blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 467 8.64k | id stringlengths 40 40 | length_bytes int64 515 49.7k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 160 3.93k | prompted_full_text stringlengths 681 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.09k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 331 8.3k | source stringclasses 1
value | source_path stringlengths 5 177 | source_repo stringlengths 6 88 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
473b7264a210362b0ce31e3450bbd7aabd145c1c | [
"t = {}\nfor n in nums:\n if n in t:\n t[n] += 1\n else:\n t[n] = 1\n if t[n] == 2:\n del t[n]\nres = []\nfor key, val in t.items():\n res.append(key)\nreturn res",
"d = set()\nfor n in nums:\n if n in d:\n d.remove(n)\n else:\n d.add(n)\nreturn list(d)"
] | <|body_start_0|>
t = {}
for n in nums:
if n in t:
t[n] += 1
else:
t[n] = 1
if t[n] == 2:
del t[n]
res = []
for key, val in t.items():
res.append(key)
return res
<|end_body_0|>
<|body_... | https://leetcode.com/problems/single-number-iii/description/ | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/single-number-iii/description/"""
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|>
<|... | stack_v2_sparse_classes_10k_train_007600 | 843 | 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 | null | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/single-number-iii/description/
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:
https://leetcode.com/problems/single-number-iii/description/
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]
<|s... | 54d3d9530b25272d4a2e5dc33e7035c44f506dc5 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/single-number-iii/description/"""
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|>
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
"""https://leetcode.com/problems/single-number-iii/description/"""
def singleNumber(self, nums):
""":type nums: List[int] :rtype: List[int]"""
t = {}
for n in nums:
if n in t:
t[n] += 1
else:
t[n] = 1
if... | the_stack_v2_python_sparse | old/Session002/Arrays/SingleNumberIII.py | MaxIakovliev/algorithms | train | 0 |
97ba2c8dbb90199871ebead20570ddb79ccca4d5 | [
"try:\n movie = db.get_movie_by_id(list_id=list_id, movie_id=movie_id, session=session)\nexcept NoResultFound:\n raise NotFoundError('could not find movie with id %d in list %d' % (movie_id, list_id))\nreturn jsonify(movie.to_dict())",
"try:\n movie = db.get_movie_by_id(list_id=list_id, movie_id=movie_id... | <|body_start_0|>
try:
movie = db.get_movie_by_id(list_id=list_id, movie_id=movie_id, session=session)
except NoResultFound:
raise NotFoundError('could not find movie with id %d in list %d' % (movie_id, list_id))
return jsonify(movie.to_dict())
<|end_body_0|>
<|body_start... | MovieListMovieAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovieListMovieAPI:
def get(self, list_id, movie_id, session=None):
"""Get a movie by list ID and movie ID"""
<|body_0|>
def delete(self, list_id, movie_id, session=None):
"""Delete a movie by list ID and movie ID"""
<|body_1|>
def put(self, list_id, movi... | stack_v2_sparse_classes_10k_train_007601 | 12,846 | permissive | [
{
"docstring": "Get a movie by list ID and movie ID",
"name": "get",
"signature": "def get(self, list_id, movie_id, session=None)"
},
{
"docstring": "Delete a movie by list ID and movie ID",
"name": "delete",
"signature": "def delete(self, list_id, movie_id, session=None)"
},
{
"... | 3 | null | Implement the Python class `MovieListMovieAPI` described below.
Class description:
Implement the MovieListMovieAPI class.
Method signatures and docstrings:
- def get(self, list_id, movie_id, session=None): Get a movie by list ID and movie ID
- def delete(self, list_id, movie_id, session=None): Delete a movie by list ... | Implement the Python class `MovieListMovieAPI` described below.
Class description:
Implement the MovieListMovieAPI class.
Method signatures and docstrings:
- def get(self, list_id, movie_id, session=None): Get a movie by list ID and movie ID
- def delete(self, list_id, movie_id, session=None): Delete a movie by list ... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class MovieListMovieAPI:
def get(self, list_id, movie_id, session=None):
"""Get a movie by list ID and movie ID"""
<|body_0|>
def delete(self, list_id, movie_id, session=None):
"""Delete a movie by list ID and movie ID"""
<|body_1|>
def put(self, list_id, movi... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MovieListMovieAPI:
def get(self, list_id, movie_id, session=None):
"""Get a movie by list ID and movie ID"""
try:
movie = db.get_movie_by_id(list_id=list_id, movie_id=movie_id, session=session)
except NoResultFound:
raise NotFoundError('could not find movie with... | the_stack_v2_python_sparse | flexget/components/managed_lists/lists/movie_list/api.py | BrutuZ/Flexget | train | 1 | |
c7a7f5471973ac0f215bd6f89ea525ae42881c73 | [
"unit = ['', 'Satu', 'Dua', 'Tiga', 'Empat', 'Lima', 'Enam', 'Tujuh', 'Delapan', 'Sembilan', 'Sepuluh', 'Sebelas']\nresult = ' '\ntotal_terbilang = self.total_terbilang\nfor line in self:\n n = int(amount_total)\n if n >= 0 and n <= 11:\n result = result + unit[n]\n elif n < 20:\n result = to... | <|body_start_0|>
unit = ['', 'Satu', 'Dua', 'Tiga', 'Empat', 'Lima', 'Enam', 'Tujuh', 'Delapan', 'Sembilan', 'Sepuluh', 'Sebelas']
result = ' '
total_terbilang = self.total_terbilang
for line in self:
n = int(amount_total)
if n >= 0 and n <= 11:
re... | inherit model account.invoice | AccountInvoice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountInvoice:
"""inherit model account.invoice"""
def total_terbilang(self, amount_total):
"""function to converting amount total into word"""
<|body_0|>
def currency_word(self):
"""function to converting currency into word"""
<|body_1|>
def _prepa... | stack_v2_sparse_classes_10k_train_007602 | 3,798 | no_license | [
{
"docstring": "function to converting amount total into word",
"name": "total_terbilang",
"signature": "def total_terbilang(self, amount_total)"
},
{
"docstring": "function to converting currency into word",
"name": "currency_word",
"signature": "def currency_word(self)"
},
{
"d... | 5 | stack_v2_sparse_classes_30k_train_002482 | Implement the Python class `AccountInvoice` described below.
Class description:
inherit model account.invoice
Method signatures and docstrings:
- def total_terbilang(self, amount_total): function to converting amount total into word
- def currency_word(self): function to converting currency into word
- def _prepare_r... | Implement the Python class `AccountInvoice` described below.
Class description:
inherit model account.invoice
Method signatures and docstrings:
- def total_terbilang(self, amount_total): function to converting amount total into word
- def currency_word(self): function to converting currency into word
- def _prepare_r... | 976928395f3b600275f6ab53445605fe1167c6ba | <|skeleton|>
class AccountInvoice:
"""inherit model account.invoice"""
def total_terbilang(self, amount_total):
"""function to converting amount total into word"""
<|body_0|>
def currency_word(self):
"""function to converting currency into word"""
<|body_1|>
def _prepa... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AccountInvoice:
"""inherit model account.invoice"""
def total_terbilang(self, amount_total):
"""function to converting amount total into word"""
unit = ['', 'Satu', 'Dua', 'Tiga', 'Empat', 'Lima', 'Enam', 'Tujuh', 'Delapan', 'Sembilan', 'Sepuluh', 'Sebelas']
result = ' '
t... | the_stack_v2_python_sparse | pn_account/models/account_invoice.py | detian08/pennyu | train | 0 |
b904cb88c9ecceff791a4ec96939146c945b1b1c | [
"super(SoftmaxSelfAttentionEncoder, self).__init__()\nself._attention_mlp = attention_mlp\nself._is_end_padded = is_end_padded",
"att_scores = self._attention_mlp(batch_sequences)\nmask = pwF.create_mask_from_length(batch_sequence_lengths, batch_sequences.size(1), self._is_end_padded).unsqueeze(-1)\nmasked_att_sc... | <|body_start_0|>
super(SoftmaxSelfAttentionEncoder, self).__init__()
self._attention_mlp = attention_mlp
self._is_end_padded = is_end_padded
<|end_body_0|>
<|body_start_1|>
att_scores = self._attention_mlp(batch_sequences)
mask = pwF.create_mask_from_length(batch_sequence_length... | Encodes a sequence using soft-max self-attention. | SoftmaxSelfAttentionEncoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoftmaxSelfAttentionEncoder:
"""Encodes a sequence using soft-max self-attention."""
def __init__(self, attention_mlp, is_end_padded=True):
""":param attention_mlp: MLP object used to generate unnormalized attention score(s). If the last dimension of the tensor returned by the MLP is... | stack_v2_sparse_classes_10k_train_007603 | 1,994 | permissive | [
{
"docstring": ":param attention_mlp: MLP object used to generate unnormalized attention score(s). If the last dimension of the tensor returned by the MLP is larger than 1 then multi-attention is applied. :param is_end_padded: Whether to mask at the end.",
"name": "__init__",
"signature": "def __init__(... | 2 | stack_v2_sparse_classes_30k_train_006501 | Implement the Python class `SoftmaxSelfAttentionEncoder` described below.
Class description:
Encodes a sequence using soft-max self-attention.
Method signatures and docstrings:
- def __init__(self, attention_mlp, is_end_padded=True): :param attention_mlp: MLP object used to generate unnormalized attention score(s). I... | Implement the Python class `SoftmaxSelfAttentionEncoder` described below.
Class description:
Encodes a sequence using soft-max self-attention.
Method signatures and docstrings:
- def __init__(self, attention_mlp, is_end_padded=True): :param attention_mlp: MLP object used to generate unnormalized attention score(s). I... | 57c85161bd6e09961cb7a1e69debc8e3e0bf7d29 | <|skeleton|>
class SoftmaxSelfAttentionEncoder:
"""Encodes a sequence using soft-max self-attention."""
def __init__(self, attention_mlp, is_end_padded=True):
""":param attention_mlp: MLP object used to generate unnormalized attention score(s). If the last dimension of the tensor returned by the MLP is... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SoftmaxSelfAttentionEncoder:
"""Encodes a sequence using soft-max self-attention."""
def __init__(self, attention_mlp, is_end_padded=True):
""":param attention_mlp: MLP object used to generate unnormalized attention score(s). If the last dimension of the tensor returned by the MLP is larger than ... | the_stack_v2_python_sparse | pytorch_wrapper/modules/softmax_self_attention_encoder.py | ajaxis001/pytorch-wrapper | train | 1 |
4bff64b5ea30f00bceac84482cd4ab24f3a15ed2 | [
"super(DefaultStaticAssetPolicy, self).__init__(config)\nself.settings = config.registry.settings\nself.views = {}",
"cache_max_age = self.settings.get('websauna.cache_max_age_seconds')\nif cache_max_age:\n cache_max_age = int(cache_max_age)\nself.config.add_static_view(name, path, cache_max_age=cache_max_age)... | <|body_start_0|>
super(DefaultStaticAssetPolicy, self).__init__(config)
self.settings = config.registry.settings
self.views = {}
<|end_body_0|>
<|body_start_1|>
cache_max_age = self.settings.get('websauna.cache_max_age_seconds')
if cache_max_age:
cache_max_age = int(... | Default inplementation of StaticAssetPolicy. | DefaultStaticAssetPolicy | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultStaticAssetPolicy:
"""Default inplementation of StaticAssetPolicy."""
def __init__(self, config: Configurator):
"""Initialize DefaultStaticAssetPolicy. :param config: Pyramid config."""
<|body_0|>
def add_static_view(self, name: str, path: str):
"""Include... | stack_v2_sparse_classes_10k_train_007604 | 9,928 | permissive | [
{
"docstring": "Initialize DefaultStaticAssetPolicy. :param config: Pyramid config.",
"name": "__init__",
"signature": "def __init__(self, config: Configurator)"
},
{
"docstring": "Include a path in static assets and configures cache busting for it. This does not only include the static resource... | 3 | stack_v2_sparse_classes_30k_train_006696 | Implement the Python class `DefaultStaticAssetPolicy` described below.
Class description:
Default inplementation of StaticAssetPolicy.
Method signatures and docstrings:
- def __init__(self, config: Configurator): Initialize DefaultStaticAssetPolicy. :param config: Pyramid config.
- def add_static_view(self, name: str... | Implement the Python class `DefaultStaticAssetPolicy` described below.
Class description:
Default inplementation of StaticAssetPolicy.
Method signatures and docstrings:
- def __init__(self, config: Configurator): Initialize DefaultStaticAssetPolicy. :param config: Pyramid config.
- def add_static_view(self, name: str... | a57de54fb8a3fae859f24f373f0292e1e4b3c344 | <|skeleton|>
class DefaultStaticAssetPolicy:
"""Default inplementation of StaticAssetPolicy."""
def __init__(self, config: Configurator):
"""Initialize DefaultStaticAssetPolicy. :param config: Pyramid config."""
<|body_0|>
def add_static_view(self, name: str, path: str):
"""Include... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DefaultStaticAssetPolicy:
"""Default inplementation of StaticAssetPolicy."""
def __init__(self, config: Configurator):
"""Initialize DefaultStaticAssetPolicy. :param config: Pyramid config."""
super(DefaultStaticAssetPolicy, self).__init__(config)
self.settings = config.registry.s... | the_stack_v2_python_sparse | websauna/system/http/static.py | websauna/websauna | train | 294 |
fc7e7f164d6ac62856bc4f9717c4d73d7301922f | [
"queue = [root] if root else []\nres = ''\nwhile queue:\n node = queue.pop(0)\n if node:\n res += '%d,' % node.val\n queue.append(node.left)\n queue.append(node.right)\n else:\n res += 'N,'\nreturn res[:-1]",
"data_list = data.split(',')\nval = data_list.pop(0)\nif not val:\n ... | <|body_start_0|>
queue = [root] if root else []
res = ''
while queue:
node = queue.pop(0)
if node:
res += '%d,' % node.val
queue.append(node.left)
queue.append(node.right)
else:
res += 'N,'
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_007605 | 2,305 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_002767 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 95d5aa4158209ad9ab81017e3bc82ef0680ea6bd | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
queue = [root] if root else []
res = ''
while queue:
node = queue.pop(0)
if node:
res += '%d,' % node.val
queue.ap... | the_stack_v2_python_sparse | Tree/BinaryTree/297_Serialize_and_Deserialize_Binary_Tree.py | OldFuzzier/Data-Structures-and-Algorithms- | train | 0 | |
8c9c14c67e2c0dd60900ff0110504cb21224f1f2 | [
"user = create_user()\nlist_sensor = []\nsensor1 = create_sensor('test1', 'code1', user)\nlist_sensor.append(sensor1)\nsensor2 = create_sensor('test2', 'code2', user)\nlist_sensor.append(sensor2)\nself.assertEqual(len(list_sensor), 2)",
"user = create_user()\ncode_mouse_event = 'The event is attached to its targe... | <|body_start_0|>
user = create_user()
list_sensor = []
sensor1 = create_sensor('test1', 'code1', user)
list_sensor.append(sensor1)
sensor2 = create_sensor('test2', 'code2', user)
list_sensor.append(sensor2)
self.assertEqual(len(list_sensor), 2)
<|end_body_0|>
<|b... | SensorTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SensorTests:
def test_list_Sensor(self):
"""Test for a list of Sensor :return:"""
<|body_0|>
def test_create_Sensor(self):
"""Test for a created Sensor :return:"""
<|body_1|>
def test_update_Sensor(self):
"""Test for a update Sensor :return:"""
... | stack_v2_sparse_classes_10k_train_007606 | 3,684 | no_license | [
{
"docstring": "Test for a list of Sensor :return:",
"name": "test_list_Sensor",
"signature": "def test_list_Sensor(self)"
},
{
"docstring": "Test for a created Sensor :return:",
"name": "test_create_Sensor",
"signature": "def test_create_Sensor(self)"
},
{
"docstring": "Test for... | 4 | stack_v2_sparse_classes_30k_train_000165 | Implement the Python class `SensorTests` described below.
Class description:
Implement the SensorTests class.
Method signatures and docstrings:
- def test_list_Sensor(self): Test for a list of Sensor :return:
- def test_create_Sensor(self): Test for a created Sensor :return:
- def test_update_Sensor(self): Test for a... | Implement the Python class `SensorTests` described below.
Class description:
Implement the SensorTests class.
Method signatures and docstrings:
- def test_list_Sensor(self): Test for a list of Sensor :return:
- def test_create_Sensor(self): Test for a created Sensor :return:
- def test_update_Sensor(self): Test for a... | 8c8f76acb85baff1cd2d8258f686515d08678350 | <|skeleton|>
class SensorTests:
def test_list_Sensor(self):
"""Test for a list of Sensor :return:"""
<|body_0|>
def test_create_Sensor(self):
"""Test for a created Sensor :return:"""
<|body_1|>
def test_update_Sensor(self):
"""Test for a update Sensor :return:"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SensorTests:
def test_list_Sensor(self):
"""Test for a list of Sensor :return:"""
user = create_user()
list_sensor = []
sensor1 = create_sensor('test1', 'code1', user)
list_sensor.append(sensor1)
sensor2 = create_sensor('test2', 'code2', user)
list_senso... | the_stack_v2_python_sparse | probe_project/apps/probe_dispatcher/tests.py | liflab/cornipickle-probe | train | 0 | |
cc10a6146830a1de30f6f3d90ec2d7c3c2cfe1d0 | [
"super().__init__()\nself.encoder = smp.encoders.get_encoder(encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights)\nencoder_out_channels = [c * 2 for c in self.encoder.out_channels[1:]]\nencoder_out_channels.insert(0, self.encoder.out_channels[0])\ntry:\n UnetDecoder = smp.decoders... | <|body_start_0|>
super().__init__()
self.encoder = smp.encoders.get_encoder(encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights)
encoder_out_channels = [c * 2 for c in self.encoder.out_channels[1:]]
encoder_out_channels.insert(0, self.encoder.out_channels[... | Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652 | FCSiamConc | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FCSiamConc:
"""Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652"""
def __init__(self, encoder_name: str='resnet34', encoder_depth: int=5, encoder_weights: Optional[st... | stack_v2_sparse_classes_10k_train_007607 | 8,273 | permissive | [
{
"docstring": "Initialize a new FCSiamConc model. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. two times smaller in spatial dimensions ... | 2 | stack_v2_sparse_classes_30k_train_003051 | Implement the Python class `FCSiamConc` described below.
Class description:
Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652
Method signatures and docstrings:
- def __init__(self, encoder_name... | Implement the Python class `FCSiamConc` described below.
Class description:
Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652
Method signatures and docstrings:
- def __init__(self, encoder_name... | 29985861614b3b93f9ef5389469ebb98570de7dd | <|skeleton|>
class FCSiamConc:
"""Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652"""
def __init__(self, encoder_name: str='resnet34', encoder_depth: int=5, encoder_weights: Optional[st... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FCSiamConc:
"""Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652"""
def __init__(self, encoder_name: str='resnet34', encoder_depth: int=5, encoder_weights: Optional[str]='imagenet'... | the_stack_v2_python_sparse | torchgeo/models/fcsiam.py | microsoft/torchgeo | train | 1,724 |
180af0e2f8459b3b0b6b121af00fb575b9d8f0f0 | [
"if not nums:\n return 0\nm = -float('inf')\ni = 0\nwhile i < len(nums) and nums[i] == 0:\n i += 1\nif i > 0:\n m = 0\nwhile i < len(nums):\n p = 1\n for j in range(i, len(nums)):\n if nums[j] == 0:\n for k in range(i, j - 1):\n p //= nums[k]\n m = max(... | <|body_start_0|>
if not nums:
return 0
m = -float('inf')
i = 0
while i < len(nums) and nums[i] == 0:
i += 1
if i > 0:
m = 0
while i < len(nums):
p = 1
for j in range(i, len(nums)):
if nums[j] == 0... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProduct(self, nums):
"""07/27/2018 22:42"""
<|body_0|>
def maxProduct(self, nums: List[int]) -> int:
"""Time complexity: O(n) Space complexity: O(1)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not nums:
return 0
... | stack_v2_sparse_classes_10k_train_007608 | 3,384 | no_license | [
{
"docstring": "07/27/2018 22:42",
"name": "maxProduct",
"signature": "def maxProduct(self, nums)"
},
{
"docstring": "Time complexity: O(n) Space complexity: O(1)",
"name": "maxProduct",
"signature": "def maxProduct(self, nums: List[int]) -> int"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, nums): 07/27/2018 22:42
- def maxProduct(self, nums: List[int]) -> int: Time complexity: O(n) Space complexity: O(1) | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, nums): 07/27/2018 22:42
- def maxProduct(self, nums: List[int]) -> int: Time complexity: O(n) Space complexity: O(1)
<|skeleton|>
class Solution:
def m... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def maxProduct(self, nums):
"""07/27/2018 22:42"""
<|body_0|>
def maxProduct(self, nums: List[int]) -> int:
"""Time complexity: O(n) Space complexity: O(1)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProduct(self, nums):
"""07/27/2018 22:42"""
if not nums:
return 0
m = -float('inf')
i = 0
while i < len(nums) and nums[i] == 0:
i += 1
if i > 0:
m = 0
while i < len(nums):
p = 1
... | the_stack_v2_python_sparse | leetcode/solved/152_Maximum_Product_Subarray/solution.py | sungminoh/algorithms | train | 0 | |
5cdad122a09bbee668c5cdb59b18113607caf063 | [
"exp = ' A -> B\\n A.radius < B.radius\\n '\ndm = dotmotif.Motif(exp)\nself.assertEqual(len(dm.list_dynamic_node_constraints()), 1)",
"exp = ' macro(A, B) {\\n A.radius > B.radius\\n }\\n macro(A, B)\\n A -> B\\n '\ndm = dotmotif.Motif(exp)\nself.... | <|body_start_0|>
exp = ' A -> B\n A.radius < B.radius\n '
dm = dotmotif.Motif(exp)
self.assertEqual(len(dm.list_dynamic_node_constraints()), 1)
<|end_body_0|>
<|body_start_1|>
exp = ' macro(A, B) {\n A.radius > B.radius\n }\n macro(A,... | TestDynamicNodeConstraints | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDynamicNodeConstraints:
def test_dynamic_constraints(self):
"""Test that comparisons may be made between variables, e.g.: A.type != B.type"""
<|body_0|>
def test_dynamic_constraints_in_macro(self):
"""Test that comparisons may be made between variables in a macro... | stack_v2_sparse_classes_10k_train_007609 | 11,613 | permissive | [
{
"docstring": "Test that comparisons may be made between variables, e.g.: A.type != B.type",
"name": "test_dynamic_constraints",
"signature": "def test_dynamic_constraints(self)"
},
{
"docstring": "Test that comparisons may be made between variables in a macro, e.g.: A.type != B.type",
"nam... | 2 | stack_v2_sparse_classes_30k_val_000381 | Implement the Python class `TestDynamicNodeConstraints` described below.
Class description:
Implement the TestDynamicNodeConstraints class.
Method signatures and docstrings:
- def test_dynamic_constraints(self): Test that comparisons may be made between variables, e.g.: A.type != B.type
- def test_dynamic_constraints... | Implement the Python class `TestDynamicNodeConstraints` described below.
Class description:
Implement the TestDynamicNodeConstraints class.
Method signatures and docstrings:
- def test_dynamic_constraints(self): Test that comparisons may be made between variables, e.g.: A.type != B.type
- def test_dynamic_constraints... | db093ddad7308756e9cf7ee01199f0dca1369872 | <|skeleton|>
class TestDynamicNodeConstraints:
def test_dynamic_constraints(self):
"""Test that comparisons may be made between variables, e.g.: A.type != B.type"""
<|body_0|>
def test_dynamic_constraints_in_macro(self):
"""Test that comparisons may be made between variables in a macro... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestDynamicNodeConstraints:
def test_dynamic_constraints(self):
"""Test that comparisons may be made between variables, e.g.: A.type != B.type"""
exp = ' A -> B\n A.radius < B.radius\n '
dm = dotmotif.Motif(exp)
self.assertEqual(len(dm.list_dynamic_node_con... | the_stack_v2_python_sparse | dotmotif/parsers/v2/test_v2_parser.py | JuttaPig/dotmotif | train | 0 | |
aae8e91d5707b7c830fa13f71f44f3d131aa4b29 | [
"QtGui.QMainWindow.__init__(self)\nself.resize(800, 600)\nself.centralwidget = QtGui.QWidget(self)\nself.mainLayout = QtGui.QHBoxLayout(self.centralwidget)\nself.mainLayout.setSpacing(0)\nself.mainLayout.setMargin(1)\nself.frame = QtGui.QFrame(self.centralwidget)\nself.gridLayout = QtGui.QVBoxLayout(self.frame)\nse... | <|body_start_0|>
QtGui.QMainWindow.__init__(self)
self.resize(800, 600)
self.centralwidget = QtGui.QWidget(self)
self.mainLayout = QtGui.QHBoxLayout(self.centralwidget)
self.mainLayout.setSpacing(0)
self.mainLayout.setMargin(1)
self.frame = QtGui.QFrame(self.centr... | Browser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Browser:
def __init__(self, default_url='http://google.com'):
"""Initialize the browser GUI and connect the events"""
<|body_0|>
def browse(self):
"""Make a web browse on a specific url and show the page on the Webview widget."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_10k_train_007610 | 33,580 | no_license | [
{
"docstring": "Initialize the browser GUI and connect the events",
"name": "__init__",
"signature": "def __init__(self, default_url='http://google.com')"
},
{
"docstring": "Make a web browse on a specific url and show the page on the Webview widget.",
"name": "browse",
"signature": "def... | 2 | stack_v2_sparse_classes_30k_val_000169 | Implement the Python class `Browser` described below.
Class description:
Implement the Browser class.
Method signatures and docstrings:
- def __init__(self, default_url='http://google.com'): Initialize the browser GUI and connect the events
- def browse(self): Make a web browse on a specific url and show the page on ... | Implement the Python class `Browser` described below.
Class description:
Implement the Browser class.
Method signatures and docstrings:
- def __init__(self, default_url='http://google.com'): Initialize the browser GUI and connect the events
- def browse(self): Make a web browse on a specific url and show the page on ... | 211c963dbac615920051be3a57991853e2081ac0 | <|skeleton|>
class Browser:
def __init__(self, default_url='http://google.com'):
"""Initialize the browser GUI and connect the events"""
<|body_0|>
def browse(self):
"""Make a web browse on a specific url and show the page on the Webview widget."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Browser:
def __init__(self, default_url='http://google.com'):
"""Initialize the browser GUI and connect the events"""
QtGui.QMainWindow.__init__(self)
self.resize(800, 600)
self.centralwidget = QtGui.QWidget(self)
self.mainLayout = QtGui.QHBoxLayout(self.centralwidget)
... | the_stack_v2_python_sparse | pyFAI-src/integrate_widget.py | SulzmannFr/pyFAI | train | 0 | |
916150bf5326ec2632b7f2617308ef38fb5d272e | [
"total_sum = sum(arr)\nexpected_sum = (len(arr) + 1) * (len(arr) + 2) // 2\nreturn expected_sum - total_sum",
"i, j = (0, len(arr) - 1)\nexpected_sum = arr[i] + arr[j]\nwhile i < j:\n i += 1\n j -= 1\n actual_sum = arr[i] + arr[j]\n if expected_sum > actual_sum:\n return arr[j] + 1\n elif ex... | <|body_start_0|>
total_sum = sum(arr)
expected_sum = (len(arr) + 1) * (len(arr) + 2) // 2
return expected_sum - total_sum
<|end_body_0|>
<|body_start_1|>
i, j = (0, len(arr) - 1)
expected_sum = arr[i] + arr[j]
while i < j:
i += 1
j -= 1
... | MissingNumber | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MissingNumber:
def get_missing_int_in_sequence(arr: []):
"""This is a linear solution and it will traverse the array entirely :param arr: Sequence from 1 to N in an array of size N - 1. ARRAY DOES NOT HAVE TO BE SORTED :return: The missing number"""
<|body_0|>
def get_missin... | stack_v2_sparse_classes_10k_train_007611 | 1,170 | no_license | [
{
"docstring": "This is a linear solution and it will traverse the array entirely :param arr: Sequence from 1 to N in an array of size N - 1. ARRAY DOES NOT HAVE TO BE SORTED :return: The missing number",
"name": "get_missing_int_in_sequence",
"signature": "def get_missing_int_in_sequence(arr: [])"
},... | 2 | stack_v2_sparse_classes_30k_train_003892 | Implement the Python class `MissingNumber` described below.
Class description:
Implement the MissingNumber class.
Method signatures and docstrings:
- def get_missing_int_in_sequence(arr: []): This is a linear solution and it will traverse the array entirely :param arr: Sequence from 1 to N in an array of size N - 1. ... | Implement the Python class `MissingNumber` described below.
Class description:
Implement the MissingNumber class.
Method signatures and docstrings:
- def get_missing_int_in_sequence(arr: []): This is a linear solution and it will traverse the array entirely :param arr: Sequence from 1 to N in an array of size N - 1. ... | 638a1312a66805fefb2a1e1dd7b4968d2c957564 | <|skeleton|>
class MissingNumber:
def get_missing_int_in_sequence(arr: []):
"""This is a linear solution and it will traverse the array entirely :param arr: Sequence from 1 to N in an array of size N - 1. ARRAY DOES NOT HAVE TO BE SORTED :return: The missing number"""
<|body_0|>
def get_missin... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MissingNumber:
def get_missing_int_in_sequence(arr: []):
"""This is a linear solution and it will traverse the array entirely :param arr: Sequence from 1 to N in an array of size N - 1. ARRAY DOES NOT HAVE TO BE SORTED :return: The missing number"""
total_sum = sum(arr)
expected_sum = ... | the_stack_v2_python_sparse | missing_number.py | wotann07/leetcode_py | train | 0 | |
3431a82db2f3101739038a4e92529a1b825a25ca | [
"connection_created.connect(self.activate_pragmas_per_connection)\nself.activate_pragmas_on_start()\nlogger.info('Running Kolibri with the following settings: {settings}'.format(settings=os.environ['DJANGO_SETTINGS_MODULE']))\nself.check_redis_settings()\nfrom morango.models import UUIDField\nFilterSet.FILTER_DEFAU... | <|body_start_0|>
connection_created.connect(self.activate_pragmas_per_connection)
self.activate_pragmas_on_start()
logger.info('Running Kolibri with the following settings: {settings}'.format(settings=os.environ['DJANGO_SETTINGS_MODULE']))
self.check_redis_settings()
from morango... | KolibriCoreConfig | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KolibriCoreConfig:
def ready(self):
"""Sets up PRAGMAs."""
<|body_0|>
def activate_pragmas_per_connection(sender, connection, **kwargs):
"""Activate SQLite3 PRAGMAs that apply on a per-connection basis. A no-op right now, but kept around as infrastructure if we ever ... | stack_v2_sparse_classes_10k_train_007612 | 7,006 | permissive | [
{
"docstring": "Sets up PRAGMAs.",
"name": "ready",
"signature": "def ready(self)"
},
{
"docstring": "Activate SQLite3 PRAGMAs that apply on a per-connection basis. A no-op right now, but kept around as infrastructure if we ever want to add PRAGMAs in the future.",
"name": "activate_pragmas_... | 4 | stack_v2_sparse_classes_30k_train_006109 | Implement the Python class `KolibriCoreConfig` described below.
Class description:
Implement the KolibriCoreConfig class.
Method signatures and docstrings:
- def ready(self): Sets up PRAGMAs.
