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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1bf838d87df81fc399ad22ff9bcbe43e59e34449 | [
"super().__init__(auth_provider)\nself._available_users = available_users\nself._ip_address = ip_addr\nself._allow_bypass_login = allow_bypass_login",
"try:\n cast(TrustedNetworksAuthProvider, self._auth_provider).async_validate_access(self._ip_address)\nexcept InvalidAuthError:\n return self.async_abort(re... | <|body_start_0|>
super().__init__(auth_provider)
self._available_users = available_users
self._ip_address = ip_addr
self._allow_bypass_login = allow_bypass_login
<|end_body_0|>
<|body_start_1|>
try:
cast(TrustedNetworksAuthProvider, self._auth_provider).async_validat... | Handler for the login flow. | TrustedNetworksLoginFlow | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrustedNetworksLoginFlow:
"""Handler for the login flow."""
def __init__(self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: dict[str, str | None], allow_bypass_login: bool) -> None:
"""Initialize the login flow."""
<|body_0|>
async def... | stack_v2_sparse_classes_36k_train_004000 | 8,219 | permissive | [
{
"docstring": "Initialize the login flow.",
"name": "__init__",
"signature": "def __init__(self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: dict[str, str | None], allow_bypass_login: bool) -> None"
},
{
"docstring": "Handle the step of the form.",
"name... | 2 | null | Implement the Python class `TrustedNetworksLoginFlow` described below.
Class description:
Handler for the login flow.
Method signatures and docstrings:
- def __init__(self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: dict[str, str | None], allow_bypass_login: bool) -> None: Initia... | Implement the Python class `TrustedNetworksLoginFlow` described below.
Class description:
Handler for the login flow.
Method signatures and docstrings:
- def __init__(self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: dict[str, str | None], allow_bypass_login: bool) -> None: Initia... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class TrustedNetworksLoginFlow:
"""Handler for the login flow."""
def __init__(self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: dict[str, str | None], allow_bypass_login: bool) -> None:
"""Initialize the login flow."""
<|body_0|>
async def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrustedNetworksLoginFlow:
"""Handler for the login flow."""
def __init__(self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: dict[str, str | None], allow_bypass_login: bool) -> None:
"""Initialize the login flow."""
super().__init__(auth_provider)
... | the_stack_v2_python_sparse | homeassistant/auth/providers/trusted_networks.py | home-assistant/core | train | 35,501 |
4461b2eba907b9afb6292ad0ef79f692485cc5db | [
"super(RegressionTaskModel, self).__init__()\nmodel_type = model_config.get('model_type', 'transformer')\nhidden_size = model_config.get('hidden_size', 512)\nin_channels = hidden_size * 2 if model_type == 'lstm' else hidden_size\nself.fc_decoder = nn.Sequential(nn.Linear(in_features=in_channels, out_features=hidden... | <|body_start_0|>
super(RegressionTaskModel, self).__init__()
model_type = model_config.get('model_type', 'transformer')
hidden_size = model_config.get('hidden_size', 512)
in_channels = hidden_size * 2 if model_type == 'lstm' else hidden_size
self.fc_decoder = nn.Sequential(nn.Lin... | RegressionTaskModel | RegressionTaskModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegressionTaskModel:
"""RegressionTaskModel"""
def __init__(self, model_config, encoder_model):
"""__init__"""
<|body_0|>
def forward(self, input, pos):
"""forward"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(RegressionTaskModel, self).... | stack_v2_sparse_classes_36k_train_004001 | 17,522 | permissive | [
{
"docstring": "__init__",
"name": "__init__",
"signature": "def __init__(self, model_config, encoder_model)"
},
{
"docstring": "forward",
"name": "forward",
"signature": "def forward(self, input, pos)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006709 | Implement the Python class `RegressionTaskModel` described below.
Class description:
RegressionTaskModel
Method signatures and docstrings:
- def __init__(self, model_config, encoder_model): __init__
- def forward(self, input, pos): forward | Implement the Python class `RegressionTaskModel` described below.
Class description:
RegressionTaskModel
Method signatures and docstrings:
- def __init__(self, model_config, encoder_model): __init__
- def forward(self, input, pos): forward
<|skeleton|>
class RegressionTaskModel:
"""RegressionTaskModel"""
de... | e6ab0261eb719c21806bbadfd94001ecfe27de45 | <|skeleton|>
class RegressionTaskModel:
"""RegressionTaskModel"""
def __init__(self, model_config, encoder_model):
"""__init__"""
<|body_0|>
def forward(self, input, pos):
"""forward"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegressionTaskModel:
"""RegressionTaskModel"""
def __init__(self, model_config, encoder_model):
"""__init__"""
super(RegressionTaskModel, self).__init__()
model_type = model_config.get('model_type', 'transformer')
hidden_size = model_config.get('hidden_size', 512)
... | the_stack_v2_python_sparse | pahelix/model_zoo/protein_sequence_model.py | PaddlePaddle/PaddleHelix | train | 771 |
9b94e0d2934d8de56d5f1ffe11aad42d5a41cc0d | [
"GObject.GObject.__init__(self)\nself.set_transient_for(parent)\nself.set_modal(True)\nself.set_name(PROGRAM_NAME)\nself.set_version(VERSION)\nself.set_copyright(COPYRIGHT_MSG)\nself.set_artists([_(\"Much of Gramps' artwork is either from\\nthe Tango Project or derived from the Tango\\nProject. This artwork is rele... | <|body_start_0|>
GObject.GObject.__init__(self)
self.set_transient_for(parent)
self.set_modal(True)
self.set_name(PROGRAM_NAME)
self.set_version(VERSION)
self.set_copyright(COPYRIGHT_MSG)
self.set_artists([_("Much of Gramps' artwork is either from\nthe Tango Proje... | Create an About dialog with all fields set. | GrampsAboutDialog | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GrampsAboutDialog:
"""Create an About dialog with all fields set."""
def __init__(self, parent):
"""Setup all the fields shown in the About dialog."""
<|body_0|>
def get_versions(self):
"""Obtain version information of core dependencies"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k_train_004002 | 8,673 | no_license | [
{
"docstring": "Setup all the fields shown in the About dialog.",
"name": "__init__",
"signature": "def __init__(self, parent)"
},
{
"docstring": "Obtain version information of core dependencies",
"name": "get_versions",
"signature": "def get_versions(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015501 | Implement the Python class `GrampsAboutDialog` described below.
Class description:
Create an About dialog with all fields set.
Method signatures and docstrings:
- def __init__(self, parent): Setup all the fields shown in the About dialog.
- def get_versions(self): Obtain version information of core dependencies | Implement the Python class `GrampsAboutDialog` described below.
Class description:
Create an About dialog with all fields set.
Method signatures and docstrings:
- def __init__(self, parent): Setup all the fields shown in the About dialog.
- def get_versions(self): Obtain version information of core dependencies
<|sk... | 0c79561bed7ff42c88714edbc85197fa9235e188 | <|skeleton|>
class GrampsAboutDialog:
"""Create an About dialog with all fields set."""
def __init__(self, parent):
"""Setup all the fields shown in the About dialog."""
<|body_0|>
def get_versions(self):
"""Obtain version information of core dependencies"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GrampsAboutDialog:
"""Create an About dialog with all fields set."""
def __init__(self, parent):
"""Setup all the fields shown in the About dialog."""
GObject.GObject.__init__(self)
self.set_transient_for(parent)
self.set_modal(True)
self.set_name(PROGRAM_NAME)
... | the_stack_v2_python_sparse | gui/aboutdialog.py | balrok/gramps_addon | train | 2 |
60d943019663a7241697aa6a37838469a7db9581 | [
"roots = np.asarray(roots)\nif len(roots.shape) != 1:\n raise ArgumentError('one-dimensional array of roots expected.')\nself.roots = roots",
"from numpy.polynomial import Polynomial as P\np = P.fromroots(self.roots)\nreturn p.deriv(1).roots()",
"p = np.asarray(points)\nif len(p.shape) > 1:\n raise Argume... | <|body_start_0|>
roots = np.asarray(roots)
if len(roots.shape) != 1:
raise ArgumentError('one-dimensional array of roots expected.')
self.roots = roots
<|end_body_0|>
<|body_start_1|>
from numpy.polynomial import Polynomial as P
p = P.fromroots(self.roots)
re... | NormalizedRootsPolynomial | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NormalizedRootsPolynomial:
def __init__(self, roots):
"""A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left(1-\\frac{\\lambda}{\\theta_i}\\right). :param roots: array with roots :math:`\\theta_1,\\dots,\\theta_n` of the p... | stack_v2_sparse_classes_36k_train_004003 | 10,845 | permissive | [
{
"docstring": "A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\\\lambda) = \\\\prod_{i=1}^n \\\\left(1-\\\\frac{\\\\lambda}{\\\\theta_i}\\\\right). :param roots: array with roots :math:`\\\\theta_1,\\\\dots,\\\\theta_n` of the polynomial and ``roots.shape==(n,)``.",
"n... | 3 | stack_v2_sparse_classes_30k_train_007054 | Implement the Python class `NormalizedRootsPolynomial` described below.
Class description:
Implement the NormalizedRootsPolynomial class.
Method signatures and docstrings:
- def __init__(self, roots): A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left... | Implement the Python class `NormalizedRootsPolynomial` described below.
Class description:
Implement the NormalizedRootsPolynomial class.
Method signatures and docstrings:
- def __init__(self, roots): A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left... | e6af3d227f1512c84a528f9c4407934973231b42 | <|skeleton|>
class NormalizedRootsPolynomial:
def __init__(self, roots):
"""A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left(1-\\frac{\\lambda}{\\theta_i}\\right). :param roots: array with roots :math:`\\theta_1,\\dots,\\theta_n` of the p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NormalizedRootsPolynomial:
def __init__(self, roots):
"""A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left(1-\\frac{\\lambda}{\\theta_i}\\right). :param roots: array with roots :math:`\\theta_1,\\dots,\\theta_n` of the polynomial and ... | the_stack_v2_python_sparse | src/krylov/utils.py | mohamedlaminebabou/krylov | train | 0 | |
848bdea728b98ab322402d9ca52f3c750b936002 | [
"self.parameters = {'encoding': 'utf-8', 'text': True}\nself.stdout = None\nself.stderr = None\nself.returncode = None",
"from subprocess import DEVNULL, PIPE, Popen, STDOUT\n\ndef comunicate(parameters):\n popen = Popen(**parameters)\n stdout, stderr = popen.communicate()\n return (popen, stdout, stderr... | <|body_start_0|>
self.parameters = {'encoding': 'utf-8', 'text': True}
self.stdout = None
self.stderr = None
self.returncode = None
<|end_body_0|>
<|body_start_1|>
from subprocess import DEVNULL, PIPE, Popen, STDOUT
def comunicate(parameters):
popen = Popen(... | A helper class for executing a command line process. | Process | [
"MIT",
"GPL-3.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Process:
"""A helper class for executing a command line process."""
def __init__(self) -> None:
"""Initialize a Process instance."""
<|body_0|>
def execute(cls, args: list[str], shell: bool=False, capture_output: bool=True, split: bool=True, outpath: str=None) -> Process... | stack_v2_sparse_classes_36k_train_004004 | 2,615 | permissive | [
{
"docstring": "Initialize a Process instance.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Execute a command line process. Args: args (list[str]): The command line arguments to execute. shell (bool, optional): If True, execute the command line using the she... | 2 | null | Implement the Python class `Process` described below.
Class description:
A helper class for executing a command line process.
Method signatures and docstrings:
- def __init__(self) -> None: Initialize a Process instance.
- def execute(cls, args: list[str], shell: bool=False, capture_output: bool=True, split: bool=Tru... | Implement the Python class `Process` described below.
Class description:
A helper class for executing a command line process.
Method signatures and docstrings:
- def __init__(self) -> None: Initialize a Process instance.
- def execute(cls, args: list[str], shell: bool=False, capture_output: bool=True, split: bool=Tru... | 00e14fc190ebff66cf50ff911f25cf5ad3529f8f | <|skeleton|>
class Process:
"""A helper class for executing a command line process."""
def __init__(self) -> None:
"""Initialize a Process instance."""
<|body_0|>
def execute(cls, args: list[str], shell: bool=False, capture_output: bool=True, split: bool=True, outpath: str=None) -> Process... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Process:
"""A helper class for executing a command line process."""
def __init__(self) -> None:
"""Initialize a Process instance."""
self.parameters = {'encoding': 'utf-8', 'text': True}
self.stdout = None
self.stderr = None
self.returncode = None
def execute(... | the_stack_v2_python_sparse | scripts/addon_library/local/ImagePaste/imagepaste/process.py | Tilapiatsu/blender-custom_config | train | 6 |
9a3bead54105efa3ba9e4ac09ef41fc7847c1f50 | [
"if handler not in self.handlers:\n self.handlers[handler] = {}\n self.handlers[handler]['file'] = file_to_handle\n self.handlers[handler]['fh'] = open(file_to_handle, 'w', encoding='utf-8')\nreturn self.handlers[handler]['fh']",
"destination_file = '{directory}/{db}.{table}.sql'.format(directory=self.di... | <|body_start_0|>
if handler not in self.handlers:
self.handlers[handler] = {}
self.handlers[handler]['file'] = file_to_handle
self.handlers[handler]['fh'] = open(file_to_handle, 'w', encoding='utf-8')
return self.handlers[handler]['fh']
<|end_body_0|>
<|body_start_1|... | The class implement a formatter of SQL type which is able to convert a list of dict of data into one file of SQL statement | Sql | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sql:
"""The class implement a formatter of SQL type which is able to convert a list of dict of data into one file of SQL statement"""
def get_handler(self, handler=None, file_to_handle=None):
"""Return a file handler if it already exists or create a new one"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_004005 | 3,364 | permissive | [
{
"docstring": "Return a file handler if it already exists or create a new one",
"name": "get_handler",
"signature": "def get_handler(self, handler=None, file_to_handle=None)"
},
{
"docstring": "The write method which should be implemented because of ineherited Formatter class The name of the fi... | 2 | stack_v2_sparse_classes_30k_train_011612 | Implement the Python class `Sql` described below.
Class description:
The class implement a formatter of SQL type which is able to convert a list of dict of data into one file of SQL statement
Method signatures and docstrings:
- def get_handler(self, handler=None, file_to_handle=None): Return a file handler if it alre... | Implement the Python class `Sql` described below.
Class description:
The class implement a formatter of SQL type which is able to convert a list of dict of data into one file of SQL statement
Method signatures and docstrings:
- def get_handler(self, handler=None, file_to_handle=None): Return a file handler if it alre... | 73a9e6377a44b64a759f663bf99ac798e4ec026a | <|skeleton|>
class Sql:
"""The class implement a formatter of SQL type which is able to convert a list of dict of data into one file of SQL statement"""
def get_handler(self, handler=None, file_to_handle=None):
"""Return a file handler if it already exists or create a new one"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sql:
"""The class implement a formatter of SQL type which is able to convert a list of dict of data into one file of SQL statement"""
def get_handler(self, handler=None, file_to_handle=None):
"""Return a file handler if it already exists or create a new one"""
if handler not in self.handl... | the_stack_v2_python_sparse | osarchiver/destination/file/sql.py | ovh/osarchiver | train | 19 |
760d5168f4a32fc286485913717ee250692e0ba4 | [
"log = ResultLog.ResultLog()\ntry:\n br = webdriver.Firefox()\nexcept:\n log.info('浏览器初始化失败了')\nbr.get('http://www.xebest.com:8000')\nreturn br",
"log = ResultLog.ResultLog()\ntry:\n br = webdriver.Firefox()\nexcept:\n log.info('浏览器初始化失败了')\nbr.get('https://user.xebest.com:8443/loginAction!init.action... | <|body_start_0|>
log = ResultLog.ResultLog()
try:
br = webdriver.Firefox()
except:
log.info('浏览器初始化失败了')
br.get('http://www.xebest.com:8000')
return br
<|end_body_0|>
<|body_start_1|>
log = ResultLog.ResultLog()
try:
br = webdr... | Browser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Browser:
def init_browser(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
<|body_0|>
def init_browserByCustomerCenter(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
<|body_1|>
def init_browserByRegister(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
... | stack_v2_sparse_classes_36k_train_004006 | 1,205 | no_license | [
{
"docstring": "该函数主要是初始化浏览器对象并返回一个webdriver对象",
"name": "init_browser",
"signature": "def init_browser(self)"
},
{
"docstring": "该函数主要是初始化浏览器对象并返回一个webdriver对象",
"name": "init_browserByCustomerCenter",
"signature": "def init_browserByCustomerCenter(self)"
},
{
"docstring": "该函数主... | 3 | stack_v2_sparse_classes_30k_train_000727 | Implement the Python class `Browser` described below.
Class description:
Implement the Browser class.
Method signatures and docstrings:
- def init_browser(self): 该函数主要是初始化浏览器对象并返回一个webdriver对象
- def init_browserByCustomerCenter(self): 该函数主要是初始化浏览器对象并返回一个webdriver对象
- def init_browserByRegister(self): 该函数主要是初始化浏览器对象并返... | Implement the Python class `Browser` described below.
Class description:
Implement the Browser class.
Method signatures and docstrings:
- def init_browser(self): 该函数主要是初始化浏览器对象并返回一个webdriver对象
- def init_browserByCustomerCenter(self): 该函数主要是初始化浏览器对象并返回一个webdriver对象
- def init_browserByRegister(self): 该函数主要是初始化浏览器对象并返... | 4dd065806f20bfdec885fa2b40f2c22e5a8d4f15 | <|skeleton|>
class Browser:
def init_browser(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
<|body_0|>
def init_browserByCustomerCenter(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
<|body_1|>
def init_browserByRegister(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Browser:
def init_browser(self):
"""该函数主要是初始化浏览器对象并返回一个webdriver对象"""
log = ResultLog.ResultLog()
try:
br = webdriver.Firefox()
except:
log.info('浏览器初始化失败了')
br.get('http://www.xebest.com:8000')
return br
def init_browserByCustomerCe... | the_stack_v2_python_sparse | Action/Browser.py | Hardworking-tester/HuaYing | train | 0 | |
d6508d909d9da78ebf9728c295a7d38e159ac53f | [
"self.set_header('content-type', 'application/json')\ntry:\n user_list = UserDao().get_user_detail_list()\n manage_groups = GroupDao().get_manage_groups(self.group.id)\n result = [user for user in user_list if user['group_id'] in manage_groups]\n self.finish(json_dumps({'status': 200, 'msg': 'ok', 'valu... | <|body_start_0|>
self.set_header('content-type', 'application/json')
try:
user_list = UserDao().get_user_detail_list()
manage_groups = GroupDao().get_manage_groups(self.group.id)
result = [user for user in user_list if user['group_id'] in manage_groups]
se... | UserListHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserListHandler:
def get(self):
"""list all users @API summary: list all users notes: get detail of users tags: - auth responses: '200': description: users schema: $ref: '#/definitions/User' default: description: Unexcepted error schema: $ref: '#/definitions/Error'"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_004007 | 6,245 | permissive | [
{
"docstring": "list all users @API summary: list all users notes: get detail of users tags: - auth responses: '200': description: users schema: $ref: '#/definitions/User' default: description: Unexcepted error schema: $ref: '#/definitions/Error'",
"name": "get",
"signature": "def get(self)"
},
{
... | 2 | null | Implement the Python class `UserListHandler` described below.
Class description:
Implement the UserListHandler class.
Method signatures and docstrings:
- def get(self): list all users @API summary: list all users notes: get detail of users tags: - auth responses: '200': description: users schema: $ref: '#/definitions... | Implement the Python class `UserListHandler` described below.
Class description:
Implement the UserListHandler class.
Method signatures and docstrings:
- def get(self): list all users @API summary: list all users notes: get detail of users tags: - auth responses: '200': description: users schema: $ref: '#/definitions... | 2e32e6e7b225e0bd87ee8c847c22862f12c51bb1 | <|skeleton|>
class UserListHandler:
def get(self):
"""list all users @API summary: list all users notes: get detail of users tags: - auth responses: '200': description: users schema: $ref: '#/definitions/User' default: description: Unexcepted error schema: $ref: '#/definitions/Error'"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserListHandler:
def get(self):
"""list all users @API summary: list all users notes: get detail of users tags: - auth responses: '200': description: users schema: $ref: '#/definitions/User' default: description: Unexcepted error schema: $ref: '#/definitions/Error'"""
self.set_header('content-... | the_stack_v2_python_sparse | nebula/views/user.py | threathunterX/nebula_web | train | 2 | |
b41893c9d8fe370d24fde34039977d2efb92d2eb | [
"inv.Inventory.__init__(self, item_code, description, market_price, rental_price)\nself.material = material\nself.size = size",
"outputdict = {}\noutputdict['item_code'] = self.item_code\noutputdict['description'] = self.description\noutputdict['market_price'] = self.market_price\noutputdict['rental_price'] = sel... | <|body_start_0|>
inv.Inventory.__init__(self, item_code, description, market_price, rental_price)
self.material = material
self.size = size
<|end_body_0|>
<|body_start_1|>
outputdict = {}
outputdict['item_code'] = self.item_code
outputdict['description'] = self.descripti... | some stuff5 | Furniture | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Furniture:
"""some stuff5"""
def __init__(self, item_code, description, market_price, rental_price, material, size):
"""some stuff6"""
<|body_0|>
def return_as_dictionary(self):
"""some stuff7"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
inv.... | stack_v2_sparse_classes_36k_train_004008 | 899 | no_license | [
{
"docstring": "some stuff6",
"name": "__init__",
"signature": "def __init__(self, item_code, description, market_price, rental_price, material, size)"
},
{
"docstring": "some stuff7",
"name": "return_as_dictionary",
"signature": "def return_as_dictionary(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009022 | Implement the Python class `Furniture` described below.
Class description:
some stuff5
Method signatures and docstrings:
- def __init__(self, item_code, description, market_price, rental_price, material, size): some stuff6
- def return_as_dictionary(self): some stuff7 | Implement the Python class `Furniture` described below.
Class description:
some stuff5
Method signatures and docstrings:
- def __init__(self, item_code, description, market_price, rental_price, material, size): some stuff6
- def return_as_dictionary(self): some stuff7
<|skeleton|>
class Furniture:
"""some stuff5... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class Furniture:
"""some stuff5"""
def __init__(self, item_code, description, market_price, rental_price, material, size):
"""some stuff6"""
<|body_0|>
def return_as_dictionary(self):
"""some stuff7"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Furniture:
"""some stuff5"""
def __init__(self, item_code, description, market_price, rental_price, material, size):
"""some stuff6"""
inv.Inventory.__init__(self, item_code, description, market_price, rental_price)
self.material = material
self.size = size
def return... | the_stack_v2_python_sparse | students/ScotchWSplenda/lesson01/assignment/inventory_management/furniture_class.py | JavaRod/SP_Python220B_2019 | train | 1 |
f7438088a05c367827876095eefac71f75b3e724 | [
"app = self.get_argument('app', default='')\ntype = self.get_argument('type', default='')\nsimple = self.get_argument('simple', default='false')\nsimple = simple == 'true'\nself.set_header('content-type', 'application/json')\ntry:\n result = EventModelDefaultDao().list_all_models()\n if app:\n result =... | <|body_start_0|>
app = self.get_argument('app', default='')
type = self.get_argument('type', default='')
simple = self.get_argument('simple', default='false')
simple = simple == 'true'
self.set_header('content-type', 'application/json')
try:
result = EventMode... | EventModelListHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventModelListHandler:
def get(self):
"""获取相应的event配置类型 @API summary: 获取相应的event配置类型 notes: 根据app和类型来获取相应的event tags: - default parameters: - name: app in: query required: false type: string description: event的app - name: type in: query required: false type: string description: event的类型 ... | stack_v2_sparse_classes_36k_train_004009 | 8,714 | permissive | [
{
"docstring": "获取相应的event配置类型 @API summary: 获取相应的event配置类型 notes: 根据app和类型来获取相应的event tags: - default parameters: - name: app in: query required: false type: string description: event的app - name: type in: query required: false type: string description: event的类型 - name: simple in: query required: false type: bo... | 3 | stack_v2_sparse_classes_30k_train_019025 | Implement the Python class `EventModelListHandler` described below.
Class description:
Implement the EventModelListHandler class.
Method signatures and docstrings:
- def get(self): 获取相应的event配置类型 @API summary: 获取相应的event配置类型 notes: 根据app和类型来获取相应的event tags: - default parameters: - name: app in: query required: false ... | Implement the Python class `EventModelListHandler` described below.
Class description:
Implement the EventModelListHandler class.
Method signatures and docstrings:
- def get(self): 获取相应的event配置类型 @API summary: 获取相应的event配置类型 notes: 根据app和类型来获取相应的event tags: - default parameters: - name: app in: query required: false ... | 2e32e6e7b225e0bd87ee8c847c22862f12c51bb1 | <|skeleton|>
class EventModelListHandler:
def get(self):
"""获取相应的event配置类型 @API summary: 获取相应的event配置类型 notes: 根据app和类型来获取相应的event tags: - default parameters: - name: app in: query required: false type: string description: event的app - name: type in: query required: false type: string description: event的类型 ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventModelListHandler:
def get(self):
"""获取相应的event配置类型 @API summary: 获取相应的event配置类型 notes: 根据app和类型来获取相应的event tags: - default parameters: - name: app in: query required: false type: string description: event的app - name: type in: query required: false type: string description: event的类型 - name: simple... | the_stack_v2_python_sparse | nebula/views/event_model_default.py | threathunterX/nebula_web | train | 2 | |
c51c611608792ff2897a0c93478fa62d4cf29cd5 | [
"if kwargs.get('configuration') is None and kwargs.get('connection_id') is None:\n kwargs['connection_id'] = PublicId('fetchai', 'http_client', '0.1.0')\nsuper().__init__(**kwargs)\nself.channel = HTTPClientChannel(self.address, provider_address, provider_port, connection_id=self.connection_id, excluded_protocol... | <|body_start_0|>
if kwargs.get('configuration') is None and kwargs.get('connection_id') is None:
kwargs['connection_id'] = PublicId('fetchai', 'http_client', '0.1.0')
super().__init__(**kwargs)
self.channel = HTTPClientChannel(self.address, provider_address, provider_port, connection... | Proxy to the functionality of the web client. | HTTPClientConnection | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTTPClientConnection:
"""Proxy to the functionality of the web client."""
def __init__(self, provider_address: str, provider_port: int, **kwargs):
"""Initialize a connection. :param provider_address: server hostname / IP address :param provider_port: server port number"""
<|b... | stack_v2_sparse_classes_36k_train_004010 | 9,528 | permissive | [
{
"docstring": "Initialize a connection. :param provider_address: server hostname / IP address :param provider_port: server port number",
"name": "__init__",
"signature": "def __init__(self, provider_address: str, provider_port: int, **kwargs)"
},
{
"docstring": "Connect to a HTTP server. :retur... | 6 | stack_v2_sparse_classes_30k_train_007188 | Implement the Python class `HTTPClientConnection` described below.
Class description:
Proxy to the functionality of the web client.
Method signatures and docstrings:
- def __init__(self, provider_address: str, provider_port: int, **kwargs): Initialize a connection. :param provider_address: server hostname / IP addres... | Implement the Python class `HTTPClientConnection` described below.
Class description:
Proxy to the functionality of the web client.
Method signatures and docstrings:
- def __init__(self, provider_address: str, provider_port: int, **kwargs): Initialize a connection. :param provider_address: server hostname / IP addres... | 9bd1d51530fc21bf41b5adea031cda19a94b048b | <|skeleton|>
class HTTPClientConnection:
"""Proxy to the functionality of the web client."""
def __init__(self, provider_address: str, provider_port: int, **kwargs):
"""Initialize a connection. :param provider_address: server hostname / IP address :param provider_port: server port number"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HTTPClientConnection:
"""Proxy to the functionality of the web client."""
def __init__(self, provider_address: str, provider_port: int, **kwargs):
"""Initialize a connection. :param provider_address: server hostname / IP address :param provider_port: server port number"""
if kwargs.get('c... | the_stack_v2_python_sparse | packages/fetchai/connections/http_client/connection.py | pbukva/agents-aea | train | 0 |
41a99e6f5963771541a3d24bf3b54f019328a3e0 | [
"with document.file.open('rb') as file:\n text = extract_text(BytesIO(file.read()))\nreturn text",
"with document.file.open('rb') as file:\n result = mammoth.extract_raw_text(file)\nreturn result.value"
] | <|body_start_0|>
with document.file.open('rb') as file:
text = extract_text(BytesIO(file.read()))
return text
<|end_body_0|>
<|body_start_1|>
with document.file.open('rb') as file:
result = mammoth.extract_raw_text(file)
return result.value
<|end_body_1|>
| Extract files text | TextExtractor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextExtractor:
"""Extract files text"""
def from_pdf(document):
"""Extract pdf text"""
<|body_0|>
def from_docx(document):
"""Extract docx text"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
with document.file.open('rb') as file:
te... | stack_v2_sparse_classes_36k_train_004011 | 883 | permissive | [
{
"docstring": "Extract pdf text",
"name": "from_pdf",
"signature": "def from_pdf(document)"
},
{
"docstring": "Extract docx text",
"name": "from_docx",
"signature": "def from_docx(document)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001122 | Implement the Python class `TextExtractor` described below.
Class description:
Extract files text
Method signatures and docstrings:
- def from_pdf(document): Extract pdf text
- def from_docx(document): Extract docx text | Implement the Python class `TextExtractor` described below.
Class description:
Extract files text
Method signatures and docstrings:
- def from_pdf(document): Extract pdf text
- def from_docx(document): Extract docx text
<|skeleton|>
class TextExtractor:
"""Extract files text"""
def from_pdf(document):
... | 22e4afa728a851bb4c2479fbb6f5944a75984b9b | <|skeleton|>
class TextExtractor:
"""Extract files text"""
def from_pdf(document):
"""Extract pdf text"""
<|body_0|>
def from_docx(document):
"""Extract docx text"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextExtractor:
"""Extract files text"""
def from_pdf(document):
"""Extract pdf text"""
with document.file.open('rb') as file:
text = extract_text(BytesIO(file.read()))
return text
def from_docx(document):
"""Extract docx text"""
with document.file.... | the_stack_v2_python_sparse | src/backend/partaj/core/services/file_handler.py | MTES-MCT/partaj | train | 4 |
5434cb3fceed40c7c842d5f1b4b2b38b78f57d81 | [
"self.maxheap = []\nself.minheap = []\nself.median = -sys.maxint - 1",
"heapq.heappush(self.maxheap, -num)\nt = heapq.heappop(self.maxheap)\nheapq.heappush(self.minheap, -t)\nif len(self.minheap) > len(self.maxheap):\n x = heapq.heappop(self.minheap)\n heapq.heappush(self.maxheap, -x)",
"m, n = (len(self.... | <|body_start_0|>
self.maxheap = []
self.minheap = []
self.median = -sys.maxint - 1
<|end_body_0|>
<|body_start_1|>
heapq.heappush(self.maxheap, -num)
t = heapq.heappop(self.maxheap)
heapq.heappush(self.minheap, -t)
if len(self.minheap) > len(self.maxheap):
... | MedianFinder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MedianFinder:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def addNum(self, num):
"""Adds a num into the data structure. :type num: int :rtype: void"""
<|body_1|>
def findMedian(self):
"""Returns the median of current... | stack_v2_sparse_classes_36k_train_004012 | 966 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Adds a num into the data structure. :type num: int :rtype: void",
"name": "addNum",
"signature": "def addNum(self, num)"
},
{
"docstring": "Returns the ... | 3 | stack_v2_sparse_classes_30k_val_000462 | Implement the Python class `MedianFinder` described below.
Class description:
Implement the MedianFinder class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def addNum(self, num): Adds a num into the data structure. :type num: int :rtype: void
- def findMedian(self): ... | Implement the Python class `MedianFinder` described below.
Class description:
Implement the MedianFinder class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def addNum(self, num): Adds a num into the data structure. :type num: int :rtype: void
- def findMedian(self): ... | f46ab3bcafbca4d0209df3aa9114dad52bda76b2 | <|skeleton|>
class MedianFinder:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def addNum(self, num):
"""Adds a num into the data structure. :type num: int :rtype: void"""
<|body_1|>
def findMedian(self):
"""Returns the median of current... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MedianFinder:
def __init__(self):
"""Initialize your data structure here."""
self.maxheap = []
self.minheap = []
self.median = -sys.maxint - 1
def addNum(self, num):
"""Adds a num into the data structure. :type num: int :rtype: void"""
heapq.heappush(self.m... | the_stack_v2_python_sparse | findMedian.py | ankitomss/python_practice | train | 0 | |
1cac1555a7173861e64dcd0166c179adbda9b727 | [
"if compute is None:\n compute = impl.get_runtime().default_ip\nreturn tf_impl.type_factory.custom_int(bits, signed, compute)",
"frac_type = Quant.int(bits=frac, signed=signed, compute=ti.i32)\nif signed:\n scale = range / 2 ** (frac - 1)\nelse:\n scale = range / 2 ** frac\nif compute is None:\n compu... | <|body_start_0|>
if compute is None:
compute = impl.get_runtime().default_ip
return tf_impl.type_factory.custom_int(bits, signed, compute)
<|end_body_0|>
<|body_start_1|>
frac_type = Quant.int(bits=frac, signed=signed, compute=ti.i32)
if signed:
scale = range / 2... | Generator of quantized types. For more details, read https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf. | Quant | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Quant:
"""Generator of quantized types. For more details, read https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf."""
def int(bits, signed=False, compute=None):
"""Generates a quantized type for integers. Args: bits (int): Number of bits. signed (bool): Signe... | stack_v2_sparse_classes_36k_train_004013 | 2,606 | permissive | [
{
"docstring": "Generates a quantized type for integers. Args: bits (int): Number of bits. signed (bool): Signed or unsigned. compute (DataType): Type for computation. Returns: DataType: The specified type.",
"name": "int",
"signature": "def int(bits, signed=False, compute=None)"
},
{
"docstring... | 3 | stack_v2_sparse_classes_30k_train_007879 | Implement the Python class `Quant` described below.
