, -1, 0, 0 ,1], -2) [[-2, 0]]"""
if len(nums) < 2:
return []
i, j = (0, len(nums) - 1)
... | the_stack_v2_python_sparse | 4sum.py | gsy/leetcode | train | 1 | |
c4e747d728a5317e3f5d232b080730bbc6c6c114 | [
"dp = [[0] * (n + 1) for _ in range(n + 1)]\nfor i in range(n, 0, -1):\n for j in range(i + 1, n + 1):\n dp[i][j] = min((k + max(dp[i][k - 1], dp[k + 1][j]) for k in range(i, j)))\nreturn dp[1][n]",
"def search(left, right):\n if left >= right:\n return 0\n if dp[left][right]:\n retu... | <|body_start_0|>
dp = [[0] * (n + 1) for _ in range(n + 1)]
for i in range(n, 0, -1):
for j in range(i + 1, n + 1):
dp[i][j] = min((k + max(dp[i][k - 1], dp[k + 1][j]) for k in range(i, j)))
return dp[1][n]
<|end_body_0|>
<|body_start_1|>
def search(left, rig... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getMoneyAmount(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def getMoneyAmount_recursive(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
dp = [[0] * (n + 1) for _ in range(n + 1)]
fo... | stack_v2_sparse_classes_36k_train_011676 | 2,412 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "getMoneyAmount",
"signature": "def getMoneyAmount(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "getMoneyAmount_recursive",
"signature": "def getMoneyAmount_recursive(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013693 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getMoneyAmount(self, n): :type n: int :rtype: int
- def getMoneyAmount_recursive(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getMoneyAmount(self, n): :type n: int :rtype: int
- def getMoneyAmount_recursive(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def getMoneyAmount(self... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def getMoneyAmount(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def getMoneyAmount_recursive(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getMoneyAmount(self, n):
""":type n: int :rtype: int"""
dp = [[0] * (n + 1) for _ in range(n + 1)]
for i in range(n, 0, -1):
for j in range(i + 1, n + 1):
dp[i][j] = min((k + max(dp[i][k - 1], dp[k + 1][j]) for k in range(i, j)))
return... | the_stack_v2_python_sparse | src/lt_375.py | oxhead/CodingYourWay | train | 0 | |
2e16908284f82b4a9583f1cc79854abe3ea7a9b6 | [
"q = deque()\nans = 0\nM, N = (len(A), len(A[0]))\nfor j in range(N):\n for i in range(M):\n if A[i][j] != '0':\n ans += 1\n A[i][j] = '0'\n q.append((i, j))\n while q:\n it, jt = q.popleft()\n for diff in [(0, -1), (0, 1), (-1, 0), (1, 0)]:\n ... | <|body_start_0|>
q = deque()
ans = 0
M, N = (len(A), len(A[0]))
for j in range(N):
for i in range(M):
if A[i][j] != '0':
ans += 1
A[i][j] = '0'
q.append((i, j))
while q:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numIslands(self, A: List[List[str]]) -> int:
"""BFS 모든 원소가 1이여도, bfs로 진행시 대각선에 해당하는 만큼만 한번에 저장하게 되어 공간을 엄청 줄일 수 있다!!!!! O(MN) / O(min(M,N))"""
<|body_0|>
def numIslands(self, A: List[List[str]]) -> int:
"""DFS O(MN) / O(MN)"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k_train_011677 | 1,586 | no_license | [
{
"docstring": "BFS 모든 원소가 1이여도, bfs로 진행시 대각선에 해당하는 만큼만 한번에 저장하게 되어 공간을 엄청 줄일 수 있다!!!!! O(MN) / O(min(M,N))",
"name": "numIslands",
"signature": "def numIslands(self, A: List[List[str]]) -> int"
},
{
"docstring": "DFS O(MN) / O(MN)",
"name": "numIslands",
"signature": "def numIslands(sel... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numIslands(self, A: List[List[str]]) -> int: BFS 모든 원소가 1이여도, bfs로 진행시 대각선에 해당하는 만큼만 한번에 저장하게 되어 공간을 엄청 줄일 수 있다!!!!! O(MN) / O(min(M,N))
- def numIslands(self, A: List[List[s... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numIslands(self, A: List[List[str]]) -> int: BFS 모든 원소가 1이여도, bfs로 진행시 대각선에 해당하는 만큼만 한번에 저장하게 되어 공간을 엄청 줄일 수 있다!!!!! O(MN) / O(min(M,N))
- def numIslands(self, A: List[List[s... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def numIslands(self, A: List[List[str]]) -> int:
"""BFS 모든 원소가 1이여도, bfs로 진행시 대각선에 해당하는 만큼만 한번에 저장하게 되어 공간을 엄청 줄일 수 있다!!!!! O(MN) / O(min(M,N))"""
<|body_0|>
def numIslands(self, A: List[List[str]]) -> int:
"""DFS O(MN) / O(MN)"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def numIslands(self, A: List[List[str]]) -> int:
"""BFS 모든 원소가 1이여도, bfs로 진행시 대각선에 해당하는 만큼만 한번에 저장하게 되어 공간을 엄청 줄일 수 있다!!!!! O(MN) / O(min(M,N))"""
q = deque()
ans = 0
M, N = (len(A), len(A[0]))
for j in range(N):
for i in range(M):
... | the_stack_v2_python_sparse | Leetcode/Number_of_Islands.py | hanwgyu/algorithm_problem_solving | train | 5 | |
9202d2e7929f8e5c3556ea256de476a3e1ab9cf5 | [
"kwargs['type'] = 'identifier'\nif 'meta' not in kwargs:\n kwargs['meta'] = {}\nkwargs['meta']['type'] = kwargs['type']\nsuper().__init__(field_id, **kwargs)",
"unique = set()\nfor value in self.values:\n unique.add(value)\nreturn unique",
"unique = set()\nfor entry in entries:\n unique.add(entry)\nret... | <|body_start_0|>
kwargs['type'] = 'identifier'
if 'meta' not in kwargs:
kwargs['meta'] = {}
kwargs['meta']['type'] = kwargs['type']
super().__init__(field_id, **kwargs)
<|end_body_0|>
<|body_start_1|>
unique = set()
for value in self.values:
uniqu... | Class for record identifiers. | Identifier | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Identifier:
"""Class for record identifiers."""
def __init__(self, field_id, **kwargs):
"""Init Identifier class."""
<|body_0|>
def to_set(self):
"""Create a set of identifiers."""
<|body_1|>
def check_unique(entries):
"""Check all entries ar... | stack_v2_sparse_classes_36k_train_011678 | 11,877 | permissive | [
{
"docstring": "Init Identifier class.",
"name": "__init__",
"signature": "def __init__(self, field_id, **kwargs)"
},
{
"docstring": "Create a set of identifiers.",
"name": "to_set",
"signature": "def to_set(self)"
},
{
"docstring": "Check all entries are unique.",
"name": "c... | 4 | stack_v2_sparse_classes_30k_train_008310 | Implement the Python class `Identifier` described below.
Class description:
Class for record identifiers.
Method signatures and docstrings:
- def __init__(self, field_id, **kwargs): Init Identifier class.
- def to_set(self): Create a set of identifiers.
- def check_unique(entries): Check all entries are unique.
- def... | Implement the Python class `Identifier` described below.
Class description:
Class for record identifiers.
Method signatures and docstrings:
- def __init__(self, field_id, **kwargs): Init Identifier class.
- def to_set(self): Create a set of identifiers.
- def check_unique(entries): Check all entries are unique.
- def... | 052a26316d19a48981417bf340d9f57e2cdc653a | <|skeleton|>
class Identifier:
"""Class for record identifiers."""
def __init__(self, field_id, **kwargs):
"""Init Identifier class."""
<|body_0|>
def to_set(self):
"""Create a set of identifiers."""
