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
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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
18d716877f083c49a62db27fb61836cb05c93ddc | [
"l = len(nums)\ni = 0\nwhile i < l:\n j = i + 1\n while j < l:\n if nums[i] == nums[j]:\n return nums[i]\n j += 1\n i += 1\nreturn 0",
"l = len(nums)\nfast = nums[nums[0]]\nslow = nums[0]\nwhile fast != slow:\n fast = nums[nums[fast]]\n slow = nums[slow]\nfast = 0\nwhile fa... | <|body_start_0|>
l = len(nums)
i = 0
while i < l:
j = i + 1
while j < l:
if nums[i] == nums[j]:
return nums[i]
j += 1
i += 1
return 0
<|end_body_0|>
<|body_start_1|>
l = len(nums)
fas... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findDuplicate1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
l = len(nums)
i = 0
whil... | stack_v2_sparse_classes_10k_train_008000 | 1,495 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findDuplicate1",
"signature": "def findDuplicate1(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findDuplicate",
"signature": "def findDuplicate(self, nums)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDuplicate1(self, nums): :type nums: List[int] :rtype: int
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findDuplicate1(self, nums): :type nums: List[int] :rtype: int
- def findDuplicate(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def findDu... | e5b018493bbd12edcdcd0434f35d9c358106d391 | <|skeleton|>
class Solution:
def findDuplicate1(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findDuplicate(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def findDuplicate1(self, nums):
""":type nums: List[int] :rtype: int"""
l = len(nums)
i = 0
while i < l:
j = i + 1
while j < l:
if nums[i] == nums[j]:
return nums[i]
j += 1
i += 1
... | the_stack_v2_python_sparse | py/leetcode/287.py | wfeng1991/learnpy | train | 0 | |
594f253eadf5775d70fc88558aaf3ed584f45560 | [
"random.seed(102938482634)\npoint_cloud = load(os.path.join('testdata', 'AHN3.las'))\nnum_all_pc_points = len(point_cloud[keys.point]['x']['data'])\nrand_indices = [random.randint(0, num_all_pc_points) for _ in range(20)]\ntarget_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)\nradius = 2.5\nneighbo... | <|body_start_0|>
random.seed(102938482634)
point_cloud = load(os.path.join('testdata', 'AHN3.las'))
num_all_pc_points = len(point_cloud[keys.point]['x']['data'])
rand_indices = [random.randint(0, num_all_pc_points) for _ in range(20)]
target_point_cloud = utils.copy_point_cloud(p... | TestExtractEigenValues | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestExtractEigenValues:
def test_eigenvalues_in_cylinders(self):
"""Test provenance added (This should actually be part the general feature extractor test suite)."""
<|body_0|>
def test_eigenvalues_of_too_few_points_results_in_0():
"""If there are too few points to c... | stack_v2_sparse_classes_10k_train_008001 | 11,643 | permissive | [
{
"docstring": "Test provenance added (This should actually be part the general feature extractor test suite).",
"name": "test_eigenvalues_in_cylinders",
"signature": "def test_eigenvalues_in_cylinders(self)"
},
{
"docstring": "If there are too few points to calculate the eigen values don't outp... | 2 | stack_v2_sparse_classes_30k_val_000139 | Implement the Python class `TestExtractEigenValues` described below.
Class description:
Implement the TestExtractEigenValues class.
Method signatures and docstrings:
- def test_eigenvalues_in_cylinders(self): Test provenance added (This should actually be part the general feature extractor test suite).
- def test_eig... | Implement the Python class `TestExtractEigenValues` described below.
Class description:
Implement the TestExtractEigenValues class.
Method signatures and docstrings:
- def test_eigenvalues_in_cylinders(self): Test provenance added (This should actually be part the general feature extractor test suite).
- def test_eig... | f6c22841dcbd375639c7f7aecec70f2602b91ee4 | <|skeleton|>
class TestExtractEigenValues:
def test_eigenvalues_in_cylinders(self):
"""Test provenance added (This should actually be part the general feature extractor test suite)."""
<|body_0|>
def test_eigenvalues_of_too_few_points_results_in_0():
"""If there are too few points to c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestExtractEigenValues:
def test_eigenvalues_in_cylinders(self):
"""Test provenance added (This should actually be part the general feature extractor test suite)."""
random.seed(102938482634)
point_cloud = load(os.path.join('testdata', 'AHN3.las'))
num_all_pc_points = len(point... | the_stack_v2_python_sparse | laserchicken/feature_extractor/test_eigenvals_feature_extractor.py | eEcoLiDAR/laserchicken | train | 28 | |
fe73898049aeb6bce9342b9ae04cba78be372394 | [
"self.gettext_domain = 'screen-resolution-extra'\ngettext.textdomain(self.gettext_domain)\nself.init_strings()",
"result = unicode(gettext.gettext(str), 'UTF-8')\nif convert_keybindings:\n result = self.convert_keybindings(result)\nreturn result",
"self.string_permission_text = self._('Monitor Resolution Set... | <|body_start_0|>
self.gettext_domain = 'screen-resolution-extra'
gettext.textdomain(self.gettext_domain)
self.init_strings()
<|end_body_0|>
<|body_start_1|>
result = unicode(gettext.gettext(str), 'UTF-8')
if convert_keybindings:
result = self.convert_keybindings(resu... | Abstract user interface. This encapsulates the entire program logic and all strings, but does not implement any concrete user interface. | AbstractUI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbstractUI:
"""Abstract user interface. This encapsulates the entire program logic and all strings, but does not implement any concrete user interface."""
def __init__(self):
"""Initialize system."""
<|body_0|>
def _(self, str, convert_keybindings=False):
"""Keyb... | stack_v2_sparse_classes_10k_train_008002 | 2,804 | no_license | [
{
"docstring": "Initialize system.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Keyboard accelerator aware gettext() wrapper. This optionally converts keyboard accelerators to the appropriate format for the frontend. All strings in the source code should use the '_'... | 3 | null | Implement the Python class `AbstractUI` described below.
Class description:
Abstract user interface. This encapsulates the entire program logic and all strings, but does not implement any concrete user interface.
Method signatures and docstrings:
- def __init__(self): Initialize system.
- def _(self, str, convert_key... | Implement the Python class `AbstractUI` described below.
Class description:
Abstract user interface. This encapsulates the entire program logic and all strings, but does not implement any concrete user interface.
Method signatures and docstrings:
- def __init__(self): Initialize system.
- def _(self, str, convert_key... | d08f7bf370a82b6970387bb9f165d374a9d9092b | <|skeleton|>
class AbstractUI:
"""Abstract user interface. This encapsulates the entire program logic and all strings, but does not implement any concrete user interface."""
def __init__(self):
"""Initialize system."""
<|body_0|>
def _(self, str, convert_keybindings=False):
"""Keyb... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AbstractUI:
"""Abstract user interface. This encapsulates the entire program logic and all strings, but does not implement any concrete user interface."""
def __init__(self):
"""Initialize system."""
self.gettext_domain = 'screen-resolution-extra'
gettext.textdomain(self.gettext_d... | the_stack_v2_python_sparse | usr/share/pyshared/ScreenResolution/ui.py | haniokasai/netwalker-rootfs | train | 2 |
7a87f9c55b7753fd63557ef72ee8a5708aa00ffb | [
"self.num_spkrs = num_spkrs\nself.perm_choices = []\ninitial_seq = np.linspace(0, num_spkrs - 1, num_spkrs, dtype=np.int64)\nself.permutationDFS(initial_seq, 0)\nself.loss_perm_idx = np.linspace(0, num_spkrs * (num_spkrs - 1), num_spkrs, dtype=np.int64).reshape(1, num_spkrs)\nself.loss_perm_idx = (self.loss_perm_id... | <|body_start_0|>
self.num_spkrs = num_spkrs
self.perm_choices = []
initial_seq = np.linspace(0, num_spkrs - 1, num_spkrs, dtype=np.int64)
self.permutationDFS(initial_seq, 0)
self.loss_perm_idx = np.linspace(0, num_spkrs * (num_spkrs - 1), num_spkrs, dtype=np.int64).reshape(1, num... | Permutation Invariant Training (PIT) module. :parameter int num_spkrs: number of speakers for PIT process (2 or 3) | PIT | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PIT:
"""Permutation Invariant Training (PIT) module. :parameter int num_spkrs: number of speakers for PIT process (2 or 3)"""
def __init__(self, num_spkrs):
"""Initialize PIT module."""
<|body_0|>
def min_pit_sample(self, loss):
"""Compute the PIT loss for each s... | stack_v2_sparse_classes_10k_train_008003 | 30,819 | permissive | [
{
"docstring": "Initialize PIT module.",
"name": "__init__",
"signature": "def __init__(self, num_spkrs)"
},
{
"docstring": "Compute the PIT loss for each sample. :param 1-D torch.Tensor loss: list of losses for one sample, including [h1r1, h1r2, h2r1, h2r2] or [h1r1, h1r2, h1r3, h2r1, h2r2, h2r... | 4 | stack_v2_sparse_classes_30k_train_004805 | Implement the Python class `PIT` described below.
Class description:
Permutation Invariant Training (PIT) module. :parameter int num_spkrs: number of speakers for PIT process (2 or 3)
Method signatures and docstrings:
- def __init__(self, num_spkrs): Initialize PIT module.
- def min_pit_sample(self, loss): Compute th... | Implement the Python class `PIT` described below.
Class description:
Permutation Invariant Training (PIT) module. :parameter int num_spkrs: number of speakers for PIT process (2 or 3)
Method signatures and docstrings:
- def __init__(self, num_spkrs): Initialize PIT module.
- def min_pit_sample(self, loss): Compute th... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class PIT:
"""Permutation Invariant Training (PIT) module. :parameter int num_spkrs: number of speakers for PIT process (2 or 3)"""
def __init__(self, num_spkrs):
"""Initialize PIT module."""
<|body_0|>
def min_pit_sample(self, loss):
"""Compute the PIT loss for each s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PIT:
"""Permutation Invariant Training (PIT) module. :parameter int num_spkrs: number of speakers for PIT process (2 or 3)"""
def __init__(self, num_spkrs):
"""Initialize PIT module."""
self.num_spkrs = num_spkrs
self.perm_choices = []
initial_seq = np.linspace(0, num_spkr... | the_stack_v2_python_sparse | espnet/nets/pytorch_backend/e2e_asr_mix.py | espnet/espnet | train | 7,242 |
975272fb32d6b908e02ef46ab80f47a3c88b4f48 | [
"mod = 10 ** 9 + 7\ndp = [[0] * (k + 1) for _ in range(n + 1)]\nfor i in range(n + 1):\n dp[i][0] = 1\nfor i in range(2, n + 1):\n for j in range(1, k + 1):\n for p in range(max(j - i + 1, 0), j + 1):\n dp[i][j] += dp[i - 1][p]\nreturn dp[n][k] % mod",
"mod = 10 ** 9 + 7\ndp = [[0] * (k + ... | <|body_start_0|>
mod = 10 ** 9 + 7
dp = [[0] * (k + 1) for _ in range(n + 1)]
for i in range(n + 1):
dp[i][0] = 1
for i in range(2, n + 1):
for j in range(1, k + 1):
for p in range(max(j - i + 1, 0), j + 1):
dp[i][j] += dp[i - 1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def kInversePairs(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_0|>
def kInversePairs_(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
mod = 10 ** 9 + 7
dp = [... | stack_v2_sparse_classes_10k_train_008004 | 1,427 | no_license | [
{
"docstring": ":type n: int :type k: int :rtype: int",
"name": "kInversePairs",
"signature": "def kInversePairs(self, n, k)"
},
{
"docstring": ":type n: int :type k: int :rtype: int",
"name": "kInversePairs_",
"signature": "def kInversePairs_(self, n, k)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kInversePairs(self, n, k): :type n: int :type k: int :rtype: int
- def kInversePairs_(self, n, k): :type n: int :type k: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kInversePairs(self, n, k): :type n: int :type k: int :rtype: int
- def kInversePairs_(self, n, k): :type n: int :type k: int :rtype: int
<|skeleton|>
class Solution:
de... | 768edc4a5526c8972fec66c6a71a38c0b24a1451 | <|skeleton|>
class Solution:
def kInversePairs(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_0|>
def kInversePairs_(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def kInversePairs(self, n, k):
""":type n: int :type k: int :rtype: int"""
mod = 10 ** 9 + 7
dp = [[0] * (k + 1) for _ in range(n + 1)]
for i in range(n + 1):
dp[i][0] = 1
for i in range(2, n + 1):
for j in range(1, k + 1):
... | the_stack_v2_python_sparse | leetcode(多线程,DP,贪心,SQL)/二刷DP与贪心LeetCode/动态规划/629. K个逆序对数组/solution.py | faker-hong/testOne | train | 1 | |
d35203e58402430788deb9c5905e15c3a603bd1b | [
"super().__init__()\nself.name = name\nself.type = type\nself.description = description",
"result = {}\nresult['type'] = self.type\nif self.description is not None:\n result['description'] = self.description\nreturn result"
] | <|body_start_0|>
super().__init__()
self.name = name
self.type = type
self.description = description
<|end_body_0|>
<|body_start_1|>
result = {}
result['type'] = self.type
if self.description is not None:
result['description'] = self.description
... | Base class for security schemes. A security scheme is required if security requirements and security schemes should be published in OpenAPI documents. | SecurityScheme | [
"Python-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SecurityScheme:
"""Base class for security schemes. A security scheme is required if security requirements and security schemes should be published in OpenAPI documents."""
def __init__(self, name, type, *, description=None, **kwargs):
"""Initialize the security scheme. :param name: ... | stack_v2_sparse_classes_10k_train_008005 | 3,415 | permissive | [
{
"docstring": "Initialize the security scheme. :param name: The name of the security scheme. :param type: The type of security scheme.",
"name": "__init__",
"signature": "def __init__(self, name, type, *, description=None, **kwargs)"
},
{
"docstring": "JSON representation of the security scheme... | 2 | stack_v2_sparse_classes_30k_train_000179 | Implement the Python class `SecurityScheme` described below.
Class description:
Base class for security schemes. A security scheme is required if security requirements and security schemes should be published in OpenAPI documents.
Method signatures and docstrings:
- def __init__(self, name, type, *, description=None,... | Implement the Python class `SecurityScheme` described below.
Class description:
Base class for security schemes. A security scheme is required if security requirements and security schemes should be published in OpenAPI documents.
Method signatures and docstrings:
- def __init__(self, name, type, *, description=None,... | 19e8d396aa9f3b6df28f773d06846d2bb58d1674 | <|skeleton|>
class SecurityScheme:
"""Base class for security schemes. A security scheme is required if security requirements and security schemes should be published in OpenAPI documents."""
def __init__(self, name, type, *, description=None, **kwargs):
"""Initialize the security scheme. :param name: ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SecurityScheme:
"""Base class for security schemes. A security scheme is required if security requirements and security schemes should be published in OpenAPI documents."""
def __init__(self, name, type, *, description=None, **kwargs):
"""Initialize the security scheme. :param name: The name of t... | the_stack_v2_python_sparse | src/roax/security.py | lliu8080/roax | train | 0 |
ba095f8d49006181f7700d1b4b4db7099af1f932 | [
"context = super(NewUserView, self).get_context_data(**kwargs)\nif 'user_is_active' in self.request.session:\n context['user_is_active'] = self.request.session['user_is_active']\nif 'user_is_none' in self.request.session:\n context['user_is_none'] = self.request.session['user_is_none']\nreturn context",
"ne... | <|body_start_0|>
context = super(NewUserView, self).get_context_data(**kwargs)
if 'user_is_active' in self.request.session:
context['user_is_active'] = self.request.session['user_is_active']
if 'user_is_none' in self.request.session:
context['user_is_none'] = self.request... | Base generic view for user login or registration. | NewUserView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NewUserView:
"""Base generic view for user login or registration."""
def get_context_data(self, **kwargs):
"""Method for get_context_date implementing from generic.DetailView class."""
<|body_0|>
def new_user_form_valid(self, form):
"""Method for new user registr... | stack_v2_sparse_classes_10k_train_008006 | 5,315 | permissive | [
{
"docstring": "Method for get_context_date implementing from generic.DetailView class.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
},
{
"docstring": "Method for new user registration.",
"name": "new_user_form_valid",
"signature": "def new_user_form_... | 3 | stack_v2_sparse_classes_30k_train_000487 | Implement the Python class `NewUserView` described below.
Class description:
Base generic view for user login or registration.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Method for get_context_date implementing from generic.DetailView class.
- def new_user_form_valid(self, form): Method... | Implement the Python class `NewUserView` described below.
Class description:
Base generic view for user login or registration.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Method for get_context_date implementing from generic.DetailView class.
- def new_user_form_valid(self, form): Method... | 5effabfaee8ff5d1294d1b4de576cde718cd24ae | <|skeleton|>
class NewUserView:
"""Base generic view for user login or registration."""
def get_context_data(self, **kwargs):
"""Method for get_context_date implementing from generic.DetailView class."""
<|body_0|>
def new_user_form_valid(self, form):
"""Method for new user registr... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NewUserView:
"""Base generic view for user login or registration."""
def get_context_data(self, **kwargs):
"""Method for get_context_date implementing from generic.DetailView class."""
context = super(NewUserView, self).get_context_data(**kwargs)
if 'user_is_active' in self.reques... | the_stack_v2_python_sparse | user_account/views.py | piemar1/Schedule_django | train | 0 |
b39c2636ecc0a419bab3a42117f8392ed86c90ea | [
"if not isinstance(q, np.ndarray):\n q = np.array(q)\nif q[0] != 0.0 or q[-1] != 1.0:\n raise RuntimeError('Invalid quantiles boundaries [' + ','.join([str(q[i]) for i in range(q.shape[0])]) + ']')\nif np.any(q[:-1] > q[1:]):\n raise RuntimeError('Quantile edges not increasing [' + ','.join([str(q[i]) for ... | <|body_start_0|>
if not isinstance(q, np.ndarray):
q = np.array(q)
if q[0] != 0.0 or q[-1] != 1.0:
raise RuntimeError('Invalid quantiles boundaries [' + ','.join([str(q[i]) for i in range(q.shape[0])]) + ']')
if np.any(q[:-1] > q[1:]):
raise RuntimeError('Quan... | Applies quantile binning -> provide quantile boundaries and bin histogram accordingly list of quantile values need to be optimized | Quantile | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Quantile:
"""Applies quantile binning -> provide quantile boundaries and bin histogram accordingly list of quantile values need to be optimized"""
def __init__(self, h, q):
"""h : either TH1 or list of TH1 q : quantiles list [0 , ... 1]"""
<|body_0|>
def rebin_method(x, ... | stack_v2_sparse_classes_10k_train_008007 | 35,100 | no_license | [
{
"docstring": "h : either TH1 or list of TH1 q : quantiles list [0 , ... 1]",
"name": "__init__",
"signature": "def __init__(self, h, q)"
},
{
"docstring": "x: bin centers w: bin heights (bin content) q: quantiles",
"name": "rebin_method",
"signature": "def rebin_method(x, w, q)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002650 | Implement the Python class `Quantile` described below.
Class description:
Applies quantile binning -> provide quantile boundaries and bin histogram accordingly list of quantile values need to be optimized
Method signatures and docstrings:
- def __init__(self, h, q): h : either TH1 or list of TH1 q : quantiles list [0... | Implement the Python class `Quantile` described below.
Class description:
Applies quantile binning -> provide quantile boundaries and bin histogram accordingly list of quantile values need to be optimized
Method signatures and docstrings:
- def __init__(self, h, q): h : either TH1 or list of TH1 q : quantiles list [0... | 30df434202df51017309b5bf88a1d9b94041b6ef | <|skeleton|>
class Quantile:
"""Applies quantile binning -> provide quantile boundaries and bin histogram accordingly list of quantile values need to be optimized"""
def __init__(self, h, q):
"""h : either TH1 or list of TH1 q : quantiles list [0 , ... 1]"""
<|body_0|>
def rebin_method(x, ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Quantile:
"""Applies quantile binning -> provide quantile boundaries and bin histogram accordingly list of quantile values need to be optimized"""
def __init__(self, h, q):
"""h : either TH1 or list of TH1 q : quantiles list [0 , ... 1]"""
if not isinstance(q, np.ndarray):
q =... | the_stack_v2_python_sparse | ZAStatAnalysis/Rebinning.py | kjaffel/ZA_FullAnalysis | train | 11 |
71e1e1077b3efbd4d4171f92b6f98250f65a2cf9 | [
"pygame.init()\nself.surface = pygame.display.set_mode((800, 600))\nnr_molecules = 50\nself.molecules = Molecules(nr_molecules, self.surface.get_size())",
"clock = pygame.time.Clock()\nrunning = True\nwhile running:\n self.surface.fill((0, 0, 0))\n for event in pygame.event.get():\n if event.type == ... | <|body_start_0|>
pygame.init()
self.surface = pygame.display.set_mode((800, 600))
nr_molecules = 50
self.molecules = Molecules(nr_molecules, self.surface.get_size())
<|end_body_0|>
<|body_start_1|>
clock = pygame.time.Clock()
running = True
while running:
... | Gas_Simulation that simulates molecules moving and colliding | Gas_Simulation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Gas_Simulation:
"""Gas_Simulation that simulates molecules moving and colliding"""
def __init__(self):
"""Initalizes the Simulation"""
<|body_0|>
def run(self):
"""Runs the simulation until the program ends"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_10k_train_008008 | 5,895 | no_license | [
{
"docstring": "Initalizes the Simulation",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Runs the simulation until the program ends",
"name": "run",
"signature": "def run(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007112 | Implement the Python class `Gas_Simulation` described below.
Class description:
Gas_Simulation that simulates molecules moving and colliding
Method signatures and docstrings:
- def __init__(self): Initalizes the Simulation
- def run(self): Runs the simulation until the program ends | Implement the Python class `Gas_Simulation` described below.
Class description:
Gas_Simulation that simulates molecules moving and colliding
Method signatures and docstrings:
- def __init__(self): Initalizes the Simulation
- def run(self): Runs the simulation until the program ends
<|skeleton|>
class Gas_Simulation:... | dd8fec6f71b18aaa4a78e26a4afefc8c70327093 | <|skeleton|>
class Gas_Simulation:
"""Gas_Simulation that simulates molecules moving and colliding"""
def __init__(self):
"""Initalizes the Simulation"""
<|body_0|>
def run(self):
"""Runs the simulation until the program ends"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Gas_Simulation:
"""Gas_Simulation that simulates molecules moving and colliding"""
def __init__(self):
"""Initalizes the Simulation"""
pygame.init()
self.surface = pygame.display.set_mode((800, 600))
nr_molecules = 50
self.molecules = Molecules(nr_molecules, self.s... | the_stack_v2_python_sparse | Gas_Simulation.py | bterwijn/python | train | 0 |
11d226e25ba32a603eb54d13f6d6f50cd7eec202 | [
"if not root:\n return ''\nqueue = deque()\nqueue.append(root)\nres = str(root.val) + '_'\nwhile len(queue) > 0:\n node = queue.popleft()\n if node.left:\n queue.append(node.left)\n res += str(node.left.val) + '_'\n else:\n res += '#_'\n if node.right:\n queue.append(node.... | <|body_start_0|>
if not root:
return ''
queue = deque()
queue.append(root)
res = str(root.val) + '_'
while len(queue) > 0:
node = queue.popleft()
if node.left:
queue.append(node.left)
res += str(node.left.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 前序遍历进行序列化https://leetcode-cn.com/problems/serialize-and-deserialize-binary-tree/solution/er-cha-shu-de-xu-lie-hua-yu-fan-xu-lie-hua-by-leet/ :desc 对树的序列号和反序列化"""
<|body_0|>
... | stack_v2_sparse_classes_10k_train_008009 | 2,880 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str 前序遍历进行序列化https://leetcode-cn.com/problems/serialize-and-deserialize-binary-tree/solution/er-cha-shu-de-xu-lie-hua-yu-fan-xu-lie-hua-by-leet/ :desc 对树的序列号和反序列化",
"name": "serialize",
"signature": "def serialize(self, root... | 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 前序遍历进行序列化https://leetcode-cn.com/problems/serialize-and-deserialize-binary-tree/solution/... | 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 前序遍历进行序列化https://leetcode-cn.com/problems/serialize-and-deserialize-binary-tree/solution/... | 08b3d9cab3c1806c37d36587372b1e8fb1683f64 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str 前序遍历进行序列化https://leetcode-cn.com/problems/serialize-and-deserialize-binary-tree/solution/er-cha-shu-de-xu-lie-hua-yu-fan-xu-lie-hua-by-leet/ :desc 对树的序列号和反序列化"""
<|body_0|>
... | 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 前序遍历进行序列化https://leetcode-cn.com/problems/serialize-and-deserialize-binary-tree/solution/er-cha-shu-de-xu-lie-hua-yu-fan-xu-lie-hua-by-leet/ :desc 对树的序列号和反序列化"""
if not root:
re... | the_stack_v2_python_sparse | tree/297.Serialize-and-Deserialize-Binary-Tree.py | HonniLin/leetcode | train | 0 | |
7fbe69881af241448c7d33658d310a283fc0da69 | [
"with Session() as lib:\n table_context = lib.virtualfile_from_data(check_kind='vector', data=data, x=x, y=y, z=z, required_z=False)\n with table_context as infile:\n if (outgrid := kwargs.get('G')) is None:\n kwargs.update({'>': outfile})\n lib.call_module(module='triangulate', args=... | <|body_start_0|>
with Session() as lib:
table_context = lib.virtualfile_from_data(check_kind='vector', data=data, x=x, y=y, z=z, required_z=False)
with table_context as infile:
if (outgrid := kwargs.get('G')) is None:
kwargs.update({'>': outfile})
... | Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Triangulate reads in x,y[,z] data and performs Delaunay triangulation, i.e., it finds how the points should be connected to give the most equilateral triangulation possible. If a map projection (give ``region`` and ``projection``) is chosen ... | triangulate | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class triangulate:
"""Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Triangulate reads in x,y[,z] data and performs Delaunay triangulation, i.e., it finds how the points should be connected to give the most equilateral triangulation possible. If a map projection (give ``... | stack_v2_sparse_classes_10k_train_008010 | 14,373 | permissive | [
{
"docstring": "Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Must provide ``outfile`` or ``outgrid``. Full option list at :gmt-docs:`triangulate.html` {aliases} Parameters ---------- x/y/z : np.ndarray Arrays of x and y coordinates and values z of the data points. data : str or... | 3 | stack_v2_sparse_classes_30k_train_006573 | Implement the Python class `triangulate` described below.
Class description:
Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Triangulate reads in x,y[,z] data and performs Delaunay triangulation, i.e., it finds how the points should be connected to give the most equilateral triangulation... | Implement the Python class `triangulate` described below.
Class description:
Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Triangulate reads in x,y[,z] data and performs Delaunay triangulation, i.e., it finds how the points should be connected to give the most equilateral triangulation... | e4ee800e8045aa5f94ddaf7ad821421d007ab279 | <|skeleton|>
class triangulate:
"""Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Triangulate reads in x,y[,z] data and performs Delaunay triangulation, i.e., it finds how the points should be connected to give the most equilateral triangulation possible. If a map projection (give ``... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class triangulate:
"""Delaunay triangulation or Voronoi partitioning and gridding of Cartesian data. Triangulate reads in x,y[,z] data and performs Delaunay triangulation, i.e., it finds how the points should be connected to give the most equilateral triangulation possible. If a map projection (give ``region`` and ... | the_stack_v2_python_sparse | pygmt/src/triangulate.py | GenericMappingTools/pygmt | train | 490 |
b0a147986ae90d6e0274dac8275669288a44ea59 | [
"self.max_length = max_length\nself.tmto_lookup = []\nfor i in range(self.max_length + 1):\n self.tmto_lookup.append({})",
"try:\n return (True, self.custom_copy(self.tmto_lookup[length][ip_ngram][target_level]))\nexcept KeyError:\n return (False, None)",
"if ip_ngram not in self.tmto_lookup[length]:\n... | <|body_start_0|>
self.max_length = max_length
self.tmto_lookup = []
for i in range(self.max_length + 1):
self.tmto_lookup.append({})
<|end_body_0|>
<|body_start_1|>
try:
return (True, self.custom_copy(self.tmto_lookup[length][ip_ngram][target_level]))
exc... | Contains all the logic to speed up guess generation by using tmto tricks Creating this as a class so I can easily pass it around | Optimizer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Optimizer:
"""Contains all the logic to speed up guess generation by using tmto tricks Creating this as a class so I can easily pass it around"""
def __init__(self, max_length):
"""Initializes the optimizer Inputs: max_length: The maximum length of strings to optimize. Increasing thi... | stack_v2_sparse_classes_10k_train_008011 | 3,380 | no_license | [
{
"docstring": "Initializes the optimizer Inputs: max_length: The maximum length of strings to optimize. Increasing this increases memory requirements",
"name": "__init__",
"signature": "def __init__(self, max_length)"
},
{
"docstring": "Look up a previous result Inputs: ip_ngram: The initial st... | 4 | stack_v2_sparse_classes_30k_val_000152 | Implement the Python class `Optimizer` described below.
Class description:
Contains all the logic to speed up guess generation by using tmto tricks Creating this as a class so I can easily pass it around
Method signatures and docstrings:
- def __init__(self, max_length): Initializes the optimizer Inputs: max_length: ... | Implement the Python class `Optimizer` described below.
Class description:
Contains all the logic to speed up guess generation by using tmto tricks Creating this as a class so I can easily pass it around
Method signatures and docstrings:
- def __init__(self, max_length): Initializes the optimizer Inputs: max_length: ... | 6fed1047838091edec7ce96679c3d0887073ed3b | <|skeleton|>
class Optimizer:
"""Contains all the logic to speed up guess generation by using tmto tricks Creating this as a class so I can easily pass it around"""
def __init__(self, max_length):
"""Initializes the optimizer Inputs: max_length: The maximum length of strings to optimize. Increasing thi... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Optimizer:
"""Contains all the logic to speed up guess generation by using tmto tricks Creating this as a class so I can easily pass it around"""
def __init__(self, max_length):
"""Initializes the optimizer Inputs: max_length: The maximum length of strings to optimize. Increasing this increases m... | the_stack_v2_python_sparse | lib_guesser/omen/optimizer.py | lakiw/pcfg_cracker | train | 286 |
d68d59000ec06311e17d7c2fac817326491c0eb4 | [
"sigma_0 = (ScoremapFOVProjParams & 'proj_sigma < 0.00001').proj()\nlen_session = (ScoremapFOV & 'no_sessions > 0').proj()\nkeys = super().key_source & sigma_0 & len_session\nreturn keys",
"params = (ScoremapFOVProjParams & key).fetch1()\nscore_map_dict, score_map_dict_shuff = (ScoremapFOV & key).fetch1('aligned_... | <|body_start_0|>
sigma_0 = (ScoremapFOVProjParams & 'proj_sigma < 0.00001').proj()
len_session = (ScoremapFOV & 'no_sessions > 0').proj()
keys = super().key_source & sigma_0 & len_session
return keys
<|end_body_0|>
<|body_start_1|>
params = (ScoremapFOVProjParams & key).fetch1()... | ScoremapFOVMoran | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScoremapFOVMoran:
def key_source(self):
"""For Moran's I calculation do NOT take any parameter sets (ScoremapFOVProjParams) that smooth the score maps. This will lead to wrong assumptions."""
<|body_0|>
def make(self, key):
"""Perform Moran’s I global autocorrelation... | stack_v2_sparse_classes_10k_train_008012 | 42,527 | permissive | [
{
"docstring": "For Moran's I calculation do NOT take any parameter sets (ScoremapFOVProjParams) that smooth the score maps. This will lead to wrong assumptions.",
"name": "key_source",
"signature": "def key_source(self)"
},
{
"docstring": "Perform Moran’s I global autocorrelation statistic calc... | 2 | stack_v2_sparse_classes_30k_train_001453 | Implement the Python class `ScoremapFOVMoran` described below.