- def activate_pragmas_per_connection(sender, connection, **kwargs): Activate SQLite3 PRAGMAs that apply on a per-connection b... | Implement the Python class `KolibriCoreConfig` described below.
Class description:
Implement the KolibriCoreConfig class.
Method signatures and docstrings:
- def ready(self): Sets up PRAGMAs.
- def activate_pragmas_per_connection(sender, connection, **kwargs): Activate SQLite3 PRAGMAs that apply on a per-connection b... | cc9da2a6acd139acac3cd71c4cb05c15d4465712 | <|skeleton|>
class KolibriCoreConfig:
def ready(self):
"""Sets up PRAGMAs."""
<|body_0|>
def activate_pragmas_per_connection(sender, connection, **kwargs):
"""Activate SQLite3 PRAGMAs that apply on a per-connection basis. A no-op right now, but kept around as infrastructure if we ever ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class KolibriCoreConfig:
def ready(self):
"""Sets up PRAGMAs."""
connection_created.connect(self.activate_pragmas_per_connection)
self.activate_pragmas_on_start()
logger.info('Running Kolibri with the following settings: {settings}'.format(settings=os.environ['DJANGO_SETTINGS_MODULE'... | the_stack_v2_python_sparse | kolibri/core/apps.py | learningequality/kolibri | train | 689 | |
1dcb7afdfb5aa1df0f97431d261f89594d143751 | [
"if k == 1:\n return [i for i in range(1, n + 1)]\nans = [n]\ndec = True\nfor diff in range(k, 0, -1):\n if dec:\n ans.append(ans[len(ans) - 1] - diff)\n dec = False\n else:\n ans.append(ans[len(ans) - 1] + diff)\n dec = True\nlast = ans[len(ans) - 1]\nif last - 2 > 0:\n ans.... | <|body_start_0|>
if k == 1:
return [i for i in range(1, n + 1)]
ans = [n]
dec = True
for diff in range(k, 0, -1):
if dec:
ans.append(ans[len(ans) - 1] - diff)
dec = False
else:
ans.append(ans[len(ans) - 1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def constructArray(self, n, k):
""":type n: int :type k: int :rtype: List[int]"""
<|body_0|>
def constructArray2(self, n, k):
"""We need k+1 numbers to generate k distinct differences. Put first (n-k-1) elements in order: [1, 2, ..., n-k-1], then for the re... | stack_v2_sparse_classes_10k_train_007613 | 1,609 | no_license | [
{
"docstring": ":type n: int :type k: int :rtype: List[int]",
"name": "constructArray",
"signature": "def constructArray(self, n, k)"
},
{
"docstring": "We need k+1 numbers to generate k distinct differences. Put first (n-k-1) elements in order: [1, 2, ..., n-k-1], then for the remaining [n-k, n... | 2 | stack_v2_sparse_classes_30k_train_000403 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def constructArray(self, n, k): :type n: int :type k: int :rtype: List[int]
- def constructArray2(self, n, k): We need k+1 numbers to generate k distinct differences. Put first (... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def constructArray(self, n, k): :type n: int :type k: int :rtype: List[int]
- def constructArray2(self, n, k): We need k+1 numbers to generate k distinct differences. Put first (... | 143aa25f92f3827aa379f29c67a9b7ec3757fef9 | <|skeleton|>
class Solution:
def constructArray(self, n, k):
""":type n: int :type k: int :rtype: List[int]"""
<|body_0|>
def constructArray2(self, n, k):
"""We need k+1 numbers to generate k distinct differences. Put first (n-k-1) elements in order: [1, 2, ..., n-k-1], then for the re... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def constructArray(self, n, k):
""":type n: int :type k: int :rtype: List[int]"""
if k == 1:
return [i for i in range(1, n + 1)]
ans = [n]
dec = True
for diff in range(k, 0, -1):
if dec:
ans.append(ans[len(ans) - 1] - di... | the_stack_v2_python_sparse | py/leetcode_py/667.py | imsure/tech-interview-prep | train | 0 | |
c051275ca47b9101d783269539d6b03c1b6b7a11 | [
"end = res = 0\nwhile end < T:\n tmp = max([r for l, r in clips if l <= end] or [0])\n if tmp == end:\n return -1\n end = tmp\n res += 1\nreturn res",
"T += 1\ndp = [-1] * T\ndp[0] = 0\nclips = sorted(clips, key=lambda a: a[0])\nfor c in clips:\n if c[0] >= T:\n break\n if dp[c[0]]... | <|body_start_0|>
end = res = 0
while end < T:
tmp = max([r for l, r in clips if l <= end] or [0])
if tmp == end:
return -1
end = tmp
res += 1
return res
<|end_body_0|>
<|body_start_1|>
T += 1
dp = [-1] * T
d... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def videoStitching(self, clips, T):
""":type clips: List[List[int]] :type T: int :rtype: int"""
<|body_0|>
def videoStitching2(self, clips, T):
""":type clips: List[List[int]] :type T: int :rtype: int"""
<|body_1|>
def videoStitching3(self, cli... | stack_v2_sparse_classes_10k_train_007614 | 3,920 | no_license | [
{
"docstring": ":type clips: List[List[int]] :type T: int :rtype: int",
"name": "videoStitching",
"signature": "def videoStitching(self, clips, T)"
},
{
"docstring": ":type clips: List[List[int]] :type T: int :rtype: int",
"name": "videoStitching2",
"signature": "def videoStitching2(self... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def videoStitching(self, clips, T): :type clips: List[List[int]] :type T: int :rtype: int
- def videoStitching2(self, clips, T): :type clips: List[List[int]] :type T: int :rtype:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def videoStitching(self, clips, T): :type clips: List[List[int]] :type T: int :rtype: int
- def videoStitching2(self, clips, T): :type clips: List[List[int]] :type T: int :rtype:... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def videoStitching(self, clips, T):
""":type clips: List[List[int]] :type T: int :rtype: int"""
<|body_0|>
def videoStitching2(self, clips, T):
""":type clips: List[List[int]] :type T: int :rtype: int"""
<|body_1|>
def videoStitching3(self, cli... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def videoStitching(self, clips, T):
""":type clips: List[List[int]] :type T: int :rtype: int"""
end = res = 0
while end < T:
tmp = max([r for l, r in clips if l <= end] or [0])
if tmp == end:
return -1
end = tmp
... | the_stack_v2_python_sparse | 1024_视频拼接.py | lovehhf/LeetCode | train | 0 | |
9981a9d733723cae2a904efb9b12018279e61c85 | [
"url = '{0}/consumers'.format(container_ref)\nresp = self.client.post(url, request_model=model, extra_headers=extra_headers, user_name=user_name, use_auth=use_auth)\nif resp.status_code == 401 and (not use_auth):\n return (resp, None)\nif resp.status_code == 200:\n if admin is None:\n admin = user_name... | <|body_start_0|>
url = '{0}/consumers'.format(container_ref)
resp = self.client.post(url, request_model=model, extra_headers=extra_headers, user_name=user_name, use_auth=use_auth)
if resp.status_code == 401 and (not use_auth):
return (resp, None)
if resp.status_code == 200:
... | ConsumerBehaviors | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConsumerBehaviors:
def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True):
"""Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: ... | stack_v2_sparse_classes_10k_train_007615 | 4,859 | permissive | [
{
"docstring": "Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: Any additional headers to pass to the request :param user_name: The user name used to create the consumer :param admin: The user with permissi... | 4 | stack_v2_sparse_classes_30k_train_005656 | Implement the Python class `ConsumerBehaviors` described below.
Class description:
Implement the ConsumerBehaviors class.
Method signatures and docstrings:
- def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True): Register a consumer to a container. :param model... | Implement the Python class `ConsumerBehaviors` described below.
Class description:
Implement the ConsumerBehaviors class.
Method signatures and docstrings:
- def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True): Register a consumer to a container. :param model... | c8e3dc14e6225f1d400131434e8afec0aa410ae7 | <|skeleton|>
class ConsumerBehaviors:
def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True):
"""Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ConsumerBehaviors:
def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True):
"""Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: Any additional... | the_stack_v2_python_sparse | functionaltests/api/v1/behaviors/consumer_behaviors.py | openstack/barbican | train | 189 | |
d59c5bcbf86acd4fe52fa3f4d700dfa09fb8783a | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn Run()",
"from ..entity import Entity\nfrom .lifecycle_workflow_processing_status import LifecycleWorkflowProcessingStatus\nfrom .task_processing_result import TaskProcessingResult\nfrom .user_processing_result import UserProcessingResu... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return Run()
<|end_body_0|>
<|body_start_1|>
from ..entity import Entity
from .lifecycle_workflow_processing_status import LifecycleWorkflowProcessingStatus
from .task_processing_result... | Run | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Run:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Run:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Run"""
<|b... | stack_v2_sparse_classes_10k_train_007616 | 7,160 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Run",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(parse_nod... | 3 | stack_v2_sparse_classes_30k_train_002715 | Implement the Python class `Run` described below.
Class description:
Implement the Run class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Run: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse n... | Implement the Python class `Run` described below.
Class description:
Implement the Run class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Run: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse n... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class Run:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Run:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Run"""
<|b... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Run:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Run:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Run"""
if not parse_node... | the_stack_v2_python_sparse | msgraph/generated/models/identity_governance/run.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
f9eda54446842508b1e43dd70e0451c368dec8e4 | [
"sketch = Sketch.query.get_with_acl(sketch_id)\nif not sketch:\n abort(HTTP_STATUS_CODE_NOT_FOUND, 'No sketch found with this ID.')\ngraphs = sketch.graphs\nreturn self.to_json(graphs)",
"sketch = Sketch.query.get_with_acl(sketch_id)\nif not sketch:\n abort(HTTP_STATUS_CODE_NOT_FOUND, 'No sketch found with ... | <|body_start_0|>
sketch = Sketch.query.get_with_acl(sketch_id)
if not sketch:
abort(HTTP_STATUS_CODE_NOT_FOUND, 'No sketch found with this ID.')
graphs = sketch.graphs
return self.to_json(graphs)
<|end_body_0|>
<|body_start_1|>
sketch = Sketch.query.get_with_acl(sket... | Resource to get all saved graphs for a sketch. | GraphListResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphListResource:
"""Resource to get all saved graphs for a sketch."""
def get(self, sketch_id):
"""Handles GET request to the resource. Returns: List of graphs in JSON (instance of flask.wrappers.Response)"""
<|body_0|>
def post(self, sketch_id):
"""Handles POS... | stack_v2_sparse_classes_10k_train_007617 | 12,247 | permissive | [
{
"docstring": "Handles GET request to the resource. Returns: List of graphs in JSON (instance of flask.wrappers.Response)",
"name": "get",
"signature": "def get(self, sketch_id)"
},
{
"docstring": "Handles POST request to the resource.",
"name": "post",
"signature": "def post(self, sket... | 2 | stack_v2_sparse_classes_30k_train_007200 | Implement the Python class `GraphListResource` described below.
Class description:
Resource to get all saved graphs for a sketch.
Method signatures and docstrings:
- def get(self, sketch_id): Handles GET request to the resource. Returns: List of graphs in JSON (instance of flask.wrappers.Response)
- def post(self, sk... | Implement the Python class `GraphListResource` described below.
Class description:
Resource to get all saved graphs for a sketch.
Method signatures and docstrings:
- def get(self, sketch_id): Handles GET request to the resource. Returns: List of graphs in JSON (instance of flask.wrappers.Response)
- def post(self, sk... | 24f471b58ca4a87cb053961b5f05c07a544ca7b8 | <|skeleton|>
class GraphListResource:
"""Resource to get all saved graphs for a sketch."""
def get(self, sketch_id):
"""Handles GET request to the resource. Returns: List of graphs in JSON (instance of flask.wrappers.Response)"""
<|body_0|>
def post(self, sketch_id):
"""Handles POS... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GraphListResource:
"""Resource to get all saved graphs for a sketch."""
def get(self, sketch_id):
"""Handles GET request to the resource. Returns: List of graphs in JSON (instance of flask.wrappers.Response)"""
sketch = Sketch.query.get_with_acl(sketch_id)
if not sketch:
... | the_stack_v2_python_sparse | timesketch/api/v1/resources/graph.py | google/timesketch | train | 2,263 |
a336ef000ab596e522c1b2ebb922b1fc3207b773 | [
"super().__init__(source_image_path, source_image_size, output_image_path, output_image_size)\nself.__levels = len(neighbourhood_padding)\nself.__neighbourhood_padding = neighbourhood_padding\nself.__tsvq_branching_factor = tsvq_branching_factor\nassert self.__levels > 0, 'At least one neighbourhood padding size mu... | <|body_start_0|>
super().__init__(source_image_path, source_image_size, output_image_path, output_image_size)
self.__levels = len(neighbourhood_padding)
self.__neighbourhood_padding = neighbourhood_padding
self.__tsvq_branching_factor = tsvq_branching_factor
assert self.__levels ... | A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve the pixels one-by-one in scanline order by finding the best ne... | RasterPixelNeighbourhoodSynthesizer | [
"MIT",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RasterPixelNeighbourhoodSynthesizer:
"""A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve... | stack_v2_sparse_classes_10k_train_007618 | 6,847 | permissive | [
{
"docstring": "Constructs a new RasterPixelNeighbourhoodSynthesizer object with the given TextureSynthesizer parameters and neighbourhood padding. Args: source_image_path: See TextureSynthesizer.__init__(). source_image_size: See TextureSynthesizer.__init__(). output_image_path: See TextureSynthesizer.__init__... | 5 | stack_v2_sparse_classes_30k_test_000181 | Implement the Python class `RasterPixelNeighbourhoodSynthesizer` described below.
Class description:
A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise... | Implement the Python class `RasterPixelNeighbourhoodSynthesizer` described below.
Class description:
A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise... | 7e7282698befd53383cbd6566039340babb0a289 | <|skeleton|>
class RasterPixelNeighbourhoodSynthesizer:
"""A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RasterPixelNeighbourhoodSynthesizer:
"""A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve the pixels o... | the_stack_v2_python_sparse | sandbox/synthesizers/raster_pixel_neighbourhood_synthesizer.py | Mandrenkov/SVBRDF-Texture-Synthesis | train | 3 |
2858c938ca3a0c39bdec654ba994f789328e59bb | [
"self.mins = mins\nself.maxs = maxs\nself.children = []",
"for axis in range(3):\n if coord[axis] < self.mins[axis] or coord[axis] > self.maxs[axis]:\n return False\nreturn True",
"for corner in itertools.product(*zip(self.mins, self.maxs)):\n if manhattan_dist(nanobot.coord, corner) > nanobot.r:\n... | <|body_start_0|>
self.mins = mins
self.maxs = maxs
self.children = []
<|end_body_0|>
<|body_start_1|>
for axis in range(3):
if coord[axis] < self.mins[axis] or coord[axis] > self.maxs[axis]:
return False
return True
<|end_body_1|>
<|body_start_2|>
... | Node in an octree | OctreeNode | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OctreeNode:
"""Node in an octree"""
def __init__(self, mins, maxs):
"""Constructor mins: [x, y, z] lower bound maxs: [x, y, z] (inclusive) upper-bound"""
<|body_0|>
def in_node(self, coord):
"""Return True if coord is in this node"""
<|body_1|>
def n... | stack_v2_sparse_classes_10k_train_007619 | 8,665 | permissive | [
{
"docstring": "Constructor mins: [x, y, z] lower bound maxs: [x, y, z] (inclusive) upper-bound",
"name": "__init__",
"signature": "def __init__(self, mins, maxs)"
},
{
"docstring": "Return True if coord is in this node",
"name": "in_node",
"signature": "def in_node(self, coord)"
},
... | 4 | stack_v2_sparse_classes_30k_train_003242 | Implement the Python class `OctreeNode` described below.
Class description:
Node in an octree
Method signatures and docstrings:
- def __init__(self, mins, maxs): Constructor mins: [x, y, z] lower bound maxs: [x, y, z] (inclusive) upper-bound
- def in_node(self, coord): Return True if coord is in this node
- def nanob... | Implement the Python class `OctreeNode` described below.
Class description:
Node in an octree
Method signatures and docstrings:
- def __init__(self, mins, maxs): Constructor mins: [x, y, z] lower bound maxs: [x, y, z] (inclusive) upper-bound
- def in_node(self, coord): Return True if coord is in this node
- def nanob... | 6671ef8c16a837f697bb3fb91004d1bd892814ba | <|skeleton|>
class OctreeNode:
"""Node in an octree"""
def __init__(self, mins, maxs):
"""Constructor mins: [x, y, z] lower bound maxs: [x, y, z] (inclusive) upper-bound"""
<|body_0|>
def in_node(self, coord):
"""Return True if coord is in this node"""
<|body_1|>
def n... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OctreeNode:
"""Node in an octree"""
def __init__(self, mins, maxs):
"""Constructor mins: [x, y, z] lower bound maxs: [x, y, z] (inclusive) upper-bound"""
self.mins = mins
self.maxs = maxs
self.children = []
def in_node(self, coord):
"""Return True if coord is ... | the_stack_v2_python_sparse | 2018/day23/challenge.py | ericgreveson/adventofcode | train | 0 |
3e05973f6ac5ea06f6720404166b62a28046930a | [
"left = []\nself.rt = 0\n\ndef dfs(root, pos, level):\n if not root:\n return\n if len(left) < level + 1:\n left.append(pos)\n self.rt = max(self.rt, pos - left[level] + 1)\n dfs(root.left, pos * 2 + 1, level + 1)\n dfs(root.right, pos * 2 + 2, level + 1)\ndfs(root, 0, 0)\nreturn self.r... | <|body_start_0|>
left = []
self.rt = 0
def dfs(root, pos, level):
if not root:
return
if len(left) < level + 1:
left.append(pos)
self.rt = max(self.rt, pos - left[level] + 1)
dfs(root.left, pos * 2 + 1, level + 1)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def widthOfBinaryTree_dfs(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def widthOfBinaryTree_bfs(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left = []
self.rt =... | stack_v2_sparse_classes_10k_train_007620 | 1,553 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "widthOfBinaryTree_dfs",
"signature": "def widthOfBinaryTree_dfs(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "widthOfBinaryTree_bfs",
"signature": "def widthOfBinaryTree_bfs(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def widthOfBinaryTree_dfs(self, root): :type root: TreeNode :rtype: int
- def widthOfBinaryTree_bfs(self, root): :type root: TreeNode :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def widthOfBinaryTree_dfs(self, root): :type root: TreeNode :rtype: int
- def widthOfBinaryTree_bfs(self, root): :type root: TreeNode :rtype: int
<|skeleton|>
class Solution:
... | 0e99f9a5226507706b3ee66fd04bae813755ef40 | <|skeleton|>
class Solution:
def widthOfBinaryTree_dfs(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def widthOfBinaryTree_bfs(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def widthOfBinaryTree_dfs(self, root):
""":type root: TreeNode :rtype: int"""
left = []
self.rt = 0
def dfs(root, pos, level):
if not root:
return
if len(left) < level + 1:
left.append(pos)
self.rt =... | the_stack_v2_python_sparse | medium/tree/test_662_Maximum_Width_of_Binary_Tree.py | wuxu1019/leetcode_sophia | train | 1 | |
b9b2002d877d4ba901c817d04112a42d2e3d500f | [
"super(CTCModule, self).__init__()\nself.pred_output_position_inclu_blank = nn.LSTM(in_dim, out_seq_len + 1, num_layers=2, batch_first=True)\nself.out_seq_len = out_seq_len\nself.softmax = nn.Softmax(dim=2)",
"pred_output_position_inclu_blank, _ = self.pred_output_position_inclu_blank(x)\nprob_pred_output_positio... | <|body_start_0|>
super(CTCModule, self).__init__()
self.pred_output_position_inclu_blank = nn.LSTM(in_dim, out_seq_len + 1, num_layers=2, batch_first=True)
self.out_seq_len = out_seq_len
self.softmax = nn.Softmax(dim=2)
<|end_body_0|>
<|body_start_1|>
pred_output_position_inclu_... | CTCModule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CTCModule:
def __init__(self, in_dim, out_seq_len):
"""This module is performing alignment from A (e.g., audio) to B (e.g., text). :param in_dim: Dimension for input modality A :param out_seq_len: Sequence length for output modality B"""
<|body_0|>
def forward(self, x):
... | stack_v2_sparse_classes_10k_train_007621 | 25,941 | no_license | [
{
"docstring": "This module is performing alignment from A (e.g., audio) to B (e.g., text). :param in_dim: Dimension for input modality A :param out_seq_len: Sequence length for output modality B",
"name": "__init__",
"signature": "def __init__(self, in_dim, out_seq_len)"
},
{
"docstring": ":inp... | 2 | null | Implement the Python class `CTCModule` described below.
Class description:
Implement the CTCModule class.
Method signatures and docstrings:
- def __init__(self, in_dim, out_seq_len): This module is performing alignment from A (e.g., audio) to B (e.g., text). :param in_dim: Dimension for input modality A :param out_se... | Implement the Python class `CTCModule` described below.
Class description:
Implement the CTCModule class.
Method signatures and docstrings:
- def __init__(self, in_dim, out_seq_len): This module is performing alignment from A (e.g., audio) to B (e.g., text). :param in_dim: Dimension for input modality A :param out_se... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class CTCModule:
def __init__(self, in_dim, out_seq_len):
"""This module is performing alignment from A (e.g., audio) to B (e.g., text). :param in_dim: Dimension for input modality A :param out_seq_len: Sequence length for output modality B"""
<|body_0|>
def forward(self, x):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CTCModule:
def __init__(self, in_dim, out_seq_len):
"""This module is performing alignment from A (e.g., audio) to B (e.g., text). :param in_dim: Dimension for input modality A :param out_seq_len: Sequence length for output modality B"""
super(CTCModule, self).__init__()
self.pred_outp... | the_stack_v2_python_sparse | generated/test_yaohungt_Multimodal_Transformer.py | jansel/pytorch-jit-paritybench | train | 35 | |
80d599a72b2874f7c907b3cc362274669670f885 | [
"super().__init__(input_key, output_key)\nlambda_fn = lambda_fn or (lambda x: x)\nself.lambda_fn = functools.partial(lambda_fn, **kwargs)",
"if self.input_key is not None:\n element = element[self.input_key]\noutput = self.lambda_fn(element)\nif self.output_key is not None:\n output = {self.output_key: outp... | <|body_start_0|>
super().__init__(input_key, output_key)
lambda_fn = lambda_fn or (lambda x: x)
self.lambda_fn = functools.partial(lambda_fn, **kwargs)
<|end_body_0|>
<|body_start_1|>
if self.input_key is not None:
element = element[self.input_key]
output = self.lamb... | Reader abstraction with an lambda encoders. Can read an elem from dataset and apply `encode_fn` function to it. | LambdaReader | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LambdaReader:
"""Reader abstraction with an lambda encoders. Can read an elem from dataset and apply `encode_fn` function to it."""
def __init__(self, input_key: str, output_key: Optional[str]=None, lambda_fn: Optional[Callable]=None, **kwargs):
"""Args: input_key: input key to use f... | stack_v2_sparse_classes_10k_train_007622 | 5,229 | permissive | [
{
"docstring": "Args: input_key: input key to use from annotation dict output_key: output key to use to store the result lambda_fn: encode function to use to prepare your data (for example convert chars/words/tokens to indices, etc) kwargs: kwargs for encode function",
"name": "__init__",
"signature": "... | 2 | stack_v2_sparse_classes_30k_train_005682 | Implement the Python class `LambdaReader` described below.
Class description:
Reader abstraction with an lambda encoders. Can read an elem from dataset and apply `encode_fn` function to it.
Method signatures and docstrings:
- def __init__(self, input_key: str, output_key: Optional[str]=None, lambda_fn: Optional[Calla... | Implement the Python class `LambdaReader` described below.
Class description:
Reader abstraction with an lambda encoders. Can read an elem from dataset and apply `encode_fn` function to it.
Method signatures and docstrings:
- def __init__(self, input_key: str, output_key: Optional[str]=None, lambda_fn: Optional[Calla... | e99f90655d0efcf22559a46e928f0f98c9807ebf | <|skeleton|>
class LambdaReader:
"""Reader abstraction with an lambda encoders. Can read an elem from dataset and apply `encode_fn` function to it."""
def __init__(self, input_key: str, output_key: Optional[str]=None, lambda_fn: Optional[Callable]=None, **kwargs):
"""Args: input_key: input key to use f... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LambdaReader:
"""Reader abstraction with an lambda encoders. Can read an elem from dataset and apply `encode_fn` function to it."""
def __init__(self, input_key: str, output_key: Optional[str]=None, lambda_fn: Optional[Callable]=None, **kwargs):
"""Args: input_key: input key to use from annotatio... | the_stack_v2_python_sparse | catalyst/contrib/data/reader.py | catalyst-team/catalyst | train | 3,038 |
852d747b43a63cccaedfd6a77a1248f772c33014 | [
"len_n, right_most = (len(nums), 0)\nfor i in range(len_n):\n if i <= right_most:\n right_most = max(nums[i] + i, right_most)\n if right_most >= len_n - 1:\n return True\nreturn False",
"len_n = len(nums)\ndp = [False] * len_n\ndp[0] = True\nfor i in range(len_n):\n if dp[i]:\n ... | <|body_start_0|>
len_n, right_most = (len(nums), 0)
for i in range(len_n):
if i <= right_most:
right_most = max(nums[i] + i, right_most)
if right_most >= len_n - 1:
return True
return False
<|end_body_0|>
<|body_start_1|>
l... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canJump(self, nums: List[int]) -> bool:
"""执行用时： 52 ms , 在所有 Python3 提交中击败了 52.08% 的用户 内存消耗： 16 MB , 在所有 Python3 提交中击败了 73.49% 的用户"""
<|body_0|>
def canJump1(self, nums: List[int]) -> bool:
"""超时"""
<|body_1|>
def jump1(self, nums: List[int... | stack_v2_sparse_classes_10k_train_007623 | 4,093 | no_license | [
{
"docstring": "执行用时： 52 ms , 在所有 Python3 提交中击败了 52.08% 的用户 内存消耗： 16 MB , 在所有 Python3 提交中击败了 73.49% 的用户",
"name": "canJump",
"signature": "def canJump(self, nums: List[int]) -> bool"
},
{
"docstring": "超时",
"name": "canJump1",
"signature": "def canJump1(self, nums: List[int]) -> bool"
... | 4 | stack_v2_sparse_classes_30k_train_005577 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canJump(self, nums: List[int]) -> bool: 执行用时： 52 ms , 在所有 Python3 提交中击败了 52.08% 的用户 内存消耗： 16 MB , 在所有 Python3 提交中击败了 73.49% 的用户
- def canJump1(self, nums: List[int]) -> bool:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canJump(self, nums: List[int]) -> bool: 执行用时： 52 ms , 在所有 Python3 提交中击败了 52.08% 的用户 内存消耗： 16 MB , 在所有 Python3 提交中击败了 73.49% 的用户
- def canJump1(self, nums: List[int]) -> bool:... | d613ed8a5a2c15ace7d513965b372d128845d66a | <|skeleton|>
class Solution:
def canJump(self, nums: List[int]) -> bool:
"""执行用时： 52 ms , 在所有 Python3 提交中击败了 52.08% 的用户 内存消耗： 16 MB , 在所有 Python3 提交中击败了 73.49% 的用户"""
<|body_0|>
def canJump1(self, nums: List[int]) -> bool:
"""超时"""
<|body_1|>
def jump1(self, nums: List[int... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def canJump(self, nums: List[int]) -> bool:
"""执行用时： 52 ms , 在所有 Python3 提交中击败了 52.08% 的用户 内存消耗： 16 MB , 在所有 Python3 提交中击败了 73.49% 的用户"""
len_n, right_most = (len(nums), 0)
for i in range(len_n):
if i <= right_most:
right_most = max(nums[i] + i, ri... | the_stack_v2_python_sparse | 跳跃游戏1&2.py | nomboy/leetcode | train | 0 | |
f1e13f61e172db31f48c1bbaf280d7f24ffb5310 | [
"import math\nfor i in range(int(math.sqrt(c)) + 1):\n left, right = (0, int(math.sqrt(c - i ** 2)) + 1)\n while left < right:\n mid = left + right >> 1\n res = mid ** 2 + i ** 2\n if res > c:\n right = mid - 1\n elif res == c:\n return True\n else:\n ... | <|body_start_0|>
import math
for i in range(int(math.sqrt(c)) + 1):
left, right = (0, int(math.sqrt(c - i ** 2)) + 1)
while left < right:
mid = left + right >> 1
res = mid ** 2 + i ** 2
if res > c:
right = mid - ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def judgeSquareSum(self, c: int) -> bool:
"""双指针 + 二分查找，范围为 左指针范围[1, sqrt(c)] 右指针[1, sqrt(c - i ** 2)] :param c: :return:"""
<|body_0|>
def judgeSquareSum2(self, c: int) -> bool:
"""双指针 :param c: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_10k_train_007624 | 1,521 | no_license | [
{
"docstring": "双指针 + 二分查找，范围为 左指针范围[1, sqrt(c)] 右指针[1, sqrt(c - i ** 2)] :param c: :return:",
"name": "judgeSquareSum",
"signature": "def judgeSquareSum(self, c: int) -> bool"
},
{
"docstring": "双指针 :param c: :return:",
"name": "judgeSquareSum2",
"signature": "def judgeSquareSum2(self, ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def judgeSquareSum(self, c: int) -> bool: 双指针 + 二分查找，范围为 左指针范围[1, sqrt(c)] 右指针[1, sqrt(c - i ** 2)] :param c: :return:
- def judgeSquareSum2(self, c: int) -> bool: 双指针 :param c: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def judgeSquareSum(self, c: int) -> bool: 双指针 + 二分查找，范围为 左指针范围[1, sqrt(c)] 右指针[1, sqrt(c - i ** 2)] :param c: :return:
- def judgeSquareSum2(self, c: int) -> bool: 双指针 :param c: ... | 9acba92695c06406f12f997a720bfe1deb9464a8 | <|skeleton|>
class Solution:
def judgeSquareSum(self, c: int) -> bool:
"""双指针 + 二分查找，范围为 左指针范围[1, sqrt(c)] 右指针[1, sqrt(c - i ** 2)] :param c: :return:"""
<|body_0|>
def judgeSquareSum2(self, c: int) -> bool:
"""双指针 :param c: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def judgeSquareSum(self, c: int) -> bool:
"""双指针 + 二分查找，范围为 左指针范围[1, sqrt(c)] 右指针[1, sqrt(c - i ** 2)] :param c: :return:"""
import math
for i in range(int(math.sqrt(c)) + 1):
left, right = (0, int(math.sqrt(c - i ** 2)) + 1)
while left < right:
... | the_stack_v2_python_sparse | Interview_preparation/tencent/JudgeSquareSum.py | yinhuax/leet_code | train | 0 | |
bd771c814da2ec9ba4337f24a133ee01a51fccb8 | [
"params = Parameters.instance().place_params\ntransmission = params['place_transmission']\nplace_idx = place.place_type.value - 1\ntry:\n num_groups = params['mean_group_size'][place_idx]\nexcept IndexError:\n num_groups = 1\nplace_inf = 0 if hasattr(infector.microcell, 'closure_start_time') and infector.is_p... | <|body_start_0|>
params = Parameters.instance().place_params
transmission = params['place_transmission']
place_idx = place.place_type.value - 1
try:
num_groups = params['mean_group_size'][place_idx]
except IndexError:
num_groups = 1
place_inf = 0 i... | Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places. | PlaceInfection | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlaceInfection:
"""Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places."""
def place_inf(place, infector, time: float):
"""Calculate the infectiousness of a place. Does not include interventions such as isolation, or... | stack_v2_sparse_classes_10k_train_007625 | 6,023 | permissive | [
{
"docstring": "Calculate the infectiousness of a place. Does not include interventions such as isolation, or whether individual is a carehome resident. Does not yet differentiate between places as we have not decided which places to implement, and what transmission to give them. Parameters ---------- place : P... | 3 | stack_v2_sparse_classes_30k_train_005064 | Implement the Python class `PlaceInfection` described below.