<|body_1|>
def check_unique(entries):
"""Check all entries ar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Identifier:
"""Class for record identifiers."""
def __init__(self, field_id, **kwargs):
"""Init Identifier class."""
kwargs['type'] = 'identifier'
if 'meta' not in kwargs:
kwargs['meta'] = {}
kwargs['meta']['type'] = kwargs['type']
super().__init__(fiel... | the_stack_v2_python_sparse | src/blobtools/lib/field.py | blobtoolkit/blobtoolkit | train | 32 |
f6a9da15cd7d656815adf5d4625f848a44487e39 | [
"if la:\n return la.size() ** 2 + la[0]\nelse:\n return 0",
"if sexpr == 0:\n return self(0)\nif sexpr.support() == [[]]:\n return self._from_dict({self.one_basis(): sexpr.coefficient([])}, remove_zeros=False)\nout = self.zero()\nwhile sexpr:\n mup = max(sexpr.support(), key=self._my_key)\n out ... | <|body_start_0|>
if la:
return la.size() ** 2 + la[0]
else:
return 0
<|end_body_0|>
<|body_start_1|>
if sexpr == 0:
return self(0)
if sexpr.support() == [[]]:
return self._from_dict({self.one_basis(): sexpr.coefficient([])}, remove_zeros=F... | generic_character | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class generic_character:
def _my_key(self, la):
"""A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\lambda|^2 + \\lambda_0` and using the ``max`` function on a list of Partitions. Of course it i... | stack_v2_sparse_classes_36k_train_011679 | 16,482 | no_license | [
{
"docstring": "A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\\\lambda|^2 + \\\\lambda_0` and using the ``max`` function on a list of Partitions. Of course it is possible that this rank function is equal for s... | 2 | stack_v2_sparse_classes_30k_train_010104 | Implement the Python class `generic_character` described below.
Class description:
Implement the generic_character class.
Method signatures and docstrings:
- def _my_key(self, la): A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key v... | Implement the Python class `generic_character` described below.
Class description:
Implement the generic_character class.
Method signatures and docstrings:
- def _my_key(self, la): A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key v... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class generic_character:
def _my_key(self, la):
"""A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\lambda|^2 + \\lambda_0` and using the ``max`` function on a list of Partitions. Of course it i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class generic_character:
def _my_key(self, la):
"""A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\lambda|^2 + \\lambda_0` and using the ``max`` function on a list of Partitions. Of course it is possible tha... | the_stack_v2_python_sparse | sage/src/sage/combinat/sf/character.py | bopopescu/geosci | train | 0 | |
a46302460e41d7e6228f31f04df0902e6bd490ba | [
"UserModel = get_user_model()\nemail = self.cleaned_data['email']\nself.users_cache = UserModel._default_manager.filter(email__iexact=email, is_active=True)\nif not len(self.users_cache):\n raise forms.ValidationError(self.error_messages['unknown'])\nif any((not is_password_usable(user.password) for user in self... | <|body_start_0|>
UserModel = get_user_model()
email = self.cleaned_data['email']
self.users_cache = UserModel._default_manager.filter(email__iexact=email, is_active=True)
if not len(self.users_cache):
raise forms.ValidationError(self.error_messages['unknown'])
if any(... | EmailPasswordResetForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailPasswordResetForm:
def clean_email(self):
"""Validates that an active user exists with the given email address."""
<|body_0|>
def save(self, domain_override=None, email_template_name='registration/password_reset_email.html', use_https=False, token_generator=default_toke... | stack_v2_sparse_classes_36k_train_011680 | 25,410 | no_license | [
{
"docstring": "Validates that an active user exists with the given email address.",
"name": "clean_email",
"signature": "def clean_email(self)"
},
{
"docstring": "Generates a one-use only link for resetting password and sends to the user.",
"name": "save",
"signature": "def save(self, d... | 2 | stack_v2_sparse_classes_30k_train_018260 | Implement the Python class `EmailPasswordResetForm` described below.
Class description:
Implement the EmailPasswordResetForm class.
Method signatures and docstrings:
- def clean_email(self): Validates that an active user exists with the given email address.
- def save(self, domain_override=None, email_template_name='... | Implement the Python class `EmailPasswordResetForm` described below.
Class description:
Implement the EmailPasswordResetForm class.
Method signatures and docstrings:
- def clean_email(self): Validates that an active user exists with the given email address.
- def save(self, domain_override=None, email_template_name='... | 61e082814d25c81007a2ff0cfae7f3a06c8c291d | <|skeleton|>
class EmailPasswordResetForm:
def clean_email(self):
"""Validates that an active user exists with the given email address."""
<|body_0|>
def save(self, domain_override=None, email_template_name='registration/password_reset_email.html', use_https=False, token_generator=default_toke... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmailPasswordResetForm:
def clean_email(self):
"""Validates that an active user exists with the given email address."""
UserModel = get_user_model()
email = self.cleaned_data['email']
self.users_cache = UserModel._default_manager.filter(email__iexact=email, is_active=True)
... | the_stack_v2_python_sparse | accounts/forms.py | cash2one/source | train | 0 | |
3aebbe5d5b739303dd15b72ea10cfe8bd40ff2db | [
"if len(matrix) == 0 or len(matrix[0]) == 0:\n return 0\nretangle = []\nres = 0\nfor arr in matrix:\n if len(retangle) == 0:\n retangle = [int(x) for x in arr]\n else:\n for j in range(len(arr)):\n if arr[j] == '0':\n retangle[j] = 0\n else:\n ... | <|body_start_0|>
if len(matrix) == 0 or len(matrix[0]) == 0:
return 0
retangle = []
res = 0
for arr in matrix:
if len(retangle) == 0:
retangle = [int(x) for x in arr]
else:
for j in range(len(arr)):
i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maximalRectangle(self, matrix):
"""classic approach: dynamic programming + max area within a histogram(leetcode 84) - similar to lc1504 e.g. [ [1,0,1,0,0], [1,0,1,1,1], [1,1,1,1,1], [1,0,0,1,0], ] for each row, we accumulate the ones on the grids on position in previous row... | stack_v2_sparse_classes_36k_train_011681 | 2,976 | no_license | [
{
"docstring": "classic approach: dynamic programming + max area within a histogram(leetcode 84) - similar to lc1504 e.g. [ [1,0,1,0,0], [1,0,1,1,1], [1,1,1,1,1], [1,0,0,1,0], ] for each row, we accumulate the ones on the grids on position in previous row and find the area of the current histogram [1,0,1,0,0] <... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maximalRectangle(self, matrix): classic approach: dynamic programming + max area within a histogram(leetcode 84) - similar to lc1504 e.g. [ [1,0,1,0,0], [1,0,1,1,1], [1,1,1,1... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maximalRectangle(self, matrix): classic approach: dynamic programming + max area within a histogram(leetcode 84) - similar to lc1504 e.g. [ [1,0,1,0,0], [1,0,1,1,1], [1,1,1,1... | 1bd17e867d1d557a6ebbbd99f693d5fbd9f5b61e | <|skeleton|>
class Solution:
def maximalRectangle(self, matrix):
"""classic approach: dynamic programming + max area within a histogram(leetcode 84) - similar to lc1504 e.g. [ [1,0,1,0,0], [1,0,1,1,1], [1,1,1,1,1], [1,0,0,1,0], ] for each row, we accumulate the ones on the grids on position in previous row... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maximalRectangle(self, matrix):
"""classic approach: dynamic programming + max area within a histogram(leetcode 84) - similar to lc1504 e.g. [ [1,0,1,0,0], [1,0,1,1,1], [1,1,1,1,1], [1,0,0,1,0], ] for each row, we accumulate the ones on the grids on position in previous row and find the ... | the_stack_v2_python_sparse | leetcode/85-maximal-retangle/main.py | shriharshs/AlgoDaily | train | 0 | |
65e156ce4e5dfb4474607b009d4a81dcd7204be5 | [
"ObjectManager.__init__(self)\nself.getters.update({'session_template': 'get_foreign_key', 'max': 'get_general', 'min': 'get_general', 'resource_type': 'get_foreign_key'})\nself.setters.update({'session_template': 'set_foreign_key', 'max': 'set_general', 'min': 'set_general', 'resource_type': 'set_foreign_key'})\ns... | <|body_start_0|>
ObjectManager.__init__(self)
self.getters.update({'session_template': 'get_foreign_key', 'max': 'get_general', 'min': 'get_general', 'resource_type': 'get_foreign_key'})
self.setters.update({'session_template': 'set_foreign_key', 'max': 'set_general', 'min': 'set_general', 'reso... | Manage SessionTemplateResourceTypeRequirements in the Power Reg system | SessionTemplateResourceTypeRequirementManager | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SessionTemplateResourceTypeRequirementManager:
"""Manage SessionTemplateResourceTypeRequirements in the Power Reg system"""
def __init__(self):
"""constructor"""
<|body_0|>
def create(self, auth_token, session_template_id, resource_type_id, min, max):
"""Create a... | stack_v2_sparse_classes_36k_train_011682 | 2,098 | permissive | [
{
"docstring": "constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Create a new SessionTemplateResourceTypeRequirement @param session_template_id Foreign key for an session_template @param resource_type_id Foreign key for an resource_type @param min Minimum nu... | 2 | stack_v2_sparse_classes_30k_train_012084 | Implement the Python class `SessionTemplateResourceTypeRequirementManager` described below.