Class description:
Implement the ScoremapFOVMoran class.
Method signatures and docstrings:
- def key_source(self): For Moran's I calculation do NOT take any parameter sets (ScoremapFOVProjParams) that smooth the score maps. This will lead to wrong assumpt... | Implement the Python class `ScoremapFOVMoran` described below.
Class description:
Implement the ScoremapFOVMoran class.
Method signatures and docstrings:
- def key_source(self): For Moran's I calculation do NOT take any parameter sets (ScoremapFOVProjParams) that smooth the score maps. This will lead to wrong assumpt... | 83c2dfc8597f9b7f4918f27b735420c4a0cc3415 | <|skeleton|>
class ScoremapFOVMoran:
def key_source(self):
"""For Moran's I calculation do NOT take any parameter sets (ScoremapFOVProjParams) that smooth the score maps. This will lead to wrong assumptions."""
<|body_0|>
def make(self, key):
"""Perform Moran’s I global autocorrelation... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ScoremapFOVMoran:
def key_source(self):
"""For Moran's I calculation do NOT take any parameter sets (ScoremapFOVProjParams) that smooth the score maps. This will lead to wrong assumptions."""
sigma_0 = (ScoremapFOVProjParams & 'proj_sigma < 0.00001').proj()
len_session = (ScoremapFOV &... | the_stack_v2_python_sparse | dj_schemas/anatomical_alignment.py | kavli-ntnu/mini2p_topography | train | 2 | |
af213e2ea812ebfbb776c17c400731a5544f6bf7 | [
"self.matrix = matrix\nself.m, self.n = (len(matrix), len(matrix[0]))\nself.right = [[0] * self.n for i in range(self.m)]\nfor i in range(self.n - 1, -1, -1):\n for j in range(self.m):\n if i == self.n - 1:\n self.right[j][i] = self.matrix[j][i]\n else:\n self.right[j][i] = se... | <|body_start_0|>
self.matrix = matrix
self.m, self.n = (len(matrix), len(matrix[0]))
self.right = [[0] * self.n for i in range(self.m)]
for i in range(self.n - 1, -1, -1):
for j in range(self.m):
if i == self.n - 1:
self.right[j][i] = self.... | NumMatrix | [] | 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_008013 | 1,544 | no_license | [
{
"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:... | 6e18c5d257840489cc3fb1079ae3804c743982a4 | <|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.matrix = matrix
self.m, self.n = (len(matrix), len(matrix[0]))
self.right = [[0] * self.n for i in range(self.m)]
for i in range(self.n - 1, -1, -1):
for j in range(self.m):
... | the_stack_v2_python_sparse | 剑指 Offer II 013. 二维子矩阵的和.py | yangyuxiang1996/leetcode | train | 0 | |
8706418d80a22006c4432b5d9cb8e0e556ea38bd | [
"self.learning_rate = learning_rate\nself.mu = mu\nself.rho = rho\nself.k = 0\nself.first_moment = 0\nself.second_moment = 0\nself.epsilon = np.finfo(float).eps",
"self.k += 1\nself.first_moment = self.mu * self.first_moment + (1 - self.mu) * gradient_tensor\nself.second_moment = self.rho * self.second_moment + (... | <|body_start_0|>
self.learning_rate = learning_rate
self.mu = mu
self.rho = rho
self.k = 0
self.first_moment = 0
self.second_moment = 0
self.epsilon = np.finfo(float).eps
<|end_body_0|>
<|body_start_1|>
self.k += 1
self.first_moment = self.mu * se... | Implementation of the "Adaptive Moment Estimation (Adam)"-method. | Adam | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Adam:
"""Implementation of the "Adaptive Moment Estimation (Adam)"-method."""
def __init__(self, learning_rate, mu, rho):
"""Constructor for the "Adaptive Moment Estimation (Adam)"-method. :param learning_rate: Learning rate. :param mu: Hyperparameter for the calculation of the first... | stack_v2_sparse_classes_10k_train_008014 | 3,815 | no_license | [
{
"docstring": "Constructor for the \"Adaptive Moment Estimation (Adam)\"-method. :param learning_rate: Learning rate. :param mu: Hyperparameter for the calculation of the first moment (v). :param rho: Hyperparamter for the calculation of the second moment (r).",
"name": "__init__",
"signature": "def __... | 2 | stack_v2_sparse_classes_30k_train_002791 | Implement the Python class `Adam` described below.
Class description:
Implementation of the "Adaptive Moment Estimation (Adam)"-method.
Method signatures and docstrings:
- def __init__(self, learning_rate, mu, rho): Constructor for the "Adaptive Moment Estimation (Adam)"-method. :param learning_rate: Learning rate. :... | Implement the Python class `Adam` described below.
Class description:
Implementation of the "Adaptive Moment Estimation (Adam)"-method.
Method signatures and docstrings:
- def __init__(self, learning_rate, mu, rho): Constructor for the "Adaptive Moment Estimation (Adam)"-method. :param learning_rate: Learning rate. :... | 1d2d990c75bb7977d76430a50a31bd9ce31da37d | <|skeleton|>
class Adam:
"""Implementation of the "Adaptive Moment Estimation (Adam)"-method."""
def __init__(self, learning_rate, mu, rho):
"""Constructor for the "Adaptive Moment Estimation (Adam)"-method. :param learning_rate: Learning rate. :param mu: Hyperparameter for the calculation of the first... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Adam:
"""Implementation of the "Adaptive Moment Estimation (Adam)"-method."""
def __init__(self, learning_rate, mu, rho):
"""Constructor for the "Adaptive Moment Estimation (Adam)"-method. :param learning_rate: Learning rate. :param mu: Hyperparameter for the calculation of the first moment (v). ... | the_stack_v2_python_sparse | Exercise 2/src_to_implement/Optimization/Optimizers.py | StefanFischer/Deep-Learning-Framework | train | 0 |
d8a27c8ecacd6d6c983ab807a9cdcc471b726370 | [
"method = 'chart.gettopartists'\nurl_params = {'method': method}\nif page is not None:\n url_params['page'] = page\nif limit is not None:\n url_params['limit'] = limit\nquery = parse.urlencode(url_params)\nreturn query",
"method = 'chart.gettoptags'\nurl_params = {'method': method}\nif page is not None:\n ... | <|body_start_0|>
method = 'chart.gettopartists'
url_params = {'method': method}
if page is not None:
url_params['page'] = page
if limit is not None:
url_params['limit'] = limit
query = parse.urlencode(url_params)
return query
<|end_body_0|>
<|body... | ChartClient | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChartClient:
def get_top_artists(self, page=None, limit=10):
"""Get the top artists chart Params: page (Optional) : The page number to fetch. Defaults to first page. limit (Optional) : The number of results to fetch per page."""
<|body_0|>
def get_top_tags(self, page=None, l... | stack_v2_sparse_classes_10k_train_008015 | 1,751 | no_license | [
{
"docstring": "Get the top artists chart Params: page (Optional) : The page number to fetch. Defaults to first page. limit (Optional) : The number of results to fetch per page.",
"name": "get_top_artists",
"signature": "def get_top_artists(self, page=None, limit=10)"
},
{
"docstring": "Get the ... | 3 | stack_v2_sparse_classes_30k_train_001068 | Implement the Python class `ChartClient` described below.
Class description:
Implement the ChartClient class.
Method signatures and docstrings:
- def get_top_artists(self, page=None, limit=10): Get the top artists chart Params: page (Optional) : The page number to fetch. Defaults to first page. limit (Optional) : The... | Implement the Python class `ChartClient` described below.
Class description:
Implement the ChartClient class.
Method signatures and docstrings:
- def get_top_artists(self, page=None, limit=10): Get the top artists chart Params: page (Optional) : The page number to fetch. Defaults to first page. limit (Optional) : The... | 5311236c98274d4e4a6f2c09c6bf021b3f195e66 | <|skeleton|>
class ChartClient:
def get_top_artists(self, page=None, limit=10):
"""Get the top artists chart Params: page (Optional) : The page number to fetch. Defaults to first page. limit (Optional) : The number of results to fetch per page."""
<|body_0|>
def get_top_tags(self, page=None, l... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ChartClient:
def get_top_artists(self, page=None, limit=10):
"""Get the top artists chart Params: page (Optional) : The page number to fetch. Defaults to first page. limit (Optional) : The number of results to fetch per page."""
method = 'chart.gettopartists'
url_params = {'method': me... | the_stack_v2_python_sparse | apiClients/chart_client.py | zflake1208/last-fm-analyzer | train | 0 | |
da86dbe5eaadb3c7609d51c6d8365536a2a5f98a | [
"sk_idx = {v: i for i, v in enumerate(req_skills)}\ndp = {0: []}\nfor i, p in enumerate(people):\n sks = 0\n for sk in p:\n if sk in sk_idx:\n sks |= 1 << sk_idx[sk]\n for k, v in list(dp.items()):\n incld = k | sks\n if incld != k and (incld not in dp or len(dp[incld]) > le... | <|body_start_0|>
sk_idx = {v: i for i, v in enumerate(req_skills)}
dp = {0: []}
for i, p in enumerate(people):
sks = 0
for sk in p:
if sk in sk_idx:
sks |= 1 << sk_idx[sk]
for k, v in list(dp.items()):
incld ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def smallestSufficientTeam(self, req_skills, people):
""":type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]"""
<|body_0|>
def smallestSufficientTeam2(self, req_skills, people):
""":type req_skills: List[str] :type people: List[List[... | stack_v2_sparse_classes_10k_train_008016 | 1,971 | no_license | [
{
"docstring": ":type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]",
"name": "smallestSufficientTeam",
"signature": "def smallestSufficientTeam(self, req_skills, people)"
},
{
"docstring": ":type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]",
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def smallestSufficientTeam(self, req_skills, people): :type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]
- def smallestSufficientTeam2(self, req_skills, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def smallestSufficientTeam(self, req_skills, people): :type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]
- def smallestSufficientTeam2(self, req_skills, ... | dbdb227e12f329e4ca064b338f1fbdca42f3a848 | <|skeleton|>
class Solution:
def smallestSufficientTeam(self, req_skills, people):
""":type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]"""
<|body_0|>
def smallestSufficientTeam2(self, req_skills, people):
""":type req_skills: List[str] :type people: List[List[... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def smallestSufficientTeam(self, req_skills, people):
""":type req_skills: List[str] :type people: List[List[str]] :rtype: List[int]"""
sk_idx = {v: i for i, v in enumerate(req_skills)}
dp = {0: []}
for i, p in enumerate(people):
sks = 0
for sk... | the_stack_v2_python_sparse | LC1125.py | Qiao-Liang/LeetCode | train | 0 | |
10ec79b81992964c09d8dd1830229b7b322274fc | [
"super().__init__()\nself.lstm_cell = tf.keras.layers.LSTMCell(lstm_units, dropout=0.3, kernel_regularizer=tf.keras.regularizers.l2(l=0.02), recurrent_regularizer=tf.keras.regularizers.l2(l=0.02))\nself.dense1 = tf.keras.layers.Dense(1)\nself.dense2 = tf.keras.layers.Dense(10, activation='relu', kernel_regularizer=... | <|body_start_0|>
super().__init__()
self.lstm_cell = tf.keras.layers.LSTMCell(lstm_units, dropout=0.3, kernel_regularizer=tf.keras.regularizers.l2(l=0.02), recurrent_regularizer=tf.keras.regularizers.l2(l=0.02))
self.dense1 = tf.keras.layers.Dense(1)
self.dense2 = tf.keras.layers.Dense(1... | Simple RNN based CF model. | RNNCFModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNCFModel:
"""Simple RNN based CF model."""
def __init__(self, maxhd, maxv, mina, maxa, lstm_units=20, dt=0.1):
"""Inits RNN based CF model. Args: maxhd: max headway (for nomalization of inputs) maxv: max velocity (for nomalization of inputs) mina: minimum acceleration (for nomaliza... | stack_v2_sparse_classes_10k_train_008017 | 20,223 | permissive | [
{
"docstring": "Inits RNN based CF model. Args: maxhd: max headway (for nomalization of inputs) maxv: max velocity (for nomalization of inputs) mina: minimum acceleration (for nomalization of outputs) maxa: maximum acceleration (for nomalization of outputs) dt: timestep",
"name": "__init__",
"signature"... | 2 | stack_v2_sparse_classes_30k_train_001650 | Implement the Python class `RNNCFModel` described below.
Class description:
Simple RNN based CF model.
Method signatures and docstrings:
- def __init__(self, maxhd, maxv, mina, maxa, lstm_units=20, dt=0.1): Inits RNN based CF model. Args: maxhd: max headway (for nomalization of inputs) maxv: max velocity (for nomaliz... | Implement the Python class `RNNCFModel` described below.
Class description:
Simple RNN based CF model.
Method signatures and docstrings:
- def __init__(self, maxhd, maxv, mina, maxa, lstm_units=20, dt=0.1): Inits RNN based CF model. Args: maxhd: max headway (for nomalization of inputs) maxv: max velocity (for nomaliz... | 0aaf9674e987822ff2dc90c74613d5e68e8ef0ce | <|skeleton|>
class RNNCFModel:
"""Simple RNN based CF model."""
def __init__(self, maxhd, maxv, mina, maxa, lstm_units=20, dt=0.1):
"""Inits RNN based CF model. Args: maxhd: max headway (for nomalization of inputs) maxv: max velocity (for nomalization of inputs) mina: minimum acceleration (for nomaliza... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RNNCFModel:
"""Simple RNN based CF model."""
def __init__(self, maxhd, maxv, mina, maxa, lstm_units=20, dt=0.1):
"""Inits RNN based CF model. Args: maxhd: max headway (for nomalization of inputs) maxv: max velocity (for nomalization of inputs) mina: minimum acceleration (for nomalization of outpu... | the_stack_v2_python_sparse | scripts/meng/deep learning/DL2_relax.py | seccode/havsim | train | 0 |
8a105f7e25566198b7b23bb0f50aae758ce128e3 | [
"super(ResNet_Det, self).__init__()\nself.net_desc = desc\nself.depth = desc['depth']\nself.num_stages = desc['num_stages'] if 'num_stages' in desc else 4\nself.strides = desc['strides'] if 'strides' in desc else (1, 2, 2, 2)\nself.dilations = desc['dilations'] if 'dilations' in desc else (1, 1, 1, 1)\nself.out_ind... | <|body_start_0|>
super(ResNet_Det, self).__init__()
self.net_desc = desc
self.depth = desc['depth']
self.num_stages = desc['num_stages'] if 'num_stages' in desc else 4
self.strides = desc['strides'] if 'strides' in desc else (1, 2, 2, 2)
self.dilations = desc['dilations']... | ResNet for detection. | ResNet_Det | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResNet_Det:
"""ResNet for detection."""
def __init__(self, desc):
"""Init ResNet."""
<|body_0|>
def _make_stem_layer(self):
"""Make stem layer."""
<|body_1|>
def _freeze_stages(self):
"""Freeze stages."""
<|body_2|>
def init_weig... | stack_v2_sparse_classes_10k_train_008018 | 6,387 | permissive | [
{
"docstring": "Init ResNet.",
"name": "__init__",
"signature": "def __init__(self, desc)"
},
{
"docstring": "Make stem layer.",
"name": "_make_stem_layer",
"signature": "def _make_stem_layer(self)"
},
{
"docstring": "Freeze stages.",
"name": "_freeze_stages",
"signature"... | 6 | null | Implement the Python class `ResNet_Det` described below.
Class description:
ResNet for detection.
Method signatures and docstrings:
- def __init__(self, desc): Init ResNet.
- def _make_stem_layer(self): Make stem layer.
- def _freeze_stages(self): Freeze stages.
- def init_weights(self, pretrained=None): Init weight.... | Implement the Python class `ResNet_Det` described below.
Class description:
ResNet for detection.
Method signatures and docstrings:
- def __init__(self, desc): Init ResNet.
- def _make_stem_layer(self): Make stem layer.
- def _freeze_stages(self): Freeze stages.
- def init_weights(self, pretrained=None): Init weight.... | df51ed9c1d6dbde1deef63f2a037a369f8554406 | <|skeleton|>
class ResNet_Det:
"""ResNet for detection."""
def __init__(self, desc):
"""Init ResNet."""
<|body_0|>
def _make_stem_layer(self):
"""Make stem layer."""
<|body_1|>
def _freeze_stages(self):
"""Freeze stages."""
<|body_2|>
def init_weig... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ResNet_Det:
"""ResNet for detection."""
def __init__(self, desc):
"""Init ResNet."""
super(ResNet_Det, self).__init__()
self.net_desc = desc
self.depth = desc['depth']
self.num_stages = desc['num_stages'] if 'num_stages' in desc else 4
self.strides = desc['... | the_stack_v2_python_sparse | built-in/TensorFlow/Research/cv/image_classification/Cars_for_TensorFlow/automl/vega/search_space/networks/pytorch/backbones/resnet_det.py | Huawei-Ascend/modelzoo | train | 1 |
221e8ebeb1db9a394dc7f7653982818ff025649d | [
"self.meta = {}\nself.meta['ra'] = dict(ext=0, card='RA')\nself.meta['dec'] = dict(ext=0, card='DEC')\nself.meta['target'] = dict(ext=0, card='OBJECT')\nself.meta['decker'] = dict(ext=0, card=None, default='default')\nself.meta['dichroic'] = dict(ext=0, card=None, default='default')\nself.meta['binning'] = dict(ext... | <|body_start_0|>
self.meta = {}
self.meta['ra'] = dict(ext=0, card='RA')
self.meta['dec'] = dict(ext=0, card='DEC')
self.meta['target'] = dict(ext=0, card='OBJECT')
self.meta['decker'] = dict(ext=0, card=None, default='default')
self.meta['dichroic'] = dict(ext=0, card=No... | Child to handle Magellan/FIRE specific code .. note:: For FIRE Echelle, we usually use high gain and SUTR read mode. The exposure time is usually around 900s. The detector parameters below are based on such mode. Standard star and calibrations are usually use Fowler 1 read mode in which case the read noise is ~20 elect... | MagellanFIRESpectrograph | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MagellanFIRESpectrograph:
"""Child to handle Magellan/FIRE specific code .. note:: For FIRE Echelle, we usually use high gain and SUTR read mode. The exposure time is usually around 900s. The detector parameters below are based on such mode. Standard star and calibrations are usually use Fowler 1... | stack_v2_sparse_classes_10k_train_008019 | 18,946 | permissive | [
{
"docstring": "Define how metadata are derived from the spectrograph files. That is, this associates the PypeIt-specific metadata keywords with the instrument-specific header cards using :attr:`meta`.",
"name": "init_meta",
"signature": "def init_meta(self)"
},
{
"docstring": "Define the list o... | 2 | stack_v2_sparse_classes_30k_val_000145 | Implement the Python class `MagellanFIRESpectrograph` described below.
Class description:
Child to handle Magellan/FIRE specific code .. note:: For FIRE Echelle, we usually use high gain and SUTR read mode. The exposure time is usually around 900s. The detector parameters below are based on such mode. Standard star an... | Implement the Python class `MagellanFIRESpectrograph` described below.
Class description:
Child to handle Magellan/FIRE specific code .. note:: For FIRE Echelle, we usually use high gain and SUTR read mode. The exposure time is usually around 900s. The detector parameters below are based on such mode. Standard star an... | 0d2e2196afc6904050b1af4d572f5c643bb07e38 | <|skeleton|>
class MagellanFIRESpectrograph:
"""Child to handle Magellan/FIRE specific code .. note:: For FIRE Echelle, we usually use high gain and SUTR read mode. The exposure time is usually around 900s. The detector parameters below are based on such mode. Standard star and calibrations are usually use Fowler 1... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MagellanFIRESpectrograph:
"""Child to handle Magellan/FIRE specific code .. note:: For FIRE Echelle, we usually use high gain and SUTR read mode. The exposure time is usually around 900s. The detector parameters below are based on such mode. Standard star and calibrations are usually use Fowler 1 read mode in... | the_stack_v2_python_sparse | pypeit/spectrographs/magellan_fire.py | pypeit/PypeIt | train | 136 |
8730d3e1b6dfef1fcbd2adfda165902a4367bae6 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | LegoCaptureServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LegoCaptureServicer:
"""Missing associated documentation comment in .proto file."""
def CollectImages(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def CollectCroppedImages(self, request, context):
"""Missing a... | stack_v2_sparse_classes_10k_train_008020 | 4,069 | no_license | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "CollectImages",
"signature": "def CollectImages(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "CollectCroppedImages",
"signature": "def Col... | 2 | stack_v2_sparse_classes_30k_train_002717 | Implement the Python class `LegoCaptureServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def CollectImages(self, request, context): Missing associated documentation comment in .proto file.
- def CollectCroppedImages(self, request... | Implement the Python class `LegoCaptureServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def CollectImages(self, request, context): Missing associated documentation comment in .proto file.
- def CollectCroppedImages(self, request... | 2fca34564d865bbf28c4cddc7665d0276f7e0cf2 | <|skeleton|>
class LegoCaptureServicer:
"""Missing associated documentation comment in .proto file."""
def CollectImages(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def CollectCroppedImages(self, request, context):
"""Missing a... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LegoCaptureServicer:
"""Missing associated documentation comment in .proto file."""
def CollectImages(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented... | the_stack_v2_python_sparse | lego_sorter_server/generated/LegoCapture_pb2_grpc.py | etusien/LegoSorterServer | train | 0 |
d9e557ec3e189281715e55d81fa18c5f3dcfe623 | [
"super().__init__()\nif dim == 1:\n conv = nn.Conv1d\n bn = nn.BatchNorm1d\nelif dim == 2:\n conv = nn.Conv2d\n bn = nn.BatchNorm2d\nelse:\n raise ValueError\nif not isinstance(out_channels, (list, tuple)):\n out_channels = [out_channels]\nlayers = []\nfor oc in out_channels:\n layers.extend([c... | <|body_start_0|>
super().__init__()
if dim == 1:
conv = nn.Conv1d
bn = nn.BatchNorm1d
elif dim == 2:
conv = nn.Conv2d
bn = nn.BatchNorm2d
else:
raise ValueError
if not isinstance(out_channels, (list, tuple)):
... | SharedMLP Module, comprising Conv2d, BatchNorm and ReLU blocks. | SharedMLP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SharedMLP:
"""SharedMLP Module, comprising Conv2d, BatchNorm and ReLU blocks."""
def __init__(self, in_channels, out_channels, dim=1):
"""Constructor for SharedMLP Block. Args: in_channels: Number of input channels. out_channels: Number of output channels. dim: Input dimension"""
... | stack_v2_sparse_classes_10k_train_008021 | 22,879 | permissive | [
{
"docstring": "Constructor for SharedMLP Block. Args: in_channels: Number of input channels. out_channels: Number of output channels. dim: Input dimension",
"name": "__init__",
"signature": "def __init__(self, in_channels, out_channels, dim=1)"
},
{
"docstring": "Forward pass for SharedMLP Args... | 2 | stack_v2_sparse_classes_30k_train_000005 | Implement the Python class `SharedMLP` described below.
Class description:
SharedMLP Module, comprising Conv2d, BatchNorm and ReLU blocks.
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, dim=1): Constructor for SharedMLP Block. Args: in_channels: Number of input channels. out_channel... | Implement the Python class `SharedMLP` described below.
Class description:
SharedMLP Module, comprising Conv2d, BatchNorm and ReLU blocks.
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, dim=1): Constructor for SharedMLP Block. Args: in_channels: Number of input channels. out_channel... | 51482281dc180786e7563c73c12ac5df89289748 | <|skeleton|>
class SharedMLP:
"""SharedMLP Module, comprising Conv2d, BatchNorm and ReLU blocks."""
def __init__(self, in_channels, out_channels, dim=1):
"""Constructor for SharedMLP Block. Args: in_channels: Number of input channels. out_channels: Number of output channels. dim: Input dimension"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SharedMLP:
"""SharedMLP Module, comprising Conv2d, BatchNorm and ReLU blocks."""
def __init__(self, in_channels, out_channels, dim=1):
"""Constructor for SharedMLP Block. Args: in_channels: Number of input channels. out_channels: Number of output channels. dim: Input dimension"""
super().... | the_stack_v2_python_sparse | ml3d/torch/models/pvcnn.py | CosmosHua/Open3D-ML | train | 0 |
2c6248d15f6e826b54efdc24b94d6134a0146782 | [
"if not heights:\n return 0\nminl = []\nminh = float('inf')\nfor i in range(len(heights)):\n if heights[i] < minh:\n minh = heights[i]\n minl = [i]\n elif heights[i] == minh:\n minl.append(i)\narea_list = [minh * len(heights), self.largestRectangleArea(heights[:minl[0]]), self.largestR... | <|body_start_0|>
if not heights:
return 0
minl = []
minh = float('inf')
for i in range(len(heights)):
if heights[i] < minh:
minh = heights[i]
minl = [i]
elif heights[i] == minh:
minl.append(i)
are... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def largestRectangleArea1(self, heights) -> int:
""":param heights: :return: int 用小数分割递归求解，OT"""
<|body_0|>
def largestRectangleArea(self, heights):
""":type heights: List[int] :rtype: int 利用栈构造升序序列，若下一个数是升序则进栈，否则开始退栈，直到出现小于等于当前数的元素。注意栈内是升序序列， 这就找到了介于两个小数之间... | stack_v2_sparse_classes_10k_train_008022 | 2,140 | no_license | [
{
"docstring": ":param heights: :return: int 用小数分割递归求解，OT",
"name": "largestRectangleArea1",
"signature": "def largestRectangleArea1(self, heights) -> int"
},
{
"docstring": ":type heights: List[int] :rtype: int 利用栈构造升序序列，若下一个数是升序则进栈，否则开始退栈，直到出现小于等于当前数的元素。注意栈内是升序序列， 这就找到了介于两个小数之间的一个序列，计算这个序列的面积，... | 2 | stack_v2_sparse_classes_30k_train_003915 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestRectangleArea1(self, heights) -> int: :param heights: :return: int 用小数分割递归求解，OT
- def largestRectangleArea(self, heights): :type heights: List[int] :rtype: int 利用栈构造升序... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largestRectangleArea1(self, heights) -> int: :param heights: :return: int 用小数分割递归求解，OT
- def largestRectangleArea(self, heights): :type heights: List[int] :rtype: int 利用栈构造升序... | 2306c494ea8f754aa4b954732f1331f3922235c1 | <|skeleton|>
class Solution:
def largestRectangleArea1(self, heights) -> int:
""":param heights: :return: int 用小数分割递归求解，OT"""
<|body_0|>
def largestRectangleArea(self, heights):
""":type heights: List[int] :rtype: int 利用栈构造升序序列，若下一个数是升序则进栈，否则开始退栈，直到出现小于等于当前数的元素。注意栈内是升序序列， 这就找到了介于两个小数之间... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def largestRectangleArea1(self, heights) -> int:
""":param heights: :return: int 用小数分割递归求解，OT"""
if not heights:
return 0
minl = []
minh = float('inf')
for i in range(len(heights)):
if heights[i] < minh:
minh = heights[i... | the_stack_v2_python_sparse | 84_LargestRectangleInHistogram.py | ZhangNANPy/LeetCode | train | 0 | |
1afa1c6a694d315ade76cf6f35a6e7ac68991a10 | [
"for parent_id_option_expression in PARENT_ID_OPTION_EXPRESSIONS:\n if parent_id_option_expression.match(option):\n return True\nreturn False",
"for parent_id_option_expression in PARENT_ID_OPTION_EXPRESSIONS:\n match = parent_id_option_expression.match(option)\n if match is not None:\n ret... | <|body_start_0|>
for parent_id_option_expression in PARENT_ID_OPTION_EXPRESSIONS:
if parent_id_option_expression.match(option):
return True
return False
<|end_body_0|>
<|body_start_1|>
for parent_id_option_expression in PARENT_ID_OPTION_EXPRESSIONS:
match... | OptionHelper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OptionHelper:
def is_parent_id_option(option):
"""Checks if the given option name is a reference to a parent entity."""
<|body_0|>
def get_parent_id_type(option):
"""Extracts the name of the type from an option that is a reference to a parent entity. For example, if ... | stack_v2_sparse_classes_10k_train_008023 | 1,932 | permissive | [
{
"docstring": "Checks if the given option name is a reference to a parent entity.",
"name": "is_parent_id_option",
"signature": "def is_parent_id_option(option)"
},
{
"docstring": "Extracts the name of the type from an option that is a reference to a parent entity. For example, if the option is... | 2 | stack_v2_sparse_classes_30k_train_004312 | Implement the Python class `OptionHelper` described below.
Class description:
Implement the OptionHelper class.
Method signatures and docstrings:
- def is_parent_id_option(option): Checks if the given option name is a reference to a parent entity.
- def get_parent_id_type(option): Extracts the name of the type from a... | Implement the Python class `OptionHelper` described below.
Class description:
Implement the OptionHelper class.
Method signatures and docstrings:
- def is_parent_id_option(option): Checks if the given option name is a reference to a parent entity.
- def get_parent_id_type(option): Extracts the name of the type from a... | 422d70e1dc422f0ca248abea47a472e3605caa4b | <|skeleton|>
class OptionHelper:
def is_parent_id_option(option):
"""Checks if the given option name is a reference to a parent entity."""
<|body_0|>
def get_parent_id_type(option):
"""Extracts the name of the type from an option that is a reference to a parent entity. For example, if ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class OptionHelper:
def is_parent_id_option(option):
"""Checks if the given option name is a reference to a parent entity."""
for parent_id_option_expression in PARENT_ID_OPTION_EXPRESSIONS:
if parent_id_option_expression.match(option):
return True
return False
... | the_stack_v2_python_sparse | src/ovirtcli/utils/optionhelper.py | minqf/ovirt-engine-cli | train | 0 | |
2dd4f6e20db4d88065973a37c85d97d1f5c66709 | [
"self.conf = conf\nself.transport = transport\nself.target = target\nself.RPC = self.target.topic_class",
"check_interval = self.conf.messaging_server.check_interval\ntime.sleep(check_interval)\nif self.conf.messaging_server.debug:\n LOG.debug('Checking status for message {} method {} on topic {}'.format(rpc_i... | <|body_start_0|>
self.conf = conf
self.transport = transport
self.target = target
self.RPC = self.target.topic_class
<|end_body_0|>
<|body_start_1|>
check_interval = self.conf.messaging_server.check_interval
time.sleep(check_interval)
if self.conf.messaging_serve... | Returns an RPC client using Music as a transport. The RPC client is responsible for sending method invocations to remote servers via a messaging transport. A method invocation consists of a request context dictionary a method name, and a dictionary of arguments. A cast() invocation just sends the request and returns im... | RPCClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RPCClient:
"""Returns an RPC client using Music as a transport. The RPC client is responsible for sending method invocations to remote servers via a messaging transport. A method invocation consists of a request context dictionary a method name, and a dictionary of arguments. A cast() invocation ... | stack_v2_sparse_classes_10k_train_008024 | 18,894 | permissive | [
{
"docstring": "Set the transport and target",
"name": "__init__",
"signature": "def __init__(self, conf, transport, target)"
},
{
"docstring": "Check status for a given message id",
"name": "__check_rpc_status",
"signature": "def __check_rpc_status(self, rpc_id, rpc_method)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_000163 | Implement the Python class `RPCClient` described below.
Class description:
Returns an RPC client using Music as a transport. The RPC client is responsible for sending method invocations to remote servers via a messaging transport. A method invocation consists of a request context dictionary a method name, and a dictio... | Implement the Python class `RPCClient` described below.