Class description:
Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places.
Method signatures and docstrings:
- def place_inf(place, infector, time: float): Calculate the infectiousness of a pl... | Implement the Python class `PlaceInfection` described below.
Class description:
Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places.
Method signatures and docstrings:
- def place_inf(place, infector, time: float): Calculate the infectiousness of a pl... | c11de122c6bfdf9103162e4045758808da5df322 | <|skeleton|>
class PlaceInfection:
"""Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places."""
def place_inf(place, infector, time: float):
"""Calculate the infectiousness of a place. Does not include interventions such as isolation, or... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PlaceInfection:
"""Class to calculate the infectiousness and susceptibility parameters for the force of infection parameter, within places."""
def place_inf(place, infector, time: float):
"""Calculate the infectiousness of a place. Does not include interventions such as isolation, or whether indi... | the_stack_v2_python_sparse | pyEpiabm/pyEpiabm/property/place_foi.py | SABS-R3-Epidemiology/epiabm | train | 21 |
2810122f26f673f977042e805b49b7a230765355 | [
"if not matrix or not matrix[0]:\n return\nrows, self.cols = (len(matrix), len(matrix[0]))\nfor r in range(rows):\n for c in range(1, self.cols):\n matrix[r][c] += matrix[r][c - 1]\nself.matrix = matrix",
"prev = self.matrix[row][col]\nif col != 0:\n prev -= self.matrix[row][col - 1]\ndiff = val -... | <|body_start_0|>
if not matrix or not matrix[0]:
return
rows, self.cols = (len(matrix), len(matrix[0]))
for r in range(rows):
for c in range(1, self.cols):
matrix[r][c] += matrix[r][c - 1]
self.matrix = matrix
<|end_body_0|>
<|body_start_1|>
... | NumMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def update(self, row, col, val):
""":type row: int :type col: int :type val: int :rtype: void"""
<|body_1|>
def sumRegion(self, row1, col1, row2, col2):
""":typ... | stack_v2_sparse_classes_10k_train_007626 | 1,888 | no_license | [
{
"docstring": ":type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": ":type row: int :type col: int :type val: int :rtype: void",
"name": "update",
"signature": "def update(self, row, col, val)"
},
{
"docstring": ":type r... | 3 | stack_v2_sparse_classes_30k_train_001178 | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def update(self, row, col, val): :type row: int :type col: int :type val: int :rtype: void
- def sumRegion(self, row... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def update(self, row, col, val): :type row: int :type col: int :type val: int :rtype: void
- def sumRegion(self, row... | 05e0beff0047f0ad399d0b46d625bb8d3459814e | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def update(self, row, col, val):
""":type row: int :type col: int :type val: int :rtype: void"""
<|body_1|>
def sumRegion(self, row1, col1, row2, col2):
""":typ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
if not matrix or not matrix[0]:
return
rows, self.cols = (len(matrix), len(matrix[0]))
for r in range(rows):
for c in range(1, self.cols):
matrix[r][c] += matrix[r... | the_stack_v2_python_sparse | python_1_to_1000/308_Range_Sum_Query_2D-Mutable.py | jakehoare/leetcode | train | 58 | |
c627d91b66216d6c8f763a0b794ca6667ebeed2e | [
"nums = sorted(nums)\nsize = len(nums)\ndiff = sys.maxsize\nfor i in range(2, size):\n remaining_target = target - nums[i]\n cur_diff = self.explore(nums, 0, i - 1, remaining_target)\n if abs(cur_diff) < abs(diff):\n diff = cur_diff\nreturn target - diff",
"diff = sys.maxsize\nwhile left < right:\... | <|body_start_0|>
nums = sorted(nums)
size = len(nums)
diff = sys.maxsize
for i in range(2, size):
remaining_target = target - nums[i]
cur_diff = self.explore(nums, 0, i - 1, remaining_target)
if abs(cur_diff) < abs(diff):
diff = cur_dif... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def threeSumClosest(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def explore(self, nums, left, right, target):
"""return the smallest diff found (target - (nums[i] + nums[j]))"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k_train_007627 | 1,168 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "threeSumClosest",
"signature": "def threeSumClosest(self, nums, target)"
},
{
"docstring": "return the smallest diff found (target - (nums[i] + nums[j]))",
"name": "explore",
"signature": "def explore(self, nu... | 2 | stack_v2_sparse_classes_30k_train_003301 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSumClosest(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def explore(self, nums, left, right, target): return the smallest diff found (targe... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSumClosest(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def explore(self, nums, left, right, target): return the smallest diff found (targe... | 78a8b27ee108ba93aa7b659665976112f48fc2c2 | <|skeleton|>
class Solution:
def threeSumClosest(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def explore(self, nums, left, right, target):
"""return the smallest diff found (target - (nums[i] + nums[j]))"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def threeSumClosest(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
nums = sorted(nums)
size = len(nums)
diff = sys.maxsize
for i in range(2, size):
remaining_target = target - nums[i]
cur_diff = self.expl... | the_stack_v2_python_sparse | companies/airbnb/p16/Solution.py | pololee/oj-leetcode | train | 0 | |
f0d86abd0b544c2067bcc2dadf9d6d615a2b08cd | [
"super(ItemThread, self).__init__(name=name)\nself.domain_name = domain_name\nself.conn = SDBConnection()\nself.item_names = item_names\nself.items = []",
"for item_name in self.item_names:\n item = self.conn.get_attributes(self.domain_name, item_name)\n self.items.append(item)"
] | <|body_start_0|>
super(ItemThread, self).__init__(name=name)
self.domain_name = domain_name
self.conn = SDBConnection()
self.item_names = item_names
self.items = []
<|end_body_0|>
<|body_start_1|>
for item_name in self.item_names:
item = self.conn.get_attribu... | A threaded :class:`Item <boto.sdb.item.Item>` retriever utility class. Retrieved :class:`Item <boto.sdb.item.Item>` objects are stored in the ``items`` instance variable after :py:meth:`run() <run>` is called. .. tip:: The item retrieval will not start until the :func:`run() <boto.sdb.connection.ItemThread.run>` method... | ItemThread | [
"CC-BY-3.0",
"LicenseRef-scancode-other-copyleft",
"LicenseRef-scancode-unknown-license-reference",
"ZPL-2.0",
"Unlicense",
"LGPL-3.0-only",
"CC0-1.0",
"LicenseRef-scancode-other-permissive",
"CNRI-Python",
"LicenseRef-scancode-warranty-disclaimer",
"GPL-2.0-or-later",
"Python-2.0",
"GPL-3.0... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ItemThread:
"""A threaded :class:`Item <boto.sdb.item.Item>` retriever utility class. Retrieved :class:`Item <boto.sdb.item.Item>` objects are stored in the ``items`` instance variable after :py:meth:`run() <run>` is called. .. tip:: The item retrieval will not start until the :func:`run() <boto.... | stack_v2_sparse_classes_10k_train_007628 | 26,088 | permissive | [
{
"docstring": ":param str name: A thread name. Used for identification. :param str domain_name: The name of a SimpleDB :class:`Domain <boto.sdb.domain.Domain>` :type item_names: string or list of strings :param item_names: The name(s) of the items to retrieve from the specified :class:`Domain <boto.sdb.domain.... | 2 | stack_v2_sparse_classes_30k_train_002256 | Implement the Python class `ItemThread` described below.
Class description:
A threaded :class:`Item <boto.sdb.item.Item>` retriever utility class. Retrieved :class:`Item <boto.sdb.item.Item>` objects are stored in the ``items`` instance variable after :py:meth:`run() <run>` is called. .. tip:: The item retrieval will ... | Implement the Python class `ItemThread` described below.
Class description:
A threaded :class:`Item <boto.sdb.item.Item>` retriever utility class. Retrieved :class:`Item <boto.sdb.item.Item>` objects are stored in the ``items`` instance variable after :py:meth:`run() <run>` is called. .. tip:: The item retrieval will ... | dccb9467675c67b9c3399fc76c5de6d31bfb8255 | <|skeleton|>
class ItemThread:
"""A threaded :class:`Item <boto.sdb.item.Item>` retriever utility class. Retrieved :class:`Item <boto.sdb.item.Item>` objects are stored in the ``items`` instance variable after :py:meth:`run() <run>` is called. .. tip:: The item retrieval will not start until the :func:`run() <boto.... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ItemThread:
"""A threaded :class:`Item <boto.sdb.item.Item>` retriever utility class. Retrieved :class:`Item <boto.sdb.item.Item>` objects are stored in the ``items`` instance variable after :py:meth:`run() <run>` is called. .. tip:: The item retrieval will not start until the :func:`run() <boto.sdb.connectio... | the_stack_v2_python_sparse | desktop/core/ext-py3/boto-2.49.0/boto/sdb/connection.py | cloudera/hue | train | 5,655 |
513a297965d3db04bdbaf65093dd086512482eb4 | [
"if head is None:\n return None\nslow = head.next\nif slow is None:\n return None\nfast = slow.next\nwhile slow is not None and fast is not None:\n if slow == fast:\n return fast\n slow = slow.next\n fast = fast.next\n if fast is not None:\n fast = fast.next\nreturn None",
"meet_no... | <|body_start_0|>
if head is None:
return None
slow = head.next
if slow is None:
return None
fast = slow.next
while slow is not None and fast is not None:
if slow == fast:
return fast
slow = slow.next
fast... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def meeting_node(self, head):
"""在链表存在环的前提下找到一快一慢指针相遇的结点, :param head: 链表的头结点 :return:"""
<|body_0|>
def entry_node_of_loop(self, head):
"""找到环的入口结点 :param head: 链表头结点 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if head is Non... | stack_v2_sparse_classes_10k_train_007629 | 1,932 | no_license | [
{
"docstring": "在链表存在环的前提下找到一快一慢指针相遇的结点, :param head: 链表的头结点 :return:",
"name": "meeting_node",
"signature": "def meeting_node(self, head)"
},
{
"docstring": "找到环的入口结点 :param head: 链表头结点 :return:",
"name": "entry_node_of_loop",
"signature": "def entry_node_of_loop(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001410 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def meeting_node(self, head): 在链表存在环的前提下找到一快一慢指针相遇的结点, :param head: 链表的头结点 :return:
- def entry_node_of_loop(self, head): 找到环的入口结点 :param head: 链表头结点 :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def meeting_node(self, head): 在链表存在环的前提下找到一快一慢指针相遇的结点, :param head: 链表的头结点 :return:
- def entry_node_of_loop(self, head): 找到环的入口结点 :param head: 链表头结点 :return:
<|skeleton|>
class... | 51e6d72bfc631fa96e5a8ed6e4e55cd240ad47d9 | <|skeleton|>
class Solution:
def meeting_node(self, head):
"""在链表存在环的前提下找到一快一慢指针相遇的结点, :param head: 链表的头结点 :return:"""
<|body_0|>
def entry_node_of_loop(self, head):
"""找到环的入口结点 :param head: 链表头结点 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def meeting_node(self, head):
"""在链表存在环的前提下找到一快一慢指针相遇的结点, :param head: 链表的头结点 :return:"""
if head is None:
return None
slow = head.next
if slow is None:
return None
fast = slow.next
while slow is not None and fast is not None:
... | the_stack_v2_python_sparse | 剑指Offer/23.链表中环的入口结点.py | CodingBuye/PythonForNowcoder | train | 2 | |
8b50a8804e58a0b95aac9e05e870fe0678a42afd | [
"if stanza_type is None:\n if element is None:\n raise ValueError('Missing iq type')\nelif stanza_type not in IQ_TYPES:\n raise ValueError('Bad iq type')\nif element is None and stanza_id is None and (stanza_type in ('get', 'set')):\n stanza_id = self.gen_id()\nif element is None:\n element = 'iq... | <|body_start_0|>
if stanza_type is None:
if element is None:
raise ValueError('Missing iq type')
elif stanza_type not in IQ_TYPES:
raise ValueError('Bad iq type')
if element is None and stanza_id is None and (stanza_type in ('get', 'set')):
sta... | <message /> stanza class. | Iq | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Iq:
"""<message /> stanza class."""
def __init__(self, element=None, from_jid=None, to_jid=None, stanza_type=None, stanza_id=None, error=None, error_cond=None, return_path=None, language=None):
"""Initialize an `Iq` object. :Parameters: - `element`: XML element of this stanza. - `fro... | stack_v2_sparse_classes_10k_train_007630 | 6,218 | permissive | [
{
"docstring": "Initialize an `Iq` object. :Parameters: - `element`: XML element of this stanza. - `from_jid`: sender JID. - `to_jid`: recipient JID. - `stanza_type`: staza type: one of: \"get\", \"set\", \"response\" or \"error\". - `stanza_id`: stanza id -- value of stanza's \"id\" attribute. If not given, th... | 5 | stack_v2_sparse_classes_30k_train_000306 | Implement the Python class `Iq` described below.
Class description:
<message /> stanza class.
Method signatures and docstrings:
- def __init__(self, element=None, from_jid=None, to_jid=None, stanza_type=None, stanza_id=None, error=None, error_cond=None, return_path=None, language=None): Initialize an `Iq` object. :Pa... | Implement the Python class `Iq` described below.
Class description:
<message /> stanza class.
Method signatures and docstrings:
- def __init__(self, element=None, from_jid=None, to_jid=None, stanza_type=None, stanza_id=None, error=None, error_cond=None, return_path=None, language=None): Initialize an `Iq` object. :Pa... | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | <|skeleton|>
class Iq:
"""<message /> stanza class."""
def __init__(self, element=None, from_jid=None, to_jid=None, stanza_type=None, stanza_id=None, error=None, error_cond=None, return_path=None, language=None):
"""Initialize an `Iq` object. :Parameters: - `element`: XML element of this stanza. - `fro... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Iq:
"""<message /> stanza class."""
def __init__(self, element=None, from_jid=None, to_jid=None, stanza_type=None, stanza_id=None, error=None, error_cond=None, return_path=None, language=None):
"""Initialize an `Iq` object. :Parameters: - `element`: XML element of this stanza. - `from_jid`: sende... | the_stack_v2_python_sparse | python3-alpha/python-libs/pyxmpp2/iq.py | kuri65536/python-for-android | train | 280 |
9ac60113e50b2b027b65a49adebdea59f22d16e5 | [
"try:\n runner_view, __ = self._get_producer(operation_type)\nexcept ValueError:\n return redirect('home')\nreturn runner_view(request, operation_type=operation_type, **kwargs)",
"try:\n __, runner_cls = self._get_producer(action_type)\nexcept ValueError:\n return redirect('home')\nreturn runner_cls()... | <|body_start_0|>
try:
runner_view, __ = self._get_producer(operation_type)
except ValueError:
return redirect('home')
return runner_view(request, operation_type=operation_type, **kwargs)
<|end_body_0|>
<|body_start_1|>
try:
__, runner_cls = self._get_... | Factory to manage scheduling of action runs. Producer stores a tuple with: - Class.as_view() for view processing - Class to execute other methods | ActionRunFactory | [
"LGPL-2.0-or-later",
"BSD-3-Clause",
"MIT",
"Apache-2.0",
"LGPL-2.1-only",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ActionRunFactory:
"""Factory to manage scheduling of action runs. Producer stores a tuple with: - Class.as_view() for view processing - Class to execute other methods"""
def process_request(self, request: http.HttpRequest, operation_type: int, **kwargs) -> http.HttpResponse:
"""Execu... | stack_v2_sparse_classes_10k_train_007631 | 7,115 | permissive | [
{
"docstring": "Execute function to process a run request. :param request: Http Request received (get or post) :param operation_type: Type of action being run. :param kwargs: Dictionary with action :return: HttpResponse",
"name": "process_request",
"signature": "def process_request(self, request: http.H... | 2 | stack_v2_sparse_classes_30k_train_003875 | Implement the Python class `ActionRunFactory` described below.
Class description:
Factory to manage scheduling of action runs. Producer stores a tuple with: - Class.as_view() for view processing - Class to execute other methods
Method signatures and docstrings:
- def process_request(self, request: http.HttpRequest, o... | Implement the Python class `ActionRunFactory` described below.
Class description:
Factory to manage scheduling of action runs. Producer stores a tuple with: - Class.as_view() for view processing - Class to execute other methods
Method signatures and docstrings:
- def process_request(self, request: http.HttpRequest, o... | c432745dfff932cbe7397100422d49df78f0a882 | <|skeleton|>
class ActionRunFactory:
"""Factory to manage scheduling of action runs. Producer stores a tuple with: - Class.as_view() for view processing - Class to execute other methods"""
def process_request(self, request: http.HttpRequest, operation_type: int, **kwargs) -> http.HttpResponse:
"""Execu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ActionRunFactory:
"""Factory to manage scheduling of action runs. Producer stores a tuple with: - Class.as_view() for view processing - Class to execute other methods"""
def process_request(self, request: http.HttpRequest, operation_type: int, **kwargs) -> http.HttpResponse:
"""Execute function t... | the_stack_v2_python_sparse | ontask/action/services/run_factory.py | abelardopardo/ontask_b | train | 43 |
32b1f1e35a1ebb6bf6cc9caba8c37ee4db8c757b | [
"self.filepath = filepath\nself.interval = interval\nself.verbose = verbose\nself.kmodel = kmodel\nself.total_steps = 0",
"self.total_steps += 1\nif self.total_steps % self.interval != 0:\n return\nfilepath = self.filepath.format(step=self.total_steps, **logs)\nif self.verbose > 0:\n print('\\nStep {}: savi... | <|body_start_0|>
self.filepath = filepath
self.interval = interval
self.verbose = verbose
self.kmodel = kmodel
self.total_steps = 0
<|end_body_0|>
<|body_start_1|>
self.total_steps += 1
if self.total_steps % self.interval != 0:
return
filepath... | ModelIntervalCheck | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelIntervalCheck:
def __init__(self, filepath, interval, verbose=0, kmodel=None):
"""save every x steps"""
<|body_0|>
def on_step_end(self, step, logs={}):
"""Save weights at interval steps during training"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_007632 | 2,749 | no_license | [
{
"docstring": "save every x steps",
"name": "__init__",
"signature": "def __init__(self, filepath, interval, verbose=0, kmodel=None)"
},
{
"docstring": "Save weights at interval steps during training",
"name": "on_step_end",
"signature": "def on_step_end(self, step, logs={})"
}
] | 2 | stack_v2_sparse_classes_30k_train_004988 | Implement the Python class `ModelIntervalCheck` described below.
Class description:
Implement the ModelIntervalCheck class.
Method signatures and docstrings:
- def __init__(self, filepath, interval, verbose=0, kmodel=None): save every x steps
- def on_step_end(self, step, logs={}): Save weights at interval steps duri... | Implement the Python class `ModelIntervalCheck` described below.
Class description:
Implement the ModelIntervalCheck class.
Method signatures and docstrings:
- def __init__(self, filepath, interval, verbose=0, kmodel=None): save every x steps
- def on_step_end(self, step, logs={}): Save weights at interval steps duri... | a49eb348ff994f35b0efbbd5ac3ac8ae8ccb57d2 | <|skeleton|>
class ModelIntervalCheck:
def __init__(self, filepath, interval, verbose=0, kmodel=None):
"""save every x steps"""
<|body_0|>
def on_step_end(self, step, logs={}):
"""Save weights at interval steps during training"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ModelIntervalCheck:
def __init__(self, filepath, interval, verbose=0, kmodel=None):
"""save every x steps"""
self.filepath = filepath
self.interval = interval
self.verbose = verbose
self.kmodel = kmodel
self.total_steps = 0
def on_step_end(self, step, logs=... | the_stack_v2_python_sparse | reinforcement_learning/0x01-deep_q_learning/train.py | salmenz/holbertonschool-machine_learning | train | 4 | |
bd06bad255a2b08ec7786dc1613d94c7874d2793 | [
"self._episode_length = episode_length\nself._use_actions_for_distance = use_actions_for_distance\nself._vectorized_demonstrations = self._vectorize(demonstrations_it)\natom_dims = self._vectorized_demonstrations.shape[1]\nself._reward_sigma = beta * self._episode_length / np.sqrt(atom_dims)\nself._reward_scale = a... | <|body_start_0|>
self._episode_length = episode_length
self._use_actions_for_distance = use_actions_for_distance
self._vectorized_demonstrations = self._vectorize(demonstrations_it)
atom_dims = self._vectorized_demonstrations.shape[1]
self._reward_sigma = beta * self._episode_len... | Computes PWIL rewards along a trajectory. The rewards measure similarity to the demonstration transitions and are based on a greedy approximation to the Wasserstein distance between trajectories. | WassersteinDistanceRewarder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WassersteinDistanceRewarder:
"""Computes PWIL rewards along a trajectory. The rewards measure similarity to the demonstration transitions and are based on a greedy approximation to the Wasserstein distance between trajectories."""
def __init__(self, demonstrations_it: Iterator[types.Transiti... | stack_v2_sparse_classes_10k_train_007633 | 6,140 | permissive | [
{
"docstring": "Initializes the rewarder. Args: demonstrations_it: An iterator over acme.types.Transition. episode_length: a target episode length (policies will be encouraged by the imitation reward to have that length). use_actions_for_distance: whether to use action to compute reward. alpha: float scaling th... | 4 | stack_v2_sparse_classes_30k_train_004686 | Implement the Python class `WassersteinDistanceRewarder` described below.
Class description:
Computes PWIL rewards along a trajectory. The rewards measure similarity to the demonstration transitions and are based on a greedy approximation to the Wasserstein distance between trajectories.
Method signatures and docstri... | Implement the Python class `WassersteinDistanceRewarder` described below.
Class description:
Computes PWIL rewards along a trajectory. The rewards measure similarity to the demonstration transitions and are based on a greedy approximation to the Wasserstein distance between trajectories.
Method signatures and docstri... | 97c50eaa62c039d8f4b9efa3e80c4d80e6f40c4c | <|skeleton|>
class WassersteinDistanceRewarder:
"""Computes PWIL rewards along a trajectory. The rewards measure similarity to the demonstration transitions and are based on a greedy approximation to the Wasserstein distance between trajectories."""
def __init__(self, demonstrations_it: Iterator[types.Transiti... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WassersteinDistanceRewarder:
"""Computes PWIL rewards along a trajectory. The rewards measure similarity to the demonstration transitions and are based on a greedy approximation to the Wasserstein distance between trajectories."""
def __init__(self, demonstrations_it: Iterator[types.Transition], episode_... | the_stack_v2_python_sparse | acme/agents/jax/pwil/rewarder.py | RaoulDrake/acme | train | 0 |
5c34c03d9c7799f25d0266f603218f87ec677b3e | [
"super().__init__()\nself.layer_norm_att = normalization_class(size, **normalization_args)\nself.layer_norm_ffn = normalization_class(size, **normalization_args)\nself.att = SelfAttention(size, attention_size, context_size, block_id, num_blocks)\nself.dropout_att = torch.nn.Dropout(p=att_dropout_rate)\nself.ffn = F... | <|body_start_0|>
super().__init__()
self.layer_norm_att = normalization_class(size, **normalization_args)
self.layer_norm_ffn = normalization_class(size, **normalization_args)
self.att = SelfAttention(size, attention_size, context_size, block_id, num_blocks)
self.dropout_att = to... | RWKV module. Args: size: Input/Output size. linear_size: Feed-forward hidden size. attention_size: SelfAttention hidden size. context_size: Context size for WKV computation. block_id: Block index. num_blocks: Number of blocks in the architecture. normalization_class: Normalization layer class. normalization_args: Norma... | RWKV | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RWKV:
"""RWKV module. Args: size: Input/Output size. linear_size: Feed-forward hidden size. attention_size: SelfAttention hidden size. context_size: Context size for WKV computation. block_id: Block index. num_blocks: Number of blocks in the architecture. normalization_class: Normalization layer ... | stack_v2_sparse_classes_10k_train_007634 | 2,602 | permissive | [
{
"docstring": "Construct a RWKV object.",
"name": "__init__",
"signature": "def __init__(self, size: int, linear_size: int, attention_size: int, context_size: int, block_id: int, num_blocks: int, normalization_class: torch.nn.Module=torch.nn.LayerNorm, normalization_args: Dict={}, att_dropout_rate: flo... | 2 | null | Implement the Python class `RWKV` described below.
Class description:
RWKV module. Args: size: Input/Output size. linear_size: Feed-forward hidden size. attention_size: SelfAttention hidden size. context_size: Context size for WKV computation. block_id: Block index. num_blocks: Number of blocks in the architecture. no... | Implement the Python class `RWKV` described below.
Class description:
RWKV module. Args: size: Input/Output size. linear_size: Feed-forward hidden size. attention_size: SelfAttention hidden size. context_size: Context size for WKV computation. block_id: Block index. num_blocks: Number of blocks in the architecture. no... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class RWKV:
"""RWKV module. Args: size: Input/Output size. linear_size: Feed-forward hidden size. attention_size: SelfAttention hidden size. context_size: Context size for WKV computation. block_id: Block index. num_blocks: Number of blocks in the architecture. normalization_class: Normalization layer ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RWKV:
"""RWKV module. Args: size: Input/Output size. linear_size: Feed-forward hidden size. attention_size: SelfAttention hidden size. context_size: Context size for WKV computation. block_id: Block index. num_blocks: Number of blocks in the architecture. normalization_class: Normalization layer class. normal... | the_stack_v2_python_sparse | espnet2/asr_transducer/decoder/blocks/rwkv.py | espnet/espnet | train | 7,242 |
fc7185e1a1f2145302ffafc4bffe2b5cb5a15e44 | [
"if not self.base_directory.exists():\n raise TestConnectionError(f'Path: {self.base_directory.resolve()} does not exist.')\nif self.assets and test_assets:\n for asset in self.assets:\n asset.test_connection()",
"if kwargs:\n raise TypeError(f'_build_data_connector() got unexpected keyword argume... | <|body_start_0|>
if not self.base_directory.exists():
raise TestConnectionError(f'Path: {self.base_directory.resolve()} does not exist.')
if self.assets and test_assets:
for asset in self.assets:
asset.test_connection()
<|end_body_0|>
<|body_start_1|>
if ... | Pandas based Datasource for filesystem based data assets. | PandasFilesystemDatasource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PandasFilesystemDatasource:
"""Pandas based Datasource for filesystem based data assets."""
def test_connection(self, test_assets: bool=True) -> None:
"""Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatas... | stack_v2_sparse_classes_10k_train_007635 | 3,249 | permissive | [
{
"docstring": "Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatasource, whether to test them as well. Raises: TestConnectionError: If the connection test fails.",
"name": "test_connection",
"signature": "def test_connection... | 2 | null | Implement the Python class `PandasFilesystemDatasource` described below.
Class description:
Pandas based Datasource for filesystem based data assets.
Method signatures and docstrings:
- def test_connection(self, test_assets: bool=True) -> None: Test the connection for the PandasFilesystemDatasource. Args: test_assets... | Implement the Python class `PandasFilesystemDatasource` described below.
Class description:
Pandas based Datasource for filesystem based data assets.
Method signatures and docstrings:
- def test_connection(self, test_assets: bool=True) -> None: Test the connection for the PandasFilesystemDatasource. Args: test_assets... | b0290e2fd2aa05aec6d7d8871b91cb4478e9501d | <|skeleton|>
class PandasFilesystemDatasource:
"""Pandas based Datasource for filesystem based data assets."""
def test_connection(self, test_assets: bool=True) -> None:
"""Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatas... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PandasFilesystemDatasource:
"""Pandas based Datasource for filesystem based data assets."""
def test_connection(self, test_assets: bool=True) -> None:
"""Test the connection for the PandasFilesystemDatasource. Args: test_assets: If assets have been passed to the PandasFilesystemDatasource, whethe... | the_stack_v2_python_sparse | great_expectations/datasource/fluent/pandas_filesystem_datasource.py | great-expectations/great_expectations | train | 8,931 |
6d11d78fff02fb7571563af224e71110fffe64cf | [
"super(FuturesSession, self).__init__(*args, **kwargs)\nif executor is None:\n executor = ThreadPoolExecutor(max_workers=max_workers)\n if max_workers > DEFAULT_POOLSIZE:\n adapter_kwargs = dict(pool_connections=max_workers, pool_maxsize=max_workers)\n self.mount('https://', HTTPAdapter(**adapte... | <|body_start_0|>
super(FuturesSession, self).__init__(*args, **kwargs)
if executor is None:
executor = ThreadPoolExecutor(max_workers=max_workers)
if max_workers > DEFAULT_POOLSIZE:
adapter_kwargs = dict(pool_connections=max_workers, pool_maxsize=max_workers)
... | FuturesSession | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FuturesSession:
def __init__(self, executor=None, max_workers=2, *args, **kwargs):
"""Creates a FuturesSession Notes ~~~~~ * ProcessPoolExecutor is not supported b/c Response objects are not picklable. * If you provide both `executor` and `max_workers`, the latter is ignored and provided... | stack_v2_sparse_classes_10k_train_007636 | 5,711 | no_license | [
{
"docstring": "Creates a FuturesSession Notes ~~~~~ * ProcessPoolExecutor is not supported b/c Response objects are not picklable. * If you provide both `executor` and `max_workers`, the latter is ignored and provided executor is used as is.",
"name": "__init__",
"signature": "def __init__(self, execut... | 2 | stack_v2_sparse_classes_30k_test_000350 | Implement the Python class `FuturesSession` described below.
Class description:
Implement the FuturesSession class.
Method signatures and docstrings:
- def __init__(self, executor=None, max_workers=2, *args, **kwargs): Creates a FuturesSession Notes ~~~~~ * ProcessPoolExecutor is not supported b/c Response objects ar... | Implement the Python class `FuturesSession` described below.
Class description:
Implement the FuturesSession class.