Class description:
Manage SessionTemplateResourceTypeRequirements in the Power Reg system
Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, session_template_id, resource_type_id... | Implement the Python class `SessionTemplateResourceTypeRequirementManager` described below.
Class description:
Manage SessionTemplateResourceTypeRequirements in the Power Reg system
Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, session_template_id, resource_type_id... | a59457bc37f0501aea1f54d006a6de94ff80511c | <|skeleton|>
class SessionTemplateResourceTypeRequirementManager:
"""Manage SessionTemplateResourceTypeRequirements in the Power Reg system"""
def __init__(self):
"""constructor"""
<|body_0|>
def create(self, auth_token, session_template_id, resource_type_id, min, max):
"""Create a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SessionTemplateResourceTypeRequirementManager:
"""Manage SessionTemplateResourceTypeRequirements in the Power Reg system"""
def __init__(self):
"""constructor"""
ObjectManager.__init__(self)
self.getters.update({'session_template': 'get_foreign_key', 'max': 'get_general', 'min': '... | the_stack_v2_python_sparse | pr_services/event_system/session_template_resource_type_requirement_manager.py | ninemoreminutes/openassign-server | train | 0 |
b5558d4c02340a7240356d68b7833e1dcf71dda0 | [
"self._graph = graph\nself._type = t\nself._batch_size = batch_size\nself._strategy = strategy\nself._client = self._graph.get_client()\nself._node_from = node_from\nif self._node_from == pywrap.NodeFrom.NODE:\n if self._type not in self._graph.get_node_decoders().keys():\n raise ValueError('Graph has no ... | <|body_start_0|>
self._graph = graph
self._type = t
self._batch_size = batch_size
self._strategy = strategy
self._client = self._graph.get_client()
self._node_from = node_from
if self._node_from == pywrap.NodeFrom.NODE:
if self._type not in self._graph... | Sampling nodes from graph. | NodeSampler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NodeSampler:
"""Sampling nodes from graph."""
def __init__(self, graph, t, batch_size, strategy='by_order', node_from=pywrap.NodeFrom.NODE):
"""Create a Base NodeSampler.. Args: graph (`Graph` object): The graph which sample from. t (string): type of node or egde. If t is a type of n... | stack_v2_sparse_classes_36k_train_011683 | 4,632 | permissive | [
{
"docstring": "Create a Base NodeSampler.. Args: graph (`Graph` object): The graph which sample from. t (string): type of node or egde. If t is a type of node, then `NodeSampler` will sample from node source. Else if `t` is a type of edge, then `node_from=EDGE_SRC` indicates that the nodes will be sampled from... | 2 | stack_v2_sparse_classes_30k_train_018014 | Implement the Python class `NodeSampler` described below.
Class description:
Sampling nodes from graph.
Method signatures and docstrings:
- def __init__(self, graph, t, batch_size, strategy='by_order', node_from=pywrap.NodeFrom.NODE): Create a Base NodeSampler.. Args: graph (`Graph` object): The graph which sample fr... | Implement the Python class `NodeSampler` described below.
Class description:
Sampling nodes from graph.
Method signatures and docstrings:
- def __init__(self, graph, t, batch_size, strategy='by_order', node_from=pywrap.NodeFrom.NODE): Create a Base NodeSampler.. Args: graph (`Graph` object): The graph which sample fr... | 1827c28c570c355e513f24b6a61a88457cfecdaf | <|skeleton|>
class NodeSampler:
"""Sampling nodes from graph."""
def __init__(self, graph, t, batch_size, strategy='by_order', node_from=pywrap.NodeFrom.NODE):
"""Create a Base NodeSampler.. Args: graph (`Graph` object): The graph which sample from. t (string): type of node or egde. If t is a type of n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NodeSampler:
"""Sampling nodes from graph."""
def __init__(self, graph, t, batch_size, strategy='by_order', node_from=pywrap.NodeFrom.NODE):
"""Create a Base NodeSampler.. Args: graph (`Graph` object): The graph which sample from. t (string): type of node or egde. If t is a type of node, then `No... | the_stack_v2_python_sparse | graphlearn/python/sampler/node_sampler.py | lorinlee/graph-learn | train | 0 |
43c98885deaf83d02e1da14a2443529426613e01 | [
"self.stack = []\ntemp = root\nwhile temp != None:\n self.stack.append(temp)\n temp = temp.left",
"if len(self.stack) != 0:\n return True\nelse:\n return False",
"if len(self.stack) != 0:\n curr = self.stack.pop()\n nextsmall = curr\n if curr.right != None:\n curr = curr.right\n ... | <|body_start_0|>
self.stack = []
temp = root
while temp != None:
self.stack.append(temp)
temp = temp.left
<|end_body_0|>
<|body_start_1|>
if len(self.stack) != 0:
return True
else:
return False
<|end_body_1|>
<|body_start_2|>
... | BSTIterator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BSTIterator:
def __init__(self, root):
""":type root: TreeNode"""
<|body_0|>
def hasNext(self):
""":rtype: bool"""
<|body_1|>
def next(self):
""":rtype: int"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
self.stack = []
... | stack_v2_sparse_classes_36k_train_011684 | 1,326 | no_license | [
{
"docstring": ":type root: TreeNode",
"name": "__init__",
"signature": "def __init__(self, root)"
},
{
"docstring": ":rtype: bool",
"name": "hasNext",
"signature": "def hasNext(self)"
},
{
"docstring": ":rtype: int",
"name": "next",
"signature": "def next(self)"
}
] | 3 | stack_v2_sparse_classes_30k_train_002266 | Implement the Python class `BSTIterator` described below.
Class description:
Implement the BSTIterator class.
Method signatures and docstrings:
- def __init__(self, root): :type root: TreeNode
- def hasNext(self): :rtype: bool
- def next(self): :rtype: int | Implement the Python class `BSTIterator` described below.
Class description:
Implement the BSTIterator class.