Class description:
Returns an RPC client using Music as a transport. The RPC client is responsible for sending method invocations to remote servers via a messaging transport. A method invocation consists of a request context dictionary a method name, and a dictio... | 9c828b70d48d6c7d6668eda4d61239cfb2b28570 | <|skeleton|>
class RPCClient:
"""Returns an RPC client using Music as a transport. The RPC client is responsible for sending method invocations to remote servers via a messaging transport. A method invocation consists of a request context dictionary a method name, and a dictionary of arguments. A cast() invocation ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RPCClient:
"""Returns an RPC client using Music as a transport. The RPC client is responsible for sending method invocations to remote servers via a messaging transport. A method invocation consists of a request context dictionary a method name, and a dictionary of arguments. A cast() invocation just sends th... | the_stack_v2_python_sparse | conductor/conductor/common/music/messaging/component.py | onap/optf-has | train | 5 |
71938574137ab9b0df5c1b16301b1795a23a8fea | [
"if self.master:\n if hasattr(self.master, 'notify_task'):\n self.master.notify_task(_task, _progress)\n else:\n print(self.__class__.__name__ + ': Internal deficiency, ' + self.master.__class__.__name__ + ' should have a notify_task function\\nTask:\\n' + _task + '\\n' + str(_progress))\nelse:\... | <|body_start_0|>
if self.master:
if hasattr(self.master, 'notify_task'):
self.master.notify_task(_task, _progress)
else:
print(self.__class__.__name__ + ': Internal deficiency, ' + self.master.__class__.__name__ + ' should have a notify_task function\nTask... | This class introduces all properties that a frame in BPM tools should hold. | BPMFrame | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BPMFrame:
"""This class introduces all properties that a frame in BPM tools should hold."""
def notify_task(self, _task, _progress):
"""This function checks if the master widget has a notify_task function, and if so, calls it. This way, notifications travel upwards in the widget stru... | stack_v2_sparse_classes_10k_train_008025 | 11,721 | permissive | [
{
"docstring": "This function checks if the master widget has a notify_task function, and if so, calls it. This way, notifications travel upwards in the widget structure until someone has a notify_task that's different. See the main_tk_replicator.ReplicatorMain for an example, :param _task: Text that defines th... | 2 | stack_v2_sparse_classes_30k_train_001792 | Implement the Python class `BPMFrame` described below.
Class description:
This class introduces all properties that a frame in BPM tools should hold.
Method signatures and docstrings:
- def notify_task(self, _task, _progress): This function checks if the master widget has a notify_task function, and if so, calls it. ... | Implement the Python class `BPMFrame` described below.
Class description:
This class introduces all properties that a frame in BPM tools should hold.
Method signatures and docstrings:
- def notify_task(self, _task, _progress): This function checks if the master widget has a notify_task function, and if so, calls it. ... | 4d7a31c0d68042b4110e1fa3e733711e0fdd473e | <|skeleton|>
class BPMFrame:
"""This class introduces all properties that a frame in BPM tools should hold."""
def notify_task(self, _task, _progress):
"""This function checks if the master widget has a notify_task function, and if so, calls it. This way, notifications travel upwards in the widget stru... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BPMFrame:
"""This class introduces all properties that a frame in BPM tools should hold."""
def notify_task(self, _task, _progress):
"""This function checks if the master widget has a notify_task function, and if so, calls it. This way, notifications travel upwards in the widget structure until s... | the_stack_v2_python_sparse | qal/tools/gui/widgets_misc.py | OptimalBPM/qal | train | 3 |
09acf4bd8667cd25a043564452c6daf34b567b14 | [
"m, n = pic_dt.shape\nif resize is None:\n n_new, m_new = (np.round(x_scale * n).astype(int), np.round(y_scale * m).astype(int))\nelse:\n n_new, m_new = resize\nfx, fy = (n / n_new, m / m_new)\nidx_x_orign = np.array(list(range(n_new)) * m_new).reshape(m_new, n_new)\nidx_y_orign = np.repeat(list(range(m_new))... | <|body_start_0|>
m, n = pic_dt.shape
if resize is None:
n_new, m_new = (np.round(x_scale * n).astype(int), np.round(y_scale * m).astype(int))
else:
n_new, m_new = resize
fx, fy = (n / n_new, m / m_new)
idx_x_orign = np.array(list(range(n_new)) * m_new).res... | 图片缩放：包含两个方法 INTER_NEAREST 最近邻插值 INTER_LINEAR 双线性插值 例子： pic_sc = pic_scale() pic_sc.pic_resize(img_dt, resize, fx=None, fy=None, interpolation = pic_sc.INTER_LINEAR) | pic_scale | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class pic_scale:
"""图片缩放：包含两个方法 INTER_NEAREST 最近邻插值 INTER_LINEAR 双线性插值 例子： pic_sc = pic_scale() pic_sc.pic_resize(img_dt, resize, fx=None, fy=None, interpolation = pic_sc.INTER_LINEAR)"""
def INTER_NEAREST(self, pic_dt, resize, x_scale=None, y_scale=None):
"""最近邻插值（图片 m * n * 图层） param pic... | stack_v2_sparse_classes_10k_train_008026 | 5,079 | no_license | [
{
"docstring": "最近邻插值（图片 m * n * 图层） param pic_dt: 为一个图片的一个图层的数据 len(pic_dt) == 2 param resize: set (长, 宽) param x_scale: float 长度缩放大小 param y_scale: float 宽带缩放大小",
"name": "INTER_NEAREST",
"signature": "def INTER_NEAREST(self, pic_dt, resize, x_scale=None, y_scale=None)"
},
{
"docstring": "找位置，... | 4 | stack_v2_sparse_classes_30k_train_004928 | Implement the Python class `pic_scale` described below.
Class description:
图片缩放：包含两个方法 INTER_NEAREST 最近邻插值 INTER_LINEAR 双线性插值 例子： pic_sc = pic_scale() pic_sc.pic_resize(img_dt, resize, fx=None, fy=None, interpolation = pic_sc.INTER_LINEAR)
Method signatures and docstrings:
- def INTER_NEAREST(self, pic_dt, resize, x_... | Implement the Python class `pic_scale` described below.
Class description:
图片缩放：包含两个方法 INTER_NEAREST 最近邻插值 INTER_LINEAR 双线性插值 例子： pic_sc = pic_scale() pic_sc.pic_resize(img_dt, resize, fx=None, fy=None, interpolation = pic_sc.INTER_LINEAR)
Method signatures and docstrings:
- def INTER_NEAREST(self, pic_dt, resize, x_... | 122c43776c2b10bd5f9b9a299bdbf9739173ad46 | <|skeleton|>
class pic_scale:
"""图片缩放：包含两个方法 INTER_NEAREST 最近邻插值 INTER_LINEAR 双线性插值 例子： pic_sc = pic_scale() pic_sc.pic_resize(img_dt, resize, fx=None, fy=None, interpolation = pic_sc.INTER_LINEAR)"""
def INTER_NEAREST(self, pic_dt, resize, x_scale=None, y_scale=None):
"""最近邻插值（图片 m * n * 图层） param pic... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class pic_scale:
"""图片缩放：包含两个方法 INTER_NEAREST 最近邻插值 INTER_LINEAR 双线性插值 例子： pic_sc = pic_scale() pic_sc.pic_resize(img_dt, resize, fx=None, fy=None, interpolation = pic_sc.INTER_LINEAR)"""
def INTER_NEAREST(self, pic_dt, resize, x_scale=None, y_scale=None):
"""最近邻插值（图片 m * n * 图层） param pic_dt: 为一个图片的一个... | the_stack_v2_python_sparse | DataWhale计算机视觉入门/图片缩放.py | scchy/My_Learn | train | 4 |
1e7c09fbab53137d472dc26fb752c5fe458a4540 | [
"BaseResource.__init__(self, *args, **kw)\nself._rsc = eval(open('%s.txt' % self.basesourcefile, 'r').read().replace('\\r\\n', '\\n'))\nself._code = open('%s.py' % self.basesourcefile, 'r').read()",
"fle = open(os.path.join(basedir, self.name) + '.rsrc.py', 'w')\nlog.info(\"Writing '%s'\" % os.path.join(basedir, ... | <|body_start_0|>
BaseResource.__init__(self, *args, **kw)
self._rsc = eval(open('%s.txt' % self.basesourcefile, 'r').read().replace('\r\n', '\n'))
self._code = open('%s.py' % self.basesourcefile, 'r').read()
<|end_body_0|>
<|body_start_1|>
fle = open(os.path.join(basedir, self.name) + '... | Represents a Python Card resource object | Resource | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Resource:
"""Represents a Python Card resource object"""
def __init__(self, *args, **kw):
"""Initialize the PythonCard resource"""
<|body_0|>
def writeToFile(self, basedir, write_code=0):
"""Write ourselves out to a directory"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_10k_train_008027 | 2,155 | permissive | [
{
"docstring": "Initialize the PythonCard resource",
"name": "__init__",
"signature": "def __init__(self, *args, **kw)"
},
{
"docstring": "Write ourselves out to a directory",
"name": "writeToFile",
"signature": "def writeToFile(self, basedir, write_code=0)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001905 | Implement the Python class `Resource` described below.
Class description:
Represents a Python Card resource object
Method signatures and docstrings:
- def __init__(self, *args, **kw): Initialize the PythonCard resource
- def writeToFile(self, basedir, write_code=0): Write ourselves out to a directory | Implement the Python class `Resource` described below.
Class description:
Represents a Python Card resource object
Method signatures and docstrings:
- def __init__(self, *args, **kw): Initialize the PythonCard resource
- def writeToFile(self, basedir, write_code=0): Write ourselves out to a directory
<|skeleton|>
cl... | 847ce71e85093ea5ee668ec61dbfba760ffa6bbd | <|skeleton|>
class Resource:
"""Represents a Python Card resource object"""
def __init__(self, *args, **kw):
"""Initialize the PythonCard resource"""
<|body_0|>
def writeToFile(self, basedir, write_code=0):
"""Write ourselves out to a directory"""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Resource:
"""Represents a Python Card resource object"""
def __init__(self, *args, **kw):
"""Initialize the PythonCard resource"""
BaseResource.__init__(self, *args, **kw)
self._rsc = eval(open('%s.txt' % self.basesourcefile, 'r').read().replace('\r\n', '\n'))
self._code =... | the_stack_v2_python_sparse | vb2py/targets/pythoncard/resource.py | rayzamgh/sumurProjection | train | 1 |
82ae17cdc5863bf5bf0f52f8fd4bc6763614ff28 | [
"self.parent = parent\nself.press = None\nself.background = None\nself.y = y\nself.line = Axis.axhline(y)\nself.line.set_linewidth(0.2)\nself.connect()",
"self.cidpress1 = self.line.figure.canvas.mpl_connect('button_press_event', self.on_press)\nself.cidrelease1 = self.line.figure.canvas.mpl_connect('button_relea... | <|body_start_0|>
self.parent = parent
self.press = None
self.background = None
self.y = y
self.line = Axis.axhline(y)
self.line.set_linewidth(0.2)
self.connect()
<|end_body_0|>
<|body_start_1|>
self.cidpress1 = self.line.figure.canvas.mpl_connect('button_... | This is a class to create an horizontal line that can be drag in a vertical fashion. Documentation of this part is not torough as it does not come from me. I took some part of another code and adapted it to this one. Attributes: parent : tkinter frame in which the matplotlib pyplot is gridded into. press : This attribu... | HorizontalDraggableLine | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HorizontalDraggableLine:
"""This is a class to create an horizontal line that can be drag in a vertical fashion. Documentation of this part is not torough as it does not come from me. I took some part of another code and adapted it to this one. Attributes: parent : tkinter frame in which the matp... | stack_v2_sparse_classes_10k_train_008028 | 32,818 | no_license | [
{
"docstring": "The constructor for the HorizontalDraggableLine Class. Parameters: parent : tkinter Frame object where the object is placed in. y : Initial position of your horizontal line on the y axis Axis : axis of your pyplot graphic that will contain this line.",
"name": "__init__",
"signature": "d... | 6 | stack_v2_sparse_classes_30k_train_000460 | Implement the Python class `HorizontalDraggableLine` described below.
Class description:
This is a class to create an horizontal line that can be drag in a vertical fashion. Documentation of this part is not torough as it does not come from me. I took some part of another code and adapted it to this one. Attributes: p... | Implement the Python class `HorizontalDraggableLine` described below.
Class description:
This is a class to create an horizontal line that can be drag in a vertical fashion. Documentation of this part is not torough as it does not come from me. I took some part of another code and adapted it to this one. Attributes: p... | 6e479850a21c337c0c47379538f002bca97e03f1 | <|skeleton|>
class HorizontalDraggableLine:
"""This is a class to create an horizontal line that can be drag in a vertical fashion. Documentation of this part is not torough as it does not come from me. I took some part of another code and adapted it to this one. Attributes: parent : tkinter frame in which the matp... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class HorizontalDraggableLine:
"""This is a class to create an horizontal line that can be drag in a vertical fashion. Documentation of this part is not torough as it does not come from me. I took some part of another code and adapted it to this one. Attributes: parent : tkinter frame in which the matplotlib pyplot... | the_stack_v2_python_sparse | Labo_Env/ultrafastGUI/Graphic.py | UltraFastQ/femtoQ-Intruments | train | 2 |
8827f0c25cbdc0b7e78b07033b627e2cf07a33a2 | [
"fn_id = id(fn)\nif cls.tick_threads.get(fn_id, None) is not None:\n logger.warning('{} already registered with PythonTickNotifier'.format(str(fn)))\n return None\nrepeater = cls._tick(fn_id)\ncls.tick_threads[fn_id] = (fn, repeater)\nreturn fn_id",
"pair = cls.tick_threads.get(token, None)\nif pair:\n p... | <|body_start_0|>
fn_id = id(fn)
if cls.tick_threads.get(fn_id, None) is not None:
logger.warning('{} already registered with PythonTickNotifier'.format(str(fn)))
return None
repeater = cls._tick(fn_id)
cls.tick_threads[fn_id] = (fn, repeater)
return fn_id
... | This notifier implements a Tick notifier | PythonTickCallback | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PythonTickCallback:
"""This notifier implements a Tick notifier"""
def register(cls, fn):
"""Register the given Python function :param fn: function, python function to register :return: token of non defined type to later unregister the function"""
<|body_0|>
def unregist... | stack_v2_sparse_classes_10k_train_008029 | 21,288 | permissive | [
{
"docstring": "Register the given Python function :param fn: function, python function to register :return: token of non defined type to later unregister the function",
"name": "register",
"signature": "def register(cls, fn)"
},
{
"docstring": "Unregister the given Python function :param token:... | 3 | stack_v2_sparse_classes_30k_train_000739 | Implement the Python class `PythonTickCallback` described below.
Class description:
This notifier implements a Tick notifier
Method signatures and docstrings:
- def register(cls, fn): Register the given Python function :param fn: function, python function to register :return: token of non defined type to later unregi... | Implement the Python class `PythonTickCallback` described below.
Class description:
This notifier implements a Tick notifier
Method signatures and docstrings:
- def register(cls, fn): Register the given Python function :param fn: function, python function to register :return: token of non defined type to later unregi... | a4f77a3fdd981eac494331e429c92bd3e4a87d3b | <|skeleton|>
class PythonTickCallback:
"""This notifier implements a Tick notifier"""
def register(cls, fn):
"""Register the given Python function :param fn: function, python function to register :return: token of non defined type to later unregister the function"""
<|body_0|>
def unregist... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PythonTickCallback:
"""This notifier implements a Tick notifier"""
def register(cls, fn):
"""Register the given Python function :param fn: function, python function to register :return: token of non defined type to later unregister the function"""
fn_id = id(fn)
if cls.tick_thread... | the_stack_v2_python_sparse | tpDcc/abstract/callback.py | OmniZ3D/tpDcc-core | train | 0 |
b2e0fe2f6f67ebeabd8733a92d188cd605929882 | [
"parser = ParlaiParser(True, True, 'Generate Dense Embs')\nparser.add_argument('--passages-file', type=str, help='file containing passages to encode. file should be a tsv file.')\nparser.add_argument('--outfile', type=str, help='where to save the passage embeddings')\nparser.add_argument('--num-shards', type=int, d... | <|body_start_0|>
parser = ParlaiParser(True, True, 'Generate Dense Embs')
parser.add_argument('--passages-file', type=str, help='file containing passages to encode. file should be a tsv file.')
parser.add_argument('--outfile', type=str, help='where to save the passage embeddings')
parser... | Generate Dense Embeddings. | Generator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
"""Generate Dense Embeddings."""
def setup_args(cls):
"""File in/out args, and sharding args."""
<|body_0|>
def run(self):
"""1) load model 2) generate embeddings 3) save embeddings."""
<|body_1|>
def encode_passages(self, agent: TorchRank... | stack_v2_sparse_classes_10k_train_008030 | 7,678 | permissive | [
{
"docstring": "File in/out args, and sharding args.",
"name": "setup_args",
"signature": "def setup_args(cls)"
},
{
"docstring": "1) load model 2) generate embeddings 3) save embeddings.",
"name": "run",
"signature": "def run(self)"
},
{
"docstring": "Encode passages with model,... | 5 | null | Implement the Python class `Generator` described below.
Class description:
Generate Dense Embeddings.
Method signatures and docstrings:
- def setup_args(cls): File in/out args, and sharding args.
- def run(self): 1) load model 2) generate embeddings 3) save embeddings.
- def encode_passages(self, agent: TorchRankerAg... | Implement the Python class `Generator` described below.
Class description:
Generate Dense Embeddings.
Method signatures and docstrings:
- def setup_args(cls): File in/out args, and sharding args.
- def run(self): 1) load model 2) generate embeddings 3) save embeddings.
- def encode_passages(self, agent: TorchRankerAg... | e1d899edfb92471552bae153f59ad30aa7fca468 | <|skeleton|>
class Generator:
"""Generate Dense Embeddings."""
def setup_args(cls):
"""File in/out args, and sharding args."""
<|body_0|>
def run(self):
"""1) load model 2) generate embeddings 3) save embeddings."""
<|body_1|>
def encode_passages(self, agent: TorchRank... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Generator:
"""Generate Dense Embeddings."""
def setup_args(cls):
"""File in/out args, and sharding args."""
parser = ParlaiParser(True, True, 'Generate Dense Embs')
parser.add_argument('--passages-file', type=str, help='file containing passages to encode. file should be a tsv file... | the_stack_v2_python_sparse | parlai/agents/rag/scripts/generate_dense_embeddings.py | facebookresearch/ParlAI | train | 10,943 |
5387360372fa674be695cc9aa2ef1c0cb4c8a047 | [
"self.assertTrue(geneutil.longestRun('AAAAA', 'A') == 5)\nself.assertTrue(geneutil.longestRun('AAATAA', 'A', 1) == 6)\nself.assertTrue(geneutil.longestRun('AAATTAA', 'A', 1) == 3)\nself.assertTrue(geneutil.longestRun('AAATTAA', 'A', 2) == 7)\nself.assertTrue(geneutil.longestRun('TAAATAA', 'A', 1) == 6)\nself.assert... | <|body_start_0|>
self.assertTrue(geneutil.longestRun('AAAAA', 'A') == 5)
self.assertTrue(geneutil.longestRun('AAATAA', 'A', 1) == 6)
self.assertTrue(geneutil.longestRun('AAATTAA', 'A', 1) == 3)
self.assertTrue(geneutil.longestRun('AAATTAA', 'A', 2) == 7)
self.assertTrue(geneutil.... | test001 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test001:
def test_longest_run(self):
"""Longest run testcases"""
<|body_0|>
def test_longest_run_mult(self):
"""Longest run testcases with more than one target"""
<|body_1|>
def test_max_sliding_count(self):
"""Max Sliding Count testcases"""
... | stack_v2_sparse_classes_10k_train_008031 | 2,692 | no_license | [
{
"docstring": "Longest run testcases",
"name": "test_longest_run",
"signature": "def test_longest_run(self)"
},
{
"docstring": "Longest run testcases with more than one target",
"name": "test_longest_run_mult",
"signature": "def test_longest_run_mult(self)"
},
{
"docstring": "Ma... | 3 | stack_v2_sparse_classes_30k_train_004017 | Implement the Python class `test001` described below.
Class description:
Implement the test001 class.
Method signatures and docstrings:
- def test_longest_run(self): Longest run testcases
- def test_longest_run_mult(self): Longest run testcases with more than one target
- def test_max_sliding_count(self): Max Sliding... | Implement the Python class `test001` described below.
Class description:
Implement the test001 class.
Method signatures and docstrings:
- def test_longest_run(self): Longest run testcases
- def test_longest_run_mult(self): Longest run testcases with more than one target
- def test_max_sliding_count(self): Max Sliding... | d7ddd2b585a841c6d986974a24a53e4d1abe71ba | <|skeleton|>
class test001:
def test_longest_run(self):
"""Longest run testcases"""
<|body_0|>
def test_longest_run_mult(self):
"""Longest run testcases with more than one target"""
<|body_1|>
def test_max_sliding_count(self):
"""Max Sliding Count testcases"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class test001:
def test_longest_run(self):
"""Longest run testcases"""
self.assertTrue(geneutil.longestRun('AAAAA', 'A') == 5)
self.assertTrue(geneutil.longestRun('AAATAA', 'A', 1) == 6)
self.assertTrue(geneutil.longestRun('AAATTAA', 'A', 1) == 3)
self.assertTrue(geneutil.lon... | the_stack_v2_python_sparse | src/geneutil_test.py | dad/base | train | 0 | |
33142880108efb5e0838fa3eb24363da09e0985a | [
"left = 0\nright = len(s) - 1\nif not s == s[::-1]:\n while left < right:\n if s[left] == s[right]:\n left += 1\n right -= 1\n else:\n str1 = s[:left] + s[left + 1:]\n str2 = s[:right] + s[right + 1:]\n return str1 == str1[::-1] or str2 == str2... | <|body_start_0|>
left = 0
right = len(s) - 1
if not s == s[::-1]:
while left < right:
if s[left] == s[right]:
left += 1
right -= 1
else:
str1 = s[:left] + s[left + 1:]
str2... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def validPalindrome(self, s):
""":type s: str :rtype: bool"""
<|body_0|>
def validPalindrome2(self, s):
""":type s: str :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left = 0
right = len(s) - 1
if not s == s[... | stack_v2_sparse_classes_10k_train_008032 | 2,011 | no_license | [
{
"docstring": ":type s: str :rtype: bool",
"name": "validPalindrome",
"signature": "def validPalindrome(self, s)"
},
{
"docstring": ":type s: str :rtype: bool",
"name": "validPalindrome2",
"signature": "def validPalindrome2(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validPalindrome(self, s): :type s: str :rtype: bool
- def validPalindrome2(self, s): :type s: str :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validPalindrome(self, s): :type s: str :rtype: bool
- def validPalindrome2(self, s): :type s: str :rtype: bool
<|skeleton|>
class Solution:
def validPalindrome(self, s)... | 3f0ffd519404165fd1a735441b212c801fd1ad1e | <|skeleton|>
class Solution:
def validPalindrome(self, s):
""":type s: str :rtype: bool"""
<|body_0|>
def validPalindrome2(self, s):
""":type s: str :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def validPalindrome(self, s):
""":type s: str :rtype: bool"""
left = 0
right = len(s) - 1
if not s == s[::-1]:
while left < right:
if s[left] == s[right]:
left += 1
right -= 1
else:
... | the_stack_v2_python_sparse | Problems/0600_0699/0680_Valid_Palindrome2/Project_Python3/Valid_Palindrome2.py | NobuyukiInoue/LeetCode | train | 0 | |
1c8c929ab841d566a7478ce99b3b02fe5071e1b9 | [
"output_buffers = {}\nfor name, shape in output_shapes.items():\n ort_type = TypeHelper.get_output_type(ort_session, name)\n torch_type = TypeHelper.ort_type_to_torch_type(ort_type)\n output_buffers[name] = torch.empty(numpy.prod(shape), dtype=torch_type, device=device)\nreturn output_buffers",
"if name_... | <|body_start_0|>
output_buffers = {}
for name, shape in output_shapes.items():
ort_type = TypeHelper.get_output_type(ort_session, name)
torch_type = TypeHelper.ort_type_to_torch_type(ort_type)
output_buffers[name] = torch.empty(numpy.prod(shape), dtype=torch_type, dev... | IOBindingHelper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IOBindingHelper:
def get_output_buffers(ort_session: InferenceSession, output_shapes, device):
"""Returns a dictionary of output name as key, and 1D tensor as value. The tensor has enough space for given shape."""
<|body_0|>
def prepare_io_binding(ort_session, input_ids: tor... | stack_v2_sparse_classes_10k_train_008033 | 12,305 | permissive | [
{
"docstring": "Returns a dictionary of output name as key, and 1D tensor as value. The tensor has enough space for given shape.",
"name": "get_output_buffers",
"signature": "def get_output_buffers(ort_session: InferenceSession, output_shapes, device)"
},
{
"docstring": "Returnas IO binding obje... | 3 | null | Implement the Python class `IOBindingHelper` described below.
Class description:
Implement the IOBindingHelper class.
Method signatures and docstrings:
- def get_output_buffers(ort_session: InferenceSession, output_shapes, device): Returns a dictionary of output name as key, and 1D tensor as value. The tensor has eno... | Implement the Python class `IOBindingHelper` described below.
Class description:
Implement the IOBindingHelper class.
Method signatures and docstrings:
- def get_output_buffers(ort_session: InferenceSession, output_shapes, device): Returns a dictionary of output name as key, and 1D tensor as value. The tensor has eno... | 5e747071be882efd6b54d7a7421042e68dcd6aff | <|skeleton|>
class IOBindingHelper:
def get_output_buffers(ort_session: InferenceSession, output_shapes, device):
"""Returns a dictionary of output name as key, and 1D tensor as value. The tensor has enough space for given shape."""
<|body_0|>
def prepare_io_binding(ort_session, input_ids: tor... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class IOBindingHelper:
def get_output_buffers(ort_session: InferenceSession, output_shapes, device):
"""Returns a dictionary of output name as key, and 1D tensor as value. The tensor has enough space for given shape."""
output_buffers = {}
for name, shape in output_shapes.items():
... | the_stack_v2_python_sparse | onnxruntime/python/tools/transformers/io_binding_helper.py | microsoft/onnxruntime | train | 9,912 | |
d5bf0f7dde159b5b620f956cacabdaa0a3c3f8a2 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn EventPropagationResult()",
"from .event_propagation_status import EventPropagationStatus\nfrom .event_propagation_status import EventPropagationStatus\nfields: Dict[str, Callable[[Any], None]] = {'location': lambda n: setattr(self, 'lo... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return EventPropagationResult()
<|end_body_0|>
<|body_start_1|>
from .event_propagation_status import EventPropagationStatus
from .event_propagation_status import EventPropagationStatus
... | EventPropagationResult | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventPropagationResult:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EventPropagationResult:
"""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 ... | stack_v2_sparse_classes_10k_train_008034 | 3,642 | 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: EventPropagationResult",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrimina... | 3 | null | Implement the Python class `EventPropagationResult` described below.
Class description:
Implement the EventPropagationResult class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EventPropagationResult: Creates a new instance of the appropriate class b... | Implement the Python class `EventPropagationResult` described below.
Class description:
Implement the EventPropagationResult class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EventPropagationResult: Creates a new instance of the appropriate class b... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class EventPropagationResult:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EventPropagationResult:
"""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 ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EventPropagationResult:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> EventPropagationResult:
"""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 Ret... | the_stack_v2_python_sparse | msgraph/generated/models/security/event_propagation_result.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
cd9aaae27ac8885e03fe3de25078bc4c80ae173e | [
"match = self.rx_interface_name.match(s)\nif not match:\n raise InterfaceTypeError(\"Invalid interface '%s'\" % s)\nt = match.group(1)[:2]\nif t.lower() == 'bu':\n t = 'BE'\nreturn '%s%s' % (t, match.group(2))",
"me = ' %s'\nmne = ' %s le %d'\nr = []\nfor prefix, min_len, max_len in pl:\n if min_len == m... | <|body_start_0|>
match = self.rx_interface_name.match(s)
if not match:
raise InterfaceTypeError("Invalid interface '%s'" % s)
t = match.group(1)[:2]
if t.lower() == 'bu':
t = 'BE'
return '%s%s' % (t, match.group(2))
<|end_body_0|>
<|body_start_1|>
... | Profile | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Profile:
def convert_interface_name(self, s):
"""MgmtEth0/1/CPU0/0 GigabitEthernet0/2/0/0.1000 Te0/0/1/3 Bundle-Ether1.5011035 Bundle-Ether1.10231016.ip18484"""
<|body_0|>
def generate_prefix_list(self, name, pl):
"""Generate prefix list _name_. pl is a list of (pref... | stack_v2_sparse_classes_10k_train_008035 | 2,215 | permissive | [
{
"docstring": "MgmtEth0/1/CPU0/0 GigabitEthernet0/2/0/0.1000 Te0/0/1/3 Bundle-Ether1.5011035 Bundle-Ether1.10231016.ip18484",
"name": "convert_interface_name",
"signature": "def convert_interface_name(self, s)"
},
{
"docstring": "Generate prefix list _name_. pl is a list of (prefix, min_len, ma... | 2 | stack_v2_sparse_classes_30k_train_004032 | Implement the Python class `Profile` described below.
Class description:
Implement the Profile class.
Method signatures and docstrings:
- def convert_interface_name(self, s): MgmtEth0/1/CPU0/0 GigabitEthernet0/2/0/0.1000 Te0/0/1/3 Bundle-Ether1.5011035 Bundle-Ether1.10231016.ip18484
- def generate_prefix_list(self, n... | Implement the Python class `Profile` described below.
Class description:
Implement the Profile class.
Method signatures and docstrings:
- def convert_interface_name(self, s): MgmtEth0/1/CPU0/0 GigabitEthernet0/2/0/0.1000 Te0/0/1/3 Bundle-Ether1.5011035 Bundle-Ether1.10231016.ip18484
- def generate_prefix_list(self, n... | aba08dc328296bb0e8e181c2ac9a766e1ec2a0bb | <|skeleton|>
class Profile:
def convert_interface_name(self, s):
"""MgmtEth0/1/CPU0/0 GigabitEthernet0/2/0/0.1000 Te0/0/1/3 Bundle-Ether1.5011035 Bundle-Ether1.10231016.ip18484"""
<|body_0|>
def generate_prefix_list(self, name, pl):
"""Generate prefix list _name_. pl is a list of (pref... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Profile:
def convert_interface_name(self, s):
"""MgmtEth0/1/CPU0/0 GigabitEthernet0/2/0/0.1000 Te0/0/1/3 Bundle-Ether1.5011035 Bundle-Ether1.10231016.ip18484"""
match = self.rx_interface_name.match(s)
if not match:
raise InterfaceTypeError("Invalid interface '%s'" % s)
... | the_stack_v2_python_sparse | sa/profiles/Cisco/IOSXR/profile.py | ewwwcha/noc | train | 1 | |
80de1261b3d77078d8170d185334f9fb64778e32 | [
"try:\n return_data = WorkFlowSimpleManager().create_workflow(nnid, wfver, request.data['type'])\n return Response(json.dumps(return_data))\nexcept Exception as e:\n return_data = {'status': '404', 'result': str(e)}\n return Response(json.dumps(return_data))",
"try:\n return_data = NNCommonManager(... | <|body_start_0|>
try:
return_data = WorkFlowSimpleManager().create_workflow(nnid, wfver, request.data['type'])
return Response(json.dumps(return_data))
except Exception as e:
return_data = {'status': '404', 'result': str(e)}
return Response(json.dumps(retu... | WorkFlowInitSimple | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkFlowInitSimple:
def post(self, request, nnid, wfver):
"""Simply initialize fixed graph flow which is preefined You can choose process with network id and data type There are several processes already designed --- # Class Name : WorkFlowInitSimple # Description: Set graph flow with gi... | stack_v2_sparse_classes_10k_train_008036 | 2,447 | permissive | [
{
"docstring": "Simply initialize fixed graph flow which is preefined You can choose process with network id and data type There are several processes already designed --- # Class Name : WorkFlowInitSimple # Description: Set graph flow with given name and data type",
"name": "post",
"signature": "def po... | 3 | stack_v2_sparse_classes_30k_test_000165 | Implement the Python class `WorkFlowInitSimple` described below.
Class description:
Implement the WorkFlowInitSimple class.