Method signatures and docstrings:
- def __init__(self, executor=None, max_workers=2, *args, **kwargs): Creates a FuturesSession Notes ~~~~~ * ProcessPoolExecutor is not supported b/c Response objects ar... | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | <|skeleton|>
class FuturesSession:
def __init__(self, executor=None, max_workers=2, *args, **kwargs):
"""Creates a FuturesSession Notes ~~~~~ * ProcessPoolExecutor is not supported b/c Response objects are not picklable. * If you provide both `executor` and `max_workers`, the latter is ignored and provided... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FuturesSession:
def __init__(self, executor=None, max_workers=2, *args, **kwargs):
"""Creates a FuturesSession Notes ~~~~~ * ProcessPoolExecutor is not supported b/c Response objects are not picklable. * If you provide both `executor` and `max_workers`, the latter is ignored and provided executor is u... | the_stack_v2_python_sparse | repoData/ross-requests-futures/allPythonContent.py | aCoffeeYin/pyreco | train | 0 | |
e04f4ba55e5799f7c6d4990e9c3306dc8d85cbf0 | [
"def preorder(root):\n if not root:\n return ['null']\n return [str(root.val)] + preorder(root.left) + preorder(root.right)\nreturn '[' + ','.join(preorder(root)) + ']'",
"arr = data[1:-1].split(',')\nself.index = 0\n\ndef construct():\n if arr[self.index] == 'null':\n self.index += 1\n ... | <|body_start_0|>
def preorder(root):
if not root:
return ['null']
return [str(root.val)] + preorder(root.left) + preorder(root.right)
return '[' + ','.join(preorder(root)) + ']'
<|end_body_0|>
<|body_start_1|>
arr = data[1:-1].split(',')
self.inde... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_007637 | 1,360 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_002163 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 19be0766f6b9c298fb32754f66416f79567843c1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def preorder(root):
if not root:
return ['null']
return [str(root.val)] + preorder(root.left) + preorder(root.right)
return '[' + ','.join... | the_stack_v2_python_sparse | leetcode/Problems/297--Serialize-and-Deserialize-Binary-Tree-Hard.py | niteesh2268/coding-prepation | train | 0 | |
2b51311d07af84be25efbf7608db02b427566682 | [
"mocked_input_list = (n for n in command_list)\nprint_argument = 'Spam & Eggs'\n\ndef mocked_input(*_):\n return next(mocked_input_list)\nmocker.patch.object(mailroom, 'input', new=mocked_input)\nmocked_print = mocker.patch.object(mailroom, 'print')\nmocked_thank_you = mocker.patch.object(mailroom, 'thank_you', ... | <|body_start_0|>
mocked_input_list = (n for n in command_list)
print_argument = 'Spam & Eggs'
def mocked_input(*_):
return next(mocked_input_list)
mocker.patch.object(mailroom, 'input', new=mocked_input)
mocked_print = mocker.patch.object(mailroom, 'print')
m... | Tests the mailroom.thank_you_cli function. Ensures that the user selection loop runs as expected __builtins__.input() is mocked to simulate user-interaction __builtins__.print() is mocked to simulate user-interaction | Test_Thank_You_CLI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_Thank_You_CLI:
"""Tests the mailroom.thank_you_cli function. Ensures that the user selection loop runs as expected __builtins__.input() is mocked to simulate user-interaction __builtins__.print() is mocked to simulate user-interaction"""
def test_thank_you_cli_name_number(self, mocker, ... | stack_v2_sparse_classes_10k_train_007638 | 17,359 | no_license | [
{
"docstring": "Positive-Test-Cases",
"name": "test_thank_you_cli_name_number",
"signature": "def test_thank_you_cli_name_number(self, mocker, command_list)"
},
{
"docstring": "Positive-Test-Cases",
"name": "test_thank_you_cli_list_quit",
"signature": "def test_thank_you_cli_list_quit(se... | 3 | null | Implement the Python class `Test_Thank_You_CLI` described below.
Class description:
Tests the mailroom.thank_you_cli function. Ensures that the user selection loop runs as expected __builtins__.input() is mocked to simulate user-interaction __builtins__.print() is mocked to simulate user-interaction
Method signatures... | Implement the Python class `Test_Thank_You_CLI` described below.
Class description:
Tests the mailroom.thank_you_cli function. Ensures that the user selection loop runs as expected __builtins__.input() is mocked to simulate user-interaction __builtins__.print() is mocked to simulate user-interaction
Method signatures... | 76224d0fb871d0bf0b838f3fccf01022edd70f82 | <|skeleton|>
class Test_Thank_You_CLI:
"""Tests the mailroom.thank_you_cli function. Ensures that the user selection loop runs as expected __builtins__.input() is mocked to simulate user-interaction __builtins__.print() is mocked to simulate user-interaction"""
def test_thank_you_cli_name_number(self, mocker, ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Test_Thank_You_CLI:
"""Tests the mailroom.thank_you_cli function. Ensures that the user selection loop runs as expected __builtins__.input() is mocked to simulate user-interaction __builtins__.print() is mocked to simulate user-interaction"""
def test_thank_you_cli_name_number(self, mocker, command_list)... | the_stack_v2_python_sparse | students/jerickson/Lesson6/test_mailroom.py | UWPCE-PythonCert-ClassRepos/SP_Online_PY210 | train | 19 |
3d58f8291db8462e35e8f461da0100080a13a27c | [
"host = self._zapi.host.get({'output': 'extend', 'selectParentTemplates': ['name'], 'filter': {search_key: host_identifier}, 'selectInventory': host_inventory})\nif len(host) < 1:\n self._module.fail_json(msg='Host not found: %s' % host_identifier)\nelse:\n return host[0]",
"output = 'extend'\ntriggers_list... | <|body_start_0|>
host = self._zapi.host.get({'output': 'extend', 'selectParentTemplates': ['name'], 'filter': {search_key: host_identifier}, 'selectInventory': host_inventory})
if len(host) < 1:
self._module.fail_json(msg='Host not found: %s' % host_identifier)
else:
retu... | Host | [
"MIT",
"GPL-3.0-only",
"LicenseRef-scancode-unknown-license-reference",
"GPL-3.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Host:
def get_host(self, host_identifier, host_inventory, search_key):
"""Get host by hostname|visible_name|hostid"""
<|body_0|>
def get_triggers_by_host_id_in_problem_state(self, host_id, trigger_severity):
"""Get triggers in problem state from a hostid"""
<... | stack_v2_sparse_classes_10k_train_007639 | 10,249 | permissive | [
{
"docstring": "Get host by hostname|visible_name|hostid",
"name": "get_host",
"signature": "def get_host(self, host_identifier, host_inventory, search_key)"
},
{
"docstring": "Get triggers in problem state from a hostid",
"name": "get_triggers_by_host_id_in_problem_state",
"signature": ... | 3 | null | Implement the Python class `Host` described below.
Class description:
Implement the Host class.
Method signatures and docstrings:
- def get_host(self, host_identifier, host_inventory, search_key): Get host by hostname|visible_name|hostid
- def get_triggers_by_host_id_in_problem_state(self, host_id, trigger_severity):... | Implement the Python class `Host` described below.
Class description:
Implement the Host class.
Method signatures and docstrings:
- def get_host(self, host_identifier, host_inventory, search_key): Get host by hostname|visible_name|hostid
- def get_triggers_by_host_id_in_problem_state(self, host_id, trigger_severity):... | 2ea7d4f00212f502bc684ac257371ada73da1ca9 | <|skeleton|>
class Host:
def get_host(self, host_identifier, host_inventory, search_key):
"""Get host by hostname|visible_name|hostid"""
<|body_0|>
def get_triggers_by_host_id_in_problem_state(self, host_id, trigger_severity):
"""Get triggers in problem state from a hostid"""
<... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Host:
def get_host(self, host_identifier, host_inventory, search_key):
"""Get host by hostname|visible_name|hostid"""
host = self._zapi.host.get({'output': 'extend', 'selectParentTemplates': ['name'], 'filter': {search_key: host_identifier}, 'selectInventory': host_inventory})
if len(h... | the_stack_v2_python_sparse | intro-ansible/venv3/lib/python3.8/site-packages/ansible_collections/community/zabbix/plugins/modules/zabbix_host_events_info.py | SimonFangCisco/dne-dna-code | train | 0 | |
320fdc1518fb6449ed3f8f9856ea07816755a960 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn EdiscoveryExportOperation()",
"from .case_operation import CaseOperation\nfrom .ediscovery_review_set import EdiscoveryReviewSet\nfrom .ediscovery_review_set_query import EdiscoveryReviewSetQuery\nfrom .export_file_metadata import Expo... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return EdiscoveryExportOperation()
<|end_body_0|>
<|body_start_1|>
from .case_operation import CaseOperation
from .ediscovery_review_set import EdiscoveryReviewSet
from .ediscovery_revi... | EdiscoveryExportOperation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EdiscoveryExportOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryExportOperation:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and c... | stack_v2_sparse_classes_10k_train_007640 | 4,962 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: EdiscoveryExportOperation",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrim... | 3 | stack_v2_sparse_classes_30k_val_000371 | Implement the Python class `EdiscoveryExportOperation` described below.
Class description:
Implement the EdiscoveryExportOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryExportOperation: Creates a new instance of the appropriat... | Implement the Python class `EdiscoveryExportOperation` described below.
Class description:
Implement the EdiscoveryExportOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryExportOperation: Creates a new instance of the appropriat... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class EdiscoveryExportOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryExportOperation:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EdiscoveryExportOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EdiscoveryExportOperation:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the obje... | the_stack_v2_python_sparse | msgraph/generated/models/security/ediscovery_export_operation.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
548bd0c95c9774f2920849080ee3229c9b8cf9ad | [
"q = deque([root])\nlev = 0\nwhile q:\n nxt = deque()\n filled = True\n n = len(q)\n for i in range(len(q)):\n node = q.popleft()\n if node.left:\n if not filled:\n return False\n nxt.append(node.left)\n else:\n filled = False\n ... | <|body_start_0|>
q = deque([root])
lev = 0
while q:
nxt = deque()
filled = True
n = len(q)
for i in range(len(q)):
node = q.popleft()
if node.left:
if not filled:
return Fa... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isCompleteTree(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isCompleteTreeAC(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
q = deque([root])
lev = 0
... | stack_v2_sparse_classes_10k_train_007641 | 2,907 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isCompleteTree",
"signature": "def isCompleteTree(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isCompleteTreeAC",
"signature": "def isCompleteTreeAC(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root): :type root: TreeNode :rtype: bool
- def isCompleteTreeAC(self, root): :type root: TreeNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isCompleteTree(self, root): :type root: TreeNode :rtype: bool
- def isCompleteTreeAC(self, root): :type root: TreeNode :rtype: bool
<|skeleton|>
class Solution:
def isC... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def isCompleteTree(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_0|>
def isCompleteTreeAC(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isCompleteTree(self, root):
""":type root: TreeNode :rtype: bool"""
q = deque([root])
lev = 0
while q:
nxt = deque()
filled = True
n = len(q)
for i in range(len(q)):
node = q.popleft()
... | the_stack_v2_python_sparse | C/CheckCompletenessofaBinaryTree.py | bssrdf/pyleet | train | 2 | |
e8c339aa2d79fc5be8e4267acd9b40b784f4382b | [
"def rserialize(root, s):\n if not root:\n s += 'null,'\n return s\n s += '{},'.format(root.val)\n s = rserialize(root.left, s)\n s = rserialize(root.right, s)\n return s\nreturn rserialize(root, '')",
"l = []\n_tmp = data.split(',')\nfor item in _tmp:\n if item:\n l.append(... | <|body_start_0|>
def rserialize(root, s):
if not root:
s += 'null,'
return s
s += '{},'.format(root.val)
s = rserialize(root.left, s)
s = rserialize(root.right, s)
return s
return rserialize(root, '')
<|end_body_... | Codec | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_007642 | 1,475 | permissive | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_004387 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | ad5e724d20a8492b8eba03fc0f24e4ff5964b3ea | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def rserialize(root, s):
if not root:
s += 'null,'
return s
s += '{},'.format(root.val)
s = rserialize(root.left, s)
... | the_stack_v2_python_sparse | dailyQuestion/2020/2020-06/06-15/python/solution_dfs.py | russellgao/algorithm | train | 3 | |
07c50ab56308c6a681a4d6a999190e9b85907a60 | [
"q = [(-nums[i], i) for i in range(k)]\nheapq.heapify(q)\nret = [-q[0][0]]\nfor i in range(k, len(nums)):\n while q and q[0][1] <= i - k:\n heapq.heappop(q)\n heapq.heappush(q, (-nums[i], i))\n ret.append(-q[0][0])\nreturn ret",
"if k == 1:\n return nums\nif k == len(nums):\n return [max(num... | <|body_start_0|>
q = [(-nums[i], i) for i in range(k)]
heapq.heapify(q)
ret = [-q[0][0]]
for i in range(k, len(nums)):
while q and q[0][1] <= i - k:
heapq.heappop(q)
heapq.heappush(q, (-nums[i], i))
ret.append(-q[0][0])
return r... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSlidingWindow2(self, nums: List[int], k: int) -> List[int]:
"""Use python heapq Runtime: 1976 ms, faster than 43.70% Memory Usage: 39 MB, less than 5.23% 1 <= nums.length <= 10^5 -10^4 <= nums[i] <= 10^4 1 <= k <= nums.length :param nums: :param k: :return:"""
<|... | stack_v2_sparse_classes_10k_train_007643 | 2,696 | permissive | [
{
"docstring": "Use python heapq Runtime: 1976 ms, faster than 43.70% Memory Usage: 39 MB, less than 5.23% 1 <= nums.length <= 10^5 -10^4 <= nums[i] <= 10^4 1 <= k <= nums.length :param nums: :param k: :return:",
"name": "maxSlidingWindow2",
"signature": "def maxSlidingWindow2(self, nums: List[int], k: ... | 2 | stack_v2_sparse_classes_30k_train_007106 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow2(self, nums: List[int], k: int) -> List[int]: Use python heapq Runtime: 1976 ms, faster than 43.70% Memory Usage: 39 MB, less than 5.23% 1 <= nums.length <= ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow2(self, nums: List[int], k: int) -> List[int]: Use python heapq Runtime: 1976 ms, faster than 43.70% Memory Usage: 39 MB, less than 5.23% 1 <= nums.length <= ... | 4dd1e54d8d08f7e6590bc76abd08ecaacaf775e5 | <|skeleton|>
class Solution:
def maxSlidingWindow2(self, nums: List[int], k: int) -> List[int]:
"""Use python heapq Runtime: 1976 ms, faster than 43.70% Memory Usage: 39 MB, less than 5.23% 1 <= nums.length <= 10^5 -10^4 <= nums[i] <= 10^4 1 <= k <= nums.length :param nums: :param k: :return:"""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSlidingWindow2(self, nums: List[int], k: int) -> List[int]:
"""Use python heapq Runtime: 1976 ms, faster than 43.70% Memory Usage: 39 MB, less than 5.23% 1 <= nums.length <= 10^5 -10^4 <= nums[i] <= 10^4 1 <= k <= nums.length :param nums: :param k: :return:"""
q = [(-nums[i], ... | the_stack_v2_python_sparse | src/todo/239-SlidingWindowMaximum.py | Jiezhi/myleetcode | train | 1 | |
6c952ce6ef498b3213542d60cb26c72a2df90e6d | [
"self.X = X\nself.fs = fs\nself.N = 2 * (len(self.X) - 1)",
"x = np.zeros(self.N)\nfor n in range(self.N):\n x[n] = 1 / np.sqrt(self.N) * self.X[0] * np.exp(1j * 2 * cmath.pi * 0 * n / self.N)\n for k in range(1, int(self.N / 2)):\n x[n] = x[n] + 1 / np.sqrt(self.N) * self.X[k] * np.exp(1j * 2 * cmat... | <|body_start_0|>
self.X = X
self.fs = fs
self.N = 2 * (len(self.X) - 1)
<|end_body_0|>
<|body_start_1|>
x = np.zeros(self.N)
for n in range(self.N):
x[n] = 1 / np.sqrt(self.N) * self.X[0] * np.exp(1j * 2 * cmath.pi * 0 * n / self.N)
for k in range(1, int(... | idft Inverse Discrete Fourier transform. | idft_p11 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class idft_p11:
"""idft Inverse Discrete Fourier transform."""
def __init__(self, X, fs):
""":param X: Input DFT X :param fs: Input integer fs contains the sample frequency"""
<|body_0|>
def solve(self):
"""\\\\\\ METHOD: Compute the iDFT with truncated N/2+1 coefficie... | stack_v2_sparse_classes_10k_train_007644 | 25,417 | no_license | [
{
"docstring": ":param X: Input DFT X :param fs: Input integer fs contains the sample frequency",
"name": "__init__",
"signature": "def __init__(self, X, fs)"
},
{
"docstring": "\\\\\\\\\\\\ METHOD: Compute the iDFT with truncated N/2+1 coefficients :return iDFT x of duration N from partial DFT ... | 2 | stack_v2_sparse_classes_30k_train_002343 | Implement the Python class `idft_p11` described below.
Class description:
idft Inverse Discrete Fourier transform.
Method signatures and docstrings:
- def __init__(self, X, fs): :param X: Input DFT X :param fs: Input integer fs contains the sample frequency
- def solve(self): \\\\\\ METHOD: Compute the iDFT with trun... | Implement the Python class `idft_p11` described below.
Class description:
idft Inverse Discrete Fourier transform.
Method signatures and docstrings:
- def __init__(self, X, fs): :param X: Input DFT X :param fs: Input integer fs contains the sample frequency
- def solve(self): \\\\\\ METHOD: Compute the iDFT with trun... | b72322cfc6d81c996117cea2160ee312da62d3ed | <|skeleton|>
class idft_p11:
"""idft Inverse Discrete Fourier transform."""
def __init__(self, X, fs):
""":param X: Input DFT X :param fs: Input integer fs contains the sample frequency"""
<|body_0|>
def solve(self):
"""\\\\\\ METHOD: Compute the iDFT with truncated N/2+1 coefficie... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class idft_p11:
"""idft Inverse Discrete Fourier transform."""
def __init__(self, X, fs):
""":param X: Input DFT X :param fs: Input integer fs contains the sample frequency"""
self.X = X
self.fs = fs
self.N = 2 * (len(self.X) - 1)
def solve(self):
"""\\\\\\ METHOD: ... | the_stack_v2_python_sparse | Inverse Discrete Fourier Transform/iDFT_main.py | FG-14/Signals-and-Information-Processing-DSP- | train | 0 |
3c5ccabd61ebb30610d25a6c995ccbfc4b730de5 | [
"pytest.importorskip('pysteps')\nshape = (30, 30)\nearlier_cube = set_up_test_cube(np.zeros(shape, dtype=np.float32), name='lwe_precipitation_rate', units='m s-1', time=datetime(2018, 2, 20, 4, 15))\nlater_cube = set_up_test_cube(np.zeros(shape, dtype=np.float32), name='lwe_precipitation_rate', units='m s-1', time=... | <|body_start_0|>
pytest.importorskip('pysteps')
shape = (30, 30)
earlier_cube = set_up_test_cube(np.zeros(shape, dtype=np.float32), name='lwe_precipitation_rate', units='m s-1', time=datetime(2018, 2, 20, 4, 15))
later_cube = set_up_test_cube(np.zeros(shape, dtype=np.float32), name='lwe_... | Tests for the generate_advection_velocities_from_winds function. Optical flow velocity values are tested within the Test_optical_flow module; this class tests metadata only. | Test_generate_advection_velocities_from_winds | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_generate_advection_velocities_from_winds:
"""Tests for the generate_advection_velocities_from_winds function. Optical flow velocity values are tested within the Test_optical_flow module; this class tests metadata only."""
def setUp(self):
"""Set up test input cubes"""
<|... | stack_v2_sparse_classes_10k_train_007645 | 5,214 | permissive | [
{
"docstring": "Set up test input cubes",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test function returns a cubelist with the expected components",
"name": "test_basic",
"signature": "def test_basic(self)"
},
{
"docstring": "Test output time coordinates a... | 4 | stack_v2_sparse_classes_30k_train_006436 | Implement the Python class `Test_generate_advection_velocities_from_winds` described below.
Class description:
Tests for the generate_advection_velocities_from_winds function. Optical flow velocity values are tested within the Test_optical_flow module; this class tests metadata only.
Method signatures and docstrings:... | Implement the Python class `Test_generate_advection_velocities_from_winds` described below.
Class description:
Tests for the generate_advection_velocities_from_winds function. Optical flow velocity values are tested within the Test_optical_flow module; this class tests metadata only.
Method signatures and docstrings:... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_generate_advection_velocities_from_winds:
"""Tests for the generate_advection_velocities_from_winds function. Optical flow velocity values are tested within the Test_optical_flow module; this class tests metadata only."""
def setUp(self):
"""Set up test input cubes"""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Test_generate_advection_velocities_from_winds:
"""Tests for the generate_advection_velocities_from_winds function. Optical flow velocity values are tested within the Test_optical_flow module; this class tests metadata only."""
def setUp(self):
"""Set up test input cubes"""
pytest.importor... | the_stack_v2_python_sparse | improver_tests/nowcasting/optical_flow/test_generate_advection_velocities_from_winds.py | metoppv/improver | train | 101 |
2ac79297d833e31d205c61d8eb0ecbc295ee55b7 | [
"datas1 = []\ndatas2 = []\nfor data in datas:\n dataF = FilterData.filterFunction_key_words(data)\n if dataF != None:\n datas1.append(dataF)\n else:\n datas2.append(data)\nreturn (datas1, datas2)",
"datas1 = []\ndatas2 = []\nfor data in datas:\n dataF = FilterData.filterFunction_site(dat... | <|body_start_0|>
datas1 = []
datas2 = []
for data in datas:
dataF = FilterData.filterFunction_key_words(data)
if dataF != None:
datas1.append(dataF)
else:
datas2.append(data)
return (datas1, datas2)
<|end_body_0|>
<|bod... | DataCuter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataCuter:
def dataCuterByKey(self, datas):
"""关键词过滤器"""
<|body_0|>
def dataCuterBySite(self, datas):
"""位置过滤器"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
datas1 = []
datas2 = []
for data in datas:
dataF = FilterData.... | stack_v2_sparse_classes_10k_train_007646 | 899 | no_license | [
{
"docstring": "关键词过滤器",
"name": "dataCuterByKey",
"signature": "def dataCuterByKey(self, datas)"
},
{
"docstring": "位置过滤器",
"name": "dataCuterBySite",
"signature": "def dataCuterBySite(self, datas)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000201 | Implement the Python class `DataCuter` described below.
Class description:
Implement the DataCuter class.
Method signatures and docstrings:
- def dataCuterByKey(self, datas): 关键词过滤器
- def dataCuterBySite(self, datas): 位置过滤器 | Implement the Python class `DataCuter` described below.
Class description:
Implement the DataCuter class.
Method signatures and docstrings:
- def dataCuterByKey(self, datas): 关键词过滤器
- def dataCuterBySite(self, datas): 位置过滤器
<|skeleton|>
class DataCuter:
def dataCuterByKey(self, datas):
"""关键词过滤器"""
... | 5eda21e66f65dd6f7f79e56441073bdcb7f18bdf | <|skeleton|>
class DataCuter:
def dataCuterByKey(self, datas):
"""关键词过滤器"""
<|body_0|>
def dataCuterBySite(self, datas):
"""位置过滤器"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DataCuter:
def dataCuterByKey(self, datas):
"""关键词过滤器"""
datas1 = []
datas2 = []
for data in datas:
dataF = FilterData.filterFunction_key_words(data)
if dataF != None:
datas1.append(dataF)
else:
datas2.append(d... | the_stack_v2_python_sparse | clientScrapySystem/DataFilterSystem/src/tool/DataCuter.py | jhfwb/Web-spiders | train | 0 | |
0bc268e0959ebd52db661aadc09388190f61175c | [
"super(BasicLinker, self).__init__()\nself.config = config\nself.encoder = encoder\nself.entity_embeddings = nn.Embedding(self.config.entity_size, self.config.embedding_dim)\nself.relu = nn.ReLU()",
"context_representation_affined = self.encoder(padded_left_contexts, left_context_lens, padded_right_contexts, righ... | <|body_start_0|>
super(BasicLinker, self).__init__()
self.config = config
self.encoder = encoder
self.entity_embeddings = nn.Embedding(self.config.entity_size, self.config.embedding_dim)
self.relu = nn.ReLU()
<|end_body_0|>
<|body_start_1|>
context_representation_affined... | BasicLinker | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicLinker:
def __init__(self, config, encoder):
""":param config: A config object that specifies the hyperparameters of the model :param encoder: A Encoder for encoding mentions"""
<|body_0|>
def forward(self, padded_left_contexts, left_context_lens, padded_right_contexts,... | stack_v2_sparse_classes_10k_train_007647 | 42,719 | permissive | [
{
"docstring": ":param config: A config object that specifies the hyperparameters of the model :param encoder: A Encoder for encoding mentions",
"name": "__init__",
"signature": "def __init__(self, config, encoder)"
},
{
"docstring": ":param mention: A mention object :return: unnormalized log pr... | 2 | stack_v2_sparse_classes_30k_train_006386 | Implement the Python class `BasicLinker` described below.
Class description:
Implement the BasicLinker class.
Method signatures and docstrings:
- def __init__(self, config, encoder): :param config: A config object that specifies the hyperparameters of the model :param encoder: A Encoder for encoding mentions
- def fo... | Implement the Python class `BasicLinker` described below.
Class description:
Implement the BasicLinker class.
Method signatures and docstrings:
- def __init__(self, config, encoder): :param config: A config object that specifies the hyperparameters of the model :param encoder: A Encoder for encoding mentions
- def fo... | 6a7dcd7d3756327c61ef949e5b4f6af6e2849187 | <|skeleton|>
class BasicLinker:
def __init__(self, config, encoder):
""":param config: A config object that specifies the hyperparameters of the model :param encoder: A Encoder for encoding mentions"""
<|body_0|>
def forward(self, padded_left_contexts, left_context_lens, padded_right_contexts,... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BasicLinker:
def __init__(self, config, encoder):
""":param config: A config object that specifies the hyperparameters of the model :param encoder: A Encoder for encoding mentions"""
super(BasicLinker, self).__init__()
self.config = config
self.encoder = encoder
self.en... | the_stack_v2_python_sparse | typenet/src/model.py | dhruvdcoder/dl-with-constraints | train | 0 | |
b4e0bf1f4f27cd4638cd77fd5aa0efdf7ace359f | [
"if not s:\n return True\nidx = t.find(s[0])\nt = t[idx:]\nm, n = (len(s), len(t))\ndp = [[False] * (n + 1) for _ in range(m + 1)]\ndp[0][0] = True\nfor i in range(1, m + 1):\n for j in range(1, n + 1):\n dp[i][j] = dp[i - 1][j - 1] or dp[i][j - 1] if s[i - 1] == t[j - 1] else dp[i][j - 1]\nreturn dp[-... | <|body_start_0|>
if not s:
return True
idx = t.find(s[0])
t = t[idx:]
m, n = (len(s), len(t))
dp = [[False] * (n + 1) for _ in range(m + 1)]
dp[0][0] = True
for i in range(1, m + 1):
for j in range(1, n + 1):
dp[i][j] = dp[i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSubsequence(self, s: str, t: str) -> bool:
"""先找出t中第一个s[0]出现的位置idx t=t[idx:] dp[i][j] 表示s前i个字符和t前j个字符是否匹配 abc ajhbdbc dp[i][j] =dp[i-1_最短回文串.py][j-1_最短回文串.py] or dp[i][j-1_最短回文串.py] if s[i-1_最短回文串.py]==t[j-1_最短回文串.py] =dp[i][j-1_最短回文串.py] dp[0][j] = False dp[i][0] = False... | stack_v2_sparse_classes_10k_train_007648 | 2,070 | no_license | [
{
"docstring": "先找出t中第一个s[0]出现的位置idx t=t[idx:] dp[i][j] 表示s前i个字符和t前j个字符是否匹配 abc ajhbdbc dp[i][j] =dp[i-1_最短回文串.py][j-1_最短回文串.py] or dp[i][j-1_最短回文串.py] if s[i-1_最短回文串.py]==t[j-1_最短回文串.py] =dp[i][j-1_最短回文串.py] dp[0][j] = False dp[i][0] = False dp[0][0] = True res = dp[-1_最短回文串.py][-1_最短回文串.py]",
"name": "isS... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSubsequence(self, s: str, t: str) -> bool: 先找出t中第一个s[0]出现的位置idx t=t[idx:] dp[i][j] 表示s前i个字符和t前j个字符是否匹配 abc ajhbdbc dp[i][j] =dp[i-1_最短回文串.py][j-1_最短回文串.py] or dp[i][j-1_最短回... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSubsequence(self, s: str, t: str) -> bool: 先找出t中第一个s[0]出现的位置idx t=t[idx:] dp[i][j] 表示s前i个字符和t前j个字符是否匹配 abc ajhbdbc dp[i][j] =dp[i-1_最短回文串.py][j-1_最短回文串.py] or dp[i][j-1_最短回... | 57f303aa6e76f7c5292fa60bffdfddcb4ff9ddfb | <|skeleton|>
class Solution:
def isSubsequence(self, s: str, t: str) -> bool:
"""先找出t中第一个s[0]出现的位置idx t=t[idx:] dp[i][j] 表示s前i个字符和t前j个字符是否匹配 abc ajhbdbc dp[i][j] =dp[i-1_最短回文串.py][j-1_最短回文串.py] or dp[i][j-1_最短回文串.py] if s[i-1_最短回文串.py]==t[j-1_最短回文串.py] =dp[i][j-1_最短回文串.py] dp[0][j] = False dp[i][0] = False... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isSubsequence(self, s: str, t: str) -> bool:
"""先找出t中第一个s[0]出现的位置idx t=t[idx:] dp[i][j] 表示s前i个字符和t前j个字符是否匹配 abc ajhbdbc dp[i][j] =dp[i-1_最短回文串.py][j-1_最短回文串.py] or dp[i][j-1_最短回文串.py] if s[i-1_最短回文串.py]==t[j-1_最短回文串.py] =dp[i][j-1_最短回文串.py] dp[0][j] = False dp[i][0] = False dp[0][0] = Tr... | the_stack_v2_python_sparse | 4_LEETCODE/2_DP/字符串匹配问题/392_判断子序列.py | fzingithub/SwordRefers2Offer | train | 1 | |
48c24ab6ba1f56814b406a9ddbcdf1235913f441 | [
"msg = cast(DefaultMessage, msg)\ndefault_msg = default_pb2.DefaultMessage()\ndefault_msg.message_id = msg.message_id\ndialogue_reference = msg.dialogue_reference\ndefault_msg.dialogue_starter_reference = dialogue_reference[0]\ndefault_msg.dialogue_responder_reference = dialogue_reference[1]\ndefault_msg.target = m... | <|body_start_0|>
msg = cast(DefaultMessage, msg)
default_msg = default_pb2.DefaultMessage()
default_msg.message_id = msg.message_id
dialogue_reference = msg.dialogue_reference
default_msg.dialogue_starter_reference = dialogue_reference[0]
default_msg.dialogue_responder_re... | Serialization for the 'default' protocol. | DefaultSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultSerializer:
"""Serialization for the 'default' protocol."""
def encode(msg: Message) -> bytes:
"""Encode a 'Default' message into bytes. :param msg: the message object. :return: the bytes."""
<|body_0|>
def decode(obj: bytes) -> Message:
"""Decode bytes in... | stack_v2_sparse_classes_10k_train_007649 | 4,510 | permissive | [
{
"docstring": "Encode a 'Default' message into bytes. :param msg: the message object. :return: the bytes.",
"name": "encode",
"signature": "def encode(msg: Message) -> bytes"
},
{
"docstring": "Decode bytes into a 'Default' message. :param obj: the bytes object. :return: the 'Default' message."... | 2 | stack_v2_sparse_classes_30k_train_000865 | Implement the Python class `DefaultSerializer` described below.
Class description:
Serialization for the 'default' protocol.
Method signatures and docstrings:
- def encode(msg: Message) -> bytes: Encode a 'Default' message into bytes. :param msg: the message object. :return: the bytes.
- def decode(obj: bytes) -> Mes... | Implement the Python class `DefaultSerializer` described below.
Class description:
Serialization for the 'default' protocol.
Method signatures and docstrings:
- def encode(msg: Message) -> bytes: Encode a 'Default' message into bytes. :param msg: the message object. :return: the bytes.
- def decode(obj: bytes) -> Mes... | 6411fcba8af2cdf55a3005939ae8129df92e8c3e | <|skeleton|>
class DefaultSerializer:
"""Serialization for the 'default' protocol."""
def encode(msg: Message) -> bytes:
"""Encode a 'Default' message into bytes. :param msg: the message object. :return: the bytes."""