Method signatures and docstrings:
- def __init__(self, root): :type root: TreeNode
- def hasNext(self): :rtype: bool
- def next(self): :rtype: int
<|skeleton|>
class BSTIterator:
def __init__(self, root... | 466dbaef48b28ad1b44944cb3eb830b0cd7c7c97 | <|skeleton|>
class BSTIterator:
def __init__(self, root):
""":type root: TreeNode"""
<|body_0|>
def hasNext(self):
""":rtype: bool"""
<|body_1|>
def next(self):
""":rtype: int"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BSTIterator:
def __init__(self, root):
""":type root: TreeNode"""
self.stack = []
temp = root
while temp != None:
self.stack.append(temp)
temp = temp.left
def hasNext(self):
""":rtype: bool"""
if len(self.stack) != 0:
ret... | the_stack_v2_python_sparse | Pythons Solutions/Trees/BST_iterator.py | dexteridea22/Interviewbit-Selected_Problems | train | 1 | |
01853c6bdbaf75a647589944a91b0f565170fd0e | [
"parser.add_argument('--lambda_side', type=float, default=1.0, help='weight for side output loss')\nparser.add_argument('--lambda_fused', type=float, default=1.0, help='weight for fused loss')\nreturn parser",
"BaseModel.__init__(self, opt)\nself.loss_names = ['side', 'fused', 'total']\nself.display_sides = opt.d... | <|body_start_0|>
parser.add_argument('--lambda_side', type=float, default=1.0, help='weight for side output loss')
parser.add_argument('--lambda_fused', type=float, default=1.0, help='weight for fused loss')
return parser
<|end_body_0|>
<|body_start_1|>
BaseModel.__init__(self, opt)
... | This class implements the DeepCrack model. DeepCrack paper: https://www.sciencedirect.com/science/article/pii/S0925231219300566 | DeepCrackModel | [
"BSD-3-Clause",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeepCrackModel:
"""This class implements the DeepCrack model. DeepCrack paper: https://www.sciencedirect.com/science/article/pii/S0925231219300566"""
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing op... | stack_v2_sparse_classes_36k_train_011685 | 5,693 | permissive | [
{
"docstring": "Add new dataset-specific options, and rewrite default values for existing options.",
"name": "modify_commandline_options",
"signature": "def modify_commandline_options(parser, is_train=True)"
},
{
"docstring": "Initialize the DeepCrack class. Parameters: opt (Option class)-- stor... | 6 | stack_v2_sparse_classes_30k_train_021379 | Implement the Python class `DeepCrackModel` described below.
Class description:
This class implements the DeepCrack model. DeepCrack paper: https://www.sciencedirect.com/science/article/pii/S0925231219300566
Method signatures and docstrings:
- def modify_commandline_options(parser, is_train=True): Add new dataset-spe... | Implement the Python class `DeepCrackModel` described below.
Class description:
This class implements the DeepCrack model. DeepCrack paper: https://www.sciencedirect.com/science/article/pii/S0925231219300566
Method signatures and docstrings:
- def modify_commandline_options(parser, is_train=True): Add new dataset-spe... | e23af48e7c155b04a9c40013fb5a6616e4102484 | <|skeleton|>
class DeepCrackModel:
"""This class implements the DeepCrack model. DeepCrack paper: https://www.sciencedirect.com/science/article/pii/S0925231219300566"""
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing op... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeepCrackModel:
"""This class implements the DeepCrack model. DeepCrack paper: https://www.sciencedirect.com/science/article/pii/S0925231219300566"""
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing options."""
... | the_stack_v2_python_sparse | models/deepcrack_model.py | yhlleo/DeepSegmentor | train | 228 |
1f8ca18039b8aa35ca6fbf9aae0fd16af10e45e0 | [
"func = chain_transform(self._default_units())\ndata_pack = data_pack.apply_on_text(func, verbose=verbose)\nvocab_unit = build_vocab_unit(data_pack, verbose=verbose)\nself._context['vocab_unit'] = vocab_unit\nreturn self",
"data_pack = data_pack.copy()\nunits_ = self._default_units()\nunits_.append(self._context[... | <|body_start_0|>
func = chain_transform(self._default_units())
data_pack = data_pack.apply_on_text(func, verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
return self
<|end_body_0|>
<|body_start_1|>
data_p... | Naive preprocessor. Example: >>> import matchzoo as mz >>> train_data = mz.datasets.toy.load_data() >>> test_data = mz.datasets.toy.load_data(stage='test') >>> preprocessor = mz.preprocessors.NaivePreprocessor() >>> train_data_processed = preprocessor.fit_transform(train_data, ... verbose=0) >>> type(train_data_process... | NaivePreprocessor | [
"MIT",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.1-or-later",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NaivePreprocessor:
"""Naive preprocessor. Example: >>> import matchzoo as mz >>> train_data = mz.datasets.toy.load_data() >>> test_data = mz.datasets.toy.load_data(stage='test') >>> preprocessor = mz.preprocessors.NaivePreprocessor() >>> train_data_processed = preprocessor.fit_transform(train_dat... | stack_v2_sparse_classes_36k_train_011686 | 2,402 | permissive | [
{
"docstring": "Fit pre-processing context for transformation. :param data_pack: data_pack to be preprocessed. :param verbose: Verbosity. :return: class:`NaivePreprocessor` instance.",
"name": "fit",
"signature": "def fit(self, data_pack: DataPack, verbose: int=1)"
},
{
"docstring": "Apply trans... | 2 | null | Implement the Python class `NaivePreprocessor` described below.
Class description:
Naive preprocessor. Example: >>> import matchzoo as mz >>> train_data = mz.datasets.toy.load_data() >>> test_data = mz.datasets.toy.load_data(stage='test') >>> preprocessor = mz.preprocessors.NaivePreprocessor() >>> train_data_processed... | Implement the Python class `NaivePreprocessor` described below.
Class description:
Naive preprocessor. Example: >>> import matchzoo as mz >>> train_data = mz.datasets.toy.load_data() >>> test_data = mz.datasets.toy.load_data(stage='test') >>> preprocessor = mz.preprocessors.NaivePreprocessor() >>> train_data_processed... | 4198ebce942f4afe7ddca6a96ab6f4464ade4518 | <|skeleton|>
class NaivePreprocessor:
"""Naive preprocessor. Example: >>> import matchzoo as mz >>> train_data = mz.datasets.toy.load_data() >>> test_data = mz.datasets.toy.load_data(stage='test') >>> preprocessor = mz.preprocessors.NaivePreprocessor() >>> train_data_processed = preprocessor.fit_transform(train_dat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NaivePreprocessor:
"""Naive preprocessor. Example: >>> import matchzoo as mz >>> train_data = mz.datasets.toy.load_data() >>> test_data = mz.datasets.toy.load_data(stage='test') >>> preprocessor = mz.preprocessors.NaivePreprocessor() >>> train_data_processed = preprocessor.fit_transform(train_data, ... verbos... | the_stack_v2_python_sparse | poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/naive_preprocessor.py | microsoft/ContextualSP | train | 332 |
61d160842ef84f27c2cb9d56eadb23b74f3ad5ea | [
"self.plugin_bundle_id = plugin_bundle_id\nself.filter_browsers = filter_browsers\nself.filter_sockets = filter_sockets\nself.vendor_config = vendor_config",
"if dictionary is None:\n return None\nvendor_config = None\nif dictionary.get('VendorConfig') != None:\n vendor_config = list()\n for structure in... | <|body_start_0|>
self.plugin_bundle_id = plugin_bundle_id
self.filter_browsers = filter_browsers
self.filter_sockets = filter_sockets
self.vendor_config = vendor_config
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
vendor_config = None
... | Implementation of the 'addNetworkSmProfileClarity' model. TODO: type model description here. Attributes: plugin_bundle_id (string): The bundle ID of the application, defaults to com.cisco.ciscosecurity.app filter_browsers (bool): Whether or not to enable browser traffic filtering (one of true, false). Defaults to true ... | AddNetworkSmProfileClarityModel | [
"MIT",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AddNetworkSmProfileClarityModel:
"""Implementation of the 'addNetworkSmProfileClarity' model. TODO: type model description here. Attributes: plugin_bundle_id (string): The bundle ID of the application, defaults to com.cisco.ciscosecurity.app filter_browsers (bool): Whether or not to enable browse... | stack_v2_sparse_classes_36k_train_011687 | 3,010 | permissive | [
{
"docstring": "Constructor for the AddNetworkSmProfileClarityModel class",
"name": "__init__",
"signature": "def __init__(self, vendor_config=None, plugin_bundle_id=None, filter_browsers=None, filter_sockets=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dic... | 2 | null | Implement the Python class `AddNetworkSmProfileClarityModel` described below.
Class description:
Implementation of the 'addNetworkSmProfileClarity' model. TODO: type model description here. Attributes: plugin_bundle_id (string): The bundle ID of the application, defaults to com.cisco.ciscosecurity.app filter_browsers ... | Implement the Python class `AddNetworkSmProfileClarityModel` described below.