Method signatures and docstrings:
- def post(self, request, nnid, wfver): Simply initialize fixed graph flow which is preefined You can choose process with network id and data type There are sev... | Implement the Python class `WorkFlowInitSimple` described below.
Class description:
Implement the WorkFlowInitSimple class.
Method signatures and docstrings:
- def post(self, request, nnid, wfver): Simply initialize fixed graph flow which is preefined You can choose process with network id and data type There are sev... | 6ad2fbc7384e4dbe7e3e63bdb44c8ce0387f4b7f | <|skeleton|>
class WorkFlowInitSimple:
def post(self, request, nnid, wfver):
"""Simply initialize fixed graph flow which is preefined You can choose process with network id and data type There are several processes already designed --- # Class Name : WorkFlowInitSimple # Description: Set graph flow with gi... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WorkFlowInitSimple:
def post(self, request, nnid, wfver):
"""Simply initialize fixed graph flow which is preefined You can choose process with network id and data type There are several processes already designed --- # Class Name : WorkFlowInitSimple # Description: Set graph flow with given name and d... | the_stack_v2_python_sparse | api/views/workflow_init_simple.py | yurimkoo/tensormsa | train | 1 | |
c194d7084b8230c7c9c73e2f2d8d4015261c2c79 | [
"self.SetStartDate(2010, 1, 1)\nself.SetEndDate(2013, 12, 31)\nself.SetCash(100000)\nIntrinioConfig.SetUserAndPassword('intrinio-username', 'intrinio-password')\nIntrinioConfig.SetTimeIntervalBetweenCalls(timedelta(minutes=1))\nself.uso = self.AddEquity('USO', Resolution.Daily).Symbol\nself.Securities[self.uso].Set... | <|body_start_0|>
self.SetStartDate(2010, 1, 1)
self.SetEndDate(2013, 12, 31)
self.SetCash(100000)
IntrinioConfig.SetUserAndPassword('intrinio-username', 'intrinio-password')
IntrinioConfig.SetTimeIntervalBetweenCalls(timedelta(minutes=1))
self.uso = self.AddEquity('USO', ... | BasicTemplateIntrinioEconomicData | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicTemplateIntrinioEconomicData:
def Initialize(self):
"""Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized."""
<|body_0|>
def OnData(self, slice):
"""OnData event is the primary... | stack_v2_sparse_classes_10k_train_008037 | 2,830 | permissive | [
{
"docstring": "Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.",
"name": "Initialize",
"signature": "def Initialize(self)"
},
{
"docstring": "OnData event is the primary entry point for your algorithm. Eac... | 2 | null | Implement the Python class `BasicTemplateIntrinioEconomicData` described below.
Class description:
Implement the BasicTemplateIntrinioEconomicData class.
Method signatures and docstrings:
- def Initialize(self): Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. A... | Implement the Python class `BasicTemplateIntrinioEconomicData` described below.
Class description:
Implement the BasicTemplateIntrinioEconomicData class.
Method signatures and docstrings:
- def Initialize(self): Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. A... | b33dd3bc140e14b883f39ecf848a793cf7292277 | <|skeleton|>
class BasicTemplateIntrinioEconomicData:
def Initialize(self):
"""Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized."""
<|body_0|>
def OnData(self, slice):
"""OnData event is the primary... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BasicTemplateIntrinioEconomicData:
def Initialize(self):
"""Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized."""
self.SetStartDate(2010, 1, 1)
self.SetEndDate(2013, 12, 31)
self.SetCash(1000... | the_stack_v2_python_sparse | Algorithm.Python/BasicTemplateIntrinioEconomicData.py | Capnode/Algoloop | train | 87 | |
60e1981a427bc6783307ac4b7812390681fd4801 | [
"if data is None:\n if lambtha <= 0:\n raise ValueError('lambtha must be a positive value')\n else:\n self.lambtha = float(lambtha)\nelif type(data) is not list:\n raise TypeError('data must be a list')\nelif len(data) < 2:\n raise ValueError('data must contain multiple values')\nelse:\n ... | <|body_start_0|>
if data is None:
if lambtha <= 0:
raise ValueError('lambtha must be a positive value')
else:
self.lambtha = float(lambtha)
elif type(data) is not list:
raise TypeError('data must be a list')
elif len(data) < 2:
... | This class represents an exponential distribution | Exponential | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Exponential:
"""This class represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""This function initializes the Exponential class"""
<|body_0|>
def pdf(self, x):
"""Calculates the value of the PDF for a given time period"""
... | stack_v2_sparse_classes_10k_train_008038 | 1,261 | no_license | [
{
"docstring": "This function initializes the Exponential class",
"name": "__init__",
"signature": "def __init__(self, data=None, lambtha=1.0)"
},
{
"docstring": "Calculates the value of the PDF for a given time period",
"name": "pdf",
"signature": "def pdf(self, x)"
},
{
"docstr... | 3 | stack_v2_sparse_classes_30k_train_006638 | Implement the Python class `Exponential` described below.
Class description:
This class represents an exponential distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): This function initializes the Exponential class
- def pdf(self, x): Calculates the value of the PDF for a given... | Implement the Python class `Exponential` described below.
Class description:
This class represents an exponential distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): This function initializes the Exponential class
- def pdf(self, x): Calculates the value of the PDF for a given... | ac4f79965e65b7716029cd31a9b026c904bdef09 | <|skeleton|>
class Exponential:
"""This class represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""This function initializes the Exponential class"""
<|body_0|>
def pdf(self, x):
"""Calculates the value of the PDF for a given time period"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Exponential:
"""This class represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""This function initializes the Exponential class"""
if data is None:
if lambtha <= 0:
raise ValueError('lambtha must be a positive value')
... | the_stack_v2_python_sparse | math/0x03-probability/exponential.py | tyedge/holbertonschool-machine_learning | train | 0 |
800e89db03c0091e696d9ee58a97ffd90de94cbb | [
"if isinstance(key, int):\n return TransType(key)\nif key not in TransType._member_map_:\n extend_enum(TransType, key, default)\nreturn TransType[key]",
"if not (isinstance(value, int) and 0 <= value <= 255):\n raise ValueError('%r is not a valid %s' % (value, cls.__name__))\nif 144 <= value <= 252:\n ... | <|body_start_0|>
if isinstance(key, int):
return TransType(key)
if key not in TransType._member_map_:
extend_enum(TransType, key, default)
return TransType[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) and 0 <= value <= 255):
rai... | [TransType] Transport Layer Protocol Numbers | TransType | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransType:
"""[TransType] Transport Layer Protocol Numbers"""
def get(key, default=-1):
"""Backport support for original codes."""
<|body_0|>
def _missing_(cls, value):
"""Lookup function used when value is not found."""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_10k_train_008039 | 13,924 | permissive | [
{
"docstring": "Backport support for original codes.",
"name": "get",
"signature": "def get(key, default=-1)"
},
{
"docstring": "Lookup function used when value is not found.",
"name": "_missing_",
"signature": "def _missing_(cls, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006854 | Implement the Python class `TransType` described below.
Class description:
[TransType] Transport Layer Protocol Numbers
Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found. | Implement the Python class `TransType` described below.
Class description:
[TransType] Transport Layer Protocol Numbers
Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found.
<|skeleton|>
class Tran... | 71363d7948003fec88cedcf5bc80b6befa2ba244 | <|skeleton|>
class TransType:
"""[TransType] Transport Layer Protocol Numbers"""
def get(key, default=-1):
"""Backport support for original codes."""
<|body_0|>
def _missing_(cls, value):
"""Lookup function used when value is not found."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TransType:
"""[TransType] Transport Layer Protocol Numbers"""
def get(key, default=-1):
"""Backport support for original codes."""
if isinstance(key, int):
return TransType(key)
if key not in TransType._member_map_:
extend_enum(TransType, key, default)
... | the_stack_v2_python_sparse | pcapkit/const/reg/transtype.py | hiok2000/PyPCAPKit | train | 0 |
7217847836d8fea86df27c06c7dfa923abecadbe | [
"self.zeroth_coefficient = zeroth_coefficient\nself.zeroth_signal_scale = zeroth_signal_scale\nsuper().__init__(inner_coefficient=inner_coefficient, outer_coefficient=outer_coefficient, signal_scale=signal_scale)",
"regularization_weights = self.regularization_weights_from(linear_obj=linear_obj)\npix_sub_weights_... | <|body_start_0|>
self.zeroth_coefficient = zeroth_coefficient
self.zeroth_signal_scale = zeroth_signal_scale
super().__init__(inner_coefficient=inner_coefficient, outer_coefficient=outer_coefficient, signal_scale=signal_scale)
<|end_body_0|>
<|body_start_1|>
regularization_weights = sel... | AdaptiveBrightnessSplitZeroth | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdaptiveBrightnessSplitZeroth:
def __init__(self, zeroth_coefficient: float=1.0, zeroth_signal_scale: float=1.0, inner_coefficient: float=1.0, outer_coefficient: float=1.0, signal_scale: float=1.0):
"""An adaptive regularization scheme which splits every source pixel into a cross of four... | stack_v2_sparse_classes_10k_train_008040 | 5,208 | permissive | [
{
"docstring": "An adaptive regularization scheme which splits every source pixel into a cross of four regularization points (regularization is described in the `Regularization` class above) and interpolates to these points in order to apply smoothing on the solution of an `Inversion`. The size of this cross is... | 2 | stack_v2_sparse_classes_30k_train_001542 | Implement the Python class `AdaptiveBrightnessSplitZeroth` described below.
Class description:
Implement the AdaptiveBrightnessSplitZeroth class.
Method signatures and docstrings:
- def __init__(self, zeroth_coefficient: float=1.0, zeroth_signal_scale: float=1.0, inner_coefficient: float=1.0, outer_coefficient: float... | Implement the Python class `AdaptiveBrightnessSplitZeroth` described below.
Class description:
Implement the AdaptiveBrightnessSplitZeroth class.
Method signatures and docstrings:
- def __init__(self, zeroth_coefficient: float=1.0, zeroth_signal_scale: float=1.0, inner_coefficient: float=1.0, outer_coefficient: float... | 6639dd86d21ea28e942155753ec556752735b4e4 | <|skeleton|>
class AdaptiveBrightnessSplitZeroth:
def __init__(self, zeroth_coefficient: float=1.0, zeroth_signal_scale: float=1.0, inner_coefficient: float=1.0, outer_coefficient: float=1.0, signal_scale: float=1.0):
"""An adaptive regularization scheme which splits every source pixel into a cross of four... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AdaptiveBrightnessSplitZeroth:
def __init__(self, zeroth_coefficient: float=1.0, zeroth_signal_scale: float=1.0, inner_coefficient: float=1.0, outer_coefficient: float=1.0, signal_scale: float=1.0):
"""An adaptive regularization scheme which splits every source pixel into a cross of four regularizatio... | the_stack_v2_python_sparse | autoarray/inversion/regularization/adaptive_brightness_split_zeroth.py | Jammy2211/PyAutoArray | train | 6 | |
435f48322403ca8e571f3bccfe8cc3a0a1677b7e | [
"super().__init__()\ncheck_boundaries(boundaries)\nself.boundaries = boundaries\nself.frequencies = frequencies",
"self.randomize(None)\nself.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])\nself.freqs = self.R.uniform(low=self.frequencies[0], high=self.frequencies[1])\nlength = signal... | <|body_start_0|>
super().__init__()
check_boundaries(boundaries)
self.boundaries = boundaries
self.frequencies = frequencies
<|end_body_0|>
<|body_start_1|>
self.randomize(None)
self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])
self... | Add a random sinusoidal signal to the input signal | SignalRandAddSine | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignalRandAddSine:
"""Add a random sinusoidal signal to the input signal"""
def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lowe... | stack_v2_sparse_classes_10k_train_008041 | 16,322 | permissive | [
{
"docstring": "Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lower and upper values need to be positive ,default : ``[0.1, 0.3]`` frequencies: list defining lower and upper frequencies for sinusoidal signal generation ,default : ``[0.001, 0.02]``",
"name": "__init... | 2 | stack_v2_sparse_classes_30k_val_000395 | Implement the Python class `SignalRandAddSine` described below.
Class description:
Add a random sinusoidal signal to the input signal
Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None: Args: boundaries: list defining lowe... | Implement the Python class `SignalRandAddSine` described below.
Class description:
Add a random sinusoidal signal to the input signal
Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None: Args: boundaries: list defining lowe... | e48c3e2c741fa3fc705c4425d17ac4a5afac6c47 | <|skeleton|>
class SignalRandAddSine:
"""Add a random sinusoidal signal to the input signal"""
def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lowe... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SignalRandAddSine:
"""Add a random sinusoidal signal to the input signal"""
def __init__(self, boundaries: Sequence[float]=(0.1, 0.3), frequencies: Sequence[float]=(0.001, 0.02)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the sinusoidal magnitude, lower and upper v... | the_stack_v2_python_sparse | monai/transforms/signal/array.py | Project-MONAI/MONAI | train | 4,805 |
4f17f068614e5feb117feee18b2a075bc575141c | [
"for row in matrix:\n for col in range(1, len(row)):\n row[col] += row[col - 1]\nself.matrix = matrix",
"original = self.matrix[row][col]\nif col != 0:\n original -= self.matrix[row][col - 1]\ndiff = original - val\nfor y in range(col, len(self.matrix[0])):\n self.matrix[row][y] -= diff",
"sum_ ... | <|body_start_0|>
for row in matrix:
for col in range(1, len(row)):
row[col] += row[col - 1]
self.matrix = matrix
<|end_body_0|>
<|body_start_1|>
original = self.matrix[row][col]
if col != 0:
original -= self.matrix[row][col - 1]
diff = ori... | NumMatrix1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix1:
def __init__(self, matrix):
"""initialize your data structure here."""
<|body_0|>
def update(self, row, col, val):
"""update the element at matrix[row,col] to val."""
<|body_1|>
def sumRegion(self, row1, col1, row2, col2):
"""sum of e... | stack_v2_sparse_classes_10k_train_008042 | 3,097 | no_license | [
{
"docstring": "initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": "update the element at matrix[row,col] to val.",
"name": "update",
"signature": "def update(self, row, col, val)"
},
{
"docstring": "sum of eleme... | 3 | stack_v2_sparse_classes_30k_train_004612 | Implement the Python class `NumMatrix1` described below.
Class description:
Implement the NumMatrix1 class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here.
- def update(self, row, col, val): update the element at matrix[row,col] to val.
- def sumRegion(self, row1, ... | Implement the Python class `NumMatrix1` described below.
Class description:
Implement the NumMatrix1 class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here.
- def update(self, row, col, val): update the element at matrix[row,col] to val.
- def sumRegion(self, row1, ... | 502e121cc25fcd81afe3d029145aeee56db794f0 | <|skeleton|>
class NumMatrix1:
def __init__(self, matrix):
"""initialize your data structure here."""
<|body_0|>
def update(self, row, col, val):
"""update the element at matrix[row,col] to val."""
<|body_1|>
def sumRegion(self, row1, col1, row2, col2):
"""sum of e... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NumMatrix1:
def __init__(self, matrix):
"""initialize your data structure here."""
for row in matrix:
for col in range(1, len(row)):
row[col] += row[col - 1]
self.matrix = matrix
def update(self, row, col, val):
"""update the element at matrix[r... | the_stack_v2_python_sparse | 308NumMatrix.py | qinzhouhit/leetcode | train | 0 | |
ff79daf1a4ef52b2731218f8f93fbd105208a5a0 | [
"self.data_dir = data_dir\nself.dims = dims\nself.channels = channels",
"keys_to_features = {'volume': tf.FixedLenFeature(self.dims + [1], tf.float32), 'label': tf.FixedLenFeature(self.dims + [self.channels], tf.float32)}\nparsed = tf.parse_single_example(value, keys_to_features)\nprint(parsed['volume'].shape)\np... | <|body_start_0|>
self.data_dir = data_dir
self.dims = dims
self.channels = channels
<|end_body_0|>
<|body_start_1|>
keys_to_features = {'volume': tf.FixedLenFeature(self.dims + [1], tf.float32), 'label': tf.FixedLenFeature(self.dims + [self.channels], tf.float32)}
parsed = tf.pa... | Reader reads from a tfrecord file to produce an image. | Reader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Reader:
"""Reader reads from a tfrecord file to produce an image."""
def __init__(self, data_dir, dims, channels):
"""initialize the reader with a tfrecord dir and dims."""
<|body_0|>
def dataset_parser(self, value):
"""parse the tfrecords."""
<|body_1|>
... | stack_v2_sparse_classes_10k_train_008043 | 2,755 | no_license | [
{
"docstring": "initialize the reader with a tfrecord dir and dims.",
"name": "__init__",
"signature": "def __init__(self, data_dir, dims, channels)"
},
{
"docstring": "parse the tfrecords.",
"name": "dataset_parser",
"signature": "def dataset_parser(self, value)"
},
{
"docstring... | 4 | stack_v2_sparse_classes_30k_train_001164 | Implement the Python class `Reader` described below.
Class description:
Reader reads from a tfrecord file to produce an image.
Method signatures and docstrings:
- def __init__(self, data_dir, dims, channels): initialize the reader with a tfrecord dir and dims.
- def dataset_parser(self, value): parse the tfrecords.
-... | Implement the Python class `Reader` described below.
Class description:
Reader reads from a tfrecord file to produce an image.
Method signatures and docstrings:
- def __init__(self, data_dir, dims, channels): initialize the reader with a tfrecord dir and dims.
- def dataset_parser(self, value): parse the tfrecords.
-... | a7273c01d02528f5c547992fda482bbfb690fa6c | <|skeleton|>
class Reader:
"""Reader reads from a tfrecord file to produce an image."""
def __init__(self, data_dir, dims, channels):
"""initialize the reader with a tfrecord dir and dims."""
<|body_0|>
def dataset_parser(self, value):
"""parse the tfrecords."""
<|body_1|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Reader:
"""Reader reads from a tfrecord file to produce an image."""
def __init__(self, data_dir, dims, channels):
"""initialize the reader with a tfrecord dir and dims."""
self.data_dir = data_dir
self.dims = dims
self.channels = channels
def dataset_parser(self, val... | the_stack_v2_python_sparse | records.py | drewlinsley/tpu_connectomics | train | 0 |
1fd41dbcbb07d51ddf961e62787d70ad5a2a23e1 | [
"m, n = (len(grid), len(grid[0]))\ndirs = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n\ndef dfs(cur, pre, visited, mark):\n visited[cur[0]][cur[1]] = True\n for dx, dy in dirs:\n i, j = (cur[0] + dx, cur[1] + dy)\n if i < 0 or i >= m or j < 0 or (j >= n) or (grid[i][j] != mark):\n continue\n ... | <|body_start_0|>
m, n = (len(grid), len(grid[0]))
dirs = [(-1, 0), (1, 0), (0, -1), (0, 1)]
def dfs(cur, pre, visited, mark):
visited[cur[0]][cur[1]] = True
for dx, dy in dirs:
i, j = (cur[0] + dx, cur[1] + dy)
if i < 0 or i >= m or j < 0 ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsCycle(self, grid):
""":type grid: List[List[str]] :rtype: bool"""
<|body_0|>
def containsCycleUF(self, grid):
""":type grid: List[List[str]] :rtype: bool"""
<|body_1|>
def containsCycleTLE(self, grid):
""":type grid: List[Li... | stack_v2_sparse_classes_10k_train_008044 | 28,242 | no_license | [
{
"docstring": ":type grid: List[List[str]] :rtype: bool",
"name": "containsCycle",
"signature": "def containsCycle(self, grid)"
},
{
"docstring": ":type grid: List[List[str]] :rtype: bool",
"name": "containsCycleUF",
"signature": "def containsCycleUF(self, grid)"
},
{
"docstring... | 3 | stack_v2_sparse_classes_30k_train_004617 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsCycle(self, grid): :type grid: List[List[str]] :rtype: bool
- def containsCycleUF(self, grid): :type grid: List[List[str]] :rtype: bool
- def containsCycleTLE(self, g... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsCycle(self, grid): :type grid: List[List[str]] :rtype: bool
- def containsCycleUF(self, grid): :type grid: List[List[str]] :rtype: bool
- def containsCycleTLE(self, g... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def containsCycle(self, grid):
""":type grid: List[List[str]] :rtype: bool"""
<|body_0|>
def containsCycleUF(self, grid):
""":type grid: List[List[str]] :rtype: bool"""
<|body_1|>
def containsCycleTLE(self, grid):
""":type grid: List[Li... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def containsCycle(self, grid):
""":type grid: List[List[str]] :rtype: bool"""
m, n = (len(grid), len(grid[0]))
dirs = [(-1, 0), (1, 0), (0, -1), (0, 1)]
def dfs(cur, pre, visited, mark):
visited[cur[0]][cur[1]] = True
for dx, dy in dirs:
... | the_stack_v2_python_sparse | D/DetectCyclesin2DGrid.py | bssrdf/pyleet | train | 2 | |
706e065d5a7f1fe0b5b92beff9432613340340a9 | [
"data = []\ndata.append(scheduler_id)\nres = requests.post(url=enable_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))\nself.assertEqual(res.status_code, 204, msg='启用计划接口调用失败')",
"data = []\ndata.append(scheduler_id)\nres = requests.post(url=disable_scheduler_url, headers=get_headers(HOST_189)... | <|body_start_0|>
data = []
data.append(scheduler_id)
res = requests.post(url=enable_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
self.assertEqual(res.status_code, 204, msg='启用计划接口调用失败')
<|end_body_0|>
<|body_start_1|>
data = []
data.append(schedul... | 测试启用停用、批量删除schedulers接口 | EnableDisable | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnableDisable:
"""测试启用停用、批量删除schedulers接口"""
def test_case01(self):
"""启用计划"""
<|body_0|>
def test_case02(self):
"""停用计划"""
<|body_1|>
def test_case03(self):
"""批量删除计划"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
data = [... | stack_v2_sparse_classes_10k_train_008045 | 15,511 | no_license | [
{
"docstring": "启用计划",
"name": "test_case01",
"signature": "def test_case01(self)"
},
{
"docstring": "停用计划",
"name": "test_case02",
"signature": "def test_case02(self)"
},
{
"docstring": "批量删除计划",
"name": "test_case03",
"signature": "def test_case03(self)"
}
] | 3 | stack_v2_sparse_classes_30k_train_004650 | Implement the Python class `EnableDisable` described below.
Class description:
测试启用停用、批量删除schedulers接口
Method signatures and docstrings:
- def test_case01(self): 启用计划
- def test_case02(self): 停用计划
- def test_case03(self): 批量删除计划 | Implement the Python class `EnableDisable` described below.
Class description:
测试启用停用、批量删除schedulers接口
Method signatures and docstrings:
- def test_case01(self): 启用计划
- def test_case02(self): 停用计划
- def test_case03(self): 批量删除计划
<|skeleton|>
class EnableDisable:
"""测试启用停用、批量删除schedulers接口"""
def test_case01... | fc41513af3063169ff1b17d6f01f7074057ceb1f | <|skeleton|>
class EnableDisable:
"""测试启用停用、批量删除schedulers接口"""
def test_case01(self):
"""启用计划"""
<|body_0|>
def test_case02(self):
"""停用计划"""
<|body_1|>
def test_case03(self):
"""批量删除计划"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EnableDisable:
"""测试启用停用、批量删除schedulers接口"""
def test_case01(self):
"""启用计划"""
data = []
data.append(scheduler_id)
res = requests.post(url=enable_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
self.assertEqual(res.status_code, 204, msg='启用计划接口... | the_stack_v2_python_sparse | singl_api/api_test_cases/cases_for_schedulers_api.py | bingjiegu/For_API | train | 0 |
6de0436abd47ba94fac9bb05fdbe77550bf7c91f | [
"self.action: Action = kwargs.pop('action', None)\nself.column_name: str = kwargs.pop('column_name', None)\nself.exclude_init: List[str] = kwargs.pop('exclude_values', list)\nsuper().__init__(form_data, *args, **kwargs)\nself.set_field_from_dict('exclude_values')\nself.fields['exclude_values'].choices = get_rows(se... | <|body_start_0|>
self.action: Action = kwargs.pop('action', None)
self.column_name: str = kwargs.pop('column_name', None)
self.exclude_init: List[str] = kwargs.pop('exclude_values', list)
super().__init__(form_data, *args, **kwargs)
self.set_field_from_dict('exclude_values')
... | Form to select a few fields to exclude. | ValueExcludeForm | [
"MIT",
"LGPL-2.0-or-later",
"Python-2.0",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValueExcludeForm:
"""Form to select a few fields to exclude."""
def __init__(self, form_data, *args, **kwargs):
"""Store action, column name and exclude init, adjust fields."""
<|body_0|>
def clean(self):
"""Store the values in the field in the dictionary."""
... | stack_v2_sparse_classes_10k_train_008046 | 20,237 | permissive | [
{
"docstring": "Store action, column name and exclude init, adjust fields.",
"name": "__init__",
"signature": "def __init__(self, form_data, *args, **kwargs)"
},
{
"docstring": "Store the values in the field in the dictionary.",
"name": "clean",
"signature": "def clean(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000388 | Implement the Python class `ValueExcludeForm` described below.
Class description:
Form to select a few fields to exclude.
Method signatures and docstrings:
- def __init__(self, form_data, *args, **kwargs): Store action, column name and exclude init, adjust fields.
- def clean(self): Store the values in the field in t... | Implement the Python class `ValueExcludeForm` described below.
Class description:
Form to select a few fields to exclude.
Method signatures and docstrings:
- def __init__(self, form_data, *args, **kwargs): Store action, column name and exclude init, adjust fields.
- def clean(self): Store the values in the field in t... | 5473e9faa24c71a2a1102d47ebc2cbf27608e42a | <|skeleton|>
class ValueExcludeForm:
"""Form to select a few fields to exclude."""
def __init__(self, form_data, *args, **kwargs):
"""Store action, column name and exclude init, adjust fields."""
<|body_0|>
def clean(self):
"""Store the values in the field in the dictionary."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ValueExcludeForm:
"""Form to select a few fields to exclude."""
def __init__(self, form_data, *args, **kwargs):
"""Store action, column name and exclude init, adjust fields."""
self.action: Action = kwargs.pop('action', None)
self.column_name: str = kwargs.pop('column_name', None)... | the_stack_v2_python_sparse | ontask/action/forms/run.py | LucasFranciscoCorreia/ontask_b | train | 0 |
76f8c05b4d88a698dd9f078ca275b3866ebba2b8 | [
"Parametre.__init__(self, 'emote', 'emote')\nself.schema = '<nom_familier> <message>'\nself.aide_courte = 'fait agir votre familier'\nself.aide_longue = \"Cette commande est identique à la commande %emote% mais pour votre familier : elle vous permet de donner un ordre, comme si le familier faisait un %emote%. Les m... | <|body_start_0|>
Parametre.__init__(self, 'emote', 'emote')
self.schema = '<nom_familier> <message>'
self.aide_courte = 'fait agir votre familier'
self.aide_longue = "Cette commande est identique à la commande %emote% mais pour votre familier : elle vous permet de donner un ordre, comme ... | Commande 'familier emote'. | PrmEmote | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmEmote:
"""Commande 'familier emote'."""
def __init__(self):
"""Constructeur du paramètre"""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Interprétation du paramètre"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Parametre.__... | stack_v2_sparse_classes_10k_train_008047 | 3,084 | permissive | [
{
"docstring": "Constructeur du paramètre",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Interprétation du paramètre",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | null | Implement the Python class `PrmEmote` described below.
Class description:
Commande 'familier emote'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre | Implement the Python class `PrmEmote` described below.
Class description:
Commande 'familier emote'.
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre
<|skeleton|>
class PrmEmote:
"""Commande 'familier e... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmEmote:
"""Commande 'familier emote'."""
def __init__(self):
"""Constructeur du paramètre"""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Interprétation du paramètre"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PrmEmote:
"""Commande 'familier emote'."""
def __init__(self):
"""Constructeur du paramètre"""
Parametre.__init__(self, 'emote', 'emote')
self.schema = '<nom_familier> <message>'
self.aide_courte = 'fait agir votre familier'
self.aide_longue = "Cette commande est i... | the_stack_v2_python_sparse | src/secondaires/familier/commandes/familier/emote.py | vincent-lg/tsunami | train | 5 |
ba27ba73c8af398383de5b82b993ee9c2890c3cc | [
"for k, v in params.items():\n if v == str(None):\n params[k] = None\nnetworks = cxmate.Adapter.to_networkx(input_stream)\nnodedata_tmp = []\nnet = networks[0]\nnet.graph['label'] = OUTPUT_LABEL\nif params['prog'] == 'twopi' and 'root' in net.graph.keys():\n params['root'] = net.graph['root']\nfor n, n... | <|body_start_0|>
for k, v in params.items():
if v == str(None):
params[k] = None
networks = cxmate.Adapter.to_networkx(input_stream)
nodedata_tmp = []
net = networks[0]
net.graph['label'] = OUTPUT_LABEL
if params['prog'] == 'twopi' and 'root' i... | NxLayoutService | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NxLayoutService:
def process(self, params, input_stream):
"""CI service for creating node positions for the given network data using Graphviz."""
<|body_0|>
def outputStream(self, networks, pos):
"""Creates a CX element generator added cartesianCoordinate from a list... | stack_v2_sparse_classes_10k_train_008048 | 2,430 | permissive | [
{
"docstring": "CI service for creating node positions for the given network data using Graphviz.",
"name": "process",
"signature": "def process(self, params, input_stream)"
},
{
"docstring": "Creates a CX element generator added cartesianCoordinate from a list of networkx objects. :params netwo... | 2 | stack_v2_sparse_classes_30k_train_005738 | Implement the Python class `NxLayoutService` described below.
Class description:
Implement the NxLayoutService class.
Method signatures and docstrings:
- def process(self, params, input_stream): CI service for creating node positions for the given network data using Graphviz.
- def outputStream(self, networks, pos): ... | Implement the Python class `NxLayoutService` described below.
Class description:
Implement the NxLayoutService class.
Method signatures and docstrings:
- def process(self, params, input_stream): CI service for creating node positions for the given network data using Graphviz.
- def outputStream(self, networks, pos): ... | 97c0237381528ac72f76c600071573fd1cbcdce6 | <|skeleton|>
class NxLayoutService:
def process(self, params, input_stream):
"""CI service for creating node positions for the given network data using Graphviz."""
<|body_0|>
def outputStream(self, networks, pos):
"""Creates a CX element generator added cartesianCoordinate from a list... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NxLayoutService:
def process(self, params, input_stream):
"""CI service for creating node positions for the given network data using Graphviz."""
for k, v in params.items():
if v == str(None):
params[k] = None
networks = cxmate.Adapter.to_networkx(input_stre... | the_stack_v2_python_sparse | services/nx_layout/service/service.py | idekerlab/graph-services | train | 0 | |
c3e0ec8e769fac78c27f034ae5662ddd58b220d0 | [
"super().__init__(parent, store)\nself.frame1_label = tk.Label(self.frame1, text='Login with password', font=('Arial', 20, 'bold'))\nself.password_label = tk.Label(self.frame1, text='Password: ')\nself.password_field = tk.StringVar()\nself.password_entry = tk.Entry(self.frame1, textvariable=self.password_field, sho... | <|body_start_0|>
super().__init__(parent, store)
self.frame1_label = tk.Label(self.frame1, text='Login with password', font=('Arial', 20, 'bold'))
self.password_label = tk.Label(self.frame1, text='Password: ')
self.password_field = tk.StringVar()
self.password_entry = tk.Entry(se... | The second screen when the app opens. It has 2 frame, the left frame is for login normally, and the right frame is for login with face. User can click on the open cam button to use this feature. Attributes: ---------- frame1 : tkinter frame The container for the left page frame2 : tkinter frame The container for the ri... | Screen2 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Screen2:
"""The second screen when the app opens. It has 2 frame, the left frame is for login normally, and the right frame is for login with face. User can click on the open cam button to use this feature. Attributes: ---------- frame1 : tkinter frame The container for the left page frame2 : tki... | stack_v2_sparse_classes_10k_train_008049 | 8,263 | permissive | [
{
"docstring": "Init the screen 2 with 2 frame on. The first frame is for login with password and the second frame is for login with face. This only load the recognizer and pca if the user has the face added and the device has camera. Params: ------- parent: tkinter frame or tk() The parent frame attach to this... | 4 | stack_v2_sparse_classes_30k_train_005762 | Implement the Python class `Screen2` described below.