<|body_0|>
def decode(obj: bytes) -> Message:
"""Decode bytes in... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DefaultSerializer:
"""Serialization for the 'default' protocol."""
def encode(msg: Message) -> bytes:
"""Encode a 'Default' message into bytes. :param msg: the message object. :return: the bytes."""
msg = cast(DefaultMessage, msg)
default_msg = default_pb2.DefaultMessage()
... | the_stack_v2_python_sparse | aea/protocols/default/serialization.py | ejfitzgerald/agents-aea | train | 0 |
62e1b3920cfb42c82371c959dcab2fee493d3d9a | [
"self.n_rows = n_rows\nself.n_cols = n_cols\nself.num = []\nfor i in range(n_rows):\n self.num.append([0] * n_cols)",
"sum_num = sum((sum(i) for i in self.num))\nif sum_num == self.n_rows * self.n_cols:\n return\nimport random\nwhile True:\n x = random.randint(0, self.n_rows - 1)\n y = random.randint(... | <|body_start_0|>
self.n_rows = n_rows
self.n_cols = n_cols
self.num = []
for i in range(n_rows):
self.num.append([0] * n_cols)
<|end_body_0|>
<|body_start_1|>
sum_num = sum((sum(i) for i in self.num))
if sum_num == self.n_rows * self.n_cols:
retur... | Solution1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution1:
def __init__(self, n_rows, n_cols):
""":type n_rows: int :type n_cols: int"""
<|body_0|>
def flip(self):
""":rtype: List[int]"""
<|body_1|>
def reset(self):
""":rtype: None"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_007650 | 1,777 | no_license | [
{
"docstring": ":type n_rows: int :type n_cols: int",
"name": "__init__",
"signature": "def __init__(self, n_rows, n_cols)"
},
{
"docstring": ":rtype: List[int]",
"name": "flip",
"signature": "def flip(self)"
},
{
"docstring": ":rtype: None",
"name": "reset",
"signature":... | 3 | stack_v2_sparse_classes_30k_val_000203 | Implement the Python class `Solution1` described below.
Class description:
Implement the Solution1 class.
Method signatures and docstrings:
- def __init__(self, n_rows, n_cols): :type n_rows: int :type n_cols: int
- def flip(self): :rtype: List[int]
- def reset(self): :rtype: None | Implement the Python class `Solution1` described below.
Class description:
Implement the Solution1 class.
Method signatures and docstrings:
- def __init__(self, n_rows, n_cols): :type n_rows: int :type n_cols: int
- def flip(self): :rtype: List[int]
- def reset(self): :rtype: None
<|skeleton|>
class Solution1:
... | 176cc1db3291843fb068f06d0180766dd8c3122c | <|skeleton|>
class Solution1:
def __init__(self, n_rows, n_cols):
""":type n_rows: int :type n_cols: int"""
<|body_0|>
def flip(self):
""":rtype: List[int]"""
<|body_1|>
def reset(self):
""":rtype: None"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution1:
def __init__(self, n_rows, n_cols):
""":type n_rows: int :type n_cols: int"""
self.n_rows = n_rows
self.n_cols = n_cols
self.num = []
for i in range(n_rows):
self.num.append([0] * n_cols)
def flip(self):
""":rtype: List[int]"""
... | the_stack_v2_python_sparse | 2019/sampling/random_flip_matrix_519.py | yehongyu/acode | train | 0 | |
79018f99c43d9acb3717d20a86edbd2675c4c362 | [
"super(SubGraphLayer, self).__init__()\nself.mlp_input_size = feature_length\nself.mlp_output_size = feature_length\nself.hidden_size = 64\nself.node_encoder = nn.Sequential(nn.Linear(self.mlp_input_size, self.hidden_size), nn.LayerNorm(self.hidden_size), nn.ReLU(True), nn.Linear(self.hidden_size, self.mlp_output_s... | <|body_start_0|>
super(SubGraphLayer, self).__init__()
self.mlp_input_size = feature_length
self.mlp_output_size = feature_length
self.hidden_size = 64
self.node_encoder = nn.Sequential(nn.Linear(self.mlp_input_size, self.hidden_size), nn.LayerNorm(self.hidden_size), nn.ReLU(True... | One layer of subgraph, include the MLP of g_enc. The calculation detail in this paper's 3.2 section. Input some vectors with 'feature_length' length, the output's length is '2*feature_length'(because of concat operator). | SubGraphLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubGraphLayer:
"""One layer of subgraph, include the MLP of g_enc. The calculation detail in this paper's 3.2 section. Input some vectors with 'feature_length' length, the output's length is '2*feature_length'(because of concat operator)."""
def __init__(self, feature_length):
""":pa... | stack_v2_sparse_classes_10k_train_007651 | 3,612 | no_license | [
{
"docstring": ":param feature_length: the length of input vector.",
"name": "__init__",
"signature": "def __init__(self, feature_length)"
},
{
"docstring": ":param x: A number of vectors. x.shape = [batch size, vNumber, feature_length] :return: All processed vectors with shape [batch size, vNum... | 2 | stack_v2_sparse_classes_30k_test_000300 | Implement the Python class `SubGraphLayer` described below.
Class description:
One layer of subgraph, include the MLP of g_enc. The calculation detail in this paper's 3.2 section. Input some vectors with 'feature_length' length, the output's length is '2*feature_length'(because of concat operator).
Method signatures ... | Implement the Python class `SubGraphLayer` described below.
Class description:
One layer of subgraph, include the MLP of g_enc. The calculation detail in this paper's 3.2 section. Input some vectors with 'feature_length' length, the output's length is '2*feature_length'(because of concat operator).
Method signatures ... | 0a314f7bdfc6db0247c92bc2c5c3806fdd18b885 | <|skeleton|>
class SubGraphLayer:
"""One layer of subgraph, include the MLP of g_enc. The calculation detail in this paper's 3.2 section. Input some vectors with 'feature_length' length, the output's length is '2*feature_length'(because of concat operator)."""
def __init__(self, feature_length):
""":pa... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SubGraphLayer:
"""One layer of subgraph, include the MLP of g_enc. The calculation detail in this paper's 3.2 section. Input some vectors with 'feature_length' length, the output's length is '2*feature_length'(because of concat operator)."""
def __init__(self, feature_length):
""":param feature_l... | the_stack_v2_python_sparse | sub_graph.py | JieFeng-cse/dynamic_driving | train | 1 |
73dc5a7d0ef009b7289a3b07bea55fc9b7055afd | [
"super(LinearND, self).__init__()\nself.dropout = dropout\nwith self.init_scope():\n self.fc = L.Linear(*size, nobias=not bias, initialW=None, initial_bias=None)\n if use_cuda:\n self.fc.to_gpu()",
"size = list(xs.shape)\nxs = xs.reshape(np.prod(size[:-1]), size[-1])\nxs = self.fc(xs)\nif self.dropou... | <|body_start_0|>
super(LinearND, self).__init__()
self.dropout = dropout
with self.init_scope():
self.fc = L.Linear(*size, nobias=not bias, initialW=None, initial_bias=None)
if use_cuda:
self.fc.to_gpu()
<|end_body_0|>
<|body_start_1|>
size = list... | LinearND | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearND:
def __init__(self, *size, bias=True, dropout=0, use_cuda=False):
"""A chainer.links.Linear layer modified to accept ND arrays. The function treats the last dimension of the input as the hidden dimension. Args: size (): bias (bool, optional): dropout (float, optional): use_cuda ... | stack_v2_sparse_classes_10k_train_007652 | 5,435 | no_license | [
{
"docstring": "A chainer.links.Linear layer modified to accept ND arrays. The function treats the last dimension of the input as the hidden dimension. Args: size (): bias (bool, optional): dropout (float, optional): use_cuda (bool, optional): if True, use GPUs",
"name": "__init__",
"signature": "def __... | 2 | stack_v2_sparse_classes_30k_train_006212 | Implement the Python class `LinearND` described below.
Class description:
Implement the LinearND class.
Method signatures and docstrings:
- def __init__(self, *size, bias=True, dropout=0, use_cuda=False): A chainer.links.Linear layer modified to accept ND arrays. The function treats the last dimension of the input as... | Implement the Python class `LinearND` described below.
Class description:
Implement the LinearND class.
Method signatures and docstrings:
- def __init__(self, *size, bias=True, dropout=0, use_cuda=False): A chainer.links.Linear layer modified to accept ND arrays. The function treats the last dimension of the input as... | b6b60a338d65bb369d0034f423feb09db10db8b7 | <|skeleton|>
class LinearND:
def __init__(self, *size, bias=True, dropout=0, use_cuda=False):
"""A chainer.links.Linear layer modified to accept ND arrays. The function treats the last dimension of the input as the hidden dimension. Args: size (): bias (bool, optional): dropout (float, optional): use_cuda ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LinearND:
def __init__(self, *size, bias=True, dropout=0, use_cuda=False):
"""A chainer.links.Linear layer modified to accept ND arrays. The function treats the last dimension of the input as the hidden dimension. Args: size (): bias (bool, optional): dropout (float, optional): use_cuda (bool, optiona... | the_stack_v2_python_sparse | models/chainer/linear.py | carolinebear/pytorch_end2end_speech_recognition | train | 0 | |
4a0521e733d7580ef3eba6519f3e26a369b68637 | [
"self.ratio = ratio\nself.kernel_size = 4\nsuper().__init__()",
"x, _ = equiangular_calculator(x, self.ratio)\nx = x.permute(0, 3, 1, 2)\nx = F.interpolate(x, scale_factor=(self.kernel_size, self.kernel_size), mode='nearest')\nx = reformat(x)\nreturn x"
] | <|body_start_0|>
self.ratio = ratio
self.kernel_size = 4
super().__init__()
<|end_body_0|>
<|body_start_1|>
x, _ = equiangular_calculator(x, self.ratio)
x = x.permute(0, 3, 1, 2)
x = F.interpolate(x, scale_factor=(self.kernel_size, self.kernel_size), mode='nearest')
... | EquiAngular Average Unpooling version 1 using the interpolate function when unpooling | EquiangularAvgUnpool | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EquiangularAvgUnpool:
"""EquiAngular Average Unpooling version 1 using the interpolate function when unpooling"""
def __init__(self, ratio):
"""Initialization Args: ratio (float): ratio between latitude and longitude dimensions of the data"""
<|body_0|>
def forward(self,... | stack_v2_sparse_classes_10k_train_007653 | 41,403 | no_license | [
{
"docstring": "Initialization Args: ratio (float): ratio between latitude and longitude dimensions of the data",
"name": "__init__",
"signature": "def __init__(self, ratio)"
},
{
"docstring": "calls pytorch's interpolate function to create the values while unpooling based on the nearby values A... | 2 | null | Implement the Python class `EquiangularAvgUnpool` described below.
Class description:
EquiAngular Average Unpooling version 1 using the interpolate function when unpooling
Method signatures and docstrings:
- def __init__(self, ratio): Initialization Args: ratio (float): ratio between latitude and longitude dimensions... | Implement the Python class `EquiangularAvgUnpool` described below.
Class description:
EquiAngular Average Unpooling version 1 using the interpolate function when unpooling
Method signatures and docstrings:
- def __init__(self, ratio): Initialization Args: ratio (float): ratio between latitude and longitude dimensions... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class EquiangularAvgUnpool:
"""EquiAngular Average Unpooling version 1 using the interpolate function when unpooling"""
def __init__(self, ratio):
"""Initialization Args: ratio (float): ratio between latitude and longitude dimensions of the data"""
<|body_0|>
def forward(self,... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EquiangularAvgUnpool:
"""EquiAngular Average Unpooling version 1 using the interpolate function when unpooling"""
def __init__(self, ratio):
"""Initialization Args: ratio (float): ratio between latitude and longitude dimensions of the data"""
self.ratio = ratio
self.kernel_size = ... | the_stack_v2_python_sparse | generated/test_deepsphere_deepsphere_pytorch.py | jansel/pytorch-jit-paritybench | train | 35 |
cef60fd4ba8cfacbde5250863002e2f5baea7091 | [
"def dist(p1, p2):\n return abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])\nheap = []\ngraph = {}\nfor i in range(len(points)):\n for j in range(i + 1, len(points)):\n d = dist(points[i], points[j])\n graph.setdefault(i, {})[j] = graph.setdefault(j, {})[i] = d\n heappush(heap, (d, i, j))\n\nclas... | <|body_start_0|>
def dist(p1, p2):
return abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])
heap = []
graph = {}
for i in range(len(points)):
for j in range(i + 1, len(points)):
d = dist(points[i], points[j])
graph.setdefault(i, {})[j] = grap... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minCostConnectPoints(self, points: List[List[int]]) -> int:
"""Time complexity: O(n^2*log(n^2)) Space complexity: O(n^2)"""
<|body_0|>
def minCostConnectPoints(self, points: List[List[int]]) -> int:
"""Time complexity: O(n^2) Space complexity: O(n^2)"""... | stack_v2_sparse_classes_10k_train_007654 | 6,484 | no_license | [
{
"docstring": "Time complexity: O(n^2*log(n^2)) Space complexity: O(n^2)",
"name": "minCostConnectPoints",
"signature": "def minCostConnectPoints(self, points: List[List[int]]) -> int"
},
{
"docstring": "Time complexity: O(n^2) Space complexity: O(n^2)",
"name": "minCostConnectPoints",
... | 3 | stack_v2_sparse_classes_30k_train_004816 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minCostConnectPoints(self, points: List[List[int]]) -> int: Time complexity: O(n^2*log(n^2)) Space complexity: O(n^2)
- def minCostConnectPoints(self, points: List[List[int]]... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minCostConnectPoints(self, points: List[List[int]]) -> int: Time complexity: O(n^2*log(n^2)) Space complexity: O(n^2)
- def minCostConnectPoints(self, points: List[List[int]]... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def minCostConnectPoints(self, points: List[List[int]]) -> int:
"""Time complexity: O(n^2*log(n^2)) Space complexity: O(n^2)"""
<|body_0|>
def minCostConnectPoints(self, points: List[List[int]]) -> int:
"""Time complexity: O(n^2) Space complexity: O(n^2)"""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def minCostConnectPoints(self, points: List[List[int]]) -> int:
"""Time complexity: O(n^2*log(n^2)) Space complexity: O(n^2)"""
def dist(p1, p2):
return abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])
heap = []
graph = {}
for i in range(len(points)):
... | the_stack_v2_python_sparse | leetcode/solved/1706_Min_Cost_to_Connect_All_Points/solution.py | sungminoh/algorithms | train | 0 | |
9f66a29b51c9a3ff4895581fd03cdeb5e6075cf4 | [
"result = []\nsynsets = wn.synsets(word)\nfor synset in synsets:\n hypernyms = synset.hypernyms()\n for hyp in hypernyms:\n result.append(hyp.lemmas()[0].name())\nreturn set(result)",
"results = []\nfor word in words:\n hypernyms = self.get_hypernyms_for_single_word(word)\n for hypernym in hype... | <|body_start_0|>
result = []
synsets = wn.synsets(word)
for synset in synsets:
hypernyms = synset.hypernyms()
for hyp in hypernyms:
result.append(hyp.lemmas()[0].name())
return set(result)
<|end_body_0|>
<|body_start_1|>
results = []
... | Generate labels for the topic models with wordnet. | ExtrensicTopicLabeler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtrensicTopicLabeler:
"""Generate labels for the topic models with wordnet."""
def get_hypernyms_for_single_word(self, word):
"""Get all the hypernyms for the synsets of a given word of a certain topic :param word: a word from a topic :type string :return: set of hypernyms for the g... | stack_v2_sparse_classes_10k_train_007655 | 3,555 | no_license | [
{
"docstring": "Get all the hypernyms for the synsets of a given word of a certain topic :param word: a word from a topic :type string :return: set of hypernyms for the given word",
"name": "get_hypernyms_for_single_word",
"signature": "def get_hypernyms_for_single_word(self, word)"
},
{
"docstr... | 4 | stack_v2_sparse_classes_30k_train_004641 | Implement the Python class `ExtrensicTopicLabeler` described below.
Class description:
Generate labels for the topic models with wordnet.
Method signatures and docstrings:
- def get_hypernyms_for_single_word(self, word): Get all the hypernyms for the synsets of a given word of a certain topic :param word: a word from... | Implement the Python class `ExtrensicTopicLabeler` described below.
Class description:
Generate labels for the topic models with wordnet.
Method signatures and docstrings:
- def get_hypernyms_for_single_word(self, word): Get all the hypernyms for the synsets of a given word of a certain topic :param word: a word from... | 34cdff618595bdb495d0cd7b23e543e089ea0c41 | <|skeleton|>
class ExtrensicTopicLabeler:
"""Generate labels for the topic models with wordnet."""
def get_hypernyms_for_single_word(self, word):
"""Get all the hypernyms for the synsets of a given word of a certain topic :param word: a word from a topic :type string :return: set of hypernyms for the g... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ExtrensicTopicLabeler:
"""Generate labels for the topic models with wordnet."""
def get_hypernyms_for_single_word(self, word):
"""Get all the hypernyms for the synsets of a given word of a certain topic :param word: a word from a topic :type string :return: set of hypernyms for the given word"""
... | the_stack_v2_python_sparse | src/Automati_Topic_Labeling_Wordnet/extrinsic_topic_labler.py | ga65duy/Topic-Modeling | train | 0 |
c2d9c3916d607173ab3c2bfd8cf464ac770f52ae | [
"self.continue_on_error = continue_on_error\nself.is_active = is_active\nself.script_params = script_params\nself.script_path = script_path\nself.timeout_secs = timeout_secs",
"if dictionary is None:\n return None\ncontinue_on_error = dictionary.get('continueOnError')\nis_active = dictionary.get('isActive')\ns... | <|body_start_0|>
self.continue_on_error = continue_on_error
self.is_active = is_active
self.script_params = script_params
self.script_path = script_path
self.timeout_secs = timeout_secs
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
... | Implementation of the 'RemoteScriptPathAndParams' model. Specifies the path to the remote script and any parameters expected by the remote script. Attributes: continue_on_error (bool): Specifies if the script needs to continue even if there is an occurence of an error. If this flag is set to true, then backup job will ... | RemoteScriptPathAndParams | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoteScriptPathAndParams:
"""Implementation of the 'RemoteScriptPathAndParams' model. Specifies the path to the remote script and any parameters expected by the remote script. Attributes: continue_on_error (bool): Specifies if the script needs to continue even if there is an occurence of an erro... | stack_v2_sparse_classes_10k_train_007656 | 3,261 | permissive | [
{
"docstring": "Constructor for the RemoteScriptPathAndParams class",
"name": "__init__",
"signature": "def __init__(self, continue_on_error=None, is_active=None, script_params=None, script_path=None, timeout_secs=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args... | 2 | stack_v2_sparse_classes_30k_train_001214 | Implement the Python class `RemoteScriptPathAndParams` described below.
Class description:
Implementation of the 'RemoteScriptPathAndParams' model. Specifies the path to the remote script and any parameters expected by the remote script. Attributes: continue_on_error (bool): Specifies if the script needs to continue e... | Implement the Python class `RemoteScriptPathAndParams` described below.
Class description:
Implementation of the 'RemoteScriptPathAndParams' model. Specifies the path to the remote script and any parameters expected by the remote script. Attributes: continue_on_error (bool): Specifies if the script needs to continue e... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RemoteScriptPathAndParams:
"""Implementation of the 'RemoteScriptPathAndParams' model. Specifies the path to the remote script and any parameters expected by the remote script. Attributes: continue_on_error (bool): Specifies if the script needs to continue even if there is an occurence of an erro... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RemoteScriptPathAndParams:
"""Implementation of the 'RemoteScriptPathAndParams' model. Specifies the path to the remote script and any parameters expected by the remote script. Attributes: continue_on_error (bool): Specifies if the script needs to continue even if there is an occurence of an error. If this fl... | the_stack_v2_python_sparse | cohesity_management_sdk/models/remote_script_path_and_params.py | cohesity/management-sdk-python | train | 24 |
86ea5b412a5051862aa5a8479f9359323b11a33f | [
"nodes_val = []\nstack = [root]\nwhile stack:\n node = stack.pop()\n nodes_val.append(node.val)\n if node.left:\n stack.append(node.left)\n if node.right:\n stack.append(node.right)\nnodes_val.sort()\nreturn min((nodes_val[i] - nodes_val[i - 1] for i in range(1, len(nodes_val))))",
"node... | <|body_start_0|>
nodes_val = []
stack = [root]
while stack:
node = stack.pop()
nodes_val.append(node.val)
if node.left:
stack.append(node.left)
if node.right:
stack.append(node.right)
nodes_val.sort()
... | Solution | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getMinimumDifference(self, root):
"""a general case, 52ms"""
<|body_0|>
def getMinimumDifference2(self, root):
"""BST, 48ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nodes_val = []
stack = [root]
while stack:
... | stack_v2_sparse_classes_10k_train_007657 | 1,556 | permissive | [
{
"docstring": "a general case, 52ms",
"name": "getMinimumDifference",
"signature": "def getMinimumDifference(self, root)"
},
{
"docstring": "BST, 48ms",
"name": "getMinimumDifference2",
"signature": "def getMinimumDifference2(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getMinimumDifference(self, root): a general case, 52ms
- def getMinimumDifference2(self, root): BST, 48ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getMinimumDifference(self, root): a general case, 52ms
- def getMinimumDifference2(self, root): BST, 48ms
<|skeleton|>
class Solution:
def getMinimumDifference(self, ro... | 49a0b03c55d8a702785888d473ef96539265ce9c | <|skeleton|>
class Solution:
def getMinimumDifference(self, root):
"""a general case, 52ms"""
<|body_0|>
def getMinimumDifference2(self, root):
"""BST, 48ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def getMinimumDifference(self, root):
"""a general case, 52ms"""
nodes_val = []
stack = [root]
while stack:
node = stack.pop()
nodes_val.append(node.val)
if node.left:
stack.append(node.left)
if node.righ... | the_stack_v2_python_sparse | leetcode/0530_minimum_absolute_difference_in_bst.py | chaosWsF/Python-Practice | train | 1 | |
78e5409add72f1ae97417a4b3d4b401c9ddd9989 | [
"Frame.__init__(self, master)\nself.master.rowconfigure(0, weight=1)\nself.master.columnconfigure(0, weight=1)\nself.grid(sticky=N + S + E + W)\nl0 = Label(self, text='Email Database Search', font=('Helvetica', 16))\nl0.grid(row=0, column=1, columnspan=2)\nl1 = Label(self, text='Not Before (yyy-mm-dd):')\nl1.grid(r... | <|body_start_0|>
Frame.__init__(self, master)
self.master.rowconfigure(0, weight=1)
self.master.columnconfigure(0, weight=1)
self.grid(sticky=N + S + E + W)
l0 = Label(self, text='Email Database Search', font=('Helvetica', 16))
l0.grid(row=0, column=1, columnspan=2)
... | Create window app signature | Application | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Application:
"""Create window app signature"""
def __init__(self, master=None):
"""Establish the window structure, leaving some widgets accessible as app instance variables. Connect button clicks to search_mail method and subject double-clicks to display_mail method."""
<|bod... | stack_v2_sparse_classes_10k_train_007658 | 5,775 | no_license | [
{
"docstring": "Establish the window structure, leaving some widgets accessible as app instance variables. Connect button clicks to search_mail method and subject double-clicks to display_mail method.",
"name": "__init__",
"signature": "def __init__(self, master=None)"
},
{
"docstring": "Take th... | 3 | stack_v2_sparse_classes_30k_train_003395 | Implement the Python class `Application` described below.
Class description:
Create window app signature
Method signatures and docstrings:
- def __init__(self, master=None): Establish the window structure, leaving some widgets accessible as app instance variables. Connect button clicks to search_mail method and subje... | Implement the Python class `Application` described below.
Class description:
Create window app signature
Method signatures and docstrings:
- def __init__(self, master=None): Establish the window structure, leaving some widgets accessible as app instance variables. Connect button clicks to search_mail method and subje... | 06c45545ed064d0e9c4fd15cc81cf454cb079c9d | <|skeleton|>
class Application:
"""Create window app signature"""
def __init__(self, master=None):
"""Establish the window structure, leaving some widgets accessible as app instance variables. Connect button clicks to search_mail method and subject double-clicks to display_mail method."""
<|bod... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Application:
"""Create window app signature"""
def __init__(self, master=None):
"""Establish the window structure, leaving some widgets accessible as app instance variables. Connect button clicks to search_mail method and subject double-clicks to display_mail method."""
Frame.__init__(sel... | the_stack_v2_python_sparse | Lesson 13 - Email Search and Display/mailgui.py | jmwoloso/Python_2 | train | 0 |
500e8ca776184cac5ece24ee08aa1b26a15bee2a | [
"points.sort()\nif len(points) < 4:\n return 0\nMAX = 10000\ndict_X = set()\nres = float('inf')\nfor i in range(len(points)):\n for j in range(len(points)):\n if points[i][0] == points[j][0] or points[i][1] == points[j][1]:\n continue\n if points[i][0] * MAX + points[j][1] in dict_X a... | <|body_start_0|>
points.sort()
if len(points) < 4:
return 0
MAX = 10000
dict_X = set()
res = float('inf')
for i in range(len(points)):
for j in range(len(points)):
if points[i][0] == points[j][0] or points[i][1] == points[j][1]:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minAreaRect(self, points):
""":type points: List[List[int]] :rtype: int"""
<|body_0|>
def minAreaRect2(self, points):
""":type points: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
points.sort()
if... | stack_v2_sparse_classes_10k_train_007659 | 1,437 | no_license | [
{
"docstring": ":type points: List[List[int]] :rtype: int",
"name": "minAreaRect",
"signature": "def minAreaRect(self, points)"
},
{
"docstring": ":type points: List[List[int]] :rtype: int",
"name": "minAreaRect2",
"signature": "def minAreaRect2(self, points)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000921 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAreaRect(self, points): :type points: List[List[int]] :rtype: int
- def minAreaRect2(self, points): :type points: List[List[int]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minAreaRect(self, points): :type points: List[List[int]] :rtype: int
- def minAreaRect2(self, points): :type points: List[List[int]] :rtype: int
<|skeleton|>
class Solution:... | 4105e18050b15fc0409c75353ad31be17187dd34 | <|skeleton|>
class Solution:
def minAreaRect(self, points):
""":type points: List[List[int]] :rtype: int"""
<|body_0|>
def minAreaRect2(self, points):
""":type points: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def minAreaRect(self, points):
""":type points: List[List[int]] :rtype: int"""
points.sort()
if len(points) < 4:
return 0
MAX = 10000
dict_X = set()
res = float('inf')
for i in range(len(points)):
for j in range(len(poin... | the_stack_v2_python_sparse | minAreaRect.py | NeilWangziyu/Leetcode_py | train | 2 | |
e1b7d4eb0e8d63e8c2b87014d01f9ed063b0139b | [
"super(ParsingFilter, self).__init__()\nself.config = config\ntry:\n if self.config['filter']['whitelist'] and self.config['filter']['blacklist']:\n _LOGGER.warning(_('Both whitelist and blacklist filters found in configuration. Only one can be used at a time - only the whitelist filter will be used.'))\n... | <|body_start_0|>
super(ParsingFilter, self).__init__()
self.config = config
try:
if self.config['filter']['whitelist'] and self.config['filter']['blacklist']:
_LOGGER.warning(_('Both whitelist and blacklist filters found in configuration. Only one can be used at a tim... | Class that filters logs. | ParsingFilter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParsingFilter:
"""Class that filters logs."""
def __init__(self, config, *parse_list):
"""Create object to implement filtering."""
<|body_0|>
def filter(self, record):
"""Apply filter to the log message. This is a subset of Logger.filter, this method applies the ... | stack_v2_sparse_classes_10k_train_007660 | 6,253 | permissive | [
{
"docstring": "Create object to implement filtering.",
"name": "__init__",
"signature": "def __init__(self, config, *parse_list)"
},
{
"docstring": "Apply filter to the log message. This is a subset of Logger.filter, this method applies the logger filters and returns a bool. If the value is tru... | 2 | stack_v2_sparse_classes_30k_test_000103 | Implement the Python class `ParsingFilter` described below.
Class description:
Class that filters logs.
Method signatures and docstrings:
- def __init__(self, config, *parse_list): Create object to implement filtering.
- def filter(self, record): Apply filter to the log message. This is a subset of Logger.filter, thi... | Implement the Python class `ParsingFilter` described below.
Class description:
Class that filters logs.
Method signatures and docstrings:
- def __init__(self, config, *parse_list): Create object to implement filtering.
- def filter(self, record): Apply filter to the log message. This is a subset of Logger.filter, thi... | 41246da2f6f379a889dadd1d3b4e139b65d3c9fb | <|skeleton|>
class ParsingFilter:
"""Class that filters logs."""
def __init__(self, config, *parse_list):
"""Create object to implement filtering."""
<|body_0|>
def filter(self, record):
"""Apply filter to the log message. This is a subset of Logger.filter, this method applies the ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ParsingFilter:
"""Class that filters logs."""
def __init__(self, config, *parse_list):
"""Create object to implement filtering."""
super(ParsingFilter, self).__init__()
self.config = config
try:
if self.config['filter']['whitelist'] and self.config['filter']['b... | the_stack_v2_python_sparse | opsdroid/logging.py | opsdroid/opsdroid | train | 835 |
0e103175b0aa04e41769df1903e096d4ac2d6596 | [
"print()\nprint('-+- ' * 40)\nlog.debug('ROUTE class : %s', self.__class__.__name__)\nlog.debug('payload : \\n{}'.format(pformat(ns.payload)))\nclaims = get_jwt_claims()\nlog.debug('claims : \\n %s', pformat(claims))\nupdated_doc, response_code = Query_db_update(ns, models, document_type, doc_id, claims, roles_for_... | <|body_start_0|>
print()
print('-+- ' * 40)
log.debug('ROUTE class : %s', self.__class__.__name__)
log.debug('payload : \n{}'.format(pformat(ns.payload)))
claims = get_jwt_claims()
log.debug('claims : \n %s', pformat(claims))
updated_doc, response_code = Query_db_... | rec edition : PUT - Updates document's infos DELETE - Let you delete document | Rec_edit | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rec_edit:
"""rec edition : PUT - Updates document's infos DELETE - Let you delete document"""
def put(self, doc_id):
"""Update a dmf in db > --- needs : a valid access_token in the header, field_to_update, field_value >>> returns : msg, doc data"""
<|body_0|>
def delete(... | stack_v2_sparse_classes_10k_train_007661 | 3,280 | permissive | [
{
"docstring": "Update a dmf in db > --- needs : a valid access_token in the header, field_to_update, field_value >>> returns : msg, doc data",
"name": "put",
"signature": "def put(self, doc_id)"
},
{
"docstring": "delete a rec in db > --- needs : a valid access_token (as admin or current user) ... | 2 | stack_v2_sparse_classes_30k_train_006403 | Implement the Python class `Rec_edit` described below.
Class description:
rec edition : PUT - Updates document's infos DELETE - Let you delete document
Method signatures and docstrings:
- def put(self, doc_id): Update a dmf in db > --- needs : a valid access_token in the header, field_to_update, field_value >>> retur... | Implement the Python class `Rec_edit` described below.