Class description:
Generator of quantized types. For more details, read https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf.
Method signatures and docstrings:
- def int(bits, signed=False, compute=None): Generates a quantized type for integer... | Implement the Python class `Quant` described below.
Class description:
Generator of quantized types. For more details, read https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf.
Method signatures and docstrings:
- def int(bits, signed=False, compute=None): Generates a quantized type for integer... | c9b8166d7b019734438232d9b247eb3555e0d6f0 | <|skeleton|>
class Quant:
"""Generator of quantized types. For more details, read https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf."""
def int(bits, signed=False, compute=None):
"""Generates a quantized type for integers. Args: bits (int): Number of bits. signed (bool): Signe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Quant:
"""Generator of quantized types. For more details, read https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf."""
def int(bits, signed=False, compute=None):
"""Generates a quantized type for integers. Args: bits (int): Number of bits. signed (bool): Signed or unsigned... | the_stack_v2_python_sparse | python/taichi/lang/quant_impl.py | ljcc0930/taichi | train | 2 |
227d3b2c5ec60b422a3a2448e92725b3408b44e0 | [
"if isinstance(obs, np.ndarray) and obs.ndim == 2:\n obs = [obs[:, i] for i in range(obs.shape[1])]\nif isinstance(sigs, np.ndarray) and sigs.ndim == 2:\n sigs = [sigs[:, i] for i in range(sigs.shape[1])]\nif (ms_obs or ms_feat) and ms_warn:\n PE.warn(PE.PyAValError(f'Be aware: Mean will be subtracted from... | <|body_start_0|>
if isinstance(obs, np.ndarray) and obs.ndim == 2:
obs = [obs[:, i] for i in range(obs.shape[1])]
if isinstance(sigs, np.ndarray) and sigs.ndim == 2:
sigs = [sigs[:, i] for i in range(sigs.shape[1])]
if (ms_obs or ms_feat) and ms_warn:
PE.warn(... | SysRem | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SysRem:
def __init__(self, obs, sigs, ms_obs=True, ms_feat=False, a0=None, ms_warn=True):
"""Implementation of the SysRem algorithm. SysRem was described by `Tamuz et al. 2005 (MNRAS 356, 1466) <https://ui.adsabs.harvard.edu/abs/2005MNRAS.356.1466T/abstract>`_ in the context of correctin... | stack_v2_sparse_classes_36k_train_004014 | 11,569 | permissive | [
{
"docstring": "Implementation of the SysRem algorithm. SysRem was described by `Tamuz et al. 2005 (MNRAS 356, 1466) <https://ui.adsabs.harvard.edu/abs/2005MNRAS.356.1466T/abstract>`_ in the context of correcting systematic effects in samples of light curves, but has been applied in other areas such as planetar... | 4 | stack_v2_sparse_classes_30k_train_015726 | Implement the Python class `SysRem` described below.
Class description:
Implement the SysRem class.
Method signatures and docstrings:
- def __init__(self, obs, sigs, ms_obs=True, ms_feat=False, a0=None, ms_warn=True): Implementation of the SysRem algorithm. SysRem was described by `Tamuz et al. 2005 (MNRAS 356, 1466)... | Implement the Python class `SysRem` described below.
Class description:
Implement the SysRem class.
Method signatures and docstrings:
- def __init__(self, obs, sigs, ms_obs=True, ms_feat=False, a0=None, ms_warn=True): Implementation of the SysRem algorithm. SysRem was described by `Tamuz et al. 2005 (MNRAS 356, 1466)... | e85314678882624baf870443c670b4f5abb70e7d | <|skeleton|>
class SysRem:
def __init__(self, obs, sigs, ms_obs=True, ms_feat=False, a0=None, ms_warn=True):
"""Implementation of the SysRem algorithm. SysRem was described by `Tamuz et al. 2005 (MNRAS 356, 1466) <https://ui.adsabs.harvard.edu/abs/2005MNRAS.356.1466T/abstract>`_ in the context of correctin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SysRem:
def __init__(self, obs, sigs, ms_obs=True, ms_feat=False, a0=None, ms_warn=True):
"""Implementation of the SysRem algorithm. SysRem was described by `Tamuz et al. 2005 (MNRAS 356, 1466) <https://ui.adsabs.harvard.edu/abs/2005MNRAS.356.1466T/abstract>`_ in the context of correcting systematic e... | the_stack_v2_python_sparse | src/pyasl/asl/aslExt_1/sysrem.py | sczesla/PyAstronomy | train | 129 | |
496d6c0f27d5661dc42b57e1d0732a8168fe47b3 | [
"self.__spiDAC = spidev.SpiDev()\nself.__spiDAC.open(0, 1)\nself.__spiDAC.max_speed_hz = 20000000\nif gainFactor != 1 and gainFactor != 2:\n raise ValueError('DAC __init__: Invalid gain factor. Must be 1 or 2')\nelse:\n self.gain = gainFactor\n self.maxdacvoltage = self.__dacMax... | <|body_start_0|>
self.__spiDAC = spidev.SpiDev()
self.__spiDAC.open(0, 1)
self.__spiDAC.max_speed_hz = 20000000
if gainFactor != 1 and gainFactor != 2:
raise ValueError('DAC __init__: Invalid gain factor. Must be 1 or 2')
else:
... | Based on the Microchip MCP4822 Define SPI bus and init | DAC | [
"Apache-2.0",
"GPL-2.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DAC:
"""Based on the Microchip MCP4822 Define SPI bus and init"""
def __init__(self, gainFactor=1):
"""Class Constructor gainFactor -- Set the DAC's gain factor. The value should be 1 or 2. Gain factor is used to determine output voltage from the formula: Vout = G * Vref * D/4096 Whe... | stack_v2_sparse_classes_36k_train_004015 | 31,508 | permissive | [
{
"docstring": "Class Constructor gainFactor -- Set the DAC's gain factor. The value should be 1 or 2. Gain factor is used to determine output voltage from the formula: Vout = G * Vref * D/4096 Where G is gain factor, Vref (for this chip) is 2.048 and D is the 12-bit digital value",
"name": "__init__",
... | 3 | stack_v2_sparse_classes_30k_train_006430 | Implement the Python class `DAC` described below.
Class description:
Based on the Microchip MCP4822 Define SPI bus and init
Method signatures and docstrings:
- def __init__(self, gainFactor=1): Class Constructor gainFactor -- Set the DAC's gain factor. The value should be 1 or 2. Gain factor is used to determine outp... | Implement the Python class `DAC` described below.
Class description:
Based on the Microchip MCP4822 Define SPI bus and init
Method signatures and docstrings:
- def __init__(self, gainFactor=1): Class Constructor gainFactor -- Set the DAC's gain factor. The value should be 1 or 2. Gain factor is used to determine outp... | 2529ca149d7f584ede780de1cb695a2f55b7031f | <|skeleton|>
class DAC:
"""Based on the Microchip MCP4822 Define SPI bus and init"""
def __init__(self, gainFactor=1):
"""Class Constructor gainFactor -- Set the DAC's gain factor. The value should be 1 or 2. Gain factor is used to determine output voltage from the formula: Vout = G * Vref * D/4096 Whe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DAC:
"""Based on the Microchip MCP4822 Define SPI bus and init"""
def __init__(self, gainFactor=1):
"""Class Constructor gainFactor -- Set the DAC's gain factor. The value should be 1 or 2. Gain factor is used to determine output voltage from the formula: Vout = G * Vref * D/4096 Where G is gain ... | the_stack_v2_python_sparse | reinvent-2020/RhythmCloud/lib/ABElectronics_Python_Libraries/ExpanderPi/ExpanderPi.py | aws-samples/aws-builders-fair-projects | train | 89 |
72ac68bffa1d84db40cb76f5cdc7565ea8dd241e | [
"coleccion = Coleccion\ncoleccion.dic_imgs = {0: ('suj1', 'img1'), 1: ('suj1', 'img2'), 2: ('suj2', 'img1'), 3: ('suj2', 'img2')}\ncoleccion.total_sujs = 2\ncoleccion.total_imgs = 4\nentrenamiento = object.__new__(Entrenamiento)\nentrenamiento.obt_indices_entrenamiento(coleccion, 70)\nself.assertTrue(len(entrenamie... | <|body_start_0|>
coleccion = Coleccion
coleccion.dic_imgs = {0: ('suj1', 'img1'), 1: ('suj1', 'img2'), 2: ('suj2', 'img1'), 3: ('suj2', 'img2')}
coleccion.total_sujs = 2
coleccion.total_imgs = 4
entrenamiento = object.__new__(Entrenamiento)
entrenamiento.obt_indices_entre... | Clase encargada de probar funciones y fragmentos importantes de código para el modulo modelo.entrenamiento | TestEntrenamiento | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEntrenamiento:
"""Clase encargada de probar funciones y fragmentos importantes de código para el modulo modelo.entrenamiento"""
def test_obt_indices_entrenamiento(self):
"""Entradas: Coleccion y el porcentaje de la coleccion que vamos a utilizar para el entrenamiento Resultado es... | stack_v2_sparse_classes_36k_train_004016 | 6,512 | no_license | [
{
"docstring": "Entradas: Coleccion y el porcentaje de la coleccion que vamos a utilizar para el entrenamiento Resultado esperado: Lista de indices que representan una imagen dentro de la coleccion. Se debe haber tomado igual cantidad de imagenes para cada sujeto @param Sin parametros @return Sin retorno",
... | 5 | stack_v2_sparse_classes_30k_train_005587 | Implement the Python class `TestEntrenamiento` described below.
Class description:
Clase encargada de probar funciones y fragmentos importantes de código para el modulo modelo.entrenamiento
Method signatures and docstrings:
- def test_obt_indices_entrenamiento(self): Entradas: Coleccion y el porcentaje de la coleccio... | Implement the Python class `TestEntrenamiento` described below.
Class description:
Clase encargada de probar funciones y fragmentos importantes de código para el modulo modelo.entrenamiento
Method signatures and docstrings:
- def test_obt_indices_entrenamiento(self): Entradas: Coleccion y el porcentaje de la coleccio... | 30513a8b81cbb97ee475855c75628419a207c0e0 | <|skeleton|>
class TestEntrenamiento:
"""Clase encargada de probar funciones y fragmentos importantes de código para el modulo modelo.entrenamiento"""
def test_obt_indices_entrenamiento(self):
"""Entradas: Coleccion y el porcentaje de la coleccion que vamos a utilizar para el entrenamiento Resultado es... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestEntrenamiento:
"""Clase encargada de probar funciones y fragmentos importantes de código para el modulo modelo.entrenamiento"""
def test_obt_indices_entrenamiento(self):
"""Entradas: Coleccion y el porcentaje de la coleccion que vamos a utilizar para el entrenamiento Resultado esperado: Lista... | the_stack_v2_python_sparse | Codigo/pruebas/unitarias/test_entrenamiento.py | JulianSalinas/eigenfaces-qa | train | 0 |
7c8a599d62fc0b5e9012f440a9613baf220951de | [
"queryset = self.queryset.filter(reply_type=kwargs['reply_type'])\nreply_account = self.request.query_params.get('reply_account')\nif reply_account:\n queryset = queryset.filter(reply_account=reply_account)\nif queryset.first() is None:\n return Response(dict(data=None))\nelse:\n serializer = self.get_seri... | <|body_start_0|>
queryset = self.queryset.filter(reply_type=kwargs['reply_type'])
reply_account = self.request.query_params.get('reply_account')
if reply_account:
queryset = queryset.filter(reply_account=reply_account)
if queryset.first() is None:
return Response(... | 关注回复或消息回复 | MessageAndAttentionReplyViewManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MessageAndAttentionReplyViewManager:
"""关注回复或消息回复"""
def retrieve(self, request, *args, **kwargs):
"""检索关注回复或消息回复"""
<|body_0|>
def create_or_update(self, request, *args, **kwargs):
"""新建关注回复或消息回复"""
<|body_1|>
def update_status(self, request, *args,... | stack_v2_sparse_classes_36k_train_004017 | 5,675 | no_license | [
{
"docstring": "检索关注回复或消息回复",
"name": "retrieve",
"signature": "def retrieve(self, request, *args, **kwargs)"
},
{
"docstring": "新建关注回复或消息回复",
"name": "create_or_update",
"signature": "def create_or_update(self, request, *args, **kwargs)"
},
{
"docstring": "删除关注回复或消息回复",
"nam... | 3 | stack_v2_sparse_classes_30k_train_019616 | Implement the Python class `MessageAndAttentionReplyViewManager` described below.
Class description:
关注回复或消息回复
Method signatures and docstrings:
- def retrieve(self, request, *args, **kwargs): 检索关注回复或消息回复
- def create_or_update(self, request, *args, **kwargs): 新建关注回复或消息回复
- def update_status(self, request, *args, **k... | Implement the Python class `MessageAndAttentionReplyViewManager` described below.
Class description:
关注回复或消息回复
Method signatures and docstrings:
- def retrieve(self, request, *args, **kwargs): 检索关注回复或消息回复
- def create_or_update(self, request, *args, **kwargs): 新建关注回复或消息回复
- def update_status(self, request, *args, **k... | 0d32f98f42591b43e0b4da5e978b627da517f758 | <|skeleton|>
class MessageAndAttentionReplyViewManager:
"""关注回复或消息回复"""
def retrieve(self, request, *args, **kwargs):
"""检索关注回复或消息回复"""
<|body_0|>
def create_or_update(self, request, *args, **kwargs):
"""新建关注回复或消息回复"""
<|body_1|>
def update_status(self, request, *args,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MessageAndAttentionReplyViewManager:
"""关注回复或消息回复"""
def retrieve(self, request, *args, **kwargs):
"""检索关注回复或消息回复"""
queryset = self.queryset.filter(reply_type=kwargs['reply_type'])
reply_account = self.request.query_params.get('reply_account')
if reply_account:
... | the_stack_v2_python_sparse | payserver/padmin/views/subscription_account_manage.py | yiyuhao/FukuanUnion | train | 0 |
d73fbd63a078effc9dcfc66f1b37f5122cfa1984 | [
"print(f'Algorithm: {params.algorithm_key}')\nprint(f'Hyperparamters: {params.algorithm_hyperparameters}')\nvideo_record_path = params.save_folder if params.video_record_test else None\nif params.algorithm_key in ['DDPG', 'DQN', 'TD3']:\n print('Setting number of train environments to 1 because apparently', para... | <|body_start_0|>
print(f'Algorithm: {params.algorithm_key}')
print(f'Hyperparamters: {params.algorithm_hyperparameters}')
video_record_path = params.save_folder if params.video_record_test else None
if params.algorithm_key in ['DDPG', 'DQN', 'TD3']:
print('Setting number of t... | Trainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trainer:
def prepare(self, params: ConfigParams):
"""Prepares parameters required for training :param params: :return:"""
<|body_0|>
def train(self, **kwargs):
"""Starts training procedure :param kwargs: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_004018 | 3,310 | no_license | [
{
"docstring": "Prepares parameters required for training :param params: :return:",
"name": "prepare",
"signature": "def prepare(self, params: ConfigParams)"
},
{
"docstring": "Starts training procedure :param kwargs: :return:",
"name": "train",
"signature": "def train(self, **kwargs)"
... | 2 | stack_v2_sparse_classes_30k_train_013776 | Implement the Python class `Trainer` described below.
Class description:
Implement the Trainer class.
Method signatures and docstrings:
- def prepare(self, params: ConfigParams): Prepares parameters required for training :param params: :return:
- def train(self, **kwargs): Starts training procedure :param kwargs: :re... | Implement the Python class `Trainer` described below.
Class description:
Implement the Trainer class.
Method signatures and docstrings:
- def prepare(self, params: ConfigParams): Prepares parameters required for training :param params: :return:
- def train(self, **kwargs): Starts training procedure :param kwargs: :re... | a6135e1b1836266beacc8ec0a5c6c3b5bbaa2b97 | <|skeleton|>
class Trainer:
def prepare(self, params: ConfigParams):
"""Prepares parameters required for training :param params: :return:"""
<|body_0|>
def train(self, **kwargs):
"""Starts training procedure :param kwargs: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Trainer:
def prepare(self, params: ConfigParams):
"""Prepares parameters required for training :param params: :return:"""
print(f'Algorithm: {params.algorithm_key}')
print(f'Hyperparamters: {params.algorithm_hyperparameters}')
video_record_path = params.save_folder if params.vi... | the_stack_v2_python_sparse | exp/training/trainer.py | bartekwojcik/SportBettingRL | train | 0 | |
71d4689d054126195b899474efa7654b0eec14bb | [
"s = 'nothingremovedhere'\nresult = findbestmatch._clean_non_chars(s)\nself.assertEqual(s, result)",
"s = '#$%#^$%&**'\nresult = findbestmatch._clean_non_chars(s)\nself.assertEqual('', result)",
"s = ''\nresult = findbestmatch._clean_non_chars(s)\nself.assertEqual('', result)"
] | <|body_start_0|>
s = 'nothingremovedhere'
result = findbestmatch._clean_non_chars(s)
self.assertEqual(s, result)
<|end_body_0|>
<|body_start_1|>
s = '#$%#^$%&**'
result = findbestmatch._clean_non_chars(s)
self.assertEqual('', result)
<|end_body_1|>
<|body_start_2|>
... | TestFindBestMatch | [
"BSD-3-Clause",
"LGPL-2.1-or-later",
"LGPL-2.1-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFindBestMatch:
def testclean_text_1(self):
"""Test for _clean_non_chars (alphanumeric symbols)"""
<|body_0|>
def testclean_text_2(self):
"""Test for _clean_non_chars (special symbols)"""
<|body_1|>
def testclean_text_3(self):
"""Test for _cle... | stack_v2_sparse_classes_36k_train_004019 | 4,823 | permissive | [
{
"docstring": "Test for _clean_non_chars (alphanumeric symbols)",
"name": "testclean_text_1",
"signature": "def testclean_text_1(self)"
},
{
"docstring": "Test for _clean_non_chars (special symbols)",
"name": "testclean_text_2",
"signature": "def testclean_text_2(self)"
},
{
"do... | 3 | stack_v2_sparse_classes_30k_train_016581 | Implement the Python class `TestFindBestMatch` described below.
Class description:
Implement the TestFindBestMatch class.
Method signatures and docstrings:
- def testclean_text_1(self): Test for _clean_non_chars (alphanumeric symbols)
- def testclean_text_2(self): Test for _clean_non_chars (special symbols)
- def tes... | Implement the Python class `TestFindBestMatch` described below.
Class description:
Implement the TestFindBestMatch class.
Method signatures and docstrings:
- def testclean_text_1(self): Test for _clean_non_chars (alphanumeric symbols)
- def testclean_text_2(self): Test for _clean_non_chars (special symbols)
- def tes... | bf7f789d01b7c66ccd0c213db0a029da7e588c9e | <|skeleton|>
class TestFindBestMatch:
def testclean_text_1(self):
"""Test for _clean_non_chars (alphanumeric symbols)"""
<|body_0|>
def testclean_text_2(self):
"""Test for _clean_non_chars (special symbols)"""
<|body_1|>
def testclean_text_3(self):
"""Test for _cle... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestFindBestMatch:
def testclean_text_1(self):
"""Test for _clean_non_chars (alphanumeric symbols)"""
s = 'nothingremovedhere'
result = findbestmatch._clean_non_chars(s)
self.assertEqual(s, result)
def testclean_text_2(self):
"""Test for _clean_non_chars (special s... | the_stack_v2_python_sparse | pywinauto/unittests/test_findbestmatch.py | pywinauto/pywinauto | train | 4,466 | |
4c8b0a33e51f216919a8f2a7ebb33e8e2cc477dd | [
"self.model = model\nself.pca_components = pca_components\nself.model_params = model_params",
"self.model_final = self.model.set_params(**self.model_params)\nself.pca = PCA(n_components=self.pca_components)\nX_small = self.pca.fit_transform(X)\nself.model_final.fit(X_small, y)",
"X_small = self.pca.transform(X)... | <|body_start_0|>
self.model = model
self.pca_components = pca_components
self.model_params = model_params
<|end_body_0|>
<|body_start_1|>
self.model_final = self.model.set_params(**self.model_params)
self.pca = PCA(n_components=self.pca_components)
X_small = self.pca.fit... | Model that first do PCA on the data before it fits's data on the model | Model | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
"""Model that first do PCA on the data before it fits's data on the model"""
def __init__(self, model=None, pca_components=1, model_params={}):
""":param model: model to fit, predict :param pca_components: param for PCA :param model_params: parameters for the model"""
... | stack_v2_sparse_classes_36k_train_004020 | 4,723 | no_license | [
{
"docstring": ":param model: model to fit, predict :param pca_components: param for PCA :param model_params: parameters for the model",
"name": "__init__",
"signature": "def __init__(self, model=None, pca_components=1, model_params={})"
},
{
"docstring": "First transform the data with PCA and t... | 3 | stack_v2_sparse_classes_30k_train_005929 | Implement the Python class `Model` described below.
Class description:
Model that first do PCA on the data before it fits's data on the model
Method signatures and docstrings:
- def __init__(self, model=None, pca_components=1, model_params={}): :param model: model to fit, predict :param pca_components: param for PCA ... | Implement the Python class `Model` described below.
Class description:
Model that first do PCA on the data before it fits's data on the model
Method signatures and docstrings:
- def __init__(self, model=None, pca_components=1, model_params={}): :param model: model to fit, predict :param pca_components: param for PCA ... | 62e386d81ffc5dab7165ea228f62861c4bbee57b | <|skeleton|>
class Model:
"""Model that first do PCA on the data before it fits's data on the model"""
def __init__(self, model=None, pca_components=1, model_params={}):
""":param model: model to fit, predict :param pca_components: param for PCA :param model_params: parameters for the model"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Model:
"""Model that first do PCA on the data before it fits's data on the model"""
def __init__(self, model=None, pca_components=1, model_params={}):
""":param model: model to fit, predict :param pca_components: param for PCA :param model_params: parameters for the model"""
self.model = ... | the_stack_v2_python_sparse | src/plot_quality_prediction/quality_prediction.py | AndrejHafner/how-good-is-my-plot | train | 3 |
9df8371d80eaab48fc3f2ccc9e817943a7e4ea1a | [
"try:\n if isinstance(lvl, int) and min(_utils.LEVELS) <= lvl <= max(_utils.LEVELS):\n return _utils.REPR_INT\n if isinstance(lvl, str):\n if lvl in _utils.REVERSE_NAMES:\n return _utils.REPR_NAME\n elif min(_utils.LEVELS) <= int(lvl) <= max(_utils.LEVELS):\n return ... | <|body_start_0|>
try:
if isinstance(lvl, int) and min(_utils.LEVELS) <= lvl <= max(_utils.LEVELS):
return _utils.REPR_INT
if isinstance(lvl, str):
if lvl in _utils.REVERSE_NAMES:
return _utils.REPR_NAME
elif min(_utils.L... | _utils | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _utils:
def getReprType(lvl):
"""For the given log level argument, return its type: REPR_INT, REPR_NAME, or REPR_STR. For example: 2 -> REPR_INT "INFO" -> REPR_NAME "2" -> REPR_STR If `lvl` is not a valid log level, return None."""
<|body_0|>
def normalize(lvl):
"""C... | stack_v2_sparse_classes_36k_train_004021 | 34,035 | permissive | [
{
"docstring": "For the given log level argument, return its type: REPR_INT, REPR_NAME, or REPR_STR. For example: 2 -> REPR_INT \"INFO\" -> REPR_NAME \"2\" -> REPR_STR If `lvl` is not a valid log level, return None.",
"name": "getReprType",
"signature": "def getReprType(lvl)"
},
{
"docstring": "... | 3 | stack_v2_sparse_classes_30k_train_003033 | Implement the Python class `_utils` described below.
Class description:
Implement the _utils class.
Method signatures and docstrings:
- def getReprType(lvl): For the given log level argument, return its type: REPR_INT, REPR_NAME, or REPR_STR. For example: 2 -> REPR_INT "INFO" -> REPR_NAME "2" -> REPR_STR If `lvl` is ... | Implement the Python class `_utils` described below.
Class description:
Implement the _utils class.
Method signatures and docstrings:
- def getReprType(lvl): For the given log level argument, return its type: REPR_INT, REPR_NAME, or REPR_STR. For example: 2 -> REPR_INT "INFO" -> REPR_NAME "2" -> REPR_STR If `lvl` is ... | 6b873defc9327d6f8b51a826dd7a7ef6c3e41396 | <|skeleton|>
class _utils:
def getReprType(lvl):
"""For the given log level argument, return its type: REPR_INT, REPR_NAME, or REPR_STR. For example: 2 -> REPR_INT "INFO" -> REPR_NAME "2" -> REPR_STR If `lvl` is not a valid log level, return None."""
<|body_0|>
def normalize(lvl):
"""C... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _utils:
def getReprType(lvl):
"""For the given log level argument, return its type: REPR_INT, REPR_NAME, or REPR_STR. For example: 2 -> REPR_INT "INFO" -> REPR_NAME "2" -> REPR_STR If `lvl` is not a valid log level, return None."""
try:
if isinstance(lvl, int) and min(_utils.LEVELS... | the_stack_v2_python_sparse | shared/log.py | joseangel-sc/conducto | train | 1 | |
f5814a14cac21a6a84bc92c72c371ecb179231b1 | [
"engine = db_connect()\ncreate_nieuws_table(engine)\nself.Session = sessionmaker(bind=engine)",
"session = self.Session()\nnieuws = Nieuws(**item)\ntry:\n session.add(nieuws)\n session.commit()\nexcept:\n session.rollback()\n raise\nfinally:\n session.close()\nreturn item"
] | <|body_start_0|>
engine = db_connect()
create_nieuws_table(engine)
self.Session = sessionmaker(bind=engine)
<|end_body_0|>
<|body_start_1|>
session = self.Session()
nieuws = Nieuws(**item)
try:
session.add(nieuws)
session.commit()
except:
... | Nieuws pipeline for storing scraped items in the database | NieuwsPipeline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NieuwsPipeline:
"""Nieuws pipeline for storing scraped items in the database"""
def __init__(self):
"""Initializes database connection and sessionmaker. Creates nieuws table."""
<|body_0|>
def process_item(self, item, spider):
"""Save nieuws in the database. This... | stack_v2_sparse_classes_36k_train_004022 | 882 | no_license | [
{
"docstring": "Initializes database connection and sessionmaker. Creates nieuws table.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Save nieuws in the database. This method is called for every item pipeline component.",
"name": "process_item",
"signature": ... | 2 | stack_v2_sparse_classes_30k_val_000887 | Implement the Python class `NieuwsPipeline` described below.
Class description:
Nieuws pipeline for storing scraped items in the database
Method signatures and docstrings:
- def __init__(self): Initializes database connection and sessionmaker. Creates nieuws table.
- def process_item(self, item, spider): Save nieuws ... | Implement the Python class `NieuwsPipeline` described below.
Class description:
Nieuws pipeline for storing scraped items in the database
Method signatures and docstrings:
- def __init__(self): Initializes database connection and sessionmaker. Creates nieuws table.
- def process_item(self, item, spider): Save nieuws ... | 14e4427ae3e1bc047f7b747b07eb65201643892d | <|skeleton|>
class NieuwsPipeline:
"""Nieuws pipeline for storing scraped items in the database"""
def __init__(self):
"""Initializes database connection and sessionmaker. Creates nieuws table."""
<|body_0|>
def process_item(self, item, spider):
"""Save nieuws in the database. This... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NieuwsPipeline:
"""Nieuws pipeline for storing scraped items in the database"""
def __init__(self):
"""Initializes database connection and sessionmaker. Creates nieuws table."""
engine = db_connect()
create_nieuws_table(engine)
self.Session = sessionmaker(bind=engine)
... | the_stack_v2_python_sparse | scrapers/nieuws/pipelines.py | timostrating/ponypicpy | train | 1 |
5ffe2ba1dd325b0a8488a81e4a0f539e86c069b4 | [
"self.sumList = []\na = 0\nfor num in nums:\n a += num\n self.sumList.append(a)",
"if i == 0:\n return self.sumList[j]\nreturn self.sumList[j] - self.sumList[i - 1]"
] | <|body_start_0|>
self.sumList = []
a = 0
for num in nums:
a += num
self.sumList.append(a)
<|end_body_0|>
<|body_start_1|>
if i == 0:
return self.sumList[j]
return self.sumList[j] - self.sumList[i - 1]
<|end_body_1|>
| 记录一个累和数组 | NumArray | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumArray:
"""记录一个累和数组"""
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.sumList = []
a = 0
... | stack_v2_sparse_classes_36k_train_004023 | 645 | no_license | [
{
"docstring": ":type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": ":type i: int :type j: int :rtype: int",
"name": "sumRange",
"signature": "def sumRange(self, i, j)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003483 | Implement the Python class `NumArray` described below.
Class description:
记录一个累和数组
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def sumRange(self, i, j): :type i: int :type j: int :rtype: int | Implement the Python class `NumArray` described below.
Class description:
记录一个累和数组
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def sumRange(self, i, j): :type i: int :type j: int :rtype: int
<|skeleton|>
class NumArray:
"""记录一个累和数组"""
def __init__(self, nums):
... | 7167f1a7c6cb16cca63675c80037682752ee2a7d | <|skeleton|>
class NumArray:
"""记录一个累和数组"""
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumArray:
"""记录一个累和数组"""
def __init__(self, nums):
""":type nums: List[int]"""
self.sumList = []
a = 0
for num in nums:
a += num
self.sumList.append(a)
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
if i =... | the_stack_v2_python_sparse | Everyday/No303.py | kikihiter/LeetCode2 | train | 4 |
4a269258154b8515146db12d6e7ca6d32b89b919 | [
"rs_field = hyperopt_utils.get_search_algorithm_random_state_field(self.type)\nif rs_field is not None and self.__getattribute__(rs_field) is None:\n self.__setattr__(rs_field, ludwig_random_state)",
"missing_packages = []\nmissing_installs = []\nfor package_name, install_name in hyperopt_utils.get_search_algo... | <|body_start_0|>
rs_field = hyperopt_utils.get_search_algorithm_random_state_field(self.type)
if rs_field is not None and self.__getattribute__(rs_field) is None:
self.__setattr__(rs_field, ludwig_random_state)
<|end_body_0|>
<|body_start_1|>
missing_packages = []
missing_in... | Basic search algorithm settings. | BaseSearchAlgorithmConfig | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseSearchAlgorithmConfig:
"""Basic search algorithm settings."""
def set_random_state(self, ludwig_random_state: int) -> None:
"""Overwrite the config random state. Search algorithms refer to random state by different names, however we want to overwrite unset random states with the ... | stack_v2_sparse_classes_36k_train_004024 | 22,089 | permissive | [
{
"docstring": "Overwrite the config random state. Search algorithms refer to random state by different names, however we want to overwrite unset random states with the Ludwig random state. This method uses a registry of random state field names to provide a single interface across all search algorithms.",
... | 2 | null | Implement the Python class `BaseSearchAlgorithmConfig` described below.
Class description:
Basic search algorithm settings.
Method signatures and docstrings:
- def set_random_state(self, ludwig_random_state: int) -> None: Overwrite the config random state. Search algorithms refer to random state by different names, h... | Implement the Python class `BaseSearchAlgorithmConfig` described below.
Class description:
Basic search algorithm settings.
Method signatures and docstrings:
- def set_random_state(self, ludwig_random_state: int) -> None: Overwrite the config random state. Search algorithms refer to random state by different names, h... | e1d023e41606c9b76b35e1d231c2f13368a30eca | <|skeleton|>
class BaseSearchAlgorithmConfig:
"""Basic search algorithm settings."""
def set_random_state(self, ludwig_random_state: int) -> None:
"""Overwrite the config random state. Search algorithms refer to random state by different names, however we want to overwrite unset random states with the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseSearchAlgorithmConfig:
"""Basic search algorithm settings."""
def set_random_state(self, ludwig_random_state: int) -> None:
"""Overwrite the config random state. Search algorithms refer to random state by different names, however we want to overwrite unset random states with the Ludwig random... | the_stack_v2_python_sparse | ludwig/schema/hyperopt/search_algorithm.py | ludwig-ai/ludwig | train | 2,567 |
4f42e2b8293b02fa3d604beea0422a1c3d918f1b | [
"filename = os.path.join(TMP_FOLDER, out_file + '.' + cls.filetype)\nparsed_schema = schema.parse(schema_str)\nrec_writer = io.DatumWriter(parsed_schema)\nfile_writer = datafile.DataFileWriter(open(filename, 'wb'), rec_writer, parsed_schema)\nfor _ in range(num_rows):\n data = {}\n data['name'] = ''.join((ran... | <|body_start_0|>
filename = os.path.join(TMP_FOLDER, out_file + '.' + cls.filetype)
parsed_schema = schema.parse(schema_str)
rec_writer = io.DatumWriter(parsed_schema)
file_writer = datafile.DataFileWriter(open(filename, 'wb'), rec_writer, parsed_schema)
for _ in range(num_rows):... | TestAvroParser | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"Elastic-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestAvroParser:
def generate_avro_file(cls, schema_str: str, out_file, num_rows: int) -> str:
"""Creates an avro file and saves to tmp folder to be used by test cases :param schema_str: valid avro schema as a string :param out_file: name of file to be created :param num_rows: number of r... | stack_v2_sparse_classes_36k_train_004025 | 4,125 | permissive | [
{
"docstring": "Creates an avro file and saves to tmp folder to be used by test cases :param schema_str: valid avro schema as a string :param out_file: name of file to be created :param num_rows: number of rows to be generated :return: string with path to the file created",
"name": "generate_avro_file",
... | 2 | null | Implement the Python class `TestAvroParser` described below.