Class description:
The second screen when the app opens. It has 2 frame, the left frame is for login normally, and the right frame is for login with face. User can click on the open cam button to use this feature. Attributes: ---------- frame1 : tkinter frame The c... | Implement the Python class `Screen2` described below.
Class description:
The second screen when the app opens. It has 2 frame, the left frame is for login normally, and the right frame is for login with face. User can click on the open cam button to use this feature. Attributes: ---------- frame1 : tkinter frame The c... | e4478a6ca8ca15ab99fc251b40e64307302bffeb | <|skeleton|>
class Screen2:
"""The second screen when the app opens. It has 2 frame, the left frame is for login normally, and the right frame is for login with face. User can click on the open cam button to use this feature. Attributes: ---------- frame1 : tkinter frame The container for the left page frame2 : tki... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Screen2:
"""The second screen when the app opens. It has 2 frame, the left frame is for login normally, and the right frame is for login with face. User can click on the open cam button to use this feature. Attributes: ---------- frame1 : tkinter frame The container for the left page frame2 : tkinter frame Th... | the_stack_v2_python_sparse | screen2.py | athan37/LoginSystemWithFace | train | 1 |
6686b9ffc6573f146a957b5eb063297d49c57622 | [
"result = {'result': 'NG'}\ncontent = CtrlDSSection().get_usecase_by_doc_id(doc_id, 'USERCASE')\nif content:\n result = {'result': 'OK', 'content': content}\nreturn result",
"result = {'result': 'NG', 'error': ''}\ndata_json = request.get_json()\nsec_obj = CtrlDSSection()\nflag, error = sec_obj.usecase_add(dat... | <|body_start_0|>
result = {'result': 'NG'}
content = CtrlDSSection().get_usecase_by_doc_id(doc_id, 'USERCASE')
if content:
result = {'result': 'OK', 'content': content}
return result
<|end_body_0|>
<|body_start_1|>
result = {'result': 'NG', 'error': ''}
data_... | ApiDSDocUsecase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiDSDocUsecase:
def get(self, doc_id):
"""获取文档下所有usecase的说明 :param doc_id: :return:"""
<|body_0|>
def post(self):
"""保存和修改文档下usecase的说明 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = {'result': 'NG'}
content = CtrlDSSecti... | stack_v2_sparse_classes_10k_train_008050 | 31,026 | no_license | [
{
"docstring": "获取文档下所有usecase的说明 :param doc_id: :return:",
"name": "get",
"signature": "def get(self, doc_id)"
},
{
"docstring": "保存和修改文档下usecase的说明 :return:",
"name": "post",
"signature": "def post(self)"
}
] | 2 | null | Implement the Python class `ApiDSDocUsecase` described below.
Class description:
Implement the ApiDSDocUsecase class.
Method signatures and docstrings:
- def get(self, doc_id): 获取文档下所有usecase的说明 :param doc_id: :return:
- def post(self): 保存和修改文档下usecase的说明 :return: | Implement the Python class `ApiDSDocUsecase` described below.
Class description:
Implement the ApiDSDocUsecase class.
Method signatures and docstrings:
- def get(self, doc_id): 获取文档下所有usecase的说明 :param doc_id: :return:
- def post(self): 保存和修改文档下usecase的说明 :return:
<|skeleton|>
class ApiDSDocUsecase:
def get(sel... | 64b31e7bdfcb8a4c95f0a8a607f0bcff576cec11 | <|skeleton|>
class ApiDSDocUsecase:
def get(self, doc_id):
"""获取文档下所有usecase的说明 :param doc_id: :return:"""
<|body_0|>
def post(self):
"""保存和修改文档下usecase的说明 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ApiDSDocUsecase:
def get(self, doc_id):
"""获取文档下所有usecase的说明 :param doc_id: :return:"""
result = {'result': 'NG'}
content = CtrlDSSection().get_usecase_by_doc_id(doc_id, 'USERCASE')
if content:
result = {'result': 'OK', 'content': content}
return result
... | the_stack_v2_python_sparse | Source/collaboration_2/app/api_1_0/api_ds_doc.py | lsn1183/web_project | train | 0 | |
efa8c265f300f619381be34ccb5b36ef6605feb4 | [
"self.event_threshold = event_threshold\nself._label_indices = {name: i for i, name in enumerate(label_names)}\nself.perf_data = {}\nfor label in label_names:\n for bench_name, bench_iterations in benchmark_names_and_iterations:\n for i in xrange(bench_iterations):\n report = read_perf_report(l... | <|body_start_0|>
self.event_threshold = event_threshold
self._label_indices = {name: i for i, name in enumerate(label_names)}
self.perf_data = {}
for label in label_names:
for bench_name, bench_iterations in benchmark_names_and_iterations:
for i in xrange(benc... | Generates dicts from a perf table. Dicts look like: {'benchmark_name': {'perf_event_name': [LabelData]}} where LabelData is a list of perf dicts, each perf dict coming from the same label. Each perf dict looks like {'function_name': 0.10, ...} (where 0.10 is the percentage of time spent in function_name). | _PerfTable | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _PerfTable:
"""Generates dicts from a perf table. Dicts look like: {'benchmark_name': {'perf_event_name': [LabelData]}} where LabelData is a list of perf dicts, each perf dict coming from the same label. Each perf dict looks like {'function_name': 0.10, ...} (where 0.10 is the percentage of time ... | stack_v2_sparse_classes_10k_train_008051 | 25,882 | permissive | [
{
"docstring": "Constructor. read_perf_report is a function that takes a label name, benchmark name, and benchmark iteration, and returns a dictionary describing the perf output for that given run.",
"name": "__init__",
"signature": "def __init__(self, benchmark_names_and_iterations, label_names, read_p... | 2 | null | Implement the Python class `_PerfTable` described below.
Class description:
Generates dicts from a perf table. Dicts look like: {'benchmark_name': {'perf_event_name': [LabelData]}} where LabelData is a list of perf dicts, each perf dict coming from the same label. Each perf dict looks like {'function_name': 0.10, ...}... | Implement the Python class `_PerfTable` described below.
Class description:
Generates dicts from a perf table. Dicts look like: {'benchmark_name': {'perf_event_name': [LabelData]}} where LabelData is a list of perf dicts, each perf dict coming from the same label. Each perf dict looks like {'function_name': 0.10, ...}... | e2745b756317aac3c7a27a4c10bdfe0921a82a1c | <|skeleton|>
class _PerfTable:
"""Generates dicts from a perf table. Dicts look like: {'benchmark_name': {'perf_event_name': [LabelData]}} where LabelData is a list of perf dicts, each perf dict coming from the same label. Each perf dict looks like {'function_name': 0.10, ...} (where 0.10 is the percentage of time ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _PerfTable:
"""Generates dicts from a perf table. Dicts look like: {'benchmark_name': {'perf_event_name': [LabelData]}} where LabelData is a list of perf dicts, each perf dict coming from the same label. Each perf dict looks like {'function_name': 0.10, ...} (where 0.10 is the percentage of time spent in func... | the_stack_v2_python_sparse | app/src/main/java/com/syd/source/aosp/external/toolchain-utils/crosperf/results_report.py | lz-purple/Source | train | 4 |
b194fd6ef5edaca01839b82883d5fd101d0e7493 | [
"self.cache = {}\nself.order = []\nself.capacity = capacity",
"if key in self.cache:\n if key != self.order[-1]:\n self.order.remove(key)\n self.order.append(key)\n return self.cache[key]\nelse:\n return -1",
"if key in self.cache:\n self.cache[key] = value\n if key != self.order[-1... | <|body_start_0|>
self.cache = {}
self.order = []
self.capacity = capacity
<|end_body_0|>
<|body_start_1|>
if key in self.cache:
if key != self.order[-1]:
self.order.remove(key)
self.order.append(key)
return self.cache[key]
... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k_train_008052 | 2,489 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | null | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
<|sk... | 7a459e9742958e63be8886874904e5ab2489411a | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.cache = {}
self.order = []
self.capacity = capacity
def get(self, key):
""":type key: int :rtype: int"""
if key in self.cache:
if key != self.order[-1]:
self.... | the_stack_v2_python_sparse | Hard/146.py | Hellofafar/Leetcode | train | 6 | |
89f507bc0e205ae3fc33ab4b8d80c3be9424c360 | [
"super(MidasNet_ASPP, self).__init__()\nuse_pretrained = False if path else True\nself.pretrained, self.scratch = _make_encoder('resnext101_wsl_aspp', features, use_pretrained)\nself.scratch.refinenet4 = FeatureFusionBlock(features)\nself.scratch.refinenet3 = FeatureFusionBlock(features)\nself.scratch.refinenet2 = ... | <|body_start_0|>
super(MidasNet_ASPP, self).__init__()
use_pretrained = False if path else True
self.pretrained, self.scratch = _make_encoder('resnext101_wsl_aspp', features, use_pretrained)
self.scratch.refinenet4 = FeatureFusionBlock(features)
self.scratch.refinenet3 = FeatureF... | Network for monocular depth estimation. | MidasNet_ASPP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MidasNet_ASPP:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. backbone (str, optional): Backbone netw... | stack_v2_sparse_classes_10k_train_008053 | 13,019 | permissive | [
{
"docstring": "Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. backbone (str, optional): Backbone network for encoder. Defaults to resnet50",
"name": "__init__",
"signature": "def __init__(self, path=None, features=... | 2 | null | Implement the Python class `MidasNet_ASPP` described below.
Class description:
Network for monocular depth estimation.
Method signatures and docstrings:
- def __init__(self, path=None, features=256): Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features.... | Implement the Python class `MidasNet_ASPP` described below.
Class description:
Network for monocular depth estimation.
Method signatures and docstrings:
- def __init__(self, path=None, features=256): Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features.... | a00c3619bf4042e446e1919087f0b09fe9fa3a65 | <|skeleton|>
class MidasNet_ASPP:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. backbone (str, optional): Backbone netw... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MidasNet_ASPP:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. backbone (str, optional): Backbone network for encod... | the_stack_v2_python_sparse | nasws/cnn/search_space/monodepth/models/midas_net.py | kcyu2014/nas-landmarkreg | train | 10 |
caf54cd1807dcacbf822584c08e45d9933187b6e | [
"if n == 1:\n return 1\nfirst, second = (1, 2)\nfor i in range(3, n + 1):\n third = first + second\n first = second\n second = third\nreturn second",
"cache = {0: 1, 1: 1}\nfor i in range(2, n + 1):\n cache[i] = cache[i - 1] + cache[i - 2]\nreturn cache[n]",
"if n in self.cache:\n return self.... | <|body_start_0|>
if n == 1:
return 1
first, second = (1, 2)
for i in range(3, n + 1):
third = first + second
first = second
second = third
return second
<|end_body_0|>
<|body_start_1|>
cache = {0: 1, 1: 1}
for i in range(2,... | ClimbingStairs | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClimbingStairs:
def total_ways(self, n: int) -> int:
"""Approach: Fibonacci Number Time Complexity: O(N) Space Complexity: O(1) :param n: :param int: :return:"""
<|body_0|>
def total_ways_(self, n: int) -> int:
"""Approach: DP Time Complexity: O(N) Space Complexity: ... | stack_v2_sparse_classes_10k_train_008054 | 1,757 | no_license | [
{
"docstring": "Approach: Fibonacci Number Time Complexity: O(N) Space Complexity: O(1) :param n: :param int: :return:",
"name": "total_ways",
"signature": "def total_ways(self, n: int) -> int"
},
{
"docstring": "Approach: DP Time Complexity: O(N) Space Complexity: O(N) :param n: :return:",
... | 4 | null | Implement the Python class `ClimbingStairs` described below.
Class description:
Implement the ClimbingStairs class.
Method signatures and docstrings:
- def total_ways(self, n: int) -> int: Approach: Fibonacci Number Time Complexity: O(N) Space Complexity: O(1) :param n: :param int: :return:
- def total_ways_(self, n:... | Implement the Python class `ClimbingStairs` described below.
Class description:
Implement the ClimbingStairs class.
Method signatures and docstrings:
- def total_ways(self, n: int) -> int: Approach: Fibonacci Number Time Complexity: O(N) Space Complexity: O(1) :param n: :param int: :return:
- def total_ways_(self, n:... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class ClimbingStairs:
def total_ways(self, n: int) -> int:
"""Approach: Fibonacci Number Time Complexity: O(N) Space Complexity: O(1) :param n: :param int: :return:"""
<|body_0|>
def total_ways_(self, n: int) -> int:
"""Approach: DP Time Complexity: O(N) Space Complexity: ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ClimbingStairs:
def total_ways(self, n: int) -> int:
"""Approach: Fibonacci Number Time Complexity: O(N) Space Complexity: O(1) :param n: :param int: :return:"""
if n == 1:
return 1
first, second = (1, 2)
for i in range(3, n + 1):
third = first + second
... | the_stack_v2_python_sparse | revisited_2021/dp/climbing_stairs.py | Shiv2157k/leet_code | train | 1 | |
2f2dfb0b8031d58b5d6d87465367928fd01a259d | [
"try:\n value = get_user_preference(request.user, preference_key, username=username)\n if value is None:\n return Response(status=status.HTTP_404_NOT_FOUND)\nexcept UserNotAuthorized:\n return Response(status=status.HTTP_403_FORBIDDEN)\nexcept UserNotFound:\n return Response(status=status.HTTP_40... | <|body_start_0|>
try:
value = get_user_preference(request.user, preference_key, username=username)
if value is None:
return Response(status=status.HTTP_404_NOT_FOUND)
except UserNotAuthorized:
return Response(status=status.HTTP_403_FORBIDDEN)
e... | **Use Cases** Get, create, update, or delete a specific user preference. **Example Requests** GET /api/user/v1/preferences/{username}/{preference_key} PUT /api/user/v1/preferences/{username}/{preference_key} DELETE /api/user/v1/preferences/{username}/{preference_key} **Response Values for GET** If the specified usernam... | PreferencesDetailView | [
"AGPL-3.0-only",
"AGPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreferencesDetailView:
"""**Use Cases** Get, create, update, or delete a specific user preference. **Example Requests** GET /api/user/v1/preferences/{username}/{preference_key} PUT /api/user/v1/preferences/{username}/{preference_key} DELETE /api/user/v1/preferences/{username}/{preference_key} **R... | stack_v2_sparse_classes_10k_train_008055 | 11,020 | permissive | [
{
"docstring": "GET /api/user/v1/preferences/{username}/{preference_key}",
"name": "get",
"signature": "def get(self, request, username, preference_key)"
},
{
"docstring": "PUT /api/user/v1/preferences/{username}/{preference_key}",
"name": "put",
"signature": "def put(self, request, user... | 3 | null | Implement the Python class `PreferencesDetailView` described below.
Class description:
**Use Cases** Get, create, update, or delete a specific user preference. **Example Requests** GET /api/user/v1/preferences/{username}/{preference_key} PUT /api/user/v1/preferences/{username}/{preference_key} DELETE /api/user/v1/pref... | Implement the Python class `PreferencesDetailView` described below.
Class description:
**Use Cases** Get, create, update, or delete a specific user preference. **Example Requests** GET /api/user/v1/preferences/{username}/{preference_key} PUT /api/user/v1/preferences/{username}/{preference_key} DELETE /api/user/v1/pref... | 5809eaca7079a15ee56b0b7fcfea425337046c97 | <|skeleton|>
class PreferencesDetailView:
"""**Use Cases** Get, create, update, or delete a specific user preference. **Example Requests** GET /api/user/v1/preferences/{username}/{preference_key} PUT /api/user/v1/preferences/{username}/{preference_key} DELETE /api/user/v1/preferences/{username}/{preference_key} **R... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PreferencesDetailView:
"""**Use Cases** Get, create, update, or delete a specific user preference. **Example Requests** GET /api/user/v1/preferences/{username}/{preference_key} PUT /api/user/v1/preferences/{username}/{preference_key} DELETE /api/user/v1/preferences/{username}/{preference_key} **Response Value... | the_stack_v2_python_sparse | Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/user_api/preferences/views.py | luque/better-ways-of-thinking-about-software | train | 3 |
df1d3f4f4ff2800213a045e5c8f45224dfbb726f | [
"key = cls.des_key\nlength = len(reqdata)\nif length < cls.block_size:\n add = cls.block_size - length\nelif length > cls.block_size:\n add = cls.block_size - length % cls.block_size\nelse:\n add = 8\nreqdata = reqdata + cls.pad_str[add - 1] * add\ndes = DES.new(key, DES.MODE_ECB)\nencrypt_data = des.encry... | <|body_start_0|>
key = cls.des_key
length = len(reqdata)
if length < cls.block_size:
add = cls.block_size - length
elif length > cls.block_size:
add = cls.block_size - length % cls.block_size
else:
add = 8
reqdata = reqdata + cls.pad_st... | 加密和解密工具类 | Crypt | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Crypt:
"""加密和解密工具类"""
def des_base64_encrypt(cls, reqdata):
"""基于DES和base64的加密算法 @:param reqdata 需要加密的请求数据"""
<|body_0|>
def des_base64_decrypt(cls, retdata):
"""DES解密 @:param retdata: lakala reponse retData"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_10k_train_008056 | 1,265 | no_license | [
{
"docstring": "基于DES和base64的加密算法 @:param reqdata 需要加密的请求数据",
"name": "des_base64_encrypt",
"signature": "def des_base64_encrypt(cls, reqdata)"
},
{
"docstring": "DES解密 @:param retdata: lakala reponse retData",
"name": "des_base64_decrypt",
"signature": "def des_base64_decrypt(cls, retda... | 2 | stack_v2_sparse_classes_30k_train_006604 | Implement the Python class `Crypt` described below.
Class description:
加密和解密工具类
Method signatures and docstrings:
- def des_base64_encrypt(cls, reqdata): 基于DES和base64的加密算法 @:param reqdata 需要加密的请求数据
- def des_base64_decrypt(cls, retdata): DES解密 @:param retdata: lakala reponse retData | Implement the Python class `Crypt` described below.
Class description:
加密和解密工具类
Method signatures and docstrings:
- def des_base64_encrypt(cls, reqdata): 基于DES和base64的加密算法 @:param reqdata 需要加密的请求数据
- def des_base64_decrypt(cls, retdata): DES解密 @:param retdata: lakala reponse retData
<|skeleton|>
class Crypt:
"""... | 92358511e1de06d8bf93888128576c32f226a26b | <|skeleton|>
class Crypt:
"""加密和解密工具类"""
def des_base64_encrypt(cls, reqdata):
"""基于DES和base64的加密算法 @:param reqdata 需要加密的请求数据"""
<|body_0|>
def des_base64_decrypt(cls, retdata):
"""DES解密 @:param retdata: lakala reponse retData"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Crypt:
"""加密和解密工具类"""
def des_base64_encrypt(cls, reqdata):
"""基于DES和base64的加密算法 @:param reqdata 需要加密的请求数据"""
key = cls.des_key
length = len(reqdata)
if length < cls.block_size:
add = cls.block_size - length
elif length > cls.block_size:
add... | the_stack_v2_python_sparse | 加密隐藏/DES/des_ecb.py | KnowNo/CTF-LEARN | train | 0 |
8f8df6a5d0f59e6f22f400809bd4bf2087368e4a | [
"self.image_list = image_list\nself.base_num = base_num\nself.pxpys = pxpys\nself.bss = bss\nself.scale = scale\nself.shift = shift\nself.method = method\nself.fit = Fitting(fit_method=fit_method, shift=self.shift, fit_range=self.scale).fit()\nself.one_point_calculation = self.with_eec if eec else self.without_eec\... | <|body_start_0|>
self.image_list = image_list
self.base_num = base_num
self.pxpys = pxpys
self.bss = bss
self.scale = scale
self.shift = shift
self.method = method
self.fit = Fitting(fit_method=fit_method, shift=self.shift, fit_range=self.scale).fit()
... | 変位計測用クラス | Displacement | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Displacement:
"""変位計測用クラス"""
def __init__(self, image_list, base_num, pxpys, bss, scale, shift, fit_method='para', eec=True, method='abs'):
""":param image_list: 画像のファイルパスリスト :param base_num: 基準フレームの番号 :param pxpys: 計測点の座標 :param bss: ブロックサイズ :param scale: 画像拡大率 :param shift: 探索範囲 :p... | stack_v2_sparse_classes_10k_train_008057 | 16,682 | no_license | [
{
"docstring": ":param image_list: 画像のファイルパスリスト :param base_num: 基準フレームの番号 :param pxpys: 計測点の座標 :param bss: ブロックサイズ :param scale: 画像拡大率 :param shift: 探索範囲 :param fit_method: フィッティング手法 :param eec: Estimate Error Cancellation :param method: 基準フレームからの絶対変位 or 前後フレームでの相対変位",
"name": "__init__",
"signature": ... | 6 | stack_v2_sparse_classes_30k_train_002605 | Implement the Python class `Displacement` described below.
Class description:
変位計測用クラス
Method signatures and docstrings:
- def __init__(self, image_list, base_num, pxpys, bss, scale, shift, fit_method='para', eec=True, method='abs'): :param image_list: 画像のファイルパスリスト :param base_num: 基準フレームの番号 :param pxpys: 計測点の座標 :par... | Implement the Python class `Displacement` described below.
Class description:
変位計測用クラス
Method signatures and docstrings:
- def __init__(self, image_list, base_num, pxpys, bss, scale, shift, fit_method='para', eec=True, method='abs'): :param image_list: 画像のファイルパスリスト :param base_num: 基準フレームの番号 :param pxpys: 計測点の座標 :par... | dd0b0f1310c7a3eb5a5a589bb86d24486cbb37b1 | <|skeleton|>
class Displacement:
"""変位計測用クラス"""
def __init__(self, image_list, base_num, pxpys, bss, scale, shift, fit_method='para', eec=True, method='abs'):
""":param image_list: 画像のファイルパスリスト :param base_num: 基準フレームの番号 :param pxpys: 計測点の座標 :param bss: ブロックサイズ :param scale: 画像拡大率 :param shift: 探索範囲 :p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Displacement:
"""変位計測用クラス"""
def __init__(self, image_list, base_num, pxpys, bss, scale, shift, fit_method='para', eec=True, method='abs'):
""":param image_list: 画像のファイルパスリスト :param base_num: 基準フレームの番号 :param pxpys: 計測点の座標 :param bss: ブロックサイズ :param scale: 画像拡大率 :param shift: 探索範囲 :param fit_meth... | the_stack_v2_python_sparse | my_utils/displacement/displacement.py | salem7mg/test4 | train | 0 |
503c72178d8d2931ae0e3700e7ee3a0b5478a821 | [
"result = {'result': 'NG'}\ndata = request.get_json(force=True)\nif data:\n succsee, message = CtrlQuotations().add_quotation(data)\n if succsee:\n result = {'result': 'OK', 'content': message}\n else:\n result['error'] = message\nelse:\n result['error'] = '请不要传空数据'\nreturn result",
"res... | <|body_start_0|>
result = {'result': 'NG'}
data = request.get_json(force=True)
if data:
succsee, message = CtrlQuotations().add_quotation(data)
if succsee:
result = {'result': 'OK', 'content': message}
else:
result['error'] = me... | ApiQuotationInfo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiQuotationInfo:
def post(self):
"""发起报价 :return:"""
<|body_0|>
def get(self, pro_id):
"""获取所有base版本 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = {'result': 'NG'}
data = request.get_json(force=True)
if data:
... | stack_v2_sparse_classes_10k_train_008058 | 10,406 | no_license | [
{
"docstring": "发起报价 :return:",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "获取所有base版本 :return:",
"name": "get",
"signature": "def get(self, pro_id)"
}
] | 2 | null | Implement the Python class `ApiQuotationInfo` described below.
Class description:
Implement the ApiQuotationInfo class.
Method signatures and docstrings:
- def post(self): 发起报价 :return:
- def get(self, pro_id): 获取所有base版本 :return: | Implement the Python class `ApiQuotationInfo` described below.
Class description:
Implement the ApiQuotationInfo class.
Method signatures and docstrings:
- def post(self): 发起报价 :return:
- def get(self, pro_id): 获取所有base版本 :return:
<|skeleton|>
class ApiQuotationInfo:
def post(self):
"""发起报价 :return:"""
... | 64b31e7bdfcb8a4c95f0a8a607f0bcff576cec11 | <|skeleton|>
class ApiQuotationInfo:
def post(self):
"""发起报价 :return:"""
<|body_0|>
def get(self, pro_id):
"""获取所有base版本 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ApiQuotationInfo:
def post(self):
"""发起报价 :return:"""
result = {'result': 'NG'}
data = request.get_json(force=True)
if data:
succsee, message = CtrlQuotations().add_quotation(data)
if succsee:
result = {'result': 'OK', 'content': message}... | the_stack_v2_python_sparse | koala/koala_server/app/api_1_0/api_quotations.py | lsn1183/web_project | train | 0 | |
cd35bc9d28049344fd3cfaaf3b6885ad02ac5583 | [
"profile.addListsToCraftTypeRepository('skeinforge_tools.craft_plugins.feed.html', self)\nself.fileNameInput = settings.FileNameInput().getFromFileName(interpret.getGNUTranslatorGcodeFileTypeTuples(), 'Open File for Feed', self, '')\nself.activateFeed = settings.BooleanSetting().getFromValue('Activate Feed:', self,... | <|body_start_0|>
profile.addListsToCraftTypeRepository('skeinforge_tools.craft_plugins.feed.html', self)
self.fileNameInput = settings.FileNameInput().getFromFileName(interpret.getGNUTranslatorGcodeFileTypeTuples(), 'Open File for Feed', self, '')
self.activateFeed = settings.BooleanSetting().ge... | A class to handle the feed settings. | FeedRepository | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeedRepository:
"""A class to handle the feed settings."""
def __init__(self):
"""Set the default settings, execute title & settings fileName."""
<|body_0|>
def execute(self):
"""Feed button has been clicked."""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_10k_train_008059 | 8,389 | no_license | [
{
"docstring": "Set the default settings, execute title & settings fileName.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Feed button has been clicked.",
"name": "execute",
"signature": "def execute(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001937 | Implement the Python class `FeedRepository` described below.
Class description:
A class to handle the feed settings.
Method signatures and docstrings:
- def __init__(self): Set the default settings, execute title & settings fileName.
- def execute(self): Feed button has been clicked. | Implement the Python class `FeedRepository` described below.
Class description:
A class to handle the feed settings.
Method signatures and docstrings:
- def __init__(self): Set the default settings, execute title & settings fileName.
- def execute(self): Feed button has been clicked.
<|skeleton|>
class FeedRepositor... | fd69d8e856780c826386dc973ceabcc03623f3e8 | <|skeleton|>
class FeedRepository:
"""A class to handle the feed settings."""
def __init__(self):
"""Set the default settings, execute title & settings fileName."""
<|body_0|>
def execute(self):
"""Feed button has been clicked."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FeedRepository:
"""A class to handle the feed settings."""
def __init__(self):
"""Set the default settings, execute title & settings fileName."""
profile.addListsToCraftTypeRepository('skeinforge_tools.craft_plugins.feed.html', self)
self.fileNameInput = settings.FileNameInput().g... | the_stack_v2_python_sparse | skeinforge_tools/craft_plugins/feed.py | bmander/skeinforge | train | 34 |
33dba0c9cc5193a336cd06e95c3352ab78557a52 | [
"for i in range(len(nums)):\n for j in range(i + 1, i + k + 1):\n if j >= len(nums):\n break\n if abs(nums[i] - nums[j]) <= t:\n return True\nreturn False",
"if t < 0:\n return False\nbucket = {}\nw = t + 1\nfor i in range(len(nums)):\n m = nums[i] // w\n if m in bu... | <|body_start_0|>
for i in range(len(nums)):
for j in range(i + 1, i + k + 1):
if j >= len(nums):
break
if abs(nums[i] - nums[j]) <= t:
return True
return False
<|end_body_0|>
<|body_start_1|>
if t < 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
"""brute force :type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
"""bucket :type nums: List[int] :type k: int :type t: int :r... | stack_v2_sparse_classes_10k_train_008060 | 1,195 | no_license | [
{
"docstring": "brute force :type nums: List[int] :type k: int :type t: int :rtype: bool",
"name": "containsNearbyAlmostDuplicate",
"signature": "def containsNearbyAlmostDuplicate(self, nums, k, t)"
},
{
"docstring": "bucket :type nums: List[int] :type k: int :type t: int :rtype: bool",
"nam... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): brute force :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): brute force :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, ... | e16702d2b3ec4e5054baad56f4320bc3b31676ad | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
"""brute force :type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
"""bucket :type nums: List[int] :type k: int :type t: int :r... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
"""brute force :type nums: List[int] :type k: int :type t: int :rtype: bool"""
for i in range(len(nums)):
for j in range(i + 1, i + k + 1):
if j >= len(nums):
break
if... | the_stack_v2_python_sparse | leetcode/hard/contains_duplicate3.py | SuperMartinYang/learning_algorithm | train | 0 | |
fe26f067060659ce6fe991b23286025bff38467f | [
"self._embeddings_index = {}\nself.embedding_dimension = embedding_dimension\nself.embedding_matrix = np.zeros((len(word_idx) + 1, self.embedding_dimension))\nself.__read_vectors__()\nself.__populate_embedding__(word_idx)",
"path = get_file(self.__GLOVE_FILE__, origin=self.__URL__, cache_subdir=self.__EMBEDDING_C... | <|body_start_0|>
self._embeddings_index = {}
self.embedding_dimension = embedding_dimension
self.embedding_matrix = np.zeros((len(word_idx) + 1, self.embedding_dimension))
self.__read_vectors__()
self.__populate_embedding__(word_idx)
<|end_body_0|>
<|body_start_1|>
path ... | GloveLoader | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GloveLoader:
def __init__(self, word_idx, embedding_dimension=100):
"""Read glove vectors from file. It creates a cache on home folder using Keras function :param embedding_dimension: (dimensions of glove 50,100,)"""
<|body_0|>
def __read_vectors__(self):
"""Read vec... | stack_v2_sparse_classes_10k_train_008061 | 1,919 | permissive | [
{
"docstring": "Read glove vectors from file. It creates a cache on home folder using Keras function :param embedding_dimension: (dimensions of glove 50,100,)",
"name": "__init__",
"signature": "def __init__(self, word_idx, embedding_dimension=100)"
},
{
"docstring": "Read vectors from glove",
... | 3 | stack_v2_sparse_classes_30k_train_001423 | Implement the Python class `GloveLoader` described below.
Class description:
Implement the GloveLoader class.
Method signatures and docstrings:
- def __init__(self, word_idx, embedding_dimension=100): Read glove vectors from file. It creates a cache on home folder using Keras function :param embedding_dimension: (dim... | Implement the Python class `GloveLoader` described below.
Class description:
Implement the GloveLoader class.