Class description:
rec edition : PUT - Updates document's infos DELETE - Let you delete document
Method signatures and docstrings:
- def put(self, doc_id): Update a dmf in db > --- needs : a valid access_token in the header, field_to_update, field_value >>> retur... | 08ba9151069f2f633461f5166b1954fdeac7854a | <|skeleton|>
class Rec_edit:
"""rec edition : PUT - Updates document's infos DELETE - Let you delete document"""
def put(self, doc_id):
"""Update a dmf in db > --- needs : a valid access_token in the header, field_to_update, field_value >>> returns : msg, doc data"""
<|body_0|>
def delete(... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Rec_edit:
"""rec edition : PUT - Updates document's infos DELETE - Let you delete document"""
def put(self, doc_id):
"""Update a dmf in db > --- needs : a valid access_token in the header, field_to_update, field_value >>> returns : msg, doc data"""
print()
print('-+- ' * 40)
... | the_stack_v2_python_sparse | solidata_api/api/api_recipes/endpoint_rec_edit.py | entrepreneur-interet-general/solidata_backend | train | 9 |
c4eeb9b0f2227bc32a04f2175c8f590c52cdba29 | [
"self.restore_to_original_site = restore_to_original_site\nself.site_owner_list = site_owner_list\nself.target_document_library_name = target_document_library_name\nself.target_document_library_prefix = target_document_library_prefix\nself.target_site = target_site",
"if dictionary is None:\n return None\nrest... | <|body_start_0|>
self.restore_to_original_site = restore_to_original_site
self.site_owner_list = site_owner_list
self.target_document_library_name = target_document_library_name
self.target_document_library_prefix = target_document_library_prefix
self.target_site = target_site
<|... | Implementation of the 'SharePointRestoreParameters' model. Specifies information needed for recovering SharePoint Site and items. Attributes: restore_to_original_site (bool): Specifies whether the objects are to be restored to the original drive. site_owner_list (list of SiteOwner): Specifies the list of SharePoint Sit... | SharePointRestoreParameters | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SharePointRestoreParameters:
"""Implementation of the 'SharePointRestoreParameters' model. Specifies information needed for recovering SharePoint Site and items. Attributes: restore_to_original_site (bool): Specifies whether the objects are to be restored to the original drive. site_owner_list (l... | stack_v2_sparse_classes_10k_train_007662 | 3,700 | permissive | [
{
"docstring": "Constructor for the SharePointRestoreParameters class",
"name": "__init__",
"signature": "def __init__(self, restore_to_original_site=None, site_owner_list=None, target_document_library_name=None, target_document_library_prefix=None, target_site=None)"
},
{
"docstring": "Creates ... | 2 | stack_v2_sparse_classes_30k_val_000359 | Implement the Python class `SharePointRestoreParameters` described below.
Class description:
Implementation of the 'SharePointRestoreParameters' model. Specifies information needed for recovering SharePoint Site and items. Attributes: restore_to_original_site (bool): Specifies whether the objects are to be restored to... | Implement the Python class `SharePointRestoreParameters` described below.
Class description:
Implementation of the 'SharePointRestoreParameters' model. Specifies information needed for recovering SharePoint Site and items. Attributes: restore_to_original_site (bool): Specifies whether the objects are to be restored to... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SharePointRestoreParameters:
"""Implementation of the 'SharePointRestoreParameters' model. Specifies information needed for recovering SharePoint Site and items. Attributes: restore_to_original_site (bool): Specifies whether the objects are to be restored to the original drive. site_owner_list (l... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SharePointRestoreParameters:
"""Implementation of the 'SharePointRestoreParameters' model. Specifies information needed for recovering SharePoint Site and items. Attributes: restore_to_original_site (bool): Specifies whether the objects are to be restored to the original drive. site_owner_list (list of SiteOw... | the_stack_v2_python_sparse | cohesity_management_sdk/models/share_point_restore_parameters.py | cohesity/management-sdk-python | train | 24 |
f68f8a4871ae7c81ef230e96f4cf236968a7e00d | [
"\"\"\":field\n Byte data of the sound.\n \"\"\"\nself.bytes: bytes = bytes(np.array(snd * 32767, dtype='int16'))\n':field\\n A base64 string of the sound. Send this to the build.\\n '\nself.wav_str = base64.b64encode(self.bytes).decode('utf-8')\n':field\\n The length of the byte ... | <|body_start_0|>
""":field
Byte data of the sound.
"""
self.bytes: bytes = bytes(np.array(snd * 32767, dtype='int16'))
':field\n A base64 string of the sound. Send this to the build.\n '
self.wav_str = base64.b64encode(self.bytes).decode('utf... | This class is used only in PyImpact, which has been deprecated. See: [`Clatter`](../add_ons/clatter.md). A sound encoded as a base64 string. | Base64Sound | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Base64Sound:
"""This class is used only in PyImpact, which has been deprecated. See: [`Clatter`](../add_ons/clatter.md). A sound encoded as a base64 string."""
def __init__(self, snd: np.ndarray):
""":param snd: The sound byte array."""
<|body_0|>
def write(self, path: U... | stack_v2_sparse_classes_10k_train_007663 | 1,305 | permissive | [
{
"docstring": ":param snd: The sound byte array.",
"name": "__init__",
"signature": "def __init__(self, snd: np.ndarray)"
},
{
"docstring": "Write audio to disk. :param path: The path to the .wav file.",
"name": "write",
"signature": "def write(self, path: Union[str, Path]) -> None"
}... | 2 | null | Implement the Python class `Base64Sound` described below.
Class description:
This class is used only in PyImpact, which has been deprecated. See: [`Clatter`](../add_ons/clatter.md). A sound encoded as a base64 string.
Method signatures and docstrings:
- def __init__(self, snd: np.ndarray): :param snd: The sound byte ... | Implement the Python class `Base64Sound` described below.
Class description:
This class is used only in PyImpact, which has been deprecated. See: [`Clatter`](../add_ons/clatter.md). A sound encoded as a base64 string.
Method signatures and docstrings:
- def __init__(self, snd: np.ndarray): :param snd: The sound byte ... | 9df96fba455b327bb360d8dd5886d8754046c690 | <|skeleton|>
class Base64Sound:
"""This class is used only in PyImpact, which has been deprecated. See: [`Clatter`](../add_ons/clatter.md). A sound encoded as a base64 string."""
def __init__(self, snd: np.ndarray):
""":param snd: The sound byte array."""
<|body_0|>
def write(self, path: U... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Base64Sound:
"""This class is used only in PyImpact, which has been deprecated. See: [`Clatter`](../add_ons/clatter.md). A sound encoded as a base64 string."""
def __init__(self, snd: np.ndarray):
""":param snd: The sound byte array."""
""":field
Byte data of the sound.
... | the_stack_v2_python_sparse | Python/tdw/physics_audio/base64_sound.py | threedworld-mit/tdw | train | 427 |
b2b1e3f407fe13f0ee61d12232288fbade0d0cb0 | [
"from collections import deque\nstack = deque()\nif root == None:\n return\nstack.appendleft(root)\nprev = None\nwhile len(stack) > 0:\n node = stack.popleft()\n if node.right != None:\n rnode = node.right\n stack.appendleft(rnode)\n if node.left != None:\n stack.appendleft(node.lef... | <|body_start_0|>
from collections import deque
stack = deque()
if root == None:
return
stack.appendleft(root)
prev = None
while len(stack) > 0:
node = stack.popleft()
if node.right != None:
rnode = node.right
... | Ex114 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ex114:
def flatten(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
<|body_0|>
def flatten0(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
<|... | stack_v2_sparse_classes_10k_train_007664 | 2,377 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead.",
"name": "flatten",
"signature": "def flatten(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead.",
"name": "flatten0",... | 2 | stack_v2_sparse_classes_30k_train_005104 | Implement the Python class `Ex114` described below.
Class description:
Implement the Ex114 class.
Method signatures and docstrings:
- def flatten(self, root): :type root: TreeNode :rtype: void Do not return anything, modify root in-place instead.
- def flatten0(self, root): :type root: TreeNode :rtype: void Do not re... | Implement the Python class `Ex114` described below.
Class description:
Implement the Ex114 class.
Method signatures and docstrings:
- def flatten(self, root): :type root: TreeNode :rtype: void Do not return anything, modify root in-place instead.
- def flatten0(self, root): :type root: TreeNode :rtype: void Do not re... | 8f9327a1879949f61b462cc6c82e00e7c27b8b07 | <|skeleton|>
class Ex114:
def flatten(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
<|body_0|>
def flatten0(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Ex114:
def flatten(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
from collections import deque
stack = deque()
if root == None:
return
stack.appendleft(root)
prev = None
while len(s... | the_stack_v2_python_sparse | LeetCode/Ex100/Ex114.py | JasonVann/CrackingCodingInterview | train | 0 | |
5a96c15aba770dd768ce1a2343c71c1dc6d79302 | [
"self.enable_worm_on_external_target = enable_worm_on_external_target\nself.policy_type = policy_type\nself.retention_secs = retention_secs\nself.version = version",
"if dictionary is None:\n return None\nenable_worm_on_external_target = dictionary.get('enableWormOnExternalTarget')\npolicy_type = dictionary.ge... | <|body_start_0|>
self.enable_worm_on_external_target = enable_worm_on_external_target
self.policy_type = policy_type
self.retention_secs = retention_secs
self.version = version
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
enable_worm_on_... | Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited from policy at successful backup run completion..... | WormRetentionProto | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WormRetentionProto:
"""Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited fro... | stack_v2_sparse_classes_10k_train_007665 | 3,033 | permissive | [
{
"docstring": "Constructor for the WormRetentionProto class",
"name": "__init__",
"signature": "def __init__(self, enable_worm_on_external_target=None, policy_type=None, retention_secs=None, version=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (... | 2 | stack_v2_sparse_classes_30k_train_005737 | Implement the Python class `WormRetentionProto` described below.
Class description:
Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. ba... | Implement the Python class `WormRetentionProto` described below.
Class description:
Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. ba... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class WormRetentionProto:
"""Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited fro... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WormRetentionProto:
"""Implementation of the 'WormRetentionProto' model. Message that specifies the WORM attributes. WORM attributes can be associated with any of the following: 1. backup policy: compliance or administrative policy with worm retention. 2. backup runs: worm retention inherited from policy at s... | the_stack_v2_python_sparse | cohesity_management_sdk/models/worm_retention_proto.py | cohesity/management-sdk-python | train | 24 |
84efe49c9c908c131952f8070c2d00f556ce3776 | [
"self.url = api_reverse('authentication:email_password')\nself.register_url = api_reverse('authentication:user-registration')\nself.user = {'user': {'username': 'kevin', 'email': 'koechkevin92@gmail.com', 'password': 'Kevin12345'}}\nself.client.post(self.register_url, self.user, format='json')\nUser.is_active = Tru... | <|body_start_0|>
self.url = api_reverse('authentication:email_password')
self.register_url = api_reverse('authentication:user-registration')
self.user = {'user': {'username': 'kevin', 'email': 'koechkevin92@gmail.com', 'password': 'Kevin12345'}}
self.client.post(self.register_url, self.u... | test for class to send password reset link to email | TestEmailSent | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEmailSent:
"""test for class to send password reset link to email"""
def setUp(self):
"""set up method to test email to be sent endpoint"""
<|body_0|>
def test_unregistered_email(self):
"""case where unregistered user tries to request a password"""
<|... | stack_v2_sparse_classes_10k_train_007666 | 7,001 | permissive | [
{
"docstring": "set up method to test email to be sent endpoint",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "case where unregistered user tries to request a password",
"name": "test_unregistered_email",
"signature": "def test_unregistered_email(self)"
},
{
... | 6 | stack_v2_sparse_classes_30k_train_006443 | Implement the Python class `TestEmailSent` described below.
Class description:
test for class to send password reset link to email
Method signatures and docstrings:
- def setUp(self): set up method to test email to be sent endpoint
- def test_unregistered_email(self): case where unregistered user tries to request a p... | Implement the Python class `TestEmailSent` described below.
Class description:
test for class to send password reset link to email
Method signatures and docstrings:
- def setUp(self): set up method to test email to be sent endpoint
- def test_unregistered_email(self): case where unregistered user tries to request a p... | a14ffcac494053ff338aa7f0a5524062964a49cc | <|skeleton|>
class TestEmailSent:
"""test for class to send password reset link to email"""
def setUp(self):
"""set up method to test email to be sent endpoint"""
<|body_0|>
def test_unregistered_email(self):
"""case where unregistered user tries to request a password"""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestEmailSent:
"""test for class to send password reset link to email"""
def setUp(self):
"""set up method to test email to be sent endpoint"""
self.url = api_reverse('authentication:email_password')
self.register_url = api_reverse('authentication:user-registration')
self.... | the_stack_v2_python_sparse | authors/apps/authentication/test_password_reset.py | andela/ah-shakas | train | 1 |
c7343e36430c63026d8a3c6cd5fe11726cb84ca3 | [
"logging.debug('Enabling user to execute test as root...')\ncommand = \"sed -i -e '$a{mark}\\\\n{user} ALL=NOPASSWD: /usr/bin/python' {filename}\".format(mark=self.MARK, user=os.getenv('SUDO_USER'), script=os.path.realpath(__file__), filename=self.SUDOERS)\nCommand(command, verbose=False).run()",
"logging.debug('... | <|body_start_0|>
logging.debug('Enabling user to execute test as root...')
command = "sed -i -e '$a{mark}\\n{user} ALL=NOPASSWD: /usr/bin/python' {filename}".format(mark=self.MARK, user=os.getenv('SUDO_USER'), script=os.path.realpath(__file__), filename=self.SUDOERS)
Command(command, verbose=Fal... | Enable/disable reboot test to be executed as root to make sure that reboot test works properly | SudoersConfigurator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SudoersConfigurator:
"""Enable/disable reboot test to be executed as root to make sure that reboot test works properly"""
def enable(self):
"""Make sure that user will be allowed to execute reboot test as root"""
<|body_0|>
def disable(self):
"""Revert sudoers co... | stack_v2_sparse_classes_10k_train_007667 | 33,067 | permissive | [
{
"docstring": "Make sure that user will be allowed to execute reboot test as root",
"name": "enable",
"signature": "def enable(self)"
},
{
"docstring": "Revert sudoers configuration changes",
"name": "disable",
"signature": "def disable(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000101 | Implement the Python class `SudoersConfigurator` described below.
Class description:
Enable/disable reboot test to be executed as root to make sure that reboot test works properly
Method signatures and docstrings:
- def enable(self): Make sure that user will be allowed to execute reboot test as root
- def disable(sel... | Implement the Python class `SudoersConfigurator` described below.
Class description:
Enable/disable reboot test to be executed as root to make sure that reboot test works properly
Method signatures and docstrings:
- def enable(self): Make sure that user will be allowed to execute reboot test as root
- def disable(sel... | 40ceac081f5181d01e188a5a1c40463d891203e6 | <|skeleton|>
class SudoersConfigurator:
"""Enable/disable reboot test to be executed as root to make sure that reboot test works properly"""
def enable(self):
"""Make sure that user will be allowed to execute reboot test as root"""
<|body_0|>
def disable(self):
"""Revert sudoers co... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SudoersConfigurator:
"""Enable/disable reboot test to be executed as root to make sure that reboot test works properly"""
def enable(self):
"""Make sure that user will be allowed to execute reboot test as root"""
logging.debug('Enabling user to execute test as root...')
command = ... | the_stack_v2_python_sparse | work/pm.py | sebastian-code/ideas_sueltas | train | 0 |
093cb63b9bbe69ba7e87c92ff6cdc3111d86a772 | [
"fields = list(super().get_fields(request, model_instance))\nordered_field_names = reversed(['notes', 'type', 'title', 'slug', 'summary', 'certainty', 'elaboration'])\nfor field_name in ordered_field_names:\n if field_name in fields:\n fields.remove(field_name)\n fields.insert(0, field_name)\nretur... | <|body_start_0|>
fields = list(super().get_fields(request, model_instance))
ordered_field_names = reversed(['notes', 'type', 'title', 'slug', 'summary', 'certainty', 'elaboration'])
for field_name in ordered_field_names:
if field_name in fields:
fields.remove(field_na... | Model admin for searchable models. | SearchableModelAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SearchableModelAdmin:
"""Model admin for searchable models."""
def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]:
"""Return reordered fields to be displayed in the admin."""
<|body_0|>
def get_fieldsets(self, requ... | stack_v2_sparse_classes_10k_train_007668 | 4,473 | no_license | [
{
"docstring": "Return reordered fields to be displayed in the admin.",
"name": "get_fields",
"signature": "def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]"
},
{
"docstring": "Return the fieldsets to be displayed in the admin form.",
... | 3 | null | Implement the Python class `SearchableModelAdmin` described below.
Class description:
Model admin for searchable models.
Method signatures and docstrings:
- def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]: Return reordered fields to be displayed in the admin... | Implement the Python class `SearchableModelAdmin` described below.
Class description:
Model admin for searchable models.
Method signatures and docstrings:
- def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]: Return reordered fields to be displayed in the admin... | 8bbdc8eec3622af22c17214051c34e36bea8e05a | <|skeleton|>
class SearchableModelAdmin:
"""Model admin for searchable models."""
def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]:
"""Return reordered fields to be displayed in the admin."""
<|body_0|>
def get_fieldsets(self, requ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SearchableModelAdmin:
"""Model admin for searchable models."""
def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]:
"""Return reordered fields to be displayed in the admin."""
fields = list(super().get_fields(request, model_instance)... | the_stack_v2_python_sparse | apps/search/admin.py | abdulwahed-mansour/modularhistory | train | 1 |
d8f9cbbdd2cd224519603aa535f863b40799f92c | [
"URLPlayer.__init__(self, show_lyrics=show_lyrics, dont_cache_search=dont_cache_search, no_cache=no_cache)\nNamePlayer.__init__(self, show_lyrics=show_lyrics, dont_cache_search=dont_cache_search, no_cache=no_cache, disable_kw=disable_kw)\nself._iterable_list = []\nself.data = data\nself.datatype = datatype\nself.pl... | <|body_start_0|>
URLPlayer.__init__(self, show_lyrics=show_lyrics, dont_cache_search=dont_cache_search, no_cache=no_cache)
NamePlayer.__init__(self, show_lyrics=show_lyrics, dont_cache_search=dont_cache_search, no_cache=no_cache, disable_kw=disable_kw)
self._iterable_list = []
self.data ... | Base class to play songs. Player will take different types of data, recognize them and play accordingly. Supported data types would be: Playlist URL Songname | Player | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Player:
"""Base class to play songs. Player will take different types of data, recognize them and play accordingly. Supported data types would be: Playlist URL Songname"""
def __init__(self, data, on_repeat, datatype=None, playlisttype=None, show_lyrics=False, dont_cache_search=False, no_cac... | stack_v2_sparse_classes_10k_train_007669 | 12,182 | permissive | [
{
"docstring": "data can be anything of the above supported types. If playlist then it is iterated over, if it is some other type then its simply sent to be played according to the player. datatype supports the following types: - playlist - song - URL",
"name": "__init__",
"signature": "def __init__(sel... | 6 | stack_v2_sparse_classes_30k_test_000217 | Implement the Python class `Player` described below.
Class description:
Base class to play songs. Player will take different types of data, recognize them and play accordingly. Supported data types would be: Playlist URL Songname
Method signatures and docstrings:
- def __init__(self, data, on_repeat, datatype=None, p... | Implement the Python class `Player` described below.
Class description:
Base class to play songs. Player will take different types of data, recognize them and play accordingly. Supported data types would be: Playlist URL Songname
Method signatures and docstrings:
- def __init__(self, data, on_repeat, datatype=None, p... | 9050f0c5f9fef7b9c9b14a7f26a055684e260d4c | <|skeleton|>
class Player:
"""Base class to play songs. Player will take different types of data, recognize them and play accordingly. Supported data types would be: Playlist URL Songname"""
def __init__(self, data, on_repeat, datatype=None, playlisttype=None, show_lyrics=False, dont_cache_search=False, no_cac... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Player:
"""Base class to play songs. Player will take different types of data, recognize them and play accordingly. Supported data types would be: Playlist URL Songname"""
def __init__(self, data, on_repeat, datatype=None, playlisttype=None, show_lyrics=False, dont_cache_search=False, no_cache=False, no_... | the_stack_v2_python_sparse | playx/player.py | NISH1001/playx | train | 229 |
2b87986897b4c1a914fc6d44e0fd0a9501f56877 | [
"self.num_eval_symbols = num_eval_symbols\nself.remove_end_of_line = remove_end_of_line\nresponse = requests.get(self.ENWIKI_URL, stream=True)\nwith ZipFile(BytesIO(response.content), 'r') as z:\n train, val, test = self._process(z.read('enwik8'))\nsuper().__init__(train, val, test, cache=cache, transform=transf... | <|body_start_0|>
self.num_eval_symbols = num_eval_symbols
self.remove_end_of_line = remove_end_of_line
response = requests.get(self.ENWIKI_URL, stream=True)
with ZipFile(BytesIO(response.content), 'r') as z:
train, val, test = self._process(z.read('enwik8'))
super()._... | The official WikiText103 dataset. | Enwiki8 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Enwiki8:
"""The official WikiText103 dataset."""
def __init__(self, num_eval_symbols: int=5000000, remove_end_of_line: bool=False, cache: bool=False, transform: Dict[str, Union[Field, Dict]]=None) -> None:
"""Initialize the Wiki103 built-in. Parameters ----------. num_eval_symbols: i... | stack_v2_sparse_classes_10k_train_007670 | 6,879 | permissive | [
{
"docstring": "Initialize the Wiki103 built-in. Parameters ----------. num_eval_symbols: int, optional The number of symbols to use for seach of validation, and testing. Default ``5000000``. remove_end_of_line: bool, optional If True, remove end of line tokens. Default ``True``. see TabularDataset for other ar... | 2 | stack_v2_sparse_classes_30k_train_003753 | Implement the Python class `Enwiki8` described below.
Class description:
The official WikiText103 dataset.
Method signatures and docstrings:
- def __init__(self, num_eval_symbols: int=5000000, remove_end_of_line: bool=False, cache: bool=False, transform: Dict[str, Union[Field, Dict]]=None) -> None: Initialize the Wik... | Implement the Python class `Enwiki8` described below.
Class description:
The official WikiText103 dataset.
Method signatures and docstrings:
- def __init__(self, num_eval_symbols: int=5000000, remove_end_of_line: bool=False, cache: bool=False, transform: Dict[str, Union[Field, Dict]]=None) -> None: Initialize the Wik... | 0dc2f5b2b286694defe8abf450fe5be9ae12c097 | <|skeleton|>
class Enwiki8:
"""The official WikiText103 dataset."""
def __init__(self, num_eval_symbols: int=5000000, remove_end_of_line: bool=False, cache: bool=False, transform: Dict[str, Union[Field, Dict]]=None) -> None:
"""Initialize the Wiki103 built-in. Parameters ----------. num_eval_symbols: i... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Enwiki8:
"""The official WikiText103 dataset."""
def __init__(self, num_eval_symbols: int=5000000, remove_end_of_line: bool=False, cache: bool=False, transform: Dict[str, Union[Field, Dict]]=None) -> None:
"""Initialize the Wiki103 built-in. Parameters ----------. num_eval_symbols: int, optional ... | the_stack_v2_python_sparse | flambe/nlp/language_modeling/datasets.py | cle-ros/flambe | train | 1 |
13322461e8e0238b7b5b14f835abcbe5fad75e0b | [
"self.courseID = courseID\nself.jobID = jobID\nself.submissionLanguage = submissionLanguage\nself.directoryFormat = directoryFormat\nself.baseFile = baseFile\nself.userEmail = userEmail\nself.toggleEmail = toggleEmail\nself.fRoot = fRoot\nself.fOut = fOut",
"path = os.path.join(dirname, 'folderizer.py')\nprint('s... | <|body_start_0|>
self.courseID = courseID
self.jobID = jobID
self.submissionLanguage = submissionLanguage
self.directoryFormat = directoryFormat
self.baseFile = baseFile
self.userEmail = userEmail
self.toggleEmail = toggleEmail
self.fRoot = fRoot
s... | The function of the jobRequest class is to unzip submission files, form the appropriate MOSS shell command, exceute it and receive output. The class handles the various states of the MOSS server. | jobRequest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class jobRequest:
"""The function of the jobRequest class is to unzip submission files, form the appropriate MOSS shell command, exceute it and receive output. The class handles the various states of the MOSS server."""
def __init__(self, courseID, jobID, submissionLanguage, directoryFormat, baseF... | stack_v2_sparse_classes_10k_train_007671 | 6,136 | no_license | [
{
"docstring": "Constructor of the Job Request that we submit to the MOSS server The job object takes 8 parameters namely: CourseID(course associated with user account) jobID(unique identifier of submitted job) submissionLanguage - Coding language utilised for code file submissions to MOSS directoryFormat - For... | 6 | stack_v2_sparse_classes_30k_train_004257 | Implement the Python class `jobRequest` described below.
Class description:
The function of the jobRequest class is to unzip submission files, form the appropriate MOSS shell command, exceute it and receive output. The class handles the various states of the MOSS server.
Method signatures and docstrings:
- def __init... | Implement the Python class `jobRequest` described below.
Class description:
The function of the jobRequest class is to unzip submission files, form the appropriate MOSS shell command, exceute it and receive output. The class handles the various states of the MOSS server.
Method signatures and docstrings:
- def __init... | 2c0d408a5ff930c34be669737ff4b6d0ae13da2f | <|skeleton|>
class jobRequest:
"""The function of the jobRequest class is to unzip submission files, form the appropriate MOSS shell command, exceute it and receive output. The class handles the various states of the MOSS server."""
def __init__(self, courseID, jobID, submissionLanguage, directoryFormat, baseF... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class jobRequest:
"""The function of the jobRequest class is to unzip submission files, form the appropriate MOSS shell command, exceute it and receive output. The class handles the various states of the MOSS server."""
def __init__(self, courseID, jobID, submissionLanguage, directoryFormat, baseFile, userEmai... | the_stack_v2_python_sparse | backend/SSRG/Jobs/MossBackendJobs/jobRequest.py | Suvanth/SSRG-Django-Website | train | 0 |
6a49f704aa0351a21a848b0fc40c7b451f35bdbd | [
"validated_data['bundle_name'] = validated_data.pop('name')\nvalidated_data['bundle_note'] = validated_data.pop('note')\nreturn Bundle.UploadNew(**validated_data)",
"project_name = validated_data.pop('project_name')\nbundle_name = instance.name\ndata = {'dst_bundle_name': validated_data.pop('new_name', None), 'no... | <|body_start_0|>
validated_data['bundle_name'] = validated_data.pop('name')
validated_data['bundle_note'] = validated_data.pop('note')
return Bundle.UploadNew(**validated_data)
<|end_body_0|>
<|body_start_1|>
project_name = validated_data.pop('project_name')
bundle_name = instan... | Serialize or deserialize Bundle objects. | BundleSerializer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BundleSerializer:
"""Serialize or deserialize Bundle objects."""
def create(self, validated_data):
"""Override parent's method."""
<|body_0|>
def update(self, instance, validated_data):
"""Override parent's method."""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_007672 | 6,625 | permissive | [
{
"docstring": "Override parent's method.",
"name": "create",
"signature": "def create(self, validated_data)"
},
{
"docstring": "Override parent's method.",
"name": "update",
"signature": "def update(self, instance, validated_data)"
}
] | 2 | null | Implement the Python class `BundleSerializer` described below.
Class description:
Serialize or deserialize Bundle objects.
Method signatures and docstrings:
- def create(self, validated_data): Override parent's method.
- def update(self, instance, validated_data): Override parent's method. | Implement the Python class `BundleSerializer` described below.
Class description:
Serialize or deserialize Bundle objects.
Method signatures and docstrings:
- def create(self, validated_data): Override parent's method.
- def update(self, instance, validated_data): Override parent's method.
<|skeleton|>
class BundleS... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class BundleSerializer:
"""Serialize or deserialize Bundle objects."""
def create(self, validated_data):
"""Override parent's method."""
<|body_0|>
def update(self, instance, validated_data):
"""Override parent's method."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BundleSerializer:
"""Serialize or deserialize Bundle objects."""
def create(self, validated_data):
"""Override parent's method."""
validated_data['bundle_name'] = validated_data.pop('name')
validated_data['bundle_note'] = validated_data.pop('note')
return Bundle.UploadNew(... | the_stack_v2_python_sparse | py/dome/backend/serializers.py | bridder/factory | train | 0 |
952c47525b3cb8ed2d97ec70f31695ae0a766477 | [
"seed(datetime.now())\nheight = randint(HEIGHT[0], HEIGHT[1])\nscratch = AVLHandler.from_scratch(height, POINT_CAP)\nstate = scratch.get_gamestate()\nhandler = AVLHandler.from_graph(state)\nreturn handler",
"successes = 0\nfailures = 0\niterations = NUM_CALLS\nfor _ in range(iterations):\n handler = self.new_h... | <|body_start_0|>
seed(datetime.now())
height = randint(HEIGHT[0], HEIGHT[1])
scratch = AVLHandler.from_scratch(height, POINT_CAP)
state = scratch.get_gamestate()
handler = AVLHandler.from_graph(state)
return handler
<|end_body_0|>
<|body_start_1|>
successes = 0
... | Test the state of the AVL tree upon generation from deserialization | AVLOldGeneration | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AVLOldGeneration:
"""Test the state of the AVL tree upon generation from deserialization"""
def new_handler():
"""create new handler to test"""
<|body_0|>
def test_golden_old(self):
"""make sure new avl is generated with correct golden node"""
<|body_1|>
... | stack_v2_sparse_classes_10k_train_007673 | 20,558 | permissive | [
{
"docstring": "create new handler to test",
"name": "new_handler",
"signature": "def new_handler()"
},
{
"docstring": "make sure new avl is generated with correct golden node",
"name": "test_golden_old",
"signature": "def test_golden_old(self)"
},
{
"docstring": "make sure nodes... | 4 | stack_v2_sparse_classes_30k_train_007017 | Implement the Python class `AVLOldGeneration` described below.
Class description:
Test the state of the AVL tree upon generation from deserialization
Method signatures and docstrings:
- def new_handler(): create new handler to test
- def test_golden_old(self): make sure new avl is generated with correct golden node
-... | Implement the Python class `AVLOldGeneration` described below.
Class description:
Test the state of the AVL tree upon generation from deserialization
Method signatures and docstrings:
- def new_handler(): create new handler to test
- def test_golden_old(self): make sure new avl is generated with correct golden node
-... | a47c849ea97763eff1005273a58aa3d8ab663ff2 | <|skeleton|>
class AVLOldGeneration:
"""Test the state of the AVL tree upon generation from deserialization"""
def new_handler():
"""create new handler to test"""
<|body_0|>
def test_golden_old(self):
"""make sure new avl is generated with correct golden node"""
<|body_1|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AVLOldGeneration:
"""Test the state of the AVL tree upon generation from deserialization"""
def new_handler():
"""create new handler to test"""
seed(datetime.now())
height = randint(HEIGHT[0], HEIGHT[1])
scratch = AVLHandler.from_scratch(height, POINT_CAP)
state = ... | the_stack_v2_python_sparse | game_board/avl/test_avl.py | Plongesam/data-structures-game | train | 2 |
ae30ef96654b959357e94a65d26d48da6c244672 | [
"self.image = tf.placeholder(dtype=tf.float32, shape=dataset.shape)\nself.model = model_class(tf.expand_dims(self.image, axis=0), trainable=False, num_classes=dataset.num_classes, **params)\nself.logits = self.model.logits[0]\nself.num_classes = self.logits.shape.as_list()[0]\nself.logits_grad = jacobian(self.logit... | <|body_start_0|>
self.image = tf.placeholder(dtype=tf.float32, shape=dataset.shape)
self.model = model_class(tf.expand_dims(self.image, axis=0), trainable=False, num_classes=dataset.num_classes, **params)
self.logits = self.model.logits[0]
self.num_classes = self.logits.shape.as_list()[0... | DeepfoolOp | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeepfoolOp:
def __init__(self, model_class, dataset, params):
"""Creates necessary ops for deepfool in the tensorflow graph Args: model_class: The class of the model to construct. Expects subclass of BasicModel dataset: The dataset to use. Only necessary for shape and number of classes p... | stack_v2_sparse_classes_10k_train_007674 | 3,133 | permissive | [
{
"docstring": "Creates necessary ops for deepfool in the tensorflow graph Args: model_class: The class of the model to construct. Expects subclass of BasicModel dataset: The dataset to use. Only necessary for shape and number of classes params: Additional parameters to pass to the model init",
"name": "__i... | 2 | stack_v2_sparse_classes_30k_train_002671 | Implement the Python class `DeepfoolOp` described below.