Class description:
Implementation of the 'addNetworkSmProfileClarity' model. TODO: type model description here. Attributes: plugin_bundle_id (string): The bundle ID of the application, defaults to com.cisco.ciscosecurity.app filter_browsers ... | 9894089eb013318243ae48869cc5130eb37f80c0 | <|skeleton|>
class AddNetworkSmProfileClarityModel:
"""Implementation of the 'addNetworkSmProfileClarity' model. TODO: type model description here. Attributes: plugin_bundle_id (string): The bundle ID of the application, defaults to com.cisco.ciscosecurity.app filter_browsers (bool): Whether or not to enable browse... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AddNetworkSmProfileClarityModel:
"""Implementation of the 'addNetworkSmProfileClarity' model. TODO: type model description here. Attributes: plugin_bundle_id (string): The bundle ID of the application, defaults to com.cisco.ciscosecurity.app filter_browsers (bool): Whether or not to enable browser traffic fil... | the_stack_v2_python_sparse | meraki_sdk/models/add_network_sm_profile_clarity_model.py | RaulCatalano/meraki-python-sdk | train | 1 |
fa089dd1fa2f10166899721113db99d56d7e047a | [
"application_session = self.fetch_and_populate_application_session(token, application_session_id)\nnamespace = self.get_application_session_namespace(application_session)\nself.ensure_namespace(namespace)\nself.logger.info('provisioning %s in namespace %s on cluster %s', application_session['name'], namespace, self... | <|body_start_0|>
application_session = self.fetch_and_populate_application_session(token, application_session_id)
namespace = self.get_application_session_namespace(application_session)
self.ensure_namespace(namespace)
self.logger.info('provisioning %s in namespace %s on cluster %s', app... | OpenShift Template Driver allows provisioning application_sessions in an existing OpenShift cluster, using an Openshift Template. All the templates require a label defined in the template, like - "label: app: <app_label>" Similar to the openshift driver, it needs credentials for the cluster in the cluster config Since ... | OpenShiftTemplateDriver | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OpenShiftTemplateDriver:
"""OpenShift Template Driver allows provisioning application_sessions in an existing OpenShift cluster, using an Openshift Template. All the templates require a label defined in the template, like - "label: app: <app_label>" Similar to the openshift driver, it needs crede... | stack_v2_sparse_classes_36k_train_011688 | 9,209 | permissive | [
{
"docstring": "Implements provisioning, called by superclass. A namespace is created if necessary.",
"name": "do_provision",
"signature": "def do_provision(self, token, application_session_id)"
},
{
"docstring": "Implements readiness checking, called by superclass. Checks that all the pods are ... | 4 | stack_v2_sparse_classes_30k_train_020430 | Implement the Python class `OpenShiftTemplateDriver` described below.
Class description:
OpenShift Template Driver allows provisioning application_sessions in an existing OpenShift cluster, using an Openshift Template. All the templates require a label defined in the template, like - "label: app: <app_label>" Similar ... | Implement the Python class `OpenShiftTemplateDriver` described below.
Class description:
OpenShift Template Driver allows provisioning application_sessions in an existing OpenShift cluster, using an Openshift Template. All the templates require a label defined in the template, like - "label: app: <app_label>" Similar ... | 438afb30f826d401bbf1f4d569d57e1c9e71761f | <|skeleton|>
class OpenShiftTemplateDriver:
"""OpenShift Template Driver allows provisioning application_sessions in an existing OpenShift cluster, using an Openshift Template. All the templates require a label defined in the template, like - "label: app: <app_label>" Similar to the openshift driver, it needs crede... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OpenShiftTemplateDriver:
"""OpenShift Template Driver allows provisioning application_sessions in an existing OpenShift cluster, using an Openshift Template. All the templates require a label defined in the template, like - "label: app: <app_label>" Similar to the openshift driver, it needs credentials for th... | the_stack_v2_python_sparse | pebbles/drivers/provisioning/openshift_template_driver.py | CSCfi/pebbles | train | 4 |
351ab2297381688a4177253566b4ff1c76897526 | [
"cost_s = sorted(costs)\nans = 0\nfor i in range(len(cost_s)):\n if coins >= cost_s[i]:\n coins -= cost_s[i]\n ans += 1\nreturn ans",
"def select_sort(l):\n \"\"\"\n\n :param l: 需要排序的列\n :return: 有序列\n \"\"\"\n for i in range(len(l) - 1):\n min_index ... | <|body_start_0|>
cost_s = sorted(costs)
ans = 0
for i in range(len(cost_s)):
if coins >= cost_s[i]:
coins -= cost_s[i]
ans += 1
return ans
<|end_body_0|>
<|body_start_1|>
def select_sort(l):
"""
:param ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxIceCream(self, costs: List[int], coins: int) -> int:
"""不能重复购买 先排序costs 优先从便宜的购买 :param costs: 雪糕价格表 :param coins: 手头的现金 :return: 能买到的最多雪糕数"""
<|body_0|>
def maxIceCream(self, costs: List[int], coins: int) -> int:
"""二分排序 :param costs: :param coins: ... | stack_v2_sparse_classes_36k_train_011689 | 2,488 | no_license | [
{
"docstring": "不能重复购买 先排序costs 优先从便宜的购买 :param costs: 雪糕价格表 :param coins: 手头的现金 :return: 能买到的最多雪糕数",
"name": "maxIceCream",
"signature": "def maxIceCream(self, costs: List[int], coins: int) -> int"
},
{
"docstring": "二分排序 :param costs: :param coins: :return:",
"name": "maxIceCream",
"si... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxIceCream(self, costs: List[int], coins: int) -> int: 不能重复购买 先排序costs 优先从便宜的购买 :param costs: 雪糕价格表 :param coins: 手头的现金 :return: 能买到的最多雪糕数
- def maxIceCream(self, costs: Lis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxIceCream(self, costs: List[int], coins: int) -> int: 不能重复购买 先排序costs 优先从便宜的购买 :param costs: 雪糕价格表 :param coins: 手头的现金 :return: 能买到的最多雪糕数
- def maxIceCream(self, costs: Lis... | b1680014ce3f55ba952a1e64241c0cbb783cc436 | <|skeleton|>
class Solution:
def maxIceCream(self, costs: List[int], coins: int) -> int:
"""不能重复购买 先排序costs 优先从便宜的购买 :param costs: 雪糕价格表 :param coins: 手头的现金 :return: 能买到的最多雪糕数"""
<|body_0|>
def maxIceCream(self, costs: List[int], coins: int) -> int:
"""二分排序 :param costs: :param coins: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxIceCream(self, costs: List[int], coins: int) -> int:
"""不能重复购买 先排序costs 优先从便宜的购买 :param costs: 雪糕价格表 :param coins: 手头的现金 :return: 能买到的最多雪糕数"""
cost_s = sorted(costs)
ans = 0
for i in range(len(cost_s)):
if coins >= cost_s[i]:
coins -... | the_stack_v2_python_sparse | a_1833.py | sun510001/leetcode_jianzhi_offer_2 | train | 0 | |
42f403058a24f9aebf8858fed3d72445fb5957e4 | [
"self.num_dim = 2\nself.reflect_index = [1, 2]\nsuper(SharpClawSolver2D, self).__init__(riemann_solver, claw_package)",
"self._apply_bcs(state)\nq = self.qbc\ngrid = state.grid\nnum_ghost = self.num_ghost\nmx = grid.num_cells[0]\nmy = grid.num_cells[1]\nmaxm = max(mx, my)\nif self.kernel_language == 'Fortran':\n ... | <|body_start_0|>
self.num_dim = 2
self.reflect_index = [1, 2]
super(SharpClawSolver2D, self).__init__(riemann_solver, claw_package)
<|end_body_0|>
<|body_start_1|>
self._apply_bcs(state)
q = self.qbc
grid = state.grid
num_ghost = self.num_ghost
mx = grid.... | Two Dimensional SharpClawSolver | SharpClawSolver2D | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SharpClawSolver2D:
"""Two Dimensional SharpClawSolver"""
def __init__(self, riemann_solver=None, claw_package=None):
"""Create 2D SharpClaw solver See :class:`SharpClawSolver2D` for more info."""