Method signatures and docstrings:
- def __init__(self, word_idx, embedding_dimension=100): Read glove vectors from file. It creates a cache on home folder using Keras function :param embedding_dimension: (dim... | 01214cf44f9e69b64c341c4b6676db73e5ca7966 | <|skeleton|>
class GloveLoader:
def __init__(self, word_idx, embedding_dimension=100):
"""Read glove vectors from file. It creates a cache on home folder using Keras function :param embedding_dimension: (dimensions of glove 50,100,)"""
<|body_0|>
def __read_vectors__(self):
"""Read vec... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GloveLoader:
def __init__(self, word_idx, embedding_dimension=100):
"""Read glove vectors from file. It creates a cache on home folder using Keras function :param embedding_dimension: (dimensions of glove 50,100,)"""
self._embeddings_index = {}
self.embedding_dimension = embedding_dime... | the_stack_v2_python_sparse | pypagai/util/glove.py | gcouti/pypagAI | train | 1 | |
bdf1846a15040aad5e9e839466171a5537aa62b8 | [
"m, n = (len(text1), len(text2))\ndp = [[0] * (n + 1) for _ in range(m + 1)]\nfor i in range(1, m + 1):\n for j in range(1, n + 1):\n if text1[i - 1] == text2[j - 1]:\n dp[i][j] = 1 + dp[i - 1][j - 1]\n else:\n dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])\nreturn dp[-1][-1]",
... | <|body_start_0|>
m, n = (len(text1), len(text2))
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
if text1[i - 1] == text2[j - 1]:
dp[i][j] = 1 + dp[i - 1][j - 1]
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestCommonSubsequence(self, text1, text2):
""":type text1: str :type text2: str :rtype: int :desc 最长公共子序列 不连续"""
<|body_0|>
def LCstring(string1, string2):
"""最长公共子串 连续"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
m, n = (len(tex... | stack_v2_sparse_classes_10k_train_008062 | 1,358 | no_license | [
{
"docstring": ":type text1: str :type text2: str :rtype: int :desc 最长公共子序列 不连续",
"name": "longestCommonSubsequence",
"signature": "def longestCommonSubsequence(self, text1, text2)"
},
{
"docstring": "最长公共子串 连续",
"name": "LCstring",
"signature": "def LCstring(string1, string2)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestCommonSubsequence(self, text1, text2): :type text1: str :type text2: str :rtype: int :desc 最长公共子序列 不连续
- def LCstring(string1, string2): 最长公共子串 连续 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestCommonSubsequence(self, text1, text2): :type text1: str :type text2: str :rtype: int :desc 最长公共子序列 不连续
- def LCstring(string1, string2): 最长公共子串 连续
<|skeleton|>
class ... | 08b3d9cab3c1806c37d36587372b1e8fb1683f64 | <|skeleton|>
class Solution:
def longestCommonSubsequence(self, text1, text2):
""":type text1: str :type text2: str :rtype: int :desc 最长公共子序列 不连续"""
<|body_0|>
def LCstring(string1, string2):
"""最长公共子串 连续"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def longestCommonSubsequence(self, text1, text2):
""":type text1: str :type text2: str :rtype: int :desc 最长公共子序列 不连续"""
m, n = (len(text1), len(text2))
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
... | the_stack_v2_python_sparse | history/1143.Longest-Common-Subsequence.py | HonniLin/leetcode | train | 0 | |
0d0002a74f37d3a84129a14afe25fe6cb477cfc8 | [
"super(PrintPopulationTypeClusters, self).__init__(experiment, name='PrintPopulationTypeClusters', label=label)\nself.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0)\nself.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end', default=self.experiment.config.ge... | <|body_start_0|>
super(PrintPopulationTypeClusters, self).__init__(experiment, name='PrintPopulationTypeClusters', label=label)
self.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0)
self.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end',... | Write a data file containing the numbers of clusters of each Cell type, as well as their mean size and the standard deviation in size Configuration is done in the [PrintPopulationTypeClusters] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to s... | PrintPopulationTypeClusters | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrintPopulationTypeClusters:
"""Write a data file containing the numbers of clusters of each Cell type, as well as their mean size and the standard deviation in size Configuration is done in the [PrintPopulationTypeClusters] section Configuration Options: epoch_start The epoch at which to start e... | stack_v2_sparse_classes_10k_train_008063 | 4,832 | permissive | [
{
"docstring": "Initialize the PrintPopulationTypeClusters Action",
"name": "__init__",
"signature": "def __init__(self, experiment, label=None)"
},
{
"docstring": "Execute the Action",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006874 | Implement the Python class `PrintPopulationTypeClusters` described below.
Class description:
Write a data file containing the numbers of clusters of each Cell type, as well as their mean size and the standard deviation in size Configuration is done in the [PrintPopulationTypeClusters] section Configuration Options: ep... | Implement the Python class `PrintPopulationTypeClusters` described below.
Class description:
Write a data file containing the numbers of clusters of each Cell type, as well as their mean size and the standard deviation in size Configuration is done in the [PrintPopulationTypeClusters] section Configuration Options: ep... | a114ac66e62a960e18127faf52cff9e48831e212 | <|skeleton|>
class PrintPopulationTypeClusters:
"""Write a data file containing the numbers of clusters of each Cell type, as well as their mean size and the standard deviation in size Configuration is done in the [PrintPopulationTypeClusters] section Configuration Options: epoch_start The epoch at which to start e... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PrintPopulationTypeClusters:
"""Write a data file containing the numbers of clusters of each Cell type, as well as their mean size and the standard deviation in size Configuration is done in the [PrintPopulationTypeClusters] section Configuration Options: epoch_start The epoch at which to start executing (def... | the_stack_v2_python_sparse | seeds/plugins/action/PrintPopulationTypeClusters.py | namlehai/seeds | train | 0 |
ed0af6b1fc923c0c58411385cac89997e2a9a99c | [
"self.log = logging.getLogger('lapis.gyazo')\nself.headers = {'User-Agent': useragent}\nself.regex = re.compile('^(.*?\\\\.)?gyazo\\\\.com$')",
"try:\n if not self.regex.match(urlsplit(submission.url).netloc):\n return None\n data = {'author': 'a gyazo.com user', 'source': submission.url, 'importer_d... | <|body_start_0|>
self.log = logging.getLogger('lapis.gyazo')
self.headers = {'User-Agent': useragent}
self.regex = re.compile('^(.*?\\.)?gyazo\\.com$')
<|end_body_0|>
<|body_start_1|>
try:
if not self.regex.match(urlsplit(submission.url).netloc):
return None
... | A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots. | GyazoPlugin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GyazoPlugin:
"""A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots."""
def __init__(self, useragent: str, **options):
"""Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the con... | stack_v2_sparse_classes_10k_train_008064 | 2,843 | permissive | [
{
"docstring": "Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the configuration. Ignored.",
"name": "__init__",
"signature": "def __init__(self, useragent: str, **options)"
},
{
"docstring": "Import a submission from ... | 2 | stack_v2_sparse_classes_30k_val_000287 | Implement the Python class `GyazoPlugin` described below.
Class description:
A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots.
Method signatures and docstrings:
- def __init__(self, useragent: str, **options): Initialize the gyazo import API. :param useragent: The useragent to use for... | Implement the Python class `GyazoPlugin` described below.
Class description:
A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots.
Method signatures and docstrings:
- def __init__(self, useragent: str, **options): Initialize the gyazo import API. :param useragent: The useragent to use for... | 29503bb70b7b9e47a5cea1ea03098543b1a01efb | <|skeleton|>
class GyazoPlugin:
"""A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots."""
def __init__(self, useragent: str, **options):
"""Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the con... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GyazoPlugin:
"""A gyazo.com import plugin. gyazo.com is a site for quickly uploading screen shots."""
def __init__(self, useragent: str, **options):
"""Initialize the gyazo import API. :param useragent: The useragent to use for querying gyazo. :param options: Other options in the configuration. I... | the_stack_v2_python_sparse | plugins/gyazo.py | spiral6/VelvetBot | train | 0 |
25d97f1cabf98aeee8ec31a1a997c9826212b5d9 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Auth Registry API The Auth Registry API is meant to as registry to obtain information of available auth providers. For example, to use OIDC or Kerberos for authentication. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document ar... | AuthRegistryServiceServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthRegistryServiceServicer:
"""Auth Registry API The Auth Registry API is meant to as registry to obtain information of available auth providers. For example, to use OIDC or Kerberos for authentication. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "... | stack_v2_sparse_classes_10k_train_008065 | 4,150 | no_license | [
{
"docstring": "Returns the auth provider that is reponsible for the given resource reference. MUST return CODE_NOT_FOUND if the reference does not exist.",
"name": "GetAuthProvider",
"signature": "def GetAuthProvider(self, request, context)"
},
{
"docstring": "Returns a list of the available au... | 2 | stack_v2_sparse_classes_30k_train_002906 | Implement the Python class `AuthRegistryServiceServicer` described below.
Class description:
Auth Registry API The Auth Registry API is meant to as registry to obtain information of available auth providers. For example, to use OIDC or Kerberos for authentication. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL",... | Implement the Python class `AuthRegistryServiceServicer` described below.
Class description:
Auth Registry API The Auth Registry API is meant to as registry to obtain information of available auth providers. For example, to use OIDC or Kerberos for authentication. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL",... | dad1a042b38db5f8bedcac3b6af25066f4d6eef9 | <|skeleton|>
class AuthRegistryServiceServicer:
"""Auth Registry API The Auth Registry API is meant to as registry to obtain information of available auth providers. For example, to use OIDC or Kerberos for authentication. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AuthRegistryServiceServicer:
"""Auth Registry API The Auth Registry API is meant to as registry to obtain information of available auth providers. For example, to use OIDC or Kerberos for authentication. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED",... | the_stack_v2_python_sparse | cs3/authregistry/v0alpha/authregistry_pb2_grpc.py | SamuAlfageme/python-cs3apis | train | 0 |
c6ede396fad99534e4d0a7f6161c53c2b2640c5d | [
"super(Actor, self).__init__()\nself.state_dim = state_dim\nself.action_dim = action_dim\nself.action_lim = action_lim\nself.hidden = 128\nself.usecuda = usecuda\nself.rnn = nn.LSTMCell(self.state_dim, self.hidden, bias=True)\nself.fc1 = nn.Linear(self.hidden, action_dim)\nself.fc1.weight.data.uniform_(-EPS, EPS)\n... | <|body_start_0|>
super(Actor, self).__init__()
self.state_dim = state_dim
self.action_dim = action_dim
self.action_lim = action_lim
self.hidden = 128
self.usecuda = usecuda
self.rnn = nn.LSTMCell(self.state_dim, self.hidden, bias=True)
self.fc1 = nn.Linear... | Actor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Actor:
def __init__(self, state_dim, action_dim, action_lim, usecuda=False):
"""Special method for class initialisation. :param state_dim: Dimension of input state. :type state_dim: int. :param action_dim: Dimension of output action. :type action_dim: int. :param action_lim: Used to limi... | stack_v2_sparse_classes_10k_train_008066 | 3,704 | permissive | [
{
"docstring": "Special method for class initialisation. :param state_dim: Dimension of input state. :type state_dim: int. :param action_dim: Dimension of output action. :type action_dim: int. :param action_lim: Used to limit action. :type action_lim: float. :return:",
"name": "__init__",
"signature": "... | 3 | stack_v2_sparse_classes_30k_test_000322 | Implement the Python class `Actor` described below.
Class description:
Implement the Actor class.
Method signatures and docstrings:
- def __init__(self, state_dim, action_dim, action_lim, usecuda=False): Special method for class initialisation. :param state_dim: Dimension of input state. :type state_dim: int. :param ... | Implement the Python class `Actor` described below.
Class description:
Implement the Actor class.
Method signatures and docstrings:
- def __init__(self, state_dim, action_dim, action_lim, usecuda=False): Special method for class initialisation. :param state_dim: Dimension of input state. :type state_dim: int. :param ... | a02bdb1754e9bae1c2448e4bccec795c739b3e6f | <|skeleton|>
class Actor:
def __init__(self, state_dim, action_dim, action_lim, usecuda=False):
"""Special method for class initialisation. :param state_dim: Dimension of input state. :type state_dim: int. :param action_dim: Dimension of output action. :type action_dim: int. :param action_lim: Used to limi... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Actor:
def __init__(self, state_dim, action_dim, action_lim, usecuda=False):
"""Special method for class initialisation. :param state_dim: Dimension of input state. :type state_dim: int. :param action_dim: Dimension of output action. :type action_dim: int. :param action_lim: Used to limit action. :typ... | the_stack_v2_python_sparse | notebook/njord-ddpg/model.py | LUOFENGZHOU/njord | train | 0 | |
74f615ec2c283c321e8402c2c06786ff109a796f | [
"kwargs['max_digits'] = kwargs.get('max_digits', 19)\nself.decimal_places = kwargs['decimal_places'] = kwargs.get('decimal_places', 6)\nkwargs['required'] = kwargs.get('required', False)\nsuper().__init__(*args, **kwargs)",
"amount = super(DecimalField, self).get_value(data)\nif len(str(amount).strip()) == 0:\n ... | <|body_start_0|>
kwargs['max_digits'] = kwargs.get('max_digits', 19)
self.decimal_places = kwargs['decimal_places'] = kwargs.get('decimal_places', 6)
kwargs['required'] = kwargs.get('required', False)
super().__init__(*args, **kwargs)
<|end_body_0|>
<|body_start_1|>
amount = sup... | Custom serializer for 'MoneyField', which ensures that passed values are numerically valid. Ref: https://github.com/django-money/django-money/blob/master/djmoney/contrib/django_rest_framework/fields.py | InvenTreeMoneySerializer | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InvenTreeMoneySerializer:
"""Custom serializer for 'MoneyField', which ensures that passed values are numerically valid. Ref: https://github.com/django-money/django-money/blob/master/djmoney/contrib/django_rest_framework/fields.py"""
def __init__(self, *args, **kwargs):
"""Override d... | stack_v2_sparse_classes_10k_train_008067 | 22,975 | permissive | [
{
"docstring": "Override default values.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Test that the returned amount is a valid Decimal.",
"name": "get_value",
"signature": "def get_value(self, data)"
}
] | 2 | null | Implement the Python class `InvenTreeMoneySerializer` described below.
Class description:
Custom serializer for 'MoneyField', which ensures that passed values are numerically valid. Ref: https://github.com/django-money/django-money/blob/master/djmoney/contrib/django_rest_framework/fields.py
Method signatures and docs... | Implement the Python class `InvenTreeMoneySerializer` described below.
Class description:
Custom serializer for 'MoneyField', which ensures that passed values are numerically valid. Ref: https://github.com/django-money/django-money/blob/master/djmoney/contrib/django_rest_framework/fields.py
Method signatures and docs... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class InvenTreeMoneySerializer:
"""Custom serializer for 'MoneyField', which ensures that passed values are numerically valid. Ref: https://github.com/django-money/django-money/blob/master/djmoney/contrib/django_rest_framework/fields.py"""
def __init__(self, *args, **kwargs):
"""Override d... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class InvenTreeMoneySerializer:
"""Custom serializer for 'MoneyField', which ensures that passed values are numerically valid. Ref: https://github.com/django-money/django-money/blob/master/djmoney/contrib/django_rest_framework/fields.py"""
def __init__(self, *args, **kwargs):
"""Override default values... | the_stack_v2_python_sparse | InvenTree/InvenTree/serializers.py | inventree/InvenTree | train | 3,077 |
48966475e1d1fa8435d560a77c1235ed4d73ef1c | [
"super().__init__()\nself.experiment_name = experiment_name\nself.default_rec_name = default_rec_name\nself.train_func = train_func\nself._call_in_subproc = call_in_subproc",
"if isinstance(tasks, dict):\n tasks = [tasks]\nif len(tasks) == 0:\n return []\nif train_func is None:\n train_func = self.train_... | <|body_start_0|>
super().__init__()
self.experiment_name = experiment_name
self.default_rec_name = default_rec_name
self.train_func = train_func
self._call_in_subproc = call_in_subproc
<|end_body_0|>
<|body_start_1|>
if isinstance(tasks, dict):
tasks = [tasks... | Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way. Assumption: models were defined by `task` and the results will be saved to `Recorder`. | TrainerR | [
"LicenseRef-scancode-generic-cla",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrainerR:
"""Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way. Assumption: models were defined by `task` and the results will be saved to `Recorder`."""
def __init__(self, experiment_name: Optional[str]=None, train_func: Callable... | stack_v2_sparse_classes_10k_train_008068 | 22,767 | permissive | [
{
"docstring": "Init TrainerR. Args: experiment_name (str, optional): the default name of experiment. train_func (Callable, optional): default training method. Defaults to `task_train`. call_in_subproc (bool): call the process in subprocess to force memory release",
"name": "__init__",
"signature": "def... | 3 | stack_v2_sparse_classes_30k_test_000265 | Implement the Python class `TrainerR` described below.
Class description:
Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way. Assumption: models were defined by `task` and the results will be saved to `Recorder`.
Method signatures and docstrings:
- def __in... | Implement the Python class `TrainerR` described below.
Class description:
Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way. Assumption: models were defined by `task` and the results will be saved to `Recorder`.
Method signatures and docstrings:
- def __in... | 4c30e5827b74bcc45f14cf3ae0c1715459ed09ae | <|skeleton|>
class TrainerR:
"""Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way. Assumption: models were defined by `task` and the results will be saved to `Recorder`."""
def __init__(self, experiment_name: Optional[str]=None, train_func: Callable... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TrainerR:
"""Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way. Assumption: models were defined by `task` and the results will be saved to `Recorder`."""
def __init__(self, experiment_name: Optional[str]=None, train_func: Callable=task_train, ... | the_stack_v2_python_sparse | qlib/model/trainer.py | microsoft/qlib | train | 12,822 |
56a0455a817f3a826b40815bfa69794b0c496e07 | [
"pre = ListNode(-1)\npre.next = head\ncurr = pre\nwhile curr.next and curr.next.next:\n forward = curr.next\n curr.next = forward.next\n forward.next = forward.next.next\n curr.next.next = forward\n curr = forward\nreturn pre.next",
"ret_head = head.next if head and head.next else head\npp_head = N... | <|body_start_0|>
pre = ListNode(-1)
pre.next = head
curr = pre
while curr.next and curr.next.next:
forward = curr.next
curr.next = forward.next
forward.next = forward.next.next
curr.next.next = forward
curr = forward
ret... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def swapPairs(self, head):
""":type head: ListNode :rtype: ListNode Fast solution, but hard to fathom."""
<|body_0|>
def rewrite(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
def rewrite2(self, head):
""":type head... | stack_v2_sparse_classes_10k_train_008069 | 3,942 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode Fast solution, but hard to fathom.",
"name": "swapPairs",
"signature": "def swapPairs(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "rewrite",
"signature": "def rewrite(self, head)"
},
{
"docs... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def swapPairs(self, head): :type head: ListNode :rtype: ListNode Fast solution, but hard to fathom.
- def rewrite(self, head): :type head: ListNode :rtype: ListNode
- def rewrite... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def swapPairs(self, head): :type head: ListNode :rtype: ListNode Fast solution, but hard to fathom.
- def rewrite(self, head): :type head: ListNode :rtype: ListNode
- def rewrite... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def swapPairs(self, head):
""":type head: ListNode :rtype: ListNode Fast solution, but hard to fathom."""
<|body_0|>
def rewrite(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
def rewrite2(self, head):
""":type head... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def swapPairs(self, head):
""":type head: ListNode :rtype: ListNode Fast solution, but hard to fathom."""
pre = ListNode(-1)
pre.next = head
curr = pre
while curr.next and curr.next.next:
forward = curr.next
curr.next = forward.next
... | the_stack_v2_python_sparse | co_fb/24_Swap_Nodes_in_Pairs.py | vsdrun/lc_public | train | 6 | |
64c2d34275e82de1920fa87b88c8f5db79128076 | [
"def driver(start, target, cnt):\n res = []\n if cnt == 0 and target > 0:\n res.append([])\n for i in range(start, len(nums)):\n subsets = driver(i + 1, target - nums[i], cnt - 1)\n for subset in subsets:\n subset.append(i)\n res.extend(subsets)\n return res\nres =... | <|body_start_0|>
def driver(start, target, cnt):
res = []
if cnt == 0 and target > 0:
res.append([])
for i in range(start, len(nums)):
subsets = driver(i + 1, target - nums[i], cnt - 1)
for subset in subsets:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def threeSumSmaller(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def threeSumSmaller2(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
def threeSumSmallerTwoP... | stack_v2_sparse_classes_10k_train_008070 | 2,140 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "threeSumSmaller",
"signature": "def threeSumSmaller(self, nums, target)"
},
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "threeSumSmaller2",
"signature": "def threeSumSmaller2(... | 3 | stack_v2_sparse_classes_30k_train_004716 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSumSmaller(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def threeSumSmaller2(self, nums, target): :type nums: List[int] :type target: int :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def threeSumSmaller(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def threeSumSmaller2(self, nums, target): :type nums: List[int] :type target: int :... | 11d6bf2ba7b50c07e048df37c4e05c8f46b92241 | <|skeleton|>
class Solution:
def threeSumSmaller(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def threeSumSmaller2(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
def threeSumSmallerTwoP... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def threeSumSmaller(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
def driver(start, target, cnt):
res = []
if cnt == 0 and target > 0:
res.append([])
for i in range(start, len(nums)):
... | the_stack_v2_python_sparse | LeetCodes/Google/3Sum Smaller.py | chutianwen/LeetCodes | train | 0 | |
dafb1f7aa0db15d840753ed05a1c2566698d1113 | [
"result = []\nroot = {'parent': None, 'state': list(), 'positives': nums, 'negatives': list()}\ntemp_node = root\nwhile temp_node is not None:\n if temp_node['positives']:\n item = temp_node['positives'].pop()\n next_node = {'parent': temp_node, 'state': temp_node['state'] + [item], 'positives': te... | <|body_start_0|>
result = []
root = {'parent': None, 'state': list(), 'positives': nums, 'negatives': list()}
temp_node = root
while temp_node is not None:
if temp_node['positives']:
item = temp_node['positives'].pop()
next_node = {'parent': te... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def permute_backtracking(self, nums):
"""根据题目的提示，可以使用回溯法来生成所有的可能的结果。 :type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def permute(self, nums):
"""从我们的第一直觉上来说，permutations很简单，只不过是所有数字的排列而已， 因此总共有len(nums)!种结果。现在的问题是怎么将这么多种结果生成出来。 在这里借用beam search的... | stack_v2_sparse_classes_10k_train_008071 | 2,369 | no_license | [
{
"docstring": "根据题目的提示，可以使用回溯法来生成所有的可能的结果。 :type nums: List[int] :rtype: List[List[int]]",
"name": "permute_backtracking",
"signature": "def permute_backtracking(self, nums)"
},
{
"docstring": "从我们的第一直觉上来说，permutations很简单，只不过是所有数字的排列而已， 因此总共有len(nums)!种结果。现在的问题是怎么将这么多种结果生成出来。 在这里借用beam search的方... | 2 | stack_v2_sparse_classes_30k_train_001797 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def permute_backtracking(self, nums): 根据题目的提示，可以使用回溯法来生成所有的可能的结果。 :type nums: List[int] :rtype: List[List[int]]
- def permute(self, nums): 从我们的第一直觉上来说，permutations很简单，只不过是所有数字的排列... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def permute_backtracking(self, nums): 根据题目的提示，可以使用回溯法来生成所有的可能的结果。 :type nums: List[int] :rtype: List[List[int]]
- def permute(self, nums): 从我们的第一直觉上来说，permutations很简单，只不过是所有数字的排列... | 45d116d790075b1583af6aecd00f8babfe2c3107 | <|skeleton|>
class Solution:
def permute_backtracking(self, nums):
"""根据题目的提示，可以使用回溯法来生成所有的可能的结果。 :type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def permute(self, nums):
"""从我们的第一直觉上来说，permutations很简单，只不过是所有数字的排列而已， 因此总共有len(nums)!种结果。现在的问题是怎么将这么多种结果生成出来。 在这里借用beam search的... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def permute_backtracking(self, nums):
"""根据题目的提示，可以使用回溯法来生成所有的可能的结果。 :type nums: List[int] :rtype: List[List[int]]"""
result = []
root = {'parent': None, 'state': list(), 'positives': nums, 'negatives': list()}
temp_node = root
while temp_node is not None:
... | the_stack_v2_python_sparse | leetcode/misc/exercise_46.py | YinongLong/Data-Structures-and-Algorithm-Analysis | train | 0 | |
5542b90fccd52b06da458f3baf60faf126d05868 | [
"self.time = deque()\nself.lookup = defaultdict(int)\nself.now = 0",
"while self.time and timestamp - self.time[-1] + 1 > 300:\n self.lookup.pop(self.time.pop())\nif timestamp > self.now:\n self.time.appendleft(timestamp)\n self.now = timestamp\nself.lookup[timestamp] += 1",
"while self.time and timest... | <|body_start_0|>
self.time = deque()
self.lookup = defaultdict(int)
self.now = 0
<|end_body_0|>
<|body_start_1|>
while self.time and timestamp - self.time[-1] + 1 > 300:
self.lookup.pop(self.time.pop())
if timestamp > self.now:
self.time.appendleft(timest... | HitCounter1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HitCounter1:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def hit(self, timestamp: int) -> None:
"""Record a hit. @param timestamp - The current timestamp (in seconds granularity)."""
<|body_1|>
def getHits(self, timestamp: i... | stack_v2_sparse_classes_10k_train_008072 | 1,884 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Record a hit. @param timestamp - The current timestamp (in seconds granularity).",
"name": "hit",
"signature": "def hit(self, timestamp: int) -> None"
},
{
... | 3 | stack_v2_sparse_classes_30k_val_000390 | Implement the Python class `HitCounter1` described below.
Class description:
Implement the HitCounter1 class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def hit(self, timestamp: int) -> None: Record a hit. @param timestamp - The current timestamp (in seconds granula... | Implement the Python class `HitCounter1` described below.
Class description:
Implement the HitCounter1 class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def hit(self, timestamp: int) -> None: Record a hit. @param timestamp - The current timestamp (in seconds granula... | 631df2ce6892a6fbb3e435f57e90d85f8200d125 | <|skeleton|>
class HitCounter1:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def hit(self, timestamp: int) -> None:
"""Record a hit. @param timestamp - The current timestamp (in seconds granularity)."""
<|body_1|>
def getHits(self, timestamp: i... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class HitCounter1:
def __init__(self):
"""Initialize your data structure here."""
self.time = deque()
self.lookup = defaultdict(int)
self.now = 0
def hit(self, timestamp: int) -> None:
"""Record a hit. @param timestamp - The current timestamp (in seconds granularity)."""... | the_stack_v2_python_sparse | 362. Design Hit Counter.py | c940606/leetcode | train | 3 | |
aa23c08a92b938eed93e0900debebda960f76beb | [
"txt.replace('&', '&')\nfor entity, code in XML.entities.items():\n txt.replace(entity, code)\nreturn txt",
"for entity, code in XML.entities.items():\n txt.replace(code, entity)\ntxt.replace('&', '&')\nreturn txt"
] | <|body_start_0|>
txt.replace('&', '&')
for entity, code in XML.entities.items():
txt.replace(entity, code)
return txt
<|end_body_0|>
<|body_start_1|>
for entity, code in XML.entities.items():
txt.replace(code, entity)
txt.replace('&', '&')
... | a class containing usefull method for XML | XML | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XML:
"""a class containing usefull method for XML"""
def encode(txt):
"""a method to replace XML entities by XML representation"""
<|body_0|>
def decode(txt):
"""a method to replace XML representation of entities by the original entities"""
<|body_1|>
<|... | stack_v2_sparse_classes_10k_train_008073 | 1,137 | permissive | [
{
"docstring": "a method to replace XML entities by XML representation",
"name": "encode",
"signature": "def encode(txt)"
},
{
"docstring": "a method to replace XML representation of entities by the original entities",
"name": "decode",
"signature": "def decode(txt)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006259 | Implement the Python class `XML` described below.
Class description:
a class containing usefull method for XML
Method signatures and docstrings:
- def encode(txt): a method to replace XML entities by XML representation
- def decode(txt): a method to replace XML representation of entities by the original entities | Implement the Python class `XML` described below.
Class description:
a class containing usefull method for XML
Method signatures and docstrings:
- def encode(txt): a method to replace XML entities by XML representation
- def decode(txt): a method to replace XML representation of entities by the original entities
<|s... | 39082e7833383bbe7dd414381f1b295e3b778439 | <|skeleton|>
class XML:
"""a class containing usefull method for XML"""
def encode(txt):
"""a method to replace XML entities by XML representation"""
<|body_0|>
def decode(txt):
"""a method to replace XML representation of entities by the original entities"""
<|body_1|>
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class XML:
"""a class containing usefull method for XML"""
def encode(txt):
"""a method to replace XML entities by XML representation"""
txt.replace('&', '&')
for entity, code in XML.entities.items():
txt.replace(entity, code)
return txt
def decode(txt):
... | the_stack_v2_python_sparse | usefullFunctions.py | chankeh/Blender-Render-Manager | train | 0 |
90b380c2f84d1b9d56d68224aaae9d58e31ae7ba | [
"if 0 <= x < len(self.grid) and 0 <= y < len(self.grid[0]):\n if self.grid[x][y] == '1':\n queue.append((x, y))",
"queue = deque()\nqueue.append((row, col))\nwhile queue:\n x, y = queue.pop()\n self.grid[x][y] = '2'\n self.append_if(queue, x - 1, y)\n self.append_if(queue, x, y - 1)\n sel... | <|body_start_0|>
if 0 <= x < len(self.grid) and 0 <= y < len(self.grid[0]):
if self.grid[x][y] == '1':
queue.append((x, y))
<|end_body_0|>
<|body_start_1|>
queue = deque()
queue.append((row, col))
while queue:
x, y = queue.pop()
self.g... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def append_if(self, queue, x, y):
"""Append to the queue only if in bounds of the grid and the cell value is 1."""
<|body_0|>
def mark_neighbors(self, row, col):
"""Mark all the cells in the current island with value = 2. Breadth-first search."""
<|... | stack_v2_sparse_classes_10k_train_008074 | 1,558 | no_license | [
{
"docstring": "Append to the queue only if in bounds of the grid and the cell value is 1.",
"name": "append_if",
"signature": "def append_if(self, queue, x, y)"
},
{
"docstring": "Mark all the cells in the current island with value = 2. Breadth-first search.",
"name": "mark_neighbors",
... | 3 | stack_v2_sparse_classes_30k_train_000458 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def append_if(self, queue, x, y): Append to the queue only if in bounds of the grid and the cell value is 1.
- def mark_neighbors(self, row, col): Mark all the cells in the curre... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def append_if(self, queue, x, y): Append to the queue only if in bounds of the grid and the cell value is 1.
- def mark_neighbors(self, row, col): Mark all the cells in the curre... | 0864b4f8a52d9463d09def8d54a9b852e4073dcc | <|skeleton|>
class Solution:
def append_if(self, queue, x, y):
"""Append to the queue only if in bounds of the grid and the cell value is 1."""
<|body_0|>
def mark_neighbors(self, row, col):
"""Mark all the cells in the current island with value = 2. Breadth-first search."""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def append_if(self, queue, x, y):
"""Append to the queue only if in bounds of the grid and the cell value is 1."""
if 0 <= x < len(self.grid) and 0 <= y < len(self.grid[0]):
if self.grid[x][y] == '1':
queue.append((x, y))
def mark_neighbors(self, row,... | the_stack_v2_python_sparse | islands2.py | Sammyuel/LeetcodeSolutions | train | 0 | |
11d7ad0146b63656733bc07d42cab8febb365031 | [
"super().__init__()\nself._cardinality = cardinality\nself._width = width\nself._start_filts = start_filts\nself._num_classes = num_classes\nself._block = block\nself._block.start_filts = start_filts\nself._layers = layers\nself.inplanes = copy.deepcopy(self._start_filts)\nself.conv1 = _StartConv(n_dim, norm_layer,... | <|body_start_0|>
super().__init__()
self._cardinality = cardinality
self._width = width
self._start_filts = start_filts
self._num_classes = num_classes
self._block = block
self._block.start_filts = start_filts
self._layers = layers
self.inplanes = ... | ResNeXt model architecture | _ResNeXt | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ResNeXt:
"""ResNeXt model architecture"""
def __init__(self, block: torch.nn.Module, layers: Sequence[int], num_classes: int, in_channels: int, cardinality: int, width: int=4, start_filts: int=64, start_mode: str='7x7', n_dim: int=2, norm_layer: str='Batch'):
"""Parameters ---------... | stack_v2_sparse_classes_10k_train_008075 | 12,047 | permissive | [
{
"docstring": "Parameters ---------- block : nn.Module ResNeXt block used to build network layers : list of int defines how many blocks should be used in each stage num_classes : int number of classes in_channels : int number of input channels cardinality : int cardinality (number of groups) width : int width ... | 3 | stack_v2_sparse_classes_30k_train_006037 | Implement the Python class `_ResNeXt` described below.