Class description:
Implement the DeepfoolOp class.
Method signatures and docstrings:
- def __init__(self, model_class, dataset, params): Creates necessary ops for deepfool in the tensorflow graph Args: model_class: The class of the model to construct. Expects s... | Implement the Python class `DeepfoolOp` described below.
Class description:
Implement the DeepfoolOp class.
Method signatures and docstrings:
- def __init__(self, model_class, dataset, params): Creates necessary ops for deepfool in the tensorflow graph Args: model_class: The class of the model to construct. Expects s... | 2aea27d28746c0726c2da6be21a51c92bf120b70 | <|skeleton|>
class DeepfoolOp:
def __init__(self, model_class, dataset, params):
"""Creates necessary ops for deepfool in the tensorflow graph Args: model_class: The class of the model to construct. Expects subclass of BasicModel dataset: The dataset to use. Only necessary for shape and number of classes p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DeepfoolOp:
def __init__(self, model_class, dataset, params):
"""Creates necessary ops for deepfool in the tensorflow graph Args: model_class: The class of the model to construct. Expects subclass of BasicModel dataset: The dataset to use. Only necessary for shape and number of classes params: Additio... | the_stack_v2_python_sparse | code/attacks/deepfool.py | Ichbinhippo/Adversarial-Attacks-on-CapsNets | train | 0 | |
462db16007ab5dca10b5b6043d85e46b16de3ffa | [
"result = {}\ntesting_mode = PyFunceble.cli.utils.testing.get_testing_mode()\nif testing_mode not in self.SUPPORTED_TEST_MODES:\n raise ValueError('<testing_mode> ({testing_mode!r}) is not supported.')\nfor registrar, value in self.dataset['counter'].items():\n if registrar == 'total':\n continue\n ... | <|body_start_0|>
result = {}
testing_mode = PyFunceble.cli.utils.testing.get_testing_mode()
if testing_mode not in self.SUPPORTED_TEST_MODES:
raise ValueError('<testing_mode> ({testing_mode!r}) is not supported.')
for registrar, value in self.dataset['counter'].items():
... | Provides our registrar stats counter. | RegistrarCounter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegistrarCounter:
"""Provides our registrar stats counter."""
def get_dataset_for_printer(self, *, limit: Optional[int]=15) -> List[Dict[str, Union[str, int]]]:
"""Provides the dataset that the printer may understand. :param limit: Maximum number of registrars to display. .. warning:... | stack_v2_sparse_classes_10k_train_007675 | 5,731 | permissive | [
{
"docstring": "Provides the dataset that the printer may understand. :param limit: Maximum number of registrars to display. .. warning:: If set to :code:`None`, all registrars will be displayed. :raise ValueError: When the current testing mode is not supported (yet?).",
"name": "get_dataset_for_printer",
... | 2 | stack_v2_sparse_classes_30k_train_004522 | Implement the Python class `RegistrarCounter` described below.
Class description:
Provides our registrar stats counter.
Method signatures and docstrings:
- def get_dataset_for_printer(self, *, limit: Optional[int]=15) -> List[Dict[str, Union[str, int]]]: Provides the dataset that the printer may understand. :param li... | Implement the Python class `RegistrarCounter` described below.
Class description:
Provides our registrar stats counter.
Method signatures and docstrings:
- def get_dataset_for_printer(self, *, limit: Optional[int]=15) -> List[Dict[str, Union[str, int]]]: Provides the dataset that the printer may understand. :param li... | 214a57d0eca3df7c4ed3421937aaff9998452ba6 | <|skeleton|>
class RegistrarCounter:
"""Provides our registrar stats counter."""
def get_dataset_for_printer(self, *, limit: Optional[int]=15) -> List[Dict[str, Union[str, int]]]:
"""Provides the dataset that the printer may understand. :param limit: Maximum number of registrars to display. .. warning:... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RegistrarCounter:
"""Provides our registrar stats counter."""
def get_dataset_for_printer(self, *, limit: Optional[int]=15) -> List[Dict[str, Union[str, int]]]:
"""Provides the dataset that the printer may understand. :param limit: Maximum number of registrars to display. .. warning:: If set to :... | the_stack_v2_python_sparse | PyFunceble/cli/filesystem/registrar_counter.py | funilrys/PyFunceble | train | 267 |
2ea15cf44b4fd5622fc0e931fe4ed287652c1b6c | [
"self.trec_eval_command = trec_eval_command\nself.relevant_metrics = relevant_metrics\nself.q_rel_file = q_rel_file\nself.temp_run_file = '/tmp/temp_run_by_carlos.run'\nself.run_file_exporter = RUN_File_Transform_Exporter(self.temp_run_file, model_name='temp_model_by_carlos')",
"self.run_file_exporter(samples)\nr... | <|body_start_0|>
self.trec_eval_command = trec_eval_command
self.relevant_metrics = relevant_metrics
self.q_rel_file = q_rel_file
self.temp_run_file = '/tmp/temp_run_by_carlos.run'
self.run_file_exporter = RUN_File_Transform_Exporter(self.temp_run_file, model_name='temp_model_by_... | TREC_Eval_Command_Experiment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TREC_Eval_Command_Experiment:
def __init__(self, trec_eval_command='trec_eval -q -c -M1000 -m ndcg_cut.3,5,10,15,20,100,1000 -m all_trec qRELS RUN_FILE', relevant_metrics=['ndcg_cut_3', 'ndcg_cut_5', 'ndcg_cut_1000', 'map_cut_1000', 'recall_500', 'recall_1000'], q_rel_file='datasets/TREC_CAsT/2... | stack_v2_sparse_classes_10k_train_007676 | 13,210 | no_license | [
{
"docstring": "This is an experiment transform that uses the official trec_eval command to compute scores for each query and return valid results according to the command specified.",
"name": "__init__",
"signature": "def __init__(self, trec_eval_command='trec_eval -q -c -M1000 -m ndcg_cut.3,5,10,15,2... | 2 | stack_v2_sparse_classes_30k_train_003255 | Implement the Python class `TREC_Eval_Command_Experiment` described below.
Class description:
Implement the TREC_Eval_Command_Experiment class.
Method signatures and docstrings:
- def __init__(self, trec_eval_command='trec_eval -q -c -M1000 -m ndcg_cut.3,5,10,15,20,100,1000 -m all_trec qRELS RUN_FILE', relevant_metr... | Implement the Python class `TREC_Eval_Command_Experiment` described below.
Class description:
Implement the TREC_Eval_Command_Experiment class.
Method signatures and docstrings:
- def __init__(self, trec_eval_command='trec_eval -q -c -M1000 -m ndcg_cut.3,5,10,15,20,100,1000 -m all_trec qRELS RUN_FILE', relevant_metr... | 92dd4d41ad6f2be5b5c4e296e2a355bb14b9a1db | <|skeleton|>
class TREC_Eval_Command_Experiment:
def __init__(self, trec_eval_command='trec_eval -q -c -M1000 -m ndcg_cut.3,5,10,15,20,100,1000 -m all_trec qRELS RUN_FILE', relevant_metrics=['ndcg_cut_3', 'ndcg_cut_5', 'ndcg_cut_1000', 'map_cut_1000', 'recall_500', 'recall_1000'], q_rel_file='datasets/TREC_CAsT/2... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TREC_Eval_Command_Experiment:
def __init__(self, trec_eval_command='trec_eval -q -c -M1000 -m ndcg_cut.3,5,10,15,20,100,1000 -m all_trec qRELS RUN_FILE', relevant_metrics=['ndcg_cut_3', 'ndcg_cut_5', 'ndcg_cut_1000', 'map_cut_1000', 'recall_500', 'recall_1000'], q_rel_file='datasets/TREC_CAsT/2020qrels.txt')... | the_stack_v2_python_sparse | notebooks/src/Experiments.py | carlos-gemmell/Glasgow-NLP | train | 0 | |
106f72fc320b7c2f5c3743091c3f0dfeb99a35b6 | [
"Version.objects.all().delete()\nTestModel.objects.all().delete()\nTestManyToManyModel.objects.all().delete()\nreversion.register(TestModel, follow=('testmanytomanymodel_set',))\nreversion.register(TestManyToManyModel, follow=('relations',))",
"with reversion.revision:\n test1 = TestModel.objects.create(name='... | <|body_start_0|>
Version.objects.all().delete()
TestModel.objects.all().delete()
TestManyToManyModel.objects.all().delete()
reversion.register(TestModel, follow=('testmanytomanymodel_set',))
reversion.register(TestManyToManyModel, follow=('relations',))
<|end_body_0|>
<|body_sta... | Tests the ManyToMany support. | ReversionManyToManyTest | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReversionManyToManyTest:
"""Tests the ManyToMany support."""
def setUp(self):
"""Sets up the TestModel."""
<|body_0|>
def testCanCreateRevision(self):
"""Tests that a revision containing both models is created."""
<|body_1|>
def testCanCreateRevision... | stack_v2_sparse_classes_10k_train_007677 | 24,355 | permissive | [
{
"docstring": "Sets up the TestModel.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Tests that a revision containing both models is created.",
"name": "testCanCreateRevision",
"signature": "def testCanCreateRevision(self)"
},
{
"docstring": "Tests that a r... | 6 | null | Implement the Python class `ReversionManyToManyTest` described below.
Class description:
Tests the ManyToMany support.
Method signatures and docstrings:
- def setUp(self): Sets up the TestModel.
- def testCanCreateRevision(self): Tests that a revision containing both models is created.
- def testCanCreateRevisionRela... | Implement the Python class `ReversionManyToManyTest` described below.
Class description:
Tests the ManyToMany support.
Method signatures and docstrings:
- def setUp(self): Sets up the TestModel.
- def testCanCreateRevision(self): Tests that a revision containing both models is created.
- def testCanCreateRevisionRela... | abc3fbfb34f791f84e9a9d4dc522966421778ab2 | <|skeleton|>
class ReversionManyToManyTest:
"""Tests the ManyToMany support."""
def setUp(self):
"""Sets up the TestModel."""
<|body_0|>
def testCanCreateRevision(self):
"""Tests that a revision containing both models is created."""
<|body_1|>
def testCanCreateRevision... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ReversionManyToManyTest:
"""Tests the ManyToMany support."""
def setUp(self):
"""Sets up the TestModel."""
Version.objects.all().delete()
TestModel.objects.all().delete()
TestManyToManyModel.objects.all().delete()
reversion.register(TestModel, follow=('testmanytoma... | the_stack_v2_python_sparse | py/django_tools/django-reversion/src/reversion/tests.py | marceltoben/evandrix.github.com | train | 3 |
9bc0e5788345cca6d77b7b1463622f186d7ab828 | [
"method_map = kwargs['method_map'] if kwargs.get('method_map', None) else self.method_map\nfor request_method, mapped_method in method_map.items():\n mapped_method = getattr(self, mapped_method)\n method_proxy = self.view_proxy(mapped_method)\n setattr(self, request_method, method_proxy)\nsuper(APIMethodMa... | <|body_start_0|>
method_map = kwargs['method_map'] if kwargs.get('method_map', None) else self.method_map
for request_method, mapped_method in method_map.items():
mapped_method = getattr(self, mapped_method)
method_proxy = self.view_proxy(mapped_method)
setattr(self, ... | 将请求方法映射到子类属性上 :method_map: dict, 方法映射字典。 如将 get 方法映射到 list 方法，其值则为 {'get':'list'} | APIMethodMapMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class APIMethodMapMixin:
"""将请求方法映射到子类属性上 :method_map: dict, 方法映射字典。 如将 get 方法映射到 list 方法，其值则为 {'get':'list'}"""
def __init__(self, *args, **kwargs):
"""映射请求方法。会从传入子类的关键字参数中寻找 method_map 参数，期望值为 dict类型。寻找对应参数值。 若在类属性和传入参数中同时定义了 method_map ，则以传入参数为准。 :param args: 传入的位置参数 :param kwargs: 传入的关... | stack_v2_sparse_classes_10k_train_007678 | 9,950 | no_license | [
{
"docstring": "映射请求方法。会从传入子类的关键字参数中寻找 method_map 参数，期望值为 dict类型。寻找对应参数值。 若在类属性和传入参数中同时定义了 method_map ，则以传入参数为准。 :param args: 传入的位置参数 :param kwargs: 传入的关键字参数",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "代理被映射方法，并代理接收传入视图函数的其他参数。 :param mapped_method... | 2 | stack_v2_sparse_classes_30k_train_003179 | Implement the Python class `APIMethodMapMixin` described below.
Class description:
将请求方法映射到子类属性上 :method_map: dict, 方法映射字典。 如将 get 方法映射到 list 方法，其值则为 {'get':'list'}
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): 映射请求方法。会从传入子类的关键字参数中寻找 method_map 参数，期望值为 dict类型。寻找对应参数值。 若在类属性和传入参数中同时定义了 metho... | Implement the Python class `APIMethodMapMixin` described below.
Class description:
将请求方法映射到子类属性上 :method_map: dict, 方法映射字典。 如将 get 方法映射到 list 方法，其值则为 {'get':'list'}
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): 映射请求方法。会从传入子类的关键字参数中寻找 method_map 参数，期望值为 dict类型。寻找对应参数值。 若在类属性和传入参数中同时定义了 metho... | 9821516dc739a1fdeca688454831728b64461999 | <|skeleton|>
class APIMethodMapMixin:
"""将请求方法映射到子类属性上 :method_map: dict, 方法映射字典。 如将 get 方法映射到 list 方法，其值则为 {'get':'list'}"""
def __init__(self, *args, **kwargs):
"""映射请求方法。会从传入子类的关键字参数中寻找 method_map 参数，期望值为 dict类型。寻找对应参数值。 若在类属性和传入参数中同时定义了 method_map ，则以传入参数为准。 :param args: 传入的位置参数 :param kwargs: 传入的关... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class APIMethodMapMixin:
"""将请求方法映射到子类属性上 :method_map: dict, 方法映射字典。 如将 get 方法映射到 list 方法，其值则为 {'get':'list'}"""
def __init__(self, *args, **kwargs):
"""映射请求方法。会从传入子类的关键字参数中寻找 method_map 参数，期望值为 dict类型。寻找对应参数值。 若在类属性和传入参数中同时定义了 method_map ，则以传入参数为准。 :param args: 传入的位置参数 :param kwargs: 传入的关键字参数"""
... | the_stack_v2_python_sparse | online_intepreter/mixins.py | Arithmeticjia/MyBlog | train | 5 |
600b474162e535fa590fe388601d73e476261085 | [
"super().__init__()\nself.add_module('conv', Conv(in_channels=in_feats, out_channels=out_feats, kernel_size=3, stride=1, padding=1, bias=False))\nself.add_module('norm', Norm(out_feats))\nself.add_module('relu', nn.ReLU(inplace=True))",
"x = self.conv(x)\nx = self.norm(x)\nx = self.relu(x)\nreturn x"
] | <|body_start_0|>
super().__init__()
self.add_module('conv', Conv(in_channels=in_feats, out_channels=out_feats, kernel_size=3, stride=1, padding=1, bias=False))
self.add_module('norm', Norm(out_feats))
self.add_module('relu', nn.ReLU(inplace=True))
<|end_body_0|>
<|body_start_1|>
... | A block consisting of a convolutional layer followed by batch normalization and ReLU activation. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Module): A convolutional layer. | _ConvBlock | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ConvBlock:
"""A block consisting of a convolutional layer followed by batch normalization and ReLU activation. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Module): A convolutional layer."""
def... | stack_v2_sparse_classes_10k_train_007679 | 24,719 | permissive | [
{
"docstring": "Initializes the ConvBlock. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Module): A convolutional layer.",
"name": "__init__",
"signature": "def __init__(self, in_feats, out_feats, Norm, Conv)... | 2 | stack_v2_sparse_classes_30k_train_000712 | Implement the Python class `_ConvBlock` described below.
Class description:
A block consisting of a convolutional layer followed by batch normalization and ReLU activation. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Modu... | Implement the Python class `_ConvBlock` described below.
Class description:
A block consisting of a convolutional layer followed by batch normalization and ReLU activation. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Modu... | 72eb99f68205afd5f8d49a3bb6cfc08cfd467582 | <|skeleton|>
class _ConvBlock:
"""A block consisting of a convolutional layer followed by batch normalization and ReLU activation. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Module): A convolutional layer."""
def... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _ConvBlock:
"""A block consisting of a convolutional layer followed by batch normalization and ReLU activation. Args: in_feats (int): Number of input features. out_feats (int): Number of output features. Norm (nn.Module): A normalization layer. Conv (nn.Module): A convolutional layer."""
def __init__(sel... | the_stack_v2_python_sparse | GANDLF/models/unetr.py | mlcommons/GaNDLF | train | 45 |
802b140501e2e4fa4a08c5199ede2efb439cba17 | [
"dp = [i for i in range(n + 1)]\nsquares = [i ** 2 for i in range(1, int(n ** 0.5) + 1)]\nfor i in range(1, n + 1):\n for s in squares:\n if i < s:\n break\n dp[i] = min(dp[i], dp[i - s] + 1)\nreturn dp[-1]",
"visited = [False] * (n + 1)\nqueue = [n]\nsquares = {i: i ** 2 for i in rang... | <|body_start_0|>
dp = [i for i in range(n + 1)]
squares = [i ** 2 for i in range(1, int(n ** 0.5) + 1)]
for i in range(1, n + 1):
for s in squares:
if i < s:
break
dp[i] = min(dp[i], dp[i - s] + 1)
return dp[-1]
<|end_body_0... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numSquares(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numSquaresBFS(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
dp = [i for i in range(n + 1)]
squares = [i ** 2 for i in ra... | stack_v2_sparse_classes_10k_train_007680 | 1,657 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "numSquares",
"signature": "def numSquares(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "numSquaresBFS",
"signature": "def numSquaresBFS(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000548 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares(self, n): :type n: int :rtype: int
- def numSquaresBFS(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares(self, n): :type n: int :rtype: int
- def numSquaresBFS(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def numSquares(self, n):
""":t... | ac53dd9bf2c4c9d17c9dc5f7fdda32e386658fdd | <|skeleton|>
class Solution:
def numSquares(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numSquaresBFS(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def numSquares(self, n):
""":type n: int :rtype: int"""
dp = [i for i in range(n + 1)]
squares = [i ** 2 for i in range(1, int(n ** 0.5) + 1)]
for i in range(1, n + 1):
for s in squares:
if i < s:
break
d... | the_stack_v2_python_sparse | cs_notes/BFS/perfect_squares.py | hwc1824/LeetCodeSolution | train | 0 | |
a4bd136857769352d181965d0072472887e8c4bd | [
"self.batch_size = batch_size\nself.is_training = is_training\nself.latent_dim = latent_dim\nself.output_cells_dim = output_cells_dim\nself.var_scope = var_scope\nself.gen_layers = gen_layers\nself.output_lsn = output_lsn\nself.gen_cond_type = gen_cond_type\nself.clusters_no = clusters_no\nself.input_clusters = inp... | <|body_start_0|>
self.batch_size = batch_size
self.is_training = is_training
self.latent_dim = latent_dim
self.output_cells_dim = output_cells_dim
self.var_scope = var_scope
self.gen_layers = gen_layers
self.output_lsn = output_lsn
self.gen_cond_type = gen... | Generic class for the Generator network. | Generator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
"""Generic class for the Generator network."""
def __init__(self, fake_outputs, batch_size, latent_dim, output_cells_dim, var_scope, gen_layers, output_lsn, gen_cond_type=None, is_training=None, clusters_ratios=None, clusters_no=None, input_clusters=None, reuse=None):
"""D... | stack_v2_sparse_classes_10k_train_007681 | 29,633 | permissive | [
{
"docstring": "Default constructor. Parameters ---------- fake_outputs : Tensor Tensor holding the output of the generator. batch_size : int Batch size used during the training. latent_dim : int Dimension of the latent space used from which the input noise of the generator is sampled. output_cells_dim : int Di... | 3 | stack_v2_sparse_classes_30k_train_006613 | Implement the Python class `Generator` described below.
Class description:
Generic class for the Generator network.
Method signatures and docstrings:
- def __init__(self, fake_outputs, batch_size, latent_dim, output_cells_dim, var_scope, gen_layers, output_lsn, gen_cond_type=None, is_training=None, clusters_ratios=No... | Implement the Python class `Generator` described below.
Class description:
Generic class for the Generator network.
Method signatures and docstrings:
- def __init__(self, fake_outputs, batch_size, latent_dim, output_cells_dim, var_scope, gen_layers, output_lsn, gen_cond_type=None, is_training=None, clusters_ratios=No... | a06f8ccd6a071d57e591dacd6164c9f78987a794 | <|skeleton|>
class Generator:
"""Generic class for the Generator network."""
def __init__(self, fake_outputs, batch_size, latent_dim, output_cells_dim, var_scope, gen_layers, output_lsn, gen_cond_type=None, is_training=None, clusters_ratios=None, clusters_no=None, input_clusters=None, reuse=None):
"""D... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Generator:
"""Generic class for the Generator network."""
def __init__(self, fake_outputs, batch_size, latent_dim, output_cells_dim, var_scope, gen_layers, output_lsn, gen_cond_type=None, is_training=None, clusters_ratios=None, clusters_no=None, input_clusters=None, reuse=None):
"""Default constr... | the_stack_v2_python_sparse | estimators/utilities.py | imsb-uke/scGAN | train | 73 |
93e5989775c1779db6095dc63156b6743d503b4e | [
"author = g.user\ntag = TagModel.query.get(tag_id)\nif not tag:\n abort(404, error=f'Tag with id={tag_id} not found')\nif tag.author != author:\n abort(403, error=f'Access denied to tag with id={tag_id}')\nreturn (tag, 200)",
"author = g.user\ntag = TagModel.query.get(tag_id)\nif not tag:\n abort(404, er... | <|body_start_0|>
author = g.user
tag = TagModel.query.get(tag_id)
if not tag:
abort(404, error=f'Tag with id={tag_id} not found')
if tag.author != author:
abort(403, error=f'Access denied to tag with id={tag_id}')
return (tag, 200)
<|end_body_0|>
<|body_s... | TagResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TagResource:
def get(self, tag_id):
"""Возвращает тег по id. Требуется аутентификация. :param tag_id: id тега :return: тег"""
<|body_0|>
def put(self, tag_id, **kwargs):
"""Изменяет тег по id. Требуется аутентификация. :param tag_id: id тега :param kwargs: параметры ... | stack_v2_sparse_classes_10k_train_007682 | 3,928 | no_license | [
{
"docstring": "Возвращает тег по id. Требуется аутентификация. :param tag_id: id тега :return: тег",
"name": "get",
"signature": "def get(self, tag_id)"
},
{
"docstring": "Изменяет тег по id. Требуется аутентификация. :param tag_id: id тега :param kwargs: параметры для изменения тега :return: т... | 3 | stack_v2_sparse_classes_30k_val_000114 | Implement the Python class `TagResource` described below.
Class description:
Implement the TagResource class.
Method signatures and docstrings:
- def get(self, tag_id): Возвращает тег по id. Требуется аутентификация. :param tag_id: id тега :return: тег
- def put(self, tag_id, **kwargs): Изменяет тег по id. Требуется ... | Implement the Python class `TagResource` described below.
Class description:
Implement the TagResource class.
Method signatures and docstrings:
- def get(self, tag_id): Возвращает тег по id. Требуется аутентификация. :param tag_id: id тега :return: тег
- def put(self, tag_id, **kwargs): Изменяет тег по id. Требуется ... | adb9a3f4524ab76e8ba656344e2ed452e87b577c | <|skeleton|>
class TagResource:
def get(self, tag_id):
"""Возвращает тег по id. Требуется аутентификация. :param tag_id: id тега :return: тег"""
<|body_0|>
def put(self, tag_id, **kwargs):
"""Изменяет тег по id. Требуется аутентификация. :param tag_id: id тега :param kwargs: параметры ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TagResource:
def get(self, tag_id):
"""Возвращает тег по id. Требуется аутентификация. :param tag_id: id тега :return: тег"""
author = g.user
tag = TagModel.query.get(tag_id)
if not tag:
abort(404, error=f'Tag with id={tag_id} not found')
if tag.author != au... | the_stack_v2_python_sparse | api/resources/tag.py | UshakovAleksandr/Blog | train | 1 | |
fce13c1fc668522367ea9f8bbe0eb33620fc9437 | [
"self.webAddress = webAddress\nself.redirectAddress = redirectAddress\nself.clientId = clientId\nself.clientSecret = clientSecret\nif type(scopes) == list:\n self.scopes = ''\n for scope in scopes:\n self.scopes += '{} '.format(scope)\nelif type(scopes) == str:\n self.scopes = scopes\nelse:\n rai... | <|body_start_0|>
self.webAddress = webAddress
self.redirectAddress = redirectAddress
self.clientId = clientId
self.clientSecret = clientSecret
if type(scopes) == list:
self.scopes = ''
for scope in scopes:
self.scopes += '{} '.format(scope)... | Class to negotiate the authentication with the Autodesk Forge Api Authentication servers. | OAuth2Negotiator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OAuth2Negotiator:
"""Class to negotiate the authentication with the Autodesk Forge Api Authentication servers."""
def __init__(self, webAddress, clientId, clientSecret, scopes, redirectAddress=None, endAddress=None, timeout=1):
"""Initialize the OAuth2Negotiator class and assign the ... | stack_v2_sparse_classes_10k_train_007683 | 3,381 | permissive | [
{
"docstring": "Initialize the OAuth2Negotiator class and assign the needed parameters for authentication. Args: webAddress (str): Web address for the Autodesk Forge API authentication server. clientId (str): Client id for the Forge App this authentication is used for. clientSecret (str): Client secret for the ... | 2 | stack_v2_sparse_classes_30k_train_005956 | Implement the Python class `OAuth2Negotiator` described below.
Class description:
Class to negotiate the authentication with the Autodesk Forge Api Authentication servers.
Method signatures and docstrings:
- def __init__(self, webAddress, clientId, clientSecret, scopes, redirectAddress=None, endAddress=None, timeout=... | Implement the Python class `OAuth2Negotiator` described below.
Class description:
Class to negotiate the authentication with the Autodesk Forge Api Authentication servers.
Method signatures and docstrings:
- def __init__(self, webAddress, clientId, clientSecret, scopes, redirectAddress=None, endAddress=None, timeout=... | 50a95378e2cf37df210269c8576bdae57d232c3b | <|skeleton|>
class OAuth2Negotiator:
"""Class to negotiate the authentication with the Autodesk Forge Api Authentication servers."""
def __init__(self, webAddress, clientId, clientSecret, scopes, redirectAddress=None, endAddress=None, timeout=1):
"""Initialize the OAuth2Negotiator class and assign the ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OAuth2Negotiator:
"""Class to negotiate the authentication with the Autodesk Forge Api Authentication servers."""
def __init__(self, webAddress, clientId, clientSecret, scopes, redirectAddress=None, endAddress=None, timeout=1):
"""Initialize the OAuth2Negotiator class and assign the needed parame... | the_stack_v2_python_sparse | PyForge/AuthNegotiator.py | eduardhendriksen/PyForge | train | 2 |
7d1b9baa94cd53c41b2e78671dae21d926f7bd39 | [
"self.lrow = len(matrix)\nif self.lrow == 0:\n self.dp = [[]]\n return\nself.lcol = len(matrix[0])\nself.dp = [[0 for _ in range(self.lcol)] for _ in range(self.lrow)]\nfor i in range(self.lrow):\n for j in range(self.lcol):\n self.dp[i][j] = self.dp[i][j - 1] + matrix[i][j]",
"r = 0\nfor row in r... | <|body_start_0|>
self.lrow = len(matrix)
if self.lrow == 0:
self.dp = [[]]
return
self.lcol = len(matrix[0])
self.dp = [[0 for _ in range(self.lcol)] for _ in range(self.lrow)]
for i in range(self.lrow):
for j in range(self.lcol):
... | NumMatrix | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
""":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_10k_train_007684 | 1,038 | permissive | [
{
"docstring": ":type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": ":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int",
"name": "sumRegion",
"signature": "def sumRegion(self, row1, col1, row2, col2)"
... | 2 | null | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype:... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype:... | 65549f72c565d9f11641c86d6cef9c7988805817 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
""":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
self.lrow = len(matrix)
if self.lrow == 0:
self.dp = [[]]
return
self.lcol = len(matrix[0])
self.dp = [[0 for _ in range(self.lcol)] for _ in range(self.lrow)]
for... | the_stack_v2_python_sparse | utils/numSumMatrix.py | wisesky/LeetCode-Practice | train | 0 | |
8e419dd4daffbd51e106252a8ad6b1d0425b18a9 | [
"future_question = create_question(question_text='Future question.', days=5)\nresponse = self.client.get(reverse('polls:detail', args=(future_question.id,)))\nself.assertEqual(response.status_code, 404)",
"past_question = create_question(question_text='Past Question.', days=-5)\nresponse = self.client.get(reverse... | <|body_start_0|>
future_question = create_question(question_text='Future question.', days=5)
response = self.client.get(reverse('polls:detail', args=(future_question.id,)))
self.assertEqual(response.status_code, 404)
<|end_body_0|>
<|body_start_1|>
past_question = create_question(questi... | QuestionIndexDetailTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QuestionIndexDetailTests:
def test_detail_view_with_a_future_question(self):
"""The detail view of a question with a pub_date in the future should return a 404 not found."""
<|body_0|>
def test_detail_view_with_a_past_question(self):
"""The detail view of a question ... | stack_v2_sparse_classes_10k_train_007685 | 12,478 | no_license | [
{
"docstring": "The detail view of a question with a pub_date in the future should return a 404 not found.",
"name": "test_detail_view_with_a_future_question",
"signature": "def test_detail_view_with_a_future_question(self)"
},
{
"docstring": "The detail view of a question with a pub_date in the... | 2 | stack_v2_sparse_classes_30k_train_002619 | Implement the Python class `QuestionIndexDetailTests` described below.
Class description:
Implement the QuestionIndexDetailTests class.
Method signatures and docstrings:
- def test_detail_view_with_a_future_question(self): The detail view of a question with a pub_date in the future should return a 404 not found.
- de... | Implement the Python class `QuestionIndexDetailTests` described below.
Class description:
Implement the QuestionIndexDetailTests class.
Method signatures and docstrings:
- def test_detail_view_with_a_future_question(self): The detail view of a question with a pub_date in the future should return a 404 not found.
- de... | 43bca49c77eb16de7580e55f7418cdab92a9a596 | <|skeleton|>
class QuestionIndexDetailTests:
def test_detail_view_with_a_future_question(self):
"""The detail view of a question with a pub_date in the future should return a 404 not found."""