Class description:
Implement the TestAvroParser class.
Method signatures and docstrings:
- def generate_avro_file(cls, schema_str: str, out_file, num_rows: int) -> str: Creates an avro file and saves to tmp folder to be used by test cases :param schema_str:... | Implement the Python class `TestAvroParser` described below.
Class description:
Implement the TestAvroParser class.
Method signatures and docstrings:
- def generate_avro_file(cls, schema_str: str, out_file, num_rows: int) -> str: Creates an avro file and saves to tmp folder to be used by test cases :param schema_str:... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class TestAvroParser:
def generate_avro_file(cls, schema_str: str, out_file, num_rows: int) -> str:
"""Creates an avro file and saves to tmp folder to be used by test cases :param schema_str: valid avro schema as a string :param out_file: name of file to be created :param num_rows: number of r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestAvroParser:
def generate_avro_file(cls, schema_str: str, out_file, num_rows: int) -> str:
"""Creates an avro file and saves to tmp folder to be used by test cases :param schema_str: valid avro schema as a string :param out_file: name of file to be created :param num_rows: number of rows to be gene... | the_stack_v2_python_sparse | dts/airbyte/airbyte-integrations/connectors/source-s3/unit_tests/test_avro_parser.py | alldatacenter/alldata | train | 774 | |
d45833cd406bed8d7365cbd9e7c0bc0c4124e17b | [
"super().save(*args, **kwargs)\nself.book_list.updated_date = timezone.now()\nself.book_list.save(broadcast=False, update_fields=['updated_date'])",
"if self.book_list.user == viewer:\n return\nis_group_member = GroupMember.objects.filter(group=self.book_list.group, user=viewer).exists()\nif is_group_member:\n... | <|body_start_0|>
super().save(*args, **kwargs)
self.book_list.updated_date = timezone.now()
self.book_list.save(broadcast=False, update_fields=['updated_date'])
<|end_body_0|>
<|body_start_1|>
if self.book_list.user == viewer:
return
is_group_member = GroupMember.obj... | ok | ListItem | [
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListItem:
"""ok"""
def save(self, *args, **kwargs):
"""Update the list's date"""
<|body_0|>
def raise_not_deletable(self, viewer):
"""the associated user OR the list owner can delete"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super().save(*... | stack_v2_sparse_classes_36k_train_004026 | 5,915 | no_license | [
{
"docstring": "Update the list's date",
"name": "save",
"signature": "def save(self, *args, **kwargs)"
},
{
"docstring": "the associated user OR the list owner can delete",
"name": "raise_not_deletable",
"signature": "def raise_not_deletable(self, viewer)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011866 | Implement the Python class `ListItem` described below.
Class description:
ok
Method signatures and docstrings:
- def save(self, *args, **kwargs): Update the list's date
- def raise_not_deletable(self, viewer): the associated user OR the list owner can delete | Implement the Python class `ListItem` described below.
Class description:
ok
Method signatures and docstrings:
- def save(self, *args, **kwargs): Update the list's date
- def raise_not_deletable(self, viewer): the associated user OR the list owner can delete
<|skeleton|>
class ListItem:
"""ok"""
def save(se... | 0f8da5b738047f3c34d60d93f59bdedd8f797224 | <|skeleton|>
class ListItem:
"""ok"""
def save(self, *args, **kwargs):
"""Update the list's date"""
<|body_0|>
def raise_not_deletable(self, viewer):
"""the associated user OR the list owner can delete"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ListItem:
"""ok"""
def save(self, *args, **kwargs):
"""Update the list's date"""
super().save(*args, **kwargs)
self.book_list.updated_date = timezone.now()
self.book_list.save(broadcast=False, update_fields=['updated_date'])
def raise_not_deletable(self, viewer):
... | the_stack_v2_python_sparse | bookwyrm/models/list.py | bookwyrm-social/bookwyrm | train | 1,398 |
2006349a88ac9f86999dbcd899d206755ed8d7df | [
"related_models = [f.related_model for f in Cell._meta.get_fields() if f.one_to_one]\nrelated_qs = [cls.objects.filter(cell=self) for cls in related_models if len(cls.objects.filter(cell=self)) > 0]\nif len(related_qs) > 1:\n raise DashBoardException('Cell data with id %d assosiated with multiple Cell types' % s... | <|body_start_0|>
related_models = [f.related_model for f in Cell._meta.get_fields() if f.one_to_one]
related_qs = [cls.objects.filter(cell=self) for cls in related_models if len(cls.objects.filter(cell=self)) > 0]
if len(related_qs) > 1:
raise DashBoardException('Cell data with id %d... | Cell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cell:
def get_related_object(self):
"""Get the related model instance object for this model via a onetoone relationship."""
<|body_0|>
def get_related_model(cls, name):
"""Given a name, get the related model that matches the name"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_004027 | 2,344 | no_license | [
{
"docstring": "Get the related model instance object for this model via a onetoone relationship.",
"name": "get_related_object",
"signature": "def get_related_object(self)"
},
{
"docstring": "Given a name, get the related model that matches the name",
"name": "get_related_model",
"signa... | 2 | stack_v2_sparse_classes_30k_train_001810 | Implement the Python class `Cell` described below.
Class description:
Implement the Cell class.
Method signatures and docstrings:
- def get_related_object(self): Get the related model instance object for this model via a onetoone relationship.
- def get_related_model(cls, name): Given a name, get the related model th... | Implement the Python class `Cell` described below.
Class description:
Implement the Cell class.
Method signatures and docstrings:
- def get_related_object(self): Get the related model instance object for this model via a onetoone relationship.
- def get_related_model(cls, name): Given a name, get the related model th... | 825c64f0148767883272c5be1e867660c969ab56 | <|skeleton|>
class Cell:
def get_related_object(self):
"""Get the related model instance object for this model via a onetoone relationship."""
<|body_0|>
def get_related_model(cls, name):
"""Given a name, get the related model that matches the name"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cell:
def get_related_object(self):
"""Get the related model instance object for this model via a onetoone relationship."""
related_models = [f.related_model for f in Cell._meta.get_fields() if f.one_to_one]
related_qs = [cls.objects.filter(cell=self) for cls in related_models if len(c... | the_stack_v2_python_sparse | p3app/dash_board/models.py | vdazrat/id8 | train | 0 | |
da3965619727141edbc97cb4a29100fcc56fe2fb | [
"super(Highlighter, self).__init__(parent)\nself.highlighting_rules = []\nblack_bold_format = QTextCharFormat()\nblack_bold_format.setFontWeight(QFont.Bold)\nself.highlighting_rules = [(QRegExp(pattern, cs=Qt.CaseInsensitive), black_bold_format) for pattern in blackbold_patterns]\nred_bold_format = QTextCharFormat(... | <|body_start_0|>
super(Highlighter, self).__init__(parent)
self.highlighting_rules = []
black_bold_format = QTextCharFormat()
black_bold_format.setFontWeight(QFont.Bold)
self.highlighting_rules = [(QRegExp(pattern, cs=Qt.CaseInsensitive), black_bold_format) for pattern in blackbo... | Class for handling syntax highlighting in editable text | Highlighter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Highlighter:
"""Class for handling syntax highlighting in editable text"""
def __init__(self, parent, blackbold_patterns, redbold_patterns):
"""Define highlighting rules - inputs = lists of patterns"""
<|body_0|>
def highlightBlock(self, text):
"""Redefined funct... | stack_v2_sparse_classes_36k_train_004028 | 19,107 | no_license | [
{
"docstring": "Define highlighting rules - inputs = lists of patterns",
"name": "__init__",
"signature": "def __init__(self, parent, blackbold_patterns, redbold_patterns)"
},
{
"docstring": "Redefined function to apply the highlighting rules",
"name": "highlightBlock",
"signature": "def... | 2 | stack_v2_sparse_classes_30k_train_009539 | Implement the Python class `Highlighter` described below.
Class description:
Class for handling syntax highlighting in editable text
Method signatures and docstrings:
- def __init__(self, parent, blackbold_patterns, redbold_patterns): Define highlighting rules - inputs = lists of patterns
- def highlightBlock(self, t... | Implement the Python class `Highlighter` described below.
Class description:
Class for handling syntax highlighting in editable text
Method signatures and docstrings:
- def __init__(self, parent, blackbold_patterns, redbold_patterns): Define highlighting rules - inputs = lists of patterns
- def highlightBlock(self, t... | 61920e434ab79114d977f499d6e1c801b23e81fe | <|skeleton|>
class Highlighter:
"""Class for handling syntax highlighting in editable text"""
def __init__(self, parent, blackbold_patterns, redbold_patterns):
"""Define highlighting rules - inputs = lists of patterns"""
<|body_0|>
def highlightBlock(self, text):
"""Redefined funct... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Highlighter:
"""Class for handling syntax highlighting in editable text"""
def __init__(self, parent, blackbold_patterns, redbold_patterns):
"""Define highlighting rules - inputs = lists of patterns"""
super(Highlighter, self).__init__(parent)
self.highlighting_rules = []
... | the_stack_v2_python_sparse | bipeditor/TextInteraction.py | BlasTJSN/BIPEditor | train | 0 |
b48093fd46576645ea8d51f9fe496fab1ef1cc78 | [
"ret = build_pb2.BuildInfra()\nret.ParseFromString(self.infra)\nreturn ret",
"proto = self.parse()\nyield proto\nself.infra = proto.SerializeToString()"
] | <|body_start_0|>
ret = build_pb2.BuildInfra()
ret.ParseFromString(self.infra)
return ret
<|end_body_0|>
<|body_start_1|>
proto = self.parse()
yield proto
self.infra = proto.SerializeToString()
<|end_body_1|>
| Stores buildbucket.v2.Build.infra. | BuildInfra | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildInfra:
"""Stores buildbucket.v2.Build.infra."""
def parse(self):
"""Deserializes infra."""
<|body_0|>
def mutate(self):
"""Returns a context manager that provides a mutable BuildInfra proto. Deserializes infra, yields it, and serializes back."""
<|bo... | stack_v2_sparse_classes_36k_train_004029 | 23,989 | permissive | [
{
"docstring": "Deserializes infra.",
"name": "parse",
"signature": "def parse(self)"
},
{
"docstring": "Returns a context manager that provides a mutable BuildInfra proto. Deserializes infra, yields it, and serializes back.",
"name": "mutate",
"signature": "def mutate(self)"
}
] | 2 | null | Implement the Python class `BuildInfra` described below.
Class description:
Stores buildbucket.v2.Build.infra.
Method signatures and docstrings:
- def parse(self): Deserializes infra.
- def mutate(self): Returns a context manager that provides a mutable BuildInfra proto. Deserializes infra, yields it, and serializes ... | Implement the Python class `BuildInfra` described below.
Class description:
Stores buildbucket.v2.Build.infra.
Method signatures and docstrings:
- def parse(self): Deserializes infra.
- def mutate(self): Returns a context manager that provides a mutable BuildInfra proto. Deserializes infra, yields it, and serializes ... | b5d4783f99461438ca9e6a477535617fadab6ba3 | <|skeleton|>
class BuildInfra:
"""Stores buildbucket.v2.Build.infra."""
def parse(self):
"""Deserializes infra."""
<|body_0|>
def mutate(self):
"""Returns a context manager that provides a mutable BuildInfra proto. Deserializes infra, yields it, and serializes back."""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BuildInfra:
"""Stores buildbucket.v2.Build.infra."""
def parse(self):
"""Deserializes infra."""
ret = build_pb2.BuildInfra()
ret.ParseFromString(self.infra)
return ret
def mutate(self):
"""Returns a context manager that provides a mutable BuildInfra proto. Des... | the_stack_v2_python_sparse | appengine/cr-buildbucket/model.py | xinghun61/infra | train | 2 |
bac192a712995eb015617aa410cf905788ae7070 | [
"super().__init__()\nself.input_dim = input_dim\nself.hidden_size = hidden_size\nself.num_layers = num_layers\nself.num_classes = num_classes\nself.output_dim = output_dim\nself.lstm_layer = nn.LSTM(input_dim, hidden_size, num_layers, batch_first=True)\nself.linear = nn.Linear(self.hidden_size, output_dim)\nself.ou... | <|body_start_0|>
super().__init__()
self.input_dim = input_dim
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_classes = num_classes
self.output_dim = output_dim
self.lstm_layer = nn.LSTM(input_dim, hidden_size, num_layers, batch_first=True)
... | Simple LSTM decoder. | LSTM_attention_embedding_decoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSTM_attention_embedding_decoder:
"""Simple LSTM decoder."""
def __init__(self, input_dim, hidden_size, output_dim, num_classes, num_layers=1):
"""Initialize model with params."""
<|body_0|>
def forward(self, inp, hidden):
"""Forward pass through LSTM layer. shap... | stack_v2_sparse_classes_36k_train_004030 | 1,863 | permissive | [
{
"docstring": "Initialize model with params.",
"name": "__init__",
"signature": "def __init__(self, input_dim, hidden_size, output_dim, num_classes, num_layers=1)"
},
{
"docstring": "Forward pass through LSTM layer. shape of lstm_out: [input_size, batch_size, hidden_dim] shape of self.hidden: (... | 2 | stack_v2_sparse_classes_30k_val_001160 | Implement the Python class `LSTM_attention_embedding_decoder` described below.
Class description:
Simple LSTM decoder.
Method signatures and docstrings:
- def __init__(self, input_dim, hidden_size, output_dim, num_classes, num_layers=1): Initialize model with params.
- def forward(self, inp, hidden): Forward pass thr... | Implement the Python class `LSTM_attention_embedding_decoder` described below.
Class description:
Simple LSTM decoder.
Method signatures and docstrings:
- def __init__(self, input_dim, hidden_size, output_dim, num_classes, num_layers=1): Initialize model with params.
- def forward(self, inp, hidden): Forward pass thr... | 9cdbf270487751a0ad6862b2fea2ccc0e23a0b67 | <|skeleton|>
class LSTM_attention_embedding_decoder:
"""Simple LSTM decoder."""
def __init__(self, input_dim, hidden_size, output_dim, num_classes, num_layers=1):
"""Initialize model with params."""
<|body_0|>
def forward(self, inp, hidden):
"""Forward pass through LSTM layer. shap... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LSTM_attention_embedding_decoder:
"""Simple LSTM decoder."""
def __init__(self, input_dim, hidden_size, output_dim, num_classes, num_layers=1):
"""Initialize model with params."""
super().__init__()
self.input_dim = input_dim
self.hidden_size = hidden_size
self.num... | the_stack_v2_python_sparse | caspr/models/lstm_decoder.py | microsoft/CASPR | train | 29 |
d8594a4753883f61dd150a247c7d777996d68b8f | [
"self._string_list_parser = ListParser()\nself._boolean_list_parser = BooleanListParser()\nself._metric_names = metric_names\nself._maximize_fitnesses = maximize_fitnesses\nself._objective_dictionary_list = objective_dictionary_list",
"objectives = self.parse_fitness_objectives()\nobjectives_list = copy.copy(obje... | <|body_start_0|>
self._string_list_parser = ListParser()
self._boolean_list_parser = BooleanListParser()
self._metric_names = metric_names
self._maximize_fitnesses = maximize_fitnesses
self._objective_dictionary_list = objective_dictionary_list
<|end_body_0|>
<|body_start_1|>
... | Class which keeps track of all the Fitness Objective data for an experiment. The idea behind this class is that this guy: 1. Parses fitness information from common injected properties 2. Centralizes the access to fitness information 3. Is dependency injected into other policy classes' constructors as this information i... | FitnessObjectivesBuilder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FitnessObjectivesBuilder:
"""Class which keeps track of all the Fitness Objective data for an experiment. The idea behind this class is that this guy: 1. Parses fitness information from common injected properties 2. Centralizes the access to fitness information 3. Is dependency injected into othe... | stack_v2_sparse_classes_36k_train_004031 | 6,107 | no_license | [
{
"docstring": "Constructor. :param metric_names: a single space-delimited string containing the one or more field names of the Metrics Record that correspond to fitness objectives. :param maximize_fitnesses: a single space-delimited string containing one or more booleans which describe whether the metric name ... | 6 | stack_v2_sparse_classes_30k_train_012289 | Implement the Python class `FitnessObjectivesBuilder` described below.
Class description:
Class which keeps track of all the Fitness Objective data for an experiment. The idea behind this class is that this guy: 1. Parses fitness information from common injected properties 2. Centralizes the access to fitness informat... | Implement the Python class `FitnessObjectivesBuilder` described below.
Class description:
Class which keeps track of all the Fitness Objective data for an experiment. The idea behind this class is that this guy: 1. Parses fitness information from common injected properties 2. Centralizes the access to fitness informat... | 99c2f401d6c4b203ee439ed607985a918d0c3c7e | <|skeleton|>
class FitnessObjectivesBuilder:
"""Class which keeps track of all the Fitness Objective data for an experiment. The idea behind this class is that this guy: 1. Parses fitness information from common injected properties 2. Centralizes the access to fitness information 3. Is dependency injected into othe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FitnessObjectivesBuilder:
"""Class which keeps track of all the Fitness Objective data for an experiment. The idea behind this class is that this guy: 1. Parses fitness information from common injected properties 2. Centralizes the access to fitness information 3. Is dependency injected into other policy clas... | the_stack_v2_python_sparse | servicecommon/fitness/fitness_objectives_builder.py | Cognizant-CDB-AIA-BAI-AI-OI/LEAF-ENN-Training-V2 | train | 0 |
11ef01f03025f4049d8a9c4b631680f48a632216 | [
"self.operands: List[Operand] = list(operands)\nfor i in range(len(self.operands)):\n self.operands[i] = Operand.validate_operand(self.operands[i])\nsuper().__init__()",
"incomplete_expression = False\nfor operand in self.operands:\n if not issubclass(type(operand), Operand):\n raise RuntimeError(f'O... | <|body_start_0|>
self.operands: List[Operand] = list(operands)
for i in range(len(self.operands)):
self.operands[i] = Operand.validate_operand(self.operands[i])
super().__init__()
<|end_body_0|>
<|body_start_1|>
incomplete_expression = False
for operand in self.opera... | Or operator class for filtering JumpStart content. | Or | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Or:
"""Or operator class for filtering JumpStart content."""
def __init__(self, *operands: Union[Operand, str]) -> None:
"""Instantiates Or object. Args: operands (Operand): Operand for Or-ing. Raises: RuntimeError: If the operands cannot be validated."""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_004032 | 16,623 | permissive | [
{
"docstring": "Instantiates Or object. Args: operands (Operand): Operand for Or-ing. Raises: RuntimeError: If the operands cannot be validated.",
"name": "__init__",
"signature": "def __init__(self, *operands: Union[Operand, str]) -> None"
},
{
"docstring": "Evaluates operator. Raises: RuntimeE... | 3 | stack_v2_sparse_classes_30k_train_002104 | Implement the Python class `Or` described below.
Class description:
Or operator class for filtering JumpStart content.
Method signatures and docstrings:
- def __init__(self, *operands: Union[Operand, str]) -> None: Instantiates Or object. Args: operands (Operand): Operand for Or-ing. Raises: RuntimeError: If the oper... | Implement the Python class `Or` described below.
Class description:
Or operator class for filtering JumpStart content.
Method signatures and docstrings:
- def __init__(self, *operands: Union[Operand, str]) -> None: Instantiates Or object. Args: operands (Operand): Operand for Or-ing. Raises: RuntimeError: If the oper... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class Or:
"""Or operator class for filtering JumpStart content."""
def __init__(self, *operands: Union[Operand, str]) -> None:
"""Instantiates Or object. Args: operands (Operand): Operand for Or-ing. Raises: RuntimeError: If the operands cannot be validated."""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Or:
"""Or operator class for filtering JumpStart content."""
def __init__(self, *operands: Union[Operand, str]) -> None:
"""Instantiates Or object. Args: operands (Operand): Operand for Or-ing. Raises: RuntimeError: If the operands cannot be validated."""
self.operands: List[Operand] = li... | the_stack_v2_python_sparse | src/sagemaker/jumpstart/filters.py | aws/sagemaker-python-sdk | train | 2,050 |
2b5718603423712bade7ba9528f3b40731036501 | [
"acting_user = UserFactory.create()\norder = OrderFactory.create()\noriginal_before_json = serialize_model_object(order)\noriginal_before_json['lines'] = []\nassert OrderAudit.objects.count() == 0\norder.save_and_log(acting_user)\nassert OrderAudit.objects.count() == 1\noriginal_after_json = serialize_model_object(... | <|body_start_0|>
acting_user = UserFactory.create()
order = OrderFactory.create()
original_before_json = serialize_model_object(order)
original_before_json['lines'] = []
assert OrderAudit.objects.count() == 0
order.save_and_log(acting_user)
assert OrderAudit.objec... | Tests for abstract models | ModelsTests | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelsTests:
"""Tests for abstract models"""
def test_save_and_log(self):
"""Tests that save_and_log() creates an audit record with the correct information."""
<|body_0|>
def test_to_dict(self):
"""assert output of to_dict"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_004033 | 2,214 | permissive | [
{
"docstring": "Tests that save_and_log() creates an audit record with the correct information.",
"name": "test_save_and_log",
"signature": "def test_save_and_log(self)"
},
{
"docstring": "assert output of to_dict",
"name": "test_to_dict",
"signature": "def test_to_dict(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002313 | Implement the Python class `ModelsTests` described below.
Class description:
Tests for abstract models
Method signatures and docstrings:
- def test_save_and_log(self): Tests that save_and_log() creates an audit record with the correct information.
- def test_to_dict(self): assert output of to_dict | Implement the Python class `ModelsTests` described below.
Class description:
Tests for abstract models
Method signatures and docstrings:
- def test_save_and_log(self): Tests that save_and_log() creates an audit record with the correct information.
- def test_to_dict(self): assert output of to_dict
<|skeleton|>
class... | 339c67b84b661a37ffe32580da72383d95666c5c | <|skeleton|>
class ModelsTests:
"""Tests for abstract models"""
def test_save_and_log(self):
"""Tests that save_and_log() creates an audit record with the correct information."""
<|body_0|>
def test_to_dict(self):
"""assert output of to_dict"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelsTests:
"""Tests for abstract models"""
def test_save_and_log(self):
"""Tests that save_and_log() creates an audit record with the correct information."""
acting_user = UserFactory.create()
order = OrderFactory.create()
original_before_json = serialize_model_object(or... | the_stack_v2_python_sparse | main/models_test.py | mitodl/bootcamp-ecommerce | train | 6 |
236e095119e026f3d122a24d26c154997b812528 | [
"super(LCNN, self).__init__()\nmodules = [LCNNBlock(n_occupancy * n_neighbor_sites, n_features)]\nfor i in range(n_conv - 1):\n modules.append(LCNNBlock(n_features * n_neighbor_sites, n_features, n_permutation))\nself.LCNN_blocks = nn.Sequential(*modules)\nself.Atom_wise_Conv = Atom_Wise_Convolution(n_features, ... | <|body_start_0|>
super(LCNN, self).__init__()
modules = [LCNNBlock(n_occupancy * n_neighbor_sites, n_features)]
for i in range(n_conv - 1):
modules.append(LCNNBlock(n_features * n_neighbor_sites, n_features, n_permutation))
self.LCNN_blocks = nn.Sequential(*modules)
s... | The Lattice Convolution Neural Network (LCNN) This model takes lattice representation of Adsorbate Surface to predict coverage effects taking into consideration the adjacent elements interaction energies. The model follows the following steps [1] It performs n lattice convolution operations. For more details look at th... | LCNN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LCNN:
"""The Lattice Convolution Neural Network (LCNN) This model takes lattice representation of Adsorbate Surface to predict coverage effects taking into consideration the adjacent elements interaction energies. The model follows the following steps [1] It performs n lattice convolution operati... | stack_v2_sparse_classes_36k_train_004034 | 18,579 | permissive | [
{
"docstring": "Parameters ---------- n_occupancy: int, default 3 number of possible occupancy n_neighbor_sites_list: int, default 19 Number of neighbors of each site. n_permutation: int, default 6 Diffrent permutations taken along diffrent directions. n_task: int, default 1 Number of tasks dropout_rate: float,... | 2 | stack_v2_sparse_classes_30k_train_000560 | Implement the Python class `LCNN` described below.
Class description:
The Lattice Convolution Neural Network (LCNN) This model takes lattice representation of Adsorbate Surface to predict coverage effects taking into consideration the adjacent elements interaction energies. The model follows the following steps [1] It... | Implement the Python class `LCNN` described below.
Class description:
The Lattice Convolution Neural Network (LCNN) This model takes lattice representation of Adsorbate Surface to predict coverage effects taking into consideration the adjacent elements interaction energies. The model follows the following steps [1] It... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class LCNN:
"""The Lattice Convolution Neural Network (LCNN) This model takes lattice representation of Adsorbate Surface to predict coverage effects taking into consideration the adjacent elements interaction energies. The model follows the following steps [1] It performs n lattice convolution operati... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LCNN:
"""The Lattice Convolution Neural Network (LCNN) This model takes lattice representation of Adsorbate Surface to predict coverage effects taking into consideration the adjacent elements interaction energies. The model follows the following steps [1] It performs n lattice convolution operations. For more... | the_stack_v2_python_sparse | deepchem/models/torch_models/lcnn.py | deepchem/deepchem | train | 4,876 |
65af826a38b585370c8030a8852221ba9a78c0aa | [
"N = len(nums)\ntotal = 0\nS = [0]\nfor num in nums:\n total += num\n S.append(total)\nfor i in xrange(0, N - 1, 1):\n for j in xrange(i + 2, N + 1, 1):\n tmp = S[j] - S[i]\n if k == 0 and tmp == 0:\n return True\n elif k != 0 and tmp % k == 0:\n return True\nretu... | <|body_start_0|>
N = len(nums)
total = 0
S = [0]
for num in nums:
total += num
S.append(total)
for i in xrange(0, N - 1, 1):
for j in xrange(i + 2, N + 1, 1):
tmp = S[j] - S[i]
if k == 0 and tmp == 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def checkSubarraySum(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_0|>
def checkSubarraySum2(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_004035 | 1,274 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: bool",
"name": "checkSubarraySum",
"signature": "def checkSubarraySum(self, nums, k)"
},
{
"docstring": ":type nums: List[int] :type k: int :rtype: bool",
"name": "checkSubarraySum2",
"signature": "def checkSubarraySum2(self, num... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkSubarraySum(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def checkSubarraySum2(self, nums, k): :type nums: List[int] :type k: int :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkSubarraySum(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def checkSubarraySum2(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
<|sk... | 70a580603d996d9843cda3c167c6e63c29df6656 | <|skeleton|>
class Solution:
def checkSubarraySum(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_0|>
def checkSubarraySum2(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def checkSubarraySum(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
N = len(nums)
total = 0
S = [0]
for num in nums:
total += num
S.append(total)
for i in xrange(0, N - 1, 1):
for j in xrange(i... | the_stack_v2_python_sparse | src/code/code_523.py | fanzijian/leet-code-practice | train | 1 | |
101f3f4be9889cb1673103418b4a0cb12404702a | [
"super().__init__(definition, cutoff_point, features_interval, prediction_start_day, prediction_interval)\nself.scale = scale\nself.denoising = denoising",
"X_train, y_train = self._resample(X_train, y_train)\nautoencoder = AutoEncoderTraining(self.batch_size, torch.cuda.is_available(), self.scale, flatten=False)... | <|body_start_0|>
super().__init__(definition, cutoff_point, features_interval, prediction_start_day, prediction_interval)
self.scale = scale
self.denoising = denoising
<|end_body_0|>
<|body_start_1|>
X_train, y_train = self._resample(X_train, y_train)
autoencoder = AutoEncoderTr... | XGBoostAutoencodersPredict | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XGBoostAutoencodersPredict:
def __init__(self, definition, cutoff_point, features_interval, prediction_start_day, prediction_interval, scale, denoising):
"""The constructor of the XGBoostPredict class Parameters ---------- definition: string The definition that you will get the labels fo... | stack_v2_sparse_classes_36k_train_004036 | 7,854 | no_license | [
{
"docstring": "The constructor of the XGBoostPredict class Parameters ---------- definition: string The definition that you will get the labels for cutoff_point: int The maximum cutoff_point where you will look your time series features_interval: int The number of days in the past that you will look the time s... | 2 | stack_v2_sparse_classes_30k_val_000215 | Implement the Python class `XGBoostAutoencodersPredict` described below.
Class description:
Implement the XGBoostAutoencodersPredict class.
Method signatures and docstrings:
- def __init__(self, definition, cutoff_point, features_interval, prediction_start_day, prediction_interval, scale, denoising): The constructor ... | Implement the Python class `XGBoostAutoencodersPredict` described below.
Class description:
Implement the XGBoostAutoencodersPredict class.
Method signatures and docstrings:
- def __init__(self, definition, cutoff_point, features_interval, prediction_start_day, prediction_interval, scale, denoising): The constructor ... | d7e42676b64d177ded11d4731e11130c129d477b | <|skeleton|>
class XGBoostAutoencodersPredict:
def __init__(self, definition, cutoff_point, features_interval, prediction_start_day, prediction_interval, scale, denoising):
"""The constructor of the XGBoostPredict class Parameters ---------- definition: string The definition that you will get the labels fo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XGBoostAutoencodersPredict:
def __init__(self, definition, cutoff_point, features_interval, prediction_start_day, prediction_interval, scale, denoising):
"""The constructor of the XGBoostPredict class Parameters ---------- definition: string The definition that you will get the labels for cutoff_point... | the_stack_v2_python_sparse | code/prediction/xgboost_prediction_autoencoders.py | mcbuehler/ssw-prediction | train | 1 | |
bcf1b9c13fa954c345b9ae9778b1cea8e402d049 | [
"super(ToKlein, self).__init__()\nself.min_norm = min_norm\nself.sum = ReduceSum(keep_dims=True)\nself.klein_constraint = KleinConstraint(self.min_norm)",
"x_2 = self.sum(x * x, -1)\nx_klein = 2 * x / (1.0 + x_2)\nx_klein = self.klein_constraint(x_klein)\nreturn x_klein"
] | <|body_start_0|>
super(ToKlein, self).__init__()
self.min_norm = min_norm
self.sum = ReduceSum(keep_dims=True)
self.klein_constraint = KleinConstraint(self.min_norm)
<|end_body_0|>
<|body_start_1|>
x_2 = self.sum(x * x, -1)
x_klein = 2 * x / (1.0 + x_2)
x_klein =... | to klein class | ToKlein | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ToKlein:
"""to klein class"""
def __init__(self, min_norm):
"""init fun"""
<|body_0|>
def construct(self, x, c):
"""class construction"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(ToKlein, self).__init__()
self.min_norm = min_no... | stack_v2_sparse_classes_36k_train_004037 | 8,596 | permissive | [
{
"docstring": "init fun",
"name": "__init__",
"signature": "def __init__(self, min_norm)"
},
{
"docstring": "class construction",
"name": "construct",
"signature": "def construct(self, x, c)"
}
] | 2 | null | Implement the Python class `ToKlein` described below.
Class description:
to klein class
Method signatures and docstrings:
- def __init__(self, min_norm): init fun
- def construct(self, x, c): class construction | Implement the Python class `ToKlein` described below.