Class description:
ResNeXt model architecture
Method signatures and docstrings:
- def __init__(self, block: torch.nn.Module, layers: Sequence[int], num_classes: int, in_channels: int, cardinality: int, width: int=4, start_filts: int=64, start_mode: str='7x7', n_d... | Implement the Python class `_ResNeXt` described below.
Class description:
ResNeXt model architecture
Method signatures and docstrings:
- def __init__(self, block: torch.nn.Module, layers: Sequence[int], num_classes: int, in_channels: int, cardinality: int, width: int=4, start_filts: int=64, start_mode: str='7x7', n_d... | 1078f5030b8aac2bf022daf5fa14d66f74c3c893 | <|skeleton|>
class _ResNeXt:
"""ResNeXt model architecture"""
def __init__(self, block: torch.nn.Module, layers: Sequence[int], num_classes: int, in_channels: int, cardinality: int, width: int=4, start_filts: int=64, start_mode: str='7x7', n_dim: int=2, norm_layer: str='Batch'):
"""Parameters ---------... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _ResNeXt:
"""ResNeXt model architecture"""
def __init__(self, block: torch.nn.Module, layers: Sequence[int], num_classes: int, in_channels: int, cardinality: int, width: int=4, start_filts: int=64, start_mode: str='7x7', n_dim: int=2, norm_layer: str='Batch'):
"""Parameters ---------- block : nn.... | the_stack_v2_python_sparse | dlutils/models/resnext.py | justusschock/dl-utils | train | 15 |
c5531fb392267ddeeff02183b2cc622e0ffd8e01 | [
"try:\n ectool_output = self._device.CallOutput(['ectool', 'pwmgetfanrpm'] + (['%d' % fan_id] if fan_id is not None else []))\n return [int(rpm[1]) for rpm in self.GET_FAN_SPEED_RE.findall(ectool_output)]\nexcept Exception as e:\n raise self.Error('Unable to get fan speed: %s' % e)",
"try:\n if rpm ==... | <|body_start_0|>
try:
ectool_output = self._device.CallOutput(['ectool', 'pwmgetfanrpm'] + (['%d' % fan_id] if fan_id is not None else []))
return [int(rpm[1]) for rpm in self.GET_FAN_SPEED_RE.findall(ectool_output)]
except Exception as e:
raise self.Error('Unable to ... | System module for thermal control (temperature sensors, fans). Implementation for systems with 'ectool' and able to control thermal with EC. | ECToolFanControl | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ECToolFanControl:
"""System module for thermal control (temperature sensors, fans). Implementation for systems with 'ectool' and able to control thermal with EC."""
def GetFanRPM(self, fan_id=None):
"""Gets the fan RPM. Args: fan_id: The id of the fan. Returns: A list of int indicati... | stack_v2_sparse_classes_10k_train_008076 | 5,738 | permissive | [
{
"docstring": "Gets the fan RPM. Args: fan_id: The id of the fan. Returns: A list of int indicating the RPM of each fan.",
"name": "GetFanRPM",
"signature": "def GetFanRPM(self, fan_id=None)"
},
{
"docstring": "Sets the target fan RPM. Args: rpm: Target fan RPM, or FanControl.AUTO for auto fan ... | 2 | stack_v2_sparse_classes_30k_val_000140 | Implement the Python class `ECToolFanControl` described below.
Class description:
System module for thermal control (temperature sensors, fans). Implementation for systems with 'ectool' and able to control thermal with EC.
Method signatures and docstrings:
- def GetFanRPM(self, fan_id=None): Gets the fan RPM. Args: f... | Implement the Python class `ECToolFanControl` described below.
Class description:
System module for thermal control (temperature sensors, fans). Implementation for systems with 'ectool' and able to control thermal with EC.
Method signatures and docstrings:
- def GetFanRPM(self, fan_id=None): Gets the fan RPM. Args: f... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class ECToolFanControl:
"""System module for thermal control (temperature sensors, fans). Implementation for systems with 'ectool' and able to control thermal with EC."""
def GetFanRPM(self, fan_id=None):
"""Gets the fan RPM. Args: fan_id: The id of the fan. Returns: A list of int indicati... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ECToolFanControl:
"""System module for thermal control (temperature sensors, fans). Implementation for systems with 'ectool' and able to control thermal with EC."""
def GetFanRPM(self, fan_id=None):
"""Gets the fan RPM. Args: fan_id: The id of the fan. Returns: A list of int indicating the RPM of... | the_stack_v2_python_sparse | py/device/fan.py | bridder/factory | train | 0 |
d3282307ef6ab6885b00a49eea9b3477644a4f65 | [
"if nums == []:\n return\nroot_val = max(nums)\nroot_idx = nums.index(root_val)\nroot = TreeNode(root_val)\nroot.left = self.constructMaximumBinaryTree(nums[:root_idx])\nroot.right = self.constructMaximumBinaryTree(nums[root_idx + 1:])\nreturn root",
"if len(nums) == 0:\n return None\nstack = []\nfor num in... | <|body_start_0|>
if nums == []:
return
root_val = max(nums)
root_idx = nums.index(root_val)
root = TreeNode(root_val)
root.left = self.constructMaximumBinaryTree(nums[:root_idx])
root.right = self.constructMaximumBinaryTree(nums[root_idx + 1:])
return ... | Recursive solution Stats: O(n^2) runtime -- need to find max for every single num in worse case | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Recursive solution Stats: O(n^2) runtime -- need to find max for every single num in worse case"""
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_0|>
def constructMaximumBinaryTree(self, nums):
""":typ... | stack_v2_sparse_classes_10k_train_008077 | 1,897 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: TreeNode",
"name": "constructMaximumBinaryTree",
"signature": "def constructMaximumBinaryTree(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: TreeNode",
"name": "constructMaximumBinaryTree",
"signature": "def constructMaximumBin... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Recursive solution Stats: O(n^2) runtime -- need to find max for every single num in worse case
Method signatures and docstrings:
- def constructMaximumBinaryTree(self, nums): :type nums: List[int] :rtype: TreeNode
- def constructMaximumBinaryT... | Implement the Python class `Solution` described below.
Class description:
Recursive solution Stats: O(n^2) runtime -- need to find max for every single num in worse case
Method signatures and docstrings:
- def constructMaximumBinaryTree(self, nums): :type nums: List[int] :rtype: TreeNode
- def constructMaximumBinaryT... | 844f502da4d6fb9cd69cf0a1ef71da3385a4d2b4 | <|skeleton|>
class Solution:
"""Recursive solution Stats: O(n^2) runtime -- need to find max for every single num in worse case"""
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_0|>
def constructMaximumBinaryTree(self, nums):
""":typ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
"""Recursive solution Stats: O(n^2) runtime -- need to find max for every single num in worse case"""
def constructMaximumBinaryTree(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
if nums == []:
return
root_val = max(nums)
root_idx = nums.i... | the_stack_v2_python_sparse | 654-max-binary_tree.py | stevestar888/leetcode-problems | train | 2 |
d441b85e15392c2057bfe64dde8c30c8d627129b | [
"subcategory = self.cleaned_data['subcategory']\nif subcategory.parent_category is None:\n raise forms.ValidationError(INVALID_SUBCATEGORY, code='invalid')\nreturn subcategory",
"images = self.data.get('images', [])\ncurrent_images = Image.objects.filter(offer_id=offer.id)\nif current_images:\n excluded_ima... | <|body_start_0|>
subcategory = self.cleaned_data['subcategory']
if subcategory.parent_category is None:
raise forms.ValidationError(INVALID_SUBCATEGORY, code='invalid')
return subcategory
<|end_body_0|>
<|body_start_1|>
images = self.data.get('images', [])
current_im... | Form to create an offer from admin | AdminCreateOfferForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdminCreateOfferForm:
"""Form to create an offer from admin"""
def clean_subcategory(self):
"""Validate subcategory is actually a subcategory, not a parent category"""
<|body_0|>
def clean_images(self, offer):
"""Clean images"""
<|body_1|>
def clean_... | stack_v2_sparse_classes_10k_train_008078 | 8,911 | no_license | [
{
"docstring": "Validate subcategory is actually a subcategory, not a parent category",
"name": "clean_subcategory",
"signature": "def clean_subcategory(self)"
},
{
"docstring": "Clean images",
"name": "clean_images",
"signature": "def clean_images(self, offer)"
},
{
"docstring":... | 4 | stack_v2_sparse_classes_30k_train_006687 | Implement the Python class `AdminCreateOfferForm` described below.
Class description:
Form to create an offer from admin
Method signatures and docstrings:
- def clean_subcategory(self): Validate subcategory is actually a subcategory, not a parent category
- def clean_images(self, offer): Clean images
- def clean_mate... | Implement the Python class `AdminCreateOfferForm` described below.
Class description:
Form to create an offer from admin
Method signatures and docstrings:
- def clean_subcategory(self): Validate subcategory is actually a subcategory, not a parent category
- def clean_images(self, offer): Clean images
- def clean_mate... | 4dc6362ef624eb6591aad9d5c7de95eee40a01c9 | <|skeleton|>
class AdminCreateOfferForm:
"""Form to create an offer from admin"""
def clean_subcategory(self):
"""Validate subcategory is actually a subcategory, not a parent category"""
<|body_0|>
def clean_images(self, offer):
"""Clean images"""
<|body_1|>
def clean_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AdminCreateOfferForm:
"""Form to create an offer from admin"""
def clean_subcategory(self):
"""Validate subcategory is actually a subcategory, not a parent category"""
subcategory = self.cleaned_data['subcategory']
if subcategory.parent_category is None:
raise forms.Va... | the_stack_v2_python_sparse | app/offers/forms.py | arielMilan1899/orbita-api | train | 0 |
7fecd064d69a3d30af08fe173ca7eb6930b698a9 | [
"if filename is None:\n self.filename = DEFAULT_FILENAME\nelse:\n self.filename = filename\nself.settings = {}\nself.config_parser = configparser.ConfigParser()",
"if not os.path.isfile(self.filename):\n return\nself.config_parser.read(self.filename)\nif 'default' in self.config_parser.sections():\n c... | <|body_start_0|>
if filename is None:
self.filename = DEFAULT_FILENAME
else:
self.filename = filename
self.settings = {}
self.config_parser = configparser.ConfigParser()
<|end_body_0|>
<|body_start_1|>
if not os.path.isfile(self.filename):
ret... | Class representing a user config file The config file format should look like: [default] circuit_drawer = mpl circuit_mpl_style = default | UserConfig | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserConfig:
"""Class representing a user config file The config file format should look like: [default] circuit_drawer = mpl circuit_mpl_style = default"""
def __init__(self, filename=None):
"""Create a UserConfig Args: filename (str): The path to the user config file. If one isn't s... | stack_v2_sparse_classes_10k_train_008079 | 3,452 | permissive | [
{
"docstring": "Create a UserConfig Args: filename (str): The path to the user config file. If one isn't specified ~/.qiskit/settings.conf is used.",
"name": "__init__",
"signature": "def __init__(self, filename=None)"
},
{
"docstring": "Read config file and parse the contents into the settings ... | 2 | null | Implement the Python class `UserConfig` described below.
Class description:
Class representing a user config file The config file format should look like: [default] circuit_drawer = mpl circuit_mpl_style = default
Method signatures and docstrings:
- def __init__(self, filename=None): Create a UserConfig Args: filenam... | Implement the Python class `UserConfig` described below.
Class description:
Class representing a user config file The config file format should look like: [default] circuit_drawer = mpl circuit_mpl_style = default
Method signatures and docstrings:
- def __init__(self, filename=None): Create a UserConfig Args: filenam... | abf6c23d4ab6c63f9c01c7434fb46321e6a69200 | <|skeleton|>
class UserConfig:
"""Class representing a user config file The config file format should look like: [default] circuit_drawer = mpl circuit_mpl_style = default"""
def __init__(self, filename=None):
"""Create a UserConfig Args: filename (str): The path to the user config file. If one isn't s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserConfig:
"""Class representing a user config file The config file format should look like: [default] circuit_drawer = mpl circuit_mpl_style = default"""
def __init__(self, filename=None):
"""Create a UserConfig Args: filename (str): The path to the user config file. If one isn't specified ~/.q... | the_stack_v2_python_sparse | qiskit/user_config.py | indian-institute-of-science-qc/qiskit-aakash | train | 37 |
2968db61b8fc3770939093a7c18a4e2a1e312257 | [
"from collections import deque\ntree_list = deque()\ntree_list.append(root)\ns = []\nwhile tree_list:\n if tree_list[0]:\n s.append(str(tree_list[0].val))\n tree_list.append(tree_list[0].left)\n tree_list.append(tree_list[0].right)\n else:\n s.append('null')\n tree_list.popleft(... | <|body_start_0|>
from collections import deque
tree_list = deque()
tree_list.append(root)
s = []
while tree_list:
if tree_list[0]:
s.append(str(tree_list[0].val))
tree_list.append(tree_list[0].left)
tree_list.append(tree... | 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_008080 | 2,902 | 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_002183 | 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:... | 2d7cb909535c6ea88c3b1c5e3123f75debaae188 | <|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"""
from collections import deque
tree_list = deque()
tree_list.append(root)
s = []
while tree_list:
if tree_list[0]:
s.append(str... | the_stack_v2_python_sparse | LeetCode297.py | wangyu33/LeetCode | train | 0 | |
ece536c11d7a919bca43def97670c44dd71fee57 | [
"ans = 0\nfor i in range(32):\n sm = 0\n for j in nums:\n sm += j >> i & 1\n ans |= sm % 3 << i\nreturn ans - 2 ** 32 if ans >= 2 ** 31 else ans",
"one = 0\ntwo = 0\nthree = 0\nfor i in range(len(nums)):\n two |= one & nums[i]\n one ^= nums[i]\n three = one & two\n one &= ~three\n t... | <|body_start_0|>
ans = 0
for i in range(32):
sm = 0
for j in nums:
sm += j >> i & 1
ans |= sm % 3 << i
return ans - 2 ** 32 if ans >= 2 ** 31 else ans
<|end_body_0|>
<|body_start_1|>
one = 0
two = 0
three = 0
fo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumberSlow(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def singleNumberGeneral(self, nums):
""":type nums: List[int] :rtype: ... | stack_v2_sparse_classes_10k_train_008081 | 1,972 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumberSlow",
"signature": "def singleNumberSlow(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber",
"signature": "def singleNumber(self, nums)"
},
{
"docstring": ":type nums... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumberSlow(self, nums): :type nums: List[int] :rtype: int
- def singleNumber(self, nums): :type nums: List[int] :rtype: int
- def singleNumberGeneral(self, nums): :type... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumberSlow(self, nums): :type nums: List[int] :rtype: int
- def singleNumber(self, nums): :type nums: List[int] :rtype: int
- def singleNumberGeneral(self, nums): :type... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def singleNumberSlow(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
def singleNumberGeneral(self, nums):
""":type nums: List[int] :rtype: ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def singleNumberSlow(self, nums):
""":type nums: List[int] :rtype: int"""
ans = 0
for i in range(32):
sm = 0
for j in nums:
sm += j >> i & 1
ans |= sm % 3 << i
return ans - 2 ** 32 if ans >= 2 ** 31 else ans
def... | the_stack_v2_python_sparse | S/SingleNumberII.py | bssrdf/pyleet | train | 2 | |
876d1c05948421aead858f1c7a1a03ed90a14848 | [
"minheap = []\nM, N = (len(matrix), len(matrix[0]))\nfor r in range(min(k, M)):\n heapq.heappush(minheap, (matrix[r][0], r, 0))\nfor i in range(k):\n ans, r, c = heapq.heappop(minheap)\n if c + 1 < N:\n heapq.heappush(minheap, (matrix[r][c + 1], r, c + 1))\nreturn ans",
"M, N = (len(matrix), len(m... | <|body_start_0|>
minheap = []
M, N = (len(matrix), len(matrix[0]))
for r in range(min(k, M)):
heapq.heappush(minheap, (matrix[r][0], r, 0))
for i in range(k):
ans, r, c = heapq.heappop(minheap)
if c + 1 < N:
heapq.heappush(minheap, (mat... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def kthSmallest(self, matrix: List[List[int]], k: int) -> int:
"""한 줄을 더해놓고, 그로부터 오른쪽으로 이동하게 heap을 구현 O(klogk) / O(k)"""
<|body_0|>
def kthSmallest(self, matrix: List[List[int]], k: int) -> int:
"""Binary Search 로 최대 범위의 값을 이동해나아가면서 그 값이 몇번째인지 매번 계산 O((M+N)... | stack_v2_sparse_classes_10k_train_008082 | 1,440 | no_license | [
{
"docstring": "한 줄을 더해놓고, 그로부터 오른쪽으로 이동하게 heap을 구현 O(klogk) / O(k)",
"name": "kthSmallest",
"signature": "def kthSmallest(self, matrix: List[List[int]], k: int) -> int"
},
{
"docstring": "Binary Search 로 최대 범위의 값을 이동해나아가면서 그 값이 몇번째인지 매번 계산 O((M+N)logD) / O(1)",
"name": "kthSmallest",
"s... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest(self, matrix: List[List[int]], k: int) -> int: 한 줄을 더해놓고, 그로부터 오른쪽으로 이동하게 heap을 구현 O(klogk) / O(k)
- def kthSmallest(self, matrix: List[List[int]], k: int) -> int... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest(self, matrix: List[List[int]], k: int) -> int: 한 줄을 더해놓고, 그로부터 오른쪽으로 이동하게 heap을 구현 O(klogk) / O(k)
- def kthSmallest(self, matrix: List[List[int]], k: int) -> int... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def kthSmallest(self, matrix: List[List[int]], k: int) -> int:
"""한 줄을 더해놓고, 그로부터 오른쪽으로 이동하게 heap을 구현 O(klogk) / O(k)"""
<|body_0|>
def kthSmallest(self, matrix: List[List[int]], k: int) -> int:
"""Binary Search 로 최대 범위의 값을 이동해나아가면서 그 값이 몇번째인지 매번 계산 O((M+N)... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def kthSmallest(self, matrix: List[List[int]], k: int) -> int:
"""한 줄을 더해놓고, 그로부터 오른쪽으로 이동하게 heap을 구현 O(klogk) / O(k)"""
minheap = []
M, N = (len(matrix), len(matrix[0]))
for r in range(min(k, M)):
heapq.heappush(minheap, (matrix[r][0], r, 0))
for ... | the_stack_v2_python_sparse | Leetcode/378.py | hanwgyu/algorithm_problem_solving | train | 5 | |
286d748e23d5e038635e94147652c49421c6195f | [
"m = max(persons) + 1\nself.v = [0] * m\nself.t = []\nself.s = min(persons)\nmax_c = 0\nmax_t = min(persons)\nfor i in range(len(persons)):\n self.v[persons[i]] += 1\n if self.v[persons[i]] >= max_c:\n max_t = persons[i]\n max_c = self.v[persons[i]]\n self.t.append(max_t)\nself.n = len(times)... | <|body_start_0|>
m = max(persons) + 1
self.v = [0] * m
self.t = []
self.s = min(persons)
max_c = 0
max_t = min(persons)
for i in range(len(persons)):
self.v[persons[i]] += 1
if self.v[persons[i]] >= max_c:
max_t = persons[i]... | TopVotedCandidate | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TopVotedCandidate:
def __init__(self, persons, times):
""":type persons: List[int] :type times: List[int]"""
<|body_0|>
def q(self, t):
""":type t: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
m = max(persons) + 1
self.v =... | stack_v2_sparse_classes_10k_train_008083 | 1,161 | no_license | [
{
"docstring": ":type persons: List[int] :type times: List[int]",
"name": "__init__",
"signature": "def __init__(self, persons, times)"
},
{
"docstring": ":type t: int :rtype: int",
"name": "q",
"signature": "def q(self, t)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002030 | Implement the Python class `TopVotedCandidate` described below.
Class description:
Implement the TopVotedCandidate class.
Method signatures and docstrings:
- def __init__(self, persons, times): :type persons: List[int] :type times: List[int]
- def q(self, t): :type t: int :rtype: int | Implement the Python class `TopVotedCandidate` described below.
Class description:
Implement the TopVotedCandidate class.
Method signatures and docstrings:
- def __init__(self, persons, times): :type persons: List[int] :type times: List[int]
- def q(self, t): :type t: int :rtype: int
<|skeleton|>
class TopVotedCandi... | 72d172ea25777980a49439042dbc39448fcad73d | <|skeleton|>
class TopVotedCandidate:
def __init__(self, persons, times):
""":type persons: List[int] :type times: List[int]"""
<|body_0|>
def q(self, t):
""":type t: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TopVotedCandidate:
def __init__(self, persons, times):
""":type persons: List[int] :type times: List[int]"""
m = max(persons) + 1
self.v = [0] * m
self.t = []
self.s = min(persons)
max_c = 0
max_t = min(persons)
for i in range(len(persons)):
... | the_stack_v2_python_sparse | src/leetcode/P911.py | stupidchen/leetcode | train | 7 | |
f13262169ff2b2ebb71dd80fd461a27bfaa49897 | [
"self.network = network\nself.planes = planes\nself.preimages = preimages\nself.partially_computed = False\nself.transformed_planes = None\nself.computed = False\nself.classifications = None",
"if self.partially_computed:\n return\nself.transformed_planes = self.network.transform_planes(self.planes, self.preim... | <|body_start_0|>
self.network = network
self.planes = planes
self.preimages = preimages
self.partially_computed = False
self.transformed_planes = None
self.computed = False
self.classifications = None
<|end_body_0|>
<|body_start_1|>
if self.partially_comp... | Handles classifying a set of planes using SyReNN. | PlanesClassifier | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlanesClassifier:
"""Handles classifying a set of planes using SyReNN."""
def __init__(self, network, planes, preimages=True):
"""Creates a new PlanesClassifier for the given @network and @planes. @planes should be a list of Numpy arrays with each one representing a V-representation ... | stack_v2_sparse_classes_10k_train_008084 | 3,410 | permissive | [
{
"docstring": "Creates a new PlanesClassifier for the given @network and @planes. @planes should be a list of Numpy arrays with each one representing a V-representation polytope with (n_vertices, n_dims). If preimages=True is set, preimages of the endpoints of each classification region will be returned (other... | 4 | stack_v2_sparse_classes_30k_train_000820 | Implement the Python class `PlanesClassifier` described below.
Class description:
Handles classifying a set of planes using SyReNN.
Method signatures and docstrings:
- def __init__(self, network, planes, preimages=True): Creates a new PlanesClassifier for the given @network and @planes. @planes should be a list of Nu... | Implement the Python class `PlanesClassifier` described below.
Class description:
Handles classifying a set of planes using SyReNN.
Method signatures and docstrings:
- def __init__(self, network, planes, preimages=True): Creates a new PlanesClassifier for the given @network and @planes. @planes should be a list of Nu... | 19abf589e84ee67317134573054c648bb25c244d | <|skeleton|>
class PlanesClassifier:
"""Handles classifying a set of planes using SyReNN."""
def __init__(self, network, planes, preimages=True):
"""Creates a new PlanesClassifier for the given @network and @planes. @planes should be a list of Numpy arrays with each one representing a V-representation ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PlanesClassifier:
"""Handles classifying a set of planes using SyReNN."""
def __init__(self, network, planes, preimages=True):
"""Creates a new PlanesClassifier for the given @network and @planes. @planes should be a list of Numpy arrays with each one representing a V-representation polytope with... | the_stack_v2_python_sparse | pysyrenn/helpers/classify_planes.py | 95616ARG/SyReNN | train | 38 |
fb804ebc23fc595a76f2a18d3ba4d87bc9b9b9fa | [
"AGG_LIST_SIZE = 50\nes_client = elasticsearch_factory.get_client()\nes_profile_search = {'query': {'bool': {'must': [{'term': {'author_id': profile_id}}]}}}\nes_search_result = es_client.search(index=settings.ES_RECOMMEND_USER, body=es_profile_search)\nagg_query_term = {}\nif len(es_search_result['hits']['hits']) ... | <|body_start_0|>
AGG_LIST_SIZE = 50
es_client = elasticsearch_factory.get_client()
es_profile_search = {'query': {'bool': {'must': [{'term': {'author_id': profile_id}}]}}}
es_search_result = es_client.search(index=settings.ES_RECOMMEND_USER, body=es_profile_search)
agg_query_term... | Elasticsearch User based recommendation engine Steps: - Perform aggregations on data to obtain recommendation list - Need to ensure that user apps and bookmarked apps are not in list - Output with query and put into recommendation table: See: https://github.com/aml-development/ozp-backend/wiki/Elasticsearch-Recommendat... | ElasticsearchUserBaseRecommender | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ElasticsearchUserBaseRecommender:
"""Elasticsearch User based recommendation engine Steps: - Perform aggregations on data to obtain recommendation list - Need to ensure that user apps and bookmarked apps are not in list - Output with query and put into recommendation table: See: https://github.co... | stack_v2_sparse_classes_10k_train_008085 | 31,247 | permissive | [
{
"docstring": "Recommendation Logic for Collaborative/User Based Recommendations: Recommendation logic - Take profile id passed in - Get User Profile information based on id - Get Categories, Bookmarks, Rated Apps (all and ones only greater than MIN_ES_RATING) - Compose Query to match profile of bookmarked and... | 2 | null | Implement the Python class `ElasticsearchUserBaseRecommender` described below.
Class description:
Elasticsearch User based recommendation engine Steps: - Perform aggregations on data to obtain recommendation list - Need to ensure that user apps and bookmarked apps are not in list - Output with query and put into recom... | Implement the Python class `ElasticsearchUserBaseRecommender` described below.
Class description:
Elasticsearch User based recommendation engine Steps: - Perform aggregations on data to obtain recommendation list - Need to ensure that user apps and bookmarked apps are not in list - Output with query and put into recom... | d31d00bb8a28a8d0c999813f616b398f41516244 | <|skeleton|>
class ElasticsearchUserBaseRecommender:
"""Elasticsearch User based recommendation engine Steps: - Perform aggregations on data to obtain recommendation list - Need to ensure that user apps and bookmarked apps are not in list - Output with query and put into recommendation table: See: https://github.co... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ElasticsearchUserBaseRecommender:
"""Elasticsearch User based recommendation engine Steps: - Perform aggregations on data to obtain recommendation list - Need to ensure that user apps and bookmarked apps are not in list - Output with query and put into recommendation table: See: https://github.com/aml-develop... | the_stack_v2_python_sparse | ozpcenter/recommend/recommend_es.py | ozoneplatform/ozp-backend | train | 1 |
11af807d4c795e1dc52c10bc098ecc1c3c2b8c65 | [
"if delta_r is None:\n delta_r = 0\nif offset is None:\n offset = Point(0, 0, 0)\nif angle is None:\n angle = 0\nif weld_params is None:\n weld_params = WeldingState()\nself.offset = offset\nself.delta_r = delta_r\nself.angle = angle\nself.welding_parameters = weld_params",
"if not mod.offset.is_zero(... | <|body_start_0|>
if delta_r is None:
delta_r = 0
if offset is None:
offset = Point(0, 0, 0)
if angle is None:
angle = 0
if weld_params is None:
weld_params = WeldingState()
self.offset = offset
self.delta_r = delta_r
... | Modification | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Modification:
def __init__(self, offset=None, delta_r=None, angle=None, weld_params=None):
"""Initalize a Modification object Arguments: object {Modification} -- self Keyword Arguments: offset {Point} -- offset (x,y,z) in meters (default: Point(0,0,0)) delta_r {float} -- delta r angle (d... | stack_v2_sparse_classes_10k_train_008086 | 2,560 | permissive | [
{
"docstring": "Initalize a Modification object Arguments: object {Modification} -- self Keyword Arguments: offset {Point} -- offset (x,y,z) in meters (default: Point(0,0,0)) delta_r {float} -- delta r angle (default: 0) angle {float} -- torch angle (default: 0) weld_params {WeldingState} -- welding parameters ... | 2 | stack_v2_sparse_classes_30k_test_000194 | Implement the Python class `Modification` described below.
Class description:
Implement the Modification class.
Method signatures and docstrings:
- def __init__(self, offset=None, delta_r=None, angle=None, weld_params=None): Initalize a Modification object Arguments: object {Modification} -- self Keyword Arguments: o... | Implement the Python class `Modification` described below.
Class description:
Implement the Modification class.
Method signatures and docstrings:
- def __init__(self, offset=None, delta_r=None, angle=None, weld_params=None): Initalize a Modification object Arguments: object {Modification} -- self Keyword Arguments: o... | 7c39e1e5a6e98fc6c8dfcae6abf033f5d961b16c | <|skeleton|>
class Modification:
def __init__(self, offset=None, delta_r=None, angle=None, weld_params=None):
"""Initalize a Modification object Arguments: object {Modification} -- self Keyword Arguments: offset {Point} -- offset (x,y,z) in meters (default: Point(0,0,0)) delta_r {float} -- delta r angle (d... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Modification:
def __init__(self, offset=None, delta_r=None, angle=None, weld_params=None):
"""Initalize a Modification object Arguments: object {Modification} -- self Keyword Arguments: offset {Point} -- offset (x,y,z) in meters (default: Point(0,0,0)) delta_r {float} -- delta r angle (default: 0) ang... | the_stack_v2_python_sparse | src/rosweld/modification.py | HuaiLeiTang/rosweld_tools | train | 0 | |
324403c2c62528c8abb75599efdfa27c65cb4b32 | [
"if not cls.objects.filter(user=user, external_id_type__name=type_name).exists():\n return False\nreturn True",
"try:\n type_obj = ExternalIdType.objects.get(name=type_name)\nexcept ExternalIdType.DoesNotExist:\n LOGGER.info('External ID Creation failed for user {user}, no external id type of {type}'.for... | <|body_start_0|>
if not cls.objects.filter(user=user, external_id_type__name=type_name).exists():
return False
return True
<|end_body_0|>
<|body_start_1|>
try:
type_obj = ExternalIdType.objects.get(name=type_name)
except ExternalIdType.DoesNotExist:
L... | External ids are sent to systems or companies outside of Open edX. This allows us to limit the exposure of any given id. An external id is linked to an internal id, so that users may be re-identified if the external id is sent back to Open edX. .. no_pii: We store external_user_id here, but do not consider that PII und... | ExternalId | [
"AGPL-3.0-only",
"AGPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExternalId:
"""External ids are sent to systems or companies outside of Open edX. This allows us to limit the exposure of any given id. An external id is linked to an internal id, so that users may be re-identified if the external id is sent back to Open edX. .. no_pii: We store external_user_id ... | stack_v2_sparse_classes_10k_train_008087 | 5,662 | permissive | [
{
"docstring": "Checks if a user has an ExternalId of the type_name provided Arguments: user: User to search for type_name (str): Name of the type of ExternalId Returns: (Bool): True if the user already has an external ID, False otherwise.",
"name": "user_has_external_id",
"signature": "def user_has_ext... | 3 | stack_v2_sparse_classes_30k_train_001906 | Implement the Python class `ExternalId` described below.
Class description:
External ids are sent to systems or companies outside of Open edX. This allows us to limit the exposure of any given id. An external id is linked to an internal id, so that users may be re-identified if the external id is sent back to Open edX... | Implement the Python class `ExternalId` described below.