<|body_0|>
def dq_hyperbolic(self, state):
"""Compute dq/dt * (delt... | stack_v2_sparse_classes_36k_train_011690 | 38,052 | permissive | [
{
"docstring": "Create 2D SharpClaw solver See :class:`SharpClawSolver2D` for more info.",
"name": "__init__",
"signature": "def __init__(self, riemann_solver=None, claw_package=None)"
},
{
"docstring": "Compute dq/dt * (delta t) for the hyperbolic hyperbolic system Note that the capa array, if ... | 2 | stack_v2_sparse_classes_30k_train_018207 | Implement the Python class `SharpClawSolver2D` described below.
Class description:
Two Dimensional SharpClawSolver
Method signatures and docstrings:
- def __init__(self, riemann_solver=None, claw_package=None): Create 2D SharpClaw solver See :class:`SharpClawSolver2D` for more info.
- def dq_hyperbolic(self, state): ... | Implement the Python class `SharpClawSolver2D` described below.
Class description:
Two Dimensional SharpClawSolver
Method signatures and docstrings:
- def __init__(self, riemann_solver=None, claw_package=None): Create 2D SharpClaw solver See :class:`SharpClawSolver2D` for more info.
- def dq_hyperbolic(self, state): ... | 6323b7295b80f33285b958b1a2144f88f51be4b1 | <|skeleton|>
class SharpClawSolver2D:
"""Two Dimensional SharpClawSolver"""
def __init__(self, riemann_solver=None, claw_package=None):
"""Create 2D SharpClaw solver See :class:`SharpClawSolver2D` for more info."""
<|body_0|>
def dq_hyperbolic(self, state):
"""Compute dq/dt * (delt... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SharpClawSolver2D:
"""Two Dimensional SharpClawSolver"""
def __init__(self, riemann_solver=None, claw_package=None):
"""Create 2D SharpClaw solver See :class:`SharpClawSolver2D` for more info."""
self.num_dim = 2
self.reflect_index = [1, 2]
super(SharpClawSolver2D, self)._... | the_stack_v2_python_sparse | src/pyclaw/sharpclaw/solver.py | clawpack/pyclaw | train | 124 |
bf3389342c767b67123b2a4e7b4514c6ed6210b7 | [
"time_tolerance = time_tolerance if time_tolerance is not None else math.inf\nif time in self._items:\n return self._items[time]\nelif time_tolerance > 0.0:\n return self._get_interpolated_value(time, time_tolerance)\nelse:\n raise KeyError",
"time_list = list(self._items.keys())\ntime_list.sort()\nserie... | <|body_start_0|>
time_tolerance = time_tolerance if time_tolerance is not None else math.inf
if time in self._items:
return self._items[time]
elif time_tolerance > 0.0:
return self._get_interpolated_value(time, time_tolerance)
else:
raise KeyError
<|en... | Interpolate Time Series It is possible to interpolate values for times not defined in the series | InterpolatedTimeSeries | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InterpolatedTimeSeries:
"""Interpolate Time Series It is possible to interpolate values for times not defined in the series"""
def get_value(self, time, time_tolerance=None, **kwargs):
"""Get item's value at the specified time If the specified time isn't defined in the series, the it... | stack_v2_sparse_classes_36k_train_011691 | 3,918 | no_license | [
{
"docstring": "Get item's value at the specified time If the specified time isn't defined in the series, the item's value is calculated based on interpolation of two consecutive values v1 and v2 with times t1 and t2 respectively such that (t1 <= time <= t2). The interpolation is calculated only if (time - t1 <... | 3 | null | Implement the Python class `InterpolatedTimeSeries` described below.
Class description:
Interpolate Time Series It is possible to interpolate values for times not defined in the series
Method signatures and docstrings:
- def get_value(self, time, time_tolerance=None, **kwargs): Get item's value at the specified time ... | Implement the Python class `InterpolatedTimeSeries` described below.
Class description:
Interpolate Time Series It is possible to interpolate values for times not defined in the series
Method signatures and docstrings:
- def get_value(self, time, time_tolerance=None, **kwargs): Get item's value at the specified time ... | ce7045918f60c92ce1ed5ca4389b969bf28e6b82 | <|skeleton|>
class InterpolatedTimeSeries:
"""Interpolate Time Series It is possible to interpolate values for times not defined in the series"""
def get_value(self, time, time_tolerance=None, **kwargs):
"""Get item's value at the specified time If the specified time isn't defined in the series, the it... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InterpolatedTimeSeries:
"""Interpolate Time Series It is possible to interpolate values for times not defined in the series"""
def get_value(self, time, time_tolerance=None, **kwargs):
"""Get item's value at the specified time If the specified time isn't defined in the series, the item's value is... | the_stack_v2_python_sparse | sp/core/time_series/interpolated.py | adysonmaia/phd-sp-dynamic | train | 0 |
f3089e133b65d4afec817a342fb66fe627ab2005 | [
"def serializeHelper(node):\n if not node:\n vals.append('#')\n return\n vals.append(str(node.val))\n serializeHelper(node.left)\n serializeHelper(node.right)\nvals = []\nserializeHelper(root)\nreturn ' '.join(vals)",
"def deserializeHelper():\n val = next(vals)\n if val == '#':\n ... | <|body_start_0|>
def serializeHelper(node):
if not node:
vals.append('#')
return
vals.append(str(node.val))
serializeHelper(node.left)
serializeHelper(node.right)
vals = []
serializeHelper(root)
return ' '.jo... | Codec | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_011692 | 2,569 | permissive | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 4dc4e6642dc92f1983c13564cc0fd99917cab358 | <|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"""
def serializeHelper(node):
if not node:
vals.append('#')
return
vals.append(str(node.val))
serializeHelper(node.left)
... | the_stack_v2_python_sparse | Python/serialize-and-deserialize-binary-tree.py | kamyu104/LeetCode-Solutions | train | 4,549 | |
82c1c5a9a0a9878ff8adf882f7e9760f46f95427 | [
"obj = structures.DeepReferenceDict({'key': {'sub_key': {'value': 'foo'}}})\nself.assertEqual('foo', obj['key']['sub_key']['value'])\nself.assertEqual('foo', obj['key.sub_key.value'])",
"obj = structures.DeepReferenceDict({'key': {}})\nwith self.assertRaises(KeyError):\n _ = obj['key.sub_key.value']"
] | <|body_start_0|>
obj = structures.DeepReferenceDict({'key': {'sub_key': {'value': 'foo'}}})
self.assertEqual('foo', obj['key']['sub_key']['value'])
self.assertEqual('foo', obj['key.sub_key.value'])
<|end_body_0|>
<|body_start_1|>
obj = structures.DeepReferenceDict({'key': {}})
w... | Test the deep reference dict structure. | DeepReferenceDictTestCase | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeepReferenceDictTestCase:
"""Test the deep reference dict structure."""
def test_deep_reference(self):
"""Delimited keys are accessible."""
<|body_0|>
def test_deep_reference_error(self):
"""Missing keys raise error."""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_011693 | 6,032 | permissive | [
{
"docstring": "Delimited keys are accessible.",
"name": "test_deep_reference",
"signature": "def test_deep_reference(self)"
},
{
"docstring": "Missing keys raise error.",
"name": "test_deep_reference_error",
"signature": "def test_deep_reference_error(self)"
}
] | 2 | null | Implement the Python class `DeepReferenceDictTestCase` described below.
Class description:
Test the deep reference dict structure.
Method signatures and docstrings:
- def test_deep_reference(self): Delimited keys are accessible.
- def test_deep_reference_error(self): Missing keys raise error. | Implement the Python class `DeepReferenceDictTestCase` described below.
Class description:
Test the deep reference dict structure.
Method signatures and docstrings:
- def test_deep_reference(self): Delimited keys are accessible.
- def test_deep_reference_error(self): Missing keys raise error.
<|skeleton|>
class Deep... | 17471c436621ebfd978b51225fa4de05367a53e1 | <|skeleton|>
class DeepReferenceDictTestCase:
"""Test the deep reference dict structure."""
def test_deep_reference(self):
"""Delimited keys are accessible."""