Class description:
to klein class
Method signatures and docstrings:
- def __init__(self, min_norm): init fun
- def construct(self, x, c): class construction
<|skeleton|>
class ToKlein:
"""to klein class"""
def __init__(self, min_norm):
"""init fu... | eab643f51336dbf7d711f02d27e6516e5affee59 | <|skeleton|>
class ToKlein:
"""to klein class"""
def __init__(self, min_norm):
"""init fun"""
<|body_0|>
def construct(self, x, c):
"""class construction"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ToKlein:
"""to klein class"""
def __init__(self, min_norm):
"""init fun"""
super(ToKlein, self).__init__()
self.min_norm = min_norm
self.sum = ReduceSum(keep_dims=True)
self.klein_constraint = KleinConstraint(self.min_norm)
def construct(self, x, c):
"... | the_stack_v2_python_sparse | research/nlp/hypertext/src/poincare.py | mindspore-ai/models | train | 301 |
59b31be914a2537691382f86b0e408af190c0fa4 | [
"if not nums:\n return 0\ndp = []\nfor i in range(len(nums)):\n dp.append(1)\n for j in range(i):\n if nums[i] > nums[j]:\n dp[i] = max(dp[i], dp[j] + 1)\nreturn max(dp)",
"dp = {1: 1, 2: 2}\nfor i in range(3, n + 1):\n dp[i] = dp[i - 1] + dp[i - 2]\nreturn dp[n]",
"dp = [[0, 0]] *... | <|body_start_0|>
if not nums:
return 0
dp = []
for i in range(len(nums)):
dp.append(1)
for j in range(i):
if nums[i] > nums[j]:
dp[i] = max(dp[i], dp[j] + 1)
return max(dp)
<|end_body_0|>
<|body_start_1|>
dp... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums: List[int]) -> int:
"""300. 最长递增子序列 给你一个整数数组 nums ，找到其中最长严格递增子序列的长度。 子序列是由数组派生而来的序列，删除（或不删除）数组中的元素而不改变其余元素的顺序。 例如，[3,6,2,7] 是数组 [0,3,1,6,2,2,7] 的子序列。"""
<|body_0|>
def climb(self, n: int) -> int:
"""70. 爬楼梯问题 一次只能爬一步或者两步，一共n阶台阶 转移... | stack_v2_sparse_classes_36k_train_004038 | 3,592 | no_license | [
{
"docstring": "300. 最长递增子序列 给你一个整数数组 nums ，找到其中最长严格递增子序列的长度。 子序列是由数组派生而来的序列，删除（或不删除）数组中的元素而不改变其余元素的顺序。 例如，[3,6,2,7] 是数组 [0,3,1,6,2,2,7] 的子序列。",
"name": "lengthOfLIS",
"signature": "def lengthOfLIS(self, nums: List[int]) -> int"
},
{
"docstring": "70. 爬楼梯问题 一次只能爬一步或者两步，一共n阶台阶 转移方程 f(i) = f(i - 1... | 4 | stack_v2_sparse_classes_30k_train_021266 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLIS(self, nums: List[int]) -> int: 300. 最长递增子序列 给你一个整数数组 nums ，找到其中最长严格递增子序列的长度。 子序列是由数组派生而来的序列，删除（或不删除）数组中的元素而不改变其余元素的顺序。 例如，[3,6,2,7] 是数组 [0,3,1,6,2,2,7] 的子序列。
- de... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLIS(self, nums: List[int]) -> int: 300. 最长递增子序列 给你一个整数数组 nums ，找到其中最长严格递增子序列的长度。 子序列是由数组派生而来的序列，删除（或不删除）数组中的元素而不改变其余元素的顺序。 例如，[3,6,2,7] 是数组 [0,3,1,6,2,2,7] 的子序列。
- de... | 330330ef6bc42eeb17f4dea53c30d230506b4e8f | <|skeleton|>
class Solution:
def lengthOfLIS(self, nums: List[int]) -> int:
"""300. 最长递增子序列 给你一个整数数组 nums ，找到其中最长严格递增子序列的长度。 子序列是由数组派生而来的序列，删除（或不删除）数组中的元素而不改变其余元素的顺序。 例如，[3,6,2,7] 是数组 [0,3,1,6,2,2,7] 的子序列。"""
<|body_0|>
def climb(self, n: int) -> int:
"""70. 爬楼梯问题 一次只能爬一步或者两步，一共n阶台阶 转移... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def lengthOfLIS(self, nums: List[int]) -> int:
"""300. 最长递增子序列 给你一个整数数组 nums ，找到其中最长严格递增子序列的长度。 子序列是由数组派生而来的序列，删除（或不删除）数组中的元素而不改变其余元素的顺序。 例如，[3,6,2,7] 是数组 [0,3,1,6,2,2,7] 的子序列。"""
if not nums:
return 0
dp = []
for i in range(len(nums)):
dp.appe... | the_stack_v2_python_sparse | Code/leetcode_everyday/0310.py | NiceToMeeetU/ToGetReady | train | 0 | |
3880ab9a7769fed7f48a901f05c43c93616a002e | [
"self.word_size_bits = word_size_bits\nself.word_size = word_size = 1 << word_size_bits\nself.offset_mask = (1 << (word_size << 2)) - 1\nself.word_fmt = b'%%0%dx' % word_size\nif size is None:\n size = self.offset_mask + 1\nsuper(OffsetsStream, self).__init__(size)",
"offset = self._offset\noffset_mask = self.... | <|body_start_0|>
self.word_size_bits = word_size_bits
self.word_size = word_size = 1 << word_size_bits
self.offset_mask = (1 << (word_size << 2)) - 1
self.word_fmt = b'%%0%dx' % word_size
if size is None:
size = self.offset_mask + 1
super(OffsetsStream, self).... | >>> s = OffsetsStream(word_size_bits = 3) >>> # this bytes comparison way supports doctest under both Py2 & Py3 >>> s.read(0) == b'' True >>> s.read(1) == b'0' True >>> s.read(7) == b'0000000' True >>> s.tell() 8 >>> s.read(8) == b'00000008' True >>> s.seek(0xDEADBEEF) == 3735928559 True >>> # ... b'deadbee0', b'deadbe... | OffsetsStream | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OffsetsStream:
""">>> s = OffsetsStream(word_size_bits = 3) >>> # this bytes comparison way supports doctest under both Py2 & Py3 >>> s.read(0) == b'' True >>> s.read(1) == b'0' True >>> s.read(7) == b'0000000' True >>> s.tell() 8 >>> s.read(8) == b'00000008' True >>> s.seek(0xDEADBEEF) == 373592... | stack_v2_sparse_classes_36k_train_004039 | 2,618 | permissive | [
{
"docstring": ":param word_size_bits: log2 of word size. A word is hexadecimal ASCII representation of that word offset. :param size: logical size of the stream. `None`: size is big enough to fit all words.",
"name": "__init__",
"signature": "def __init__(self, word_size_bits=4, size=None)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_019236 | Implement the Python class `OffsetsStream` described below.
Class description:
>>> s = OffsetsStream(word_size_bits = 3) >>> # this bytes comparison way supports doctest under both Py2 & Py3 >>> s.read(0) == b'' True >>> s.read(1) == b'0' True >>> s.read(7) == b'0000000' True >>> s.tell() 8 >>> s.read(8) == b'00000008... | Implement the Python class `OffsetsStream` described below.
Class description:
>>> s = OffsetsStream(word_size_bits = 3) >>> # this bytes comparison way supports doctest under both Py2 & Py3 >>> s.read(0) == b'' True >>> s.read(1) == b'0' True >>> s.read(7) == b'0000000' True >>> s.tell() 8 >>> s.read(8) == b'00000008... | 93e03c2b3f880f5c7c9f90e1ba5593dbf602bdb9 | <|skeleton|>
class OffsetsStream:
""">>> s = OffsetsStream(word_size_bits = 3) >>> # this bytes comparison way supports doctest under both Py2 & Py3 >>> s.read(0) == b'' True >>> s.read(1) == b'0' True >>> s.read(7) == b'0000000' True >>> s.tell() 8 >>> s.read(8) == b'00000008' True >>> s.seek(0xDEADBEEF) == 373592... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OffsetsStream:
""">>> s = OffsetsStream(word_size_bits = 3) >>> # this bytes comparison way supports doctest under both Py2 & Py3 >>> s.read(0) == b'' True >>> s.read(1) == b'0' True >>> s.read(7) == b'0000000' True >>> s.tell() 8 >>> s.read(8) == b'00000008' True >>> s.seek(0xDEADBEEF) == 3735928559 True >>>... | the_stack_v2_python_sparse | common/offsets_stream.py | ispras/qdt | train | 38 |
bdfb529d93d4abd67815271d4954078cb8124a6d | [
"super().__init__(out_dir=out_dir, model=model, test_loader=test_loader)\nassert inspect.ismethod(model.predict_3D), 'model must have the method `predict_3D`'\nif pseudo_3D:\n assert inspect.ismethod(model.predict_3D_pseudo3D_2Dconv), 'model must have the method `predict_3D_pseudo3D_2Dconv`'\nself.pseudo_3D = ps... | <|body_start_0|>
super().__init__(out_dir=out_dir, model=model, test_loader=test_loader)
assert inspect.ismethod(model.predict_3D), 'model must have the method `predict_3D`'
if pseudo_3D:
assert inspect.ismethod(model.predict_3D_pseudo3D_2Dconv), 'model must have the method `predict_... | Inference for a single model for every file generated by `test_loader`. Predictions are saved in `out_dir`. Predictions are done on the resized predictions. Post-processing only includes removing small 3D connected components. | General3DPredictor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class General3DPredictor:
"""Inference for a single model for every file generated by `test_loader`. Predictions are saved in `out_dir`. Predictions are done on the resized predictions. Post-processing only includes removing small 3D connected components."""
def __init__(self, out_dir, model, test... | stack_v2_sparse_classes_36k_train_004040 | 2,477 | permissive | [
{
"docstring": "Attributes out_dir (str): path to the output directory to store predictions model (torch.nn.Module): class with the `predict_3D` method for predicting a single patient volume. test_loader: Iterable instance for generating data (pref. torch DataLoader) must have the __len__ arg. pred_3D_params (d... | 2 | stack_v2_sparse_classes_30k_train_018066 | Implement the Python class `General3DPredictor` described below.
Class description:
Inference for a single model for every file generated by `test_loader`. Predictions are saved in `out_dir`. Predictions are done on the resized predictions. Post-processing only includes removing small 3D connected components.
Method ... | Implement the Python class `General3DPredictor` described below.
Class description:
Inference for a single model for every file generated by `test_loader`. Predictions are saved in `out_dir`. Predictions are done on the resized predictions. Post-processing only includes removing small 3D connected components.
Method ... | 81d7413022220ea86a23212737b3682e84ae74a4 | <|skeleton|>
class General3DPredictor:
"""Inference for a single model for every file generated by `test_loader`. Predictions are saved in `out_dir`. Predictions are done on the resized predictions. Post-processing only includes removing small 3D connected components."""
def __init__(self, out_dir, model, test... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class General3DPredictor:
"""Inference for a single model for every file generated by `test_loader`. Predictions are saved in `out_dir`. Predictions are done on the resized predictions. Post-processing only includes removing small 3D connected components."""
def __init__(self, out_dir, model, test_loader, pseu... | the_stack_v2_python_sparse | kits19cnn/inference/general_predictors.py | jchen42703/kits19-2d-reproduce | train | 9 |
1bebf5d0ceac2ebb9379f272ee52d5b9dac018d6 | [
"library_key = LibraryLocatorV2.from_string(lib_key_str)\napi.require_permission_for_library_key(library_key, request.user, permissions.CAN_VIEW_THIS_CONTENT_LIBRARY)\nqueryset = api.ContentLibrary.objects.get_by_key(library_key).import_tasks\nresult = ContentLibraryBlockImportTaskSerializer(queryset, many=True).da... | <|body_start_0|>
library_key = LibraryLocatorV2.from_string(lib_key_str)
api.require_permission_for_library_key(library_key, request.user, permissions.CAN_VIEW_THIS_CONTENT_LIBRARY)
queryset = api.ContentLibrary.objects.get_by_key(library_key).import_tasks
result = ContentLibraryBlockImp... | Import blocks from Courseware through modulestore. | LibraryImportTaskViewSet | [
"AGPL-3.0-only",
"AGPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LibraryImportTaskViewSet:
"""Import blocks from Courseware through modulestore."""
def list(self, request, lib_key_str):
"""List all import tasks for this library."""
<|body_0|>
def create(self, request, lib_key_str):
"""Create and queue an import tasks for this ... | stack_v2_sparse_classes_36k_train_004041 | 42,120 | permissive | [
{
"docstring": "List all import tasks for this library.",
"name": "list",
"signature": "def list(self, request, lib_key_str)"
},
{
"docstring": "Create and queue an import tasks for this library.",
"name": "create",
"signature": "def create(self, request, lib_key_str)"
},
{
"docs... | 3 | null | Implement the Python class `LibraryImportTaskViewSet` described below.
Class description:
Import blocks from Courseware through modulestore.
Method signatures and docstrings:
- def list(self, request, lib_key_str): List all import tasks for this library.
- def create(self, request, lib_key_str): Create and queue an i... | Implement the Python class `LibraryImportTaskViewSet` described below.
Class description:
Import blocks from Courseware through modulestore.
Method signatures and docstrings:
- def list(self, request, lib_key_str): List all import tasks for this library.
- def create(self, request, lib_key_str): Create and queue an i... | 5809eaca7079a15ee56b0b7fcfea425337046c97 | <|skeleton|>
class LibraryImportTaskViewSet:
"""Import blocks from Courseware through modulestore."""
def list(self, request, lib_key_str):
"""List all import tasks for this library."""
<|body_0|>
def create(self, request, lib_key_str):
"""Create and queue an import tasks for this ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LibraryImportTaskViewSet:
"""Import blocks from Courseware through modulestore."""
def list(self, request, lib_key_str):
"""List all import tasks for this library."""
library_key = LibraryLocatorV2.from_string(lib_key_str)
api.require_permission_for_library_key(library_key, reques... | the_stack_v2_python_sparse | Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/content_libraries/views.py | luque/better-ways-of-thinking-about-software | train | 3 |
5c247065e6b7794a8becc9aa5e092f7d5e2dd1bf | [
"super(MonomialBasisFunctionsMethod, self).__init__(A, B=B, p=p, ti=ti, options=options, verbose=verbose)\nif ti == []:\n self.t1 = 1.0 / self.tau0\nelse:\n if isinstance(ti, list):\n ti = numpy.array(ti)\n elif isinstance(ti, Number):\n ti = numpy.array([ti])\n if ti.size != 1:\n r... | <|body_start_0|>
super(MonomialBasisFunctionsMethod, self).__init__(A, B=B, p=p, ti=ti, options=options, verbose=verbose)
if ti == []:
self.t1 = 1.0 / self.tau0
else:
if isinstance(ti, list):
ti = numpy.array(ti)
elif isinstance(ti, Number):
... | Interpolate Schatten norm (or anti-norm) of an affine matrix function using monomial basis functions (MBF) method. This class accepts only *one* interpolant point (:math:`q = 1`). That is, the argument ``ti`` should be only one number or a list of the length 1. A better method is ``'imbf'`` which accepts arbitrary numb... | MonomialBasisFunctionsMethod | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MonomialBasisFunctionsMethod:
"""Interpolate Schatten norm (or anti-norm) of an affine matrix function using monomial basis functions (MBF) method. This class accepts only *one* interpolant point (:math:`q = 1`). That is, the argument ``ti`` should be only one number or a list of the length 1. A ... | stack_v2_sparse_classes_36k_train_004042 | 14,850 | permissive | [
{
"docstring": "Initializes the base class and attributes, namely, the trace at the interpolant point.",
"name": "__init__",
"signature": "def __init__(self, A, B=None, p=2, options={}, verbose=False, ti=[])"
},
{
"docstring": "Computes the trace at the interpolant point. This function is used i... | 3 | stack_v2_sparse_classes_30k_train_013004 | Implement the Python class `MonomialBasisFunctionsMethod` described below.
Class description:
Interpolate Schatten norm (or anti-norm) of an affine matrix function using monomial basis functions (MBF) method. This class accepts only *one* interpolant point (:math:`q = 1`). That is, the argument ``ti`` should be only o... | Implement the Python class `MonomialBasisFunctionsMethod` described below.
Class description:
Interpolate Schatten norm (or anti-norm) of an affine matrix function using monomial basis functions (MBF) method. This class accepts only *one* interpolant point (:math:`q = 1`). That is, the argument ``ti`` should be only o... | de867f131a4cda7d60a68bf0558e896fae89d776 | <|skeleton|>
class MonomialBasisFunctionsMethod:
"""Interpolate Schatten norm (or anti-norm) of an affine matrix function using monomial basis functions (MBF) method. This class accepts only *one* interpolant point (:math:`q = 1`). That is, the argument ``ti`` should be only one number or a list of the length 1. A ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MonomialBasisFunctionsMethod:
"""Interpolate Schatten norm (or anti-norm) of an affine matrix function using monomial basis functions (MBF) method. This class accepts only *one* interpolant point (:math:`q = 1`). That is, the argument ``ti`` should be only one number or a list of the length 1. A better method... | the_stack_v2_python_sparse | imate/interpolator/_monomial_basis_functions_method.py | ameli/imate | train | 5 |
486bf90aaebe5f015056c626ce14a695a50a6c63 | [
"super().__init__()\nself.overhead_time_queue = queue\nself.data_incoming = True\nConsumerThread.id_counter += 1\nself.id = ConsumerThread.id_counter",
"if len(self.overhead_time_queue) == 0:\n print(f'Consumer {self.id} is sleeping since queue is empty')\n time.sleep(0.75)\nwhile self.data_incoming or len(... | <|body_start_0|>
super().__init__()
self.overhead_time_queue = queue
self.data_incoming = True
ConsumerThread.id_counter += 1
self.id = ConsumerThread.id_counter
<|end_body_0|>
<|body_start_1|>
if len(self.overhead_time_queue) == 0:
print(f'Consumer {self.id}... | Consumes data from CityOverheadTimeQueue. | ConsumerThread | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConsumerThread:
"""Consumes data from CityOverheadTimeQueue."""
def __init__(self, queue: CityOverheadTimeQueue):
""":param queue: a CityOverheadTimeQueue"""
<|body_0|>
def run(self) -> None:
"""Gets data from the queue and prints it."""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k_train_004043 | 3,229 | no_license | [
{
"docstring": ":param queue: a CityOverheadTimeQueue",
"name": "__init__",
"signature": "def __init__(self, queue: CityOverheadTimeQueue)"
},
{
"docstring": "Gets data from the queue and prints it.",
"name": "run",
"signature": "def run(self) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_021627 | Implement the Python class `ConsumerThread` described below.
Class description:
Consumes data from CityOverheadTimeQueue.
Method signatures and docstrings:
- def __init__(self, queue: CityOverheadTimeQueue): :param queue: a CityOverheadTimeQueue
- def run(self) -> None: Gets data from the queue and prints it. | Implement the Python class `ConsumerThread` described below.
Class description:
Consumes data from CityOverheadTimeQueue.
Method signatures and docstrings:
- def __init__(self, queue: CityOverheadTimeQueue): :param queue: a CityOverheadTimeQueue
- def run(self) -> None: Gets data from the queue and prints it.
<|skel... | 11c3806aee78fa0e78bdf8037d4c203645df5500 | <|skeleton|>
class ConsumerThread:
"""Consumes data from CityOverheadTimeQueue."""
def __init__(self, queue: CityOverheadTimeQueue):
""":param queue: a CityOverheadTimeQueue"""
<|body_0|>
def run(self) -> None:
"""Gets data from the queue and prints it."""
<|body_1|>
<|end... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConsumerThread:
"""Consumes data from CityOverheadTimeQueue."""
def __init__(self, queue: CityOverheadTimeQueue):
""":param queue: a CityOverheadTimeQueue"""
super().__init__()
self.overhead_time_queue = queue
self.data_incoming = True
ConsumerThread.id_counter += ... | the_stack_v2_python_sparse | Labs/Lab10/producer_consumer.py | chrisyandev/PythonAssignments | train | 0 |
99fd14e433ce72ec855a699c8327fc057f812260 | [
"super().__init__()\nself.resizable(width=False, height=False)\nself.geometry()\nself.title('Zip File Maker')\nself.treeview_frame = ttk.Frame(self)\nself.button_row_frame = ttk.Frame(self)\nself.files_treeview = ttk.Treeview(self.treeview_frame, columns=('path',), selectmode='browse', show='tree')\nself.files_tree... | <|body_start_0|>
super().__init__()
self.resizable(width=False, height=False)
self.geometry()
self.title('Zip File Maker')
self.treeview_frame = ttk.Frame(self)
self.button_row_frame = ttk.Frame(self)
self.files_treeview = ttk.Treeview(self.treeview_frame, columns... | The class for interacting with tkinter. | MainWindow | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MainWindow:
"""The class for interacting with tkinter."""
def __init__(self):
"""Initialize main window."""
<|body_0|>
def add_files(self):
"""Ask user to give file path and save new file."""
<|body_1|>
def remove_file(self):
"""Remove curren... | stack_v2_sparse_classes_36k_train_004044 | 4,820 | permissive | [
{
"docstring": "Initialize main window.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Ask user to give file path and save new file.",
"name": "add_files",
"signature": "def add_files(self)"
},
{
"docstring": "Remove currently selected file.",
"nam... | 4 | stack_v2_sparse_classes_30k_train_000892 | Implement the Python class `MainWindow` described below.
Class description:
The class for interacting with tkinter.
Method signatures and docstrings:
- def __init__(self): Initialize main window.
- def add_files(self): Ask user to give file path and save new file.
- def remove_file(self): Remove currently selected fi... | Implement the Python class `MainWindow` described below.
Class description:
The class for interacting with tkinter.
Method signatures and docstrings:
- def __init__(self): Initialize main window.
- def add_files(self): Ask user to give file path and save new file.
- def remove_file(self): Remove currently selected fi... | 73b554d9796510fc86e5fc55016732aa866266c6 | <|skeleton|>
class MainWindow:
"""The class for interacting with tkinter."""
def __init__(self):
"""Initialize main window."""
<|body_0|>
def add_files(self):
"""Ask user to give file path and save new file."""
<|body_1|>
def remove_file(self):
"""Remove curren... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MainWindow:
"""The class for interacting with tkinter."""
def __init__(self):
"""Initialize main window."""
super().__init__()
self.resizable(width=False, height=False)
self.geometry()
self.title('Zip File Maker')
self.treeview_frame = ttk.Frame(self)
... | the_stack_v2_python_sparse | Files/Zip File Maker/zip_file_maker.pyw | fossabot/IdeaBag2-Solutions | train | 0 |
1d4abea7461146eea951c58128195b614eddf9a1 | [
"args = ['modeling/airports.csv']\nopts = {}\ncall_command('import_airport', *args, **opts)",
"args = ['modeling/boards.csv']\nopts = {}\ncall_command('import_board', *args, **opts)",
"args = ['modeling/hotels.csv']\nopts = {}\ncall_command('import_hotel', *args, **opts)",
"args = ['modeling/markets.csv']\nop... | <|body_start_0|>
args = ['modeling/airports.csv']
opts = {}
call_command('import_airport', *args, **opts)
<|end_body_0|>
<|body_start_1|>
args = ['modeling/boards.csv']
opts = {}
call_command('import_board', *args, **opts)
<|end_body_1|>
<|body_start_2|>
args = ... | CommandsTestCase | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommandsTestCase:
def test_import_airport(self):
"""Test import_airport command."""
<|body_0|>
def test_import_board(self):
"""Test import_board command."""
<|body_1|>
def test_import_hotel(self):
"""Test import_hotel command."""
<|body_2... | stack_v2_sparse_classes_36k_train_004045 | 5,159 | permissive | [
{
"docstring": "Test import_airport command.",
"name": "test_import_airport",
"signature": "def test_import_airport(self)"
},
{
"docstring": "Test import_board command.",
"name": "test_import_board",
"signature": "def test_import_board(self)"
},
{
"docstring": "Test import_hotel ... | 6 | stack_v2_sparse_classes_30k_train_019017 | Implement the Python class `CommandsTestCase` described below.
Class description:
Implement the CommandsTestCase class.
Method signatures and docstrings:
- def test_import_airport(self): Test import_airport command.
- def test_import_board(self): Test import_board command.
- def test_import_hotel(self): Test import_h... | Implement the Python class `CommandsTestCase` described below.
Class description:
Implement the CommandsTestCase class.
Method signatures and docstrings:
- def test_import_airport(self): Test import_airport command.
- def test_import_board(self): Test import_board command.
- def test_import_hotel(self): Test import_h... | 0abcb82bf30540ac5cd57d5ec9178e692a1a2ca6 | <|skeleton|>
class CommandsTestCase:
def test_import_airport(self):
"""Test import_airport command."""
<|body_0|>
def test_import_board(self):
"""Test import_board command."""
<|body_1|>
def test_import_hotel(self):
"""Test import_hotel command."""
<|body_2... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommandsTestCase:
def test_import_airport(self):
"""Test import_airport command."""
args = ['modeling/airports.csv']
opts = {}
call_command('import_airport', *args, **opts)
def test_import_board(self):
"""Test import_board command."""
args = ['modeling/boar... | the_stack_v2_python_sparse | mdm/test.py | lordoftheflies/gargantula-scrapersite | train | 0 | |
5b7253d5f0c707b4dd5cfa1bf0ebb5a437c24aed | [
"self.capacity = capacity\nself.current = 0\nself.start = Node(0, 0)\nself.end = Node(0, 0)\nself.start.next = self.end\nself.end.prev = self.start\nself.node_map = {}",
"if key not in self.node_map:\n return -1\nnode = self.node_map[key]\nnode.next.prev = node.prev\nnode.prev.next = node.next\nnode.next = sel... | <|body_start_0|>
self.capacity = capacity
self.current = 0
self.start = Node(0, 0)
self.end = Node(0, 0)
self.start.next = self.end
self.end.prev = self.start
self.node_map = {}
<|end_body_0|>
<|body_start_1|>
if key not in self.node_map:
retu... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k_train_004046 | 1,953 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: None",
"name": "pu... | 3 | null | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None
<|sk... | 30bfafb6a7727c9305b22933b63d9d645182c633 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.current = 0
self.start = Node(0, 0)
self.end = Node(0, 0)
self.start.next = self.end
self.end.prev = self.start
self.node_map = {}
def get(self, ... | the_stack_v2_python_sparse | leetcode/Design/lru-cache.py | iCodeIN/competitive-programming-5 | train | 0 | |
0cabedfadb79d035c5e8bbd8a8b5155911fe6fe4 | [
"super().__init__(img=bullet_img, x=x, y=y)\nself.set_sprite_center()\nself.speed = 300\nself.visible = True",
"if self.visible:\n self.move(self.speed * dt)\n if self.x < 0 or self.x > 1024 or self.y > 768:\n self.visible = False"
] | <|body_start_0|>
super().__init__(img=bullet_img, x=x, y=y)
self.set_sprite_center()
self.speed = 300
self.visible = True
<|end_body_0|>
<|body_start_1|>
if self.visible:
self.move(self.speed * dt)
if self.x < 0 or self.x > 1024 or self.y > 768:
... | 炮弹精灵 | BulletSprite | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BulletSprite:
"""炮弹精灵"""
def __init__(self, x=0, y=0):
"""初始化"""
<|body_0|>
def fire_move(self, dt):
"""移动炮弹"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super().__init__(img=bullet_img, x=x, y=y)
self.set_sprite_center()
self... | stack_v2_sparse_classes_36k_train_004047 | 4,509 | no_license | [
{
"docstring": "初始化",
"name": "__init__",
"signature": "def __init__(self, x=0, y=0)"
},
{
"docstring": "移动炮弹",
"name": "fire_move",
"signature": "def fire_move(self, dt)"
}
] | 2 | null | Implement the Python class `BulletSprite` described below.
Class description:
炮弹精灵
Method signatures and docstrings:
- def __init__(self, x=0, y=0): 初始化
- def fire_move(self, dt): 移动炮弹 | Implement the Python class `BulletSprite` described below.
Class description:
炮弹精灵
Method signatures and docstrings:
- def __init__(self, x=0, y=0): 初始化
- def fire_move(self, dt): 移动炮弹
<|skeleton|>
class BulletSprite:
"""炮弹精灵"""
def __init__(self, x=0, y=0):
"""初始化"""
<|body_0|>
def fir... | 941e29d5f39092b02f8486a435e61c7ec2bdcdb6 | <|skeleton|>
class BulletSprite:
"""炮弹精灵"""
def __init__(self, x=0, y=0):
"""初始化"""
<|body_0|>
def fire_move(self, dt):
"""移动炮弹"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BulletSprite:
"""炮弹精灵"""
def __init__(self, x=0, y=0):
"""初始化"""
super().__init__(img=bullet_img, x=x, y=y)
self.set_sprite_center()
self.speed = 300
self.visible = True
def fire_move(self, dt):
"""移动炮弹"""
if self.visible:
self.move... | the_stack_v2_python_sparse | Python趣味编程：从入门到人工智能/第31课_捕鱼达人/示例程序/version3/game_sprites.py | zhy0313/children-python | train | 0 |
83e7fb03dcb79eb6b1b7ca25aeac32d5380d25e9 | [
"if num_splits is None:\n num_splits = len(self.list_of_blocks)\nif other_axis_partition is not None:\n return self._wrap_partitions(self.deploy_func_between_two_axis_partitions(self.axis, func, num_splits, len(self.list_of_blocks), kwargs, *tuple(self.list_of_blocks + other_axis_partition.list_of_blocks)))\n... | <|body_start_0|>
if num_splits is None:
num_splits = len(self.list_of_blocks)
if other_axis_partition is not None:
return self._wrap_partitions(self.deploy_func_between_two_axis_partitions(self.axis, func, num_splits, len(self.list_of_blocks), kwargs, *tuple(self.list_of_blocks +... | This abstract class is created to simplify and consolidate the code for AxisPartitions that run pandas. Because much of the code is similar, this allows us to reuse this code. Subclasses must implement `list_of_blocks` which unwraps the `RemotePartition` objects and creates something interpretable as a pandas DataFrame... | PandasFrameAxisPartition | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PandasFrameAxisPartition:
"""This abstract class is created to simplify and consolidate the code for AxisPartitions that run pandas. Because much of the code is similar, this allows us to reuse this code. Subclasses must implement `list_of_blocks` which unwraps the `RemotePartition` objects and c... | stack_v2_sparse_classes_36k_train_004048 | 10,118 | permissive | [
{
"docstring": "Applies func to the object in the plasma store. See notes in Parent class about this method. Args: func: The function to apply. num_splits: The number of times to split the result object. other_axis_partition: Another `PandasOnRayFrameAxisPartition` object to apply to func with this one. maintai... | 4 | stack_v2_sparse_classes_30k_train_006845 | Implement the Python class `PandasFrameAxisPartition` described below.
Class description:
This abstract class is created to simplify and consolidate the code for AxisPartitions that run pandas. Because much of the code is similar, this allows us to reuse this code. Subclasses must implement `list_of_blocks` which unwr... | Implement the Python class `PandasFrameAxisPartition` described below.
Class description:
This abstract class is created to simplify and consolidate the code for AxisPartitions that run pandas. Because much of the code is similar, this allows us to reuse this code. Subclasses must implement `list_of_blocks` which unwr... | 90191f115e0ad57394598551fd2fd2ee8f70ed43 | <|skeleton|>
class PandasFrameAxisPartition:
"""This abstract class is created to simplify and consolidate the code for AxisPartitions that run pandas. Because much of the code is similar, this allows us to reuse this code. Subclasses must implement `list_of_blocks` which unwraps the `RemotePartition` objects and c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PandasFrameAxisPartition:
"""This abstract class is created to simplify and consolidate the code for AxisPartitions that run pandas. Because much of the code is similar, this allows us to reuse this code. Subclasses must implement `list_of_blocks` which unwraps the `RemotePartition` objects and creates someth... | the_stack_v2_python_sparse | modin/engines/base/frame/axis_partition.py | devin-petersohn/modin | train | 2 |
2d0a694ebbca739474979d7b314d7748d2cde069 | [
"budget_pool = self.pool.get('account.budget')\nbudget_line_pool = self.pool.get('account.budget.lines')\nfor r in self.browse(cr, uid, ids, context=context):\n if r.type == 'transfer' and (not r.line_ids):\n raise osv.except_osv(_('Error!'), _('You cannot complete Transfer Operations without any Budget l... | <|body_start_0|>
budget_pool = self.pool.get('account.budget')
budget_line_pool = self.pool.get('account.budget.lines')
for r in self.browse(cr, uid, ids, context=context):
if r.type == 'transfer' and (not r.line_ids):
raise osv.except_osv(_('Error!'), _('You cannot c... | Account Budget Operation. Allow accountant to transfer special amount from multiple budget lines to another for cash/plan budgets, Beside budget increase operation. | account_budget_operation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class account_budget_operation:
"""Account Budget Operation. Allow accountant to transfer special amount from multiple budget lines to another for cash/plan budgets, Beside budget increase operation."""
def complete(self, cr, uid, ids, context={}):
"""Workflow function change state to comp... | stack_v2_sparse_classes_36k_train_004049 | 5,063 | no_license | [
{
"docstring": "Workflow function change state to complete and compute amount value & set operation number @return: True",
"name": "complete",
"signature": "def complete(self, cr, uid, ids, context={})"
},
{
"docstring": "Execute the operation by calling transfer function in budget line and chan... | 2 | stack_v2_sparse_classes_30k_train_003685 | Implement the Python class `account_budget_operation` described below.
Class description:
Account Budget Operation. Allow accountant to transfer special amount from multiple budget lines to another for cash/plan budgets, Beside budget increase operation.
Method signatures and docstrings:
- def complete(self, cr, uid,... | Implement the Python class `account_budget_operation` described below.
Class description:
Account Budget Operation. Allow accountant to transfer special amount from multiple budget lines to another for cash/plan budgets, Beside budget increase operation.