<|body_0|>
def test_detail_view_with_a_past_question(self):
"""The detail view of a question ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class QuestionIndexDetailTests:
def test_detail_view_with_a_future_question(self):
"""The detail view of a question with a pub_date in the future should return a 404 not found."""
future_question = create_question(question_text='Future question.', days=5)
response = self.client.get(reverse('... | the_stack_v2_python_sparse | sdd_project copy/labhelpers/tests.py | katrusso/LabHelpers | train | 0 | |
3659d158856a2feeb2e0680b0bf2eb2142f1c3a5 | [
"k = None\ngo = True\nfor i in range(len(nums) - 1, -1, -1):\n for j in range(i + 1, len(nums)):\n if nums[i] < nums[j] and (k is None or nums[j] < nums[k]):\n k = j\n if k is not None:\n nums[i], nums[k] = (nums[k], nums[i])\n nums[i + 1:] = sorted(nums[i + 1:])\n break... | <|body_start_0|>
k = None
go = True
for i in range(len(nums) - 1, -1, -1):
for j in range(i + 1, len(nums)):
if nums[i] < nums[j] and (k is None or nums[j] < nums[k]):
k = j
if k is not None:
nums[i], nums[k] = (nums[k],... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_0|>
def permuteUnique(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_10k_train_007686 | 2,067 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.",
"name": "nextPermutation",
"signature": "def nextPermutation(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[List[int]]",
"name": "permuteUnique",
"signature": "d... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextPermutation(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def permuteUnique(self, nums): :type nums: List[int] :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextPermutation(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def permuteUnique(self, nums): :type nums: List[int] :... | c026f2969c784827fac702b34b07a9268b70b62a | <|skeleton|>
class Solution:
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_0|>
def permuteUnique(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
k = None
go = True
for i in range(len(nums) - 1, -1, -1):
for j in range(i + 1, len(nums)):
if nums[i] < nums[j]... | the_stack_v2_python_sparse | codes/contest/leetcode/permutations-ii.py | jiluhu/dirtysalt.github.io | train | 0 | |
5ee937ced5a77d338602e7d14205dbeaa6cf50e5 | [
"if user_input is None:\n user_input = {}\nreturn self.async_show_form(step_id='user', data_schema=vol.Schema({vol.Required(CONF_STATION_CODE, default=user_input.get(CONF_STATION_CODE, '')): str}), errors=errors or {})",
"errors = {}\nif user_input is None:\n return self._show_setup_form(user_input, errors)... | <|body_start_0|>
if user_input is None:
user_input = {}
return self.async_show_form(step_id='user', data_schema=vol.Schema({vol.Required(CONF_STATION_CODE, default=user_input.get(CONF_STATION_CODE, '')): str}), errors=errors or {})
<|end_body_0|>
<|body_start_1|>
errors = {}
... | Handle a Meteoclimatic config flow. | MeteoclimaticFlowHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MeteoclimaticFlowHandler:
"""Handle a Meteoclimatic config flow."""
def _show_setup_form(self, user_input=None, errors=None):
"""Show the setup form to the user."""
<|body_0|>
async def async_step_user(self, user_input=None):
"""Handle a flow initiated by the use... | stack_v2_sparse_classes_10k_train_007687 | 2,082 | permissive | [
{
"docstring": "Show the setup form to the user.",
"name": "_show_setup_form",
"signature": "def _show_setup_form(self, user_input=None, errors=None)"
},
{
"docstring": "Handle a flow initiated by the user.",
"name": "async_step_user",
"signature": "async def async_step_user(self, user_i... | 2 | stack_v2_sparse_classes_30k_train_004874 | Implement the Python class `MeteoclimaticFlowHandler` described below.
Class description:
Handle a Meteoclimatic config flow.
Method signatures and docstrings:
- def _show_setup_form(self, user_input=None, errors=None): Show the setup form to the user.
- async def async_step_user(self, user_input=None): Handle a flow... | Implement the Python class `MeteoclimaticFlowHandler` described below.
Class description:
Handle a Meteoclimatic config flow.
Method signatures and docstrings:
- def _show_setup_form(self, user_input=None, errors=None): Show the setup form to the user.
- async def async_step_user(self, user_input=None): Handle a flow... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class MeteoclimaticFlowHandler:
"""Handle a Meteoclimatic config flow."""
def _show_setup_form(self, user_input=None, errors=None):
"""Show the setup form to the user."""
<|body_0|>
async def async_step_user(self, user_input=None):
"""Handle a flow initiated by the use... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MeteoclimaticFlowHandler:
"""Handle a Meteoclimatic config flow."""
def _show_setup_form(self, user_input=None, errors=None):
"""Show the setup form to the user."""
if user_input is None:
user_input = {}
return self.async_show_form(step_id='user', data_schema=vol.Schem... | the_stack_v2_python_sparse | homeassistant/components/meteoclimatic/config_flow.py | home-assistant/core | train | 35,501 |
24581175401848fac323e00d17629aabd49187d7 | [
"results = super(CompanyLDAP, self).get_ldap_dicts()\nldaps = self.sudo().search([('ldap_server', '!=', False)], order='sequence')\ncacert_paths = ldaps.read(['cacert_path'])\nfor i in range(len(results)):\n results[i].update(cacert_paths[i])\nreturn results",
"uri = 'ldap://%s:%d' % (conf['ldap_server'], conf... | <|body_start_0|>
results = super(CompanyLDAP, self).get_ldap_dicts()
ldaps = self.sudo().search([('ldap_server', '!=', False)], order='sequence')
cacert_paths = ldaps.read(['cacert_path'])
for i in range(len(results)):
results[i].update(cacert_paths[i])
return results... | CompanyLDAP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompanyLDAP:
def get_ldap_dicts(self):
"""Add cacert_path to ldap_dicts"""
<|body_0|>
def connect(self, conf):
"""Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ldap configuration dictionary. :param dict conf: LDAP configurat... | stack_v2_sparse_classes_10k_train_007688 | 1,325 | no_license | [
{
"docstring": "Add cacert_path to ldap_dicts",
"name": "get_ldap_dicts",
"signature": "def get_ldap_dicts(self)"
},
{
"docstring": "Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ldap configuration dictionary. :param dict conf: LDAP configuration :retur... | 2 | stack_v2_sparse_classes_30k_train_006833 | Implement the Python class `CompanyLDAP` described below.
Class description:
Implement the CompanyLDAP class.
Method signatures and docstrings:
- def get_ldap_dicts(self): Add cacert_path to ldap_dicts
- def connect(self, conf): Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ... | Implement the Python class `CompanyLDAP` described below.
Class description:
Implement the CompanyLDAP class.
Method signatures and docstrings:
- def get_ldap_dicts(self): Add cacert_path to ldap_dicts
- def connect(self, conf): Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ... | c355e18aeb3e7123fe184fcc7ec06485ab498343 | <|skeleton|>
class CompanyLDAP:
def get_ldap_dicts(self):
"""Add cacert_path to ldap_dicts"""
<|body_0|>
def connect(self, conf):
"""Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ldap configuration dictionary. :param dict conf: LDAP configurat... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CompanyLDAP:
def get_ldap_dicts(self):
"""Add cacert_path to ldap_dicts"""
results = super(CompanyLDAP, self).get_ldap_dicts()
ldaps = self.sudo().search([('ldap_server', '!=', False)], order='sequence')
cacert_paths = ldaps.read(['cacert_path'])
for i in range(len(resu... | the_stack_v2_python_sparse | auth_ldap_tls/models/res_company_ldap.py | rythe77/odoo11_customized | train | 5 | |
ee7b5825add6c424d2cd9ae8e2a32436bdd3bde4 | [
"output = []\nif root is None:\n return str([])\nqueue = deque()\nqueue.append(root)\nwhile queue:\n cur = queue.popleft()\n if cur is None:\n output.append('#')\n continue\n output.append(cur.val)\n queue.extend([cur.left, cur.right])\nreturn str(output)",
"if data == '[]':\n retu... | <|body_start_0|>
output = []
if root is None:
return str([])
queue = deque()
queue.append(root)
while queue:
cur = queue.popleft()
if cur is None:
output.append('#')
continue
output.append(cur.val)
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_007689 | 1,846 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 61923f8714f21e8dd5ebafa89b2c3929cff3adf1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
output = []
if root is None:
return str([])
queue = deque()
queue.append(root)
while queue:
cur = queue.popleft()
if c... | the_stack_v2_python_sparse | Leetcode/Hard/BSTSerialize.py | pumbaacave/atcoder | train | 1 | |
6260698a778091b1c0d836752927da6b53939092 | [
"snap = super(FlowArea, self).snapshot()\nsnap['direction'] = self.direction\nsnap['align'] = self.align\nsnap['horizontal_spacing'] = self.horizontal_spacing\nsnap['vertical_spacing'] = self.vertical_spacing\nsnap['margins'] = self.margins\nreturn snap",
"super(FlowArea, self).bind()\nattrs = ('direction', 'alig... | <|body_start_0|>
snap = super(FlowArea, self).snapshot()
snap['direction'] = self.direction
snap['align'] = self.align
snap['horizontal_spacing'] = self.horizontal_spacing
snap['vertical_spacing'] = self.vertical_spacing
snap['margins'] = self.margins
return snap
... | A widget which lays out its children in flowing manner, wrapping around at the end of the available space. | FlowArea | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlowArea:
"""A widget which lays out its children in flowing manner, wrapping around at the end of the available space."""
def snapshot(self):
"""Returns the snapshot dict for the FlowArea."""
<|body_0|>
def bind(self):
"""Bind the change handler for the FlowItem... | stack_v2_sparse_classes_10k_train_007690 | 2,869 | permissive | [
{
"docstring": "Returns the snapshot dict for the FlowArea.",
"name": "snapshot",
"signature": "def snapshot(self)"
},
{
"docstring": "Bind the change handler for the FlowItem.",
"name": "bind",
"signature": "def bind(self)"
},
{
"docstring": "The getter for the 'flow_items' prop... | 3 | null | Implement the Python class `FlowArea` described below.
Class description:
A widget which lays out its children in flowing manner, wrapping around at the end of the available space.
Method signatures and docstrings:
- def snapshot(self): Returns the snapshot dict for the FlowArea.
- def bind(self): Bind the change han... | Implement the Python class `FlowArea` described below.
Class description:
A widget which lays out its children in flowing manner, wrapping around at the end of the available space.
Method signatures and docstrings:
- def snapshot(self): Returns the snapshot dict for the FlowArea.
- def bind(self): Bind the change han... | 424bba29219de58fe9e47196de6763de8b2009f2 | <|skeleton|>
class FlowArea:
"""A widget which lays out its children in flowing manner, wrapping around at the end of the available space."""
def snapshot(self):
"""Returns the snapshot dict for the FlowArea."""
<|body_0|>
def bind(self):
"""Bind the change handler for the FlowItem... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FlowArea:
"""A widget which lays out its children in flowing manner, wrapping around at the end of the available space."""
def snapshot(self):
"""Returns the snapshot dict for the FlowArea."""
snap = super(FlowArea, self).snapshot()
snap['direction'] = self.direction
snap[... | the_stack_v2_python_sparse | enaml/widgets/flow_area.py | enthought/enaml | train | 17 |
046d76758e1b005faef5dcfa3d772c11ab50a958 | [
"self.horizontal_line = [ShapePoint(start_end_points[0]), ShapePoint(start_end_points[1])]\nself.colors = colors or list()\nself.bins = bins\ncolumn_count = 2\ncolumns_width = (0.8, 0.2)\nself.text = text\nself.text_scale = 0.6\nself.start_dots_values = start_dots_values\nself.default_color = 'red'\nself.customers,... | <|body_start_0|>
self.horizontal_line = [ShapePoint(start_end_points[0]), ShapePoint(start_end_points[1])]
self.colors = colors or list()
self.bins = bins
column_count = 2
columns_width = (0.8, 0.2)
self.text = text
self.text_scale = 0.6
self.start_dots_va... | Overridden Table class. Custom text and dots were added. | CustomersTable | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomersTable:
"""Overridden Table class. Custom text and dots were added."""
def __init__(self, start_end_points: Tuple[tuple, tuple], row_count: int=0, row_height: Union[int, float]=0.5, visible_row_count: int=0, colors: list=None, bins: Union[int, float]=0, text: str='', start_dots_value... | stack_v2_sparse_classes_10k_train_007691 | 9,066 | no_license | [
{
"docstring": "Class initialization. Args: start_end_points (Tuple[tuple, tuple]): Left top and right top points. ((x1,y1), (x2,y2)). row_count (int, optional): Table row count. Defaults to 0. row_height (Union[int, float], optional): Table row height. Defaults to 0.2. visible_row_count (int, optional): Table ... | 2 | stack_v2_sparse_classes_30k_train_006738 | Implement the Python class `CustomersTable` described below.
Class description:
Overridden Table class. Custom text and dots were added.
Method signatures and docstrings:
- def __init__(self, start_end_points: Tuple[tuple, tuple], row_count: int=0, row_height: Union[int, float]=0.5, visible_row_count: int=0, colors: ... | Implement the Python class `CustomersTable` described below.
Class description:
Overridden Table class. Custom text and dots were added.
Method signatures and docstrings:
- def __init__(self, start_end_points: Tuple[tuple, tuple], row_count: int=0, row_height: Union[int, float]=0.5, visible_row_count: int=0, colors: ... | 290bf56ef888283a0225939ed8b1f87445e506d0 | <|skeleton|>
class CustomersTable:
"""Overridden Table class. Custom text and dots were added."""
def __init__(self, start_end_points: Tuple[tuple, tuple], row_count: int=0, row_height: Union[int, float]=0.5, visible_row_count: int=0, colors: list=None, bins: Union[int, float]=0, text: str='', start_dots_value... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CustomersTable:
"""Overridden Table class. Custom text and dots were added."""
def __init__(self, start_end_points: Tuple[tuple, tuple], row_count: int=0, row_height: Union[int, float]=0.5, visible_row_count: int=0, colors: list=None, bins: Union[int, float]=0, text: str='', start_dots_values: list=None)... | the_stack_v2_python_sparse | classes/table.py | mohovkm/habr_manim | train | 0 |
b1d217c48da1f80ffbbea6749dd6d83afb775db1 | [
"include_inactive = request.args.get('include_inactive', '0') != '0'\nget_users_response = InternalApi().get(url_for('flexmeasures_api_v2_0.get_users', include_inactive=include_inactive))\nusers = [process_internal_api_response(user, make_obj=True) for user in get_users_response.json()]\nreturn render_flexmeasures_... | <|body_start_0|>
include_inactive = request.args.get('include_inactive', '0') != '0'
get_users_response = InternalApi().get(url_for('flexmeasures_api_v2_0.get_users', include_inactive=include_inactive))
users = [process_internal_api_response(user, make_obj=True) for user in get_users_response.js... | UserCrudUI | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserCrudUI:
def index(self):
"""/users"""
<|body_0|>
def get(self, id: str):
"""GET from /users/<id>"""
<|body_1|>
def toggle_active(self, id: str):
"""Toggle activation status via /users/toggle_active/<id>"""
<|body_2|>
def reset_pa... | stack_v2_sparse_classes_10k_train_007692 | 4,885 | permissive | [
{
"docstring": "/users",
"name": "index",
"signature": "def index(self)"
},
{
"docstring": "GET from /users/<id>",
"name": "get",
"signature": "def get(self, id: str)"
},
{
"docstring": "Toggle activation status via /users/toggle_active/<id>",
"name": "toggle_active",
"si... | 4 | null | Implement the Python class `UserCrudUI` described below.
Class description:
Implement the UserCrudUI class.
Method signatures and docstrings:
- def index(self): /users
- def get(self, id: str): GET from /users/<id>
- def toggle_active(self, id: str): Toggle activation status via /users/toggle_active/<id>
- def reset_... | Implement the Python class `UserCrudUI` described below.
Class description:
Implement the UserCrudUI class.
Method signatures and docstrings:
- def index(self): /users
- def get(self, id: str): GET from /users/<id>
- def toggle_active(self, id: str): Toggle activation status via /users/toggle_active/<id>
- def reset_... | 6ba518bae7e9b8a715b9a05f6fae19f5e4ade791 | <|skeleton|>
class UserCrudUI:
def index(self):
"""/users"""
<|body_0|>
def get(self, id: str):
"""GET from /users/<id>"""
<|body_1|>
def toggle_active(self, id: str):
"""Toggle activation status via /users/toggle_active/<id>"""
<|body_2|>
def reset_pa... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserCrudUI:
def index(self):
"""/users"""
include_inactive = request.args.get('include_inactive', '0') != '0'
get_users_response = InternalApi().get(url_for('flexmeasures_api_v2_0.get_users', include_inactive=include_inactive))
users = [process_internal_api_response(user, make_... | the_stack_v2_python_sparse | flexmeasures/ui/crud/users.py | meeseeksmachine/flexmeasures | train | 0 | |
aab53c42c26c9d1287effbd607bd152114a4056c | [
"req_body = cli.make_body(sp=sp, ipPort=ip_port, ipAddress=ip_address, netmask=netmask, v6PrefixLength=v6_prefix_length, gateway=gateway, vlanId=vlan_id)\nresp = cli.post(cls().resource_class, **req_body)\nresp.raise_if_err()\nreturn cls.get(cli, resp.resource_id)",
"req_body = self._cli.make_body(sp=sp, ipPort=i... | <|body_start_0|>
req_body = cli.make_body(sp=sp, ipPort=ip_port, ipAddress=ip_address, netmask=netmask, v6PrefixLength=v6_prefix_length, gateway=gateway, vlanId=vlan_id)
resp = cli.post(cls().resource_class, **req_body)
resp.raise_if_err()
return cls.get(cli, resp.resource_id)
<|end_body... | UnityReplicationInterface | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnityReplicationInterface:
def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None):
"""Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on... | stack_v2_sparse_classes_10k_train_007693 | 3,507 | permissive | [
{
"docstring": "Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on which the replication interface is running. :param ip_port: `UnityIpPort` object. Physical port or link aggregation on the storage processor on which... | 2 | null | Implement the Python class `UnityReplicationInterface` described below.
Class description:
Implement the UnityReplicationInterface class.
Method signatures and docstrings:
- def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None): Creates a replication interface.... | Implement the Python class `UnityReplicationInterface` described below.
Class description:
Implement the UnityReplicationInterface class.
Method signatures and docstrings:
- def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None): Creates a replication interface.... | ccfccba0bceda34c0d5dc8105c95731036f4e955 | <|skeleton|>
class UnityReplicationInterface:
def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None):
"""Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UnityReplicationInterface:
def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None):
"""Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on which the rep... | the_stack_v2_python_sparse | storops/unity/resource/replication_interface.py | emc-openstack/storops | train | 61 | |
a7761fd27dde869d14df70a5edc58025834e9163 | [
"self.data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)\nself.data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)\na, b = self.tokenize_dataset(self.data_train)\nself.tokenizer_en = b\nself.tokenizer_pt = a",
"pp = []\nee = []\nfor p... | <|body_start_0|>
self.data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
self.data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)
a, b = self.tokenize_dataset(self.data_train)
self.tokenizer_en = b
se... | Class methods | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Class methods"""
def __init__(self):
"""class constructor"""
<|body_0|>
def tokenize_dataset(self, data):
"""creates sub-word tokenizers for our dataset"""
<|body_1|>
def encode(self, pt, en):
"""encodes a translation into tokens"... | stack_v2_sparse_classes_10k_train_007694 | 1,609 | no_license | [
{
"docstring": "class constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "creates sub-word tokenizers for our dataset",
"name": "tokenize_dataset",
"signature": "def tokenize_dataset(self, data)"
},
{
"docstring": "encodes a translation into tok... | 3 | stack_v2_sparse_classes_30k_test_000377 | Implement the Python class `Dataset` described below.
Class description:
Class methods
Method signatures and docstrings:
- def __init__(self): class constructor
- def tokenize_dataset(self, data): creates sub-word tokenizers for our dataset
- def encode(self, pt, en): encodes a translation into tokens | Implement the Python class `Dataset` described below.
Class description:
Class methods
Method signatures and docstrings:
- def __init__(self): class constructor
- def tokenize_dataset(self, data): creates sub-word tokenizers for our dataset
- def encode(self, pt, en): encodes a translation into tokens
<|skeleton|>
c... | 91300120d38acb6440a6dbb8c408b1193c07de88 | <|skeleton|>
class Dataset:
"""Class methods"""
def __init__(self):
"""class constructor"""
<|body_0|>
def tokenize_dataset(self, data):
"""creates sub-word tokenizers for our dataset"""
<|body_1|>
def encode(self, pt, en):
"""encodes a translation into tokens"... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Dataset:
"""Class methods"""
def __init__(self):
"""class constructor"""
self.data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
self.data_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation', as_supervised=True)
a, b = ... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/1-dataset.py | anaruzz/holbertonschool-machine_learning | train | 0 |
8a82340e52e1e486e613389d71969df2d28f9ea7 | [
"super().__init__(kwargs)\nself.desc = copy.deepcopy(kwargs)\nself.fm = NormalizedWeightedFMLayer(input_dim4lookup=kwargs['input_dim4lookup'], alpha_init_mean=kwargs['alpha_init_mean'], alpha_init_radius=0.0001, alpha_activation=kwargs['alpha_activation'], selected_pairs=kwargs['selected_pairs'])\nself.l1_cover_par... | <|body_start_0|>
super().__init__(kwargs)
self.desc = copy.deepcopy(kwargs)
self.fm = NormalizedWeightedFMLayer(input_dim4lookup=kwargs['input_dim4lookup'], alpha_init_mean=kwargs['alpha_init_mean'], alpha_init_radius=0.0001, alpha_activation=kwargs['alpha_activation'], selected_pairs=kwargs['se... | Automatic Feature Interaction Selection (FIS) For DeepFM. :param input_dim: feature space of dataset :type input_dim: int :param input_dim4lookup: feature number in `feature_id`, usually equals to number of non-zero features :type input_dim4lookup: int :param embed_dim: length of each feature's latent vector(embedding ... | AutoGateModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutoGateModel:
"""Automatic Feature Interaction Selection (FIS) For DeepFM. :param input_dim: feature space of dataset :type input_dim: int :param input_dim4lookup: feature number in `feature_id`, usually equals to number of non-zero features :type input_dim4lookup: int :param embed_dim: length o... | stack_v2_sparse_classes_10k_train_007695 | 3,679 | permissive | [
{
"docstring": "Construct the AutoGateModel class. :param net_desc: config of the structure :type net_desc: class object :return: return AutoGateModel class :rtype: class object",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "Retrieve feature interaction scor... | 2 | null | Implement the Python class `AutoGateModel` described below.
Class description:
Automatic Feature Interaction Selection (FIS) For DeepFM. :param input_dim: feature space of dataset :type input_dim: int :param input_dim4lookup: feature number in `feature_id`, usually equals to number of non-zero features :type input_dim... | Implement the Python class `AutoGateModel` described below.
Class description:
Automatic Feature Interaction Selection (FIS) For DeepFM. :param input_dim: feature space of dataset :type input_dim: int :param input_dim4lookup: feature number in `feature_id`, usually equals to number of non-zero features :type input_dim... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class AutoGateModel:
"""Automatic Feature Interaction Selection (FIS) For DeepFM. :param input_dim: feature space of dataset :type input_dim: int :param input_dim4lookup: feature number in `feature_id`, usually equals to number of non-zero features :type input_dim4lookup: int :param embed_dim: length o... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AutoGateModel:
"""Automatic Feature Interaction Selection (FIS) For DeepFM. :param input_dim: feature space of dataset :type input_dim: int :param input_dim4lookup: feature number in `feature_id`, usually equals to number of non-zero features :type input_dim4lookup: int :param embed_dim: length of each featur... | the_stack_v2_python_sparse | zeus/networks/pytorch/customs/autogate.py | huawei-noah/xingtian | train | 308 |
708d26e2ea4e4ff93db62ddf7d60a9b528bdc111 | [
"topicTitle = request.args.get('topicTitle', '', type=str)\nreportId = request.args.get('reportId', 0, type=int)\nalgorithm = request.args.get('algorithm', '', type=str)\nthreshold = request.args.get('threshold', 0, type=float)\npage = request.args.get('page', 1, type=int)\nper_page = request.args.get('per_page', 1... | <|body_start_0|>
topicTitle = request.args.get('topicTitle', '', type=str)
reportId = request.args.get('reportId', 0, type=int)
algorithm = request.args.get('algorithm', '', type=str)
threshold = request.args.get('threshold', 0, type=float)
page = request.args.get('page', 1, type... | EmotionAnalyzerController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmotionAnalyzerController:
def get(self):
"""Returns a page of tweet with emotions"""
<|body_0|>
def post(self):
"""Analyses the emotion of tweets generated by a similarity algorithm."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
topicTitle = requ... | stack_v2_sparse_classes_10k_train_007696 | 2,883 | no_license | [
{
"docstring": "Returns a page of tweet with emotions",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Analyses the emotion of tweets generated by a similarity algorithm.",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003468 | Implement the Python class `EmotionAnalyzerController` described below.
Class description:
Implement the EmotionAnalyzerController class.
Method signatures and docstrings:
- def get(self): Returns a page of tweet with emotions
- def post(self): Analyses the emotion of tweets generated by a similarity algorithm. | Implement the Python class `EmotionAnalyzerController` described below.
Class description:
Implement the EmotionAnalyzerController class.
Method signatures and docstrings:
- def get(self): Returns a page of tweet with emotions
- def post(self): Analyses the emotion of tweets generated by a similarity algorithm.
<|sk... | e8d5fd562724df1ad26b90d0c731e133b052df24 | <|skeleton|>
class EmotionAnalyzerController:
def get(self):
"""Returns a page of tweet with emotions"""
<|body_0|>
def post(self):
"""Analyses the emotion of tweets generated by a similarity algorithm."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EmotionAnalyzerController:
def get(self):
"""Returns a page of tweet with emotions"""
topicTitle = request.args.get('topicTitle', '', type=str)
reportId = request.args.get('reportId', 0, type=int)
algorithm = request.args.get('algorithm', '', type=str)
threshold = reque... | the_stack_v2_python_sparse | app/main/controllers/emotionAnalyzerController.py | ProyectoFinal2020/TweetAnalyzer-Backend | train | 0 | |
a2b22a60dab12bf7e7554da6c77aeab2692b3b5f | [
"n = len(triangle)\ndp = [[0] * n for _ in range(n)]\nfor i in range(n - 1, -1, -1):\n for j in range(len(triangle[i]) - 1, -1, -1):\n if i == n - 1:\n dp[i][j] = triangle[i][j]\n else:\n dp[i][j] = min(dp[i + 1][j + 1], dp[i + 1][j]) + triangle[i][j]\nreturn dp[0][0]",
"if ... | <|body_start_0|>
n = len(triangle)
dp = [[0] * n for _ in range(n)]
for i in range(n - 1, -1, -1):
for j in range(len(triangle[i]) - 1, -1, -1):
if i == n - 1:
dp[i][j] = triangle[i][j]
else:
dp[i][j] = min(dp[i ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal2(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n = len(triang... | stack_v2_sparse_classes_10k_train_007697 | 3,302 | no_license | [
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal",
"signature": "def minimumTotal(self, triangle)"
},
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal2",
"signature": "def minimumTotal2(self, triangle)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000391 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal2(self, triangle): :type triangle: List[List[int]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal2(self, triangle): :type triangle: List[List[int]] :rtype: int
<|skeleton|>
class... | 690b685048c8e89d26047b6bc48b5f9af7d59cbb | <|skeleton|>
class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal2(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
n = len(triangle)
dp = [[0] * n for _ in range(n)]
for i in range(n - 1, -1, -1):
for j in range(len(triangle[i]) - 1, -1, -1):
if i == n - 1:
... | the_stack_v2_python_sparse | 数组/120. 三角形最小路径和.py | SimmonsChen/LeetCode | train | 0 | |
dd9bf306928f5cb5cc4ac537661e6a284ab1b507 | [
"super().__init__()\nimport sklearn\nimport sklearn.linear_model\nself.model = sklearn.linear_model.LarsCV",
"specs = super(LarsCV, cls).getInputSpecification()\nspecs.description = 'The \\\\xmlNode{LarsCV} is Cross-validated \\\\textit{Least Angle Regression model} model\\n is a regression... | <|body_start_0|>
super().__init__()
import sklearn
import sklearn.linear_model
self.model = sklearn.linear_model.LarsCV
<|end_body_0|>
<|body_start_1|>
specs = super(LarsCV, cls).getInputSpecification()
specs.description = 'The \\xmlNode{LarsCV} is Cross-validated \\text... | Cross-validated Least Angle Regression model. | LarsCV | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-2-Clause",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LarsCV:
"""Cross-validated Least Angle Regression model."""
def __init__(self):
"""Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None"""
<|body_0|>
def getInputSpecification(cls):
"""Method to get a reference to a c... | stack_v2_sparse_classes_10k_train_007698 | 6,409 | permissive | [
{
"docstring": "Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Method to get a reference to a class that specifies the input data for class cls. @ In, cls, the class for... | 3 | null | Implement the Python class `LarsCV` described below.
Class description:
Cross-validated Least Angle Regression model.
Method signatures and docstrings:
- def __init__(self): Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None
- def getInputSpecification(cls): Method to g... | Implement the Python class `LarsCV` described below.
Class description:
Cross-validated Least Angle Regression model.
Method signatures and docstrings:
- def __init__(self): Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None
- def getInputSpecification(cls): Method to g... | 2b16e7aa3325fe84cab2477947a951414c635381 | <|skeleton|>
class LarsCV:
"""Cross-validated Least Angle Regression model."""
def __init__(self):
"""Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None"""
<|body_0|>
def getInputSpecification(cls):
"""Method to get a reference to a c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LarsCV:
"""Cross-validated Least Angle Regression model."""
def __init__(self):
"""Constructor that will appropriately initialize a supervised learning object @ In, None @ Out, None"""
super().__init__()
import sklearn
import sklearn.linear_model
self.model = sklea... | the_stack_v2_python_sparse | ravenframework/SupervisedLearning/ScikitLearn/LinearModel/LarsCV.py | idaholab/raven | train | 201 |
2f8e94dd4de6db0b49673e27b220bd8d13830f0a | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn WorkbookFilter()",
"from .entity import Entity\nfrom .workbook_filter_criteria import WorkbookFilterCriteria\nfrom .entity import Entity\nfrom .workbook_filter_criteria import WorkbookFilterCriteria\nfields: Dict[str, Callable[[Any], N... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return WorkbookFilter()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .workbook_filter_criteria import WorkbookFilterCriteria
from .entity import Entity
from .... | WorkbookFilter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkbookFilter:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WorkbookFilter:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Retur... | stack_v2_sparse_classes_10k_train_007699 | 2,232 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: WorkbookFilter",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_valu... | 3 | null | Implement the Python class `WorkbookFilter` described below.
Class description:
Implement the WorkbookFilter class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WorkbookFilter: Creates a new instance of the appropriate class based on discriminator va... | Implement the Python class `WorkbookFilter` described below.
Class description:
Implement the WorkbookFilter class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WorkbookFilter: Creates a new instance of the appropriate class based on discriminator va... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class WorkbookFilter:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WorkbookFilter:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Retur... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WorkbookFilter:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> WorkbookFilter:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: WorkbookFi... | the_stack_v2_python_sparse | msgraph/generated/models/workbook_filter.py | microsoftgraph/msgraph-sdk-python | train | 135 |
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