Class description:
External ids are sent to systems or companies outside of Open edX. This allows us to limit the exposure of any given id. An external id is linked to an internal id, so that users may be re-identified if the external id is sent back to Open edX... | 5809eaca7079a15ee56b0b7fcfea425337046c97 | <|skeleton|>
class ExternalId:
"""External ids are sent to systems or companies outside of Open edX. This allows us to limit the exposure of any given id. An external id is linked to an internal id, so that users may be re-identified if the external id is sent back to Open edX. .. no_pii: We store external_user_id ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ExternalId:
"""External ids are sent to systems or companies outside of Open edX. This allows us to limit the exposure of any given id. An external id is linked to an internal id, so that users may be re-identified if the external id is sent back to Open edX. .. no_pii: We store external_user_id here, but do ... | the_stack_v2_python_sparse | Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/external_user_ids/models.py | luque/better-ways-of-thinking-about-software | train | 3 |
2d3f210806d7d8e410e5754bd532370f6c94fc0c | [
"self.basedir = util.createdir(basedir)\nself.decoder = decoder if decoder is not None else default_decoder\nself.encoder = encoder if encoder is not None else DefaultEncoder",
"filename = os.path.join(self.basedir, FILE(column_id, row_id))\nif os.path.exists(filename):\n with open(filename, 'r') as f:\n ... | <|body_start_0|>
self.basedir = util.createdir(basedir)
self.decoder = decoder if decoder is not None else default_decoder
self.encoder = encoder if encoder is not None else DefaultEncoder
<|end_body_0|>
<|body_start_1|>
filename = os.path.join(self.basedir, FILE(column_id, row_id))
... | Metadata store that maintains annotations for a dataset snapshot in JSON files with a given base directory. The files that maintain annotations are named using the FileSystemMetadataStoreFactory resource identifier. The following are the file names of metadata files for different types of resources: - ds.json: Dataset ... | FileSystemMetadataStore | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileSystemMetadataStore:
"""Metadata store that maintains annotations for a dataset snapshot in JSON files with a given base directory. The files that maintain annotations are named using the FileSystemMetadataStoreFactory resource identifier. The following are the file names of metadata files fo... | stack_v2_sparse_classes_10k_train_008088 | 7,066 | permissive | [
{
"docstring": "Initialize the base directory and the optional JSON encoder and decoder. Parameters ---------- basedir: string Path to the base directory for all annotation files. The directory is created if it does not exist. encoder: json.JSONEncoder, default=None Encoder for JSON objects. decoder: callable: ... | 3 | stack_v2_sparse_classes_30k_train_004784 | Implement the Python class `FileSystemMetadataStore` described below.
Class description:
Metadata store that maintains annotations for a dataset snapshot in JSON files with a given base directory. The files that maintain annotations are named using the FileSystemMetadataStoreFactory resource identifier. The following ... | Implement the Python class `FileSystemMetadataStore` described below.
Class description:
Metadata store that maintains annotations for a dataset snapshot in JSON files with a given base directory. The files that maintain annotations are named using the FileSystemMetadataStoreFactory resource identifier. The following ... | e3d0e04f90468c49f29ca53edc2feb12465c24d5 | <|skeleton|>
class FileSystemMetadataStore:
"""Metadata store that maintains annotations for a dataset snapshot in JSON files with a given base directory. The files that maintain annotations are named using the FileSystemMetadataStoreFactory resource identifier. The following are the file names of metadata files fo... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FileSystemMetadataStore:
"""Metadata store that maintains annotations for a dataset snapshot in JSON files with a given base directory. The files that maintain annotations are named using the FileSystemMetadataStoreFactory resource identifier. The following are the file names of metadata files for different t... | the_stack_v2_python_sparse | openclean/data/metadata/fs.py | Denisfench/openclean-core | train | 0 |
dd61656fd3c151a1e02beae894ea6e432780eaa2 | [
"super(Bottleneck, self).__init__()\nself.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False, groups=args.num_groups)\nself.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False, groups=args.num_groups)\nself.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False,... | <|body_start_0|>
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False, groups=args.num_groups)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False, groups=args.num_groups)
self.conv3 = nn.Conv2d(plane... | A bottleneck block for Resnets. Used in Resnet-50, Resnet-101, and Resnet-152. | Bottleneck | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bottleneck:
"""A bottleneck block for Resnets. Used in Resnet-50, Resnet-101, and Resnet-152."""
def __init__(self, args, inplanes, planes, stride=1, downsample=None):
"""Initializes the Bottleneck. Arguments: inplanes(int): The depth (number of channels) of the input. planes(int): T... | stack_v2_sparse_classes_10k_train_008089 | 2,091 | permissive | [
{
"docstring": "Initializes the Bottleneck. Arguments: inplanes(int): The depth (number of channels) of the input. planes(int): The number of filters to use in convolutions and therefore the depth (number of channels) of the output. stride(int): The stride to use in the convolutions. downsample(func): The downs... | 2 | stack_v2_sparse_classes_30k_train_006281 | Implement the Python class `Bottleneck` described below.
Class description:
A bottleneck block for Resnets. Used in Resnet-50, Resnet-101, and Resnet-152.
Method signatures and docstrings:
- def __init__(self, args, inplanes, planes, stride=1, downsample=None): Initializes the Bottleneck. Arguments: inplanes(int): Th... | Implement the Python class `Bottleneck` described below.
Class description:
A bottleneck block for Resnets. Used in Resnet-50, Resnet-101, and Resnet-152.
Method signatures and docstrings:
- def __init__(self, args, inplanes, planes, stride=1, downsample=None): Initializes the Bottleneck. Arguments: inplanes(int): Th... | 12bace8fd6ce9c5bb129fd0d30a46a00a2f7b054 | <|skeleton|>
class Bottleneck:
"""A bottleneck block for Resnets. Used in Resnet-50, Resnet-101, and Resnet-152."""
def __init__(self, args, inplanes, planes, stride=1, downsample=None):
"""Initializes the Bottleneck. Arguments: inplanes(int): The depth (number of channels) of the input. planes(int): T... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Bottleneck:
"""A bottleneck block for Resnets. Used in Resnet-50, Resnet-101, and Resnet-152."""
def __init__(self, args, inplanes, planes, stride=1, downsample=None):
"""Initializes the Bottleneck. Arguments: inplanes(int): The depth (number of channels) of the input. planes(int): The number of ... | the_stack_v2_python_sparse | onconet/models/blocks/bottleneck.py | yala/Mirai | train | 66 |
601f92dec65beefe5d0cdc00b202c756c69ae660 | [
"official_account = OfficialAccount.manager.add(level=OfficialAccount.LEVEL_3, name='name', email='email@email.com', original='original', wechat='wechat')\nrule = Rule.manager.add(official_account=official_account, name='rule test', reply_pattern=Rule.REPLY_PATTERN_ALL)\nkeyword = Keyword.manager.add(rule, keyword=... | <|body_start_0|>
official_account = OfficialAccount.manager.add(level=OfficialAccount.LEVEL_3, name='name', email='email@email.com', original='original', wechat='wechat')
rule = Rule.manager.add(official_account=official_account, name='rule test', reply_pattern=Rule.REPLY_PATTERN_ALL)
keyword = ... | KeywordTest | [
"BSD-3-Clause",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KeywordTest:
def test_add_keyword(self):
"""测试添加关键字"""
<|body_0|>
def test_keyword_search(self):
"""测试关键字搜索"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
official_account = OfficialAccount.manager.add(level=OfficialAccount.LEVEL_3, name='name', em... | stack_v2_sparse_classes_10k_train_008090 | 4,271 | permissive | [
{
"docstring": "测试添加关键字",
"name": "test_add_keyword",
"signature": "def test_add_keyword(self)"
},
{
"docstring": "测试关键字搜索",
"name": "test_keyword_search",
"signature": "def test_keyword_search(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002928 | Implement the Python class `KeywordTest` described below.
Class description:
Implement the KeywordTest class.
Method signatures and docstrings:
- def test_add_keyword(self): 测试添加关键字
- def test_keyword_search(self): 测试关键字搜索 | Implement the Python class `KeywordTest` described below.
Class description:
Implement the KeywordTest class.
Method signatures and docstrings:
- def test_add_keyword(self): 测试添加关键字
- def test_keyword_search(self): 测试关键字搜索
<|skeleton|>
class KeywordTest:
def test_add_keyword(self):
"""测试添加关键字"""
... | 37a6cd54584a2e1229c943c569daed7227d9f318 | <|skeleton|>
class KeywordTest:
def test_add_keyword(self):
"""测试添加关键字"""
<|body_0|>
def test_keyword_search(self):
"""测试关键字搜索"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class KeywordTest:
def test_add_keyword(self):
"""测试添加关键字"""
official_account = OfficialAccount.manager.add(level=OfficialAccount.LEVEL_3, name='name', email='email@email.com', original='original', wechat='wechat')
rule = Rule.manager.add(official_account=official_account, name='rule test', ... | the_stack_v2_python_sparse | wechat_platform/system/keyword/tests.py | qiyeboy/wechat-platform | train | 1 | |
fffed213ed11b43a5328b06caca1320208cf87be | [
"self.allowed_items = {}\nself.matches = {}\nself.allowed_items['Category'] = PartCategory.objects.all()\nself.matches['Category'] = ['name__contains']\nself.allowed_items['default_location'] = StockLocation.objects.all()\nself.matches['default_location'] = ['name__contains']\nself.allowed_items['default_supplier']... | <|body_start_0|>
self.allowed_items = {}
self.matches = {}
self.allowed_items['Category'] = PartCategory.objects.all()
self.matches['Category'] = ['name__contains']
self.allowed_items['default_location'] = StockLocation.objects.all()
self.matches['default_location'] = ['n... | Part: Upload file, match to fields and import parts(using multi-Step form) | PartImport | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PartImport:
"""Part: Upload file, match to fields and import parts(using multi-Step form)"""
def get_field_selection(self):
"""Fill the form fields for step 3."""
<|body_0|>
def done(self, form_list, **kwargs):
"""Create items."""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_10k_train_008091 | 28,283 | permissive | [
{
"docstring": "Fill the form fields for step 3.",
"name": "get_field_selection",
"signature": "def get_field_selection(self)"
},
{
"docstring": "Create items.",
"name": "done",
"signature": "def done(self, form_list, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004337 | Implement the Python class `PartImport` described below.
Class description:
Part: Upload file, match to fields and import parts(using multi-Step form)
Method signatures and docstrings:
- def get_field_selection(self): Fill the form fields for step 3.
- def done(self, form_list, **kwargs): Create items. | Implement the Python class `PartImport` described below.
Class description:
Part: Upload file, match to fields and import parts(using multi-Step form)
Method signatures and docstrings:
- def get_field_selection(self): Fill the form fields for step 3.
- def done(self, form_list, **kwargs): Create items.
<|skeleton|>
... | 5a08ef908dd5344b4433436a4679d122f7f99e41 | <|skeleton|>
class PartImport:
"""Part: Upload file, match to fields and import parts(using multi-Step form)"""
def get_field_selection(self):
"""Fill the form fields for step 3."""
<|body_0|>
def done(self, form_list, **kwargs):
"""Create items."""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PartImport:
"""Part: Upload file, match to fields and import parts(using multi-Step form)"""
def get_field_selection(self):
"""Fill the form fields for step 3."""
self.allowed_items = {}
self.matches = {}
self.allowed_items['Category'] = PartCategory.objects.all()
... | the_stack_v2_python_sparse | InvenTree/part/views.py | onurtatli/InvenTree | train | 0 |
2a5f15a9846f30d15ab904103118b4c1aaa34bb8 | [
"vocab_size = 100\nsequence_length = 512\ntest_network = networks.BertEncoder(vocab_size=vocab_size, num_layers=2, hidden_size=hidden_size, sequence_length=sequence_length, dict_outputs=True)\ndual_encoder_model = dual_encoder.DualEncoder(test_network, max_seq_length=sequence_length, output=output)\nleft_word_ids =... | <|body_start_0|>
vocab_size = 100
sequence_length = 512
test_network = networks.BertEncoder(vocab_size=vocab_size, num_layers=2, hidden_size=hidden_size, sequence_length=sequence_length, dict_outputs=True)
dual_encoder_model = dual_encoder.DualEncoder(test_network, max_seq_length=sequenc... | DualEncoderTest | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DualEncoderTest:
def test_dual_encoder(self, hidden_size, output):
"""Validate that the Keras object can be created."""
<|body_0|>
def test_dual_encoder_tensor_call(self, hidden_size, output):
"""Validate that the Keras object can be invoked."""
<|body_1|>
... | stack_v2_sparse_classes_10k_train_008092 | 5,709 | permissive | [
{
"docstring": "Validate that the Keras object can be created.",
"name": "test_dual_encoder",
"signature": "def test_dual_encoder(self, hidden_size, output)"
},
{
"docstring": "Validate that the Keras object can be invoked.",
"name": "test_dual_encoder_tensor_call",
"signature": "def tes... | 3 | stack_v2_sparse_classes_30k_train_003864 | Implement the Python class `DualEncoderTest` described below.
Class description:
Implement the DualEncoderTest class.
Method signatures and docstrings:
- def test_dual_encoder(self, hidden_size, output): Validate that the Keras object can be created.
- def test_dual_encoder_tensor_call(self, hidden_size, output): Val... | Implement the Python class `DualEncoderTest` described below.
Class description:
Implement the DualEncoderTest class.
Method signatures and docstrings:
- def test_dual_encoder(self, hidden_size, output): Validate that the Keras object can be created.
- def test_dual_encoder_tensor_call(self, hidden_size, output): Val... | 6fc53292b1d3ce3c0340ce724c2c11c77e663d27 | <|skeleton|>
class DualEncoderTest:
def test_dual_encoder(self, hidden_size, output):
"""Validate that the Keras object can be created."""
<|body_0|>
def test_dual_encoder_tensor_call(self, hidden_size, output):
"""Validate that the Keras object can be invoked."""
<|body_1|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DualEncoderTest:
def test_dual_encoder(self, hidden_size, output):
"""Validate that the Keras object can be created."""
vocab_size = 100
sequence_length = 512
test_network = networks.BertEncoder(vocab_size=vocab_size, num_layers=2, hidden_size=hidden_size, sequence_length=seque... | the_stack_v2_python_sparse | models/official/nlp/modeling/models/dual_encoder_test.py | aboerzel/German_License_Plate_Recognition | train | 34 | |
bd4e9bd5fa99ae3f31eeb99e60615c067f8daf18 | [
"self._dt_fmt = dt_fmt\nself._data_header = data_header\nself._footer_header = footer_header\nself._columns = columns\nself._sep = sep\nself._nr_cols = 3\nself._current_line = None",
"self._current_line = 0\nwith open(file_name, 'r', encoding=encoding) as agt_file:\n self._parse_meta_data(agt_file)\n df = s... | <|body_start_0|>
self._dt_fmt = dt_fmt
self._data_header = data_header
self._footer_header = footer_header
self._columns = columns
self._sep = sep
self._nr_cols = 3
self._current_line = None
<|end_body_0|>
<|body_start_1|>
self._current_line = 0
w... | Parser for agt files that contain sensor data, i.e., timestamps, temerature, and pressure. These files also contain meta-inforamtion that is ignored. | AgtParser | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AgtParser:
"""Parser for agt files that contain sensor data, i.e., timestamps, temerature, and pressure. These files also contain meta-inforamtion that is ignored."""
def __init__(self, dt_fmt='%Y/%m/%d %H:%M:%S', sep=';', data_header='[Data]', footer_header='END OF DATA', columns=('pressure... | stack_v2_sparse_classes_10k_train_008093 | 6,235 | permissive | [
{
"docstring": "Constructor for a new parser, optionally specify a timestamp format.",
"name": "__init__",
"signature": "def __init__(self, dt_fmt='%Y/%m/%d %H:%M:%S', sep=';', data_header='[Data]', footer_header='END OF DATA', columns=('pressure', 'temperature'))"
},
{
"docstring": "parse the f... | 6 | null | Implement the Python class `AgtParser` described below.
Class description:
Parser for agt files that contain sensor data, i.e., timestamps, temerature, and pressure. These files also contain meta-inforamtion that is ignored.
Method signatures and docstrings:
- def __init__(self, dt_fmt='%Y/%m/%d %H:%M:%S', sep=';', d... | Implement the Python class `AgtParser` described below.
Class description:
Parser for agt files that contain sensor data, i.e., timestamps, temerature, and pressure. These files also contain meta-inforamtion that is ignored.
Method signatures and docstrings:
- def __init__(self, dt_fmt='%Y/%m/%d %H:%M:%S', sep=';', d... | e748466a2af9f3388a8b0ed091aa061dbfc752d6 | <|skeleton|>
class AgtParser:
"""Parser for agt files that contain sensor data, i.e., timestamps, temerature, and pressure. These files also contain meta-inforamtion that is ignored."""
def __init__(self, dt_fmt='%Y/%m/%d %H:%M:%S', sep=';', data_header='[Data]', footer_header='END OF DATA', columns=('pressure... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AgtParser:
"""Parser for agt files that contain sensor data, i.e., timestamps, temerature, and pressure. These files also contain meta-inforamtion that is ignored."""
def __init__(self, dt_fmt='%Y/%m/%d %H:%M:%S', sep=';', data_header='[Data]', footer_header='END OF DATA', columns=('pressure', 'temperatu... | the_stack_v2_python_sparse | Python/DataFormats/agt_parser.py | gjbex/training-material | train | 130 |
a83cd5f7267b566e3b482e1ab6acec71a54a0d8c | [
"data = parse(filename)\ntotal = 0\nfor equation in data:\n total += p1_eval(equation)\nreturn total",
"data = parse(filename)\ntotal = 0\nfor equation in data:\n total += p2_eval(equation)\nreturn total"
] | <|body_start_0|>
data = parse(filename)
total = 0
for equation in data:
total += p1_eval(equation)
return total
<|end_body_0|>
<|body_start_1|>
data = parse(filename)
total = 0
for equation in data:
total += p2_eval(equation)
retur... | AoC 2020 Day 18 | Day18 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Day18:
"""AoC 2020 Day 18"""
def part1(filename: str) -> int:
"""Given a filename, solve 2020 day 18 part 1"""
<|body_0|>
def part2(filename: str) -> int:
"""Given a filename, solve 2020 day 18 part 2"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_008094 | 3,361 | no_license | [
{
"docstring": "Given a filename, solve 2020 day 18 part 1",
"name": "part1",
"signature": "def part1(filename: str) -> int"
},
{
"docstring": "Given a filename, solve 2020 day 18 part 2",
"name": "part2",
"signature": "def part2(filename: str) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_000479 | Implement the Python class `Day18` described below.
Class description:
AoC 2020 Day 18
Method signatures and docstrings:
- def part1(filename: str) -> int: Given a filename, solve 2020 day 18 part 1
- def part2(filename: str) -> int: Given a filename, solve 2020 day 18 part 2 | Implement the Python class `Day18` described below.
Class description:
AoC 2020 Day 18
Method signatures and docstrings:
- def part1(filename: str) -> int: Given a filename, solve 2020 day 18 part 1
- def part2(filename: str) -> int: Given a filename, solve 2020 day 18 part 2
<|skeleton|>
class Day18:
"""AoC 202... | e89db235837d2d05848210a18c9c2a4456085570 | <|skeleton|>
class Day18:
"""AoC 2020 Day 18"""
def part1(filename: str) -> int:
"""Given a filename, solve 2020 day 18 part 1"""
<|body_0|>
def part2(filename: str) -> int:
"""Given a filename, solve 2020 day 18 part 2"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Day18:
"""AoC 2020 Day 18"""
def part1(filename: str) -> int:
"""Given a filename, solve 2020 day 18 part 1"""
data = parse(filename)
total = 0
for equation in data:
total += p1_eval(equation)
return total
def part2(filename: str) -> int:
"... | the_stack_v2_python_sparse | 2020/python2020/aoc/day18.py | mreishus/aoc | train | 16 |
f4d8b32220926433d2d1a23a2e1371ff284c648b | [
"super(ClassificationModelWrapper, self).__init__()\nself.model: SwinTransformerV2 = model\nself.pooling: nn.Module = nn.AdaptiveAvgPool2d(1)\nself.classification_head: nn.Module = nn.Linear(in_features=output_channels, out_features=number_of_classes)",
"features: List[torch.Tensor] = self.model(input)\nclassific... | <|body_start_0|>
super(ClassificationModelWrapper, self).__init__()
self.model: SwinTransformerV2 = model
self.pooling: nn.Module = nn.AdaptiveAvgPool2d(1)
self.classification_head: nn.Module = nn.Linear(in_features=output_channels, out_features=number_of_classes)
<|end_body_0|>
<|body_... | Wraps a Swin Transformer V2 model to perform image classification. | ClassificationModelWrapper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassificationModelWrapper:
"""Wraps a Swin Transformer V2 model to perform image classification."""
def __init__(self, model: SwinTransformerV2, number_of_classes: int=10, output_channels: int=768) -> None:
"""Constructor method :param model: (SwinTransformerV2) Swin Transformer V2 ... | stack_v2_sparse_classes_10k_train_008095 | 41,159 | no_license | [
{
"docstring": "Constructor method :param model: (SwinTransformerV2) Swin Transformer V2 model :param number_of_classes: (int) Number of classes to predict :param output_channels: (int) Output channels of the last feature map of the Swin Transformer V2 model",
"name": "__init__",
"signature": "def __ini... | 2 | stack_v2_sparse_classes_30k_train_002301 | Implement the Python class `ClassificationModelWrapper` described below.
Class description:
Wraps a Swin Transformer V2 model to perform image classification.
Method signatures and docstrings:
- def __init__(self, model: SwinTransformerV2, number_of_classes: int=10, output_channels: int=768) -> None: Constructor meth... | Implement the Python class `ClassificationModelWrapper` described below.
Class description:
Wraps a Swin Transformer V2 model to perform image classification.
Method signatures and docstrings:
- def __init__(self, model: SwinTransformerV2, number_of_classes: int=10, output_channels: int=768) -> None: Constructor meth... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class ClassificationModelWrapper:
"""Wraps a Swin Transformer V2 model to perform image classification."""
def __init__(self, model: SwinTransformerV2, number_of_classes: int=10, output_channels: int=768) -> None:
"""Constructor method :param model: (SwinTransformerV2) Swin Transformer V2 ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ClassificationModelWrapper:
"""Wraps a Swin Transformer V2 model to perform image classification."""
def __init__(self, model: SwinTransformerV2, number_of_classes: int=10, output_channels: int=768) -> None:
"""Constructor method :param model: (SwinTransformerV2) Swin Transformer V2 model :param ... | the_stack_v2_python_sparse | generated/test_ChristophReich1996_Swin_Transformer_V2.py | jansel/pytorch-jit-paritybench | train | 35 |
919f38b4ae8c468d533312f6e7077cc19dc6fa1b | [
"try:\n state = self.add_model_schema.load(request.json)\n key = CreateDashboardPermalinkCommand(dashboard_id=pk, state=state).run()\n http_origin = request.headers.environ.get('HTTP_ORIGIN')\n url = f'{http_origin}/superset/dashboard/p/{key}/'\n return self.response(201, key=key, url=url)\nexcept (V... | <|body_start_0|>
try:
state = self.add_model_schema.load(request.json)
key = CreateDashboardPermalinkCommand(dashboard_id=pk, state=state).run()
http_origin = request.headers.environ.get('HTTP_ORIGIN')
url = f'{http_origin}/superset/dashboard/p/{key}/'
... | DashboardPermalinkRestApi | [
"Apache-2.0",
"OFL-1.1"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DashboardPermalinkRestApi:
def post(self, pk: str) -> Response:
"""Stores a new permanent link. --- post: description: >- Stores a new permanent link. parameters: - in: path schema: type: string name: pk requestBody: required: true content: application/json: schema: $ref: '#/components/s... | stack_v2_sparse_classes_10k_train_008096 | 6,224 | permissive | [
{
"docstring": "Stores a new permanent link. --- post: description: >- Stores a new permanent link. parameters: - in: path schema: type: string name: pk requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/DashboardPermalinkPostSchema' responses: 201: description: The perma... | 2 | stack_v2_sparse_classes_30k_train_006377 | Implement the Python class `DashboardPermalinkRestApi` described below.
Class description:
Implement the DashboardPermalinkRestApi class.
Method signatures and docstrings:
- def post(self, pk: str) -> Response: Stores a new permanent link. --- post: description: >- Stores a new permanent link. parameters: - in: path ... | Implement the Python class `DashboardPermalinkRestApi` described below.
Class description:
Implement the DashboardPermalinkRestApi class.
Method signatures and docstrings:
- def post(self, pk: str) -> Response: Stores a new permanent link. --- post: description: >- Stores a new permanent link. parameters: - in: path ... | 0945d4a2f46667aebb9b93d0d7685215627ad237 | <|skeleton|>
class DashboardPermalinkRestApi:
def post(self, pk: str) -> Response:
"""Stores a new permanent link. --- post: description: >- Stores a new permanent link. parameters: - in: path schema: type: string name: pk requestBody: required: true content: application/json: schema: $ref: '#/components/s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DashboardPermalinkRestApi:
def post(self, pk: str) -> Response:
"""Stores a new permanent link. --- post: description: >- Stores a new permanent link. parameters: - in: path schema: type: string name: pk requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/Dashboa... | the_stack_v2_python_sparse | superset/dashboards/permalink/api.py | apache-superset/incubator-superset | train | 21 | |
b7fb5916f850092f066f97069ca3f8650d7dae24 | [
"self.locale = locale\nself.participant = participant\nself.utterancetierTypes = utterancetierTypes\nself.wordtierTypes = wordtierTypes\nself.postierTypes = postierTypes\nself.morphemetierTypes = None\nself.glosstierTypes = None\nself.translationtierTypes = translationtierTypes\nself.interlineartype = POS\nself.ann... | <|body_start_0|>
self.locale = locale
self.participant = participant
self.utterancetierTypes = utterancetierTypes
self.wordtierTypes = wordtierTypes
self.postierTypes = postierTypes
self.morphemetierTypes = None
self.glosstierTypes = None
self.translationt... | The class EafPosCorpusReader implements a part of the corpus reader API described in the Natual Language Toolkit (NLTK). The class reads in all the .eaf files (from the linguistics annotation software called Elan) in a given directory and makes this data accessible through several functions. The data contains "tags", w... | PosCorpusReader | [
"CC-BY-3.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PosCorpusReader:
"""The class EafPosCorpusReader implements a part of the corpus reader API described in the Natual Language Toolkit (NLTK). The class reads in all the .eaf files (from the linguistics annotation software called Elan) in a given directory and makes this data accessible through sev... | stack_v2_sparse_classes_10k_train_008097 | 20,569 | permissive | [
{
"docstring": "root: is the directory where your .eaf files are stored. Only the files in the given directory are read, there is no recursive reading right now. This parameter is obligatory. files: a regular expression for the filenames to read. The default value is \"*.eaf\" locale: restricts the corpus data ... | 4 | stack_v2_sparse_classes_30k_train_006641 | Implement the Python class `PosCorpusReader` described below.
Class description:
The class EafPosCorpusReader implements a part of the corpus reader API described in the Natual Language Toolkit (NLTK). The class reads in all the .eaf files (from the linguistics annotation software called Elan) in a given directory and... | Implement the Python class `PosCorpusReader` described below.
Class description:
The class EafPosCorpusReader implements a part of the corpus reader API described in the Natual Language Toolkit (NLTK). The class reads in all the .eaf files (from the linguistics annotation software called Elan) in a given directory and... | ac2bed9b6e759033d17b6ed9e8fa1e79dad68ae6 | <|skeleton|>
class PosCorpusReader:
"""The class EafPosCorpusReader implements a part of the corpus reader API described in the Natual Language Toolkit (NLTK). The class reads in all the .eaf files (from the linguistics annotation software called Elan) in a given directory and makes this data accessible through sev... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PosCorpusReader:
"""The class EafPosCorpusReader implements a part of the corpus reader API described in the Natual Language Toolkit (NLTK). The class reads in all the .eaf files (from the linguistics annotation software called Elan) in a given directory and makes this data accessible through several function... | the_stack_v2_python_sparse | src/poioapi/corpusreader.py | IgorBMSTU/poio-api | train | 0 |
8df1f5685a66075b63f50c260dcaf7e6c866f776 | [
"self.types = {}\nwith open('resources/types.txt') as file:\n for line in file:\n if not line.startswith('#'):\n tokens = line.split('\\t')\n self.types[tokens[0]] = tokens[1].strip()",
"clausetypes = {'p': 'dcl', 's': 'dcl', 'f': 'dcl', 'r': 'rel', 'd': 'dep'}\nclausetype = clause... | <|body_start_0|>
self.types = {}
with open('resources/types.txt') as file:
for line in file:
if not line.startswith('#'):
tokens = line.split('\t')
self.types[tokens[0]] = tokens[1].strip()
<|end_body_0|>
<|body_start_1|>
claus... | Adds CCG features | CCGTyper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CCGTyper:
"""Adds CCG features"""
def __init__(self):
"""Adds CCG features"""
<|body_0|>
def type_verb(self, surface, pos, tag):
"""Adds CCG features"""
<|body_1|>
def type(self, surface, pos, tag):
"""Retypes it as a verb if it's a verb, cop... | stack_v2_sparse_classes_10k_train_008098 | 24,263 | no_license | [
{
"docstring": "Adds CCG features",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Adds CCG features",
"name": "type_verb",
"signature": "def type_verb(self, surface, pos, tag)"
},
{
"docstring": "Retypes it as a verb if it's a verb, copula or verbal nou... | 3 | stack_v2_sparse_classes_30k_train_007338 | Implement the Python class `CCGTyper` described below.
Class description:
Adds CCG features
Method signatures and docstrings:
- def __init__(self): Adds CCG features
- def type_verb(self, surface, pos, tag): Adds CCG features
- def type(self, surface, pos, tag): Retypes it as a verb if it's a verb, copula or verbal n... | Implement the Python class `CCGTyper` described below.
Class description:
Adds CCG features
Method signatures and docstrings:
- def __init__(self): Adds CCG features
- def type_verb(self, surface, pos, tag): Adds CCG features
- def type(self, surface, pos, tag): Retypes it as a verb if it's a verb, copula or verbal n... | 0f6e943c2bf639f2bdad278145f6fa25502a02df | <|skeleton|>
class CCGTyper:
"""Adds CCG features"""
def __init__(self):
"""Adds CCG features"""
<|body_0|>
def type_verb(self, surface, pos, tag):
"""Adds CCG features"""
<|body_1|>
def type(self, surface, pos, tag):
"""Retypes it as a verb if it's a verb, cop... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CCGTyper:
"""Adds CCG features"""
def __init__(self):
"""Adds CCG features"""
self.types = {}
with open('resources/types.txt') as file:
for line in file:
if not line.startswith('#'):
tokens = line.split('\t')
self... | the_stack_v2_python_sparse | innealan/acainn.py | colinbatchelor/gdbank | train | 4 |
b21620639463b2e4632c167fabfe9d5edc10f3c7 | [
"nums.sort()\nidx = 1\nwhile idx < len(nums):\n if nums[idx] != nums[idx - 1]:\n return nums[idx - 1]\n else:\n idx += 2\nelse:\n return nums[-1]",
"sumof = 0\ni = 0\nwhile i < len(nums):\n sumof = sumof ^ nums[i]\n print(sumof)\n i = i + 1\nreturn sumof"
] | <|body_start_0|>
nums.sort()
idx = 1
while idx < len(nums):
if nums[idx] != nums[idx - 1]:
return nums[idx - 1]
else:
idx += 2
else:
return nums[-1]
<|end_body_0|>
<|body_start_1|>
sumof = 0
i = 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def _singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nums.sort()
idx = 1
while ... | stack_v2_sparse_classes_10k_train_008099 | 1,064 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber",
"signature": "def singleNumber(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "_singleNumber",
"signature": "def _singleNumber(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000240 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: int
- def _singleNumber(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: int
- def _singleNumber(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def singleNu... | 3e72dcaa579f4ae6f587898dd316fce8189b3d6a | <|skeleton|>
class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def _singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
nums.sort()
idx = 1
while idx < len(nums):
if nums[idx] != nums[idx - 1]:
return nums[idx - 1]
else:
idx += 2
else:
return... | the_stack_v2_python_sparse | problems100_200/136_Single_Number.py | Provinm/leetcode_archive | train | 0 |
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