Method signatures and docstrings:
- def complete(self, cr, uid,... | 0b997095c260d58b026440967fea3a202bef7efb | <|skeleton|>
class account_budget_operation:
"""Account Budget Operation. Allow accountant to transfer special amount from multiple budget lines to another for cash/plan budgets, Beside budget increase operation."""
def complete(self, cr, uid, ids, context={}):
"""Workflow function change state to comp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class account_budget_operation:
"""Account Budget Operation. Allow accountant to transfer special amount from multiple budget lines to another for cash/plan budgets, Beside budget increase operation."""
def complete(self, cr, uid, ids, context={}):
"""Workflow function change state to complete and comp... | the_stack_v2_python_sparse | v_7/GDS/common_shamil_v3/account_budget_cash/account_budget_operation.py | musabahmed/baba | train | 0 |
1aa1a0058a6e7d11a04fac660de3b22d5d2813f1 | [
"super().__init__(params)\nself.lr = lr\nself.momentum = momentum\nself.v_weights = [np.zeros_like(t.weights) for t in self.params]\nself.v_bias = [np.zeros_like(t.bias) for t in self.params]",
"for i, object in enumerate(self.params):\n self.v_weights[i] = self.momentum * self.v_weights[i]\n self.v_bias[i]... | <|body_start_0|>
super().__init__(params)
self.lr = lr
self.momentum = momentum
self.v_weights = [np.zeros_like(t.weights) for t in self.params]
self.v_bias = [np.zeros_like(t.bias) for t in self.params]
<|end_body_0|>
<|body_start_1|>
for i, object in enumerate(self.par... | SGD | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SGD:
def __init__(self, params, lr=0.001, momentum=0.0):
"""The Stochastic Gradient Descent optimzer (SGD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent"""
<|body_0|>
def step(self):
... | stack_v2_sparse_classes_36k_train_004050 | 3,421 | no_license | [
{
"docstring": "The Stochastic Gradient Descent optimzer (SGD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent",
"name": "__init__",
"signature": "def __init__(self, params, lr=0.001, momentum=0.0)"
},
{
"docstr... | 2 | stack_v2_sparse_classes_30k_val_000149 | Implement the Python class `SGD` described below.
Class description:
Implement the SGD class.
Method signatures and docstrings:
- def __init__(self, params, lr=0.001, momentum=0.0): The Stochastic Gradient Descent optimzer (SGD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Dete... | Implement the Python class `SGD` described below.
Class description:
Implement the SGD class.
Method signatures and docstrings:
- def __init__(self, params, lr=0.001, momentum=0.0): The Stochastic Gradient Descent optimzer (SGD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Dete... | 07ff58ae68264e3a9b820d10e84d82f8a3ca99b5 | <|skeleton|>
class SGD:
def __init__(self, params, lr=0.001, momentum=0.0):
"""The Stochastic Gradient Descent optimzer (SGD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent"""
<|body_0|>
def step(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SGD:
def __init__(self, params, lr=0.001, momentum=0.0):
"""The Stochastic Gradient Descent optimzer (SGD) :param lr: the learning rate to use :param momentum: Value variable between 0 and 1. Determines the velocity of the gradient descent"""
super().__init__(params)
self.lr = lr
... | the_stack_v2_python_sparse | wavegrad/optimizers.py | vlnraf/WaveGrad | train | 1 | |
51c504ccd7085f189941e9a1e48b821439d5c1cc | [
"import rdkit.Chem\nimport logging\nrdmol = rdkit.Chem.MolFromSmiles('CCC')\nmessage = debug_rdkit_mol(rdmol, level=logging.INFO)\nself.assertIsNotNone(message)",
"mol = Molecule().from_adjacency_list('\\n1 C u0 p0 c0 {2,D} {3,S} {4,S}\\n2 O u0 p2 c0 {1,D}\\n3 H u0 p0 c0 {1,S}\\n4 H u0 p0 c0 {1,S}\\n')\nrdmol = t... | <|body_start_0|>
import rdkit.Chem
import logging
rdmol = rdkit.Chem.MolFromSmiles('CCC')
message = debug_rdkit_mol(rdmol, level=logging.INFO)
self.assertIsNotNone(message)
<|end_body_0|>
<|body_start_1|>
mol = Molecule().from_adjacency_list('\n1 C u0 p0 c0 {2,D} {3,S} {... | RDKitTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RDKitTest:
def test_debugger(self):
"""Test the debug_rdkit_mol(rdmol) function doesn't crash We can't really test it in the unit testing framework, because that already captures and redirects standard output, and that conflicts with the function, but this checks it doesn't crash."""
... | stack_v2_sparse_classes_36k_train_004051 | 7,587 | permissive | [
{
"docstring": "Test the debug_rdkit_mol(rdmol) function doesn't crash We can't really test it in the unit testing framework, because that already captures and redirects standard output, and that conflicts with the function, but this checks it doesn't crash.",
"name": "test_debugger",
"signature": "def ... | 4 | null | Implement the Python class `RDKitTest` described below.
Class description:
Implement the RDKitTest class.
Method signatures and docstrings:
- def test_debugger(self): Test the debug_rdkit_mol(rdmol) function doesn't crash We can't really test it in the unit testing framework, because that already captures and redirec... | Implement the Python class `RDKitTest` described below.
Class description:
Implement the RDKitTest class.
Method signatures and docstrings:
- def test_debugger(self): Test the debug_rdkit_mol(rdmol) function doesn't crash We can't really test it in the unit testing framework, because that already captures and redirec... | 349a4af759cf8877197772cd7eaca1e51d46eff5 | <|skeleton|>
class RDKitTest:
def test_debugger(self):
"""Test the debug_rdkit_mol(rdmol) function doesn't crash We can't really test it in the unit testing framework, because that already captures and redirects standard output, and that conflicts with the function, but this checks it doesn't crash."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RDKitTest:
def test_debugger(self):
"""Test the debug_rdkit_mol(rdmol) function doesn't crash We can't really test it in the unit testing framework, because that already captures and redirects standard output, and that conflicts with the function, but this checks it doesn't crash."""
import rd... | the_stack_v2_python_sparse | rmgpy/molecule/converterTest.py | CanePan-cc/CanePanWorkshop | train | 2 | |
f976c2242466f0bb05e759080fbbf959bb7ef18e | [
"x = [i.replace(',', '') for i in list]\ny = [i for i in x if i != '--']\nz = [i for i in y if i != '0']\nints = [int(i) for i in z]\nreturn ints",
"x = [i for i in list if i != '--']\ny = [i for i in x if i != '0']\nreal = [float(i) for i in y]\nreturn real"
] | <|body_start_0|>
x = [i.replace(',', '') for i in list]
y = [i for i in x if i != '--']
z = [i for i in y if i != '0']
ints = [int(i) for i in z]
return ints
<|end_body_0|>
<|body_start_1|>
x = [i for i in list if i != '--']
y = [i for i in x if i != '0']
... | Used to process a list of strings to a list of usable integers or floats. | DataTypes | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataTypes:
"""Used to process a list of strings to a list of usable integers or floats."""
def integers(cls, list):
"""Returns a list of integers, from a list of strings"""
<|body_0|>
def floats(cls, list):
"""Returns a list of floats from a list of strings"""
... | stack_v2_sparse_classes_36k_train_004052 | 48,979 | no_license | [
{
"docstring": "Returns a list of integers, from a list of strings",
"name": "integers",
"signature": "def integers(cls, list)"
},
{
"docstring": "Returns a list of floats from a list of strings",
"name": "floats",
"signature": "def floats(cls, list)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018919 | Implement the Python class `DataTypes` described below.
Class description:
Used to process a list of strings to a list of usable integers or floats.
Method signatures and docstrings:
- def integers(cls, list): Returns a list of integers, from a list of strings
- def floats(cls, list): Returns a list of floats from a ... | Implement the Python class `DataTypes` described below.
Class description:
Used to process a list of strings to a list of usable integers or floats.
Method signatures and docstrings:
- def integers(cls, list): Returns a list of integers, from a list of strings
- def floats(cls, list): Returns a list of floats from a ... | 8004577bd11d60534d6106fb1893209431a70697 | <|skeleton|>
class DataTypes:
"""Used to process a list of strings to a list of usable integers or floats."""
def integers(cls, list):
"""Returns a list of integers, from a list of strings"""
<|body_0|>
def floats(cls, list):
"""Returns a list of floats from a list of strings"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataTypes:
"""Used to process a list of strings to a list of usable integers or floats."""
def integers(cls, list):
"""Returns a list of integers, from a list of strings"""
x = [i.replace(',', '') for i in list]
y = [i for i in x if i != '--']
z = [i for i in y if i != '0'... | the_stack_v2_python_sparse | main/data_functions.py | ytrevor81/NFL-Stats-Library | train | 1 |
f022a6d164c5e16b5ae514cd391b1e5e01c2c93c | [
"pobj = pickle.dumps(obj, protocol)\nzobj = zlib.compress(pobj)\nreturn self.send(zobj, flags=flags)",
"zobj = self.recv(flags)\npobj = zlib.decompress(zobj)\nreturn pickle.loads(pobj)",
"md = dict(dtype=str(A.dtype), shape=A.shape)\nself.send_json(md, flags | zmq.SNDMORE)\nreturn self.send(A, flags, copy=copy,... | <|body_start_0|>
pobj = pickle.dumps(obj, protocol)
zobj = zlib.compress(pobj)
return self.send(zobj, flags=flags)
<|end_body_0|>
<|body_start_1|>
zobj = self.recv(flags)
pobj = zlib.decompress(zobj)
return pickle.loads(pobj)
<|end_body_1|>
<|body_start_2|>
md =... | A class with some extra serialization methods send_zipped_pickle is just like send_pyobj, but uses zlib to compress the stream before sending. send_array sends numpy arrays with metadata necessary for reconstructing the array on the other side (dtype,shape). | SerializingSocket | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SerializingSocket:
"""A class with some extra serialization methods send_zipped_pickle is just like send_pyobj, but uses zlib to compress the stream before sending. send_array sends numpy arrays with metadata necessary for reconstructing the array on the other side (dtype,shape)."""
def send... | stack_v2_sparse_classes_36k_train_004053 | 3,404 | permissive | [
{
"docstring": "pack and compress an object with pickle and zlib.",
"name": "send_zipped_pickle",
"signature": "def send_zipped_pickle(self, obj, flags=0, protocol=-1)"
},
{
"docstring": "reconstruct a Python object sent with zipped_pickle",
"name": "recv_zipped_pickle",
"signature": "de... | 4 | stack_v2_sparse_classes_30k_train_018520 | Implement the Python class `SerializingSocket` described below.
Class description:
A class with some extra serialization methods send_zipped_pickle is just like send_pyobj, but uses zlib to compress the stream before sending. send_array sends numpy arrays with metadata necessary for reconstructing the array on the oth... | Implement the Python class `SerializingSocket` described below.
Class description:
A class with some extra serialization methods send_zipped_pickle is just like send_pyobj, but uses zlib to compress the stream before sending. send_array sends numpy arrays with metadata necessary for reconstructing the array on the oth... | e1c3c9717e9d0d3667ca7e4809eab167a726e1ff | <|skeleton|>
class SerializingSocket:
"""A class with some extra serialization methods send_zipped_pickle is just like send_pyobj, but uses zlib to compress the stream before sending. send_array sends numpy arrays with metadata necessary for reconstructing the array on the other side (dtype,shape)."""
def send... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SerializingSocket:
"""A class with some extra serialization methods send_zipped_pickle is just like send_pyobj, but uses zlib to compress the stream before sending. send_array sends numpy arrays with metadata necessary for reconstructing the array on the other side (dtype,shape)."""
def send_zipped_pickl... | the_stack_v2_python_sparse | python/uptune/template/pipeline.py | cornell-zhang/uptune | train | 32 |
be4fb3ce40811c90b808da75c0fd9695b1d5f9cb | [
"super().__init__()\nself.embedding = tf.keras.layers.Embedding(input_dim=vocab, output_dim=embedding)\nself.gru = tf.keras.layers.GRU(units=units, kernel_initializer='glorot_uniform', return_sequences=True, return_state=True)\nself.F = tf.keras.layers.Dense(units=vocab)\nself.attention = SelfAttention(units)",
"... | <|body_start_0|>
super().__init__()
self.embedding = tf.keras.layers.Embedding(input_dim=vocab, output_dim=embedding)
self.gru = tf.keras.layers.GRU(units=units, kernel_initializer='glorot_uniform', return_sequences=True, return_state=True)
self.F = tf.keras.layers.Dense(units=vocab)
... | class RNNDecoder | RNNDecoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNDecoder:
"""class RNNDecoder"""
def __init__(self, vocab, embedding, units, batch):
"""Initializer. Args: vocab: (int) representing the size of the output vocabulary. embedding: (int) representing the dimensionality of the embedding vector. units: (int) representing the number of ... | stack_v2_sparse_classes_36k_train_004054 | 2,152 | no_license | [
{
"docstring": "Initializer. Args: vocab: (int) representing the size of the output vocabulary. embedding: (int) representing the dimensionality of the embedding vector. units: (int) representing the number of hidden units in the RNN cell. batch: (int) representing the batch size.",
"name": "__init__",
... | 2 | stack_v2_sparse_classes_30k_train_010472 | Implement the Python class `RNNDecoder` described below.
Class description:
class RNNDecoder
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): Initializer. Args: vocab: (int) representing the size of the output vocabulary. embedding: (int) representing the dimensionality of the e... | Implement the Python class `RNNDecoder` described below.
Class description:
class RNNDecoder
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): Initializer. Args: vocab: (int) representing the size of the output vocabulary. embedding: (int) representing the dimensionality of the e... | 75274394adb52d740f6cd4000cc00bbde44b9b72 | <|skeleton|>
class RNNDecoder:
"""class RNNDecoder"""
def __init__(self, vocab, embedding, units, batch):
"""Initializer. Args: vocab: (int) representing the size of the output vocabulary. embedding: (int) representing the dimensionality of the embedding vector. units: (int) representing the number of ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RNNDecoder:
"""class RNNDecoder"""
def __init__(self, vocab, embedding, units, batch):
"""Initializer. Args: vocab: (int) representing the size of the output vocabulary. embedding: (int) representing the dimensionality of the embedding vector. units: (int) representing the number of hidden units ... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/2-rnn_decoder.py | jdarangop/holbertonschool-machine_learning | train | 2 |
5f39c69c3a1ec7529867363b981096bb2f006489 | [
"orig = forward = head\ncnt = 0\nwhile forward.next and cnt < n:\n forward = forward.next\n cnt += 1\nif cnt == n - 1:\n orig = orig.next\nelif cnt < n:\n return orig\nelse:\n while forward.next:\n head = head.next\n forward = forward.next\n head.next = head.next.next\nreturn orig",
... | <|body_start_0|>
orig = forward = head
cnt = 0
while forward.next and cnt < n:
forward = forward.next
cnt += 1
if cnt == n - 1:
orig = orig.next
elif cnt < n:
return orig
else:
while forward.next:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_0|>
def rewrite(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_1|>
def rewrite2(self, head, n):
""":ty... | stack_v2_sparse_classes_36k_train_004055 | 3,246 | no_license | [
{
"docstring": ":type head: ListNode :type n: int :rtype: ListNode",
"name": "removeNthFromEnd",
"signature": "def removeNthFromEnd(self, head, n)"
},
{
"docstring": ":type head: ListNode :type n: int :rtype: ListNode",
"name": "rewrite",
"signature": "def rewrite(self, head, n)"
},
... | 3 | stack_v2_sparse_classes_30k_train_006688 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head, n): :type head: ListNode :type n: int :rtype: ListNode
- def rewrite(self, head, n): :type head: ListNode :type n: int :rtype: ListNode
- def rew... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head, n): :type head: ListNode :type n: int :rtype: ListNode
- def rewrite(self, head, n): :type head: ListNode :type n: int :rtype: ListNode
- def rew... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_0|>
def rewrite(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_1|>
def rewrite2(self, head, n):
""":ty... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeNthFromEnd(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
orig = forward = head
cnt = 0
while forward.next and cnt < n:
forward = forward.next
cnt += 1
if cnt == n - 1:
orig = orig.next
... | the_stack_v2_python_sparse | co_fb/19_Remove_Nth_Node_From_End_of_List.py | vsdrun/lc_public | train | 6 | |
4380bb6366fcb206205af5758f11ae8b49c5573c | [
"units = _get_test_data_dir('crash-corpus')\ncoverage_dir = _make_coverage_dir(tmp_path)\nprofraw_file = os.path.join(coverage_dir, 'test_crash.profraw')\ncrashes_dir = _make_crashes_dir(tmp_path)\nrun_coverage.do_coverage_run(self.COVERAGE_BINARY_PATH, units, profraw_file, crashes_dir)\nassert os.listdir(crashes_d... | <|body_start_0|>
units = _get_test_data_dir('crash-corpus')
coverage_dir = _make_coverage_dir(tmp_path)
profraw_file = os.path.join(coverage_dir, 'test_crash.profraw')
crashes_dir = _make_crashes_dir(tmp_path)
run_coverage.do_coverage_run(self.COVERAGE_BINARY_PATH, units, profraw... | Integration tests for run_coverage.py | TestIntegrationRunCoverage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIntegrationRunCoverage:
"""Integration tests for run_coverage.py"""
def test_integration_do_coverage_run_crash(self, tmp_path):
"""Test that do_coverage_run returns crashing inputs."""
<|body_0|>
def test_integration_do_coverage_run_no_crash(self, tmp_path):
... | stack_v2_sparse_classes_36k_train_004056 | 4,054 | permissive | [
{
"docstring": "Test that do_coverage_run returns crashing inputs.",
"name": "test_integration_do_coverage_run_crash",
"signature": "def test_integration_do_coverage_run_crash(self, tmp_path)"
},
{
"docstring": "Test that do_coverage_run doesn't return crashing inputs when there are none.",
... | 3 | stack_v2_sparse_classes_30k_train_017688 | Implement the Python class `TestIntegrationRunCoverage` described below.
Class description:
Integration tests for run_coverage.py
Method signatures and docstrings:
- def test_integration_do_coverage_run_crash(self, tmp_path): Test that do_coverage_run returns crashing inputs.
- def test_integration_do_coverage_run_no... | Implement the Python class `TestIntegrationRunCoverage` described below.
Class description:
Integration tests for run_coverage.py
Method signatures and docstrings:
- def test_integration_do_coverage_run_crash(self, tmp_path): Test that do_coverage_run returns crashing inputs.
- def test_integration_do_coverage_run_no... | ff8ef0c6da62268521061a432c5b9e228c2f53dc | <|skeleton|>
class TestIntegrationRunCoverage:
"""Integration tests for run_coverage.py"""
def test_integration_do_coverage_run_crash(self, tmp_path):
"""Test that do_coverage_run returns crashing inputs."""
<|body_0|>
def test_integration_do_coverage_run_no_crash(self, tmp_path):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestIntegrationRunCoverage:
"""Integration tests for run_coverage.py"""
def test_integration_do_coverage_run_crash(self, tmp_path):
"""Test that do_coverage_run returns crashing inputs."""
units = _get_test_data_dir('crash-corpus')
coverage_dir = _make_coverage_dir(tmp_path)
... | the_stack_v2_python_sparse | experiment/measurer/test_run_coverage.py | google/fuzzbench | train | 1,005 |
47b1e796708ee421f462e4a68b1e71be170df340 | [
"super().__init__(**kwargs)\nself.identifier_required = identifier_required\nself.disallowed_asset_types = disallowed_asset_types\nself.coingecko_obj = coingecko\nself.cryptocompare_obj = cryptocompare",
"asset_type = data.pop('asset_type')\nif self.disallowed_asset_types is not None and asset_type in self.disall... | <|body_start_0|>
super().__init__(**kwargs)
self.identifier_required = identifier_required
self.disallowed_asset_types = disallowed_asset_types
self.coingecko_obj = coingecko
self.cryptocompare_obj = cryptocompare
<|end_body_0|>
<|body_start_1|>
asset_type = data.pop('as... | AssetSchema | [
"AGPL-3.0-only",
"AGPL-3.0-or-later",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AssetSchema:
def __init__(self, identifier_required: bool, disallowed_asset_types: Optional[list[AssetType]]=None, coingecko: Optional['Coingecko']=None, cryptocompare: Optional['Cryptocompare']=None, **kwargs: Any) -> None:
"""Initializes an asset schema depending on the given asset typ... | stack_v2_sparse_classes_36k_train_004057 | 13,942 | permissive | [
{
"docstring": "Initializes an asset schema depending on the given asset type. If identifier_required is True then the identifier field is required. Provided asset_type must not be in disallowed_asset_types list. If coingecko is not None then the coingecko identifier has to be valid. If cryptocompare is not Non... | 2 | stack_v2_sparse_classes_30k_train_014135 | Implement the Python class `AssetSchema` described below.
Class description:
Implement the AssetSchema class.
Method signatures and docstrings:
- def __init__(self, identifier_required: bool, disallowed_asset_types: Optional[list[AssetType]]=None, coingecko: Optional['Coingecko']=None, cryptocompare: Optional['Crypto... | Implement the Python class `AssetSchema` described below.
Class description:
Implement the AssetSchema class.
Method signatures and docstrings:
- def __init__(self, identifier_required: bool, disallowed_asset_types: Optional[list[AssetType]]=None, coingecko: Optional['Coingecko']=None, cryptocompare: Optional['Crypto... | 496948458b89afc41458f19d1cba0e971ab67c8b | <|skeleton|>
class AssetSchema:
def __init__(self, identifier_required: bool, disallowed_asset_types: Optional[list[AssetType]]=None, coingecko: Optional['Coingecko']=None, cryptocompare: Optional['Cryptocompare']=None, **kwargs: Any) -> None:
"""Initializes an asset schema depending on the given asset typ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AssetSchema:
def __init__(self, identifier_required: bool, disallowed_asset_types: Optional[list[AssetType]]=None, coingecko: Optional['Coingecko']=None, cryptocompare: Optional['Cryptocompare']=None, **kwargs: Any) -> None:
"""Initializes an asset schema depending on the given asset type. If identifi... | the_stack_v2_python_sparse | rotkehlchen/serialization/schemas.py | LefterisJP/rotkehlchen | train | 0 | |
e2cfdcc2649cf467072e1c1931367c1dff6129ef | [
"if instance.form_definition.form_template_name:\n self.render_template = instance.form_definition.form_template_name\nelse:\n self.render_template = settings.DEFAULT_FORM_TEMPLATE\nreturn self.render_template",
"sekizai_varname = sekizai_get_varname()\ncontext = {'instance': instance, 'config': config, sek... | <|body_start_0|>
if instance.form_definition.form_template_name:
self.render_template = instance.form_definition.form_template_name
else:
self.render_template = settings.DEFAULT_FORM_TEMPLATE
return self.render_template
<|end_body_0|>
<|body_start_1|>
sekizai_var... | Plugin for rendering Form designer form RUS: Класс плагин Дизайнера форм FormDesignerPlugin | FormDesignerPlugin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FormDesignerPlugin:
"""Plugin for rendering Form designer form RUS: Класс плагин Дизайнера форм FormDesignerPlugin"""
def get_render_template(self, request, instance, **kwargs):
"""RUS: Возвращает выбранный из списка шаблон формы для рендеринга, если он не выбран - используется шабло... | stack_v2_sparse_classes_36k_train_004058 | 2,528 | permissive | [
{
"docstring": "RUS: Возвращает выбранный из списка шаблон формы для рендеринга, если он не выбран - используется шаблон формы по умолчанию.",
"name": "get_render_template",
"signature": "def get_render_template(self, request, instance, **kwargs)"
},
{
"docstring": "Return the context to use in ... | 2 | stack_v2_sparse_classes_30k_val_000224 | Implement the Python class `FormDesignerPlugin` described below.
Class description:
Plugin for rendering Form designer form RUS: Класс плагин Дизайнера форм FormDesignerPlugin
Method signatures and docstrings:
- def get_render_template(self, request, instance, **kwargs): RUS: Возвращает выбранный из списка шаблон фор... | Implement the Python class `FormDesignerPlugin` described below.
Class description:
Plugin for rendering Form designer form RUS: Класс плагин Дизайнера форм FormDesignerPlugin
Method signatures and docstrings:
- def get_render_template(self, request, instance, **kwargs): RUS: Возвращает выбранный из списка шаблон фор... | 186c783b4e4ed58199db7165703253cac1189c5a | <|skeleton|>
class FormDesignerPlugin:
"""Plugin for rendering Form designer form RUS: Класс плагин Дизайнера форм FormDesignerPlugin"""
def get_render_template(self, request, instance, **kwargs):
"""RUS: Возвращает выбранный из списка шаблон формы для рендеринга, если он не выбран - используется шабло... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FormDesignerPlugin:
"""Plugin for rendering Form designer form RUS: Класс плагин Дизайнера форм FormDesignerPlugin"""
def get_render_template(self, request, instance, **kwargs):
"""RUS: Возвращает выбранный из списка шаблон формы для рендеринга, если он не выбран - используется шаблон формы по ум... | the_stack_v2_python_sparse | edw_fluent/plugins/form_designer_plugin/content_plugins.py | infolabs/django-edw-fluent | train | 0 |
99ec9eea977b63fa6bc30eb49ff79122b177b0d0 | [
"self.input_size = input_size\nself.output_size = output_size\nself.alpha = 0.01\nself.alpha_decay = 0.01\nself.gamma = 1",
"neural_net = Sequential()\nneural_net.add(Dense(52, input_dim=self.input_size, activation='tanh'))\nneural_net.add(Dense(128, activation='tanh'))\nneural_net.add(Dense(self.output_size * 2 ... | <|body_start_0|>
self.input_size = input_size
self.output_size = output_size
self.alpha = 0.01
self.alpha_decay = 0.01
self.gamma = 1
<|end_body_0|>
<|body_start_1|>
neural_net = Sequential()
neural_net.add(Dense(52, input_dim=self.input_size, activation='tanh'))... | 'Controller' class that manages the updating of neural networks for a list of agents. | Controller | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Controller:
"""'Controller' class that manages the updating of neural networks for a list of agents."""
def __init__(self, input_size: int, output_size: int):
"""Initialise the class with learning parameters."""
<|body_0|>
def make_agent(self):
"""Create agent us... | stack_v2_sparse_classes_36k_train_004059 | 4,546 | no_license | [
{
"docstring": "Initialise the class with learning parameters.",
"name": "__init__",
"signature": "def __init__(self, input_size: int, output_size: int)"
},
{
"docstring": "Create agent using Keras neural network.",
"name": "make_agent",
"signature": "def make_agent(self)"
},
{
"... | 4 | stack_v2_sparse_classes_30k_val_000710 | Implement the Python class `Controller` described below.
Class description:
'Controller' class that manages the updating of neural networks for a list of agents.
Method signatures and docstrings:
- def __init__(self, input_size: int, output_size: int): Initialise the class with learning parameters.
- def make_agent(s... | Implement the Python class `Controller` described below.
Class description:
'Controller' class that manages the updating of neural networks for a list of agents.
Method signatures and docstrings:
- def __init__(self, input_size: int, output_size: int): Initialise the class with learning parameters.
- def make_agent(s... | c72db39f7e49bd2c4ba9d8446f6ac7b3678928fd | <|skeleton|>
class Controller:
"""'Controller' class that manages the updating of neural networks for a list of agents."""
def __init__(self, input_size: int, output_size: int):
"""Initialise the class with learning parameters."""
<|body_0|>
def make_agent(self):
"""Create agent us... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Controller:
"""'Controller' class that manages the updating of neural networks for a list of agents."""
def __init__(self, input_size: int, output_size: int):
"""Initialise the class with learning parameters."""
self.input_size = input_size
self.output_size = output_size
s... | the_stack_v2_python_sparse | Machine_Learning/DeepQ/RLController.py | JamesNunns/Robotics-Group-Studies | train | 8 |
7a5a7bd6c640be015635d1ebcce9f243eb59337d | [
"super(LCAModel, self).__init__()\nself.lca_layer = lca_layer\nself.num_lca_dim = num_lca_dim\nself.num_simulations = num_simulations\nself.num_time_steps = num_time_steps\nself.save_activities = save_activities",
"dev = 'cuda:0'\nactive = torch.ones(size=(self.num_simulations, 1), device=dev)\nif self.save_activ... | <|body_start_0|>
super(LCAModel, self).__init__()
self.lca_layer = lca_layer
self.num_lca_dim = num_lca_dim
self.num_simulations = num_simulations
self.num_time_steps = num_time_steps
self.save_activities = save_activities
<|end_body_0|>
<|body_start_1|>
dev = 'c... | LCAModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LCAModel:
def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000, save_activities: bool=False):
"""A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time cours... | stack_v2_sparse_classes_36k_train_004060 | 12,763 | permissive | [
{
"docstring": "A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time course of perceptual choice: the leaky, competing accumulator model. Psychol Rev. 2001 Jul;108(3):550-92. doi: 10.1037/0033-295x.108.3.550. PMID: 11488378. Args: lca_la... | 2 | stack_v2_sparse_classes_30k_train_005390 | Implement the Python class `LCAModel` described below.
Class description:
Implement the LCAModel class.
Method signatures and docstrings:
- def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000, save_activities: bool=False): A model that simulates a leaky compe... | Implement the Python class `LCAModel` described below.
Class description:
Implement the LCAModel class.
Method signatures and docstrings:
- def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000, save_activities: bool=False): A model that simulates a leaky compe... | 424971b04d55a2cddbae4c05a0aae2d7b3502c20 | <|skeleton|>
class LCAModel:
def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000, save_activities: bool=False):
"""A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time cours... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LCAModel:
def __init__(self, lca_layer: LCALayer, num_lca_dim: int, num_simulations: int=10000, num_time_steps: int=3000, save_activities: bool=False):
"""A model that simulates a leaky competing accumulator model (Usher and McClelland). References: Usher M, McClelland JL. The time course of perceptua... | the_stack_v2_python_sparse | Scripts/Debug/stability_flexibility/pytorch_lca.py | PrincetonUniversity/PsyNeuLink | train | 79 | |
2600e73c375869a188c306f8f26c7160346612a5 | [
"self.name = name\nself.sequence = sequence\nself.codon_table = create_codon_table()\nself.codon_to_bases = {str(value): key for key, value in create_codon_index().items()}\nself.acid_table = create_aminoacids_table()\nself.acid_to_codon_table = create_aminoacids_to_codon_index_table()\nself.example_gene_representa... | <|body_start_0|>
self.name = name
self.sequence = sequence
self.codon_table = create_codon_table()
self.codon_to_bases = {str(value): key for key, value in create_codon_index().items()}
self.acid_table = create_aminoacids_table()
self.acid_to_codon_table = create_aminoaci... | A class for a particular protein sequence (made from amino acids) The space is all synonymous sequences of genes representing this amino acid sequence (represented in terms of bases) | ProteinBaseParameter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProteinBaseParameter:
"""A class for a particular protein sequence (made from amino acids) The space is all synonymous sequences of genes representing this amino acid sequence (represented in terms of bases)"""
def __init__(self, name: str, sequence: str):
""":param name: Name of par... | stack_v2_sparse_classes_36k_train_004061 | 4,499 | permissive | [
{
"docstring": ":param name: Name of parameter :Sequence: input gene",
"name": "__init__",
"signature": "def __init__(self, name: str, sequence: str)"
},
{
"docstring": "Generates multiple random gene representations of the amino acid :param point_count: number of data points to generate. :retur... | 2 | stack_v2_sparse_classes_30k_train_016334 | Implement the Python class `ProteinBaseParameter` described below.
Class description:
A class for a particular protein sequence (made from amino acids) The space is all synonymous sequences of genes representing this amino acid sequence (represented in terms of bases)
Method signatures and docstrings:
- def __init__(... | Implement the Python class `ProteinBaseParameter` described below.
Class description:
A class for a particular protein sequence (made from amino acids) The space is all synonymous sequences of genes representing this amino acid sequence (represented in terms of bases)
Method signatures and docstrings:
- def __init__(... | f19eaf7231ed007cce9e12fba0f7f936eb48cfdb | <|skeleton|>
class ProteinBaseParameter:
"""A class for a particular protein sequence (made from amino acids) The space is all synonymous sequences of genes representing this amino acid sequence (represented in terms of bases)"""
def __init__(self, name: str, sequence: str):
""":param name: Name of par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProteinBaseParameter:
"""A class for a particular protein sequence (made from amino acids) The space is all synonymous sequences of genes representing this amino acid sequence (represented in terms of bases)"""
def __init__(self, name: str, sequence: str):
""":param name: Name of parameter :Seque... | the_stack_v2_python_sparse | boss/code/parameters/protein_base_parameter.py | henrymoss/BOSS | train | 25 |
7171f5552b963e13550383d4b5229120d2230f9b | [
"if isinstance(id, int):\n return session.query(Dataset).filter(Dataset.id == id).one()\nraise ValueError('id must be integer')",
"if isinstance(id, int):\n return session.query(Dataset).filter(Dataset.problem_id == id).all()\nraise ValueError('id must be integer')"
] | <|body_start_0|>
if isinstance(id, int):
return session.query(Dataset).filter(Dataset.id == id).one()
raise ValueError('id must be integer')
<|end_body_0|>
<|body_start_1|>
if isinstance(id, int):
return session.query(Dataset).filter(Dataset.problem_id == id).all()
... | DatasetRepository | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatasetRepository:
def get_by_id(session, id):
"""Gets a dataset with the specified id :param session: :param id: :return: a dataset"""
<|body_0|>
def get_by_problem_id(session, id):
"""Gets a dataset for the problem with the specified id :param session: :param id: :... | stack_v2_sparse_classes_36k_train_004062 | 1,004 | permissive | [
{
"docstring": "Gets a dataset with the specified id :param session: :param id: :return: a dataset",
"name": "get_by_id",
"signature": "def get_by_id(session, id)"
},
{
"docstring": "Gets a dataset for the problem with the specified id :param session: :param id: :return: a dataset",
"name": ... | 2 | stack_v2_sparse_classes_30k_train_008395 | Implement the Python class `DatasetRepository` described below.