<|body_0|>
def test_deep_reference_error(self):
"""Missing keys raise error."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeepReferenceDictTestCase:
"""Test the deep reference dict structure."""
def test_deep_reference(self):
"""Delimited keys are accessible."""
obj = structures.DeepReferenceDict({'key': {'sub_key': {'value': 'foo'}}})
self.assertEqual('foo', obj['key']['sub_key']['value'])
s... | the_stack_v2_python_sparse | grow/common/structures_test.py | grow/grow | train | 352 |
3ea83523a12430699362eb30ec88df8fe7caa889 | [
"try:\n super().perform_authentication(request)\nexcept Exception:\n pass",
"s = CartSelectAllSerializer(data=request.data)\ns.is_valid(raise_exception=True)\nselected = s.validated_data.get('selected')\nuser = request.user\nif user.is_authenticated():\n strict_redis = get_redis_connection('carts')\n ... | <|body_start_0|>
try:
super().perform_authentication(request)
except Exception:
pass
<|end_body_0|>
<|body_start_1|>
s = CartSelectAllSerializer(data=request.data)
s.is_valid(raise_exception=True)
selected = s.validated_data.get('selected')
user =... | 购物车全选和全不选 | CartSelectAllView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CartSelectAllView:
"""购物车全选和全不选"""
def perform_authentication(self, request):
"""drf框架在视图执行前会调用此方法进行身份认证(jwt认证) 如果认证不通过,则会抛异常返回401状态码 问题: 抛异常会导致视图无法执行 解决: 捕获异常即可"""
<|body_0|>
def put(self, request):
"""全选或全不选"""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_36k_train_011694 | 12,969 | no_license | [
{
"docstring": "drf框架在视图执行前会调用此方法进行身份认证(jwt认证) 如果认证不通过,则会抛异常返回401状态码 问题: 抛异常会导致视图无法执行 解决: 捕获异常即可",
"name": "perform_authentication",
"signature": "def perform_authentication(self, request)"
},
{
"docstring": "全选或全不选",
"name": "put",
"signature": "def put(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006039 | Implement the Python class `CartSelectAllView` described below.
Class description:
购物车全选和全不选
Method signatures and docstrings:
- def perform_authentication(self, request): drf框架在视图执行前会调用此方法进行身份认证(jwt认证) 如果认证不通过,则会抛异常返回401状态码 问题: 抛异常会导致视图无法执行 解决: 捕获异常即可
- def put(self, request): 全选或全不选 | Implement the Python class `CartSelectAllView` described below.
Class description:
购物车全选和全不选
Method signatures and docstrings:
- def perform_authentication(self, request): drf框架在视图执行前会调用此方法进行身份认证(jwt认证) 如果认证不通过,则会抛异常返回401状态码 问题: 抛异常会导致视图无法执行 解决: 捕获异常即可
- def put(self, request): 全选或全不选
<|skeleton|>
class CartSelectAl... | 12b52f21a4ec20b4853870468c28d2385dc185a8 | <|skeleton|>
class CartSelectAllView:
"""购物车全选和全不选"""
def perform_authentication(self, request):
"""drf框架在视图执行前会调用此方法进行身份认证(jwt认证) 如果认证不通过,则会抛异常返回401状态码 问题: 抛异常会导致视图无法执行 解决: 捕获异常即可"""
<|body_0|>
def put(self, request):
"""全选或全不选"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CartSelectAllView:
"""购物车全选和全不选"""
def perform_authentication(self, request):
"""drf框架在视图执行前会调用此方法进行身份认证(jwt认证) 如果认证不通过,则会抛异常返回401状态码 问题: 抛异常会导致视图无法执行 解决: 捕获异常即可"""
try:
super().perform_authentication(request)
except Exception:
pass
def put(self, reque... | the_stack_v2_python_sparse | django_prj/meiduo/meiduo_mall/meiduo_mall/apps/carts/views.py | 123wuyu/demo_prj | train | 1 |
6985b6c65debb674fa4b3f3e4ff38a644d1f69aa | [
"self.x = TT.matrix('x')\nif sparse_input:\n self.x = SS.csr_matrix('x')\nself.targets = TT.matrix('targets')\nself.weights = TT.matrix('weights')\nif self.weighted:\n return [self.x, self.targets, self.weights]\nreturn [self.x, self.targets]",
"err = outputs[self.output_name()] - self.targets\nif self.weig... | <|body_start_0|>
self.x = TT.matrix('x')
if sparse_input:
self.x = SS.csr_matrix('x')
self.targets = TT.matrix('targets')
self.weights = TT.matrix('weights')
if self.weighted:
return [self.x, self.targets, self.weights]
return [self.x, self.targets... | A regression model attempts to produce a target output. Regression models are trained by optimizing a (possibly regularized) loss that centers around some measurement of error with respect to the target outputs. This regression model implementation uses the mean squared error. If we have a labeled dataset containing :m... | Regressor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Regressor:
"""A regression model attempts to produce a target output. Regression models are trained by optimizing a (possibly regularized) loss that centers around some measurement of error with respect to the target outputs. This regression model implementation uses the mean squared error. If we... | stack_v2_sparse_classes_36k_train_011695 | 18,849 | permissive | [
{
"docstring": "Setup Theano variables for our network. Parameters ---------- sparse_input : bool If True, create an input variable that can hold a sparse matrix. Defaults to False, which assumes all arrays are dense. Returns ------- vars : list of theano variables A list of the variables that this network requ... | 2 | stack_v2_sparse_classes_30k_train_020339 | Implement the Python class `Regressor` described below.
Class description:
A regression model attempts to produce a target output. Regression models are trained by optimizing a (possibly regularized) loss that centers around some measurement of error with respect to the target outputs. This regression model implementa... | Implement the Python class `Regressor` described below.
Class description:
A regression model attempts to produce a target output. Regression models are trained by optimizing a (possibly regularized) loss that centers around some measurement of error with respect to the target outputs. This regression model implementa... | 06b5b6f5de05220702f34c943f309d8188010b57 | <|skeleton|>
class Regressor:
"""A regression model attempts to produce a target output. Regression models are trained by optimizing a (possibly regularized) loss that centers around some measurement of error with respect to the target outputs. This regression model implementation uses the mean squared error. If we... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Regressor:
"""A regression model attempts to produce a target output. Regression models are trained by optimizing a (possibly regularized) loss that centers around some measurement of error with respect to the target outputs. This regression model implementation uses the mean squared error. If we have a label... | the_stack_v2_python_sparse | code/seq2seq_graph/src/theanets-0.6.1/theanets/feedforward.py | scylla/masters_thesis | train | 2 |
f48c8bed0752f6a8c52f88ff97e588ac951420da | [
"Offsets.__init__(self, **kwargs)\nself.center = center\nself.target = target",
"target = [image.header[x] for x in self.target]\ncenter = [image.header[x] for x in self.center]\nimage.meta['offsets'] = np.subtract(target, center)\nreturn image"
] | <|body_start_0|>
Offsets.__init__(self, **kwargs)
self.center = center
self.target = target
<|end_body_0|>
<|body_start_1|>
target = [image.header[x] for x in self.target]
center = [image.header[x] for x in self.center]
image.meta['offsets'] = np.subtract(target, center)... | An offset-calculation method based on fits headers. | FitsHeaderOffsets | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FitsHeaderOffsets:
"""An offset-calculation method based on fits headers."""
def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any):
"""Initializes new fits header offsets."""
<|body_0|>
async def __call__(self, image... | stack_v2_sparse_classes_36k_train_011696 | 1,149 | permissive | [
{
"docstring": "Initializes new fits header offsets.",
"name": "__init__",
"signature": "def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any)"
},
{
"docstring": "Processes an image and sets x/y pixel offset to reference in offset attribute.... | 2 | stack_v2_sparse_classes_30k_train_013075 | Implement the Python class `FitsHeaderOffsets` described below.
Class description:
An offset-calculation method based on fits headers.
Method signatures and docstrings:
- def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any): Initializes new fits header offsets.
... | Implement the Python class `FitsHeaderOffsets` described below.
Class description:
An offset-calculation method based on fits headers.
Method signatures and docstrings:
- def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any): Initializes new fits header offsets.
... | 2d7a06e5485b61b6ca7e51d99b08651ea6021086 | <|skeleton|>
class FitsHeaderOffsets:
"""An offset-calculation method based on fits headers."""
def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any):
"""Initializes new fits header offsets."""