Class description:
Implement the DatasetRepository class.
Method signatures and docstrings:
- def get_by_id(session, id): Gets a dataset with the specified id :param session: :param id: :return: a dataset
- def get_by_problem_id(session, id): Gets a data... | Implement the Python class `DatasetRepository` described below.
Class description:
Implement the DatasetRepository class.
Method signatures and docstrings:
- def get_by_id(session, id): Gets a dataset with the specified id :param session: :param id: :return: a dataset
- def get_by_problem_id(session, id): Gets a data... | 428719b8589b3ca9922ae0c6fa527f47f8a98690 | <|skeleton|>
class DatasetRepository:
def get_by_id(session, id):
"""Gets a dataset with the specified id :param session: :param id: :return: a dataset"""
<|body_0|>
def get_by_problem_id(session, id):
"""Gets a dataset for the problem with the specified id :param session: :param id: :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatasetRepository:
def get_by_id(session, id):
"""Gets a dataset with the specified id :param session: :param id: :return: a dataset"""
if isinstance(id, int):
return session.query(Dataset).filter(Dataset.id == id).one()
raise ValueError('id must be integer')
def get_b... | the_stack_v2_python_sparse | DB/Repositories/DatasetRepository.py | valiro21/mlc | train | 0 | |
8b6f68877cc8ea312f3b73537d8b62a24c12554c | [
"req = query_production.parse_args(strict=True)\ncondition = []\nif req.get('merchant_code'):\n condition.append(TbMerchant.code == req['merchant_code'])\nmerchants = TbMerchant.query.filter(*condition).paginate(page=req['page'], per_page=req['page_size'])\nreturn ({'results': [{'merchant_name': i.name, 'merchan... | <|body_start_0|>
req = query_production.parse_args(strict=True)
condition = []
if req.get('merchant_code'):
condition.append(TbMerchant.code == req['merchant_code'])
merchants = TbMerchant.query.filter(*condition).paginate(page=req['page'], per_page=req['page_size'])
... | Production | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Production:
def get(self):
"""获取产品列表"""
<|body_0|>
def put(self):
"""更新产品信息"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
req = query_production.parse_args(strict=True)
condition = []
if req.get('merchant_code'):
condit... | stack_v2_sparse_classes_36k_train_004063 | 9,260 | no_license | [
{
"docstring": "获取产品列表",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "更新产品信息",
"name": "put",
"signature": "def put(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011801 | Implement the Python class `Production` described below.
Class description:
Implement the Production class.
Method signatures and docstrings:
- def get(self): 获取产品列表
- def put(self): 更新产品信息 | Implement the Python class `Production` described below.
Class description:
Implement the Production class.
Method signatures and docstrings:
- def get(self): 获取产品列表
- def put(self): 更新产品信息
<|skeleton|>
class Production:
def get(self):
"""获取产品列表"""
<|body_0|>
def put(self):
"""更新产品信... | 9f4553a6ea6d703f0ff3e330b090ee10e2b9a12a | <|skeleton|>
class Production:
def get(self):
"""获取产品列表"""
<|body_0|>
def put(self):
"""更新产品信息"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Production:
def get(self):
"""获取产品列表"""
req = query_production.parse_args(strict=True)
condition = []
if req.get('merchant_code'):
condition.append(TbMerchant.code == req['merchant_code'])
merchants = TbMerchant.query.filter(*condition).paginate(page=req['pa... | the_stack_v2_python_sparse | xxw/support/src/modules/business_user/public.py | GSIL-Monitor/xxw | train | 0 | |
54c4f3520d5d633aec41806b924b17ff1faf61a8 | [
"QtWidgets.QDialog.__init__(self)\nself.df = pandaTable\nself.layout = QtWidgets.QGridLayout(self)\nself.columnSelect = QtWidgets.QComboBox()\nself.columnSelect.addItems(self.df.columns.values)\nself.layout.addWidget(self.columnSelect, 0, 1)\nself.layout.addWidget(QtWidgets.QLabel('Column:'), 0, 0)\nself.separatorL... | <|body_start_0|>
QtWidgets.QDialog.__init__(self)
self.df = pandaTable
self.layout = QtWidgets.QGridLayout(self)
self.columnSelect = QtWidgets.QComboBox()
self.columnSelect.addItems(self.df.columns.values)
self.layout.addWidget(self.columnSelect, 0, 1)
self.layout... | A dialog box to get the information required by the newRowsOnSeparator function. | NewRowsOnSeparatorDialogBox | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NewRowsOnSeparatorDialogBox:
"""A dialog box to get the information required by the newRowsOnSeparator function."""
def __init__(self, pandaTable, parent):
"""Initializes the UI and sets the two dropdowns to display column names of the active Panda."""
<|body_0|>
def get... | stack_v2_sparse_classes_36k_train_004064 | 29,548 | no_license | [
{
"docstring": "Initializes the UI and sets the two dropdowns to display column names of the active Panda.",
"name": "__init__",
"signature": "def __init__(self, pandaTable, parent)"
},
{
"docstring": "Returns the user's input",
"name": "getResults",
"signature": "def getResults(self, pa... | 2 | stack_v2_sparse_classes_30k_train_016191 | Implement the Python class `NewRowsOnSeparatorDialogBox` described below.
Class description:
A dialog box to get the information required by the newRowsOnSeparator function.
Method signatures and docstrings:
- def __init__(self, pandaTable, parent): Initializes the UI and sets the two dropdowns to display column name... | Implement the Python class `NewRowsOnSeparatorDialogBox` described below.
Class description:
A dialog box to get the information required by the newRowsOnSeparator function.
Method signatures and docstrings:
- def __init__(self, pandaTable, parent): Initializes the UI and sets the two dropdowns to display column name... | 1a3c5ad967472faf66236a311cc07a5128f5f911 | <|skeleton|>
class NewRowsOnSeparatorDialogBox:
"""A dialog box to get the information required by the newRowsOnSeparator function."""
def __init__(self, pandaTable, parent):
"""Initializes the UI and sets the two dropdowns to display column names of the active Panda."""
<|body_0|>
def get... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NewRowsOnSeparatorDialogBox:
"""A dialog box to get the information required by the newRowsOnSeparator function."""
def __init__(self, pandaTable, parent):
"""Initializes the UI and sets the two dropdowns to display column names of the active Panda."""
QtWidgets.QDialog.__init__(self)
... | the_stack_v2_python_sparse | datatool/gui/Model.py | scottawalton/datatool | train | 0 |
76234a31da876af5664d45294a19409368869f3d | [
"cfg = '# auto-generated by RADVD service (utility.py)\\n'\nfor iface in node.get_ifaces(control=False):\n prefixes = list(map(cls.subnetentry, iface.ips()))\n if len(prefixes) < 1:\n continue\n cfg += f'interface {iface.name}\\n{{\\n AdvSendAdvert on;\\n MinRtrAdvInterval 3;\\n ... | <|body_start_0|>
cfg = '# auto-generated by RADVD service (utility.py)\n'
for iface in node.get_ifaces(control=False):
prefixes = list(map(cls.subnetentry, iface.ips()))
if len(prefixes) < 1:
continue
cfg += f'interface {iface.name}\n{{\n AdvSen... | RadvdService | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RadvdService:
def generate_config(cls, node: CoreNode, filename: str) -> str:
"""Generate a RADVD router advertisement daemon config file using the network address of each interface."""
<|body_0|>
def subnetentry(ip: netaddr.IPNetwork) -> str:
"""Generate a subnet de... | stack_v2_sparse_classes_36k_train_004065 | 19,801 | permissive | [
{
"docstring": "Generate a RADVD router advertisement daemon config file using the network address of each interface.",
"name": "generate_config",
"signature": "def generate_config(cls, node: CoreNode, filename: str) -> str"
},
{
"docstring": "Generate a subnet declaration block given an IPv6 pr... | 2 | null | Implement the Python class `RadvdService` described below.
Class description:
Implement the RadvdService class.
Method signatures and docstrings:
- def generate_config(cls, node: CoreNode, filename: str) -> str: Generate a RADVD router advertisement daemon config file using the network address of each interface.
- de... | Implement the Python class `RadvdService` described below.
Class description:
Implement the RadvdService class.
Method signatures and docstrings:
- def generate_config(cls, node: CoreNode, filename: str) -> str: Generate a RADVD router advertisement daemon config file using the network address of each interface.
- de... | 20071eed2e73a2287aa385698dd604f4933ae7ff | <|skeleton|>
class RadvdService:
def generate_config(cls, node: CoreNode, filename: str) -> str:
"""Generate a RADVD router advertisement daemon config file using the network address of each interface."""
<|body_0|>
def subnetentry(ip: netaddr.IPNetwork) -> str:
"""Generate a subnet de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RadvdService:
def generate_config(cls, node: CoreNode, filename: str) -> str:
"""Generate a RADVD router advertisement daemon config file using the network address of each interface."""
cfg = '# auto-generated by RADVD service (utility.py)\n'
for iface in node.get_ifaces(control=False)... | the_stack_v2_python_sparse | daemon/core/services/utility.py | coreemu/core | train | 606 | |
955f644102887748654b61f89170995d69784322 | [
"self.mean = np.zeros(shape, np.float64)\nself.var = np.ones(shape, np.float64)\nself.count = epsilon",
"batch_mean = np.mean(arr, axis=0)\nbatch_var = np.var(arr, axis=0)\nbatch_count = arr.shape[0]\nself.update_from_moments(batch_mean, batch_var, batch_count)",
"delta = batch_mean - self.mean\ntot_count = sel... | <|body_start_0|>
self.mean = np.zeros(shape, np.float64)
self.var = np.ones(shape, np.float64)
self.count = epsilon
<|end_body_0|>
<|body_start_1|>
batch_mean = np.mean(arr, axis=0)
batch_var = np.var(arr, axis=0)
batch_count = arr.shape[0]
self.update_from_momen... | Calulates the running mean and std of a data stream. Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam. | RunningMeanStd | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RunningMeanStd:
"""Calulates the running mean and std of a data stream. Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam."""
def __init__(self, epsilon=0.0001, shape=()):
"""Initializes containers for ... | stack_v2_sparse_classes_36k_train_004066 | 6,361 | permissive | [
{
"docstring": "Initializes containers for data mean and variance. Args: epsilon (float): helps with arithmetic issues. shape (tuple): the shape of the data stream's output.",
"name": "__init__",
"signature": "def __init__(self, epsilon=0.0001, shape=())"
},
{
"docstring": "Update current stats ... | 3 | null | Implement the Python class `RunningMeanStd` described below.
Class description:
Calulates the running mean and std of a data stream. Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam.
Method signatures and docstrings:
- def __init__(sel... | Implement the Python class `RunningMeanStd` described below.
Class description:
Calulates the running mean and std of a data stream. Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam.
Method signatures and docstrings:
- def __init__(sel... | 140ed17dbd91d73a1f6537520b610adff732b9aa | <|skeleton|>
class RunningMeanStd:
"""Calulates the running mean and std of a data stream. Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam."""
def __init__(self, epsilon=0.0001, shape=()):
"""Initializes containers for ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RunningMeanStd:
"""Calulates the running mean and std of a data stream. Attributes: mean (np.array): mean of data stream. var (np.array): variance of data stream. count (float): total count of data steam."""
def __init__(self, epsilon=0.0001, shape=()):
"""Initializes containers for data mean and... | the_stack_v2_python_sparse | safe_control_gym/math_and_models/normalization.py | utiasDSL/safe-control-gym | train | 387 |
a3cbf372835466e3515f5c5f93ccf9b95bbf026d | [
"with patch('modules.exercises.mod_11_testing.process.MyConnection') as con_class:\n mock_db = MagicMock(name='db_mock')\n con_class.return_value = mock_db\n mock_db.get_book.side_effect = [{'book_id': '10', 'author_name': 'test__another_1', 'name': 'name_1'}, {'book_id': '11', 'author_name': 'test__anothe... | <|body_start_0|>
with patch('modules.exercises.mod_11_testing.process.MyConnection') as con_class:
mock_db = MagicMock(name='db_mock')
con_class.return_value = mock_db
mock_db.get_book.side_effect = [{'book_id': '10', 'author_name': 'test__another_1', 'name': 'name_1'}, {'boo... | Test class for get_info_list method of GetBookAuthor. We will increase coverage of this method. We do not inheritate for TestCase as we dont want to be discovered by nosetests, just for checking | TestGetAllBookAuthor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestGetAllBookAuthor:
"""Test class for get_info_list method of GetBookAuthor. We will increase coverage of this method. We do not inheritate for TestCase as we dont want to be discovered by nosetests, just for checking"""
def test_get_all_books_work(self):
"""When database module is... | stack_v2_sparse_classes_36k_train_004067 | 3,022 | no_license | [
{
"docstring": "When database module is working we check that our process return a valid list",
"name": "test_get_all_books_work",
"signature": "def test_get_all_books_work(self)"
},
{
"docstring": "TEst that database error is handled in our process module and we dont raise any exception up",
... | 2 | null | Implement the Python class `TestGetAllBookAuthor` described below.
Class description:
Test class for get_info_list method of GetBookAuthor. We will increase coverage of this method. We do not inheritate for TestCase as we dont want to be discovered by nosetests, just for checking
Method signatures and docstrings:
- d... | Implement the Python class `TestGetAllBookAuthor` described below.
Class description:
Test class for get_info_list method of GetBookAuthor. We will increase coverage of this method. We do not inheritate for TestCase as we dont want to be discovered by nosetests, just for checking
Method signatures and docstrings:
- d... | 8f082201e24f0f2b991d9388500fdbf95d6f073d | <|skeleton|>
class TestGetAllBookAuthor:
"""Test class for get_info_list method of GetBookAuthor. We will increase coverage of this method. We do not inheritate for TestCase as we dont want to be discovered by nosetests, just for checking"""
def test_get_all_books_work(self):
"""When database module is... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestGetAllBookAuthor:
"""Test class for get_info_list method of GetBookAuthor. We will increase coverage of this method. We do not inheritate for TestCase as we dont want to be discovered by nosetests, just for checking"""
def test_get_all_books_work(self):
"""When database module is working we c... | the_stack_v2_python_sparse | modules/exercises/mod_11_testing/solution.py | garciacastano09/pycourse | train | 0 |
b35564543de5a9afcb9b650a03bb42d0e97a2cd1 | [
"self.env_type = env_type\nself.protected_count = protected_count\nself.protected_size_bytes = protected_size_bytes\nself.unprotected_count = unprotected_count\nself.unprotected_size_bytes = unprotected_size_bytes",
"if dictionary is None:\n return None\nenv_type = dictionary.get('envType')\nprotected_count = ... | <|body_start_0|>
self.env_type = env_type
self.protected_count = protected_count
self.protected_size_bytes = protected_size_bytes
self.unprotected_count = unprotected_count
self.unprotected_size_bytes = unprotected_size_bytes
<|end_body_0|>
<|body_start_1|>
if dictionary... | Implementation of the 'ProtectedObjectsByEnv' model. Number of Protected Objects by Type. Attributes: env_type (string): Environment Type. protected_count (int): Number of Protected Objects. protected_size_bytes (long|int): Size of Protected Objects. unprotected_count (int): Number of Unprotected Objects. unprotected_s... | ProtectedObjectsByEnv | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProtectedObjectsByEnv:
"""Implementation of the 'ProtectedObjectsByEnv' model. Number of Protected Objects by Type. Attributes: env_type (string): Environment Type. protected_count (int): Number of Protected Objects. protected_size_bytes (long|int): Size of Protected Objects. unprotected_count (i... | stack_v2_sparse_classes_36k_train_004068 | 2,553 | permissive | [
{
"docstring": "Constructor for the ProtectedObjectsByEnv class",
"name": "__init__",
"signature": "def __init__(self, env_type=None, protected_count=None, protected_size_bytes=None, unprotected_count=None, unprotected_size_bytes=None)"
},
{
"docstring": "Creates an instance of this model from a... | 2 | stack_v2_sparse_classes_30k_train_012558 | Implement the Python class `ProtectedObjectsByEnv` described below.
Class description:
Implementation of the 'ProtectedObjectsByEnv' model. Number of Protected Objects by Type. Attributes: env_type (string): Environment Type. protected_count (int): Number of Protected Objects. protected_size_bytes (long|int): Size of ... | Implement the Python class `ProtectedObjectsByEnv` described below.
Class description:
Implementation of the 'ProtectedObjectsByEnv' model. Number of Protected Objects by Type. Attributes: env_type (string): Environment Type. protected_count (int): Number of Protected Objects. protected_size_bytes (long|int): Size of ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class ProtectedObjectsByEnv:
"""Implementation of the 'ProtectedObjectsByEnv' model. Number of Protected Objects by Type. Attributes: env_type (string): Environment Type. protected_count (int): Number of Protected Objects. protected_size_bytes (long|int): Size of Protected Objects. unprotected_count (i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProtectedObjectsByEnv:
"""Implementation of the 'ProtectedObjectsByEnv' model. Number of Protected Objects by Type. Attributes: env_type (string): Environment Type. protected_count (int): Number of Protected Objects. protected_size_bytes (long|int): Size of Protected Objects. unprotected_count (int): Number o... | the_stack_v2_python_sparse | cohesity_management_sdk/models/protected_objects_by_env.py | cohesity/management-sdk-python | train | 24 |
12bea1e6d507b0dd669f8d71a32d5741dcbc2555 | [
"rx_time = time.time()\nmsg_type, rem_ch, loc_ch = struct.unpack('>III', data[0:12])\nif msg_type == 1 and rem_ch == 0:\n logging.warning('Client should not get INIT messages')\n return\nledbattest = self._tests.get(rem_ch)\nif ledbattest is None:\n logging.warning('Could not find ledbat test with our id: ... | <|body_start_0|>
rx_time = time.time()
msg_type, rem_ch, loc_ch = struct.unpack('>III', data[0:12])
if msg_type == 1 and rem_ch == 0:
logging.warning('Client should not get INIT messages')
return
ledbattest = self._tests.get(rem_ch)
if ledbattest is None:
... | description of class | ClientRole | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClientRole:
"""description of class"""
def datagram_received(self, data, addr):
"""Process the received datagram"""
<|body_0|>
def start_client(self, **kwargs):
"""Start the functioning of the client by starting a new test"""
<|body_1|>
def _stop_tes... | stack_v2_sparse_classes_36k_train_004069 | 3,998 | permissive | [
{
"docstring": "Process the received datagram",
"name": "datagram_received",
"signature": "def datagram_received(self, data, addr)"
},
{
"docstring": "Start the functioning of the client by starting a new test",
"name": "start_client",
"signature": "def start_client(self, **kwargs)"
},... | 5 | stack_v2_sparse_classes_30k_train_007164 | Implement the Python class `ClientRole` described below.
Class description:
description of class
Method signatures and docstrings:
- def datagram_received(self, data, addr): Process the received datagram
- def start_client(self, **kwargs): Start the functioning of the client by starting a new test
- def _stop_test(se... | Implement the Python class `ClientRole` described below.
Class description:
description of class
Method signatures and docstrings:
- def datagram_received(self, data, addr): Process the received datagram
- def start_client(self, **kwargs): Start the functioning of the client by starting a new test
- def _stop_test(se... | 16d61db9d8838ec3f4088c8a04ec9f5daa1ab532 | <|skeleton|>
class ClientRole:
"""description of class"""
def datagram_received(self, data, addr):
"""Process the received datagram"""
<|body_0|>
def start_client(self, **kwargs):
"""Start the functioning of the client by starting a new test"""
<|body_1|>
def _stop_tes... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClientRole:
"""description of class"""
def datagram_received(self, data, addr):
"""Process the received datagram"""
rx_time = time.time()
msg_type, rem_ch, loc_ch = struct.unpack('>III', data[0:12])
if msg_type == 1 and rem_ch == 0:
logging.warning('Client shou... | the_stack_v2_python_sparse | pyledbat/testledbat/clientrole.py | justas-/pyledbat | train | 10 |
72de027ad380186edcd59b98e76f4f1eb3effbe9 | [
"self.layers = layers\nself.features = features\nself.codebook = {}",
"if codeword in self.codebook:\n return self.codebook[codeword]\ncount = len(self.codebook)\nif count >= self.features:\n return hash(codeword) % self.features\nelse:\n self.codebook[codeword] = count\n return count",
"scaled_floa... | <|body_start_0|>
self.layers = layers
self.features = features
self.codebook = {}
<|end_body_0|>
<|body_start_1|>
if codeword in self.codebook:
return self.codebook[codeword]
count = len(self.codebook)
if count >= self.features:
return hash(codewo... | 砖瓦编码 | TileCoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TileCoder:
"""砖瓦编码"""
def __init__(self, layers, features):
"""layers 要用到几层砖瓦编码 features 砖瓦编码应该得到多少特征"""
<|body_0|>
def get_feature(self, codeword):
"""codeword 数据坐标(层数 坐标 坐标 动作)"""
<|body_1|>
def __call__(self, floats=(), ints=()):
"""将观测值向量... | stack_v2_sparse_classes_36k_train_004070 | 22,277 | no_license | [
{
"docstring": "layers 要用到几层砖瓦编码 features 砖瓦编码应该得到多少特征",
"name": "__init__",
"signature": "def __init__(self, layers, features)"
},
{
"docstring": "codeword 数据坐标(层数 坐标 坐标 动作)",
"name": "get_feature",
"signature": "def get_feature(self, codeword)"
},
{
"docstring": "将观测值向量转化为 坐标 f... | 3 | stack_v2_sparse_classes_30k_train_011581 | Implement the Python class `TileCoder` described below.
Class description:
砖瓦编码
Method signatures and docstrings:
- def __init__(self, layers, features): layers 要用到几层砖瓦编码 features 砖瓦编码应该得到多少特征
- def get_feature(self, codeword): codeword 数据坐标(层数 坐标 坐标 动作)
- def __call__(self, floats=(), ints=()): 将观测值向量转化为 坐标 floats 特... | Implement the Python class `TileCoder` described below.
Class description:
砖瓦编码
Method signatures and docstrings:
- def __init__(self, layers, features): layers 要用到几层砖瓦编码 features 砖瓦编码应该得到多少特征
- def get_feature(self, codeword): codeword 数据坐标(层数 坐标 坐标 动作)
- def __call__(self, floats=(), ints=()): 将观测值向量转化为 坐标 floats 特... | e6526e9e38fcb5be91b46cb40715c15242198a0b | <|skeleton|>
class TileCoder:
"""砖瓦编码"""
def __init__(self, layers, features):
"""layers 要用到几层砖瓦编码 features 砖瓦编码应该得到多少特征"""
<|body_0|>
def get_feature(self, codeword):
"""codeword 数据坐标(层数 坐标 坐标 动作)"""
<|body_1|>
def __call__(self, floats=(), ints=()):
"""将观测值向量... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TileCoder:
"""砖瓦编码"""
def __init__(self, layers, features):
"""layers 要用到几层砖瓦编码 features 砖瓦编码应该得到多少特征"""
self.layers = layers
self.features = features
self.codebook = {}
def get_feature(self, codeword):
"""codeword 数据坐标(层数 坐标 坐标 动作)"""
if codeword in s... | the_stack_v2_python_sparse | mountain_car/function_approx.py | lwzswufe/gym_learning | train | 0 |
1ee789cf35ba4d00e8154c7c287cd1646c73f0c9 | [
"self.sckt = sckt\nself.msg_cnt = 0\nself.client_private_key = None\nself.client_public_key = None\nself.shared_key = None",
"if self.msg_cnt == 0:\n new_msg = bytes('Hello'.encode('utf-8'))\n self.msg_cnt += 1\nelif self.msg_cnt == 1:\n txt1 = msg[:268]\n txt2 = msg[268:]\n parameters = load_der_p... | <|body_start_0|>
self.sckt = sckt
self.msg_cnt = 0
self.client_private_key = None
self.client_public_key = None
self.shared_key = None
<|end_body_0|>
<|body_start_1|>
if self.msg_cnt == 0:
new_msg = bytes('Hello'.encode('utf-8'))
self.msg_cnt += 1... | Classe que implementa a funcionalidade de um CLIENTE. | Client | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Client:
"""Classe que implementa a funcionalidade de um CLIENTE."""
def __init__(self, sckt=None):
"""Construtor da classe."""
<|body_0|>
def process(self, msg=b''):
"""Processa uma mensagem (`bytestring`) enviada pelo SERVIDOR. Retorna a mensagem a transmitir co... | stack_v2_sparse_classes_36k_train_004071 | 3,319 | no_license | [
{
"docstring": "Construtor da classe.",
"name": "__init__",
"signature": "def __init__(self, sckt=None)"
},
{
"docstring": "Processa uma mensagem (`bytestring`) enviada pelo SERVIDOR. Retorna a mensagem a transmitir como resposta (`None` para finalizar ligação)",
"name": "process",
"sign... | 2 | stack_v2_sparse_classes_30k_train_021522 | Implement the Python class `Client` described below.
Class description:
Classe que implementa a funcionalidade de um CLIENTE.
Method signatures and docstrings:
- def __init__(self, sckt=None): Construtor da classe.
- def process(self, msg=b''): Processa uma mensagem (`bytestring`) enviada pelo SERVIDOR. Retorna a men... | Implement the Python class `Client` described below.
Class description:
Classe que implementa a funcionalidade de um CLIENTE.
Method signatures and docstrings:
- def __init__(self, sckt=None): Construtor da classe.
- def process(self, msg=b''): Processa uma mensagem (`bytestring`) enviada pelo SERVIDOR. Retorna a men... | 719c1336eec8653987f2a39e93150649752cdd04 | <|skeleton|>
class Client:
"""Classe que implementa a funcionalidade de um CLIENTE."""
def __init__(self, sckt=None):
"""Construtor da classe."""
<|body_0|>
def process(self, msg=b''):
"""Processa uma mensagem (`bytestring`) enviada pelo SERVIDOR. Retorna a mensagem a transmitir co... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Client:
"""Classe que implementa a funcionalidade de um CLIENTE."""
def __init__(self, sckt=None):
"""Construtor da classe."""
self.sckt = sckt
self.msg_cnt = 0
self.client_private_key = None
self.client_public_key = None
self.shared_key = None
def pro... | the_stack_v2_python_sparse | Criptografia e Segurança de Informação/Tecnologias Criptográficas/G6/Client.py | JoelGama/Projects | train | 0 |
937f2ec2754e48e80fd878e044c2ba0e7f7f3a62 | [
"for book in Book.objects.all():\n if book.ISBNCode == validated_data['ISBNCode']:\n return Response(status=status.HTTP_400_BAD_REQUEST)\nreturn Book.objects.create(**validated_data)",
"instance.isbncode = validated_data.get('isbncode', instance.title)\ninstance.title = validated_data.get('title', insta... | <|body_start_0|>
for book in Book.objects.all():
if book.ISBNCode == validated_data['ISBNCode']:
return Response(status=status.HTTP_400_BAD_REQUEST)
return Book.objects.create(**validated_data)
<|end_body_0|>
<|body_start_1|>
instance.isbncode = validated_data.get('i... | BookSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BookSerializer:
def create(self, validated_data):
"""Create and return a new `Book` instance, given the validated data."""
<|body_0|>
def update(self, instance, validated_data):
"""Update and return an existing `Book` instance, given the validated data."""
<|... | stack_v2_sparse_classes_36k_train_004072 | 1,288 | no_license | [
{
"docstring": "Create and return a new `Book` instance, given the validated data.",
"name": "create",
"signature": "def create(self, validated_data)"
},
{
"docstring": "Update and return an existing `Book` instance, given the validated data.",
"name": "update",
"signature": "def update(... | 2 | stack_v2_sparse_classes_30k_train_000456 | Implement the Python class `BookSerializer` described below.
Class description:
Implement the BookSerializer class.
Method signatures and docstrings:
- def create(self, validated_data): Create and return a new `Book` instance, given the validated data.
- def update(self, instance, validated_data): Update and return a... | Implement the Python class `BookSerializer` described below.
Class description:
Implement the BookSerializer class.
Method signatures and docstrings:
- def create(self, validated_data): Create and return a new `Book` instance, given the validated data.
- def update(self, instance, validated_data): Update and return a... | c0da48013a6aa4d5ee3638c60109ceaf938f7af5 | <|skeleton|>
class BookSerializer:
def create(self, validated_data):
"""Create and return a new `Book` instance, given the validated data."""
<|body_0|>
def update(self, instance, validated_data):
"""Update and return an existing `Book` instance, given the validated data."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BookSerializer:
def create(self, validated_data):
"""Create and return a new `Book` instance, given the validated data."""
for book in Book.objects.all():
if book.ISBNCode == validated_data['ISBNCode']:
return Response(status=status.HTTP_400_BAD_REQUEST)
ret... | the_stack_v2_python_sparse | Django API/TransparentCDNApi/book/serializers.py | rafagarciac/TransparentCDNProject | train | 3 | |
66c68f37af977b34b03765b3fd8e7a51f9c3243c | [
"try:\n self.object = User.objects.get(username=self.request.user)\n print(self.object)\n return self.object\nexcept:\n return None",
"obj = self.get_object()\nprint(obj)\nif obj is not None:\n initial_data = model_to_dict(obj)\n print(initial_data)\n initial_data.update(model_to_dict(obj))\n... | <|body_start_0|>
try:
self.object = User.objects.get(username=self.request.user)
print(self.object)
return self.object
except:
return None
<|end_body_0|>
<|body_start_1|>
obj = self.get_object()
print(obj)
if obj is not None:
... | UploadProfilePic | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UploadProfilePic:
def get_object(self):
"""Check if data already exists"""
<|body_0|>
def get_initial(self):
"""Pre-fill the form if data exists"""
<|body_1|>
def form_valid(self, form):
"""Save to the database. If data exists, update else create... | stack_v2_sparse_classes_36k_train_004073 | 6,927 | no_license | [
{
"docstring": "Check if data already exists",
"name": "get_object",
"signature": "def get_object(self)"
},
{
"docstring": "Pre-fill the form if data exists",
"name": "get_initial",
"signature": "def get_initial(self)"
},
{
"docstring": "Save to the database. If data exists, upda... | 3 | stack_v2_sparse_classes_30k_train_015200 | Implement the Python class `UploadProfilePic` described below.
Class description:
Implement the UploadProfilePic class.
Method signatures and docstrings:
- def get_object(self): Check if data already exists
- def get_initial(self): Pre-fill the form if data exists
- def form_valid(self, form): Save to the database. I... | Implement the Python class `UploadProfilePic` described below.
Class description:
Implement the UploadProfilePic class.
Method signatures and docstrings:
- def get_object(self): Check if data already exists
- def get_initial(self): Pre-fill the form if data exists
- def form_valid(self, form): Save to the database. I... | 4e466eefaac29d9aebd162a320be32785f221d24 | <|skeleton|>
class UploadProfilePic:
def get_object(self):
"""Check if data already exists"""
<|body_0|>
def get_initial(self):
"""Pre-fill the form if data exists"""
<|body_1|>
def form_valid(self, form):
"""Save to the database. If data exists, update else create... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UploadProfilePic:
def get_object(self):
"""Check if data already exists"""
try:
self.object = User.objects.get(username=self.request.user)
print(self.object)
return self.object
except:
return None
def get_initial(self):
"""Pr... | the_stack_v2_python_sparse | SocialNetwork/dashboard/views.py | Nitu22499/SocialMediaClone | train | 0 | |
cfd67a60b14509ef84e84268f2c29e507bf2585f | [
"self._io_stream = io_stream\nself._output_format = output_format\nBase.__init__(self, **kw)",
"while True:\n batch = self._batch_q.pop()\n if batch is None:\n break\n self._io_stream.write(batch.formatted_str(self._output_format))\n self._io_stream.flush()"
] | <|body_start_0|>
self._io_stream = io_stream
self._output_format = output_format
Base.__init__(self, **kw)
<|end_body_0|>
<|body_start_1|>
while True:
batch = self._batch_q.pop()
if batch is None:
break
self._io_stream.write(batch.form... | Output records into IO stream | IoStream | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IoStream:
"""Output records into IO stream"""
def __init__(self, io_stream, output_format='json', **kw):
"""Setup ostream :param io_stream: stream to `write()` :param output_format: ouput format of each record. One of 'json', 'csv' are supported. :param **kw: passed to :func:`Base.__... | stack_v2_sparse_classes_36k_train_004074 | 991 | permissive | [
{
"docstring": "Setup ostream :param io_stream: stream to `write()` :param output_format: ouput format of each record. One of 'json', 'csv' are supported. :param **kw: passed to :func:`Base.__init__()`",
"name": "__init__",
"signature": "def __init__(self, io_stream, output_format='json', **kw)"
},
... | 2 | stack_v2_sparse_classes_30k_train_006173 | Implement the Python class `IoStream` described below.
Class description:
Output records into IO stream
Method signatures and docstrings:
- def __init__(self, io_stream, output_format='json', **kw): Setup ostream :param io_stream: stream to `write()` :param output_format: ouput format of each record. One of 'json', '... | Implement the Python class `IoStream` described below.
Class description:
Output records into IO stream
Method signatures and docstrings:
- def __init__(self, io_stream, output_format='json', **kw): Setup ostream :param io_stream: stream to `write()` :param output_format: ouput format of each record. One of 'json', '... | a1e34af507b94d51ba588ad4a039ce0115b46475 | <|skeleton|>
class IoStream:
"""Output records into IO stream"""
def __init__(self, io_stream, output_format='json', **kw):
"""Setup ostream :param io_stream: stream to `write()` :param output_format: ouput format of each record. One of 'json', 'csv' are supported. :param **kw: passed to :func:`Base.__... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IoStream:
"""Output records into IO stream"""
def __init__(self, io_stream, output_format='json', **kw):
"""Setup ostream :param io_stream: stream to `write()` :param output_format: ouput format of each record. One of 'json', 'csv' are supported. :param **kw: passed to :func:`Base.__init__()`"""
... | the_stack_v2_python_sparse | shellstreaming/ostream/io_stream.py | laysakura/shellstreaming | train | 1 |
c24a8a62f0be9ef21da40a74873c7cf08a57ecb6 | [
"super().__init__(attacker, defender, enemy=enemy)\nself._move_file_name = join('moves', 'ice_beam.png')\nself._fps = 20\nif enemy:\n self._particle_systems = [MoveLinearParticleSystem(self._move_file_name, 1, (140, 70), 60, dx=-4, dy=2, duration=1), MoveLinearParticleSystem(self._move_file_name, 1, (140, 40), 6... | <|body_start_0|>
super().__init__(attacker, defender, enemy=enemy)
self._move_file_name = join('moves', 'ice_beam.png')
self._fps = 20
if enemy:
self._particle_systems = [MoveLinearParticleSystem(self._move_file_name, 1, (140, 70), 60, dx=-4, dy=2, duration=1), MoveLinearPart... | IceBeam | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IceBeam:
def __init__(self, attacker, defender, enemy=False):
"""Displays the ice beam animation. This animation consists of two particle systems that generate crystals that follow a linear path towards the opponent. The player jiggles and is tinted while it is getting hit by the beam. T... | stack_v2_sparse_classes_36k_train_004075 | 4,101 | no_license | [
{
"docstring": "Displays the ice beam animation. This animation consists of two particle systems that generate crystals that follow a linear path towards the opponent. The player jiggles and is tinted while it is getting hit by the beam. The tint expires at the end of the move. Ice shards are played after the b... | 3 | stack_v2_sparse_classes_30k_train_018467 | Implement the Python class `IceBeam` described below.
Class description:
Implement the IceBeam class.
Method signatures and docstrings:
- def __init__(self, attacker, defender, enemy=False): Displays the ice beam animation. This animation consists of two particle systems that generate crystals that follow a linear pa... | Implement the Python class `IceBeam` described below.
Class description:
Implement the IceBeam class.
Method signatures and docstrings:
- def __init__(self, attacker, defender, enemy=False): Displays the ice beam animation. This animation consists of two particle systems that generate crystals that follow a linear pa... | 6718fdb6555d87f0b7b331c10d64a604431f8e81 | <|skeleton|>
class IceBeam:
def __init__(self, attacker, defender, enemy=False):
"""Displays the ice beam animation. This animation consists of two particle systems that generate crystals that follow a linear path towards the opponent. The player jiggles and is tinted while it is getting hit by the beam. T... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IceBeam:
def __init__(self, attacker, defender, enemy=False):
"""Displays the ice beam animation. This animation consists of two particle systems that generate crystals that follow a linear path towards the opponent. The player jiggles and is tinted while it is getting hit by the beam. The tint expire... | the_stack_v2_python_sparse | pokered/modules/animations/moves/ice_beam.py | surranc20/pokered | train | 44 | |
f8bcf9c38da426f45171d975250c44d716df0c5f | [
"super().__init__()\nself.label_arr = np.asarray(['NULL'] + classes)\npath_to_ckpt = inference_graph\nself.detection_graph = tf.Graph()\nwith self.detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(path_to_ckpt, 'rb') as fid:\n serialized_graph = fid.read()\n od_g... | <|body_start_0|>
super().__init__()
self.label_arr = np.asarray(['NULL'] + classes)
path_to_ckpt = inference_graph
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(path_to_ckpt, 'rb'... | TFDetector | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TFDetector:
def __init__(self, classes, inference_graph='frozen_graph.pb'):
"""Initialize Detector Object"""
<|body_0|>
def predict(self, images_data, batch_size=10, min_confidence=0.7):
"""Predict results from list of images to list of boxes"""
<|body_1|>
<... | stack_v2_sparse_classes_36k_train_004076 | 2,196 | permissive | [
{
"docstring": "Initialize Detector Object",
"name": "__init__",
"signature": "def __init__(self, classes, inference_graph='frozen_graph.pb')"
},
{
"docstring": "Predict results from list of images to list of boxes",
"name": "predict",
"signature": "def predict(self, images_data, batch_s... | 2 | stack_v2_sparse_classes_30k_train_008391 | Implement the Python class `TFDetector` described below.
Class description:
Implement the TFDetector class.
Method signatures and docstrings:
- def __init__(self, classes, inference_graph='frozen_graph.pb'): Initialize Detector Object
- def predict(self, images_data, batch_size=10, min_confidence=0.7): Predict result... | Implement the Python class `TFDetector` described below.
Class description:
Implement the TFDetector class.
Method signatures and docstrings:
- def __init__(self, classes, inference_graph='frozen_graph.pb'): Initialize Detector Object
- def predict(self, images_data, batch_size=10, min_confidence=0.7): Predict result... | 7a20d4350c630017d11f964a4996dce8b9a8251b | <|skeleton|>
class TFDetector:
def __init__(self, classes, inference_graph='frozen_graph.pb'):
"""Initialize Detector Object"""
<|body_0|>
def predict(self, images_data, batch_size=10, min_confidence=0.7):
"""Predict results from list of images to list of boxes"""
<|body_1|>
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TFDetector:
def __init__(self, classes, inference_graph='frozen_graph.pb'):
"""Initialize Detector Object"""
super().__init__()
self.label_arr = np.asarray(['NULL'] + classes)
path_to_ckpt = inference_graph
self.detection_graph = tf.Graph()
with self.detection_g... | the_stack_v2_python_sparse | train/tf_detector.py | CatalystCode/active-learning-detect | train | 5 | |
059e83a3f1c7a999df0f2ab18dc8000302497dbf | [
"main_data = self.get_main_data(imdb_id, api_data)\nratings_data = self.get_ratings_data(imdb_id, api_data)\nif ratings_data and main_data:\n return {'omdb_main': main_data, 'omdb_ratings': ratings_data}\nelse:\n raise GatherException(imdb_id, 'Failed standardise')",
"try:\n main_data = [{'imdb_id': imdb... | <|body_start_0|>
main_data = self.get_main_data(imdb_id, api_data)
ratings_data = self.get_ratings_data(imdb_id, api_data)
if ratings_data and main_data:
return {'omdb_main': main_data, 'omdb_ratings': ratings_data}
else:
raise GatherException(imdb_id, 'Failed sta... | This class standardises the response returned from the OMDB API, removing unwanted data, and structuring the remaining data so that it is easier to handle in later processes. | StandardiseResponse | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StandardiseResponse:
"""This class standardises the response returned from the OMDB API, removing unwanted data, and structuring the remaining data so that it is easier to handle in later processes."""
def standardise(self, imdb_id, api_data):
"""Constructs a new dictionary from the ... | stack_v2_sparse_classes_36k_train_004077 | 5,419 | permissive | [
{
"docstring": "Constructs a new dictionary from the API data. :param imdb_id: The imdb_id for the requested film :param api_data: The raw response from the OMDB API :return: A standardised dictionary.",
"name": "standardise",
"signature": "def standardise(self, imdb_id, api_data)"
},
{
"docstri... | 3 | stack_v2_sparse_classes_30k_test_000859 | Implement the Python class `StandardiseResponse` described below.
Class description:
This class standardises the response returned from the OMDB API, removing unwanted data, and structuring the remaining data so that it is easier to handle in later processes.
Method signatures and docstrings:
- def standardise(self, ... | Implement the Python class `StandardiseResponse` described below.
Class description:
This class standardises the response returned from the OMDB API, removing unwanted data, and structuring the remaining data so that it is easier to handle in later processes.
Method signatures and docstrings:
- def standardise(self, ... | cd6974764f8136529e5d4a3c191ad34865bfe732 | <|skeleton|>
class StandardiseResponse:
"""This class standardises the response returned from the OMDB API, removing unwanted data, and structuring the remaining data so that it is easier to handle in later processes."""
def standardise(self, imdb_id, api_data):
"""Constructs a new dictionary from the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StandardiseResponse:
"""This class standardises the response returned from the OMDB API, removing unwanted data, and structuring the remaining data so that it is easier to handle in later processes."""
def standardise(self, imdb_id, api_data):
"""Constructs a new dictionary from the API data. :pa... | the_stack_v2_python_sparse | processes/get_omdb.py | kinoreel/kino-gather | train | 0 |
bd106e2ad64713b98c60a2cda940d4b6d2ccfd16 | [
"pk = uuid.uuid4()\nrequest = self.context['request']\nif not request.data.get('path'):\n raise serializers.ValidationError('没有上传文件')\nreturn PriorScheme.objects.create(pk=pk, **validated_data)",
"instance.staff_id = validated_data.get('staff_id', instance.staff_id)\ninstance.name = validated_data.get('name', ... | <|body_start_0|>
pk = uuid.uuid4()
request = self.context['request']
if not request.data.get('path'):
raise serializers.ValidationError('没有上传文件')
return PriorScheme.objects.create(pk=pk, **validated_data)
<|end_body_0|>
<|body_start_1|>
instance.staff_id = validated_... | 应急方案序列化器 | PriorSchemeSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PriorSchemeSerializer:
"""应急方案序列化器"""
def create(self, validated_data):
"""新建"""
<|body_0|>
def update(self, instance, validated_data):
"""更新，instance为要更新的对象实例"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pk = uuid.uuid4()
request = s... | stack_v2_sparse_classes_36k_train_004078 | 1,631 | no_license | [
{
"docstring": "新建",
"name": "create",
"signature": "def create(self, validated_data)"
},
{
"docstring": "更新，instance为要更新的对象实例",
"name": "update",
"signature": "def update(self, instance, validated_data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003726 | Implement the Python class `PriorSchemeSerializer` described below.
Class description:
应急方案序列化器
Method signatures and docstrings:
- def create(self, validated_data): 新建
- def update(self, instance, validated_data): 更新，instance为要更新的对象实例 | Implement the Python class `PriorSchemeSerializer` described below.
Class description:
应急方案序列化器
Method signatures and docstrings:
- def create(self, validated_data): 新建
- def update(self, instance, validated_data): 更新，instance为要更新的对象实例
<|skeleton|>
class PriorSchemeSerializer:
"""应急方案序列化器"""
def create(self... | 3645bc3a396727af208db924c6fdee38abc0f894 | <|skeleton|>
class PriorSchemeSerializer:
"""应急方案序列化器"""
def create(self, validated_data):
"""新建"""
<|body_0|>
def update(self, instance, validated_data):
"""更新，instance为要更新的对象实例"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PriorSchemeSerializer:
"""应急方案序列化器"""
def create(self, validated_data):
"""新建"""
pk = uuid.uuid4()
request = self.context['request']
if not request.data.get('path'):
raise serializers.ValidationError('没有上传文件')
return PriorScheme.objects.create(pk=pk, **... | the_stack_v2_python_sparse | ruidun_system/safe/serializers/priorscheme_serializer.py | TingxieLi/django-restframework | train | 0 |
40680345831dfd7d498593132e85cfbaf1febdc7 | [
"path = self._get_path()\nfor trait_name, value in self._changed.items():\n if self._is_preference_trait(trait_name):\n self.preferences.set('%s.%s' % (path, trait_name), value)\nself._changed.clear()\nreturn",
"if trait_name.endswith('_items'):\n trait_name = trait_name[:-6]\n if self._is_prefere... | <|body_start_0|>
path = self._get_path()
for trait_name, value in self._changed.items():
if self._is_preference_trait(trait_name):
self.preferences.set('%s.%s' % (path, trait_name), value)
self._changed.clear()
return
<|end_body_0|>
<|body_start_1|>
i... | A page in a preferences dialog. | PreferencesPage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreferencesPage:
"""A page in a preferences dialog."""
def apply(self):
"""Apply the page's preferences."""
<|body_0|>
def _anytrait_changed(self, trait_name, old, new):
"""Static trait change handler. This is an important override! In the base-class when a trait... | stack_v2_sparse_classes_36k_train_004079 | 4,275 | no_license | [
{
"docstring": "Apply the page's preferences.",
"name": "apply",
"signature": "def apply(self)"
},
{
"docstring": "Static trait change handler. This is an important override! In the base-class when a trait is changed the preferences node is updated too. Here, we stop that from happening and just... | 3 | null | Implement the Python class `PreferencesPage` described below.
Class description:
A page in a preferences dialog.
Method signatures and docstrings:
- def apply(self): Apply the page's preferences.
- def _anytrait_changed(self, trait_name, old, new): Static trait change handler. This is an important override! In the ba... | Implement the Python class `PreferencesPage` described below.
Class description:
A page in a preferences dialog.
Method signatures and docstrings:
- def apply(self): Apply the page's preferences.
- def _anytrait_changed(self, trait_name, old, new): Static trait change handler. This is an important override! In the ba... | 5466f5858dbd2f1f082fa0d7417b57c8fb068fad | <|skeleton|>
class PreferencesPage:
"""A page in a preferences dialog."""
def apply(self):
"""Apply the page's preferences."""
<|body_0|>
def _anytrait_changed(self, trait_name, old, new):
"""Static trait change handler. This is an important override! In the base-class when a trait... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PreferencesPage:
"""A page in a preferences dialog."""
def apply(self):
"""Apply the page's preferences."""
path = self._get_path()
for trait_name, value in self._changed.items():
if self._is_preference_trait(trait_name):
self.preferences.set('%s.%s' % ... | the_stack_v2_python_sparse | maps/build/AppTools/enthought/preferences/ui/preferences_page.py | m-elhussieny/code | train | 0 |
fec9cb5ca579b887938c1288af05664db07ba9bd | [
"self.s = compressedString\nself.p = 0\nself.num = 0\nself.ch = ''",
"if not self.hasNext():\n return ' '\nif self.num == 0:\n self.ch = self.s[self.p]\n self.p += 1\n while self.p < len(self.s) and (not self.s[self.p].isalpha()):\n self.num = self.num * 10 + int(self.s[self.p])\n self.p... | <|body_start_0|>
self.s = compressedString
self.p = 0
self.num = 0
self.ch = ''
<|end_body_0|>
<|body_start_1|>
if not self.hasNext():
return ' '
if self.num == 0:
self.ch = self.s[self.p]
self.p += 1
while self.p < len(sel... | StringIterator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StringIterator:
def __init__(self, compressedString):
""":type compressedString: str"""
<|body_0|>
def next(self):
""":rtype: str"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
s... | stack_v2_sparse_classes_36k_train_004080 | 1,101 | no_license | [
{
"docstring": ":type compressedString: str",
"name": "__init__",
"signature": "def __init__(self, compressedString)"
},
{
"docstring": ":rtype: str",
"name": "next",
"signature": "def next(self)"
},
{
"docstring": ":rtype: bool",
"name": "hasNext",
"signature": "def hasN... | 3 | null | Implement the Python class `StringIterator` described below.
Class description:
Implement the StringIterator class.
Method signatures and docstrings:
- def __init__(self, compressedString): :type compressedString: str
- def next(self): :rtype: str
- def hasNext(self): :rtype: bool | Implement the Python class `StringIterator` described below.
Class description:
Implement the StringIterator class.
Method signatures and docstrings:
- def __init__(self, compressedString): :type compressedString: str
- def next(self): :rtype: str
- def hasNext(self): :rtype: bool
<|skeleton|>
class StringIterator:
... | 36cb33af758b1d01da35982481a8bbfbee5c2810 | <|skeleton|>
class StringIterator:
def __init__(self, compressedString):
""":type compressedString: str"""
<|body_0|>
def next(self):
""":rtype: str"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StringIterator:
def __init__(self, compressedString):
""":type compressedString: str"""
self.s = compressedString
self.p = 0
self.num = 0
self.ch = ''
def next(self):
""":rtype: str"""
if not self.hasNext():
return ' '
if self.nu... | the_stack_v2_python_sparse | LeetCode/designCompressedStringIterator.py | dicao425/algorithmExercise | train | 0 | |
b05c89fdba66c10dbccbcac279ac924066219267 | [
"query = g.db.query(MatchTeam)\nquery = query.filter(MatchTeam.match_id == match_id)\nrows = query.all()\nret = []\nfor row in rows:\n record = row.as_dict()\n record['url'] = url_for('matches.team', match_id=match_id, team_id=row.team_id, _external=True)\n ret.append(record)\nreturn jsonify(ret)",
"args... | <|body_start_0|>
query = g.db.query(MatchTeam)
query = query.filter(MatchTeam.match_id == match_id)
rows = query.all()
ret = []
for row in rows:
record = row.as_dict()
record['url'] = url_for('matches.team', match_id=match_id, team_id=row.team_id, _externa... | All teams in a match | MatchTeamsAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MatchTeamsAPI:
"""All teams in a match"""
def get(self, match_id):
"""Find teams by match"""
<|body_0|>
def post(self, match_id):
"""Add a team to a match"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
query = g.db.query(MatchTeam)
quer... | stack_v2_sparse_classes_36k_train_004081 | 24,829 | permissive | [
{
"docstring": "Find teams by match",
"name": "get",
"signature": "def get(self, match_id)"
},
{
"docstring": "Add a team to a match",
"name": "post",
"signature": "def post(self, match_id)"
}
] | 2 | null | Implement the Python class `MatchTeamsAPI` described below.
Class description:
All teams in a match
Method signatures and docstrings:
- def get(self, match_id): Find teams by match
- def post(self, match_id): Add a team to a match | Implement the Python class `MatchTeamsAPI` described below.
Class description:
All teams in a match
Method signatures and docstrings:
- def get(self, match_id): Find teams by match
- def post(self, match_id): Add a team to a match
<|skeleton|>
class MatchTeamsAPI:
"""All teams in a match"""
def get(self, ma... | 9825cb22b26b577b715f2ce95453363bf90ecc7e | <|skeleton|>
class MatchTeamsAPI:
"""All teams in a match"""
def get(self, match_id):
"""Find teams by match"""
<|body_0|>
def post(self, match_id):
"""Add a team to a match"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MatchTeamsAPI:
"""All teams in a match"""
def get(self, match_id):
"""Find teams by match"""
query = g.db.query(MatchTeam)
query = query.filter(MatchTeam.match_id == match_id)
rows = query.all()
ret = []
for row in rows:
record = row.as_dict()
... | the_stack_v2_python_sparse | driftbase/api/matches.py | dgnorth/drift-base | train | 1 |
a92161ab392b15224d8d167017d8b28dd13796c6 | [
"super().__init__(renderer, player_id, goal)\nself.difficulty = difficulty\nif difficulty > 5:\n difficulty = 5\nself._number_of_scenarios = {0: 5, 1: 10, 2: 25, 3: 50, 4: 100, 5: 150}[difficulty]",
"scenarios = []\nbest_score = 0\nbest_scenario_index = 0\nfor scenario_number in range(self._number_of_scenarios... | <|body_start_0|>
super().__init__(renderer, player_id, goal)
self.difficulty = difficulty
if difficulty > 5:
difficulty = 5
self._number_of_scenarios = {0: 5, 1: 10, 2: 25, 3: 50, 4: 100, 5: 150}[difficulty]
<|end_body_0|>
<|body_start_1|>
scenarios = []
best... | A smart player that searches through many random moves and picks the best scoring scenario. This player is runned by the computer. The amount of ramdom scenarios that this player will look through depends on the difficulty attributed to itself. These are the amount of moves that this player will consider, according to ... | SmartPlayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmartPlayer:
"""A smart player that searches through many random moves and picks the best scoring scenario. This player is runned by the computer. The amount of ramdom scenarios that this player will look through depends on the difficulty attributed to itself. These are the amount of moves that t... | stack_v2_sparse_classes_36k_train_004082 | 13,079 | no_license | [
{
"docstring": "Initialize this SmartPlayer.",
"name": "__init__",
"signature": "def __init__(self, renderer: Renderer, player_id: int, goal: Goal, difficulty: int) -> None"
},
{
"docstring": "Make a smart move, which is based on the difficulty level. The difficulty level is the amount of random... | 2 | stack_v2_sparse_classes_30k_train_011998 | Implement the Python class `SmartPlayer` described below.
Class description:
A smart player that searches through many random moves and picks the best scoring scenario. This player is runned by the computer. The amount of ramdom scenarios that this player will look through depends on the difficulty attributed to itsel... | Implement the Python class `SmartPlayer` described below.
Class description:
A smart player that searches through many random moves and picks the best scoring scenario. This player is runned by the computer. The amount of ramdom scenarios that this player will look through depends on the difficulty attributed to itsel... | 01185e1eab994b42d7e0ec33223eed742b83233e | <|skeleton|>
class SmartPlayer:
"""A smart player that searches through many random moves and picks the best scoring scenario. This player is runned by the computer. The amount of ramdom scenarios that this player will look through depends on the difficulty attributed to itself. These are the amount of moves that t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmartPlayer:
"""A smart player that searches through many random moves and picks the best scoring scenario. This player is runned by the computer. The amount of ramdom scenarios that this player will look through depends on the difficulty attributed to itself. These are the amount of moves that this player wi... | the_stack_v2_python_sparse | CSC148/assignments/a2/backup/player.py | rcase31/UofTCourses | train | 1 |
f98024562c6b8058f65e26c6435b8143352ad026 | [
"backend = self.get_backend_for_app(app_id)\nauth_client = get_backend_authenticated_client(request.user.username, backend)\nenv_vars = auth_client.get_application_env_variables(app_id)\nreturn self.respond(env_vars)",
"env_vars = json.loads(request.body)\nbackend = self.get_backend_for_app(app_id)\nauth_client =... | <|body_start_0|>
backend = self.get_backend_for_app(app_id)
auth_client = get_backend_authenticated_client(request.user.username, backend)
env_vars = auth_client.get_application_env_variables(app_id)
return self.respond(env_vars)
<|end_body_0|>
<|body_start_1|>
env_vars = json.l... | AppEnvVariablesApiView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppEnvVariablesApiView:
def get(self, request, app_id):
"""Get the environmental variables :param django.http.HttpRequest request: the request object :param str app_id: the ID of the app :rtype: django.http.HttpResponse"""
<|body_0|>
def post(self, request, app_id):
... | stack_v2_sparse_classes_36k_train_004083 | 1,963 | no_license | [
{
"docstring": "Get the environmental variables :param django.http.HttpRequest request: the request object :param str app_id: the ID of the app :rtype: django.http.HttpResponse",
"name": "get",
"signature": "def get(self, request, app_id)"
},
{
"docstring": "Set the environmental variables The b... | 2 | stack_v2_sparse_classes_30k_test_000070 | Implement the Python class `AppEnvVariablesApiView` described below.
Class description:
Implement the AppEnvVariablesApiView class.
Method signatures and docstrings:
- def get(self, request, app_id): Get the environmental variables :param django.http.HttpRequest request: the request object :param str app_id: the ID o... | Implement the Python class `AppEnvVariablesApiView` described below.
Class description:
Implement the AppEnvVariablesApiView class.
Method signatures and docstrings:
- def get(self, request, app_id): Get the environmental variables :param django.http.HttpRequest request: the request object :param str app_id: the ID o... | df2bbc2c0f7b593930a5c5bc038232f66394f8c5 | <|skeleton|>
class AppEnvVariablesApiView:
def get(self, request, app_id):
"""Get the environmental variables :param django.http.HttpRequest request: the request object :param str app_id: the ID of the app :rtype: django.http.HttpResponse"""
<|body_0|>
def post(self, request, app_id):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AppEnvVariablesApiView:
def get(self, request, app_id):
"""Get the environmental variables :param django.http.HttpRequest request: the request object :param str app_id: the ID of the app :rtype: django.http.HttpResponse"""
backend = self.get_backend_for_app(app_id)
auth_client = get_ba... | the_stack_v2_python_sparse | web/api_server/api/app_env_variables_api_view.py | TigerAppsOrg/TigerHost | train | 0 | |
bdd311797f7708a40ec246fa8eda22177c619d1a | [
"if not head or not head.next or k == 0:\n return head\ntail = head\ncount = 1\nwhile tail.next:\n count += 1\n tail = tail.next\nif count == k:\n return head\nnode = head\nfor _ in range(count - k % count - 1):\n node = node.next\ntail.next = head\nhead = node.next\nnode.next = None\nreturn head",
... | <|body_start_0|>
if not head or not head.next or k == 0:
return head
tail = head
count = 1
while tail.next:
count += 1
tail = tail.next
if count == k:
return head
node = head
for _ in range(count - k % count - 1):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rotateRight(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_0|>
def rotateRight_cycle(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
def rotateRight_v2(self, head, k):
... | stack_v2_sparse_classes_36k_train_004084 | 2,578 | no_license | [
{
"docstring": ":type head: ListNode :type k: int :rtype: ListNode",
"name": "rotateRight",
"signature": "def rotateRight(self, head, k)"
},
{
"docstring": ":type head: ListNode :type k: int :rtype: ListNode",
"name": "rotateRight_cycle",
"signature": "def rotateRight_cycle(self, head, k... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotateRight(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
- def rotateRight_cycle(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
- de... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotateRight(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
- def rotateRight_cycle(self, head, k): :type head: ListNode :type k: int :rtype: ListNode
- de... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def rotateRight(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_0|>
def rotateRight_cycle(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
def rotateRight_v2(self, head, k):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rotateRight(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
if not head or not head.next or k == 0:
return head
tail = head
count = 1
while tail.next:
count += 1
tail = tail.next
if count... | the_stack_v2_python_sparse | src/lt_61.py | oxhead/CodingYourWay | train | 0 | |
2ba9f5d4290faf4b5e6844c81ce59fc68627cdcc | [
"self.ip_address = ip_address\nself._sshtun_port = _sshtun_port\nself.ssh_user = ssh_user\nself.ssh_user_pass = ssh_user_pass",
"client = paramiko.SSHClient()\nclient.set_missing_host_key_policy(paramiko.AutoAddPolicy())\nclient.connect(self.ip_address, self._sshtun_port, self.ssh_user, self.ssh_user_pass)\nshell... | <|body_start_0|>
self.ip_address = ip_address
self._sshtun_port = _sshtun_port
self.ssh_user = ssh_user
self.ssh_user_pass = ssh_user_pass
<|end_body_0|>
<|body_start_1|>
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
c... | RemoteConnectSSH | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoteConnectSSH:
def __init__(self, ip_address, _sshtun_port, ssh_user, ssh_user_pass):
"""Initialization Args: ip_address: port: ssh_user: ssh_user_pass: Return:"""
<|body_0|>
def execute_ssh_command(self, command):
"""Executes command on switch. Args: command(str)... | stack_v2_sparse_classes_36k_train_004085 | 2,809 | no_license | [
{
"docstring": "Initialization Args: ip_address: port: ssh_user: ssh_user_pass: Return:",
"name": "__init__",
"signature": "def __init__(self, ip_address, _sshtun_port, ssh_user, ssh_user_pass)"
},
{
"docstring": "Executes command on switch. Args: command(str): ssh command to execute :return:",
... | 4 | null | Implement the Python class `RemoteConnectSSH` described below.
Class description:
Implement the RemoteConnectSSH class.
Method signatures and docstrings:
- def __init__(self, ip_address, _sshtun_port, ssh_user, ssh_user_pass): Initialization Args: ip_address: port: ssh_user: ssh_user_pass: Return:
- def execute_ssh_c... | Implement the Python class `RemoteConnectSSH` described below.
Class description:
Implement the RemoteConnectSSH class.
Method signatures and docstrings:
- def __init__(self, ip_address, _sshtun_port, ssh_user, ssh_user_pass): Initialization Args: ip_address: port: ssh_user: ssh_user_pass: Return:
- def execute_ssh_c... | 0637a465088b468d6fdb6d1bb6f7b087547cec56 | <|skeleton|>
class RemoteConnectSSH:
def __init__(self, ip_address, _sshtun_port, ssh_user, ssh_user_pass):
"""Initialization Args: ip_address: port: ssh_user: ssh_user_pass: Return:"""
<|body_0|>
def execute_ssh_command(self, command):
"""Executes command on switch. Args: command(str)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RemoteConnectSSH:
def __init__(self, ip_address, _sshtun_port, ssh_user, ssh_user_pass):
"""Initialization Args: ip_address: port: ssh_user: ssh_user_pass: Return:"""
self.ip_address = ip_address
self._sshtun_port = _sshtun_port
self.ssh_user = ssh_user
self.ssh_user_pa... | the_stack_v2_python_sparse | remote_qa/remote_connect.py | Krishnaarunangsu/XpressoDataHandling | train | 0 | |
bfd659120433c047fed1377ba51dcf22745b0574 | [
"self.model_id_attr = model_id_attr\nself.t_at_ccl = Cube(None)\nself.p_at_ccl = Cube(None)\nself.temperature = Cube(None)\nself.minimum_t_diff = 4",
"cct = np.ma.masked_array(self.t_at_ccl.data.copy())\nq_at_ccl = saturated_humidity(self.t_at_ccl.data, self.p_at_ccl.data)\nccl_with_mask = np.ma.masked_array(self... | <|body_start_0|>
self.model_id_attr = model_id_attr
self.t_at_ccl = Cube(None)
self.p_at_ccl = Cube(None)
self.temperature = Cube(None)
self.minimum_t_diff = 4
<|end_body_0|>
<|body_start_1|>
cct = np.ma.masked_array(self.t_at_ccl.data.copy())
q_at_ccl = saturate... | Plugin to calculate the convective cloud top temperature from the cloud condensation level temperature and pressure, and temperature on pressure levels data using saturated ascent. The temperature is that of the parcel after saturated ascent at the last pressure level where the parcel is buoyant. The interpolation requ... | CloudTopTemperature | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CloudTopTemperature:
"""Plugin to calculate the convective cloud top temperature from the cloud condensation level temperature and pressure, and temperature on pressure levels data using saturated ascent. The temperature is that of the parcel after saturated ascent at the last pressure level wher... | stack_v2_sparse_classes_36k_train_004086 | 6,910 | permissive | [
{
"docstring": "Set up class Args: model_id_attr: Name of model ID attribute to be copied from source cubes to output cube",
"name": "__init__",
"signature": "def __init__(self, model_id_attr: str=None)"
},
{
"docstring": "Ascends through the pressure levels (decreasing pressure) calculating the... | 4 | null | Implement the Python class `CloudTopTemperature` described below.
Class description:
Plugin to calculate the convective cloud top temperature from the cloud condensation level temperature and pressure, and temperature on pressure levels data using saturated ascent. The temperature is that of the parcel after saturated... | Implement the Python class `CloudTopTemperature` described below.