<|body_0|>
async def __call__(self, image... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FitsHeaderOffsets:
"""An offset-calculation method based on fits headers."""
def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any):
"""Initializes new fits header offsets."""
Offsets.__init__(self, **kwargs)
self.center = cent... | the_stack_v2_python_sparse | pyobs/images/processors/offsets/fitsheader.py | pyobs/pyobs-core | train | 9 |
635c27c54eaab446f8dd127f7ec00278f2a4194f | [
"self._verbose = rospy.get_param('~verbose', False)\ntime_limit = rospy.get_param('~time_limit', 10)\nplanner_commands = rospy.get_param('~planner_commands')\nself.verbose_print(planner_commands)\nif not planner_commands:\n raise Exception('Command to execute planner not provided')\nself._action_server = actionl... | <|body_start_0|>
self._verbose = rospy.get_param('~verbose', False)
time_limit = rospy.get_param('~time_limit', 10)
planner_commands = rospy.get_param('~planner_commands')
self.verbose_print(planner_commands)
if not planner_commands:
raise Exception('Command to execut... | A wrapper around planner which calls the appropriate scripts with appropriate parameters wrapped inside an action server. | TaskPlannerServer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskPlannerServer:
"""A wrapper around planner which calls the appropriate scripts with appropriate parameters wrapped inside an action server."""
def __init__(self):
"""- initialising subscribers, publishers and action server, - reading ros parameters."""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_011697 | 4,837 | no_license | [
{
"docstring": "- initialising subscribers, publishers and action server, - reading ros parameters.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Callback function to handle plan action clients' goals. :goal: PlanGoal :returns: None",
"name": "_plan_execute_cb",
... | 4 | stack_v2_sparse_classes_30k_train_011493 | Implement the Python class `TaskPlannerServer` described below.
Class description:
A wrapper around planner which calls the appropriate scripts with appropriate parameters wrapped inside an action server.
Method signatures and docstrings:
- def __init__(self): - initialising subscribers, publishers and action server,... | Implement the Python class `TaskPlannerServer` described below.
Class description:
A wrapper around planner which calls the appropriate scripts with appropriate parameters wrapped inside an action server.
Method signatures and docstrings:
- def __init__(self): - initialising subscribers, publishers and action server,... | 8129cd48351159508cae3438a8b8b3d776c771ca | <|skeleton|>
class TaskPlannerServer:
"""A wrapper around planner which calls the appropriate scripts with appropriate parameters wrapped inside an action server."""
def __init__(self):
"""- initialising subscribers, publishers and action server, - reading ros parameters."""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaskPlannerServer:
"""A wrapper around planner which calls the appropriate scripts with appropriate parameters wrapped inside an action server."""
def __init__(self):
"""- initialising subscribers, publishers and action server, - reading ros parameters."""
self._verbose = rospy.get_param(... | the_stack_v2_python_sparse | mir_planning/mir_task_planning/ros/scripts/task_planner_server | b-it-bots/mas_industrial_robotics | train | 25 |
edc08358dfe490040078f3a0ff0f77674aa04d46 | [
"self._server = server\nself._port = port\nself._password = password\nself.cmus = None",
"try:\n self.cmus = remote.PyCmus(server=self._server, port=self._port, password=self._password)\nexcept exceptions.InvalidPassword:\n _LOGGER.error('The provided password was rejected by cmus')"
] | <|body_start_0|>
self._server = server
self._port = port
self._password = password
self.cmus = None
<|end_body_0|>
<|body_start_1|>
try:
self.cmus = remote.PyCmus(server=self._server, port=self._port, password=self._password)
except exceptions.InvalidPassword... | Representation of a cmus connection. | CmusRemote | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CmusRemote:
"""Representation of a cmus connection."""
def __init__(self, server, port, password):
"""Initialize the cmus remote."""
<|body_0|>
def connect(self):
"""Connect to the cmus server."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
sel... | stack_v2_sparse_classes_36k_train_011698 | 7,140 | permissive | [
{
"docstring": "Initialize the cmus remote.",
"name": "__init__",
"signature": "def __init__(self, server, port, password)"
},
{
"docstring": "Connect to the cmus server.",
"name": "connect",
"signature": "def connect(self)"
}
] | 2 | null | Implement the Python class `CmusRemote` described below.
Class description:
Representation of a cmus connection.
Method signatures and docstrings:
- def __init__(self, server, port, password): Initialize the cmus remote.
- def connect(self): Connect to the cmus server. | Implement the Python class `CmusRemote` described below.
Class description:
Representation of a cmus connection.
Method signatures and docstrings:
- def __init__(self, server, port, password): Initialize the cmus remote.
- def connect(self): Connect to the cmus server.
<|skeleton|>
class CmusRemote:
"""Represent... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class CmusRemote:
"""Representation of a cmus connection."""
def __init__(self, server, port, password):
"""Initialize the cmus remote."""
<|body_0|>
def connect(self):
"""Connect to the cmus server."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CmusRemote:
"""Representation of a cmus connection."""
def __init__(self, server, port, password):
"""Initialize the cmus remote."""
self._server = server
self._port = port
self._password = password
self.cmus = None
def connect(self):
"""Connect to the... | the_stack_v2_python_sparse | homeassistant/components/cmus/media_player.py | home-assistant/core | train | 35,501 |
4037a4e74a43efd5962495331eec325c0a98f2c3 | [
"super(ScalingUpExecuteWebhookTest, self).setUp()\nself.create_group_response = self.autoscale_behaviors.create_scaling_group_given(gc_min_entities=self.gc_min_entities_alt)\nself.group = self.create_group_response.entity\nself.resources.add(self.group, self.empty_scaling_group)",
"policy_up = {'change': 1}\nexec... | <|body_start_0|>
super(ScalingUpExecuteWebhookTest, self).setUp()
self.create_group_response = self.autoscale_behaviors.create_scaling_group_given(gc_min_entities=self.gc_min_entities_alt)
self.group = self.create_group_response.entity
self.resources.add(self.group, self.empty_scaling_gr... | System tests to verify execute scaling policies scenarios | ScalingUpExecuteWebhookTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScalingUpExecuteWebhookTest:
"""System tests to verify execute scaling policies scenarios"""
def setUp(self):
"""Create a scaling group with minentities over zero"""
<|body_0|>
def test_system_execute_webhook_scale_up_change(self):
"""Create a scale up policy wit... | stack_v2_sparse_classes_36k_train_011699 | 3,280 | permissive | [
{
"docstring": "Create a scaling group with minentities over zero",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Create a scale up policy with change and execute its webhook",
"name": "test_system_execute_webhook_scale_up_change",
"signature": "def test_system_execu... | 4 | null | Implement the Python class `ScalingUpExecuteWebhookTest` described below.
Class description:
System tests to verify execute scaling policies scenarios
Method signatures and docstrings:
- def setUp(self): Create a scaling group with minentities over zero
- def test_system_execute_webhook_scale_up_change(self): Create ... | Implement the Python class `ScalingUpExecuteWebhookTest` described below.
Class description:
System tests to verify execute scaling policies scenarios
Method signatures and docstrings:
- def setUp(self): Create a scaling group with minentities over zero
- def test_system_execute_webhook_scale_up_change(self): Create ... | 7199cdd67255fe116dbcbedea660c13453671134 | <|skeleton|>
class ScalingUpExecuteWebhookTest:
"""System tests to verify execute scaling policies scenarios"""
def setUp(self):
"""Create a scaling group with minentities over zero"""
<|body_0|>
def test_system_execute_webhook_scale_up_change(self):
"""Create a scale up policy wit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScalingUpExecuteWebhookTest:
"""System tests to verify execute scaling policies scenarios"""
def setUp(self):
"""Create a scaling group with minentities over zero"""
super(ScalingUpExecuteWebhookTest, self).setUp()
self.create_group_response = self.autoscale_behaviors.create_scali... | the_stack_v2_python_sparse | autoscale_cloudroast/test_repo/autoscale/system/policies/test_system_execute_webhook_scaleup.py | rackerlabs/otter | train | 20 |
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