Class description:
Plugin to calculate the convective cloud top temperature from the cloud condensation level temperature and pressure, and temperature on pressure levels data using saturated ascent. The temperature is that of the parcel after saturated... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class CloudTopTemperature:
"""Plugin to calculate the convective cloud top temperature from the cloud condensation level temperature and pressure, and temperature on pressure levels data using saturated ascent. The temperature is that of the parcel after saturated ascent at the last pressure level wher... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CloudTopTemperature:
"""Plugin to calculate the convective cloud top temperature from the cloud condensation level temperature and pressure, and temperature on pressure levels data using saturated ascent. The temperature is that of the parcel after saturated ascent at the last pressure level where the parcel ... | the_stack_v2_python_sparse | improver/psychrometric_calculations/cloud_top_temperature.py | metoppv/improver | train | 101 |
ee5d565fff69ae732bc18fc77aff830b29e453ad | [
"dic = {}\nfor n in nums:\n if n not in dic:\n dic[n] = 1\n else:\n dic[n] += 1\nres = []\nfor k, v in dic.iteritems():\n if v == 1:\n res.append(k)\nreturn res",
"nums.sort()\nres = []\ni = 0\nwhile i < len(nums) - 2:\n if nums[i] != nums[i + 1]:\n res.append(nums[i])\n ... | <|body_start_0|>
dic = {}
for n in nums:
if n not in dic:
dic[n] = 1
else:
dic[n] += 1
res = []
for k, v in dic.iteritems():
if v == 1:
res.append(k)
return res
<|end_body_0|>
<|body_start_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def singleNumber2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
dic = {}
for n in nums... | stack_v2_sparse_classes_36k_train_004087 | 1,346 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "singleNumber",
"signature": "def singleNumber(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "singleNumber2",
"signature": "def singleNumber2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002477 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: List[int]
- def singleNumber2(self, nums): :type nums: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: List[int]
- def singleNumber2(self, nums): :type nums: List[int] :rtype: List[int]
<|skeleton|>
class Solution:
... | 31b2b4dc1e5c3b1c53b333fe30b98ed04b0bdacc | <|skeleton|>
class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_0|>
def singleNumber2(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
dic = {}
for n in nums:
if n not in dic:
dic[n] = 1
else:
dic[n] += 1
res = []
for k, v in dic.iteritems():
if v == ... | the_stack_v2_python_sparse | prob260_single_number3.py | Hu-Wenchao/leetcode | train | 0 | |
d1eeeef9328629e21653fd9f3130ad41dd8fc7d6 | [
"self._use_polld = use_polld\nself._server = None\nif use_polld:\n remote = 'http://%s:%s' % (host, tcp_port)\n self._server = net_utils.TimeoutXMLRPCServerProxy(remote, timeout=timeout, verbose=verbose)",
"if edge not in self.GPIO_EDGE_LIST:\n raise GpioManagerError('Invalid edge %r. Valid values: %r' %... | <|body_start_0|>
self._use_polld = use_polld
self._server = None
if use_polld:
remote = 'http://%s:%s' % (host, tcp_port)
self._server = net_utils.TimeoutXMLRPCServerProxy(remote, timeout=timeout, verbose=verbose)
<|end_body_0|>
<|body_start_1|>
if edge not in se... | GPIO monitor and control manager. | GpioManager | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GpioManager:
"""GPIO monitor and control manager."""
def __init__(self, use_polld, host=None, tcp_port=None, timeout=10, verbose=False):
"""Constructor. Args: use_polld: True to use polld to manage GPIO on remote server, or False to manage local GPIO port directly. host: Name or IP a... | stack_v2_sparse_classes_36k_train_004088 | 11,503 | permissive | [
{
"docstring": "Constructor. Args: use_polld: True to use polld to manage GPIO on remote server, or False to manage local GPIO port directly. host: Name or IP address of servo server host. tcp_port: TCP port on which servod is listening on. timeout: Timeout for HTTP connection. verbose: Enables verbose messagin... | 4 | stack_v2_sparse_classes_30k_train_001915 | Implement the Python class `GpioManager` described below.
Class description:
GPIO monitor and control manager.
Method signatures and docstrings:
- def __init__(self, use_polld, host=None, tcp_port=None, timeout=10, verbose=False): Constructor. Args: use_polld: True to use polld to manage GPIO on remote server, or Fal... | Implement the Python class `GpioManager` described below.
Class description:
GPIO monitor and control manager.
Method signatures and docstrings:
- def __init__(self, use_polld, host=None, tcp_port=None, timeout=10, verbose=False): Constructor. Args: use_polld: True to use polld to manage GPIO on remote server, or Fal... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class GpioManager:
"""GPIO monitor and control manager."""
def __init__(self, use_polld, host=None, tcp_port=None, timeout=10, verbose=False):
"""Constructor. Args: use_polld: True to use polld to manage GPIO on remote server, or False to manage local GPIO port directly. host: Name or IP a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GpioManager:
"""GPIO monitor and control manager."""
def __init__(self, use_polld, host=None, tcp_port=None, timeout=10, verbose=False):
"""Constructor. Args: use_polld: True to use polld to manage GPIO on remote server, or False to manage local GPIO port directly. host: Name or IP address of ser... | the_stack_v2_python_sparse | py/utils/gpio_utils.py | bridder/factory | train | 0 |
bb70e4e0c2476bc4f6955e358c2f08be80a92daf | [
"notebook = get_object_or_404(Notebook, slug=notebook_slug)\ncontent = request.data.get('content')\ntitle = request.data.get('title', '')\ntags = request.data.get('tags')\ntype = request.data.get('type', '')\ndate = request.data.get('date', '')\nif content:\n kwargs = {'content': content, 'author': request.user,... | <|body_start_0|>
notebook = get_object_or_404(Notebook, slug=notebook_slug)
content = request.data.get('content')
title = request.data.get('title', '')
tags = request.data.get('tags')
type = request.data.get('type', '')
date = request.data.get('date', '')
if conte... | # Entries - **GET** List the entries for the current notebook. - **POST** Create a new entry attached to the current notebook. - **PUT** Edit the current notebook's name, status, group, or sections. - **DELETE** Delete the current notebook (sets the status to deleted). | EntryListAPIView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntryListAPIView:
"""# Entries - **GET** List the entries for the current notebook. - **POST** Create a new entry attached to the current notebook. - **PUT** Edit the current notebook's name, status, group, or sections. - **DELETE** Delete the current notebook (sets the status to deleted)."""
... | stack_v2_sparse_classes_36k_train_004089 | 19,450 | no_license | [
{
"docstring": "Create a new Entry.",
"name": "post",
"signature": "def post(self, request, notebook_slug)"
},
{
"docstring": "Update an existing Notebook.",
"name": "put",
"signature": "def put(self, request, notebook_slug)"
},
{
"docstring": "Delete a Notebook. Sets the status ... | 3 | stack_v2_sparse_classes_30k_train_004867 | Implement the Python class `EntryListAPIView` described below.
Class description:
# Entries - **GET** List the entries for the current notebook. - **POST** Create a new entry attached to the current notebook. - **PUT** Edit the current notebook's name, status, group, or sections. - **DELETE** Delete the current notebo... | Implement the Python class `EntryListAPIView` described below.
Class description:
# Entries - **GET** List the entries for the current notebook. - **POST** Create a new entry attached to the current notebook. - **PUT** Edit the current notebook's name, status, group, or sections. - **DELETE** Delete the current notebo... | b8c4ceff83c92e83a2297b17ad29e29b3b8fdc3c | <|skeleton|>
class EntryListAPIView:
"""# Entries - **GET** List the entries for the current notebook. - **POST** Create a new entry attached to the current notebook. - **PUT** Edit the current notebook's name, status, group, or sections. - **DELETE** Delete the current notebook (sets the status to deleted)."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntryListAPIView:
"""# Entries - **GET** List the entries for the current notebook. - **POST** Create a new entry attached to the current notebook. - **PUT** Edit the current notebook's name, status, group, or sections. - **DELETE** Delete the current notebook (sets the status to deleted)."""
def post(se... | the_stack_v2_python_sparse | vinci/views/apis.py | mod2/vinci | train | 1 |
65d476eadb6b2d358c08cbe9c88cf6a5797a4b2a | [
"print('**kwargs', kwargs)\nif cls not in cls._instance:\n cls._instance[cls] = super(MetaClass, cls).__call__(*args, **kwargs)\n return cls._instance[cls]",
"if cls.__name__[0].isupper():\n ' Create class only if First Letter is Capital '\n for k, v in attr.items():\n if hasattr(v, '__call_... | <|body_start_0|>
print('**kwargs', kwargs)
if cls not in cls._instance:
cls._instance[cls] = super(MetaClass, cls).__call__(*args, **kwargs)
return cls._instance[cls]
<|end_body_0|>
<|body_start_1|>
if cls.__name__[0].isupper():
' Create class only if First L... | Meta class | MetaClass | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MetaClass:
"""Meta class"""
def __call__(cls, *args, **kwargs):
"""Implementing Singleton Design Pattern"""
<|body_0|>
def __init__(cls, name, base, attr):
"""Defining Your Own Rules"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
print('**kwarg... | stack_v2_sparse_classes_36k_train_004090 | 1,990 | no_license | [
{
"docstring": "Implementing Singleton Design Pattern",
"name": "__call__",
"signature": "def __call__(cls, *args, **kwargs)"
},
{
"docstring": "Defining Your Own Rules",
"name": "__init__",
"signature": "def __init__(cls, name, base, attr)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005676 | Implement the Python class `MetaClass` described below.
Class description:
Meta class
Method signatures and docstrings:
- def __call__(cls, *args, **kwargs): Implementing Singleton Design Pattern
- def __init__(cls, name, base, attr): Defining Your Own Rules | Implement the Python class `MetaClass` described below.
Class description:
Meta class
Method signatures and docstrings:
- def __call__(cls, *args, **kwargs): Implementing Singleton Design Pattern
- def __init__(cls, name, base, attr): Defining Your Own Rules
<|skeleton|>
class MetaClass:
"""Meta class"""
de... | 41c4346132d84c00ee2163f14d5a47b052716663 | <|skeleton|>
class MetaClass:
"""Meta class"""
def __call__(cls, *args, **kwargs):
"""Implementing Singleton Design Pattern"""
<|body_0|>
def __init__(cls, name, base, attr):
"""Defining Your Own Rules"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MetaClass:
"""Meta class"""
def __call__(cls, *args, **kwargs):
"""Implementing Singleton Design Pattern"""
print('**kwargs', kwargs)
if cls not in cls._instance:
cls._instance[cls] = super(MetaClass, cls).__call__(*args, **kwargs)
return cls._instance[cls]... | the_stack_v2_python_sparse | Metaclass/MetaClassMaster.py | soumilshah1995/Data-Structure-and-Algorithm-and-Meta-class | train | 1 |
75a2af4fa6a4557a64082fdee5b9b2b2d49ee7ed | [
"wx.Panel.__init__(self, parent)\nself.number_of_grids = 0\nself.frame = parent\nself.mainSizer = wx.BoxSizer(wx.VERTICAL)\ncontrolSizer = wx.BoxSizer(wx.HORIZONTAL)\nself.widgetSizer = wx.BoxSizer(wx.VERTICAL)\nself.addButton = wx.Button(self, label='Add')\nself.addButton.Bind(wx.EVT_BUTTON, self.onAddWidget)\ncon... | <|body_start_0|>
wx.Panel.__init__(self, parent)
self.number_of_grids = 0
self.frame = parent
self.mainSizer = wx.BoxSizer(wx.VERTICAL)
controlSizer = wx.BoxSizer(wx.HORIZONTAL)
self.widgetSizer = wx.BoxSizer(wx.VERTICAL)
self.addButton = wx.Button(self, label='Ad... | MyPanel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyPanel:
def __init__(self, parent):
"""Constructor"""
<|body_0|>
def onAddWidget(self, event):
"""Add widget."""
<|body_1|>
def onRemoveWidget(self, event):
"""Remove widget."""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
wx.P... | stack_v2_sparse_classes_36k_train_004091 | 6,784 | permissive | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, parent)"
},
{
"docstring": "Add widget.",
"name": "onAddWidget",
"signature": "def onAddWidget(self, event)"
},
{
"docstring": "Remove widget.",
"name": "onRemoveWidget",
"signature": "def ... | 3 | stack_v2_sparse_classes_30k_train_006660 | Implement the Python class `MyPanel` described below.
Class description:
Implement the MyPanel class.
Method signatures and docstrings:
- def __init__(self, parent): Constructor
- def onAddWidget(self, event): Add widget.
- def onRemoveWidget(self, event): Remove widget. | Implement the Python class `MyPanel` described below.
Class description:
Implement the MyPanel class.
Method signatures and docstrings:
- def __init__(self, parent): Constructor
- def onAddWidget(self, event): Add widget.
- def onRemoveWidget(self, event): Remove widget.
<|skeleton|>
class MyPanel:
def __init__... | 5a07e02588b1b7c8ebf7458b10e81b8ecf84ad13 | <|skeleton|>
class MyPanel:
def __init__(self, parent):
"""Constructor"""
<|body_0|>
def onAddWidget(self, event):
"""Add widget."""
<|body_1|>
def onRemoveWidget(self, event):
"""Remove widget."""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyPanel:
def __init__(self, parent):
"""Constructor"""
wx.Panel.__init__(self, parent)
self.number_of_grids = 0
self.frame = parent
self.mainSizer = wx.BoxSizer(wx.VERTICAL)
controlSizer = wx.BoxSizer(wx.HORIZONTAL)
self.widgetSizer = wx.BoxSizer(wx.VERT... | the_stack_v2_python_sparse | sandbox/dynamic_widgets2.py | baluneboy/pims | train | 0 | |
607d40807b6dac66d5e3a77b88ef132d598ba0e7 | [
"res = []\nfor i in range(len(words)):\n for j in range(len(words)):\n if i == j:\n continue\n concat_words = words[i] + words[j]\n if concat_words == concat_words[::-1]:\n res.append((i, j))\nreturn res",
"d = {w: i for i, w in enumerate(words)}\nres = []\nfor i, w i... | <|body_start_0|>
res = []
for i in range(len(words)):
for j in range(len(words)):
if i == j:
continue
concat_words = words[i] + words[j]
if concat_words == concat_words[::-1]:
res.append((i, j))
r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def palindromePairs(self, words: List[str]) -> List[List[int]]:
"""Brute force method O(n^2)"""
<|body_0|>
def palindromePairs(self, words: List[str]) -> List[List[int]]:
"""optimized O(N*k^2), iterate over all words, iterate over all characters, check pali... | stack_v2_sparse_classes_36k_train_004092 | 2,241 | no_license | [
{
"docstring": "Brute force method O(n^2)",
"name": "palindromePairs",
"signature": "def palindromePairs(self, words: List[str]) -> List[List[int]]"
},
{
"docstring": "optimized O(N*k^2), iterate over all words, iterate over all characters, check palindrome 4 different cases: Case 1: If s2 is th... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def palindromePairs(self, words: List[str]) -> List[List[int]]: Brute force method O(n^2)
- def palindromePairs(self, words: List[str]) -> List[List[int]]: optimized O(N*k^2), it... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def palindromePairs(self, words: List[str]) -> List[List[int]]: Brute force method O(n^2)
- def palindromePairs(self, words: List[str]) -> List[List[int]]: optimized O(N*k^2), it... | e50dc0642f087f37ab3234390be3d8a0ed48fe62 | <|skeleton|>
class Solution:
def palindromePairs(self, words: List[str]) -> List[List[int]]:
"""Brute force method O(n^2)"""
<|body_0|>
def palindromePairs(self, words: List[str]) -> List[List[int]]:
"""optimized O(N*k^2), iterate over all words, iterate over all characters, check pali... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def palindromePairs(self, words: List[str]) -> List[List[int]]:
"""Brute force method O(n^2)"""
res = []
for i in range(len(words)):
for j in range(len(words)):
if i == j:
continue
concat_words = words[i] + words... | the_stack_v2_python_sparse | Leetcode/ByteDance/336. Palindrome Pairs.py | brlala/Educative-Grokking-Coding-Exercise | train | 3 | |
56f9f4c476195a198682891f1558760e85252574 | [
"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... | Service defines a gRPC service for interacting with transactions. | ServiceServicer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServiceServicer:
"""Service defines a gRPC service for interacting with transactions."""
def Simulate(self, request, context):
"""Simulate simulates executing a transaction for estimating gas usage."""
<|body_0|>
def GetTx(self, request, context):
"""GetTx fetche... | stack_v2_sparse_classes_36k_train_004093 | 7,930 | permissive | [
{
"docstring": "Simulate simulates executing a transaction for estimating gas usage.",
"name": "Simulate",
"signature": "def Simulate(self, request, context)"
},
{
"docstring": "GetTx fetches a tx by hash.",
"name": "GetTx",
"signature": "def GetTx(self, request, context)"
},
{
"... | 4 | stack_v2_sparse_classes_30k_train_020947 | Implement the Python class `ServiceServicer` described below.
Class description:
Service defines a gRPC service for interacting with transactions.
Method signatures and docstrings:
- def Simulate(self, request, context): Simulate simulates executing a transaction for estimating gas usage.
- def GetTx(self, request, c... | Implement the Python class `ServiceServicer` described below.
Class description:
Service defines a gRPC service for interacting with transactions.
Method signatures and docstrings:
- def Simulate(self, request, context): Simulate simulates executing a transaction for estimating gas usage.
- def GetTx(self, request, c... | c38a07458a36305457680196e8c47372008db5ab | <|skeleton|>
class ServiceServicer:
"""Service defines a gRPC service for interacting with transactions."""
def Simulate(self, request, context):
"""Simulate simulates executing a transaction for estimating gas usage."""
<|body_0|>
def GetTx(self, request, context):
"""GetTx fetche... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ServiceServicer:
"""Service defines a gRPC service for interacting with transactions."""
def Simulate(self, request, context):
"""Simulate simulates executing a transaction for estimating gas usage."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not... | the_stack_v2_python_sparse | bluzelle/codec/cosmos/tx/v1beta1/service_pb2_grpc.py | hhio618/bluzelle-py | train | 3 |
247291bd3923b474de92021a5db93c110c0d7e9a | [
"form = super(CommonGeoDatasetEditView, self).get_form()\nform.fields['categories'].queryset = form.fields['categories'].queryset.filter(organization=self.request.organization)\nreturn form",
"if form.instance.pk:\n created = False\nelse:\n created = True\nresponse = super(CommonGeoDatasetEditView, self).fo... | <|body_start_0|>
form = super(CommonGeoDatasetEditView, self).get_form()
form.fields['categories'].queryset = form.fields['categories'].queryset.filter(organization=self.request.organization)
return form
<|end_body_0|>
<|body_start_1|>
if form.instance.pk:
created = False
... | Create or edit a geodataset | CommonGeoDatasetEditView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonGeoDatasetEditView:
"""Create or edit a geodataset"""
def get_form(self):
"""Get the form"""
<|body_0|>
def form_valid(self, form):
"""Handle a valid form"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
form = super(CommonGeoDatasetEditVie... | stack_v2_sparse_classes_36k_train_004094 | 7,482 | permissive | [
{
"docstring": "Get the form",
"name": "get_form",
"signature": "def get_form(self)"
},
{
"docstring": "Handle a valid form",
"name": "form_valid",
"signature": "def form_valid(self, form)"
}
] | 2 | null | Implement the Python class `CommonGeoDatasetEditView` described below.
Class description:
Create or edit a geodataset
Method signatures and docstrings:
- def get_form(self): Get the form
- def form_valid(self, form): Handle a valid form | Implement the Python class `CommonGeoDatasetEditView` described below.
Class description:
Create or edit a geodataset
Method signatures and docstrings:
- def get_form(self): Get the form
- def form_valid(self, form): Handle a valid form
<|skeleton|>
class CommonGeoDatasetEditView:
"""Create or edit a geodataset"... | 3af6bc9f3ff4e5dfdbb118209e877379428bc06c | <|skeleton|>
class CommonGeoDatasetEditView:
"""Create or edit a geodataset"""
def get_form(self):
"""Get the form"""
<|body_0|>
def form_valid(self, form):
"""Handle a valid form"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommonGeoDatasetEditView:
"""Create or edit a geodataset"""
def get_form(self):
"""Get the form"""
form = super(CommonGeoDatasetEditView, self).get_form()
form.fields['categories'].queryset = form.fields['categories'].queryset.filter(organization=self.request.organization)
... | the_stack_v2_python_sparse | geodataset/views.py | ofa/everyvoter | train | 7 |
0623dfd86a435ef1695bfa540bec1d19ccbe511e | [
"name = 'masDASdk213aksd123Saad'\nself.assertRaises(ProposedCardInfo.DoesNotExist, ProposedCardInfo.objects.get, name=name)\neffect_without_modifiers = CardEffect.objects.filter(has_modifier=False).first()\neffect_with_modifiers = CardEffect.objects.filter(has_modifier=True).first()\nself.assertIsNotNone(effect_wit... | <|body_start_0|>
name = 'masDASdk213aksd123Saad'
self.assertRaises(ProposedCardInfo.DoesNotExist, ProposedCardInfo.objects.get, name=name)
effect_without_modifiers = CardEffect.objects.filter(has_modifier=False).first()
effect_with_modifiers = CardEffect.objects.filter(has_modifier=True)... | WholeProposedCardListTestCase | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WholeProposedCardListTestCase:
def test_post1(self):
"""Scenario: POST request is made with correct data. Expected result: Proposed card is created correctly."""
<|body_0|>
def test_post2(self):
"""Scenario: POST request is made with levels array provided, but empty.... | stack_v2_sparse_classes_36k_train_004095 | 42,884 | permissive | [
{
"docstring": "Scenario: POST request is made with correct data. Expected result: Proposed card is created correctly.",
"name": "test_post1",
"signature": "def test_post1(self)"
},
{
"docstring": "Scenario: POST request is made with levels array provided, but empty. Expected result: Proposed ca... | 2 | stack_v2_sparse_classes_30k_train_001204 | Implement the Python class `WholeProposedCardListTestCase` described below.
Class description:
Implement the WholeProposedCardListTestCase class.
Method signatures and docstrings:
- def test_post1(self): Scenario: POST request is made with correct data. Expected result: Proposed card is created correctly.
- def test_... | Implement the Python class `WholeProposedCardListTestCase` described below.
Class description:
Implement the WholeProposedCardListTestCase class.
Method signatures and docstrings:
- def test_post1(self): Scenario: POST request is made with correct data. Expected result: Proposed card is created correctly.
- def test_... | ea812b13de0cd6c47c541cbede2d016a7837b4b8 | <|skeleton|>
class WholeProposedCardListTestCase:
def test_post1(self):
"""Scenario: POST request is made with correct data. Expected result: Proposed card is created correctly."""
<|body_0|>
def test_post2(self):
"""Scenario: POST request is made with levels array provided, but empty.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WholeProposedCardListTestCase:
def test_post1(self):
"""Scenario: POST request is made with correct data. Expected result: Proposed card is created correctly."""
name = 'masDASdk213aksd123Saad'
self.assertRaises(ProposedCardInfo.DoesNotExist, ProposedCardInfo.objects.get, name=name)
... | the_stack_v2_python_sparse | WMIAdventure/backend/WMIAdventure_backend/proposed_content/tests.py | Michal-Czekanski/WMIAdventure-1 | train | 0 | |
bc573e9fe7b5d7a29a24d324b6fa18f5d4ab5d93 | [
"p = len(nums) - 1\nfor i in range(len(nums) - 1, -1, -1):\n if i + nums[i] >= p:\n p = i\nreturn p == 0",
"reachable = [True] + [False] * (len(nums) - 1)\nfor i, n in enumerate(nums):\n if reachable[i]:\n for j in range(i, min(len(nums), i + n + 1)):\n reachable[j] = True\nreturn r... | <|body_start_0|>
p = len(nums) - 1
for i in range(len(nums) - 1, -1, -1):
if i + nums[i] >= p:
p = i
return p == 0
<|end_body_0|>
<|body_start_1|>
reachable = [True] + [False] * (len(nums) - 1)
for i, n in enumerate(nums):
if reachable[i]:... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canJump(self, nums):
""":type nums: List[int] :rtype: bool 這個做法的想法是從終點往回看每一個格子是不是有機會走到終點 如果有機會，就將p更新到那個位子 然後再繼續往回看每一個剩下的格子能不能走到更新後的p點 如果到最後 p 點被更新到 0, 也就是起點 就表示這個是可以從起點走到終點的, 所以最後是回傳 p == 0"""
<|body_0|>
def canJumpSlow(self, nums):
""":type nums: List[... | stack_v2_sparse_classes_36k_train_004096 | 1,531 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool 這個做法的想法是從終點往回看每一個格子是不是有機會走到終點 如果有機會，就將p更新到那個位子 然後再繼續往回看每一個剩下的格子能不能走到更新後的p點 如果到最後 p 點被更新到 0, 也就是起點 就表示這個是可以從起點走到終點的, 所以最後是回傳 p == 0",
"name": "canJump",
"signature": "def canJump(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool"... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canJump(self, nums): :type nums: List[int] :rtype: bool 這個做法的想法是從終點往回看每一個格子是不是有機會走到終點 如果有機會，就將p更新到那個位子 然後再繼續往回看每一個剩下的格子能不能走到更新後的p點 如果到最後 p 點被更新到 0, 也就是起點 就表示這個是可以從起點走到終點的, 所以... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canJump(self, nums): :type nums: List[int] :rtype: bool 這個做法的想法是從終點往回看每一個格子是不是有機會走到終點 如果有機會，就將p更新到那個位子 然後再繼續往回看每一個剩下的格子能不能走到更新後的p點 如果到最後 p 點被更新到 0, 也就是起點 就表示這個是可以從起點走到終點的, 所以... | ac53dd9bf2c4c9d17c9dc5f7fdda32e386658fdd | <|skeleton|>
class Solution:
def canJump(self, nums):
""":type nums: List[int] :rtype: bool 這個做法的想法是從終點往回看每一個格子是不是有機會走到終點 如果有機會，就將p更新到那個位子 然後再繼續往回看每一個剩下的格子能不能走到更新後的p點 如果到最後 p 點被更新到 0, 也就是起點 就表示這個是可以從起點走到終點的, 所以最後是回傳 p == 0"""
<|body_0|>
def canJumpSlow(self, nums):
""":type nums: List[... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canJump(self, nums):
""":type nums: List[int] :rtype: bool 這個做法的想法是從終點往回看每一個格子是不是有機會走到終點 如果有機會，就將p更新到那個位子 然後再繼續往回看每一個剩下的格子能不能走到更新後的p點 如果到最後 p 點被更新到 0, 也就是起點 就表示這個是可以從起點走到終點的, 所以最後是回傳 p == 0"""
p = len(nums) - 1
for i in range(len(nums) - 1, -1, -1):
if i + num... | the_stack_v2_python_sparse | cs_notes/arrays/jump_game.py | hwc1824/LeetCodeSolution | train | 0 | |
bea510e3466d9c17839bd176c8024a8cb589664a | [
"if asyncEstimate:\n task = self._coreEstimator.asyncEstimate(warp.warpedImage.coreImage)\n return AsyncTask(task, POST_PROCESSING.postProcessing)\nerror, estimation = self._coreEstimator.estimate(warp.warpedImage.coreImage)\nreturn POST_PROCESSING.postProcessing(error, estimation)",
"coreImages = [warp.war... | <|body_start_0|>
if asyncEstimate:
task = self._coreEstimator.asyncEstimate(warp.warpedImage.coreImage)
return AsyncTask(task, POST_PROCESSING.postProcessing)
error, estimation = self._coreEstimator.estimate(warp.warpedImage.coreImage)
return POST_PROCESSING.postProcessin... | Image color type estimator. Work on face detections. Allowed types see `ImageColorSchema`. | ImageColorTypeEstimator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageColorTypeEstimator:
"""Image color type estimator. Work on face detections. Allowed types see `ImageColorSchema`."""
def estimate(self, warp: Union[FaceWarp, FaceWarpedImage], asyncEstimate: bool=False) -> Union[ImageColorType, AsyncTask[ImageColorType]]:
"""Estimate image color... | stack_v2_sparse_classes_36k_train_004097 | 4,296 | permissive | [
{
"docstring": "Estimate image color type on warp. Args: warp: warped image asyncEstimate: estimate or run estimation in background Returns: estimated image color type if asyncEstimate is false otherwise async task Raises: LunaSDKException: if estimation failed",
"name": "estimate",
"signature": "def es... | 2 | stack_v2_sparse_classes_30k_train_011864 | Implement the Python class `ImageColorTypeEstimator` described below.
Class description:
Image color type estimator. Work on face detections. Allowed types see `ImageColorSchema`.
Method signatures and docstrings:
- def estimate(self, warp: Union[FaceWarp, FaceWarpedImage], asyncEstimate: bool=False) -> Union[ImageCo... | Implement the Python class `ImageColorTypeEstimator` described below.
Class description:
Image color type estimator. Work on face detections. Allowed types see `ImageColorSchema`.
Method signatures and docstrings:
- def estimate(self, warp: Union[FaceWarp, FaceWarpedImage], asyncEstimate: bool=False) -> Union[ImageCo... | 7a4bebc92ae7a96d8d9c18a024208308942f90cd | <|skeleton|>
class ImageColorTypeEstimator:
"""Image color type estimator. Work on face detections. Allowed types see `ImageColorSchema`."""
def estimate(self, warp: Union[FaceWarp, FaceWarpedImage], asyncEstimate: bool=False) -> Union[ImageColorType, AsyncTask[ImageColorType]]:
"""Estimate image color... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageColorTypeEstimator:
"""Image color type estimator. Work on face detections. Allowed types see `ImageColorSchema`."""
def estimate(self, warp: Union[FaceWarp, FaceWarpedImage], asyncEstimate: bool=False) -> Union[ImageColorType, AsyncTask[ImageColorType]]:
"""Estimate image color type on warp... | the_stack_v2_python_sparse | lunavl/sdk/estimators/face_estimators/image_type.py | matemax/lunasdk | train | 16 |
2cef6fb2b668c333be11e85155c5bdaf29d0dfcd | [
"super().__init__()\nself.criterion = criterion\nself.weight = weight\nself.independent_perm = independent_perm\nself.solver = PITSolver(criterion, weight, independent_perm)\nself.layer_weights = layer_weights",
"losses = 0.0\nif not isinstance(infs[0], (tuple, list)) and len(infs) == len(ref):\n loss, stats, ... | <|body_start_0|>
super().__init__()
self.criterion = criterion
self.weight = weight
self.independent_perm = independent_perm
self.solver = PITSolver(criterion, weight, independent_perm)
self.layer_weights = layer_weights
<|end_body_0|>
<|body_start_1|>
losses = 0... | MultiLayerPITSolver | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiLayerPITSolver:
def __init__(self, criterion: AbsEnhLoss, weight=1.0, independent_perm=True, layer_weights=None):
"""Multi-Layer Permutation Invariant Training Solver. Compute the PIT loss given inferences of multiple layers and a single reference. It also support single inference a... | stack_v2_sparse_classes_36k_train_004098 | 3,042 | permissive | [
{
"docstring": "Multi-Layer Permutation Invariant Training Solver. Compute the PIT loss given inferences of multiple layers and a single reference. It also support single inference and single reference in evaluation stage. Args: criterion (AbsEnhLoss): an instance of AbsEnhLoss weight (float): weight (between 0... | 2 | null | Implement the Python class `MultiLayerPITSolver` described below.
Class description:
Implement the MultiLayerPITSolver class.
Method signatures and docstrings:
- def __init__(self, criterion: AbsEnhLoss, weight=1.0, independent_perm=True, layer_weights=None): Multi-Layer Permutation Invariant Training Solver. Compute... | Implement the Python class `MultiLayerPITSolver` described below.
Class description:
Implement the MultiLayerPITSolver class.
Method signatures and docstrings:
- def __init__(self, criterion: AbsEnhLoss, weight=1.0, independent_perm=True, layer_weights=None): Multi-Layer Permutation Invariant Training Solver. Compute... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class MultiLayerPITSolver:
def __init__(self, criterion: AbsEnhLoss, weight=1.0, independent_perm=True, layer_weights=None):
"""Multi-Layer Permutation Invariant Training Solver. Compute the PIT loss given inferences of multiple layers and a single reference. It also support single inference a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiLayerPITSolver:
def __init__(self, criterion: AbsEnhLoss, weight=1.0, independent_perm=True, layer_weights=None):
"""Multi-Layer Permutation Invariant Training Solver. Compute the PIT loss given inferences of multiple layers and a single reference. It also support single inference and single refe... | the_stack_v2_python_sparse | espnet2/enh/loss/wrappers/multilayer_pit_solver.py | espnet/espnet | train | 7,242 | |
6d3c29363a5e25edf0c0dfa091737648b4bcb7bd | [
"user = User.objects.create_user('user', 'user@test.com')\nactivity = Activity.objects.create(title='Test activity', slug='test-activity', description='Testing!', duration=10, point_value=10, pub_date=datetime.datetime.today(), expire_date=datetime.datetime.today() + datetime.timedelta(days=7), confirm_type='text',... | <|body_start_0|>
user = User.objects.create_user('user', 'user@test.com')
activity = Activity.objects.create(title='Test activity', slug='test-activity', description='Testing!', duration=10, point_value=10, pub_date=datetime.datetime.today(), expire_date=datetime.datetime.today() + datetime.timedelta(da... | MissionTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MissionTest:
def testGroupMissionCompletion(self):
"""Test that a group mission is completed when its related activity is completed."""
<|body_0|>
def testGroupMissionKarma(self):
"""Test that karma for a group activity is handled properly."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_004099 | 7,469 | no_license | [
{
"docstring": "Test that a group mission is completed when its related activity is completed.",
"name": "testGroupMissionCompletion",
"signature": "def testGroupMissionCompletion(self)"
},
{
"docstring": "Test that karma for a group activity is handled properly.",
"name": "testGroupMissionK... | 4 | null | Implement the Python class `MissionTest` described below.
Class description:
Implement the MissionTest class.
Method signatures and docstrings:
- def testGroupMissionCompletion(self): Test that a group mission is completed when its related activity is completed.
- def testGroupMissionKarma(self): Test that karma for ... | Implement the Python class `MissionTest` described below.
Class description:
Implement the MissionTest class.
Method signatures and docstrings:
- def testGroupMissionCompletion(self): Test that a group mission is completed when its related activity is completed.
- def testGroupMissionKarma(self): Test that karma for ... | 783db33ed0b38fb4dccc371c426265f7028a2d13 | <|skeleton|>
class MissionTest:
def testGroupMissionCompletion(self):
"""Test that a group mission is completed when its related activity is completed."""
<|body_0|>
def testGroupMissionKarma(self):
"""Test that karma for a group activity is handled properly."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MissionTest:
def testGroupMissionCompletion(self):
"""Test that a group mission is completed when its related activity is completed."""
user = User.objects.create_user('user', 'user@test.com')
activity = Activity.objects.create(title='Test activity', slug='test-activity', description='... | the_stack_v2_python_sparse | makahiki/apps/components/canopy/tests.py | keokilee/makahiki | train | 2 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.